Lockhartcaldwell3376

Deep learning (DL) offers an unprecedented opportunity to revolutionize the landscape of toxicity prediction based on quantitative structure-activity relationship (QSAR) studies in the big data era. However, the structural description in the reported DL-QSAR models is still restricted to the two-dimensional level. Inspired by point clouds, a type of geometric data structure, a novel three-dimensional (3D) molecular surface point cloud with electrostatic potential (SepPC) was proposed to describe chemical structures. Each surface point of a chemical is assigned its 3D coordinate and molecular electrostatic potential. A novel DL architecture SepPCNET was then introduced to directly consume unordered SepPC data for toxicity classification. The SepPCNET model was trained on 1317 chemicals tested in a battery of 18 estrogen receptor-related assays of the ToxCast program. The obtained model recognized the active and inactive chemicals at accuracies of 82.8 and 88.9%, respectively, with a total accuracy of 88.3% on the internal test set and 92.5% on the external test set, which outperformed other up-to-date machine learning models and succeeded in recognizing the difference in the activity of isomers. Additional insights into the toxicity mechanism were also gained by visualizing critical points and extracting data-driven point features of active chemicals.Benefiting from the merits of high stability and superior activity, nanozymes are recognized as promising alternatives to natural enzymes. Despite the great leaps in the field of therapy and colorimetric sensing, the development of highly sensitive nanozyme-involved photoelectrochemical (PEC) biosensors is still in its infancy. Specifically, the investigation of multifunctional nanozymes facilitating different catalytic reactions remains largely unexplored due to the difficulty in synergistically amplifying the PEC signals. In this work, mesoporous trimetallic AuPtPd nanospheres were synthesized with both efficient oxidase and peroxidase-like activities, which can synergistically catalyze the oxidation of 4-chloro-1-naphthol to produce benzo-4-chlorohexadienone precipitation on the surface of photoactive materials, and thus lead to the decreased photocurrent as well as increased charge-transfer resistance. Inspired by the proton-dependent catalytic activity of nanozymes, a self-regulated dual-modal PEC and electrochemical bioassay of urease activity was innovatively established by in situ regulating the activity of AuPtPd nanozymes through urease-mediated proton-consuming enzymatic reactions, which can remarkably improve the accuracy of the assay. Meanwhile, the determination of urease activity in spiked human saliva samples was successfully realized, indicating the reliability of the biosensor and its application prospects in clinical diagnosis.Electrification of delivery fleets has emerged as an important opportunity to reduce the transportation sector's environmental impact, including reducing greenhouse gas (GHG) emissions. When, where, and how vehicles are charged, however, impact the reduction potential. Not only does the carbon intensity of the grid vary across time and space, but charging decisions also influence battery degradation rates, resulting in more or less frequent battery replacement. Here, we propose a model that accounts for the spatial and temporal differences in charging emissions using marginal emission factors and degradation-induced differences in production emissions using a semi-empirical degradation model. We analyze four different charging strategies and demonstrate that a baseline charging scenario, in which a vehicle is fully charged immediately upon returning to a central depot, results in the highest emissions and employing alternative charging methods can reduce emissions by 8-37%. We show that when, where, and how batteries are charged also impact the total cost of ownership. Although the lowest cost and the lowest emitting charging strategies often align, the lowest cost deployment location for electric delivery vehicles may not be in the same location that maximizes environmental benefits.Developing high-efficiency dual-functional catalysts to promote oxygen electrode reactions is critical for achieving high-performance aprotic lithium-oxygen (Li-O2) batteries. Herein, Sr and Fe cation-codoped LaCoO3 perovskite (La0.8Sr0.2Co0.8Fe0.2O3-σ, LSCFO) porous nanoparticles are fabricated as promising electrocatalysts for Li-O2 cells. The results demonstrate that the LSCFO-based Li-O2 batteries exhibit an extremely low overpotential of 0.32 V, ultrahigh specific capacity of 26 833 mA h g-1, and superior long-term cycling stability (200 cycles at 300 mA g-1). These prominent performances can be partially attributed to the existence of abundant coordination unsaturated sites caused by oxygen vacancies in LSCFO. Most importantly, density functional theory (DFT) calculations reveal that codoping of Sr and Fe cations in LaCoO3 results in the increased covalency of Co 3d-O 2p bonds and the transition of Co3+ from an ordinary low-spin state to an intermediate-spin state, eventually resulting in the transformation from nonconductor LCO to metallic LSCFO. In addition, based on the theoretical calculations, it is found that the inherent adsorption capability of LSCFO toward the LiO2 intermediate is reduced due to the increased covalency of Co 3d-O 2p bonds, leading to the formation of large granule-like Li2O2, which can be effectively decomposed on the LSCFO surface during the charging process. Notably, this work demonstrates a unique insight into the design of advanced perovskite oxide catalysts via adjusting the covalency of transition-metal-oxygen bonds for high-performance metal-air batteries.People whose cells express mutated forms of the BRCA1 tumor suppressor are at a higher risk for developing cancer. BRCA1-deficient cells are defective in DNA double-strand break repair. The inhibition of poly(ADP-ribose) polymerase 1 in such cells is a synthetically lethal, cytotoxic effect that has been exploited to produce anticancer drugs such as Olaparib. However, alternative synthetic lethal approaches are necessary. We report that DNA polymerase β (Pol β) forms a synthetically lethal interaction with BRCA1. The SiRNA knockdown of Pol β or the treatment with a Pol β pro-inhibitor (pro-1) is cytotoxic in BRCA1-deficient ovarian cancer cells. selleck kinase inhibitor BRCA1-complemented cells are significantly less susceptible to either treatment. pro-1 is also toxic to BRCA1-deficient breast cancer cells, and its toxicity in BRCA1-deficient cells is comparable to that of Olaparib. These experiments establish Pol β as a synthetically lethal target within BRCA1-deficient cells and a potentially useful one for treating cancer.