The Many Faces of Guilt: A Review Mapping Unique and Overlapping Expressions in OCD and Depression.

Background: Guilt, a complex emotional experience pervading many lives, takes on an intricate form when intertwined with psychiatric conditions. As a multifaceted concept, guilt represents a key diagnostic feature in depression and is an integral part of obsessive compulsive disorder (OCD).

Methods: This systematic review aimed to synthesize empirical research on the varied dimensions of guilt across these two mental illnesses, where guilt is emphasized as a pathognomonic factor.

Evaluation of Multiplex loop-mediated isothermal amplification assay for the detection of complex from clinically suspected cases of pulmonary tuberculosis.

Tuberculosis (TB) is the second leading cause of death from a single infectious agent worldwide. Bangladesh ranks 7th among the 30 high TB burdened countries in the world. Accurate detection of complex (MTBC) is challenging for developing countries as most of the resource poor settings are not suitable to perform molecular techniques.

lociPARSE: A Locality-aware Invariant Point Attention Model for Scoring RNA 3D Structures.

A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently available machine learning-based approaches. Here, we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures.

Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models.

Ground-breaking progress has been made in structure prediction of biomolecular assemblies, including the recent breakthrough of AlphaFold 3. However, it remains challenging for AlphaFold 3 and other state-of-the-art deep learning-based methods to accurately predict protein-RNA complex structures, in part due to the limited availability of evolutionary and structural information related to protein-RNA interactions that are used as inputs to the existing approaches. Here, we introduce ProRNA3D-single, a new deep-learning framework for protein-RNA complex structure prediction with only single-sequence input.

The landscape of RNA 3D structure modeling with transformer networks.

Transformers are a powerful subclass of neural networks catalyzing the development of a growing number of computational methods for RNA structure modeling. Here, we conduct an objective and empirical study of the predictive modeling accuracy of the emerging transformer-based methods for RNA structure prediction. Our study reveals multi-faceted complementarity between the methods and underscores some key aspects that affect the prediction accuracy.