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High-performance catalysts are extremely required for controlling NO emission via selective catalytic reduction (SCR), and to acquire a common structural feature of catalytic sites is one key prerequisite for developing such catalysts. We design a single-atom catalyst system and achieve a generic characteristic of highly active SCR catalytic sites. A single-atom Mo 1 /Fe 2 O 3 catalyst is developed by anchoring single acidic Mo ions on (001) surfaces of reducible α-Fe 2 O 3 , and the individual Mo ion and one neighboring Fe ion are thus constructed as one dinuclear site. As the number of the dinuclear sites increases, SCR rates increase linearly but the apparent activation energy remains almost unchanged, evidencing the identity of the dinuclear active sites. We further design W 1 /Fe 2 O 3 and Fe 1 /WO 3 and find that tuning acid or/and redox properties of dinuclear sites can alter SCR rates. Therefore, this work provides a design strategy for developing improved SCR catalysts via optimizing acid-redox properties of dinuclear sites. Identification of active sites is one key prerequisite for rational design of efficient catalysts. Here, the authors achieve a common feature of catalytic active sites for NO selective reduction with NH 3 , which assists precise identification of active sites and effective design of optimal catalysts.

Background Immune checkpoint inhibitor-related cardiotoxicity is one of the most lethal adverse effects, and thus, the identification of underlying mechanisms for developing strategies to overcome it has clinical importance. This study aimed to investigate whether microbiota-host interactions contribute to PD-1/PD-L1 inhibitor-related cardiotoxicity. Methods A mouse model of immune checkpoint inhibitor-related cardiotoxicity was constructed by PD-1/PD-L1 inhibitor BMS-1 (5 and 10 mg/kg), and cardiomyocyte apoptosis and cardiotoxicity were determined by hematoxylin and eosin, Masson’s trichome and TUNEL assays. 16S rRNA sequencing was used to define the gut microbiota composition. Gut microbiota metabolites short-chain fatty acids (SCFAs) were determined by HPLC. The serum levels of myocardial enzymes (creatine kinase, aspartate transaminase, creatine kinase-MB and lactate dehydrogenase) and the production of M1 factors (TNF-α and IL-1β) were measured by ELISA. The colonic macrophage phenotype was measured by mmunofluorescence and qPCR. The expression of Claudin-1, Occludin, ZO-1 and p-p65 was measured by western blot. The gene expression of peroxisome proliferator-activated receptor α (PPARα) and cytochrome P450 (CYP) 4X1 was determined using qPCR. Statistical analyses were performed using Student’s t-test for two-group comparisons, and one-way ANOVA followed by Student–Newman–Keul test for multiple-group comparisons. Results We observed intestinal barrier injury and gut microbiota dysbiosis characterized by Prevotellaceae and Rikenellaceae genus depletion and Escherichia-Shigella and Ruminococcaceae genus enrichment, accompanied by low butyrate production and M1-like polarization of colonic macrophages in BMS-1 (5 and 10 mg/kg)-induced cardiotoxicity. Fecal microbiota transplantation mirrored the effect of BMS-1 on cardiomyocyte apoptosis and cardiotoxicity, while macrophage depletion and neutralization of TNF-α and IL-1β greatly attenuated BMS-1-induced cardiotoxicity. Importantly, Prevotella loescheii recolonization and butyrate supplementation alleviated PD-1/PD-L1 inhibitor-related cardiotoxicity. Mechanistically, gut microbiota dysbiosis promoted M1-like polarization of colonic macrophages and the production of proinflammatory factors TNF-α and IL-1β through downregulation of PPARα-CYP4X1 axis. Conclusions Intestinal barrier dysfunction amplifies PD-1/PD-L1 inhibitor-related cardiotoxicity by upregulating proinflammatory factors TNF-α and IL-1β in colonic macrophages via downregulation of butyrate-PPARα-CYP4X1 axis. Thus, targeting gut microbiota to polarize colonic macrophages away from the M1-like phenotype could provide a potential therapeutic strategy for PD-1/PD-L1 inhibitor-related cardiotoxicity. Graphical abstract

Gastric cancer often shows malignant growth and insensitivity to chemotherapeutic drugs due to the regulation of complex molecular mechanisms, which results in poor prognosis for patients. However, the relevant molecular mechanisms remain unclear. In this study, we reported that family with sequence similarity 117, member B (FAM117B), promoted the growth of gastric cancer cells and reduced the sensitivity of cells to chemotherapeutic drugs. Mechanistically, FAM117B competed with nuclear factor E2-related factor 2 (NRF2) for Kelch-like ECH-associated protein 1 (KEAP1) binding, reduced the ubiquitination degradation of NRF2, and activated the KEAP1/NRF2 signaling pathway. Moreover, FAM117B-induced growth and chemoresistance of gastric cancer cells were NRF2 dependent. We found that FAM117B and NRF2 protein levels were highly expressed in tumor tissues of patients with gastric cancer and their co-overexpression represented an independent factor for poor prognosis. Collectively, our findings reveal that FAM117B is involved in promoting gastric cancer growth and drug resistance, and it could be exploited as a cancer therapeutic target.

Clinical decision support systems (CDSSs) are increasingly integrated into healthcare settings to improve patient outcomes, reduce medical errors and enhance clinical efficiency by providing clinicians with evidence-based recommendations at the point of care. However, the adoption and optimisation of these systems remain a challenge. This review aims to provide an overview of the current state of CDSS, discussing their development, implementation, benefits, limitations and future directions. We also explore the potential for enhancing their effectiveness and provide an outlook for future developments in this field. There are several challenges in CDSS implementation, including data privacy concerns, system integration and clinician acceptance. While CDSS have demonstrated significant potential, their adoption and optimisation remain a challenge.

Satellite imaging mission planning is used to optimize satellites to obtain target images efficiently. Many evolutionary algorithms (EAs) have been proposed for satellite mission planning. EAs typically require evolutionary parameters, such as the crossover and mutation rates. The performance of EAs is considerably affected by parameter setting. However, most parameter configuration methods of the current EAs are artificially set and lack the overall consideration of multiple parameters. Thus, parameter configuration becomes suboptimal and EAs cannot be effectively utilized. To obtain satisfactory optimization results, the EA comp ensates by extending the evolutionary generation or improving the evolutionary strategy, but it significantly increases the computational consumption. In this study, a multi-objective learning evolutionary algorithm (MOLEA) was proposed to solve the optimal configuration problem of multiple evolutionary parameters and used to solve effective imaging satellite task planning for region mapping. In the MOLEA, population state encoding provided comprehensive population information on the configuration of evolutionary parameters. The evolutionary parameters of each generation were configured autonomously through deep reinforcement learning (DRL), enabling each generation of parameters to gain the best evolutionary benefits for future evolution. Furthermore, the HV of the multi-objective evolutionary algorithm (MOEA) was used to guide reinforcement learning. The superiority of the proposed MOLEA was verified by comparing the optimization performance, stability, and running time of the MOLEA with existing multi-objective optimization algorithms by using four satellites to image two regions of Hubei and Congo (K). The experimental results showed that the optimization performance of the MOLEA was significantly improved, and better imaging satellite task planning solutions were obtained.

Background Arachidonic acid (AA) metabolic enzymes including cyclooxygenase-2 (COX-2), microsomal prostaglandin E synthase-1 (mPGES-1) and cytochrome P450 (CYP) 4A11 play important roles in glioma angiogenesis. Thus, there is an urgent need to identify the underlying mechanisms and develop strategies to overcome them. Methods A homology model of human CYP4A11 was constructed using SYBYL-X 2.0. Structure-based virtual screening against COX-2, mPGES-1 and CYP4A11was performed using the Surflex-Dock of the SYBYL suite. The candidates were further evaluated their antiangiogenic activities in a zebrafish embryo and rabbit corneal angiogenesis model. Laser doppler analysis was used to measure tumor perfusion. The expression of CD31 and α-SMA was measured by immunofluorescence. Western blot was used to measure the expression of HIF-1, Akt and p-Akt. The gene expression of FGF-2, G-CSF, PDGF, TGF-β, Tie-2, VEGF, lncRNA NEAT1 and miR-194-5p were determined using qPCR. The production of FGF-2, TGF-β and VEGF were analyzed using ELISA. Bioinformatic analysis and luciferase reporter assays confirmed the interaction between lncRNA NEAT1 and miR-194-5p. Results The nearly 36,043 compounds from the Traditional Chinese Medicine (TCM) database were screened against COX-2, mPGES-1 and CYP4A11 3D models, and the 17 top flavonoids were identified. In zebrafish screening, isoliquiritigenin (ISL) exhibited the most potent antiangiogenic activities with the EC 50 values of 5.9 μM. Conversely, the antiangiogenic effects of ISL in the zebrafish and rabbit corneal models were partly reversed by 20-hydroxyeicosatetraenoic acid (20-HETE) or prostaglandin E2 (PGE 2 ). ISL normalized glioma vasculature and improved the efficacy of temozolomide therapy in the rat C6 glioma model. Inhibition of COX-2, mPGES-1 and CYP4A by ISL decreased FGF-2, TGF-β and VEGF production in the C6 and U87 glioma cells with p-Akt downregulation, which was reversed by Akt overexpression. Furthermore, ISL downregulated lncRNA NEAT1 but upregulated miR-194-5p in the U87 glioma cell. Importantly, lncRNA NEAT1 overexpression reversed ISL-mediated increase in miR-194-5p expression, and thereby attenuated FGF-2, TGF-β and VEGF production. Conclusions Reprogramming COX-2, mPGES-1 and CYP4A mediated-AA metabolism in glioma by flavonoid ISL inhibits the angiogenic Akt- FGF-2/TGF-β/VEGF signaling through ceRNA effect of miR-194-5p and lncRNA NEAT1, and may serve as a novel therapeutic strategy for human glioma.

Drought stress severely threatens the yield of cereal crops. Therefore, understanding the molecular mechanism of drought stress response of plants is crucial for developing drought-tolerant cultivars. NAC transcription factors (TFs) play important roles in abiotic stress of plants, but the functions of NAC TFs in sorghum are largely unknown. Here, we characterized a sorghum NAC gene, SbNAC9, and found that SbNAC9 can be highly induced by polyethylene glycol (PEG)-simulated dehydration treatments. We therefore investigated the function of SbNAC9 in drought stress response. Sorghum seedlings overexpressing SbNAC9 showed enhanced drought-stress tolerance with higher chlorophyll content and photochemical efficiency of PSII, stronger root systems, and higher reactive oxygen species (ROS) scavenging capability than wild-type. In contrast, sorghum seedlings with silenced SbNAC9 by virus-induced gene silencing (VIGS) showed weakened drought stress tolerance. Furthermore, SbNAC9 can directly activate a putative peroxidase gene SbC5YQ75 and a putative ABA biosynthesis gene SbNCED3. Silencing SbC5YQ75 and SbNCED3 led to compromised drought tolerance and reduced ABA content of sorghum seedlings, respectively. Therefore, our findings revealed the important role of SbNAC9 in response to drought stress in sorghum and may shed light on genetic improvement of other crop species under drought-stress conditions.

With satellite quantity and quality development in recent years, remote sensing products in vast areas are becoming widely used in more and more fields. The acquisition of large regional images requires the scientific and efficient utilization of satellite resources through imaging satellite task planning technology. However, for imaging satellite task planning in a vast area, a large number of decision variables are introduced into the imaging satellite task planning model, making it difficult for existing optimization algorithms to obtain reliable solutions. This is because the search space of the solution increases the exponential growth with the increase in the number of decision variables, which causes the search performance of optimization algorithms to decrease significantly. This paper proposes a large-scale multi-objective optimization algorithm based on efficient competition learning and improved non-dominated sorting (ECL-INS-LMOA) to efficiently obtain satellite imaging schemes for large areas. ECL-INS-LMOA adopted the idea of two-stage evolution to meet the different needs in different evolutionary stages. In the early stage, the proposed efficient competitive learning particle update strategy (ECLUS) and the improved NSGA-II were run alternately. In the later stage, only the improved NSGA-II was run. The proposed ECLUS guarantees the rapid convergence of ECL-INS-LMOA in the early evolution by accelerating particle update, introducing flight time, and proposing a binary competitive swarm optimizer BCSO. The results of the simulation imaging experiments on five large areas with different scales of decision variables show that ECL-INS-LMOA can always obtain the imaging satellite mission planning scheme with the highest regional coverage and the lowest satellite resource consumption within the limited evaluation times. The experiments verify the excellent performance of ECL-INS-LMOA in solving vast area mapping planning problems.

Catalytic activity of metal particles is reported to originate from the appearance of nonmetallic states, but conductive metallic particles, as an electron reservoir, should render electron delivery between reactants more favorably so as to have higher activity. We present that metallic rhodium particle catalysts are highly active in the low-temperature oxidation of carbon monoxide, whereas nonmetallic rhodium clusters or monoatoms on alumina remain catalytically inert. Experimental and theoretical results evidence the presence of electronic communications in between vertex atom active sites of individual metallic particles in the reaction. The electronic communications dramatically lower apparent activation energies via coupling two electrochemical-like half-reactions occurring on different active sites, which enable the metallic particles to show turnover frequencies at least four orders of magnitude higher than the nonmetallic clusters or monoatoms. Similar results are found for other metallic particle catalysts, implying the importance of electronic communications between active sites in heterogeneous catalysis. The role of electronic states in catalytic performance remains a topic of debate. In this study, the authors show that electronic interactions between active sites can facilitate the coupling of two half-reactions, thereby enhancing reaction rates.

Background While circulating metabolites have been increasingly linked to cancer risk, the causality underlying these associations remains largely uninterrogated. Methods We conducted a comprehensive 2-sample Mendelian randomization (MR) study to evaluate the potential causal relationship between 913 plasma metabolites and the risk of seven cancers among European-ancestry individuals. Data on variant-metabolite associations were obtained from a genome-wide association study (GWAS) of plasma metabolites among 14,296 subjects. Data on variant-cancer associations were gathered from large-scale GWAS consortia for breast ( N  = 266,081), colorectal ( N  = 185,616), lung ( N  = 85,716), ovarian ( N  = 63,347), prostate ( N  = 140,306), renal cell ( N  = 31,190), and testicular germ cell ( N  = 28,135) cancers. MR analyses were performed with the inverse variance-weighted (IVW) method as the primary strategy to identify significant associations at Bonferroni-corrected P  < 0.05 for each cancer type separately. Significant associations were subjected to additional scrutiny via weighted median MR, Egger regression, MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO), and reverse MR analyses. Replication analyses were performed using an independent dataset from a plasma metabolite GWAS including 8,129 participants of European ancestry. Results We identified 94 significant associations, suggesting putative causal associations between 66 distinct plasma metabolites and the risk of seven cancers. Remarkably, 68.2% (45) of these metabolites were each associated with the risk of a specific cancer. Among the 66 metabolites, O-methylcatechol sulfate and 4-vinylphenol sulfate demonstrated the most pronounced positive and negative associations with cancer risk, respectively. Genetically proxied plasma levels of these two metabolites were significantly associated with the risk of lung cancer and renal cell cancer, with an odds ratio and 95% confidence interval of 2.81 (2.33–3.37) and 0.49 (0.40–0.61), respectively. None of these 94 associations was biased by weak instruments, horizontal pleiotropy, or reverse causation. Further, 64 of these 94 were eligible for replication analyses, and 54 (84.4%) showed P  < 0.05 with association patterns consistent with those shown in primary analyses. Conclusions Our study unveils plausible causal relationships between 66 plasma metabolites and cancer risk, expanding our understanding of the role of circulating metabolites in cancer genetics and etiology. These findings hold promise for enhancing cancer risk assessment and prevention strategies, meriting further exploration.