dataesr/scientific-paragraphs-categorization huggingface dataset:
Although not significant, depression scores differed between preHD and controls (see Supporting Information tables S1-S3). See Supporting Information methods for detailed inclusion/exclusion criteria. Evaluation for psychiatric symptoms was performed on the day of MRI scanning by a neurologist or psychiatrist using the Hospital anxiety and depression score (HADS), the Baltimore apathy and irritability scale, and the Beck's depression inventory-2 (BDI-II). See Supporting Information tables S4 and S5 for clinical breakdowns of the BDI-II and HADS depression (HADS-D) and anxiety scores (HADS-A). The self-reported Baltimore apathy scale was chosen as the apathy measure of interest due to incomplete data for the companion reported Baltimore apathy scale. While we acknowledge the possibility of bias or cognitive deficit in self-reports, comparison of companion reported and selfreported apathy scores in Huntington's disease shows high correlation suggesting validity of self-reported apathy in Huntington's disease  [Mason and Barker, 2015].
MRI Acquisition Track-on HD 3T MRI data were acquired on two different scanners (Philips Achieva at Leiden and Vancouver and Siemens TIM Trio at London and Paris). Diffusion-weighted images were acquired with 42 unique gradient directions (b 5 1,000 sec/mm 2 ). Eight images with no diffusion weighting (b 5 0 sec/mm 2 ) and one image with no diffusion weighting (b 5 0 sec/mm 2 ) were acquired from the Siemens and Philips scanners, respectively. Voxel size for Siemens scans was 2 3 2 3 2 mm 3 and for Phillips 1. 96 3 1. 96 3 2 mm 3. For resting state fMRI, 165 whole-brain volumes were acquired at a repetition time of 3s using a T2*weighted echo planar imaging (EPI) sequence. Voxel size for both Siemens and Philips scans was 3. 3 x 3. 3 x 3. 3mm 3. Please see Supporting Information Methods for detailed acquisition parameters.
MRI Acquisition Track-HD 3T MRI data were acquired were acquired on Siemens (London and Paris) and Philips (Leiden) 3T MRI scanners. Diffusion-weighted images with 42 unique gradient directions (b 5 1,000 sec/mm 2 ) were collected with either seven images (Siemens) with no diffusion weighting or one image with no diffusion weighting (Phillips). Voxel size for Siemens scans was 2 3 2 3 2 mm 3 and for Phillips 1. 96 3 1. 96 3 2 mm 3. Please see Supporting Information Methods for detailed acquisition parameters.
Data pre-processing Preprocessing was performed using SPM8 and the CONN functional connectivity toolbox version 14 (https://www. nitrc. org/projects/conn/) [Whitfield-Gabrieli and Nieto-Castanon, 2012]  running under MATLAB v8. 3. Segmented images were used to create an improved anatomical scan for coregistration. The first four EPI images were discarded to allow for steady state equilibrium. Functional images were first realigned, incorporating field maps for inhomogeneity correction whenever available and then coregistered to the new anatomical image. Freesurfer ROIs were also coregistered to the anatomical image using NiftyReg  [Modat et al. , 2010]. In CONN regression of noise ROIs (without global signal regression) was carried out using the anatomical Compcorr method  [Behzadi et al. , 2007] , along with six movement parameters, followed by band-pass filtering between 0.
Cortical modules and depression A cortical module associated with depression was first identified. This was done by calculating an average functional connectivity matrix across participants, for cortical regions only. This was then decomposed in a data-driven manner using the Louvain method for community detection  (Blondel et al. , 2008]  as implemented in the Brain connectivity toolbox  [Rubinov and Sporns, 2010]  (See Supporting Information methods for additional information). Pearson and Spearman rank partial correlations were then performed between depression scores HADS-D and BDI-II and the total number of functional connections within each module. Age, gender, site, and CAG repeat length were included as covariates. The brain regions in the module showing correlations with depression were then combined with the caudate and thalamus, as these regions are implicated in depression  [Gong and He, 2015]  and are selectively vulnerable in preHD  [McColgan et al.
ACKNOWLEDGMENTS We would like to thank  Dr. Philippe Zizzari  for his contribution in preparing the manuscript. We are grateful to  Alice Cougnon  and  Chloé Got  and the  Plateau d'Exploration Fonctionnelle du Petit Animal in the Center for Psychiatry and Neurosciences  for the care of the animals. We are grateful to  Bertin Pharma  ( Montigny-le-Bretonneaux, France ) for providing the AG and DAG assays.
FUNDING This work was supported by an  Agence Nationale de la Recherche (ANR) Jeunes Chercheuses Jeunes Chercheurs   ANR-12-JSV1-0013-01  grant to  VT, University Paris Descartes Sorbonne Paris Cité ,  Institut National de la Santé et de la Recherche Médicale (INSERM)  and  Ecole Supérieure de Physique et de Chimie Industrielles (ESPCI) Paris.
Acknowledgements We thank  Yannick Hommet  for his excellent technical assistance. We are grateful to  Eloïse Lemarchand  for her contribution to this work. This study was supported by grants from the  Institut National de la Santé Et de la Recherche Médicale (INSERM)  and the  University of Caen-Normandy , and the  Regional Council of Normandy. AB received a fellowship from  Fondation Liliane Betancourt-Ecole de l'Inserm.
Data Analysis Theoretical and experimental data were analyzed using Kruskal-Wallis rank sum test between the three conditions (saline (C0), muscimol (C1) or saline following muscimol (C2)) for the 6 samples (12×10 first trials of C0 (control), 12×10 last trials of C0 (control), 12×10 first trials of C1 (GPi Off/muscimol); 12×10 last trials of C1(GPi OFF/muscimol); 12×10 first trails of C2(GPi On/saline); 12×10 last trials of C2(GPi On/saline)) with posthoc pairwise comparisons using Dunn's-test for multiple 436 comparisons of independent samples. P-values have been adjusted according to the false discovery rate (FDR) procedure 438 of Benjamini-Hochberg. Results were obtained from raw data 439 using the PMCMR R package  (Pohlert, 2014)  We tested our hypothesis on the model using 12 different ses-.
Inter-regional functional connectivity was estimated using the pairwise Pearson correlation statistics, resulting in 4005 ([90 × 89]/2) correlation coefficients for each participant. We examined group effects on functional connectivity of individual links by performing multiple linear regression analysis 4005 times. Each regression model included 5 independent factors (i. e. , group effects of each thought domain), with each controlled for the others, and 1 dependent variable (i. e. , the functional connectivity value of an individual link). Our resting-state data were aggregated from three unpublished datasets (see Methods section). Therefore, we added the "dataset" as a covariate in the regression models to control for any variability across datasets. The analyses yielded 2 significant functional links for the thought domain of AUDI/LANG, corrected for multiple comparisons at a false discovery rate (FDR) of 0.
e. , multiple comparisons) in conventional univariate multiple regression can be inherently eliminated. This analysis was chosen to account for the relationships among several dependent variables and conduct tests of the coefficients across different variables. Our model included 5 independent factors (i. e. , group effects of each thought domain), 1 repeated factor (i. e. , time: 1 st half vs. 2 nd half of the time series data), 1 covariate (i. e. , dataset), and functional connectivity values for a set of links (either all decreasing links or all increasing links) as dependent variables. The multivariate multiple regression analysis yielded two outputs: 1) results of multivariate analysis of variance that tested the overall group effects on functional connectivity across all dependent variables; and 2) results of univariate analysis that examined the group effect on the functional connectivity of each individual dependent variable for each thought domain.
In the current study, the fMRI time points were divided into two halves, which revealed an overall trend of decreased functional connectivity for the 11 domain-specific links from the 1 st to the 2 nd half. Future research might include recently developed fMRI protocols of higher temporal sampling and advanced analytic procedures  62  aimed at deconstructing time points into more non-overlapping segments, which may lead to more accurate assessment of dynamic changes in functional connectivity due to mind wandering. As an exploratory analysis, we divided our dataset 2 (i. e. , the one with the longest resting-state fMRI scan time of 520 sec: Supplementary Table  S3 ) into four 130-sec segments for segment-specific connectivity measurement. Across the 11 domain-specific functional links of dataset 2, connectivity of the 1 st quarter of time series data was significantly more positive (or less negative) compared to the other 3 quarters (p < 0.
0001), and the connectivity was not significantly different among the 2 nd , 3 rd , and 4 th quarters (Supplementary Fig. S1 ), suggesting that dynamic changes of functional connectivity in these links likely occurred between the 1 st and 2 nd quarters (i. e. , centered around 130 sec after fMRI scans began). This observation could also explain why the maintenance of mind wandering was most pronounced in the 2 nd half of the scan because the 2 nd half of time series data for all 3 datasets began after 130 sec. Overall, our findings associated with temporal differences in connectivity are consistent with the concept that non-static functional connectivity existed in a number of connections  [23] [24] [25] 27. Fur her, our findings suggest that the temporal variations in functional connectivity are to some degree of neuronal origin and could be linked with the continuity of mind wandering.
Methods Participants. The study's participants included 72 right-handed healthy adults (mean age 55 ± 18 years, 37 males) from three unpublished datasets. Participants were excluded if they had a history of neurological or psychiatric disorder; unstable or untreated medical illness (including uncontrolled diabetes or treatment-resistant hypertension); any contraindication to MRI, such as claustrophobia or metallic implants; or raw score less than 27 on the Mini-Mental State Exam  67. The institutional review board at Duke University Medical Center approved the study, and all data collection and analyses were carried out in accordance with the approved study protocol and the guidelines of the Helsinki Declaration. All of the participants provided written informed consent prior to participation.
Imaging protocols. Resting-state-fMRI was performed on a 3 Tesla GE scanner. Each participant was scanned on a single occasion, lying still with eyes fixated on a centrally located crosshair. Participants were scanned using three echo planar imaging (EPI) protocols summarized in Supplementary Table  S3. The potential influence of confounders (i. e. , dataset and age) was controlled in the following analysis. fMRI data analysis. The preprocessing of fMRI data was conducted through the Duke Brain Imaging and Analysis Center preprocessing pipelines based on the tools from the Oxford Centre for Functional MRI of the Brain's Software Library (FSL version 5. 0. 1, www. fmrib. ox. ac. uk/fsl) and locally developed Matlab codes (Mathworks, Natick, MA, USA). The first 4 volumes were discarded in order to reach the T1 steady state.
The data were corrected for slice-timing differences and motion (six parameters: three translations and three rotations), and were registered to the Montreal Neurological Institute (MNI) 152 template using a 12 degrees of freedom affine transformation implemented in FSL's Linear Image Registration Tool. All subsequent analyses were conducted in the MNI standard space. We regressed out the 6-parameter rigid body head motion (obtained from motion correction), the averaged time course profiles in the white matter, and the averaged time course profiles in the cerebrospinal fluid regions to reduce non-neuronal contributions to BOLD correlations  68. We also removed constant offsets and linear drift. Time domain signals with their frequencies less than 0. 08 Hz were retained. A schematic diagram of our matrix-based functional connectivity analysis procedures is illustrated in Fig.
Statistical analyses. The first goal of the study was to identify functional connections that exhibited a significant group effect (i. e. , higher percentage group vs. lower percentage group) on functional connectivity derived from the whole resting-state fMRI time series data (see Part I in Fig. 2 ). For each individual connection, we performed a multiple linear regression analysis to examine the group effects on functional connectivity. Each regression model included 5 independent factors (i. e. , group effects of each thought domain), 1 covariate (i. e. , dataset), and 1 dependent variable (i. e. , functional connectivity value). The independent factor was coded as -1 (lower percentage group) and 1 (higher percentage group). The regression model was performed 4005 times, and multiple comparisons were corrected by an FDR of 0.
The regression model included 5 independent factors (i. e. , group effects of each thought domain), 1 repeated factor (i. e. , time: 1 st half vs. 2 nd half of the time series data), 1 covariate (i. e. , dataset), and functional connectivity values of a set of links (decreasing or increasing links) as dependent variables. The multivariate multiple regression analysis yielded two outputs: 1) results of multivariate analysis of variance that tested the overall group effects on functional connectivity across all dependent variables; and 2) results of univariate analysis that examined the group effect on functional connectivity of each individual dependent variable for each thought domain.
This work was granted access to the HPC resources of CCRT/CINES under the allocation 2016-[076644] made by GENCI (Grand Equipement National de Calcul Intensif). We acknowledge PRACE for granting access to resource SuperMUC based in Germany at the Leibniz Supercomputing Centre (LRZ, Leibniz-Rechenzentrum). Financial support from the International Center for Frontier Research in Chemistry (icFRC) and the Agence Nationale de la Recherche (ANR) through the LabEx project Chemistry of Complex Systems (CSC-MCE-13) to MC are gratefully acknowledged. providing an atomistic description of the regulatory intracellular (IC) domain  [12] ; and shedding light onto pLGICs activation/deactivation by agonist versus antagonist binding  [13, 14]. The significant amount of structural information available in pLGICs provides opportunities to explore gating by all-atom Molecular Dynamics (MD)  [16].
For this purpose, a series of snapshots of the protein taken every 0. 2 ps were processed by the program HOLE  [21]. The resulting profiles averaged over 2-ps windows are shown in Fig 3. The timeresolved analysis indicates that at the end of the relaxation the constriction point is located at the position 9 0 , fully consistent with the X-ray structure of GluCl apo. Also, it shows that although 50% of closing occurs within the first 2 ns of simulation, where the pore radius at 9 0 drops from 3. 4 Å (X-ray active) to 1. 9 Å (MD with IVM bound), the rest of the transition takes several hundreds of ns. Strikingly, the superimposition of the pore-lining helices M2 on the Xray structure of GluCl apo shows that in the absence of IVM the TM domain has relaxed to the resting state in less than 1 μs; see Fig 3.
Preparation of structures A detailed description of the preparation of the active state model with and without IVM starting with the X-ray structure of GluCl with L-glutamate and ivermectin bound (PDB 3RIF)  [7]  is given in Ref. [18]. The L-Glu Ã simulation was started from the model built with IVM bound. Parameters for IVM were assigned using the CGenFF software  [32, 33]. An atomistic model of the resting state was prepared starting from the X-ray structure of GluCl apo (PDB 4TNV)  [8]  and following the same procedure. Three missing residues per subunits (i. e. 103-105), which form a loop inside the lumen of the channel in the EC domain were reconstructed by MODELLER  [34]  using the X-ray structure of GluCl with IVM bound as a template. Two intra-subunit disulphide bridges between the cysteine residues 130 and 144, and residues 191 and 202 were built in all subunits as done for the active state.
056 atoms. The energetics were modeled using the all-atom CHARMM27 force field (i. e. CHARMM22  [38]  with CMAP corrections for backbone dihedrals  [39] ) for the protein and CHARMM36 for the lipids  [40]. The modified water model TIP3P  [41]  and the NaCl parameters from Roux and coworkers  [42]  were used for the solvent. The simulations were carried out in the isothermal-isobaric (NPT) ensemble using the highly scalable NAMD package  [43]. The pressure was maintained constant to 1. 01325 bar by the Berendsen barostat  [44] , the temperature was controlled by the Langevin thermostat  [45]  at 300 K. A cutoff of 12 Å was used for the electrostatic interactions with a switch at 10 Å. The electrostatics were computed every two steps and the time step used was 2 fs. All covalent bonds involving hydrogen atoms were constrained with the SHAKE algorithm  [46].
Structural and dynamic observables Five observables were used to characterize the functional state of the pLGIC: the global twisting of the receptor, the tilting of the β-sandwiches in the EC domain, the configuration of the ion pore, its ion and water permeability, and the configuration of the orthosteric neurotransmitter site and the allosteric transmembrane site. All observables have been implemented in the program Wordom (version 0. 23-rc1 available at https://sourceforge. net/p/wordom/ codehg) to allow for efficient analysis of long MD trajectories. The global twisting (τ) was evaluated per subunit and defined as the angle spanned by the projections of the geometrical centers of the EC and the TM portions of each subunit on the pseudo-symmetry axis of the receptor. Geometrically, this angle measures the torsion of the EC domain relative the TM domain of the receptor around the pore axis.
The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement No. 604102  ( Human Brain Project ) and from the  European Unions Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 720270  ( HBP SGA1 ). Codesign  project  6  ( CDP6 ) is a recipient of the Human provide.
Odorant binding proteins (OBPs) and chemosensory proteins (CSPs) of arthropods are thought to be involved in chemical recognition which regulates pivotal behaviors including host choice, copulation and reproduction. In insects, OBPs and CSPs located mainly in the antenna but they have not been systematically characterized yet in Bemisia tabaci which is a cryptic species complex and could damage more than 600 plant species. In this study, among the 106,893 transcripts in the head assembly, 8 OBPs and 13 CSPs were identified in B. tabaci MED based on head transcriptomes of adults. Phylogenetic analyses were conducted to investigate the relationships of B. tabaci OBPs and CSPs with those from several other important Hemipteran species, and the motif-patterns between Hemiptera OBPs and CSPs were also compared by MEME.
De novo assembly of short reads and gene annotation Clean short reads were obtained by removing those containing an adapter or poly-N and of low quality from the raw reads. Transcriptome de novo assembly was carried out with the short read assembling program Trinity (r20140413p1) by using default parameters  [42, 43]. The resulting transcripts that were larger than 150 bp were first aligned by Blastx to protein databases, including NCBI Nr, Swiss-Prot, KEGG, and COG (E-value < 10 -5 ), retrieving proteins with the highest sequence similarity for each transcript along with their protein functional annotations. Then, we used the Blast2GO program  [44]  to obtain a GO annotation of the transcripts, and GO functional classification with the WEGO software  [45].
We separated PCR products and purified the products of expected size with the Wizard DNA purification system (Promega, WI, USA). The DNA fragments from PCR amplification were cloned into a pMD18-T (TaKaRa, Dalian, China) and sequenced by Sunbiotech (Beijing, China). All candidate OBPs and CSPs were cloned and sequenced using the primers of validation (S1 Table ). Putative N terminal signal peptides of BtabOBPs and BtabCSPs were predicted by Signal IP 4. 1 (http://www. cbs. dtu. dk/services/SignalP/) [46]. We adopted nomenclature for the BtabOBPs and BtabCSPs that are analogous to those deposited in GenBank (http://www. ncbi. nlm. nih. gov/genbank/) and the rest of them were named based on their order in the head transcriptome data. Based on previous studies, BtabOBPs were divided into two groups: Classic OBPs, characterized by 6 cysteine residues at conserved positions and Minus-C OBPs, which are missing cysteine residues, generally C2 and C5  [5].
Phylogenetic and motif analysis of the OBP and CSP genes The phylogenetic trees were reconstructed for the analyses of BtabOBPs and BtabCSPs, using these genes (the signal peptides of sequences were removed from OBPs and CSPs) as well as sequences from other insects. The OBP dataset contained 8 sequences from B. tabaci and 72 from other hemipteran insects. The CSP dataset contained 13 sequences from B. tabaci and 53 from other Hemipteran species. The amino acid sequences of the genes used for phylogenetic tree construction are listed in supporting materials (S1 File). Amino acid sequences were aligned with Clustal X  [47]  and unrooted trees were constructed with MEGA5. 1  [48]  using the neighbor joining method, with Poisson correction of distances (OBPs and CSPs). A total of 160 OBPs and 102 CSPs from different Hemiptera species were used for comparing the motif-pattern between Hemiptera OBPs and CSPs.
Spatial expression analysis of B. tabaci MED OBPs and CSPs RT-qPCR analysis was performed using gene-specific primers and SYBR Premix EX TaqTM (TaKaRa, Dalian, China) with three biological replicates in an ABI 7500 (Applied Biosystems, Foster City, CA, USA). Two housekeeping genes, β-actin (Accession number: EE600682) and EF-1α (AF071908), from B. tabaci were used as a reference  [50]  and 21 pairs primers for RT-qPCR were used (S2 Table ). The RT-qPCR was carried out in 20 μl reactions containing 2 μl cDNA (200 ng/ul), 10 μl SYBR Premix Ex TaqTM (TaKaRa, Dalian, China), 1 μl forward primer (10 μM), 1 μl reverse primer (10 μM), 0. 4 μl Rox Reference Dye II and 5. 6 μl nuclease free water. Thermal cycling conditions were: 95˚C for 30s, 40 cycles of 95˚C for 5s, 62˚C for 34s. After the cycling protocol, a melting curve analysis from 60˚C to 95˚C was applied to all reactions to verify a single PCR product.
This was followed by the measurement of fluorescence during 55-95˚C melting curve in order to detect a single gene-specific peak and to check the absence of primer dimer peaks. A single and discrete peak was detected for all primers tested. Negative controls were non template reactions (replacing cDNA with ddH2O). The results were analyzed using the ABI 7500 analysis software SDS1. 4. Quantification of transcript level of all BtabOBPs and BtabCSPs was conducted in terms of the 2 -ΔΔCt method  [51]. Expression levels of these genes were calculated relative to the two housekeeping genes using the Q-Gene method in Microsoft Excel-based software of Visual Basic  [52, 53]. For each sample, three biological replications were performed with each biological replication measured in three technique replications.