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.

Emergent constraint on equilibrium climate sensitivity from global temperature variability.

Equilibrium climate sensitivity (ECS) remains one of the most important unknowns in climate change science. ECS is defined as the global mean warming that would occur if the atmospheric carbon dioxide (CO2) concentration were instantly doubled and the climate were then brought to equilibrium with that new level of CO2. Despite its rather idealized definition, ECS has continuing relevance for international climate change agreements, which are often framed in terms of stabilization of global warming relative to the pre-industrial climate. However, the 'likely' range of ECS as stated by the Intergovernmental Panel on Climate Change (IPCC) has remained at 1.5-4.5 degrees Celsius for more than 25 years. The possibility of a value of ECS towards the upper end of this range reduces the feasibility of avoiding 2 degrees Celsius of global warming, as required by the Paris Agreement. Here we present a new emergent constraint on ECS that yields a central estimate of 2.8 degrees Celsius with 66 per cent confidence limits (equivalent to the IPCC 'likely' range) of 2.2-3.4 degrees Celsius. Our approach is to focus on the variability of temperature about long-term historical warming, rather than on the warming trend itself. We use an ensemble of climate models to define an emergent relationship between ECS and a theoretically informed metric of global temperature variability. This metric of variability can also be calculated from observational records of global warming, which enables tighter constraints to be placed on ECS, reducing the probability of ECS being less than 1.5 degrees Celsius to less than 3 per cent, and the probability of ECS exceeding 4.5 degrees Celsius to less than 1 per cent.

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.

Modes of action of the archaeal Mre11/Rad50 DNA-repair complex revealed by fast-scan atomic force microscopy.

Mre11 and Rad50 (M/R) proteins are part of an evolutionarily conserved macromolecular apparatus that maintains genomic integrity through repair pathways. Prior structural studies have revealed that this apparatus is extremely dynamic, displaying flexibility in the long coiled-coil regions of Rad50, a member of the structural maintenance of chromosome (SMC) superfamily of ATPases. However, many details of the mechanics of M/R chromosomal manipulation during DNA-repair events remain unclear. Here, we investigate the properties of the thermostable M/R complex from the archaeon Sulfolobus acidocaldarius using atomic force microscopy (AFM) to understand how this macromolecular machinery orchestrates DNA repair. While previous studies have observed canonical interactions between the globular domains of M/R and DNA, we observe transient interactions between DNA substrates and the Rad50 coiled coils. Fast-scan AFM videos (at 1-2 frames per second) of M/R complexes reveal that these interactions result in manipulation and translocation of the DNA substrates. Our study also shows dramatic and unprecedented ATP-dependent DNA unwinding events by the M/R complex, which extend hundreds of base pairs in length. Supported by molecular dynamic simulations, we propose a model for M/R recognition at DNA breaks in which the Rad50 coiled coils aid movement along DNA substrates until a DNA end is encountered, after which the DNA unwinding activity potentiates the downstream homologous recombination (HR)-mediated DNA repair.

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.

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.

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.

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.

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.

Extinction and climate change.

Arising from F. He & S. P. Hubbell 473, 368-371 (2011). Statistical relationships between habitat area and the number of species observed (species-area relationships, SARs) are sometimes used to assess extinction risks following habitat destruction or loss of climatic suitability. He and Hubbell argue that the numbers of species confined to-rather than observed in-different areas (endemics-area relationships, EARs) should be used instead of SARs, and that SAR-based extinction estimates in the literature are too high. We suggest that He and Hubbell's SAR estimates are biased, that the empirical data they use are not appropriate to calculate extinction risks, and that their statements about extinction risks from climate change do not take into account non-SAR-based estimates or recent observations. Species have already responded to climate change in a manner consistent with high future extinction risks.

Methotrexate-conjugated PEGylated dendrimers show differential patterns of deposition and activity in tumor-burdened lymph nodes after intravenous and subcutaneous administration in rats.

The current study sought to explore whether the subcutaneous administration of lymph targeted dendrimers, conjugated with a model chemotherapeutic (methotrexate, MTX), was able to enhance anticancer activity against lymph node metastases. The lymphatic pharmacokinetics and antitumor activity of PEGylated polylysine dendrimers conjugated to MTX [D-MTX(OH)] via a tumor-labile hexapeptide linker was examined in rats and compared to a similar system where MTX was α-carboxyl O-tert-butylated [D-MTX(OtBu)]. The latter has previously been shown to exhibit longer plasma circulation times. D-MTX(OtBu) was well absorbed from the subcutaneous injection site via the lymph, and 3 to 4%/g of the dose was retained by sentinel lymph nodes. In contrast, D-MTX(OH) showed limited absorption from the subcutaneous injection site, but absorption was almost exclusively via the lymph. The retention of D-MTX(OH) by sentinel lymph nodes was also significantly elevated (approximately 30% dose/g). MTX alone was not absorbed into the lymph. All dendrimers displayed lower lymph node targeting after intravenous administration. Despite significant differences in the lymph node retention of D-MTX(OH) and D-MTX(OtBu) after subcutaneous and intravenous administration, the growth of lymph node metastases was similarly inhibited. In contrast, the administration of MTX alone did not significantly reduce lymph node tumor growth. Subcutaneous administration of drug-conjugated dendrimers therefore provides an opportunity to improve drug deposition in downstream tumor-burdened lymph nodes. In this case, however, increased lymph node biodistribution did not correlate well with antitumor activity, possibly suggesting constrained drug release at the site of action.

Molecular weight (hydrodynamic volume) dictates the systemic pharmacokinetics and tumour disposition of PolyPEG star polymers.

Herein we report for the first time the biological fate of poly[(oligoethylene glycol) acrylate] (POEGA) star polymers synthesised via a versatile arm-first reversible addition-fragmentation chain transfer (RAFT) polymerisation approach. The biopharmaceutical behaviour of three different molecular weight (49, 64 and 94kDa) POEGA stars was evaluated in rats and nude mice bearing human MDA MB-231 tumours after intravenous administration. The 94kDa star polymer exhibited a longer plasma exposure time than the 49kDa or 64kDa star polymer; an observation attributable to differences in the rates of both polymer biodegradation and urinary excretion. Tumour biodistribution also correlated with molecular weight and was greatest for the longest circulating 94kDa star. Different patterns of liver and spleen biodistribution were observed between mice and rats for the different sized polymers. The polymers were also well-tolerated in vivo and in vitro at therapeutic concentrations. FROM THE CLINICAL EDITOR: Advances in nanotechnology has enabled scientists to produce nanoparticle as drug carriers in cancer therapeutics. In this article, the authors studied the biological fate of poly[(oligoethylene glycol) acrylate] (POEGA) star polymers of different size, after intravenous injections. This would allow the subsequent comparison to other drug delivery systems for better drug delivery.

Detection of bifurcations in noisy coupled systems from multiple time series.

We generalize a method of detecting an approaching bifurcation in a time series of a noisy system from the special case of one dynamical variable to multiple dynamical variables. For a system described by a stochastic differential equation consisting of an autonomous deterministic part with one dynamical variable and an additive white noise term, small perturbations away from the system's fixed point will decay slower the closer the system is to a bifurcation. This phenomenon is known as critical slowing down and all such systems exhibit this decay-type behaviour. However, when the deterministic part has multiple coupled dynamical variables, the possible dynamics can be much richer, exhibiting oscillatory and chaotic behaviour. In our generalization to the multi-variable case, we find additional indicators to decay rate, such as frequency of oscillation. In the case of approaching a homoclinic bifurcation, there is no change in decay rate but there is a decrease in frequency of oscillations. The expanded method therefore adds extra tools to help detect and classify approaching bifurcations given multiple time series, where the underlying dynamics are not fully known. Our generalisation also allows bifurcation detection to be applied spatially if one treats each spatial location as a new dynamical variable. One may then determine the unstable spatial mode(s). This is also something that has not been possible with the single variable method. The method is applicable to any set of time series regardless of its origin, but may be particularly useful when anticipating abrupt changes in the multi-dimensional climate system.

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.

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

LEVEL OF EVIDENCE: IV.

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.

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.

Prediction of cytochrome P450 xenobiotic metabolism: tethered docking and reactivity derived from ligand molecular orbital analysis.

Metabolism of xenobiotic and endogenous compounds is frequently complex, not completely elucidated, and therefore often ambiguous. The prediction of sites of metabolism (SoM) can be particularly helpful as a first step toward the identification of metabolites, a process especially relevant to drug discovery. This paper describes a reactivity approach for predicting SoM whereby reactivity is derived directly from the ground state ligand molecular orbital analysis, calculated using Density Functional Theory, using a novel implementation of the average local ionization energy. Thus each potential SoM is sampled in the context of the whole ligand, in contrast to other popular approaches where activation energies are calculated for a predefined database of molecular fragments and assigned to matching moieties in a query ligand. In addition, one of the first descriptions of molecular dynamics of cytochrome P450 (CYP) isoforms 3A4, 2D6, and 2C9 in their Compound I state is reported, and, from the representative protein structures obtained, an analysis and evaluation of various docking approaches using GOLD is performed. In particular, a covalent docking approach is described coupled with the modeling of important electrostatic interactions between CYP and ligand using spherical constraints. Combining the docking and reactivity results, obtained using standard functionality from common docking and quantum chemical applications, enables a SoM to be identified in the top 2 predictions for 75%, 80%, and 78% of the data sets for 3A4, 2D6, and 2C9, respectively, results that are accessible and competitive with other recently published prediction tools.

FAst MEtabolizer (FAME): A rapid and accurate predictor of sites of metabolism in multiple species by endogenous enzymes.

FAst MEtabolizer (FAME) is a fast and accurate predictor of sites of metabolism (SoMs). It is based on a collection of random forest models trained on diverse chemical data sets of more than 20 000 molecules annotated with their experimentally determined SoMs. Using a comprehensive set of available data, FAME aims to assess metabolic processes from a holistic point of view. It is not limited to a specific enzyme family or species. Besides a global model, dedicated models are available for human, rat, and dog metabolism; specific prediction of phase I and II metabolism is also supported. FAME is able to identify at least one known SoM among the top-1, top-2, and top-3 highest ranked atom positions in up to 71%, 81%, and 87% of all cases tested, respectively. These prediction rates are comparable to or better than SoM predictors focused on specific enzyme families (such as cytochrome P450s), despite the fact that FAME uses only seven chemical descriptors. FAME covers a very broad chemical space, which together with its inter- and extrapolation power makes it applicable to a wide range of chemicals. Predictions take less than 2.5 s per molecule in batch mode on an Ultrabook. Results are visualized using Jmol, with the most likely SoMs highlighted.