dataesr/scientific-paragraphs-categorization huggingface dataset:
Environmental variability of the studied landscape The variability of soil physicochemical properties across the studied area was assessed by subjecting the data to principal component analysis (PCA). Land management practices over the entire landscape were summarized by performing a factor analysis for mixed data to define land management clusters using the FactoMineR package  (Lê et al. 2008 ) with land use, soil tillage, crop rotation diversity (number of plant types in the crop rotation), and the pesticide treatment frequency index, as data input. These clusters followed a gradient in cropping intensity based on soil disturbance and in the diversity and persistence of plant cover that is, Forest (forest, no-tillage, no catchcrop, n = 44); Perennial plant cover (three frequently mown) grasslands, three blackcurrant (Ribes nigrum) and one Miscanthus (Miscanthus giganteus), n = 7); Catch Crop (agricultural plot, minimum tillage, catch-crop, n = 22); Minimum tillage (agricultural plot, minimum tillage, no catch-crop, n = 57); Mechanical hoeing (agricultural plot, mechanical hoeing, no catch-crop, n = 33) and Conventional tillage (agricultural plot, conventional tillage, no catch-crop, n = 104).
If the fitted model provides a valid representation of the spatial variation of the soil or microbial property, then these errors display a v 2 distribution which has a mean of 1 and median 0. 455  (Lark 2002). The mean and median values of the SSPE were also calculated for 1000 simulations of the fitted model to determine the 95% confidence limits. An ordinary kriging estimation was performed in the standardized-rank space and the kriging estimates were then back-transformed into the original space. The geostatistical analysis gstat and GeoR R package for variograms analysis and kriging were used  (Ribiero and Diggle 2001).
Quantitative response and explanatory data were, respectively, log-transformed and standardized to provide an approximated Gaussian and homoscedastic residual distribution. For each taxon, physicochemical and land management variables were selected by multiple regression analysis using a stepwise selection procedure, which maximized the adjusted R² (in order to maximize the explained variation by the model) and minimized the Akaike Information Criterion (AIC, in order to discard previously retained variables that reduced the overall predictive power). Spatial descriptors were then selected from the model residuals, in order to strictly identify the spatial autocorrelation that did not correspond to spatially structured environmental variables. These selection steps enabled us to exclude those variables that did not contribute significantly to the explained variation (P < 0.
001), thereby limiting overfitting and problems due to co-linear variables  (Ramette 2007). The respective effects of each explanatory variable, or combinations thereof, were determined by (1) partial regression for the relative abundance of taxa and (2) distance-based redundancy analysis (db-RDA,  Ramette and Tiedje 2007; Bru et al. 2010 ). The statistical significance was assessed by 999 permutations of the reduced model. All these analyses were performed with R (http://www. r-project. org/) using the vegan package  (Oksanen et al. 2011).
The respective significant contribution of each variable is represented by the height of the shape and was calculated by taking into account all other variables using partial regression models and adjusting the R 2 values. The color was scaled to depict the value of the standardized partial regression coefficients (green, positive, red negative effect). c-Proteobacteria and Acidobacteria are not represented since no significant contribution of any physicochemical or land management variables explained their variations in the data set. Bacterial taxa are ranked according to their overall relative abundance in the data set. (from forest to conventional tillage) and variations in bacterial and archaeal taxa belonging to Bacteroidetes, Firmicutes, Chloroflexi, Gemmatimonadetes, Thaumarchaeota, Crenarchaeota, and Fibrobacteres (Fig.
Acknowledgments This work, through the involvement of technical facilities of the  GenoSol platform of the infrastructure ANAEE-France , received a grant from the  French  state through the  National Agency for Research under the program  " Investments for the Future"  (reference  ANR-11-INBS-0001 ), as well as a grant from the  Regional Council of Burgundy  and of the ANR ADVERB. We thank the farmers of F enay for providing the agricultural practices and for allowing us to perform soil sampling on their fields. Thanks are also extended to  D. Warwick  for her comments to the manuscript.
Surface velocities Information on the dynamic state of the entire Antarctic ice sheet was recently brought together and presented in a single database by  Rignot et al. (2011b). Surface velocity magnitudes were inferred from synthetic aperture radar (SAR) imagery acquired between 1996 and 2009 with five different satellite-mounted sensors. The coverage of this velocity mosaic is almost complete and errors in the inferred flow field fall below 10 m a -1 over most of Antarctica. We do not use the later released high-resolution data but base ourselves on the earlier product with a 900 m sample spacing, because the inversion is limited by the 1 km grid of the Bedmap2 geometry. The velocity observations are not interpolated onto the model grid, because the underlying finite element approach intrinsically allows us to compute the velocity solution at any location.
g. Gillet-Chaulet et al. , 2012; Morlighem et al. , 2013; Arthern et al. , 2015). The data assimilation presented here has reached a quality such that it can serve to identify inconsistencies in and between the velocity and geometry data sets. As the optimisation primarily addresses velocity differences, our assimilation reveals inhomogeneities in the surface velocity mosaic. However, more importantly it points towards regions where observed ice flow is not reconcilable with the geometric database. This is especially the case for pinning points which can alter the dynamics of a larger area. Consequently, ice flow models require the location and extent of these pinning areas as input in order to be able to reproduce observed velocities. Only then could realistic stress distributions be inferred, which would make it possible to quantify the resistance that ice shelves exert on the upstream flow.
tensif/Modélisation/Expérimentation Numérique et Technologique) infrastructure, which is supported by the Rhône-Alpes region (grant CP ER07 1 3 CIRA), the OSUG@2020 labex (reference ANR10 LABX56) and the Equip@Meso project (reference ANR-10-EQPX-29-01) of the programme Investissements d'Avenir supervised by the Agence Nationale pour la Recherche. The other part of the simulations were conducted at the CINES (Centre Informatique National de l'Enseignement Supérieur) computing centre under allocation 2015-016066 from GENCI (Grand Equipement National de Calcul Intensif). Results presented here also received support and benefited from the development team at the CSC-IT Center for Science Ltd. (Finland). Edited by: G. H. Gudmundsson.
Results The combined bird data set consisted of 6849 individuals (4248 and 2601 pre-and post-logging, respectively) from 202 species (178 pre-logging, 148 post-logging). Point counts yielded 5561 individuals from 143 species, and 1288 birds from 59 species were captured in mist nets. The bat data set comprised 1144 (692 pre-logging, 452 post-logging) bat captures from 50 species (40 pre-logging, 39 post-logging), and 442 (271 pre-logging, 171 post-logging) detections of target species were recorded for large mammals. Bird and bat rarefaction curves began to level off for the assemblages and their partitions, indicating that the communities were comprehensively sampled (Fig. S1 , Supporting information). Confidence intervals of the curves overlapped for all partitions with the exception of insectivorous birds, where post-logging diversity was lower.
was provided by Iwokrama International Centre (IIC) staff, and we are particularly grateful to J. Honorio for conducting the bird point counts. J. Bicknell was supported by a University of Kent 50th Anniversary Scholarship. Fieldwork was funded by the UNDP-Guiana Shield Initiative, WWF Guianas, the Inter-American Development Bank and the IIC. Research permission was granted by the Guyana Environmental Protection Agency, permit number 251111 BR173.
These proportions were not statistically significantly different from the null expectation (P >. 05). Meta-analysis of SNP*SNP interaction test results from the iCOGS dataset with those from 72 014 additional women in BCAC yielded similar results (Supplementary Table  7 , available online). Given that no SNP pair showed strong evidence for departure from the multiplicative model, subsequent analyses were based on a PRS that included the main effects of SNPs but no SNP*SNP interaction terms.
By contrast, the low absolute risk of breast cancer among women at the lowest end of the risk distribution raises the possibility that such women might be recommended more limited surveillance. Women at different levels of the PRS reach the same 10-year risk threshold at different ages, supporting the notion that using SNP profiles rather than age alone as a criterion to offer routine mammographic screening could lead to more effective screening programs  (6). The utility of such an approach (SNPs) that reached genome-wide statistical significance in a meta-analysis of iCOGS and previous combined genome-wide association studies were included in the risk score, and the effect sizes for each SNP were estimated using iCOGS database minus pKARMA (Supplementary Table  9 , available online). PRS = polygenic risk score; OR = odds ratio.
Our sample was derived from studies in Europe, North America, and Australia and restricted to women of European origin. While the results should be widely applicable in these populations, additional studies will be required to develop and validate genetic profiles for other populations, in particular Asian and African populations, where SNP associations, background incidence rates and distribution of tumour characteristics are substantially different. Our analysis summarized family history in terms of a single binary variable, but familial risk of breast cancer also depends on the number of affected and unaffected relatives and their ages. Risk prediction algorithms that combine full family history data with a polygenic component perform better than simpler models  (22). It is possible to incorporate the current PRS into family-history based models for breast cancer, such BOADICEA, to improve genetic risk prediction  (23).
The COGS project includes the largest set of breast cancer studies with both phenotype and genotype information, and our analysis utilized by far the largest number of SNPs with confirmed associations with breast cancer, including all SNPs discovered to date. Further refinement of the risk stratification should be possible through incorporating additional SNPs exhibiting evidence for association, but not at formal genome-wide The risk discrimination provided by the genetic profile, summarised in the PRS and family history, should be further improved by combining, with lifestyle risk factors, benign breast disease, and mammographic density  (24, 25, 28). Although we did not consider lifestyle factors explicitly in this dataset, other large studies have found no good evidence for interactions between common susceptibility SNPs and lifestyle factors for breast cancer, suggesting that SNPs generally combined multiplicatively  (26, 27).
Funding This work was supported by  Cancer Research-UK  (grant numbers  C1287/A10118 ,  C1287/A12014 ) and the  European Community 's  Seventh Framework Programme  ( 223175 [HEALTH-F2-2009-223175 ]) (COGS). Genotyping of the iCOGS array was funded by the  European Union  ( HEALTH-F2-2009-223175 ),  Cancer Research UK  ( C1287/A10710 ), the  Canadian Institutes of Health Research (CIHR)  for the " CIHR Team in Familial Risks of Breast Cancer" program , and the  Ministry of Economic Development, Innovation and Export Trade of Quebec  ( PSR-SIIRI-701 ). This work was also supported by  Breakthrough Breast Cancer funding  (to MGC). Analysis was supported in part by the  National Institutes of Health Post-Genome Wide Association initiative  ( 1U19CA148065  (DRIVE) and  1U19CA148537 (ELLIPSE) ).
Laboratory infrastructure was funded by  Cancer Research UK  ( C8197/A10123 ). This work was also supported by the  Government of Canada through Genome Canada  and the  Canadian Institutes of Health Research and the Ministère de  l'enseignement supérieur , de la recherche, de la science et de la technologie du Québec through Génome Québec  for the  PERSPECTIVE  project. Breast Cancer Association Consortium meetings were funded by the  European Union European Cooperation in Science and Technology (COST)  programme ( BM0606 ). The  Australian Breast Cancer Family Study (ABCFS) ,  Northern California Breast Cancer Family Registry (NC-BCFR)  and  Ontario Familial Breast Cancer Registry  (OFBCR) studies were supported by the  US National Cancer Institute  ( UM1 CA164920 ). The ABCFS was also supported by the  National Health and Medical Research Council of Australia , the  New South Wales Cancer Council , the  Victorian Health Promotion Foundation (Australia) , and the  Victorian Breast Cancer Research Consortium.
The  Australian Breast Cancer Tissue Bank  (ABCTB) study was supported by the  National Health and Medical Research Council of Australia , The  Cancer Institute New South Wales  and the  National Breast Cancer Foundation. The  Bavarian Breast Cancer Cases and Controls (BBCC)  study was partly funded by  ELAN-Fond of the University Hospital of Erlangen. The  British Breast Cancer Study (BBCS)  was funded by  Cancer Research UK  and  Breakthrough Breast Cancer and acknowledges National Health Service  funding to the  National Institutes for Health Research Biomedical Research Centre , and the  National Cancer Research Network (NCRN). The BBCS GWAS received funding from  The Institut National de Cancer. The  Breast Cancer In Galway Genetic Study (BIGGS)  was supported by  National Institutes for Health Research Comprehensive Biomedical Research Centre, Guy's & St.
Thomas' NHS Foundation Trust  in partnership with King's  College London, United Kingdom (ES ), and the  Oxford Biomedical Research Centre (IT). The  Breast Cancer Study of the University Clinic Heidelberg (BSUCH)  was supported by the  Dietmar-Hopp Foundation , the  Helmholtz Society , and the  German Cancer Research Center (DKFZ). The  CECILE Breast Cancer Study (CECILE)  was funded by  Fondation de France ,  Institut National du Cancer (INCa) ,  Ligue Nationale contre le Cancer, Ligue contre le Cancer Grand Ouest, Agence Nationale de Sécurité Sanitaire (ANSES) ,  Agence Nationale de la Recherche (ANR). The  Copenhagen General Population Study (CGPS)  was supported by the  Chief Physician Johan Boserup and Lise Boserup Fund , the  Danish Medical Research Council , and  Herlev Hospital. The  Spanish National Cancer Centre Breast Cancer Study (CNIO-BCS)  was supported by the  Genome Spain Foundation , the  Red Temática de Investigación Cooperativa en Cáncer , and by grants from the  Asociación Española Contra el Cáncer  and the  Fondo de Investigación Sanitario  ( PI11/00923 ,  PI081120 ).
The California Teachers Study (CTS) was initially supported by the  California Breast Cancer Act of 1993  and the  California Breast Cancer Research Fund  (contract  97-10500 ) and is currently funded through the  National Institutes of Health  ( R01 CA77398 ). The CTS study was also funded by the  Lon V. Smith Foundation  ( LVS39420 ) to HAC. Collection of cancer incidence data was supported by the  California Department of Public Health  as part of the statewide cancer reporting program mandated by  California Health and Safety Code Section 103885. For the DietCompLyf Breast Cancer Survival Study (DBCSS) the University of Westminster  curated the DietCompLyf database, created by and funded by  Against Breast Cancer Registered Charity  No. 1121258. The  University of Westminster's Against Breast Cancer Research Unit  acknowledges funding from the  charity Against Breast Cancer  (Registered Charity Number  1121258 ).
The  Esther Breast Cancer Study (ESTHER)  was supported by a grant from the  Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the  German Cancer Aid (Deutsche Krebshilfe ). The  Familial Breast Cancer Study (FBCS) study  was supported by funds from  Cancer Research UK  ( C8620/A8372 ,  C8620/ A8857 ), a  US Military Acquisition (ACQ) Activity , an  Era of Hope Award  ( W81XWH-05-1-0204 ), and the  Institute of Cancer Research UK. CT is funded by a  Medical Research Council (UK) Clinical Research Fellowship. The  FBCS acknowledges National Health Service (NHS)  funding to the  Royal Marsden / Institute of Cancer Research National Institutes for Health Research (NIHR) Specialist Cancer Biomedical Research Centre.
The  German Consortium for Hereditary Breast & Ovarian Cancer  (GC-HBOC) was supported by  Deutsche Krebshilfe  ( 107 352 ). Gene Environment Interaction and Breast Cancer in Germany (GENICA)  was funded by the  Federal Ministry of Education and Research (BMBF) Germany  ( 01KW9975/5 ,  01KW9976/8 ,  01KW9977/0 ,  01KW0114 ), the  Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ) ,  Heidelberg, Institute for Prevention and Occupational Medicine of the German Social Accident Insurance (IPA) ,  Bochum , and the  Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany. The  Genetic Epidemiology Study of Breast Cancer by Age 50 (GESBC) study  was supported by the  Deutsche Krebshilfe e. V. ( 70492 ) and the  German Cancer Research Center (DKFZ).
The  kConFab Clinical Follow Up Study  was funded by the  National Health and Medical Research Council  ( 145684 ,  288704 ,  454508 ). RB was a  Cancer Institute NSW Fellow. The AOCS study was supported by the  United States Army Medical Research and Materiel Command  ( DAMD17-01-1-0729 ), the  Cancer Council of Tasmania , the  Cancer Foundation of Western Australia  and the  National Health and Medical Research Council  ( 199600 ), and a  National Health and Medical Research Council  grant to GCT. Mavaddat et al. | 12 of 15 article The  Leuven Multidisciplinary Breast Centre (LMBC) study is supported by the "Stichting tegen Kanker " and by the  FWO  and the  KULPFV/10/016-SymBioSysII  to DL. The  Mammary Carcinoma Risk Factor Investigation (MARIE) study  was supported by the  Deutsche Krebshilfe e.
and Margaret T. Grohne Family Foundation  and the  Ting Tsung and Wei Fong Chao Foundation. The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further supported by  Australian National Health and Medical Research Council  ( 209057 ,  251553 ,  504711 ) and by infrastructure provided by  Cancer Council Victoria. The Multi-ethnic Cohort (MEC) was supported by the  National Institutes of Health  ( CA63464 ,  CA54281 ,  CA098758 , and  CA132839 ). The  Memorial Sloan-Kettering Cancer Center (MSKCC)  was supported by grants from the  Breast Cancer Research Foundation  and the  Robert and Kate Niehaus Clinical Cancer Genetics Initiative. The work of  Montreal Gene-Environment Breast Cancer Study (MTLGEBCS)  was supported by the  Quebec Breast Cancer Foundation , the  Canadian Institutes of Health Research for the "CIHR Team in Familial Risks of Breast Cancer" program  ( CRN-87521 ), and the  Ministry of Economic Development, Innovation and Export Trade  ( PSR-SIIRI-701 ).
MG received an  Investigator Award  from the  CIHR  and a  Health Scholar Award  from the  Fonds de la recherche en santé du Québec. J Simard  is chairholder of the  Canada Research Chair in Oncogenetics. The  Norwegian Breast Cancer Study (NBCS)  was supported by grants from the  Norwegian Research council  ( 155218/V40 ,  175240/S10  to ALBD,  FUGE-NFR 181600/V11 ) to VNK and a  Swizz Bridge Award  to ALBD. The  Nashville Breast Health Study (NBHS)  was supported by  National Institutes of Health  (grant  R01CA100374 ). The  Nurses Health Study (NHS)  was funded by  National Institutes of Health  ( PO1 CA87969 ), and this project was (in part) supported by the  Genetic Associations and Mechanisms in Oncology (GAME-ON) Network  ( U19 CA148065 ). The  Oulu Breast Cancer Study (OBCS)  was supported by research grants from the  Finnish Cancer Foundation , the  Academy of Finland , the  University of Oulu , and the  Oulu University Hospital.
The  Rotterdam Breast Cancer Study (RBCS)  was funded by the  Dutch Cancer Society  ( DDHK 2004-3124 ,  DDHK 2009-4318 ). The  Singapore and Sweden Breast Cancer Study  (SASBAC) was supported by the  Agency for Science, Technology and Research of Singapore (A*STAR) , the  National Institutes of Health , and the  Susan G. Komen Breast Cancer Foundation. The  Sheffield Breast Cancer Study (SBCS)  was supported by  Yorkshire Cancer Research  ( S295 ,  S299 ,  S305PA ) and the  Sheffield Experimental Cancer Medicine Centre. The  Southern Community Cohort Study (SCCS)  is funded by  National Institutes of Health  ( R01 CA092447 ). The  Arkansas Central Cancer Registry  is fully funded by a grant from the  National Program of Cancer Registries, Centers for Disease Control and Prevention (CDC). Study of Epidemiology and Risk factors in Cancer Heredity (SEARCH)  was funded by a programme grant from  Cancer Research UK  ( C490/A10124 ), the  UK National Institute for Health Research Biomedical Research Centre  at the  University of Cambridge , and a  Cancer Research UK  grant ( C8197/A10123 ) to.
Group structure and cohesion along with their changes over time play an important role in the success of missions where crew members spend prolonged periods of time under conditions of isolation and confinement. Therefore, an objective system for unobtrusive monitoring of crew cohesion and possible individual stress reactions is of high interest. For this purpose, an experimental wireless group structure (WLGS) monitoring system integrated into a mobile psychophysiological system was developed. In the presented study the WLGS module was evaluated separately in six male subjects (27-38 years old) participating in a 520-day simulated mission to Mars. Two days per week, each crew member wore a small sensor that registered the presence and distance of the sensors either worn by the other subjects or strategically placed throughout the isolation facility.
Another approach to assess social relationships is based on speech registrations by mobile systems  [32] [33] [34]. One of the latest developments is ActiWatch that registers proximity of other watches through Blue-Tooth technology (http://www. actigraphcorp. com/products/wactisleep-bt-monitor/). During the same period statistical methods for analyzing social relationships showed an enormous development, specifically the "statnet" package for the data analysis system R. (for details see http://www. r-project. org). The statnet core team (David R. Hunter, Carter T. Butts, Steven M. Goodreau, Pavel N. Krivitsky, Skye Bender-deMoll, Martina Morris) published theoretical foundations, analyses of applied data as well as software descriptions and tutorials  [35] [36] [37] [38] [39] [40]. Snijder and his group worked on stochastic models for network dynamics  [41] [42].