I think this is what I find most important to know about the situation, I think anyone who looks critically at MBTI or a test of its kind would see some unrealistic ideas, but they're portraying themselves as professionals in the field, that they have a real benefit to knowing your results. People don't think to check when they believe it is an industry standard, and a lot of people haven't had access to classes teaching how to research.

This discusses the power and control Spaniards had over the Indians giving them a name "indios" and erasing their cultural backgrounds.

Where settlement first began.

Interesting that the MOI has no impact of the efficacy of the edit with the lac promoter. Additionally, the variability at 10 MOI for lac is extreme, it seems like a large portion of them outright failed. What is it about lac that prevents increasing the MOI from increasing the efficiency? What is it about lac that causes such a high variability for lac 10 MOI?

Cas13a being used its intended way (to destroy a virus) as part of an experimental screening is interesting. I wonder what applications the Cas proteins have while serving their native functions.

An edited population percent of ~0.005% is a pretty low efficiency. That is 1/20,000 cells. Is this sufficient for downstream applications? The specificity is good though. Perhaps increase the efficiency may cause an increase in off target effects.

https://www.facebook.com/reel/1632254748516251

Beau L'Amour does a tour of his father Louis L'Amour's typewriters:<br /> - Brown Olympia SM3, used in the 50s, on a trip or in a car primarily<br /> - Small gray portable typewriter (Hermes ultraportable?) <br /> - IBM Model B, purchased in 58 or 59, the majority of his writing work was produced on this machine<br /> - IBM Selectric (II or III), this became his primary machine once it was released and he began using it <br /> - SMC Electra 120

This shows a change in reason toward cooking and even though it isn't nor fun. She realized that sometimes you just have to do it.

She points out that cooking is an actual skill and take a lot of practice and time and you have to make mistakes to get better

She try's to find a way to joke about how she had no other option but to cook.

She explains that cooking isn't fun and take a lot of effort and she doesn't think its worth it.

This brings the readers attention and makes them ask themselves how she hasn't cooked dinner for herself till she was 33

eLife Assessment

Using a range of complementary approaches, this study examines how type I and type II interferon (IFN) programs exert opposing effects on macrophage responses relevant to tuberculosis. The work addresses a key biological question and provides valuable mechanistic insights. However, the data to support several key conclusions is incomplete and requires further strengthening; in particular, the role of ferritin needs to be established more definitively, the TNF stimulation findings should be validated in the context of M. tuberculosis infection, and in vivo evidence is needed to support the proposed therapeutic strategy. Additionally, aspects of iron metabolism, lipid peroxidation, and the translatability of the findings between mouse and human macrophages would benefit from greater clarity and deeper mechanistic investigation.

Reviewer #1 (Public review):

Summary:

This study examines how type I IFN and IFN-γ exert opposing effects on macrophage responses relevant to TB. Using bone marrow-derived macrophages from genetically susceptible B6.Sst1S mice, the authors describe a persistent pathological activation state induced by TNF and characterized by sustained type I IFN signalling, oxidative stress and lipid peroxidation. They show that IFN-γ priming limits several features of this state and propose altered iron metabolism as one mechanism underlying this protective effect. They then use a computational cell-state approach to identify pharmacological interventions that may mimic aspects of IFN-γ activity. In particular, CDK4/6 inhibition with trilaciclib and activation of retinoic acid signalling with ATRA appear to act through complementary mechanisms and, when combined at low concentrations, improve control of intracellular M. tuberculosis.

Strengths:

A major strength of the study is the combination of several complementary approaches, including genetic susceptibility, cytokine signalling, oxidative stress, iron and lipid metabolism, transcriptomics, computational modelling and pharmacological perturbation. Together, these experiments build a coherent model of macrophage dysfunction.

The evidence that type I IFN signalling contributes to maintenance of the pathological state is particularly convincing within the TNF stimulation model. Blocking the type I IFN receptor after the phenotype has developed restores responsiveness to IFN-γ and prevents further accumulation of lipid-peroxidation products. The authors also provide evidence that persistence does not simply reflect continued TNF signalling, since blockade of the TNF receptor after 24 h does not abolish the elevated lipid-peroxidation phenotype. Another strength is that the computational analysis generates experimentally testable predictions, and two mechanistically distinct interventions identified by this approach are subsequently validated in macrophages.

Weaknesses:

There are, however, several limitations that affect the strength and scope of the conclusions.

(1) First, the use of the terms "persistent" and especially "self-sustaining" would be better supported by a more complete time-course analysis.

(2) Second, the proposed central role of ferritin-mediated iron sequestration in the protective effect of IFN-γ is not yet demonstrated directly. The data clearly link IFN-γ treatment to ferritin induction and reduced labile iron, but the causal contribution of ferritin itself remains to be established.

(3) Third, an important limitation is the connection between the mechanistic model developed with TNF stimulation and actual M. tuberculosis infection. Most of the mechanistic analysis, including type I IFN super-induction, lipid peroxidation, ferritin induction, labile iron and HIF1α regulation, is performed in TNF-stimulated macrophages. The infection experiments show that IFN-γ improves bacterial control and that low-dose trilaciclib plus ATRA reduces intracellular bacterial burden, but they do not establish that M. tuberculosis infection induces the same pathological circuit, or that these interventions improve bacterial control by acting through that circuit. The study therefore defines a convincing TNF-driven macrophage phenotype with relevance to bacterial control, but the broader conclusion that this mechanism underlies IFN-dependent susceptibility to TB remains only partially supported.

(4) Finally, the therapeutic implications go beyond the experimental evidence currently presented, since all of the pharmacological experiments are performed in cultured macrophages and there is no in vivo validation.

Conclusion:

Overall, this study proposes an interesting framework for understanding how inflammatory activation may become maladaptive in susceptible macrophages and how IFN-γ may combine antimicrobial activation with protection from oxidative damage. The convergence between IFN-γ, iron metabolism, lipid peroxidation and the pharmacological perturbations identified computationally is a clear strength. However, the causal role of ferritin, the operation of the proposed circuit during M. tuberculosis infection, and the in vivo relevance of the pharmacological strategy remain to be established. These limitations leave the mechanistic and translational evidence incomplete, while the study itself remains potentially important.

Reviewer #2 (Public review):

Summary:

The authors have carried out extensive transcriptomic, phenotypic and modelling-based analyses to provide novel insights into the interaction of the type I and II interferon programs in the determination of macrophage activation status and resistance to Mtb infection and infection-mediated damage. They demonstrate how an antagonistic effect between the two programs goes beyond classical downstream immune signalling pathways to lipid peroxidation maintained in a sustained autocrine manner and generation of a persistent pathological activation state (pPAS). Based on these analyses, the authors propose a therapeutic strategy of boosting specific pathways that increase oxidative stress resilience to reduce inflammatory pathology without suppressing host defenses for bacterial control and resisting pPAS. The conceptual framework may prove applicable to interferonopathies and to other bacterial and viral infections, though this remains to be tested.

Strengths:

(1) The study uses macrophages from a disease-relevant genetic murine model in which the sst1 locus drives the formation of necrotic pulmonary granulomas resembling human TB lesions- pathology not seen in standard C57BL/6 mice. This provides a genetically defined comparison between susceptible and resistant macrophages on an otherwise identical background, allowing the authors to attribute differences in activation state to a single locus rather than to strain-level variation.

(2) The experimental design isolates the phenomenon of interest: the TNF withdrawal and restimulation scheme allows the authors to establish that the aberrant activation state persists after removal of the initiating stimulus, rather than simply reflecting ongoing stimulation. The timed IFNAR blockade at 2, 12 and 24 h similarly separates initiation of the state from its maintenance.

(3) Lipid peroxidation is assessed through two orthogonal readouts: 4-HNE immunostaining for accumulated adducts and linoleamide alkyne click chemistry for ongoing synthesis. These, coupled with ROS and labile iron pool measurements, isotype antibody controls, parallel B6 and B6.Sst1S comparisons, and an anti-TNFR control, help in establishing that the phenotype is independent of continued TNF signalling. The convergence of these independent measures gives confidence in the peroxidation phenotype itself.

(4) The cSTAR analysis is applied here using regression rather than classification, generating a continuous DPD_TB score that correlates with measured Mtb fold change and thus provides a quantitative transcriptomic metric of macrophage priming state. Critically, the pathway predictions arising from this analysis (CDK4/6 inhibition and RAR activation) were tested and confirmed experimentally. The inferred network topology further predicted synergy between these two interventions, which bore out experimentally as an approximately ten-fold reduction in the effective dose of each agent in controlling Mtb during infection.

Weaknesses:

(1) Figure 2C is difficult to interpret as presented. The row labels ("No TNF"/"TNF") use different terminology from the corresponding conditions in panel A ("TNF withdrawal"/"TNF restimulated"), and "TNF" appears on both axes referring to different phases of the experiment; no timepoint is given on the panel itself, unlike neighbouring panels. Harmonising the labels with panel A and stating the harvest timepoint would help the reader. More substantively, the remaining lipid peroxidation readouts in this figure (panels D-G) are all at early timepoints of TNF stimulation (2-24 h) rather than during withdrawal, which limits what they can say about sustained autocrine signalling. Extending these assays to the later timepoints used in Figure 1 would considerably strengthen the claim that IFN-I maintains, rather than only initiates, the pathological state. The same applies to Figure 2G, where the contribution of itaconate to 4-HNE accumulation over time is not yet resolved.

(2) Several of the pathways implicated here are reported to behave differently between murine and human macrophages, and between macrophage subsets (alveolar versus monocyte-derived macrophages), during Mtb infection. This does not diminish the findings in this model, but it does bear on how broadly they can be generalised.

a) Type I interferon responses differ by species and by macrophage subset across multiple reports. Since the proposed model depends on autocrine IFN-I signalling reaching a threshold sufficient to sustain the pathological state, these differences in IFN-I output are worth keeping in mind when interpreting the wider significance of the findings.

b) Similarly, itaconate production in murine BMDMs is 20-fold higher than in LPS-activated human monocyte-derived macrophages and 50-fold higher than in LPS-activated alveolar macrophage-like cells, and Mtb infection of these human macrophages very weakly induces ACOD1 with almost no detectable itaconate (PMID 41797714). This is relevant to the Acod1/4-OI arm of the mechanism.

c) In a cross-species comparison of Mtb-infected macrophages, cholesterol homeostasis genes (including HMGCS1, IDI1, LSS) were significantly upregulated in human alveolar macrophages but downregulated in subcutaneous BCG-exposed murine alveolar macrophages (PMID 41208107)- the opposite direction to the lipid biosynthesis suppression treated here as a defining pPAS feature. The same group reports that murine AMs lack c-Maf and IL-10 whereas murine BMDMs express both (PMID 40073087), indicating that the autocrine anti-inflammatory brake on IFN-I responses is itself subset-dependent.

d) Finally, the cSTAR network predictions were inferred from human THP-1 perturbation data, but tested only in murine BMDMs. Establishing how this circuit operates in human macrophages, and during Mtb infection rather than TNF stimulation alone, would be a valuable extension of the work.

(3) The causal relationships linking IFN-I, lipid peroxidation, ROS and loss of IFNγ responsiveness could be drawn together more clearly. These elements are each established, but the connections between them are not always demonstrated directly. IFN-I appears to promote peroxidation through Acod1/itaconate and suppression of lipid biosynthesis rather than through iron, since neither IFNβ nor IFNAR blockade alters the labile iron pool. This would suggest two separable inputs to 4-HNE rather than a single pathway. This raises a further question about the persistent state itself: the labile iron pool rise appears to be TNF-driven, yet all labile iron measurements are made at 24 h in the continued presence of TNF and none under the withdrawal condition, so it is unclear whether elevated catalytic iron is sustained once the initiating stimulus is removed. Similarly, while IFNAR blockade reduces peroxidation, the reciprocal arm is not tested. An antioxidant or iron chelator could be used to ask whether peroxidation in turn drives Ifnb1 super-induction. In the absence of this information, the proposed feedback loop remains partly inferred. It would considerably strengthen the manuscript if the authors could clarify, through additional experiments or in the text, how the labile iron pool and lipid peroxidation relate to one another and what sustains each of them after TNF withdrawal.

(4) Reading across the manuscript, the labile iron pool emerges as the variable most consistently associated with the phenotype. Every protective intervention tested converges on it. By contrast, the alternative candidate mechanisms do not track with outcome. Lipid biosynthesis genes are suppressed by IFNγ yet induced by both trilaciclib and ATRA, all three of which are protective. GPX4 is unchanged under IFNγ and trilaciclib. The Acod1/itaconate axis cannot account for it since IFNγ priming blocks 4-HNE accumulation induced by exogenous itaconate. Yet, a direct causal role for iron is never tested. Additionally, IFNβ induces 4-HNE with the labile iron pool entirely unchanged, indicating at least one route to lipid peroxidation that bypasses catalytic iron. Focusing on iron handling would make the manuscript's message more coherent and its therapeutic argument more compelling.

Reviewer #3 (Public review):

Summary:

Araveti et al. demonstrate that Type I Interferon (IFN-I) and Interferon-gamma (IFN-γ) play opposing roles in regulating lipid peroxidation and host resistance during Mycobacterium tuberculosis (Mtb) infection. While both the two pathways drive inflammation, they differ fundamentally in cell protection. IFN-I signaling triggers a destructive, self-amplifying cycle catalysing the generation of reactive oxygen species (ROS) and lipid peroxidation, which ultimately impairs the host's ability to clear Mtb. Conversely, the authors demonstrate that IFN-γ couples antimicrobial activation with cytoprotection. It primes macrophages to fight the mycobacteria while simultaneously shielding them from oxidative stress. It achieves this by sequestering iron, which successfully prevents ROS from converting into damaging lipid peroxidation products.

Strengths:

Ultimately, this study highlights a critical biological distinction: IFN-γ safely balances inflammatory activation with cellular defense, whereas IFN-I promotes uncontrolled pathological damage. This divergent coupling of inflammation and cytoprotection carries major consequences for disease progression and host survival.

Weaknesses:

This study demonstrates all the findings in specific mouse strains. How these translate in human macrophages is not well characterised, thus raising the issue of its overall impact in tuberculosis disease.

Ted struggles to realize everyone is always worried about something and has their own issues. I think this is a key point in the story, showing a major disconnect between Ted and his sister. Ted lacks self-awareness and needs to realize that things he says can be very hurtful, especially towards family.

Ted's conspiracies and mental illness are appearing to cause his siblings severe distress. His alarming statements, comments, and beliefs are so unrealistic to his family that they struggle to engage with him.

what

ummm

This hooks the reader and grabs their attention because it makes you think how have they never had a Mcrib before.

He is explaining that a bad experience meant more than just whether food looks and taste good or bad

He uses a funny and weird comparison to show how disgusting he thought the McRib looked.

He explains how he feels about the experience and give a clear resolution and how the story ends

This helps show the reader or readers how curious he became about trying the Mcrib

se da a entender que la ciencia busca construir sus mediciones sobre verdades universales de la naturaleza y no depender de objetos que podrían llegar a sufrir cambios a medida del tiempo

contexto?

he is mainly saying for mustarded to give him his hand to scratch his face which is honestly a random and comedic request.

"This moment is part of a larger scene where Titania is in a tender, almost romantic mood, lavishing affection on Bottom. She agrees to let him sleep, wrapping him in her arms and sending away the fairies. The line about “an exposition of sleep” is a poetic way of saying he is deeply asleep, and the request for dried peas is a humorous, rustic preference that contrasts with the fairy world’s magic and romance"

This is a good explanation of this scene, but it takes away from the emotional side of titania almost having maternal instincts for him and wanting to care.

she is caressing his cheek while waiting for a kiss. She is extremely in love with him. I feel as if this part of the play should be look at extremely romantically with bottom starting to fall for her as well.

fictitious glory

study guides

eLife Assessment

This valuable study provides a proof of concept for the utility of transcriptomic methods to develop novel diagnostic tests for bovine tuberculosis. A comprehensive analysis using a suite of machine learning models provides convincing evidence for the use of mRNA biomarkers to distinguish between infected and disease-free animals. The analysis also provides major insights into both the levels of individual variation in expression between animals as well as systematic changes with respect to the time from infection.

Reviewer #1 (Public review):

Summary:

The control of bovine tuberculosis in managed populations such as Ireland and Great Britain is unusual in that demonstrably sick animals are rarely, if ever, seen in herds. Control is therefore focused on the identification and removal of animals that test positive to the tuberculin skin test (the legal definition of infection). Despite over a century of study, the relationship between tuberculin test status, infection and most importantly infectiousness is still poorly quantified. Different formats of the tuberculin skin test are acknowledged to have both poor sensitivity and compromised specificity, although the characteristics of these tests are likely to vary considerably between contexts due to both biological variation and discretion in measurements by testers. There is an urgent need for new, more reliable and cheaper diagnostics to address the failures of existing control programs and to enable control in emerging markets that do not currently control the disease.

Strengths:

A key strength of this study is the use of samples from both naturally infected and experimentally infected animals. This data set is used to perform a careful and exhaustive evaluation of the extent to which patterns of transcriptomic expression can be used to classify between disease free animals and those infected with bovine tuberculosis.

The experimentally infected animal samples provide evidence that expression patterns of infected animals vary with respect to the time from infection. The authors highlight that this suggests transcriptomic markers may be able to detect infection earlier than tuberculin and IGRA tests that target cell-mediated immune responses. However, these methods could potentially provide a valuable new tool for quantifying the role of individual variation and progression for a disease where the individual life-history is still frustratingly mysterious.

Weaknesses:

However, the high levels of individual variation - and in particular differences in patterns of expression between naturally and experimentally infected animals do raise questions about how diagnostic tests developed from these tools would be used in practice. In particular, while many of the models considered achieved high sensitivity - estimated specificity is consistently lower than current diagnostic tests and considerably lower than that necessary for screening tests given the frequency of testing carried out as part of statutory control programs.

Expanding the number of samples may help to address these issues, but I would have liked to see some discussion of the extent to which the level of biological variation observed in this study may limit the precision of diagnostic tests developed using these tools. Given the likely characteristics of tests based on these methods, I would be interested to hear how the authors think they could fit within current statutory programs, either as supplementary or replacement tests?

Reviewer #2 (Public review):

Summary:

This study evaluates whether peripheral blood transcriptomic profiles can be used to classify cattle infected with Mycobacterium bovis using a range of machine-learning approaches. By integrating RNA-seq datasets from naturally and experimentally infected animals, the authors develop and test predictive models capable of distinguishing infected from uninfected cattle and assess their ability to differentiate bovine tuberculosis from other infectious diseases. The study addresses an important challenge in bovine tuberculosis control and presents evidence that host transcriptional signatures may have utility as an adjunct diagnostic approach.

Strengths:

- The study combines data from multiple independent cohorts, including both naturally and experimentally infected cattle, which increases the biological relevance of the findings.

- The analytical workflow is comprehensive, scientifically sound and clearly described. Multiple machine-learning approaches are evaluated and compared rather than relying on a single modelling strategy.

- The inclusion of a held-out test set, especially because such data is limited, provides a useful assessment of model performance beyond cross-validation alone.<br /> - Thorough evaluation against datasets from cattle infected with MAP, BoHV-1 and BRSV is a valuable addition and provides useful information regarding the specificity of the identified transcriptional signatures.

- The authors acknowledge important limitations, including batch effects and the need for additional validation.

- All underlying data and code are made publicly available

Weaknesses:

- My main concern relates to generalisability. Although a separate testing dataset was used, the training and testing datasets were generated through random partitioning of samples from the same underlying studies. As a result, classifier performance in a completely independent external cohort remains unclear. Discussion of this limitation, and whether alternative validation strategies such as leave-one-study-out analyses were considered, would strengthen the manuscript.

- The authors identify substantial study-specific batch effects following dataset integration and appropriately account for these in the modelling framework. However, given the magnitude of the reported batch structure, additional discussion regarding the potential influence of residual between-study variation on classifier performance would be helpful.

- The manuscript is framed in the context of global bovine tuberculosis control, yet the practical implementation of a transcriptomic diagnostic approach is not discussed in great detail. Since bovine tuberculosis remains a significant challenge in many low- and middle-income settings, further consideration of the feasibility, cost, infrastructure requirements, and potential translation of these signatures into more deployable diagnostic platforms would improve the broader relevance of the study.

- The datasets used for classifier development are derived primarily from Ireland, the UK and the United States. It would be useful to discuss whether differences in circulating M. bovis lineages, cattle populations, management systems, or co-infection pressures could influence host transcriptional responses and therefore the performance of the proposed classifiers in other epidemiological settings. This ties to the previous comment, since epidemiological settings in LMIC countries with a high burden of M.bovis disease would be vastly different from where the data was sourced.

Reviewer #3 (Public review):

Summary:

This is an excellent piece of work which sheds greater light on the responses of cattle to both experimental and natural infection in cattle with Mycobacterium bovis infection, using data from different experimental and field groups.

Strengths:

The work is based on robust analysis of a range of highly relevant experimental and field sample sets, using transcriptomic approaches. It provides insight into pathogenesis and disease responses, as well as some evidence regarding potential future diagnostic advances.

Weaknesses:

I have some simple, but important comments on how the work is discussed. (Consequently, most comments focus on the discussion section). In particular, I suggest that the authors have confused or conflated the great progress that they have made in improving the understanding of the responses to M. bovis infection with an improved ability to practically improve the diagnosis of the infection in the field. Minor differences in specificity and sensitivity - and predictive values - of tests or assays being used can have profound effects in different prevalence settings on the farm, and I don't feel that this understanding is adequately reflected in the discussion in particular. In this respect, the authors really should, in my view, focus not on the outstanding results that come in or from their model fitting approaches, to focus on their model testing results in relation to extrapolated meaning / external validity. The text uses words like 'robust' and 'highly accurate' which are meaningless in the context of test interpretation in the field.

I get their enthusiasm, based on really interesting findings in relation to disease progression and immune and inflammatory responses, but these indistinct claims rather devalue the quality of the rest of their work in my view.

One great challenge in work of this nature, using natural cases from farms, is that there is no gold standard for identifying the cases that current diagnostic approaches miss and which they hope their new approaches can help with. This is not discussed or mentioned in their enthusiasm for what they have achieved. Their field datasets are based on the current, insensitively detected cases. It misses the 'occult' cases that are present but undiagnosed.

isso indica que a gente atende em todos esses lugares, mas já sabe que o povo anda pirangueiro e caloterio até demais esse ano! tão pedindo evento em Porto e João Pessoa querendo que a gente cobre o mínimo, aí complica pq ninguém tem carro ne

gostei do conceito, talvez da pra reformular um pouco

essa parte como eu falei antes não faz sentido, talvez uns vídeos curtos

acho que toda essa parte poderia ser substituída por fotos ou vídeos, não precisa de tanto texto. mesmo pq a galera não liga de quem é a cabine e quem é o técnico

talvez a gente poderia seguir o modelo atual de ter um tipo de portfolio logo de primeira, sem ser link

esse tá bom, talvez só reorganizar um pouco a frase depois

é o q eu disse, não tem como garantir a dupla, principalmente se for espanhol mas eu gostei do conceito da "dupla fixa". talvex poderia focar mais que a gente é especialista em acompanhamento, consecutiva e tem experpiência com eventos remotos e essas coisas mais difíceis

não vou mais pagar essa merda dessa abrates. muito spam e só os velhos chorando de saudade da época de ouro, e a interactio é que exigia e não chama já faz 1 ano

achei legal essa parte

a questão é que isso não dá pra garantir 100%, mas gostei do conceito por trás dessa frase

aqui tb complica, pq a gente não tem conta no zoom, nem tá trabalhando com essas aí (interactio já era, as outras nunca trabalhei). de graça só tem o Teams, que anda uma fuleiragem só

tem mais de 30, mas tem novamente a questão do tamanho do evento. poderia colocar tipo "atendemos eventos com até 50 pessoas", sei lá

achei estranho o termo, o povo usa muito essa expressão? poderia colocar "com 20 anos de carreira" talvez

isso acho q já tá meio q subentendido, principalmente pra quem contrata e não quer saber desses detalhes. Talvez o foco poderia ser em eventos pequenos, pra ninguém encher o saco querendo orçamento pros grandes.

a) the topology is not determined by the metric b) this fact is highly non obvious and if I didnt already know some version of this it would probably just confuse me on a first reading

Interpretation matters a lot, which is why it is important to have explanantion on why you might think that way. Providing a "why?" in English Literature helps other readers see your view, and maybe open up a new one for others.

Has a lot to do with comprehension and perception.

Being able to change the Anthology and advocate for writers and their texts who we think are worthy is a privilege

Its interesting to combine the past, present, and future of literature and not being able to find a correct or moral definition.

How can we define "American" when learning about our land beign stolen.

The conections to the past is what makes literature matter today, and who we truly recognize today.

Discrimination against authors

Is there even a defined answer?

It helps us understand the past and where this literature began.

We question the location, author, and the context of the text to further examine weather or not it is consered literature. On the other hand, we question what CAN we qualify as literature and why we are the ones who get to decide.

I noticed that The Waste Land's original title (the epigraph about the Sibyl who can't die even though she desperately wants to) reminded me a lot of Dickens' Our Mutual Friend. Both deal with a similar idea: a society that's technically alive but has lost any real ability to renew itself. What I found interesting is how Dickens shows this through institutions. The systems that are supposed to help poor people actually end up wearing them down and humiliating them instead. It's not that anyone is truly dying, but the whole structure around them makes recovery or dignity feel out of reach. Once I read that next to Eliot's poem, I started noticing that same Sibyl curse throughout The Waste Land, too. Life just keeps going, but mechanically, with no real vitality or chance of rebirth.

The first thirty lines of TWL provide a fairly concrete setting and tone for the beginning of the poem. Eliot plays with the concept of time and new beginnings, mentioning flowers growing out of the "dead land" which could be interpreted as the waste land. He also writes about the mixing of "memory and desire" and "Dull roots with spring rain" which draw parallels to past (memory, dull roots) and future (desire, spring rain). Within the first 20 lines, the story progresses from spring, to summer, and into winter. From lines 20-30, Eliot mentions "Son of man" which is almost certainly referring to Jesus, as that is one of his meny titles in the Bible. Eliot actually borrows the images of fear and dust, as well as burdensome insects, from Ecclesiastes 12, which talks about how dust shall return to the earth when people fear God again. It also says how dust will return to the earth one the grasshopper becomes burdensome and man goes to his home. This runs parallel to Eliot's cricket that provides no relief and his characters search for shelter, shade and rest in the lines in the mid twenties. The first thirty lines conclude with Eliot writing on how he will show someone fear in a handful of dust, which circles back to the passages in Ecclesiastes.

This phrase seems evocative of a particular line from Ezekiel: "the spirit entered into me when he spake unto me, and set me upon my feet, that I heard him that spake unto me." (S2). The intimacy established between the intervention of a voice and the physical proximity of that voice is striking. In order for a spirit to connect with the human conscious, it must also enter into the body. The function of the red rock seems similar. First, the shadow's presence is noted underneath it, though Eliot's inclusion of a parenthetical the following line implies that the enclosed meaning can also be derived from its predecessor. So, the acknowledgement of the rock is not simply a detail, but a communication, a voice that demands physical proximity in order to adequately impose itself. Ezekiel also seems to project a passivity onto the role of humanity during the reception of divine language. This is indicated by the following: "I will make thy tongue cleave to the roof of thy mouth, that thou shalt be dumb, and shalt not be to them a reprover: for they are a rebellious house." (S3). The tongue is essential to the process of human communication, though the word worthy of most attention is "cleave." A lingering ambiguity enshrouds it, as it could either be interpreted as evoking the tongue's adhesion to the mouth (signaling silence) or even perhaps as the tongue's cutting away, its freedom from captivity at the roof of the mouth. Hence, uncensored human voice could be a purveyor of diving wrath (bringing "rebellion") in addition to human silence. Between the two conditions remains the only ground known not to incite the anger of God: passivity and subservience. The role of humanity is not to talk or to be silent, but merely to listen, to absorb, to establish proximity with a voice that is not their own, not directly associated with their action. The red rock provides this.

Eliot is portraying a strong connection to death and despair. Death becomes a form of escape, for the Sibyl, as death would free her from an endless existence. Conrad presents Kurtz as physically alive but destroyed in a spiritual sense before he actually dies. Both Eliot and Conrad offer a life without meaning as a way of a living death. Sibyl wishes for death because being alive has been unbearable. On a similar page Kurtz reaches death after a horrifying reality check about himself and the world around him.

DLT its meeee

Helloooooooo

meeee bored

i need to do 50 lessons today aiii aiiiiii aaaaaiiiiiiiiii

Think this should also be arranged further to the top.

These are the numbered topics right? like 1. Customer Programs

maybe we can highlight light Red and 2 above light green.

Is this shown in training? or how

typo Included projects pull sub projects..

Should this be inserted before Types and Hierarchy? Bc I see it as the overview of the page then to be broken down by statuses,etc.

Bold, highlight, can be easily lost

1st entry could be the left side view briefly mentioning the All Open, Latest activity views and so on..

Ok here would be good to mention that Ops will not consider STORY and BUG

Bug and story are not mentioned what should the reader think abt these? Ignore?

I think this should be first part in How our instance is structured Bc its a higher level view than previously mentioned topics.

Also maybe worth Highlighting?

on the top right of the screen

which means?

bold or highlight?

throw people off

first choice

My first choice

Choice First My

My first choice

my first choice

Social work as a profession has also been contending with this shift.

Potentially, I could make these the same, just one box rather than two, but then people can fill in the "team options" within that interface.

tooltips should explain the translation into percentiles

make it clear that 1. these are translated to percentiles and 2. the "quick ratings" are less weighted/noted separately as 'quick ratings'

clothes. Robin picks the wrong man off an outfit. Oberon said find an Athenian but never said exactly who. Because of his poor request, the wrong person received the spell. So basically, it is his fault

Try to convey this in a less dense way. Consider whether some of these are redundant or overlap others, etc.

this is just 'show below' -- make it a link instead

most "expandable" are arrows, but others are boxes

Despite the hardships that come with raising a child in her situation, it feels she was still able to teach strong values and ideals to Noah.

I feel Noah’s mom did a great job maintaining hope for a better situation.

I like his mother’s mentality towards building Noah’s ambition and it supports her philosophy Of a simple name without double meaning.

It’s seams like they uses women’s straight when it’s convenient but when it gooses against what they want in the household they expect women to be submissive. That’s seams a bit hipacriticul..

Aging Noah shows us an example of how as a kid he didn’t fully comprehend the works around him and is able to reflect on that as an adult, obviously, he doesn’t think she’s like a computer now, But he made the comparison to represent how his interactions went when he was a kid.

Was there anything Put in place to avoid things like this or was it more just not enforced because of the area they lived in?.

Here’s an example of a harmful stereotype That is still exacerbated by Cultural ideal And it makes me think when they abolished Apartides how long did it take befor its stoped shaping day to day life.

I feel like he’s spending a lot of time developing his mother. What for? Is he building trying to build up to an empathetic response later in the book? Or is he building So we are able to better understand her up coming actions?.

I would think the double standers and miss treatment reenforces the discrimination not only in legislation but also culture..

It’s quite somber to think how these government studied other forms of oppression to make sure they did it best for their desired outcome. And even nowadays, knowing what we do, I would presume they’re still Reminence of these laws in affect or even seen in culture..

He doses a great job developing his mother as a character and giving enough context So Also readers can understand her actions and motives behind them..

It’s interesting to see how even today he holds an a satiation with second hand cars and poor out comes, it makes sense with what he outlines here but logically the second hand car in only a minor factor in his misfortune..

eLife Assessment

In this fundamental manuscript, Richter et al. present a thorough anatomical characterization of the Drosophila melanogaster larval pharyngeal sensory system, which is involved in taste-guided behaviors.This study fills a major gap in the larval sensory map, providing a detailed neuroanatomical foundation for future investigations into sensory circuits and behavior. The exceptional data will significantly enrich the field of Drosophila neurobiology.

Reviewer #2 (Public review):

Summary:

The authors wanted to achieve a detailed ultrastructural reconstruction of the gustatory sensory organs in the Drosophila pharynx. Using serial EM and the associated bioinformatics tools they have achieved their goal.

Strengths:

Given the dataset, finding presented are solid and will be an important work of reference for the future.

Comments on revised version.

The authors have well responded to my previous comments and added text and figure material.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors provide a detailed ultrastructural analysis of the larval pharyngeal sensory organs, including the dorsal pharyngeal sensilla, dorsal pharyngeal organ, ventral pharyngeal sensilla, and posterior pharyngeal sensilla. Using electron microscopy and 3D reconstruction, Richter et al., present a comprehensive mapping and classification of pharyngeal sensory structures, defining the morphological type of pharyngeal sensilla based on ultrastructure and generating a neuron-to-sensillum map. These findings significantly advance our understanding of internal larval sensory systems and establish a robust framework for future functional studies in coordination with external sensory systems.

Strengths:

The application of high-resolution electron microscopy and 3D imaging analysis successfully overcomes technical challenges associated with visualizing deep internal structures. This enables an unprecedented level of anatomical detail of the larval pharyngeal sensory system. Thus, the study complements and completes existing maps of larval sensory circuits, contributing a comprehensive neuroanatomical characterization of larval sensory input pathways. These insights will inform future studies on larval behavior, sensory processing, and may also have applied relevance for insect control strategies.

Weaknesses:

While the manuscript is concise, clearly written, and methodologically rigorous, it primarily addresses a specialized readership with expertise in insect neuroanatomy.

We thank the reviewer for the positive assessment of our study and for the helpful suggestions. In response, we have clarified the visual presentation of the pharyngeal sense organs in Figure 1, expanded the discussion of adult pharyngeal sensory systems, briefly broadened the comparison to other insect species, checked and corrected the scale bars, and added further methodological detail where appropriate.

Reviewer #2 (Public review):

Summary:

This manuscript documents the structure of the pharyngeal nervous system of the Drosophila larva. The authors wanted to achieve a detailed ultrastructural reconstruction of the gustatory sensory organs in the Drosophila pharynx. Using serial EM and the associated bioinformatics tools, they have achieved their goal. The paper is written clearly and illustrated beautifully with 3D models and annotated sections. The data will significantly enrich the field of Drosophila neurobiology.

Strengths:

Given the dataset, the findings presented are solid and will be an important work of reference for the future.

Weaknesses:

Previous work, including EM, on the pharyngeal sensory organ is not sufficiently referenced and used for comparison with the data presented in this study.

We are grateful for the reviewer’s thoughtful comments and for the suggestion to strengthen the historical and comparative context of the work. We have revised the introduction to better acknowledge and discuss the relevant previous EM-based literature on adult and larval internal gustatory sensilla, clarified the organization of the shared pore structure in T1–T3, highlighted the DPO multidendritic neurons more explicitly, and added a comparison that emphasizes the added value of the complete serial EM dataset.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) For improved clarity, highlight the pharyngeal sense organs in Figure 1B. Consider using the color schemes to differentiate between peripheral and internal sensory organs.

We thank the reviewer for this helpful suggestion. We have revised Figure 1 to more clearly separate the pharyngeal sense organs from the external sense organs in the head region. This revision improves visual clarity and accessibility for readers.

(2) In reference to lines 80-84, expand the discussion to address how future studies could explore the conserved morphological and functional characterization of the adult pharyngeal sensory system.

We appreciate this suggestion and have expanded the discussion accordingly. We now briefly address how future work could compare the larval and adult pharyngeal sensory systems to examine conserved morphological and functional features.

(3) To broaden the manuscript's appeal and emphasize its relevance beyond Drosophila, briefly discuss similarities, differences, or conserved roles of pharyngeal sensory systems in other insect species.

Thank you for this valuable recommendation. We have added a paragraph placing the Drosophila pharyngeal sensory system in a broader insect context, including similarities, differences and potential conservation across species.

(4) Recheck the scale bars in all figures, including the supplemental material.

We thank the reviewer for pointing this out. We carefully rechecked all scale bars across the main and supplemental figures and corrected the missing ones.

(5) Consider including additional details on image processing or provide appropriate citations for further reading.

We appreciate this suggestion. We have expanded the methods section to include additional information on technical details and provide the relevant reference for further reading.

Reviewer #2 (Recommendations for the authors):

(1) Line 57ff: The previous literature describes internal gustatory sensilla in considerable detail.

(a) Adult: These sensilla form three complexes, the labral sensory organ, and the ventral and dorsal cibarial sensory organ (Nayak & Singh, 1983, 1985; Singh, 1997; Stocker & Schorderet, 1981; Kendroud et al., 2017). The work by Nayak and Sing includes TEM and presents detailed EM-based schematics. This should be referenced and discussed.

(b) Larva: Gendre et al. 2004, describes the internal gustatory organs and relates them to their adult counterparts:

- Dorsal pharyngeal sense organ (DPS) and dorsal pharyngeal organ DPO) are the forerunners of adult labral and ventral cibarial sensory organs

- Posterior pharyngeal sensory organ (PPS) is the forerunner of the adult dorsal cibarial sensory organ

- Ventral pharyngeal sensory organ (VPS), derived from the labial segment, undergoes apoptosis during metamorphosis

This work, connecting larva and adult (and containing detailed diagrams comparing adult and larval pharyngeal sensilla) should be presented in the introduction.

We thank the reviewer for this important comment. We have revised the introduction to better cite and discuss previous EM-based studies of internal gustatory sensilla in both adult and larval stages, and we now place our findings more explicitly in the context of this prior work.

(2) Line 180: the relationship between the ending of T1-T3 in one shared pore, and the individually wrapped sensilla should be explained; maybe a simple diagram would help. I did not understand how it works. Normally, in a gustatory sensillum, you have one or more sensory neurons, surrounded by thecogen, trichogen, and tormogen cells. The trichogen generates the shaft with the pore at its tip. Now here, in T1-T3, you have three sets of thecogen/trichogen/tormogen. Do all three trichogen cells somehow participate in the shaft with the common pore? Or only a single one, and the other two generate no shaft? It is possible this cannot be resolved, but the authors should address the problem and suggest a possible scenario.

We appreciate the reviewer’s concern and agree that this point required clarification. We have revised the relevant text to better explain the organization of T1-T3 and their shared pore and the organization of the support cells.

(3) Line 205: the DPO multidendritic neurons with dendrites into the hemolymph should be shown; in Figure S4G, I could see only cell bodies. These MD neurons in the gustatory system are, I believe, a true novelty and should be emphasized more if the material allows (text figure!)

Thank you for highlighting this point. We have revised the results and supplementary material to show these neurons more clearly and to emphasize their novelty and potential relevance to the pharyngeal sensory system.

(4) A somewhat detailed comparison between the ultrastructure of the DPS as extracted from the serial EM stack of this study, and the conclusions of Nayak and Singh 1983 as depicted in their diagram Figure 7a would be productive. The idea being: what additional details can (only) a complete EM stack provide, compared to conventional EM.

We appreciate this suggestion. Rather than directly comparing larval and adult structures in detail, we now emphasize what the complete serial EM dataset adds beyond conventional single-section EM, namely a more comprehensive and complete reconstruction of the sensory organs and associated cell types (multidendritic neurons, papilla sensilla, and chordotonal organs that were not described before, organization of support cells)

(5) To round off the work and connect it to the previously published analysis of gustatory terminal arborizations and connectivity in the brain (Miroschnikow et al.,2018), it would be helpful to add an analysis of the distribution of axons from the different sensilla in the nerves. Miroschnikow analyzes the central terminations of the same sense for which the peripheral structure is described here, only that in their L1 connectome, the periphery was cut off. Do the findings of the current study match their predictions, as to the number of sensory neurons, etc? It should be possible to follow, even at the lower resolution of the dataset presented here, to follow axons of sensory neurons through the nerves to the neuropil entry, and thereby make the connection. I consider this to be of great importance for the field, for authors who want to use the data of this study, and the Miroschnikow et al analysis, for their own studies.

We thank the reviewer for this thoughtful and constructive suggestion. We fully agree that linking the peripheral sensory anatomy described in this study to the central projections analyzed by Miroschnikow et al. would be highly valuable and of broad interest. However, a systematic analysis of axon distributions from the different sensilla through the nerves to their neuropil entry points is beyond the scope of the present work. Owing especially to the dataset’s resolution and inherent limitations, tracing the connections from sensory organs through the nerves to their projections in the brain is technically highly challenging and extremely time-consuming, since much of the process would need to be performed manually. We therefore do not include a detailed comparison with the predictions from Miroschnikow et al. in this manuscript. Nevertheless, we appreciate that such an analysis would be an important next step for the field and a useful resource for future studies.

We are grateful for the reviewers’ thoughtful feedback, which has helped us improve the manuscript substantially. We hope that the revised version addresses the concerns raised and better conveys the significance of our work.

Fix ai tics language like this

Link and tooltip explain what this question is, also give an in-text abbreviated meaningful name for this question

lets just say VPN, nothing else

do we need this here? this is the english study. if anything talk about labels in general, not german.

Why aren't we showing the AI answer like we did everywhere else? Ideally we show article on the left → AI answer on the right. Or do we not have the evidence in a screenshot?

remove

I wonder if this section makese the study stornger or weaker. "This is the affiliate model, not advertising: these publishers test products and earn a commission on purchases." I mean... are these articles incentivized by different advertisers? we prob can't know... do you think the section should stay as is?

Dont think it's clear when you say German half of the courpus. I dont think we have until now discussed how we counducted the study in German as well. Maybe you say that earlier in some place above where it fits?

Remove

Visually this doenst look great. COuld be better aligned.

us masterheads as a term clear to everyone? i didn't know it but Im also not a native english speaker

Remove.

💻/asus/🧊/me/📓/2026/9/📁/🎞/@/youtube.com/All You Need is Love：Peter Russell - YouTube (14_09_2026%2014：07：24).html

eLife Assessment

This study presents a potentially valuable approach to generate a systems vaccinology framework for enabling the design of optimized poxvirus-based MVA vaccines. The developed Boolean modelling framework provides a novel computational approach to predict immune response dynamics against vaccine candidates, which is validated against previously published data and used to test/predict the response for new genetically modified MVA vaccine candidates. However, the strength of evidence is currently incomplete, as key aspects of model construction, calibration, interpretation, validation, and reproducibility, as well as comparability of the used vaccine candidates, require further clarification and supporting information.

Reviewer #1 (Public review):

Summary:

The manuscript "A predictive systems vaccinology framework enables rational optimization of MVA-based vaccines" by Deman and co-workers presents an approach to use Boolean models for the optimization of MVA for vaccinations. Different Boolean models are derived/inferred to perform in silico testing, e.g., of knock-outs.

Strengths:

The optimization of vaccine platforms is very important, and model-based approaches have proved a powerful framework for in silico testing. As far as I'm aware, this is the first time a comprehensive Boolean model is used for this. The authors make an effort to inform this model from available information and experimental data, using state-of-the-art calibration pipelines.

Weaknesses:

(1) Lines 154-158: "Because certain biological processes represented in KEGG (e.g., phosphorylation or ubiquitination) do not have direct logical equivalents, this conversion of signaling pathways into a Boolean network can lead to information loss and disconnection of nodes from the rest of the network. To mitigate this issue, we reconnected isolated nodes back to the main structure using oriented protein-protein interaction (PPI) data from 69, thereby restoring connectivity while preserving directionality of regulation." It is not clear to me how the reconnection addresses the described issue that not all processes can be represented in the selected modelling framework. In this context, I would also appreciate it if the authors could clarify the meaning of your states. Is it the presence of a protein (relating to low/high abundance), the activation status (relating to low/high phosphorylation), or a combination? Depending on this, different Boolean representations should be chosen, and different process information can be used.

(2) Lines159-160: "To enhance immediate readability and interpretability, we connected the resulting network with the corresponding cellular population abundances analyzed by cytometry in the samples." I would appreciate it if the authors could clarify how the cellular layer and the population layers were connected. Is this related to proliferative potential?

(3) Line 168++: It is unclear to me which parts of the Boolean network described in the section "Boolean naïve network construction" have been calibrated. Among other things, it would also be interesting to know how many logical expressions were changed by ZhegAlCal compared to the naive model and how these expressions were selected. Is there a regularization aiming to minimize the number of changes? In this context, I would also appreciate a clarification of the data processing. The current text mentions a 20% change compared to baseline, a threshold of 0.05, and a 2-means clustering strategy, yet it is unclear how they interact to obtain the binarized training and validation data.

(4) Line 267++: The model constructed by the authors describes cell-level processes in infected cells. Yet, the data used in the study - which have previously been published in reference 47 - seem to rather capture population averages over heterogeneous, partially non-infected cells. It is unclear to me why / how this can be compared. I would appreciate a clarification, potentially including a more detailed description of the employed datasets.

(5) Lines 758-759: "The networks generated and analysed during this study are publicly available in the CellCellective repository (MVA 3 pathways, MVA 6 pathways, YF17D)." I searched for the research but did not find it. In my opinion, it would be important to make the models as well as the implementations for calibration, etc. available. Without this, value and reproducibility are limited. I would encourage the authors to provide a detailed human-readable model description in the supplement.

(6) Lines 783-785: The GO analysis seems to be performed in comparison to the human genome. Yet, the model contains only 200 nodes, so a substantially reduced fraction. I was wondering if this was considered in the analysis process and if the authors checked how often the enrichments for multiple pathways were driven by the same genes.

(7) Figure 3: It appears as if the number of considered "network updates" was set to 10 (0 to 9) and that this somehow maps to the experimental time. Yet, the experimental observation times are far from uniform.

Reviewer #2 (Public review):

Summary

Boolean network modeling is more commonly used in cancer and developmental biology than in vaccine research. Deman et al. apply this framework to a practical vaccinology problem: they aimed to build a mechanistic, executable computational framework capable of both explaining and predicting how the early innate immune response to the MVA vaccine changes when specific viral genes are altered, with the longer-term goal of using that framework to guide the rational design of improved MVA-based vaccines. The authors aimed to: (i) construct and calibrate a Boolean network model of the MVA-induced immune response against real longitudinal non-human primate (NHP) data; (ii) test whether the calibrated model, without being fit to this new data, could reproduce the outcomes of several previously published MVA gene-deletion mutants; and (iii) compare this MVA model to an analogous model of the well-established YF-17D yellow fever vaccine, in the hope of identifying specific molecular targets that could reorient the MVA response toward the durable, single-dose protection YF-17D is known to provide.

In pursuit of these aims, the authors construct an executable Boolean network of the innate immune response to the MVA vaccine by merging three KEGG pathways (cytosolic DNA-sensing, apoptosis, NF-κB signaling) with cell-population data, and calibrate it against a small NHP dataset (n=3 macaques, 7 time points; Rosenbaum et al., ref. 47). They show the calibrated network reproduces 86-87% of the observed binarized states, and that forcing the network to mimic known MVA deletion mutants (e.g., the triple mutant ΔC6L/ΔK7R/ΔA46R) reproduces qualitative features (e.g., early IFN-β, TNF-α, and IL-6 upregulation) reported in independent published mouse and cell-line studies. They then build a second, six-pathway "consensus" network shared between MVA and YF-17D, compare the two networks' topology and dynamics, and use this comparison, together with a graph-theoretic search for "highly effective" signaling paths, to propose two previously untested MVA deletion mutants (ΔK7R and ΔF17R) predicted to shift the MVA response toward more YF-17D-like features.

Strengths

The overall workflow (Figure 1) is clearly described. The calibration approach-binarizing longitudinal cellular and transcriptomic data and fitting network trajectories with the ZhegAlCal algorithm (a Zhegalkin-polynomial/SAT-solving-based method for fitting Boolean trajectories to binarized time-series data)-appears to be a defensible, well-reasoned way to translate a literature-derived network into real kinetic data.

The retrospective validation against independent published MVA mutants (deletions in C6L, K7R, A46R, and N2L, and separately an A21L point-mutant with three alanine substitutions rather than a deletion) is a genuine strength: the model's qualitative behavior (upregulation of IFN-β, TNF-α, IL-6; limited change in RIG-I) aligns with what those studies reported. We also appreciated that the authors report instances of partial disagreement alongside their successes (e.g., CCL5/RANTES) - that kind of candor about where the model doesn't quite line up is exactly what gives the parts that do line up more credibility.

The static topological analysis - hub identification, "determinative power" and "vertex betweenness" (two complementary measures of how much a node's state constrains, or lies on paths between, the rest of the network), and "effective graphs" (a measure of how deterministically an edge's regulator sets its target's state) - is a thoughtful use of graph theory to complement the dynamic simulations.

The authors also clearly discuss the Boolean formalism's core approximation, i.e., that binary on/off states can dilute real but subtle quantitative differences (lines 679-688), and that this work was based on modeling blood-only responses rather than those responses that occur at the vaccination site or draining lymph nodes (lines 709-714).

Weaknesses

A few things gave us pause as we read, which we raise here in the spirit of strengthening what already strikes us as a promising framework.

The MVA/YF-17D comparison starts from two vaccines that are already known to differ substantially. The manuscript uses the divergence between the MVA and YF-17D Boolean networks as an entry point for identifying "MVA optimization" opportunities, but MVA and YF-17D are, on their face, very different vaccine platforms. MVA is a non/limited-replicating DNA poxvirus vector, sensed mainly through cytosolic DNA/cGAS-STING pathways, dosed intradermally, and typically requiring two doses for optimal protection. YF-17D, by contrast, is a live, replicating, attenuated RNA flavivirus, sensed through multiple TLR/RIG-I pathways, and given as a single subcutaneous dose that confers durable, often lifelong, protection (see refs 21, 31, 38-44 in the manuscript). Given this, it's not surprising that the authors themselves report "almost opposite behaviors" for several core cell populations - classical monocytes, B cells, NK cells, and CD4/CD8 T cells - between the two calibrated networks (lines 647-657).

This stated motivation made us question how much of that divergence reflects a real, actionable difference in vaccine-induced immune programming (the paper's implicit premise) versus differences in virus biology, dosing route, or study/technical design (different sampling schedules, microarray vs. RNA-seq, n=3 vs. n=12 animals) that a Boolean network comparison can't easily tease apart. To their credit, the authors' Discussion is candid on this point-stating directly that "no clear optimization strategies for the MVA viral vector came to mind from the comparison" (lines 664-666)-a useful signal that the comparison's direct yield was limited. The two candidate mutants that emerged instead came from intersecting the model's high-impact nodes with a pre-existing, literature-curated list of MVA immunomodulatory genes (yielding five candidate deletions), which were then individually simulated and narrowed down to the two, ΔK7R and ΔF17R, that produced notable changes - not from the MVA/YF-17D comparison alone.

The motivation for choosing Boolean modeling over ODE/PDE approaches could be clearer. The choice is motivated mainly by precedent - the authors note it "has rarely been used to model vaccine-induced immune responses, in contrast to statistical modeling or ordinary differential equation (ODE)-based modeling" (lines 100-105) - and by practical considerations, such as not requiring kinetic parameters and being tractable at the scale of networks with hundreds of nodes. What we found ourselves wanting was a more explicit account of the trade-off: ODE and PDE models already simplify the true, spatially resolved, continuously varying underlying biology, and Boolean modeling is a further simplification on top of that. A clearer statement of why this additional simplification is acceptable, or even preferable, for this application (for example: the scale of the curated network, the absence of measured rate constants for most edges, or the interpretability of discrete states) would greatly improve this work.

We found the manuscript dense and long relative to the size of its central, generalizable findings. This is a presentation issue rather than an evidentiary one. The Results section narrates GO-enrichment interpretation update-by-update for four separate network trajectories (unperturbed MVA, perturbed MVA mutants, the MVA arm of the consensus network, and YF-17D), much of which restates what the (extensive) supplementary figures already show.

The paper's only prospective predictions are experimentally untested. The two new mutants proposed at the end of the paper (ΔK7R, ΔF17R) are in silico predictions only; they haven't been constructed or tested experimentally in this study. We read the title's claim of a "predictive" framework and the paper's "rational design" framing as best describing a hypothesis-generation tool validated by retrodiction of previously published phenotypes, rather than a demonstration that these two newly proposed mutants will behave as predicted in vivo.

Did the authors achieve their aims, and do the results support their conclusions?

Looking at each aim in turn, the picture that emerges is mixed. The first aim - building and calibrating a Boolean network model of the MVA-induced immune response - is convincingly achieved: the calibrated network fits the underlying data well (86-98% of binarized states correctly reproduced, depending on which of the two networks is considered), and its successive states correspond to biologically sensible processes (early chemotaxis, then T-cell activation, then antiviral/ROS-related signatures) that track what is independently known about the innate response to poxvirus vaccination. The second aim-showing the calibrated model can reproduce, without being fit to it, the outcomes of previously published MVA mutants-is also substantially achieved, with the caveat noted above that agreement is qualitative and directional rather than exact, and not uniform across every marker tested.

The third aim is where the results, in our reading, support the paper's conclusions least well. The stated purpose of comparing the MVA and YF-17D networks was to identify actionable strategies for reorienting MVA's response, but the authors themselves report that the comparison alone yielded no clear optimization strategy (lines 664-666); as noted above, the two candidates that are ultimately proposed came instead from that separate gene-list intersection and simulation step - not from the YF-17D comparison in the more direct way the framing implies. Given that, the paper's headline conclusion - that this framework "enables rational optimization of MVA-based vaccines" - reads to us as only partially supported by what is actually shown: the framework is well demonstrated as a tool for capturing and reproducing known immune biology, but its capacity to prospectively guide the design of a better vaccine remains, at this point, an untested hypothesis rather than a demonstrated result.

Likely impact and utility to the community:

The most durable contribution of this paper, regardless of how the two specific candidate mutants eventually fare in the laboratory, strikes us as methodological: the explicit workflow for merging curated signaling pathways into a large executable Boolean network and calibrating it against longitudinal experimental data (building on the authors' own previously published calibration method, reference 75) is clearly described and, together with the deposition of the calibrated networks on the public CellCollective platform, should be usable by other groups working on other vaccines or viral vectors. That reusability is a genuine and useful contribution to the systems-vaccinology toolkit, independent of whether MVA specifically benefits from it.

Its more immediate, practical utility is harder to gauge from the paper alone. For vaccine developers specifically interested in MVA, the value of this work currently lies in the two testable hypotheses it generates (ΔK7R, ΔF17R) rather than in validated design guidance, since neither candidate has been built or tested here. It's also worth flagging that the framework's generalizability beyond MVA and poxviruses is untested within this paper - the approach is demonstrated for one vector and one comparator vaccine, so readers working on other vaccine platforms may want to treat it as a promising template to adapt and validate for their own systems, rather than as a result that has already been shown to transfer.

They would be patient, knowledgeable, kind and easy to talk to.

"Dear Professor __"

Be prepared- go over notes and write down the questions you want to ask.

Establishing a "mentor" relationship. Could possibly ask for a reference letter in the future. They may know of more jobs or internships.

eLife Assessment

This study presents a valuable finding on a potential new regulatory mechanism for cellular proliferation in the mammalian cochlea, taking advantage of the organoid-forming potential of cells from the greater epithelial ridge (GER), or Kolliker's organ, a transient structure in the developing cochlea. The authors highlight three potential regulators - galectin 1, galectin 3, and Myc - providing solid evidence for potential roles in modulating the proliferation of cochlear GER cells using gene expression studies, single-cell profiling, pharmacological inhibition, and genetic overexpression both in vitro and in vivo. However, some of the analyses and interpretations are incomplete, and a better understanding of how the compounds and the gene overexpression studies affect the cells and modulate cell survival/death would aid a fuller interpretation of the data.

Reviewer #1 (Public review):

Summary:

The overall aims of this study are a bit unclear. The first experiments use organoids derived from cochlear GER cells in combination with single-cell RNA-seq to try to identify factors that might be important in the initiation of cellular proliferation, although the definition of proliferation is a bit loose and includes the number of organoids, the size of organoids, cell viability, and/or expression of Mki67.

Based on those results, the authors chose to focus on galectins 1 and 3 and Myc. The reasoning for these choices is a bit unclear, as their ranks in the DE gene list are 51 and 67, and the fold change for each is less than 2. Regardless, the subsequent experiments use inhibitors to examine the effects of galectins and Myc on proliferation of organoids. The results of these experiments do show an effect for inhibition of Lgals1 and Myc, although not Lgals3, but it was unclear whether the effects of these factors on growth could be separated from toxicity treatment, as both OTC008 and 10058-F4 seemed to lead to cell death.

Next, overexpression of Lgals1, 3 and Myc was actuated in organoids using AAV viruses. The results do show an effect on proliferation, but the results are confusing in that the mRNA expression profiles for two of the transgenes are markedly different in terms of timing, which would not be predicted based on similarities in the constructs. Also, while showing comparable results in some assays, the Myc vector is apparently toxic, killing ~25% of the cells by D9 even though mRNA levels are steady between D5 and D9 in those cells.

Finally, an in vivo model is used to kill several different types of cochlear cells followed by inhibition of Lgals1. The results of these experiments show a strong inhibition of expression of Ki67 following treatment with OTX008, which is intriguing. However, OTX008 was administered IP, and it does not appear that the ability of OTX008 to cross the blood-labyrinth or even blood-brain barrier has been examined. So it isn't clear whether the results of these experiments indicate a direct or indirect role for OTX008 and galectin-1 in cochlear proliferation. These issues need to be addressed.

Strengths:

The results present evidence for potential roles for galectins and myc in the modulation of proliferation of cochlear GER cells. In vitro and in vivo approaches are combined with single-cell profiling to provide a comprehensive analysis.

Weaknesses:

(1) Multiple transgenic mouse lines are used in this study, but there are no citations as to where these lines came from, how they were validated, and, for some inducible Cre lines, when the injections of tamoxifen were made.

(2) Sixty-four organoids were formed per well, but from an average of how many seeded single GER cells? This is not clear (page 5, third paragraph).

(3) Page 6: Why was cluster 7 grouped with clusters 1,2 and 3? Most cluster 7 cells are from D1.

(3) In Figure 3A, there does not appear to be a correlation between expression of either galectin-1 or galectin-3 and expression of Mki67, which I would expect would be predicted if these markers play a role in proliferation.

(4) Figure 3C: A more direct way to examine this would be immunofluorescence for galectin-1 and galectin-3 on cochlear tissue. This would also indicate whether galectin expression correlates with the Sox2+/Fgfr3- population of GER cells.

(5) For the data shown in Figure 4, what were the experimental conditions? In particular, how long in culture? One interpretation of the data in 4B and E is a decreased increase in the number of organoids, but an alternative is that the treatments are toxic and the organoids are dying. Based on a comparison with the results for myc inhibition, isn't cell toxicity in response to treatment with OTX008 or GB1107 the more likely explanation?

(6) I think the data in Figure 5 show that the inhibitor experiment demonstrates that the inhibitors, or their targets, are required for organoid survival, as the number of organoids drops to 0, which must be below the starting value.

(7) It is suggested (page 11, third paragraph) that galectins and myc could be linked or independent effectors of organoids. But couldn't this be tested by combining the inhibitors in the same experiment?

(8) On page 12, it seems AAV infection of the target cell population prevented organoid formation? This could be a major concern. If nothing else, doesn't this suggest that the effects observed in these experiments might be a result of induced organoid formation from other cochlear duct cells? Also, was expression of the transgenes (Lgals or Myc) confirmed in a cell type that is normally negative for those genes?

(9) The data in Figure 6C are confusing. The rate of mRNA expression from the AAV transgene should be comparable regardless of the construct given that the promoter is the same. But the results suggest a significant difference in the behavior of the two vectors, with Myc levels reaching a 15-fold increase in just three days while the Lgals vector is at only half that level after 7 days.

(10) An increase that is not significant is not an increase and should not be described as one (page 13 in the first paragraph).

(11) In the AAV-Myc experiments, the overall level of mRNA for Mki67 on D9 is comparable to that in the AAV-lgals1 AAV (Figure 6B), but 25% of the cells are dead (page 13, first paragraph)? Similarly, in Figures 6E and 6F, the number of organoids in the AAV-Myc samples is significantly larger than in either control or Lgals, but are most of those cells dead, then?

(12) Regarding the isolation process in Figure 7A, I am concerned this will also isolate cells from the stria vascularis? Do they retain a greater potential for growth that might lead to their predominance in the growth assay?

(13) Was the Ki67creERT2 used to label a subset of cells for FACS (page 14)? If not, why was this included? If so, when was the induction made? And doesn't this bias the selection to cells that were proliferating at the time of the induction?

(14) It is stated that "proliferation is most active at P4 with robust cycling of cells observed in the lateral GER". But then on the following page (page 15), it's stated that the single cell data indicates essentially no proliferating cells in the control, even though there are a lot of lateral GER cells. Can the authors give an explanation for this discrepancy?

(15) In the first figures in the study, the isolation approach collected lateral GER cells and identified Lgals and Myc as important for organoid expansion (page 15). In Figure 8, there appears to be no change in Lgals or Myc expression in lateral GER cells in response to the damage. Instead, it is medial GER cells that appear to have increased Lgals1 and Myc. And from Figure 8H, are those increases significant?

(16) A quick search of the literature suggests that there is no evidence that OTX008 can cross the blood-labyrinth or blood-brain barrier (page 15). Was this examined by the authors?

Reviewer #2 (Public review):

Summary:

The study uncovers novel factors driving proliferation in the greater epithelial ridge (GER), a proliferative tissue in the neonatal cochlea that may hold important clues on the quest for hair cell regeneration via proliferative means in the adult cochlea.

Strengths:

The strengths include the use of both cochlear organoids and in vivo mouse models combined with pharmacological and genetic approaches to inhibit or overexpress proliferative targets identified in the RNA-Seq analysis from the FACS-sorted GER cells. The genetic and pharmacologic manipulation experiments are very strong and convincingly demonstrate that galectins 1 and 4 and Myc are necessary (and in some cases sufficient) to drive cell proliferation in cochlear tissue. However, the real treasure trove is the carefully generated RNA-Seq dataset itself, which offers a wealth of additional differentially-expressed genes that likely contribute to cochlear cell proliferation.

Weaknesses:

The primary weakness is that the initial genes studied here, galectins 1 and 3 and Myc, are all associated with tumor formation or cancer progression, so targeting these genes raises concerns about tumor formation in the cochlea.

https://search.brave.com/search?q=peter+russell+global+brain

However, there is trade-off between safety with high levels of capital and the return on equity (ROE) for holders of bank capital

but also diversify the sources of funds.

Balance the need for liquidity against the larger returns from less liquid assets.

Lower the credit risk (i.e., the default risk) of their asset portfolio by diversifying and also lower the interest-rate risk of their asset portfolio.

i.e., diversifying between different types of securities.

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link to financial regulation

it must be (adequacy) in brackets

net worth

corporate, bank and government bonds

(restrictive covenants).

Issuing marketable debt and equity securities (bonds and stocks) is not the primary funding source for businesses.

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reformular como problema de ecuaciones diferenciales y pasar a Práctico 9.

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Antigone not being surprised by Haemon asking her to marry him makes me wonder if she is settling for him. It is clear that she was found sitting by herself while he was dancing with someone else all night.

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💻/asus/🧊/me/📓/2026/9/📁/🎞/@/youtube.com/What＂Mindfulness＂ReallyMeans in the Original Teaching - YouTube (14_09_2026%2009：19：35).html

German translation? Chng to tool or system?

chng to standard practices?

comments like this maybe could be in bold or even highlighted. Again, would stand out as a key takeaway for the reader.

Add with bullet points or numbered list it would stick out better for the reader

Maybe we add this at the beginning?

the following points transition into What we track and isnt smooth for the reader

Maybe make a separate paragraph bc of the transition "It is not.." then to 3 points that are tracked is a bit confusing

stocks

longer than 10 years

What Mindfulness really means What "Mindfulness" Really Means in the Original Teaching - YouTube

eLife Assessment

This is an important study that uses a tripartite transdiagnostic framework to separate depression-specific, anxiety-specific, and shared psychopathology dimensions and relate them to mood variability and mood reactivity to reward prediction errors across several large non-clinical cohorts and a clinical sample. The evidence is compelling: large samples, a well-characterised gambling task, rigorous computational and psychometric analyses (i.e., split-half replication of the factor structure, convergent results with non-orthogonalised factors, an explicitly specified risk-attitude model, diagnostic breakdown and power analyses for the clinical cohort) and replication of the depression-specific blunting of reward prediction error sensitivity in patients. Anxiety-specific associations emerge reliably only when data are pooled and are likely underpowered clinically given co-morbid anxious depression, a constraint the authors now state explicitly. The work advances a mechanistic account of how distinct symptom dimensions shape reward-based mood updating.

Reviewer #1 (Public review):

Summary of strengths:

Thank you very much for giving me the opportunity to review this very interesting paper. The research question is intriguing, allowing to address commonly observed co-morbidities between depression and anxiety and their dissociable and opposite relationship to mood fluctuations and sensitivity to reward prediction errors. The computational analyses are very in-depth, including many state of the art checks and validations. Finally, another strength is the inclusion of several large or very large samples, including a patient sample in addition to the general population sample.

Comments on revised version.

I want to thank the authors for taking the time to answer all my questions. Their answers were very thoughtful and well argued. I found the theoretical explanations very helpful for explaining their approach and ideas further. In particular, it was fascinating to see how including a single non-orthogonalized depression or anxiety scored show no effect, but including them in simultaneously revealed their previously observed patterns.

Reviewer #2 (Public review):

Summary:

Despite their common co-occurrence, depression and anxiety are known to alter mood fluctuations in opposite ways. Here, the authors aimed at distinguishing depression-specific from anxiety-specific from psychopathology-general effects of reward processing on mood fluctuations, focusing on reward prediction errors (RPE) which are known to be linked to mood fluctuations. This mechanistic study aims at uncovering the process through which these psychopathologies are associated with mood modulations. The authors were able to appropriately test their hypothesis and obtained results corroborating their conclusions.

This work provides a convincing demonstration of the relevance of computational psychiatry (Huys et al, 2016) and the use of decision neuroscience to shed light on the interplay of anxiety and depression and mood.

Comments on revised version.

(1) Methodological & Theoretical Framework: The authors used a tripartite model to effectively distinguish depression vs anxiety dimensions from broad psychopathology/distress.

(2) Possible theoretical confounds: This manuscript addressed adequately the concerns one would have regarding risk-attitudes.

(3) Computational Rigor: The computational model elegantly separates reward expectations (EV in the model) from outcome processing through RPE, which are two sequential cognitive processes, providing a fine-grained mechanistic account of mood fluctuations.

(4) Clear & Logical Results Structure: In response to feedback provided during the previous round of review (previously cited as a recommendation for authors), the authors re-organized the Results section into three distinct, easy-to-navigate subsections (Depression, Anxiety, and Depression vs. Anxiety), which substantially improves readability and clarity.

(5) Neurobiological Context: The Discussion has been enriched with a well-integrated overview of the neural circuits (striatal-midbrain dopaminergic, vmPFC, OFC, and anterior insula) likely underpinning RPE-driven mood updates, which is sure to improve the translational interest of this work.

(6) Transparent Reporting: The authors had already provided a trustworthy writing approach when referring to trending statistical results. In this revised manuscript, they have been exceptionally transparent regarding study limitations, data collection timelines (addressing potential AI-related artifacts), and statistical power constraints.

Status of Previous Weaknesses & Suggested Revisions

(1) Clinical Sample Size and Anxiety-Specific Effects

Previous Concern: The sample size of the clinical sample (N=116) may not be sufficient to detect anxiety-specific effects due to the high rate of comorbid anxious depression. It would be beneficial to include the number of MDD vs GAD vs anxious depression diagnoses in the clinical population as this would be likely to shine light on the power limitations.

• Author Revision: The authors have fully addressed this point by adding a diagnostic breakdown in Table S8 which details the diagnosis, illness duration, and medication status. They also included details of the power analysis (Discussion, pages 17-18) putting into perspective their results and provided two possible literature-informed interpretations of their findings. This is also reflected in their updated Abstract.

(2) Re-organization of Results

Previous Suggestion: The results sections 2 (depression) and 3 (anxiety) could be improved by reducing the back and forth between factors throughout the results. It may be useful to split them into 3 sections: depression only, anxiety only, depression vs anxiety.

• Author Revision: The Results section was restructured as suggested, cleanly isolating depression-specific associations, anxiety-specific associations, and direct statistical comparisons between the two ("differential associations" in the manuscript).

(3) Neurocircuitry Discussion

Previous Suggestion: In the discussion, authors could have mentioned the brain areas most likely to be involved in these processes, both cognitive and psychopathological, as previous studies (such as Cecchi et al, 2022) have aimed at identifying regions involved in RPE processing while modulating mood in health. A short section on this would be useful to the neuropsychiatric community.

• Author Revision: A concise section was added to the Discussion mapping computational parameters onto striatal, prefrontal, and insular circuitry (see Strengths #5).

Conclusion:

The authors have satisfactorily resolved all minor-to-moderate issues raised in the previous review.

Reviewer #3 (Public review):

Summary:

In this submission Wang and colleagues jointly examine the association between depression and anxiety symptoms and individuals' affective reactivity to reward prediction errors in Ruttledge et al.'s gambling paradigm. Taking a bifactor approach to anxiety and depression in several non-clinical (and one clinical sample), the authors find that anxiety-specific symptoms relate to over-reactivity of mood to reward prediction errors (RPEs) as well as heightened mood variability , while depression-specific symptoms relate to blunted mood sensitivity to RPEs. These depression-, but not-anxiety specific relationships replicated in patient samples.

Strengths:

I was impressed that the data-driven, transdiagnostic approach employed by the authors uncovered specific relationships between anxiety and depression-specific factors and RPE reactivity in a well characterized task and computational model, especially in a non-clinical sample. This sheds new light on how these affective processes may be perturbed-and importantly, in different ways-by anxiety and depression symptoms. Likewise, the replication of the depression-specific finding (RPE hypo-reactivity) in a clinical sample was nice to see.

Weaknesses:

While the anxiety- and depression-specific factors had differential effects on mood variability (Fig 2A-D) and RPE reactivity (Fig 2E-G) in all samples, such that the correlations between the two factors and these mood parameters were significantly different, the anxiety factor was not consistently (significantly) associated with either mood-related parameter across samples. However, the authors resolve anxiety-specific predictive effects when they collapse across datasets. While it is intuitive that achieving a larger effective sample size would afford the power necessary to detect such individual differences, this struck me as a major caveat for this set of results.

The associations the authors observe between the 'common factor' of depression and anxiety and risk-aptitudes tendencies-presumably the alpha (exponent) parameter in a prospect theory-type subjective value model. But where is this analysis explained? (i.e. how was this model formulated and how were risk attitude parameters estimated?) And what is the interpretation of this finding-is there precedent for looking at risk attitudes in this task? And why would these predictive effects only be observed in relation to the common, but not unique factors of anxiety and depression?

Comments on revised version.

I believe the weaknesses identified in the previous round of review have been adequately addressed by the authors, and my suggestions concerning clarity of presentation have by and large been implemented by the authors.

Author response:

The following is the authors’ response to the original reviews.

eLife Assessment

This important study uses a tripartite transdiagnostic computational framework to distinguish depression-specific, anxiety-specific, and shared psychopathology dimensions, in their relationships to mood variability and mood reactivity to reward prediction errors across multiple large non-clinical cohorts and a clinical sample. The evidence is convincing overall because the study combines large samples, a well-characterized gambling task and in-depth computational and psychometric analyses, and it replicates the depression-specific association with blunted reward prediction error-sensitivity in a clinical sample. However, the anxiety-specific effects are less consistently supported across individual datasets, may be underpowered in the clinical cohort because of comorbidity, and some aspects of the factor-analytic, risk-attitude, and mediation analyses would benefit from clearer explanation. These findings advance a mechanistic account of how distinct symptom dimensions differentially shape reward-based mood updating and variability, providing a principled framework for future transdiagnostic modeling.

We thank the editors and reviewers for this important assessment.

Regarding inconsistent results for anxiety-related effects in healthy datasets. Although the anxiety-specific factor showed associations in the expected direction across healthy datasets, these associations were not significant in several individual datasets. Specifically, anxiety-specific scores were positively correlated with mood variation (laboratory dataset: r = 0.10, p = 0.531; online dataset 1: r = 0.08, p = 0.026; online dataset 2: r = 0.19, p = 0.004) and with RPE-related mood sensitivity (laboratory dataset: r = 0.04, p = 0.820; online dataset 1: r = 0.05, p = 0.216; online dataset 2: r = 0.19, p = 0.004; Figures 2A–C and 2E–G). This pattern may partly reflect limited statistical power at the single-dataset level. Because these datasets used comparable task and questionnaire procedures and showed positive effect directions, we conducted pooled analyses to obtain a more stable estimate. Importantly, these analyses included dataset as a random intercept in mixed-effects models to account for between-dataset differences. Thus, the pooled analysis provides an integrated estimate across samples, conceptually similar to an individual-participant-data meta-analytic approach. The pooled results provided evidence for the expected anxiety-specific associations with greater mood variability and heightened RPE-related mood sensitivity in non-clinical participants (mood variation: t = 3.46, p < 0.001; RPE-related mood sensitivity: t = 2.60, p = 0.009). In addition, we conducted a mini meta-analysis, and results support that anxiety is associated with intensified mood fluctuations and increased mood sensitivity to RPE in non-clinical participants. We have clarified this point below:

Pages 10-11:

“Correlations between the anxiety-specific factor and mood variation were positive in direction across datasets, although they were not statistically significant in several datasets (the laboratory dataset: r = 0.10, p = 0.531; the online dataset 1: r = 0.08, p = 0.026; the online dataset 2: r = 0.19, p = 0.004). Similarly, correlations between the anxiety-specific factor and β_RPE were positive in direction but statistically inconsistent across datasets (the laboratory dataset: r = 0.04, p = 0.820; the online dataset 1: r = 0.05, p = 0.216; the online dataset 2: r = 0.19, p = 0.004; Figure 2A-C & 2E-G). Because these datasets used comparable task and questionnaire procedures and showed positive effect directions, and because reliable individual differences often require large samples to detect[48], we combined the laboratory dataset, online dataset 1, and online dataset 2 (total N = 1,026). This approach is analogous to an individual-participant-data meta-analytic analysis. We fitted linear mixed-effects models predicting mood variation and β_RPE from the three bifactor scores, with dataset included as a random intercept to account for dataset-level variability. For mood variation, the anxiety-specific factor was positively associated with mood variation (t = 3.46, p < 0.001), whereas the depression-specific factor was negatively associated with mood variation (t = -6.13, p < 0.001). For RPE-related mood sensitivity, the anxiety-specific factor was positively associated with β_RPE (t = 2.60, p = 0.009), whereas the depression-specific factor was negatively associated with β_RPE (t = -5.30, p < 0.001). These associations remained significant after controlling for gender, age, task earnings, and mood drift. In addition, we performed a mini meta-analysis on these correlation coefficients[49]. Results showed significant positive correlation for both mood variation and RPE-related mood sensitivity (mood variation: Z = 3.399, 95 % CI for correlation coefficient r [0.045, 0.166]; RPE-related mood sensitivity: Z = 2.618, 95 % CI for correlation coefficient r [0.021, 0.143]), supporting that anxiety is associated with intensified mood fluctuations and increased mood sensitivity to RPE in non-clinical participants.”

We also admit that the anxiety-related effects were less robust than the depression-related effects and were detectable only in the pooled dataset (n = 1,026); therefore, they require further replication in larger samples.

Page 17:

“Notably, the anxiety-related effects were less robust than the depression-related effects and were detectable only in the pooled dataset (n = 1,026); therefore, they require further replication in larger samples.”

Regarding be underpowered sample size in the clinical cohort. We agree that the clinical sample may have been underpowered to detect anxiety-specific effects, especially given the high comorbidity between anxiety and depression in affective disorders (Table S8). Based on the effect size observed in the non-clinical datasets (r = 0.079), we estimated that a sample size of 1,226 would be required to detect this effect with 80% statistical power using a two-tailed test with α = .05. This estimate is substantially larger than the current clinical sample size (n = 116). Although these covariate analyses support the robustness of the depression-related effect, they do not resolve whether the absence of the anxiety-related effect reflects limited power or true clinical discontinuity. We also revised the Discussion to explicitly acknowledge that the anxiety-related effect observed in the pooled non-clinical dataset was not replicated in the clinical sample. We now note two possible interpretations. First, this discontinuity may reflect limited statistical power in the clinical sample. Second, and more speculatively, it may reflect a disruption of mood homeostasis in affective disorders (Paulus, 2007). In non-clinical individuals, the counterbalancing associations of depression- and anxiety-related traits with mood variation may contribute to emotional equilibrium. In contrast, affective disorders may involve a loss of this regulatory balance, reducing the ability to stabilize mood in the face of competing depression- and anxiety-related affective signals. We have revised the manuscript as follows:

Pages 12-13:

“To test whether abnormalities in RPE-driven mood fluctuations can serve as clinically relevant computational markers of depression- and anxiety-related symptom dimensions, we recruited patients with affective disorders (n = 116) to complete the same questionnaire battery and gambling task with momentary mood ratings (Figure 1). Demographic, psychological, and clinical characteristics are summarized in Table 1 and Table S8. We observed significant negative correlations between depression-specific scores and both mood variation (r = -0.239, p = 0.009) and RPE-related mood sensitivity (β_RPE; r = -0.216, p = 0.020). These associations remained significant after controlling for demographic and clinical covariates, task earnings, and mood drift (ps < 0.05). Bootstrap validation yielded consistent results. Mediation analyses further showed that reduced mood sensitivity to RPEs statistically mediated the association between depression-specific scores and lower mood fluctuations (a × b = -0.141, 95% CI = [-0.261, -0.038], p = 0.021; Figure 3). However, we did not observe significant correlation with anxiety (mood variation: r = -0.092, p = 0.327; β_RPE: r = -0.095, p = 0.311).”

Pages 17-18:

“Notably, the pattern of heightened RPE sensitivity observed in the pooled non-clinical dataset was not observed in the clinical sample. On the one hand, this discontinuity may reflect that the clinical sample was underpowered to detect anxiety-specific effects, especially given the high comorbidity between anxiety and depression in affective disorders (Table S8). Based on the effect size observed in the non-clinical datasets (r = 0.079), we estimated that a sample size of 1,226 would be required to detect this effect with 80% statistical power using a two-tailed test with α = .05. This estimate is substantially larger than the current clinical sample size (n = 116). On the other hand, it may reflect a disruption of mood homeostasis in clinical populations[41,58]. In non-clinical individuals, counterbalancing associations of depression- and anxiety-related traits with mood variation may help maintain emotional equilibrium. In contrast, affective disorders may involve a loss of such regulatory balance, reducing the ability to stabilize mood in the face of competing depression- and anxiety-related affective signals.”

Abstract:

“Results showed that depression was associated with dampened mood fluctuations due to mood hyposensitivity to RPE. Importantly, this pattern was also found in patients with affective disorders. In contrast, anxiety correlated with heightened mood fluctuations stemming from mood hypersensitivity to RPE in non-clinical participants.”

We have also revised the manuscript accordingly to make the factor-analytic, risk-attitude, and mediation analyses clear.

Reviewer #1 (Public review):

Summary:

This is a very interesting paper. The research question is intriguing, allowing the authors to address commonly observed comorbidities between depression and anxiety and their dissociable and opposite relationship to mood fluctuations and sensitivity to reward prediction errors. The computational analyses are very in-depth, including many state-of-the-art checks and validations. Another strength is the inclusion of several large or very large samples, including a patient sample in addition to the general population sample.

I have the following questions:

(1) Factor analysis

I found the hierarchical organization of the factors interesting. While this is a very common procedure in, for example, the field of intelligence (producing sub-scores and a general g factor), it is not yet very commonly used in the field of computational psychiatry (though it has been validated before for anxiety/depression, so it is used here with good reason). I was also impressed by the methodological depth. In particular, it was of note how thoroughly done it was (for example, repeating the EFA on the second half of the data set). I have one question though: is the sample size too small for the exploratory analyses, given the number of items? Given the stability across the half-split, I imagine it is not. Perhaps the authors could spell out how many items, what would be the recommended standard for a subject-to-item ratio, and comment on this. A very technical point, the authors should specify how they extracted the factor scores from the other data sets (is it using the Thurstone or Bartlett method)? From experience (though not doing a hierarchical factor analysis), Bartlett can be somewhat better compared to the default (Thurstone) - better as in the resulting factors more closely recapitulating the factor correlations in the original sample (and independence of responses of other participants in a sample for computing a person's factor score). Could you also comment on similarities or divergences in this hierarchical factor analysis approach from another one recently used transdiagnostically in Wise et al. (2026, Translational Psychiatry)?

We thank the Reviewer for the positive evaluation of our hierarchical factor-analytic approach and for recognizing the methodological depth of our analyses. We are particularly grateful for the Reviewer’s constructive suggestions regarding the participant-to-item ratio, factor score extraction, and the relation between our approach and recent transdiagnostic hierarchical factor-analytic work.

First, hierarchical organization. As the Reviewer noted, hierarchical and bifactor representations have a well-established tradition in intelligence research, where they are used to model the g factor alongside domain-specific abilities (e.g., Reise, 2012; Rodriguez et al., 2016). Crucially, the use of such hierarchical structures in the present study was motivated primarily by theory and evidence from anxiety and depression research, rather than by analogy to intelligence research alone. This tradition can be traced back to the tripartite model of anxiety and depression (Clark & Watson, 1991), which distinguished a broad shared component of general distress or negative affect from more specific anxiety- and depression-related components. Subsequent psychometric work has further supported bifactor and hierarchical representations of anxiety and depression symptoms, including models that separate a general internalizing/distress factor from symptom-specific dimensions (e.g., Simms et al., 2008). More recently, similar hierarchical symptom structures have also been adopted in computational psychiatry to relate shared and specific affective symptom dimensions to task-derived computational parameters (Gagne et al., 2020, 2022; see Wise et al., 2023 for a review). Thus, the bifactor structure used here provides a theoretically motivated way to capture both the variance shared by anxiety and depression and the symptom-specific variance relevant to our computational analyses. We have clarified this point in the revised manuscript as follows:

Pages 3-4:

“Recent work has used bifactor models of the tripartite model of depression and anxiety to clarify their distinct features and differential influences on decision-making[31,32]. The tripartite model of anxiety and depression proposes that these two symptom dimensions share a broad general distress or negative affect component while also including symptom-specific components: low positive affect/anhedonia is more specific to depression, whereas physiological hyperarousal is more specific to anxiety[30,33,34]. Bifactor analysis offers a way to model this structure statistically. In a bifactor model, symptoms load on a general factor reflecting their shared variance and on specific factors capturing residual variance in narrower symptom dimensions after accounting for the general factor. Although bifactor and hierarchical models have long been used in psychometrics, e.g., intelligence research[35,36], their application to anxiety and depression is grounded in the tripartite model and subsequent psychometric work distinguishing general internalizing/distress from symptom-specific dimensions. This framework has recently been extended to computational psychiatry, where shared and specific affective symptom dimensions have been linked to task-derived computational parameters. For example, Gagne et al. (2022) used bifactor analysis to show that depression was associated with weaker prior beliefs, whereas anxiety was associated with a stronger negative bias in belief updatin31.”

Second, the participant-to-item ratio. We agree that the ratio of 450 participants to 128 items in the EFA split-half sample, approximately 3.5:1, is below some conventional sample-size recommendations for exploratory factor analysis, including the often-cited recommendation of five participants per item (Costello & Osborne, 2005). However, as the Reviewer noted, the split-half analysis showed a stable factor structure, and the independent CFA in the other split-half sample further supported the robustness of the solution. Importantly, participant-to-item ratios are only one criterion for evaluating factor recovery. De Winter, Dodou, and Wieringa (2009) demonstrated that reliable EFA solutions may be obtained even with relatively small samples when the data are well-conditioned, such as when factor loadings are high, the number of factors is small, and each factor is defined by multiple items. These conditions were largely met in our data. We have clarified this point in the revised manuscript as follows:

Supplementary Page 3:

“In addition, the ratio of 450 participants to 128 items in the EFA split-half sample, approximately 3.5:1, is below some conventional sample-size recommendations for EFA, including the often-cited recommendation of five participants per item[8]. However, the split-half EFA yielded a stable factor structure, and the independent CFA further supported the robustness of this solution. Moreover, reliable EFA solutions may be obtained even with relatively small samples when the data are well-conditioned, such as when factor loadings are high, the number of factors is small, and each factor is defined by multiple items9. These conditions were largely met in our data.”

Next, factor score extraction. We followed prior work using bifactor modeling in computational psychiatry (Gagne et al., 2020) and extracted factor scores with the Anderson–Rubin method, implemented using psych::factor.scores with method = "Anderson". This approach yields standardized and mutually orthogonal factor scores, which is particularly appropriate for our subsequent correlation analyses because it produces orthogonal scores and therefore avoids multicollinearity among the general, depression-specific, and anxiety-specific factors. In addition, as suggested by the Reviewer, we extracted factor scores using the Bartlett method from an oblique bifactor model, which allowed the depression- and anxiety-specific factors to correlate. In the combined dataset (n = 1,026), anxiety- and depression-specific scores were significantly correlated when extracted using the Bartlett method (r = 0.638, p < 0.001), whereas, as expected, they were effectively uncorrelated when extracted using the Anderson–Rubin method (r < 0.001, p = 1.000). This comparison suggests that the Anderson–Rubin method is more appropriate for our analytic aim of estimating the unique associations of shared and symptom-specific components with task-derived parameters, because it separates the general distress/internalizing factor from the statistically separable residual anxiety- and depression-specific components. For this reason, we retained the Anderson–Rubin factor scores in the main analyses. We have clarified this point in the revised manuscript as follows:

Supplementary Page 3:

“For factor score extraction, we followed prior work using bifactor modeling in computational psychiatry[10] and extracted factor scores with the Anderson–Rubin method, implemented using psych::factor.scores with method = "Anderson". This approach yields standardized and mutually orthogonal factor scores, which is particularly appropriate for our subsequent correlation analyses because it avoids multicollinearity among the general, depression-specific, and anxiety-specific factors. As a robustness check, we also extracted factor scores using the Bartlett method from an oblique bifactor model, which allowed the depression- and anxiety-specific factors to correlate. In the combined dataset (n = 1,026), anxiety- and depression-specific scores were significantly correlated when extracted using the Bartlett method (r = 0.638, p < 0.001), whereas, as expected, they were effectively uncorrelated when extracted using the Anderson–Rubin method (r < 0.001, p = 1.000). This comparison suggests that the Anderson–Rubin method is more appropriate for our analytic aim of isolating the unique contributions of shared and symptom-specific variance, because it separates the general distress/internalizing factor from residual anxiety- and depression-specific components. Thus, we retained the Anderson–Rubin factor scores in the main analyses.”

Finally, the similarities and differences between our hierarchical factor-analytic approach and the recent transdiagnostic hierarchical factor-analytic approach of Wise et al. (2026). Both approaches fit EFA models with different numbers of factors and use cross-level correlations to characterize hierarchical symptom structure. However, Wise et al. (2026) applied this framework to a broader symptom battery covering transdiagnostic and neurodevelopmental dimensions, identifying a hierarchy that included a general psychopathology factor and more specific dimensions such as internalizing, externalizing, inattentive/neurodevelopmental, mood/anxiety, and withdrawal. In contrast, our study focused more narrowly on anxiety and depression dimensions, with the goal of deriving symptom factors that could be linked to task-derived computational parameters. Accordingly, whether the current findings are specific to anxiety- and depression-related symptom dimensions or instead reflect broader transdiagnostic psychopathology or nonspecific response-related variance remains unknown. Future studies should include measures covering a wider range of psychiatric dimensions, such as internalizing, externalizing, inattentive/neurodevelopmental, mood/anxiety, and withdrawal dimensions identified by Wise et al. (2026), to better determine whether the links among symptom dimensions, RPE-related mood sensitivity, and mood variability are disorder-specific or transdiagnostic. We have discussed this point in the revised manuscript as follows: Page 19:

“Future studies should include measures covering a wider range of psychiatric dimensions, such as internalizing, externalizing, inattentive/neurodevelopmental, mood/anxiety, and withdrawal dimensions identified by Wise et al. (2026)[59], to better characterize whether links among symptom dimensions, RPE sensitivity, and mood variability are disorder-specific or transdiagnostic.”

(2) Linking factors to task parameters

As I understand it, the authors relate the orthogonalized depression/anxiety to task parameters (sensitivity to RPEs on mood and mood variations) using correlations. In order to have a better understanding of how this relates to other commonly used approaches, I would pose two questions:

(i) What are the correlations when the full (non-orthogonalized) factor scores for depression and anxiety are used? Are the signs the same?

(ii) What are the results when, instead of the independent correlations, the authors perform b_RPE ~ anxiety + depression (again using the non-orthogonalized factors)? I'm assuming all of these analyses should give the same results if the authors' hypothesis of opposing effects of anxiety and depression holds true.

We thank the Reviewer for these helpful comments. Our original analyses used orthogonalized depression- and anxiety-specific factor scores because this approach is aligned with our analytic aim of separating shared and symptom-specific variance within the tripartite/bifactor framework of anxiety and depression (Clark & Watson, 1991), and has been used in prior work (Gagne et al., 2020, 2022; see Wise et al., 2023 for a review). Orthogonalization allows us to statistically separate the shared distress component from the symptom-specific components of anxiety and depression, which was central to our hypothesis regarding their opposing associations with task-derived parameters. As expected, the orthogonalized anxiety- and depression-specific factor scores were uncorrelated in the combined dataset (r < 0.001, p = 1.000; n = 1,026). By contrast, the full non-orthogonalized depression and anxiety scores retained substantial shared variance and were highly correlated (r = 0.638, p < 0.001; n = 1,026), making their separate associations less straightforward to interpret.

Nevertheless, we agree that analyses using the full non-orthogonalized depression and anxiety scores provide an important comparison with more commonly used non-orthogonal symptom-score approaches. We therefore conducted the analyses suggested by the Reviewer. When the full depression and anxiety scores were entered separately into linear mixed-effects models predicting RPE-related mood sensitivity, with dataset included as a random intercept, the anxiety association was not significant (anxiety: b = 0.002, t = 1.130, p = 0.259; depression: b = -0.008, t = -3.732, p < 0.001). By contrast, when the full non-orthogonalized anxiety and depression scores were entered simultaneously in the same linear mixed-effects model, the original pattern was replicated: anxiety and depression showed opposing associations with RPE-related mood sensitivity (anxiety: b = 0.012, t = 4.619, p < 0.001; depression: b = -0.017, t = -5.851, p < 0.001). This pattern is consistent with a mutual suppression effect: shared variance between anxiety and depression may obscure their unique associations when examined separately, whereas the simultaneous regression model reveals their opposing symptom-specific associations.

Together, these supplementary analyses support our original interpretation that RPE-related mood sensitivity is associated with the separable anxiety- and depression-specific components in opposite directions. We have revised the manuscript as follows:

Supplementary Pages 3-4:

“We further analyzed non-orthogonalized full depression and anxiety scores to assess the robustness of our results. When full depression and anxiety scores were entered in separate linear mixed-effects models predicting RPE-related mood sensitivity, with dataset included as a random intercept, the anxiety association was not significant (anxiety: b = 0.002, t = 1.130, p = 0.259; depression: b = -0.008, t = -3.732, p < 0.001). By contrast, when the full non-orthogonalized anxiety and depression scores were entered simultaneously in the same linear mixed-effects model, the original pattern was replicated: anxiety and depression showed opposing associations with RPE-related mood sensitivity (anxiety: b = 0.012, t = 4.619, p < 0.001; depression: b = -0.017, t = -5.851, p < 0.001). This pattern is consistent with a mutual suppression effect: shared variance between anxiety and depression may obscure their unique associations when examined separately, whereas simultaneous regression reveals their opposing symptom-specific associations. These results support our interpretation that RPE-related mood sensitivity is linked to the separable anxiety- and depression-specific components.”

Minor comments:

(1) The authors should write down when the data were collected for each study. This is because AI capabilities have massively increased since ~2020 in quite specific steps (with the public release of new AI models), meaning that AI is likely to have been able to do tasks and questionnaires without detection if data were collected recently.

We thank the Reviewer for this important comment. We have now added the data collection periods for each dataset in Table 1. The laboratory and clinical dataset were collected in a controlled laboratory setting rather than through online testing. As shown in Table 1, all online experiments were conducted before November 2022, prior to the public release of ChatGPT and its broad entry into public awareness. Therefore, our data were unlikely to have been substantially affected by AI-assisted responding. We have clarified this point in the revised manuscript as follows:

Page 20:

“See Table 1 for demographic information and data collection periods. Because online data collection may raise concerns about AI-generated responses, we note that artificial intelligence tools, such as ChatGPT, became widely known to the public in November 2022, whereas all online experiments in the present study were conducted before November 2022 (see Table 1). Therefore, these data were unlikely to have been substantially affected by participants’ use of AI tools.”

(2) The authors should include a statement in the methods section that checks for AI were done. If none yet, could you do any? Recent papers (Westwood, PNAS 2025; van der Stigchel PNAS, 2026) point to the risk since at least the release of o4-mini (used in the cited paper to create very human-like behaviour).

We thank the Reviewer for this helpful comment. We have clarified this point in the revised manuscript as follows:

Page 20:

“See Table 1 for demographic information and data collection periods. Because online data collection may raise concerns about AI-generated responses, we note that artificial intelligence tools, such as ChatGPT, became widely known to the public in November 2022, whereas all online experiments in the present study were conducted before November 2022 (see Table 1). Therefore, these data were unlikely to have been substantially affected by participants’ use of AI tools.”

(3) It would have been good to collect questionnaires of other, thought to be unrelated psychiatric traits, like compulsivity or schizophrenia symptoms, to check the specificity of the results, also under the assumption that higher scores on either of these skewed questionnaires can pick up individual differences in 'bad questionnaire completion'. The authors should comment on the absence of other questionnaires in the discussion in the limitations section.

We thank the Reviewer for this helpful comment. We agree that the absence of broader psychiatric trait measures limits our ability to evaluate the specificity of the observed associations. Our symptom assessment focused specifically on anxiety and depression because the study was motivated by hypotheses about their potentially opposing links with RPE sensitivity and mood variability. Although previous research has shown intact mood sensitivity to RPEs in individuals with suicidal thoughts and behaviors (Wang et al., 2026), we cannot determine whether the current findings are specific to anxiety- and depression-related symptom dimensions or instead reflect broader transdiagnostic psychopathology or nonspecific response-style variance.

Regarding the concern that higher scores on symptom questionnaires with skewed score distributions may partly capture individual differences in poor-quality questionnaire responding, we note that we implemented strict data-quality procedures for both questionnaire and task data. Four attention-check items were embedded throughout the questionnaire battery, requiring participants to select a prespecified response, for example, “Please select the second option for this item.” Similarly, four attention-check trials were embedded throughout the gambling task. For example, participants were asked to choose between a certain gain of 20 points and a gamble with possible outcomes of 35 and 55 points, for which the dominant response was to choose the gamble option. Participants who failed any of these attention checks were excluded. In addition, our behavioral and mood data reproduced key patterns reported in previous studies (Rutledge et al., 2014 & 2015) using momentary mood ratings during gambling tasks, including higher mood following gains than following losses and systematic mood drift over time (all ps < 0.001). These procedures and validation checks reduce the likelihood that the present findings were driven by poor questionnaire or task completion. We have clarified this point in the revised manuscript as follows:

Page 19:

“Second, our symptom assessment focused specifically on anxiety and depression. This choice was motivated by our primary hypotheses, but it limits our ability to evaluate the specificity of the observed associations. Recent work has shown that individuals with suicidal thoughts and behaviors exhibit reduced mood sensitivity to certain rewards (CR), but not to RPEs[49], suggesting that the current RPE-related effects are not driven by suicide-related processes. However, because we did not assess other psychiatric dimensions, such as compulsivity or schizophrenia-spectrum symptoms, we cannot determine whether the current findings are specific to anxiety- and depression-related symptom dimensions or instead reflect broader transdiagnostic psychopathology or nonspecific response-related variance. Future studies should include measures covering a wider range of psychiatric dimensions, such as internalizing, externalizing, inattentive/neurodevelopmental, mood/anxiety, and withdrawal dimensions identified by Wise et al. (2026)[59], to better characterize whether links among symptom dimensions, RPE sensitivity, and mood variability are disorder-specific or transdiagnostic.”

Page 20：

“Participants were excluded if 1) they failed any of the attentional checks (4 items); 2) they made the same choices for all items; 3) they responded with extreme inconsistency in two similar questionnaires (difference in z-scores out of ±2).”

Page 21:

“There were four items for attentional checks, which required the participants to make a specific choice and were embedded in the entire measurements, e.g., ‘please select the second option for this item’.”

Page 22:

“We also set 4 trials embedded in the entire task for attentional checks. For example, participants were asked to make a choice between a certain gain 20 and a gamble 35/55, where the correct response for this trial was the gamble choice.”

Page 5:

“Choice data (e.g., gambling rates) and mood data (e.g., initial mood, mean mood, and mood variation) showed patterns similar to those reported in previous studies measuring momentary mood during gambling tasks (Figure S2 & S3)[10,45]. We also replicated established effects on momentary mood: mood was higher following gains than following losses, and mood drifted over time (all ps < 0.001; Figure S4).”

(4) The authors could include a more explicit sentence in the abstract stating that the anxiety result did not hold up in the clinical population.

We thank the Reviewer for this helpful comment. We have clarified this point in the revised manuscript as follows:

Abstract:

“Results showed that depression was associated with dampened mood fluctuations due to mood hyposensitivity to RPE. Importantly, this pattern was also found in patients with affective disorders. In contrast, anxiety correlated with heightened mood fluctuations stemming from mood hypersensitivity to RPE in non-clinical participants.”

Reviewer #2 (Public review):

Summary:

Despite their common co-occurrence, depression and anxiety are known to alter mood fluctuations in opposite ways. Here, the authors aimed at distinguishing depression-specific from anxiety-specific from psychopathology-general effects of reward processing on mood fluctuations, focusing on reward prediction errors (RPEs), which are known to be linked to mood fluctuations. This mechanistic study aims at uncovering the process through which these psychopathologies are associated with mood modulations. The authors were able to appropriately test their hypothesis and obtained results corroborating their conclusions.

This work provides a convincing demonstration of the relevance of computational psychiatry (Huys et al, 2016) and the use of decision neuroscience to shed light on the interplay of anxiety, depression, and mood.

Strengths:

The authors used a tripartite model to distinguish depression vs anxiety, as well as a computational model distinguishing reward expectation (EV in the model) from outcome processing through RPE, which are two sequential cognitive processes.

The manuscript adequately addresses the concerns one would have regarding risk-attitudes and regarding referring to trending statistical results.

Weaknesses:

The sample size of the clinical sample (N=116) may not be sufficient to detect anxiety-specific effects due to the high rate of comorbid anxious depression. It would be beneficial to include the number of MDD vs GAD vs anxious depression diagnoses in the clinical population, as this would likely shine light on the power limitations.

We thank the Reviewer for this helpful comment. We agree that the clinical sample may have been underpowered to detect anxiety-specific effects, especially given the high comorbidity between anxiety and depression in affective disorders (see Table S8 for diagnosis, illness duration, and medication status). Based on the effect size observed in the non-clinical datasets (r = 0.079), we estimated that a sample size of 1,226 would be required to detect this effect with 80% statistical power using a two-tailed test with α = .05. This estimate is substantially larger than the current clinical sample size (n = 116). Although these covariate analyses support the robustness of the depression-related effect, they do not resolve whether the absence of the anxiety-related effect reflects limited power or true clinical discontinuity.

We also revised the Discussion to explicitly acknowledge that the anxiety-related effect observed in the pooled non-clinical dataset was not replicated in the clinical sample. We now note two possible interpretations. First, this discontinuity may reflect limited statistical power in the clinical sample. Second, and more speculatively, it may reflect a disruption of mood homeostasis in affective disorders (Paulus, 2007). In non-clinical individuals, the counterbalancing associations of depression- and anxiety-related traits with mood variation may contribute to emotional equilibrium. In contrast, affective disorders may involve a loss of this regulatory balance, reducing the ability to stabilize mood in the face of competing depression- and anxiety-related affective signals. We have revised the manuscript as follows:

Pages 12-13:

“To test whether abnormalities in RPE-driven mood fluctuations can serve as clinically relevant computational markers of depression- and anxiety-related symptom dimensions, we recruited patients with affective disorders (n = 116) to complete the same questionnaire battery and gambling task with momentary mood ratings (Figure 1). Demographic, psychological, and clinical characteristics are summarized in Table 1 and Table S8. We observed significant negative correlations between depression-specific scores and both mood variation (r = -0.239, p = 0.009) and RPE-related mood sensitivity (β_RPE; r = -0.216, p = 0.020). These associations remained significant after controlling for demographic and clinical covariates, task earnings, and mood drift (ps < 0.05). Bootstrap validation yielded consistent results. Mediation analyses further showed that reduced mood sensitivity to RPEs statistically mediated the association between depression-specific scores and lower mood fluctuations (a × b = -0.141, 95% CI = [-0.261, -0.038], p = 0.021; Figure 3). However, we did not observe significant correlation with anxiety (mood variation: r = -0.092, p = 0.327; β_RPE: r = -0.095, p = 0.311).”

Pages 17-18:

“Notably, the pattern of heightened RPE sensitivity observed in the pooled non-clinical dataset was not observed in the clinical sample. On the one hand, this discontinuity may reflect that the clinical sample was underpowered to detect anxiety-specific effects, especially given the high comorbidity between anxiety and depression in affective disorders (Table S8). Based on the effect size observed in the non-clinical datasets (r = 0.079), we estimated that a sample size of 1,226 would be required to detect this effect with 80% statistical power using a two-tailed test with α = .05. This estimate is substantially larger than the current clinical sample size (n = 116). On the other hand, it may reflect a disruption of mood homeostasis in clinical populations[41,58]. In non-clinical individuals, counterbalancing associations of depression- and anxiety-related traits with mood variation may help maintain emotional equilibrium. In contrast, affective disorders may involve a loss of such regulatory balance, reducing the ability to stabilize mood in the face of competing depression- and anxiety-related affective signals.”

Abstract:

“Results showed that depression was associated with dampened mood fluctuations due to mood hyposensitivity to RPE. Importantly, this pattern was also found in patients with affective disorders. In contrast, anxiety correlated with heightened mood fluctuations stemming from mood hypersensitivity to RPE in non-clinical participants.”

Reviewer #3 (Public review):

Summary:

In this submission, Wang and colleagues jointly examine the association between depression and anxiety symptoms and individuals' affective reactivity to reward prediction errors in Ruttledge et al.'s gambling paradigm. Taking a bifactor approach to anxiety and depression in several non-clinical (and one clinical sample), the authors find that anxiety-specific symptoms relate to over-reactivity of mood to reward prediction errors (RPEs) as well as heightened mood variability, while depression-specific symptoms relate to blunted mood sensitivity to RPEs. These depression- but not anxiety-specific relationships replicated in patient samples.

Strengths:

I was impressed that the data-driven, transdiagnostic approach employed by the authors uncovered specific relationships between anxiety and depression-specific factors and RPE reactivity in a well characterized task and computational model, especially in a non-clinical sample. This sheds new light on how these affective processes may be perturbed-and importantly, in different ways-by anxiety and depression symptoms. Likewise, the replication of the depression-specific finding (RPE hypo-reactivity) in a clinical sample was nice to see.

Weaknesses:

(1) While the anxiety- and depression-specific factors had differential effects on mood variability (Figure 2A-D) and RPE reactivity (Figure 2E-G) in all samples, such that the correlations between the two factors and these mood parameters were significantly different, the anxiety factor was not consistently (significantly) associated with either mood-related parameter across samples. However, the authors resolve anxiety-specific predictive effects when they collapse across datasets. While it is intuitive that achieving a larger effective sample size would afford the power necessary to detect such individual differences, this struck me as a major caveat for this set of results.

We thank the Reviewer for this important comment. Although the anxiety-specific factor showed associations in the expected direction across datasets, these associations were not significant in several individual datasets. Specifically, anxiety-specific scores were positively correlated with mood variation (laboratory dataset: r = 0.10, p = 0.531; online dataset 1: r = 0.08, p = 0.026; online dataset 2: r = 0.19, p = 0.004) and with RPE-related mood sensitivity (laboratory dataset: r = 0.04, p = 0.820; online dataset 1: r = 0.05, p = 0.216; online dataset 2: r = 0.19, p = 0.004; Figures 2A–C and 2E–G). This pattern may partly reflect limited statistical power at the single-dataset level.

Because these datasets used comparable task and questionnaire procedures and showed positive effect directions, we conducted pooled analyses to obtain a more stable estimate. Importantly, these analyses included dataset as a random intercept in mixed-effects models to account for between-dataset differences. Thus, the pooled analysis provides an integrated estimate across samples, conceptually similar to an individual-participant-data meta-analytic approach. The pooled results provided evidence for the expected anxiety-specific associations with greater mood variability and heightened RPE-related mood sensitivity (mood variation: t = 3.46, p < 0.001; RPE-related mood sensitivity: t = 2.60, p = 0.009). In addition, we conducted a mini meta-analysis, and results support that anxiety is associated with intensified mood fluctuations and increased mood sensitivity to RPE.

However, we have clarified in the revised manuscript that the anxiety-related effects were less robust than the depression-related effects and require further replication in larger samples.

Pages 10-11:

“Correlations between the anxiety-specific factor and mood variation were positive in direction across datasets, although they were not statistically significant in several datasets (the laboratory dataset: r = 0.10, p = 0.531; the online dataset 1: r = 0.08, p = 0.026; the online dataset 2: r = 0.19, p = 0.004). Similarly, correlations between the anxiety-specific factor and β_RPE were positive in direction but statistically inconsistent across datasets (the laboratory dataset: r = 0.04, p = 0.820; the online dataset 1: r = 0.05, p = 0.216; the online dataset 2: r = 0.19, p = 0.004; Figure 2A-C & 2E-G). Because these datasets used comparable task and questionnaire procedures and showed positive effect directions, and because reliable individual differences often require large samples to detect[48], we combined the laboratory dataset, online dataset 1, and online dataset 2 (total N = 1,026). This approach is analogous to an individual-participant-data meta-analytic analysis. We fitted linear mixed-effects models predicting mood variation and β_RPE from the three bifactor scores, with dataset included as a random intercept to account for dataset-level variability. For mood variation, the anxiety-specific factor was positively associated with mood variation (t = 3.46, p < 0.001), whereas the depression-specific factor was negatively associated with mood variation (t = -6.13, p < 0.001). For RPE-related mood sensitivity, the anxiety-specific factor was positively associated with β_RPE (t = 2.60, p = 0.009), whereas the depression-specific factor was negatively associated with β_RPE (t = -5.30, p < 0.001). These associations remained significant after controlling for gender, age, task earnings, and mood drift. In addition, we performed a mini meta-analysis on these correlation coefficients[49]. Results showed significant positive correlation for both mood variation and RPE-related mood sensitivity (mood variation: Z = 3.399, 95 % CI for correlation coefficient r [0.045, 0.166]; RPE-related mood sensitivity: Z = 2.618, 95 % CI for correlation coefficient r [0.021, 0.143]), supporting that anxiety is associated with intensified mood fluctuations and increased mood sensitivity to RPE.”

Page 17:

“Notably, the anxiety-related effects were less robust than the depression-related effects and were detectable only in the pooled dataset (n = 1,026); therefore, they require further replication in larger samples.”

(2) The authors observe associations between the 'common factor' of depression and anxiety and risk-attitude tendencies, presumably the alpha (exponent) parameter in a prospect theory-type subjective value model. But where is this analysis explained? (i.e. how was this model formulated and how were risk attitude parameters estimated?) And what is the interpretation of this finding - is there precedent for looking at risk attitudes in this task? And why would these predictive effects only be observed in relation to the common, but not unique, factors of anxiety and depression?

We apologize for the unclear statement. We have added a description of the computational modeling of choice behavior. Please see our revisions below:

Page 13:

“Choice parameters were estimated using an established approach–avoidance prospect theory model[10,45,49], which included loss aversion, domain-specific risk attitude parameters in the gain and loss domains, and value-independent Pavlovian approach and avoidance parameters (see Supplementary Note 7 for details of the computational choice models). In this model, risk attitude was quantified by the exponent parameter α in a prospect-theory-inspired subjective value function. Lower α values reflect greater risk aversion, whereas values closer to or above 1 reflect more linear or risk-seeking valuation.”

Supplementary Pages 8-9:

Note 7: Computational model of gambling choice

To quantify how different events impacted participants’ momentary moods during the gambling In line with previous studies[14,15], our choice model space included expected value model (cM1), prospect theory model (cM2)[16], and approach-avoidance prospect theory model (cM3)[14]. For cM2 (Equations 6-9), there were 3 parameters, including risk aversion (α, range: [0.3, 1.3]), loss aversion (λ: [0.5, 5]), and inverse temperature (μ: [0, 10]).

Where V_gain and V_loss are the objective gain and loss from a gamble, respectively. Please note thatV_gain is 0 in loss trials and V_loss is 0 in gain trials. V_certain is the objective value for the certain option. U_gamble and U_certain denote the subjective utilities of the gamble and the certain option, respectively. Choice probability for gamble (P_gamble) is determined by the softmax rule. Building on cM2, cM3 decomposes the decision process into risk-attitude-driven valuation (e.g., loss and risk aversion) and value-insensitive motivational components (Equations 6-8 & 10-12). That is, choice probability for P_gamble in cM3 is jointly determined by the softmax rule and approach/avoidance parameters (β_gain: [-1, 1], β_loss: [-1, 1]). Approach/avoidance parameters are not applied in mixed trials. Please note that a higher gambling rate does not imply a change in risk attitude per se: it can arise from an increased value-insensitive approach bias even when risk-attitude parameters are comparable between groups. Risk attitude is indeed conceptualized in economics as the curvature of the utility function (i.e., the subjective value) of the objective outcomes, with concave curves associated with risk aversion, and convex curves associated with risk seeking[17,18]. By contrast, the approach or avoidance bias apply to all the value. A possible interpretation of the approach bias is that participant approach the option with the highest possible gain (the lottery) in the gain frame; the avoidance bias would then reflect a tendency to systematically avoid the highest potential losses (the lottery) in the loss frame.

Model comparison using BIC revealed that the winning model for each dataset was the approach-avoidance prospect theory model (cM3; mean R² = 0.51 for the laboratory dataset, 0.49 for the online dataset1, 0.54 for the online dataset 2, and 0.40 for the clinical dataset; Table S9).

Please also see our interpretation of this finding below:

Page 18:

“With respect to decision-making, prior literature using risky decision-making tasks without feedback has linked pathological anxiety to greater risk aversion[58]. In line with this, our results from a risky decision-making task with feedback suggest that the common factor, rather than anxiety-specific variance per se, is more consistently associated with risk aversion. This suggests that heightened gain-domain risk aversion may be a transdiagnostic feature of internalizing psychopathology, rather than being uniquely attributable to anxiety.”

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Thank you very much for giving me the opportunity to review this very interesting paper.

Recommendations:

(1) Add more specific ethics information than "study was approved by ethics committee of Beijing normal university".

We thank the Reviewer for this important comment. We have added approval number. Please see our revision below:

Page 20:

“The study was approved by the Ethics Committee of Beijing Normal University (approve number: ICBIR_A_0016_028). Written or electronic informed consent was obtained from all participants before participation.”

(2) Add information on how participants were recruited. I think the websites listed only hosted the experiment/questionnaires?

We thank the Reviewer for pointing this out. We have revised the relevant text as follows:

Page 20:

“A total of 2634 participants via online platforms (questionnaires from https://www.wjx.cn and tasks from https://www.naodao.com) took part in five experiments, including a psychometric experiment, a laboratory experiment, two online replication experiments. Participants were recruited through participant pools and study advertisement. For online experiments, interested participants accessed the study through an online link and completed the questionnaires and task remotely. For the laboratory experiment, participants completed the study in a controlled laboratory setting.”

(3) Typo in Figure 1A, grey panel - psychometric.

We apologize for the typo. We have corrected typographical errors throughout the manuscript.

(4) In the Discussion, there is a section on r-to-z transformations, and I was not quite sure what in the Results this links to.

We thank the Reviewer for pointing out the unclear statement. Please see our revision below:

Page 18:

“First, although anxiety- and depression-related associations differed consistently, the anxiety-specific associations themselves were less robust across datasets.”

**Reviewer #2 (Recommendations for the authors):&&

The Results sections 2 (depression) and 3 (anxiety) could be improved by reducing the back and forth between factors throughout the results. It may be useful to split them into 3 sections: depression only, anxiety only, and depression vs anxiety.

We thank the Reviewer for this helpful suggestion. As suggested, we have reorganized this part into three sections: depression, anxiety, and depression versus anxiety. Please see our revision below:

Page 12:

“Differential associations of depression and anxiety with mood fluctuations. To directly test whether depression- and anxiety-specific factors differed in their associations with mood dynamics, we compared the corresponding correlations. These comparisons showed that depression-specific associations were significantly more negative than anxiety-specific associations for both mood variation (laboratory dataset: Z = -1.84, p = 0.033; online dataset 1: Z = -5.36, p < 0.001; online dataset 2: Z = -3.42, p < 0.001) and β_RPE (laboratory dataset: Z = -1.77, p = 0.038; online dataset 1: Z = -3.67, p < 0.001; online dataset 2: Z = -4.00, p < 0.001; Figures 2A–C and 2E–G). These results support distinct associations of depression- and anxiety-specific factors with RPE-related mood dynamics.”

In the discussion, the authors could have mentioned the brain areas most likely to be involved in these processes, both cognitive and psychopathological, as previous studies (such as Cecchi et al, 2022) have aimed at identifying regions involved in RPE processing while modulating mood in health. A short section on this would be useful to the neuropsychiatric community.

We thank the Reviewer for this helpful suggestion. We agree that the Discussion would benefit from a more explicit consideration of the neural systems that may support RPE-related mood updating and their relevance to psychopathology. We have revised the Discussion accordingly, as shown below:

Page 16:

“Although the present study did not include neuroimaging, the observed computational dissociation may map onto partially distinct neural systems involved in reward learning, mood updating, and affective psychopathology. RPE processing has been consistently linked to striatal–midbrain dopaminergic reward-learning circuits[8,50]. The integration of these reward-learning signals into subjective mood and value-based decision-making may further involve the ventral medial prefrontal cortex and orbitofrontal cortex[44]. In addition, the anterior insula may be particularly relevant for integrating feedback-related signals with affective and interoceptive states[8,44], potentially linking RPE processing to anxiety- and depression-related mood dynamics. Consistent with this view, Cecchi et al. (2022)[51] used intracranial EEG to show that feedback-related neural activity tracks mood fluctuations and risky choice. Future neuroimaging studies should test whether depression-related reductions and anxiety-related increases in RPE-related mood sensitivity are associated with altered interactions among striatal, prefrontal, and insular circuits.”

Reviewer #3 (Recommendations for the authors):

(1) The authors need to present a clearer definition of the terms "bifactor analysis" and "tripartite model" in the Introduction. What does tripartite mean in this context? What are the assumptions of such bifactor analyses (e.g. as used in Gagne et al. and the present work) and how, in broad strokes, are they carried out? These are important constructs to clarify for readers outside the computational psychiatry niche.

We thank the Reviewer for this helpful suggestion. We have revised the Introduction accordingly, as shown below:

Page 3:

“Recent work has used bifactor models of the tripartite model of depression and anxiety to clarify their distinct features and differential influences on decision-making[31,32]. The tripartite model of anxiety and depression proposes that these two symptom dimensions share a broad general distress or negative affect component while also including symptom-specific components: low positive affect/anhedonia is more specific to depression, whereas physiological hyperarousal is more specific to anxiety[30,33,34]. Bifactor analysis offers a way to model this structure statistically. In a bifactor model, symptoms load on a general factor reflecting their shared variance and on specific factors capturing residual variance in narrower symptom dimensions after accounting for the general factor.”

(2) Previous examinations of depression and RPE reactivity in this task paradigm, as the authors note (e.g. Rutledge et al., 2017), observed that individuals diagnosed with depression showed an intact association between RPEs and mood. In other words, there was no previously observed relationship between depression and affective reactivity to RPEs in this task context. Here, the authors find that the "unique" depression factor identified by the authors (in a non-clinical sample) is associated with blunted RPE sensitivity - this is worth commenting on specifically.

We thank the Reviewer for this helpful suggestion. We have discussed this point in the Discussion. Please also see it below:

Pages 15-16:

“Our computational model not only replicates the important role of RPEs in mood dynamics but also highlights the divergent mediating roles of RPE-related mood sensitivity in the associations of depression and anxiety with mood fluctuations. The opposite associations of depression and anxiety with mood sensitivity to RPEs complement previous findings of apparently intact RPE-related mood sensitivity in depression[12,25,37]. These findings further underscore the necessity of decomposing shared and specific components of depression and anxiety in studies of mood dynamics, which can enhance our understanding of their distinct associations with emotion processing and cognitive flexibility. This point is consistent with bifactor-based work showing that shared and specific symptom dimensions can have different computational correlates. For example, Gagne et al. (2020) showed that bifactor-derived symptom dimensions differentially relate to maladaptation to environmental volatility[32], complementing previous findings that trait anxiety is associated with inflexible adjustment to volatility[32].”

(3) There is a note (line 247) about the interpretation of the correlations in Figure 2, which attempts to explain away the inconsistent relationships between the anxiety-specific factor and mood variability as well as RPE reactivity observed in Figure 2. I can't say I understand the authors' point here about "signs of positive correlations", so I would say the authors need to clarify their logic here. More to the point, the authors only resolve anxiety-specific predictive effects when they collapse across these datasets. As discussed above (see 'weaknesses'), this is a serious limitation in my view and needs to be discussed as such in the paper.

We apologize for the unclear statement. Although the anxiety-specific factor showed associations in the expected direction across datasets, these associations were not significant in several individual datasets. Specifically, anxiety-specific scores were positively correlated with mood variation (laboratory dataset: r = 0.10, p = 0.531; online dataset 1: r = 0.08, p = 0.026; online dataset 2: r = 0.19, p = 0.004) and with RPE-related mood sensitivity (laboratory dataset: r = 0.04, p = 0.820; online dataset 1: r = 0.05, p = 0.216; online dataset 2: r = 0.19, p = 0.004; Figures 2A–C and 2E–G). This pattern may partly reflect limited statistical power at the single-dataset level.

Because these datasets used comparable task and questionnaire procedures and showed positive effect directions, we conducted pooled analyses to obtain a more stable estimate. Importantly, these analyses included dataset as a random intercept in mixed-effects models to account for between-dataset differences. Thus, the pooled analysis provides an integrated estimate across samples, conceptually similar to an individual-participant-data meta-analytic approach. The pooled results provided evidence for the expected anxiety-specific associations with greater mood variability and heightened RPE-related mood sensitivity (mood variation: t = 3.46, p < 0.001; RPE-related mood sensitivity: t = 2.60, p = 0.009). We have revised it to make it clear. Please see our revisions below:

Pages 10-11:

“Correlations between the anxiety-specific factor and mood variation were positive in direction across datasets, although they were not statistically significant in several datasets (the laboratory dataset: r = 0.10, p = 0.531; the online dataset 1: r = 0.08, p = 0.026; the online dataset 2: r = 0.19, p = 0.004). Similarly, correlations between the anxiety-specific factor and β_RPE were positive in direction but statistically inconsistent across datasets (the laboratory dataset: r = 0.04, p = 0.820; the online dataset 1: r = 0.05, p = 0.216; the online dataset 2: r = 0.19, p = 0.004; Figure 2A-C & 2E-G). Because these datasets used comparable task and questionnaire procedures and showed positive effect directions, and because reliable individual differences often require large samples to detect, we combined the laboratory dataset, online dataset 1, and online dataset 2 (total N = 1,026). This approach is analogous to an individual-participant-data meta-analytic analysis while accounting for dataset-level variability. Because these datasets used comparable task and questionnaire procedures and showed positive effect directions, and because reliable individual differences often require large samples to detect[48], we combined the laboratory dataset, online dataset 1, and online dataset 2 (total N = 1,026). This approach is analogous to an individual-participant-data meta-analytic analysis while accounting for dataset-level variability. We fitted linear mixed-effects models predicting mood variation and β_RPE from the three bifactor scores, with dataset included as a random intercept. For mood variation, the anxiety-specific factor was positively associated with mood variation (t = 3.46, p < 0.001), whereas the depression-specific factor was negatively associated with mood variation (t = -6.13, p < 0.001). For RPE-related mood sensitivity, the anxiety-specific factor was positively associated with β_RPE (t = 2.60, p = 0.009), whereas the depression-specific factor was negatively associated with β_RPE (t = -5.30, p < 0.001).”

Page 17:

“Notably, the anxiety-related effects were less robust than the depression-related effects and were detectable only in the pooled dataset (n = 1,026); therefore, they require further replication in larger samples.”

(4) I expected to see that the authors would also investigate relationships between anxiety/depression related factors and the decay (gamma) parameter in the 'Happiness equation', which is presumably estimated from the data here. While I don't have a strong intuition about directions of (or presence of) predictive relationships here, doesn't it stand to reason that different aspects of psychopathology examined here might map onto how long- (versus short-) lasting the effects of, say, RPEs are, upon mood?

We thank the Reviewer for this important comment. In the healthy datasets, we fitted a linear mixed-effects model predicting the decay parameter (γ) from the three bifactor scores, with dataset included as a random intercept. None of the factors showed a significant association with γ (common: t = 0.708, p = 0.479; anxiety: t = 0.564, p = 0.573; depression: t = 1.146, p = 0.252). In the clinical dataset, we fitted a linear model predicting γ from the three bifactor scores and again found no significant associations (common: t = -0.036, p = 0.972; anxiety: t = -0.047, p = 0.963; depression: t = 0.052, p = 0.959). We have clarified this point in the revised manuscript as follows:

Supplementary Page 5:

“In the healthy datasets, we conducted a linear mixed-effect model against decay parameter (gamma) with all three factors, with dataset as a random factor. Results did not show significant effect (common: t = 0.708, p = 0.479; anxiety: t = 0.564, p = 0.573; depression: t = 1.146, p = 0.252). In the clinical dataset, we conducted a linear model against decay parameter (gamma) with all three factors and found no significant effect (common: t = -0.036, p = 0.972; anxiety: t = -0.047, p = 0.963; depression: t = 0.052, p = 0.959).”

(5) The rationale for and interpretation of the mediation model, which presumably aims to explain the relationships between anxiety- and depression-specific factors, RPE reactivity, and mood variability was barely explained by the authors. At present, I'm not sure what the added value of this analysis is. The authors should either remove or explain/motivate the mediation more clearly.

We thank the Reviewer for this helpful comment. The rationale for the mediation analysis is that mood variability in the task is not only a descriptive behavioral outcome, but may also arise from the degree to which momentary mood is updated by RPEs. Therefore, if depression is associated with reduced RPE-related mood sensitivity and anxiety with increased RPE-related mood sensitivity, these alterations should statistically account for their opposite associations with mood variability. The mediation model directly tested this possibility by examining whether RPE-related mood sensitivity accounted for the association between symptom-specific factors and mood variability. We have clarified this rationale in the revised manuscript as follows:

Page 10:

“Given the strong correlation between β_RPE and mood variation (rs > 0.67, ps < 0.001), we further conducted a mediation analysis to examine whether individual differences in RPE-related mood sensitivity statistically accounted for the association between depression loading and mood variation. This analysis was motivated by the hypothesis that depression-related dampening of mood variability may arise, at least in part, from reduced mood sensitivity to RPEs.”

(6) This submission would benefit from extensive English language copy editing. There are many passages in the paper (in fact, too many to list here) that suffer from either grammatical errors or clarity issues.

We apologize for these mistakes. We have corrected the typographical errors throughout the manuscript.

Application: Investing in the Stock Market • Recommendations from investment advisors cannot help us outperform the market. • A hot tip is probably based on information that is already contained in the price of the stock. • Stock prices respond to announcements only when the information is new and unexpected. • A “buy-and-hold” strategy is the most sensible strategy for the small investor (savings of transaction costs) or buy shares into a mutual fund.

coupon or dividend

X-X^e

optimal forecast using all available information

expectation of the variable that is being forecast

return on investment in equity

the price of the stock is only determined by the present value of the future dividend stream

The requirements provided here are more extensive than those stipulated by Art. 28 para. 1 AMLO-FINMA and margin no. 53 of the FINMA circular 2016/7 as in force (and presumably also as to be amended); specifically, "adding one copy of page 10 of the application form for each of the individuals involved" will de facto rule out sub-contracting to larger providers, e.g. such employing dozens of employees.

Also see Section 38 of the Polyreg regulations as referred to in the Polyreg application form.

Scaling synthesis

Tools for Thought Rocks fission

Multiplayer Tools for Thought

.

Vgl. Art. 28 GwV-FINMA sowie S. 10 Polyreg-Aufnahmegesuch-Antrag sowie Rn. 53 des FINMA-Rundschreibens 2016/7 "Video- und Online-Identifizierung" in der aktuellen sowie – voraussichtlich – künftigen Fassung.

.

.

Pathos + Imagery

Lion

보고서 보고서

Should they do that even if I select "AI ratings only" in the Rating source option above?

My sense is that it might be clearer just to show the numbers for quick votes (rather than the signs that may be read differently by people who don't read instructions carefully) (?)

I think this is called "Score weights" now?

I might misunderstand this, but should there be an assessment of prompt and run stability (rather than just an explanation of what it means)?

fair transaction

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Resource: GraphPad Prism (RRID:SCR_002798)

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SciCrunch record: RRID:SCR_002798

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What is this?