dataesr/scientific-paragraphs-categorization huggingface dataset:
vital-it. ch/software/ BOX_form. html). To create the Oct1 crystal structure images, the Protein Data Bank (PDB) ID 1Oct (http://dx. doi. org/10. 2210/ pdb1oct/pdb;  Klemm et al. 1994 ) was downloaded from the PDB  (Berman et al. 2000) , and images were created using the University of California at San Francisco Chimera 1. 11. 2 package  (Pettersen et al. 2004 ). Amino acids were numbered according to mouse BRN2 sequence and the mouse OCT1 variant NCBI reference NP_035267. 2.
GSVA analyses were performed using the Bioconductor package GSVA  (Hänzelman et al. 2013). The gene sets used were obtained from the Molecular Signatures Database  (Subramanian et al. 2005). The GSVA matrix was then clustered and displayed as a heat map using Pheatmap (https://cran. r-project. org/web/packages/pheatmap/index. html).
TCGA bioinformatics analysis Files with FPKM (fragments per kilobase per million mapped fragment) gene expression measurements were downloaded on November 20, 2017 from TCGA (https://portal. gdc. cancer. gov/ projects/TCGA-SKCM) with the following filters: project ID: TCGA-SKCM; sample type: metastatic, primary tumor, additional metastatic; workflow type: HTSeq-FPKM; data category: transcriptome profiling. We added log 2 (FPKM + 1) values for genes POU3F2 (BRN2, ENSG00000184486), POU4F1 (BRN3a, ENSG00000152192), and POU4F2 (BRN3b, ENSG00000151615) as variables to the negative binomial model previously published  (Robles-Espinoza et al. 2016). This left 271 samples in the model, as two did not have available FPKM measurements (TCGA-EE-A3AE and TCGA-GN-A269). We also added log 2 (mean FPKM [MITF target genes]) as a variable, where MITF target genes = MITF (ENSG00000187098), BCL2 (ENSG00000171791), CDK2 (ENSG00000123374), CDK4 (ENSG00000135446), DCT (ENSG 00000080166), HIF1A (ENSG00000100644), MLANA (ENSG000 00120215), PMEL (ENSG00000185664), PPARGC1A (ENSG000 00109819), PTEN (ENSG00000171862), RAB27A (ENSG00000 069974), SHC4 (ENSG00000185634), TRPM1 (ENSG0000013 4160), TYR (ENSG00000077498), TBX2 (ENSG00000121068), MCOLN1 (ENSG00000090674), DIAPH1 (ENSG00000131504), and MET (ENSG00000105976).
Data analysis Visualization of data and statistical analysis other than that generated by AP-MS or RNA-seq was performed using Prism7 software (GraphPad Software, Inc. ). FACS data analysis was performed by ordinary two-way ANOVA and Sidak's multiple comparisons test. Data RNA-seq data have been deposited in the Gene Expression Omnibus (GEO) with accession number GSE124761. MS data are available at MassIVE (http://massive. ucsd. edu; MSV000080598; ftp://massive. ucsd. edu/MSV000080598) and the ProteomeX-change Consortium (http://proteomecentral. proteomexchange. org; PXD006017).
Acknowledgments We thank  Amélie Fradet-Turcotte  for her insightful comments on the manuscript. Proteomics work was performed in part at the  Network Biology Collaborative Centre at the Lunenfeld-Tanenbaum Research Institute , a facility supported by the  Canada Foundation for Innovation funding , the  Ontarian Government , and  Genome Canada  and  Ontario Genomics. This work was funded by a  Wellcome Trust  fellowship ( 106288/Z/14/Z  to K. H. ), the  Ludwig Institute for Cancer Research  (C. R. G. , R. B. , J. C. , and P. L. ), a  National Institutes of Health  grant ( PO1 CA128814-06A1  to P. F. ), and a  Canadian Institutes of Health Research (CIHR) Foundation  grant ( FDN 143301  to A. -C. G). M. R. M. was supported by the  National Institute for Health Research Biomedical Research Centre ,  Oxford , and C.
D. R-E. was supported by a  Wellcome Trust Seed Award in Science  ( 204562/Z/16/Z ) and a  Universidad Nacional Autonoma de Mexico   PAPIIT grant  ( IA200318 ). H. K. was supported by a fellowship from the  Deutsche Forschungsgemeinschaft  (project no. 392470008 ). A. -C. G. holds the  Canada Research Chair in Functional Proteomics. J. -P. L. was funded by the  Cancer Research Society  ( 22779 ) and a Junior 1 salary award from the  Fonds de Recherche du Québec-Santé  (FRQ-S). L. L. was supported by the  Fondation ARC  project labellisé,  La Ligue National Contre le Cancer , the  Institut National du Cancer , and the  Institut Thématique Multi-Organisme Cancer. J. -Y. M. holds a  Fonds de Recherche du Québec-Santé Chair in Genome stability. Work in J. -Y. M. 's laboratory was supported by a  CIHR Foundation  grant.
Sample Due to the implications from previous research, we selected a large-scale dataset from a reward-based platform, Kickstarter. The data was obtained using a self-programmed web-crawler collecting a wide set of variables a project initiator can affect projects from Kickstarter's initial start in 2009 to the end of 2016. Overall, 294,150 valid projects were retrieved. Film or video projects (19%, n = 54,525) are most frequent, followed by music (16%, n = 45,606), publishing (11%, n = 31,255), games (8%, n = 23,964), and technology (8%, n = 22,584). Projects have an average funding period of 34. 32 days (SD = 13. 10) and an average goal of 45,961. 35 USD (SD = 1,139,705. 09) and are backed by 90. 61 investors on average (SD = 782. 16) that pledge a mean of 7440. 47 USD (SD = 75,234. 47).
Models Non-linear effects can be incorporated into a variety of model types. However, incorporating non-linear effect terms in linear models, usually via polynomials, requires hypotheses about their nature (e. g. , exponential, cubic) and successive significance testing. Since significance testing is not advisable in large-scale datasets  [34] , our approach is inherently explorative. As there is no theoretical assumption which variable follows which non-linear pattern, a family of non-linear models is chosen which resembles this exploratory approach. Generalized additive models (GAM)  [35]  try to find segments with (unique) non-linear patterns and aggregate these segments to a continuous function. Recent developments  [36]  have improved GAMs substantially and eased its application to the present type of datasets.
Goodness of fit and explained variance In order to evaluate the advantage or disadvantage of all types of models, we apply global as well as local criteria. Fit tests like a chi-square-based likelihood test are inappropriate because of the different model assumptions as well as the large dataset. We thus apply the information criteria BIC and log-likelihood. Further, we rerun isolated effect models to obtain estimates for the relative contribution of each of the six variables. Table  2  summarizes the model comparisons. Overall, both non-linear models were substantially better able to predict success through updates and comments while being on par or slightly better than a linear model for all other variables (rewards, Facebook friends and shares, Twitter tweets). Consequentially, model prediction improved from.
Quality control, alignment on P. acuta genome and methylome description All data treatment was carried out under a local galaxy instance (Goecks et al. , 2010) (http://bioinfo. univ-perp. fr). Fastq Groomer v1. 0 was used for verification of the fastqsanger format, and the FASTX-Toolkit v0. 0. 13 (Compute quality statistics, Draw quality score boxplot, Draw nucleotides distribution chart) was used for initial quality control. First and last five nucleotides for all reads were removed using the TRIM tool from the FASTX-Toolkit because of lower quality. Overall, read quality was judged sufficiently good (the majority of reads showed a quality score above 26 for the non-trimmed position) and no further quality filter was applied. Read alignment on the P. acuta reference genome (Vidal-Dupiol et al.
in preparation) was done in paired-end using Bismark v2. 74  (Krueger and Andrews, 2011)  and Bowtie2  (Langmead and Salzberg, 2012)  with default parameters. We only kept alignments for which both read mates were successfully aligned and fragment size was between 150 and 500 bp. BAM files output from Bismark were sorted and converted to SAM format using Samtools v0. 1. 19 (Li et al. , 2009). SAM files were loaded into the R package methylKit v0. 5. 6  (Akalin et al. , 2012).
Individual numerical data for panels C-H are provided in Supporting information, S3 Data. ampl. , amplitude; autIPSC, autaptic inhibitory postsynaptic current; BQA, Bayesian quantal analysis; IPSC, inhibitory postsynaptic current; max, maximal; PV, parvalbumin; synIPSC, synaptic inhibitory postsynaptic current. https://doi. org/10. 1371/journal. pbio. 3000419. g003 aminophenoxy)ethane-N,N,N 0 ,N 0 -tetraacetic acid (BAPTA, 20 mM) in the presence of 2 mM Ca 2+. Diffusion of BAPTA to autaptic contacts progressively reduced the size of autIPSCs until a complete block of autaptic neurotransmission was typically achieved within 20 min following whole-cell break in (Fig  4A ). In order to rule out that this time-dependent reduction of autaptic responses was due to nonspecific rundown, we performed some control experiments, in which autIPSCs were recorded with an intracellular solution containing low concentration (1 mM) of the slow Ca 2+ chelator EGTA, mimicking endogenous Ca 2+ buffering by PV  [25].
e. , before and after potential autaptic transmission block-is shown on the summary plot. In PV cells with evoked autaptic IPSC, the frequency strongly decreases in the presence of intracellular BAPTA (n = 7) but not EGTA (n = 7), whereas in PV cells with no autaptic transmission, the frequency did not change in both conditions (BAPTA, n = 6; EGTA, n = 4) ( ��� p < 0. 001). Individual numerical data for panels C and I are provided in Supporting information, S4 Data. ACSF, artificial cerebrospinal fluid; autIPSC, autaptic inhibitory postsynaptic current; BAPTA, 1,2-bis(oaminophenoxy)ethane-N,N,N 0 ,N 0 -tetraacetic acid; IPSC, inhibitory postsynaptic current; mIPSC, miniature IPSC; PN, pyramidal neuron; PV, parvalbumin; TTX, tetrodotoxin. https://doi. org/10. 1371/journal. pbio. 3000419. g004 versus 5.
(E, F) Same as (C) and (D) but for the PV cell represented with the blue trace in (A) and (B). Note the appearance of spike doublets (E), yielding multimodal ISI distribution (F, left) and broader phase histogram (right). (G, H) Same as (C-F) but for the cell represented with green trace in (A) and (B). Note the appearance of high-frequency bursts (G) yielding a large peak in the ISI distribution at faster intervals than the oscillation period. Furthermore, note that the phase histogram yielded an even broader profile. (I, J) Same as in (C-H) but for the PV cell intracellularly perfused with BAPTA, illustrated with a red trace in (A) and (B). Note the similar firing behavior of the EGTA cell characterized by the fast AHP and burst firing (green traces in [A, B, G, and H]). Individual numerical data for panel B are provided in Supporting information, S5 Data.
Photo-stimulation Photo-stimulation was induced using a blue LED (λ = 470 nm, OptoLED Lite, Cairn Research, Faversham, UK) collimated and coupled to the epifluorescence path of the microscope (BX51WI; Olympus). Light was delivered through a 60× (1. 0 NA) water immersion lens, centered on layer II/III. The light intensity and waveform were controlled by the analog output of a digitizer (Digidata 1440A, Molecular Devices, San Jose, CA, United States). Light ramps had a duration of 1-3 s and a slope of 0. 1-0. 8 mW s -1 , started at zero intensity, and reached a final intensity of 0. 3-1. 6 mW s -1. The slope was adjusted in each slice to obtain a robust rhythmic activity in the gamma frequency range with a stable power for the entire duration of the stimulus. Light ramps were repeated with a 60-s interval.
Data acquisition and analysis Signals were amplified using a Multiclamp 700B patch-clamp amplifier (Molecular Devices, USA), sampled at 20 KHz, and filtered at 2 KHz or 10 KHz in voltage-clamp and currentclamp modes, respectively. Voltage measurements were not corrected for liquid junction potential. Access resistance was <20 MΩ and monitored throughout the experiment. Recordings were discarded from analysis if the resistance changed by >20% over the course of the experiment. Data were analyzed using pClamp (Molecular Devices, USA), Origin (OriginLab, Northampton, MA, USA), MATLAB (MathWorks, Natick, MA, USA), and custom written scripts and software. Unitary autIPSCs and synIPSCs. A brief (0. 2-0. 6 ms) depolarizing current step was injected in the presynaptic PV interneuron from PV-PV or PV-PN pairs.
Data were analyzed using pClamp (Molecular Devices, USA), Origin (Microcal, USA), and MATLAB (Mathworks, USA) software. BQA. Quantal parameters were estimated using an improved implementation of BQA  [24]  as described previously  [46, 47]. Briefly, synaptic or autaptic currents were measured in the presence of two different Ca 2+ concentrations (1. 5 and 2 mM) corresponding to intermediate and high release probabilities. BQA was performed only for experiments in which at least 50 stable responses per condition could be recorded, followed (in the case of autaptic connections) by application of gabazine in the presence of both Ca 2+ concentrations in order to subtract the profile of the action currents, which is subject to changes following reduction of Ca 2+. In contrast to multiple probability fluctuation analysis (MPFA)  [48] , which relies on parabolic fits to the variance-mean relationship of synaptic currents, BQA models the distribution of all amplitudes observed at different release probabilities.
ACSF containing a high concentration of K + (approximately 20 mM) was applied using a pressure system (puff) through a glass pipette located near the cell body of the recorded PV interneuron to depolarize axon terminals impinging onto the recorded neuron. High-K + puffs induced global asynchronous release of GABA that could be detected as a substantial increase in the frequency of mIPSCs. mIPSCs were recorded during successive sequences of baseline activity and throughout puff application (3-6 s, 1 puff/min) for at least 20 min. Miniature GABAergic events (induced by high-K + puffs and asynchronous release) were detected using a custom written software (Detector, courtesy J. R. Huguenard, Stanford University; S1 and S2 Figs). Briefly, individual events were detected on a differentiated copy of the raw current trace with a threshold-triggered process.
Rhythmic synaptic events evoked by light stimulation were detected using Detector software as described above for mIPSCs. Spikes of layer V PV cells were extracted using a threshold of -10 mV on the membrane potential trace. PSC cycles were measured, and the timing of each spike in the PV neuron was expressed as a phase relative to the peak of each γ-oscillation. ISIs and phase distributions were computed for each cell using custom written scripts (MATLAB). Variability of firing was evaluated using the entropy of the log interval distribution  [49, 50] , whereas the dispersion of pericycle spike times was quantified using the entropy of the corresponding phase distribution  [49]. The circular statistic toolbox of MATLAB was used to compute parameters of phase distributions and their associated statistical tests, as indicated in the text.
AHP duration of single APs was measured as the 10%-90% decay time setting the baseline right before the spike (5-ms window). Hence, isolated spikes were selected for this analysis because the decay time of AP being part of doublets or burst could be contaminated by the generation of the following one. The average value of the AHP decay time of 10-20 spikes was considered for each PV cell. Statistics. Because most data distributions were not normal, unless indicated in the text, we used nonparametric significance test, Wilcoxon's signed-rank test, and Wilcoxon's ranksum test for paired and unpaired data, respectively.
Acknowledgments We thank all members of the laboratory for their critical input throughout the execution of this project. We thank  Frederic Manseau ,  Pasqualina Farisello , and  Tommaso Fellin  for their initial involvement in this project and  Geeske M. van Woerden  for help with in utero electroporation. We are grateful to  Javier Zorrilla de San Martin ,  Nelson Rebola , and  David DiGregorio  for critically reading this manuscript.
This work was supported by  European Research Council (ERC)  under the European Community's  7th Framework Programmme  ( FP7/ 2007-2013) / ERC  grant agreement No  200808 ; " Investissements d'avenir "  ANR-10-IAIHU-06 ;  Agence Nationale de la Recherche  ( ANR-13-BSV4-0015-01 ,  ANR - FRONTELS , and  ANR-NanoSynDiv) ,  Fondation Recherche Medicale (Equipe FRM   DEQ20150331684 ),  NARSAD  independent investigator grant, and a grant from the  Institut du Cerveau et de la Moelle epinière (Paris)  (AB) and by a  Leverhulme Trust   Research grant  ( RPG-2013-176 ), a  Biotechnology and Biological Sciences Research Council  Grant ( BB/L001454 ) and a  Medical research council (MRC)  grant number  MR/ R011494/1  to MB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In this article, we report the study of a patient who has ID associated with speech delay. According to the breakpoints of the translocation t(3;5), the predicted TAD in chromosome 3 only contains CNTN6 and CETN3 gene. Few studies described patients with ID carrying microdeletions/microduplications containing CNTN6  [23]. Still these CNVs have also been reported in some phenotypically normal individuals in the databases of genomic variants. They are mostly inherited from healthy parents and no patient has been identified with a point mutation of CNTN6 (ClinGen Dosage Sensitivity Map Curation https://www. ncbi. nlm. nih. gov/projects/dbvar/clingen/). To date CETN3 is not described in human disease. In chromosome 5, we identify a possible new TAD encompassing MEF2C and LINC01266. Our results of RNA quantification showed a clear significant overexpression of MEF2C in the patient's lymphoblastoid cell line.
Acknowledgements We deeply thank the patient and her family for their participation in this study. We thank  Mrs. D. Mechin  and  Mrs. A. Fabre  for sanger sequencing. We thank the microarray  Core Facility of the Institute of Research on biotherapy, CHRU-INSERM-UM1 Montpellier , http://irb. chu-montpellier. frand the CHRO-MOSTEM platform (http://www. chu-montpellier. fr/fr/chercheurs/plateformes/les-plateformes-recherche/chromostem/).
| Wolbachia screening and multilocus sequence typing analysis Wolbachia infection in adult females was detected by nested PCR amplification of a Wolbachia-specific 16S rDNA fragment (~400 bp) using 2 μl of host genomic DNA, according to the protocol developed in Catteruccia's laboratory  (Shaw et al. , 2016). Amplification of this 16S rDNA fragment in infected Aedes albopictus and Culex pipiens genomic DNA (data not shown) confirmed the performance of this nested PCR protocol to detect Wolbachia in many different mosquito species  (Shaw et al. , 2016). To detect potential contaminations, Ae. albopictus and Culex quinquefasciatus from Gabon were used as positive controls, and water and Ae. aegypti as negative controls. Moreover, PCR amplifications for each species were carried out independently and on different days.
| Phylogenetic and statistical analysis All Wolbachia sequences for the 16S, coxA, fbpA and ftsZ gene fragments and for Anopheles COII were manually corrected using Geneious R10  (Kearse et al. , 2012). The resulting consensus sequences for each gene were aligned with sequences that represent the main known Wolbachia supergroups obtained from GenBank (see Table  S1 ). Only unique haplotypes for each species were included in the analysis (haplotype was defined as a unique allelic profile for each examined locus). Inference of phylogenetic trees was performed using the maximum likelihood (ML) method and RAxML  (Stamatakis, 2014 ) with a substitution model GTR + CAT  (Stamatakis, 2006)  and 1,000 bootstrapping replicates. Finally, all MLST Wolbachia sequences were used to build phylogenetic trees using RAxML (GTR + CAT model, 1,000 bootstrapping replicates).
DATA AVA I L A B I L I T Y Data for this study are available at the Dryad digital Repository: https ://doi. org/10. 5061/dryad. sn81548 (Ayala et al. , 2019). DNA sequences of Wolbachia and Anopheles recovered in this study and of those used as references for phylogenetic analyses are submitted at Genbank (MK755460-MK755837).
Instrumentation An automated pipetting system Eppendorf epMotion®5075 was used to carry out the bioassays. A microplate reader (Spectramax Plus 384, Molecular Device, Wokingham, Berkshire, UK) was used to measure absorbance values. Data were acquired with the SoftMaxPro software (Molecular Devices, Wokingham, Berkshire, UK) and the Prism software (GraphPad Software, La Jolla, California, CA, USA) was used to calculate inhibition percentages. The results are presented as inhibition percentages (I%) calculated as follows (for the DPPH radical scavenging assay, and tyrosinase, lipoxygenase and elastase assays): I% = OD control -OD sample OD control × 100 or as follows (for the hyaluronidase and collagenase assays): I% = OD sample OD blank -OD control × 100 Similarly, all OD (apart the hyaluronidase ones) were corrected with the blank measurement corresponding to the absorbance of the sample before addition of the substrate.
Acknowledgements We would like to thank all of the participants for their excellent submissions and thoughtful commentary. We would also like to acknowledge  Simon Harold , who originally created the competition, the  BMC marketing department  and the  BMC Communications team. All images published in this Editorial are released under a  Creative Commons Attribution License (CC BY)  to ensure credit with proper attribution [6]. If you wish to re-distribute or re-use any images published in this editorial, please credit individual winners as the image licensee.
Methods The PROSPECT Working Group consists of anaesthetists and surgeons with broad international representation [10]. The Working Group meets face-to-face twice a year, with other communications performed via email correspondence. During the meetings, commonly performed but painful surgical procedures are identified for review. Once the PROSPECT Working Group identifies the surgical procedure (new or update) to be reviewed, a sub-group is selected, with one lead from the Working Group. The sub-group consists of at least two members of the Working Group and co-opted external members as required (e. g. surgeons and/or anaesthetists) with specific expertise in the surgical procedure to be reviewed. In addition, specialists in literature searches and/or data analysis may also be included in the sub-group.
The studies undergo quality assessments which are used to assign the level of evidence (Table  1 ). Criteria employed for quality assessments include allocation concealment of treatment assignment by those involved in recruitment (A, adequate; B, unclear; C, inadequate; D, not used), quality scoring to assess randomisation using the Jadad score (1-5)  [12] , assessment of blinding, reporting of the flow of patients and participant follow-up of greater or less than 80%  [13]. 1 ). The proposed recommendations are then sent to all members of the Working Group along with data extraction files, included studies and excluded studies with reasons for exclusion, level of evidence of the included studies and reasons for recommending or not recommending interventions. Members of the Working Group also have access to the publications of included studies so that they can review the evidence for themselves.