dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design and setting This is a cross-sectional study using data collected by Agrocalidad, the regulatory and control agency for animal and plant health in Ecuador. 1  Ecuador has a population of approximately 17 million and is divided into 24 provinces, which can be grouped under three regions: the Amazon basin, the Andean highlands, and the coastal region. Poultry production is one of Ecuador's most important industries: in 2019, all farms together produced around 281 million chickens. The industry is concentrated in the provinces of Guayas, Santo Domingo, and Pichincha and supplies nearly the entire national demand (11). There are 546 registered public and private slaughterhouses in the country. Slaughterhouses are regulated by Agrocalidad, which is part of the Ministry of Agriculture and Livestock, one of the three ministries involved in the implementation of the National Plan for the Prevention and Control of AMR  (9).
was confirmed through biochemical testing. For antimicrobial susceptibility testing (AST), the Kirby Bauer method, double disk test (for confirmation of extended-spectrum beta-lactamase-producing strains), and the broth microdilution test (for colistin) were used. AST was interpreted in line with the Clinical and Laboratory Standards Institute (CLSI) Guidelines  (15). The dataset used in this article can be made available, on request, by the corresponding author. Among isolates with E. coli, resistance to erythromycin was highest 112 (76%) followed by tetracycline 105 (71%). Nearly half of the isolates were resistant to trimethoprim/sulphamethoxazole (84; 57%), penicillins (71; 48%), or ciprofloxacin (57; 39%). All E. coli isolates were susceptible to carbapenems (Table  1 ). Extended-spectrum beta-lactamases (ESBL) were detected in 32 (22%) of E.
Data on antimicrobial use in animals Agrocalidad regulates the registration, control, marketing, and use of veterinary products. Via an online platform, manufacturers and distributors declare product name, class, and amount in kg of antimicrobial agents used in animals for human consumption according to type of use: veterinary medical use (including prevention), growth promotion, or both  (17). For the purpose of this study, we obtained the number of antimicrobials imported or produced (in kg) in Ecuador during 2019 from the report sent by Agrocalidad to WHOA in 2020.
This can be done the isolates were all obtained from poultry, the WOAH report includes antimicrobial use in the animal production sectors as a whole. Therefore, it is not possible to attribute AMR found in this study to high levels of use of any particular antimicrobial. As a next step, antimicrobial use in different animal production industries can be monitored in actual field settings and then triangulated with data on local AMR patterns. The WOAH report also showed that colistin was the antibiotic mostly commonly used for growth promotion. There are several reports in Ecuador and Latin America about dissemination and high levels of colistin resistance in human, animals, and the environment  (25, 26). Consequently, in 2019 Agrocalidad issued a ban on the use of this antibiotic in animals in Ecuador  (27).
Acknowledgments. This research protocol was developed through the  Structured Operational Research and Training Initiative (SORT IT ), a global partnership coordinated by  TDR ,  UNICEF ,  UNDP ,  World Bank ,  WHO Special Programme for Research and Training in Tropical Diseases  hosted at the  World Health Organization. The specific SORT IT program that led to this study protocol included an implementation partnership of  TDR  and the  Pan American Health Organization (PAHO) , the  PAHO  country offices in Colombia and Ecuador; the  Ministry of Agriculture and Livestock of Ecuador ;  World Organization for Animal Health of Ecuador; Agency for Plant and Animal Health Regulation and Control-Agrocalidad , Ecuador; The  International Union Against Tuberculosis and Lung Disease headquarters in Paris, France , and  Southeast Asia office, India ;  Institute of Tropical Medicine, Antwerp, Belgium ;  Damien Foundation ,  Belgium ;  Indian Council of Medical Research-National Institute of Epidemiology; Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER) ;  GMERS Medical College Gotri Vadodara Gujarat, India; India Medical College Baroda, Gujarat, India ;  Sri Manakula Vinayagar Medical College, India ;  Universidade Federal de Ciências de Saúde de Porto Alegre, Brazil ;  Universidade de Brasília, Brazil; Autonomous University of Yucatán, Mexico.
Acknowledgements This research was undertaken as part of the  ASPIRE Innovative Training Network , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Grant  Agreement No. 674960. HG and RH acknowledge ESR fellowships provided by Aspire. We are grateful for the provision of beamtime by  Synchrotron Soleil  (beamtime Proposal No. 20151368 ,  20161224 ,  20181872 ) and we thank the technical staff at  Soleil , and especially  J. -F. Gil , for their support and for the smooth operation of the facility.
Relation between eugenol content and technological maturity in berry skin We established significant negative correlations (p = 0. 05) between the total eugenol content in berry skin and the Technological Maturity Index TMI in the fruit (Figure  6 ). Overall, most interestingly, when all the data were pooled, the negative correlation was highly significant between the total eugenol content in berry skin and the TMI, i. e. , R = -0. 90 (R 2 = 0. 81), p-value < 2. 2 × 10 -16. (Figure  6 ). Thus, the riper the Baco blanc berries are, the lower the concentration of total eugenol quantified in their skin is. By using the CLONES40 data only, the negative Pearson's correlation coefficient was very close to being significant, i. e. , R = -0. 89 (R 2 = 0. 79), p-value = 0. 05. For the PREMS32 data, Pearson's negative correlation coefficient was highly significant between the total eugenol content in berry skin and the TMI, i.
Field measurements Different field measurements, as described in the Materials and Methods section, were performed. These results are presented in the supplementary data (Supplementary Table  1 ). In most cases, no significant differences were identified between treatments except for two circumstances. First, in the PREMS32 plot, leaf-removed vines showed a significantly lower NDVI value than control vines. Second, in the CLONES40 plot, two clones presented a bunch compactness significantly different from the other clones.
ACKNOWLEDGEMENTS This research was supported by the  Bureau National Interprofessionnel de l'Armagnac  and the  Region Occitanie as part of the programme "Actions de Recherche-Expérimentations" within the "Baco blanc " project. The authors are grateful to the winegrowers of the studied plots for their help as well as the following important collaborators:  Thierry Dufourcq  ( Institut Français de la Vigne et du Vin ),  Mathilde Guinoiseau  ( Chambre d'Agriculture du Gers ),  Magaly Farel  ( Chambre d'Agriculture des Landes ),  Sarah Lacquemant  (Domaine d'Ognoas),  Christelle Boisbineuf  for her historical research on the origin of the Baco blanc cultivar,  Jean Roudet ,  Isabelle Demeaux ,  Laure Dubois  and  Anaïs Poirier  for their technical support during all the experimentations. The authors acknowledge also the  Bordeaux INRAE-BSA-IFV Joint Technology Unit UMT SEVEN  for its support, as this study was carried out partly within the UMT SEVEN framework.
XRD analysis The phase and crystalline structure of Al 2 O 3 -NPs were analyzed using XRD with an PANalytical X'Pert Pro instrument. The measurements were taken using Cu K radiation at 76 kV and a wavelength λ = 1. 54056 A, at 2θ angles between 10 and 100° with a resolution of 0. 03° and an integration time of 384 s. The crystalline phases were identified by comparing the obtained result with references from the International Centre for Diffraction Data (ICDD).
Statistical analysis The statistical significance of variations between treatments and controls in mitotic index and chromosomal abnormality frequency was examined. Each treatment was performed three times. The significance levels of p ≤ 0. 05 and p ≤ 0. 01 were determined using one-way ANOVA (Sigma Plot 13. 0 software) and SPSS for statistical analysis. As illustrated in Fig. 2 , the green-synthesized Al 2 O 3 -NPs showed a maximum absorbance peak at 264 nm. The absorption position of Al 2 O 3 -NPs is based on the particle size and shape  [21]. From the literature, we can observe that the absorption position depends on the sources used for the synthesis of Al 2 O 3 -NPs, for example, macroalgae Sargassum ilicifolium extract (227 nm)  [58] , Citrus aurantium L extract (322 nm)  [24] , and Al foil waste (237 nm)  [21].
16S and ITS2 rRNA sequence analysis The 16S rRNA sequences were demultiplexed and quality filtered using the QIIME version 2. 1. 0 software package  (27). The sequences were then assigned to ASVs (amplicon sequence variant) using the DADA2 algorithm  (28)  with a 97% pairwise identity threshold and classified taxonomically using the SILVA reference database (version 13. 8) for bacteria  (29). For the ITS2 rRNA sequences, data were processed using the FROGS pipeline  (30)  for sequence quality control, and filtering and affiliation of taxa was performed with the UNITE ITS database (version 8_2) (31) using the FROGS guidelines for ITS data. 1 Rarefaction analysis was performed and used to compare the relative abundance of OTUs across samples. α-diversity was estimated using the Shannon diversity index or the number of observed species.
β-diversity was measured using the Jaccard distance matrix and was used to build principal coordinates analysis (PCoA) plots. Using these tools, we did not observe any cage effect in our different sets of experiments. Differential abundance analysis based on the negative binomial distribution (DESeq2) algorithm was used to identify taxa that were specific to the treatment  (32). The raw sequence data were deposited in the SRA database from the NCBI under the following accession numbers (PRJNA896895).
Using the DESeq2 R package for differential analysis, we identified 6 families that were significantly impacted by S. boulardii: (p value adjusted <0. 05): Atheliaceae, Debaryomycetaceae, Erysiphaceae, Hypocreales, Pleosporaceae and Tremellaceae. However, while the higher relative abundance cannot be contested for Tremellaceae, the reduced relative abundance could be due only to the over-representation of S. boulardii in the total sequences. Additional quantitative real time PCR or CFU would be necessary for the absolute abundance characterization in order to account for actual decrease of fungi families. Nevertheless, S. boulardii slightly increased the levels of the Tremellaceae family (Figure  1E ) and seemed to contained the development of five specific families that would need further investigation using absolute quantification.
Acknowledgments We thank the employees of the animal facility at the  IERP platform ,  INRAE , UE0907 ( Jouy en Josas, France)  for their technical assistance. This work has benefited from the facilities and expertise of @BRIDGe for 16S rRNA sequencing (Universite Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en -Josas , France). 10. 3389/fmed. 2023. 1087715 Frontiers in Medicine 13 frontiersin. org.
Queries by the sponsor or its representatives must be checked by authorised persons using the source data and answered directly in the eCRF. Any resulting corrections must be made in the eCRF. The data management plan describes the trial-specific approach of the individual processes for traceability and completeness of the relevant data (see Supplementary materials). The persons responsible for data management are responsible for data administration and processing. This is done by using electronic data capture software for clinical trials that meets the requirements of the applicable laws and guidelines (especially good scientific practice). The scope of database access and the associated authorisations are regulated by appropriate user roles. The data is checked for completeness, plausibility and consistency by means of programmed checks directly in the eCRF and by additional manual checks outside the eCRF.
Any queries arising in the process are sent to the respective trial site directly in the eCRF. Queries as well responses or corrections are made directly in the eCRF. Changes to the data are reproduced in the audit trail. The data is backed up on a daily basis. The data storage facilities are located in a locked room of the Medical Faculty of TU Dresden, to which only the responsible system administrators have access. At the end of the trial, the database will be closed after all data relevant to the trial protocol have been entered and all queries have been clarified. Subsequent changes to the data can only be made with the consent of the principal coordinating investigator. Records and documents related to the trial or distribution of investigational medicinal products (e. g. data collection forms, informed consent forms, drug accountability log and other relevant documents) must be retained at the trial site in accordance with the regulatory requirements but for at least 25 years.
Confidentiality {27} The collection, transfer, storage and evaluation of personal data within clinical trial is carried out in accordance with the applicable legal regulations (e. g. EU General Data Protection Regulation, EU reg. 2016/679 GDPR). Data collected during this clinical trial will be recorded on electronic data carriers, treated in strict confidence and only passed on to the sponsor of the trial for scientific evaluation and the assessment of AEs and the responsible supervisory authorities, the ethics commissions and the European database to verify the proper conduct of the trial and to evaluate trial results and AEs. To the extent necessary for the review of the clinical trial, authorised representatives of the sponsor (monitoring and auditing) and/or the regulatory authority, who are bound to secrecy, may inspect the personal data available at the trial site.
Analysis of baseline parameters and concomitant medications Baseline parameters, medical history and concomitant medication will be documented during screening and throughout the trial until the last visit. Enrolment, protocol deviations and discontinuations from the study drug and the study will be summarised. Demographics (e. g. age, race, ethnicity and sex) and medical history and concomitant medication will also be summarised by treatment group. For qualitative variables (e. g. sex), absolute (n=x) and relative frequencies will be calculated per treatment group. Data will be visualised by bar plots. For quantitative data (e. g. age), the number of valid observations (n=x), mean, standard deviation, standard error, median, minimum and maximum will be calculated for each treatment group and each time point separately.
To explore the composite endpoint further, time-to-event endpoint considering the time from randomisation till time of first event will be analysed. We will visualise these data by presenting Kaplan-Meier curves for each treatment arm. Furthermore, Coxregressions models will be performed adjusting for the same factors and covariates as used in the logistic regression models. To address the repetitive nature of the events used in the definition of the primary endpoint, we will also perform supportive analyses for recurrent event data. We will fit a negative binomial regression model for the events of the composite endpoints accounting for the time a patient is under risk. Furthermore, we will perform the counting process model of Andersen-Gill and frailty models. To assess the impact of the individual components of the composite endpoints, each component will be analysed descriptively.
In case of premature termination of trial therapy according to the active group for a patient, the patient will continue the study following the protocol of the control arm. Further visits and trial-specific procedures will be continued. The documentation of all subsequent visits should still be pursued. Should the study be discontinued prematurely for a patient, all study materials will be retained. Data will be collected to the point of withdrawal and used for the intention-to-treat analysis if the subject consents. There will be no replacement of withdrawn subjects. For subjects withdrawing prematurely from the clinical trial, a final visit should be sought.
Methods for additional analyses (e. g. subgroup analyses) {20b} The primary analysis will be performed on a modified intention-to-treat principle. Sensitivity analyses will be performed according to intention-to-treat and per protocol analysis, and the dataset will be restricted to adherent patients. Subgroup analysis will be performed in patients at risk for immunological (re-transplantation) and infectious (diabetes mellitus) events and according to age group (>55 years of age), gender and study centre. If appropriate, these factors will be included as additional covariates in the regression models as described above.
Methods in analysis to handle protocol non-adherence and any statistical methods to handle missing data {20c} Medical adherence will be defined by patient diary (see main protocol section 5. 10). Adherent patients will be analysed within the adherent set. For the primary analysis, missing values will be considered treatment failures in the composite primary endpoint. Additionally, meaningful missing values will be imputed by statistical models for the data according to the underlying mechanism of missing data. Sensitivity analyses will be performed for imputed data.
Plans to give access to the full protocol, participant-level data and statistical code {31c} The full trial database and analysis dataset will be reported to the competent regulatory authorities and, as far as possible, made available to the Open Research Data Pilot (https:// ec. europa. eu/ resea rch/ parti cipan ts/ docs/ h2020-fundi ng-guide/ cross-cutti ng-issues/ open-access-disse minat ion_ en. htm). The trial management team providing day-to-day support for the trial consists of the project coordinator, the trial manager and the project administrators. They will meet on a regular basis every month or ad hoc if necessary.
Dissemination plans {31a} All essential results of the clinical trial will be submitted to the responsible higher federal authority and the ethics committee within 1 year after the end of the clinical trial. In addition, the results and, if necessary, other necessary information will be published in the European database. Links to publications will be provided to the trial registry. The trial results will be reported on the trial webpage and to the institutional review boards, the funding agency and the consortium partners of the Horizon2020 project. The full trial database and analysis dataset will be reported to the competent regulatory authorities and, as far as possible, made available to the Open Research Data Pilot. All results will be published open access. Wherever possible, the 'gold' open access route will be preferred.
Infectious events as primary and secondary endpoints The primary endpoint is designed to detect consequences of too-intense immunosuppression that might be reduced by personalised TTV-guided immunosuppression. Infectious disease events are scored by assessors blinded to the allocation sequence. Diagnosis is based on the Infectious Diseases Guidelines 2019 published by the American Society according to the data provided in the eCRF. Assessors have the possibility to post queries to the investigator in case of missing information. Investigators are advised to not unblind investigators within their correspondence. Any insignificant treatment, e. g. topical antifungal treatment of minor localised cutaneous disease, or supportive treatment during viral respiratory infections, e. g. antiseptic lozenge, is excluded.
Meteorology and soil electrical conductivity The weather station (METRE Group, Inc. , WA, USA) installed in the solar greenhouse recorded air temperature (Ta, • C), relative humidity (RH, %), and solar radiation (Rs, W m -2 ) across the whole growth period of the tomato plants. Vapor pressure deficit (VPD, kPa) was calculated based on Ta and RH  (Allen et al. , 1998). These data were collected every 5 s by the sensor, and the values were averaged every 15 min and recorded in the EM50 data logger. Daily-scale data in the solar greenhouse during the whole growth period is shown in Fig. 2. Soil samples were collected from the upper and lower soil layers of the pot prior to transporting and after harvest and they were air-dried, Fig. 1. The site of the experimental station (A, B) and the schematic diagram of the experiment in the solar greenhouse, including the whole arrangement of the pot and drip irrigation system (C), partial schematic diagram of the arrangement of row (D) and column (E) of pots.
Data analysis Two-way analysis of variance (ANOVA) was used to determine the main effects of water, salinity, and interaction on yield, biomass, WUE, gas exchange and fruit quality parameters using SPSS software (Version 26. 0, IBM Corp. ). One-way ANOVA was used to determine the significant difference between water treatments under the same salinity level and the significant difference between salinity treatments under the same water regime. Principal component analysis (PCA) and correlation analysis (CA) of all parameters were performed using OriginPro 2022 to explore the relationships among different traits and the effects of reduced water and salinity treatments. Linear regression or quadratic function fitted the relationship between g s and P n , g s and iWUE, EC e and WUE, and EC e and fruit quality parameters at a given water regime.
Acknowledgments This work was supported by the  National Natural Science Foundation of China  ( 51790534 ,  51725904 ,  51861125103 ) and the  Discipline Innovative Engineering Plan  ( 111 Program ,  B14002 ). HL appreciates the funding from  China Scholarship Council  ( CSC202206350001 ) for supporting his study at INRAE in France. Authors also appreciate the editors and the reviewers for their insightful and constructive comments and suggestions.
Methods We investigate partial melting of five rock types (three metabasic and two metasedimentary compositions) over a wide range of pressure-temperature conditions. We also model equilibrium crystallization of intermediate and felsic granitoids at mid-crustal depths (0. 6 GPa). Results of the modelling are combined with trace element partitioning using lattice strain models (e. g. Blundy and Wood, 1994)  when possible, and with a formulation for the Eu partitioning anomaly between melt and zircon  (Trail et al. , 2012)  to evaluate the Eu/Eu* of zircon in equilibrium with the system as a function of pressure, temperature, mineral assemblage, and oxygen fugacity. Phase equilibrium modelling was conducted using the Thermocalc v. 3. 50 software package  (Powell and Holland, 1988) , the  Holland and Powell (2011)  internally consistent database, and specifically calibrated activity-composition models for each rock type.
This was computed using the μO values from the Thermocalc output combined with the Gibbs free energy (G) values of nickel and nickel oxide from the database of  Holland and Powell (2011). The concentrations of Sm, Eu and Gd in melt-either anatectic melt in the metamorphic system or residual melt in the crystallization models-were computed using a mass balance approach with mineral/melt partition coefficients  (Hanson, 1980). More details are found in the Supplementary Material. For all minerals except zircon, we treat Eu 2+ and Eu 3+ separately because they partition differently into various minerals. First, we calculate the ratio of Eu 2+ /Eu 3+ in the system as a function of f O2, temperature and melt composition using the formulation of  Burnham et al. (2015) ; this is the same approach applied by  Holder et al.