Psychological research on self-control-the forgoing of immediate rewards in favor of global goals-focuses largely on how people monitor and control their thoughts, feelings, and behavior. Comparatively less work has examined the regulation of motivational states. This is surprising given the motivational roots of self-control dilemmas: people desire an immediate reward on the one hand, but also recognize that this reward precludes the ability to attain higher-priority concerns on the other. We describe an emerging perspective that highlights the monitoring and control of one's motivational states; i.e., metamotivation. We distinguish this approach from similar approaches (e.g., cognitive control, emotion regulation) and review initial supporting empirical results. Studying metamotivation is essential if we are to gain a comprehensive understanding into the questions of who, when, and why people succeed or fail at self-control.
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http://dx.doi.org/10.1016/j.copsyc.2024.101883 | DOI Listing |
Bioinform Adv
December 2024
Computer Science Department, Indiana University, Bloomington, IN 47408, United States.
Motivation: Microbial signatures in the human microbiome are closely associated with various human diseases, driving the development of machine learning models for microbiome-based disease prediction. Despite progress, challenges remain in enhancing prediction accuracy, generalizability, and interpretability. Confounding factors, such as host's gender, age, and body mass index, significantly influence the human microbiome, complicating microbiome-based predictions.
View Article and Find Full Text PDFBioinform Adv
December 2024
Laboratory of Experimental Biophysics, Center for Advanced Technologies, Tashkent, 100174, Uzbekistan.
Motivation: Understanding the conformational landscape of protein-ligand interactions is critical for elucidating the binding mechanisms that govern these interactions. Traditional methods like molecular dynamics (MD) simulations are computationally intensive, leading to a demand for more efficient approaches. This study explores how multiple sequence alignment (MSA) clustering enhance AF-Multimer's ability to predict conformational landscapes, particularly for proteins with multiple conformational states.
View Article and Find Full Text PDFBioinform Adv
December 2024
Department of Protein Evolution, Max Planck Institute for Biology, Tübingen 72076, Germany.
Motivation: Coiled coils are a widespread structural motif consisting of multiple α-helices that wind around a central axis to bury their hydrophobic core. While AlphaFold has emerged as an effective coiled-coil modeling tool, capable of accurately predicting changes in periodicity and core geometry along coiled-coil stalks, it is not without limitations, such as the generation of spuriously bent models and the inability to effectively model globally non-canonical-coiled coils. To overcome these limitations, we investigated whether dividing full-length sequences into fragments would result in better models.
View Article and Find Full Text PDFNetw Neurosci
December 2024
Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. The brain connectivity of different modalities provides an insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multimodal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs).
View Article and Find Full Text PDFJ Med Imaging (Bellingham)
December 2024
University of Chicago, Department of Radiology, Chicago, Illinois, United States.
Purpose: High soft-tissue contrast imaging is essential for effective radiotherapy treatment. This could potentially be realized using both megavoltage and kilovoltage x-ray sources available on some therapy treatment systems to perform "MV-kV" dual-energy (DE) computed tomography (CT). However, noisy megavoltage images obtained with existing energy-integrating detectors (EIDs) are a limiting factor for clinical translation.
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