dataesr/scientific-paragraphs-categorization huggingface dataset:
Bruitparif processed and homogenized all these layers according to the Environmental Noise Directive, in order to create a noise database at the scale of the Ile-de-France region. From the vector layers of noise points or noise lines and the raster layer provided by local authorities and government services with noise levels between 30 and 80 dB(A), Bruitparif generated a layer of polygons of noise levels with a subdivision in 5 to 5 dB(A) classes (55 -<60; 60 -<65; 65 -<70; 70 -<75; and 75-< 80). The raw data of the final Bruitparif map were collected between 2007 and 2011. The geographical processing of the noise database was performed with the ArcInfo 10 Geographic Information System. The Environmental Noise Directive established that noise levels equal to or above 55 dB(A) could have an impact on human health.
Then according to the proportion of the surface of each class of noise that fell into the buffer of each participant, the following noise variables were defined with the SAS software: 25th percentile of noise in the buffer of each participant, median noise value, and 75th percentile of the noise value in the buffer of each participant. The median was used rather than the average, because it was found relevant to consider different percentiles in order to take into account the variability of noise exposure into the buffer for people going through it and to identify people exposed to high levels of noise that would not be captured by an indicator of central tendency. The areas with modeled information on noise did not cover the entire territory of the Ile-de-France region and the areas with modeled information on noise differed in their coverage according to the type of transportation mode.
Road traffic noise were estimated at the place of residence in Models 2A (N = 6194) and as the median value of 500 m radius street network buffers around the place of residence in model 2B (N = 6539). Multilevel logistic regression models were estimated after excluding individuals with missing values for road traffic noise variables. These models were estimated between standardized continuous noise variables and annoyance due to road traffic, adjusted for individual/neighborhood factors of basic model 1 (Table  2 ). Road traffic noise was estimated in 500 m radius street network buffers around the place of residence. Abbreviation: B-N: between-neighborhood. aircraft noise  [9]. Calculating the total length of railways and roads from data of the Institute of Urban Planning of the Ile-de-France region and of the National Geographic Institute, we found that the total length of the rail network represents only 2.
Acknowledgements We are grateful to  INPES (and Pierre Arwidson)  for its continued support since the beginning of the study. We are also grateful to Insee, the  French National Institute of Statistics and Economic Studies , which provided support for the geocoding of the RECORD participants and allowed us to access to relevant geographical data (with special thanks to  Pascale Breuil ). We thank Geoconcept for allowing us to access to the  Universal Geocoder software. We also thank the  Caisse Nationale d'Assurance Maladie des Travailleurs Salariés (France)  and the  Caisse Primaire d'Assurance Maladie de Paris (France)  for helping make this study possible. Bruitparif is also warmly thanked for providing access to the noise data and for the invaluable assistance and advices received ( Marie-Alice d'Orléans, Fanny Mietlicki ).
In fact, Indian health ministry's working group on noncommunicable disease burden  [3]  noted that "India will soon become home to the second largest number of elderly in the world. The challenges are unique with this population in India. " In accordance, Kumar and colleagues at the Dharma Foundation of India (DFI) conducted a preliminary cross-sectional study on 95 elderly (age > 60 years) subjects from New Delhi and Gurgaon, India  [6]. They investigated psychomotor aspects of depression where they found a negative correlation between the Berg Balance Scale (BBS) score  [7]  and the Geriatric Depression Scale (GDS) score  [8]. Also, they found that as age increased, the BBS score decreased and the GDS score increased, leading to poorer balance and depression. Following this alarming insight, DFI conducted a subsampling of 24 community dwelling elderly from their database in India that covered a wide range of BBS and GDS scores, in order to evaluate a low-cost POCT  [9]  system for the psychomotor symptoms of depression.
Point-of-Care Testing (POCT) of Psychomotor Symptoms of Depression. Following the preliminary cross-sectional study by Kumar and colleagues  [6] , a convenient subsampling of the community dwelling elderly in India was conducted to select 24 elderly (>60 years) subjects, such that they covered a wide range of BBS and GDS scores. All subjects gave their informed consent for the experiments in compliance with the Helsinki Declaration. The objective was to evaluate a low-cost POCT system to investigate the psychomotor symptoms of depression affecting standing balance. The experimental setup is shown in Figure  1. The centre of pressure (CoP) was monitored using a Wii Balance Board (Nintendo, USA)  [13]  and custom software developed using the WiiBrew toolkit  [14]. The Wii Balance Board (Wii BB) relayed the 2D ground reaction force information to the PC via Bluetooth [14], while Emotiv EEG Neuroheadset (Emotiv, Australia) relayed EEG data to the PC via their proprietary wireless protocol  [12].
Offline Analysis of Baseline Resting State EEG. Emotiv EEG Neuroheadset wirelessly relayed EEG data to the PC from 14 channels (International 10-20 system)-AF3, AF4, F3, F4, F7, F8, FC5, FC6, P3 (CMS), P4 (DRL), P7, P8, T7, T8, O1, and O2. Resting state EEG was recorded in eyesopen and eyes-closed conditions during quiet standing for at least 3 minutes. The EEG data were analysed using EEGlab software  [15]. The eye-blink artifacts were rejected using EEGlab functions, then the artifact-free continuous EEG was divided into 4. 096 seconds epochs using a Hanning time window (epochs were overlapped by 50%), and an estimation of the power spectra was found for the absolute theta (4-7. 5 Hz), alpha (7. 5-14 Hz), and beta (14-20 Hz) frequency bands for QEEG analysis  [16]. Following Grin-Yatsenko and colleagues  [16] , a threeway analysis of variance ("anovan" in MATLAB R2010a, The MathWorks, Inc.
, USA) of the absolute EEG power in eyesclosed condition was conducted with the following factors: GDS score (three levels: 0-9, 10-19, 20-30), EEG electrode locations (14 levels), and frequency band (three levels: theta, alpha, and beta). We then performed cluster analysis ("clusterdata" in Matlab R2010a, The MathWorks, Inc. , USA) to compute the Euclidean distances between items in the data set and create a hierarchical cluster tree from the data set. Also, Linear Discriminant Classification ("ClassificationDiscriminant. fit" in Matlab R2010a, The MathWorks, Inc. , USA) was investigated to evaluate the possibility of classification of depressive patients using eyes-closed EEG power spectra at discriminatory frequency bands and electrode locations, as found using multiple comparison tests ("multcompare" in Matlab R2010a, The MathWorks, Inc.
The PT/OT conducting the FRT reported BBS and GDS scores of each elderly subject from a preliminary cross-sectional study (Section 2. 1). The EEG data from 14 channels (International 10-20 system), AF3, AF4, F3, F4, F7, F8, FC5, FC6, P3 (CMS), P4 (DRL), P7, P8, T7, T8, O1, O2, was analysed using EEGlab software  [15]. The eye-blink artifacts were rejected using EEGlab functions, then the artifact-free continuous EEG was divided into 4. 096 seconds epochs using a Hanning time window (epochs were overlapped by 50%), and the power spectra was estimated for the theta, alpha, and beta frequency bands for QEEG analysis  [18]. The evolution of the baselinenormalized EEG power in different frequency bands (normalized by their corresponding baseline eyes-open value) during quiet standing in-between FRT was investigated for different GDS scores, EEG electrode locations, and baselinenormalized cued response time.
Here, the POCT system may also be used to provide real-time feedback and monitoring of the biomarker, where a comprehensive therapy program can be developed using functional reach training in conjunction with pharmacological interventions. Furthermore, the frequency of falls can be monitored with a freely available mobile phone-based application, FallAlert  [24] , during such a comprehensive therapy program where the propensity of falls during periods of psychomotor symptoms of depression in elderly can be investigated in an interventional study.
Acknowledgments Authors acknowledge their gratitude to the reviewers for their insightful comments that helped in improving the paper. R. Kumar is highly indebted to  Mrs. Ruby Aikat ,  ISIC Institute of Rehabilitation Sciences, New Delhi, India , for her invaluable support, inspiring guidance, suggestions, and constant encouragement during this study. R. Kumar  would also like to thank  Dr. R. K. Sharma  ( Occupational Therapy Department, Safdarjang Hospital, New Delhi ) and  Ms. Nidhi Vinaykya  for their motivational support. R. Kumar  also extends his gratitude to  Dharma Foundation of India (Nonprofitable and Charitable Trust)  for supporting this cross-sectional study under their " Active Ageing Project. " The voluntary help received from all the subjects is gratefully acknowledged. Also, the technical help received from  Mr.
12, 15  FEV 1 , 80% predicted is considered abnormal. Th se definitions are also used by the European Coal and Steel Community (ECSC) for reference equations. 1, 3, 15, 17  To ascertain reversibility of airway obstruction, we measured FEV 1 after inhalation of a bronchodilator. Impr ement in FEV 1 after bronchodilator use is a hallmark of asthma, but can also be found in COPD. 1  There are no previous surveys in Syria on CRD prevalence and risk factors in patients managed at PHCs. Syria is the first country in the eastern Mediterranean region to administer this GARD multicenter survey to collect data and implement evidence-based interventions on prevention and control of CRD. The GARD survey is a particularly useful tool to study the role of smoking as a risk factor in females 6 years and older presenting to primary care in Syria.
statistical analysis Data were expressed as percentages for discrete variables and as means (standard deviations) for continuous variables. The chi-square test and t-test were used when analyzing differences between groups. Analysis of variance was used to compare continuous variables between the three studied groups. Logistic regression models were fitted using respiratory symptoms or diseases or lung functions as the dependent variable and active or passive smoking as the independent variable. All possible interactions between the explanatory variables were tested and found to be statistically nonsignificant. We considered P , 0. 05 to be significant. Statistical analysis was carried out using the Stata software package (version 6; StataCorp, College Station, TX, USA).
Acknowledgments We thank the  Syria WHO GARD Working Group  members for their contribution:  Dr Fatmeh Yassine ,  Dr Jamal Allouch ,  Dr Nasser Daaboul ,  Dr Daed Taha ,  Dr Samir Sabba ,  Dr Gayath Dyban ,  Dr Ramsa Halloum ,  Dr Mahmoud Bakir ,  Dr Abdullah Khouri ,  Dr Annas Naem , and  Dr Mayhoob Husein , for their efforts as trainers to GPs;  Dr Fatmeh El-Dmeirawi  and  Dr Gazal Dib , pulmonary pediatricians;  Dr Kinaz Al-Sheih ,  Dr Hala-Al-Khaer , and  Dr Mohammad Al-Bittar , directors in Ministry of Health programs, for their public health views and advice;  Dr Mazen Dayoub ,  Dr Mais Khaddouj ,  Dr Dalal Kheirbek ,  Dr Younes Nizar ,  Dr Hassan Mansour ,  Dr Majdouline Youness ,  Dr Rami Razzouk , and  Manaf Ajlouny , for their help with data collection; and  Dr Abdulfattah Abbas ,  Dr Husein Said ,  Dr Ibrahim Suleiman , and  Dr Mounif Meri , for their administrative assistance.
The nine residues in the mammalian P2X7Rs (10 in the zebrafish P2X4. 1R, including P2X7Rs are aligned with the sequences of the truncated zebrafish Leu217) involved in forming the inter-subunit ATP-binding site (shown in P2X4. 1Rs with determined atomic structures (Hattori and Gouaux, 2012). Figure The ∆P2X4. 1-B 2 (B2) consisting of Ser28-Phe381 with changes at three positions (C51F, N78K, and N187R) and the ∆P2X4. 1-C (C) Ser28-Lys365 with changes at two positions (N78K and N187R) were used to determine the structures in the closed (PDB accession number: 4DW0) and ATP-bound open states (4DW1), respectively. The species abbreviations are: H, human; MM, macaque monkey; D, dog; R, rat; M, mouse; GP, guinea pig. The sequences in italics are absent in the atomic structures or structural models.
Study design This randomised controlled pragmatic study was performed in two parallel cohorts of IBS patients enrolled by 87 general practitioners from December 2009 to May 2011 in France. Written informed consent was obtained from each patient. The protocol was conducted in accordance with the Declaration of Helsinki and European GCP for biomedical research, and was approved by the local Ethics Committee ('Comit e de Protection des Personnes' Sud-M editerran ee I') of Marseille University Hospital (France). The study was registered with the EudraCT identifier 2009-013049-27 and Clinicaltrials. gov Identifier: NCT01404923. General practitioners were randomised to one of the two treatment strategy arms: either an ondemand treatment with ACS or the other arm, where they were allowed to decide which usual treatment was needed by the patients.
Data collection After the inclusion visit, follow-up visits were scheduled at months 1 and 3 prior to a final visit at   (31)  were completed at baseline, then at months 3 and 6. IBS-SSS was filled in prior to inclusion, then at months 1, 3 and 6. Concomitant treatments and adverse events were collected at months 1, 3 and 6 visits. The delivery of treatment units (on-demand ACS group) or the drug prescriptions (usual treatment group) were renewed at months 1 and 3.
The hospitalisation costs were calculated according to the duration of stay and based on the mean hospitalisation daily price at the 'Assistance Publique des Hopitaux de Marseille' (Marseille University Hospital). Indirect costs (days off work) were calculated based on the mean net salary quoted from the French National Institute of Economics and Statistics (INSEE). Direct costs were calculated for each treatment group as the sum of the treatments, physician's visits, investigations and hospitalisations costs related to IBS. Total cost was the sum of indirect (days off work) and direct costs. Costs were compared between treatment groups (Mann-Whitney test). All statistical tests were bilateral at the 0. 05 level. Analyses were performed using SAS version 9. 2 (SAS Institute, Inc. , Cary, NC).
Methods: We pooled individual-level data from 7 cohort studies comprising 102 128 men and women who were free of existing coronary artery disease at baseline (1985-2000). Questionnaires were used to measure job strain (yes v. no) and 4 lifestyle risk factors: current smoking, physical inactivity, heavy drinking and obesity. We grouped participants into 3 lifestyle categories: healthy (no lifestyle risk factors), moderately unhealthy (1 risk factor) and unhealthy (2-4 risk factors). The primary outcome was incident coronary artery disease (defined as first nonfatal myocardial infarction or cardiac-related death). Results: There were 1086 incident events in 743 948 person-years at risk during a mean follow-up of 7. 3 years. The risk of coronary artery disease among people who had an unhealthy lifestyle compared with those who had a healthy lifestyle (hazard ratio [HR] 2.
Study population We used pooled individual-level data from 7 European prospective cohort studies participating in the Individual-participant-data Meta-analysis in Working Populations (IPD-Work) Consortium:  7  the Whitehall II Study in the United Kingdom;  12  the GAZEL Study in France;  13  the Belgian Job Stress Project (Belstress);  14  the Work, Lipids, and Fibrinogen Study in Stockholm (WOLF-S)  15  and in Norrland (WOLF-N),  16  Sweden; the Finnish Public Sector Study;  17  and the Health and Social Support (HeSSup) Study in Finland. 18 etails of the study design and participants have been published previously and are summarized in Appendix 1 (available at www. cmaj. ca/lookup ] [14] [15] [16] [17] [18]  For our meta-analysis, we included all men and women from the cohort studies who were free of coronary artery disease at baseline (1985-2000 depending on the year recruitment began) and for whom complete data on job strain, lifestyle risk factors and coronary artery disease were available.
Statistical analysis We pooled the data from the participating cohort studies after harmonization of all measures. To examine the absolute difference in risk between groups, we computed age-, sex-and cohortadjusted 10-year incidence of coronary artery disease by baseline measures of job strain, lifestyle risk factors and combinations of both. Hazard ratios (HRs) and 95% confidence intervals (CIs) were computed using Cox regression. To examine whether the associations of the combinations of job strain and lifestyle risk factors with the risk of coronary artery disease were confounded by social patterning of these characteristics, we adjusted the models for socioeconomic status. To account for differences between countries in which the cohort studies were conducted, the models were adjusted for country.
Strengths and limitations Our study has some notable strengths. We extracted data from studies in 2 stages: the exposure was defined and harmonized across cohorts, with investigators masked to the health outcome; outcome data were obtained after this stage to minimize investigator bias. 24 We calculated population attributable risk and absolute differences in incidence rates between groups, in addition to HRs, because absolute differences in disease risk are considered to be particularly useful for planning public health policy. Our results also have limitations. Job strain, as well as smoking status, alcohol intake and physical activity, were self-reported by participants; therefore, some misclassification of these lifestyle indicators may have occurred. Our study was based on observational data; thus, conclusions about causal associations with coronary artery disease are precluded because we cannot rule out residual or unmeasured confounding.
Funding: The IPD-Work Consortium is supported by the EU New OSH ERA Research Programme (funded by the Finnish Work Environment Fund, the Swedish Council for Working Life and Social Research, the German Social Accident Insurance and the Danish National Research Centre for the Working Environment), the Academy of Finland (grant no. 132944), the Bupa Foundation in the UK, and the Ministry of Social Affairs and Employment in the Netherlands. The German National Accident Insurance supports analyses in the frame of the OSH ERA project. Funding bodies for participating cohort studies are listed on their websites. The study sponsors had no role in the design of the study, the collection, analysis or interpretation of data, the writing of the report or the decision to submit the article for publication.
Study design and sample We conducted a descriptive cross-sectional study of supply of several measures of acute care services in a convenience sample of seven cities with a population of at least 100,000 from different geographic and economic strata: on the basis of World Bank criteria, two cities from high-income countries (Boston, USA and Paris, France), three from upper middle-income countries (Bogota, Colombia; Recife, Brazil; and Liaocheng, China), and two from lower-middle-income countries (Chennai, India and Kumasi, Ghana). We defined supply of acute care services as the number of each service per 100,000 population and per population-adjusted measure of disease burden. With local collaborators, we determined supply from data provided by local and regional authorities and conducted primary data collection when required.
We abstracted data from the 2008 GBD project update  [7] , which estimates the number of deaths per country in multiple categories of diseases. The 2008 data are the latest available at the required level of granularity. Two investigators (NKJA, SM) independently selected all communicable and non-communicable diseases whose burden would potentially be mitigated by acute care services. Disagreements were resolved by consensus of all study investigators. We selected the following illnesses: respiratory infections (otitis media, lower, and upper respiratory infections), injuries (road traffic accidents, poisoning, falls, fires, drowning, self-inflicted injuries, violence due to war and civil conflicts), and other acute illnesses (tuberculosis, diarrheal diseases, childhoodcluster diseases, meningitis, malaria, tropical-cluster diseases, dengue, Japanese encephalitis, nephritis and nephrosis, cardiovascular and respiratory diseases, maternal conditions, and diabetes mellitus).
g. , ability to measure central venous pressure), and invasive mechanical ventilation. Self-defined ICU beds: as defined by the region or hospital, regardless of whether the definition agreed with our standardized definition. Ambulances: vehicles (including those operated by the fire department) that transport acutely ill patients from home to hospitals and vice versa, excluding vehicles that solely transport patients between hospitals. To obtain data, we identified local collaborators in each city. We circulated data collection instruments via email and held monthly conference calls to provide clarification and updates on the data collection process. Each collaborator collected data between January and September 2012. Collaborators first attempted to obtain data from government sources. If data were unavailable, collaborators conducted web searches and contacted other local data sources or individual hospitals, as required.
Analysis For each city, we calculated acute care services supply (hospitals, hospital beds, ICU beds, and ambulances) per 100,000 population and per 100 deaths due to acute illnesses. We also calculated disease burden as the total deaths due to all acute illnesses, respiratory infection deaths, and deaths due to injury per 100,000 population. For each disease burden category, we also calculated subtotals by age. We tested using regression statistics for any linear or non-linear relationship between supply of acute care services per 100 deaths and national gross domestic product (GDP) per capita, obtained from the World Bank  [18]. Unless otherwise stated, when comparing ICU beds, we used the standardized rather than 'self-defined' count. We enumerated the barriers to data collection identified by our local collaborators.
Third, even though we standardized definitions of data elements, the method of data collection (e. g. , use of existing centralized data vs. manual data collection) varied depending on availability. Although we attempted to verify all data, it is possible that the different methods biased our results. We relied on GBD project data for our measure of disease burden. GBD also suffers from variable data collection methodologies across regions and relies heavily on cause-of-death data, which are prone to error and capture only some aspects of disease burden. Differences in the mortality rate across cities may have led to inaccuracy in projecting the overall burden of a given disease. Furthermore, we selected only those deaths attributed to diseases we considered likely to require acute care. Our selection criteria were based on expert opinion, and are somewhat arbitrary, although we note that others recently proposed a similar approach  [25].