Due to its importance and wide adoption, wheat cultivation is promptly required to shift towards sustainable practices, reducing the dependency on chemical components. Among bio-based solutions aimed at securing the sustainability of wheat cultivation, biostimulants offer a versatile platform of eco-friendly tools assuring sustainability and profitability. Microalgae present a concrete example of a biostimulant source due to their richness in metabolites and high value products. Therefore, this study evaluated the biostimulant potential of eleven eco-extracts prepared from soil-isolated microalgae strains. Eco-extracts applied via soil drench at low dose (0.1 g/L) were investigated for their biostimulant effects on wheat growth, physiology, yield, and quality under controlled conditions. Results demonstrated significant ameliorations in treated plants as compared to the control, with no phytoinhibitory effects. Remarkable enhancements were notable in growth parameters such as shoot and root lengths (+40-70%), physiological traits such as total chlorophyll and stomatal conductance (+7-52%), yield components in the example of grain number per spike and thousand grain weight (+17-103%), and grain quality namely protein and polyphenol content (+2-fold to 4-fold). Similarly, phosphorus accumulation and uptake were significantly improved, while soil physicochemical status was ameliorated, indicating enhanced fertility. Multivariate analysis and composite index ranking marked Chlorella sp. GA18, Chlorella sp. GA65, Scenedesmus sp. GA69, and Chlorococcum sp. GA63 as eco-extracts with consistent performances across all plant traits. These findings highlighted the promising potential of integrating microalgae-based eco-friendly extracts in sustainable wheat cultivation.
Amer Chabili, Z. Hakkoum, F. Minaoui et al.· Plant Science· 1 citation
Control electrical stimulation significantly enhanced mucus secretion under optimized parameters of 10 V and 15 cycles, resulting in a 3.59-fold increase in mucus-associated protein output and clear electrophoretic protein profiles.
Xing Lu, Yong Long, Xiaozhen He et al.· Comparative Biochemistry and...· 0 citations
Evaluating the effectiveness of embeddings from five Protein Language Models, including ProtBERT-BFD, ESM-2, ProtALBERT, ProLLaMA, and ProtGPT-2, as input features for various machine learning classifiers suggests that while current embeddings offer strong performance, further advancements in feature extraction and model architectures are needed to significantly boost strict accuracy.
Karthik Avinash, S. Tejas, Sriram Mamidala et al.· Analytical Biochemistry· 0 citations
This study constructed a pH-responsive P-TN/SF@Fe-Cur composite coating that demonstrated significant anti-infective, anti-inflammatory, antioxidant, pro-angiogenic, and pro-osteogenic effects in rat subcutaneous infection and femoral defect models.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.