An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver

<p>Abstract</p> <p>Background</p> <p>Many groups, including our own, have proposed the use of DNA methylation profiles as biomarkers for various disease states. While much research has been done identifying DNA methylation signatures in cancer vs. normal etc., we still lack sufficient knowledge of the role that differential methylation plays during normal cellular differentiation and tissue specification. We also need thorough, genome level studies to determine the meaning of methylation of individual CpG dinucleotides in terms of gene expression.</p> <p>Results</p> <p>In this study, we have used (insert statistical method here) to compile unique DNA methylation signatures from normal human heart, lung, and kidney using the Illumina Infinium 27 K methylation arraysand compared those to gene expression by RNA sequencing. We have identified unique signatures of global DNA methylation for human heart, kidney and liver, and showed that DNA methylation data can be used to correctly classify various tissues. It indicates that DNA methylation reflects tissue specificity and may play an important role in tissue differentiation. The integrative analysis of methylation and RNA-Seq data showed that gene methylation and its transcriptional levels were comprehensively correlated. The location of methylation markers in terms of distance to transcription start site and CpG island showed no effects on the regulation of gene expression by DNA methylation in normal tissues.</p> <p>Conclusions</p> <p>This study showed that an integrative analysis of methylation array and RNA-Seq data can be utilized to discover the global regulation of gene expression by DNA methylation and suggests that DNA methylation plays an important role in normal tissue differentiation via modulation of gene expression.</p

Influence of the Kuroshio interannual variability on the summertime precipitation over the East China Sea and adjacent area

Author Posting. © American Meteorological Society, 2019. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 32(8), (2019): 2185-2205. doi:10.1175/JCLI-D-18-0538.1.Much attention has been paid to the climatic impacts of changes in the Kuroshio Extension, instead of the Kuroshio in the East China Sea (ECS). This study, however, reveals the prominent influences of the lateral shift of the Kuroshio at interannual time scale in late spring [April–June (AMJ)] on the sea surface temperature (SST) and precipitation in summer around the ECS, based on high-resolution satellite observations and ERA-Interim. A persistent offshore displacement of the Kuroshio during AMJ can result in cold SST anomalies in the northern ECS and the Japan/East Sea until late summer, which correspondingly causes anomalous cooling of the lower troposphere. Consequently, the anomalous cold SST in the northern ECS acts as a key driver to robustly enhance the precipitation from the Yangtze River delta to Kyushu in early summer (May–August) and over the central ECS in late summer (July–September). In view of the moisture budget analysis, two different physical processes modulated by the lateral shift of the Kuroshio are identified to account for the distinct responses of precipitation in early and late summer, respectively. First, the anomalous cold SST in the northern ECS induced by the Kuroshio offshore shift is likely conducive to the earlier arrival of the mei-yu–baiu front at 30°–32°N and its subsequent slower northward movement, which may prolong the local rainy season, leading to the increased rain belt in early summer. Second, the persistent cold SST anomalies in late summer strengthen the near-surface baroclinicity and the associated strong atmospheric fronts embedded in the extratropical cyclones over the central ECS, which in turn enhances the local rainfall.We appreciate three anonymous reviewers for their thoughtful and constructive comments. This work is supported by the National Key Research and Development Program of China (2016YFA0601804), the National Natural Science Foundation of China (NSFC) Projects (91858102, 41490643, 41490640, 41506009, U1606402) and the OUC–WHOI joint research program (21366).2019-10-0

Long-term SST variability on the Northwest Atlantic continental shelf and slope

Author Posting. © American Geophysical Union, 2020. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 47(1), (2020): e2019GL085455, doi:10.1029/2019GL085455.The meridional coherence, connectivity, and regional inhomogeneity in long‐term sea surface temperature (SST) variability over the Northwest Atlantic continental shelf and slope from 1982–2018 are investigated using observational data sets. A meridionally concurrent large SST warming trend is identified as the dominant signal over the length of the continental shelf and slope between Cape Hatteras in North Carolina and Cape Chidley, Newfoundland and Labrador, Canada. The linear trends are 0.37 ± 0.06 and 0.39 ± 0.06 °C/decade for the shelf and slope regions, respectively. These meridionally averaged SST time series over the shelf and slope are consistent with each other and across multiple longer observational data sets with records dating back to 1900. The coherence between the long‐term meridionally averaged time series over the shelf and slope and basin‐wide averaged SST in the North Atlantic implies approximately two thirds of the warming trend during 1982–2018 may be attributed to natural climate variability and the rest to externally forced change including anthropogenic warming.We are grateful to the Editor Dr. Kathleen Donohue and two anonymous reviewers. This work was supported by NOAA's Climate Program Office's Modeling, Analysis, Predictions, and Projections (MAPP) program (NA19OAR4320074). We acknowledge our participation in MAPP's Marine Prediction Task Force. The data of NOAA OISST used in this study are available at NOAA Earth System Research Laboratory (https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html). The HadISST data set is available at Met Office, Hadley Centre (https://www.metoffice.gov.uk/hadobs/hadisst/). The COBE SST and NOAA ERSST data sets are available at NOAA Earth System Research Laboratory's Physical Sciences Division (https://www.esrl.noaa.gov/psd/data/gridded/data.cobe.html; https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html). The near‐surface air temperature is available at Global Historical Climatology Network‐Monthly Database (https://www.ncdc.noaa.gov/data‐access/land‐based‐station‐data/land‐based‐datasets/global‐historical‐climatology‐network‐monthly‐version‐4). The data of SSH are available at Copernicus Marine Environment Monitoring Service (http://marine.copernicus.eu/services‐portfolio/access‐to‐products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_ L4_REP_OBSERVATIONS_008_047).2020-07-0

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Seasonal prediction of bottom temperature on the Northeast U.S. Continental Shelf

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chen, Z., Kwon, Y.-O., Chen, K., Fratantoni, P., Gawarkiewicz, G., Joyce, T. M., Miller, T. J., Nye, J. A., Saba, V. S., & Stock, B. C. Seasonal prediction of bottom temperature on the Northeast U.S. Continental Shelf. Journal of Geophysical Research: Oceans, 126(5), (2021): e2021JC017187, https://doi.org/10.1029/2021JC017187.The Northeast U.S. shelf (NES) is an oceanographically dynamic marine ecosystem and supports some of the most valuable demersal fisheries in the world. A reliable prediction of NES environmental variables, particularly ocean bottom temperature, could lead to a significant improvement in demersal fisheries management. However, the current generation of climate model-based seasonal-to-interannual predictions exhibits limited prediction skill in this continental shelf environment. Here, we have developed a hierarchy of statistical seasonal predictions for NES bottom temperatures using an eddy-resolving ocean reanalysis data set. A simple, damped local persistence prediction model produces significant skill for lead times up to ∼5 months in the Mid-Atlantic Bight and up to ∼10 months in the Gulf of Maine, although the prediction skill varies notably by season. Considering temperature from a nearby or upstream (i.e., more poleward) region as an additional predictor generally improves prediction skill, presumably as a result of advective processes. Large-scale atmospheric and oceanic indices, such as Gulf Stream path indices (GSIs) and the North Atlantic Oscillation Index, are also tested as predictors for NES bottom temperatures. Only the GSI constructed from temperature observed at 200 m depth significantly improves the prediction skill relative to local persistence. However, the prediction skill from this GSI is not larger than that gained using models incorporating nearby or upstream shelf/slope temperatures. Based on these results, a simplified statistical model has been developed, which can be tailored to fisheries management for the NES.This work was supported by NOAA's Climate Program Office's Modeling, Analysis, Predictions, and Projections (MAPP) Program (NA17OAR4310111, NA19OAR4320074), and Climate Program Office's Climate Variability and Predictability (CVP) Program (NA20OAR4310482). We acknowledge our participation in MAPP's Marine Prediction Task Force

Identification of the factors associated with outcomes in a condition management programme

&lt;p&gt;Background: A requirement of the Government’s Pathways to Work (PtW) agenda was to introduce a Condition Management Programme (CMP). The aim of the present study was to identify the differences between those who engaged and made progress in this telephone-based biopsychosocial intervention, in terms of their health, and those who did not and to determine the client and practitioner characteristics and programme elements associated with success in a programme aimed at improving health.&lt;/p&gt; &lt;p&gt;Methods: Data were obtained from the CMP electronic spreadsheets and clients paper-based case records. CMP standard practice was that questionnaires were administered during the pre- and post-assessment phases over the telephone. Each client’s record contains their socio-demographic data, their primary health condition, as well as the pre- and post-intervention scores of the health assessment tool administered. Univariate and multivariate statistical analysis was used to investigate the relationships between the database variables. Clients were included in the study if their records were available for analysis from July 2006 to December 2007.&lt;/p&gt; &lt;p&gt; Results: On average there were 112 referrals per month, totalling 2016 referrals during the evaluation period. The majority (62.8%) of clients had a mental-health condition. Successful completion of the programme was 28.5% (575 “completers”; 144 “discharges”). Several factors, such as age, health condition, mode of contact, and practitioner characteristics, were significant determinants of participation and completion of the programme. The results showed that completion of the CMP was associated with a better mental-health status, by reducing the number of clients that were either anxious, depressed or both, before undertaking the programme, from 74% to 32.5%.&lt;/p&gt; &lt;p&gt;Conclusions: Our findings showed that an individual's characteristics are associated with success in the programme, defined as completing the intervention and demonstrating an improved health status. This study provides some evidence that the systematic evaluation of such programmes and interventions could identify ways in which they could be improved.&lt;/p&gt

Mesoscopic models for DNA stretching under force: new results and comparison to experiments

Single molecule experiments on B-DNA stretching have revealed one or two structural transitions, when increasing the external force. They are characterized by a sudden increase of DNA contour length and a decrease of the bending rigidity. It has been proposed that the first transition, at forces of 60--80 pN, is a transition from B to S-DNA, viewed as a stretched duplex DNA, while the second one, at stronger forces, is a strand peeling resulting in single stranded DNAs (ssDNA), similar to thermal denaturation. But due to experimental conditions these two transitions can overlap, for instance for poly(dA-dT). We derive analytical formula using a coupled discrete worm like chain-Ising model. Our model takes into account bending rigidity, discreteness of the chain, linear and non-linear (for ssDNA) bond stretching. In the limit of zero force, this model simplifies into a coupled model already developed by us for studying thermal DNA melting, establishing a connexion with previous fitting parameter values for denaturation profiles. We find that: (i) ssDNA is fitted, using an analytical formula, over a nanoNewton range with only three free parameters, the contour length, the bending modulus and the monomer size; (ii) a surprisingly good fit on this force range is possible only by choosing a monomer size of 0.2 nm, almost 4 times smaller than the ssDNA nucleobase length; (iii) mesoscopic models are not able to fit B to ssDNA (or S to ss) transitions; (iv) an analytical formula for fitting B to S transitions is derived in the strong force approximation and for long DNAs, which is in excellent agreement with exact transfer matrix calculations; (v) this formula fits perfectly well poly(dG-dC) and $\lambda$-DNA force-extension curves with consistent parameter values; (vi) a coherent picture, where S to ssDNA transitions are much more sensitive to base-pair sequence than the B to S one, emerges.Comment: 14 pages, 9 figure

Effect of different rubber materials on husking dynamics of paddy rice

The conventional way to husk rice is to pass it between two rubber rollers that are rotating with a surface speed differential. The resulting normal pressure and shear stress causes the husk to be peeled away from the kernel. The process is suited to high-rice flow rates, but is energy intensive and can result in considerable wear to the surfaces of the rollers. The operating parameters for machines of this design are usually determined and set empirically. In this article, some experiments and calculations had been carried out in order to explore the mechanisms involved in husking rice grains using this method. A simple sliding friction rig with load cell and high-speed camera was used to observe the mechanisms that occur during husking. The husking performance of different rubbers was compared for changes in the applied normal load. It was found that grains rotate between the rubber counterfaces on initial motion before being husked. In addition, harder rubbers were found to husk a higher proportion of entrained grains at lower applied normal load. By measuring the coefficient of friction between rice and rubber samples, the shear force required to husk a given percentage of grains could be calculated and was shown to be constant regardless of rubber type. Based on the mechanism seen in the high-speed video, it was evident that there was a limiting shear stress that was the governing factor over the husked ratio

Effect of different rubber materials on husking dynamics of paddy rice

The conventional way to husk rice is to pass it between two rubber rollers that are rotating with a surface speed differential. The resulting normal pressure and shear stress causes the husk to be peeled away from the kernel. The process is suited to high-rice flow rates, but is energy intensive and can result in considerable wear to the surfaces of the rollers. The operating parameters for machines of this design are usually determined and set empirically. In this article, some experiments and calculations had been carried out in order to explore the mechanisms involved in husking rice grains using this method. A simple sliding friction rig with load cell and high-speed camera was used to observe the mechanisms that occur during husking. The husking performance of different rubbers was compared for changes in the applied normal load. It was found that grains rotate between the rubber counterfaces on initial motion before being husked. In addition, harder rubbers were found to husk a higher proportion of entrained grains at lower applied normal load. By measuring the coefficient of friction between rice and rubber samples, the shear force required to husk a given percentage of grains could be calculated and was shown to be constant regardless of rubber type. Based on the mechanism seen in the high-speed video, it was evident that there was a limiting shear stress that was the governing factor over the husked ratio