dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors would like to thank the other researchers involved in the ORSOSA Study. Coordinators:  A Sobaszek ,  R de Gaudemaris. Scientific Board :  T Lang ; Groupe ORSOSA:  V Bonneterre ,  F Balducci ,  S Caroly ,  V Ehlinger ,  L Larabi ,  JM Soulat ,  G Chatellier. Regional coordinators:  JM Soulat ,  A Trichard ,  P Gabinski ,  M Druet-Cabanac ,  MC Vignaud ,  N Broessel ; and research assistants:  C Lecornet ,  J Goddard ,  M Tournegros ,  L Ferrand ,  I Kerjean ,  L Goddard ,  C Lecornet ,  M Le Guen ,  M Lorian ,  M Randrianarivo ,  M Dubuy ,  M Assimi ,  V Cottel ,  A Didry. Caisse Nationale de Retraites des Agents des Collectivités Locales  (CNRACL) supported this research. The CNRACL was not involved in the study design, data collection, analyses, data interpretation, or writing of the report.
Results and discussion Hypomethylation and expression of MAEL and SYCE1 define molecular markers for ICF1 syndrome Peripheral blood samples were obtained from healthy donors and ICF patients. We assessed profiles of gene expression focusing on the germline genes transcriptional signature that we previously established in a mouse model for ICF1  [23, 24]. As expected from their expression primarily in germ cells (Additional file 5; and RNAseq and exon arrays datasets on normal tissues publically available at UCSC genome browser) and function in meiosis, expression of the germline genes tested was undetectable in blood cells from healthy controls, although that of SYCE1 showed some degree of variation between healthy donors that did not seem to correlate with age or sex (Figure  1  and Additional file 6).
Acknowledgements The authors wish to thank all patients and family members for their participation in this study. We are grateful to  Evani Viegas-Péquignot  and  Fabienne Nigon  for assistance with registries and tissue collections collected in the frame of the previous "ICF consortium", and to  Florent Hubé  for critical reading of the manuscript. This work was supported by  ANR (Agence Nationale pour la Recherche ;  ANR-09-GENO-035 ); ELW was supported by the  French Ministry of Research and Ligue Nationale Contre le Cancer.
Study design and population The IeDEA West African data collection began in 2006, based on existing data issued from operational cohorts offering ART in 10 clinical centres in 7 countries in West Africa (Benin, Burkina Faso, Cote d'Ivoire, Gambia, Ghana, Mali and Senegal) that contribute standardized data to the pWADA collaboration  [20, 21]. All HIV-infected children under age 16, starting ART in these programmes from January 2000 to December 2009, were included. In 2010, a total of 2883 children under 15 at enrolment were included, of whom 1416 were younger than 5. The Mali and Cote d'Ivoire cohorts represented 74% of the cohort of children younger than five years at that time. In these two latter countries, we nested a retrospective cohort in all HIV-infected children who initiated ART before their fifth birthday, between January 2004 and June 2009 and followed up in the five clinics contributing to the pWADA collaboration: four in Abidjan, Cote d'Ivoire (CEPREF, CIRBA, CHU Yopougon, MTCT' Abobo-Avocatier) and one in Bamako, Mali (Gabriel Toure hospital).
Study monitoring and data collection The inclusion date was the date of initiation of ART. Follow-up ended at death, at the last clinical visit prior to transfer to a different care provider or loss to follow-up, or 30 June 2010. If children withdrew from care or could not be located by three tracking attempts after a missed visit, with no clinical contact up to six months, they were classified as lost to follow-up. At ART initiation, and every three months thereafter, children underwent clinical monitoring and education about adherence to treatment. Additional unscheduled visits were made by children needing acute care. Clinical monitoring took into account all clinical events occurring after ART initiation, especially the occurrence of opportunistic infections and AIDS-defining clinical events.
Clinical and laboratory data were documented by physicians during patient visits using standardized forms. They were updated in the IeDEA paediatric database every six months. For this study, information on the PMTCT exposure was updated through a direct review of children's medical records. When exposure to PMTCT intervention was documented, we investigated whether NVP had been used or not. Children were assigned as PMTCT-exposed or unexposed if this information was specified in the medical record, and PMTCT exposure was considered unknown otherwise. The following baseline information was collected: date of birth, sex, clinical centre, date of ART initiation, weight, cotrimoxazole prophylaxis, CD4 count and per cent, WHO staging 3 or 4 event, and first-line ART regimen. Additionally, we collected information about CD4 count and per cent, WHO staging 3 or 4 event, and ART regimen for the six-and twelve-month visits and the date of latest news.
Statistical analysis Descriptive statistics were presented as median values with inter-quartile ranges (IQR) for continuous variables. Categorized variables were presented as number or proportions (%) with their 95% confidence intervals. Chi-square test or Fischer exact test were used to compare baseline categorical variables and Kruskal-Wallis test was used to compare medians of baseline continuous variables. The z-scores of weight for age (WAZ) were calculated using SAS programme based on the Centers for Disease Control (CDC) 2000 growth reference year. The 12-month probabilities of survival were computed using the Kaplan-Meier estimates for all children. The Logrank test was used to compare survival curves according to PMTCT exposure categories. Baseline factors associated with treatment failure at 12 months after ART initiation were analyzed using univariate analysis and then a multivariate Cox proportional-hazards model.
Variables with p50. 20 in the univariate analysis were included in a full multivariate analysis. A reduced adjusted model was ultimately created in which variables were excluded using a backward procedure. The main analysis was conducted in the overall cohort and the secondary one compared the two known cohorts for PMTCT exposure. To understand whether missing data for PMTCT exposure had introduced selection bias in our analysis, we performed a multivariate sensitivity analysis with the hypothesis of maximum bias. First, all children with unknown exposure to PMTCT were assigned to the PMTCT-exposed group. Then, they were assigned to PMTCT-not-exposed group. All statistical analyses were assessed for statistical significance at the pB0. 05 threshold and were performed using SAS 9. 01 software.
Study cohort The retrospective data collection was carried out for the 1035 eligible children younger than five years at ART initiation (48% in Mali); PMTCT exposure was documented for only 353 children (34%) of whom 73 were exposed and 280 unexposed, and it remained unknown for 682 children (66%). Although the unknown cohort appears similar to the unexposed cohort, the characteristics of the three cohorts were different, except for sex (Table  1 ). Thus, the analysis of outcomes according to PMTCT exposure was performed among all children, in order to present a ''real-life'' cohort.
The lack of data on PMTCT exposure could be explained first by the long delay between birth of HIVinfected children and their first contact with a paediatrician. Second, PMTCT information was usually traced on handwritten maternal medical records and was not electronically recorded. Third, there was no systematic and organized linkage between children's and maternal records. This lack of data completeness has important implications for the use and interpretation of routine observational databases for research and audit, and highlights the need for regular data validation of these databases  [42]. Different solutions to improve the quality of medical records and thus the health management system could be envisaged, such as implementing provider-based electronic medical records that improve the quality of data collected with a significant reduction in missing and incorrect information  [43].
'zoreKangah (Abidjan, Cote d'Ivoire), Denis Malvy (Bordeaux, France), David Meless (Abidjan, Cote d'Ivoire), Aida Mounkaila-Harouna (Bordeaux, France), Camille Ndondoki (Bordeaux, France), Caroline Shiboski (San Francisco USA), Rodolphe Thiebaut (Bordeaux, France). Funding: The National Cancer Institute (NCI), the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD) and the National Institute of Allergy and Infectious Diseases (NIAID) of the U. S. National Institutes of Health (NIH), as part of the International Epidemiologic Databases to Evaluate AIDS (IeDEA) under Award Number U01AI069919. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Coordinating Centre: ISPED, Univ Bordeaux Segalen, Bordeaux, France.
N 'zoreKangah (Abidjan, Cote d'Ivoire), Denis Malvy (Bordeaux, France), David Meless (Abidjan, Cote d'Ivoire), Aida Mounkaila-Harouna (Bordeaux, France), Camille Ndondoki (Bordeaux, France), Caroline Shiboski (San Francisco USA), Rodolphe Thiebaut (Bordeaux, France). Funding: The National Cancer Institute (NCI), the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD) and the National Institute of Allergy and Infectious Diseases (NIAID) of the U. S. National Institutes of Health (NIH), as part of the International Epidemiologic Databases to Evaluate AIDS (IeDEA) under Award Number U01AI069919. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Coordinating Centre: ISPED, Univ Bordeaux Segalen, Bordeaux, France.
It is worth noting that one participant who later progressed to a diagnosis of mild cognitive impairment (MCI) responded negatively to the robot. The authors found that difficulties maneuvering the robot around the environment were possible barriers to acceptance of the device. Heerink et al  10  developed and validated a new theoretical model of assistive social agent acceptance, adapted from the unified theory of acceptance and use of technology model. They used controlled experiments with longitudinal data collected regarding three different social agents at elderly care facilities and at the homes of older adults. They found that the influential strength of the processes leading to acceptance differed between systems. It was revealed that "perceived usefulness" and "attitude" are the most significant influences on intention to use a robot or a screen agent appearing in a computer display.
Usability-performance measures To study if participants could learn how to use the robot, we recorded time and number of errors and aids required in realizing ten tasks (to look up calendar, to program an appointment in calendar, to check emails, to write an email, to prepare a shopping list, to check weather forecast, to play a cognitive game, to check medication reminder, to make a Skype video conference call, and to activate a musicbroadcast program). Performances at the third session after training were compared to those at the beginning of the fourth session. By doing so, we could evaluate the memorability of the system (how easily participants could reestablish proficiency when they use a system again after a period of not using it).
Analyses For the robot-acceptance questionnaire and performance measures, we first performed descriptive analysis. Nonparametric statistical analysis was conducted, due to the inability to assume normal distribution with the small sample size. Comparisons between groups (MCI versus CIH subjects) were determined using the Mann-Whitney U-test, and comparisons within groups (first session versus fourth session) were determined using the Wilcoxon matched-pairs test. All tests were two-sided, and P-values below 0. 05 were considered to denote statistical significance. All the statistical analyses were performed using SPSS 17. 0 (IBM Corporation, Armonk, NY, USA) software. Semistructured interviews were audiotaped and then transcribed. Then, the analyses of the transcripts were performed according to inductive thematic analysis.
Acknowledgments This work was supported by the  French National Research Agency (ANR) , the  National Solidarity Fund for Autonomy (CNSA) , and the  General Directorate for Armament (DGA)  through the  TECSAN program  ( ANR-09-TECS-012 ). We also thank  Fanny Lorentz  and  Véronique Ferracci  for their help in the management of projects.
Ten protein kinase C (PKC) isozymes play divergent roles in signal transduction. Because of sequence similarities, it is particularly difficult to generate isozyme-selective small molecule inhibitors. In order to identify such a selective binder, we derived a pharmacophore model from the peptide EAVSLKPT, a fragment of PKCε that inhibits the interaction of PKCε and receptor for activated C-kinase 2 (RACK2). A database of 330 000 molecules was screened in silico, leading to the discovery of a series of thienoquinolines that disrupt the interaction of PKCε with RACK2 in vitro. The most active molecule, N-(3-acetylphenyl)-9amino-2,3-dihydro-1,4-dioxino[2,3-g]thieno[2,3-b]quinoline-8-carboxamide (8), inhibited this interaction with a measured IC 50 of 5. 9 μM and the phosphorylation of downstream target Elk-1 in HeLa cells with an IC 50 of 11.
It has been shown that small molecule inhibitors of protein-protein interactions are useful for pharmacological purposes. 20, 21 A cell-permeable inhibitor of PKCε signal transduction could help to explain the exact function of PKCε and could probably also be developed further for pharmaceutical purposes. ■ RESULTS Molecular Modeling. The lack of any known nonpeptidic inhibitor of the PKCε/RACK2 interaction and the very limited structural data available render rational drug discovery of an inhibitor of this protein-protein interaction a difficult task. A crystal structure is only available for the N-terminal-C2-like domain of PKCε (PDB entry 1GMI), but no structural data are available on RACK2. The PKCε protein segment EAVSLKPT, which as a peptide is known to disrupt PKCε/RACK2 interaction, is highly solvent-exposed and expected to undergo conformational changes during the interaction with RACK2.
The interface is large (with respect to the size of small molecule inhibitors), only vaguely characterized, and interaction hot spots remain speculative. Conformations observed for EAVSLKPT (in particular Glu14 and Lys19; numbering according to the crystal structure) in the crystal structure are likely to be influenced by crystal packing. Lacking known ligands and structural data for modeling, several pharmacophore models were derived directly from EAVSLKPT in its crystal structure conformation (Figure  1A ). One pharmacophore model obtaining comparably high specificity is reported in Figure  1B. It consists of three hydrogen bonding features and one hydrophobic feature. The latter attempts to resemble the hydrophobic area presented to RACK2 by Val16, while the hydrogen bonding features characterize the side chain properties of Glu14, Ser17, and Lys19.
1 H NMR, ESI-MS, and HPLC analyses of 8 are shown in the Supporting Information Figures  3 4 5 6. Computational Approaches. PDB entry 1GMI was used as a basis for the definition of structure-based pharmacophores of the PKCε protein fragment EAVSLKPT. Catalyst (version 4. 11, Accelrys Inc. , San Diego, CA, USA) was employed for pharmacophore modeling and screening. Multiconformational databases of the Asinex Gold and Platinum libraries (Asinex Ltd. , Moscow, Russia) were generated applying the FAST conformational search algorithm with a maximum of 250 conformers created per molecule. The two databases were screened using the Catalyst FAST algorithm. Cells and Cell Proliferation. HeLa cells containing a tetracycline/ doxycycline-inducible constitutively active PKCε were described previously.
Each injection of one partner on the other induces the formation of supramolecules, thus releasing an amount of heat which is proportional to the binding enthalpy and to the amount of complexes formed during this addition. The binding isotherm is then obtained by recording heats for all injections. At this stage, a fitting procedure between experimental and theoretical data allows the evaluation of various thermodynamic parameters. Alternatively, a competitive experiment may also be undertaken to investigate the titrant-titrand supramolecule, even if it is more scarcely used  [12, 13]. In this protocol, the titrant injections are applied to a solution containing both the titrand and a competitive molecule. Such an experiment allows the characterization of the titrant-titrand complex, provided that a preliminary titration between the titrand and the competitive molecule (this time in the syringe) has been realized and analyzed.
Acknowledgements The authors wish to thank  Prof. Sophie Fourmentin  for helpful discussions. UCEIV participates in the  Institut de Recherche en ENvironnement Industriel (IRENI)  financed by the  Communauté Urbaine de Dunkerque , the  Région Nord Pas-de-Calais , the  Ministère de l'Enseignement Supérieur et de la Recherche , the  CNRS  and the  European Regional Development Fund (ERDF).
Recently, tissue-based methods for proteomic analysis have been used in clinical research and appear reliable for digestive, brain, lymphomatous, and lung cancers classification. However simple, tissue-based methods that couple signal analysis to tissue imaging are time consuming. To assess the reliability of a method involving rapid tissue preparation and analysis to discriminate cancerous from non-cancerous tissues, we tested 141 lung cancer/non-tumor pairs and 8 unique lung cancer samples among the stored frozen samples of 138 patients operated on during 2012. Samples were crushed in water, and 1. 5 ml was spotted onto a steel target for analysis with the Microflex LT analyzer (Bruker Daltonics). Spectra were analyzed using ClinProTools software. A set of samples was used to generate a random classification model on the basis of a list of discriminant peaks sorted with the k-nearest neighbor genetic algorithm.
Mass Spectrometry Measurements were performed with a Microflex LT (Bruker Daltonics, Bremen, Germany) mass spectrometer laser. Spectra were recorded in the positive linear mode (delay: 170 ns; ion source 1 (IS1) voltage: 20 kV; ion source 2 (IS2) voltage: 16. 65 kV; lens voltage: 7. 20 kV; mass range: 2 kDa to 20 kDa). Each spectrum was obtained after 6640 shots (240 shots) in automatic mode at a variable laser power, and the acquisition time ranged from 60-120 seconds per spot. All signals with resolution $ 400 were automatically acquired using AutoXecute acquisition control in FlexControl software version 3. 0. The spectra of the 4 spots for each tissue mix were imported into BioTyper-RTC version 3. 0 software (Bruker Daltonik GmbH).
Statistical analysis ClinProTools v2. 2 software uses the data generated from spectra including spectra pretreatment, peak picking, and peak calculation operation. The peak definition, the normalization of the area to the total ion count end point level and the mass recalibration (maximal peak shift of 1000 ppm) were taken into account, and the sort mode using the t-test p-value from the Wilcoxon/Kruskal-Wallis test was used. Differences in classes analyzed were assessed on the basis of a discriminant peak identification list. To create the list of discriminant peaks, we used the k-nearest neighbor genetic algorithm (GA) implemented in this software. This algorithm is based on probability estimates for classification. We first searched for a model able to correctly discriminate the 2 classes, Cancer and Non-tumor, and second for a model able to correctly discriminate Primary lung cancer and Metastasis.
To find the most discriminant model, GA was trained with the Reference pool, and internal validation was processed (10-fold crossvalidation). The performance of the model was evaluated by recognition capability (RC) and positive predictive value (PPV): RC = TP/n where TP is the number of true positives (correctly classified) in a data set, n is the number of samples in the data set, and PPV = TP/(TP + FP) where FP is the number of false positives (misclassified). In a second step, the spectra from the Blinded samples were used to verify the classification ability of the generated model. The effective Sensitivity, Specificity and Accuracy of a model were calculated from the results obtained for the Blinded samples versus the reference histological diagnosis as the Gold standard using standard formulas (Sensitivity = TP/TP + FN; Specificity = TN/TN + FP; Accuracy = TP + TN/n).
Related literature For applications of metal-organic coordination materials, see:  Yaghi et al. (2003) ;  Janiak (2003) ;  Lalart et al. (1981) ;  Mori et al. (2005 Mori et al. ( , 2006)) ;  Dybtsev et al. (2004). For studies and properties of oxonic acid, see:  Lalart et al. (1981) ;  Pancheva (1977) ;  Cihak et al. (1968). For comparable interatomic distances in related structures, see:  Sheldrick & Poonia (1986) ;  Cuesta et al. (2003) ;  Pike (1976). Forstacking interactions, see:  Janiak (2000). For a multipolar atom model transfered from the ELMAM2 electron density database, see:  Domagała et al. (2012). For fractal analysis of the residual electron density, see:  Meindl & Henn (2008).
Experimental Crystal data  Hydrogen-bond geometry (A ˚, ). [K 3 (C 4 H 2 N 3 O 4 ) 3 (H 2 O) 2 ] M r = D-HÁ Á ÁA D -H HÁ Á ÁA D Á Á ÁA D -HÁ Á ÁA N3_3-H3_3Á Á ÁO9_3 1. (i) x; y þ 1; z; (ii) Àx þ 2; y; Àz þ 1 2 ; (iii) x; y À 1; z; (iv) Àx þ 1; y À 1; Àz þ 1 2 ; (v) Àx þ 1; y; Àz þ 1 2. Data collection: APEX2  (Bruker, 2012) ; cell refinement: SAINT  (Bruker, 2012) ; data reduction: SAINT; program(s) used to solve structure: MoPro  (Jelsch et al. , 2005) ; program(s) used to refine structure: MoPro; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012) ; software used to prepare material for publication: MoPro.
Refinement After initial refinement with SHELXL97, the structure was further refined with the program MoPro  (Jelsch et al. , 2005)  using a multipolar atom model transfered from the ELMAM2 electron density database  (Domagała et al. , 2012). The R(F) factor improved from 4. 3 to 3. 4%. The residual difference electron density showed a positive/negative peak when the nitrogen atom N12_4 was modeled as deprotonated or fully protonated, respectively. Due to the twofold symmetry of this anion the hydrogen atom H12_4 was modelled with half-occupancy on the two crystallographically equivalent sites. The other H atom positions were refined using distance restraints; the target values were 1. 01 (2) and 0. 97 (2) Å for N-H and O-H bond lengths, respectively. In the oxonate moieties, angle similarity restraints (σ = 0.
The theory predicts a À9 cm À1 isotope shift (experimental À9 cm À1 ) for 5-PMUA featuring 18 Op1 and 18 Op2 peroxide oxygen atoms (orange trace). Overall, the calculations agree remarkably well with the experiment, identifying the 605 cm À1 5-PMUA Raman band as a signature for its peroxide state. Exposure to X-rays can lead to specific modifications in proteins and nucleic acids, includ-  ing bond rupture. [9] In both UOX:5-PMUA and UOX:5-PIU crystals, we observed that the C5 À Op1 bond is susceptible to selective radiolysis at low X-ray doses (Movie S3). We tracked peroxide radiolysis quantitatively using online Raman-assisted crystallography by performing multiple data collections interspersed by spectrophotometric measurements on a single UOX:5-PMUA crystal. In the case of UOX:5-PIU, only multiple Xray data collections were performed.
The Cryobench is a platform of the Grenoble Instruct centre ( ISBG UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  ESRF, FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Partnership for Structural Biology  (PSB). is available on the WWW under http://dx.