A T-stub Square Ring Resonator (SRR) based Ultra-Wide Band (UWB) Band Pass Filter (BPF) is studied and investigated in this paper. The proposed filter is based on coupled feed line connected to the ...T-stub SRR. Ultra-wideband characteristics can be realized by adjusting the T-stub lengths and coupling the gaps between both sides of waveguides and SRR. The characteristics of the T-stub SRR show that the miniaturized UWB BPF can be operated at THz frequencies. The proposed UWB filter is simulated and analyzed using the Finite Differential Time Domain (FDTD) solver-based Computer Simulation Technology (CST) studio suite. The resonance conditions are explained and the transmission performance of the filter agrees with the simulated and theoretical calculations. The proposed filter is best suitable for Electronic-Plasmonic Integrated Circuits (EPICs).
Manganese superoxide dismutase (MnSOD) is a mitochondrially localized primary antioxidant enzyme, known to be essential for the survival of aerobic life and to have important roles in tumorigenesis. ...Here, we show that MnSOD deficiency in skin tissues of MnSOD-heterozygous knockout (Sod2(+/-)) mice leads to increased expresson of uncoupling proteins (UCPs). When MnSOD is deficient, superoxide radical and its resulting reactive oxygen species (ROS) activate ligand binding to peroxisome proliferator-activated receptor alpha (PPARα), suggesting that the activation of PPARα signaling is a major mechanism underlying MnSOD-dependent UCPs expression that consequently triggers the PI3K/Akt/mTOR pathway, leading to increased aerobic glycolysis. Knockdown of UCPs and mTOR suppresses lactate production and increases ATP levels, suggesting that UCPs contribute to increased glycolysis. These results highlight the existence of a free radical-mediated mechanism that activates mitochondria uncoupling to reduce ROS production, which precedes the glycolytic adaptation described as the Warburg Effect.
Abstract Doxorubicin (Dox) is a potent, broad-spectrum chemotherapeutic drug used around the world. Despite its effectiveness, it has a wide range of toxic side effects, many of which most likely ...result from its inherent pro-oxidant activity. It has been reported that Dox has toxic effects on normal tissues, including brain tissue. The present study tested the protective effect of a xanthone derivative of Garcinia Mangostana against Dox-induced neuronal toxicity. Xanthone can prevent Dox from causing mononuclear cells to increase the level of tumor necrosis factor-alpha (TNFα). We show that xanthone given to mice before Dox administration suppresses protein carbonyl, nitrotyrosine and 4-hydroxy-2′-nonenal (4HNE)-adducted proteins in brain tissue. The levels of the pro-apoptotic proteins p53 and Bax and the anti-apoptotic protein Bcl-xL were significantly increased in Dox-treated mice compared with the control group. Consistent with the increase of apoptotic markers, the levels of caspase-3 activity and TUNEL-positive cells were also increased in Dox-treated mice. Pretreatment with xanthone suppressed Dox-induced increases in all indicators of injury tested. Together, the results suggest that xanthone prevents Dox-induced central nervous system toxicity, at least in part, by suppression of Dox-mediated increases in circulating TNFα. Thus, xanthone is a good candidate for prevention of systemic effects resulting from reactive oxygen generating anticancer therapeutics.
Lysophosphatidic acid (LPA), its sphingolipid homolog sphingosine 1-phosphate (S1P) and several other related molecules constitute a family of bioactive lipid phosphoric acids that function as ...receptor-active mediators with roles in cell growth, differentiation, inflammation, immunomodulation, apoptosis and development. LPA and S1P are present in physiologically relevant concentrations in the circulation. In isolated cell culture systems or animal models, these lipids exert a range of effects that suggest that S1P and LPA could play important roles in maintaining normal vascular homeostasis and in vascular injury responses. LPA and S1P act on a series of G protein-coupled receptors, and LPA may also be an endogenous regulator of PPARgamma activity. In this review, we discuss potential roles for lysolipid signaling in the vasculature and mechanisms by which these bioactive lipids could contribute to cardiovascular disease.
One of the standard approaches for data analysis in unsupervised machine learning techniques is cluster analysis or clustering, where the data possessing similar features are grouped into a certain ...number of clusters. Among several significant ways of performing clustering, Fuzzy C-means (FCM) is a methodology, where every data point is hypothesized to be associated with all the clusters through a fuzzy membership function value. FCM is performed by minimizing an objective functional by optimally estimating the decision variables namely, the membership function values and cluster representatives, under a constrained environment. With this approach, a marginal increase in the number of data points leads to an enormous increase in the size of decision variables. This explosion, in turn, prevents the application of evolutionary optimization solvers in FCM, which thereby leads to inefficient data clustering. In this paper, a Neuro-Fuzzy C-Means Clustering algorithm (NFCM) is presented to resolve the issues mentioned above by adopting a novel Artificial Neural Network (ANN) based clustering approach. In NFCM, a functional map is constructed between the data points and membership function values, which enables a significant reduction in the number of decision variables. Additionally, NFCM implements an intelligent framework to optimally design the ANN structure, as a result of which, the optimal number of clusters is identified. Results of 9 different data sets with dimensions ranging from 2 to 30 are presented along with a comprehensive comparison with the current state-of-the-art clustering methods to demonstrate the efficacy of the proposed algorithm.
The capability of wind power to meet the energy demand inspired researchers to develop techniques for harnessing this clean and renewable energy. As a primary step, accurate forecasting of wind ...characteristics by modelling the stochastic nature of wind is done. Although, statistical methods provide good results in forecasting, they are inferior to Deep Learning based tools while handling extreme nonlinearities in wind characteristics. The authors in this work implemented Long Short Term Memory (LSTM) networks, a deep learning based tool, for modelling wind time series data due to its efficiency in handling long term dependencies. However, several hyper-parameters like activation function and design are chosen heuristically, making the modelling process tedious and inefficient. In this study, novel multi objective optimization formulation, driven by NSGA II, is proposed to design the LSTM networks with respect to the conflicting objectives of accuracy and parsimony. The resultant optimal LSTM models are used for long term forecasting (2 years) of wind characteristics data, with an accuracy of 97%, obtained from a real wind farm in France. To demonstrate the importance of this forecasting, a study of wind power calculations on a real wind farm is conducted. For a given layout, the effect of wind frequency scenarios, generated from the time series data of wind, on the annual power calculations, is determined. The existing and forecasted values of wind speed and direction over longer periods of time resulted in realistic values of expected power from wind farm. This study demonstrates the importance of forecasting while evaluating the power which can impact research in fields such as wind farm layout optimization and control under uncertainty.
Owing to the generation of vast amount of unlabelled dynamic data and the need to analyze them, deep unsupervised learning based clustering algorithms are gaining importance in the field of data ...science. Since the task of automated feature extraction is proficiently combined with the machine learning models in deep unsupervised learning algorithms, they are identified to be superior as compared to conventional dynamic similarity measure based clustering methods. In this context, the authors present a recurrent neural network (RNN) based clustering algorithm optimization, where the vital information representing the dynamic data (or time-series data) is extracted first and subsequently clustered using a soft clustering algorithm. This methodology not only ensures dynamic component extraction in terms of static features but also clusters them efficiently using an evolutionary clustering algorithm called Neuro-Fuzzy C-Means (NFCM) clustering, which reduces the large-scale optimization problem of FCM to small-scale along-with identification of optimal number of clusters. The proposed algorithm has been implemented on three different test data sets collected from machine learning repository and it was found that the results are 98-100% accurate.
Calcium spiking can be used for drug screening studies in pharmaceutical industries. However, performing experiments for multiple drugs and doses are highly expensive. The oscillatory behavior of ...calcium spiking data demonstrates extreme nonlinearity and phase singularity. This makes it more challenging to construct physics-based models for the experimental observations. In this scenario, data based modelling, such as Artificial Neural Networks (ANN), and thereafter the model based prediction of calcium profiles may offer a cost-effective and time saving solution. Therefore, a novel ANN building algorithm is presented in the current work, where data based simultaneous estimation of ANN architecture and nonlinear activation function stands out as the main highlight. The resultant ANN was then used to learn the oscillatory behavior in calcium ion concentration data, obtained from hippocampal neurons of rats by fluorescent labelling and confocal imaging. The paper shows that the novel technique can be used in general for emulating biochemical oscillations (with or without drug injection) and can be implemented to predict the cell-drug responses for intermediated doses. The proposed algorithm can also be used for obtaining high resolution data from low resolution experimental measurements.
Neural synchronicity plays a vital role in monitoring the functions that are cognitive. Any disturbance identified in the neural synchrony might lead to a diseased state. In the case of in vitro cell ...recordings, the neurons demonstrate significant heterogeneity in the firing pattern. Thus, the task of automated identification of synchronous and asynchronous neurons from a large population of neuronal cells remains challenging. To address this issue, an efficient unsupervised machine learning approach has been proposed for a system of primary cultures of hippocampal neurons. Here, a confocal microscope is used for imaging of intracellular calcium using Fluo-4 as the fluorescent indicator. The obtained static images are transformed into time-varying data of cytosolic calcium. Subsequently, an intelligent artificial neural network (ANN) assisted fuzzy clustering algorithm is proposed for grouping the synchronous neurons from a heterogeneous set of calcium data that are spiking in nature. This novel algorithm enables a drastic variable reduction followed by the implementation of a global optimization algorithm to solve the problem in Fuzzy C-means (FCM) clustering. Additionally, the proposed technique computes the optimal cluster number and the hyper-parameters involved in ANNs. To validate the result obtained from ANN assisted FCM, a correlation coefficient, and a spiking pattern plot is analyzed for both the synchronous and asynchronous neuronal cells. Besides this, the proposed algorithm is compared with the traditional FCM, where the solution quality is found to be improved along-with an 88% reduction in decision variable count. The complete novel framework combines the aspects of calcium imaging, ANN-assisted FCM, validation, and comparison, which as a whole, can be used for quick and effective quantification of synchronicity.
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