Cancer is one of the top causes of mortality, and it arises when cells in the body grow abnormally, like in the case of breast cancer. For people all around the world, it has now become a huge issue and a threat to their safety and wellbeing. Breast cancer is one of the major causes of death among females all over the globe, and it is particularly prevalent in the United States. It is possible to diagnose breast cancer using a variety of imaging modalities including mammography, computerized tomography (CT), magnetic resonance imaging (MRI), ultrasound, and biopsies, among others. To analyze the picture, a histopathology study (biopsy) is often performed, which assists in the diagnosis of breast cancer. The goal of this study is to develop improved strategies for various CAD phases that will play a critical role in minimizing the variability gap between and among observers. It created an automatic segmentation approach that is then followed by self-driven post-processing activities to successfully identify the Fourier Transform based Segmentation in the CAD system to improve its performance. When compared to existing techniques, the proposed segmentation technique has several advantages: spatial information is incorporated, there is no need to set any initial parameters beforehand, it is independent of magnification, it automatically determines the inputs for morphological operations to enhance segmented images so that pathologists can analyze the image with greater clarity, and it is fast. Extensive tests were conducted to determine the most effective feature extraction techniques and to investigate how textural, morphological, and graph characteristics impact the accuracy of categorization classification. In addition, a classification strategy for breast cancer detection has been developed that is based on weighted feature selection and uses an upgraded version of the Genetic Algorithm in conjunction with a Convolutional Neural Network Classifier. The practical application of the suggested improved segmentation and classification algorithms for the CAD framework may reduce the number of incorrect diagnoses and increase the accuracy of classification. So, it may serve as a second opinion tool for pathologists and aid in the early detection of diseases.

Multiple FDA-approved SARS-CoV-2 vaccines currently provide excellent protection against severe disease. Despite this, immunity can wane relatively fast, particularly in the elderly and novel viral variants capable of evading infection- and vaccination-induced immunity continue to emerge. Intranasal (IN) vaccination more effectively induces mucosal immune responses than parenteral vaccines, which would improve protection and reduce viral transmission. Here, we developed a rationally designed IN adjuvant consisting of a combined nanoemulsion (NE)-based adjuvant and an RNA-based RIG-I agonist (IVT DI) to drive more robust, broadly protective antibody and T cell responses. We previously demonstrated this combination adjuvant (NE/IVT) potently induces protective immunity through synergistic activation of an array of innate receptors. We now demonstrate that NE/IVT with the SARS-CoV-2 receptor binding domain (RBD), induces robust and durable humoral, mucosal, and cellular immune responses of equivalent magnitude and quality in young and aged mice. This contrasted with the MF59-like intramuscular adjuvant, Addavax, which showed a decrease in immunogenicity with age. Robust antigen-specific IFN-γ/IL-2/TNF-α was induced in both young and aged NE/IVT-immunized animals, which is significant as their reduced production is associated with suboptimal protective immunity in the elderly. These findings highlight the potential of adjuvanted mucosal vaccines for improving protection against COVID-19.

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Handwritten recognition has been one of the active and challenging research areas in the field of image processing. In this, paper we are going to proposed to recognize handwritten Sanskrit word using a Prewitt's operator for the edge detection. However, most of the current work in these areas is limited to English and a few oriental languages. The lack of efficient solutions for Indic scripts and languages such as Sanskrit has hampered information extraction from a large body of documents of cultural and historical importance. In this we use Freeman chain code(FCC)as the representation technique of an image character. Chain code gives the boundary of a character image in which the codes represents the direction of where is the location of the next pixel. Randomized algorithm is used to generate the FCC. After that, features vector is built. The criteria of features to input the classification is the chain code that converted to various features. And genetic algorithm is applied to evaluate the initial population to find out non-linear segmentation path in the possible segmentation zone. Accordingly, several generations are performed to evaluate the individuals with maximum fitness value. Support vector machine (SVM) is chosen for the classification step.

New data from 400-GeV p-nucleus interactions are analyzed to investigate the variation of normalized created-charged-particle multiplicity (R3) as a function of effective number of projectile encounters ( (v')) calculated by using the additive quark model. The data, combined with other available data from pnucleus and ?T--nucleus reactions, confirm the projectile independence of the R3-( v ' ) relationship, first observed by Kumar et al.