Competitor-Weighted Centrality and Small-World Clusters in Competition Networks on Firms' Innovation Ambidexterity: Evidence from the Wind Energy Industry.

A firm's embedding structures in a technology competition network can influence its propensity for innovation ambidexterity. Using PCT (patent cooperation treaty) patent data of wind energy companies between 2010 and 2019, we adopted social network analysis and fixed-effects panel negative binomial regression to examine the impacts of network structural features on firm innovation ambidexterity. The results show that competitor-weighted centrality contributes to a firm's propensities for both incremental and radical green innovation. In contrast, a firm's embeddedness in small-world clusters can moderate the effect of the firm's competitor-weighted centrality positively on its incremental innovation but negatively on its radical innovation. The study makes three theoretical contributions. First, it enriches the understanding of how the competition network affects innovation ambidexterity. Second, it provides new insights into the relationship between competition network structures and technology innovation strategy. Finally, it contributes to bridging the research on the social embeddedness perspective and green innovation literature. The findings of this study have important implications for enterprises in the wind energy sector regarding how competitive relationships affect green technology innovation. The study underscores the importance of considering the competitiveness of a firm's rivals and the embedded structural features when devising green innovation strategies.

Scratch-AID, a deep learning-based system for automatic detection of mouse scratching behavior with high accuracy.

Mice are the most commonly used model animals for itch research and for development of anti-itch drugs. Most laboratories manually quantify mouse scratching behavior to assess itch intensity. This process is labor-intensive and limits large-scale genetic or drug screenings. In this study, we developed a new system, Scratch-AID (Automatic Itch Detection), which could automatically identify and quantify mouse scratching behavior with high accuracy. Our system included a custom-designed videotaping box to ensure high-quality and replicable mouse behavior recording and a convolutional recurrent neural network trained with frame-labeled mouse scratching behavior videos, induced by nape injection of chloroquine. The best trained network achieved 97.6% recall and 96.9% precision on previously unseen test videos. Remarkably, Scratch-AID could reliably identify scratching behavior in other major mouse itch models, including the acute cheek model, the histaminergic model, and a chronic itch model. Moreover, our system detected significant differences in scratching behavior between control and mice treated with an anti-itch drug. Taken together, we have established a novel deep learning-based system that could replace manual quantification for mouse scratching behavior in different itch models and for drug screening.

Machine learning-based clustering and classification of mouse behaviors via respiratory patterns.

Breathing is dynamically modulated by metabolic needs as well as by emotional states. Even though rodents are invaluable models for investigating the neural control of respiration, current literature lacks systematic characterization of breathing dynamics across a broad spectrum of rodent behaviors. Here we uncover a wide diversity in breathing patterns across spontaneous, attractive odor-, stress-, and fear-induced behaviors in mice. Direct recordings of intranasal pressure afford more detailed respiratory information than more traditional whole-body plethysmography. K-means clustering groups 11 well-defined behavioral states into four clusters with distinct key respiratory features. Furthermore, we implement RUSBoost (random undersampling boost) classification, a supervised machine learning model, and find that breathing patterns can separate these behaviors with an accuracy of 80%. Taken together, our findings highlight the tight relationship between breathing and behavior and the potential use of breathing patterns to aid in distinguishing similar behaviors and inform about their internal states.