Chronic Effects of a High Sucrose Diet on Murine Gastrointestinal Nutrient Sensor Gene and Protein Expression Levels and Lipid Metabolism

The gastrointestinal tract (GIT) plays a key role in regulating nutrient metabolism and appetite responses. This study aimed to identify changes in the GIT that are important in the development of diet related obesity and diabetes. GIT samples were obtained from C57BL/6J male mice chronically fed a control diet or a high sucrose diet (HSD) and analysed for changes in gene, protein and metabolite levels. In HSD mice, GIT expression levels of fat oxidation genes were reduced, and increased de novo lipogenesis was evident in ileum. Gene expression levels of the putative sugar sensor, slc5a4a and slc5a4b, and fat sensor, cd36, were downregulated in the small intestines of HSD mice. In HSD mice, there was also evidence of bacterial overgrowth and a lipopolysaccharide activated inflammatory pathway involving inducible nitric oxide synthase (iNOS). In Caco-2 cells, sucrose significantly increased the expression levels of the nos2, iNOS and nitric oxide (NO) gas levels. In conclusion, sucrose fed induced obesity/diabetes is associated with changes in GI macronutrient sensing, appetite regulation and nutrient metabolism and intestinal microflora. These may be important drivers, and thus therapeutic targets, of diet-related metabolic disease.     

Investigation on thermo-mechanical behavior of SMA spring under the influence of different actuation medium

Microsystem Technologies - In this paper different actuation media and its impact on the morphology of structure of NiTi SMA, is presented. The functional properties of a NiTi component can be...     

Free convective magnetohydrodynamic flow over an exponentially stretching sheet with radiation

The study explores a steady two‐dimensional magnetohydrodynamic boundary layer flow phenomenon of an incompressible viscous fluid with buoyancy‐driven force over an exponentially stretching sheet. In addition to that, the interaction of thermal radiation in conjunction with dissipative effects, that is, viscous and Joule dissipation is also considered, which is justified due to the presence of magnetic field. The boundary layer equations governed by the flow phenomena are transformed into ordinary differential equations by a suitable choice of similarity transformation. Numerical methods, such as fourth‐order Runge‐Kutta scheme in association with the shooting technique is employed to get an approximate solution of these transformed equations. The numerical computations for the wall shear stress and the heat transfer coefficients are obtained, analyzed, and then discussed. Furthermore, the major findings are pick‐in velocity distribution near the plate is marked with an increase in buoyancy parameter and the rate of heat transfer profile is linear in its boundary layer for low Prandtl number.     

Role of metallic and ceramic supports on enhanced microwave heating processes

Analysis has been carried out to study efficient heating due to microwaves (MWs) for the samples placed on metallic and ceramic supports (Al₂O₃, SiC). The analysis is carried out for water which exhibits greater dielectric loss and oil which possesses small dielectric loss. A preliminary analysis on enhanced MW heating of samples has been carried out via average power within a sample vs. sample thickness diagram for various cases. The maxima in power, also termed as ‘resonances’ are observed for specific sample thicknesses and the two consecutive maxima in average power are termed as R₁ and R₂ modes. During both R₁ and R₂ modes, the greater intensity of resonances for power absorption in samples occurs in presence of metallic support. For a specific resonance mode in water samples, the maxima with metallic supports correspond to smaller sample thickness only. In contrast, for oil samples, the metallic support corresponds to greater average power for all sample thicknesses (>1 cm). Ceramic supports correspond to lower average power and Al₂O₃ is a suitable support for water whereas SiC support may cause local runaway heating for oil sample. The spatial distributions of power illustrate that the regime connected with the metallic support is heated at a lower rate specially for oil samples. The efficient and enhanced heating strategy is further exercised with metallic-ceramic composite supports. It is observed that Al₂O₃-metallic for water and SiC-metallic for oil are optimal support assemblies for efficient heating. In addition, the optimal SiC-metallic support would avoid the local runaway heating of oil samples. The choice of support may not be trivial due to complex dielectric response of sample-support assembly. Our analysis is carried out for two limiting cases due to water and oil, and we have recommended efficient heating strategy for both water and oil. The heating strategy can be suitably extended for heating of any materials on a support in custom MW ovens.     

Spatial location is critical for conditioning place preference with visual but not tactile stimuli.

The roles of visual, tactile, and spatial location cues were studied in 6 conditioned place preference (CPP) experiments with ethanol (2 g/kg) in mice (of the DBA/2J strain). Visual cues were effective conditioned stimuli (CSs) when consistently presented in the same spatial location, but not when the same cue was presented in two different locations during training. In contrast, tactile CSs were effective regardless of spatial location during training. Moreover, spatial location controlled CPP expression when visual cues were used but not when tactile cues were used. However, spatial location per se was not an effective CS. These studies suggest that CPP conditioned to tactile cues is mediated by brain systems different from those mediating CPP conditioned to visual-spatial cues. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Activities and immiscibility in the system Cu-Rh

The thermodynamic activity of rhodium in solid Cu-Rh alloys is measured by the electromotive force method in the temperature range from 1050 to 1325 K with a solid-state cell:

Self-Powered Red/UV Narrowband Photodetector by Unbalanced Charge Carrier Transport Strategy

Narrowband photodetector (NB-PD) with selective light detection is critical for artificial vision and imaging. Intrinsic (optical-filter-free) NB-PDs using conjugated organics or halide perovskite materials have been developed for eliminating the current complex filtering systems in NB-PDs. However, the poor performance and external driving circuit of organic NB-PDs as well as complex doping and uncontrollable recombination reactions in typical perovskite NB-PDs have limited their applicational diversification. A p-type self-doped perovskite for intrinsic NB detection is reported which exhibits unique unbalanced electron–hole transfer kinetics. In conjunction with the optical field distribution, an unbalanced charge transport within the self-doped perovskite triggers a wavelength-dependent photo-carrier collection, resulting in a novel spontaneous internal quantum efficiency narrowing mechanism. As a result, by reverting the device architectural polarity, an NB detection at a monochromic light of either red or UV is observed. Using such a revertible asymmetric device design, self-powered NB-PDs are successfully achieved. Briefly, the corresponding NB-PDs exhibit excellent narrow response with a response window of ≈100 nm, high detectivity ≈10¹¹ Jones, and fast response speed (f_−3dB ≈ 60 kHz) at zero bias. These results demonstrate a new strategy of manipulating internal charge transport to realize power-free and filter-free intrinsic NB-PDs.     

All Electrospray Printing of Carbon-Based Cost-Effective Perovskite Solar Cells

With the power conversion efficiencies of perovskite solar cells (PSCs) exceeding 25%, the PSCs are a step closer to initial industrialization. Prior to transferring from laboratory fabrication to industrial manufacturing, issues such as scalability, material cost, and production line compatibility that significantly impact the manufacturing remain to be addressed. Here, breakthroughs on all these fronts are reported. Carbon-based PSCs with architecture fluorine doped tin oxide (FTO)/electron transport layer/perovskite/carbon, that eliminate the need for the hole transport layer and noble metal electrode, provide ultralow-cost configuration. This PSC architecture is manufactured using a scalable and industrially compatible electrospray (ES) technique, which enables continuous printing of all the cell layers. The ES deposited electron transport layer and perovskite layer exhibit properties comparable to that of the laboratory-scale spin coating method. The ES deposited carbon electrode layer exhibits superior conductivity and interfacial microstructure in comparison to films synthesized using the conventional doctor blading technique. As a result, the fully ES printed carbon-based PSCs show a record 14.41% power conversion efficiency, rivaling the state-of-the-art hole transporter-free PSCs. These results will immediately have an impact on the scalable production of PSCs.     

A Profile-Based Novel Framework for Detecting EDoS Attacks in the Cloud Environment

The future of information technology mainly depends upon cloud computing. Hence security in cloud computing is highly essential for the consumers as well as the service providers of the particular cloud environment. There are many security threats are challenging the current cloud environment. One of the important security threat ever in cloud environment is considered to be the Distributed Denial of Service (DDoS) attack. Where cloud is of greater benefit in terms of providing on-demand services, a certain kind of attack named as Economic Denial of Sustainability (EDoS) occurs in pay per use payment model. Due to the occurrence of this attack the consumers are forced to pay additional amount for the services offered. EDoS attacks are similar to that of DDoS attacks Which is classified as-attacks associated with bandwidth consuming, application targeted attacks and the exhaustion of the connection layer. The main objective of the proposed work is to design a profile-based novel framework for maximizing the detection of various types of EDoS attacks. During this process, the proposed framework consisting Feature Classification (FC) algorithm ensures that false positives and negatives along with bandwidth and memory consumption are highly minimized. The proposed algorithm allows only the limited resources for allocation to the available virtual machines which increases the chances of the detecting the attack and preventing the misuse propagation of resources. The accuracy and efficiency of this approach is proven to be higher with lesser computational complexity when compare to the existing approaches.

     

Multi-cell nuclei segmentation in cervical cancer images by integrated feature vectors

Automated analysis of cervical cancer images is considered as an attractive research in the biological fields. Due to the intensive advances in the digital technology and the light microscopy, the cellular imaging requires the continuous growing importance. Sophisticated methods are adopted to isolate the nuclei from the cytoplasm based on the boundary estimation and the analysis of intensity of blue cells to improve the abnormality prediction. The cell-based segmentation evolved in research studies assures the automatic assistance with an assumption of a single cell. Previous work [21] concentrates on the segmentation of abnormal region on single-cell images. This paper extends that work into the multi-cell images with the addition of geometrical features. The complex cell structure, poor contrast, and overlapping affect the cell segmentation performance. This paper enhances the performance of single-cell segmentation with the integrated feature vectors of geometrical (area, cell size, cell intensity and the maximum intensity) and Gray level Co-occurrence Matrix (GLCM) to improve the abnormality level prediction.. Initially, the Neighborhood Concentric Filtering (NCF) is applied on the input slides to remove the noise present in the image and enhance the intensity level. Then, the initial level cluster formation and masking are performed on the noise-free image. The Optimal Weight Updating with the Multi-Level set (OWU-ML) estimates the Region of Interest (ROI) and segments the blue cell and cytoplasm. The clear analysis of blue cell indicates the exact classification of abnormal levels in the images. The combination of geometrical and GLCM extracts the texture pattern features of the blue cell, cytoplasm and the nucleus portions in the form of angle variations. Finally, the Neural Network-based RVM classifier predicts the classes of (normal and abnormal) cervical images. The integration of novel methods such as OWU-ML segmentation, GLCM + geometrical feature extraction and NN-RVM classification improves the abnormal prediction performance and assures the suitability in multi-cell cervical image handling in biological applications.