dataesr/scientific-paragraphs-categorization huggingface dataset:
7i  and Fig. 9c ), and contained one RUNX1 specific peak in its intronic region. Finally, 274 genes were the closest genes to peaks for both RUNX1 and FOXL2, with the majority of them (197 genes) nearest to overlapping peaks for RUNX1 and FOXL2 (Fig. 9a ). Most of these genes (74%; 146/197 genes) were downregulated in Runx1/Foxl2 DKO ovaries (Dataset S7). For instance, the granulosa cell enriched genes Fst and Itpr2, both downregulated in Runx1/Foxl2 single and double KO ovaries (Fig. 7d  and 9d ), contained common binding peaks for FOXL2 and RUNX1 (Fig. 9a  and 9d ). For Fst, this binding of RUNX1 and FOXL2 was located in its first intron, in the previously identified regulatory region that contributes to its expression  41, 46. On the other hand, the Sertoli cell gene Dmrt1, strongly upregulated in Runx1/Foxl2 DKO (Fig.
, Carlsbad, CA). Gene expression was analyzed by real-time PCR using Bio-Rad CFX96TM Real-Time PCR Detection system. Gene expression was normalized to Gapdh. The Taqman probes and primers used to detect transcript expression are listed in Table  S1. Data were analyzed using Prism GraphPad Software by unpaired Student's t-test or by ANOVA p<0. 05. Values are presented as mean ± s. e. m.
Arrays were scanned in an Affymetrix Scanner 3000 and data was obtained using the GeneChip® Command Console Software (AGCC; Version 3. 2) and Expression Console (Version 1. 2). Microarray data have been deposited in GEO under accession code GSE129038. Gene expression analyses were conducted with Partek software (St. Louis, Missouri) using a one-way ANOVA comparing the RMA normalized log2 intensities. A full dataset Excel file containing the normalized log2 intensity of all genes for each genotype, and a graphic view of their expression is provided in Dataset S2. In order to identify differentially expressed genes, analysis of variance (ANOVA) was used to determine if there was a statistical difference between the means of groups and the gene lists were filtered with p<0. 05 and fold-change cutoff of 1.
Duplicate mapped reads were removed using Picard tools MarkDuplicates. jar (v1. 110). The number of uniquely-mapped non-duplicate reads for each biological replicate was 8,932,674 and 15,036,698. After merging the replicate datasets, binding regions were identified by peak calling using HOMER v. 4. 9 66 with FDR<1e-5. Called peaks were subsequently redefined as 300mers centered on the called peak midpoints and filtered for a 4-fold enrichment over input and over local signal. Genomic distribution of RUNX1-bound regions was determined based on Refseq gene models as downloaded from the UCSC Genome Browser as of August 09, 2017. Enriched motifs were identified using HOMER findMotifsGenome. pl de novo motif analysis with parameter "-size given". For RUNX1 and FOXL2 ChIP-seq comparisons, binding peaks that had at least 1 bp in common were considered overlapping.
Peaks were assigned to the nearest gene based on RefSeq. Gene lists were analyzed for enrichment using the online tool EnrichR  67. The ChIP-seq data are available in the ReproGenomics Viewer (https://rgv. genouest. org)  68, 69  and Gene Expression Omnibus (GSE128767; http://www. ncbi. nlm. nih. gov/geo/). Groner and Ditsa Levanon (The Weizmann Institute of Science, Israel) for the RUNX1 antibody, David Zarkower (University of Minnesota, USA) for the DMRT1 antibody, Ken Morohashi (Kyushu University, Japan) for the SF1 and SOX9 antibodies, and Dagmar Wilhelm (University of Melbourne, Australia) for the SRY antibody. We are grateful for the NIEHS Molecular Genomics Core, Integrative Bioinformatics Support Group, Comparative Medicine Branch, and Cellular and Molecular Pathology Branch for their services.
Funding This work was supported in part by the  Intramural Research Program  ( ES102965 ) of the  NIH, National Institute of Environmental Health Sciences  in the U. S. The goat study was supported by  Agence Nationale de la Recherche in France  ( ANR-16-CE14-0020 ). The human fetal gonads study was supported by l 'Institut national de la santé et de la recherche médicale (Inserm) , l' Université de Rennes 1 and l'Ecole des hautes études en santé publique (EHESP) in France.
ACKNOWLEDGEMENTS O. B. acknowledges HPC resources from TGCC under the gencmip6 allocation provided by  GENCI  ( Grand Equipement National de Calcul Intensif ). J. S. , C. W. S. , G. M. , Ø. H. , and B. H. S. are supported by the projects NAPEX (grant no. 229778 ) and  SUPER  (grant no. 250573 ) funded by the  Norwegian Research Council. T. A. was supported by the  Met Office Hadley Centre Climate Programme  funded by  BEIS and Defra. A. V. and M. R. K. were supported by the  Natural Environment Research Council  (grant  NE/ K500872/1 ). T. B. R. was supported by a  NERC CASE  award  NE/K007483/1  in collaboration with the  Met Office  and  NERC  grant  NE/N006038/1. Simulations with HadGEM3-GA4 were performed using the MONSooN system, a collaborative facility supplied under the  Joint Weather and Climate Research Programme , which is a strategic partnership between the Met Office and the  Natural Environment Research Council.
2-2-SNP Selection and Genotyping The SNPs were selected according to the previous GWAS  [22] [23] [24] 30]. These genetic variants were discovered to be associated with SWB or personality traits. The minor allele frequency (MAF) should be higher than 0. 05 in East Asians. We included five SNPs (rs1426371, rs2164273, rs322931, rs3756290, rs490647). All SNPs are located in the intron region except for the downstream rs490647. More detailed information about the SNPs analysed is provided in Table  1. Genotyping of all five SNPs applied the matrix-assisted laser desorption/ionization time of flight(MALDI-TOF) mass spectrometer on MassARRAY® Analyzer 4 platform (Sequenom, San Diego, CA). Assay Design Suite v2. 0 from the Mysequenom online software was employed in probes and primers design. a According to the dbSNP database.
2-4-Statistical Analysis SHEsisPlus (http://shesisplus. bio-x. cn/SHEsis. html) was applied to analyse allelic and genotypic distributions and Hardy-Weinberg equilibrium  [32]. We detected the association between the candidate SNPs with behavioural measures (i. e. personality and SWB) in five genetic models by "SNPassoc" R package (http://www. r-project. org/). Heat map illustrating correlations was also achieved on R software through "Corrplot" package. Hierarchical regression analysis was performed using SPSS ® Statistics version 24. 0. For all analysis, two tailed p values were adapted and statistical significance was set for p < 0. 05. Research methodology is summarized in Figure  1.
RNa ISolatIoN aND SeQUeNCINg aND MRNa eXpReSSIoN Total RNA was extracted with TRIzol Reagent (Life Technologies), purified using the PureLink RNA mini kit (Ambion), and treated with RNase-Free DNase (Qiagen) with an on-column DNase treatment. For mRNA sequencing, 100-bp single-end RNA-sequencing (RNA-seq) libraries were prepared using the Illumina TruSeq Stranded mRNA and the Illumina TruSeq SR Cluster Kit v4 reagents (Illumina). Sequencing was performed with Illumina HiSeq 2500 in a 100-bp reads run. RNA-seq data are available in the Gene Expression Omnibus (GSE87560). Detailed bioinformatic analyses are in the Supporting Information. mRNA was converted into complementary DNA (cDNA) using the SuperScript II reverse transcriptase kit (Invitrogen). Quantitative real-time PCR was performed using SYBR green supermix (Applied Biosystems).
Acknowledgment: We thank  J. Dubrot  for helping with fluorescence-activated cell sorting studies;  R. Vernet ,  S. Offner , and  I. Vidal  for technical assistance;  J. F. Henry ,  N. Aguilera , and  J. L. Thoumas  ( Plateau de Biologie Experimentale de la Souris ,  ENS de Lyon)  and  A. Olivier  for technical help in handling mice;  A. -L. Rougemont  for histological studies;  J. Duc  for data analyses; the core facilities at  UNIL ,  UNIGE , and  EPFL  for various services; the vectorology platform from Inserm  U1089  ( Nantes, France ) for the production of the Adeno-urokinase plasminogen activator vector;  N. Gadot  ( Plateforme Anatomopathologie Recherche, Lyon, France ) for the immunohistochemical analysis; and the members of the  Trono lab  for discussions.
Supported by grants from the Swiss National Science Foundation and the European Research Council (ERC-2010-AdG-268721-KR ABnKAP, to D. T. ), the Novartis Health Foundation (to M. L. ), the FP7 Marie Skłodowska-Curie Actions Project (UE7-CN-I LIVE-625689) and Novartis Health Foundation (to C. U. ); the Clinical Research Center, University Hospital and Faculty of Medicine (Geneva) and the Louis-Jeantet Foundation (to B. W. ). F. L. C. was supported by the French Agence Nationale de la Recherche sur le SIDA et les hépatites virales, the European Research Council (ERC-2008-AdG-233130-HEPCENT), and the LabEx Ecofect (ANR-11-LABX-0048).
Numerical modeling To reproduce this fluid propagation process from a geo-hydromechanical point of view, a discrete element model has been set up using the three-dimensional software 3DEC  [7]. Besides the model containing the totality of the fractures identified crossing the injection chamber (Fig. 2a , case a), the contribution of each fracture family's orientation is also tested. More specifically, the secondary fault plane (i. e. the brown plane Fig. 2a, cases a, b  and c ) is modeled together with the sub-vertical fracture family only in a second configuration (Fig. 2b , case b) and in a third configuration, the fault is modeled with the sub-horizontal fracture family only (Fig. 2c, case c ). In all cases, the fracture plane that was mostly affected by the fluid pressurization is the secondary fault, i.
This paper reports the results of the first experiment dealing with the challenges of building a machine translation system for usergenerated content involving a complex South Slavic language. We focus on translation of English IMDb user movie reviews into Serbian, in a low-resource scenario. We explore potentials and limits of (i) phrase-based and neural machine translation systems trained on out-of-domain clean parallel data from news articles (ii) creating additional synthetic indomain parallel corpus by machine-translating the English IMDb corpus into Serbian. Our main findings are that morphology and syntax are better handled by the neural approach than by the phrase-based approach even in this lowresource mismatched domain scenario, however the situation is different for the lexical aspect, especially for person names.
Therefore, the challenge of translating user-generated texts is threefold, and if the target language is complex, then fourfold. In this work, we focus on neural machine translation of English IMDb movie reviews into Serbian, a morpho-syntactically complex South Slavic language. To the best of our knowledge, this is the first experiment dealing with machine translation of user-generated content involving a South Slavic language. The main questions of our research described in this work are (i) What performance can be expected of an English-to-Serbian machine translation system trained on news articles and applied to movie reviews? (ii) Can this performance be improved by translating the monolingual English movie reviews into Serbian thus creating additional synthetic in-domain bilingual data? (iii) What are the main issues and what are the most important directions for the next experiments? In order to answer these questions, we build a neural (NMT) machine system on the publicly available clean out-of-domain news corpus, and a phrase-based (PBMT) system trained on the same data in order to compare the two approaches in this specific scenario.
Data Sets We carried out our experiments using the publicly available "Large Movie Review Dataset" 2   (Maas et al. , 2011)  which contains 50, 000 IMDb user movie reviews in English. The data set is mainly intended for sentiment analysis research, so each review is associated with its binary sentiment polarity label "positive" or "negative". Negative reviews have a score ≤4 out of 10, positive reviews have a score ≥7 out of 10 and the reviews with more neutral ratings are not included. The overall distribution of labels is balanced, namely 25k positive and 25k negative reviews. In the entire collection, no more than 30 reviews are allowed for any particular movie. For our experiments, we kept 200 reviews (100 positive and 100 negative) containing about 2, 500 sentences for testing purposes, and used the remaining 49, 800 reviews (about 500k sentences) for training.
Expanding English IMDb Reviews into a Bilingual Training Corpus The Serbian language is generally not very well supported in terms of NLP resources. The English-Serbian publicly available parallel OPUS data 3 consists mostly of subtitles, which are rather noisy. The only really clean parallel corpus there is "SEtimes", which is the reason why we used it for the baseline system in our first experiments -we wanted to avoid any effects of noisy data. To the best of our knowledge, there are no publicly available parallel corpora containing usergenerated texts in Serbian. Therefore, we created synthetic IMDb parallel corpus by translating English IMDb reviews into Serbian using our baseline systems. This technique is shown to be very helpful for NMT systems  (Sennrich et al. , 2016; Poncelas et al.
• Train one more NMT system using SEtimes corpus enriched with both IMDb pbmt and IMDb nmt corpora (IMDb joint ). Table  1  shows the statistics for each of the three training corpora (SEtimes, IMDb pbmt and IMDb nmt ), for the development set, as well as for the test set. First, it can be noticed that the IMDb training corpus contains more than twice segments and running words than the English part of the SEtimes corpus, and it has a much larger vocabulary. Another fact is that, due to the rich morphology, the Serbian SEtimes vocabulary is almost twice as large as the English one. Nevertheless, this is not the case for the synthetic IMDb data, where the Serbian vocabulary is only barely larger or even comparable to the English one. This confirms the intuition about sub-optimal forward translation mentioned in the previous section -machine translated data generally exhibit less lexical and syntactic variety than natural data  (Burlot and   , and here we are additionally dealing with a scarce out-of-domain MT system translating into a more complex language.
As for the test set, it could be expected that the scores will be worse than for the development set. However, several interesting tendencies can be observed. First of all, the baseline NMT system outperforms the baseline PBMT system despite the scarcity of the training corpus and domain mismatch  (Koehn and Knowles, 2017) , however only in terms of word-level scores -both characterlevel scores are better for the PBMT system. Furthermore, adding IMDb pbmt data deteriorates all word-level scores and improves both characterlevel scores. On the other hand, adding IMDb nmt data improves all baseline scores, but the improvements of the character-based scores are smaller than those yielded by adding the IMDb pbmt corpus. Finally, using all synthetic data IMDb joint improves all scores (except BLEU) over the baseline, however the improvements are smaller than the improvements of each individual synthetic data sets (IMDb nmt for word-level scores and IMDb pbmt for character-level scores).
Possible reason is the free word order in the Serbian language, so that the system trained on IMDb pbmt data simply generated the word order closest to the one in the reference translation. As for the lexical errors, it can be seen that the lexical error rate is much higher for the baseline NMT system than for the baseline PBMT system, which corresponds to the domain-mismatch challenge for NMT  (Koehn and Knowles, 2017). Furthermore, the highest reduction of this error type is achieved when the IMDb pbmt corpus is added.
Summary and Outlook In this work, we focused on the task of building an English-to-Serbian machine translation system for IMDb reviews. We first trained a phrase-based and a neural model on out-of-domain clean parallel data and used it as baselines. We then generated additional synthetic in-domain parallel data by translating the English IMDb reviews into Serbian using the two baseline machine translation systems. This "forward-translation" technique improved the baseline results, although "backtranslation" (translating natural Serbian texts into English) would be more helpful. Further analysis shown that morphology and syntax are better handled by the neural approach than by the phrasebased approach, whereas the situation is different for the lexical aspect, especially for person names. This finding also indicates that in general, machine translation of person names into Slavic languages (especially those which require/allow transcription) should be investigated more systematically.
Over the course of 3 weeks, containers were inspected daily for emergence of A. lopezi parasitic wasps. Parasitism levels of A. lopezi (per tip and per site) were calculated. Next, for sites where A. lopezi was found, we analyzed field-level P. manihoti abundance with A. lopezi parasitism rate with linear regression (see also ref. 18  ). The latter analysis can reflect the degree of parasitoid control over its host, and give an initial appreciation of the extent of A. lopezi-mediated mealybug population suppression. Variables were log-transformed to meet assumptions of normality and homoscedasticity, and all statistical analyses were conducted using SPSS (PASW Statistics 18). Country-specific cassava production and trade trends. To assess how mealybug invasion and ensuing parasitoid-mediated cassava yield recovery affected cassava production and trade, we examined country-level production and inter-country trade for cassava-derived commodities.
More specifically, we contrasted cassava yield and production trends with inter-country trade flows over periods spanning the 2008 P. manihoti invasion, the 2009 A. lopezi introduction into Thailand and the subsequent (natural, and human-aided) region-wide distribution of A. lopezi (mid-2010 onward). Our assessments detailed shifts in cassava production (harvested area, ha) and yearly trade flows (quantity) of cassava-derived commodities into Thailand from neighboring countries within the P. manihoti invaded range. Crop production statistics for Thailand were obtained through the Office of Agricultural Economics (OAE), Ministry of Agriculture & Cooperatives (Bangkok, Thailand). Furthermore, country-specific patterns of cassava production (harvested area, ha) and yield (t ha -1 ) were obtained for Vietnam, Myanmar, Lao PDR, and Cambodia via the FAO STAT database (http://www.
fao. org/faostat/). To assess structural changes in the inter-country trade of cassava-derived commodities, we extracted data from the United Nations Comtrade database (https://comtrade. un. org/). Over a 2006-2016 time period, we recorded the following evolutions in terms of quantity (tonnes): global annual imports of cassava-derived commodities to Thailand (reporting) and China, from ''All'' trade partner countries. More specifically, we queried the database for bilateral trade records of three cassavaderived commodities and associated Harmonized System (HS) codes: "Cassava whether or not sliced-as pellets, fresh or dried" (71410), "Tapioca & substitutes prepared from starch" (1903), and "Cassava starch" (110814). Given the occasional inconsistencies in country-reported trade volumes or values in either FAO STAT or Comtrade databases, cross-checks were made with databases from the Thai Tapioca Starch Association (TTSA) and corrections were made accordingly.
Country-specific trends in forest loss vs. cassava area growth. To infer the likely impact of cassava area growth on forest loss in different Southeast Asian countries, we obtained data from both a near-real-time vegetation monitoring system, Terra-i (https://www. terra-i. org) and deforestation data from Global Forest Watch 52 (https://www. globalforestwatch. org/). Terra-i relies upon satellite-derived rainfall and vegetation data obtained through TRMM sensor data (Tropical Rainfall Monitoring Mission) and MODIS MOD13Q1, respectively, to detect deviations from natural vegetation phenology patterns that cannot be explained by climatic events. More specifically, Terra-i adopts computational neural networks to detect how vegetation vigor behaves at a given site over a period of time in relation to observed rainfall, and thus identifies certain anomalies while accounting for the effects of drought, flooding and cloud cover or other image ''noise.
'' Changes in vegetation greenness at the landscape level are recorded on the Terra-i platform on a biweekly basis. Terra-i outputs have been validated through comparison with the Global Forest Change data and the PRODES system in Brazil. These datasets showed similar values as the average KAPPA coefficient at 0. 96 ± 0. 004. Furthermore, the average recall value for detection of events with an area of 90% to 100% of a MODIS pixel is of 0. 9 ± 0. 05, which shows that Terra-i detects the large-size events. However, an average recall of 0. 28 ± 0. 13 has been observed when the event size is about 10% of a MODIS pixel, showing a limitation of Terra-I to detect smaller size tree cover clearance. Country-level deforestation statistics over a 10year time period were extracted from this platform for Lao PDR, Myanmar, Vietnam, and Cambodia, and data were compiled on a province-level for each year from 2005 to 2013.
Next, yearly province-level records of cassava (harvested) area were compiled for each of the different countries by accessing FAO STAT, the Cambodia 2013 agriculture census and primary datasets as facilitated through national authorities and the International Food Policy Research Institute (IFPRI), Washington DC, USA. For Lao PDR, province-level records were only available on cultivated area of all root crops combined. Here, we assumed that major inter-annual changes in harvested area of root crops in Lao PDR can be ascribed to cassava as other locally important root crops, such as yam and sweetpotato are mostly grown for subsistence purposes and are less subject to major inter-annual area shifts. No continuous yearly datasets on local cassava cultivation area were available for Cambodia, and no province-level cassava cultivation records could be accessed for Myanmar.
Because of these variations in data availability, some analyses were carried out over different periods (see below). To quantify the extent to which forest loss was related to cassava area expansion, two types of analyses were conducted. First, we used linear regression to relate province-level increases in harvested cassava cropping area with forest loss during that same period for all countries (i. e. , Cambodia, Lao PDR, Vietnam), over three different time frames: 2005-2010, 2010-2013, and 2005-2013. Second, as complete annual records on (province-level) cassava cultivation were available for Vietnam, linear regression analysis allowed annual province-level trends in forest loss to be related to cassava expansion for individual years (2009-2013). To meet assumptions of normality and heteroscedasticity, data were subject to log-normal (for cassava area records) or rank-based inversed normal transformation (for deforestation rates and records).
Acknowledgements This manuscript uses original datasets that resulted from collaborative research, with trials jointly conceptualized, defined, and executed by national program staff and international cooperators. We thank  G. Hyman ,  J. Newby , and  L. K. Palao  for providing supporting information, and are grateful to  Lao Thao  and  Sok Sophearith  for assistance with field work. We are equally thankful to Thailand's  Department of Agricultural Extension (DoAE)  for coordinating nation-wide farmer survey work, and to  Department of Agriculture (DoA) -Information and Communication Technology Center  for facilitating mealybug survey records and maps.