Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics.

: The research addresses algorithmic bias in deep learning models for cardiovascular risk prediction, focusing on fairness across demographic and socioeconomic groups to mitigate health disparities. It integrates fairness-aware algorithms, susceptible carrier-infected-recovered (SCIR) models, and interpretability frameworks to combine fairness with actionable AI insights supported by robust segmentation and classification metrics. : The research utilised quantitative 3D/4D heart magnetic resonance imaging and tabular datasets from the Cardiac Atlas Project's (CAP) open challenges to explore AI-driven methodologies for mitigating algorithmic bias in cardiac imaging.

Structure of the aminoterminal domain of the birnaviral multifunctional VP3 protein and its unexplored critical role.

To overcome their limited genetic capacity, numerous viruses encode multifunctional proteins. The birnavirus VP3 protein plays key roles during infection, including scaffolding of the viral capsid during morphogenesis, recruitment, and regulation of the viral RNA polymerase, shielding of the double-stranded RNA genome and targeting of host endosomes for genome replication, and immune evasion. The dimeric form of VP3 is critical for these functions.

Site-Specific Incorporation of Fluorinated Prolines into Proteins and Their Impact on Neighbouring Residues.

The incorporation of fluorinated amino acids into proteins provides new opportunities to study biomolecular structure-function relationships in an elegant manner. The available strategies to incorporate the majority of fluorinated amino acids are not site-specific or imply important structural modifications. Here, we present a chemical biology approach for the site-specific incorporation of three commercially available C-modified fluoroprolines that has been validated using a non-pathogenic version of huntingtin exon-1 (HttExon-1).

Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks.

Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series.