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Petterson Mccormick posted an update 6 months ago
Many human activities are tactile. Recognizing how a person touches an object or a surface surrounding them is an active area of research and it has generated keen interest within the interactive surface community. In this paper, we compare two machine learning techniques, namely Artificial Neural Network (ANN) and Hidden Markov Models (HMM), as they are some of the most common techniques with low computational cost used to classify an acoustic-based input. We employ a small and low-cost hardware design composed of a microphone, a stethoscope, a conditioning circuit, and a microcontroller. Together with an appropriate surface, we integrated these components into a passive gesture recognition input system for experimental evaluation. To perform the evaluation, we acquire the signals using a small microphone and send it through the microcontroller to MATLAB’s toolboxes to implement and evaluate the ANN and HMM models. We also present the hardware and software implementation and discuss the advantages and limitations of these techniques in gesture recognition while using a simple alphabet of three geometrical figures circle, square, and triangle. The results validate the robustness of the HMM technique that achieved a success rate of 90%, with a shorter training time than the ANN.Tissue engineering has recently emerged as a novel strategy for the regeneration of damaged skeletal muscle tissues due to its ability to regenerate tissue. However, tissue engineering is challenging due to the need for state-of-the-art interdisciplinary studies involving material science, biochemistry, and mechanical engineering. For this reason, electrospinning and three-dimensional (3D) printing methods have been widely studied because they can insert embedded muscle cells into an extracellular-matrix-mimicking microenvironment, which helps the growth of seeded or laden cells and cell signals by modulating cell-cell interaction and cell-matrix interaction. In this mini review, the recent research trends in scaffold fabrication for skeletal muscle tissue regeneration using advanced techniques, such as electrospinning and 3D bioprinting, are summarized. In conclusion, the further development of skeletal muscle tissue engineering techniques may provide innovative results with clinical potential for skeletal muscle regeneration.Nosema disease is a major disease of honey bees caused by two species of microsporidia, Nosema apis and N. ceranae. Current control involves using antibiotics, which is undesirable because of possible antibiotic resistance and contamination. In this study, flagellin, zymosan, chitosan, and peptidoglycan were investigated as alternatives for controlling N. ceranae infections and for their effect on bee survivorship and behaviors. Chitosan and peptidoglycan significantly reduced the infection, and significantly increased survivorship of infected bees, with chitosan being more effective. However, neither compound altered the bees’ hygienic behavior, which was also not affected by the infection. Chitosan significantly increased pollen foraging and both compounds significantly increased non-pollen foraging compared to healthy and infected bees. Memory retention, evaluated with the proboscis extension reflex assay, was temporarily impaired by chitosan but was not affected by peptidoglycan, nor was it affected by N. ceranae infection compared to the non-infected bees. This study indicates that chitosan and peptidoglycan provide benefits by partially reducing N. ceranae spore numbers while increasing survivorship compared to N. ceranae infected bees. Also, chitosan and peptidoglycan improved aspects of foraging behavior even more than in healthy bees, showing that they may act as stimulators of important honey bee behaviors.In this study, the effect of cutting parameters on the quality of an Al/CFRP sandwich structure (aluminium alloy-carbon fibre reinforced polymer) after milling with uncoated and TiAlN-coated tools was examined. The results of the cutting force were also investigated. The research was conducted in a VMC 800 HS vertical machining centre with a variable cutting speed and feed. The milling process was carried out using a non-coated, two-blade carbide milling cutter with a 35° helix angle and an analogous tool with a TiAlN coating. The surface quality was characterised in terms of the height deviation, which is one of the shape deviations after machining hybrid materials. The research showed that the maximum (77.60 µm) and minimum (1.78 µm) values of the height deviations were obtained using the tool with a TiAlN coating. It was found that the tested factors had significant effects on the height deviation, where the feed had the greatest influence and the cutting speed had the lowest influence on the surface quality. Oligomycin A The tested factors were not statistically significant in terms of the cutting force.Neurofibromatosis type (NF1) is a syndrome characterized by varied symptoms, ranging from mild to more aggressive phenotypes. The variation is not explained only by genetic and epigenetic changes in the NF1 gene and the concept of phenotype-modifier genes in extensively discussed in an attempt to explain this variability. Many datasets and tools are already available to explore the relationship between genetic variation and disease, including systems biology and expression data. To suggest potential NF1 modifier genes, we selected proteins related to NF1 phenotype and NF1 gene ontologies. Protein-protein interaction (PPI) networks were assembled, and network statistics were obtained by using forward and reverse genetics strategies. We also evaluated the heterogeneous networks comprising the phenotype ontologies selected, gene expression data, and the PPI network. Finally, the hypothesized phenotype-modifier genes were verified by a random-walk mathematical model. The network statistics analyses combined with the forward and reverse genetics strategies, and the assembly of heterogeneous networks, resulted in ten potential phenotype-modifier genes AKT1, BRAF, EGFR, LIMK1, PAK1, PTEN, RAF1, SDC2, SMARCA4, and VCP. Mathematical models using the random-walk approach suggested SDC2 and VCP as the main candidate genes for phenotype-modifiers.
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