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Humans can portray different expressions contrary to their emotional state of mind. Therefore, it is difficult to judge humans’ real emotional state simply by judging their physical appearance. Although researchers are working on facial expressions analysis, voice recognition, and gesture recognition; the accuracy levels of such analysis are much less and the results are not reliable. Hence, it becomes vital to have realistic emotion detector. Electroencephalogram (EEG) signals remain neutral to the external appearance and behavior of the human and help in ensuring accurate analysis of the state of mind. The EEG signals from various electrodes in different scalp regions are studied for performance. Hence, EEG has gained attention over time to obtain accurate results for the classification of emotional states in human beings for human–machine interaction as well as to design a program where an individual could perform a self-analysis of his emotional state. In the proposed scheme, we extract power spectral densities of multivariate EEG signals from different sections of the brain. From the extracted power spectral density (PSD), the features which provide a better feature for classification are selected and classified using long short-term memory (LSTM) and bi-directional long short-term memory (Bi-LSTM). The 2-D emotion model considered for the classification of frontal, parietal, temporal, and occipital is studied. The region-based classification is performed by considering positive and negative emotions. The performance accuracy of our previous model’s results of artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (K-NN), and LSTM was compared and 94.95% accuracy was received using Bi-LSTM considering four prefrontal electrodes.

Advancements in the Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS) along with recent developments in omics sciences have resulted in a better understanding of molecular mechanisms and pathways associated with the physio-pathological state of the animal. Metabolomics is a post-genomics tool that deals with small molecular metabolites in a given set of time which provides clear information about the status of an organism. Recently many researchers mainly focus their research on metabolomics studies due to its valuable information in the various fields of livestock management and precision dairying. The main aim of the present review is to provide an insight into the current research output from different sources and application of metabolomics in various areas of livestock including nutri-metabolomics, disease diagnosis advancements, reproductive disorders, pharmaco-metabolomics, genomics studies, and dairy production studies. The present review would be helpful in understanding the metabolomics methodologies and use of livestock metabolomics in various areas in a brief way. [ABSTRACT FROM AUTHOR]

The central dogma of molecular biology is responsible for the crucial flow of genetic information from DNA to protein through the transcription and translation process. Although the sequence of DNA is constant in all organs, the difference in protein and variation in the phenotype is mainly due to the quality and quantity of tissue-specific gene expression and methylation pattern. The term methylation has been defined and redefined by various scientists in the last fifty years. There is always huge excitement around this field because the inheritance of something is beyond its DNA sequence. Advanced gene methylation studies have redefined molecular genetics and these tools are considered de novo in alleviating challenges of animal disease and production. Recent emerging evidence has shown that the impact of DNA, RNA, and protein methylation is crucial for embryonic development, cell proliferation, cell differentiation, and phenotype production. Currently, many researchers are focusing their work on methylation to understand its significant role in expression, disease-resistant traits, productivity, and longevity. The main aim of the present review is to provide an overview of DNA, RNA, and protein methylation, current research output from different sources, methodologies, factors responsible for methylation of genes, and future prospects in animal genetics. [ABSTRACT FROM AUTHOR]

One of the most promising and widely used additive manufacturing technologies, fused deposition modelling (FDM), is based on material extrusion and is most commonly used for producing thermoplastic parts for functional applications with the objectives of low cost, minimal waste and ease of material conversion. Considering that pure thermoplastic materials have a significantly poor mechanical performance, it is necessary to enhance the mechanical properties of thermoplastic parts generated using FDM technology. One of the conceivable techniques is to incorporate reinforcing materials such as short glass fibre (SGF) into the thermoplastic matrix in order to produce a polymer composite that can be used in engineering applications, such as structural applications. The morphological and mechanical properties of SGF (short glass fibre) reinforced ABS- (Acrylonitrile Butadiene Styrene) based polymer composites created via the method of FDM (fused deposition modelling) were investigated in this work. Properties were evaluated at three different weight percentages (0, 15 and 30 wt%). The composite filaments were developed using the process of twin screw extrusion. The comparison was made between ABS + SGF (short glass fibre) composites and pure ABS of mechanical properties that include surface roughness, tensile strength and low-velocity impact. The tests were carried out to analyze the properties as per ASTM standards. It has been found that the impact strength and tensile strength show an improvement in glass fibre inclusion; moreover, alongside the direction of build, the surface roughness had been reduced. The studies also focused on studying the dispersion characters of SGF in ABS matrix and its impact on the properties. Strength and modulus of SGF reinforced ABS composite has been significantly improved along with reduction of ductility. A 57% increase in tensile strength has been noted for 30 wt% addition of SGF to ABS in comparison to pure ABS. It was also interesting to note the reduction in surface roughness with every incremental addition of SGF to ABS. A 40% reduction in surface roughness has been observed with a 30 wt% addition of SGF to ABS in comparison to pure ABS.

In data mining, classification is one of the most critical steps in predicting the target class. It is performed by an improved model in existing work in which feature selection is performed based on the bat optimization method to increase the classification accuracy. This study uses an enhanced neural network for classification, including intuitive, interpretable correlated-contours fuzzy rules. Further, a practical model is created based on the extraction of fuzzy rules, where data partitioning is performed via a similarity-based directional component. However, the dataset used for experimentation is noisy and incomplete data values. Due to incompleteness, knowledge discovery is obstructed, and the classification results are affected. Here bat provides very slow convergence and easily falls into local optima. To solve this issue, an improved framework is introduced in which missing value imputation is performed by using k means clustering, and then for feature selection, an improved cuckoo search optimization is used. An enhanced classifier based on fuzzy logic and alex net neural network structure (F-ANNS) is used for classification, and hybrid ant colony particle swarm optimization (HASO) is used for optimizing parameters of the alex net neural network classifier. The results show that the proposed work is more effective in precision, recall, accuracy, and f-measure as shown by experimental results. [ABSTRACT FROM AUTHOR]

Abstract We evaluated the cardioprotective effect of Amalaki Rasayana (AR), a rejuvenating Ayurvedic drug prepared from Phyllanthus emblica fruits in the reversal of remodeling changes in pressure overload left ventricular cardiac hypertrophy (LVH) and age-associated cardiac dysfunction in male Wistar rats. Six groups (aging groups) of 3 months old animals were given either AR or ghee and honey (GH) orally; seventh group was untreated. Ascending aorta was constricted using titanium clips in 3 months old rats (N = 24; AC groups) and after 6 months, AR or GH was given for further 12 months to two groups; one group was untreated. Histology, gene and protein expression analysis were done in heart tissues. Chemical composition of AR was analyzed by HPLC, HPTLC and LC-MS. AR intake improved (P

Millions of lives have been impacted by COVID-19, which has spread rapidly. Several vaccines have been developed to curb the severe prognosis induced by the virus. However, a part of the population (elderly and patients with coexisting conditions) is still at risk. It is crucial to identify these patients early since appropriate treatments can be provided to them to prevent the onset of severe symptoms such as breathlessness and hypoxia. Hence, this study utilizes machine learning and explainable artificial intelligence (XAI) to predict COVID-19 severity using biochemical, haematological and inflammatory markers. The patients are grouped into three classes: mild, moderate and severe. Four nature-inspired techniques have been utilized to select the best markers. The final stacked model obtained a maximum accuracy of 84. Demystifying the models has been done using four XAI techniques, including Shapley additive values (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Lactate dehydrogenase (LDH), albumin, D-Dimer, c-reactive protein (CRP) and lymphocytes were considered important, according to them. The classifiers can be utilized as a prognostic decision support framework to aid the medical personnel in classifying COVID-19 patients. [ABSTRACT FROM AUTHOR]

The theoretical investigation of five 3,3'-(phenylmethylene)bis(1H-indole) derivatives (BIM1, BIM2, BIM3, BIM4 and BIM5) with β-cyclodextrin (β-CD) were studied using density functional theory B3LYP 3-21G method as basis set. Substituents (–OCH2COOH and -OCH3) at the ortho-, para- and meta- positions in the phenyl ring of BIMs (BIM 1–5) has changed the stability of inclusion complexes BIMs:β-CD with forming 1:1 stoichiometric ratio. To access the chemistry behind this orientation and composition, we have studied the geometrical parameters, complexation energy, HOMO-LUMO energy gap, chemical reactivity, thermodynamic parameters, Mulliken charges and molecular electrostatic potential (MEP) map for all inclusion complexes (BIMs:β-CD) with stable orientation. Natural bond orbital (NBO), NCI-RDG and QTAIM analysis revealed the prospects of hydrogen bonds (=N–H–O and –O–H–O) between BIMs and β-CD. [ABSTRACT FROM AUTHOR]