Studying the human protein targets implicated in the dengue-human protein interactions is crucial since it will help to identify suitable drugs that could either inhibit the dengue proteins from connecting with the host or other-wise alter those path/interactions. This paper uses several machine learning algorithms—including logistic regression, random forests, support vector machines, extra trees, Naive Baye’s, Adaboost, XGBoost, among others—to find patterns and relationships within the data connected to the defining traits of the human proteins already known in dengue-human protein-protein interaction network (PPIN) and confirmed to participate in the path/interactions so that new proteins can be forecasted that could influence the path/interactions and so offer a direction for more research improving the efficiency. Finding new proteins will assist in highlighting new targets for therapeutic medications, which will therefore aid to improve the life of patients resulting in lowering the death rate. Diverse algorithms and thorough data help us to build strong predictive models. Promising machine learning methods provide researchers useful tools to improve their discoveries. The results of this study highlight the possibility of machine learning in solving one of the most important worldwide health issues of our day.
Galvanic Skin Response (GSR) is a widely used physiological parameter that reflects variations in skin conductance arising from sweat gland activity. Since these glands are under the control of the sympathetic nervous system, changes in GSR provide a reliable indication of emotional arousal, stress, and other autonomic reactions. The measurement is typically made in regions where the density of sweat glands is high, allowing small fluctuations in sympathetic activity to produce detectable changes in voltage output due to change in skin resistance. To obtain dependable readings from such subtle variations, a stable and well- defined excitation source is essential. For this purpose, GSR systems commonly employ a constant current source. By delivering a small, steady current through the skin, the circuit ensures that any change in voltage across the electrodes truly represents a physiological change rather than a variation in the measuring device. This approach improves linearity, reduces the influence of electrode polarization, and enhances the overall accuracy of the measurement. For its sensitivity and simplicity, the GSR technique has become an important tool in psychological studies, biofeedback training, and clinical monitoring of autonomic function.
Data center placement in a network plays a vital role for different online applications like VoIP, cloud computing, etc. However, disasters can affect their functionality leading to huge disruption in service. Not only this, network load balancing is another major concern nowadays, the improper management of which can hamper the network throughput and quality of service. In this paper, a new routing, spectrum and core allocation (RSCA) heuristic has been developed to balance the network load by imposing labels to the data centers based on their usage in dynamic space division multiplexing-based elastic optical network (SDM-EON). In this context, two data center selection strategies are introduced which are tested and analysed on two well- known topologies against different parameters, proving their efficacy over each other.
A voltage source inverter (VSI) with a three-phase system and based on fuzzy logic direct power control is used to inject solar power into the grid. The configuration of the inverter is very important to ensure optimal power flow. This paper presents a new maximum power point tracking system based on fuzzy logic that is superior to traditional MPPT techniques such as the incremental conductance method. This MPPT uses fuzzy logic control for adaptive optimization of the operating point. The proposed control strategy provides efficient energy harvesting and synchronization, which can be done at a unity power factor (UPF). The simulation results obtained using MATLAB/SIMULINK software prove the effectiveness of fuzzy logic control (FLC) compared to conventional DPC and INC in constant and changing weather conditions. The results obtained are compared with IEEE standards, which provide information on the reliability and accuracy of the system.
Glioblastoma multiforme (GBM) is a seriously harmful and fast, dividing tumor that develops in the spinal or brain cord and is generally the result of the transformation of astrocytes, helpful cells that maintain the neural balance. GBM is a serious clinical challenge as it has the potential to spread and consume healthy tissue. Molecule wise, it is a disorder caused by alterations of amino acid residues in the target proteins that change their function along with the structure and make the tumor grow faster. The mutations can be either driver or passenger. Driver mutations are the alterations in the genes that make cancer develop and proliferate. They are often present in several patient samples, and are confirmed by the experimental results. Passenger mutations, meanwhile, are biologically inert. They occur in cancer cells that are genetically unstable, but they do not directly lead to tumor formation. Here we have significantly analyzed a dataset containing 18,115 protein samples with 8,728 passenger (neutral) mutations and 9,386 verified driver mutations. This selection is made based on their functional and recurrence annotations. We built a framework, based on machine learning, that features different physicochemical properties (at the residue and protein levels) and sequence, based contextual features to distinguish them. Our machine learning model accurately classifies driver mutations and can be pivotal in identifying novel therapeutic targets through these extensive feature sets. The approach not only reveals the mutations in glioblastoma but also offers a path to the mutations which are beleived to be most likely to affect the progression of the disease. This, in turn, will lead us to devise personalized and targeted therapy plans.
