Topical Anti-Inflammatory and Analgesic Activities of Citrullus colocynthis Extract Cream in Rats.

Background and objectives:Citrullus colocynthis (CC), known as bitter apple, is used to treat diabetes in Iranian traditional medicine. The aim of this study is to evaluate the anti-inflammatory and analgesic activities of CC cream in rats. Materials and Methods: The carrageenan-induced edema in a rat hind paw was carried out to evaluate the topical anti-inflammatory effect of the CC fruit extract cream (2⁻8%) and the tissue levels of IL-6 and TNF-α were estimated by using a commercial ELISA kit. The topical antinociceptive activity of CC cream (2⁻8%) was evaluated in the rat formalin test. To determine the role of opioid receptors in the local antinociceptive effect of the CC cream, naloxone (20 μg/paw, i.pl.), a non-selective opioid antagonist, was used. Results: The results showed that the CC cream (2⁻8%) dose-dependently reduced the carrageenan-induced paw edema and reversed the changes in the level of TNF-α and IL-6 due to carrageenan-induced edema (p < 0.01). The anti-inflammatory effect of CC cream 8% was comparable to that of hydrocortisone ointment 1%. Furthermore, the application of CC cream (2⁻8%) dose-dependently inhibited both first and second phases of the formalin test (p < 0.05). The antinociceptive effect of the CC cream (8%) was comparable to that of methyl salicylate cream 30%. Moreover, the administration of naloxone significantly reversed the topical antinociceptive effect of the CC cream (p < 0.05). Conclusions: For the first time, this study indicated that the topical application of CC cream possesses significant anti-inflammatory and antinociceptive activities in animal models, which were probably mediated by opioid receptors and the suppression of pro-inflammatory cytokines (TNF-α and IL-6). Thus, the CC cream can be used to treat inflammatory pain and inflammatory diseases

An advanced short-term wind power forecasting framework based on the optimized deep neural network models

With the continued growth of wind power penetration into conventional power grid systems, wind power forecasting plays an increasingly competitive role in organizing and deploying electrical and energy systems. The wind power time series, though, often present non-linear and non-stationary characteristics, allowing them quite challenging to estimate precisely. The aim of this paper is in proposing a novel hybrid model named Evol-CNN in order to predict the short-term wind power at 10-min interval up to 3-hr based on deep convolutional neural network (CNN) and evolutionary search optimizer. Specifically, we develop an improved version of Grey Wolf Optimization (GWO) algorithm by incorporating two effective modifications in its original structure. The proposed GWO algorithm is more effective than the original version due to performing in a faster way and the ability to escape from local optima. The proposed GWO algorithm is utilized to find the optimal values of hyperparameters for deep CNN model. Moreover, the optimal CNN model is employed to predict wind power time series. The main advantage of the proposed Evol-CNN model is to enhance the capability of time series forecasting models in obtaining more accurate predictions. Several forecasting benchmarks are compared with the Evol-CNN model to address its effectiveness. The simulation results indicate that the Evol-CNN has a significant advantage over the competitive benchmarks and also, has the minimum error regarding of 10-min, 1-hr and 3-hr ahead forecasting.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Comprehensive phylogenetic, similarity and allergenicity analysis of Boophilus genus tick Tropomyosin protein

     Boophilus genus ticks are responsible for transferring some pathogens and reducing production factors in cattle. Tropomysin (TPM) protein has actin regulator activity and playing important role in immune and allergic reactions. The main goal is to determine different aspects of phylogenetic, similarity, homology, structure and allergenicity of TPM protein. In prior study, we identified TPM by using Mass-spectrometry in Boophilus anulatus larva proteins extraction. Analysis by NCBI and Mascot software showed complete similarity of this protein with Boophilus microplus. TPM Blasting, invertebrates TPM sequences retrieval, aligning and analyzing of conserved and variable regions along sequences were next steps. Also, construction the phylogenetic tree, overall mean distances estimation, homology protein secondary structure, allergencity analysis was achieved. The most similar sequences to Boophilus genus TPM are Haemaphysalis sp., Scolopendra sp. and etc., respectively. The multiple sequence alignment showed that conserved and variable regions stretched in different part of TPM. The close relationships in Phylogenetic tree between Ticks and Mites were seen, although the TPM sequences in ticks are more similar to each other than to mites and assume as the nearest relatives. Insects TPM like worms, located in two separated clades, and Trichinella spiralis in worm clades are more related taxa to members of ticks and mites groups. Furthermore, overall mean distances over sequence pairs reflects TPM conservation during speciation. TPM has high homology in different species and has two domain of α-helix that cannot form disulfide bonds. Finally, allergenicity analysis by separated and hybrid approach showed it undoubted is allergen and candidates some peptides as responsible for allergenicity of TPM. The comprehensive analysis of TPM has never been easy, especially when we attempt to make statements from different aspects about this protein.  Our study revealed the some unique and valuable aspects of TPM protein of Boophilus genus, and will help to further studies on mentioned protein

Learning Deep Architectures for Power Systems Operation and Analysis

With the rapid increase in size and computational complexities of power systems, the need for powerful computational models to capture strong patterns from energy datasets is emerged. In this thesis, we provide a comprehensive review on recent advances in deep neural architectures that lead to significant improvements in classification and regression problems in the area of power engineering. Furthermore, we introduce our novel deep learning methodologies proposed for a large variety of applications in this area. First, we present the interval deep probabilistic modeling for wind speed forecasting. Incorporating the Rough Set Theory into deep neural networks, we create an accurate interval model for point prediction of intermittent wind speed datasets. Then, we develop a graph convolutional neural network for the spatiotemporal prediction of wind speed values in multiple neighboring wind sites. Our provided numerical results show the great improvement of prediction accuracy compared to classic deep learning. Using the concept of graph convolutions, we also develop a new conditional graph variational autoencoder to learn the probability density of future solar irradiance given the historical solar irradiance of multiple photovoltaic energy sites. This study led to the state-of-the-art performance in probabilistic solar prediction in power systems domain. Moreover, we introduced a novel multimodal deep recurrent structure that makes use of both system-wide power and voltage measurements as well as load parameters for accurate real-time load modeling. The numerical results show the significant improvement of this method compared to classic deep learning in estimating dynamic load parameters of smart grids. Moreover, we develop deep dictionary learning as a new paradigm in machine learning for energy disaggregation and behind-the-meter net load decomposition. The presented work leads to the best accuracy in comparison with recent sparse coding and dictionary learning-based decomposition methods in the literature. Finally, a novel deep generative model is introduced to learn the probability density of the measurements on the nodes and edges of a power grid. Using this model, we take a large number of samples from the probability distribution of the structure of power systems, hence, generating synthetic power networks with the same topological and physical behaviors as the original power system. Our simulation results on real-world datasets show the great improvements of the proposed approach compared to the data-driven approaches in the recent literature

Deep Neural Networks in Power Systems: A Review

Identifying statistical trends for a wide range of practical power system applications, including sustainable energy forecasting, demand response, energy decomposition, and state estimation, is regarded as a significant task given the rapid expansion of power system measurements in terms of scale and complexity. In the last decade, deep learning has arisen as a new kind of artificial intelligence technique that expresses power grid datasets via an extensive hypothesis space, resulting in an outstanding performance in comparison with the majority of recent algorithms. This paper investigates the theoretical benefits of deep data representation in the study of power networks. We examine deep learning techniques described and deployed in a variety of supervised, unsupervised, and reinforcement learning scenarios. We explore different scenarios in which discriminative deep frameworks, such as Stacked Autoencoder networks and Convolution Networks, and generative deep architectures, including Deep Belief Networks and Variational Autoencoders, solve problems. This study’s empirical and theoretical evaluation of deep learning encourages long-term studies on improving this modern category of methods to accomplish substantial advancements in the future of electrical systems

Topical Anti-Inflammatory and Analgesic Activities of Citrullus colocynthis Extract Cream in Rats

Background and objectives:Citrullus colocynthis (CC), known as bitter apple, is used to treat diabetes in Iranian traditional medicine. The aim of this study is to evaluate the anti-inflammatory and analgesic activities of CC cream in rats. Materials and Methods: The carrageenan-induced edema in a rat hind paw was carried out to evaluate the topical anti-inflammatory effect of the CC fruit extract cream (2&ndash;8%) and the tissue levels of IL-6 and TNF-&alpha; were estimated by using a commercial ELISA kit. The topical antinociceptive activity of CC cream (2&ndash;8%) was evaluated in the rat formalin test. To determine the role of opioid receptors in the local antinociceptive effect of the CC cream, naloxone (20 &mu;g/paw, i.pl.), a non-selective opioid antagonist, was used. Results: The results showed that the CC cream (2&ndash;8%) dose-dependently reduced the carrageenan-induced paw edema and reversed the changes in the level of TNF-&alpha; and IL-6 due to carrageenan-induced edema (p &lt; 0.01). The anti-inflammatory effect of CC cream 8% was comparable to that of hydrocortisone ointment 1%. Furthermore, the application of CC cream (2&ndash;8%) dose-dependently inhibited both first and second phases of the formalin test (p &lt; 0.05). The antinociceptive effect of the CC cream (8%) was comparable to that of methyl salicylate cream 30%. Moreover, the administration of naloxone significantly reversed the topical antinociceptive effect of the CC cream (p &lt; 0.05). Conclusions: For the first time, this study indicated that the topical application of CC cream possesses significant anti-inflammatory and antinociceptive activities in animal models, which were probably mediated by opioid receptors and the suppression of pro-inflammatory cytokines (TNF-&alpha; and IL-6). Thus, the CC cream can be used to treat inflammatory pain and inflammatory diseases

Maximum Relevance Minimum Redundancy Dropout with Informative Kernel Determinantal Point Process

In recent years, deep neural networks have shown significant progress in computer vision due to their large generalization capacity; however, the overfitting problem ubiquitously threatens the learning process of these highly nonlinear architectures. Dropout is a recent solution to mitigate overfitting that has witnessed significant success in various classification applications. Recently, many efforts have been made to improve the Standard dropout using an unsupervised merit-based semantic selection of neurons in the latent space. However, these studies do not consider the task-relevant information quality and quantity and the diversity of the latent kernels. To solve the challenge of dropping less informative neurons in deep learning, we propose an efficient end-to-end dropout algorithm that selects the most informative neurons with the highest correlation with the target output considering the sparsity in its selection procedure. First, to promote activation diversity, we devise an approach to select the most diverse set of neurons by making use of determinantal point process (DPP) sampling. Furthermore, to incorporate task specificity into deep latent features, a mutual information (MI)-based merit function is developed. Leveraging the proposed MI with DPP sampling, we introduce the novel DPPMI dropout that adaptively adjusts the retention rate of neurons based on their contribution to the neural network task. Empirical studies on real-world classification benchmarks including, MNIST, SVHN, CIFAR10, CIFAR100, demonstrate the superiority of our proposed method over recent state-of-the-art dropout algorithms in the literature