dataesr/scientific-paragraphs-categorization huggingface dataset:
MLST Typing and SNP Detection To understand the genetic diversity of all the isolates multi-locus sequence typing (MLST) was performed using BioNumerics 1. The MLST method is known to have a higher discriminatory power for S. aureus strains than PFGE  (Peacock et al. , 2002a). For classical MLST typing seven housekeeping genes and their various alleles were used to define strain relatedness 2   (Jolley et al. , 2018). A phylogenetic tree was constructed by executing the Linux based stand-alone source code of kSNP3  (Gardner et al. , 2015) , which identified core genome SNP counts and provided a consensus parsimony phylogenetic tree. The kmer size was set to 19, the optimum size estimated by the kSNP3 utility program Kchooser  (Gardner et al. , 2015). Pairwise SNP distances between later stage isolates as compared to early stage isolates from each individual were calculated to define mutation over time.
Resistance and Virulence Gene Identification All the genomes were screened for the presence of 40 known and putative virulence genes 3   (Shukla et al. , 2010)  (enterotoxin genes, exotoxin genes, leucocidin genes, hemolysin genes, surface protein genes, and putative virulence genes) and the S. aureus antibiotic resistance genes available in the ResFinder database 4. Those 40 genes are grouped as classical staphylococcal. The Linux-based command line tool known as Abricate was downloaded 5  to perform additional mass screening for antimicrobial resistance or virulence genes. All the identified resistance and virulence genes in the dataset were summarized in Supplementary Table  3. Additionally, in silico-based mapping of the scn gene using BioNumerics was carried out to determine the presence of IEC  (van Wamel et al.
Quality Testing of Genome Datasets Genome sizes varied from 2,647 to 2,827 Kilo base (kb). The average number of contigs generated per genome was 64 contigs (ranging from 40 to 315 contigs). The average N50 contig length was 171778 bp (Supplementary Table  2 ). Isolates (and hence their genomes) from a single individual are expected to be part of a single clade as predicted by the MLST data and phylogenetic clustering (Figures  1, 2 ). (2007: 17. 60%; 2010: 30%)  were found to be the dominant MLST types followed by ST8 and ST5 (18% each) in 2007 and ST22 and ST7 (20% each) in the 2010 subgroups (Table  1 ). Over the period of 3 years some of the strains were replaced by different sequence type strains within a same carrier. For instance isolates from carrier B and D in 2007 were ST30 and ST8 but in 2010, isolates from the same carries were ST7 and ST22, respectively.
Mutational Analyses of Strains From Artificially Colonized Humans All strains were of ST8. No considerable identity was observed with resistance genes (Supplementary Table  3 ) from the database which was in agreement with the pan-susceptibility of the isolates. The overall core genome SNP counts among the isolates ranged from 0 to 7 SNPs (average 2) (Figure  5 ). The maximum range of pairwise SNP distances between the isolates within a host was 0 to 5 SNPs (median SNP distance 2) after 28 days of colonization in S. aureus nasal carriers (Table  2 ). Fourteen virulence genes  (sea, hla, hlb, hld, hlgB, clfA, clfB, fnbA, fnbB, icaA, sdrC, sdrD, sdrE, and tsst-1)  were identified in the current sequence dataset (Supplementary Table  3 ). The virulence factor fnbA was not found in isolates from host B and was also missing in one of the isolates from carrier F (1410060).
On the other hand, several authors reported a log K′Cu-L value for phytoplankton between 10 and 13  (Leal et al. , 1999; Soldo et al. , 2005; Vasconselos et al. , 2002; Muller et al. , 2005). The biofilm was grown with a Cu concentration corresponding to 1 % growth inhibition (Supplement D). Because our experiments were performed without added Cu in solution, the exudates produced by the biofilm correspond to a low-stress condition. The log K′Cu-L of Cu-DOM complexes used in the vMINTEQ simulation was fixed at 6. 0 and 12 in order to test 2 extreme scenarios thus encompassing a wide range of possible aquatic environments (Supplement E). Simulations using the software vMINTEQ were performed to determine the speciation of.
Acknowledgments: We are thankful to two anonymous reviewers who contributed to the manuscript improvement through their constructive comments. Support from the  GAGILAU grant of Midi-Pyrenean Regional Council, France  (No  DAER-R9390173 ),  MiPy  project of the  CTP  (No  12051637 ) and  SOIL-TAKE-CARE EC  grant are acknowledged. We thank  Jasmine Berg  for her editorial advice and English corrections.
Bioinformatics Base-calling was performed by using the Real-Time Analysis software sequence pipeline (2. 7. 7) with default parameters. Sequence reads were mapped onto the human genome build (hg38/GRCh38) by using the Burrows-Wheeler Aligner (BWA) tool. Duplicated reads (e. g. paired-end reads in which the insert DNA molecule showed identical start and end locations in the human genome) were removed using sambamba tools. The Whole Exome Sequencing metrics are provided in Table  S1. Variant calling, allowing the identification of SNV (Single Nucleotide Variations) and small insertions/deletions (up to 20 bp) was performed via the Broad Institute GATK Haplotype Caller GVCF tool (3. 7) for constitutional DNA. Ensembl's VEP (Variant Effect Predictor, release 87) program was used for initial variant annotation.
This tool takes into account data available in dbSNP (dbSNP147), the 1000 Genomes Project (1000G_phase3), the Exome Variant Server (ESP6500SI-V2-SSA137), the Exome Aggregation Consortium (ExAC r3. 0), and IntegraGen inhouse databases. For missense changes, two bioinformatic predictions for pathogenicity are available: SIFT (SIFT. 2. 2), PolyPhen (2. 2. 2). Other information like quality score, homozygote/heterozygote status, count of variant allele reads, presence of the variant in the COSMIC database (version79) are reported. Additional annotation data was retrieved using dbNSFP (version 3. 4, https://sites. google. com/site/jpopgen/ dbNSFP) and Varsome (https://varsome. com/). Minor allele frequencies were manually verified on GnomAD (http://gnomad. broadinstitute. org), ISB Kaviar (http://db.
systemsbiology. net/kaviar/), and Great Middle Eastern variant database GME Variome: (http://igm. ucsd. edu/ gme/). Variant filtering was performed on the following criteria: minimum depth at variant position of 5, correct segregation in the family, on the basis of homozygosity by descent: variants should be homozygous in both affected sisters III-10 and III-11, heterozygous in the mother II-8 (obligate carrier), heterozygous or homozygous for Reference allele in the fertile sister III-13, absence in unrelated in-house fertile controls, -Minor Allele Frequency (MAF) below 1% both in global and in each population in the GnomAD database, presence in the coding sequence (i. e not in UTRs, introns, intergenic, …) high predicted functional impact on the protein. Impact was evaluated based on the nine predictors included in dbNSFP  [14] [15] [16]  plus M-CAP  [17]  (considered as pathogenic when the majority of the predictors agreed).
Cell culture and minigene assays HeLa cells were used for minigene assays (CLS Cat# 300194/ p772_HeLa, RRID:CVCL_0030). Cells were grown in DMEM-F12 medium (Gibco®, Life Technologies, Grand Island, NY, USA), supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin (Invitrogen-Gibco, Life Technologies, Grand Island, NY, USA). Cells were seeded 16 h before transfection to be at 80% of confluence at the time of transfection and transfected with 6250 ng of total DNA per well (6-well plates) using the calcium phosphate method  [19]  and rinsed 24 h after transfection. Forty-eight hours after transfection, cells were washed with phosphate-buffered saline before mRNA extraction with TRI Reagent® (MRC, Euromedex) according to the manufacturer's instructions. cDNA synthesis was performed using M-MLV (Invitrogen) and random hexamers.
Expression analysis Nested PCR was used to evaluate the expression profile of the wildtype and mutated transcripts. PCR were carried out with two nested pairs of primers to amplify the MEIOB cDNA between exons 11 and 14, expected to amplify 501 bp (external PCR, wild-type allele) and 386 bp (nested PCR, wild-type allele, see Table  S3 ). Amplification was performed for 40 cycles, 94 °C for 30 s, 55 °C for 45 s and 72 °C for 1 min. PCR products were verified by Sanger sequencing. Longer fragments of the sequencing chromatograms are displayed in Fig. S2. 2. 6. Biochemical assays 2. 6. 1. Cell culture and plasmid transfections Cells were cultivated in a 5% CO 2 humidified incubator at 37 °C. Human embryonic kidney cells (HEK-293, ATCC, CLS Cat# 300192, RRID:CVCL_0045) and HeLa cells were grown in DMEM supplemented with 10% FBS (Gibco).
Before staining, cells were blocked with PBS-S/0. 1% Tween 20 (PBS-S-T) containing 5% BSA. Cells were incubated with primary antibodies (Table  S4 ) in PBS-S-T/5% BSA and then washed with PBS-S-T and incubated with appropriate fluorescent dye-coupled secondary antibody. After washes in PBS-S-T, coverslips were mounted on glass slides using ProLong® Gold antifade reagent with DAPI (Life Technologies). Imaging was performed using an AX70 epi-fluorescence microscope (Olympus) equipped with a Photometrics CoolSNAP MYO CCD camera. Images were analysed using ImageJ software.
Variant calling identified over 100,000 variants per sample (Tables  S1-S2 ). As it is frequent in consanguineous families, a recessive pathology can emerge because of identity by descent: the children of a consanguineous marriage inherit two copies of an ancestral variant, one through the maternal branch and the other through the paternal branch. Therefore, we filtered variants on the basis of their homozygosity in both patients, heterozygosity in the mother, heterozygosity or absence in the nonaffected sister and absence in unrelated fertile in-house controls. Furthermore, we kept variants with a possible impact on the coding sequence and a minor allele frequency (MAF) b 0. 01 in databases such as GnomAD. Further filtering on available functional data regarding a possible role in reproduction and fertility yielded only one plausible candidate variant (Table  S2 ).
This variant is rs1010446295 in MEIOB (annotated as chr16:g. 1839255CNT (hg38), NM_001163560. 2: c. 1218GNA, Fig. 2a ), which is predicted to alter splicing of exon 12 with high probability (i. e. , p = 0. 99 according to AdaBoost and 1 according to Random Forest)  [18]. Its MAF in GnomAD database is 0. 000018, with only 4 heterozygous individuals and no homozygotes detected in N110,000 genomes. It was absent from the GME Variome dedicated to Middle East populations. Sanger sequencing confirmed that the two affected sisters were indeed homozygous for the MEIOB variant, whereas the unaffected family members were either heterozygous or homozygous for the wild-type allele (Fig. S1 ). MEIOB encodes a meiosis-specific factor that binds ssDNA. Our previous expression data in human and mouse show that MEIOB/Meiob is strongly expressed in the developing ovary during meiosis  [9].
• Data storage: Confidentiality: while storing WBAN data, it should be encrypted and protected by access control lists. Integrity: WBAN data should be kept intact, unbroken while storage is performed. Dependability: WBAN data should be recovered after network incidents (link failure, deleted data, etc. ). Privacy: unauthorized access to the stored WBAN data should be prohibited by the data storage policy. • Data access: Accountability: every malicious WBAN, that would access illegally to others' secured data, should be retrieved and held responsible of his actions. Revocability: malicious WBANs should be underprivileged at once. Non-repudiation: a source WBAN should not deny the generation of its own data. • Other security requirements: Authentication: any source WBAN should be authenticated by the destination, before sending the sensing data.
Availability: the WBAN data should be available and accessible whenever it is needed. Then, specific mechanisms should be implemented to ensure a secure environment around the WBAN, which will detect, prevent and reinforce the WBAN against attacks. Existing security mechanisms for WBANs could be developed into seven aspects: 1. Cryptography: cryptographic functions are used to ensure the safety and security of the information collected by sensor nodes (vital signs of a patient). Choosing the cryptographic system depends on the WBAN application and its energy consumption. 2. Key management: cryptographic keys are generated and shared to the WBANs to ensure secure communications. Three types of key management protocols can be found: the trusted server, considering a trusty base station to validate the key agreement, the key pre-distribution or the symmetric key cryptography, and finally, the public-key infrastructure.
Intended to scale to high-density networks, connecting a wide range of heterogeneous devices, CodeBlue places the security issue on the spotlight of interest and implements a flexible and decentralized security model that allows the emergency medical technicians to be authenticated by the network before receiving patient information. A handoff mechanism is also performed to ensure transparent authentication when the patient is transferred to the hospital. The Biosec project  [81]  also addresses the security constraints of the communications among biosensors operating in extremely stringent environments. The idea is to derive biometric inputs from the human body for the security module. Therefore, keying material is computed using biometrics from the body, which would drop the security overhead for the benefit of reliable sensing and energy saving.
50h, Wayne Rasband National Institutes of Health, USA): from 100 frames (fps = 100 s -1 ) the standard deviation is calculated. The standard deviation image is a quantification of the movement of the bacteria, where white areas indicate high movement and black areas low movement. The gray-intensity profile of the standard deviation image is then plotted as function of the position in the frame, with the vertical values averaged, with ImageJ 'Plot Profile' function. in this way, the band profile is obtained and then it is fitted with Matlab (Version R2017a, Mathworks) with a Laplace distribution. The 10x magnification videos are pre-processed with ImageJ for background subtraction and quality improvement, and then they are tracked with a custom-made tracking algorithm  [13]. The 4th-order velocities are extracted from the trajectories with a custom-made Matlab program.
EBSD analysis Electron backscattered diffraction (EBSD) analysis was performed to determine the grain morphology and grain orientation of the samples. The EBSD characterisations were carried out on a Zeiss Sigma HD scanning electron microscope (SEM) operated at 20 kV. The EBSD data acquisition was performed using a Bruker e-Flash HR detector, and was processed by Esprit Bruker software packages. The step size was 0. 47 mm. The average grain size measurements were performed with a grain tolerance angle of 10°. The twin boundaries were defined as coincident site lattice boundaries for 60°rotation on the 〈111i direction. The mechanically polished samples were electropolished using a perchloric acid solution to remove irregularities and any deformation layer from the surface.
TEM Microstructural examinations were performed using a Thermo Fischer Scientific Tecnai G20 transmission electron microscope (TEM) equipped with a LaB 6 filament operated at 200 keV and a charged coupled device camera for images and diffraction patterns acquisition. Energy dispersive spectroscopy (EDS) was also conducted in the TEM for chemical analysis using a synergie 4 /Bruker Silicon drift EDS detector. STEM images were recorded using high angular annular dark field (HAADF) and bright field (BF) detectors and a low camera length was used to minimise the diffraction contrast. The statistical analysis of the materials porosity was determined by image analysis using Thermo Fischer Scientific Visilog Software.
Selection of winter oilseed rape (WOSR) genotypes. A total of 116 cultivars of winter oilseed rape (Brassica napus), registered in France between the 1950's and the 2000's, were gathered from the CRB BraCySol (INRA, IGEPP, France) to represent the diversity of inbred lines cultivated in Western Europe. All cultivars were genotyped with the infinium Brassica 60K SNP array  (Clarke et al. , 2016). A set of 628 polymorphic SNPs presenting a low level of linkage disequilibrium (r²<0. 2) were selected to analyze the structure of the genetic diversity within the accessions. The structure of the collection was studied using admixture  (Alexander et al. , 2009)  and ten cross-validations. The genetic diversity of the collection was analyzed using Darwin software  (Perrier & Bonnot, 2003). Sokal and Michener genetic distances were estimated between each couple of cultivars, and then clustering was performed using UPGMA.
Seed phenotyping. Seed imbibition, germination, and early radicle growth were monitored in vitro with an automated phenotyping platform (Phenotic, SFR Quasav, Angers, France), which is described in detail in  Ducournau et al. (2004 Ducournau et al. ( , 2005) )  and  Wagner et al. (2012). Briefly, four subsamples of 25 seeds by genotype and year (Y1 and Y2) were incubated at 20°C on germination test paper (GE Healthcare, type 3644) continuously moistened for 5 days and image acquisition was performed every two hours. Image acquisition, image analysis and data analysis methods are described in  Demilly et al. (2014). The following parameters were determined: seed area (Area; in mm 2 ), volume 8h after initiation of imbibition (Imb_Vol; in mm 3 ), radicle elongation after 8h (Elon_germ, in mm), mean germination time (germination, in hours), and time to reach 50% of germination per seed subsample (T50, in hours).
Sequence processing. Fastq files were processed with DADA2 version 1. 6. 0 (Callahan et al. , 2016) , using the parameters described in the workflow for "Big Data: Paired-end" (https://benjjneb. github. io/dada2/bigdata_paired. html). The only modification made relative to this protocol was a change in the truncLen argument according to the quality of the sequencing run. Taxonomic affiliations for amplicon sequence variants (ASV) generated with DADA2 were assigned with a naive Bayesian classifier  (Wang et al. , 2007)  on an inhouse gyrB database  (Bartoli et al. , 2018)  and the UNITE v7. 1 fungal database  (Abarenkov et al. , 2010).
Microbial community analyses. Analyses of diversity were conducted with the R package Phyloseq version 1. 22. 3 (McMurdie & Holmes, 2013). Data were normalized based on sequencing depth, using a rarefaction of 1,100 and 15,000 sequences per sample for gyrB and ITS1, respectively. Observed richness (number of ASV per sample), estimated richness (Chao1 index), and inverse Simpson's index were calculated with Phyloseq. Faith's phylogenetic diversity was calculated on gyrB dataset with the R package picante version 1. 6-2  (Kembel et al. , 2010). Differences in alpha-diversity estimators between years and genotypes were assessed with Kruskal-Wallis tests. Differences were considered as significant at a p-value < 0. 01. Changes in microbial assemblage composition were assessed with Bray-Curtis (BC) index and weighted UniFrac (wUF) distance  (Lozupone & Knight, 2005).
Diversity of the seed microbiota of B. napus. Overall 4,451 bacterial and 359 fungal ASVs were detected within gyrB and ITS1 datasets. ASV provided a finer resolution than OTU  (Callahan et al. , 2017) , this descriptor was therefore used for estimating the taxonomic structure of the seed microbiota of B. napus. According to coverage estimates, the number of gyrB sequences was not sufficient for reaching the plateau after rarefaction, while saturation was achieved for fungal reads. Hence, predicted richness (Chao1 index) was assessed for bacterial assemblages, while observed richness (number of ASVs) was measured for fungal assemblages. Although bacterial and fungal richness were significantly different between the two harvesting years (P < 0. 01), seed samples collected in Y2 contained on average fewer bacterial and more fungal ASVs than Y1 seed sample (Fig.
Therefore, because of the most probable assumption of microbiota involvement, further experiments are required to investigate further the implication of microbial assemblages in germination or extended seed phenotypes variations. In conclusion, the microbial assemblages of B. napus seeds are mainly influenced by the environment but to a lesser extent by the plant genotype. The inheritance of some abundant bacterial and fungal ASVs also differs between genotypes. Further studies will explore more extensively the host genetic determinants driving the microbial assemblages and the impact of such assemblages on seed vigor. Observed richness (number of detected ASVs) and (E) diversity (Inverse Simpson's index) were assessed with ITS1 ASVs for fungi. ASVs was performed with an in-house gyrB database  (Bartoli et al.
, 2018)  and the UNITE v7. 1 database  (Abarenkov et al. , 2010) , respectively. Unknown taxa represented ASVs that are not affiliated at the order level. et al. , 2004 , 2005; Wagner et al. , 2012). Four subsamples of 25 seeds by genotype and year (Y1 and Y2) were incubated at 20°C on germination paper (GE Healthcare, type 3644) continuously moistened for 5 days and image acquisition was performed every two hours. Europe, among the 5 WOSR genetic groups. Cultivars were genotyped using 628 SNP  (Clarke et al. , 2016). The structure of the collection was studied using ADMIXTURE  (Alexander et al. , 2009). Each genetic group is represented with a colour. A cultivar belongs to the group of which the colour is majority. The 9 selected genotypes of the study are marked with a yellow star. S1 ).
In the first step, the inputs and the outputs of the system are observed and acquired and will be used throughout all further steps. The system model hypothesis is vital because it needs to describe faithfully the behavior of the observed system, otherwise it will be rejected after the test and validation phases. Au (  5 ) Cu (  40 ) Au (  40 ) Ta (  5 ) Resist (  40 ) Among models used to describe the nonlinear dynamics of a system, different model classes have been reported  [20] [21] [22] [23] [24] [25] [26] [27]. Continuous-time polynomials represent the first class of models where, by using Ordinary Differential Equations (ODEs), the acquired data can be fitted  [21]. The examples of nonlinear models described with ODEs are Lorenz  [23]  and Rössler systems  [24] ; the Lotka-Volterra model  [28]  suited for ecological systems as it efficiently describes the interaction of different species competing in nature  [29] ; the Kuramoto-Sivashinsky equation for chemical reaction modeling  [21].
The whole process is summarized by a flowchart diagram in Figure  3. The obtained echo state network can be successfully used to model one time series. In the NCVO case, one time series corresponds to one set of magnetization signals (M x (t), M y (t) and M z (t)) obtained for one value of the input DC current. Therefore, it becomes impossible to capture all the sets of magnetization signals of the NCVO device using a single system. To do that, separately trained ESNs are needed. In other words, the number of echo state networks should be equal to the number of input datasets. In the next section, we expose a new methodology of training recurrent neural networks, which, by its memory, assures the easy switching of the different input datasets under the same weights. Consequently, the magnetization signals M x (t), M y (t), and M z (t) can be captured at different DC current values, previously trained on, using a single system.