eLife assessment

The authors' innovative use of single-cell sequencing combined with physiological phenotyping of 5 different Parkinsons models in Drosophila provides compelling support for the important conclusion that these different models have a shared convergent effect on olfactory projection neuron (OPN) dysfunction. The effect on OPN occurs early in disease progression, and likely underlies anosmia observed as an early symptom of PD. Additional experiments and analysis are required to support the authors' suggestions that: (a) the defect in these models is specific to cholinergic OPNs; (b) that OPN degeneration is (causally) connected to dopaminergic neuron (DAN) degeneration; and also (c) that observed motor defects are reasonable measure of DAN dysfunction.

Reviewer #1 (Public Review):

This is a fantastic, comprehensive, timely, and landmark pan-species work that demonstrates the convergence of multiple familial PD mutations onto a synaptic program. It is extremely well written and I have only a few comments that do not require additional data collection.

Major Comments:

(1) In the functional experiments performing calcium imaging on projection neurons I could not find a count of cell bodies across conditions. Since the loss of OPNs could explain the reduced calcium signal, this is a critical control to perform. A differential abundance test on the single-cell data would also suffice here and be easy for the authors to perform with their existing data.

(2) One of the authors' conclusions is that cholinergic neurons and the olfactory system are acutely impacted by these PD mutations. However, I wonder if this is the case:<br /> a. Most Drosophila excitatory neurons are cholinergic and only a subpopulation appear to be dysregulated by these mutations. The authors point out that visual neurons also have many DEGs, couldn't the visual system also be dysregulated in these flies? Is there something special about these cholinergic neurons versus other cholinergic neurons in the fly brain? I wonder if they can leverage their nice dataset to say something about vulnerability.<br /> b. As far as I can tell, the cross-species analysis of DEGs (Figure 3) is agnostic to neuronal cell type, although the conclusion seems to suggest only cholinergic neurons were contrasted. Is this correct? Could you please clarify this in the text as it's an important detail. If not, Have the authors tried comparing only cholinergic neuron DEGs across species? That would lend strength to their specificity argument. The results for the NBM are impressive. Could the authors add more detail to the main text here about other regions to the main text?<br /> c. Uniquely within the human data, are cholinergic neurons more dysregulated than others? I understand this is not an early timepoint but would still be useful to discuss.<br /> d. In the discussion, the authors say that olfactory neurons are uniquely poised to be dysregulated as they are large and have high activity. Is this really true compared to other circuits? I didn't find the references convincing and I am not sure this has been borne out in electron microscopy reconstructions for anatomy.

Reviewer #2 (Public Review):

Summary:

Pech et al selected 5 Parkinson's disease-causing genes, and generated multiple Drosophila lines by replacing the Drosophila lrrk, rab39, auxilin (aux), synaptojanin (synj), and Pink1 genes with wild-type and pathogenic mutant human or Drosophila cDNA sequences. First, the authors performed a panel of assays to characterize the phenotypes of the models mentioned above. Next, by using single-cell RNA-seq and comparing fly data with human postmortem tissue data, the authors identified multiple cell clusters being commonly dysregulated in these models, highlighting the olfactory projection neurons. Next, by using selective expression of Ca2+-sensor GCaMP3 in the OPN, the authors confirmed the synaptic impairment in these models, which was further strengthened by olfactory performance defects.

Strengths:

The authors overall investigated the functionality of PD-related mutations at endogenous levels and found a very interesting shared pathway through single-cell analysis, more importantly, they performed nice follow-up work using multiple assays.

Weaknesses:

While the authors state this is a new collection of five familial PD knock-in models, the AuxR927G model has been published and carefully characterized in Jacquemyn et al., 2023. ERG has been performed for Aux R927G in Jacquemyn et al., 2023, but the findings are different from what's shown in Figure 1b and Supplementary Figure 1d, which the authors should try to explain. Moreover, according to the authors, the hPINK1control was the expression of human PINK1 with UAS-hPINK1 and nsyb-Gal4 due to technical obstacles.  Having PINK1 WT being an overexpression model, makes it difficult to explain PINK1 mutant phenotypes. It will be strengthened if the authors use UAS-hPINK1 and nsyb-Gal4 (or maybe ubiquitous Gal4) to rescue hPink1L347P and hPink1P399L phenotypes. In addition, although the authors picked these models targeting different biology/ pathways, however, Aux and Synj both act in related steps of Clathrin-mediated endocytosis, with LRRK2 being their accessory regulatory proteins. Therefore, is the data set more favorable in identifying synaptic-related defects?

GH146-GAL4+ PNs are derived from three neuroblast lineages, producing both cholinergic and GABAergic inhibitory PNs (Li et al, 2017). Therefore, OPN neurons have more than "cholinergic projection neurons". How do we know from single-cell data that cholinergic neurons were more vulnerable across 5 models?

In Figure 1b, the authors assumed that locomotion defects were caused by dopaminergic neuron dysfunction. However, to better support it, the author should perform rescue experiments using dopaminergic neuron-specific Gal4 drivers. Otherwise, the authors may consider staining DA neurons and performing cell counting. Furthermore, the authors stated in the discussion, that "We now place cholinergic failure firmly ahead of dopaminergic system failure in flies", which feels rushed and insufficient to draw such a conclusion, especially given no experimental evidence was provided, particularly related to DA neuron dysfunction, in this manuscript.

It is interesting to see that different familial PD mutations converge onto synapses. The authors have suggested that different mechanisms may be involved directly through regulating synaptic functions, or indirectly through mitochondria or transport. It will be improved if the authors extend their analysis on Figure 3, and better utilize their single-cell data to dissect the mechanisms. For example, for all the candidates listed in Figure 3C, are they all altered in the same direction across 5 models?

While this approach is carefully performed, the authors should state in the discussions the strengths and the caveats of the current strategy. For example, what kind of knowledge have we gained by introducing these mutations at an endogenous locus? Are there any caveats of having scRNAseq at day 5 only but being compared with postmortem human disease tissue?

Reviewer #3 (Public Review):

Summary:

This study investigates the cellular and molecular events leading to hyposmia, an early dysfunction in Parkinson's disease (PD), which develops up to 10 years prior to motor symptoms. The authors use five Drosophila knock-in models of familial PD genes (LRRK2, RAB39B, PINK1, DNAJC6 (Aux), and SYNJ1 (Synj)), three expressing human genes and two Drosophila genes with equivalent mutations.

The authors carry out single-cell RNA sequencing of young fly brains and single-nucleus RNA sequencing of human brain samples. The authors found that cholinergic olfactory projection neurons (OPN) were consistently affected across the fly models, showing synaptic dysfunction before the onset of motor deficits, known to be associated with dopaminergic neuron (DAN) dysfunction.

Single-cell RNA sequencing revealed significant transcriptional deregulation of synaptic genes in OPNs across all five fly PD models. This synaptic dysfunction was confirmed by impaired calcium signalling and morphological changes in synaptic OPN terminals. Furthermore, these young PD flies exhibited olfactory behavioural deficits that were rescued by selective expression of wild-type genes in OPNs.

Single-nucleus RNA sequencing of post-mortem brain samples from PD patients with LRRK2 risk mutations revealed similar synaptic gene deregulation in cholinergic neurons, particularly in the nucleus basalis of Meynert (NBM). Gene ontology analysis highlighted enrichment for processes related to presynaptic function, protein homeostasis, RNA regulation, and mitochondrial function.

This study provides compelling evidence for the early and primary involvement of cholinergic dysfunction in PD pathogenesis, preceding the canonical DAN degeneration. The convergence of familial PD mutations on synaptic dysfunction in cholinergic projection neurons suggests a common mechanism contributing to early non-motor symptoms like hyposmia. The authors also emphasise the potential of targeting cholinergic neurons for early diagnosis and intervention in PD.

Strengths:

This study presents a novel approach, combining multiple mutants to identify salient disease mechanisms. The quality of the data and analysis is of a high standard, providing compelling evidence for the role of OPN neurons in olfactory dysfunction in PD. The comprehensive single-cell RNA sequencing data from both flies and humans is a valuable resource for the research community. The identification of consistent impairments in cholinergic olfactory neurons, at early disease stages, is a powerful finding that highlights the convergent nature of PD progression. The comparison between fly models and human patients' brains provides strong evidence of the conservation of molecular mechanisms of disease, which can be built upon in further studies using flies to prove causal relationships between the defects described here and neurodegeneration.

The identification of specific neurons involved in olfactory dysfunction opens up potential avenues for diagnostic and therapeutic interventions.

Weaknesses:

The causal relationship between early olfactory dysfunction and later motor symptoms in PD remains unclear. It is also uncertain whether this early defect contributes to neurodegeneration or is simply a reflection of the sensitivity of olfactory neurons to cellular impairments. The study does not investigate whether the observed early olfactory impairment in flies leads to later DAN deficits. Additionally, the single-cell RNA sequencing analysis reveals several affected neuronal populations that are not further explored. The main weakness of the paper is the lack of conclusive evidence linking early olfactory dysfunction to later disease progression. The rationale behind the selection of specific mutants and neuronal populations for further analysis could be better qualified.

eLife assessment

This study uses state-of-the-art methods to label endogenous dopamine receptors in a subset of Drosophila mushroom body neuronal types. The authors report that DopR1 and Dop2R receptors, which have opposing effects in intracellular cAMP, are present in axons termini of Kenyon cells, as well as those of two classes of dopaminergic neurons that innervate the mushroom body indicative of autocrine modulation by dopaminergic neurons. Additional experiments showing opposing effects of starvation on DopR1 and DopR2 levels in mushroom body neurons are consistent with a role for dopamine receptor levels increasing the efficiency of learned food-odour associations in starved flies. Supported by solid data, this is a valuable contribution to the field.

Reviewer #1 (Public Review):

Summary:

This is an important and interesting study that uses the split-GFP approach. Localization of receptors and correlating them to function is important in understanding the circuit basis of behavior.

Strengths:

The split-GFP approach allows visualization of subcellular enrichment of dopamine receptors in the plasma membrane of GAL4-expressing neurons allowing for a high level of specificity.

The authors resolve the presynaptic localization of DopR1 and Dop2R, in "giant" Drosophila neurons differentiated from cytokinesis-arrested neuroblasts in culture as it is not clear in the lobes and calyx.

Starvation-induced opposite responses of dopamine receptor expression in the PPL1 and PAM DANs provide key insights into models of appetitive learning.

Starvation-induced increase in D2R allows for increased negative feedback that the authors test in D2R knockout flies where appetitive memory is diminished.

This dual autoreceptor system is an attractive model for how amplitude and kinetics of dopamine release can be fine-tuned and controlled depending on the cellular function and this paper presents a good methodology to do it and a good system where the dynamics of dopamine release can be tested at the level of behavior.

Weaknesses:

LI measurements of Kenyon cells and lobes indicate that Dop2R was approximately twice as enriched in the lobe as the average density across the whole neuron, while the lobe enrichment of Dop1R1 was about 1.5 times the average, are these levels consistent during different times of the day and the state of the animal. How were these conditions controlled and how sensitive are receptor expression to the time of day of dissection, staining, etc.

The authors assume without discussion as to why and how presynaptic enrichment of these receptors is similar in giant neurons and MB.

Figures 1-3 show the expensive expression of receptors in alpha and beta lobes while Figure 5 focusses on PAM and localization in γ and β' projections of PAM leading to the conclusion that pre-synaptic dopamine neurons express these and have feedback regulation. Consistency between lobes or discussion of these differences is important to consider.

Receptor expression in any learning-related MBONs is not discussed, and it would be intriguing as how receptors are organized in those cells. Given that these PAMs input to both KCs and MBONs these will have to work in some coordination.

Although authors use the D2R enhancement post starvation to show that knocking down receptors eliminated appetitive memory, the knocking out is affecting multiple neurons within this circuit including PAMs and KCs. How does that account for the observed effect? Are those not important for appetitive learning?

The evidence for fine-tuning is completely based on receptor expression and one behavioral outcome which could result from many possibilities. It is not clear if this fine-tuning and presynaptic feedback regulation-based dopamine release is a clear possibility. Alternate hypotheses and outcomes could be considered in the model as it is not completely substantiated by data at least as presented.

Reviewer #2 (Public Review):

Summary:

Hiramatsu et al. investigated how cognate neurotransmitter receptors with antagonizing downstream effects localize within neurons when co-expressed. They focus on mapping the localization of the dopaminergic Dop1R1 and Dop2R receptors, which correspond to the mammalian D1- and D2-like dopamine receptors, which have opposing effects on intracellular cAMP levels, in neurons of the Drosophila mushroom body (MB). To visualize specific receptors in single neuron types within the crowded MB neuropil, the authors use existing dopamine receptor alleles tagged with 7 copies of split GFP to target reconstitution of GFP tags only in the neurons of interest as a read-out of receptor localization. The authors show that both Dop1R1 and Dop2R, with differing degrees, are enriched in axonal compartments of both the Kenyon Cells cholinergic presynaptic inputs and in different dopamine neurons (DANs), which project axons to the MB. Co-localization studies of dopamine receptors with the presynaptic marker Brp suggest that Dop1R1 and, to a larger extent Dop2R, localize in the proximity of release sites. This localization pattern in DANs suggests that Dop1R1 and Dop2R work in dual-feedback regulation as autoreceptors. Finally, they provide evidence that the balance of Dop1R1 and Dop2R in the axons of two different DAN populations is differentially modulated by starvation and that this regulation plays a role in regulating appetitive behaviors.

Strengths:

The authors use reconstitution of GFP fluorescence of split GFP tags knocked into the endogenous locus at the C-terminus of the dopamine receptors as a readout of dopamine receptor localization. This elegant approach preserves the endogenous transcriptional and post-transcriptional regulation of the receptor, which is essential for studies of protein localization.

The study focuses on mapping the localization of dopamine receptors in neurons of the mushroom body. This is an excellent choice of system to address the question posed in this study, as the neurons are well-studied, and their connections are carefully reconstructed in the mushroom body connectome. Furthermore, the role of this circuit in different behaviors and associative memory permits the linking of patterns of receptor localization to circuit function and resulting behavior. Because of these features, the authors can provide evidence that two antagonizing dopamine receptors can act as autoreceptors within the axonal compartment of MB innervating DANs. The differential regulation of the balance of the two receptors under starvation in two distinct DAN innervations provides evidence of the role that regulation of this balance can play in circuit function and behavioral output.

Weaknesses:

The approach of using endogenously tagged alleles to study localization is a strength of this study, but the authors do not provide sufficient evidence that the insertion of 7 copies of split GFP to the C terminus of the dopamine receptors does not interfere with the endogenous localization pattern or function. Both sets of tagged alleles (1X Venus and 7X split GFP tagged) were previously reported (Kondo et al., 2020), but only the 1X Venus tagged alleles were further functionally validated in assays of olfactory appetitive memory. Despite the smaller size of the 7X split-GFP array tag knocked into the same location as the 1X venus tag, the reconstitution of 7 copies of GFP at the C terminus of the dopamine receptor, might substantially increase the molecular bulk at this site, potentially impeding the function of the receptor more significantly than the smaller, single Venus tag. The data presented by Kondo et al. 2020, is insufficient to conclude that the two alleles are equivalent.

The authors' conclusion that the receptors localize to presynaptic sites is weak. The analysis of the colocalization of the active zone marker Brp whole-brain staining with dopamine receptors labeled in specific neurons is insufficient to conclude that the receptors are localized at presynaptic sites. Given the highly crowded neuropil environment, the data cannot differentiate between the receptor localization postsynaptic to a dopamine release site or at a presynaptic site within the same neuron. The known distribution of presynaptic sites within the neurons analyzed in the study provides evidence that the receptors are enriched in axonal compartments, but co-labeling of presynaptic sites and receptors in the same neuron or super-resolution methods are needed to provide evidence of receptor localization at active zones. The data presented in Figures 5K-5L provides compelling evidence that the receptors localize to neuronal varicosities in DANs where the receptors could play a role as autoreceptors.

Given the highly crowded environment of the mushroom body neuropil, the analysis of dopamine receptor localization in Kenyon cells is not conclusive. The data is sufficient to conclude that the receptors are preferentially localizing to the axonal compartment of Kenyon cells, but co-localization with brain-wide Brp active zone immunostaining is not sufficient to determine if the receptor localizes juxtaposed to dopaminergic release sites, in proximity of release sites in Kenyon cells, or both.

eLife assessment

This important work, leveraging state-of-the-art whole-night sleep EEG-fMRI methods, advances our understanding of the brain states underlying sleep and wakefulness. Despite a small sample size, the authors present convincing evidence for substates within N2 and REM sleep stages, with reliable transition structure, supporting the perspective that there are more than the five canonical sleep/wake states.

Reviewer #1 (Public Review):

Summary:

The study made fundamental findings in investigations of the dynamic functional states during sleep. Twenty-one HMM states were revealed from the fMRI data, surpassing the number of EEG-defined sleep stages, which can define sub-states of N2 and REM. Importantly, these findings were reproducible over two nights, shedding new light on the dynamics of brain function during sleep.

Strengths:

The study provides the most compelling evidence on the sub-states of both REM and N2 sleep. Moreover, they showed these findings on dynamics states and their transitions were reproducible over two nights of sleep. These novel findings offered unique information in the field of sleep neuroimaging.

Weaknesses:

The only weakness of this study has been acknowledged by the authors: limited sample size.

Reviewer #2 (Public Review):

Summary:

Yang and colleagues used a Hidden Markov Model (HMM) on whole-night fMRI to isolate sleep and wake brain states in a data-driven fashion. They identify more brain states (21) than the five sleep/wake stages described in conventional PSG-based sleep staging, show that the identified brain states are stable across nights, and characterize the brain states in terms of which networks they primarily engage.

Strengths:

This work's primary strengths are its dataset of two nights of whole-night concurrent EEG-fMRI (including REM sleep), and its sound methodology.

Weaknesses:

The study's weaknesses are its small sample size and the limited attempts at relating the identified fMRI brain states back to EEG.

General appraisal:

The paper's conclusions are generally well-supported, but some additional analyses and discussions could improve the work.

The authors' main focus lies in identifying fMRI-based brain states, and they succeed at demonstrating both the presence and robustness of these states in terms of cross-night stability. Additional characterization of brain states in terms of which networks these brain states primarily engage adds additional insights.

A somewhat missed opportunity is the absence of more analyses relating the HMM states back to EEG. It would be very helpful to the sleep field to see how EEG spectra of, say, different N2-related HMM states compare. Similarly, it is presently unclear whether anything noticeable happens within the EEG time course at the moment of an HMM class switch (particularly when the PSG stage remains stable). While the authors did look at slow wave density and various physiological signals in different HMM states, a characterization of the EEG itself in terms of spectral features is missing. Such analyses might have shown that fMRI-based brain states map onto familiar EEG substates, or reveal novel EEG changes that have so far gone unnoticed.

It is unclear how the presently identified HMM brain states relate to the previously identified NREM and wake states by Stevner et al. (2019), who used a roughly similar approach. This is important, as similar brain states across studies would suggest reproducibility, whereas large discrepancies could indicate a large dependence on particular methods and/or the sample (also see later point regarding generalizability).

More justice could be done to previous EEG-based efforts moving beyond conventional AASM-defined sleep/wake states. Various EEG studies performed data-driven clustering of brain states, typically indicating more than 5 traditional brain states (e.g., Koch et al. 2014, Christensen et al. 2019, Decat. et al 2022). Beyond that, countless subdivisions of classical sleep stages have been proposed (e.g., phasic/tonic REM, N2 with/without spindles, N3 with global/local slow waves, cyclic alternating patterns, and many more). While these aren't incorporated into standard sleep stage classification, the current manuscript could be misinterpreted to suggest that improved/data-driven classifications cannot be achieved from EEG, which is incorrect.

More discussion of the limitations of the current sample and generalizability would be helpful. A sample of N=12 is no doubt impressive for two nights of concurrent whole-night EEG-fMRI. Still, any data-driven approach can only capture the brain states that are present in the sample, and 12 individuals are unlikely to express all brain states present in the population of young healthy individuals. Add to that all the potentially different or altered brain states that come with healthy ageing, other demographic variables, and numerous clinical disorders. How do the authors expect their results to change with larger samples and/or varying these factors? Perhaps most importantly, I think it's important to mention that the particular number of identified brain states (here 21, and e.g. 19 in Stevner) is not set in stone and will likely vary as a function of many sample- and methods-related factors.

Reviewer #1 (Public Review):

Summary:

This work studies representations in a network with one recurrent layer and one output layer that needs to path-integrate so that its position can be accurately decoded from its output. To formalise this problem, the authors define a cost function consisting of the decoding error and a regularisation term. They specify a decoding procedure that at a given time averages the output unit center locations, weighted by the activity of the unit at that time. The network is initialised without position information, and only receives a velocity signal (and a context signal to index the environment) at each timestep, so to achieve low decoding error it needs to infer its position and keep it updated with respect to its velocity by path integration.

The authors take the trained network and let it explore a series of environments with different geometries while collecting unit activities to probe learned representations. They find localised responses in the output units (resembling place fields) and border responses in the recurrent units. Across environments, the output units show global remapping and the recurrent units show rate remapping. Stretching the environment generally produces stretched responses in output and recurrent units. Ratemaps remain stable within environments and stabilise after noise injection. Low-dimensional projections of the recurrent population activity forms environment-specific clusters that reflect the environment's geometry, which suggests independent rather than generalised representations. Finally, the authors discover that the centers of the output unit ratemaps cluster together on a triangular lattice (like the receptive fields of a single grid cell), and find significant clustering of place cell centers in empirical data as well.

The model setup and simulations are clearly described, and are an interesting exploration of the consequences of a particular set of training requirements - here: path integration and decodability. But it is not obvious to what extent the modelling choices are a realistic reflection of how the brain solves navigation. Therefore it is not clear whether the results generalize beyond the specifics of the setup here.

Strengths:

The authors introduce a very minimal set of model requirements, assumptions, and constraints. In that sense, the model can function as a useful 'baseline', that shows how spatial representations and remapping properties can emerge from the requirement of path integration and decodability alone. Moreover, the authors use the same formalism to relate their setup to existing spatial navigation models, which is informative.

The global remapping that the authors show is convincing and well-supported by their analyses. The geometric manipulations and the resulting stretching of place responses, without additional training, are interesting. They seem to suggest that the recurrent network may scale the velocity input by the environment dimensions so that the exact same path integrator-output mappings remain valid (but maybe there are other mechanisms too that achieve the same).

The clustering of place cell peaks on a triangular lattice is intriguing, given there is no grid cell input. It could have something to do with the fact that a triangular lattice provides optimal coverage of 2d space? The included comparison with empirical data is valuable, although the authors only show significant clustering - there is no analysis of its grid-like regularity.

Weaknesses:

The navigation problem that needs to be solved by the model is a bit of an odd one. Without any initial position information, the network needs to figure out where it is, and then path-integrate with respect to a velocity signal. As the authors remark in Methods 4.2, without additional input, the only way to infer location is from border interactions. It is like navigating in absolute darkness. Therefore, it seems likely that the salient wall representations found in the recurrent units are just a consequence of the specific navigation task here; it is unclear if the same would apply in natural navigation. In natural navigation, there are many more sensory cues that help inferring location, most importantly vision, but also smell and whiskers/touch (which provides a more direct wall interaction; here, wall interactions are indirect by constraining velocity vectors). There is a similar but weaker concern about whether the (place cell like) localised firing fields of the output units are a direct consequence of the decoding procedure that only considers activity center locations.

The conclusion that 'contexts are attractive' (heading of section 2) is not well-supported. The authors show 'attractor-like behaviour' within a single context, but there could be alternative explanations for the recovery of stable ratemaps after noise injection. For example, the noise injection could scramble the network's currently inferred position, so that it would need to re-infer its position from boundary interactions along the trajectory. In that case the stabilisation would be driven by the input, not just internal attractor dynamics. Moreover, the authors show that different contexts occupy different regions in the space of low-dimensional projections of recurrent activity, but not that these regions are attractive.

The authors report empirical data that shows clustering of place cell centers like they find for their output units. They report that 'there appears to be a tendency for the clusters to arrange in hexagonal fashion, similar to our computational findings'. They only quantify the clustering, but not the arrangement. Moreover, in Figure 7e they only plot data from a single animal, then plot all other animals in the supplementary. Does the analysis of Fig 7f include all animals, or just the one for which the data is plotted in 7e? If so, why that animal? As Appendix C mentions that the ratemap for the plotted animal 'has a hexagonal resemblance' whereas other have 'no clear pattern in their center arrangements', it feels like cherrypicking to only analyse one animal without further justification.

Reviewer #2 (Public Review):

Summary:<br /> The authors proposed a neural network model to explore the spatial representations of the hippocampal CA1 and entorhinal cortex (EC) and the remapping of these representations when multiple environments are learned. The model consists of a recurrent network and output units (a decoder) mimicking the EC and CA1, respectively. The major results of this study are: the EC network generates cells with their receptive fields tuned to a border of the arena; decoder develops neuron clusters arranged in a hexagonal lattice. Thus, the model accounts for entrohinal border cells and CA1 place cells. The authors also suggested the remapping of place cells occurs between different environments through state transitions corresponding to unstable dynamical modes in the recurrent network.

Strengths:<br /> The authors found a spatial arrangement of receptive fields similar to their model's prediction in experimental data recorded from CA1. Thus, the model proposes a plausible mechanisms to generate hippocampal spatial representations without relying on grid cells. This result is consistent with the observation that grid cells are unnecessary to generate CA1 place cells.

The suggestion about the remapping mechanism shows an interesting theoretical possibility.

Weaknesses:<br /> The explicit mechanisms of generating border cells and place cells and those underlying remapping were not clarified at a satisfactory level.

The model cannot generate entorhinal grid cells. Therefore, how the proposed model is integrated into the entire picture of the hippocampal mechanism of meｍory processing remains elusive.

Reviewer #3 (Public Review):

Summary:

The authors used recurrent neural network modelling of spatial navigation tasks to investigate border and place cell behaviour during remapping phenomena.

Strengths:

The neural network training seemed for the most part (see comments later) well-performed, and the analyses used to make the points were thorough.

The paper and ideas were well explained.

Figure 4 contained some interesting and strong evidence for map-like generalisation as environmental geometry was warped.

Figure 7 was striking, and potentially very interesting.

It was impressive that the RNN path-integration error stayed low for so long (Fig A1), given that normally networks that only work with dead-reckoning have errors that compound. I would have loved to know how the network was doing this, given that borders did not provide sensory input to the network. I could not think of many other plausible explanations... It would be even more impressive if it was preserved when the network was slightly noisy.

Weaknesses:

I felt that the stated neuroscience interpretations were not well supported by the presented evidence, for a few reasons I'll now detail.

First, I was unconvinced by the interpretation of the reported recurrent cells as border cells. An equally likely hypothesis seemed to be that they were positions cells that are linearly encoding the x and y position, which when your environment only contains external linear boundaries, look the same. As in figure 4, in environments with internal boundaries the cells do not encode them, they encode (x,y) position. Further, if I'm not misunderstanding, there is, throughout, a confusing case of broken symmetry. The cells appear to code not for any random linear direction, but for either the x or y axis (i.e. there are x cells and y cells). These look like border cells in environments in which the boundaries are external only, and align with the axes (like square and rectangular ones), but the same also appears to be true in the rotationally symmetric circular environment, which strikes me as very odd. I can't think of a good reason why the cells in circular environments should care about the particular choice of (x,y) axes... unless the choice of position encoding scheme is leaking influence throughout. A good test of these would be differently oriented (45 degree rotated square) or more geometrically complicated (two diamonds connected) environments in which the difference between a pure (x,y) code and a border code are more obvious.

Next, the decoding mechanism used seems to have forced the representation to learn place cells (no other cell type is going to be usefully decodable?). That is, in itself, not a problem. It just changes the interpretation of the results. To be a normative interpretation for place cells you need to show some evidence that this decoding mechanism is relevant for the brain, since this seems to be where they are coming from in this model. Instead, this is a model with place cells built into it, which can then be used for studying things like remapping, which is a reasonable stance.

However, the remapping results were also puzzling. The authors present convincing evidence that the recurrent units effectively form 6 different maps of the 6 different environments (e.g. the sparsity of the cod, or fig 6a), with the place cells remapping between environments. Yet, as the authors point out, in neural data the finding is that some cells generalise their co-firing patterns across environments (e.g. grid cells, border cells), while place cells remap, making it unclear what correspondence to make between the authors network and the brain. There are existing normative models that capture both entorhinal's consistent and hippocampus' less consistent neural remapping behaviour (Whittington et al. and probably others), what have we then learnt from this exercise?

One striking result was figure 7, the hexagonal arrangement of place cell centres. I had one question that I couldn't find the answer to in the paper, which would change my interpretation. Are place cell centres within a single clusters of points in figure 7a, for example, from one cell across the 100 trajectories, or from many? If each cluster belongs to a different place cell then the interpretation seems like some kind of optimal packing/coding of 2D space by a set of place cells, an interesting prediction. If multiple place cells fall within a single cluster then that's a very puzzling suggestion about the grouping of place cells into these discrete clusters. From figure 7c I guess that the former is the likely interpretation, from the fact that clusters appear to maintain the same colour, and are unlikely to be co-remapping place cells, but I would like to know for sure!

I felt that the neural data analysis was unconvincing. Most notably, the statistical effect was found in only one of seven animals. Random noise is likely to pass statistical tests 1 in 20 times (at 0.05 p value), this seems like it could have been something similar? Further, the data was compared to a null model in which place cell fields were randomly distributed. The authors claim place cell fields have two properties that the random model doesn't (1) clustering to edges (as experimentally reported) and (2) much more provocatively, a hexagonal lattice arrangement. The test seems to collude the two; I think that nearby ball radii could be overrepresented, as in figure 7f, due to either effect. I would have liked to see a computation of the statistic for a null model in which place cells were random but with a bias towards to boundaries of the environment that matches the observed changing density, to distinguish these two hypotheses.

Some smaller weaknesses:<br /> - Had the models trained to convergence? From the loss plot it seemed like not, and when including regularisors recent work (grokking phenomena, e.g. Nanda et al. 2023) has shown the importance of letting the regularisor minimise completely to see the resulting effect. Else you are interpreting representations that are likely still being learnt, a dangerous business.<br /> - Since RNNs are nonlinear it seems that eigenvalues larger than 1 doesn't necessarily mean unstable?<br /> - Why do you not include a bias in the networks? ReLU networks without bias are not universal function approximators, so it is a real change in architecture that doesn't seem to have any positives?<br /> - The claim that this work provided a mathematical formalism of the intuitive idea of a cognitive map seems strange, given that upwards of 10 of the works this paper cite also mathematically formalise a cognitive map into a similar integration loss for a neural network.

Aim Achieved? Impact/Utility/Context of Work

Given the listed weaknesses, I think this was a thorough exploration of how this network with these losses is able to path-integrate its position and remap. This is useful, it is good to know how another neural network with slightly different constraints learns to perform these behaviours. That said, I do not think the link to neuroscience was convincing, and as such, it has not achieved its stated aim of explaining these phenomena in biology. The mechanism for remapping in the entorhinal module seemed fundamentally different to the brain's, instead using completely disjoint maps; the recurrent cell types described seemed to match no described cell type (no bad thing in itself, but it does limit the permissible neuroscience claims) either in tuning or remapping properties, with a potentially worrying link between an arbitrary encoding choice and the responses; and the striking place cell prediction was unconvincingly matched by neural data. Further, this is a busy field in which many remapping results have been shown before by similar models, limiting the impact of this work. For example, George et al. and Whittington et al. show remapping of place cells across environments; Whittington et al. study remapping of entorhinal codes; and Rajkumar Vasudeva et al. 2022 show similar place cell stretching results under environmental shifts. As such, this papers contribution is muddied significantly.

Across all the articles depression is mentioned a lot and seeing that children take antidepressants is truly shocking

This tells us that mental illness has worsened over the years

More side effects means more drugs prescribed. Does this mean more opportunities to earn more profit? It is evident that psychoactive drugs are in demand yet healthcare system fails to make affordable

Seeing as how symptoms can relapse from withdrawal, this clarifies my understanding of how much more effective it can be to combine psychotherapy and medication than depending on one treatment alone

I am of the opinion that certain medications that are used in the field of mental health can and do cause further issues in the brain and its regular functioning. On the basis of a few of the advertisements, the majority of pharmaceuticals, if not all of them, have adverse consequences. Therefore, we cannot assert that prescription medication is always the solution. When it comes to mental health, the provision of medications has shifted from assisting people to earning a profit.

A issue in and of itself is the misconception that medical professionals are more inclined to prescribe medication than they are to genuinely deliver therapeutic care. Instead of giving tools and other resources that are not related to drugs, they are essentially concerned with applying a bandage to the problem. It is true that some people require medicines in order to cope, but not everyone.

eLife assessment

This study presents a valuable finding that PRMT inhibitors may exert synergistic effects with PARP inhibitors to eliminate ovarian and triple-negative cancer cells in vitro and in vivo using preclinical mouse models. The evidence supporting the claims of the authors is solid, although the inclusion of novelty justification would have strengthened the study. The work will be of interest to scientists working on breast cancer and ovarian cancer.

Reviewer #1 (Public Review):

Summary:

The authors aimed to enhance the effectiveness of PARP inhibitors (PARPi) in treating high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC) by inhibiting PRMT1/5 enzymes. They conducted a drug screen combining PARPi with 74 epigenetic modulators to identify promising combinations.

Zhang et al. reported that protein arginine methyltransferase (PRMT) 1/5 inhibition acts synergistically to enhance the sensitivity of Poly (ADP-ribose) polymerase inhibitors (PARPi) in high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC) cells. The authors are the first to perform a drug screen by combining PARPi with 74 well-characterized epigenetic modulators that target five major classes of epigenetic enzymes. Their drug screen identified both PRMT1/5 inhibitors with high combination and clinical priority scores in PARPi treatment. Notably, PRMT1/5 inhibitors significantly enhance PARPi treatment-induced DNA damage in HR-proficient HGSOC and TNBC cells through enhanced maintenance of gene expression associated with DNA damage repair, BRCAness, and intrinsic innate immune pathways in cancer cells. Additionally, bioinformatic analysis of large-scale genomic and functional profiles from TCGA and DepMap further supports that PRMT1/5 are potential therapeutic targets in oncology, including HGSOC and TNBC. These results provide a strong rationale for the clinical application of a combination of PRMT and PARP inhibitors in patients with HR-proficient ovarian and breast cancer. Thus, this discovery has a high impact on developing novel therapeutic approaches to overcome resistance to PARPi in clinical cancer therapy. The data and presentation in this manuscript are straightforward and reliable.

Strengths:

(1) Innovative Approach: First to screen PARPi with a large panel of epigenetic modulators.<br /> (2) Significant Results: Found that PRMT1/5 inhibitors significantly boost PARPi effectiveness in HR-proficient HGSOC and TNBC cells.<br /> (3) Mechanistic Insights: Showed how PRMT1/5 inhibitors enhance DNA damage repair and immune pathways.<br /> (4) Robust Data: Supported by extensive bioinformatic analysis from large genomic databases.

Weaknesses:

(1) Novelty Clarification: Needs clearer comparison to existing studies showing similar effects.<br /> (2) Unclear Mechanisms: More investigation is needed on how MYC targets correlate with PRMT1/5.<br /> (3) Inconsistent Data: ERCC1 expression results varied across cell lines.<br /> (4) Limited Immune Study: Using immunodeficient mice does not fully explore immune responses.<br /> (5) Statistical Methods: Should use one-way ANOVA instead of a two-tailed Student's t-test for multiple comparisons.

Reviewer #2 (Public Review):

Summary:

The authors show that a combination of arginine methyltransferase inhibitors synergize with PARP inhibitors to kill ovarian and triple-negative cancer cell lines in vitro and in vivo using preclinical mouse models.

PARP inhibitors have been the common targeted-therapy options to treat high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC). PRMTs are oncological therapeutic targets and specific inhibitors have been developed. However, due to the insufficiency of PRMTi or PARPi single treatment for HGSOC and TNBC, designing novel combinations of existing inhibitors is necessary. In previous studies, the authors and others developed an "induced PARPi sensitivity by epigenetic modulation" strategy to target resistant tumors. In this study, the authors presented a triple combination of PRMT1i, PRMT5i and PARPi that synergistically kills TNBC cells. A drug screen and RNA-seq analysis were performed to indicate cancer cell growth dependency of PRMT1 and PRMT5, and their CRISPR/Cas9 knockout sensitizes cancer cells to PARPi treatment. It was shown that the cells accumulate DNA damage and have increased caspase 3/7 activity. RNA-seq analysis identified BRCAness genes, and the authors closely studied a top hit ERCC1 as a downregulated DNA damage protein in PRMT inhibitor treatments. ERCC1 is known to be synthetic lethal with PARP inhibitors. Thus, the authors add back ERCC1 and reduce the effects of PRMT inhibitors suggesting PRMT inhibitors mediate, in part, their effect via ERCC1 downregulation. The combination therapy (PRMT/PARP) is validated in 2D cultures of cell lines (OVCAR3, 8 and MDA-MB-231) and has shown to be effective in nude mice with MDA-MB-231 xenograph models.

Strengths and weaknesses:

Overall, the data is well-presented. The experiments are well-performed, convincing, and have the appropriate controls (using inhibitors and genetic deletions) and statistics.

They identify the DNA damage protein ERCC1 to be reduced in expression with PRMT inhibitors. As ERCC1 is known to be synthetic lethal with PARPi, this provides a mechanism for the synergy. They use cell lines only for their study in 2D as well as xenograph models.

measures in developing M&E

This is so interesting! Does that mean these things could be genetic? Or is this a result of our own environmental factors?

I believe it would be more effective to implement school-based programs and influence youths as they are still in early development

Two reasons this could be: (1) maybe it is better to start with low dosage than risking intense side effects that can emerge as other disorders AND (2) as stated earlier, primary care physical contact decreased as prescribing of antidepressants increased, so patients do not necessarily know if they need to adjust treatment

I would like to learn more about the long-term impact of these stressors on potential increase of mental disorders. I would assume that mental health issues can arise from these stressful events, especially from social issues that can result in personal discriminatory experiences. Like I stated earlier, this is why I believe free health care is the common denominator that would address some parts of these issues.

Frequent check-ups while taking medication is important to track symptoms and side effects and address them as they manifest.

Here, it is quite clear that the majority of nations offer inadequate therapy, and that this treatment is not adequately directed toward those who are in need. In particular, when it comes to mental illness, it is important to remember that every single person is unique and should be treated as such. For those who are afflicted with mental disease, there is no universally applicable treatment. This ought to be seen as a mental epidemic that is occurring all throughout the world.

Just in the United States of America, the fact that there is evidence pointing to low quality of mental health treatment is quite troubling. The fact that this is the case demonstrates that the United States has not taken mental illness and the treatment of persons with it seriously enough. In addition to being unsatisfactory, the fact that forty percent of patients report receiving only minimum therapy ought to be brought to the forefront.

I'm not very surprised by this unfortunately but I am surprised that while this was true, the president of the APA in 1905 was a woman.

I find this tricky that the perspective is hypothesizing a true disposition for "good" and continuing to move up in the heirarchy of needs throughout our lives. Unfortunately I think many people who have all of their physiological, security, and social needs met still find their self-worth and confidence in the form of putting others down. I think there is a fine line between positive self-worth and an inflated self-worth derived from negative thoughts or behaviors. While I can understand the heirarchy of needs, I'm not sure I see how it fits into the idea that it shows a true disposition for "good".

I find it surprising that he viewed the mind in this way and I can imagine that it can be difficult to study the unconscious mind because we aren't consciously thinking about it. I also wonder if his findings would be accurate at in his idea of the unconscious mind.

This is a tricky topic to wrap my head around because as they are stating that our thoughts that we constantly isn't matter or energy. I believe that they are stating that the y usually have to use experiments based on observation but to what extent does that limit the results of an experiment?

I find it interesting that through many experiments, scientist were able to identify what psychology is exactly and how to study it

shouldve been aemon as maester smh

so he led to elia's death...kill him

wtf am i reading

oh no...

oh he still remembers him

NOO

ugh give the sword back

insane

nvm bro why ruin the moment

HELP WHAT LMAO

ok but why are they a funny pair

ok thats iconic

16?? oh nahh

theres something sad about that

its raining rn

sons plural?

i like her (i'll ignore the earlier stuff)

black stone? hmm

atleast he's embarressed

freaks all of them FREAKS

oh her name's asha here

yeah you do

ohh nvm thats yara isn't it

so he wants to be dominated...

phew its goo that she's not yara

thats yara isnt it

such a small amry..

doesnt the winds of winter cover have a horn

shouldve gotten rid of the boltons

and he's right

because yk its wrong to live a women in this situation where her baby will be killed

the truama dump is insane

My name is Katelyn Hoesche, I am in the Rarotonga Tribe and I like supreme pizza

Hey guys! My name is Abby Trausch and I am apart of the Manihiki tribe. My preferred pizza toppings are pepperoni, mushrooms, and green peppers.

Hi my name is Aida Burks. I am in the Puka Puka tribe! My favorite pizza topping is Pepperoni!

Delete.

Mention and link to Weatherbench2 here.

replace climate with atmosphere in this paragraph. ERA5 is an atmospheric reanalysis.

The announcement has already happened with Xvec :) . Instead I would phrase as "describing how the Xarray ecosystem supports vector geometries".

Also I'd mention xvec in this first paragraph.

Can you install jupyterlab-code-formatter and ruff and/or black. It'll auto-format these cells to multiple lines for you.

"time.season"

Plotting

This is quite powerful, I'd add a bit about .xvec.query in the introduction.

AND here, you are showing how the geometry dimension is special, and enables nice interfaces

The time dimension is a speicfic example here. More importantly, you can do the "usual" multi-dimensional array things along the non-geometry dimensions. I would make that point.

.idxmax()

"Creating a vector data cube from a raster data cube"

set_coords(["city", "country"])

If you set them as coords, I bet you wouldn't need this.

I would also rename geometry -> city, and city->name maybe?

lat, lon are redundant with "geometry", I'd drop them to illustrate your points better

I couldn't run this notebook because I didn't have this function

To me, Polygons of interest are where things get interesting.

Points are reasonably handled without these geometries. But a polygon is a meaningful increase in metadata associated with an observation.

nice

I would consider illustrating the poiints here with a 5 row DataFrame.

"In memory, geometries are commonly represented as Shapely geometries"

This column can also be the index no?

Use "table" instead of "data frame"

When you use this kind of construction, always use the same verb i.e. "viewed", and the same ordering. For example - BAD: "A is better than B, while D is worse than C" - GOOD: "A is better than B, and C is better than D".

When you use the same ordering and minimize differences, it becomes a lot easier for the brain to parse.

You'll need to explain what a vector data cube is up top.

Long sentence, I would delete this.

Should check that these will render properly. Usually you need :tada:

This is so unfortunate for these children. Their placements being restricted due to the level of their mental and behavioral disorders, something that they did not ask for.

This fact made me question why Illinois has had consistent issues with the care of children in psychiatric hospitals? What about the state of Illinois makes it so difficult to release these children versus a state like Nevada?

How is this situation that much different from a jail sentence? Awaiting behind bars and not receiving support, this limits the growth of these individuals and their ability to make it in the real world once they’re released.

Admitting fault is a good step in the right direction, but actions speak louder than words. These children are supposed to be in care but this institution has dealt with years of recurring neglect. As a taxpayer, I do not want my money contributing towards the unnecessary hospitalization of these children who should be released.

This is an example of restricting these children of their human rights. Especially when they’re cleared and being kept for a prolonged amount of time. Lack of social interaction and educational support will severely set back these children in the future.

YES if the statement agrees with the claims of the writer

NO if the statement contradicts the claims of the writer

NOT GIVEN if it is impossible to say what the writer thinks about this

37 Campaigns designed to correct misinformation will fail to achieve their purpose if people are unable to understand them.

Q37: YES

Đoạn 7: For corrective campaigns to be persuasive,audiences need to be able to comprehend them

keyword của Q37

H. mental operation

Q38: Attempts to teach elementary school students about misinformation have been opposed. -> as early as elementary school.

Leyendo el texto, a mí opinión, el siguiente párrafo es sacado del documento para hacer referencia a cuál es el objetivo general: Yo creo que el punto más impor- tante y diferenciador de la epistemología cualitativa es, como te decía, el carácter constructivo-interpretativo del conocimiento que orienta la investigación cuali- tativa concreta. ¿En qué sentido esto ocurre? En la construcción de indicadores que nos permiten ir avanzando, no por expresiones explícitas de las personas estudiadas, sino por elementos indirectos que van tomando un valor en la construcción del inves- tigador, y que nos permiten generar hipótesis para producir un saber sobre la subjetividad, para construir un saber sobre el cual esta epistemología se desarrolló, y que nos permite construcciones que están más allá de la conciencia, de la intención y del lenguaje intencional de las personas estudiadas. Esto es un gran desafío, estos son caminos difíciles de inteligibilidad. Sin embargo, nos permiten saberes para explicar problemas que las otras teorías y epistemologías no nos posibilitaron.

Esta epistemología busca comprender y aprovecharla para obtener un conocimiento más profundo y matizado de la realidad social. La subjetividad no se ve como un obstáculo, sino como una fuente valiosa de información que puede revelar aspectos de la realidad social que no son accesibles a través de métodos puramente objetivos.

Argumentos que Soportan el Texto

1. Carácter Constructivo-Interpretativo: El argumento central del texto es que la epistemología cualitativa tiene un carácter constructivo-interpretativo. Esto significa que el conocimiento se construye a través de la interpretación de los datos, en lugar de simplemente recolectarlos y analizarlos de manera objetiva. Esta interpretación permite al investigador generar hipótesis y construir un saber que va más allá de lo explícitamente expresado por los sujetos estudiados.

2. Indicadores Indirectos: Otro argumento clave es la importancia de los indicadores indirectos en la investigación cualitativa. A diferencia de los enfoques tradicionales que se centran en las expresiones directas y explícitas de los sujetos, la epistemología cualitativa se enfoca en elementos indirectos que pueden no ser evidentes a simple vista. Estos indicadores indirectos son esenciales para comprender la subjetividad de los sujetos y para construir un conocimiento más profundo y complejo.

3. Más Allá de la Conciencia y el Lenguaje Intencional: El texto argumenta que el conocimiento sobre la subjetividad no puede limitarse a lo que los sujetos son conscientes o a lo que expresan intencionalmente. La epistemología cualitativa permite acceder a niveles más profundos de la subjetividad que están más allá de la conciencia y el lenguaje intencional de los sujetos. Esto amplía las posibilidades de comprensión y explicación de los fenómenos sociales.

4. Desafíos y Caminos de Inteligibilidad: Finalmente, el texto reconoce que esta aproximación presenta grandes desafíos y que los caminos para alcanzar esta comprensión profunda son difíciles de inteligibilidad. Sin embargo, sostiene que estos caminos permiten obtener saberes que son cruciales para explicar problemas que las teorías y epistemologías tradicionales no han podido resolver.

ID: 851661

adjective

1. intended to explain or describe something:

Earlier, I mentioned that there appears to be an issue inside the Department of Children and Family Services (DCFS) with the leadership and staff. It's possible that the entire organization has to undergo a complete makeover and reorganization. Can this be considered a solution? It is not clear to me, but something needs to be altered.

There is an issue within the Department of Children and Family Services, and the primary reason for this is that Marc Smith is the thirteenth leader in the decade. There is a need to conduct research in this particular field.

This makes it abundantly evident that there was no plan A, B, or C in place at the time that hundreds of residential treatment beds and 2,000 foster homes were taken away. What prevented this from being clearly thought out? The fact that they were cut demonstrates a blatant disrespect for the mental health of these youngsters with regard to their mental health.

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DOI: 10.1186/s12915-023-01636-9

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DOI: 10.1016/j.celrep.2024.114464

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What is this?

DOI: 10.3389/fcell.2023.1103923

Resource: (BDSC Cat# 64349,RRID:BDSC_64349)

Curator: @mpairish

SciCrunch record: RRID:BDSC_64349

What is this?

DOI: 10.3389/fcell.2023.1103923

Resource: (BDSC Cat# 3605,RRID:BDSC_3605)

Curator: @mpairish

SciCrunch record: RRID:BDSC_3605

What is this?

DOI: 10.1093/genetics/iyad004

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @mpairish

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1016/j.celrep.2024.114417

Resource: (KCB Cat# KCB 200970YJ, RRID:CVCL_0336)

Curator: @vtello

SciCrunch record: RRID:CVCL_0336

What is this?

DOI: 10.1016/j.celrep.2024.114417

Resource: CVCL_2749

Curator: @vtello

SciCrunch record: RRID:CVCL_2749

What is this?

DOI: 10.1016/j.stem.2024.06.004

Resource: (RRID:CVCL_9771)

Curator: @vtello

SciCrunch record: RRID:CVCL_9771

What is this?

DOI: 10.1016/j.stem.2024.06.004

Resource: (RRID:CVCL_9773)

Curator: @vtello

SciCrunch record: RRID:CVCL_9773

What is this?

DOI: 10.1016/j.molcel.2024.06.009

Resource: (Millipore Cat# SCC066, RRID:CVCL_U985)

Curator: @vtello

SciCrunch record: RRID:CVCL_U985

What is this?

DOI: 10.1097/WNR.0000000000001936

Resource: BDSC_77170

Curator: @anisehay

SciCrunch record: RRID:BDSC_77170

What is this?

DOI: 10.1097/WNR.0000000000001936

Resource: BDSC_36007

Curator: @anisehay

SciCrunch record: RRID:BDSC_36007

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: (BDSC Cat# 7018,RRID:BDSC_7018)

Curator: @anisehay

SciCrunch record: RRID:BDSC_7018

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: (BDSC Cat# 6,RRID:BDSC_6)

Curator: @anisehay

SciCrunch record: RRID:BDSC_6

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: BDSC_59269

Curator: @anisehay

SciCrunch record: RRID:BDSC_59269

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: RRID:BDSC_93857

Curator: @anisehay

SciCrunch record: RRID:BDSC_93857

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: (BDSC Cat# 34086,RRID:BDSC_34086)

Curator: @anisehay

SciCrunch record: RRID:BDSC_34086

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: (BDSC Cat# 34070,RRID:BDSC_34070)

Curator: @anisehay

SciCrunch record: RRID:BDSC_34070

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: BDSC_38254

Curator: @anisehay

SciCrunch record: RRID:BDSC_38254

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: (BDSC Cat# 28943,RRID:BDSC_28943)

Curator: @anisehay

SciCrunch record: RRID:BDSC_28943

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: BDSC_57184

Curator: @anisehay

SciCrunch record: RRID:BDSC_57184

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: (BDSC Cat# 33383,RRID:BDSC_33383)

Curator: @anisehay

SciCrunch record: RRID:BDSC_33383

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: (BDSC Cat# 38907,RRID:BDSC_38907)

Curator: @anisehay

SciCrunch record: RRID:BDSC_38907

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: (BDSC Cat# 67877,RRID:BDSC_67877)

Curator: @anisehay

SciCrunch record: RRID:BDSC_67877

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: (BDSC Cat# 67877,RRID:BDSC_67877)

Curator: @anisehay

SciCrunch record: RRID:BDSC_67877

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: (BDSC Cat# 3605,RRID:BDSC_3605)

Curator: @anisehay

SciCrunch record: RRID:BDSC_3605

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: (BDSC Cat# 25710,RRID:BDSC_25710)

Curator: @anisehay

SciCrunch record: RRID:BDSC_25710

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: (BDSC Cat# 25709,RRID:BDSC_25709)

Curator: @anisehay

SciCrunch record: RRID:BDSC_25709

What is this?

DOI: 10.1016/j.isci.2023.107335

Resource: (BDSC Cat# 38933,RRID:BDSC_38933)

Curator: @anisehay

SciCrunch record: RRID:BDSC_38933

What is this?

DOI: 10.1101/2023.01.16.524277

Resource: (BDSC Cat# 14091,RRID:BDSC_14091)

Curator: @mpairish

SciCrunch record: RRID:BDSC_14091

What is this?

DOI: 10.1016/j.xcrm.2024.101653

Resource: (IMSR Cat# CRL_027,RRID:IMSR_CRL:027)

Curator: @vtello

SciCrunch record: RRID:IMSR_CRL:027

What is this?

DOI: 10.1371/journal.pone.0280652

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @mpairish

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.3390/ijms24032128

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @mpairish

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1093/genetics/iyad008

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @mpairish

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1093/genetics/iyad008

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @mpairish

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1093/genetics/iyad008

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @mpairish

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1093/genetics/iyad008

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @mpairish

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1093/genetics/iyad008

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @mpairish

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1093/genetics/iyad008

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @mpairish

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1016/j.cels.2022.12.008

Resource: RRID:BDSC_27388

Curator: @mpairish

SciCrunch record: RRID:BDSC_27388

What is this?

DOI: 10.1016/j.cels.2022.12.008

Resource: (BDSC Cat# 51324,RRID:BDSC_51324)

Curator: @mpairish

SciCrunch record: RRID:BDSC_51324

What is this?

DOI: 10.1016/j.virusres.2024.199430

Resource: (KCB Cat# KCB 200970YJ, RRID:CVCL_0336)

Curator: @vtello

SciCrunch record: RRID:CVCL_0336

What is this?

DOI: 10.1016/j.virusres.2024.199430

Resource: (BCRC Cat# 60005, RRID:CVCL_0030)

Curator: @vtello

SciCrunch record: RRID:CVCL_0030

What is this?

DOI: 10.7554/eLife.83385

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @anisehay

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.7554/eLife.83385

Resource: (BDSC Cat# 51647,RRID:BDSC_51647)

Curator: @anisehay

SciCrunch record: RRID:BDSC_51647

What is this?

DOI: 10.1371/journal.pone.0262792

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @mpairish

SciCrunch record: RRID:SCR_006457

What is this?