Alcoholism and Diabetes Mellitus

Chronic use of alcohol is considered to be a potential risk factor for the incidence of type 2 diabetes mellitus (T2DM), which causes insulin resistance and pancreatic β-cell dysfunction that is a prerequisite for the development of diabetes. However, alcohol consumption in diabetes has been controversial and more detailed information on the diabetogenic impact of alcohol seems warranted. Diabetes, especially T2DM, causes dysregulation of various metabolic processes, which includes a defect in the insulin-mediated glucose function of adipocytes, and an impaired insulin action in the liver. In addition, neurobiological profiles of alcoholism are linked to the effects of a disruption of glucose homeostasis and of insulin resistance, which are affected by altered appetite that regulates the peptides and neurotrophic factors. Since conditions, which precede the onset of diabetes that are associated with alcoholism is one of the crucial public problems, researches in efforts to prevent and treat diabetes with alcohol dependence, receives special clinical interest. Therefore, the purpose of this mini-review is to provide the recent progress and current theories in the interplay between alcoholism and diabetes. Further, the purpose of this study also includes summarizing the pathophysiological mechanisms in the neurobiology of alcoholism

Is there a shared neurobiology between aggression and Internet addiction disorder?

Purpose: Evidences indicate that Internet addiction disorder (IAD) has a higher risk of developing aggression and violent behavior. A few correlation studies between IAD and aggression have implicated a common biological mechanism. However, neurobiological approaches to IAD and aggression have not yet been studied. Methods: A literature search for studies for Internet addiction disorder or aggression was performed in the PubMed database and we selected articles about neurobiology of IAD or aggression. Results: This review includes (a) common neural substrates such as the prefrontal cortex and the limbic system between aggression and IAD; (b) common neuromodulators such as dopamine, norepinephrine, serotonin, opiate and nicotine between aggression and IAD. Conclusions: Through reviewing the relevant literature, we suggested the possibility of common neurobiology between the two psychiatric phenomena and direction of research on aggression in IAD

Synthesis of VO_2 Nanowire and Observation of the Metal-Insulator Transition

We have fabricated crystalline nanowires of VO_2 using a new synthetic method. A nanowire synthesized at 650^oC shows the semiconducting behavior and a nanowire at 670^oC exhibits the first-order metal-insulator transition which is not the one-dimensional property. The temperature coefficient of resistance in the semiconducting nanowire is 7.06 %/K at 300 K, which is higher than that of commercial bolometer.Comment: 3 pages, 4 figures, This was presented in NANOMAT 2006 "International workshop on nanostructed materials" on June 21-23th of 2006 in Antalya/TURKE

Chronic alcohol consumption, type 2 diabetes mellitus, insulin-like growth factor-I (IGF-I), and growth hormone (GH) in ethanol-treated diabetic rats

AbstractAimsAlcohol has deleterious influences on glucose metabolism which may contribute to the development of type 2 diabetes mellitus (T2DM). Insulin-like growth factor I (IGF-I) and growth hormone (GH), which interact with insulin to modulate metabolic control, have been shown to be related to impaired glucose tolerance. This study was conducted to assess the possibility that altered circulating IGF-I and GH levels contribute to the exacerbation of T2DM by alcohol use in type 2 diabetic Otsuka Long-Evans Tokushima Fatty (OLETF) rats and non-diabetic Long-Evans Tokushima Otsuka (LETO) rats.Main methodOLETF rats were pair-fed a Lieber-DeCarli Regular Ethanol diet and LETO rats were pair-fed a control diet for 6weeks. At 6weeks, an Intraperitoneal Glucose Tolerance Test (IP-GTT) was performed and IGF-I and GH levels were evaluated.Key findingsPrior to an IP-GTT, OLETF-Ethanol (O-E) group had significantly a decrease in the mean glucose levels compared to OLETF-Control (O-C) group. At 120min post IP-GTT, the O-E group had significantly an increase in the mean glucose levels compared to O-C group. The serum IGF-I levels were significantly lower and the serum GH levels were significantly higher in the O-E group than in L-C group.SignificanceThese results suggest that IGF-I and GH are prominent in defining the risk and development of T2DM, and may be adversely affected by heavy alcohol use, possibly mediating its diabetogenic effects. Thus, the overall glucose intolerance in the setting of alcoholism may be attributable to inappropriate alteration of IGF-I and GH levels

Retrieval of NO2 Column Amounts from Ground-Based Hyperspectral Imaging Sensor Measurements

Total column amounts of NO2 (TCN) were estimated from ground-based hyperspectral imaging sensor (HIS) measurements in a polluted urban area (Seoul, Korea) by applying the radiance ratio fitting method with five wavelength pairs from 400 to 460 nm. We quantified the uncertainty of the retrieved TCN based on several factors. The estimated TCN uncertainty was up to 0.09 Dobson unit (DU), equivalent to 2.687 ?? 1020 molecules m???2) given a 1?? error for the observation geometries, including the solar zenith angle, viewing zenith angle, and relative azimuth angle. About 0.1 DU (6.8%) was estimated for an aerosol optical depth (AOD) uncertainty of 0.01. In addition, the uncertainty due to the NO2 vertical profile was 14% to 22%. Compared with the co-located Pandora spectrophotometer measurements, the HIS captured the temporal variation of the TCN during the intensive observation period. The correlation between the TCN from the HIS and Pandora also showed good agreement, with a slight positive bias (bias: 0.6 DU, root mean square error: 0.7 DU)

Enhancing the Mass Sensitivity of Graphene Nanoresonators Via Nonlinear Oscillations: The Effective Strain Mechanism

We perform classical molecular dynamics simulations to investigate the enhancement of the mass sensitivity and resonant frequency of graphene nanomechanical resonators that is achieved by driving them into the nonlinear oscillation regime. The mass sensitivity as measured by the resonant frequency shift is found to triple if the actuation energy is about 2.5 times the initial kinetic energy of the nanoresonator. The mechanism underlying the enhanced mass sensitivity is found to be the effective strain that is induced in the nanoresonator due to the nonlinear oscillations, where we obtain an analytic relationship between the induced effective strain and the actuation energy that is applied to the graphene nanoresonator. An important implication of this work is that there is no need for experimentalists to apply tensile strain to the resonators before actuation in order to enhance the mass sensitivity. Instead, enhanced mass sensitivity can be obtained by the far simpler technique of actuating nonlinear oscillations of an existing graphene nanoresonator.Comment: published versio

Resting-state EEG activity related to impulsivity in gambling disorder

Background and aims Impulsivity is a core feature of gambling disorder (GD) and is related to the treatment response. Thus, it is of interest to determine objective neurobiological markers associated with impulsivity in GD. We explored resting-state electroencephalographic (EEG) activity in patients with GD according to the degree of impulsivity. Methods In total, 109 GD subjects were divided into three groups according to Barratt impulsiveness scale-11 (BIS-11) scores: high (HI; 25th percentile of BIS-11 scores, n = 29), middle (MI; 26th–74th percentile, n = 57), and low-impulsivity (LI) groups (75th percentile, n = 23). We used generalized estimating equations to analyze differences in EEG absolute power considering group (HI, MI, and LI), brain region (frontal, central, and posterior), and hemisphere (left, midline, and right) for each frequency band (delta, theta, alpha, beta, and gamma). Results The results indicated that GD patients in the HI group showed decreased theta absolute power, and decreased alpha and beta absolute power in the left, right, particularly midline frontocentral regions. Discussion and conclusions This study is a novel attempt to reveal impulsive features in GD by neurophysiological methods. The results suggest different EEG patterns among GD patients according to the degree of impulsivity, raising the possibility of neurophysiological objective features in GD and helping clinicians in treating GD patients with impulsive features

Petuum: A New Platform for Distributed Machine Learning on Big Data

What is a systematic way to efficiently apply a wide spectrum of advanced ML programs to industrial scale problems, using Big Models (up to 100s of billions of parameters) on Big Data (up to terabytes or petabytes)? Modern parallelization strategies employ fine-grained operations and scheduling beyond the classic bulk-synchronous processing paradigm popularized by MapReduce, or even specialized graph-based execution that relies on graph representations of ML programs. The variety of approaches tends to pull systems and algorithms design in different directions, and it remains difficult to find a universal platform applicable to a wide range of ML programs at scale. We propose a general-purpose framework that systematically addresses data- and model-parallel challenges in large-scale ML, by observing that many ML programs are fundamentally optimization-centric and admit error-tolerant, iterative-convergent algorithmic solutions. This presents unique opportunities for an integrative system design, such as bounded-error network synchronization and dynamic scheduling based on ML program structure. We demonstrate the efficacy of these system designs versus well-known implementations of modern ML algorithms, allowing ML programs to run in much less time and at considerably larger model sizes, even on modestly-sized compute clusters.Comment: 15 pages, 10 figures, final version in KDD 2015 under the same titl

Smartphone dependence classification using tensor factorization

Excessive smartphone use causes personal and social problems. To address this issue, we sought to derive usage patterns that were directly correlated with smartphone dependence based on usage data. This study attempted to classify smartphone dependence using a data-driven prediction algorithm. We developed a mobile application to collect smartphone usage data. A total of 41,683 logs of 48 smartphone users were collected from March 8, 2015, to January 8, 2016. The participants were classified into the control group (SUC) or the addiction group (SUD) using the Korean Smartphone Addiction Proneness Scale for Adults (S-Scale) and a face-to-face offline interview by a psychiatrist and a clinical psychologist (SUC = 23 and SUD = 25). We derived usage patterns using tensor factorization and found the following six optimal usage patterns: 1) social networking services (SNS) during daytime, 2) web surfing, 3) SNS at night, 4) mobile shopping, 5) entertainment, and 6) gaming at night. The membership vectors of the six patterns obtained a significantly better prediction performance than the raw data. For all patterns, the usage times of the SUD were much longer than those of the SUC. From our findings, we concluded that usage patterns and membership vectors were effective tools to assess and predict smartphone dependence and could provide an intervention guideline to predict and treat smartphone dependence based on usage data.112Ysciescopu