dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study had financial support from the  São Paulo Government (São Paulo Research Foundation -FAPESP  through the grants numbers  2019/ 21722-0 ,  2018/22097-0 ,  2018/22100-0 ,  2017/03787-2 , and  2016/ 16528-2. RMR is a recipient of a  CNPq Productivity  fellowship.
The study is welcomed since in contrast to genetic studies, the role of environmental factors in cholestatic diseases has been poorly explored. One clinical and one basic article are related to NAFLD. The study by Linge et al. 9  provides a comprehensive assessment of the potential role of sarcopenia and myosteatosis in NAFLD. Sarcopenia (progressive and generalized loss of skeletal muscle mass, strength, and/or physical performance) is strongly associated with cirrhosis and commonly observed in end-stage liver disease. However, studies reporting associations between sarcopenia and NAFLD/NASH are not yet fully understood. Using data from a large UK Biobank imaging study, the authors investigate the associations of MRI-measured adverse muscle composition (i. e low muscle volume and high muscle fat) with poor function, sarcopenia and metabolic comorbidity within NAFLD.
Although previous data argued for a role of TSP1 in NASH, the current study provides novel insights into the role of TSP1 and SMPDL3B in the regulation of macrophages' inflammatory phenotype. Additional articles in this issue include a study on inequalities in acute liver failure patients who underwent a liver transplant and a report of the role of de novo HBc synthesis in the maintenance and transcriptional regulation of HBV covalently closed circular DNA (cccDNA). Nephew et al. 11 used the US Scientific Registry of Transplant Recipient Database to assess whether potential sex differences could be identified in waitlist mortality, liver transplant and post-transplant 1-year survival in patients with acute liver failure. On multivariate analysis, women were not more likely to die on the waiting list than men and their chances of undergoing liver transplant were similar to those of men.
Methods: We extracted metadata for all biomedical journals referenced in the National Library of Medicine, with any attributed Broad Subject Terms, and at least 50 authored (i. e. by at least one author) articles between 2015 and 2019, identifying the most prolific author (i. e. the person who signed the most papers in each particular journal). We calculated the PPMP and the 2015-2019 Gini index for the distribution of articles across authors. When the relevant information was reported, we also computed the median publication lag (time between submission and acceptance).
The PPMP is also in line with studies on resource distribution in economics. A highly prolific author who is an outlier on the PPMP measure monopolizes a large part of a journal's publications. This analogy supports another, more complex measure, the Gini index  [3] , used in econometrics to describe resource distribution inequalities. Applied to our context, it could be used to quantify imbalances in the patterns of authorship within a 3/39 journal. We set out to apply these two indices to a very large dataset of biomedical journals over a 5-year timeframe, to describe outliers using these indices and to describe time intervals between submission and acceptance dates as a potential surrogate for unfair or partisan editorial practices.
Limitations Our descriptive and exploratory survey, based on a large available database, provides information about the broad scene of 'nepotistic journals' but it may miss some finer points, especially concerning the quality of articles published in these journals. The quality of a scientific article is a difficult concept to measure and it cannot be easily summarized in quantitative metrics. In addition, we restricted our analysis to journals indexed in the National Library of Medicine (NLM) under one or more of the existing broad terms. Some journals are registered without broad terms, requiring a manual pick-up by the NLM. Consequently, our survey may have preferentially included the more established journals indexed in the NLM, with a durable presence in the database, and hence likely to have a better-quality global and editorial conduct than non-indexed journals.
Data extraction The eligibility criteria for the selection of journals were (i) a biomedical journal referenced in the National Library of Medicine (NLM), (ii) having at least one 'Broad Subject Term' and, (iii) having published more than 50 papers between January 2015 and December 2019. Broad Subject Terms are Medical Subject Headings (MeSH), terms used to describe a journal's overall scope, and they are defined by the NLM for journals in the MEDLINE database. [12] The 2015-2019 period was chosen, as this 5year window enables a smoothing of random variations and description of recent practices. One author (AS) searched for changes to journal names during the 2015-2019 period and, in cases of renaming, pooled the articles published under the different names. To identify eligible journals, we used the Entrez programming utilities (E-utilities) which enable queries to the National Center for Biotechnology Information (NCBI) databases.
The search query -presented in Web-Appendix 8 -was used to identify all biomedical journals in the NLM catalogue having at least one of the 'Broad Subject Terms' listed. Then, for each journal, article metadata was automatically collected 11/39 with E-utilities. On account of technical restrictions, querying for article metadata was run from 2015 up to the date of extraction and then restricted to the period January 2015 to December 2019. To manage articles without an author name, the third selection criterion was slightly modified to focus on journals with at least 50 'authored articles' -i. e. articles with at least one identified author -over the 2015-2019 period (see 'protocol changes'). Publications reprinted in several journals (e. g. PRISMA statements published in 6 different journals to promote dissemination) did not receive special treatment, and no correction was applied, as each article was only examined in relation to its publication journal.
Data analysis A descriptive analysis was performed using median, range, and quartiles for continuous variables, and counts and percentages for categorical variables. Descriptions for the 100 outlier journals were computed overall and with respect to membership of any of the most prolific author(s) on the editorial board. Correlations were computed using Pearson's coefficient, with 95% confidence interval (CI). To explore field-specific variations, the distribution of the two indices within each 'Broad Subject term' was graphically displayed. The yearly and overall distribution of the percentage of papers by each author and the Lorenz curve -a graphic representation of the cumulative distribution of appearances as an author -were presented for each of the potential outliers identified above.
In section 4, we show that our theory can rationalize recent credit-easing policy interventions, such as the TALF, which has the features of our optimal subsidy. In general, it is difficult to empirically distinguish models of SCE based on incomplete knowledge, from models of multiple REE based on payoff externalities (as in Bebchuk and Goldstein (2011)). To overcome this difficulty, we collected micro data relative to issuance and riskiness of AAA-rated auto loan ABS before and after the implementation of the TALF. In this market, the likelihood that an auto loan is repaid is not influenced by the likelihood that other auto loans are repaid. Thus, an observed change of regime from high-risk-high-interest to low-risklow-interest, which we show to be independent from aggregate conditions, can only be attributed to a self-confirming mechanism, as payoff externalities do not exist in this market.
Our unique dataset provides information on the effect of the TALF on the riskness of the underlying retail market of auto loans. Initially, we observe a high-risk-highinterest regime. Our theory would say that, before the TALF, the market was in a risky SCE, where, given the information available to lenders, increasing interest rates was an optimal choice to minimize losses. This is what we find in an econometric analysis of the pre-TALF sample: higher interest rates are associated with smaller losses. Then, the introduction of the TALF made the interest rates on the newly generated Auto ABS, and consequently on the underlying Auto loans, to fall dramatically and stayed low even after the end of the TALF. Our theory would say that this is the effect of learning that sufficiently low interest rates generate smaller losses.
This is indeed what we find looking at the post-TALF sample: lower interest rates are associated now with smaller losses. We interpret the information embedded in these two subsamples as capturing the beliefs of Auto companies before and after the TALF, beliefs which were justified by their own experience in the market. We finally run a regression discontinuity analysis that uses the whole dataset and find evidence of a switch in the effect of interest rates on losses, after the introduction of the TALF. This last exercise demonstrates that the riskiness of Auto loans was indeed affected in a non-linear way by interest rates, which in our model is a condition for the emergence of a self-confirming credit crisis. Other related work. This paper belongs to a broad research agenda on financial market crises and related policy interventions.
The case of TALF in the Automotive ABS market The objective of this section is to provide evidence in support of the self-confirming mechanism implied by our theory, which cannot be explained by alternative mechanisms of self-fulfilling liquidity traps. To this aim, we collect and analyze micro-data on the effect of the 2009 TALF -the most significant (and controversial) US crediteasing intervention -on the evolution of riskiness and market value of the underlying securities.
Dataset on Automotive AAA-rated ABS We collected data on Asset Backed Securities (ABS) in the US automotive sector for 9 different issuers in the automotive sector who appear in the balance sheet of the New York Fed as being accepted as part of the Term Asset-Backed Securities Loan Facility (TALF) program starting in March 2009. In particular, we collected all the available free, online information on Trusts issued from 2007 to 2012, a time span which includes the introduction of TALF 25. Table  1 : List of tranches for every issuing company (in bold the Trusts' eligible collateral under the Federal Reserve Bank's Term Asset-Backed Securities Loan Facility (TALF)). Table  1  reports the tranches sorted by issuer for which we have found information. All the issuing entities listed above have benefited from the TALF programme imple-mented by the Federal Reserve (those reported in bold in Table  1 ).
The number of tranches that have been eligible varies by issuer. The program started in March 2009; however, the loans covered by TALF have been extended by each issuer at different points in time within the year 2009. 26 The sum of the amounts of the loans mentioned above represents around 46,5% of all ABS-Auto covered by TALF. The dataset 27  includes information on the following three dimensions: 28  1. Principal amounts in US dollars. For each of the trust we report the total value of the the credit pool gathered by the asset. Moreover, we have the breakdown for different "Classes" (or Asset Backed Notes) of riskiness, going from the more secure A1, to the riskiest, C. Only the Asset Backed Notes A1, A2, A3 and A4 are the ones classified as AAA by rating agencies (i. e. they are above the minimal level of FICO points credit scores to get the AAA label), hence, the ABS itself to be rated AAA must be almost entirely (but not exclusively) composed by A-rated Asset Backed Notes.
The evolution of the total principal amounts in our sample is plotted in figure  8  where a different solid line denotes a different A-rated Asset Backed Notes. Note the collapse of issuance at the end of 2008 and the following recovery in 2009 during the TALF period. Contrast this with the course of the dashed green line, which denotes the total value (y-axis on the left) of minimal risk loan issued by US banks in the same period (source: ST. Louis FRED dataset). The contrast highlights the fact that the recovery in the automotive market during the TALF intervention coincides with the deepest depression of the safest credit market in the US economy. 2. Interest Rates. Each tranche for each company is characterized by an average fixed interest rate for each category where the underlying pool of credit has been signed.
29 The evolution of the weighted average of the interest rate spreads -i. e. interest rate minus 1-month Libor -fixed at the time of the issuance is plotted in figure  9. A different solid line denotes a different A-rated Asset Backed Notes. Note the sharp increase of interest rates at the end of 2008 and the following decrease in 2009 during the TALF period. Contrast this with the evolution of the dotted green dashed, which denotes the interest rate on minimal risk loan issued by US banks in the same period (source: ST. Louis FRED dataset). From the comparison we note that the decrease of interest rates in the automotive market during the TALF intervention is in stark contrast with a permanent increase in interest rates in the safest credit 3. Losses with respect to the original pool balance.
Econometric analysis We run two econometric exercises in order to distinguish between local and global knowledge. By global knowledge we mean the best available knowledge when using all the information in our dataset, accounting for all possible non-linearities present in the data; this captures the objective probability distribution φ in our theory. By local knowledge we mean the best knowledge that firms could had at the time of the introduction of the TALF, compared with what they learned after the introduction of the TALF, considering pre-and post-TALF as two isolated regimes and neglecting non-linearities as in a first order approximation; this captures the subjective probability distribution β in our theory, around a neighborhood of the equilibrium. We show that i) the outcome produced by the TALF contradicts what could have been predicted extrapolating from all the information available before TALF, and ii) what companies could learn from the two partial subsamples is consistent with what can be learned using all the information ex-post available in this market.
Assessing local knowledge We run two regressions on two different subsamples of our dataset, with a similar number of observations. The first sample -Sample I -consists of all the data available before 25 March 2009, which is the date of implementation of TALF. The second, Sample II, consists of all the data relative to tranches issued after 25 March 2009 for the following two years, i. e. until 1 April 2011. On each sample we run the following linear regressions: Y i,t,T = β 0 + β 1 X i,T +β 2 Val i,T + β 3 Lib T + S i + + β 4 Lib t + β 5 u t + β 6 Inf t + β 7 Gdp t + β 8 Vix t + i,t,T where Y i,t,T denotes the first differences in monthly cumulative losses (with respect to initial pool balance) occurring at time t relative to the tranche issued by company i at time T ; X i,T stands for the differential of the average interest rate relative to the tranche (i, T ) with the correspondent one-month Libor value at time T ; Val i,T is the amount issued, which is relative to the tranche (i, T ), valued in current US dollars.
S i are company fixed-effects that control for all the time-invariant unobserved company characteristics. Finally, a set of covariates that control for the business cycle are included: u t is the US monthly unemployment rate; Vix t is the monthly VIX index; Lib t is the one-month Libor at time t; Lib T is the one-month Libor at the time of the issuance of the relative tranche T ; Inf t is the monthly US inflation rate; Gdp t is the monthly US national GDP growth rate. The regression aims at capturing the linear relation between interest spreads, which is a choice variable of the financing companies, and resulting losses, controlling for a number of tranche-specific factors, and business-cycle variables. This specification does not capture eventual non-linearities in our micro-data or in business-cycle series, as if we were looking at local information produced by first-order perturbations around an equilibrium.
Assessing global knowledge Our second econometric exercise is designed to capture the point of view of an external observer that wants to ex-post assess how the introduction of the TALF affected the impact of the interest spread on the losses. We still run a linear regression using the whole dataset, but now we introduce time-fixed effects, instead of business-cycle series, in order to capture any potential non-linearity in time affecting all companies and all tranches. Our specification is the following: Y i,t,T = β 0 + β 1 D + β 2 X i,T +β 3 DX i,T + β 4 Val i,T + S i + S t + S p + i,t,T , where we introduce new variables: D, S t and S p. D denotes a dummy which is 1 when the payment belongs to a tranche issued during or after the TALF period, and 0 otherwise. Thus, β 3 measures the differential effect of spreads on losses that the introduction of the TALF generated in the newly issued trust.
B. 1 Data on ABS in the Automotive Industry The data collected are information on Asset Backed Securities (ABS) in the US automotive sector. The data include information on: Cumulative Losses with respect to the original Pool Balance, Interest Rates, Principal Amounts in US dollars. The information is collected for 9 issuers in the automotive sector, namely: BMW Vehicle Lease Trust, CarMax Auto Owner Trust, Ford credit Auto Owner Trust, Harley-Davidson  Motorcycle Trust, Honda Auto Receivables Owner Trust, Hyundai Auto Receivables  Trust, Nissan Lease Trust, Nissan Auto Receivables Owner Trust, World Omni Auto  Receivables Trust. Every year each of these companies delivers onto the market a variable number of Trusts (or tranches). For instance, World Omni in 2008 extended loans in two tranches (2008-A, 2008-B), while the same company extended only one tranche in 2009 (2009-A).
All the issuing entities listed in bold in Table  1  was eligible for the TALF program. The number of tranches that were eligible varies by issuer. The program started in March 2009; however, the loans covered by TALF have been extended by each issuer at different points in time within year 2009. The tranches covered by TALF programme that are included in the database are sufficiently representative of the whole sample of Asset Backed Securities covered by TALF in the US automotive sector, since the sum of the loan amounts mentioned above represents around 46,5% of all ABS covered by TALF (calculations are our own, reference "TERM ASSET-BACKED SE-CURITIES LOAN FACILITY DATA" from FED). The data have been collected one piece of information at a time from prospectuses publicly available online.
The major source utilized is https://www. bamsec. com/companies/6189/208 where the majority of observations are available. The other sources are the issuers' websites which sometimes contain the Trusts' prospectuses. Concerning the controls for the business cycle, the data were collected from several sources: the monthly US Inflation is calculated as (CPI t -CPI t-1 )/CPI t-1 and the US Consumer Price Index (all items) are taken from OECD (MEI); The US Civilian Unemployment rate not seasonally adjusted comes from the US Bureau of Labor Statistics; the CBOE Volatility Index (VIX) along with the one-month Libor correspondent to the 15th day of each month are from the St. Louis FED data. Lastly, the monthly GDP Index used to calculate the GDP growth rate is from Monthly GDP Index -Macroeconomic Advisers.
work have been presented, for their comments. We thank  Mauro Lanati  for excellent research assistance. We acknowledge financial support by the  Fondation Banque de France  and of the  ADEMU  project, " A Dynamic Economic and Monetary Union ", funded by the  European Union 's  Horizon 2020 Program  under grant agreement No  649396. The views expressed in this paper do not necessarily reflect those of  Banque de France  or  European Commission. The ADEMU Working Paper Series is being supported by the  European Commission Horizon 2020 European Union funding for Research & Innovation , grant agreement No  649396.
Study context We draw on qualitative data that were collected as part of a mixed-methods study that investigated drug resistance to first-line ART and associated factors amongst hospitalized patients with advanced HIV disease in two hospitals supported by Médecins sans Frontières (MSF) in Homa Bay, Kenya and Kinshasa, DRC (Table  1 ). Advanced HIV was defined as CD4 ≤ 350 cells/µL. The quantitative component of this study measured the proportion of treatment failure (virological failure and major drug resistance mutations) among these patients using a cross-sectional design. Despite the differences between the settings, both hospitals faced high rates of advanced HIV amongst patients who were admitted with an HIV diagnosis. In both sites, around half of all patients admitted to hospital with HIV had been on ART for at least 6 months, and the majority were admitted with advanced HIV.
Data generation In-depth interviews took place between October and November 2017 in Kinshasa, and between February and April 2018 in Homa Bay. In Kinshasa, interviews were conducted by a trained, non-clinical male Congolese research assistant in Lingala or French in a quiet, private room inside the ward. In Homa Bay, interviews were conducted in Dholou (often with segments of English and Swahili) by a non-clinical, female, Kenyan social scientist in a private outdoors location on the hospital grounds. Interviews lasted for between 20 and 120 minutes, and covered participants' lived experiences of illness since their HIV diagnosis, their health journeys, experiences with treatment and pathways of care. Regular pauses were proposed during the interview to enable the participant to rest.
Data management and analysis Interviews were audio-recorded with consent, transcribed and translated into either French (Kinshasa) or English (Homa Bay) by a trained staff member. Transcripts were reviewed for quality, and any discrepancies in translation were verified and corrected with the assistance of the research assistants. Data from transcripts were coded manually in Kinshasa and with the aid of NVivo 11 software in Homa Bay. A thematic analysis was undertaken by the research lead in each site, drawing on the principles of grounded theory, whereby codes were raised to a conceptual level considering their underlying meaning and their interrelationship  (Charmaz, 2014; Green & Thorogood, 2004). Detailed analytical summaries of the emerging themes and sub-themes were shared regularly between the research leads in each site.
Acknowledgments The authors are grateful to all the participants who contributed to this study as well as their caretakers, doctors and nurses, and support staff at the  Centre Hospitalier de Kabinda ,  DRC  and  Homabay County Teaching and Referral Hospital, Kenya. We also thank the study teams in both sites, in particular  Grace Akinyi  (transcriber/translator in Kenya) and  Rebecca Harrison  (epidemiologist in DRC) and the teams supporting the cross-sectional study.