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The urgent need to address environmental issues associated with the use of conventional fossil fuels has driven the rapid evolution of the global energy landscape. This review explores the background and significance of 3-G biofuel production, emphasizing the shift towards sustainable alternatives amidst escalating greenhouse gas emissions. While various renewable energy sources have gained prominence, biofuels have emerged as a promising solution for the transportation and industrial sectors, particularly from microalgal biomass. The rationale for focusing on microalgal biomass is based on its technical and environmental advantages. Unlike traditional feedstocks, microalgae boast a high lipid content, enhancing biofuel production efficiency. Their rapid growth rates and efficient carbon dioxide sequestration make microalgae frontrunners in scalable and sustainable biofuel production. This review aims to comprehensively analyze recent breakthroughs in 3-G biofuel production from microalgal biomass, filling gaps in the existing literature. The topics covered included species diversity, cultivation techniques, harvesting, pretreatment, lipid extraction methods, and biofuel production pathways. Genetic engineering, downstream processing, energy-efficient practices, and emerging trends, such as artificial intelligence and cross-disciplinary collaboration, will be explored. This study aims to consolidate recent research findings, identify challenges and opportunities, and guide future directions in microalgal biomass-based biofuel production. By synthesizing unpublished research, this review seeks to advance our knowledge and provide insights for researchers to foster sustainable and efficient 3-G biofuel production.

The experiment was conducted from April to May 2024 at the Centre for Excellence in Biotechnology in Research and Training (CEBRT) lab of Amicable Knowledge Solution University (AKS) University, Satna. In the present study, a simple, routine, and reproducible RP-HPLC method was employed for estimating the levels of Ferulic acid (FA) in different treatments of maize that is essential in the phytochemical analysis of this plant, has been assessed based on ferulic acid (FA). The method used in this study was based on HPLC on a C18 column using a gradient of water, methanol, and acetic acid. The calibration curve showed optimum linearity from 10 to 100 µg ml-1, with R²=0.981. This method is used to quantify the concentration, of FA. This methodology was used to detect FA in maize plants with different treatments: T1: control plants, T2: drought-induced plants, T3: plants inoculated with PKR1, T4: plants inoculated with PKR5, T5: plants inoculated with PKR6. The increase in FA was observed in all three Plant Growth Promoting Rhizobacteria (PGPR)-treated plants (T3, T4, and T5) as compared to T1: control plant and T2: drought-induced plants. Among these, maximum FA was found in T4 treatment, and the maximum peak height and area of T4 were 229259 and 10884, respectively. The Reverse-phase high-performance liquid chromatography (RP-HPLC) RP-HPLC analytical methodology was validated for reliable analysis of FA content in maize leaves, contributing to phytochemical studies and quality assurance in different plant treatments and their derived products.

In the real-world, there are various situations when all units are not accessible of the respondent called unit non-response. The effect of unit non-response is a tricky matter for estimating the total number of unit. The present work highlights the interest about subpopulations (domains) in two affairs: i. if domains total of the supportive information is accessible ii. if domains total of the supportive variable does not access. The government needs to be introducing the actual facilities in these small domains. The supportive information is used to find out the estimate of the non respondent information and to apply this information for desired domains. Sometimes, it has been found that the accessible auxiliary variable for the domains might be positive shape. Therefore, it develops an appropriate model that has positive skewness. The present context highlighted the indirect method using a power-based estimation with calibration approach. By combining power based estimation and calibration technique, it is possible to obtain more accurate estimates for intended small domains. Even the supportive information is positively biased. This approach helps us in mitigating the effect of non-respondent and improving the overall reliability of the estimators. The simulation was conducted for different sizes 70 and 90 when nonresponse variable in the study variable. The results show that investigated power-based estimate provides better option over relevant exponential, ratio, and generalized regression estimators for intended domains.

Non-rhizobial endophytes (NREs) are active colonizers inhabiting the root nodules. Though their active role in the lentil agroecosystem is not well defined, here we observed that these NREs might promote the growth of lentils, modulate rhizospheric community structure and could be used as promising organisms for optimal use of rice fallow soil. NREs from root nodules of lentils were isolated and examined for plant growth-promoting traits, exopolysaccharide (EPS) and biofilm production, root metabolites, and the presence of nifH and nifK elements. The greenhouse experiment with the chosen NREs, i.e., Serratia plymuthica 33GS and Serratia sp. R6 significantly increased the germination rate, vigour index, development of nodules (in non-sterile soil) and fresh weight of nodules (33GS 94%, R6 61% growth) and length of the shoot (33GS 86%, R6 51.16%) as well as chlorophyll levels when compared to the uninoculated control. Scanning Electron Microscopy (SEM) revealed that both isolates could successfully colonize the roots and elicit root hair growth. The inoculation of the NREs resulted in specific changes in root exudation patterns. The plants with 33GS and R6 treatment significantly stimulated the exudation of triterpenes, fatty acids, and their methyl esters in comparison to the uninoculated plants, altering the rhizospheric microbial community structure. Proteobacteria dominated the rhizospheric microbiota in all the treatments. Treatment with 33GS or R6 also enhanced the relative abundance of other favourable microbes, including Rhizobium, Mesorhizobium, and Bradyrhizobium. The correlation network analysis of relative abundances resulted in numerous bacterial taxa, which were in cooperation with each other, having a possible role in plant growth promotion. The results indicate the significant role of NREs as plant growth promoters, which also includes their role in root exudation patterns, enhancement of soil nutrient status and modulation of rhizospheric microbiota, suggesting their prospects in sustainable, and bio-based agriculture.

Hot springs are considered to be a unique environment with extremophiles, that are sources of industrially important enzymes, and other biotechnological products. The objective of this study was to undertake, analyze, and characterize the microbiome of two major hot springs located in the state of Madhya Pradesh explicitly, Chhoti Anhoni (Hotspring 1), and Badi Anhoni (Hotspring 2) to find out the inhabitant microbial population, and their functional characteristics. The taxonomic analysis of the microbiome of the hot springs revealed the phylum Proteobacteria was the most abundant taxa in both the hot-springs, however, its abundance in hot-spring 1 (~88%) was more than the hot-spring 2 (~52%). The phylum Bacteroides (~10–22%) was found to be the second most abundant group in the hot-springs followed by Spirocheates (~2–11%), Firmicutes (~6–8%), Chloroflexi (1–5%), etc. The functional analysis of the microbiome revealed different features related to several functions including metabolism of organics and degradation of xenobiotic compounds. The functional analysis showed that most of the attributes of the microbiome was related to metabolism, followed by cellular processes and environmental information processing functions. The functional annotation of the microbiomes at KEGG level 3 annotated the sequences into 279 active features that showed variation in abundance between the hot spring samples, where hot-spring 1 was functionally more diverse. Interestingly, the abundance of functional genes from methanogenic bacteria, was higher in the hot-spring 2, which may be related to the relatively higher pH and temperature than Hotspring 1. The study showed the presence of different unassigned bacterial taxa with high abundance which indicates the potential of novel genera or phylotypes. Culturable isolates (28) were bio-prospected for industrially important enzymes including amylase, protease, lipase, gelatinase, pectinase, cellulase, lecithinase, and xylanase. Seven isolates (25%) had shown positive results for all the enzyme activities whereas 23 isolates (82%) produced Protease, 27 isolates (96%) produced lipase, 27 isolates produced amylase, 26 isolates (92%) produced cellulase, 19 isolates (67%) produced pectinase, 19 isolates (67%) could produce lecithinase, and 13 isolates (46%) produced gelatinase. The seven isolates, positive for all the enzymes were analyzed further for quantitative analysis and identified through molecular characterization.

This paper analyzes the time to event data in the presence of collinearity. To address collinearity, the ridge regression estimator was applied in multiple and logistic regression as an alternative to the maximum likelihood estimator (MLE), among others. It has a smaller mean square error (MSE) and is therefore more precise. This paper generalizes the approach to address collinearity in the frailty model, which is a random effect model for the time variable. A simulation study is conducted to evaluate its performance. Furthermore, the proposed method is applied on real life data taken from the largest sample survey of India, i.e., national family health survey (2005–2006 ) data to evaluate the association of different determinants on infant mortality in India.