dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Hydration is fundamental for proteins, and a common approach, amongst a wide list of methods, focuses on anomalous diffusion of water near the protein, hydration sites, residence time, etc. [24] [25] [26]. The present study takes a complementary point of view, more oriented towards the solutes. It yields a detailed description of the pores, water channels in the protein, and of their evolution. Attention is payed, not to the individual molecule motion, but to the protein-solvent interface as a complete, complex and evolving surface. To do so, the VLDP software was used to analyse a series of MD snapshots from a fully solvated system. Compared to other grid or Voronoi based algorithms defining pores as empty places in the protein structure (CAVER, HOLE, MOLE, etc. ), our method finds water-filled channels and pockets, which is an advantage for soluble molecules like enterobactin, sensitive to the physico-chemical properties, e.
Methods Simulation protocol. All Molecular Dynamics (MD) simulations were performed using the version 3. 1. 4 of the GROMACS simulation package (www. gromacs. org) with an extended united atom version of the GROMOS96 force field ffG45a3  30, 31. For consistency, the GROMACS version used is the same as in a previous reference work  18  for which the system was set up and subsequently extended, although this version is now deprecated. The enterobactin parameters and details on its parametrisation are available for download at the URL http://www. baaden. ibpc. fr/pub/other/ebact. zip. The SPC water model  32  was employed. The simulation protocol was similar to that used in previous studies of related iron transporters such as FhuA  16, 33. No attempt was made to enforce the iron-enterobactin transport by artificial forces or constraints.
The reason why we use the Laguerre weighted tessellation rather than the simple Voronoi one (which is the special case where all weights are equal) is that the geometry, in particular the volumes, are more coherent when changing the description from all-atom to the residue scale, and closer to experimental data  4, 38, 41. These weights fine-tune the position of the protein-solvent interface. The water weight value was determined by minimising the following function: ∑ σ σ = - + - f r v v ( ) (( ) ( ) ) , (1) i n i i r ref 2 ref 2 where v i is the average Laguerre volume of water O in snapshot i, the average being taken over the water layer at the water-solute interface (solute is either the protein or lipids); the reference v ref is the mean Laguerre volume of surface water molecules evaluated in a preliminary computation that included all water hydrogen atoms; σ i ,σ ref are the corresponding volume standard deviations.
This envelope is the set of residues that remain in contact with enterobactin in all snapshots with ligand. It defines the enterobactin site in FepA even when the enterobactin ligand is absent. Software. The topological and geometrical analyses have been carried out by VLDP (Voronoi Laguerre Delaunay Protein)  [4] [5] [6]  , a program developed at the LPTM (Cergy-Pontoise, F). The views of the molecules, polyhedra, channels, etc. were produced using Pymol (version 1. 0r2)  43. Parts of Fig. 1  were produced using HyperBalls  44. The plots were produced by Gnu R  45.
However, the still large computational requirements limit their usage for statistical studies. The aim of this work is to forecast and illustrate realistic selection functions for the eRASS cluster and point-source population. It relies on multiple realizations of selected areas of the eROSITA sky, with X-ray emitting sources described by controlled parametric inputs. For instance, the galaxy cluster population is uniquely described by its apparent flux and size on the sky. We bring special attention into reproducing the main spectro-photometric features of the extragalactic point-source population (AGN). For the first time, we process eRASS simulation fields with the eROSITA source detection software (preliminary version). We derive realistic detection lists, similar to the real detection lists expected for scientific use.
Source detection and characterization The source detection and characterization procedure used in this work is a preliminary version of the source detection tool in the eROSITA Science Analysis Software System (eSASS) package. It builds upon the source detection algorithm used in the XMM-Newton Science Analysis System (XMM-SAS) with several revisions and upgrades. The detection procedure is based on the sliding-cell method. As a first step, this algorithm scans an X-ray image with a sliding square box, and if the signal-to-noise in the box is greater than a specified threshold value it is marked as a source candidate. The signal is calculated from the pixel values inside the cell, and the background is estimated from the neighboring pixels. Then, the candidate objects are re-moved from the image creating a source-free image which is interpolated by a spline function to create a smooth background map.
The z-CR-HR method The z-CR-HR method is based on the premise that the raw X-ray data of a galaxy cluster contains significant information about its redshift, luminosity and temperature and that this information can be statistically extracted. The cosmological analysis is then simplified by basing it on purely the cluster redshift and quantities which are directly observable in X-rays, namely the count-rate in the 0. 5 -2 keV band (CR) and the hardness ratio (HR), which is the ratio of the count-rates measured in the 1 -2 keV and 0. 5 -1 keV bands. A particular advantage of this method is that it bypasses the need of having to derive individual cluster masses, X-ray luminosities and temperatures and that the scaling relations between mass and its X-ray proxies can be constrained simultaneously with the cosmological parameters.
, 2002). Such strains cannot always be differentiated in sufficient detail by means of the classical DNA-based methods  (Sabat et al. , 2013). More recently, improvements in both the time-to-results and the affordability of whole-genome sequencing (WGS) promised to overcome this limitation by providing access to complete microbial genomes  (Harris et al. , 2010; Salipante et al. , 2015). Both the WGS discriminatory power and its versatility helped to highlight transmission pathways intractable by the legacy Gold Standard methods  (Köser et al. , 2012a; Gordon et al. , 2014). Prior studies have shown the feasibility of large-scale genomic epidemiology studies in the public health and nosocomial context, leading to the generation of valuable databases covering the global epidemiology of MRSA.
Strains may also be defined based on the absence or presence of additional characteristic genes (toxins, resistance markers, etc. ). In our study, we used the SA DNA microArray (Alere) profiles and lineage assignment to identify the clonal lineages. It should be noted that the DNA microarray assignment is based on the richness of the Alere database using public sequences from both MRSA and MSSA strains, which have previously been described as new clonal lineages. Below we will describe sub-collections in more detail.
Data Analysis The sequencing data were processed through the EpiSeq TM V1 2  workflow. This is a reference-free approach and the workflow begins at de novo assembly. EpiSeq TM V1 uses the A5-MiSeq open-source pipeline  (Coil et al. , 2015). Read cleaning consisted in removing the sequencing adapters using Trimmomatic  (Lohse et al. , 2012). Then, the reads were filtered and trimmed further according to quality and length criteria (Supplementary Table  S2 ) using Trimmomatic and the pre-process function of String Graph Assembler SGA (Supplementary Table  S2 )  (Simpson and Durbin, 2012). Finally, SGA was used to correct errors in the reads by a k-mer frequency-based method. After being quality filtered and error corrected, the reads were assembled by the IDBA-UD500 with k-mer lengths from 35 to read length -1  (Peng et al.
When no match was found, we considered the allele as new and therefore no classical MLST profile could be assigned. The Wg-MLST profiles of the strains are provided in Supplementary Table  S1. The classical spa scheme for S. aureus was downloaded from the Ridom Spa website spaserver. ridom. de. As for Wg-MLST, we first extracted the spa sequence of each isolate using BLASTN against an in-house database of representative spa sequences. Then, we scanned the sequence for consecutive occurrence of the known short repeats and recorded the longest one as the spa motif for the isolate. The Wg-spa profiles of the strains are provided in Supplementary Table  S1. EpiSeq TM V1 uses SNPs in 2,158 core-genes (including the seven genes of the MLST scheme) to establish the phylogeny of the strains. The core-genes were selected from a pangenome constructed from 29 completely assembled S.
aureus genomes (Supplementary Table  S3 ). Orthologs from the pangenome were identified using Proteinortho5  (Lechner et al. , 2011)  using a cutoff of 70% of protein sequence similarity. Then the ortholog nucleic sequences were clustered with cd-hit (80% homology;  Li and Godzik, 2006). For each cluster one or several representative sequences were selected with a minimum sequence homology of 90%. This core genome set was validated using 1,557 genomes from both the public domain and internal sequenced genomes. This dataset includes 924 genomes from the study of  Gordon et al. (2014) ; 420 genomes from the NCBI Refseq genome database; 213 strains sequenced in house. Core-genes were excluded if their detection rates were low in the validation set (less than 5%) and/or the multiple-alignment of the validation sequences showed unexpected complexity (indels, inversions that could lead to alignments errors), or if they clustered with resistance or virulence markers via cd-hit (80% homology).
Spa type information is provided near the strain identifier. The sample origin is labeled by a symbol (see legend). The dots indicate nodes supported by a bootstrap score over 80, based on 100 repetitions. The larger the dot, the higher the score. The scale represents the number of SNPs in 2,131 core genes. sets. Bioinformatics platforms have to be developed to allow intuitive data management and exploration. These data sets should conform to internationally curated standards for sets of genes and mutations that are recognized as key virulence or resistance determinants. Consequently, the greatest limitation of WGS analysis is often not the software or the sequencing technology but (i) the completeness and the correctness of the reference database being used and (ii) the conversion of floods of data into actionable results  (Sabat et al.
, 2013; Gilchrist et al. , 2015). Standard tools for whole-genome alignment can be used to compare assembled genomes to databases of microbial genomes, which are available from sources such as the NCBI and the Integrated Microbial Genomes (IMG) database  (Markowitz et al. , 2014). Ideally, these networks will be based on a shared bioinformatic infrastructure that links genome sequences and metadata (time, location, method of isolation, clinical details, and additional variables) with tools that help appraise the clinical and public-health relevance of any given entry  (Grad and Lipsitch, 2014). However, keeping databases constantly up-to-date is difficult but remains a critical element in NGS analysis. The generic WGS bioinformatics' workflow (EpiSeq TM ) we use here can easily generate a uniform database and associated metadata, and therefore can be adapted to unforeseen evolution.
CONCLUSION Throughout our retrospective study, we have demonstrated that compared to conventional typing methods, WGS provides true additional insights into the evolutionary and transmission dynamics of S. aureus. The usefulness of WGS for outbreak investigation is currently still limited by the lack of real-time deployment of this technology. In addition, the enormous volume of data generated by NGS technology constitutes a major challenge to microbiology laboratories lacking dedicated bioinformatic support. In this study, we moved a step forward toward improving the accessibility of WGS by using a bioinformatics pipeline based on de novo assembly and by the inclusion of epidemiological markers. In parallel, and independently to the changes in sequencing technology, a strong requirement for global curated databases of phylogenetic, resistance, and virulence markers is mandatory to ease both sharing of experience and data exchange between laboratories as well as between hospitals.
ACKNOWLEDGMENTS We thank  Christine Courtier ,  Christine Gardon , and  Caroline Bouveyron  for their technical assistance at the National Reference Center for Staphylococci. We also thank  Nicole Desserrières ,  Régine Cuziat , and  Jean François Gorse  for their technical assistance at bioMérieux. We are grateful to  Marie-Cécile Ploy ,  Philippe Lehours ,  Veronique Duval ,  Sylvie Ledru ,  Didier Lepelletier , and  Chantal Pinchon  for providing the Staphylococcus aureus strains and the associated data.
g. 22). Existing samples for both types of study are limited in size, with previous work based on relatively shallow observations of pre-selected (and possibly biased) galaxy samples. MIGHTEE will provide a unique opportunity to revolutionise this field through a deep, wide-area dataset, with an unbiased sample of radio-selected AGN and a rich suite of ancillary data not normally available in wider 21-cm surveys. Using both direct H I emission detections, and stacking analyses, the H I gas content of samples of galaxies selected to display AGN activity will be compared against samples of non-AGN matched in mass, environment, and other properties. This will allow the investigation of the origin of the AGN activity and the influence of the AGN on its host galaxy. At the sensitivity of MIGH-TEE, it will be possible to detect H I masses down to around 2 × 10 9 M out to z = 0.
AGN polarisation as a probe of environment Radio-loud AGN are intrinsically highly polarised; the fractional polarisation in high-resolution, high-frequency images can approach the theoretical maximum of ∼ 70%  (57). MIGHTEE will provide extremely sensitive, broad-band L-band observations, supplemented in some cases with S-band data as described in Section 3. 1, and so will give measurements of integrated or moderately resolved polarisation as a function of frequency for many thousands of radio-loud AGN, allowing samples to be subdivided by e. g. luminosity and redshift to measure the evolution of intrinsic and environmental properties. Among other projects, MIGHTEE's polarisation surveys, coupled with total intensity information and optical identifications/redshifts for the target sources, will allow us to (i) statistically relate observed (de)polarisation to source environment, by combining with optical and (where possible) X-ray data available in the target fields; (ii) investigate the dependence of observed (de)polarisation on physical size, testing the prediction of models that sources observed at low resolution become more polarised as they become physically larger, so that the average Faraday depth to/dispersion in front of the lobes becomes smaller  (25) ; it does indeed appear to be the cases that sources of larger angular size are more polarised in deep surveys (59) but MIGHTEE will give much larger samples and constrain the physical size scale on which this takes place, and hence the physical size of the hot-gas halo; (iii) carry out largescale statistical studies of the Laing-Garrington effect for a large number of moderately resolved AGN, probing physical conditions in the centre of the host environment and giving a prior for every source on the angle to the line of sight; and (iv) carry out a statistical test of the (  13 ) depolarisation law and hence probe the fine structure and possibly the power spectrum of the magnetic field in the intra-cluster medium (ICM).
MIGHTEE scientific data products We plan to provide multiple intermediate MIGHTEE data releases of the reduced data following qualification of the data products for scientific use. Data will be released along with ancillary data where appropriate. Visibility data will be processed into images and catalogues using the data-centric processing facility at the South African Inter-University Institute for Data Intensive Astronomy (IDIA). The data processing pipeline is under development by a coordinated development team using the IDIA cloud-based development framework. We envisage that the MIGHTEE team will provide a data release 18 months after observations of a field are completed in either L-or S-band. The release will include data cubes for spectral line work, band-averaged multi-frequency synthesis total intensity images, full-Stokes intensity and RM synthesis cubes.
Added value of this study The most important changes in GBD 2017 are the independent estimation of population and a comprehensive update on fertility, which are described in a separate paper. There are several countries with significant differences in population size between the UNPOP estimates and the new GBD estimates. Since population is the denominator for mortality calculations, this leads to substantial changes in life expectancy and age-specific mortality rates in several countries. There were four major data additions and improvements that related to the estimation of mortality. First, for the estimation of population size, we systematically searched for census data and found data from 1257 censuses, which are now used in the analysis and which enabled an extended analysis of completeness using death distribution methods in more locations than previous iterations.
Second, in the estimation of adult mortality, we included data from 31 Demographic Surveillance Sites (DSS) which were adjusted based on the relationship between DSS under-5 death rates and national under-5 death rates. Third, we used published sources to create a database of fatal discontinuities from conflicts and natural disasters that extends back to 1950; each fatal discontinuity has been given a unique ID that tags the reported deaths to a location, date, and type of discontinuity. Fourth, GBD 2017 included an additional 622 data sources that were not available for GBD 2016 and which do not fall into the three categories already described. The main methodological improvements fall into two categories: the first category is enhancements to the modelling framework, which improved the estimation of both child mortality, defined as the probability of death below the age of 5 years, and adult mortality, a term we use to refer to the probability of death between ages 15 and 60 years.
For child mortality, we standardised hyperparameter selection for the spatiotemporal Gaussian process regression models, which enhances the comparability of results between locations and across time. For adult mortality, we also standardised hyperparameter selection and added child mortality as a covariate to the model. These changes had minimal effect on the mean estimate but changed the width of the uncertainty intervals in small populations and locations with sparse data. The second category encompasses three substantial improvements to the GBD model life table system: first, we revised the entire database to reflect the change in population counts. Second, each life table in the database was assigned a quality score using explicit criteria related to the variance in the slope of the death rate with respect to age, reductions in mortality at older ages compared with younger ages (age >60 years), and other unexpected crossovers.
Overview As with GBD 2016, this analysis adheres to the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) standards developed by WHO and others. 32 A table detailing our adherence to GATHER is included in appendix 1; statistical code used in the entire process is publicly available online. Analyses were done with Python versions 2. 5. 4 and 2. 7. 3, Stata version 13. 1, and R version 3. 1. 2. The methods used to produce estimates of age-specific mortality remain similar to those used in GBD 2016. Here we provide a broad overview and highlight the major changes since GBD 2016. All other details are included in appendix 1.
Data and data processing In the estimation of age-specific mortality for GBD 2017, we used five types of data. These were data from VR systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites (DSS). The most robust source for estimating age-specific mortality is a VR system that records all deaths by age, sex, and location. Our analysis of mortality starts with collating all publicly available VR data plus data shared directly by governments or GBD collaborators from VR systems. We evaluate the completeness of VR data separately for deaths under the age of 5 years and deaths over the age of 15 years. For under-5 deaths, we statistically compare VR-based death rates with those recorded in censuses or surveys.
This information is called a summary birth history and can yield an unbiased assessment of the trend in the under-5 death rate. 39  Assessments of adult mortality, in addition to VR and sample registration data, use survey data collected on sibling histories. A sibling history means that a respondent is asked to report on the survival or death of each of their siblings; in other words, the respondent provides a complete birth history for their mother. Sibling histories are subject to survivor bias and recall bias. Sibling history data are processed for GBD using methods that address these limitations. 40  Some surveys and some censuses also use information on deaths in a household over some recent time interval-for example, the past 12 months. Studies suggest that respondents can over-report or under-report deaths of household members.
41  We apply death distribution methods to assess completeness, which can be greater than 100% due to tele scoping of event reporting, which happens when a respondent reports an event that happened before the recall period as if it happened during the recall period. For GBD 2017, we also included DSS data on adult mortality for the first time, specifically on the probability of death between the ages of 15 and 60 years (45q15), from local communities that are under direct surveillance. Because these DSS communities are not nationally representative, we adjusted the level of 45q15 based on the ratio of the probability of death from birth to age 5 years (5q0) from the DSS to the national 5q0, taking into account that the relationship between 5q0 and 45q15 changes as the level of 5q0 declines because, on average, there are larger declines in 5q0 than in 45q15 over time.
New data for GBD 2017 compared to GBD 2016 In GBD 2017, we have added 458 location-years of VR data at the national level and 9 location-years of VR data at the subnational level compared with GBD 2016. We also included an additional 62 complete birth history sources at the national level, 12 complete birth history sources at the subnational level, 72 national summary birth history sources, and 16 subnational summary birth history data sources. 11 national and seven subnational sibling history surveys were also added. We included 1529 datapoints from DSSs in 15 countries. The total numbers of datapoints used were 181 625 for under-5 mortality estimation and 63 234 for adult mortality estimation. We also used 35 177 empirical life tables in the all-cause mortality database for GBD 2017. Appendix 1 provides complete lists of data availability and data sources by location; these are also available using our online source tool, the Global Health Data Exchange.