Reviewer #1:

Using two behavioral experiments, the authors partially replicate known effects that rotated faces decrease the benefit of visual speech on auditory speech processing.

As reported by the authors, Experiment 1 suffers from a design flaw considering that a temporal drift occurred in the course of the experiment. This clearly invalidates the reliability of the results and this experiment should be properly calibrated and redone. There is otherwise well-known literature on the topic.

Experiment 2 should be discussed in the context of divided attention tasks previously reported by researchers so as to better emphasize how and whether this is a novel observation.

Additionally:

-The question being addressed is narrowly and ill-construed: numerous authoritative statements in the introduction should reference existing work. For instance, seminal models of Bayesian perception (audiovisual speech processing especially) should be attributed to Dominic Massaro. Such statements as "studies fail to distinguish between binding and late integration" are surprising considering that the fields of multisensory integration and audiovisual speech processing have essentially and traditionally consisted in discussing these specific issues. To name a few researchers in the audiovisual speech domain: the work of Ruth Campbell, Ken Grant, and Jean-Luc Schwartz have largely contributed to refine debates on the implication of attentional resources to audiovisual speech processing using behavioral, neuropsychology, and neuroimaging methods. In light of the additional statements of the kind "The importance of temporal coherence for binding has not previously been established for speech", I would highly recommend the authors to do a thorough literature search of their topic (below some possible references as a start).

-What the authors understand to be "linguistic cues" should be better defined. For instance, the inverted face experiment aimed at dissociating whether visemic processing depends on face recognition (i.e. on holistic processing) or whether it depends on featural processing (and it does constitute a test, as suggested by the authors, of whether viseme recognition is a linguistic process per se).

Some references:

-Alsius, A., Möttönen, R., Sams, M. E., Soto-Faraco, S., & Tiippana, K. (2014). Effect of attentional load on audiovisual speech perception: evidence from ERPs. Frontiers in psychology, 5, 727.

-Chandrasekaran, C., Trubanova, A., Stillittano, S., Caplier, A., & Ghazanfar, A. A. (2009). The natural statistics of audiovisual speech. PLoS Comput Biol, 5(7), e1000436.

-Jordan, T. R., & Bevan, K. (1997). Seeing and hearing rotated faces: Influences of facial orientation on visual and audiovisual speech recognition. Journal of Experimental Psychology: Human Perception and Performance, 23(2), 388.

-Grant, K. W., & Seitz, P. F. (2000). The use of visible speech cues for improving auditory detection of spoken sentences. The Journal of the Acoustical Society of America, 108(3), 1197-1208.

-Grant, K. W., Van Wassenhove, V., & Poeppel, D. (2004). Detection of auditory (cross-spectral) and auditory-visual (cross-modal) synchrony. Speech Communication, 44(1-4), 43-53.

-Schwartz, J. L., Berthommier, F., & Savariaux, C. (2002). Audio-visual scene analysis: evidence for a" very-early" integration process in audio-visual speech perception. In Seventh International Conference on Spoken Language Processing.

-Schwartz, J. L., Berthommier, F., & Savariaux, C. (2004). Seeing to hear better: evidence for early audio-visual interactions in speech identification. Cognition, 93(2), B69-B78.

-Tiippana, Kaisa, T. S. Andersen, and Mikko Sams. (2004) "Visual attention modulates audiovisual speech perception." European Journal of Cognitive Psychology 16.3: 457-472.

-van Wassenhove, V. (2013). Speech through ears and eyes: interfacing the senses with the supramodal brain. Frontiers in psychology, 4, 388.

-Van Wassenhove, V., Grant, K. W., & Poeppel, D. (2007). Temporal window of integration in auditory-visual speech perception. Neuropsychologia, 45(3), 598-607.

Reviewer #1:

This work claims to show that learning of word associations during sleep can impair learning of similar material during wakefulness. The effect of sleep on learning depended on whether slow-wave sleep peaks were present during the presentation of that material during sleep. This is an interesting finding, but I have a lot of questions about the methods that temper my enthusiasm.

1) The proposed mechanism doesn't make sense to me: "synaptic down-scaling of hippocampal and neocortical language-related neurons, which were then too saturated for further potentiation required for the wake-relearning of the same vocabulary". Also lines 105-122. What is 'synaptic down-scaling'? what are 'language related neurons'? ' How were they 'saturated'? What is 'deficient synaptic renormalization'? How can the authors be sure that there are 'neurons that generated the sleep- and ensuing wake-learning of ... semantic associations'? How can inferences about neuronal mechanisms (ie mechanisms within neurons) be drawn from what is a behavioural study?

2) On line 54 the authors say "Here, we present additional data from a subset of participants of our previous study in whom we investigated how sleep-formed memories interact with wake-learning." It isn't clear what criteria were used to choose this 'subset of participants'. Were they chosen randomly? Why were only a subset chosen, anyway?

3) The authors do not appear to have checked whether their nappers had explicit memory of the word pairs that had been presented. Why was this not checked, and couldn't explicit memory explain the implicit memory traces described in lines 66-70 (guessing would be above chance if the participants actually remembered the associations).

Reviewer #1:

In the present manuscript, Evans and Burgess present a computational model of the entorhinal-hippocampal network that enables self-localization by learning the correspondence between stimulus position in the environment and internal metric system generated by path integration. Their model is composed of two separate modules, observation and transition, which inform about the relationship between environmental features and the internal metric system, and update the internal metric system between two consecutive positions, respectively. The observation module would correspond to projection from hippocampal place cells (PCs) to entorhinal grid cells (GCs), while the transition module would just update the GCs based on animal's movement. The authors suggest that the system can achieve fast and reliable learning by combining online learning (during exploration) and offline learning (when the animal stops or rests). While online learning only updates the observation model, offline learning could update both modules. The authors then test their model on several environmental manipulations. Finally, they discuss how offline learning could correspond to spontaneous replay in the entorhinal-hippocampal network. While the work will certainly be of great interest to the community, the authors should improve the presentation of their manuscript, and make sure they clearly define the key concepts of their study.

Online learning is clearly explained in the manuscript (e.g. l.101). Both environment structure (PC-PC connections) and the observation models (PC->GC synapses) are learned online, and this leads to stable grid cells. Then, the authors suggest that prediction error between the observation and transition models triggers offline inference that can update both models simultaneously. However, it is hard to figure out what offline learning is exactly. The section "Offline inference: The hippocampus as a probabilistic graph" is quite impossible to follow. Before explicitly defining offline learning the authors introduce a spring model of mutual connection between feature locations, but it is not clearly explained if this network is optimized online or offline.

The end of this section is particularly difficult to follow (line 180): "In this context, learning the PC-GC weights (modifying the observation model) during online localization corresponds to forming spatial priors over feature locations which anchor the structure, which would otherwise be translation or rotation invariant (since measurements are relative), learned during offline inference to constant locations on the grid-map.".

What really triggers offline inference is only explained much further in the manuscript, l. 366. Interestingly, this section refers to Fig. 1G for the first time, and should naturally be moved at the beginning of the manuscript (where Fig.1 is described)

Along the same lines, the role of offline learning should be made much more explicit in Fig. 2.

The frequent references to the method section too often break the flow of paper and make it difficult to follow. The authors should start their manuscript with a clear and simple definition of the core idea and concepts, almost in lay terms and only introducing a few annotations, using Fig. 1 (perhaps with some modification and focusing especially on panels A and F) as a visual support, and to move mathematical equations such as Eq. 3 to the supplementary information.

The authors have tested their model on various manipulations that have been previously carried out in freely moving animals, such as change in visual gain and in environmental geometry. These sections are interesting but, again, would be much clearer if presented after a clear explanation of online and offline learning procedures, not in between.

Finally, the authors discuss the relationship between offline inference and neuronal replay, as observed experimentally in vivo (Figs 6&7). This is interesting but would perhaps benefit from some graphical explanation. It is not immediately obvious to understand the fundamental difference between message passing (Fig. 6A) and simple synaptic propagation of activity among connected PC in CA3. Fig. 7 is actually a nice illustration of the phenomenon and should perhaps be presented before Fig. 6.

Reviewer #1:

The authors present a workflow based on targeted Nanopore DNA sequencing, in which they amplify and sequence nearly full-length 16S rRNA genes, to analyze surface water samples from the Cam river in Cambridge. They first identify a taxonomic classification tool, out of twelve studied, that performs best with their data. They detect a core microbiome and temporal gradients in their samples and analyze the presence of potential pathogens, obtaining species level resolution and sewage signals. The manuscript is well written and contains sufficient information for others to carry out a similar analysis with a strategy that the authors claim will be more accessible to users around the world, and particularly useful for freshwater surveillance and tracing of potential pathogens.

The work is sufficiently well-documented and timely in its use of nanopore sequencing to profile environmental microbial communities. However, given that the authors claim to provide a simple, fast and optimized workflow it would be good to mention how this workflow differs or provides faster and better analysis than previous work using amplicon sequencing with a MinION sequencer.

Many of the June samples failed to provide sufficient sequence information. Could the authors comment on why these samples failed? While some samples did indeed have low yields, this was not the case for all (supp table 2 and supp figure 5) and it could be interesting to know if they think additional water parameters or extraction conditions could have affected yields and subsequent sequencing depth.

One of the advantages of nanopore sequencing is that you can obtain species-level information. It would therefore be helpful if the authors could include information on how many of their sequenced 16S amplicons provided species-level identification.

While the overall analysis of microbial communities is well done, it is not entirely clear how the authors define their core microbiome. Are they reporting mainly the most abundant taxa (dominant core microbiome), and would this change if you look at a taxonomic rank below the family level? How does the core compare, for example, with other studies of this same river?

This is a page note. I can write overall comments about the pre-print here.

Tags can also be added below.

Reviewer #1:

This study takes two existing models of hippocampal theta rhythm generation, a reduced one with two populations of Izhikevich neurons, and a detailed one with numerous biophysically detailed neuronal models. The authors do some parameter variation on 3 parameters in the reduced model and ask which are sensitive control parameters. They then examine control of theta frequency through a phase response curve and propose an inhibition-based tuning mechanism. They then map between the reduced and detailed model, and find that connectivity but not synaptic weights are consistent. They take a subset of the detailed model and do a 2 parameter exploration of rhythm generation. They compare phenomenological outcomes of the model with results from an optogenetic experiment to support their interpretation of an inhibition-based tuning mechanism for intrinsic generation of theta rhythm in the hippocampus.

General comments:

1) The paper shows the existence of potential rhythm mechanisms, but the approach is illustrative rather than definitive. For example, in a very lengthy section on parameter exploration in the reduced model, the authors find some domains which do and don't exhibit rhythms. Lacking further exploration or analytic results, it is hard to see if their interpretations are conclusive.

2) The authors present too much detail on too few dimensions of parameters. An exhaustive parameter search would normally go systematically through all parameters, and be digested in an automated manner. For reporting this, a condensed summary would be presented. Here the authors look at 3 parameters for the reduced model and 2 parameters in the detailed one - far fewer than the available parameter set. They discuss the properties of these parameter choices at length, but then pick out a couple of illustrative points in the parameter domain for further pursuit. This leaves the reader rather overwhelmed on the one hand, and is not a convincing thorough exploration of all parameters of the system on the other.

3) I wonder if the 'minimal' model is minimal enough. Clearly it is well- supplied with free parameters. Is there a simpler mapping to rate models or even dynamical systems that might provide more complete insights, albeit at the risk of further abstraction?

4) Around line 560 and Fig 12 the authors conclude that only case a) is consistent with experiment. While it is important to match data to experiment, here the match is phenomenological. It misses the opportunity to do a quantitative match which could be done by taking advantage of the biological detail in the model.

5) The paper is far too long and is a difficult read. Many items of discussion are interspersed in the results, for example around line 335 among many others.

Reviewer #1:

Studies in mouse models and humans show synapse loss and dysfunction that precede neurodegeneration, raising questions about timing and mechanisms. Using longitudinal in vivo 2-photon imaging, Jackson et al., investigate pre- and post-synaptic changes in rTg4510 mice, a widely used mouse model of tauopathy. Consistent with cross sectional studies, the authors observed a reduction in density of presynaptic axons and dendritic spines in layer 1 cortex that relate to degeneration of neurites and dendrites over time. Taking advantage of an inducible model to overexpress tau p301L, they show that reducing expression of tau by DOX early in disease progression, resulted in amelioration of synapse loss, also consistent with other studies. Interestingly, the authors observed a significant reduction of dendritic spines less than a week before dendrite degeneration. In contrast, they observed plasticity and turnover of presynaptic structures weeks before axonal degeneration, suggesting different mechanisms.

Overall the results are interesting and largely consistent with previous findings. The new findings shown in Figures 5 and 6 address the timing of pre and postsynaptic loss and structural plasticity and reveal interesting differences; however, the data are highly variable and there are several issues that diminish enthusiasm as outlined below. Moreover, this study does not include new biological or mechanistic insight into the differences in pre- and post-synaptic changes from previous work in the field.

The main weakness relates to the significance and relevance beyond this specific mouse model and brain region. I appreciate the strengths but also technical challenges of in vivo longitudinal imaging, including a small field of view. Thus, the rationale and choice of model and brain region, and validation of key findings is critical to support conclusions. In this case, the tau model, although used by others, has several caveats relevant to the investigation of synapse loss (see point 2 below) that weaken this study and its impact.

1) Most of the work in the model related to synapse loss and dysfunction have been carried out in hippocampus and other regions of cortex in this model and tau and amyloid models. Here the authors focused on layer 1 of (somatosensory) cortex and followed neurites of pyramidal cells labeled with AAV:GFP, an approach that does not enable one image and track axons and dendrites from large numbers of neurons. They observed divergent dynamics in spine and presynaptic TBS of individual dendrites and axons. Given the small number of neurons sampled, significant noise in their imaging data, these findings need more validation using other approaches. This is particularly important for the data and conclusion drawn from Figures 5 and 6 (see point 3).

To estimate the overall effect of genotype the authors fitted Generalized Additive Mixed Models (GAMMS) to their data given the variability in the data within animals and genotype. It would be helpful to those less familiar to provide more comparisons of data using additional statistical tests and analyses along with power analyses calculations.

2) Major caveat with inducible Tau mode Tg4510. While this inducible model has the advantage of controlling timing of tau overexpression in neurons, a recent study by Gamache et al (PMID: 31685653) demonstrated that there are issues with the transgene insertion site and factors other than tau expression are actually what is driving the phenotype. Thus, differences in synaptic and behavioral phenotypes are based on the mouse line used and this needs to be carefully controlled. This was not addressed or discussed. See https://pubmed.ncbi.nlm.nih.gov/31171783/ and https://pubmed.ncbi.nlm.nih.gov/30659012/

3) The interesting new findings presented in Figures 5 and 6 that address timing and differences in axonal and dendritic/spine plasticity and loss need to be validated with more neurons and animals. The sample size is small ( i.e. n= 18 axons from 7 animals and not clear how many neurons. Given the significant variability of the data even within animals, these experiments and data are considered preliminary.

4) How does anesthesia influence these changes in structural plasticity observed? This was not addressed or discussed.

Reviewer #1:

Xu and colleagues compared the intersubject correlation (ISC) and intersubject functional connectivity (ISFC) of participants listening to narrative and argumentative texts while undergoing fMRI. Replicating earlier findings, they show that ISC in the DMN was greater when participants listened to an intact narrative than when they listened to a sentence-scrambled version of the same narrative. Listening to a sentence-scrambled argument elicited ISC in language and control regions of the brain, though interestingly, there was no region in the brain where ISC was greater when participants listened to an intact version of the argument. Instead, there was greater ISFC between the IPS and language areas of the brain when participants listened to the intact argument than when they listened to the scrambled argument. The authors interpret their results as suggesting that listening to the intact argument did not recruit additional brain systems, but instead promoted the cooperation between regions that were already involved in processing the argument.

Most prior work using "naturalistic stimuli" has examined the neural responses to narratives. This manuscript extends this work in an important way by examining how the brain responds to arguments, which comprise a non-trivial proportion of the linguistic content people are exposed to on a daily basis. The ISFC results (Fig. 7) are particularly noteworthy and novel. My main concerns have to do with the possibility that ISC for the scrambled argument seems to be stronger and more extensive than that for the intact argument, and how this might affect the authors' interpretation of their results. Below are some suggestions and comments which I think the paper could benefit from considering further:

1) I think it would be helpful to run the Scrambled Argument > Intact Argument ISC contrast. Visual inspection of Figure 2 suggests that ISC for the scrambled argument might be stronger than that for the intact argument, especially in control regions. If this is truly the case, I think the authors should discuss what this might imply about what is happening during the scrambled condition and if this affects thinking of the scrambled condition as a control for low-level linguistic features. In particular, the 2.97 out of 5 comprehensibility rating of the scrambled arguments suggests that participants might have understood the scrambled arguments. If participants are actively trying to make sense of the scrambled argument text, it seems like this could then drive observed differences in ISFC between the intact and scrambled arguments as well (e.g., decreased connectivity between control and language regions when trying to make sense of scrambled text, rather than increased connectivity between control and language regions when processing an intact argument).

2) More broadly, I think the authors need to make sure their effects aren't driven by the scrambled conditions. For example, for Figure 2 - figure supplement 2, the (Intact Narrative - Scrambled Narrative) > (Intact Argument - Scrambled Argument) contrast can be driven by high ISC in the Scrambled Argument condition, which would suggest a different interpretation of the results. My suggestion would be to run the contrast as (Intact Narrative - Scrambled Narrative) > max((Intact Argument - Scrambled Argument),0) to make sure that the contrast isn't driven by a negative value on the right hand side of the inequality.

3) Point 2 also applies to Figures 6 and 7. Relatedly, the rightmost panel of Figure 6C suggests that the analysis is indeed capturing some edges where the SES of the Scrambled Argument is greater than that of the Intact Argument.

4) How well do the vertexes identified in Figure 7D overlap with the Intact Argument > Resting map? Given the authors interpretation that the ISFC results suggest cooperation between areas involved in processing the intact stimulus, I think this should be properly assessed.

5) Both ISC and ISFC capture only signal that is shared across participants. Most narratives are crafted such that all listeners have a similar interpretation. This is unlike arguments, where different listeners might agree with an argument to a different extent. If listeners had differing interpretations of the argument, ISC/ISFC would miss brain activity/connectivity involved in processing an argument. I think this possibility should be considered and discussed, especially given the null DMN finding for the argumentative texts.

6) For the t-tests on the behavioral ratings , it looks like the authors collapsed over the two texts within a category. This doesn't seem right, given that the ratings for each text are dependent. A mixed model approach would be more appropriate. I doubt this will change the results, but I think it would be good to follow best practices when possible.

Reviewer #1:

Major issues:

I have two major comments on the work.

1) The authors motivate the work from the use of naturalistic speech, and the application of the developed method to investigate, for instance, speech-in-noise deficits. But they do not discuss how comprehensible the peaky speech in fact is. I would therefore like to see behavioural experiments that quantitatively compare speech-in-noise comprehension, for example SRTs, for the unaltered speech and the peaky speech. Without such a quantification, it is impossible to fully judge the usefulness of the reported method for further research and clinical applications.

2) The neural responses to unaltered speech and to peaky speech are analysed by two different methods. For unaltered speech, the authors use the half-wave rectified waveform as the regressor. For peaky speech, however, the regressor is a series of spikes that are located at the timings of the glottal pulses. Due to this rather different analysis, it is impossible to know to which degree the differences in the neural responses to the two types of speech that the authors report are due to the different speech types, or due to the different analysis techniques. The authors should therefore use the same analysis technique for both types of speech. It might be most sensible to analyse the unaltered speech through a regressor with spikes at the glottal pulses a well. In addition, it would be good to see a comparison, say of a SNR, when the peaky speech is analysed through the half-wave rectified waveform and through the series of spikes. This would also further motivate the usage of the regressor with the series of spikes.

Reviewer #1:

The authors studied the over-representation of imprinted genes in the mouse brain by using fifteen single-cell RNA sequencing datasets. The analysis was performed at three levels 1) whole-tissue level, 2) brain-region level, and 3) region-specific cell subpopulation level. Based on the over-representation and gene-enrichment analyses, they interpreted hypothalamic neuroendocrine populations and monoaminergic hindbrain neurons as specific hotspots of imprinted gene expression in the brain.

Objective:

Though the study is potentially interesting, the expression of imprinted genes in the brain and hypothalamus is already known (Davies W et al., 20005, Shing O et al. 2019, Gregg et al, 2010 including many other studies cited in the paper). However, the authors put forth two objectives, the first being whether imprinted gene expression is actually enriched in the brain compared to other adult tissues, where they did find brain as one of the tissues with over-represented imprinted genes. Secondly, whether the imprinted genes are enriched in specific brain regions. The study objectives cannot qualify as completely novel as it is the validation of most of what is already known using scRNA-seq datasets.

Methods and Results

Pros:

-15 scRNA-seq datasets were analysed independently and they were processed as in the original publication.

-Two enrichment methods used to find tissue-specific enrichment of imprinted genes and appropriate statistics applied wherever necessary.

Concerns:

-It is not clear how the over-representation using fisher's exact test was calculated? It would be appropriate to include the name of the software or R package, if used, in the basic workflow section of Materials and methods.

-Why did authors particularly use Liger in R for GSEA analysis?

-GSEA plots generated using Liger and represented for each analysis in the paper by itself does not look informative. For eg. in figure 4 and other GSEA plots in the paper- i) Which 'score' does the Y-axis represent? Include x-axis label and mention corrected GSEA q value either in the legend or the figure. ii) Was the normalized enrichment score (NES) calculated? What genes in the cluster represent maximum enrichment? A heat map of the imprinted genes contributing to the cell cluster will add more clarity to the GSEA plots.

-Apart from the tissue-specific enrichment of gene sets, a functional GO/pathways enrichment of the group of imprinted genes will strengthen the connection of these genes with feeding, parental behavior and sleep.

-Are these imprinted genes coexpressed across the analyzed brain structures, as the authors repeatedly stress on the functioning of imprinted genes as a group?

-A basic workflow schematic might be necessary for an easy and quick understanding of the methods.

Overall, the study gives some insight into the brain regions, particularly cell clusters in the brain where imprinted genes could be enriched. However, the nature of the study is preliminary and validates most of previous studies. The authors have already highlighted some of the limitations of the study in the discussion.

Reviewer #1:

The manuscript “A computationally designed fluorescent biosensor for D-serine" by Vongsouthi et al. reports the engineering of a fluorescent biosensor for D-serine using the D-alanine-specific solute-binding protein from Salmonella enterica (DalS) as a template. The authors engineer a DalS construct that has the enhanced cyan fluorescent protein (ECFP) and the Venus fluorescent protein (Venus) as terminal fusions, which serve as donor and acceptor fluorophores in resonance energy transfer (FRET) experiments. The reporters should monitor a conformational change induced by solute binding through a change of the FRET signal. The authors combine homology-guided rational protein engineering, in-silico ligand docking and computationally guided, stabilizing mutagenesis to transform DalS into a D-serine-specific biosensor applying iterative mutagenesis experiments. Functionality and solute affinity of modified DalS is probed using FRET assays. Vongsouthi et al. assess the applicability of the finally generated D-serine selective biosensor (D-SerFS) in-situ and in-vivo using fluorescence microscopy.

Ionotropic glutamate receptors are ligand-gated ion channels that are importantly involved in brain development, learning, memory and disease. D-serine is a co-agonist of ionotropic glutamate receptors of the NMDA subtype. The modulation of NMDA signalling in the central nervous system through D-serine is hardly understood. Optical biosensors that can detect D-serine are lacking and the development of such sensors, as proposed in the present study, is an important target in biomedical research.

The manuscript is well written and the data are clearly presented and discussed. The authors appear to have succeeded in the development of D-serine-selective fluorescent biosensor. But some questions arose concerning experimental design. Moreover, not all conclusions are fully supported by the data presented. I have the following comments.

1) In the homology-guided design two residues in the binding site were mutated to the ones of the D-serine specific homologue NR1 (i.e. F117L and A147S), which lead to a significant increase of affinity to D-serine, as desired. The third residue, however, was mutated to glutamine (Y148Q) instead of the homologous valine (V), which resulted in a substantial loss of affinity to D-serine (Table 1). This "bad" mutation was carried through in consecutive optimization steps. Did the authors also try the homologous Y148V mutation? On page 5 the authors argue that Q instead of V would increase the size of the side chain pocket. But the opposite is true: the side chain of Q is more bulky than the one of V, which may explain the dramatic loss of affinity to D-serine. Mutation Y148V may be beneficial.

2) Stabilities of constructs were estimated from melting temperatures (Tm) measured using thermal denaturation probed using the FRET signal of ECFP/Venus fusions. I am not sure if this methodology is appropriate to determine thermal stabilities of DalS and mutants thereof. Thermal unfolding of the fluorescence labels ECFP and Venus and their intrinsic, supposedly strongly temperature-dependent fluorescence emission intensities will interfere. A deconvolution of signals will be difficult. It would be helpful to see raw data from these measurements. All stabilities are reported in terms of deltaTm. What is the absolute Tm of the reference protein DalS? How does the thermal stability of DalS compare to thermal stabilities of ECFP and Venus? A more reliable probe for thermal stability would be the far-UV circular dichroism (CD) spectroscopic signal of DalS without fusions. DalS is a largely helical domain and will show a strong CD signal.

3) The final construct D-SerFS has a dynamic range of only 7%, which is a low value. It seems that the FRET signal change caused by ligand binding to the construct is weak. Is it sufficient to reliably measure D-serine levels in-situ and in-vivo? In Figure 5H in-vivo signal changes show large errors and the signal of the positive sample is hardly above error compared to the signal of the control. Figure 5G is unclear. What does the fluorescence image show? Work presented in this manuscript that assesses functionality and applicability of the developed sensor in-situ and in-vivo is limited compared to the work showing its design. For example, control experiments showing FRET signal changes of the wild-type ECFP-DalS-Venus construct in comparison to the designed D-SerFS would be helpful to assess the outcome.

4) The FRET spectra shown in Supplementary Figure 2, which exemplify the measurement of fluorescence ratios of ECFP/Venus, are confusing. I cannot see a significant change of FRET upon application of ligand. The ratios of the peak fluorescence intensities of ECFP and Venus (scanned from the data shown in Supplementary Figure 2) are the same for apo states and the ligand-saturated states. Instead what happens is that fluorescence emission intensities of both the donor and the acceptor bands are reduced upon application of ligand.

much more positive/optimistic outlook than hobbes

If women corrupted the corruption will grow in society, because women shape the half of society, beside they playing an important role in families. so it shows that when a women corrupted then his son can also follow his path.

what does Krishna meant by three world? is it the three world that they believe to the life after death?

Arjuna stated that there are all my kinsman such as: teachers, fathers, sons i do not want to go the battle and kill them. is Krishna meant only his kinsman the reason to not fight or he was not able to do it?

Krishna state that if i want kingship, it is not because of victory or pleasure. then what Krishna meant by delight and life itself in the statement?

Arjuna stated in this part to Krishna about his weakness that he is able to fight, he do not have power to fight in the battle.

This helps to address the institutional type question

Dominance through Elites. This is a grievous danger. We've seen a constant perversion of Genesis 1:27-28 (fruitful, multiply, dominate earth) by those who see this charge as an exclusive office rather than a "law" to be governed through 3rd density complexes! Dominion was designed to be a service to 2nd density and the way toward 4th Density for those in 3rd: NOT SERVICE TO SELF!

DOI: 10.1111/bph.15156

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Curator comments: allele name: c264Tg Danio rerio ZFIN Cat# ZDB-ALT-070316-1

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DOI: 10.1016/j.celrep.2020.03.024

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What is this?

DOI: 10.1016/j.celrep.2020.03.024

Resource: ZDB-ALT-180625-1

Curator: @ethanbadger

SciCrunch record: RRID:ZDB-ALT-180625-1

What is this?

DOI: 10.7554/eLife.55913

Resource: (ZFIN Cat# ZDB-GENO-060919-1,RRID:ZFIN_ZDB-GENO-060919-1)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-GENO-060919-1

What is this?

(Gilgamesh) lets (no) girl go free to (her bridegroom) What the author meant by this sentence?

(he was the vanguard) Gilgamesh was brave.

DOI: 10.1016/j.cels.2020.02.007

Resource: (ZFIN Cat# ZDB-ALT-100402-1,RRID:ZFIN_ZDB-ALT-100402-1)

Curator: @ethanbadger

SciCrunch record: RRID:ZFIN_ZDB-ALT-100402-1

What is this?

variant id lookup result: http://localhost:8001/test.html

Monarch lookup result: http://localhost:8001/test.html

DOI: 10.7554/eLife.47699

Resource: (ZFIN Cat# ZDB-ALT-100721-1,RRID:ZFIN_ZDB-ALT-100721-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-100721-1

What is this?

DOI: 10.2337/db19-0508

Resource: (IMSR Cat# KOMP_VG15351-1-Vlcg,RRID:IMSR_KOMP:VG15351-1-Vlcg)

Curator: @Naa003

SciCrunch record: RRID:IMSR_KOMP:VG15351-1-Vlcg

What is this?

DOI: 10.7554/eLife.48914

Resource: (ZFIN Cat# ZDB-ALT-130514-1,RRID:ZFIN_ZDB-ALT-130514-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-130514-1

What is this?

DOI: 10.7554/eLife.48914

Resource: (ZFIN Cat# ZDB-ALT-141008-1,RRID:ZFIN_ZDB-ALT-141008-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-141008-1

What is this?

DOI: 10.7554/eLife.54935

Resource: (ZFIN Cat# ZDB-ALT-120412-1,RRID:ZFIN_ZDB-ALT-120412-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-120412-1

What is this?

DOI: 10.1016/j.devcel.2020.03.017

Resource: (ZFIN Cat# ZDB-ALT-110705-1,RRID:ZFIN_ZDB-ALT-110705-1)

Curator: @ethanbadger

SciCrunch record: RRID:ZFIN_ZDB-ALT-110705-1

What is this?

DOI: 10.1016/j.devcel.2020.03.017

Resource: (ZFIN Cat# ZDB-ALT-150904-1,RRID:ZFIN_ZDB-ALT-150904-1)

Curator: @ethanbadger

SciCrunch record: RRID:ZFIN_ZDB-ALT-150904-1

What is this?

DOI: 10.1016/j.devcel.2020.03.017

Resource: (ZFIN Cat# ZDB-ALT-110421-1,RRID:ZFIN_ZDB-ALT-110421-1)

Curator: @ethanbadger

SciCrunch record: RRID:ZFIN_ZDB-ALT-110421-1

What is this?

DOI: 10.7554/eLife.54937

Resource: (ZFIN Cat# ZDB-ALT-080625-1,RRID:ZFIN_ZDB-ALT-080625-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-080625-1

What is this?

DOI: 10.7554/eLife.54937

Resource: (ZFIN Cat# ZDB-ALT-080528-1,RRID:ZFIN_ZDB-ALT-080528-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-080528-1

What is this?

DOI: 10.7554/eLife.54937

Resource: (ZFIN Cat# ZDB-ALT-080528-1,RRID:ZFIN_ZDB-ALT-080528-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-080528-1

What is this?

DOI: 10.7554/eLife.54937

Resource: (ZFIN Cat# ZDB-ALT-160120-1,RRID:ZFIN_ZDB-ALT-160120-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-160120-1

What is this?

DOI: 10.1016/j.cub.2020.04.020

Resource: (ZFIN Cat# ZDB-ALT-130506-1,RRID:ZFIN_ZDB-ALT-130506-1)

Curator: @ethanbadger

SciCrunch record: RRID:ZFIN_ZDB-ALT-130506-1

What is this?

DOI: 10.1016/j.cub.2020.04.020

Resource: (ZFIN Cat# ZDB-ALT-120117-1,RRID:ZFIN_ZDB-ALT-120117-1)

Curator: @ethanbadger

SciCrunch record: RRID:ZFIN_ZDB-ALT-120117-1

What is this?

DOI: 10.7554/eLife.53995

Resource: (ZFIN Cat# ZDB-ALT-181113-1,RRID:ZFIN_ZDB-ALT-181113-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-181113-1

Curator comments: ZFIN Cat# ZDB-ALT-181113-1

What is this?

DOI: 10.1016/j.celrep.2020.107790

Resource: (ZFIN Cat# ZDB-ALT-121009-1,RRID:ZFIN_ZDB-ALT-121009-1)

Curator: @ethanbadger

SciCrunch record: RRID:ZFIN_ZDB-ALT-121009-1

Curator comments: Zebrafish: Tg(hcrt:nfsB-EGFP)

What is this?

This poem is a sonnet with 3 quatrains (lines of 4) and a couplet (set of lines), so in order to help your comprehension, you want to divide the stanzas by the structure.

Themes in poetry show up in the last lines or last stanza. A theme is a universal statement about what the author believes.

If we consider that poems are either a MEMORY or an EMOTION we can use word choice to figure out the theme. This poem is about emotion, particularly about not giving in to an enemy.

So universal theme would be...

symbolism - McKay calls his enemies "monsters", but he doesn't seem to be fighting fictional beasts in the poem, so not literal monsters.

Poetry 101:

Step 1 - Read this poem. Consider this just your first run through. Take notice of punctation (periods, comma, etc.). That's where you stop when reading a poem.

Step 2 - Read the poem again - But now you want to think in Costa's Level 2. So, notice rhetorical devices and what they are doing in the text. Divide your stanzas so you can chunk information and analyze in pieces.

Step 3 - Find the theme. Theme is a universal statement that tells us how the author feels about a topic. This is not a subject - like the beach - but what does the beach, or the setting, mean to the author. Themes are found in the last lines or the last stanza of a poem.

The world continues to change, warp, consume, ebb, and flow around us - good and bad directions.

So if we write a universal theme...

It's easy to ignore the word "but"; however, this is a conjunction that tells you the author is about to flip everything they've been saying.

It's a tiny word with a major impact. So this is a good place to make a division in a poem. To see what was being established, and how the author now wants to change it.

DOI: 10.1016/j.cub.2020.05.016

Resource: (ZFIN Cat# ZDB-ALT-150717-1,RRID:ZFIN_ZDB-ALT-150717-1)

Curator: @ethanbadger

SciCrunch record: RRID:ZFIN_ZDB-ALT-150717-1

What is this?

We mus examine life to be able to choose. We are born taught things that sometimes we don't understand but we just know because it was passed down to us, but it is up to us to experience these things t be able to decide for ourselves.

definition

• Point = That of which there is no part.
• Line = a length without breadth.

Ahuja, A. (2020, June 16). The two-metre rule and other imperfect Covid-19 risk calculations | Free to read. https://www.ft.com/content/30adbfa1-178f-47e2-97a8-bd41ca590999

the reason why the author wrote this article

Avis personnel de l'auteur et vision assez limitée du champ des possibles qu'offre une (des) rencontre(s) qu'elle soit virtuelle ou non.

L'auteur ne va t-il pas un peu vite dans sa déduction , le communautarisme comme il est évoqué ici appartient à un autre débat

Perception personnelle et réductrice de l'auteur qui ne représente pas la réalité. Les utilisateurs doivent-ils justifier d'une attente particulière pour motiver leur inscription ?

L'auteur se confère des qualités d'expert en évoquant une série d'hypothétiques troubles névrotiques liés à l'utilisation des sites de rencontres. Il vulgarise et banalise des pathologies sérieuses.

L'usage pathologique d'internet est réel, mais l'auteur invoque ici un problème de taille sans s'appuyer sur une source solide illustratrant la réalité des risques encourus par les utilisateurs des sites de rencontres.

Voici le premier argument. En effet, il nous est dit que ce phénomène de dispersion atteint son paroxysme avec les femmes actives qui doivent gérer vie professionnelle et vie de famille, mais il serait bon d'approfondir d'avantage. Nous pouvons nous demander pourquoi cela se produit chez les femmes actives. Nous pouvons donc réfléchir si cela n'est pas dû à ce qu'on appelle la charge mentale, puisque ce sont majoritairement les femmes qui s'occupent du foyer et des enfants. A l'inverse, nous pouvons nous demander si la dispersion n'atteint pas son paroxysme parce qu'elles sont d'avantage multitâches que les hommes. En effet, elles sont capables de faire plusieurs tâches simultanément, et cela peut aussi engendrer une dose supplémentaire de travail. Ce phénomène de multitâche peut donc entraîner d'avantage de dispersion de leur part.

I would hazard that US is far above optimal state size for wellbeing and wisdom.

But why does it rain money on them? And who else does that?

This is the scope of Test 2.

Figaro est sarcastique sur la censure

Il y a du mouvement. Il s'assoit et se lève

Il est dégoûté de lui-même et veut avoir une carrière honnête

Il parle de malhonnêteté et de ses avantages

Il parlait de ses différentes professions.

Et ainsi commence le plaidoyer de l'auteur et de son point de vue rhétorique, mettant bien en avant la puissance du numerique sur le biologique dans l'idée collective

optimal questions

YES I THOUGHT THE EXACT SAME FUCKING THING

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Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001263

variant id lookup result: https://www.ncbi.nlm.nih.gov/clinvar/variation/72912/

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0002591

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0000463

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0003196

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allele id lookup result: https://reg.clinicalgenome.org/redmine/projects/registry/genboree_registry/by_caid?caid=CA414193321

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001166

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Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001263

DOI: 10.7554/eLife.46275

Resource: (ZFIN Cat# ZDB-GENO-090127-1,RRID:ZFIN_ZDB-GENO-090127-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-GENO-090127-1

What is this?

DOI: 10.7554/eLife.46275

Resource: (ZFIN Cat# ZDB-GENO-071218-1,RRID:ZFIN_ZDB-GENO-071218-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-GENO-071218-1

What is this?

DOI: 10.7554/eLife.42455

Resource: (ZFIN Cat# ZDB-ALT-090917-1,RRID:ZFIN_ZDB-ALT-090917-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-090917-1

What is this?

DOI: 10.1111/bph.14614

Resource: (IMSR Cat# KOMP_VG14098-1-Vlcg,RRID:IMSR_KOMP:VG14098-1-Vlcg)

Curator: @ethanbadger

SciCrunch record: RRID:IMSR_KOMP:VG14098-1-Vlcg

What is this?

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0410211

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allele id lookup result: http://reg.clinicalgenome.org/redmine/projects/registry/genboree_registry/by_caid?caid=CA399185143

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variant id lookup result: https://www.ncbi.nlm.nih.gov/clinvar/variation/692266/

variant id lookup result: https://www.ncbi.nlm.nih.gov/clinvar/variation/692268/

allele id lookup result: http://reg.clinicalgenome.org/redmine/projects/registry/genboree_registry/by_caid?caid=CA8805224

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Big plus one and resolving disagreements in core beliefs is really hard esp when at 1st tier levels. This needs major transformation of being work.

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001290