dataesr/scientific-paragraphs-categorization huggingface dataset:
Given this evidence, our model predicts that stocks with higher fund ownership should experience greater increases in liquidity with more frequent disclosure. To test this prediction, we employ a difference-in-differences approach to examine the change in stock liquidity during the two-year period around the SEC rule change in May 2004. In particular, we examine how changes in stock liquidity (first difference) vary with the ownership of actively managed domestic equity mutual funds (second difference). Ge and Zheng (2006)  document that some funds voluntarily disclose their portfolios. We carefully identify such funds that disclose to different sources (Morningstar and Thomson Reuters) in addition to the SEC EDGAR (Electronic Data Gathering, Analysis, and Retrieval) database (see Section IV for more details).
7 See the SEC Final Rule IC-26372 on May 10, 2004 at http://www. sec. gov/rules/final/33-8393. htm. To illustrate the SEC regulation change in 2004, we present in Table  I  the total number of mutual fund holdings reports in each year from 1994 to 2011, the period over which data is electronically available from the EDGAR database. We find that the total number of filings almost doubled from 6,474 in 2003 to 12,438 in 2005 as shown in the last column. We break down the numbers for each form type and find that this dramatic increase in the total number of filings is completely due to the introduction of the N-Q form in 2004. The N-Q forms account for about half of all filings from 2005 onward. [Insert Table  I Here]  Individual funds can voluntarily report their portfolio information more frequently than is mandated by the SEC.
A. Data description We start by identifying the mutual funds that increased their portfolio disclosure frequency due to the SEC regulation change in 2004. For this purpose, we first obtain funds' portfolio disclosure dates from three major data sources: the SEC EDGAR, Morningstar, and Thomson Reuters S12. 9 We manually match the funds across these sources using fund names, tickers, and CUSIPs. Finally, we merge the lists of disclosure dates and remove any duplicates. This gives us a comprehensive list of all unique instances of disclosure for each fund over time. The above procedure helps us identify the funds affected by the regulation change. Next, we obtain the portfolio holdings data of these funds from the Thomson Reuters S12 database for our empirical analyses. We further merge the resulting data with the CRSP mutual fund data using the Wharton Research Data Services' (WRDS) MFLINKS tables to obtain fund returns and characteristics such as total assets under management, expense ratio, load, and turnover.
B. Variable construction B. 1 Stock-Level Variables We construct our sample of stocks from the CRSP stock database. We consider all common stocks in CRSP over the period May 2003 to April 2005 in our main analyses. We choose this period so that we have one year prior to and one year after the SEC disclosure regulation change in May 2004 to examine the changes in stock liquidity and fund 9 There are differences in mutual funds' portfolio disclosure dates to the SEC and to Thomson Reuters  (Schwarz and Potter (2014) ). Therefore, we take a comprehensive approach and combine the portfolio disclosure dates from the SEC and the two major mutual fund database vendors (Morningstar and Thomson Reuters). 10 Our sample of 1,459 funds comes from a total of 2,063 actively managed domestic equity funds before the 2004 regulation change.
We use these non-mutual-fund institutions as a control group to identify the effects of the SEC rule change. For this purpose, we define Non-MF Ownership as the quarterly aggregate institutional ownership from Thomson Reuters Institutional Holdings (S34), excluding mutual funds and asset management companies. In addition, we isolate hedge funds from the non-mutual-fund institutions to form another control group because they are arguably the most actively managed institutions. We define Hedge Fund Ownership as the quarterly aggregate hedge fund ownership in the Thomson Reuters S34 database. Classification of institutional investors and hedge funds follows that in  Agarwal et al. (2013). Lastly, we also use U. S. index equity funds as a control group because index funds are passive investors and by definition their disclosed portfolios should not contain any private information.
We construct Index Fund Ownership as the ownership of index funds that we identify from the CRSP Mutual Fund Database. Next, we construct a number of variables to proxy for stock liquidity. The first measure is the Amihud illiquidity measure  (Amihud (2002) ), calculated as the monthly average of daily Amihud measures. Specifically, we construct this measure as follows,   Vol is the daily volume. Our model suggests that an increase in disclosure frequency should result in lower adverse selection costs for the market maker and thus lower bid-ask spreads. Therefore, we also use the high-frequency Trade and Quote (TAQ) data to compute three bid-ask spread measures widely used in previous studies (e. g. ,  Chordia, Roll, and Subrahmanyam (2000, 2001) ,  Goyenko, Holden, and Trzcinka (2009) ,  Nimalendran and Ray (2014) ).
For 5-factor alpha, we also include the  Pástor and Stambaugh (2003)  liquidity factor in equation (  6 ) above. To compute the DGTW-adjusted return, we follow  Daniel et al. (1997)  to sort stocks into 5×5×5 portfolios based on the size, book-to-market, and momentum quintiles. Then, we calculate the benchmark-adjusted returns for each stock position in a fund's portfolio and construct the value-weighted average at the fund level using the portfolio weights. Lastly, we compute the cumulative benchmark-adjusted returns between two successive report dates in the Thomson Reuters S12 database and then divide them by the number of months in the period to obtain a monthly DGTW-adjusted return. We compute the Liquidity-adjusted DGTW return as a modified version of the DGTW-adjusted return. To ensure that we have a sufficient number of stocks in each portfolio, we sort stocks into terciles instead of quintiles.
fund performance;  Jianfeng Hu  for TAQ order imbalance data;  Lei Jiang ,  Mahendrarajah (Nimal) Nimalendran , and  Sugata Ray  for data on TAQ liquidity measures; and  Christopher Schwarz  for data on mutual fund holdings. We would also like to thank  Muneem Ahad  and  Ashutosh Tyagi  for excellent research assistance. Kevin Mullally  thanks the  Center for Economic Analysis of Risk (CEAR) at Georgia State University  for their financial support. Yuehua Tang  acknowledges the D. S. Lee Foundation Fellowship from  Singapore Management University.
Background: The objectives of this study were to describe hospital stays related to HZ and to evaluate the direct and indirect cost of hospitalizations due to HZ among patients aged over 50 years. Methods: The hospitalizations of people aged over 50 years were selected from the French national hospital 2011 database (PMSI) using ICD-10 diagnosis codes for HZ. Firstly, stays with HZ as principal or related diagnostic were described through the patient characteristics, type of hospitalization and the related costs. Secondly, a retrospective case-control analysis was performed on stays with HZ as comorbidity in 5 main hospitalizations causes (circulatory, respiratory, osteo-articular, digestive systems and diabetes) to assess the impact of HZ as co-morbidity on the length of stay, mortality rate and costs.
HZ epidemiological data are annually estimated by the French sentinel surveillance reporting system (based on data derived from consultations of a sample of French GPs). Moreover, HZ can be a repetitive history: in contrast with previous assessments, rates of HZ recurrence appear to be comparable to rates of first occurrence in immunocompetent individuals  [15]. In France, estimated HZ annual incidences were 346,988 cases in 2009  [16] , 269,833 cases in 2010  [17] , 219,823 cases in 2011  [18]  and 303,625 cases in 2012  [19]. Based on the sentinel surveillance reporting system, Gonzalez Chiappe et al. , estimated the French 2010 HZ incidence rate to be 382 cases/100,000 inhabitants. The article also studies herpes zoster hospitalizations trough the PMSI database between 2000 and 2006. Over the seven years, 61,429 hospital stays were collected with a mean annual of 8,728 stays per year.
Data sources The French Medical Information System (Programme de Médicalisation des Systèmes d'Information -PMSI) is an exhaustive medico-administrative hospital discharge database that covers all public and private hospitals in France as well as those in the French Territories  [21] [22] [23] [24] [25] [26]. Diagnoses identified during admission are coded using the International Classification of Diseases, 10th revision (ICD-10) by the physician. PMSI includes a compilation of standard discharge summaries ("Résumé Standard de Sortie", RSS) for every admission. Anonymised data ("Résumé Standardisé Anonymisé", RSA) with limited socio-demographic information (gender, age, residence code) and medical information on the main diagnosis that led to hospital admission, the nature of treatments received and examinations carried out, underlying comorbidities and possible complications, are made available for epidemiologic studies.
Each patient's stay is classified by Diagnosis Related Group (DRG) (Groupe Homogène de Séjours) according to the information documented by the physician. The economic burden of hospitalized cases would be expected to be well documented within the PMSI database because since the introduction of a DRGbased prospective payment system (the "Tarification à l'Activité") in 2005, the PMSI database has been used as the basis for the funding of services in all hospitals, with each hospital receiving DRG-based payments according to the national tariff. Thus, data extracted from this database is exhaustive (all public and private hospitals are included and no sampling is done) and of high quality, with limited coding errors.
Data collection of herpes zoster All hospital stays with a primary, related and associated herpes zoster specific code were selected from the 2011 PMSI database using the ICD-10 codes B02* ("Zoster)". In order to evaluate the hospitalization economic impact of HZ among patients aged over 50 years in France and to assess the impact on death, hospitalization's length of stay and hospitalization's cost of HZ when this latter is comorbidity in other hospitalization's causes, two different data collection were done from the PMSI database. For the first analysis, a conservative approach was done by selecting only those hospital stays that had a primary diagnosis or had direct link with HZ for patients aged over 50 years. HZ ICD 10 codes were selected based on Gonzalez Chiappe et al. in 2010 publication, by seven codes: B020 "HZ encephalitis", B021 "HZ meningitis", B022 "HZ with neurological disease", B023 "HZ Ophthalmicus", B027 "Disseminated HZ", B028 "HZ with other complications" and B029 "HZ with no complications"  [20].
Ethics and consent This non-interventional study does not fall under the scope of the law and does not require any ethics committee submission (Law 88-1138 relative to Biomedical Research of December 20, 1988, modified on  August 9, 2004). The ATIH (Agence Technique de l'Information sur l'Hospitalisation) is responsible for managing the finalized database each year under approval by the CNIL (National commission for data processing and civil liberties).
Burden of HZ hospitalization Hospital stay characteristics A total number of 7,389 stays for HZ were identified in the PMSI 2011 database for patients aged over 50 years. A medical interpretation by a DIM physician classified the selection into 2 groups, 2,571 hospital stays (35 %), related to 2,392 patients, were directly related to HZ (2,362 and 209 stays were in public and private hospitals, respectively), and 4,818 stays (65 %) were indirectly related to HZ. This last group was excluded (4,047 and 771 stays were in public and private hospitals, respectively) [Fig. 1 ]. A conservative approach was taken by selecting only those hospital stays that had a primary diagnosis of HZ or had direct link with HZ. Over the 2,571 hospital stays observed during the study period for HZ, stays for B029 "HZ without complications" was mainly represented with 911 hospital stays (33.
Impact of HZ comorbidity in hospitalizations for other reasons Hospital stay characteristics A total number of 4,818 hospital stays with a zona comorbidity among patients aged over 50 years without  In order to evaluate the statistical impact of zona as comorbidity on length of stay, death rate, and cost, a separately case control was performed for each disease. Chi-square tests showed that age and sex distribution were significantly different between the 2 cohorts for each disease (p < 0. 001). As a consequence, extracted data were adjusted by age and sex for all disease [Table  4 ]. A case-cohort stay was matched with 3 randomly chosen control-cohort stays for the same age group, sex and pathology. The co-morbidity analysis was based on the selection of the 1. 717 stays for patients aged over 50 years in cases cohort and of 5,151 stays out of the 3,665,088 stays of patients aged over 50 years in control cohort [Table  4 ].
Discussion The current PMSI 2011 analysis identified 2,571 stays of patients aged over 50 years hospitalized for HZ corresponding of 0. 9 % of the 2011 recorded total stays, while diabetes represented 95,204 stays (62 times more) and degenerative disease represented 48,291 stays (19 times more). The HZ represents a subsequent economic burden of approximately Clinical and costs input data were derived from a cohort of 2,392 patients, with 60 % of women and with a mean age of 76. 3 years. Death occurs in 1. 6 % of cases of patients hospitalized with zoster. B020 "HZ encephalitis" and B021 "HZ meningitis" stays characteristics involved older patients, longer LOS, higher rates of transfer to another health care institution and higher death rates even though the related number of extracted stays was the lowest (63 and 44 stays respectively).
Herpes Zoster significantly increases length of hospitalization (p < 0. 001) leading to subsequent economic burden for the French health system. It is expected that this extension of LOS also has an impact on the cost of the management of the disease. Herpes Zoster doesn't significantly increase the number of deaths. This review has various limitations. First of all, PMSI database is the basis of hospital funding and not an epidemiological register. The prospective payment is directly linked to the coding process leading to optimize this latter. Nevertheless the high exhaustiveness and quality of information (including some medical information) allows using the PMSI database to estimate various indicators by disease, like the number of patients hospitalized annually, the number of stays per patient and the total cost of hospitalization  [24] [25] [26].
1 Reward pathways extend from the ventral tegmental area to the nucleus accumbens (NAc), the dorsal striatum, the hippocampus, and the prefrontal cortex (PfC) through dopaminergic projections. 2 Because cocaine and amphetamines increase the amount of dopamine in the synaptic cleft through diverse mechanisms, it is possible that distinct molecular adaptations might occur in response to different psychostimulants. This sentence highlights the need for comprehensive studies of these drugs of abuse. Here, we focus our attention on data specific for AMPH and its highly addictive analog, METH. AMPH and METH activate neurotransmitter release by reversing monoamine transport, whereas cocaine acts as a monoamine reuptake inhibitor. 1 AMPH and METH display very similar molecular effects, notwithstanding some pharmacodynamic variations; for example, at the same dose, METH is described as a more potent stimulant.
To investigate whether tropical forest dwellers have developed specific biological adaptations to this harsh environment, we searched for genome-wide signals of positive selection in populations from the Americas and Africa, specifically aiming at identifying convergent evolution signals, that is a significant signal of positive selection occurring at the same genomic region or biological pathway in populations belonging to two distinct evolutionary lineages. To that effect, we investigated populations living in tropical forests and others, genetically related, living outside these environments using publicly available genome-wide SNP data and a robust and sensitive F ST -based method for inference of positive selection that explicitly includes a convergent selection model.
Populations and samples Genome-wide single nucleotide polymorphism (SNP) data were downloaded for seven populations included in the Human Genetic Diversity Panel database (HGDP;  [10, 11] ). Two African (Biaka (n = 30) and Mbuti pygmies (n = 15)) and two American (Surui (n = 21) and Karitiana (n = 24)) tropical forest populations were selected, as well as three other populations from these continents to serve as non-tropical forest comparisons (Mandenka (n = 24) and Yoruba (n = 24) in Africa; Pima (n = 25) in America). Considering the genetic similarity between Mandenka and Yoruba  [12] , these populations were grouped into a single set, hereafter called "West Africa", to increase sample size and the statistical power of the analyses. More information on the chosen populations can be found in  [3, [10] [11] [12] [13] [14]  and S1 Table.
The results of this analysis are the core of this study, but since we had a single non-forest dwelling Amerindian population, we performed a supplementary analysis with a similar sampling design as described above, except that we replaced the Mexican Pima by the Zapotec. The rationale behind this approach is to check whether the signal observed using the core analyses is not related to any adaptive evolutionary phenomenon that would be restricted to Pima (nonforest) rather than to the forest dwellers Karitiana and Surui. Thus, by replacing Pima by Zapotec-another non-forest agriculturalist population-we should be able to check if the reported signals hold. This analysis is performed separately, because the number of shared SNP for this dataset (364, 470 SNPs) is much lower than that of the HGDP database (660,918 SNPs).
Genetic data Data on 660,918 SNPs (Illumina HumanHap 650K Beadchips) were downloaded from the Stanford University HGDP-CEPH SNP genotyping data supplement 1 (  [12] ; <ftp://ftp. cephb. fr/hgdp_supp1/>). We initially discarded 250 markers that were monomorphic in all populations, those that presented only missing data, and those located on either the Y-chromosome (due to low SNP density in the chip), the pseudoautosomal region of the sex chromosomes, or the mtDNA, leaving us with 660,668 SNPs. Within each PS defined above, those markers with minor allele frequency below 5% in all populations joined into one were discarded, yielding 582,074, 581,855, 584,205, and 577,345 SNPs for population sets PS1-PS4, respectively. The supplementary dataset includes merged data from seven different sources,  [18].
The Zapotec sample was genotyped on Illumina HumanHap 550 V3. 0 arrays obtained partially from MGDP  [19]  and partially from samples newly genotyped by Reich et al. [18]. Surui data includes samples genotyped by HGDP  [10, 11]  and Kidd Lab  [18]  on Illumina 650Y arrays. Data for the remaining populations are from the same source as the core analysis, i. e. HGDP  [10, 11]. This merged dataset was compiled and processed according to stringent data curation and validation procedures. For our study, we used data on segments of potentially non-native origin that were originally masked by Reich et al. [18]  with a local ancestry inference software. This yielded a sample size of 43 individuals for Zapotec (merging Zapotec1 and Zapotec2), 13 for Karitiana and 24 to Surui. Readers are referred to the source publications for further details of genotyping, masking, and filtering  [18, 19].
Gene and regulatory elements annotation All SNPs were assigned to genes using PLINK v1. 07 (  [26] ; available at http://pngu. mgh. harvard. edu/purcell/plink). SNPs not present in coding regions were assigned to a given gene if located less than 50 kb away from it. When more than one gene was within this range, the closest gene was chosen for the subsequent analyses. This software was also used for indicating the attributes of the functional SNPs, i. e. if they are nonsense (stop codon), missense (non-synonymous), frameshift or splice-site mutations, based on information from dbSNP build 129 available with PLINK v1. 07. The amount of outlier SNPs falling in genic regions (or < 50 kb away from them) was compared to the amount of outlier SNPs in non-genic regions first taking into account only protein coding genes and then all regulatory elements as described below.
We assessed the p-value by taking the proportion of occasions that resulted in an overlap equal or larger than the overlap we found. We followed the same procedure when testing the overlap of outlier genes. The hg19 assembly coordinates (NCBI Build 37. 3) of 19,683 protein-coding genes located on the human autosomal and X chromosomes were obtained from the NCBI Entrez Gene website (  [28] ; <http://www. ncbi. nlm. nih. gov/gene>, accessed on February 7, 2013). Seventeen genes were annotated with multiple locations (caused by merging of gene records in the NCBI database); in these cases we took the outermost start and end positions. The original positions of the SNPs on the hg18 reference genome (NCBI Built 36. 3) obtained from the original dataset (  [26] ; <ftp://ftp. cephb. fr/hgdp_supp1/>) were remapped on hg19 with the NCBI Genome Remapping Service (<http://www.
For each gene and each selection model, we took the highest selection score of all SNPs assigned to this gene. After removing 1,922 genes with no SNPs assigned, a list of 17,761 genes remained for PS1 and PS4, 17,766 for PS2, and 17,769 for PS3. We collected 2,336 gene sets from the NCBI Biosystems database (  [31] , http://www. ncbi. nlm. nih. gov/biosystems, downloaded 3 Sep 2013). After removing genes that were not part of the gene list mentioned above, excluding gene sets containing less than 10 genes and pooling (nearly) identical gene sets into union sets, 1,216 (PS1 and PS2) and 1,217 (PS3 and PS4) sets remained and they were used as input in the enrichment tests. We computed the SUMSTAT  [32]  score for each gene set, which is the sum of either the p af , p am, p co, or p sl values of all genes in a set, depending on the selection model.
The sliding-window procedure yielded 440, 399, 505, and 471 SNPs with a significant signal of positive selection for each PS (S1 File). From these, 19 are found to be functional non-synonymous mutations (missense) in one or two PSs (S2 Table ). There was no instance of a significant SNP defined as nonsense, frameshift, or splice-site mutation. Outlier SNPs are significantly enriched (Fisher exact test, p-value < 0. 022) for genic SNPs in comparison to the whole HGDP SNP set. On average 72. 8% of the outlier SNPs after the sliding-window procedure are located in or less than 50 kb away from protein-coding regions considering all four PSs, while for all SNPs in the HGDP database this proportion is only 63. 6%. This enrichment is not due to potential LD between outlier SNPs, as it still holds when randomly sampling one outlier SNP and one non-outlier SNP per 100 kb window (N = 1000, mean p-value = 0.