Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter.

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%.

Fabrication of Nd3+ and Yb3+ doped NIR emitting nano fluorescent probe: A candidate for bioimaging applications.

The intentional design of rare earth doped luminescent architecture exhibits unique optical properties and it can be considered as a promising and potential probe for optical imaging applications. Calcium fluoride (CaF2) nanoparticles doped with optimum concentration of Nd3+ and Yb3+ as sensitizer and activator, respectively, were synthesized by wet precipitation method and characterized by x-ray diffraction (XRD) and photoluminescence. In spite of the fact that the energy transfer takes place from Nd3+ to Yb3+, the luminescence intensity was found to be weak due to the lattice defects generated from the doping of trivalent cations (Nd3+ and Yb3+) for divalent host cations (Ca2+). These defect centres were tailored via charge compensation approach by co-doping Na+ ion and by optimizing its concentration and heat treatment duration. CaF2 doped with 5 mol% Nd3+, 3 mol% Yb3+ and 4 mol% Na+ after heat treatment for 2 h exhibited significantly enhanced emission intensity and life time. The ex vivo fluorescence imaging experiment was done at various thickness of chicken breast tissue. The maximum theoretical depth penetration of the NIR light was calculated and the value is 14 mm. The fabricated phosphor can serve as contrast agent for deep tissue near infrared (NIR) light imaging.

Effect of entomopathogenic nematode of Heterorhabditis indica infection on immune and antioxidant system in lepidopteran pest Spodoptera litura (Lepidoptera: Noctuidae).

Entomopathogenic nematodes form excellent tools to study insect immunity in response to during infection. Insects activate as several defense mechanisms, namely Phenoloxidase, haemocytes, detoxification and antioxidant enzymes. However little mechanistic information is available about the sublethal effects of entomopathogenic nematodes infection on detoxification and immune mechanisms in lepidopteran insects. In the present study, the effects of infection on antioxidant, detoxification and immune systems of Spodoptera litura larvae were studied. Results show a significant reduction in Total Haemocyte Count observed after 3 h of infection. A significant increase Superoxide dismutase, Catalase, Glutathione S-transferase, Glutathione Peroxidase and Acid phosphatase were observed 6 h after infection and, progressive decrease in Peroxidase, Alkaline phosphatase and Lipid peroxidation was also observed. This study shows that increased detoxification enzyme levels in response to nematode infection are a protective mechanism in insects. Nematode infection suppresses insect immune response, which is evident from low haemocyte count and Phenoloxidase levels to ultimately cause larval mortality.