dataesr/scientific-paragraphs-categorization huggingface dataset:
The influence of resources used for automated domain annotation of protein sequences was strongly emphasised. A variety of resources is needed to overcome the presence of potential bias in the set of initial domains used as models, and resources such as CDD  5  or Interpro 6 should be considered for comprehensive domain-based analyses. The relevance of some of the current classical domain databases was acknowledged, as they contributed to 73. 51% (computed from our dataset) of the overall sequence annotations (excluding overlapping annotations with PDB and PMP databases). V, I, L, F, M, Y, W ) and protein sequence energy (dark sequences after TREMOLO-HCA filtering). Frequency and folding energy (HP-struct) were computed for the four final groups of HCA domains from the dark proteomes after Tremolo-HCA filtering.
The colormap corresponds to the probability density computed from a Gaussian kernel estimation over the data points. The four groups of HCA domains have distributions similar to those obtained with the initial groups of sequences (dark proteins and regions, gray regions, PDB regions). The segments of proteins, or entire proteins, without any annotation are not uniformly distributed between kingdoms; eukaryotic and viral sequences especially possess a large reservoir of un-annotated domains, making them interesting targets for further investigations. Based on the protein sequence annotations, parts of the sequences were separated into four categories according to the domain annotation source: PDB regions (sequences annotated from direct structural information), gray regions (sequences annotated from domain family information), dark regions (un-annotated sequence part while other parts of the protein sequence are annotated), dark proteins (proteins without any annotation).
Then, to further detect new protein domains, the hydrophobic cluster analysis (HCA) methodology was used. The method was able to extract, in a large number of protein sequences, regions (called here HCA domains) that have a content in hydrophobic residues consistent with a possible ability to fold into compact 3D structures. The remaining parts of these sequences without any HCA domain, or other annotation, do correspond to truly disordered sequences, which appear to be less abundant than expected (4. 7% of all of the amino acids). The relative limited amount of sequences used in this study, restricted to Uniprot/Swissprot, probably underestimate the proportion of HCA domains from the dark proteomes sharing no obvious similarity with any other sequence. However, the quality of the sequences from the Uniprot/Swissprot dataset strongly limited the potential bias from artifactual proteins.
Methods Dataset. The set of protein sequences considered in this study was extracted from Uniprot/Swissprot, release of November 2015  27. The Swissprot dataset has the advantage of being meticulously curated with reliable annotations and the proteins of this set are less likely to correspond to artifactual constructs. Another advantage is to allow comparison of our results with the recent study of Perdigão and colleagues  23  , who used the same set of proteins. The Uniprot/Swissprot dataset is composed of 549 832 proteins. The dataset is separated by kingdoms, with 332 280 protein sequences from Bacteria, 181 584 sequences from Eukaryota, 19 369 sequences from Archaea, and 16 599 sequences from Viruses. Annotation. Protein domain annotation was computed for each protein sequence using different resources.
A correspondence map exists between the protein structure entries of the Protein Databank Bank  26  and the protein sequences of Uniprot. This correspondence map was used to annotate the Uniprot/Swissprot sequences with highly reliable structural information. Three different annotation sources were used to annotate the gray protein universe: the ProteinModelPortal (PMP)  60  , the Pfam-A version 28  8  , and the Conserved Domain Database (CDD)  5. The annotations were performed using the extracted PMP mapping to Uniprot/Swissprot proteins, the scanning tool from the Pfam utilities, and the CDD web server using default parameters. Annotations of the PDB group were extracted from the PDB/Uniprot mapping available on the Uniprot website. Physico-chemical features. Several statistical analyses were conducted after annotation.
For the different groups considered here, comparisons were performed to highlight possible differences at the sequence level through evaluation of the amino acid composition. A particular attention was also given to the physico-chemical properties of the studied sequences and to their characterization through hydrophobic cluster analysis (HCA). Protein sequence description. The sequences from dark regions and dark proteins were investigated using the HCA methodology  14, 25, 28, 29  which is presented in Fig. S13  in more details. Particularly, long segments with high density in hydrophobic clusters (typical of folded domains) were searched using the SEG-HCA software  14. A minimal size threshold of 30 residues was applied to consider the segments found by SEG-HCA as potential foldable domains that can behave as autonomous folding units.
Thus, the dark regions were separated into two different groups, likely foldable regions (presence of a hydrophobic core) and too short or too hydrophilic segments. The likely foldable sequences are referred to as the HCA domains. These sequences were compared to HCA domains extracted from the sequences of the PDB regions and gray regions in order to understand why these regions still don't have any domain annotation. This step allows a fair comparison between groups insofar as HCA domains have previously been compared to annotated domains and seen to cover them very well  14. Different descriptors were compared between sequences of each group: domain lengths, amino acid frequencies, content in hydrophobic amino acids, intrinsic disorder (estimated through the consensus field of the MobiDB 34 database), sites to undergo disorder-to-order transition upon binding (predicted by ANCHOR  35, 36  ), and the percentage of residues predicted in transmembrane regions (computed from the transmembrane field of the Uniprot sequence description).
As the domain detection by SEG-HCA heavily relies on the presence of strong hydrophobic amino acids (V, I, L, F, M, Y, W), a topological analysis of the hydrophobic clusters was performed. To that aim, the distances between hydrophobic clusters were calculated (between the last amino acid of a first cluster and the first amino acid of a second cluster, but also between the first amino acid of the same first cluster and the last amino acid of the same second cluster) and used as a proxy for hydrophobic cluster topology. Foldability of sequences from the dark proteome. Domains detected by SEG-HCA have the general characteristics of folded domains. To go further in the comparison of the sequences of the different groups, simulation using HP-lattice models were conducted using the HPstruct tool from the CPSP package  61.
Difference in energy relative to the composition in hydrophobic amino acids could indicate a general difference in topology that may explain the lack of annotation for the SEG-HCA domains included in the dark proteome. 5000 protein sequences of each group were randomly chosen and drawn, as a comprehensive calculation is not possible, due to computing limitations. Statistical tests. Distributions were compared using the two sample Kolmogorov-Smirnov test as the distributions do not appear following a Gaussian law. However, hypothesis testing is very sensitive to even small deviations when comparing large datasets as here. Therefore, our analyses focused on the observed differences between the groups and their interpretations, rather than on the computed p-values. However, some p-values are given when judged necessary.
Similarity searches. Absence of annotations for the domains of the dark proteomes described as foldable by the SEG-HCA software may originate from multiple causes. A deeper investigation of the potential similarities that the dark sequences may share with other sequences was therefore conducted. Several methodologies described in, Fig. S14 , were deployed to further characterize HCA domains belonging to the dark regions and to the dark proteins. First, as a general method to eliminate potential miss-annotations of sequences within the dark proteome, PSI-BLAST 62 searches were performed using the HCA domain sequences of the dark proteome as queries against sequences of the gray and PDB groups. HCA domain sequences with matches characterized by an E-value below 0. 001 and a coverage greater than 80% were considered as miss-detected known domains.
Second, the remaining HCA domains from the dark region and dark protein sequences were then pulled together and a psi-cd-hit  63, 64  analysis was performed (local alignment parameters with successive cutoffs of 90, 60 and 30 sequence identity). This method was used to evaluate the number of potential, still undescribed families within the dark regions. Finally, HCA domain sequences which do not belong to any cluster were then further analyzed using TREMOLO-HCA, a tool designed to unravel remote relationships that HCA domains sequences of the dark proteome may share with other proteins  30. Briefly, TREMOLO-HCA performs a HHblits search  39  followed by a CDD annotation, thus associating remote homology detection and domain context. In this study, the uni-prot_20 database (release of 2015) was used as the target database.
Therefore, smoking cessation is the single most important and effective way to reduce a person's risk of cancer  [29, 30]. In the present study, we examined the lifestyle factors (smoking, alcohol consumption, various salted and preserved food consumption, pre-chewing of food, and weaning practices) involved in the development of NPC in the Singaporean population. Since Singapore is a highrisk area for the development of NPC, studying the risk factors in this population provides more data for future studies of NPC in other Southeast Asian countries. The National Cancer Centre of Singapore (NCCS) is the country's largest tertiary referral center for NPC patients; as such, NPC patients recruited from there are representative of all Singaporean patients diagnosed with NPC.
Study population Between 2008 and 2012, 300 patients between 21 and 80 years of age who were Singaporeans or permanent residents of Singapore were recruited from the NCCS. All patients were newly diagnosed with histologically confirmed NPC. The controls were friends of patients, support group volunteers in hospitals and other organizations, members of religious societies, and NCCS staff members. Since Singapore is a small country with a fairly homogenous population, people's geographic location does not affect demographics and their exposure to the risk factors studied. Controls and patients were recruited simultaneously, and a total of 310 controls were recruited to match the cases. From the databank of 310 controls, one control was selected randomly and matched with each case in terms of age (±5 years), sex, and ethnicity using statistical software.
Data collection Data were collected from patients and controls via faceto-face interviews conducted in the NCCS by trained researchers using a standardized lifestyle questionnaire. This questionnaire was adapted from the International Agency for Research on Cancer (IARC) as part of a jointcollaboration in a wide study of the genetic epidemiology of NPC in Southeast Asia  [7]. Information collected included demographics, tobacco smoking, alcohol drinking, dietary habits (consumption frequency of traditional preserved food), and history of weaning (which included history of being breastfed, specific food items consumed, and whether food was pre-chewed). Date of diagnosis and clinical variables were obtained from patients' clinical records.
Data analysis Descriptive statistics were used to summarize the general demographic characteristics of the study population, stratified by cases and controls. The frequency of the food consumed was classified into three categories: never or rarely consumed, consumed at least once a month (monthly basis), and consumed at least once a week (weekly/daily basis). Where appropriate, categorical variables were assessed by the Chi square test or Fisher's exact test; continuous variables were assessed by t tests. The matched-pair method was used to analyze the data  [31]. Associations between the risk factors and NPC were examined singly using conditional logistic regression to obtain the crude odds ratio (OR) and its corresponding 95% confidence interval (CI), followed by multivariable analysis, adjusting for covariates and interaction effects if necessary  [32].
Adjusted ORs were reported from the multivariate model that was best fit obtained during statistical analysis. Additionally, to assess the possibility of dose-response relationships with risk of NPC, overall trend (P-trend) for the consumption of the food items was computed. Case-control pairs that had missing values in either case or control were excluded from the analysis. Unconditional logistic regression adjusting for age, sex, and ethnicity was also performed; the results were similar to those observed when conditional logistic regression was used. All statistics presented were obtained by conditional logistic regression. All statistical analyses were performed using STATA version 12. 0 software (Stata Corporation, College Station, TX, USA). Two-sided tests with P values less than 0. 05 were considered statistically significant.
We tried minimizing recall bias by asking confirmation questions and, on dietary questions, having several options in terms of consumption frequency. Second, our data collection was limited by the questions in the questionnaire asked directly about the study participants, particularly with regards to lifestyle, dietary information, and weaning practices. Data on weaning are ideally obtained from the mother of the study participant, rather than the participant himself or herself. But given that cancer is a late-onset disease, the mother may no longer be alive or may not remember this information, so the information was self-reported by the participant where possible. Notably, the amount of missing data on weaning was similar across cases and controls. Nonetheless, none of the weaning variables was conclusive, and these variables were not included in the multivariate model.
Acknowledgements We thank  Dr. Soong Yoke Lim  for reviewing this manuscript and giving valuable comments to improve it;  Dr. Paul Brennan  for enabling the collaboration between IARC WHO and NCCS; and  Ms. Ding Ying Wei  for collecting data in the early phase of the study. We also thank the staffs at the  NCCS Department of Radiation Oncology  and the  NCCS Specialist Clinic  who assisted with data collection.
Flow cytometric assay (FCA) As previously described  [5] [6] [7] , FCA profiles of samples were obtained by using a combination of GFP green fluorescence (viable cells expressing GFP) and propidium iodide (PI) red fluorescence for cells with damaged membranes. Flow cytometric measurements were performed using a BD FACSCalibur instrument (Becton Dickinson Biosciences) equipped with an aircooled argon laser (488-nm emission; 20 mW). The green fluorescent emission from GFP was collected in the FL1 channel (500 to 560 nm), and the red fluorescence from PI was collected in the FL3 channel (670 nm). A threshold was applied to the FL1 channel to eliminate background signals. Analyses were performed at a low-flow-rate setting. The results were analysed with Cell Quest Pro software (Becton Dickinson Biosciences).
Acknowledgments We would like to thank  (i) Dr. C. Ginevra  and  Dr. S. Jarraud  from the  French Reference  Center for Legionella for providing us with the GFP-modified strain, and (ii) Sophia Deleage and  Maxime Tarrit  for the validation of the model during their internships. This study was supported by a grant from  Jean Monnet University, France.
Initially, four hearts were stimulated at a 7 Hz basic frequency from either the right atrium (RA) or the LV endocardial surface at the apex. Once it was established that ventricular transmural conduction velocity (CV) was not different between these two pacing sites (summarized in Supplementary material online, Figure  S2 ), pacing was performed exclusively at the LV endocardial apex. Hearts from three Scn5a þ/À mice were unable to tolerate the baseline pacing frequency of 7 Hz used throughout the study, with no signs of ischemia or perfusion issues. The maximum pacing frequency tolerated by these hearts was recorded (see Figure  2C ) after which they were excluded from all subsequent analysis. Data were collected while hearts were paced at 7 Hz initially and then the highest stable pacing frequency possible, defined as the highest frequency where 1:1 capture could be maintained.
Data analysis The 10% point of the AP rising phase relative to stimulus pulse was used as an index of tissue activation at each transmural layer, allowing calculation of transmural CV and beat-to-beat activation variability. AP duration (APD) was calculated at 30, 50, and 80% repolarization levels. WF and 2 P data were analysed using custom written software in Delphi (Borland, Austin, TX, USA) and Matlab (Mathworks, Natick, MA, USA), respectively. Data are expressed as mean ± standard error. Statistical analysis was performed in Graphpad prism 7 (La Jolla, CA, USA) using Student's t-test (paired where appropriate), two-way analysis of variance (ANOVA) or repeated measures two-way ANOVA with Bonferroni multiple comparisons test, as indicated in figure legends. P <0. 05 were considered significant.
Study limitations Transmural electrophysiological data were acquired sequentially as simultaneously examining many layers in intact tissue using 2P imaging was not possible, limiting analysis to steady state behaviour synchronized to the stimulus pulse. Nevertheless, it enabled average transmural conduction behaviour of both ventricles to be examined within the same preparation with minimal physical tissue disruption, maintaining the preparation as close to physiological as possible. Ventricular wedge preparations are routinely used for optical interrogation of transmural electrophysiology  39  ; however, the small size of the mouse heart precludes use of such an approach. Computational modelling of the ventricular wall in 2 D utilized a verified mouse ventricular cell model to mirror the intact mouse heart used experimentally.
Acknowledgements The authors would like to thank  Karin Solvang-Garten ,  Nathan Scrimgeour ,  Ragnhild Rosbjørgen ,  Toril Rolfseng  ( NTNU; Trondheim, Norway ) and  Agnes Carcouet  (l'institut du thorax, Nantes, France) for their expert technical assistance. Two photon imaging was performed through the  Cellular & Molecular Imaging Core facility  at  NTNU.
Funding This work was funded by research grants from the  Nasjonal Forskning pa Hjerte og Karsykdommer (UNIKARD) , the  Norwegian Council for Cardiac Research, Norway , and the  British Heart Foundation  under project grant number  PG/14/66/30927. This work was also supported by a  Medical Research Council New Investigator  Grant (MR/N011007/1-to M. B. ) and by a grant from the  Agence Nationale de la Recherche  ( ANR-12-BSV1-0013-01-to F. C ).
Here, we identified subtelomeric duplication blocks based on pairwise comparisons of different subtelomeres within the same strain (Fig. 5a  and Supplementary Data Set 6). In total, we identified 173 pairs of subtelomeric duplication blocks across the 12 strains, with 8-26 pairs for each strain (Supplementary Table  15 ). Among the 16 pairs of subtelomeric duplication blocks previously identified in S288C (mentioned above), all the 12 larger pairs passed our filtering criteria. Interestingly, the Hawaiian S. paradoxus UWOPS91-917. 1 has the most subtelomeric duplication blocks and half of these are strain-specific, suggesting unique subtelomere evolution in this strain. The duplicated segments always maintained the same centromere-telomere orientation, supporting a mutational mechanism of double-strand break (DSB) repair as previously suggested in other species40,41.
We further summarized those 173 pairs of duplication blocks based on the orthologous subtelomeres involved. This led to 75 unique duplicated subtelomere pairs, 59 (78. 7%) of which are new compared to what was previously identified in S288C (Supplementary Data Set 7). We found 31 (41. 3%) of these unique pairs to be shared between strains, or even between species with highly dynamic strain-sharing patterns (Fig. 5b  and Supplementary Fig. 8a ). Most (87. 1%) of this sharing pattern could not be explained by the strain phylogeny (Supplementary Data Set 7). This suggests a constant gain and loss process of subtelomeric duplications throughout evolutionary history. Given the rampant subtelomere reshuffling, we investigated to what extent the similarity in orthologous subtelomere composition reflects the intra-species phylogenies.
Discussion The landscape of genetic variation is shaped by multiple evolutionary processes, including mutation, drift, recombination, gene flow, natural selection and demographic history. The combined effect of these factors can vary considerably both across the genome and between species, resulting in different patterns of evolutionary dynamics. The complete genome assemblies that we generated for multiple strains from both domesticated and wild yeasts provide a unique dataset for exploring such patterns with unprecedented resolution. Considering the evolutionary dynamics across the genome, eukaryotic subtelomeres are exceptionally variable compared to chromosomal cores40,53,54, with accelerated evolution manifested by extensive CNV accumulation, rampant ectopic reshuffling, and rapid functional divergence6,41,55-57.
Assembly evaluation and manual refinement We retrieved the reference genomes (Supplementary Note) for both species to assess the quality of our PacBio assemblies. For each polished PacBio assembly, we first used RepeatMasker (v4. 0. 5) (see URLs ) to soft-mask repetitive regions (option: -species fungi -xsmall -gff). The soft-masked assemblies were subsequently aligned to the reference genomes using the nucmer program from MUMmer (v3. 23)66 for chromosome assignment. For most chromosomes, we have single contigs covering the entire chromosomes. For the cases where internal assembly gaps occurred, we performed manual gap closing by consulting the assemblies generated in the pilot phase of this project. The only gap that we were unable to close is the highly repetitive rDNA array (usually consisting 100-200 copies of 9.
Annotation of the protein-coding genes, tRNA genes, and other genomic features For nuclear genomes, we assembled an integrative pipeline that combines three existing annotation tools to form an evidence-leveraged protein-coding gene annotation. First, we used the RATT package77 for directly transferring the non-dubious S. cerevisiae reference gene annotations to our PacBio assemblies based on whole genome alignments. Furthermore, we used the Yeast Genome Annotation Pipeline (YGAP)78 to annotate our PacBio assemblies (default options without scaffolds reordering) based on gene sequence homology and synteny conservation. A custom Perl script was used to remove redundant, truncated, or frameshifted genes annotated by YGAP. Finally, we used the Maker pipeline (v2. 31. 8)79 to perform de novo gene discovery with EST/protein alignment support (Supplementary Note).