Prominent structural models of depression and anxiety arise from 2 traditions: (a) the tripartite/integrative hierarchical model based on symptom dimensions, and (b) the fear/anxious-misery model based on diagnostic comorbidity data. The tri-level model of depression and anxiety was developed to synthesize these structural models, postulating that narrow (disorder-specific), intermediate (fear and anxious-misery), and broad (general distress) structural factors are needed to most fully account for covariation among these symptoms. Although this model has received preliminary support (Prenoveau et al., 2010), the current study compares it with the above established models and seeks to validate the best-fitting structure. We evaluated the tri-level model and alternative structural models in a large clinical sample (N = 1,000) using bifactor analysis. In exploratory and confirmatory subsamples, the tri-level model provided a good fit to the data and each of the 3 levels (narrow, intermediate, and broad) accounted for substantial variance; this model provided a superior fit relative to more parsimonious competing structural models. Furthermore, impairment was independently associated with all 3 levels of the tri-level model, comorbidity was most closely linked to the broad tri-level dimensions, and the factors generally showed the expected convergent/discriminant associations with diagnoses. Results suggested several revisions to prior research: (a) worry may be best modeled at the broadest structural level, rather than as an indicator of anxious-misery or fear; (b) social interaction anxiety may belong with anxious-misery, rather than fear; and (c) obsessive-compulsive disorder is generally associated with fear disorders, but hoarding is associated with both fear and anxious-misery. (PsycINFO Database Record
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http://dx.doi.org/10.1037/abn0000197 | DOI Listing |
Bioinformatics
January 2025
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
Motivation: The accurate prediction of O-GlcNAcylation sites is crucial for understanding disease mechanisms and developing effective treatments. Previous machine learning models primarily relied on primary or secondary protein structural and related properties, which have limitations in capturing the spatial interactions of neighboring amino acids. This study introduces local environmental features as a novel approach that incorporates three-dimensional spatial information, significantly improving model performance by considering the spatial context around the target site.
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Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, College of Biological and Environment Engineering, Zhejiang Shuren University, Hangzhou, 310015, China.
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January 2025
Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
DNA is not only a centrally important molecule in biology: the specificity of bonding that allows it to be the primary information storage medium for life has also allowed it to become one of the most promising materials for designing intricate, self-assembling structures at the nanoscale. While the applications of these structures are both broad and highly promising, the self-assembly process itself has attracted interest not only for the practical applications of designing structures with more efficient assembly pathways, but also due to a desire to understand the principles underlying self-assembling systems more generally, of which DNA-based systems provide intriguing and unique examples. Here, we review the fundamental physical principles that underpin the self-assembly process in the field of DNA nanotechnology, with a specific focus on simulation and modelling and what we can learn from them.
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January 2025
Institute of Inorganic Chemistry, Slovak Academy of Sciences, Dúbravská cesta 9, SK-84536 Bratislava, Slovakia.
The solvent effect on the indirect J(M-P) spin-spin coupling constant in phosphine selenoether -substituted acenaphthene complexes LMCl is studied at the PP86 level of nonrelativistic and four-component relativistic density functional theory. Depending on the metal, the solvent effect can amount to as much as 50% or more of the total -value. This explains the previously found disagreement between the J(Hg-P) coupling in LHgCl, observed experimentally and calculated without considering solvent effects.
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March 2025
Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
Magnetic resonance imaging (MRI) is a powerful tool to identify the structural and functional correlates of neurological illness but provides limited insight into molecular neurobiology. Using rat genetic models of autism spectrum disorder, we show that image texture-processed neurite orientation dispersion and density imaging (NODDI) diffusion MRI possesses an intrinsic relationship with gene expression that corresponds to the biophysically modeled cellular compartments of the NODDI diffusion signal. Specifically, we demonstrate that neurite density index and orientation dispersion index signals are correlated with intracellular and extracellular gene expression, respectively.
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