dataesr/scientific-paragraphs-categorization huggingface dataset:
Specifically, each land cover class was classified according to pre-defined rules. The implementation of these rules was based on thresholds (Table  1 ). As Anopheles breed in various areas, four land cover classes were identified as areas for mosquito development that were considered potential breeding sites: rice field, water body, hydrographic network, wet cultivation  [45]. A post-classification phase was performed to assess the accuracy of the classification results. The ground truth data were obtained from field missions in five districts of the CHL during rainy season. Three confusion matrices were generated in order to represent the misclassification, as well as classification accuracy for 2014, 2015, and 2016. Cohen's Kappa index was calculated for each confusion matrix to test for the concordance of the classification.
Criteria Identification and Differentiation The choice of the criteria was based on a literature review and seven expert opinions (epidemiologists, entomologist, a doctor, geomaticians, and a modeler). Inhabited zone: Malaria only occurs in inhabited areas as humans are the only reservoir of malaria. Village locations, in vector format (point type), were extracted from the database of the Malagasy Cartographic Institute named "Foiben-Taosarintanin'i Madagasikara" (FTM) in 2000. In order to obtain updated village locations, these were combined with data gathered from OpenStreetMap (https://www. openstreetmap. org/) and data obtained from 2013 to 2016 field missions. A 1 km buffer, representing inhabited areas, was created from the previously combined data and then rasterized to a 30 m spatial resolution.
Population density: The last national population census campaigns took place in 1993 and 2018, but data from the latter is not yet accessible. Given the lack of updated data, the population density was extracted from the WorldPop project database (www. worldpop. org. uk). WorldPop is an open-access library providing population data with a spatial resolution of 100 m. The 2010 population density was used for the 2014 model, while data of 2015 were used for 2015 and 2016 models. Since data with better resolution do not exist at the present time, a resampling to 30 m was performed. Elevation: Elevation is important in malaria risk assessment because it is directly related to temperature, and impacts the life cycle of the mosquito vectors  [46] [47] [48]  and the development of Plasmodium species, which are the parasites responsible for malaria disease.
Elevation data were obtained from digital terrain models provided by the Shuttle Radar Topography Mission (SRTM) campaign held in 2000. The SRTM data for CHL, with a spatial resolution of 30 m, was downloaded from the USGS Earth explorer website (https://earthexplorer. usgs. gov/). Two tile sets of SRTM images (SRTM_46_16 and SRTM_46_17) were used to cover all CHL. Temperature: Temperature influences mosquito development and longevity  [49, 50]. The hot and rainy season lasts from October to May in Madagascar. During this period, the population densities of Anopheles and malaria cases are high. Mean temperatures for this timeframe, from 2014 to 2016, were computed. The USGS, through the International Research Institute (IRI) library.
Elevation data were obtained from digital terrain models provided by the Shuttle Radar Topography Mission (SRTM) campaign held in 2000. The SRTM data for CHL, with a spatial resolution of 30 m, was downloaded from the USGS Earth explorer website (https://earthexplorer. usgs. gov/). Two tile sets of SRTM images (SRTM_46_16 and SRTM_46_17) were used to cover all CHL. Temperature: Temperature influences mosquito development and longevity  [49, 50]. The hot and rainy season lasts from October to May in Madagascar. During this period, the population densities of Anopheles and malaria cases are high. Mean temperatures for this timeframe, from 2014 to 2016, were computed. The USGS, through the International Research Institute (IRI) library (http://iridl. ldeo. columbia. edu/), provides temperature data every eight days with a spatial resolution of 1 km.
This source platform allowed extracting a 30 m spatial resolution through its expert mode tool. Precipitation: Rainfall influences the availability of mosquito breeding sites; it may predict vector abundance  [51, 52]. Rain increases the chance of larval habitat availability; however, excessive rainfall can cause leaching of larval breeding sites. Daily precipitation with a spatial resolution of 12 km data was obtained from the IRI website (http://iridl. ldeo. columbia. edu/) provided by the United States National Oceanic and Atmospheric Administration (US NOAA) agency. These were GeoTIFF raster images, in which values are expressed in millimeters of rain. Using the same expert mode feature as for temperature, a resolution of 30 m was extracted. Total precipitation during the October to May period was calculated for each studied year by adding daily values during the eight months.
Validation of the Model Framework Model output was compared to data on Annual Parasite Incidence (API) provided by the NMCP. These data record the incidence rate of malaria per commune and per year. API validation data was only available for 208 communes and for 2015. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to assess the model performance. The perfect model shows a value of 1. 0 in the ROC curve  [61]. We used the 2015 API data to validate the output of the model for the 2016 IRS campaign. The 2015 API in each commune, corresponding to observed values, was recoded as a binary variable to compute the AUC ROC: API <1% was considered as low incidence and API ≥1% as high incidence. Sensitivity and specificity were computed using the best cut-off of the classified malaria risk map (Figure  4 ).
Plugin Development In order to provide a dynamic, simple, and free decision support tool for the NMCP, an extension of the QGIS 2. x software, called "MCE for Public Health", was developed. The Python programming language was used because it is the most suitable language for QGIS. To make the tool more efficient, all calculations were performed with the application programming interface and Geospatial Data Abstraction Library (GDAL) of QGIS. Qt Designer Software was used to design the graphical user interface (GUI)  [65]. It was designed to be ergonomic, easy to use, and to prevent user typing errors  [65].
"MCE for Public Health" Plugin The plugin is an interactive tool, adapted for the QGIS 2. x version: a dynamic, free, and open source semi-automatic tool. It groups the main steps for MCE evaluation: reclassification of constraints, normalization, computing weight for each factor using a pair-wise comparison matrix, and aggregation of factors. The plugin runs for a maximum of 10 constraints and 15 factors. It can support four raster formats of input and output data: rst (idrisi format), gtiff (GeoTIFF format), img (erdas imagine format), and jpeg. The Full MCE plugin file is available on GitHub (https://github. com/SaGEOTeam/FullMCE). The plugin is presented both in French and English. Figure  7  shows the main interface of the developed plugin.
Discussion The MCE method had previously been carried out in six areas of the Malagasy CHL  [22]. Here, the model is improved by including all areas of the CHL and by taking into account rainfall and population distribution. The comparison of available API data with the risk model enabled an assessment of the accuracy of MCE in this study. Validation of the malaria risk model showed a good result. For the first time, to our knowledge, the development of the "Full MCE" plugin enables non-specialists, such as staff of the Malagasy NMCP, to carry out a complete MCE approach with a free spatial decision tool. This initiative allowed the use of a single software platform to perform all the MCE treatments. In April 2019, NMCP and other stakeholders (Ministry of Public Health, Ministry of Agriculture, universities and research institutes) from 10 countries were trained to use the plugin and the tool was distributed to them.
An update with the latest version 3. x of QGIS is currently in progress to allow a wider diffusion. The availability of Landsat 8 images allowed updating the Malagasy CHL land cover map with a medium spatial resolution of 30 m. The previous land cover map had dated from 2009. Although the OBIA approach is better suited for images at a high spatial resolution, it is also currently used for images with medium spatial resolution such as Landsat images  [69, 70]. At the scale of the present study (i. e. , commune scale), the results obtained from the Landsat 8 images seem appropriate. The MCE approach is a knowledge-based and pragmatic method adapted to situations in which there is a lack of data. This method is rapid and easy to implement  [36]. It is an alternative to a statistical method requiring large datasets and is adaptable and/or transposable to different areas  [36]  with particular aspects of malaria transmission.
Data collection on climate and demography was difficult in Madagascar, whether free or paid. Indeed, the number of meteorological stations is not sufficient for national use, and their interpolation may have a direct impact on the result. The scarcity of meteorological stations imposed us to use remotely sensed substitute data. The IRI for Climate and Society provided a solution to this issue by integrating climate and environmental data from various sources  [77]. Forecast climate data was previously used to predict malaria risks in the African region  [28, 77, 78]. Dambach et al. confirmed the role of land surface temperature and rainfall in mosquito density  [79]. Datasets used to compute malaria risk were not always available at similar scales. Population data was only available with a spatial scale of 100 m.
Lack of access to high spatial resolution data is frequent in low income countries and considerably hinders the quality of produced risk maps. Furthermore, using datasets with different spatial resolutions is clearly not ideal, and it is recommended to downgrade the resolution of all datasets to the lowest spatial resolution. Here, we took the risk to resample datasets to 30 m but finally produced an operational risk map at the commune level (i. e. , with a much coarser resolution than the one used for the datasets). The latest general population censuses in Madagascar were conducted in 1993 and in 2018 but results of the latter are not yet available. When the 2018 Malagasy population census data is released, updated population density maps with finer resolution will be produced and should considerably improve incidence and prevalence estimates, as well as risk maps produced in Madagascar (including the malaria maps derived from this work).
Study Population The GERIA-COVID study consisted of a longitudinal observational study in one French geriatric acute care unit dedicated to COVID-19 patients during the first wave (ClinicalTrials. gov Identifier: NCT04560608). Data of the GERIA-COVID study were retrospectively collected from hospital records. The inclusion criteria in the study were as follows: (1) patients hospitalized in the geriatric acute care unit of the University Hospital of Angers, France, in March-June 2020; (2) no objection from the patient and/or relatives to the use of anonymized clinical and biological data for research purpose. The inclusion criteria for the present analysis were as follows: (1) diagnosis of COVID-19 with RT-PCR and/or chest CT-scan; (2) age 80 and over; (3) data available on the regular use of VKA and on the heart rhythm on hospital admission; (4) no introduction of VKA during the hospitalization for COVID-19; (5) data available on the vital status within 7 days after the diagnosis of COVID-19.
Scaled Schoenfeld residuals were computed to check the proportionality assumption. A statistical test was performed to validate the non-proportionality combined with a plot of these residuals against time. Hazard ratios (HR) with 95% confidence intervals (95% CI) were reported. Finally, the relapsed time to death was illustrated by survival curves computed without propensity score adjustment, according to Kaplan-Meier estimator. p-Values < 0. 05 were considered significant. All statistics were performed using SAS ® version 9. 4 software (SAS Institute Inc. ) and R (R core team, 2018).
Acknowledgments: The authors wish to thank the  GERIA-COVID study group. GERIA-COVID study group :  Cédric Annweiler ,  Marine Asfar ,  Mélinda Beaudenon ,  Jean Barré ,  Antoine Brangier ,  Mathieu Corvaisier ,  Guillaume Duval ,  Jennifer Gautier ,  Mialy Guenet ,  Jocelyne Loison ,  Frédéric Noublanche ,  Marie Otekpo ,  Hélène Rivière ,  Guillaume Sacco ,  Romain Simon. Affiliation:  Department of Geriatric Medicine, University Hospital, Angers, France. The authors have listed everyone who con-tributed significantly to the work in the Acknowledgments section. Permission was obtained from all persons named in the Acknowledgments section. There was no compensation for this contribution.
, 2019 (Mandal et al. , , 2016)). The phenomenological approach gives the bead step amplitude   and the rigidity index  defined as  = (∫   () )/(  )  0 so that  → 0 corresponds to a very soft and deformable cytoplasm while  → 1 corresponds to a very stiff and rigid cytoplasm. The power law analysis yields the complex shear modulus  =  ′ + ′′ with ′ the storage modulus (elastic-like behaviour) and ′′ the loss modulus (viscous-like behaviour). The amplitude of the complex shear modulus is given by  0 = √′ 2 + ′′ 2 The SLL model yields the spring constant  and the viscosity. Oscillatory intracellular rheology (Fig. S1C ) was performed by applying a   = 0. 5 µmamplitude sinusoidal displacement at 0. 8 Hz for 40 sec of the stage. The displacements of a bead trapped in the optical tweezers (1 W laser output power, corresponding to 150 mW on the sample) and of a reference bead located in the cytoplasm away from the optical trap were recorded at 120 frames/s in the resonant mode of the confocal scanner for 40 s using the NIS Nikon software.
Quantifications of cytoskeleton proteins from immunofluorescence images Images of fixed cells were acquired as confocal z-stacks separated by 0. 2 μm in the galvanometric mode of the confocal scanner with a 4-or 8-frames averaging using the NIS-Elements Nikon software. Z-stacks were processed with a sum intensity projection using ImageJ. Cell contours were drawn manually and the mean fluorescence intensity inside the contour was measured.
Intracellular spatial organization on micropatterns Images of fixed cells were acquired as z-stacks separated by 0. 2 μm with a Delta-Vision microscope using the Softworks software (objectives 60X or 100X). Images were deconvoluted using an enhanced-ratio 15. For each cell, the deconvoluted images were automatically aligned and processed with a maximum intensity projection using a customwritten ImageJ macro. The final image was obtained with an average z-stack projection of all the cells and displayed using an inverted look-up table (LUT). Averaged fluorescence profiles of intracellular organelles (Golgi apparatus, lysosomes, nucleus) were obtained by averaging the fluorescence intensity profiles from individual maximum intensity projections along a line aligned with the axis of the crossbow-shaped micropattern and of the same width as the micropattern (61 µm for U373 grade III cells and 50 µm for U87 grade IV cells).
Acknowledgements We thank  Dalila Labiod  for providing F98 cells. We thank  Raffaële Attia  for her help in photomask design. We thank  Cécile Leduc ,  Kerren Murray  and  Chiara de Pascalis  for help with primary rat astrocytes and transfection reagents. We thank  Mathieu Coppey ,  Chloé Geller  and  Fiona Francis  for stimulating discussions. We thank  Sébastien Manneville ,  Sandra Lerouge  and  Sophie Asnacios  for technical assistance. We thank  Kalpana Mandal  for technical assistance. C. A was funded by a 3 years grant from  UPMC   University Paris 6 (Programme Doctoral "Interfaces Pour le Vivant ") and a 6 months grant from  La Ligue Contre le Cancer. Other fundings were from  Institut Curie ,  CNRS , INSERM Plan Cancer 2009-2013  INSERM -CEA Tecsan  (grant number  PC201125 ), and the labex «Who AM I ?», labex  ANR-11-LABX-0071  and the  Université de Paris , Idex  ANR-18-IDEX-0001  funded by the  French Government  through its  «investments for the future» program.
Materials and methods This study specifically received approval from the Ethics Committee at the University of Aix-Marseille on May 29, 2018 (file reference: 2018-24-05-007). The CIL (Correspondant Informatique et Libertes) was made aware of the study complying with French law (reference: DSN_2018-07-27_7419). Data were collected from anonymized medical records. A 4-year retrospective observational study was carried out in Southern France between 1 January 2014 and 31 December 2017. This regional study included the University Teaching Hospitals (UTHs) in Marseille (UTH Nord and UTH Timone) and Nice (UTH Archet and UTH Lenval) as well as their three Multidisciplinary Prenatal Diagnosis Centres (MPDCs) located at the UTH Nord, the UTH Timone in Marseille, and the UTH Archet in Nice. The data were collected retrospectively from computerised patient medical files by two researchers and anonymized.
Data collected For each foetus included, the data collected were the gestational age at diagnosis, the existence of polymalformative syndrome (PMS) or an associated genetic anomaly, the occurrence of foetal death, and the realisation of a termination of pregnancy (TOP) with an analysis of the gestational age and aetiology of it. In cases of a postnatal diagnosis, the parameters studied were the age at the time of diagnosis and the discovery of an associated PMS or genetic anomaly. For all children born with CHD, the following data were collected: the requirement for hospitalisation in the neonatal intensive care unit and the duration in number of days, the existence of PMS or an associated genetic anomaly, and the occurrence of death.
Population Among 249070 deliveries during the study period, 772 CHD cases were identified. Seventyone cases were excluded because the data on pregnancy outcomes were incomplete, and 24 more were excluded because they did not meet the inclusion criteria (Fig  2 ). In total, 677 CHD cases were included, out of which 134 (19. 8%) were classified into group 1, 206 (30. 4%) into group 2 and 337 (49. 8%) into group 3. The most commonly identified CHDs were ventricular septal defects (21. 3%), tetralogy of Fallot (10. 5%), coarctations of the aorta (10. 5%) and atrioventricular septal defects (9. 5%) (Table  1 ). The screening rates per year were 69. 2%, 68. 2%, 82. 9% and 71. 1%, respectively, from 2014 to 2017.
Acknowledgements This article is a part of a series on health economics, based on the  EULAR Health Economics in Rheumatology course , held in 2016 and 2018. Contributors MdW prepared the first draft of this manuscript with input from FG, BF, SG, MJ and AB. All authors approved the revised version. Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
POTENTIAL THERAPEUTIC TARGETS AND CONCLUSION (SEE FIGURE 2) On May 12, 2020, using the term "COVID, " an unbiased search of already registered trials on https://clinicaltrials. gov/ retrieved 1,409 hits, and, when refined with "double blind/placebo, " 119 hits were found. Although the number of trials that are ongoing or "under recruitment" is expectedly very high, the range of molecules tested is relatively narrow and aimed at targeting mainly antivirals. These include remdesivir (21 hits), lopinavir/ritonavir (also used in AIDS), as well as interferons (46 hits). Also falling in that category are trials testing molecules aiming to block viral entry at the cellular surface by targeting ACE-inhibitors (32 hits) or the membrane proteases of the TTSP family (see above) using camostat mesilate (5 hits).
Regardless, the ultimate prize in the fight against COVID-19 (or further SARS-CoV infections) undoubtedly lies with the future generation of effective vaccines and the development of neutralizing antibodies  (91, 92). Unfortunately, coronavirus vaccines in general have attracted less attention compared to the effort dedicated to vaccines against other potential pandemic viruses such as Influenza. For example, from 2012 onwards, few SARS-CoV vaccines reached phase 1 clinical trials for lack of interest from the pharmaceutical industry when it became evident that the virus was not making a "comeback" after its initial appearance. However, although probably too late for affecting the current "first wave" of SARS-CoV-2 pandemic, many pharmaceutical companies and research laboratories are now working on a plethora of vaccine formulations [for a review, see  (91)  and https://clinicaltrials.
Ageing of steels during operation could possibly modifythe transition temperatures, as well as the upper shelf energy. These shifts are measures of the embrittlement of the material and must be carefully determined to assess the ability of a component to fulfil its function in operating or accidental conditions. Therefore, the knowledge of the uncertainties in the determination of the transition temperatures and upper shelf energy is of the utmost importance for safe operations. Uncertainties and confidence intervals on the transition temperatures could be estimatedfrom the statistics of the parameters of the fit function and are dependant of the dataset  [2] [3] [4] [5]. This means that, in this case, the uncertainties are not related only to the methodology of measure but also to the result of the measure itself.
Representation of the data Because of the unbalanced number of results for the different test temperatures, an analytic description of the database is used instead of the individual values. The Charpy fracture energies are usually assumed to obey a Weibull distribution in the lower part of the transition region  [10] [11] [12] [13]. However, it is known that the standard deviation of the fracture energy goes through a maximum, approximatively at T t , associated with the possible development of a bimodality. Therefore, we choose here to use bimodal distributions to represent the variation of the data independently at each test temperature. These distributions result from the mixing of two Weibull distributions: P f E E f ; T À Á ¼ g 1 À exp E f E l m l ! þ 1 À g ð Þ 1 À exp E f E h m h ! : ð2Þ At the test temperature T, g is the fraction of the specimens failing with a "low" energy, E l , m l are the parameters of the distribution of the fracture energy E f for the specimens failing with low energy and E h , m h are the parameters for the specimens failing with higher energy.
(  1 )) parametrized by least squares method on the data set obtained in step 1. -Steps 1 and 2 are repeated 10000 times thanks to an Octave script  [14]. -Then the transition temperatures are statistically characterized through their mean, standard deviation and other statistical characteristics of their distributions. Two quantities are considered to derive the uncertainty: the precision and the accuracy. The precision s is quantified by the standard deviation of the mean value of T28, T41, T56, T68 and USE of the sampled data set. The accuracy (or error) e is the difference between the true values and computed values of transition temperaturesmeans; i. e. , the mean value of T28, T41, T56, T68 and USE derived from resampled data points, which follows equation  (2). Therefore, the uncertainty u of a transition temperature T k is the sum in quadrature of these two quantities: u T k ð Þ ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi s 2 þ e 2 p : ð3Þ.