dataesr/scientific-paragraphs-categorization huggingface dataset:
Only SNPs within a 1Mb distance from each probe are available. This dataset is available on the SMR website (cnsgenomics. com/software/smr). The chromatin state was from the Roadmap Epigenomics Consortium  (Roadmap Epigenomics Consortium et al. 2015)  and inferred by ChromHMM  (Ernst and Kellis 2012)  in various brain tissues.
9772. Additionally, we note in GTEx that rs160459 and several SNPs of the GWS locus block are splicing QTLs (sQTLs) of KIAA0586 and PSMA3-AS1 in several non-brain tissues (Table  S7 ). Nearby gene expression across the lifespan. Fig. S7  presents the expression levels of genes within +/-1 MB of the GWS locus, across brain developmental stages, from Brainspan  (Miller et al. 2014 ). Among these genes, ACTR10, RTN1 and DACT1 showed the highest expression level, with RTN1 mainly expressed in brain tissues (GTEx data not shown). The normalized expression of the genes at least moderately expressed in the brain is reported in Fig. 1C. We note that DACT1 and DAAM1 are mainly expressed during early brain development in the following distinct periods: high expression from the early to late mid-prenatal period (8 to 24 weeks post-conception (pcw), no data before 8 pcw), followed by a decrease from the late prenatal period (26 pcw) until the end of the first year, and minimal expression after early childhood (from 2 years of age).
Codes are available for download at https://github. com/dehurtado/NonOhmicConduction. For the sake of comparison, we also consider the case of uniform (Ohmic) gap junction conductivity, i. e. , s x; u ð Þ ¼ s c ; x 2 B cyt ε;d ; bds g ; x 2 B gap ε;d : 8 > > > < > > > : ð13Þ Following standard asymptotic-analysis arguments for linear systems  [11] , one can show that for the case of the piecewise uniform conductivity tensor defined in (  13 ) the effective conductivity tensor takes the form ŝ ¼ 1 s c þ 1 bs g ( ) À 1 : ð14Þ We remark that in this case the macroscopic conductivity is not dependent on the voltage gradient. Further, we note that (  14 ) is also obtained as a particular case of the NOHM when the microscopic conductivity (  13 ) is assumed, see S1 Appendix. We refer to the system of Eqs (  11 ) and (  12 ) that considers the uniform effective conductivity tensor  (14)  as the Linear Homogenization Model (LHM).
Data analysis In the BISECTION-PRE and BISECTION-POST phases, we recorded participants' verbal responses for every trial ("Is the tick positioned to the right or to the left of the veridical segment midline?"). We then calculated the proportion of 'RIGHT' responses in function of the offset separately in each block (one single block in BISECTION-PRE and 5 blocks in BISECTION-POST). This S-shape function saturated at 0% for large negative (left) offsets and at 100% for large positive offsets (right). To quantify the offset that corresponded to chance level (50%), i. e. the subjective perceptual estimation of the line center, we fitted logistic functions in each block, and for each participant separately (r 2 =0. 91 on average), ݂ ൌ ଵ ଵା షೌ మ ሺషೌ భ ሻ , where a 1 and a 2 were regressed and t corresponds to the offset.
Direction error of each movement was defined as the angle between straight ahead and the segment connecting the start position to the position of the cursor 150 ms after movement onset. Quantile-quantile plots were used to assess normality of the data before using parametric statistical tests. Variables of interest were submitted to different statistical models (repeated measures ANOVAs) according to the effects analyzed. Independent t-tests were conducted to compare data between groups and paired t-tests were used to compare different conditions within groups. We also report partial eta-squared for significant results to account for effect size. Data processing and statistical analyses were done using custom routines in Matlab (The Mathworks, Chicago, IL).
Acknowledgments This research was supported by the  « Institut National de la Santé et de la Recherche Médicale » (INSERM) , the  « Conseil Général de Bourgogne » (France)  and the  « Fonds Européen de Développement Régional » (FEDER). The authors wish to thank  J. Ferrera  for her efficient assistance to collect data for the technical validation of the setup.
The private egocentric network can also help to focus the study of the private network in the space of its neighbours across the layers especially in the context of very large-scale open networks. 4. We define layer and inter-layer reachability metrics of a given sub-network: this measure is based on the private egocentric network and help to appreciate the connectivity strength of private data across layers. We illustrate our methodology through a biological application. The open multilayer network is constructed from open databases where weighted interactions between proteins-proteins, metabolites-metabolites and proteins-metabolites are given. The private data is a set of proteins and metabolites collected experimentally and present a set of nodes in the open multilayer network. We show how the private network is constructed, analysed and compared to the whole (open) network.
The private egocentric network is analysed and the layers reachability metrics are computed and discussed. Pathways between pairs of private proteins are then analysed and classified according to their location in the open network  (private, egocentric or extra-egocentric). The KEGG (Kyoto Encyclopedia of Genes and Genomes) open data set  (Kanehisa and Goto 2000)  is also used to describe pathways. By applying this methodology on the biological data we show how it can help biologist to complete, assess and interpret their private data by using the open network: weighted interactions between private collected molecules are added by using the open network. The connectivity between the molecules inter-layers and across layers are computed and the distribution of the identified molecules in the open network are observed and interpreted, Reachabilities across layer is computed in addition shortest paths which are biologically meaningful are also analysed and classified.
Measures for the whole networks are then computed by aggregating both precedent measures. In many other work  (Battiston et al. 2014) , a monoplex network is constructed by aggregating data from the different layers of a multilayer network, the classical definition of node degree is then applied to the resulting monoplex network. However, network aggregation leads to a loss of information. In Some other work, the distinction of the layers is maintained and the degree of node is represented by a vector. It is also possible to define degree and neighbourhood in terms of a focal node and any subset of the layers  (Berlingerio et al. 2013). On the other hand, we defined layer and inter-layer reachability metrics of a given sub-network this measure is based on the private egocentric network and help to appreciate the connectivity strength of private data across layers (see "Layer and inter-layer reachability of a subnetwork" section).
Biological application The aim of this application is to study several sets of biological data collected in experimentally related samples (i. e. cannabis samples). Identified molecules (proteins and metabolites) are measured form the biological collected data in different "omics" experiments: transcriptomics, proteomics and metabolomics. In their experiments, biologist measured at several time points, contigs: each one quantify genes, spots: each one quantify one or more proteins, and metabolites. Each gene produce typically one protein but sometimes more proteins. At this point we only have nodes (but no edges), corresponding to molecules measured in the experiments. To get relationships biologist frequently used the open STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database  (Szklarczyk et al.
2019) , which is the main protein-protein (and so also gene-gene) interactions database as well as the STITCH (Search Tool for InTeractions of CHemicals) one. STITCH  (Szklarczyk et al. 2016 ) is a twin database including edges between metabolites and metabolites, and also between proteins and metabolites (see Fig. 8 ). Each interaction in both databases is based on the presence of experimental, coexpression (similar behaviour across several public available experiment), text mining (appearing in the same phrase),pathway (participating to the same known biological network). A combined score aggregating all these types of interactions whose value is are between 0 and 1000 is added to both databases (see Tables  1, 2  and 3 ). The open multilayer network is constructed form the open STRING and STITCH databases (see Fig.
Analysis of each layer (proteins and metabolites layers) (a) The layer is constructed from the open data base (STRING and STITCH). Private network is also constructed from the set of identified molecules (proteins and metabolites) collected from the cannabis samples experiment, where biological data are collected in different "omics" experiments: transcriptomics, proteomics and metabolomics. (Kanehisa and Goto 2000)  is also used to describe pathways. Open biological databases are very big in relation to the average high throughput biological experiment. In our case the proteins in the experiment represent only form 0. 58% to 0. 74% of the total number of proteins in the whole network. The metabolites in the experiment represent less than 0. 05% of the total number of metabolites in the whole network.
Only few of them (3,37 %) can be outside the egocentric networks. These few long shortest paths can be isolated and studied by biologists in order to understand the molecule interactions into these paths. Table  11  shows two shortest paths pf length 6 composed of proteins and metabolites: one have some nodes outside the egocentric network and the other one is completely inside the egocentric network and the other one  Proteins pathways analysis using the KEGG data base KEGG (Kyoto Encyclopedia of Genes and Genomes) is an open database resource that integrates genomic, chemical and systemic functional information. In particular, gene catalogs from completely sequenced genomes are linked to higher-level systemic functions of the cell, the organism and the ecosystem  (Kanehisa and Goto 2000).
Major efforts have been undertaken to manually create a knowledge base for such systemic functions by capturing and organising experimental knowledge in computable forms; namely, in the forms of molecular networks called KEGG pathway maps, BRITE functional hierarchies and KEGG modules. From the KEGG data set we extract 4692 proteins which are associated to Pathway identifiers. Each identifier is also related to a pathway name that characterised the molecules (see Tables  12  and 13 ). There are 238 pathways. Our goal is to use this data set in order to characterise the set of private proteins extracted from the experiment. We represent the affiliation of private proteins by an affiliation network extracted from the KEGG database and modelled by a bipartite graph containing the set of private proteins connected to pathway identifiers (see Fig.
56% to 0. 7%). • Analysing shortest paths which are biologically meaningful between pairs of private proteins could be very helpful for biologists. We propose to classify them according to their location in the open network (private, egocentric or extra-egocentric). In our case, we notice that the majority of found shortest paths belong to the egocentric network (or the private one) so they are in the neighbours of the private network nodes. Only few of them (3,37 % ) can be outside the egocentric networks. These few long shortest paths can be isolated and studied by biologists in order to understand the molecule interactions into these paths. • By using the KEGG database  (Kanehisa and Goto 2000) , we proposed to characterise the set of private proteins identified from the experiment by pathways description presented as a list of pathway names.
Conclusion and perspectives We presented in this paper a methodology including measures and methods which helps domain experts to understand, evaluate and complete their private data by comparing and/or combining them with open data, when both are modelled by multilayer networks. We proposed a new formalism for multilayer network that allows to carry out fine analysis by considering two levels: the intra-layer level and the inter-layer one. We introduced the notions of private multilayer network and private egocentric network which is defined around the private multilayer network. The private egocentric network is used to evaluate the connectivity strength between the different layers of private data in comparison to the open network. We showed how we can use these notions to define the layer and inter-layer reachability metrics of a given sub-network.
We illustrated our methodology through a biological application where interactions between molecules (proteins and metabolites) are extracted from open databases and modelled by a multilayer network. The private data is a set of proteins and metabolites collected experimentally and presented a set of nodes in the whole multilayer network. Current experimental results are relevant from biologists point of view. We showed that the application of this methodology allows biologists to compare and assess identified molecules and private networks with the open one. (Blondel et al. 2008) , we obtain a modularity of is 0. 46. 39 communities are detected from the principal connected component for the minimum combined score: • 22 have their cardinalities between 3 and 28 • 11 have their cardinalities between 1000 and 10000 • 6 have their cardinalities between 15000 and 30000 We show in Table  3   Appendix C: Proteins-metabolites network analysis • Networks construction: Table  21  shows global measures values of the bipartite component, the whole networks, the private one and the private egocentric network according to combined score percentiles.
Examples for metabolites interactions extracted from the open STRING database: used to construct metabolites-proteins interactions Global and local measures are compared and discussed for open and private networks, we apply the Louvain algorithm (Blondel et al. 2008) in order to detect communities, private (identified from experiment) molecules distribution is studied according to the detected communities. The bipartite component containing the interactions proteins-metabolites is constructed from the open STITCH data base. The whole multilayer networks, the private multilayer network as well as the private egocentric networks are also constructed. (b) Networks global and local measures are compared and discussed. (c) Layer reachablities from metabolites to proteins and from proteins to metabolites are computed and discussed.
Examples of pairs of pathways identifiers and proteins extracted from the KEGG database : measures the rate of private proteins pathways among the pathways included in the community. 2. The experiment recall given by PathC i ∩PathE PathE : measures the rate of private proteins pathways among all the private proteins pathways. 3. The jaccard Index given by PathC i ∩PathE PathC i ∪PathC i : measures the rate of private proteins included in this community and that have an affiliation.
Availability of data and materials There are two types of data that are analysed in this study public datasets and private datasets. The public datasets analysed during the current study are available in the: • STRING DataBase version 11. 0: https://string-db. org/ • STITCH Data set: http://stitch. embl. de/ • KEGG Pathway database: https://www. genome. jp/kegg/pathway. html The private datasets used and/or analysed during the current study are available for the published part, and from the corresponding author on reasonable request.
(d) Shortest paths between pairs of private proteins are then analysed and classified according to their location in the open network (private, egocentric or extra-egocentric). The KEGG open data set Chemical Protein Experimental Prediction Database Textmining combined _score CIDs91758425 AT1G09340. 1 0 0 0 250 250 CIDs91758425 AT2G42600. 1 0 0 0 300 300 CIDs91758423 AT1G04070. 1 0 0 0 153 153.
Introduction Racemic fenfluramine has long been considered to act primarily through a serotonergic pharmacological mechanism of action  [1] [2] [3]. In recent phase 3 randomized controlled clinical trials (NCT02926898 and NCT02682927/NCT02826863), fenfluramine demonstrated robust efficacy as an add-on antiepileptic drug (AED) for treating patients with Dravet syndrome  [4, 5] , the presumed activity of which is mostly due to decreases in neuronal excitability by pharmacological modulation of calcium channels and/ or gamma aminobutyric acid-ergic (GABAergic) neurotransmission  [6, 7]. Further, in an open-label extension study (OLE; NCT02823145), patients with at least 1 year of fenfluramine treatment and meaningful seizure reduction also showed improvements in executive functions (behavior, emotions, and cognition), compared with prerandomization baseline  [8].
Step-through latency (i. e. , the latency spent to enter the dark compartment) and level of sensitivity to the shock (0 = no sign; 1 = flinching reactions; 2 = flinching and vocalization reactions) were recorded. Twenty-four hours after training, retention tests were performed by placing each mouse into the white compartment for 5 s, raising the door, and recording step-through latency (max, 300 s). Results were expressed as median and interquartile (25%-75%) range because the data are nonparametric with an established upper limit.
In vitro functional activity assays To determine whether the identified receptor binding resulted in functional agonist or antagonist responses at clinically achievable concentrations at the receptor or cellular level, a series of in vitro assays was performed. The assays selected were specific to each receptor subtype (Supplemental Table  1 , middle column). Sodium channel profiling, including Nav1. 1, 1. 2, 1. 3, 1. 4, 1. 5, 1. 6, 1. 7, and 1. 8, showed no significant responses, while β 2 ARs, muscarinic M 1 receptors, and 5-HT 1A receptors did not show functional results within clinically relevant ranges (IC 50 b 1 μM) (data not shown). Regarding putative σ 1 activity, the drugs were first tested for the modulation of electrically stimulated contractions of the guinea-pig vas deferens, a classical σ receptor functional assay  [34, 35].
2D ) nor did it change the increased activity induced by dizocilpine, a well-known psychostimulant (Fig. 2E, F ). In the passive avoidance test, fenfluramine, without having an effect alone (Fig. 2G ), dizocilpine-induced deficits in a bell-shaped manner, with significant attenuation at all doses tested (Fig. 2H ). Because fenfluramine is known to produce analgesic effects, shock sensitivity was measured during training. Fenfluramine treatment did not affect shock sensitivity in the dose-range tested (ANOVA for shock sensitivity in Fig. 2G : H = 0. 713, P N 0. 05; data not shown). A marked analgesic effect was observed only with some mice tested with the dizocilpine-fenfluramine combination at 10 mg/kg (data not shown), with a shock sensitivity of 0. 8 ± 0. 5 vs 1. 7 ± 0. 1 for controls, suggesting that the antiamnesic effect of fenfluramine was disconnected from any analgesic effects.
In vitro evaluation of total ROS and mitochondrial superoxide in SCs WT SCs were pre-treated with curcumin (0. 001, 0. 01, 0. 1, 1 or 10 μM) (Sigma) or with Nano-Cur (0. 001, 0. 01, 0. ) and pictures were captured using a Nikon digital camera (Nikon, Champigny-sur-Marne, France). All images were processed using the NIS-element software (Nikon, Champigny-sur-Marne, France). To quantify positive cells for each marker, 7 fields were counted using ImageJ software (NIH, USA). The "stained cells/number of total cells" ratio was calculated for each field to evaluate the percentage of ROS-positive cells.
Cells were fixed with 4% Formaldehyde in PBS at RT for 15 min and then washed with PBS three times. After permeabilizing with 0. 1% Triton in PBS and washing, cells were blocked with 1% BSA in PBS for 15 min and incubated with primary antibodies: rabbit anti-NFM Ab (1:1000, Abcam, ab8135) and mouse anti-MBP Ab (1:1000, Sigma-Aldrich, SMI 99) overnight at 4 °C. Anti-rabbit Alexa Fluor-488 and anti-mouse Alexa Fluor-594 fluorescent secondary antibodies were used for primary Ab detection (1:800, Thermo Fisher Scientific) and were incubated for 1 h at RT. After three washes with PBS and once with distilled water, coverslips were mounted using mounting solution with DAPI for nuclei staining. For each sample, images were acquired using a Nikon optical microscope H600L (Nikon, Tokyo, Japan). Fluorescence intensity was quantified by ImageJ software (NIH, USA).
Animals The generation of heterozygous CMT1A rats has been previously described by Prof Michael Sereda  [28]. These rats harbor 3 copies of murine Pmp22 gene. Experiments were performed on 60 one-month-old male rats (30 heterozygous CMT1A rats and 30 Sprague Dawley wildtype rats, body weight 200-220 g). Animals were housed in cages with an enriched environment and maintained on a 12 h light/dark cycle, at RT of 22 °C and with ad libitum access to food and water. All rats were observed daily for general well-being, and their weight was measured weekly (data not shown). All animal experiments were performed according to recommendations of the European Directive of 22 September 2010 (2010/63/EU) on the protection of animals used for scientific purposes. Experimental protocols were specifically approved by the Regional Animal Experimentation Ethics committee (CREEAL n°  16-2013-16).
Detection was carried out with a 4000 QTRAP hybrid triple quadrupole linear ion trap mass spectrometer (Sciex) equipped with a Turbo Ion Spray electrospray ionization (ESI) source and controlled by the Analyst software version 1. 6. 2 (Sciex). Ionization was achieved with electrospray in the negative ionization mode. The MRM transitions of m/z (367. 1 → 133. 9, 367. corresponding peak area ratio to that of the IS (internal standard) using a quadratic regression with 1/x weighting to the calibration curve.
Data and statistical analysis All data are expressed as mean ± SEM (Standard Error of the Mean). If normality (Shapiro-Wilk test) and equal variance (Brown-Forsythe test) were observed, data were then compared using either one or two-way analysis of variance (ANOVAs) with repeated measures, followed by post-hoc Tukey's or Dunnett's comparison test. If normality and equal variance were not observed, data were compared using the Kruskal-Wallis test followed by post-hoc Dunn's test. All statistical analyses were performed with GraphPad statistical software (GraphPad Software, Inc. , La Jolla, CA, USA). Differences were considered significant when p was < 0. 05, < 0. 01, or < 0. 001.
Regarding PMP22 (Fig. 6C ), decreased expression was observed in CMT1A/Saline and CMT1A/Nano homogenates when compared with those of the WT/Saline animals. However, Fig. 4. Nano-Cur treatment improves the phenotype of CMT1A rats. (A) Grip strength of hind limbs (g). (B) Balance performance evaluated as fall latency time (s). (C) Mechanical withdrawal threshold using von Frey's filaments (g). (D) Thermal withdrawal threshold evaluated as withdrawal latency time (s) on a hotplate. Data are expressed as mean ± SEM. n = 10 rats/group. Results were compared using two-way ANOVA (repeated measures) and post-hoc Dunnett's test ( * : p < 0. 05, * * : p < 0. 01 and * * * : p < 0. 001 vs. WT/Saline group) and with two-way ANOVA followed by post-hoc Tukey's test (#: p < 0. 05, ##: p < 0. 01 and ###: p < 0.