dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Normality of data distribution was assessed using the Shapiro-Wilk test and by visually checking each variable's distribution (histogram). Data are expressed as mean ± SD when normally distributed or as median [IQR] when non-normally distributed. Proportions were used as descriptive statistics for categorical variables. Comparisons of values between independent groups were performed by the 2-tailed Student t test or the Mann-Whitney U test, as appropriate. Analysis of the discrete data was performed by χ2 test or Fisher exact test when the numbers were small. There were missing data (missing at random) for interleukin 6 (3. 6%), ferritin (1. 8%), D-dimer (1%), fibrinogen (8. 2%), aptt (9. 1%), and INR (9. 1%) that were imputed using multiple imputations with 50 imputed datasets.
The potential problem of co-linearity was evaluated using Spearman or Pearson correlation coefficient before running the analysis. Subhazard ratios (SHRs) and 95% confidence intervals were summarized. A value of p < 0. 05 was considered statistically significant, and all reported p values are twosided. Statistical analyses were performed using Stata/SE 14. 2 software for Windows (Stata Corp LLC, TX 77845, USA).
Experimental treatment Individuals were randomly assigned to the control (n = 22) or water-deprived (n = 21) hydric treatment. For the waterdeprived group, drinking water was removed for 15 days, which is grossly similar to the mild, chronic summer droughts repeatedly occurring over the past decade in western France. At the end of the treatment period, water-deprived snakes were provided water ad libitum immediately after blood-sampling (see below). Water-deprived and control individuals did not differ in BM or SVL at the beginning of the experiment (linear models, lm, package stats; initial BM, Treatment: F 1,41 = 0. 1, P = 0. 78; SVL, Treatment: F 1,41 = 0. 04, P = 0. 84). The experimental treatment occurred during early pregnancy for all individuals (onset of treatment: 18. 4 ± 8. 2% of developmental time; end of treatment: 38.
Statistical analyses All statistical analyses were performed using R software version 3. 6. 3 (R Core Team, 2020). To investigate how viper morphology (BM, TW), physiology (plasma osmolality, haematocrit, baseline CORT, ROMs, OXY) and body temperature (T b ) were affected by experimental conditions, we used univariate linear mixed effects models fitted with the lme function of the nlme package  (Pinheiro et al. , 2019). For each univariate model, treatment group (control or water deprived), session (effect of time), females' reproductive effort (number of living embryos at the onset of the treatment period) and their interaction were included as fixed factors. Individual identity was treated as a random effect. We dropped all non-significant interactions from these models using marginal F tests (anova, package stats).
We used the family quasibinomial rather than binomial because our model was slightly overdispersed (ϕ = 2. 96). In order to determine if individual reproductive effort and physiological response traits ( osmolality, Hct, CORT, ROMs, OXY) best predicted variation in embryonic mortality, we built six independent generalized linear models where each of these variables was included as a covariate. We next compared all models using a quasi-AIC (package AICcmodavg) model selection approach.
Acknowledgement The authors thank  Donatien François  for help with snake captures,  Annelise Roustaing  and  Romane Schaeffer  who contributed to data acquisition and care of vipers,  Charline Parenteau  and  Emmanuelle Seghrouchni  for their help with the corticosterone assay and  Robin Viton  and  Elsa Daniaud  for logistic support. The authors thank  Sydney Hope  for helpful comments on the English of the manuscript and one anonymous reviewer and  Emily Taylor  for their constructive comments on an earlier draft of the manuscript.
Funding This work was supported by the  Agence Nationale de la Recherche  under the ' Aquatherm' project  ( ANR-17-CE02-0013  to J. -F. L. G. and O. L. ),  Region Nouvelle-Aquitaine  under the 'Aquastress' project ( 2018-1R20214  to O. L. ) and the  Departmental Council of Gironde  under the ' Climate Sentinels ' project (coordinators:  Cistude Nature, Fanny Mallard ). M. D. is supported by a doctoral grant from ED 227  Sorbonne University.
Patients with and without AKI were eligible. The SARS-CoV-2 reverse-transcriptase PCR (RT-PCR) was performed on nasopharyngeal swabs using the Cobas SARS-CoV-2 assay (cobas 6800; Roche), which targets E and ORF1ab genes  (30, 31). A control group of five patients without COVID-19 was used to evaluate the COVID-19-induced histologic lesions. Four patients with an antemortem diagnosis of sepsis and/or ARDS, who underwent an autopsy within 24 hours, were identified in the database of the Department of Pathology (i. e. , patients R1-R4). One control patient died from pulmonary sepsis not related to COVID-19 in ICU during the present prospective recruitment (patient P1) and was PCR negative for SARS-CoV-2. The immunocompromised status was defined as having a history of cancer or active cancer, transplantation, HIV infection, type 1 diabetes, or inflammatory disease.
Clinical, Biologic, and Radiologic Parameters Clinical data were collected from medical records and anonymized: age, weight, height, body mass index, history of hypertension, history of diabetes, active or previous cancer, active smoking, and CKD. All biologic data were obtained from the same laboratory. Blood levels of C-reactive protein, procalcitonin, creatinine, lactate dehydrogenase, and potassium were determined using the Abbott Alinity instrument. Lymphocytes and platelet measurement were obtained using the Sysmex SE-9000 Hematology analyzer. Plasma D-Dimer levels were measured with the Innovance D-Dimer kit on the automated Siemens CS5100. The number of red blood cells in the urine was automatically evaluated using a sediMAX. The eGFR was calculated using the CKDepidemiology equation  (32).
Statistical Analyses Data were expressed as mean (minimum value; maximum value) when the distribution was normal, and as median with interquartile range (IQR) (quartile 1; quartile 3) if not. Normality was assessed by the Shapiro-Wilk test. Categorical variables were expressed as percentages. Statistical difference was evaluated by a t test in the case of normally distributed variables or by a rank test where continuous variables were not normally distributed and by Fisher's exact test for categorical variables. The Spearman rankorder correlation coefficient was used to measure the strength and direction of association between two variables measured on an ordinal or continuous scale. Analyses were conducted using STATA (version 15; StataCorp, College Station, TX).
Acknowledgments The authors sincerely thank all physicians and nurses who take care of patients with COVID-19 in the clinical wards and ICUs. The authors also thank all members of the department of  Medico-Economic Information Service, Laurence Poma (ULi ege Cardiovascular Sciences) ,  Francois Maclot  ( ULi ege Academic Hospital, Department of Clinical Microbiology ), and  Michael Sarlet  ( ULi ege, School of Veterinary Medicine ) for their technical assistance. Our manuscript has been reviewed by a native English speaker,  Enda Breen  (enda. breen4@mail. dcu. ie),who is a member of the  Irish Translators' and Interpreters' Association. P. Erpicum ,  J. Huart , and  F. Jouret  are Fellows of the  Fonds National de la Recherche Scientifique, Brussels, Belgium.
1 Gy isodose (Dcoverage in our case) to the 11. 55 isodose (50% of Dcoverage). The larger the value, the shallower is the gradient. Statistical analyses were performed using R v2. 15. 1 (http://www. cran. r-project. org). To explore changes across the four sets of data obtained (DCA, VMATc Eclipse , VMATnc Eclipse and VMATnc Elements ), Kruskal-Wallis tests were performed, followed by Dunn tests for pairwise comparisons. If the associated p-value was under the significance level (˛ = 0. 05), it was assumed that there was a statistically significant difference between the data sets compared.
Materials and Methods The data used in this study was part of the database of the Digi-NewB (https: //www. digi-newb. eu/) observational cohort (NCT02863978, Horizon 2020 European project GA-689260). This cohort prospectively included newborn infants hospitalized in the neonatal units of six university hospitals in the western region of France (University Hospitals of Rennes, Angers, Nantes, Brest, Poitiers, and Tours) in 2017-2019. The collection of the data was carried out after approval by the ethics committee (CPP Ouest IV 34/16), the national agency for the safety of medicines and health products (Authorization number: 2016062400181) and informed parental consent. The Digi-NewB project is aimed at improving clinical intervention in neonatal units by developing a new generation of non-invasive perinatal health monitoring based on a multi-parametric approach.
Study Population This study included very preterm infants (VPI) born before 31 weeks gestational age (GA) between 1 November 2016 and 3 October 2019. All the newborns included in this study were clinically suspected of LOS and received more than five days of antibiotics (or die before the course is completed), beginning at least 72 h after birth. For each VPI, only the first episode of LOS was studied. Maternal, obstetrical, and birth data, as well as the clinical, biological, and microbiological data surrounding LOS suspicion, were prospectively collected. The presentation of all these data was prepared in a user-friendly presentation by the investigators to facilitate the case analyses by the experts. All the characteristics of each very preterm infant were checked, and curves presenting the evolution of the studied parameters were prepared.
Variables Studied Maternal, obstetrical and birth data, as well as the data collected around LOS suspicion, were provided to the adjudication committee. These data had been prospectively collected continuously in 6 h periods. The period surrounding the infection was defined as five days before and five days after the start of antibiotic administration (T0). The 13 clinical parameters used were collected from electronic case report forms or from analyses of heart rate, respiratory rate and transcutaneous oxygen saturation traces obtained from the monitors. All occurrences of abnormal clinical or biological signs were annotated with regard to T0. The clinical signs studied were as follows: cardio-respiratory events, bradycardias, increase in oxygen or ventilatory assistance, peripheral hemodynamic (cutaneous refilling time >3 s and/or occurrence of pallor or marbling), central hemodynamic (heart rate, blood pressure, need for hemodynamic drug or volume expansion), digestive symptoms (abdominal distension, feeding cessation), body temperature (<36 • C or >38 • C or unstable), abnormal neurological examination, and presence of painful symptoms.
Adjudication Committee An independent committee was composed of 5 high-level experts well recognized for their in-depth knowledge of LOS, with four experts in neonatology and one expert in microbiology involved in the evaluation of nosocomial infections occurring in neonatology. These experts were asked to independently qualify their assessment of each recorded case according to a three-level Likert scale as "certain or very probable", "probable," or "doubtful" infection. The descriptive files provided included the clinical, biological and bacteriological data of all patients grouped in a table together with a slide show containing the detailed individual clinical course of each patient. Temperature, cardiorespiratory events frequency, bradycardia frequency, mean heart rate, mean respiratory rate, FiO2-P respiratory score were presented as individual curves with a time scale based on 6 h-periods (example in Figure  1 ).
A documentary database was also given  [14, [27] [28] [29]. the detailed individual clinical course of each patient. Temperature, cardiorespiratory events frequency, bradycardia frequency, mean heart rate, mean respiratory rate, FiO2-P respiratory score were presented as individual curves with a time scale based on 6 h-periods (example in Figure  1 ). A documentary database was also given  [14, [27] [28] [29]. The information concerning the prognostic composite criteria was blinded to the expert as well as the results of the HRV analysis. At the end of the adjudication process, the cases were post hoc classified by the investigators into two groups. Newborns were considered "infected" if there were at least 3 "probable" or "very probable" ratings out of the five and "doubtful" in other cases.
The results are presented in number and % or median (interquartile range, IQR) or mean (standard deviation, SD) depending on the distribution of the variables. Comparisons were made with Mann-Whitney U-tests and Mac Nemar's Chi 2 test using Statistica 13. 2 software (StatSoft Inc. , Tulsa, USA). In order to answer our main hypothesis, we studied inter-rater reproducibility by comparing the LOS evaluations of each expert 2 to 2 by Cohen's Kappa tests. A Fleiss kappa was also used to calculate the overall reproducibility of the LOS ranking by the 5 experts. This work was also carried out for the international classifications. We also calculated the Cohen Kappa between the synthetic general classification resulting from the analysis of the results of the adjudication committee and the results of the international classifications.
Population During the study period, 60 preterm infants born before 31 weeks gestation who had received more than five days of antibiotics were included in the Digi-NewB cohort. Twelve patients were excluded from the current study because nine had received antibiotics started before 48 h of life, and three had not enough data available at the time of the suspected infection to be reliably characterized. One infected preterm infant who died 24 h after the start of antibiotics was included in the study. The characteristics of the 48 infants included in the study population are presented in Table  1. The median gestational age at birth was 26 weeks + 3 days (IQR: 25 w + 2d-27 w + 5d) with a birth weight of 785 g (IQR: 726-911) and a sex M/F ratio of 1. 4. Adaptation at birth was good, with an Apgar score greater than 7 at 5 min of life for 38 infants (79%).
Acknowledgments: We thank  Solène Blache ,  Edouard Boustouler ,  Sandie Cabon ,  Charlotte Cameli ,  Camille Chaillous ,  Mélanie Corre ,  Magalie Fauchère ,  Géraldine Favrais ,  Loïc Fin ,  Cyril Flamant ,  Florence Geslin ,  Alfredo Hernandez ,  Carole Jeanneaud ,  Thomas Janvier ,  Laurence Jay Passot ,  Maude Luherne ,  Sophie Marzin ,  Guillaume Ménard ,  Romain Muraz ,  Sandrine Roudaut ,  Raphael Weber  and all the families of preterm infants for their participation and help in this study.
• Fast (real-time), simple operation, • Able to detect all foreseeable weather conditions using a single method, • Use a camera with the standard settings found on roadside traffic monitoring cameras, • Able to quantify various levels of intensity for each detected weather condition. The following section contains a review of the state-of-the-art. A new database  [5]  was used for the purpose of implementing and evaluating our detection system. This database is described in detail in Section 3 with an evaluation of state-of-the-art databases. Then, we propose a selection of three Deep Convolutional Neural Network (DCNN) architectures in Section 4 and present a method for analysing them in Section 5. Lastly, Section 6 presents the results obtained, and Section 7 contains our conclusions.
Using this base, the algorithm was able to classify the aforementioned conditions with a 0. 92 success rate. There are two main limitations with this method. Firstly, the database was highly heterogeneous, including weather conditions without any physical measurement. These conditions may sometimes be simulated (cinema scene) and in all cases are not described by physical meteorological sensors. Secondly, the proposed method uses a combination of algorithms, rendering it hard to roll out for other weather conditions such as night-time conditions, for example. The method described in  [18]  was based on image descriptors and an SVM designed to classify images in two weather categories: cloudy and sunny. Furthermore, the authors of  [19]  used a similar method to classify weather in three categories: sunny, cloudy and overcast.
Strictly speaking, these methods are not able to detect degraded weather conditions. Other more recent methods operate using convolutional neural networks  [4, 20]. These deep learning-based methods generally yield better results but are costly in computing time. The methods described thus far are not able to classify degraded weather conditions; rather, they sort images into two classes: sunny vs. cloudy  [4]  with an accuracy of 82. 2%, or estimate seasons and temperatures based on images  [20]  with 63% accuracy of season classification. It can be seen that the methods identified in the review of the state-of-the-art only partially address the issue of describing degraded weather conditions in road environments. The following section evaluates existing databases and their limitations and introduces a new database used to learn our classification system in this specific context.
Rain Rain is composed of free-falling drops of water in the atmosphere. It is characterised from a microscopic point of view (size of the drops) and from a macroscopic point of view (rainfall rate). In the construction of our weather database, only rainfall rate will be taken into account to characterize rain conditions. The rainfall rate is noted R R and it is expressed in mm•h -1. According to the French standard NF P 99-320  [21] , there are different types of rain classified according to their intensity (Table  1 ). These rain intensities correspond to values commonly encountered in metropolitan France. Table  1. Different rain classes according to French standard NF P 99-320  [21].
Existing Databases and Their Limitations Weather information can be an important factor for many computer vision applications, such as object detection and recognition or scene categorisation. For this, we need image databases including weather information. There are only a few publicly available databases for meteorological classification, and they contain a limited number of images  [24]. The database provided in  [2]  includes clear weather, light rain and heavy rain for rain classification. A second database, including only rain conditions, was developed by  [25]  in which the Cerema-AWR (Adverse Weather Rain) database was constructed with 30,400 images divided into two types of rain: natural and simulated for different camera settings. Like rain, the authors of  [26]  focused on the study of different fog levels (clear weather, light fog and heavy fog) using a database containing 321 images collected from six cameras installed on three different weather stations.
The images were taken from static and fixed platforms. Works of  [18]  provided a meteorological classification database with more real scenes, but only sunny and cloudy images are involved. The weather dataset contains 10,000 images divided into two classes. In  [27] , the authors described the Multi-class Weather Image database (MWI), which contains 20,000 images divided into four weather conditions classes, such as sunny, rainy, snowy and haze, and which are collected from web albums and films like Flickr, Picasa, MojiWeather, Poco, Fengniao. This database is annotated manually. In  [24] , the authors present a 65,000-image database collected from Flickr, divided into six weather classes: sunny, cloudy, rainy, snowy, foggy and stormy. This database is called MWD (Multi-class Weather Dataset).
Also in  [28] , the authors created the Image2Weather database with 183,000 images and divided them into five classes: sun, cloud, snow, rain and fog. In  [29] , the Archive of Many Outdoor Scenes (AMOS + C) database has been proposed as the first large-scale image dataset associated with meteorological information, which has been extended from the AMOS database by collecting meteorological data via time stamping and geolocation of images. The authors of  [30]  introduced the Seeing Through Fog database, which contains 12,000 samples acquired under different weather and illumination conditions and 1500 measurements acquired in a fog chamber. Furthermore, the dataset contains accurate human annotations for objects labelled with 2D and 3D bounding boxes and frame tags describing the weather, daytime and street conditions.
There exists another database called Cerema-AWP  [5] , which contains adverse weather conditions, such as fog and rain with two different intensities for both. These conditions are captured during day and night and were used to evaluate pedestrian detection algorithms under these degraded conditions. Fog and rain in this database were produced artificially in the French Cerema PAVIN BP platform, which is the unique European platform allowing the production of controlled fog and rain. We refer to  [31, 32]  for more details on this platform. During the study of state-of-the-art databases, we encountered a lot of difficulties. Indeed, there are many articles that do not present the experimental approach used for weather classification. They are not well documented and do not contain explanations that would allow readers to use the databases reliably.
As a consequence, it becomes very complicated to compare our work with previous works with satisfactory results. There are other difficulties that are related to weather databases. Either they are not public such as  [26, 27] , or, if they are accessible, they do not have all the information that makes it possible to study them (for example, the meteorological spatial and temporal conditions in which they were taken). In this context, a qualitative database analysis was presented in  [33]. The authors focused on the assessment of the documentation quality of the Cerema-AWP database  [5]. However, this analysis does not discuss the qualitative or experimental side of the database linked to meteorological data. Existing meteorological databases cannot be used rigorously. Indeed, most of them are collected from websites like Google Image or Flicker, and these images are then labelled manually, such as the MWD database  [24].
Furthermore, I2W database images are collected from the web, even if they are automatically labelled with meteorological information taken by sensors, and this always contains ambiguous images (the database available publicly is not the same as the one presented in  [28] : the authors added a 6th class called "Others" which contains images presenting classification doubts). The different filtering steps are not described precisely and there are therefore ambiguities. Another problem encountered concerning the I2W database is the imbalance between meteorological classes where there is a large difference in the number of images (70,501 images for sunny class and 357 images for fog class). This imbalance can lead to difficulties for the neural networks during the learning phase. Through these labelling problems, and since weather classification requires at least reliable data, we have built a rigorous and automatically annotated database that is based on sensors for meteorological classes allocation installed on the same site as the acquisition camera.