dataesr/scientific-paragraphs-categorization huggingface dataset:
Prospective habitat suitability (HS > 0. 5) was used to express the amount of potentially suitable areas for each species according to the built SDMs. Binomial tests were used to highlight SDM areas where the target species has actually been registered. We used the coefficient of determination to assess similarity (p < 0. 05) between the predicted HS maps (pairwise) obtained through Maxent for each species across the study area  [8]. GIS-modelling was accomplished using SAGA GIS, DivaGis, and QGis  [49]. Linear regression analysis of SDM grid of P. glenii as dependent and multiple grids (SDM T. cristatus and/or L. vulgaris) as independent (predictor) variables was carried out using the program SAGA GIS. Details of the regression/correlation analysis are provided in the supplement. Optionally the regression model is used to create a new grid (SDM P.
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/d15020201/s1, Figure  S1   S1 ) and the most important factors. Areas of the highest habitat suitability (>0. 5) are colored in red and areas of the lowest (<0. 1)-in blue; Figure  S3 : The result of pairwise comparison of SDM grids (Potential (probabilistic) model, coefficient of determination (R2), SAGA GIS)-dependence graphs of 3 species: A-T. cristatus/P. glenii; B-L. vulgaris/P. glenii; C-T. cristatus/L. vulgaris; world expansion built in the Maxent program based on the 19 environmental variables (EarthEnv NGEI, Table  S1 ); areas of the highest habitat suitability (>0. 5) are colored in red and areas of the lowest (<0. 1)-in blue; Figure  S4 : Maps of the joint habitation of the newts with P.
glenii (red bold line shows the areas of cohabitation of the fish with the newts): A-with T. cristatus; B-with L. vulgaris; Table  S1 : Bioclimatic variables (35) from the CliMond dataset and 19 environmental variables (Long-term hydroclimatic variables) from the Near-global environmental information (EarthEnv NGEI) for freshwater ecosystems in 1 km resolution.
Acknowledgments: The authors are grateful to  Natalia Filimonova  ( I. I. Schmalhausen Institute of Zoology NAS of Ukraine ) for assistance with correction of English language of the paper. We thank the project #  16-00-F02201-000002  ( Rural Support Service of Latvia, European Maritime and Fisheries Fund, Action Program for Fisheries Development ) for the possibility of using a Daugavpils University mobile complex of pond aquaculture laboratories for the purposes of our study.
European Pesticides Database The raw data were retrieved from the European Pesticides Database v3. 1. This database lists all the substances approved as well as those where approval is pending and those not approved  [7]. The collection method was therefore a full analysis of the PPP regulation regarding each AS regulation activity since 2011: entry in vigour, extension of the expiration date, renewal and non-renewal, end of approval, changes of regulatory status, type of AS or residue management. The status of the active substances in this database is crucial since no regulations are involved when AS are only subjected to "end of approval" abandoned by the applicant, thus without specific regulation. Further calculations were carried out from this constituted database like total number of AS, chemical AS, BCA AS, AS and residue type, BCA category, BCA over chemical AS ratio and their evolution through time.
Directives and Regulations Regulation (EC) No 1107/2009  [6]  is the main and original document dealing with PPPs and substances (pesticides) since 2009. Implementing Regulation (EU) No 540/2011  [5, 8]  is the main companion of the PPP regulation as it regards the list of approved active substances. Following this, Regulation (EC) No 396/2005  [11]  manages the rules on maximum residue levels of pesticides in or on food as well as plant and animal feed. Subsequently, all the information on one active substance is centralized in the pesticide database, including review reports that contain the GAP usage tables dedicated to field applications. Agricultural uses  [12]  or usages in plant protection are defined by cultivation practices and roughly managed by the coupled crop vs. bioagressor (insect/pathogen), linked to function, and are listed in the corresponding Good Agricultural Practices (GAP) Table in the Review Reports in the EU Pesticide database  [7].
Increase in BCA AS Following the initial situation where BCAs were considered as AS substitutions for chemical AS, it should be noted that until the end of 2022, the increase in BCA AS was relatively significant, regular and still positive, but at the beginning of 2023 we observe a stagnation of precise abandonment (end of approval without a regulatory event), which is typically what our database is capable of tracking, knowing that the updating of the Pesticide database is quite slow. The corresponding evolution of BCA AS is exhibited in Figure  9 , showing not only the level but also a decline in BCA AS in 2023. It is therefore appropriate to confirm that the (regulatory) movements of the BCAs definitely do not cor-.
Increase in BCA AS Following the initial situation where BCAs were considered as AS substitutions for chemical AS, it should be noted that until the end of 2022, the increase in BCA AS was relatively significant, regular and still positive, but at the beginning of 2023 we observe a stagnation of precise abandonment (end of approval without a regulatory event), which is typically what our database is capable of tracking, knowing that the updating of the Pesticide database is quite slow. The corresponding evolution of BCA AS is exhibited in Figure  9 , showing not only the level but also a decline in BCA AS in 2023. It is therefore appropriate to confirm that the (regulatory) movements of the BCAs definitely do not correspond to organized "replacements" because they do not correspond in particular to the same functions.
Our primary aim was to collect eye-movement data using a portable eye-tracking device and to subsequently create a machine learning algorithmic model capable of using eye-movement data exclusively to screen stroke disease. Eye tracking is a new technology for objectively measuring visual physiological parameters. The device sends infrared rays to irradiate the eyes, collects the angle of light reflected by the pupil and cornea through the built-in camera, and then determines the coordinates of the viewpoint using mathematical calculation. 25  Hence, the eye movements are measured without contact. Eye tracking for capturing dynamic eye behaviors has been increasingly applied in disease diagnosis because of its noninvasive, convenient, and flexible use. 26e28 Thus, it can be potentially used as an objective diagnostic tool for stroke, but the diagnostic efficacy of machine learning models based on eye tracking in stroke remains to be elucidated.
Feature extraction In daily visual behavior, eye movements mainly include saccades and fixation. Saccades help the visual system look around the environment and transform the information approximately 3 times per second. 35  Saccades bring relevant information glimpsed and selected by the eyes into the fovea area where the vision is the most acute, whereas fixation focuses on such information and further processes its details. 36, 37  Therefore, the saccade and fixation movements are interactive (Fig. 4 ) and cooperate to complete the visual exploration function. 38  In this study, MATLAB 2018a was adopted to extract the data features of the original eye movements to obtain the eye-movement feature values of the fixation-related variables and saccade-related variables of the two groups of subjects.
Feature selection We selected the features with a statistically significant difference. We used SPSS 26. 0 to distinguish and evaluate the differences in eye-movement features between stroke patients and healthy participants in the five-color stimulus task. Considering that the study incorporated multiple trial data, relying solely on eye-movement characteristic data used once may be influenced by a greater number of incidental factors, potentially failing to accurately reflect the similarities and differences between the 2 groups for each index. In this study, we used a comprehensive measurement approach using 5 trials under the five-color task. If each indicator satisfies the normality and sphericity tests for each trial, we opted for repeated measures analysis of variance. A statistical test was performed on the average value of each subject across the 5 trials if these conditions were not met.
Model training and evaluation Supervised learning involves instructing a machine to understand the characteristics of the data upon which the learning process relies. 43  Initially, the training data are used, with the input dataset containing essential feature labels. The model and learned parameters are subsequently applied to previously unseen or new data to predict class labels for this new information. In this study, a supervised machine learning model was used to classify 2 groupsdstroke patients and healthy participantsdby using select eye-movement features. Six supervised algorithms with strong performance were chosen, namely, random forest (RF), k-nearest neighbors (KNN), decision tree (DT), gradient boosting classifier (GBC), XGBoost, and CatBoost. These classifiers use the extracted eye-movement features as input to train the model, enabling stroke recognition.
All machine learning algorithms used in this study were implemented through the Scikit-Learn, XGBoost, and Catboost algorithm libraries in Python 3. 8. 44  The default parameters for model training were used, and specific parameter adjustments for stroke diseases were not  performed within the algorithm model. In this study, the dataset was divided into a training set and a test set at a ratio of 8:2. The training dataset was used, which comprised a significant proportion of the total data volume, for model training. The performance of the output model was then evaluated on the test set, and the efficacy of the trained model in the actual application was calculated. For the trained model, the classification results were evaluated as true positive (TP), false positive (FP), false negative (FN), and true negative (TN).
Moreover, recall, precision, and F1 score were calculated. The precision and recall were negatively correlated, and their average can be reconciled by the F1 score, which can evaluate a classifier more comprehensively. The recall, precision, F1 score, and accuracy were calculated in Equations (  1 )e(4), respectively. Recall ¼ TP TP þ FN ; (1) Precision ¼ TP TP þ FP ; ( 2 ) F1 score ¼ 2$Precision$Recall Precision þ Recall ; (3) Accuracy ¼ TP þ TN TP þ FP þ TN þ FN : (4) When machine learning (ML) models are trained repeatedly on the same data, they may overfit the characteristics of the provided data, consequently losing reliability when applied to new datasets. To address this challenge, we used a fivefold crossvalidation method (Fig. 5 ) to bolster the reliability of the model. Specifically, we divided the sample into 5 independent subsets.
29, 77  Therefore, it is of research value whether a color will have different eye-movement features in healthy groups and stroke patients and whether the changes in eye-movement features caused by color stimuli can realize the rapid and accurate recognition of stroke machine learning models. In this study, the models trained on eye-movement features evoked by five-color vision all exhibit good performance, demonstrating the significance of combining eye tracking with the five-color theory of TCM to realize the rapid recognition of stroke. Although the results showed that the recognition accuracy rates of the model under the green, red, and yellow stimuli were higher than those of the white and black stimuli, the corresponding relationship with stroke viscera needs to be further verified in a wider multisource dataset.
55  This is a novel point of this study. Although eye diagnosis under TCM has achieved breakthroughs in recognizing static characteristics such as eye color and shape by combining certain technologies such as microscopy and artificial intelligence algorithms,  78  this method fails to fully reflect the features of the dynamic eye diagnosis under the TCM because it overlooks the extremely important dynamic eye properties. In the future, existing eye-tracking technology can be used to obtain objective and quantitative dynamic eye information, and the eyemovement database of stroke patients should be continuously developed to collect more comprehensive objective data and clinical information on dynamic eye diagnosis. The research data obtained in this study provided a possibility to realize stroke diagnosis in a larger range of potential disease populations.
Characterization of Farming Systems and Their Diversity To determine to what extent the study region met the objective of CSA, the diversity of existing farming systems was first characterized with a farm typology. Crop rotations represent the outputs of farmers' decisions based on their objectives, constraints and biophysical context. To build the farming system typology, data were used on crop acreages, slopes and farm size, which are easily available worldwide based on surveys or databases, such as the IACS for the European Union  [28]. In this study, the 2010 governmental farmers' declaration data of areas and crop rotations of the farming system (methodology described in Todoroff et al. [29] ) was used as input data to build up a typology, which represented approximately 80% of the agricultural fields of the archipelago.
When experimental data are missing, those variables should be estimated from the literature. For the purpose of this study, this indicator was calculated with the Morgwanik model described by Sierra et al. [26]. We applied this indicator to each farm's cropping system for a whole crop rotation over a 30-year period. Description of the gas flows that were taken into account for estimating greenhouse gas emissions ("GHG emissions") of the farm represented with the orange line. Black, green and red dotted arrows refer to inputs, gas emissions and CO 2 sequestration respectively. Synt. Fert. , Org. fert. and Crop res. refer to Organic fertilizers, Synthetic fertilizers and Crop residues respectively. The variation (∆) of soil organic carbon was accounted for separately with the indicator "SOC variation".
When experimental data are missing, those variables should be estimated from the literature. For the purpose of this study, this indicator was calculated with the Morgwanik model described by Sierra et al. [26]. We applied this indicator to each farm's cropping system for a whole crop rotation over a 30-year period. In this study, the current (initial) SOC stock (C 0 ) was determined according to the Soil Database of the Agricultural Engineering Office (SDAEO) CaribAgro in Guadeloupe  [26]. To convert the soil carbon variation into CO 2eq , we approximated that CO 2 was the only gas emitted from agricultural soils  [67]. In doing so, both "GHG emissions" and "SOC variation" could be subtracted to estimate the GHG balance. SOC variation = (C n -C 0 ) * 44 12 C n+1 = C n -(K m * C n ) -(K e * C n ) + (K h * A m ) + (K h * Res) Cn: soil organic carbon for the year n (t/ha) C 0 : soil organic carbon for the year 0 (t/ha) km: soil mineralization rate (%) Ke: soil erosion rate (%) Kh: amendment humification ratio (%) Kh': crop residues humification ratio (%) Am: carbon inputs from amendment (t/ha) Res: carbon inputs from crop residues (t/ha) 3-Plowing (number/year) The indicator proposed here addresses the frequency of plowing.
Data Sources Data quality and availability are important factors orienting indicator construction or selection. The survey of the 12 farmers provided data on material and energy flows. When data were missing, e. g. , the farmer was not able to provide information about a specific yield or input, they were substituted for data from local "gray" literature and, as a last resort, peer-reviewed literature. For each farm, the current (initial) SOC stock was determined according to the Soil Database of the Agricultural Engineering Office (SDAEO) CaribAgro in Guadeloupe  [26]. The ADEME and IPCC reports  [64, 71]  provided the data for GHG emissions factors used in tier 1 and tier 2 approaches. Emergy assessment publications provided recommendations for the accounting methodology and specific UEVs used in this study  [59, 60].
INSPIRES ( ClinicalTrials. gov NCT03635749 ) is a randomised, double-blind, placebo-controlled, multicentre 2×2 factorial trial. It will assess whether intensive antiplatelet therapy and/or statin therapy, initiated within 72 hours after ischaemic stroke or high-risk TIA of atherosclerotic origin, will decrease the risk of stroke recurrence within 90 days. There will be enrolment of patients from 222 hospitals in China.
Visits and data collection Each subject will be seen in person on the day of randomisation, day 7, day 14 or discharge day, and day 90 with 1-year follow-up by a telephone visit. Fasting venous blood samples will be collected from all subjects at baseline as well as day 90 for genetic analyses and measurement of biomarkers. For a detailed study plan, see online supplemental table  S4. Neurological assessments including NIHSS, ABCD 2 and mRS scores are performed by local investigators. All local investigators received standardised training in order to ensure the accuracy and consistency of assessments. Eligibility on brain imaging including the diagnosis of stroke or TIA, the degree of arterial stenosis and the infarction pattern will be first determined by local investigators.
Study organisation The steering committee will provide scientific and strategic recommendations, and ensure the quality of the study. The executive committee will collect blinded data according to the study protocol, and conduct appropriate guidance based on the progress of the study. An independent clinical research organisation will review the execution process of the trial as well as all data collected. The data safety and monitoring board, consisting of independent academic members and statisticians who are not involved in the study, will monitor the trial's progress on a regular basis to guarantee that the trial is conducted according to the highest ethics and patient safety standards. The Clinical Event Adjudication Committee, composed of independent experts with extensive clinical experience, will review all clinical outcome events (including stroke, MI, death and moderate to severe bleeding) and safety outcome events and make the final adjudications.
Besides, for patients with multiple acute infarctions in territories of different large arteries but without stenosis, transoesophageal echocardiography is necessary. Patients with detected emboli from the aortic arch will be allowed to be included in the trial. In this trial, a more precise DAPT during the acute phase and based on the stroke aetiology would be explored first. The data will help provide evidence for a more individualised antiplatelet strategy. The benefit and safety of aspirin combined with ticagrelor has been investigated in the THALES and CHANCE-2 trials, especially for patients of atherosclerotic origin. 2 3 29 30  However, enrolment in INSPIRES had already begun before the results of these two studies were published, such that aspirin combined with ticagrelor was not available as DAPT in INSPIRES.
Flow simulations in mathematical models In a preliminary stage, flow conditions inside the cone and the test tube during aspiration or discharge were inferred from simplified flow simulations using the Flow Simulation tool embedded in Solidworks 2020 CAD software. A detailed geometric model of the tube and cone assembly was first generated in Solidworks. This geometry is automatically meshed in the simulation tool and standard wall laws are applied depending on the mesh refinement. Secondly, steady continuous flow conditions were applied between the tube and cone top openings in order to generate either a continuous aspiration or a continuous discharge. Once the computation converged, streamlines were generated, and animations thereof were recorded for further analysis. The same procedure and tools were used to outline the generic features of the final release device.
After development of the new design, detailed flow conditions inside the release device were determined by computational fluid dynamics (CFD) simulations using Fluent 2020R2 finite volume software package (ANSYS Inc. , Canonsburg, PA, USA). The simulations were performed in two consecutive steps. The first one consisted of a simple transient viscous laminar simulation with periodic flow conditions between the inlet and outlet of a single chamber geometry. The boundary conditions were set in terms of pressure gradient similar to the one derived from previous assessments of platelet production under different flow rates. This process resulted in a hybrid mesh with a total of 5322,000 elements -5964,000 nodes. The results were used to initiate the second step consisting of a steady turbulent flow simulation.
Background The European registry for individuals with GSD5 and other muscle glycogenosis (EUROMAC) was launched to register rare muscle glycogenosis in Europe, to facilitate recruitment for research trials and to learn about the phenotypes and disseminate knowledge about the diseases. A network of twenty collaborating partners from eight European countries and the US contributed data on rare muscle glycogenosis in the EUROMAC registry. Methods Following the initial report on demographics, neuromuscular features and comorbidity (2020), we here present the data on social participation, previous and current treatments (medication, supplements, diet and rehabilitation) and limitations. Furthermore, the following questionnaires were used: Fatigue severity scale (FSS), WHO Disability Assessment Scale (DAS 2.
The European registry for individuals with GSD5 and other muscle glycogenosis (EUROMAC) was launched to raise awareness of diagnostic accuracy of muscle GSDs, improve care, and collect important clinical and epidemiological data for use in future clinical trials by Scalco et al. [3, 4]  The first two EUROMAC reports focused on the design of the registry and the clinical and genetic characteristics and comorbidities. The main findings were a high frequency of fixed weakness, normal CK values in a minority of participants, body mass index above background population and a high prevalence of hypothyroidism and coronary heart disease. [3, 4]  We here present the additional data collected in the EUROMAC registry, including scores on social participation, previous and current treatments, functional limitations, fatigue, health disability, quality of life and physical activity.
Participants and data entry All participants consented in writing before inclusion in the registry. Any clinician working at a European institution was able to register on the EUROMAC platform and enter patient data after joining the EUROMAC partnership. Inclusion criteria were individuals with a diagnosis of one of the 14 known muscle GSDs either verified by genetic testing or enzymatic testing of the muscle biopsy. Individuals with Pompe disease were excluded as there was a well-established registry for this disease already. Following informed consent from the participant, data were pseudonymized and uploaded onto a safe, encrypted web-based registry. Recruited participants were able to log in, review their own information and complete selected sections with their personal experiences. None of the recruited participants or participating clinicians were allowed to see data from other participants.