dataesr/scientific-paragraphs-categorization huggingface dataset:
[27]n reads were removed from raw FASTQ files by mapping to the human reference genome GRCH38 with bowtie2[27]version 2. 3. 2 (http://bowtie-bio. sourceforge. net/bowtie2/ index. shtml). Reads that did not map to human genome were output to separate FASTQ files and used for all subsequent analyses. Illumina data was analyzed using the pipeline described in detail at https://github. com/lmoncla/illumina_pipeline. Briefly, raw FASTQ files were fastq output. fastq SLIDINGWINDOW:5:30 MINLEN:100. Trimmed reads were mapped to consen- sus sequences previously derived[22] using bowtie2[27] version 2. 3. 2 (http://bowtie-bio. sourceforge. net/bowtie2/index. shtml), using the following command: bowtie2 -x reference_se- quence. fasta -U read1. trimmed. fastq,read2. trimmed. fastq -S output. sam-local.
Duplicate reads were removed with Picard (http://broadinstitute. github. io/picard/) with: java -jar picard. jar MarkDuplicates I = input. sam O = output. sam REMOVE_DUPLICATES = true. Mapped reads were imported into Geneious (https://www. geneious. com/) for visual inspection and consensus calling. Consensus sequences were called by reporting the majority base at each site. [28]med using Trimmomatic[28](http://www. usadellab. org/cms/?page=trimmomatic), trimming in sliding windows of 5 base pairs and requiring a minimum Q-score of 30. Reads that were trimmed to a length of <100 base pairs were discarded. Trimming was performed with the following command: java -jar Trimmomatic-0. 36/trimmomatic-0. 36. jar SE input.
However, these studies did not consider all stages and fitness components of the life cycle and did not directly test for a possible effect of the load accumulated during range expansion. Here, we conducted an experiment under controlled environmental conditions following the whole life cycle of Cbp accessions whose number of potentially deleterious mutations had been estimated from whole genome sequence data  [15]. Life-history traits, fertility, viability of the progeny, phenological traits (germination time, flowering start) and vegetative traits (growth of the rosette) were assessed in Cbp accessions coming from the three main genetic clusters. In contrast with most studies based on one or a few life-history traits, we recorded the impact of different densities of competitors on traits associated with the complete life cycle, i.
(ii) Capsella bursa-pastoris performances To assess viability and fertility under different competition intensities, phenotypic traits related to vegetative growth, phenology and fitness were recorded (electronic supplementary material, figure  S1 ). First, the diameter of the rosette, i. e. the largest distance between opposite leaves, of each focal plant (in cm) was measured at 14 (Dt 1 ) and 21 (Dt 2 ) days after transplantation. The measurement was taken from pictures, using the program ImageJ  [24]. Rosette growth rate (Δ growth ) was then computed as Δ growth = Dt 2 -Dt 1. Second, we recorded three phenological traits: (i) flowering start (FS), i. e. the date of the first flower appearance, (ii) flowering timespan (FT), i. e. the number of days between FS and plant death, and (iii) lifetime (LT), i.
After 7 days of stratification (24 h darkness, 4°C), Petri dishes and pots were moved into growth chambers (12 : 12 h light : darkness, 22°C). We studied the germination dynamic (GD) by recording the germination rate after 1, 2 and 7 days for the seeds germinated in agar plates (GD AGAR ) and after 2, 5, 7 and 14 days for seeds sown in pots (GD SOIL ). The number of seed germinated divided by the number of seeds sown on the last day of each treatment was considered as the progeny germination rate (GR AGAR and GR SOIL ). Then, for each accession and three of the competition treatments (no competitor, two competitors and eight competitors), one seedling was transplanted from the agar plate into individual pots. The seedlings were grown until they started flowering (FS prog ). (c) Data analysis (i) Genetic load estimation Kryvokhyzha et al.
[15, 25]  estimated genetic load between populations of Cbp by classifying mutations into tolerated and deleterious ones using SIFT4G  [26]. Briefly, SIFT4G classifies mutations according to conservation scores based on sequence alignments including a reference species and related species. Both A. thaliana and C. rubella reference partition databases were used to classify the single nucleotide polymorphisms (SNPs). To get only the annotation of the mutations that occurred after speciation of Cbp, the mutations with reconstructed ancestral sequences were polarized as in Kryvokhyzha et al. [15]  and only derived mutations were analysed. This was done for the two subgenomes of Cbp the one derived from C. grandiflora and the one derived from C. orientalis, leading to two estimates of the proportion of deleterious mutations, μ delCg and μ delCo , respectively.
(ii) Principal component analysis To obtain a synthetic view of the relationship between the three main clusters, with respect to fitness components, all the traits were jointly analysed with a principal component analysis (PCA, implemented in 'FactoMineR', R package v. 2. 41-3  [27] ). We thereafter focused on the first four principal components ('corrplot', R package v. 2. 41-3  [28] ), which captured most of the variation (85%).
(iv) Other traits For the phenotypic traits (vegetative-and fitness-related), linear mixed models and generalized linear mixed models (LMM and GLMM, lme4 package v. 1. 1-21, R  [31] ) were adjusted to the data to assess the effect of geographic origin, number of competitors and their interaction, while controlling for block effect: g ijkl ¼ C j þ G k þ ðC j Â G k Þ þ b i þ a l þ 1 ijk : ð2:1Þ For a given trait, γ is the observation for an accession l from geographic origin k, in block i, and with j competitors. Upper and lower case letters denote fixed and random effects, respectively. C is the competition-specific fixed effect, G is the geographic origin fixed effect (a three-level factor corresponding to the AS, EU and ME genetic clusters), (C j × G k ) is the interaction between competition and geographic origin, and ε is the error term (table  1 ).
SUPPLEMENTARY DATA Although many mHealth tools are available for the assessment of AR, E1 only MASK has reported data on medications that can be used in RWE. MASK, a new development of ARIA, is an information and communication technology system centered around the patient in adolescents and adults. E2,E3 MASK, which is freely available in the Google Play and Apple stores, can inform patient decisions on the basis of a self-care plan proposed by the health care professional. The MASK 2016 study indicated low adherence and allowed comparative efficacy of medications by using a novel approach.
MASK treatment study E14 Objectives. A cross-sectional real-world observational study was undertaken in 22 countries to complement the 2017 pilot study. E7  Methods. MASK was used to collect data of daily VAS scores for (1) overall allergic symptoms; (2) nasal, ocular, and asthma symptoms; and (3) work, as well as medication use. The 3 most common intranasal medications containing INCSs (fluticasone furoate, mometasone furoate, and fluticasone propionate), MPAzeFlu, and 8 oral H 1 -antihistamines were studied. The study included some of the users of the pilot study (to achieve a sufficient number of users per drug),  E7   The 2017 MASK treatment study confirms MASK's usefulness in assessing behavior in patients with allergic rhinitis. A ranking of medications was possible and confirmed the MASK 2016 study.
80 workers per colony. They were provided by an Argentinian company that started breeding this species in captivity recently. The original idea of the study was to simulate realistic scenarios that are common among apple growers. In this sense, the introduction of honeybee colonies is a standard management practice, which is implemented by default by apple producers. Therefore, we decided to use this situation as our baseline scenario and avoid the scenario with bumblebees alone. In each orchard, we selected 1-ha plots and both honeybee hives and bumblebee colonies were deployed along one edge of the plots. Apple trees were planted in rows with the same variety (Red Delicious), which was interdispersed with rows of the pollen donor variety. We chose five trees per plot from which to collect data (detailed below), which were located c.
| Farmer's profit We estimated the profit of farmers from apples in the two types of farms (with honeybees vs. with honeybees and bumblebees). Fruit price ranged according to three individual weight categories (small: <130 g; medium: 130-170 g; large: >170 g). The prices were 0. 38, 0. 66 and 0. 80 $USD/kg for small, medium and large apples respectively  (Geslin, Aizen, et al. , 2017). We estimated at the farm level the proportion of harvested fruits that belong to each of the three weight categories. The average costs associated with the farm inputs, excluding the price of honeybee and bumblebee colonies, were 0. 28 $USD/kg and approximately 80% of apple production was sold. All economic data was estimated by  Geslin, Aizen, et al. (2017)  for the same area in the previous year  (2015) , and thus, we used the same cost structure.
ACK N OWLED G EM ENTS We thank  P. Hünicken  for assistance with database management and for providing valuable information about the study system. We are also indebted to the owners and managers of the farms, who provided us with all the facilities to perform this study. Funding was provided by  Consejo Nacional de Investigaciones Científicas y Técnicas  (PDTS,  Res 298/14 ,  Exp 8383/13 ) and  Universidad Nacional de Río Negro  ( PI 40-B-399  and  PI 40-B-567 ) and the  National Fund for Research  ( PICT 2015(PICT -2333 )). We acknowledge  Brometan  for providing the bumblebee colonies. The manuscript benefited from constructive comments of  J. P González-Varo.
Basic knowledge of transcriptional regulation is needed to understand the mechanisms governing biological processes, i. e. , nitrogen fixation by Rhizobiales bacteria in symbiosis with leguminous plants. The RhizoBindingSites database is a computerassisted framework providing motif-gene-associated conserved sequences potentially implicated in transcriptional regulation in nine symbiotic species. A dyad analysis algorithm was used to deduce motifs in the upstream regulatory region of orthologous genes, and only motifs also located in the gene seed promoter with a p-value of 1e-4 were accepted. A genomic scan analysis of the upstoream sequences with these motifs was performed. These predicted binding sites were categorized according to low, medium and high homology between the matrix and the upstream regulatory sequence.
On average, 62. 7% of the genes had a motif, accounting for 80. 44% of the genes per genome, with 19613 matrices (a matrix is a representation of a motif). The RhizoBindingSites database provides motif and gene information, motif conservation in the order Rhizobiales, matrices, motif logos, regulatory networks constructed from theoretical or experimental data, a criterion for selecting motifs and a guide for users. The RhizoBindingSites database is freely available online at rhizobindingsites. ccg. unam. mx.
We use TFBS to refer to all motifs located in the upstream regions of genes or operons, and this information can be used to build TRNs or to infer regulons (groups of genes regulated by the same TF). Phylogenetic footprinting has been successfully used to complement TRNs in other bacteria, such as Escherichia coli K12  (McCue et al. , 2001; Freyre-González et al. , 2008) , and is particularly useful when the studied TF binding motif is well conserved across a group of related organisms. It must be noted that one key point in the applicability of phylogenetic footprinting in bacterial genomes is the organization of bacterial genomes into operons and the general lack of distal regulation (e. g. , enhancers). Variants of a phylogenetic footprinting method have been used to construct databases with experimental transcriptional regulation and extended data (motifs deduced in silico from experimental data) of Rhizobium species, such as RhizoRegNet 1  , which is based on published regulatory interaction data  (Krol et al.
, 2011). PRODORIC v. 8. 9 2  contains a collection of TFBSs with experimental evidence from the literature  (Grote et al. , 2009). RegPrecise 3  is a program based on an expanded reference collection of manually curated regulons  (Novichkov et al. , 2013). RegTransBase 4  captures the knowledge from published scientific literature for the verification of predictions  (Cipriano et al. , 2013). However, for Rhizobium species, only a few TFs with motifs are known, and these databases contain limited genomic information and cannot infer regulons from coexpressed proteins, which are groups of co-regulated operons of the response system in bacterial cells  (McGuire et al. , 2000; Liu et al. , 2016). To complement the missing information, we developed the RhizoBindingSites database 5  , a computational genomic framework of transcriptional regulation for nine symbiotic species of the order Rhizobiales that provides information for inferring transcriptional regulation in bacteria for better experimental designs on transcriptional regulation.
The RhizoBindingSites database includes information about numerous organisms, namely, Rhizobium etli CFN42, R. etli Mim1, Bradyrhizobium diazoefficiens USDA110, Sinorhizobium fredii NGR234, Sinorhizobium meliloti 1021, Rhizobium leguminosarum bv. viciae 3841, Bradyrhizobium sp. BTAi, Azorhizobium caulinodans ORS 571 and Mesorhizobium japonicum MAFF303099, and covers a great number of host plants in symbiotic relationships.
Motif Discovery Position-specific scoring matrices (PSSMs), hereafter called motifs, were identified using the program RSAT for footprint discovery  (Janky and van Helden, 2008). Briefly, the input for this program includes the name of an organism, one or more gene names and the name of a taxon (see the command in Appendix A). The program searches the orthologous genes in the given taxon for each gene of the desired organism with a best bidirectional hit and an E-value threshold 1. 0e-5. For each selected ortholog, the program obtains the upstream sequences (-400 to -1) relative to the translation start site. These sequences are masked in the redundant fragments, and a motif discovery algorithm is applied to these sequences using dyad-analysis in  RSAT (van Helden et al. , 2000). This program looks for overrepresented variable-spaced motifs, which is the case for most bacterial TFs that bind DNA as dimers.
Selection of Motifs and Genomic Scan Given that the motifs were discovered from the promoters of the orthologs of each gene, it is possible that some discovered motifs do not exist in the organism of interest. Some of the discovered motifs are likely to be artifacts (i. e. , motifs reflecting the taxonwise nucleotide frequencies and that do not correspond to a bona fide TF binding motif from the gene seed). Then, for selection of relevant motifs, matrices representing the motifs were used to scan the promoter regions of their gene seeds using the RSAT program matrix-scan  (Defrance et al. , 2008; Thomas-Chollier et al. , 2008; command in Appendix B). We selected the motifs predicting a TFBS in the gene seed promoter with a p-value ≤ 1e-4, where the p-value evaluates the weight of the site.
Motif Information The motif information window in RhizoBindingSites contains data from a genomic matrix-scan analysis with the matrices from each gene. These data represent an expected regulon that will hereafter be referred to as an "e-regulon" to distinguish it from the regulon deduced experimentally. These genes sharing one or more motifs from a gene in their upstream promoter region are shown; the presented information includes the locus tag of the gene; strand of the gene location; associated motif, which is identified by the locus name or locus tag of the gene followed by the letter "m" with the number of the matrix (i. e. , RHE_RS30790_m1); strand of the motif in the upstream region; start/end position of the motif; sequence of the site motif; and weight, p-value and significance for the site (Figure  1 ;  Medina-Rivera et al.
Gene Information The gene information window in the RhizoBindingSites database shows unique genes from an e-regulon (Figure  2 ). It includes the identifier locus tag (when there is no function annotated) or the start-stop position sequence of the gene, gene strand location, gene ID, name of the locus, identifier locus tag, protein product identifier, number of amino acids in the protein, COG number, COG group, and number of the gene in the genome; an equal number is used for genes near the three genes according to the number of the genes in the genome (see below), and the total number of unique genes is included at the bottom of column 1 (Figure  2 ). Note that some genes lack functional information although they are grouped according to shared motifs.
Motif Conservation in the Taxon Rhizobiales All methods for predicting TFBSs produce false positives. To determine how conserved motifs are in the taxa and gain more confidence in these data, the RhizoBindingSites database makes it easier for users to search for predicted motifs in the upstream sequences of orthologous genes in the order Rhizobiales. Detection of conserved motifs across taxa was performed using the RSAT command matrix-scan (command Appendix C; Figure  1 ;  Medina-Rivera et al. , 2015; Nguyen et al. , 2018).
Comparison of Reported and Predicted Data of Transcriptional Regulation We searched for coincidence of the published TF target genes in experimental transcriptional regulation data from R. etli CFN42, S. meliloti 1021, B. diazoefficiens USDA110, R. leguminosarum bv. viciae 3841, and S. fredii NGR234 taken from the RegTransBase database  (Cipriano et al. , 2013). We used the target genes of the predicted TFBSs to build putative regulons, which were then compared against the predicted data from RhizoBindingSites database (Supplementary Table  9 ).
Some Examples of How to Use the RhizoBindingSites Database for a Computational Study An Inferred Transcriptional Regulatory Network for the Synthesis of a Nodulation Factor From R. etli CFN42 We carried out an in silico search to determine how the genes involved in the synthesis of a nodulation factor are regulated. It is recognized that the transcriptional regulator nodD activates the transcription of genes for the synthesis of the nodulation factor  (Rossen et al. , 1985; Laranjo et al. , 2014). The nodulation factor excreted to the rhizosphere promotes the first changes in Phaseolus vulgaris roots to establish symbiosis with R. etli CFN42  (Winsor, 1989; Cárdenas et al. , 1995; Wang et al. , 2018). A list of genes annotated for the synthesis of the nodulation factor was made according to the annotation of the genome of R.
RESULTS AND DISCUSSION RhizoBindingSites (see text footnote 5) is a database of predicted DNA-binding motif regulatory regions associated with TF and non-TF genes from nine genomes of Rhizobiales (consult the user guide in the RhizoBindingSites database). Presumption of a motif in the gene seed may help avoid detecting motifs by chance and raise the possibility of finding genes involved in the gene regulatory process. The goal of RhizoBindingSites is to provide TF and non-TF binding motifs and gene information for each gene at three levels of confidence and to facilitate motif conservation analysis in the order Rhizobiales. In addition, the user can obtain the motif logo and regulons from predicted or experimental data. Regulatory sequences analysis tools footprint discovery data showed that in the nine genomes, 62.
9e-4 is acceptable with a low identity, probably including more false positives than at the medium level of restriction (p-value of 1. 0e-5 to 9. 9e-5) or even the stricter level (pvalue of 1. 0e-6 or lower) in the R. etli CFN42 genome. This is a general observation for all genomes in this study. Accordingly, the RSAT matrix-scan program output data had the lowest p-value per matrix  (Thomas-Chollier et al. , 2008). Motifs with a p-value of 1. 0e-4 may be relevant for relaxed consensus promoters, such as sigA gene from R. etli CFN42  (Ramírez-Romero et al. , 2006). It is worth noting that the motif information window contains predicted motifs of TF and non-TF genes. For matrixscan data of non-TF genes, the genomic matrix-scan output contains all genes sharing one or some motifs of a gene, based on the principle that most of the genes that encode enzymatic or structural proteins have orthologs and conserved sequences between them, probably involved in the transcriptional regulation of the gene.
The users may choose the p-value for motif prediction in the motif information window from the RhizoBindingSites database (Figure  1 ). After exploring possible functional data, it is recommended to select data with the lowest p-value level, since the p-value evaluates the weight of the site (see "Materials and Methods"). Moreover, for better selection of predicted data, it is necessary to consider the following in the gene information window of RhizoBindingSites (Figure  2 ): the function of the query protein, the COG classification  (Galperin et al. , 2015) , the preponderancy of functions in the group of genes sharing the same motif (see the chart located in the gene information window), and other TFs considering vicinity  (Bowers et al. , 2004; Figure 2, column 12). It is worth noting that the user will have a set of genes per genome sharing a motif from a TF from the RhizoBindingSites database or an experimental profile of proteins with coexpressed TFs.
In those cases, the presence of the regulons can be inferred with the application "Prediction of regulons" (located in the motif information window for each species). Additionally, the user may enrich their data by searching for the genomic context of the query and target genes on the Gene Context Tool NG v3 website at http://bioinfo. ibt. unam. mx/ gecont  (Martinez-Guerrero et al. , 2008). The user may be able to determine how conserved a motif is in the order Rhizobiales by conducting a matrix-scan analysis  (Thomas-Chollier et al. , 2008) , specifically, by clicking the motif map button in the motif information window of the RhizoBindingSites database. This option shows a matrix-scan analysis with the matrix selected and upstream sequences (-400 to -1) of orthologs of the gene query. The output is a was to determine the transcriptional regulators involved in the synthesis of a nodulation factor from R.
These data showed that among nine TFs, only the matrix RHE_RS26495_m3 of the TF RHE_RS26495 was involved in the transcriptional regulation of the non-TF gene RHE_RS00040. The process is detailed in the user guide ("Materials and Methods"). In the third example, since for matrix-scan output data from RHE_RS00040, a non-TF gene, nine TFs shared one or more motifs, transcriptional regulation was inferred only between these TFs, with the lowest p-value for a TF-TF relationship. As was hypothesized, a Cytoscape graph  (Shannon et al. , 2003)  showed interregulation between the TFs, with the TF RHE_RS27920 located at the top of the network with the greatest number of connections. The analysis and graph are described in detail in the user guide ("Materials and Methods"). The RhizoBindingSites database (see text footnote 5) provides bioinformatic information that may contribute to the design of experiments on the transcriptional regulation of nitrogen-fixing symbiotic species which covers a great number of host plants and is thus useful for inferring regulons and providing information for regulatory networks.