dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This study is part of the  Public Private Partnership   LWV19175  " Improving low-protein diets to prevent behavioural problems in pigs " and funded by the  Dutch Ministry of Agriculture, Nature and Food, ForFarmers ,  De Heus , and  METEX NØØVISTAGO. The authors would like to thank  Jan Fledderus  for the diet formulation and the farmers where the experiment took place (ForFarmers). The authors also want to thank  Gisabeth Binnendijk  of  Wageningen Livestock Research ,  Bjorge Larsson ,  Monique Ooms , and  Manon van Marwijk  of the  Adaptation Physiology group of Wageningen University  for their involvement in the trial. They are also grateful for the help of the students involved in the experiment:  Renske Oppers ,  Madeleine de Groot , and  Sven Beekman.
Background: Intestinal parasitosis constitute a major public health issue, particularly in subtropical and tropical areas. Even though they are classified as neglected tropical diseases, no national study has been carried out recently in Guinea to estimate the prevalence of intestinal parasitosis. Objective: A systematic review and meta-analysis aimed to determine the overall prevalence of intestinal parasitosis in Guinea. Method: The PRISMA method was used to perform a systematic review and meta-analysis. The studies carried out in order to study intestinal parasitosis in Guinea and published between 2010 and 2020 were searched in online public databases. The prevalence of parasitosis was calculated by a random-effects meta-analysis. Subgroup comparisons were performed using Q-tests. Statistical analyses were performed with the R software.
This review was registered with PROSPERO under the identification number CRD42022349743. Results: 69 studies were selected out of 1230 studies identified in online public databases. The meta-analysis involved 44,186 people with an overall prevalence of intestinal parasitic infections of 52%. Conclusions: This is the first study in Guinea to assess the prevalence of intestinal parasitic infections in different regions of the country. It was found that intestinal parasitosis are a real health problem in Guinea, hence, the need to put in place national strategies for regular control.
Documentary Search Strategy A systematic review of the literature and a meta-analysis were carried out using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) method  [18]. We searched in the PubMed/Medline, Google Scholar, and ResearchGate databases (for peer-reviewed journal articles) and in the Library of Gamal Abdel Nasser University (UGAN) in Conakry (for dissertations or theses of graduate students in the departments of medicine, pharmacy, and biomedical sciences) to compile all the studies carried out on intestinal parasitosis in Guinea and published between 2010 and 2020. Two reasons motivated the choice of this period: (i) Theses prior to 2010 were not accessible in the database of the Library of Gamal Abdel Nasser University in Conakry, and although the search was done in a digital catalog, only the papers formats of these dissertations were accessible in the library of the UGAN.
; (ii) The National NTD Control Program, which defines national strategic guidelines, began after 2010. For the documentary search in each database, the keyword pair "Guinea" and "Intestinal parasitosis" was initially used. Then, in a second step, the keyword "Guinea" was used in association with each intestinal parasite or parasitic infection (i. e. , "Guinea & A. duodenale" or "Guinea & Ancylostomiasis". ). The exhaustive list of the parasites sought was: Hookworms (A. duodenale and Necator americanus), Eel (Strongyloides stercoralis), A. lumbricoides, Whipworm (T. trichiura), Pinworm (E. vermicularis), Trichina (Trichinella spiralis), S. mansoni (S. mansoni, S. intercalatum, S. japonicum and S. mekongi), Tapeworms (Taenia saginata, and Taenia-solium, Hymenolepis diminuta, Hymenolepis nana), Trichomonas intestinalis, Balantidium coli, Cryptosporidium, Cryptosporidium hominis, Cryptosporidium parvum, Blastocystis, Cystoisospora belli, Isospora belli, Entamoeba histolytica, Fasciolopsis buski, G.
Search Strategy The articles and theses/dissertations selected were read and analyzed in accordance with a reading grid, and the extracted data included: (i) name of authors; (ii) diploma obtained as a result of the study in the case of theses/dissertations (MD: medical doctor; PharmD: pharmacy doctor; master or MSc: master in sciences), and the reference number of the study; (iii) year of publication (year of publication by the editor or year of submission of the thesis to the university library); (iv) year of observation (period of beginning of inclusions or selection of the study population); (v) age range of the study population (according to age, the study population was divided into 5 groups: children between 0 and 5 years old, children between 0 and 17 years old, adults, mixed population (children and adults), and pregnant women); (vi) study setting; (vii) study site (area corresponding to the place of residence of the population included in the study.
Data Extraction Studies were screened independently by two authors, first based on titles and abstracts. The full texts of papers identified as being potentially relevant for inclusion were retrieved and independently assessed by the same collaborators. Data extraction format for the pooled prevalence was facilitated using a Microsoft Excel sheet where data pertaining to the authors' names, publication year, study design, study setting, study area, region, technique of stool examination, sample size, and prevalence of intestinal parasitic infection were listed.
Statistical Analysis The prevalence of parasitosis was calculated by a random-effects meta-analysis (heterogeneity between studies was expected). A logistic regression model with random intercept was used to estimate prevalence, with logit transformation. Confidence intervals of the estimates for each study were calculated with the Clopper-Pearson method (exact binomial interval). Subgroup comparisons were performed using Q-tests. The general significance level was set at a p-value below 0. 05. Statistical analyses were performed with the R software version 4. 0. 5  [19]. The R code used for the analysis and the database were added as Supplementary File S1.
Characteristics of the Included Articles A total of 1230 studies were initially identified in the queried databases. Overall, 1128 studies were excluded during the first selection phase according to the following reasons: exclusion of articles published before 2010, dissertations or theses submitted at the library before 2010, publications not related to intestinal parasitosis, and duplicates. Out of the 102 publications or documents, after a second selection phase, 69 studies met the study criteria (Figure  1 ). erogeneity between studies was expected). A logistic regression model with random tercept was used to estimate prevalence, with logit transformation. Confidence interv of the estimates for each study were calculated with the Clopper-Pearson method (ex binomial interval). Subgroup comparisons were performed using Q-tests.
Characteristics of the Included Articles A total of 1230 studies were initially identified in the queried databases. Overall, 11 studies were excluded during the first selection phase according to the following reaso exclusion of articles published before 2010, dissertations or theses submitted at the libra before 2010, publications not related to intestinal parasitosis, and duplicates. Out of 102 publications or documents, after a second selection phase, 69 studies met the stu criteria (Figure  1 ). In total, 100% of selected studies were cross-sectional, and 68% described the f quency of intestinal parasitosis in people consulting a health care structure; 32% were p formed in institutions such as schools, orphanages, universities, or jails. Parasitologi examination techniques were not described in 10 studies.
Strengths and Limitations of the Study A key strength of this systematic review and meta-analysis is that is the first to our knowledge to determine the pooled prevalence estimates of intestinal parasitic infections in Guinea. In addition, a rigorous search of several databases and other sources to identify eligible studies can also be considered one of the strengths of this review. On the other hand, one of the main limitations relates to a selection bias due to the fact that sampling in the majority of the studies was performed in healthcare structures since data from the general population in Guinea were limited and not available. The heterogeneity of the studies was also a limitation of the research. Furthermore, concerning diagnostic tests, microscopic examination of fresh stools was the most commonly used method.
• Types of articles: both published and unpublished including dissertations or theses accessible in the database of the UGAN in Conakry. •Types of publication: peer-reviewed full-text articles. • Exclusion criteria: • Publications dating before 2010 • Publications reporting prevalence after antiparasitic treatment. • Duplicates articles • Dissertations of graduate students deposited in university libraries in Conakry before 2010 • Articles and dissertations on intestinal parasitosis carried out outside Guinea.
Tiny devices now allow extensive data collection even on small animals. While the applications of bio-logging have grown in ecology, its use in management and industrial applications allows novel information on animals' ecology, physiology, and health status to be gained. In our Special Issue, we collected papers that investigated the use of accelerometers and other remote tracking systems in captive and wild animals, as well as farm and game animals with potential applications for wild animals. Eager et al. [9]  developed a system (i. e. , the IsoLynx system) to track greyhounds during races and reduce their race-related injuries. Similar techniques (i. e. , video-or image-based animal tracking) have been used to track wild animals  [10] [11] [12] [13] [14]. Nekaris et al. [15]  used triaxial accelerometers to predict the behaviors of a captive individual of a nocturnal primate, the Bengal slow loris, via a random forest model.
Methods Our research was limited to preclinical studies investigating the neurorespiratory effects produced by fentanyl and its analogs. Databases including PubMed, Google Scholar, Cochrane Library, ISI Web of Science, Embase and Scopus were systematically used for searching scientific articles written in English and French languages and published from 1980/01/01 until 2023/01/31, by using combinations of the following keywords: "Fentanyl" OR "Carfentanil" OR "Sufentanil" OR "Alfentanil" OR "Opiates" OR "Naloxone" OR "Morphine" AND ("Intoxication" OR "Overdose" OR "Neurorespiratory" OR "Treatment" OR "Analogs". The reference lists of the retrieved publications were thoroughly checked to complete the search. After the exclusion of duplicates, related articles were identified based on title and abstract examination.
Then, full-text articles were downloaded and evaluated by two investigators, independently. The references cited in the retrieved articles as well as review articles published in the field were checked to find additional relevant studies. We included in the review all articles reporting preclinical experimental data on the neurorespiratory effects of fentanyl and its analogs. We excluded all articles reporting human data or experimental data focused on non-neurorespiratory effects. In this narrative review, we first focused on the neurorespiratory effects resulting from fentanyl and analogs, based on the available experimental data, mainly obtained with fentanyl. Then, we considered the specificities attributed to the main fentanyl analogs if reported. Finally, we discussed the expected response of fentanyl-induced neurorespiratory effects to naloxone and the possible benefits of alternative strategies to reverse CNS toxicity.
Interval training and the 2,400 m performance test During some IT sessions (W1, W3, W6, and W8), horses were equipped with heart rate monitors, which also collected GPS data (Equimetre, Arioneo, France). For the 2,400 m performance tests carried out on weeks 0, 3, 6, and 8, the rider wore a GPS watch (Forerunner 500, Garmin, France) to record the speed continuously and the time required to complete the distance.
Incremental continuous exercise test The ICET performed on W0 and W9 was preceded by a warmup on the path at walk for 15 min, followed by a trot for 3 min at 4. 2 m. s -1. Then, the horses started their test with 3 min of trotting at 5. 6 m. s -1 on the pathway that leads to the track. Once on the track, the rider was instructed to increase the speed by 1. 4 m. s -1 (5 km. h -1 ) every 2 minutes in successive stages until the horses could no longer maintain the requested speed at trot. The speed of the last completed stage was considered the maximum aerobic speed (MAS) at trot. During the ICET, horses were equipped with a heart rate monitor that also collected GPS data in order to follow the actual speed at each stage (Polar Equine H10, Kemple, Finland). In addition, horses wore an Equimask (Cosmed, Italy) linked to a portable gas analysis system (K4b 2 , Cosmed, Italy) to monitor respiratory parameters.
Data processing During the IT sessions, maximal peak velocity (V peak ) achieved at trotting was recorded. The linear acceleration from 5. 0 m. s -1 to V peak was calculated as the rate of change of the velocity in the given duration to reach V peak. The maximal acceleration per horse and training session was used for statistical analysis. The average speed during the 2,400 m test was calculated from the time needed to cover the distance. In addition, the maximum heart rate value (HR peak ) achieved during the test was also measured. Data recorded during the ICET with the gas analysis system were downloaded using the K4b 2 management data software (Cosmed, Italy). Breath-by-breath data were filtered with a 5-s average filter, and data were filtered for abnormalities on the K4b 2 software. Data were then transferred to Excel files to convert _ VE, _ VO 2 and _ VCO 2 into mL.
kg -1. min -1 to calculate the respiratory exchange ratio (RER) and the quotients of _ VE/ _ VCO 2 and _ VE/ _ VO 2. The different stages of the test were identified from data points. Cardiorespiratory values were calculated as the averages over the last minute of each stage. Additionally, peak data achieved during the test were recorded during the final stage of the test, even if the stage was incomplete. Whole body substrate oxidation was determined by calculating carbohydrate oxidation (CHOox) and lipid oxidation during exercise using the following equations  (Frayn, 1983) : CHOox g. min -1 4. 585 × _ VCO 2 -3. 2255 × _ VO 2 , Lipid oxidation g. min -1 1. 7012 × _ VO 2 -1. 694× _ VCO 2 , where _ VO 2 and _ VCO 2 are expressed in litres per minute. We assumed that protein oxidation made negligible contribution to _ VO 2 and _ VCO 2 in horses and that these equations developed for use in humans were also applicable to the horse  (Geor et al.
Statistical analysis Statistical analysis was performed with SAS software (SAS Studio, SAS Institute Inc. , United States). The effects of diet, week, and the interaction diet * week on changes in BM, body condition score, thickness of the GM, maximal peak speed and acceleration measured during IT, average speed and HR peak measured during the 2400 m tests, MAS, and cardiorespiratory variables (HR, _ VE, _ VO 2 , _ VCO 2 , RER, _ VE/ _ VCO 2 , _ VE/ _ VO 2 , CHOox, and lipid oxidation) measured during ICET were assessed using mixed-effect models with a normal distribution of errors in a MIXED procedure. The model included the horse as a random effect and weeks as repeated measures. For respiratory variables recorded at each stage during the ICET, the effect of speed and the interaction between speed and diet were also included in the MIXED model.
In our trial, horses followed a training program that included three types of exercises. Two riders were involved in this training programme: one was in charge of all the interval trainings, one of the two other exercises, and all the performance tests. This was set up to avoid any rider effect on the measurements. Furthermore, all exercise tests were performed in early morning with dry weather conditions to minimise the influence of environmental conditions, which can interact with physiological responses  (Hargreaves et al. , 1999). Finally, the riders trained daily to monitor speed during exercises to maintain horses at the targeted intensities. This made it possible to respect the speeds imposed during the ICET strictly. Thanks to these standardised conditions, performance and cardiorespiratory data were comparable from one test to the other for each horse.
The cardiorespiratory data we obtained were within expected ranges: HR ranged from 149 to 221 bpm depending on the intensity, _ VO 2 between 58 and 132 mL. kg -1. min -1 , and _ VCO2 between 49 and 132 mL. kg -1. min -1. We did not observe the underestimation of _ VCO 2 reported during some field tests with trotters  (van Erck et al. , 2007) , possibly because of the technical evolutions in CO 2 concentration measurement in the K4b 2 system. To assess the performance and energy metabolism during exercise, horses were regularly monitored during some determined interval training sessions to measure their maximal speed and acceleration (Figure  1 ). In addition, two field tests were also set up and carried out before and after the 8 weeks of training. The first performance test was designed to mimic a 2,400 m race's effort, a standard racing distance in Europe for trotters.
Acknowledgments The authors would like to thank the technical staff of  Plateforme NSP  for taking care of all horses during the experimental trial and contributing to the measurements ( Ludivine Gadrey ,  Manon Guy-Coquille ,  Charly Poillot ,  Marine Colpart ,  Julie Laclau , and  Marie Plessard ). They are grateful to  Nicolas Mary  and  Lionel Pierron , who trained the horses and carried out the riding tests.
1, UN GHS classification for chemicals causing irreversible effects on the eye / serious damage to the eye; Cat. 2, UN GHS classification for chemicals causing reversible effects on the eye/eye irritation, subcategorized in 2A (irritant to eyes, eye effects are not fully reversible within 7 days of observation) and 2B (mild irritant to eyes, eye effects fully reversible within 7 days of observation); CO, Corneal Opacity; CON4EI, CONsortium for in vitro Eye Irritation testing strategy; DA, Defined Approach; DASF, Defined Approach for Surfactants; DRD, Draize eye test Reference Database; EIT, Eye Irritation Test; EIVS, Eye Irritation Validation Study; HCE, Human Corneal Epithelium; IATA, Integrated Approach on Testing and Assessment; LLBO, Laser Light-Based Opacitometer; NAM, New Approach Methodologies; No Cat.
liquids while the BCOP based on the LLBO opacity endpoint is used to identify Cat. 1 liquids. Surfactants were also excluded based on the same reason described for DAL-1. Furthermore, non-surfactant solids were excluded since those chemicals are outside the applicability domain of the STE test method when used to identify No Cat. (OECD, 2020b). The purpose of the current study was to develop a defined approach for eye hazard identification according to the three categories of the UN GHS for liquid, semi-solid and solid chemicals having surfactant properties. 1 Initial analyses which are based on the original dataset used to develop DAL-1 and DAL-2, showed that a combination of the RhCE test methods with a modification of the STE test method (testing an additional concentration of 0. 5%) seemed the most promising approach.
Data sources The historical Draize eye test data on surfactants were collected from different databases, i. e. (1) the Draize eye test Reference Database (DRD;  Barroso et al. , 2017) , (2), the database from the Colgate-Palmolive's programme used for the validation of the EpiOcular™ model  (Blazka et al. , 2000) , and (3) the database used for the evaluation of the Hen's Egg Test Chorioallantoic Membrane (HET-CAM) test method  (Scheel et al. , 2011). For each Draize eye test study, detailed information on the ocular tissue effects that drive classification in vivo was available. Data on the SkinEthic™ HCE EIT and the EpiOcular™ EIT test methods were derived from several peer-reviewed publications  (Kaluzhny et al. , 2011; Hayashi et al. , 2012; Alépée et al. , 2016; Kandarova et al. , 2018; Alépée et al.
, 2019a Alépée et al. , , 2019b) )  and from validation studies  (Barroso et al. , 2014). The results from the STE test method were obtained from Kao Corporation background review document  (Takahashi et al. , 2009; Sakaguchi et al. , 2011; Kojima et al. , 2013)  and Abo and co-authors  (Abo et al. , 2018). After integration of all in vitro data into a single database, remaining data gaps were identified and additional surfactants were tested within the   Test, n. d). The surfactants tested by the STE test method were tested simultaneously at 5%, 0. 5% and 0. 05% for the newly generated data, as well as for the existing data. The results for the 0. 5% had not been previously published. Because the testing at the three concentrations was performed simultaneously, the prediction model according to TG 491 and the modified STE test method could be used.
Evaluation of the performance of the DAs The performance of the DASF was assessed by comparing the prediction results with the classification based on historical in vivo Draize eye test data. For each chemical the predicted class was obtained by considering all available results of each in vitro test method. The performance of the DASF to distinguish between the three UN GHS categories was compared against minimum performance values for each UN GHS category that were accepted by the OECD Expert Group on Eye/ Skin Irritation/Corrosion and Phototoxicity (OECD SD, 2022b). The percentage of correct predictions should be least 75% for Cat. 1, 50% for Cat. 2 and 70% for No Cat. (Table  3 ). For overall measure of performance, the balanced accuracy was reported since it takes imbalances of the dataset into account whereas accuracy does not.
2 classification. The Cosmetics Europe Draize eye test Reference Database (DRD) contained only 8 Cat. 2 surfactants of which only 6 were available for testing  (Barroso et al. , 2017). Two additional Cat. 2 surfactants with historical Draize eye test were identified from other databases  (Blazka et al. , 2000 and Scheel et al. , 2011). As such, only 8 Cat. 2 surfactants were available for assessing the predictive performance of the DASF for this category. Further efforts were made by consulting regulatory agencies, stakeholders, and mining of the Cosing (Cosmetic Ingredient) database, but so far no additional Category 2 surfactants which meet the predefined requirements could be identified. Taken together, only surfactants for which individual raw data of the Draize eye study are available and that have no conflicting data in case multiple in vivo studies are available, should be considered as candidates for assessing the performance of the DASF.
A minimum of 12 patients are targeted for enrolment for each antifungal agent, across 12 countries and 30 intensive care units to perform descriptive pharmacokinetics. Pharmacokinetic sampling will occur during two dosing intervals (occasions): firstly, between days 1 and 3, and secondly, between days 4 and 7 of the antifungal course, collecting three samples per occasion. Patients' demographic and clinical data will be collected. Main outcome measures: The primary endpoint of the study is attainment of pharmacokinetic/pharmacodynamic target exposures that are associated with optimal efficacy. Thirty-day mortality will also be measured. Results and conclusions: This study will describe whether contemporary antifungal drug dosing achieves drug exposures associated with optimal outcomes. Data will also be used for the development of.