dataesr/scientific-paragraphs-categorization huggingface dataset:
The autopilot system used in this study is composed of and modified from the open-source APM (ArduPilot Mega 2. 6 autopilot) firmware and open-source software Mission Planner, transmitted by ground-air XBee telemetry. In order to generate a high-resolution DSM (defines the terrain data including buildings and tree canopy) and mosaic orthorectified images, a total of 3767 photos were gathered by Sony ILCE-QX1 camera mounted on the crafts, during five fly missions, for a total coverage area of 33 km 2 with 8 cm ground sam-pling distance. The coverage of the adjacent photos is kept at 85 % overlap and 45 % side lap. The raw images are processed by ContextCapture and Pix4Dmapper software mutually; the data sets generated in this study are orthoimages and DSMs, with a grid spacing of 10 cm. Prior to the morphotectonic analysis (e.
g. ,  Deffontaines and Chorowicz, 1991; Deffontaines et al. , 1993 Deffontaines et al. , , 1994 Deffontaines et al. , , 1997 Deffontaines et al. , , 2017;; Pubellier et al. , 1994) , the quality of the data set (see whole data set in Fig. 2 ) needs to be evaluated. Eighteen ground control points are extracted with grid size of 2 m resolution of airborne light detection and ranging (lidar) data set and from the airborne lidar-associated 25 cm resolution orthorectified image. Most of the ground control points are situated on crossroads, targeted and georeferenced from orthorectified images, and the elevation is from the airborne lidar data. The comparison of the UAS DSM with airborne lidar data gives a root-meansquare deviation of 4. 1 cm with maximum error of 42. 5 cm from 26 sites of open, bare ground area, e.
g. , roads, school playgrounds, unvegetated terrains and parking lots. The elevation of the check points is averaged from an area of 4 m 2 , equal to the grid size of airborne lidar data. The distribution of the ground control points and checked points are located in Fig. 2b. Figure  3  highlights the precision and resolution difference on the available topographic data sets. The 40 m grid DEM was generated in 1986 by the Aerial Survey Office from aerial photos, the 5 m grid DEM was generated by several aerial photogrammetric agencies in 2004, whereas most of the aerial photos were acquired in 2003. The project to create a 5 m DEM data set was funded by the Taiwan Min-istry of the Interior. The airborne lidar data were acquired in 2011 and used to generated a 2 m grid DTM (defines the geomorphologic elevation after removing the buildings, trees and vehicles) and DSM, but this data set is not authorized to be published even though it was generated by the authors.
The ground control points used in this study were extracted from the airborne lidar data set we acquired some years ago. Figure  3  highlights visually the difference of quality, precision and resolution of each digital topographic data set. One can see the low quality of the 40 m DEM in contrast to the precision and resolution of our UAS DSM. Figure  4  illustrates some case examples of the quality of both the UAS DSM as well as the orthorectified image of the Hengchun Fault. The morphostructures analyses based on photo interpretation are conducted accordingly (Fig. 4c, d, g, h, k,  and i ). 3 Geology of the Hengchun area.
Inputs of PS-InSAR interferometry on the Hengchun Fault In order to deduce the regional tectonic activity of the Hengchun Fault, we processed 13 radar images (ALOS PAL-SAR images acquired in L-band; λ = 23. 6 cm, average incidence angle = 34. 3 • ) in ascending mode (flight direction 350 • E of N) between 17 January 2007 and 28 January 2011, thus providing 4 years of monitoring, through StaMPS software  (Hooper et al. , 2007). A series of 12 interferograms has been generated, all with respect to a unique "super master" as a reference image situated in the mid-time series that minimizes both spatial and temporal decorrelation  (Pathier et al. , 2003; Champenois, 2011 , Champenois et al. , 2011 , 2012 , 2014, Fig. 8 ). Note that their perpendicular baselines situated between -1194 and 2417 m are significantly below the critical limit where all interferometric coherence is lost  (Hooper, 2009).
Regarding the Kenting Fault, with the data used in this study we see no gradient of PS LOS velocity nor noticeable differential displacements on the GPS stations situated around the Kenting Fault (see Fig. 9  and profiles of Fig. 10ab ). Moreover, from the geological mapping analysis  (Chang et al. , 2003) , the Kenting Fault has a sinusoidal planimetric geometry that follows more or less the topographic isocontour of the relief. So the Kenting Fault is a low-dipping thrust fault. Consequently, this fault may be either locked at the surface or an inherited inactive fault, or it may have a tectonic activity that we do not observe on this PS-InSAR data set during the monitoring time period. We note a great coherence in between the PS and the available GPS measurements (colored large dots in Fig.
Acknowledgements. We are grateful to  Andy Hooper  for having developed and permitting to use the " StaMPS/MTI " algorithms. We thank also the  Japan Aerospace Exploration Agency (JAXA)  for the ALOS data in the scope of PI 112- 0001  project (project no. 5226904000 ;  PI Benoit Deffontaines). Erwan Pathier  ( UJF Grenoble ) and  Benedicte Fruneau  (UPE) are also deeply thanked for their support during  Johann Champenois 's PhD thesis. This project was partially supported by LIA ADEPT (now  D3E ) no. 536 ,  French CNRS-NSC  (now MOST) and  Taiwan MOST   105-2116-M-027-003. Edited by:  Yuichi S. Hayakawa Reviewed  by: four anonymous referees.
Cost measures Resource use data were collected by questionnaire at 24-28 weeks, 35-37 weeks, and 24-48 after delivery. Additional resource use data concerning the delivery and other delivery-related in-hospital services were collected from hospital records. Costs were measured from a societal perspective and included intervention, medical, absenteeism, and travel costs. All costs were expressed in Euros 2012  [23]. Due to a limited availability of unit costs reflecting the "true cost" of a good or service for most of the participating countries, unit costs were based on Dutch costing data. To correct for the fact that the relative prices of factors differ across countries, unit costs were adjusted per country using purchasing power parities  [24, 25]. An overview of the unit costs per country can be found in Additional file 3.
Intervention costs included all costs related to the development, implementation, and execution of the interventions, and were estimated using a bottom-up micro-costing approach. The latter means that intervention costs were estimated by collecting detailed information regarding the quantities of resources consumed while implementing and executing the interventions as well as their respective unit prices. Frequency and duration of face-to-face and telephone sessions were based on pilot study data  [13]. Time investments of intervention providers were valued using their gross hourly salaries. Material and website hosting costs were estimated using invoices. Medical costs included costs related to the use of primary healthcare (i. e. care by a GP, a midwife, a dietician, and a diabetes counselor), secondary healthcare (i.
Statistical analysis Analyses were performed according to the intention-totreat principle. Missing data were replaced using multiple imputation, stratified by treatment group. The imputation model included variables related to the "missingness" of data or the outcome measure, and variables that differed at baseline between groups  [29]. Using Predictive Mean Matching, 20 complete data sets were created to keep the loss-of-efficiency below 5%  [29]. Datasets were analyzed separately as specified below. Pooled estimates were calculated using Rubin's rules  [30]. Effects on gestational weight gain, fasting glucose, and HOMA-IR at 35-37 weeks and the effect on QALYs gained from baseline till after delivery were analyzed using multilevel analyses, adjusted for baseline values and follow-up duration (in weeks).
Costeffectiveness acceptability curves (CEACs) were constructed, showing the interventions' probability of being cost-effective compared with usual care at various willingness-to-pay (WTP) values. Analyses were performed using Stata 12. 0. Statistical significance was set at p < 0. 05. Four sensitivity analyses (SAs) were performed. In SA1, national tariffs were used for estimating QALYs  [31] [32] [33] [34] [35] , instead of the European tariff. In SA2, United Kingdom (UK) unit costs data were used for valuing resource use, instead of Dutch ones. UK unit costs were used in SA2, as previous research indicates that a large share of European economic evaluations is carried out in the UK, and thus relies on UK costing data as well  [36]. UK unit costs were derived from previously published studies, international databases, hospital records, and published UK pricing lists  [37] [38] [39] [40].
Funding The project described received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 242187. In the Netherlands, additional funding was provided by the  Netherlands Organisation for Health Research and Development (ZonMw)  (grant  200310013 ). In Poland, additional funding was obtained from  Polish Ministry of Science  (grants  2203/7 ,  PR/2011/2 ). In Denmark , additional funding was provided by  Odense University Free Research Fund. In the.
The system's performance was: accuracy = 88. 2% (95% CI: 82. 4-92. 2%); PPV = 76. 5% (95% CI: 70. 6-82. 4%). This level of prediction made it suitable for further analysis of the entire dataset. Based on this automatic detection of e-IDS, we created two sub-classes for maternal vocalization: e-IDS versus non e-IDS. Two variables were derived: e-IDS Ratio (duration of IDS vocalization/duration of interaction) and non e-IDS Ratio (duration of non e-IDS vocalization/duration of interaction). We also derived two Infant response to maternal vocalization ratios: Infant response to maternal vocalization e-IDS Ratio, Infant response to maternal vocalization non e-IDS Ratio which reflect the ratio of time during which the infant vocalizes in response to his/her mother's e-IDS and non e-IDS).
Acknowledgments This work has been partially supported by the  European Commission  under the 7th  Framework Programme  through the  COMET -COordination and iMplementation of a pan-European instrumenT for radioecology , grant agreement no:  604974. The authors would like to thank the other members of the  ALLIANCE SRA/roadmap Working Group  for the fruitful discussion all along our advances,  C. Adam-Guillermin ,  T. Arnold ,  C. Duffa ,  N. Horemans ,  O. Masson ,  S. Sachs ,  J. Tschiersch ,  C. Berthomieu ,  L. Currivan ,  P. Krajewski ,  F. Legarda ,  B. Michalik ,  M. J. Madruga ,  M. Merroun ,  J. Popic ,  P. Nogueira ,  B. Salbu ,  M. Steiner ,  M. Vidal ,  J. Vives I Batlle. Authors are also grateful to Mike Thorne for his high availability and very helpful and relevant advice. We are also thankful to the anonymous referees whose recommendations helped to make this position paper clearer.
Materials and Methods The LMT-S tool is formatted as a questionnaire embedded in a spreadsheet (Microsoft Excel 2007). Responses entered by assessors are automatically converted into an overall safety and biosecurity score. The calculated score is accompanied by tabulated summaries and graphic depictions of the laboratory's safety and biosecurity strengths and weaknesses. The LMT-S, which was modeled after the FAO LMT-Core module, has questions which specifically target laboratory safety, biosafety, and biosecurity in the context of administrative activities, operational procedures, engineering, and personal protective equipment (PPE) used to control accidental or intentional release of biological materials and to respond to potential adverse events in the laboratory. The LMT-S was designed to be used either by trained external assessors during on-site assessments used for establishing baseline laboratory safety and biosecurity performance and for measuring progress of post-intervention activities, or for use by laboratories in self-assessment, monitoring, and continuous improvement of their own safety and biosecurity-related practices.
The sixth tab provides FAO copyright and associated information related to development and rights to access the LMT-S module. Scoring within the LMT-S is based on a percentage of an optimum score of 100%, which represents an ideal situation, i. e. , a laboratory that would score 4 for all the sub-categories assessed. A confidence score is additionally calculated from the percentage of subcategory questions that are not completed (i. e. , left blank) by the assessor. Completion of 0-69% of the questionnaire provides a low confidence score, 70-89% a medium confidence score, and 90-100% completion of the questionnaire is ranked as reliable. The confidence score is reported as a component of the summary results. The laboratory safety and biosecurity data is tabulated and also graphically depicted using a color-coded radar-style chart, with rankings allocated to 0-20%, 20-40%, 40-60%, 60-80% and 80-100% as compared to the optimum 100% benchmark score for each area and category (Figure  1 ).
When multiple assessments are provided (e. g. , over time, by different assessors, before and after intervention activities), the results are tabulated by assessment, by area and by category, and additionally presented graphically by area and by category as a compilation allowing for comparison of up to three assessments. A confidence score is additionally calculated from the percentage of subcategory questions that are not completed (i. e. , left blank) by the assessor. Completion of 0-69% of the questionnaire provides a low confidence score, 70-89% a medium confidence score, and 90-100% completion of the questionnaire is ranked as reliable. The confidence score is reported as a component of the summary results. The laboratory safety and biosecurity data is tabulated and also graphically depicted using a color-coded radar-style chart, with rankings allocated to 0-20%, 20-40%, 40-60%, 60-80% and 80-100% as compared to the optimum 100% benchmark score for each area and category (Figure  1 ).
Results Of the 34 laboratories participating in the pilot use of the LMT-S, 17 were located in geopolitical region A and 17 in region B. A total of 19 (region A = 4, region B = 15) countries participated in the evaluation and use of the LMT-S during calendar years 2015 through 2017. Follow-up or postintervention assessments were not completed for all laboratories, so only initial assessment findings are analyzed and reported here. Training on use of the LMT-S followed by laboratory assessment missions were conducted in two geographically distinct global regions, with a total of 19 countries and 34 laboratories participating. Assessor training in a third geographic region was scheduled, but not completed for inclusion in the current data analysis. The laboratories evaluated included the range of facilities, including well-funded and well-resourced regional and national laboratories, as well as laboratories that would be classified as resource-limited.
In a similar vein of providing focused and valuable detailed assessment of veterinary laboratory safety and biosecurity, the LMT-S can provide standardized data necessary for informing the Global Health Security Agenda Joint External Evaluation (GHSA JEE). The GHSA JEE is a well-recognized comprehensive assessment with a One Health focus that is performed at a national level, and used by countries to identify the strengths and weaknesses within their health system (i. e. , veterinary, public, environmental) in order to prioritize and access opportunities, including funding for capacity development in disease prevention, detection, and response. With regional biosafety programmes currently implemented in both region A and B, the results of the standardized LMT-S assessments can be used to document safety and biosecurity compliance, and can be shared between countries as they participate in national and regional discussions on priorities for capacity development, resource sharing, and intervention strategies.
A goal of the LMT Web-portal is to provide laboratory's further incentive for improving laboratory functionality and capacity, and ultimately help laboratories enhance their profile nationally, regionally, and globally. The LMT-S was specifically developed with the goal of ascertaining and improving the level of laboratory safety and biosecurity in support of global disease detection and response capacity-building. The LMT-S has been demonstrated during pilot testing to be an accessible multipurpose tool for documenting and improving laboratory safety and biosecurity. The tool fills an important niche in helping to provide a safe and biosecure laboratory environment through standardized assessment and documentation, which in turn provide critical awareness and objective data useful in national and international health, including laboratory funding and prioritization activities.
Acknowledgments: This work was supported by the  Food and Agriculture Organization of the United Nations (UN-FAO)  with funding from the  United States Agency for International Development (USAID)  under the Emerging Pandemics Threats (EPT)-1/ IDENTIFY  project  OSRO/INT/902/USA  and  EPT-2  project  OSRO/GLO/507/USA. Author S. Blacksell  was additionally supported by the  Wellcome Trust. The content of this article is the responsibility of the authors and does not necessarily reflect the views of  UN-FAO, USAID  or the  United States Government. For their valued contributions to LMT-S development and implementation, the authors would like to thank  J. Kimutai ,  F. Baurier ,  N. Bloch ,  E. Fabregas ,  E. Hansson ,  J. Kozlovac ,  S. Letellier ,  K. Martin ,  T. McNunn ,  C. Morrissy ,  J.
Introduction Seven institutes † in Europe have combined their expertise in the field of exoplanetary research to develop the European Horizon-2020 ExoplANETS-A ‡ project under the coordination of CEA Saclay. In the framework of the project, novel data calibration and spectral extraction tools, as well as novel retrieval tools, based on 3D models of exoplanet atmospheres, will be developed to exploit archival data from space-and ground-based observatories, and produce a homogeneous and reliable characterization of the atmospheres of transiting exoplanets. Additionally, to model successfully the exoplanet atmosphere, it is necessary to have a sound knowledge of the host star. To this end, we will collect a coherent and uniform database of the relevant properties of host stars from online archives (e.
The sample The sample of exoplanets and host stars considered by the project comprises all transiting-exoplanet systems observed by HST or Spitzer. Currently, this corresponds to 135 exoplanets, of which 85 have HST data; the associated number of stars is 113, with 76 having HST data (Fig. 1(b) ). For the host stars, the primary online archival databases ExoplANETS-A 3 to be used are HST (for UV spectra), XMM-Newton (for X-ray), Gaia (for astrometric properties) and SIMBAD (for spectral types etc). The searches also include: GALEX (UV photometry); Chandra, ROSAT and other X-ray catalogues, together with results retrieved from the published literature. At X-ray wavelengths, most of the detections are from the 3XMM (DR8) serendipitous source catalogue  (Rosen et al. 2016). In summary, to date (November 2018), we have the following statistics: • X-ray (3XMM-DR8 cat.
Visible-light photometry from Kepler/K2 and TESS will also be used to help characterise the stars in terms of rotational and aperiodic variability. One third of our sources have Kepler light-curves while TESS will probably provide data for the remainder. The ExoplANETS-A database of stellar properties and spectral models will be made publicly available, via a web-based interface, by the end of 2020.
For example, full-length rat DNA polymerase β consists of three domains (DNA polymerase β N-terminal; DNA polymerase β and DNA polymerase β catalytic). Crystal structures are available for full-length protein (PDB id: 1BPD) and the two C-terminal domains (PDB id: 1RPL) (Fig  1 ). For the study, we have compared the properties of the second and third domains in the two crystal forms. This comparison allowed us to study the influence of the first domain on the second as well as the third domains. Further comparative dynamics analyses of homologous protein domains were carried out to understand the functional relevance of tethering of domains. Analyses reveal an intricate coupling between the domains in multi-domain systems leading to alteration in dynamics in 18 protein pairs. Structural and energetics differences were observed in half the numbers of cases studied.
Flexibility and coupling of fluctuations of residues change on tethering Normal mode analysis was used to study the extent of influence of tethering on the dynamics of the constituent domains. Normal modes, accounting for 80% variance of the protein motion, were calculated for each MD and ID of the 20 protein domain pairs. To compare the flexibility of MD and ID normalized summed square fluctuation values were compared. The flexibility profiles were observed to be statistically different for all the domain pairs, except two (Fig  4A  and 4B , two-sample KS test, p-value < 2. 2e-16). To ensure that the differences are not an artefact of crystal packing, flexibility profiles of ID and MD were compared with two control datasets namely control dataset 2 and control dataset 3 respectively. The control dataset 2 was generated by in silico removal of the tethered domains from MD.
The domains in the control dataset 2 (referred to as AD) are essentially identical to ID in sequence as well as length. The flexibility profiles of ID and AD were observed to be similar (S3A Fig). The control dataset 3 was generated by in silico ligation of the ID with the tethered domain of MD. This was achieved by superimposing the ID onto MD, followed by in silico removal of the common domain from MD and then ligation of the remaining domains of MD with ID. The multi- domains in the control dataset 3 (referred to as swapped domain) are essentially identical to MD in sequence and length. The flexibility profiles of MD and swapped domains were also observed to be similar (S3B Fig). The similarity of the flexibility profiles of the protein domain pairs and the control datasets ensured that the differences observed in the flexibility profiles of MD and ID are a consequence of the tethering of the domains in multi-domain systems than a crystallization artefact.
A majority of interface residues (~70%) have higher flexibility in ID than in MD. But interestingly, ~30% of interface residues have comparable flexibility in MD and ID. Thus, some of the interface residues retain their rigidity in the isolated state as well. ~36% of the functional residues have higher flexibility in ID than MD while ~18% have higher flexibility in MD than ID ( Molecular dynamic studies were carried out for 3 domain pairs from the dataset to study the synchronization of motions in the domain at all-atom level. These 3 pairs of domains were selected based on the number of interfacial residues between the domains. Tight coupling of motions was observed not only between the C-alpha of residues but also between the sidechains of residues in MD (S6 Fig). While weak or no coupling was observed for side-chains of residues in ID.