Recovery, interrupted: persisting symptoms after concussion and the power of iatrogenesis.

Persisting symptoms after concussion (PSaC) affect up to 30% of children, adolescents, and young adults beyond 1 month post-injury, posing challenges in clinical care. This retrospective study examined 54 patients referred for neuropsychological evaluation due to PSaC, exploring factors contributing to symptom persistence. Results showed that 75.

Data Generation for Machine Learning Interatomic Potentials and Beyond.

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data.

Spathaspora marinasilvae sp. nov., a xylose-fermenting yeast isolated from galleries of passalid beetles and rotting wood in the Amazonian rainforest biome.

Four yeast isolates were obtained from rotting wood and galleries of passalid beetles collected in different sites of the Brazilian Amazonian Rainforest in Brazil. This yeast produces unconjugated allantoid asci each with a single elongated ascospore with curved ends. Sequence analysis of the internal transcribed spacer-5.

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential.

Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. For this reason, chemistry and materials science would greatly benefit from a general reactive MLIP, that is, an MLIP that is applicable to a broad range of reactive chemistry without the need for refitting.