Synthesis and Characterization of Sol–Gel Screen Printed Gd:ZnO (GZO) Film Towards Opto-Electronic Applications

Semiconductors - In this study, Gd0.02Zn0.98O (GZO) film was coated onto ordinary glass substrate by unusual sol–gel screen printing procedure. Gadolinium was successfully integrated into ZnO...     

A Parametric Study of Spontaneous Ignition in Large Coal Stockpiles

Self-heating of coal during its storage and transportation has been a serious problem for decades. Coal stored in large piles for long duration is subjected to weathering by atmospheric air that prevails with different temperatures and moisture content. Chemisorption of atmospheric oxygen results in low-temperature oxidation of pile, which generates heat due to exothermic reactions. If the local heat release rate is higher as compared to the heat dissipated, a significant increase in temperature is possible and this results in spontaneous ignition of the pile. The presence of moisture in coal delays the occurrence of self-heating. This motivates to analyze a scenario of using moist coal to delay or even prevent the self-ignition in dry coal until a given time period of its storage. The main objective of this work is to investigate the critical conditions, which may lead to spontaneous ignition in large coal stockpiles containing dry and moist coal layers. A one-dimensional numerical model is used for this purpose. A parametric study is carried out considering different porosity, superficial air velocity and reactivity values. The time period of coal pile storage is fixed as 360 days. The location and time taken for self-ignition in the pile within this period is reported for each case. In summary, considering several cases, the simulations systematically reveal that highly reactive coal with high pile porosity and higher superficial gas velocity takes the least time to reach the self-ignition temperature.

     

Pluripotent Stem Cell-Derived Hepatocytes Phenotypic Screening Reveals Small Molecules Targeting the CDK2/4-C/EBPα/DGAT2 Pathway Preventing ER-Stress Induced Lipid Accumulation

Non-alcoholic fatty liver disease (NAFLD) has a large impact on global health. At the onset of disease, NAFLD is characterized by hepatic steatosis defined by the accumulation of triglycerides stored as lipid droplets. Developing therapeutics against NAFLD and progression to non-alcoholic steatohepatitis (NASH) remains a high priority in the medical and scientific community. Drug discovery programs to identify potential therapeutic compounds have supported high throughput/high-content screening of in vitro human-relevant models of NAFLD to accelerate development of efficacious anti-steatotic medicines. Human induced pluripotent stem cell (hiPSC) technology is a powerful platform for disease modeling and therapeutic assessment for cell-based therapy and personalized medicine. In this study, we applied AstraZeneca’s chemogenomic library, hiPSC technology and multiplexed high content screening to identify compounds that significantly reduced intracellular neutral lipid content. Among 13,000 compounds screened, we identified hits that protect against hiPSC-derived hepatic endoplasmic reticulum stress-induced steatosis by a mechanism of action including inhibition of the cyclin D3-cyclin-dependent kinase 2-4 (CDK2-4)/CCAAT-enhancer-binding proteins (C/EBPα)/diacylglycerol acyltransferase 2 (DGAT2) pathway, followed by alteration of the expression of downstream genes related to NAFLD. These findings demonstrate that our phenotypic platform provides a reliable approach in drug discovery, to identify novel drugs for treatment of fatty liver disease as well as to elucidate their underlying mechanisms.     

Ginsenoside M1 Induces Apoptosis and Inhibits the Migration of Human Oral Cancer Cells

Oral squamous cell carcinoma (OSCC) accounts for 5.8% of all malignancies in Taiwan, and the incidence of OSCC is on the rise. OSCC is also a common malignancy worldwide, and the five-year survival rate remains poor. Therefore, new and effective treatments are needed to control OSCC. In the present study, we prepared ginsenoside M1 (20-O-beta-d-glucopyranosyl-20(S)-protopanaxadiol), a major deglycosylated metabolite of ginsenoside, through the biotransformation of Panax notoginseng leaves by the fungus SP-LSL-002. We investigated the anti-OSCC activity and associated mechanisms of ginsenoside M1 in vitro and in vivo. We demonstrated that ginsenoside M1 dose-dependently inhibited the viability of human OSCC SAS and OEC-M1 cells. To gain further insight into the mode of action of ginsenoside M1, we demonstrated that ginsenoside M1 increased the expression levels of Bak, Bad, and p53 and induced apoptotic DNA breaks, G1 phase arrest, PI/Annexin V double-positive staining, and caspase-3/9 activation. In addition, we demonstrated that ginsenoside M1 dose-dependently inhibited the colony formation and migration ability of SAS and OEC-M1 cells and reduced the expression of metastasis-related protein vimentin. Furthermore, oral administration or subcutaneous injection of ginsenoside M1 significantly reduced tumor growth in SAS xenograft mice. These results indicate that ginsenoside M1 can be translated into a potential therapeutic against OSCC.     

A survey of interactive mesh‐cutting techniques and a new method for implementing generalized interactive mesh cutting using virtual tools‡

In our experience, mesh‐cutting methods can be distinguished by how their solutions address the following major issues: definition of the cut path, primitive removal and re‐meshing, number of new primitives created, when re‐meshing is performed, and representation of the cutting tool. Many researches have developed schemes for interactive mesh cutting with the goals of reducing the number of new primitives created, creating new primitives with good aspect ratios, avoiding a disconnected mesh structure between primitives in the cut path, and representing the path traversed by the tool as accurately as possible. The goal of this paper is to explain how, by using a very simple framework, one can build a generalized cutting scheme. This method allows for any arbitrary cut to be made within a virtual object, and can simulate cutting surface, layered surface or tetrahedral objects using a virtual scalpel, scissors, or loop cautery tool. This method has been implemented in a real‐time, haptic‐rate surgical simulation system allowing arbitrary cuts to be made on high‐resolution patient‐specific models. Published in 2002 by John Wiley & Sons, Ltd.     

An intelligent hierarchical residual attention learning-based conjoined twin neural network for Alzheimer's stage detection and prediction

Alzheimer's disorder (AD) causes permanent impairment in the brain's memory of the cellular system, leading to the initiation of dementia. Earlier detection of Alzheimer's disease in the initial stages is challenging for researchers. Deep learning and machine learning-based techniques can help resolve many issues associated with brain imaging exploration. Brain MR Images (Brain-MRI) are used to detect Alzheimer's in computable research work. To correctly categorize the stages of Alzheimer's disease, discriminative features need to be extracted from the MR images. Recently, many studies have used deep learning methods for the early detection of this disorder. However, overfitting degrades the deep learning method's performance because the dataset's selection images are smaller and imbalanced. Some studies could not reach more discriminative and effectual attention-aware features for Alzheimer's stage classification to increase the model performance. In this paper, we develop a novel hierarchical residual attention learning-inspired multistage conjoined twin network (HRAL-CTNN) to classify the stages of Alzheimer's. We used augmentation approaches to scale insufficient and imbalanced data. The HRAL-CTNN is efficiently overcoming the issues of not obtaining efficient attention-aware and generative features for Alzheimer's stage classification. The proposed model solved the problem of redundant features by extracting attentive discriminant features, and scaling imbalance data by data augmentation, after that training and validation using HRAL-CTNN. The execution of this proposed work has been performed on the ADNI MRI dataset. This work achieved outstanding accuracy of 99.97 $\pm$ 0.01% and F1 score of 99.30 $\pm$ 0.02% for Alzheimer's stage classification. This model proposed by our group outperformed the existing related studies in terms of the model's performance score.     

Accelerating earthquake simulations on general‐purpose graphics processors

Parallelization strategies are presented for Virtual Quake, a numerical simulation code for earthquakes based on topologically realistic systems of interacting earthquake faults. One of the demands placed upon the simulation is the accurate reproduction of the observed earthquake statistics over three to four decades. This requires the use of a high‐resolution fault model in computations, which demands computational power that is well beyond the scope of off‐the‐shelf multi‐core CPU computers. However, the recent advances in general‐purpose graphic processing units have the potential to address this problem at moderate cost increments. A functional decomposition of Virtual Quake is performed, and opportunities for parallelization are discussed in this work. Computationally intensive modules are identified, and these are implemented on graphics processing units, significantly speeding up earthquake simulations. In the current best case scenario, a computer with six graphics processing units can simulate 500 years of fault activity in California at 1.5 km × 1.5 km element resolution in less than 1 hour, whereas a single CPU requires more than 2 days to perform the same simulation. Copyright © 2015 John Wiley & Sons, Ltd.