dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Ilycaffé  for cup tasting and  Exportadora Atlantic S. A (ECOM Company)  for its technical support in the field in Nicaragua. This research was partially funded by the  BREEDCAFS  project, which was supported by the  European Commission  under the  Horizon 2020-Research and Innovation Programme ,  H2020-SFS-2016-2 , grant agreement number:  727934.
Study Design and Patients From 1 August 2017 to 31 March 2018, 187 consecutive patients underwent TAVR at our institution (Nouvel Hôpital Civil, Université de Strasbourg, France). The indication for TAVR and vascular access sites were assessed by the local heart team. The protocol was approved by the local ethics committee. All participants gave their informed written consent before the procedure and agreed to the anonymous processing of their data (France 2 Registry). Patients received aspirin (75 mg to 160 mg) and clopidogrel (300 mg for loading dose and 75 mg/day for maintenance dose) before TAVR, with ongoing DAPT after the procedure for 3 months. The loading of clopidogrel was not performed when the patient was under chronic clopidogrel therapy. In patients treated with OAC, clopidogrel was not administered, and OAC plus aspirin were continued for 3 months.
Routinely, OAC therapy was discontinued 5 days prior to the procedure. CT imaging was planned at discharge. Of these patients, 90 had high-resolution cardiac CT performed at various times after TAVR. In the presence of HAM, OAC therapy was initiated and a new contrast MDCT was performed in the follow up to assess consistent regression of HAM images. Ninety-seven patients did not perform post-TAVR contrast MDCT due to death, severe impaired renal function (glomerular filtration rate ≤30 mL/min/1. 73 m 2 ) or loss of follow-up (Figure  1 ). agreed to the anonymous processing of their data (France 2 Registry). Patients received aspirin (75 mg to 160 mg) and clopidogrel (300 mg for loading dose and 75mg/day for maintenance dose) before TAVR, with ongoing DAPT after the procedure for 3 months. The loading of clopidogrel was not performed when the patient was under chronic clopidogrel therapy.
Aortic valve calcium score and aortic valve annulus sizing were determined on the pre-TAVR CT by one radiologist using Vitrea software in version 6. 6 (Vital Imaging, Minnetonka, MN, USA). CT imaging definition of SLT was defined according to previous definitions  [6]. We first began to evaluate leaflet thickening (i. e. , HALT) in diastole in two reconstructed planes. In the event of HALT, maximal leaflet thickening and the number of leaflets implicated were recorded. RELM was determined using cine reconstructions. Post-TAVR CT analysis was done in a consensus reading session with 1 experienced cardiac radiologist (MO, with 12 years of experience in CT) and 1 cardiologist (CJ, with 1 year of experience in CT) using a dedicated workstation (Vitrea version 6. 6).
Collection of Data All data, at baseline and during follow-up, were collected retrospectively according to the Valve Academic Research Consortium-2 criteria  [11]. The primary endpoint of the study was to determine the prevalence of SLT (HAM ± postprocedural elevation in transvalvular gradient) according to anticoagulant and antiplatelet therapy. The secondary endpoint was to determine predictive factors of HAM. In addition, we investigated the prevalence of HALT. In the event of HAM, antithrombotic treatment (VKA or NOACs) was initiated and a new CT scan was performed few months later to assess consistent regression of HAM images and restored leaflet motion.
Statistical Analysis Categorical variables are expressed as numbers (%), and continuous variables are expressed as mean ± SD or median and interquartile values. Differences between the groups, consisting of HAM+ vs. HAM-and HALT+ vs. HALT-were assessed with χ 2 tests for categorical variables. Unpaired Student's t-test was used to analyse continuous variables that showed normal distributions, and the Mann-Whitney test was used to analyse continuous variables with skewed distributions. Univariate and multivariate analysis was performed to identify independent predictors of HAM+. Variables with a univariate p < 0. 05 were considered for subsequent multivariate models. A p-value < 0. 05 was considered significant. Statistical analysis was performed using SPSS 17. 0 for Windows (SPSS Inc. , Chicago, IL, USA).
Another crucial issue relies on the clinical impact of valve thrombosis on thromboembolic events. The pioneering work by Chakravarty  [12]  has suggested that SLT was associated with increased rates of transient ischemic attack (TIA). Conversely, in a large registry comprising 754 patients (among them 120 patients with valve thrombosis), no differences in overall mortality and stroke/TIA rates could be demonstrated after a 406-day follow-up. However, it should be emphasized that rates of stroke/TIA were surprisingly extremely low (<2%) for a TAVR population  [28] , which probably reflects the German policy characterized by a more liberal use of TAVR implantation that includes lower risk patients. By contrast, insights from the US FDA MAUDE database has highlighted that leaflet thrombosis is associated with adverse outcomes including stroke, cardiogenic shock and death  [29].
The definition of optimal antithrombotic therapies following TAVR remains a matter of important controversies. Whereas the frequency of subclinical leaflet thrombosis and the possible link between SLT and valve deterioration advocate for a liberal use of OAC, safety concerns and the assessment of bleeding events remain key elements when prescribing OAC. Several groups including ours have recently underlined the paramount importance of bleeding events that significantly outweighed ischemic concerns  [23] [24] [25] [26] [27] [28] [29] [30]. Of major importance, late bleeding is considered as a main determinant of adverse outcome in the frail TAVR population. Ongoing trials (ATLANTIS trial NCT02943785, POPular-TAVI trial NCT02247128, ENVISAGE-TAVI trial NCT02943785, AUREA trial NCT01642134, and AVATAR trial NCT02735902) will provide important insights on the efficacy but also safety profile of OAC including NOACS.
Data analysis: The results were analyzed using the Quantisoft® software (Bio-Rad) to determine the fraction of PCR-positive droplets in the original sample. Poisson statistical analysis of the number of positive and negative reactions permitted the calculation of the absolute amount of WT or mutated alleles in the starting template without standard curves  (12, 16, 17). The results were reported in fractional abundance (FA) corresponding to the percentage of the mutated alleles out of the total alleles (ratio mutated alleles / mutated + WT alleles). The statistical comparison between methods was performed using a two-tailed Fisher's exact test performed on the Prism 6 Software (Graphpad, San Diego, USA).
Acknowledgments: We offer our gratitude to the patients and their doctors. We particularly thank the pediatric clinicians and the endocrinologist,  Pr. A. S. Parent ,  Dr. S. Epstein ,  Dr. E. Marquant ,  Dr. H. Bellon ,  Dr. N. Faure-Gallon ,  Dr. C. Borrione ,  Dr. C. Brue , and  Pr. T Brue.
Introduction A principal aim of the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) was to document changes in treatment practice for patients with newly diagnosed atrial fibrillation during an era when non-vitamin K antagonist oral anticoagulants (NOACs) were becoming more widely adopted. In these analyses, the key factors which determined the choice between NOACs and vitamin K antagonists (VKAs) are explored. Methods Logistic least absolute shrinkage and selection operator regression determined predictors of NOAC and VKA use. Data were collected from 24,137 patients who were initiated on AC ± antiplatelet (AP) therapy (NOAC [51. 4%] or VKA [48. 6%]) between April 2013 and August 2016.
Results The most significant predictors of AC therapy were country, enrolment year, care setting at diagnosis, AF type, concomitant AP, and kidney disease. Patients enrolled in emergency care or in the outpatient setting were more likely to receive a NOAC than those enrolled in hospital (OR 1. 16 [95% CI: 1. 04-1. 30], OR: 1. 15 [95% CI: 1. 05-1. 25], respectively). NOAC prescribing seemed to be favored in lower-risk groups, namely, patients with paroxysmal AF, normotensive patients, and those with moderate alcohol consumption, but also the elderly and patients with acute coronary syndrome. By contrast, VKAs were preferentially used in patients with permanent AF, moderate to severe kidney disease, heart failure, vascular disease, and diabetes and with concomitant AP. Conclusion GARFIELD-AF data highlight marked heterogeneity in stroke prevention strategies globally.
8, 9 ] [12]  The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF; ClinicalTrials. gov Identifier: NCT01090362) provided an opportunity to record prospectively the choice of the anticoagulant (AC) first prescribed in patients with newly diagnosed AF. Initial analyses revealed an increase in the use of ACs from 57. 4% to 71. 2% of patients between 2010 and 2016. 13, 14 he aim of this analysis is to evaluate the factors (patient characteristics, year of enrolment by cohort, care settings, and geographic location) which have played a role in determining the choice of the AC type during these years of changing treatment practice.
8% to 62. 6% of patients on AC therapy. Cross-sectional data at baseline including treatment patterns (NOAC relative to VKA) for all patients on an AC with or without antiplatelet (AP) therapy are reported. The data were extracted from the study database on October 2017. This article has been written according to the standards outlined by the Strengthening the Reporting of Observational Studies in Epidemiology statement. The registry is being conducted in accordance with the principles of the Declaration of Helsinki, local regulatory requirements, and the International Conference on Harmonization-Good Pharmacoepidemiological and Clinical Practice guidelines. GARFIELD-AF data are captured using an electronic case report form designed by Dendrite Clinical Systems Ltd (Henley-on-Thames, UK). Data management and quality assurance processes have been described previously.
The linearity assumption was evaluated for each continuous measure by applying restricted cubic splines. The association between date of enrolment and NOAC use versus VKA use was estimated using a restricted cubic spline. The predicted values and 95% CIs from the unadjusted models displayed the relationship of increasing NOAC use with time. Data analysis was performed with SAS version 9. 4 (SAS Institute Inc, Cary, NC, USA). This work was supported by an unrestricted research grant from Bayer AG (Berlin, Germany) to the Thrombosis Research Institute (London, UK), which sponsors the GARFIELD-AF registry. The funding source had no involvement in the data collection, data analysis, or data interpretation. The authors are solely responsible for the design and conduct of this study, all study analyses, the drafting and editing of the paper, and its final contents.
Discussion There is still much to learn about how NOACs are being used in clinical practice and their impact on short-and long-term outcomes in patients with AF. A principal aim of the GARFIELD-AF registry was to document changes in treatment practice at a national and global level for patients with newly diagnosed AF during an era when NOACs are becoming more widely adopted. 14, 15 Although the indications and eligibility of patients for NOACs and VKAs are well codified by prescribing information, clinical guidelines, expert opinion, and a wealth of published data,  7, [17] [18] [19] [20]  the large global data set from GARFIELD-AF provides an opportunity to evaluate the key drivers of NOAC and VKA prescribing across a range of care settings in everyday practice. Unsurprisingly, the registry recorded enormous variability in prescribing practice reflecting the heterogeneity in the organization of health care across the globe.
Our analyses also provide evidence about ongoing concerns regarding the prescription of NOACs for patients with renal dysfunction. However, it may only be a question of time before the favorable findings from recent randomized clinical trials (RCTs) with NOACs are influencing clinical practice. Subgroup analyses from RCTs with rivaroxaban or apixaban have shown the benefits of these NOACs when compared to warfarin. 44, 45 Data from phase III trials also suggest that the preservation of renal function was significantly better with rivaroxaban than warfarin. 46 hese findings were corroborated by Yao et al (2017) in a US retrospective cohort analysis of the Opted-Labs Data Warehouse database,  47  which compared the impact of NOACs (apixaban, dabigatran, and rivaroxaban) and warfarin on renal outcomes.
Strengths and limitations This study describes real-world clinical practices in AC prescribing for a well-defined cohort of patients with newly diagnosed nonvalvular AF and at least 1 risk factor for stroke. However, in contrast to RCTs, AF patients with many concomitant diseases were not excluded from this noninterventional registry. This observational study was sufficiently large to identify hospital and other care settings which were representative in each country. With patients recruited within 6 weeks of diagnosis of AF, the registry provided a record of almost all patients treated in each participating center with nonvalvular AF, that is, including some high-risk patients who died shortly after diagnosis. GARFIELD-AF mitigated some of the limitations inherent to observational studies through the standardization of clinical definitions and the rigorous audit (using both remote and onsite monitoring) to ensure the completeness and accuracy of the data collected.
This is a unique feature because GARFIELD-AF is a prospective global disease registry and regional differences and country-related variations of drug prescribing patterns had been anticipated. Other studies addressing the predictors of NOAC use where smaller, retrospective, limited to selected patient populations, limited to single countries or regions within a country, or monocenter observations. A small study of Guerriero et al was limited to 967 patients in South Italy and assessed the predictive role of age, gender, and number and type of co-treatments for NOAC versus warfarin prescription in elderly patients. In contrast to our findings, age was negatively associated with NOAC initiation. 51 A German claim data analysis of 16,804 elderly nursing home residents identified previous stroke, bleeding events, and a recent hospitalization as predictors for NOAC prescription instead of VKA.
52 An analysis of health insurance data of the Canadian Province of Quebec showed that initiation with NOACs was less likely for patients ≥80 years old (OR 0. 55, 95% CI 0. 41-0. 73) or with CHA 2 DS 2 -VASc ≥2 (OR 0. 49, 95% CI 0. 42-0. 57). 53 Another group of authors addressed the effect of NOACs on prescribing practices for AF in older adults by using the database of their hospital, the academic medical center in St Louis, MO, USA. Consistent with our results, they observed an increase of NOAC use over, time but in contrast to GARFIELD-AF, the rate of NOAC uptake was attenuated by increasing age. 54 The group of Urbaniak et al used the Norwegian Prescription Database to analyze the prescription patterns of NOACs in Norway, and they report that AF patients b70 years old had higher odds of NOAC prescribing (OR 1.
The Dog10K effort to provide deep and comprehensive genome sequence data will further enable an improved inference of evolutionary rates in canids, a parameter that has been challenging to estimate  [38, 42, 43]. This effort will reduce the confidence intervals surrounding inferred divergence times, effective population sizes and other demographic parameters. We will estimate mutation rates for a diverse array of molecular features, including SNVs, CNVs and mobile element insertions, and test whether mutational events cluster along the genome. These efforts will improve our understanding of the mutational process in dogs and wolves, and how mutations are affected by sequence composition, tandem repeats, CpG sites, chromatin accessibility and the unique nature of canine recombination hotspots  [68].
Dog10K's emphasis on providing WGS from aged, healthy individuals will also benefit disease studies. These data are often missing from current studies as a result of cost constraints, which have led investigators to preferentially sequence diseased individuals and rely on publicly available sequence databases to determine breed allele frequencies. However, the populations sampled in public databases are not representative of all breeds and data are lacking even for breeds where disease frequency is high. Dog10K will address this information gap by sequencing aged healthy individuals representing breeds whose relative risk for a heritable disease is high, such as a particular type of cancer  [92] [93] [94] , e. g. Bernese Mountain Dogs and histiocytic sarcoma  [95] [96] [97] , Scottish Terriers and bladder cancer  [98] , Golden Retrievers and lymphoma  [99] , and Irish Wolfhounds and osteosarcoma  [100].
Equally important are breeds with a predisposition to cardiac issues, neurological, neuromuscular and autoimmune disorders, deafness and ocular disease, etc. , and breeds that experience multiple distinct diseases at a high frequency. Reference sequence data on all breeds will provide the scientific community with an ability to perform matched WGS-based case-control analysis, either directly or through imputation. This aim is important, as it is the link between canine and human health that has generated the most interest on the part of the biomedical community. WGS data can also be used to determine the genes that contain the lowest levels of polymorphism across canids. Such genes are likely to be under strong purifying selection in canids. We can intersect the resultant list with a similar database derived from humans.
These comparisons will reveal the extent to which purifying selection has shifted across the mammalian phylogeny. The same dataset will be used to screen mutations assigned 'variant of unknown significance' status in human disease association studies. Variants found in the Dog10K catalog in breeds that are not at increased risk for the disease in question, or that are found frequently across breeds, are unlikely to be disease-associated, whereas those that are present in at-risk breeds not only provide useful data for human genetic studies, but also suggest an animal model for the development of therapeutics. The challenge will be in setting thresholds, as both neutral and disease alleles of varying types are nearing fixation in a variety of breeds. Despite that difficulty, Dog10K may benefit the human medical community by providing foundational data and resources for diseases of interest, for both human and veterinary disorders.
The first three genes mentioned above are themselves excellent candidates for the identification of additional human OCD genes, since they play a role in both brain development and synaptic plasticity. Further exploration of canine OCD by Dog10K may prove crucial for future studies that address human and animal mental health. Since Dog10K will focus on mapping breedspecific behaviors, we face challenges in accurate phenotypic dissection of complex behavioral traits, especially those that are breed-related  [112]. Incorporating experts in behavioral assessment and data storage will be key in disentangling the genetics of breed behaviors, and understanding how identical genes and pathways function in humans. Dog10K will therefore employ a behavioral scientist to collect and quantitate data. Finally, we are mindful that epigenetic variation also plays a highly relevant and underappreciated role in behavior, and focused studies will be needed to tackle related questions  [113, 114].
In addition, we will select 25 popular breeds (e. g. German Shepherd Dog, Golden Retriever, Bulldog and Poodle) for which we will thoroughly explore intra-breed variation by sequencing a minimum of 50 dogs in each (n breeds 3 = 1250). We will emphasize individual selection to maximize phenotypic variation (i. e. disease susceptibility and morphology) and minimize relatedness. This has already been done for the Yorkshire Terrier, and is available in the public 722 WGS data described below. Though most of canine phenotypic variation is found within the registered breed dogs, many specialized traits are uniquely found among isolated indigenous populations. Thus, we will additionally sample five unrelated individuals from 20 niche or non-breed populations (n niche = 100). Further, since 47% of all dogs in the US are categorized as either mixed-breed or designer-breed dogs  [118] , we will sequence 10 unrelated 'mixed' breed individuals from 30 different locations (n mixed = 300).
While blood samples are desirable, samples from some dogs may only be available using buccal swabs. The numbers presented here for phases 1 and 2 are in addition to the 722 WGS samples already released and cataloged by members of the community, producing over 91 million variants (https:// research. nhgri. nih. gov/dog genome/data release/ index. shtml [4] ; see also ncbi. nlm. nih. gov/sra). All of the sequence data produced and made public by the general canine genome community will be integrated into the total dataset. The initial 5450 samples selected here (Table  2 ) represent about one-half of the proposed 10 000 samples. Subsequent expan-sion will focus on rare breeds, comprehensive village dog sampling, disease phenotypes, robust withinbreed sampling and geographical representation to highlight subtle phenotypic variation.
Online resources and data sharing Raw sequence data along with sample metadata, including sample name, breed, sampling location and any disease phenotypes, will be deposited in the Genome Sequence Archive (GSA) (http://gsa. big. ac. cn/), as well the International Nucleotide Sequence Database Collaboration (INSDC, a collaboration that includes DNA Data Bank of Japan (DDBJ), European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) and National Center for Biotechnology Information (NCBI)), which will enable automatic synchronization across the three repositories. Variant call files, as well information on coverage and genome quality control, will be made available through the project website and individually submitted to the Ensembl Variation database. Revised genome assemblies and associated annotations will be deposited in the GSA and INSDC databases, along with required files REVIEW for exploring the data through existing genome browsers.
The web sites that will be maintained as part of this project include: the Dog10K website (http://www. dog10kgenomes. org), the iDog database  [120]  (http://bigd. big. ac. cn/idog), the DogSD database  [121]  (http://dogsd. big. ac. cn/), https://research. nhgri. nih. gov/dog genome/data release/index. shtml, https://ncbi. nlm. nih. gov/sra, https://midgard. nhgri. nih. gov/dog genome/index. shtml and the European Consortium; NCBI and European Nucleotide Archive (ENA) for FASTQ files.
ACKNOWLEDGEMENTS Dog10K gratefully acknowledges our many colleagues and trainees who have provided samples including:  Guo-Dong Wang  ( Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China), Hannes Lohi (University of Helsinki, Helsinki, Finland ),  Tosso Leeb  ( University of Bern, Bern, Switzerland ),  Elaine A. Ostrander  ( National Institutes of Health, Bethesda, MD, USA ),  Christophe Hitte  ( University of Rennes, Rennes, France ),  Peter Savolainen  ( Royal Institute of Technology, Stockholm, Sweden ),  Greger Larson  ( University of Oxford, Oxford, UK ) and Bridgett vonHoldt (Princeton University, Princeton, NJ, USA). We also thank those who have provided expertise or participated in sequencing efforts that have contributed to Dog10K. We thank many members of our laboratories who provided figures or data for figures.