I totally agree with this. When a student submits AI-generated work as their own, it misuses the instructor’s time and undermines the trust that’s essential in a learning environment. Instructors spend significant energy reading, assessing, and giving feedback with the assumption that the writing represents a student’s genuine thinking. I feel like many professors were in school themselves at one more and put forward the effort to get where they are today and expect that from students aswell

The biggest claim here is that humans are uniquely able to imagine, infer, and reason beyond what we’ve already seen. This suggests that genuine creativity and forward-thinking are human strengths that cannot be outsourced, no matter how advanced AI becomes

This makes me reflect on how often I skim or rely on shortcuts instead of engaging with full texts. The paragraph also hints at a bigger concern: if students become too dependent on AI to simplify everything, they may lose the stamina and skill needed for close reading, critical thinking, and grappling with nuance. In this way, the issue isn’t just about AI use, it’s about how our reading habits and expectations are being reshaped, possibly at the cost of deeper comprehension.

This shows the importance of implementing some co created rules and expectations in the classroom

There have been many times where I use AI to create lesson plans for rubrics for my class. Since students may use this in the future, it is important for them to know how to use it properly which is where we come in as educators

This comment pushes me to think about the concept of “authentic voice.” Lin implies that AI-generated text can flatten individuality, making everyone’s writing sound the same. AI usually defaults to generic, polished, overly formal

AI does speed up tasks. But if we simply outsource all time-consuming work to AI, we sacrificing our own learning and it will end up taking more time since you will have to edit the AI work.

The researchers found that the sponges retained about 6–7×10⁶ E. coli cells per hour for every 1 cm³ of sponge tissue, even though their clearance rates were relatively low. What does this high retention activity suggest about the sponge’s ability in integrated aquaculture systems?

The study began with very high concentrations of E. coli, measuring 7.3×10⁶ cells/mL and 2.3×10⁷ cells/mL. How quickly were the sponges able to reduce these bacterial levels over time?

This section concerns me. It details that C. nucula has all the capacity required to become an invasive species. My concern is that the sponges planted for bioremediation might grow out of control and present an entirely new set of issues.

It’s interesting that the sponge didn’t have super high clearance rates but still managed to hold onto a ton of bacteria. That makes me think that maybe clearance isn’t the best thing to judge these sponges by. Honestly, retention seems way more important if the goal is bioremediation, so I like that the authors highlight that difference.

These methods seem really controlled and organized, especially with the sterile seawater and the consistent amount of E. coli added. Part of me wonders if using only E. coli gives the full picture, since real seawater has a lot more going on.

The authors point out how sponges can filter a ton of water and hold onto most of the particles, which already makes them seem pretty useful for pollution issues. It made me wonder could Chondrilla nucula actually work better than some of the tools we already use in aquaculture?

With such great amounts of variability, would this sponge actually make a good candidate for bioremediation? Or would it fluctuate too much to truly help?

4<br /> hermeticism

google.com/search?q=role+of+the+individual+in+the+hermetic+tradition&

4<br /> hermeticism

The DARES volunteers are actively preparing for emergency situations. Veiligheidsregio's sign convenants with them.

nRF52 is more power efficient, but only has BT, no wifi. Check which one of the boards mentioned provides wired ports?

ESP32 MCU supports both wifi and BT. Higher power consumption though.

In general, as this network is supposed to provide alternative comms when regular networks break down: how reliant on battery / solar powered devices is the network?

https://web.archive.org/web/20251205130615/https://meshtastic.org/blog/a-major-solution-to-a-miner-problem/

Bitcoin miners have deployed LoRa for mining networks in the first 2020s. Currently most are no longer in use.

Learn how to build a HIPAA-aligned telemedicine and digital pharmacy app for Saudi Arabia (KSA), from market outlook and regulatory requirements to architecture, AI integration, and cost estimates.

Meshtastic LLC is registered in Sante Fe, New Mexico, USA. That information is not contained in the meshtastic website.

The head office address given is that of a registration agent though: New Mexico Registered Agent by High Desert Corporate Filings LLC

Meshtastic trademark owned/controlled by Meshtastic LLC. US based I presume (given that examples I saw on the site take USA as default location).

https://opencorporates.com/companies?utf8=%E2%9C%93&utf8=%E2%9C%93&q=Meshtastic+LLC&jurisdiction_code=&type=companies

Meshtastic LLC (and Meshtastic Solutions LLC, a service provider on top of the network it seems) are USA, New Mexico registered corporate limited liability entities.

You use your phone or 'companion app'(?) to send a msg to a radio (over BT, wifi or wire). The radio broadcasts incoming messages, including the one you provide through the app.

Msgs that are not acknowledged by another radion will be send at most 3 times. (will you be able to see it has not propagated?)

A radio that receives msgs already received will not rebroadcast it. Any broadcasted msg has a 'hop limit' and if it hits 0 it will not be rebroadcast. This limits the spread of a message, no? What is the default hop limit? Otoh the hoplimit does not limit the initial number of paths for broadcasting. So it's an attenuation over paths.

Theoretically in a dense network, my msg may reach Y number of other radios that all start out with the same hop limit.

I do not see here yet how you could intentionally set and reach a specific recipient. This description provides attenuated propagation of messages but no direction/addressee?

Meshtastic positions itself as a radio protocol without need for licensing such as in ham radio. Meaning it's fully in parallel to ham emergency networks like the Dutch DARES.

From GUI, right?

^

eLife Assessment

This important study shows that different forms and mixtures of cardenolide toxins in tropical milkweed, especially nitrogen- and sulfur-containing types, change how monarch caterpillars eat, grow, and store these chemicals under laboratory conditions. It provides solid evidence to demonstrate that chemical diversity within a single group of plant toxins (cardenolides) can have combined effects on even highly specialized herbivores that are different from what one would expect from each toxin alone. However, as all experiments used leaf-disc assays with fixed "natural" toxin ratios and only one adapted herbivore species, tests on living plants, other mixture designs, and non-adapted herbivores would make the broader conclusions stronger.

Reviewer #1 (Public review):

Summary:

In the ecological interactions between wild plants and specialized herbivorous insects, structural innovation-based diversification of secondary metabolites often occurs. In this study, Agrawal et al. utilized two milkweed species (Asclepias curassavica and Asclepias incarnata) and the specialist Monarch butterfly (Danaus plexippus) as a model system to investigate the effects of two N,S-cardenolides - formed through structural diversification and innovation in A. curassavica-on the growth, feeding, and chemical sequestration of D. plexippus, compared to other conventional cardenolides. Additionally, the study examined how cardenolide diversification resulting from the formation of N,S-cardenolides influences the growth and sequestration of D. plexippus. On this basis, the research elucidates the ecophysiological impact of toxin diversity in wild plants on the detoxification and transport mechanisms of highly adapted herbivores.

Strengths:

The study is characterized by the use of milkweed plants and the specialist Monarch butterfly, which represent a well-established model in chemical ecology research. On one hand, these two organisms have undergone extensive co-evolutionary interactions; on the other hand, the butterfly has developed a remarkable capacity for toxin sequestration. The authors, building upon their substantial prior research in this field and earlier observations of structural evolutionary innovation in cardenolides in A. curassavica, proposed two novel ecological hypotheses. While experimentally validating these hypotheses, they introduced the intriguing concept of a "non-additive diversity effect" of trace plant secondary metabolites when mixed, contrasting with traditional synergistic perspectives, in their impact on herbivores.

Weaknesses:

The manuscript has two main weaknesses. First, as a study reliant on the control of compound concentrations, the authors did not provide sufficient or persuasive justification for their selection of the natural proportions (and concentrations) of cardenolides. The ratios of these compounds likely vary significantly across different environmental conditions, developmental stages, pre- and post-herbivory, and different plant tissues. The ecological relevance of the "natural proportions" emphasized by the authors remains questionable. Furthermore, the same compound may even exert different effects on herbivorous insects at different concentrations. The authors should address this issue in detail within the Introduction, Methods, or Discussion sections.

Second, the study was conducted using leaf discs in an in vitro setting, which may not accurately reflect the responses of Monarch butterflies on living plants. This limitation undermines the foundation for the novel ecological theory proposed by the authors. If the observed phenomena could be validated using specifically engineered plant lines-such as those created through gene editing, knockdown, or overexpression of key enzymes involved in the synthesis of specific N,S-cardenolides - the findings would be substantially more compelling.

Reviewer #2 (Public review):

This study examined the effects of several cardenolides, including N,S-ring containing variants, on sequestration and performance metrics in monarch larvae. The authors confirm that some cardenolides, which are toxic to non-adapted herbivores, are sequestered by monarchs and enhance performance. Interestingly, N,S-ring-containing cardenolides did not have the same effects and were poorly sequestered, with minimal recovery in frass, suggesting an alternate detoxification or metabolic strategy. These N,S-containing compounds are also known to be less potent defences against non-adapted herbivores. The authors further report that mixtures of cardenolides reduce herbivore performance and sequestration compared to single compounds, highlighting the important role of phytochemical diversity in shaping plant-herbivore interactions.

Overall, this study is clearly written, well-conducted and has the potential to make a valuable contribution to the field. However, I have one major concern regarding the interpretations of the mixture results. From what I understand of the methods, all tested mixtures contain all five compounds. As such, it is not possible to determine whether reduced performance and sequestration result from the complete mixture or from the presence of a single compound, such as voruscharin for performance and uscharin for sequestration. For instance, if all compounds except voruscharin (or uscharin) were combined, would the same pattern emerge? I suspect not, since the effects of the individual N,S-containing compounds alone are generally similar to those of the full mixture (Figure S3). By taking the average of all single compounds, the individual effects of the N,S-containing ones are being inflated by the non-N,S-containing ones (in the main text, Figure 4). In the mix, of course, they are not being 'diluted', as they are always present. This interpretation is further supported by the fact that in the equimolar mix, the relative proportion of voruscharin decreases (from 50% in the 'real mix'), and the target measurements of performance and sequestration tend to increase in the equimolar mix compared to the real mix.

Despite this issue, the discussion of mixtures in the context of plant defence against both adapted and non-adapted herbivores is fascinating and convincing. The rationale that mixtures may serve as a chemical tool-kit that targets different sets of herbivores is compelling. The non-N,S cardenolides are effective against non-adapted herbivores and the N,S-containing cardenolides are effective against adapted herbivores. However, the current experiments focus exclusively on an adapted species. It would be especially interesting to test whether such mixtures reduce overall herbivory when both adapted and non-adapted species are present.

It remains possible that mixtures, even in the absence of voruscharin or uscharin, genuinely reduce sequestration or performance; however, this would need to be tested directly to address the abovementioned concern.

Author response:

Thanks for these insightful reviews and your summary assessment. We certainly agree that ours was a laboratory study with a single specialized insect, and both mixtures types had all five compounds (controlling for total toxin concentration). Thus, our conclusion that combined effects of naturally occurring toxins (within the cardenolide class) have non-additive effects for the specialized sequestering monarch are constrained by our experimental conditions. In our assay we used two mixture types, equimolar and “natural” proportions. We acknowledge that the natural proportions will vary with plant age, damage history, etc. of the host plant, Asclepias curassavica. Our proportions were based on growing the plants a few different times under variable conditions. Although we did not conduct these experiments on non-adapted insects, we discuss a related experiment that was conducted with wild-type and genetically engineered Drosophila (Lopez-Goldar et al. 2024, PNAS). In sum, we appreciate the reviewers’ comments.

Equating videogames = vice, and war nationalism = virtue. One is framed as slacking while the other one is framed as serving your country.

Drone cams with joysticks and first person recordings are also becoming popular, and are game-like.

But also its dissinterest in games and play, as forms of culture. Culture is more often than not, anti-hegemonic, and Russian university students that aspire to design games could flip the purposes of this games engine. Anyhow, it's not like they couldn't use Open Source ones...

So, as I understand it, we should change ourselves to embrace more anti-consumerist anti-globalisation strategies (favouring more close "around the fireplace" gatherings) whilst we simultaneously create new spaces removing old ones, for migrating communities to arise (whilst simultaenously avoiding their uncritical reproduction that could lead to hegemonic fearmongering).

He's talking about self-censorship, burying the past, avoiding historical memory. The fears are in place, I'd argue, but simultaneously, for a society to really move on it must reconcile with its past and repair those who had been wronged, else the "survivors" remain silenced, displaced, existing but unseen... and this too can be the seed of radicalisation and accumulating hatred.

Sure, infantilisation besets resentment... and skewed representations may co-opt/tokenise marginalised individuals as diversity hires... but don't we need to give visibility to them as a means to redistribute power? What is it we white males can do, if not taking a step back, staying in silence, and giving space?

I wonder, did you share your strategy with them and explain why you thought it could be more effective?

hi

https://web.archive.org/web/20251205111520/https://www.tomshardware.com/tech-industry/artificial-intelligence/googles-agentic-ai-wipes-users-entire-hard-drive-without-permission-after-misinterpreting-instructions-to-clear-a-cache-i-am-deeply-deeply-sorry-this-is-a-critical-failure-on-my-part Agentic AI deletes entire HDD, when it was supposed to only delete a cache folder (rmdir in root folder, not the projectfolder....bc it does not know about the world)

eLife Assessment

This study provides a useful advance in generating mouse oligodendrocytes by direct lineage conversion from cortical astrocytes. The authors demonstrate that Sox10 converts astrocytes to MBP+ oligodendrocytes, whereas Olig2 expression converts astrocytes to PDFRalpha+ oligodendrocyte progenitor cells. The data supporting the conclusions are solid, but there are concerns regarding select figures and the absence of functional validation.

Reviewer #1 (Public review):

Bajohr and colleagues propose a transcription factor-driven approach to generating bonafide oligodendrocyte lineage cells (OLCs) from primary mouse astrocytes. Ectopic expression of Olig2, Sox10, or Nkx6.2 in isolated astrocytes produced a range of OLC-like cell states, with Sox10 emerging from lineage tracing and single cell RNA sequencing experiments as the most successful transcription factor in driving direct lineage reprogramming. The authors strengthened their claims with an unbiased, deep learning perturbation model to predict genetic drivers of the astrocyte cluster to OLC cluster transition observed in their scRNA seq dataset. Here, Sox10 surfaced in the top ten correlated genes, and the top transcription factor, mediating this fate shift. Altogether, this paper presents an interesting approach to generate OLCs, a cell type historically difficult to procure, from primary mouse astrocytes to study this lineage in development and disease and perhaps repopulate it in dysmyelinating conditions. While this certainly addresses a technical gap in the field, authors defined iOLCs as ones with lineage-specific gene expression and morphological characteristics, lacking any functional analysis to assess the reprogrammed cells' capacity to myelinate. This comment and other critiques are discussed below.

While Sox10 and Mbp expression in iOLCs, as confirmed by IHC, is a promising result suggesting that ectopic Sox10 instructs transduced cells to develop into cells of myelinating potential, functional confirmation is essential. As mentioned in the discussion, the absence of a substrate for myelination may have also contributed to the low DLR efficiency. Co-culturing Sox10 iOLCs with primary neurons and examining the cells' potential to engage and enwrap axons would greatly strengthen the authors' claim that this could be an effective therapeutic approach to myelin regeneration in vivo, or even a technical approach to studying myelin dynamics in vitro.

In Figure 1B, it appears that Mbp expression in tdTomato+ cells decreases in Sox10 transduced iOLs during the observed time period. Can the authors elaborate on this result, given that MBP expression is crucial for myelination and should, if anything, increase with time?

The authors acknowledge that there is a conversion of tdTomato- zsGreen+ cells with an astrocyte-like morphology to OLC cells expressing Mbp following Sox10 induction (Supplementary figure 5C,D). While they note the diversity of the astrocyte lineage in the discussion, further analysis should be applied to this subset of cells to confirm the subset of astrocyte or progenitor-like cell type that gives rise to their cell endpoint of interest (Sox10-driven Mbp+ iOLs).

Finally, ectopic expression of Olig2 and Sox10 in primary astrocytes resulted in very different OLC subtypes, as evidenced by OLC marker expression seen in IHC and the subclustering of these cell types in scRNA seq. Although this diversity in OLC type and generation efficiency follows with previous reports showing that these two transcription factors vary in effect, might the authors further discuss this discrepancy given that the two transcription factors regulate one another (as mentioned in the introduction) and should theoretically give rise to more similar cells? Perhaps due to the lower specificity of Olig2 in marking a pure OLC population relative to Sox10?

Author response:

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

Reviewer #1 (Public Review):

Faiz et al. investigate small molecule-driven direct lineage reprogramming of mouse postnatal mouse astrocytes to oligodendrocyte lineage cells (OLCs). They use a combination of in vitro, in vivo, and computational approaches to confirm lineage conversion and to examine the key underlying transcription factors and signaling pathways. Lentiviral delivery of transcription factors previously reported to be essential in OLC fate determination-Sox10, Olig2, and Nkx2.2-to astrocytes allows for lineage tracing. They found that these transcription factors are sufficient in reprogramming astrocytes to iOLCs, but that the OLCs range in maturity level depending on which factor they are transfected with. They followed up with scRNA-seq analysis of transfected and control cultures 14DPT, confirming that TF-induced astrocytes take on canonical OLC gene signatures. By performing astrocyte lineage fate mapping, they further confirmed that TF-induced astrocytes give rise to iOLCs. Finally, they examined the distinct genetic drivers of this fate conversion using scRNA-seq and deep learning models of Sox10- astrocytes at multiple time points throughout the reprogramming. These findings are certainly relevant to diseases characterized by the perturbation of OLC maturation and/or myelination, such as Multiple Sclerosis and Alzheimer's Disease. Their application of such a wide array of experimental approaches gives more weight to their findings and allows for the identification of additional genetic drivers of astrocyte to iOLC conversion that could be explored in future studies. Overall, I find this manuscript thoughtfully constructed and only have a few questions to be addressed. 

(1) The authors suggest that Sox10- and Olig2- transduced astrocytes result in distinct subpopulations iOLCs. Considering it was discussed in the introduction that these TFs cyclically regulate one another throughout differentiation, could they speculate as to why such varying iOLCs resulted from the induction of these two TFs? 

We thank the Reviewer for the opportunity to speculate. We hypothesize that Sox10 and Olig2 may induce different OLCs as a result of differential activation of downstream genes within the gene regulatory network, which are important for OPC, committed OLC and mature OL identity [1]. In support of this, we found different expression levels of genes involved in downstream OLC specification networks [1], including Sox6, Tcfl2 and Myrf, at D14 (Author response image 1), following further analysis of our RNA-seq data.

Author response image 1.

Expression of OLC regulatory network genes in Sox10- and Olig2- cultures. Violin plots show gene expression levels (log-normalized) of downstream OLC regulatory genes (Sox6, Zeb2, Tcf7l2, Myrf, Zfp488, Nfatc2, Hes5, Id2) between Sox10 and Olig2 treated OLCs at 14 days post transduction. Analysis was performed on oligodendrocyte progenitor and mature oligodendrocyte clusters (from Manuscript Figure 1D, clusters 3 and 8).

(2) In Figure 1B it appears that the Sox10- MBP+ tdTomato+ cells decreases from D12 to D14. Does this make sense considering MBP is a marker of more mature OLCs? 

Thank you for this comment. To address this, we compared the number of MBP+tdTomato+ Sox10 cells across reprogramming timepoints. We saw no difference between the number of MBP+tdTomato+ OLs at D12 and D14 (Author response image 2, p = 0.2314). However,  we do see a [nonsignificant] decrease in MBP+tdTomato+ Sox10 cells from D12 to D22 (Manuscript Supplementary Figure 3B, Author response image 2, p= 0.0543), which suggests that culture conditions are not optimal for longer-term cell survival [2], [3], [4].  

Author response image 2.

Comparison of Sox10- induced MBP+tdTomato+ iOLCs over time. Quantification of MBP⁺tdTomato⁺ iOLs in Sox10 cultures at D8 (n=5), D10 (n=5), D12 (n=5), D14 (n=7) and D22 (n=3) post transduction. Data are presented as mean ± SEM, each data point represents one individual cell culture experiment, Brown-Forsythe and Welch ANOVA on transformed percentages with Dunnett’s T3 multiple comparisons test (*= p<0.05).  

(3) Previous studies have shown that MBP expression and myelination in vitro occurs at the earliest around 4-6 weeks of culturing. When assessing whether further maturation would increase MBP positivity, authors only cultured cells up to 22 DPT and saw no significant increase. Has a lengthier culture timeline been attempted? 

We agree with the Reviewer that previous studies of pluripotent stem cell derived (hESCs or iPSCs) have shown MBP+ OLCs in vitro around 4-6 weeks [5], [6], [7]. However,  studies of neural stem cells [8] or fibroblasts [9] conversion show OLC appearance after 7 and 24 days, respectively, demonstrating that OLCs can be generated in vitro within 1-3 weeks of plating. Moreover, as noted above in response to #2, we see fewer MBP+ cells at  22DPT, suggesting that extended time in culture may require additional factors for support. Therefore, we did not attempt longer timepoints. 

(4) Figure S4D is described as "examples of tdTomatonegzsGreen+OLCmarker+ cells that arose from a tdTomatoneg cell with an astrocyte morphology." The zsGreen+ tdTomato- cell is not convincingly of "astrocyte morphology"; it could be a bipolar OLC. To strengthen the conclusions and remove this subjectivity, more extensive characterizations of astrocyte versus OLC morphology in the introduction or results are warranted. This would make this observation more convincing since there is clearly an overlap in the characteristics of these cell types.  

We thank the reviewer for this excellent suggestion. To assess astrocyte morphology, we measured the cell size, nucleus size, number of branches and branch thickness of 70 Aldh1l1+tdTomato+ astrocytes in tamoxifen-labelled Aldh1l1-CreERT2;Ai14 cultures (new Supplemental Table 1). To assess OPC morphology, we  performed IHC for PDGFRa in iOLC cultures and measured the same parameters in 70 PDGFRa+ OPCs (new Supplemental Table 1).  We found that astrocytes were characterized by larger branch thickness, cell length and nucleus size, while OPCs showed a larger number of branches (new Supplemental Figure 1, and Author response image 3 below). Based on this framework, the AAV9-GFAP::zsGreen^posAldh1l1-tdTomato^neg and AAV9-GFAP::zsGreen^posAldh1l1-tdTomato^posstarting cells tracked fall within the bounds of ‘astrocytes’. We have revised the manuscript to include this more rigorous characterization (Line 119-124, Page 4; Line 307-312, Page 9; Line 323-326, Page 9). We also demonstrate (below) that the GFAP::zsGreen^pos Aldh1l1-tdTomato^pos and GFAP::zsGreen^posAldh1l1-tdTomato^neg starting cell depicted in Figure 2G and Supplemental Figure 5D is consistent with astrocyte morphology (Author response image 3). 

Author response image 3.

Morphological characterization of astrocytes, oligodendrocyte lineage cells, and starting cells. Quantification of the (A) cell length, (B) nucleus size, (C) number of branches, and (D) branch thickness iAldh1l1+tdTomato+ and PDGFRα+ OPCs (n= 70 per cell type, data are presented as mean ± SEM). Orange line indicates parameter value for GFAP::zsGreen^posAldh1l1-tdTomato^pos starting cell in Figure 2G. Green line indicates parameter value for GFAP::zsGreen^pos Aldh1l1-tdTomato^neg starting cell in Supplemental Figure 5D.

Reviewer #2 (Public Review):             

The study by Bajohr investigates the important question of whether astrocytes can generate oligodendrocytes by direct lineage conversion (DLR). The authors ectopically express three transcription factors - Sox10, Olig2 and Nkx6.2 - in cultured postnatal mouse astrocytes and use a combination of Aldh1|1-astrocyte fate mapping and live cell imaging to demonstrate that Sox10 converts astrocytes to MBP+ oligodendrocytes, whereas Olig2 expression converts astrocytes to PDFRalpha+ oligodendrocyte progenitor cells. Nkx6.2 does not induce lineage conversion. The authors use single-cell RNAseq over 14 days post-transduction to uncover molecular signatures of newly generated iOLs.  

The potential to convert astrocytes to oligodendrocytes has been previously analyzed and demonstrated. Despite the extensive molecular characterization of the direct astrocyteoligodendrocyte lineage conversion, the paper by Bajohr et al. does not represent significant progress. The entire study is performed in cultured cells, and it is not demonstrated whether this lineage conversion can be induced in astrocytes in vivo, particularly at which developmental stage (postnatal, adult?) and in which brain region. The authors also state that generating oligodendrocytes from astrocytes could be relevant for oligodendrocyte regeneration and myelin repair, but they don't demonstrate that lineage conversion can be induced under pathological conditions, particularly after white matter demyelination. Specific issues are outlined below. 

We thank the reviewer for this summary. We agree that there are a handful of reports of astrocytelike cells to OLC conversion [10], [11]. However, our study is the first study to confirm bonafide astrocyte to OLC conversion, which is important given the recent controversy in the field of in vivo astrocyte to neuron reprogramming [12]. In addition, the extensive characterization of the molecular timeline of reprogramming, highlights that although conversion of astrocytes is possible by ectopic expression of any of the three factors, the subtypes of astrocytes converted and maturity of OLCs produced may vary depending on the choice of TF delivered. Our findings will inform future in vivo studies of iOLC generation that aim to understand the impact of brain region, age, pathology, and sex, which are especially important given the diversity of astrocyte responses to disease [13], [14], [15].

(1) The authors perform an extensive characterization of Sox10-mediated DLR by scRNAseq and demonstrate a clear trajectory of lineage conversion from astrocytes to terminally differentiated MBP+ iOLCs. A similar type of analysis should be performed after Olig2 transduction, to determine whether transcriptomics of olig2 conversion overlaps with any phase of sox10 conversion.

We thank the Reviewer for this excellent comment. We chose to include an in-depth analysis of Sox10 in the manuscript, as Sox10-transduced cultures showed a higher percentage of mature iOLCs compared to Olig2 in our studies. We have added this specific rationale to the manuscript (Line 329-330-Page 9). 

Nonetheless, we also agree that understanding the underpinnings of Olig2-mediated conversion is important. Therefore, we used Cell Oracle [16] to understand the regulation of cell identity by Olig2.  in silico overexpression of Olig2 in our control time course dataset (D0, D3, D8 and D14) showed cell movement from cluster 1, characterized by astrocyte genes [Mmd2[17], Entpd2[18], H2-D1[19]], towards cluster 5, characterized by OPC genes [Pdgfra[20], Myt1[21]] validating astrocyte to OLC conversion by Olig2 (Author response image 4).

We hypothesize that reprogramming via Sox10 and Olig2 take different conversion paths to oligodendrocytes for the following reasons. 

(1) Differential astrocyte gene expression at D14 when cells are exposed to Sox10 and Olig2 (Manuscript Figure 1D-E [Sox10 characterized by Lcn2[19], C3[19]; Olig2 characterized by Slc6a11[22], Slc1a2[23]].

(2) Differential expression of key OLC gene regulatory network genes at D14 between cells treated with Sox10 and Olig2 (Author response image 1). 

Author response image 4.

in silico modeling of Olig2 reprogramming (A) UMAP clustering of Cre control treated cells from 0, 3, 8, and 14 days post transduction (DPT). (B) UMAP clustering from (A) overlayed with timepoint and treatment group. (C) Cell Oracle modeling of predicted cell trajectories following Olig2 knock in (KI), overlaid onto UMAP plot. Arrows indicate cell movement prediction with Olig2 KI perturbation.  

(2) A complete immunohistochemical characterization of the cultures should be performed at different time points after Sox10 and Olig2 transduction to confirm OL lineage cell phenotypes. 

We performed a complete immunohistochemical characterization of Ai14 cultures transduced with GFAP::Sox10-Cre and GFAP::Olig2-Cre. This system allows permanent labelling and therefore, enabled the tracking of transduced cells through the process or DLR, which we believe is the most appropriate way to characterize iOLC conversion efficiencies. We then confirmed the conversion of Aldh1l1+ astrocytes in Aldh1l1-CreERT2;Ai14 cultures transduced with GFAP::Sox10-zsGreen and GFAP::Olig2-zsGreen. In this system, GFAP drives the expression of zsGreen, and therefore, may not faithfully track all cells and lead to an underestimate of the numbers of converted cells. For example, iOLCs from Aldh1l1^neg astrocytes or iOLCs that have lost zsGreen expression following conversion. Therefore we use this system only to confirm astrocyte origin.

Nonetheless, we appreciate this comment and recognize that there may be differences in conversion efficiencies when analyzing Aldh1l1+ astrocytes versus all transduced cells. Therefore, we have softened the language in the manuscript (see below) regarding Olig2 and Sox10 generating different OLC phenotypes and now claim iOLC generation from both Sox10 and Olig2. We thank the Reviewer for this comment, and believe it has strengthened the discussion. 

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References

(1) E. Sock and M. Wegner, “Using the lineage determinants Olig2 and Sox10 to explore transcriptional regulation of oligodendrocyte development,” Dev Neurobiol, vol. 81, no. 7, pp. 892–901, Oct. 2021, doi: 10.1002/dneu.22849.

(2) B. A. Barres, M. D. Jacobson, R. Schmid, M. Sendtner, and M. C. Raff, “Does oligodendrocyte survival depend on axons?,” Current Biology, vol. 3, no. 8, pp. 489–497, Aug. 1993, doi: 10.1016/0960-9822(93)90039-Q.

(3) A.-N. Cho et al., “Aligned Brain Extracellular Matrix Promotes Differentiation and Myelination of Human-Induced Pluripotent Stem Cell-Derived Oligodendrocytes,” ACS Appl. Mater. Interfaces, vol. 11, no. 17, pp. 15344–15353, May 2019, doi: 10.1021/acsami.9b03242.

(4) E. G. Hughes and M. E. Stockton, “Premyelinating Oligodendrocytes: Mechanisms Underlying Cell Survival and Integration,” Front. Cell Dev. Biol., vol. 9, Jul. 2021, doi: 10.3389/fcell.2021.714169.

(5) M. Ehrlich et al., “Rapid and efficient generation of oligodendrocytes from human induced pluripotent stem cells using transcription factors,” Proc Natl Acad Sci U S A, vol. 114, no. 11, pp. E2243–E2252, Mar. 2017, doi: 10.1073/pnas.1614412114.

(6) Y. Liu, P. Jiang, and W. Deng, “OLIG gene targeting in human pluripotent stem cells for motor neuron and oligodendrocyte differentiation,” Nat Protoc, vol. 6, no. 5, pp. 640–655, May 2011, doi: 10.1038/nprot.2011.310.

(7) S. A. Goldman and N. J. Kuypers, “How to make an oligodendrocyte,” Development, vol. 142, no. 23, pp. 3983–3995, Dec. 2015, doi: 10.1242/dev.126409.

(8) M. Faiz, N. Sachewsky, S. Gascón, K. W. A. Bang, C. M. Morshead, and A. Nagy, “Adult Neural Stem Cells from the Subventricular Zone Give Rise to Reactive Astrocytes in the Cortex after Stroke,” Cell Stem Cell, vol. 17, no. 5, pp. 624–634, Nov. 2015, doi:10.1016/j.stem.2015.08.002.

(9) F. J. Najm et al., “Transcription factor–mediated reprogramming of fibroblasts to expandable, myelinogenic oligodendrocyte progenitor cells,” Nat Biotechnol, vol. 31, no. 5, pp. 426–433, May 2013, doi: 10.1038/nbt.2561.

(10) A. Mokhtarzadeh Khanghahi, L. Satarian, W. Deng, H. Baharvand, and M. Javan, “In vivo conversion of astrocytes into oligodendrocyte lineage cells with transcription factor Sox10; Promise for myelin repair in multiple sclerosis,” PLoS One, vol. 13, no. 9, p. e0203785, Sep. 2018, doi: 10.1371/journal.pone.0203785.

(11) S. Farhangi, S. Dehghan, M. Totonchi, and M. Javan, “In vivo conversion of astrocytes to oligodendrocyte lineage cells in adult mice demyelinated brains by Sox2,” Mult Scler Relat Disord, vol. 28, pp. 263–272, Feb. 2019, doi: 10.1016/j.msard.2018.12.041.

(12) L.-L. Wang, C. Serrano, X. Zhong, S. Ma, Y. Zou, and C.-L. Zhang, “Revisiting astrocyte to neuron conversion with lineage tracing in vivo,” Cell, vol. 184, no. 21, pp. 5465-5481.e16, Oct. 2021, doi: 10.1016/j.cell.2021.09.005.

(13) I  Matias, J. Morgado, and F. C. A. Gomes, “Astrocyte Heterogeneity: Impact to Brain Aging and Disease,” Front. Aging Neurosci., vol. 11, Mar. 2019, doi: 10.3389/fnagi.2019.00059.

(14) N. Habib et al., “Disease-associated astrocytes in Alzheimer’s disease and aging,” Nat Neurosci, vol. 23, no. 6, pp. 701–706, Jun. 2020, doi: 10.1038/s41593-020-0624-8.

(15)  M. A. Wheeler et al., “MAFG-driven astrocytes promote CNS inflammation,” Nature, vol. 578, no. 7796, pp. 593–599, Feb. 2020, doi: 10.1038/s41586-020-1999-0.

(16) K. Kamimoto, B. Stringa, C. M. Hoffmann, K. Jindal, L. Solnica-Krezel, and S. A. Morris, “Dissecting cell identity via network inference and in silico gene perturbation,” Nature, vol. 614, no. 7949, pp. 742–751, Feb. 2023, doi: 10.1038/s41586-022-05688-9.

(17) P. Kang et al., “Sox9 and NFIA coordinate a transcriptional regulatory cascade during the initiation of gliogenesis,” Neuron, vol. 74, no. 1, pp. 79–94, Apr. 2012, doi:10.1016/j.neuron.2012.01.024.

(18) K. Saito et al., “Microglia sense astrocyte dysfunction and prevent disease progression in an Alexander disease model,” Brain, vol. 147, no. 2, pp. 698–716, Nov. 2023, doi:10.1093/brain/awad358.

(19) S. A. Liddelow et al., “Neurotoxic reactive astrocytes are induced by activated microglia,” Nature, vol. 541, no. 7638, pp. 481–487, Jan. 2017, doi: 10.1038/nature21029.

(20) Q. Zhu et al., “Genetic evidence that Nkx2.2 and Pdgfra are major determinants of the timing of oligodendrocyte differentiation in the developing CNS,” Development, vol. 141, no. 3, pp. 548–555, Feb. 2014, doi: 10.1242/dev.095323.

(21) J. A. Nielsen, J. A. Berndt, L. D. Hudson, and R. C. Armstrong, “Myelin transcription factor 1 (Myt1) modulates the proliferation and differentiation of oligodendrocyte lineage cells,” Mol Cell Neurosci, vol. 25, no. 1, pp. 111–123, Jan. 2004, doi:10.1016/j.mcn.2003.10.001.

(22) J. Liu, X. Feng, Y. Wang, X. Xia, and J. C. Zheng, “Astrocytes: GABAceptive and GABAergic Cells in the Brain,” Front. Cell. Neurosci., vol. 16, Jun. 2022, doi:10.3389/fncel.2022.892497.

(23) A. Sharma et al., “Divergent roles of astrocytic versus neuronal EAAT2 deficiency on cognition and overlap with aging and Alzheimer’s molecular signatures,” Proceedings of the National Academy of Sciences, vol. 116, no. 43, pp. 21800–21811, Oct. 2019, doi:10.1073/pnas.1903566116

eLife Assessment

In this valuable study, Wandler et al. provide convincing theoretical evidence for alternate mechanisms of rhythm generation by CPGs. Their model shows that cell-type-specific connectivity and an inhibitory drive could underlie rhythm generation. Excitatory input could act to enhance the frequency range of these rhythms. This modeling study could motivate further experimental investigation of these mechanisms to understand CPG rhythmogenesis.

Reviewer #1 (Public review):

This study explores the connectivity patterns that could lead to fast and slow undulating swim patterns in larval zebrafish using a simplified theoretical framework. The authors show that a pattern of connectivity based only on inhibition is sufficient to produce realistic patterns with a single frequency. Two such networks couple with inhibition but with distinct time constants can produce a range of frequencies. Adding excitatory connections further increases the range of obtainable frequencies, albeit at the expense of sudden transitions in mid-frequency range.

Strengths:

(1) This is an eloquent approach to answering the question of how spinal locomotor circuits generate coordinated activity using a theoretical approach based on moving bump models of brain activity.

(2) The models make specific predictions on patterns of connectivity while discounting the role of connectivity strength or neuronal intrinsic properties in shaping the pattern.

(3) The models also propose that there is an important association between cell-type-specific intersegmental patterns and the recruitment of speed-selective subpopulations of interneurons.

(4) Having a hierarchy of models creates a compelling argument for explaining rhythmicity at the network level. Each model builds on the last and reveals a new perspective on how network dynamics can control rhythmicity. I liked that each model can be used to probe questions in the next/previous model.

Comments on revisions:

I am very happy to see the simplified biophysical model supporting the original findings. The authors have done an excellent job addressing my comments.

Just a small note, please change C. Elegans to C. elegans.

Reviewer #2 (Public review):

Summary:

The authors aimed to show that connectivity patterns within spinal circuits composed of specific excitatory and inhibitory connectivity and with varying degrees of modularity could achieve tail beats at various frequencies as well as proper left-right coordination and rostrocaudal propagation speeds.

Strengths:

The model is simple and the connectivity patterns explored are well supported by the literature

The conclusions are intuitive and support many experimental studies on zebrafish spinal circuits for swimming. The simulations provide strong support for the sufficiency of connectivity patterns to produce and control many hallmark features of swimming in zebrafish

Weaknesses:

The authors have addressed my previous concerns well. I have no further concerns.

Reviewer #3 (Public review):

Summary:

Central pattern generator (CPG) circuits underly rhythmic motor behaviors. Till date, it is thought that these CPG networks are rather local and multiple CPG circuits are serially connected to allow locomotion across the entire body. Distributed CPG networks that incorporate long-range connections have not been proposed although such connectivity has been experimentally shown for several different spinal populations. In this manuscript, the authors use this existing literature on long-range spinal interneuron connectivity to build a new computational model that reproduces basic features of locomotion like left-right alternation, rostrocaudal propagation and independent control of frequency and amplitude. Interestingly, the authors show that a model solely based on inhibitory neurons can recapitulate these basic locomotor features. Excitatory sources were then added that increased the dynamic range of frequencies generated. Finally, the authors were also able to reproduce experimentally observed consequences of cell-type-specific ablations showing that local and long range, cell-type-specific connectivity could be sufficient for generating locomotion.

Strengths:

This work is novel, providing an interesting alternative of distributed CPGs to the local networks traditionally predicted. It shows cell type-specific network connectivity is as important if not more than intrinsic cell properties for rhythmogenesis and that inhibition plays a crucial role in shaping locomotor features. Given the importance of local CPGs in understanding motor control, this alternative concept will be of broad interest to the larger motor control field including invertebrate and vertebrate species.

Weaknesses:

The main weaknesses were addressed in the revision.

Author response:

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

Reviewer #1 (Public review):

(1)How is this simplified model representative of what is observed biologically? A bump model does not naturally produce oscillations. How would the dynamics of a rhythm generator interact with this simplistic model?

Bump models naturally produce sequential activity, and can be engineered to repeat this sequential activity periodically (Zhang, 1996; Samsonovich and McNaughton, 1997; Murray and Escola, 2017). This is the basis for the oscillatory behavior in the model presented here. As we describe in our paper, such a model is consistent with numerous neurobiological observations about cell-type-specific connectivity patterns. The reviewer is, however, correct to point out that our model does not incorporate other key neurobiological features--in particular, intracellular dynamical properties--that have been shown to play important roles in rhythm generation. Our aim in this work is to establish a circuit-level mechanism for rhythm generation, complementary to classical models that rely on intracellular dynamics for rhythm generation. Whether and how these mechanisms work together is something that we plan to explore in future work, and we have added a sentence to the Discussion to this effect.

(2) Would this theoretical construct survive being expressed in a biophysical model? It seems that it should, but even a simple biological model with the basic patterns of connectivity shown here would greatly increase confidence in the biological plausibility of the theory.

We thank the reviewer for pointing out this way to strengthen our paper. We implemented the connectivity developed in the rate models in a spiking neuron model which used EI-balanced Poisson noise as input drive. We found that we could reproduce all the main results of our analysis. In particular, with a realistic number of neurons, we observed swimming activity characterized by (i) left-right alternation, (ii) rostal-caudal propagation, and (iii) variable speed control with constant phase lag. The spiking model demonstrates that the connectivity-motif based mechanisms for rhythmogenesis that we propose are robust in a biophysical setting.

We included these results in the updated manuscript in a new Results subsection titled “Robustness in a biophysical model.”

(3) How stable is this model in its output patterns? Is it robust to noise? Does noise, in fact, smooth out the abrupt transitions in frequency in the middle range?

The newly added spiking model implementation of the network demonstrates that the core mechanisms of our models are robust to noise,  since the connectivity is randomly chosen and the input drive is Poisson noise.

To test the effect of noise as it is parametrically varied, we also added noise directly to the rate models in the form of white noise input to each unit. Namely, the rate model was adapted to obey the stochastic differential equation

\[

\tau_i \frac{dr_i(t)}{dt} = -r_i(t) + \left[ \sum_j W_{ij} r_j(t - \Delta_{ij}) + D_i + \sigma\xi_t \right]_+

\]

Here $\xi_t$ is a standard Gaussian white noise and $\sigma$ sets the strength of the noise. We found that the swimming patterns were robust at all frequencies up to $\sigma =  0.05$. Above this level, coherent oscillations started to break down for some swim frequencies. To investigate whether the noise smoothed out abrupt transitions, we swept through different values of noise and modularity of excitatory connections. The results showed very minor improvement in controllability (see figure below), but this was not significant enough to include in the manuscript.

Author response image 1.

(4) All figure captions are inadequate. They should have enough information for the reader to understand the figure and the point that was meant to be conveyed. For example, Figure 1 does not explain what the red dot is, what is black, what is white, or what the gradations of gray are. Or even if this is a representative connectivity of one node, or if this shows all the connections? The authors should not leave the reader guessing.

All figure captions have been updated to enhance clarity and address these concerns.

Reviewer #2 (Public review):

(1) Figure 1A, if I interpret Figure 1B correctly, should there not be long descending projections as well that don't seem to be illustrated?

Thank you for highlighting this potential point of confusion. The diagram in question was only intended to be a rough schematic of the types of connections present in the model. We have added additional descending connections as requested

(2)Page 5, It would be good to define what is meant by slow and fast here, as this definition changes with age in zebrafish (what developmental age)?

We have updated the manuscript to include the sentence: “These values were chosen to coincide with observed ranges from larval zebrafish.” with appropriate citation.

Reviewer #3 (Public review):

(1) The authors describe a single unit as a neuron, be it excitatory or inhibitory, and the output of the simulation is the firing rate of these neurons. Experimentally and in other modeling studies, motor neurons are incorporated in the model, and the output of the network is based on motor neuron firing rate, not the interneurons themselves. Why did the authors choose to build the model this way?

We chose to leave out the motor neurons from our models for a few reasons. While motor neurons read out the rhythmic activity generated by the interneurons and may provide some feedback, they are not required for rhythmogenesis. In fact, interneuron activity (especially in the excitatory V2a neurons (Agha et al., 2024)) is highly correlated with the ventral root bursts within the same segment. This suggests that motor neurons are primarily a local readout of the rhythmic activity of interneurons; therefore, the rhythmic swimming activity can be deduced directly from the interneurons themselves.

Moreover, there is a lack of experimental observation of the connectivity between all the cell types considered in our model and motor neurons. Hence, it was unclear how we should include them in the model. To address this, we are currently developing a data-driven approach that will determine the proper connectivity between the motor neurons and the interneurons, including intrasegmental connections.

(2) In the single population model (Figure 1), the authors use ipsilateral inhibitory connections that are long-range in an ascending direction. Experimentally, these connections have been shown to be local, while long-range ipsilateral connections have been shown to be descending. What were the reasons the authors chose this connectivity? Do the authors think local ascending inhibitions contribute to rostrocaudal propagation, and how?

The long-range ascending ipsilateral inhibitory connections arises from a limitation of our modeling framework. The V1 neurons that provide these connections have been shown experimentally to fire later than other neurons (especially descending V2a  neurons) within the same hemisegment (Jay et al., J Neurosci, 2023); however, our model can only produce synchronized local activity. Hence, we replace local phase offsets with spatial offsets to produce correctly structured recurrent phasic inputs. We are currently investigating a data-driven method for determining intrasegmental connectivity which should be able to produce the local phase offset and address this concern; however, this is beyond the scope of the current paper.

(3) In the two-population model, the authors show independent control of frequency and rhythm, as has been reported experimentally. However, in these previous experimental studies, frequency and amplitude are regulated by different neurons, suggesting different networks dedicated to frequency and amplitude control. However, in the current model, the same population with the same connections can contribute to frequency or amplitude depending on relative tonic drive. Can the authors please address these differences either by changes in the model or by adding to the Discussion?

Our prior  experimental results that suggested a separation of frequency and amplitude control circuits focus on motor neuron recruitment, instead of interneuron activity (Jay et al., J Neurosci 2023; Menelaou and McLean, Nat Commun 2019). To avoid potential confusion about amplitudes of interneurons vs. of motor neurons, we have removed the results from Figure 3 about control of amplitude in the 2-population model, instead focusing this figure on the control of frequency via speed-module recruitment. For the same reason, we have removed the panel showing the effects of targeted ablations on interneuron amplitudes in Figure 7. We have kept the result about amplitude control in our Supplemental Figure S2 for the 8-population model, but we try to make it clear in the text that any relationship between interneuron amplitude and motor neuron amplitude would depend on how motor neurons are modeled, which we do not pursue in this work.

(4) It would be helpful to add a paragraph in the Discussion on how these results could be applicable to other model systems beyond zebrafish. Cell intrinsic rhythmogenesis is a popular concept in the field, and these results show an interesting and novel alternative. It would help to know if there is any experimental evidence suggesting such network-based propagation in other systems, invertebrates, or vertebrates.

We have expanded a paragraph in the Discussion to address these questions. In particular, we highlight how a recent study of mouse locomotor circuits produced a model with similar key features (Komi et al., 2024). These authors made direct use of experimentally determined connectivity structure and cell-type distributions, which informed a model that produced purely network-based rhythmogenesis. We also point out that inhibition-dominated connectivity has been used for understanding oscillatory behavior in neural circuits outside the context of motor control (Zhang, 1996; Samsonovich and McNaughton, 1997; Murray and Escola, 2017). Finally, we address a study that used the cell-type specific connectivity within the C. Elegans locomotor circuit as the architecture for an artificial motor control system and found that the resulting system could more efficiently learn motor control tasks than general machine learning architectures (Bhattasali et al. 2022). Like our model, the Komi et al. and Bhattasali et al. models generate rhythm via structured connectivity motifs rather than via intracellular dynamical properties, suggesting that these may be a key mechanism underlying locomotion across species.

Reviewer #1 (Recommendations for the authors):

(1) Express this modeling construct in a simple biophysical model.

See the new Results subsection titled “Robustness in a biophysical model.”

(2) Please cite the classic models of Kopell, Ermentrout, Williams, Sigvardt etc., especially where you say "classic models".

We have added relevant citations including the mentioned authors.

(3) "Rhythmogenesis remain incompletely understood" changed to "Rhythmogenesis remains incompletely understood".

We chose not to make this change since the ‘remain’ refers to the plural ‘core mechanisms’ not the singular ‘rhythmogenesis’.

Reviewer #3 (Recommendations for the authors):

(1) The figures are well made; however, it would help to add more details to the figure legends. For example, what neuron's firing rate is shown in Figure 1C? What is the red dot in 1B? Figures 3E,F,G: what is being plotted? Mean and SD? Blue dot in Figure 5C?

All figure captions have been updated to enhance clarity and address these concerns.

(2) A, B text missing in Figure 7.

We have revised this figure and its caption; please see our response to Comment 3 above.

(3) It would be nice to see the tonic drive pattern that is fed to the model for each case, along with the different firing rates in the figures. It would help understand how the tonic drive is changed to rhythmic activity.

The tonic drive in the rate models is implemented as a constant excitatory input that is uniform across all units within the same speed-population. There is no patterning in time or location to this drive.

References

(1) Moneeza A Agha, Sandeep Kishore, and David L McLean. Cell-type-specific origins of locomotor rhythmicity at different speeds in larval zebrafish. eLife, July 2024

(2) Nikhil Bhattasali, Anthony M Zador, and Tatiana Engel. Neural circuit architectural priors for embodied control. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 12744–12759. Curran Associates, Inc., 2022.

(3) Salif Komi, August Winther, Grace A. Houser, Roar Jakob Sørensen, Silas Dalum Larsen, Madelaine C. Adamssom Bonfils, Guanghui Li, and Rune W. Berg. Spatial and network principles behind neural generation of locomotion. bioRxiv, 2024

(4) James M Murray and G Sean Escola. Learning multiple variable-speed sequences in striatum via cortical tutoring. eLife, 6:e26084, May 2017.

(5) Alexei Samsonovich and Bruce L McNaughton. Path integration and cognitive mapping in a continuous attractor neural network model. Journal of Neuroscience, 17(15):5900–5920, 1997.

(6) K Zhang. Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory. Journal of Neuroscience, 16(6):2112–2126, 1996.

eLife Assessment

This global study compares environmental niche model outputs of avian influenza pathogen niche constructed for two distinct periods, and uses differences between those outputs to suggest that the changed case numbers and distribution relate to intensification of chicken and duck farming, and extensive cultivation. While a useful update to existing niche models of highly pathogenic avian influenza, the justification for the use of environmental niche models to explore land cover change as a driver of changed case epidemiology is incomplete.

Reviewer #1 (Public review):

The authors aim to predict ecological suitability for transmission of highly pathogenic avian influenza (HPAI) using ecological niche models. This class of models identify correlations between the locations of species or disease detections and the environment. These correlations are then used to predict habitat suitability (in this work, ecological suitability for disease transmission) in locations where surveillance of the species or disease has not been conducted. The authors fit separate models for HPAI detections in wild birds and farmed birds, for two strains of HPAI (H5N1 and H5Nx) and for two time periods, pre- and post-2020. The authors also validate models fitted to disease occurrence data from pre-2020 using post-2020 occurrence data.

Reviewer #2 (Public review):

Summary:

The geographic range of highly pathogenic avian influenza cases changed substantially around the period 2020, and there is much interest in understanding why. Since 2020 the pathogen irrupted in the Americas and the distribution in Asia changed dramatically. This study aimed to determine which spatial factors (environmental, agronomic and socio-economic) explain the change in numbers and locations of cases reported since 2020 (2020--2023). That's a causal question which they address by applying correlative environmental niche modelling (ENM) approach to the avian influenza case data before (2015--2020) and after 2020 (2020--2023) and separately for confirmed cases in wild and domestic birds. To address their questions they compare the outputs of the respective models, and those of the first global model of the HPAI niche published by Dhingra et al 2016.

ENM is a correlative approach useful for extrapolating understandings based on sparse geographically referenced observational data over un- or under-sampled areas with similar environmental characteristics in the form of a continuous map. In this case, because the selected covariates about land cover, use, population and environment are broadly available over the entire world, modelled associations between the response and those covariates can be projected (predicted) back to space in the form of a continuous map of the HPAI niche for the entire world.

Strengths:

The authors are clear about expected bias in the detection of cases, such geographic variation in surveillance effort (testing of symptomatic or dead wildlife, testing domestic flocks) and in general more detections near areas of higher human population density (because if a tree falls in a forest and there is no-one there, etc), and take steps to ameliorate those. The authors use boosted regression trees to implement the ENM, which typically feature among the best performing models for this application (also known as habitat suitability models). They ran replicate sets of the analysis for each of their model targets (wild/domestic x pathogen variant), which can help produce stable predictions. Their code and data is provided, though I did not verify that the work was reproducible.

The paper can be read as a partial update to the first global model of H5Nx transmission by Dhingra and others published in 2016 and explicitly follows many methodological elements. Because they use the same covariate sets as used by Dhingra et al 2016 (including the comparisons of the performance of the sets in spatial cross-validation) and for both time periods of interest in the current work, comparison of model outputs is possible. The authors further facilitate those comparisons with clear graphics and supplementary analyses and presentation. The models can also be explored interactively at a weblink provided in text, though it would be good to see the model training data there too.

The authors' comparison of ENM model outputs generated from the distinct HPAI case datasets is interesting and worthwhile, though for me, only as a response to differently framed research questions.

Weaknesses:

This well-presented and technically well-executed paper has one major weakness to my mind. I don't believe that ENM models were an appropriate tool to address their stated goal, which was to identify the factors that "explain" changing HPAI epidemiology.

Comments on the revised version from the editors:

We are extremely grateful to the authors for presenting a thoughtful and respectful point by point rebuttal to the prior reviewers' comments. After reading these comments carefully, we conclude that there is a straightforward strongly held disagreement between the authors and the reviewers as to the validity of the methods (Ecological Niche Modeling) for this particular dataset. Please note that the two reviewers have substantial expertise in the area of Ecologic Niche Modeling. We elected not to reach out to the reviewers for a third set of comments as we do not think their overall opinions will change, and wish to be respectful of their time.

To allow readers a balanced assessment of the paper, we intend to publish your rebuttal comments in full. It is our hope that interested readers can weigh both sides of this respectful and interesting debate in order to reach their own conclusions about the strength of evidence presented in your manuscript.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

We thank the Reviewers for their thorough attention to our paper and the interesting discussion about the findings. Before responding to more specific comments, here some general points we would like to clarify:

(1) Ecological niche models are indeed correlative models, and we used them to highlight environmental factors associated with HPAI outbreaks within two host groups. We will further revise the terminology that could still unintentionally suggest causal inference. The few remaining ambiguities were mainly in the Discussion section, where our intent was to interpret the results in light of the broader scientific literature. Particularly, we will change the following expressions:

-  “Which factors can explain…” to  “Which factors are associated with…” (line 75);

-  “the environmental and anthropogenic factors influencing” to “the environmental and anthropogenic factors that are correlated with” (line 273);

-  “underscoring the influence” to “underscoring the strong association” (line 282).

(2) We respectfully disagree with the suggestion that an ecological niche modelling (ENM) approach is not appropriate for this work and the research question addressed therein. Ecological niche models are specifically designed to estimate the spatial distribution of the environmental suitability of species and pathogens, making them well suited to our research questions. In our study, we have also explicitly detailed the known limitations of ecological niche models in the Discussion section, in line with prior literature, to ensure their appropriate interpretation in the context of HPAI.

(3) The environmental layers used in our models were restricted to those available at a global scale, as listed in Supplementary Information Resources S1 (https://github.com/sdellicour/h5nx\_risk\_mapping/blob/master/Scripts\_%26\_data/SI\_Resource\_S1.xlsx). Naturally, not all potentially relevant environmental factors could be included, but the selected layers are explicitly documented and only these were assessed for their importance. Despite this limitation, the performance metrics indicate that the models performed well, suggesting that the chosen covariates capture meaningful associations with HPAI occurrence at a global scale.

Reviewer #1 (Public review):

The authors aim to predict ecological suitability for transmission of highly pathogenic avian influenza (HPAI) using ecological niche models. This class of models identify correlations between the locations of species or disease detections and the environment. These correlations are then used to predict habitat suitability (in this work, ecological suitability for disease transmission) in locations where surveillance of the species or disease has not been conducted. The authors fit separate models for HPAI detections in wild birds and farmed birds, for two strains of HPAI (H5N1 and H5Nx) and for two time periods, pre- and post-2020. The authors also validate models fitted to disease occurrence data from pre-2020 using post-2020 occurrence data. I thank the authors for taking the time to respond to my initial review and I provide some follow-up below.

Detailed comments:

In my review, I asked the authors to clarify the meaning of "spillover" within the HPAI transmission cycle. This term is still not entirely clear: at lines 409-410, the authors use the term with reference to transmission between wild birds and farmed birds, as distinct to transmission between farmed birds. It is implied but not explicitly stated that "spillover" is relevant to the transmission cycle in farmed birds only. The sentence, "we developed separate ecological niche models for wild and domestic bird HPAI occurrences ..." could have been supported by a clear sentence describing the transmission cycle, to prime the reader for why two separate models were necessary.

We respectfully disagree that the term “spillover” is unclear in the manuscript. In both the Methods and Discussion sections (lines 387-391 and 409-414), we explicitly define “spillover” as the introduction of HPAI viruses from wild birds into domestic poultry, and we distinguish this from secondary farm-to-farm transmission. Our use of separate ecological niche models for wild and domestic outbreaks reflects not only the distinction between primary spillover and secondary transmission, but also the fundamentally different ecological processes, surveillance systems, and management implications that shape outbreaks in these two groups. We will clarify this choice in the revised manuscript when introducing the separate models. Furthermore, on line 83, we will add “as these two groups are influenced by different ecological processes, surveillance biases, and management contexts”.

I also queried the importance of (dead-end) mammalian infections to a model of the HPAI transmission risk, to which the authors responded: "While spillover events of HPAI into mammals have been documented, these detections are generally considered dead-end infections and do not currently represent sustained transmission chains. As such, they fall outside the scope of our study, which focuses on avian hosts and models ecological suitability for outbreaks in wild and domestic birds." I would argue that any infections, whether they are in dead-end or competent hosts, represent the presence of environmental conditions to support transmission so are certainly relevant to a niche model and therefore within scope. It is certainly understandable if the authors have not been able to access data of mammalian infections, but it is an oversight to dismiss these infections as irrelevant.

We understand the Reviewer’s point, but our study was designed to model HPAI occurrence in avian hosts only. We therefore restricted our analysis to wild birds and domestic poultry, which represent the primary hosts for HPAI circulation and the focus of surveillance and control measures. While mammalian detections have been reported, they are outside the scope of this work.

Correlative ecological niche models, including BRTs, learn relationships between occurrence data and covariate data to make predictions, irrespective of correlations between covariates. I am not convinced that the authors can make any "interpretation" (line 298) that the covariates that are most informative to their models have any "influence" (line 282) on their response variable. Indeed, the observation that "land-use and climatic predictors do not play an important role in the niche ecological models" (line 286), while "intensive chicken population density emerges as a significant predictor" (line 282) begs the question: from an operational perspective, is the best (e.g., most interpretable and quickest to generate) model of HPAI risk a map of poultry farming intensity?

We agree that poultry density may partly reflect reporting bias, but we also assumed it a meaningful predictor of HPAI risk. Its importance in our models is therefore expected. Importantly, our BRT framework does more than reproduce poultry distribution: it captures non-linear relationships and interactions with other covariates, allowing a more nuanced characterisation of risk than a simple poultry density map. Note also that we distinguished in our models intensive and extensive chicken poultry density and duck density. Therefore, it is not a “map of poultry farming intensity”. 

At line 282, we used the word “influence” while fully recognising that correlative models cannot establish causality. Indeed, in our analyses, “relative influence” refers to the importance metric produced by the BRT algorithm (Ridgeway, 2020), which measures correlative associations between environmental factors and outbreak occurrences. These scores are interpreted in light of the broader scientific literature, therefore our interpretations build on both our results and existing evidence, rather than on our models alone. However, in the next version of the paper, we will revise the sentence as: “underscoring the strong association of poultry farming practices with HPAI spread (Dhingra et al., 2016)”. 

I have more significant concerns about the authors' treatment of sampling bias: "We agree with the Reviewer's comment that poultry density could have potentially been considered to guide the sampling effort of the pseudo-absences to consider when training domestic bird models. We however prefer to keep using a human population density layer as a proxy for surveillance bias to define the relative probability to sample pseudo-absence points in the different pixels of the background area considered when training our ecological niche models. Indeed, given that poultry density is precisely one of the predictors that we aim to test, considering this environmental layer for defining the relative probability to sample pseudo-absences would introduce a certain level of circularity in our analytical procedure, e.g. by artificially increasing to influence of that particular variable in our models." The authors have elected to ignore a fundamental feature of distribution modelling with occurrence-only data: if we include a source of sampling bias as a covariate and do not include it when we sample background data, then that covariate would appear to be correlated with presence. They acknowledge this later in their response to my review: "...assuming a sampling bias correlated with poultry density would result in reducing its effect as a risk factor." In other words, the apparent predictive capacity of poultry density is a function of how the authors have constructed the sampling bias for their models. A reader of the manuscript can reasonably ask the question: to what degree are is the model a model of HPAI transmission risk, and to what degree is the model a model of the observation process? The sentence at lines 474-477 is a helpful addition, however the preceding sentence, "Another approach to sampling pseudo-absences would have been to distribute them according to the density of domestic poultry," (line 474) is included without acknowledgement of the flow-on consequence to one of the key findings of the manuscript, that "...intensive chicken population density emerges as a significant predictor..." (line 282). The additional context on the EMPRES-i dataset at line 475-476 ("the locations of outbreaks ... are often georeferenced using place name nomenclatures") is in conflict with the description of the dataset at line 407 ("precise location coordinates"). Ultimately, the choices that the authors have made are entirely defensible through a clear, concise description of model features and assumptions, and precise language to guide the reader through interpretation of results. I am not satisfied that this is provided in the revised manuscript.

We thank the Reviewer for this important point. To address it, we compared model predictive performance and covariate relative influences obtained when pseudo-absences were weighted by poultry density versus human population density (Author response table 1). The results show that differences between the two approaches are marginal, both in predictive performance (ΔAUC ranging from -0.013 to +0.002) and in the ranking of key predictors (see below Author response images 1 and 2). For instance, intensive chicken density consistently emerged as an important predictor regardless of the bias layer used.

Note: the comparison was conducted using a simplified BRT configuration for computational efficiency (fewer trees, fixed 5-fold random cross-validation, and standardised parameters). Therefore, absolute values of AUC and variable importance may differ slightly from those in the manuscript, but the relative ranking of predictors and the overall conclusions remain consistent.

Given these small differences, we retained the approach using human population density. We agree that poultry density partly reflects surveillance bias as well as true epidemiological risk, and we will clarify this in the revised manuscript by noting that the predictive role of poultry density reflects both biological processes and surveillance systems. Furthermore, on line 289, we will add “We note, however, that intensive poultry density may reflect both surveillance intensity and epidemiological risk, and its predictive role in our models should be interpreted in light of both processes”.

Author response table 1.

Comparison of model predictive performances (AUC) between pseudo-absence sampling were weighted by poultry density and by human population density across host groups, virus types, and time periods. Differences in AUC values are shown as the value for poultry-weighted minus human-weighted pseudo-absences.

Author response image 1.

Comparison of variable relative influence (%) between models trained with pseudo-absences weighted by poultry density (red) and human population density (blue) for domestic bird outbreaks. Results are shown for four datasets: H5N1 (<2020), H5N1 (>2020), H5Nx (<2020), and H5Nx (>2020).

Author response image 2.

Comparison of variable relative influence (%) between models trained with pseudo-absences weighted by poultry density (red) and human population density (blue) for wild bird outbreaks. Results are shown for three datasets: H5N1 (>2020), H5Nx (<2020), and H5Nx (>2020).

The authors have slightly misunderstood my comment on "extrapolation": I referred to "environmental extrapolation" in my review without being particularly explicit about my meaning. By "environmental extrapolation", I meant to ask whether the models were predicting to environments that are outside the extent of environments included in the occurrence data used in the manuscript. The authors appear to have understood this to be a comment on geographic extrapolation, or predicting to areas outside the geographic extent included in occurrence data, e.g.: "For H5Nx post-2020, areas of high predicted ecological suitability, such as Brazil, Bolivia, the Caribbean islands, and Jilin province in China, likely result from extrapolations, as these regions reported few or no outbreaks in the training data" (lines 195-197). Is the model extrapolating in environmental space in these regions? This is unclear. I do not suggest that the authors should carry out further analysis, but the multivariate environmental similarly surface (MESS; see Elith et al., 2010) is a useful tool to visualise environmental extrapolation and aid model interpretation.

On the subject of "extrapolation", I am also concerned by the additions at lines 362-370: "...our models extrapolate environmental suitability for H5Nx in wild birds in areas where few or no outbreaks have been reported. This discrepancy may be explained by limited surveillance or underreporting in those regions." The "discrepancy" cited here is a feature of the input dataset, a function of the observation distribution that should be captured in pseudo-absence data. The authors state that Kazakhstan and Central Asia are areas of interest, and that the environments in this region are outside the extent of environments captured in the occurrence dataset, although it is unclear whether "extrapolation" is informed by a quantitative tool like a MESS or judged by some other qualitative test. The authors then cite Australia as an example of a region with some predicted suitability but no HPAI outbreaks to date, however this discussion point is not linked to the idea that the presence of environmental conditions to support transmission need not imply the occurrence of transmission (as in the addition, "...spatial isolation may imply a lower risk of actual occurrences..." at line 214). Ultimately, the authors have not added any clear comment on model uncertainty (e.g., variation between replicated BRTs) as I suggested might be helpful to support their description of model predictions.

Many thanks for the clarification. Indeed, we interpreted your previous comments in terms of geographic extrapolations. We thank the Reviewer for these observations. We will adjust the wording to further clarify that predictions of ecological suitability in areas with few or no reported outbreaks (e.g., Central Asia, Australia) are not model errors but expected extrapolations, since ecological suitability does not imply confirmed transmission (for instance, on Line 362: “our models extrapolate environmental suitability” will be changed to “Interestingly, our models extrapolate geographical”). These predictions indicate potential environments favorable to circulation if the virus were introduced.

In our study, model uncertainty is formally assessed when comparing the predictive performances of our models (Fig. S3, Table S1), the relative influence (Table S3) and response curves (Fig. 2) associated with each environmental factor (Table S2). All the results confirming a good converge between these replicates. Finally, we indeed did not use a quantitative tool such as a MESS to assess extrapolation but did rely on qualitative interpretation of model outputs.

All of my criticisms are, of course, applied with the understanding that niche modelling is imperfect for a disease like HPAI, and that data may be biased/incomplete, etc.: these caveats are common across the niche modelling literature. However, if language around the transmission cycle, the niche, and the interpretation of any of the models is imprecise, which I find it to be in the revised manuscript, it undermines all of the science that is presented in this work.

We respectfully disagree with this comment. The scope of our study and the methods employed are clearly defined in the manuscript, and the limitations of ecological niche modelling in this context are explicitly acknowledged in the Discussion section. While we appreciate the Reviewer’s concern, the comment does not provide specific examples of unclear or imprecise language regarding the transmission cycle, niche, or interpretation of the models. Without such examples, it is difficult to identify further revisions that would improve clarity.

Reviewer #2 (Public review):

The geographic range of highly pathogenic avian influenza cases changed substantially around the period 2020, and there is much interest in understanding why. Since 2020 the pathogen irrupted in the Americas and the distribution in Asia changed dramatically. This study aimed to determine which spatial factors (environmental, agronomic and socio-economic) explain the change in numbers and locations of cases reported since 2020 (2020--2023). That's a causal question which they address by applying correlative environmental niche modelling (ENM) approach to the avian influenza case data before (2015--2020) and after 2020 (2020--2023) and separately for confirmed cases in wild and domestic birds. To address their questions they compare the outputs of the respective models, and those of the first global model of the HPAI niche published by Dhingra et al 2016.

We do not agree with this comment. In the manuscript, it is well established that we are quantitatively assessing factors that are associated with occurrences data before and after 2020. We do not claim to determine the causality. One sentence of the Introduction section (lines 75-76) could be confusing, so we intend to modify it in the final revision of our manuscript. 

ENM is a correlative approach useful for extrapolating understandings based on sparse geographically referenced observational data over un- or under-sampled areas with similar environmental characteristics in the form of a continuous map. In this case, because the selected covariates about land cover, use, population and environment are broadly available over the entire world, modelled associations between the response and those covariates can be projected (predicted) back to space in the form of a continuous map of the HPAI niche for the entire world.

We fully agree with this assessment of ENM approaches.

Strengths:

The authors are clear about expected bias in the detection of cases, such geographic variation in surveillance effort (testing of symptomatic or dead wildlife, testing domestic flocks) and in general more detections near areas of higher human population density (because if a tree falls in a forest and there is no-one there, etc), and take steps to ameliorate those. The authors use boosted regression trees to implement the ENM, which typically feature among the best performing models for this application (also known as habitat suitability models). They ran replicate sets of the analysis for each of their model targets (wild/domestic x pathogen variant), which can help produce stable predictions. Their code and data is provided, though I did not verify that the work was reproducible.

The paper can be read as a partial update to the first global model of H5Nx transmission by Dhingra and others published in 2016 and explicitly follows many methodological elements. Because they use the same covariate sets as used by Dhingra et al 2016 (including the comparisons of the performance of the sets in spatial cross-validation) and for both time periods of interest in the current work, comparison of model outputs is possible. The authors further facilitate those comparisons with clear graphics and supplementary analyses and presentation. The models can also be explored interactively at a weblink provided in text, though it would be good to see the model training data there too.

The authors' comparison of ENM model outputs generated from the distinct HPAI case datasets is interesting and worthwhile, though for me, only as a response to differently framed research questions.

Weaknesses:

This well-presented and technically well-executed paper has one major weakness to my mind. I don't believe that ENM models were an appropriate tool to address their stated goal, which was to identify the factors that "explain" changing HPAI epidemiology.

Here is how I understand and unpack that weakness:

(1) Because of their fundamentally correlative nature, ENMs are not a strong candidate for exploring or inferring causal relationships.

(2) Generating ENMs for a species whose distribution is undergoing broad scale range change is complicated and requires particular caution and nuance in interpretation (e.g., Elith et al, 2010, an important general assumption of environmental niche models is that the target species is at some kind of distributional equilibrium (at time scales relevant to the model application). In practice that means the species has had an opportunity to reach all suitable habitats and therefore its absence from some can be interpreted as either unfavourable environment or interactions with other species). Here data sets for the response (N5H1 or N5Hx case data in domestic or wild birds ) were divided into two periods; 2015--2020, and 2020--2023 based on the rationale that the geographic locations and host-species profile of cases detected in the latter period was suggestive of changed epidemiology. In comparing outputs from multiple ENMs for the same target from distinct time periods the authors are expertly working in, or even dancing around, what is a known grey area, and they need to make the necessary assumptions and caveats obvious to readers.

We thank the Reviewer for this observation. First, we constrained pseudo-absence sampling to countries and regions where outbreaks had been reported, reducing the risk of interpreting non-affected areas as environmentally unsuitable. Second, we deliberately split the outbreak data into two periods (2015-2020 and 2020-2023) because we do not assume a single stable equilibrium across the full study timeframe. This division reflects known epidemiological changes around 2020 and allows each period to be modeled independently. Within each period, ENM outputs are interpreted as associations between outbreaks and covariates, not as equilibrium distributions. Finally, by testing prediction across periods, we assessed both niche stability and potential niche shifts. These clarifications will be added to the manuscript to make our assumptions and limitations explicit.

Line 66, we will add: “Ecological niche model outputs for range-shifting pathogens must therefore be interpreted with caution (Elith et al., 2010). Despite this limitation, correlative ecological niche models  remain useful for identifying broad-scale associations and potential shifts in distribution. To account for this, we analysed two distinct time periods (2015-2020 and 2020-2023).”

Line 123, we will revise “These findings underscore the ability of pre-2020 models in forecasting the recent geographic distribution of ecological suitability for H5Nx and H5N1 occurrences” to “These results suggest that pre-2020 models captured broad patterns of suitability for H5Nx and H5N1 outbreaks, while post-2020 models provided a closer fit to the more recent epidemiological situation”.

(3) To generate global prediction maps via ENM, only variables that exist at appropriate resolution over the desired area can be supplied as covariates. What processes could influence changing epidemiology of a pathogen and are their covariates that represent them? Introduction to a new geographic area (continent) with naive population, immunity in previously exposed populations, control measures to limit spread such as vaccination or destruction of vulnerable populations or flocks? Might those control measures be more or less likely depending on the country as a function of its resources and governance? There aren't globally available datasets that speak to those factors, so the question is not why were they omitted but rather was the authors decision to choose ENMs given their question justified? How valuable are insights based on patterns of correlation change when considering different temporal sets of HPAI cases in relation to a common and somewhat anachronistic set of covariates?

We agree that the ecological niche models trained in our study are limited to environmental and host factors, as described in the Methods section with the selection of predictors. While such models cannot capture causality or represent processes such as immunity, control measures, or governance, they remain a useful tool for identifying broad associations between outbreak occurrence and environmental context. Our study cannot infer the full mechanisms driving changes in HPAI epidemiology, but it does provide a globally consistent framework to examine how associations with available covariates vary across time periods.

(4) In general the study is somewhat incoherent with respect to time. Though the case data come from different time periods, each response dataset was modelled separately using exactly the same covariate dataset that predated both sets. That decision should be understood as a strong assumption on the part of the authors that conditions the interpretation: the world (as represented by the covariate set) is immutable, so the model has to return different correlative associations between the case data and the covariates to explain the new data. While the world represented by the selected covariates \*may\* be relatively stable (could be statistically confirmed), what about the world not represented by the covariates (see point 3)?

We used the same covariate layers for both periods, which indeed assumes that these environmental and host factors are relatively stable at the global scale over the short timeframe considered. We believe this assumption is reasonable, as poultry density, land cover, and climate baselines do not change drastically between 2015 and 2023 at the resolution of our analysis. We agree, however, that unmeasured processes such as control measures, immunity, or governance may have changed during this time and are not captured by our covariates.

Recommendations for the Authors:

Reviewer #1 (Recommendations for the authors):

- Line 400-401: "over the 2003-2016 periods" has an extra "s"; "two host species" (with reference to wild and domestic birds) would be more precise as "two host groups".

- Remove comma line 404

Many thanks for these comments, we have modified the text accordingly.

Reviewer #2 (Recommendations for the authors):

Most of my work this round is encapsulated in the public part of the review.

The authors responded positively to the review efforts from the previous round, but I was underwhelmed with the changes to the text that resulted. Particularly in regard to limiting assumptions - the way that they augmented the text to refer to limitations raised in review downplayed the importance of the assumptions they've made. So they acknowledge the significance of the limitation in their rejoinder, but in the amended text merely note the limitation without giving any sense of what it means for their interpretation of the findings of this study.

The abstract and findings are essentially unchanged from the previous draft.

I still feel the near causal statements of interpretation about the covariates are concerning. These models really are not a good candidate for supporting the inference that they are making and there seem to be very strong arguments in favour of adding covariates that are not globally available.

We never claimed causal interpretation, and we have consistently framed our analyses in terms of associations rather than mechanisms. We acknowledge that one phrasing in the research questions (“Which factors can explain…”) could be misinterpreted, and we are correcting this in the revised version to read “Which factors are associated with…”. Our approach follows standard ecological niche modelling practice, which identifies statistical associations between occurrence data and covariates. As noted in the Discussion section, these associations should not be interpreted as direct causal mechanisms. Finally, all interpretive points in the manuscript are supported by published literature, and we consider this framing both appropriate and consistent with best practice in ecological niche modelling (ENM) studies.

We assessed predictor contributions using the “relative influence” metric, the terminology reported by the R package “gbm” (Ridgeway, 2020). This metric quantifies the contribution of each variable to model fit across all trees, rescaled to sum to 100%, and should be interpreted as an association rather than a causal effect.

L65-66 The general difficulty of interpreting ENM output with range-shifting species should be cited here to alert readers that they should not blithely attempt what follows at home.

I believe that their analysis is interesting and technically very well executed, so it has been a disappointment and hard work to write this assessment. My rough-cut last paragraph of a reframed intro would go something like - there are many reasons in the literature not to do what we are about to do, but here's why we think it can be instructive and informative, within certain guardrails.

To acknowledge this comment and the previous one, we revised lines 65-66 to: “However, recent outbreaks raise questions about whether earlier ecological niche models still accurately predict the current distribution of areas ecologically suitable for the local circulation of HPAI H5 viruses. Ecological niche model outputs for range-shifting pathogens must therefore be interpreted with caution (Elith et al., 2010). Despite this limitation, correlative ecological niche models  remain useful for identifying broad-scale associations and potential shifts in distribution.”

We respectfully disagree with the Reviewer’s statement that “there are many reasons in the literature not to do what we are about to do”. All modeling approaches, including mechanistic ones, have limitations, and the literature is clear on both the strengths and constraints of ecological niche models. Our manuscript openly acknowledges these limits and frames our findings accordingly. We therefore believe that our use of an ENM approach is justified and contributes valuable insights within these well-defined boundaries.

Reference: Ridgeway, G. (2007). Generalized Boosted Models: A guide to the gbm package. Update, 1(1), 2007.

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I could say my Canvas is a blank slate then :(

eLife Assessment

Davis and colleagues describe findings that are fundamental to the understanding of pressure mechanosensation in lymphatic vessels and are of significant importance to other areas of mechanosensory physiology. Based on many different knockout mouse models and rigorous state-of-the-art pressure myography recordings, they present compelling evidence that mechano-activation of GNAQ/GNA11-coupled GPCRs generates IP3, which induces Ca2+ release from internal stores through IP3R1 and drives depolarization through the activation of ANO1 Cl- channels to induce lymphatic vessel contractility. Nevertheless, some aspects of the manuscript are incomplete. The specific identity of the GPCR(s) involved remains to be uncovered, as evidence of frequency-pressure impairment is only demonstrated with abolition of GNAQ/GNA11action, not the receptors per se.

Reviewer #1 (Public review):

Summary:

Davis and co-authors used many mouse models to investigate mechanisms that regulate the contractility of mouse popliteal collecting vessels, primarily chronotropy. Many of the mechanisms studied were previously shown to regulate pressure-induced constriction in small arteries. The authors use prior literature from the vasculature as a framework to test similar concepts in lymphatic vessels. The mouse models used provide evidence for and against the involvement of multiple proteins in regulating chronotropy and other contractile properties in lymphatic vessels. They propose that mechano-activation of GNAQ/GNA11-coupled GPCRs generates IP3, which induces Ca2+ release through IP3R1 and drives depolarization through the activation of ANO1 Cl- channels. Major concerns include the author's major conclusion that GNAQ/GNA11-coupled GPCRs contribute to chronotropy. This conclusion is not supported by the data presented.

Strengths:

One major strength of the study lies in the vast number of mouse knockout models that were used to test the importance of ion channels and G protein signaling pathways in the regulation of lymphatic vessel contractility. In this regard, the study is a valiant effort. The authors achieved several objectives to find that ANO1 and IP3R1 regulate chronotropy, and many other potential proteins do not regulate chronotropy. This study will have a major impact on the field if additional support for G proteins is provided.

Weaknesses:

Major conclusions concerning the involvement of G proteins are drawn from the global Gna11 knockout mouse models. This conclusion is weak. Global Gna11 knockout mice are highly likely to have a multifactorial phenotype that could create significant differences in the data. Control experiments need to be performed on vessels from the global knockout mice if these major conclusions are to be made. Similarly, pharmacological tools or alternative approaches to manipulate G proteins should be used to support the data from these mouse models to draw these major conclusions.

The Gnaq smKO mice are the most specific G protein model studied here. However, there is no phenotype. Do not discuss trends in the data. If the data are not significant, conclude so. If more experiments are required to reach significance, provide more data in the manuscript.

The conclusions repeatedly refer to a signaling pathway wherein the upstream component is GPCRs, which activate G proteins. While this may be the case, no GPCRs were identified here, and the involvement of G proteins is questionable, as the authors outline in lines 693-695 and noted above. The conclusions should be tempered, including in the abstract, unless additional experiments are performed to support the involvement of G proteins. Perhaps then the authors may be able to infer that GPCRs are involved.

Line 318. The point regarding the choice to use popliteal vessels versus IALVs will be unclear to the uninitiated, particularly as the authors previously used IALVs. Including additional justification in the text and/or data from IALVs in Figure 1, which compares IALVs to popliteal vessels, would better explain the logic.

The conclusions drawn for TRPC6 and TRPC3 are less convincing. Germline global knockout mice, which are known to undergo compensation, were used, and high data variability is apparent. Using TRPC3 and TRPC6 blockers in the mouse models studied in Figure 4 would strengthen the arguments made regarding these proteins.

Did you perform power analysis to ensure that experimental numbers were sufficient to conclude that no statistical difference exists between datasets? If not, this needs to be done. For example, data shown in Figure 5C for tone and 6C for frequency and tone appear to be significantly different, but are concluded not to be so.

At the end of each result section, a concluding statement is made regarding the effects on pressure-induced chronotrophy. In many cases, there are additional effects of manipulating protein expression on other contractile properties. One example is for TRPC3 and TRPC6 (lines 414-416), but others are TRPV4, TRPV3, ENaC, Kir, Cav3.1/3.2, etc. Some interpretation is in the Discussion, but the concluding statements at the end of each result section should be expanded to summarize what the authors think the other significant differences in the data represent.

Kv7.4 channels. You state you have data (not shown) with linopiridine and XE991. Why not show those results here to support the experiments with the Kcnq4 smKO mice? Otherwise, I suggest you remove the statement from the unpublished data.

Figure 13A. Kcnj2 is modestly expressed in LECs, but very little is present in LMCs. This likely underlies the effect of barium. If you remove the endothelium, does the effect of barium disappear? While this is not the major focus of the study, the effects of barium are dramatic, and it should be made clear whether this is due to inhibition of Kir channels in smooth muscle or endothelial cells.

Figure 18C tone. Several values for losartan look different but are not labelled as such. Please clarify and discuss if different.

The manuscript should include raw data traces in figures that show the major pathways that you conclude regulate chronotropy.

Reviewer #2 (Public review):

Summary:

In this study, Davis et al. embarked on the quest for the molecular elements responsible for the regulation of lymphatic phasic contractile activity in response to variation of transmural pressure, a mechanism (termed pressure-induced lymphatic chronotropy by the authors) critical for drainage of interstitial fluid from the tissue and transport of lymph back to the blood circulation. Their aim was to investigate the mechanism(s) involved in the pressure-induced regulation of lymphatic pumping, and test whether activation of cation channels, shown in other systems to play mechanosensitive roles are directly at play, and/or whether mechano-activation of GNAQ/GNA11-coupled GPCRs is necessary to generate second messengers to activate those channels, as it has been suggested for the regulation of myogenic tone in arteries. To achieve their goal, the authors used their well-described, highly reliable protocols of mouse lymphatic vessel isolation, pressure myography, and data acquisition to obtain frequency-pressure relationships and other contractile function parameters from transgenic mice where specific channels or molecular elements of interest have been ablated. They combined these data with scRNAseq analysis of these gene targets to determine their respective role and levels of expression in lymphatic muscle cells. Their conclusion is that none of the exhaustive list of tested ion channels was critical, except ANO1 Cl channels, part of the contractile pacemaker mechanism, but that transmural pressure activates GNAQ/GNA11-coupled GPCRs, which generate IP3 to induce SR Ca2+ release through IP3R1 and activate ANO1-mediated depolarization.

Strengths:

The manuscript's strengths reside primarily in very robust, clean, and unequivocal pressure myography data and analysis. The research team is mastering these techniques they developed more than a decade ago and have implemented in mouse lymphatics to study their contractile properties, with consistent and convincing outcomes. They also provide data from an impressive list of transgenic mice in order to determine the role of the targeted gene in pressure-induced lymphatic chronotropy, relying on pharmacological small molecule inhibitors only when necessary. Finally, the use of scRNAseq analysis they gathered from previously published datasets brings novelty with respect to the expression of the genes of interest in all populations of cells comprising the lymphatic vessels, but more critically, to validate or contrast the potential impact of genetic alteration of the given gene on the ability of lymphatic muscles to respond to a change in pressure.

Weaknesses:

The main weakness may reside in the fact that while the authors provide a convincing demonstration that GNAQ/GNA11 are involved in the regulation of the F-P relationship, they give little evidence of the involvement of "upstream" receptors. Indeed, inhibition of AT1R, shown to be involved in myogenic regulation of arteries (a phenomenon the authors rightfully compare to pressure-induced lymphatic chronotropy), didn't lead toa similar effect (decrease in F-P) in lymphatic vessels. Arguably, other GPCRs might be involved in lymphatic vessels, but as such information is not provided in the manuscript, the author's conclusions should be dampened. More in-depth discussion would be required. In fact, it can be argued that the discussion is very restricted with respect to the amount of data and information the manuscript provides.

Overall, the authors convincingly achieved their aim by performing an impressive number of technically challenging experiments, leading to solid datasets. While these support their main conclusions, a more elaborate discussion might be required to refine them.

This study is likely to have an important impact on the field as it provides some answers to the lingering question of how lymphatic vessels regulate their contractile activity to variation in transmural pressure and certainly proposes an experimental means to further explore and address that question.

Reviewer #3 (Public review):

In this manuscript, Davis and colleagues aimed to identify the molecular sensors and signaling cascade that enable collecting lymphatic vessels to increase their spontaneous contraction frequency in response to intraluminal pressure (pressure-induced chronotropy). They tested whether the process is similar to blood vessel myogenic constriction by relying on cation channels (TRPC6, TRPM4, PKD2, PIEZO1, etc.) or instead require the activation of G-protein-coupled receptors (presumably mechanosensitive GNAQ/GNA11-coupled receptors), using ex vivo pressure myography of mouse popliteal lymphatics, smooth muscle-specific conditional knockouts, quantitative PCR validation, and single-cell RNA sequencing for target prioritization. The authors convincingly demonstrate that pressure-induced chronotropy does not require the cation channels implicated in arterial myogenic tone but is blunted by deletion of GNAQ/GNA11 or IP3 receptor 1, supporting a model of GPCR > IP3 > Ca2+ release > Cl⁻ channel activation > depolarization. The core conclusion is robust. The work redefines lymphatic pacemaking as G-protein-coupled receptor-dependent mechanotransduction, distinct from arterial mechanisms, and provides a genetically validated toolkit that is useful for studying lymphatic function and dysfunction.

Strengths:

(1) The data are of high quality and highly sensitive functional readouts

(2) The systematic genetic targeting is a major strength that overcomes pharmacological artifacts

(3) Careful quantitative analyses of frequency-pressure slopes

Weaknesses:

(1) The use of inguinal-axillary vessels for single-cell RNA sequencing rather than the popliteal segment studied functionally.

(2) No direct testing of the specific G-protein-coupled receptor involved.

Author response:

We thank the reviewers and editors for their insightful comments on our manuscript. We intend to submit a revised manuscript that addresses all concerns raised by the reviewers. A major limitation identified by the reviewers was our inability to identify one or more specific mechanosensitive GPCRs in lymphatic muscle cells (LMCs). To address this concern, we plan to include several additional figures in the revised manuscript. One figure will list the 136 GPCRs identified in LMCs by our scRNAseq analysis, based on the list of validated GPCRs in https://esbl.nhlbi.nih.gov/Databases/GPCRs/index.html and olfactory GPCRs listed in https://esbl.nhlbi.nih.gov/Databases/GPCRs/MouseHumanRatORs.html. We plan to arrange the data in a hierarchical manner according to their expression level and denote their heterotrimeric GTP-binding protein alpha subunit(s), if known. To reinforce our finding that pressure-induced chronotropy in LMCs is mediated through Gq/11, we will present additional data testing the effects of acute Gq/11  inhibition with YM-254890 (a selective Gq/11 inhibitor) on the frequency-pressure relationship of popliteal vessels, as suggested by one reviewer. We will address concerns regarding the potential regional differences in lymphatic contractile regulation arising from our use of popliteal lymphatic vessels for contraction assays and expression analysis of LMCs obtained from Inguinal-Axillary lymphatic vessels (IALVs). To account for possible differences between the two, we will test pressure responses of IALVs from double Gq/11 knockout mice and test responses of wild-type IALVs to acute administration of YM-25489.

Our preliminary analysis of the 136 GPCRs in LMCs revealed a shorter list of 10 GPCRs that are expressed in at least 50% of LMCs (based on the IALV scRNAseq dataset). Since existing evidence from our studies, and those of other investigators, suggests that any LMC is capable of initiating pacemaking, we consider it reasonable to impose this requirement.

Author response table 1.

We plan to use pharmacologic inhibitors to test as many of these candidates as possible. Unfortunately, inhibitors are not available for many of the GPCRs listed above, but we will test Npr3, Npy1R, and Ednra; a negative result for Tbxa2r has already been documented in a previous study (Schulz et al. ATVB 2025). Even if this strategy does not lead to identification of one or more specific GPCRs involved in LMC pressure transduction, it will narrow the list of possible candidates that need to be tested in future experiments.

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Celohliníkové nůžkové stany Lehká a odolná konstrukce bez volných dílů- žádné montování a spojovaní, žádné nářadí. Stan stačí rozložit během 60 s! Nepromokavá látka a podlepené švy pro absolutní ochranu před nepříznivým počasím. +change the text in video for "Nůžkové stany

pictogram + info about the only octagonal profile on CZ/SK market

For this and subsequent figures, some quantification or multiple cells rather than images of a single cell would be more convincing (even just a line scan of fluorescence intensity along the length of the flagella in each channel, for spatial coincidence). Single channel grayscale would also make it visually more clear. It’s a bit difficult to discern as shown.

There is also an IFT20-mCherry strain available in the chlamy center. It’s expressed in the ift20 mutant background. Curious also what the chr1 localization is in ift20 mutants.

Since you have pre-immune serum, I’m guessing you generated this chlamy-specific ift-20 antibody yourself? Wild type and IFT-20 mutant (either ift20-1 or a CLiP mutant) lysate by side on the same blot would be a convincing demonstration of antibody specificity.

What is the source of these antibodies?

De drie die je moet onthouden en gebruiken

eLife Assessment

This study offers important insights into how outer membrane vesicles (OMVs) secreted by Serratia marcescens, which carry various virulence factors, contribute to pathogenicity. The experiments provide solid preliminary support for OMV-mediated pathogenic effects, with a critical role for the metalloprotease virulence factor PrtA. However, the evidence remains incomplete, and the current level of validation limits confidence in the strength of the conclusions.

Reviewer #1 (Public review):

Summary:

The work of Bechara Rahme and colleagues provides an explanation as to how bacterially infected flies eventually die. While widespread tissue and multiorgan damage are to be expected in the latest stages of a systemic infection, the mechanisms leading to the host's death remain unresolved. To this end, this work illustrates the role of PrtA, a metalloproteinase found within Outer Membrane Vesicles (OMVs) secreted by Serratia marcescens, in inducing neuronal apoptosis and paralysis before death. Another interesting aspect of the work is the compromise of blood blood-brain barrier (BBB) by OMVs. BBB is different between mammals and flies; however, it merits scientific attention.

Strengths:

The strength of evidence lies in a wealth of experiments involving disparate innate immune mechanisms that either contribute (Imd, PPO1/2, Nox, Duox, SOD2) or oppose (hemocytes and Hayan protease) host defense. Moreover, the role of neuronal JNK and apoptic signaling is shown to contribute to host death.

Genetics is supported by experiments using chemical treatments (Vitamin C and mito-TEMPO) as host-protecting antioxidants, and the biochemical purification and quantification of OMVs and the PrtA protease.

Weaknesses:

However, the reliance on non-isogenised flies to provide quantitative data is unsafe, and at this point, the strength of the evidenceis apparently incomplete. The mutant flies used for the genes Key, Myd88, Hayan, and Nos are doubtfully comparable to the control fly strains used in terms of the general genetic background. The latter is of utmost importance in assessing quantitative traits.

The general background difference between control and test flies is also an issue when using tissue-specific expression via GAL4/UAS, because the UAS lines used are only apparently but not truly isogenic to the w flies used as controls.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigate the mechanisms underlying the virulence of OMVs using a Drosophila model. They reveal a complex interplay between host defenses and OMV pathogenicity. Although the study enhances our understanding of Drosophila innate immunity, additional evidence is needed to strengthen the conclusions.

Strengths:

(1) In Figure 1, Toll pathway mutants infected with OMVs displayed three distinct phenotypic outcomes: mildly enhanced resistance to OMV infection, a response similar to that of the control, or increased susceptibility. Therefore, in addition to Imd and Kenny mutants from the Imd pathway, further mutants, such as Relish and PGRP-LC, should be examined to assess whether the Imd pathway is involved in host defense against OMVs.

(2) Plasmatocytes clear particles via phagocytosis or endocytosis. However, flies lacking all hemocytes showed increased resistance to OMV challenge, raising the question of whether hemocytes actually aid the pathogen. To explore this hypothesis, the uptake of fluorescently tagged OMVs should be examined.

(3) Hayan cleaves PPO into active PO. However, Hayan and PPO mutants exhibit opposite phenotypes upon OMV injection, raising the question of whether OMV-induced pathogenesis is linked to melanization.

(4) Puckered mRNA levels were used as a read-out for JNK pathway activity. A transient induction of the JNK pathway was observed in head and thorax tissues. It would be beneficial if the authors could directly examine JNK activation in neuronal cells using immunostaining for pJNK.

(5) In Figure 4B, the kayak was knocked down using the pan-neuronal driver elav-Gal4. To confirm the specificity and validity of this observation, the experiment should be repeated using another neural-specific driver.

Weaknesses:

It is unclear how many Serratia marcescens cells a 69 nL injection of 0.1 ng/nL OMVs corresponds to.

Reviewer #3 (Public review):

Summary:

The authors investigate deficiencies in various immune responses, and also the prtA toxin's role in OMV toxicity. Some key interpretations are that the Imd pathway contributes to preventing OMV toxicity, but not Toll, and that Hayan and Eater somehow mediate OMV or PrtA toxicity. This descriptive effort is a solid set of experiments, although some experimental results may require further validation.

Strengths:

The breadth of experiments tests multiple immune parameters, providing a systematic effort that ensures a number of potentially relevant interactions can be recovered. Certain findings, such as the PrtA toxicity to flies, appear solid, and some interesting findings regarding Hayan and eater will be of interest to the fly immunity field.

Weaknesses:

It appears almost all results rely on the use of a single mutant representing the deletion of the gene. It's not clear if the mutations are always in the same genetic background, but this can be clarified. There are a couple of results that are confusing and may be internally contradicting, and should be additionally validated and clarified.

Author response:

We thank the reviewers and editors for the careful evaluation of our manuscript. Below, we provide a first refutation of some of the concerns expressed by reviewers.

Both reviewer 1 &3 underscore the importance of controlling for genetic backgrounds. This is actually an issue only for a limited part of the study and this criticism should not apply to major findings of this study, with some exceptions, as detailed below.

It is important to note that we have identified ourselves several of the mutant lines we have been using. For instance, key and MyD88 mutant alleles have been identified in the Exelixis transposon insertion collection that we have screened in collaboration with this firm (e.g., [3, 4, 5]). This resource has been generated in a isogenized w [A5001] strain[6], which we are using as matched control for these mutants (Figs 1B,D). Of note, while they share a common genetic background, the phenotypes of key and MyD88 are opposite in terms of sensitivity to OMV challenge. The imd^shadok null allele had been identified during our chemical mutagenesis screen with EMS in a yw cn bw background [5, 7, 8, 9], which was used as a control (FigS1A).

With respect to Hayan (Fig. 2C, Fig. S2C) and eater (Fig. S2A-B) mutants[10, 11, 12], we find a similarly strong phenotype with two independent mutants in distinct genetic backgrounds (actually three for Hayan, as we have not included in our original manuscript the Hayan^SK3allele generated in the Lemaitre laboratory in which OMVs displayed also impaired virulence). We have shown that the Hayan mutants do display the expected phenotype in terms of PPO cleavage (Fig. S2D). Please, also note that in Fig. S2C the two mutant alleles are tested in the same experiment: even though there is some variation between the w¹¹¹⁸ and the w[A5001] strains, the two mutants behave in a remarkably similar manner. As regards the role of the cellular response, we note that we obtained results similar to those obtained with eater mutants using genetic ablation of hemocytes (Fig. 2A) or by saturating the phagocytosis apparatus (Fig. 2B), a confirmation by two totally-independent approaches.

Of note, the observed eater and Hayan phenotypes are strong and not relatively small and thus unlikely to be due to the genetic background.

The PPO mutants have been isogenized in the w¹¹¹⁸ by the lab of Bruno Lemaitre[13, 14] and are also validated biochemically in Fig. S2D. These mutants have been extensively tested in the Lemaitre laboratory[13, 14, 15].

With respect to RNAi silencing driven ubiquitously or in specific tissues using the UAS-Gal4 system, we have mostly used transgenes from the Trip collection and have used as a control the mCherry RNAi provided by this resource[16]. As the RNAi transgenes have been generated in the same genetic background, it follows that independently of the driver used, the genetic background used in mCherry and genes-of-interest (Duox, Nox, Jafrac2) silenced flies is controlled for (Fig. 3D,E).

For UAS-Gal4-mediated overexpression of fly superoxide dismutase genes, we have used SOD1 and SOD2 transgenes that have both been generated by the same laboratory (Phillips laboratory, University of Guelph) presumably in the same genetic background. Using two distinct drivers we find a strongly enhanced susceptibility phenotype when using UAS-SOD2 but not UAS-SOD1 transgenes (Fig. 3F, Fig. 4E). Importantly, the former is associated with mitochondria whereas the other is expressed in the endoplasmic reticulum: we independently confirm this phenotype using the mitoTempo mitochondrial ROS inhibitor.

We shall thus address the criticism with NOS mutants, where genetic background control is indeed critical and for the UAS-kay RNAi line using a Trip line and its associated mCherry RNAi control transgene.

With respect to the Toll pathway mutants, we agree that some of the variability of the phenotypes may be due to the genetic background, especially as regards tube and pelle. The SPE and grass mutants have been retrieved in a screen performed by the group of Jean-Marc Reichhart in our Research Unit. They thus have been generated in the same genetic background, yet grass displays a mildly decreased virulence of injected OMVs whereas SPE mutants display an opposite phenotype (compare Fig. S1E to S1I; the survival experiment shave been performed in the same set of experiments and have been separated for clarity). We do not intend to analyze further the mutants of the Toll pathway as our data suggest that the canonical Toll pathway, likely activated through psh (Fig. S1F) appears to be activated to detectable levels too late by comparison with the time course of OMV pathogenicity. In our opinion, the contribution of the Toll pathway in the host defense against OMV pathogenicity is minor, albeit we acknowledge that some of the findings, especially with SPE are puzzling.

With respect to the IMD pathway, we shall test also PGRP-LC and Relish mutants, as suggested by reviewers 2&3.

Reviewer 2 query: “It is unclear how many Serratia marcescens cells a 69 nL injection of 0.1 ng/nL OMVs corresponds to.”

OMVs were purified from 600 mL of SmDb11 cultures grown to an average OD₆₀₀ of 2.0. Based on a cell density of 0.8 × 10⁸ cells/mL per OD unit, this corresponds to approximately 9.6 × 10¹⁰ total bacterial cells.

Each OMV preparation was concentrated into a final volume of 400 µL, resulting in a concentration factor of ~1500× relative to the original culture. Therefore, an injection dose of 69 nL of OMVs is equivalent to 0.1 mL of the starting bacterial culture, which corresponds to:

0.2 OD units

Approximately 1.6 × 10⁷ bacterial cells

It is likely that such high concentrations occur only toward the end of the infection, if OMVs are produced at the same rate in the host and in vitro.

With respect to other Reviewer 2 queries, we shall give a try at labeling OMVs with the FM4-64 lipophilic dye and examining whether they are taken up by hemocytes. However, an issue may arise with potentially high background, which has been encountered in cell culture. Of note, OMVs are known to attack cultured human THP1 cells, a monocyte cell line [17].Of note, determining whether OMVs are taken up by hemocytes may only be a starting point to understand how they promote the pathogenicity of OMVs. This question constitutes the topic of a full study that we are currently unable to undertake.

We shall also test whether we can document phospho-JNK expression in neural tissues.

Finally, we shall also confirm the data obtained with two elav-Gal4 drivers (including an inducible one) with the nsyb-Gal4 driver line.

References

(1) Xu R, et al. The Toll pathway mediates Drosophila resilience to Aspergillus mycotoxins through specific Bomanins. EMBO Rep 24, e56036 (2023).

(2) Huang J, et al. A Toll pathway effector protects Drosophila specifically from distinct toxins secreted by a fungus or a bacterium. Proc Natl Acad Sci U S A 120, e2205140120 (2023).

(3) Gobert V, et al. Dual Activation of the Drosophila Toll Pathway by Two Pattern Recognition Receptors. Science 302, 2126-2130 (2003).

(4) Gottar M, et al. Dual Detection of Fungal Infections in Drosophila via Recognition of Glucans and Sensing of Virulence Factors. Cell 127, 1425-1437 (2006).

(5) Gottar M, et al. The Drosophila immune response against Gram-negative bacteria is mediated by a peptidoglycan recognition protein. Nature 416, 640-644 (2002).

(6) Thibault ST, et al. A complementary transposon tool kit for Drosophila melanogaster using P and piggyBac. Nat Genet 36, 283-287 (2004).

(7) Rutschmann S, Jung AC, Hetru C, Reichhart J-M, Hoffmann  JA, Ferrandon D. The Rel protein DIF mediates the antifungal, but not the antibacterial,  response in Drosophila. Immunity 12, 569-580 (2000).

(8) Rutschmann S, Jung AC, Rui Z, Silverman N, Hoffmann JA, Ferrandon D. Role of Drosophila IKKg in a Toll-independent antibacterial immune response. Nat Immunology 1, 342-347 (2000).

(9) Jung A, Criqui M-C, Rutschmann S, Hoffmann J-A, Ferrandon D. A microfluorometer assay to measure the expression of ß-galactosidase and GFP reporter genes in single Drosophila flies. Biotechniques 30, 594- 601 (2001).

(10) Nam HJ, Jang IH, You H, Lee KA, Lee WJ. Genetic evidence of a redox-dependent systemic wound response via Hayan protease-phenoloxidase system in Drosophila. Embo J 31, 1253-1265 (2012).

(11) Kocks C, et al. Eater, a transmembrane protein mediating phagocytosis of bacterial pathogens in Drosophila. Cell 123, 335-346 (2005).

(12) Bretscher AJ, et al. The Nimrod transmembrane receptor Eater is required for hemocyte attachment to the sessile compartment in Drosophila melanogaster. Biology open 4, 355-363 (2015).

(13) Binggeli O, Neyen C, Poidevin M, Lemaitre B. Prophenoloxidase activation is required for survival to microbial infections in Drosophila. PLoS Pathog 10, e1004067 (2014).

(14) Dudzic JP, Kondo S, Ueda R, Bergman CM, Lemaitre B. Drosophila innate immunity: regional and functional specialization of prophenoloxidases. BMC Biol 13, 81 (2015).

(15) Dudzic JP, Hanson MA, Iatsenko I, Kondo S, Lemaitre B. More Than Black or White: Melanization and Toll Share Regulatory Serine Proteases in Drosophila. Cell reports 27, 1050-1061 e1053 (2019).

(16) Perkins LA, et al. The Transgenic RNAi Project at Harvard Medical School: Resources and Validation. Genetics 201, 843-852 (2015).

(17) Goman A, et al. Uncovering a new family of conserved virulence factors that promote the production of host-damaging outer membrane vesicles in gram-negative bacteria. J Extracell Vesicles 14, e270032 (2025).

eLife Assessment

This valuable study presents an analysis of the gene regulatory networks that contribute to tumour heterogeneity and tumor plasticity in Ewing sarcoma, with key implications for other fusion-driven sarcomas. The authors convincingly employed orthogonal approaches, including single-cell sequencing and xenografts, to reveal the existence and plasticity of specific gene regulatory networks (e.g., TGF-beta signaling) within Ewing sarcoma, as well as significant differences that exist between cell lines and patient tumors.

Reviewer #1 (Public review):

The investigators elegantly utilized a single-cell co-assay of RNA and ATAC seq to unveil the heterogeneous gene regulatory networks in Ewing sarcoma. The authors should be commended on their ability to identify multiple unique modules of gene regulation of Ewing sarcoma utilizing complex computational methods between numerous Ewing sarcoma cell lines. Additionally, they complemented their single-cell findings with xenografts as well as primary Ewing sarcoma patient tumors - validating the intratumoral heterogeneous gene regulatory networks of Ewing sarcoma. More importantly, they have revealed that exogenous TGF-β may modify these distinct epigenetic and transcriptional signatures within Ewing sarcoma tumors. Overall, the manuscript highlights an important discovery of the heterogenous gene regulatory programming of Ewing sarcoma and further highlights the role that TGFB plays within the tumor microenvironment of Ewing sarcoma. There are some areas of ambiguity that require clarification to increase the impact of the manuscript.

Reviewer #2 (Public review):

Summary:

This work by Waltner et. al. provides a comprehensive single-cell multiomics analysis of plasticity in gene regulatory networks present in Ewing sarcoma using single-cell RNA-sequencing (scRNA-seq) and single-cell assay for transposase accessible chromatin with sequencing (scATAC-seq). They find that Ewing sarcoma cell line models have distinct patterns of chromatin accessibility compared to non-Ewing sarcoma models, and that there is significant variability across Ewing sarcoma cell lines, and sometimes within a single cell line. These differences across models are linked to 3 distinct gene regulatory modules, 2 of which are present across the range of model systems studied here. The first modules present across models are activated when the fusion is expressed and include genes enriched for the known EWSR1::FLI1 response element, GGAA microsatellites, along with other neural crest transcription factors. The other module primarily consists of genes repressed by EWSR1::FLI1, which are activated in EWSR1::FLI1-low states. Interestingly, EWSR1::FLI1-low cells have already been tied to more migratory and metastatic phenotypes, and the data here suggest these cells are more responsive to external signals from TGF-β, and this may be mediated through FOSL2-mediated gene regulation. While there are some minor additional validation studies that can be performed to strengthen a few individual analyses, this is a technically rigorous study, with a variety of different analytical techniques used to address similar questions, and this approach elevates confidence in the answers provided. This is further strengthened by the diverse set of model systems used, including patient-derived cell lines, cell line xenograft models, patient-derived xenografts, mining available single-cell data from patient samples, and validation of the gene modules identified in a larger set of patient microarray samples. In whole, this study provides a valuable resource for understanding heterogeneity, plasticity, and gene expression networks in Ewing sarcoma. This may be useful for future studies of metastatic disease and may also provide a framework for similar questions in other fusion-driven sarcomas.

Strengths:

There are a few core strengths in this study. First is the number and diversity of Ewing sarcoma models studied, spanning commonly used cell lines, patient-derived xenografts, and patient samples. The second is the large array of rigorous and orthogonal approaches used to uncover the identity and function of various gene modules. This includes an array of informatics techniques, as well as specific modulation of cell line models in culture. A third is confirmation that different gene expression programs are present in the same tumor using spatial transcriptomic analysis. Lastly, the authors have made all of their data and code accessible, enabling continued use of this dataset as a resource for others.

Weaknesses:

As highlighted by the authors, this study is somewhat limited by the small number of single-cell data from patient samples that are publicly available. Much of the analysis comes from cell lines. Additionally, they focus only on one type of signal that may modulate cell plasticity, and there are likely to be many others. Lastly, there are a few weak spots in the data. Some of this likely arises from the underlying complexity of the data, the generally sparse nature of scATAC data, and the biological heterogeneity present in the cell lines studied. The most pronounced weakness was in the analysis of transcription factors that dictate gene expression in the distinct modules, as well as the response to TGF-β. While some specific transcription factors showed module-specific expression consistent with the computational prediction in Figure 2, others did not likely due to additional factors not tested here. Likewise, the same transcription factors did not always show consistent enrichment in the gene modules that responded to TGF-β treatment when analyzed across cell lines. On the whole, these are relatively minor weaknesses and do not diminish the value of this study.

the pictures show a perspective from the protesters; see many signs that state things like "Focused on the wrong 1%" and "Love thy neighbor. no exceptions", shows how this bill is affecting people

Implements fear for what could happen if you go against the policy change; will result in repercussions ( lack of evidence to back this statement up)

Meest gebruikt

This is an issue in that elites stay the same. There is no real room for growth from the outside or for people to climb into that since elites are already selected out.

Here is one of the main problems with military power. It comes down to using it correctly. Using it incorrectly can lead to more conflict within and exterior.

I like how this shows how support comes in different shapes and sizes. From a pin all the way to a flag. This goes both ways support for positive and support for negative.

I think this depends how just how broken a marriage is and if it really is due for a divorce. I know many people who face relief when their parent's finally split because they dont need to deal with endless conflict and stress. Yes, no one wants their parents to split but I feel like that's not the kids place to choose in the relationship.

use the banner "Grafický návrh zdarma" e.g. from https://test2025.mitkoforevents.cz/nuzkove-stany/3x3/

Stan Dome +change the headline in the video as well Unikátní kopulový tvar umožňuje maximální využití zastřešeného prostoru. Konstrukce se snadno montuje bez použití jakéhokoliv nářadí! (next paragraph)Velká plocha opláštění je ideální pro přípravu atraktivního designu potisku, který realizujeme moderní metodou digitální sublimace.

VIP stan DOME +correct the name in all other web occurences incl. nav. menu etc.

I think it's so sad how often parents lack the ability to care for themselves to the point where they have no other choice but to abandon their own child or children. It's sad that people aren't as careful and aware of what consequences they will face when being immature. And the cost of that is going to be a future traumatized and broken child because that child will always know neglect and abandonment.

Im not suprised by this stat. And with that 10 percent decrease from 1980, I feel like in today's world the decrease is going to get significantly worse. I feel like the economic and political state of the world plays a role in that but also what the societal standards are now, especially for young adults.

This can be a really dangerous thing for marriages, but for couples in general who live with each other. If one person revolves their value around their successes and achievements, there will be a power trip. This may be even more dangerous when children are involved and the cost of that power because anger and rage when things go wrong.

Many of these ideas that carry these characteristics end up sticking with people. This means some people end up taking to heart these negative ideas do not benefit anyone but those in power.

To think that a belief system would be genocidal is telling. It shows how badly priorities are set and that is never good. How would a system like this ever even work out?

This is honestly really crazy how they would even consider this type of footage "ASMR" or even remotely relaxing. This is treating human beings as an object, as simple entertainment which is wrong in so many different ways.

this statement by the governor and the language used shows a non neutral language for everyone and can only appeal to one narrative of the story. Perspective found from the direct governor that is implementing these laws

this shows framing because when the article says a specific word, from the Supreme Court decision, a “setback,” it signals the article’s perspective. This can often make the audience read this as a issue that is important.

in these lines three stategies listed below are pre drafting activities .mean before the drafting ,understanding of rhetorical situation ,analyz other multimodal texts,gather content ,media,and tools

combining each mode to create a clear communicative essay osten involves the writing process like revision and drafting and thoughtful writer good consider how final product dose or dose not .creating text tell us about conceder , purpose and context

My question is, how close is the current United States administration to securing control of all mainstream media sources?

This portion speaks about the poverty aspect of this topic as many individuals with dark skin talk about feeling targeted for being poor and being poor because of systemic racism.

This is a definitive example of the effects of poverty to children and families

This suggests a definitive opinion that drifting towards SAE is the preferred outcome for students.

This references that the influence of poverty can impact school readiness across early elementary grades but were not part of this research, and also likely contributed to the 60% variance in reading scores.

I'm assuming they're saying because it's more deliberate of speaking in one language for one session of speech and then switching to another-- that is going beyond code-switching which includes words of multiple dialects in one sentence.

This suggests a nuance in what the goal is; Do we want the most success academically, or do we want cooperative connections and learner acceptance in class rooms?

This study observed the value of motivation students have with translanguaging

Participating students enjoyed the instructor's lesson plan to have them have class discussions in Arabic and write their EFL activities in English. This is a tangible example of what was done in the study.

This is where it's referenced students being motivated intrinsically or for their own satisfaction.

Students would start with the intention of learning English for their career or social recognition, but it would often change later for the people and culture behind the language.

This study primarily focused on the self-determination of students to want to learn, but some factors did influence students' decision to commit such as being able to speak their native tongue and understanding the value of the language being learned.

Two-thirds of students reported having a high-level of proficiency in English because of the allowed use of Arabic in their class.

This is the proficiency for English with translanguage

Reference about accent observed in the activities which isn't very important as much as their learned English for professional and academic settings.

This overall says the students report the African American Vernacular English is just as good as the fancy English but being able to perform the privileged English is necessary to be successful in academic and professional settings.

It's important to maintain an optimistic attitude towards your students because it affects their learning ability.

The Court is reminding us that education isn’t just another public service but it’s the foundation for everything else in life. School is where kids learn how to navigate society, build skills, and prepare for adulthood. So if a child is denied a fair shot at school because of segregation, it’s not a small issue but it affects their entire future. That’s why the Court treats this as such a big constitutional problem.

This sentence explains the Court’s central legal reasoning in Brown v. Board of Education: segregation in public schools denied Black children equal protection under the law, which the 14th Amendment guarantees. By declaring this practice unconstitutional, the Court overturned decades of legalized racial separation in education.

The Hansa was a group of merchants who worked together to keep trade safe and organized. Lübeck became a key city because it was in a good spot for shipping between the North Sea and the Baltic Sea, helping the Hansa become very powerful in northern Europe.

Venice became very important for Mediterranean trade, while in northern Europe, merchants formed alliances to control trade in the Baltic Sea. This shows that trade networks were growing in many parts of Europe at the same time.

Safe trade routes made long journeys possible, letting Europeans like Marco Polo travel all the way to China. Without secure paths, such trips would have been too dangerous to attempt.

Al-Idrisi was a Muslim scholar who worked for a Christian king, showing that religious tolerance allowed talented people to contribute to society. His studies and travels helped him make maps that connected different regions and cultures.

Sicily under the Normans was a place where Muslims and Christians lived together peacefully. This shows that even during times of conquest, people from different religions could cooperate and respect each other.

The yellow robe showed he was now emperor, and the previous ruler stepped down without fighting, making the transition smooth.

Zhao Kuangyin was a talented general who became emperor without major fighting. He used his influence over the army to take control in a clever and careful way, showing that sometimes political skill was more important than winning battles. His rise also marked the start of a dynasty that would last over 300 years.

After the Tang Dynasty fell, China broke into many small kingdoms that often fought each other. This period was very unstable, with frequent changes in rulers. The Song Dynasty reunited much of China and created a stronger, centralized government, which helped bring peace and stability after decades of chaos.

Trade worked well because middlemen helped move goods between faraway places and used a simple shared language so everyone could understand a little. Standard weights and Arabic numbers made it easy to measure and price things correctly, letting merchants from different countries trade without confusion.

Baghdad was a busy city for both government and learning. The Abbasids learned how to make paper from the Chinese, which let more people read and write, helping the empire run more smoothly and share knowledge.

The disease spread because rats carrying infected fleas traveled on ships, showing how trade and travel could unintentionally move deadly illnesses across regions. Ports like Pelusium became starting points for outbreaks because they connected many places through commerce.

Theodoric grew up in Constantinople, learning Roman politics, culture, and military skills. This education helped him rule Italy effectively and navigate both Ostrogothic and Roman populations when he became king.

Odoacer ended the Western Roman Empire by removing its last emperor. Even though he ruled as king, he still recognized the Eastern Roman Empire, which shows that he wanted authority but also legitimacy from Rome.

Saladin was a skilled leader who brought stability to Egypt when it was in chaos. His actions helped protect the region from Crusader attacks and strengthened his reputation as a powerful and respected ruler.

I really liked how they framed this, I wish administrators would think of digital learning tools through this frame rather than having to filter everything

This has been a major headache in my time on this earth. It also speaks to the fact that companies have Finite resources with which to plug up any voids or responsibility- centered abdications.

It seems like one side is arguing in favor a fevered Utopia dream-like environment, and the other is accounting for the limitations of the human experience.

FINALLY! SOMEONE in Academia with a pragmatic view and a spine.

They regularly switch between three languages. People mix languages to build community and avoid sounding rude or too direct.

The researchers found that people switch between Catalan, English, and Spanish depending on who they’re talking to (their audience).

Language choice online is not random but helps build relationships.

I should identify my short-term changes and actually make the changes happen.

It has almost no impact on my studies and academic performance. Some times my grades might drop a little due to the stress but not much.

I can relate to this because I feel like getting better at things requires finding out the process of them and figuring it out, but also failing in the process to better improve yourself. In order to improve, you must first learn from your failures in the past.

consider making all six of these small-font items into two-line blurbs in which the length of the first line is closer to that of the second.

(note: this isn't an issue with all window / screen sizes)

this wording doesn't quite reflect what's in the details below. something more data-focused would be a better fit, especially if that is the source of your value.

"signals" makes sense, but "explainable signals" doesn't work here. "contextualized", for example, may work.

"which allocators and managers are likely to be most relevant to your goals at each event"

"events" is the least sophisticated of the three words. I would put it in the middle -- "which managers, events, and allocators matter most to engage".

"that extend the network" doesn't seem to have discernible meaning. "extend your network" or "extend your team's network" would be more clear.

"focus on details" makes more sense as the enabling factor of the other two. "We focus on details, developing superior products and scaling success" may work, or the addition of another value add (with the choice to keep or move the detail bit elsewhere)

this implies large-cap clients, but I find that Jeff's bio makes it more explicit with the "to high-net-worth investors" inclusion. Add just a bit more oomph.

This reads as more of a parent company than a slogan where it's currently situated. but "built for alternatives" doesn't return anything related on Google. If a slogan, consider italicizing and putting the brand name on top.

perhaps nit-picky, but I find the use of a shortened "west" and long-form "street" to be asynchronous. I would do "West" and "Street", personally,

The combination of MaSIF-mimicry and GluePCA provides a powerful framework for mapping the latent CRBN-MGD interactome, and the scale at which you identify both ZF and non-ZF binders is impressive. The work makes a strong case that CRBN’s binding landscape is far broader than its known degradome.

I have a few questions about the bonsai library, its expression in yeast, and the interpretation of the latent interactome:

Because all bonsai constructs are expressed in yeast, some human ZFs and non-ZF fragments may misfold, fail to coordinate Zn²⁺, or depend on mammalian PTMs or chaperones. These would appear as GluePCA negatives even if they are true binders in human cells. Have you assessed folding in yeast, estimated how much of the “non-binder” space may reflect yeast-specific false negatives, or tested any GluePCA-negative constructs in a mammalian binding assay?

GluePCA reports proximity-driven binding, but degradation requires a very specific geometry relative to the CRL4^CRBN ubiquitination machinery. Many binders may be sterically dead: capable of engaging CRBN-MGD but oriented such that lysines are inaccessible to the E2. Have you explored whether certain ZF orientations, linker patterns, lysine positions, or accessory elements distinguish productive binders from non-productive ones? And do you think the dataset is sufficient to computationally separate these classes? Such an analysis could help predict productively oriented binders that are more amenable to rational MGD design.

Does this imply proteins must be able to traverse large defects in fitness landscapes through co-occurence of multiple mutations / rearrangements to arrive at new structures?

Is this magnitude shift ~1.2x NRF2 nuclear expression consistent with expected disease biology. That is, what magnitude of protein level change would you expect this expression shift to affect and is this sufficient to alter disease

Its interesting to me that algae is the largest producer of oxygen on the planet, but in order for it to survive in rural areas, it harms the water, making it a trade off of one necessary resource for another

https://koha-community.org/

No other articles mentioned the word suicide they instead referred to the accident as a self inflicted gun shot wound.

This headline assumes that the audience already knows about the incident

Since you are dealing with a homogeneous population and regressing out ancestry components, this may not be as large of an issue as you suspect. It would be nice to see some simulations of the false positive rate you expect when doing this. I imagine there are also a lot of important, true interactions within-chromosome.

Another idea for a way to partition PGSs is by the sign of the PGS SNP effect size. Perhaps if a SNP significantly interacts with negative-effect-size PGS SNPs (but not positive), or vice-versa, this could help with placing causal SNPs/genes relative to other known players in a pathway or distinguishing between a pathways' functions in contrasting diseases. However this may not work since networks are complex and involve maybe activating and inhibitory interactions.

Perhaps you could reduce complexity even further by building a PGS per chromosome, testing all chromosome PGS x chromosome PGS interactions, and then doing a subsequent SNPxSNP GWAS between the SNPs on the implicated chromosomes. I'm not sure how many SNPs are on each chromosome, but this could potentially reduce computational needs and the multiple testing burden since the number of epistatic interactions per trait is very small.

If you didn't regress out ancestry, could the PGS term already sufficiently account for population structure? It may not be necessary to remove ancestry components if the PGS term absorbs polygenic background and therefore ancestry, allowing you to use a larger and more diverse population to estimate the SNPxPGS term. It's unclear to me whether it would fully account for this, but may be something to try.

Without knowing it, Dickinson might have predicted what Franco Cambi wrote in his essay. It is very cheap to travel through books, because books "bear" the Human Soul.

A Page is a fundamental part of a Book, so the previous note about the Book still stands.

The Book is one of the main characters of cultural places. Books are places where culture nourishes one's soul and helps one's soul to develop. Think about these poems, are they not changing your perspective?

Heaven might also be considered a spiritual place, but Franco Cambi never mentions it in his essay as this, in my opinion, is a different topic altogether.

The Dome is also a spiritual place, but, as previously said in the other annotations, there is no need to go there, because her Home is as good as, or even better than, a Church or a Dome.

This is also a personal place that stands also as a spiritual place from Dickinson's point of view.

This is considered a personal place, but, in this case, she finds his spiritual place in a personal place as she believes that there is no need to go to church in order to pray, praying God can also be done at home by praying with sincerity.

The Church is the perfect spiritual place, in this place, through the soul, one connects with God and in the silence of a prayer, the soul speaks and is nourished with and by God.

delete "and NJIT" insert instead: "and with Artificial Intelligence courses at the New Jersey Institute of Technology (NJIT)"

add below this line the line NJIT Data Science Faculty (link it to: https://ds.njit.edu/administration-and-faculty)

The Gate is more like a liminal space, so this place does not count.

Emily Dickinson treats her soul as if it were a building with doors, and I personally believe that, to be specific, the door belongs with a house. Aren't our souls houses that store who we really are?

Society is clearly a collective place, but in this case Dickinson wants to personally select what is the Society that can walk through the door of her soul.

The Soul is the quintessential place of the soul, a personal place that, in this poem, shuts the door to the collective places on the outside. The Soul is the place that is nourished, cherished, and where all the developing of the self takes place.

Surprisingly, the second natural place mentioned is not capitalized. Yet, she also compares this place to the Brain.

The Sky is the first natural place mentioned. Dickinson uses this place in order to compare it to the Brain.

The capitalized words by Emily Dickinson are not a coincidence. The Brain is the first "place" identified.

Can God be considered a place? Not sure, but praying to God might be a personal place where one is in a conversation with Him, a moment of reflection that nourishes one's soul.

I hate the fact that Deaf/hard of hearing people have been told that they have got to read lips, which is hard to do and they won't get the whole message. We can learn sign language

NUMBERS ! AT LAST!

this paragraph stuck out to me and this line really caught my eye. when it comes to writing something it is our job catch the eye of the reader and draw them in. when writing about something it needs to be executed correctly or it might be "pushed" aside.

personally archive @gyuri https://bafybeicid7zd2l56yipy5nxra2qttovqqjh46x3cqu4qsmlhfevsku5lcq.ipfs.dweb.link?filename=Release%20v0.47.0%20%C2%B7%20ipfs_ipfs-desktop%20%C2%B7%20GitHub%20(30_11_2025%2012%EF%BC%9A29%EF%BC%9A13).html

and annotated it with https://hypothes.is

and the self-hosted archved page snarfed with single File has been instantly available!

A real Milestone

Hughes announces the new movement in art that is based on racial pride. This quote is significant because it shaped the cultural identity of the Harlem Renaissance. He gives Black artists the power to reject white approval and claim full freedom in their cultural expression.

This is when Hughes criticizes internalized racism, but the passage is based on the male point of view. We don't hear much from Black women, queer artists, or people living in rural Southern areas. Their lives would help us understand race and art in a deeper way.

For me at least, this helps me understand by anthropic might e interested in Bun

This is like how online platforms promised to demonetise bad actors online but never quite get round to doing so once The attention has waned.

This source heavily relies on the drivers military background where other articles mention his background much less.

This is hinting at a possible tie between these two events where multiple other sources say that there is no link between these two events.

This lead does not explain the occurrence but instead dives right into who the driver was.

This headline is different since it immediately states the drivers self-inflicted gun shot, and not mentioning the explosion.

Is this not how things work in Texas though with connect and manage?

Zis is true

How does Scandal deal with other black folk entering the picture

The trust audiences have with Olivia and Tati, expanding past black woman

I will do more research on this but if that's true, that is so hurtful.

process by which cells absorb molecules from outside by engulfing them with the cell membrane to form a vesicle

glycoproteins bind to/fuse with host cell membranes, releasing the viruses genetic material

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