dataesr/scientific-paragraphs-categorization huggingface dataset:
The values of soil variables ρ, C, and θ above were extracted from the WISE30sec dataset using corresponding geographic coordinates. Total C density in forest ecosystems was estimated during the monitoring years using Eq. (  11 ): CD eco = CD tree + CD shrub + CD herb + CD litter + SOCD (11) where CD eco (Mg C ha -1 ) is C density in forest ecosystems, and the other Y. Liu et al. variables are as defined above.
Acknowledgments We thank  Qiang Ren  from  Jinhe Forestry Bureaus of Inner Mongolia ,  Changlei Wei  from  Greater Khingan Mountains A&F Academy , and  Qibao Zhang  from  Inner Mongolia Agricultural University  for providing some of the data used in this study. We greatly appreciate the anonymous reviewers' valuable comments and suggestions on the manuscript. This study was funded by the  National Natural Science Foundation of China  (  32160363 ), the  Natural Science Foundation of Inner Mongolia Autonomous Region  ( 2021MS03096 ), the  Science and Technology Planning Program of Inner Mongolia Autonomous Region  ( 2020GG0067 ), the  Foundation for Science and Technology (FCT)  of Portugal through projects  UIDB/04326/2020 ,  UIDP/04326/2020 ,  LA/ P/0101/2020 ,  PTDC/BIA-CBI/6515/2020 , and the Individual Call to Scientific Employment Stimulus  2022.
Acknowledgements This work was funded by  Agence Nationale pour la Recherche  ( ANR-16-CE12-0003 ) and  La Ligue Nationale Contre le Cancer (LNCC)-Comité Ile de France. AC was supported by  MESR  and LNCC. We would like to thank  A. Bonnet-Garnier  and  A. Jouneau  for the gift of  Suv39dKO KO ESCs  and advice. We are also grateful to image core microscopy facilities of IRSL, Paris, in particular  N. Setterblad  for his assistance with live imaging. We warmly thank  V. Lallemand-Breitenbach  and  H. de Thé  ( Collège de France, Paris, France ) for the sharing of PML antibody and helpful advice. We kindly thank  P. Lesage  for her critical readings of the manuscript, and other members of the team for helpful advice. We finally thank support services of IRSL. The authors declare no conflict of interest.
Moreover, the program is mandatory under the law, thereby reducing selection bias. We use the National Population Health Survey (NPHS) longitudinal data from 1994 to 2003. Using a difference-in-difference (DD) model, we compare individuals in Quebec with similar individuals in the remaining Canadian provinces. To the best of our knowledge, this study is the first to investigate the impact of a universal drug insurance policy on health behaviors and mental health measures in a public health care system with free physician and hospital services. Fixed effects models suggest that the reform increased drug insurance coverage by 15. 9% points. The policy has also had a positive and significant impact on medication use. Although we find a significant increase in the number of GP visits and overall health, there is no evidence of an improvement in mental health.
| DATA We use confidential micro-data from the NPHS, a biennial longitudinal survey conducted by Statistics Canada. The NPHS started in 1994 -1995 and ended in 2010 -2011. One member of each household, aged 12 or older, was randomly selected to answer a series of questions about their health status, use of health care services, and different factors that influence their health. 7  In this study, we use the  1994/1995, 1996/1997, 1998/1999, 2000/2001 and 2002/2003  cycles of NPHS. The sample includes individuals aged 18-64. We excluded respondents who were younger than 18 years old because of study of risky behaviors (e. g. , smoking, alcohol, BMI). We also excluded respondents older than 64 because the drug insurance policy was different for the elderly and the non-elderly. We then eliminated individuals reporting welfare benefits as their income source in any of the survey cycles.
All provinces and territories provide free access to physician and hospital services. Two specifications are presented for the main estimates. The first includes only province and year dummies. The second adds additional control variables (i. e. , individual characteristics). Controlling for additional individual characteristics can allow for accounting for compositional changes in the treated and control groups and improve efficiency by reducing the variance of the error term. We exclude observations with missing data for any of the control variables. Standard errors are clustered at the individual level. For the Poisson models (with individual fixed effects), we report the bootstrapped standard errors. We use the longitudinal weights provided by Statistics Canada in the data. 19.
This paper is not without limitations, which lead to suggestions for future research. First, due to the constraints of the database, the study does not distinguish between prescribed drugs and non-prescribed drugs. However, in Figure  A1 , we show that the trend of total drug expenditure is very similar to total prescription drug expenditures, both before and after 1997. However, non-prescription drug expenditures in Quebec were lower than the rest of Canada before and after 1997. Moreover, Canada reports a very low share of over-the-counter medicines in the overall total of drug spending (more than 91% of total drug spending is for prescribed drugs)  (OECD, 2016). Second, for the therapeutic drug class, the NPHS only includes information on the extensive margin (consumed or not). No information on quantity of use for each drug therapeutic class is available.
The Fair PharmaCare was implemented in British Columbia in May 2003 and is offered to low-income households. However, its implementation took place at the very end of our period study (NPHS data collection in 2003 ended in June). 18 The 2003 First Ministers Health Accord consists of a federal investment of more than $16 billion, with the following priorities: primary health care, catastrophic drug coverage, home care, development and implementation of the electronic health record, and access to diagnostic/medical equipment. 19 As a robustness test, we remove the sample weights and show that, in general, unweighted estimates remain similar to weighted estimates (available on request). 20 In NPHS data, we have no information about the type of drug insurance: private, government, or employer-paid plans.
To support space omics, NASA, European Space Agency (ESA), and Japan Aerospace Exploration Agency (JAXA) communities have promoted several initiatives, such as GeneLab led by NASA, 1 the Space Omics Topical Team funded by ESA, 2,3 and International Standards for Space Omics Processing (ISSOP 4 ) that integrates a multi-national force of scientists interested in sample processing standardization and metadata normalization of space omics research, including those from JAXA. The European life science community in space research has always been limited in numbers but fully devoted to microgravity research, leading international progress in particular model organisms. In the last fifteen years, the financial crisis, together with the emergence of private partners for NASA, made ESA-related researchers vulnerable to the lack of crewed mission launch capabilities.
At the same time, it is important to work in international research announcements to maximize the results of large, reference experiments required to be performed in key model systems. ESA has provided a strategic roadmap for the future, but the continuous redefinition of those working plans, with little call for opportunities for scientists, does not allow concrete actions. On the contrary, NASA is promoting Technology and Integrated Discipline Engineering Services, including a continuum of support to the space research community including spaceflight opportunities, funding for laboratory activities, and even data processing support via GeneLab. As an ESA-funded Space Omics Topical Team, we have previously highlighted how the different funding scheme/s within the European Union (EU) is complex.
The ISSOP consortium has highlighted that the reliance of space biologists leveraging omics approaches is increasing. 4  One reason is that performing omic analysis on pre-collected and correctly stored samples is possible and maximizes the knowledge gained from spaceflight experiments. Therefore, with a combination of multiple omics approaches, traditional and novel sequencing, and sophisticated downstream bioinformatics tools, research could achieve more precise, robust, and significant conclusions on the impact of different parameters that condition biology and life in the space environment. Afterward, following protocols for analysis standardization and dissemination of these discoveries, these data provide actionable scientific insights that enhance our comprehension of the (patho)physiology and biology during short-and longer-term space travel.
International cooperation and standardization European researchers, due to their leadership coordinating international space research consortiums from major European research institutes, pioneered studies using omics data to assist in the comprehension of the cellular events induced by the extraterrestrial environment. 3  Some cases of synergistic partnerships are ESA and CLAIRE, Europe's largest artificial intelligence research network. 5 Furthermore, European scientists in space have access to state-of-art facilities and resources such those available in the European Molecular Biology-European Bioinformatics Institute (EMBL-EBI) located in Hinxton (UK), which in addition to research activities maintains services and databases such as Ensembl, UniProt, AlphaFold, BioStudies, MGnify, Expression Atlas, or PRIDE, to name only a few.
Currently, Europe provides a wide range of great opportunities for synergies and networking at a regional, national, and European level for states that are both members of ESA and EMBL. This is enhanced by the state-of-the-art sample and data collections, in addition to the outstanding bioinformatics expertise available, the continuous crosstalk and exchange of initiatives with GeneLab and ISSOP, as well as the prominent engagement in international space biology studies. As a result, European scientists provide significant contributions on data and analysis standardization,  6  as well as engagement in international agreements for standardization from other fields. Hence, developing standard operating procedures across all member states of ESA would contribute to perform more sophisticated comparisons of datasets to be applicable in translational studies.
Consequently, and to capitalize on the application of (multi-)omics approaches in space biology, we recommend and encourage ESA to significantly increase its funding investment and initiative that supports omics-based investigations and the generation of a European space omics database. This recommendation is not only regarding physical institutions but also ''virtual'' institutes focused on space-related projects, particularly if the critical mass of researchers is evenly distributed in Europe. Our Topical Team also suggests that ESA should take into consideration that there exists a chance for ESA to take a leading role in space biology publications due to the excellent research centers in all member states. However, unlike USA-based scientists that have been fully supported by NASA and federal administration, Principal Investigators in Europe are mostly isolated without a supportive critical mass of other scientists in their institutions or in other nearby research centers, which prevents multi-disciplinary coordinated investigations.
Ways to improve Space omics in Europe A collaboration effort from Europe to promote Space Omics Standards principal source for training on big data in biology and together with the European Molecular Biology Organization (EMBO) provides training on omics experimentation and bioinformatic analysis in the continent. 8, 9  There are also numerous educational programs focusing on space life sciences, e. g. , summer courses funded by ESA for more than two decades ago that evolved into the summer university currently promoted from the ESA Education office in Belgium. These summer courses engage students into Science, Technology, Engineering, and Math careers but there is not a next step in their curricula should they wish to continue engaging space biology. However, the only formal training on space omics that the authors are aware of occurs as part of ISU Master of Space Studies, being delivered continuously since the academic year of 2020/2021.
It is unclear whether ESA will promote the future construction of a single building for cutting-edge space biology research in Europe, with the additional mission to become a reference in implementing standardization protocols worldwide as well as coordinate key collaborative initiatives with public and commercial space industry for Europe to capitalize the race to space. Related to this, the lack of development and integration in the current databases from space biology experiments at ESA constitutes an opportunity to generate a large database that includes omics, the details of the sample repository, images captured during orbital activities, as well as valuable metadata information captured from in flight operations at User Support and Operations Centers facilities. For this endeavor, the experience of ELIXIR (https://elixir-europe.
org) on developing life science resources across Europe could be crucial. The goal of ELIXIR is to coordinate these resources so that they form a single infrastructure and bring together more than 220 European organizations. In summary, there is excellent expertise in Europe from which ESA could benefit from to manage the huge increase of space life sciences data that is expected in the future, and to establish the necessary bioinformatics infrastructure, taking advantage of existing infrastructure. ESA Science Data Centers should collect inputs from ESA and European principle investigators to achieve a similar level of integration to other databases including GeneLab (space omics repository, not existing at ESA), ALSDA (Ames Life Science Data Archive, equivalent to ESA ERASMUS archives in Europe), BSP (Biospecimen Sharing Program), NBISC (NASA Biological Institutional Scientific Collection), and PSI (Physical Sciences Informatics).
This does not necessarily mean to establish new databases with added value, but to create stable links and cooperation between the existing archive at ESA and both European (EMBL/EBI) and American (GeneLab) sources of space omics datasets. We also propose to assume a major role in the development of European sample biobank (for example, the UK Biobank) to integrate European samples/data into NASA repositories in case an ESA/NASA agreement is not feasible. The analysis of the whole genome data is not trivial and requires computing infrastructure and databases, as well as advanced bioinformatics solutions and expertise for large-scale data analysis. Prioritization in large-scale space technology research programs is inevitable and is already happening in NASA-supported research. NASA established GeneLab, an interactive and publicly available resource where scientists can store, share, and analyze the data from space-related experiments (Ray et al.
2019). JAXA also opened the Integrated Biobank for Space Life Science (ibSLS, https://ibsls. megabank. tohoku. ac. jp/), an interactive omics database and sample sharing system of JAXA's space mouse missions. 17  ESA is currently defining a new scientific program and it is a unique moment to create new tools and lead new initiatives in space sciences for the whole genome data. Storing the data on European servers is important for several reasons. Firstly, it helps ensure that the data are subject to EU regulations and laws, such as the General Data Protection Regulation, which are designed to protect the privacy and security. Secondly, it reduces the risk of the data being accessed or hacked by unauthorized third parties, including those who may mishandle the data. Thirdly, it helps support the EU's and ESA's efforts to promote growth and competitiveness of the European cosmic sector by providing a secure and reliable infrastructure for businesses to operate in.
While the existing resources contain mainly bulk genomic profiles, it would be complementary to create an additional infrastructure dedicated to single-cell and spatial analysis. 18  Although bulk analyses have broad utilities, single-cell analyses provide a more precise understanding of the cell-type-specific adaptations to environmental challenges as demonstrated for instance by the Human Cell Atlas. There is a need for a wide initiative that is going to advance space science and provide means to recognize and trace outer spacerelated abnormalities at the single-cell and spatial level. Understanding the body's response to various external factors, such as those occurring in microgravity, requires a thorough understanding of the reactions taking place in individual cells. This leads to the collection of huge amounts of data and specialized computational analysis using machine learning and artificial intelligence methods.
European scientific groups have been successfully conducting both molecular and computational research at the single-cell level on Earth  19  and spatially resolved transcriptome profiling. 20, 21  Now, ESA has the chance to capitalize and could lead the adaptation of existing methods and the development of space-specific single-cell methodologies and pipelines that could be integrated into a robust, standardized approach. In addition, to provide interpretable and accountable computational solutions and allow researchers to benefit from the already existing datasets, machine learning and artificial intelligence methods should be considered. Such a new dedicated platform could provide a technical framework for integrating various types of molecular and imaging datasets with health records facilitating joint analyses.
Maximizing opportunities afforded by human space omics Collecting omics routinely from humans entering space presents a golden opportunity for European space research; policy pertaining to human omics studies in spaceflight will be an important issue to reach a consensus on among European and international stakeholders. The NASA Twins study showcased the feasibility of collecting omics data from astronauts throughout the whole course of a long-duration mission, including in-flight measures with minimally invasive biopsies such as blood, and non-invasive harvesting of samples like urine. 23  Similar human multi-omic studies are now occurring internationally through commercial ventures (e. g. , SpaceX Inspiration4 and Polaris Dawn) and via space agencies (e. g. , JAXA Cell-Free Epigenome). It is essential for Europe to rise to this emergent opportunity, leveraging considerable experience and expertise in designing human performance studies and model organism omic studies in space.
24  Secondly, human omics studies in space could accelerate the understanding of the impact of spaceflight directly on human biology, such as by elucidating mechanisms of spaceflight-associated neuro-ocular syndrome. Thus, collecting human space omics is immediately actionable while also presenting a path for future advanced space biology research. Thorough discussion regarding appropriate policy today could lead to the generation of datasets designed with future research and analysis capabilities in mind. For example, by collecting correlated metadata and functional data (e. g. , exercise performance) alongside omics, machine learning and artificial intelligence approaches could be applied to identify significant relationships between spaceflight-induced omic changes and phenotypic changes.
In order to deploy a routine program, we must consider practicalities and logistics in detail, to design a cost-effective and standardized program. As such, in a recent manuscript from members of the ESA-funded Space Omics Topical Team, a number of considerations were introduced and a set of questions were suggested to motivate further discussion among experts and stakeholders. 25  The first subset of questions aims to initiate discussion of the possible collection routines from astronauts and spaceflight participants, decide on the omics data to collect, from which samples, and at which time points. These questions and some initial thoughts are listed below. (1) Which omic types and technologies have the highest potential for scientific return and clinical actionability (with additional consideration to multi-omic combinations)? -Genomic sequencing is likely to be the most immediately clinically actionable.
Team are European affiliated scientists landing at the Space Biology research field as Principal Investigators from spaceflight biological experiments or as bioinformatics experts. The list of members contributing to this article are Daniela Bezdan, Joseph Borg, Thomas Cahill, Eugenie Carnero-Diaz, Henry Cope, Colleen S. Deane, Timothy Etheridge, Stefania Giacomello, Gary Hardiman, Raul Herranz, Natalie Leys, Pedro Madrigal, Aranzazu Manzano, Felice Mastroleo, F. Javier Medina, Jakub Mieczkowski, Manuel A. Fernandez-Rojo, Keith Siew, Willian A. da Silveira, Nathaniel J. Szewczyk, Stephen B. Walsh, and Silvio Weging. ACKNOWLEDGMENTS All listed authors are members of the ESA Space Omics Topical Team, funded by the ESA grant/contract 4000131202/20/NL/PG/pt ''Space Omics: Toward an integrated ESA/NASA -omics database for space- flight and ground facilities experiments'' awarded to RH, which was the main funding source for this work.
Osteopathic Medicine. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. Potential risk Recommendations No co-ordination at international level to align A common policy on data sharing, including human subject's data, should ESA and state member policies. be established at ESA level. Members from European research institutes A virtual Space Omics research center (network) should be established. remain isolated in non-thematic centers. A specific space omic/database community (similar to GeneLab) is missing in Europe. Single state members have different ESA should consider mechanisms to ensure funding for multi-national mechanisms to fund space scientists. research collaborations/teams.
Pollinator insects play a crucial role in maintaining biodiversity and agricultural production worldwide. Yet they are subject to various infectious and parasitic agents (IPAs). To better assess their exposure to IPAs, discriminative and quantitative molecular methods have been developed. These tools produce large datasets that need to be summarised so as to be interpreted. In this paper, we described the calculation of three types of composite indices (numerical, ordinal,  nominal)  to characterize the honey bee exposure to IPAs in 128 European sites. Our summarizing methods are based on component-based factorial analyses. The indices summarised the dataset of eight IPAs quantified at two sampling times, into synthetic values providing different yet complementary information. Because our dataset included two sampling times, we used Multiple Factor Analysis (MFA) to synthetize the information.
e. insecticides, herbicides, fungicides), the depletion and poor quality of food sources, inappropriate beekeeping practices, and meteorological or climatic causes, as well as the interaction between them, weakens bee health and generates a worrying colony over-mortality. Consequently, studying the stress effects on bee health is becoming a crucial economic and ecological issue. The advent of new technologies made possible for biologists the collection of large datasets integrating information from multiple sources. Therefore, researchers need methods that summarise the information contained in these large datasets in order to perform statistical analyses and get meaningful insights from data. Only a few methods are proposed in the literature to build indices for animal health. In most animal breeding, many models developed to characterize population health use methods that are either arbitrary  [5]  or not easily transferable to another animal species.