Particle Swarm Optimization to solve Economic Dispatch considering Generator Constraints

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Cell Stress and Cell Death

Editorial: This special issue on Cell Stress and Cell Death is aimed at bringing together recent developments in the fields of cellular stress and cell death and, in particular, the interplay between cell stress responses and cell death. The special issue opens with a review by S. Fulda et al. which provides an overview of how cells can respond to stress in a variety of ways ranging from the activation of survival pathways to the initiation of cell death that eventually eliminates damaged cells. Whether cells mount a protective response or succumb to death depends to a large extent on the nature and duration of the stress as well as the cell type. For example, milder stresses can lead to protection through activation of the heat shock response or the unfolded protein response (UPR). This review also describes several types of cell death (e.g., apoptosis, necrosis, pyroptosis, or autophagic cell death) and the mechanism by which a cell dies often depends on various exogenous factors as well as the cell’s ability to handle the stress to which it is exposed. The implications of cellular stress responses for human physiology and disease are multifold and are discussed in this review in the context of some major world health issues such as diabetes, Parkinson’s disease, myocardial infarction, and cancer. ..

Engineering a Dimeric Caspase-9: A Re-evaluation of the Induced Proximity Model for Caspase Activation

Caspases are responsible for the execution of programmed cell death (apoptosis) and must undergo proteolytic activation, in response to apoptotic stimuli, to function. The mechanism of initiator caspase activation has been generalized by the induced proximity model, which is thought to drive dimerization-mediated activation of caspases. The initiator caspase, caspase-9, exists predominantly as a monomer in solution. To examine the induced proximity model, we engineered a constitutively dimeric caspase-9 by relieving steric hindrance at the dimer interface. Crystal structure of the engineered caspase-9 closely resembles that of the wild-type (WT) caspase-9, including all relevant structural details and the asymmetric nature of two monomers. Compared to the WT caspase-9, this engineered dimer exhibits a higher level of catalytic activity in vitro and induces more efficient cell death when expressed. However, the catalytic activity of the dimeric caspase-9 is only a small fraction of that for the Apaf-1-activated caspase-9. Furthermore, in contrast to the WT caspase-9, the activity of the dimeric caspase-9 can no longer be significantly enhanced in an Apaf-1-dependent manner. These findings suggest that dimerization of caspase-9 may be qualitatively different from its activation by Apaf-1, and in conjunction with other evidence, posit an induced conformation model for the activation of initiator caspases

Characterization of a novel and specific inhibitor for the pro-apoptotic protease Omi/HtrA2

Omi/HtrA2 is a mammalian serine protease with high homology to bacterial HtrA chaperones. Omi/HtrA2 is localized in mitochondria and is released to the cytoplasm in response to apoptotic stimuli. Omi/HtrA2 induces cell death in a caspase-dependent manner by interacting with the inhibitor of apoptosis protein as well as in a caspase-independent manner that relies on its protease activity. We describe the identification and characterization of a novel compound as a specific inhibitor of the proteolytic activity of Omi/HtrA2. This compound (ucf-101) was isolated in a high throughput screening of a combinatorial library using bacterially made Omi-(134-458) protease and fluorescein-casein as a generic substrate. ucf-101 showed specific activity against Omi/HtrA2 and very little activity against various other serine proteases. This compound has a natural fluorescence that was used to monitor its ability to enter mammalian cells. ucf-101, when tested in caspase-9 (-/-) null fibroblasts, was found to inhibit Omi/HtrA2-induced cell death

Neural network modeling of distribution transformer with internal winding faults using double Fourier series

ABSTRACT An efficient transformer model is required to characterize the transformer internal faults for its condition assessment, which is experimentally very costly. This paper discusses the application of Neural Network (NN) techniques in the modeling of a distribution transformer with internal short-circuit winding faults. A transformer model can be viewed as a functional approximator constructing an input-output mapping between some specific variables and the terminal behaviors of the transformer. Neural network model takes fault specification and energized voltage as the inputs and the output voltage or terminal currents as the outputs. A major kind of neural network, i.e. back-propagation feed-forward network (BPFN), is used to model the faults in distribution transformers. The NN models are trained offline using training sets generated by a field based model, i.e. Double Fourier Series based field (DFSF) models. These models are implemented using MATLAB. The comparison between some simulation cases and corresponding experimental results shows that the well-trained neural networks can accurately simulate the terminal behavior of distribution transformers with internal short circuit faults

Predicting The Strength Properties of Self Healing Concrete Using Artificial Neural Network

An extensive simulation program is used in this study to discover the best ANN model for predicting the compressive strength of concrete with respect to the percentage of mineral admixture and percentage of crystalline admixture. To accomplish this, an experimental database of 100 samples is compiled from the literature and utilized to find the best ANN architecture. The main aim of this paper was to predict the strength properties of self-healing concrete (SHC) with crystalline admixture and different mineral admixtures using an artificial neural network (ANN). The samples, 100 in Number, with different mixes, were analyzed after 28 days of curing of the samples. ANN was fed with the experimental data containing four input parameters: mineral admixture (MA), percentage of mineral admixture (PMA), Percentage of crystalline admixture (PCA), and type of exposure (TE). Correspondingly, strength (Fc) was the output parameter. The experimental data showed a good correlation with the values predicted by ANN. In conclusion, ANN could be used to accurately evaluate SHC strength characteristics

Zero Crossing Point Detection in a Distorted Sinusoidal Signal Using Decision Tree Classifier

Zero-crossing point detection in a sinusoidal signal is essential in the case of various power systems and power electronics applications like power system protection and power converters controller design. In this paper, 96 data sets are created from a distorted sinusoidal signal based on MATLAB simulation. Dis- torted sinusoidal signals are generated in MATLAB with various noise and harmonic levels. In this pa- per, a decision tree classi er is used to predict the zero crossing point in a distorted signal based on input fea- tures like slope, intercept, correlation and Root Mean Square Error (RMSE). Decision tree classi er model is trained and tested in the Google Colab environment. As per simulation results, it is observed that decision tree classi er is able to predict the zero-crossing points in a distorted signal with maximum accuracy of 98.3 % for noise signals and 100 % for harmonic distorted signals

Editorial: Cell stress and cell death

金沢大学医薬保健研究域医学

Characterization of two receptors for TRAIL.

Two receptors for TRAIL, designated TRAIL-R2 and TRAIL-R3, have been identified. Both are members of the tumor necrosis factor receptor family. TRAIL-R2 is structurally similar to the death-domain-containing receptor TRAIL-R1 (DR-4), and is capable of inducing apoptosis. In contrast, TRAIL-R3 does not promote cell death. TRAIL-R3 is highly glycosylated and is membrane bound via a putative phosphatidylinositol anchor. The extended structure of TRAIL-R3 is due to the presence of multiple threonine-, alanine-, proline- and glutamine-rich repeats (TAPE repeats). TRAIL-R2 shows a broad tissue distribution, whereas the expression of TRAIL-R3 is restricted to peripheral blood lymphocytes (PBLs) and skeletal muscle. All three TRAIL receptors bind TRAIL with similar affinity, suggesting a complex regulation of TRAIL-mediated signals