dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional annotation and analysis of proteins that displayed significant changes between WT and Shank3 11 -/-mutant mice was performed using DAVID Bioinformatics Resources (version 6. 7,) 3 (Huang da et al. , 2009a,b). To identify autismassociated gene products among the pool of changed proteins in the Shank3 11 -/-mutant PSD from either brain region, proteins were individually matched with the SFARI (Simons Foundation Autism Research Initiative) autism gene database 4  (Banerjee-Basu and Packer, 2010). For the evaluation of known and predicted protein-protein interactions among these proteins, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database v10. 0 5  was used  (Szklarczyk et al. , 2015). Protein-protein interactions were visualized with the Gephi software v0.
For example, this included a decrease of the AMPAR subunits GluA1 and GluA2, the NMDAR subunits GluN1 and GluN2B and the kainate receptor GluK5 (Supplementary Table  S2 ). Contrary to that, the same type of analysis revealed that in the hippocampal Shank3 11 -/-mutant PSD proteins involved in cytoskeleton organization such as Abi1, Gelsolin or Profilin2 were predominantly changed following loss of Shank3 (Figures  2C, D ; Supplementary Table  S2 ). The Striatal PSD Proteome of Shank3 11 -/-Mutants Comprises More Altered Proteins Encoded by ASD Susceptibility Genes than the Hippocampal One Similar to the GO-term based protein enrichment analysis, comparison of the quantitative datasets of altered proteins in the striatal or hippocampal PSD proteome of Shank3 11 -/- mutant animals with the 826 autism-associated primary target genes presently listed in the SFARI autism gene database also rendered distinct results: 14 of the 61 proteins (23%) altered in the striatal and 8 of the 55 proteins (15%) altered in the hippocampal Shank3 11 -/-mutant PSD matched with proteins encoded by SFARI autism genes (Figures  3A, B ).
The only converging molecular alteration in this context was a decrease of Homer1 in both Shank3 11 -/-mutant PSD proteomes, while all other changes were unique to each brain region. Importantly, all molecules changed in the striatal Shank3 11 -/-mutant PSD  that matched with the SFARI autism gene database were reduced and comprised the murine homologs of proteins encoded by several major ASD candidates (Figure  3A ) including NCKAP1, GRIN2B and TRIO, 3 of the 65 high-risk ASD TADA genes  (De Rubeis et al. , 2014; Sanders et al. , 2015). Based on these results, we generated minimal distance interaction networks that showed a high degree of interactions among these molecules almost exclusively in the striatal Shank3 11 -/-mutant PSD (Figures  3C, D ). The core of this network comprises known direct and indirect Shank interactors  (Han et al.
DISCUSSION In this study, we used ion-mobility enhanced data-independent label-free LC-MS/MS to generate an unbiased dataset of in vivo changes in the PSD fraction from both striatum and hippocampus of adult Shank3 11 -/-mutant mice lacking major isoforms of Shank3  (Schmeisser et al. , 2012; Vicidomini et al. , 2016). We have previously shown by the same methodological approach (biochemical isolation followed by LC-MS/MS) that in this fraction, PSD-specific proteins were highly enriched whereas contaminating proteins (i. e. , mitochondrial or presynaptic ones) were efficiently decreased  (Distler et al. , 2014b). Our comprehensive analysis quantified approximately 2 500 proteins in the striatal and 2 400 proteins in the hippocampal Shank3 11 -/-mutant PSD and identified several significantly regulated proteins largely distinct for either brain region.
, 2012; Han et al. , 2013)  In addition, loss of differential Shank3 isoforms with distinct functions from striatal or hippocampal PSDs in a varying degree depending on their physiological expression pattern and levels throughout the brain might play a role in this context  (Wang et al. , 2014). Based on the fact that approximately 0. 7% of individuals with ASD exhibit a mutation in SHANK3  (Leblond et al. , 2014) , we further compared our datasets with the SFARI autism gene database to identify molecular patterns that could be of relevance for a better understanding of ASD-associated pathomechanisms in our model. Interestingly, we only found one protein to be altered in the Shank3 11 -/-mutant PSD of both brain regions: Homer1. This post-synaptic scaffold protein and C-terminal Shank interactor, which interconnects the latter with group I metabotropic glutamate receptors and regulators of post-synaptic calcium signaling  (Tu et al.
This finding subsequently calls for a detailed analysis of ASD-like behaviors and the underlying molecular pathomechanisms in Homer1 mutants. Intriguingly, we also found that almost 25% of the altered proteins in the striatal Shank3 11 -/-mutant PSD matched with the SFARI autism gene database. These molecules were all reduced and comprised several major ASD candidates including three proteins encoded by the murine homologs of the TADA genes NCKAP1, GRIN2B and TRIO, which are highly relevant for ASD formation in humans  (De Rubeis et al. , 2014; Sanders et al. , 2015). Minimal distance interaction network analysis further showed that the majority of these proteins interact with each other pointing toward a defined ASD-associated molecular network that is disrupted specifically at the PSD of Shank3 11 -/-mutant corticostriatal synapses.
Study design In this experimental human laboratory study, participants were randomized to one of four conditions, balanced by gender, using a 2 (unit information: present vs. absent) × 2 (calorie information: present vs. absent) between-subjects design. The volume of beer consumed during an ad libitum taste test was the primary outcome measure. The study protocol including data analysis plan was pre-registered on the Open Science Framework (https://osf. io/58rw7/).
Participants We recruited social alcohol consumers (defined as drinking at least two units per week and no more than 35 units per week if female or 50 units per week if male). Participants were recruited from a database at the University of Bristol, which included students, staff, and the public. Participants were required to be at least 18 years of age, in good psychiatric and physical health, and to like beer. Exclusion criteria included use of illicit drugs (except cannabis), family history of alcoholism and recent consumption of alcohol (within 24 h of the test session). Participants were reimbursed £5. The study was approved by the Faculty of Science Research Ethics Committee at the University of Bristol (ethics approval code: 24,091,526,481).
Randomization was determined using a pre-assigned code using random number generation in MATLAB  (Mathworks, version 2015a). Participants were informed they would have 10 min to complete the taste test on the computer, and to drink as much or as little as they would like to make their ratings. After 10 min, the experimenter returned to remove the beer glasses and envelope. Participants were then asked to complete various questionnaires (AUQ, PANAS, AUDIT and TFEQ), during which time the experimenter left the room, and measured the beer remaining. Participants then completed the funnelled debrief questionnaire and a manipulation check required participants to report, or guess, the number of units and calories in each beer. Participants then completed the questions on how unit and calorie information would influence their behaviour.
| RNA-seq Total RNA was extracted from each of the 23 samples using an RNeasy Mini kit (Qiagen). RNA concentration was measured with a high-sensitivity QuBit fluorometric assay (Life Technologies), and its quality was tested with a denaturing MOPS gel electrophoresis assay. From total RNA, mRNA was isolated and fragmented, and Sequenced reads were quality-checked and used for de novo transcriptome assembly and quantification of gene expression following the pipeline in De Wit,  Pespeni, and Ladner (2012)  and the associated open-access scripts (https://github. com/DeWitP/SFG; downloaded on 13 January 2016). In short, low-quality sequenced bases (Phred score < 20) were removed using the Fastx-toolkit (0. 0. 13), as were adapter sequences. Duplicate reads were identified and counted using MarkDuplicates (Picard tools 2.
7. 12) with the following settings: maxDiff = 0. 01, maxSeedDiff = 5, seedLen = 30, and nThrds = 2. As a proxy of gene expression, the counts of uniquely mapped reads mapping to each contig were compiled for each sample using a custom script from De  Wit et al. (2012)  with a mapping quality threshold of 20 and a read length threshold of 20 bp. The proportion of duplicate reads was high. Removal of 110 contigs which matched known C. glacialis ribosomal sequences (1,006 sequences, downloaded from NCBI on 12 April 2016) reduced the number of duplicates, resulting in 45%-93% duplicate reads per sample. As duplicate reads may arise from highly expressed genes and may have biological significance, they were not removed  (Parekh, Ziegenhain, Vieth, Enard, & Hellmann, 2015). To associate physiological function to each of the contigs, the proteins for which the contigs coded were identified by searching the arthropod sequences in NCBI's nonredundant (nr) protein database and the entire UniProt protein database (downloaded on July 6 2016) (e-value cutoff = 1 × 10 -5 ; Blastx 2.
3. 0+). Hypothetical or predicted protein annotations in nr were excluded by discarding matches with the following words: "hypothetical," "predicted," "unknown," and "putative. " The sequences corresponding to known proteins were annotated with biological process and molecular function Gene Ontologies (GO), a classification of physiological function spanning from very broad to specific descriptions for each protein, as well as Kyoto Encylopedia of Genes and Genomes (KEGG) Gene ID using their Uniprot ID and the Uniprot flatfile database (uniprot. org, downloaded on 13 July 2016, http://www. genome. jp/kegg). For each contig with KEGG Gene ID, the KEGG orthologs and their associated KEGG pathways were downloaded from the KEGG REST API using the R package KEGGREST  (Tenenbaum, 2016). KEGG orthologs, and the KEGG pathways they are associated with, are not species specific and are thus designed for cross-species comparisons, allowing their application in nonmodel species like C.
| Gene expression analyses Differentially expressed contigs (DECs), those whose expression varied significantly with pH (treated as a continuous variable), were identified with the R package DESeq2 (version 1. 10. 1,  Love, Huber, & Anders, 2014a). DESeq2 outperforms nearly all other HTS gene expression R packages in detecting significantly differently expressed genes  (Seyednasrollah, Laiho, & Elo, 2013; Love, Huber, & Anders, 2014b; Schurch, Schofield, & Gierliński, 2016) , taking into account common sources of error such as low sample size, low-count genes and high variability using shrinkage estimators of dispersion and fold change  (Love et al. , 2014a). Contig expression is discussed here as gene expression. To determine whether gene expression varied significantly along a continuous pH scale, DESeq2 fits generalized linear models (GLMs) to raw counts of each gene, with a logarithmic link (log2) and negative binomial distribution using a shrunken dispersion estimate  (Love et al.
, 2014a). The GLM fit produces the overall expression strength of the gene and the log fold change (LFC) per unit of pH. LFCs are then shrunk to reduce heteroscedasticity caused by low-count genes showing high LFCs, with low-count, low sample size, or high-dispersion genes shrunk the most. Ward tests with Benjamini-Hochberg procedure are used to adjust for multiple testing in DESeq2. Significant differently expressed contigs were defined as those with |LFC| > 1 and adjusted p-value (false discovery rate, FDR) <0. 1. To visualize the similarity of expression of the DECs across samples, a heatmap was created, with the raw contig counts transformed using the rlog function in DESeq2 and plotted using "aheatmap" in the R package NMF, which centers and scales expression of each contig (0. 17. 6,  Gaujoux & Seoighe, 2010).
By measuring the response to low pH at multiple levels of biological organization, both organismal and molecular, we detected physiological changes in response to low pH that were undetected in organismal measures. Additionally, there continues to be considerable F I G U R E 3 Cytoscape network of the 350 most significant downregulated biological process Gene Ontology (GO) terms in C. glacialis, linked by SimRel semantic similarity of the terms by ReViGO. The color of each GO term node (from white to dark red) indicates the adjusted p-value, with more significant terms in darker red. Likewise, the larger the label name, the more significant the adjusted p-value. The size of each node indicates the size of the GO term in the entire UniProt database inconsistency in the measured responses of marine organisms to low pH, even within taxa  (Kroeker et al.
ACKNOWLEDGMENTS The authors would like to acknowledge support from  Science for Life Laboratory , the  National Genomics Infrastructure, NGI , and  Uppmax  for providing assistance in massive parallel sequencing and computational infrastructure. We thank  NOTUR  ( UNINETT Sigma2)  for computing time (project  NN9428K ) and support on the high performance computing cluster Stallo ( UiT The Arctic University of Norway)  and the  Institute of Marine Research's marine laboratory at Austevoll, Norway , for housing and supporting the copepod cultures. We also thank  Mats Töpel  at  University of Gothenburg  for providing computing infrastructure (Albiorix). This research was supported by the  Research Council of Norway  ( HAVKYST  Project # 225279  to PT, HH, and HIB), the  Institute of Marine Research  (Project # 83192-01  to HIB), and  Fram Centre (FRAM-High North Research Centre for Climate and the Environment)  funding (Project # 14591-02  to AB, HIB, HH, and PT).
the complementary DNA (cDNA) libraries were synthesized according to the TruSeq RNA sample preparation v2 low sample protocol (Illumina). Concentrations of cDNA were measured with the highsensitivity QuBit fluorometric assay. Barcoded index adapters were ligated, and samples were pooled equimolarly (5-6 samples per pool) and sequenced single-end, with a 50 bp read length in four lanes in an Illumina HiSeq 2500 high-throughput sequencing machine (High Output mode) at the Swedish National Genomics Infrastructure (SNP & SEQ platform) in Uppsala, Sweden. All raw data were submitted to National Center for Biotechnology Information's (NCBI) Short Read Archive (SRA, accession numbers SAMN05990738-SAMN05990760; BioProject ID: PRJNA352656).
Acknowledgments We would like to thank  Stephanie Schöppenthau  and  Sébastien Debilly  for MRI data acquisition,  Thomas Bielser  for MRI data management and pre-processing, and  Vanessa De Barros  for animal colony maintenance. We also thank  Eric Kremer  and  CNRS  for providing the CAV2-Cre virus, and the  Hall Lab  for their invaluable comments on the manuscript.
The injector and detector temperature was 230 °C. Data were processed using a Varian Star Workstation v 6. 30 (Varian Inc. , Walnut Creek, CA, USA). Results were expressed as the relative percentage of each individual FA present in the sample. Sterol composition was determined using method ISO 6799 (1991), after trimethylsilylation of the crude sterol fraction, using a Varian 3800 instrument equipped with a VF-1 ms column (30 m • 0. 25 mm i. d. ) and helium (flow rate 1. 6 mL/mn) as carrier gas, column temperature was isothermal at 270 °C, injector and detector temperature was 300 °C. Injected quantity was 1 lL for each analysis. Data were processed using Varian Star Workstation v 6. 30 (Varian Inc. , Walnut Creek, CA, USA). Tocopherols composition was determined using the method ISO  9936 (2006).
The psychiatric nurse collected information about changes in demographic data occurring between the intake and the follow-up visit; in addition, information was gathered about relevant clinical events, such as number of hospitalizations owing to mental health issues, suicide attempts, structured psychotherapy, and pharmacological treatment for depression during the follow-up period. The Mood Disorder module of SCID-I was administered to collect information about the presence of mood symptoms during the follow-up interval. Though recall of previous episodes is often a challenge for individuals suffering from depression, every effort was made to ascertain periods of remission of symptoms, recovery, and recurrences after remission or recovery. The chronology of events was registered as accurately as possible using the Life Chart method  [41].
Statistical analysis Statistical analyses were carried out using the software Statistical Package for the Social Sciences (SPSS), Version 23. 0 for Windows (IBM Corp. , Armonk, NY, USA). Univariate statistics (chi-square, t-test) were used to compare demographic and clinical characteristics between patients who participated in the follow-up study and those who did not participate. For the present analysis, indicators of the course of depression were defined as follows  [18]. Recovery was defined as full symptom remission from an MDE for 8 weeks or more; this class included patients with no residual symptoms. Remission included patients who did not meet the full criteria for MDE for 8 weeks or more, but presented with residual symptoms. Recurrence was defined as the development of a new mood episode meeting the full criteria for MDE during recovery or remission.
There is also some evidence that depressed patients tend to underreport past depressive episodes, with greater recall failure over time  [87]. The Life Chart, used to retrospectively assess the course of depression, has proved to be a reliable instrument that can reduce recall bias  [88]. A final limitation was that we did not use a structured family history interview to record participant family history, which may have reduced the reliability of our data  [89]. However, highly experienced staff collected collateral information about mood disorders affecting first-degree relatives.
Acknowledgments The authors gratefully acknowledge for their professional, dedicated, and passionate work all the members of the ATC team. Particular thanks to  Connie Dalton, PhD , CPsych;  Donna Horner , MSW, RSW;  Amanda Telford , MSW, RSW;  Rosemarie Lidstone ,  OT, Reg(ON) ;  Doreen Parker , RN, CPMHN(C), CPRP;  Darlene Morris , administrative assistant, and all the participants who made this study possible.
Analytical methods The metal analyses were performed by inductively coupled plasma mass spectrometer (ICP-MS) (X series 2, Thermo Scientific, USA) and inductively coupled plasma optical emission spectrometer (ICP-OES) (Perkin Elmer, Optima 8300, USA)  [41]. Analytical grade chemicals and ultrapure (MiliQ) water were used. Alternatively, periodic metal measurements were carried out by atomic absorption spectrophotometer (AAS-F) (Varian 200, USA). The wavelengths (nm) for Cu and Au were 324. 752 and 267. 595, respectively. The analyzing blanks and calibration standards were performed periodically to establish the reproducibility of the data and assure the analytical quality standards (QA/QC). The statistical analysis of the results was carried out by MINITAB 17 and Microsoft Excel 2016.
Statistical analyses Growth performance, total tract digestibility, haptoglobin concentration and intestinal morphology were analysed using the Proc Mixed of SAS (SAS Institute Inc. , Cary, NC, USA). The pig was considered as the experimental unit. The statistical model included the fixed effects of diet and block. Weaning weight was added as a covariable for growth performance analysis. The Shapiro-Wilk's normality test was performed on the residuals for each trait. Data for G : F from 28 to 42 days were transformed logarithmically before the statistical analysis to validate this assumption. Diarrhoea incidence was analysed by a χ 2 contingency test using the Proc Freq of SAS (SAS Institute. Inc. ). Probability values under 0. 05 were considered to be significant and probability values under 0.
Acknowledgements The authors thank  M. Alix ,  G. Guillemois ,  Y. Jaguelin-Peyraud ,  J. Liger ,  A. Marchais ,  V. Piedvache ,  J. F. Rouaud  and  P. Touanel  for skilful technical assistance in animal care and laboratory analyses. This study was financially supported by  Cooperl Arc Atlantique (Lamballe, France ).
[3] , supported by the same set of synapomorphies. The two groups Hypoperlidae and Miomoptera fall together in the same clade, but with a trichotomy, supported by the characters '47, state 1' and '48, state 1' that concern the particular shape of the pterostigmata. The polytomy is due to the absence of data on the character '56' (cerci) for Hypoperla. Using a Bayesian analysis, we obtained 75,000 cladograms, with a topology of the 50% consensus (see Additional file 4) very close to that of the MP strict consensus cladogram. The unique difference is Hypoperla as sister group of crown Acercaria and (Idelopsocus + Palaeomantis) as sister group of (Hypoperla + crown Acercaria). This second analysis shows that the relative positions of the Miomoptera and Hypoperlidae are uncertain. The Miomoptera are likely to be the sister group of the Hypoperlidae, or the sister group of the (Hypoperlidae + crown Acercaria), or even the two groups are paraphyletic.
Line drawings and photographs The venation patterns were drawn directly using a camera lucida. Photographs were taken with digital camera Canon D550 with reverse lens MP-E 65 mm. Original photographs were processed using the image-editing software Adobe Photoshop CS4, and for some images were processed by the focus-stacking software Helicon Focus Pro. Scanning electron micrographs of Delopsocus latus were taken by an environmental electron microscope Hitachi S-3700 N in the National Museum in Prague.
Acknowledgements The author cordially thanks to  Susan Butts  and  Jessica Utrup  for loan of Delopsocus latus from  Yale Peabody Museum (New Haven, CT, USA ),  Paul Mayer  for access to the collection The  Field Museum of Natural History (Chicago, IL, USA) ,  Ricardo Perez de la Fuente  for the photographs of  Delopterum minutum from the Museum of Comparative Zoology collection (Cambridge, USA) , and  Alexander Rasnitsyn  for access to the collection and kind help in  Laboratory of Paleoentomology at Russian Academy of Sciences (Moscow, Russia ). The authors thank  Lenka Váchová  and  Jiří Kvaček  (both  National Museum in Prague ) for their help with the application of the ESEM. We thank the  President and Fellows of Harvard College  for permission to use MCZ copyrighted material. We are grateful to three anonymous referees for their very insightful comments, which improved the manuscript.
Funding The first author (J. P. ) acknowledges the financial support of the  Grant Agency of the Czech Republic  (No. 14-03847  J). This work was also partially benefitted from the project of ICCM, who was supported by a grant from  Agence Nationale de la Recherche  under the LabEx  ANR-10-LABX-0003-BCDiv , in the program " Investissements d'avenir "  ANR-11-IDEX-0004-02.
For the purpose of this work, we study all identified impact craters with diameters ≥1 km in the northern lowlands with coverage of CRISM targeted L-detector data as of November 2015. We focus on the well-calibrated 1. 0-2. 6 μm channels from the L-detector because both mafic and hydrated minerals are expressed in the spectral range; in locations with hydrated mineral detections, we examined select S-detector data (covering 0. 4-1. 0 μm) to search for crystalline iron oxides but did not find them. The region of interest is shown in Figure  1 , with the outline of the study region bounded by contour lines along the dichotomy boundary, following  Tanaka et al. [2005]  and excluding the northern polar cap. A total of 18,293 impact craters ranging in diameter from 1 km to 376 km lie within the proposed study region, documented in Mars' global crater database  [Robbins and Hynek, 2012].