dataesr/scientific-paragraphs-categorization huggingface dataset:
Population stratification is not present in the current study since a white population has been used and allele frequencies between the different cohorts are very similar. Additionally, pleiotropy and the potentially confounding effects of linkage disequilibrium are likely avoided owing to the use of multiple genetic variants in the GRS and the use of the trait specific scores. Nevertheless, canalization cannot be completely excluded as a limitation of the present study. Another limitation of our study, linked also to Mendelian randomization assumption two, is that that serum lipid levels were only available for a small proportion of the sample (227 LOAD cases, 196 controls and 127 MCI from the ANM cohort). We used the 550 participants from ANM dataset to calculate the GRS-lipid trait association (first stage equation) for the individual level data instrumental variable analysis in order to derive these estimates from a sample nested within our total sample.
Conclusions There is no dispute over the involvement of lipid metabolism in the pathophysiology of LOAD. However, the results of our study do not support a causal role for genetically increased plasma cholesterol in LOAD and suggest that epidemiological associations between peripheral lipids and LOAD may be confounded by secondary disease processes. Future studies should focus on large LOAD datasets with longitudinal peripheral lipid measures and other markers of lipid metabolism. allele refers to the allele associated with increasing LDL-c, TC, and TG levels and decreasing HDL-c levels. Analyses are performed with respect to the risk allele. Risk allele frequency refers to the frequency of the risk allele used in the analysis. For the four full GRSs all SNPs associated with the specific phenotype at p,5610 28  were used.
Acknowledgments We thank the individuals and families who took part in this research. We would like to acknowledge the use of the computational Linux cluster and the  Biomedical Research Centre Nucleus Informatics Team  at the  National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre  and Dementia Unit at  South London and Maudsley NHS Foundation Trust and [Institute of Psychiatry] King's College London. Alzheimer's Disease Neuroimaging Initiative (ADNI).
1 ( GERAD1) Consortium , the AddNeuroMed, This work was supported by the  National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre and Dementia Unit at South London and Maudsley NHS Foundation Trust and [Institute of Psychiatry] King's College London , the 7th  Framework Programme  of the  European Union  ( ADAMS  project,  HEALTH-F4-2009-242257 ), the  Alzheimer's Society, Alzheimer's Research UK , and the  European Molecular Biology Organization (EMBO ;  ASTF 440-2011 ). Petroula Proitsi  is an Alzheimer's  Society Post-Doctoral Research Fellow. The fructosamine testing performed in this study was funded by the  Psychiatry Research Trust. The computational Linux cluster and the  Biomedical Research Centre Nucleus Informatics Team  are supported by the  National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre and Dementia Unit at South London and Maudsley NHS Foundation Trust and [Institute of Psychiatry] King 's September 2014 | Volume 11 | Issue 9 | e1001713  College London.
Alzheimer's Disease Neuroimaging Initiative  (ADNI) acknowledgments: Data collection and sharing for this project was funded by the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ) and  DOD ADNI (Department of Defense  award number  W81XWH-12-2-0012 ). ADNI is funded by the  National Institute on Aging , the  National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following:  Alzheimer's Association; Alzheimer's Drug Discovery Foundation ;  BioClinica, Inc. ;  Biogen Idec Inc. ;  Bristol-Myers Squibb Company ;  Eisai Inc. ;  Elan Pharmaceuticals, Inc. ;  Eli Lilly and Company; F. Hoffmann-La Roche Ltd  and its affiliated company  Genentech, Inc. ;  GE Healthcare; Innogenetics, N. V.
The  Charles Wolfson Charitable Trust  supported the OPTIMA group. Washington University was funded by  NIH  grants,  Barnes Jewish Foundation , and the  Charles and Joanne Knight Alzheimer's Research Initiative. Patient recruitment for the  MRC Prion Unit/UCL Department of Neurodegenerative Disease collection  was supported by the  UCLH/UCL Biomedical Centre. LASER-AD was funded by  Lundbeck SA. The Bonn group was supported by the  German Federal Ministry of Education and Research (BMBF) ,  Competence Network Dementia and Competence Network Degenerative Dementia , and by the  Alfried Krupp von Bohlen und Halbach-Stiftung. The  GERAD1  Consortium also used samples ascertained by the  NIMH AD Genetics Initiative. The AddNeuroMed study was supported by funds from the  National Institutes for Health Research Biomedical Research Centre for Mental Health  at the  South London and Maudsley National Health Service Foundation Trust and Institute of Psychiatry, King's College London.
Chronic dietary TCDD exposure was conducted by exposing juvenile zebrafish to dietary TCDD at 0, 0. 1, 1, 10 or 100 ppb TCDD for 42 d. Accumulations of TCDD in fish were measured in whole fish bodies. TCDD-induced toxicities were investigated using histopathologic analysis, and global gene expression in fish treated with TCDD was analyzed through microarray experiments. TCDD accumulation was dose-and time-dependent in zebrafish, although no significant mortality was observed even at accumulated doses up to 10 ppb. Juvenile zebrafish showed pathologic alterations due to TCDD exposure at 28 and 42 d, especially in disruption of ovarian development and degeneration of nasal epithelium. Functional analysis of microarray data from TCDDtreated fish revealed a correlation between gene expression and histopathologic outcomes.
The concentration of Cy3-labeled DNA was determined using a ND-1000 NanoDrop Spectrophotometer. Cy3-labeled double stranded DNA was hybridized to a NimbleGen 126135 K array using a NimbleGen hybridization kit; the hybridization was performed in the NimbleGen Hybridization System (NimbleGen, WI) at 42uC for 16 h. Arrays were washed with NimbleGen Wash buffers and 1 M DTT, and then spin dried in a NimbleGen microarray drier. Hybridized microarrays were sent to MOgene, LC (St, Louis, MO) for scanning. The arrays were scanned at 2 mm resolution on a NimbleGen MS200 scanner with auto-gain adjust. The TIFF images were gridded and signals extracted using NimbleScan v. 2. 6. Expression data were normalized with the Robust Multichip Average (RMA) algorithm as described previously  [18].
Microarray data analysis Differential expression analysis was performed with Multiple Array Viewer (MeV, Dana-Farber Cancer Institute, MA) software, version 4. 8. 1  [19, 20]. Three statistical methods were reviewed and tested for analysis: Bayesian Estimation of Temporal Regulation (BETR), one-way ANOVA, and Significance Analysis of Microarrays (SAM). False discovery rate (FDR) was controlled with the Benjamini and Hochberg procedure  [21]. For both experiments and all expression data, BETR consistently predicted the lowest FDR of the three methods, and was therefore selected for differential expression analysis. Statistical significance was set at FDR ,0. 05. We applied K-means clustering using MeV software to cluster genes based on their expression profiles across doses and time. Gene Ontology (GO) annotations (categories of biological process [BP]) were achieved by performing BLASTX between array sequences against all protein sequences of the Amigo database at amigo.
geneontology. org [22]. Zebrafish sequences were extracted to align against the Amigo database based on their accession numbers on the array from different sources, including the NCBI (http://www. ncbi. nlm. nih. gov/UniGene/), VEGA (http://vega. sanger. ac. uk/Danio_rerio/) and Ensembl (http:// www. ensembl. org/Danio_rerio). The ''GOHyperGAll'' script from BioConductor was employed to conduct GO enrichment analysis for functional annotations of dysregulated genes. Enriched GO terms are statistically significant and representative in dysreuglated genes against the whole array. Ingenuity Pathways Analysis (IPA) software (Ingenuity Systems, www. ingenuity. com) was used to analyze IPA Canonical pathways and generate IPA Tox lists. Canonical pathways analysis identified the most significant pathways from the IPA library from the input data set.
Tox lists were most significant toxicity lists identified from the IPA library  [23] , and were associated with key toxicological responses to xenobiotic insult. The p values were calculated by Fisher's exact test, which calculates the probability of a gene in the dataset of a given size due to chance alone. Since IPA software does not support zebrafish data, we used human orthologs of dysregulated genes from NimbleGen geneID file for knowledgebased pathway and functional analysis. Microarray data (both preand post-normalized) have been deposited in the Gene Expression Omnibus (GEO) database (http://www. ncbi. nlm. nih. gov/geo/). The data is accessible through GEO Series accession number GSE47434 for the dose-dependent experiment, and GSE47547 for the time-dependent experiment.
Perinucleolar and cortical alveolar stages are two of the oocyte developmental stages that occur prior to the vitellogenic stage. In the perinucleolar stage, follicular cells are monolayer and oocytes are large. In cortical alveolar stages, yolk vesicles (cortical Figure  3. Assimilation of TCDD in female and male zebrafish during 42-d of dietary exposure. TCDD from extraction of pooled fish (up to 5) were analyzed by gas chromatography coupled to high-resolution mass spectrometry (GC-HRMS). The data (ppb) represent the observed TCDD (ng) verse wet weight of fish (g). The asterisk (*) means failed analysis and no more samples to be detected in fish treated with 1 ppb TCDD at 14 d; the ''#'' means no fish in 100 ppb group were identified as female by physical observation at 42 d. doi:10. 1371/journal.
Cluster 4 contains 469 genes that were up-regulated in the 0. 1, 1 and 10 ppb groups but down-regulated in the 100 ppb group, with the enriched GO terms being myofibril assembly, cellular component assembly involved in morphogenesis, actomyosin structure organization, striated muscle cell development and muscle cell development. Cluster 5 contains 262 genes that were up-regulated at each concentration of TCDD, and these genes are primarily involved in cellular response to stimulus. These dysregulated genes were analyzed by IPA software and groups of genes were mapped to IPA Canonical pathways and Tox Lists (Table  S11 ). A series of IPA Tox lists was identified between female and male fish, such as renal necrosis/cell death, NRF2-mediated oxidative stress response, and cell cycle regulation; and common significant Canonical pathways were cardiac hypertrophy signaling, glycolysis/gluconeogenesis, p53 signaling.
This work was funded by the  University of Wisconsin Sea Grant Institute  under grants from the  National Sea Grant College Program, National Oceanic and Atmospheric Administration, U. S. Department of Commerce , and from the  State of Wisconsin. Federal grant number  NA060AR4170011 , project number  R/BT-21. This work was also supported by the  UWM Children's Environmental Health Sciences Core Center  ( NIEHS 2P30ES004184 ), and the  Shaw Fund of the Greater Milwaukee Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
X-Ray Microtomography and Histomorphometry Formaldehyde-fixed femurs were scanned using a SkyScan 1174 v. 2 micro computed tomography (micro-CT) device (SkyScan, Belgium, http://www. skyscan. be/home. htm) with a voltage source of 50 keV, current of 800 mA, and 6. 74 mm isotropic resolution. All scans were three dimensionally reconstructed and analyzed with the same parameter setup (NRecon v. 1. 6 and CTAn v. 1. 11 software; SkyScan) to separate mineralized elements from background using the software histogram tool to threshold graylevel values. In the validation step of the tail-suspended mouse model, bone mass-defined as the BV/TV-was quantified in the cancellous bone region located in the secondary metaphyseal area of the distal end of the femur in a standardized zone positioned 100 mm below the growth cartilage and 100 mm from the cortices.
CONCLUSION In contrast to previously published data showing that hypoxic treatment initiated immediately after bone-defect surgery is detrimental to bone repair  [24, 25] , our data indicated that delayed hypoxia applied during the remodeling process enhanced bonehealing efficiency. As assessed by micro-CT measurements, only 4 days of delayed hypobaric hypoxia promoted the closing of the cortical defect (+55%) and the filling of the medullary cavity with trabecular new bone (+35%) in our drilled-hole-defect mouse model. Our proteome analysis and histomorphometry data suggested that the bone-repair improvement originates from an acceleration of the natural bone-healing process. We provide further support for previously published studies showing that the bone-repair mechanism involves large mobilization of both MSCs (osteoblast progenitors) and HSCs/HPCs (osteoclast progenitors) from undamaged bones into the lesioned femur via blood circulation.
ACKNOWLEDGMENTS We thank  Catherine Andr  é,  Dominique Bonneau ,  Xavier Butigieg ,  Bernadette Guerton , and  Sylvie Renault  for their valuable technical assistance in our laboratory. The authors are also grateful to  Dr. Veronique Chastres  for her statistical expertise and  Dr. Adrienne Anginot  for reading of the manuscript. The authors extend their thanks to  Dr. Frances Ash  for her helpful language assistance. This work was supported by a grant from the  D élégation Gén érale pour l'Armement  (Grant  PDH-1-SMO-0205  to J. -J. L. and X. H. ) of the  French Ministry of Defense.
© 2014 Bonnemains et al. ; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/2. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
Acknowledgements. We thank  Torge Martin  and  Alistair Adcroft  for providing their code as a basis for the ICB module. Funding to couple NEMO with the icebergs module was provided by the  UK Natural Environment Research Council  (grant number  NE/H021396/1 ) for the project " A century of variability in Greenland melting and iceberg calving ". We are grateful to three anonymous reviewers and the topic editor for a wide range of helpful comments and insights. Edited by:  D. Goldberg.
Computations of tortuosity, effective thermal conductivity and permeability tensors The full 3-D tensors of tortuosity τ (dimensionless) of effective thermal conductivity k (in W m -1 K -1 ) and of intrinsic permeability K (in m 2  ) were computed from 3-D images. For that purpose, specific boundary value problems arising from the homogenization process  (Auriault et al. , 2009; Calonne et al. , 2014)  have been numerically solved on representative elementary volumes (REVs) extracted from 3-D images of snow by using the software Geodict 2 , based on a finite difference method  (Thoemen et al. , 2008). We define the REV with a side length l by wherein i and a are the domains occupied by the ice and the air, respectively, and where denotes the common boundary. To compute the effective thermal conductivity tensor k, the following boundary value problem was solved  (Auriault et al.
In summary, this study presents numerical tools to quantitatively monitor snow properties using 3-D images and provides a detailed database describing snow under a TG metamorphism. In particular, it provides a quantification of snow anisotropy, which is a key -but challenging -parameter to access directly with physical measurements during such a process. Used as a guideline or a validation tool, this database offers new outlooks for the development of microand macroscale snow models. The Supplement related to this article is available online at doi:10. 5194/tc-8-2255-2014-supplement.
Acknowledgements. We thank  Henning Löwe ,  Martin Schneebeli , and three anonymous reviewers for their contributions in the improvement of this paper. Funding by  Météo-France ,  INSU-LEFE and DigitalSnow  ( ANR-11-BS02-009-03 ) is acknowledged. We thank  P. Charrier  and  J. Desrues  of the 3SR laboratory, where the 3-D images were obtained. We are also grateful to  J. Roulle ,  J. -M. Panel ,  P. Puglièse ,  C. Carmagnola  and  S. Morin  of the  CNRM-GAME  for their support during the experiment. N. Calonne  thanks  S. Morin  for his co-supervision of her master's internship, during which the TG experiment has been realized. CNRM-GAME/CEN is part of the  Labex OSUG@2020  ( ANR10 LABX56 ). The laboratory  3SR  is part of the  LabEx Tec 21  ( Investissements d'Avenir -grant  agreement  ANR11LABX0030 ).
Similar spaceborne observations of a maritime mesoscale cloud system (MCS) located off the Brazilian coast between 0 • and 3 • N latitude on 20 June 2008 are then analysed. Near cloud-top backscatter anomalies are evidenced in a region which corresponds to the coldest temperatures with maximum cloud top altitudes derived from collocated CALIPSO/IIR and Meteosat-9/SEVIRI infrared brightness temperatures. The interpretation of CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization) data highlights signifi-.
Retrieved MCS cloud properties at usual cruise altitudes of commercial aircraft In this section the cloud ice water content (IWC) and effective radius (R eff ) retrieved with both the CloudSat 2B-IWC algorithm  (Benedetti et al. , 2003, called C2B hereafter)   the DARDAR (raDAR/liDAR, called DAR hereafter) icecloud products  (Delanoë and Hogan, 2008, 2010)  are analysed. The C2B retrieval is designed to use CloudSat Radar (CPR) radar reflectivity and visible optical depth if both data streams are available. The source for the visible optical depth is the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on the Aqua platform. The retrieval technique described in detail by  Benedetti et al. (2003)  was adapted from the study by  Austin and Stephens (2001). This technique is based on minimization of a cost function which calculates the distance between the model-derived and the observations variables.
constrained CO emissions are validated through comparison of the respective simulations with independent data of ground-based CO measurements at the ZOTTO site. Using our optimal regional-scale estimates of the conversion factors (which are found to be in agreement with earlier published estimates obtained from local measurements of experimental fires), the total CO 2 emissions from wildfires in Siberia in 2012 are estimated to be in the range from 280 to 550 Tg C, with the optimal (maximum likelihood) value of 392 Tg C. Sensitivity test cases featuring different assumptions regarding the injection height and diurnal variations of emissions indicate that the derived estimates of the total CO 2 emissions in Siberia are robust with respect to the modeling options (the different estimates vary within less than 15 % of their magnitude).
, 2007). Accurate estimation of such climatic feedbacks through fires can hardly be possible without adequate quantitative knowledge of the CO 2 emissions from wildfires. Presently, estimates of emissions of CO 2 and other species from wildfires and other types of open biomass burning are available on the global scale from several "bottom-up" emission inventories, such as, e. g. , the Global Fire Emission Database (GFED)  (van der Werf et al. , 2010; Giglio et al. , 2013) , the Wildland Fire Emission Inventory (WFEI)  (Urbanski et al. , 2011) , the Emissions for Atmospheric Chemistry and Climate Model Intercomparison Project (AC-CMIP) inventory  (Lamarque et al. , 2010) , the Fire INventory from NCAR (FINN)  (Wiedinmyer et al. , 2011) , and the Global Fire Assimilation System (GFAS) emission data set  (Kaiser et al.
Taking into account the large uncertainties in the available estimates of emission factors (see Sect. 2. 5), we considered only three aggregated vegetative land cover categories, i. e. , forest (including both coniferous and broadleaf forests), grass (including shrubs), and agricultural land. The fraction of each category per grid cell was calculated by using the Global Land Cover Facility (GLCF) database  (Hansen and Reed, 2000) , which originally distinguishes 14 land cover classes. Furthermore, the FRP-to-BBR conversion factors as well as the diurnal variations of FRP and emissions for fires in agricultural land and grass fires were assumed to be the same. This assumption seems to be reasonable in view of the large uncertainties in the obtained estimates of the conversion factor for the "grass" category (see Sect.
(  13 ), (  14 ), and the CO 2 emission factors, β CO 2 , also varied within their uncertainty range in accordance with the corresponding lognormal probability distribution. The Monte Carlo experiment performed in this study included 1000 iterations. Note that due to covariation of errors in α co l and α aod l (c = 0), the uncertainty in α cmb l can be larger compared to the case when the errors are independent. As a potential source of the error covariation, we attempted to take into account possible common model errors in transport and emissions of CO and aerosol (see Sect. 2. 3. 3). However, since the exact nature and characteristics of uncertainties in the input data for our analysis are not known (as it is common for virtually any "real world" application of the inverse modeling approach), the uncertainties reported below for our estimates of the conversion factors and CO 2 emissions should be considered with caution.
7. This threshold value (which is exceeded in 58 % of the retrievals in the region and period considered) is a compromise to avoid getting larger uncertainties in our emission estimates due to a smaller contribution of the boundary layer to the CO columns or due to insufficient amount of the selected data (with large DOFS). The sensitivity of the results of this study to the threshold value was examined and found to be small compared to other uncertainties. In addition to satellite CO measurements, we used the ground-based measurements of near-surface CO concentrations at the Zotino Tall Tower Observatory (ZOTTO) site  (Schulze et al. , 2002; Lloyd et al. , 2002; Chi et al. , 2013;  http://www. zottoproject. org/) situated in central Siberia (89. 35 • E, 60. 80 • N). We used the daily mean CO concentrations obtained by averaging the original hourly data.
Aerosol optical depth (AOD) measurements As a source of information on the aerosol content in the atmosphere, we used satellite retrievals of AOD at 550 nm in May-September 2012. The daily AOD data retrieved from MODIS measurements onboard the AQUA and TERRA satellites were obtained as the L3 MYD08_D3/MOD08_D3 data product from the NASA Giovanni-Interactive Visualization and Analysis system (http://daac. gsfc. nasa. gov/ giovanni/). The spatial resolution of the AOD data is 1 • × 1 •. The retrieval algorithm is described in  Kaufman et al. (1997)  and  Remer et al. (2005). The relative uncertainty of the MODIS AOD data over land is estimated to be about 20 %  (Ichoku et al. , 2005).