dataesr/scientific-paragraphs-categorization huggingface dataset:
Laboratory data were analyzed both with full descriptive statistical analysis and considering out-of-range values according to the patient's age. Out-of-range values were defined according to Nelson Textbook of Pediatrics Reference intervals for laboratory tests. [8] atients were stratified into different illness categories (asymptomatic, mild illness, moderate illness, severe illness, critical illness) according to the American National Institutes of Health (NIH) Guidelines. [9] Age categories were grouped as 0 to 2 (infancy), from 2 to 12 (childhood), from 12 to 17 (early and middle adolescence) years old according to the American Academy of Pediatrics classification. [10] The sample size was determined by the number of positive patients that accessed the Pediatric ED of the CHU of Amiens-Picardie from March 20, 2020, to April 28, 2021.
Clustering was obtained using the complete linkage agglomerative hierarchical clustering method. Estimation of the optimal number of clusters was performed using the Silhouette method (Supplemental Digital Content 8, http://links. lww. com/MD/G876). Missing data were dealt with complete-case data analysis. We used Excel (Microsoft, Redmond, WA) for descriptive statistics and R [R Core Team version 4. 1. 1 (2021), Vienna, Austria]  [11]  for plotting and advanced statistical analysis, with packages including tidyverse, ggplot2, ggrepel, dendextend, gplots, and UpSetR. The study was approved by the CHU-Amiens, Haute Picardie ethics committee with the protocol number: PI2021_843_0052 and performed in the respect of the Helsinki declaration. Informed consent to use recorded health data and publication was obtained from caregivers of all individual participants included in the study after the ethics committee approved the present study.
Viral load Values for VL were collected from 41 children: the median value was 23. 39 (IQR 15. 64-29. 30), with a descending trend with the increase of illness category severity. Our data show a quadratic curvilinear association between age and CT-VL (Supplemental Digital Content 7, http://links. lww. com/MD/G876 ). Given the limits of our data that will be discussed in the limitation further in our article, it is not possible to infer further.
The main example in this context is the framework developed by the VAM (vulnerability analysis and monitoring) team of the World Food Programme (WFP-VAM, 2019). Such a framework integrates machine and deep learning techniques on heterogeneous data (i. e. , satellite images at different spatial resolutions and GPS points) to predict   and  in several countries. Nevertheless, the results strongly varied from country to country, and the predictions were generally not accurate enough to be used in operational cases. Lentz et al. (2019)  also predict   and  with linear regressions using mainly open and free data as predictors. The response variables are from the Living Standards Measurement Surveys (LSMS) conducted in Malawi in 2010 and 2013. The data used are from diverse sources: meteorology, precipitation, market prices and soil quality.
To address heterogeneous data with machine learning, it is necessary to apply data fusion methods because there is no generic machine learning method suited to all types of problems and data but rather methods that are better tailored to the specificities of each problem (e. g. , classification, regression, clustering, and anomaly detection) and data source (e. g. , quantitative variables, time series, images, and text)  (Alzubi et al. , 2018). Data fusion is the process of combining multiple data sources to produce more accurate, robust, and reliable and less redundant information than by considering each data source individually  (Chandrasekaran et al. , 2017; Khaleghi et al. , 2013). Data can be merged at 3 levels  (Brena et al. , 2020; Hall & Llinas, 2017) : (1) at the data level (early fusion) by simply concatenating the initial variables to obtain a dataset with a larger amount of information that can be input into a machine learning algorithm, (2) at the feature level (feature fusion) by extracting more relevant features from each data source and concatenating them to be input into a machine learning algorithm, and (3) at the decision level (late fusion) by aggregating the predictions from models associated with each data source into a global prediction.
Response data The response variables, or FS indicators, are derived from the Permanent Agricultural Survey, which has been conducted annually as part of the routine of the Burkinabe Ministry of Agriculture since 1982 in Burkina Faso  (Permanent Agricultural Survey, 2015). The survey was conducted using two-stage stratified sampling (villages and households). The sampling frame for the first stage was obtained from the agricultural module of the 2006 general population census. This frame yielded a list of villages (7871 villages and areas) containing 1,219,241 agricultural households (in 2008, a total of 1,424,909 households were agricultural, representing 81. 5 percent of households Bureau central du recensement généal de l'agriculture, 2011). The sampling frame for farm households is created in each sampled village (selected with probability proportional to the number of farm households) from a household list drawn up each year by enumerating all farm households in the village.
For this study, we take into account the data that are available from 2009 to 2018. 1  The resulting dataset contains information from 46,400 farm households, i. e. , an average of 4640 farm households per year distributed in 344 of the 351 communes illustrated in Fig. 1. A farm household is defined as a household practising one of the following activities: temporary crops (rainfed and off-season crops), fruit growing, and animal husbandry. As in most surveys, the quality of data obtained from household surveys can be affected by biases. Nonobservation bias related to the failure to acquire information (coverage and sampling bias, nonconsent bias, nonresponse bias)  (Lepkowski, 2001)  and measurement bias due to measurement error during data collection (linked to the interviewer, the respondent or the questionnaire)  (Kasprzyk, 2001)  are the two main types of bias.
First, FS proxies are preprocessed to extract relevant explanatory variables at the commune scale, which is the smallest administrative boundary for which the response variables are spatialized, enabling the number of examples for model learning to be maximized. Some proxies from raster data or GPS points have a finer granularity and must be aggregated by commune by summing (rainfall), averaging (minimum and maximum temperatures, soil quality), counting (number of hospitals, schools and violent events), taking the maximum (smoothed brightness temperature (SMT), elevation) or more complex aggregations (Gini coefficient, autocorrelation, and differential entropy of population rasters). Other proxies (meteorological data accessible by stations and commodity prices accessible by markets) are available at a coarser granularity and must be interpolated on every commune: in these cases, we chose to use K-nearest neighbour interpolation, which is an accurate and rapid technique.
05). The information on each dataset is available in Table  3 ; for more details on the variety of data used in the models, see Appendix. To address the structural heterogeneity of the data, the selected explanatory variables are classified into 4 groups with a similar structure to treat each group with an appropriate machine learning method: • Time series that have multiple values per year and one value per commune. They are aggregated into monthly time series (May to November of the year in which the FS indicator is collected and of the previous year) • Conjunctural data that have one value per year and one value per commune. 2 Culture mask: S2 prototype land cover map at 20 m in Africa 2016. • Spatial data that have one value per commune and are invariant per year. • High spatial resolution (HSR) data that have multiple values per commune.
Competing methods and ablations To evaluate the performance of the    framework, we compare it to the performance of several baselines, ablations and competing methods. We use the following two independent studies as our competing methods: • As a first competing method, we select the WFP framework introduced in Section 2. They use linear regressions and compare the performance of their models using only open and free data and adding data from the previous LSMS survey. In our study, we take into account the performance of models that incorporate only open data in accordance with our objectives. The data used are from diverse sources: meteorology, precipitation, market prices and soil quality. We also define three baseline models to test simpler machine and deep learning techniques on different subsets of explanatory variables: • Type (c) Model: We apply an RF directly on initial variables: time series only, CS variables only and both types of data.
Experimental setting The    framework has been implemented using TensorFlow 1. 15 under Python 3. 7, and the code is publicly available on GitHub. 3 he RF is set up with 900 trees with a maximum depth of 20. The LSTM is parameterized with 2 layers of 64 neurons. The cost function used is the mean square error, and the optimizer is based on the FTRL algorithm. The CNN is configured with 3 convolution layers with 32, 64 and 128 filters. A max pooling of dimension 2 is placed after each convolution layer. The cost function used is the mean square error, and the optimizer is based on the Adam algorithm. For model (b) using feature fusion, 64 features are extracted from the LSTM, and 128 features are extracted from the CNN. LSTM and CNN are trained from scratch using a batch size of 250 as well as 1000 and 100 epochs, respectively.
Baseline models We observe that the models of type (c) (only based on the use of RF directly on explanatory variables) give performances that are already significant and close to those of more sophisticated models; by integrating only the CS variables, we obtain Rˆ2 of 0. 414 and 0. 401 for   and , respectively. This validates the data source selection and preprocessing applied to the data in use. The type (d1) and (d2) models aim at processing data with complex structures (time series and HSR images) with a suitable deep learning method. The LSTM does not succeed in highlighting the sequential aspect of time series, offering lower performance than the RF on the same data (i. e. , model (c) on the time series): 0. 232 vs. 0. 241 for   and 0. 223 vs 0. 237 for. Our hypothesis is that in the present case, where we have noisy response variables, the LSTM capable of detecting complex patterns in sequential data overintercepts the noise despite our settings and therefore overfits the data.
Several methods have been recently developed to explain the predictions of neural network models, by identifying the input variables (e. g. , pixels, time series items) that have most contributed to the model decision  (Khormuji & Rostami, 2021; Montavon et al. , 2018) , e. g. , sensitivity analysis, occlusion analysis and layer-wise relevance propagation (LRP). However, these methods are not suitable for our experimental setting, since they are tailored to classification problems, while our study focuses on regression. For CS variables that are directly processed by the RF, the significance of variables can be approached by the permutation importance. The top 10 ranks of CS variables according to their permutation importance for   and  are shown in Table  5. We note that variables from multiple domains are included in these two top 10 lists: landscape structure (3 variables), population dynamics (2 variables), soil quality (2 variables), meteorological (2 variables), vegetation (1 variable), insecurity (1 variable), sanitary (1 variable) or economic (1 variable) variables, which confirms the importance of the combined use of data sources from multiple domains.
Conclusion This study proposes the    framework, which uses machine learning methods to predict by regressions two key FS indicators that are normally obtainable through costly and lengthy household surveys. To validate our models, we used as labels a database including households from across Burkina Faso from 2009 to 2018 using mostly global public data as explanatory variables to ensure the replicability and generalizability of our methods to other countries. We faced two scientific obstacles: (1) The multifactorial aspect of FS implies the choice of heterogeneous input data (at the thematic, structural and spatiotemporal scale levels) and suitable preprocessing to maximize the contribution of each data point. To take into account a maximum number of facets of FS, we integrated data from different topics (landscape structure,  (2) Machine learning methods suitable for each type of variable had to be chosen and combined.
We have seen that the performance obtained by our models (Rˆ2) is not high, not exceeding 0. 469 or 0. 434 for Rˆ2 in the prediction of   and , respectively. However, the results of this study are superior to most of the rare works we were able to compare it with. These results indicate that the prediction of these FS indicators is a complex issue. This study adds another layer to this little-studied issue. We have also observed the modest but significant contribution of deep learning models (CNNs) to the processing of HSR data. The use of this type of data in the context of FS is therefore a relevant avenue for future work. However, the use of deep learning models (LSTM) for time series processing did not yield significant results, and future research will include better treatment of time series because the temporal aspect plays an important role in FS.
Acknowledgements This study was conducted with the help of the  Ministry of Agriculture, Water Resources, Sanitation and Food Security of Burkina Faso , the  World Food Programme (WFP)  and the  Société Nationale de Gestion du Stock de Sécurité Alimentaire (SONAGESS) , which provided data. This work was supported by the  French National Research Agency  under the Investments for the  Future Programme #DigitAg , referred to as  ANR-16-CONV-0004.
Aim of the work: The aim of our study is to investigate the effect of a high-energy diet on renal histopathological changes, T and B lymphocytes infiltration and expression levels of caspase-3 in apoptotic kidney cells of Psammomys obesus, an animal model that intimately mimics human diabetic complications. Material and methods: After 6 months of feeding with the high energy diet, metabolic parameters were evaluated using an enzymatic colorimetric kit, the renal histopathological changes were examined by histological methods, whereas the expression levels of lymphocyte cells and caspase-3 were evaluated with immunohistochemical methods. To compare the expression levels of caspase-3, T and B lymphocytes in control and experimental animals groups, we quantified immunohistochemistry images using free software ImageJ Fiji.
To compare the expression levels of caspase-3, CD20 and CD3 of the two animal groups (control and experimental), we quantified immunohistochemistry images by converting the original images into RBG images which were deconvolved by ImageJ using the color deconvolution plugin (free software ImageJ Fiji version 1. 2; WS Rasband, National Institute of Health, Bethesda, MD), https://imagej. net/Fiji/Downloads.
Materials and Methods This was a prospective, randomized, controlled, single-blinded study conducted in two centers: Armand Trousseau Academic Hospital (Paris, France) and Tenon Academic Hospital (Paris, France). The trial was approved by the Ethics Committee for the Protection of Persons (CPP Sud-Ouest et Outre-Mer, Number 1-18-38, 9 July 2018), and registered at clinicaltrials. gov (NCT03498820) before the first inclusion. Written informed consent was obtained from each patient prior to enrollment.
Data Collection Intraoperative data: Baseline HR and SBP were obtained just before skin incision. Then, HR, SBP, BIS, propofol Ce, remifentanil Ce and ANI were collected every five min from skin incision until wound closure. For example, during a 3-h procedure, 36 sets of data were obtained. At the end of the procedure, total doses of remifentanil and propofol were recorded, as well as the number of atropine, ephedrine and nicardipine boluses. Surgeons were also asked to rate their overall satisfaction on a 4-points scale (0 = not satisfied at all, 3 = very satisfied). All intraoperative data were collected by an investigator dedicated to the protocol. This investigator could not be blinded to the patient's group. Postoperative data: HR, SBP, SpO 2 and pain score (Visual Analogue Scale, VAS) were collected every 15 min in PACU.
Post hoc analyses included the number of patients requiring ephedrine, atropine and nicardipine and descriptive plots of remifentanil consumption over surgical time. Analyses were performed with SAS software version 9•4 (SAS Institute Inc. , Cary, NC, USA), except for 95% CI differences estimated with R Studio version 1. 2. 5019 (R: A Language and Environment for Statistical Computing, R Core Team, R Foundation for Statistical Computing, Vienna, Austria, https://www. R-project. org). All tests were two-sided and a p value of less than 0. 05 indicated statistical significance.
Population Eighty patients were included between November 2018 and December 2019. Among them, 78 underwent the surgical procedure (hysterectomy, n = 35; myomectomy, n = 23; adnexectomy, n = 7, other, n = 13; two procedures were cancelled) and were included in the ITT analysis. ANI guidance was interrupted in three patients of the ANI group because they received ephedrine or atropine. The flowchart is provided in Figure  1 ; patients' characteristics are provided in Table  1. In the 78 patients who underwent surgery, a total of 1864 intraoperative datasets were analyzed between skin incision and wound closure. Mood method and Exact method, respectively. In each group, 95% CIs were estimated by normal approximation for means, by distribution-free method for medians and by Exact method for proportions.
Population Eighty patients were included between November 2018 and December 2019. Among them, 78 underwent the surgical procedure (hysterectomy, n = 35; myomectomy, n = 23; adnexectomy, n = 7, other, n = 13; two procedures were cancelled) and were included in the ITT analysis. ANI guidance was interrupted in three patients of the ANI group because they received ephedrine or atropine. The flowchart is provided in Figure  1 ; patients' characteristics are provided in Table  1. In the 78 patients who underwent surgery, a total of 1864 intraoperative datasets were analyzed between skin incision and wound closure.
Construction of SARS-CoV-2 nsp10 and nsp14 bacterial expression plasmids The SARS-CoV-2 nsp10 and nsp14 genes were codon optimized for expression in E. coli and obtained from Genescript. The amino acid sequences for nsp10 and nsp14 were derived from SARS-CoV-2 isolate 2019-nCoV/USA-WA1/2020 (GenBank MN985325. 1). The genes were amplified by PCR using Phusion DNA polymerase. The nsp10 gene was amplified using the synthetic nsp10 gene as template and DNA oligonucleotides (5 -GAACAG ATTGGAGGTGCCGGGAATGCTACGGAA-3 and 5 -CCGCAAGCTTGTCGACTTATCATTGAAGCATA GGTTCACGCAA-3 ). The nsp14 gene was amplified using the synthetic nsp14 gene as template and DNA oligonucleotides (5 -GAACAGATTGGAGGTGCGGA GAACGTTACAGGT and 5 -CCGCAAGCTTGTCGAC TTATCATTGAAGGCCAGTAAACGTATTCCA-3 ). The PCR products were gel purified and cloned into either the pAviTag-pSUMO bacterial expression plasmids using BsaI and SalI.
Patient population A single-centre observational cohort study was conducted at Georges Pompidou European Hospital, a tertiary teaching hospital in Paris (France). All adult patients (aged ≥ 18 years) who required hospitalization for at least 48 hours between 13 March and 30 April 2020 for COVID-19, confirmed by reverse transcription-polymerase chain reaction, and with a chest CT scan at admission were eligible. Patients with a history of ischaemic stroke, coronary artery disease or peripheral artery disease (i. e. ASCVD) were excluded. This study is part of the French COVID cohort study (a prospective cohort study conducted to determine COVID-19 physiopathology; ClinicalTrials. gov identifier: NCT04262921). All patients provided written informed consent before enrolment, and were informed that their medical data could be used for research purposes in accordance with the General Data Protection Regulation (EU 2016/679).
Data collection Data were collected from the centre's electronic medical records, and included patient demographic characteristics (age and sex), cardiovascular risk factors, past medical history, presenting vital signs and symptoms, laboratory findings, treatment before admission, complications and outcomes. Cardiovascular risks factors were defined using international recommendations, and obesity was defined as body mass index > 30 kg/m 2  [13]. From the medical history collected, we grouped both active cancers and those in remission in the cancer category. Participants were followed up by telephone by local trial personnel at the end of October 2020, after at least a 6-month period for all patients.
Biological data and cardioprotective treatments For each patient, a biological workup, including complete blood count, ionogram, creatinine, C-reactive protein, brain natriuretic peptide and troponin, was collected at admission. The high-sensitivity troponin I value at admission and the peak value were recorded. Myocardial injury was defined by elevated cardiac troponin concentrations above the 99th percentile upper reference limit. Myocardial infarction was defined as a significant modification of the electrocardiogram, associated with an elevation of troponin I value or typical chest pain. Cardioprotective treatments assessed in this study were antiplatelet therapies (aspirin 75 mg or 100 mg or clopidogrel 75 mg per day), statins, renin-angiotensin system (RAS) blockers (including angiotensin-converting enzyme inhibitors [ACE inhibitors] and angiotensin-II receptor blockers [ARBs]) and beta-blockers used for chronic heart failure (carvedilol, metoprolol, bisoprolol and nebivolol).
International scientific societies now recommend continuation of these treatments, however, after several observational studies demonstrated no association between the use of RAS inhibitors and the risk or severity of infection with SARS-CoV-2  [33, [35] [36] [37] [38] [39] [40] [41]. These data were confirmed by the BRACE CORONA trial, the first randomized cohort study evaluating the clinical impact of discontinuing or continuing ACE inhibitors or ARBs in COVID-19. For the 659 patients involved, no significant difference was found in the number of days of out-of-hospital survival at 30 days between stopping or continuing ACE inhibitors/ARBs in patients hospitalized with COVID-19  [42] , suggesting that these treatments should be continued during hospitalization, at least in mild and moderate forms of COVID-19.
The dose calculations for workers were performed using the ICARE (Internal Dose Calculation for Radionuclide Exposure) software  [23]. ICARE was developed as a tool for the calculation of reference and specific dose coefficients, retention and excretion functions for internal exposure to radionuclides. The current commercially available version of ICARE, which implements all biokinetic and dosimetric models used for the calculation of dose coefficients in ICRP Publication 119  [24] , was updated as part of this study to include the most recent ICRP models and thus the specific dose coefficients calculated here are directly comparable with the reference dose coefficients of ICRP Publication 134  [9].