dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulation results The main focus of this work is on building the system architecture and validating the communication pattern. Therefore, simulation experiments are performed with simulated data rather than real sensor data. In particular, different sample data from different open access databases are considered. Portions of these data are fed to the client to simulate real sensor measurements. Concerning ECG monitoring, we use data from the Physiobank archive  [17]. Within this archive, the MIT-BIH Arrhythmia Database  [18]  is considered. This database consists of 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects. By using this dataset, the possibility of detecting supraventricular premature or ectopic beats (atrial or nodal)  [14]  is considered.
Another challenge that could be explored in the future is the use of advanced techniques for data mining that draw from the fields of big data, artificial intelligence, machine learning, statistics and database systems by using a knowledge base. Optimisation methods and software libraries similar to the ones proposed in  [21]  may be adopted. In the near future, we expect wearable technology to deliver a key interface and input into the industrial Internet of Things (IoT). The proposed system contributes towards the fulfillment of this idea.
PCRs began with a hot start at 95°C for 5 min and continued as follows: for multiplexes-95°C for 30 s/63-57°C for 90 s/72°C for 30 s, for 35 cycles and 60°C for 30 min according to the QIAGEN protocol; for TB01, 95°C for 45 s/55-50°C for 45 s/72°C for 25 s, for 35 cycles and 72°C for 7 min. The PCR products were resolved using an ABI 3500xL (fluorescence-based DNA analysis instrument using capillary electrophoresis technology with 24 capillaries from Applied Biosystems, Waltham, MA, USA) and scored manually. The loci D0MST presented critical amplification failures and was eventually removed from data, leaving nine loci for further data analysis.
For microsatellite data, the expected heterozygosity (H E ) under Hardy-Weinberg expectations, observed heterozygosity (H O ) and estimators by  Weir and Cockerham (1984)  of Wright's F-statistics were calculated using GENETIX 4. 05  (Belkhir, Borsa, Chikhi, Raufaste, & Bonhomme, 1996 -2004). The significance levels of F IS and F ST were assessed via 1,000 permutations. Allelic richness after rarefaction for the smallest entire sample size (N = 13, Australia) was calculated using FSTAT  (Goudet, 1995). F ST p-values for multiple comparisons were also assessed using FDR by SGoF+. We used the program POWSIM 4. 1 from  Ryman and Palm (2006)  to determine the statistical power of our markers to detect genetic differentiation given differences in their levels of allelic diversity and sample sizes.
As POWSIM computes Nei's F ST  (Nei, 1987; Nei & Chesser, 1983 ) "modified to be independent of the number of subpopulations" (POWSIM manual), we used GENETIX and mmod package 1. 3. 3 (Winter, 2012)  implemented within the statistical software R 3. 2. 0 (R Core Team 2015) to determine the Nei's F ST values for both mitochondrial and microsatellites data sets. POWSIM enables to determine with chi-square and Fisher tests if the data set power to detect the corresponding F ST is sufficient, that is, superior to 80%. The parameters of the Markov Chain were fixed to 10,000, 1,000 and 10,000 for, respectively, dememorizations (burn-ins), batches and iterations per run, for a total of 1,000 runs each.
In addition, a discriminant analysis of principal components (DACP,  Jombart, Devillard, & Balloux, 2010)  was performed. DAPC is a multivariate analysis that integrates principal component analysis  (PCA)  with discriminant analysis to summarize genetic differentiation between groups  (Jombart, 2008;  (adegenet package v2. 0. 1,  Jombart, 2008). Sampling location was used as prior. While STRUCTURE forms genetic clusters of individuals by minimizing departure from Hardy-Weinberg and linkage disequilibria, DAPC maximizes genetic separation among groups and minimizes variation within groups  (Jombart et al. , 2010) , which may constitute a more accurate approach for species exhibiting potentially high gene flow. A haplotype network aiming to construct the shortest possible tree of haplotypes was computed using Network 4.
ACKNOWLEDGEMENTS We thanks the editor and the anonymous reviewers for their very helpful comments and suggestions. We thank the associations  Stellaris and Seaquarium (Aquarium of Le Grau du Roi)  for their help in gathering samples, as well as  Malcom Francis ,  Julian Pepperell  (for collects during fishing tournaments in New South Wales, Australia, funded by the  New South Wales Recreational Fishing Trusts) ,  Pedro Afonso ,  Nota Peristeraki ,  Sebastián Biton Porsmoguer ,  Sarah Mutoni  and  Greg Nowell , and also  Serge Planes  for his role as a hub for the shipping and conservation of shark samples. This article has been produced with the financial assistance of the  European Union  ( GCP/ INT/233 /EC -Population structure of IOTC species in the Indian Ocean). The views expressed herein can in no way be taken to reflect the official opinion of the  European Union.
This work was funded by  FFP ("France Filière Pêche")  and managed by  AMOP ("Association Méditerranéenne des Organisations de Producteurs"). We are grateful to the skippers of the domestic longline fleet for their continued support of the "SELPAL" project. The data used in this work were (partly) produced through the genotyping and sequencing facilities of the  Labex Cemeb (Centre Méditerranéen Environnement Biodiversité). We thank  Margaux Denamiel ,  Louise Vernier  and  Geoffrey Bled-Defruit  as Intechmer internships. We thank  Bo Peng  and  Solenn Stoeckel  for their help in finalizing the python script using simuPOP. Finally, we thank  Pierre-Alexandre Gagnaire  and the participants of the  GDR Marco  for fruitful discussions.
9 The external validity in NOWAC is considered high as the performed validation study showed that the distribution of exposures was independent of the response rate and the observed cumulative incidence of cancer vs expected national figures from the Cancer Registry of Norway showed no substantial differences. 10 Details on the design, materials and procedures of the NOWAC study have been described elsewhere. 11 n the present study, 145,658 women who returned Q1 between 1991 and 2005 were considered eligible for inclusion (Fig. 1 ). We excluded women who had emigrated or died before Q1 was registered in the study database (n = 30), women who were diagnosed with cancer (other than non-melanoma skin cancer) prior to Q1 (n = 5112), and women with missing weight in both Q1 and Q2 (n = 1678). Women who reported implausible weight values ( < 30 or > 200 kg), height values ( < 100 or > 230 cm) (n = 4) or age at menopause ( < 25 or > 60 years) (n = 88) in either questionnaire were also excluded.
Thus, our final analytical study sample consisted of 138,746 women: 40% enroled in 1991-1992, 31% enroled in 1996-1997 and 29% enroled in 2003-2005. BMI and weight change analyses were carried out in subsamples of the final analytical study sample. In the BMI analysis, we excluded women with < 2 years of follow-up after Q1 to reduce the possible influence of reverse causality from the effects of pre-clinical cancer on weight (n = 1 565), and women with missing weight or height in Q1 (n = 1473). In the weight change analysis, we excluded women who did not return Q2 (n = 51 637). Women who returned Q2 were younger, had lower body weight and were less likely to use hormone therapy (HT) compared with women who completed only Q1. Furthermore, we excluded women who emigrated or died before Q2 was registered in the study database (n = 8).
32 In our study, we assume that the potential misclassification due to this information bias was non-differential between cases and non-cases. Therefore, our risk estimates may have been underestimated. Furthermore, a validation study of BMI has been conducted in the NOWAC study and showed substantial agreement between self-reports and objective measurements. 33 n addition, the covariate physical activity was also self-reported and displayed a moderate significant correlation with heart rate and movement in a previous validation study. 34 Total energy intake was omitted from the analyses because the foodfrequency questionnaire was not provided to all participants in this study, leading to a large amount of missing data (61%), and because of known biases with respect to obesity. 35 Finally, as mentioned above, the lack of information on intentionality of weight loss to avoid reverse causality hampered the weight loss analysis.
ACKNOWLEDGEMENTS We thank the staff and participants of the NOWAC study for their valuable contributions. MdS, EW, IL and CR are supported by the  Medical Faculty, UiT The Arctic University of Norway. The Medical Faculty,  UiT The Arctic University of Norway  did not contribute to the study design, data collection or analysis, nor did it influence the decision to submit the manuscript for publication. The authors did not receive external funding for the preparation of the manuscript.
Among them, 5,867 proteins have no homologue in Arabidopsis thaliana. For these proteins, no functional information is available from closely related species and experimental evidence will be required to explore their role in roses. We also looked at whether the difference in the predicted number of proteins was owing to protein family expansion. Such a scenario was detected for some protein families, including nucleotide-binding site leucine-rich repeat (NBS-LRR) and cytochrome P450 (Supplementary Fig. 3 ). Transposable elements. The REPET package  23  was used to generate a genome-wide annotation of repetitive sequences of the HapOB genome (see Methods for details). Retrotransposons, also called class I elements, represent the largest transposable element genomic fraction (35. 1% of the sequenced genome), of which long terminal repeat (LTR) retrotransposons represent 28.
8 software. Genome assembly, polishing and contamination assessment. All sequence data generated that were derived from 27 SMRT cells containing 19. 2 Gb of reads larger than 500 bp were assembled with CANU hierarchical assembler v1. 4 (ref. 16 ) (version release r8046). In general, default settings were used except 'corMinCoverage' , which was changed from 4 to 3, 'minOverLapLength' , which was increased from 500 to 1,000, and 'errorRate' , which was adjusted to 0. 015. The assembly was completed on the Dutch National e-Infrastructure with the support of SURF Cooperative using 2,024 CPU hours (Intel Xeon Haswell 2. 6 GHz) for the complete CANU process. Illumina paired-end (2 × 125 bp) reads were mapped onto the genome assembly using Burrow-Wheeler aligner maximum exact match (BWA-MEM)  59.
Pilon 60 was then used to error correct the assembly. This procedure was repeated three times iteratively. For contamination assessment, prokaryotic genes were predicted on the contigs using MetaGeneAnnotator 61. The number of genes per nucleotide was computed for every contig. Furthermore, Illumina reads were mapped on the contigs using BWA-MEM  62. The number of mapped reads per nucleotide was computed for every contig. Contigs with a low Illumina read mapping frequency were aligned against the GenBank non-redundant protein database using BLASTX.
GWAS analysis. The GWAS analyses for petal numbers and prickle density were performed in TASSEL 3. 0 (ref. 66 ) as described previously  33. Trait marker association for petal number was analysed using the mixed linear model (MLM) and 39,831 markers (petal as a quantitative trait with the Q + K model), including a fixed effect as the population structure matrix (Q) and random effect as the kinship matrix (K). Significance thresholds were corrected for multiple testing by the Bonferroni method using the number of contigs (19,083) as a correction factor, resulting in a significance threshold of 1. 78 × 10 -6. The kinship matrix used in the MLM was calculated for 10,000 SNP markers with the software SPAGeDi 1. 5 (Zitat) as described previously  33. For the GWAS analysis of prickles and petals with the general linear model (GLM) in TASSEL 3.
However, for some contigs, genetic maps did not resolve the orientation problems. In these situations, we used the synteny between Rosa and F. vesca  10. The strategy used to position and orient contigs is described in Supplementary Fig. 11. The position and orientation of the contigs are listed in Supplementary Table  13. Concerning the K5 integrated genetic map, among the 25,695 SNP markers present, 20,706 SNPs (80. 6%) could be positioned on the HapOB genome sequence by BLAST of the SNP-flanking marker sequences (Supplementary Fig. 2a ). Centromere region identification and FISH. Three complementary tools were used to identify centromeric tandem repeats and to estimate their abundance in the R. chinensis 'Old Blush' genome: Tandem Repeat Finder (TRF  68  ), TAREAN  69  and RepeatExplorer  70  , each with default settings, and the output was parsed using custom python scripts.
71 ) with parameter -k 1 to select read pairs with high similarity to the CL226 repeat. Selected read pairs were then split into two groups: reads that matched the CL226 repeat sequence itself and reads that matched the flanking genome sequence. Both groups of reads were separately mapped onto the genomic scaffolds using Bowtie with parameters -a 1 and -N 1. The distribution of the two sets of CL226 reads was visualized using the circlize package  72  of R Bioconductor  73. Mitotic chromosome slides were prepared with the 'SteamDrop' method  74  using young root meristems of R. chinensis 'Old Blush'. Two oligonucleotide probes (5′ -TTGCGTTGTTCTAGTGACATTCA-TAMRA-3′ ; 5′ -ACCCTAGAAGCGAGAAGTTTGG-TAMRA-3′ ) were used for FISH, as previously described  75. DRAWID  76  was used for chromosome and signal analysis.
Acknowledgements We thank the  ImHorPhen team of IRHS  and the experimental unit ( UE Horti ) for their technical assistance in plant management. We thank the  PTM ANAN (M. Bahut) of the SFR Quasav  and the  Gentyane platforms (especially C. Poncet)  for the SSR and SNP analyses, respectively. We acknowledge  A. Chauveau  and  I. Le Clainche  for libraries preparation and  E. Marquand  and  A. Canaguier  for data processing. This work was supported by  CEA-IG/CNG , by conducting the DNA quality control and by providing access to the  INRA-EPGV group  for their Illumina Sequencing Platform. We acknowledge  J. -L. Gaignard  (from the communication service of the  INRA ) for his help to fund the project. We thank the GDR team, and particularly  P. Zheng ,  S. Jung  and  D. Main , for management of the genome sequence at the GDR database.
We thank ' Région Pays de la Loire' for funding the sequencing of HapOB  ( Rose Genome  Project), the resequencing of eight wild species ( Genorose project in the framework of RFI 'Objectif Végétal ') and for the EPICENTER ConnecTalent grant of the  Pays de la Loire  (N. D. and E. B. ). F. F. and L. H. S. -O. thank the  ANR  for funding the genetic determinism of flower development ( ANR-13-BSV7-0014 ). K. K. thanks the  JSPS  for funding the analysis of the S-locus (JSPS KAKENHI no. 17H04616 ). T. D. thanks the  German Ministry of Economic Affairs  for funding the GWAS analysis (Aif programme ZI) and the  Deutsche Forschungsgemeinschaft  for the RNA-seq data generation (DFG program  GRK1798 ). The development of the high-density SNP maps was partly funded by  TTI Green Genetics  and by the  TKI Polyploids projects  ( BO-26.
Code availability. The R code used for pairwise maximum likelihood recombination and lod score calculations is available through CRAN (https://CRAN. R-project. org/package= polymapR). The R code used to infer phylogenetic relationships is available on request from the corresponding author  90. The python scripts used for centromeric region identification are available on request from the corresponding author. Data availability. All the genome data have been made available on a genome browser (https://iris. angers. inra. fr/obh/) and in the public GDR database (https://www. rosaceae. org/species/rosa/chinensis/genome_v1. 0) 91. FASTA files of chromosomes and genes (mRNA, proteins and non-coding RNA) and gff files for gene models and structural features (transposable element) can be downloaded from both the previously mentioned websites.
Raw data (PacBio and Illumina reads) are available under the accession number PRJNA445774. RNA-seq data used for genome annotation are available under the following SRA accession numbers: SRP128461 for 91/100-5 leaves infected with blackspot and SRP133785 for R. wichurana and 'Yesterday' leaves infected with two powdery mildew pathotypes. Raw data of resequencing of the eight wild Rosa species are available under the SRA accession number SRP143586.
Methods This was a single-center, prospective, unblinded trial (ClinicalTrials. gov base identifier: NCT03010943), which was performed in compliance with all regulatory and ethical guidelines for clinical research. All patients signed a written informed consent. The inclusion criterion was patients aged >18 years who were hospitalized for treatment of a tumor or any type of surgical lesion near the language region of the brain. The exclusion criteria were all contraindications to an awake surgery (cognitive impairment, aphasia, and morbid anxiety). The main objective was to assess procedural feasibility and safety. This study was performed using a Samsung Gear VR combined with a Samsung S7 smartphone (android platform) and headphones. After general and local anesthesia, the patient was positioned lying on his side, with a rigid pin fixation of the head.
Once the craniotomy was completed and the dura was opened, we awakened the patient. Electroencephalography signals were recorded using a subdural electrode. After the cortex was exposed, language mapping was performed by a neuropsychologist using an image denomination task on a digital tablet. The mapping took place as previously described  [10, 11]. DES was applied with a bipolar electrode delivering a biphasic current (60Hz, 1 ms pulse width, current amplitude ranging from 2 to 8 mA over 2-3 s). To prevent interference with the routine procedure of awake craniotomy and language brain mapping, we decided to duplicate the image naming task viewed in VRH (two dimensions, 2D) and then in stereoscopy (three dimensions, 3D; an app based on Unity 3D software with an interface allowing VRH communication via a computer and Bluetooth connection).
Results A total of 3 patients used the vTime app during wound closure (2 males and one female; mean age, 54 years). Only 1 participant had a previous VR experience. Before the surgery, all the patients were trained without any issues. Patients used a standard avatar and an account opened by the Department of Neurosurgery to preserve anonymity. They interacted with an avatar piloted by the neuropsychologist, who also wore a VRH, under the control of a physician who participated in the meeting and controlled the scene on a smartphone connected to the app. This allowed continual monitoring of the operation (Multimedia Appendix 1). The mean time of connection and interaction was 10 minutes. During DES of the left inferior frontal gyrus (pars opercularis), all patients failed to perform the 2D and 3D language and motor tasks.
Interestingly, these platforms take different approaches for conveying nonverbal language: arms, hands, head, and mouth movement and gaze. For our trial, we chose Samsung VR, a low-cost, high-quality, customizable wireless device, with an optional pad control and a game controller. The VR social network vTime is compatible with the Samsung VRH  [12]. The vTime app allows interaction with several avatars and positional control in different virtual environments. The avatar can point anywhere within the scene and produce gestural expressions such as OK, Thumbs Up, Clap, Thumbs Down, Blow Kiss, etc  [12]. It is also possible for the user to touch other avatars or to take control of his or her personal space. We demonstrated that patients undergoing awake craniotomies can wear a VRH and interact with an avatar piloted by a neuropsychologist.
| Literature search We conducted a systematic literature search in two major databases, SCOPUS and Web of Science (WoS) Core Collection Database (SCI-EXPANDED index), for studies that compared ES or biodiversity with different vegetation management (initial database query 25 January 2016; detailed search terms in Appendix S1). This resulted in a total number of 1,429 publications. After screening those papers by title 489 articles remained and after reading the abstracts for their relevance, 157 articles remained for full-text screening. Abstract screening was performed by two persons in parallel to cover different fields of expertise and to discuss which articles to include (for the detailed selection process see the PRISMA flow diagram in Figure  S1 ). In the next step articles were screened based on a predefined set of inclusion and exclusion criteria.
Only empirical datasets were included that compared at least two different soil or vegetation management treatments. Studies that included (1) less than three spatially independent replicates per treatment level, (2) vineyards under plastic or in greenhouses, and (3) treatments not directly manipulating soil or vegetation management in the vineyards (e. g. application of synthetic or external mulches or the use of different fungicide or insecticide treatments) were excluded. In addition, only studies, which reported means and any dispersion measure of the dependent variable (e. g. SD or SEM), were used. We contacted the authors of recently published papers with missing data of variance or additional information like irrigation regime of the treatments. Thereof, authors of 11 articles sent adequate datasets for the inclusion in this meta-analysis.
| Data extraction In full-text screening and the follow-up data extraction the coauthors participated according to their expertise in viticulture (DPo), pest control (DPa), biodiversity (SK, SW, JZ), microbiology (BL) and hydrology/soil sciences (TB, PS, GG, JG). Each expert needed to document why an article was excluded (most frequently due to missing measures of variation or insufficient spatial replication); and if inclusion criteria were met, data and covariates were collected in a common database. If studies reported the outcome of several different treatments, which differed in species diversity, we only included the treatment with the largest contrast to the control, for example, bare soil vs. cover crop mixtures with highest number of plant species. As an exclusion of those datasets (n = 11 studies) did not change overall effect size considerably, this approach did not bias results.
The results are reported as the back-transformed values of the relative percentage of increase (positive values) or decrease (negative values) in comparison to the control treatment. We chose to analyse data with hierarchical mixed-effects meta-analysis models that allow incorporating fixed (moderators), true random effects as well as a nesting factor for effect sizes in the respective sources or articles. As several data points were extracted from a single article, we used the article ID as a nesting factor to avoid violating the assumption that effect sizes are independent from each other. We used the rma. mv function of the metafor package  (Viechtbauer, 2010)  for r (R Development Core Team, 2017) to fit mixed-effects models to incorporate the true variation in the effect size variation across studies and the fixed effects by adding moderators  (Borenstein et al.