dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2000). Likewise, it provided all possible groups with an equal chance of being selected. They comprised full time and part-time workers, shift workers, zero-hours/casual workers, night workers and manual and non-manual workers. In the end, we identified 375 employees from the six groups as our sample. Among them, 200 employees (with the support of their supervisor) agreed to participate (85% female), with an average age of 46. 72 and an average tenure of 9. 38 years. The response rate was 53% for the employee survey and 100% for the manager survey. Incomplete surveys were omitted, and we obtained complete data on a total of 161 supervisor-employee dyads for the final analysis. All the variables were adopted from existing studies and measured using a multi-item, Likert-type scale ranging from disagree strongly (1), disagree (2), disagree slightly (3), neutral (4), agree slightly (5) and agree (6), to agree strongly (7).
Main findings We employed SPSS to estimate ordinary least-squares models to test our hypotheses  (Hayes, 2018). Table  2  presents the results of our multivariate regression analysis. H1 predicts that SPPO mediates the relationship between HWC and EAB. Three conditions need to exist to confirm the mediating effect  (Hayes, 2018). In essence, 1) the effect of the independent variable on the mediator is significant, 2) the effect of the mediator on the dependent variable is significant when accounting for the effect of the independent variable, and 3) the indirect effect in mediation is significant. In our analysis, HWC (predictor variable) shows significant impact on SPPO (mediator) (Model 1: β =. 29, p =. 00). Thus, condition 1 is supported. Furthermore, the effect of SPPO (mediator) on EAB (outcome variable) is significant when accounting for the effect of HWC (predictor variable).
NETWORKS WITH A NESTED NODE SET In this section, we elaborate the two modeling principles, mentioned above, for analyzing a network with a nested node set. For the sake of concreteness, we shall already employ some of the specifics of the dataset further described in Section 3. The data are constituted by two repeated observations of a social network with a node set containing four levels of nesting: individuals, in households, in compounds, and in villages. The compound is an extended family of several households living closely together. Of these levels, the village level is mainly important for the network structure, while the other levels are important for moderating the effects of covariates.
Can you name the people in the village community whom you regularly visit? These name generators, common to many sociability surveys  (Marsden, 2011) , are essential regular exchanges for members of this community. We define the relation defined in this way as a sociability relation. The second wave of data collection took place in February and March 2016. Due to death, emigration, or transhumance, not everybody could be interviewed. The data used is the network of all 406 individuals who were interviewed in both waves. Within-village outdegrees ranged from 0 to 21, and their means in the two waves were 6. 0 and 4. 1; between-village outdegrees ranged from 0 to 14, with means 0. 8 and 0. 7. The individuals lived in 135 households, and these were grouped in 54 compounds.
Estimation Estimation in this model is conditional on y(t 1 ). For this article, estimation was done by the method of moments ("MoM"); this is explained in  Snijders (2001 Snijders ( , 2017)) , and sketched below. Maximum likelihood estimation is also possible  (Snijders, Koskinen, & Schweinberger, 2010 ), but would be too time consuming for this large dataset. Denote the parameter by  = (, ). We treat the MoM only for the case of two waves, two dependent networks, and a constant rate function  (k)  i (y) =  (k). The dependent variable is the pair of networks Y (t) = ( Y (1) (t), Y (2) (t) ) , observed for t = t 1 , t 2. The MoM estimate is defined, for a suitable statistic z (y(t 1 ), y(t 2 )), as the solution θ of E θ {z (Y (t 1 ), Y (t 2 )) |Y (t 1 ) = y(t 1 )} = z (y(t 1 ), y(t 2 )). ( 7 ) The vector z (y(t 1 ), y(t 2 )) has the same dimensionality as ; to each one-dimensional parameter in  corresponds one one-dimensional statistic.
The focus group experience was recorded to facilitate transcription of the discussion. The focus groups were useful to deepen the results of the interviews. With awareness of the small sample size, the empirical evidence was checked, comparing it among individuals from different backgrounds to enrich the empirical materials. In each country there was a multiplier event with the principal aim of presenting and discussing the empirical results with new companies. The data collection had an explorative aim, because a further step of the TA VIE project will be to conduct interviews with students focusing more on the role of international mobility. Finally, a design thinking session was conducted with the participation of all partners to create a framework of competencies for engineering considering previous work in all countries (see Figure  2 , research logic).
Outcomes from Interviews and Focus Group This section presents the empirical results of the analysis conducted in Madrid. The UPM team collected data from six interviews with participants from two Spanish companies and four international companies. All of them recruit engineers of different fields. The HR specialists highlighted that companies consider the competency communicating in a foreign language a must, and they do not recruit engineers who have not achieved the required foreign language level. To analyze the results, only the competencies that were highlighted at least by four companies were considered. These are presented in Table  2. Once the interviews were finished, results were contrasted in a focus group with 18 participants from six companies and the UPM. The focus group was led by working in three different teams.
Outcomes from Interviews Royal Institute of Technology, KTH's TA VIE team, collected data from seven HR specialists in Stockholm, five of whom were from organizations of different sizes, professional fields, and levels of internationalization, and two from recruitment agencies for engineers. While none of the suggested items were deemed completely unimportant, some strongly varied in their valuation by respondents. The skills most valued by the respondents in Stockholm: are presented in Table  2. When taking a closer look at these highly ranked items and those additionally brought up by the respondents, it appears that these relate to personal, interactional, and organizational dimensions. Engineers should have certain personal qualities, be able to interact with others and create additional value for their organization.
Clinical Application: Long-Term CCB Use and Outcomes of COVID-19 in Patients With High Blood Pressure The clinical goal of this case study was to evaluate the potential effects of CCBs on in-hospital mortality related to COVID-19  [4]. To achieve this goal, we used two different sources of data. The first source was two elements of structured data: International Classification of Disease, Tenth Revision (ICD-10) codes and medication prescriptions from an electronic prescription system. The second source was information on medications and comorbidities extracted by the NLP pipeline from nonstructured fields in the EHR. The inclusion criterion for patients was COVID-19 disease confirmed by reverse transcriptase-polymerase chain reaction (RT-PCR). We considered a patient as receiving long-term treatment with CCBs (Multimedia Appendix 4) if there were at least two mentions (in structured data or extracted with NLP, respectively) in the last 6 months.
Discussion In this paper, we investigated the potential utility of biomedical NLP in the context of a rapidly emerging novel disease. To do this, we asked a specific question: Does the leveraging of unstructured textual information via NLP yield clinically actionable information? To answer this question, we used NLP to extract information about hypertension and a medication for treating it from the EHRs of patients with COVID-19. The results showed that an NLP pipeline can be adapted quickly to the domain of a novel disease, it can perform well enough to extract useful information, and when that information is used to supplement the structured data that is already available, the sample size can be increased sufficiently to see treatment effects that were not previously statistically detectable.
Acknowledgments The authors thank the EDS AP-HP COVID consortium integrating the AP-HP Health Data Warehouse team as well as all the  AP-HP  staff and volunteers who contributed to the implementation of the EDS-COVID database and operating solutions for the database. The authors would like to acknowledge  John Bennett  for his thorough editing. This work was supported by state funding from the  French National Research Agency (Agence Nationale de la Recherche, ANR)  under the " Investissements d'Avenir" program  (reference:  ANR-10-IAHU-01 ) and an  ANR   PractikPharma grant  ( ANR-15-CE23-0028 ). The collaborators associated with  AP-HP/Universities/INSERM COVID-19  Research Collaboration:  AP-HP COVID CDR Initiative, Paris, France , are as follows:  Pierre-Yves Ancel ,  Alain Bauchet ,  Nathanaël Beeker ,  Vincent Benoit ,  Mélodie Bernaux ,  Ali Bellamine ,  Romain Bey ,  Aurélie Bourmaud ,  Stéphane Breant ,  Anita Burgun ,  Fabrice Carrat ,  Charlotte Caucheteux ,  Julien Champ ,  Sylvie Cormont ,  Christel Daniel ,  Julien Dubiel ,  Catherine Duclos ,  Loic Esteve ,  Marie Frank ,  Nicolas Garcelon ,  Alexandre Gramfort ,  Nicolas Griffon ,  Olivier Grisel ,  Martin Guilbaud ,  Claire Hassen-Khodja ,  François Hemery ,  Martin Hilka ,  Anne Sophie Jannot ,  Jerome Lambert ,  Richard Layese ,  Judith Leblanc ,  Léo Lebouter ,  Guillaume Lemaitre ,  Damien Leprovost ,  Ivan Lerner ,  Kankoe Levi Sallah ,  Aurélien Maire ,  Marie-France Mamzer ,  Patricia Martel ,  Arthur Mensch ,  Thomas Moreau ,  Antoine Neuraz ,  Nina Orlova ,  Nicolas Paris ,  Bastien Rance ,  Hélène Ravera ,  Antoine Rozes ,  Elisa Salamanca ,  Arnaud Sandrin ,  Patricia Serre ,  Xavier Tannier ,  Jean-Marc Treluyer ,  Damien van Gysel ,  Gaël Varoquaux ,  Jill Jen Vie ,  Maxime Wack ,  Perceval Wajsburt ,  Demian Wassermann  and  Eric Zapletal.
The cumulative number of patients tested is currently not sufficient (n = 103) to estimate the frequency of SARS-CoV-2 in semen. Moreover, the viral load, duration of excretion and infectivity of the viral material detected in semen are unknown. Importantly also, it remains to be determined whether the cellular components of semen (leukocytes, epithelial cells, and spermatozoa) are infected. Based on known mechanisms of SARS-CoV-2 entry into cells (i. e. requirement for dual expression of the ACE2 receptor and protease TMPRSS2), transcriptomic and protein databases were screened for relevant gene expression in genital organs. Overall, it appears that while ACE2 is highly expressed in the testis, its co-expression with TMPRSS2 transcripts is rare among the cell types tested (less than 0. 1%)  [7].
The female gamete, the oocyte, is out of reach by natural means. During oocyte retrieval for ART, fluids from mature follicles are aspirated to harvest cumulus oocyte complexes where the oocytes are individually surrounded by cumulus cells. E-mail address: Frida. ENTEZAMI@ahparis. org (F. Entezami). 1  The expression of ACE2 mRNA has been found in human ovarian tissue based again on RNAseq databases. Stanley and colleagues reported on the wide expression of ACE2 and absence or very low expression of TMPRSS2 transcripts in human cumulus cells  [7]. Conversely, BSG and CTSL were both expressed at high levels, and as they are possible targets for SARS-CoV-2 entry into the cells, cumulus cells display some potential to be infected  [7]. Therefore human cumulus cells may not act as a barrier to entry of the virus in the oocyte, as has been suggested.
As shown in the literature, adult height is negatively correlated with socio-economic deprivation in the early years of a child's life (controlling for other genetic and non-genetic determinants of height). This is why the adult height of individuals born at the beginning of transition can serve as a proxy for the quality of life in the early transition years. Given that transition took place in the early 1990s, the novel anthropometric data collected within the LiTS offer the first opportunity to examine whether transition permanently affected the height of individuals who were young children at the time it occurred. A few years ago, those cohorts were still growing up. It is only now that we can compare their adult height to that of their older and younger peers, and thus rigorously evaluate whether the early reform years were indeed accompanied by intense hardship.
We find that the negative impact on people's height was homogenenous; it affected individuals born in advantaged and disadvantaged families equally, as well as men and women, or people born in urban and rural areas. To ensure that our results are robust, we replicate the analysis using another household survey, the Russia Longitudinal Monitoring Survey of the Higher School of Economics (RLMS-HSE), a rich individual panel dataset comprising several survey rounds from 1994 to 2016. Although it covers only Russia, the survey is unique as it collects information on multiple household members, thus allowing us to control for the genetic determinants of height and to account for endogeneity in fertility choices by comparing siblings in terms of their anthropometric measures. Results confirm that cohorts born or one year old during price liberalisation in Russia are today 1.
Given the absence of accurate and comprehensive data on health facilities and medical staff for the pre-transition period, we utilise a proxy obtained from the UN DESA Population Division: infant mortality rates. Furthermore, we control for war and violent conflict from the UCDP/PRIO Armed Conflict Dataset and the Correlates of War project, which enable us to disentangle the impact of transition from any possible consequences of war or conflict that might have taken place at the same time. As an extension to our analysis, we use another household survey, the Russia Longitudinal Monitoring Survey of the Higher School of Economics (RLMS-HSE), a panel dataset consisting of various survey rounds from 1994 to 2016. Although RLMS-HSE covers only one transition country (Russia), it has three distinct advantages compared to LiTS III.
These controls include gender, whether the respondent was born in an urban or rural locality, religion, and parental background (maternal and paternal education and sector of employment). Country fixed effects (α c ) capture any time-invariant country characteristics, while country-specific linear time trends θ c y allow us to control for the "natural" increase in height over time prevailing in most middle-income countries. Survey weights, which ensure that the data are representative at the country level, are included in all specifications. We also control for country-and birth-year-specific factors x c y which are likely to affect the outcome of interest. In particular, we control for the incidence of war and log GDP per capita. The war variable indicates whether the country was involved in a conflict that caused at least 25 casualties per year in the relevant time period.
9 inally, we use the RLMS-HSE data to evaluate the effects of transition on height at the preadulthood stage by computing height-for-age z-scores that compare children to their peers in other countries, in particular to those in the United States. All the analyses based on the RLMS-HSE data utilise specification (1), but models of life satisfaction include age and survey year controls since we observe individuals in different waves. Each individual only contributes one observation to the models. We use height-for-age z-scores instead of adult height to study the impact of transition on people that have not yet reached their adult height. The fixed effects are calculated at region (oblast) level, and the linear time trends at the country level. Results are robust to the inclusion of trends at the region level.
Life satisfaction and attitudes The results presented in the previous section provide evidence of the hardship associated with the early years of the transition reforms. While the data show that people born at the onset of transition are today shorter than their counterparts, it is important to investigate whether impacts go beyond physical well-being and extend, for instance, to life satisfaction and attitudes. If deprivation was substantial, individuals who lived during the transition period could still be affected today; for instance, they could have lower objective and/or subjective well-being than their peers. We include all individuals surveyed by the LiTS who were 21 and above at the time of the interview, and control for age and age squared. Table  4  shows no lasting negative impact of transition on the current life satisfaction of cohorts born around the start of transition; on the contrary, we find that individuals who were in their infancy when transition occurred in fact report higher levels of life satisfaction than their peers.
As pointed out earlier, LiTS data on comparator countries shows that when GDP per capita reaches around USD 10,000 to 15,000, additional economic growth has very limited impact on average height. In order to determine whether this is also the case for the post-communist countries, we split them into two groups that fall below or above a certain per capita GDP threshold. As very few countries in our sample recorded such high GDP per capita values, we choose the median income per capita in the sample, USD 8,630. We evaluate the impact of transition on height separately for individuals in these two groups of countries. Our findings, presented in Table  A8  and Table A9 , suggest that the effect is driven by countries with GDP per capita below the median threshold, which seems to confirm that the height satiation already observed in the comparator countries was also present in some post-communist countries.
Weight In addition to height, we collect data on weight and therefore can check if transition had an impact on BMI and the probability of being underweight or overweight. Table  A21  reports the results for BMI for those who were at least 21 years old when they participated in the survey. We focus on this sample since height, along with weight, is a component of BMI, and we therefore exclude those individuals who might have not yet reached their adult height when the survey was implemented. Columns (1)-(3) show that individuals who were born, one or two years old at the onset of transition have today a lower BMI than their older and younger peers, and that this effect is either largely (columns (4)-(  5 )) or fully (column (  6 )) explained by the fall in GDP per capita. 15 or adults, a normal BMI ranges from 18.
Specifications (4)-(  6 ) also control for maternal education (reference is no educaiton) and height. Children height is converted into height-for-age z-scores after subtracting the average and dividing by the standard deviation of the height of US children of the same age, using the WHO Global Database based on US population data. A Online Appendix (Not for Publication) All specifications control for country fixed effects and birth year fixed effects. Additionally, gender of the respondent, whether the respondent was born in an urban or rural locality, religion, parental education, and the incidence of war are included as controls. Specifications (  4 )-(  6 ) also control for parental sector of employment and log GDP per capita. The sample is composed of respondents aged 21 or above at the time of the interview.
Robust standard errors in parentheses. ***, ** and * indicate significance at 1%, 5% and 10% level, respectively. All specifications control for region fixed effects and country linear time trends. Additionally, gender of the respondent and whether the respondent was born in an urban or rural locality, maternal education (reference is no educaiton) and maternal height are included as controls. Children height is converted into height-for-age z-scores after subtracting the average and dividing by the standard deviation of the height of US children of the same age, using the WHO Global Database based on US population data. country time trend Robust standard errors in parentheses. ***, ** and * indicate significance at 1%, 5% and 10% level, respectively. Gender of the respondent and whether the respondent was born in an urban or rural locality are included as controls.
Specifications include mother fixed effects and robust errors. columns (1) to (3) include all waves and add control for old child when comparing children within a family. Columns (4) to (6) restrict observations to 1994-2010 waves and include linear trend. Children height is converted into height-for-age z-scores after subtracting the average and dividing by the standard deviation of the height of US children of the same age, using the WHO Global Database based on US population data.
Robust standard errors in parentheses. ***, ** and * indicate significance at 1%, 5% and 10% level, respectively. All specifications control for region fixed effects and country linear time trends. Additionally, gender of the respondent and whether the respondent was born in an urban or rural locality, and maternal height are included as controls. Columns (1) to (3) controlfor whether the houshehold owns a dacha and columns (4) to (6) control for consumption of meat and meat products per capita (kg) in the oblast when respondent was born (Data available only for cohorts born in 1990 onward). Children height is converted into height-for-age z-scores after subtracting the average and dividing by the standard deviation of the height of US children of the same age, using the WHO Global Database based on US population data.
Life in Transition Survey III, EBRD Transition Indicators, Gapminder, Correlates of War Data, UCDP/PRIO Armed Conflict Dataset, and authors' calculations. Notes: Standard errors in parentheses are clustered at the country level. ***, ** and * indicate significance at 1%, 5% and 10% level, respectively. All specifications control for country fixed effects as well as age and age squared. Additionally, gender of the respondent, whether the respondent was born in an urban or rural locality, religion, parental education, and the incidence of war are included as controls. The sample is composed of respondents aged 21 or above at the time of the interview.
019  *  *  * 1. 018  *  *  * 1. 013  *  *  * 0. 984  *  *  * 0. 980  *  *  * 0. 973  *  *  * (0. 310) (0. 311) (0. 311) (0. 309) (0. 310) (0. 310) Father completed tertiary education 1. 533  *  *  * 1. 535  *  *  * 1. 531  *  *  * 1. 512  *  *  * 1. 510  *  *  * 1. 502  *  *  * (0. 313) (0. 314) (0. 314) (0. 307) (0. 307) (0. 307) Observations 40910 40910 40910 39943 39943 39943 R 2 0. 385 0. 385 0. 385 0. 387 0. 387 0. 387 Other controls Yes Yes Yes Yes Yes Yes Country fixed effects Yes Yes No No No No Time trend Linear Linear Linear Linear Linear Linear Source: Life in Transition Survey III, EBRD Transition Indicators, Gapminder, Correlates of War Data, UCDP/PRIO Armed Conflict Dataset, and authors' calculations. Notes: Standard errors in parentheses are clustered at the country level. ***, ** and * indicate significance at 1%, 5% and 10% level, respectively.