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Herein, we review the magnetic, superconducting and structural phase diagrams of the title systems, with an emphasis on our recent results form magnetic and structural neutron diffraction, magnetic susceptibility and system, and indicate the occurrence of frustrated phase separation on a nanoscopic length scale in the La2-xSrxCuO4 system with O < x ≲ 0.05. 139La nuclear quadrupole resonance measurements. The results clarify the miscibility gap in the La2CuO4+δ. © 1994.

Imaginary-time path integral (PI) is a rigorous tool to treat nuclear quantum effects in static properties. However, with its high computational demand, it is crucial to devise precise estimators. We introduce generalized PI estimators for the energy and heat capacity that utilize coordinate mapping. While it can reduce to the standard thermodynamic and centroid virial (CVir) estimators, the formulation can also take advantage of harmonic character of quantum oscillators and crystals to construct a coordinate mapping. The method is not applicable to fluids or systems with imaginary modes such as double-well potentials. This yields harmonically mapped averaging (HMA) estimators, with mappings that decouple (HMAc) or couple (HMAq) the centroid and internal modes. The HMAq is constructed with normal mode coordinates (HMAq-NM) with quadratic scaling of cost or harmonic oscillator staging (HMAq-SG) coordinates with linear scaling. The estimator performance is examined for a 1D anharmonic oscillator and a 3D Lennard-Jones crystal using path integral molecular dynamics (PIMD) simulation. The HMA estimators consistently provide more precise estimates compared to CVir, with the best performance obtained by HMAq-NM, followed by HMAq-SG, and then HMAc. We also examine the effect of anharmonicity (for AO), intrinsic quantumness, and Trotter number. The HMA formulation introduced assumes the availability of forces and Hessian matrix; however, an equally efficient finite difference alternative is possible when these derivatives are inaccessible. The remarkable improvement in precision offered by HMAq estimators provides a framework for efficient PI simulation of more challenging systems, such as those based on ab initio calculations.

The Boyle temperature, TB, for an n-segment polymer in solution is the temperature where the second osmotic virial coefficient, A2, is zero. This characteristic is of interest for its connection to the polymer condensation critical temperature, particularly for n → ∞. TB can be measured experimentally or computed for a given model macromolecule. For the latter, we present and examine two approaches, both based on the Mayer-sampling Monte Carlo (MSMC) method, to calculate Boyle temperatures as a function of model parameters. In one approach, we use MSMC calculations to search for TB, as guided by the evaluation of temperature derivatives of A2. The second approach involves numerical integration of an ordinary differential equation describing how TB varies with a model parameter, starting from a known TB. Unlike general MSMC calculations, these adaptations are appealing because they neither invoke a reference for the calculation nor use special averages needed to avoid bias when computing A2 directly. We demonstrate these methods by computing TB lines for off-lattice linear Lennard-Jones polymers as a function of chain stiffness, considering chains of length n ranging from 2 to 512 monomers. We additionally perform calculations of single-molecule radius of gyration Rg and determine the temperatures Tθ, where linear scaling of Rg2 with n is observed, as if the polymers were long random-walk chains. We find that Tθ and TB seem to differ by 6% in the n → ∞ limit, which is beyond the statistical uncertainties of our computational methodology. However, we cannot rule out systematic error relating to our extrapolation procedure as being the source of this discrepancy., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

Purpose: We describe the implementation and ongoing maintenance of CYP2C19 and CYP2D6 focused pharmacogenetic (PGx) testing to guide antidepressant and antianxiety medication prescriptions in a large rural, nonprofit health system., Summary: Depression and anxiety are common psychiatric conditions. Sanford Health implemented PGx testing for metabolism of cytochrome P450 (CYP) isozymes 2C19 and 2D6 in 2014 to inform prescribing for multiple medications, including antidepressant and antianxiety therapies. As guidelines, genotype to phenotype translation, panel offerings, and other resources are updated, we adapt our approach. We make educational and informational materials available to providers and patients. Pharmacogenomic clinical pharmacists review PGx results with discrete values and provide guidance documentation in the electronic medical record. A robust clinical decision support system is in place to provide interruptive alerts, noninterruptive alerts, and genomic indicators. A referral-based interdisciplinary clinic is also available to provide in-depth education to patients regarding PGx results and implications. Additionally, partnering with our health plan has expanded access to PGx testing for patients with anxiety or depression., Conclusion: The implementation and maintenance of Sanford Health's PGx program to guide antidepressant and antianxiety medication use continues to evolve and requires a multipronged approach relying on both human and informatics-based resources., (© American Society of Health-System Pharmacists 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Imaginary-time path integral (PI) is a rigorous quantum mechanical tool to compute static properties at finite temperatures. However, the stiff nature of the internal PI modes poses a sampling challenge. This is commonly tackled using staging coordinates, in which the free particle (FP) contribution of the PI action is diagonalized. We introduce novel and simple staging coordinates that diagonalize the entire action of the harmonic oscillator (HO) model, rendering it efficiently applicable to (exclusively) systems with harmonic character, such as quantum oscillators and crystals. The method is not applicable to fluids or systems with imaginary modes. Unlike FP staging, the HO staging provides a unique treatment of the centroid mode. We provide implementation schemes for PIMC and PIMD simulations in NVT ensemble. Sampling efficiency is assessed in terms of the precision and accuracy of estimating the energy and heat capacity of a one-dimensional HO and an asymmetric anharmonic oscillator (AO). In PIMC, the HO coordinates propose collective moves that perfectly sample the HO contribution, then (for AO) the residual anharmonic term is sampled using standard Metropolis method. This results in a high acceptance rate and, hence, high precision, in comparison to the FP staging. In PIMD, the HO coordinates naturally prescribe definitions for the fictitious masses, yielding equal frequencies of all modes when applied to the HO model. This allows for a substantially larger time step sizes relative to standard staging, without affecting accuracy or integrator stability. For completeness, we also present results using normal mode (NM) coordinates, based on both HO and FP models. While staging and NM coordinates show similar performance (for FP or HO), staging is computationally preferable due to its cheaper scaling with the number of beads. The simplicity and the enhanced sampling gained by the HO coordinates open avenues for efficient estimation of nuclear quantum effects in more complex systems with harmonic character, such as real molecular bonds and quantum crystals.

A robust and simple implementation of the generalized Einstein formulation using single equilibrium molecular dynamics simulation is introduced to compute diffusion and shear viscosity. The unique features underlying this framework are as follows: (1) The use of a simple binary-based method to sample time-dependent transport coefficients results in a uniform distribution of data on a logarithmic time scale. Although we sample "on-the-fly," the algorithm is readily applicable for post-processing analysis. Overlapping same-length segments are not sampled as they indicate strong correlations. (2) Transport coefficients are estimated using a power law fitting function, a generalization of the standard linear relation, that accurately describes the long-time plateau. (3) The use of a generalized least squares (GLS) fitting estimator to explicitly consider correlations between fitted data points results in a reliable estimate of the statistical uncertainties in a single run. (4) The covariance matrix for the GLS method is estimated analytically using the Wiener process statistics and computed variances. (5) We provide a Python script to perform the fits and automate the procedure to determine the optimal fitting domain. The framework is applied to two fluids, binary hard sphere and a Lennard-Jones near the triple point, and the validity of the single-run estimates is verified against multiple independent runs. The approach should be applicable to other transport coefficients since the diffusive limit is universal to all of them. Given its rigor and simplicity, this methodology can be readily incorporated into standard molecular dynamics packages using on-the-fly or post-processing analysis., (© 2023 Author(s). Published under an exclusive license by AIP Publishing.)

We describe an extension of the ZENO program for polymer and nanoparticle characterization that allows for precise calculation of the virial coefficients, with uncertainty estimates, of polymeric structures described by arbitrary rigid configurations of hard spheres. The probabilistic method of virial computation used for this extension employs a previously developed Mayer-sampling Monte Carlo method with overlap sampling that allows for a reduction of bias in the Monte Carlo averaging. This capability is an extension of ZENO in the sense that the existing program is also based on probabilistic sampling methods and involves the same input file formats describing polymer and nanoparticle structures. We illustrate the extension's capabilities, demonstrate its accuracy, and quantify the efficiency of this extension of ZENO by computing the second, third, and fourth virial coefficients and metrics quantifying the difficulty of their calculation, for model polymeric structures having several different shapes. We obtain good agreement with literature estimates available for some of the model structures considered.

The virial equation of state (VEOS) provides a rigorous bridge between molecular interactions and thermodynamic properties. The past decade has seen renewed interest in the VEOS due to advances in theory, algorithms, computing power, and quality of molecular models. Now, with the emergence of increasingly accurate first-principles computational chemistry methods, and machine-learning techniques to generate potential-energy surfaces from them, VEOS is poised to play a larger role in modeling and computing properties. Its scope of application is limited to where the density series converges, but this still admits a useful range of conditions and applications, and there is potential to expand this range further. Recent applications have shown that for simple molecules, VEOS can provide first-principles thermodynamic property data that are competitive in quality with experiment. Moreover, VEOS provides a focused and actionable test of molecular models and first-principles calculations via comparison to experiment. This Perspective presents an overview of recent advances and suggests areas of focus for further progress.

The koala, one of the most iconic Australian wildlife species, is facing several concomitant threats that are driving population declines. Some threats are well known and have clear methods of prevention (e.g., habitat loss can be reduced with stronger land-clearing control), whereas others are less easily addressed. One of the major current threats to koalas is chlamydial disease, which can have major impacts on individual survival and reproduction rates and can translate into population declines. Effective management strategies for the disease in the wild are currently lacking, and, to date, we know little about the determinants of individual susceptibility to disease. Here, we investigated the genetic basis of variation in susceptibility to chlamydia using one of the most intensively studied wild koala populations. We combined data from veterinary examinations, chlamydia testing, genetic sampling and movement monitoring. Out of our sample of 342 wild koalas, 60 were found to have chlamydia. Using genotype information on 5007 SNPs to investigate the role of genetic variation in determining disease status, we found no evidence of inbreeding depression, but a heritability of 0.11 (95% CI: 0.06-0.23) for the probability that koalas had chlamydia. Heritability of susceptibility to chlamydia could be relevant for future disease management, as it suggests adaptive potential for the population., (© 2022 The Authors. Molecular Ecology published by John Wiley & Sons Ltd.)

Effective conservation requires accurate data on population genetic diversity, inbreeding, and genetic structure. Increasingly, scientists are adopting genetic non-invasive sampling (gNIS) as a cost-effective population-wide genetic monitoring approach. gNIS has, however, known limitations which may impact the accuracy of downstream genetic analyses. Here, using high-quality single nucleotide polymorphism (SNP) data from blood/tissue sampling of a free-ranging koala population ( n  = 430), we investigated how the reduced SNP panel size and call rate typical of genetic non-invasive samples (derived from experimental and field trials) impacts the accuracy of genetic measures, and also the effect of sampling intensity on these measures. We found that gNIS at small sample sizes (14% of population) can provide accurate population diversity measures, but slightly underestimated population inbreeding coefficients. Accurate measures of internal relatedness required at least 33% of the population to be sampled. Accurate geographic and genetic spatial autocorrelation analysis requires between 28% and 51% of the population to be sampled. We show that gNIS at low sample sizes can provide a powerful tool to aid conservation decision-making and provide recommendations for researchers looking to apply these techniques to free-ranging systems., Competing Interests: The authors declare no conflict of interest., (© 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

We use molecular simulation to study the wetting behavior of antifouling polymer-tethered membranes. We obtain the interfacial properties (e.g., contact angle) of water at various temperatures for five polymer membranes, including a base polysulfone (PSF) membrane and four other PSF membranes grafted with antifouling polymers (two poly(ethylene glycol) (PEG) tethers and two zwitterionic tethers). We implement a coupled Monte Carlo (MC)/molecular dynamics (MD) approach to determine the interface potentials of water on the membrane surfaces in an efficient manner. Within this method, short MC and MD simulations are performed in cycles to collect the surface excess free energy of a thin water film on polymer membrane surfaces. Simulation results show that the grafting of zwitterionic tethers provides a more significant enhancement in the hydrophilicity of the PSF membrane than that of the PEG tethers. Water completely wets the surface of zwitterionic polymer membranes.

We present a method for computing osmotic virial coefficients in explicit solvent via simulation in a restricted Gibbs ensemble. Two equivalent phases are simulated at once, each in a separate box at constant volume and temperature and each in equilibrium with a solvent reservoir. For osmotic coefficient B N , a total of N solutes are individually exchanged back and forth between the boxes, and the average distribution of solute numbers between the boxes provides the key information needed to compute B N . Separately, expressions are developed for B N as a series in solvent reservoir density ρ 1 , with the coefficients of the series expressed in terms of the usual gas-phase mixture coefficients B ij . Normally, the B ij are defined for an infinite volume, but we suggest that the observed dependence of B ij on system size L can be used to estimate L dependence of the B N , allowing them to be computed accurately at L → ∞ while simulating much smaller system sizes than otherwise possible. The methods for N = 2 and 3 are demonstrated for two-component mixtures of size-asymmetric additive hard spheres. The proposed methods are demonstrated to have greater precision than established techniques, for a given amount of computational effort. The ρ 1 series for B N when applied by itself is (for this noncondensing model) found to be the most efficient in computing accurate osmotic coefficients for the solvent densities considered here.

Egress from host cells is an essential step in the lytic cycle of T. gondii and other apicomplexan parasites; however, only a few parasite secretory proteins are known to affect this process. The putative metalloproteinase toxolysin 4 (TLN4) was previously shown to be an extensively processed microneme protein, but further characterization was impeded by the inability to genetically ablate TLN4 . Here, we show that TLN4 has the structural properties of an M16 family metalloproteinase, that it possesses proteolytic activity on a model substrate, and that genetic disruption of TLN4 reduces the efficiency of egress from host cells. Complementation of the knockout strain with the TLN4 coding sequence significantly restored egress competency, affirming that the phenotype of the Δ tln4 parasite was due to the absence of TLN4. This work identifies TLN4 as the first metalloproteinase and the second microneme protein to function in T. gondii egress. The study also lays a foundation for future mechanistic studies defining the precise role of TLN4 in parasite exit from host cells. IMPORTANCE After replicating within infected host cells, the single-celled parasite Toxoplasma gondii must rupture out of such cells in a process termed egress. Although it is known that T. gondii egress is an active event that involves disruption of host-derived membranes surrounding the parasite, very few proteins that are released by the parasite are known to facilitate egress. In this study, we identify a parasite secretory protease that is necessary for efficient and timely egress, laying the foundation for understanding precisely how this protease facilitates T. gondii exit from host cells.

Aim: This manuscript describes implementation of clinical decision support for providers concerned with perioperative complications of malignant hyperthermia susceptibility. Materials & methods: Clinical decision support for malignant hyperthermia susceptibility was implemented in 2018 based around our pre-emptive genotyping platform. We completed a brief descriptive review of patients who underwent pre-emptive testing, focused particularly on RYR1 and CACNA1S genes. Results: To date, we have completed pre-emptive genetic testing on more than 10,000 patients; 13 patients having been identified as a carrier of a pathogenic or likely pathogenic variant of RYR1 or CACNA1S . Conclusion: An alert system for malignant hyperthermia susceptibility - as an extension of our pre-emptive genomics platform - was implemented successfully. Implementation strategies and lessons learned are discussed herein.

Habitat destruction and fragmentation are increasing globally, forcing surviving species into small, isolated populations. Isolated populations typically experience heightened inbreeding risk and associated inbreeding depression and population decline; although individuals in these populations may mitigate these risks through inbreeding avoidance strategies. For koalas, as dietary specialists already under threat in the northern parts of their range, increased habitat fragmentation and associated inbreeding costs are of great conservation concern. Koalas are known to display passive inbreeding avoidance through sex-biased dispersal, although population isolation will reduce dispersal pathways. We tested whether free-ranging koalas display active inbreeding avoidance behaviours. We used VHF tracking data, parentage reconstruction, and veterinary examination results to test whether free-ranging female koalas avoid mating with (a) more closely related males; and (b) males infected with sexually transmitted Chlamydia pecorum. We found no evidence that female koalas avoid mating with relatively more related available mates. In fact, as the relatedness of potential mates increases, so did inbreeding events. We also found no evidence that female koalas can avoid mating with males infected with C. pecorum. The absence of active inbreeding avoidance mechanisms in koalas is concerning from a conservation perspective, as small, isolated populations may be at even higher risk of inbreeding depression than expected. At risk koala populations may require urgent conservation interventions to augment gene flow and reduce inbreeding risks. Similarly, if koalas are not avoiding mating with individuals with chlamydial disease, populations may be at higher risk from disease than anticipated, further impacting population viability., (© 2020 John Wiley & Sons Ltd.)

Osteosarcoma and chondrosarcoma are sarcomas of the bone and the cartilage that are primarily treated by surgical intervention combined with high toxicity chemotherapy. In search of alternative metabolic approaches to address the challenges in treating bone sarcomas, we assessed the growth dependence of these cancers on leucine, one of the branched-chain amino acids (BCAAs), and BCAA metabolism. Tumor biopsies from bone sarcoma patients revealed differential expression of BCAA metabolic enzymes. The cytosolic branched-chain aminotransferase (BCATc) that is commonly overexpressed in cancer cells, was down-regulated in chondrosarcoma (SW1353) in contrast with osteosarcoma (143B) cells that expressed both BCATc and its mitochondrial isoform BCATm. Treating SW1353 cells with gabapentin, a selective inhibitor of BCATc, further revealed that these cells failed to respond to gabapentin. Application of the structural analog of leucine, N-acetyl-leucine amide (NALA) to disrupt leucine uptake, indicated that all bone sarcoma cells used leucine to support their energy metabolism and biosynthetic demands. This was evident from the increased activity of the energy sensor AMP-activated protein kinase (AMPK), down-regulation of complex 1 of the mammalian target of rapamycin (mTORC1), and reduced cell viability in response to NALA. The observed changes were most profound in the 143B cells, which appeared highly dependent on cytosolic and mitochondrial BCAA metabolism. This study thus demonstrates that bone sarcomas rely on leucine and BCAA metabolism for energy and growth; however, the differential expression of BCAA enzymes and the presence of other carbon sources may dictate how efficiently these cancer cells take advantage of BCAA metabolism., (© 2020 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.)

We present a concise, general, and efficient procedure for calculating the cluster integrals that relate thermodynamic virial coefficients to molecular interactions. The approach encompasses nonpairwise intermolecular potentials generated from quantum chemistry or other sources; a simple extension permits efficient evaluation of temperature and other derivatives of the virial coefficients. We demonstrate with a polarizable model of water. We argue that cluster-integral methods are a potent yet underutilized instrument for the development and application of first-principles molecular models and methods.

Implementation of the harmonically mapped averaging (HMA) framework in the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) is presented for on-the-fly computations of the energy, pressure, and heat capacity of crystalline systems during canonical molecular dynamics simulations. HMA has a low central processing unit and storage requirements and is straightforward to use. As a case study, the properties of the Lennard-Jones and embedded-atom model (parameterized for nickel) crystals are computed. The results demonstrate the higher efficiency of the new class compared to the inbuilt LAMMPS classes for calculating these properties. However, HMA loses its effectiveness in systems where diffusion occurs in the crystal, and an example is presented to allow this behavior to be recognized. In addition to its improved precision, HMA is less affected by small errors introduced by having a larger time step in molecular dynamics simulations. We also present an analysis of the effect of potential truncation on anharmonic properties, and show that artifacts of truncation on the HMA averages can be eliminated simply by shifting the potential energy to zero at the truncation radius. Full properties can be obtained by adding easily computed values for the lattice and harmonic properties using the untruncated potential.

In Paper I [J. R. Elliott, A. J. Schultz, and D. A. Kofke, J. Chem. Phys. 143, 114110 (2015)] of this series, a methodology was presented for computing the coefficients of a power series of the Helmholtz energy in reciprocal temperature, β, through density series based on cluster integral expansions. Previously, power series in β were evaluated by thermodynamic perturbation theory (TPT) using molecular simulation of a reference fluid. The present methodology uses cluster integrals to evaluate coefficients of the density expansion at each individual order of temperature. While Paper I [J. R. Elliott, A. J. Schultz, and D. A. Kofke, J. Chem. Phys. 143, 114110 (2015)] developed this methodology for square well (SW) spheres, the present work extends the methodology to Lennard-Jones (LJ) spheres, where the reference fluid is the Weeks-Chandler-Andersen potential. Comparisons of TPT coefficients computed from cluster integrals to those from molecular simulation show good agreement through third order in β when coefficients are expressed with effective approximants. Notably, the agreement for LJ spheres is much better than for SW spheres although fewer coefficients of the density series (B 2 -B 5 ) are available than for SW spheres (B 2 -B 6 ). The coefficients for B i (β) of the reference fluid are shown to follow a simple relationship to the virial coefficients of hard sphere fluids, corrected for the temperature dependency of the equivalent hard sphere diameter. This lays the foundation for a correlation of the second virial coefficient of LJ spheres B 2 (β) that extrapolates to infinite order in temperature. This correlation of B 2 (β) provides a basis for estimating the low density limit of TPT coefficients at all orders in temperature, facilitating a recursive extrapolation formula to estimate TPT coefficients of fourth order and higher over the entire density range. The applicability of the resulting equation of state is demonstrated by computing the thermodynamic properties for LJ spheres and comparing to standard simulation results.

Toxoplasma gondii is a ubiquitous pathogen that can cause encephalitis, congenital defects, and ocular disease. T. gondii has also been implicated as a risk factor for mental illness in humans. The parasite persists in the brain as slow-growing bradyzoites contained within intracellular cysts. No treatments exist to eliminate this form of parasite. Although proteolytic degradation within the parasite lysosome-like vacuolar compartment (VAC) is critical for bradyzoite viability, whether other aspects of the VAC are important for parasite persistence remains unknown. An ortholog of Plasmodium falciparum chloroquine resistance transporter (CRT), TgCRT, has previously been identified in T. gondii To interrogate the function of TgCRT in chronic-stage bradyzoites and its role in persistence, we knocked out TgCRT in a cystogenic strain and assessed VAC size, VAC digestion of host-derived proteins and parasite autophagosomes, and the viability of in vitro and in vivo bradyzoites. We found that whereas parasites deficient in TgCRT exhibit normal digestion within the VAC, they display a markedly distended VAC and their viability is compromised both in vitro and in vivo Interestingly, impairing VAC proteolysis in TgCRT-deficient bradyzoites restored VAC size, consistent with a role for TgCRT as a transporter of products of digestion from the VAC. In conjunction with earlier studies, our current findings suggest a functional link between TgCRT and VAC proteolysis. This study provides further evidence of a crucial role for the VAC in bradyzoite persistence and a new potential VAC target to abate chronic Toxoplasma infection. IMPORTANCE Individuals chronically infected with the intracellular parasite Toxoplasma gondii are at risk of experiencing reactivated disease that can result in progressive loss of vision. No effective treatments exist for chronic toxoplasmosis due in part to a poor understanding of the biology underlying chronic infection and a lack of well-validated potential targets. We show here that a T. gondii transporter is functionally linked to protein digestion within the parasite lysosome-like organelle and that this transporter is necessary to sustain chronic infection in culture and in experimentally infected mice. Ablating the transporter results in severe bloating of the lysosome-like organelle. Together with earlier work, this study suggests the parasite's lysosome-like organelle is vital for parasite survival, thus rendering it a potential target for diminishing infection and reducing the risk of reactivated disease., (Copyright © 2019 Kannan et al.)

We introduce a method to construct the interface potential from a series of molecular dynamics simulations conducted within the canonical ensemble. The interface potential provides the surface excess free energy associated with the growth of a fluid film from a surface. We collect the force that the fluid exerts on the surface (disjoining pressure) at a series of film thicknesses. These force data are then integrated to obtain the interface potential. "Spreading" and "drying" versions of the general approach are considered. The spreading approach focuses on the growth of a thin liquid film from a solid substrate in a mother vapor. The drying approach focuses on the growth of a thin vapor film on a solid substrate in a mother liquid. The methods provide a means to compute the contact angle of a fluid droplet in contact with the surface. The general method is applied to two model systems: (1) a monatomic Lennard-Jones fluid in contact with atomistically detailed face centered cubic (FCC) substrate and (2) TIP4P/2005 water in contact with a rigid silica surface. For the Lennard-Jones model system, we generate results with both the drying and spreading methods at various temperatures and substrate strengths. These results are compared to those from previous simulation studies. For the water system, the drying method is used to obtain wetting properties over a range of temperatures. The water system also highlights challenges associated with application of the spreading method within the framework pursued here.

The process of developing new compounds and materials is increasingly driven by computational modeling and simulation, which allow us to characterize candidates before pursuing them in the laboratory. One of the non-trivial properties of interest for organic materials is their packing in the bulk, which is highly dependent on their molecular structure. By controlling the latter, we can realize materials with a desired density (as well as other target properties). Molecular dynamics simulations are a popular and reasonably accurate way to compute the bulk density of molecules, however, since these calculations are computationally intensive, they are not a practically viable option for high-throughput screening studies that assess material candidates on a massive scale. In this work, we employ machine learning to develop a data-derived prediction model that is an alternative to physics-based simulations, and we utilize it for the hyperscreening of 1.5 million small organic molecules as well as to gain insights into the relationship between structural makeup and packing density. We also use this study to analyze the learning curve of the employed neural network approach and gain empirical data on the dependence of model performance and training data size, which will inform future investigations., (This journal is © The Royal Society of Chemistry 2019.)

Wildlife diseases are a recognized driver of global biodiversity loss, have substantial economic impacts, and are increasingly becoming a threat to human health. Disease surveillance is critical but remains difficult in the wild due to the substantial costs and potential biases associated with most disease detection methods. Noninvasive scat surveys have been proposed as a health monitoring methodology to overcome some of these limitations. Here, we use the known threat of Chlamydia disease to the iconic, yet vulnerable, koala Phascolarctos cinereus to compare three methods for Chlamydia detection in scats: multiplex quantitative PCR, next generation sequencing, and a detection dog specifically trained on scats from Chlamydia-infected koalas. All three methods demonstrated 100% specificity, while sensitivity was variable. Of particular interest is the variable sensitivity of these diagnostic tests to detect sick individuals (i.e., not only infection as confirmed by Chlamydia-positive swabs, but with observable clinical signs of the disease); for koalas with urogenital tract disease signs, sensitivity was 78% with quantitative PCR, 50% with next generation genotyping and 100% with the detection dog method. This may be due to molecular methods having to rely on high-quality DNA whereas the dog most likely detects volatile organic compounds. The most appropriate diagnostic test will vary with disease prevalence and the specific aims of disease surveillance. Acknowledging that detection dogs might not be easily accessible to all, the future development of affordable and portable "artificial noses" to detect diseases from scats in the field might enable cost-effective, rapid and large-scale disease surveillance., (© 2019 John Wiley & Sons Ltd.)

We introduce a means to implement the interface potential approach for computing wetting properties within a molecular dynamics framework. The general approach provides a means to determine the contact angle of a liquid droplet on a solid substrate in a mother vapor. We present a framework for implementing "spreading" and "drying" versions of the method within an isothermal-isobaric ensemble. Two free energy methods are considered: cumulative integration of average force profile and multistate Bennett acceptance ratio. An umbrella sampling strategy is used to restrain volume fluctuations and to ensure adequate sampling of a broad volume range. We explore implementation of the approach with the GROningen MAchine for Chemical Simulations and the Large-scale Atomic/Molecular Massively Parallel Simulator. We test the accuracy and efficiency of the method with models consisting of a monoatomic Lennard-Jones fluid in the vicinity of a structureless or atomistically detailed substrate. Our results show that one can successfully generate the drying potential within the framework pursued here. The efficiency of the method is strongly dependent upon how one handles the dynamics of the two confining walls. These decisions impact the rate of volume fluctuations, and therefore, the quality of the volume distributions collected. Our efforts to implement the spreading method with molecular dynamics alone proved unsuccessful. The rate at which the configuration space of the vapor phase evolves is insufficient. We show how one can overcome this challenge by implementing a coupled molecular dynamics/Monte Carlo approach. Finally, we show how one can determine the variation in interfacial properties with temperature and substrate strength.

We compute the vapor-liquid critical coordinates of a model of helium in which nuclear quantum effects are absent. We employ highly accurate ab initio pair and three-body potentials and calculate the critical parameters rigorously in two ways. First, we calculate the virial coefficients up to the seventh and find the point where an isotherm satisfies the critical conditions. Second, we use Gibbs Ensemble Monte Carlo (GEMC) to calculate the vapor-liquid equilibrium, and extrapolate the phase envelope to the critical point. Both methods yield results that are consistent within their uncertainties. The critical temperature of "classical helium" is 13.0 K (compared to 5.2 K for real helium), the critical pressure is 0.93 MPa, and the critical density is 28.4 mol·L -1 , with expanded uncertainties (corresponding to a 95% confidence interval) on the order of 0.1 K, 0.02 MPa, and 0.5 mol·L -1 , respectively. The effect of three-body interactions on the location of the critical point is small (lowering the critical temperature by roughly 0.1 K), suggesting that we are justified in ignoring four-body and higher interactions in our calculations. This work is motivated by the use of corresponding-states models for mixtures containing helium (such as some natural gases) at higher temperatures where quantum effects are expected to be negligible; in these situations, the distortion of the critical properties by quantum effects causes problems for the corresponding-states treatment.

We report equilibrium molecular simulation data for the classical Lennard-Jones (LJ) model, covering all thermodynamic states where the crystal is stable, as well as fluid states near coexistence with the crystal; both fcc and hcp polymorphs are considered. These data are used to compute coexistence lines and triple points for equilibrium among the fcc, hcp, and fluid phases. All results are obtained with very high accuracy and precision such that coexistence conditions are obtained with one to two significant figures more than previously reported. All properties are computed in the limit of an infinite cutoff radius of the LJ potential and in the limit of an infinite number of atoms; furthermore, the effect of vacancy defects on the free energy of the crystals is included. Data are fit to a semi-empirical equation of state to within their estimated precision, and convenient formulas for the thermodynamic and coexistence properties are provided. Of particular interest is the liquid-vapor-fcc triple point temperature, which we compute to be 0.694 55 ± 0.000 02 (in LJ units).

The precision and accuracy of the anharmonic energy calculated in the canonical (NVT) ensemble using three different thermostats (viz., Andersen, Langevin, and Nosé-Hoover) along with no thermostat (i.e., microcanonical, NVE) are compared via application to aluminum crystals at ≈100 GPa for temperatures up to melting (4000 K) using ab initio molecular dynamics (AIMD) simulation. In addition to the role of the thermostat, the effect of using either conventional or the recently introduced harmonically mapped averaging (HMA) method is considered. The effect of AIMD time-step size Δ t on the ensemble averages gauges accuracy, while for a given Δ t , the stochastic uncertainty (computed using block averaging) provides the metric for precision. We identify the rate of convergence of block averages (with respect to block size) as an important issue in this context, as it imposes a minimum simulation length required to achieve reliable statistics, and it differs considerably among the methods. We observe that HMA with a Langevin thermostat in an NVT simulation shows the best performance, from the point of view of accuracy, precision, and simulation length. In addition, we introduce a novel HMA-based ensemble average for the temperature. In application to NVE simulations, the new formulation exhibits much smaller fluctuations compared to the conventional kinetic-energy approach; however, it provides only marginal improvement in uncertainty due to strong negative correlations exhibited by the conventional form (which acts to reduce its uncertainty but also slows convergence of the block averages).

Time-resolved diffraction has become a vital tool for probing dynamic responses to an applied stimulus. Such experiments traditionally use hardware solutions to histogram measured data into their respective bin. We will show that a major advantage of event-based data acquisition, which time-stamps measured diffraction data with 100 ns accuracy, is much preferred over hardware histogramming of the data by enabling postprocessing for advanced custom binning using a software solution. This approach is made even more powerful by coupling measured diffraction data with metadata about the applied stimuli and material response. In this work, we present a time-filter approach that leverages the power of event-based diffraction collection to reduce stroboscopic data measured over many hours into equally weighted segments that represent subsets of the response to a single cycle of the applied stimulus. We demonstrate this approach by observing ferroelectric/ferroelastic domain wall motion during electric field cycling of BaTiO 3 . The developed approach can readily be expanded to investigate other dynamic phenomena using complex sample environments.

New molecular modeling data show that the entropy of bcc iron exhibits no system-size anomalies, implying that it should be feasible to compute accurate free energies of this system using first-principles methods without requiring a prohibitively large number of atoms. Conclusions are based on rigorous calculations of size-dependent free energies for a Sutton-Chen model of iron previously fit to ab initio calculations, and refute statements recently appearing in the literature indicating that the size of the simulation cell is critical for stabilization of the bcc phase.

Egress is a crucial phase of the Toxoplasma gondii intracellular lytic cycle. This is a process that drives inflammation and is strongly associated with the pathogenesis observed during toxoplasmosis. Despite the link between this process and virulence, little is known about egress on a mechanistic or descriptive level. Previously published work has suggested that a phospholipase, lecithin-cholesterol acyltransferase (LCAT), secreted from the parasite's dense granules contributes to parasite growth, virulence, and egress. Here we present evidence from several independent mutant parasite lines confirming a role for LCAT in efficient egress, although no defects in growth or virulence were apparent. We also show via genetic complementation that the catalytic activity of LCAT is required for its role in parasite egress. This work solidifies the contribution of LCAT to egress of T. gondii tachyzoites. IMPORTANCE Toxoplasma gondii is one of the most successful human pathogens, infecting an estimated 2.5 billion people across the globe. Pathogenesis seen during acute or reactivated toxoplasmosis has been closely tied to the parasite's intracellular lytic life cycle, which culminates in an event called egress that results in the release of freshly replicated parasites from the infected host cell. Despite the highly destructive, cytolytic nature of this event and its downstream consequences, very little is known about how the parasite accomplishes this step. Previous work has suggested a role for a secreted phospholipase, LCAT, in Toxoplasma egress and roles in cell traversal and egress in the Plasmodium species orthologue. We confirm here that LCAT-deficient tachyzoites are unable to efficiently egress from infected monolayers, and we provide evidence that LCAT catalytic activity is required for its role in egress.

Maintaining genetic diversity is a crucial component in conserving threatened species. For the iconic Australian koala, there is little genetic information on wild populations that is not either skewed by biased sampling methods (e.g., sampling effort skewed toward urban areas) or of limited usefulness due to low numbers of microsatellites used. The ability to genotype DNA extracted from koala scats using next-generation sequencing technology will not only help resolve location sample bias but also improve the accuracy and scope of genetic analyses (e.g., neutral vs. adaptive genetic diversity, inbreeding, and effective population size). Here, we present the successful SNP genotyping (1272 SNP loci) of koala DNA extracted from scat, using a proprietary DArTseq ™ protocol. We compare genotype results from two-day-old scat DNA and 14-day-old scat DNA to a blood DNA template, to test accuracy of scat genotyping. We find that DNA from fresher scat results in fewer loci with missing information than DNA from older scat; however, 14-day-old scat can still provide useful genetic information, depending on the research question. We also find that a subset of 209 conserved loci can accurately identify individual koalas, even from older scat samples. In addition, we find that DNA sequences identified from scat samples through the DArTseq ™ process can provide genetic identification of koala diet species, bacterial and viral pathogens, and parasitic organisms.

The quantitative assessment of uncertainty and sampling quality is essential in molecular simulation. Many systems of interest are highly complex, often at the edge of current computational capabilities. Modelers must therefore analyze and communicate statistical uncertainties so that "consumers" of simulated data understand its significance and limitations. This article covers key analyses appropriate for trajectory data generated by conventional simulation methods such as molecular dynamics and (single Markov chain) Monte Carlo. It also provides guidance for analyzing some 'enhanced' sampling approaches. We do not discuss systematic errors arising, e.g., from inaccuracy in the chosen model or force field.

Hard polyhedra are a natural extension of the hard sphere model for simple fluids, but there is no general scheme for predicting the effect of shape on thermodynamic properties, even in moderate-density fluids. Only the second virial coefficient is known analytically for general convex shapes, so higher-order equations of state have been elusive. Here we investigate high-precision state functions in the fluid phase of 14 representative polyhedra with different assembly behaviors. We discuss historic efforts in analytically approximating virial coefficients up to B 4 and numerically evaluating them to B 8 . Using virial coefficients as inputs, we show the convergence properties for four equations of state for hard convex bodies. In particular, the exponential approximant of Barlow et al. (J. Chem. Phys. 2012, 137, 204102) is found to be useful up to the first ordering transition for most polyhedra. The convergence behavior we explore can guide choices in expending additional resources for improved estimates. Fluids of arbitrary hard convex bodies are too complicated to be described in a general way at high densities, so the high-precision state data we provide can serve as a reference for future work in calculating state data or as a basis for thermodynamic integration.

Stretch-sensitive Ia afferent monosynaptic connections with motoneurons form the stretch reflex circuit. After nerve transection, Ia afferent synapses and stretch reflexes are permanently lost, even after regeneration and reinnervation of muscle by motor and sensory afferents is completed in the periphery. This loss greatly affects full recovery of motor function. However, after nerve crush, reflex muscle forces during stretch do recover after muscle reinnervation and reportedly exceed 140% baseline values. This difference might be explained by structural preservation after crush of Ia afferent synapses on regenerating motoneurons and decreased presynaptic inhibitory control. We tested these possibilities in rats after crushing the tibial nerve (TN), and using Vesicular GLUtamate Transporter 1 (VGLUT1) and the 65 kDa isoform of glutamic acid-decarboxylase (GAD65) as markers of, respectively, Ia afferent synapses and presynaptic inhibition (P-boutons) on retrogradely labeled motoneurons. We analyzed motoneurons during regeneration (21 days post crush) and after they reinnervate muscle (3 months). The results demonstrate a significant loss of VGLUT1 terminals on dendrites and cell bodies at both 21 days and 3 months post-crush. However, in both cellular compartments, the reductions were small compared to those observed after TN full transection. In addition, we found a significant decrease in the number of GAD65 P-boutons per VGLUT1 terminal and their coverage of VGLUT1 boutons. The results support the hypothesis that better preservation of Ia afferent synapses and a change in presynaptic inhibition could contribute to maintain or even increase the stretch reflex after nerve crush and by difference to nerve transection., (© 2017 Wiley Periodicals, Inc.)

Globally, nearly 2 billion people are infected with the intracellular protozoan Toxoplasma gondii 1 . This persistent infection can cause severe disease in immunocompromised people and is epidemiologically linked to major mental illnesses 2 and cognitive impairment 3 . There are currently no options for curing this infection. The lack of effective therapeutics is due partly to a poor understanding of the essential pathways that maintain long-term infection. Although it is known that Toxoplasma replicates slowly within intracellular cysts demarcated with a cyst wall, precisely how it sustains itself and remodels organelles in this niche is unknown. Here, we identify a key role for proteolysis within the parasite lysosomal organelle (the vacuolar compartment or VAC) in turnover of autophagosomes and persistence during neural infection. We found that disrupting a VAC-localized cysteine protease compromised VAC digestive function and markedly reduced chronic infection. Death of parasites lacking the VAC protease was preceded by accumulation of undigested autophagosomes in the parasite cytoplasm. These findings suggest an unanticipated function for parasite lysosomal degradation in chronic infection, and identify an intrinsic role for autophagy in the T. gondii parasite and its close relatives. This work also identifies a key element of Toxoplasma persistence and suggests that VAC proteolysis is a prospective target for pharmacological development.

Four methods for calculation of the classical free energy of crystalline systems are compared with respect to their efficiency and accuracy. Two of the methods involve thermodynamic integration along an unphysical path (λ integration, λI), and two involve integration in temperature from the low-temperature harmonic limit (T integration, TI). Specifically, the methods considered are (1) Frenkel-Ladd integration from a noninteracting Einstein crystal reference (ECR-λI); (2) conventional integration in temperature (Conv-TI); (3) integration from an interacting quasi-harmonic reference (QHR-λI); and (4) temperature integration using harmonically mapped averaging to evaluate the integrand (HMA-TI). The latter two methods are "harmonically assisted", meaning that they exploit the harmonic nature of the crystal to greatly reduce fluctuations in the relevant averages. This feature allows them to deliver a result of much higher precision for a given computational effort, compared to ECR-λI and Conv-TI, and with no less accuracy. Regarding the harmonically assisted methods, HMA-TI has several advantages over QHR-λI with respect to the simplicity of the integration path (which promotes a more accurate result), ease of implementation, and usefulness of the data recorded along the integration path.

Accurate virial coefficients B&#95;{N}(λ,ɛ) (where ɛ is the well depth) for the three-dimensional square-well and square-step potentials are calculated for orders N=5-9 and well widths λ=1.1-2.0 using a very fast recursive method. The efficiency of the algorithm is enhanced significantly by exploiting permutation symmetry and by storing integrands for reuse during the calculation. For N=9 the storage requirements become sufficiently large that a parallel algorithm is developed. The methodology is general and is applicable to other discrete potentials. The computed coefficients are precise even near the critical temperature, and thus open up possibilities for analysis of criticality of the system, which is currently not accessible by any other means.

A general framework is established for reformulation of the ensemble averages commonly encountered in statistical mechanics. This "mapped-averaging" scheme allows approximate theoretical results that have been derived from statistical mechanics to be reintroduced into the underlying formalism, yielding new ensemble averages that represent exactly the error in the theory. The result represents a distinct alternative to perturbation theory for methodically employing tractable systems as a starting point for describing complex systems. Molecular simulation is shown to provide one appealing route to exploit this advance. Calculation of the reformulated averages by molecular simulation can proceed without contamination by noise produced by behavior that has already been captured by the approximate theory. Consequently, accurate and precise values of properties can be obtained while using less computational effort, in favorable cases, many orders of magnitude less. The treatment is demonstrated using three examples: (1) calculation of the heat capacity of an embedded-atom model of iron, (2) calculation of the dielectric constant of the Stockmayer model of dipolar molecules, and (3) calculation of the pressure of a Lennard-Jones fluid. It is observed that improvement in computational efficiency is related to the appropriateness of the underlying theory for the condition being simulated; the accuracy of the result is however not impacted by this. The framework opens many avenues for further development, both as a means to improve simulation methodology and as a new basis to develop theories for thermophysical properties.

We report on the intriguing evolution of the dynamical spin correlations of the frustrated spinel ZnMn2O4. Inelastic neutron scattering and magnetization studies reveal that the dynamical correlations at high temperatures are 1D. At lower temperature, these dynamical correlations become 2D. Surprisingly, the dynamical correlations condense into a quasi 2D Ising-like ordered state, making this a rare observation of two dimensional order on the spinel lattice. Remarkably, 3D ordering is not observed down to temperatures as low as 300 mK. This unprecedented dimensional crossover stems from frustrated exchange couplings due to the huge Jahn-Teller distortions around Mn(3+) ions on the spinel lattice.

Knowledge of approximate harmonic behavior of crystals is introduced into a new "mapped averaging" framework to yield alternative expressions for the thermodynamic properties of crystalline systems. The expressions separate the known harmonic behavior from residual averages, which thus encapsulate anharmonic contributions to the properties. With harmonic contributions removed, direct measurement of these anharmonic contributions by molecular simulation can be accomplished without contamination by noise produced by the already-known harmonic behavior. We show with application to the Lennard-Jones model that first-derivative properties (pressure, energy) can be obtained to a given precision via this harmonically mapped averaging at least 10 times faster than by using conventional averaging, and second-derivative properties (e.g., heat capacity) are obtained at least 100 times faster; in more favorable cases, the speedup exceeds a millionfold. Free-energy calculations are accelerated by 50 to 1000 times. Data obtained using these formulations are rigorous and not subject to any added approximation, and in fact are less sensitive to inaccuracies relating to finite-size effects, potential truncation, equilibration, and similar considerations. Moreover, the approach does not require any alteration in how sampling is performed during the simulation, so it may be used with standard Monte Carlo or molecular dynamics methods. However, the mapped averages do require evaluation of first and second derivatives of the intermolecular potential, for evaluation of first and second thermodynamic-derivative properties, respectively. Apart from its usefulness to simulation, the formalism developed here may constitute a basis for new theoretical treatments of crystals.

Cluster integrals are evaluated for the coefficients of the combined temperature- and density-expansion of pressure: Z = 1 + B2(β) η + B3(β) η(2) + B4(β) η(3) + ⋯, where Z is the compressibility factor, η is the packing fraction, and the B(i)(β) coefficients are expanded as a power series in reciprocal temperature, β, about β = 0. The methodology is demonstrated for square-well spheres with λ = [1.2-2.0], where λ is the well diameter relative to the hard core. For this model, the B(i) coefficients can be expressed in closed form as a function of β, and we develop appropriate expressions for i = 2-6; these expressions facilitate derivation of the coefficients of the β series. Expanding the B(i) coefficients in β provides a correspondence between the power series in density (typically called the virial series) and the power series in β (typically called thermodynamic perturbation theory, TPT). The coefficients of the β series result in expressions for the Helmholtz energy that can be compared to recent computations of TPT coefficients to fourth order in β. These comparisons show good agreement at first order in β, suggesting that the virial series converges for this term. Discrepancies for higher-order terms suggest that convergence of the density series depends on the order in β. With selection of an appropriate approximant, the treatment of Helmholtz energy that is second order in β appears to be stable and convergent at least to the critical density, but higher-order coefficients are needed to determine how far this behavior extends into the liquid.

The mathematical structure imposed by the thermodynamic critical point motivates an approximant that synthesizes two theoretically sound equations of state: the parametric and the virial. The former is constructed to describe the critical region, incorporating all scaling laws; the latter is an expansion about zero density, developed from molecular considerations. The approximant is shown to yield an equation of state capable of accurately describing properties over a large portion of the thermodynamic parameter space, far greater than that covered by each treatment alone.

We calculated virial coefficients BN, 8 ≤ N ≤ 16, of the Lennard-Jones (LJ) model using both the Mayer-sampling Monte Carlo method and direct generation of configurations, with Wheatley's algorithm for summation of clusters. For N = 8, 24 values are reported, and for N = 9, 12 values are reported, both for temperatures T in the range 0.6 ≤ T ≤ 40.0 (in LJ units). For each N in 10 ≤ N ≤ 16, one to four values are reported for 0.6 ≤ T ≤ 0.9. An approximate functional form for the temperature dependence of BN was developed, and fits of LJ BN(T) based on this form are presented for each coefficient, 4 ≤ N ≤ 9, using new and previously reported data.

Hydrogen bonds profoundly influence the architecture and activity of biological macromolecules. Deep appreciation of hydrogen bond contributions to biomolecular function thus requires a detailed understanding of hydrogen bond structure and energetics and the relationship between these properties. Hydrogen bond formation energies (ΔGf) are enormously more favorable in aprotic solvents than in water, and two classes of contributing factors have been proposed to explain this energetic difference, focusing respectively on the isolated and hydrogen-bonded species: (I) water stabilizes the dissociated donor and acceptor groups much better than aprotic solvents, thereby reducing the driving force for hydrogen bond formation; and (II) water lengthens hydrogen bonds compared to aprotic environments, thereby decreasing the potential energy within the hydrogen bond. Each model has been proposed to provide a dominant contribution to ΔGf, but incisive tests that distinguish the importance of these contributions are lacking. Here we directly test the structural basis of model II. Neutron crystallography, NMR spectroscopy, and quantum mechanical calculations demonstrate that O-H···O hydrogen bonds in crystals, chloroform, acetone, and water have nearly identical lengths and very similar potential energy surfaces despite ΔGf differences >8 kcal/mol across these solvents. These results rule out a substantial contribution from solvent-dependent differences in hydrogen bond structure and potential energy after association (model II) and thus support the conclusion that differences in hydrogen bond ΔGf are predominantly determined by solvent interactions with the dissociated groups (model I). These findings advance our understanding of universal hydrogen-bonding interactions and have important implications for biology and engineering.

We describe the design of an object-oriented library of software components that are suitable for constructing simulations of systems of interacting particles. The emphasis of the discussion is on the general design of the components and how they interact, and less on details of the programming interface or its implementation. Example code is provided as an aid to understanding object-oriented programming structures and to demonstrate how the framework is applied., (© 2015 Wiley Periodicals, Inc.)

We propose a measure that quantifies the effort needed to compute a given stochastic average, considered in the context of molecular modeling. This "difficulty index" is defined in terms of CPU time, observed uncertainty, and a characteristic scale for the quantity being computed. This metric provides a focus for optimization and comparison of methods and, if applied broadly, could aid understanding of the impact of models, algorithms, implementations, and platforms on performance of molecular modeling techniques.