Simultaneous Sparse Approximation Using an Iterative Method with Adaptive Thresholding

This paper studies the problem of Simultaneous Sparse Approximation (SSA). This problem arises in many applications which work with multiple signals maintaining some degree of dependency such as radar and sensor networks. In this paper, we introduce a new method towards joint recovery of several independent sparse signals with the same support. We provide an analytical discussion on the convergence of our method called Simultaneous Iterative Method with Adaptive Thresholding (SIMAT). Additionally, we compare our method with other group-sparse reconstruction techniques, i.e., Simultaneous Orthogonal Matching Pursuit (SOMP), and Block Iterative Method with Adaptive Thresholding (BIMAT) through numerical experiments. The simulation results demonstrate that SIMAT outperforms these algorithms in terms of the metrics Signal to Noise Ratio (SNR) and Success Rate (SR). Moreover, SIMAT is considerably less complicated than BIMAT, which makes it feasible for practical applications such as implementation in MIMO radar systems

Possible mechanism(s) for relaxant effect of aqueous and macerated extracts from Nigella sativa on tracheal chains of guinea pig

BACKGROUND: In previous studies, the relaxant, anticholinergic (functional antagonism) and antihistaminic effects of Nigella sativa have been demonstrated on guinea pig tracheal chains. To elucidate the other mechanisms responsible for the relaxant effect of this plant, its inhibitory effect on the calcium channel was examined in this study. RESULTS: The inhibitory effects of both concentrations of diltiazem in all three groups of experiments were significantly greater than those of saline (p < 0.01 to P < 0.001). The inhibitory of two larger concentrations of aqueous extracts in group 1 and 2 were significantly greater than those of saline (p < 0.01 to P < 0.001). The effect of two larger concentrations of macerated extract in group 1 and all concentrations of this extract in group 2 were also significantly greater than those of saline (p < 0.01 to P < 0.001). However, the extract of Nigella sativa did not show any inhibitory effect in group 3. There was a significant correlation between inhibitory effect and increasing concentrations for both extracts and diltiazem in groups 1 and 2 (p < 0.05 to p < 0.005). CONCLUSION: Although the extracts of Nigella sativa showed inhibitory effects on pre-contracted tracheal chains in the presence of both ordinary and calcium free Krebs solution, the absence of inhibitory effects of the extracts on KCl induced contraction of tracheal chains suggest that the calcium channel blocking effect of this plant dose not contribute to the relaxant effect of this plant on the tracheal chains of guinea pigs

Spoken Persian digits recognition using deep learning

Classification of isolated digits is a fundamental challenge for many speech classification systems. Previous works on spoken digits have been limited to the numbers 0 to 9. In this paper, we propose two deep learning-based models for spoken digit recognition in the range of 0 to 599. The first model is a Convolutional Neural Network (CNN) model that uses the Mel spectrogram obtained from the audio data. The second model uses the recent advances in deep sequential models, especially the Transformer model followed by a Long Short-Term Memory (LSTM) Network and a classifier. Moreover, we also collected a dataset, including audio data by a contribution of 145 people, covering the numerical range from 0 to 599. The experimental results on the collected dataset indicate a validation accuracy of 98.03%

Brief Azacytidine Step Allows The Conversion of Suspension Human Fibroblasts into Neural Progenitor-Like Cells

In recent years transdifferentiation technology has enabled direct conversion of human fibroblasts to become a valuable, abundant and accessible cell source for patient-specific induced cell generation in biomedical research. The majority of transdifferentiation approaches rely upon viral gene delivery which due to random integration with the host genome can cause genome instability and tumorigenesis upon transplantation. Here, we provide a simple way to induce neural progenitor-like cells from human fibroblasts without genetic manipulation by changing physicochemical culture properties from monolayer culture into a suspension in the presence of a chemical DNA methyltransferase inhibitor agent, Azacytidine. We have demonstrated the expression of neural progenitor-like markers, morphology and the ability to spontaneously differentiate into neural-like cells. This approach is simple, inexpensive, lacks genetic manipulation and could be a foundation for future chemical neural transdifferentiation and a safe induction of neural progenitor cells from human fibroblasts for clinical applications

Comprehensive Gene Expression Analysis of Human Embryonic Stem Cells during Differentiation into Neural Cells

Global gene expression analysis of human embryonic stem cells (hESCs) that differentiate into neural cells would help to further define the molecular mechanisms involved in neurogenesis in humans. We performed a comprehensive transcripteome analysis of hESC differentiation at three different stages: early neural differentiation, neural ectoderm, and differentiated neurons. We identified and validated time-dependent gene expression patterns and showed that the gene expression patterns reflect early ESC differentiation. Sets of genes are induced in primary ectodermal lineages and then in differentiated neurons, constituting consecutive waves of known and novel genes. Pathway analysis revealed dynamic expression patterns of members of several signaling pathways, including NOTCH, mTOR and Toll like receptors (TLR), during neural differentiation. An interaction network analysis revealed that the TGFβ family of genes, including LEFTY1, ID1 and ID2, are possible key players in the proliferation and maintenance of neural ectoderm. Collectively, these results enhance our understanding of the molecular dynamics underlying neural commitment and differentiation

Spinal Cord Injury Affects Gene Expression of Transmembrane Proteins in Tissue and Release of Extracellular Vesicle in Blood: In Silico and In Vivo Analysis

Objective: Spinal cord injury (SCI) can disrupt membrane transmission by affecting transmembrane channels orneurotransmitter release. This study aimed to explore gene expression changes of transmembrane proteins underlyingSCI through bioinformatics approaches and confirming in SCI model in rats.Materials and Methods: In this experimental study, the differentially expressed genes (DEGs) in acute and subacuteSCI were obtained based on microarray data downloaded from the gene expression omnibus (GEO). Transmembraneproteins of DEGs were recognized by using the UniProt annotation and transmembrane helices prediction (TMHMM)methods. The model of SCI was established through a weight-dropping procedure in rats. To confirm the SCI model,hematoxylin and eosin (H&E) staining was performed. Total mRNA was extracted from spinal cord tissues, and the RNAexpression profile of some of the significantly changed genes in the previous part that has been confirmed by real-timepolymerase chain reaction (PCR). Blood was collected from rats before sacrificing. Extracellular vesicles (EVs) wereisolated by high-speed centrifugation from plasma. For the assessment of protein expression, western blotting wasused.Results: Based on bioinformatics analysis, we candidated a set of membrane proteins in SCI’s acute and sub-acutephases, and confirmed significant upregulation in Grm1, Nrg1, CD63, Enpp3, and Cxcr4 between the acute and controlgroups and downregulation in Enpp3 between acute and subacute groups at the RNA level. Considering CD63 as anEV marker, we examined the protein expression of CD9 and CD63 in the plasma-derived EVs, and CD9 has significantexpression between acute and control groups. We also demonstrate no significant CD63 and Cxcr4 expressionsbetween groups.Conclusion: Our results provide new insight into the relationship between candidate transmembrane protein expressionand different stages of SCI using in-silico approaches. Also, results show the release of EVs in blood in each group afterSCI helping enlarge strategies to enhance recovery following SCI

Interfacial Tension Hysteresis of Eutectic Gallium-Indium

When in a pristine state, gallium and its alloys have the largest interfacial tensions of any liquid at room temperature. Nonetheless, applying as little as 0.8 V of electric potential across eutectic gallium indium (EGaIn) placed within aqueous NaOH (or other electrolyte) solution will cause the metal to behave as if its interfacial tension is near zero. The mechanism behind this phenomenon has remained poorly understood because NaOH dissolves the oxide species, making it difficult to directly measure the concentration, thickness, or chemical composition of the film that forms at the interface. In addition, the oxide layers formed are atomically-thin. Here, we present a suite of techniques which allow us to simultaneously measure both electrical and interfacial properties as a function of applied electric potential, allowing for new insights into the mechanisms which cause the dramatic decrease in interfacial tension. A key discovery from this work is that the interfacial tension displays hysteresis while lowering the applied potential. We combine these observations with electrochemical impedance spectroscopy to evaluate how these changes in interfacial tension arise from chemical, electrical, and mechanical changes on the interface, and close with ideas for how to build a free energy model to predict these changes from first principles

A Comprehensive Review on the Metabolic Cooperation Role of Nuclear Factor E2-Related Factor 2 and Fibroblast Growth Factor 21 against Homeostasis Changes in Diabetes

Objective: Type 1 and type 2 diabetes are associated with metabolic disorders including hyperglycemia, hyperlipidemia, and inflammation, leading to the production of reactive oxygen species and nitrogen activators. In these cases, some of the body’s innate factors are activated to cope with these dangerous situations. The purpose of the review is to explain the collaboration between the nuclear factor E2-related factor 2 (NRF2) and fibroblast growth factor 21 (FGF21) in homeostasis and body metabolism with a focus on diabetes. Materials and methods: This review is based on searching the PubMed database, SCOPUS, Elsevier and citation lists of relevant publications. Subject heading and key words used include diabetes, oxidative stress, inflammation, NRF2, and FGF21. Only articles in English were included. Results: NRF2 and FGF21 are two attractive biomarkers for the diagnosis of specific metabolic disorders and therapeutic targets, which have been implicated as therapeutic targets for the management of diabetic complications. The combination of both factors leads to the regulation of antioxidant and anti-inflammatory responses and metabolic pathways. Conclusions: Given most studies of NRF2- and FGF21-based therapeutic interventions in animal models and the possibility of not achieving the same results in humans, further clinical studies are needed to determine the efficacy of NRF2 and FGF21 in treatment of patients with diabetes

Application of artificial intelligence techniques for automated detection of myocardial infarction: A review

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and other biophysical signals.Comment: 16 pages, 8 figure