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.

Physically interpretable approximations of many-body spectral functions.

The rational function approximation provides a natural and interpretable representation of response functions such as the many-body spectral functions. We apply the vector fitting (VFIT) algorithm to fit a variety of spectral functions calculated from the Holstein model of electron-phonon interactions. We show that the resulting rational functions are highly efficient in their fitting of sharp features in the spectral functions, and could provide a means to infer physically relevant information from a spectral data set.

Oxygenation influences xylose fermentation and gene expression in the yeast genera Spathaspora and Scheffersomyces.

Background: Cost-effective production of biofuels from lignocellulose requires the fermentation of D-xylose. Many yeast species within and closely related to the genera Spathaspora and Scheffersomyces (both of the order Serinales) natively assimilate and ferment xylose. Other species consume xylose inefficiently, leading to extracellular accumulation of xylitol.

Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment.

Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data have a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. We investigate a training procedure based on iterative Boltzmann inversion that produces a pair potential correction to an existing MLP using equilibrium radial distribution function data.

Uncertainty-driven dynamics for active learning of interatomic potentials.

Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, the ML model provides an uncertainty estimate along with its prediction for each new atomic configuration.

Machine Learning Framework for Modeling Exciton Polaritons in Molecular Materials.

A light-matter hybrid quasiparticle, called a polariton, is formed when molecules are strongly coupled to an optical cavity. Recent experiments have shown that polariton chemistry can manipulate chemical reactions. Polariton chemistry is a collective phenomenon, and its effects increase with the number of molecules in a cavity.

sp. nov., a predacious yeast isolated from soil and rotten wood in an Amazonian rainforest biome.

Three yeast isolates were obtained from soil and rotting wood samples collected in an Amazonian rainforest biome in Brazil. Comparison of the intergenic spacer 5.8S region and the D1/D2 domains of the large subunit rRNA gene showed that the isolates represent a novel species of the genus .

Yueomyces silvicola sp. nov., a novel ascomycetous yeast species unable to utilize ammonium, glutamate, and glutamine as sole nitrogen sources.

Five yeast strains isolated from tree bark and rotten wood collected in central and southwestern China, together with four Brazilian strains (three from soil and rotting wood collected in an Amazonian rainforest biome and one from Bromeliad collected in Alagoas state) and one Costa Rican strain isolated from a flower beetle, represent a new species closely related with Yueomyces sinensis in Saccharomycetaceae, as revealed by the 26S ribosomal RNA gene D1/D2 domain and the internal transcribed spacer region sequence analysis. The name Yueomyces silvicola sp. nov.

Synergy of semiempirical models and machine learning in computational chemistry.

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g.

Unusual presentation of disseminated cryptococcal infection complicated by myocarditis in a heart transplant recipient.

Background: Cryptococcus neoformans is the third most common cause of invasive fungal infection in solid organ transplant (SOT) recipients. While cryptococcal infection can involve any organ, cases of myocarditis are exceedingly rare.

Methods: A retrospective chart review was completed for this case report.

CP skyrmions and skyrmion crystals in realistic quantum magnets.

Magnetic skyrmions are nanoscale topological textures that have been recently observed in different families of quantum magnets. These objects are called CP skyrmions because they are built from dipoles-the target manifold is the 1D complex projective space, CP ≅ S. Here we report the emergence of magnetic CP skyrmions in a realistic spin-1 model, which includes both dipole and quadrupole moments.

Flexible class of exact Hubbard-Stratonovich transformations.

We consider a class of Hubbard-Stratonovich transformations suitable for treating Hubbard interactions in the context of quantum Monte Carlo simulations. A tunable parameter p allows us to continuously vary from a discrete Ising auxiliary field (p=∞) to a compact auxiliary field that couples to electrons sinusoidally (p=0). In tests on the single-band square and triangular Hubbard models, we find that the severity of the sign problem decreases systematically with increasing p.

Effect of reclaimed water and dehydrated sludge on the morpho-physiology and yield of sorghum.

The effect on the morpho-physiological parameters and yield of sorghum cultivated in a greenhouse with reclaimed water (RW) and (dehydrated sludge (DS) obtained in a sewage treatment plant, was evaluated. Six treatments (T), with five repetitions each, were carried out in entirely randomized blocks. Water (W) was used in T1 (W) (control), T2 (W + NPK), and T3 (W + DS); RW was used in T4 (RW), T5 (RW + P), and T6 (RW + DS).

Semi-Empirical Shadow Molecular Dynamics: A PyTorch Implementation.

Extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) in its most recent shadow potential energy version has been implemented in the semiempirical PyTorch-based software PySeQM. The implementation includes finite electronic temperatures, canonical density matrix perturbation theory, and an adaptive Krylov subspace approximation for the integration of the electronic equations of motion within the XL-BOMB approach (KSA-XL-BOMD). The PyTorch implementation leverages the use of GPU and machine learning hardware accelerators for the simulations.

Lightweight and effective tensor sensitivity for atomistic neural networks.

Atomistic machine learning focuses on the creation of models that obey fundamental symmetries of atomistic configurations, such as permutation, translation, and rotation invariances. In many of these schemes, translation and rotation invariance are achieved by building on scalar invariants, e.g.

Extending machine learning beyond interatomic potentials for predicting molecular properties.

Machine learning (ML) is becoming a method of choice for modelling complex chemical processes and materials. ML provides a surrogate model trained on a reference dataset that can be used to establish a relationship between a molecular structure and its chemical properties. This Review highlights developments in the use of ML to evaluate chemical properties such as partial atomic charges, dipole moments, spin and electron densities, and chemical bonding, as well as to obtain a reduced quantum-mechanical description.

Donnan Dialysis for Recovering Ammonium from Fermentation Solutions Rich in Volatile Fatty Acids.

For the production of polyhydroxyalkanoates (PHA) using nitrogen-rich feedstocks (e.g., protein-rich resources), the typical strategy of restricting cell growth as a means to enhance overall PHA productivity by nitrogen limitation is not applicable.

Anti-human immunodeficiency virus-1 activity of MoMo30 protein isolated from the traditional African medicinal plant Momordica balsamina.

Background: Plants are used in traditional healing practices of many cultures worldwide. Momordica balsamina is a plant commonly used by traditional African healers as a part of a treatment for HIV/AIDS. It is typically given as a tea to patients with HIV/AIDS.

sp. nov. and sp. nov., two d-xylose-fermenting ascosporogenous yeasts from Amazonian Forest biomes.

Four isolates of species were recovered from rotting wood collected in two Brazilian Amazonian biomes. The isolates produced unconjugated allantoid asci with a single elongated ascospore with curved ends. Sequence analysis of the ITS-5.