This dataset contains processed transcriptomic and proteomic data generated from inguinal white adipose tissue collected from control mice, obesity-associated PCOS model mice, and PCOS mice treated with high-dose Atractylodis Rhizoma-derived exosome-like nanovesicles (AR-ELNs). These data support the multi-omics analyses presented in the associated manuscript and were used to characterize adipose metabolic-endocrine remodeling, with particular focus on kynurenine pathway activation and AhR/AR-related gene and protein programs in obesity-associated PCOS. The dataset includes processed RNA-seq differential expression tables and quantitative proteomics differential expression tables for the following comparisons: PCOS_vs_CTRL_iWAT_RNAseq_DEG_all_genes.xlsx Gene-level differential expression results comparing inguinal white adipose tissue from obesity-associated PCOS model mice with control mice. PCOS_H-ELNs_vs_PCOS_iWAT_RNAseq_DEG_all_genes.xlsx Gene-level differential expression results comparing inguinal white adipose tissue from PCOS mice treated with high-dose AR-ELNs with untreated PCOS model mice. PCOS_vs_CTRL_iWAT_Proteomics_DEP_all_proteins.xlsx Protein-level differential expression results comparing inguinal white adipose tissue from obesity-associated PCOS model mice with control mice. PCOS_H-ELNs_vs_PCOS_iWAT_Proteomics_DEP_all_proteins.xlsx Protein-level differential expression results comparing inguinal white adipose tissue from PCOS mice treated with high-dose AR-ELNs with untreated PCOS model mice. These processed datasets include gene- or protein-level identifiers, expression or abundance changes, fold-change values, statistical significance values, and annotation fields used for downstream pathway enrichment, integrated transcriptomic-proteomic analysis, and candidate pathway selection. The data are provided to support transparency and reproducibility of the multi-omics analyses reported in the manuscript.
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.