The effect of multimorbidity patterns on physical and cognitive function in diabetes patients: a longitudinal cohort of middle-aged and older adults in China

BackgroundThe prevalence of diabetes has increased rapidly, and comorbid chronic conditions are common among diabetes patients. However, little is known about the pattern of multimorbidity in diabetes patients and the effect on physical and cognitive function. This study aimed to assess the disease clusters and patterns of multimorbidity in diabetes patients using a novel latent class analysis (LCA) approach in middle-aged and older adults and explore the association between different clusters of multimorbidity in diabetes and the effect on physical and cognitive function.MethodsThis national observational study included 1,985 diabetes patients from the four waves of the China Health and Retirement Longitudinal Study (CHARLS) in 2011 to 2018. Thirteen chronic diseases were used in latent class analysis to identify the patterns of multimorbidity in diabetes, which span the cardiovascular, physical, psychological, and metabolic systems. Cognitive function is assessed via a structured questionnaire in three domains: memory, executive function, and orientation. We combined activities of daily living (ADL) with instrumental activities of daily living (IADL) to measure physical function. Linear mixed models and negative binomial regression models were used to analyze the association between patterns of multimorbidity in diabetes and the effect on cognitive function and disability, respectively.ResultsA sample of 1,985 diabetic patients was identified, of which 1,889 (95.2%) had multimorbidity; their average age was 60.6 years (standard deviation (SD) = 9.5), and 53.1% were women. Three clusters were identified: “cardio-metabolic” (n = 972, 51.5%), “mental-dyslipidemia-arthritis” (n = 584, 30.9%), and “multisystem morbidity” (n = 333, 17.6%). Compared with diabetes alone, the “multisystem morbidity” class had an increased association with global cognitive decline. All patterns of multimorbidity were associated with an increased risk of memory decline and disability; however, the “multisystem morbidity” group also had the strongest association and presented a higher ADL-IADL disability (ratio = 4.22, 95% CI = 2.52, 7.08) and decline in memory Z scores (β = −0.322, 95% CI = −0.550, −0.095, p = 0.0058).ConclusionSignificant longitudinal associations between different patterns of multimorbidity in diabetes patients and memory decline and disability were observed in this study. Future studies are needed to understand the underlying mechanisms and common risk factors for multimorbidity in diabetes patients and to propose treatments that are more effective

Relaxed Adaptive Lasso and Its Asymptotic Results

This article introduces a novel two-stage variable selection method to solve the common asymmetry problem between the response variable and its influencing factors. In practical applications, we cannot correctly extract important factors from a large amount of complex and redundant data. However, the proposed method based on the relaxed lasso and the adaptive lasso, namely, the relaxed adaptive lasso, can achieve information symmetry because the variables it selects contain all the important information about the response variables. The goal of this paper is to preserve the relaxed lasso’s superior variable selection speed while imposing varying penalties on different coefficients. Additionally, the proposed method enjoys favorable asymptotic properties, that is, consistency with a fast rate of convergence with Opn−1. The simulation demonstrates that the proper variable recovery, i.e., the number of significant variables selected, and prediction accuracy of the relaxed adaptive lasso in a limited sample is superior to the regular lasso, relaxed lasso and adaptive lasso estimators

Relaxed Adaptive Lasso and Its Asymptotic Results

This article introduces a novel two-stage variable selection method to solve the common asymmetry problem between the response variable and its influencing factors. In practical applications, we cannot correctly extract important factors from a large amount of complex and redundant data. However, the proposed method based on the relaxed lasso and the adaptive lasso, namely, the relaxed adaptive lasso, can achieve information symmetry because the variables it selects contain all the important information about the response variables. The goal of this paper is to preserve the relaxed lasso’s superior variable selection speed while imposing varying penalties on different coefficients. Additionally, the proposed method enjoys favorable asymptotic properties, that is, consistency with a fast rate of convergence with Opn−1. The simulation demonstrates that the proper variable recovery, i.e., the number of significant variables selected, and prediction accuracy of the relaxed adaptive lasso in a limited sample is superior to the regular lasso, relaxed lasso and adaptive lasso estimators

Evaluating the Safety Control Scheme of Railway Centralized Traffic Control (CTC) System with Coloured Petri Nets

The Centralized Traffic Control (CTC) system plays an important role in ensuring safe and efficient rail transportation operations. It is mainly responsible for the implementation and adjustment of the train operation schedule through the automatic control of the station signalling equipment. The major task of the CTC system is to achieve a high rail transportation operation efficiency under the precondition of safety. For this purpose, it is necessary to select appropriate safety control schemes for the CTC system. In this paper, a formal approach is proposed to quantitatively evaluate the operation efficiencies of the CTC system with respect to different safety control schemes. The proposed approach adopts stochastic coloured Petri nets as the means of description for the system model, and evaluates the operation efficiency of the CTC system based on the data collected during the simulation of the system model. To exemplify the proposed approach, the safety control scheme of prohibiting a passenger train from passing a freight train through adjacent rail tracks between two adjacent stations is studied. The results of the case study show the feasibility of the proposed approach

Fuzzy multi-objective linear programming applying to crop area planning

Crop area planning plays significant role in agricultural water management. During the planning, because of ambiguous or uncertain information caused by the vagueness of decision makers' subjective preference or the uncertainty of objective information, conventional multi-objective linear programming (MOLP) model is not suitable for such decision-making in such fuzzy environment. In this study, we proposed the fuzzy multi-objective linear programming (FMOLP) model with triangular fuzzy numbers and transformed the FMOLP model and its corresponding fuzzy goal programming (FGP) problem to crisp ones which can be solved by the conventional programming methods. The FMOLP model was applied to crop area planning of Liang Zhou region, Gansu province of northwest China, and then the optimal cropping patterns under different water-saving levels and satisfaction grades for water resources availability of the decision makers (DM) were obtained. Compared to the MOLP model, the FMOLP model itself expresses the fuzzy information effectively, and its solutions can represent the DMs satisfactory degree of the subjective preference and propose alternative solutions for better decision support when applied in the crop area planning.Fuzzy multi-objective linear programming Fuzzy numbers Fuzzy goal programming Crop area planning

Robust Variable Selection Based on Relaxed Lad Lasso

Least absolute deviation is proposed as a robust estimator to solve the problem when the error has an asymmetric heavy-tailed distribution or outliers. In order to be insensitive to the above situation and select the truly important variables from a large number of predictors in the linear regression, this paper introduces a two-stage variable selection method named relaxed lad lasso, which enables the model to obtain robust sparse solutions in the presence of outliers or heavy-tailed errors by combining least absolute deviation with relaxed lasso. Compared with lasso, this method is not only immune to the rapid growth of noise variables but also maintains a better convergence rate, which is Opn−1/2. In addition, we prove that the relaxed lad lasso estimator has the property of consistency at large samples; that is, the model selects the number of important variables with a high probability of convergence to one. Through the simulation and empirical results, we further verify the outstanding performance of relaxed lad lasso in terms of prediction accuracy and the correct selection of informative variables under the heavy-tailed distribution

Robust Variable Selection Based on Relaxed Lad Lasso

Least absolute deviation is proposed as a robust estimator to solve the problem when the error has an asymmetric heavy-tailed distribution or outliers. In order to be insensitive to the above situation and select the truly important variables from a large number of predictors in the linear regression, this paper introduces a two-stage variable selection method named relaxed lad lasso, which enables the model to obtain robust sparse solutions in the presence of outliers or heavy-tailed errors by combining least absolute deviation with relaxed lasso. Compared with lasso, this method is not only immune to the rapid growth of noise variables but also maintains a better convergence rate, which is Opn−1/2. In addition, we prove that the relaxed lad lasso estimator has the property of consistency at large samples; that is, the model selects the number of important variables with a high probability of convergence to one. Through the simulation and empirical results, we further verify the outstanding performance of relaxed lad lasso in terms of prediction accuracy and the correct selection of informative variables under the heavy-tailed distribution

Molecular Effects of Irradiation (Cobalt-60) on the Control of Panonychus citri (Acari: Tetranychidae)

The effective dose of irradiation to control pest mites in quarantine has been studied extensively, but the molecular mechanisms underlying the effects of the irradiation on mites are largely unknown. In this study, exposure to 400 Gy of γ rays had significant (p < 0.05) effects on the adult survival, fecundity and egg viability of Panonychus citri. The irradiation caused the degradation of the DNA of P. citri adults and damaged the plasma membrane system of the egg, which led to condensed nucleoli and gathered yolk. Additionally, the transcriptomes and gene expression profiles between irradiated and non-irradiated mites were compared, and three digital gene expression libraries were assembled and analyzed. The differentially expressed genes were putatively involved in apoptosis, cell death and the cell cycle. Finally, the expression profiles of some related genes were studied using quantitative real-time PCR. Our study provides valuable information on the changes in the transcriptome of irradiated P. citri, which will facilitate a better understanding of the molecular mechanisms that cause the sterility induced by irradiation