Vijai Kumar Gupta

Fusarium proliferatum causes banana crown rot and produces fumonisins, posing a threat to environmental safety and public health. Here, we report the 43.6-Mb complete genome sequence of F. proliferatum, containing 3026 genes encoding putative small secreted protein (SSP) candidates including 206 effectors. A list of pathogenesis-related genes was identified based on the transcriptome of F. proliferatum during infecting banana fruit. Furthermore, 10 putative SSPs were validated as secreted proteins using a yeast secretion system. Among these, Fp_G08764 and Fp_G03587 triggered cell death, while Fp_G13932 inhibited cell death in Nicotiana benthamiana leaves. Other candidates (Fp_G11318, Fp_G11726, Fp_G09727, Fp_G13547, Fp_G09042, Fp_G08494, and Fp_G11459) exhibited no significant effect on cell death. Additionally, Fp_G11318, Fp_G08764, and Fp_G13932-deletion mutants exhibited reduced pathogenicity on banana peel and N. benthamiana, indicating their essential roles in full virulence. Our results shed more light on the infection mechanism of F. proliferatum, contributing to develop effective techniques to control fungal disease.

Efficient valorization of lignocellulosic biomass into high‐value lignin monomers is a cornerstone of sustainable biorefineries, yet the complexity of optimizing reductive catalytic fractionation limits industrial scalability. This study presents a machine learning (ML)‐driven framework that harnesses 3,451 experimental data points from 54 peer‐reviewed studies to model and optimize lignin monomer production. Among four advanced ML models developed, eXtreme Gradient Boosting Regression is found to achieve the highest predictive accuracy (R = 0.80–0.86) with low prediction errors (root mean square error: 3.99–8.31; mean absolute error: 2.85–6.90) for monomer production. Feature importance analysis reveals that operational parameters account for the largest influence (40–57%), followed by substrate content (25–43%) and catalyst‐solvent properties (14–21%). The error between experimental and ML‐predicted total monomer yields ranges from 2% to 2.6%, demonstrating robust performance of the model. Scaling this approach has the potential to process 140 million tons of aspen biomass annually, can reduce CO ₂ emissions by 20.6 million tons, and yield $4,729 million in socioeconomic savings. This ML‐enhanced strategy offers a scalable and environmentally viable pathway for data‐driven lignocellulose valorization, advancing the development of low‐carbon, economically competitive biorefineries.

Food loss and waste (FLW) valorization remains challenging due to mixed properties and composition arising from seasonal and regional variations in food production. Here we examine the capacities of 3D printing for valorizing FLW streams, with a focus on FLW-based bioinks. We consider how waste management practices, 3D printing technology and emerging FLW valorization techniques could address challenges concerning raw material sourcing, improved material printability and suitable mechanical properties. Bioink ingredients incorporating biologically active compounds derived from FLW streams could offer tailored functionalities, supporting food preservation and economic, health and environmental sustainability benefits in line with the Sustainable Development Goals.

This review analyzes biphasic fractionation of lignocellulose using statistical analysis and DFT simulations, focusing on enzymatic hydrolysis, phase roles, solvent-catalyst impacts, and biphasic system sustainability with its industrial potential.

Natural emulsifiers originating from unconventional sources have attracted attention due to their exceptional functional properties. Bael fruit shell is typically considered an agricultural byproduct and is often discarded as waste. This research, therefore, focuses on valorizing it by developing bael fruit shell extract (BFSE). The utilization of bael fruit shells makes this approach both a sustainable and functional innovation. Proximate analysis showed that bael fruit shell powder (BFSP) is rich in carbohydrates (57.89 ± 0.32%) and proteins (7.53 ± 0.90%). Compounds, including polysaccharides, proteins, and other bioactive components, were extracted from BFSP at different pH levels (4, 5, 6, and 7) with microwave treatment (950 W for 3 min). The optimal pH was identified as 5, yielding the highest soluble fractions (80.08 ± 0.46%), polysaccharide, and protein content (180.98 ± 0.34 and 56.98 ± 0.37 mg/g). The optimized BFSE powder (pH 5) showed a porous structure, excellent thermal stability, high water and oil absorption capacities, and excellent emulsifying activity and stability. The BFSE nanoemulsion showed excellent stability compared to the conventional lecithin‐based nanoemulsion. The BFSE‐based nanoemulsion effectively reduced the viability of S. aureus , E. coli , and Candida albicans , suggesting its potential as an antimicrobial agent that could be utilized for enhancing food safety and extending shelf life in food preservation.