Analysis of Barriers to Health Information Seeking and Utilizing in Patients With Diabetes

As one of the three chronic diseases, diabetes’s high prevalence and multiple complications bring a heavy burden to diabetes patients and the society. In order to better manage and control diabetes, it is essential for diabetes patients and their families to seek and utilize diabetes health information themselves, but there are many factors that affect diabetes patients to access and use health information. This article aims to explore the barrier factors that affect diabetes patients to seek and utilize diabetes health information, and to provide targeted measures to better manage and control diabetes

Energy Risks Zoning and Demand Forecasting in Jiangsu rovince

AbstractThis paper takes Jiangsu province as an example, divides the zone of energy risks and uses GM (1, 1) and the combination of BP network model to forecast energy demand in this region.Finally, we adopt ARCEngine secondary development achieving the system simulation, and putting forward a strategic suggestion on energy problem of Jiangsu province. The study provides the scientific data support for making energy policy rationally, reducing the increasingly prominent phenomenon of energy demand and offer support for different levels in different departments.It can provide the scientific basis for risk prevention and comprehensive risk management plan.© 2011 Published by Elsevier Ltd. Selection and peer-review under responsibility of RIUD

A gene based approach to test genetic association based on an optimally weighted combination of multiple traits.

There is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases for which multiple correlated traits are often measured. Joint analysis of multiple traits could increase statistical power by aggregating multiple weak effects. Existing methods for multiple trait association tests usually study each of the multiple traits separately and then combine the univariate test statistics or combine p-values of the univariate tests for identifying disease associated genetic variants. However, ignoring correlation between phenotypes may cause power loss. Additionally, the genetic variants in one gene (including common and rare variants) are often viewed as a whole that affects the underlying disease since the basic functional unit of inheritance is a gene rather than a genetic variant. Thus, results from gene level association tests can be more readily integrated with downstream functional and pathogenic investigation, whereas many existing methods for multiple trait association tests only focus on testing a single common variant rather than a gene. In this article, we propose a statistical method by Testing an Optimally Weighted Combination of Multiple traits (TOW-CM) to test the association between multiple traits and multiple variants in a genomic region (a gene or pathway). We investigate the performance of the proposed method through extensive simulation studies. Our simulation studies show that the proposed method has correct type I error rates and is either the most powerful test or comparable with the most powerful tests. Additionally, we illustrate the usefulness of TOW-CM based on a COPDGene study

Gene-based association tests using GWAS summary statistics and incorporating eQTL

Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with

An Analysis of Mode III Doubly Periodic Crack-Tip Field of Orthotropic Composite Materials

The mechanical behavior near the doubly periodic crack tips for orthotropic composite materials plate subjected to antiplane shear loading is studied. This is done by complex function theory and conformal mapping of the Jacobi elliptic function with the help of boundary conditions. The analytical solution of the crack-tips stress intensity factor and the expression of stress fields are obtained. Numerical examples are given to analyze the impact of the different transverse spacing, longitudinal spacing, and the ratio of cracks periods on stress intensity factors. The results show that the crack-tip field increases with reducing either the transverse spacing or the longitudinal spacing. At the same time, the crack-tip field increases with the decrease of the ratio of cracks periods. This shows that the distribution form makes an important effect on the crack-tip field, but the crack density parameter is not the only cause

Phylogeny of the genus Morus (Urticales: Moraceae) inferred from ITS and trnL-F sequences

Both nuclear ribosomal ITS and chloroplast trnL-F sequences were acquired from 13 mulberry genotypes belonging to nine species and three varieties, and one paper mulberry. The later belongs to genus B. papyrifera, designed as outgroup, and were analyzed. Within the genus Morus, the sequence diversity of ITS was much higher than that of trnL-F. The results of phylogenetic analyses based on these data (separately or combined) show that the genus Morus is monophyletic group. Strict consensus tree obtained through the Neighbor-joining method can be divided into five major clades in the genus Morus, according to combined sequence data. M. bombycis, M. alba var. venose formed clades A and B, respectively. Clade C comprises of 5 species; M. rotundiloba, M. atropurpurea, M. mongolica, M. australi, and M. mongolica var. diabolica. Clade D comprises of 3 species; M. wittiorum, M. laevigata, and M. alba. Clade E comprises of 2 species; M. multicaulis, and M.alba var. macrophylla. The results from cluster analysis were basically in agreement with the existing morphologic classification.African Journal of Biotechnology Vol. 4 (6), pp. 563-569, 200

Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines

<p>Abstract</p> <p>Background</p> <p>Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occur approximately every 1000 bases in the overall human population. The non-synonymous SNPs (nsSNPs) that lead to amino acid changes in the protein product may account for nearly half of the known genetic variations linked to inherited human diseases. One of the key problems of medical genetics today is to identify nsSNPs that underlie disease-related phenotypes in humans. As such, the development of computational tools that can identify such nsSNPs would enhance our understanding of genetic diseases and help predict the disease.</p> <p>Results</p> <p>We propose a method, named Parepro (Predicting the amino acid replacement probability), to identify nsSNPs having either deleterious or neutral effects on the resulting protein function. Two independent datasets, HumVar and NewHumVar, taken from the PhD-SNP server, were applied to train the model and test the robustness of Parepro. Using a 20-fold cross validation test on the HumVar dataset, Parepro achieved a Matthews correlation coefficient (MCC) of 50% and an overall accuracy (Q2) of 76%, both of which were higher than those predicted by the methods, such as PolyPhen, SIFT, and HydridMeth. Further analysis on an additional dataset (NewHumVar) using Parepro yielded similar results.</p> <p>Conclusion</p> <p>The performance of Parepro indicates that it is a powerful tool for predicting the effect of nsSNPs on protein function and would be useful for large-scale analysis of genomic nsSNP data.</p

Testing optimally weighted combination of variants for hypertension

© 2014 Zhao et al.; licensee BioMed Central Ltd. Testing rare variants directly is possible with next-generation sequencing technology. In this article, we propose a sliding-window-based optimal-weighted approach to test for the effects of both rare and common variants across the whole genome. We measured the genetic association between a disease and a combination of variants of a single-nucleotide polymorphism window using the newly developed tests TOW and VW-TOW and performed a sliding-window technique to detect disease-susceptible windows. By applying the new approach to unrelated individuals of Genetic Analysis Workshop 18 on replicate 1 chromosome 3, we detected 3 highly susceptible windows across chromosome 3 for diastolic blood pressure and identified 10 of 48,176 windows as the most promising for both diastolic and systolic blood pressure. Seven of 9 top variants influencing diastolic blood pressure and 8 of 9 top variants influencing systolic blood pressure were found in or close to our top 10 windows

Prognostic implications of left ventricular ejection fraction trajectory changes in heart failure

AimsThe latest guidelines recommended to assess the trajectory of left ventricular ejection fraction (LVEF) in patients with heart failure (HF). However, there is limited data on the trajectory of LVEF in real-world settings. In this study, we investigated the frequency and prognostic implications of changes in LVEF trajectory.MethodsPatients were divided into intensified LVEF, static LVEF, and worsening LVEF groups based on the transitions of HF types from baseline to follow-up. The intensified and worsening LVEF groups were further subdivided into mild (≤10% absolute changes of LVEF) and significant (&gt;10% absolute changes of LVEF) increase or decrease groups according to the magnitude of change. The incidences and associations of changes in LVEF with patient outcomes were analyzed.ResultsAmong the 2,429 patients in the study cohort, 38.3% of HF with reduced ejection fraction (HFrEF) and 37.6% of HF with mildly reduced ejection fraction (HFmrEF) showed an improvement in their LVEF. In contrast, a decline in LVEF was observed in 19.3% of HF patients with preserved ejection fraction (HFpEF) and 34.9% of those with HFmrEF. Cox regression analysis showed that the intensified LVEF group was associated with a lower risk of composite endpoints, while the worsening LVEF group yielded opposite findings. Subgroup analysis revealed that compared to those with mild changes in LVEF, baseline HFrEF patients with significant increase showed a lower risk of composite outcome, while baseline HFpEF patients were the opposite.ConclusionsThe trajectories of LVEF changes are strongly correlated with outcomes in patients with HF who had prior history of HF admission. The most significant prognostic implications observed in patients with significant LVEF changes. Trajectory LVEF and type of HF changes are useful tools recommended for prognostication