Mitochondrial Dysfunction and β-Cell Failure in Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is the most common human endocrine disease and is characterized by peripheral insulin resistance and pancreatic islet β-cell failure. Accumulating evidence indicates that mitochondrial dysfunction is a central contributor to β-cell failure in the evolution of T2DM. As reviewed elsewhere, reactive oxygen species (ROS) produced by β-cell mitochondria as a result of metabolic stress activate several stress-response pathways. This paper focuses on mechanisms whereby ROS affect mitochondrial structure and function and lead to β-cell failure. ROS activate UCP2, which results in proton leak across the mitochondrial inner membrane, and this leads to reduced β-cell ATP synthesis and content, which is a critical parameter in regulating glucose-stimulated insulin secretion. In addition, ROS oxidize polyunsaturated fatty acids in mitochondrial cardiolipin and other phospholipids, and this impairs membrane integrity and leads to cytochrome c release into cytosol and apoptosis. Group VIA phospholipase A2 (iPLA2β) appears to be a component of a mechanism for repairing mitochondrial phospholipids that contain oxidized fatty acid substituents, and genetic or acquired iPLA2β-deficiency increases β-cell mitochondrial susceptibility to injury from ROS and predisposes to developing T2DM. Interventions that attenuate ROS effects on β-cell mitochondrial phospholipids might prevent or retard development of T2DM

Adherence to a behavioral weight loss treatment program enhances weight loss and improvements in biomarkers

Objectives: To describe participants' adherence to multiple components (attendance, energy intake, fat gram, exercise goals, and self-monitoring eating and exercise behaviors) of a standard behavioral treatment program (SBT) for weight loss and how adherence to these components may influence weight loss and biomarkers (triglycerides, low density lipoproteins [LDL], high density lipoprotein, and insulin) during the intensive and less-intensive intervention phases. Methods: A secondary analysis of a randomized clinical trial consisting of a SBT with either fat-restricted standard or lacto-ovo vegetarian diet. The 12-month intervention was delivered in 33 group sessions. The first six months reflected the intensive phase; the second six months, the less-intensive intervention phase. We conducted the analysis without regard to treatment assignment. Eligible participants included overweight/obese adults (N = 176; mean body mass index = 34.0 kg/m2). The sample was 86.9% female, 70.5% White, and 44.4 ± 8.6 years old. The outcome measures included weight and biomarkers. Results: There was a significant decline in adherence to each treatment component over time (P < 0.0001). In the first six months, adherence to attendance, self-monitoring and the energy goal were significantly associated with greater weight loss (P < 0.05). Adherence to attendance and exercise remained significantly associated with weight loss in the second six months (P < 0.05). Adherence to attendance, self-monitoring and exercise had indirect effects through weight loss on LDL, triglycerides, and insulin (P < 0.05). Conclusions: We observed a decline in adherence to each treatment component as the intervention intensity was reduced. Adherence to multiple treatment components was associated with greater weight loss and improvements in biomarkers. Future research needs to focus on improving and maintaining adherence to all components of the treatment protocol to promote weight loss and maintenance. © 2009 Acharya et al

A Family of Maximum Margin Criterion for Adaptive Learning

In recent years, pattern analysis plays an important role in data mining and recognition, and many variants have been proposed to handle complicated scenarios. In the literature, it has been quite familiar with high dimensionality of data samples, but either such characteristics or large data have become usual sense in real-world applications. In this work, an improved maximum margin criterion (MMC) method is introduced firstly. With the new definition of MMC, several variants of MMC, including random MMC, layered MMC, 2D^2 MMC, are designed to make adaptive learning applicable. Particularly, the MMC network is developed to learn deep features of images in light of simple deep networks. Experimental results on a diversity of data sets demonstrate the discriminant ability of proposed MMC methods are compenent to be adopted in complicated application scenarios.Comment: 14 page

Diagnostic value of tumour markers CA-125 and CEA in the diagnostics of malignant pleural fluids

Copyright: Copyright 2016 Elsevier B.V., All rights reserved.The significance of carbohydrate (cancer) antigen 125 (Ca-125) and carcinoembryonic antigen (CEA) tumour markers levels in differential diagnostics of malignant and benign pleural effusion was studied. Within this prospective study, 121 patients with fluids of various aetiology in the pleural cavity were analysed. Malignant pleural effusion was detected in 55 patients, parapneumonic effusion in 28 patients, transudative effusion of cardiac origin in 31 patients, pancreatitis in one patient and tuberculous pleurisy in five patients. The highest accuracy in diagnosis of malignancy was observed for Ca-125 and CEA levels in the pleural fluid: 75.2% at cut-off value ≥1452 U/mL and 76.9% at cut-off value ≥6.58 ng/mL, respectively. We conclude that the level of tumour markers in pleural fluid has additional diagnostic significance in the differential diagnosis of malignant and benign pleural effusions.publishersversionPeer reviewe

The thoughtful self

The relationship between a concept in the external world (e.g., the self), and its representation in cognition

Bicontinuous minimal surface nanostructures for polymer blend solar cells

This paper presents the first examination of the potential for bicontinuous structures such as the gyroid structure to produce high efficiency solar cells based on conjugated polymers. The solar cell characteristics are predicted by a simulation model that shows how the morphology influences device performance through integration of all the processes occurring in organic photocells in a specified morphology. In bicontinuous phases, the surface de. ning the interface between the electron and hole transporting phases divides the volume into two disjoint subvolumes. Exciton loss is reduced because the interface at which charge separation occurs permeates the device so excitons have only a short distance to reach the interface. As each of the component phases is connected, charges will be able to reach the electrodes more easily. In simulations of the current-voltage characteristics of organic cells with gyroid, disordered blend and vertical rod (rods normal to the electrodes) morphologies, we find that gyroids have a lower than anticipated performance advantage over disordered blends, and that vertical rods are superior. These results are explored thoroughly, with geminate recombination, i.e. recombination of charges originating from the same exciton, identified as the primary source of loss. Thus, if an appropriate materials choice could reduce geminate recombination, gyroids show great promise for future research and applications

Relaxed 2-D Principal Component Analysis by LpL_p Norm for Face Recognition

A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition. Different to the 2DPCA, 2DPCA-$L_1$ and G2DPCA, the R2DPCA utilizes the label information (if known) of training samples to calculate a relaxation vector and presents a weight to each subset of training data. A new relaxed scatter matrix is defined and the computed projection axes are able to increase the accuracy of face recognition. The optimal $L_p$-norms are selected in a reasonable range. Numerical experiments on practical face databased indicate that the R2DPCA has high generalization ability and can achieve a higher recognition rate than state-of-the-art methods.Comment: 19 pages, 11 figure

Divided attention selectively impairs memory for self-relevant information

Information that is relevant to oneself tends to be remembered more than information that relates to other people, but the role of attention in eliciting this "self-reference effect" is unclear. In the present study, we assessed the importance of attention in self-referential encoding using an ownership paradigm, which required participants to encode items under conditions of imagined ownership by themselves or by another person. Previous work has established that this paradigm elicits a robust self-reference effect, with more "self-owned" items being remembered than "other-owned" items. Access to attentional resources was manipulated using divided-attention tasks at encoding. A significant self-reference effect emerged under full-attention conditions and was related to an increase in episodic recollection for self-owned items, but dividing attention eliminated this memory advantage. These findings are discussed in relation to the nature of self-referential cognition and the importance of attentional resources at encoding in the manifestation of the self-reference effect in memory

Multi-scale initial conditions for cosmological simulations

We discuss a new algorithm to generate multi-scale initial conditions with multiple levels of refinements for cosmological "zoom-in" simulations. The method uses an adaptive convolution of Gaussian white noise with a real space transfer function kernel together with an adaptive multi-grid Poisson solver to generate displacements and velocities following first (1LPT) or second order Lagrangian perturbation theory (2LPT). The new algorithm achieves RMS relative errors of order 10^(-4) for displacements and velocities in the refinement region and thus improves in terms of errors by about two orders of magnitude over previous approaches. In addition, errors are localized at coarse-fine boundaries and do not suffer from Fourier-space induced interference ringing. An optional hybrid multi-grid and Fast Fourier Transform (FFT) based scheme is introduced which has identical Fourier space behaviour as traditional approaches. Using a suite of re-simulations of a galaxy cluster halo our real space based approach is found to reproduce correlation functions, density profiles, key halo properties and subhalo abundances with per cent level accuracy. Finally, we generalize our approach for two-component baryon and dark-matter simulations and demonstrate that the power spectrum evolution is in excellent agreement with linear perturbation theory. For initial baryon density fields, it is suggested to use the local Lagrangian approximation in order to generate a density field for mesh based codes that is consistent with Lagrangian perturbation theory instead of the current practice of using the Eulerian linearly scaled densities.Comment: 22 pages, 24 figures. MNRAS in press. Updated affiliation