Dynamics Calculations of the Flexibility and Vibrational Spectrum of the Linear Alkane CH, Based on Machine-Learned Potentials.

Hydrocarbons are the central feedstock of fuels, solvents, lubricants, and the starting materials for many synthetic materials, and thus the physical properties of hydrocarbons have received intense study. Among these, the molecular flexibility and the power and infrared spectroscopies are the focus of this paper. These are examined for the linear alkane CH using molecular dynamics (MD) calculations and recent machine-learned potentials.

The effects of maternal flow on placental diffusion-weighted MRI and intravoxel incoherent motion parameters.

Purpose: To investigate and explain observed features of the placental DWI signal in healthy and compromised pregnancies using a mathematical model of maternal blood flow.

Methods: Thirteen healthy and nine compromised third trimester pregnancies underwent pulse gradient spin echo DWI MRI, with the results compared to MRI data simulated from a 2D mathematical model of maternal blood flow through the placenta. Both sets of data were fitted to an intravoxel incoherent motion (IVIM) model, and a rebound model (defined within text), which described voxels that did not decay monotonically.

DFT-Based Permutationally Invariant Polynomial Potentials Capture the Twists and Turns of CH.

Hydrocarbons are ubiquitous as fuels, solvents, lubricants, and as the principal components of plastics and fibers, yet our ability to predict their dynamical properties is limited to force-field mechanics. Here, we report two machine-learned potential energy surfaces (PESs) for the linear 44-atom hydrocarbon CH using an extensive data set of roughly 250,000 density functional theory (DFT) (B3LYP) energies for a large variety of configurations, obtained using MM3 direct-dynamics calculations at 500, 1000, and 2500 K. The surfaces, based on Permutationally Invariant Polynomials (PIPs) and using both a many-body expansion approach and a fragmented-basis approach, produce precise fits for energies and forces and also produce excellent out-of-sample agreement with direct DFT calculations for torsional and dihedral angle potentials.

Calculations of Dissociation Dynamics of CHOH on a Global Potential Energy Surface Reveal the Mechanism for the Formation of HCOH; Roaming Plays a Role.

The experimental observation of hydroxymethylene, HCOH, following excitation of methanol at 193 nm, was reported recently (Hockey, E. K.; McLane, N.

Dosimetric comparison of hippocampal-sparing technologies in patients with low-grade glioma.

Background: Radiotherapy (RT) plays an integral role in the management of low-grade gliomas (LGG). Late toxicity from RT can cause progressive neurocognitive dysfunction. Radiation-induced damage to the hippocampus (HCP) plays a considerable role in memory decline.

Adaptive radiotherapy for muscle invasive bladder cancer: a retrospective audit of two bladder filling protocols.

Background: Radical radiotherapy for muscle-invasive bladder cancer (MIBC) is challenging due to large variations in bladder shape, size and volume during treatment, with drinking protocols often employed to mitigate geometric uncertainties. Utilising adaptive radiotherapy together with CBCT imaging to select a treatment plan that best fits the bladder target and reduce normal tissue irradiation is an attractive option to compensate for anatomical changes. The aim of this retrospective study was to compare a bladder empty (BE) protocol to a bladder filling (BF) protocol with regards to variations in target volumes, plan of the day (PoD) selection and plan dosimetry throughout treatment.

COVID-19 Incidence, Risk Factors, Impact, and Related Stigma Among a Cohort of Persons With HIV in Washington, DC.

Background: Studies on the incidence of COVID-19 among persons with HIV (PWHs) present varied results. Few studies have investigated the impact of COVID-19 infection on health and socioeconomic factors or COVID-19 stigma. We sought to measure the incidence and severity of COVID-19 infection among a cohort of PWHs, characterize associated risk factors and impact, and document perceptions of COVID-19-related stigma.

Efficient High-Order Space-Angle-Energy Polytopic Discontinuous Galerkin Finite Element Methods for Linear Boltzmann Transport.

We introduce an -version discontinuous Galerkin finite element method (DGFEM) for the linear Boltzmann transport problem. A key feature of this new method is that, while offering arbitrary order convergence rates, it may be implemented in an almost identical form to standard multigroup discrete ordinates methods, meaning that solutions can be computed efficiently with high accuracy and in parallel within existing software. This method provides a unified discretisation of the space, angle, and energy domains of the underlying integro-differential equation and naturally incorporates both local mesh and local polynomial degree variation within each of these computational domains.

Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine.

Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PESs) in many chemical simulations. The MLPs are often evaluated with the root-mean-square errors on the test set drawn from the same distribution as the training data. Here, we systematically investigate the relationship between such test errors and the simulation accuracy with MLPs on an example of a full-dimensional, global PES for the glycine amino acid.

Leveraging human-centered design and causal pathway diagramming toward enhanced specification and development of innovative implementation strategies: a case example of an outreach tool to address racial inequities in breast cancer screening.

Background: Implementation strategies are strategies to improve uptake of evidence-based practices or interventions and are essential to implementation science. Developing or tailoring implementation strategies may benefit from integrating approaches from other disciplines; yet current guidance on how to effectively incorporate methods from other disciplines to develop and refine innovative implementation strategies is limited. We describe an approach that combines community-engaged methods, human-centered design (HCD) methods, and causal pathway diagramming (CPD)-an implementation science tool to map an implementation strategy as it is intended to work-to develop innovative implementation strategies.

Formic Acid-Ammonia Heterodimer: A New Δ-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer.

The formic acid-ammonia dimer is an important example of a hydrogen-bonded complex in which a double proton transfer can occur. Its microwave spectrum has recently been reported and rotational constants and quadrupole coupling constants were determined. Calculated estimates of the double-well barrier and the internal barriers to rotation were also reported.

Changes in alcohol consumption during the COVID-19 pandemic: a longitudinal cohort study using smart-breathalyzer data.

Previous studies relying on alcohol sales, alcohol-related injuries, and surveys have suggested that alcohol consumption increased during the COVID-19 pandemic. We sought to leverage over 1 million Breath Alcohol Concentration (BrAC) measurements from Bluetooth-enabled breathalyzers to conduct an objective and longitudinal assessment of alcohol use during the pandemic. Serial BrAC measurements revealed a decrease in drinking between January 1, 2020 and March 30, 2020, an increase between March 30, 2020 and May 25, 2020, a statistically insignificant decrease between May 25, 2020 and January 1, 2021, and an increase again between January 1, 2021 and June 4, 2021.

Ab Initio Potential Energy Surface for NaCl-H with Correct Long-Range Behavior.

We report a full dimensional ab initio potential energy surface for NaCl-H based on precise fitting of a large data set of CCSD(T)/aug-cc-pVTZ energies. A major goal of this fit is to describe the very long-range interaction accurately. This is done in this instance via the dipole-quadrupole interaction.

A New Method to Avoid Calculation of Negligible Hamiltonian Matrix Elements in CI Calculation.

Hamiltonian matrices typically contain many elements that are negligibly small compared to the diagonal elements, even with methods to prune the underlying basis. Because for general potentials the calculation of -matrix elements is a major part of the computational effort to obtain eigenvalues and eigenfunctions of the Hamiltonian, there is strong motivation to investigate locating these negligible elements without calculating them or at least avoid calculating them. We recently demonstrated an effective means to "learn" negligible elements using machine learning classification ( , 159, 071101).

A Status Report on "Gold Standard" Machine-Learned Potentials for Water.

Owing to the central importance of water to life as well as its unusual properties, potentials for water have been the subject of extensive research over the past 50 years. Recently, five potentials based on different machine learning approaches have been reported that are at or near the "gold standard" CCSD(T) level of theory. The development of such high-level potentials enables efficient and accurate simulations of water systems using classical and quantum dynamical approaches.

Machine learning classification can significantly reduce the cost of calculating the Hamiltonian matrix in CI calculations.

Hamiltonian matrices in electronic and nuclear contexts are highly computation intensive to calculate, mainly due to the cost for the potential matrix. Typically, these matrices contain many off-diagonal elements that are orders of magnitude smaller than diagonal elements. We illustrate that here for vibrational H-matrices of H2O, C2H3 (vinyl), and C2H5NO2 (glycine) using full-dimensional ab initio-based potential surfaces.

Benchmarking Tests of Contemporary SRS Platforms: Have Technological Developments Resulted in Improved Treatment Plan Quality?

Purpose: Stereotactic radiosurgery treatment delivery can be performed with a range of devices, each of which have evolved over recent years. We sought to evaluate the differences in performance of contemporary stereotactic radiosurgery platforms and also to compare them with earlier platform iterations from a previous benchmarking study.

Methods And Materials: The following platforms were selected as "state of the art" in 2022: Gamma Knife Icon (GK), CyberKnife S7 (CK), Brainlab Elements (Elekta VersaHD and Varian TrueBeam), Varian Edge with HyperArc (HA), and Zap-X.

Interfacing q-AQUA with a Polarizable Force Field: The Best of Both Worlds.

Polarizable force fields are pervasive in the fields of computational chemistry and biochemistry; however, their empirical or semiempirical nature gives them both weaknesses and strengths. Here, we have developed a hybrid water potential, named q-AQUA-pol, by combining our recent q-AQUA potential with the TTM3-F water potential. The new potential demonstrates unprecedented accuracy ranging from gas-phase clusters, e.

Ring-Polymer Instanton Tunneling Splittings of Tropolone and Isotopomers using a Δ-Machine Learned CCSD(T) Potential: Theory and Experiment Shake Hands.

Tropolone, a 15-atom cyclic molecule, has received much interest both experimentally and theoretically due to its H-transfer tunneling dynamics. An accurate theoretical description is challenging owing to the need to develop a high-level potential energy surface (PES) and then to simulate quantum-mechanical tunneling on this PES in full dimensionality. Here, we tackle both aspects of this challenge and make detailed comparisons with experiments for numerous isotopomers.