Hydrogen Sulfide Metabolite,Sodium Thiosulfate: Clinical Applications and Underlying Molecular Mechanisms

Thiosulfate in the form of sodium thiosulfate (STS) is a major oxidation product of hydrogen sulfide (H₂S), an endogenous signaling molecule and the third member of the gasotransmitter family. STS is currently used in the clinical treatment of acute cyanide poisoning, cisplatin toxicities in cancer therapy, and calciphylaxis in dialysis patients. Burgeoning evidence show that STS has antioxidant and anti-inflammatory properties, making it a potential therapeutic candidate molecule that can target multiple molecular pathways in various diseases and drug-induced toxicities. This review discusses the biochemical and molecular pathways in the generation of STS from H₂S, its clinical usefulness, and potential clinical applications, as well as the molecular mechanisms underlying these clinical applications and a future perspective in kidney transplantation.     

Design of interval networks based on neural network and Choquet integral

Design and learning of networks best suited for a particular application is a never-ending process. But this process is restricted due to problems like stability, plasticity, computation and memory consumption. In this paper, we try to overcome these problems by proposing two interval networks (INs), based on a simple feed-forward neural network (NN) and Choquet integral (CI). They have simple structures that reduce the problems of computation and memory consumption. The use of Lyapunov stability (LS) in combination with fuzzy difference (FD) based learning algorithm evolve the converging and diverging process which in turn assures the stability. FD gives a range of variation of parameters having the lower and the upper bounds within which the system is stable thus defining the plasticity. Effectiveness and applicability of the NN and CI based network models are investigated on several benchmark problems dealing with both identification and control.     

Identification of Natural Products as Potential Pharmacological Chaperones for Protein Misfolding Diseases

Defective protein folding and accumulation of misfolded proteins is associated with neurodegenerative, cardiovascular, secretory, and metabolic disorders. Efforts are being made to identify small-molecule modulators or structural-correctors for conformationally destabilized proteins implicated in various protein aggregation diseases. Using a metastable-reporter-based primary screen, we evaluated pharmacological chaperone activity of a diverse class of natural products. We found that a flavonoid glycoside ( C-10 , chrysoeriol-7-O-β-D-glucopyranoside) stabilizes metastable proteins, prevents its aggregation, and remodels the oligomers into protease-sensitive species. Data was corroborated with additional secondary screen with disease-specific pathogenic protein. In vitro and cell-based experiments showed that C-10 inhibits α-synuclein aggregation which is implicated in synucleinopathies-related neurodegeneration. C-10 interferes in its structural transition into β-sheeted fibrils and mitigates α-synuclein aggregation-associated cytotoxic effects. Computational modeling suggests that C-10 binds to unique sites in α-synuclein which may interfere in its aggregation amplification. These findings open an avenue for comprehensive SAR development for flavonoid glycosides as pharmacological chaperones for metastable and aggregation-prone proteins implicated in protein conformational diseases.     

The use of background and ability profiles to predict college student outcomes.

To determine whether profiles of predictor variables provide incremental prediction of college student outcomes, the authors 1st applied an empirical clustering method to profiles based on the scores of 2,771 entering college students on a battery of biographical data and situational judgment measures, along with SAT and American College Test scores and high school grade point average, which resulted in 5 student groups. Performance of the students in these clusters was meaningfully different on a set of external variables, including college grade point average, self-rated performance, class absenteeism, organizational citizenship behavior, intent to quit their university, and satisfaction with college. The 14 variables in the profile were all significantly correlated with 1 or more of the outcome measures; however, nonlinear prediction of these outcomes on the basis of cluster membership did not add incrementally to a linear-regression-based combination of these 14 variables as predictors. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Potential Role of Kallistatin in the Development of Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA) is a vascular condition that causes permanent dilation of the abdominal aorta, which can lead to death due to aortic rupture. The only treatment for AAA is surgical repair, and there is no current drug treatment for AAA. Aortic inflammation, vascular smooth muscle cell apoptosis, angiogenesis, oxidative stress and vascular remodeling are implicated in AAA pathogenesis. Kallistatin is a serine proteinase inhibitor, which has been shown to have a variety of functions, potentially relevant in AAA pathogenesis. Kallistatin has been reported to have inhibitory effects on tumor necrosis factor alpha (TNF-α) signaling induced oxidative stress and apoptosis. Kallistatin also inhibits vascular endothelial growth factor (VEGF) and Wnt canonical signaling, which promote inflammation, angiogenesis, and vascular remodeling in various pre-clinical experimental models. This review explores the potential protective role of kallistatin in AAA pathogenesis.     

ABC transporters Cdr1p, Cdr2p and Cdr3p of a human pathogen Candida albicans are general phospholipid translocators

We have used fluorescent 7-nitrobenz-2-oxa-1,3-diazol-4-yl (NBD)-tagged phospholipid analogues, NBD-PE (phosphatidylethanolamine), NBD-PC (phosphatidylcholine) and NBD-PS (phosphatidylserine), to demonstrate that Cdr1p and its other homologues, Cdr2p and Cdr3p, belonging to the ATP-binding cassette (ABC) superfamily behave as general phospholipid translocators. Interestingly, CDR1 and CDR2, whose overexpression leads to azole resistance in C. albicans, elicit in-to-out transbilayer phospholipid movement, while CDR3, which is not involved in drug resistance, carries out-to-in translocation of phospholipids between the two monolayers of plasma membrane. Cdr1p, Cdr2p and Cdr3p could be further distinguished on the basis of their sensitivities to different inhibitors. For example, the in-to-out activity associated with Cdr1p and Cdr2p is energy-dependent and sensitive to sulphydryl blocking agents such as N-ethylmaleimide (NEM) and cytoskeleton disrupting agent cytochalasin E, while Cdr3p-associated out-to-in activity is energy-dependent but insensitive to NEM and cytochalasin E. We found that certain drugs, such as fluconazole, cycloheximide and miconazole, to which Cdr1p confers resistance could also affect in-to-out transbilayer movement of NBD-PE, while the same drugs had no effect on Cdr3p-mediated out-to-in translocation of NBD-PE. The ineffectiveness of these drugs to affect Cdr3p mediated out-to-in phospholipid translocation further confirms the inherent difference in the directionality of phospholipid translocation between these pumps. Notwithstanding the role of some of the Cdrps in drug resistance, this study clearly demonstrates that these ABC transporters of C. albicans are phospholipid translocators and this function could represent one of the physiological functions of such large family of proteins.     

Modeling of non‐linear systems by FWNNs and their intelligent control

By utilizing some of the important properties of wavelets like denoising, compression, multiresolution along with the concepts of fuzzy logic and neural network, two fuzzy wavelet neural networks (FWNNs) are proposed for approximating any arbitrary non‐linear function, hence, identifying a non‐linear system. We have fuzzified the output of DWT block, which receives the given inputs, in the proposed two methods: one using compression property and other using multiresolution property. We present a new type of fuzzy neuron model, each non‐linear synapse of which is characterized by a set of fuzzy implication rules with singleton weights in their consequents. It is shown that noise and disturbance in the reference signal are reduced with wavelets and also the variation of somatic gain, the parameter that controls the slope of the activation function in the neural network, leads to more accurate output. Identification results are found to be accurate and speed of their convergence is fast. Next, we simulate a control system for keeping output at a desired level by using the identified models. Two self‐learning controllers are designed in this simulation. One is a self‐learning fuzzy PI controller and other is a NN controller. Simulation results show that the NN controller is more adaptive and fast. Copyright © 2005 John Wiley & Sons, Ltd.     

Optimal Non-Invasive Fault Classification Model for Packaged Ceramic Tile Quality Monitoring Using MMW Imaging

Millimeter wave (MMW) frequency has emerged as an efficient tool for different stand-off imaging applications. In this paper, we have dealt with a novel MMW imaging application, i.e., non-invasive packaged goods quality estimation for industrial quality monitoring applications. An active MMW imaging radar operating at 60 GHz has been ingeniously designed for concealed fault estimation. Ceramic tiles covered with commonly used packaging cardboard were used as concealed targets for undercover fault classification. A comparison of computer vision-based state-of-the-art feature extraction techniques, viz, discrete Fourier transform (DFT), wavelet transform (WT), principal component analysis (PCA), gray level co-occurrence texture (GLCM), and histogram of oriented gradient (HOG) has been done with respect to their efficient and differentiable feature vector generation capability for undercover target fault classification. An extensive number of experiments were performed with different ceramic tile fault configurations, viz., vertical crack, horizontal crack, random crack, diagonal crack along with the non-faulty tiles. Further, an independent algorithm validation was done demonstrating classification accuracy: 80, 86.67, 73.33, and 93.33 % for DFT, WT, PCA, GLCM, and HOG feature-based artificial neural network (ANN) classifier models, respectively. Classification results show good capability for HOG feature extraction technique towards non-destructive quality inspection with appreciably low false alarm as compared to other techniques. Thereby, a robust and optimal image feature-based neural network classification model has been proposed for non-invasive, automatic fault monitoring for a financially and commercially competent industrial growth.     

A fast-sensing readout circuit in 240 Hz enabled by code division multiple access (CDMA) implemented for an ultra-thin on-cell flexible capacitive touch panel

A new fast readout circuit employing the known coding scheme of code division multiple access (CDMA) is successfully designed and applied to a 7-inch ultra-thin, flexible on-cell touch screen panel (TSP). The adopted CDMA is known originally as a coding scheme for data communication, which is applied in this study to address the sensing electrodes of the ultra-thin flexible touch panel. Due to the orthogonality between the driving signals to the touch panel coded by Walsh transform, one type of CDMA, the interference noises between sensing electrodes can be reduced effectively to render accurate touch sensing results. The electromagnetic interference from the flexible display can also be filtered out as baseline component in the output signal. And the frame time of touch reporting can be substantially shortened. Following the sensing electrode is a new readout designed of the switched-capacitor (SC) circuit, to avoid distributing sample signals from parasitic capacitance and also to enlarge the voltage changes due to the capacitance changes caused by touches. A 12-bit analog-to-digital converter (ADC) is orchestrated after the SC circuit to transform the front-end analog signal to digital codes. The digital part of the designed readout adopts a correction algorithm to eliminate the background signals from the display, and also a moving average algorithm to minimize the higher-frequency noises from the display and other electrodes. Experiments are conducted to validate the expected performance. It is evidenced that the Walsh code driving algorithm improves the quality of the readout output signal to be in 42 dB SNR, the report rate to a fast 240 Hz, and a power consumption of 0.39 mW by each sensing channel.

     

Pattern‐Similarity‐Based Model for Time Series Prediction

This research proposes a pattern/shape‐similarity‐based clustering approach for time series prediction. This article uses single hidden Markov model (HMM) for clustering and combines it with soft computing techniques (fuzzy inference system/artificial neural network) for the prediction of time series. Instead of using distance function as an index of similarity, here shape/pattern of the sequence is used as the similarity index for clustering, which overcomes few of the shortcomings associated with distance‐based clustering approaches. Underlying hidden properties of time series are captured with the help of HMM. The prediction method used here exploits the pattern identification prowess of the HMM for cluster selection and the generalization and nonlinear modeling capabilities of soft computing methods to predict the output of the system. To see the validity of the proposed method in the real‐life scenario, it is tested on four different time series. The first is a benchmark Mackey–Glass time series, which is tested for delay parameters τ = 17 and τ = 30. The remaining time series are monthly sunspot data time series, Laser data time series and the last is Lorenz attractor time series. Simulation results show that the proposed method provide a better prediction performance in comparison with the existing methods.