Bioinformatics: Computational Approaches for Genomics and Proteomics

Bioinformatics is a fast evolving field that combines biology, computer science, and statistics to analyze and comprehend enormous volumes of biological data. As a result of the introduction of high-throughput technologies like next-generation sequencing and mass spectrometry, genomic and proteomics research has generated enormous volumes of data, necessitating the development of computational tools to process and extract useful insights from these datasets. This presentation presents a survey of computational approaches in bioinformatics with a particular emphasis on their application to genomics and proteomics. The study of the entire genome is a topic covered in the discipline of genomics, which also includes genome annotation, assembly, and comparative genomics. Proteomics focuses on the investigation of proteins, including their identification, quantification, structural analysis, and functional characterization. Consequently, the importance of the area of bioinformatics has increased

Absence of the common Insulin-like growth factor-1 19-repeat allele is associated with early age at breast cancer diagnosis in multiparous women

Multiparity decreases the risk of breast cancer in white women, whereas it is a risk factor in black women <50 years. Early-onset breast cancer (<50 years) has been associated with high insulin-like growth factor-1 (IGF-1) levels. Absence of the common IGF1 19 cytosine-adenine (CA)-repeat allele (IGF1-19/-19) inverts the effect of several non-genetic factors on breast cancer risk but the interaction between IGF1-19/-19 and multiparity on breast cancer risk is unknown. As IGF1-19/-19, multiparity and early-onset breast cancer are more common in black than in white women, we aimed to study whether multiparity combined with IGF1-19/-19 increases the risk of early-onset breast cancer. Four hundred and three breast cancer patients diagnosed in Lund, Sweden, at age 25–99 years were genotyped for the IGF1 CA-repeat length using fragment analysis. Overall, 12.9% carried the IGF1-19/-19 genotype. There was a highly significant interaction between multiparity and IGF1-19/-19 on age at breast cancer diagnosis (P=0.007). Among IGF1-19/-19 patients, multiparity was associated with a 9.2 year earlier age at diagnosis compared with uniparity or nulliparity (P=0.006). Multiparity combined with IGF1-19/-19 was associated with an early age at breast cancer diagnosis. If confirmed, IGF1-19/-19 may help identify a subgroup of women for earlier breast cancer screening

Calculation of the efficacy of vaccines against tick infestations on cattle

Articles in International JournalsCattle ticks are responsible for great economic losses in cattle farming worldwide, and their main control method, chemicals, has been showing problems, whether resulting from the development of resistant strains of ticks or environmental contamination. Research studies directed toward developing vaccines against ticks are emerging. One way to evaluate those vaccines is to calculate the percentage of efficacy. The aim of this study was to analyze scientific publications archived in PubMed that used this method of assessment and discuss the main factors that may affect its calculation. Thus, 25 articles addressing this subject were selected. The percentage of efficacy was usually calculated in one of two ways, with one considering the reduced fertility of eggs and the other not. The latter method may underestimate the vaccine efficacy, and the most complete formula for calculating the efficacy reflects how much the vaccine actually affects the infestation. In our view, the use of the complete formula for calculating the percentage of efficacy is broader and more representative of the vaccine effect on the tick population.RESUMO - Carrapatos de bovinos são responsáveis por grandes perdas econômicas para a pecuária bovina mundial e seu principal método de controle, o químico, vem apresentando problemas, seja pelo desenvolvimento de amostras de carrapatos resistentes ou pela contaminação ambiental. Na tentativa de diminuir a utilização dos acaricidas, surgem pesquisas direcionadas ao desenvolvimento de vacinas contra carrapatos. Uma maneira de avaliar essas vacinas é pelo cálculo de percentagem de eficácia. O objetivo deste trabalho foi analisar as publicações científicas indexadas no PubMed que utilizaram este método de avaliação e discutir os principais fatores que podem interferir no seu cálculo. Dessa maneira, selecionaram-se 25 artigos que tratavam desse assunto. A percentagem de eficácia apareceu sendo calculada de duas formas, uma considerando a redução da fertilidade dos ovos e a outra não. Essa última pode subestimar a eficiência da vacina, e a fórmula de cálculo da eficácia mais completa representa o quanto da infestação a vacina realmente reduziu. Em nosso entendimento, a utilização da fórmula completa para o cálculo da percentagem de eficácia é mais abrangente e representativa do efeito da vacina na população de carrapatos

Socially and biologically inspired computing for self-organizing communications networks

The design and development of future communications networks call for a careful examination of biological and social systems. New technological developments like self-driving cars, wireless sensor networks, drones swarm, Internet of Things, Big Data, and Blockchain are promoting an integration process that will bring together all those technologies in a large-scale heterogeneous network. Most of the challenges related to these new developments cannot be faced using traditional approaches, and require to explore novel paradigms for building computational mechanisms that allow us to deal with the emergent complexity of these new applications. In this article, we show that it is possible to use biologically and socially inspired computing for designing and implementing self-organizing communication systems. We argue that an abstract analysis of biological and social phenomena can be made to develop computational models that provide a suitable conceptual framework for building new networking technologies: biologically inspired computing for achieving efficient and scalable networking under uncertain environments; socially inspired computing for increasing the capacity of a system for solving problems through collective actions. We aim to enhance the state-of-the-art of these approaches and encourage other researchers to use these models in their future work