dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of the performance of the WRF model is carried out for simulating the surface winds and the diurnal cycle of wind speed for the small island developing state of Fiji at a 1. 33 km by 1. 33 km grid resolution using 1deg gridded data from NCEP-FNL. Simulations are performed for an austral summer (January 2017) and an austral winter (July 2017) month using the dynamical downscaling and the two-way nested approach. A set of physics parameterization schemes together with topo_wind = 1, 2 and ysu_topdown_pblmix = 1 physics settings associated with YSU PBL scheme are used to correct the surface winds and the diurnal cycle of wind speed. The results reveal that the WRF model is able to capture the surface winds and the diurnal cycle of wind speed on the windward side. Surface winds on the leeward side and the outer islands, show positive bias especially at nighttime for January and at both the day and night time for July.
Model and simulation setup The study used the Advanced Research WRF (ARW)  [19, 20]  atmospheric mesoscale model version 3. 9. 1. The dynamical downscaling method was used for running the simulations using the WRF model. This method uses the output from General Circulation Models (GCM) as input initial and boundary conditions to drive a regional numerical model to simulate atmospheric parameters at a high spatial resolution via direct nesting, taking into account the local conditions  [21, 22]. The initial and boundary conditions for Fiji were obtained from 6-hourly NCEP-FNL (Final) Operational Global Analysis data at 1°× 1° grid resolution  [23]. The static fields for topography was obtained from USGS GMTED2010, land-water masks, land use/land cover classification and albedo, etc. , were obtained and interpolated from the 21-class MODIS and MODIS FPAR, all these made available from the National Center for Atmospheric Research (NCAR) database, at a resolution of 30arc-seconds.
Long observational datasets available in the order of 5 -8 years have similar diurnal cycle patterns for the months of January and July of 2017 used in this study. The WRF model is able to simulate the full diurnal cycle of wind speed on the windward side stations for both January and July. For the leeward side and the outer island stations, the model is able to simulate the daytime wind speed but there is a positive bias in the nighttime for January. For July, the modelled wind speeds for both the outer islands and the leeward side, show poor diurnal cycle predictions, and a positive bias for both the day and the nighttime. The possible reasons for the observed bias include the limitations of the model in terms of the errors associated with the coarse resolution input data used as initial and boundary conditions, resolution of the domain, terrain and vegetation characteristics and the complexity of the terrain and its representation in the model.
Conclusions An evaluation of the WRF model to simulate the surface winds and the diurnal cycle of wind speed has been carried out for the SIDS of Fiji at a high spatial resolution of 1. 33 km by 1. 33 km. The NCEP-FNL 1deg atmospheric input data was used to drive the WRF model simulations via two-way nested approach for an austral summer (January) and an austral winter (July) month of 2017. A set of physics parameterization schemes were tested with additional physics settings associated with the YSU PBL scheme to correct the surface winds and the diurnal cycle of wind speed against measurements from AWSs. The results showed that the WRF model is able to accurately capture the surface winds and the diurnal cycle of wind speed on the windward side. Surface winds on the leeward side and the outer islands, show positive bias especially at night for January and both the day and night for July, despite adjusting some of the physics settings, showing the limitations of the model.
Acknowledgement This study is part of a PhD research project, for which the lead author is the recipient of scholarship funding from the  New Zealand Ministry of Foreign Affairs and Trade (NZMFAT). The authors gratefully acknowledges the  Fiji Meteorological Services  for providing the ground-based wind data measurements from AWSs in Fiji and the  New Zealand eScience Infrastructure (NeSI)  for use of the high-performance computing facilities in this research.
Acknowledgements This work is included in the  MORSE  project supported by the  LaSIPS (Paris-Saclay University). The sample fabrication was performed at the  Plateforme de Micro-Nano-Technologie/C2N , which was partially funded by the  "Conseil Général de l'Essonne ". This work was also partly supported by the  French RENATECH network. Data presented in this manuscript are available in 34.
Acknowledgments The authors thank the  American Institute of Mathematics  for hosting the workshop " Lipschitz metric on Teichmüller space " and the  Mathematical Sciences Research Institute  for hosting the semester program " Dynamics on Moduli Spaces of Geometric Structures " where some of the work reported here was completed. The authors gratefully acknowledge grant support from  NSF   DMS 0952869  and  DMS 1709877  (DD),  NSF   DMS 1611758  (JT),  NSERC RGPIN 435885 (KR) , and from  NSF   DMS 1107452 ,  1107263 ,  1107367  " RNMS: GEometric structures And Representation varieties " (the  GEAR Network ). The authors also thank  François Guéritaud  for helpful conversations related to this work, and specifically for suggesting the statement of Theorem 6. 8. Finally, the authors thank the anonymous referees for their careful reading of the paper and for helpful comments and corrections.
Statistical Analyses Data analysis was performed using non-parametric methods. Behavioral changes assessed by the ADOS and DD-C-GAS were evaluated using Friedman's rank sum test for repeated measurements and Wilcoxon signed rank tests for comparisons between two time points. If the Friedman test indicated significance, Wilcoxon signed rank tests with adjustment for multiple testing by Bonferroni-Holm were performed between each pair of two time points. The outcome measures were changes in ADOS total and severity scores, the DD-C-GAS subdomain scores, autism symptom level, and treatment adherence scores. In order to evaluate the impact of either age or autism symptom severity at baseline on outcome parameters, analyses of change were performed by age category (<=43 months; >43 months) and baseline ADOS total score category (</=20; >20) respectively.
Background and Demographic Data We analyzed the data of 32 children (28 males, 4 females) who had been treated at the FIAS center between January 2011 and July 2017 and who had completed their 2-year follow-up assessment. The children's mean age when the FIAS intervention started was 44. 0 ± 8. 3 months. The mean ADOS total score at baseline was 20. 0 ± 3. 46, while the mean severity score was 7. 0 ± 1. 43 indicating moderate to high average levels of autism-related symptoms. Mean DD-C-GAS subdomain scores at baseline were similar for self-care (30. 6 ± 14. 21) and communication (31. 0 ± 12. 05), slightly lower for social behavior (27. 0 ± 10. 44), and higher for intellectual skills (42. 1 ± 16. 17). Scores at baseline indicated significant special needs in all four domains, especially in the areas of social behavior and communication.
ADOS Findings by Time Point and Changes From Baseline Mean ADOS total scores decreased from 20. 0 ± 3. 46 at baseline to 18. 5 ± 5. 91 at 1-year follow-up and statistically significantly to 17. 1 ± 6. 16 at 2-year follow-up (p = 0. 031). Mean severity scores decreased numerically from 7. 0 ± 1. 43 at baseline to 6. 8 ± 2. 01 at 1-year follow-up and to 6. 4 ± 1. 83 at 2-year follow-up (see Table  3 ). The possible impact of age, total ADOS scores, and ADOS severity scores at baseline were estimated by comparing subgroups above and below the respective median values: no statistically significant or relevant subgroup differences were noted for any of these baseline variables (data not shown).
There was a trend towards greater improvement on the DD-C-GAS intellectual skills domain in the lower ADOS total score category at baseline. DD-C-GAS subdomain scores were highly intercorrelated. We observed moderately strong negative correlations between ADOS total scores and DD-C-GAS subdomain scores at baseline. At 1-year follow-up, there were highly significant negative correlations for all subdomains except self-care. At 2year follow-up, negative correlations were moderately significant for ADOS total score and all DD-C-GAS subdomain scores (data not shown). In contrast, there were no relevant correlations between the two instruments in terms of change from baseline. Scatterplots of changes in ADOS total as well as ADOS severity scores versus DD-C-GAS subdomains illustrate the heterogeneity of the individual results (see Figures  5  and 6 ).
0-based servitization and the quality of supplier-customer relationship. For instance, in their exploratory qualitative study,  Rymaszewska and her co-authors (2017)  report the that support to customer success, which indirectly augments also the relationship quality between partners, is a central element of the value creation achieved by the supplier via IoT-based servitization. Digital technologies for servitization can be exploited, at a basic level, in order to support and make some manual activities more efficient, as happens for the management of CRM software and the creation of customers' databases  (Frank et al. , 2019). Servitization, usually powered by IoT, intensifies the strength and thus also the quality of relationships between manufacturers and customers. Indeed, industrial firms need to increase their commitment (e.
0  (M€ uller et al. , 2018). C-level managers belonging to the different case companies were contacted, targeting both top managers (CEOs, and GMs) and specific function managers presumably directly involved in the transformation driven by I4. 0 (such as CTOs, CIOs, etc. ). An introductive phone call by the researchers to the interviewees was made in order to explain the nature and aims of the research, to assess the actual firm's willingness to participate in the investigation and to identify which managers and other positions were specifically to be involved in the interviews. Then, data was collected by in-depth face-to-face semi-structured interviews with firms' key informants and top managers in charge of activities heavily impacted by I4. 0related technologies. In many cases, two or more people have been involved in the interviews, for a total duration of approximately 51.
In fact, PBC benchmarks revenues to given agreed-upon equipment's efficiency measures (i. e. uptime level), adopting a risk-andgain sharing approach based on ad hoc and recursively negotiable contractual agreements. The adoption of a complete technological outfit (IoT, Cloud, BD, DA) is mandatory for this third level of digital servitization (all the firms are accumulating data but only one of them may say to leverage BD at present). Various capabilities of smart and connected productssuch as remote condition monitoring, remote control, remote optimization and device autonomyplay here a significant role in enabling sophisticated revenue models (such as performance-based contracts) and make them sustainable. At a technological level, while the simplest of those capabilities may be conveniently provided by automated platform services like in level 1 (in particular, remote monitoring and control), unlocking the most advanced features of this level, inherently oriented to process optimization and outcome guarantee, requests customer-specific investments in terms of both organizational and technological resources and capabilities (from dedicated human resources to ad hoc technological infrastructures).
Methods The MEDLINE database was systematically reviewed for papers on human subjects published in English between December 2014 and November 2019. We used the MeSH terms diabetes, gestational, obesity, pregnancy, gestational weight gain, treatment, neonate, and offspring. For this review, we selected meta-analyses that addressed lifestyle interventions for the prevention of GDM or for the improvement of pregnancy outcomes in the case of GDM and meta-analyses that specifically addressed measures to limit GWG. We also looked at meta-analyses that addressed the effect of bariatric surgery on pregnancy outcomes. Only analyses that included more than two trials were recorded. One hundred and eighty-eight records were identified and there were 157 records after exclusion of duplicates. Forty-three were screened and 26 full-text articles were assessed for eligibility.
Data from seven clinical centers that conducted separate RCT to test different lifestyle intervention strategies to modify GWG were combined to conduct an individual-participant data (IPD) meta-analysis. This included 1150 women with BMI ≥ 25 kg/m 2 randomized either to the group with interventions targeting diet, physical activity, and behavioral strategies or to the group with standard of care  [19]. Mean total GWG was 1. 6 kg less for the intervention group (-1. 58, 95% CI -2. 18, -0. 99; p < 10 -3 ) and the percentage of women with GWG per week below Institute of Medicine (IOM) guidelines was significantly higher in the intervention group than the standard care group (20. 6% vs. 14. 2%; p = 0. 002, OR 1. 65, 95% CI 1. 20, 2. 27). There was no difference between groups for the incidence of GDM (OR 0.
Characteristics of the Groups During the process of recruitment, we obtained 139 applications for the control group. Among those, 132 met the required criteria. We randomly selected 100 participants among those eligible. One menstrual cycle was monitored, and all qualified participants took part in the study. The willingness to participate in the study group was expressed by 224 women. During verification process, 208 women were qualified to participate. Three cycles were monitored in the study group. In this manuscript we reported only first from monitored cycles. Due to incomplete data three participants were excluded from the study. The application for the study was voluntary. After providing full information on the form, course of the study, and possible risk, none of the participant refused to participate.
EURION facilitates collaboration and data sharing between the 8 projects and prevents research overlaps. In ERGO, we address additional challenges identified by the EC and OECD. In both EU and other international legislations, regulatory procedures for the identification and assessment of EDs are separated for human health and the environment. Consequently, useful data obtained from non-mammalian vertebrate tests (e. g. , fish and amphibians) are disregarded, or not given sufficient weight, in human health assessments and vice versa, even though the endocrine system is highly conserved among vertebrate classes  [5] [6] [7] [8] [9] [10] [11]. The international workshops pointed out thyroid hormone disruption (THD) as a focus area, because existing vertebrate in vivo tests are not environmentally protective enough and validated in vitro tests for THD are not yet available.
Nonetheless, as reported by  [21] , even EU REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) dossiers of high tonnage chemicals (above 100 and 1000 tonnes/year) are lacking measured developmental toxicity data. Often, animal tests have been waived through read-across and the exploitation of existing data, but not by employing alternative test methods  [22]. Additionally, ED-related endpoints and effects regarding the environment are still missing in REACH standard data requirements. With respect to this, it is currently discussed to amend the REACH standard data sets, to explicitly cover the ED mode of actions and correlated adverse effects. To efficiently cover ED in the tiered REACH testing approach, it is of utmost importance to use human health data for environmental assessment and vice versa.
ERGO (acronym for EndocRine Guideline Optimization) approach for testing and crossclass extrapolation of THD (thyroid hormone disruption) effects between mammalian and nonmammalian test guidelines. Based on existing information, chemicals with known molecular initiating events (MIE), key event (KE), endocrine activity (EA) and adverse effect (AE) in mammals will be investigated in silico, in vitro and in vivo, in fish and amphibians. A battery of biomarkers and endpoints (B/E) will be tested for EA and AE confirmation in the in vivo studies. Biotransformation data will be obtained when EA and/or AE cannot be confirmed. All existing and new data will be used for AOP (adverse outcome pathway) network development. Chemicals with unknown effects in mammals but known EA and AE in non-mammalian vertebrates will be investigated in silico, in vitro and for differences in biotransformation among mammals and non-mammalian vertebrates.
Teleost fish, the phylogenetically oldest and largest group of vertebrates, will be used to achieve this goal. Specifically, zebrafish (Danio rerio), the most popular fish model for toxicological and vertebrate developmental research, is being used. A strong basis of information on the regulation of specific developmental processes by THs  [28, 29]  has made zebrafish an important biomedical model for TH-related diseases, including obesity, cardiovascular diseases and diabetes  [30]. Thus, a comprehensive database on structure and function of the zebrafish thyroid system is available; the ontogenetic patterns of thyroid receptors (TR) L-TRαL-Thraa, S-Thraa, Thrab ERGO (acronym for EndocRine Guideline Optimization) approach for testing and cross-class extrapolation of THD (thyroid hormone disruption) effects between mammalian and non-mammalian test guidelines.
Based on existing information, chemicals with known molecular initiating events (MIE), key event (KE), endocrine activity (EA) and adverse effect (AE) in mammals will be investigated in silico, in vitro and in vivo, in fish and amphibians. A battery of biomarkers and endpoints (B/E) will be tested for EA and AE confirmation in the in vivo studies. Biotransformation data will be obtained when EA and/or AE cannot be confirmed. All existing and new data will be used for AOP (adverse outcome pathway) network development. Chemicals with unknown effects in mammals but known EA and AE in non-mammalian vertebrates will be investigated in silico, in vitro and for differences in biotransformation among mammals and non-mammalian vertebrates. The existing separation in ED testing between mammals and non-mammals is clearly outlined in the OECD Conceptual Framework for Testing and Assessment of Endocrine Disrupters, where all in vivo testing is divided into mammalian and non-mammalian toxicology test guidelines  [23].
Specifically, zebrafish (Danio rerio), the most popular fish model for toxicological and vertebrate developmental research, is being used. A strong basis of information on the regulation of specific developmental processes by THs  [28, 29]  has made zebrafish an important biomedical model for TH-related diseases, including obesity, cardiovascular diseases and diabetes  [30]. Thus, a comprehensive database on structure and function of the zebrafish thyroid system is available; the ontogenetic patterns of thyroid receptors (TR) L-TRαL-Thraa, S-Thraa, Thrab and Thrb are fully described  [26, 31, 32] , and the function of the zebrafish thyroid system appears to be fully comparable to that of higher vertebrates and humans. For instance, the molecular mechanisms of thyroid organogenesis and the role of TR signaling during embryogenesis are well documented for zebrafish and seem to be highly conserved between zebrafish and mammalian models  [26, 31].
WP2 Knowledge Management and Data Basing To integrate the work performed within the different WPs and to align the scientific tasks with the overarching strategy of the ERGO project, structured and efficient data management is crucial. A searchable and well-structured electronic database (Figure  4 ), containing test data as well as literature data and relevant meta information, supports decision making on relevant project issues, like the selection of adequate case study chemicals at the beginning of the project. Additionally, it is essential for the success of the project to involve all interested stakeholders from the beginning to integrate their knowledge and needs. This is done by establishing a user reference group compiled of experts from industry, regulatory bodies and scientists.
WP3 Adverse Outcome Pathway (AOP) Network Development Based on comparative data compilation and comprehensive scientific evidence, an AOP network covering multiple modes of THD will be developed. Structuring all available scientific evidence according to formal OECD AOP development principles and following the OECD review process will support the prioritization of assays for identifying THDs across vertebrate classes for validation. Both experimental data from literature (WP2) and new data generated by the consortium (WP4, 5, 6) are used. As a first step, existing data on the TH regulation of eye development of fish have been summarized and a new, hypothesized AOP network is currently being developed and regularly updated with new data generated in ERGO experiments. Evidence will be added to existing AOPs (see Figure  2 ) and new AOPs will be developed if gaps exist, e.
g. , interference with thyroid serum-binding protein as a MIE. WP3 will amend the relevant AOPs in the AOP-Wiki to promote international collaboration and visibility. Due to the principle of re-usability of KEs included in individual AOPs, this will de facto lead to the construction of an AOP network in the AOP-Wiki. In addition, ERGO leads the AOP working group (WG) of the EURION cluster. There are currently around 50 individual WG members, with representatives from each of the eight cluster projects. The mission of the AOP WG is twofold: to support and facilitate AOP-related activities in the EURION projects, and to bring together AOP-structured information and data across projects. The WG is organizing AOP training workshops as a function of needs within the projects, AOP development workshops as data becomes available, and regular meetings and teleconferences to align AOP-related activities and stimulate collaboration across projects.
The initial focal points were to populate our database with literature-known TH disruptors, to characterize the chemical domain of our ERGO reference compounds as related to the EINECS inventory (see below), to undertake computational docking with reference ligands, and to profile biotransformation through S9 enzyme assays derived from rat hepatocytes (such as the S9 mix used for the Ames test). Respective biotransformation studies include a fish-specific S9 enzyme assay (OECD 319B)  [49]  and the results feed the PBTK modelling regarding metabolism.
WP5 Case Studies for Thyroid-Related Endpoints and Biomarkers in ED Test Systems By far, the largest and most experimental WP in ERGO. Based on an analysis of gaps in the database on effects of THDs in vertebrates, ERGO WP5 will (1) identify suitable B/E for THD across several vertebrate classes and, thus, (2) provide the experimental data required to implement THD as an endpoint in existing or novel OECD TGs. Figure  5  outlines a potential case study with chemical X in WP5. Such case studies are performed with a battery of THDs, with six different defined MOAs (Appendix A): (1) NIS inhibition, (2) THR interaction, (3) TPO inhibition, (4) DIO inhibition, (5) TTR/TBG interaction, (6) TH liver clearance. For each MOA, different model compounds have been selected based on available information from literature, which provides sufficient evidence for THD-specific MOAs of these compounds, without undesired side effects.