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Objective: Type 2 diabetes mellitus (T2DM) is associated with a high prevalence of depression, and depression is influenced by personality traits; however, these psychological factors have not been well studied in individuals with T2DM. The association between the use of a 5-factor model of personality and depressive symptoms was examined in individuals with T2DM. Methods: The subjects were 435 T2DM patients (mean age 63.1±12.6 years). Depressive status and personality traits were assessed using the Center for Epidemiologic Studies Depression Scale and the Ten-Item Personality Inventory, Japanese version, respectively. Lifestyle factors and glycated hemoglobin A1c levels in the patients were also included in the analyses. Results: Among the 435 subjects with T2DM, 130 (29.9%) exhibited possible depression, and 68 (15.6%) exhibited probable depression. After adjustment for confounders, Extraversion, Agreeableness and Neuroticism were found to be significantly associated with the presence of depression. No relationships were found between depression and HbA1c. Conclusion: These findings indicate that Extraversion and Agreeableness are protective factors, and Neuroticism is a risk factor for depression in T2DM patients. Psychological therapy focusing on personality may reduce depressive symptoms. Additional studies are needed to examine the relationships between psychological factors and depressive symptoms using a longitudinal study design.

Aims/Introduction Insomnia is associated with type 2 diabetes mellitus, and results in a low quality of life. There are several known relationships between insomnia and personality. Thus, we clarified the association between some personality traits and insomnia among Japanese type 2 diabetes mellitus patients. Materials and Methods The participants were 504 type 2 diabetes mellitus patients (mean age 63.9 ± 12.5 years). Sleep disturbance and personality traits were evaluated using the Pittsburgh Sleep Quality Index‐Japanese version and the Ten‐Item Personality Inventory Japanese version, respectively. Lifestyle factors, glycated hemoglobin levels and depressive status of the patients were also included in the analyses. Results Among the 504 participants with type 2 diabetes mellitus, 154 (30.6%) showed probable insomnia. After adjustment for confounders, being female, living alone, high body mass index and “high neuroticism” were found to be significantly correlated with current insomnia. No other relationships between insomnia and glycated hemoglobin or lifestyle factors, such as smoking, drinking alcohol or exercise frequency, were found. Conclusions The prevalence of insomnia in individuals with type 2 diabetes mellitus was high, and the risk factors included some personality factors. Future prospective studies are required to confirm the therapeutic effects of behavioral interventions for insomnia in patients with type 2 diabetes mellitus.

Kazutaka Yoshida,1 Hideyuki Otaka,2 Hiroshi Murakami,2 Hirofumi Nakayama,1 Masaya Murabayashi,2 Satoru Mizushiri,2 Koki Matsumura,2 Jutaro Tanabe,2 Yuki Matsuhashi,2 Miyuki Yanagimachi,2 Norio Sugawara,3 Kazuhiko Nakamura,1 Makoto Daimon,2 Norio Yasui-Furukori1 1Department of Neuropsychiatry, Hirosaki University Graduate School of Medicine, Hirosaki, Japan; 2Department of Endocrinology and Metabolism, Hirosaki University Graduate School of Medicine, Hirosaki, Japan; 3Department of Clinical Epidemiology, Translational Medical Center, National Center of Neurology and Psychiatry, Tokyo, Japan Purpose: Insomnia, which is associated with type 2 diabetes mellitus (DM), results in a low quality of life, and several relationships exist between insomnia and coping style. Thus, we clarified the association between some coping styles and insomnia among Japanese type 2 DM patients. Subjects and methods: The subjects included 503 type 2 DM patients (mean age 63.9±12.5years). Sleep disturbance and personality traits were evaluated using the Japanese version of the Pittsburgh Sleep Quality Index and the Brief Scale for Coping Profile, respectively. Lifestyle factors, glycated hemoglobin A1c (HbA1c) levels, and the depression statuses of the patients were also included in the analyses. Results: Among the 503 subjects with type 2 DM, 141 (28.0%) subjects exhibited probable insomnia. After adjusting for confounders, being female, living alone, and using “avoidance and suppression” were significantly correlated with current insomnia. No other relationships were found between insomnia and HbA1c or lifestyle factors, such as smoking, drinking alcohol, and exercise frequency. Conclusion: The prevalence of insomnia in individuals with type 2 DM was high, and the protective factors included some emotion-focused coping styles. Future prospective studies are required to confirm the therapeutic effects of behavioral interventions on insomnia in patients with type 2 DM. Keywords: cross-sectional studies, coping style, Japanese, insomnia, type 2 DM

Objective Glycemic control varies based on lifestyle factors and stress coping mechanisms, which are influenced by personality. The psychological factors associated with glycemic control have not yet been established in patients with type 2 diabetes mellitus (T2DM). The relationship between a 5-factor model of personality and glycemic control was evaluated in individuals with T2DM. Methods The subjects were 503 Japanese outpatients with T2DM. Glycated hemoglobin A1c (HbA1c) levels, depressive status, insomnia and personality traits were assessed. Lifestyle factors of the patients, such as habitual alcohol consumption and smoking, were also included in the analyses. Results Because the influence of insulin therapy on HbA1c is so strong, we stratified the patients according to insulin use. Simple regression analysis showed a significant correlation between HbA1c and neuroticism in patients who did not use insulin. After adjustment for confounders, multiple regression analyses revealed that none of the personality factors, including neuroticism, were found to be associated with HbA1c. Conclusion These findings suggest that personality traits do not have a large impact on glycemic control. Further studies are required to confirm the relationships between psychological factors and glycemic control using a longitudinal study design.

Aims/Objectives Glycemic control varies according to stress level and the efficacy of control measures, affecting the outcomes of diabetes. Although detailed coping styles have not been well studied in patients with type 2 diabetes mellitus, problem‐focused coping strategies are believed to be related to better control of blood glucose. Associations between coping profiles/dimensions and blood glucose control were examined in individuals with type 2 diabetes. Materials and Methods The participants included 503 Japanese patients (mean age 63.9 ± 12.6 years) with type 2 diabetes. The average glycated hemoglobin A1c (HbA1c) levels were calculated from HbA1c levels measured more than four times within the 12 months before the assessment. Coping profiles were assessed using the Brief Scale for Coping Profile. Lifestyle factors were also included in the analyses. Results Factors other than age were not associated with HbA1c levels in patients who used insulin. Conversely, habitual alcohol consumption, single status, the adaptive emotion‐focused coping dimension, and changing mood and changing one’s point of view profiles were associated with HbA1c levels. Conclusions These findings suggest that adaptive emotion‐focused coping supports glycemic control in type 2 diabetes patients who do not use insulin. Additional studies using a longitudinal design are required to further examine the relationships between psychological factors and glycemic control., This study investigated coping styles associated with glucose control in people with type 2 diabetes. An adaptive emotion‐focused coping dimension supports glycemic control in people with type 2 diabetes who do not use insulin.

Objective Type 2 diabetes mellitus (T2DM) is associated with a high prevalence of depression, which is influenced by personality traits and coping style. However, these psychological factors have not been well studied in individuals with T2DM. The association between coping behaviors and the reported levels of depressive symptoms was examined in individuals with T2DM. Methods The subjects were 435 T2DM patients (mean age 63.1 ± 12.6 years). Depressive status, personality traits and coping behaviors were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D) and the Brief Scale for Coping Profile (BSCP). Lifestyle factors and glycated hemoglobin A1c (HbA1c) levels in the patients were also included in the analyses. Results Among the 435 subjects with T2DM, 130 (29.9%) exhibited possible depression, and 68 (15.6%) displayed probable depression. After adjustment for confounders, logistic and multiple regression analyses revealed that certain coping profile scores, such as Changing one’s point of view, Emotional expression involving others and Avoidance and suppression, were consistently and significantly associated with the presence and severity of depression. No relationship was found between depression and HbA1c. Conclusion These findings indicate that Maladaptive emotion-focused coping strategies, such as Emotional expression involving others and Avoidance and suppression, are protective factors and that Adaptive emotion-focused coping, such as Changing one’s point of view, is a risk factor for depression in T2DM patients. Psychological intervention focusing on the coping profile may reduce depressive symptoms. Additional studies are needed to examine the relationships between psychological factors and depressive symptoms using a longitudinal study design.

Aim: Sterol regulatory element-binding protein (SREBP)-1c is the dominant liver insulin-stimulated isoform and strongly correlates with diabetic dyslipidemia characterized by hyperinsulinemia [i.e., high-density lipoprotein cholesterol (HDL-C) levels and hypertriglyceridemia]. MicroRNA (miRNA) 33b is harbored in the intron of SREBP-1c and represses ATP-binding cassette, sub-family A, and member 1 (ABCA1) expression, essential for HDL formation. We measured plasma miRNA33b levels as possible biomarkers for diabetic dyslipidemia in patients with type 2 diabetes mellitus (T2DM) showing insulin resistance. Methods: The participants included 50 patients with T2DM (M/F 31/19) enrolled in an educational program for controlling blood glucose levels at Hirosaki University Hospital. HbA1c, fasting plasma glucose, insulin, and lipid levels were determined. Plasma miRNA33b, miRNA33a and miRNA148a were quantified using a TaqMan® MicroRNA Assay, and values were corrected with reference to miRNA16. Results: Mean BMI of participants were 28.2 ± 6.6 (kg/m2) and the Homeostasis Model Assessment of Insulin Resistance was 4.3 ± 2.7. Patients' laboratory findings indicated diabetic dyslipidemia with insulin resistance. Plasma miRNA33b/16 levels revealed a positive correlation with plasma insulin level (r = 0.326, P = 0.021), serum C-peptide (r = 0.280, P = 0.049), and triglyceride (r = 0.351, P = 0.012), but no association with HDL-C (r = −0.210, P = 0.143). The blood level of miRNA33a was approximately 1/150th of that of miRNA33b and was not correlated with the above parameters. Conclusion: We postulated that plasma miRNA33b may be useful as a new metabolic biomarker of dyslipidemia in patients with T2DM as well as metabolic syndrome via an insulin/SREBP-1c/miRNA33b/ABCA1 pathway.

SUMMARY This paper proposes a new optimizing system for stock portfolios that uses evolutionary computation techniques to derive a highly suitable combination and investment ratio of brands as well as an appropriate trading-strategy tree. Accurately predicting price trends in the stock market is a difficult task to achieve with the result that investors often suffer great losses. Because stock portfolios are thought to be a valid means of avoiding such risks in terms of financial engineering, they have the effect of reducing risk by diversifying investment into several different brands. Based on this, it was attempted to determine an optimal combination of brands that constitute a portfolio and to derive the investment ratio using a multiobjective genetic algorithm, and also to optimize a trading strategy tree using genetic programming. When a performance evaluation was carried out, the system was found to generally obtain the operative results by making it possible to obtain stable profits using a combination of low risk brands. The system was also able to realize low risk investments in all test periods.

There are a lot of constructional differences in an English-Japanese phrase template, and that often makes the act of translation difficult. Moreover, there exist various and tremendous phrase templates and sentence to be refered to. It is not easy to prepare the corpus that covers the all. Therefore, it is very significant to generate the translation pattern of the sentence pattern automatically from a viewpoint of the translation success rate and the capacity of the pattern dictionary. Then, for the purpose of realizing the automatic generation of the translation pattern, this paper proposed the new method for the generation of the translation pattern by using the genetic programming technique (GP). The technique tries to generate the translation pattern of various sentences which are not registered in the phrase template dictionary automatically by giving the genetic operation to the parsing tree of a basic pattern. The tree consists of the pair of the English-Japanese sentence generated as the first stage population. The analysis tree data base with 50,100,150,200 pairs was prepared as the first stage population. And this system was applied and executed for an English input of 1,555 sentences. As a result, the analysis tree increases from 200 to 517, and the accuracy rate of the translation pattern has improved from 42.57% to 70.10%. And, 86.71% of the generated translations was successfully done, whose meanings are enough acceptable and understandable. It seemed that this proposal technique became a clue to raise the translation success rate, and to find the possibility of the reduction of the analysis tree data base.

We previously proposed a new way of automatically generating membership functions that play an important role in improving the accuracy of fuzzy control by using genetic programming (GP). In this paper, we report 2 improvements for achieving better fuzzy robot intelligence. First, we made the mutation rate change dynamically based on a coupled chaotic system. Secondly, we introduced population partitioning using deme by parallel processing. The effectiveness of these improvements is demonstrated through computer simulation.

This paper describes a negotiation agent system based on the fuzzy decision making. The method of seeking appropriate membership functions and a reasonable agreement point was examined by means of the genetic programming technique with the coupled chaos system, which is an intelligent principle. The negotiation rule is based on the negotiation model expressed by the utility theory in the process of decision making. And the concession process was modified with the opponent’s movement and the persistence of each negotiator. In order to search for a membership function more efficiently, the dynamic state of symbiosis between individuals, which was caused by the coupled chaos system, was taken advantage of. Then the effectiveness of the technique was examined by applying it to a practical negotiation case which needs cooperative decision making. As a result, the following findings were obtained. This technique helps discover practicable membership functions in a vast search area, and achieve the solution search with high efficiency. This technique is also considered to be applied to the negotiation support easily.

Aims Cholesterol efflux with high-density lipoprotein (HDL) particles has an important role in the first step of reverse cholesterol transport (RCT). However, HDL function in type 2 diabetes has not been well investigated thoroughly. We measured cholesterol efflux in 36 patients with type 2 diabetes compared with 9 controls without diabetes. Methods The HDL fraction was separated with polyacrylamide gel and recovered using the protein recovery system. Concentration adjusted HDL fraction was used to determine HDL-mediated cholesterol efflux (Efflux-hdl) from THP-1 derived macrophages. We measured paraoxonase-1 (PON 1) activity to determine antioxidation capacity, serum amyloid A protein (SAA) to determine inflammatory response, and carboxymethyl-lysin (CML) to determine antiglucoxidative capacity. Results Efflux-hdl demonstrated no correlation with plasma apoprotein A-1 (ApoA-I) or HDL-cholesterol in patients with diabetes. PON1 activity in the patients’ HDL fraction was positively correlated with Efflux-hdl (r = 0.39, p = 0.02), and showed a negative tendency with HbA1c levels (r = −0.28, p = 0.10). SAA and CML levels did not demonstrate correlation with Efflux-hdl in patients with diabetes. Conclusion We confirmed the functional changes in HDL particles in the patients. Efflux-hdl from macrophages was reduced depending upon the decrease in PON1 activity, which was inversely related to HbA1c levels.

Recently, we proposed a genetic network inference method using linear programming machines (LPMs). As this method infers genetic networks by solving linear programming problems, its computational time is very short. However, generic networks inferred by the method using the LPMs often contain a large number of false-positive regulations. When we try to apply the inference method to actual problems, we must experimentally validate the inferred regulations. Therefore, it is important to reduce the number of false-positive regulations. To decrease the number of regulations we must validate, this study assigns confidence values to all of the possible regulations. For this purpose, we combine a bootstrap method and the method using the LPMs. Through numerical experiments on artificial genetic network inference problems, we check the effectiveness of assessing the confidence values of the regulations.

The inference of genetic networks using a model based on a set of differential equations is generally time-consuming. In order to decrease its computational time, we have proposed the inference method using a normalized Gaussian network (NGnet) model. The inferred models however contain many false-positive regulations when we apply the NGnet approach to the genetic network inference problems. This paper proposes the reduced NGnet model and the gradual reduction strategy to overcome the drawbacks of the NGnet approach. Then, in order to verify their effectiveness, we apply the inference method using the proposed techniques to several artificial genetic network inference problems.