dataesr/scientific-paragraphs-categorization huggingface dataset:
Unfortunately, although our data show that entrained high-beta neural activity in the right FEF (modulated by focal rhythmic TMS bursts) increased synchronization between the stimulated site and electrodes positioned over right parietal regions, the limited spatial resolution of our EEG montage (60 electrodes) does not enable us to pinpoint which specific parietal regions got synchronized during right FEF rhythmic stimulation patterns. However, previous results employing diffusion imaging with participants who underwent similar non-invasive stimulation patterns and behavioral tasks  (Quentin et al. , 2014 (Quentin et al. , , 2015) )  suggest that fronto-parietal synchronization could operate along the 1st branch of the Superior Longitudinal Fasciculus (SLF I), part of the white matter bundle subtendng the dorsal attention network linking the FEF and the posterior intraparietal sulcus (IPS).
Statistical analyses on topographic maps also suggest that random TMS patterns (comparison between random active and random sham conditions) increased synchronization around 30 Hz between the right FEF and fronto-parietal regions in the left hemisphere (contralateral to the stimulation). Hence, in an attempt to refine the spatial distribution of such increased frontoparietal synchronization observed in response to rhythmic or random TMS, we defined two regions of interest comprised of left or right parietal and parieto-occipital scalp EEG contacts. Statistical analyses on time-frequency datasets (Fig 3) confirmed that, compared to random TMS bursts, rhythmic TMS patterns increased right fronto-parietal synchrony only during the delivery of active stimulation and that such effects operated within a frequency band restricted to high-beta (24-45.
Acknowledgement Chloé Stengel was supported by a  PhD fellowship  from the  University Pierre and Marie Curie. Marine Vernet  was supported by a fellowship from the  Fondation pour la Recherche Medicale. Julià L. Amengual was supported by a fellowship from the  Fondation Fyssen. The activities of the laboratory of  Dr. Valero-Cabré  are supported by research grants  IHU-A-ICM-Translationnel ,  ANR  projet  Générique OSCILOSCOPUS  and Flag-era-JTC-2017 CAUSALTOMICS. The authors would also like to thank  Romain Quentin ,  Juliette Godard  and  Laura Fernandez  for providing help during data acquisition and the  Naturalia & Biologia Foundation  for financial assistance for traveling and attending meetings.
A better knowledge of the bovine milk proteome and its main drivers is a prerequisite for the modulation of bioactive proteins in milk for human nutrition, as well as for the discovery of biomarkers that are useful in husbandry and veterinary medicine. Milk composition is affected by lactation stage and reflects, in part, the energy balance of dairy cows. We aggregated the cow milk proteins reported in 20 recent proteomics publications to produce an atlas of 4654 unique proteins. A multistep assessment was applied to the milk proteome datasets according to lactation stages and milk fractions, including annotations, pathway analysis and literature mining. Fifty-nine proteins were exclusively detected in milk from early lactation. Among them, we propose six milk proteins as putative biomarkers of negative energy balance based on their implication in metabolic adaptative pathways.
Construction of the milk proteome atlas. A computational workflow was used to aggregate data from publications reporting cow milk proteome to create an atlas of all proteins present, independently of their abundance and without statistical analysis. Briefly, we collected publications on bovine milk by a literature search on Bos taurus using PubMed. gov (NCBI) and the Web of Science (Clarivate Analytics) search engines up to February 2018. The search provided 87 milk proteome publications that were reviewed and curated based on the availability Figure  3. Venn diagram of the 43 gene names from early milk proteins that were previously identified in the liver  24  , adipose tissue  25  and mammary gland 23 of dairy cows. of information, such as the days in milk and the health status of the cows, as well as accessible supplementary materials.
Milk protein data could come from tank samples or individual milking but only from cows without mammary infection according to the somatic cell counts or as declared by the authors. Twenty publications complied with the selection criteria:  10, 13, 15, 17, 22, [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53]. The main objective of this computational data aggregation from 20 publications, with 35 datasets (Table  2 ), is to obtain an overview of milk proteins independently of breed, age, country and methodologies of protein isolation and identification. Methods for protein isolation were density gradient ultracentrifugation  15, 39  , centrifugation and washing  10, 22, 40  , (ultra)centrifugation with acidification  48  and with major proteins depletion  42, 45, 46  , or (ultra)centrifugation without acidification  13, 17, 41, 43, 44, 47, [49] [50] [51] [52] [53].
Methods for protein identification were FASP, dimethyl labelling, LC-LTQ-Orbitrap/MS  44  , electrophoresis gel LC-MS/MS 52 , iTRAQ labelling, SCX and LC-MS/MS  10, 22, 39, 43, 48  , Maldi-(TOF/TOF)-MS detection  42, 47, 49, 50  and from 1 to 15 repetitions of (nano)LC-MS/MS runs  13, 15, 17, 40, 41, 45, 46, 51, 53. Protein identifiers (ID) reported in the publications were extracted from tables in Portable Document Format (PDF) or from supplementary data files using Tabula software (www. tabula. technology, Last update February 11, 2017). All collected IDs were aggregated in an atlas of proteins using Excel software (2016). Each protein ID was annotated according to i) milk: fraction, processing, and period of lactation; ii) animal phenotype: breed, health status, and production parameters; and iii) protein identification: number of proteins and proteomic methods used.
Because publications report different protein nomenclatures, all protein IDs were changed by the corresponding gene names (GN), with the Retrieve/ ID Mapping tool of the UniProt database (The UniProt  54  ), the Protein Identifier Cross-Reference service  55  and/ or the ProteCONVERT tool of the ProteINSIDE web interface  56  being used to homogenize data and to generate an atlas of GN. Categorization of the milk proteome atlas. For the categorization of the database, four milk fractions were defined based on the protein isolation techniques used in laboratories. The milk fractions were i) skimmed milk, with aggregating proteins isolated by centrifugation under 100 000 g combined with or without casein depletion by acidification (14 datasets); ii) whey, with aggregating proteins isolated by centrifugation over 100 000 g (13 datasets); iii) MFGM, with aggregating proteins isolated from cream milk (4 datasets); and iv) exosomes, with aggregating proteins isolated from skimmed milk with a protocol based on a sucrose gradient 12 (4 datasets).
Five lactation stages were defined according to the DIM, to account for differences in the physiological status and energy balance of the dairy cow. The lactation stages were the i) colostrum period, with aggregating proteins detected in colostrum (samples collected during the first 5 days post-partum; 12 datasets); ii) early lactation (between 6 and 21 DIM, 7 datasets); iii) peak lactation (between 22 and 80 DIM, 6 datasets); iv) mid-lactation corresponding to post-peak lactation (after 81 DIM, but not during the dry-off period, 8 datasets); and v) drying-off (from milk collected at 3 and 8 days after stopping regular milking, 2 datasets). The early lactation stage was defined as 6 to 21 DIM, corresponding to classic physiological NEB  6, [57] [58] [59]  and excluding the colostrum period  60.
The categorization of the data from the 20 publications using criteria for the milk fractions and lactation stages produced 35 datasets (Table  2 ) from different cow breeds, experiments and countries. Among the data, the Holstein-Friesian cows were mainly represented. One-half of studies originated from USA, Australia and Denmark. Finally, we discarded those GN identified more than once in each dataset. Based on the 35 datasets, the atlas aggregated 8841 GN corresponding to proteins.
In this instance, patients will be counselled and asked to provide an informed consent for the trial. Even if patients are not on a clinical trial, full informed consent for treatment and also for data reporting is an EBMT requirement for good clinical practice and data protection. In order for the transplant community to gain a better understanding of the long-term side-effects of this treatment, it is important that transplant centres report data relating to transplant treatments to the EBMT or an equivalent registry. In the EBMT, patient data will be handled in an anonymous (and/or pseudonymised) fashion and in line with current general data protection regulation (GDPR). Figure  3a -d summarises recent activity and JACIE accreditation status in EBMT reporting centres for autologous HSCT procedures in ADs, both overall and for specific disease categories.
Objective. To present the most recent national estimates of lung cancer burden globally in 185 countries and tobacco smoking prevalence (%) by sex. Materials and methods. Estimates of lung cancer incidence and mortality for 2018 were extracted from the Globocan database; observed incidence, from the last volume of Cancer Incidence in Five Continents, and tobacco prevalence, from the World Health Observatory/WHO database. Results. In 2018, over two million new lung cancer cases and 1. 7 million deaths were estimated to occur worldwide, representing 14% of the new cancer cases and 20% of the cancer deaths. Incidence rates showed marked variation between countries. Stable or decreasing incidence rates were predominant among males, while among females increasing rates were common. Conclusion. The continuing rise in lung cancer among women reinforces the need for strengthening implementation of the preventive actions committed to by governments in the WHO Framework Convention for Tobacco Control.
Data sources and population Estimates of lung cancer incidence for 185 countries in the year 2018 were extracted from Globocan database. The methods of national estimation rely upon the most reliable sources of cancer incidence and mortality data of high quality available at national or subnational level. Although population-based cancer registries (PBCR) may be national in terms of coverage, they often cover subnational areas; a detailed account of the methods is provided by Ferlay and colleagues elsewhere. 6 e extracted recorded incidence and population data as reported in the latest volume of Cancer Incidence in Five Continents (CI5, Volume XI),  7  a compendium of high quality data from PBCR worldwide, for 343 cancer registries in 62 countries worldwide, predominantly for the years of diagnosis 2008-2012.
Acknowledgements The work reported here was undertaken during the tenure of an  IARC Postdoctoral Fellowship , partially supported by the  European Commission FP7 Marie Curie -Actions -People -co -funding regional, national and international programmes (Cofund). We would like to thank the Directors and staff of the population-based cancer registries worldwide who compiled and submitted their data for the CI5 and Globocan projects used in this paper. Declaration of conflict of interests. The authors declare that they have no conflict of interests.
microti (spoligopattern SB0423; Mycobacterium bovis spoligotype database, http://www. mbovis. org). In the other 2 cases, the laboratory was unable to determine the species because of low DNA load from the sample. This result likely was due to the slow in vitro growth rate of M. microti in infected animals, which makes it difficult to isolate in routine diagnostic laboratories. Spoligopattern SB0423 is included in a phylogenetic cluster with spoligopattern SB0112, also associated with M. microti, on the basis of neighbor joining. Both spoligotypes are localized in the eastern French Pyrenees, close to the borders with Spain and Andorra (3). Most M. microti cases in France, found in cats, dogs, and llamas during 2005-2016 and, since 2015, in wild boars and badgers, have been found within 50 km of the outbreak area in Spain.
Acknowledgments We thank the forest guard body of the Catalan Government and Vedat Pirineus S. L. for wildlife sample collection. We are also grateful to  Maite Martín ,  Zoraida Cervera ,  Sierra Espinar , and  Mónica Pérez  for their outstanding technical assistance. This work was funded by the  Department of Agriculture, Livestock, Fisheries and Food (DARP)  of the  Government of Catalonia. IRTA is supported by  Centres de Recerca de Catalunya (CERCA) Programme/Generalitat de Catalunya  (www. cerca. cat).
9-r2). mainly was used to assess the extent and quality of the ORF in a transcript and search the sequences with the NCBI eukaryotes' protein database with the e-value of '1e-10'. Each transcript was translated in all three possible reading frames and used PFAM  SCAN (v. 1. 3) to identify occurrence of any of the known protein family domains documented in the Pfam database (Pfam A and Pfam B), by default parameters of ÀE 0. 001 -domE 0. 001. Transcripts predicted with coding potential by either/all of the three tools above were filtered out, and those without coding potential were the candidate set of lncRNAs. Genome-wide tRNA genes were predicted using the tRNAscan-SE web server  (Lowe & Chan, 2016). The genomic loci for circRNA, lncRNA and tRNA were deposited at CNGB Nucleotide Sequence Archive (https://db.
MeDIP-Seq sequence alignments and data analysis Raw sequencing data were filtered by removing the adaptors and discarding low-quality reads. Clean reads were aligned to the reference genome of S. japonica with BOWTIE's  (Langmead & Salzberg, 2012)  best mode using default parameters. Read depths of each sample were normalized to eliminate the influence via differences of total read numbers between samples (Fig. 1a ). After using input reads to eliminate the background noise in three MG samples, peaks were called using 'MACS2 callpeak'  (Zhang et al. , 2008)  (Fig. S2A ). According to GTF annotation, the normalized absolute read depth (NARD) between genomic components were calculated, including GENOME (genome-wide), TSSUP (transcript start site), TESDOWN (transcript stop site), exon, intron, transposable elements (TEs), genes encoding for lncRNAs, circRNAs and tRNAs.
Clustering and data analysis of WGBS Clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v. 3-cBot-HS (Illumina) according to the manufacturer's instructions. After cluster generation, the library preparations were sequenced on an Illumina Hiseq 2500 platform and 125-bp paired-end reads were generated. Image analysis and base calling were performed with the Illumina CASAVA pipeline, and finally 125 bp paired-end reads were generated. BISMARK software (v. 0. 16. 1)  (Krueger & Andrews, 2011)  was used to perform alignments of bisulfite-treated reads to a reference genome using default parameters. Results of the methylation extractor were transformed to BIGWIG format for visualization using the IGV browser  (Thorvaldsd ottir et al.
g. tRNAs, lncRNAs) divided by the sum of cytosines and guanines of this component. The approach also was used to calculate the methylation level of the whole genome (MLwg; Fig. 2b ). To remove experimental artifacts, noise and any batch effects, MLgc were normalized according to MLwg, which is defined as the normalized methylation level of genomic components (NMLgc; Fig. 2c )  (Xiang et al. , 2010). Differentially methylated sites (DMSs: a DMS corresponds to a single nucleotide), regions (DMRs) and promoters (DMPs) of WGBS The DMRs were identified using the SWDMR software (http:// 122. 228. 158. 106/swDMR/), which uses a sliding-window approach. The window size of 1000 bp using a step length of 100 bp was used. To avoid overlap of neighbouring DMRs, the sliding-window always started downstream of already identied DMRs.
Fisher's exact test was implemented to detect DMRs. The areaStat value that combines both the P-values and foldchange between two experimental groups were introduced to identify the DMR (see details for areaStat: http://www. biocond uctor. org/packages/release/bioc/vignettes/DSS/inst/doc/DSS. html#11_background). A larger absolute value of the areaStat is more likely to be a DMR. The minimal absolute value of areaStat in the present study is 40, resulting in a greater than six-fold difference in methylation level between two experimental groups (n = 3) with P < 0. 05. Customized scripts were used to identify DMSs using Fisher's exact test with FDR multiple test correction. DMSs with P < 0. 05 and a fold-change of > 2 in methylation level between two experimental groups (n = 3) were considered candidate DMSs.
Positive clones were screened and sequenced (Sunny, Shanghai, China). Up to 30 randomly picked clones for each amplified locus were sequenced using the ABI3730xl platform. Phylogenetic analysis Given the large diversity of DNA methyl transferases (DNMTs), only the DNA methylase motif region (PF00145) was used for constructing phylogenetic trees after sequence alignment using MUSCLE 3. 8. 31 (Edgar, 2004). Maximum-likehood (ML) phylogentic trees were constructed using MEGA6 with the LG + G + F model, predicted as best model by the 'Find best DNA/protein models' module of MEGA6  (Tamura, 2013). Motif structures of genes were visualized using the Interactive Tree of Life (iTOL)  (Ivica & Peer, 2016). 3D structures of DNMT proteins were visualized using X-ray structure models from SWISS-MODEL https://www.
swiss model. expasy. org/. The data of methylation level of each mC (MLmc) and its context attribute is obtained from the direct result BISMARK (Supporting Information Datasets S1-S9). The white spots of the violin plots stand for the median value. (c) Methylation level of genomic components (MLgc) of TSSUP (transcript start site), TESDOWN (transcript stop site), exon, intron, transposable elements (TEs), genes encoding for long noncoding RNAs (lncRNAs), circular RNAs (circRNAs) and transfer RNAs (tRNAs). To eliminate the variability among samples, all MLgc are normalized by the intrasample MLwg (GENOME). Error bars indicate AE SD. (d) The distribution of CG ratios (number of G and C residues/total number of residues) of all elements for all life stages. Upper panel: density plot lines of different types (in different colours) show the distribution of CG ratios of all element types.
Cytosine methylome landscapes based on WGBS Overall, only c. 1. 4% of cytosines in the genome of S. japonica were methylated based on WGBS. At such low levels of DNA methylation, a few sequencing errors can significantly impact the results. Thus, to minimize false positives and negatives in the present dataset, three biological replicates were sequenced for each life-cycle stage of S. japonica using WGBS (Fig. 1 ; Table  S1 ). By calculating the percentage of cytosines sequenced at cytosine reference positions in the lambda genome, a final conversion rate of > 99. 95% was identified for unmethylated cytosines. By combining nine individual methylome profiles, it was possible to obtain a high-quality, high-resolution kelp methylome with a final sequence yield of 92. 4 Gbp covering 86% of all cytosines in the genome (Table  S1 ).
To minimize the number of false positives, the lower threshold for identification of methylated cytosines was set at four sequence reads per methylated cytosine (Table  S1 ; Datasets S1-S9). Most of the chromosomes had the highest MLgf in MG with additional hypermethylated chromosomes 4, 17 and regions on chromosomes 9, 12, making MG the most methylated life-cycle stage, followed by FG and SP (Figs 1, S4, S5B; Table  S3 ). Although chromosomes 7 and 29 had a higher MLgf in FG compared to MG (Figs 1, S4, S5B), sex-determining genes were not found to be localized on these chromosomes  (Lipinska et al. , 2017)  (Tables  S3-S4 ). Approximately 57% of the methylated cytosines in the genome were methylated in a CHH context, whereas 19% and 24% were in CHG or CpG contexts, respectively (Fig. 2a ; Table  S4 ).
DNA methyltransferases and demethylases in S. japonica The DNMTs and demethylases (DNDMTs) are involved in the establishment of tissue and cell-type-specific methylation patterns during developmental processes in most multicellular organisms  (Meissner et al. , 2008; Kohli & Zhang, 2013; Satg e et al. , 2016; Iurlaro et al. , 2017). As the overall DNA methylation was relatively low in S. japonica, a homology-based search was performed in the S. japonica genome to identify all possible DNMTs and DNDMTs. Although the DNA-methylase domain PF00145 is common in all DNMTs, effective DNA methylation requires the following additional domains, which occur only in the DNMT1/3/4/5/6 family: ADD (IPR025766), PWWP (PF00855), PHD (IPR001965) and a bromo-domain (PF00439)  (Ponger & Li, 2005). All of the six DNMTs found in S.
japonica had the highest similarity to members of the DNMT2 family without any of the domains required for efficient DNA methylation (Figs 4a,b, S38). There were no homologues found with significant similarity to any of the other DNMTs such as 1, 3, 4, 5 or 6 (Fig. S37 ). Interestingly, ADD, PWWP, PHD and bromo-domains were found at other loci in the genome of S. japonica, but none of them were found to be DNMT2. In order to identify proteins involved in active DNA demethylation (DNDMTs), a search was carried out for RRM-fold domains (PF15628), DNA glycosylase domains (IPR011257), DME, DML, ROS, TET and MBD domains, all of which were reported previously to catalyse the removal of the 5mC base  (Meissner et al. , 2008; Kohli & Zhang, 2013; Su et al. , 2014; Lipinska et al. , 2017; Teng et al.
, 2017). Only one potential candidate was found in the genome, which included a DNA glycosylase and the methyl-CpG-binding domain protein 2 (MBD2, PF01429) (Figs S41-42). The MBD2 domain was reported to catalyze the removal of a methyl group  (Wu & Zhang, 2014). To have six genes from the DNMT2 family encoded in the S. japonica genome with all other DNMTs missing may indicate that DNMT2 methylates DNA in the structural context of tRNAs  (Kaiser et al. , 2017). Thus, the MLgf of tRNA genes encoded in the mitochondrial, chloroplast and nuclear genomes of S. japonica were analyzed for all three life stages individually (Fig. S43 ; Table  S25 ). Overall, the sporophyte had the lowest MLgf of tRNA genes for all three genomes. However, tRNA genes of the nuclear genome of the sporophyte were significantly more methylated than in the organellar genomes, which was not noticeable in the other life stages as tRNA genes in their organellar genomes were much more methylated than their counterparts in sporophytes.
S1-S9 and their source Illumina clean data for methylome have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession no. GSE117191 (https://www. ncbi. nlm. nih. gov/geo/query/acc. c gi?acc=GSE117191). The Illumina raw data for transcriptomes have been deposited at the NCBI database (the SRA accession numbers are SRR5860561-SRR5860568). The Illumina data of the MeDIP-seq experiments and the WGS data were.