dataesr/scientific-paragraphs-categorization huggingface dataset:
24. 56. 2456 in 2014 where the ARD content of different North American soils was analysed using functional metagenomics The screening of the clones was performed on aztreonam, chloramphenicol, ciprofloxacin, colistin, cefepime, cefotaxime, cefoxitin, D-cycloserine, ceftazidime, gentamicin, meropenem, penicillin, piperacillin, piperacillin-tazobactam, tetracycline, tigecycline, trimethoprim and trimethoprim-sulfamethoxazole (cotrimoxazole). For the present study, we collected the nucleotide sequences of the inserts deposited on Genbank (KJ691878-KJ696532). The sequence translation of the open reading frames was performed by Prodigal (using default parameters) A total of 4,654 sequences of inserts were collected, in which 12,904 amino acid sequences were predicted. We then searched for ARDs belonging to the relevant ARD families according to the antibiotics used for the screening of the clones: beta-lactamases (all classes), APH, ANT, AAC(2), AAC(3)-I, AAC(3)-II, AAC.
Metabolomics Samples were prepared using a single-extraction method. Global untargeted metabolomics was performed using three platforms (ultra-HPLC [UHPLC]-MS in electrospray ionisation-positive and -negative modes, and GC-MS in electrospray ionisation-positive mode) to semi-quantitatively measure a diverse set of 542 serum metabolites. Targeted metabolomics was performed using an isotope-dilution UHPLC-MS/MS assay for the absolute quantification of 26 metabolites. Metabolites were identified by automated spectral comparison with a standard library and missing values were imputed using minimum non-missing measurement  [14, 17]. See ESM Methods for further details. The targeted and untargeted metabolomics data were further standardised to zero mean and unit variance per metabolite, and combined into a single data matrix containing 568 metabolite measurements from 543 samples.
Limitations of the study Distinct predictive signatures may be discovered in different studies due to differences in the metabolites measured, or differences in the genetic and environmental background of the study population. The relatively large overlap of our biomarker panel with known markers of type 2 diabetes, however, suggests that our results are robust and stable. Replication of our findings in the DESIR data also shows that the predictive model trained using Finnish individuals generalised to independent French individuals, despite the potential variability due to the demographic difference. α-HB was quantitatively measured in the BPS dataset, but using an untargeted platform in the DESIR dataset. Moreover, three of the metabolic markers, [Hyp3]-BK, X-12063 and X-13435, were not measured in the DESIR study.
Study population and study design The study population included all consecutive adult patients who underwent cardiac surgery with cardiopulmonary bypass (CPB) over a 7-year period between January 2006 and December 2012 (7161 patients enrolled) in our institution. Since 2006, patients, surgery characteristics as well as in-hospital outcomes were prospectively entered into an institutional database by trained research assistants (S1 Table ). The study was approved by Bichat hospital Institutional Review Board ("Comited'Evaluation des Projets de Recherche Biomedicale Paris Nord"; IRB 00006477). It waived the need for patient's informed consent because the data were analyzed anonymously. In accordance with the French Law, the registry was approved by the Advisory Committee for Information Processing in Health Research (ComiteConsultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Sante, Paris, France) and the French National Commission on Computing and Liberty (Commission Nationale Informatique et Liberte, Paris, France).
Reporting of the study complies with the Strengthening the Reporting of Observational studies in Epidemiology recommendations statement for reporting. Although data were prospectively acquired for a research purpose, the study should be considered as a retrospective study. Analyses were performed for all types of surgical procedures including emergency. The types of surgery were defined as follows: isolated coronary artery bypass (CABG), isolated valvular surgery or valvular surgery combined with CABG and other procedures. The only exclusion criteria were patients less than 18 years old and cardiac transplantations. The primary endpoint was in-hospital mortality. In-hospital mortality was defined as death occurring at any time in the hospital where the surgery was performed before discharge from hospital.
Nevertheless, multifactorial risk models developed through multiple logistic regression analyses have been proposed to improve risk prediction, The term of reference for the ideal risk score should be the simplicity of use (restricted numbers of parameters) and a robust PP. The Society of Thoracic Surgeons (STS) has developed risk models to predict mortality and morbidity for CABG, valve, and combined valve and CABG operations from a large multicenter dataset of patients. The STS risk is regularly updated and used an on-line risk calculator including a large number of covariates. Even though it is rather time consuming to calculate it for each patient, the STS score seems to be a more reliable tool to assess the OM of the high risk patient than the ES I  [18]. The ES II scoring system  [8]  was derived from a multicenter database of more than 20000 consecutive patients.
What reasons could be put forward to explain such a failure of risk scoring among the elderly? Experts explain this lack of performance by the fact that octogenarians are underrepresented in contemporary dataset with patients' characteristics and operative techniques moving over time  [17]. Moreover, specifics risks factors (extensive ascending aortic or valvular calcifications, tissue fragility) among the elderly have relevant impact on OM, but are not applicable to the general population. We could speculate that the high rate of patients (more than 25%) who died in the operating theater or very soon thereafter from direct surgical causes illustrate these unpredictable risks. Finally, assessment of cognitive and functional capacity and indices of frailty are not evaluated by current risk scores.
M. aurum genome sequencing and assembly A Pacific Biosciences RSII system (PacBio, University of Washington, Seattle, WA) was used  [29]  to obtain the complete genome sequence of M. aurum. A 3 to 20 kb library of this strain was prepared and sequenced using C2 chemistry kits on one single-molecule real-time (SMRT) cell with a 90-min collection protocol, achieving an average genome coverage of >100× for the strain. The 3 to 20-kb continuous-long-read (CLR) data were assembled de novo using the PacBio Hierarchical Genome Assembly Process 2 (HGAP2)/Quiver software package. The 5′ and 3′ 10 kb regions were investigated and aligned using Geneious (version 7. 1) to identified primer sequence and overlapping regions.
M. aurum genome annotation The complete genome sequence of M. aurum was submitted to the online Rapid Annotation Subsystems Technology (RAST) server  [30, 31]. The tRNA and rRNA genes were verified using the tRNAscan-SE search server  [32]  and RNammer  [33]. This genome annotation was used to identify the gene dnaA, which was used as the first gene of the M. aurum genome. The last annotated gene was rpmH gene. This genome manipulation was done using Geneious (version 7. 1). The annotation of M. aurum using RAST and Prokka (data not shown), did not lead to the identification of any plasmid coding sequences. In addition, we analyzed the genome sequence using PlasmidFinder (version 1. 3) that also showed no plasmid sequences in the M. aurum genome.
Comparative genomics All open reading frames (ORFs) annotated in the genome sequences of M. tuberculosis H37Rv, M. smegmatis, and M. marinum were retrieved from the National Center for Biotechnology Information (NCBI) website. These ORFs were combined with those identified in the genome sequence of M. aurum and compared using a Reciprocal Best Hit (RBHs) strategy with an E-value threshold of up to 100 to maximize our chances of finding homologs. We used UBLAST, as implemented in the sequence analysis multitool USEARCH (version 8. 1. 1812). Several python scripts were written to compare and manage the generated data. Data visualization was made using BRIG  [31].
Functional classification using Tuberculist and KEGG pathways All functional categories were obtained using the Kyoto Encyclopedia of Genes and Genomes (KEGG)  [34]  and Tuberculist (http://tuberculist. epfl. ch/). A python script was written to retrieve the Tuberculist and KEGG functional categories relative to M. tuberculosis H37Rv. Several python scripts were written to visualize the different data as a treemap.
Acknowledgments We would like to thank the BIOBANQUES at the  Institut Pasteur in Paris  for providing us Mycobacterium aurum, strain ATCC23366 registered under the number  CIP104465T. We also thank  Roland Brosch  and  Roxane Simeone  from the  Integrated Mycobacterial Pathogenomics Unit at the Institut Pasteur in Paris  for providing us Mycobacterium marinum, strain ATCC BAA-535. Finally, we thank  Hélène Munier-Lehman  and  Olivier Helynck  from the group Biochemistry and screening at the  Institut Pasteur in Paris  for their assistance in setting up the rezasurine assay and scaling it up to high-throughput screening.
Funding This work is part of the  Nanotherapeutics for Antibiotic Resistant Emerging Bacterial pathogens (NAREB) European Research Network  ( Collaborative Project ), supported by the  European Union 's  Seventh Framework Program  for research, technological development and demonstration under grant agreement no  604237. In addition, this work was funded by the  ERA-NET   Pathogenomics  project  GeMoA  registered under the number  PIM2010EPA-00719.
Timing and setting Studies with baseline and/or follow-up data for CCL18 level, chitotriosidase activity, and one or more prespecified outcomes will be included. Because patients were enrolled at varying time from disease onset across studies, we will collect the age at GD diagnosis and at evaluation. We anticipate that the length of follow-up ranges between 6 and 12 months for most randomized controlled trials. However, longer follow-up times are expected for patients enrolled in observational prospective cohort studies, study extensions, or ongoing registries. There will be no restrictions by type of setting.
Information sources Electronic databases Studies will be identified by searching Medline via PubMed (1995 onwards), EMBASE via Ovid (1995 onwards), and Cochrane Central Register of Controlled Trials (CENTRAL) via Wiley interface (current issue). The first report on increased plasma chitotriosidase activity in patients with GD was published in 1994  [11]. The first paper reporting on increased chemokine CCL18/PARC level in Gaucher disease was published by the same research group, 10 years later  [17]. Because a direct comparison of the accuracy for these two biomarkers was unlikely to be published before this date, our searches have been restricted to 1995 onwards. Our preliminary searches did not identify any relevant citation prior to 2004.
Other information sources We will supplement the electronic search by scanning the reference lists of retrieved original articles and previously published review articles for additional studies. We will search clinicaltrials. gov and the WHO international clinical trials registry platform in order to identify enzyme replacement and/or substrate reduction therapy trials that routinely evaluated chitotriosidase activity and CCL18 as surrogate biochemical markers of GD activity. Additionally, we will contact research groups, authors of relevant articles, and prominent clinicians within the field in order to identify ongoing or completed but not yet published relevant studies.
Search strategy Electronic search strategies will be developed by a member from the coordinating group and critically reviewed by a health sciences librarian, using the Peer Review of Electronic Search Strategies (PRESS)-2015 Guideline Statement  [26]. The search concepts will include plasma chitotriosidase activity, CCL18, biological markers, enzyme replacement therapy, and Gaucher disease. We will use both standardized medical subject heading (MeSH) and text words (Additional file 1: Appendix 1). No type of document restrictions will be applied and no methodology filters will be used. After the Medline search strategy is finalized, adjustments will be made to account for differences in syntax and subject headings (EMTREE) across electronic bibliographic databases. A member from the coordinating group with experience of searching for systematic reviews will execute electronic searches.
Selection process Two review authors will independently screen citation titles and abstracts, where available, yielded by the literature search against pre-specified eligibility criteria. The screening process will assess whether the citation (1) reported data from an original research study, (2) focused on patients with GD, (3) evaluated plasma chitotriosidase activity, (4) evaluated serum CCL18 level, and (5) captured one or more pre-specified outcomes of interest. The two review authors will rate each citation using a "relevant," "irrelevant," "unsure" (whether relevant or irrelevant) designation. Full-text articles will be retrieved for citations that received a "relevant" or "unsure" classification from at least one of the two review authors. Records rated "irrelevant" by the two review authors will be discarded for further review.
Data collection Two review authors will independently perform qualitative and quantitative data extraction. For this purpose, a data extraction form will be developed, pretested, and adjusted as necessary. Disagreements will be resolved by discussion between the two review authors. Where possible, IPD will be extracted from published articles and entered into a database. A hematologist with expertise in GD research and clinical management will invite corresponding authors and/or principal investigators of eligible primary studies to collaborate in this systematic review project by providing us with IPD. Each corresponding author and/or principal investigator will be first approached during international conferences or contacted by both regular and electronic mails. Contact information will be retrieved from published articles and online search.
Outcomes The pre-specified outcomes are derived from liver volume, spleen volume, hemoglobin concentration, platelet count, and symptomatic bone events with imaging confirmation (Table  2 ). The primary outcome is a composite of hepatomegaly, splenomegaly, anemia, and thrombocytopenia. The secondary outcomes include symptomatic bone manifestations with imaging confirmation, a composite of massive hepatomegaly or splenomegaly, severe anemia or thrombocytopenia, and individual components of the primary and secondary composite outcomes. Cut-off values for continuous parameter are set according to published guidelines or previous studies  [28, 29]. We anticipate that organ volumetric data will be derived by computed tomography, magnetic resonance, or ultrasound imaging, depending on the technology available.
Difference in areas under the ROC curve estimates will be pooled using random-effects meta-analysis models for plasma chitotriosidase activity and serum CCL18 level  [35]. Organ volumes will be expressed as multiples of normal (MN) adjusted for body weight. The normal spleen volume will be computed as 2 mL/kg multiplied by weight in kilograms in patient who did not undergo any splenectomy procedure. The normal liver volume will be computed as 25 mL/ kg multiplied by weight in kilograms (see the "Methods" section) One-stage approach synthesizes individual patient data in a single step, using a multilevel mixed-effects regression modeling that accounts for patient clustering within primary studies. This approach allows incorporation of patient-level covariates and exploration of potential interactions involving patient-or study-level covariates.
IPD meta-analysis collaborative group The IPD meta-analysis collaborative group will include the members of coordinating group and a representative from each primary study that provided individual participant data. Clinicians with field expertise may be invited to join the IPD meta-analysis collaborative group. Updated lists of the IPD meta-analysis collaborative group members will be available on the Prospective Registry of Systematic Reviews (PROSPERO) website throughout the conduct of the study. The IPD metaanalysis collaborative group members will be consulted at key stages of the project and given the opportunity to be involved in data analysis, interpretation of the findings, and critical review of the final manuscript.
Responsible research is the key Some guiding principles already exist to help radiomics scientists to further implement the responsible research paradigm into their current practice. For one, the Responsible Research and Innovation website (rri-tools. eu) provides useful guidelines. Furthermore, a concise set of principles for better scientific data management and stewardship has been defined, the "FAIR Guiding Principles"  (23) , stating that all research objects should be: i) Findable; ii) Accessible; iii) Interoperable; and iv) Reusable. Implementation of the FAIR principles within the radiomics field can facilitate its faster clinical translation. Many research tools and online repositories already implement a variety of aspects of the FAIR principles  (23) , and we can add two other tools of interest: i) The Cancer Imaging Archive (cancerimagingarchive.
net), a service which de-identifies and hosts medical images accessible for public download; and ii) The Radiomics Ontology (bioportal. bioontology. org/ontologies/RO), a repository on the National Center for Biomedical Ontology BioPortal aiming to improve the interoperability of radiomics analyses via consistent tagging of radiomics features, segmentation algorithms and imaging filters. This ontology could provide a standardized way of reporting on radiomics data and methods and would summarize the implementation details of a given radiomics workflow (e. g. , Table  1 ) in a more concise manner. To conclude, initial pioneer studies in radiomics have paved the way to an exciting field and to most promising methods to better personalize cancer treatments. Yet, better standardization, transparency and sharing practices in the radiomics community are required to improve the quality of  Multivariable analysis integrates variables other than radiomics features (e.
FEATURE SELECTION Feature robustness The robustness of features against segmentation variations and varying imaging settings (e. g. , noise fluctuations, inter-scanner differences, etc. ) is evaluated. Unreliable features are discarded. Feature complementarity The inter-correlation of features is evaluated. Redundant features are discarded. MODEL ASSESSMENT False discovery corrections Correction for multiple testing comparisons (e. g. , Bonferroni or Benjamini- Hochberg) is applied in univariate analysis. Estimation of model performance The teaching dataset is separated into training and validation set(s) to estimate optimal model parameters. Example methods include bootstrapping, cross- validation, random sub-sampling, etc. Independent testing A testing set distinct from the teaching set is used to evaluate the performance of complete models (i.
Gene expression studies Total RNA was extracted with TRIzol 1 reagent (Invitrogen). For real-time quantitative PCR (qPCR), total RNA samples (2 μg) were reverse-transcribed using High Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Primers for SYBR Green assays are presented in S3 Table. Amplifications were performed on an ABI Prism 7300 Real Time PCR System (Applied Biosystems). QPCR data were normalized by TATA-box binding protein (Tbp) mRNA levels and analyzed with LinRegPCR.
Acknowledgments We are very grateful to  Colette Be  ´toulières for animal care and technical assistance for animal experiments. We thank  Xavier Blanc  and his group ( UPAE, INRA, Jouy-en-Josas ) for the design and preparation of the experimental diets. We thank the staff members of the following GenoToul core facilities for technical assistance:  MetaToul/Lipidomic ,  GenoToul/Trix , Anexplo/Histopathology and Phenotyping. We particularly thank  Veronique Roques  for her help in the Lipidomic facility and  Yannick Lippi  for his help with high-throughput qPCR in the Trix facility. We thank  Dr. D. J. Mangelsdorf  ( Howard Hughes Medical Institute, Dallas, TX ) for providing us with the LXR-deficient mice and for constructive discussions. We thank  Dr W. Wahli  for constructive discussions.
Randomization In Study 1, patients were not randomized but directly entered in the treatment groups when inclusion/exclusion criteria were met. Before proceeding from one cohort to the next, safety and efficacy data were reviewed by a Data Monitoring Committee (DMC) and the following group was started only when predefined limits were met. In Study 2, randomization to treatment allocations followed a 2:2:1 ratio: either once daily for 3 days, twice daily for 3 days or to the comparator drug. Randomization was done by an interactive voice/web response system on day 1. The system generated the randomization lists of the patients and assigned treatment number and the respective treatment for the patient accordingly. Randomization was not stratified by site and the study was not-blinded. To compensate for this a DMC assessed safety and activity independently from investigator and sponsor.
A retrospective study of severely agitated geriatric patients was conducted using existing medical records and electronic database comparing a group who received early geriatric intervention with a group of patients who received standard of care. Forty-six patients were analyzed, 24 patients received early geriatric intervention and 22 patients received standard care. There was reduced number (1. 42 versus 2. 54, p = 0. 002) and types of antipsychotic medications used in the early geriatric intervention group compared with the control group. The use of behavior chart was higher in the early geriatric intervention group (100% versus 41%, p < 0. 01). There were more patients who required 5 or more code blacks in the control group. There was no significant difference in length of hospital stay, total number of code blacks, institutionalization, mortality, use of restraints, and safety incidents.
Statistical Analysis and Modeling Nonlinear regressions from experimental points regarding the evolution of the free endotoxin concentration according to time were performed by using GraphPad Prism version 5. 00 (GraphPad Software, La Jolla, California) with the following equations (where y = endotoxin concentration, x = time): the data obtained with Ceftriaxone treatment were ruled by an exponential curve {y = y 0. e kx }, amikacin and phages were ruled by a reverse exponential curve {y = y 0 +(plateau-y o ). (1-e -kx )}, whereas imipenem and cefoxitin were ruled by a sigmoid function {y = (V max. x h )/(K+x h )} (see Supplementary Tables  1  and 2 ). The areas under the concentration of free endotoxin vs time curve (AUC) were calculated using the trapeze method with MatLab 8. 3 (MathWorks, France).