dataesr/scientific-paragraphs-categorization huggingface dataset:
The ENIGMA-Laterality group is studying global trends in the profile of left-right differences in brain structure, and whether they relate to handedness, sex, and disease status, in over 15,000 people  (Guadalupe et al. , 2015 , submitted for publication). Reduced or abnormal brain asymmetry has been reported in many brain disorders  (Okada et al. , in press ), but the scope and generality of these differences is not yet understood. Also, many important aspects of human brain function show lateralization in terms of the underlying processing networks, but the biology of this specialization is poorly understood, as are factors that influence it. Whether brain asymmetry measures add value as diagnostic predictors, will be testable across ENIGMA. ENIGMA-EEG is studying the influence of genetic variants on brain functional activity measured with scalp recorded electrical signals, in a combined dataset from 10,155 individuals, ranging from 5 to 74 years of age.
EEG metrics of brain function mature rapidly with age, and relate to aspects of cognition such as the brain's processing efficiency; they also show abnormalities across many neurodevelopmental and psychiatric disorders. Combining data from several large twin and family datasets, the ENIGMA-EEG working group is performing a genome-wide association analysis of brain oscillatory power -a highly heritable trait -before proceeding to in-depth analyses of lateralized activity, brain connectivity, and network properties.
, 2013 (Chen et al. , , 2015)). Based on the notion of genetic correlation, brain regions or sets of voxels can be grouped into clusters with similar genetic determination. The standard decomposition of the brain into regions may be adapted to include genetic clusters, or new regions where genome-wide association may be more efficient  (Chiang et al. , 2012). This approach has already been applied to create genetic partitions of the cortex; initial work in ENIGMA will overlay pre-made partitions on the cortical data from each site. Genetic correlations can now be computed rapidly from GWAS summary statistics (B. Bulik-Sullivan et al. , 2015; B. K. Bulik-Sullivan et al. , 2015)  making it feasible to compute and perform clustering on matrices of "genetic connectivity" whose entries are genetic correlations.
, 2013a,b; de Reus et al. , 2015). On the genetic side, ENIGMA working groups have also formed to assess other kinds of genetic variation, including copy number variants (CNVs), where abnormalities have been reported in autism, schizophrenia, and learning disabilities. The ENIGMA CNV helpdesk is now supervising supervising an initial analysis of CNV data in 13,057 people from 24 cohorts worldwide, after developing harmonized protocols for CNV "calling" and quality control. Participating cohorts include groups from Japan, Mexican-Americans, and people of Western European, Nordic or Swedish ancestry. Initial efforts are evaluating known "psychiatric" CNVs as predictors of MRI and DTI phenotypes computed in other ENIGMA projects. Challenges include the pooling of data from genotyping chips with different coverage; some have sparse coverage of SNPs in regions with segmental duplications or complex CNVs.
In a complementary initiative, the ENIGMA-Epigenetics working group is studying epigenetic processes such as methylation, which is an index of biological aging and lifecourse 'stress' that may explain an important proportion of the gene-environment contribution to expression of many common diseases such as stroke and dementia. The group is now performing epigenome-wide association studies (EWAS), across 14 cohorts from Asia, Australia, North America, and Western Europe, to test associations between DNA methylation and brain measures, initially focusing on total brain volume, subcortical volumes and cortical thickness and surface areas. The working group is analyzing methylation data from 9,000 people, of whom 5,000 have both methylation data and MRI. In addition, the ENIGMA-Epigenetics group is prioritizing the analysis of DNA methylation sites based on their effects on gene expression or association with stress-and anxiety-related phenotypes.
Relevance to Individual Evaluation, and Longitudinal Assessment ENIGMA was not designed to make predictions about individuals based on their scans and genomic data. As in most epidemiological studies, the power lies in aggregating so much individual data that subtle effects on the brain can be detected, including findings that each cohort's data were insufficient to detect. In other words, its primary goal has been to relate brain measures to disease and treatment effects, and to variants in the genome. With the aggregated data, it has been possible to determine how reproducible these patterns are worldwide. Also, for the study of treatment effects, ENIGMA does not have the ideal design. Ideally, one would prefer to have pre-post treatment longitudinal designs instead of the cross-sectional comparisons in ENIGMA, where medication status is often confounded by age, disease duration, comorbidity and disease severity.
Even if a large data sample is needed to discover a factor that influences the brain, it does not mean that it is irrelevant to individuals; APOE is one such example, discovered in 1993 by linkage analysis in pedigrees. More recently, a rare variant in the TREM2 gene  (Jonsson et al. , 2013; Rajagopalan et al. , 2013)  was found to affect Alzheimer's disease risk and accelerate brain tissue loss as we age -perhaps doubling loss rates in old age and increasing AD risk by a factor of 2-4. This gene variant is undoubtedly important for those who carry it: it is found in a little under 1% of controls and a little over 1% of AD patients.
Understanding how APOE4 and other major risk genes shift the lifetime trajectory of brain measures will also help determine how much they will help when used for clinical trial stratification. This is a goal of the ENIGMA-Lifespan group  (Dima et al. , 2015). Clearly, any predictors of suicidal behavior would be very important in the management and follow-up of patients with psychiatric disorders  (Mathews et al. , 2013) , and a secondary project on suicidality was started within the ENIGMA-Depression working group  (Rentería et al. , submitted for publication). Similarly, factors that predict whether ADHD in a child will persist into adulthood, will have clinical utility  (Hoogman et al. , 2015). Ultimately, the stratification or clustering of ENIGMA cohort data into subtypes, based on imaging, clinical or behavioral data, may point to distinctions that help us understand the heterogeneity of these disorders.
Normative Data Across the Human Lifespan One effort where ENIGMA may contribute to individual prediction and evaluation -albeit with some caveats -is the ENIGMA-Lifespan project  (Dima et al. , 2015). In this work, ENIGMA cohorts are invited to contribute volumetric measures from normal individuals in their samples, which span the age range from 2 to 92 years of age. Although some cohort studies focus on children or the elderly, many scan people across the lifespan, allowing the computation of age-trajectories for several key brain measures; the results show a remarkable difference in the maturational trajectory of different structures, supporting many earlier neurodevelopmental reports on the sequence of brain development  (Gogtay et al. , 2004; Sowell et al. , 2004). To cope with the non-uniform sampling density of the cohorts, these overall trajectories must be interpreted cautiously; clearly some parts of the lifespan are better sampled than others, and unmodeled effects of scan site, demographics, and even cultural or environmental differences may drive some of the effects.
Clearly, disentangling the driving factors is statistically complex, but the potential is there, to derive normative measures and models of our path through life, in cohort studies as diverse as ENIGMA. The life span analyses (and normative curves) are also highly relevant for neurodevelopmental disorders such as OCD, ADHD, autism, etc. -for early detection, and secondary prevention in at-risk populations. Eventually, there may even be efforts to train individuals in specific domains, to stimulate the maturation of specific brain areas that appear to be deviant from the norm curves. Such normative data have possible applications for individual assessment, if used judiciously. In pediatrics, growth charts for height and weight offer metrics of where a child stands relative to others of the same age, as a Z-score for example.
Machine Learning, Big Data, and Individual Prediction With the advent of very large neuroimaging datasets, we can fit predictive models to the data and test them for their robustness. Our models of how diseases and genes affect the brain are constantly being tested and improved, especially in situations where statistical effects have previously been too small to discover, or have been confounded by factors that cannot be adjusted for. In GWAS for example, there are known genetic differences in allele frequencies across populations, and if these are not accurately modeled based on much larger datasets, and adjusted for using multidimensional scaling, they will confound the analysis and lead to spurious results -many more SNPs will show "effects" on the brain, ultimately turning out to be false positives.
Given the sample sizes attained, ENIGMA offers a framework not only for unrestricted searches, but also to test more focused hypotheses and provide internal replication using, for example, cross-validation methods. So far, the Working Groups have over 30 "secondary proposals": many study clinical measures, disease subtypes, and patterns of behavior such as suicidality or negative symptoms, or other differences that might contribute to the heterogeneity of brain disease and outcomes. One such project, in the ENIGMA-Major Depression group, assesses the effects of childhood trauma on depression-related brain measures, a factor that may be modeled effectively by comparisons with data from the ENIGMA-PTSD group, where childhood trauma is also a major predictive factor. Partnerships between ENIGMA groups may resolve some sources of brain differences that are difficult to disentangle.
Past efforts to combine imaging and genomic data for outcome prediction suggest that imaging measures may be much more predictive of future clinical decline than genomic measures, but both are complementary (Peters and the Alzheimer's Disease DREAM Challenge, submitted for publication). Predictive models should improve as they draw on more data, and the larger ENIGMA GWAS studies are now discovering more genetic markers that can be used in predictive models for brain measures  (Hibar and the CHARGE and ENIGMA2 Consortia, submitted for publication; Hibar et al. , 2015a,b, in press ; Adams and the CHARGE and ENIGMA2 Consortia, submitted for publication). However, compelling as these approaches are and not wishing to dampen the enthusiasm for these very promising techniques, the image measurements being predicted generally require a human check and correction if necessary, particularly in datasets with complex imaging features such as occur in older patients with stroke -machine learning analysis algorithms still cannot reliably separate the hyperintensity due to a small cortical infarct from that due to a white matter hyperintensity or artifact, reliably.
Also, the variants driving the heritability of disease risk are only just beginning to be discovered for many of the major brain diseases studied within and outside of ENIGMA. Unsupervised learning is also relevant for understanding the heterogeneity of diseases, which has made it harder to discover their causes and mechanisms. Brodersen et al. (2013)  argued that one could use unsupervised learning on imaging, clinical and genetic data to see whether subtypes (or clusters) can be identified within a disease, and whether these data cluster together in agreement (or disagreement) with current diagnostic classifications. In conclusion, we have reviewed current work by the ENIGMA Consortium. ENIGMA began in 2009, and is now a distributed effort, with over 30 working groups (see Table  1 ), coordinated from many centers worldwide.
As we noted, ENIGMA's main goals have been to detect effects of disease and genetic variants on the brain, to see how consistent these effects are worldwide, and to study what modulates these effects. On the genetic side, it may soon be possible for polygenic scoring to produce predictors that are routinely used in brain imaging studies, explaining some of the observed variance. This may make other effects easier to detect. On the disease side, we are beginning to identify and confirm distinctive patterns of brain differences in each of a range of brain diseases, along with a better understanding of which patterns are specific to given disorders, which patterns tend to generalize, and what factors account for the heterogeneity across cohorts. This will help us understand the situations where predictive models can be used, for diagnostic classification, outcome prediction, and norming of individual data against appropriate reference populations.
We end with a note in praise of small studies. Like any consortium, ENIGMA would be impossible without the cohort studies and all the individuals who contribute; most of the data analyzed in ENIGMA came from cohorts with relatively modest sample sizes. Inevitably, many hypotheses are not addressable on a large scale, and some questionsespecially causal questions -involve targeted interventions or phenotypic assessments with a depth or sophistication not likely to be attained at every site. As Aristotle said, "Nobody has the ability to work everything out, but everyone has something useful to say; working together, the whole vast world of science is within our reach. " (ἐκ πάντων δὲ συναθροιζομένων γίγνεσθαί τι μέγεθος; Aristotle, Metaphysics α, c. 350 BCE). (Nalls et al. , 2015) , in 13,708 cases and 95,282 controls (red SNPs = genome-wide significant signals).
Acknowledgments This work was supported in part by a  Consortium grant  ( U54 EB 020403 ) from the  NIH Institutes contributing  to the  Big Data to Knowledge (BD2K) Initiative , including the  NIBIB. Funding for individual consortium authors is listed in Hibar et al. , Nature, 2015 and in other papers cited here. This paper was collaboratively written on Google Docs by all authors, over a period of several weeks. We thank  Josh Faskowitz  for making Fig. 3, the ENIGMA "roadmap".
Background: There have been few reports on programmatic experience of viral hepatitis testing and treatment in resource-limited settings. To inform the development of the 2017 World Health Organization (WHO) viral hepatitis testing guidance and in particular the feasibility of proposed recommendations, we undertook a survey across a range of organisations engaged with hepatitis testing in low-and middle-income countries (LMICs). Our objective was to describe current hepatitis B and C testing practices across a range of settings in different countries, as well as key barriers or challenges encountered and proposed solutions to promote testing scale-up. Methods: Hepatitis testing programmes in predominantly LMICs were identified from the WHO Global Hepatitis Programme contacts database and through WHO regional offices, and invited to participate.
Survey sites Potential hepatitis testing and treatment programme sites in LMICs were identified from the various contacts databases of the WHO Global Hepatitis Programme (GHP) and of key WHO implementing partners [Médecins du Monde (MdM), Médecins sans Frontières (MsF) and Forum for Collaborative Research]. Twenty-two programmes from Asia, Sub-Saharan Africa and Eastern Europe that were broadly representative of different types of testing programmes ie. governmental or nongovernmental organisations; hospital or community based testing; general or specific target populations; and from different geographic regions, were invited to participate in the survey.
Acknowledgements We would like to acknowledge to all experts kindly participated to this feasibility survey:  Albert Minga  ( National Program Ivory Coast, Abidjan, Côte d'Ivoire ),  Ali Mahamat Moussa  ( Esther/Expertise France, Ndjamena, Chad ),  Boubakar Madougou  ( Esther/Expertise France, Niamey, Niger ),  George Macharia Wambugu  ( Médicins du Monde, Nairobi, Kenya), Henry Ly Chan (The Chinese University of Hong Kong, Hong Kong, China), Ina Inaridze (Médicins du Monde, Tbilisi, Georgia ),  Isabelle Andrieux-Meyer  ( Médecins Sans Frontières, Geneva, Switherland ),  Janus Ong  ( Philippine General Hospital, Manila, Philippines ),  Joseph Larmarange  (Agence nationale de recherche sur le sida et les hépatites virales, Côte d'Ivoire),  Kizys Herbocszek  ( Médecins Sans Frontières, Manipur, India ),  Mai Ling Perman  ( Colonial War Memorial Hospital, Suva, Fiji ),  Maud Lemoine  ( Imperial College, London, United Kingdom ),  Naranbaatar Dashdorj (Onom Foundation ,  Ulan Bator , Mongolia),  Nguyen Vu Trung  ( National Hospital for Tropical Disease, Hanoi, Vietnam ),  Paata Sabelashvili  ( National Member of HCV elimination Comission, Georgia), Pham Van Anh (Haiphong medical and pharmaceutical university, Haiphong, Vietnam ),  Ponsiano Ocama  ( Makerere University, Kampla, Uganda ),  Nicolas Durier  ( Treat Asia/amfAR, Bangkok, Thailand ),  Tatiana Barnard  ( Alliance, Kiev, Ukraine ),  Than Min Htike  ( Médicins du Monde, Yangoon, Myanmar ),  Xavier Assamagan (Esther/Expertise France, Lome, Togo)  and  Zin Mar Han  ( Médicins du Monde, Dar es Salam, Tanzania ).
Water potential was then measured on these bagged leaves right before the scan with a Scholander-type pressure chamber (Precis 2000, Gradignan, France). Current-year shoots were scanned using a high flux (3. 1011 photons mm -2 ) 25 keV monochromatic X-ray beam. The projections were recorded with a Hamamatsu Orca Flash sCMOS camera equipped with a 250 µm thick LuAG scintillator and visible light optics providing an effective pixel size of 3 microns. The complete tomographic scan included 1,500 projections, 50 ms each, for a 180° rotation. Samples were exposed for 75 s to the X-ray beam. Tomographic reconstructions were performed using PyHST2 software  (Mirone et al. , 2014)  using the Paganin method  (Paganin, 2006). The loss of theoretical conductance k was determined from a transverse 2D micro-CT slice taken from the centre of the scan volume by estimating the theoretical k of each sapling stem based on the conduit dimensions of embolised and functional conduits.
Acknowledgments We thank the  Experimental Unit of Pierroton ,  UE 0570 ,  INRA ,  69  route d'Arcachon, 33612  CESTAS (France) , and the  Blue Mountains Botanic Garden  staff and  Robert Spooner-Hart  for providing material and technical support. For fluorescent microscopy, we thank the  Bordeaux Imaging Center (CNRS-INSERM) and Bordeaux University. Sébastien Marais  is acknowledged for technical support with fluorescent microscopy. CMP-D was supported by a scholarship from the  Spanish Ministry of Economy and Competitiveness  ( EEBB-I-15-09191 ). We thank the PSICHE beamline (SOLEIL synchrotron facility, project  20141229 ). This work was supported by the programme ' Investments for the Future '  ANR-10-EQPX-16 , XYLOFOREST) from the  French National Agency for Research. BC was supported by an  Australian Research Council  ( Future Fellowship  grant no.
For COI amplification, conditions included: (1) initial denaturation step at 95 °C for 15 min, then 5 cycles at 95 °C for 40 s, 45 °C for 40 s, 72 °C for 1 min, were followed by 45 cycles at 95 °C for 40 s, 50 °C for 40 s, 72 °C for 1 min and a final extension step at 72 °C for 20 min for C1J1718/C1N2191, and (2) initial denaturation step at 94 °C for 3 min, 5 cycles of denaturation at 94 °C for 30 s, annealing at 45 °C for 90 s and extension at 68 °C for 60 s were followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at 51 °C for 90 s, and extension at 68 °C for 60 s and a final extension at 68 °C for 10 min for LepF1/LepR. Amplicons were analysed by electrophoresis in 1. 5% agarose gel stained with 0. 1% ethidium bromide. All sequences obtained are available in GenBank (Table  1 ).
01 and ***p < 0. 001-Student's t-test followed by Holm-Sidak correction). ( † indicates the loss of animal from group). b Representative colonic pathology as assessed by epithelial cell turnover (Ki67 staining-left panels) and goblet cell density (PAS staining-right panels) in Cre -(upper panels) and Cre + mice (lower panels). Arrows indicate loss of goblet cells. Scale bar, 1 mm. c Experimental scheme for the transfer of splenic T cells from Cre -or Cre + mice into congenic WT recipients. d Survival of WT recipients given T cells from Cre -ro Cre + mice. Data are pooled from two experiments with a total of seven (Cre -) or eight (Cre + ) mice per group. e Frequency of CD45. 2 + donor T cells among total blood T cells (left) and the frequency of donor cells within the CD4 + and CD8 + T-cell compartments (centre and right) at 1, 2 and 3 weeks post transfer in the mice from c, d above.
To distinguish between these ideas, we sought surrogate markers that were not affected by TGFβR1 deficiency and that might allow us to identify cells within the putative CD103 + CD11b + DC lineage without using CD103 itself. As a first step in this process, we used microarray analysis to compare the transcriptomes of all four of Rag1 -/-Cre - Rag1 -/-Cre + Rag1 -/- Cre - the CD103/CD11b-defined DC subsets from WT small intestine, as this information was not available from existing databases. Hierarchical clustering analysis demonstrated that the DC subsets segregated clearly from each other and from CD64 + mϕ (Fig. 5a ). Rag1 -/- Cre + Rag1 -/- Cre - Rag1 -/- Cre + Rag1 -/-Cre - Rag1 -/-Cre + As before, we excluded the small CD103 -CD11b -population from this analysis and to visualise the differences between the remaining three DC populations, we plotted each gene in a graph comprising one axis per DC subset placed at a 120°angle to each other, creating a hexagonal 'Triwise' plot (Fig.
Following incubation with purified anti-CD16/CD32 for 10 min at 4 °C, 1-10 × 10 6 cells were stained at 4 °C in the dark as described previously  63, 64  using the antibodies listed in Supplementary Table  6  and analysed using an LSR II or FACSAriaI/III cytometer (BD Biosciences) and FlowJo software (Tree Star). Genomic PCR. Cre-mediated deletion of Tgfbr1 was analysed by genomic PCR  65. The floxed third exon of Tgfbr1 was identified by a 250-bp band, whereas excision was demonstrated by a 350-bp band (see Supplementary Table  7 ). Quantitative RT-PCR. mRNA was extracted from cells using the GenElute mammalian total RNA Miniprep kit (Sigma-Aldrich) and complementary DNA was synthesized with SuperScript II reverse transcriptase (Invitrogen), according to the manufacturer's protocol. TaqMan real-time quantitative PCR assays were designed using Universal ProbeLibrary (Roche) to determine transcript levels of the indicated genes using the primers listed in Supplementary Table  8.
25 ng total RNA was used for each sample for synthesis of biotinylated double stranded cDNA, using the NuGEN Ovation Pico WTA System V2 kit and the NuGEN Encore Biotin Module kit, according to the manufacturer's recommendations. Following fragmentation and end-labelling, 2 µg of cDNA were hybridized for 16 h at 45 °C on GeneChip Mouse Gene 1. 0 ST arrays (Affymetrix), interrogating 28,853 genes represented by ~27 probes spread across the full length of the gene. The chips were washed, stained in the GeneChip Fluidic Station 450 (Affymetrix) and scanned with the GeneChip Scanner 3,000 7 G (Affymetrix) at a resolution of 0. 7 µm. Raw data (. cel intensity files) were extracted from the scanned images using the Affymetrix GeneChip Command Console version 3. 2. After quality control using R/Bioconductor, the Robust Multi-array Average procedure was used to normalize data within arrays (probeset summarization, background correction and log2-transformation) and between arrays (quantile normalization).