Could NCOA5 a novel candidate gene for multiple sclerosis susceptibility?

BACKGROUND: Multiple sclerosis (MS) is an inflammatory immune-mediated demyelinating disease that causes a challenging and disabling condition. Environmental and genetic factors play a role in appearing the state of the disease. Recent studies have shown that nuclear cofactor genes may play a role in the pathogenesis of MS. NCOA5 is a nuclear receptor coactivator independent of AF2 that modulates ERa-mediated transcription. This gene is involved in the pathogenesis of diseases such as psoriasis, Behcet's disease, and cancer. METHODS AND RESULTS: We investigated the relationship between the rs2903908 polymorphism of the NCOA5 gene and MS among 157 unrelated MS patients and 160 healthy controls by RT-PCR. The frequencies of the CC, CT, and TT genotypes were 19.87%, 37.82%, and 42.31%, respectively, for the MS group and 5.63%, 43.75%, and 50.62%, respectively, for the control group. The CC genotype and the C allele were found to be significantly higher in the patient group (the p values were 0.0002 and 0.003, respectively). CONCLUSIONS: The fact that the CC genotype was found to be significantly higher in the patient group compared to the control group (p = 0.0002) and that it had a statistically significantly higher OR value (OR, 95% CI = 4.16, 1.91-9.05) suggests that the C allele may recessively predispose to MS for this polymorphism. These results suggest for the first time that the NCOA5 gene may have an effect on the occurrence of MS through different molecular pathways, which are discussed in the manuscript.

Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking.

One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in the literature for neural network training. An important group of metaheuristic algorithms is swarm-intelligent-based optimization algorithms. In this study, feed-forward neural network training is carried out for maximum power point tracking by using 13 swarm-intelligent-based optimization algorithms. These algorithms are artificial bee colony, butterfly optimization, cuckoo search, chicken swarm optimization, dragonfly algorithm, firefly algorithm, grasshopper optimization algorithm, krill herd algorithm, particle swarm optimization, salp swarm algorithm, selfish herd optimizer, tunicate swarm algorithm, and tuna swarm optimization. Mean squared error is used as the error metric, and the performances of the algorithms in different network structures are evaluated. Considering the results, a success ranking score is obtained for each algorithm. The three most successful algorithms in both training and testing processes are the firefly algorithm, selfish herd optimizer, and grasshopper optimization algorithm, respectively. The training error values obtained with these algorithms are 4.5 × 10-4, 1.6 × 10-3, and 2.3 × 10-3, respectively. The test error values are 4.6 × 10-4, 1.6 × 10-3, and 2.4 × 10-3, respectively. With these algorithms, effective results have been achieved in a low number of evaluations. In addition to these three algorithms, other algorithms have also achieved mostly acceptable results. This shows that the related algorithms are generally successful ANFIS training algorithms for maximum power point tracking.

A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning.

This survey aims to identify commonly used methods, datasets, future trends, knowledge gaps, constraints, and limitations in the field to provide an overview of current solutions used in medical image analysis in parallel with the rapid developments in transfer learning (TL). Unlike previous studies, this survey grouped the last five years of current studies for the period between January 2017 and February 2021 according to different anatomical regions and detailed the modality, medical task, TL method, source data, target data, and public or private datasets used in medical imaging. Also, it provides readers with detailed information on technical challenges, opportunities, and future research trends. In this way, an overview of recent developments is provided to help researchers to select the most effective and efficient methods and access widely used and publicly available medical datasets, research gaps, and limitations of the available literature.

Deep Learning Methods for Multi-Channel EEG-Based Emotion Recognition.

Currently, Fourier-based, wavelet-based, and Hilbert-based time-frequency techniques have generated considerable interest in classification studies for emotion recognition in human-computer interface investigations. Empirical mode decomposition (EMD), one of the Hilbert-based time-frequency techniques, has been developed as a tool for adaptive signal processing. Additionally, the multi-variate version strongly influences designing the common oscillation structure of a multi-channel signal by utilizing the common instantaneous concepts of frequency and bandwidth. Additionally, electroencephalographic (EEG) signals are strongly preferred for comprehending emotion recognition perspectives in human-machine interactions. This study aims to herald an emotion detection design via EEG signal decomposition using multi-variate empirical mode decomposition (MEMD). For emotion recognition, the SJTU emotion EEG dataset (SEED) is classified using deep learning methods. Convolutional neural networks (AlexNet, DenseNet-201, ResNet-101, and ResNet50) and AutoKeras architectures are selected for image classification. The proposed framework reaches 99% and 100% classification accuracy when transfer learning methods and the AutoKeras method are used, respectively.

3-State Protein Secondary Structure Prediction based on SCOPe Classes

Abstract Improving the accuracy of protein secondary structure prediction has been an important task in bioinformatics since it is not only the starting point in obtaining tertiary structure in hierarchical modeling but also enhances sequence analysis and sequence-structure threading to help determine structure and function. Herein we present a model based on DSPRED classifier, a hybrid method composed of dynamic Bayesian networks and a support vector machine to predict 3-state secondary structure information of proteins. We used the SCOPe (Structural Classification of Proteins-extended) database to train and test the model. The results show that DSPRED reached a Q3 accuracy rate of 82.36% when trained and tested using proteins from all SCOPe classes. We compared our method with the popular PSIPRED on the SCOPe test datasets and found that our method outperformed PSIPRED.