On 2019-03-21 17:36:33, user Valerie Wood wrote:

Now published in Open Biology<br /> https://royalsocietypublish...

On 2019-03-21 15:15:48, user Howard Salis wrote:

Overall, this is a well-written & intriguing manuscript with a substantial & diverse collection of supporting data that will add to the ongoing debate over the translational ramp hypothesis. However, I have some simple questions, and even though no one really uses the comments section in bioRxiv, I'll take the dive.

1. In the abstract, the authors state that "The observed difference [in protein abundance] is not dependent on tRNA abundance, efficiency of translation initiation, or overall mRNA structure." but in the main text, they write "In summary, we show that the efficiency of protein synthesis in addition to overall mRNA structure and codon content is strongly dependent on the nucleotide sequence positions 7-15 and the resulting protein amino acid positions 3-5." These two sentences are quite dissimilar, and only the conclusion in the main text is supported by the authors' data.

2. The stability of mRNA structures located solely within the N-terminal CDS sequence are known to alter the rate of translation initiation and protein synthesis rate [https://academic.oup.com/na...]. Further, if the first 15 nucleotides of a CDS are altered, it's very possible that those nucleotides will base pair with the 5' UTR sequence to form additional mRNA structures that will inhibit translation initiation [https://www.nature.com/arti...]. The effect sizes are very large; the presence of one stable mRNA structure (dG = -15 kcal/mol) within 15 nucleotides of the start codon will repress translation initiation by about 850-fold.

3. The authors mention mRNA structure several times, but the manuscript's main text does not include any mRNA structure calculations that test whether their presence & stability could explain the differences in protein abundance or expression level data.

4. Whenever a physical phenomenon has multiple overlapping mechanisms [as is very likely the case here], it's always difficult to design experiments that clearly distinguish the mechanisms and quantify their effects separately. The authors' in vitro data using FRET and mis-charged tRNAs are a step in the right direction towards sussing out these mechanisms. But it's important to place these additional measurements in a systems-wide perspective. For example, if the FRET measurements report an increase in the abundance of ribosome initiation complex, then the cause of that increase could be either an increase in the rate of ribosome recruitment/initiation OR a slow down in the transition to the processive elongation phase. Either cause would have the same effect. This can be clearly shown by deriving a simple kinetic model of the process.

5. The measurements using mischarged tRNAs are great, though error bars and an F-test are absolutely needed to determine whether the differences are significant. Protein abundances are varying by >100-fold, but the measurements of translation processivity are changing by 1.4-fold. Again, multiple mechanisms could be responsible for the overall large change in protein expression levels, and amino acid content in the ramp has the potential to contribute, but it's important to quantify its magnitude.

6. If there are special mRNA and amino acid motifs that specifically alter translation elongation rates, why would they only have an effect when present within the first 5 codons? The ribosome very quickly transitions to a processive state after initiation. Is the argument that a still-bound initiation factor is responsible for mediating these effects, which are lost after the initiation factor dissociates? If so, that would require some proof!

On 2019-03-21 09:56:19, user didier Fesquet wrote:

together with https://journals.plos.org/p...<br /> a functional role for the isoform specific Nter domain of FBW7a

On 2019-03-20 17:14:20, user Aaron Liston wrote:

You should cite the paper where the concept of 'genome skimming' was first described and the term was coined.<br /> https://bsapubs.onlinelibra...

On 2019-03-20 16:25:43, user Rosa Fasching wrote:

That's why the pioneers (#KrebstherapiederAltmeister) said long time ago, no matter what kind of diet it is (Bircher-Benner, Gerson, Mayer, Kuhl etc.) it all depends on the microbes in your gut. They are the key players and "chefs".

On 2019-03-20 15:52:51, user Lawrie wrote:

A novel twist on Coley's toxins/vaccine, that was curing some cancers over 120 years ago in London using the same principle of bacteria triggering the immune response.

On 2019-03-20 15:27:31, user Claudiu Bandea wrote:

Free-Living Chlamydiae?

The finding that new chlamydial lineages, identified as metagenome-assembled genomes (MAGs), dominate the microbial communities in sediment cores from a region surrounding Loki’s Castle hydrothermal vent field (1) is a remarkable discovery.

Based on estimates of genome replication rate using the iRep algorithm (2), and in context of results indicating the absence of putative eukaryotic host cells in the sediments, the authors proposed that their new MAGs are derived from actively dividing chlamydia not associated with eukaryotic hosts. If true, this represents the first examples of ‘free-living chlamydiae’, an extraordinary finding given that all previously studied chlamydial lineages, both pathogenic and environmental, are obligate intracellular organisms (3,4).

However, the authors’ interpretation of the iRep results and suggestion of actively dividing extracellular chlamydiae might be questionable. A more likely explanation is simply that the chlamydial EBs, the extracellular spore-like cells in the chlamydial life cycle, contain a chlamydial genome at various stages of replication.

It is highly conceivable that during the differentiation of the intracellular dividing chlamydial cells, the RBs, into EBs, the chlamydial genomes is in process of replicating, which is ‘arrested’ during the differentiation process. Addressing this hypothesis, which is relevant for the entire chlamydial field, is relatively straightforward: perform a MAG-like sequencing experiment and an iRep study on a population of purified EBs from chlamydial lineages that can be grown in culture.

One additional comment on authors’ interpretation of the results and their proposal of putative free-living chlamydiae. Apparently, the authors failed to consider that the genome size of the new chlamydial MAGs is intermediary between that of environmental chlamydiae and the pathogenic chlamydiae, all of which are obligate intracellular lineages. Given that thousands of intracellular parasitic or symbiotic lineages have a smaller genome/proteome compared to that of their free-living relatives (4), it is very likely that the newly discovered marine sediment chlamydiae followed a similar evolutionary pathway (see also Ref. 5).

References

1. Dharamshi J, Tamarit D, Eme L et al. 2019. Marine sediments illuminate Chlamydiae diversity and evolution. bioRxiv: doi: https://doi.org/10.1101/577767 ;

2. Brown CT, Olm MR, Thomas BC, Banfield JF. 2016. Measurement of bacterial replication rates in microbial communities. Nat Biotechnol. 34(12):1256-1263; https://www.ncbi.nlm.nih.go...

3. Subtil A, Collingro A, Horn M. 2014.Tracing the primordial Chlamydiae: extinct parasites of plants? Trends Plant Sci. 19(1):36-43; https://www.ncbi.nlm.nih.go...

4. Taylor-Brown A, Vaughan L, Greub G, Timms P, Polkinghorne A. 2015. Twenty years of research into Chlamydia-like organisms: a revolution in our understanding of the biology and pathogenicity of members of the phylum Chlamydiae. Pathog Dis.;73(1):1-15. https://www.ncbi.nlm.nih.go...

5. Bandea C. Evolution of giant viruses from larger ancestors. 2018. Comment in bioRxiv on “Virus genomes from deep sea sediments expand the ocean megavirome and support independent origins of viral gigantism”; doi: https://doi.org/10.1101/469... https://www.biorxiv.org/con...

On 2019-03-20 14:42:42, user Maria Carolina Medina Gomez wrote:

Could not be the improvment driven also by the difference in number of haplotypes in the reference panels, as the ethnically similar one contained more haplotypes... I would even try to combine the 2 reference panels to see if by using a MIXED but larger reference panel actually resulted in less imputation quality of rare variants and declined the number of associations detected

On 2019-03-20 09:41:33, user Michael Hochberg wrote:

We found broadly similar conclusions (major reductions in drug levels) in a study of how information about tumor growth could be used to optimize treatment strategies. See <br /> 10.7554/eLife.06266

On 2019-03-20 09:09:31, user Roland Krämer wrote:

Final peer-reviewed manuscript published in BioScience https://doi.org/10.1093/bio...

On 2019-03-20 04:26:52, user anonymous wrote:

The title doesn't accurately convey just how interesting this is. Perhaps alter it to something like "Evolutionary loss of spindle assembly checkpoint function in chordate early development"?

On 2019-03-20 00:30:07, user Charles Warden wrote:

I have not submitted something to F1000 Research myself, but I thought the article would be posted while you were awaiting reviewers.

While I admittedly don't see a "pysradb" article in F1000 research, I thought it was a little strange for the bioRxiv pre-print to be formatted as a F1000 pre-submission. I know of one article that was submitted to PLOS ONE that automatically got deposited into bioRxiv, but I didn't think F1000 did something similar. Is there a reason this pre-print is formatted as a F1000 pre-submission?

On 2019-03-19 19:55:41, user JULIUS ADLER wrote:

As previously reported (bioRxiv October 1, 2018), in

Drosophila Mutants that Are Motile but Respond Poorly to All Stimuli Tested:<br /> Mutants in RNA Splicing and RNA Helices, Mutants in The Boss<br /> Lar L. Vang and Julius Adler

a mutant of Drosophila was isolated that is motile but lacks responses to all attractants and repellents tested. We finally decided to call that gene after The Boss because it was stated in bioRxiv October 1, 2018,

“Do organisms have a director? Another case where a single mutation would cause a great many different defects in behavior is 'The Boss’. The idea of The Boss was introduced by Adler in ‘My Life with Nature’, p. 60 of Annual Review of Biochemistry 2011: 'The Boss is the thing inside every organism - human, other animals, plants, microorganisms - that is in <br /> charge of the organism. All things that an organism does are controlled by The Boss: etc."

But naming this mutant after The Boss (it’s just a name) does not guarantee that The Boss really exists. Other than the fact that all responses are missing in this mutant, there is not yet any evidence that The Boss exists. Further work will need to be done to find out if it exists.

On 2019-03-11 20:52:31, user Julius Adler wrote:

March 11, 2019

Add the following to the end of the Discussion of October 1, 2018

Drosophila Mutants that Are Motile but Respond Poorly to All Stimuli Tested:<br /> Mutants in RNA Splicing and RNA Helicase, Mutants in The Boss<br /> Lar L. Vang and Julius Adler

Relation of this paper (October 1, 2018) to the idea of generalized arousal:

Specific responses have now been recognized as different from a generalized response, also known as generalized arousal.

The idea of a generalized arousal was described by Donald Pfaff, Lars Westberg, and Lee-Ming Kow (2005): “Many of the major advances in neurobiology during past decades dealt with specific central nervous system responses to particular environmental or internal stimuli…Fundamental to all emotional and motivational states is the concept of ‘arousal’ of the central nervous system, the 'activation of behavior.' Going under different names - elementary , fundamental, global, or generalized arousal - all of these terms refer to the same primitive capacity of the vertebrate central nervous system. Arousal underlies all mammalian behaviors…Gene/behavior thinking, even in Drosophila, is moving beyond single gene effects (Robert Anholt, 2004).”

As to arousal in Drosophila, Bruno van Swinderen and Rozi Andretic (2003) have written: “Changes in levels of arousal, such as occur during sleep or attention, most likely accomplish adaptive functions common to most animals. Recent evidence demonstrating changing arousal states in Drosophila melanogaster complements other behavioral research in this model organism. Herein we review the methodology related to the study of circadian rhythms, sleep and anesthesia where arousal, or lack of it, plays an essential role…Wakefulness is a state of increased arousal compared to sleep.”

The Boss (Adler, p. 60 of 2011) is related to generalized arousal but The Boss goes further, see October 1, 2018, above.

Adler J ( 2011) My life with nature. Annu Rev Biochem. 80:42-70.

Anholt RRH (2004) Genetic nodules and networks for behavior: lessons from Drosophila. BioEssays 26:1299-1306.

Pfaff D, Westberg L, Kow L-M (2005) Generalized arousal of mammalian central nervous system. J Comp Neurol 493:1-22.

van Swinderen B, Andretic R (2003) Arousal in Drosophila. Behav Proc 64:133-144.

Vang LL, Adler J (October 1, 2018) Drosophila mutants that are motile but respond poorly to all stimuli tested: Mutants in RNA splicing and RNA helicase, mutants in The Boss. bioRxiv

On 2019-03-19 17:53:15, user Fraser Lab wrote:

The major goal of this paper is to create a predictive model for RNA tertiary interactions. The authors use an on-chip method to monitor the interaction between two separate RNAs (the tectoRNA heterodimer), one flowed in and one bound to the chip. The observe a surprising effect on the exact helical conformation of the non contacting parts of the complex. This led them to develop a new model for how the precise helical conformation can alter the dG of binding. Particularly cool aspects of the paper are the ensemble nature of the prediction, the blinded predictions, and the ability to iterate to identify corrections to outliers (figure 5).

This is a very well written, clear and important paper. It solves a hard problem that few have approached with such thoroughness and quantification.

My only suggestions are:

Not to bury the somewhat orthogonal structure prediction contained in Supplementary FIgures 12 and 13 - as they go a long way to convince me that there is going to be some generality to this method.

and<br /> Perhaps it's more completely explained in previous papers - but is there any validation of the dG obtained by the on chip experiments vs more traditional (low throughput?) solution methods? There are some small idiosyncrasies of the on chip methods (e.g. lack of diffusion of the chip bound RNA) that could create small, correctable biases. A small discussion or pointer to previous references in the second paragraph of results, which demonstrate the reproducibility of the results would suffice.

I review non-anonymously: James Fraser, UCSF. Will be posting this review on BioRxiv

On 2019-03-19 06:33:52, user Xing Xu wrote:

I will suggest putting APOE somewhere in the title. It will give the article a huge boost, considering how notorious APOE4 is in Alzheimer's community.

On 2019-03-19 00:40:56, user Andy Gross wrote:

Thank you for this very valuable resource. Could you please include additional information on the "SR background noise filter" as described in Module 06 of supplementary figure 2. Understanding the full filtering pipeline is essential to using this as a resource for annotating variants based on this data. As it stands, it is not currently possible to distinguish between variants that are common but filtered, and variants that are rare.

On 2019-03-18 20:58:48, user Tulsi Ram Damase wrote:

Great work. Better to cite this paper,

https://www.future-science....

Purification of single-stranded DNA by co-polymerization with acrylamide and electrophoresis.

On 2019-03-18 16:33:25, user Stephen Floor wrote:

Very curious what diffusivity looks like using SMdM if you deplete ATP!

On 2019-03-18 10:32:07, user Federico Giorgi wrote:

Are SHANK2 SVs homozygous or heterozygous?

On 2019-03-17 21:14:15, user Arie Horowitz wrote:

There is no access to the supplementary files. Please upload them. Thank you<br /> Arie Horowitz

On 2019-03-17 19:46:32, user Connor Miller wrote:

Dear colleagues,

I'm heavily involved in the development of methods for genome-based species delineation and I'm sorry that I have to report several flawed assumptions in your method paper which affect the overall conclusion of your work. You can of course ignore this comment, but please keep in mind that the below points are crucial and ultimately affect how your study will be accepted, used and cited in the scientific community. The below points hold true regardless of whether or not you succeed with the publication in one or the other journal.

1) line 26: average nucleotide identity is not the "method of choice" and it is not the "gold standard" as you also claim further below. That is, if the authors had carefully studied the literature, especially the paper introducing the term OGRI (doi: 10.1099/ijs.0.054171-0), they would have learned that digital DDH (doi: 10.1186/1471-2105-14-60 and 10.4056/sigs.531120) is another important method in the field that was shown to outperform several ANI implementations (doi: 10.1186/1471-2105-14-60 and 10.4056/sigs.531120). How come that crucial methods with partly over 1000 citations are overlooked resulting in such plainly false statements?

2) line 31: if the authors want to propose a new method for species delineation, they have to provide evidence that the method correlates well with datasets of empirical DDH. This requirement was explicitly stated by the "Report of the ad hoc committee for the re-evaluation of the species definition in bacteriology" in 2002 (doi: 10.1099/00207713-52-3-1043):

"[...] Investigators are encouraged to propose new species based upon other genomic methods or techniques provided that they can demonstrate that, within the taxa studied, there is a sufficient degree of congruence between the technique used and DNA:DNA reassociation. [...]"

Correlation to empirical DDH is in fact the one and only optimality criterion against which *any* new method has to be benchmarked against. Assessing predictive accuracy is not asked for in this context because this can greatly distort the conclusions as one is not evaluating the OGRI methods on their full scales via correlation but rather compares them on a binary "same species/distinct species" level, that way loosing a lot of information.

The reason why a good correlation to empirical DDH is so important is that the pragmatic species concept of prokaryotes is ultimately based on conventional DDH for decades now. To safeguard consistency in prokaryotic species delineation, new methods have to show sufficiently high correlation values and also have to compare with other methods in that field. So the question is not how "fast and simple" a particular new method is but primarily how well does it mimic DDH without mimicking its pitfalls.

3) line 55: "Average nucleotidic identity (ANI) is nowadays the mostly acknowledged OGRI for assessing relatedness between genomic sequences." -> This is utterly wrong (see doi: 10.1186/1471-2105-14-60 and 10.4056/sigs.531120). Did no one check the literature before submitting the work?

4) line 63: calculation time is a secondary criterion. The primary criterion is whether or not the species delineation method is sound from a methodological perspective and whether or not it correlates well with conventional DDH. In the age of multicore processors, cloud computing, and high throughput computing clusters, the question whether a method takes 1, 2 or 3 seconds is rather negligible. An emphasis on speed and simplicity is indeed counterproductive unless the authors show that their method outperforms existing methods in the field via the previously mentioned optimality criterion.

5) line 66: That the authors' method is faster than e.g. ANI is obvious because they operate on k-mers and not on raw sequence data, that way reducing complexity. But is a reduction in complexity what we want, i.e., discarding the information provided by the full sequences and the resulting alignments?

6) line 90: How can benchmark dataset restricted to a single genus (Pseudomonas) be valid for establishing a universal method for the species delineation in Bacteria and Archaea??? What if your method works well for Pseudomonas but not so for, say, Burkholderia?

7) line 95: At the core of the authors' method is the percentage of shared k-mers between two respective genome sequences. However, earlier studies already reported a significantly lower performance of methods based on maximal unique matches (which is a special case of a k-mers). Moreover, why don't the authors discuss that the k-mer approach doesn't allow for any filtering by e-value or any other statistical criterion (as BLAST does)?

8) line 132: Again, the authors claim that ANIb is a the best method for genome-based species delineation. That is simply not true (see doi: 10.1186/1471-2105-14-60 and 10.4056/sigs.531120). Apart from that you have to compare to empirical DDH data, not ANI values. The latter only results in an increased level of indirection and and increased risk for an error of propagation.

9) line 187: Again, no, ANIb is not the current "gold standard" (see doi: 10.1186/1471-2105-14-60 and 10.4056/sigs.531120). Apart from being misleading, the term "gold standard" can only apply to conventional DDH to which all novel in silico methods have to compare to (see above).

10) line 217: Apart from the problems mentioned above, the authors claim that the approach could provide a rapid complementary approach for bacterial classification. But how can it be complementary if it is not universal because it was only validated against a Pseudomonas dataset? In line 198 even state: "[...] it is likely that the percentage of shared k-mers has to be adapted when investigating other bacterial genera [...]". If at all, the scientific community needs a universal tool, not a tool totally detached from DDH (see above) and only valid for a specific genus.

11) Language is not good. Authors should consider proof-reading by a native speaker

12) some typos I found (not complete list):

line 62: NBCI -> NCBI<br /> line 55: nucleotidic -> nucleotide<br /> line 138: "genomes sequences" -> genome sequences<br /> line 143: "is closed to" -> is close to<br /> line 228: "Finer-grained ..." -> A more fine-grained

On 2019-03-17 09:47:14, user Tobias Aurelius Knoch wrote:

the paper is a nice attempt, however, it misses (or deliberately neglects) important papers both in respect of the history (e.g. we even hold the patent for T2C often related to captureC) of the field as well as the results and their impact (all is on the internet available !!!):

Wachsmuth, M., Knoch, T. A. & Rippe, K. Dynamic properties of independent chromatin domains measured by correlation spectroscopy in living cells. Epigenetics & Chromatin 9:57, 1-20, 2016.

Knoch, T. A.@, Wachsmuth, M., Kepper, N., Lesnussa, M., Abuseiris, A., A. M. Ali Imam, Kolovos, P., Zuin, J., Kockx, C. E. M., Brouwer, R. W. W., van de Werken, H. J. G., van IJcken, W. F. J., Wendt, K. S. & Grosveld, F. G. The detailed 3D multi-loop aggregate/rosette chromatin architecture and functional dynamic organization of the human and mouse genomes. Epigenetics & Chromatin 9:58, 1-22, 2016.

Jhunjhunwala, S., van Zelm, M. C., Peak, M. M., Cutchin, S., Riblet, R., van Dongen, J. J. M., Grosveld, F. G., Knoch, T. A.+ & Murre, C.+ The 3D-structure of the Immunoglobulin Heavy Chain Locus: implications for long-range genomic interactions. Cell 133(2), 265-279, 2008.

Rauch, J.*, Knoch,T. A.*, Solovei, I., Teller, K. Stein, S., Buiting, K., Horsthemke, B., Langowski, J., Cremer, T., Hausmann, M. & Cremer, C. Lightoptical precision measurements of the Prader- Willi/Angelman Syndrome imprinting locus in human cell nuclei indicate maximum condensation changes in the few hundred nanometer range. Differentiation 76(1), 66-82, 2008.

Knoch, T. A. Simulation of different three-dimensional models of interphase chro­mosomes compared to experiments - an evaluation and review framework of the 3D genome organization. Seminars in Cell and Developmental Biology, 2018.

Knoch, T. A. Dreidimensionale Organisation von Chromosomen-Domänen in Simulation und Experiment. (Three-dimensional organization of chromosome domains in simulation and experi­ment.) Diploma Thesis, Faculty for Physics and Astronomy, Ruperto-Carola University, Heidelberg, Germany, 1998 and TAK†Press, Tobias A. Knoch, Mannheim, Germany, ISBN 3-00-010685-5 and ISBN 978-3-00-010685-9 (soft cover, 2rd ed.), ISBN 3-00-035857-9 and ISBN 978-3-00-0358857-0 (hard cover, 2rd ed.), ISBN 3-00-035858-7 and ISBN 978-3-00-035858-6 (DVD, 2rd ed.), 1998.

Knoch, T. A. Approaching the three-dimensional organization of the human genome: structural-, scaling- and dynamic properties in the simulation of interphase chromosomes and cell nuclei, long- range correlations in complete genomes, in vivo quantification of the chromatin distribution, con­struct conversions in simultaneous co-transfections. Dissertation, Ruperto-Carola University, Hei­delberg, Germany, and TAK†Press, Tobias A. Knoch, Mannheim, Germany, ISBN 3-00-009959-X and ISBN 978-3-00-009959-5 (soft cover, 3rd ed.), ISBN 3-00-009960-3 and ISBN 978-3-00-009960-1 (hard cover, 3rd ed.), ISBN 3-00-035856-9 and ISBN 978-3-00-010685-9 (DVD, 3rd ed.) 2002.

On 2019-03-17 06:50:00, user Simen Sandve wrote:

Interesting paper! I found a tiny detail you should correct in the next version. You refer to a ‘rainbow trout WGD’, but there is no such thing. Rainbowtrout is a salmonid fish, and all salmonid species share an ancestral WGD about 80-100 MYA. Hence, you should refer to this WGD event as the ‘salmonid WGD’.

On 2019-03-16 10:58:46, user David Peters wrote:

The study is missing several basal crocodylomorphs: Junggarsuchus, Pseundhesperosuchus, Trialestes, Lewisuchus, Gracilisuchus, Saltopus, Scleromochlus and Terrestrisuchus.

On 2019-03-16 02:10:00, user Kim Park wrote:

The results will be more convincing in brain tissues combined with single cell RNA-seq and single cell ATAC-seq, Dr. Li-Huei Tsai's recent work on AD using single cell techniques reveal interesting findings.

On 2019-03-15 20:32:32, user Patrick Hu wrote:

Very interesting! We also identified a complex rearrangement in a mutant that emerged from a screen for suppressors of the eak-7;akt-1 dauer-constitutive phenotype. The dauer suppression is the consequence of akt-2 duplication. G3 doi: 10.1534/g3.115.024257

On 2019-03-15 12:15:38, user nelzo ereful wrote:

Just curious -- you mentioned you used GATK ASEReadCounter at its default parameters. This filters out duplicated reads. Why is this so? What if the duplicate read(s) is/are authentic expression copies of a gene? thanks...

On 2019-02-22 14:14:01, user Simon Castellano wrote:

"Expression levels for allele-specific analyses were represented as read counts overlapping informative SNP positions."

There may be instances when reads contain two or more SNPs (don't know if this happens in stickle fish datasets but it happens between inter-specifically related rice genotypes). Ideally, a read contains one SNP (1:1). What did you do to avoid overestimating the number of reads containing 2 or more SNPs?

Also, how do you confirm whether the asymmetrical expression is due to cis/trans interactions and not because of allele-specific copy number variations in the heterozygote? cheers

On 2019-02-22 10:24:52, user Simon Castellano wrote:

Good paper. <br /> "We tested for ASE in each informative SNP position using a binomial exact test in R<br /> and an FDR level of 10%."<br /> why the FDR is high (10%)? usually it is 5% or 0.5% (e.g. McManus et al. 2010)

On 2019-03-15 11:32:42, user Vladimir Chubanov wrote:

Very nice work, congratulation! The suggested link between tissue<br /> calcification and magnesium balance via renal TRPM6 is very exciting. However, please<br /> note that mice with kidney-restricted KO of Trpm6 displayed unchanged magnesium<br /> homeostasis (Chubanov et al, eLife 2016).

On 2019-03-14 20:49:17, user Tara Thorpe wrote:

Isn’t cannabis sativa technically European hemp? The cannabis we consume would technically be cannabis Indica & afghanica. Please correct me if I’m wrong, my sources are loose- although I am a cannabis industry professional.

Thank you!

On 2019-03-14 19:50:02, user Charles Warden wrote:

Is there a typo in "We filtered for samples with ≤ 0.1 missingness, sites with = 0.0 missingness, and MAF ≥ 0.05."?

For example, do you mean you filtered samples with "> 0.1 missingness" overall?

On 2019-03-14 14:15:02, user Venkat Krishnan wrote:

Very interesting work !! All the best with your publication

just a minor mistake in page 3 : LXR agonist is GW3965 and not GW2965 :)

On 2019-03-14 06:11:14, user Trudy Oliver wrote:

Look forward to diving into this. Thanks for sharing on BioRxiv! Btw, we wish for you to refer to our mouse model (RPM) as having "over-expression of MYC" rather than "MYC amplification" to be clear that this is not genomic amplification. We should also keep in mind the mouse harbors point mutant MYC T58A that doesn't occur in human SCLC, but we do think it does a good job mimicking high levels of MYC protein.

On 2019-03-14 01:10:12, user Charles Warden wrote:

I noticed that the earlier EDGE paper wasn't cited in this pre-print (or listed in the "software" section of the Storey website):

https://academic.oup.com/bi...

Perhaps this citation should be added?

I thought the "ODP in EDGE (Storey et al. 2005)" plot in Figure 2 of the previous EDGE publication reminded me of the mODP plots in Figure 3 this pre-print. It looks like the "UW Biostatistics Working Paper Series" citation in the previous EDGE paper later became the peer-reviewed citation [3] in this pre-print (and I'm sure you understand everything better than I do). However, I thought the main result that caught my eye was Figure 2 in the earlier EDGE paper, so I thought I should say something.

I hope everything is going great with you!

On 2019-03-13 22:10:07, user Nicolas Jorge Betancourt wrote:

Is it possible to add my ORCID?

Nicolas Betancourt: 0000-0002-8768-9829

On 2019-03-13 15:27:32, user Rocky Fan wrote:

Interesting study, but I am wondering if there is any example of silencers that are H3K4me1 (and even H3K27ac) positive.

On 2019-03-13 15:18:48, user Takashi Koyama wrote:

Hello, I have a question about the number of cycles in sequencing step.

In the excellent manuscript, the author mentioned that the read1 sequencing was performed for 6-21 cycles and read2 for 54-70 cycles. However, the R1 and R2 fastq data deposited in the EMBL databank seem to have apx. 20- and 60-nt in length, respectively. I am not sure but the author might use different parameter of the cycles for the deposited data.

I would like to set up BRB-seq in our lab and hence would like to know exact parameter of cycles for R1, R2, idx1 and idx2.

Thank you for your kind helps.

Regards<br /> Takashi

On 2019-03-13 14:56:53, user Iain Wilson wrote:

It is difficult to assess the glycomic information without the supplement and it would be easier if the origin of the cell lines (e.g., insect, which is biologically meaningful) is more specifically integrated into the main text. Nevertheless, it is clear that glycosylation must have a role in host/vector-virus interactions; perhaps also consider citing work by Gavin Screaton on interactions of lectins with Dengue virus derived from different cells.

On 2019-03-13 13:49:21, user Carlos Eduardo Wetzel wrote:

This set a very harmful precedent concerning the rules of plant nomenclature (your species is already invalid one). This should not be allowed by BioRxiv. Check some rules for validation of taxon names here: https://www.iapt-taxon.org/...

On 2019-03-13 13:07:13, user sandeep chakraborty wrote:

Not just DNA templates,

both AAV and the Cas9 integrate in their genomes.

Data from 5 diff studies (will be adding more as and when I find more).

Should be really unacceptable.

https://sanchakblog.wordpre...

On 2019-03-13 11:53:07, user Giorgio Cattoretti wrote:

The method proposed for AF subtraction is in fact a method for object subtraction, based on thresholding and segmenting and results in loss of substantial information from the image.<br /> Fig.2 indeed shows entire macrophages removed.

Prior art, not quoted by the Authors, has a more efficient and intelligent method of subtracting the AF signal pixel by pixel, maintaining the full information even in autofluorescent objects and does not need sophisticate equipment or software.<br /> Pang, Z, et al Autofluorescence removal using a customized filter set. Microsc Res Tech. 2013;76:1007–1015. DOI: 10.1002/jemt.22261<br /> Pang, Z, et al. Dark pixel intensity determination and its applications in normalizing different exposure time and autofluorescence removal. J Microsc. 2012;246:1–10. DOI: 10.1111/j.1365-2818.2011.03581.x<br /> Van de Lest, CH, et al. Elimination of autofluorescence in immunofluorescence microscopy with digital image processing. J Histochem Cytochem. 1995;43:727–30. https://doi.org/10.1177/43....

We used extensively and published a method based on those refs:<br /> Bolognesi MM et al, JoHC 65 (8):431-444, 2017<br /> https://doi.org/10.1369/002.... (see Suppl. Fig. 2)<br /> In essence, in AF objects, the level of AF in each pixel is subtracted from the signal+AF level, leaving a 0 pixel background value and only the specific signal value. No information is lost from the image.<br /> Although the automation proposed by the Authors is quite welcome, the loss of information may not be acceptable, except for selected aesthetic purposes.<br /> Best regards

On 2019-03-13 05:08:47, user Ilaria Russo wrote:

The truth is that the universities are just playing the lottery using the life’s of early researchers to get funds that are for them. Mostly they have no interest to support them till the money come in. They are not investing in them, except very poorly and insuffiently. They only use these lifes to get money in. The very day some of the young PI secures large funds, you will be surprised how suddenly that hosting university realises that that is a person to support and not just a mere bet placed at the office of the funding agencies.

On 2019-03-12 19:00:38, user CombinedGIF wrote:

Cool work! Very interesting!

On 2019-03-12 18:24:14, user Rayna wrote:

I noticed that this manuscript references the Future of Research in the abstract, but none of the manuscripts published by the organizers of the Future of Research are cited. It would be great to include and cite some of their findings. The full list of publications can be found at https://www.futureofresearc...

On 2019-03-12 02:57:25, user Huiguang Yi wrote:

Have you compare it to other metagenomics reads classification tools ? what is its superiority over Kraken?

On 2019-03-11 23:19:53, user Paul Macklin wrote:

Follow-up work just posted as a preprint here: <br /> https://www.biorxiv.org/con...

On 2019-03-11 22:10:23, user Paul Macklin wrote:

This preprint (now in review) continues our recent work in https://bmcbioinformatics.b...

On 2019-03-11 17:12:47, user alyona_minina wrote:

Our prepprint is finally out! Thanks to the massive efforts of Adrian and Co! Feedback is greatly appreciated!)

On 2019-03-11 16:38:20, user Claudiu Bandea wrote:

Great study, but please consider that if the ‘huge phages’ have a ‘biology’ that is analogous to that of symbiotic (and parasitic) bacteria, which originated from larger, free-living ancestors by reductive evolution, then, these phages may have also originated following a similar evolutionary pathway (i.e. genome/proteome reduction). See http://precedings.nature.co... and my recent comment on another bioRxiv article: https://www.biorxiv.org/con...

On 2019-03-11 15:05:00, user Peris_D wrote:

Was anyone able to install BITE from the source and the GenABEL package? I would like to try the package in R.

On 2019-03-11 12:27:06, user Andrew Millar wrote:

This preprint is now published in FEBS Letters, see 10.1002/1873-3468.13311

On 2019-03-11 05:01:41, user Benjamin Michael Marshall wrote:

Hi all,<br /> Extremely interesting paper with important implications for baselines. <br /> I wanted to ask how you think the possible disconnect between historical occurrence records (the 1500-1950 data-sets) and the modern Worldclim 2.0 climate records (1970-2000) could have affected the habitat suitability model estimations? I'm not sure if climatic shifts in South Africa have been sufficient to change species distributions enough to weaken the connection between occurrence location and the local climatic values. <br /> Hope you can shed some light on the impacts, if any.<br /> Thank you for sharing your work.<br /> Benjamin Marshall

On 2019-03-10 16:56:15, user David Curtis wrote:

You've somehow managed to cite our paper using weighted burden analysis to implicate FYN without mentioning material which seems very relevant to your report.

Our paper is here: https://link.springer.com/a...<br /> Since then, we have written further on FYN and NMDAR here: https://journals.lww.com/ps...

One thing you have omitted to mention is that our analyses were based on the same dataset as you have used, except that yours is a subset of ours and ours was about twice as large. It would make sense to explain this properly.

The other thing you have omitted to mention is that the method we reported in that paper consisted of a weighted burden analysis which is quite similar in approach to yours. This was fully explained and properly cited in our paper so it is difficult to understand why you would not have been aware of this. There are several papers describing this methodology which you should cite.

The main description of the methodology is in these papers:

https://www.ncbi.nlm.nih.go...<br /> https://onlinelibrary.wiley...<br /> https://www.ingentaconnect....

You should probably also refer to these later papers which are clearly relevant:<br /> https://onlinelibrary.wiley...<br /> https://www.nature.com/arti...

On 2019-03-10 00:15:11, user eight oh wrote:

This is an interesting paper, but users should know that MAGIC induces extremely unrealistic correlations among genes. After running it, a large fraction of genes have a correlation > 0.99 across cells. If you run it on shuffled data (which should have no covariance structure) it still induces tons of correlations with extremely high coefficients.

I am surprised that these points either escaped the authors' attention or were dismissed by them. I have tried contacting them directly, but they did not seem to think it was a problem.

You can see for yourself by following their tutorial on GitHub:<br /> `# run MAGIC on their test dataset

library(Rmagic)<br /> data(magic_testdata)<br /> MAGIC_data <- magic(magic_testdata)

calculate all gene-gene correlations

imputed_data <- MAGIC_data$result<br /> coefs = cor(imputed_data)<br /> round(quantile(abs(as.numeric(coefs)), na.rm=T), 2)<br /> 0% 25% 50% 75% 100% <br /> 0.00 0.69 0.91 0.98 1.00`

In other words, even on their GitHub tutorial, after running MAGIC on their own dataset using the exact commands they suggest, over 50% of the gene-gene correlations in their dataset have a magnitude exceeding 0.90. Over 25% of the gene-gene correlations have a magnitude exceeding 0.98. Therefore, it is not surprising that CDH1 and VIM are perfectly correlated - you will get this relationship for virtually any pair of genes.

Using MAGIC with published single cell datasets is not any better. I have tried running it on several datasets and for many sets of parameters - in every case, it returns extremely unrealistic results that are all but unusable for any application.

On 2019-03-09 21:03:52, user Peter White wrote:

Will the data used for the Uniform Manifold Approximation and Projection (UMAP) plot analysis be made available? We are interested in taking a VCF from one of our patients and seeing which population group they would cluster with from your data. While releasing the individual SNP results would probably have issues with privacy, is it possible to provide the locations used and the PCA values?

On 2019-02-01 08:25:39, user Agustín Ruiz wrote:

Awesome dataset. Many questions can be interrogated. Big big science.

On 2019-03-08 17:41:13, user Ben Carter wrote:

Hi, all. The manuscript was uploaded with an old, incorrect addgene accession number for the PA-Tnp vector. The correct accession number is 121137. We just gave addgene clearance to make it publicly available, so it should be searchable shortly. A revised version of the manuscript with the correct number will be submitted tonight. Sorry for any confusion!

-Ben C.

On 2019-03-08 17:35:15, user Francisco J Cantu Or wrote:

This paper studies factors that influence countries' scientific production and ways to improve it. It is centered on Mexico and its strategic partners. Use 10 years data obtained from Scopus (2007-2016) and applies Panel data to treat countries scientific output as a time series....

On 2019-03-08 13:34:36, user Laura Sanchez wrote:

Dear Geier et al, this preprint was discussed in a lab meeting and we would like to offer the following for review. Thank you for posting this very interesting manuscript. Best, The Sanchez Lab:

The manuscript by Geier at all explores an interesting application of MALDI imaging mass spectrometry with FISH to correlate the spatial distributions of specific microbial genera with metabolites from the same tissue section in an animal host. The transparency of a very thorough SI was greatly appreciated and necessary to allow other readers to evaluate the data. It is clear that this methodology will greatly enhance our ability to probe microbial-host chemistry by keeping the tissues intact to generate hypotheses about how metabolites might be utilized in specific microenvironments of a host. It was also appreciated that the authors use a biologically relevant system and were able to offer hypotheses to test about the metabolites that were observed in different spatial clusters and how this may relate to the biology of the system on the whole. We offer the following as major and minor critiques that may improve the manuscript:

Major<br /> MALDI- FISH previous applications. Can the authors better delineate how this is an improvement on the existing methods, it is briefly stated but if it could be more clearly laid out that would be more helpful in clearly evaluating the innovative aspect of this approach. For instance, can the authors comment on how they were able to achieve 3 micron crystals when others were limited to 50 microns, is the laser more finely tuned or is it simply the matrix application? Or why is separate sections be non-desirable? For the presented method of washing the matrix followed by FISH can the authors comment on whether they believe this may impact the FISH results at all?

This paper should be cited in the MALDI-FISH discussion as a previous application. https://www.pnas.org/conten...

MS1, network and identities. We had a brief misunderstanding over this section. It had seemed that the molecules were assigned based solely on MS1 and the molecular formula, but it was clear that the authors have used MS/MS to verify the metaspace annotations. If this was more clearly delineated with the use of terminology from the ‘Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)’ could improve the transparency and all the hard work done in this section.

The MF ratio calculation was slightly confusing. How were these assigned in lines 146-150? How do you really know where the metabolites are coming from, is this all based on FISH? MF ratio is confusing to a non-computational specialist. It seems contradictory between the SI and main paper. If a cell is 1 micron, 3 microns could encompass way more than a microbial cell.

Figure legends.For example, figure 3B, grey scale, should this be magenta? Why was grey used here and magenta in other images? Consistency across the figures could enhance the readability of the manuscript more. For instance, Figure 1 has no color legend, but it appears that it is this supposed to match Figure 3? Can the authors please be more consistent across figures with color legends?

Figure 4 is difficult to interpret. With the large numbers of colors and shapes used, it is difficult to infer what the meaning is meant to be and when printed, the shapes can’t be distinguished at this definition.

GNPS networks. In general for the type of high resolution mass data presented here, a 2Da parent mass tolerance isn’t appropriate and the same for a 0.5 for the ion tolerance (MS2). This means the real windows being examined are 4 Da and 1 Da for HR-MS data likely leading to artificial connections in a molecular networking or poor consensus nodes. Is there also a reason why the full GNPS network was not included as a figure in the SI? Why were only selected networks highlighted?

Along these lines and the 577 and 800 compounds, were the authors able to find literature precedent for these outside of mass spectrometry databases such as Marinlit, Antimarin, NP Atlas, Dictionary of Natural Products, ect? GNPS doesn’t have everything. For the 800 compound- was the dereplication workflow attempted on gnps.ucsd.edu?

Last paragraph in the conclusion was arguably the strongest tie to how this method may be used and applied to other systems. The is a history of targeting the wrong organism is powerful to mention in the intro and could possibly bring the manuscript full circle. <br /> Final thoughts, this almost seemed like two papers, the discovery aspect is important, but as it stands, it is underdeveloped. It seems like some of the SI material and more clear discussion on how the technique expands on existing methods could strengthen the manuscript. For instance, with the molecular formula, as it stands, it seems there are between 12-13 degrees of unsaturation (the reported formula for the left over atoms does not yield a whole number which is slightly confusing) which would mean this metabolite does have a strong chromophore, the authors allude to the fact, but no UV-vis trace was in the SI, could this help with the dereplication process? This can be arguably more diagnostic for a molecule than the MS at times.

Minor<br /> There were some issues with referencing or figures, please carefully proofread.

Line 291. Homolog is strictly a phylogeny term; should this be analogue or constitutional isomer?

Can the authors define why the range 400-1200 was chosen? Why not larger or smaller?

Why were lipids extracted from the gills? Were any other extractions used, could this have impacted the LC-MS/MS analyses?

Can the authors comment on whether bringing the mussels up from depth might have impacted any of the chemistry. This seems like a large potential variable.

The authors reference non-fragmented metabolites, we assume this refers to the in-source fragments that may arise during AP-MALDI ionizations. Can the authors more clearly delineate how they could tell in source fragments apart from intact molecules?

On 2019-03-08 10:46:51, user David Gfeller wrote:

EPIC: Not a great acronym, since EPIC is a published method for cell fraction predictions developed in my lab (https://elifesciences.org/a..., and that we are also actively developing HLA ligand predictors (MixMHCpred and MixMHC2pred).

On 2019-03-07 19:45:46, user Guest wrote:

B cell editing is becoming a fast-growing field with a lot of potential, while this paper is probably the most advanced in the field, I'd still like to discuss the weaknesses in the methodology and scientific approach it presents.

While the single chain "eMAb" seems like an elegant solution, during the results it became clearer and clearer that it does not respond to the problem. First, while it is true that Moffett et al. have cited Delpy & al., it is not clear that in this model the editing of the J4-iEmu intron actually inhibits the endogenous heavy chain. Indeed, as seen in fig.4c, Moffett et al. actually show that cells that were all IgL+ do not lose their endogenous lambda light chain following editing which could be explained (in contrast to the simplistic bi allelic expression given in the paper) by poly transcriptional start site on a single allele. Thus, both RNA products would include the constant from the same allele. A simple experiment to show that bi-allelic expression occurs would have been to take IgL+ sorted cells (as in fig.4) and edit them as in fig.4d, then the double positive cells would be analysed for IgK/IgL, in these cells only IgK+ population would be visible if Moffett et al. hypothesis on bi-allelic expression is correct. This experiment, which seems obvious to do on a first glimpse, is however lacking in this draft.

Hartwigger et al. also uploaded a draft last month on B cell editing (in bioRxiv), in their paper it seems they respond to the endogenous chain expression by using a PolyA signal upstream their cassette. It would also be interesting to see this approach in Moffett et al. draft. Finally, on this point, it seems possible for the endogenous light chain (which is still expressed in the edited cells) to bind to the eMAb, using the conventional disulfide bounding in the CH1, leading to a steric interference and miss-folding of the antibody.

Did Moffett et al. analyse this possibility? The linker used in the eMAb seem to be a very good target for an immune response. Did Moffet et al. analyze such a response occurring in the recipient mice? What is the fate of the edited B cells in such cases?

In fig.6 it is indicated that only edited cells were injected to recipient mice, how did the cells were separated? By FACS using the tagged antigen? Then wouldn't the sorting itself activate the cells prior to injection? If so, these B cells would behave as antigen presenting cells (APC) by presenting the antigen on MHC, a process that by itself could activate the immune response. Moffett et al. did not delve on this point which is crucial to determine the pathway of response in their approach.

The described AAV infection methodology in Moffett et al. is somewhat disturbing, no MOI quantification, only volumetric reference based on high variance titer.

The authors did not use proper negative controls in adoptive transfer experiments or flow cytometry experiments. In both cases, AAV transduced and Cas9 transfected cells should be the negative controls, as both processes greatly influence membrane proteins. Moreover, an immune response towards MHC presented Cas9 peptides on the edited B cells could by itself change the immune reaction (serving as an adjuvant) to impact the outcomes of fig.6c and fig.6e.

On 2019-03-07 10:10:21, user Joe Flood wrote:

Not forgetting of course that 98% of what makes a European is from an archaic African hominid.

On 2019-03-07 06:15:44, user Kwok Wai LO wrote:

Just note that the YAP1 and MAML2 genes are not in chromosome 3, but in 11q of human genome.

On 2019-03-06 21:05:53, user Victor Cueto wrote:

This article was published in the journal Ornitologia Neotropical. The complete cite is:

Cueto, V.R. & C.A. Gorosito. 2018. Seasonal changes in bird assemblages of a forest-steppe ecotone in North Patagonia. Ornitología Neotropical 29: 349-358

The article can be downland in:

http://journals.sfu.ca/ornn...

On 2019-03-06 19:44:39, user Kiya Govek wrote:

Great paper! Could there be a typo in the “Data integration for reference assembly” methods section, where B should actually be defined as B = Y[,a] - X[,a] to be consistent with subsequent equations?

On 2019-03-06 18:08:24, user John Dziak wrote:

I apologize to readers for an error in a formula in our manuscript. Expression 3 on page 12 should read B[ij] = Pr(y|Mi)/Pr(y|Mj), not Pr(Mi|y)/Pr(Mj|y). Under the assumption that Pr(Mi)=Pr(Mj), using Bayes theorem it can be shown that Pr(Mi|y)/Pr(Mj|y)=Pr(y|Mi)/Pr(y|Mj), because Pr(y) cancels out from the numerator and denominator.<br /> However, the actual definition of the Bayes factor is B[ij] = Pr(y|Mi)/Pr(y|Mj).<br /> -- John Dziak

On 2019-03-06 17:18:38, user Marc Scheetz wrote:

First time publishing a pre-print... interested to see how this goes. Vancomycin + PipTazo increased AKI is a myth! interested in the discussion!

On 2019-03-06 16:48:32, user Daniel Karl wrote:

Thank you for this paper. I found it very helpful as I consider some of my spike-in normalization methods. My biggest concern is using the loess normalization on a spike-in chromatin subset as implemented in the affy package. This method is fantastic for normalization when the signal is within the dynamic range of the subset. However, the spike-in a small fraction of the total reads, so the dynamic range of the max value in a 1kb window often does not approach the value of the IP counts. In particular, the affy package defaults to putting 0 weight on loess normalization outside of the subset. Is this what you found as well? Did you correct the extreme weights in any way? I also fear that this model would not be robust at the high IP count bins. However, the effect would be to very nicely correct the background IP signal, as you nicely demonstrate. Scaling or subsampling into the spike-in range may work as an alternative, but would introduce biases at the low count bins. I'm curious to hear your thoughts and how you dealt with this.

On 2019-03-06 06:40:11, user Debarun Acharya wrote:

Please correct the spelling of pseudogenization in page 3 line 18.

On 2019-03-06 05:52:24, user Hawk eey wrote:

This paper has been published at Nature Methods:<br /> https://doi.org/10.1038/s41...

On 2019-03-06 03:32:58, user swellsh wrote:

Looks great! So proud of you!

On 2019-03-05 23:02:30, user Theo Sanderson wrote:

The values provided for the F1-score seem to be a factor of 2 lower than those calculated using the harmonic mean of the precision and the recall.

On 2019-03-05 22:48:17, user Robert Policastro wrote:

The hPRINT website seems to have been down the last two days. Has anyone been able to access it recently?

genemania.org might be a worthwhile addition to the list of databases as well.

On 2019-03-05 20:26:26, user Simon Chamaillé wrote:

Published (under a revised form) in Journal of Animal Ecology: doi:10.1111/1365-2656.12910

On 2019-03-05 10:44:11, user Akshay Kumar wrote:

Hi. This is a graduate student from India. Can't help but notice that the supplementary files are missing. Is there any other portal where there are accessible to public?<br /> Thank you in advance

On 2019-03-04 19:54:01, user Firas Bou Daher wrote:

Very interesting paper and nice work! I was wondering about Figure1 D; the cell length data presented seem very different from what know in the literature and the means (somewhere between 8 and 25µm) are are much shorter than what you would expect. Also in Figure1 E, how was the cell number quantified? We know that each file of cells contains about 18-24 cells depending on whether it is a file of dividing or non-dividing cells. Are these values the total number of epidermal cells (they seem to be between 100 and 400 approximately!)?

On 2019-03-04 17:47:19, user Adema Ribic wrote:

But are the left-handed fighters natural lefties? Most of the southpaws are not natural-they've been trained into it because of the competitive advantage, so the results are not surprising at all. How many of those in this study are actual, natural lefties?

On 2019-03-02 08:19:16, user thtsrghtgrbge wrote:

Fail to report other published thermophilic Cas9 (e.g. https://www.ncbi.nlm.nih.go... and would like to see actual data (gels) for Figure 4 when you report activity of these orthologs of thermophilic Cas9s

On 2019-03-01 19:43:17, user Misha Monahov wrote:

Dear Dr Gomez-Barriocanal,<br /> what is the experimental evidence that a voltage exists on ER membrane?

On 2019-03-01 19:20:58, user Nathan Good wrote:

This is a pre-print of an article to be published in Scientific Reports. The final authenticated version will be available online at: https://doi.org/10.1038/s41...

publication date pending

On 2019-03-01 17:41:05, user James Chataway wrote:

Hi there, I am planning on doing a two-sample MR study using these MS SNPs as instrumental variables. However, I'm confused on which SNPs to use from your supplementary tables 6/7. In supplementary table 6 the "discovery SNP" and "effect SNP" differ from one another as do their alleles. Which should I use and should I use the OR(joined) and P(joined). Or should I be using the SNPs from supplementary table 7? Any help appreciated, thanks

On 2019-02-28 22:29:59, user Roberto F Delgadillo wrote:

https://journals.plos.org/p...

On 2019-02-28 14:07:32, user Nicholas Stroustrup wrote:

The authors may have inadvertently missed prior work on automated lifespan assays. At least two high-throughput automated technologies have been developed and are currently widely used.<br /> For example:<br /> https://www.ncbi.nlm.nih.go...<br /> https://www.nature.com/arti...

On 2019-02-28 13:48:49, user Pat Pataranutaporn wrote:

Related project

https://dl.acm.org/citation...<br /> https://dl.acm.org/citation...<br /> https://ieeexplore.ieee.org...

On 2019-02-28 11:44:05, user Kasper Rasmussen wrote:

NB: This manuscript has undergone major changes since the initial submission to BioRxiv. To see the final version published in Genome Research, please go to doi: 10.1101/gr.239277.118

On 2019-02-28 07:10:41, user David Posada wrote:

very nice job, but the question is, are bulks monophyletic?

On 2019-02-28 00:59:38, user Robert Policastro wrote:

Hello, It looks like there are chunks of text highlighted in the manuscript.

On 2019-02-27 20:17:20, user HannahWaterman wrote:

I am looking to use DeepSignal for my Master thesis. Can I talk to anyone that has used it before to help me understand how to get started?

On 2019-02-27 16:55:56, user Huiwang Ai wrote:

The final version of this paper is availiable from ACS Biochemistry: https://pubs.acs.org/doi/10...<br /> Please refer to the ACS Biochemistry version for the most acurate presentation.

On 2019-02-27 16:08:28, user HYC wrote:

Excellent work, congrats Lorenzo @lorenzpasquali, Mireia @mireia_bioinfo and all the team!!

On 2019-02-27 16:01:34, user PMI Journal Club at MPIPZ wrote:

We recently reviewed this paper in our pre-print journal club at the Max Planck Institute for Plant Breeding Research. The review can be found here: https://www.authorea.com/35...

On 2019-02-27 11:07:05, user Giovanni Levi wrote:

This exceptional paper will set a new standard of analysis of mouse behavior in groups. It shows clearly and reproducibly a new tool that will soon be implemented by many laboratories in the world. The open-source, free and simple 3D" tracking system is very convivial and easy to use.

On 2019-02-27 09:40:01, user Thomas Weissensteiner wrote:

Could inherited differences in amplification efficiency result from intra-molecular duplex formation between barcode and variable TCR sequences?

On 2019-02-27 00:49:16, user Farrukh-Baig wrote:

Nice findings.... I could not find in materials and methods, for ytube experiments how did you inoculate nectars ? and most importantly how much volume did you use ? <br /> I've done similar work with beetles & found differential preferences (just like yours). But I've figured out the reason behind this.

On 2019-02-26 19:05:22, user Partisan Puff wrote:

There is an error in figure 2b. For 'evidence of translation' the authors use 'iii' instead of 'ii'

On 2019-02-26 18:42:12, user ivan wrote:

nice work!

On 2019-02-26 18:13:54, user Cory Sheffield wrote:

Also, there is some discussion on large bees with respect to Megachile pluto. So does IT distance reflect overall size (body mass) or just length? Is a long skinny wasps more likely to emerge later than a more robust, but shorter bee?

On 2019-02-26 18:08:53, user Cory Sheffield wrote:

Did you look for differences between males and females for each species? Not only are males typically smaller, but emerge faster (i.e., from eggs which are laid last in the tunnel), which seemingly would support your trend. But this occurs in both late emerging species (i.e., those wintering as mature larvae) which have more variation in emergence time, and in those early spring emerging species with narrow emergence times. So, is the early emergence of males only because they are smaller, or is there something else involved? What about larger males?

Also, did you look for differences in body size based on the size of nesting tunnel the occupants were in? Tunnel diameter will influence body size, so it would appear that when a species nests in a smaller diameter tunnel, it will emerge faster than a conspecific from a larger tunnel. Was this the case?

Why not look to see if the pattern is supported within a taxon (ie Megachile). Megachile inermis is are largest native Megachile species in Canada that uses trap nests, but you have several smaller species that emerge later in your figure. Thus, how do you know the variation is not due to something other than body size? Perhaps timing of emergence is based on synchrony with floral hosts for species with more dietary restrictions, or for parasitic taxa whose emergence times are typically later than their hosts? Could food quality influence emergence time? Are cleptos larger than their hosts to emerge later?

On 2019-02-26 12:09:58, user Eliécer E. Gutiérrez wrote:

A bit dissapointed not to see our article (see below), which seems highly relevant to this awesome research, cited:

Gutiérrez EE, Boria RA, Anderson RP. 2014. Can biotic interactions cause allopatry? niche models, competition, and distributions of South American mouse opossums. Ecography 37: 741–753. DOI: 10.1111/ecog.00620

On 2019-02-26 10:23:22, user p_peterlongo wrote:

Hi there.

Thanks for this nice piece of work.

I am suprised that you did not mention existing state of the art reference free approaches for short variants (snps & indel) detection. Mainly, I would expect comparative results with our approach (discoSnp++ ) or Zamin ones (cortex).

Best regards, Pierre Peterlongo.

On 2019-02-26 09:21:36, user Levi Yant wrote:

Final published version available via SharedIt read only link: https://rdcu.be/borZY <br /> DOI: 10.1038/s41559-019-0807-4. <br /> https://www.nature.com/arti...

On 2019-02-26 03:49:11, user Ja S wrote:

Good job!

On 2019-02-26 02:25:25, user Jondice wrote:

It would be great to know which implementation (and version) of SteadyCom was used!

On 2019-02-25 21:50:26, user Jason Kwan wrote:

UPDATE: A revised version of this paper has been accepted for publication in Nucleic Acids Research. In response to peer review, that version includes validation with extra sequencing datasets derived from synthetic metagenomes. Stand by for DOI and other citation info when it becomes available.

On 2019-02-25 19:30:01, user Xuhua Xia wrote:

This paper is obsolete and contains lousy/erroneous statements. It is replaced by a more extensive study submitted to AIMS Genetics

On 2019-02-25 15:30:02, user Jun Yu wrote:

This is a very nice article.<br /> All peaks/regions called by HMMRATAC, F-Seq, and MACS2, as well as the various "gold standard" files, can be downloaded from the following URL: http://biomisc.org/download....

Would you please check the url still working?

Thank you! Again, nice work!

On 2019-02-25 13:04:08, user Matthias M. Fischer wrote:

In his comment, Dr. Pouwels has expressed concern regarding the magnitudes of the correlation between rates of infection with antibiotic-resistant microbes and the use of antibiotics in the primary care vs. the hospital sector. He re-analysed a small subset of the data with a fixed-effects generalised linear model that is not further specified and compared p and R-squared values as a proxy of their biological significance.

In our view, the analysis he presented is inappropriate for two major reasons. First, the focus on a small subset of the data, in his case only 21 observations, leads to reduced statistical power, and thereby unreliable statistical estimates, which becomes apparent by the high standard errors and consequently higher p values Dr. Pouwels has reported.

Second and more important, by fitting only a simple fixed-effects model, strong confounding differences between the individual EU member states are missed. Important confounders which are not corrected for this way are for example the average yearly temperature of a country and its population density -- two factors that exert strong effects on antibiotic resistance rates (see references Bruinsma et al. (2003) and MacFadden et al. (2018) in our manuscript).

Additionally, the comparison of p values of predictor variables to assess their biological significance is debatable. It is well-known that statistical significance does not necessarily translate to biological significance, i.e. a higher or lower effect size of a predictor variable. Similarly, coefficients of determination, such as R-squared values, do not quantify the effect a predictor exerts on a dependent variable. For this reason, we instead consider the comparison of the partial regression coefficients of the different predictors (after properly controlling for confounding variables) the most meaningful way of quantifying biological significance.

As Prof. van Schaik correctly points out, we have only analysed the data for two bacterial species, which additionally are closely related to each other.

In case of the analysed datasets from the European Union, the exclusion of the data for the other three bacterial species was necessary. If one worked with data for two or even only one class of antimicrobial agents, the resulting statistical model would be strongly underpowered and not able to properly control for occurring confounding factors. Consequently, the estimates obtained by such a model would come with a high amount of uncertainty and would therefore be highly unreliable and potentially misleading.

We do agree with Prof. van Schaik that our results are not a final and definite proof, and we have explained the limitations of our approach in the discussion part of the manuscript. We have also made clear in the discussion that our analysis should be perceived as a starting point for further analyses of both theoretical and microbiological nature. Current ongoing research in our lab is aimed at compiling a more comprehensive dataset for more in-depth analyses also taking into account other bacterial species. Nonetheless, we believe that it is important to quickly disseminate our first findings to encourage further research and to provide a fresh perspective on this important topic. Further analysis will indeed reveal if hospital use of antibiotics is the main driver of population-level infections with bacteria resistant to other classes of antibiotics and with other pathogens as well.

Matthias M. Fischer, Matthias Bild

On 2019-02-22 22:39:37, user Willem van Schaik wrote:

This paper is interesting and could potentially lead to identifying the most important drivers for the emergence and spread of antibiotic resistance. The authors are to be commended for tackling this complex issue. However, there are some methodological aspects of this study which I would like to comment on.

The authors focus this study on Escherichia coli and Klebsiella pneumoniae, because for these bacteria the data covers 'the highest number of different antimicrobial classes (four<br /> or three classes of antibiotics, respectively). In contrast, the datasets for P. aeruginosa, Acinetobacter sp. and S. pneumoniae only reported data for two, two and one class of antimicrobial agents respectively, so they were excluded from further analyses due to reasons of statistical power.' I do not agree with the decision to exclude datasets for P. aeruginosa, Acinetobacter sp. and S. pneumoniae. Even if these datasets cover resistance to only one or two classes of antibiotics, they are surely relevant to analyse as well, because they contribute to our understanding of the spread of antibiotic resistance. Indeed, only studying two biologically related bacteria (E. coli and K. pneumoniae are both gut bacteria from the family Enterobacteriaceae) might skew the data and the interpretation of the data. I would advise the authors to broaden their analyses to other opportunistic pathogens, including P. aeruginosa, Acinetobacter sp. and S. pneumoniae, Staphylococcus aureus (particularly MRSA) and enterococci.

The authors make a far-reaching claim ('Hospital use of antibiotics as the main driver of infections with antibiotic-resistant bacteria') but due to the limited datasets (only covering two bacteria) that were analysed in this study, this claim seems to be poorly supported by the evidence provided here.

On 2019-02-21 12:52:12, user Koen B Pouwels wrote:

I do agree that it is an important question what is driving antibiotic resistance levels. However, I did a quick re-analysis of the data showing quite different results. For simplicity I only focused on antibiotic use in the hospital and the community (both expressed in DDD per 1000 inhabitants per day). When focusing on fluoroquinolone resistance among E. coli samples the adjustedR2 for a robust linear model (to reduce the potential influence of outliers) with only hospital use of quinolones is lower than for a model with only community use of quinolones (0.45 vs 0.53). When including both in the model community use has a p-value of 0.04, while hospital use has a p-value of 0.11. And yes, hospital use has a larger coefficient (probably partly due to the much lower total hospital use compared to community use and smaller variation on a DDD/1000/day scale), but the standard error is also huge. When looking at 'any antibiotic' use the adjustedR2 is 0.02 for hospital use alone and 0.18 for community use alone. When including both predictors (which lowers the adjustedR2) the p-value for community use is 0.03, while hospital use has a p-value of 0.67. In contrast to this paper I didn't include a random intercept because I only have one measurement per country. There is still a lot of confounding and one could argue that I should have excluded a particular country, etc, but I think my re-analysis indicates that it's not that certain that hospital use is a bigger contributor than community use.

On 2019-02-20 20:50:28, user Eric Batard wrote:

I don't understand what is the outcome for E. coli : which resistance did you model ?

On 2019-02-19 02:34:48, user Mark A Girard wrote:

Hospital use of antibiotics is also the main driver of the enormous number of people directly harmed by antibiotics as well. People routinely get poisoned by antibiotics, in particular the fluoroquinolones, and then doctors misdiagnose the patients with lupus, fibromyalgia, ALS, Parkinson's, MS and hundreds of other wrong conditions. This is a disaster of almost unimaginable scope and scale, the Thalidomide story of our era, times ten thousand, spilling into the news cycle soon.

On 2019-02-24 17:29:03, user Anders Sejr Hansen wrote:

Please see the updated version of this preprint: https://www.biorxiv.org/con...<br /> The numbers for CTCF in mESCs and human U2OS cells are essentially unchanged. <br /> But the estimate for cohesin (through the Rad21 subunit) has changed significantly (new estimate 109k proteins/cell; old estimate was 87k proteins/cell). <br /> Main change: estimates have now been cross-validated using calibrated FCS-imaging in collaboration with the Ellenberg lab (EMBL Heidelberg).

On 2019-02-24 14:00:39, user aquape wrote:

Very interesting paper, thanks a lot, but unfortunately it generally follows the usual traditional paleo-anthropological interpretations, which are not based on comparative biology.<br /> The most thermo-active sweat-glands are not only seen in humans, but also in fur-seals: overheated furseals on land sweat abundantly from their naked hind-flippers, which are abundantly supplied with sweat-glands (Bartholomew & Wilke 1956 "Body temperature in the northern fur seal Callorhinus ursinus" J.Mammal. 37:327-333). All Carnivora have eccrine plantar glands, so likely all or part of the thermo-active sweat-glands on the furseals' flippers are eccrine.<br /> Early-Pleistocene "archaic" Homo spread intercontinentally, not running over open plains where salt & water (sweat) are scarce, but simply following the coasts of Africa & southern Eurasia (e.g. Verhaegen & Munro 2002 "The continental shelf hypothesis" Nutr.Health Nutr.Health 16:25-27) and eventually from the coasts also went inland along the rivers etc. (google e.g. "Coastal Dispersal 2018 Verhaegen" PPT).<br /> This Coastal Dispersal Model (Munro 2010 "Molluscs as ecological indicators in palaeo-anthropological contexts" PhD thesis Austr.Nat.Univ. Canberra) most parsimoniously explains a number of evolutionary innovations in Homo erectus etc., such as pachy-osteo-sclerosis, platymeria, platycephaly and supraorbital torus, and dramatic brain expansion (cf. the abundance of brain-specific nutrients such as DHA, taurine & iodine in littoral foods, e.g. Cunnane & Stewart eds 2010 "Human brain evolution: the Influence of freshwater and marine food resources" John Wiley NJ). If we just use the available comparative evidence of other animals, it is unnecessary to invoke unique ("just-so", or anthropocentric) explanations for the evolution of human abundant thermo-active sweat-glands.<br /> The independent evolution of (1) hair folicles & (2) eccrine glands, which you describe in your paper, is best explained IMO by<br /> (1) the aquarboreal theory of Mio-Pliocene hominoids (vs monkeys),<br /> (2) the littoral theory of Pleistocene Homo (vs apes-australopiths-habilis),<br /> google e.g. "Ape and Human evolution 2018 Verhaegen".

On 2019-02-24 08:03:23, user Afif Elghraoui wrote:

Can you achieve a similar effect with single-pass reads if you use the consensus of the subreads coming from the same ZMW for alignment/assembly?

On 2019-02-23 17:36:29, user λeo wrote:

Why did not you check Erlang?

On 2019-02-23 12:03:23, user Vanessa Ribes wrote:

Dullard attenuates Smad activity in cardiac crest cells and thereby controls the tempo of their aggregation! Well done @DarriJf, @cadotbrun!

On 2019-02-22 23:07:45, user Mario dos Reis wrote:

The correct citation for tAI is dos Reis et al. 2004 NAR, 32 (17), 5036-5044

On 2019-02-21 08:08:28, user Danna Gifford wrote:

''a 20 phylogeny of related Pseudomonads" should this be a 20 spp or strain phylogeny? (i.e. is there a missing word after 20)

On 2019-02-22 19:00:15, user GuyguyKabundi Tshima wrote:

Integrating knowledge from the different specialities involved in malaria research

On 2019-02-22 18:22:46, user Stefan Taube wrote:

Awesome paper. Beautiful pictures and movies. Looking forward to the follow up for MNV ;-) Also check out the TWIV discussion.

On 2019-02-22 17:01:14, user Rocío Deanna wrote:

Now published in American Journal of Botany:<br /> https://bsapubs.onlinelibra...

On 2019-02-22 16:06:50, user Rafat Merchant wrote:

It is very intresting to learn

On 2019-02-21 23:19:13, user Ildus Ahmetov wrote:

Re: Caster Semenya case<br /> The study involved 133 endurance athletes from the National team (average period of competition activity: 2004-2012). <br /> All of these 133 athletes were tested negative for doping substances.<br /> Of those, 56 athletes were specialized in middle distances and 5 of them in 800 m running.<br /> Highly elite long-distance (1.62 (0.55) vs 1.78 (0.87) nmol/L, P = 0.601) and middle-distance (1.09 (0.47) vs 1.57 (0.88) nmol/L, P = 0.235) athletes tended to have lower T levels than less successful athletes. <br /> Among 800 m runners, highly elite runners tended to have lower T levels than less successful athletes (1.02 (0.10) vs 1.71 (0.68) nmol/L, P = 0.2692).<br /> This observation was confirmed when we have correlated athletes' 800 m running times with their testosterone levels (r=0.4, P=0.5167), meaning that the higher testosterone level in women, the worse their performance.

On 2019-02-21 20:49:58, user Leonida Fusani wrote:

Interesting work. You might want to have a look at our recent paper in Behavioral Ecology:

https://doi.org/10.1093/beh...

On 2019-02-05 10:21:11, user Sascha Rösner wrote:

https://uploads.disquscdn.c...

The results of this study are very interesting. Especially as we are dealing here with a long lived habitat specialist with very limited dispersal abilities. The ecological or conservation consequences might be crucial in the case of e.g. increased habitat fragmentation. In a study, where similar questions were asked, we found somewhat similar result in terms of relationship with significant closer relationship of males up to 5km I guess.

https://www.researchgate.ne...

On 2019-02-21 14:56:28, user Matheus LC wrote:

Congratulations Ricardo Righetto

On 2019-02-21 12:53:53, user Kateryna Kondratska wrote:

An interesting study on the dynamics of calcium signaling in Drosophila embryo throughout embryogenesis. Along with calcium transients, the study reports, for the first time, endogenous calcium waves during embryogenesis of insects. The authors suggested that these calcium waves are potentially linked to such developmental events as tissue extension, cuticle deposition gene expression and development of neuronal activity. Further studies are needed to elucidate the specific molecular players/mechanisms mediating this dynamic calcium signaling during Drosophila embryogenesis.

On 2019-02-21 07:07:24, user Martin Smith wrote:

Profile-based methods (20, 23) such as<br /> Nofold annotate and cluster sequences against a CM database<br /> of known families, therefore their applicability is limited to<br /> already known families and cannot be used for de novo family<br /> or motif discovery.

Nofold uses vector quantization and unsupervised clustering from the CM profiles against which it scores query sequences. It is thus perfectly suitable for de novo RNA structure family or motif discovery, albeit the clustering accuracy may be limited to the CMs used to calculate distance vectors.

On 2019-02-21 00:21:29, user GuyguyKabundi Tshima wrote:

I would like to share with you the POSTER 421 on page 224 in the ABSTRACT BOOK MEDICINE AND HEALTH IN THE TROPICS Marseille-France 11-15 September 2005. I have been involved in the study. So, I travelled in the selected DRC cities : Kinshasa, Kimpese, Kisangani, Lubumbashi,... for the study supported by USAID via the school of public health of the University of Kinshasa.<br /> Take home message:<br /> A coverage of 60% ITN in a village may be suitable enough to try and manage the prevailing effects of malaria parasite because it may offer a general protection ( insecticides in mosquito nets may kill mosquito in houses and reduce their number in the community level), but the coverage was very low in the DRC meaning that we were so far to reach the coverage of 60% ITN in each surveyed village. A vaste coverage should drastically reduce the prevailing effects of malaria parasite.<br /> Reference: https://festmih.eu/wp-conte....<br /> IMPORTANT NOTICE: The abstracts included in this book are the proceedings of the Medicine and Health in the Tropics‚ Congress, as provided by the authors, without modification or copy-editing. The organizers of the Congress are, therefore, in no way responsible for abstract presentation or scientific content.<br /> P421<br /> MONITORING NET COVERAGE FOR MALARIA CONTROL IN THE DEMOCRATIC REPUBLIC OF THE CONGO<br /> BOBANGA L.T.2, WOLKOM A.3, HAWLEY W.3, BEACH R.3, DOTSON E.3, TSHEFU K.A.4, MULUMBA M.P.2, KABUYA W.1, MWAMBA R.1, GIKAPA J.6, TSHIMA K.2<br /> 1. Basics DRC, KINSHASA, DEMOCRATIC REPUBLIC OF THE CONGO<br /> 2. Service of parasitology, Kinshasa School of Medecine, KINSHASA, DEMOCRATIC REPUBLIC OF THE CONGO<br /> 3. Division of Parasitic Diseases, Centers of diseases control and prevention, ATLANTA GA, UNITED STATES<br /> 4. Kinshasa school of public health, KINSHASA, REPUBLIC DEMOCRATIC OF THE CONGO<br /> 5. School of medecine, KINSHASA, DEMOCRATIC REPUBLIC OF THE CONGO<br /> 6. Santé Rurale(SANRU), KINSHASA, DEMOCRATIC REPUBLIC OF THE CONGO<br /> Background.<br /> In DRC, malaria is endemic and a significant source of morbidity and mortality. In 2001, DRC endorsed Abuja Declaration and the National Malaria Control Program (PNLP) initiated to protect children and pregnant women and to reduce poverty in DRC . Objectives are 60% Household with at least one ITN, 60% children sleeping under ITN and 60% Pregnant Women sleeping under ITN. With some partners the ministry of Health are implementing ITN in some health zones for more than 1 year. Differents distribution approaches used by partners. Than evaluation of these appears necessary.<br /> Objectives.<br /> To evaluate coverage and equity of distribution.To identify factors influencing use of net, and strengths and weakness of different programmatic approaches.<br /> Methodology.<br /> Surveys Conduct community-based surveys in 9 Health Zones (Kinshasa, Mbuji Mayi, Tshikaji, Pawa, Kisangani, Kimpese, Lodja, Vanga and Lubumbashi) Interviews and documentary review Health zones responsible interviewed.<br /> Results.<br /> ITN household possession: 14-49%. Proportion of pregnant women using ITN: 5-49%. Proportion of children sleeping under ITN: 5- 36%. Malaria prevention is the principal factor influencing ITN use preceding nuisance. Cost is the principal barrier to ITN acquisition.<br /> Conclusion.<br /> Different partners use different approaches. Distribution is not equitable in different groups. Coverage in progress in DRC but new consensus is needed between PNLP and partners.

On 2019-02-20 22:53:46, user GuyguyKabundi Tshima wrote:

The link between malaria and climate in Kinshasa, Democratic Republic of the Congo in the bioRxiv preprint:<br /> What is the explanation for Plasmodium vivax malarial recurrence? Experience of Parasitology Unit of Kinshasa University Hospital of 1982-1983 and 2000-2009.<br /> From the preprint, I highlighted the link between malaria and climate:<br /> OBJECTIVE<br /> I wanted to highlight the link between the rainiest month and positive microscopy for malaria control purposes<br /> RESULTS<br /> November 2001 had the high number of positive samples.<br /> CONCLUSION<br /> Efforts for malaria control should be focus on the rain months.<br /> DISCUSSION<br /> The number of positive cases was recorded in 2001. 2001 was marked by the beginning of the resistance on antimalarials drugs involving a change towards the artemisinin derivatives, but it was in 2005 that the national malaria control programme PNLP introduced the combination of artesunate-amodiaquine to treat cases of uncomplicated malaria or simple malaria forms, also Artemether-Lumefantrine and Dihydroartemisinin-piperaquine for complicate malaria forms. The old combination was sulfadoxine and pyrimethamine for uncomplicate malaria and quinine for complicate malaria forms .<br /> It was also observed in the last quarter of the year with a pic or the highest number of confirmed samples at the month of November (Figure 5).<br /> In Kinshasa, the last three months of the year is the period of heavy rain with temperatures between 30 ° C and 38 ° C. These conditions are favorable to the proliferation of Anopheles that would promote the transmission of malaria during this time of the year without the use of mosquito preventive measures.<br /> RECOMMENDATION<br /> We promoted the use of the insecticide impregnated nets.

William Seriki's recommended solution:

Malaria parasite does thrive in areas that are habitable for it. Therefore, it’s not suitable enough to try and manage the prevailing effects of malaria parasite (such like buying and distributing mosquito nets and insecticides), but instead, a diagnostic approach should be taken to drastically reduce (more like eradicating) the prevailing effects of malaria parasite.<br /> With my factors; why Malaria is common among impoverished communities, I will recommend that the ‘Top-down & Bottom-up’ model of approach should be adopted. Whereby situations that have not really helped in the eradication of malaria parasite can be effectively approached.<br /> Now,<br /> taking a critical look at those factors one after the other. The ‘Top-Down’ approach helps your diagnosis to say if some of those factors are due to government negligence and therefore, appropriate steps can be taken by the government to ensure those factors do not exist anymore for malaria parasite to thrive.<br /> The ‘Bottom-Up‘ approach helps your diagnosis to say if some of those factors are due to community/Individual negligence and therefore, appropriate steps can be taken by the individuals (within the community) to ensure those factors are no longer in existence.<br /> And I think this solution in over all, will also help to close the social, health and economic gap impacting on community health.

On 2019-02-02 23:45:22, user GuyguyKabundi Tshima wrote:

Citation: Preprints deposited in bioRxiv can be cited using their digital object identifier (doi).<br /> Example: Tshima KG. 2019. What is the explanation for Plasmodium vivax malarial recurrence? Experience of Parasitology Unit of Kinshasa University Hospital of 1982-1983 and 2000-2009. bioRxiv doi: 10.1101/537027<br /> Using this preprint that can be downoloaded for free, I will give my humble contribution on which method to choose in different settings:<br /> • In Africa, Asia and South America:<br /> - In rural areas: RDTs +++ , Microscopy ???<br /> - In urban areas:<br /> * if hospital structure (University Hospital): Microscopy +++, RDTs +++<br /> * if research institute : Microscopy, RDTs, PCR<br /> • In Europe and North America: Microscopy, RDTs and PCR.<br /> A.1. A.2.A.3.= Comments on ??? Microscopy use in rural areas highlights the Advantages or interests and disadvantages or limitations of biological diagnosis of malaria access. According to the points A.1., A.2. and A.3. developed below: RDTs are more suitable for EFFECTIVE identification of presence of malaria parasites (only for P.falciparum) by rural health care practitioners<br /> A.1. Microscopy<br /> ADVANTAGES<br /> • Good sensitivity<br /> • Determination of mixed infections<br /> • Post-treatment evaluation<br /> • Retrospective reading and quality control<br /> DISADVANTAGES<br /> • Qualified staff<br /> • Color quality<br /> A.2. Rapid diagnostic test (HRP-2 / Aldolase / pLDH)<br /> Rapid diagnostic test (HRP-2 / Aldolase / pLDH)<br /> DETECTION OF PARASITIC ANTIGENS<br /> ADVANTAGES (INTERESTS)<br /> • Rapid diagnosis<br /> • Detection even in the event of sequestration<br /> • Therapeutic treatment<br /> DISADVANTAGES (LIMITS)<br /> • No quantification<br /> • Lower sensitivity for P.vivax, P.malariae and P.ovale<br /> • Persistence of HRP-2 antigens after treatment<br /> • Problem of quality control<br /> • Heat<br /> • Less sensitive in stable transmission zone<br /> A.3. Detection of parasite genetic material (PCR)<br /> INTERESTS (ADVANTAGES)<br /> • Sensitive<br /> • Specific<br /> • Differentiation of species<br /> • Diagnosis of re-infection and relapse and possibility of degenotyping<br /> LIMITS (DISADVANTAGES)<br /> • Costly<br /> • Slow<br /> • Contamination<br /> • Specialized equipment<br /> • Specialized staff, not available<br /> • Not yet for routine diagnosis<br /> TAKE HOME MESSAGE:<br /> The reference method for the diagnosis of malaria is microscopy<br /> • But the combination of microscopy and a rapid diagnostic test minimizes the risk of missing a vital diagnosis for the patient<br /> • Biological diagnosis remains an important element for the patient under antimalarial to avoid the phenomenon of resistance.<br /> TO REMEMBER:<br /> What to remember<br /> • Mandatory biological diagnosis for any suspicion of malaria<br /> • More suspected cases in stable transmission but few cases diagnosed with microscopy<br /> • Education of the population for obtaining in all the cases of fever a biological confirmation for a fast, early and adequate therapeutic process.<br /> In ​​terms of public health<br /> - to know the species of malaria in question<br /> - to evaluate the malaria control strategy<br /> - malaria eradication surveillance<br /> - to avoid the misuse of antimalarial drugs Resistance<br /> - Evaluating Efficacy of Treatment<br /> - Cost<br /> - Socio-Cultural Factors<br /> .<br /> In the preprint, I would like to study the environment factor in genetic of populations. I am interested on asian populations living in africa for a long period. I took the example of P.vivax that is more prevalent in Asia than in Africa, I would like to know its prevalence for a ten year period of 2000-2009 and later compare with 2010-2019 using surveillance data.

It is clear for me that I should use microscopy results.

Guyguy.

On 2019-02-20 19:21:09, user Arjan Boonman wrote:

Correct, however, an infinite number of scatters (a lawn would have a lot) would lead to a white noise spectrum, so no deep troughs anymore. Our paper only calculates up to 300 scatterers (effect on spectrum shown in figure 2). Leafy bush would limit the number of scatterers, so still give rise to deeply modulated spectra. We're looking into that at the moment. However, the pulses of bats closer to vegetation tend to be no longer narrowband so this topic departs from the subject of the article which is optimization of echo detection in noise by means of bio-sonar. Many narrowband echolocators (open space hawkers) still modulate (by 3-8kHz) their pulses even when foraging very high in the sky (incl species of Emballonuridae) (Table 1 this paper). We hope to be able to confirm this behavior in more species of bat that fly above sonar contact with the ground (as revealed by combined GPS and onboard recordings).<br /> The Doppler effect at 8.5m/s (likely max speed) gives rise to 5% increase in bandwidth so Figure 3 in our paper can be used to assess the small additional beneficial effect such increases may afford.<br /> For all clarity to any other reader: of course the extremely narrowband CF signals used by Rhinolophus and Hipposideros are NOT optimized for detection in open space, but for detection of Doppler shifts and wing-flutter in cluttered-space (see review by Denzinger and Schnitzler 2011).

On 2019-02-20 14:19:41, user Justin Halls wrote:

Presumably this also applies to other large diffuse targets consisting of multiple scatterers such as a field of grass, a leafy bush or a twiggy tree. It would also be nice to consider the effects of Doppler on the signals which, for a randomly moving swarm of insects would result in a certain amount of bandspreading. For the case of two-tone bats the situation may be more complicated, depending on species and echolocation strategy - Barbastelles for example use one note forward directed for hunting and one ground directed for navigation, and a similar hypothesis was extended to Saccopteryx when David Pye observed that the frequency difference of the two notes corresponded to the Doppler shift due to the bat's flight speed - also implying the use of Doppler compensation in some short-cf bats.