Olesenbraswell5505

Physical processes that occur within porous materials have wide-ranging applications including - but not limited to - carbon sequestration, battery technology, membranes, oil and gas, geothermal energy, nuclear waste disposal, water resource management. The equations that describe these physical processes have been studied extensively; however, approximating them numerically requires immense computational resources due to the complex behavior that arises from the geometrically-intricate solid boundary conditions in porous materials. Here, we introduce a new dataset of unprecedented scale and breadth, DRP-372 a catalog of 3D geometries, simulation results, and structural properties of samples hosted on the Digital Rocks Portal. The dataset includes 1736 flow and electrical simulation results on 217 samples, which required more than 500 core years of computation. This data can be used for many purposes, such as constructing empirical models, validating new simulation codes, and developing machine learning algorithms that closely match the extensive purely-physical simulation. This article offers a detailed description of the contents of the dataset including the data collection, simulation schemes, and data validation.Deoxygenation of aldehydes and their tautomers to alkenes and alkanes has implications in refining biomass-derived fuels for use as transportation fuel. Electrochemical deoxygenation in ambient, aqueous solution is also a potential green synthesis strategy for terminal olefins. In this manuscript, direct electrochemical conversion of vinyl alcohol and acetaldehyde on polycrystalline Cu to ethanol, ethylene and ethane; and propenol and propionaldehyde to propanol, propene and propane is reported. Sensitive detection was achieved using a rotating disk electrode coupled with gas chromatography-mass spectrometry. In-situ attenuated total reflection surface-enhanced infrared absorption spectroscopy, and in-situ Raman spectroscopy confirmed the adsorption of the vinyl alcohol. Calculations using canonical and grand-canonical density functional theory and experimental findings suggest that the rate-determining step for ethylene and ethane formation is an electron transfer step to the adsorbed vinyl alcohol. Finally, we extend our conclusions to the enol reaction from higher-order soluble aldehyde and ketone. The products observed from the reduction reaction also sheds insights into plausible reaction pathways of CO2 to C2 and C3 products.Primary sclerosing cholangitis (PSC) is an idiopathic cholestatic liver disease characterized by chronic inflammation and progressive fibrosis of intra- and extrahepatic bile ducts. Osteoporosis is a frequent comorbidity in PSC, and we could previously demonstrate that IL17-dependent activation of bone resorption is the predominant driver of bone loss in PSC. Since we additionally observed an unexpected heterogeneity of bone mineral density in our cohort of 238 PSC patients, the present study focused on a comparative analysis of affected individuals with diagnosed osteoporosis (PSCOPO, n = 10) or high bone mass (PSCHBM, n = 7). The two groups were not distinguishable by various baseline characteristics, including liver fibrosis or serum parameters for hepatic function. In contrast, quantification of serum bile acid concentrations identified significant increases in the PSCOPO group, including glycoursodeoxycholic acid (GUDCA), an exogenous bile acid administered to both patient groups. Although cell culture experiments did not support the hypothesis that an increase in circulating bile levels is a primary cause of PSC-associated osteoporosis, the remarkable differences of endogenous bile acids and GUDCA in the serum of PSCOPO patients strongly suggest a yet unknown impairment of biliary metabolism and/or hepatic bile acid clearance in this patient subgroup, which is independent of liver fibrosis.What is an optimal parameter landscape and geometric layout for a quantum processor so that its qubits are sufficiently protected for idling and simultaneously responsive enough for fast entangling gates? Quantum engineers pondering the dilemma might want to take a look on tools developed for many-body localization.Achieving food security in sub-Saharan Africa (SSA) is a multidimensional challenge. SSA reliance on food imports is expected to grow in the coming decades to meet the population's demand, projected to double to over 2 billion people by 2050. In addition, climate change is already affecting food production and supply chains across the region. Addressing these multiple food security challenges will necessitate rapid enhancements in agricultural productivity, which is influenced by a host of demographic, agronomic, and climatic factors. We use statistical approaches to examine rainfed maize in Kenya, where maize cultivation and consumption are widespread and central to livelihoods and national food security. We find that improving a suite of agronomic factors, such as applying fertilizer, planting certified seeds, and extension services, will have a greater effect on rainfed maize productivity than demographics and can offset the effects of climate change. These findings could also offer insights into similar challenges for other crops in Kenya and other SSA countries.Solar hydrogen production is one of the ultimate technologies needed to realize a carbon-neutral, sustainable society. However, an energy-intensive water oxidation half-reaction together with the poor performance of conventional inorganic photocatalysts have been big hurdles for practical solar hydrogen production. Here we present a photoelectrochemical cell with a record high photocurrent density of 19.8 mA cm-2 for hydrogen production by utilizing a high-performance organic-inorganic halide perovskite as a panchromatic absorber and lignocellulosic biomass as an alternative source of electrons working at lower potentials. In addition, value-added chemicals such as vanillin and acetovanillone are produced via the selective depolymerization of lignin in lignocellulosic biomass while cellulose remains close to intact for further utilization. This study paves the way to improve solar hydrogen productivity and simultaneously realize the effective use of lignocellulosic biomass.Lethal giant larvae homolog 2 (LLGL2) and solute carrier family 7 member 5 (SLC7A5) have been reported to be involved in resistance to endocrine therapy. This study aimed to assess the effects of LLGL2/SLC7A5 co-expression in predicting prognosis and response to tamoxifen therapy in ERα-positive breast cancer patients according to LLGL2/SLC7A5 mRNA and protein expression in long-term follow-up invasive breast cancer tissues. We identified that low LLGL2/SLC7A5 mRNA co-expression (LLGL2low/SLC7A5low) was associated with disease-free survival (DFS) compared with other combination groups in all breast cancer patients. In ERα-positive breast cancer patients, LLGL2low/SLC7A5low showed longer DFS and overall survival (OS) compared with LLGL2high/SLC7A5high and a positive trend of longer survival compared with the other combination groups. We also observed that LLGL2low/SLC7A5low showed longer survival compared with LLGL2high/SLC7A5high in ERα-positive breast cancer patients receiving adjuvant tamoxifen therapy. Multivariate analysis demonstrated that LLGL2low/SLC7A5low was an independent favorable prognostic factor of both DFS and OS, not only in all breast cancer patients, but also in ERα-positive breast cancer patients. High co-expression of LLGL2 and SLC7A5 protein showed a positive trend of shorter survival. Our study showed that co-expression of LLGL2 and SLC7A5 mRNA is a promising candidate biomarker in early breast cancer patients.A wearable silent speech interface (SSI) is a promising platform that enables verbal communication without vocalization. The most widely studied methodology for SSI focuses on surface electromyography (sEMG). selleck However, sEMG suffers from low scalability because of signal quality-related issues, including signal-to-noise ratio and interelectrode interference. Hence, here, we present a novel SSI by utilizing crystalline-silicon-based strain sensors combined with a 3D convolutional deep learning algorithm. Two perpendicularly placed strain gauges with minimized cell dimension ( less then 0.1 mm2) could effectively capture the biaxial strain information with high reliability. We attached four strain sensors near the subject's mouths and collected strain data of unprecedently large wordsets (100 words), which our SSI can classify at a high accuracy rate (87.53%). Several analysis methods were demonstrated to verify the system's reliability, as well as the performance comparison with another SSI using sEMG electrodes with the same dimension, which exhibited a relatively low accuracy rate (42.60%).Spatial light modulators (SLMs) play essential roles in various free-space optical technologies, offering spatio-temporal control of amplitude, phase, or polarization of light. Beyond conventional SLMs based on liquid crystals or microelectromechanical systems, active metasurfaces are considered as promising SLM platforms because they could simultaneously provide high-speed and small pixel size. However, the active metasurfaces reported so far have achieved either limited phase modulation or low efficiency. Here, we propose nano-electromechanically tunable asymmetric dielectric metasurfaces as a platform for reflective SLMs. Exploiting the strong asymmetric radiation of perturbed high-order Mie resonances, the metasurfaces experimentally achieve a phase-shift close to 290∘, over 50% reflectivity, and a wavelength-scale pixel size. Electrical control of diffraction patterns is also achieved by displacing the Mie resonators using nano-electro-mechanical forces. This work paves the ways for future exploration of the asymmetric metasurfaces and for their application to the next-generation SLMs.Histologic grading of breast cancer involves review and scoring of three well-established morphologic features mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We first evaluate model performance using pathologist-based reference standards for each component. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. The individual component models perform at or above published benchmarks for algorithm-based grading approaches, achieving high concordance rates with pathologist grading. Further, prognostic performance using deep learning-based grading is on par with that of pathologists performing review of matched slides. By providing scores for each component feature, the deep-learning based approach also provides the potential to identify the grading components contributing most to prognostic value. This may enable optimized prognostic models, opportunities to improve access to consistent grading, and approaches to better understand the links between histologic features and clinical outcomes in breast cancer.