dataesr/scientific-paragraphs-categorization huggingface dataset:
Domain II is a transitional regime between these two situations. In the case of our study, we distinguish between the two scalings we have used for D 50 calculation (Figs. 7D  and 7F ). We note that E/Ψ varies over 4 orders of magnitudes between these different studies. Only our results, when using the D 50 scaling set from field observations, and the data from DiBiase and  Whipple (2011)  in the San Gabriel Mountains are clearly in threshold dominated regime I. It appears that all cases where the incision threshold is a free parameter for model optimization, with either a constant or variable τ c , fall into the transitional regime II. The two cases we consider for our Massif Central dataset illustrate the very strong influence of hypothesis made on bedload size in influencing threshold effects and associated regime, as there is a factor 2 in D 50 in values between the 2 scalings, but a corresponding order of magnitude difference in the E/Ψ ratios.
Following  Lague (2014)  we also consider the implications of the relative influence of incision thresholds on the recurrence time t r for incision events, as a function of the critical normalized discharge Q * c = Q c / Q (equation 11) and the discharge variability parameter k (equation 12), t r (Q * c ) = Γ(k/Q * c , k + 1) -1 , ( 17 ) where Γ is the incomplete Gamma function. We observe that studies where the threshold is considered as a free parameter set by model optimization imply very low t r < 1 week which, in the case of our study area in the Massif Central, is not consistent by the model optimization over their dataset. In the case of a spatially variable τ c , our results illustrate a situation where using such optimization approach improve the quality of the fit to the erosion rate data (Fig.
Competing interests. The authors declare that they have no conflict of interest. Acknowledgements. This research has been supported by the Agence Nationale de la Recherche (project ANR-18-CE01-0017 (Topo-Extreme)). C. Desormeaux PhD is supported by a fellowship from the French Ministry for Higher Education. The 10 Be measurements were 510 performed at the ASTER AMS national facility (CEREGE, Aix en Provence) which is supported by the INSU/CNRS, the ANR through the "Projets thématiques d'excellence" program for the "Equipements d'excellence" ASTER-CEREGE action and IRD.
•Figure 8. 8 Figure 8. Comparison between our dataset and published data of normalized channel steepness index (θ ref = 0. 45) versus basin-wide denudation rate calculated from 10 Be concentrations and incision rates calculated from terraces in different climatic and tectonic contexts. Black solid line is a power law fit to the aggregated denudation rates dataset. Original data for the Ecuadorian Andes are from Vanacker et al. (2015) as reported in Campforts et al. (2020). Original data for the San Gabriel Mountains are from DiBiase et al. (2010) as reported in DiBiase and Whipple (2011). Original data for the Himalayas and Eastern Tibet are from Scherler et al. (2014); Wobus et al. (2005); Godard et al. (2010, 2012, 2014); Finnegan et al. (2008); Ouimet et al. (2009); Harkins et al. (2007), as compiled byScherler et al.
Prompt identification of patients at risk of disease worsening is a challenging issue  [23, 24]. Machine learning is a valuable tool to respond to big COVID-19 datasets, which may contribute to further improvement of the accuracy in the identification of the most Machine learning is a valuable tool to respond to big COVID-19 datasets, which may contribute to further improvement of the accuracy in the identification of the most susceptible patients who could benefit from prompt personalized therapeutic interventions  [25] [26] [27]. The prospective, observational study ROADMAP-COVID-19 (pROspective Risk Assessment anD bioMArkers of hyPercoagulability for the identification of patients with COVID-19 at risk for disease worsening) was conducted aiming to derive a prognostic score for the risk of intubation or death in patients with critical COVID-19 by assessing biomarkers of hypercoagulability, endothelial cell activation and inflammation and a large panel of clinical analytes.
Ethics: The study was conducted in accordance with the Helsinki declaration, and all patients received healthcare according to the recommended institutional practice during COVID-19 pandemic. All hematological tests were performed in the frame of routine monitoring of patients. This is a non-interventional data-based study, using the care data collected during patients stay, involving all the consecutive critically ill COVID-19 patients admitted to the ICU in Tenon Hospital during the pandemic. There is no processing of indirectly identifiable data, or chaining with data from other sources, or long-term patient follow-up for this research. This study was approved by the institutional review board of Sorbonne University (Reference CER-2020-54) according to the French regulations. All participants (or their relatives) gave consent to participate.
Statistical Analysis A number of patients had to provide statistical power for the derivation of an accurate score by responding to the following criteria: (a) the model had to be constructed according to the rule of thumb, the so-called events per variable (EPV) 10-1, (b) less than 10 variables should be included in the model in order for it to be easy to use  [30] [31] [32]. Patients with missing data (clinical variables or biomarkers) were excluded from the analysis. Values of biomarkers measured in the cohort of patients were compared against values measured in the control group. When comparisons were required, continuous variables were assessed using Mann-Whitney U tests; a p-value < 0. 05 was considered statistically significant. Separate random forest classifiers were trained on variables to predict intubation and mortality.
During training, the random forest classifier performed an implicit feature selection; the top features are those that appear highest ranked in the most trees. To reduce overfitting, the number of trees were limited to ten, the maximum depth of each tree was limited to three, and a three-fold cross validation approach was used  [33] [34] [35]. To remain conservative and to limit the risk of overfitting further, no hyperparameters were tuned or optimized by design and intent. The samples were split into two stratified training (70%) and validation sets (30%), one set for intubation and another for mortality with their respective features. A Boruta feature reduction method, based on random forest classifiers, was then used to develop two models, one for intubation and one for mortality, using the training dataset  [36].
The latter provided the two respective models with the least number of features providing the most accurate prediction ability. The reduced variable sets were then visualized with a nonlinear dimensionality reduction on the full data matrix using the t-distributed stochastic nearest neighbor embedding (t-SNE) algorithm  [37]. The t-SNE assumes that the "optimal" representation of the data lies on a manifold with complex geometry, but with low dimension, embedded in the full-dimensional space of the raw data. Receiver operating characteristic (ROC) curves were conducted with the random forest to determine sensitivity and specificity of the reduced variable sets for predicting their binary outcome, either intubation or mortality. To avoid circular analysis and reduce overfitting, the reduced model was tested using the validation dataset and a random forest classifier with ten trees, maximum depth of three, and three-fold cross validation.
Hypercoagulability, Cellular Activation and Inflammation Biomarkers The derivation cohort consisted of 118 ICU patients who had no missing data out of 130 patients admitted in ICU during the study period. Baseline demographics and comorbidities of patients prior to hospital admission are reported in Table  1. The intubation rate was 70% and the mortality rate was 18% in the studied cohort, being similar in the ensemble of the ICU-admitted patients. The levels of biomarkers of hypercoagulability, cellular activation and inflammation in patients and controls are reported in Table  2.
The increased levels of D-Dimers document an environment of sustained fibrin formation and lysis. Two systematic reviews highlight that D-dimer values are higher in non-survivors as well as in patients with severe COVID-19 than in those with mild disease showing the association between D-dimer levels and disease severity, or death  [43, 44]. Nevertheless, our data show that the predictive value of D-Dimers alone for intubation was weak. This finding is in line with the concept that D-dimers cannot be used as a stand-alone test for the evaluation of the risk of COVID-19 aggravation and mortality  [44, 45]. The derivation group was homogenous regarding the timing of the assessment (all patients were enrolled on the first day of ICU admission) and representative of the population of patients with severe COVID-19, since very limited exclusion criteria were applied.
Pollen samples Figure  1  shows scanning electron microscopic images of the four studied pollen species, namely ragweed, ash, birch and pine. These microscopic images allow the size and the shape of these pollen to be characterized: each pollen taxon exhibits a very characteristic shape comprising grains presenting a quasi-monodisperse size distribution. The four considered species clearly differ in size and shape, from nearly spherical with a 20 µm volume equivalent diameter for ragweed to highly irregular shapes with a diameter larger than 60 µm for pine. Small-scale features exist on each pollen surface, such as granularity or apertures, which show the great complexity of these microscopic objects. These specific sizes and shapes are used for pollen recognition as extensively detailed in palynological databases  (PalDat, 2021) , as recalled in the introduction.
However, it remains challenging and at least non-intuitive to identify a pollen taxon based on this set of 40 intricate scattering matrix elements. In the literature, decision trees have been applied for classification, but overlaps exist due to experimental uncertainties, which limit the application of this classification methodology. Rather, we here propose to reduce the dimensionality of our system to a 2D-representation plane while taking into account experimental uncertainties. Therefore, the goal of this paragraph is to provide a simpler lightscattering criterion for classifying among the four considered pollen taxa, based on a 2D-representation plane. To reduce the dimension of our four-pollen-taxa dataset, a principal component analysis (PCA) has been performed. A linear discriminant analysis (LDA) may appear to be more suited for labelled classes.
However, as published by  Martinez and Kak (2001) , PCA might outperform LDA when the number of samples per class is small, and in our methodology, each class (pollen taxon) is represented by a single point as being representative of the distinct size and shape of each pollen taxon. Applying a LDA would require having more than one single point per class. PCA is a statistical method for dimension reduction and feature extraction  (Jolliffe, 2005). There, each pollen is described by its two principal components, hereafter called PC 1 and PC 2 , which form an orthogonal basis of two uncorrelated components. Each measured scattering pattern can then be defined in terms of a combination of the PCs. Interestingly, besides the advantage of a dimensionality reduction, the PCA maximizes the variance in the (PC 1 , PC 2 ) representation of the dataset.
For that, we considered the explained variance, which measures the proportion to which a mathematical model accounts for the dispersion of a given dataset. In our case, a threshold of 99 % explained variance is reached by taking into account only two principal components. As a result, Fig. 6  presents the projection of each pollen taxon in the new (PC 1 , PC 2 )-orthogonal basis. The newly obtained coordinates PC 1 and PC 2 of each pollen account for our 10 retrieved scattering matrix elements (5 per wavelength per pollen sample), and the corresponding https://doi. org/10. 5194/amt-15-1021-2022 Atmos. Meas. Tech. , 15, 1021-1032, 2022   45+ ) and (RC) at wavelength λ VIS (in green) and wavelength λ IR (in red). The detected light intensity is normalized so that a i × I λ 0 = 1, as detailed in Sect.
3. 3. A break has been inserted to ease the reading. Each measurement data point is obtained by repeating 400 lasers shots, and the uncertainty is calculated by following the methodology described in Sect. 3. 4. error bars in Fig. 6  have been evaluated by propagating the uncertainties of each f λ ij. The explained variance of PC 1 is 78. 8 %, while that of PC 2 reaches 20. 3 % so that the dimension reduction still accounts for 99. 1 % of our experimental variance. Based on Fig. 6 , a classification criterion among our four-pollen-taxa dataset is to assign to each pollen taxon the area delimited by its error bar. To each measured scattering pattern (i. e. each set of 10 evaluated f λ ij matrix elements) there is a corresponding single point in the (PC 1 , P C 2 ) basis, which allows the pollen to be identified if the evaluated  point lies in one of the Fig.
Foliard, Les vies du «trésor de Ségou», "Revue historique" 2018, Vol. 4(688), pp. 869-898. Décret du 28 mai 1895 portant règlement sur le service des armées en campagne, Imprimerie Nationale, Paris 1895. Ius praedae colonial. Enjeux juridiques autour des objets de la discorde, in: C. Bories et al. (eds. ), op. cit. , pp. 185-211. II) with Respect to the Laws and Customs of War on Land and its Annex: Regulations concerning the Laws and Customs of War on Land. The Hague, 29 July 1899, https://ihl-databases. icrc. org/applic/ihl/ihl. nsf/Treaty. xsp?documentId=CD0F6C83F96FB459C12563CD002D66A1&action=open-Document [accessed: 06. 05. 2022]. Imperialism and War. Essays on Colonial Wars in Asia and Africa, E. J. Brill, Leiden 1989. For the related concept of "savage war", see K. A. Wagner, Savage Warfare: Violence and the Rule of Colonial Difference in Early BritishCounterinsurgency, "History Workshop Journal" 2018, Vol.
Acknowledgements We thank  Prof. Céline Pulcini,MD MSc PhD ,  MoH Coordinator  of the  French AMR National Action Plan , Ministère des Solidarités et de la Santé, Paris, France  for her critical review of the manuscript. We are also grateful to  Bérengère Gall ,  Julien Vivant  and  Victoire Sevenet , from the  BVA  group for their help in conceiving the surveys and data analyses. Funding.
Ascorbic Acid Depletion Assay Particles were incubated for 4 h at 37 • C in an ascorbic acid solution (200 µM) prepared in 0. 9% NaCl at pH 7. 4. Samples were then centrifuged 5 min (12,000× g, 4 • C) to remove particles and the supernatants were filtered (Nylon 0. 45 µm syringe filter). The final concentrations of AA were quantified with an Agilent High Pressure Liquid Chromatography Infinity II equipped with DAD detector (Agilent Technologies, USA) and a Poroshell 120 EC-C18 (3 × 150 mm, 2. 7 µm) column (Agilent Technologies, USA) equipped with guard EC-C18 (3 mm) maintained at 25 • C. The mobile phase (potassium hydrogen phosphate 10 mM, pH 3) flow rate was 0. 6 mL/min. UV data were collected at 220 nm. Percentage of depletion was calculated as described below: OP AA = 100 -(AA concentration in the sample/AA concentration in the blank) × 100.
Acknowledgments: Thanks to  Didier GOUX  ( Biological Microscopy Facility Center, CMAbio3, Normandie Univ, UNICAEN ) for technical TEM support for microscopy observations and to  Sophie Pricot  for proofreading the English. Thanks to  E. Brotin  ( ImpedenCELL platform, UNICAEN, France ) for access to the IncuCyte ® S3 device, as part of the  European project  " DILEMI ", co-funded by the  Normandy County Council , the  European Union  within the framework of the  Operational Programme ERDF/ESF   2014-2020.
Literature search In order to identify potential items for the questionnaire, a systematic literature search was performed in the Medline database using the PRISMA methodology  [26]. The goal of this search was to identify clinical situations in which the use of aripiprazole in schizophrenia in adults had been evaluated. All formulations of aripiprazole (oral tablets, intramuscular injectable and depot formulations) were considered in the literature review. The search terms used are listed in Table  1  and a PRISMA flow diagram of the selection process is provided in the Supplementary Information (Additional Fig. 1 ). Titles and abstracts of the identified publications were screened for relevance by two independent reviewers. Full texts of retained publications were individually reviewed to assess eligibility and the selection validated by an independent reviewer.
Implementation of the survey The Delphi questionnaires were administered and the data collected by an independent moderator, who was the only person who knew the identity of the panellists and had access to the individual votes. The moderator used a web-based survey software (Survey Monkey ® ) to invite psychiatrists to participate, to deliver the questionnaire and to collect and compile the ratings. The study questionnaire opened with a series of screening questions to ensure that the panellist had the required experience. In order to proceed with the survey, panellists were required to confirm that they were a psychiatrist treating adult patients with schizophrenia, prescribing atypical antipsychotics, working in the hospital sector, and seeing patients in at least three of the five clinical domains of interest in the survey.
Mortality data The all-cause individual mortality data are also open and published by INE. They are derived from death certificates, and include information on place of birth and residence, type of place of death (i. e. hospital, home …), age and sex. I employed mortality data for the period between January 1st, 2015 and December 31st, 2020 for residents in Spain (2,584,378 deaths). Covid-19 mortality presents an age-specific pattern that is more concentrated at older ages than other causes mortality: only 120 (0. 4%) of the 28,643 reported Covid-19 deaths until the end of May 2020 in Spain concerned individuals aged under 40. As a comparison, 1. 5% of the total deaths in Spain in the year 2019 were of people aged under 40. Because of this age specificity of mortality from Covid-19, I only consider deaths among those aged 40 or older for this study.
β 2n+3 represents the mortality increment during the Covid-19 epidemic as compared to the expected mortality level for 2020. Thus, the MRR for each region of birth r and large age group g associated with the epidemic is: MRR g,r = exp(β 2n+3 ) (2) Year of death was included in order to separate the diminishing mortality trend in the 2015-2019 period from direct and indirect mortality due to the 2020 epidemic. Eventually, the data were also stratified by region of residence (HIR, LIR) for wave 1, to measure different mortality increases in high and low-impact regions.
Nowadays, the importance of detection of myositis-specific antibodies (MSA) and myositis-associated antibodies (MAA) in diagnosis and in delineating disease subsets of idiopathic inflammatory myopathy (IIM) is highly acknowledged by IIM experts. Consequently, MSA/MAA are increasingly integrated in expert-based myositis (sub)classification criteria as well as in routine diagnostics. In contrast, MSA/MAA are under-represented in databased (sub)classification criteria, mostly related to the lack of sufficient data on the wide spectrum of MSA/MAA in large multicenter cohorts. Unfortunately, the current commercially available assays to detect MSA/MAA show variable analytical and clinical performance characteristics. This challenges the design of prospective multicenter studies on MSA/MAA as well as the optimization of their routine clinical use.
Technical investigations included electromyography and determination of the muscle enzymes (e. g. creatine kinase [CK], lactate dehydrogenase [LDH], aldolase, transaminases). The presence of classical DM skin rashes differentiated between DM and PM  [3, 4]. IBM was later incorporated in the Dalakas diagnostic criteria of 1991, which were in fact an expert-opinion based fine-tuning of the Peter and Bohan criteria  [5]. More widely used clinico-pathological expert-based criteria for IBM are the Griggs criteria as proposed in 1995  [6] , which were later updated by the use of unbiased machine learning approaches on patient data  [7]. Of note, Dalakas already anticipated on the potential role of autoantibody detection in diagnosis, clinical description and immunopathogenesis of IIM, but this idea was denominated as yet premature  [5].
In this context, the concerns raised tempt the design of retrospective studies on large datasets missing details on the MSA/MAA detection method (e. g. for defining future classification/disease criteria). Moreover, even if this information is readily available this may not overcome the hurdles as it will be provocative how to handle the variability. For future prospective studies variability of assays should be taken into account and anticipated upon already when designing the study protocol. Thereby, close collaborations between IIM clinical experts, laboratory professionals and diagnostic companies will be of utmost importance. Some authors already suggested minimal actions to improve the quality of the assays such as the integration of quality control procedures (e. g. participation in external quality control programs and the inclusion of internal quality control material)  [57, 58] , adapting the cut-off values  [38, 45, 57, 59 ] and correlating with the IFA pattern  [40, 59].
Perennial crops are differentiated between orchards (including olive groves) and vineyards. Information regarding organic versus conventional farming is included for both annual and perennial crops. To do so, we use data from EIDER and Agreste (former and current statistical databases from the French Ministry of Environment) at the department level for the period 2000-2020, and from Eurostat at the NUTS2 level for the 2 years 2013 and 2016. Specific information for forests is taken from sustainable management labels (PEFC or FSC). Agronomic yield and input use (pesticides and fertilizers) data are derived from the Annual Agricultural Statistics of Agreste. Complementary data are obtained from ADEME and FranceAgrimer reports. The ecosystem service function is calibrated with data from numerous works focusing on the impact of pollination on fruits and rapeseed  (Klein et al.
Economic data Spatially differentiated economic data are used within our bioeconomic model. Agricultural economic data come from the Annual Agricultural Statistics of Agreste, the European database of Eurostat and the European Farm Accountancy Data Network FADN. Specific additional information regarding forests emerges from the French National Forestry Office (NFO). The rigidity parameters are the same as in  Mouysset et al. (2019)  when applicable, and the others are set with historical data (time series between 2000 and 2019). Such constraints ensure realistic but flexible dynamics of LUC.
Climate data The climate data come from Météo France measurement stations, which are spatially interpolated on a regular 8 km grid using the SAFRAN surface model. We use the average daily temperature and daily rain precipitation data for the 2000-2020 period to create relevant year-SAR-aggregated ecological variables. The forecast data come from DriaslesfutursduclimatⒸ which provides access to the different climate evolutions simulated for the current century in France. Many explanatory climate variables can be constructed to assess the impact of climate change on biodiversity. Based on a review of the recent literature on the study of the impacts of climate change on the distribution areas of terrestrial biodiversity and the abundance of species, we selected some relevant variables to capture the average climate trend effects and extreme climate events: Mean Annual.