DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment

We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on a hierarchical tree constructed from attention values extracted from a graph neural network (GNN), which was trained to predict atomic partial charges from accurate quantum-mechanical (QM) calculations. The resulting dynamic attention-based substructure hierarchy (DASH) approach provides fast assignment of partial charges with the same accuracy as the GNN itself, is software-independent, and can easily be integrated in existing parametrization pipelines as shown for the Open force field (OpenFF). The implementation of the DASH workflow, the final DASH tree, and the training set are available as open source / open data from public repositories

Point-of-Care Ultrasound Diagnosis of Acute High Altitude Illness: A Case Report

With the advent of high-quality portable ultrasound machines, point-of-care ultrasound (POCUS) has gained interest as a promising diagnostic tool for patients with high altitude illness. Although POCUS is used successfully in hospital environments to detect interstitial pulmonary edema and increased intracranial pressure, the relationship between specific sonographic criteria and high altitude illness is still unclear. We report the case of a healthy 32-y-old male who developed acute respiratory distress and neurologic impairment at 4321 m while participating in a high altitude medical research expedition. We discuss the potential of POCUS to diagnose acute high altitude illness by lung ultrasound, optic nerve sheath diameter measurement, and echocardiography. Ultrasound in combination with clinical findings helped us to exclude relevant differential diagnoses, start on-site treatment, and organize an evacuation. We used serial clinical and ultrasound examinations to assess the patient over time. Although its role in high altitude medicine needs further investigation, we believe that POCUS can be a valuable tool to aid clinical decision-making in remote, high altitude environments

DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment

We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on a hierarchical tree constructed from attention values extracted from a graph neural network (GNN), which was trained to predict atomic partial charges from accurate quantum-mechanical (QM) calculations. The resulting dynamic attention-based substructure hierarchy (DASH) approach provides fast assignment of partial charges with the same accuracy as the GNN itself, is software-independent, and can easily be integrated in existing parametrization pipelines, as shown for the Open force field (OpenFF). The implementation of the DASH workflow, the final DASH tree, and the training set are available as open source/open data from public repositories

Multiplexed genome engineering by Cas12a and CRISPR arrays encoded on single transcripts

The ability to modify multiple genetic elements simultaneously would help to elucidate and control the gene interactions and networks underlying complex cellular functions. However, current genome engineering technologies are limited in both the number and the type of perturbations that can be performed simultaneously. Here, we demonstrate that both Cas12a and a clustered regularly interspaced short palindromic repeat (CRISPR) array can be encoded in a single transcript by adding a stabilizer tertiary RNA structure. By leveraging this system, we illustrate constitutive, conditional, inducible, orthogonal and multiplexed genome engineering of endogenous targets using up to 25 individual CRISPR RNAs delivered on a single plasmid. Our method provides a powerful platform to investigate and orchestrate the sophisticated genetic programs underlying complex cell behaviors