dataesr/scientific-paragraphs-categorization huggingface dataset:
Instead, the regions we identify here may be sites generated by genetic drift. Therefore, we further assessed the likelihood that these ROH hotspots are caused by selection or drift. From our simulations, we conclude that the majority of the ROH hotspots observed are unlikely to be caused by strong selection. Inclusion of strong selection in the Rum population scenario increased the ROH hotspot threshold over that in the empirical dataset (Fig. 4A ). We cannot, however, conclude the ROH hotspots are purely a result of genetic drift, as simulations suggest that ROH hotspots are equally likely to occur as a result of neutral processes as under weak selection (Fig. 4A ) We tentatively suggest that the maximum peaks in ROH density in the Rum data (i. e. the two highest ROH hotspots on chromosomes 15 and 28, Fig.
Difference between map positions used within each population was only significant in the Rum dataset. One-way ANOVA of map on ROH length, p-values: Rum dataset <2. 2e-16 and Argyll dataset <0. 1. b Difference between map positions used within each population were significant. One-way ANOVA of map on number of ROH per ID, p-values: Rum dataset <2. 2e-16 and Argyll dataset <9e-12. Differences between populations within maps were significant. One-way ANOVA of population on number of ROH per ID, p-value <2. 2e-16 for both maps. cDifference between map positions used within each population were significant. One-way ANOVA of map on F ROH , p values: Rum dataset <2. 2e-16 and Argyll dataset <3e-7. Differences between populations within maps were significant. One-way ANOVA of population on F ROH , p-value < 2.
ACKNOWLEDGEMENTS We thank NatureScot for permission to work on the  Isle of Rum. We thank the many field workers that have helped to collect samples, particularly Alison and  Sean Morris. The long-term project and this research were funded by the  UK Natural Environment Research Council , and most of the SNP genotyping was supported by a  European Research Council   Advanced Grant  to  J. M. Pemberton. A. Hewett  is supported by an  E4   NERC DTP studentship  ( NE/S007407/1 ). M. Stoffel is funded by The  Leverhulme Trust  ( RPG-2019-072 ). S. E. Johnston is supported by a  Royal Society   University Research Fellowship  ( UF150448 ). The Argyll sample genotypes were contributed by  Eryn McFarlane. Genotyping  was conducted at the  Wellcome Trust Clinical Research Facility Genetics Core. We are grateful to the  Darwin Tree of Life  project for the physical map positions used.
a Difference between map positions used within each population was only significant in the Rum dataset. One-way ANOVA of map on ROH length, p-values: Rum dataset <2. 2e-16 and Argyll dataset <0. 1. b Difference between map positions used within each population were significant. One-way ANOVA of map on number of ROH per ID, p-values: Rum dataset <2. 2e-16 and Argyll dataset <9e-12. Differences between populations within maps were significant. One-way ANOVA of population on number of ROH per ID, p-value <2. 2e-16 for both maps. cDifference between map positions used within each population were significant. One-way ANOVA of map on F ROH , p values: Rum dataset <2. 2e-16 and Argyll dataset <3e-7. Differences between populations within maps were significant. One-way ANOVA of population on F ROH , p-value < 2.
(B) Sensitivity to the pyrethroid deltamethrin (0. 05%) was measured in dsGFP and dsPara by exposing the mosquitoes to the impregnated papers for 7 min, and 24h post-exposure, the mortality rate was counted in each group. The 7 min time exposure choice was made to avoid saturation of the para channels by the insecticide, and for which the control group is reaching close to 100% death. Mortality rate was significantly reduced in dsPara background at 4 and 6-days post-exposure, but not at 10 days. Table  2. Statistical analysis of mortality phenotypes post-deltamethrin exposure following gene silencing. Summary data for all experimental replicates testing the effect of para gene silencing compared to control treatment with dsGFP on mosquito mortality rate post-deltamethrin exposure. Individual p-values were calculated per replicate by Chi2 statistical comparison between dsPara and dsGFP control conditions.
While the false negative rate is difficult to determine since so little is known about the pharmacology of our system, we note that of the three small molecules we previously established to alter dark flash habituation that were included in the screen (Clozapine, Haloperidol and Pimozide), two were identified among our hits. To explore the pharmaco-behavioural space in our dataset we clustered small molecules based on their behavioural phenotypes (Figure  2A ). This strategy can identify drugs that share common pharmacological targets, or perhaps distinct pharmacological targets that result in convergent behavioural effects Bruni et al. (2016) ; Rihel et al. (2010). Indeed, drugs known to target the same molecular pathways often showed similar behavioural fingerprints lying proximal on the linkage tree, indicating that our dataset contains sufficient signal-to-noise to recover consistent pharmaco-behaviour relationships.
(2019) , we found that different aspects of the response were independently affected pharmacologically, resulting in distinctive correlated groupings within the correlogram. While we previously found that O-Bend response Probability and Latency habituate independently in individual fish Randlett et al. (2019) , in our drug screen data these appear to be tightly coupled (Figure  2B , green cluster). This may result from their reliance on a common molecular-level mechanism that occurs in distinct circuit loci. However, the performance of the animals in the OMR assay under different treatments was also associated with O-bend Probability and Latency, suggesting that pharmacological modulation of general visual acuity could drive these correlations within the drug screen dataset. These analyses confirm habituation behaviour manifests from multiple distinct molecular mechanisms that independently modulate different behavioural outputs.
-figure Supplement 1). To explore the spatial patterns in these datasets we used a 2-dimensional lookup table to simultaneously visualize the correlation and bias of Ca 2+ fluctuations with regressors representing either DF stimuli or movement (Fig-  ure 5E, F ). This revealed segregated populations of neurons coding for the DFs (pink) and movement (green/teal). Similar patterns are visible in individuals and across the entire population of 34 larvae. As expected, DF-tuned neurons were located predominantly in visual sensory areas in the midbrain (tectum) and diencephalon (pretectum and thalamus). Motor-coding neurons dominated in the hindbrain, with the exception of the cerebellum and inferior olive, which was predominantly tuned to the sensory stimulus. Some neurons did show approximately equal correlation values to both stimuli, as evidenced by the blue-ish hues.
Conclusion Habituation is the simplest form of learning, yet despite its presumed simplicity a model of how this process is regulated in the vertebrate brain is still emerging. Here we have combined two powerful methods offered by the larval zebrafish model: high-throughput behavioural screening and whole brain functional imaging. By applying these methods to long-term habituation, we identified dozens of pharmacological agents that strongly modulate habituation learning and distinct classes of neurons that are activated by DFs and are modulated during learning. The systematic datasets we generated contain large amounts of additional information that await future validation and integration into a unified model for DF habituation. Nonetheless they yielded a multitude of hypotheses as to the molecular and circuit mechanisms of habituation that can be followed up in future studies.
5 cm peak to peak, translating at 5. 5 cm per second. Direction of motion was switched every 30 s, for a total testing period of 1 hour, and performance was scored as the average change in heading direction towards the direction of motion during these 30s epocs. Acoustic tap stimuli were delivered using a Solenoid (ROB-10391, Sparkfun). The behavioural paradigm was designed to be symmetrical such that 1hr worth of stimulation was followed by 1hr worth of rest (Figure  1B ), allowing us to alternate the view of the camera between two plates using 45-degree incidence hot mirrors (43-958, Edmund Optics) mounted on stepper motors (Figure  1A ,  Sparkfun) , driven by an EasyDriver (ROB-12779, Sparkfun). Apparatus were controlled using arduino microcontrollers (Teensy 2. 0 and 3. 2, PJRC) interfaced with custom written software (Multi-Fish-Tracker), available here: github.
Behavioural analyses The behaviour of the fish was tracked online at 28 hz, and 1-second long videos at 560 hz were recorded in response to DF and Acoustic Tap stimuli. Offline tracking on recorded videos was performed in MATLAB (Mathworks) using the script "TrackMultiTrackerTiffStacks_ParallelOnFrames. m", as described previously, to track larval posture Randlett et al. (2019). Tracks were then analyzed using Python. Analysis code available here: github. com/owenrandlett/lamire_2022. Responses to DFs and to taps were identified as movement events that had a bend amplitude greater than 3 and 1, respectively. Behavioural fingerprints were created by first calculating the average value for each fish reflecting either the DF response during the specified time period (Naive = DFs 1-5, Training = DFs 6-240, Test = DFs 241-300), or the average response during the entire stimulus period (Acoustic Taps, OMR, Free Swimming).
MAP-Mapping Immunuostaining was performed as previously described Randlett et al. (2015) , on wild type zebrafish bleached with 1. 5% H 2 O 2 , 1% KOH. Confocal stacks were aligned to the Z-Brain atlas coordinates using the computational morphometry toolkit (CMTK, nitrc. org/projects/cmtk/), and calculations of the pairwise differences in pERK/tERK staining were performed as described in, code available here: github. com/owenrandlett/Z-Brain. Regional analyses ( Figure  4A -K(insets), L, M) were performed on regions of the brain as defined by the updated MECE (Mutually Exclusive, Collectively Exhaustive) regional definitions, which unambiguously assign each voxel in the Z-Brain to a single region arranged in a hierarchical ontology  (Vorha et al, unpublished)  ; available here: https://zebrafishatlas.
Acknowledgments We thank the Randlett, Granato and Engert group members for helpful advice regarding the manuscript and work,  Gregory Forkin  for help in zebrafish husbandry, and the Burgess and Baier groups for sharing anatomical data from the  Zebrafish Brain Browser  and mapZebrain atlases. This work was supported by funding from the  ATIP-Avenir program  of the  CNRS and Inserm  (O. R. ), a  Fondation Fyssen  research grant (O. R. ), the  IDEX-Impulsion initiative of the University of Lyon  (O. R. ), the  NIH   RO1  grants  MH109498  (M. G. ),  NS123887  (M. H. ), and the  NIH Brain Initiative  grants  U19NS104653 ,  R24 NS086601 , and  R43OD024879 , as well as Simons Foundation grants SCGB nos. 542973 and 325207 (F. E. ).
Large-scale brain networks reveal structural connections as well as functional synchronization between distinct regions of the brain. The latter, referred to as functional connectivity (FC), can be derived from neuroimaging techniques such as functional magnetic resonance imaging (fMRI). FC studies have shown that brain networks are severely disrupted by stroke. However, since FC data are usually large and high-dimensional, extracting clinically useful information from this vast amount of data is still a great challenge, and our understanding of the functional consequences of stroke remains limited. Here, we propose a dimensionality reduction approach to simplify the analysis of this complex neural data. By using autoencoders, we find a low-dimensional representation encoding the fMRI data which preserves the typical FC anomalies known to be present in stroke patients.
Neuroimaging acquisition and preprocessing. We use data from the Washington University Stroke Cohort, extensively described in previous articles  13, [21] [22] [23] [24]. A brief description of the data acquisition and preprocessing follows. A complete description of it is explained in detail in a previous publication  14. Neuroimaging data were collected at the Washington University School of Medicine using a Siemens 3T Tim-Trio scanner with a 12-channel head coil. It was obtained sagittal T1-weighted MP-RAGE (TR = 1950 ms; TE = 2. 26 ms, flip angle = 90 degrees; voxel dimensions = 1. 0 × 1. 0 × 1. 0 mm), and gradient echo EPI (TR = 2000 ms; TE = 2 ms; 32 contiguous slices; 4 × 4 mm in-plane resolution) resting-state functional MRI scans from each subject. Participants were instructed to fixate on a small centrally located white fixation cross that was presented against a black background on a screen at the back of the magnet bore.
Seven resting-state scans (128 volumes each) were obtained from each participant (~ 30 min total) giving a total of 896 time points for each participant. Resting-state fMRI preprocessing included (i) regression of head motion, signal from ventricles and CSF, signal from white matter, global signal, (ii) temporal filtering retaining frequencies in 0. 009-0. 08 Hz band. This frequency band is a standard for rs-fMRI studies and is the default band used in the most widespread software studies and connectivity, the CONN toolbox  25  ; and (iii) frame censoring, FD = 0. 5 mm. Finally, the resulting time series were projected on the cortical and subcortical surface of each subject divided into 235 ROIs (200 cortical plus 35 subcortical). The atlas and the functional labels for each ROI can be obtained together with the scripts used in the current manuscript at: https:// github.
Previous analyses of edge time series data have shown that transient periods of high-amplitude activity make disproportionately large contributions to the time-averaged functional connectivity  [33] [34] [35] [36]. In other words, data selected from specific temporal slices can be used to reconstruct a similarity matrix with a high correspondence to the functional connectivity matrix constructed from the full dataset  37, 38. In the current study, we applied a peak detection algorithm as previously implemented  39. The collective co-fluctuations of brain regions were estimated as the root sum square (RSS) of co-fluctuations between all pairs of brain regions (edges) at every given time point. Next, BOLD time series were randomly shifted (using MATLAB's circshift operator), hence approximately preserving each node's autocorrelation, while randomizing the cross-correlation across nodes.
This null model was iterated 1000 times. Time points in the original time series for which the empirically observed RSS amplitude exceeded the null model (P < 0. 001) were maintained. The resulting peaks in the original RSS at the corresponding time points were considered as significant events. RSS peaks that exceed extreme z-score values (above or below 4. 5 deviations) were excluded from the analysis. These peaks do not occur frequently (at most once per 1100 frames). The correlation between the FC created by the timepoints containing peaks, and the original FC, gives an indication of how much information is contained in these specific points  23. Edge-centric analysis has been applied to stroke datasets in a previous study  23. This work demonstrated that edge-centric measures, such as normalized entropy or high-amplitude co-fluctuations (transient periods of high-amplitude activity), can be used as indicators of lesion severity and recovery  23.
A dimensionality reduction was applied to the individual test data in each domain using principal component analysis as in  21  , yielding summary domain scores: Language, MotorR and MotorL (one score per side of the body), AttentionVF (visuospatial field bias), Average performance (overall performance and reaction times on attention tasks), and AttentionValDis (the ability to re-orient attention to unattended stimuli), Memory V (composite verbal memory score) and MemoryS (composite spatial memory score). Finally, patients' behavioral scores were z-scored with regards to controls' scores, to highlight behavioral impairments. In addition to domain-specific scores, the patients' clinical severity was assessed through the National Institutes of Health Stroke Scale (NIHSS)  29  that includes 15 subtests addressing: level of consciousness (LOC), gaze and visual field deficits, facial palsy, upper and lower motor deficits, limb ataxia, sensory impairment, inattention, dysarthria and language deficits.
In all cases, the features were maintained, and in some cases, enhanced. For some of the analysis, we focus on a specific dimensionality, with 6 dimensions, based on the results provided by the reconstruction error calculation (Supp. Fig. 2a ). It is worth clarifying that as the latent reduction requires a division between training and testing dataset, the amount of subjects used for the latent space is slightly lower, as visible in the different degrees of freedom. The same analyses were performed using a classical linear dimensionality reduction method, principal component analysis (PCA). To compare PCA with the latent representation obtained through the autoencoder, we used the same number of dimensions for the two methods, considering the 6 first principal components. These components accounted for only 85 percent of the total variance, implying that the autoencoder achieved a more efficient degree of dimensional compression.
Discussion Deep learning models are being increasingly used in precision medicine thanks to their ability to provide accurate predictions of clinical outcomes from large-scale datasets of patients' records. However, in the case of brain disorders, the deep learning approach is still limited, since clinical neuroimaging datasets typically have a small sample size. Thus, data scarcity has forced the adoption of simpler feature extraction methods, which are less prone to overfitting. (B) Correlation between SC and FC of healthy controls and stroke patients (at each measurement stage) revealing the increase at the remote stage, showing a similar value to controls after 1 year of the incident (p < 0. 01), while it is not the case after 2 weeks and 3 months (p = 0. 86). (C) Prediction of recovery using as input of the classifier the FC and reversibility matrix of the source space and the FC and reversibility matrix of the latent space.
One future direction is to explore how autoencoder-based dimension reduction can be employed in conjunction with whole-brain models and enhanced by using larger datasets (or simulated data). The mechanisms underlying the emergence of different brain states can be probed using whole-brain models based on conceptually simple local dynamical rules coupled according to empirical measurements of anatomical connectivity, for instance, by coupling nonlinear oscillators with the long-range white matter tracts inferred from diffusion tensor imaging (DTI)  30. Previous studies have already demonstrated the utility of whole-brain models in stroke research  23, 67, 68. The output of these models could similarly be embedded in a low-dimensional space, which could be analyzed using similar procedures to the ones described in this project.
Our novel approach may be fruitfully applied to a wide array of brain disorders, subserving both the theoretical goal of a clearer understanding of these diseases, and at the same time, the clinical goal of maximizing patients' classification, diagnosis, and prognosis. In the present study, we reached high classification accuracies by only relying on functional data. In addition to the classification of low dimensional description of functional data, not only fMRI, but also EEG, could be quite helpful to select target for non-invasive brain stimulation. Currently, there have been proposals to use patterns of functional connectivity  79  to guide invasive and non-invasive brain stimulation. However, high dimensional data sets like resting-state fMRI connectivity patterns are difficult to collapse in a small set of coordinates.
The low dimensional embedding coupled with classification methods to highlight the most predictive components could be a strategy to select sensitive targets. It is important to note that nonlinear dimensionality reduction methods are often fragile in the presence of noise 3 in the presence of low data quality, which limits their use when statistics is limited. However, datasets with sufficient length, such as the one presented in this article, can avoid this problem. Therefore, before proceeding to nonlinear methods, it is worthwhile to ensure a dense enough sampling of the high-dimensional space such that local neighborhoods include data points from different trajectories. In our current study, we used an autoencoder model because of its ease-of-use and flexibility. However, there are other interpretable variants that have been proposed to enable the inspection of embedded information  80, 81.
Even though this metric is outperformed in literature by other stroke-related metrics  13  , we used it in order to be comparable to the performance in the latent representation, as when data gets reduced, the anatomical properties such as space localization, get lost. Lastly, whole-brain models introducing anatomical information 24 could be applied directly to the latent representation in order to add anatomical restrictions and enrich the information available for the proposed analysis. In conclusion, this study adds new evidence for the relevance of low-dimensional embeddings for fMRI signals and proposes a non-linear dimensionality reduction approach as a promising tool to explore altered brain dynamics after stroke. In addition, it reveals how complexity metrics such as brain signal reversibility provide indicators that relate to lesion severity and predict lesion recovery, making it the first study of this type with applications to longitudinal stroke studies.
Funding S. I is supported by the  EU- project  euSNN  ( MSCA-ITN-ETN H2020-860563 ). G. D. received funding from the  Horizon EU   ERC Synergy Grant  Project ID:  101071900 ;  Spanish national research  project (ref. PID2019-105772GB-I00/AEI/10. 13039/501100011033 ) funded by the  Spanish Ministry of Science, Innovation and Universities (MCIU). MC is supported by  Fondazione Cassa di Risparmio di Padova e Rovigo (CARIPARO)  (GA number  55403 );  Ministry of Health Italy :  NEUROCONN  ( RF-2008-12366899 );  H2020 European School of Network Neuroscience-euSNN ,  H2020-SC5-2019-2 , (Grant Agreement number  869505 );  Ministry of Health Italy :  EYEMOVINSTROKE  ( RF-2019-12369300 ).
Anti-SwgA Antibodies Rabbits were immunized against two different peptides SWLKRDTLKPFQEG-Cys and Cys-STRTRATDTDDVWE designed based on sequence data (ProteoGenix). Coupling of the peptide through cysteine to m-maleimidobenzoyl-N-hydroxy-succinimide ester, immunization of the animal, and titer determination of the antiserum was performed by ProteoGenix SAS (Schiltigheim, France). Immunopurification of the specific antipeptide antibodies was done after coupling the peptide to epoxy-activated Sepharose (Amersham Pharmacia Biotech, Orsay, France) following the instructions of the manufacturer. Immunolabeling of blots was done using the ECL Western blotting detection procedure of Amersham Pharmacia Biotech.
Funding: This research was funded by l 'Agence Nationale pour la Recherche (Afulnf   ANR-16-CE92-0039 ), la  Fondation pour la Recherche Médicale  ( DEQ20150331722 ),  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant  ANR-10-LABX-62-IBEID ), and an  AIC grant. This work was also supported by the  Wellcome  trust grant  208396/Z/17/Z.