Adverse events and perceived abandonment: learning from patients' accounts of medical mishaps.

Background: Adverse medical events affect 10% of American households annually, inducing a variety of harms and attitudinal changes. The impact of adverse events on perceived abandonment by patients and their care partners has not been methodically assessed.

Objective: To identify ways in which providers, patients and families responded to medical mishaps, linking these qualitatively and statistically to reported feelings of abandonment and sequelae induced by perceived abandonment.

Neural Network Corrections to Intermolecular Interaction Terms of a Molecular Force Field Capture Nuclear Quantum Effects in Calculations of Liquid Thermodynamic Properties.

We incorporate nuclear quantum effects (NQE) in condensed matter simulations by introducing short-range neural network (NN) corrections to the ab initio fitted molecular force field ARROW. Force field NN corrections are fitted to average interaction energies and forces of molecular dimers, which are simulated using the Path Integral Molecular Dynamics (PIMD) technique with restrained centroid positions. The NN-corrected force field allows reproduction of the NQE for computed liquid water and methane properties such as density, radial distribution function (RDF), heat of evaporation (HVAP), and solvation free energy.

Combining Force Fields and Neural Networks for an Accurate Representation of Bonded Interactions.

We present a formalism of a neural network encoding bonded interactions in molecules. This intramolecular encoding is consistent with the models of intermolecular interactions previously designed by this group. Variants of the encoding fed into a corresponding neural network may be used to economically improve the representation of torsional degrees of freedom in any force field.

Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions.

A key goal of molecular modeling is the accurate reproduction of the true quantum mechanical potential energy of arbitrary molecular ensembles with a tractable classical approximation. The challenges are that analytical expressions found in general purpose force fields struggle to faithfully represent the intermolecular quantum potential energy surface at close distances and in strong interaction regimes; that the more accurate neural network approximations do not capture crucial physics concepts, e.g.

Protein-Ligand Binding Free-Energy Calculations with ARROW─A Purely First-Principles Parameterized Polarizable Force Field.

Protein-ligand binding free-energy calculations using molecular dynamics (MD) simulations have emerged as a powerful tool for in silico drug design. Here, we present results obtained with the ARROW force field (FF)─a multipolar polarizable and physics-based model with all parameters fitted entirely to high-level ab initio quantum mechanical (QM) calculations. ARROW has already proven its ability to determine solvation free energy of arbitrary neutral compounds with unprecedented accuracy.

Neuromorphic object localization using resistive memories and ultrasonic transducers.

Real-world sensory-processing applications require compact, low-latency, and low-power computing systems. Enabled by their in-memory event-driven computing abilities, hybrid memristive-Complementary Metal-Oxide Semiconductor neuromorphic architectures provide an ideal hardware substrate for such tasks. To demonstrate the full potential of such systems, we propose and experimentally demonstrate an end-to-end sensory processing solution for a real-world object localization application.

Accurate determination of solvation free energies of neutral organic compounds from first principles.

The main goal of molecular simulation is to accurately predict experimental observables of molecular systems. Another long-standing goal is to devise models for arbitrary neutral organic molecules with little or no reliance on experimental data. While separately these goals have been met to various degrees, for an arbitrary system of molecules they have not been achieved simultaneously.

[Treatment of refractory epilepsy. A comparison between classic ketogenic diet and modified Atkins diet in terms of efficacy, adherence, and undesirable effects].

Background: the ketogenic diet (CD) is an established, effective non-pharmacological treatment for refractory epilepsy in childhood. Aim: the objective of this study was to compare the efficacy, the presence of undesirable effects, and adherence between the classic ketogenic diet (DCC) and the modified Atkins diet (DAM). Materials and methods: a retrospective and comparative investigation was carried out to evaluate the medical records of all the patients who started treatment with a ketogenic diet by the same team between 2008 and 2018.

Initial Inoculum and the Severity of COVID-19: A Mathematical Modeling Study of the Dose-Response of SARS-CoV-2 Infections.

SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) causes a variety of responses in those who contract the virus, ranging from asymptomatic infections to acute respiratory failure and death. While there are likely multiple mechanisms triggering severe disease, one potential cause of severe disease is the size of the initial inoculum. For other respiratory diseases, larger initial doses lead to more severe outcomes.

Real Space Observation of Electronic Coupling between Self-Assembled Quantum Dots.

The control of quantum coupling between nano-objects is essential to quantum technologies. Confined nanostructures, such as cavities, resonators, or quantum dots, are designed to enhance interactions between electrons, photons, or phonons, giving rise to new properties, on which devices are developed. The nature and strength of these interactions are often measured indirectly on an assembly of dissimilar objects.

On the importance of accounting for nuclear quantum effects in ab initio calibrated force fields in biological simulations.

In many important processes in chemistry, physics, and biology the nuclear degrees of freedom cannot be described using the laws of classical mechanics. At the same time, the vast majority of molecular simulations that employ wide-coverage force fields treat atomic motion classically. In light of the increasing desire for and accelerated development of quantum mechanics (QM)-parameterized interaction models, we reexamine whether the classical treatment is sufficient for a simple but crucial chemical species: alkanes.

Prediction of cyclohexane-water distribution coefficient for SAMPL5 drug-like compounds with the QMPFF3 and ARROW polarizable force fields.

We present the performance of blind predictions of water-cyclohexane distribution coefficients for 53 drug-like compounds in the SAMPL5 challenge by three methods currently in use within our group. Two of them utilize QMPFF3 and ARROW, polarizable force-fields of varying complexity, and the third uses the General Amber Force-Field (GAFF). The polarizable FF's are implemented in an in-house MD package, Arbalest.

Facilitating access to antiviral medications and information during an influenza pandemic: engaging with the public on possible new strategies.

Antiviral medications can decrease the severity and duration of influenza, but they are most effective if started within 48 hours of the onset of symptoms. In a severe influenza pandemic, normal channels of obtaining prescriptions and medications could become overwhelmed. To assess public perception of the acceptability and feasibility of alternative strategies for prescribing, distributing, and dispensing antivirals and disseminating information about influenza and its treatment, the Institute of Medicine, with technical assistance from the Centers for Disease Control and Prevention (CDC), convened public engagement events in 3 demographically and geographically diverse communities: Fort Benton, MT; Chattanooga, TN; and Los Angeles, CA.

Discretization of electronic states in large InAsP/InP multilevel quantum dots probed by scanning tunneling spectroscopy.

The topography and the electronic structure of InAsP/InP quantum dots are probed by cross-sectional scanning tunneling microscopy and spectroscopy. The study of the local density of states in such large quantum dots confirms the discrete nature of the electronic levels whose wave functions are measured by differential conductivity mapping. Because of their large dimensions, the energy separation between the discrete electronic levels is low, allowing for quantization in both the lateral and growth directions as well as the observation of the harmonicity of the dot lateral potential.

Improved protein structure selection using decoy-dependent discriminatory functions.

Background: A key component in protein structure prediction is a scoring or discriminatory function that can distinguish near-native conformations from misfolded ones. Various types of scoring functions have been developed to accomplish this goal, but their performance is not adequate to solve the structure selection problem. In addition, there is poor correlation between the scores and the accuracy of the generated conformations.

Funnel sculpting for in silico assembly of secondary structure elements of proteins.

We present a method for designing a funnel-shaped free-energy surface that reproducibly assembles secondary structure elements of proteins into their native conformations from a random extended configuration. Assuming a priori knowledge of secondary structure, our method can design a funnel-shaped surface for folding of alpha, beta, and alphabeta structures individually. We design energy surfaces that fold up to five unrelated sequences with the same energy parameters.

Automatic classification of protein structure by using Gauss integrals.

We introduce a method of looking at, analyzing, and comparing protein structures. The topology of a protein is captured by 30 numbers inspired by Vassiliev knot invariants. To illustrate the simplicity and power of this topological approach, we construct a measure (scaled Gauss metric, SGM) of similarity of protein shapes.

Design of an optimal Chebyshev-expanded discrimination function for globular proteins.

We describe the construction of a scoring function designed to model the free energy of protein folding. An optimization technique is used to determine the best functional forms of the hydrophobic, residue-residue and hydrogen-bonding components of the potential. The scoring function is expanded by use of Chebyshev polynomials, the coefficients of which are determined by minimizing the score, in units of standard deviation, of native structures in the ensembles of alternate decoy conformations.

Conformations of closed DNA.

We examine the conformations of a model for a short segment of closed DNA. The molecule is represented as a cylindrically symmetric elastic rod with a constraint corresponding to a specification of the linking number. We obtain analytic expressions leading to the spatial configuration of a family of solutions representing distortions that interpolate between the circular form of DNA and a figure-eight form that represents the onset of interwinding.