dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to all participants who consented to be part of this study. We thank  Sandrine Schmutz  and  Sophie Novault  for their help with single-cell sorting (CB-UTechS,  Institut Pasteur ),  Sylviane Hamon  for providing commensal bacteria (CRBIP,  Institut Pasteur ),  Laurence Ma ,  Christiane Bouchier  and  Cedric Fund  for performing the NGS experiments (Biomics,  Institut Pasteur ). We also thank the  NIH AIDS Reagent Program (division of AIDS, NIAID, NIH ) for contributing reagents and the  Agence National de Recherche sur le SIDA   et les hépatites virales (ANRS)  for an equipment grant. H. M. received core grants from the  G5 Institut Pasteur Program , the  Milieu Intérieur Program  ( ANR-10-LABX-69-01 ) and  INSERM. This work was funded by the  European Research Council (ERC) -Seventh Framework Program  ( ERC-2013-StG 337146 ).
| Patient cohort Patients data were collected between May 2020 and March 2021 by contacting all University Medical Centers in the Netherlands, personal communication with collaborating colleagues in Europe, and by contacting medical centers that previously published about KBG syndrome. For each center, one clinical geneticist or (pediatric) neurologist was responsible for the patient inclusion and data collection in their center. The single inclusion criterion was a confirmed genetic diagnosis of KBG syndrome based on a (likely) pathogenic variant in ANKRD11 or a chromosomal 16q24. 3 microdeletion including (part of) ANKRD11. To prevent duplicates, we asked whether patients had been included in a previous study. Included patients were classified into an "epilepsy group"-when having (a history of) epilepsy-or a "non-epilepsy group".
| Genotyping ANKRD11 variants were all described with reference to transcript "NM_013275. 6". Chromosomal microdeletions were reported with reference to the Genome Reference Consortium Human Reference sequence version 37 (GRCh37/hg19). Pathogenicity of variants was determined in accordance with the American College of Medical Genetics and Genomics guidelines. 21  For missense and splice-site mutations, pathogenicity was predicted by using in silico prediction tools (Align GVGD, conserving scores, Grantham, SIFT, PolyPhen, NNsplice, MaxENT, and Splice Site Finder; see Table  S1 ). We classified all nonsense variants, frameshift variants, and partial ANKRD11 deletions as truncating variants.
| Phenotyping We collected data from medical records, genetic testing, electroencephalogram (EEG), and neuroimaging results of all patients. All EEG results were analyzed by a pediatric neurologist. A diagnosis of epilepsy was made according to the International League Against Epilepsy (ILAE) 2014 definition. 22  Seizure types, epilepsy types, and epilepsy syndromes were classified in accordance with the 2017 and 2022 ILAE classifications. [23] [24] [25] [26] [27] [28]  The epilepsy type was classified as "combined focal and generalized epilepsy" if seizures or EEG findings together showed features of both a focal and a generalized epilepsy. Seizure freedom was defined as having no seizures for at least 1 year and drug-resistant epilepsy as failure of adequate trials of two antiepileptic drug schedules to achieve sustained seizure freedom, in accordance with the ILAE definition.
| Historical cohort We performed a literature search in June 2022 using PubMed to identify previously reported patients with a diagnosis of KBG syndrome, confirmed by the identification of a (likely) pathogenic ANKRD11 variant or a 16q24. 3 microdeletion including ANKRD11, and a (former) diagnosis of epilepsy (see Figure  S1  for the flowchart). If data about epilepsy characteristics were incomplete, we contacted the corresponding authors to collect additional data.
| Data analysis IBM SPSS statistics 26 was used for statistical analysis of our data. Baseline characteristics were analyzed using descriptive statistics. Differences in prevalence between groups (epilepsy vs no epilepsy group) were examined by Fisher's exact and Pearson's chi-squared tests. Relative risks (RR) were calculated with subsequent 95% confidence intervals (CI). Logistic regression analysis was used to perform genotype-phenotype correlation analysis. P-values were multiplied with the number of tests (n = 10) to correct for multiple testing (Bonferroni correction). P-values <0. 05 were considered statistically significant.
Introduction Forests in the tropics are composed of a matrix of different forests types. Whilst the widespread lowland evergreen rain forests are thought to be phosphorus (P) limited  [1] , other tropical forests such as heath and montane, are considered nitrogen (N) poor  [2] [3] [4]. So, N poor forests may respond differently to increases in N inputs than lowland evergreen rain forests. The use of fertilisers, fossil fuel and biomass burning have been increasing the concentration of atmospheric reactive N (defined as oxide gases, anions and amine derivatives) since the mid-20th century in the industrialised northern hemisphere, with a consequent increase in N deposition  [5, 6]. The global dataset of  [7]  allows a comparison of worldwide N deposition rates ranging from 0. 17 kg N ha -1 year -1 in Alaska to 27.
We chose these wind heights because the smoke released from fires in Borneo has been estimated to reach heights between 523 m and 993 m  [34]. Nonetheless, smoke is concentrated at a lower height so we also added the 50 m trajectory. We obtained the fire maps from NASA's FIRMS system, which provides data from active fires detected by MODIS C6 satellites (https://earthdata. nasa. gov/earth-observation-data/near-real-time/firms accessed on 3 December 2018). Using these data, we counted how many fires the winds arriving at our study site passed over every two weeks. We used only fire points which had a detection confidence over 30%, as suggested by the MODIS website (https://www. earthdata. nasa. gov/faq/firms-faq#ed-confidence accessed on 22 December 2019). The smoke from each fire becomes less dense with increasing distance from the fire itself.
Acknowledgments: We would like to thank the  Sabah Biodiversity Council, the Soil Chemistry section, Ecology section and Herbarium  staff from  Sabah Forest Research Centre  and in particular the research assistants  Spincer Sitim ,  Lioba Sawadon  and  Yun Len Lee. We are indebted  Marijn Bauters  for valuable suggestions,  David Gaveau  and  Erik Meijaard  for the help in obtaining the land use map and  Pierre Gloaguen  for the assistance with R coding.
First, changes in grassland growth and conditions due to drought and precipitation occur at daily temporal scales  (Salehnia et al. , 2018; Wang et al. , 2015). Consequently, the impacts of short-duration droughts (i. e. , fewer than 30 days) will not be properly estimated by the monthly classic SPEI, especially when such brief drought events are distributed between two consecutive months. Accordingly, we used daily climate data and integrated for a given day the difference between P and PET over the 15, 30, 60, 90, or 120 preceding days. Second, the small number of D observations can lead to a weak goodness-of-fit in the probability distribution step. In climate studies, the World Meteorological Organization (WMO) recommended a 30-year period of climatic data when establishing climatic normal  (Marchi et al.
Standardized anomalies of vegetation reflectance over the 1985-2019 period Similar to the estimation of drought severity with a modified version of the SPEI, we computed the standardized anomalies of local vegetation reflectance indices. We first extracted the reflectance bands of Landsat 4, 5, 7, and 8 over the period of 1985-2019 for each of the 394 vegetation plots from Google Earth Engine  (Gorelick et al. , 2017)  using the reticulate package in R  (Ushey et al. , 2022). Landsat images offer a sufficiently fine spatial resolution (30 m x 30 m) to account for vegetation heterogeneityin other words, they discriminate between different vegetation plots within the same parcel, as depicted in Fig. 1 ), and temporal resolution (16 days) to monitor vegetation reflectance changes over the course of a growing season.
We then summarized these data to obtain: (i) the total amount of nitrogen fertilization from the applied organic and inorganic nitrogen, expressed in kg ha -1 ; (ii) the average number of uses per year based on the number of grazing rotations and harvesting dates; (iii) the prominent type of use, computed as the difference between the total number of grazing and mowing events for a two-year period, with positive values indicating the predominance of grazing, negative values the predominance of mowing, and zero equal numbers of grazing and mowing events; and (iv) the date of first use expressed in cumulative growing degree days. This was computed as the sum of the growing degree days of the date of first grazing or mowing event recorded for two years of monitoring and then averaged. Expressing the date of first use in thermal time instead of Julian days allowed the comparison between vegetation plots distributed along a large elevation gradient  (Perronne et al.
, 2019) , and minimize the effect of between-year variability of meteorological conditions. Indeed, the farmers manage their parcels according to the grass growth which may lead to variation in calendar dates of management events between years but not in cumulative growing degree days, or at least to a lesser extent. Vegetation diversity. Botanical surveys were conducted at the level of vegetation plots, in which all species were identified, and their local abundances were estimated visually. From these relevés (surveys), we first derived taxonomic diversity indices: species richness, the Shannon diversity index, and Simpson's diversity index. Then, we used a trait database compiled for 1300 plant species of open habitats of the Massif central (Baseflor in  Julve, 1998; DIVGRASS in Carboni et al.
, 2019; Hossain and Li, 2021; Lu et al. , 2021; Ma et al. , 2016; Maurer et al. , 2020). Other studies that scrutinized multiple timescales considered much coarser ones, than we did, with monthly meteorological data  (Almeida-Ñauñay et al. , 2022; Li et al. , 2015; Liu et al. , 2017; Xu et al. , 2021). Despite recent developments, satellite-based assessments of vegetation response to drought may still suffer from a few limitations. First, the relationships between VI anomalies and the modified SPEI were noisy overall. Indeed, anomalies of grassland reflectance may arise from multiple natural phenomena, including pest attacks (e. g. , voles increasing bare soil), vegetation diseases, or compositional changes in the vegetation. Anomalies of the climatic water balance index (SPEI) were computed from the SAFRAN data with fine daily temporal resolution but coarse spatial resolution (8 km x 8 km grid).
Acknowledgements The authors acknowledge the  Agence Nationale de la Recherche of the French government  for the support received through the program " Investissements d'Avenir " ( 16-IDEX-0001CAP20-25 ), all the people and funders who were involved in the collection of ground data during the Prairies AOP, ATOUS, and  AEOLE program  and projects, and to the  PhilFrance scholarship program. Special appreciation is given to  INRAE AgroClim service unit and MeteoFrance  for the field and SAFRAN meteorological data access. Lastly, the authors express their gratitude to the two anonymous reviewers for taking the time and effort necessary to provide constructive comments and suggestions, which helped us to improve the quality of the manuscript.
Genome Sequencing The genomes of the two not-yet-sequenced AE-STEC O80:H2 and of the E. coli O80:H26 and O78:H4 were sequenced and analyzed to confirm the presence or absence of stx, eae, pS88-located and cma/iha genes using Virulence Finder 2. 0 (https://cge. food. dtu. dk/services/VirulenceFinder/; accessed on 23 March 2023) and their O:H serotypes using SeroType Finder 2. 0 (https://cge. food. dtu. dk/services/SerotypeFinder/; accessed on 23 March 2023), as previously described  [21, 34]. Sequencing data were submitted as NCBI BioProject PRJNA973567. The Genbank accession numbers are SAMN35130762 (O78C), SAMN35130763 (EH3161), SAMN35130764 (SES5320) and SAMN35130765 (SES5363).
Statistical Analysis All statistical analyses were performed using R software with "Rcmdr v2. 6-0" and "survival" packages (https://www. john-fox. ca/RCommander/index. html; accessed on 1 April 2023) after pooling the different groups of larvae per strain, virulotype, or serotype. Kaplan-Meier curves were created to assess the survival of the larvae according to the inoculation doses of each bacterial strain. Log-rank tests were carried out to highlight significant differences in survival rates between the groups of larvae inoculated with the different concentrations (log1 to log6) of each E. coli strain and the PBS group. Hazard ratios (HRs) with a 95% confidence interval (HR-95%) were calculated to give the relative measure of the risk factor for death for the larvae when comparing the same concentration of two different E.
mellonella larvae. The serotypes of the E. coli O80:H26 and O78:H4 of the laboratory serotype collection were also confirmed (Tables  1  and 3 ). The stx genes and the LEE-located genes were not detected, identifying them as neither AE-STEC nor EPEC. Conversely, the pS88 plasmidlocated genes, including the etsC/iucC genes, were detected in the E. coli O78:H4 strain, but not in the E. coli O80:H26 strain (Tables  1  and 3 ), whereas the cma/iha chromosomal genes were not detected. Since the E. coli O78:H4 was pathogenic for larvae of G. mellonella in preliminary testing and harbored one pS88-like plasmid, this strain was added as a non-O80 pS88 plasmid-positive control. 1 all but stx1a AE-STEC SES5320 and SES5363 strains were previously sequenced  [21]. The Genbank accession numbers are SAMN35130764 (SES5320) and SAMN35130765 (SES5363).
2   EH numbers are listed in  [21] ; SES numbers are the new laboratory collection references. 3 all three E. coli O80:H6 and O80:H45 strains were previously sequenced  [34]. 4 The Genbank accession numbers are SAMN35130763 (EH3161) and SAMN35130762 (O78C). 5 stx1a AE-STEC EH2282 strain was erroneously referred to as stx2a instead of stx1a in Figure  1  of reference  [21]. 6 +: gene detected; empty box: gene not detected. Vet. Sci. 2023, 10, 420 9 of 22.
Acknowledgments: The authors wish to thank Jean-Noël Duprez (Bacteriology, Department of Infectious Diseases, Faculty of Veterinary Medicine, ULiège, Liège, Belgium, for his skillful technical help; Marc Saulmont of ARSIA ("Association Régionale de Santé et d'Identification Animales") for providing the two additional AE-STEC O80:H2; and the company SYNGULON, Seraing, Belgium (https://syngulon. com/) for providing different laboratory E. coli K12 strains. M. A. was a trainee veterinary student from Osaka Prefecture University, Osaka, Japan, and C. K. was a trainee veterinary student from University of Liege.
Clinical Outcomes The primary outcome was a composite of time to either first hospitalization for HF (regardless of prior history of hospitalization for HF) or all-cause mortality. Follow-up was conducted through a clinic visit at 6 months after baseline assessment and structured phone interviews with the participants at the 1-and 2-year timepoints. Data in patients who were lost to follow-up were censored at the date when the patient was last known to be alive and had not experienced an event.
CMR analysis Image analysis was performed using CVI42 software (Circle Cardiovascular Imaging, Calgary, Canada) by trained imaging fellows at the National Heart Research Institute of Singapore CMR Core Laboratory, who were blinded to the clinical information of the patients. Ventricular volumes, mass and ejection fraction were measured from the short-axis cine stack, using manual contouring of the left ventricle in end-diastole and end-systole, excluding papillary muscles, as detailed previously  [13]. LV volumes and mass data were indexed to body surface area. The presence of LGE was assessed qualitatively by two readers according to the recommendations by the Society of CMR  [15]. Average native and postcontrast myocardial T1 values were measured by placing a region of interest (ROI) within the middle third of the short-axis myocardial wall at the basal-and mid-ventricular levels, while avoiding regions of focal LGE.
Statistics Normality was assessed for continuous variables using the Shapiro-Wilk test. Normally distributed data are presented as mean ± standard deviation. Non-normally distributed data are presented as median [interquartile range]. Comparisons were performed for continuous variables using the parametric Student t-test or the non-parametric Mann-Whitney U test. Categorical variables are presented as number (percentage) and compared using the χ 2 test. Univariable Cox regression analysis was performed to identify prognostic variables in the entire cohort and also in the diabetes subgroup. Clinically relevant variables (age, sex, body mass index, diabetes, smoking, coronary artery disease, hypertension, New York Heart Association functional class, systolic blood pressure, NT-proBNP and hs-TnT) and CMR markers of function, remodelling and fibrosis (LV end diastolic volume index, mass index, ejection fraction, GLS, LGE, Native T1, ECV) were tested in the Cox models.
Covariates with a p-value <0. 05 in univariable analyses were entered into the multivariable Cox model to identify independently prognostic variables, using forward stepwise selection. Event-free survival curves were examined using the Kaplan-Meier method and compared with the logrank test. Statistical analyses were performed using SPSS Version 28 (Statistical Package for the Social Sciences, International Business Machines, Inc. , Armonk, New York, USA) and GraphPad Prism 9. 4. 1 (GraphPad Software, Inc. , San Diego, California, USA). A two-sided p-value <0. 05 was considered as statistically significant.
The results from the current study raise the possibility of GLS-guided risk stratification and management in patients with HF and diabetes. Indeed, there is emerging evidence of improvement in GLS with antidiabetic medications which may enable this in future  [19] [20] [21]. GLS by CMR is well-suited for routine clinical use. The technique relies on software packages which track the endocardial and epicardial borders, and reference values are specific for the software used for analysis. STE has a number of limitations, including dependence on high quality 2D images, and results are often affected by foreshortening, dropout of the apical and anterolateral segments on apical views, and/or suboptimal acoustic windows  [12, 22, 23]. GLS by CMR overcomes these limitations and can be measured from routinely acquired bright-blood, steady-state free precession long-axis cine imaging.
Patients with HF were recruited prospectively across six tertiary cardiac centres in Singapore (Asian neTwork for Translational Research and Cardiovascular Trials [ATTRaCT], ClinicalTrials. gov NCT02791009). Patients were included if they presented to hospital with a primary diagnosis of HF, or if they attended a hospital clinic within 6 months of an episode of decompensated HF (requiring hospitalization or treatment in an out-patient setting). In all cases, a trained cardiologist adjudicated the clinical diagnosis of HF. The exclusion criteria were: HF primarily due to severe valve disease, HF due to acute coronary syndrome resulting in a transient episode of acute pulmonary oedema, severe renal failure (estimated glomerular filtration rate < 15 mL/min per 1. 73m 2 ), specific causes of HF (constrictive pericarditis, complex adult congenital heart disease, hypertrophic cardiomyopathy, eosinophilic myocarditis, cardiac amyloidosis, and acute chemotherapy-induced cardiomyopathy), isolated right heart failure, and life threatening non-cardiac co-morbidity with life expectancy of <1 year.
Histograms show the mean G SEM. Number of tumors/CD8 + T cells/myeloid cells for each model: human, 5/12068/2976; HGS2, 9/1800/ 3303; 60577, 5/476/1215. The standard displacement is the distance between a cell's initial and last positions. Therefore, we used the MaxDis rather than the standard displacement classifying the cells that migrated back to their initial position as non-migrating cells (Figure  3C ). To semi-automate this analysis we established a pipeline in R. This aggregated all movement metrics of interest, computed the maximum displacement of all cells for all time-lapse experiments, merged the cell populations of two duplicate time-lapse movies and distinguished the static, wobbling, migrating, and long migrating cell populations from each other. The script produced. csv outputs to plot each metric of interest and calculated the proportions of cells belonging to each behavior category.
Behaviour analysis Time-lapse recordings were projected to a single z-plane (maximum intensity projection) with the Nikon NIS-Elements software and imported in Imaris 9. 1 (Oxford Instruments, Bitplane). Each recording was visualized and checked for drifting. When needed, the drifting was corrected in Imaris using the Correct 3D drift ImageJ plugin. If the fibronectin channel did not display any movement, it was used as reference channel for drift correction. If this channel couldn't be used, we instead searched for a static point in the other channels. This point was tracked through time in Imaris using the surface or spot function, masked into an additional channel and this channel was used as reference in the ImageJ plugin. Recordings were then analysed using the spot function with tracking overtime allowed, estimated diameter 10mm for myeloid and 7mm for CD8 + T cells, without region growing but with background subtraction for improved signal detection.
Behaviour data processing All statistics for each recording were routinely exported into a folder. Using the statistical programming language R (version 4. 0. 4 and R studio version 1. 4. 1106), we have automated the aggregation of the statistics and the analysis of the different behaviours. In short, for each recording, the script computes the maximum displacement, aggregates the statistics of interest and pools the cells (myeloid or CD8 + T cells) from two recordings of the same tumour together. It then aggregates all samples together, splits the static, wobbling, migrating and long migrating populations (Figure  3C ), computes the percentage of the proportions for each behaviour population, extracts the statistics (proportion, trajectory length, mean speed, straightness) into individual.
Cells were then washed twice with PBS containing 2. 5% BSA and 2mmol/L EDTA, fixed in 2% formalin saline, washed and suspended in 200mL PBS containing 2. 5% BSA and 2mmol/L EDTA. Appropriate florescence minus one (FMO) controls were used. Flow cytometry data were acquired on LSR fortessa cell analyser (BD Bioscience). Data analysis was done in FlowJo Version 10. 7. 3.
ACKNOWLEDGMENTS The authors would like to thank the surgeons of  Barts Trust Oncology  for their help in supplying human tumors. We wish to acknowledge  Nadia Rahman  and the  BCI  pathology core,  Dr. Linda Hammond  and the  BCI microscopy core and the Biological Services Unit. We thank  Dr. Chiara Berlato  and  Dr. Ganga Gopinathan  for their technical help and  Dr. Eleni Maniati  for her help with R. We would also like to acknowledge Pr. Victoria Sanz-Moreno  for sharing her expertise with cell movement. Finally, we are extremely grateful to the patients who donated samples and made this study possible. This work was funded by  Cancer Research UK  program grants ( A16354 ,  A25714 , to F. R. B), by an  Ovarian Cancer Research Alliance  grant ( 648516  to B. M. and F. R. B), by  Wellbeing of Women  grants ( ELS906  and  RTF1103  to S.