Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys

The distinctive characteristics of alloy catalysts, encompassing composition, structure, and modifiable adsorption sites, present significant potential for the development of highly efficient electrocatalysts for oxygen evolution/reduction reactions [oxygen evolution reactions (OERs)/oxygen reduction reactions (ORRs)]. Machine learning (ML) methods can quickly establish the relationship between material features and catalytic activity, thus accelerating the development of alloy electrocatalysts. However, the current abundance of features presents a crucial challenge in selecting the most pertinent ones. In this study, we explored seven intrinsic features directly derived from the material's structure, with a specific focus on the chemical environment of active sites and their nearest neighbors. An accurate and efficient ML model to predict potential bifunctional oxygen electrocatalysts based on the intrinsic features of AB-type alloy active sites and intermediate free energies in the OERs/ORRs was established. These features possess clear physical and chemical meanings, closely linked to the electronic and geometric structures of active sites and neighboring atoms, thereby providing indispensable insights for the discovery of high-performance electrocatalysts. The ML model achieved R2 scores of 0.827, 0.913, and 0.711 for the predicted values of the three intermediate (OH, O, OOH) free energies, with corresponding mean absolute errors of 0.175, 0.242, and 0.200 eV, respectively. These results indicate that the ML model exhibits high accuracy in predicting the intermediate free energies. Furthermore, the ML model exhibited a prediction efficiency 150,000 times faster than traditional density functional theory calculations. This work will offer valuable insights and a framework for facilitating the rapid design of potential catalysts by ML methods.

Picky Eating in Children: A Scoping Review to Examine Its Intrinsic and Extrinsic Features and How They Relate to Identification

The health benefits and importance of family mealtimes have been extensively documented. Picky eating can impact this complex activity and has numerous extrinsic (or external) and intrinsic (or internal) features. Occupational therapists work with children and their families by looking at both intrinsic and extrinsic influences and are therefore well-placed to work within this context. This scoping review comprises a comprehensive search of key health industry databases using pre-determined search terms. A robust screening process took place using the authors pre-agreed inclusion and exclusion criteria. There were 80 studies that met the inclusion criteria, which were then mapped using content analysis. The most common assessments used to identify picky eating relied on parental reports and recall. Often additional assessments were included in studies to identify both the intrinsic and extrinsic features and presentation. The most common reported intrinsic features of the child who is a picky eater included increased sensitivity particularly to taste and smell and the child's personality. Extrinsic features which appear to increase the likelihood of picky eating are authoritarian parenting, rewards for eating, and pressuring the child to eat. Most commonly reported extrinsic features that decrease the likelihood of picky eating are family meals, responsive parents, and involving the child in the preparation of food. In conclusion, there is a lack of published papers addressing the role of occupational therapists in the assessment and identification of picky eating in children. There appears to be a complex interplay between intrinsic and extrinsic features which impact caregiver responses and therefore on the picky eater.

Direct Antimicrobial Susceptibility Testing on Clinical Urine Samples by Optical Tracking of Single Cell Division Events

With the increasing prevalence of antibiotic resistance, the need to develop antimicrobial susceptibility testing (AST) technologies is urgent. The current challenge has been to perform the antibiotic susceptibility testing in short time, directly with clinical samples, and with antibiotics over a broad dynamic range of clinically relevant concentrations. Here, a technology for point-of-care diagnosis of antimicrobial-resistant bacteria in urinary tract infections, by imaging the clinical urine samples directly with an innovative large volume solution scattering imaging (LVSi) system and analyzing the image sequences with a single-cell division tracking method is developed. The high sensitivity of single-cell division tracking associated with large volume imaging enables rapid antibiotic susceptibility testing directly on the clinical urine samples. The results demonstrate direct detection of bacterial infections in 60 clinical urine samples with a 60 min LVSi video, and digital AST of 30 positive clinical samples with 100% categorical agreement with both the clinical culture results and the on-site agar plating validation results. This technology provides opportunities for precise antibiotic prescription and proper treatment of the patient within a single clinic visit.