One-Carbon Metabolism Nutrients, Genetic Variation, and Diabetes Mellitus.

Diabetes mellitus (DM) affects about 9.3% of the population globally. Hyperhomocysteinemia (HHcy) has been implicated in the pathogenesis of DM, owing to its promotion of oxidative stress, ß-cell dysfunction, and insulin resistance. HHcy can result from low status of one-carbon metabolism (OCM) nutrients (e.g., folate, choline, betaine, vitamin B6, B12), which work together to degrade homocysteine by methylation. The etiology of HHcy may also involve genetic variation encoding key enzymes in OCM. This review aimed to provide an overview of the existing literature assessing the link between OCM nutrients status, related genetic factors, and incident DM. We also discussed possible mechanisms underlying the role of OCM in DM development and provided recommendations for future research and practice. Even though the available evidence remains inconsistent, some studies support the potential beneficial effects of intakes or blood levels of OCM nutrients on DM development. Moreover, certain variants in OCM-related genes may influence metabolic handling of methyl-donors and presumably incidental DM. Future studies are warranted to establish the causal inference between OCM and DM and examine the interaction of OCM nutrients and genetic factors with DM development, which will inform the personalized recommendations for OCM nutrients intakes on DM prevention.

A Core-Shell Nanoreinforced Ion-Conductive Implantable Hydrogel Bioelectronic Patch with High Sensitivity and Bioactivity for Real-Time Synchronous Heart Monitoring and Repairing.

To achieve synchronous repair and real-time monitoring the infarcted myocardium based on an integrated ion-conductive hydrogel patch is challenging yet intriguing. Herein, a novel synthetic strategy is reported based on core-shell-structured curcumin-nanocomposite-reinforced ion-conductive hydrogel for synchronous heart electrophysiological signal monitoring and infarcted heart repair. The nanoreinforcement and multisite cross-linking of bioactive curcumin nanoparticles enable well elasticity with negligible hysteresis, implantability, ultrahigh mechanoelectrical sensitivity (37 ms), and reliable sensing capacity (over 3000 cycles) for the nanoreinforced hydrogel. Results of in vitro and in vivo experiments demonstrate that such solely physical microenvironment of electrophysiological and biomechanical characteristics combining with the role of bioactive curcumin exert the synchronous benefit of regulating inflammatory microenvironment, promoting angiogenesis, and reducing myocardial fibrosis for effective myocardial infarction (MI) repair. Especially, the hydrogel sensors offer the access for achieving accurate acquisition of cardiac signals, thus monitoring the whole MI healing process. This novel bioactive and electrophysiological-sensing ion-conductive hydrogel cardiac patch highlights a versatile strategy promising for synchronous integration of in vivo real-time monitoring the MI status and excellent MI repair performance.

Asymptotic Properties of Neural Network Sieve Estimators.

Neural networks have become one of the most popularly used methods in machine learning and artificial intelligence. Due to the universal approximation theorem (Hornik et al., 1989), a neural network with one hidden layer can approximate any continuous function on compact support as long as the number of hidden units is sufficiently large. Statistically, a neural network can be classified into a nonlinear regression framework. However, if we consider it parametrically, due to the unidentifiability of the parameters, it is difficult to derive its asymptotic properties. Instead, we consider the estimation problem in a nonparametric regression framework and use the results from sieve estimation to establish the consistency, the rates of convergence and the asymptotic normality of the neural network estimators. We also illustrate the validity of the theories via simulations.

Fast heritability estimation based on MINQUE and batch training.

Heritability, the proportion of phenotypic variance explained by genome-wide single nucleotide polymorphisms (SNPs) in unrelated individuals, is an important measure of the genetic contribution to human diseases and plays a critical role in studying the genetic architecture of human diseases. Linear mixed model (LMM) has been widely used for SNP heritability estimation, where variance component parameters are commonly estimated by using a restricted maximum likelihood (REML) method. REML is an iterative optimization algorithm, which is computationally intensive when applied to large-scale datasets (e.g. UK Biobank). To facilitate the heritability analysis of large-scale genetic datasets, we develop a fast approach, minimum norm quadratic unbiased estimator (MINQUE) with batch training, to estimate variance components from LMM (LMM.MNQ.BCH). In LMM.MNQ.BCH, the parameters are estimated by MINQUE, which has a closed-form solution for fast computation and has no convergence issue. Batch training has also been adopted in LMM.MNQ.BCH to accelerate the computation for large-scale genetic datasets. Through simulations and real data analysis, we demonstrate that LMM.MNQ.BCH is much faster than two existing approaches, GCTA and BOLT-REML.

A conditional autoregressive model for genetic association analysis accounting for genetic heterogeneity.

Converging evidence from genetic studies and population genetics theory suggest that complex diseases are characterized by remarkable genetic heterogeneity, and individual rare mutations with different effects could collectively play an important role in human diseases. Many existing statistical models for association analysis assume homogeneous effects of genetic variants across all individuals, and could be subject to power loss in the presence of genetic heterogeneity. To consider possible heterogeneous genetic effects among individuals, we propose a conditional autoregressive model. In the proposed method, the genetic effect is considered as a random effect and a score test is developed to test the variance component of genetic random effect. Through simulations, we compare the type I error and power performance of the proposed method with those of the generalized genetic random field and the sequence kernel association test methods under different disease scenarios. We find that our method outperforms the other two methods when (i) the rare variants have the major contribution to the disease, or (ii) the genetic effects vary in different individuals or subgroups of individuals. Finally, we illustrate the new method by applying it to the whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative.

A goodness-of-fit test based on neural network sieve estimators.

Neural networks have become increasingly popular in the field of machine learning and have been successfully used in many applied fields (e.g., imaging recognition). With more and more research has been conducted on neural networks, we have a better understanding of the statistical proprieties of neural networks. While many studies focus on bounding the prediction error of neural network estimators, limited research has been done on the statistical inference of neural networks. From a statistical point of view, it is of great interest to investigate the statistical inference of neural networks as it could facilitate hypothesis testing in many fields (e.g., genetics, epidemiology, and medical science). In this paper, we propose a goodness-of-fit test statistic based on neural network sieve estimators. The test statistic follows an asymptotic distribution, which makes it easy to use in practice. We have also verified the theoretical asymptotic results via simulation studies and a real data application.

Joint analysis of genetic and epigenetic data using a conditional autoregressive model.

BACKGROUND: Rapidly evolving high-throughput technology has made it cost-effective to collect multilevel omic data in clinical and biological studies. Different types of omic data collected from these studies provide both shared and complementary information, and can be integrated into association analysis to enhance the power of identifying novel disease-associated biomarkers. To model the joint effect of genetic markers and DNA methylation on the phenotype of interest, we propose a joint conditional autoregressive (JCAR) model. A linear score test is used for hypothesis testing and the corresponding p value can be obtained using the Davies method. RESULTS: The JCAR model was applied to the GAW20 data from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study. In our application of the JCAR model, we consider a baseline model and a full model. In the baseline model, we consider 3 different scenarios: a model with only genetic information, a model with only DNA methylation information at visit 2, and a model using both genetic and DNA methylation information at visit 2. For the full model, we consider both genetic and DNA methylation information at visit 2 and visit 4. The top 10 significant genes are reported for each model. Based on the results, we found that the gene MYO3B was significant as long as the methylation information was considered in the analysis. CONCLUSIONS: JCAR is a useful tool for joint association analysis of genetic and epigenetic data. It is easy to implement and is computationally efficient. It can also be extended to analyze other types of omic data.

Genetic risk prediction using a spatial autoregressive model with adaptive lasso.

With rapidly evolving high-throughput technologies, studies are being initiated to accelerate the process toward precision medicine. The collection of the vast amounts of sequencing data provides us with great opportunities to systematically study the role of a deep catalog of sequencing variants in risk prediction. Nevertheless, the massive amount of noise signals and low frequencies of rare variants in sequencing data pose great analytical challenges on risk prediction modeling. Motivated by the development in spatial statistics, we propose a spatial autoregressive model with adaptive lasso (SARAL) for risk prediction modeling using high-dimensional sequencing data. The SARAL is a set-based approach, and thus, it reduces the data dimension and accumulates genetic effects within a single-nucleotide variant (SNV) set. Moreover, it allows different SNV sets having various magnitudes and directions of effect sizes, which reflects the nature of complex diseases. With the adaptive lasso implemented, SARAL can shrink the effects of noise SNV sets to be zero and, thus, further improve prediction accuracy. Through simulation studies, we demonstrate that, overall, SARAL is comparable to, if not better than, the genomic best linear unbiased prediction method. The method is further illustrated by an application to the sequencing data from the Alzheimer's Disease Neuroimaging Initiative.

Direct Care Workforce Training: Internet Accessibility and Acceptance.

Evidence exists that web-based learning for health care professionals can improve topic-specific knowledge, increase access to training, and lower training costs. However, limited information exists on the value of online education for improving hands-on skills as part of personal care aide (PCA) training. New PCA training programs are emerging that are fully online or hybrid models that blend online with in-person instruction. Such programs require access to a computer and high-speed internet, which could prove difficult for low-income PCAs who are less likely to own a computer. The current study evaluated a PCA training demonstration that examined issues of internet access, use, and acceptability for PCA training. Results show most trainees prefer a hybrid online/in-person model, but there are gaps in access and acceptability, particularly related to reading ability. These findings have implications for health care providers who deliver training programs aimed at developing a qualified PCA workforce capable of providing competent care to older adults. [Journal of Gerontological Nursing, 43(6), 46-52.].

Do Canes or Walkers Make Any Difference? NonUse and Fall Injuries.

Purpose of the Study: Examine patterns of cane and walker use as related to falls and fall injuries. Hypotheses: Among people who fall at home, most do not have an assistive device with them when they fall. Nonusers who fall sustain more severe injuries. Design and Methods: This was a cross-sectional study using a self-administered written survey completed by 262 people aged 60 and older who were community dwelling, cognitively intact, and current cane/walker users with a history of falls. They were recruited through clinical practice sites, churches, and senior housing in central Michigan. Outcomes of interest included patterns of device use, reasons for nonuse, device use at time of fall, and fall-related injuries. Results: Seventy-five percent of respondents who fell were not using their device at the time of fall despite stating that canes help prevent falls. Reasons for nonuse included believing it was not needed, forgetfulness, the device made them feel old, and inaccessibility. Perceived risk was not high enough to engage in self-protective behavior. However, nonuse led to a significantly higher proportion of falls resulting in surgery than among device users. Among respondents requiring surgery, 100% were nonusers. Most respondents never received a home safety evaluation (68%) and only 50% received training on proper device use. Implications: Providers must place increased emphasis on the importance of cane/walker use for injury prevention through patient education to promote personal relevance, proper fitting, and training. New strategies are needed to improve device acceptability and accessibility.

Chaotic operation and chaos control of travelling wave ultrasonic motor.

The travelling wave ultrasonic motor, which is a nonlinear dynamic system, has complex chaotic phenomenon with some certain choices of system parameters and external inputs, and its chaotic characteristics have not been studied until now. In this paper, the preliminary study of the chaos phenomenon in ultrasonic motor driving system has been done. The experiment of speed closed-loop control is designed to obtain several groups of time sampling data sequence of the amplitude of driving voltage, and phase-space reconstruction is used to analyze the chaos characteristics of these time sequences. The largest Lyapunov index is calculated and the result is positive, which shows that the travelling wave ultrasonic motor has chaotic characteristics in a certain working condition Then, the nonlinear characteristics of travelling wave ultrasonic motor are analyzed which includes Lyapunov exponent map, the bifurcation diagram and the locus of voltage relative to speed based on the nonlinear chaos model of a travelling wave ultrasonic motor. After that, two kinds of adaptive delay feedback controllers are designed in this paper to control and suppress chaos in USM speed control system. Simulation results show that the method can control unstable periodic orbits, suppress chaos in USM control system. Proportion-delayed feedback controller was designed following and arithmetic of fuzzy logic was used to adaptively adjust the delay time online. Simulation results show that this method could fast and effectively change the chaos movement into periodic or fixed-point movement and make the system enter into stable state from chaos state. Finally the chaos behavior was controlled.