Immune digital twins for complex human pathologies: applications, limitations, and challenges.

Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins.

STK19 facilitates the clearance of lesion-stalled RNAPII during transcription-coupled DNA repair.

Transcription-coupled DNA repair (TCR) removes bulky DNA lesions impeding RNA polymerase II (RNAPII) transcription. Recent studies have outlined the stepwise assembly of TCR factors CSB, CSA, UVSSA, and transcription factor IIH (TFIIH) around lesion-stalled RNAPII. However, the mechanism and factors required for the transition to downstream repair steps, including RNAPII removal to provide repair proteins access to the DNA lesion, remain unclear.

Deep Learning Methods to Help Predict Properties of Molecules from SMILES.

Machine learning methods have been proposed in lieu of simulations to predict chemical properties of molecules. The trade-off here is paying for the training time once, in exchange for instant predictions on the input data. However, many of these methods rely heavily on feature engineering to prepare the data for these models.

ArcaDB: A Disaggregated Query Engine for Heterogenous Computational Environments.

Modern enterprises rely on data management systems to collect, store, and analyze vast amounts of data related to their operations. Nowadays, clusters and hardware accelerators (e.g.