dataesr/scientific-paragraphs-categorization huggingface dataset:
Interannual Variability Ciliate communities were consistently dominated by "Other Spirotrichea" with annual mean abundance of 9. 4 ± 2% relative to the total microbial eukaryotic reads. "Other ciliates" (1. 7 ± 1%), Strombidium sp. , Pelagostrombidium sp. , "Other Oligohymenophorea, " "Other Litostomatea, " Askenasia sp. , and Monodinium sp. abundances ranged from 0. 01 to 1. 5% of total microbial eukaryote relative abundance (Supplementary Figure  4A ). The most abundant dinoflagellate OTUs were from Gymnodiniales, Blastodinium-like, "Arctic clade 1, " Gyrodinium, Adenoides-like, and unclassified Dinoflagellates, each accounting for 1-6% of the total microbial eukaryotic reads (Supplementary Figure  4B ). Here, using our refined taxonomic database we found that microzooplankton community composition also changed, especially in summer 2008.
DISCUSSION Temporal and distributional studies of ciliates and dinoflagellates are usually restricted to morphologically recognizable species  (  Montagnes, 1996; Levinsen and Nielsen, 2002). The use of HTS coupled with the improved reference database provided higher resolution of potential species that suggests high diversity compared to previous reports from the Arctic. Overall, we discriminated around 30 taxa per 100 reads, with the total number of reads always higher for dinoflagellates than for ciliates. This translated into 251 dinoflagellates and 141 ciliate species based on OTUs defined as 98% similar as estimated using Choa1. These species estimates are considerably higher than the 55 ciliate species reported in the Western Canada Basin  (Jiang et al. , 2013)  and the <20 dinoflagellate species reported in the Beaufort Sea  (Okolodkov and Dodgeb, 1996)  based on microscopy.
ACKNOWLEDGMENTS The authors acknowledge the help and assistance of staff at the  Institute of Ocean Sciences, Department of Fisheries and Oceans Canada , the  Captains and Crews of CCGS Amundsen ,  Louis S St. Laurent , and  Wilfred Laurier. We are also grateful to  Jonathan Gagnon  and  Jean-Éric Tremblay  for the nutrient data, and to  Ramon Terrado  colleagues from  ICM ,  CSIC Barcelona  and  Marianne Potvin  in carrying out some of the sampling and laboratory work.
As such, because our model selection is done independently from the property we are interested in, we can analyze whether rules). the properties of the set of words that we obtain through simulation differ from what we observe in the real lexicon, and in what direction. To accurately capture the phonotactic processes at play in real language, we built several generative models of lexicons: n-grams over phones, n-grams over syllables, and a PCFG over syllables. After training, we evaluated each model on a held-out dataset to determine which most accurately captured each language. The best model was used as the statistical baseline with which real lexicons are compared. We studied monomorphemes of Dutch, English, German and French. Because our baseline models capture effects of phonotactics, we are able to assess pressures for clumpiness or dispersion over and above phonotactic and morphological regularities.
Selection of the best model To evaluate the ability of each model to capture the structure of the real lexicon, we trained each model on 75% of the lexicon (the training set) and evaluated the probability of generating the remaining 25% of the lexicon (the validation set). This process was repeated over 30 random splits of the dataset into training and validation sets. For each model type, we smoothed the probability distribution by assigning non-zero probability to unseen n-grams or rules in the case of the PCFG. This was to allow us to derive a likelihood for unseen but possible sequences of phonemes in the held-out set. Various smoothing techniques exist, but we focus on Witten-Bell smoothing and Laplace smoothing which are straightforward to implement in our case. 10 All smoothing techniques were combined with a backoff procedure (though not for the PCFG), such that if the context AB of a unit U has never been observed (p(U|AB) = 0) then we can use the distribution of the lower context (p(U|B)).
g. ,  Cole et al. , 1978; Martin & Peperkamp, 2015; Miller & Nicely, 1955). In addition, we looked only at the most used contrasts and not at the whole range of contrasts available. Thus, it could still be the case that a more perceptual and language-specific measure of phoneme confusability-looking at a broader range of possible contrastswould be a better predictor of clumpiness. If the lexicons prefer minimal pairs to be distinctive then we should observe more minimal pairs with easily perceptible contrasts than with confusable contrasts. In order to investigate this possibility, we looked at minimal pairs in English, for which confusability data between phonemes are readily available  (Miller & Nicely, 1955). We computed the distance between the mean number of minimal pairs in our simulated lexicons and the number of minimal pairs in the real lexicon for each of the 120 contrasts present in the Miller and Nicely dataset.
e. , by removing the infinitival ending from the verbs). One might imagine that, because most verbs end in -er in French, for instance, these words have fewer degrees of freedom and thus edit distances will be smaller across the lexicon. In our analysis using just root forms, however, the results were qualitatively the same as when we used lemmas in their infinitive form, likely because the generative models already capture this regularity. That is, our baseline models too have a disproportionate number of words ending in -er in French and -en in German and Dutch. Because the presence of these infinitival stems does not substantially alter the result, we chose to keep them in the main analysis so as to be consistent with the standard databases we used (CELEX and Lexique).
Acknowledgements We thank  Benoit Crabbé ,  Emmanuel Dupoux  and all members of Tedlab, the audience at AMLaP 2014, and the audience at CUNY 2013 for helpful comments. Research reported in this publication was supported by  ANR-10-LABX-0087 ,  ANR-10-IDEX-0001-02 ,  ANR-13-APPR-0012 , the  Eunice Kennedy Shriver National Institute Of Child Health & Human Development of the National Institutes of Health  under Award Number  F32HD070544  as well as the post-graduate fellowship from the  Fyssen  to ID and an  NDSEG  graduate fellowship and an  NSF   Doctoral Dissertation Improvement Grant  in linguistics to KM. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Akap4 is transcribed only in the post-meiotic phase of spermatogenesis and is a cytoskeletal structure present in the principal piece of the sperm flagellum  [76]. Targeted disruption of Akap4 results in abnormal flagella, and hence motility of spermatozoa  [76, 77]. To further document what stage of spermatogenesis might be interrupted in NBMs, we imported the list of spermatogenesis-related gene clusters that show specific expression at several different stages of mouse spermatogenesis (mitotic, meiotic, post-meiotic, and somatic clusters,) from the Germonline database (Additional file 30)  [78]. DEGs belonging to all four clusters tended to show higher expression in BMs than in NBMs, but this difference was more pronounced for post-meiotic genes (Figure  4D ). More specifically, 10% (55 out of the 522) of up-regulated DEGs in BM belonged to the post-meiotic cluster, while only 2% of down-regulated DEGs in BM (5 out of 258) belonged to this category.
RNA sequence (RNA-seq) analysis The quality of the generated RNA sequence was evaluated using FastQC (version 0. In short, read counts were normalized using EDASeq R package, and "in-silico empirical" negative controls genes, for RUVseq package (RUVg normalization) were obtained by taking least significantly DEGs based on a first-pass differential expression analysis performed prior to RUVg normalization. RUVseq package (RUVg normalization) was then performed using the "in-silico empirical" negative controls genes. Differential expression was performed using edgeR R package (version 3. 10. 5) [130], using the negative binomial GLM approach, edgeR normalization on raw counts, and by considering a design matrix that includes both the covariates of interest and the factors of unwanted variation.
Histology of testes and ovaries Gonadal tissue samples were fixed by immersion in 4% paraformaldehyde in saline within 15 minutes of collection for a minimum of seven days. After fixation tissue samples were dehydrated and cleared using standard histological methodology, before embedding in paraffin wax. Sections of 5-8 µm were cut and stained for light microscopy with haematoxylin-eosin. Photomicrographs of sections were captured with a QIClick™ CCD Camera (01-QICLICK-R-F-CLR-12; QImaging) linked to a DMRA2 light microscope (Leica), using Volocity® v. 6. 3. 1 image analysis software (Perkin-Elmer) running on an iMac computer (27-inch with OS X Yosemite, version 10. 10).
Statistical information used to generate the graph including the p-value and other enriched terms are provided in Additional file 32A. Supplementary Figure  13 : Gene enrichment for DEGs that also show lower expression in BM testes. A) Heatmap of enriched terms (Canonical Pathways, GO Biological Processes, Hallmark Gene Sets, KEGG Pathway) for DEGs that show lower expression in BMs (generated using Metascape). The color code on the y-axis is used to show the clustering and relation of enriched networks (Supplementary Figure  13B ). Detailed list of enriched terms and genes that belong to these terms is provided in Additional file 30B. B) Network of enriched terms found in above colored by cluster ID, indicating the relationship between the different enriched terms (Supplementary Figure  13A ).
Acknowledgments The authors would like to acknowledge the  Cell and Tissue Imaging Platform of the Genetics and Developmental Biology Department  (UMR3215/U934) of  Institut Curie , member of  France-Bioimaging  (supported by  ANR-10-INSB-04 ), particularly  Olivier Renaud  for help with light microscopy. We thank all members of the  Edith Heard  team for stimulating discussions, technical and conceptual advice, notably  Mikael Attia  and  Ronan Chaligné. We thank  Deborah Bourc 'his,  Nicolas Servant , and  Emmanuel Barillot  from  Institut Curie  for their insightful comments on the manuscript. We would like to acknowledge the  Biological Services Unit  staff at  QMUL  ( Richard Rountree ,  Arthur Gatward ,  Paul Sroka ) for care and maintenance of animals and  Cécile Reyes  ( Institut Curie, Genomics facility ) for tissue fragmentation and RNA extraction, and  Professor Jenny Jarvis  ( University of Cape Town ).
Funding This work was supported by the  International Blaise Pascal Chair  to EHB, financed by the  Région Ile-de-France  and administered by the  Fondation de l'Ecole normale supérieure. The Cell and  Tissue Imaging Platform of the Genetics and Developmental Biology Department  was supported by  ANR-10-INSB-04. The  Institut Curie High -throughput sequencing platform (NGS platform) was supported by  ANR-10-EQPX-03  and  ANR10-INBS-09-08  from the  Agence Nationale de le Recherche  and by the  Canceropôle Ile-de-France ). L. M-P was funded by an  FRM fellowship  ( SPF20151234950 ). Funding to EH was from  Labex DEEP  ( ANR-11-LBX-0044 ) part of the  IDEX Idex PSL  ( ANR-10-IDEX-0001-02 PSL ) and  ABS4NGS  ( ANR-11-BINF-0001 ).
11. 2) [119]. Sequence adapters, low quality reads, and overrepresented sequences (e. g. mitochondrial sequences) in the reads were removed using Trimmomatic (version 0. 32) [120], and the quality of the reads was checked again using FastQC. Sequence that passed the quality assessment were aligned to the NMR genome (hetGla2, Broad institute, 2012) using tophat2 (version 2. 0. 6, default parameters) [121], with bowtie2 (version 2. 1. 0) [122]. For each sample genome guided de-novo transcriptome assembly was performed using Cufflinks (version 2. 2. 1, default parameters) [123] and assembled transcript from all samples were merged using cuffmerge (cufflinks) to generate a master reference transcripts. Merged transcripts (master transcripts) were annotated to gene name using mouse transcripts.
Mouse cDNA were obtained from Ensembl [124], and a mouse cDNA blast database was generated using blast (version 2. 2. 25) [125, 126]. Assembled and merged NMR transcripts (master transcript) were searched against mouse cDNA blast database, and transcripts with e val <= 10^-5, with a length of >200bp were retained. Transcript abundance level was generated using master reference transcripts generated by cufflinks and HTSeq (version 0. 5. 3p9) [127]. The transcript level quantified using HTSeq was used as an input for further processing using R software environment for statistical computing and graphics (version 3. 2. 2). Data normalization, removal of batch effect and other variant was performed using EDASeq R package (version 2. 2. 0) [128] and RUVseq package (Remove Unwanted Variation from RNA-Seq package) [129].
Identification of Tfcp2l1 target genes To identify transcriptional targets of Tfcp2l1 that might explain IC regulation, we used an integrative approach that combined high-throughput data from two independent assays: (1) We found 843 differentially expressed genes in a comparison of EIIA-Cre;Tfcp2l1 f/f knockout and Tfcp2l1 f/f control P1 mouse kidneys (Affymetrix Microarrays analyzed by limma, n = 3, p<0. 05). According to GUDMAP, the comprehensive genitourinary development database  (Harding et al. , 2011, www. gudmap. org) , our analysis showed that 62% of downregulated genes were expressed either by UB, cortical or medullary collecting ducts or by the TALH including multiple subunits of the H + ATPase complex (Atp6v0d2; Atp6b1; Atp6v1c2) and IC specific Foxi1, Ca12, Ca2, Aqp6 and Oxgr1 (Figure  5A , Figure 5-figure supplements 1 and 2; Supplementary file 1); (2) We found 6564 Tfcp2l1-bound genomic regions in P1 mouse kidney using ChIP-Seq (Figure  5A ; Supplementary file 2).
Comparing the two datasets we found that Tfcp2l1 bound both up and down regulated genes at the TSS (Figure  5A, B ). In fact, in a global analysis of all Tfcp2l1 peaks, 30% accumulated near the TSS within known promoters (Figure  5C ). In addition these peaks demonstrated the established Tfcp2l1 motif (Supplementary file 4)  (Chen et al. , 2008). Moreover, cell type specific actions of Tfcp2l1 were suggested in a comparison with Tfcp2l1 ChIP-Seq from ES  (Chen et al. , 2008)  and from Grhl2 ChIP-Seq in kidney  (Werth et al. , 2010)  which revealed little overlap with Tfcp2l1 ChIP-Seq in kidney (Figure  5D ). In contrast, a random set of genes that were not modulated by the Tfcp2l failed to show TSS enrichment (Figure  5B ). The comparison between the two datasets also allowed us to identify the critical transcriptional targets of Tfcp2l1.
Tfcp2l1 regulates Jag1 and Notch signaling in the collecting duct The analysis of our two datasets demonstrated significant over-representation of components of the Notch pathway suggesting that Tfcp2l1 might regulate Notch signaling in ICs or in PCs. We analyzed the Notch pathway using well characterized antibodies for Notch Ligands (Jag1, Jag2, Dll1, Dll4), Receptors (Notch1-4) and Notch Signaling States ('ON': characterized by detection of cleaved Notch and its target, nuclear Hes1, and 'OFF': characterized by detection of uncleaved Notch). We found that Jag1 marked the development of ICs: Jag1 and Atp6b1 co-localize with Ktr8 in IC-PC 'double positive cells' at E18 (Figure  6A ) and in the adult Jag1 was specifically expressed by a subset of ICs called Pendrin + b-ICs (Figure  6B ). The localization of Jag1 was specific because, a-ICs and PCs were Jag1 -(Figure  6C ).
RNA extraction and cDNA synthesis, Affymetrix Arrays We isolated RNA using Qiagen RNAeasy micro kit, analyzed the isolate by Bioanalyzer (Agilent, Santa Clara, California), and synthesized cDNA using kits from Applied Biosystems. Preparation of templates and hybridization on Affymetrix Arrays was performed at the Columbia Genome Center. Differential gene expression was identified using R Limma (https://bioconductor. org/packages/ limma). Induced genes were validated by QPCR using Sybr Green (Applied Biosystems, Forster City, California) and relative levels of mRNA expression were normalized to b-actin mRNA. Primer pairs were designed using Primer3 software (Whitehead Institute, MIT) and validated by product size (Supplementary file 5). P-values cut-offs (0. 05 and 0. 01) were chosen to generate datasets of comparable sizes.
Our data has been deposited with GEO, Accession Number GSE87769 (and Super Series: Accession Number GSE85325, GSE87744, GSE87752). Tfcp2l1 ChIP-Seq data from ES cells was obtained from GEO, Accession Number GSE11431. Grhl2 ChIP-Seq data from the kidney was obtained from GEO, Accession Number GSE24295.
In situ hybridization and immunocytochemistry Tissues were prepared by intracardial perfusion with PBS followed by 1% PFA/PBS, 30% Sucrose/ PBS, OCT (Tissue-Tek) and cryosectioned at 50 mm. Blocking and staining was performed in PBS/3% BSA/0. 2% Triton X-100 solution. Controls and experimental samples were always placed on the same slide, stained in parallel and imaged with the same parameters using confocal Zeiss LSM510 Confocal Microscope (Columbia Core Facilities). We used confocal microscopy to reconstruct whole cells from the image stacks and estimated the amount of protein per cell by measuring total fluorescent signal. Antibodies are listed in Supplementary file 5. Data were analyzed using Fiji (ImageJ). Digoxigenin-labeled antisense riboprobes were generated from cDNAs using reverse primers containing a 5' T7 polymerase promoter sequence (Supplementary file 5;  Schmidt-Ott et al.
Figure11. Models. (A) Development of cellular diversity in the collecting duct. Initially, we found a monotonous expression of PC proteins at E13, and then a transitional stage (E15-E18) characterized by the appearance of 'double-positive' cells. After birth, these cells achieved distinct identities and assumed rosette-like patterning. Deletion of Tfcp2l1 resulted in a monotonic cell type expressing PC cell, but not IC cell markers. Inactivation of Jag1 in contrast resulted in the loss of cell identity and patterning, increasing the number of 'double-positive' cells typified by E18 collecting ducts. (B) Proposed model of cell-autonomous and non-cell-autonomous actions of Tfcp2l1. Tfcp2l1 induces the expression of IC genes, including Jag1. Expression of Jag1 triggers Notch signaling in adjacent PC cells.
Methods We carried out a cross-sectional survey from January 2013 to October 2015. An online questionnaire was designed by the EFPT Psychotherapy Working Group, as a result of collective debates considering previous studies  [5, 6]  and psychotherapy training-related European recommendations  [2] [3] [4]. The target population was meant to be psychiatrists in training or those within 5 years from qualification. We established national coordinators for each country to spread the questionnaire via local trainee networks, In some large countries there was no way for them to estimate accurately how many trainees there were but we estimate that there might be 20 000 psychiatric trainees in Europe. Two reminders were sent out across the survey period for each participating country. The questionnaire was implemented using Open Source software -Limesurvey.
The saturation property has also been observed in multiple other fields, and other studies from different disciplines have led to similar conclusions  (5). In spite of the saturation property of distance metrics, identifying horseshoes is still highly useful for identifying patterns concerning niche differentiation. These insights can ultimately guide additional statistical analyses, such as network analyses and indicator taxon analyses, to facilitate the targeted characterization of microbial niches. Methodology. All analyses can be found under https://github. com/knightlabanalyses/horseshoe-analyses. The mean gradient used for the 2 case studies was calculated as follows: g x ϭ ⌺ iϭ1 N g i x i ⌺ jϭ1 D x j (1) where x i is the proportion of OTU x in sample i, g x is the mean gradient of OTU x, and g i is the sample gradient at sample i.
This calculation can be found in the gneiss package under the function mean_niche_estimator. The function used to sort the tables in Fig. 2c  used niche_sort. In the 88-soil-sample study, the table was sorted by sample pH and mean pH of the samples in which the organisms were observed. In the postmortem mouse study, the table was sorted by the number of days of decomposition and the mean number of days that the organisms were observed in the samples. The data represented in the heat map in Fig. 2f  and the abundances in Fig. S2  were normalized using the center log ratio transformation given by the following equation: Analyses were performed using Scipy, Numpy, Matplotlib, Seaborn, Scikit-bio, and Gneiss.
ACKNOWLEDGMENTS We thank  Noah Fierer  for his input on the analysis of the 88 soil samples and  Dan Knights  for discussion on the EMBAD metric. We also acknowledge  Amnon Amir  and  Tomasz Kosciolek  for their insights into the horseshoe effect. J. T. M. was funded by  NSF   GRFP DGE-1144086. J. L. M. and R. K. were funded by the  Office of Justice Programs National Institute of Justice  (award  NIJ-2011-DN-BX-K533 ). Opinion/Hypothesis January/February 2017 Volume 2 Issue 1 e00166-16 msystems. asm. org6.
Design and study population The study used a cross-sectional design with data from the SILNE (Smoking Inequalities: Learning from Natural Experiments) survey, conducted in 2013. The SILNE survey was primarily aimed at studying socioeconomic inequalities in smoking in different European cities  [19] , but it also included variables on alcohol consumption. The SILNE survey sample included 50 secondary schools from six European cities: Namur (Belgium), Tampere (Finland), Hannover (Germany), Latina (Italy), Amersfoort (the Netherlands) and Coimbra (Portugal) (n = 11,015; 79. 4% response rate). All data were self-reported by the students. Ethical approval was obtained in all countries. Methodological details on the SILNE survey have been published elsewhere  [19]. For this study, we excluded students under 14 and over 17 (n = 424) and those with missing information on both alcohol measures (n = 12), demographic variables (n = 178) and parental and adolescent SEP variables (n = 1696).
Second, parental SEP was reported by students instead of parents themselves. As a consequence, we could not ask in detail about the family income or parental occupation status. Moreover, parental education level was the only variable with a relevant proportion of missing data (around 12%). This may introduce bias, as non-response rates tend to be higher among people with low SEP  [28]. However, we conducted a sensitivity analysis based on multiple imputation of missing values, and we found that the results were not sensitive to the way in which missing values were dealt with. Finally, the fact that all SEP measures were self-reported may also be a source of social desirability bias, as children may report higher levels of SEP to gain social approval.
of the Tampere region (Finland) (Favourable Statement reference number: 10/2012), the Ethics committee of Martin-Luther-University Halle-Wittenberg (Germany) (Approval's reference number: 2012-112, approved on 13/12/ 2012), the commission d'Éthique Biomédicale (Belgium) (Approval's reference number: 2012/09OCT/461) and the General Directorate for Education (Portugal) (Approval's reference number: Ref number 0338600001, approved on 02/11/2012). Consent to participate was obtained from participants and their parents in all cities (more detailed data on reference  [19] ). Data on the SILNE project are not openly available, but the owners of the SILNE data set are represented in the team of authors and M Bosque-Prous received their permission to use the data for the purposes of this paper.