dataesr/scientific-paragraphs-categorization huggingface dataset:
The NORMAN Joint Programme of Activities promotes this type of study as a way to identify candidate compounds for further investigation  [41]. Finally, as part of its latest Joint Programme of Activities, NORMAN will develop a bioactivity database. This project aims to support the interpretation of effect-based monitoring data for mixture toxicity modelling. A richer set of bioactivity data will be crucial to understanding the contribution of detected chemicals to the observed effect in the different assays. Currently, the lack of effect data for the detected chemicals in different assays is a major limitation and more data is needed for a significant improvement of mixture modelling and elucidation of drivers of toxicity. This database will be essential to reveal CEC-induced bioassay activity that cannot be explained by the measured concentrations of the few individual chemicals for which effect data are already known  [41].
Rationale All state-of-the-art tools presented here have been developed and tested within large-scale European projects (e. g. FP7 SOLUTIONS, EDA-EMERGE FP7-PEOPLE-2011-ITN/290100, ANSWER H2020-MSCA-ITN-2015/675530, NEREUS COST Action ES1403, APEX LIFE17 ENV/SK/000355). NORMAN works in close cooperation with international river basin organisations (e. g. the International Commission for the Protection of the Danube River (ICPDR) ± 14 European countries and the EU; organising Joint Danube Surveys every 6 years), sea conventions (e. g. Black Sea Commission; OSPAR), environmental specimen banks and environmental authorities in various Member States (e. g. France, Germany, Nordic countries, The Netherlands). In 2019 the NORMAN Association received funding from the ICPDR as a contribution in support of its participation in the experimental activities of the 4th Joint Danube Survey (JDS4).
Experimental setup Hands and trunk movements were recorded with 8 infrared optical cameras from the Vicon Motion Capture System (Vantage V5, lens 8. 5 mm, Oxford Metrics, UK). The sampling frequency was set at 100 Hz. VICON Nexus 2 software was used to save time series of each marker. Experimenter placed markers on the target and the manubrium, and for each body side on the 1 st metacarpal, the lateral epicondyle of the humerus, the acromion process, and the iliospinal. For each side, we corrected iliospinal marker position before data analysis to correspond to the anatomical centre of hips joints.
Data analysis Data analysis was processed with SciLab 6. 0. 2. A low-pass Butterworth digital filter of 2nd order of 5 Hz and a cut-off frequency of 30Hz were applied on the signal. First, we computed the beginning and end of each reaching movement from the hand maker time series. The beginning of the movement was when the Euclidean velocity became positive and remained positive until the maximum velocity. The end of the movement was when the Euclidean distance to the target reached its minimum. Second, we computed Proximal Arm Use (PAU) for each trial as (hand displacement -trunk displacement) / hand displacement. We finally computed PANU as Maximal PAU minus Spontaneous PAU  (Bakhti et al. , 2017). Median PANU trial was retained for data analysis. Accuracy was assessed through end point error, computed as the Euclidean distance between the active hand and the target at the end of the reaching movement.
Methods Argus was used in 148 healthcare facilities from May 2016 to July 2018, expanding to 185 healthcare facilities from July 2018. Data from week 21 of 2016 to week 12 of 2019 was extracted from the Argus database and analysed. An assessment mission took place in August 2016 to collect users' satisfaction, to estimate the concordance of the received data with the collected data, and to estimate the time required to report data with Argus.
Furthermore, we were unable to identify a single tool which would allow each level of the system to review and analyse data from its area in a workflow respecting the IDSR procedures. WHO has therefore developed "Argus", an IT tool to facilitate public health surveillance for early detection and response of events in compliance with IDSR procedures (http://www. argus. community/). It uses Short Message Service (SMS) technology for the transmission of aggregated weekly reports and alerts of serious or unexpected events between healthcare facilities and all levels of the public health surveillance system via a mobile phone application. A web platform complements the phone application for data management and analysis at each level of the system. To finalize the development of Argus and assess if it could improve public health surveillance in Togo, Argus had been pilot tested in two regions of the country since May 2016.
A new paper form was developed to support tallying of the weekly figures based on the healthcare facility registry. Data was to be reported with Argus before the paper form was archived in the healthcare facility. Alert thresholds at healthcare facility level were defined for nine diseases and conditions (acute flaccid paralysis: 1, acute haemorrhagic fever: 1, cholera: 1, human anthrax: 1, human rabies: 1, measles: 3, meningitis: 2, neonatal tetanus: 1, yellow fever: 1). If the weekly number of cases reached an alert threshold, a warning message appeared on the Argus Android Client application at the healthcare facility level and the disease or condition was highlighted on the aggregated report displayed on the Argus web platform. Argus duties at each level of the surveillance system were defined as follows, based upon the IDSR guidelines: • Healthcare facility: send the weekly aggregated report from the previous week on Monday before four PM.
e. percentage of uptime of the Argus web platform); and the costs of implementation and operation. The full list of indicators used to assess and describe the Argus pilot test results in Togo is presented in Table  1. To assess data reporting and data review and to describe public health events identified by the system we extracted data from epidemiological week 21 of 2016 to epidemiological week 12 of 2019 from the Argus database. We monitored the percentage of uptime of the Argus web platform through an online monitoring service (https://www. site24x7. com). To describe the • general appearance of Argus Android Client at healthcare facilities; • available documentation for Argus Android Client used at healthcare facilities; • general appearance of Argus web platform at intermediate and central levels; • available documentation for Argus web platform at intermediate and central levels; • usefulness of Argus Android Client used for data reporting; • usefulness of Argus web platform for data review; • usefulness of Argus web platform for data analysis; • overall simplicity of use of Argus Android client; • simplicity of use of Argus Android client for data reporting; • overall simplicity of use of Argus web platform; • simplicity of use of Argus web platform for data review; • simplicity of use of Argus web platform to monitor the completeness and timeliness of data reporting.
Sum of ratings assigned by respondents (from 1 worst to 5 best) / No. of respondents. costs of the implementation and operation of the system we used the invoices paid for the project and extracted the number of SMS exchanged from the Argus database. One assessment mission took place from the 4 th to 12 th August 2016 to collect users' satisfaction, to estimate the concordance of the received data with the collected data, and to estimate the time required to report data with Argus. This was done through eight sessions organized with all users of the system, each session was composed of: a 20-minute presentation on the current use of Argus in Togo; a 40-minute slot for participants to fill in a user-satisfaction questionnaire (S1 Questionnaires); a 1-hour structured discussion with all participants; and a 30-minute structured discussion with surveillance officers at intermediate and central levels.
Costs To estimate the concordance of the received data with the collected data, participants were asked to bring the paper forms archived at the healthcare facilities and used to tally the weekly figures. A copy of each form was collected and its data entered twice into a database using the Epidata software. To estimate the time required to report data with Argus, participants in four of the eight evaluation sessions were provided with a mock paper tallying form and asked to report it using Argus. The amount of time it took each participant to enter the information and send the report was measured and registered by one surveyor. All data analyses were performed using the R statistical software  [14].
Argus included several components (http://www. argus. community/): • A custom Argus Android Client application installed on Android mobile phones, Fig 1. • An Argus server located at the Togolese Ministry of Health in charge of data collection and management. It displayed an online web platform to review the collected data and identify potential acute public health events through weekly epidemiological summaries and dedicated dashboards, Fig 1. The Argus server was composed of three PHP applications exchanging information through XML files and web services, a web front end developed with the AngularJS framework, and an instance of the R statistical software.
Paper forms. W21 2016 -W31 (No. of data having the same value in the system and on the paper forms) / (No. of data Argus database. 2016 collected in the system) � 100. Data review by surveillance % of uptime of the Argus web platform. Monitoring service b. 30/05/2016-01/04/ officers (duration of Argus web platform online and available to users / duration of Argus web 2019 platform offline) � 100. Evolution of completeness and timeliness of data review at each level. Argus database. W21 2016 -W12 By level: (No. of validated or rejected reports / No. of reports received) � 100. 2019 (No. of reports received and validated or rejected on time / No. of reports received on time) � 100. Argus database. W21 2016 -W12 2019 Number of immediate notifications of serious or unexpected events received. Argus database.
In addition, as this was a pre-study for a fall prevention programme, the professionals who recruited participants were asked to select individuals with a presumptive increased risk of falling (e. g. indicated by experienced falls in the previous year, balance impairment, having difficulties walking). However, this criterion appeared to be complicated to apply, for example, because fall-proneness was not registered in their database or because professionals found it awkward to ask. As fall risk was not the main focus of the present study, we did not screen participants ourselves; hence, we cannot be certain whether they had an increased risk of falling. However, it is likely that our sample had impaired vitality (e. g. indicated by using a wheeled walker). The average age of the participants was 76.
The interviews were conducted by one of four researchers of the University of Applied Sciences Utrecht (including the authors AJB and AR, both female, and two male colleagues), assisted by a second researcher from the project team. The second researcher observed and made notes. Participants gave written informed consent before the start of the interview. All interviews were audio-recorded and transcribed verbatim. Interviewees were not asked for additional information afterwards. Data were analysed thematically. The authors AJB and AR created the initial code scheme after reading transcripts of the interviews, and tested the code scheme by coding all interviews. After discussing the coding process, one code was added, and other codes were clarified in more detail; resulting in a code tree consisting of nineteen main themes and 35 subthemes.
The final coding of all interviews was done by one researcher (AJB). The data were manually coded in the program Atlas. ti 8. The analysis started by discussing initial insights among all researchers involved. Then, AJB started analysing the coded data, starting with the codes that came up from the discussion, which were 'paying attention to or talking to others about eating behaviour', and 'loneliness' in combination with 'being alone'. Next, text fragments belonging to related codes were analysed in order to link them to each other. Finally, all coded text fragments were analysed, to prevent missing relevant insights. Insights were discussed with AR and ML, and, eventually, four key topics were identified in relation to interpersonal determinants of eating. The findings were compared with the results of the observations, checked with the other interviewers, and supported with illustrative quotations (translated from Dutch to English).
Background: Studies interested in patients coping with a cardiac illness usually focus on children, teenagers, and adults above the age of 55. Apart from the field of congenital heart diseases, there is a general lack of literature regarding young adult cardiac patients (18-55 years old) who seem to cope with psychosocial issues. Therefore, the objective of this paper was to gather all the research carried out concerning the psychological experiences of young adult cardiac patients. Methods and Results: A comprehensive, systematic review was conducted on quantitative, qualitative, and mixed-method studies in PsycINFO, PubMed, ScienceDirect, and Cochrane Library databases. Out of the 10,747 articles found, 32 were included. While we aimed to include many cardiac diseases, coronary patients dominated the data.
Search Strategy and Eligibility We conducted our search on PsycINFO, PubMed, ScienceDirect, and Cochrane library databases, the most relevant ones regarding our topic. Several searches were run on these databases before coming up with the appropriate keywords. The keywords retained related to three categories (both in French and in English): (1) cardiac diseases, (2) young adults, and (3) psychology and patients' experience. The final list was discussed with a cardiologist in order to make sure that no important condition was missing. As we wanted to focus only on cardiac diseases, it was decided not to include the following words (also entered in French): stroke, brain, and cerebral. Our search had to be adapted to the specific constraints of each database. For example, on PsycINFO and PubMed, the search was limited to "human research" (this filter was not available otherwise).
On ScienceDirect, the search was limited to titles, abstracts, and keywords among research, review, and data articles. The search terms and equations are available in the Supplementary Material. Articles published in English or French up to March 1, 2019 were included. We included (1) quantitative, (2) qualitative, and (3) mixed-method articles, which are (4) available online and (5) focusing on the psychological issues (6) of young patients coping with a cardiac illness. Research dealing with patients on a wider age range than 18-55 years old was included only if part of the data concerned our target age range. When necessary, the authors were contacted to request the full texts or to receive more information (mainly regarding the exact age of the participants). Those that were excluded were classified as: (1) non-cardiac-related articles, (2) theses, (3) dissertations, (4) purely medical studies, (5) risk factor studies, (6) congenital heart disease studies, and (7) studies mixing their data with children, (8) adolescents, and (9) adults over 55 years old.
Themes Each article was thoroughly analyzed and its data of interest was extracted. Data were gathered in themes and then clustered into superordinate themes. Five main subjects of interest were revealed: (1) emotional states, (2) quality of life, (3) adjusting to the medical environment, (4) social life, and (5) identity. Each theme included different subthemes that are presented below. The questionnaires used in each quantitative study are presented in Table  3. The themes explored in each qualitative study are presented in Table  4.
Stress Stress was rarely properly defined, even though it was accounted for in 53% of our selection. Most of the articles referred to stress as a risk factor and in relation to work, children, finances, and lifestyle. Informative data were mainly found in quantitative articles. Out of the 17 articles with a focus on stress, "perceived stress" was only investigated in six articles. Stressful life events were separately assessed in two studies. Two studies assessed stress in experimental conditions  (Vaccarino et al. , 2014 (Vaccarino et al. , , 2016)) : they used blood pressure and heart rate measures as they intentionally induced mental stress with a standardized public speaking task. Surprisingly, post-traumatic stress was never explored in these studies. After significant stress during hospitalization for a cardiac event, stress decreased over time, both 1 month later and 2 years later  (Brummett et al.
, 2008; Starrenburg et al. , 2014; Ooi et al. , 2016)  and by the multiple social roles women have to handle, such as parenting and housekeeping, as well as developing their career  (Vazquez et al. , 2008). Although this systematic review highlighted several specificities of YAC patients, it also brought to light the difficulties encountered to aggregate data on this subject. Indeed different methods (quantitative, qualitative, mixed) and also different tools (specific validated questionnaires, more general ones with only some items of interest, or questionnaires created for the study) were used to assess the psychosocial variables. Furthermore, different populations (MI, coronary artery disease, cardiac arrest, catheterization, and heart transplant) and different times of measures (during hospitalization or years later) were studied.
Data extraction and quality assessment The first reviewer (TAA) independently extracted the data, the second reviewer (SP) verified the accuracy and completeness of the extracted data. The extracted data included information on sampled population, weight change measurement, the finding of interest, and covariates. Several study authors were contacted to clarify study details and request results for those aged ≥65 years separately. Study quality was assessed independently by two reviewers (TAA and SP) using the Newcastle-Ottawa Scale (NOS) scored based on the Agency for Healthcare Research and Quality (AHRQ) guidelines  [10, 11]. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework was used to evaluate the quality of evidence, where we assessed risk of bias; consistency of results (based on heterogeneity); directness (applicability of population differences); precision (summary estimate CIs) and publication bias (funnel plots)  [12].
Data analysis Meta-analysis was performed using a random effects model of pooled HRs or RRs and 95% CIs of allcause mortality and weight change  [13]. Separate analyses were performed for studies that measured weight/BMI loss, weight/BMI gain or weight/BMI fluctuation (loss and regain of weight within a specific period) compared with a reference category. Where studies investigated more than one subgroup of weight/BMI loss/gain, the largest weight/BMI change groups were used, and the most fully adjusted analysis used for meta-analysis, an approach used previously  [14]. Heterogeneity across studies was assessed using the I² statistic [15] and p values <0. 05 considered statistically significant. Sub-group analyses were identified a priori (age, gender, length of follow up, study quality) or following data extraction (physical activity adjustment, study origin and weight change methodology) and performed to determine whether associations differed by participant characteristics or study design.
Data collection Two investigators collected the data. They were medical students at the end of their training in the Faculty of Medicine of Parakou University and had been trained on the questionnaire before the survey. The data was collected by using 2 questionnaires: a Stroke Screening Questionnaire in a door-todoor survey and a Stroke Confirmation Questionnaire. The 2 questionnaires were constructed based on the modified WHO questionnaire on neurological disorders  [19, 20]  which were used previously in Benin after back translation  [9]. The screening questionnaire contained eight questions and any positive response was considered as a suspected case. Any suspected case underwent the neurological examination to confirm the diagnosis of stroke. During this exam the second part of the questionnaire was used and all data concerning the clinical features or the history of the stroke occurrence was recorded.
The screening questionnaire contained also socio-demographic data, vascular risk factors, anthropometrics data (height and weight) and blood pressure. For the last, each subject had been lying for at least 5 minutes in a room in the house and three blood pressure shots were taken on one arm. The last value of the arterial pressure was retained. A systolic value more than 160 mmHg and/or a diastolic value 90 mmHg were defined as elevated blood pressure. The weight was taken using a weight scale (SECA*) with a precision of 100 g for each slightly dressed person. For size one toise was used with a precision of 1 cm. The body mass index (BMI) was calculated by dividing the weight (kg) by the square of size (m). The BMI less than 18. 5 kg/m 2 was considered as thinness, BMI between 18. 5-24. 9 kg/m 2 as normal weight, between 25 kg/m 2 and 29.
Statistical analysis The Stata version 21. 0 software (Stata Press, 4905 Lakeway Drive, College Station, Texas 77,845) was used to enter data and for statistical analysis. The categorical variables were described as number and percentage with 95% confidence interval, continuous variables by using mean with one standard deviation. The stroke prevalence (old and new cases) was estimated and age-standardized prevalence to World Health Organization (WHO) and SEGI type population was computed by using the direct standardisation method. The association between stroke and socio-demographic data and vascular risk factors was estimated using prevalence ratio. The Chi 2 test or exact Fisher test were used to compare percentages. All variables with a P-value of 0. 1 or less were introduced in multivariate analysis by using step-by-step background logistic regression.
5% and 8. 5%. The mean values of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were respectively 120. 9 AE 10. 7 mmHg (range: 90-211 mmHg) and 79. 3 AE 6. 8 mmHg (53-125 mmHg), respectively. Of the 4671 subjects, 331 had a SBP ! 140 mmHg and or a DBP ! 90 mmHg. mmHg. The incidence of elevated BP was 7. 1%. Among the 4671 people screened 161 were suspected of stroke and underwent neurological examination (Fig. 1 ). Only 54 cases were confirmed. CT-scan data were obtained in 10 cases. The overall prevalence of stroke was 1156 per 100,000 persons with the 95% confidence interval of 850 to 1462 per 100,000 inhabitants. The prevalence of stroke increased significantly with age (P = 0. 00001). The age-standardized prevalence of stroke according to WHO type population was 3223 cases per 100,000 population.
Databases Two sound emission databases for light and heavy vehicles registered on different roads in France were used for the present study. One of the databases consisted of measurements made on tracks  (2001) (2002) (2003) (2004) (2005) (2006)  and the other of on-site measurements on public roads  (2007) (2008) (2009) (2010) (2011) (2012) (2013). Light vehicles were monitored in 57 locations (3 on track and 54 on public roads) and heavy vehicles in 25 locations (2 on track and 23 on public roads), constituting a set of 82 measurement campaigns (see Tables  1  and 2  -Supplementary Material). Ontrack measurements were made in controlled conditions (controlled pass-by procedure NF S 31 119-2, and on-site measurements were made in real traffic conditions (statistical pass-by procedure NF S 31 119; ISO 11819-1.