Objectives: This study applies three latent interaction models in the theory of planned behaviour (TPB; Ajzen, 1988, Attitudes, personality, and behavior. Homewood, IL: Dorsey Press; Ajzen, 1991, Organ. Behav. Hum. Decis. Process., 50, 179) to quitting smoking: (1) attitude × perceived behavioural control on intention; (2) subjective norms (SN) × attitude on intention; and (3) perceived behavioural control × intention on quitting behaviour.
Methods: The data derive from a longitudinal Internet survey of 939 smokers aged 15-74 over a period of 4 months. Latent interaction effects were estimated using the double-mean-centred unconstrained approach (Lin et al., 2010, Struct. Equ. Modeling, 17, 374) in LISREL.
Results: Attitude × SN and attitude × perceived behavioural control both showed a significant interaction effect on intention. No significant interaction effect was found for perceived behavioural control × intention on quitting.
Conclusions: The latent interaction approach is a useful method for investigating specific conditions between TPB components in the context of quitting behaviour. Theoretical and practical implications of the results are discussed.
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http://dx.doi.org/10.1111/bjhp.12034 | DOI Listing |
Sci Adv
January 2025
Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Designing binders to target undruggable proteins presents a formidable challenge in drug discovery. In this work, we provide an algorithmic framework to design short, target-binding linear peptides, requiring only the amino acid sequence of the target protein. To do this, we propose a process to generate naturalistic peptide candidates through Gaussian perturbation of the peptidic latent space of the ESM-2 protein language model and subsequently screen these novel sequences for target-selective interaction activity via a contrastive language-image pretraining (CLIP)-based contrastive learning architecture.
View Article and Find Full Text PDFHumans excel at applying learned behavior to unlearned situations. A crucial component of this generalization behavior is our ability to compose/decompose a whole into reusable parts, an attribute known as compositionality. One of the fundamental questions in robotics concerns this characteristic: How can linguistic compositionality be developed concomitantly with sensorimotor skills through associative learning, particularly when individuals only learn partial linguistic compositions and their corresponding sensorimotor patterns? To address this question, we propose a brain-inspired neural network model that integrates vision, proprioception, and language into a framework of predictive coding and active inference on the basis of the free-energy principle.
View Article and Find Full Text PDFProteins
January 2025
Department of Statistics, Florida State University, Tallahassee, Florida, USA.
The structures of metalloproteins are essential for comprehending their functions and interactions. The breakthrough of AlphaFold has made it possible to predict protein structures with experimental accuracy. However, the type of metal ion that a metalloprotein binds and the binding structure are still not readily available, even with the predicted protein structure.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Public Health, Tzu Chi University, No.701, Sec. 3, Zhongyang Rd., Hualien County, Hualien City, 970, Taiwan.
Background: Humans experience functioning difficulties in daily life, which are dependent on the interaction between health conditions and barriers in life. In general, functioning is an umbrella term and a dynamic concept. Thus, identifying the factors associated with long-term functioning would be beneficial to the development of specific health policies and quality of life for people with disabilities.
View Article and Find Full Text PDFCommun Biol
January 2025
Dept. Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA.
Predicting novel mutations has long-lasting impacts on life science research. Traditionally, this problem is addressed through wet-lab experiments, which are often expensive and time consuming. The recent advancement in neural language models has provided stunning results in modeling and deciphering sequences.
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