dataesr/scientific-paragraphs-categorization huggingface dataset:
Heritability of self-employment and the degree of variance that is accounted for by common SNPs We used data from the Swedish Twin Registry (STR) and the classical twin design to estimate the heritability of the tendency to engage in self-employment. We computed the tetrachoric correlations between the tendencies to engage in self-employment within monozygotic (MZ) and dizygotic (DZ) twin pairs. Table  1  indicates that the correlations within the MZ twin pairs were consistently higher than within the DZ twin pairs for males only, for females only, and for pooled males and females. We note that the correlation within DZ twin pairs in the pooled sample was higher than for the DZ correlations in males and females when the two sexes are considered separately. This effect most likely results from imprecise estimation of the tetrachoric correlations due to the small number of cases.
[52]  was employed to estimate the degree of variance in the tendency to engage in self-employment that is explained by all of the genotyped autosomal SNPs in the GWAS datasets. The proportion of the explained variance was estimated for pooled males and females, males only, and females only. To maximize the power of the analysis, we used a combined sample of one of the discovery studies (Rotterdam Study Baseline [RS-I]) and the STR. We estimated that 25% (p = 0. 032) of the variance in the tendency to engage in self-employment could be explained by the common genotyped autosomal SNPs for pooled males and females (Table  3 ). The variance that could be explained for males only and for females only was 25% (p = 0. 152) and 0% (p = 0. 499), respectively. The estimates for males and females separately were not significantly different from one other.
Meta-analyses of genome-wide association studies We performed genome-wide association analyses of selfemployment using the data from sixteen discovery studies. These studies comprised 7,734 participants who had been self-employed at least once and 42,893 participants who did not report being selfemployed. Table  4  includes the descriptive statistics for the studies. The mean ages in the pooled samples of males and females ranged from 31 to 68. 8 years, and the average age across all of the studies was 53. 4 years. Following independent association analyses for each study, we performed a fixed-effect meta-analysis of the studylevel results for approximately 2. 4 million SNPs using a pooled zscore approach. The discovery meta-analysis Q-Q plot (Figure  1A ) did not indicate a strong deviation for the lowest p-values.
Predicting self-employment from genotype data We examined whether the results from the discovery metaanalyses could be used to predict self-employment in the replication study  [76]. We pruned the set of autosomal SNPs to a subset of approximately 120,000 SNPs that are in approximate linkage equilibrium. In an initial prediction analysis, we included only the subset of these 120,000 SNPs that reached a 1% significance level. We calculated a predictive score for each individual in the replication study by determining, for each SNP, the product of the individual's number of effect alleles and the estimated regression coefficient from the discovery meta-analysis. This product was then summed across the included SNPs and divided by the number of included SNPs. We evaluated the predictive power of the SNPs by calculating the degree of variance in the tendency to engage in self-employment that was explained by the score and the area under the receiver operating characteristic curve (AUC).
Third, we perform the first meta-analysis of GWASs of an economic behavior (i. e. , self-employment) using data from sixteen studies that together comprise approximately 50,000 participants. The discovery stage had 80% power to detect a variant at genomewide significance with a minor allele frequency of 0. 25 and odds ratios of approximately 1. 11 for pooled males and females, 1. 15 for males only, and 1. 17 for females only  [87] , assuming we had a non-noisy, harmonized measure of self-employment across studies. Yet, we do not identify genome-wide significant associations. This result suggests that there are no common SNPs for selfemployment with moderate to large effect sizes, thus placing an upper bound on the effect sizes of common SNPs that we can expect to exist. Gene-based tests for approximately 17,700 genes, including several candidate genes for entrepreneurship that have been previously suggested in the literature  [48, 79] , do not reveal significant associations.
We opted to maximize the available sample size in this study and operationalized entrepreneurship as self-employment, which is also the most frequently used measure of entrepreneurship in the economics literature  [102]. We included every study we were aware of in the analysis that included a measure of self-employment and which was willing to contribute data, although this approach necessitated that data from diverse populations (e. g. , Eastern German self-employed individuals and US business owners) were pooled. The available measures of self-employment varied across studies, including different single-and multiple-item measures, data from standalone surveys, and data from repeated measures or retrospective employment histories of the participants. For a number of studies, this approach resulted in a lack of detailed and reliable data regarding work-life history.
Acknowledgments We are grateful to  Peter Visscher  for his helpful comments and suggestions; AGES: The researchers are indebted to the participants for their willingness to participate in the study; ASPS: The authors thank the staff and the participants of the ASPS for their valuable contributions. We thank  Birgit Reinhart  for her long-term administrative commitment and  Ing Johann Semmler  for technical assistance with the creation of the DNA bank; ERF: We are grateful to all of the patients and their relatives, as well as to the general practitioners and the neurologists for their contributions. We are also thankful to  P. Veraart  for her assistance in matters pertaining to genealogy,  Jeannette Vergeer  for supervision of the laboratory work, and  P. Snijders  for his assistance in data collection; GHS: We thank all of the study participants and all of the colleagues that are involved in the GHS; H2000: We would like to thank all of the Health 2000 Survey participants; HBCS: We thank all of the study participants as well as everyone who is involved in the Helsinki Birth Cohort Study; NFBC1966: We thank  Professor Paula Rantakallio  (launch of  NFBC1966  and initial data collection),  Ms.
Sarianna Vaara  (data collection),  Ms. Tuula Ylitalo  (administration),  Mr. Markku Koiranen  (data management),  Ms. Outi Tornwall  and  Ms. Minttu Jussila  (DNA biobanking); NTR: We thank all of the participating twin families for their cooperation; RS: We thank  Pascal Arp ,  Mila Jhamai ,  Dr. Michael Moorhouse ,  Marijn Verkerk , and  Sander Bervoets  for their assistance in creating the GWAS database. The authors are very grateful to the participants and staff from the  Rotterdam Study , the participating general practitioners and the pharmacists. We would also like to thank  Dr. Tobias A. Knoch ,  Luc V. de Zeeuw ,  Anis Abuseiris , and  Rob de Graaf , as well as their institutions: the  Erasmus Computing Grid ,  Rotterdam ,  The Netherlands , and especially the national  German MediGRID  and ;  Services@MediGRID  part of the  German D-Grid  for access to their grid resources; SardiNIA: We thank all of the volunteers who participated in the study,  Monsignore Piseddu ,  Bishop of Ogliastra , the mayors and citizens of the participating Sardinian towns ( Lanusei, Ilbono, Arzana, and Elini ), the head of the Public Health Unit ASL4 for their volunteer work and cooperation, and the team of biologists, physicians, nurses, and the recruitment personnel; SHIP: The contributions to data collection made by the field workers, the study physicians, the ultrasound technicians, the interviewers, and the computer assistants are gratefully acknowledged; STR: The STR thanks the  SNP&SEQ Technology Platform, Uppsala  for their genotyping assistance; THISEAS: The  Hellenic study of Interactions between SNPs and Eating in Atherosclerosis Susceptibility (THISEAS ) thanks the genotyping facility at the  Wellcome Trust Sanger Institute  for typing the THISEAS samples and, in particular,  Sarah Edkins  and  Cordelia Langford.
The  AGES-Reykjavik Study  is funded by  National Institutes of Health  contract  N01-AG-12100 , the  NIA   Intramural Research Program ,  Hjartavernd  (the  Icelandic Heart Association) , and the  Althingi  (the Icelandic Parliament); ASPS: The research reported in this article was funded by the  Austrian Science Fond (FWF)  grant number  P20545-P05  and  P13180. The  Medical University of Graz  supports the databank of the ASPS; ERF: The genotyping for the ERF study was supported by  EUROSPAN (European Special Populations Research Network)  and the  European Commission   FP6 STRP grant  ( 018947 ;  LSHG-CT-2006-01947 ). The ERF study was further supported by grants from the  Netherlands Organization for Scientific Research, Erasmus MC , the  Centre for Medical Systems Biology (CMSB)  and the  Netherlands Brain Foundation  ( Hersenstichting Nederland ); GHS: This work/the  Gutenberg Health Study is funded through the government of Rheinland-Pfalz (''Stiftung Rheinland Pfalz fu ¨r Innovation '', contract number  AZ 961-386261/733 ), the  research programs ''Wissen schafft Zukunft'' and ''Schwerpunkt Vaskula ¨re Pra ¨vention'' of the Johannes Gutenberg-University of Mainz  and its contract with  Boehringer Ingelheim  and  Philips Medical Systems  including an unrestricted grant for the Gutenberg Health Study; H2000: The study was funded mainly by the budgetary funds of  National Institute for Health and Welfare (THL).
Markus Perola  is partly financially supported for this work by the  Finnish Academy SALVE program  ''Pubgensense'' 129322; HRS: The Health and Retirement Study is sponsored by the  National Institute on Aging  (grant number  NIA U01AG009740 ) and is conducted by the  University of Michigan ; KORA S4: The KORA Augsburg studies were financed by the  Helmholtz Zentrum Mu ¨nchen, German Research Center for Environmental Health, Neuherberg, Germany  and supported by grants from the  German Federal Ministry of Education and Research (BMBF). Part of this work was financed by the  German National Genome Research Network (NGFN). Our research was supported within the  Munich Center of Health Sciences (MC Health ) as part of LMUinnovativ;  NFBC1966 :  Academy of Finland  [project grants  104781 ,  120315 ,  129418  and  Center of Excellence in Complex Disease Genetics and SALVE], University Hospital Oulu, Biocenter, University of Oulu, Finland  ( 75617 ), the  European Commission [EURO-BLCS ,  Framework 5  award  QLG1-CT-2000-01643 ],  NHLBI  [ 5R01HL087679-02 ] through the  STAMPEED program  [ 1RL1MH083268-01 ],  NIH/NIMH  [ 5R01MH63706:02 ],  ENGAGE  project and grant agreement [HEALTH-F4-2007-201413], and the  Medical Research Council, UK  [ G0500539 ,  G0600705 , PrevMetSyn/SALVE]; NTR: The Netherlands Twin Register data collection and genotyping has been funded by the  Netherlands Organization for Scientific Research (NWO: MagW/ZonMW  grants  904-61-090 ,  985-10-002 , 904-61-193 , 480-04-004 ,  400-05-717 ,  Addiction-31160008 Middelgroot-911-09-032 ,  Spinozapremie 56-464-14192 ),  Center for Medical Systems Biology (CSMB, NWO Genomics) ,  NBIC/BioAssist/RK(2008.
024 ),  Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL ,  184. 021. 007 ), the  VU University's Institute for Health and Care Research (EMGO+)  and  Neuroscience Campus Amsterdam (NCA) , the  European Science Foundation  ( ESF ,  EU/QLRT-2001-01254 ), the  European Community 's  Seventh Framework Program  (FP7/2007-2013),  ENGAGE  ( HEALTH-F4-2007-201413 ); the  European Science Council (ERC Advanced ,  230374 ),  Rutgers University Cell and DNA Repository  ( NIMH U24 MH068457-06 ), the  Avera Institute ,  Sioux Falls ,  South Dakota (USA)  and the  National Institutes of Health (NIH ,  R01D0042157-01A ). Part of the genotyping and analyses were funded by the  Genetic Association Information Network (GAIN)  of the Foundation for the  US National Institutes of Health , the ( NIMH ,  MH081802 ) and by the Grand Opportunity grants  1RC2MH089951-01  and  1RC2 MH089995-01  from the  NIMH ; RS: The Rotterdam.
Para se verificar se o processo de chegada de chamadas pode ser considerado como um processo de Poisson (ver hipótese 2 do modelo hipercubo na seção3), testes de aderência foram realizados utilizando-se o software BestFit. Esses testes mostraram que, com um nível de significância de 5%, não se pode rejeitar a hipótese de que os intervalos entre chegadas sucessivas tenham distribuição exponencial nos três períodos do dia. O gráfico ilustrado na Figura 6 mostra a porcentagem do número de chamados em cada subátomo nos três períodos: manhã, tarde e noite. A Tabela 2 mostra os números de chamados e as proporções de chegadas destes chamados em cada subátomo do sistema, assim como as taxas médias de chegada (chamados por hora) em cada subátomo, em cada período do dia.
Tempos de atendimento dos servidores Os tempos de atendimento foram obtidos a partir do intervalo de tempo entre os instantes de saída e retorno à base. Esses valores estão disponíveis nas fichas de regulação médica. Os tempos de atendimento incluem o tempo de preparo da equipe, o tempo de viagem até o local da ocorrência, o tempo em cena e o tempo de viagem de retorno à base. Os valores estão apresentados na Tabela 3 (em minutos). Para verificar estatisticamente a hipótese do modelo hipercubo de que os tempos de atendimento sejam exponencialmente distribuídos (ver hipótese 8 na seção 3), foi aplicado o teste de Kolmogorov-Smirnov do software BestFit. Esta hipótese foi rejeitada com α = 5%. Desta maneira, a utilização de tempos de atendimento com distribuições exponenciais no modelo hipercubo é uma simplificação que pode comprometer os resultados da análise, o que é estudado na próxima Tabela 4.
Os resultados da análise do SAMU-RP apresentados a seguir foram obtidos por meio da solução desse sistema de equações de equilíbrio para cada um dos períodos: manhã, tarde e noite. Um modelo de simulação detalhado foi desenvolvido e implementado utilizando o software ARENA ® para se avaliar os possíveis desvios dos resultados aproximados do modelo hipercubo. Este modelo de simulação representa um sistema de atendimento emergencial com todas as características do SAMU-RP, descritas na Seção 2. A simulação foi realizada considerando as distribuições mais aderentes para os tempos de atendimentos, além de considerar prioridade na fila de espera por classe de usuário. Por exemplo, nos testes realizados com o software BestFit ® , a distribuição Lognormal foi a que melhor significativos (menores que 10 -2 minutos).
Agradecimentos Os autores agradecem aos dois revisores anônimos os úteis comentários e sugestões. Também agradecem ao SAMU-RP a colaboração com esta pesquisa e ao CNPq o apoio financeiro. SAMU-RP apresenta a taxa média de utilização de 0,34. Os resultados mostram que, quando a taxa de utilização ultrapassa 50%, as diferenças dos tempos médio de espera para os chamados das três classes começam a ser significativas. Note que os tempos de espera são superestimados no modelo hipercubo para os chamados das classes a e b, enquanto que para os chamados da classe c estes tempos são subestimados. Essa análise reforça a importância de considerar prioridade na fila do modelo hipercubo em sistemas em que a taxa de utilização é alta o suficiente para a formação de fila.
Second, we investigate the link between financial literacy and the propensity to plan for retirement in France. Our empirical analysis is based on data from the PATER (PATrimoines et Préférences face au TEmps et au Risque) household survey, an original survey that mainly aims to measure households' saving preferences. The 2011 wave surveys a representative sample of French adults and includes questions to assess respondents' understanding of basic financial concepts (interest, inflation, and risk), following the methodology proposed by Lusardi and Mitchell (2011c), in addition to questions on financial planning (as suggested in Ameriks et al. 2003). Based on this survey, we assess levels of financial literacy in France. The results we obtain suggest that financial literacy should not be taken for granted, as most respondents do not exhibit mastery of the fundamentals of economics and finance.
5) Describe the success criteria to be achieved for drug repositioning and reformulation, from a payor perspective. 6) Evaluate the impact of pricing and reimbursement requirements, regulations, and evaluations for the market access of repositioned drugs and reformulated drugs, and if these can be forecasted. 7) Evaluate the potential lost benefit for patients and the society as a whole in cases where pricing or reimbursement cannot be achieved. The present study focuses on objectives 1, 2, and 3 in order to evaluate the consistency of nomenclature of drug reformulation and repositioning cases. The literature review was conducted from May 14, 2012, to May 25, 2012. The search was conducted by screening the Embase online database. Given the wide scope of this project, a review of the literature listed in Google Scholar and Google, as well as some proprietary data sources, was also performed.
Overview of literature search results The search in the Embase database sourced 505 publications. After screening for duplicates and analyzing titles and abstracts, 87 publications were selected. The reasons for rejection were recorded (Fig. 1 ). From the selected publications, 19 were not available in full text format, leading to 68 full articles being retrieved. The second selection process excluded 24 more publications, leading to selection of 44 full articles from Embase (Supplemental material 1). The ad hoc search allowed for the inclusion of eight more pertinent articles and four proprietary reports, which resulted in a total of 56 publications for data extraction.
Le protocole de recherche La méthodologie s'appuie sur une double approche : une revue de littérature et une étude empirique à dimension comparative. L'extraction des documents formant le corpus qui a servi à la revue de littérature a été opérée sur plusieurs bases de données complémentaires : Science Direct, IEEE 2 et Ebsco. Cette approche relève du choix d'ouvrir le spectre de recherche afin d'obtenir un corpus conséquent et multidisciplinaire. Comme la définition de l'éco-conception reste implicite dans la littérature, notre cadre analytique a été construit et objectivé en intégrant des synonymes ou des concepts proches tels que design for environment, ecodesign, environmentally benign design, sustainable engineering, environmentally conscious design. Par ses différentes échelles territoriales (Monde, grande région, État, collectivités territoriales) et son approche méthodologique (desk-research internationale et enquêtes de terrain), ce projet se positionne aux côtés des études macro-analytiques sur les freins et les leviers de l'éco-conception et sur le rôle des parties prenantes  (Le Pochat, 2005 ; Mathieux, 2001 ; Tukker et al.
Treatments and Road Transportation The duration of the different transportation treatments were selected according to the results of a survey previously carried out in hatcheries in France about the common practices in chick transportation (data not shown). Hatcheries representing 40% of the total production of 1-d-old broiler chicks in France responded to the survey. Chick transportation of 4 and 10 h corresponded to the average and maximum durations, respectively. For this reason, the control group (T00) considered as nontransported chicks was taken directly from the hatchery to the barn, a distance of about 200 m, within less than 5 min. The second (T04) and third (T10) groups were transported in a truck for 4 or 10 h, respectively. Transportation conducted under commercial conditions, using a semi-trailer truck with air conditioning set at 28 to 29°C and 34 to 36% RH (first.
Statistical Analysis Data were analyzed using GLM (RVAideMemoire package) of the R (version 2. 15. 1) for Windows platform (R Development Core Team, 2012). The first model assessed the effect of transportation duration on hematocrit, BW, feed uptake, and FCR during the growout period up to slaughter age, for each experiment separately. The second model assessed the effect of breeder age on incubation results such as fertility, hatchability, chick quality score, culls, hematocrit, and BW at 1 d old before transportation. Model 1: Y i = μ + TRD i + ε i ; model 2: Y j = μ + BRD j + ε j , where Y i and Y j : the studied effect; μ: mean; TRD j : effect of transportation duration; BRD i : effect of breeder age; and ε i and ε j : residual errors. The experimental unit differed according to the measured parameter; it was each bird for chick quality score, BW, and hematocrit; each pen for feed uptake, FCR, and mortality; and each tray of 150 eggs for incubation parameters (fertility, hatchability, and percentage of culls).
Viability Viability results are presented in Figure  1. The results of the Cox model showed no significant effect of a,b Within a row, means of the same factor that do not share a common superscript differ significantly (P ≤ 0. 05). 1 Hematocrit was assessed on 44, 64, and 84 chicks for the first, second, and third experiments consecutively for each transportation duration. T00 = control group; T04 and T10 = transported in a truck for 4 or 10 h, respectively. a-c Within a row, means for breeder age and transport duration, respectively, of the same factor that do not share a common superscript differ significantly (P ≤ 0. 05). 1  Data are shown as the mean of 30 individual measurements per pen. T00 = control group; T04 and T10 = transported in a truck for 4 or 10 h, respectively. transportation duration on the viability of broilers up to slaughter age in either the first (P = 0.
ACKNOWLEDGMENTS The authors thank  S. Bougeard  ( ANSES: Ploufragan, France ) and  M. Hervé  ( UMR1349   INRA-Agrocampus Ouest: Rennes, France ) for their help in the statistical analyses,  Syndicat National des Accouveurs (Rennes, France)  and  France AgriMer (Montreuil-sous-Bois, France)  for their funding, and the staff from  AN-SES experimental station (Ploufragan, France)  for their help in the experiments.
ELISPOT assay Recipient splenocytes were co-cultured with irradiated donor splenocytes in MultiScreen 96-Well Plates (Millipore Corporation, Billerica, MA) precoated with an IFNg antibody (AN18, MABTECH AB, Nacka Strand, Sweden). IFNg spots were detected by a detection antibody (R4-6A2-biotin, MABTECH AB) and visualized using streptavidin-ALP (MABTECH AB) followed by adding substrate solution (BCIP/NBT, MABTECH AB). Spots were counted using the AID EliSpot Software Version 5. 0 (AID GmbH, Ebingerstrasse, Germany).
Morphometric analysis Functioning heart allografts retrieved 100 days after transplantation were embedded in OCT-compound (Tissue-Tek, Sakura, The Netherlands) and sectioned at a thickness of 6 mm by using a Bright 5040 Cryostat. These sections were stained with Elastin (VMR International Ltd, Lutterworth, UK) and van Gieson (Raymond A. Lamb Ltd, East Sussex, UK) stain and photographed using light microscopy (Nikon, Tokyo, Japan) and a Coolpix digital camera (Nikon). Intimal expansion rate was calculated as the average from 3 and 5 levels within the heart allograft, with 200 mm of distance between each level. The data from each level were calculated from more than five coronary arteries with more than 75 mm diameter in the heart graft  (15). The rate of intimal expansion (current open area/previous open area) was calculated by using Adobe Photoshop CS3 extended (Version 10.