dataesr/scientific-paragraphs-categorization huggingface dataset:
Androgen influence on gene expression in a human neural stem cell model To gain insight into the impact of androgens on gene expression in embryonic development, we examined data from a recent RNA-seq experiment whereby human neural stem cells (hNSC) derived from embryonic stem cells were treated with 100 nM of a potent non-aromatizable androgen, dihydrotestosterone (DHT) or a control treatment (dimethyl sulfoxide; DMSO)  [28]  (GEO accession number: GSE86457). The cell line used was line SA001 from a male donor, obtained from Cellartis (Goteborg, Sweden). The work was supervised by the French Bioethics Agency (Agreement number NOR AFSB 12002 43S). hNSCs were derived as described in Boissart et al. [36]. Read counts obtained from RNA sequencing of three batches (biological replicates) of treated hNSCs derived from SA001 (DMSO (n = 4;3;3) vs.
DHT 100 nM (n = 3;3;3)) were analyzed, scaling them by library size using the trimmed mean of M values (TMM) method, implemented with the calcNormFactors function in the edgeR library  [37]. Low expressing genes were removed if there was not two or more samples with more than 100 reads while the previous analysis  [28]  filtered out genes with less than 100 reads normalized and divided by gene length in kb for each condition and below the 80th percentile in one of the condition studied (DMSO or DHT 100 nM 24 h). This filtering left a total of 13,284 genes for further downstream analysis. Batch effects were removed using the ComBat function within the sva R library. The software utilized in the prior paper  [28]  for DE analysis was DESeq2  [38]. This study used the voom function from the limma library in R to estimate precision weights for linear modeling of differential expression (DE) that will account for mean-variance trends  [39].
Isolating DMN-relevant genes based on spatial expression similarity to rsfMRI components We next wanted to isolate genes with high-relevance to our specific large-scale neural circuits identified by rsfMRI ICA analysis. To achieve this aim, we used the gene expression decoding functionality within Neurosynth and NeuroVault  [41]  to identify genes whose spatial expression patterns are consistently similar across subjects to our rsfMRI IC maps. This decoding analysis utilizes the six donor brains from the Allen Institute Human Brain Gene Expression atlas  [42, 43]. The analysis first utilizes a linear model to compute similarity between the observed rsfMRI IC map and spatial patterns of gene expression for each of the six brains in the Allen Institute dataset. The slopes of these subject-specific linear models encode how similar each gene's spatial expression pattern is with our rsfMRI IC maps.
Gene set enrichment analyses Analyses examining enrichment (i. e. , overlap) between two lists of genes, was implemented using the sum(dhyper) function in R. For enrichments with Neurosynth Gene Expression Decoding analyses, the background set size for all enrichment analyses was set to the total number of genes considered for Neurosynth Gene Expression Decoding analyses (i. e. , 20,787). We also examined enrichment with autism-associated genes listed under SFARI Gene Scoring categories (lists downloaded on 10/10/2017). The background set size for this analysis was set to the total number of genes included in the DE analysis  (13, 284). All enrichment analyses using Gene Ontology (GO) database was implemented with AmiGO 2 (http://amigo. geneontology. org/amigo). Here we used a custom background list, which was the total number of genes included in the DE analysis (i.
Developmental trajectory of MEF2C expression RNA-seq data from the Allen Institute BrainSpan Human Brain Gene Expression atlas was utilized for this analysis. We examined medial prefrontal cortex (MFC) in BrainSpan as it is the most prominent region from the IC01 functional connectivity map (see Results). We tested hypotheses about whether there is developmental upregulation of MEF2C and enhanced variability in prenatal vs. postnatal development. To test this, we used a permutation test (100,000 permutations) whereby on each permutation we randomized prenatal or postnatal labels and then re-calculated the mean difference or difference in standard deviation in expression between prenatal and postnatal (after birth) periods. We then compared the observed mean or standard deviation difference statistics to their null distributions to compute the p-value for each comparison.
MEF2C expression in an iPSC model of autism MEF2C expression was examined from the RNA-seq data from Marchetto et al. [10]. This dataset comprises expression measured from induced pluripotent stem cells (iPSC), neural progenitor cells, and neurons grown from fibroblasts of typically developing controls or patients with autism. Analyses were specifically focused on MEF2C expression and used a linear mixed-effect model ANOVA (lme function from the nlme R library) modeling diagnosis, RIN, cell type, and diagnosis⁎cell type interaction as fixed effects and subject identifier as a random effect. This ANOVA was followed-up by specific tests (lm function in R) of betweengroup difference within each cell type, covarying for RIN.
3d ). Finally, we tested whether MEF2C is dysregulated in induced pluripotent stem cells (iPSC) from male patients with autism and whether the directionality of such dysregulation is congruent with the directionality of DHTinfluence on MEF2C expression. To achieve this aim, we re-examined a recent RNA-seq dataset, which examined iPSC, neural progenitor cells, and neurons grown from fibroblasts of male cases of autism  [10]. An ANOVA examining all cell types identified a Diagnosis⁎Cell Type interaction (F = 6. 89, p = 0. 0047). Congruent with the directionality of DHT upregulation, MEF2C is also upregulated in iPSCs from male patients with autism (t = 3. 68, p = 0. 0035, Cohen's d = 1. 42). In contrast, no dysregulation is observed in neural progenitor cells (t = 0. 12, p = 0. 90, Cohen's d = 0.
Discussion In this study, we have shown that variation in testosterone during midgestational prenatal development has longlasting effects on how specific networks comprising the core of the social brain, the DMN, are organized. In line with hypotheses about sex-specific mechanisms, we identified that the fetal programming impact of testosterone on later circuit-level organization of the DMN depends on whether an individual is male or female. These findings are compatible with other normative largescale work on sex differences in DMN connectivity. Two large independent studies on adults utilizing the 1000 Functional Connectomes or UK Biobank datasets now replicably show that default mode connectivity is subtly on-average stronger in females than in males  [49, 50]. While these prior and much larger studies find these subtle DMN sex differences, it is noteworthy that sex differences in DMN connectivity were not apparent in the current dataset (t = 0.
Imperative in working towards this translational goal is the need to understand how FT might have male-specific biological effects on developing neural circuits for the social brain, such as the DMN. Using a human neural stem cell model, we identified a subset of genes that are differentially expressed after androgen treatment. In an important link to autism, this set of genes is enriched in a number of highimpact genes known to be causes of syndromic autism and intellectual disability. This finding underscores a prior paper  [28]  utilizing the same dataset that came across similar findings albeit with a different approach for differential expression analysis. However, this finding makes some specific inferences with regards to enrichments with the ''syndromic'' category of variants (n = 102) labeled within SFARI Gene, whereas the prior paper looked for enrichment with any of n = 235 genes annotated within SFARI Gene.
Thus, important future work could examine AR targeting of MEF2C and its effect on early neurogenesis and differentiation processes. Finally, while available data suggests that rare deleterious MEF2C mutations are not more prevalent in males vs. females with autism or intellectual disability  [70] [71] [72] , it is unclear whether sex effects would exist in more subtle missense mutations or whether effects might appear with much larger sample size. It will be important in future work to examine these questions with larger datasets. There are some important caveats and limitations to be discussed. As illustrated in Fig. 1c , it is noteworthy that FT exhibits a much larger range of variability in males than females. From the current data we cannot determine whether females might show a similar effect if FT variability was enhanced to a similar range as that of males.
Acknowledgements This work was supported by a  Wellcome Trust  ( 091774/Z/10/Z ) project grant to SB-C and ETB and grants from the  MRC, Autism Research Trust , and the  Templeton World Charitable Foundation  to SBC. This work is also supported by the  National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) East of England at Cambridgeshire  and  Peterborough NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR , or the Department of Health. MVL and BA were supported by the  Wellcome Trust. MVL was also supported by the  British Academy ,  Jesus College Cambridge , and the  European Research Council  (ERC;  755816 ) during this period of work. AP and J-LM were supported by a grant from the  Agence Nationale de la Recherche (HARTAGeNe).
Bacterial communities DNA was extracted using a PowerSoil DNA Extraction Kit (Qiagen, Venlo, The Netherlands), following the manufacturer's instructions. DNA was quantified using QuantIT dsDNA assay (Thermo Scientific, Waltham, Massachusetts, SI Fig  S1 ). The DNA extracts were diluted to 5 ng/µL with nuclease-free water. Samples with concentrations <10 ng/ µL were diluted 1:1. The V3 region of the 16S ribosomal RNA gene was targeted in PCR amplification (341F; 518R, SI Table  S1 ) to characterize BCS  [44] , and quality of amplification was visually checked by gel electrophoresis. Amplicons were cleaned and normalized using SequalPrep Normalization Plate Kit (Thermo Scientific), then pooled, and further purified using a QIAquick Gel Extraction Kit (Qiagen, Venlo, The Netherlands). The resulting pooled library was sequenced on an Illumina MiSeq with V2 chemistry and 2 × 150 bp paired-end reads (BioSample accession numbers SAMN07445364-SAMN07445398 and SAMN07445422).
All bioinformatics scripts are available as a Jupyter notebook at https://bitbucket. org/smonteux/monteux_deep_ thaw. Merging, quality filtering, and 97% de novo operational taxonomic unit (OTU) clustering were performed with VSEARCH v1. 10. 2  [45] , chimera removal with UCHIME, and the GOLD database  [46, 47]. OTUs that were abundant in the technical control sample (>5%) were removed, alike in ref. [48]. PyNAST, FastTree, the RDPnaive Bayesian classifier, and Greengenes 13. 8 database were used in QIIME v1. 9. 1  [49] [50] [51] [52]  to obtain OTUs taxonomy, filtering out OTUs present in <10% of the samples to exclude highly variable OTUs that may inflate the number of differentially abundant OTUs (adapted from ref. [53] ). Weighted UniFrac distances  [54]  were computed on an abundance table averaged from 100 rarefactions at 899 sequences depth.
Statistical analyses All statistical analyses were carried out using R v3. 4. 1  [73] , and the script used to produce figures and tables is available at https://bitbucket. org/smonteux/monteux_deep_thaw. Effects of the deep-thaw treatment and soil depth on potential respiration at 11 °C, Q 10 , and soil abiotic variables (OMC, pH, NH 4 + , and soil moisture) were assessed using analysis of variance with linear mixed-effects models (nlme package  [74] ), using treatment and depth as fixed factors, and core as a random factor, followed by pairwise contrasts when appropriate (lsmeans package  [75] ). NH 4 + and both bulk and intrinsic potential respiration were log-transformed, and OMC was square-root-transformed to improve normality of residuals. The effects of depth and treatment on BCS were assessed by testing for deviation of each OTU against a negativebinomial distribution with generalized linear models (manyglm(), mvabund package  [76] ), which confounds location and dispersion effects less than distance-based approaches (e.
g. , PERMANOVA  [77] ). A principal coordinates analysis (PCoA) ordination of weighted UniFrac distances was computed from the averaged abundance table and associations with root density and soil variables were tested using envfit() (vegan package  [78] ). To explore relationships between potential respiration on one hand, and soil chemistry, root density, and BCS (first PCoA axis, henceforth bacterial PCo1) on the other hand, we used multiple linear regressions. Soil chemistry variables showed multi-collinearity, therefore we used principal component analysis to summarize variability in soil chemistry, with all input variables scaled and centered. The first principal component (soil PC1) was mostly driven by positive OMC and soil moisture (SI Fig. S2 ) and was used as a proxy for soil chemistry in the multiple regressions.
Regression models were fit separately for bulk and intrinsic potential respiration, using soil PC1 only, or adding bacterial PCo1 and/or root density (scaled and centered) as predictors. To investigate how predictors of potential respiration differed between control and deep-thaw soils, we included treatment and its interactions with the other independent predictors in all models. We further fitted models with bacterial PCo1 alone to compare the relative importance of soil PC1 and bacterial PCo1. For both bulk and intrinsic potential respiration, we compared regression models based on their second-order Akaike's Information Criterion (AICc, AICcmodavg package  [79] ).
Abiotic and biotic drivers of potential respiration Including the experimental treatment alongside soil chemistry (soil PC1), BCS (bacterial PCo1), and/or root density improved all regression models for both bulk and intrinsic potential respiration (SI Table  S6 ). Bulk respiration models best fit the data when adding treatment without interactions (treatment effect on intercept) while for intrinsic respiration all interactions between treatment and other predictors   S4 ). b Mean relative abundance of phyla in each depth × treatment combination; dashed rectangles enclose permanently frozen samples. c Phylum distribution, abundance, and abundance change of the 149 OTUs that are different in the intermediate layer between the deep-thaw and control treatments (DEseq2 NB Wald test). Each bar represents the log2 of the fold change between control and deep-thaw samples for one OTU; dots represent the relative abundance of the OTU in the entire rarefied dataset, black dots indicate abundant OTUs (>0.
Altogether, our results show that permafrost thaw indirectly affects SOM decomposition through large effects on its drivers, such as BCS, which might prove important in understanding and predicting the permafrost carbon feedback. The data supporting the findings of this study are available in figshare with the identifier https://doi. org/10. 6084/ m9. figshare. 5977471; the sequence data have been deposited in Sequence Read Archive with accession numbers SAMN07445364-SAMN07445398 and SAMN07445422; the entire bioinformatics and statistical analysis pipeline is available at https://bitbucket. org/smonteux/monteux_deep_ thaw/.
Acknowledgements We thank  Tuukka Mäkiranta ,  Pia Bartels , and the Arctic Ecosystems students (2015) for help during field work; and the  Abisko Scientific Research Station  for practical support. Funding: This study was funded by a  Wallenberg Academy Fellowship  ( KAW 2012. 0152 ), Swedish Research Council  ( Dnr 621-2011-5444 ), and  Formas  ( Dnr 214-2011-788 ) grants all attributed to ED. JTW was supported by a postdoctoral fellowship from the  Flemish Science Foundation (FWO).
Sample preparation In the first dataset, we use cell lines stably expressing fluorescently tagged proteins in order to minimize the cell-to-cell variability in fluorescence signal. HeLa cells stably expressing fluorescently tagged GFP-RAB6A were previously generated in the Lab at Institut Curie  (Teber et al. , 2005). They were maintained in DMEM supplemented with 10% fetal bovine serum. Cells were then spread onto fibronectin Cytoo chips (Cytoo Cell Architect) 4 to 5 hr before imaging. Cell adhesion on micro-patterns both constrains the cells in terms of lateral movement and averages their size and shape (diskshaped and crossbow-shaped, Cytoo Cell Architect, 1100 m 2 ). As a control of patterning effect, the same cell line was grown under the same culture conditions, and spread on regular glass coverslips, 4 to 5 hr before imaging.
Data acquisition For Rab6-positive membranes in unconstrained cells, videos were recorded with an epifluorescence video automated system composed of a Ti Eclipse inverted microscope equipped with a 100x objective Plan NA (1. 4) and a piezo stage for 3D acquisitions (Nikon, S. A, France). The fluorescence was collected using a 512 Â 512 EM-CCD (Evolve, Photometric, USA) and driven through the Metamorph software (Molecular Devices). 18 series of 120 Z image stacks of 10 frames were recorded at a rate of about 1 stack/s. The volume rendering of one image from this dataset is shown in Appendix 1-figure  1a. For Rab6-positive membranes on micro-patterns, the 488 nm laser of a spinning-disk confocal microscope (Ti Eclipse, Nikon, S. A, France equipped with spinning disk system, a 100x/1. 4 oil objective and CoolSnap HQ2 CCD, from Roper Scientific S.
A. R. L, France) was used to acquire 3D 380 Â 380 Â 8 stacks at a rate of one stack per second. 18 image sequences with crossbow-shaped cells and 22 image sequences with disk-shaped cells were acquired. The system was driven by the Metamorph software (Molecular Devices). The volume rendering of two images from this dataset are shown in Appendix 1-figure  1b-c. For the Rab11 dataset, live-cell imaging was performed using simultaneous dual color Total Internal Reflection Fluorescence (TIRF) microscopy. All imagings were performed in full conditioned medium at 37 o C and 5% CO2 unless otherwise indicated. Simultaneous dual color TIRF microscopy sequences were acquired on a Nikon TE2000 inverted microscope equipped with a 100x TIRF objective (NA = 1. 49), an azimuthal TIRF module (Ilas2, Roper Scientifc), an image splitter (DV, Roper Scientific) installed in front of an EMCCD camera (Evolve, Photometrics) that can be bypassed or not, depending on the experimental conditions, as indicated in the text, and a temperature controller (LIS).
As an example, let us consider a study aimed at analyzing the effects of a drug on a sample of normal individuals. To evaluate the drug efficiency, a comparison between normal individuals and individuals that were administered the drug is conducted. Let us assume that the drug is effective on half the individuals. Consequently, normal individuals are compared to a mix of normal individuals and individuals with the drug effects. This comparison should not be statistically significant as the drug is not efficient on all individuals. However, the effects on the individuals for which the drug is efficient might hide the fact that it is not efficient on all individuals if all the data are pooled together. In what follows, we propose to compute a distance between all experiments instead of a distance between conditions.
Acknowledgements This work was supported by the  France-BioImaging infrastructure  ( ANR-10-INBS-04 ). The Cell and  Tissue Imaging Facility  at  Institut Curie  is a member of  France-BioImaging. We thank  Sabine Bardin  for regularly testing mycoplasma infection and for the sample preparation of experiments with Rab6 proteins. We thank  Jean-Yves Tinevez  for his help to implement the QuantEv analysis module for TrackMate and the Icy track processor.
Code availability The jar file of the QuantEv Icy plugin is available at http://icy. bioimageanalysis. org/plugin/QuantEv. The jar file of the QuantEv track processor is available at http://icy. bioimageanalysis. org/plugin/ QuantEv _(track_processor). The source codes can be extracted from the jar files. The QuantEv analysis module for TrackMate is available on GitHub (Pecot, 2018; copy archived at https://github. com/elifesciences-publications/QuantEvForTrackMate). These codes are released under the GNU Affero General Public License v3. 0.
X-ray Diffraction Analysis The structure of the membranes were investigated at room temperature by Bruker D8 DISCOVER diffractometer (Karlsruhe, Germany) (40 kV, CuK α radiation, step size 0. 05 • , 40 mA, scan rate 5 s/step) for 2θ = 5 ÷ 70 • in the Bragg-Brentano geometry. The SAXS data were analyzed using ATSAS software package  [57].
Study of the Membrane Structure by FTIR Spectroscopy FTIR spectroscopy was applied to study the nature of interaction between the components of the hybrid membrane (PVA, fullerenol, chitosan). It should be mentioned that all membranes additionally contained maleic acid (35 wt %) as a cross-linking agent. The FTIR spectra for PVA-0-20 dense and PVA-5-20 dense membranes are presented in Figure  3. The data demonstrated that there was a wide peak in the region 3200-3500 cm -1 for PVA-0-20 dense membrane, which corresponded to the oscillations of -OH groups of PVA (Figure  3a ). While for PVA-5-20 dense membrane the intensity of a given peak decreased due to the interaction between hydroxyl groups of PVA and fullerenol (Figure  3b )  [36]  with the formation of ether bonds as well as ester bonds, related to the interaction of PVA and MA, and also cross-linking between MA and fullerenol.
Contact Angle Measurements The study of the changes in the surface morphology of the dense membranes based on PVA, PVA-chitosan, and PVA-chitosan-fullerenol composites was carried out by the static sessile drop method to measure the contact angles by water. The water was chosen as a standard liquid to determine the hydrophilicity of the membrane surfaces. The values of the obtained contact angles for the dense membranes after the bulk modifications with chitosan and fullerenol are presented in Table  3. The presented data in Table  3  demonstrated that the introduction of 20 wt % chitosan in the PVA matrix did not lead to a change in the hydrophilic surface properties of the membranes, while increasing of fullerenol concentration into the PVA-chitosan matrix led to a decrease of the contact angle, namely to the surface hydrophilization, due to the rise of the quantity of polar groups (-OH) on the surface of the membrane.
Study of the Swelling Degree The degree of swelling in water has been studied for the developed dense membranes based on PVA, PVA-chitosan, and PVA-fullerenol-chitosan composites by the gravimetric method. The swelling characteristics were studied to assess the cross-linking degree of PVA membranes after carrying out bulk modifications as well as to study the degree of swelling in water, since the first stage of the pervaporation mechanism is sorption. The values of the swelling degree are necessary for understanding the transport mechanism of low-molecular penetrants through the membrane. Table  4  presents the data of swelling degree in water for the dense PVA-based membranes.
Acknowledgments: This work was supported by  Russian Science Foundation  (grant no. 17-73-20060 ). The experimental work of this study was facilitated by  CNRS  and equipment from the  Resource Centers of GEOMODEL, Center for X-ray Diffraction Methods , the  Chemical Analysis and Materials Research Centre , and the  Interdisciplinary Resource Center for Nano Technology at St. Petersburg State University. The authors thank  Nikolay Solovyev  for reading the manuscript.