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Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development. PLAIN LANGUAGE SUMMARY: The application of vaccines is one of the most promising treatments for numerous infectious diseases. However, the design and development of effective vaccines involve huge investments and resources, and only a handful of candidates successfully reach the market. Only relying on traditional methods is both time-consuming and expensive. Various computational tools and software have been developed to accelerate the vaccine design and development. Further, AI-enabled computational tools have revolutionized the field of vaccine design and development by creating predictive models and data-driven decision-making processes. Therefore, information and awareness of these AI-enabled computational resources will immensely facilitate the development of vaccines against emerging pathogens. In this review, we have meticulously summarized the available computational tools for each step of in-silico vaccine design and development, delving into the transformative applications of AI and ML in this domain, which would help to choose appropriate tools for each step during vaccine development, and also highlighting the limitations of these tools to facilitate the selection of appropriate tools for each step of vaccine design. [ABSTRACT FROM AUTHOR]

The cattle production system focuses on maintaining an animal-based food supply with a lower number of cattle. However, the fecundity of dairy cows has declined worldwide. The reproductive tract microbiome is one of the important factors which can influence bovine fecundity. Therefore, reproductive tract microbiomes have been explored during the estrus cycle, artificial insemination, gestation, and postpartum to establish a link between the micro-communities and reproductive performance. These investigations suggested that microbial dysbiosis in the reproductive tract may be associated with declined fertility. However, there is a scarcity of comprehensive investigations to understand microbial diversity, abundance, shift, and host-microbiome interplay for bovine infertility cases such as repeat breeding syndrome (RBS). This review summarizes the occurrence and persistence of microbial taxa to gain a better understanding of reproductive performance and its implications. Further, we also discuss the possibilities of microbiome manipulation strategies to enhance bovine fecundity. [ABSTRACT FROM AUTHOR]

Challenges of climate change and growth population are exacerbated by noticeable environmental changes, which can increase the range of plant diseases, for instance, net blotch (NB), a foliar disease which significantly decreases barley (Hordeum vulgare L.) grain yield and quality. A resistant germplasm is usually identified through visual observation and the scoring of disease symptoms; however, this is subjective and time-consuming. Thus, automated, non-destructive, and low-cost disease-scoring approaches are highly relevant to barley breeding. This study presents a novel screening method for evaluating NB severity in barley. The proposed method uses an automated RGB imaging system, together with machine learning, to evaluate different symptoms and the severity of NB. The study was performed on three barley cultivars with distinct levels of resistance to NB (resistant, moderately resistant, and susceptible). The tested approach showed mean precision of 99% for various categories of NB severity (chlorotic, necrotic, and fungal lesions, along with leaf tip necrosis). The results demonstrate that the proposed method could be effective in assessing NB from barley leaves and specifying the level of NB severity; this type of information could be pivotal to precise selection for NB resistance in barley breeding. [ABSTRACT FROM AUTHOR]

Anaerobic digestion produces biogas, which is a proven bioprocess for generating energy, recovering nutrients, and reusing waste materials. Generally, the biogas generated contains methane (CH4) and carbon dioxide (CO2) in a 3:2 ratio, which limits the usage of the biogas to only cooking gas. To further enhance the application of biogas to vehicular fuel and natural gas grids, CO2 must be removed for an enhanced calorific value. This study seeks to lower greenhouse gas emissions by sequestering carbon dioxide from biogas. CO2 sequestration by microorganisms to upgrade the biogas and simultaneously convert the CO2 into acetic acid is a less explored area of research. Therefore, this research focuses mainly on the analysis of CO2 consumption % and acetic acid yield by novel isolated bacteria from fruit waste and mixed consortia obtained from cow dung and digested samples. The research finding states that there was a 32% increase in methane yield shown by isolated strain A1, i.e., CH4% was increased from 60% to 90%, whereas only an 11% increase was shown by consortia, which was an increase from 60% to 80%. The highest biogas upgradation was shown by the A1 strain at 30 °C incubation temperature and pH 8. The A1 strain demonstrated the highest recorded yield of acetic acid, reaching a concentration of 2215 mg/L at pH 8. A pH range of 7–8 was found to be the best-suited pH, and a mesophilic temperature was optimum for CO2 consumption and acetic acid production. The major objective is to create an effective method for improving biogas so that it is acceptable for different energy applications by lowering the carbon dioxide content and raising the methane content. This development signifies a significant advancement in the enhancement of biogas upgradation, as well as the concurrent generation of value-added goods, thereby establishing a sustainable platform technology. [ABSTRACT FROM AUTHOR]