dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis The semi-structured interviews were recorded and then fully transcribed. A thematic content analysis of the interviews was performed using QDA Miner 4. This qualitative analysis software facilitates the management of data and counts the occurrence of codes and categories. The coding process was performed as the data were collected, making it possible to determine when theoretical and empirical saturation had been reached  [38]. The analysis took place in 5 phases based on the work of Creswell  [42]. However, given the semiinductive research approach used, the coding was based on a list of ICT characteristics, technostressers and symptoms of psychological distress drawn from the literature review, which was then enriched during the coding process. This analytic strategy was carried out iteratively  [43].
At this level, the above equations are coupled with the hydrodynamic code. In particular, the two-dimensional density map generated by a hydrodynamic code in cylindrical geometry is used as an input to Eq. 6. In order to increase the calculation speed, the code uses a dedicated FORTRAN library, written for such a purpose, that performs all the calculations in parallel mode via OpenMP library. The transfer matrix is later used to generate the synthetic image considering all the parameters of the real imaging system, namely polychromaticity, source and detector PSF. Regarding the polychromaticity, for a polychromatic source the final image is a weighted sum of the monochromatic ones  32, 40  : ∑ λ = λ I wE I ( ) ( ) (7) poly mono image analysis. The condition required to use the Eq. 4 together with that of object homogeneity, and an X-ray photon energy below 50 keV allow phase retrieving from the measured image, by using the Transport Intensity Equation (TIE)  46, 47.
Acknowledgements The authors acknowledge the support of the PHELIX staff at GSI. The authors thank  Dr M. Griffa  for the useful discussion. This work has been carried out within the framework of the EUROfusion Enabling Research Project:  AWP17-ENR-IFE-CEA-01  " Preparation and Realization of Eu-ropean Shock Ignition Experiments " and has received funding from the  Euratom  research and training pro-gramme  2014-2018  under grant agreement No. 633053. The views and opinions expressed herein do not necessar-ily reflect those of the  European Commission. The research leading to these results has received funding from  LASERLAB-EUROPE  (grant agreement No. 654148 , Eu-ropean Union's  Horizon 2020 research and innovation pro-gramme ). The work was also supported by the  Competitiveness Program of NRNU MEPhI, Russia.
Here, we used full-length scRNA-seq to characterize the HEV transcriptome in adult mouse PLNs at steady state, during inflammation, and after inhibition of LTbR signaling. We show that PLN HEVs exhibit cellular and spatial heterogeneity during homeostasis and are defined by molecular signatures reflecting cellular activation. We provide a comprehensive view of LTbR-dependent genes and pathways in HECs, and we demonstrate that antigenic stimulation and inflammation have a major influence on the HEC transcriptome. Lastly, we report important differences in the regulation of genes that control HEV-mediated lymphocyte trafficking, and we identify genes that are co-regulated or correlated with critical HEV genes. This dataset constitutes a useful resource for further phenotypic and functional characterization of HEVs.
Selected genes are labeled for reference. (J) t-SNE analysis of all cells (n = 220). Each point represents an individual cell. Single-cell data are pooled from 5 or 10 mice per experiment and represent 5 (hHECs), 2 (bECs), 1 (dHECs), or 3 (iHECs) individual experiments. (K) Venn diagram of differentially expressed genes between hHECs and the three other conditions, and expression distribution (violin plots) of representative genes from each set. Gene expression values were calculated as fragments per kilobase of transcript per million (FPKM). Each symbol in (H) represents an individual mouse; small horizontal lines represent the mean (± SD). NS, not significant (p = 0. 9019); *p = 0. 0002; **p < 0. 0001 (unpaired two-tailed Student's t test). Data are representative of two experiments (H; number of total cells; mice per group: n = 5), five experiments (H; percentages; mice per group: n = 5), or are pooled from 5-10 mice per experiment in (C), (F), and (G).
, 2016; Webster et al. , 2006)  and maintenance of high levels of CCL21, despite some reduction after antigenic stimulation, are other important elements likely to contribute to the efficient recruitment of naive lymphocytes into inflamed PLNs. We believe that the dataset that we have generated constitutes a useful resource for further phenotypic and functional characterization of HEVs in future studies. By combining different experimental conditions, we have identified genes closely associated with the functional HEV phenotype. Some of the genes that were co-regulated or correlated with critical HEV genes under these experimental conditions could turn out to encode important regulators of HEV-mediated lymphocyte trafficking. These candidate regulators include enzymes, transcriptional and translational regulators, secreted factors, signaling components, and proteins modulating cell adhesion.
, 2016) , allowed us to generate a high-quality HEC scRNA-seq dataset. This dataset is characterized by a high sequencing depth (>1. 6 million mapped reads), high sensitivity (mean number of genes detected >5,000), and full-length transcriptome coverage. This will be undoubtedly useful in future studies, for instance to analyze splicing events and/or long non-coding RNAs in HECs. However, it is important to point out that our study has several limitations, including the relatively low number of high-quality cells sequenced, and the possibility that technical variability could explain some cell-tocell variability, particularly for lower expressed genes  (Hicks et al. , 2018). Although the distinction between technical variation and true biological variation is a challenge in scRNA-seq experiments  (Hicks et al.
, 2018) , we have no doubts that the heterogeneity of HECs that we describe in the present manuscript is due to biological variation and not to technical variation, because we have confirmed the scRNA-seq results with independent techniques, including RNA-FISH, immunohistofluorescence, and flow cytometry. In conclusion, our results provide important insights into the phenotypic diversity, heterogeneity, and plasticity of PLN HEVs and their regulation in homeostasis and inflammation. The dataset comprehensively describes the full-length HEV transcriptome on a genome-wide scale in different experimental conditions and constitutes a useful resource for further characterization of HEV regulation and function. Future work should also aim at defining the phenotype of HEVs at single-cell resolution in other mouse or human lymphoid organs and to compare it to potentially related endothelial cell types.
QUANTIFICATION AND STATISTICAL ANALYSIS Read alignments and gene-expression estimation Paired-end reads were trimmed using trimmomatic-0. 32  (Bolger et al. , 2014)  using the parameters: (ILLUMINACLIP:$(adapters): 2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36). It removed Nextera adapters from read ends, removed low quality bases from edges and removed any reads that were shorter than 36 reads. Trimmed reads were then aligned to the UCSC mm10 mouse genome using RSEM v1. 2. 18  (Li and Dewey, 2011)  with default settings. The gtf file of mm10 was slightly modified to remove Ccl21a pseudogenes (Gm1987, Gm10591, Gm13304, and Gm21541) as multiple mapping caused reads to be discarded. Gene expression values were calculated as Fragments Per Kilobase of transcript per Million (FPKM) and were obtained from resulting RSEM output.
(I) Variability plot of hHECs represented as mean against squared coefficient of variation (cv 2 ). Blue dots represent genes determined by Monocle to be differentially expressed between hHECs and bECs (p < 0. 001; multiple testing corrected), green dots represent commonly described endothelial cell genes, and purple dots represent stably expressed or ribosome-associated genes. Selected genes are labeled for reference. (J) t-SNE analysis of all cells (n = 220). Each point represents an individual cell. Single-cell data are pooled from 5 or 10 mice per experiment and represent 5 (hHECs), 2 (bECs), 1 (dHECs), or 3 (iHECs) individual experiments. (K) Venn diagram of differentially expressed genes between hHECs and the three other conditions, and expression distribution (violin plots) of representative genes from each set.
Gene expression values were calculated as fragments per kilobase of transcript per million (FPKM). Each symbol in (H) represents an individual mouse; small horizontal lines represent the mean (± SD). NS, not significant (p = 0. 9019); *p = 0. 0002; **p < 0. 0001 (unpaired two-tailed Student's t test). Data are representative of two experiments (H; number of total cells; mice per group: n = 5), five experiments (H; percentages; mice per group: n = 5), or are pooled from 5-10 mice per experiment in (C), (F), and (G). See also FiguresS1, S2, and S3.
ACKNOWLEDGMENTS We thank  E. Bellard  for intravital microscopy experiments and members of the  Girard laboratory  for helpful discussions. We are grateful to  Genotoul GeT-Sante, GeT-PlaGe, Bioinfo, ANEXPLO-IPBS , cell sorting  TRI-I2MC , and  Imaging Core Facility TRI-IPBS. We acknowledge the help of  E. Naser  and  A. Zakaroff  for cell sorting;  J. J. Maoret ,  C. Genthon , and  O. Bouchez  for Fluidigm C1 and RNA-seq;  C. Klopp  and  C. Gaspin  for initial bioinformatics analyses;  F. Viala  for iconography; and  N. Ortega  for animal ethics. We thank  S. Rosen  for the generous gift of anti-GlyCAM-1 antibodies. This work was supported by grants from  Fondation ARC pour la Recherche sur le Cancer  ( SL220110603471  and  PGA 120150202411  to J. -P. G. ),  Agence Nationale de la Recherche  ( ANR-12-BSV1-0006-01  to J.
-P. G. ),  Institut National du Cancer  ( INCa_6584  and  PLBIO13-096  to J. -P. G. ), and  Laboratoire d'Excellence Toulouse Cancer  (LABEX TOUCAN). K. V. was supported by  Agence Nationale de la Recherche and Institut National du Cancer , C. T. by fellowships from  LABEX TOUCAN and Fondation ARC  ( DOC20170505820 ), and J. C. by a fellowship from the  French Ministry of Research. The Imaging Core Facility TRI-IPBS received financial support from  ITMO Cancer Aviesan (Alliance Nationale Pour les Sciences de la Vie et de la Sante, National Alliance for Life Science and Health)  within the framework of  Cancer Plan.
Microcosm construction and incubation Microcosms were constructed using a non-drought-acclimatised agricultural soil (0 -15 cm depth, pH 6. 5) collected from field plots at SRUC, Craibstone, Scotland (grid reference NJ872104). Details of the sampling site and other soil characteristics are described by  Kemp et al. (1992). Soil was air-dried at 25 o C for 4 days, sieved (3. 35-mm mesh) and stored at 4 o C for 4 weeks. Soil pH and moisture content were determined as described by  Nicol et al. (2005)  and initial soil moisture content decreased from 27. 7% (field moisture content) to 10% after air-drying. A soil water retention curve, assessing the relationship between soil matric potential and soil moisture, was determined on independent soil samples, measuring matric potential using a WP4C water potentiometer (Decagon, Pullman, UK) (data not shown).
Statistical analysis All statistical analyses were computed using the program R 3. 2. 2 (R Development Core Team, 2015), using agricolae and nlstools packages. AOA and AOB abundances were normalised by log10 transformation and mean z-score-transformed. For the soil microcosm experiment, the effects of matric potential, NH4 + amendment and time, and their interactions, on soil pH, NH4 + -N and NO3 --N concentrations and AOA and AOB amoA abundances during incubation of soil microcosms were analysed using three-way ANOVAs. (All independent variables were categorical and destructive sampling allowed analysis of time as an independent fixed factor. ) Generalised linear models were used to explore further the correlations between AOA and AOB abundances, as dependent variables, and matric potential and time as categorical independent variables and measured soil NH4 + -N concentrations and pH as continuous independent variables.
Children that fit the inclusion criteria (local healthy individual aged between 6 months and 12 years old with a normal weight that has given consent and completed all enrolment procedures) were invited to participate in the cohort study. Children with a chronic illness or permanent disability, living in a remote area and children that were not permanent residents were excluded. All eligible and consenting children were enrolled. Participants were then actively followed for a period of 11 months at 4-weekly intervals, apart from a longer period (up to 6 weeks) between the 7th and 8th visit due to the Christmas holiday season. During each active case detection (ACD) visit, information on LLIN usage the night before by mother and child was recorded. The reported LLIN usage by the participants and their mothers was very similar and thus was combined and averaged the LLIN usage data from both mother and child during cohort.
The genotyped samples were submitted for capillary electrophoresis, followed by fragment analysis using GeneMapper software (Applied Biosystems, USA), as described previously  [33]. From the genotyping data, the multiplicity of infection (MOI) and molecular force of blood-stage infection ( mol FOB) were determined  [35, 36]. Briefly, mol FOB is the observed number of new blood-stage infections, as identified by individual Plasmodium spp. genotypes, divided by the time-at-risk (i. e. the incidence of new blood-stage infections). An observed infection, as identified by a specific genotype, was considered to be 'new' if the same genotype (of any molecular marker) had not been seen in the two previous active or passive case detection visits. As such, mol FOB can be determined for each study participant or as a sum over the entire study population or subpopulations.
This approach was taken due to the irregular data structure that included missing data. The prevalence of P. vivax infection (1) throughout the period of the study was analysed using logistic regression based on the XTLOGIT command in STATA. Only the ACD visits data were included in the analysis of prevalence as the study was estimating the prevalence at each visit. The incidence of new P. vivax clones acquired during cohort (2) was assessed using a negative binomial regression approach based on the XTNBREG command in STATA  [35]. New P. vivax clones acquired during PCD, as determined using the technique described above were binned into the following ACD visit interval for consistency. Plasmodium vivax log-transformed infection densities (3) were analysed by linear regression method using the XTREG command in STATA.
Study demographics A total of 1111 children aged 6 months to 12 years old from 20 villages in Ngella (Fig. 1 ) were enrolled during May 2013. Of these, data from 860 children was kept for analysis (Fig. 2 ). Among the 251 excluded children, 28 children were excluded post-enrolment (refer to Fig. 2  for details), 63 children withdrew from the study; and 160 children were excluded post hoc due to inconsistency in follow-up attendance. The excluded participants (Additional file 2) were similar in gender and age to those included, but due to problems with accessibility of villages a significantly higher number of children residing in the North Coast region were excluded as compared to the Bay region (p-value < 0. 001). The 860 children included in the final analyses had a minimum of 6 ACD attendances (Additional file 3).
Conclusions This study complements the previous work by Robert et al. [24]. This study presents a macroand micro-analysis of the deformation bands during Boixols thrust and fold growth in order to test the hypothesis of a shallow occurrence of these bands (Tremp basin, Spain)  [24]. Numerical simulations were made using the LAM with OptumG2 software in order to follow the stress evolution and to predict the band distribution through 2D cross sections with an imposed fault-propagation fold kinematic. Our simulations show that the thrust is guiding the high values of stress, which implies relatively high values of the Q/P ratio (between 0. 7 and 1. 2) at the back of the fold and ahead of the thrust tip. Below the thrust and far away, in front of the deformed area, this ratio is decreasing to lower values, although close to 0.
The aim of this review was to analyze the existing knowledge of the anthropometric and physiological characteristics of MMA (mixed martial arts). A computerized search was performed in the PubMed and Google Scholar databases for English-language, peer-reviewed investigations using the term "mixed martial arts" or "MMA" alone and in combination with "physiological characteristics", "physiological profile", "body composition", "strength", "power", "endurance", "aerobic", and "anaerobic". The main findings of this review suggested an overall profile of low body fat, high flexibility, muscle strength, muscle endurance, and anaerobic power, and average cardiovascular endurance. Nevertheless, no differences in anthropometric and physiological characteristics by performance level of the athletes were observed.
Materials and Methods A computerized search was performed (January 2019) in the PubMed and Google Scholar databases for peer-reviewed investigations using the term "mixed martial arts" or "MMA" alone and in combination with "physiological characteristics", "physiological profile", "body composition", "strength", "power", "endurance", "aerobic", and "anaerobic". The abstracts of the relevant titles were then read, and if the study included anthropometric and/or physiological evaluation(s) for MMA athlete(s), the full text was retrieved. Manual searches were also done using reference lists from the recovered articles.
ABCA4 gene sequencing and identification of variants ABCA4 sequencing was performed for 1,054 genetically unsolved STGD and STGD-like patients. This revealed 323 unique (likely) pathogenic SNVs and 11 SVs in 1,144 alleles (Supplemental Table  S6 ). Sixtyfour of 323 SNVs (26%) and all 11 SVs were novel. Details are provided in Table  1A -C. Thirteen percent of these alleles were represented by DI variants and SVs and another 10% accounted for NCSS variants (Fig. 1A ). All variants and the respective cases were uploaded into the ABCA4 variant and STGD1 cases database LOVD, at www. lovd. nl/ABCA4. Two (likely) pathogenic variants were found in 326 probands, three of them carrying p. Asn1868Ile in a homozygous manner, and one (likely) causal variant in trans with p. Asn1868Ile was found in 125 probands.
As shown in Supplemental Fig. S10 , these SVs are spread over the entire gene. All SVs are rare, except for deletions spanning exons 20-22 and exon 6, both found in 6 probands, in Belgium/Germany/Netherlands and from Iberic origin, respectively, suggesting founder effects. Fifteen large rearrangements in ABCA4 have also been reported in the general population databases gnomAD and Database of Genomic Variants (DGV) (Supplemental Table  S18 ). While their sizes range from 53 bp to 45. 75 kb, we consider only five of these to have clinical significance as they span one or more exons. One deletion spanning the 5' UTR, exon 1 and intron 1, was present in gnomAD and DGV with allele frequencies of 4. 7 x 10 -5 and 2. 5 x 10 - 4 , respectively. In STGD1 cases, the allele frequency of this SV was 4 x 10 -4.
Database (gnomAD; http://gnomad. broadinstitute. org/) and Genome of the Netherlands (GoNL). We then applied a filter based on a 'quality score' of >200, based on the average number of reads targeting each genomic position in a single run and other standard filters based on the position of SNVs (e. g. exons, canonical splice sites and known NCSS variants). A number of previously listed causal variants with relatively high AFs such as c. 2588G>G, c. 5461-10T>C, c. 5603A>T and c. 5882G>A (AFs in gnomAD: 0. 00430, 0. 00022, 0. 04219 and 0. 00456 respectively) were selected separately  (Cremers et al. 1998; Cornelis et al. 2017; Schulz et al. 2017; Zernant et al. 2017). To assess the variants, in silico analysis was performed using online pathogenicity assessment tools such as Sorting Intolerant From Tolerant (SIFT; https://sift.
bii. a-star. edu. sg) (Kumar et al. 2009) , Polymorphism Phenotyping v2 (PolyPhen-2)  (Adzhubei et al. 2010) , and Mutation Taster  (Schwarz et al. 2014 ). Among others, Grantham and PhyloP scores were also assessed to predict the physiochemical effect of nucleotide changes on ABCA4 protein and conservation of nucleotide positions, respectively. For the selection of known coding and non-coding causal variants, ABCA4 variant databases, such as the Leiden Open Variation database (LOVD; www. lovd. nl/ABCA4) (Cornelis et al. 2017) , and the Human Genome Mutation Database (HGMD; http://www. hgmd. cf. ac. uk/ac/index. php), were used. We supplemented the list of SNVs and small indels with additional variants reported in the literature and employed a script to identify these in an automated manner.
Candidate splice defect variant selection Intronic variants located at positions -14 to -3 upstream of exons and at positions +3 to +6 downstream of exons were considered NCSS variants, and variants outside these positions were considered DI variants. Exonic ABCA4 NCSS variants can also be found within the first or the last two nucleotides of an exon  (Cornelis et al, 2017). To select novel NCSS and DI variants for in vitro splice assays, the following selection criteria were used: i) minor allele frequency <0. 005 in general population databases such as dbSNP, gnomAD and GoNL, ii) difference in wild-type (WT) and mutant splice site scores obtained by in silico analysis performed by using five different algorithms including SpliceSiteFinderlike (SSFL), MaxEntScan, NNSPLICE, GeneSplicer, Human Splicing Finder (HSF) via Alamut Visual software version 2.
7 (Biosoftware, 2014; Interactive Biosoftware, Rouen, France; www. interactivebiosoftware)  (Reese et al. 1997; Pertea et al. 2001; Cartegni et al. 2003; Yeo and Burge 2004; Desmet et al. 2009) , iii) additional splice site score comparison between WT and mutant was made by using the SpliceAI program, which unlike other programs searches for altered splice sites up to 5 kb upstream and 5 kb downstream of the variant  (Jaganathan et al. 2019) , and iv) enrichment of variants' allele frequencies in the patient cohort compared to general population databases such as gnomAD and GoNL. A list of all the selected candidates for in vitro splice assays and details of in silico analysis are provided in Supplemental Table  S21.
Copy number variant analysis An excel script was employed to detect copy number variants (CNVs) using smMIP read depth. Only large (>400 bp) deletions and duplications in principle can be identified, but not insertions and inversions. Therefore the term CNV instead of SV was used in the following section. The read coverage of a single target was first normalized in comparison to all 3,375 targets by dividing the number of reads for a single target by the total number of reads for a given DNA sample. The coverage of each target of a patient was subsequently compared with the coverage of each target in all probands of that series using the following ratio: R = average coverage of each target of a proband/average coverage of each target of all probands in the run. In order to identify large deletions and duplications, the following ranges were defined: homozygous deletions, ratios (R) <0.