dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This work made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https: //www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. Most of the authors are current or past members of the  ESA Gaia mission team and of the Gaia DPAC  and their work has been supported by the  French Centre National de la Recherche Scien-tifique (CNRS) , the  Centre National d' Etudes Spatiales (CNES) , the  L'Agence Nationale de la Recherche , the  Région Aquitaine , the  Université de Bordeaux , the  Utinam Institute of the Université de Franche-Comté , and the  Institut des Sciences de l' Univers (INSU) ; the  United Kingdom Particle Physics and Astronomy Research Council (PPARC) , the  United Kingdom Science and Technology Facilities Council (STFC) , and the  United Kingdom Space Agency (UKSA)  (through the following grants to the  University of Bristol , the  University of Cambridge , the  University of Edinburgh , the  University of Leicester , the  Mullard Space Sciences Laboratory of University College London , and the  United Kingdom Rutherford Appleton Laboratory (RAL) :  PP/D006511/1 ,  PP/D006546/1 ,  PP/D006570/1 ,  ST/I000852/1 ,  ST/J005045/1 ,  ST/K00056X/1 ,  ST/K000209/1 ,  ST/K000756/1 ,  ST/L006561/1 ,  ST/N000595/1 ,  ST/N000641/1 ,  ST/N000978/1 ,  ST/N001117/1 ,  ST/S000089/1 ,  ST/S000976/1 ,  ST/S001123/1 ,  ST/S001948/1 ,  ST/S002103/1 , and  ST/V000969/1 ) ; the  Belgian Federal Science Policy Office (BELSPO)  through various  Programme de Développement d'Expériences Scientifiques (PRODEX)  grants; the  German Aerospace Agency (Deutsches Zentrum fur Luft -und Raumfahrt e.
1 < R 0 < 2) and from the analytical predictions (T, continuous lines). Analytical predictions are approximated for the exponential phases, although at the epidemic peak the solution is no longer exponential. Nonetheless, this approximation well predicts the lag between the deterministic and the stochastic EPT. To compare this with real data, we consider the Italian regional infection counts collected by the Italian Protezione Civile (https://github. com/pcm-dpc/COVID-19) and report the EPT distribution in Fig. 5. For each region, the first epidemic wave started after the first observed case (a). For the second wave (b), we took the day with the lowest number of infections between the first wave to the second peak. The empirical distribution function (EDF) of the EPT for the first and the second wave is fitted using maximum likelihood estimates of the theoretical EPT distribution defined in equations in the equations in the supplementary material; we assumed in this analysis that each region has the same population, 2 × 10 6 people, the average population of the Italian regions.
Purpose: We aimed to determine the risk factors of sleepiness at the wheel among patients with obstructive sleep apnea (OSA) and to determine factors that were independently associated with reported sleep-related near-miss accidents or car accidents. Patients and Methods: This retrospective study was conducted on 843 OSA patients from the French Pays de la Loire sleep cohort database. Each patient completed surveys including anthropometric data, medical history, professional status, and data on alcohol and tobacco use. Epworth sleepiness scale (ESS) and sleep quality questionnaires were administered. Regarding driving, data were collected on occurrence of sleepiness-related near-misses or car accidents, and on distance driven per year. The primary dependent variable of interest was reported sleepiness at the wheel.
Study Design This retrospective study was conducted on the Pays de la Loire sleep cohort database. As previously described,  20  this multicenter clinic-based French cohort has been recruiting patients ≥ 18 years investigated for suspected OSA. Patients with mild-to-severe OSA (apnea-hypopnea index [AHI] ≥5 events/h), diagnosed between 2007 and 2017 at the Sleep Medicine Center of the University Hospital of Angers, were included in this study. Patients with learning difficulties, who were unable to fill in the questionnaires, or read and/or speak French, patients with neuromuscular diseases or chronic respiratory failure, and those without a driver license were excluded from the cohort. Approval was obtained from the University of Angers Ethics Committee and the "Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Santé (C.
Sleep Studies Patients were investigated by polysomnography (PSG) or home sleep apnea testing (HSAT) according to pretest clinical probability of OSA, 21 using CID102L8D TM and CID102L TM devices (CIDELEC, Sainte-Gemmes-sur-Loire, France), respectively, for PSG and HSAT. Recorded data included oronasal airflow, tracheal sounds and suprasternal pressure, chest and abdominal wall motion using respiratory inductance plethysmography belts, arterial oxygen saturation (pulse oximetry), body position and electrophysiological signals for sleep evaluation (for PSG only). However, only data, from the PSG and HSAT recordings, relevant to OSA severity, was explored for this study. Sleep stages and other sleeprelated data were not analyzed as it was not available in patients investigated by HSAT. Respiratory events were scored manually using the AASM recommended criteria  22  and the apnea hypopnea index (AHI) was included in the analysis.
Baseline Evaluation and Questionnaires Each patient completed surveys including anthropometric data, medical history, and professional status according to a specifically designed and validated self-administered questionnaires from the "Institut National de la Statistique et des Etudes Economiques" (INSEE). 20 atients were asked to specify whether they were shift workers, and if so, to state their shift schedule. Data on clinical diagnosis of depression requiring drug treatment and data on alcohol and tobacco use were documented. The validated French version of the Epworth sleepiness scale  24  (ESS) was used to assess EDS. In addition, subjects were asked about symptoms of insomnia (difficulty falling asleep, difficulty maintaining sleep and early morning awakening), and about sleep habits (sleep duration on workdays and sleep duration on non-workdays).
Regarding driving, the patients were asked two questions concerning the occurrence of sleeprelated near misses or car accidents in the last year. A near-miss accident was defined as an event of limited impact, perceived by the subject as being at risk of an accident (eg, deviation from the lane, crossing a white line) but having caused no damage. An accident was defined as an event that caused injury and/or material damage. Finally, data on distance driven per year came from patients' self-reported information through questionnaires.
Study Population Among 1014 eligible OSA patients from the Pays de la Loire sleep cohort, recruited at the Angers University Hospital, 171 were excluded due to missing data (n=96) or absence of driving license (n=75). The final sample size comprised 843 patients including 87 professional drivers. The studied patients were majority men (63%) with typical mild-to-severe OSA (mean (SD) apnea-hypopnea index [AHI] and ESS: 25. 3 (23. 3) events/h and 10. 1 (5. 2) respectively), with frequent obesity (mean (SD) body mass index [BMI]: 29. 6 (6. 6)), systemic hypertension (32. 6%), diabetes (12. 9%), cardiovascular diseases (16. 2%) and diagnosed depression (25. 6%). Of the 843 subjects in our sample, 298 (35. 3%) reported having fallen asleep or felt sleepy at the wheel in the past year. Frequency of sleepiness occurrence was homogeneously distributed with approximately 31.
DovePress and reporting difficulty maintaining sleep were at higher risk of sleepiness while driving. Overall, nocturnal hypoxemia assessed by T90 was the only OSA severity index associated with reported sleepiness at the wheel. These findings, except for T90, could help identify OSA patients at risk for sleepiness at the wheel and car accidents as early as the first visit at the sleep medicine clinic and before sleep study results. Our data show that one-third of OSA patients, from this French cohort, reports sleepiness while driving. This prevalence is in agreement with a recent French study of more than 50,000 subjects referred to the sleep clinic. 31 In the general population, the prevalence of sleepiness at the wheel is usually lower, with 17% of Europeans reporting an episode of sleepiness at the wheel over the last two years  32  and 28% of French drivers reporting a severe episode of sleepiness requiring a driving break over the last year.
Limitations and Biases Our study has several limitations. Our sample concerns only OSA patients living in France and having consulted at a specific university hospital sleep center, introducing a selection bias, and making the extrapolation to the general population difficult. In addition, our data were collected retrospectively, which may introduce a bias in data collection. A large part of the data is declarative and is therefore exposed to recall bias. Furthermore, our investigation did not include any questions about countermeasures against sleepy driving. Nevertheless, these different biases are counterbalanced by an adjustment of confounding factors through multivariate analysis and inclusion of a relatively large number of subjects. These measures make the strengths of our study. Concerning sleepiness while driving, our study is one of the few to include clinical data as well as data from questionnaires and sleep study recordings, giving us a complete profile of OSA patients who are vulnerable to sleepiness while driving and are at risk of road accidents.
Procedures Study drugs were administered twice daily in equally divided doses with a faster titration schedule than prior studies: dosages started at 5 mg/kg/day and reached 25 mg/kg/day on day 9 and 50 mg/kg/day on day 29 (eFigure 2 in Supplement 1). The number and type of seizures and status epilepticus episodes were reported daily using an interactive voice-response system, and adverse events and medications were recorded using a paper-based diary. Details of the trial procedures are available in the protocol (Supplement 2). Patients who completed treatment were eligible to enter an open-label extension phase (NCT02544750). Patients who withdrew from the study or did not enter the openlabel extension were seen at 4 weeks after the last dose. An independent safety monitoring committee approved the higher dosages used in this trial and monitored patient safety.
Type I error was controlled by a hierarchical gatekeeping procedure (eTable 2 in Supplement 1), wherein each successive end point was tested for inferential statistical significance only if the preceding comparison was statistically significant; otherwise, resultant P values were designated as nominal and used descriptively. All statistical analyses were done using SAS version 9. 3 or higher (SAS Institute), and the threshold of significance was P <. 05. ; UK, 7) to the CBD25 group (n = 75), the CBD50 group (n = 73), and the placebo group (n = 76) (Figure  1 ). Not all patients reached or remained at their assigned dosage (eTable 3 in Supplement 1); 87% (65 of 75 patients) in the CBD25 group and 53% (39 of 73 patients) in the CBD50 group were receiving their target dosage at treatment end.
II. Monitoring of pharmaceuticals in Monitoring strategies can be aimed to generate a comprehensive dataset of the contamination  (Biel-Maeso et al. , 2018; Kötke et al. , 2019; Zhao et al. , 2017)  or to be a preliminary study intended as a first-step exploratory survey establishing baseline levels which will facilitate comparisons through long-term monitoring  (Long et al. , 2013). The objective is sometimes to fill data gaps or to complete the results from previous research  (Ali et al. , 2018; Borecka et al. , 2015). The identification of the compounds and the observed concentration ranges are defined for one  (Ali et al. , 2017; Alygizakis et al. , 2016)  or several compartments in a view to assess the distribution (or partition) of the compounds between the different matrices, for example dissolved and suspended particulate matter  (Cantwell et al.
As few pharmaceuticals are regulated, the selection of contaminants may be pre-defined by existing lists of compounds defined as of possible concern for aquatic environments, published by national environmental agencies or international institutions, for example as the OSPAR Commission, the European Commission or the United States' Environmental Protection Agency (US EPA). The identification of priority substances might permit to fill knowledge gaps concerning their occurrence and, furthermore, their impacts on living organisms. One may refer to the established lists by the Watch List of the WFD, by the OSPAR Commission (lists of substances of possible concern) and the NORMAN Network (210 pharmaceuticals in the listed emerging substances 1 ). These lists are built upon prioritization methodologies that permit to select the substances in relation with the lack of data concerning their occurrence, analytical performance, and potential hazard upon living organisms.
Data are mainly obtained at national scale  (Matongo et al. , 2015; McEneff et al. , 2014; Paíga et al. , 2015). For example  (Cantwell et al. , 2017)  selected 15 pharmaceuticals highly prescribed in the US and investigated their occurrence in estuarine waters of the Narragansett Bay in Rhode Island. Of the 15 active pharmaceutical ingredients measured, 14 were consistently present in the dissolved phase, and 8 were bound to suspended particulate matter collected by sediment traps over the studied period. 1 https://www. norman-network. net/?q=node/81#sub23 2 https://www. epa. gov/ccl/contaminant-candidate-list-3-ccl-3 Another inclusion criterion can be their previously reported occurrence directly in receiving marine environment. For example, in the California Pilot  Study (2009 Study ( -2010) )  of the USA Mussel Watch Program,  (Maruya et al.
This recent dataset enables to draw up a first preliminary global contamination profile of marine waters according to therapeutic classes and marine water locations. All the therapeutic classes (AINS, analgesics, antibiotics, central nervous system (CNS) drugs, cardiovascular system (CVS) drugs, lipidlowering and the miscellaneous pharmaceutical molecules class named 'others') are occurring at once in estuaries, coastal and offshore waters. A significative difference between both estuary and coastal concentrations and offshore mean concentrations (Wilcoxon rank sum test, p-value<0. 05, Supplementary Material 2) might be explained by pharmaceutical dilution in the water column at offshore locations, with mean maximal concentrations of 492. 14±1,641. 92 ng/L, 96. 36±155. 48 ng/L and 12. 7±7.
III. Monitoring of pharmaceuticals in the marine environment: conclusion and perspectives Since 2012; an increasing number of monitoring strategies included or focused on pharmaceutical contamination of the marine environment despite the lack of international guidelines. Most often, data available in the literature are generated by one-time surveys. These are intensive monitoring programs 33 Norman Network Databases : https://www. norman-network. net/?q=node/24 set up during a punctual period of time, to measure and observe the quality of the aquatic environment for a specific purpose. The broad occurrence and variability of the marine environment pharmaceutical contamination highlighted by recent studies  (Arpin-Pont et al. , 2016)  suggest that pharmaceutical compounds should be more systematically included both in large  (UNESCO and HELCOM, 2017)  and in routine marine environment monitoring programs.
Prioritization methodologies permitted to set up watch lists of targeted molecules by some international institutions on which certain monitoring are based. At the local scale, the specificities of the study area might be considered i. e. the local pharmaceutical sources. Drug local consumption patterns might be identified (examples of high occurrence of antiretroviral and antimalarial in Africa  (K'oreje et al. , 2020) ), or data gathered from the pollution sources as WWTP effluents. The priority substance lists might be built in the prospect to be regularly monitored to set up databases and to allow analysis of temporal trends. Sampling strategy should be site-specific and preceded by a pilot study to choose the most relevant and cost-effective sampling locations and frequency. Marine sites configuration (offshore, bay, gulf, beaches, etc.
) plays a crucial role in hydrodynamics thus in pharmaceutical concentrations especially when using PS techniques. Seawater is the systematically sampled matrix as pharmaceuticals are rather polar, and sediments and biota are the subject of several studies as their sampling give complementary information to better assess the overall contamination. Thanks to the development of HRMS techniques, highly sensitive analyses can be conducted to quantify trace concentrations of pharmaceuticals diluted in the seawater column. Non-target analysis represent a promising approach to identify fate of known contaminants, new molecules of interest and to redefine the monitoring by updating the target list of compounds  (Tian et al. , 2019). In the light of this dataset, and to go along with future protective measures to limit the evidenced overall contamination by these compounds  (Arpin-Pont et al.
Sample To test our research hypotheses, we use an unbalanced panel dataset that covers the innovation activity of 9,126 UK firms constructed from six consecutive waves of a community innovation survey (UKIS) and Business Structure Database (BSD) known as Business Register during 2002-2014 and annual business registry survey during 2002-2014. We collected and matched UKIS data to the initial year of BSD data for  2002, 2004, 2006, 2008, 2010, and 2012. The UKIS includes innovation input and output data, barriers to innovation, innovation mechanisms, innovation sales, R&D and software expenditure, knowledge collaboration, etc. The BSD variables describe the firm's legal status, ownership (foreign or national firm), alliance information (firm belongs to a larger enterprise network), export, turnover, employment, the industry at five-digit level, and a firm location the postcode.
The Business Structure Databases could raise the measurement problem as a significant share of firms registered in the BSD is self-employed with zero employees. Altogether self-employed with zero employees and micro-firms make up to 97% of the BSD sample in different years. A vast literature models entrepreneurship as occupational choice and assumes that individuals differ in the characteristics that are relevant to perform the entrepreneurial function  (Hudik and Bylund, 2021). Research focuses on whether actors are self-employed (entrepreneurs) or employed in a firm (non-entrepreneurs or intrapreneurs). For this study, we are interested in researching entrepreneurial firms, rather than self-employed entrepreneurs, and firms that include such characteristics as the ability to respond to opportunities to innovate  (Holmes and Schmitz, 1990) , the ability to recognize and combine talents  (Lazear, 2005) , risk aversion, and initial wealth  (Kihlstrom and Laffont, 1979).
Our sample is reduced to 13,712 observations and 9,126 firms. Given the availability of data, we created two distinctive samples. The first sample includes data on innovation performance proxied by a share of new to market sales (13,712 observations) for firms with at least six employees, which also excludes all self-employed. Our second sample excludes London-based firms or firms that are located elsewhere with the headquarters (HQs) in London with 13,552 observations. We start by analyzing our sample of 13,712 observations and 9,126 firms. Under-represented sectors are mining and quarrying (<1%) and utility electricity (<1%). Industries with the highest share in a sample are high-tech manufacturing (20. 90%), real estate and other business activities (11. 44%), wholesale, retail trade (16.
Startups (BSD) Binary variable equal to one if a firm is from 0-3 years old since establishment has maximum (50 employees at establishment) and is not part of an enterprise group, including no units at establishment, zero otherwise Uncertainty (UKIS) How important has been the uncertain demand for innovative goods or services as constraint to innovation and activities in influencing a decision to innovate (0none; 3very high)? Risk (UKIS) How important has been an excessive perceived economic risk as constraints to innovation and activities in influencing a decision to innovate (0none; 3very high)? Control variables EQI The regional European Quality of Government Index (EQI) which includes corruption, impartiality and rule of law pillars  (Charron et al. , 2013 (Charron et al. , , 2020) )  Collaboration regional (UKIS)  The degree of effectiveness of various legal and strategic methods for maintaining or increasing the competitiveness of product and process innovations rescaled from zero to one using the data on: patents, copyright, trademarks, secrecy, first entry (0not applicable or important to 3high)? Software (UKIS) The amount of expenditure for purchasing advanced machinery, equipment and software (000s) to total sales (000s pound sterling) Source: UKIS -UK Innovation survey; BSD -Business Structure Database.
64 13 -Education 210 1. 53 16 -Other community, social activity 335 2. 44 UKIS4 (2005) 5,594 40. 79 UKIS5 (2007) 1,784 13. 01 UKIS6 (2009) 1,983 14. 46 UKIS7 (2011) 1,847 13. 46 UKIS8 (2013) 1,335 9. 73 UKIS9 (2015) 1,169 8. 52 Total observations 13,712 100 Total 13,712 Source: Office for National Statistics. (2017a). UK Innovation Survey, 1994-2016: Secure Access. [data collection]. 6th Edition. UK Data Service. SN: 6699, http://doi. org/10. 5255/UKDA-SN-6699-6 (hereinafter UKIS-UK Innovation survey). Office for National Statistics. (2017b). Business Structure Database, 1997-2017: Secure Access. [data collection]. 9th Edition. UK Data Service. SN: 6697, http://doi. org/10. 5255/UKDA-SN-6697-9 (hereinafter BSD-Business Structure Database).
06*** (1. 21) -2. 95** (1. 22) -3. 17** (1. 23) Industry fixed effects Yes Yes Yes Yes Region fixed effects Yes Yes No No Number of obs. 13712 13712 13712 13712 F stat 43. 56 38. 76 34. 70 37. 66 R 2 0. 15 0. 22 0. 22 0. 21 RMSE 10. 90 10. 22 10. 20 10. 25 Dependent variable: Innovation sales % to total sales. Note: Reference category for legal status is Company (limited liability company), industry (mining), region (North East of England). Robust standard errors are in parenthesis. Significance level: *p < 0. 05; **p < 0. 01, ***p < 0. 001 Source: UKIS -UK Innovation survey; BSD -Business Structure Database.
When considering the ecological features of fungi, such as a broad diversity of lineages and lifestyles in nature, fungal mitogenomes can provide crucial information on evolutionary adaptations and divergences of the mitochondria in different environments  [1, 6]. According to the recent rapid developments of high-throughput sequencing technologies, fungal organelle genome databases have increasingly accumulated, however, these are still less abundant than those of animals and plants  [4, 7]. Thus far, most studies of fungal mitogenomes have focused on the  (1)  sequencing and characterization of individuals and (2) the elucidation of their phylogenetic positions within its relatives to support the potentials for one of the significant molecular markers identifying fungal species. Until recently, few studies have reported on comparative analyses of mitogenomes, ranging from a taxa level for specific fungal groups, under evolutionary viewpoints  [8] [9] [10] [11].