dataesr/scientific-paragraphs-categorization huggingface dataset:
Remote data collection tools (remote interviews, journals, social media, and photo journaling) were useful in some contexts, but not a sufficient part of the rapid qualitative research toolkit in epidemics. We also noted that although most rapid tools that the Methods Subgroup examined were being used in LMICs, very often studies using these tools were led by 'global health' actors, not local researchers. It is possible that this is a result of publication bias, but our investigation did include grey literature. The implication of local actors in development of the tools was unclear as a result.
Simultaneous data collection and analysis Although only one of our sources explicitly sought to undertake data collection and analysis simultaneously, the Methods Subgroup contended that it could enhance quality and rapidity of an RQM. Rapid qualitative assessments required data collection and analysis to occur simultaneously and iteratively, to ensure that data collection responds over time to new findings  [32]. Resources should be devoted to transcription software and/or team so that transcriptions can be done as rapidly possible. During the implementation process of an RQM, research teams scheduled frequent debriefings, wrote analytical memos, adjusted numbers and characteristics of included participants, questions asked and tools used, and triangulated with other types of data. Periodic memos or reports on the bases of these debriefings can help track changing research priorities in response to realities on the ground.
Acknowledgments We are grateful to members of the  COVID-19 Social Sciences Research Roadmap , as well as  João Rangel de Almeida  and  Dayo Spencer-Walters  for their feedback on this study. We also thank  Anthony Billaud ,  Renata Gabriela Cortez Go  ´mez,  Ximena Pamela Dı ´az Bermudez , and  Ruben Muñoz Martı  ´nez for their important contributions to this study. Finally, we thank  A. David Napier  for his suggestions during the planning stages of the study.
Data Transformation In the proposed method, changes are detected in the short-time Fourier transform (STFT) of the raw acceleration/angular velocity signals recorded on the lower back of healthy subjects. This data transformation has been successfully used in multiple publications as it enables visualizing activity changes but also speed changes or event slope changes  [21] [22] [23]. More specifically, two signals of interest are extracted from the raw data (IMU recordings): the craniocaudal angular velocity (gCC) and the anteroposterior acceleration (aAP). These signals were chosen as they are directly influenced by the changes that can be observed during the execution of the protocol (beginning and end of gait, short activities, half turns, and turns). Studies have already used these signals to meet objectives similar to ours (turn detection, detection of activities of daily life)  [24, 25].
Calibration of β Instead of manually calibrating this parameter by trial and error, a supervised approach, described in previous studies  [29, 30] , is adopted in this article to learn an optimal value. This method takes as input an annotated database of physiological signals where the relevant breakpoints have been annotated by an expert, and learns the adequate parameter value that is able to reproduce the segmentation strategy of the expert on new signals. Formally, the input is a collection of N labeled signals y (1) ,. , y (N) , and for each y (i) , an expert manually provided the set of true change point indexes {t (i) k } k. The optimal smoothing parameter, denoted βopt , is such that the risk of the true expert segmentation on all signals R β y (i) , {t (i) k } k is closest to the one of the predicted segmentation on all signals R β y (i) , { t(i) k } k : βopt := arg min β>0 1 N N ∑ i=1 R β y (i) , {t (i) k } k -R β y (i) , { t(i) k } k.
Our classification procedure is therefore based on feature extraction coupled with supervised learning. However, contrary to state-of-the-art methods used for HAR, instead of using (possibly overlapping) frames, we propose to perform this classification at the regime level. The advantages are twofold: first, because of the segmentation procedure, we know that each regime is stationary, which is a valuable theoretical property for computing robust features. Second, the average length of the regimes is often longer than typical frame durations, which provides more data for computing features. First, we extract for each detected regime an extensive list of both temporal and frequency features such as variances, means, dominant frequencies, and power at dominant frequencies. These features have been chosen and selected in accordance with a recent stateof-the-art article  [10]  dedicated to activity classification from IMU signals.
Step 4: Score Generation and Graphical Feedback Using a database of healthy walking phases taken from the healthy subjects from the recorded cohort, statistics for the different features are computed (means, percentiles, etc. ). These models are then used to assess each novel walking phase with the scoring procedure described as follows. Considering a feature with mean µ and standard deviation σ on all walking regimes from healthy subjects, we compute the z-score normalized feature z = x -µ σ. ( 7 ) The z-score normalized features are then displayed with a color bar of boundaries [x min , x max ] where x min and x max are, respectively, the 10% and 90% percentiles of the normalized features on the healthy subjects. Slightly below healthy values correspond to regimes with z-scores just below those obtained for the 10th percentile of healthy subjects, and slightly above healthy values correspond to regimes with z-scores just above those obtained for the 90th percentile of healthy subjects.
Evaluation Metrics In the next section, we conduct a step-by-step assessment of each step of the pipeline with adapted metrics. All simulations are run with 3-fold cross-validation. To that aim, we split the dataset into three balanced sets (two training sets and one validation set) of seven healthy subjects and three pathological subjects (two participants having undergone or about to undergo orthopedical surgery and one neurological patient). This cross-validation allows one to verify that the algorithm developed in this study can be used on new unseen data to apply the desired segmentation.
Measurement of radioactivity in the air In the present study, and without loss of generality of the proposed method, we will focus on the analysis of the aerosol samples measured at IRSN 1  for validation purposes. More precisely, as part of its monitoring brief, IRSN carries out regular measurements of radioactivity levels in the environment, employing high-performance investigation means for the quantification of trace amounts. As part of the OPERA-Air network (permanent radioactivity observatory), the data measured at IRSN also serves as a support to the numerous research and expertise studies on the time and spatial variations of radionuclides in the environment. This paper focuses on the 10 high flow air samplers (300-900 m3. h-1), whose filters are sampled and measured by gamma-ray spectrometry on a weekly basis.
17 In the final model, only significant variables were retained. Odds ratios with corresponding 95% confidence intervals were computed. Interactions between variables were tested. 17 Missing data are described in the tables and no imputations were made. 18  The additional free-text responses were examined using a method of thematic analysis for trends and categorised by two authors. As it was a survey, no sample size calculation was performed. All reported P-values were two-sided, and statistical significance was set at P<0. 05. All analyses were performed using statistical software (SAS Enterprise Guide, version 7. 13; SAS Institute; Cary, NC, USA).
Our survey has several limitations. First, as data were collected on the basis of a declarative survey there is necessarily a self-selection bias. 31 Second, some interesting points could not be addressed in our questionnaire, as we had to limit the number of items (there were 29). We were not able to separate respective information rates from paediatric only and adult only anaesthesiologists, as most anaesthesiologists were providing both paediatric and adult care. It would have been interesting to study more precisely differences in habits and practice between adults and children. Third, we only asked anaesthesiologists (physicians) and did not address information provided by anaesthetist nurses and other operative theatre and surgical wards staff. Fourth, there are probably important differences in relatives' needs and expectations within the same country and between different countries.
Study Participants This study was approved by the Independent Bioethics Committee for Scientific Research at Medical University of Gda ńsk (number NKBBN/1043/2021-2022) and is retrospectively registered at ClinicalTrials. gov (NCT05637372). Patients received verbal and written information describing the trial and gave their consent to participate in this study. Subjects were qualified for this study based on an initial interview including the following criteria: age 21-23 (+/-1), no history of trauma, orthodontic treatment, or TMD. Previous studies by Sulün et al. confirmed that the articular eminence reaches its full size between 21 and 30 years of age in healthy patients and decreases after the age of 31  [25]. Similarly, the articular condyle is fully developed between the ages of 21 and 22  [26].
Additionally, third-the CD measurement, which indicates angle between Frankfurt plane and a line connecting the highest point C of the condyle to point D, which is below the highest point of the articular tubercle (B), considering the change in the thickness of the disc above the condyle during protrusive movement (Figure  2D ). The distance BD reflects intermediate discal space that in vertical dimension is equal to ASS. The numerical data were collected for both joints of each patient and tabulated for further analyses. Likewise, the data concerning the incisors were collected. The cross-section view of left and right incisors was examined, and, for each patient, the pair of central incisors presenting the higher value of IGA was selected for further measurements of the incisal features  [19, 29].
The distance BD reflects intermediate discal space that in vertical dimension is equal to ASS. The numerical data were collected for both joints of each patient and tabulated for further analyses. Likewise, the data concerning the incisors were collected. The cross-section view of left and right incisors was examined, and, for each patient, the pair of central incisors presenting the higher value of IGA was selected for further measurements of the incisal features  [19, 29]. We measured the incisal guidance angle-the angle between the line connecting the incisal margin of the maxillary and mandibular incisors with the FHP (Figure  3A ); the interincisal angle-between long axis of upper and lower incisors (Figure  3B ); overbite-vertical and perpendicular to FHP distance between incisal margins; and overjet-horizontal and parallel to FHP distance between incisal margin of upper incisor and transverse to the FHP projection of the lower incisal edge (Figure  3C ).
Afterward, intraoral scans of qualified patients were conducted with the use of the Carestream 3600 scanner. The obtained STL files were transferred to the Modjaw ® measuring device to record the mandible movements in real time (Figure  4 ). The Modjaw ® was designed as a substitute for the facebow, axiograph, and mechanical articulator at the same time, with the possibility to transfer obtained data along with the patient's individual reference plane to CAD/CAM software (Exocad GmbH, Darmstadt, Germany)  [30]. To obtain an individual value of the SCGA, the following actions were performed: fixing the hinge axis by means of repetitive opening and closing movements with the tongue positioned on the palate, and protrusive movement from MIP to edge-to-edge position. The SCGA during function for each patient was computed in the Modjaw ® software between the path of the moving condyle and the individual reference plane set by the line connecting the hinge axis to the line created between the Nasion and Subnasale point.
Statistical Analysis All calculations have been carried out by means of Microsoft Excel 2019 spreadsheet and STATISTICA (TIBCO Software Inc. , Palo Alto, CA, USA; 2020) Data Science Workbench, version 14. In the statistical description of quantitative data, classical measures of location, such as arithmetic means and median, and measures of variation, such as standard deviation and range, were used. The normality of distribution of the variables was tested using Shapiro-Wilk's test. To assess the linear correlation between two variables, Pearson's correlation and Spearman's correlation were used with respect to the type of distribution of the variables tested. To estimate best predictors for a particular dependent variable, multiple regression models were created. In all the calculations, the statistical significance level was set to p < 0.
05. Afterward, intraoral scans of qualified patients were conducted with the use of the Carestream 3600 scanner. The obtained STL files were transferred to the Modjaw ® measuring device to record the mandible movements in real time (Figure  4 ). The Modjaw ® was designed as a substitute for the facebow, axiograph, and mechanical articulator at the same time, with the possibility to transfer obtained data along with the patient's individual reference plane to CAD/CAM software (Exocad GmbH, Darmstadt, Germany)  [30]. To obtain an individual value of the SCGA, the following actions were performed: fixing the hinge axis by means of repetitive opening and closing movements with the tongue positioned on the palate, and protrusive movement from MIP to edge-to-edge position. The SCGA during function for each patient was computed in the Modjaw ® software between the path of the moving condyle and the individual reference plane set by the line connecting the hinge axis to the line created between the Nasion and Subnasale point.
In this study we estimate the potential contribution of maize intensification with mineral fertilizer to increase maize production and household food security in Uganda and Tanzania. Using existing datasets across SSA on maize fertilizer trials  (Bonilla Cedrez et al. , 2021; Kihara et al. , 2017; Kihara et al. , 2016) , fertilizer and maize price  (Bonilla Cedrez et al. , 2020a , 2020b) , and nationally representative household surveys in Uganda and Tanzania  (Kilic et al. , 2015) , we explore the spatial variation in maize response to nutrient inputs, maize prices and fertilizer costs, and household characteristics. We estimate the contribution to national food security, that is, the amount of surplus production that farm households can produce, to be sold and consumed by others. We explore the three following research questions: i) What is the potential increase in household-level food security by intensification of maize production? ii) What is the relative importance of increasing maize production in already food secure and currently food insecure maize growers for national maize production? and iii) Are the regions where household-level food security would benefit most, also the regions where national-level production would increase most? In line with the recent finding that small farms contribute only marginally to global food production  (Lowder et al.
Overall approach Our study assessed the contribution of the intensification of maize production to food security in three main steps. First, we trained a statistical model with fertilizer trial data to predict maize grain yield responses to N, P and K fertilizer application for the soil and climate conditions prevailing across SSA, and predicted the spatial variation in maize grain yield response to N, P and K fertilizer in Uganda and Tanzania. Then, we computed a simple food security indicator at household level (the food availability ratio, FA, see below) for a nationally representative set of farm households in Uganda and Tanzania. In a last step, we used the predicted maize yield responses to N, and spatial data on fertilizer and maize prices, to estimate i) the profitmaximizing N fertilizer input for each household, ii) the additional maize production from using this amount of fertilizer, and its effect on the household food availability ratio, and iii) the increase in the overall regional and national maize grain supply.
Maize yield response to mineral fertilizer 2. 2. 1. Experimental data from maize fertilizer trials We combined data from 15,952 maize fertilizer trials conducted at 1352 locations in SSA from 1969 to 2017 (see Appendix 1 in Supplementary materials for details on the composition of the dataset). The dataset included information on trial location (longitude, latitude), maize grain yield, N, P and K fertilizer application rates, topsoil (0-30 cm) characteristics such as soil organic carbon, sand, available P, exchangeable K, Ca and Mg contents and pH, and climate variables such as growing-season rainfall, rainfall intensity and temperature, and number of consecutive dry days over the maize growing season. The trials were rainfed, implying that there may have been water stress in some growing seasons and/or in some locations.
Spatial prediction of maize response to N, P and K fertilizer We used a Random Forest (RF) algorithm to predict maize grain yield as a function of nutrient inputs (N, P, K), soil properties (soil organic carbon, sand, available P, exchangeable K, Ca and Mg content and pH), and climatic factors (seasonal rainfall, average growing season temperature, number of consecutive dry spell during the season and daily rainfall intensity during the season). We tuned three hyperparameters (parameters that are calibrated during the learning process of the RF algorithm, i. e. the number of trees, the number of candidate predictor variables at each node and the minimum number of samples necessary to split a nonterminal node) based on 70% of the dataset (randomly selected) and an 8-fold cross validation with two replicates.
We explored 30 combinations of the hyperparameters obtained by maximizing the coverage of the parameter space. The criterion maximized during the training procedure was the cross-validated proportion of the explained maize yield variance (R 2 ) averaged over the two replicates. The performance of the best RF model (with optimal combination of hyperparameters) was then evaluated on the 30% hold-out test dataset. Feature analysis (i. e. partial dependence plot and variable importance) was performed on the model fit with the full dataset. Variable importance values were calculated based on a metric that captures the increase in mean squared error (MSE), calculated from out-of-sample predictions, after randomly permuting the values of the respective predictors  (Breiman, 2001) , using the R package vip  (Greenwell et al.
, 2020). Functional relationships between predictors and maize yield were analyzed through partial dependence plots using the pdp R package  (Greenwell, 2017). A partial dependence plot shows the marginal effect of one input on the model prediction, averaged across the values taken by the other inputs. In order to test the contribution of climate factors, we built one model that included climate variables, and another model that did not include these, and compared the predictive capacity of the two models. The RF model was then used to predict maize control yield (0 N, 0 P, 0 K) in Uganda and Tanzania at a spatial resolution of 250 m using soil data (topsoil organic carbon, sand, available P, exchangeable K, Ca and Mg content and pH) from Africa SoilGrid  (Hengl et al. , 2015 (Hengl et al.
Farm household survey data We used the Living Standard Measurement Study -Integrated Surveys on Agriculture (LSMS-ISA) of 2010-2011 for Uganda and Tanzania  (Kilic et al. , 2015). These household surveys are nationally representative and cover the main regions of Uganda (World Bank, 2021b) and Tanzania  (World Bank, 2021c). These surveys included 2716 households in Uganda and 3440 in Tanzania. Households reporting no crop production and households with missing location coordinates were excluded from the analysis, leading to a reduced sample of 1753 farm households in Uganda and 2435 in Tanzania. We used the following variables: household location (with a 10-km offset), household composition, farmer-reported total cropped land and area for the different crops, farmer-reported total crop production and sales of crop production, mineral fertilizer use on crops, total livestock production (meat and milk), sales of livestock products, and off-farm income.
Additional labor cost for increased fertilizer application was not considered because of lack of reliable data. N-AE was computed as follows  (Vanlauwe et al. , 2011) : NAE i = (Yi -Y0N ) i (2) where Yi is the maize grain yield with N input rate i and Y 0N is the maize grain yield with no N fertilizer input. The market maize price and N fertilizer cost at a given household location was extracted from the spatial dataset of Bonilla  Cedrez et al. (2020a Cedrez et al. ( , 2020b) )  (Appendix 2 in Supplementary materials describes the model development procedure of these two studies).
It will however, be challenging to do this with an empirical model as there is less data available for these types of crops than for maize. The accuracy of the RF model of our study (R 2 = 0. 82) was better than that of a model trained on yields measured in 601 field trials in Tanzania (R 2 = 0. 24,  Palmas and Chamberlin, 2020) , highlighting the importance of training the model on a large dataset. The main features of our model were in line with the findings of a meta-analysis of 71 studies across SSA that showed that soil texture, pH and exchangeable K were the main factors explaining variability in N-AE  (Ichami et al. , 2019). Soils can vary substantially in macro and micro-nutrient content at short distancesowing to past crop management by farmers  (Falconnier et al. , 2016). The variations of crop responses to nutrient inputs depending on rainfall season (short vs long in area with two growing seasons per year) would also deserve further analysis.
Discussion We hypothesize that a training program for nurses on the AVF approach in procedural simulation versus theoretical input alone would decrease the adverse events related to AVF punctures and would be beneficial for the patient. This study is innovative for several reasons. First, simulation-based training in continuing education among professionals is not widely used. Furthermore, training allows for the standardization of practices within the team, both technically and relationally. Trial registration ClinicalTrials. gov NCT05 302505.
[17]  showed that training through simulation versus interactive seminars increased the care performance of senior physicians at the end of extracorporeal circulation procedures in the operating room. Finally, in a recent study, Ajmi et al. [18]  showed that after a hybrid simulation training program on the management of stroke patients, the time from admission to fibrinolysis, as well as the number of patients with a poor vital prognosis, was reduced. All of these studies show real benefit in patient management. The review of the literature does not show a consensus on how to approach AVF or how to teach the procedure. Thus, we conducted a feasibility, monocentric, prospective, observational, before/after simulation training pilot study, SIMFAV (NCT03680209), at the University Hospital of Caen (unpublished data).