dataesr/scientific-paragraphs-categorization huggingface dataset:
Extracellular proteins The mass peaks of the peptides obtained from the medium Fig. 2. Biological functions associated with 99 proteins identified in extracellular fraction. Number (blue) and percentages (orange) of the identified proteins associated with different biological functions. "Other functions" encompasses 36 functions for which 2 or fewer proteins were identified where no functional category contained more 2% of the total number of proteins. were compared with protein sequences in the NCBI and UniProt databases. A total of 99 proteins of the extracellular proteome were identified (Table  A2 ). The database ID, MW, pI value, function, biological process and cell location were obtained by interrogation of the Uniprot database (Table  A2 ). The proteins identified outside cells are from several different functional classes, acting in cell division, fatty acid biosynthesis, glycolysis, GMP biosynthesis, pathogenesis, protein biosynthesis, transcription, translation, and 36 other minor functions.
Most proteins (16%) are related to translation activities (Fig. 2 ). However no associated function was found for as many as 10% of the identified proteins in either of the two databases interrogated. The locations of the 99 identified proteins were predicted by PSORTb. Most proteins, 61 of them, were predicted to be cytoplasmic. For the other proteins, 4 were predicted to be located in plasma membranes, 4 in the cell wall and 13 in the extracellular medium. No location was predicted for 17 proteins. Like other Gram-positive bacteria, the pathogenicity of S. aureus is largely dependent on the secretion of numerous extracellular virulence factors. Therefore, the clinical performance of an antibiotic in treating S. aureus infection is determined not only by its bactericidal or bacteriostatic activities, but also by how it affects the release of virulence factors.
Proteins secreted by this pathway are translocated across the cytoplasmic membrane in an unfolded state. Subsequent processing and folding of these proteins take place in the cell wall environment on the trans-side of the membrane  [19]. The results of our study are in accordance with those of Wooldridge et al. [19]  who showed using Secreto-meP software that most S. aureus expo-proteins are exported from the cytoplasm via the Sec pathway. Some studies suggest that MRSA-specific proteins do not follow the signal peptide-dependent secretion pathways, which SecretomeP would predict to be secreted via the classical pathway  [20, 21]. Two of the predicted extracellular proteins are known to be related to virulence: the immunoglobulin-binding protein Sbi (A7 Â 659, Table  1 ) and leukocidin F-subunit (H7G6G2).
Among the extracellular proteins, we identified two putative uncharacterized proteins (H7G701 and D2FNS2). In the present study, IsdA (H7G3A4, Table  1 ), a protein that also acts as an adhesin and promotes cell binding to immobilized whole cells proteins, was identified  [24]. The IsdA protein is expressed at the S. aureus surface during infection required for human nasal colonization or survival on human skin  [24]. All protein accession numbers, from both proteome fractions, were input into statistical Venn diagram software, to compare the 236 accession identified in the cell surface fraction with the 99 from the extracellular fraction. The output reveals that 37 proteins are common to both proteomes (Fig. 3 ). These common proteins were analyzed by PSORTb software to predict their location into the cell.
STRAP software was used to present the data according to biological processes in which the proteins are implicated (Fig. 3 )  [25]. As expected, the extracellular proteome is less weighted towards cellular processes but more involved in regulation and metabolic processes and interactions with cells and organisms. The cell surface fraction contained more different kinds of proteins, more than 70% of which are involved in cellular processes. The proteins common to both fractions were mainly related to cellular processes, then to regulation. The presence of cytosolic proteins in both fractions complicates this kind of analysis, but it is still possible to differentiate which processes are most related to different proteomes or subproteomes. All 37 common proteins were predicted to be cytosolic proteins.
Conclusions The protein profile of MRSA C2355 showed evidence of the microorganism's phenotypic characteristics, consistent our current knowledge of the S. aureus genome with the overall metabolic regulation and cellular physiology expected for MRSA ST398. Identifying bacterial infection proteins like catalase and IsdA in the extracellular proteome is further evidence of the invasiveness of MRSA ST398. Future studies may identify novel determinants for this strain and elucidate the patho-physiological processes that facilitate the survival and replication of MRSA ST398 within the host that can be targeted to find treatments. The moonlight activity of some essential proteins is a challenge for proteomics, then the experimental confirmation of these moonlighting proteins will extend the localization data in databases.
ERA-Interim/Land is a global land surface reanalysis data set covering the period 1979-2010. It describes the evolution of soil moisture, soil temperature and snowpack. ERA-Interim/Land is the result of a single 32-year simulation with the latest ECMWF (European Centre for Medium-Range Weather Forecasts) land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanalysis and precipitation adjustments based on monthly GPCP v2. 1 (Global Precipitation Climatology Project). The horizontal resolution is about 80 km and the time frequency is 3-hourly. ERA-Interim/Land includes a number of parameterization improvements in the land surface scheme with respect to the original ERA-Interim data set, which makes it more suitable for climate studies involving land water resources.
Data set and methods The experimental set-up makes use of offline (or stand-alone) land simulations, which represent a convenient framework for isolating benefits and deficiencies of different land surface parameterizations  (Polcher et al. , 1998). In addition, given the complexity of the coupling with the atmosphere, offline simulations are much more cost-effective (faster) to run than a coupled atmosphere-land assimilation system. In this study, offline runs are performed both at the global and point scales. All the 3-hourly meteorological forcing parameters were linearly interpolated in time to the land surface model integration time step of 30 min. The land-use information has been derived from the United States Geophysical Survey-Global Land Cover Classification (USGS-GLCC) and the United Nations-Food and Agriculture Organization (UN-FAO) data set at the same resolution as the forcing data.
FLUXNET land energy fluxes FLUXNET is a global surface energy, water, and CO 2 flux observation network and consists of a collection of regional networks  (Baldocchi et al. , 2001 ; http://fluxnet. ornl. gov). Additionally, observational data for the year 2006 from the Boreal Ecosystem Research and Monitoring Sites (BERMS;  Betts et al. , 2006) , and the Coordinated Energy and water cycle Observations Project (CEOP) were used in this study. The FLUXNET observations are part of the La Thuile data set, which provides flux tower measurements of latent heat flux (LE), sensible heat flux (H ) and net ecosystem exchange (NEE) at high temporal resolution (30-60 min). For verification purposes, hourly observations from the year 2004 were selected from the original observational archive (excluding gap filled values) with a high-quality flag only (see Table  1 ).
The GTS-SYNOP observing network The GTS-SYNOP (Global Telecommunications Systemsurface SYNOPtic observation) is an operationally maintained data set under coordination of the World Meteorological Organization (WMO), which provides daily groundbased observations of the main weather parameters and selected land surface quantities, such as snow depth, at a large number of sites worldwide. The snow data are acquired at a minimum frequency of once a day and represent the only quantitative snow-depth measurements in contrast to remote sensing observations, which have limited information on snow depth. These data are operationally used at ECMWF for the daily global snow analysis as described in Drusch et al. (  2004 ) and de Rosnay et al. (  2014 ).
The GRDC river discharge data set The Global Runoff Data Centre (GRDC) operates under the auspice of the World Meteorological Organization and provides data for verification of atmospheric and hydrologic models. The GRDC database is updated continuously and contains daily and monthly discharge data information for over 3000 hydrologic stations in river basins located in 143 countries. Over the GSWP-2 period, the runoff data of verification of the soil hydrology  (Balsamo et al. , 2009). Pappenberger et al. (2009)  and  Balsamo et al. (2010b)  used the GRDC discharge to evaluate a coupled land surface-river discharge scheme for river flood prediction.
The experimental equivalence of offline and atmospheric coupled land data assimilation  (Balsamo et al. , 2007; Mahfouf et al. , 2008)  offers also in this case a 2 orders of magnitude computational saving. This is expected to provide a fast land surface reanalysis as envisaged within the EUfunded ERA-CLIM2 project. Moreover, it can open up new possibilities of more advanced data assimilation schemes (e. g. Fowler and van Leeuwen, 2012) , especially designed for non-linear systems.
Acknowledgements. The authors thank  R. Riddaway  from  ECMWF  for his valuable comments on the text, and  C. O'Sullivan ,  A. Bowen ,  S. Witter  for their help preparing the figures. This work used eddy covariance data acquired by the FLUXNET community that is gratefully acknowledged. TU Wien  provided the ISMN data for soil moisture verification and we thank them for their important effort. We thank the  GRDC  for data provision of global river discharge. The  ECMWF User-Support team  is acknowledged for making the data easily accessible. Edited by:  B. Su.
Methods and analysis: Seven databases with PubMed, EMBASE, the Cochrane Library, Web of Science, CINAHL, LiLACS and PsycINFO will be searched from 1966 to December 2013 (updated to May, 2015). There are no restrictions on language or type of publication. Randomised clinical trials assessing first-generation and newer-generation antidepressant medications against active comparator or placebo as acute treatment for depressive disorders in children and adolescents (under 18 years of age) will be included. The primary outcome for efficacy will be mean improvement in depressive symptoms, as measured by the mean change score of a depression rating scale from baseline to post-treatment. The tolerability of treatment will be defined as side effect discontinuation, as defined by the proportion of patients who discontinued treatment due to adverse events during the trial.
Types of participants Children and adolescents (aged from 6 to 18 years when they initially enrolled in the trials) with a primary diagnosis of current major depressive disorder according to standardised diagnostic interviews, for example, the Diagnostic and Statistical Manual of Mental Disorders (DSM)  [30] [31] [32]  or the International Classification of Diseases (ICD),  33 34  will be included. Where a trial contains a portion of participants who are over 18 years of age, we will contact the trial authors in order to obtain data for only those participants within our age range. We will exclude trials focusing on child or adolescent bipolar disorder, but will include trials involving patients with comorbid general psychiatric disorders, such as attention deficit hyperactivity disorder, anxiety disorder and substance-related disorder.
Types of outcome measures The acute phase will be defined as from 4 to 16 weeks, and if a trial presents data beyond 16 weeks or for more than one time period within our predefined acute phase periods, we will take the 8-week or close to 8-week time point. 35 We will exclude trials with treatment duration of <4 weeks. Where depression symptoms are measured using more than one depression scale in a trial, we will extract data from the depressive scales on the basis of a hierarchy of rating scales. This hierarchy will be based on psychometric properties and appropriateness for use with children and adolescents, and for consistency of use across trials (referred from the Hetrick 14 et al study) (table  1 ). The Children's Depression Rating Scale Revised (CDRS-R)  36  is adapted for children and adolescents from the Hamilton Depression Rating Scale (HAMD),  37  a tool validated and commonly used in adult populations.
Suicide-related outcomes Suicide-related dichotomous and continuous outcomes will be measured. If data are available, we will extract the number of participants with suicide-related events (combined suicidal ideation and suicidal behaviour) during the acute treatment, as measured on a standardised, validated and reliable rating scale, or reported cases of suicidality. 42 In addition, we will also collect data on suicidal ideation as a continuous outcome where a standardised, validated and reliable rating scale, such as the Suicidal Ideation Questionnaire-Junior High School version (SIQ-JR),  43  has been used.
Data sources and search strategy Seven electronic databases (PubMed, EMBASE, the Cochrane Library, Web of Science, CINAHL, LiLACS and PsycINFO) will be searched from 1966 to December 2013 (updated to May, 2015), with Medical Subject Headings (MeSH) and text words: 'depress*' or 'dysthymi*' or 'mood disorder*' or 'affective disorder*' and 'selective serotonin reuptake inhibitor*' or 'SSRIs' or gov, WHO's trial portal, and US FDA reports will be reviewed. There are no restrictions on language or type of publication. Additional studies will be searched in the reference lists of all identified publications including relevant meta-analyses and systematic reviews. All relevant authors and principal manufacturers will be contacted to supplement incomplete reports of the original papers or to provide new data for unpublished trials.
Data extraction and risk of bias assessment Two independent reviewers (YL, BQ) will independently extract the key trial parameters using a standardised data abstraction form and assess the risk of bias. The standardised data extraction forms will include the trial characteristics (eg, first listed author, publication year, journal, country, institution and sponsor), patient characteristics (eg, diagnostic criteria for depression, the type of patients, the number of patients, level of depressive symptoms, comorbidities, the age of patients and the gender of patients), intervention details (eg, antidepressant type, dose of antidepressant and the duration of treatment) and outcome measures (efficacy, tolerability, acceptability and suicide-related outcome). The risk of bias in trials will be assessed by the Cochrane risk of bias tool.
05 can be assumed if 95% CrIs do not include 0. If means and SDs are not provided, we will calculate them from the p value or other statistical indices as described elsewhere. 44 45 Results from intention-to-treat analysis (ITT) or modified ITT will be preferred over results from completer analyses, while we will also consider the data set for the means and SDs that are presented in the literature. The pooled estimates will be obtained using the Markov Chains Monte Carlo method. Two Markov chains will be run simultaneously with different arbitrarily chosen initial values. To ensure convergence, trace plots and the Brooks-Gelman-Rubin statistic will be assessed. 46 onvergence will be found to be adequate after running 50 000 samples for both chains. These samples will be then discarded as 'burn-in', and posterior summaries will be based on 100 000 subsequent simulations.
The node splitting method will be used to calculate the inconsistency of the model, which separates evidence on a particular comparison into direct and indirect evidence. 47 robability values will be summarised and reported as surface under the cumulative ranking curve (SUCRA) and rankograms, a simple transformation of the mean rank used to provide a hierarchy of the treatments and accounts for both the location and the variance of all relative treatment effects. 48 Network meta-analysis will be performed using the WinBUGS software package (V. 1. 4. 3, MRC Biostatistics Unit, Cambridge, UK) with random-effects models for multiarm trials. The other analyses will be performed and presented by the Stata V. 11. 0 and R 2. 11. 1 software packages.
Subgroup analyses The antidepressant medications will be coded according to clinical characteristics, risk of bias and sample size. We will conduct the subgroup analyses of data in primary outcome for efficacy. We will perform the following subgroup analyses by using the meta-regression model, and calculate Somer's D (a correlation coefficient for a dichotomous and an ordinal variable):  49  (1) sex ratio (male-to-female ratio >1 vs male-to-female ratio <1); (2) age group; (3) treatment duration; (4) severity of depressive symptom (mild-to-moderate vs moderate-to-severe); (5) comorbid general psychiatric disorders (with vs without comorbidity); (6) risk of bias ('high risk' literature vs 'unclear and low risk' literature); (7) sample size; (8) company sponsor (with vs without sponsor); and (9) the type of trials ( published literature vs unpublished literature).
Also, ClinicalTrials. Depression symptom severity measurement Hierarchy scales Abbreviation 1 Children's Depression Rating CDRS Scale 2 Hamilton Depression Rating HAMD Scale 3 Montgomery Asberg MADRS Depression Rating Scale 4 Beck Depression Inventory BDI 5 Children's Depression CDI Inventory 6 Schedule for Affective K-SADS Disorders and Schizophrenia for School Aged Children 7 Mood and Feeling MFQ Questionnaire 8 Reynolds Adolescent RADS Depression Scale 9 Bellevue Index of Depression BID 10 Child Depression Scale CDS 11 Centre for Epidemiologic CESD Studies Depression Scale 12 Child Assessment Schedule CAS 13 Child Behaviour Checklist- CBCL-D Depression Zhou X, et al. BMJ Open 2015;5:e007768. doi:10. 1136/bmjopen-2015-007768'serotonin norepinephrine reuptake inhibitor*' or 'SNRIs'.
The day-to-day variability of pollution levels is large, and strongly influenced by local and large-scale weather patterns. The European Copernicus programme (http://www. copernicus. eu) is focusing on Earth observation activities in the field of land, marine, atmosphere, emergency monitoring, climate change, and security. This programme includes a series of satellite missions -the so-called sentinels. Sentinel 5 precursor  (Veefkind et al. , 2012; launch planned in 2016) , Sentinel 4, and Sentinel 5 are missions dedicated to the atmosphere. The atmospheric component of the Copernicus programme is the Copernicus Atmosphere Monitoring Service (CAMS). This service has been established to help Europe respond to air quality problems and a changing climate. The purpose of the CAMS and the precursor project MACC (Monitoring Atmospheric Composition and Climate) is to combine satellite and other observations into a data assimilation modelling system in order to provide daily analyses and forecasts of the variability in atmospheric pollutant concentrations.
These reports  (Eskes et al. , 2014a)  are available on the MACC web-site at http://macc. copernicus-atmosphere. eu/services/aqac/ global_verification/validation_reports/. The VAL sub-project also provided a validation report for the MACC 30year ozone column reanalysis (the Multi-Sensor Reanalysis (MSR);  van der A et al. , 2010) , which is available on the MACC website. The VAL sub-project is maintaining a set of web pages with more detailed verification plots for individual seasons, months, or days (http://www. copernicus-atmosphere. eu/services/aqac/global_verification/). Some of these pages are based on near-real-time data, and they are complemented by the near-real-time (NRT) monitoring information from the data assimilation system. For a good understanding of the quality of the MACC system, it is important to consider which species in the global assimilation system are constrained by the observations, and which species are covered by the validation data sets used; this is summarized in Table  1.
The MACC aerosol and reactive gas models contain on the order of 100 species with global coverage and range from the surface into the mesosphere. Clearly, only a small fraction of this is observed and constrained by the available observations. -Assimilation: the MACC assimilation is focusing on aerosol optical depth (AOD), ozone, CO, NO 2 , and SO 2. Note that the species are treated in a univariate way and correlations in background errors of different species are neglected  (Inness et al. , 2015). An analysis update of one trace gas will nevertheless influence others through the chemical reactions. -Validation: the validation is also constrained by the limited amount of trace gas and aerosol properties for which validation data are available. Furthermore, validation is limited by the amount of external data that are available in real time or at least within a few weeks after measurement, and with a reasonable global coverage.
, 2009). Satellite AOD measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) are assimilated in this system  (Benedetti et al. , 2009). Changes of the operational system compared to the aerosol model described in the above papers can be found on the MACC website or in the VAL reports. The aerosol system is based on one model  (Morcrette et al. , 2009) , and there is no stand-alone version of the model operated without data assimilation. The reactive gas and aerosol modelling systems use realtime aerosol fire emissions from the Global Fire Assimilation System (GFASv1;  Kaiser et al. , 2012)  developed within GEMS and MACC. The VAL project evaluates all of these model configurations. For the near-real-time reports  (Eskes et al. , 2014a)  three model configurations are considered: the o-suite, the free-running IFS-MOZART, and free-running IFS-TM5 coupled systems.
Both model configurations are evaluated by the VAL team. A precursor of the C-IFS (CB05) system without data assimilation was producing daily forecasts from December 2012 to September 2014. This version was also evaluated by the VAL team, and results for this version are shown below. We remind the reader that o-suite always refers to the IFSbased analysis and forecast system including the assimilation of the full suite of aerosol, chemical, and meteorological observations.
Measurements used for validation The following independent data sets are presently used (year 2014) to produce the validation reports. Typical uncertainties and geographical details are provided in  -AOD and Ångström exponent (AE) data sets from the AERONET sun photometer network. NRT level 1. 5 data are made available on a monthly basis by NASA Goddard  (Holben et al. , 2001; Smirnov et al. , 2000)  and are used for a real-time verification of the analyses and forecasts. Supporting graphs were generated with the Ae-roCom tools (http://aerocom. met. no/cgi-bin/aerocom/ surfobs_annualrs. pl?Project=MACC). -AOD, AE and dust aerosol optical depth (DOD) from 36 AERONET stations, combined with AOD from MODIS (Aqua) and with lidar vertical extinction profiles at Tenerife station. These data sets are used for the quarterly assessments of mineral dust content, and analyses of outstanding dust events over northern Africa, Middle East, and Europe.