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We introduce Gaussian Process Regression (GPR) as an enhanced method of thermodynamic extrapolation and interpolation. The heteroscedastic GPR models that we introduce automatically weight provided information by its estimated uncertainty, allowing for the incorporation of highly uncertain, high-order derivative information. By the linearity of the derivative operator, GPR models naturally handle derivative information and, with appropriate likelihood models that incorporate heterogeneous uncertainties, are able to identify estimates of functions for which the provided observations and derivatives are inconsistent due to the sampling bias that is common in molecular simulations. Since we utilize kernels that form complete bases on the function space to be learned, the estimated uncertainty in the model takes into account that of the functional form itself, in contrast to polynomial interpolation, which explicitly assumes the functional form to be fixed. We apply GPR models to a variety of data sources and assess various active learning strategies, identifying when specific options will be most useful. Our active-learning data collection based on GPR models incorporating derivative information is finally applied to tracing vapor-liquid equilibrium for a single-component Lennard-Jones fluid, which we show represents a powerful generalization to previous extrapolation strategies and Gibbs-Duhem integration. A suite of tools implementing these methods is provided at https://github.com/usnistgov/thermo-extrap., (© 2023 Public Domain.)

Variational autoencoders (VAEs) are rapidly gaining popularity within molecular simulation for discovering low-dimensional, or latent, representations, which are critical for both analyzing and accelerating simulations. However, it remains unclear how the information a VAE learns is connected to its probabilistic structure and, in turn, its loss function. Previous studies have focused on feature engineering, ad hoc modifications to loss functions, or adjustment of the prior to enforce desirable latent space properties. By applying effectively arbitrarily flexible priors via normalizing flows, we focus instead on how adjusting the structure of the decoding model impacts the learned latent coordinate. We systematically adjust the power and flexibility of the decoding distribution, observing that this has a significant impact on the structure of the latent space as measured by a suite of metrics developed in this work. By also varying weights on separate terms within each VAE loss function, we show that the level of detail encoded can be further tuned. This provides practical guidance for utilizing VAEs to extract varying resolutions of low-dimensional information from molecular dynamics and Monte Carlo simulations.

Discovering meaningful collective variables for enhancing sampling, via applied biasing potentials or tailored MC move sets, remains a major challenge within molecular simulation. While recent studies identifying collective variables with variational autoencoders (VAEs) have focused on the encoding and latent space discovered by a VAE, the impact of the decoding and its ability to act as a generative model remains unexplored. We demonstrate how VAEs may be used to learn (on-the-fly and with minimal human intervention) highly efficient, collective Monte Carlo moves that accelerate sampling along the learned collective variable. In contrast to many machine learning-based efforts to bias sampling and generate novel configurations, our methods result in exact sampling in the ensemble of interest and do not require reweighting. In fact, we show that the acceptance rates of our moves approach unity for a perfect VAE model. While this is never observed in practice, VAE-based Monte Carlo moves still enhance sampling of new configurations. We demonstrate, however, that the form of the encoding and decoding distributions, in particular the extent to which the decoder reflects the underlying physics, greatly impacts the performance of the trained VAE.

Purpose: To develop a volume-independent conformity metric called the Gaussian Weighted Conformity Index (GWCI) to evaluate stereotactic radiosurgery/radiotherapy (SRS/SRT) plans for small brain tumors., Methods: A signed bi-directional local distance (BLD) between the prescription isodose line and the target contour is determined for each point along the tumor contour (positive distance represents under-coverage). A similarity score function (SF) is derived from Gaussian function, penalizing under- and over-coverage at each point by assigning standard deviations of the Gaussian function. Each point along the dose line contour is scored with this SF. The average of the similarity scores determines the GWCI. A total of 40 targets from 18 patients who received Gamma-Knife SRS/SRT treatments were analyzed to determine appropriate penalty criteria. The resulting GWCIs for test cases already deemed clinically acceptable are presented and compared to the same cases scored with the New Conformity Index to determine the influence of tumor volumes on the two conformity indices (CIs)., Results: A total of four penalty combinations were tested based on the signed BLDs from the 40 targets. A GWCI of 0.9 is proposed as a cutoff for plan acceptability. The GWCI exhibits no target volume dependency as designed., Conclusion: A limitation of current CIs, volume dependency, becomes apparent when applied to SRS/SRT plans. The GWCI appears to be a more robust index, which penalizes over- and under-coverage of tumors and is not skewed by the tumor volume., (© 2022 American Association of Physicists in Medicine.)

At aqueous interfaces, the distribution and dynamics of adsorbates are modulated by the behavior of interfacial water. Hydration of a hydrophobic surface can store entropy via the ordering of interfacial water, which contributes to the Gibbs energy of solute binding. However, there is little experimental evidence for the existence of such entropic reservoirs, and virtually no precedent for their rational design in systems involving extended interfaces. In this study, two series of mesoporous silicas were modified in distinct ways: (1) progressively deeper thermal dehydroxylation, via condensation of surface silanols, and (2) increasing incorporation of nonpolar organic linkers into the silica framework. Both approaches result in decreasing average surface polarity, manifested in a blue-shift in the fluorescence of an adsorbed dye. For the inorganic silicas, hydrogen-bonding of water becomes less extensive as the number of surface silanols decreases. Overhauser dynamic nuclear polarization (ODNP) relaxometry indicates enhanced surface water diffusivity, reflecting a loss of enthalpic hydration. In contrast, organosilicas show a monotonic decrease in surface water diffusivity with decreasing polarity, reflecting enhanced hydrophobic hydration. Molecular dynamics simulations predict increased tetrahedrality of interfacial water for the organosilicas, implying increased ordering near the nm-size organic domains (relative to inorganic silicas, which necessarily lack such domains). These findings validate the prediction that hydrophobic hydration at interfaces is controlled by the microscopic length scale of the hydrophobic regions. They further suggest that the hydration thermodynamics of structurally heterogeneous silica surfaces can be tuned to promote adsorption, which in turn tunes the selectivity in catalytic reactions.

Performance of membranes for water purification is highly influenced by the interactions of solvated species with membrane surfaces, including surface adsorption of solutes upon fouling. Current efforts toward fouling-resistant membranes often pursue surface hydrophilization, frequently motivated by macroscopic measures of hydrophilicity, because hydrophobicity is thought to increase solute-surface affinity. While this heuristic has driven diverse membrane functionalization strategies, here we build on advances in the theory of hydrophobicity to critically examine the relevance of macroscopic characterizations of solute-surface affinity. Specifically, we use molecular simulations to quantify the affinities to model hydroxyl- and methyl-functionalized surfaces of small, chemically diverse, charge-neutral solutes represented in produced water. We show that surface affinities correlate poorly with two conventional measures of solute hydrophobicity, gas-phase water solubility and oil-water partitioning. Moreover, we find that all solutes show attraction to the hydrophobic surface and most to the hydrophilic one, in contrast to macroscopically based hydrophobicity heuristics. We explain these results by decomposing affinities into direct solute interaction energies (which dominate on hydroxyl surfaces) and water restructuring penalties (which dominate on methyl surfaces). Finally, we use an inverse design algorithm to show how heterogeneous surfaces, with multiple functional groups, can be patterned to manipulate solute affinity and selectivity. These findings, importantly based on a range of solute and surface chemistries, illustrate that conventional macroscopic hydrophobicity metrics can fail to predict solute-surface affinity, and that molecular-scale surface chemical patterning significantly influences affinity-suggesting design opportunities for water purification membranes and other engineered interfaces involving aqueous solute-surface interactions., Competing Interests: The authors declare no competing interest.

Purpose: To reduce patient and procedure identification errors by human interactions in radiotherapy delivery and surgery, a Biometric Automated Patient and Procedure Identification System (BAPPIS) was developed. BAPPIS is a patient identification and treatment procedure verification system using fingerprints., Methods: The system was developed using C++, the Microsoft Foundation Class Library, the Oracle database system, and a fingerprint scanner. To register a patient, the BAPPIS system requires three steps: capturing a photograph using a web camera for photo identification, taking at least two fingerprints, and recording other specific patient information including name, date of birth, allergies, etc . To identify a patient, the BAPPIS reads a fingerprint, identifies the patient, verifies with a second fingerprint to confirm when multiple patients have same fingerprint features, and connects to the patient's record in electronic medical record (EMR) systems. To validate the system, 143 and 21 patients ranging from 36 to 98 years of ages were recruited from radiotherapy and breast surgery, respectively. The registration process for surgery patients includes an additional module, which has a 3D patient model. A surgeon could mark 'O' on the model and save a snap shot of patient in the preparation room. In the surgery room, a webcam displayed the patient's real-time image next to the 3D model. This may prevent a possible surgical mistake., Results: 1,271 (96.9%) of 1,311 fingerprints were verified by BAPPIS using patients' 2 nd fingerprints from 143 patients as the system designed. A false positive recognition was not reported. The 96.9% completion ratio is because the operator did not verify with another fingerprint after identifying the first fingerprint. The reason may be due to lack of training at the beginning of the study., Conclusion: We successfully demonstrated the use of BAPPIS to correctly identify and recall patient's record in EMR. BAPPIS may significantly reduce errors by limiting the number of non-automated steps., Competing Interests: SP was employed by Cure-In Incorporated. TM was employed by Carlow International Incorporated. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Sohn, Kim, Park, Lee, Monroe, Malone, Kinsella, Yao, Kunos, Lo, Shenk and Machtay.)

Purpose: To assess stereotactic radiotherapy (SRT)/stereotactic body radiotherapy (SBRT) practices by polling clinics participating in multi-institutional clinical trials., Methods: The NRG Oncology Medical Physics Subcommittee distributed a survey consisting of 23 questions, which covered general technologies, policies, and procedures used in the Radiation Oncology field for the delivery of SRT/SBRT (9 questions), and site-specific questions for brain SRT, lung SBRT, and prostate SBRT (14 questions). Surveys were distributed to 1,996 radiotherapy institutions included on the membership rosters of the five National Clinical Trials Network (NCTN) groups. Patient setup, motion management, target localization, prescriptions, and treatment delivery technique data were reported back by 568 institutions (28%)., Results: 97.5% of respondents treat lung SBRT patients, 77.0% perform brain SRT, and 29.1% deliver prostate SBRT. 48.8% of clinics require a physicist present for every fraction of SBRT, 18.5% require a physicist present for the initial SBRT fraction only, and 14.9% require a physicist present for the entire first fraction, including set-up approval for all subsequent fractions. 55.3% require physician approval for all fractions, and 86.7% do not reposition without x-ray imaging. For brain SRT, most institutions (83.9%) use a planning target volume (PTV) margin of 2 mm or less. Lung SBRT PTV margins of 3 mm or more are used in 80.6% of clinics. Volumetric modulated arc therapy (VMAT) is the dominant delivery method in 62.8% of SRT treatments, 70.9% of lung SBRT, and 68.3% of prostate SBRT., Conclusion: This report characterizes SRT/SBRT practices in radiotherapy clinics participating in clinical trials. Data made available here allows the radiotherapy community to compare their practice with that of other clinics, determine what is achievable, and assess areas for improvement., Competing Interests: DF reports grants from NCI, during the conduct of the study. IC reports grants from Varian Medical Systems and Philips HealthCare, outside the submitted work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Chetvertkov, Monroe, Boparai, Solberg, Pafundi, Ruo, Gladstone, Yin, Chetty, Benedict, Followill, Xiao and Sohn.)

Thermodynamic extrapolation has previously been used to predict arbitrary structural observables in molecular simulations at temperatures (or relative chemical potentials in open-system mixtures) different from those at which the simulation was performed. This greatly reduces the computational cost in mapping out phase and structural transitions. In this work, we explore the limitations and accuracy of thermodynamic extrapolation applied to water, where qualitative shifts from anomalous to simple-fluid-like behavior are manifested through shifts in the liquid structure that occur as a function of both temperature and density. We present formulas for extrapolating in volume for canonical ensembles and demonstrate that linear extrapolations of water's structural properties are only accurate over a limited density range. On the other hand, linear extrapolation in temperature can be accurate across the entire liquid state. We contrast these extrapolations with classical perturbation theory techniques, which are more conservative and slowly converging. Indeed, we show that such behavior is expected by demonstrating exact relationships between extrapolation of free energies and well-known techniques to predict free energy differences. An ideal gas in an external field is also studied to more clearly explain these results for a toy system with fully analytical solutions. We also present a recursive interpolation strategy for predicting arbitrary structural properties of molecular fluids over a predefined range of state conditions, demonstrating its success in mapping qualitative shifts in water structure with density.

The decoupling approach to solvation free energy calculations requires scaling the interactions between the solute and the solution with all intramolecular interactions preserved. This paper reports a new procedure that makes it possible to these calculations in LAMMPS. The procedure is tested against built-in GROMACS capabilities. The model compounds chosen to test our methodology are ethanol and biphenyl. The LAMMPS and GROMACS results obtained are in good agreement with each other. This work should help perform solvation free energy calculations in LAMMPS and/or other molecular dynamics software having no built-in functions to implement the decoupling approach.

A tetrahedral structure resulting from hydrogen bonding is a hallmark of liquid water and plays a significant role in determining its unique thermophysical properties. This water feature has helped understand anomalous properties and physically interpret and model hydrophobic solvation thermodynamics. Tetrahedrality is well described by the geometric relationship of any central water molecule with two of its nearest neighbors in the first coordination shell, as defined by the corresponding "three-body" angle. While order parameters and even full water models have been developed using specific or average features of the three-body angle distribution, here we examine the distribution holistically, tracking its response to changes in temperature, density, and the presence of model solutes. Surprisingly, we find that the three-body distribution responds by varying primarily along a single degree of freedom, suggesting a remarkably simplified view of water structure. We characterize three-body angle distributions across temperature and density space and identify principal components of the variations with state conditions. We show that these principal components embed physical significance and trace out transitions between tetrahedral and simple-fluid-like behavior. Moreover, we find that the ways three-body angles vary within the hydration shells of model colloids of different types and sizes are nearly identical to the variations seen in bulk water across density and temperature. Importantly, through the principal directions of these variations, we find that perturbations to the hydration-water distributions well predict the thermodynamics associated with colloid solvation, in particular, the relative entropy of this process that captures indirect, solvent-mediated contributions to the hydration free energy.

This document provides a starting point for approaching molecular simulations, guiding beginning practitioners to what issues they need to know about before and while starting their first simulations, and why those issues are so critical. This document makes no claims to provide an adequate introduction to the subject on its own. Instead, our goal is to help people know what issues are critical before beginning, and to provide references to good resources on those topics. We also provide a checklist of key issues to consider before and while setting up molecular simulations which may serve as a foundation for other best practices documents.

Purpose: A survey was created by NRG to assess a medical physicists' percent full time equivalent (FTE) contribution to multi-institutional clinical trials. A 2012 American Society for Radiation Oncology report, "Safety Is No Accident," quantified medical physics staffing contributions in FTE factors for clinical departments. No quantification of FTE effort associated with clinical trials was included., Methods: To address this lack of information, the NRG Medical Physics Subcommittee decided to obtain manpower data from the medical physics community to quantify the amount of time medical physicists spent supporting clinical trials. A survey, consisting of 16 questions, was designed to obtain information regarding physicists' time spent supporting clinical trials. The survey was distributed to medical physicists at 1996 radiation therapy institutions included on the membership rosters of the 5 National Clinical Trials Network clinical trial groups., Results: Of the 451 institutions who responded, 50% (226) reported currently participating in radiation therapy trials. On average, the designated physicist at each institution spent 2.4 hours (standard deviation [SD], 5.5) per week supervising or interacting with clinical trial staff. On average, 1.2 hours (SD, 3.1), 1.8 hours (SD, 3.9), and 0.6 hours (SD, 1.1) per week were spent on trial patient simulations, treatment plan reviews, and maintaining a Digital Imaging and Communications in Medicine server, respectively. For all trial credentialing activities, physicists spent an average of 32 hours (SD, 57.2) yearly. Reading protocols and supporting dosimetrists, clinicians, and therapists took an average of 2.1 hours (SD, 3.4) per week. Physicists also attended clinical trial meetings, on average, 1.2 hours (SD, 1.9) per month., Conclusion: On average, physicist spent a nontrivial total of 9 hours per week (0.21 FTE) supporting an average of 10 active clinical trials. This time commitment indicates the complexity of radiation therapy clinical trials and should be taken into account when staffing radiation therapy institutions., (Copyright © 2018. Published by Elsevier Inc.)

The interactions of water with solid surfaces govern their apparent hydrophobicity/hydrophilicity, influenced at the molecular scale by surface coverage of chemical groups of varied nonpolar/polar character. Recently, it has become clear that the precise patterning of surface groups, and not simply average surface coverage, has a significant impact on the structure and thermodynamics of hydration layer water, and, in turn, on macroscopic interfacial properties. Here we show that patterning also controls the dynamics of hydration water, a behavior frequently thought to be leveraged by biomolecules to influence functional dynamics, but yet to be generalized. To uncover the role of surface heterogeneities, we couple a genetic algorithm to iterative molecular dynamics simulations to design the patterning of surface functional groups, at fixed coverage, to either minimize or maximize proximal water diffusivity. Optimized surface configurations reveal that clustering of hydrophilic groups increases hydration water mobility, while dispersing them decreases it, but only if hydrophilic moieties interact with water through directional, hydrogen-bonding interactions. Remarkably, we find that, across different surfaces, coverages, and patterns, both the chemical potential for inserting a methane-sized hydrophobe near the interface and, in particular, the hydration water orientational entropy serve as strong predictors for hydration water diffusivity, suggesting that these simple thermodynamic quantities encode the way surfaces control water dynamics. These results suggest a deep and intriguing connection between hydration water thermodynamics and dynamics, demonstrating that subnanometer chemical surface patterning is an important design parameter for engineering solid-water interfaces with applications spanning separations to catalysis., Competing Interests: The authors declare no conflict of interest.

An in-depth knowledge of the interaction of water with amorphous silica is critical to fundamental studies of interfacial hydration water, as well as to industrial processes such as catalysis, nanofabrication, and chromatography. Silica has a tunable surface comprising hydrophilic silanol groups and moderately hydrophobic siloxane groups that can be interchanged through thermal and chemical treatments. Despite extensive studies of silica surfaces, the influence of surface hydrophilicity and chemical topology on the molecular properties of interfacial water is not well understood. In this work, we controllably altered the surface silanol density, and measured surface water diffusivity using Overhauser dynamic nuclear polarization (ODNP) and complementary silica-silica interaction forces across water using a surface forces apparatus (SFA). The results show that increased silanol density generally leads to slower water diffusivity and stronger silica-silica repulsion at short aqueous separations (less than ∼4 nm). Both techniques show sharp changes in hydration properties at intermediate silanol densities (2.0-2.9 nm -2 ). Molecular dynamics simulations of model silica-water interfaces corroborate the increase in water diffusivity with silanol density, and furthermore show that even on a smooth and crystalline surface at a fixed silanol density, adjusting the spatial distribution of silanols results in a range of surface water diffusivities spanning ∼10%. We speculate that a critical silanol cluster size or connectivity parameter could explain the sharp transition in our results, and can modulate wettability, colloidal interactions, and surface reactions, and thus is a phenomenon worth further investigation on silica and chemically heterogeneous surfaces., Competing Interests: The authors declare no conflict of interest.

Interactions between hydrophobic moieties steer ubiquitous processes in aqueous media, including the self-organization of biologic matter. Recent decades have seen tremendous progress in understanding these for macroscopic hydrophobic interfaces. Yet, it is still a challenge to experimentally measure hydrophobic interactions (HIs) at the single-molecule scale and thus to compare with theory. Here, we present a combined experimental-simulation approach to directly measure and quantify the sequence dependence and additivity of HIs in peptide systems at the single-molecule scale. We combine dynamic single-molecule force spectroscopy on model peptides with fully atomistic, both equilibrium and nonequilibrium, molecular dynamics (MD) simulations of the same systems. Specifically, we mutate a flexible (GS) 5 peptide scaffold with increasing numbers of hydrophobic leucine monomers and measure the peptides' desorption from hydrophobic self-assembled monolayer surfaces. Based on the analysis of nonequilibrium work-trajectories, we measure an interaction free energy that scales linearly with 3.0-3.4 k B T per leucine. In good agreement, simulations indicate a similar trend with 2.1 k B T per leucine, while also providing a detailed molecular view into HIs. This approach potentially provides a roadmap for directly extracting qualitative and quantitative single-molecule interactions at solid/liquid interfaces in a wide range of fields, including interactions at biointerfaces and adhesive interactions in industrial applications.

Implicit solvation models have long been sought as routes to significantly increase the speed and capabilities of biomolecular simulations. However, it has not always been clear that force fields developed independently of solvation models can together accurately predict secondary structure and folding, and whether the separate influences of the solvation and force field models can be described as independent and additive (versus synergistic). Here, we test two implicit solvation models with several recently developed protein force fields, within the AMBER simulation package. We create a representative set of five helical and five hairpin peptides, 11-20 amino acid residues in length, and calculate folded structures using replica exchange molecular dynamics simulations for all force field/solvent/peptide combinations, each with two instances using distinct starting configurations. In general, we find that no force field/solvent combination successfully folds all peptides and that the hairpin peptides are more difficult to capture. That being said, the older ff96/igb5* combination does a reasonable job in folding multiple secondary structures, while ff14SB/igb5* and ff14ipq/igb8 work well for helical and hairpin motifs, respectively. All combinations give rise to similar numbers of salt bridges, except for solvent models paired with ff14ipq, which slightly enhances them. Interestingly, we are unable statistically to decouple the effects of force field, solvent model, and peptide secondary structure on performance, such that particular combinations can have specific effects. These results suggest that future efforts might benefit from codevelopment of implicit models with force fields or from the use of emerging coarse-graining strategies that extract solvation effects in a bottom-up manner.

This article proposes quantitative analysis tools and digital phantoms to quantify intrinsic errors of deformable image registration (DIR) systems and establish quality assurance (QA) procedures for clinical use of DIR systems utilizing local and global error analysis methods with clinically realistic digital image phantoms. Landmark-based image registration verifications are suitable only for images with significant feature points. To address this shortfall, we adapted a deformation vector field (DVF) comparison approach with new analysis techniques to quantify the results. Digital image phantoms are derived from data sets of actual patient images (a reference image set, R, a test image set, T). Image sets from the same patient taken at different times are registered with deformable methods producing a reference DVFref. Applying DVFref to the original reference image deforms T into a new image R'. The data set, R', T, and DVFref, is from a realistic truth set and therefore can be used to analyze any DIR system and expose intrinsic errors by comparing DVFref and DVFtest. For quantitative error analysis, calculating and delineating differences between DVFs, 2 methods were used, (1) a local error analysis tool that displays deformation error magnitudes with color mapping on each image slice and (2) a global error analysis tool that calculates a deformation error histogram, which describes a cumulative probability function of errors for each anatomical structure. Three digital image phantoms were generated from three patients with a head and neck, a lung and a liver cancer. The DIR QA was evaluated using the case with head and neck., (© The Author(s) 2014.)

Purpose: A quantitative and objective metric, the medical similarity index (MSI), has been developed for evaluating the accuracy of a medical image segmentation relative to a reference segmentation. The MSI uses the medical consideration function (MCF) as its basis., Methods: Currently, no indices provide quantitative evaluations of segmentation accuracy with medical considerations. Variations in segmentation can occur due to individual skill levels and medical relevance--curable or palliative intent, boundary uncertainty due to volume averaging, contrast levels, spatial resolution, and unresolved motion all affect the accuracy of a patient segmentation. Current accuracy measuring indices are not medically relevant. For example, undercontouring the tumor volume is not differentiated from overcontouring tumor. Dice similarity coefficient (DSC) and Hausdorff distance (HD) are two similarity measures often used. However, these metrics consider only geometric difference without considering medical implications. Two segments (under- vs overcontouring tumor) with similar DSC and HD measures could produce significantly different medical treatment results. The authors are proposing a MSI involving a user-defined MCF derived from an asymmetric Gaussian function. The shape of the MCF can be determined by a user, reflecting the anatomical location and characteristics of a particular tissue, organ, or tumor type. The peak of MCF is set along the reference contour; the inner and outer slopes are selected by the user. The discrepancy between the test and reference contours is calculated at each pixel by using a bidirectional local distance measure. The MCF value corresponding to that distance is summed and averaged to produce the MSI. Synthetic segmentations and clinical data from a 15 multi-institutional trial for a head-and-neck case are scored and compared by using MSI, DSC, and Hausdorff distance., Results: The MSI was shown to reflect medical considerations through the choice of MCF penalties for under- and overcontouring. Existing similarity scores were either insensitive to medical realities or simply inaccurate., Conclusions: The medical similarity index, a segmentation evaluation metric based on medical considerations, has been proposed, developed, and tested to incorporate clinically relevant considerations beyond geometric parameters alone.

Molecular containers such as cucurbit[7]uril (CB7) and the octa-acid (OA) host are ideal simplified model test systems for optimizing and analyzing methods for computing free energies of binding intended for use with biologically relevant protein-ligand complexes. To this end, we have performed initially blind free energy calculations to determine the free energies of binding for ligands of both the CB7 and OA hosts. A subset of the selected guest molecules were those included in the SAMPL4 prediction challenge. Using expanded ensemble simulations in the dimension of coupling host-guest intermolecular interactions, we are able to show that our estimates in most cases can be demonstrated to fully converge and that the errors in our estimates are due almost entirely to the assigned force field parameters and the choice of environmental conditions used to model experiment. We confirm the convergence through the use of alternative simulation methodologies and thermodynamic pathways, analyzing sampled conformations, and directly observing changes of the free energy with respect to simulation time. Our results demonstrate the benefits of enhanced sampling of multiple local free energy minima made possible by the use of expanded ensemble molecular dynamics and may indicate the presence of significant problems with current transferable force fields for organic molecules when used for calculating binding affinities, especially in non-protein chemistries.

Inhibiting HIV reverse transcriptase through the use of nonnucleoside reverse transcriptase inhibitors (NNRTIs) has become an essential component in drug regimens for the treatment of HIV. Older NNRTIs, such as nevirapine, are structurally rigid, exhibiting decreased inhibitory function on development of common mutations in the NNRTI-binding pocket, which is located around 10 Å from the catalytically active binding site. The newer generation of drugs, such as rilpivirine, are more flexible and resistant to binding pocket mutations but the mechanism by which they actually inhibit protein function and avoid mutations is not well-understood. To this end, we have performed 2-2.4 µs simulations with explicit solvent in an isobaric-isothermal ensemble of six different systems: apo wild-type, apo K103N/Y181C mutant, nevirapine-bound wild-type, nevirapine-bound mutant, rilpivirine-bound wild type, and rilpivirine-bound mutant. Analysis of protein conformations, principal components of motion, and mutual information between residues points to an inhibitory mechanism in which the primer grip stretches away from the catalytic triad of aspartic acids necessary for polymerization of HIV-encoding DNA, but is still unable to reveal a specific structural mechanism behind mutation resistance., (Copyright © 2013 Wiley Periodicals, Inc.)

Purpose: To accurately quantify the local difference between two contour surfaces in two- or three-dimensional space, a new, robust point-to-surface distance measure is developed., Methods: To evaluate and visualize the local surface differences, point-to-surface distance measures have been utilized. However, previously well-known point-to-surface distance measures have critical shortfalls. Previous distance measures termed "normal distance (ND)," "radial distance," or "minimum distance (MD)" can report erroneous results at certain points where the surfaces under comparison meet certain conditions. These skewed results are due to the monodirectional characteristics of these methods. ComGrad distance was also proposed to overcome asymmetric characteristics of previous point-to-surface distance measures, but their critical incapability of dealing with a fold or concave contours. In this regard, a new distance measure termed the bidirectional local distance (BLD) is proposed which minimizes errors of the previous methods by taking into account the bidirectional characteristics with the forward and backward directions. BLD measure works through three steps which calculate the maximum value between the forward minimum distance (FMinD) and the backward maximum distance (BMaxD) at each point. The first step calculates the FMinD as the minimum distance to the test surface from a point, p(ref) on the reference surface. The second step involves calculating the minimum distances at every point on the test surface to the reference surface. During the last step, the BMaxD is calculated as the maximum distance among the minimum distances found at p(ref) on the reference surface. Tests are performed on two- and three-dimensional artificial contour sets in comparison to MD and ND measure techniques. Three-dimensional tests performed on actual liver and head-and-neck cancer patients., Results: The proposed BLD measure provides local distances between segmentations, even in situations where ND, MD, or ComGrad measures fail. In particular, the standard deviation measure is not distorted at certain geometries where ND, MD, and ComGrad measures report skewed results., Conclusions: The proposed measure provides more reliable statistics on contour comparisons. From the statistics, specific local and global distances can be extracted. Bidirectional local distance is a reliable distance measure in comparing two- or three-dimensional organ segmentations.

Composite plans created from different image sets are generated through Deformable Image Registration (DIR) and present a challenge in accurately presenting uncertainties, which vary with anatomy. Our effort focuses on the application of Fuzzy Set theory to provide an accurate dose representation of such a composite treatment plan. The accuracy of the DIR is generally verified through geometrical visual checks, including the confirmation of the corresponding anatomies with edge features, such as bone or organ boundaries. However, the remaining volume of the image (mostly soft tissues) has few significant image features and therefore greater uncertainty. We fuzzified the deformation vector and derived a Fuzzy composite dose. The fuzzification was implemented using Gaussian functions based on the varying uncertainties in the DIR. After establishing the theoretical basis for this new approach, we present two-and three-dimensional examples as proof-of-concept. Using Fuzzy Set theory, composite dose plans displaying locality-based uncertainties were successfully created, providing information previously unavailable to clinicians. Previous to Fuzzy Set dose presentations, clinicians had no measure of confidence in the accuracy of a composite dose plan. Using fuzzified composite dose presentations, clinicians can determine a safe additional dose to previously treated anatomy. This will possibly increase the treatment success rate and reduce the rate of complications.

Purpose: To develop a new metric for image registration that incorporates the (sub)pixelwise differential importance along spatial location and to demonstrate its application for image guided radiation therapy (IGRT)., Methods: It is well known that rigid-body image registration with mutual information is dependent on the size and location of the image subset on which the alignment analysis is based [the designated region of interest (ROI)]. Therefore, careful review and manual adjustments of the resulting registration are frequently necessary. Although there were some investigations of weighted mutual information (WMI), these efforts could not apply the differential importance to a particular spatial location since WMI only applies the weight to the joint histogram space. The authors developed the spatially weighted mutual information (SWMI) metric by incorporating an adaptable weight function with spatial localization into mutual information. SWMI enables the user to apply the selected transform to medically "important" areas such as tumors and critical structures, so SWMI is neither dominated by, nor neglects the neighboring structures. Since SWMI can be utilized with any weight function form, the authors presented two examples of weight functions for IGRT application: A Gaussian-shaped weight function (GW) applied to a user-defined location and a structures-of-interest (SOI) based weight function. An image registration example using a synthesized 2D image is presented to illustrate the efficacy of SWMI. The convergence and feasibility of the registration method as applied to clinical imaging is illustrated by fusing a prostate treatment planning CT with a clinical cone beam CT (CBCT) image set acquired for patient alignment. Forty-one trials are run to test the speed of convergence. The authors also applied SWMI registration using two types of weight functions to two head and neck cases and a prostate case with clinically acquired CBCT/ MVCT image sets. The SWMI registration with a Gaussian weight function (SWMI-GW) was tested between two different imaging modalities: CT and MRI image sets., Results: SWMI-GW converges 10% faster than registration using mutual information with an ROI. SWMI-GW as well as SWMI with SOI-based weight function (SWMI-SOI) shows better compensation of the target organ's deformation and neighboring critical organs' deformation. SWMI-GW was also used to successfully fuse MRI and CT images., Conclusions: Rigid-body image registration using our SWMI-GW and SWMI-SOI as cost functions can achieve better registration results in (a) designated image region(s) as well as faster convergence. With the theoretical foundation established, we believe SWMI could be extended to larger clinical testing.

A dosimetric study of a 103Pd seed for permanent interstitial brachytherapy, the Theragenics Corporation Model 200 (TheraSeed), has been undertaken utilizing Monte Carlo photon-transport (MCPT) simulations. All dosimetric quantities recommended by the American Association of Physicists in Medicine (AAPM) Task Group 43 (TG-43) [Med. Phys. 22, 209-234 (1995)] report have been calculated. This source contains graphite pellets coated with palladium metal, within which the radioactive 103Pd is distributed. Due to the significant influence of this metal coating thickness on the dose distribution, two coating thicknesses, 2.2 microm (light seed, representing currently available seeds) and 10.5 microm (heavy seed, representing reactor-produced seeds available before 1994), were analyzed. Quantities determined are the following: dose rate constant, radial dose function, anisotropy function, anisotropy factor, anisotropy constant, and "along and away" dose tables. The National Institute of Standards and Technology (NIST) Wide Angle Free Air Chamber (WAFAC) standard for air-kerma strength (SK,N99) was simulated, allowing a comparison to measured dosimetry data normalized to SKN99. The calculated dose-rate constants are 0.691 (light seed) and 0.694 cGy h(-1) U(-1) (heavy seed), where 1 U= 1 microGy x m2 x h(-1), in contrast to the recommended TG-43 value of 0.74 cGy h(-1) U(-1) and the value, 0.665 cGy h(-1) U(-1), recommended by AAPM report 69 [Med. Phys. 27, 634-642 (2000)]. Anisotropy constants (1/r2 weighted average, r > or = 1 cm) are 0.862 and 0.884 for the light seed and heavy seed, respectively. A generalization of the AAPM formalism [Med. Phys. 27, 634-642] for evaluating the time-dependent ratio of an administered-to-prescribed dose is presented. The findings of this study, in combination with 5% corrections applied to WAFAC measurements performed in 1999, imply that changes in the AAPM's recommended ratios as large as 6%, are indicated.

This paper compares experimentally measured and calculated dose-rate distributions for a novel 125I liquid-filled brachytherapy balloon applicator (the GliaSite RTS), designed for the treatment of malignant brain-tumor resection-cavity margins. This work is intended to comply with the American Association of Physicists in Medicine (AAPM) Radiation Therapy Committee's recommendations [Med. Phys. 25, 2269-2270 (1998)] for dosimetric characterization of low-energy photon interstitial brachytherapy sources. Absolute low dose-rate radiochromic film (RCF) dosimetry measurements were performed in coronal planes about the applicator. The applicator was placed in a solid water phantom, machined to conform to the inflated applicator's surface. The results were used to validate the accuracy of Monte Carlo photon transport (MCPT) simulations and a point-source dose-kernel algorithm in predicting dose to water. The absolute activity of the 125I solution was determined by intercomparing a National Institute of Standards and Technology (NIST) 125I standard with a known mass of radiotherapy solution (Iotrex) in an identical vial and geometry. For the two films not in contact with applicator, the average agreement between RCF and MCPT (specified as the mean absolute deviation in successive 4 mm rings) was found to be within +/-5% at distances 0.2-25 mm from the film centers. For the two films touching the catheter, the mean agreement was +/-14.5% and 7.5% near the balloon surface but improving to 7.5% and 6% by 3.5 mm from the surface. These errors, as large as 20% in isolated pixels, are likely due to trim damage, 125I contamination, and poor conformance with the balloon. At larger distances where the radiation doses were very low, the observed discrepancies were significantly larger than expected. We hypothesize that they are due to a dose-rate dependence of the RCF response. A 1%-10% average difference between a simple one-dimensional path-length semiempirical dose-kernel model and the MCPT calculations was observed over clinically relevant distances.

The plastic scintillator (PS) is a promising dosimeter for brachytherapy and other low-energy photon applications because of its high sensitivity and approximate tissue equivalence. As part of our project to develop a new PS material which maximizes sensitivity and radiological equivalence to water, we have measured the response, epsilon (light output/unit air kerma), of PS to low-energy bremsstrahlung (20 to 57 keV average energies) x-rays as well as photons emitted by 99mTc, 192Ir, and 137Cs sources, all of which were calibrated in terms of air kerma. The PS systems studied were a standard commercial PS, BC400 (Bicron Corporation, Newbury, OH), and our new sensitive and quench-resistant scintillator (polyvinyltoluene base and binary dye system) with and without 4% Cl loading intended to match the effective atomic number of water. For low-energy x-rays, epsilon was 20-57% relative to epsilon for 192Ir photons. Chlorine loading clearly reduced the energy dependence of epsilon, which ranged from 46% to 85% relative to 192Ir. However, even after using Monte Carlo photon-transport simulation to correct for the non-air equivalence of the PS, inherent dosimetric sensitivity still varied by 30% over the 20-400 keV energy range. Our work, one of the few measurements of PS response to low-energy photons, appears to confirm Birks' 1955 finding that ionization quenching reduces sensitivity to electrons below 125 keV. However, our results cannot be explained by Birks' widely used unimolecular quenching model.