dataesr/scientific-paragraphs-categorization huggingface dataset:
For our Bayesian approach the test statistic is the average value of BF BMA over all SNPs in the cis candidate region; for the tissue-by-tissue analysis the test statistic is the minimum p{value from linear regressions performed separately in each tissue for each SNP in the cis candidate region. We translate each of these test statistics into a p{value for each gene by comparing observed values with simulated values obtained under H 0 (by permutation of individual labels). Finally we computed, for each method, the number of genes identified as having an eQTL at an FDR of 0. 05 (using the qvalue package  [31] ). Joint mapping, via BF BMA , substantially increased power to identify eQTLs compared with tissue-by-tissue analysis. For example, BF BMA identified 1022 eQTLs at FDR = 0. 05, which is 63% more than the 627 eQTLs identified by the tissue-by-tissue analysis at the same FDR (Figure  3A, 3B,  and 3C ).
Alleles were in Hardy-Weinberg equilibrium and the lower degree of freedom (df) required made it more powerful than the general genetic model. The general model, or allele-based test, requiring 2 df, was used. SNP were then analyzed by comparing differences in allele frequencies between cases and controls using a x 2 test. Statistical analyses were performed using the SPSS statistical package (SPSS Inc. , Chicago, IL, USA). Confidence intervals (CI) for proportions were calculated using the Wilson score method without continuity correction  [17]. CI for the odds ratios (OR) were calculated using the methods described by Armitage and Berry  [18].
Acknowledgments We thank the control population who participated in this study, as well as  P. Dubiez  for his great help with data collection,  Dr I. Redonnet-Vernhet  and  Dr D. Montaudon  for the homocystein and vitamin B determination,  Dr K. Titier  for the cotinin and cannabis analysis,  Dr F. Me  ´nard for her great help in MTTP genotyping, technicians from  CHU and university biochemistry laboratories of Bordeaux ,  Dr A. Doussau  and  Pr G. Che  ˆne for statistical help,  Mrs Chabaud  for administrative help.
Oocyan or blue/green eggshell colour is an autosomal dominant trait found in native chickens (Mapuche fowl) of Chile and in some of their descendants in European and North American modern breeds. We report here the identification of an endogenous avian retroviral (EAV-HP) insertion in oocyan Mapuche fowl and European breeds. Sequencing data reveals 100% retroviral identity between the Mapuche and European insertions. Quantitative real-time PCR analysis of European oocyan chicken indicates over-expression of the SLCO1B3 gene (P,0. 05) in the shell gland and oviduct. Predicted transcription factor binding sites in the long terminal repeats (LTR) indicate AhR/Ar, a modulator of oestrogen, as a possible promoter/enhancer leading to reproductive tissue-specific over-expression of the SLCO1B3 gene.
g. as in the case of human salivary amylase genes  [29, 30] ). A TFSEARCH of the U3 region of the LTR sequences described here identifies a number of predicted transcription factor binding sites that might stimulate the transcription of neighbouring genes. One of these binding sites, AhR/Ar (score 85. 4), has been linked to oestrogen metabolism and oxidative stress  [31, 32] , and has also been shown to increase solute carrier expression in the liver of mice  [33]. The oocyan Dongxiang SLCO1B3 transcript (GenBank accession no: JN381032) commences 2 bp after the predicted AhR/Ar transcription factor binding site. The results of our analyses therefore suggests that the EAV-HP insertion might enhance the expression of SLCO1B3 in a reproductive tissuespecific manner. This is possibly due to promoter and/or enhancer sequences in the LTRs, such as AhR/Ar -a mediator of oestrogen regulation.
Indeed, several studies have identified genes expressed in the chicken oviduct as being under the regulation of oestrogen  [34] [35] [36]. We suspect that the over-expression in the shell gland of SLCO1B3, for which biliverdin is a substrate, might increase the accumulation of biliverdin in the shell gland during shell matrix formation, giving rise to blue eggs. However, in absence of full transcriptome data we cannot fully exclude the possibility of other long-range cis-regulatory effects of the EAV-HP insertion, or a possible role for any of the SNPs identified in the oocyan mapping interval on the expression of the phenotype. Our findings address an important question: to what extent are endogenous retroviruses (ERVs) shaping phenotypic diversity in birds? It is not the first reported instance of an ERV insertion affecting a chicken's phenotype.
FASTA sequences have been submitted to NCBI GenBank for the Mapuche/Araucana and Dongxiang EAV-HP insertions (GenBank accession no: KC632577 and KC632578). Primers were designed to perform a multiplex PCR to screen a large number of samples for the retroviral insertion. One of the reverse primers (Multiplex PCR R2) is in the LTR sequence of EAV-HP which, with the forward primer, amplifies a 167 bp product (190 bp in Dongxiang) in the presence of the retroviral insertion. Whilst the other reverse primer (Multiplex PCR R1) with the forward primer spans the insertion breakpoint on Gga1 and amplifies a 364 bp product in the absence of the retroviral insertion. In total, 143 samples were screened (14 jungle fowl, 51 Dongxiang chickens, 54 Mapuche fowl, and 24 individuals of various modern and traditional chicken breeds) (Table  S3 ).
EAV-HP sequence analysis A BLAST of the complete c26830 sequence confirmed it to be the EAV-HP retrovirus. The heuristic approach for gene prediction  [48]  for viruses implemented in GeneMark  [49]  was used to predict genes in the complete c26830 sequence, following which, the predicted protein sequence was analysed for protein families using Pfam  [50]. Transcription factor binding sites were predicted for the LTR U3 domains of each sequence using TFSEARCH and the vertebrate TRANSFAC database  [51].
Acknowledgments Thanks to  Ana Martı ´nez Bas  and  Dr. Eva Armero Iba  ´n ˜ez ( Technical University of Cartagena, Spain ) for providing samples of the blue-egg laying Moss chicken breed from Spain, and to the  Fazanterie de Rooie Hoeve (The Netherlands)  for allowing sampling of their jungle fowl. Professor T. Burke  ( University of Sheffield, UK ) kindly stored the tissue samples used for qRT-PCR prior to their analysis in this study. We thank  Rachel Tarlinton  ( University of Nottingham ) and  Frank Ryan  ( University of Sheffield ) for stimulating discussion about retrovirus evolution. Finally a deserved acknowledgement to the  Mapuche people of Chile  who have selected and preserved the blue egg phenotype to the present.
Funding for this study was made possible through a  BBSRC PhD fellowship  to the first author and a  BBSRC research grant  ( BH/H009051/1 ) to the last author, as well as the Chinese Government contribution to the  CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources. The  Roslin Institute  is supported by a core strategic grant from the  BBSRC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In France, Norway, Utrecht, and Naples only women were recruited. Approval for this study was obtained from the ethical review boards of the International Agency for Research on Cancer (IARC) and from all local institutions where subjects had been recruited for the EPIC study: the Florence Health Authority Ethical Committee (Italy); the Norfolk Local Research Ethics Committee (UK); the Medical Ethics Committee of the Netherlands Organization for Applied Scientific Research (the Netherlands); the Ethics Committee of the Medical Association of the State of Brandenburg (Germany); and the Danish National Committee on Biomedical Research Ethics (Denmark). Written informed consent was obtained from all participants before joining EPIC study. The prediction model was derived based on data from 6 EPIC centers from 5 countries which participated in the Diet, Obesity and Genes (DiOGenes) project  [17] , namely: the United Kingdom (UK-Norfolk), the Netherlands [(NL-Doetinchem and NL-Amsterdam/Maastricht); two separate centers because of differences in follow-up assessment of anthropometry], Italy (IT-Florence), Germany (GER-Potsdam), and Denmark (DK-Copenhagen/ Aarhus).
It is defined as J = sensitivity + specificity -1 and ranges from 0 to 1, with 1 implying perfect separation of diseased and non-diseased by the continuous marker  [37]. Calibration, as a measure of how reliable the predictions are, was evaluated by using a modified version of the Hosmer-Lemeshow-Test for survival analysis introduced by D'Agostino and Nam  [34]. For this purpose, the observed probabilities of CRC at 5 years estimated by the Kaplan-Meier approach were compared with the average predicted probabilities across tenths of predicted risk which was also plotted for visualization. Statistical analyses were performed using SAS (Statistical Analysis System, version 9. 2; SAS Institute Inc, Cary, NC).
This publication arises from a collaboration of two EU projects, the  Diet, Obesity and Genes (DiOGenes)  project and the  Physical Activity, Nutrition, Alcohol, Cessation of Smoking, Eating out of Home and Obesity (PANACEA)  project. DiOGenes is a pan-European study within the  EU Sixth Framework Programme for Research and Technological Development  ( 2005-2009) (FOOD-CT-2005-513946 , http://www. diogenes. eu. org). PANACEA received funding from the  EU  in the framework of the  Public Health Programme  (project  2005328 ). This work was further supported by the  European Commission :  Public Health  and Consumer Protection Directorate 1993-2004, the  Research Directorate-General 2005 , the  Ligue contre le Cancer , the Societe  ´3M , the  Mutuelle Generale de l'Education Nationale , and the  Institut National de la Santeet de la Recherche Medicale ;  German Cancer Aid , the  German Cancer Research Center , and the  Federal Ministry of Education and Research (Germany) ; the  Danish Cancer Society (Denmark) ;  Health Research Fund (FIS)  of the  Spanish Ministry of Health RTICC 'Red Tematica de Investigacion Cooperativa en Cancer  (grant number  C03/10 ,  R06/0020 ); the participating regional governments and institutions of Spain; Cancer Research United Kingdom, the  Medical Research Council , the  Stroke Association , the  British Heart Foundation , the  Department of Health , the  Food Standards Agency , and the  Wellcome Trust  ( United Kingdom ); the  Italian Association for Research on Cancer  and the  National Research Council (Italy) ; the  Dutch Ministry of Public Health, Welfare and Sports , the  Dutch Ministry of Health, Dutch Prevention Funds, LK Research Funds , the  Dutch Zorg Onderzoek Nederland , and the  World Cancer Research Fund (Netherlands) ; the  Swedish Cancer Society , the  Swedish Scientific Council , and the  Regional Government of Skane.
Methods: We analyzed data from the Global Longitudinal Study of Osteoporosis in Women (GLOW), including women aged $55 years from the United States of America, Canada, Australia and seven European countries. Women completed questionnaires including fracture data at baseline and years 1, 2 and 3. Results: Among 60,393 postmenopausal women, 4122 incident fractures were reported (86% non-hip, non-vertebral [NHNV], 8% presumably clinical vertebral and 6% hip). Hip fractures were more likely to occur in spring, with little seasonal variation for NHNV or spine fractures. Hip fractures occurred equally inside or outside the home, whereas 65% of NHNV fractures occurred outside and 61% of vertebral fractures occurred inside the home. Falls preceded 68-86% of NHNV and 68-83% of hip fractures among women aged #64 to $85 years, increasing with age.
We evaluated data obtained from the international Global Longitudinal Study of Osteoporosis in Women (GLOW) to characterize the circumstances surrounding incident low-trauma fractures in a primary care population of postmenopausal women aged 55 years and older from the United States of America, Canada, Australia and several countries in northern and southern Europe. In this paper, we offer a descriptive analysis of when, where and how vertebral, hip, and non-hip, non-vertebral (NHNV) fractures occurred during the first 3 years of observation of the GLOW cohort.
Study Design GLOW is a longitudinal database study being conducted in physician practices at 17 sites in 10 countries (Australia, Belgium, Canada, France, Germany, Italy, The Netherlands, Spain, United Kingdom and United States of America). These sites are located in major population centers. Details of the study design and methods have previously been described  [3]. Study sites were selected to achieve a broad geographic distribution across several countries in northern and southern Europe and within the US, as well as to have participation from both Canada and Australia. All study sites had lead investigators with expertise in osteoporosis and access to a clinical research team capable of managing a large cohort of subjects. These lead investigators identified primary care practices in their region that were members of local research or administrative networks and able to supply names and addresses of their patients electronically.
Statistical Analysis Fracture rates during the first 3 years of follow-up are reported as raw numbers and percentages. Differences were tested using the chi-square test or Fisher's exact test in the case of small cell values; the Mantel-Haenszel chi-square test was used when comparing ordered age categories. Data are based upon individual subjects and not individual fractures, and reflect each woman's first incident fracture post baseline. Women with missing fracture data were counted as not fracturing. We have separated the fractures into three types: hip, vertebral and NHNV (defined as fracture of the clavicle, upper arm, wrist, rib, pelvis, ankle, upper leg, lower leg, shoulder, knee, hand, elbow or foot). If a woman had more than one fracture type in one year, she was excluded from the analysis.
The calendar season of the fracture was determined by the month in which it was reported: in all sites except Australia, the months of December, January and February were considered winter; March, April and May were considered spring; June, July and August were considered summer; and September, October and November were considered fall. In Australia, the seasons were reversed. The data were analyzed for all subjects combined, and are also described for four age subgroups: #64, 65-74, 75-84 and $85 years for the analysis of how fractures occurred. All analyses were conducted using SAS version 9. 2.
Discussion To determine when, where and how hip, spine and NHNV fractures occur in postmenopausal women, we analyzed data from the first 3 years of GLOW. In this large, international, populationbased study of women aged 55 years and older, there were 4122 self-reported first incident fractures, of which most (86%) were NHNV fractures. Hip fractures occurred nearly equally inside and outside the home, while most spine fractures occurred inside the home and most NHNV outside the home. Falls were the dominant cause for hip and NHNV fractures, and the proportion of these fractures with a fall as the proximate cause increased with increasing age. The effect of seasonality on fractures, especially hip fractures, has previously been reported from large series in Scandinavia, the United States of America and Canada.
Recent studies from different areas of Norway have found that hip fracture rates are highest during the winter  [7] , particularly for those fractures occurring outdoors  [8] , while two older studies did not see a seasonal variation of effects of cold temperature on the incidence of hip fractures in Oslo, Norway  [9]  or Malmo, Sweden  [10]. Two Medicare-based population studies conducted in the United States of America in the 1980s found that hip fracture rates  [11]  and fracture rates for hip, distal forearm, proximal humerus and ankle fractures  [12]  were highest in the winter and lowest in the summer. A study from Canada with data from 1982-1992 found hip fractures in women aged 60-74 years peaked in mid-December  [13]. The prospective data from GLOW show similar proportions of NHNV fractures throughout the four seasons, and a significantly higher number of hip fractures during spring compared with the other three seasons.
It is possible that lower levels of vitamin D resulting from a reduction in sunlight exposure over the winter and clinically manifest in spring might be a contributing factor to the occurrence of hip fracture in GLOW subjects, but there were no collections of 25-hydroxy vitamin D data in GLOW to support or refute this possibility. It is not clear why these data differ from those in previous studies, but our results may reflect the broad geographical distribution of the GLOW countries with a variety of climates, or may represent secular changes in the timing of fractures since the mid-to-late 1980s, when data for many of the previous studies were collected. In GLOW, almost twice as many NHNV fractures occurred outside the home as inside the home, a finding consistent with previous studies that have noted that the majority of NHNV fractures occur outdoors  [14, 15].
85 years), vertebral fractures were most often reported in the absence of any fall or injury. While fall prevention strategies have not been proven to lower fracture rates, it may still be desirable for patients at risk for fractures to be provided with advice on how to lower the risk of falls, and efforts to reduce falls both in the home and outside the home should be applied throughout all seasons of the year, not solely in the winter. A major strength of this study is the use of a standard questionnaire for all countries, minimizing methodology bias by collecting data in a uniform manner. Owing to the very large sample with a broad age range, GLOW provides us with information representative of women at risk of osteoporosisrelated fractures, including women younger than 65 years, as well as those who are over 85 years of age.
How does the brain integrate multiple sources of information to support normal sensorimotor and cognitive functions? To investigate this question we present an overall brain architecture (called ''the dual intertwined rings architecture'') that relates the functional specialization of cortical networks to their spatial distribution over the cerebral cortex (or ''corticotopy''). Recent results suggest that the resting state networks (RSNs) are organized into two large families: 1) a sensorimotor family that includes visual, somatic, and auditory areas and 2) a large association family that comprises parietal, temporal, and frontal regions and also includes the default mode network. We used two large databases of resting state fMRI data, from which we extracted 32 robust RSNs. We estimated: (1) the RSN functional roles by using a projection of the results on task based networks (TBNs) as referenced in large databases of fMRI activation studies; and (2) relationship of the RSNs with the Brodmann Areas.
Introduction To perform sensorimotor and cognitive functions, the brain needs to coordinate and integrate the activity of different regions that process multiple sources of information  [1] [2] [3] [4] [5]. This integration is based on correlated activities that can be seen, for example, in the coherent brain networks revealed by resting-state magnetic functional imaging  [6, 7]. Recent results suggest that these resting state networks (RSNs) are organized according to some general anatomo-functional principles  [8] [9] [10] [11] [12] [13] [14]. First, RSNs appear to be similar to the Task-Based Networks (TBNs) that emerge from the analysis of large ensembles of activation studies collected with different cognitive tasks found in very large functional imaging databases  [13, 14]. These TBNs can be used as a reference set to infer the sensorimotor and cognitive functions of cortical networks revealed by RSNs.
Then, we inferred the RSNs functional roles from the overlap of the RSNs with 18 reference TBNs extracted from large databases of fMRI activations obtained during sensorimotor and cognitive tasks  [14]. Similarly, we characterized the RSNs topography and topological features from their overlap with the Brodman Areas, in order to determine the RSNs spanning adjacent cortical regions (e. g. , BA17 and BA18), as well as those extending to distant regions (e. g. , between the BA39 parietal area and the BA46 frontal area). We then extracted functional groups of RSNs from the RSN6TBN matrix and independently extracted anatomical groups from the RSN6BA matrix, using general classification methods (such as expectation maximization, see Method section). As an advantage of this method, these two well-defined reference sets (i.
Extraction of the most representative RSNs in the group: robustness We used two independent databases of resting state activity: the first was from a population of subjects from Cambridge (N~197) and the second was from a population of subjects from Beijing (N~180). We started with the Cambridge database. We first applied spatially independent components analysis (sICA) to the voxel time series across each subject's brain. This provided one voxel factor scores matrix per subject. The matrices of all subjects were then concatenated into a group factor score matrix that was used in a hierarchical cluster analysis. From this analysis, we obtained 264 classes of voxels from which we selected 32 representative classes as the 32 most representative RSNs. These RSNs were observed in at least 10% of the subjects (see Fig.
1A  and Fig. 1B ). The first 20 classes were observed in at least 50% of the subjects (Fig. 1B ). We labeled each RSN with its representativeness rank in the population (Fig. 1B ). We then used the same general method with the independent database of subjects from Beijing. This analysis also yielded 32 RSNs. The RSNs found in the Cambridge database were very similar to those found in the Beijing database (Fig. 1C ). The similarity rate was as high as 60%, showing that the 32 identified RSNs were highly representative and robust within and between independent populations.
RSNs Specialization: overlap between RSNs and TBNs Previous results have shown that RSNs are similar to TBNs extracted from a large database of activation studies across a variety of behavioral tasks  [13, 14]. A systematic statistical analysis of thousands of datasets of brain activation scans obtained during behavioral tasks (sensory, motor, cognitive, and emotional) has previously revealed the existence of clusters of activation corresponding to TBNs (available on-line at www. brainmap. org/). We computed the overlaps between our 32 RSNs and the 18 reference TBNs (Fig. 2A ). The RSNs are represented along the vertical axis of the matrix and sorted according to their maximum overlap with the TBNs. The RSN6TBN matrix revealed a dual organization. The upper part of this matrix is nearly diagonal and reveals RSNs related to TBNs specialized in sensorimotor and cognitive functions (see Table in Fig.
2C ). The lower part of the matrix reveals RSNs that correspond mainly to the Default Mode Network (''DMN,'' TBN# 13, involved in social interactions) but also to several TBNs such as TBN number 15 (working memory and attention), 18 (language), 4 (emotion and interoception), and 2 (olfaction, taste, and reward). Since the TBNs database did not describe the specialization of subcortical structures, we focused on the cerebral cortex: RSN#10 and 12 corresponding to sub-cortical networks were eliminated from subsequent analyses. In order to further categorize the major RSNs families according to their overlap with TBNs, we used a clustering method based on mixture distributions (see Methods section). Fig. 2B  shows the RSN6TBN matrix, in which RSNs along the vertical axis are sorted on the basis of the afore mentioned clustering method.