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Major depressive disorder (MDD), the most prevalent mental disorder is a global public health issue.The aim of this study was to assess the association between low income and major depressive disorder (MDD) by age and sex.The National Health Insurance Research Database (NHIRD) of Taiwan was used to retrieve data. A total of 1,743,948 participants were eligible for the study. Low-income individuals were identified from 2001 and 2003 (specifically, Group Insurance Applicants, ie, category“51” or “52”) and followed from 2004 to 2010. MDD was identified using the ICD-9-CM 296.2 and 296.3 codes.Among non-low-income individuals, the MDD incidence rates increased with age in both males and females, that is, 0.35, 0.93, 0.97, 1.40 per 10,000 person-months for males and 0.41, 1.60, 1.89, 1.95 per 10,000 person-months for females aged 0 to 17, 18 to 44, 45 to 64, and ≥65 years, respectively. Low-income females (18–44 years) and males (45–64 years) had the highest incidence of MDD, which was 3.90 and 3.04, respectively, per 10,000 person-months. Among low and non-low-income individuals, the MDD incidence rates were higher in the females than males in all age groups. Males aged 45 to 64 and 0 to 17 years had highest hazard ratios (HR) of 2.789 (95% confidence interval [CI], 1.937–4.014) and 2.446 (95% CI, 1.603–3.732), respectively. The highest HRs for females were 2.663 (95% CI, 1.878–3.775) and 2.219 (CI, 1.821–2.705) in the 0 to 17 and 18- to 44-year age groups. Low income was not found to serve as a risk factor for the development of MDD in males and females aged ≥65 years.Among the non-low-income males and females, the incidence rates of MDD were found to increase with age. Low income was found to serve as a significant risk factor for MDD only in individuals under age 65.

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Bactrocera dorsails fat body protein 1 (Bdfbp1) cDNA was cloned (GenBank accession no. MT514270), and the complete 3,749-bp cDNA encoded a 1,152-amino acid protein. The phylogenetic relationship of dipteran fbp1s was analyzed. The sequence XP_028900815 from the insect genome project for Zeugodacus cucurbitae (LOC105219342) was proposed that two fbp1 genes were present in the sequence. The developmental transcriptional expression profiles were determined. In the larval stages, Bdfbp1 mRNA had significantly higher expression in the late third instar larvae compared with first, second, and early third instar larvae. In the pupal stages, the highest expression of Bdfbp1 mRNA was found in the newly pupated pupae and then decreased with age. In the fat body of female adults, Bdfbp1 was highly expressed in newly emerged samples and decreased rapidly over the following three days. In the fat body of male adults, Bdfbp1 was highly expressed in newly eclosed samples. RNAi treatment decreased the expression level of Bdfbp1 without statistical difference. However, RNAi treatment significantly decreased the rate of eclosion. These results suggest that Bdfbp1 may function as a storage protein and be associated with adult eclosion.

BackgroundCpG islands have been demonstrated to influence local chromatin structures and simplify the regulation of gene activity. However, the accurate and rapid determination of CpG islands for whole DNA sequences remains experimentally and computationally challenging.Methodology/Principal FindingsA novel procedure is proposed to detect CpG islands by combining clustering technology with the sliding-window method (PSO-based). Clustering technology is used to detect the locations of all possible CpG islands and process the data, thus effectively obviating the need for the extensive and unnecessary processing of DNA fragments, and thus improving the efficiency of sliding-window based particle swarm optimization (PSO) search. This proposed approach, named ClusterPSO, provides versatile and highly-sensitive detection of CpG islands in the human genome. In addition, the detection efficiency of ClusterPSO is compared with eight CpG island detection methods in the human genome. Comparison of the detection efficiency for the CpG islands in human genome, including sensitivity, specificity, accuracy, performance coefficient (PC), and correlation coefficient (CC), ClusterPSO revealed superior detection ability among all of the test methods. Moreover, the combination of clustering technology and PSO method can successfully overcome their respective drawbacks while maintaining their advantages. Thus, clustering technology could be hybridized with the optimization algorithm method to optimize CpG island detection.Conclusion/SignificanceThe prediction accuracy of ClusterPSO was quite high, indicating the combination of CpGcluster and PSO has several advantages over CpGcluster and PSO alone. In addition, ClusterPSO significantly reduced implementation time.