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Abstract Traditional alcoholic beverages have played a significant role in the cultural, social, and culinary fabric of societies worldwide for centuries. Studying the microbial community structure and their metabolic potential in such beverages is necessary to define product quality, safety, and consistency, as well as to explore associated biotechnological applications. In the present investigation, Illumina-based (MiSeq system) whole-genome shotgun sequencing was utilized to characterize the microbial diversity and conduct predictive gene function analysis of keem, a starter culture employed by the Jaunsari tribal community in India for producing various traditional alcoholic beverages. A total of 8,665,213 sequences, with an average base length of 151 bps, were analyzed using MG-RAST. The analysis revealed the dominance of bacteria (95.81%), followed by eukaryotes (4.11%), archaea (0.05%), and viruses (0.03%). At the phylum level, Actinobacteria (81.18%) was the most abundant, followed by Firmicutes (10.56%), Proteobacteria (4.00%), and Ascomycota (3.02%). The most predominant genera were Saccharopolyspora (36.31%), followed by Brevibacterium (15.49%), Streptomyces (9.52%), Staphylococcus (8.75%), Bacillus (4.59%), and Brachybacterium (3.42%). At the species level, the bacterial, fungal, and viral populations of the keem sample could be categorized into 3347, 57, and 106 species, respectively. Various functional attributes to the sequenced data were assigned using Cluster of Orthologous Groups (COG), Non-supervised Orthologous Groups (NOG), subsystem, and KEGG Orthology (KO) annotations. The most prevalent metabolic pathways included carbohydrate, lipid, and amino acid metabolism, as well as the biosynthesis of glycans, secondary metabolites, and xenobiotic biodegradation. Given the rich microbial diversity and its associated metabolic potential, investigating the transition of keem from a traditional starter culture to an industrial one presents a compelling avenue for future research.

Abstract Differentiation of Crohn’s disease (CD) from intestinal tuberculosis (ITB) is a big challenge to gastroenterologists because of their indistinguishable features and insensitive diagnostic tools. A non-invasive biomarker is urgently required to distinguish ITB/CD patients particularly in India, a TB endemic region, where CD frequency is increasing rapidly due to urbanization. Among the three differentially expressed miRNAs obtained from small RNA transcriptomic profiling of ileocaecal/terminal ileal tissue of ITB/CD patients (n = 3), only two down-regulated miRNAs, miR-31-5p, and miR-215-5p showed comparable data in qRT-PCR. Out of which, only miR-215-5p was detectable in the patient’s plasma, but there was no significant difference in expression between ITB/CD. On the other hand, miR-375-3p, the pulmonary TB specific marker was found in higher amount in the plasma of ITB patients than CD while reverse expression was observed in the ileocaecal/terminal ileal tissues of the same patients. Next, using Bioplex pro-human cytokine 48-plex screening panel, only three chemokines, Eotaxin-1/CCL11, SDF-1α/CXCL12, and G-CSF have noted significantly different levels in the serum of ITB/CD patients. ROC analysis has revealed that compared to a single molecule, a combination of miR-375-3p + Eotaxin-1/CCL11 + SDF-1α /CXCL12 + G-CSF showed a better AUC of 0.83, 95% CI (0.69–0.96) with 100% specificity and positive predictive value while sensitivity, negative predictive value, and accuracy were 56%, 69%, and 78% respectively in distinguishing ITB from CD. This study suggests that a combination of plasma markers shows better potential in differentiating ITB from CD than a single marker and this panel of markers may be used for clinical management of ITB/CD patients.

Abstract Transportation of fish seed is a complex phenomenon associated with multiple kinds of stressors that simultaneously affect the fish in a confined environment, causing stress and mortality. The present study investigated the stress-relieving effect of exogenous glucose as a water additive in different concentrations (0.1, 0.2, 0.3, and 0.4%) during simulated transportation (12 h) of L. rohita fingerlings. The integrated biomarker response (IBR) index is a holistic tool to determine the optimum dose of exogenous glucose for mitigating transportation stress in fish. Based on selected biomarkers related to the stress hormone, serum biochemistry, oxidative stress, and HSP70 mRNA expression, the IBR index is calculated for each treatment and control group. The result showed a significant change in the level of stress hormone cortisol, enzymes (SGPT, LDH, MDH, SOD, CAT) and metabolites (serum glucose, triglyceride, creatinine) along with an upregulation in liver HSP70 mRNA expression. IBR index suggests that 0.2% glucose exhibited the lowest multi-biomarker stress response in comparison to other treatments and control. Therefore, the use of 0.2% glucose as a water additive will provide a solution to transportation induced stress in L. rohita fingerling and will underwrite the success of grow-out fish culture in days to come.

Abstract The field of data exploration relies heavily on clustering techniques to organize vast datasets into meaningful subgroups, offering valuable insights across various domains. Traditional clustering algorithms face limitations in terms of performance, often getting stuck in local minima and struggling with complex datasets of varying shapes and densities. They also require prior knowledge of the number of clusters, which can be a drawback in real-world scenarios. In response to these challenges, we propose the "hybrid raven roosting intelligence framework" (HRIF) algorithm. HRIF draws inspiration from the dynamic behaviors of roosting ravens and computational intelligence. What distinguishes HRIF is its effective capacity to adeptly navigate the clustering landscape, evading local optima and converging toward optimal solutions. An essential enhancement in HRIF is the incorporation of the Gaussian mutation operator, which adds stochasticity to improve exploration and mitigate the risk of local minima. This research presents the development and evaluation of HRIF, showcasing its unique fusion of nature-inspired optimization techniques and computational intelligence. Extensive experiments with diverse benchmark datasets demonstrate HRIF's competitive performance, particularly its capability to handle complex data and avoid local minima, resulting in accurate clustering outcomes. HRIF's adaptability to challenging datasets and its potential to enhance clustering efficiency and solution quality position it as a promising solution in the world of data exploration.

Abstract Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and caused an economic downturn. The number of verified coronavirus cases is increasing daily, putting the whole human race at danger and putting strain on medical experts to eradicate the disease as rapidly as possible. As a consequence, it is vital to predict the upcoming coronavirus positive patients in order to plan actions in the future. Furthermore, it has been discovered all across the globe that asymptomatic coronavirus patients play a significant part in the disease’s transmission. This prompted us to incorporate similar examples in order to accurately forecast trends. A typical strategy for analysing the rate of pandemic infection is to use time-series forecasting technique. This would assist us in developing better decision support systems. To anticipate COVID-19 active cases for a few countries, we recommended a hybrid model utilizing a fuzzy time series (FTS) model mixed with a non-linear growth model. The coronavirus positive case outbreak has been evaluated for Italy, Brazil, India, Germany, Pakistan, and Myanmar through June 5, 2020 in phase-1, and January 15, 2022 in phase-2, and forecasts active cases for the next 26 and 14 days respectively. The proposed framework fitting effect outperforms individual logistic growth and the fuzzy time series techniques, with R-scores of 0.9992 in phase-1 and 0.9784 in phase-2. The proposed model provided in this article may be utilised to comprehend a country’s epidemic pattern and assist the government in developing better effective interventions.