Alfalfa biomass contains significant carbohydrate fractions underutilized in animal feed. This research optimized the enzymatic hydrolysis of Alfalfa biomass and evaluated single-cell protein (SCP) production using Candida utilis and Komagataella pastoris . Hydrolysis kinetics in this study showed biphasic sugar release, with enzymatic optimization increasing monomeric sugar yield from 18.5% to a maximum of 33.0% (a 78% relative increase) at 22 mg protein/g biomass enzyme loading. C. utilis demonstrated superior metabolic versatility, consuming 23% more total sugars than K. pastoris , with particularly enhanced pentose utilization showing 45% greater xylose consumption. K. pastoris achieved 51% faster growth rates than C. utilis , while both yeasts produced comparable protein yields per unit sugar consumed. SCP production enriched protein content 2.1-fold compared to raw Alfalfa biomass, reaching approximately 40% crude protein. Essential amino acid profiling (tryptophan excluded) showed that fermentation substantially improved several essential amino acids relative to FAO/WHO reference values, particularly lysine, threonine, and methionine; however, methionine + cysteine remained below the FAO reference ratio in both yeasts ( K. pastoris : 0.99; C. utilis : 0.87), indicating that sulfur-containing amino acids remain a limiting factor despite overall nutritional improvement. The integrated bioprocess achieved 27.6% carbohydrate-to-biomass conversion efficiency and 11.3% carbohydrate-to-protein conversion efficiency based on measured monomeric sugars (oligosaccharide utilization during fermentation was not independently quantified). This work demonstrates a promising lab-scale strategy for alfalfa valorization through enzymatic hydrolysis and yeast fermentation, yielding a nutritionally improved protein product; techno-economic analysis, feeding trials, and scale-up studies are required before cost-effectiveness or industrial readiness can be established.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
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.