dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses of clinical data and microarrays. Demographic, clinical, and laboratory quantitative variable results are expressed as the mean ± SD or the median (range), depending on the Gaussian nature of the distribution. Normality was assessed with the Shapiro-Wilk's test. For unsupervised analyses, gene expression data were filtered by the resulting P values at 5%, and the Benjamini and Hochberg method was used to estimate the false discovery rate. For the planned analysis of B cell-related genes and for pathway analyses, the false discovery rate was not used, given the exploratory nature of these analyses based on the whole blood in a limited sample size. We performed gene set enrichment analysis for DEGs with significant fold changes in expression of P values less than or equal to 0.
Total RNA (75 ng) was reverse transcribed to cDNA using Maxima Reverse Transcriptase Kit (Thermo Fisher Scientific). qRT-PCR was performed with the LC480 Instrument (Roche Diagnostics) according to the manufacturer's recommended protocol. Reverse-transcribed RNA was amplified using the Probe-Master kit (Roche Diagnostics) and TaqMan gene expression assay ID HS00975865_m1 for BTK (Thermo Fisher Scientific). Data were normalized with 2 reference genes (peptidylpropyl isomerase B [PPIB] and succinate dehydrogenase complex flavoprotein subunit A [SDHA]), obtained from a pilot study of housekeeping genes with the GeNORM algorithm. Housekeeping genes were measured with the SYBR Green I Master kit (Roche Diagnostics). Sequences of PCR primers of housekeeping genes were 5 0 -AGACTGCGGCAAGATCGAGGTG-3 0 (forward) and 5 0 -GGGCCTGCACAGACGGTCA-3 0 (reverse) for PPIB and 5 0 -GCATTTCTACGACACCG-3 0 (forward) and 5 0 -GTGCAATA GCGAGTGG-3 0 (reverse) for SDHA.
ACKNOWLEDGMENTS The authors acknowledge  Sarah Tubiana  ( Centre de Ressources Biologiques de l'Hôpital Bichat ),  Sebastien Jacques  and  Franck Letourneur  ( Plateforme de Transcriptomique de l'Institut Cochin ),  Aurelien Guffroy  and  Vincent Poindron  ( Department of Internal Medicine and Clinical Immunology, Strasbourg University Hospital ),  Blandine Guffroy  ( Department of Hematology, Strasbourg University Hospital), Isabelle Alamome, Laboratory Hematology, Colmar Civilian Hospital ),  Gilles Blaison  ( Department of Internal Medicine, Colmar Civilian Hospital ), and  Laurent Messer  ( Department of Rheumatology, Colmar Civilian Hospital ).
NCT03040583. The opinions and assertions contained herein are those of the authors and do not necessarily represent those of the  Innovative Medicines Initiative 2 Joint Undertaking , and the  Innovative Medicines Initiative 2 Joint Undertaking  is not responsible for any use that may be made of the information herein. Supported by the  Innovative Medicines Initiative 2 Joint Undertaking NECESSITY  (grant  806975 ). Dr. Gottenberg 's work was supported by  Bristol-Myers-Squibb  for the transcriptomic analysis of the ASSESS and Norwegian cohorts and by  Geneviève Garnier  ( Association Française du Syndrome de Gougerot-Sjögren  et des syndromes secs). The ASSESS cohort was supported by two research grants from the  French Society of Rheumatology.
These specimens have been collected following the standard ammonite zonation of the Valanginian-Hauterivian interval of the Early Cretaceous in the VB  [Reboulet et al. , 2014]. The data resulted in a taxonomic revision of the genus Castellanibelus. The individual species were then interpreted in their biostratigraphic context. Finally, the abundance patterns of Castellanibelus are compared to those of other co-occurring belemnite genera, in order to obtain quantitative distribution patterns for each ammonite zone.
Material The specimens were collected from the upper surface of beds, following the standard ammonite zones of the standard Kilian group zonation  [Reboulet et al. , 2014] , in the following Az stands for ammonite zones and Asz for ammonite subzones. Most of the specimens come from the departments of Alpes de Haute-Provence, Drôme and Hautes-Alpes. The reference specimens have been deposited in collections of the National Geological Reserve of Haute-Provence. The genus Castellanibelus, which first appeared in the Tithonian  [Combémorel, 1972] , reached its acme in the Early Valanginian and disappeared subsequently in the latest Valanginian. The biometric analysis performed in this study was carried out on 1762 rostra collected in 112 outcrops exposing Valanginian strata. The investigated exposed surface area in the VB is estimated to be around 4500 km 2 based on data from the Geoportail website of Institut national de l'information géographique et forestière  [IGN, 2022].
Measurements Biometric data have been obtained from the 1762 specimens collected from the Valanginian of the VB. The measurements were made using a digital caliper to an accuracy of 0. 01 mm. Each value was measured three times successively for assessment of measurement error. In order to obtain statistically useful data, measurements of specific features must be linked to tie points. The position of the protoconch is one such tie point used in many studies [e. g. Christensen, 1975 Christensen, , 2000]]. The position of the protoconch in Castellanibelus is easy to identify because of the regularity of its alveolar cavity. Its median position, relatively close to the apex but anterior to the maximum diameter of the posterior part, does not allow an efficient use of the protoconch-apex length (Figure  3a ).
Morphometry and statistics Biometric data provide a quantitative means of comparing and statistically testing differences among individual rostra of a belemnite assemblage. Because belemnite rostra are subject to allometric growth, biometric ratios rather than raw measurement values are commonly used in statistical analyses of belemnite assemblages comprising individuals of different sizes and ages. Ratios of values taken at the same point on rostra are therefore widely used in belemnite biometry suggesting that they are reliable tools [e. g. Spaeth, 1971 , Mutterlose et al. , 1983]. The following ratios have been computed for the present study: • ica (ha/la): anterior compression index • icp (hp/lp): posterior compression index • iddv (lp/la): dorsoventral dilation index • idlat (hp/ha): lateral dilation index • Psr (L pp /lp): posterior shape ratio • apical angle (INVTAN[(l max /2)/L pp ]) × 2)) • alveolar relative depth (L ad /L tp ) Calculations and graphs were accomplished with an Excel database.
6 ). We validated the identified spatial isoform differences by in situ sequencing (ISS) on a tissue section from another individual (Fig. 1c , Supplementary Fig. 7 ). Each spatially barcoded spot typically captures transcripts from multiple cells. However, using single cell RNA-seq data, it is possible to deconvolute the transcriptional signal into the likely constituent cell types of the spot, and to associate specific cell type(s) to isoform expression. We demonstrate this approach using a previously published MOB scRNA-seq dataset  [7]  and a deconvolution strategy based on the identification of pairwise cell correspondences  [8]. This approach identifies the myelinatingoligodendrocyte-based (Mag+) cell type within the Granule Cell layer as the predominant producer of the Plp1 standard isoform and the Olfactory Ensheathing Cell-based (Sox10+) cell type within the Olfactory Nerve Layer as the predominant producer of the truncated Plp1 isoform DM20 (Supplementary Fig.
Oxford Nanopore data processing Nanopore reads were processed according to the scNaUmi-seq protocol  [4]  (https://github. com/ucagenomix/sicelore) with slight modifications. Briefly, to eliminate reads that originate from chimeric cDNA generated during library preparation, we initially scanned reads for internal (> 200 nt from end) Template Switching Oligonucleotide (TSO, AAGCAGTGGTATCAACGCAGAGTACAT) and 3' adapter sequences (CTACACGACGCTCTTCCGATCT) flanked by a poly(T) (poly(T)-adapter). When two adjacent poly(T)-adapters, two TSOs or one TSO in proximity of a poly(T)-adapter were found, the read was split into two separate reads. Next the reads were scanned for poly(A/T) tails and the 3' adapter sequence to define the orientation of the read and strand-specificity. Scanned reads were then aligned to Mus musculus mm10 with minimap2 v2.
Count matrices and data analysis Raw gene expression matrices generated by Space Ranger were processed using R/Bioconductor (version 3. 5. 2) and the Seurat R package (version 3. 1. 4). Visualizations of spatial data were generated with the STUtility R package (version 1. 0. 0). We created Seurat objects for each of the three samples (MOB, CBS1 and CB2) with different assays for the analysis as follows: (i) "Spatial" containing genelevel raw short read data from the Space Ranger output, (ii) "ISOG" containing the gene-level nanopore long read data, (iii) "ISO" containing isoform-level transcript information where only the molecules where all exons are observed are kept, (iv) "JUNC" containing each individual exon-exon junction observation per isoform, and (v) "AtoI" containing editing sites from the RADAR database (mm9 UCSC liftover to mm10) and from the Licht study  [11] , for which we observed at least one UMI in our dataset.
Acknowledgements This project was supported by  Institut National contre le Cancer  ( PLBIO2018-156 ),  FRM  ( DEQ20180339158 ), the  Inserm Cross-cutting Scientific Program HuDeCA 2018 , the  National Infrastructure France Génomique  ( Commissariat aux Grands Investissements ,  ANR-10-INBS-09-03 ,  ANR-10-INBS-09-02 ), the  Swedish Research Council, Swedish Foundation for Strategic Research, Horizon2020 HCA discovAIR, Knut and Alice Wallenberg Foundation  ( 2018. 0172 ), Erling-Persson Family Foundation (HDCA) , and  Science for Life Laboratory. We would like to thank the  National Genomics Infrastructure (NGI) Sweden  for providing infrastructure support. We thank  Ludvig Bergenstråhle  and  Alma Andersson  for advice and helpful discussions.
Code availability All custom software used is available on Github (https://github. com/ucagenomix/sicelore). R figures and analysis scripts are available on Github (https://github. com/ucagenomix/SiT). Nanopore long reads. bam files are available within an interactive IGV web browser for visual inspection (https://www. genomique. info/SiT/). Seurat object. RDS files for the three samples are available on demand.
Adjustment of phenotypic data All phenotypic traits were independently adjusted for field microenviromental heterogeneity using the 'breedR' package  (Muñoz and Sanchez, 2022). Phenotypes were combined and adjusted by years. In the model, the genomic estimated breeding values (GEBV) for each trait were determined with the genomic best linear unbiased prediction based model (GBLUP)  (Whittaker et al. , 2000; Meuwissen et al. , 2001; Cantet et al. , 2005). Within trials, a random effect was fitted thanks to the use of the tensor product of two bisplines bases with a covariance structure for the random knot effects (RKE) to account for spatial variability along the row (r) and the column (c) of the field design  (Cantet et al. , 2005; Cappa and Cantet, 2007; Cappa et al. , 2015)  to capture the spatial heterogeneity at the plot level.
Genomic data analysis Genotyping Young leaves from individual plants were collected, ground in liquid nitrogen and total genomic DNA was isolated using the DNeasy Plant Mini Kit (QIAGEN Ltd, UK). DNA quality was checked by agarose gel electrophoresis and concentration was estimated using the QubitTM dsDNA BR Assay Kit (Invitrogen by ThermoFisher Scientific, UK) following the manufacturer's instructions. For genotyping we used the Vfaba_v2 Axiom SNP array with 50K SNP  (O'Sullivan et al. , 2019; Khazaei et al. , 2021). Seven of the 100 accessions showing poor DNA quality, as well as SNP markers with a call rate below 97% and a minor allele frequency (MAF) below 95% were excluded from the analysis. After quality control, a matrix consisting of 21,915 SNPs and 93 accessions with 0. 89% missing data was kept for further GWAS analysis.
The imputation of the missing data was performed by mean allelic frequency. To obtain the genetic position of the significant SNPs we used the information and protocols provided by  Skovbjerg et al. , 2022. Thus, 17,403  out of the 21,915 SNPs markers could be assigned to genomic positions. The extremely large size of chromosome 1 (> 3 Gbp) generated problems with various softwares and therefore the chromosome was split at the centromere to form Chr1S and Chr1L. In addition, to verify and complete the faba bean chromosomal positions, the SNPs flanking sequences were aligned to the V. faba reference genomee 1   (Jayakodi et al. , 2022)  using the Geneious v. 7. 1. 9genomee 2.
Genetic structure To estimate the number of distinct genetic clusters (K) and admixture existing in the faba bean panel a bayesian based clustering analysis was performed using FastSTRUCTURE v 1. 0  (Raj et al. , 2014). FastSTRUCTURE was run on default settings with 10-fold cross validation on the 100 accessions testing for subpopulations (K) values ranging from 1 to 10. The most likely K number was chosen by plotting the marginal likelihood of each model as a function of K and determining when the graph begins to plateau. Accessions with membership probabilities ≥ 0. 50 were considered to belong to the same group. The choice of K was further supported by applying a discriminant analysis of principal components (DAPC) based procedure for clustering using the 'fviz_pca' function in the 'factoextra' R-package  (Kassambara and Mundt, 2020).
Correlation and broad sense heritability estimates To understand the extent of the relationship among the traits, the correlation matrix for control and stress values was made by Pearson correlation analysis. Descriptive analysis and correlations were conducted in the R statistical software. The broad sense heritability (h 2 ) for the traits was estimated using the following formula: h2 = Vg=(Vg + Vsp + Vres) where V g is the genetic variance component, V sp is the spatial variance component, V res is the residual variance component. The genotypic mean value for each accession for each trait under control and stress conditions were represented by mean PCA biplot. PCA was performed in the R software package 'prcomp' and visualized with the 'fviz_pca' function.
Genome-wide association analysis Association analyses were performed in 93 faba bean accessions with 21,915 high-quality SNPs. A Multi Locus Mixed Model method (MLMM)  (Segura et al. , 2012)  was implemented in the R package 'mlmm. gwas' (Bonnafous et al. , 2019)  to evaluate the trait-SNP associations. The MLMM, is an iterative approach that improves power over single locus methods by incorporating multiple markers in the model simultaneously as covariates, to reduce the false-positive rates and to increase the detection power. In each step (maximum 10 steps), the variance components are estimated and then used to calculate p-values for the association of each SNP with the trait of interest. MLMM utilizes eBIC (extended Bayesian Information Criterion) to determine the number of steps and therefore the number of QTLs with a lambda value of 0.
Potential candidate gene The sequences flanking associated SNPs were blasted against the NCBI M. truncatula reference genome 3  to annotate potential candidate genes underlying the causal variants. Gene locations were determined using the Genome Data Viewer (GDV) 4. In addition, the sequences flanking associated SNPs were blasted against the faba bean reference genome  (Jayakodi et al. , 2022)  to verify chromosomal positions and locate those candidates that did not show significant hits in Medicago. For some of these, the use of the corresponding faba bean contig allowed to infer the Medicago ortholog and include the corresponding gene annotation.
Principal component analysis biplot The contribution of the various traits to the overall variation in the dataset was investigated by PCA (Figure  4 ). The first three principal components (PCs) with eigenvalues > 1 accounted for 74. 4% of the total variation (Supplementary Figure  1 ). Since the first two PCs showed the highest percentage of variance (62. 8%), the PCA biplot was constructed only with PC1 and PC2, showing a clear separation of control vs. drought stress data points along the main axis (Figure  4 ). PC1 explained 49. 1% of the total variability among traits or individuals and was mostly associated with SP, PP, PH, PY, SPAD2 and MAT (Figure  4 ). PC2 accounted for an additional 13. 7% of the total variability among traits and appeared to be related with HSW PRO and TSS (Figure  3 ).
( b ): In bold, loci associated with HSW, SPAD1 and PRO in both conditions. In red, significant loci associated to the traits that did not reach the Bonferroni threshold (p) > 5. 83. In italics, common loci associated with PP and SP traits. (c): Gene locations were determined using the Genome Data Viewer (GDV). ( b )In bold, loci associated with HSW in both conditions. In red, significant loci associated to the traits that did not reach the Bonferroni threshold (p) > 5. 83. ( c )Gene locations were determined using the Genome Data Viewer (GDV).
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 Programme for Research & Innovation  under grant agreement  n°727312  ( EUCLEG Project ) and  RTA2020-007  cofinanced by  ERDF. We thank the seed companies and institutes that provided us with faba bean seeds of their accessions:  INRA, Dijon (Jean-Bernard Magnin-Robert ),  ICARDA  ( Fouad Maalouf ) and IFAPA. The technical staff of IFAPA, centro  Alameda del Obispo , is thanked for their deep investment to multiply accessions and collect leaves for genotyping. The authors are grateful to  Dr. A. Di Pietro  for critical reading of the manuscript.
Image analysis and detection algorithm Calcium imaging allows for the visualization of neuronal activity within the neuronal network. In general, segmentation of cells inside a field of view and analysis of fluorescent traces were made with Calima software  [10]. For the flight experiments, some parts of the video were too noisy to be analyzed due to an unwanted motion of the microfluidic chip. Thus, a dedicated correction algorithm was applied, based on the Lucas-Kanade method using the optical flow functions of the OpenCv library on Python. This part is described in more details in section 3. 1. 2.
In the present study we used the optical flow functions of the OpenCv library on Python  [15]. First step was to detect suitable points for the tracking algorithm, in order to follow their movement. In our case, this first step was very efficient thanks to the image resolution and the shape of the objects, which made it possible to have a very good measurement of movement. The second step consisted in tracking the objects and deducing the speed associated with the movement. A typical example is shown on Fig. 3  where the undesired motion induced by the vibrations of the experimental setup was measured. This allowed to stabilize the snapshots by substracting the measured displacements to the time-series.
Acknowledgments. This work was supported by  CNES , topics " Sciences de la Vie dans l'Espace" and  " Sciences de la matière"  ( APR   CNES   2022 ). The experiments were carried out during the  CNES Parabolic  Flights VP164 campaign. We also thank the  Novespace team , and especially  Alexandra Jaquemet , for helping us to properly take into account the many constraints imposed by zero-g flights, making these experiments possible. We warmly thank  Aude Panatier  and the  Neurocentre Magendie (INSERM, University of Bordeaux)  who allowed us to access their laboratory in order to prepare and cultivate our cells on site. We would also like to thank the PMMH engineers especialy  Amaury Fourgeaud  as well as  Leïla Godinaud  from the  CEA  for her help to track neurons.
29 mm). As 3D correlation (stereo-correlation) is performed after a calibration phase using a calibration chart, the tilt angles are not an issue. U and V displacement have been recalculated in a coordinate system associated with the specimen. W displacement is calculated with a mean Z plane as a reference. to combine stereo-correlation data of the same object viewed from different perspectives, but the current study only performed stereo-correlation with a classic local subset method, using the commercial software VIC-3D-7. Figure  22  shows the main fields of interest obtained by stereo-correlation from Camera Pair #1 (Figure  21 ) on most of the skin and the stiffener flange. The U displacement field represents the useful displacement field of compression, while the Z position field allows for the observation of the buckling mode settled.
The strain field was observed on the top surface of the sample, which is a combination of the membrane strain (generated by the useful compression) and the bending strain (generated by the buckles bending). This configuration was interesting for the development of ongoing research aiming to combine stereo-correlation data of the same object viewed from different perspectives, but the current study only performed stereo-correlation with a classic local subset method, using the commercial software VIC-3D-7. Figure  22  shows the main fields of interest obtained by stereo-correlation from Camera Pair #1 (Figure  21 ) on most of the skin and the stiffener flange. The U displacement field represents the useful displacement field of compression, while the Z position field allows for the observation of the buckling mode settled.
Introduction -Making Use of Implicit Complexity Certification of any program is incomplete if it ignores resource considerations, as runtime failure will occur if usage exceeds available capacity. To address this deficiency, automatic complexity analysis produced many different implementations  [9, 13, 14, 15]  with varying features. This paper presents the development and specificities of our automatic static complexity analyzer, pymwp. The first original dimension of our tool is its inspiration, coming from Implicit Computational Complexity (ICC)  [10]. This field designs systems guaranteeing program's runtime resource usage that tend to possess practically useful properties. For this reason, it is conjectured that ICC systems could be used to achieve realistic complexity analysis  [18, p.
16]. Our series of work  [5, 6]  is testing this hypothesis, and resulted in the tool we present in this paper: pymwp is one of the first ICC-inspired applications, and the first mechanization of the specific technique it implements. Let us first exemplify what pymwp calculates. Example 1. Consider an imperative program with a fixed number of parameters: void i n c r e a s i n g ( int X1 , int X2 , int X3 ) { while ( X2 < X1 ) { X2 = X1 + X1 ; } while ( X3 < X2 ) { X3 = X2 + X2 ; } } Independently of the arguments passed (henceforth called initial values), once computation concludes, X1 will hold the same value, but the values held by X2 and X3 may have changed. By manual analysis, we can deduce that the variable values "growth bound" between the initial values X1, X2 and X3 (overloading initial values and parameter names) and their final values (denoted X1', X2' and X3'), omitting constants, is X1' = X1, X2' ≤ max(X1, X2) and X3' ≤ max(X3, X2 + X1).
4  Therefore, for all initial values, the value growth of the variable's value is bounded by a polynomial w. r. t. its initial values. Our analysis is designed to either produce such bounds, or to pinpoint variables that grow exponentially. Introducing more variables, or potentially non-terminating iteration, or complicating the logic would make manual analysis difficult. However, our static analyzer handles all those cases automatically. It determines if a program accepts at least one polynomial bounding the final value of its variables in terms of their initial values-what we call its growth bound. If a bound cannot be established, it provides feedback on sources of failure, identifying variable pairs that have "too strong" dependencies. The technique is sound  [16, p. 11] , meaning a positive result guarantees program has satisfactory value growth behavior at runtime.