Reviewer #1 (Public Review): 

Watanabe and colleagues investigated how properties intrinsic to the organism (allometry, development, morphological integration) have directed the evolution of encephalized and diverse brains in avian and non-avian dinosaurs, on the basis of 3-D imaging, high-density shape data of endocasts from 37 extant and recently extinct crown bird species, 6 non-avian coelurosaurian dinosaurs, and Archaeopteryx, as well as developmental neuroanatomical data of model archosaurs (Gallus, Alligator). Using the methods for multivariate analysis and for evaluating the pattern of integration, they demonstrate that extant birds have a distinct allometric, more integrated brain structure than non-avian dinosaurs closely related to birds. This study reveals complexity in evolutionary processes (concerted and/or mosaic patterns) that shape the evolution of encephalized and divergent brains across vertebrates, birds in particular.

The inference in the manuscript is overall clear, and the conclusions are well supported by data, which in turn will be informative for relevant or follow-up studies. Due to the paucity of the endocast data from early birds, some claims sourced from the present data could be proposed more cautiously (e.g. in the abstract), like the way on page 10.

Reviewer #1 (Public Review): 

In this study, Hale and colleagues study the extent of co-regulation of alternative splicing events by the two RNA binding proteins RBFOX1 and MBNL1. The authors use inducible expression to look at the relationship between the dose of each factor and their impact on exon inclusion. The results support a model where for a subset of splicing events, RBFOX1 can act to buffer MBNL1 dose, and in one case the authors provide more detailed mechanistic evidence that this likely occurs through a consensus RBFOX binding site (UGCAUG) that also serves as a low-affinity binding site (YGCA) for MBNL1. 

In general, I found the current study interesting and timely, highlighting a key challenge in our ability to understand and predict alternative splicing outcomes. Specifically, as the current study demonstrates, differing concentrations of RNA binding proteins that co-regulate alternative splicing events can often lead to diverse and complex regulatory outcomes, which we still do not fully understand. 

A strength of this study is that it highlights the importance of understanding alternative splicing regulation as a product of interactions between multiple RNA binding proteins, where distinct concentrations of these factors can lead to diverse outcomes on splicing patterns. The work demonstrates this concept through a combination of more detailed mechanistic work on a reporter gene and through the use of in vitro binding assays. It also begins to extend these ideas through transcriptome-wide analysis of splicing patterns in cells expressing differing concentrations of two RNA binding proteins. 

I do feel that in its current form, the study would benefit from additional genome-wide analysis to further strengthen the generality of the model put forward by the authors to explain buffering of MBNL1 dosage via the co-expression of RBFOX1. 

Overall however, this study has increased our appreciation for the importance of moving away from studying RNA binding proteins in isolation and without consideration of other cis elements and trans factors.

Reviewer #3 (Public Review): 

In this manuscript, the authors address an important problem in understanding how alternative splicing outcomes are determined. In many cases, the influence of multiple factors accounts for the relative abundance of different splice variants and these splicing factors and their target transcripts frequently change in expression over the course of a developmental program. But how are the contributions from these multiple trans-acting regulators combined to produce a binary outcome, such as inclusion or skipping of an exon? The splicing factors examined in this manuscript are the well-studied Muscleblind (MBNL) and RBFOX families, each of which has three separately encoded paralogous loci, and which change in expression during developmental processes to control splicing of large sets of exons. These factors are particularly important in the context of muscle development, and MBNL proteins are sequestered within nuclear foci driven by expanded-repeat encoded transcripts containing canonical MBNL binding sites which is a causal factor in myotonic dystrophies. Thus it is important to understand how these factors interact to control splicing in normal and disease contexts. The interactions between RBFOX and MBNL, as well as another family of splicing regulators known as CUGBP or CELF proteins in co-regulating splicing events have all been extensively documented in the literature, including in some cases examples of overlapping binding sites. 

The novel regulatory interaction that the authors set out to characterize here began with the identification of a non-canonical, overlapping binding site for MBNL embedded within an RBFOX motif controlling the splicing of exon 11 in the insulin receptor (INSR) transcript. RBFOX showed a 'buffering' response curve on minigene splicing, where at low concentrations of MBNL protein RBFOX significantly enhanced splicing, while the effect of MBNL was dominant when it was present at increased concentrations. The effects of each protein could be dissected by mutations pinpointing each motif, and recombinant MBNL exhibited a higher binding affinity for the hybrid site compared to RBFOX RRM, consistent with the dynamics seen in the minigene splicing assay. Through use of an innovative dual-inducible transgene system to independently manipulate expression of MBNL and RBFOX1, alternative splicing was measured first by performing a multipoint dose-response curve on individual endogenously expressed genes by RT-PCR, then examining the extreme end-points of the curve in a genome wide manner using RNA-seq. 

A strength of the paper is in the use of these innovative cell lines with independently-titratable MBNL and RBFOX protein expression to enable dose-response testing. This system is used in a two-step process in which the coincidence of altered splicing events detected in a simplified end-point RNA-seq experiment with the dose-dependent validation of endogenous splicing responses allows the inference of complex dose-response curves from a much simpler RNA-seq experiment. However, the main weakness is the generalization of the proposed mechanism, which affects the significance of the conclusion. Simply put, although the 'buffering' type interaction on which they focus could was found among a larger group of exons in the genomewide data, these accounted for only about 1/3 of the affected splicing events. More importantly, there was little evidence provided for the role of hybrid binding sites among these buffered splicing events, with only ~30% of the exons having an identifiable motif like the one found in INSR. While the INSR minigene experiments nicely define a mechanism responsible for the splicing of that exon, and the genomic data hint at the possibility of other such interactions, the conclusions of this manuscript in its current form do not represent a major, generalizable advancement in our understanding of splicing regulation.

Reviewer #2 (Public Review): 

In this work, Hale et al. performed systematic analysis how dosage dependent splicing regulation by MBNL1 is affected by different levels of RBFOX1 expression. Starting with analysis of INSR exon 11 using minigene splicing reporters, the authors observed that the magnitude of MBNL dependent splicing is large when RBFOX1 is low, and it is reduced when RBFOX expression is high, a phenomenon denoted "buffering" co-regulatory mechanism. By mutagenesis and in vitro binding assays, the authors proposed that the RBFOX1 binding motif UGCAUG can directly bind MBNL1 through the imperfect UGCA sequence, and an increasing level of MBNL1 can outcompete RBFOX1 binding. To generalize this observation, this study elegantly generated two cell lines. In each cell line, MBNL1 and RBFOX1 are controlled by inducible promoters, which can be independent controlled by DOX/ponA titration. Exons co-regulated by MBNL1 and RBFOX1 were identified by RNA-seq analysis and were binned based on how RBFOX1 level affects the magnitude of MBNL1-dependent splicing. The authors observed that exons subject to buffering co-regulation is most abundant. The competitive binding of RBFOX1 and MBNL1 through YGCAUG appears to account for a subset of these exons. 

Overall, systematic investigations of combinational splicing regulation by multiple RBPs are lacking and this study provided a very nice experimental system with several interesting observations. I hope the comments below can be helpful to improve the manuscript: 

1) Previous studies reported that MBNL1 and RBFOX1 frequently regulate exon inclusion or skipping in the same direction. The authors found the magnitude of MBNL1-dependent splicing changes is smaller when RBFOX1 level is high, which was denoted "buffering" co-regulation. This is certainly correct at sementic level, but this observation does not directly imply whether there is mechanistic coordination between the two RBPs or if there is, what is the nature of such coordination. 

a) This is because the study measured the magnitude of splicing regulation by delta_PSI, which is bounded by the baseline inclusion level. For example, when the baseline exon inclusion is 0.1 and the inclusion can readily increase to 0.4 when MBNL1 is induced if the exon is strongly regulated by MBNL1. However, if RBFOX1 increases the baseline exon inclusion level to 0.7, then the maximal possible delta_PSI upon MBNL1 expression will be 0.3, even when there is no direct coordination between the two RBPs. Therefore, it is not precise to claim the regulation is non-additive based on this observation. 

b) The authors might refer to Baeza-Centurion et al. 2019 Cell 176:549, which addressed this issue. There might be more rigorous theoretical framework if the authors want to argue whether the regulation is additive or coordinated. 

2) Through mutagenesis analysis of the UGCAUG elements into UCGAUG (MUT1, which disrupted the RBFOX binding site) or into CGCUUG (MUT2, which disrupted the RBFOX binding sites while creating a MBNL binding site), as well as gel shift experiment, the authors proposed that RBFOX1 and MBNL1 can compete in binding to the UGCAUG element. These data are consistent with the theory, but do not exclude other possibilities. For example, the juxtaposition of RBFOX1 and MBNL1 can stabilize the binding of the two proteins to RNA. The reduction in MBNL1-dependent splicing in MUT1 can be explained by destabilization of RNA-mediated protein-protein interactions. The restoration of splicing by MUT2 could be due to the fact that the newly created CGCU increases the affinity of MBNL1 binding to clustered YGCY motif sites, so that it is less dependent on stabilization by interacting with RBFOX1. 

3) The observation of MBNL1 binding to UGCAUG containing sequences in vitro, or the observation that it can outcompete RBFOX1 binding at high concentration is not very surprising. This result does not prove such binding/competition occurs in cells at endogenous protein level. However, it is somewhat surprising that MBNL1 can outcompete RBFOX1 RRM in binding to UGCAUG at equivalent concentration. Is it because the substrate sequence used in gel shift has three YGCY like sites (GGCU, UGCA, and UGCG)? If this is the case, how this situation can be generalized? It might be helpful to determine Kd from the gel shift and compare the values with those reported in the literature. 

4) Comparison of RBFOX1 motifs in different sets of exons. Before the analysis presented in Figure 3 E,F, it will be helpful to examine motif enrichment in RBFOX-dependent vs. independent exons in different regions (upstream intron, exon, and downstream intron), which will provide a positive control, as the expected patterns are very well established in the literature. In their analysis, the authors should distinguish upstream/downstream intron depending on RBFOX-dependent inclusion or skipping. 

Some of the requests above might go beyond the immediate scope of the study, but nevertheless the authors should probably at least discuss how these issues affect the interpretation of the data.

Reviewer #2 (Public Review): 

Overzealous inflammation is a significant clinical concern in COVID-19 patients. However, mechanisms underlying this hyperinflammatory response is unclear. In the current work by Khan and colleagues, the team investigates the role of the SARS-CoV-2 spike protein in driving inflammation and demonstrates the role of TLR2 in this mechanism. Here, the team identifies a range of inflammatory cytokines and chemokines generated following treatment of human and mouse cells with the S protein. Interestingly, the team did not observe any impacts on interferon signaling, suggesting a disconnect between the cytokine/chemokine response and interferon production. Likewise, this appears to be spike-protein dependent in the research team's hands. Biochemical studies suggest that the spike protein induces NF-kB signaling through a TLR2/MyD88 dependent mechanism. In general, the studies were well conducted and the data presented support the overall conclusions of the study. However, there are a few limitations to the work noted. These include questions associated with TLR2 heterodimer formation, the use of A549 cells that are refractory to SARS-CoV-2 infection as a model system, and clear data linking the mouse studies that are critical to the mechanism back to the human studies which lack specific assessments of TLR2/MyD88.

Reviewer #1 (Public Review): 

In this manuscript, Khan et al. investigated the roles of SARS-CoV-2 proteins on activation of immune cells. The authors found that the macrophage cell lines such as human THP-1 cells and mouse RAW 274.7 cells with recombinant viral proteins, and found that only spike proteins (S1 and S2) could potently activated macrophages to produce pro-inflammatory cytokines and chemokines. 

Strengths: 

It was Intriguing that only spike proteins (S1 and S2) could potently activate macrophages to produce pro-inflammatory cytokines and chemokines. The authors also observed that direct contact of macrophages with spike protein transfected epithelial cells, that mimic viral infection, resulted in the activation of macrophages. Detail analyses showed that spike proteins were recognized by Toll-like receptor 2 to activate NF-kB signaling. In vivo mouse experiments further supported the in vitro experiments. This study revealed a pathogenic of the SARS-CoV-2 spike proteins that is directly activating the host inflammatory responses, which therefore may have a profound impact in understanding a novel aspect of the cytokine signaling that is involved critically in the COVID-19 pathogenesis unveiled for the first time. 

Weaknesses: 

Recent report by Shirato and Kizaki (Heliyon 7(2021) e06187: 10.1016/j.heliyon.2021.e06187) showed that SARS-CoV-2 spike protein can stimulate macrophages (RAW 264.7 cells and THP-1 cells) to produce pro-inflammatory cytokines via TLR4-dependent manner. This is likely to contradict this study. The authors must thoroughly argue these controversial observations.

Reviewer #3 (Public Review): 

Khan and colleagues evaluate the ability of purified components of the SARS-CoV-2 virus to induce inflammatory responses in macrophages and epithelial cells. They observe that the spike protein drives a TLR2-dependent inflammatory response both in vitro and in vivo. There also appears to be a potential crosstalk between epithelial cells and macrophages in response to the spike protein, however the specifics of this interaction remain unresolved.

Reviewer #2 (Public Review): 

Godet et. al have attempted to identify the cellular components of what is known as an IRESome, and conclude that paraspeckles are the sites of IRES action. They found that IRES accessory proteins known as ITAFs concentrate during hypoxia at that paraspeckle sites and are important for IRES-mediated translation. The authors show that the long non-coding RNA Neat1, and in particular isoform 2 of NEAT1, is a universal essential component that can recognize almost all cellular IRESs and contributes to their translation during the stress response in angiogenesis and/or cardio-protection. In summary, the authors propose a novel and very interesting concept, but one which is still incomplete and will require additional experimentation in order to convincingly conclude that the lncRNA NEAT1 is required for IRES mediated mRNA translation activity.

Reviewer #1 (Public Review): 

The manuscript investigates a topic of general interest to translational regulation - IRES function during hypoxia. The authors propose that nuclear paraspeckles serve as areas where cellular IRESes acquire their ITAFs and that this subsequently enables them (the IRESes) to be appropriately expressed. Among the components of the paraspeckles that the authors find associated with the FGF1 IRES is the lncRNA, Neat1, and a few resident proteins. The strengths of the current study is that the presented experiments are generally well presented and described. The manuscript is well written. The experiments cover a wide breadth in the area of FGF1 IRES activity/regulation. The weaknesses lies in several instances where correlation between datasets are taken to imply direct cause-effect relationships. Some experiments take several days to set-up (eg, knock-downs) and it thus becomes difficult to establish such direct cause-effect relationships versus effects due to secondary causes.

Reviewer #3 (Public Review): 

This study searched for IRES transacting-factors (ITAFs) that control the translation of the IRES in fibroblast growth factor FGF1 mRNA during normoxia and hypoxia in mouse cardiomyocytes. Because it has been known that several ITAFs locate to nuclear paraspeckles, the authors examined roles for a long noncoding RNA, NEAT1, that is located to these speckles, in the activation of the FGF1-IRES. Using depletion studies it was shown that NEAT1 indeed modulation of FGF1 IRES activity. Using a tagged version of p54nrb, which interacts with NEAT1, several interacting proteins were discovered by mass spectrometry. SiRNA-mediated depletion of the mRNAs encoding some of these proteins (i.e. RPS2, hnRNPM, nucleolin) showed a very modest decrease in IRES activity during normoxia, but less so during hypoxia. Finally, the authors showed that effects of NEAT1 on translation were specific for IRES-containing mRNAs that function during angiogenesis and cardioprotection. While effects of NEAT1 on FGF1 translation is supported by solid data, roles for NEAT1-interacting ITAFs is less clear. However, pre-assembly of translation-competent FGF1 in nuclear paraspeckles is a novel finding that may be very relevant in cardiomyocytes.

Reviewer #2 Public Review:

In this manuscript, Ritchey and colleagues studied an intercross of two inbred mouse strains for their inflammasome response to interrogate the genetic basis for enhanced inflammasome activity. This was spurred by the observation that bone marrow-derived macrophages (BMDM) from DBA/2 mice showed an approximately 2-fold enhanced NLRP3 inflammasome response compared to BMDMs from AKR mice. To explore this phenomenon, they stimulated BMDMs from DBA/2 and AKR intercrosses (F4 generation) with NLRP3 agonists and then studied the ensuing IL-1β response. Conducting quantitative trait locus (QTL) mapping the authors then identified a region on chromosome 7 to have the highest LOD score for the phenotype studied (this region was named Irm3). The Irm3 region encompasses the 134.80-138.45 Mb interval on chromosome 7 that encodes for 66 genes. Given its established role in inflammasome signaling and also a strong cis eQTL LOD score, the authors focused on Pycard in the following. Comparing the two mouse strains, the authors noted an SNV in the 3' UTR of the Pycard gene with differing genotypes for DBA/2 and AKR mice. This SNV is located just downstream the stop codon, a region that seems to display little conservation across different mammalian species. Comparing ASC protein expression, the authors noted increased levels of ASC in BMDMs from DBA/2 mice, a finding that also translated into higher amounts of ASC speck levels following inflammasome stimulation. Subsequent experiments indicated that Pycard mRNA levels of BMDMs from DBA/2 mice displayed a longer half-life, while Pycard mRNA transcription or splicing was not affected. Modeling the 3' UTR region of interest furthermore suggested that the SNV impacts on the structure of this region. To validate the causal role of this SNV in regulating Pycard expression, the authors generated DBA/2 ES cells, in which they changed the genotype of this SNV into the corresponding AKR variant. Comparing ES-cell-derived macrophages of the parental DBA/2 genotype to the AKR-adapted Pycard genotype, the authors found that ASC expression levels were indeed decreased and that this reduced expression translated into a reduced NLRP3 inflammasome response in these cells. Altogether, these data suggest that an SNV in the 3' UTR of the murine Pycard gene impacts the stability of its mRNA, which translates into altered ASC protein levels and thereby the activity of inflammasome pathways.

Strength:

The conclusions of this paper are well supported by data and there are no major gaps or flaws in the line of reasoning. A particular stronghold is the functional validation of the here-identified SNV using a CRISPR-based point mutagenesis approach. This set of data provides a high level of confidence for the proposed model.

Weakness:

While this manuscript provides an elegant QTL mapping approach to identify differential expression of Pycard as a major regulator of inflammasome activity in murine BMDMs, the outcome of this study does not provide any new biological insight into inflammasome biology. The fact that differential expression of ASC impacts on inflammasome activity is well expected based on its firmly established role in inflammasome signaling.<br> Unfortunately, the here-identified mechanism of the differential regulation of the half-life of the Pycard mRNA is not conserved in other species, which precludes any extrapolations to other organisms. Moreover, as also correctly summarized by the authors, there is currently no evidence that genetic variants leading to differential ASC expression in humans would impact on human health or disease. These shortcomings obviously limit the conceptual advance and relevance of the here-identified mechanism.

Reviewer #1 Public Review:

Genetic differences in outbred species such as humans and differences in the epigenomic structure form the basis of the large variability in the immune response. In particular, the inflammasome is highly regulated at multiple levels, including the post-transcriptional and post-translational levels. Inflammasome responses towards a myriad of triggers are associated with disease development in murine models of disease. Furthermore, clinical trials are ongoing testing the ability of inflammasome inhibitory small molecules to prevent or ameliorate inflammasome-driven pathologies in patient populations.

This manuscript identified that a single nucleotide change in the gene encoding for the universal inflammasome adaptor protein ASC regulates mRNA stability of Pycard and thereby inflammasome function. A particular strength of this manuscript is that the authors managed to show, using genetic alterations, that the single SNP in the Pycard gene sequence (rs33183533) between AKR and DBA/2 mice is the cause of variance in inflammasome activity. Given the relevance of inflammasome for various human pathologies, this work is important for a broad readership.

Reviewer #1 (Public Review):

In the present study Giridharan et al. identify a novel role for the phosphoinositide kinase PIKfyve in endosomal recruitment of the retriever complex and recycling of integrins as well as cell migration. Inhibition of PIKfyve with a specific inhibitor reduces endosomal levels of PI3,5P2 which in turns reduces the endosomal recruitment of VPS35L and other retriever components. The sorting nexin SNX17, which preferentially binds to PI3P, is not affected by PIKfyve inhibitors but cannot recycle integrins in the absence of endosomal retriever components. Based upon various inhibitor experiments, the authors propose a model of sequential recruitment of SNX17 and the WASH complex by PI3P followed by a shift to PI3,5P2 and recruitment of retriever, thus initiating actin mediated tubule formation and endosomal exit of SNX17 bound integrins.

The manuscript is clearly written, the data are largely of sufficient quality, and the findings are certainly of interest to the endosomal research community. I also agree with the model the authors propose. One weak point of the study is the dependence on microscopic techniques to analyze integrin surface levels and endosomal recruitment of the retriever complex. The study would have benefited from additional methods to confirm the microscopy data. It would also be good if the authors could confirm their inhibitor studies with genetic suppression/deletion of PIKfyve, ideally followed by rescues with a kinase deficient mutant.

The authors rely on microscopy of integrin beta 1 for most of their data. However, SNX17 and the retriever complex are not required for the recycling of all beta 1 integrins (Steinberg et al., 2012). In HeLa cells, it is mainly integrin alpha 5/beta1 that is recycled by SNX17. Therefore, the other beta 1 integrins that recycle SNX17 independently tend to mask the recycling phenotypes caused by the loss of SNX17/retriever. I think that the authors could detect a much more pronounced recycling phenotype upon PIKfyve inhibition if they stained integrin alpha 5 instead of integrin beta 1. Does integrin alpha 5 "get stuck" in a LAMP1 positive compartment similar to what Steinberg et al., 2012 or McNally et al., 2017 describe in their studies? These two studies clearly show that almost all integrin alpha 5/beta 1 accumulates in a LAMP1 or LAMP2 positive compartment upon loss of retriever/SNX17 function. If the authors are correct in their assumptions, this should be happening upon loss of PIKfyve activity. One could use the Abcam antibody against integrin alpha 5 that was used in the McNally et al. study as it works very well.

Reviewer #2 (Public Review):

Giridharan and colleagues have sought to examine the role of phosphoinositide switching on the function of endosomes in the sorting and transport of integral proteins, in particular integrins. Their focus has been on the enzyme PIKfyve which catalyses the conversion of PI(3)P, an identity feature of early endosomes, to PI(3,5)P2, an identity cue for late endosomes. Through experiments that inhibit PIKfyve function, they have examined the resulting effects on the localization and function of components of the SNX17-retriever-CCC-WASH pathway in the sorting of internalized integrins and the impact on cell migration. They conclude that the activity of PIKfyve, along with its upstream kinase VPS34 (this generates PI(3)P), are coordinated to regulate integral protein sorting through this pathway. Strengths of the study include: the research topic, analysis of the functional significance of efficient and correct endosomal sorting is an expanding area of interest; its breath, from analysis of cell migration down to molecular analysis of integrin sorting; the combination of quantitative biochemical and imaging based analysis; and, the use of genetic tools and acute chemical inhibition. However, the data supporting the conclusions could be strengthened by additional controls and the integration of published data into the final model that argues for phosphoinositide switching in the ordered assembly of the SNX17-retriever-CCC-WASH pathway could be improved.

Reviewer #3 (Public Review):

This study extends what is known about the importance of phosophoinositides in endosomal protein sorting and will be of interest to groups studying endosomal protein sorting. It is perhaps to be expected that PIKfyve, the only PI3P 5-kinase in could have a role in this process as it is important for sorting/trafficking events at other points in the endocytic pathway.<br> It remains to be determined whether the SNX17-Retriever machinery is solely responsible for integrin recycling and it is noteworthy that some of the effects of the loss of PIKfyve function are somewhat marginal. Indeed, there is evidence that SNX27, along with the WASH complex is necessary for integrin recycling and cell migration. There is a possibility that PIKfyve has rather wide-ranging roles in endosomal protein sorting and therefore many cargo proteins will be affected to a greater or lesser degree if PIKfyve function is impaired. A question left open is what machinery is involved in forming the tubular carriers (or vesicles) that transport integrins to the cell surface as SNX17 lacks the membrane-bending BAR domains in many other sorting nexins that can drive membrane tubulation.

Reviewer #1 (Public Review): 

In this manuscript Shi and Fay investigate how natural genetic variation in cis-regulatory sequences impact gene expression dynamics, using budding yeast as a model. Much work in the field, including some landmark studies from this laboratory, have focused on allele specific expression. By contrast, relatively few have investigated the impact of natural genetic variation on the kinetics of gene expression, as the authors do here during the diauxic shift using both inter- and intra-specific hybrids. Strikingly, they find that ASE dynamics are more strongly associated with insertions and deletions than ASE levels. Using reporter assays the authors test which promoter regions and individual variants are sufficient to produce the observed dynamics of gene expression. By investigating chimeric promoter regions between species, the authors gain insight into constraints on the evolution of gene expression dynamics. This manuscript addresses an important question, the findings are novel, and the methods are appropriate. I have a couple of suggestions that I hope the authors will agree can improve their work. 

1) Line 124: I understand the focus on regulatory regions, but post transcription regulation of transcript stability can arise from many mechanisms. RNA binding proteins frequently interact with regions within an open reading frame. I understand the complications of considering coding mutations, but why exclude synonymous polymorphisms within ORFs, for example? At a bare minimum it should be noted in the text. 

2) In what is otherwise an exceptionally clear manuscript it took some time to understand on line 157 precisely how the 334 'regions' were defined from the 1,818 CREs. Some extra sentences would be very helpful to guide the reader here, perhaps with a figure panel to scaffold the logic. 

3) In figure 4 the scale of the x-axis (time) is confusing. Most of the plots don't seem to start at t=0, but it is impossible to tell from the labeling. Because the timepoints highlighted also differ depending on the message being plotted, which is of course natural, interpreting differences in slope, etc. becomes confusing. The authors should either replot with the origins at t=0 or clearly indicate that there is a break in the axis. 

4) Line 209 and 210 - I understand that the PhastCons scores did not improve the association between upstream polymorphisms and ASE dynamics, but it would be nice to hear a bit more from the authors about what this might mean. The observation is restated in the discussion but again mostly without any speculation about what it might mean before moving on to the discussion of technical limitations. If the result is true what might it mean?

Reviewer #2 (Public Review): 

Summary and Strengths: 

In this manuscript, the authors set out to determine the degree to which genetic variation among yeast strains and species influences gene expression during a large, genome-wide change in gene expression. While many studies have examined genetic influences on static expression levels, much less attention has been paid to dynamic responses. We know that different environmental conditions trigger different cellular states that engage different gene expression programs. We also know that DNA variation can have different effects in these cellular states. What we know much less about is how genetic influences shape the transition from one transcriptional state to the other. 

The authors addressed this gap with a comprehensive genomics study. First, they quantified "allele specific expression" (ASE) in several diploid hybrids among strains of the yeast Saccharomyces cerevisiae as well as hybrids between S. cerevisiae and two sister species. RNA sequencing in such hybrids can distinguish RNA molecules produced from the two parental genomes. When there are cis-acting variants influencing a given gene, their effects become detectable as a difference between the expression levels of the two parental alleles. 

The main innovation of this work is that the authors profiled ASE along a time series during a shift from fermentative yeast growth to respiration. During this shift, gene expression changes substantially. Using their time-resolved ASE profiling strategy, the authors were able to track when and how genetic differences influence these changes. This experimental design is a major strength of the paper. It is strengthened further by the inclusion of several hybrids and by dense temporal sampling. Overall, the authors succeeded in their goal of quantifying dynamic ASE. 

Second, the authors used high-throughput reporter assays to study the effects of individual DNA variants in several hundred cis-regulatory elements (CREs). Interestingly, the CREs they studied were able to capture ASE dynamics at least in part, even though the reporter system was integrated into a common locus that probably differs from the chromatin state at the native genes. This use of a complementary genomics approach is another major strength of this paper. 

One highlight result from the high-throughput assays is that many cis-regulatory elements contained multiple causal variants. Another thought-provoking result is that causal variants were neither more likely to occur at conserved nucleotides nor to cause more severe disruption of transcription factor binding sites than other variants. This result is somewhat counterintuitive given the well-established ability of conservation to mark functionally important nucleotides. As the authors state, this absence of evidence may be due to the fact that only a few handful of causal variants were found, limiting the statistical ability to detect more subtle differences in conservation or transcription factor binding sites. On the other hand, the results clearly show that there is no simple code for determining causal variants from available annotations. As the authors state, this is in line with earlier observations that much of cis-regulatory DNA variation could be evolutionarily neutral, perhaps because the effects it has on most genes are not large enough to matter for fitness. These two results are additional strengths of the paper. 

Together, the paper contains an impressive amount of work. I greatly enjoyed the complementary use of ASE and reporter assays. The experiments seem to have been executed well, and are described succinctly and clearly. The paper is an interesting overview of the effects of cis-regulatory variants on dynamic gene expression change. Its main impact on the field lies in the clear demonstration that dynamic ASE exists, as well as its quantification. 

Weaknesses: 

First, the results in the first half of the paper are not overly surprising. They boil down to "genetic variation does influence expression dynamics". This is not unexpected, given genetic variation has been shown to influence just about any cellular process studied so far. As such, the paper essentially confirms the existence of a phenomenon whose existence was not really in doubt. Fortunately, the work into causal variants in the second half of the paper does provide additional insight. 

Second, the results are somewhat descriptive. This is not uncommon for genomics work, but does leave the reader wondering how exactly a given variant may alter gene expression dynamics, especially if it neither occurs at a conserved site nor drastically changes transcription factor binding. I do understand that a deep dive into individual causal variants is outside of the already impressive scope of this paper. I nevertheless hope that one impact of this work will be future mechanistic studies of some of these variants. 

Third, the statistical model to infer ASE strikes me as suboptimal (line 420). From how I understand the Methods section, allelic read counts are transformed to an allele frequency. This frequency is assumed to be 0.5 in the absence of ASE. ASE is then modeled as deviation from 0.5, using a linear model. This last point seems problematic. First, frequencies can only range from 0 and 1, whereas a basic linear model would be allowed to infer frequencies outside of this range. It is not clear to me that this model can properly capture the bounded nature of these data. Second, RNA-Seq data is count based, and transforming to an allele frequency loses information about the accuracy of each measurement. Specifically, genes with few reads have less power due to more stochastic counting noise. Third, the choice of weighting observation simply by the raw read counts (line 422) seems ad hoc and should be justified. More broadly, the authors could have opted for more established, count-based analysis strategies for ASE data, such as binomial tests or more advanced frameworks (e.g. beta-binomial tests as in https://www.biorxiv.org/content/10.1101/699074v2 ). 

Fourth, there is only one biological replicate per hybrid, creating the risk that this one observation of the given time course may not be biologically representative. This also raises questions about how the linear model (see above) was fit without replicate data. 

My final comments (these are not weaknesses but more discussion points) are about the analyses relating the number of sequence differences at a given gene to its strength of ASE (starting at line 120). The authors report significant associations, in line with previous studies. However, it is worth pointing out that this analysis makes an implicit assumption that there are multiple causal variants with effects in the same direction such that adding each variant would increase the ASE difference. The analyses cannot account for the case of multiple causal variants with effects in opposite directions. In this case, even a large number of variants could result in no net ASE. The authors' observation that the association between the number of variants and ASE is strongest for the most closely related strain pair (line 139) may be explained by this scenario. If there are many causal variants that cancel each other, having fewer variants in closely related strains reduces the opportunity for such cancellation. Given these considerations, it is actually somewhat surprising that there is any association between the number of variants at a gene and its ASE. 

Along similar lines, the authors' point (line 226 and end of the Discussion) that inter-species chimeras should lie between the two parental species unless there are epistatic interactions misses the possibility that there could be multiple causal variants with effects in different directions. Additive combinations of these may well create phenotypes more extreme than the parents. For example, say the distal promoter of a given gene has accumulated five variants that all increase expression by the same amount x, and the proximal promoter has accumulated four variants that each decrease expression by the same amount x. The net difference between species would be an increase of one x. A chimera that only has the five distal variants would show a difference of 5x without needing to evoke epistasis.

Reviewer #2 (Public Review): 

In this study Hahn and colleagues investigate the role of Slow-oscillation spindle coupling for motor memory consolidation and the impact of brain maturation on these interactions. The authors employed a real-life gross-motor task, where adolescents and adults learned to juggle. They demonstrate that during post-learning sleep SO-spindles are stronger coupled in adults as compared to adolescents. The authors further show, that the strength of SO-spindle coupling correlates with overnight changes in the learning curve and task proficiency, indicating a role of SO-spindle coupling in motor memory consolidation. <br> Overall, the topic and the results of the present study are interesting and timely. The authors employed state of the art analyse carefully taking the general variability of oscillatory features into account. It also has to be acknowledged that the authors moved away from using rather artificial lab-tasks to study the consolidation of motor memories (as it is standard in the field), adding ecological validity to their findings. However, some features of their analyses need further clarification. 

1) Supporting and extending previous work of the authors (Hahn et al, 2020), SO-spindle coupling over centro-parietal areas was stronger in adults as compared to adolescents. Despite these differences in the EEG results the authors collapsed the data of adults and adolescents for their correlational analyses (Fig. 4a and 4b). Why would the authors think that this procedure is viable (also given the fact that different EEG systems were used to record the data)? 

2) The authors might want to explicitly show that the reported correlations (with regards to both learning curve and task proficiency change) are not driven by any outliers. 

3) The sleep data of all participants (thus from both sleep first and wake first) were used to determine the features of SO-spindle coupling in adolescents and adults. Were there any differences between groups (sleep first vs. wake first)? This might be in interesting in general but especially because only data of the sleep first group entered the subsequent correlational analyses. 

4) To allow a more comprehensive assessment of the underlying data information with regards to general sleep descriptives (minutes, per cent of time spent in different sleep stages, overall sleep time etc.) as well as related to SOs, spindles and coupled events (e.g. number, density etc.) would be needed. 

5) The authors used a partial correlations to rule out that age drove the relationship between coupling strength, learning curve and task proficiency. It seems like this analysis was done specifically for electrode C4, after having already established that coupling strength at electrode C4 correlates in general with changes in the learning curve and task proficiency. I think the claim that results were not driven by age as confounding factor would be stronger if the authors used a cluster-corrected partial correlation in the first place (just as in the main analysis).

Reviewer #1 (Public Review):

Overall, the authors have done a nice job covering the relevant literature, presenting a story out of complicated data, and performing many thoughtful analyses. 

However, I believe the paper requires quite major revisions. 

Major issues: 

I do not believe the current results present a clear, comprehensible story about sleep and motor memory consolidation. As presented, sleep predicts an increase in the subsequent learning curve, but there is a negative relationship between learning curve and task proficiency change (which is, as far as I can tell, similar to "memory retention"). This makes it seem as if sleep predicts more forgetting on initial trials within the subsequent block (or worse memory retention) - is this true? Regardless of whether it is statistically true, there appears another story in these data that is being sacrificed to fit a story about sleep. To my eye, the results may first and foremost tell a circadian (rather than sleep) story. Examining the data in Figure 2A and 2B, it appears that every AM learning period has a higher learning curve (slope) than every PM period. While this could, of course, be due to having just slept, the main story gleaned from such a result is not a sleep effect on retention, which has been the emphasis on motor memory consolidation research in the last couple of decades, but on new learning. The fact that this effect appears present in the first session (juggling blocks 1-3 in adolescents and blocks 1-5 in adults) makes this seem the more likely story here, since it has less to do with "preparing one to re-learn" and more to do with just learning and when that learning is optimal. But even if it does not reach statistical significance in the first session alone, it remains a concern and, in my opinion, should be considered a focus in the manuscript unless the authors can devise a reason to definitively rule it out. 

Here is how I recommend the authors proceed on this point: include all sessions from all subjects into a mixed effect model, predicting the slope of the learning curve with time of day and age group as fixed effects and subjects as random effects: 

learning curve slope ~ AM/PM [AM (0) or PM (1)] + age [adolescent (0) or adult (1)] + (1|subject) 

...or something similar with other regressors of interest. If this is significant for AM/PM status, they should re-try the analysis using only the first session. If this is significant, then a sleep-centric story cannot be defended here at all, in my opinion. If it is not (which could simply result from low power, but the authors could decide this), the authors should decide if they think they can rule out circadian effects and proceed accordingly. I should note that, while to many, a sleep story would be more interesting or compelling, that is not my opinion, and I would not solely opt to reject this paper if it centered a time-of-day story instead. 

The authors need to work out precisely what is happening in the behavior here, and let the physiology follow that story. They should allow themselves to consider very major revisions (and drop the physiology) if that is most consistent with the data. As presented, I am very unclear of what to take away from the study. 

If indeed the authors keep the sleep aspect of this story, here are some comments regarding the physiology. The authors present several nice analyses in Figure 3. However, given the lack of behavioral difference between adolescents and adults (Fig 2D), they combine the groups when investigating behavior-physiology relationships. In some ways, then, Figure 3 has extraneous details to the point of motor learning and retention, and I believe the paper would benefit from more focus. If the authors keep their sleep story, I believe Figure 3 and 4 should be combined and some current figure panels in Figure 3 should be removed or moved to the supplementary information. 

Why did the authors use Spearman rather than Pearson correlations in Figure 4? Was it to reduce the influence of the outlier subject? They should minimally clarify and justify this, since it is less conventional in this line of research. And it would be useful to know if the relationship is significant with Pearson correlations when robust regression is applied. I see the authors are using MATLAB, and the robustfit toolbox (https://www.mathworks.com/help/stats/robustfit.html) is a simple way to address this issue. 

Additionally, with only a single night of recording data, it is impossible to disentangle possible trait-based sleep characteristics (e.g., Subject 1 has high SO-spindle coupling in general and retains motor memories well, but these are independent of each other) from a specific, state-based account (e.g., Subject 1's high SO-spindle coupling on night 1 specifically led to their improved retention or change in learning, etc., and this is unrelated to their general SO-spindle coupling or motor performance abilities). Clearly, many studies face this limitation, but this should be acknowledged.

Evaluation Summary:

This study contributes a significant advance to the field in terms of detailed genomic epidemiology of the introductions of drug resistant typhoid into Kenya, and Africa in general. The findings highlight the role of asymptomatic carriage in the spread, drug-resistance mechanisms and the emergence of typhoid strains in Kenya. The authors also contribute a small number of sequences from both carriage and acute disease, most sequenced cases are associated with acute disease, making this deposit of publicly available carriage sequences extremely valuable. This work will be of interest to a broad readership, including but not limited to clinicians, biologists, and public health experts.

(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

Reviewer #1 (Public Review):

Kariuki et al. report a clinical, microbiological, and genomic epidemiology analysis of multidrug-resistant typhoid strains sampled from a large case-control study of children living in an informal settlement in Kenya. Using whole-genome sequencing, the authors demonstrate that the frequency and distribution of S. Typhi genotypes among cases and controls is similar, highlighting the importance of asymptomatic carriage as a reservoir for transmission. The authors also describe the presence of drug-resistant genes and their genomic localisation. Further phylodynamic and geographic analysis defined clades associated with the Kenyan S. Typhi isolates in a global context to determine potential importation and time of emergence.

The participant inclusion and exclusion criteria are sound, and the microbiological and bioinformatics analysis methods are robust and well-described.

Specific comments:

- Although this is mentioned in the referenced papers, it would be great to briefly mention the microbiological methods used for serotyping in the microbiology laboratory.

- It would be great to mention if, in general, there are any discrepancies in the isolation frequency of S. Typhi isolation between stool samples and rectal swabs. If the isolation frequency differs between the sample types, then biased sampling favouring one of these samples could affect the estimated carriage frequency.

- Details on how other bacterial species were identified are not included. It would be great to mention whether Kraken or other tools were used.

- Considering that some isolates were mistyped in the laboratory, it would be great to include some discussion on this. It seems that the inclusion criteria required all the sequencing of S. Typhi and not all other Salmonella serotypes (see lines 132 & 147). I wondered whether broadening the criteria for selecting the isolates to undergo genome sequencing would somewhat change the results, specifically the carriage frequency and distribution of genotypes (i.e., identifying more S. Typhi that would be mistyped otherwise).

- It's not clear why the IS1 insertion sequences were identified in the genomes. S. Typhi probably contain other IS elements, therefore, a brief explanation of why on the IS1 elements were identified should be provided.

- The link between the phylogenetic branch lengths and tree tips suggesting a more prolonged duration from acquisition to sampling in the clinic is not clear. Since the isolates were sampled cross-sectionally, it's not clear how this information was inferred in the absence of data on within-host diversity of the isolates.

- Some information is hidden in columns of Table S1. It would be great to present this data as a separate Excel spreadsheet file.

Reviewer #2 (Public Review):

The authors present a detailed and well-written investigation of the genomic epidemiology of MDR Typhoid in Kenya from both carriage and acute case sequences. The authors found phylogenetically interspersed carriage and acute cases that indicate a reservoir role for asymptomatic carriers. The Phylogenetic results also indicated that there wasn't a point-source short-term outbreak of MDR typhoid but rather 3 well-established east african lineages that are associated with continuous community transmission and presence in both carriage and acute cases for a long period of time.

The dataset is small but the authors account for this and include publicly available previously sequenced isolates for phylogenetic context. The phylogenetic conclusions are well supported by the presented data and appropriate methodology has been applied.

Overall, the paper is a useful addition to our knowledge on Typhoid and is of considerable interest to the field.

Reviewer #3 (Public Review):

The authors set out to describe the genomic epidemiology of Salmonella Typhi causing diarrhea among children in an informal Kenyan setting between 2013 and 2016. A unique strength of this paper is the they also analyzed contemporaneous carriage isolates from age matched controls in the same locality. Based on the phylogenetic overlap between carriage and invasive isolates, they deduce a potential role for carriage driving disease transmission community. This finding has important implications, since it places the onus on health officials to devise control strategies that encompass surveillance and potentially interventions in healthy carriers. It also signals the need to study the role of alternative reservoirs of S. typhi like the environment and animals.<br> The authors went further and probed the historical context of the isolates. They leveraged epidemiological data dating back to the 1990's and genome sequences from previous studies in Kenya to determined that most of the isolates causing disease were the offspring of multidrug resistant lineages that replaced the drug susceptible lineages in the 1990's. Their dating places the introduction of these lineages around 1990, which correlated with the first reports of these lineages in Kenya. This historical context is important in understanding the evolution of drug resistance and how the genomic landscape has changed with changing antibiotic usage patterns. This dataset adds context on the evolutionary trajectory of S. typhi in Kenya, providing a broader baseline for future studies to track the emergence of novel lineages as well as the evolution of existing lineages. Future studies will have a wider temporal spread and, with that, likely improved confidence in inferences from phylogenetic dating.

Finally, the authors studied patterns of mutations to identify genetic signatures that differentiated carriage from invasive disease isolates. They showed that a higher proportion of non-synonymous mutations likely meant positive selection and a longer duration of colonization in carriage than disease. While they identify important gene classes that had a higher proportion of non-synonymous mutations in carriers, there is little discussion on the biological implication of their findings. It is also unclear whether these findings may be biased by clonal inheritance i.e., if most a given mutation occurs on a branch or clade that is dominated by carriage isolates, the signal may be due to phylogenetic placement and not necessarily a biological adaption process. One way to overcome this would be a genome wide association study that looks at mutations, unitigs as well as whole genes, and takes phylogenetic placement into account. However, the authors rightly acknowledged that their study yielded a low number of genomes, which may not be powered for such analyses.<br> A major concern was the high proportion of isolates that were initially designated as S typhi but confirmed by genomics to be another species (34.6%). Although this could be due to low specificity of the serotyping assays or randomly picking different colonies in a mixed infection, it can also indicate sample mix ups in the dataset. It is important for the authors to clarify what steps were taken to rule out sample mix-ups and to share details on how reliable their initial serotyping was. This is compounded by the low number of genomes, which the authors acknowledge, as well as the geographic localization, which raises questions about how generalizable these findings are to the wider population in Kenya.

Reviewer #1 (Public Review):

In this study, the authors investigate the relationship between temporal predictability and linguistic predictability during speech listening.<br> They first show that language statistics and linguistic predictability influence the duration of linguistic units (syllables and words) in naturally spoken speech. Specifically, they provide evidence that words with higher word frequency have shorter durations, and that linguistic units are lengthened when the next linguistic unit is not strongly predictable.<br> Then, they test a new computational model (STiMCON), which suggests that brain-speech tracking reflects a mechanism that is not only following the acoustics of speech, but also generates a temporal phase code that is sensitive to linguistic constraints.

The manuscript addresses a very relevant and timely question: the origins of the pseudo-rhythmicity of speech, and the role of neural oscillations in the tracking of this pseudo-rhythmic input. The premise behind this manuscript is important for neuroscientists interested in the neurobiology of language. The first results show that pseudo-rhythmicity in speech is in part a consequence of top-down predictions flowing from an internal model of language, which I think is in itself a very exciting finding. The proposed STiMCON model is interesting, but some more information is needed in the manuscript to fully understand it.

Reviewer #2 (Public Review):

TenOver and Martin investigate the question of whether including linguistic predictability in an oscillatory model of speech segmentation can explain how neuronal oscillations can process quasi-periodic speech. First, they analyze data from a speech corpus to show that word-frequency, number of syllables and characters relates to syllable duration. They show that linguistic predictability affects the onset timing of words. More specifically, highly predictable words (based on a corpus pretrained RNN), had a shorter word onset time. They introduce a RNN model (STiNCON) that includes 3 layers, oscillations, connectivity between the layers, and inhibition. With the model they show how linguistic predictability can align neuronal excitability to process quasi-rhythmic speech in an oscillator model. They run STiNCON first on several speech materials (short constructed sentences, word-pairs, corpus material CGN) and use it to model a behavioral and EEG experiment that was previously published (TenOver & Sack, 2015) (here they also evaluate their model compared to other models with fewer components: suggesting that the oscillation and the feedback were crucial parameters).

The topic is highly interesting and provides new insights to the current debate about the role of oscillations and top-down predictability in speech recognition. The authors provide extensive material. The manuscript is well written and the model is sophisticated. However, given the complexity and density of the manuscript, more clarity in describing the modeling would be useful. This includes, adding methodological details and justification of choices (particularly for the model application to the EEG study).

The results support the authors conclusions. However, I have several questions/comments: (1) In the model the predictability is computed at the word-level, while the oscillator operates at the syllable level. The authors show different duration effects for syllables within words, likely related to predictability. Is there any consequence of this mismatch of scales? (2) Furthermore, could the authors clarify whether or not and how they think the model mechanism is different from top-down phase reset (e.g. l. 41). It seems that the excitability cycle at the intermediate word-level is shifted from being aligned to the 4 Hz oscillator though the linguistic feedback from layer l+1. Would that indicate a phase resetting at the word-level layer through the feedback? (3) The model shows how linguistic predictability can affect neuronal excitability in an oscillatory model, allowing to improve the processing of non-isochronous speech. I do not fully understand the claim that the linguistic predictability makes the processing (at the word-level) more isochronous, and why such isochronicity is crucial. (4) The authors showed that word frequency affects the duration of a word. Now the RNN model relates the predictability of a word (output) to the duration of the previous word W-1 (l. 187). Didn't one expect from Fig. 1B that the duration of the actually predicted word is affected? How are these two effects related?

Reviewer #3 (Public Review):

The authors have presented a unique perspective on the nature of oscillatory tracking of speech perception. A critical question in this field is how such a mechanism could handle dealing with natural deviations from rhythmicity in speech. While some researchers have sought out to show the flexible and dynamic nature of oscillations, the authors here propose that even a sinusoidal mechanism can handle pseudo-rhythmicity so long as it is driven by linguistic constraints. To that end, they show first that (at least in Dutch) the length of words is modulated by linguistic predictability of the word. Then, they build a simple model which combines an sinewave oscillation (fixed amplitude and phase) with these linguistic predictions to generate a phase code of both perception and predictability. The authors show that this model can explain variation in timing in naturalistic speech as well as explain surprising findings on the bias of perceptual content by oscillatory phase.

I find the paper to be an important contribution to the field and well thought out. I have only a few thoughts and comments.

1) The use of an RNN model to estimate the internal language model is particularly effective. While the authors acknowledge that a RNN is unlikely to capture all of the complexities of the human internal language model. I found the choice to use a simple architecture as a statistical extractor to be a nice use of a tool that can sometimes be overly convoluted.

2) An important question is how the authors relate these findings to the Giraud and Poeppel 2012 proposal which really focuses on the syllable. Would you alter the hypothesis to focus on the word level? Or remain at the syllable level and speed up and low down the oscillator depending on the predictability of each word? It would be interesting to hear the authors thoughts on how to manage the juxtaposition of syllable and word processing in this framework.

3) The authors describe the STiMCON model as having an oscillator with frequency set to the average stimulus rate of the sentence. But how an oscillator can achieve this on its own (without the hand of its overloads) is unclear particularly given a pseudo-rhythmic input. The authors freely accept this limitation. However, it is worth noting that the ability for an oscillator mechanism to do this under pseudorhythmic context is more complicated than it might seem, particularly once we include that the stimulus rate might change from the beginning to the end of a sentence and across an entire discourse.

4) The "I eat very nice cake" analysis clearly demonstrates in a simple and didactic way the fundamental behaviors of the model: that predictions of the internal model can be read out in a phase code and that deviations from rhythmicity can yield more rhythmic behavior in the brain. I applaud the authors for demonstrating the behaviors in this very simple case first before moving to the more complex naturalistic case

5) The analysis of the naturalistic dataset shows a nice correlation between the estimated time shifts predicted by the model and the true naturalistic deviations. However, I find it surprising that there is so little deviation across the parameters of the oscillator (Figure 6A). What should we take from the fact that an oscillator aligned in anti-phase from the with the stimulus (which would presumably show the phase code only stimulus offsets), still shows a near equal correlation with true timing deviations. Furthermore, while the R2 shows that the predictions of the model co-vary with the true values, I'm curious to know how accurately they are predicted overall (in terms of mean squared error for example). Does the model account for deviations from rhythmicity of the right magnitude?

6) Lastly, it is unclear to what extent the oscillator is necessary to find this relative time shift. A model comparison between the predictions of the STiMCON and the RNN predictions on their own (à la Figure 3) would help to show how much the addition of the oscillation improves our predictions. Perhaps this is what is meant by the "non-transformed R2" but this is unclear.

7) Figure 7 shows a striking result demonstrating how the model can be used to explain an interesting finding that phase of an oscillation can bias perception towards da or ga. The initial papers consider this result to be explained by delays in onset between visual and auditory stimuli whereas this result explains it in terms of the statistical likelihood each syllable. It is a nice reframing which helps me to better understand the previous result.

Reviewer #2 (Public Review): 

The authors apply neural network modeling and representational analysis of fMRI data to testing the ability of the theoretical framework under the "non-monotonic-plasticity hypothesis" to explain how hippocampal subdivisions represent similarity and distinctiveness between events. They suggest that the dentate gyrus subfield, in particular, was sensitive to the degree of overlap between experiences, and changes how it favored distinctiveness or similarity in its representation of associated stimuli in a non-monotonic manner. 

Overall, the work builds logically on prior evidence from this group focused on how cortical representations influence memory, and leverages a compelling theoretical framework to reconcile some conflict in the literature on how hippocampal representations respond to overlap. 

The primary confusion and concern with the current manuscript was on the theoretical side. It was not wholly clear from the literature review why DG was the predicted locus of the non-monotonic representational relationship observed, and how the findings fit with extant data from rodent work. 

Additionally, the theoretical model (nicely illustrated in the manuscript) is considered in a somewhat biological-network-agnostic level. Some assumption for how context changes over time, how prior representations are maintained over time, etc., are important for non-monotonic relationships between representations and memory to manifest in the model, but the manuscript does not provide much discussion of their plausibility. This was particularly notable in terms of the emphasis given in the fMRI data to different hippocampal subfields, but not much discussion given on whether/why the model framework is static across subfields (in terms of how context and item information are represented and connected). 

As such, this review was very positive, and found the methods to be sound and the conclusions to be solid. There was some room for improvement in how the theoretical foundation was presented for the hippocampal subregion fMRI predictions and for the conceptualization of the neural network memory model.

Reviewer #1 (Public Review): 

In this paper, Wammes et al. used fMRI to investigate changes in representational similarity of temporally paired images in hippocampal subfields. The stimuli were designed to parametrically vary in their visual similarity so that individual pairs covered the entire range of visual overlap, which was behaviourally validated by a separate sample of participants. The authors compared the neural patterns evoked by these pairs of stimuli before and after participants completed a statistical learning task. The findings showed that pre- to post-learning, representations in the dentate gyrus reconfigured to fit a cubic model, consistent with the non-monotonic plasticity hypothesis (NMPH). 

This is an interesting, novel approach with a clever stimulus manipulation which addresses a gap in the current literature. The study is well-motivated by theory, the analyses are appropriate and clearly described, the implemented controls are carefully designed, and the manuscript is well-written. However, it is unclear whether the same principles necessarily generalize beyond visual similarity, and whether these neural patterns meaningfully relate to behaviour.

1) The analytic approach is well-designed and the results clearly address the hypotheses. However, it seems like the conclusions might be dependent on this learning paradigm, which should be discussed in a bit more detail and made clearer. The present statistical learning approach is somewhat implicit in its nature and relies on the participants gradually recognizing the temporal links between stimuli. In contrast, in most prior studies cited in the present manuscript, participants were explicitly instructed to make associations between stimuli that either occurred on the screen simultaneously, or relatively far apart in time (i.e., not successively). This top-down influence likely plays an important role. Even beyond experimental paradigms - we often make connections between similar experiences that occurred far apart in time, and cannot always rely on temporal contingencies. The step between previous work and statistical learning needs to be made clearer and more explicit. 

2) Related to the point above - the timecourse over which such statistical learning occurs should be discussed. If I understood correctly, all of the learning occurred in the 6 scanned blocks between the two templating runs. Does the NMPH predict that the hippocampal patterns should immediately reconfigure depending on visual input, or only reconfigure once the participants encode the links between paired stimuli? If the pattern consistent with the NMPH is immediately evident, this would suggest that the present findings, while very convincing, might not be governed by the same mechanisms as integration/differentiation in memory. It seems unlikely that participants would immediately attempt to link these complex visual stimuli, especially as the cover task was orthogonal. To this end, it would be helpful to see any kind of analysis evaluating representations across the 6 statistical learning runs. 

3) In the Introduction and Discussion, the authors focus on learning and discuss the integration/differentiation of memories. To establish a link between the reported hippocampal representations and behaviour, it would be helpful to show evidence of a link between neural differentiation and measures of statistical learning such as priming. 

4) From the authors' predictions (and Fig 1), it might follow that participants who show steeper slopes in early visual regions (i.e., higher correspondence to stimulus similarity) pre-learning might also show a stronger cubic trend in the hippocampus. It would be useful to show within-participant analyses to link visual processing regions to hippocampal representations.

Reviewer #3 (Public Review):

Neurobiologists direct a fundamental level of questions to ask how the brain controls sequential patterns of muscle activation and thus organizes coordinated movements. This paper addresses that problem in an insect model system and it focuses on the behavioral sequence by which the larval fruit fly transforms to a largely inert, intermediate life stage, called the pupa. Insect development is an exceptionally useful context in which to study the sequence of events that define specific ontological epochs. Other systems share many of the same developmental sequalae, but in insects the developmental progression of events proceeds at a largely invariant pace, and so it assures a highly-predictable timetable which helps to make observations and propose correlations. The work by Elliott and colleagues takes advantage of the invariant insect development sequence to ask a neuroethological question - how does the coordination of muscle activation arise that underlies pupal ecdysis (emergence) in the fruit fly Drosophila? The authors use an innovative combination of calcium imaging, multi-viewpoint, whole animal muscle recording, genetic manipulations and convolutional analysis. They present a comprehensive overview of the individual motor elements and the emergence of regulatory coordination in neuromuscular physiology that lead invariably to the ecdysial behavioral events. This paper is successful in presenting a model whereby complex and coordinate behaviors arise during an invariant developmental sequence from what is termed seemingly uncorrelated patterns of muscle activation. Further they investigate the molecular basis for the emerging regulatory coordination and underscore the influence of specific regulatory peptides and peptide hormones. The authors do an excellent job making a detailed, data-rich and highly specific set of observations accessible to a general audience. That accessibility, and the novel approach of analyzing behavior at the resolution of single muscle cells, should promote consideration of similar developmental sequences in other model systems.

Reviewer #2 (Public Review):

The manuscript by Diao et al. is an important extension of their eLife paper of 2017. Their development of new tools that allow them to follow Ca2+ transients in single muscle fibers over the whole animal through the behavioral sequence and also to independently monitor the Ca2+ transients in the endplates of the motor neurons that innervate these muscles. Their goal is to break down the movements that control the ecdysis sequence into elemental "syllables" and then to defined the role of these syllables in constructing progressively complex behavioral programs and as targets of neuropeptide modulation.

A crucial behavior that occurs during P1 in higher flies is the movement of the gas bubble but this event is largely ignored in the paper. Prior to pupal ecdysis, gas is expelled into the posterior puparial space and then actively translocated, via muscular contractions of the body wall, to the anterior end of the puparium during the latter portion of P1 (shown nicely in the author's 2017 Video). A detailed study by C.G. Chadfield & J.C.Sparrow (1985. Dev. Genetics 5: 103) of pupal ecdysis in Drosophila emphasized the importance of this translocation for head eversion. When they simply removed the operculum at the start of bubble movement, then the gas bubble could not push the animal backwards in the puparial case and head eversion could not occur. However, they saw normal pupation and head eversion if the removed operculum was immediately replaced and sealed down with petroleum jelly.

During translocation, the bubble moves in a fragmented fashion between the pupal cuticle and the puparium. Ignoring this movement leads to statements like on line 378 "Because pupal ecdysis is independent of environmental factors and executed in the absence of competing physiological needs, it is likely that its variability is intrinsic to the ecdysis network." For the pupating animal, its "environment" is the inside of the puparial case and the moving bubble is an unpredictable variable in this environment. The trajectory and route of bubble movement is not fixed, and it is likely that variation in sensory feed-back from the gas movement explains the motor variability and reduced stereotypy during P1. The role for proprioception during this phase is likely to inform the CNS of the progression of the bubble fragments. The author's finding that the blockage of proprioceptors suppresses the behavior progression could mean that this sensory information is needed to signal that an anterior space has been produced, and without this signal, the behavior does not progress to its next phase. This should be addressed in the text if not experimentally.

Another aspect of the background that is missing is considering earlier studies on the ontogeny of behaviors leading up to ecdysis/hatching. Notable are studies of the progressive construction of the flight motor program during metamorphosis in moths (Kammer & Rheuben 1976 J. Exp. Biol. 65:65.) and a similar feature of assembly of motor programs prior to hatching in Drosophila (Crisp et al., 2008 Development 135:3707). In the moth studies, complex motor programs were gradually assembled during ontogeny with motor neurons firing but without muscle contraction (as the authors see in prepupae during P0 - Fig 2C). A lack of excitation-contraction coupling in the moth prevents muscle movement through most of development. This suppression of contraction is essential because prior to production of adult cuticle, muscle contraction would rip the developing animal apart. The same requirement to suppress muscle contraction would be seen in fly prepupa until sufficient pupal cuticle has been secreted to prevent rupture from actual muscle contractions! This should be addressed in the text.

Besides not being explicit about how the syllables combine to build the eight basic movements, it is not clear how these basic movements then combine to support the major behaviors of each phase. This is seen in P1, where we see that swing and brace movements can co-occur (e.g., Fig 3D) but is a swing on one side always associated with a brace on the other? What are their phase relationships? Does their temporal association remain stable as the bouts progress? Another example is in Phase 3. There appear to be 5 basic behaviors associated with bouts in Phase 3. The example in Fig 1H shows double peak bouts in phase 3, and the bulk Ca data show a preponderance of double peaks. The different shapes suggest that there are different movements during the two peaks. Their discussion of P3 movements (around line 273), though, does not address this feature of the double peaks. The example in Fig 7A suggests that some movements, like the PostSwing occur at half the frequency of other movements such as the PostCon and AntComp. Is this the basis of the double peaks and how is that reflected in the movements that are finally produced? This should be addressed in the text.

One approach that I did not find useful was dividing the analysis into compartments - anterior versus posterior and dorsal-lateral-ventral. This may provide a way of generating some statistical analysis, but it did not illuminate anything about the behavior. The line between anterior and posterior segments seems to be arbitrary. Of course, it is important to know if there is directionality of movement [waves going anteriorly versus posteriorly], but beyond that, I am not sure what it adds. [Indeed, it made Fig 7 very confusing!] Also, I could not see a rationale for considering separate dorsal-lateral-ventral compartments. This should be addressed in the text.

Reviewer #1 (Public Review):

This paper examines muscle activity at single muscle level during Drosophila ecdysis (adult hatching) behavior. The premise is that quantifying behavior or motor neuron activity is insufficient to understand how the CNS generates behavior - it is also critical to quantify muscle activity. They show that abdominal body wall muscles generate stereotyped patterns of activity during four developmental stages; (phase 0, stochastic activity; phase 1-3, each with different patterns of activity. Co-active groups of muscles form "syllables" which are used in different combinations to generate the stereotyped activity seen in phases 1-3. This analysis was facilitated by use of a convoluted neural network. Interestingly, they found examples where muscle contraction did not match muscle activity (GCaMP elevation), showing the importance of measuring both attributes.

In addition to mapping the stereotyped muscle activity at single muscle resolution in the generation of ecdysis behavior, they find that phase 1 and 3 are quite variable, and speculate that other constraints on the CNS output (e.g. during larval locomotion) may prevent a sharpening up of muscle patterns. They show that the hormone ETH is required for initiating phase 1, and the neuromodulators bursicon and CCAP are required for initiating phase 2. Failure to initiate either phase is lethal. Lastly, they show that in addition to initiating phase 1 or 2, the hormone/neuromodulators result in more coherent muscle activity.

Overall this study sets the stage for a detailed analysis of motor neuron function in driving muscle activity patterns, and then further into the CNS to understand the role of premotor neurons. Ecdysis behavior has the potential to be a powerful system for understanding how the CNS generates behavior at the single muscle /single motor neuron level, as well as for understanding how neuromodulators act to regulate muscle/motor neuron activity.

The figures are almost all too small to see the salient information, and the color scheme is often difficult to resolve. Please enlarge the key aspects of the figures; and try to use more distinctive colors where critical comparisons need to be made. Some examples: left/right colored lines in 1G; panel 3D; lines in 3E; all data in 5G (this is the worst for tiny data); 6C,D,J; all of 7.

unidad_COVID2019

PMID: 32219356 DOI: 10.1001/jamacardio.2020.1017

unidad_COVID2019

DOI: 10.1056/NEJMoa2001017

unidad_COVID2019

DOI: 10.1056/NEJMoa2005412

but there's no way any analysis restricted to the article text will ID this, because you have to resolve the RRID to figure that out, right?

Why would papers have a higher percentage of SCR RRIDs? Where are the other RRIDs found?

the conclusion is in the paragraphs above titled comparison. Perhaps this para should be titled "future directions" or something?

This section seems like it should be in the introduction.

correct plural is corpora

Ok so Scibot is identifying digital resources from a list & flagging when there's no RRID but there probably should be?

There are 190000 RRIDs in 13000 articles. RDW found RRIDs (doesn't say how many) in 701110/(2341133+738910+72493+151784=3304320) articles. So there are resources mentioned in about 21% of articles, based on extraction, but presuming all of the 13000 RRID containing articles were included in the 3 million, the RRID prevalence is closer to 6%, but RRIDs mentioning digital resources are 26748 or .8%. So 4/5 of articles don't mention digital resources at all?

A good description about the AIDS quilt letting people know the size of the quilt itself.

Maybe add another introduction as well to this explaining why you're doing this project

Did he attend the school or was he part of the team

On his day of death were people happy he moved on or sad that he was gone

Probably find more information on how close he was to the person that he was close to

Is there any videos that he ever did if he did explain or put out what he did

fix this

Use more formal and differentiate your sentences a little more.

Even though the panel is described might need to make more deeper description.

Might need to fix this, maybe add a comma somewhere

*Was not able to annotate Ky'Metri's panel because did not have his PSD draft".

This description gives a good idea on how the background looks and the words across the panel alone.

A good sense of a quote being that he was a Tar Heel fan.

The referent here wants to be an annotated segment of interest, to which discussion (and workflow tags) can attach.

• a little more detailed introduction
• add attribution for the photograph
• introduce your three claims at the beginning
• introduce your thesis too

• on the first slide, i suggest including an explanation of the word "do-good"
• explain the different types of research further
• create another slide to compare and contrast each researchers studies and their different impacts
• slide 3, include photo attributions

• Include Photo Attributions
• I did not understand the purpose of this slideshow, because I did not understand its' relation with your claim.

• I liked the arrangement of your slides, because it helped convey a specific message
• Great Slide
• Include Photo Attributions

• on the first slide, you need to list the quote's author to establish credibility.
• on the third slide, i suggest increasing the size of the two photographs to help fill in empty space

I believe that this is an introductory paragraph to the next paragraph because it basically give the claim for the next paragraph try combining the two paragraphs and who will have a nice flow and that way the claim is known

Im not really sure what the claim is between these two. Try making the claim a little more noticable so the reader knows exaclty what he or she is getting into when they start to read this and again you could add this to the paragraph where it starts talking about african americans because it would help elaborate.

This paragraph has the claim of spirit also helping aid the south and you could possibly add this to the other paragph where you spoke about the movement instead of making it another paragraph because the addition information at the end of the paper makes it choppy

This seems to be your claim but you only talk about your claim brefly when it could be elaborated more.

There doesnt seem to be a claim here but maybe make your claim broader in this paragraph since your talking about multiple things here and about fortune 500 companies that were housed in the city.

At first when reading this, this sentence seems to be the claim but as i kept reading the rest of the paragraph did not seem to match the claim. just elaborate some more with your claim and about the reconstruction era in atlanta.

dont forget to add a works cited to the end of your essay

change it to "the reconstruction era"

try re-wording this sentence. the part about "being a giant" could be changed to sound a little more scholarly.

playing the devils advocate someone could say that this is incorrect and the native americans were not relocated but thrown out or pushed out by the white man. have that in mind when you edit this. also check up on the citation becuase the citation seems to be biased.

whos period of growth?

instead of just adding ambrose into this try directly quoting where the author says this in your source. it could make your paper a little more diverse in what kind of sentences you have

try added other minorites into this statictic not only african americans.

*were

reword this because it sounds weird when you say it.

*was used as a

change this world to group. circle sounds a little weird when you put it into this sentence.

i've never heard of confederate government. try useing another world for the group. Try saying something liek "confederate leaders" because the confederates never actually had a government.

This seems to be the claim in this paragraph. It is a little confusing when you start reading the paragraph becuase I didnt know if you were going to talk about the amry or politics in this but it ended up being about the army and stength of it. Your evidence in this paragraph seems to back up your claim. You could try and eleborate more with your claim. Instead of having one citation that backs up your claim try adding some more to elaborate it. Your second citaion in this paragraph does not have much to do with the claim since it is about industrialization of atlanta and production of goods.

I dont see a direct claim here. You moslty talk about the begging history of atlanta. You talk about a couple of different things in this paragraph and it does not seem to flow that well due to the fact that none of the things you talked about were eleborated as well as they could have been.

SO in this paragraph im thinking that the claim is that Atlanta ia a very popular and thats because of its history. I feel like you could adress your claim in a better way but over all i get the main point of this paragraph. Since this is a "introduction" I wouldn't expect you to have many evidence but you do mention the recontruction period and the civil war. One thing that you did not do in this paragraph is cite a source.

This is one of a few incomplete pages on this site. Once the content is written in the same style and tone as the other provided content, then the completed piece will be even more successful. This particular movie is also a more contemporary work, which makes it very relevant to the topic overall.

Here invasion stories is defined by the fears it arouses within the reader. The film becomes bigger than just an invasion story where aliens attack humans; it touches on a bigger picture such as pinpointing political themes that deeply affect the people.

Here is the obvious summary of the film. This is very much needed for the reader who isn't familiar with this particular film. Avisual is provided to make the page aesthetically appealing.The author stays consistent with this effective design and style

Here is the evidence of given credit to borrowed sources. Therefore, it shows that within the content, the author has provided substantial information backed by these specific sources. The reader can further investigate the topic if he/she wanted to.

Here is the posed question that the authors intend to answer through the featured invasion stories. Even if this About Page isn't the static front page, the reader can still identify the information being presented. What the reader can take from this information is that through the sites illustrations, he/she will be able to understand what an invasion story is and what kinds of invasion stories exists.

I want to read more! But, I know that there's a word limit. I enjoyed your demonstration of critical thinking on the topic of literacy. However, in the highlighted section here, I'd suggest making a smoother transition so that the reader knows that this is the end of your essay aside from it obviously being the last para.

Because you mentioned him in the previous parag and addressed him as only Graff, I'd go back and state his first name there as well just for consistency.

Great transition here to the next para. You could either start this sentence as a new para or leave it... either way works.

May you specify the course?

Can you specify what class? That way the audience will know the extent of relevancy here.

Who was your instructor?

I'd suggest finding a synonym for one of the "courses" just for the sake of flow here

Could you add just one concise sentence here briefing over what it is exactly, but tying it back to the main point of this para?

See comment for para 3

See comment for para 3

See critique on line "In English 3090" para 3

In your first paragraph, you named the course entirely. I'd suggest being consistent throughout your essay here for the purpose of clarity and again consistency.

Again, your hyperlink here is placed at a great spot for the reader. Although in a sense it may interrupt one's reading, it does no harm here because towards the end, you make aware your point of the Marxist critique and what it is.

I like how you tie this back into your mentioning of the iPhone in the previous paragraph. I'd suggest putting this entire highlighted piece at the beginning of this particular paragraph so that it makes a smoother transition when reading it. Everything else is fine the way it is.

I absolutely enjoyed your use of hyperlinks here. They are very appropriate and convenient. I also think your audience will enjoy this.

I think this entire paragraph is awesome Daniel. for some readers it may be a bit verbose, but considering that the main audience will be the staff that's assessing it, I think it's completely fine here.