dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations We acknowledge some limitations of this study. The publicly available data we used do not contain information regarding major IMR risk factors such as gestational age at birth, weight at birth, or presence of a severe congenital malformation. 16 Thus, we were not able to explore the exact role of these factors in the recent increase of IMR, notably for the neonates who die in the early neonatal period. 31, 32 Second, previous studies conducted in Western Europe and the USA demonstrated the association between inequalities in numerous aspects of health and infant mortality. 33 Notably, in England, IMR rose sharply in the most disadvantaged areas. 28 Because information on sociodemographic factors known as risk factors for IMR (mother's age, nationality, education level, and social and geographic contexts)  16  were not available in our dataset, we could not explore these associations.
38, 42 However, recent studies showed improvement in the survival rate of very preterm children in France. 38, 42, 43 Improved care for mothers at high risk of pregnancy complications may lead to the prevention of stillbirths but this may also defer some deaths to the neonatal period and increase neonatal mortality. However, data from the Euro-Peristat network suggests an increase in the stillbirth rate in France from 4¢3% in 2010 to 4¢8% in 2015, supporting the concerns raised in this analysis of IMR. Up-to-date data on the potential factors associated with IMR should be routinely collected and analyzed. In the meantime, strategies to improve further the quality of French perinatal care, especially among infants born extremely premature are thus urgently needed. 44 he increase in IMR was also partially driven by an increase in infant male deaths.
OUTCOMES The event resulted in several tangible and prospective outcomes. Firstly, the qualitative findings from workshops were analysed thematically to create the set of guidelines shown in Table  1. These were used for a study that arose directly from the workshop: PwP wanted to explore the potential impact of drum circle therapy on their wellbeing and asked the researchers to develop new ways of collecting data that would be less arduous for them. Consequently, a feasibility study focused on a single drum circle session in a motion capture laboratory was conducted  (14, 15). In order to continue our PPI work, we included an evaluation questionnaire for that study. This feedback/feedforward approach continues to inform our studies. At present we are working with PwP to develop a new approach to quantifying clinical measures, by combining motion capture and gait mat technology, as part of a recently funded PPI project to develop a new music-based intervention for and with PwP 1.
5, mild health or perception; >3. 5, good health or positive perception. Global health and general workplace environment self-evaluations were scored for a second time with, respectively, health reports and working environment scores. In the first step of this study, in 2020, we divided the demographic outcomes to perform multivariate analysis. A multinomial logistic regression analysis was performed and the associations between risk factors and outcomes were obtained after adjustment for cofounders including, gender, age, and years at workplace. Demographic data were self-reported by the participants, including occupation, gender (male/female), age (<35, 35-44, 45-54, >55 years), and time at workplace (<5, 6-15, 16-26, >26 years). Referring to this first publication and to allow better reliability between the results of the two phases, we decide to retain such classification by categories.
Statistical Analyses Data analysis was performed using R-4. 0. 2 and R Studio software (R Foundation for Statistical Computing, Vienna, Austria). The results for continuous variables are shown as means. The ranked data, which were ranked from each part of the questionnaire, are presented as frequencies and percentages. The mean change in scores of psychological distress and physical symptoms from T1 to T2 were compared between all workers and in the subgroup of HCWs (t-test for two independent groups). The Chi 2 test was used for the comparison of demographic data between the two populations. The significance level was set at p = 0. 05.
2%) cannabis to smoke (called "CBD-rich cannabis" in this manuscript), as opposed to "regular" high THC cannabis (called "illegal cannabis" here). The survey collected self-reported data on the following: socio-demographic and substance use (cannabis, CBD, alcohol, tobacco) characteristics, preferred mode of CBD use, and primary reason for CBD use. The latter was collected using the question "In the past 30 days, why have you used CBD?". Only one answer was allowed from a list of options which included "to reduce the use of tobacco or other substances (illegal cannabis, alcohol, etc. )" (Supplementary Table  1 ). People who ticked this answer were then asked if they used CBD for illegal cannabis use reduction. Those who replied "yes", were then asked (i) to what extent CBD had an impact on their illegal cannabis use ("large reduction/moderate reduction/no effect/moderate increase/large increase/I do not know"; these answer options were dichotomized into "large reduction" vs.
In reality, smoking cannabis exposes persons to harmful substances, including carcinogens  (55) (56) (57). This route of administration is therefore inadvisable, in favor of smoke-free inhalation  (58)  or oromucosal administration  (29). Our study has several limitations. First, the nonrepresentativeness of our sample of cannabis users in France limits the generalizability of our results, and highlights the need for study duplication. For instance, participants with no job appeared over-represented. Second, we had no data to enable us to detect cannabis use disorder in our sample. However, we did have frequency of use data, which is a good proxy for problematic and low-risk cannabis use  (47, 59, 60). Third, we used self-assessed changes (reduction/no change/increase) in cannabis use and had no data on the contextual elements of these changes.
Markerless data Markerless data was processed with Theia3D, a deep learning-based commercial software. It uses deep learning techniques and inverse kinematics (IK) to estimate 3D pose of a subject of interest based on multi-camera video data and the associated calibration file  [18]. The outputs of Theia3D were composed of a 17-segment kinematic model (head, thorax, upper arms, forearms, hands, pelvis, thighs, shanks, feet, toes) with 54 degrees of freedom (DoFs) and the pose matrices associated with each segment. These outputs were exported in Visual3D (Cmotion, Germantown, USA, v2021. 11. 3) and body segment inertial parameters (BSIPsegment mass, position of segment CoM and moment of inertia) were added to the kinematic model according to Dumas and Wojtusch  [29]. The markerless and marker-based models are both described in details in  [30].
Marker-based data Marker data was first processed in QTM, including labelling and gap filling of marker trajectories. Then, labelled and cleaned marker positions were imported in Visual3D where custom models were created based on Dumas and Wojtusch  [29]. IK was finally performed using a multibody kinematics optimization  [31]. The marker-based kinematic model was built in order to match as much as possible the markerless segments' axes and kinematic model as described in Theia3D documentation. It has the same topology except that the toes are rigidly linked to the feet, and the same sex-specific BSIP.
Computing balance related quantities For both marker-based and markerless data, the following quantities were exported from Visual3D following the IK step: • the position of the CoM for each segment and the whole body; • the linear velocity of the CoM of each segment; • the angular velocity of each segment; • the rotation matrix associated to the different segments. Using MATLAB (MathWorks, USA), the positions and velocities of segments' CoM and their angular velocities were filtered using a 4 th order Butterworth filter with a 7 Hz cut-off frequency. The velocity of the global CoM was obtained by differentiating the CoM and filtering the resulting signal with the same parameters. The XCoM was computed based on Hof  [9]  as: 𝑋𝐶𝑂𝑀 ⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ = 𝐶𝑂𝑀 ⃗⃗⃗⃗⃗⃗⃗⃗⃗ + 𝑣 𝑐𝑜𝑚 ⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝜔 0 (1) where 𝐶𝑂𝑀 ⃗⃗⃗⃗⃗⃗⃗⃗⃗ is the position of the whole-body's CoM, 𝑣 𝑐𝑜𝑚 ⃗⃗⃗⃗⃗⃗⃗⃗⃗ its velocity and 𝜔 0 = √ 𝑔 𝑙 ⁄ is a constant with 𝑔 = 9.
Discussion This study aimed at assessing commonly used quantities in balance studies, CoM, XCoM and WBAM, estimated with a markerless pose estimation software, Theia3D, and comparing the results to the estimation obtained with a marker-based method. Overall, the magnitude of errors between the two measurement systems is the centimetre for CoM and XCoM and less than 10% of the amplitude for the WBAM. The RMSE obtained when comparing the CoM computed with the markerless and markerbased systems have a magnitude of around 1cm, and the associated CI remains under 2 cm across all tasks, suggesting a minimal dispersion of the results. Few studies have compared CoM computed using these two approaches. Needham et al  [23]  focused on computing the CoM during sprinting, using an open source markerless software and a full body marker set.
Moreover, for all the tasks, the smallest RMSE of the CoM and XCoM is systematically in the ML direction: as these tasks take place in the sagittal plane, there is little displacement and small velocity in the ML direction, thus the error of measurement is reduced. There is no similar phenomenon for WBAM. This should be considered when studying a task with an important velocity in one direction, as it appears that the biggest error between markerless and marker-based systems can be found in the main direction of movement. There are some limitations to this study. The first limitation is that participants were healthy young adults, and it is a possibility that the dataset used to train the neural network of the markerless system contained mainly similar subjects. It is thus relevant to wonder if the same results would be obtained with participants with different ages and/or anthropometric characteristics, or patients with body deformities.
Computational results and sensitivity analysis To survey the computational experiment on medical-entertainment tour design, the proposed model is examined based on a real example of central medical tour services in the Middle East, and the problems various sizes are solved. We code the proposed model under the OPL and scripts accessed via IBM ILOG CPLEX version 12. 8, and code the hybrid approach, which is the consolidation of PHA and GA via MATLAB R2019a. The codes are run in a computer with an Intel R ○Core TM i7-4500U CPU at 2. 39 GHz and an 8. 00 GB RAM.
Open Access This comment refers to the article available online at https:// doi. org/ 10. 1186/ s13613-022-01003-x. *Correspondence: benoit. painvin@chu-rennes. fr; jean-marc. tadie@churennes. fr Finally, as mentioned by the authors, robust prospective studies including before departure, ongoing transport and arrival data are needed to determine the timing of the transfer, the safest medical condition allowing for transfer, and whether transport impacts ICU mortality.
• Mobile Widewife (Nigeria): voice messages sent to pregnant women for follow-up pregnancy. • M-Pedigree (Kenya): drug identification. • My Healthline (Cameroon): answers to questions on sexuality, family planning and HIV/AIDS. • mHero (Liberia): information on the virus outbreak Ebola. • Djobi (Mali, Senegal): mobile application contributing to reducing infant mortality and kindergarten in Senegal and Mali through mutual health insurance. These first experiences have shown the interest that SSA has regarding the use of mobile telephony for health actions. In the following section, we detail the method that has been followed to perform the literature review. In particular, the inclusion criteria and the databases that have been looked up are described.
Included mobile-based serious games Figure  1  shows the selection process for the included articles in the literature review. Following our search using the keywords, 30 publications were found in the Scopus database, 10 in PubMed, and 20 in Google Scholar. No duplicates were found. During the title/abstract screening, we excluded 12 articles in total because they did not meet the criteria, leaving 48 for full-text screening. Upon completion of the full-text screening, 10 articles were selected. With further full-text screening, we found that only 6 articles were related to mobile serious games, education, and awareness of health information in rural Africa.
General characteristics of interventions As shown in Table  1 , interventions included in this study were implemented in Nigeria (n = 2), Kenya (n = 2), Swaziland (n = 1), and Ghana (n = 1). Only two (n = 2) games, Tumaini and SwaziYolo, were assessed through a randomized controlled trial. The serious games targeted behavior change in HIV prevention among young people (n = 3), malaria preventive behaviors (n = 2) and Ebola prevention (n = 1). All the digital solutions were designed in the English language and support only Android devices. Moreover, all of these interventions required internet access.
Limitations The main limitation of our literature review is that it was based only on three databases: PubMed, Scopus, and Google Scholar. Consequently, we were unable to include additional studies that might have been indexed by other databases or available through the gray literature. Another limitation is that our keywords are limited in scope. For example, we only used the term "mobile health" in our search string without including its other variations such as "mHealth, " "m-Health, " etc. Future work should aim to address these limitations. This may provide access to more academic papers and projects carried out by non-governmental organizations (NGOs).
Acknowledgements We would like to sincerely thank the  European Family Medicine Research Network  for its support in the survey. The authors are grateful for the work of the European research team: Lingner H,  Hoffman DR ,  Czachowski S ,  Assenova R, Koskela TH , Klemenc-Ketis Z, and Sowinska A. We would like to sincerely thank the participants for their enthusiasm. The authors are grateful for the comments and suggestions Downloaded from https://academic. oup. com/fampra/advance-article/doi/10. 1093/fampra/cmac140/6874871 by  Universiteit Antwerpen -Bibliotheek  user on 12 January 2023.
). 4   The role of anticipation in the ICC was described in a January 2, 2012, circular about the government's disaster management plan as follows: 5 'As the situation evolves, attention focuses on increasingly distant time horizons, a few hours initially, then telescoping to days or even weeks. The cascade effects of the crisis on sectors of activity other than those originally affected are analysed' (French Prime Minister's Office, 2012, 2019; p. 9). From a practical and organisational standpoint, anticipation is a 'subset' of the situation function that oversees data collection to inform the evolving situation. Meanwhile, the Ministry of the Armed Forces adopts a long-term approach to thinking about the immediate effects of a crisis. The timeframe can extend from a few weeks to a few months in terms of both spatial consequences and economic effects on a territory.
ACKNOWLEDGEMENT The authors would like to thank the 3 anonymous reviewers for the insightful comments on an earlier version. This article has been supported by the  APRIL  project funded by the  French National Research Agency (ANR ; Grant No. ANR-18-OURA-0001 ), and by the  GC-Gouv  project funded by the  Secrétariat général de la Défense et de la Sécurité nationale-the Secretariat-General for National Defence and Security (SGDSN) ,  Prime Minister's Office, France.
Estimation of the angular kinematics for the thigh The thigh movement is not measured using an inertial sensor and hence, its angular kinematics could not be estimated directly. We used a classification-based approach by using the kinematics from the shank and back to identify four classes: sitting, standing, sit-to-stand and stand-to-sit. The thigh angular kinematics were estimated for each of the classes separately because we observed from the reference data that, the kinematics for each class could be modelled by a different function. A two-tiered classification scheme was used. The first classifier distinguished between a stationary state (sitting and standing) and transition state (sit-to-stand and stand-to-sit). The second classifier classified sit-to-stand and stand-to-sit movements; and based on that, a probabilistic approach was used to determine sitting or standing state.
A threshold for classification was determined automatically from the right edge of first bin in the histogram of the peaks from feature vector. Bin width for the histogram was obtained using Scott's rule  [38]. Histogram was computed using the MATLAB (The MathWorks, Inc. , Natick, Massachusetts, US) function histogram(). The first bin of the histogram captured feature values of stationary states which were close to zero, hence the right edge of the first bin computed using Scott's rule gave the threshold value. A binary linear classification was performed using this threshold. Spurious misclassifications were identified and corrected automatically by finding one or two samples that had a different class from their neighbouring samples. Based on the classification results, a series of sit-to-stand = =90 P(Stand SiSt)=1 P(Sit StSi)=1 =0 o o | | x = 0 (StSi) x = 1 (SiSt) (t,x) t,x Stationary state Classification 2 -classifying sit-to-stand, stand-to-sit, sitting and standing states using unsupervised learning Once the stationary and transition state segments were identified, the second classification was done on the transition segments to classify sit-to-stand and stand-to-sit states.
Estimation of the thigh angular kinematics Based on the classification of each segment, we used a model similar to a single neuron of an artificial neural network with appropriate activation function to estimate the thigh kinematics for each state (see  Fig 4). We assumed that when a person is standing, the thigh angle, θ T is 0 o and when a person is sitting on a chair, θ T is 90 o. We confirmed this by observing the distribution of the thigh angles while sitting and standing from the reference data collected from the young healthy participants as detailed in the next section. The angular velocity ω T and angular acceleration α T were zero during the stationary states. To estimate stand-to-sit transition angle θ T , we used a model similar to artificial neural network with a single neuron with sigmoid activation function Eq (9)) for regression (see  Fig 4).
5, one participant had H&Y score of 2 and two participants had H&Y score of 1. 5. All the participants in these two groups were over the age of 60, were able to walk independently unaided, and reported themselves to be able to perform transfers, walking and activities in standing three times over a period of approximately one hour. People with Parkinson's disease had a diagnosis made by a specialist at least 12 months prior to the study. The data from 4 OH and 10 PwP participants was collected in their home and the data from rest of the participants was collected in the laboratory. The ethical approval for this study was obtained from the ethics committee of the University of Southampton. The participants in all the three groups gave a written informed consent prior to the participation.
Wearable sensors Wearable sensors, custom designed at the University of Reading, were used to collect the movement data in this study. Each wearable sensor consisted of a triaxial accelerometer and a triaxial gyroscope. The data were stored to the internal SD card. The sensors sampled at nominal rate of 50 Hz and ±4 g provided an actual sampling rate of 49. 985 ± 0. 016 Hz. The bandwidth at nominal sampling rate was 21 Hz with a noise density of 0. 14 mg/ √ Hz. All sensor data were resampled using video recording as an external time base. Further details of the sensors can be found in  [34]. The wearable sensors were attached to the shank and the back as shown in Fig 5. (A) (B).
Motion capture To provide the ground truth data for validating the results of the parametric estimation models, we collected motion capture data using the Codamotion 3D Motion Analysis System (Codamotion, Rothley, UK) for the young healthy (YH) participant group. The Codamotion active markers were used to track the motions of the body during data collection. The ODIN (Codamotion, Rothley, UK) software was then used to extract the body segment angular displacement, velocity and acceleration. The Codamotion sensors were placed on the leg and back as shown in Fig 5. The motion capture data was not collected for OH and PwP groups because the data recording was done at home for several participants since these groups had difficulty travelling. Rigorously validated model for kinematics estimation using motion capture data from YH was applied to OH and PwP groups.
Experimental protocol Prior to wearable sensors' data collection, the distance in meters of the inertial sensor on the shank from the ankle (L S ) and the distance of the inertial sensor on the back from the hip (L B ) were recorded for input into the modelling algorithm for each participant. The wearable inertial sensors were attached to the right shank and to the middle of the lower back using elastic straps. The active Codamotion markers were placed on the shank and back close to the inertial sensors for YH. The clusters of Codamotion markers were also attached to the thigh where there was no inertial sensor. The YH participants were then asked to perform three sets of five sit-to-stand and stand-to-sit transitions, with rest in between the sets as required. The OH and PwP participants were asked to perform a single set of three sit-to-stand and stand-to-sit transitions.
Data Processing Data obtained from the inertial sensors on the shank and the back were synchronised using ELAN software  [40]  by tapping the sensors together at the beginning of the trial and at the end of the trial. The Codamotion data in the YH participants was synchronised with the inertial sensors by moving the right foot backwards and forwards at the beginning and at the end of the experiment. After aligning the data, the Codamotion data was subsampled to 50 Hz to match the sampling rate to the inertial sensors. Also, the Codamotion data was calibrated such that the angles of the shank, thigh and back were close to 0 o in the standing position. The measurements from the x-axis and the y-axis of the accelerometer and the z-axis of the gyroscope on the shank and the back (Fig 1 ) were used as measurement input to the EKF model detailed in the previous section to obtain the shank and back angular kinematics θ i , ω i and α i , i ∈ {S, B}.
Using the angular kinematics of the shank and back, the classification was performed to obtain segments of data belonging to the standing, sitting, sit-to-stand and stand-to-sit states. For estimating thigh kinematics θ T , ω T and α T , function in Eq (8) was used as detailed in the previous section to model thigh angular kinematics. Since there was no sensor on the thigh, the estimation of the kinematics was completely dependent on the estimated kinematics from the shank and the back. The estimated body kinematics were compared against reference Codamotion data in the YH participants and the model was applied to the OH and PwP participants. All the analysis was completed using MATLAB.