Deficiency of Tristetraprolin Triggers Hyperthermia through Enhancing Hypothalamic Inflammation

Tristetraprolin (TTP), an RNA-binding protein, controls the stability of RNA by capturing AU-rich elements on their target genes. It has recently been identified that TTP serves as an anti-inflammatory protein by guiding the unstable mRNAs of pro-inflammatory proteins in multiple cells. However, it has not yet been investigated whether TTP affects the inflammatory responses in the hypothalamus. Since hypothalamic inflammation is tightly coupled to the disturbance of energy homeostasis, we designed the current study to investigate whether TTP regulates hypothalamic inflammation and thereby affects energy metabolism by utilizing TTP-deficient mice. We observed that deficiency of TTP led to enhanced hypothalamic inflammation via stimulation of a variety of pro-inflammatory genes. In addition, microglial activation occurred in the hypothalamus, which was accompanied by an enhanced inflammatory response. In line with these molecular and cellular observations, we finally confirmed that deficiency of TTP results in elevated core body temperature and energy expenditure. Taken together, our findings unmask novel roles of hypothalamic TTP on energy metabolism, which is linked to inflammatory responses in hypothalamic microglial cells.     

A Quantitative Analysis of Spectrum Efficiency of Various Diversity Combining Schemes in the Presence of Co-channel Interference

Wireless Personal Communications - Content Addressable Memory (CAM) is used in high speed searching applications and also in data compression. Recently in the network computing era, fast lookup...     

TS3FCM: trusted safe semi-supervised fuzzy clustering method for data partition with high confidence

Multimedia Tools and Applications - Data partition with high confidence is one of the main concentration of researchers in Soft Computing for many years. It is known that there may be some data...     

Supplier Selection Fuzzy Model in Supply Chain Management: Vietnamese Cafe Industry Case

Supplier selection is a vital part of the supply chain and is also a current issue that concerns businesses today as supplier quality directly affects the operations of the organization. Choosing the right supplier can help businesses increase productivity, competitiveness in the market, and profits without having to lower the quality of the products. However, choosing a supplier is not a simple matter, it requires businesses to consider many aspects about their suppliers. Therefore, the goal of this study is to propose an integrated model consisting of two models: Fuzzy Analytics Network Process (Fuzzy-ANP) model and Weighted Aggregated Sum Product Assessment (WASPAS) to solve the problem above. The Fuzzy-ANP model was developed to evaluate the weightings of the supplier selection criteria, and the WASPAS Model was used to rank the suppliers. An example of supplier selection in the coffee industry in Vietnam was studied to validate the model, namely 5 main criteria, with 16 sub-criteria, and 7 suppliers. The model test results show that the Fuzzy ANP and WASPAS integration model was suitable. In future, these developing models can apply to other industries or integrate with other models.     

Compatible natural gradient policy search

Machine Learning - Trust-region methods have yielded state-of-the-art results in policy search. A common approach is to use KL-divergence to bound the region of trust resulting in a natural...     

Diagnosis of breast cancer based on modern mammography using hybrid transfer learning

Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved.

     

Fabrication of asymmetric zinc oxide/carbon nanotubes coated polysulfone photocatalytic nanocomposite membrane for fouling mitigation

Membrane filtration is a favorable option in water reclamation from contaminated water source, nevertheless, inevitable membrane fouling which greatly shortens membrane longevity and separation efficiency. The paper aimed to mitigate the membrane fouling through the formation of an asymmetric PSF-ZnO/CNTs photocatalytic nanocomposite membrane. Instead of direct blending the photocatalyst into polymer matrix, the asymmetric nanocomposite membrane was prepared with prior formation of a self-assembled ZnO/CNTs photocatalyst layer through wet-processing technique followed with coating of PSF support layer via phase inversion method. The morphology of the nanocomposite membrane was characterized to confirm the formation of the asymmetric structure. The effect of ZnO/CNTs photocatalyst loading on the pore characteristic and antifouling properties of the PSF-ZnO/CNTs nanocomposite membrane in dye remediation were assessed. The incorporation of ZnO/CNTs layer was found to endows the membrane with ability to photodegrade methylene blue. The PSF-ZnO/CNTs membrane with 0.038 g ZnO/CNTs photocatalyst loading (M5) showed the greatest flux recovery ratio (98.46%) and the lowest irreversible fouling ratio (1.54%) while exhibited decent water permeability about 29.99 L/m²h without compromise the methylene blue rejection rate (91.04%).     

Static,free vibration,and buckling analysis of laminated composite Reissner–Mindlin plates using NURBS‐based isogeometric approach

This paper presents a novel numerical procedure based on the framework of isogeometric analysis for static, free vibration, and buckling analysis of laminated composite plates using the first‐order shear deformation theory. The isogeometric approach utilizes non‐uniform rational B‐splines to implement for the quadratic, cubic, and quartic elements. Shear locking problem still exists in the stiffness formulation, and hence, it can be significantly alleviated by a stabilization technique. Several numerical examples are presented to show the performance of the method, and the results obtained are compared with other available ones. Copyright © 2012 John Wiley & Sons, Ltd.     

Free vibration of axially loaded rectangular composite beams using refined shear deformation theory

Free vibration of axially loaded rectangular composite beams with arbitrary lay-ups using refined shear deformation theory is presented. It accounts for the parabolical variation of shear strains through the depth of beam. Three governing equations of motion are derived from the Hamilton’s principle. The resulting coupling is referred to as triply axial-flexural coupled vibration. A displacement-based one-dimensional finite element model is developed to solve the problem. Numerical results are obtained for rectangular composite beams to investigate effects of fiber orientation and modulus ratio on the natural frequencies, critical buckling loads and load–frequency curves as well as corresponding mode shapes.