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Treatment with radiation therapy (RT) can cause anxiety and distress for pediatric patients and their families. Radiation oncology teams have developed strategies to reduce the negative psychological impact. This survey study aimed to characterize these methods.
Pediatric patients with rhabdomyosarcoma (RMS) are treated with multimodal therapy, often with radiation therapy (RT) as part of local therapy. We report on the efficacy and patterns of failure after proton beam therapy (PBT) for RMS.
Not all patients with stage III non-small cell lung cancer (NSCLC) are suitable for concurrent chemoradiation therapy (CRT). Local failure rate is high for sequential concurrent CRT. As such, there is a rationale for treatment intensification.
Long-term androgen-deprivation therapy (ADT) is the standard of care in combination with radiation therapy (RT) in high-risk prostate cancer (PC), despite substantial toxicity from the resulting hypogonadism. We hypothesized that a combination of more potent but shorter-term androgen inhibition in men with intermediate- or high-risk localized PC would synergize with definitive RT to provide short-term testosterone recovery and improve disease control.
A PENTEC review of childhood cancer survivors who received brain radiation therapy (RT) was performed to develop models that aid in developing dose constraints for RT-associated central nervous system (CNS) morbidities.
Advances in germline genetic testing have led to a surge in identification of ataxia-telangiectasia mutated (ATM) variant carriers among breast cancer patients, raising numerous questions regarding use of breast radiation therapy (RT) in this population.
Cardiotoxicities induced by cancer therapy can negatively affect quality of life and survival. We investigated whether high-sensitivity cardiac troponin T (hs-cTnT) levels could serve as biomarker for early detection of cardiac adverse events (CAEs) after chemoradiation therapy (CRT) for non-small cell lung cancer (NSCLC).
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The main protease enzyme (Mpro) of coronaviruses cleaves the viral polyprotein into functional units essential for virus replication. Prior work has demonstrated that Mpro functions as a homodimer. However, studies on the mechanism of dimerization have been challenging because the purified protease is mostly dimeric, dimerization-defective mutants lack proteolytic activity, and robust cell-based assays have yet to be reported. To enable work on Mpro dimerization, we have developed a quantitative luciferase-based SARS-CoV-2 (SARS2) Mpro biosensor that accurately reports protein dimerization in living cells and, upon purification, also in vitro. Co-transfection of cells with a construct expressing Mpro fused to the 18 kDa LargeBiT of luciferase (LgBiT) and a second construct with Mpro fused to the 1 kDa SmallBiT of luciferase (SmBiT) results in a reconstitution of luciferase activity in a dose-dependent manner that requires conserved residues within the dimerization interface. Proteolytic activity is dispensable for dimerization and, uniquely, a C145A catalytically inactive mutant exhibits enhanced dimerization signal likely due to lower cytotoxicity. Mpro enzymes from multiple different coronaviruses also dimerize in this system, indicating mechanistic conservation. Interestingly, this dimerization biosensor also provides a quantitative read-out of inhibitor-facilitated dimerization. Covalent SARS2 Mpro inhibitors such as nirmatrelvir cause a 3- to 5-fold increase in luciferase activity. Together with corroborating structural, biophysical, and molecular dynamics experiments, our studies support a model in which covalent Mpro inhibitors such as nirmatrelvir simultaneously block catalytic activity and induce allosteric stabilization of the dimeric complex.
Active site occlusion by carbon species, often referred to as coke, is a common deactivation mechanism for heterogeneous catalysts. While it is known that transition metals can lower the temperature required for oxidative coke removal, the roles of ionic species and metal nanoparticles in coke removal are not well understood. This work aims to differentiate how ionic and nanoparticle Cu sites catalyze oxidative regeneration of a coked beta (BEA) zeolite catalyst. This was accomplished by synthesizing catalysts containing exclusively CuOx nanoparticles (NPs) or Cu2+ ions supported on BEA, physically mixing Cu/BEA with a coked BEA catalyst, and monitoring coke removal using in situ spectroscopy. Our results point to an improved combustion activity for CuOx NPs relative to Cu2+ ions, especially in the combustion of graphitic-type coke species. This improved understanding of coke combustion in transition metal containing catalysts informs strategies to improve catalyst regeneration, thereby increasing operational lifetimes.
We present a methodology for calibration of parametric ordinary and partial differential equation models, using off-the-shelf software for back-propagation in Neural Networks (NN). As a prototypical example, we consider calibration of a Reynolds-averaged Navier-Stokes (RANS) turbulence closure model, against ground truth data from direct numerical simulations (DNS) of two different turbulent flows. Numerical time integration is represented as a custom NN, where only the RANS model parameters are trainable. A loss function is defined to quantify the mismatch between the NN prediction and the ground truth over a predefined, finite time integration window. This loss function is then minimized using a gradient descent method utilizing the back-propagation algorithm. Furthermore, this dynamic approach to training is to be contrasted with a static approach, wherein a least square regression estimate for parameters is obtained in the limit of an infinitesimal time integration window. In a first test of static and dynamic approaches against ground truth data generated by the model, the former proves to be significantly faster and more accurate than the latter at recovering the parameters. When both calibration approaches are tested against DNS data, for which it is known that the model cannot achieve a perfect fit, the static approach yields a good prediction only for short times, while the dynamic approach results in physical and stable predictions over the entire integration window. After optimization of the dynamic approach for time step, spatial resolution, stability, and physics-based constraints, we obtain a 50% improvement of outcomes over those obtained from the existing, manually calibrated set of parameters, demonstrating the merits of this systematic and automated procedure.
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Continued urbanization has led to tremendous changes on the landscape. These changes have exacerbated the effects of extreme climatic events such as flooding because of constrained water infiltration and increased surface flow. Typical runoff control measures involve sophisticated gray infrastructure that guide excess surface flow into storage and disposal sites. In a dynamic climate system, these measures are not sustainable since they cannot be easily modified to accommodate large volumes of runoff. Green Infrastructure (GI) is an adaptable technique that can be used to minimize runoff, in addition to offering an array of additional benefits (urban heat regulation, aesthetics, improved air quality etc.). Strategic placement of GI is key to achieving maximum utility. While physical site characteristics play a major role in determining suitable GI placement sites, knowledge of future precipitation patterns is crucial to ensure successful flood mitigation. Here, suitable GI sites within the city of Knoxville, Tennessee, were determined based on potential impact of an extreme flood event as indicated by site characteristics. Then, the relative potential likelihood of a flood event was determined based on projected precipitation data and knowledge of existing flood zones. By combining potential impact with likelihood information, low, medium, and high priority GI implementation sites were established. Results indicate that high priority sites are in the central parts of the city with priority decreasing outward. The GI prioritization scheme presented here, offers valuable guidance to city planners and policy makers who wish to exploit the GI approach for flood mitigation.
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Here, the dissolution rate of spent nuclear fuel has been studied heavily to understand the impacts of a failed waste package scenario on potential radionuclide release from a geologic repository. Multiple countries have evaluated these scenarios in oxidizing and reducing environments respective to their relevant repository conditions. The repository environment (e.g., water chemistry, temperature, oxygen content) and the fuel itself (e.g., chemical content of the fuel) both heavily impact the dissolution rate of the spent fuel. This work examined the impact that rare earth element dopants (Ce, Nd, Yb) have on the fuel dissolution under repository relevant conditions with decreasing oxidizing conditions using a single pass flowthrough system. UO 2 samples were doped with Ce, Nd, or Yb with concentrations between 1 and 5 at%. The addition of dopants to the samples reduced the dissolution rates on most samples relative to pure UO 2 samples. Scoping experiments that occurred in a less oxidizing environment showed a reduction in dissolution rate compared to fully oxidizing conditions. The results within highlight the importance of dopant behavior in used fuel dissolution modeling.