ZIF-67 functionalized bacterial cellulose films for light-enhanced NaBH<sub>4</sub>-assisted catalytic reduction of nitrobenzene. [PDF]
Mhlwatika Z, Khene S, Nombona N.
europepmc +1 more source
Objective Youth who experience a sport‐related knee injury have elevated odds of becoming overweight or developing obesity in 3 to 10 years, compounding their risk for posttraumatic osteoarthritis (PTOA). To inform prevention strategies, this study compared patterns of adiposity change between youth with a sport‐related knee injury and uninjured youth ...
Justin M. Losciale +6 more
wiley +1 more source
Utilization of Recycled Brick Aggregate in Cementitious Composites: Mechanical Performance, Cement Matrix Solidification Effect and Life Cycle Assessment. [PDF]
Václavík V +9 more
europepmc +1 more source
Objective Reproductive‐age women with systemic autoimmune and rheumatic diseases (SARDs) have unique information needs related to their SARDs and reproductive health. We sought to understand their use of and receptivity to current and hypothetical generative artificial intelligence (AI) tools for health information‐seeking. Methods We conducted a cross‐
Mariam Arif +5 more
wiley +1 more source
Modulating rice starch physicochemical and <i>in vitro</i> digestive properties with ball-milled pea protein isolate: Effect of ball-milling time. [PDF]
Jiang J +6 more
europepmc +1 more source
Patients improve, patterns persist: longitudinal stability of RA joint involvement patterns
Objective Rheumatoid arthritis is a heterogeneous disease. Data‐driven approaches, from synovial histology to joint involvement patterns (JIPs), have sought to define clinically meaningful subgroups. Whether these subgroups represent stable phenotypes or transient disease states remains unclear.
Tjardo Maarseveen +24 more
wiley +1 more source
Impact of Polyimide Semi-Interpenetrating Polymer Networks on the Physicochemical Properties of Vulcanized Acrylonitrile-Butadiene Rubber. [PDF]
Kwon S +8 more
europepmc +1 more source
dynoGP: Deep Gaussian Processes for Dynamic System Identification
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source

