Cr(VI) is a priority soil contaminant, yet current earthworm toxicity assessments still rely largely on single-species apical endpoints. In this study, we systematically compared the sensitivity of 12 earthworm species, representing epigeic, endogeic and anecic life forms, to Cr(VI) using four response indicators spanning multiple levels of biological organization: growth inhibition (EC50), acetylcholinesterase (AChE) inhibition, protein secondary-structure alteration (circular dichroism, CD), and coelomic fluid osmolality. Species-specific exposure-response curves were fitted with log-logistic models to derive EC50 values for each endpoint. Species-specific EC50 values were summarized using cumulative distributions. The growth-inhibition distribution was interpreted as an SSD, whereas the biochemical and physiological endpoints were summarized as cumulative frequency distributions. Across the 12 species, EC50 values showed pronounced interspecific variability, ranging from a ∼2.6-fold span for growth inhibition to a ∼27-fold span for AChE inhibition. For AChE inhibition, the most responsive endpoint in the present dataset, Lumbricus rubellus was the most sensitive species (EC50 = 10.21 mg·kg-1), whereas Lumbricus terrestris was the least sensitive species (EC50 = 280.44 mg·kg-1). The biochemical and physiological response indicators showed lower 5th-percentile response concentrations than the 28-d growth-inhibition endpoint, with the following lower-tail response ranking: AChE inhibition < protein secondary-structure alteration < coelomic-fluid osmolality deviation < growth inhibition. The AChE-based P5 was approximately one order of magnitude lower than the growth-inhibitory HC5, indicating that the AChE biomarker responded at substantially lower nominal Cr(VI) concentrations than the 28-d growth response. Results identified the AChE-based P5 as the lowest endpoint-specific lower-tail percentile among the assessed biochemical and physiological response indicators, while the 50th-percentile comparison further supported their greater concentration-based responsiveness relative to growth inhibition, providing mechanistic information for endpoint selection in soil Cr(VI) effect assessment.
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 seque...
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.