dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS Financial disclosure: The CIBMTR is supported primarily by  Public Health Service Grant/Cooperative Agreement   U24CA076518  with the  National Cancer Institute (NCI) , the  National Heart, Lung and Blood Institute (NHLBI) , and the  National Institute of Allergy and Infectious Diseases (NIAID) ; Grant/Cooperative Agreement U24HL138660 with the  NHLBI  and  NCI ; Grants  R21 HL140314  and  U01 HL128568  from the  NHLBI ; Contract  HHSH250201700006C  with the  Health Resources and Services Administration (HRSA) ; Grants  N00014-18-1-2888  and  N00014-17-1-2850  from the  Office of Naval Research ; subaward from prime Contract Award  SC1MC31881-01-00  with the HRSA; subawards  from Prime Grant  awards  R01 HL131731  and  R01 HL126589  from the  NHLBI ; subawards from prime Grant Awards  5P01CA111412 ,  5R01 HL129472 ,  R01 CA152108 ,  1R01 HL131731 ,  1U01 AI126612 , and  1R01 CA231141  from the  NIH ; and commercial funds from  Actinium Pharmaceuticals ,  Adaptive Biotechnologies, Allovir, Amgen, Anonymous donation  to the  Medical College of Wisconsin, Anthem, Astellas Pharma US, Atara Biotherapeutics, BARDA, Be the Match Foundation, bluebird bio, Boston Children's Hospital, Bristol Myers Squibb, Celgene, Children's Hospital of Los Angeles, Chimerix, City of Hope Medical Center, CSL Behring ,  CytoSen Therapeutics ,  Daiichi Sankyo ,  Dana-Farber Cancer Institute, Enterprise Science and Computing, Fred Hutchinson Cancer Research Center ;  Gamida-Cell ,  Genzyme ,  Gilead Sciences ,  GlaxoSmithKline, HistoGenetics, Immucor, Incyte, Janssen Biotech, Janssen Pharmaceuticals, Janssen Research & Development, Janssen Scientific Affairs, Japan Hematopoietic Cell Transplantation Data Center, Jazz Pharmaceuticals, Karius, Karyopharm Therapeutics, Kite Pharma ,  Kyowa Kirin ,  Magenta Therapeutics ,  Mayo Clinic and Foundation Rochester, Medac, Mediware ,  Memorial Sloan Kettering Cancer Center ,  Merck & Company, Mesoblast, MesoScale Diagnostics, Millennium ,  Miltenyi Biotec , Mundipharma EDO,  National Marrow Donor Program ,  Novartis Oncology ,  Novartis Pharmaceuticals, Omeros, Oncoimmune, OptumHealth ,  Orca Biosystems ,  PCORI ,  Pfizer ,  Phamacyclics ,  PIRCHE, Regeneron.
The two samples were then pooled to produce a 60 µL equimass pool used for adapter ligation with 10 µL of the Adapter Mix II (AMII), 20 µL of the NEBNext Quick Ligation Reaction, and 10 µL of Quick T4 DNA ligase. After 15 min of room temperature incubation, the material was bead-purified using provided SFB and EB before the final library was mixed and loaded onto an R9. 4 flow cell (FLO-MIN106, Oxford Nanopore Technologies) and the run was performed on a MinION Mk1B device (ONT) in order to obtain 1 Gb of raw data per sample in 1. 5 h.
Genome Assembly and Phylogenetic Inference Sequencing data were basecalled with a quality score of 6 and subsequently demultiplexed using Guppy GPU basecaller and barcoder (ONT). Raw reads were trimmed of indexes, primers, and adapters using Porechop  [24]. Cleaned reads were then mapped against a custom reference of SARS-CoV-2 genome comprising 4 Chinese and 70 early French sequences using Bowtie2  [25]  and Minimap2  [26]. Finally, a consensus genome sequence based on mapped reads was generated with bcftools consensus  [27]. SARS-CoV-2 sequences were deposited on GISAID under numbers EPI_ISL_3838696 and EPI_ISL_3857106. Consensus sequences generated were aligned with 760 SARS-CoV-2 genomes representing the diversity of major lineages circulating in France from March 2020 to March 2021 using MAFFT  [28].
Acknowledgments: We wish to thank all veterinarian staff members and students from  VetAgro Sup  veterinary hospital and private clinics involved in the process of animal sampling, particularly  Lisa Le Merdy  and  Corentin Buisson  for their significant contribution,  Floriane Gucciardi  and  Ines Charif  for their excellent technical implication in the laboratory process,  Astrid Vabret  for gracious support, and  Estelle Leperchois  and  Januel Vouteau  for technical assistance.
Clinical, biochemical, and, when available, radiological and histopathological data were retrieved for all subjects. Most patients underwent previous phenotype-based genetic tests that excluded variations in genes frequently involved in myopathies. The informed consent was signed by all adult patients and the parents (or legal representatives) for children before molecular genetic testing. The study was approved on 20 July 2021, by the Institutional Review Board of Montpellier University Hospital, with the IRB project identification code: IRB-MTP_2021_07_202100912.
Bioinformatics Bioinformatics analyses for single nucleotide variant (SNV) and copy number variant (CNV) detection were performed as previously described  [9]. The generated variant call files (vcf) were analyzed using an in-house annotation software that is based on ANNO-VAR  [11]  and that uses the following databases for variant annotation: RefGen, Knowngene, clinVar, Kaviar, ExAC03, GnomAD Exome, snp138, dbsnfp, dbscsnv, and spidex.
Variant Pathogenicity Evaluation Variants were expected to be present in less than 1% of general population databases such as the Exome Aggregation Consortium (EXAC) and the Genome Aggregation Database (GnomAD). The pathogenicity of the identified variants was assessed using the MPA software  [10]  and a set of criteria reported by  Zenagui et al. 2018 [9] , according to the American College of Medical Genetics and Genomics (ACMG) guidelines  [12]. The MPA software provides a prediction score of increasing pathogenicity, where 0 predicts a neutral effect and 10 a pathogenic effect. All probable disease-causing variants were then discussed in multidisciplinary meetings to evaluate their pathogenicity by comparison with the patient phenotype and the suspected mode of inheritance. Further analyses to establish a diagnosis and define genotype-phenotype correlations included familial segregation studies and detailed phenotype analysis through additional clinical examination, whole-body muscle MRI, and retrieval of data concerning biochemical tests and muscle biopsy findings (histology, immunolabeling, and/or electron microscopy).
Results Pathogenic or likely pathogenic variants were detected in 74 of the 156 patients (47. 4%) (Supplementary Table  S2 ). No disease-causing variant was identified in 72 patients, and a variant of unknown significance (VUS) was detected in 10 patients. Among the 98 pathogenic or likely pathogenic variants, 36 were described in the literature as pathogenic, 62 had never been previously reported (Figure  1A ). Most of them were missense variants (59. 2%) (Figure  1B ). 26 different genes were affected, reflecting the genetic diversity of hereditary myopathies, with RYR1, NEB, and TTN the most frequent implicated genes (Figure  1C ). Variants were expected to be present in less than 1% of general population databases such as the Exome Aggregation Consortium (EXAC) and the Genome Aggregation Database (GnomAD).
No other abnormalities were detected in either line. X chromosome duplication is a known recurrent abnormality in human iPSCs  (Taapken et al. , 2011). This may limit the utility of these lines for some applications, although it is likely that the additional X chromosome would be inactivated by X chromosome inactivation  (Bar et al. , 2019). Analysis of the parental LCL indicated that despite having a normal karyotype at early passage, this line is prone to X chromosome duplication. Indeed, multiple attempts to generate karyotypically normal clones all yielded the same X chromosome duplication. Short tandem repeat (STR) typing was used to verify culture identity and purity; both iPSC clones matched the original parental cell line (data archived with journal). The original heterozygous ACTA1 variant (c.
Immunocytochemistrypluripotency marker expression and trilineage differentiation potential For qualitative pluripotency analysis, iPSCs (P11) were plated on Matrigel-coated 96-well Nunc polymer optical bottom plates and stained for OCT4, SSEA4, SOX2, and TRA-1-60 (Table  2 ). Trilineage differentiation potential was assessed using the STEMdiff™ Trilineage Differentiation Kit (StemCell) using StemPro™ Accutase™ (Gibco) for cell dissociation. Immunocytochemistry was performed as described in the PSC 4-marker Immunocytochemistry Kit (ThermoFisher), except that cells were incubated with primary antibodies (Table  3 ) overnight at 4 • C in 3% BSA. Nuclei were stained using NucBlue™ Fixed Cell stain (ThermoFisher). Cells were imaged using an Olympus IX71 microscope with a DP74 camera and CellSens software.
Karyotyping iPSCs were screened for the 8 most common karyotypic abnormalities at P10 using the hPSC Genetic Analysis kit (StemCell) on a ViiA 7 system (Applied Biosystems). Karyostat analysis (P10) was performed by the Ramaciotti Centre for Genomics (Sydney, NSW, Australia). Data were analyzed using Chromosome Analysis Suite 4. 2 (ThermoFisher). Gbanding (P14) was performed at PathWest Diagnostic Genomics (Perth, WA, Australia). At least 8 metaphase spreads were analyzed per cell line.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. B ) mRNA expression of pluripotency markers. qPCR data were normalised to GAPDH. C) Immunofluorescence for OCT4 and SSEA4 pluripotency markers. Scale = 100 µm. D) Immunofluorescence for SOX2 and TRA-1-60 pluripotency markers. Scale = 100 µm. E) Relative qPCR quantitation of mRNA for markers of mesoderm (TBXT, BMP4), endoderm (SOX17, GATA4), and ectoderm lineages (OTX2, PAX6), from iPSCs differentiated to each germ layer using the STEMdiff™ Trilineage differentiation kit. F) Agarose gel of PCRs for four different EBV markers (EBNA1, OriP, BZLF1, LMP1) in parental LCL (positive control) and HPIi002 lines (2-A and 2-B).
Acknowledgements This work was supported by funding from an  AFM   Telethon Trampoline Grant  ( REF-21816 ) to  Kristen Nowak , and A  Foundation Building Strength Research Grant  ( ID-A3TR22 ,  PI Nigel Laing ). We also gratefully acknowledge funding from the  Australian National Health and Medical Research Council , including a  Principal Research Fellowship  ( APP1117510 ) to  Nigel Laing  and a  Career Development Fellowship  ( APP1122952 ) to  Gianina Ravenscroft.
Being constructed upon adjusted regularizations, CHAOS-7 is not provided with uncertainties. • COV-OBS-x2  (Huder et al. 2020 ) covers the entire observatory era since 1840. Satellite data are considered by means of virtual observatories  (Hammer et al. 2021a ). The core evolution is co-estimated with the magnetospheric external dipole in dipole coordinates. It is also projected onto splines, but with a longer (2-yr) knot spacing. This compromise, chosen so as to accommodate periods with much different data coverage, renders impossible the description of fluctuations at periods shorter than 3 years or so  (Pick et al. 2019 ). The a priori information added to reduce the non-uniqueness of the inverse problem is based on stochastic processes compatible with the temporal spectrum of the field.
Rapid Secular Variation in Context The methods by which we choose to interrogate geomagnetic observations shape the insights we are able to obtain. Thus, it is prudent to understand the commonly used analysis techniques and identify aspects that lack a specific definition, thereby being highly study and dataset or model dependent. The first challenge then is to determine what constitutes a rapid SV change, especially when looking at time series of SV from observatories (both ground based and virtual). This can vary from study to study as the definition of what feature constitutes a rapid variation highly depends on the time resolution of the observations and on the smoothing that is applied to reduce the noise in the data or model. With a working definition, rapid SV variations can then for instance be analysed via time-longitude and time-latitude plots as shown in Figs.
3, 4 , to provide insight into the spatial and temporal characteristics of the variations. Further spatio-temporal insight can be gained by analysing the frequency-wavenumber spectra of the time-longitude/latitude plots  (Finlay and Jackson 2003; Chulliat et al. 2015) , which may help reveal the different waves present in the dataset. Subsequently, to characterize the difference between the distinct rapid variation occurrences, it is useful to define the amplitude of the rapid variation. In geomagnetic field models, this can simply be defined based on the SA. Meanwhile, studies based on SV time series data from observatories have used the difference in the gradient of the linear SV segments before and after sudden accidents (e. g. ,  Brown et al. 2013). However, searching for such segments around events localized in time relies on a strong a priori assumption, as processes characterized by power spectra such as (7) possibly present abrupt changes at 1 3 any epoch  (Gillet et al.
Acknowledgements NG and DJ were partially supported by the  French Centre National d'Etudes Spatiales (CNES)  for the study of Earth's core dynamics in the context of the Swarm mission of  ESA. NG and DJ contributions have been also funded by  ESA  in the framework of  EO Science for Society , through contract  4000127193/19/NL/IA  (SWARM + 4D Deep Earth: Core). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( GRACEFUL Synergy Grant  agreement No. 855677 ). RA would like to thank  Sabine Stanley  and  Ankit Barik  for fruitful discussions and comments pertaining to Sect. 3. We thank two anonymous referees for their comments that helped improve the quality of the manuscript. We thank  J.
01% (wt/vol) to stain the plant cell wall  42. Analyses were carried out using a confocal laser-scanning microscope (Carl Zeiss LSM 700, Jena, Germany). Calcofluor was excited at 405 nm with emission signal collection at 405-470 nm. For SYTO 9 and propidium iodide, an excitation wavelength of 488 and 555 nm was used to collect emission signals at 490-522 nm and 555-700 nm, respectively. Images were obtained using the ZEN 2008 software (Zeiss, Oberkochen, Germany).
Acknowledgements This study was supported by  IRD (JEAI_SymbiTrop) ; the  Suranaree University of Technology (SUT) , the  Thailand Science Research and Innovation (TSRI) , the office of national higher education science research and innovation policy council (nxpo) by  Program Management Unit (PMU-B)  project ( B05F630107 ), the  Agricultural Research Development Agency (ARDA)  ( PRP6305031340 ), the  Franco-Thai Cooperation Program in Higher Education and Research  (PHC SIAM 2017-No. 38280WF ) and by the  ANR grants 'SymEffectors'  and ' ET-Nod ' number  ANR-16-CE20-0013  and  ANR-20-CE20-0012 , respectively. We thank  Professor Kiwamu Minamisawa  for providing the Glycine max seeds and  Dr Daniel Foncéka  for providing the V. unguiculata seeds.
Bunny Barnes, an old friend of mine and surgical colleague from my days as a resident at the MGH and on the staff at Tufts, had recommended me to Howard as someone who could contribute to the study because of my newly acquired analytic skills and substantial clinical experience. I remember the interview well. In my usual blunt manner, I told Howard that I had no interest in working on malpractice! I had not made a career change and spent a year of my life learning how to do Howard Hiatt. (All rights reserved) 1 The Hidden Epidemic: The Harvard Medical Practice Study health policy research just to waste it on an issue that was so polarizing and for which I saw no reasonable prospect for change. I was cool to the whole idea. But Howard explained that the scope of the study was much bigger than malpractice in that it would collect interesting and previously undeveloped data about the substance behind malpractice, medical injury, and also measure its costs to patients.
In line with this inconclusive literature, the present paper seeks to understand why prior evidence relying on large panels led to non-linear conclusions. The core contribution of this paper is an in-depth reassesment of the mainstream empirical methodology. The present study reassesses the non-linear evidence twofold. Firstly, by using more data. The new dataset results in additional countries and observations. It extends the scope of the study up to 2015, thereby including additional post-crisis observations. Second, in reexamining the non-linear conclusion in its original methodological environment, this study also sets the focus on the soundness of the econometric methodology. The finance-growth nexus is no exception to the well known empirical struggle to identify a causal impact. Moving beyond mere statistical association is commonly achieved based on instrumental variables techniques to extract the exogenous component of financial development in a macroeconomic setup.
Data and variables The dataset is gathered from the usual sources. Throughout the study, the independent variable is economic growth, measured as the log-difference of real GDP per capita  (WDI, World Bank, 2018). The study rests on credit to the private sector by deposit money banks and other financial institutions as a ratio of GDP (private credit) as a proxy for financial depth. The finance-growth literature commonly uses the amount of credit allocated to the private sector as it is likely to be associated with the functions exerted by the financial sector that can impact an economy's growth path. 3 t is undoubtedly an imperfect substitute for the much broader concept of financial development. However, it has two main advantages. First, it has become one of the most commonly used financial depth measures and, thus, facilitates comparison with previous empirical work.
, 2004). For comparison purposes with existing literature, this study also works with an older dataset gathered from  Arcand et al. (2015). This older dataset ranges from 1960 to 2010. Besides extending the sample length, it is worth noting that the new dataset does not exactly match the former. There are inevitable data revisions, where some values are reclassified as missing, and some become available. The correlations, however, are usually close to 0. 98 within the sample (including the proxy for financial depth), except for the government consumption ratio, which is 0. 94. The new dataset results in additional countries and observations. It extends the scope of the study up to 2015, thereby including additional post-crisis observations. The paper focuses on the most extended period range.
The latter requires at least one observation at the beginning of the sample, which is not available for most countries. The selection criteria is the availability of the data. To mitigate the issue and maximize the sample size, I follow  Arcand et al. (2015)  and start a decade later, in 1970, instead of 1960. Once turning to panel estimates, this constraint no longer burdens the sample size. 6 able  1  reports various cross-country regressions. Column (1) provides a benchmark based on the old dataset. The point estimate associated with the linear term of private credit is positive, the quadratic term is negative, and both are statistically significant. It indicates that financial depth starts yielding negative returns as credit to the private sector reaches 86% of GDP. The reliability of this turning point, however, rests solely on the SLM test.
With a low p-value of 0. 02, the threshold is well identified. Now focusing on the new dataset. Using additional available countries and extending the coverage up to 2015, the non-linear finding weakens. The threshold for private credit rises to 100% of GDP with a higher p-value of 0. 08 for the SLM test. However, 96% of total observations are below this threshold. Only three countries experience a level of financial depth above the 100% of GDP  threshold. None of them reach the 180% threshold above which the marginal effect of financial depth would become both negative and statistically significant. Fig. 2  plots the quadratic fit between financial depth and growth using the new expanded data. It shows that the curvature is due to only three countries above the threshold, namely: the United-States (USA), Japan (JPN), and Switzerland (CHE).
Therefore, weak instruments should be tested for. Thus, Table  2  performs the same regressions as in Table  1 , starting with a benchmark threshold estimate from the old dataset, then with the new dataset up to 2010 and 2015. For each specification, Table  2  complements the estimates with tests for underidentification and weak instruments. For the underidentification, Table  2  reports the p-values for the  Kleibergen and Paap (2006)  heteroscedasticity robust version of the Lagrange-Multiplier (LM) test. The null hypothesis is that the structural equation is underidentified. A rejection of the null indicates that the smallest canonical correlation between the endogenous variables and the instruments is nonzero. Since the nonzero correlation condition is not enough, Table  2  also controls for weak-instruments by reporting the weak-instruments Wald statistics based on  Cragg and Donald (1993) , and its non-iid robust analog by  Kleibergen and Paap (2006).
While the first column provides a benchmark of the typical non-linear result from the old dataset, the subsequent columns report estimates based on the new dataset expanding the period and country coverage. credit. Thus, the SLM test now trivially rejects the presence of an inverted U-shape. The thresholds estimates are highly sensitive to the specific data process. The IH estimations suffer from weak instrumentation. Thus, not surprisingly, the point estimates from Table  2  are in line with those obtained from the OLS estimator in Table  1. This proximity does not point toward highly causal results. It would rather be a sign of untreated bias and persistent endogeneity as the instruments are far too weak to address these issues. By looking under the hood of the identification through heteroscedasticity, these simple tests shine brighter lights on its inability to yield a reliable identification of a causal impact from finance depth to economic growth.
All explanatory variables, however, are systematically based on the first observation of each five-year spell. The absence of averaging implies a substantial informational loss as well as a consistency loss. Excluding 80% of the observations would possibly expose the coefficient estimates to bias as it could mismeasure the true explanatory variables. Hence, is the starting point of the five-year spells influencing the results? Table  3  shows regressions for sequential anchoring of the five-year spells based on the old dataset. Column (1) provides a benchmark (typical) non-linear conclusion. Column (2) provides point estimates with a one-year forward shift for the starting point of each spell, with an identical sample of countries, and one fewer observation (916 against 917 previously) due to data availability.
The coefficients associated with the linear and quadratic term of private credit lose magnitude, and neither of them is statistically significant. The SLM test discards the inverted U-shape with a high p-value of 0. 46. Through columns (3) to (5), the same exercise goes on by shifting forward by one year the beginning of the five-year spells. In the end, out of the five possible starting points presented in columns (1-5), only one supports the existence of a threshold. Table  4  conducts the same exercise, this time based on the new dataset. The number of countries in the new dataset varies slightly from the old one. 11 The selection criteria is the availability of the data. Each column shifts forward by one year the beginning of the five-year spells. Very similar conclusions arise, as only one estimate out of the five possible anchors supports the presence of a significant threshold.