dataesr/scientific-paragraphs-categorization huggingface dataset:
Web Socket Node. js includes a library named Socket. IO and allows the deployment of a real-time bidirectional event-based communication. To do so, the destination address of the data (the address of the server hosting the website) must be declared in the Node. js application, and the IP address of the sender must be declared in the Django website. Through this method, the library Socket. IO for Python and the one for Node. js are able to communicate by using a function call.
The 3D joint coordinates received from the Kinect and the Sensors 2018, 18, 1344 6 of 21 assessment associated are saved into the database. At the end of the exercise, the patients can review their movements and have a detailed feedback on their performance. The same questionnaire as previously mentioned is asked to the user at the end of a practical session, in order to provide the therapist with information on the state of the patient after performing the physical exercises. Consult. This section allows the patients to check the results and performances of the exercises they achieved. Messaging. This section allows the patient to exchange messages with a health professional (questions, advice, etc. ).
Exercise Database The modelling of this database is as follows (see, Figure  5 ): Main table. This table synthetizes the data about the exercises completed by the patient (date, kind of exercise, stage, completion time, etc. ) and the corresponding outcome performance. During the execution of an exercise, fractions of the entire movement are assessed in real-time, in order to evaluate the correctness of the performed movement. Then, a single outcome (good, fair or bad) is produced based on the sum of these individual assessments (see, next section for more details on the assessment module). Exercises table. This table, linked to the Main table through a 1:N relationship, provides details about each exercise and a demonstration video of the movement that must be performed. Data exercise N* table.
Feature Representation The Kinect camera is used to extract the three-dimensional coordinates of the main body joints. Nevertheless, additional data are required for the feature representation. The joint positions only are not sufficient to create the motion representation. It is also necessary to consider the joint orientation data and the depth data. The joint orientation is a 4d vector (quaternion) containing the coefficients to calculate pitch, yaw and roll between two sets of coordinate systems (in this case the joint specific orientations). These rotations enable us to detect harmful movements. The depth data is needed to create the personal coordinate system so that motion can be expressed in terms of relative movement in the frontal, sagittal and horizontal planes. The proposed total feature representation is represented in Figure  7.
The transformation from joint positions to angles is one of the straight forward tasks and can be computed rapidly utilizing the cosine law  (3). The absolute lengths between joints are calculated, in order to extract an angle as feature. The first features are created to represent the relative pose of the subject. These are features 1-6 as shown in Figure  7. Cos (A) = -a 2 + b 2 + c 2 2bc (3) Then, additional vectors are computed in a similar fashion, in order to represent hip rotations in the sagittal plane (Figure  8 ). With the floor plane orientation, the Personal Coordinate System (PCS) can be constructed. The additional data required in this step are the left and right hip joint position data. The vector that crosses both these points can be translated onto the reference point. Now at this point two vectors meet at the reference point and enable the third direction to be extracted by finding the perpendicular of these directions.
The representation of the Kinect's skeletal data is based on the degrees of freedom per joints. The created feature vector contains four variables (Table  2 ). Two of these features are the absolute value of the linear speeds of the hip center joint and the shoulder center joint (features F0-F1, in Table  2 ). The speeds are calculated using a buffer of 15 frames (around 0. 25 s) as a denoising method. The speed is the average of the values contained in the buffer. This buffer gets updated at every frame by discarding the oldest data point and adding the newest one. In this sense the data stream becomes available after the buffer is filled. Also, the angles of the upper legs in frontal planes (feature F3, in Table  2 ) are integrated into the model. The angles are calculated with the cosine law.
Setup and Protocol Two patients (74 and 77 years of age) and one healthy subject participated in this experiment. The patients were attending their regular session of physiotherapy for their rehabilitation. Both patients had undergone hip replacement surgery at their right hip. This surgery took place 1. 5 and 3 weeks prior to the experiment (i. e. , early stage recovery). All the participants were informed and gave their consent on the gathering of their exercise data and later use for scientific research purposes. Ethics clearance for this study was granted by the Nova University of Lisbon. Videos of the whole session were created as well as in depth images for further extraction of the skeleton and applying the input transformation into the desired feature representation. The participants were asked to perform five different exercises, which were repeated eight times (i.
or any other incompliance that will disturb the recovery process. These errors could be handled by a pipeline of assessment, in which the most serious errors will be tackled primarily in the form of a targeted feedback for the patient. In order to facilitate this process, current work is focused on the development of an application to enable the therapists to easily create new assessment models that could be applied on any kind of rehabilitation exercises (see prototype interface in Figure  16 ). CSV files can be loaded by clicking an HMM button. Before doing so, the features that are going to be used in training need to be assigned by typing them in the features section. To analyze the optimal amount of states that a dataset requires, Bayesian Information Criteria (BIC) can be performed. This creates a score using cross validation for states 2-10 (as a HMM exists at least out of 2 states).
Data analysis Data were collected at the end of the survey. They were analysed with SPSS 21, Sigma Plot 12 and R (3. 2. 5) software. F-CDRISC scale has recently been validated for resilience measurement in health students. 43 The reliability of f-JSPE and f-TAS20 scales was re-assessed in our conditions. The item-score correlation coefficient (rIS) was calculated. As Cronbach's α coefficient is not a good estimator of internal consistency for multifactorial scales,  46  greatest lower bound (GLB) and McDonald's ω coefficient were calculated as recommended elsewhere. 47 The scale appropriateness was deduced from the Kaiser-Meyer-Olkin coefficient (KMO, optimal value above. 8) and the Bartlett's test (optimal p <. 001). The item-sampling adequacy was deduced from the anti-image correlation coefficient value calculated for each item (AIC, optimal value above.
Small, medium and large effect sizes were respectively characterized by d, η 2 ≤. 2,. 2 < d, η 2 ≤. 5 and. 5 < d, η 2. 50 ltiple linear regression analyses were performed to test whether alexithymia and empathy can predict resilience. Gender (male = 1, female = 2) and year of formation (year 1 = 1, year 2 = 2 and year 3 = 3) were also considered as potential predicting factors of resilience. Standardized regression coefficient (β) and p values were calculated to estimate the relationships between the different variables. The heterogeneity within a dataset can be ascertained by cluster analysis (CA). 51 A non-hierarchical CA (K-means) was run by using standardized scores (z scores) for f-JSPE, f-CDRISC and f-TAS20 as clustering variables. The validity of different models (from 2 to 4 clusters) was assessed.
Limitations Although significant datasets support our work, it presents several limitations. It corresponds to a monocentric study (Faculty of Medicine of Nantes). The results are based on a limited number of responses (49% of the population of residents). A selection bias may be present in the survey, as collected responses can originate from students with a positive a priori for this study. Furthermore, medical residents are potentially able to identify socially acceptable answers concerning the level of empathy associated with medical practice. Fisher and Katz describe this social desirability-bias on selfreport assessment. 79 It may be responsible for some overestimated responses and the present survey. The last limitation concerns the design of the study which corresponds to a cross-sectional survey.
Introduction: Integration of services to screen and manage mental health and substance use disorders (MSDs) into HIV care settings has been identified as a promising strategy to improve mental health and HIV treatment outcomes among people living with HIV/AIDS (PLWHA) in low-and middle-income countries (LMICs). Data on the extent to which HIV treatment sites in LMICs screen and manage MSDs are limited. The objective of this study was to assess practices for screening and treatment of MSDs at HIV clinics in LMICs participating in the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium. Methods: We surveyed a stratified random sample of 95 HIV clinics in 29 LMICs in the Caribbean, Central and South America, Asia-Pacific and sub-Saharan Africa. The survey captured information onsite characteristics and screening and treatment practices for depression, post-traumatic stress disorder (PTSD), substance use disorders (SUDs) and other mental health disorders.
Evidence-based management for many MSDs significantly improves mental health outcomes  [8] [9] [10] [11]. Integration of evidence-based interventions to manage MSDs into HIV care has been identified as a promising strategy to improve mental health and HIV treatment outcomes of PLWHA in LMICs  [12, 13]. The World Health Organization (WHO) recommends integration of or linkage to mental health services for PLWHA, where possible, and has developed the Mental Health Gap Action Programme (mhGAP) to provide evidence-based guidelines for the diagnosis and management of priority mental health disorders in routine healthcare settings  [14, 15]. The extent to which screening and management of MSDs is integrated into HIV treatment programmes in LMICs remains largely unknown  [1, 16]. The objective of this study was to describe MSD screening and management practices in a representative sample of HIV treatment sites in LMICs participating in the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium.
| Data collection and statistical analyses Data collection occurred between August 2016 and May 2017. English and French versions of the survey were available online and as paper-based instruments. The online version was implemented using REDCap (projectredcap. org), a secure web-based application hosted by the University of Bern  [22]. IeDEA research coordinators distributed surveys to sites in their region. Survey instructions explained that the survey should be completed by individual(s) knowledgeable about clinic capacity and services offered and encouraged the respondent to contact other people at the site if they did not know or were uncertain about answers to survey questions. Surveys completed on paper were entered into REDCap by regional representatives or study staff members. The survey captured information onsite characteristics (level of care, setting, patient population, sector) and screening and management practices for depression, PTSD, SUDs, including alcohol and drug use disorders, and other serious mental illnesses, including schizophrenia and bipolar disorder.
Respondents were asked separately about screening and management procedures for: depression, PTSD, SUDs and other serious mental illnesses. The survey was translated into French and sections were back-translated into English. For each MSD assessed, respondents were asked if their HIV treatment site: screened patients, had a written protocol for screening, how they screened, whom they screened, had a written protocol for management, where management was provided, and the type of staff members responsible for management. Sites and coordinating centres for all IeDEA regions had Institutional Review Board approvals that permitted collection of site-level data for this survey. This survey collected only site-level data. Descriptive statistics summarize the prevalence of screening and management of MSDs overall and by World Bank Income designation of the country in which the site was located.
Selective serotonin reuptake inhibitors (SSRIs), benzodiazepines and other psychiatric medications were reportedly available on site at 52%, 81% and 71% of sites respectively. On-site management of all MSDs assessed was significantly more commonly reported among sites that reported having staff members with mental health training. Ninety percent of sites that reported having staff members on site with mental health training reported on-site management of depression compared to 33% of sites that reported not having staff members on site with mental health training (data not shown). Similarly, 73% of sites that reported having staff members on site with mental health training reported on-site management of PTSD compared to 16% of sites that reported not having staff members on site with mental health training.
| DISCUSSION We surveyed 95 HIV treatment sites across 29 LMICs to assess practices for MSD screening and management. Our analyses suggest that gaps in screening for MSDs in HIV sites exist, especially in paediatric clinics and in screening for PTSD in urban settings and middle-income countries. Gaps in management of MSDs were most commonly reported in rural settings, paediatric clinics, health centres, private sector facilities and in lower middle-income countries. These findings highlight the need for additional attention to the mental health needs of children and adolescents living with HIV and integration of mental health services into paediatric HIV treatment programmes. While data on the prevalence of MSDs among children and adolescents living with HIV in LMICs are limited, research indicates that children and adolescents living with HIV experience increased risk of MSDs compared to non-infected peers  [23, 24].
Households were randomly selected from a phone directory, based on their address in the study area. Once a household was contacted by phone, a respondent was then randomly selected from within the household. The participant signed and returned an informed consent form by mail. Almost 40% of those contacted who refused to participate responded to a short questionnaire about their demographic and socioeconomic characteristics. It was also possible to compare the characteristics of the participants to those of people who refused to participate (non-participants), as well as to those of the study population, using data from the French national census. In total, 1244 participants (549 men and 695 women) were included in the study and responded to a questionnaire during a face-to-face interview at their place of residence in 2013.
The L Aeq,24hr , L Aeq,6hr-22hr , and L night correspond to the average of sound levels during the corresponding periods of time. 1 People randomly selected and contacted by phone, but who refused to participate. These people responded to a short questionnaire about their demographic and socioeconomic characteristics. 2 The distribution of the study population is based on data from the 1999 INSEE census, adjusted in 2007, for individuals aged 18 and over and living in one of the 161 municipalities of the study area.
Acknowledgments: The Airport Pollution Control Authority (Acnusa) requested that the  French Institute of Science and Technology for Transport, Development, and Networks (Ifsttar)  carry out this study. The authors would like to thank them for their confidence. The authors are grateful to all the participants in the study and to their interviewers. The authors also thank  Paris Airports  and the  French Civil Aviation Authority  for providing noise exposure maps and are also grateful to  Inès Khati  for her participation in the implementation of the study and to  Sylviane Lafont  for her skillful revision of the manuscript.
Permanent exits and ageing into the people living with HIV cohort had a marginal effect. Both in-migrants and outmigrants were less likely to be retained at each step of the HIV care continuum. However, their overall impact on the crosssectional cascade was limited as the effect of in-and out-migration balanced each other. The contribution of HIV seroconversions was negative in all clusters. Conclusions: In a context of high HIV incidence, the continuous flow of newly infected individuals slows down the efforts to increase ART coverage and population viral suppression, ultimately attenuating any population-level impact on HIV incidence. Clinical Trial Number: NCT01509508 (clinicalTrials. gov).
The trial area was also served by three local governmental primary care clinics of the department of health providing HIV testing, HIV care and ART according to national guidelines only  [21]. HIV-positive participants of both arms could opt to receive HIV care in primary care clinics or transfer to a trial clinic. The Biomedical Research Ethics Committee (BREC), University of KwaZulu-Natal, South Africa (BFC 104/11) and the Medicines Control Council of South Africa approved the trial. The trial was also registered on ClinicalTrials. gov: NCT01509508 and South African National Clinical Trials Register: DOH-27-0512-3974.
| Sources of data The main data source for this analysis was the trial database, which provided information on trial registrations and trial exits; uptake and results of home-based rapid HIV testing; third generation ELISA HIV serological results from DBS; and clinic visits, ART prescription and viral loads of PLWHIV seen in trial clinics. Two additional data sources were used to capture information from PLWHIV seen in local governmental clinics: (a) viral loads and CD4 counts from National Health Laboratory Service (NHLS); and (b) ART clinic visits and ART prescriptions from the AHRI clinical database (ACCDB) which is managed by the Hlabisa Department of Health and AHRI. Both NHLS and the ACCDB database contain data from Hlabisa primary care clinics since 2004  [21]. Linkage between trial, NHLS and ACCDB database used a probabilistic score based on first name, last name, date of birth, South African ID number and cell phone number.
A case-by-case investigation (including additional laboratory analysis) was performed to solve any inconsistent data. An individual was considered as HIV-negative before the last known negative status and as HIV-positive after the first known positive status. For those in whom a negative status was followed by a positive one, date of seroconversion was imputed using a random point approach (uniform distribution). For individuals entering the trial cohort, the first opportunity for the trial team to ascertain their HIV status occurred de facto after their entry. For some, a previous record was found in NHLS and/or in ACCDB database. For the others, we imputed if and when a potential unobserved HIV seroconversion occurred using the observed incidence within the same cluster and for people of the same sex.
A similar approach was used to impute a possible seroconversion before the end of trial follow-up for those whose last observed HIV status was negative, assuming they remained undiagnosed until the end of trial follow-up. Individuals with no observed HIV status (i. e. with no data on HIV status) were excluded from the analysis. HIV care statuses were defined as: (i) undiagnosed; (ii) diagnosed but not actively in care (i. e. never in care or lost to follow-up); (iii) actively in care but not on ART; (iv) on ART but not virally suppressed (undocumented viral load or viral load over 400 copies/lL); and (v) in care, on ART and documented viral suppression. Position within the HIV care continuum was considered as a score ranging from 0 (undiagnosed) to 4 (virally suppressed). We note S i,t the score of an individual i at time t.
An individual was considered as being diagnosed if he/she had at least one positive rapid test, one self-report as HIVpositive or had visited a primary care clinic. Date of HIV diagnosis was defined as the date of home-based HIV testing for individuals newly diagnosed by the trial counsellors. For those already diagnosed when they were interviewed, we considered the date of the first record in NHLS or ACCDB database, if any. It should be noted that for individuals tested in primary care clinics, a CD4 count is performed the same day in case of positive result, resulting in a record in NHLS database. For the few individuals self-reporting being HIV-positive but with no previous record in NHLS/ACCDB, we considered as a proxy of the diagnosis date the date of the self-report. Being actively in HIV care in a trial clinic was defined as not being >90 days late of a scheduled clinic appointment date  [22]  (visits were scheduled monthly if on ART, six-monthly if not yet eligible for ART in control arm).
Due to the nature of the data available in NHLS and ACCDB database with neither database being exhaustive (some individuals recorded in one database were not found in the other, in particular pre-ART patients not covered by ACCDB), we were not able to use the same definitions regarding being actively in HIV care in primary care clinic. For patients matched with ACCDB database, being actively in HIV care was defined as having a clinic visit recorded in the last four months (one month for next appointment, this database being limited to ART patients supposed to visit clinics monthly, +3 months late). For patients matched with NHLS database (which contains only laboratory test results), it was having a CD4 count or a viral load recorded in the previous 13 months, CD4 count and viral load data being considered a proxy for clinic visits, following the approach proposed by Lessel et al.
[23]. Being on ART was defined as having an ART prescription recorded in the trial or the ACCDB database in the previous 3 months or as having an undetectable viral load (<400 copies/lL) recorded in the last 13 months in the NHLS database, an undetectable viral load being considered here as a proxy for being on ART for HIV patients recorded in NHLS database but not found in ACCDB. Viral suppression was defined as a viral load fewer than 400 copies/lL. The viral load at a given date was estimated by linear interpolation using all results recorded in the trial and the NHLS database. The viral load was considered as undocumented before the first available record.
As a result, out-and in-migration had a balanced impact on the cross-sectional cascade, both effects cancelling out. Data collected through the ANRS 12249 TasP trial constitute a unique opportunity to make a fine description of individual trajectories over time through HIV services, on a daily basis, which can be done only in very few settings. However, to fully understand our results, it is crucial to understand the limitations of the data. Due to the nature of available data sources, different definitions and assumptions were required to estimate individual statuses. First, in-migration events were not directly collected by TasP fieldworkers and have therefore been derived by comparison of resident household members lists between survey rounds. Second, while HIV care statuses were measured precisely within trial clinics, we had to use proxy indicators (based on laboratory data in particular) for being in HIV care and being on ART in local governmental clinics.