Reviewer #1 (Public review):

The work presented by Cheung et al. used a quantitative proteomics method to capture molecular changes in B cells exposed to LPS and IL-4, a combination of stimuli activating naive B cells. Amino acid transporters, cholesterol biosynthetic enzymes, ribosomal components, and other proteins involved in cell proliferation were found to increase in stimulated B cells. Experiments involving genetic loss-of-function (SLC7A5), pharmacological inhibition (HMGCR, SQLE, prenylation), and functional rescue by metabolites (mevalonate, GGPP) validated the proteomics data and revealed that amino acid uptake, cholesterol/mevalonate biosynthesis, and cholesterol uptake played a crucial role in B cell proliferation, survival, biogenesis, and immunoglobulin class switching. Experiments involving cholesterol-free medium showed that both biosynthesis and LDLR-mediated uptake catered to the cholesterol demand of LPS/IL-4-stimulated B cells. A role for protein prenylation in LDLR-mediated cholesterol uptake was postulated and backed by divergent effects of GGPP rescue in the presence and absence of cholesterol in culture medium.

Strengths:

The discovery was made by proteome-wide profiling and unbiased computational analysis. The discovered proteins were functionally validated using appropriate tools and approaches. The metabolic processes identified and prioritized from this comprehensive survey and systematic validation highly likely represent mechanisms of high importance and influence. Analysis of immune cell metabolism at the protein level is relatively compared to transcriptomic and metabolomic analysis.

The conclusions from functional validation experiments were supported by clear data and based on rational interpretations. This was enabled by well-established readouts/analytical methods used to determine cell proliferation, viability, size, cholesterol content, and transporter/enzyme function. The data generated from these experiments strongly support the conclusions.

This work reveals a complex, yet intriguing, relationship between cholesterol metabolism and protein prenylation as they serve to promote B cell activation. The effects of pharmacological inhibition and metabolite replenishment on the cholesterol content and activation of B cells were determined and logically interpreted.

Weaknesses:

The findings of this study were obtained almost exclusively from ex vivo B cell stimulation experiments. Their contribution to B cell state and B cell-mediated immune responses in vivo was not explored. Without in vivo data, the study still provides valuable mechanistic information and insights, but it remains unknown, and there is no discussion about, how the identified mechanisms may play out in B cell immunity.

The role of HMGCR, SQLE, and prenylation in B cell activation was assessed using pharmacological inhibitors. Evidence from other loss-of-function approaches, which could strengthen the conclusions, does not exist. This is a moderate weakness and somewhat offset by other data, including those obtained from the tests involving multiple distinct pharmacological inhibitors and the metabolite replenishment experiments.

Reviewer #2 (Public review):

This study uses mass spectrometry to quantify how LPS + IL-4 modify the mouse B cell proteome as naïve cells undergo blastogenesis and enter the cell cycle. This analysis revealed changes in key proteins involved in amino acid transport and cholesterol biosynthesis. Genetic and pharmacological experiments indicated important roles for these metabolic processes in B cell proliferation.

This work provides new information about the regulation of TI B cell responses by changes in cell metabolism and also a comprehensive mass spectrometry dataset which will be an important general resource for future studies. The experiments are thorough and carefully carried out. The majority of conclusions are backed up by data that is shown to be highly significant statistically. The comprehensive mass spectrometry dataset will be an important general resource for future studies.

After revision, the study now includes new data showing that the up regulation of amino acid uptake and cholesterol metabolism is not restricted to LPS + IL-4 (TLR4 + IL4R) stimulation but is also observed after stimulation of TLR7, TLR9, CD40 and the BCR. This increases the impact of this work and shows that this metabolic rewiring is a common feature of B cell activation. The inclusion of inhibitor data showing important roles for MTOR and ERK/p38a MAP kinases in the metabolic changes identified and provides preliminary insights into the mechanisms involved.

Author Response:

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

Reviewer #1 (Public review):

We agree with the reviewer that a limitation of our study is its focus on cell-based assays rather than in vivo experiments. We did consider evaluating the effects of statins on B cell responses in vivo; however, this approach is complicated by findings that statins can influence antigen presentation by dendritic cells, thereby impacting antibody responses (Xia et al, 2018). We have revised the discussion section to acknowledge this points.

The reviewer also noted that our study assessed the roles of HMGCR, SQLE, and prenylation in B cell activation using pharmacological inhibitors and genetic knockdown/out approaches. Loss-of-function techniques such as RNAi, siRNA, and CRISPR can be challenging to apply to primary B cells, but we are exploring their feasibility for future revisions. While we acknowledge the limitations of using pharmacological inhibitors, we have taken several steps to mitigate these, including targeting multiple steps in the cholesterol biosynthetic pathway using structurally distinct inhibitors and conducting rescue experiments by supplementing downstream metabolites. To strengthen the results on prenylation further, we have added data using two further distinct prenylation inhibitors (revised Figure 6). To further investigate potential off-target effects of statins, we performed proteomic analysis of B cells treated with and without fluvastatin. The data suggest that fluvastatin primarily affects cholesterol metabolism and does not cause widespread off-target effects (new Supplementary Figure 9).

Reviewer #1 (Recommendations for the authors):

What signalling mechanisms link LPS sensing to proteomic and metabolic changes? Do these changes depend on specific signalling modules downstream of TLR4 (e.g., MyD88, TRIF, NF-kappaB, MAPKs)? Other receptors found to produce similar effects (TLR7, TLR9, CD40) may share these modules. This information could strengthen the conclusion by showing the chain of molecular events through which immune stimuli reprogram B cell metabolism.

Signalling through most TLRs, including TLR4, TLR7 and TLR9, requires the adaptor protein MyD88. To determine if MyD88 is required for LPS-induced signalling, we carried out immunoblotting to compare signalling in B cells between WT mice and MyD88-deficient mice. We found that phosphorylation of key downstream proteins, including p38 and ERK1/2 (MAPK signalling), Akt, p70S6K and S6 (mTOR signalling) was diminished in MyD88-deficient mice (Figure S11). These results have been added to the manuscript as Supplementary Figure 11.

We assessed the requirement of these signalling pathways for LPS-induced proliferation by treating B cells with rapamycin to block mTORC1, PD184352 for MEK1/MEK2 (the upstream activators of ERK1/2), VX745 for p38 or a combination of PD184352 and VX745. These results have been added to the manuscript as the new Figure 9. Rapamycin demonstrated the strongest inhibitory effect on proliferation, and combinatorial blocking of MAPK signalling mildly reduced proliferation (Figure 9A-B). In terms of cholesterol metabolism, treatment with all of these inhibitors reduced cholesterol levels; however, treatment with PD184352 and VX745 reduced cholesterol to the same level as naïve B cells (Figure 9F).

Other activating stimuli appear to have similar effects, we showed originally that TLR7 and TLR9 activation had a similar effect on proliferation and cholesterol to TLR4, as did activation of CD40 and the BCR (Figure 10). We have now expanded this and shown that these other receptors can also promote protein synthesis (new Supplementary Figure 4).

There seem to be errors in the manuscript text.

(1) Page 6, line 232: ssRNAseq?

We that the reviewer for spotting these issues. This has been amended to scRNAseq.

(2) Page 13, line 490: SC7A5?

This has been amended to SLC7A5

(3) The abbreviation CF (cholesterol-free?) is not defined when it first appears.

This has been amended to cholesterol-free (CF) on page 9, line 411.

Reviewer #2 (Public review):

The reviewer suggested that the study would be strengthened by determining whether the observed changes are specific to LPS + IL-4 stimulation or represent a more general B cell response to mitogenic signals. We believe that these effects are not specific to LPS and also occur with other mitogenic stimuli. We have expanded on the data in the original draft showing that other TLR agonists as well as CD40 and BCR stimulation increase both B cell proliferation and cholesterol levels and also looked at the effects of these stimuli on protein synthesis.

Reviewer #2 (Recommendations for the authors):

(1) One of the most highly enriched processes is 'response to interferon alpha'. This stands out as most of the other processes identified involve more general cellular processes (i.e., cell proliferation, cell metabolism, etc...). Minimally, interferon alpha should be discussed. It would also be interesting to test whether type I interferons regulate any of the metabolic changes identified.

Response to interferon alpha has the highest fold enrichment of 6.78. To look at this further compiled a list of proteins upregulated by IFN-α stimulation in murine B cells, derived from (Mostafavi et al, 2016) and compared these with our proteome. We found that most of the IFNα regulated genes were not significantly upregulated following LPS + IL-4 stimulation compared to naïve B cells (Figure S3A). We also measured phosphorylation of the transcription factor STAT1, which is induced by IFNα and IFNβ signalling, and found that LPS stimulation did not induce p-STAT1 (Figure S3B-C). These results have been added to the manuscript as Supplementary Figure 3. Despite this, as discussed further in the manuscript we cannot rule out a weak interferon response in the proteomics.

(2) The proteome of BCR-stimulated B cells has been analyzed by mass spectrometry. This dataset should be compared with the LPS + IL-4 dataset of the current study. This may reveal whether these two stimulations have similar or different effects on B-cell function. In particular, it is interesting to know whether BCR stimulation induces SLC7A5 expression and whether proteins involved in cholesterol metabolism are altered by BCR stimulation.

A similar study using anti-IgM and anti-CD40 to activate murine B cells has found an upregulation of amino acid transporters, including SLC7A5, in their proteomic data, suggesting that this is not a stimulus-specific effect. This has been added to the text subsection “Protein synthesis in LPS + IL-4 stimulated B cells is dependent on the uptake of amino acids.” In line with this we have also shown that stimulation of the BCR upregulates protein synthesis (new Supplementary Figure 4). We have added data on HMGCR, SQLE and LDLR form the BCR proteomics experiments to the new Supplementary Figure 13. As the BCR proteome published as a preprint (James et al 2024) is about to be resubmitted as a distinct paper that does not deal with cholesterol metabolism, we have not expanded on this dataset further.

(3) A role for mTORC1 has been shown for proteome remodelling following BCR stimulation of naïve B cells, regulating the expression of amino acid transporters. Is mTORC1 involved in any of the changes detected following LPS + IL-4 stimulation? (i.e., cell proliferation, ribosome biogenesis, amino acid transport, cholesterol biogenesis).

To determine the importance of mTORC1 for B cell function, we treated B cells with rapamycin. We found that rapamycin treatment slightly reduced protein synthesis (Figure S12A) and amino acid uptake (Figure S12B). These results have been added to the manuscript as Supplementary Figure 12. Rapamycin reduced cholesterol to almost the levels in naïve B cells (new Figure 9F) and had a significantly inhibitory effect on proliferation (new Figure 9A-B).

(4) Analysis of Slc7a5 knockout B cells showed that SLC7A5 is required for LPS-induced proliferation (Figure 4G). Is SLC7A5 required for B cell growth following LPS + IL-4 stimulation? Is SLC7A5 required for BCR-induced B cell proliferation/growth?

There appears to be a misunderstanding, as Figure 4G compares proliferation between WT and SLC7A5 KO B cells following LPS + IL-4 stimulation and not LPS stimulation alone.

Unfortunately, we no longer have access to Slc7a5fl/fl/Vav-iCre+/- mice and will not be able to measure CTV staining for proliferation following BCR stimulation. However, a similar study using anti-IgM and anti-CD40 to activate murine B cells found that B cells from Slc7a5fl/fl/Vav-iCre+/- mice were significantly smaller, had reduced expression of the chaperone protein CD98 and impaired expression of the transferrin receptor CD71, which is required for iron uptake, compared to WT B cells (James et al, 2024).

(5) The expression of several key proteins (regulating proliferation/amino acid transport/cholesterol metabolism) is shown to be significantly upregulated by LPS + IL-4 stimulation of naïve B cells. It would be interesting to determine whether these increases result from induced transcription of the relevant genes. This could initially be assessed by qRT-PCR analysis of LPS + IL-4 stimulated primary B cells, or alternatively, mining of online RNAseq datasets.

We mined RNA-Seq data from C57BL/6 mice (Tesi et al, 2019) which compared naïve B cells and B cells after 2,4, or 8 hours of LPS stimulation. We found that the transcription of genes that coded for the amino acid transporter SLC7A5/SLC3A2 (Figure S6A-B) and key genes involved in cholesterol metabolism followed the same pattern of upregulation as our proteomic data (Figure S6C-F). These results have been added to the manuscript as a new Supplementary Figure 6.

(6) Cholesterol levels are shown to be increased following resiquimod, CpG, anti-IgM, and CD40L stimulation (Figure 9). What effect do these agonists have on levels of HMGCR, SQLE, and LDLR in B cells? Is B-cell growth by these agonists impaired by Fluvastatin.

We found that stimulation of murine B cells with either IL-4, anti-IgM or anti-CD40 could increase levels of HMGCR, SQLE and LDLR, with the largest increase seen with a combination of these stimuli (Figure S13A-D) (James et al, 2024). These results have been added to the manuscript as Supplementary Figure 13.

Figures 10C-E show that B cell growth, survival and proliferation are impaired by Fluvastatin after Resiquimod, CpG, anti-IgM, and CD40L stimulation, although we do not have proteomic data from these stimuli to confirm the levels of HMGCR, SQLE and LDLR.

We carried out proteomics after 24 hours of LPS + IL-4 stimulation in normal/CF media, with or without Fluvastatin. We found that Fluvastatin treatment in normal media increased the expression of HMGCR, SQLE and LDLR. Fluvastatin treatment in CF media had the highest increase in the expression of these key proteins (Figure S9G-J). These results have been added to the manuscript as Supplementary Figure 9.

(7) Do Fluvastatin or FGTI-2734 affect early activation of signaling pathways by LPS + IL-4 stimulation of B cells? (eg. MAPKs, STATs, PI3K/AKT).

This is an interesting question, we will pursue this in our future work.

References:

James O, Sinclair LV, Lefter N, Salerno F, Brenes A & Howden AJM (2024) A proteomic map of B cell activation and its shaping by mTORC1, MYC and iron. bioRxiv 2024.12.19.629506 doi:10.1101/2024.12.19.629506

Xia Y, Xie Y, Yu Z, Xiao H, Jiang G, Zhou X, Yang Y, Li X, Zhao M, Li L, et al (2018) The Mevalonate Pathway Is a Druggable Target for Vaccine Adjuvant Discovery. Cell 175: 1059-1073.e21

Cuando es tocado

Este comentario no trata sobre errores, sino que siento que sería más acorde a la actividad, que, por ejemplo, al llegar a los 50 toques, el robot se active y muestre en pantalla un mensaje como "¡Ahora estoy vivo!", junto con una cara robótica como d[o_o]b y se quede así.

Solo lo comento como sugerencia, ya que eso añadiría más complejidad a la actividad y quizá no es lo deseado.

Al ser de protobject, creo que solo sería teléfono y computadora.

En realidad no se pinta de color rojo, sino que aumenta la opacidad del dibujo (en este caso es blanco y negro), usando el bloque "claro al (...)%"

En el código, la inclinación no se divide por 5, sino que primero es dividida por 15 en el cálculo de la posición del led, y luego a la inclinación en sí, se le suma 60 para obtener la nota musical.

En realidad el código usa el eje Y, por lo que debería ser al revés.

En lugar de decir "¿Qué es la relación de datos (...)?" creo que debiese ser "¿Cuál es la relación entre los datos (...)?"

statements that describe assertions of desirable system properties

statements that describe assertions of desirable system properties

statements that describe assertions of desirable system properties

statements that describe assertions of desirable system properties

statements that describe assertions of desirable system properties

solitude = épidémie. Argument appuyé par Murthy + neuropsy > scientifique.

// avec article de Byung-Chul Han : paradoxe car on s'éloigne alors qu'avec télétravail ou réseau sociaux on est en contact (virtuel) permanant.

Blurring/FOMO = 2 types de réactions/aux TIC

eLife Assessment

This study represents a useful finding on the social modulation of the complex repertoire of vocalizations made across a variety of strains of lab mice. The evidence supporting the claims is, at present, incomplete, as numerous concerns regarding the appropriate categorization of vocalizations, the averaging of data points with disparate levels of occurrence, the interpretation of the function of noisy calls, and a general lack of adequate analyses of experimental data were raised. With these issues addressed, the work will be of importance to scientists studying rodent vocal communication.

Reviewer #1 (Public review):

Summary:

Adult laboratory mice produce ultrasonic vocalizations during free social interactions, as well as lower-frequency, voiced calls (squeaks) during aversive contexts. The question of whether mice possess a more complex repertoire of vocalizations has been of great interest to scientists studying rodent vocal behavior. In the current study, the authors analyze the rates and acoustic features of vocalizations produced by pairs of mice that are allowed to interact across a barrier, which prevents direct physical interaction. In this context, they find that same-sex (but not opposite-sex) pairs of mice produce vocalizations that are lower in frequency than the typical 70 kHz ultrasonic vocalizations produced during free interactions and that are also distinct from squeaks. These lower frequency vocalizations were observed in both male-male and female-female pairs, as well as in same-sex pairs from multiple mouse strains. The authors also report that call rates and acoustic features are not affected in male-male pairs that have been treated with the anxiolytic drug buspirone, suggesting that anxiety is not a major driver of vocalization in this behavioral context.

Strengths:

(1) The observation that same-sex pairs of mice produce lower frequency (<70 kHz) vocalizations in this behavioral context is novel.

(2) The consideration of multiple types of pairs (female-female, male-male, and female-male), as well as the inclusion of multiple strains of mice and barriers with different hole diameters, are all strengths of the study.

(3) The authors include detailed analyses of vocalization acoustic features, as well as detailed tracking of mouse positions relative to the barrier.

Weaknesses:

The categorization applied to vocalizations based on their mean frequencies is poorly supported and ignores the distinction in laryngeal production mechanism between voiced and ultrasonic vocalizations. Specifically, the authors are likely lumping together voiced and ultrasonic vocalizations into their "low frequency" (< 30 kHz) category, while they reserve the term "ultrasonic" exclusively for the subset of ultrasonic vocalizations with the highest mean frequencies (> 50 kHz). This categorization scheme also does not align well with past work on lower frequency rodent vocalizations, which complicates the comparison of the present findings to that past work.

In some analyses, the authors report that different groups of mice produce different relative proportions of vocalization types (as defined by mean frequency) but then compare acoustic features of vocalizations between groups after pooling all vocalizations together. The analyses of acoustic features conducted in this way may be confounded by the different proportions of vocalization types across groups.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors examine vocal communication during same-sex dyadic interactions in mice, comparing periods of physical separation (with limited sensory access) to direct social contact. They report that separation dramatically alters the vocal repertoire, shifting it away from canonical ultrasonic vocalizations (USVs) toward low-frequency vocalizations (LFVs) and broadband "noisy" calls. While LFVs and noisy calls have been described previously, largely in aversive contexts, this study provides a detailed, systematic characterization of these vocalizations during social interactions, thereby extending prior work.

The authors explore several experimental manipulations and analyses, including divider hole size, strain and sex differences, anxiolytic drug treatment, and correlations with spatial proximity, to infer potential functions of these call types. Although the dataset is rich, the results are largely descriptive, and many conclusions remain tentative. Several experimental variables are not fully controlled, and in some cases, the interpretation exceeds what the data can clearly support. Nonetheless, with improved experimental framing, additional analyses of existing data, and a clearer discussion of limitations, this work has the potential to make a valuable contribution by broadening the field's focus beyond USVs to understand a wider vocal repertoire relevant to social context.

Strengths:

Much work on mouse vocal communication focuses almost exclusively on USVs. This manuscript convincingly demonstrates that non-USV vocalizations (LFVs and noisy calls) are prominent and systematically modulated by social context, highlighting an underappreciated dimension of mouse communication. Furthermore, the authors employ several experimental manipulations, including sensory access, strain, sex, and pharmacological treatment, to assess changes in vocalization repertoire. This provides a valuable resource for the field and reveals robust context dependence of vocalization. The discussion is thoughtful and integrative, particularly in its consideration of potential communicative roles of LFVs and noisy calls and their relationship to sensory constraints and signal propagation, although these ideas will require further experimental validation.

Weaknesses:

There are several concerns regarding experimental design and data interpretation that could be addressed to strengthen the manuscript.

(1) The terminology used for vocalization types is confusing and needs better clarification. The authors refer to Grimsley et al. (2016) multiple times, yet they use the same names for their vocalizations while applying different definitions. This makes it very difficult to compare the two papers. Since this study and Grimsley et al. use different mouse strains (FVB vs CBA), a direct comparison of absolute frequencies may also not be appropriate. Please explicitly clarify the definitions of the call types (e.g., frequency range, voiced vs. USV) and explain how they relate to those in the previous study earlier in the manuscript.

(2) In the initial experiment, mice always experience separation first (15 minutes), followed by unification (5 minutes), using novel same-sex dyads. Multiple factors besides physical contact could influence vocalization across this sequence, including habituation to the arena, reduced anxiety over time, or increasing familiarity with the partner despite physical separation. It is unclear whether the authors have tested the reverse order (unification first, followed by separation). If not, this limitation should be explicitly acknowledged. In addition, examining whether vocalizations or behaviors change over the course of the 15-minute separation period, for example, by comparing early vs late phases, could help disentangle effects of habituation from those of physical separation per se.

(3) The conclusion that separation-induced LFVs are unlikely to be anxiety-driven may overinterpret the buspirone experiment (Figure 8). Vehicle injections themselves produced large changes in call rate and call-type distribution, raising concerns about stress or arousal induced by the injection procedure. Comparisons between buspirone-treated animals and untreated animals are therefore problematic, as these groups differ in their experimental histories, including the number of exposures. The manuscript would benefit from independent measures confirming the anxiolytic efficacy of buspirone compared to vehicle injection in this paradigm, such as behavioral readouts of anxiety. In addition, the experimental design requires a clearer description. It is not always clear whether the same dyads were tested twice, or how social familiarity, contextual familiarity, and habituation to injections were handled. Male data comparing first and second exposures should also be included as supplementary figures to allow direct comparison with the excluded female dataset.

(4) The idea that noisy calls function to attract conspecific attention is intriguing. However, in Figure 5, all call types, including LFVs and USVs, are most likely to occur when mice are already in close proximity during separation, which seems inconsistent with a long-distance signaling role. Analyses of the temporal relationship between vocalizations and behavior would strengthen this claim. For example, it would be informative to test whether bouts of noisy calls precede approach behavior or a reduction in inter-animal distance. Examining whether calls occur before, during, or after orientation toward the partner could further clarify whether these vocalizations actively modulate social behavior.

(5) The effects of divider hole size on vocal repertoire are striking but difficult to interpret. Unexpectedly, small holes and no holes yield similar call distributions, whereas large holes produce a markedly different profile dominated by LFVs, which also differs from free interactions. If large holes allow greater tactile or close-range interaction, the reduction in USVs and MFV is counterintuitive. Incorporating behavioral metrics such as distance, orientation, or specific interaction types alongside call classification would greatly aid interpretation and help link vocal output to interaction quality rather than divider type alone.

(6) Throughout the study, vocalizations are pooled across both animals in the dyad. Because the arena is neutral rather than a home cage, either animal could be initiating vocalization. Assigning calls to individuals, where possible, using spatial or acoustic cues, would substantially strengthen functional interpretations. Even limited analyses, e.g., identifying which animal vocalizes first or whether calls precede approach by the partner, could provide important insight into the communicative role of different call types.

Author Response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Adult laboratory mice produce ultrasonic vocalizations during free social interactions, as well as lower-frequency, voiced calls (squeaks) during aversive contexts. The question of whether mice possess a more complex repertoire of vocalizations has been of great interest to scientists studying rodent vocal behavior. In the current study, the authors analyze the rates and acoustic features of vocalizations produced by pairs of mice that are allowed to interact across a barrier, which prevents direct physical interaction. In this context, they find that same-sex (but not opposite-sex) pairs of mice produce vocalizations that are lower in frequency than the typical 70 kHz ultrasonic vocalizations produced during free interactions and that are also distinct from squeaks. These lower frequency vocalizations were observed in both male-male and female-female pairs, as well as in same-sex pairs from multiple mouse strains. The authors also report that call rates and acoustic features are not affected in male-male pairs that have been treated with the anxiolytic drug buspirone, suggesting that anxiety is not a major driver of vocalization in this behavioral context.

Strengths:

(1) The observation that same-sex pairs of mice produce lower frequency (<70 kHz) vocalizations in this behavioral context is novel.

(2) The consideration of multiple types of pairs (female-female, male-male, and female-male), as well as the inclusion of multiple strains of mice and barriers with different hole diameters, are all strengths of the study.

(3) The authors include detailed analyses of vocalization acoustic features, as well as detailed tracking of mouse positions relative to the barrier.

Weaknesses:

The categorization applied to vocalizations based on their mean frequencies is poorly supported and ignores the distinction in laryngeal production mechanism between voiced and ultrasonic vocalizations. Specifically, the authors are likely lumping together voiced and ultrasonic vocalizations into their "low frequency" (< 30 kHz) category, while they reserve the term "ultrasonic" exclusively for the subset of ultrasonic vocalizations with the highest mean frequencies (> 50 kHz). This categorization scheme also does not align well with past work on lower frequency rodent vocalizations, which complicates the comparison of the present findings to that past work.

We thank the reviewer for their assessment. Firstly, we did not use mean frequencies, but peak frequencies of each single call.

The distinction between ‘voiced’ and ‘whistled’ vocalizations based on their spectral-temporal features is hardly possible. While evidence in form of audio recordings made from both deer mouse and grasshopper mouse in helium-enriched air suggests vocalizations with lower fundamental frequency being ‘voiced’ (Pasch et al., 2017; Riede et al., 2022), a computational model considering the laryngeal anatomy of Mus musculus estimates fundamental frequencies of vocalizations at subglottal phonation threshold pressures usual for USVs to be in the range of 1 – 5 kHz and approaching 10 kHz for higher subglottal pressures usually found in the production of ‘voiced’ vocalizations (Pasch et al., 2017). Furthermore, a recent study in the singing mouse (Scotinomys teguina) found minimal fundamental frequencies of single song notes, produced by a whistle mechanism, to be about 4 kHz (Zheng et al., 2025). Thus, the presence of low fundamental (peak) frequencies in mouse vocalizations alone appears to be insufficient for deducing the production mechanism of these vocalizations.

We did not observe differences in acoustic features clearly separating our ‘LFV’ calls into two groups suggestive of different production mechanisms. Thus, we cannot rule out that our ‘LFV’ class contains vocalizations produced by different mechanisms. However, we did not observe any squeaks in our experiments and can therefore rule out that this prominent type of ‘voiced’ call is lumped together with other calls in the ‘LFV’ calls.

While the questions regarding production mechanism, the neurocircuitry involved, and the context-dependent choice of which mechanism to use is intriguing/enticing, the distinction between ‘voiced’ and ‘whistled’ vocalizations lies beyond the scope of our manuscript. Instead, the neurocircuitry involved in mouse vocalization production, particularly USVs and squeaks has been revealed by other laboratories. Optogenetical activation of RAm Nts neurons elicited emission of both audible vocalizations (fundamental frequencies of 10 kHz and below) and USVs in awake mice in a stimulus-dependent manner (Veerakumar et al., 2023). Furthermore, optogenetical activation of RAm-vocalization neurons led to immediate measurable adduction of vocal folds and emission of canonical USVs (Park et al., 2024). While different populations of PAG neurons are responsible for the production both squeaks and USVs (Ziobro et al., 2024), the two input streams seem to converge on RAm vocalization neurons, as silencing the output of these neurons abolished both squeak and USV emission completely (Park et al., 2024). Thus, while near complete closing of the vocal folds is necessary for the production of canonical USVs (Mahrt et al., 2016; Park et al., 2024), it is not clear which degree of vocal fold opening would result in what fundamental frequencies.

We will add a paragraph on this issue to the discussion in the next version of the manuscript.

In some analyses, the authors report that different groups of mice produce different relative proportions of vocalization types (as defined by mean frequency) but then compare acoustic features of vocalizations between groups after pooling all vocalizations together. The analyses of acoustic features conducted in this way may be confounded by the different proportions of vocalization types across groups.

We displayed the relative distribution of the different call classes demonstrating that 80% of the call repertoire during the separation consisted of noisy calls and ‘LFV’. Thus, the per individual averaged acoustic features e.g. peak frequency would be predominantly shaped by the features of these two call classes. However, we agree with the reviewer’s criticism and will provide a more detailed display and analysis of the acoustic features of each call class.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors examine vocal communication during same-sex dyadic interactions in mice, comparing periods of physical separation (with limited sensory access) to direct social contact. They report that separation dramatically alters the vocal repertoire, shifting it away from canonical ultrasonic vocalizations (USVs) toward low-frequency vocalizations (LFVs) and broadband "noisy" calls. While LFVs and noisy calls have been described previously, largely in aversive contexts, this study provides a detailed, systematic characterization of these vocalizations during social interactions, thereby extending prior work.

The authors explore several experimental manipulations and analyses, including divider hole size, strain and sex differences, anxiolytic drug treatment, and correlations with spatial proximity, to infer potential functions of these call types. Although the dataset is rich, the results are largely descriptive, and many conclusions remain tentative. Several experimental variables are not fully controlled, and in some cases, the interpretation exceeds what the data can clearly support. Nonetheless, with improved experimental framing, additional analyses of existing data, and a clearer discussion of limitations, this work has the potential to make a valuable contribution by broadening the field's focus beyond USVs to understand a wider vocal repertoire relevant to social context.

Strengths:

Much work on mouse vocal communication focuses almost exclusively on USVs. This manuscript convincingly demonstrates that non-USV vocalizations (LFVs and noisy calls) are prominent and systematically modulated by social context, highlighting an underappreciated dimension of mouse communication. Furthermore, the authors employ several experimental manipulations, including sensory access, strain, sex, and pharmacological treatment, to assess changes in vocalization repertoire. This provides a valuable resource for the field and reveals robust context dependence of vocalization. The discussion is thoughtful and integrative, particularly in its consideration of potential communicative roles of LFVs and noisy calls and their relationship to sensory constraints and signal propagation, although these ideas will require further experimental validation.

Weaknesses:

There are several concerns regarding experimental design and data interpretation that could be addressed to strengthen the manuscript.

(1) The terminology used for vocalization types is confusing and needs better clarification. The authors refer to Grimsley et al. (2016) multiple times, yet they use the same names for their vocalizations while applying different definitions. This makes it very difficult to compare the two papers. Since this study and Grimsley et al. use different mouse strains (FVB vs CBA), a direct comparison of absolute frequencies may also not be appropriate. Please explicitly clarify the definitions of the call types (e.g., frequency range, voiced vs. USV) and explain how they relate to those in the previous study earlier in the manuscript.

The existence and use of various distinct classification systems for mouse vocalizations is well known and the need to agree on a common classification system is consensus in the field. Thus, it was not our intention to complicate mouse call classification even more.

Grimsley at al. (2016) reserve the ‘low frequency’ band solely for squeaks (or “low frequency harmonics”). Hence, it appears straight forward to name mouse calls with “mean dominant frequencies” falling between squeaks and USVs, “mid-frequency tonal vocalizations (MFVs)” (Grimsley et al., 2016). We did not observe the emission of squeaks in our experiments, but instead we observed tonal vocalizations in a peak frequency spectrum encompassing both squeaks and Grimsley and colleagues’ ‘MFVs’, representing the lowest peak frequencies we observed (< 32 kHz). Furthermore, we observed vocalizations in the range of 32 – 50 kHz (which were not low frequency components of canonical USVs) and of > 50 kHz (corresponding to canonical USVs). Leaning on the terminology of Grimsley and colleagues (2016), we thought it to be straightforward to name these call classes according to their location on the frequency spectrum: low frequency vocalizations (LFVs; up to 32 kHz), encompassing squeaks, but also Grimsley and colleagues’ MFVs, middle frequency vocalizations (MFVs; 32 – 50 kHz), and finally canonical USVs (> 50 kHz). Admittedly, choosing ‘MFVs’ for mouse calls with different acoustic features than those described by Grimsley and colleagues (2016) has caused unnecessary confusion. We therefore consider adapting our classification scheme for the next version of the manuscript.

Regarding the comparison of call classes between different mouse strains, strain differences of spectral-temporal features of call classes have been described for canonical USVs (e.g. Scattoni et al., 2008). However, the acoustic features as well as call repertoire are still quite comparable. Furthermore, we have additionally tested both CBA/J and C57BL/6J mice in our study confirming the presence of both noisy calls, ‘LFVs’, ‘MFVs’, and ‘USVs’ in the vocal repertoire of these two strains.

We will provide a more detailed display and analysis of the acoustic features of the call classes with the next version of the manuscript.

(2) In the initial experiment, mice always experience separation first (15 minutes), followed by unification (5 minutes), using novel same-sex dyads. Multiple factors besides physical contact could influence vocalization across this sequence, including habituation to the arena, reduced anxiety over time, or increasing familiarity with the partner despite physical separation. It is unclear whether the authors have tested the reverse order (unification first, followed by separation). If not, this limitation should be explicitly acknowledged. In addition, examining whether vocalizations or behaviors change over the course of the 15-minute separation period, for example, by comparing early vs late phases, could help disentangle effects of habituation from those of physical separation per se.

We had not tested mice in the reverse order, beginning with 5 minutes of unification followed by 15 minutes of separation. Therefore, we acknowledge this limitation of our study and will address it explicitly in the next version of our manuscript. We appreciate the reviewer’s note regarding the inclusion of vocalizations over time and aim to provide this analysis in the next version of the manuscript.

(3) The conclusion that separation-induced LFVs are unlikely to be anxiety-driven may overinterpret the buspirone experiment (Figure 8). Vehicle injections themselves produced large changes in call rate and call-type distribution, raising concerns about stress or arousal induced by the injection procedure. Comparisons between buspirone-treated animals and untreated animals are therefore problematic, as these groups differ in their experimental histories, including the number of exposures. The manuscript would benefit from independent measures confirming the anxiolytic efficacy of buspirone compared to vehicle injection in this paradigm, such as behavioral readouts of anxiety. In addition, the experimental design requires a clearer description. It is not always clear whether the same dyads were tested twice, or how social familiarity, contextual familiarity, and habituation to injections were handled. Male data comparing first and second exposures should also be included as supplementary figures to allow direct comparison with the excluded female dataset.

We agree with the reviewer’s point that the injection procedure itself appeared to have an impact on vocalization behavior. In fact, we had included the ‘untreated’ cohort in Fig. 8 despite their different experimental history to appreciate the potential impact of injection onto vocal behavior.

Furthermore, we appreciate the reviewer’s point of confirming the anxiolytic effect of buspirone treatment with further behavioral readouts and aim to provide such analysis in the next version of the manuscript.

Regarding the reviewer’s query for clearer experimental design description, the same dyads were tested twice. All mice lived in groups in their home cage, however, they had not met the individual they would face during the experiment before the first experiment. We will improve the description of the experimental design addressing the reviewer’s points in the next version of the manuscript.

(4) The idea that noisy calls function to attract conspecific attention is intriguing. However, in Figure 5, all call types, including LFVs and USVs, are most likely to occur when mice are already in close proximity during separation, which seems inconsistent with a long-distance signaling role. Analyses of the temporal relationship between vocalizations and behavior would strengthen this claim. For example, it would be informative to test whether bouts of noisy calls precede approach behavior or a reduction in inter-animal distance. Examining whether calls occur before, during, or after orientation toward the partner could further clarify whether these vocalizations actively modulate social behavior.

We appreciate the reviewer’s remarks regarding the apparent inconsistencies between noisy calls as conspecific attraction calls and their occurrence in close mouse-to-mouse proximity. We must concede that the size of our testing arena limited the maximum distances mice could achieve. Thus, we aim to provide a more extensive analysis including approach behavior and changes of inter-animal distances for resubmission of the manuscript as suggested by the reviewer.

(5) The effects of divider hole size on vocal repertoire are striking but difficult to interpret. Unexpectedly, small holes and no holes yield similar call distributions, whereas large holes produce a markedly different profile dominated by LFVs, which also differs from free interactions. If large holes allow greater tactile or close-range interaction, the reduction in USVs and MFV is counterintuitive. Incorporating behavioral metrics such as distance, orientation, or specific interaction types alongside call classification would greatly aid interpretation and help link vocal output to interaction quality rather than divider type alone.

We agree with the reviewer that the interpretation of the divider-hole-size-experiment are difficult and following this reviewer’s input, aim to provide additional behavioral analysis for the effect of divider hole size with the next version of the manuscript.

(6) Throughout the study, vocalizations are pooled across both animals in the dyad. Because the arena is neutral rather than a home cage, either animal could be initiating vocalization. Assigning calls to individuals, where possible, using spatial or acoustic cues, would substantially strengthen functional interpretations. Even limited analyses, e.g., identifying which animal vocalizes first or whether calls precede approach by the partner, could provide important insight into the communicative role of different call types.

We agree with the points raised by the reviewer regarding the importance of assigning recorded calls to the respective individual for deciphering the communicative role of different call types. Unfortunately, our system was only equipped with one condenser microphone therefore we are not able to assign calls to individual mice.

Literature:

Grimsley, J. M. S., Sheth, S., Vallabh, N., Grimsley, C. A., Bhattal, J., Latsko, M., Jasnow, A., & Wenstrup, J. J. (2016). Contextual Modulation of Vocal Behavior in Mouse: Newly Identified 12 kHz „Mid-Frequency“ Vocalization Emitted during Restraint. Frontiers in Behavioral Neuroscience, 10, 38. https://doi.org/10.3389/fnbeh.2016.00038

Mahrt, E., Agarwal, A., Perkel, D., Portfors, C., & Elemans, C. P. H. (2016). Mice produce ultrasonic vocalizations by intra-laryngeal planar impinging jets. Current Biology: CB, 26(19), R880–R881. https://doi.org/10.1016/j.cub.2016.08.032

Park, J., Choi, S., Takatoh, J., Zhao, S., Harrahill, A., Han, B.-X., & Wang, F. (2024). Brainstem control of vocalization and its coordination with respiration. Science (New York, N.Y.), 383(6687), eadi8081. https://doi.org/10.1126/science.adi8081

Pasch, B., Tokuda, I. T., & Riede, T. (2017). Grasshopper mice employ distinct vocal production mechanisms in different social contexts. Proceedings. Biological Sciences, 284(1859), 20171158. https://doi.org/10.1098/rspb.2017.1158

Riede, T., Kobrina, A., Bone, L., Darwaiz, T., & Pasch, B. (2022). Mechanisms of sound production in deer mice (Peromyscus spp.). The Journal of Experimental Biology, 225(9), jeb243695. https://doi.org/10.1242/jeb.243695

Scattoni, M. L., Gandhy, S. U., Ricceri, L., & Crawley, J. N. (2008). Unusual repertoire of vocalizations in the BTBR T+tf/J mouse model of autism. PloS One, 3(8), e3067. https://doi.org/10.1371/journal.pone.0003067

Veerakumar, A., Head, J. P., & Krasnow, M. A. (2023). A brainstem circuit for phonation and volume control in mice. Nature Neuroscience, 26(12), 2122–2130. https://doi.org/10.1038/s41593-023-01478-2

Zheng, X. M., Harpole, C. E., Davis, M. B., & Banerjee, A. (2025). Vocal repertoire expansion in singing mice by co-opting a conserved midbrain circuit node. Current Biology: CB, 35(23), 5762-5778.e6. https://doi.org/10.1016/j.cub.2025.10.036

Ziobro, P., Woo, Y., He, Z., & Tschida, K. (2024). Midbrain neurons important for the production of mouse ultrasonic vocalizations are not required for distress calls. Current Biology: CB, 34(5), 1107-1113.e3. https://doi.org/10.1016/j.cub.2024.01.016

eLife Assessment

In this important study, the authors demonstrate that generative AI techniques (restricted Boltzmann machine) can be used effectively to design and characterize mutational pathways of WW domains with different binding specificities. The computational studies are complemented by experimental validations, and the results provide solid evidence supporting the idea that sequence landscape holds significance in understanding protein evolution from a transition path perspective. The minor weakness of the study in the current form concerns limited success in designing variants with smoothly varying binding specificities. Nevertheless, the work will likely have a major impact on research aimed at understanding how evolution navigates fitness landscapes as well as reconstructing ancestral sequences.

Reviewer #1 (Public review):

Summary:

The authors aim to study mutational paths connecting WW domains with different binding specificities. Their approach combines an unsupervised sequence generative model based on RBMs with a path-sampling algorithm. The key result is that most intermediate sequences along the designed transition paths retain measurable binding activity in wet-lab assays, whereas paths containing the same mutations introduced in a randomized order are largely non-functional. This difference is attributed to epistatic interactions captured by the RBM model.

Strengths:

Exploring mutational paths in high-dimensional protein sequence space is a challenging problem. The computational framework used here is state-of-the-art and is strengthened by systematic experimental characterization of binding activity. The study is comprehensive in scope, including multiple transition paths both within and across WW specificity classes, and the integration of modeling with high-throughput experimental validation is a clear strength.

Weaknesses:

A major concern is whether the stated goal of specificity switching is fully achieved. Along the sampled transition paths, most intermediate variants appear to retain specificity close to either the initial or the final class, rather than exhibiting gradually shifting specificity. For example, in Figure 4G (Class I to Class II/III), binding appears largely binary, with intermediates behaving similarly to one of the endpoints. A similar pattern is observed in Figure 3H for the Class I to Class IV transition, where binding responses are close to 0 or 1. In this sense, the specificity-switching objective is only partially realized by assigning two endpoints with different specificity. This raises a broader conceptual question: is it possible that different WW specificities evolved from a common ancestor without passing through intermediates that exhibit mixed or intermediate specificity? If so, then inferring specificity-switching pathways purely from extant natural sequences may be fundamentally challenging.

Reviewer #2 (Public review):

This is an extremely important work that shows how one can use generative models to construct specificity-switching mutational paths in complex fitness landscapes. The experimental evidence is very clear, and the theoretical tools are innovative.

The work will likely have a deep impact on future research aimed at understanding how evolution navigates fitness landscapes as well as reconstructing ancestral sequences.

The manuscript is extremely clear and well written, the experimental evidence is strong, and the methods are clearly described, so I do not have major issues to raise. A few minor issues are listed below.

(1) I consider the WW domain as an 'easy' case from the point of view of generative modelling. The domain is rather short, epistatic effects are not very strong (e.g. Boltzmann learning usually converges very quickly to a very paramagnetic state), and the resulting models are well interpretable (e.g. the hidden units of the RBM correlate well with subclasses).

This is not always (not often?) the case, however. In more complex proteins, the learning procedures can be slower and the resulting models less interpretable. Just for completeness, perhaps the authors could comment on the generality of the results and what they would expect for other systems based on their experience.

(2) In Section 3.3, the authors say that direct paths connecting Class I and Class IV behave similarly to indirect paths, despite having lower scores according to the RBM. How generic is this? Does it also happen for other classes? This might be an important point to address, as direct paths are easier to sample.

(3) The path shown in Figure 4 goes through a region of non-functionality around sequences 18-19. It seems that the sample path is basically exploring the functional regions for Class I and Class II/III separately, trying to approach the other class, but then it can't really make the switch.

By contrast, the path going from Class I to Class IV seems able to perform the functional switch in a single step (20-21) without losing too much of the function.

Perhaps the authors could better comment on this? Is this a limitation of the sampling method, or a fundamental biological fact?

(4) On page 12, it is stated that the temperature was chosen to 1/3 to maximize the score. This is important and should be mentioned earlier (I didn't notice it until that point).

(5) On page 13, it is stated that: "However, the scores of the ancestral sequences along the phylogenetic pathways assigned by the RBM are significantly lower than the ones of the RBM-designed sequences. This result is expected as ASR reconstruction does not take into account epistasis, differently from RBM, and we expect ASR sequences to generally be of lesser quality."

I was very surprised by this result. My own experience with ASR shows that, on the contrary, sequences found by ASR (via maximum likelihood) tend to have high scores in the (R)BM, and tend to be more stable than extant sequences. I attribute this to the fact that ASR typically finds a "consensus" sequence that maximizes the contribution to the score coming from the fields (the profile), which is typically dominant over the epistatic signal, resulting in a bigger score. Maybe the authors did not use maximum likelihood in the ASR? Some clarification might be useful here.

eLife Assessment

This important paper substantially advances our understanding of how Molidustat may work, beyond its canonical role, by identifying its therapeutic targets in cancer. This study presents a compelling and well-structured investigation into the therapeutic vulnerabilities of APC-mutant colorectal cancer. This work will be of broad interest to the cancer community in studying small molecules and their therapeutic targets.

Reviewer #1 (Public review):

Summary:

The authors aimed to uncover novel therapeutic vulnerabilities in APC-mutant colorectal cancer (CRC), which constitutes the majority of CRC cases. They hypothesized that modulating oxygen-sensing pathways (via PHD inhibition) could disrupt adaptive stress responses in these tumours.

Strengths:

The study employs a powerful, two-pronged approach to identify Molidustat's targets. By using both Thermal Proteome Profiling (TPP) and an orthogonal chemical proteomic competition assay, the authors provide compelling evidence that GSTP1 is a genuine, direct off-target, effectively addressing the common limitation of indirect effects in proteomic screens.

Weaknesses:

(1) In Figure 1, the current data rely on a single guide RNA (sgRNA). To make the data solid, at least two independent sgRNAs targeting different regions of PHD2 should be used.

(2) Figure 3E: Asn205 site should be mutated to prove that whether Molidustat inhibits GSTP1 activity via Asn205 or not.

(3) Figure 5B and 5C: The metabolic imbalance phenotype observed upon dual knockout of PHD2 and GSTP1 requires rescue experiments to confirm on-target specificity.

Reviewer #2 (Public review):

Summary:

The authors aimed to determine Molidustat targets and the potential utility of these findings. They clearly demonstrate that Molidustat interferes with GSTP1 and some other proteins on top of PHD2. They also demonstrate that PHD2 deletion is not sufficient to recapitulate Molidustat effects in cells and proteomes. Finally, they demonstrate synthetic lethality in organoids for Molidustat and APC deletion.

Strengths:

The data on Molidustat proteomes, GSTP1 binding, inhibition and metabolic health of organoids is really clear. All biochemical, docking and omic data are really strong. The potential impact of these findings could be the use of Molidustat in APC null tumours and awareness of potential off-target effects.

Weaknesses:

A main but minor weakness is that Molidustat also inhibits other PHDs, although these are less expressed. PHD1 has been shown to control the cell cycle and be expressed in the colon, where it is needed for viability. Although this does not explain the lack of effect of other PHD inhibitors, it does warrant some discussion. The use of MTT is not very good to detect viability when it measures metabolism; this also needs to be discussed and perhaps supplemented with colony or cell number measurements.

Reviewer #3 (Public review):

In this paper, the authors revealed that Molidustat can induce a dose-dependent increase in Caspase-3/7 activity in the HT29 cell line, which is an APC-mutant colorectal cancer cell line. More importantly, they found that targeting PHD2 alone cannot cause cell death. By using thermal proteome profiling (TPP) and orthogonal chemical proteomic competition assays, they determined GTSP1 as a previously undiscovered off-target of Molidustat. They also revealed that combined PHD2 and GSTP1 loss leads to an increase in intracellular ROS and apoptosis. Moreover, they evaluated the effects of Molidustat in colonic organoids and showed that Molidustat has a high selectivity for colonic organoids with activated WNT signaling and/or KRAS pathway alterations, and this effect is not reproduced by hydroxylase inhibition alone, providing a new potential approach to targeting both PHD2 and GTSP1 for the treatment of APC-mutant CRC.

Specific comments:

(1) What is the possible molecular mechanism of dual GSTP1/PHD2 loss, inducing cell death?

(2) Can the authors mutate the binding site of Molidustat on GTSP1 to verify the in silico docking results?

(3) Evidence for Molidustat inhibiting PHD2 activity or stabilising HIF-1α should be provided.

Hackers https://www.youtube.com/watch?v=G5cFV_0sahI

This example about storing gender made me realize that data design is always a trade-off between simplicity and inclusiveness. While using fixed categories can make data easier to organize and analyze, it can also exclude people whose identities don’t fit those options. At the same time, allowing completely open input can make the data messy and harder to use. It makes me think that there is no perfect solution, and designers have to balance usability with fairness when deciding how to store information.

This section made me think about how data constraints are not just technical decisions, but also reflect power. For example, limiting what counts as a “valid” input (like restricting names or formats) means that platforms are deciding what kinds of identities or expressions are acceptable. While these constraints may improve consistency or usability, they can also exclude certain forms of representation. It makes me wonder who gets to define these rules and whether users have any say in them.

This shows how the constraints provide efficiency. For example if you add more than 280 characters, the system will be slow and not efficient.

义务类型 触发标准 对平台的要求 Knowledge-based duty 平台实际知道（actual knowledge）具体侵权内容 被动：收到通知才需行动 Standard-based duty 以行业惯例为基准，平台应当知道（constructive knowledge） 主动：须尽职调查，主动发现侵权

The author talks about the different use cases of floating points as well as explaining what they are. Floating point numbers are numbers that contain decimals, similar to integers with the exception that they can be longer and tend to be more precise forms of keeping track of numerical data; floats are often used in more complex math operations.

Array is a memory space that is widely used. It is a continuous memory meaning that it is good at finding specific data

The article discusses how Elon Musk wanted to halt his acquisition of Twitter once information regarding real users vs bots had come out. Muskl faced legal challengers but the discourse resulted in lower acquisition costs from his legal team. The article highlights in the business or economic sense how bots are viewed when it comes to platforms and how in a way, they can be a negative feature, which indicates a lack of platform use and therefore lower revenue generation for corporations.

I was reading the Julia Evans article, and what stuck out to me was specifically the odometer example. This was supposed to be a program that tracked 10,000 km, but it only actually tracked 262 km because floating-point errors kept accumulating over time. I originally thought of floating point as a super technical concept, but this example makes it seem much clearer and less technical to me, and makes me think twice about how much we should trust computers when it comes to accuracy.

This source provides a foundational summary of HyperText Markup Language (HTML), explaining that it is the standard markup language for creating web pages by describing the structure of a webpage through a series of elements. A key detail from the source is that HTML elements tell the browser how to display content, using tags like

for headings and

for paragraphs to label pieces of content.

I found this article interesting because it introduced me to points that I have never considered before. Specifically, I feel that I have seen too many of these "voluntourism" selfies on social media platforms, and I considered them normal without considering the real implications. For instance, the fact that these pictures create the generalization that all of Africa is in poverty.

While reading this post, I noticed the user is dealing with a problem where their last name is too long for the system, and others in the comments are sharing advice or similar situations. I realize how systems like TurboTax simplify real-world information by setting character limits. In reality, names don’t have a fixed length, so this can be frustrating for people with longer names. I can relate to this because my own name is also quite long, and I’ve had similar issues where some characters get cut off when entering my information. So I often have to double check multiple times to make sure everything is correct. It also shows that some users are not fully supported by these systems and I wonder why these limits exist and whether they could be made more flexible.

Design justice is an interesting concept. I feel we usually forget how the users/those having their data collected are impacted by the data collection. Also it makes me wonder how harmful is the data companies collect on us, and subsequently do we have an expectation of data privacy whenusing companies platforms.

Princípio da Exclusividade OU Pureza

• São exceções ao princípio da exclusividade:
  • Autorização para crédito suplementar (já existe dotação no orçamento, autorizando-se a suplementação se necessário)
  • Contratação de operação de crédito, ainda que ARO.

NÃO CONFUNDIR: - A abertura de créditos extraordinário ou especial não configuram como exceção ao princípio da exclusividade. Poderão, lado outro, caracterizar exceção ao princípio da anualidade em decorrência da previsão do art. 167, § 2º.

• Por essa razão, a LOA não pode veicular autorização para crédito especial, demandando-se lei a parte para se conceder a autorização legislativa.

I think it's super interesting how data is collected across various apps and also downloadable into a compact format. I recall a friend utilizing this tool after his account on TikTok was compromised in order to retain his original algorithim which was created over the course of 6 years. Data accessibility is particularly useful in enhancing quality of life through information retention in case of account breaches.

This part helped me better understand what metadata actually means. Before, I only thought about the main content like text or photos as “data,” but I didn’t realize that information like time, user, and interactions can also be very important. In real life, I feel like metadata might even reveal more about a person than the content itself. For example, when we use social media, patterns like when we post or who we interact with can say a lot about our habits. This makes me think more about privacy and how much information we are unintentionally sharing.

Learning about metadata made me realize how much information I have that is going out to the public that I do not even know about. Every photo I take shares a location and a timestamp, and most apps track when and how the app is being used, so it sort of bothers me how so much of my information and schedule is going out to these companies, which can potentially sell it to other groups or organizations.

Toqueville

Sort of talking about a council manager type beat

Maybe just say fuck it and give it all under the purview of the state

Maybe there would also be a virtuous cycle

The cost benefit is something I remain skeptical of

I'm liking the "ya diversity" standpoint

In reality the relocation and political/physical infrastructure this would take is like fucking impossible

Create a sense of shared fate between otherwise fragmented localities

Ensuring representative efficacy

And sort of a moral disregard

This is the arrow chart we say above

Which only serves to further nationalize the issue

Insular concerns

But you need diversity which is harder to get w/ small populations

The suburbs have greater social tension, but on what scale?

Are healthier places

Rehashing chapter one

filled with

Important as the building block for government action and accountability

Limiting the issues that matter at the local level

Because affluent places have disproportionate power

The citizens it actually matters to have less of a say

But cities need a certain level of jurisdiction over their own people

Not involving all the affected actors

Right so its great when its contained to EG but eventually EG wants to bug providence

Other ways to resolve conflict

Oh hello Rousseau

works on too individual a basis

Which would be bad for dsemocracy if we could actually prove that was the cause

Both arose at the same time

Unclear which way the causation flows

Also a breakdown in putmans capital

Mobilization

So optimistic

From private to public?

Maybe makes them more generous to co-citizens

Ok that seems a little grand to me but if they want me to buy it I will

In that case the suburbs are defiantly NOT good

right, the local level might be ok but step it up anymore and we've got a probelm

So rational actors who are detached is not a good theory

But probably society has some say in how you are formed, I'm following

But a different polity reaps the benefits

The urb bruh

Points out that there is going to be an inherent inequality between municipalities

Bring me back to machine politics

Which means not all municipalities have to be responsive

I mean not if you believe Hobbes

Proactive gov.

Self sorting leads to same-ness which is not a problem as we see above, might also be a problem for the causation the author wants to claim as self sorting would be selection bias. Of course ability to sort is not perfect eh.

?

This reminds me of God’s command to humans. When He gave us dominion over the earth, He did not give us the rights to do whatever we want, but to care and maintain(stewarding) the earth.

Forests disappearing seems like an easy fix for more land, however trees and forests will ultimately cause much damage due to the carbon dioxide that they provide.

To maintain a healthy/stable economy and society it is vital to keep your environment ready and able for the problems that nature might bring.

A great way to help with economic growth is utilizing the environment and finding what in your society has greatest value that other societies do not have.

This idea of combining dictionaries and lists made me realize how complex data structures can actually be in real life. At first, it sounds confusing, but when I think about social media, it makes sense. For example, a user can have multiple types of information, and some of that information (like followers or posts) is already a list. So organizing data this way feels more natural and realistic. It also shows how programming tries to model real-world relationships between people and information.

I'm surprised that the scav-5 and scav-6 paralogs were both able to reduce the large LD phenotype to the same extent as scav-4 (there doesn't appear to be significant difference between the mutants). To me this suggests either they each contribute a third of the BCFA uptake, or that they operate together to internalize BCFAs. The scav-4;scav-6 double mutant suggests the first idea isn't correct as you don't see a stronger effect there. Do you think its possible these transporters are working as a complex? I would be interested to see if you can rescue each of these mutants with scav-4 expression, or if rescue requires all receptors to be present.

It's amazing you were able to find a GOF mutation that exacerbated this phenotype! I think it would help accentuate this if you included a picture of wt animals on the JUb74 food, next to the L462F animals on JUB74 food in Fig 4A to contrast that shift in LD size.

Could you preform the experiment in C. elegans defined media supplemented with BCFAs? That would fully remove other variables from the Microbacterium and confirm this is solely due to BCFAs.

I feel like schools are the reason why i hesitate at times to turn in work on time even though I MAY have gotten the prompt down just fine for points like my zine.... it's that anxiety i get that makes me think what I've written is not enough.

eLife Assessment

This study offers valuable insights into how humans detect and adapt to regime shifts, highlighting dissociable contributions of the frontoparietal network and ventromedial prefrontal cortex to sensitivity to signal diagnosticity and transition probabilities. The combination of an innovative instructed-probability task, Bayesian behavioural modelling, and model-based fMRI analyses provides solid support for the main claims. The addition of new model-comparison figures in revision effectively addresses the previously noted potential confound between posterior switch probability and time in the neuroimaging results. At the behavioural level, while the computational model captures the pattern of "system neglect" well, qualitatively distinct mechanisms, such as hyper-prior attraction toward experiment-wise mean parameters, reporting biases, or probability-outlier underweighting, could produce similar behavioural signatures and cannot be fully disambiguated with the current design alone; however, converging evidence from the authors' prior work partially mitigates this concern.

Reviewer #1 (Public review):

Summary:

The study examines human biases in a regime-change task, in which participants have to report the probability of a regime change in the face of noisy data. The behavioral results indicate that humans display systematic biases, in particular, overreaction in stable but noisy environments and underreaction in volatile settings with more certain signals. fMRI results suggest that a frontoparietal brain network is selectively involved in representing subjective sensitivity to noise, while the vmPFC selectively represents sensitivity to the rate of change.

Strengths:

- The study relies on a task that measures regime-change detection primarily based on descriptive information about the noisiness and rate of change. This distinguishes the study from prior work using reversal-learning or change-point tasks in which participants are required to learn these parameters from experiences. The authors discuss these differences comprehensively.

- The study uses a simple Bayes-optimal model combined with model fitting, which seems to describe the data well. The model is comprehensively validated.

- The authors apply model-based fMRI analyses that provide a close link to behavioral results, offering an elegant way to examine individual biases.

Weaknesses:

The authors have adequately addressed my prior concerns.

Reviewer #3 (Public review):

This study concerns how observers (human participants) detect changes in the statistics of their environment, termed regime shifts. To make this concrete, a series of 10 balls are drawn from an urn that contains mainly red or mainly blue balls. If there is a regime shift, the urn is changed over (from mainly red to mainly blue) at some point in the 10 trials. Participants report their belief that there has been a regime shift as a % probability. Their judgement should (mathematically) depend on the prior probability of a regime shift (which is set at one of three levels) and the strength of evidence (also one of three levels, operationalized as the proportion of red balls in the mostly-blue urn and vice versa). Participants are directly instructed of the prior probability of regime shift and proportion of red balls, which are presented on-screen as numerical probabilities. The task therefore differs from most previous work on this question in that probabilities are instructed rather than learned by observation, and beliefs are reported as numerical probabilities rather than being inferred from participants' choice behaviour (as in many bandit tasks, such as Behrens 2007 Nature Neurosci).

The key behavioural finding is that participants over-estimate the prior probability of regime change when it is low, and under estimate it when it is high; and participants over-estimate the strength of evidence when it is low and under-estimate it when it is high. In other words participants make much less distinction between the different generative environments than an optimal observer would. This is termed 'system neglect'. A neuroeconomic-style mathematical model is presented and fit to data.

Functional MRI results how that strength of evidence for a regime shift (roughly, the surprise associated with a blue ball from an apparently red urn) is associated with activity in the frontal-parietal orienting network. Meanwhile at time-points where the probability of a regime shift is high, there is activity in another network including vmPFC. Both networks show individual differences effects, such that people who were more sensitive to strength of evidence and prior probability show more activity in the frontal-parietal and vmPFC-linked networks respectively.

Strengths

(1) The study provides a different task for looking at change-detection and how this depends on estimates of environmental volatility and sensory evidence strength, in which participants are directly and precisely informed of the environmental volatility and sensory evidence strength rather than inferring them through observation as in most previous studies

(2) Participants directly provide belief estimates as probabilities rather than experimenters inferring them from choice behaviour as in most previous studies

(3) The results are consistent with well-established findings that surprising sensory events activate the frontal-parietal orienting network whilst updating of beliefs about the word ('regime shift') activates vmPFC.

Weaknesses

(1) The use of numerical probabilities (both to describe the environments to participants, and for participants to report their beliefs) may be problematic because people are notoriously bad at interpreting probabilities presented in this way, and show poor ability to reason with this information (see Kahneman's classic work on probabilistic reasoning, and how it can be improved by using natural frequencies). Therefore the fact that, in the present study, people do not fully use this information, or use it inaccurately, may reflect the mode of information delivery.

In the response to this comment the authors have pointed out their own previous work showing that system neglect can occur even when numerical probabilities are not used. This is reassuring but there remains a large body of classic work showing that observers do struggle with conditional probabilities of the type presented in the task,

(2) Although a very precise model of 'system neglect' is presented, many other models could fit the data.

For example, you would get similar effects due to attraction of parameter estimates towards a global mean - essentially application of a hyper-prior in which the parameters applied by each participant in each block are attracted towards the experiment-wise mean values of these parameters. For example, the prior probability of regime shift ground-truth values [0.01, 0.05, 0.10] are mapped to subjective values of [0.037, 0.052, 0.069]; this would occur if observers apply a hyper-prior that the probability of regime shift is about 0.05 (the average value over all blocks). This 'attraction to the mean' is a well-established phenomenon and cannot be ruled out with the current data (I suppose you could rule it out by comparing to another dataset in which the mean ground-truth value was different).

More generally, any model in which participants don't fully use the numerical information they were given would produce apparent 'system neglect'. Four qualitatively different example reasons are: 1. Some individual participants completely ignored the probability values given. 2. Participants did not ignore the probability values given, but combined them with a hyperprior as above. 3. Participants had a reporting bias where their reported beliefs that a regime-change had occurred tend to be shifted towards 50% (rather than reporting 'confident' values such 5% or 95%). 4. Participants underweighted probability outliers resulting in underweighting of evidence in the 'high signal diagnosticity' environment (10.1016/j.neuron.2014.01.020 )

In summary I agree that any model that fits the data would have to capture the idea that participants don't differentiate between the different environments as much as they should, but I think there are a number of qualitatively different reasons why they might do this - of which the above are only examples.

Author response:

The following is the authors’ response to the previous reviews

eLife Assessment

This study offers valuable insights into how humans detect and adapt to regime shifts, highlighting dissociable contributions of the frontoparietal network and ventromedial prefrontal cortex to sensitivity to signal diagnosticity and transition probabilities. The combination of an innovative instructed-probability task, Bayesian behavioural modeling, and model-based fMRI analyses provides a solid foundation for the main claims; however, major interpretational limitations remain, particularly a potential confound between posterior switch probability and time in the neuroimaging results. At the behavioural level, reliance on explicitly instructed conditional probabilities leaves open alternative explanations that complicate attribution to a single computational mechanism, such that clearer disambiguation between competing accounts and stronger control of temporal and representational confounds would further strengthen the evidence.

Thank you. In this revision, we addressed Reviewer 3’s remaining concern on the potential confound between posterior probability and time in neuroimaging results. First, as suggested by the reviewer, we provided images of activations for the effect of Pt and delta Pt after controlling for intertemporal prior in GLM-2. Second, we compared the effect of Pt and delta Pt between GLM-1 (without intertemporal prior) and GLM-2 (with intertemporal prior) and showed the results in a new figure (Figure 4).

Regarding issue on reliance on explicitly instructed probabilities, we wish to point out that most of the concerns such as response mode and regression to the mean were addressed in the original behavioral paper by Massey and Wu (2005). Please see our response to this point in detail in Weakness (2) posted by Reviewer 3.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study examines human biases in a regime-change task, in which participants have to report the probability of a regime change in the face of noisy data. The behavioral results indicate that humans display systematic biases, in particular, overreaction in stable but noisy environments and underreaction in volatile settings with more certain signals. fMRI results suggest that a frontoparietal brain network is selectively involved in representing subjective sensitivity to noise, while the vmPFC selectively represents sensitivity to the rate of change.

Strengths:

- The study relies on a task that measures regime-change detection primarily based on descriptive information about the noisiness and rate of change. This distinguishes the study from prior work using reversal-learning or change-point tasks in which participants are required to learn these parameters from experiences. The authors discuss these differences comprehensively.

- The study uses a simple Bayes-optimal model combined with model fitting, which seems to describe the data well. The model is comprehensively validated.

- The authors apply model-based fMRI analyses that provide a close link to behavioral results, offering an elegant way to examine individual biases.

Weaknesses:

The authors have adequately addressed my prior concerns.

Thank you for reviewing our paper and providing constructive comments that helped us improve our paper.

Reviewer #3 (Public review):

Thank you again for reviewing the manuscript. In this revision, we focused on addressing your concern on the potential confound between posterior probability and time in neuroimaging results. First, we presented whole-brain results of subjects’ probability estimates (Pt, their subjective posterior probability of switch) after controlling for the effect of time on probability of switch (the intertemporal prior). Second, we compared the effect of probability estimates (Pt) on vmPFC and ventral striatum activity—which we found to correlate with Pt—with and without including intertemporal prior in the GLM. These results will be summarized in a new figure (Figure 4) in the revised manuscript.

As suggested by the reviewer, we also added slice-by-slice images of the whole-brain results on Pt and delta Pt in the supplement in addition to the Tables of Activation so that the activated brain regions can be clearly seen through these images.

This study concerns how observers (human participants) detect changes in the statistics of their environment, termed regime shifts. To make this concrete, a series of 10 balls are drawn from an urn that contains mainly red or mainly blue balls. If there is a regime shift, the urn is changed over (from mainly red to mainly blue) at some point in the 10 trials. Participants report their belief that there has been a regime shift as a % probability. Their judgement should (mathematically) depend on the prior probability of a regime shift (which is set at one of three levels) and the strength of evidence (also one of three levels, operationalized as the proportion of red balls in the mostly-blue urn and vice versa). Participants are directly instructed of the prior probability of regime shift and proportion of red balls, which are presented on-screen as numerical probabilities. The task therefore differs from most previous work on this question in that probabilities are instructed rather than learned by observation, and beliefs are reported as numerical probabilities rather than being inferred from participants' choice behaviour (as in many bandit tasks, such as Behrens 2007 Nature Neurosci).

The key behavioural finding is that participants over-estimate the prior probability of regime change when it is low, and under estimate it when it is high; and participants over-estimate the strength of evidence when it is low and under-estimate it when it is high. In other words participants make much less distinction between the different generative environments than an optimal observer would. This is termed 'system neglect'. A neuroeconomic-style mathematical model is presented and fit to data.

Functional MRI results how that strength of evidence for a regime shift (roughly, the surprise associated with a blue ball from an apparently red urn) is associated with activity in the frontal-parietal orienting network. Meanwhile at time-points where the probability of a regime shift is high, there is activity in another network including vmPFC. Both networks show individual differences effects, such that people who were more sensitive to strength of evidence and prior probability show more activity in the frontal-parietal and vmPFC-linked networks respectively.

Strengths

(1) The study provides a different task for looking at change-detection and how this depends on estimates of environmental volatility and sensory evidence strength, in which participants are directly and precisely informed of the environmental volatility and sensory evidence strength rather than inferring them through observation as in most previous studies

(2) Participants directly provide belief estimates as probabilities rather than experimenters inferring them from choice behaviour as in most previous studies

(3) The results are consistent with well-established findings that surprising sensory events activate the frontal-parietal orienting network whilst updating of beliefs about the word ('regime shift') activates vmPFC.

Weaknesses

(1) The use of numerical probabilities (both to describe the environments to participants, and for participants to report their beliefs) may be problematic because people are notoriously bad at interpreting probabilities presented in this way, and show poor ability to reason with this information (see Kahneman's classic work on probabilistic reasoning, and how it can be improved by using natural frequencies). Therefore the fact that, in the present study, people do not fully use this information, or use it inaccurately, may reflect the mode of information delivery.

In the response to this comment the authors have pointed out their own previous work showing that system neglect can occur even when numerical probabilities are not used. This is reassuring but there remains a large body of classic work showing that observers do struggle with conditional probabilities of the type presented in the task.

Thank you. Yes, people do struggle with conditional probabilities in many studies. However, as our previous work suggested (Massey and Wu, 2005), system-neglect was likely not due to response mode (having to enter probability estimates or making binary predictions, and etc.).

(2) Although a very precise model of 'system neglect' is presented, many other models could fit the data.

For example, you would get similar effects due to attraction of parameter estimates towards a global mean - essentially application of a hyper-prior in which the parameters applied by each participant in each block are attracted towards the experiment-wise mean values of these parameters. For example, the prior probability of regime shift ground-truth values [0.01, 0.05, 0.10] are mapped to subjective values of [0.037, 0.052, 0.069]; this would occur if observers apply a hyper-prior that the probability of regime shift is about 0.05 (the average value over all blocks). This 'attraction to the mean' is a well-established phenomenon and cannot be ruled out with the current data (I suppose you could rule it out by comparing to another dataset in which the mean ground-truth value was different).

More generally, any model in which participants don't fully use the numerical information they were given would produce apparent 'system neglect'. Four qualitatively different example reasons are: 1. Some individual participants completely ignored the probability values given. 2. Participants did not ignore the probability values given, but combined them with a hyperprior as above. 3. Participants had a reporting bias where their reported beliefs that a regime-change had occurred tend to be shifted towards 50% (rather than reporting 'confident' values such 5% or 95%). 4. Participants underweighted probability outliers, resulting in underweighting of evidence in the 'high signal diagnosticity' environment (10.1016/j.neuron.2014.01.020 )

In summary I agree that any model that fits the data would have to capture the idea that participants don't differentiate between the different environments as much as they should, but I think there are a number of qualitatively different reasons why they might do this - of which the above are only examples - hence I find it problematic that the authors present the behaviour as evidence for one extremely specific model.

We thank the reviewer for this comment. We thank you for putting out that there are alternative models that can describe the over- and underreaction seen in the dataset. Massey and Wu (2005) dealt with this possibility in their original paper. Their concern was not so much about alternative ways of modeling their results, but in terms of alternative psychological processes. For example, asymmetric noise accounts have been posited in the judgment and decision making literature as possible accounts of phenomena like over-confidence. They addressed what might be crudely called “regression/attraction to the mean” in two ways. First, they looked at median responses as well as mean responses (because medians are less affected by the regressive effect) and found the same patterns of over- and underreactions. Second, they also generated sequences that matched particular posterior probabilities (so that over- and underreaction cannot be explained by regression to the mean) and still found under- and overreactions.

We also wish to point out in the judgment and decision making literature starting from Edwards (1968), there is a long history of using normative Bayesian model as the starting model and subsequently develop quasi-Bayesian models (like the system-neglect model) to describe systematic deviations from the normative Bayesian.

Finally, we want to clarify that our primary goal is not to engage in model fitting exercise that examines different possible models. To us, what is more important is that system neglect is a psychologically motivated hypothesis. It is built on the idea that the lack of sensitivity to the system parameters is due to the fact that people focus primarily on the signals and secondarily on the system parameters that generate the signals. Massey and Wu (2005) dealt with a host of other potential explanations through experimental manipulations and data analysis. In this paper, we built on Massey and Wu to examine the neurocomputational basis that gives rise to over- and underreactions.

(3) Despite efforts to control confounds in the fMRI study, including two control experiments, I think some confounds remain.

For example, a network of regions is presented as correlating with the cumulative probability that there has been a regime shift in this block of 10 samples (Pt). However, regardless of the exact samples shown, Pt always increases with sample number (as by the time of later samples, there have been more opportunities for a regime shift)? To control for this the authors include, in a supplementary analysis, an 'intertemporal prior.' I would have preferred to see the results of this better-controlled analysis presented in the main figure. From the tables in the SI it is very difficult to tell how the results change with the includion of the control regressors.

Thank you. In response, we added a new figure, now Figure 4, showing the results of Pt and delta Pt from GLM-2 where we added the intertemporal prior as a regressor to control for temporal confounds. We compared Pt and delta Pt results in vmPFC and ventral striatum between GLM-1 and GLM-2. We also showed the results on intertemporal prior on vmPFC and ventral striatum from GLM-2.

On the other hand, two additional fMRI experiments are done as control experiments and the effect of Pt in the main study is compared to Pt in these control experiments. Whilst I admire the effort in carrying out control studies, I can't understand how these particular experiment are useful controls. For example, in experiment 3 participants simply type in numbers presented on the screen - how can we even have an estimate of Pt from this task?

We thank the reviewer for this comment. On the one hand, the effect of Pt we see in brain activity can be simply due to motor confounds and the purpose of Experiment 3 was to control for them. Our question was, if subjects saw the similar visual layout and were just instructed to press buttons to indicate two-digit numbers, would we observe the vmPFC, ventral striatum, and the frontoparietal network like what we did in the main experiment (Experiment 1)?

On the other hand, the effect of Pt can simply reflect probability estimates of that the current regime is the blue regime, and therefore not particularly about change detection. In Experiment 2, we tested that idea, namely whether what we found about Pt was unique to change detection. In Experiment 2, subjects estimated the probability that the current regime is the blue regime (just as they did in Experiment 1) except that there were no regime shifts involved. In other words, it is possible that the regions we identified were generally associated with probability estimation and not particularly about probability estimates of change. We used Experiment 2 to examine whether this were true.

To make the purpose of the two control experiments clearer, we updated the paragraph describing the control experiments on page 9:

“To establish the neural representations for regime-shift estimation, we performed three fMRI experiments (n = 30 subjects for each experiment, 90 subjects in total). Experiment 1 was the main experiment, while Experiments 2 to 3 were control experiments that ruled out two important confounds (Fig. 1E). The control experiments were designed to clarify whether any effect of subjects’ probability estimates of a regime shift, P_t, in brain activity can be uniquely attributed to change detection. Here we considered two major confounds that can contribute to the effect of P_t. First, since subjects in Experiment 1 made judgments about the probability that the current regime is the blue regime (which corresponded to probability of regime change), the effect of P_t did not particularly have to do with change detection. To address this issue, in Experiment 2 subjects made exactly the same judgments as in Experiment 1 except that the environments were stationary (no transition from one regime to another was possible), as in Edwards (1968) classic “bookbag-and-poker chip” studies. Subjects in both experiments had to estimate the probability that the current regime is the blue regime, but this estimation corresponded to the estimates of regime change only in Experiment 1. Therefore, activity that correlated with probability estimates in Experiment 1 but not in Experiment 2 can be uniquely attributed to representing regime-shift judgments. Second, the effect of P_t can be due to motor preparation and/or execution, as subjects in Experiment 1 entered two-digit numbers with button presses to indicate their probability estimates. To address this issue, in Experiment 3 subjects performed a task where they were presented with two-digit numbers and were instructed to enter the numbers with button presses. By comparing the fMRI results of these experiments, we were therefore able to establish the neural representations that can be uniquely attributed to the probability estimates of regime-shift.”

To further make sure that the probability-estimate signals in Experiment 1 were not due to motor confounds, we implemented an action-handedness regressor in the GLM, as we described below on page 19:

“Finally, we note that in GLM-1, we implemented an “action-handedness” regressor to directly address the motor-confound issue, that higher probability estimates preferentially involved right-handed responses for entering higher digits. The action-handedness regressor was parametric, coding -1 if both finger presses involved the left hand (e.g., a subject pressed “23” as her probability estimate when seeing a signal), 0 if using one left finger and one right finger (e.g., “75”), and 1 if both finger presses involved the right hand (e.g., “90”). Taken together, these results ruled out motor confounds and suggested that vmPFC and ventral striatum represent subjects’ probability estimates of change (regime shifts) and belief revision.”

(4) The Discussion is very long, and whilst a lot of related literature is cited, I found it hard to pin down within the discussion, what the key contributions of this study are. In my opinion it would be better to have a short but incisive discussion highlighting the advances in understanding that arise from the current study, rather than reviewing the field so broadly.

Thank you. We thank the reviewer for pushing us to highlight the key contributions. In response, we added a paragraph at the beginning of Discussion to better highlight our contributions:

“In this study, we investigated how humans detect changes in the environments and the neural mechanisms that contribute to how we might under- and overreact in our judgments. Combining a novel behavioral paradigm with computational modeling and fMRI, we discovered that sensitivity to environmental parameters that directly impact change detection is a key mechanism for under- and overreactions. This mechanism is implemented by distinct brain networks in the frontal and parietal cortices and in accordance with the computational roles they played in change detection. By introducing the framework in system neglect and providing evidence for its neural implementations, this study offered both theoretical and empirical insights into how systematic judgment biases arise in dynamic environments.”

Recommendations for the authors:

Reviewer #3 (Recommendations for the authors):

Thank you for pointing out the inclusion of the intertemporal prior in glm2, this seems like an important control that would address my criticism. Why not present this better-controlled analysis in the main figure, rather than the results for glm1 which has no effective control of the increasing posterior probability of a reversal with time?

Thank you for this suggestion. We added a new figure (Figure 4) that showed results of Pt and delta Pt from GLM-2. We also compared the effect of Pt and delta Pt between GLM-1 and GLM-2. We found that the effect of Pt and delta Pt did not differ between GLM-1 and GLM-2. GLM-1 and GLM-2 differed on whether various task-related regressors contributing to Pt, including the intertemporal prior, were included in the model. In GLM-1, those task-related regressors were not included. In GLM-2, the task-related regressors were included in addition to Pt and delta P.

The reason we kept results from GLM-1 (Figure 3) was primarily because we wanted to compare the effect of Pt between experiments under identical GLM. In other words, the regressors in GLM-1 was identical across all 3 experiments. In Experiments 1 and 2, Pt and delta Pt were respectively probability estimates and belief updates that current regime was the Blue regime. In Experiment 3, Pt and delta Pt were simply the number subjects were instructed to press (Pt) and change in number between successive periods (delta Pt).

Here is the section in the main text where we discussed the new Figure 4 on page 19-22:

We further examined the robustness of P_t and ∆P_t representations in vmPFC and ventral striatum in three follow-up analyses. In the first analysis, we implemented a GLM (GLM-2 in Methods) that, in addition to P_t and ∆P_t, included various task-related variables contributing to P_t as regressors. Specifically, to account for the fact that the probability of regime change increased over time, we included the intertemporal prior as a regressor in GLM-2. The intertemporal prior is the natural logarithm of the odds in favor of regime shift in the t-th period, , where q is transition probability and t = 1, …, 10is the period (Eq. 1 in Methods). It describes normatively how the prior probability of change increased over time regardless of the signals (blue and red balls) the subjects saw during a trial. Including it along with P_t would clarify whether any effect of P_t can otherwise be attributed to the intertemporal prior. We found that the results of P_t and ∆P_t in the vmPFC and ventral striatum in GLM-2 were identical to those in GLM-1 (Fig. 4): Fig. 4A was meant to depict the results in slices identical to those shown in Fig. 3B for results based on GLM-1. For slice-by-slice results, see Fig. S7 in SI for results based on GLM-1 and Fig. S9 for GLM-2. For Tables of activations, see Tables S1-S3 in SI for GLM-1 and Tables S7-S9 for GLM-2. In a separate, independent region-of-interest (ROI) analysis on vmPFC and ventral striatum (Fig. 4BC; see Independent regions-of-interest (ROIs) analysis in Methods for details), we further compared the effect of both P_t and ∆P_t between GLM-1 and GLM-2. For P_t, the difference between GLM-1 and GLM-2 was not significant (paired t-test, t(58) = −0.72, p = 0.47 in vmPFC, t(58) = −0.21, p = 0.83 in ventral striatum), while the effect of P_t from GLM-1 (one sample t-test, t(29) = −3,82, p <.01 in vmPFC; t(29) = −3.06, p <.01 in ventral striatum) and GLM-2 was significant (one-sample t-test, t(29) = −2.69, p =.01 in vmPFC; t(29) = −2.50, p .02 in ventral striatum). For ∆P_t, the difference between GLM-1 and GLM-2 was not significant (paired t-test, t(58) = −0.07, p =0.94 in vmPFC; t(58) = −0.14, p =0.88 in ventral striatum), while the effect of  from GLM-1 (one-sample t-test, t(29) = −3.12, p <.01 in vmPFC; t(29) = −4.14, p <.01 in ventral striatum) and GLM-2 was significant (one-sample t-test, t(29) = −2.92, p <.01 in vmPFC; t(29) = −3.59, p <.01 in ventral striatum). For the intertemporal prior, activity in both vmPFC and ventral striatum did not correlate significantly with the intertemporal prior (one-sample t-test, t(29) = −0.07, p =0.95 in vmPFC; t(29) = −0.53, p =0.60 in ventral striatum). All the t-tests described above were two-tailed. Taken together, these results suggest that vmPFC and ventral striatum represented P_t and ∆P_t regardless of whether the intertemporal prior and other task-related regressors contributing to P_t were included in the GLM. We also did not find that vmPFC and ventral striatum to represent the intertemporal prior. In the second analysis, we implemented a GLM that replaced P_t with the log odds of P_t, 1n (P_t/(1 - P_t)) (Fig. S10 in SI). In the third analysis, we implemented a GLM that examined P_t separately on periods when change-consistent (blue balls) and change-inconsistent (red balls) signals appeared (Fig. S11 in SI). Each of these analyses showed significant correlation with P_t in vmPFC and ventral striatum, further establishing the robustness of the P_t findings.

As a further point I could not navigate the tables of fMRI activations in SI and recommend replacing or supplementing these with images. For example I cannot actually find a vmPFC or ventral striatum cluster listed for the effect of Pt in GLM1 (version in table S1), which I thought were the main results? Beyond that, comparing how much weaker (or not) those results are when additional confound regressors are included in GLM2 seems impossible.

As suggested by the reviewer, we added slice-by-slice images showing the effect of Pt and delta Pt (Figure S9 in SI for GLM-2 and Figure S7 for GLM-1). The clusters in blue represent Pt effect, the clusters in orange represent delta Pt effect. As can be seen, both Pt and delta Pt are represented in the vmPFC and ventral striatum.

On Instagram, a single post distills complex human experiences into a structured collection of Strings (usernames and captions), Integers (like counts), DateTimes (relative post age), and Binary/Blob data (the image or video itself). These digital artifacts act as a significant simplification of reality by flattening three-dimensional, multi-sensory moments into a 2D frame that lacks the physical context, emotional depth, or the "unfiltered" events occurring just outside the camera's view. This system works exceptionally well for "influencers" and brands who benefit from highly curated, aesthetic-first storytelling that drives rapid engagement.

When considering the design of social media sites, I think the pieces of information that should be immediately visible to users are other users' display names, profile pictures, and mutual connections that they may share. I think that these are good pieces of information to be immediately visible because, it will draw the user in and get them to engage more with others profiles. By only sharing basic information, users have enough factors to judge whether or not they want to engage with this user.

TikTok only shows the number of likes as an integer data type, meaning it tells me how many people liked a video, but it does not show different emotions like Facebook, where users can react with various feelings. So we cannot really tell whether people truly enjoyed the video or just saved or liked it to share with others. It does not clearly reflect viewers’ real feelings, including mine. Another example is text data such as usernames and profile pictures which are based on users’ personal preferences and do not necessarily reflect who they are in real life. This is why there are many fake accounts on social media, created for different purposes. Sometimes when scrolling on TikTok, I wonder why I see unfamiliar videos that I have never searched for or talked about. I think this happens because the platform collects data from my followers, and if they like certain types of videos, similar content may also appear on my feed.

двигательные только в нижней ветви нижнечелюстного нерва

État des Lieux des Fermes Urbaines Sociales au Québec : Modèles, Impacts et Défis

Synthèse de direction

Le paysage de l'agriculture urbaine au Québec connaît une transformation structurelle avec l'émergence croissante des fermes urbaines sociales.

On dénombre actuellement environ 30 initiatives de ce type à travers la province (Montréal, Québec, Matanie, Mauricie).

Ces entités se distinguent par une mission hybride : la production maraîchère de haute qualité couplée à des objectifs d'innovation sociale, d'insertion socioprofessionnelle et de sécurité alimentaire.

Les principaux enseignements de l'analyse des projets leaders (Jardin des Orioles, Cuisine Collective Hochelaga-Maisonneuve et Jardin solidaire Filonem) révèlent que :

• La production est un levier social : Le jardinage sert de prétexte à l'inclusion, à l'éducation environnementale et à la reprise de pouvoir des citoyens.

• Les modèles économiques sont diversifiés : Ils oscillent entre l'organisme communautaire pur et l'entreprise d'économie sociale visant une autonomie financière partielle (jusqu'à 60 %).

• Les défis structurels persistent : Le financement pérenne des ressources humaines, la complexité logistique des sites urbains et le recrutement de personnel possédant la double compétence (agricole et sociale) constituent les principaux freins au développement.

Typologie et Missions des Fermes Urbaines Sociales

Le mouvement des fermes urbaines sociales, documenté activement depuis 2025, s'inscrit dans le programme « Cultiver la ville » du Laboratoire sur l’agriculture urbaine (AU/LAB).

Ces projets ne se limitent pas à la culture de légumes ; ils intègrent des fonctions de santé publique et de cohésion sociale.

Les porteurs de projets et leur philosophie

Les structures analysées présentent des approches de gouvernance et des philosophies distinctes :

• Le Groupe Provert (Jardin des Orioles) : Une OBNL environnementale dont la mission est d'agir pour des collectivités durables via l'économie circulaire, le verdissement et l'agriculture durable.

• La Cuisine Collective Hochelaga-Maisonneuve (CCHM) : Une entreprise d'économie sociale centrée sur l'autonomie alimentaire et l'insertion socioprofessionnelle, fonctionnant comme une « pieuvre » de services intégrés.

• Le Filon (Jardin solidaire Filonem) : Un organisme communautaire axé sur l'accueil inconditionnel et l'holocratie (pouvoir au profit de l'organisation et non des egos), utilisant l'économie comme levier social sans objectif de profit.

Analyse de l'Impact Productif et Environnemental

Malgré leur vocation sociale, ces fermes atteignent des niveaux de productivité significatifs grâce à des méthodes de culture intensives et écologiques.

Capacités de production et techniques

| Projet | Surface cultivée | Production annuelle (2024) | Méthodes culturales | | --- | --- | --- | --- | | Jardin des Orioles | 1 400 m² | 2,2 tonnes | Biointensif, maraîchage nordique en serre froide. | | CCHM | 7 sites (dont 60 000 pi² à la SAQ) | 15 tonnes | Culture en pleine terre, serres, tunnels, jardins verticaux. | | Filonem | 1 hectare cultivé | N/A | Agroécologie, sols vivants, forêt nourricière (111 arbres), non-labour. |

Distribution et sécurité alimentaire

Les fermes privilégient les circuits courts et la redistribution sociale :

• Dons aux banques alimentaires : Le Jardin des Orioles donne 1,7 tonne sur ses 2,2 tonnes produites à des partenaires comme Épicène Henri.

La CCHM consacre 30 % de sa production aux dons.

• Marchés à tarification sociale : Vente à prix modique dans les déserts alimentaires ou via des marchés mobiles pour garantir l'accessibilité.

• Transformation interne : Utilisation des surplus pour les services de traiteur, les cafétérias (ex: SAQ) ou la bocalerie (congelés, herbes séchées) afin d'éliminer le gaspillage.

Le Volet Social : Insertion, Éducation et Mobilisation

La ferme urbaine sociale agit comme un « tiers-lieu », un espace de rencontre favorisant la mixité sociale et l'apprentissage.

• Insertion socioprofessionnelle : Des programmes tels que « Je cultive mon avenir » (Provert) ou les plateaux de la CCHM accueillent des personnes en réorientation, des immigrants ou des individus avec des besoins particuliers (autisme, déficience intellectuelle).

• Éducation et Sensibilisation : Les sites reçoivent des groupes scolaires (du CPE au secondaire) pour reconnecter les jeunes à l'origine des aliments et aux enjeux écologiques (santé des sols, biodiversité).

• Engagement Citoyen : Le modèle du Filon repose sur l'implication de 250 citoyens par an, structurés en comités décisionnels, favorisant l'empowerment et l'autogestion.

Défis et Enjeux de Pérennisation

L'exploitation d'une ferme à vocation sociale en milieu urbain comporte des obstacles spécifiques identifiés par les gestionnaires.

1. Enjeux de recrutement et de main-d'œuvre

Le recrutement de « maréchers sociaux » est complexe.

Ces employés doivent posséder une expertise agricole pointue tout en étant des formateurs et des accompagnateurs compétents.

Le roulement de personnel et la difficulté de mobiliser des bénévoles au-delà du mois de mai sont des défis récurrents.

2. Viabilité financière

• Financement des salaires : Les subventions couvrent souvent l'achat de matériel, mais rarement les salaires de manière pérenne.

• Autofinancement limité : Les revenus issus de la vente de légumes (marchés, restaurants, CPE) suffisent rarement à couvrir l'ensemble des coûts opérationnels, notamment en raison de la mission sociale qui prime sur la productivité pure.

3. Contraintes physiques et logistiques

• Accès et transport : Certains sites (comme à Lévis) souffrent d'un manque d'accessibilité en transport en commun, limitant la mobilisation citoyenne.

• Multiplicité des sites : Pour la CCHM, la gestion de 7 sites distincts entraîne une logistique lourde de déplacement de matériel et de personnel.

• Qualité des sols : La remise en culture de friches urbaines ou industrielles nécessite des investissements importants pour améliorer la santé des sols (compaction, argile).

4. Tension entre missions

Il existe une tension constante entre l'objectif de rendement agricole (nécessaire pour la crédibilité et certains revenus) et l'objectif d'inclusion (qui demande de la flexibilité et peut ralentir les opérations).

Conclusion

Les fermes urbaines sociales au Québec s'affirment comme des piliers de la résilience urbaine.

Elles transforment des espaces sous-utilisés en oasis de biodiversité et de solidarité.

Le succès de ces initiatives repose sur un ancrage communautaire fort et une capacité à naviguer entre les exigences de la production maraîchère et les impératifs de l'intervention sociale.

La pérennisation de ce modèle passera par une reconnaissance accrue des bailleurs de fonds pour le volet « ressources humaines » et par une consolidation des circuits de vente locaux.

VERY GOOD SLAYYY - CLEARLY, WHILE AN ARISTOCRAT, HE LAYS CLAIM TO THE WELSH AND HOW THE ENGLISH WERE UNJUST!!!

Very very slay! It was kinda highly a religious project!!!

SLAYYY ITS ABOUT CASTELL COCHHHH!!!!

Very interesting for architecture!!

SLAYYY FOR ARCHITECTUREEE

Interestingly describes baroque and classical as 'barbarous' while gothic was usually described this way!

SLAYYY - IT NEEDED TO BE GRAND, IT WAS A NOBLE PLACE AND NEEDED TO BE NOBLE, IT NEEDED TO BE MODERN AND REFLECT THE INDUSTRIAL MIGHT OF THE BUTES!!!

slayyyy background to the architecture with burges!!!

CARDIFF CASTLE

Instead, he engaged with a maginificent architectural wonder?

interesting that the day he got his majority was the day he joined the church!

Interesting that he wished to gift this to people!

SLAYYY he felt angered at the ruins - it is thus arguable that, in seeing the state of Castell Coch, he sought to restore it to its glory as a home away from home for the rich

Could you link to the other chapter to open in a new tab?

It would be helpful to set the link to open in a new tab

Duplicate paragraph

para más detalles sobre teoría de la decisión véase

para más detalles

para más detalles

Para más detalles

enlace

ver (cita) para más detalles

agregar: para más detalles ... y añadir el libro de woodward

colocar antes de la cita: para más detalles consultar

para más detalles puede consultarse ...

añade enlace como la sección anterior

estadísticamente consistente

buscaar palabra sinónima

Prickly Ash / Vínlandsviður / Vínlandsbroddviður (Zanthoxylum americanum)

Zanthoxylum =

🩸 circulation ⚡ stimulant 😬 tingling effect

paprika / chili / cayenne (Capsicum annuum) Capsicum fyrir brjósk:

💊 sterk verkjalosun 🩸 circulation 🔥 hitandi ➡️ einkum symptom relief

Valurt (Symphytum officinale) Valurt fyrir brjósk:

🛠️ styður viðgerð vefja 🔥 minnkar bólgu 💊 minnkar verki

➡️ ein af fáum jurtum sem fara í “repair” flokk ❗ en: 👉 aðeins útvortis

Arnika (Arnica montana) Arnica: ➕ minnkar verki og bólgu ➕ hjálpar við áverka

❌ en: byggir ekki brjósk hefur ekki djúp áhrif á liðinn sjálfan

💊 pain 🔥 inflammation (topical) 🩸 bruising ❗ external use only

Ocotillo (Fouquieria splendens) Fouquieriaceae lítil og sérhæfð ætt plöntur frá þurrum svæðum (t.d. Mexíkó/eyðimörk) 🫁 lung support ❌ ekki liðir 🩸 styður blóðflæði (óbeint gagnlegt)

Haugarfi (Stellaria media) enska: Chickweed

🔥 mild bólgueyðing 💧 róar vefi

Haugarfi fyrir brjósk: ➕ mjög óbein áhrif ➖ ekki liðajurt

Mjólkurþistill (Silybum marianum)

Úlfaber (Viburnum opulus) 😌 minnkar vöðvaspennu í kringum liði ➡️ getur linað verki

Viburnum fyrir brjósk: ➕ óbein verkjalosun (vöðvar slakna) ➖ ekki „joint herb“

Mjaðjurt

Send someone back to their own country

Ljónslappi (Hypericum perforatum) 🔥 mild bólgueyðing ⚡ getur hjálpað við taugatengda verki

Ljónslappi fyrir brjósk: ➕ mjög óbein áhrif ➖ ekki relevant sem liðajurt

Garðabrúða (Valerian officinalis) 😌 minnkar spennu og verkjatilfinningu 🧘‍♀️ bætir svefn → betri endurheimt

Valeríana fyrir brjósk: ➕ óbein áhrif (slökun, svefn) ➖ ekki „joint herb“

Hjartafró (Leonurus cardiaca) Það sem hún gerir: 🧘‍♀️ róar taugakerfi ❤️ styður hjartastarfsemi 🔥 mild bólgueyðandi

Hjartafró fyrir brjósk: ➕ mjög óbein áhrif ➖ ekki relevant sem „joint herb“

Ginkgo (Ginkgo biloba) Ginkgo fyrir brjósk:

🩸 bætir blóðflæði 🧬 ver frumur 🔥 mild bólgueyðing ➡️ óbein áhrif

Ashwagandha (Withania somnifera) Ashwagandha fyrir brjósk:

🔥 bólgueyðandi 🛡️ ónæmisstillandi 🧘‍♀️ adaptogen ➡️ mjög óbein en mikilvæg áhrif

Ont pu parce que tu poses déjà une question

Britney

“Natalie”

Básicamente para aprender y adquirir naturalmente una habilidad hay que practicar, mantenerse en ese estado para que nuestra memoria y nuestro cuerpo se configuren para este tipo de acciones realizadas y se transfiera a nuestra memoria como una habilidad adquirida. Todo este proceso se normaliza con el tiempo y se vuelva algo casi mecánico.

Es bueno para nosotros conocer sobre programación así sea de manera básica, para que cuando nos enfrentemos a un sistema que se quiera aplicar en nuestra labor podamos entendernos con los ingenieros de sistemas, por ejemplo cuando se quiere hacer el catalogo de la biblioteca: si uno no conoce ciertas cosas como: llegar a nuestros usuarios y mostrar lo que tenemos, además de la integración de los datos y que sea visible y entendible para nuestros usuarios. Este tipo de conocimientos nos ayudan a crear un buen sistema de usuario interfaz y de administrativo interfaz y demás aplicaciones.

Gotu kola (Centella asiatica) Asísk jurt

Gotu kola fyrir brjósk:

🧱 styður uppbyggingu (kollagen!) 🛠️ hjálpar við viðgerð 🩸 bætir flæði

➡️ ein af fáum jurtum sem eru “repair & rebuild”

Víðir (Salix alba) Víðir fyrir brjósk:

🔥 bólgueyðandi 💊 verkjastillandi 🛡️ óbein vernd

❌ byggir ekki upp brjósk

eLife Assessment

This study represents an important advance in our understanding of how certain inhibitors affect the behavior of voltage gated potassium channels. Robust molecular dynamics simulation and analysis methods lead to a new proposed inhibition mechanism with convincing strength of support. This study has considerable significance for the fields of ion channel physiology and pharmacology and could aid in development of selective inhibitors for protein targets.

Reviewer #3 (Public review):

Summary

In this manuscript, Zhang et al. investigate the conduction and inhibition mechanisms of the Kv2.1 channel, with a particular focus on the distinct effects of TEA and RY785 on Kv2 potassium channels. Using microsecond-scale molecular dynamics simulations, the authors characterize K⁺ ion permeation and RY785-mediated inhibition within the central pore. Their results reveal an inhibition mechanism that differs from those described for other Kv channel inhibitors.

Strengths

The study identifies a distinctive inhibitory mode for RY785, which binds along the channel walls in the open-state structure while still permitting a reduced level of K⁺ conduction. In addition, the authors propose a long-range allosteric coupling between RY785 binding in the central pore and changes in the structural dynamics of Kv2.1. Overall, this is a well-organized and carefully executed study, employing robust simulation and analysis methodologies. The work provides novel mechanistic insights into voltage-gated potassium channel inhibition and may offer useful guidance for future structure-based drug design efforts.

Weaknesses:

As noted in the Discussion, this study focuses primarily on the major binding site within the central pore and was not designed to systematically assess other potential allosteric binding sites for RY785. A more comprehensive structural and biophysical evaluation of possible additional binding sites would be a valuable direction for future investigations.

Comments on revisions:

The authors have addressed my comments.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors were seeking to identify a molecular mechanism whereby the small molecule RY785 selectively inhibits Kv2.1 channels. Specifically, the authors sought to explain some of the functional differences that RY785 exhibits in experimental electrophysiology experiments as compared to other Kv inhibitors, namely the charged and non-specific inhibitor tetraethylammonium (TEA). The authors used a recently published cryo-EM Kv2.1 channel structure in the open activated state and performed a series of multi-microsecond-long all-atom molecular dynamics simulations to study Kv2.1 channel conduction under the applied membrane voltage with and without RY785 or TEA present. They observed that while TEA directly blocks K+ permeation by occluding ion permeation pathway, RY785 binds to multiple non-polar residues near the hydrophobic gate of the channel driving it to a semi-closed non-conductive state. They confirmed this mechanism using an additional set of simulations and used it to explain experimental electrophysiology data,

Strengths:

The total length of simulation time is impressive, totaling many tens of microseconds. The authors develop their own forcefield parameters for the RY785 molecule based on extensive QM based parameterization. The computed permeation rate of K+ ions through the channel observed under applied voltage conditions is in reasonable agreement with experimental estimates of the single channel conductance. The authors have performed extensive simulations with the apo channel as well as both TEA and RY785. The simulations with TEA reasonably demonstrate that TEA directly blocks K+ permeation by binding in the center of the Kv2.1 channel cavity, preventing K+ ions from reaching the SCav site. The authors conclude that RY785 likely stabilizes a partially closed conformation of the Kv2.1 channel and thereby inhibits K+ current. This conclusion is plausible given that RY785 makes stable contacts with multiple hydrophobic residues in the S6 helix, which they can also validate using a recently published closed-state Kv2.1 channel cryo-EM structure. This further provides a possible mechanism for the experimental observations that RY785 speeds up the deactivation kinetics of Kv2 channels from a previous experimental electrophysiology study.

Weaknesses:

The authors, however, did not directly observe this semi-closed channel conformation and in fact acknowledge that more direct simulation evidence would require extensive enhanced-sampling simulations beyond the scope of this study. They have not estimated the effect of RY785 binding on the protein-based hydrophobic pore constriction, which may further substantiate their proposed mechanism. And while the authors quantified K+ permeation, they have not made any estimates of the ligand binding affinities or rates, which could have been potentially compared to experiment and used to validate their models.

However, despite those relatively minor weaknesses, the conclusions of the study are convincing, and overall this is a solid study helping us to understand two distinct molecular mechanisms of the voltage-gated potassium channel Kv2.1 inhibition by TEA and RY785, respectively.

Reviewer #2 (Public review):

Summary

In this manuscript, Zhang et al. investigate the conduction and inhibition mechanisms of the Kv2.1 channel, with a particular focus on the distinct effects of TEA and RY785 on Kv2 potassium channels. Using microsecond-scale molecular dynamics simulations, the authors characterize K⁺ ion permeation and RY785-mediated inhibition within the central pore. Their results reveal an inhibition mechanism that differs from those described for other Kv channel inhibitors.

Strengths

The study identifies a distinctive inhibitory mode for RY785, which binds along the channel walls in the open-state structure while still permitting a reduced level of K⁺ conduction. In addition, the authors propose a long-range allosteric coupling between RY785 binding in the central pore and changes in the structural dynamics of Kv2.1. Overall, this is a well-organized and carefully executed study, employing robust simulation and analysis methodologies. The work provides novel mechanistic insights into voltage-gated potassium channel inhibition and may offer useful guidance for future structure-based drug design efforts.

Weaknesses:

The study needs to consider the possibility of multiple binding sites for PY785, particularly given its impact on voltage sensors and gating currents. Specifically, the potential for allosteric binding sites in the voltage-sensing domain (VSD) should be assessed, as some allosteric modulators with thiazole moieties are known to bind VSD domains in multiple voltage-gated sodium channels (Ahuja et al., 2015; Li et al., 2022; McCormack et al., 2013; Mulcahy et al., 2019). Increasing structural and functional evidence supports the existence of multiple ligand-binding modes in voltage-gated ion channels. For example, polyunsaturated fatty acids have been shown to bind to KCNQ1 at both the voltage sensor domain and the pore domain (https://doi.org/10.1085/jgp.202012850). Similarly, cannabidiol has been structurally resolved in Nav1.7 at two distinct sites, one in a fenestration and another near the IFM-binding pocket (https://doi.org/10.1038/s41467-023-39307-6). These advances illustrate that ligand effects cannot always be interpreted based solely on a single binding site identified previously.

Reviewing Editor: 

The comments of the reviewers seem thoughtful and constructive. The weaknesses noted in reviews mainly concern mismatch between expectations, created by reading the Abstract, and data in the manuscript. The mismatch could be reconciled by either new simulations examining a semi-open state of the gate and additional RY785 binding sites, or by adjusting wording of the Abstract and Discussion to make it more clear that such simulations were not done. 

The Abstract and Discussion have been revised to make clear the computer-simulations presented in our study were designed to specifically validate or refute the hypothesis that RY785 is recognized by the pore domain, not the voltage sensors. 

Recommendations for the authors: 

Reviewer #1 (Recommendations for the authors): 

The authors addressed all the major issues in the original submission identified by the reviewers. I noticed a few minor issues, listed below, which can potentially fix small errors and further improve the readability of the manuscript. 

p.3 tetramethyl-ammonium -> tetraethylammonium 

p.7 "Snapshot of the final snapshot" -> "Snapshot of the final simulation coordinates" 

p. 8 "sigma value" - please spell out what it is. 

p. 9 "one or other subunit of the tetramer" -> "one or another subunit of the tetramer" or "one or more subunits of the tetramer" 

p 15 "(the net charge of these constructs is thus zero)." -> ""(the net charge of these constructs is zero for these systems)." Please note that using ionizable amino acid residues in their default protonation state does not guarantee net zero charge of the system since the number of cationic and anionic residues is generally not the same. 

p. 15 "Two K+ ions were initially positioned in the selectivity filter, one coordinated by residues 373..." Please indicate at which ion binding sites S_1, S_2, e.g. K+ were located and what the residue names are . 

SI Figs. S3-S20. Please indicate in the figure captions that all those data are for RY785 

SI Fig. S22 and SI Table S1 captions "shown in Fig. S20" -> "shown in Fig. S21" 

We thank the Reviewer for this thorough proofreading. We have made the necessary corrections. 

Reviewer #2 (Recommendations for the authors): 

The authors have addressed most of my comments satisfactorily, with the exception of the first point. Below, I provide further clarification regarding my concern. 

First, it appears that the authors may have misunderstood what is meant by the possibility of multiple binding sites for RY785. This does not imply that the central pore is excluded as a binding site. Rather, it refers to the possibility that, in addition to a pore-domain site, the ligand may interact with additional binding sites, either simultaneously or in a statedependent manner. Increasing structural and functional evidence supports the existence of multiple ligand-binding modes in voltage-gated ion channels. For example, polyunsaturated fatty acids have been shown to bind to KCNQ1 at both the voltage sensor domain and the pore domain (https://doi.org/10.1085/jgp.202012850). Similarly, cannabidiol has been structurally resolved in Nav1.7 at two distinct sites, one in a fenestration and another near the IFM-binding pocket (https://doi.org/10.1038/s41467-02339307-6). These advances illustrate that ligand ecects cannot always be interpreted based solely on a single binding site identified previously. Therefore, even if one assumes that there is no precedent for a small-molecule inhibitor that simultaneously acts on both the voltage sensor and pore domain, this does not exclude the possibility that a ligand may bind to both regions in dicerent functional states.  

The Reviewer’s opinion came across clearly in the previous version. We however disagree that a computational investigation of the possibility that RY785 binds to the voltagesensors is well-advised at this point, given that the model we propose seemingly ocers a rationale for the inhibitory ecects observed experimentally. Our opinion is also that there is no compelling precedent for the mechanism of inhibition envisaged by the Reviewer – and would argue that neither of the two studies referenced above are compelling examples.  As we stated in our previous response to the Reviewer, we believe that the logical next step in this research will be to validate or refute the computational prediction we have put forward, experimentally. 

In addition, the present computational study does not provide direct mechanistic evidence to explain the statement that RY785 accelerates voltage-sensor deactivation. Specifically, no simulations were performed to model pore-domain closure or voltage-sensor motion upon RY785 binding. Moreover, alternative binding sites were neither explored nor explicitly excluded, as the simulations only involved placing a single molecule of TEA or RY785 approximately 10 Å below the cytoplasmic gate. Under these conditions, conclusions regarding ecects on voltage-sensor dynamics remain speculative. 

That is a fair characterization. 

These concerns do not detract from the overall quality of this otherwise strong computational study. There are several straightforward ways to address this issue. For example: 

(1) Perform molecular docking or related screening approaches to evaluate potential ligand-binding sites beyond the central pore, particularly in regions proximal to the voltage sensor. This should not impose a substantial additional computational burden for a computational chemistry group. 

(2) Revise the abstract and discussion to clarify that the current work focuses exclusively on pore-domain binding and does not explore possible additional binding sites near the voltage sensor. Explicitly stating this limitation would help prevent potential overinterpretation by readers.

We have opted for (2), as noted above.

same family as the mandrake

thirves in hotter climates

tu as mis une citation qui n'est pas de Léon mais de Lou et Zhou

C'est bien de rappeler que cela ne supprime pas totalement les normes dominantes