Metagenome-assembled genomes (MAGs) of the emerging pathogen from enrichment of coastal seawater, sediment, and algae at St. John's Island, Singapore.

Coastal water, sediment, and algae samples were collected from St. John's Island, Singapore, and enriched in either broth or agar. Metagenomic sequencing was carried out on DNA from these enrichments and analyzed.

Epidemiology of Epstein-Barr Virus Chronic High Viral Load in Kidney Transplant Recipients.

Background: Epstein-Barr virus (EBV) chronic high viral load (CHVL) may be defined by >16 000 copies/mL whole blood or >200 copies/10 peripheral blood mononuclear cells in >50% samples exceeding 6 mo. EBV CHVL has only been characterized in a few small pediatric studies, with heterogeneous results and unclear clinical significance.

Methods: This single-center observational study evaluated adult and pediatric kidney transplant recipients transplanted between 2010 and 2021 on tacrolimus/mycophenolate-based/prednisone immunosuppression.

Dynamic omniphobic surfaces enable the stable dropwise condensation of completely wetting refrigerants.

Condensation is a vital process integral to numerous industrial applications. Enhancing condensation efficiency through dropwise condensation on hydrophobic surfaces is well-documented. However, no surfaces have been able to repel liquids with extremely low surface tension, such as fluorinated solvents, during condensation, as they nucleate and completely wet even the most hydrophobic interfaces.

Diagnostic accuracy of an automated classifier for the detection of pleural effusions in patients undergoing lung ultrasound.

Rationale: Lung ultrasound, the most precise diagnostic tool for pleural effusions, is underutilized due to healthcare providers' limited proficiency. To address this, deep learning models can be trained to recognize pleural effusions. However, current models lack the ability to diagnose effusions in diverse clinical contexts, which presents significant challenges.

Predicting Carbonic Anhydrase Binding Affinity: Insights from QM Cluster Models.

A systematic series of QM cluster models has been developed to predict the trend in the carbonic anhydrase binding affinity of a structurally diverse dataset of ligands. Reference DLPNO-CCSD(T)/CBS binding energies were generated for a cluster model and used to evaluate the performance of contemporary density functional theory methods, including Grimme's "3c" DFT composite methods (rSCAN-3c and ωB97X-3c). It is demonstrated that when validated QM methods are used, the predictive power of the cluster models improves systematically with the size of the cluster models.