An Urban Neo-Poverty Population-Based Quality of Life and Related Social Characteristics Investigation from Northeast China

OBJECTIVE: To investigate quality of life (QOL) and related characteristics among an urban neo-poverty population in northeast China, and to compare this population with a traditional poverty cohort. DESIGN: The research was a cross-sectional survey executed from June 2005 to October 2007, with a sample of 2940 individuals ages 36 to 55 in three different industrial cities of northeast China. Data were collected on QOL status and sociodemographic characteristics. QOL was assessed using the 36-item Short Form Health Survey (Chinese version). Multiple regression analysis was employed to analyze association between sociodemographic variables and QOL. RESULTS: The scores for QOL in the neo-poverty group were higher than those in the traditional poverty group, but lower than those in the general population. When the neo-poverty population was divided into two subgroups by age, 36-45 years and 46-55 years, the differences in QOL scores were not significant. However, there were significant differences in several dimensions between two subgroups according to unemployment time (<5 years and >5 years). Additionally, stepwise regression analysis indicated that disease burden, including disease and medical expenditures, was a common risk factor for declining QOL in the neo-poverty group. CONCLUSIONS: Despite some limitations, this study provides initial evidence that the QOL of the urban neo-poverty population lies between that of the general population and traditional poverty. QOL of the neo-poverty group approached QOL of the traditional poverty group with increased unemployment years. In addition to decreased income, disease burden is the most important factor influencing QOL status in urban neo-poverty

A Lightweight Deep Learning Model for Profiled SCA Based on Random Convolution Kernels

In deep learning-based side-channel analysis (DL-SCA), there may be a proliferation of model parameters as the number of trace power points increases, especially in the case of raw power traces. Determining how to design a lightweight deep learning model that can handle a trace with more power points and has fewer parameters and lower time costs for profiled SCAs appears to be a challenge. In this article, a DL-SCA model is proposed by introducing a non-trained DL technique called random convolutional kernels, which allows us to extract the features of leakage like using a transformer model. The model is then processed by a classifier with an attention mechanism, which finally outputs the probability vector for the candidate keys. Moreover, we analyze the performance and complexity of the random kernels and discuss how they work in theory. On several public AES datasets, the experimental results show that the number of required profiling traces and trainable parameters reduce, respectively, by over 70% and 94% compared with state-of-the-art works, while ensuring that the number of power traces required to recover the real key is acceptable. Importantly, differing from previous SCA models, our architecture eliminates the dependency between the feature length of power traces and the number of trainable parameters, which allows for the architecture to be applied to the case of raw power traces

Associations between serum vitamins and serum lipids in healthy Northeast China adults

Abstract Objective In previous studies, serum vitamins were shown associated with lipid levels. However, evidence regarding the associations between various serum vitamins and serum lipids is limited. Therefore, the associations between serum vitamins and serum lipids were investigated in this cross-sectional study.Methods The study population included 131 adults (42 males and 89 females) ≥ 18 years of age who lived more than three years in Shenyang, Liaoning province, China. Serum lipids included total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Analysis of covariance was used to explore the associations between serum vitamins and serum lipids.Results After multiple adjustments, vitamin A and vitamin C concentrations were positively associated with LDL-C level ( P for trend &lt; 0.05). Vitamin A, vitamin B5, and vitamin C concentrations were positively associated with TG level ( P for trend &lt; 0.05). However, vitamin B1 concentration was negatively associated with TG level ( P for trend = 0.04). Vitamin E concentration was positively associated with HDL-C level ( P for trend = 0.02). No association was observed between vitamin concentrations and TC level.Conclusion The results in the present study indicate that serum vitamin concentrations are associated with serum lipid levels. Vitamin B1 and vitamin E concentrations were associated with a better status of lipid profiles. However, vitamin A, vitamin C, and vitamin B5 concentrations were associated with a worse status of lipid profiles.</jats:p

Interaction between CYP1A1/CYP17A1 polymorphisms and parental risk factors in the risk of hypospadias in a Chinese population

AbstractHypospadias (HS) is a common congenital malformation of the genitourinary tract in males and its etiology is viewed as multifactorial, and studies about gene-environment interaction in the etiology of HS are rare. A total of 152 cases and 151 controls were selected in the present study. Information before and during pregnancy from questionnaires finished by mothers of subjects were extracted, and the relating data were analyzed to determine the risk factors of HS. Meanwhile, maternal genomic DNA was genotyped for the single nucleotide polymorphisms (SNPs) of CYP1A1 rs1048943 and CYP17A1 rs4919686. Results of multivariable logistic regression analyses showed that several factors were associated with hypospadias risk. Analysis of the distributions of SNPs in CYP1A1 and CYP17A1 genes showed that the mutant genotype CC (OR = 4.87) of CYP1A1 rs1048943, and mutant genotype CC (OR = 5.82), recessive genotype AC + CC (OR = 2.17) and allele C (OR = 1.77) of CYP17A1 rs4919686 significantly increased the risk of HS. In addition, the additive gene-environment interactions were also found in several models. Several maternal risk factors that are associated with HS risk can interact with CYP1A1/CYP17A1 polymorphisms, which lead to infants vulnerable to occurrence of HS in Chinese populations.</jats:p

Descriptive statistics of general conditions.

<p>Descriptive statistics of general conditions.</p

Standardized regression coefficients from multivariate stepwise regression of factors influencing QOL in three groups.

<p>NOTE: *<i>P</i><0.05 **<i>P</i><0.01 ***<i>P</i><0.0001.</p

Comparison of people with ≤5 and>5 years unemployment [Mean (SD)].

<p>NOTE: *<i>P</i><0.05 **<i>P</i><0.01</p