dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We would like to thank  Professor Isabelle Peretz  for her valuable help in the design of the experiment. We also wish to thank the participants involved in this study for their tireless contribution,  Drs Chris James  and  Kuzma Strelnikov  for their comments on an early version of the article, and  Dr Camilla Thyregod  for her help in the statistical analysis. This study was part of  Nicolas Vannson 's final year research project for the completion of his Master of audiology, supervised by  Drs Marozeau  and  Innes-Brown.
e. of each genotype) encountered each female (i. e. of each genotype) once and no animal was tested twice consecutively (Fig. 1a ). Units of each line were recorded on separate days, except for the last recording day, where two units of the Foxp2 humΔneo line and one unit of the Foxp2 hum line were recorded. For the recordings, the microphone was connected to a preamplifier (UltraSoundGate 116, hardware and software: Avisoft Bioacoustics, Glienicke, Germany), which was connected to a computer using the recording software AVISOFT SASLAB PRO v4. 33 with a sampling frequency of 300 kHz (hardware and software: Avisoft Bioacoustics, Glienicke, Germany). Mice were housed at a 10 h/14 h dark/light cycle under standard conditions in isolated ventilated racks (lights on at 0700 h). All animal work was performed in accordance with guidelines of the Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, and federal regulations of Saxony, Germany.
Analysis of element structure The sampling frequency of 300 kHz resulted in a frequency range of 150 kHz. To reduce background noise at lower frequencies, which could negatively influence the estimation of whistle tracking algorithm, we used a high-pass FIR filter of 35 kHz. We used the whistle tracking algorithm of AVISOFT SAS LAB PRO 5. 2 (Avisoft Bioacoustics) with the following settings: monotonic, maximum change per step 8 pix = 4. 7 kHz, minimum continuity = 8 milliseconds, hold time = 15 milliseconds. These criteria were compared with previous analyses of male mouse vocalizations  (Fischer & Hammerschmidt 2011; Hammerschmidt et al. 2012a; Jamain et al. 2008). Based on these settings, we calculated the number of given call elements, inter-element interval (ICI: from the end of an element to the start of the next element) and element duration.
For the analysis of call structure, we used the AVISOFT RECORDER 4. 2 (Avisoft Bioacoustics) to store the single elements as separated wav files. We used the same whistle tracking algorithm with the same settings as described above. From the stored elements, we calculated spectrograms (frequency range: 150 kHz, frequency resolution: 293 Hz, time resolution: 0. 21 milliseconds). The spectrograms were submitted to the custom software program LMA 2012 [developed by K. Hammerschmidt, see  Fischer et al. (2013) ] to estimate characteristic acoustic parameters (Fig. 1b, c ). As mice typically concentrate the energy of their calls into one small frequency band, so-called whistles or pure tone-like sounds (see Figs. 1c, S1), we focused on the peak frequency, i. e. the loudest frequency in the spectrum, which corresponds in most cases to the fundamental frequency (F0).
e. irrespective of the specific element type ('unpartitioned vocal repertoire'). Next, we conducted the analysis at the level of different element types. We used a two-step cluster analysis (IBM SPSS STATISTICS 21, Armonk, New York, USA) with the log-likelihood distance measure and the Schwarz-Bayesian information criterion (BIC) to find the cluster solution with the best fit. First, we tested a three-cluster solution, which was the solution with the best BIC value, like in former mouse studies  (Hammerschmidt et al. 2012a (Hammerschmidt et al. ,2012b)). In addition, we chose the six-cluster solution, which turned out to be the solution with the highest number of cluster by same silhouette coefficient (S C = 0. 3). Cluster solutions with a higher number of clusters dropped clearly under S C of 0.
Further statistical procedures All data were analyzed using an LMM (IBM SPSS STATISTICS 21, USA), including genotype and line (Foxp2 hum and Foxp2 humΔneo ) as fixed factors, and the interaction between genotype and line. For the description of general vocal behavior across all call types (unpartitioned repertoire), we included encounter order as a fixed factor. Whenever we used repeated recordings per subject, we included subject as random factor. We corrected all P-values for multiple testing using Simes correction.
Patient Inclusion Criteria After screening, the female or male PLHIV who met the following criteria were included in the study: over 18 years of age, provided consent to be included in the study, presented with documented HIV infection, and were either receiving or not receiving ART during medical consultations between 1996 and 2011 in one of the three study sites during the study period. The patients' data were collected between June 1, 2010 and May 31, 2011.
General and Demographic Characteristics of the Study Population During the 12 months of data collection, 490 PLHIV were screened in the three sites chosen for the studynamely, the Fondation Jeanne Ebori Hospital (FJE), the Libreville Hospital Ambulatory treatment centers (LH-ATC), and NKEMBO Hospital (Nkembo H). From these centers, 86. 1% (422/490) patients were included in the study, 13. 9% (68/490) were excluded for several reasons including declining to provide consent (40 cases), an age less than 18 years (15 cases), or unconfirmed serology (13 cases). The 422 included patients were classified as either category B (as per the HIV/AIDS Atlanta Center for Diseases Control classification; CDC, Atlanta, USA) or as stage 3 from the World Health Organization (WHO, Geneva, Switzerland) classification; of them, 83% (350/422) were treated with antiretroviral drugs.
In all, 87 (20. 6%), 264 (62. 5%), and 71 (16. 8%) patients included in the study were recruited from the Fondation Jeanne Ebori Hospital (FJE), the Libreville Hospital Ambulatory treatment centers (LH-ATC), and NKEMBO Hospital (Nkembo H), respectively. There was an increase in the average proportion of screened patients with a positive HIV test in the three sites between 1996 and 2011: 2. 4% [10/422; range: 1. 2%-3%)], 24. 4% [103/422; range: 8. 5%-28. 7%], and 73. 2% [309/422; range: 69. 7%-90. 1%] patients between  1996-2000, 2001-2005, and 2006 -2011, respectively (Fig. 1A ). The 422 included individuals were predominantly female, representing 79. 9% (337/422), and only 20. 1% (85/422) males, corresponding to a sex ratio (M/F) equal to 0. 25 (data not shown). Their overall mean age was 34±8 years (range: 18-66 years), with the highest HIV prevalence observed in patients aged 30-39 years (44.
Whenever a certain number of sedentary minutes is recorded, SitCoach reminds the user to take a break from sitting. Reminders are given in the form of audio and tactile messages. Similarly FitBit+  [12]  aims to decrease sedentary behavior by detecting when people have been inactive for longer period and then generate messages for taking short breaks and perform some physical activity. User wears device known as Fitbit that transmits data related to steps taken, intensity of physical activity, duration of activity, distance etc. to their workstation. This data is analyzed and if user is found inactive for longer period, the alerts are generated prompting them to perform some physical activity. However, as we have identified, activeness is not just the prevention of the sedentary behavior, but inculcating the eagerness of living an active life; a goal that is more challenging and requires more treatment.
The authors linked published computer simulation models to project clinical outcomes of five PMTCT strategies  [14]. Another simulation model for MTCT is the Spectrum software package, developed by Stover et al. [15] [16] [17]. The Spectrum program is used to forecast key HIV prevalence and PMTCT rates based on national HIV surveillance and surveys, program statistics and epidemic patterns. To the best of our knowledge, to date there are no published models that simulate individually a cohort of pregnant women in Cameroon able to estimate MTCT rates accounting for both prevalent (infected at delivery) and incident (infected in the lactating period) maternal HIV infections. Thus, we developed a stochastic simulation model to estimate the MTCT risk (perinatal and postnatal) and the number of new paediatric infections based on the observed uptake of PMTCT programmes at different stages of the PMTCT cascade in Cameroon in 2011.
Model structure: calculation of mother-to-child transmission probabilities Mother-to-child-transmission (MTCT) of HIV can mainly occur during the second and third trimester of pregnancy, during delivery or breastfeeding  [1]. Indeed, HIV transmission through breastfeeding has emerged as a substantial mode of MTCT among African breastfeeding populations and can occur in two different circumstances: among HIV prevalent mothers HIV-infected at delivery and among incident mothers HIV-infected while lactating. The risk of transmission through breastfeeding is cumulative according to the duration of breastfeeding and the longer the duration of breastfeeding, the greater the transmission risks  [26] [27] [28] [29]. Thus, we estimated three MTCT probabilities, using data from MTCT studies among pregnant and breastfeeding populations in Africa: 1/ the perinatal transmission probability at 6-week, 2/ the postnatal transmission probability and 3/ the postnatal transmission probability in those born to incident HIV-mothers who seroconverted while lactating.
Sensitivity analyses Sensitivity analyses of the results of the simulation model were conducted by varying in suitable intervals some key parameters in the prevention of MTCT of HIV, as described in Table  2. Sensitivity analyses were performed by varying these parameters in specific intervals based on the lowest and highest published values. For each key parameter 1000 values were randomly drew within the corresponding range. For each of these 1000 sets of randomly drawn parameter we simulated one cohort of the population size of pregnant women expected in Cameroon in 2011 and the associated MTCT rates were calculated. In this work, all results were performed using R statistical software (R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
According to the Cameroon demographic health survey, 24 % of women still have unmet needs for family planning  [18]. Modelling studies have suggested that there will be significant reductions in the number of HIV-exposed infants if unmet needs are reduced  [37]. Thus, it is essential to strengthen programmes that offer family planning services to both HIV-infected and uninfected pregnant women. Methods for producing estimates have their limitations  [38]. First, computer simulations simplify complex biological and operational processes and many assumptions involved some uncertainty. However, our results are consistent with those previously reported by other modelling studies, including the Spectrum model. Second, we used multiple-source data from different settings that were not all issued from Cameroon.
Methods This report is a secondary analysis of a previous study designed to assess the theoretical eligibility as organ donors of patients deceased after end-of-life decisions  [25]. The study was performed in 43 French ICUs (15 units in university-affiliated centers, 28 in general hospitals). The institutional review board (CPP Paris Ile de France II, IRB registration: 00001072) approved the protocol. The study period (60 or 90 consecutive days in normal operating conditions) took place during the first half of 2013. All patients admitted to the ICU who underwent a WhWd procedure according to the terms of the French Leonetti law were enrolled in the survey. The epidemiological data recorded during the ICU stay included age, gender, medical history, reasons for admission, Simplified Acute Physiology Score (SAPS) II index  [26] , Knaus  [27] , McCabe  [28] , and Charlson  [29]  scores on admission, relevant clinical and biological characteristics, Sequential Organ Failure Assessment (SOFA) score  [30]  at the time of the WhWd decision, reasons for the decision, implemented measures, and outcome of the patient (deceased or discharged alive).
Acknowledgments This study was supported by the non-profit organizations  AADAIRC (Association d'Aide à Domicile aux Insuffisants Respiratoires Chroniques ) and Ouest-Transplant. The authors thank  Marie-Line Cras ,  Nicolas Girard , and all the contributors (cited in the next paragraph) for their help in the implementation of the survey. The EPILAT study group is composed of the following:  Olivier Lesieur , Réanimation, CH Saint-Louis, 17019  La Rochelle , France;  Maxime Leloup ,.
Data source The CONTROL (COhort oN TReatments of alcohOL dependence) study is a single center, prospective, observational study evaluating consecutive patients with alcohol dependence assessed for the first time at the Alcohol Treatment Center at Lausanne University Hospital, Lausanne, Switzerland. CONTROL included 143 participants, followed for up to 487 days. Full details of the CONTROL study design have been published previously  [26, 27]. The primary objective of CONTROL was to describe the drinking patterns of patients with alcohol dependence and their baseline predictive factors during a 12-month period. More specifically, it intended to describe the population of patients with alcohol dependence, their disease management and their evolution in terms of alcohol consumption, social consequences, quality of life and resource use, after an initial evaluation for alcohol treatment  [26].
The model takes into account the relative risk of different events over different time periods (1 day, 1 month or 6 months) in accordance with the clinical occurrence and physiopathology of each event. Another study using this microsimulation model was published previously  [16]. The same methodology was used in the present study, in terms of alcohol consumption simulation (two-part model), and event prediction (general population risks and risk ratios depending on level of alcohol consumption). However, in the present study, an observational study dataset was used to predict alcohol consumption instead of data from RCTs. Results of the two approaches are generally consistent, although the number of events is slightly higher in the present study, likely to be due to a higher variability in alcohol consumption simulations and the fact that the patient population in the current model was not selected on the basis of exclusive RCT criteria; indeed, the patient population in the current study (based on the CONTROL cohort) represents the full range of severity of alcohol dependence.
In that study cause-specific-mortality data were not available and thus it is not known whether self-rated health trajectories vary according to specific cause of death. A further limitation is that previous studies have not been able to determine the time order between the decline in self-rated health and diagnosis of the chronic disease leading to death. Thus, it is not known whether poor self-rated health follows the diagnosis leading to death or whether persons who were undiagnosed also have poor self-rated health. In this retrospective study, we utilized cause-specific mortality data and annual measurements of self-rated health in a large French occupational cohort to examine trajectories of self-rated health up to 15 years prior to death. We sought to pinpoint when perceptions of health begin to diverge among people at risk of subsequent death compared to those who survive.
Study population The GAZEL cohort was established in 1989 and comprises employees of the French national gas and electricity company: Electricitede France-Gaz de France (EDF-GDF)  [10]. At baseline, 20,624 employees (73 % men) aged 35-50 consented to participate. Information about health, lifestyle, and individual, familial, social, and occupational characteristics of the GAZEL participants is collected by annual surveys. Informed consent was obtained from all participants and the study received ethical approval from the French national ethics committee [Commission nationale de l'informatique et des libertes (CNIL)]. In this study we analyzed data from GAZEL participants who died between 2000 and 2010. We restricted inclusion in the study to those who had at least one self-reported health assessment 10-15 years prior to death.
Thus, the sample analyzed consisted of 774 men and 141 women who died at a mean age of 61. 5  (SD 4. 6). The analyses were based on a case-control design with the deceased persons being the cases and randomly selected 1-3 controls for each case. The average number of controls for each case was 2. 8. Controls were matched for sex, baseline age, occupational grade and marital status, leading to 2188 male and 390 female controls. Self-reported health for each case and control was traced backwards for 15 years and followup for controls ended when their case died. Thus, cases and controls received roughly the same number of follow-up measurements. On average, cases provided data from 10. 3 (SD 4. 6) and controls from 11. 4 (SD 4. 1) of the possible 15 study waves.
Mortality outcome Mortality data on all participants were obtained annually from EDF-GDF as it pays out retirement benefits. In the analysis we included deaths that occurred between 1 January 2000 and 31st December 2010. Cause of death was obtained from the French National Death Index. Causespecific mortality was coded using the International Classification of Diseases, Tenth Revision (ICD-10). We distinguished between deaths due to ischemic heart disease and cerebrovascular disease (I20-I25 or I46 or I60-I69), other cardiovascular disease (I00-I15 or I26-I45 or I47-I59 or I70-I99), smoking-related cancers [C00-C06 (lip and oral cavity) or C09-C15 (pharynx) or C25 (biliary tract) or C30-C34 (respiratory organs) or C38 (heart) or C64-C68 (urinary tract)], other cancer (C00-D48 excluding above-mentioned smoking-related cancers), suicides (E95 or X60-X84), other external causes (E80-E94 or E96-E99 or S00-T98 or V01-X59 or X85-Y98) and other deaths.
Acknowledgments This work was supported by the  EU 's  Era-Age 2 program  ( Academy of Finland   264944 ),  Academy of Finland  ( 286294 ) and the  Swedish Research Council for Health, Working Life and Welfare (Forte ,  2012-1661 ). Mika Kivima  ¨ki is supported by the  Medical Research Council  ( K013351 ) and a professorial fellowship from the  Economic and Social Research Council, UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Recruitment was done in general practice settings, mother-child health care centers, and in the public transit system. Semidirected, face-to-face interviews were carried out until data saturation between June and August 2010 in Paris and its suburbs. After 30 interviews, no new information emerged. The interviews were recorded and transcribed. We performed a thematic analysis within a phenomenological approach, focusing on the perceptions and the management of depression, and the reasons for not consulting a GP during a depressive episode. The transcripts were open-coded to reach a consensus definition of the categories and subcategories. This coding was done independently by two researchers (CR and PC) for triangulation. An axial coding framework was then developed by reorganizing the open codes.
This survey was conducted under the ethical responsibility of this free clinic's chief physician (JL). A questionnaire was constructed on the basis of the four main categories that emerged from the qualitative analysis. It contained 62 closed-ended questions and collected the following information and data: the patients' main sociodemographic characteristics, their responses to the questions in the Mini-International Neuropsychiatric Interview (MINI)  [17] , whether or not they were afraid to talk to the physician about their depression, the type of care sought during a depressive episode (past, present and hypothetical), and the possible reasons for not seeing a GP. The sociodemographic data included age, sex, nationality, job category status, family status, type of health insurance, cohabitation, educational level, and having or not having declared a regular GP to the Social Security health insurance system.
The chi-square test (or the Fisher's exact test for small samples) was used for comparisons of proportions. The statistical analyses were performed using Stata v. 10 software (StataCorp LP, College Station, Texas, USA). All data are available on https://mynotebook. labarchives. com/share/claire/MjAuOHw1OTcxMi8xNi0yL1RyZWVO b2RlLzI0MjMwMTAwODd8NTIuOA==.
Data sources and searches To identify relevant articles, targeted literature searches were conducted in MEDLINE, EMBASE and Cochrane Library from January 1990 to December 2013. For each condition of interest, Medical Subject Headings (MesH) and text terms related to adherence and interventions were identified. Details on the MEDLINE search strategy are presented in Table  2. The grey literature was reviewed post-hoc (opengrey. eu and greylit. org) but no references related to the subject was found. We also searched relevant citations manually in the reference list of pertinent reviews. Two trained reviewers screened independently each title and abstracts (FD and SR). All titles selected by at least one reviewer went on full-text review. Conflicts were resolved by discussion and consensus. Clinical experts also reviewed the search strategy.
Data extraction and quality assessment A trained reviewer extracted data of interest from each study that met the inclusion criteria and summarized them in a structured table. The summarized studies were checked for completeness and accuracy by a second reviewer. To estimate the magnitude of adherence (size of intervention effect), we calculated the relative risk (RR), which is the ratio of the proportion of patients improving in the intervention group divided by the proportion of patients improving in the control group. RR is easy to interpret and consistent with the way clinicians generally think. [18]. One reviewer (FD) assessed the risk of bias/study limitations of each study using the GRADE system  [19]. The five following criteria were examined for clinical trials: 1) Lack of allocation concealment; 2) Lack of blinding; 3) Incomplete accounting of patients and outcome events; 4) Selective outcome reporting bias; 5) Other limitations such as use of non-validated measures of adherence.
Data analysis-Classification of interventions A qualitative analysis of extracted data was performed. Intervention approaches were classified into four categories: educational, behavioral, cognitive behavioral, and multicomponent intervention according to Greenley et al  [16]. Educational interventions aim to enhance patient knowledge of disease and symptoms, the benefits and mechanisms of action of the medication regimen, the consequences of non-adherence and potential side effects of treatment (example: individual or group educational sessions), Behavioral interventions promote the act of medication taking and/or reinforce adherence by providing incentives for medication taking (example: text message sending, motivational interview), Cognitive behavioral interventions enhance adherence by altering thinking patterns that contribute to non-adherence while also establishing behavioral patterns that support adherence using aforementioned behavioral strategies (example: problem-solving skills training), Multicomponent interventions use multiple strategies to enhance adherence including educational, behavioral, cognitive behavioral, motivational and/or support provision strategies.