Plutonium oxide melt structure and covalency.

Advances in nuclear power reactors include the use of mixed oxide fuel, containing uranium and plutonium oxides. The high-temperature behaviour and structure of PuO2-x above 1,800 K remain largely unexplored, and these conditions must be considered for reactor design and planning for the mitigation of severe accidents. Here, we measure the atomic structure of PuO2-x through the melting transition up to 3,000 ± 50 K using X-ray scattering of aerodynamically levitated and laser-beam-heated samples, with O/Pu ranging from 1.57 to 1.76. Liquid structural models consistent with the X-ray data are developed using machine-learned interatomic potentials and density functional theory. Molten PuO1.76 contains some degree of covalent Pu-O bonding, signalled by the degeneracy of Pu 5f and O 2p orbitals. The liquid is isomorphous with molten CeO1.75, demonstrating the latter as a non-radioactive, non-toxic, structural surrogate when differences in the oxidation potentials of Pu and Ce are accounted for. These characterizations provide essential constraints for modelling pertinent to reactor safety design.

Integrating Board-Certified Psychiatric Pharmacists in a Certified Community Behavioral Health Clinic.

Demand for mental health services has dramatically increased in recent years, raising concerns about the availability of service providers to meet these increased needs. One approach to expanding access to care is the use of highly qualified board-certified psychiatric pharmacists (BCPPs). However, the implementation of programs for integrating BCPPs has not been well characterized in community mental health settings. This column describes the development and implementation of a comprehensive practice model to incorporate BCPPs in a certified community behavioral health clinic. The authors report the results from the first 14 months of BCPP integration (based on 3,221 direct patient care interventions), offer recommendations, and highlight lessons learned.

Long-term survivorship of the Herbert ulnar head prosthesis: a multicentre retrospective cohort study.

LEVEL OF EVIDENCE: IV.

Emergent constraints on carbon budgets as a function of global warming.

Earth System Models (ESMs) continue to diagnose a wide range of carbon budgets for each level of global warming. Here, we present emergent constraints on the carbon budget as a function of global warming, which combine the available ESM historical simulations and future projections for a range of scenarios, with observational estimates of global warming and anthropogenic CO2 emissions to the present day. We estimate mean and likely ranges for cumulative carbon budgets for the Paris targets of 1.5 °C and 2 °C of global warming of 812 [691, 933] PgC and 1048 [881, 1216] PgC, which are more than 10% larger than the ensemble mean values from the CMIP6 models. The linearity between cumulative emissions and global warming is found to be maintained at least until 4 °C, and is consistent with an effective Transient Climate Response to Emissions (eTCRE) of 2.1 [1.8, 2.6] °C/1000PgC, from a global warming of 1.2 °C onwards.

Total wrist arthroplasty: commentary and opinions.

Total wrist arthroplasty (TWA) is gaining interest as a management option for wrist arthritis. This review article summarizes the current evidence base for TWA, focusing on the performance and survivorship of fourth-generation implants. These appear to offer satisfactory patient-reported outcomes and survivorship over the medium term, but heterogeneity between implants and patient populations complicates data interpretation. We discuss issues facing TWA practice, including surgical competency, volume, implant development and stewardship. We acknowledge the need for further research on this topic and highlight a number of questions that need answering.

AL4GAP: Active learning workflow for generating DFT-SCAN accurate machine-learning potentials for combinatorial molten salt mixtures.

Machine learning interatomic potentials have emerged as a powerful tool for bypassing the spatiotemporal limitations of ab initio simulations, but major challenges remain in their efficient parameterization. We present AL4GAP, an ensemble active learning software workflow for generating multicomposition Gaussian approximation potentials (GAP) for arbitrary molten salt mixtures. The workflow capabilities include: (1) setting up user-defined combinatorial chemical spaces of charge neutral mixtures of arbitrary molten mixtures spanning 11 cations (Li, Na, K, Rb, Cs, Mg, Ca, Sr, Ba and two heavy species, Nd, and Th) and 4 anions (F, Cl, Br, and I), (2) configurational sampling using low-cost empirical parameterizations, (3) active learning for down-selecting configurational samples for single point density functional theory calculations at the level of Strongly Constrained and Appropriately Normed (SCAN) exchange-correlation functional, and (4) Bayesian optimization for hyperparameter tuning of two-body and many-body GAP models. We apply the AL4GAP workflow to showcase high throughput generation of five independent GAP models for multicomposition binary-mixture melts, each of increasing complexity with respect to charge valency and electronic structure, namely: LiCl-KCl, NaCl-CaCl2, KCl-NdCl3, CaCl2-NdCl3, and KCl-ThCl4. Our results indicate that GAP models can accurately predict structure for diverse molten salt mixture with density functional theory (DFT)-SCAN accuracy, capturing the intermediate range ordering characteristic of the multivalent cationic melts.

Diminished activity-dependent BDNF signaling differentially causes autism-like behavioral deficits in male and female mice.

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders with strong genetic heterogeneity and more prevalent in males than females. Recent human genetic studies have identified multiple high-risk genes for ASD, which produce similar phenotypes, indicating that diverse genetic factors converge to common molecular pathways. We and others have hypothesized that activity-dependent neural signaling is a convergent molecular pathway dysregulated in ASD. However, the causal link between diminished activity-dependent neural signaling and ASD remains unclear. Brain-derived neurotrophic factor (BDNF) is a key molecule mediating activity-dependent neural signaling. We therefore hypothesize that diminished activity-dependent BDNF signaling could confer autism-like behavioral deficits. Here, we investigated the effect of diminished activity-dependent BDNF signaling on autism-like behavioral deficits by using mice with genetic knock-in of a human BDNF methionine (Met) allele, which has decreased activity-dependent BDNF release without altering basal BDNF level. Compared with wild-type (WT) controls, diminished activity-dependent BDNF signaling similarly induced anxiety-like behaviors in male and female mice. Notably, diminished activity-dependent BDNF signaling differentially resulted in autism-like social deficits and increased self-grooming in male and female mice, and male mice were more severe than female mice. Again, sexually dimorphic spatial memory deficits were observed in female BDNF+/Met mice, but not in male BDNF+/Met mice. Our study not only reveals a causal link between diminished activity-dependent BDNF signaling and ASD-like behavioral deficits, but also identifies previously underappreciated sex-specific effect of diminished activity-dependent BDNF signaling in ASD. These mice with genetic knock-in of the human BDNF Met variant provide a distinct mouse model for studying the cellular and molecular mechanisms underlying diminished activity-dependent neural signaling, the common molecular pathway dysregulated in ASD.

Biomechanical Comparison of 5 Different Fixation Constructs in a Trapeziometacarpal Joint Arthrodesis Model.

PURPOSE: Trapeziometacarpal joint (TMC) arthrodesis has a high rate of nonunion. This biomechanical analysis sought to determine the stiffness of 5 fixation methods in a TMC joint arthrodesis model. METHODS: Five fixation constructs were tested in a validated porcine model: crossed 1.1-mm K-wires, crossed 1.6-mm K-wires, crossed headless compression screws (HCSs), compression plating (CP), and locked compression plating (LCP). The cantilever bending stiffness was measured in abduction, adduction, flexion, and extension. Samples were loaded to failure in extension, and the mode of failure was examined. RESULTS: The crossed HCSs performed consistently well in all tests. Loading to failure resulted in screw pullout. In abduction and adduction, HCS and 1.6-mm K-wires were significantly stiffer than the other constructs. The mean load to failure in extension was similar in the HCS, CP, and LCP groups (304 N/mm, 311 N/mm, and 293 N/mm, respectively). There were no differences between CP and LCP in any biomechanical tests, and the mode of failure was through plate bending. The crossed 1.1-mm K-wires performed poorly in all tests. CONCLUSIONS: Crossed HCS displayed the greatest overall stability. Standard plating in compression mode and LCP had a similar biomechanical performance. CLINICAL RELEVANCE: The ideal construct stiffness required for the successful union after TMC joint arthrodesis is unknown, but HCS has the best overall biomechanical performance and, therefore, might be considered the best choice for this clinical setting.

Criterion Validity of Radon Test Values Reported by a Commercial Laboratory versus the Environmental Protection Agency.

Objective: Radon exposure is a proven cause of lung cancer and is a possible cause of other diseases. Recently, several ecologic studies explored the correlation of county-wide incidence rates for non-lung cancers with residential radon levels, using radon data reported by a commercial laboratory. However, the validity of the commercial radon data, i.e., whether they are an accurate representation of the radon levels in the counties from which they were drawn, is unknown. Methods: We compared county-wide radon data from the commercial laboratory with corresponding measurements from the same counties reported previously by the Environmental Protection Agency (EPA). Matching data were available for four states, Iowa, North Dakota, Texas, and Wisconsin, and were compared by paired t-tests. Criterion validity of the commercial tests, i.e., how well the commercial data predicted the EPA data, was tested using non-parametric methods, Kendall's tau, Lin's concordance, and Passing−Bablok regression. Results: The commercial and EPA data pairs from the four states were significantly positively correlated, although the size of the correlations was modest (tau = 0.490, Lin = 0.600). Passing−Bablok regression indicated that the commercial radon values were significantly higher than their EPA pairs and significantly overestimated radon at low levels (<4 pCi/L, p < 0.001). Conclusions: The commercial laboratory data were moderately predictive of EPA radon levels at the county level but were significantly biased upwards at low levels. The disagreement likely has several causes, including selection bias from homes that were tested voluntarily. Ecologic studies that employ radon data obtained from commercial laboratories should be interpreted with caution.

Lead service lines and Parkinson's disease prevalence in U.S. States.

INTRODUCTION: We recently showed that the prevalence of Parkinson's Disease (PD) in U.S. states is positively associated with the quantity of acid rain. Acid rain could play an etiologic role in PD by mobilizing metals, e.g., lead, from watersheds and pipes into drinking water. We assessed the correlation of PD with lead service lines, the underground pipes that connect homes to municipal water sources, which are a major aqueous source of lead. METHODS: We used multiple regression techniques to examine PD prevalence rates by state relative to the number of lead service lines. We included known or suspected aqueous risk factors, e.g., the Acid Precipitation Index (a measure of acid rain) and well water use. RESULTS: Age-, race-, and sex-adjusted prevalence rates for PD were significantly and positively correlated with the log number of lead service lines (p = 0.0004). The effect of lead service lines remained significant after adjusting for the effects of acid rain and well water use (p = 0.0019). CONCLUSION: These findings are consistent with a role for lead in the etiology of PD. Studies of lead service line exposure in relation to PD at the individual level are warranted.

SSIPTools: Software and Methodology for Surface Site Interaction Point (SSIP) Approach and Applications.

We present the SSIPTools suite of programs. SSIPTools is a collection of software modules enabling the use of the Surface Site Interaction Point (SSIP) molecular descriptors, used for the modeling of noncovalent interactions in neutral organic molecules. It contains an implementation of the workflow for the generation of the SSIP descriptors, as well as the Functional Group Interaction Profiles (FGIPs) and Solvent Similarity Indexes (SSIs) applications, based on the SSIMPLE (Surface Site Interaction model for the Properties of Liquids at Equilibria) approach.

Success Rates for the Objectives of US State Cancer Control Plans: A First Look.

BACKGROUND: A well-designed cancer control plan is an important tool for a nation, state, or community to address the burden of cancer. Furthermore, it provides the opportunity to devise and implement measurable objectives. However, there has been little to no assessment of the success rates of such objectives. METHODS: I compared the success rate of objectives between US states' current plan and most recent past plan to determine the proportion of success in the United States overall. I also tested possible reasons for low success rates. RESULTS: The mean success rate was 20% for stringent successes (only exact matches between plans) and 28% for loose successes (exact and similar matches between plans). The magnitude of change in percentage between the baseline and target for loose objectives significantly predicted success (P = .0347). Higher change resulted in lower success. However, neither the number of objectives nor the level of overlap significantly predicted success rate. Nor was population size, region, or rural-urban status significantly related to success rate. The most successful states had high proportions of objectives that were measurable and a high number of overlapping objectives. CONCLUSION: I found that objective success rates were low for cancer control plans. To improve success rates, I suggest that future cancer control plans ensure each objective has a measurable baseline and realistically attainable target.

Cancer Mortality and Research Outcomes in a Rural State.

BACKGROUND: North Dakota is a rural state with high rates of cancer. Determining how various demographic, geographic, and funding factors contributed to cancer incidence on a state and county level helps improve cancer prevention and control. OBJECTIVES: We examined cancer incidence rate trends by demographic (sex and ethnicity) and geographic (county, population, rural/frontier status) factors. We also examined cancer funding and research output by year. METHODS: Cancer incidence rates were obtained from the North Dakota Cancer Registry and stratified by sex, ethnicity, and county. US cancer rates also were obtained for comparison. Generalized linear models were used to compare overall incidence rates and yearly trends. RESULTS: Male melanoma incidence rates increased faster than the US average across year P = 0.020). Incidence rates for prostate, lung, and colorectal cancer among American Indians/Alaska Natives (AI/AN) decreased faster than Whites across year (P < 0.001, P= 0.001, P < 0.001, respectively). Four counties-2 for breast cancer and 2 for prostate cancer-had differential trends compared to the North Dakota average across year (P = 0.011, P = 0.029; P= 0.046, P = 0.042). County-level lung cancer incidence rates were positively correlated with county population size, while rates for cervix/uteri were negatively correlated (P = 0.001, P = 0.023). Funding from the National Institutes of Health for North Dakota increased across year along with cancer papers published increased (P < 0.001, P < 0.001). CONCLUSIONS: Examining state and county data revealed several surprising trends and the need for a more fine-scale approach to cancer cause, control, and prevention.

Acid Precipitation and the Prevalence of Parkinson's Disease: An Ecologic Study in U.S. States.

Although the etiology of Parkinson's disease (PD) is unknown, potentially informative clues lie in its geographic distribution. PD prevalence rates within the U.S. are significantly higher in the Midwest and Northeast, a pattern that resembles the geographic distribution of acid precipitation ("acid rain"). Using linear and multivariable regression, we examined state-wide data on PD prevalence in relation to environmental factors including total precipitation, the acidity of precipitation, the use of well water, and industrial releases of sulfuric acid. In multivariate analyses, age-, race-, and gender-adjusted prevalence rates for PD were inversely correlated with well water use and positively correlated with industrial releases of sulfuric acid and with the quantity of acid precipitation (p < 0.0001). To our knowledge, this is the first report of an association between PD and acid rain. Because acid rain is known to leach metals from soils and pipes into drinking water, acid rain's association with PD prevalence adds support for a role for metals in the etiology of PD.

Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl.

The in silico modeling of molten salts is critical for emerging "carbon-free" energy applications but is inhibited by the cost of quantum mechanically treating the high polarizabilities of molten salts. Here, we integrate configurational sampling using classical force fields with active learning to automate and accelerate the generation of Gaussian approximation potentials (GAP) for molten salts. This methodology reduces the number of expensive ab initio evaluations required for training set generation to O(100), enabling the facile parametrization of a molten LiCl GAP model that exhibits a 19 000-fold speedup relative to AIMD. The developed molten LiCl GAP model is applied to sample extended spatiotemporal scales, permitting new physical insights into molten LiCl's coordination structure as well as experimentally validated predictions of structures, densities, self-diffusion constants, and ionic conductivities. The developed methodology significantly lowers the barrier to the in silico understanding and design of molten salts across the periodic table.

Brain cancer incidence rates and the presence of nuclear reactors in US states: a hypothesis-generating study.

BACKGROUND: The etiology of brain cancer is poorly understood. The only confirmed environmental risk factor is exposure to ionizing radiation. Because nuclear reactors emit ionizing radiation, we examined brain cancer incidence rates in the USA in relation to the presence of nuclear reactors per state. METHODS: Data on brain cancer incidence rates per state for Whites by sex for three age groups (all ages, 50 and older, and under 50) were obtained from cancer registries. The location, number, and type of nuclear reactor, i.e., power or research reactor, was obtained from public sources. We examined the association between these variables using multivariate linear regression and ANOVA. RESULTS: Brain cancer incidence rates were not associated with the number of nuclear power reactors. Conversely, incidence rates per state increased with the number of nuclear research reactors. This was significant for both sexes combined and for males in the 'all ages' category (ß = 0.08, p = 0.0319 and ß = 0.12, p = 0.0277, respectively), and for both sexes combined in the'50 and older' category (ß = 0.18, p = 0.0163). Brain cancer incidence rates for counties with research reactors were significantly higher than the corresponding rates for their states overall (p = 0.0140). These findings were not explicable by known confounders. CONCLUSIONS: Brain cancer incidence rates are positively associated with the number of nuclear research reactors per state. These findings merit further exploration and suggest new opportunities for research in brain cancer epidemiology.

United States County-level COVID-19 Death Rates and Case Fatality Rates Vary by Region and Urban Status.

COVID-19 is a global pandemic with uncertain death rates. We examined county-level population morality rates (per 100,000) and case fatality rates by US region and rural-urban classification, while controlling for demographic, socioeconomic, and hospital variables. We found that population mortality rates and case fatality rates were significantly different across region, rural-urban classification, and their interaction. All significant comparisons had p < 0.001. Northeast counties had the highest population mortality rates (27.4) but had similar case fatality rates (5.9%) compared to other regions except the Southeast, which had significantly lower rates (4.1%). Population mortality rates were highest in urban counties but conversely, case fatality rates were highest in rural counties. Death rates in the Northeast were driven by urban areas (e.g., small, East Coast states), while case fatality rates tended to be highest in the most rural counties for all regions, especially the Southwest. However, on further inspection, high case fatality rate percentages in the Southwest, as well as in overall US counties, were driven by a low case number. This makes it hard to distinguish genuinely higher mortality or an artifact of a small sample size. In summary, coronavirus deaths are not homogenous across the United States but instead vary by region and population and highlight the importance of fine-scale analysis.

Recent developments in the PySCF program package.

PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. This paper explains the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and quantum information science.

A Surface Site Interaction Point Method for Dissipative Particle Dynamics Parametrization: Application to Alkyl Ethoxylate Surfactant Self-Assembly.

Dissipative particle dynamics (DPD) is a coarse-grained approach to the simulation of large supramolecular systems, but one limitation has been that the parameters required to describe the noncovalent interactions between beads are not readily accessible. A first-principles computational method has been developed so that bead interaction parameters can be calculated directly from ab initio gas-phase molecular electrostatic potential surfaces of the molecular fragments that represent the beads. A footprinting algorithm converts the molecular electrostatic potential surfaces into a discrete set of surface site interaction points (SSIPs), and these SSIPs are used in the SSIMPLE (surface site interaction model for the properties of liquids at equilibrium) algorithm to calculate the free energies of transfer of one bead into a solution of any other bead. The bead transfer free energies are then converted into the required DPD interaction parameters for all pairwise combinations of different beads. The reliability of the parameters was demonstrated using DPD simulations of a range of alkyl ethoxylate surfactants. The simulations reproduce the experimentally determined values of the critical micelle concentration and mean aggregation number well for all 22 surfactants studied.