dataesr/scientific-paragraphs-categorization huggingface dataset:
Pipeline selection We first compared the performance of two previously published annotation pipelines used in coding gene de novo annotation in intron-rich fungal genomes using RNA-seq data. The For this analysis, we used only RNA-seq data obtained in biological triplicate from cells grown to exponential growth phase at 30°C in complete medium (YPD) (EXPO30)  (WALLACE et al. 2020). Previously described BAM files obtained after alignment of trimmed reads to the C. neoformans H99 genome were input into the BRAKER1 and Cufflink-CodingQuarry pipelines  (WALLACE et al. 2020). For the Cufflink-CodingQuarry-based analyses, we used two alternative protocols. In the first case, we first merged the BAM files from each of the three replicates (CUFF-CQ protocol). In the second case, each replicate BAM file was used to generate a unique GTF prediction file, these files were then merged using Cuffmerge and used by CodingQuarry as a single transcript source (C3Q1 protocol) (Figure  1 ).
Thus, replicate samples with 1, 5, 7. 5, 10, 15, 20, 30, and 40 million reads were used for de novo annotation of the C. neoformans H99 genome, and the quality of the gene predictions were compared. We performed this analysis using the same strategy for C. deneoformans. As shown in Figure  3 , the quality of the gene structure prediction was highly dependent on the size of the RNA-seq initial data set in both species and strongly anti-correlated with the number of Cufflinks-assembled transcripts (Table  S1 ). The highest-quality predictions were obtained with replicate samples with only 5-10 million reads. Using this adjusted read depth, the prediction showed improvement in nearly all parameters, including for missed genes, missed exons, and missed introns (Table  S1 ). We adjusted the number of reads to 7.
Exonerate-based recovery of missed genes Improvement of the pipelines was associated with an increase in the sensitivity of gene identification. In the initial C3Q1 protocol, 447 reference genes were missed, whereas only 162 H99 genes and 132 JEC21 genes were missed with the C3Q5 pipeline. Blast2GO analysis of the protein sequences encoded by the missed genes identified 16 proteins with conserved domains suggesting that it might be possible to identify some of them through comparative sequence analysis. Another 111 sequences were defined as hypothetical proteins. We first used the sequence alignment program Exonerate (SLATER AND BIRNEY 2005) and the JEC21 reference proteome as a reference to try to recover missed coding genes in H99. As expected, this analysis identified a number of missed loci, but also added a number of unpredicted loci thus reducing the quality of the annotation.
Manual curation of R265 annotation To systematically analyze critical points of the automated C. deuterogattii R265 gene prediction, four sets of data were evaluated and selected for manual correction: 1) Exonerateretrieved sequences (deleted and non-predicted), 2) predicted novel loci, 3) genes predicted in merged/split loci, and 4) small and potential pseudogenes. During the course of this manual curation of chromosomes 9 and 14, visual examination of the aligned reads revealed a number of loci at which the genome sequence did not entirely align with the RNA-seq reads, suggesting there were errors in the reference genome assembly. These errors were responsible for gene shortening or splitting and might partially explain the lower quality index score calculated for the R265 predicted annotation of chromosomes 14 and 9 compared to the quality scores obtained using similar data from JEC21 and H99.
This is likely because Cufflinks tends to make huge clusters when large data sets are input; these clusters might be eliminated during the transcript identification step. Accordingly, we found that the number of predicted transcripts decreased when too much data was used. Second, we found a nearly linear relationship between the number of introns predicted and the quality of the annotation. However, this correlation did not hold when two of the pipelines were compared; the BRAKER pipeline predicted more introns than the C3Q pipeline, along with predicting many more genes. Nevertheless, the correlation between intron number and annotation quality provided a facile way to evaluate the reliability of a de novo annotation, which might be affected, for instance, by the quality of the RNA-seq data.
Acknowledgements PAGF was supported a  CAPES exchange grant  ( Advanced Network of Computational Biology -RABICÓ -Grant  no. 23038. 010041/2013-13 ). This work was supported by a  CAPES COFECUB  grant  n°39712ZK  to GJ, by a  CNPq grant  ( 309897/2017-3 ) to CCS, and by a  NIH/NIAID   R37   MERIT  Award  AI39115-23  and a  NIH/NIAID   R01  Award  AI50113-16  to J. H. J. H. is co-director and fellow of CIFAR program Fungal Kingdom: Threats & Opportunities. S. J. P was supported by the  NIH/NIAID   F31  Fellowship  1F31AI143136-02. Members of the  Heitman laboratory  are acknowledged for valuable discussion.
Web-based survey A cover letter containing a hyperlink to the survey was sent to the directors of the Italian Neonatal Units and to one attending neonatologist at each site with expertise in neonatal cardiology, based on the database of the Italian Society of Neonatology and the ISGNC. To ensure only one response for center an unique survey link was assigned to each institution. To prevent incomplete answers, the survey form could be submitted only when completed. The first email was sent in June 2017 and a reminder in September 2017. No responders were subsequently contacted by a personalized e-mail and/or a phone call. No financial rewards were offered for taking part in the survey. Completion of the questionnaire implied consent to take part in the survey. The identity of each participant was kept confidential throughout the data collection and analysis.
Data analysis Google Forms automatically converted every questionnaire into Excel files (Microsoft, Seattle, WA). I. C. and B. F checked every questionnaire for possible inconsistencies throughout this process of conversion. Continuous variables were tested for normality using the Shapiro-Wilk test and presented as means (SD) or median (IQR) as appropriate. Categorical variables were presented as proportions. Comparisons between subgroups were conducted using a Student t test or a Mann-Whitney U test as appropriate. Categorical variables were compared using the χ 2 or Fisher exact test as appropriate. A p value of < 0. 05 was considered significant. SPSS v 20 (SPSS Inc. , Chicago, Illinois) was used to perform the statistical analysis.
RNA extraction, RT-qPCR, and Northern blot Total RNA was extracted using TRI reagent (Euromedex) followed by RNAse free-RQ1 DNAse (Promega) and proteinase K (Sigma) treatments. 1 mg of whole cell RNA was reverse transcribed with random hexamer primers using Go Script Reverse Transcriptase kit (Promega) at 42˚C for 60 min. mRNA expression was performed using the IQ Custom SYBR Green Supermix (Bio-Rad) qPCR. The relative quantification of gene expression was performed using the standard curve method with triplicates for each data point. For Northern blotting analysis, 5 mg of total RNA was fractionated by electrophoresis on a 6% polyacrylamide/7 M urea denaturing gel. Electro transferred onto a nylon membrane (Amersham Hybond-N, GE Healthcare) followed by UV crosslinking (Stratalinker). Hybridizations were carried out with 5 0 -end 32 P-labeled-DNA oligonucleotide probes.
Membranes were incubated overnight at 50˚C in 5X SSPE, 5X Denhardt's, 1% SDS, 150 mg/mL yeast tRNA and washed twice in 0. 1% SSPE, 0. 1% SDS for 15 min at room temperature. a Typhoon Biomolecular Imager (Amersham) and visualized using Multi Gauge V3. 0 software. Radioactive signals were revealed using a Typhoon Biomolecular Imager (Amersham) and visualized using Multi Gauge V3. 0 software. Primers are listed in Supplementary file 4.
For each of the five editing sites, A-to-G conversion was scored in every read with high quality (sequencing quality score !20 for the nucleotide mapped on that site). Raw data are available on Sequence Read Archive (SRA) database under the accession numbers PRJNA603261 and PRJNA603264. Scripts used for that analysis, detailed instructions and intermediary data have been deposited at https://github. com/HKeyHKey/Hebras_et_al_2020  (Seitz, 2020 ; copy archived at swh:1:rev: 1b96b6f5e8d479eb43f12f6687f04d4a60f4e305).
rRNA 2'O-methylation profiling RiboMeth-seq was performed in biological triplicates on whole adult mouse brains  (Birkedal et al. , 2015). In brief, 5 mg of total RNA was alkaline degraded and size fractionated to the range of 20-40 nt. Sequencing adaptors were ligated on to the ends of the alkaline degraded RNA using a modified tRNA ligase. The library RNA containing adaptors was reverse transcribed using Superscript IV (Thermo Fisher Scientific). The final libraries were sequenced on a PI Chip v3 (Thermo Fisher Scientific) using an Ion Proton semiconductor sequencer. Reads were mapped against curated mouse rRNA sequences  (Hebras et al. , 2020). RiboMeth-seq data are available on GEO under the accession number GSE145159.
Transcriptomic analyses Library preparation (polyA selection) and Illumina sequencing (2 Â 150 bp configuration) were performed by Genewiz. Raw data quality control was done using FASTQC (Version 0. 11. 9). Reads were aligned to reference genome (GRCm38/mm10, Dec. 2011) with STAR (Version 2. 4. 0. 1). Read counts were generated for each annotated gene (GRCm38/mm10, Ensembl gtf release 91) using HTSeqcount (Version 0. 11. 1). Read normalization and pairwise differential gene expression analysis with Hebras, Marty, et al. eLife 2020;9:e60862. DOI: https://doi. org/10. 7554/eLife. 60862 multiple testing corrections were conducted using the R Bioconductor DESeq2 package (Version 1. 26). The heat map was produced by R (heatmap. 2) using normalized RNA-seq read counts from a list of genes of interest.
Genes reads counts per sample were first normalized by the 'Relative Log Expression' normalization (RLE) implemented in the DESeq2. Each normalized count was then normalized by the mean normalized value of the gene across all samples, in order to remove differences due to the level of expression of each gene. Samples are clustered in the left side of the figure, with a dendrogram, computed with euclidian distance and the complete agglomeration method. Raw data are available on Sequence Read Archive (SRA) database under the accession number PRJNA608249.
Briefly, slides were hybridized overnight at 37˚C in 15% formamide, 2X SSPE, 10% dextran sulfate, 150 mg/mL yeast tRNA and 10 ng of Cy3-labeled SNORD115 oligonucleotide probe (TXGAGCATGAATTTXATGTCATCACCTXTCTTCATGACAXT, with X = Amino-allyl-modified nucleotides). Samples were washed at room temperature in 15% formamide, 2X SSPE (20 min, twice), then 1xXSSPE (10 min) and mounted in Moviol DAPI (0. 1 mg/mL). Images were captured with a CoolSnap HQ camera (Princeton Instruments) mounted on an inverted IX-81 microscope (Olympus) with a 1006 PL APO ON objective (NA 1. 4) and the Metavue software.
Statistical methods R and the Graphpad Prism (version 8) statistical software were used for data analysis. For multivariate comparisons, statistical significance of the observed effects was calculated using ANOVA. When ANOVA applicability conditions were not met (variance heterogeneity or non-normality of residuals), measured values were log-transformed. In a few cases (Figure  5E  and F, the two panels of Figure  5L , Figure  6E  and Figure  6G ), ANOVA applicability conditions were not fully met even after log-transformation, resulting in imprecise p-value calculation. For time-course analyses, explanatory variables were either genotype and time (Figure  6A ) or genotype and a variable depending on time by a bi-exponential function, to account for the non-linearity of glucose concentration upon time (Figures  5C  and 6C ).
Data files and R scripts for these analyses have been deposited on https:// github. com/HkeyHKey/Hebras_et_al_2020 (Seitz, 2020 ; copy archived at swh:1:rev:1b96b6f5e8-d479eb43f12f6687f04d4a60f4e305). For post-hoc tests and for univariate comparisons with two groups, significance was assessed using Student's t-test, or using the Mann-Whitney test when experimental values were not normally distributed. Data are presented as mean ± SEM and statistically significant differences are indicated as *p<0. 05,**p<0. 01, ***p<0. 001 and ****p<0. 0001.
Acknowledgements We thank the  ABC Facility of ANEXPLO Toulouse  for the mouse husbandry. We are grateful to the  GeT core facility , Toulouse, France (get@genotoul. fr) and the Phenotypage service - US006/CREFRE INSERM/UPS. We are particularly indebted to  Sophie Le Gonidec ,  Pauline Heuillard  and  Camille Eche  ´for their technical assistance and scientific support. The authors also acknowledge «the  Mouse Behavioral Core»  ( Center of Integrative Biology, Toulouse), notably Sebastien Lopez  for his advices in setting up behavioral apparatus and procedures. We thank  Xavier Fioramonti  for the gift of PFAfixed brains dissected from Pomc-eGFP transgenic mice. We also thank  G Canal  for assistance in the high-throughput analysis of A-to-I RNA editing. This work was supported by the  'Fondation pour la Recherche Medicale' (FRM ;  DEQ20160334936 ), the  Agence Nationale de la Recherche  ( ANR-18-CE12-0008-01 ) and the  Fundation for Prader-Willi Research (FPWR).
Data availability RiboMeth-seq data are available on GEO under the accession number GSE145159 Raw data (mRNAseq) are available on Sequence Read Archive (SRA) database under the accession number PRJNA608249. Raw data (A-to-I RNA editing) are available on Sequence Read Archive (SRA) database under the accession numbers PRJNA603261 and PRJNA603264. Scripts used for that analysis, detailed instructions and intermediary data have been deposited at https://github. com/HKeyHKey/ Hebras_et_al_2020 (copy archived at https://archive. softwareheritage. org/swh:1:rev:1b96b6f5e8-d479eb43f12f6687f04d4a60f4e305/). The following datasets were generated:.
(E) Black and gray bars represent expression levels (RT-qPCR relative to Gapdh) of flanking imprinted genes in the whole brain of mice bearing a paternally and maternally inherited deletion, respectively (n = 4-6 per genotype). Expression levels of WT were arbitrarily set to 1. (F) IGV screenshot showing that a targeted deletion of Snord115 gene array does not perturb the expression at the Ube3A/anti-Ube3A gene locus (mRNA-seq datasets generated from hypothalamus; supplementary file 3). (G) Methylation levels (RiboMeth-seq) at 2'-O-methylated rRNA sites in WT (black circles) and Snord115-deficient (red circles) adult brain (n = 3 per genotype). Nucleotide numbering is according to human rRNA (Krogh et al. , 2016). Asterisks indicate statistical significance levels. The online version of this article includes the following figure supplement(s) for figure 1: Hebras, Marty, et al.
A reward zone was located within each linear virtual track, at the end (0. 81-1. 15 m) or at the center (0. 34-0. 68 m) of the vertical and oblique corridors, respectively. An enriched environment, used to increase behavioral discrimination and neuronal discrimination in CA1, consisted in a substantially different virtual scene, with different visual cues and textures on the walls, but maintaining the same reward location as for the oblique corridor. For the electrophysiology experiments, two different variations of the virtual environment were used as familiar and novel environments (Figure  S4 ). The Blender Game Engine (https://www. blender. org/) was used in conjunction with the Blender Python API to drive the virtual reality system.
Imaging data processing Motion correction of the imaging data was performed using a phase-correlation algorithm built into the suite2p software  (Pachitariu et al. , 2017 ; https://github. com/cortex-lab/Suite2P). Segmentation into regions of interest (ROIs) was performed with suite2p using a singular value decomposition algorithm. To select dentate gyrus granule cells, we only used ROIs corresponding to small, densely packed cell bodies. ROIs corresponding to large isolated cell bodies were discarded in order to exclude putative interneurons and mossy cells. The neuropil signal was subtracted from the extracted fluorescence using suite2p. Neuronal activity was quantified by adapting previously published methods  (Dombeck et al. , 2010) : ''events'' were identified as contiguous regions in the normalized relative change in fluorescence (DF/F) signal exceeding a threshold of mean + 2.
Identification of spatially modulated cells and place fields To compute spatial activity maps, we used data from continuous running periods with a duration >1 s at a speed >0. 5 cm/s. For each lap crossing, spatial maps were computed by dividing the track into 50 spatial bins. We then divided the sum of events in each spatial bin by the occupancy (dwell time) of the animal in this bin. Spatial maps were smoothed with a Gaussian filter (sigma = 5 cm). We defined spatially modulated cells as those neurons that fire consistently at the same location across different lap crossings, independently of the exact shape of their place fields. Therefore, we identified spatially modulated cells by computing the mean pairwise circular cross-correlation between spatial maps across all different lap crossings within a session.
ACKNOWLEDGMENTS We thank  Josef Bischofberger ,  Remi Monasson ,  Yaniv Ziv , and  David DiGregorio  for helpful discussions and comments on the manuscript. We thank  Lucile Sontag  and  Claire Lecestre  for technical assistance. This work was supported by grants from the  ERC (StG 678790  NEWRON to C. S. -H. and  MSCA 800027 FindMEMO  to M. A. ), the  Pasteur Weizmann Council , the  E ´cole Doctorale Cerveau-Cognition-Comportement  (ED3C; ED no. 158 , contrat doctoral no. 2802/2017  to R. G. -O. ), and a  Pasteur-Roux fellowship  (to M. A. ).
Deep learning tremor The CNN model is trained on seismic data from the Canadian National Seismograph Network (CNSN)  ("Canadian National Seismograph Network", 1989). Figure  2A  shows the area analyzed: Vancouver Island on the North American Plate and the subducting Juan de Fuca plate, with a schematic of the locked and slowly slipping portions of the downgoing slab. Our ground truth for the initial training of the neural network is a tremor catalog from the Pacific Northwest Seismic Network (PNSN). The catalog was constructed using Wech's tremor identification method (multi-station, based upon 5 minute envelope correlation)  (Wech & Creager, 2008)  from the southern portion of  Vancouver  Island between October 2009 to July 2017. We build our database from 5 minute portions of single station seismograms.
For every tremor event in the PNSN catalog, we record the corresponding 5 minute single station waveform, and label it as containing tremor. For the non-tremor examples we randomly sample the seismic data on days where no tremor was identified in the PNSN catalog. This results in about 47,500 time windows of 5 minute single station waveforms labeled as containing tremor, and 47,500 windows of 5 minute single station waveforms labeled as not containing tremor. In order to leverage the ability of CNNs to extract information from images, instead of feeding raw waveforms to the CNN, we first compute the short-time Fourier transform (STFT) of the 5 minute portions of data (Fig. 1B ). The original 5 minute waveforms containing 12'000 data points are converted into spectrograms over the 5 minute interval.
These steps result in our database of 95,000 labeled 'tremor' and 'absence of tremor' examples (Fig. 1D, E ). We split our database of 95'000 time (frequency) windows into two contiguous portions for training and testing: the first 80% for training and the last 20% for validation (10%) and testing (10%). In other words, all the examples from the end of 2009 to the end of 2015 are used to train the model, the examples from the end of 2015 to the end of 2016 are used for validation, and the examples from the end of 2016 to the end of 2017 are used to test and assess the model. The split of training data into contiguous pieces is of paramount importance for time series in general, and for this problem in particular. For instance, applying a random train/test split would assign pieces of any tremor burst longer than a few minutes to both the training and the testing set, making the problem dramatically easier -the CNN would only have to memorize the examples it sees in training.
Lowering the classification threshold classifies more items as positive, thus increasing both false positives and true positives. For our purposes, true positives are catalogued events detected on a single station by our deep learning model. False positives are detections from our model of possible events that were not catalogued. Figure  2B  shows the model performance on the test set (last year of the labeled database, 2017) according to the ROC curve: with a ROC-AUC score of 0. 945, the model performs well in discriminating between positives (catalogued events) and negatives (waveforms not catalogued, and on days with no catalogued events). The confu- sion matrix in inset shows the fraction of classified noise and tremor compared to the actual labels, for a model with a default threshold of 0.
Universality of tremor characteristics Deep learning models have tremendous expression capabilities, which can lead to overfitting. In the previous section we demonstrated that our network generalizes to new seismic data from the station it was trained on, recorded later in time, suggesting that overfitting is not an issue. A powerful additional test is to see whether the network generalizes to seismic data from another station. Seismic recordings at a given station are a convolution of the source, propagation path and 'site amplification' effects, that may vary tremendously over short distances  (Aki & Richards, 2002). Generalization to another station would suggest that the network did not only learn the specifics of the seismic data it has been trained on. In Fig. 2B  we show that our trained network is robust when applied to other seismic stations on Vancouver Island.
Acknowledgments This work was funded by  Institutional Support (LDRD) at Los Alamos  and the  DOE Office of Basic Research, Geoscience Program. We thank  Romain Jolivet ,  Joan Gomberg  and  Daniel Trugman  for helpful discussions. We thank  Tim Cote ,  Xiuying Jin  and  Michal Kolaj  from the  Canadian National Seismograph Network  for helping us obtain the data and for data troubleshooting. The seismic data used were obtained from the  Canadian National Seismograph Network  (" Canadian National Seismograph Network ", 1989), and the GPS data are from the  Western Canada Deformation Array (WCDA) , processed by the  USGS Pacific Northwest NetworkS  (doi: 10. 5066/F7NG4NRK ). This work was supported by  DOE Office of Science (Geoscience Program)  grant  89233218CNA000001. CH was partly financed by  CEA/DASE  (and hosted at the  Yves Rocard Joint Laboratory ENSCNRSCEA/DASE ), and by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( Geo-4D  project, grant agreement  758210 ).
In Cry/Vip combinations, antagonism is more frequent and higher in magnitude compared to other categories. Despite a significant number of tested Bt toxin combinations, most of them have been bioassayed only against one pest species. To aid the research on Bt pesticidal protein activity, we present TOXiTAXi (http:// www. combio. pl/ toxit axi/), a universal database and a dedicated web tool to conveniently gather and analyze the existing and future bioassay results on biocidal activity of toxins against various taxonomic groups. Biological plant protection products based on Bacillus thuringiensis (Bt) insecticidal proteins provide useful pest management tools to growers all over the world. Their implementation in agriculture, horticulture and forestry has not only granted efficient control of many economically important pest species, but also allowed to reduce the usage of harmful synthetic agents 1,2.