Linear-Scaling Local Natural Orbital-Based Full Triples Treatment in Coupled-Cluster Theory.

We present an efficient, asymptotically linear-scaling implementation of the canonically coupled-cluster method with singles, doubles, and full triples excitations (CCSDT) method. We apply the domain-based local pair natural orbital (DLPNO) approach for computing CCSDT amplitudes. Our method, called DLPNO-CCSDT, uses the converged coupled-cluster amplitudes from a preceding DLPNO-CCSD(T) computation as a starting point for the solution of the CCSDT equations in the local natural orbital basis.

Accurate and efficient open-source implementation of domain-based local pair natural orbital (DLPNO) coupled-cluster theory using a t1-transformed Hamiltonian.

We present an efficient, open-source formulation for coupled-cluster theory through perturbative triples with domain-based local pair natural orbitals [DLPNO-CCSD(T)]. Similar to the implementation of the DLPNO-CCSD(T) method found in the ORCA package, the most expensive integral generation and contraction steps associated with the CCSD(T) method are linear-scaling. In this work, we show that the t1-transformed Hamiltonian allows for a less complex algorithm when evaluating the local CCSD(T) energy without compromising efficiency or accuracy.

A modular, composite framework for the utilization of reduced-scaling Coulomb and exchange construction algorithms: Design and implementation.

Multiple algorithms exist for calculating Coulomb (J) or exchange (K) contributions to Fock-like matrices, and it is beneficial to develop a framework that allows the seamless integration and combination of different J and K construction algorithms. In Psi4, we have implemented the "CompositeJK" formalism for this purpose. CompositeJK allows for the combination of any J and K construction algorithms for any quantum chemistry method formulated in terms of J-like or K-like matrices (including, but not limited to, Hartree-Fock and density functional theory) in a highly modular and intuitive fashion, which is simple to utilize for both developers and users.

Directional Δ Neural Network (DrΔ-Net): A Modular Neural Network Approach to Binding Free Energy Prediction.

The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of systems, they are generally too costly to be applied at the same frequency as end point or ligand-based methods. By contrast, these data-driven approaches are typically fast enough to address thousands of systems but with reduced transferability to unseen systems.

Tensor Hypercontraction Form of the Perturbative Triples Energy in Coupled-Cluster Theory.

We present the working equations for a reduced-scaling method of evaluating the perturbative triples (T) energy in coupled-cluster theory, through the tensor hypercontraction (THC) of the triples amplitudes (). Through our method, we can reduce the scaling of the (T) energy from the traditional to a more modest . We also discuss implementation details to aid future research, development, and software realization of this method.

Out-of-hospital management of unresponsive, apneic, witnessed opioid overdoses: a case series from a supervised consumption site.

Objectives: There are conflicting recommendations for lay rescuer management of patients who are unresponsive and apneic due to opioid overdose. We evaluated the management of such patients at an urban supervised consumption site.

Methods: At a single urban supervised consumption site in Vancouver, BC, we conducted a retrospective chart review and administrative database linkage of consecutive patients who were unresponsive and apneic following witnessed opioid overdose between January 1, 2012 and December 31, 2017.

Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge.

Atomic charges are critical quantities in molecular mechanics and molecular dynamics, but obtaining these quantities requires heuristic choices based on atom typing or relatively expensive quantum mechanical computations to generate a density to be partitioned. Most machine learning efforts in this domain ignore total molecular charges, relying on overfitting and arbitrary rescaling in order to match the total system charge. Here, we introduce the electron-passing neural network (EPNN), a fast, accurate neural network atomic charge partitioning model that conserves total molecular charge by construction.

Comparison of rates of opioid withdrawal symptoms and reversal of opioid toxicity in patients treated with two naloxone dosing regimens: a retrospective cohort study.

Introduction: When managing opioid overdose (OD) patients, the optimal naloxone regimen should rapidly reverse respiratory depression while avoiding opioid withdrawal. Published naloxone administration guidelines have not been empirically validated and most were developed before fentanyl OD was common. In this study, rates of opioid withdrawal symptoms (OW) and reversal of opioid toxicity in patients treated with two naloxone dosing regimens were evaluated.

Inequities in Pediatric Abusive Head Trauma According to Neighborhood Social and Material Deprivation: A Population-Level Study in British Columbia, Canada.

Objectives: To explore the relationship between neighborhood social and material deprivation and the rates of abusive head trauma (AHT), and whether it differs according to sex, and following the implementation of the () program.

Method: A cross-sectional study design was applied to data from children 0 to 24 months old with a confirmed AHT diagnosis between 2005 and 2017 in British Columbia. Dissemination area-based social and material deprivation scores were assigned to residential areas, where AHT cases were recorded.