dataesr/scientific-paragraphs-categorization huggingface dataset:
The total number of fragments mapping within bins spanning 10 bp genomic intervals were counted, and these counts were normalised according to the total number of fragments mapping within the surrounding 1 kb genomic interval. Nucleosome occupancy was calculated as the mean number of normalised counts in overlapping 50 bp bins centred every 10 bp, and the levels were calculated for every position from -1 kb to +1 kb surrounding refGene annotated promoters. Analysis of ChIP-MNase enrichment at promoters. The mean number of fragments per bp in each dataset mapping between -10 kb and +10 kb of each annotated refGene promoter was counted, and the fold-enrichment at each promoter was calculated by dividing this number by the mean genome-wide density of mapped fragments in the dataset (that is, the number of mapped fragments divided by the mappable genome size).
High coverage promoters, which were used in subsequent analyses, were defined as those with a mean coverage of ≥0. 05 fragments per bp in the range -1 kb to +1 kb in non-stimulated DC and fibroblast datasets (equivalent to 100 mapped fragments within the 2 kb range, or approximately 4-fold enrichment at the sequencing depths used in this study), and amounted to 13,559 promoters, or 53% of the total of 25,881 refGene annotated promoters. To ensure that this criterion did not result in exclusion of promoters of non-expressed or low expressed genes, the fraction of promoters of genes with distinct expression levels was calculated among these high coverage promoters, or among the 10% of promoters with the highest fold-enrichment (shown in Fig. 1c ). This confirmed that promoters of non-expressed genes are not excluded, although they are detectably under-represented (by 0.
Peaks with these very low prominences were excluded by applying a cut-off (excluding roughly 20% of all peaks; Supplementary Fig. 3d ); this relaxed criterion is intended to encompass moderately preferred nucleosome positions, and to allow the possibility of multiple preferred nucleosome positions at any genomic nucleosomal interval. Note that more-stringent cut-offs select higher-confidence nucleosome positions and display a strongly phased arrangement of internucleosomal spacing, but may overlook potentially valid but less-favoured positions (Supplementary Fig. 3e ). To estimate the resolution, distance discrepancies were determined between each predicted nucleosome position and the closest position that was independently predicted from a replicate biological sample: the data resolution is defined as the mean of all discrepancies.
An identical workflow was applied to genomic DNA fragmented by sonication to determine the upper limit that can be empirically measured by this approach (which is close to the resolution expected by random placement of regions at the same density; Fig. 1g , Supplementary Fig. 3f ). To determine the contribution of biological or methodological variability to the estimated resolution, sequence read data from replicate samples were combined and randomly reassorted into two mock replicates (thereby eliminating any underlying differences between samples), and the same workflow was applied to the in silico reassorted mock replicate datasets. To estimate the resolution corresponding to distinct levels of sequence coverage, genomic 4 kb intervals were assigned to groups defined by coverage level (within a twofold range for each group), and the resolution and mean coverage level was determined separately for every group.
The nucleosomal footprint was defined as the difference between the period and the offset calculated in this way. Estimation of the mean distance between peaks using a linear fit to the distribution of same-strand distances (to determine the period) and of the major peak of opposite-strand distances (to determine the nucleosomal footprint) gave comparable results (see Supplementary Fig. 4a, b, d ). Analysis of nucleosome gaps at DNase hypersensitive sites. DNase hypersensitive sites (DHS) analysed in fibroblasts correspond to ENCODE narrowPeak annotations (GEO accession number GSM1014177). To exclude promoters, only DHS midpoints >1 kb from annotated TSSs were analysed. The midpoint of each DHS was defined as the median cut site position, that is, the position at which half of all sequence tags mapped within the DHS region are mapped on either side.
Normalised nucleosome occupancies were aligned relative to this position. To identify predicted single-nucleosome-sized gaps within the ChIP-MNase data, we calculated the distances between all adjacent predicted nucleosome positions, and selected those in the range of 330-370 bp from each replicate fibroblast dataset. We considered only those gaps that were independently identified in both replicates, and that fell within 1 kb intervals with a minimum coverage of 0. 025 fragments per bp (25 mapped fragments within the 1 kb range). DNase hypersensitivity data was aligned relative to the midpoint of each gap. Gaps were deemed to correspond to DHS regions if the mean density of DNase sequence tags across the 400 bp interval centred on the nucleosome gap exceeded 0. 1 per bp (40 mapped tags per gap, corresponding to the highest 0.
Demultiplexed sequence data was aligned to the mouse mm9 genome using bowtie (with -v 2 -a -m 5 --maxins 2000 --tryhard for pairedend sequencing); alignment rates were around 78%. Excess duplicate reads that mapped to the exact same genomic location (corresponding to likely PCR duplicates) were eliminated if there were significantly more than expected based on the local read density in a range of ±2 bp at a p value of 0. 05; the level of redundant reads that were filtered out at this stage was around 6%. The total number of fragments mapping to each genomic interval were counted; fragments that mapped ambiguously to more than one genomic location were counted fractionally at all aligning locations. Determination of polymerase pause indices. The levels of RNA pol-II S7P or of unmodified RNA pol-II (using dataset GSM1624320) at promoters and within gene bodies were calculated as the mean ChIP coverage levels across the TSS ± 500 bp (1 kb interval, for promoters) or across the region between +1 and +6 kb (5 kb interval, for gene bodies).
The pause index for each promoter was defined as the ratio of pol-II levels at the promoter to the levels at the gene body. Public datasets used. Gene and promoter annotations are from refGene (refGene table downloaded 08112013). Datasets used to analyse gene expression in DCs (GSM624282), gene expression in fibroblasts (GSM970853), gene activation in LPS-stimulated DCs (GSM799198-9, 201-2, 204-5; series GSE32255  84  ), gene activation in TNF-α-stimulated fibroblasts (GSM318681-6; series GSE12697  88  ), DHSs in fibroblasts (GSM1014177), MNase-seq in fibroblasts (GSM2538320-2538343; series GSE96688), BAF-155 ChIP-seq (GSM1835955)  [89] [90] [91] [92] [93]  and Brg1 ChIP-seq (GSM1835956 and GSM1132963) were retrieved from GEO. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We are grateful to the teams of  Ulrike Bönisch  ( MPI-IE ),  Vladimir Benes  ( EMBL Gene-Core ), and the  IRCAN genomics core facility  (supported by  FEDER ,  Région PACA ,  Conseil Départmental 06 ,  ITMO Cancer Aviesan and INSERM)  for sequencing, the  MPI-IE  and IRCAN bioinformatics services for computing resources,  Hiroshi Kimura  for providing anti-H3K4me1 monoclonal antibodies and  Ritwick Sawarkar  for critical reading of the paper. This work was supported by funding from the  Max Planck Society , the  German Research Foundation , the  Ligue Contre le Cancer , and the  Agence Nationale de la Recherche. SS is supported by  INSERM.
Data availability The data that support this study are available from the corresponding author upon reasonable request. All datasets generated in this study are available from the NCBI Gene Expression Omnibus (GEO) database with accession number GSE142170. The source data underlying Figs. 1g, 2c, d,  and 5a , and Supplementary Figs. 2c, 3g and 9d, are provided as a source data file.
Objectives The main aim of this study was to determine the undergraduate dental students' self-reported effectiveness of online classes offered by various dental institutes of Pakistan. Materials and Methods A cross-sectional study was conducted using an online survey. A pretested and validated questionnaire comprising of 31 questions was circulated among dental students. The study participants were inquired about the perceived level of accessibility of resources and the effectiveness of online teaching. The data was analyzed using SPSS version 24. Mann-Whitney U test and Kruskal-Wallis test were used for statistical analysis at p ≤0. 05. Results A total of 1,207 students from all over Pakistan responded. The majority of students was dissatisfied with the institutional learning management system and quality of learning resources available.
Materials and Methods This cross-sectional study was conducted in the dental colleges of Pakistan. In Pakistan, the dental institutes pursue a 4-year undergraduate dentistry program followed by 1 year of mandatory house job before receiving their license to practice. Therefore, undergraduate dental students, from the first year to final (4th) year, who attended online classes, were included in this study. Students who declined to consent, house officers, and students of allied specialties were excluded from this study. Purposive sampling was done for sample recruitment. A pretested and validated questionnaire, comprising of 31 questions, was used. The questionnaire was made by using Google Docs (www. docs. google. com). The form was circulated among dental students of Pakistan through social media and e-mail.
Statistical Analysis Data were analyzed by using SPSS version 24. 0. Descriptive statistics for age was reported as mean and standard deviation, while frequencies and percentages were reported for all categorical characteristics (►Tables 1-3). Mann-Whitney U test was used to examine the differences in the parameters of the effectiveness of online education between public and private sector groups (►Table 4). The Kruskal-Wallis test was used to investigate the associations of characteristics like major geographic regions (provinces) of Pakistan and year of study with the parameters of effectiveness of online education (►Tables 5 and 6 ). A value of p≤0. 05 was considered significant.
The possibility of frequent power outages in hot summer weather coupled with an average performing internet connection may prove to be two most noteworthy limiting factors in determining the future efficacy of online teaching programs in Pakistan. In this survey, smartphones were rated highest among the devices used by the respondents for attending online lectures and social media was recorded as the most used mode for notification of class schedule. 7] [28]  Studies have reported that portability, time management, and easy access to data are some of the main reasons listed by the medical and dental students favoring increased usage of smartphones and mobile applications for e-learning. 26, 29 With the presence of a multitude of research evidence supporting the use of smartphones and social media technology for educational purposes, many authors recommend designing programs, materials, and activities that are compatible with smartphone technology, thereby integrating this diverse learning approach as a part of the official teaching curriculum.
Statistical Analysis Data are presented as means ± SD, median and interquartile range (for continuous variables), or percentages (for binary variables), as appropriate. Normality was assessed using Kolmogorov-Smirnov test. Comparisons across patient groups were performed using analysis of variance (ANOVA), χ² test, with or without continuity correction. Absolute agreement between young and senior operators was tested using a two-way mixed model considering average measurements. Interrater reliability was tested by Cohen's Kappa coefficient. Pearson's correlation coefficients were calculated to assess the relationships between continuous variables in data with normal distribution. ICC was computed by a single-rating, absolute-agreement, and 2-way random-effects model with 3 raters per Centre.
Statistical Analysis Data are presented as means ± SD, median and interquartile range (for continuous variables), or percentages (for binary variables), as appropriate. Normality was assessed using Kolmogorov-Smirnov test. Comparisons across patient groups were performed using analysis of variance (ANOVA), χ 2 test, with or without continuity correction. Absolute agreement between young and senior operators was tested using a two-way mixed model considering average measurements. Interrater reliability was tested by Cohen's Kappa coefficient. Pearson's correlation coefficients were calculated to assess the relationships between continuous variables in data with normal distribution. ICC was computed by a single-rating, absolute-agreement, and 2-way random-effects model with 3 raters per Centre.
General Characteristics of the Population The MASCOT HIT study enrolled 1037 subjects, of which 99 were excluded due to incomplete data provided. The final population was thus composed of 938 subjects: 309 healthy controls; 139 patients with AH, 194 patients with AS (total of 333 in the LA pressure overload condition group); 128 patients with MR and 168 patients with HF (total of 296 included in LA volume-pressure overload group). Mean population age was 59±14 years, 55. 7% males. Table  1  describes the clinical characteristics of the study population, while Table  2  presents the standard echocardiographic parameters.
93 for global QRS-LASr and 0. 90 for global P-LASr. The reproducibility of LA strain was close to the ICC value for LV GLS (0. 96). When analysing the study groups separately, we found that the reproducibility was better in pathological left atria compared to healthy individuals, with the best results in the pressure-volume overload model. All the ICC values are summarized in Table  4. Variability of QRS-LASr and P-LASr values was not affected by LA volume or E/e' ratio. Even if a trend of higher reproducibility can be found in MASCOT data both for QRS and P measurements in patients with dilated LA or elevated filling pressures, this did not reach statistical significance. Young echocardiographers were able to analyze QRS-LASr in both apical views in 90% of subjects, in only one apical view (4 or 2 chambers) in 9%, and the analyses were not obtainable in only 1% of cases.
While the current study confirms these factors as important for adherence in COPD therapy, additional study is needed to identify other factors within the WHO dimensions that may positively influence this critical aspect of patient care. True gains in improving adherence to COPD therapy and other long-term therapies will likely require integrated and holistic approaches that address all five of the WHO dimensions. Strengths of this study include robust outcome measures that identified treatment status and the reason for treatment discontinuation (physician decision or patient decision). In addition, the study comprised a large patient sample size (N = 1311) and collected data from a very large sample of physicians and pharmacists. To our knowledge, this is one of the first studies to evaluate COPD adherence in a low-middle-income country, which is representative of the high COPD prevalence in these countries.
Data collection Patients in the study attended their own treating physicians who performed COPD clinical assessments consistent with local medical practices and managed treatment guidelines. Physicians administered a questionnaire to participating patients at monthly visits and at the end of each visit; treatment status was binary categorized as treatment continued or treatment stopped. The questionnaire also collected the name and the address of the pharmacy from which the patients purchased their prescribed inhaler medication. The physician also collected data regarding patient deaths and treatment-related adverse events. Data regarding patients' social support related to assistance taking daily COPD treatment was collected as the absence of social support for the patient or patient supported by spouse, children, and siblings.
Hydroclimatic Data Nyong discharge data series come from two sources. The gauging station, as well as the methods used for reading water levels and calculating flow rates, however, remain the same from one source to another. The series obtained from the Hydrological Research Center (CRH) cover the period 1951-1987. This center manages a hydrometric database, generally with a daily time step, which contains almost all the measurements carried out on Cameroonian territory since the early 1950s, for most stations. These measurements were carried out first by ORSTOM (Office de la Recherche Scientifique et Technique Outre-Mer/Office for Scientific Research in Overseas Territories, Paris, France) researchers, currently IRD (Institut de Recherche pour le Développement/Research Institute for Development, Marseille, France) and their predecessors from EDF (Electricité De France/France Electricity, Paris, France) and, since 1980, by CRH.
No observation data is available on this river between 1988 and 1997. Indeed, after 1987, due to budgetary constraints, the hydrological service no longer guaranteed the continuity of observations. We note then the abandonment of a large number of stations observed, including those of Nyong and Ntem. Over the period 1998-2016, the series of discharges used are those of the ORE-BVET (Observatory for Environmental Research/Experimental Tropical Watersheds). Funded by the Ministry of Research and New Technologies, the National Institute of Universe Sciences, the Research Institute for Development and the Midi-Pyrénées Observatory, this observatory aims to understand the relative influence of climate variability and agriculture on water cycles and biogeochemical cycles in tropical areas. Based on long-term monitoring (>10 years) of meteorological, hydrological and geochemical variables in watersheds, it is implemented both in Cameroon and in India.
In Cameroon, the observatory is interested in the Nyong watershed. The complete series of Ntem discharges comes from the CRH, and covers the period 1953-2015, but with gaps during the 1990s and 2000s. There are no observation data on this river between the intervals  1993-2001 and 2006-2009. All of the hydrological data used in this work were collected daily. The monthly, seasonal and annual modules were calculated thereafter. The rainfall series comes from two sources. Over the period 1950-1999, the SIEREM (Système d'Informations Environnementales pour les Ressources en Eau et leur Modélisation/System of Environmental Informations on Water Resources and their Modeling) rainfall grids were used. SIEREM  [32]  is a database based on a generic scheme allowing the management of hydroclimatic but also environmental information on Africa.
This database was developed by the HydroSciences Montpellier/HSM Laboratory, from a rainfall database (around 7000 gauging stations) from Africa, inherited among others from ORSTOM, and its experience in the management of hydroclimatic databases in the intertropical region  [33, 34]. Among its recovery products, SIEREM offers monthly rainfall grids over Africa for the period 1940-1999, at the 0. 5 • square degree, approximately 2750 km 2 of the area considered  [35]. The space and time steps chosen seem to be the most appropriate resolutions to be able to appreciate, at the same time, the impacts of climate change and anthropic activities on water resources  [36, 37]. These grids are freely accessible in ASCII and NetCDF formats on the site http://www. hydrosciences. org/spip. php?article1387.
Before use, they had to be validated first by comparison with rain gauges measurements from stations. From 2000s, the choice was made for TRMM (Tropical Rainfall Measuring Mission) 3B42 V7 rainfall estimates. The TRMM platform was launched in 1997, built jointly by the United States and Japan. It offers several pre-calibrated data sets, including the daily product TRMM 3B42 V7 at a spatial resolution of 0. 25 • × 0. 25 •. These data combine syntheses of TRMM images and other satellite data  [38]. Thus, microwave sensors from AQUA, NOAA (National Oceanic and Atmospheric Administration), DMSP (Defense Meteorological Satellite Program) and visible/infrared data from geostationary satellites are taken into account in precipitation estimation algorithms. These estimates are then adjusted by incorporating the monthly climate measurements on the ground of the GPCP (Global Precipitation Climatology Project) and CAMS (Climate Assessment and Monitoring System) networks to give the monthly products TRMM 3B43.
The adjustment coefficients calculated for this product are finally applied to the daily data to give the final product TRMM 3B42, version 7  [38] , downloadable free of charge from the website https://giovanni. gsfc. nasa. gov, in ASCII format. The monthly TRMM rainfall data were first validated through comparisons with the SIEREM rainfall over the years of common availability (1998 and 1999). The correction factors (essentially between 1. 17 and 1. 4) were subsequently established for the months for which the differences between the two sources were significant (July and August). Four climatic indices (SOI: Southern Oscillation Index; DMI: Dipole Mode Index; NATL: North Tropical Atlantic sea surface temperature indices; SATL: South Tropical Atlantic sea surface temperature indices) representative of certain sources of large-scale variability (Pacific Ocean, Indian Ocean and Atlantic Ocean), likely to influence the climate of the region  [39] , were used in this study (Figure  2 ).
Spatial Data The spatial data used for the study of land use in the Nyong watershed are essentially the Landsat 8 satellite images from January 2015 and Landsat MSS from March 1973. Three Landsat scenes cover the entire watershed, corresponding to the path/row 185/57, 185/58, 184/57. The same study was carried out in the Ntem watershed, using four Landsat 8 scenes from January 2015 and Landsat MSS from February 1973, corresponding to path/row 184/58, 185/58, 184/59 and 185/59. All of these images are made available to the general public free of charge by the National Aeronautics and Space Administration (NASA), via the US Geological Survey website (https://earthexplorer. usgs. gov/), in GeoTIFF format. The downloaded images taken during the winter (December to mid-March) were preferred to that of the rainy seasons because they are less affected by cloud disturbances.
Spatial Data The supervised classification by maximum likelihood of the downloaded Landsat satellite images, using SNAP software (free access), allowed to perform a diachronic analysis of the evolution of land use in the studied watersheds. This operation was preceded by operations of preprocessing and recognition of objects in the field by photography and GPS (Global Positioning System). The preprocessing of satellite images refers to all of the processes applied to raw data to correct the geometric and radiometric errors that characterize certain satellite images. These errors are generally due to the technical problems of the satellites and to the interactions between the outgoing electromagnetic radiation and atmospheric aerosols, also called "atmospheric noise. " These atmospheric disturbances are influenced by different factors that are present on the day of acquisition, including weather, fires and other human activities.