dataesr/scientific-paragraphs-categorization huggingface dataset:
Basemap data We extracted measurements of the esker from the 1 m/pixel digital elevation model (DEM; vertical precision ±0. 492 m) and 50 cm/pixel orthorectified images generated by  Butcher et al. (2017)  from High Resolution Imaging Science Experiment  (HiRISE; McEwen et al. , 2007 ) images ESP_049573_2265 and ESP_049639_ 2265. Given the resolution of the input images, we consider the effective horizontal resolution of the DEM to be 2 m. We imported these data into ESRI ArcGIS 10. 1 software in a sinusoidal projection centred on 276. 5 • E. (the centre of the valley hosting the esker). We then derived slope, shaded relief, and aspect map products from the DEM to aid visual inspection and interpretation of the 3D surface. A list of data products used in this study is provided in Appendix A.
Following  Butcher et al. (2016) , we then calculated ridge height H for each cross-sectional transect as the difference in elevation between the ridge crest point and the bed. We calculated the bed elevation by averaging the elevations of the left and right base points. We calculated ridge width for each cross-sectional transect as the horizontal distance between the right and left base points. Post-processing of our raw 3D measurements is described in Section 4. 4. The crest points and associated raw height and width measurements, in addition to processed height and width data generated during the analyses presented in Sections 4. 4 and 4. 5, are included in supplementary dataset 1. We performed statistical analyses using R software (R Development Core Team, 2008).
Sampling We excluded cross sectional transects from the sample if: (1) the base of the ridge was obscured by proximal tributary ridges, (2) they had been visibly modified by secondary processes such as impact cratering or superposition by moraine ridges (see  Butcher et al. , 2017) , or (3) they were within 40 m of the tapering ends of the ridge segment. We excluded subzone I  (Butcher et al. , 2017)  from our morphometric analyses owing to its near-complete obscuration by superposing VFF materials. Our raw dataset comprised measurements for 457 cross-sectional transects (see Appendix B).
g. ,  Banerjee and McDonald, 1975; Brennand, 1994 Brennand, , 2000;; Burke et al. , 2008 Burke et al. , , 2010;; Cummings et al. , 2011; Burke et al. , 2012; Perkins et al. , 2013; Burke et al. , 2015; Perkins et al. , 2016). Recent studies have identified relationships between the interior sedimentology and macro-scale morphology of eskers on Earth (e. g. ,  Burke et al. , 2015; Perkins et al. , 2016). Candidate eskers on Mars are currently inaccessible except by orbital remote sensing. However, terrestrial insights can aid understanding of the dynamics of esker-forming drainage events on Mars, where metre-scale morphometric observations are enabled by high-resolution remote sensing datasets, but sub-metre-scale sedimentary observations are not currently possible. Based on terrestrial insights, we propose that the stacked morphology of the NW Tempe Terra esker reflects at least two stages of esker deposition within the esker-forming subglacial meltwater conduit, either during the same meltwater drainage episode, or during two or more distinct drainage episodes.
Acknowledgements We thank two anonymous reviewers, whose suggestions helped us to improve the clarity of the manuscript. This work was undertaken at  The Open University  as part of a PhD studentship held by FEGB and funded by the  Science Technology and Facilities Council (STFC)  grant  ST/N50421X/1. Manuscript preparation was supported by  The Open University  (internal funding) and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( ERC Advanced Grant   PALGLAC 787263 ). We also gratefully acknowledge  UK Space Agency  grants  ST/L00643X/1 ,  ST/R001413/1 , and  ST/R001383/1  (MRB);  ST/R001405/1 ,  ST/P001262/1 , and  ST/S00145X/1  (SRL); and  ST/R/001375  (AH). SJC is supported by the  French space agency, CNES.
The application allowed us to optimize the model by searching the optimum slope of the MAP curve to optimize the goodness-of-t of the model for detecting epidemics. The MEM app calculates goodness-of-t indicators in an iterative process using a cross-validation procedure (28). True positives (TP) were then de ned as values of epidemic period above the threshold, true negatives (TN) as values of the non-epidemic period below the threshold, false positives (FP) as values of the non-epidemic period above the threshold and false negatives (FN) as values of epidemic period below the threshold. The process was repeated for each season in the dataset and all TP, TN, FP and FN were pooled. To measure the performance of the threshold, the following statistics and de nitions were used (28): 1. Sensitivity: The number of epidemic weeks above the pre-epidemic threshold and above the post-epidemic threshold divided by the number of epidemic weeks (epidemic length).
Acknowledgements We would like to thank  Dr. Amgad Elkholy  and  Dr Mohamed Elhakim  from  Infectious Hazard Management (IHM) /EMRO at  World Health Organization  and  Dr Henry Laurenson-Schafer  for organizing training sessions on statistical methods for analyzing data provided by in uenza surveillance systems as well as Pr. Abderrahmane Maarou  , former Director of Epidemiology at the  Ministry of Health of Morocco.
Acknowledgments This project has received funding from the  European Union   Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement 721766 as well as from the innovation board of the  Carl Zeiss Meditec AG. T. R. is recipient of a  DOC Fellowship  of the  Austrian Academy of Sciences at the Institute of Neurology (25262). The financial support by the  Austrian Federal Ministry for Digital and Economic Affairs  and the  National Foundation for Research, Technology and Development  is gratefully acknowledged.
Ultimately, for the purpose of this study, we analyzed data only for spiders for which we were able to obtain sex-specific analytical measurements, namely, (1) orb-weaver spiders in Araneus, (2) jumping spiders in Salticidae,  (5)  funnel weavers in Tegenaria and (6) cobweb spiders of Steatoda grossa. Raw data on stoichiometry for the two remaining groups  (3, 4) , for which sex-specific information was not available, were only included in Table  A1  for reference. It is important to mention that the reproductive state of collected females was not studied here; thus, females with or without eggs in their bodies could contribute to our samples, which could have increased the variation in the measured concentrations of elements. Considering the detection limits of the analytical equipment, we measured the concentrations of the studied elements in samples comprising of one to one hundred individuals, depending on their body mass.
Sulfanilic acid was used as the reference material for the C, and N analyses, and certified reference materials (NCSDC73349, NCSZC73016 and RM8415) were used for the other elements. Principal component analysis (PCA) was employed to reconstruct the multielemental stoichiometric relations among taxa and sexes using Canoco 5  [34]. The data were log-transformed, centered and standardized by species but not by sample; thus, PCA was performed on a correlation matrix. To check for differences between sexes and taxa, we performed analysis of variance (ANOVA) independently for the 1st-and 2nd-axis scores using Statistica 13 (TIBCO Software Inc. ).
Hence, we will supplement our reasoning with available data collected for other arthropods. A study on different "populations" (natural and reared in the laboratory) of the black widow spider Latrodectus hesperus suggested between-"population" differences in C:N, C:P and N:P stoichiometries  [40]. However, only female spiders were considered. Our current study does not allow us to compare variation in stoichiometric phenotypes between sexes because of the too small number of analytical samples collected for males; however, it was previously shown, using solitary bees as model organisms, that females are more homeostatic than males in their elemental phenotype  [24]. If so, it may be possible to find statistically significant differences in stoichiometric phenotypes of females but not males associated with different populations.
Judged from these relationships, Latin America may be home to at least a third of global biodiversity. Detailed studies over the past several decades have made it possible to compare accurately the numbers of species of indigenous vascular plants in Africa, Latin America, and Southeast Asia, and the results are unexpected. In summary, the Afrotropical Region (Africa south of the Sahara with Madagascar), with an area of 22,657,000 km 2 , is home to 56,451 plant species. These were enumerated accurately following the development of the regularly updated African Plant Database  (1)  and the Madagascar Catalogue (2), both regional nomenclature repositories with standardized taxonomy. For Latin America (Mexico south, including the West Indies), with  19, 197 ,000 km 2 , a similar effort (3) has recorded 118,308 known species.
Acknowledgments: We are grateful to the following people for the invaluable discussion on data for selected groups of animals that allow for comparison with our data with plants:  A. Bauer  (reptiles) and  B. Fisher  (ants); lepidopteran data were discussed with  S. Collins ,  J. Dobson ,  J. D. Holloway ,  D. Lees ,  R. K. Rollins ,  R. Vane-Wright , and  M. Williams. We are also grateful to  D. Middleton ,  National Parks Board, Singapore , for the valuable discussions on Southeast Asia and to  S. Miller ,  U. S. National Museum, Smithsonian Institution , for additional discussions. We thank  C. Davidson  and  S. Christoph  ( Flora of The World ),  C. Davis , and  D. C. Western  for permitting use of their photographs. Funding: No grants were received for any aspect of this study. Author contributions: P.
Data, descriptive statistics and estimation methodology 2. 1 Data The study uses data from UNCTADstat and the International Trade Centre (ITC). We retrieve aggregate information on broader categories of commodities exports from 54 Commonwealth member countries at a bilateral level from UNCTADstat and use it to generate descriptive statistics on the structure, composition and historical trend of commodities exports. We employ the ITC dataset for the product-level analysis for the recent period since the onset of COVID-19. The projections and simulations are based on monthly trade flow data from the ITC. 1  Use of these two sources enables us to expand the breadth and depth of the analysis and examine commodity trade of Commonwealth countries from multiple macro and micro dimensions. Data for January-February imports by China have been downloaded from the Chinese customs website.
Bar numbers refer to deviations from baseline levels. The COVID-19 shock is assumed to last until the end of June 2020 for China and the end of August 2020 for other destinations in scenario 1. In scenario 2, the COVID-19 shock is assumed to last until the end of August 2020 for China and the end of October 2020 for other destinations. Source: Authors' simulations using ITC dataset. International Trade Working Paper 2020/14 above benchmark expected levels. This would owe essentially to a positive reaction during the pandemic of demand for palm and coconut oils from Papua New Guinea and for frozen fish from Papua New Guinea. Demand for palm oils from Papua New Guinea is also expected to move up in the UK market as a result of the pandemic. Among commodity-dependent Commonwealth countries, the largest decrease in absolute terms is obtained for exports from African members directed to the EU-27 market.
Quantification of tightening product contraction in vivo Using a DermaTop-HE acquisition system (EOTech SA, Marcoussi, France), which provided a resolution of 30 μm/pixel, calibrated images of each subject's cheek were acquired before and after application of the product, at the time points indicated in Table  1. The image sequences were used to determine the skin areal strain induced by the cosmetic application. To this end, a custom-written Python code (Python Software Foundation, Wilmington, Germany) based on  [25]  allowed identifying the location of the 8 ink dots, from which the deformation gradient tensors corresponding to the observed skin deformation at each measurement time point were estimated using least-square error minimization. Hence, the principal strains, ɛ 1 and ɛ 2 , could be computed and the areal strain was obtained as: ε Area = ( ε 1 + 1 ) • ( ε 2 + 1 ) -1.
Note, however, that the influence of gravity on the results reported in Fig. 7  a is negligible, as confirmed by comparing simulations with and without gravimetric field (data not shown). The extent of product contraction at each simulation time increment was determined by quantifying the maximum and minimum in-plane strain for each shell element, computing the 'per-element' areal strain, and finally averaging these values over the entire shell representing the cosmetic film; the configuration reached after gravimetric load application was used as reference to quantify areal strains.
Statistical methods and data representation Throughout this paper, experimental quantities are reported in terms of mean measured value and standard deviation (error bars or shadings). Statistical comparison of Cutometer® parameter values before and after application of the tightening product was based on two-sided paired t -tests; normality of the sample difference was confirmed by performing Shapiro-Wilk's test ( cf. Supplementary Material ). The threshold for significance was set to p < 0. 05, and analysis was performed using the statistical module available within the SciPy Python package (Python Software Foundation, Wilmington, Germany).
Conclusions The mechanical characteristics of a soft active biomaterial for skin anti-aging were assessed considering a wide range of experimental conditions, providing the data required to inform a numerical model of the attained cosmetic action. The corresponding in vivo effects on the tissue biomechanics were rationalized by considering the combined role of the film stiffness and of its contractile action, as well as their interplay with the substrate mechanical behavior. Moreover, wrinkle alleviation was shown to be greatly enhanced by a substrate with skin-like deformability, due to the corresponding limitations to the film contraction in plane. Finally, the developed numerical model was used for parametric studies providing insights that are relevant for optimization of this fascinating class of biomaterials.
Acknowledgements The authors wish to thank  Laura Bernardi ,  Anna Moro , and  Xi Wu  from the  Institute for Mechanical Systems (IMES) of ETH Zurich  for their invaluable help in setting up the experimental protocols, as well as the group of  Prof. Jürg Dual  ( IMES, ETH Zurich ) for the use of the white light interferometer. We also wish to thank  Alexandra Farran ,  Anne-Laure Bernard  ( L'Oréal USA ),  Pierre Dupuis  ( L'Oréal France ) and  Florian Formanek  ( HORIBA Scientific ) for useful discussions,  Hussein Jouni  ( L'Oréal France ) for his help with the statistical analysis, and  Audrey Gueniche  ( L'Oréal France ) for critical reading of the manuscript. This work was partially supported by  L'Oréal Research & Innovation  in the framework of a scientific collaboration with  ETH Zurich.
One subject became sick during the flight despite medication and was excluded from all the analyses. In addition to the main perspective-taking task, each subject completed a computerized version of the classic rod-and-frame test (RFT;  [54] ) to assess visual field dependency. The main task and the RFT were implemented in Matlab 2014b (MathWorks, Natick, USA) and ran on a Dell Alienware Cassini 15 computer.
Statistical analysis We used Matlab 2017a (MathWorks, Natick, MA, USA) and IBM SPSS Statistics 21. 0 to perform the statistical analyses. Data for each session were gathered in a matrix containing, for each condition along the first dimension and each subject along the second dimension, the average response time or success rate computed over all repetitions of the same condition. During the pre and postflight session, each condition was repeated 12 times (in a pseudo random order). During the inflight session, each condition was repeated on average 8 times. However, the number of repetitions was slightly variable across subjects due to the outlier removal procedure and because subjects did not necessarily complete the same number of trials over the 15 parabolas. Given our relatively small sample size, we often opted for non-parametric tests, i.
Performance on the ground We first analyzed performance on the ground to verify that the task engaged subjects cognitively. We used the data collected preflight (i. e. , in normal gravity conditions) and assessed response time (RT) and success rate (SR) across experimental conditions. If the task requires subjects to perform egocentric transformations with various degrees of difficulty, we expect performance to vary between conditions. Accordingly, we found that the mean RT differed across condition type (non-parametric Friedman test with repeated measures,  2 (4)=19. 85, p<0. 001). In particular, we found that subjects were significantly slower when the avatar was tilted (avatar-tilt and both-tilt) compared to the no-tilt condition (Figure  2a ; Wilcoxon sign-rank tests with Bonferroni correction and adjusted significance level of p<0.
Effect of microgravity on performance To test our main hypothesis of an effect of microgravity on perspective-taking abilities, we next compared 1g to 0g data collected during parabolas. We expected performance to be most influenced by weightlessness in tilted perspective-taking conditions, corresponding to situations where subjects mentally rotate their perspective away from the gravitational vector (roll rotations). Consistent with this prediction, we observed a specific decrease in RT in both the avatar-tilt and both-tilt conditions (difference in mean RT between 0g and 1g: 0. 24 and 0. 30 s, respectively; Figure  5a ) but almost no difference in the no-persp, no-tilt and shelf-tilt conditions between 0g and 1g (-0. 02, 0. 02 and -0. 02 s, respectively; Figure  5a ). To validate these observations statistically, we performed a 3-way ANOVA using 'gravity level', 'avatar orientation', and 'shelf orientation' as main factors (see Methods).
Acknowledgments This project was supported by the  French space agency  ( Centre National d'Etudes Spatiales, CNES, France ). We thank  Sébastien Rouquette  and  Maurice Marnat  for providing most of the hardware (laptops and virtual reality equipment). We also thank  Sébastien Barde  and  Alain Maillet  for constructive discussions in the early stages of the project, as well as  Dr. Anne Pavy-Le Traon  and  Guillemette Gauquelin-Koch  for administrative and financial aspects. Finally, we thank  Novespace (Mérignac, France ) responsible for the parabolic flight campaign, and in particular  Thomas Villatte  for his technical guidance throughout the project.
Methods and Findings: Utilizing the baseline data from the IMI DIRECT participants (n=1514) we sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Multi-omic (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, and measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI image-derived liver fat content (<5% or ³5%). We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and Random Forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operator characteristic area under the curve (ROCAUC) of 0.
METHODS AND MATERALS Participants (IMI DIRECT) The primary data utilized in this study were generated within the IMI DIRECT consortium, which includes a multicenter prospective cohort study of 3029 adults recently diagnosed with T2D (n=795) or at high risk of developing the disease (n=2234). All participants provided informed consent and the study protocol was approved by the regional research ethics committees for each clinical study center. Details of the study design and the core characteristics are explained elsewhere  (13, 14).
Measures (IMI DIRECT) A T2*-based multiecho technique was used to derive liver fat content from MRI  (15, 16)  and the percentage values were categorized into fatty (³5%) or non-fatty concentrations (<5%) to define the outcome variable. We elected not to attempt quantitative prediction of liver fat content, as this would require a much larger dataset to be adequately powered. A frequently-sampled 75g oral glucose tolerance test (OGTT) or a frequently sampled mixed-meal tolerance test (MMTT) was performed, from which measures of glucose and insulin dynamics were calculated, as previously described  (13, 14, 17). Liver fat data were available for 1514 IMI DIRECT participants  variables in the analyses. Buffy coat was separated from whole blood, and DNA was 87 then extracted and genotyped using the Illumina HumanCore array (HCE24 v1.
0); genotype imputation was performed using the Haplotype Reference Consortium (HRC) and 1000 Genome (1KG) reference panels. Details of the quality control (QC) steps for the genetic data are described elsewhere  (14). Transcriptomic data were generated using RNA-sequencing from fasting whole blood. Only protein-coding genes were included in the analyses, as reads per kilobase of transcript per million mapped reads (RPKM). The targeted metabolomic data of fasting plasma samples were generated using the Biocrates AbsoluteIDQ p150 kit. Additionally, untargeted LC/MS-based metabolomics was used to cover a broader spectrum of metabolites. A combination of technologies and quantitative panels of protein assays were used to generate 'targeted' proteomic data. This included Olink's proximity extension assays  (18) , sandwich immunoassay kits using Luminex technology (MerckMillipore and R&D Systems, Sweden), microfluidic ELISA assays (ProteinSimple, USA  (19) ), as well as protein analysis services from Myriad RBM (Myriad GmbH, Germany) and for hsCRP (MLM Medical Labs GmbH, Germany).
Minimizing the following value, LASSO excludes the least informative variables and selects those features of most importance for the outcome of interest () in a sample of n cases, each of which consists of m parameters. The penalty applied by  can be any value from zero to positive infinity and is determined through a cross-validation step  (22). • • • • • • • • • • • ••••• • • • ••••• • • • • • • • • • • • • • • •• • • • • • • •• •• • • • • ••••• • • • • • • • • • • • • • • • • • •• • • • • • • • • • • • • • • • •• • •••• • • • • ••••• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •••••• • • •••••• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • ••••• • • •••• • • • • • • • • • ••• • • • • • • • • • •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • •••••••• ••••••• •••••• ••••• •• • • • •• •• • • • • • • • • • • • • • • • •• • • • • • • •• • • • • • • • • • • • • • • ••• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • ••• • • • • • • • • • • • • • • • • • • • • • • • • • •••••• • • • • • • • • • • • • • •••• • • • • • • • • • ••• • • • • • • • • • •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • •••• • •• • ••• •• •• •••• • • ••••• • • • • • • • •• • LiverFat Age ∑ (𝑦 ( -𝑦 * +) , + 𝜆 × ∑ |𝛽 " | 0 "12 3 (12 (1) To minimize bias (for example by overfitting), we randomly divided the dataset and used 70% (n=735) for feature selection and 30% (n=314) for the model generation (see below).