dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability Data from the UV radiometers and the total sky imagers can be downloaded from Zenodo and are referenced at https://doi. org/10. 5281/zenodo. 4811488 (Lamy and Portafaix, 2021a). The data from the UV radiometers are also available on the WOUDC website at these addresses for the four stations studied here: - The TROPOMI surface UV radiation product used in this study is available at https://nsdc. fmi. fi/data/data_s5puv. php (last access: 1 June 2021,  Lindfors et al. , 2018). The AC SAF (GOME/MetOp) data can be downloaded through the website at https://acsaf. org (last access: 1 June 2021,  Kujanpää, 2021). The TEMIS data can be downloaded through the website at http://www. temis. nl/uvradiation/UVindex. html (last access: 1 June 2021,  Van Geffen et al. , 2021). The OMUVBG data can be downloaded through the website at https://disc.
gsfc. nasa. gov/datasets/OMUVBG_003 (last access: 19 August 2021,  Hovila et al. , 2021). The CAMS data can be downloaded through the website at https://apps. ecmwf. int (last access: 1 June 2021,  Pitkänen et al. , 2021). https://doi. org/10. 5194/essd-13-4275-2021 Earth Syst. Sci. Data, 13, 4275-4301, 2021 The UV-Indien network is an emerging network dedicated to UV radiation and cloud cover in the western Indian Ocean region. This network offers numerous perspectives for studies both on the variability in these parameters at each site and, for example, for the validation of climate models. The diversity of the instrumented sites provides a good representation of the different environmental conditions in the region. Our region is essentially made up of poor countries whose access is difficult.
The challenge of maintaining such a network in this region is mainly related to the regular calibration of the radiometers and also to the repair of the instruments when failures occur. A protocol including three types of calibration was established to guarantee the quality of the measurements while meeting logistical and financial constraints. A calibration plan has been established with frequencies of the order of 2 years, but logistical difficulties or health crises can modulate these forecasts. Another difficulty lies in the regular transfer of data to a centralising organisation. Nonetheless, the calibration protocol and post-processing of the data ensure the quality of the data before distribution to the Zenodo and WOUDC data repositories. In order to assess the validity of the measurements so that future users can use them with confidence, we compared these measurements with satellite estimates and model estimates of the UVI.
In clear-sky conditions, the correlation coefficient between the satellite or model estimates and the ground-based measurements was greater than 0. 9 at all stations except Mahé and for all datasets except OMUVBG. In all-sky conditions, the largest UVI median absolute difference between the satellite or model estimates and the ground-based instruments was -0. 4 (TROPOMI), -1. 4 (TROPOMI), -2. 3 (GOME), and -6. 0 (GOME) at Saint-Denis, Antananarivo, Anse Quitor, and Mahé, respectively. In clear-sky conditions, the largest UVI median absolute difference between the satellites or model estimates and the ground-based instruments was 1. 1 (TEMIS), -1. 3 (TROPOMI), -1. 7 (GOME), and -4. 6 (GOME) at Saint-Denis, Antananarivo, Anse Quitor, and Mahé, respectively. In all-sky conditions, the smallest UVI median difference between the satellites or model estimates and the measurements of ground-based instruments was 0.
Therefore, the maxima of UVI are higher at Mahé, and the resulting enhancement of UVI should also be stronger. As the phenomenon of cloud enhancement of the UV index can be observed in the radiometer data of the four stations presented in detail here, these data are suitable for the study of this phenomenon. To enable users to investigate the impact of clouds on UVI, all-sky cameras have recently been deployed at each station of the UV-Indien network. In this article, we have also proposed a first estimate of the variability in UVI at these different sites to give users a general idea of this variability. In a region where UVI values are very high all year round, these data are also useful for monitoring the intensity of ultraviolet radiation in the Indian Ocean for preventive health studies or health impact studies.
Acknowledgements. We thank the  AC SAF  project of  EUMET-SAT  for providing the GOME-2/MetOp offline surface UV products used in this paper. We also thank the  Finnish Meteorological Institute  for providing the TROPOMI UV products and especially  Jukka Kujanpää  for product development. The authors thank Reuniwatt for the support provided during the installation of the total sky cameras at the different sites of the  UV-Indien network. Finally , the authors would like to thank the  PMOD/WRC  for carrying out the calibration of the UVS-E-T-150154 radiometer in Moufia,  Saint-Denis. Financial support. The  UV-Indien  programme is supported by the  European Union  through  PO INTERREG V , by the  Reunion Island Council , and by the  French Government. The Bentham spectroradiometer and UV radiometer at Saint-Denis are managed by  OPAR  ( Observatoire de Physique de l'Atmosphère de la Réunion ), and OPAR activities are funded by the  Université de La Réunion  and  CNRS.
Many existing tools allow the users to predict TE locations in the genome, such as LTRpred  (Drost 2020) , PiRATE  (Berthelier et al. 2018)  or REPET  (Quesneville et al. 2005; Flutre et al. 2011). Others tools can reveal the relationship between TEs and host sequences at transcriptome level. LIONS  (Babaian et al. 2019 ) is one of them that detects and quantifies transposable element initiated transcription from RNA-seq. Another software that can provide the screening and selection of potentially important genomic repeats is GREAM (Genomic Repeat Element Analyzer for Mammals)  (Chandrashekar et al. 2015). However, this web-server can offer the TE-gene neighborhood only within mammalian species. There are no tools for revealing positional relationship between TEs and host coding sequences that can be applied for any species.
General workflow Two input files are needed, a GFF file annotation of the host genome and a TSV file containing information about TE annotation. The Apricot dataset (Prunus mandshurica) and two different TE annotations were used to implement our pipeline: TE annotation from the REPET package and LTR retrotransposon annotation from LTRpred software. The gene and TE annotation files are firstly optimized for data analysis, by removing duplicate lines and sorting out by chromosome and by start position in ascending order. Then, the files are processed with Python in order to analyze the relationship between nearest genes. Our pipeline could detect and report all scenarios that could occur between TEs and the closest genes in the host genome as shown in the Figure  1. The Python output result is then used in R to create tables and graphs to visualize TE-gene relationships.
Example from Figure  1 : Gene9-TE10. • check subset superset gene: this function searches for genes, which are either a subset (Gene7-TE9) or a superset (Gene6-TE7/TE8) of the TE. • calculate distance: this function allows to calculate the distance between a TE and its closest gene. • write data: this function writes an output file containing all the information about the genes detected by the previous functions. The python output is a TSV file containing the TE-gene relationship information based on the physical structure of the genome as descripted above. For each case, the start position, end position, ID and strand information are recorded. The TE ID, type, strand and position are also reported. Graphical visualization of the data is obtained by R scripts with the 4. 0. 2 version. Using the output file from the Python script, descriptive statistics of the relation between TEs and genes in the genome are presented.
The output of the R scripts is graphs and CSV files containing the values counted by the algorithm. There are three R scripts allowing users to report three different statistics: • TE statistics, which show how many TEs and what types of TEs are present in the file and the distribution of TEs between those types. • Overlap statistics, which show how many TEs have an overlap with genes, both upstream and downstream. • Distance statistics, which show the number of TEs with an upstream or downstream gene within 0-500 bp, 500-1000 bp, 1000-2000 bp and more than 2000 bp intervals. The genes reported as upstream or downstream of TEs in R scripts were defined based on the TEs' strand as described in Figure  3.
Use case In order to explain how the program works, we will use two example files named gene_testing_data and transposon_testing_data, which gather all possible scenarios of the position of TEs relative to their closest genes. These two input files can be downloaded from the github page. The following command must be run in order to execute the program: (1) python3 Multiprocessing/Create_Data_multipro. py \ -g data/Gene_testing_data. tsv \ -te data/Transposon_testing_data. tsv \ -o result/output_TE. tsv The -g argument is used to provide the gene file, the -te is used to come up with the TE file and the -o to supply the name of the result file. The output file is in TSV format. Once this file has been obtained, the R analysis can be run by adapting the following script line: ( The R scripts will provide output files with the statistics regarding overlapping genes and LTR retrotransposons as shown in Figure  4 , or distance between LTR retrotransposons and genes as shown in Figure  5 , in table format as well as graph visualization.
Conclusion Transposable elements are repetitive DNA sequences that have the ability to move within the genome. These mobile elements can play an important role in gene regulation and have a large impact on genome evolution. We have developed the first pipeline, which can report directly the relationship between TEs and its nearest genes among the genome. The accuracy of the tool has been verified by using a test dataset and running on two different TE annotation softwares. This pipeline could be useful to subsequently analyze potential effects of TEs on gene expression as well as on specific gene function.
In an attempt to identify the molecular changes that might be involved in this plasticity, we used the software package Trinity to complete a de novo assembly of a TAG transcriptome that included ganglia from adult male crickets collected one, three, and 7 days after removal of a single cercus. Control tissue was TAGs from animals in which the hair-free, distal tip of the cercus was cut one, three, and 7 days before collection. We used EdgeR and DESeq2 to complete a differential expression analysis and examined Gene Ontology (GO) terms to identify potential molecular pathways that might be involved in this plasticity. Specifically, we screened our differential results for candidates that might be expected to influence synaptic strength or the balance between excitation and inhibition. Our GO term analysis indicates that many members of the ubiquitin proteosome system (UPS), known to play a role in synaptic remodeling  [16] , and components of the chromatin-mediated transcriptional pathways were differentially expressed after deafferentation.
Results and discussion We performed next generation RNA sequencing on individual terminal ganglia (TAG) from Gryllus bimaculatus in order to assess the differential expression of genes after unilateral cercal removal. Thirty samples, each consisting of the TAG from a single adult male cricket in a control or deafferented condition, were used as the source of RNA for transcriptome assembly. Reads were collectively assembled using Trinity, a total of 838,991,089 trimmed and quality filtered paired-end reads of 150 bp in length were input into Trinity for de novo assembly. We built five de novo transcriptomes using five different k-mer lengths: 21, 25, 27, 30, and 32 (Fig. 1 ). Multiple k-mer methods are shown to be advantageous to a single short or long k-mer assembly alone, improving the diversity and contiguity of the transcripts  [17].
Differential expression during compensatory plasticity To determine transcripts that were differentially regulated at one, three, and seven days after deafferentation, the reads for each of the 30 Illumina libraries, excluding three outliers, were mapped back to our multiple k-mer transcriptome to create a counts matrix (See Supplemental Material). Outliers were determined by visualizing MDS plots of the counts data. Three samples (3D_3, 7D_2, and 7D_3) were visually distinct from the rest of the data and were removed (data not shown). Pairwise comparisons of normalized counts data from deafferented vs. control crickets were performed at each time point using both EdgeR and DESeq2 (See Supplemental Materials). We visualized the distribution of upregulated  versus downregulated transcripts between the two programs in volcano plots, which indicate that both programs identified a roughly similar proportion of detected transcripts as differentially regulated across all three time points (Fig.
BLAST and gene-ontology analysis Once we had a conservative set of transcripts predicted to be differentially regulated, we used BLAST2GO  [20]  to identify them. Transcripts were BLASTed to the "nr" database, though not all transcripts inputted into the BLAST2GO program resulted in BLAST hits and/or GO annotations (Fig. 5  and Supplemental Materials). At one day downregulated, 18% of transcripts had both BLAST and GO results (green in Fig. 5 ) and an additional 37% had only BLAST hits (blue in Fig. 5 ). At three days downregulated, 34% of transcripts had both BLAST and GO results and an additional 38% had only BLAST hits. At seven days downregulated, 31% of transcripts had BLAST and GO results, and an additional 17% had only BLAST hits. At one day upregulated, 41% of transcripts had both BLAST and GO results and an additional 16% had only BLAST hits.
At three days upregulated, 58% of Fig. 2  Volcano plots of differential gene expression in control and deafferented terminal ganglia at one, three, or seven days after deafferentation. The horizontal dotted line marks a p-value of 0. 05, and the vertical dotted line indicates no predicted fold change. Each point represents a predicted transcript determined to be differentially regulated by EdgeR (a, c, and e) or DESeq2 (b, d, and f). Blue points represent predicted transcripts determined to be significantly up-or down-regulated transcripts had both BLAST and GO results and an additional 13% had only BLAST hits. At seven days upregulated, 36% of transcripts had BLAST and GO results, and an additional 10% had only BLAST hits (Fig. 5 ). A sizeable percentage of differentially regulated transcripts did not match anything in the "nr" database (gray in Fig.
These results raise the possibility that alterations in the UPS could be responsible for the observed shift in Fig. 4  Comparison the number of differentially regulated genes at one, three, and seven days after deafferentation identified by the two analytical programs, DESeq2 and EdgeR. The number of transcripts found to be differentially regulated by both programs varied by condition, but similar trends were observed across time points. Those transcripts identified by both programs were used for further analyses Fig. 5  Distribution of transcripts with and without BLAST hits and GO terms. Percent and number of sequences with no BLAST hits, BLAST hits, and BLAST hits with GO term annotation and mapping. Distribution of sequences in each category varies across time points excitatory and inhibitory synapses on GIs after the removal of a cercus  [7] [8] [9].
Gene ontology enrichment analysis Metascape was used to integrate functional enrichment information across independent databases  [36]  to identify enriched GO terms from our differentially expressed transcript lists. We first reBLASTed our differentially expressed lists against the curated Swiss-Prot database to retrieve appropriate gene identifiers (See Supplemental Materials). Similar ratios of BLAST hit percentages across timepoints were observed using Swiss-Prot as with the "nr" database, however, the percentage of transcripts with Swiss-Prot identifiers was lower than for the "nr" database (Table  5 ). Despite the low percentage of hits in Swiss-Prot, several categories of GO terms did appear to be enriched, mainly at 3 days (Fig. 7 ). Most strikingly, a number of GO terms related to developmental processes were enriched, such as embryo development (GO: 0009790), growth (GO: 004007), tube development (GO: 0035295), and cellular component morphogenesis (GO: 0032989).
Samples were prepared according to standard Illumina paired-end library protocols prior to sequencing. Briefly, samples were normalized to 200 ng in 50uL volume. mRNA capture beads were used to select for mRNAs, followed by chemical fragmentation, cDNA synthesis with random priming, adapter ligation, and finally library PCR. Sequencing occurred on the Illumina NextSeq 550 platform running v2 chemistry on a PE150 High output flowcell v2. 5 to generate to ~35 M paired end reads of 150 bp in length for each sample. The percentage of transcripts with BLAST results in nr was higher as compared to results obtained in Swiss-Prot across all time points. Several GTPase-related GO terms were identified as differentially regulated at different time points in the terminal ganglion.
Transcriptome analysis Individual assemblies were analyzed using the TrinityStats. pl. Alignment statistics were obtained using (v 2. 3. 4. 1) to map raw reads against each de novo transcriptome. A k-mer identity number was added to each contig's Trinity ID to allow for future referencing once combined. All five assemblies were concatenated and the Evidential Gene program was used to create a single non-redundant assembly. Evidential Gene works on the longest ORFs, as obtained from the Transdecoder. LongOrfs function, removes fragments, and uses a BLAST on self to identify highly similar (98%) sequences. The main (okay) and alternative (okalt) sets output from Evidential Gene were combined into a final fasta file and used as the transcriptome for all future analyses. Bam files, sorted bam files, bam index files, and idxstats.
txt files were created using samtools  [39]. The metajinomics python mapping tools  [40]  were used to generate a counts matrix listing the number of reads mapped to each Trinity predicted contig in every cricket sample. Samtools was used to extract the sequencing depth at every base position for each contig in each cricket sample. A python script was used to extract the mean and standard deviation of depth for each contig. The program plotly in R was used to plot the depth of each cricket sample and visually compared to determine outliers. An MDS plot of the counts data was also generated and three samples (3D_3, 7D_2, and 7D_3) were visually distinct from the rest of the data and were removed (data not shown).
Differential expression Two programs, EdgeR and DESeq, were used to run the differential expression analysis  [41, 42]. Similar filtering and normalization parameters were used in both programs to exclude any contigs that did not have at least one count per million in at least two libraries. Pairwise comparisons at each time point were made between control and deafferented cricket samples to generate lists of significantly upregulated and downregulated genes with a p-value cutoff of 0. 05. We used the EnhancedVolcano package in R to visualize trends in number of differentially expressed genes per program and time point  [43]. Lists of overlapping genes between the two programs for each time point were generated from the pairwise comparisons and used for downstream analysis.
BLASTing The NCBI BLASTx local tool  [44]  was used to identify proteins similar to the translated nucleotide query sequences. An E-value cutoff of 1e-3 was used and max_tar-get_seqs was set to 1. For transcripts with multiple hits we picked the result with the lowest E-value. Query sequences were BLASTed against the entire non-redundant database downloaded from the NCBI website on August 2, 2018. BLAST results were filtered by p-value to identify transcripts that were differentially regulated at a p-value 0. 05 by both EdgeR and DESeq2 programs.
Gene ontology analysis BLAST2GO was used to provide GO term annotations for differentially regulated transcripts at each of the time points using the parameters: BLASTx-fast against the nr database, number of BLAST hits = 20, E-value of 1. 0 e -3, word size of 6, hsp length cutoff of 33, with default mapping and annotation settings. GO terms associated with various transcripts were manually grouped according to GO subtype (cellular component, biological process, or molecular function) and plotted to view the distribution across time points. Gene Ontology enrichment analysis was performed with the web-based platform Metascape. The differentially expressed transcripts were BLASTed to the Uniprot/Swissprot database downloaded February 2020 to obtain properly formatted gene ID lists accepted by Metascape.
value), sframe (Subject frame), ssciname (Subject Scientific Name), scomname (Subject Common Name), salltitles (All Subject Titles). Table Transcripts identified as significantly downregulated at or below p = 0. 05, 7 days post cercal removal by DESeq2. Column headings are as follows: qaccver (Query accesion. version) saccver (Subject accession. version), qstart (start of alignment in query), qend (end of alignment in query), sstart (start of alignment in subject), send (end of alignment in subject), bitscore, qframe (Query frame), evalue (expect value), sframe (Subject frame), ssciname (Subject Scientific Name), scomname (Subject Common Name), salltitles (All Subject Titles). Table  S8. Transcripts identified as significantly upregulated at or below p = 0. 05, 1 day post cercal removal by EdgeR.
Column headings are as follows: qaccver (Query accesion. version) saccver (Subject accession. version), qstart (start of alignment in query), qend (end of alignment in query), sstart (start of alignment in subject), send (end of alignment in subject), bitscore, qframe (Query frame), evalue (expect value), sframe (Subject frame), ssciname (Subject Scientific Name), scomname (Subject Common Name), salltitles (All Subject Titles). Table  S9. Transcripts identified as significantly upregulated at or below p = 0. 05, 3 days post cercal removal by EdgeR. Column headings are as follows: qaccver (Query accesion. version) saccver (Subject accession. version), qstart (start of alignment in query), qend (end of alignment in query), sstart (start of alignment in subject), send (end of alignment in subject), bitscore, qframe (Query frame), evalue (expect value), sframe (Subject frame), ssciname (Subject Scientific Name), scomname (Subject Common Name), salltitles (All Subject Titles).
Field measurements and inference of the rate of new infections We used a privacy-respective setup to film pedestrian flows and crowds in diverse scenarios in a discrete and passive way with a TomTom Bandit camera covered with a thin plastic layer (to deteriorate the quality of the image) and installed in a zenithal position. This privacy-respective setup was approved by the Data Protection Officer of the French National Centre for Scientific Research (CNRS). After some pre-processing with the FFMpeg software to correct the 'fish-eye' effect, downsample the video, and select the area of interest (from 8 m 2 to 100 m 2 ), the positions and head orientations of all pedestrians were manually tracked at a rate of 2 frames per second (fps) with the help of a dedicated Python software; linear interpolation then increased the rate to 10 fps.