dataesr/scientific-paragraphs-categorization huggingface dataset:
Human movement data and models We considered eight connectivity metrics in our model, all of which capture diff erent aspects of connectivity between two districts, shown to be useful for inferring regular daily commuting patterns, longer-term movements, and the general human diff usion process. 8, 18 The fi rst set of metrics included distance and travel time distance. For distance, we calculated the great circle distance between the centroids of each pair of districts using data from using data from the Database of Global Administrative Areas. For travel time distance, we calculated the travel time between each pair of districts. 19 This metric represents the underlying transport network, which has been shown to refl ect population mixing (appendix p 7). 16  We derived a second set of metrics from standard models describing human mobility.
We then disaggregated this adjacency matrix into three binary connectivity matrices with connectivity degrees of one (ie, districts that share a border), connectivity degrees of two (ie, districts that share a common neighbour), and connectivity degrees of more than two. A full list of the 11 variables used to predict the geographic expansion of yellow fever virus is available in the appendix (p 8). To calibrate the gravity and radiation models, we used aggregated and deidentifi ed mobile phone-derived mobility estimates at the constituency level from Namibia between Oct 1, 2010, and Sept 30, 2011. These data measure the proportion of time that unique subscriber identity module (SIM) cards in each constituency spend in all other constituencies, and have been described in detail by Ruktanonchai and colleagues.
22 We used data from Namibia because it directly neighbours the study area and has a similar per capita gross domestic product (GDP) to both Angola and the DR Congo and because mobile phone-derived mobility estimates were not available from the study region at the time of analysis. We then used the models to predict between-district mobility for Angola and the DR Congo using the movement package in R. We computed national adjacency networks using administrative boundary data from the Database of Global Administrative Areas dataset. For each of the 313 administrative regions in Angola and the DR Congo, we calculated the total human population size using gridded population estimates based on data from WorldPop. We tested the hypothesis that the rate of spatial expansion changed with time by introducing a timevarying binary variable that was equal to 0 before change point T and equal to 1 after T.
For forward prediction, we used the same approach to predict the possible expansion of yellow fever virus 1-2 months ahead. We fi tted the model to the entire epidemic from week 49 in 2015 to late August, 2016, then used the model parameters to simulate the geographic spread forward in time. We estimated the exponential growth rate and the doubling time of the epidemic in the early phase when the number of cases was growing exponentially. We reconstructed the generation time distribution of yellow For the movement package in R see https://github. com/SEEG-Oxford/movement logit(P(x j (t) = 1|x j (t -1) = 0)) = β 0 + j k=1 Σ β k n Y k є ,t + (t -1) = Yj k ,t Σ i i (k) A j i x A i (k) j 1 -(1 -p = 4•33 p M w ) For the WorldPop project see http://www. worldpop. org. uk/ fever virus (ie, the time lag from the infection of a case to infection of subsequent cases in the chain of transmission) from previous knowledge of the natural history of yellow fever virus infection in people and in mosquitoes.
Discussion In the context of rapidly spreading and potentially fatal infectious disease epidemics for which vaccine stockpiles are limited, such as the current yellow fever outbreak in central Africa, it is essential to establish which areas are at the greatest risk of infection to inform vaccine prioritisation decisions. This risk estimation requires an in-depth understanding of the determinants of disease spread. We integrated and analysed diverse datasets, including the size and mobility of human populations and detailed maps of vector suitability, to describe the epidemic and spatial spread of yellow fever virus. We found that the spatial spread of yellow fever virus was well explained by human mobility and vector suitability and that our approach could help to discriminate districts at high risk of invasion from others at lower risk.
Such data could inform which populations contribute most to the invasion process and might help the targeting of intervention strategies. Beyond local spread, there is a risk of international spread of the virus via air travel, as seen with the yellow fever virus cases that have been detected in travellers returning from Angola to China. 28 In the present study, we refrained from extrapolating our results to regions other than the two that are currently at the core of the aff ected countries because evidence of overland cross-border local transmissions has, so far, only been reported between Angola and the DR Congo; frequent exchange along the major road from Uige, Angola, to Kinshasa, DR Congo, has been recorded (fi gure 4). Additionally, the Angolan district of Cabinda is located entirely within the DR Congo, which results in substantial population fl ows through the western coastal corridor of the DR Congo.
Contributors MUGK and SC developed the idea and design of the study. MUGK, BN, SS, HS, OF, FMS, NRM, and MAJ gathered the data. MUGK, SC, RCR, and BN analysed the data. MUGK, SC, RCR, NRF, NG, GRWW, OF, SCH, MN, DB, RNT, HHN, BSRP, NT, EON, NRF, JSB, AJT, KK, TdO, DLS, OGP, AAS, IIB, and SIH contributed to the data interpretation. MUGK and SC wrote the fi rst draft of the manuscript. All authors contributed to writing and approved the fi nal version of the manuscript.
Acknowledgments MUGK receives funding from the  International research Consortium on Dengue Risk Assessment Management and Surveillance  (IDAMS; European Commission 7th Framework Programme [21893]). NG is supported by a  University of Melbourne McKenzie  fellowship. MAJ received partial support from the  Models of Infectious Disease Agent Study program  (Cooperative Agreement number  1U54GM088558 ). FMS acknowledges funding from the  Rhodes Trust. SIH received a grant.
This review aims to evaluate the effects of biofeedback interventions that have been used towards improving motor performance and motor learning in people with cerebral palsy. Methods: Using a customized strategy, MEDLINE, CINAHL, Embase, PsycINFO, Cochrane Central Register of Controlled Trials, SCOPUS, SPORTDiscus, and PEDro databases will be searched. Two independent reviewers will screen titles and abstracts, review full texts for inclusion criteria, and extract data from relevant articles using a standardized template. Quality of evidence and risk of bias will be assessed through the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology. Discussion: Several studies have investigated biofeedback-based interventions for people with cerebral palsy. However, there is a great variety and limited consensus regarding how to implement biofeedback mechanisms.
Data synthesis Outcome measures from each study will be categorized as either primary, that is related to functional performance metrics of daily activities, or categorized as secondary, relating to physiological changes during controlled tasks (e. g. , improved movement speed or accuracy). Within the primary and secondary outcome categories, individual outcomes will be sub-grouped to the extent possible based on their potential patient impact. A summary of findings with the related quality of evidence for each of these sub-grouped outcomes will be synthesized to reflect the current state of biofeedback interventions aiming to improve motor performance for people with cerebral palsy.
All reads were mapped to a human mitochondrial genome reference (NCBI accession J01415. 2) using the same BWA mapping method described above. Reads with a higher mapping edit-distance to human mtDNA than to bear mitochondrial genomes were considered of likely human origin and were removed from the bear mitogenome mapping results. PCR duplicates were removed with the MarkDuplicates tool in the Picard software suite v. 1. 112 using lenient validation stringency (http://broadinstitute. github. io/picard/). Consensus calling was carried out using Samtools mpileup  [49]  with default settings.
(f ) Phylogenetic analyses Complete mitochondrial genomes, partial control region sequences, and cytochrome b sequences for 11 Asian black bears, 76 American black bears, two cave bears (U. spelaeus), 200 brown bears, and 52 polar bears were obtained from GenBank (electronic supplementary material, table  S4 ). Two GenBank datasets were created: one dataset included only complete mitogenomes for the non-Tibetan/ Himalayan bears and both partial (amplicon sequences) and complete mitogenomes for Tibetan and Himalayan bear lineages, while the other dataset included both amplicon sequences and complete mitogenomes for non-Tibetan/Himalayan bears. All new sequences produced in this study were added to these two GenBank datasets and used in the phylogenetic analyses. Sloth bear (U. ursinus) and sun bear (U.
(g) Divergence time estimation Bayesian MCMC-based divergence time estimation was carried out using BEAST version 1. 8. 0 under the GTR substitution model. The dataset used for molecular dating analysis included only complete mitogenome, since shorter mtDNA regions (e. g. control region and cytochrome b) are generally associated with considerable uncertainty and may bias molecular dating analyses due to homoplasy  [10, 17]. The uncorrelated lognormal relaxed clock and the constant size coalescent prior were used. Radiocarbon dates and stratigraphically estimated dates for four ancient sequences were used to calibrate ages for terminal nodes, including three sequences from extinct bear species (U. spelaeus and U. deningeri) dated to 31. 8 thousand years (ka) BP  [56] , 44. 1 ka BP  [57] , and 409 ka BP  [42] , an approximately 120 ka BP polar bear subfossil  [38] , and seven European brown bears dated to approximately 4.
1-37 ka BP  [58]. Trees were sampled every 1000 generations from a total of 1 000 000 000 generations. The maximum clade credibility tree was generated using TreeAnnotator, implemented in the BEAST package  [59] , with 10% burn-in. Effective sampling size value greater than 200 for all parameters sampled from the MCMC and the posterior distributions were examined using Tracer v. 1. 6  [60].
, 2009]  and FTIR  [La Spina et al. , 2010]   The AERONET stations routinely measure column optical and microphysical properties of the aerosol layer, which are used to assess the downwind impact of Mt. Etna's sulphur emissions. Additionally, the Lampedusa station is equipped with complementary instrumentation, including a LiDAR which provides height resolved aerosol and clouds information. These data allow the characterization of aerosol layer and their precursors, which is fundamental for identifying downwind volcanic contribution. Ground and space observations are complemented with an atmospheric Lagrangian model (FLEXPART -FLEXible PARTicle dispersion model, https://flexpart. eu/) for inspecting the transport and dispersion of volcanic plumes over the Mediterranean region. First results have allowed the characterization of the Etnean plumes dispersion at the decadal timescale, thus quantifying the frequency of ventilation of different mediterranean sub-sectors and confirming the prevalence (about 80% frequency) of the dispersion of the plumes towards the eastern quadrant (i.
Acknowledgements All participants of the EPL-RADIO, SMED and MedSuV  WP5  projets are gratefully acknowledged, especially:  T. Caltabiano ,  E. Carboni ,  S. Corradini ,  A. di Sarra ,  P. -J. Gauthier ,  A. La Spina ,  B. Legras ,  L. Merucci ,  G. Puglisi ,  S. Scollo ,  L. Terray.
Mass spectrometry analysis Protein preparation for mass spectrometry-based proteomic analyses was as described  (50). Briefly, extracted proteins were stacked in the top of a SDS-PAGE gel (NuPAGE 4-12%, Invitrogen) before in-gel digestion using trypsin (Promega, sequencing grade). Resulting peptides were analysed by online nanoLC-MS/MS (UltiMate 3000 coupled to LTQ-Orbitrap Velos Pro and Ultimate 3000 RSLCnano coupled to Q-Exactive Plus, Thermo Scientific, for Spp1 and Mer2 interactomes analyses, respectively) using a 120-min gradient. Peptides and proteins were identified and quantified using MaxQuant (version 1. 5. 8. 3  (51) ) and SwissProt database (June 2017 version, Saccharomyces cerevisiae S288c taxonomy). Only proteins identified with a minimum of two unique + razor peptides were taken into account for further analyses.
Statistical analyses were performed using ProStaR  (52). Proteins identified in the reverse and contaminant databases, proteins only identified by site and proteins exhibiting less than 3 intensity values in one condition were discarded from the list. After log2 transformation, intensity values were normalized by median centering before missing value imputation (replacing missing values by the 2. 5 percentile value of each column); statistical testing was conducted using limma t-test. Differentially recovered proteins were sorted out using a log2(fold change) cut-off of 7 and a FDR threshold on remaining p-values of 1% using the Benjamini-Hochberg method. Co-immunoprecipitation and western blot 6. 10 8 cells were washed with PBS, and lyzed in 1. 5 ml lysis buffer (20 mM HEPES/KOH pH7. 5; 150 mM NaCl; 0.
Antibodies used were as follows: anti-H3K4me3 (MC315, Millipore, 1/5000); anti-H3K4me2 (07-030, Millipore, 1/5000); anti-PGK1 (Invitrogen, 1:20000); anti-HA (Roche, 12CA5, 1/750); anti-Myc (Santa Cruz, 9E10, 1/500); anti-Flag (Sigma, 1/1000); anti-TAP (Invitrogen, 1/2000); anti-Spp1 (rabbit polyclonal, 1/2000). The Spp1 polyclonal antibody was made in rabbit against the full-length Spp1 fused at its N-terminus with a 6His-MBP tag. Signal was detected using the SuperSignal™ West Pico Chemiluminescent Substrate (ThermoFisher) and a Chemidoc touch system (Biorad). Signal was quantified using Image J software.
Acknowledgments We thank  Scott Keeney  and  Bruce Futcher  for reagents. We thank  Corinne Grey  and  Arnaud De Muyt  for critical reading of the manuscript, and  Daniel Holoch  and  Raphaël Margueron  for discussions. We thank the recombinant proteins platform of  Institut Curie  for the Spp1 antibody, and the support of the discovery platform and informatics group at EDyP.
9] [30] [31]  Large sample sizes are required to achieve optimal power in analyses of so many genomic sites, especially if the prevalence of the exposure or outcome under study is low. Collaboration between studies and combined meta-analysis of the available data are needed to optimize the use of resources and to increase the likelihood of detecting DNA methylation differences underlying the associations of early life exposures and health outcomes. This paper describes the global Pregnancy And Childhood Epigenetics (PACE) Consortium which, to date, brings together 39 studies with over 29 000 samples and DNA methylation data in pregnant women, newborns and/or children. Besides strongly increased power to detect associations, bringing studies together in the PACE Consortium for meta-analysis greatly decreases the risk of false-positive associations.
33 Currently, the PACE Consortium includes 39 studies with genome-wide DNA methylation data from pregnancy, newborn or childhood samples and information on at least one of the exposures or outcomes of interest. A list of studies currently involved in the PACE Consortium with basic study information is shown in Table  1. More detailed descriptions of the individual cohorts can be found in the Supplementary material and Supplementary Table  1 , available at IJE online. The PACE Consortium is an open, dynamic collaboration and additional research groups are welcome to join. The Consortium structure is purposefully kept simple. The work in the Consortium is strongly researcherdriven. Any member can propose an analysis. Projects are often co-led by two or more researchers from different studies.
Subsequently, the meta-analysis centre performs quality control of the summary results files and meta-analyses all datasets, with specific 'omics' meta-analysis software, such as Metal. 34 Standard quality controls include inspection of the distribution of effect estimates and standard errors across cohorts, and Manhattan plots of individual cohort and metaanalysis results. The full process of quality control and metaanalysis is independently repeated by an analyst from one of the other participating studies (the 'second centre') as a quality control measure. As a general rule, as many studies as possible are included in the discovery meta-analysis to increase power to discover new associated DNA methylation sites. Replication of findings is then pursued in further studies that were unable to participate in the discovery meta-analysis, if available.
How often have they been followed-up? The PACE Consortium brings together a large number of cohorts, each of them with cohort-specific protocols (Table  1 , Supplementary Table  1 ). Most studies have ongoing data collection and follow-up. Many of the cohorts have multiple follow-up time points from fetal life into childhood, and several have follow-up into adolescence or early adulthood. Most have information on maternal exposures during pregnancy, including maternal smoking and body mass index. 31, 32 A number of studies also collected information on more specific exposures, such as air pollution. 35 All cohorts have collected information on child physical and/or mental development. Some studies have a particular focus, such as cleft lip and palate (NCL) or autism (SEED I), but most are population-based cohorts collecting a vast amount of data on many domains.
These include anthropometric, cardiometabolic, neurodevelopmental and respiratory measurements, as well as childhood diseases. Further details of data collection waves, follow-up and biological sample collection in all studies can be found in Supplementary Table  1. The PACE Consortium is focused around the common methylation platform. Studies commit to the PACE Consortium on a projectby-project basis. They are not necessarily involved in PACE with all their data, but rather decide per project whether or not they will participate. It is therefore possible that a particular study is not involved in a PACE project on a specific topic, for example because they decide to pursue a single-study project or because they are involved in another collaboration on that topic. In such cases, studies opt out of the project and are not involved until the work is published.
The main exposures that the PACE Consortium currently focuses on are those occurring during pregnancy; the main outcomes are childhood health parameters and diseases. An overview is given in Figure  3. Recently, working groups have formed around methodological issues, such as blood cell composition adjustment and evaluation of methods for identifying differentially methylated regions. Many of the studies involved in the PACE Consortium also have GWAS data and other types of 'omics' data, including transcriptomics and metabolomics if available, creating the possibility for integrative omics analyses. [38]  What has it found? 9] [50] [51] [52] [53] [54]  Multiple consortium projects are currently being analysed or prepared. Here, we would like to highlight the first three published reports. The first large PACE Consortium meta-analysis reported on the results of a meta-analysis on maternal smoking in relation to cord blood DNA methylation.
The longitudinal data collection from early life onwards enables us to study the role of DNA methylation in life course health trajectories. Sixth, the experience and diverse backgrounds of the PACE investigators, including epidemiologists, statisticians, geneticists, clinicians, bioinformaticians and biologists, enables sharing of methods and analytical code, quicker solutions to methodological issues and easier exchange of knowledge and skills. The experience of many PACE investigators in existing consortia, often with the same partner studies, was of great benefit at the start of the PACE Consortium. Issues that may have posed challenges to earlier consortia, such as communication between studies, harmonizing analytical methods, and authorship strategies, were hence part of the 'basic skill set' of this Consortium.
A subset of PACE cohorts have DNA methylation measured in placenta. Second, the distribution of blood cell subtypes in blood samples varies in response to a range of internal and external factors, such as infection, diseases and smoking. As DNA methylation is cell-type specific, an observed association of an exposure or an outcome with DNA methylation may be the result of changes in blood cell composition, rather than a representation of a true association. Adjustment for blood cell composition in studies using cord blood data is a challenge. So far, we have used the regression calibration method of Houseman and colleagues, which until recently has been constrained to the first available reference panel of 450 K data in white blood cell subtypes of six adult males. This panel has been shown to be suboptimal in estimating blood cell proportions in DNA from newborns.
Conversely, results from laboratory scientists may inspire new analyses of DNA methylation in human studies. Many methodological issues need to be resolved. The PACE Consortium offers a strong platform to address these points and to contribute to the field of population epigenetics in the future. Can I get hold of the data? Where can I find out more? The PACE Consortium is an open consortium and studies interested in participating in one or more analyses are welcome to join. Each individual cohort analyses its own data locally and only summary statistics, including cohortspecific effect estimates, standard errors and P-values for each CpG site, are shared for the meta-analysis. Therefore, for access to data from individual cohorts in the PACE Consortium, researchers should contact studies directly.
261357), Stockholm County Council (ALF), Swedish Foundation for Strategic Research (SSF, RBc08-0027, EpiGene project), the Strategic Research Programme (SFO) in Epidemiology at Karolinska Institutet, the Swedish Research Council Formas and the Swedish Environment Protection Agency. Funding for this work was provided by R01ES017646, R01ES01900, R01ES16443, and Autism Speaks grant #260377. Etudes des Determinants preet postnatals precoces du developpement et de la santede l'Enfant (EDEN) EDEN funding was provided by: Funds for Research in Respiratory Health, the French Ministry of Research: IFR program, INSERM Nutrition Research Program, French Ministry of Health: Perinatality Program, French National Institute for Population Health Surveillance (INVS), Paris-Sud University, French National Institute for Health Education (INPES), Nestle, Mutuelle Generale de l'Education Nationale (MGEN), French-speaking association for the study of diabetes and metabolism (Alfediam), grant # 2012/ 51290-6 Sao Paulo Research Foundation (FAPESP), EU-funded MedAll project.
The ENVIRonAGE birth cohort is funded by the European Research Council (ERC-2012-StG. 310898) and by funds of the Flemish Scientific Research Council (FWO, N1516112 / G. 0. 873. 11 N. 10). The methylation assays were funded by the European Community's Seventh Framework Programme FP7/2007-2013 project EXPOsOMICS (grant no. 308610). M. P. was supported by the People Program (Marie Curie Actions) of the European Union's Seventh Framework Program FP7/2007-2013/ under REA grant agreement n [628858]. A. V. has a PhD fellowship from Bijzonder Onderzoeksfonds (BOF) Hasselt University. Flemish Environment and Health Study I (FLEHSI) birth cohort the Australian National Health and Medical Research Council and the United States National Institute of Health. Rhea Mother-Child Cohort (Rhea) The Rhea project was financially supported by European Union (EU) grants for specific projects (EU FP6-2003-Food-3-.