dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of changes in gene expression and termination readthrough Focusing on the reads that mapped to the human genome, we used HTSeq-count (v0. 6. 1) (22)  to count the number of reads per gene and per intergenic region (up to 5000 nt downstream the annotated transcript end sites of genes). The differential expression analysis as well as the estimation of FPKM of genes and their downstream intergenic regions was done with the DESeq2 R package (23) using default parameters. A gene was considered as markedly and significantly differentially expressed if it passed the following thresholds: gene baseMean 10, gene FPKM IAV-infected 1 and/or gene FPKM mock-infected 1, adjusted P-value ≤0. 05 and |log 2 FC| ≥ 1. The same thresholds were applied to compare the control siRNA and anti-RED siRNA conditions.
Identification of changes in alternative splicing All raw reads were assembled using the KisSplice (v2. 4. 1) (24) local transcriptome assembler. This tool allows to extract splicing events that correspond to specific patterns in the De Bruijn graph, which we call bubbles. KisSplice outputs the sequences and quantification of ASEs. The following parameters were used to run KisSplice:stranded -strandedAbsoluteThreshold 0 -mismatches 2counts 2 -min overlap 5 -experimental. The sequences of the ASEs were then mapped to the human genome (hg38 using annotation Gencode v27) using STAR, with default settings. Each event was classified in a type of splicing event [alternative acceptor (altA), alternative donor (altD), exon skipping (ES), multiple exon skipping (ES-M) and intron retention (IR)] and assigned to a gene using KisSplice2RefGenome (v1.
2. 3)  (25). Finally, the differential analysis was done with the kissDE R package (v1. 1, doi: 10. 18129/B9. bioc. kissDE). KissDE outputs three important measures: percent spliced in (PSI), PSI and adjusted P-value. PSI is a measure representing the percentage of inclusion of an exonic or intronic sequence: PSI = [inclusion/(inclusion + exclusion)], where 'inclusion' corresponds to the number of reads supporting the inclusion isoform and 'exclusion' corresponds to the number of reads supporting the exclusion isoform. A PSI of 1 indicates that the exonic or intronic region is always included, while a PSI of 0 indicates that it is always spliced out in the mature RNA. PSI represents the magnitude of the change of inclusion of the ASE between the two conditions and is computed as follows: PSI = [PSI IAV-infected -PSI mock-infected ], where PSI IAV-infected and PSI mock-infected are the mean PSI values of the four biological replicates.
Principal component and GO term enrichment analyses Principal component analysis (PCA) was performed using ade4 R package  (26)  on the normalized gene count values, on the normalized intergenic count values or on the PSI values. We plotted coordinates of the eight samples on the first two principal components using the ggplot2 R package  (27). We searched for gene ontology (GO) terms enriched in the differentially expressed genes and in the genes containing a differentially spliced event using the topGO R package (doi: 10. 18129/B9. bioc. topGO). We used the elim algorithm with a user-defined score and tested only the GO terms containing at least 50 genes. For the analysis of the differentially expressed genes, the score was defined as the log 2 FC value if the adjusted P-value was lower than the threshold of 0.
FACS A549 (6 × 10 5 ) or Calu-3 (24 × 10 5 ) cells seeded in sixwell plates were mock infected or infected with the indicated viruses. Cells were harvested with trypsin, followed by fixation (4% paraformaldehyde) and permeabilization (0. 1% Triton X). Cells were then stained with a primary mouse monoclonal anti-influenza virus NP antibody (Abcam; ab20343) and a secondary anti-mouse DyLight633 (red) antibody (Thermo Fisher; 35512). The stained cells were subjected to FACS analysis (Attune NxT Flow Cytometer). Data were analyzed and processed using the FlowJo software.
Shiny interface We used RStudio's Shiny framework to develop a webbased interface that allows to browse the AS, gene expression and readthrough results generated in this study, as well as the multivariate analysis (PCA) of these three transcriptional processes. This Shiny interface is available online (http://virhostnet. prabi. fr:3838/shinyapps/flu-splicing) and is also available for download on GitHub to install locally (https://github. com/cbenoitp/flu-splicing). The users can explore and filter the genes or ASEs of interest according to several metrics and can download the list of selected genes or ASEs as an excel file. The interface also gives the possibility to plot some metrics (like PSI or log 2 FC) and to download the resulting plot. Finally, users can browse the intersection of different ASE analyses.
We focused our splicing analysis on the five major types of ASEs, i. e. altA and altD sites, ES, ES-M and IR. To filter out minor isoforms, including minor isoforms derived from highly expressed genes, we computed for each of the >66 000 ASEs that were identified by kissDE an LEE value that provides an estimation of the proportion of isoforms showing the ASE (see the 'Materials and Methods' section). Only the ASEs showing FPKM ≥ 1 and LEE ≥ 0. 5 in at least one of the two conditions (IAV-infected and/or mockinfected) were taken into consideration. The resulting reference splicing dataset comprised 25 037 ASEs corresponding to 7043 distinct genes (Figure  2A , outer circle). Upon additional filtering for ASEs showing a marked and significant change in PSI values (| PSI| ≥ 10% and P ≤ 0. 05), we identified 3969 ASEs, corresponding to 2076 genes, that are IAV sensitive (Figure  2A , inner circle, and Supplementary Table  S3 ).
A substantial proportion of these shows at least one non-annotated splice site (Figure  2B ), therefore  highlighting the added value of performing de novo calling of ASEs with KisSplice. A trend for increased exon inclusion and intron removal in IAV-infected cells, i. e. overall increased splicing, was observed. Indeed, the median PSI (PSI IAV-infected -PSI mock-infected ) value was positive for ES (+11%) and ES-M events (+10. 3%) and negative for IR events (-17. 5%) (Figure  2C ). The distribution of the lengths of IAV-sensitive ASEs is shown in Figure  2D  and Supplementary Figure  S2. Within the IAV-sensitive dataset, the median length of skipped exons was close to the median length of all exons in the human genome (127 nt versus 149 nt), whereas the median length of retained introns was smaller than the median length of all introns in the human genome (368 nt versus 2036 nt).
This observation could partly be due to the limited capacity of KisSplice to assemble long introns, since it requires that they are fully covered by reads to be correctly assembled. However, we obtained similar findings using IRFinder, a pipeline dedicated to the analysis of IR (28): the overall decrease in IR was clearly confirmed (median dPSI = -15. 4%), and the median length of retained introns in the IAV-sensitive dataset (707 nt) was higher compared with that when the KisSplice pipeline was used but clearly smaller than the median length of all introns (Supplementary Figure  S3 ).
A subset of 21 ES events, randomly selected among the IAV-sensitive events identified by RNA-seq, was characterized in the four above-described experimental conditions using RT-PCR. The increase or decrease in exon inclusion expected from the RNA-seq data was confirmed for all 21 ES events; in 15 cases, it was detected exclusively in A549 cells infected with the WSN virus and not in the other experimental conditions (Figure  3 , left panels, Supplementary    S4 ). These results indicate that influenza infection induces specific changes in the host AS program that are independent from the interferon response and distinct from a general stress response. To evaluate to what extent the observed changes in splicing were dependent on the experimental virus-cell system, a subset of eight IAV-sensitive ES events identified by RNAseq was characterized in parallel in A549 or Calu-3 cells infected at a high MOI with the WSN strain (H1N1 subtype) or with a virus representative of the circulating human seasonal IAVs (H3N2 subtype).
When our pipeline for differential splicing was applied to the RNA-seq dataset of Fabozzi et al. , only 95 cellular genes were found to exhibit significant splicing changes. Out of these 95 genes, only 27 (28%) also showed splicing changes in our dataset, which most likely relates to differences in the experimental protocol and to some degree of dependence on the virus-cell system used (A/Udorn/307/72-Beas2B versus A/WSN/33-A549). Yet, a subset of eight IAV-induced splicing changes from our dataset, which were not present in the dataset of Fabozzi et al. , was consistently observed with two distinct cell lines and two distinct viruses, suggesting that the overall degree of conservation of IAV-induced splicing changes is actually higher. Our data reveal an increase in exon inclusion and intron removal in IAV-infected cells.
Viral-induced changes in the nuclear environment of RED may affect its function in a more subtle way than depletion, e. g. only a minor fraction of RED may be bound to the viral polymerase and/or the abundant NS1 mRNA. On the other hand, the transcription slowdown in IAV-infected cells (40) may contribute to the global increase in intron removal we observed and may overcome more subtle effects such as increased IR in a specific subset of mRNAs due to altered RED function. Interestingly, in a recent preprint by  Thompson et al. ,  an approach similar to ours is used to assess to what extent hnRNP K could be mediating IAV-induced changes in ES events  (37). The findings are similar to ours, i. e. the overlap is in the same range whether the IAV infection dataset is compared with the hnRNP K depletion dataset in the study by Thompson et al.
(21% of ES events are found in common, among which 63% show a concordant PSI) or with the RED depletion dataset in our study (35% of ES events are found in common, among which 72% show a concordant PSI; Supplementary Figure  S11A ). These observations support the hypothesis that multiple splicing factors could be involved in the reprogramming of the splicing landscape in IAVinfected cells: RED, hnRNP K and potentially other noncore splicing factors that have been proposed to regulate the splicing of IAV mRNAs, such as SF2  (41)  and TRA2A  (42). There is evidence that the NS1 protein of IAVs can modulate host splicing through binding to the U6 snRNA  (43)  or by inducing a relocalization of the SRSF2 factor  (44). The recent finding that NS1 primarily binds intronic sequences (45) might contribute to the marked decrease in IR we observed upon IAV infection, also observed by Rotival et al.
in IAV-infected human macrophages  (19). Finally, our RNAseq data reveal that 74 and 34 genes corresponding to splicing factors show differential expression or splicing, respectively (Supplementary Table  S8 ), which could in turn be the cause of other splicing changes. Given the magnitude of splicing changes observed at 6 h post-infection, analyses performed at earlier time points could help elucidate the key mechanisms involved. Separate analysis of poly(A)+ and poly(A)-mRNAs from the cytoplasmic and nuclear fractions would provide a more accurate picture of the splicing landscape and changes induced by IAV infection. From a methodological perspective, our RNA-seq datasets provide a valuable basis to train and improve bioinformatic pipelines for the analysis of AS. Indeed, our high-depth sequencing uncovers a large fraction of unannotated splice sites, IRs and complex splicing events, whose identification and quantification remain challenging with the currently available softwares.
FUNDING French National Research Agency  [ ANR-16-CE23-0001  to V. L. ];  LabEx IBEID  [ 10-LABX-0062  to N. N. ];  Horizon 2020--Research and Innovation Framework Programme  [ 665807  to  U. A. as a participant in the  Pasteur-Paris University International  PhD Program];  Institut Carnot Pasteur Microbes & Santé  [to U. A. as a participant in the  Pasteur-Paris University International  PhD Program].
Forecasting The forecasting service provided by the LEMMAS is used internally by the Trust Evaluation Service and by the SMARTERCtrol system as well. The forecasting module includes several methods and a set of strategies for day-ahead and hour-ahead forecasting. Indeed, one of the most important targets of the energy operators is to be able to have a better control on the energy consumption and also being prepared for the amount of energy demand in the following hours or days. In order to provide consumption and generation forecasting for dierent short and long terms, this service should make use of various forecasting methods: Articial Neural Networks  [5] , Support Vector Machines  [26] , Fuzzy Rule Based Systems  [13] , Reinforcement Learning  [2] , Time Series Analysis  [6]  and Ensemble Methods  [23].
4  Multi-Agent Optimization and Control This section presents a multi-agent optimization and control system (SMARTER-Ctrol) for microgrids. Taking into account the constraints, limitations and user preferences, its goal is to perform an optimized management of the self-controlled resources of a microgrid. Furthermore, this system is connected to the forecasting service described previous section 3. 2. This way SMARTERCtrol enriches the quality of decisions by also considering some consumption/generation forecasts in the decision making. SMARTERCtrol is composed by two main modules, the optimization module and the control one. Each of them counts with a dierent multi-agent system model and deploys their own agents which are continuously communicating to coordinate: Control module: agents are in charge of controlling the existing devices.
(2017)  proposed a new allocation method based on biophysical parameters  (Chen et al. , 2017). Adapted from previous methods, they propose to allocate impacts in proportion to the energy needed for the growth, the maintenance and the activity of each tissue. The method has been judged as scientifically viable but also particularly difficult to apply due to the amount of necessary data and to the complexity of the calculation model. In a recent project, we de-veloped a freeware to easily calculate biophysical allocation factors as well as mass and economic factors to allow a fair comparison: MeatPartTool. We also collected data to create a dataset of mass, economic and biophysical allocation factors for a large range of beef (132 individuals), calf (54 individuals) and lamb (14 individuals) at the slaughterhouse stage.
Value of the Data • These data are useful as they move the debate on allocation factors for LCA of meat forward. There is no consensus between stakeholders on the subject when choosing between allocation methods. The lack of data however makes methods difficult to use and compare. Here is proposed an unprecedented range of mass, economic and biophysical allocation factors for meat coproducts. • This dataset will benefit to everyone who wants to practice LCA to meat products at slaughter stage. Researchers, industrials, decision-makers are interested to better understand environmental impacts of meat. If they cannot calculate their own allocation factors, they can pick the most appropriate ones in this dataset. Furthermore, by proposing both mass, economic and biophysical allocation factors, the dataset sets political questions aside but offers material to discuss.
• These data can be directly used to allocate environmental impacts between meat coproducts at slaughter stage. The vast range of individuals proposed allows the user to choose appropriate allocation factors instead of generic ones. Furthermore, as both mass, economic and biophysical allocation factors are calculated, the user will be able to easily provide sensitivity analysis when using one or another. • This dataset offers a large range of mass, economic and biophysical allocation factors that were not available so far in literature. From now, only a few ones existed, mostly generic cases. This is the beginning towards more differentiated datasets appropriated to different realities. The authors think that this dataset should be completed by other individuals, especially from different geographical areas.
Input data Primary data (i. e. all the dataset necessary to calculate allocation factors) have been collected from different sources: literature, previous projects and expert interviews. In total, the dataset comprises 132 beef, 54 calves and 14 lambs ( Table  1  ). For each species, the list of coproducts has been drawn up. For a given species, it is considered that every breed comprises the same coproducts. Each coproduct is then classified by: • Human Food • Pet Food • PAP C3 (animal by-products, blood, etc. ) • Gelatin C3 (bones, tendons) • Skin Tannery C3 (skin, mask) • Fat and Greaves C3 (fat, tallow) • C1-C2 for disposal • Spreading/Compost -Group of tissues • Carcass • GIT (stomach, intestines, etc. ) • Liver • Others • Whole Body To complete, in Europe: -C1 products are those that presents risks of: • Spongiform encephalopathy transmission; • Presence of residues of toxic substances; • Presence of environmental contaminants.
-C2 products are those coming from digestive system that present health hazards -C3 products are free of risks and used as intrants for industrial production (for example petfood or fertilizers) C1 and C2 products are generally discarded. Lists of coproducts and associated destinations and groups of tissues for bovine, calf and ovine are respectively available in Tables  2 3 4. These tables also contain, for each coproduct, the percentages of Water, Dry Matter, Lipids and Proteins. These are the same for every breed of a given species in the present dataset. Those data concerning quantity of coproducts and their physicochemical compositions were compiled by  Gac et al. (2012)  considering bibliographic references, supplemented by extrapolations and expert estimates when information was lacking.
For each coproduct, the mass fraction of the total mass is necessary. It has been calculated for each breed of each species. They can be considered as generic data to characterize coproducts. Data from  Gac et al. (2012)  are used as a reference and adapted to each breed depending on carcass yields  [2]. BW % i, j = BW % i, generic * Carcass Y iel d j Carcass Y iel d generic f or carcass coproducts BW % i, j = BW % i, generic * Carcass Y iel d generic Carcass Y iel d j f or other coproducts with BW % i, j ( Empty Body W eight ) the mass f raction of the coproduct i f rom breed j Carcass Yields are available in Tables  5 6 7. They come from Laisse et al. [3]. These table also contains the Empty Body Weight at slaughter age that differs from a breed to another. These data have been obtained on the basis of a census data extraction operated by Institut de l'Elevage (GES Division) from SPIE (the Professional Livestock Information System approved by the French State), which contains data from the BDNI (National Data Base of Identification which register all animal birth and movements), completed by the Normabev database (concerning slaughtering of bovines).
This French information system on livestock is described by Delomel and Gibon  [4]. When data were not available, mean values have been used. Next table contains a list of parameters that are identical for each breed of a given species ( Table  8  ). These parameters are used by the model developed by Chen et al. [1]  to calculate the Finally, a coefficient is used to modulate the energy required for the activity. These coefficients are specific for breeds and depend on the rearing mode. Data from IPCC (2006) are used  [6]. Data are available in Table  9. To calculate economic allocation factors, an economic dataset has been built by compiling data from ACYVIA  [7]. The dataset is available respectively for beef, calf and lamb in Tables 10-12 All these input data are also available in a complete *.
Samples and DNA Extraction Extracted from carcass tissue, shed skin and blood, and buccal swabs originating from 8 countries, 124 DNA samples were collected for this study (Figure  1  and Table  1 ) and deposited at the Collection of Genetic Resources of the Hungarian Natural History Museum in Budapest. Spatial data (longitude and latitude in decimal degrees) were recorded using a GPS device for each sampled individual. We extracted DNA from buccal swabs using the blackPREP Swab DNA Kit according to kit protocols (Analytik Jena AG, Jena, Germany). For shed skin and carcass (liver and muscle) samples, the DNEasy Blood and Tissue Kit (Qiagen, Hilden, Germany) was applied following manufacturer's instructions but adding 30 µL of Proteinase K (20 mg/µL) to increase lysis efficacy. Prior to the extraction, skin samples were soaked in water for 24 h at room temperature and manually sliced into fragments of ~5 mm to facilitate enzymatic lysis.
Samples and DNA Extraction Extracted from carcass tissue, shed skin and blood, and buccal swabs originating from 8 countries, 124 DNA samples were collected for this study (Figure  1 , Table  1 ) and deposited at the Collection of Genetic Resources of the Hungarian Natural History Museum in Budapest. Spatial data (longitude and latitude in decimal degrees) were recorded using a GPS device for each sampled individual. We extracted DNA from buccal swabs using the blackPREP Swab DNA Kit according to kit protocols (Analytik Jena AG, Jena, Germany). For shed skin and carcass (liver and muscle) samples, the DNEasy Blood and Tissue Kit (Qiagen, Hilden, Germany) was applied following manufacturer's instructions but adding 30 μL of Proteinase K (20 mg/μL) to increase lysis efficacy. Prior to the extraction, skin samples were soaked in water for 24 h at room temperature and manually sliced into fragments of ~5 mm to facilitate enzymatic lysis.
For quality trimming, process_radtags applied a sliding window analysis using the 15% of read length from 5 to 3 of each read. If the average quality of a window dropped below a phred score of 10, the read was discarded. SNPs were called from the resulting reads using the ustacks-cstacks-sstacks-rxstacks-populations pipeline within Stacks v. 1. 45 and keeping only one random SNP per RAD locus, which resulted in 24,497 SNPs. Sequence coverage depth of SNPs for each individual was calculated using VCFtools 0. 1. 16 (see https://doi. org/10. 5061/dryad. mgqnk98xmfor description of the Stacks pipeline). The functions geno and mind within Plink 1. 07  [36]  were used to remove SNPs with missing call rates exceeding 25% and to exclude individuals with over 25% missing genotypes (n = 19) from the dataset.
caspius species conservation program, the high number of samples with IBS > 0. 95 likely reflect the recapture of snakes or re-sampling of shed skins. In the Croatian islands, the snakes were surveyed annually, individually recognizing each specimen, thus re-sampling or recapture can be excluded. Despite the removal of samples, the remaining dataset included individuals from each sampled locality (Table  1 ). The final dataset for downstream analysis consisted of 53 individuals and 17,518 SNPs with a 43-fold average coverage depth over all individuals (ranging from 8-to 109-fold; Table  S1 ).
Population Summary Statistics, Structure and Differentiation The package pegas  [37]  within RStudio  [38]  was used to calculate overall expected (H E ) and observed heterozygosity (H O ), which were compared using a parametric Welch's t-test implemented in R statistical environment v. 3. 5. 1. [39]  using the t-test function. We explored the initial population structure with a Principal Component Analysis (PCA) implemented in Adegenet v. 2. 1. 0 [40]. Phylogenetic relationships between individuals were visualized in a NeighbourNet network implemented in Splitstree v. 4. 10 [41]  based on a pairwise genetic distance matrix calculated with Plink v. 1. 07. To further identify the number of ancestral populations (K) and potential admixture in D. caspius populations-Admixture v. 1. 3 [42]  was run, testing for values of K between 2 and 9.