dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  A. Skarshewski  from  AGRF  for help with raw data processing before IMGT/ HighV-QUEST analysis;  M. Tinning  from  AGRF  for helpful advice on experimental design;  A. Lucas  from the  Institute for Immunology and Infectious Diseases ,  Western Australia , for conducting HLA typing; the  AMREP Flow Cytometry Facility  for cell sorting;  S. Turner  from the  Department of Microbiology and Immunology, The University of Melbourne , for helpful discussions and reading of the manuscript;  V. Venturi  from  Complex Systems Biology Group, University of New South Wales , for helpful discussions and advice on data annotation. This work was supported by grant number  543143  from the  National Health and Medical Research Council (NHMRC) Australia  and a private donation to the  Burnet Institute.
J. J. M. is a  NHMRC Career Development Fellow  supported by a  Wales Office of Research and Development (WORD) Research Funding Scheme , S. R. B. and E. J. G. are  NHMRC Senior Research Fellows. We gratefully acknowledge the contribution to this work of the  Victorian Operational Infrastructure Support Program. IMGT is funded by the  Ministere de l'Enseignement Superieur et de la Recherche (MESR) ,  Centre National de la Recherche Scientifique (CNRS)  and  Universite Ḿontpellier 2, France. IMGT/HighV-QUEST  was granted access to the HPC resources of CINES under the allocation 2010-2013-036029 made by  GENCI  ( Grand Equipement National de Calcul Intensif ).
End-points and statistical analysis Data are presented as frequency (percent) or median (range). The end-points studied were overall survival, progression-free sur-vival, non-relapse mortality, disease progression (relapse) as well as acute and chronic graft-versus-host disease (GVHD). Progression was defined as either relapse or disease progression for patients who did not achieve complete or partial response. Non-relapse mortality was defined as death without progression. Non-relapse mortality and disease progression were analyzed in a competing risks' setting, in which each of these events was considered as a competing event for the other one. Acute and chronic GVHD were also analyzed in a competing risks' setting with death and relapse as competing events. Patients who developed GVHD after donor lymphocyte infusion were considered as not having GVHD in these analyses.
Acknowledgments The authors would like to thank the  Société Française de Greffe de Moelle et de Thérapie cellulaire (SFGM-TC)  for sharing their database and the data manager Nicole Raus. We also thank the  Agence de la Biomedecine , especially  Dr. Irina Ionescu  and  Dr. Federico Garnier , for kindly sharing their database for cord blood transplantation. We would like to acknowledge the physicians who took care of patients. We also thank  Dr. Felipe Suarez  and  Pr. Olivier Hermine  for their helpful discussions and editing the manuscript.
S1E ). In 5 mice the infection was allowed to proceed for 6 h and in the other 10 mice for 24 h. These 15 mice represent data pooled from 4 separate experiments performed at different times using skin from 7 different donors. Bacterial loads in the blood averaged 4. 8610 4 CFU/ml at 6 h and 2. 4610 4 CFU/ml at 24 h (Fig. 2A ). At 24 h 5/10 mice had no detectable bacteria in their circulation. Bacterial counts were then determined from the grafted human skin and other organs. Bacterial counts were consistently high in the grafted human skin averaging 2. 1610 4 CFU/mg of tissue after 6 h of infection, and 4. 4610 2 CFU/mg after 24 h (Fig. 2B , Hu skin). In the same mice, contralateral mouse skin contained virtually no detectable bacteria (Fig. 2B , Ms skin). As a control of the grafting procedure, skin from C57/BL6 mice was grafted in place of the human skin on SCID/Beige mice.
Microscope set-up Intravital and fluorescence confocal microscopy was performed using an inverted Nikon Eclipse Ti microscope equipped with a CSU-X1 M1 confocal head with a Yokogawa spinning disk head and an Evolve (Photometrics) camera (Roper Scientific, France). Images were captured using MetaMorph software (Molecular Devices, USA). Histology was imaged using a Nikon Eclipse TC600 with a Baumer camera and Archimed software (Microvision Instruments, France). Image processing was performed using MetaMorph and Image J  [43]. Final figures were created in Photoshop (Adobe, USA).
Acknowledgments Authors would like to thank:  Dr David Maladry  for surgical assistance,  Dr. Susanna Celli  for providing training of the grafting procedure. Dewi Vernerey  for assistance with the statistical analysis. Eric Camerer ,  Vassili Soumelis ,  Nicolas Lerolle ,  Silke Silva ,  Magali Soyer ,  Anne-Flore Imhaus ,  Valentina Lo Schiavo ,  Flore Aubey  and  Arthur Charles-Orzag  for critical reading of the manuscript.
Background: Of major concern in any febrile child presenting with a seizure is the possibility of bacterial meningitis (BM). We did a systematic review to estimate the risk of BM among various subgroups of young children with a first seizure in the context of fever, and to assess the utility of routine lumbar puncture (LP) in children with an apparent first FS. Methods/Principal Findings: MEDLINE, INIST, and the COCHRANE Library databases were searched from inception to December 2011 for published studies, supplemented by manual searches of bibliographies of potentially relevant articles and review articles. Studies reporting the prevalence of BM in young children presenting to emergency care with a first: i) ''seizure and fever'', ii) apparent simple FS, and iii) apparent complex FS were included.
Search strategy The literature search aimed to identify all studies looking at the prevalence of BM in young children presenting to emergency care with a first seizure in the context of fever. To identify eligible original articles, we searched the following electronic databases from inception to December 2011: MEDLINE via PUBMED, INIST (Scientific and Technical Information Institute) via article@inist, and COCHRANE library. In each electronic database, various combinations of the following search terms were used: ''febrile seizure'', ''febrile convulsion'', ''fever'', ''seizure'', ''convulsion'', ''meningitis'', and ''central nervous system infections''. The reference lists of potentially relevant articles and review articles were also screened for additional articles of interest. The detailed search strategy for each electronic database can be found in Text S2.
When multiple articles reported on a same study population, we included only the most detailed publication that met the inclusion criteria. When studies were identified as containing pertinent data not included in the published article (e. g. , studies that included children with a first FS, but did not differentiate between simple FS and complex FS) we contacted the authors to obtain missing data. When a response was not provided and raw data from the original study allowed us to differentiate between two groups of patients, such articles were included in the study; otherwise, they were excluded. Because of the likelihood that small studies would have overestimated event outcome rates, studies including less than 20 patients were arbitrary excluded. Case reports, review articles, editorials, comments, and clinical guidelines were excluded.
Data extraction and quality assessment Data extraction and quality assessment of the included studies were performed independently by two of the reviewers (AN, AM) using a standardized data collection form. Any disagreement was resolved unanimously by discussion. The following information was extracted from each study: first author, country of origin, dates of enrollment, type (prospective versus retrospective), clinical setting, number of patients, inclusion and exclusion criteria, method of outcome ascertainment (clinical grounds, clinical grounds with follow-up, LP), number of outcome events (CNS infections, BM), definition of FS (for studies on simple FS, and complex FS), number and outcome of patients pre-treated with antibiotics, and enrollment era (pre-vaccine versus post-vaccine era).
Data synthesis and analysis All analyses were performed separately for each group of patients: children with a ''seizure and fever'', those with an apparent first simple FS, and those with an apparent first complex FS. Studies, patients, outcome ascertainment methods, and outcome data were summarized using basic descriptive statistics (simple counts and proportions). The average prevalences of CNS infections and BM were calculated by dividing the number of children with target outcome by the number of children included in the study. As the prevalence of BM was the main focus of our study, the pooled proportion of BM was synthesized by using meta-analytic techniques. Proportions were first transformed into a quantity based upon the Freeman-Tukey variant of the arcsine square root transformed proportion  [46]  suitable for the usual fixed and random effects summaries  [47].
Cellular fractionation assay Hela cells were seeded in 6-well plates (1. 5610 5 cells per well) 24 hours before transfection, then transfected with 2 mg plasmid DNA and 4 mg of Jetprime reagent (Polyplus) per well. Cells were fractionated 24 hr post-transfection using the method firstly described by  [23]  and modified by  [15]. The total protein concentration of each fraction was determined by Bradford assay. 30 mg (for endogenous proteins) or 5mg (for ectopically expressed proteins) of each fraction were analyzed by Western blotting with anti-TOX4 antibody (Sigma HPA017880 1:1000), anti-NOVA1 antibody (Abcam Ab97368, 1:700), anti-LEDGF antibody (BD #611714 1:2000), anti-FLAG M2 antibody (Sigma, A2220 1:2000) or anti-HA antibody (Sigma, 1:2000).
Endogenous Western blot of TOX4 and NOVA1 Lysates from Hela, SHSY5Y brain, and Jurkat cell lines, and stimulated PBMC cells taken from two donors were harvested in radioimmunoprecipitation assay (RIPA) buffer (20 mM Hepes, 150 mM Tris-HCl [pH 8. 0], 150 mM NaCl, 1% [wt/vol] deoxycholate, 0. 1% [wt/vol] sodium dodecyl sulfate, 1% [vol/ vol] NP-40), 2 mM EDTA). Total protein concentration was determined by Bradford assay and 30 mg of protein was Western blotted using either anti-TOX4 antibody (Sigma HPA017880 1:1000), anti-NOVA1 antibody (Abcam Ab97368, 1:700) or anti-B-actin antibody (Sigma, 1:2000).
Epifluorescence Microscopy Hela cells were seeded in 24-well dishes on sterilized glass coverslips (8610 4 cells per well) 24 hours before transfection, then transfected with 0. 5mg total plasmid DNA and 1mg of Jetprime reagent (Polyplus). 24 hours post-transfection cells were fixed with 4% paraformaldehyde for 10 min. Cells were then washed twice in PBS and permeabilized in 0. 1% (vol/vol) Triton X-100 in PBS for 10 min. Cells were blocked for 30 minutes at 37uC in blocking buffer containing 10% FCS, 3% BSA, 0. 1% (vol/vol) Triton X-100 in PBS. Cells were stained with primary antibodies for 30 minutes at 37uC, followed by washing (36 10 min in PBS-Triton X-100 0. 1%), and detection with secondary antibodies coupled with Alexa dyes (A488, A555 or A647) (Molecular Probe, dilution 1:2000). Primary antibody dilutions were as follows; anti-TOX4 antibody (Sigma HPA017880 1:200), anti-NOVA1 antibody (Abcam Ab97368, 1:200), anti-LEDGF mouse antibody (BD #611714 1:200), anti-LEDGF rabbit (Bethyl A300-848 1:200), anti-SC35 (Abcam Ab11826, 1:200), anti-Coilin (Abcam Ab87913, 1:200), anti-FLAG M2 antibody (Sigma, A2220 1:1000), anti-HA rat (Roche clone 3F10, 1:500).
After secondary incubation coverslips were washed again (3610 min in PBS-Triton X-100 0. 1%), rinsed in ddH2O and briefly dipped in 100% ethanol. After drying, coverslips were mounted in Fluromount G medium (Electron Microscopy Science) containing 400 ng/ml DAPI (Sigma). Z-stack images taken at 200 nm steps (pixel size 102 nm) were captured with an Axio-Imager microscope (Zeiss) with a 636 oil objective (NA = 1. 4). Images were cropped using Image J (v1. 47p ImageJ, US NIH, Bethesda, MD, http://rsb. info. nih. gov/ij/). , then deconvolved using Metamorph version 7. 1 software (Meinel algorithm, 7 iterations). Co-localization analysis was perfomed using the JACoP plugin in Image J. Images represented in figures are a maximum z-projection of 5 deconvolved z-stack images surrounding the focal plane.
Selected fractions were dialyzed against 150 mM NaCl, 25 mM Tris-HCl pH 7. 4, 1 mM EDTA, 1 mM DTT and 0. 2 mM PMSF and then against the same buffer supplemented with 20% glycerol. An additional size exclusion chromatography was performed on Superdex S200 10/300 GL for TOX4 PIR and Superdex S75 10/300 GL for NOVA1 PIR in the same buffer according to the protocol described by the manufacturer (GE Healthcare). The quality of purified PIRs was checked by SDS-PAGE electrophoresis and either coomassie staining or Western blotting and hybridization with anti-His (Sigma, H1029), anti-TOX4 (Sigma, HPA017880) or anti NOVA1 (Abcam, ab77594) antibody. Polynucleosomes were assembled by salt dialysis on the 2. 6 kbp 5SG5E4 DNA using native Hela histones as described in previous studies  [49]. GST pull down were performed as described in  [32]  and adapted in  [15].
Acknowledgments We would like to thank  Monsef Benkirane ,  Yaı ¨r Botbol ,  Elodie Chatre ,  Camille Martel ,  Gregory Neveu  and  Kyu-Ho Park  for scientific discussions,  Philippe Souque  for technical help with production of lentiviral virions,  Karine Monier ,  Xavier Gaume  and the  Platim (SFR Bioscience Gerland -Lyon Sud)  for their advices in cell imaging and Felix Rey and  Bertrand Fourcade  for supporting and hosting these projects in their laboratories.
This work was supported by grants from the  Agence Nationale de la Recherche  sur le SIDA et les hepatites ( AO 2011-1 ),  Centre National de la Recherche Scientifique  ( CNRS, Missions Interdisciplinaires ) and  Ecole Normale Superieure de Lyon (ENS). MM was funded by the  Ministe  `re de la recherche et de l'enseignement superieur,  Pierre Berge  ´and  Pasteur Weizman  fundations. MN was funded by the  CNRS  and  ENS  and FDN by SIDACTION. JX is funded by the  ANRS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Study design, sites and population We conducted a retrospective cohort study based on routine data from a patient tracing program at Lighthouse Trust's two public ART clinics in urban Malawi, Lighthouse and Martin Preuss Center (MPC). All confirmed LTFU patients were traced by either phone or home visit. Lighthouse and MPC ART clinics are located in Lilongwe, Malawi's capital city. On average, Lighthouse and MPC manage 8,300 and 11,200 patients a month, respectively, using a real-time, point of care electronic data system (EDS) from which the data for this study came  [13]. All HIV-infected individuals aged 15 years or older who received ART at Lighthouse and MPC between January 2006 and December 2010 were eligible for inclusion in this study.
Data collection As is routine practice at Lighthouse and MPC, during the study period all HIV diagnosed individuals were registered in the EDS and asked for consent at registration to be traced in case of LTFU. HIV-infected adults were started on ART if their CD4 count was equal to or below 250 cells/µl or they were in WHO clinical stage 3 or 4 in accordance with Malawi National ART guidelines during the study period  [14]. During the study period, one generic fixed dose combination of treatment (stavudine, lamivudine, nevirapine) was used as first line regimen. Routine return visits were scheduled monthly during the first six months on ART and every two months thereafter if no clinical complications occurred. At each ART visit, adherence was assessed using pill counts and entered in the EDS.
Additionally, as mentioned above, ART programs should consider providing larger quantities of ARVs per visit for stable patients to reduce the number of clinics visits, reducing transport costs, decrease waiting times and better supply those with unexpected travel. Lastly, a previous study noted patient fear or concerns about provider reactions to their return to care as reason for ART discontinuation  [23]. Although not specifically addressed in our study, additional efforts to sensitize healthcare staff to be more welcoming when patients return after missing or delaying appointments is critical to the success of any intervention. Our study should be considered with the following limitations. First, we used routine program data collected from our patient tracing program: some information was missing.
Background: Several attempts have been made to determine HIV-1 resistance from genotype resistance testing. We compare scoring methods for building weighted genotyping scores and commonly used systems to determine whether the virus of a HIV-infected patient is resistant. Methods and Principal Findings: Three statistical methods (linear discriminant analysis, support vector machine and logistic regression) are used to determine the weight of mutations involved in HIV resistance. We compared these weighted scores with known interpretation systems (ANRS, REGA and Stanford HIV-db) to classify patients as resistant or not. Our methodology is illustrated on the Forum for Collaborative HIV Research didanosine database (N = 1453). The database was divided into four samples according to the country of enrolment (France, USA/Canada, Italy and Spain/UK/Switzerland).
The weights assigned are estimated through statistical methods in which the parameters and decision rules are directly available. It is well known that a score created from a particular data set will, with that data set, always outperform a score created using a different data set as there is always some degree of model overfitting. We therefore divided our data set into four country-based samples to investigate whether country-based scores provided better performance on external data sets than existing IS. As an illustrative example, we used the Forum for Collaborative HIV Research didanosine database  [9]. Section 2 presents the data set used for this study, the Linear Discriminant Analysis, Logistic Regression and Support Vector Machine used in the scoring framework and the criteria for comparing the different systems.
Data set We used the FORUM database for didanosine (ddI) based on 13 studies (clinical trials and clinic-based cohorts). Briefly, this database, which has been described elsewhere  [9, 24] , includes data for 1453 drug-experienced patients who had viral load. 500 copies/mL and who underwent a genotypic resistance test when beginning a ddI-containing therapy. The median number of previously used antiretroviral drugs was four (range 1-12), including three NRTIs (0-6) and one protease inhibitor (0-4). Thirty-one percent (31%) of the patients had been pretreated with ddI before inclusion. The sample was split into four independent samples based on country of enrolment: France (n = 474), USA/ Canada (219), Italy (440) and S/U/S (Spain/UK/Switzerland n = 320). The virological outcome was a change in HIV-1 RNA between baseline and week 8, and a response was defined as a HIV-1 RNA reduction of 0.
Note that estimated coefficients associated with mutations reflect their importance for the resistant/sensitive classification. These approaches are complementary and can reveal different aspects of modeled scenarios. For a given sample, the three methods thus provide three independent scores leading to three different classifications. Didanosine resistance IS were evaluated on the entire sequence for the RT region. The ANRS and REGA algorithm (version October 2011 and 8. 0. 2, respectively), HIV-db score system (version 29 May 2012) and a recent ddI algorithm developed with the same Forum data set  [24]  that we called LDVD (Large Derivation and Validation Data sets) were studied. ANRS, REGA and LDVD classify patients into three levels of inferred drug resistance: 'sensitive', 'intermediate resistant' and 'resistant'.
Each country-based score was applied to the data subset used to train it and then to the three other data subsets. We also compared performance of global scores and existing IS on the four country-based samples. Global scores for the whole data set are not independent of country-based data subsets but existing IS are also not independent of the studies included in the FORUM database. Data included in the FORUM database on didanosine were pooled from 13 trials or cohorts representing knowledge about didanosine resistance that was directly or indirectly used to build existing IS. For example, analysis of the Jaguar trial provided a genotypic score that was included in the ANRS algorithm  [28]. Analyses aiming to improve the prediction of virological response, often use sensitivity, specificity (respectively defined as true positive rate and 1-false positive rate) and the Receiver Operating Characteristic (ROC) curves.
Results The prevalence of each of the 32 mutations included in the analysis is shown in Figure  1. The most frequent mutations in the entire data set are: M184V (prevalence 58. 9%), R211K (44. 5%), M41L (43. 7%), T215Y (41. 9%), D67N (34. 8%), L210W (28. 5%), Q207E (18. 4%), K103N (17. 9%) and V118I (16. 0%). Overall, the median number of mutations was 3 (interquartile range, 2-6) and 84 patients carried none of the 32 mutations included in the study. The French data set has the highest prevalence for seven of the nine mutations with prevalence higher than 15% (M41L, D67N, K103N, V118I, M184V, L210W and T215Y). Twenty-two of the 32 mutations were more prevalent in the French than in the other three data subsets Most of the patients were men (78%) and 995 (68. 5%) patients were defined as 'responders' (HIV-1 reduction greater than 0.
Classification for the two other methods is not so simple; nevertheless mutations F77L, Q207K and K223Q have the greatest weight in defining a patient as 'resistant'. Discordance between SVM and both LogReg and LDA was higher than that between LogReg and LDA (Table  2 ). Overall, 103 patients were classified as 'resistant' by all three methods and 88 were classified as 'resistant' by LDA and LogReg but as 'sensitive' by SVM (Table  2 ). The precision of each of the three methods and existing IS applied to the global dataset are reported in Table  3. Overall 201, 193 and 152 patients were classified as 'resistant' with the LDA, LogReg and SVM methods, respectively. As expected, the scoring methods performed better than already existing IS because the global data set was used both for training and validating these scores.
Scoring methods classified approximately 60% of the patients without virological response at week 8 as 'resistant' with no significant difference between the three methods. The precision of existing IS was, on average, of 40% (range 37% for HIV-db I to 58% for LDVD II). LDVD II, however, classified only 67 patients as 'resistant' such that the confidence in this the precision value was poor (58. 2%, 95% confidence interval 45. 5 to 70. 2%). It appears that there is a correlation between the precision and the number of patients classified as 'resistant' since the ANRS I and II provided a good performance (49%) with approximately 370 patients classified as resistant while HIV-db I provided the poorest performance (37%) with more than 1000 patients defined as 'resistant' Global scores and existing IS were also applied to each countrybased data set (Table  4 ).
Precision was generally much better for scoring methods than for existing IS. Weighted means of the precision were around 60% for scoring methods and were between 37 and 49% for existing IS (except for LDVD II but, again, with a large 95% confidence interval as explained above). The French data set provided the best performance, and this was a consequence of the high prevalence of most of the mutations contributing to global scores and used by existing IS. For the three other subsamples, the precisions were better than or similar to those for any existing IS. The SVM method performed better than the two other methods for the Italian subsample, but the inverse was observed for the S/U/S subsample. There was no significant difference between the three statistical methods for French and USA/Canadian data subsets.