dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Analysis Using Normative Modeling All statistical data analyses were performed using Python 3. 7  (58)  [numpy 1. 17. 2 (59, 60) and scipy 1. 3. 1  (61) ]. Normative modeling (NM) provides a metric similar to a z score, but accounts for the underlying structure of the population across multiple covariates. NM uses Gaussian processes (GP) to model the distribution of control group measures while estimating separately the overall trajectory in the covariate space, the heterogeneity in the population, and the uncertainty of the fit  (62). The Python code is available in open access at https://github. com/GHFC/SoNeTAA/.
FUNDING This work was supported by the funding of the  Institut Pasteur , the  INSERM , the  Fondation FondaMental , the  APHP , the  DHU Protect , the  Fondation Bettencourt-Schueller , the  Fondation Cognacq Jay , the  Fondation Conny-Maeva , the  Fondation de France , and the  Labex BioPsy. FB was funded by the  Fondation FondaMental  and the  Congrès Français de Psychiatrie. GD is funded by the  Institute for Data Valorization (IVADO) ,  Montreal , and the  Fonds de recherche du Québec (FRQ). JK is funded by  NIMH  grant  MH080838.
Participants This analysis utilized data from Bulgarian adults (N = 1013, aged 18 years and over), obtained between April and July 2018 for a multinational (Argentina, Australia, Bulgaria, Canada, Denmark, France, Germany, Mexico, Singapore, Spain, the UK, and the US) Internet-based randomized experiment on FOPL, described in detail elsewhere  [15]. Briefly, the same ISO-accredited international web panel provider called PureProfile was used for the recruitment of participants across the 12 countries. PureProfile (https://business. pureprofile. com/) is headquartered in New South Wales, Australia, and is one of the web's oldest survey panel providers, working with brands, publishers and research groups worldwide. It employs direct-to-consumer technology platforms and recruits survey participants via online and mass media advertising and word-of-mouth referrals.
Covariates Upon enrollment in the trial, participants completed a short online questionnaire, providing information about their age, sex, household income, educational level (up to high school, trade certification or equivalent, undergraduate degree, graduate degree), presence of children < 14 years in the household, grocery shopping responsibility (yes, no, shared), knowledge about nutrition (none, very limited, average, extensive) and perceived quality of their own diet (very unhealthy, mostly unhealthy, mostly healthy, very healthy). Self-reported annual household income in Bulgarian leva was assigned to one of three categories (low, medium, high) using 2017 data on the Bulgarian median income (3590 €, exchange rate = 1. 735) from the World Income Inequality Database  [27].
Bioinformatics analysis The quality of the data was assessed using FastQC from the Babraham Institute. Potential contaminants were investigated with the FastQ Screen software  [32]  from the Babraham Institute. RNA-seq 50 nt reads were aligned to the Ae. aegypti assembly (AaegL5. 0) with a set of gene model annotations (GCF_002204515. 2_AaegL5. 0_ genomic. gff downloaded from NCBI on 10 July 2018), using the splice junction mapper TopHat v2. 1. 1  [33] , which used bowtie 2. 2. 8  [34]. Final read alignments having more than 3 mismatches were discarded. Gene counting was performed using HTSeq-count  [35]  version 0. 9. 0 (union mode). Since data come from a strand-specific assay, the read has to be mapped to the opposite strand of the gene. Before statistical analysis, genes with less than 15 reads (cumulating all the analysed samples) were filtered out.
Dataset was then transformed in log CPM (counts per million) and a trimmed mean of M-values normalization was used to correct for libraries size effect. Description of the samples are present in https ://githu b. com/loire /CCS_RNAse q_analy sis. A multidimensional scaling (MDS) was used to represent the bray-curtis distances calculated between all replicates and samples on the 100 most expressed genes.
Statistical analysis The following steps were followed: (i) fit a quasi-likelihood negative binomial generalized log-linear model to count data; genewise statistical tests were conducted for a given coefficient or contrast (glmQLfit) (quasi-likelihood (QL) F-test against the FC threshold); (ii) genewise statistical tests were conducted for a given coefficient or contrast relative to a specified fold-change threshold (here a 2-fold change in expression threshold) (function glmTreat); (iii) differentially expressed (DE) genes were identified using the Bioconductor  [36]  package edgeR 3. 20. 1  [37]. Data were normalized using the relative log expression (RLE)  [38]  normalization factors. Genes with adjusted P-value less than 5% (according to the FDR, Benjamini-Hochberg method) were declared differentially expressed.
High quality of mRNA sequence dataset generated To obtain a broad picture of the host response to DENV and RVFV infections, RNA-Seq was used to analyze differential gene expression at the mRNA level. The mRNA was isolated and purified from Mock, RVFV and DENV infected cells at 1 and 6 days post-infection to assess for early and late detection genes expression. High-throughput sequencing generated an average of 11,021,430 million reads per sample. Approximately 94. 63% of the reads were mapped to the Ae. aegypti genome (GCF_002204515. 2_AaegL5. 0_genomic. gff, [39] ) and there were anywhere between 7028 and 9565 genes expressed per replicate at each time point. A total of 9580 genes expressed above 0. 5 count per million in at least 3 samples were subsequently analyzed.
Dataset exploration Selected samples exhibit a good homogeneity among samples after normalization. A nice dataset, with replicates well grouped and a net separation of groups of samples was observed. One of the RVF replicate at 24 h post-infection was discarded due to its failure to the quality control check (library size < 5 millions of reads) (Fig. 1 ). The first dimension separates mock infection from viral infection, and the second dimension separates early (24 h post-infection) and late (6 days post-infection) responses. Additionally, late responses to viral and mock infections are similar as well as early responses to viral and mock infections, indicating the possibility to conduct a direct comparison between them to search for common differential expression of genes in response to both viruses.
Funding This study was funded by  INTERREG FEDER   TROI 2015-2017  under the  DP One Health Indian Ocean  (http://www. onehealth-oi. org)and the project  MOM (Ministère des Outre-Mer ,  2013-2014 ). MGX acknowledges financial support from  France Génomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ).
Overall, in the experiments undertaken in this paper, the proposed method achieved a mean overall accuracy of 97. 27% for cloud detection, with a mean precision of 96. 02% and a mean recall of 95. 86%. For cloud shadow detection, the proposed method achieved a mean precision of 95. 92% and a mean recall of 92. 86%. Experimental results on two validation datasets (GF-1 WFV validation data and ZY-3 validation data) show that the proposed multiscale-3D-CNN method achieved good performance with limited spectral ranges.
In this paper, an end-to-end multiscale-3D-CNN architecture is proposed for cloud and cloud shadow detection in high resolution multispectral imagery. The proposed method enjoys the benefit from end-to-end deep learning and the performance of the proposed method is not dependent on superpixels segmentation accuracy. Specifically, the multiscale learning module is designed to extract cloud and cloud shadow contextual information of different levels. In order to fully explore the joint spatial-spectral correlations feature, a spectral-spatial information of 3D-convolution layer is developed for high resolution multispectral imagery. Experimental results on two validation datasets (GF-1 WFV validation data and ZY-3 validation data) show that the proposed multiscale 3D-CNN model achieves good performance and it does not require additional superpixels segmentation processing.
III. EXPERIMENT AND ANALYSIS A. DATA SET AND TRAINING In order to evaluate the proposed multiscale-3D-CNN model and examine at its performance under the condition of limited spectral range, we use the two validation datasets (GF-1 WFV validation data and ZY-3 validation data). In order to make full use of information, the four band-combination images are inputted into the proposed multiscale-3D-CNN. The truth cloud and cloud shadows (reference images) are obtained by manually marking on the ENVI software platform. The FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) method is used for GF-1 WFV and ZY-3 images. The radiometric calibration coefficient of GF-1 WFV and ZY-3 images FLAASH atmospheric correction can be downloaded from http://www. cresda. com/CN/Downloads /dbcs/index.
C. COMPARISON WITH OTHER METHODS In this paper, in order to verify the performance of the proposed method, we compared the proposed method to the MFC method  [9]  (http://sendimage. whu. edu. cn/en/mfc/), Deeplab method  [20]  (https://github. com/ rishizek/ /tensorflowdeeplab-v3), and PSPnet method  [21]  (https://hszhao. github. io/projects/pspnet/). We propose multiscale 3D-CNN to learn the multilevel semantic information of various clouds and cloud shadows. The MFC method is a good performance traditional threshold cloud detection method. The MFC method is  based on spectral information similarity to detect each pixel. Both the Deeplab and the PSPNet are good performed deep semantic segmentation networks for natural images. Fig. 5  shows some of cloud and cloud shadow detection results of example images generated by different methods on GF-1 WFV validation data.
In order to make full use of band information, the four band-combination images are inputted into the multiscale 3D-CNN. In addition, the feature fusion module helps to handle features of different levels. Experimental results on two validation datasets (GF-1 WFV validation data and ZY-3 validation data) show that the proposed multiscale 3D-CNN model achieves a high accuracy with limited spectral information and it does not require additional superpixels segmentation processing. In the future work, we will explore the possibility of using our multiscale-3D-CNN model to detect cloud and cloud shadow from different types of sensor images (such as Sentinel-2, Landsat, and Quickbird).
black box), 256+3D-CNN (Fig. 3red box), 128+3D-CNN (Fig. 3blue box), and FCN-8 (https://github. com/shelhamer/fcn. berkeleyvision. org). FCN-8 is a well-known FCN, where the output scoremap is 1/8 × 1/8 size of the input image [40]. To visually compare the cloud and cloud shadow detection results, Fig. 4shows some cloud and cloud shadow detection results of example images generated by different CNN structure on ZY-3 WFV validation data. Fig. 4(g) shows ground truth (red region are cloud and gray region represents cloud shadow).
The mutation (G / T) results in a stop codon (TAG) after 133 amino acids, resulting in a truncated MreB5 protein in ASP-I cells instead of the 352-amino-acid wild-type MreB5 protein  (38. 5 kDa). Whole-genome sequencing of ASP-I strain (Genome sequence SRA: PRJNA635252) confirmed the point mutation in mreB5 and the absence of additional mreB sequences other than mreB1-5 in the genome. Further, we performed mass spectrometry analysis of trypsinized samples of the cytosolic and membrane/Triton X-114 solubilized fractions from the two cell types. The peptide fragments of all MreBs, including MreB5, were found in both cytosolic and membrane-enriched fractions of wild-type (GII-3) cells (Figure  1G ). However, no MreB5 peptides were detected in the cytosolic fractions of ASP-I cells although two peptides corresponding to a 4.
35 ± 0. 62 mm) was shorter than that of GII-3 (6. 21 ± 2. 02 mm) and ASP-I MreB5 (5. 26 ± 2. 37 mm; Figure  2E ). In late exponential phase, the mean cell lengths observed were 2. 77 ± 0. 81 mm (ASP-I), 7. 92 ± 2. 34 mm (GII-3), and 5. 24 ± 2. 61 mm (ASP-I MreB5 ; Figure  2E ), demonstrating that ASP-I cells, in the absence of MreB5, grew to shorter lengths compared to GII-3 and ASP-I MreB5. It is noteworthy that none of the other four MreBs could compensate for the absence of MreB5 for retaining helicity and motility in ASP-I cells. This shows that at least a few of the MreBs may not be redundant in function and demonstrates the plausible existence of ''division of labor'' among the multiple MreB paralogs in Spiroplasma. Analysis of all Spiroplasma genomes in the database revealed that they encode a minimum of 5 and maximum of 8 paralogs of mreB gene (Figure  3A ).
These findings indicate that each MreB might be expressed independently and at different levels. To understand the structural features of MreB from a cell-walldeficient bacteria, we determined the crystal structure of Spiroplasma citri MreB5 (a construct with a C-terminal hexahistidine tag denoted as MreB5-His 6 ; PDB: 7BVY; Figure  4A ; Table  S2 ). The crystal packing showed a single protofilament interface for MreB5-His 6 , conserved among other structurally characterized MreBs from cell-walled organisms (e. g. , PDB: 1JCE of Thermotoga maritima MreB). Electron microscopy observations suggest that MreB5-His 6 forms antiparallel double protofilaments, a characteristic of MreBs  [30, 31]  (Figures  4B  and 4C ). A double protofilament structure generated by superposition of MreB5 monomers on Caulobacter crescentus MreB (PDB: 4CZE; Figure  4C ) clearly shows that the projection in the 2D class averages corresponds to the antiparallel double protofilament organization.
ImageJ 1. 52p (USA)  [52]  was used for the measurement of cell contour (L1) and the shortest distance between two points along cell body length (L2) from electron microscopy images. The cell length refers to the contour length L1 at all instances in the text, while cell width is the extent of cells along a cross section approximately perpendicular to the cell contour line. The data is expressed as the mean ± standard deviation (SD). Statistical significance was estimated by the unpaired t test, two tailed. GraphPad Prism 5. 00 for Windows (GraphPad Software, San Diego California USA, https://www. graphpad. com:443/) was used for statistical analysis of the data and plotting the graphs.
Whole genome sequencing Whole genome sequencing of S. citri ASP-I was performed at the Genome Transcriptome facility of Bordeaux (https://pgtb. cgfb. u-bordeaux. fr/) using a combination of Oxford Nanopore (GridION sequencer) and paired-end 250 bp Illumina (MiSeq sequencer) reads. The genome was assembled into 4 main contigs using both short and long reads and the Unicycler v0. 4. 6 pipeline (PMID: 28594827). One contig of 7999 bp corresponded to the previously described plasmid pSciA (GenBank accession AJ966734. 1). The three chromosomal contigs came up to a total of 1493377 bp.
nLC-MS/MS analysis Peptide mixture was analyzed on a Ultimate 3000 nanoLC system (Dionex, Amsterdam, the Netherlands) coupled to a Electrospray Orbitrap Fusion Lumos Tribrid Mass Spectrometer (Thermo Fisher Scientific, San Jose, CA). 10 mL of peptide digests were loaded onto a 300 mm (inner diameter) x 5 mm C18 PepMapTM trap column (LC Packings) at a flow rate of 10 mL/min. The peptides were eluted from the trap column onto an analytical 75 mm (inner diameter) x 50 cm C18 Pep-Map column (LC Packings) with a 4 -40% linear gradient of solvent B in 45 min (solvent A was 0. 1% formic acid and solvent B was 0. 1% formic acid in 80% ACN). The separation flow rate was set to 300 nL/min. The mass spectrometer operated in positive ion mode at a 1. 9 kV needle voltage. Data were acquired using Xcalibur 4.
Database search and results processing Data were searched by SEQUEST through Proteome Discoverer 2. 3 (Thermo Fisher Scientific Inc. ) against homemade protein databases consisting of products of S. citri ASP-I and GII-3 CDS (Chromatography Data System). Spectra from peptides higher than 5000 dalton (Da) or lower than 350 Da were rejected. The search parameters were as follows: mass accuracy of the monoisotopic peptide precursor and peptide fragments was set to 10 parts per million (ppm) and 0. 6 Da respectively. Only b-and y-ions were considered for mass calculation. Oxidation of methionines (+ 16 Da) and protein N-terminal modifications (Acetylation + 42 Da) were considered as variable modifications and carbamidomethylation of cysteines (+ 57 Da) as fixed modifications. Two missed trypsin cleavages were allowed.
The Nikon oil immersion microscope objective was a 60X with a numerical aperture (N. A. ) of 0. 80. Pictures were taken with a camera Nikon Digital Sight DS-Qi1Mc (1280 3 1024 pixels). Motility was favored by increasing the viscosity of the medium following the protocol described in  [71] : during the exponential growth phase of the bacteria, one volume of the culture was diluted with one volume of methyl cellulose dissolved in modified SP4. Here, 1% (w/v) methyl cellulose [from Sigma (M7027), Molecular weight 14000 g/mol] was used to adjust the viscosity of the solutions. Bacterial solutions were prepared between two sealed microscope slides, with a liquid thickness of 15 mm. Videos were recorded at a frame rate of 10 frames per second (fps) using the software NIS-Elements Br  (Nikon). Spiroplasma cell velocities were estimated from videos using the 'Manual Tracking' plugin in ImageJ  [52].
Similarly, cell lengths and widths were measured from images using ImageJ. The data is expressed as the mean ± standard deviation (SD). Statistical significance was estimated by the unpaired t test, two tailed. GraphPad Prism 5. 00 for Windows (GraphPad Software, San Diego California USA, https:// www. graphpad. com:443/) was used for statistical analysis of the data and plotting the graphs. MreB sequences analysis and classification mreB gene and protein sequences were obtained from NCBI (https://www. ncbi. nlm. nih. gov/). The genes from each species were labeled in order of their appearance in the genome with respect to dnaA, with the closest mreB at the 3 0 of dnaA being assigned number 1. All the MreB protein sequences were aligned using Clustal Omega  [23]  with default settings. A phylogenetic tree was prepared using all the aligned MreB sequences with the help of NGPhylogeny  [21]  using default settings and visualized using IToL  [22].
Identification of mreB promoter regions Nucleotide sequence corresponding to 100 basepairs (bp) region upstream (before the 5 0 end) of each mreB gene of Spiroplasma species under consideration was obtained from NCBI. The À35 and À10 elements from 100 bp regions of all mreBs were identified using BPROM software  [24]. The identified À35 and À10 sequences for all MreBs from a cluster (grouped on the basis of phylogenetic tree) across species were used for generating a Weblogo using WebLogo 3  [54].
(C) A view of the double protofilament structure of MreB5-His 6 , modeled based on Caulobacter crescentus MreB (PDB: 4CZE), matches with the projection view of the 2D class averages in (B). The subdomains are color-coded as in (A). The view corresponds to an orientation with 1A and 1B subdomains behind the plane of 2A and 2B subdomains. The orientation of the protofilaments is highlighted by the black arrows, marked according to the convention in (A). (D) A representative 12% SDS-PAGE gel showing sedimentation assays of untagged fibril, MreB5-His 6 , and MreB5-His 6 in presence of fibril (untagged). I, P, and S, respectively, represent input, pellet, and supernatant fractions upon sedimentation at 159,000 3 g. Red and blue * symbols show the full-length bands of fibril and MreB5, respectively, and the lower bands are the impurity or degradation bands obtained during fibril purification.
ACKNOWLEDGMENTS Work in the lab of P. G. is supported by funds from  Department of Science and Technology INSPIRE Faculty Fellowship  ( IFA12/LSBM-52 ),  Innovative Young Biotechnologist Award  ( BT/07/IYBA/2013 ),  Department of Biotechnology Membrane Structural Biology Programme  grant ( BT/PR28833/BRB/10/1705/ 2018 ), and  IISER Pune. Common facilities at  IISER Pune  and the proteome and genome-transcriptome platforms of  University of Bordeaux , France are acknowledged. We thank  Pascal Sirand-Pugnet  and  Vincent Baby  at  INRAE, Bordeaux, France and Charlotte Mouden at the Genome Transcriptome facility (PGTB), Bordeaux, France  for their help in handling genomic data. Help received from  K. R. Vinothkumar  ( NCBS, Bangalore, India ) and  Mamta Bangera  ( InStem, Bangalore, India ),  Piotr Szwedziak  and  Martin Pilhofer  ( ETH, Zu ¨rich ), and  Armel Bezault  ( IECB, Bordeaux ) for processing of electron microscopy samples at  National Electron Cryomicroscopy Facility, Bangalore Life Sciences Cluster, India ; ScopeM, ETH, Zu ¨rich; and  IECB, Bordeaux , respectively, and access to the facilities is acknowledged.
S. citri ASP-I whole genome This paper  Bioproject PRJNA635252 Experimental Models: Organisms/Strains S. citri GII-3 Joe ¨l Renaudin , [48] DSMZ 21846 S. citri  ASP-I Joe ¨l Renaudin , [18] N/A Oligonucleotides Oligonucleotides used in this work This work See Table S3.
| Patients Patients referred for molecular genetic testing for suspected NLS/ severe serine biosynthesis disorder or with a diagnosis of such a disease established by whole exome sequencing (WES) were eligible for this study. All molecular testing was performed in a diagnostic context after obtaining informed consent according to the respective national regulations for genetic testing. Specific written parental permission was obtained for publication of photographs presented in this article. The study cohort included a total of 19 patients, 10 females and 9 males, from 15 unrelated families. Three of these families had a history of another probably affected offspring, but no details were available from these. The clinical data of all patients were collected using a questionnaire. Families were of various ethnic origin (Table  1 ).
| Molecular analysis DNA was extracted from blood samples of the patients, except for families 4 and 11, where only FFPE (formalin-fixed, paraffinembedded) tissue from the affected fetus was available. From three families, DNA samples of affected fetuses (patients 5a/5b, 12, and 14) were not available, and therefore parental DNA was tested assuming heterozygous carrier status (Table  1 ). In family 5, a chorionic villus sample DNA (CVS DNA) was analyzed to provide prenatal diagnosis in a family with a previous child clinically diagnosed with NLS. In the majority of samples, targeted Sanger sequencing was performed for all coding exons and flanking intronic regions of the known causative genes for NLS (PHGDH [ENST00000369409]; PSAT1 [ENST00000376588]; PSPH [ENST00000395471]). Sequence data were generated using the ABI BigDye Terminator Cycle Sequencing Kit (Applied Biosystems, Germany) and an automated capillary sequencer (ABI 3500; Applied Biosystems), and Sequence Pilot software (JSI medical systems, Germany) was used for analysis.
Mutated protein stability was also analyzed in silico using an integrated predictor for protein stability change upon single mutation (http://predictor. nchu. edu. tw/ istable/). The level of evidence for pathogenicity of all observed variants was classification according to the recommendations of the American College of Medical Genetics and Genomics (ACMG;  Richards et al. , 2015). All the variants were submitted to the genetic Leiden Open Variation Database, LOVD v. 3. 0 Build 23 (https://www. lovd. nl/).
| Structural analysis and molecular modeling The structural analysis of the protein variants was performed based on the crystal structures of PHGDH (PDB code: 2G76) and PSAT1 (PDB code: 3E77). Missense changes were modeled with SwissModel  (Guex & Peitsch, 1997)  and RasMol  (Sayle & Milner-White, 1995)  was used for structure analysis and visualization.