Explore the vast range of titles available.

Atomically dispersed metal catalysts maximize atom efficiency and display unique catalytic properties compared with regular metal nanoparticles. However, achieving high reactivity while preserving high stability at appreciable loadings remains challenging. Here we solve the challenge by synergizing metal–support interactions and spatial confinement, which enables the fabrication of highly loaded atomic nickel (3.1 wt%) along with dense atomic copper grippers (8.1 wt%) on a graphitic carbon nitride support. For the semi-hydrogenation of acetylene in excess ethylene, the fabricated catalyst shows extraordinary catalytic performance in terms of activity, selectivity and stability—far superior to supported atomic nickel alone in the absence of a synergizing effect. Comprehensive characterization and theoretical calculations reveal that the active nickel site confined in two stable hydroxylated copper grippers dynamically changes by breaking the interfacial nickel–support bonds on reactant adsorption and making these bonds on product desorption. Such a dynamic effect confers high catalytic performance, providing an avenue to rationally design efficient, stable and highly loaded, yet atomically dispersed, catalysts. Synergizing metal–support interactions and spatial confinement through atomic copper grippers boost the dynamics of highly loaded atomic nickel for high activity, high thermal/chemical stability and unprecedented coke inhibition in hydrogenation reactions.

Pterostilbene, as attracting much attention for its diversified pharmacological activities including anti-inflammation and anti-oxidation, has great potential in the pharmaceutical application. In this study, PEG-conjugated pterostilbene was designed to improve the poor solubility and stability of pterostilbene aiming to achieve the controlled release. Twelve different types of amino acid and succinyl were used as the linking arm between PEG and pterostilbene in the prepared conjugates. The prepared PEG-amino acidyl-pterostilbene conjugates were characterized by 1HNMR, IR and DSC. Solubility, free pterostilbene and loading capability of the prepared conjugates were determined by UV and HPLC method. In vitro release of the conjugates was also evaluated by using dialysis method. The results indicated that Twelve different types of PEG-amino acidyl-pterostilbene conjugates were successfully synthesized. Water solubility of the conjugates were all greater than 600 mg·mL-1. The prepared PEG conjugates showed good ability of controlled release. Of all the prepared conjugates, PEGsuccinyl-lysine-pterostilbene showed the best performance in in vitro release with lipase and PEG-succinyl-lysine-pterostilbene showed the best performance in in vitro release without lipase. The highest accumulative release rate of PEG-succinyl-lysine-pterostilbene was of 47% with lipase while 40% of PEG-succinyl-methionine-pterostilbene without lipase at pH 7.4 buffer within 72 h.

● Fungi are more sensitive to precipitation than bacteria. ● The susceptibility of abundant and rare bacteria to precipitation has no difference. ● Abundant taxa of fungi are more vulnerable to precipitation than rare ones. Precipitation scenario alteration leads to grievous ecological consequences in ecosystems, especially on the Qinghai-Tibet Plateau. Bacterial and fungal community and their abundant and rare taxa in soil ecosystems may respond differently to the changed precipitation. Therefore, more attention needs to be paid to the sensitivity of bacteria and fungi and their abundant and rare taxa to precipitation shifts. The responses of bacterial and fungal populations and their abundant and rare taxa concerning diversity, assembly, and interactions to manipulative changes of precipitation were explored via imitating no precipitation, little precipitation, and medium precipitation using 16S rRNA gene and ITS amplicon sequencing. The results indicated that the change rate of fungal Simpson diversity with precipitation was higher than that of bacteria. The slope of the modified stochasticity ratio (MST) value of fungi to precipitation was steeper than that of bacteria. The Simpson diversity and the MST value of abundant and rare taxa within bacteria had no difference with precipitation. In contrast, those of abundant taxa within fungi varied more than rare ones with precipitation. By unveiling the differential responses of microbial populations with discrepant characteristics, this study allowed us to understand the different microbial communities responding to rainfall on the Qinghai-Tibet Plateau.

To clarify the potential role of selenium (Se) on cerebral ischemia/reperfusion (I/R) injury, we utilized mouse middle cerebral artery occlusion (MCAO) followed by reperfusion as an animal model and oxygen–glucose deprivation and reoxygenation (OGD/R) to treat N2a cells as a cell model, respectively. MCAO model was established in mice and then divided into different groups with or without Se treatment. TTC staining was used to observe whether the cerebral I/R modeling was successful, and the apoptosis level was determined by TUNEL staining. The expression of GPx-4 and p22phox was assessed by western blot. In vitro experiments, the OGD/R induced oxidative stress in N2a cells was assessed by levels of GSH/GSSG, malondialdehyde, superoxide dismutase and iron content, respectively. QRT-PCR was used to detect the mRNA levels of Cox-2, Fth1, Mfn1 and mtDNA in N2a cells. JC-1 staining and flow cytometry was performed to detect the mitochondrial membrane potential. Se treatment alleviated cerebral I/R injury and improved the survival rate of mice. Additionally, Se treatment apparently attenuated oxidative stress and inhibited iron accumulation in MCAO model mice and OGD/R model of N2a cells. In terms of its mechanism, Se could up-regulate Mfn1 expression to alleviate oxidative stress and ferroptosis by promoting mitochondrial fusion in vivo and vitro. These findings suggest that Se may have great potential in alleviating cerebral I/R injury.

Abdominal aortic aneurysm (AAA) is the most common aortic disease occurring below the renal arteries, caused by multiple etiologies. Currently, no effective drug treatment exists, and the specific pathogenesis remains unclear. Due to its insidious onset and diagnostic challenges, AAA often culminates in aortic rupture, which has a high mortality rate. During AAA development, vascular smooth muscle cells (VSMCs) undergo significant pathological alterations, including contractile dysfunction, phenotypic modulation, cellular degradation, and heightened inflammatory and oxidative stress responses. In particular, emerging evidence implicates vascular smooth muscle cell (VSMC) metabolic dysregulation and mitochondrial dysfunction as key contributors to AAA progression. In this review, we systematically summarize the current understanding of VSMC biology, including their developmental origins, structural characteristics, and functional roles in aortic wall homeostasis, along with the regulatory networks governing the VSMC phenotype and functional maintenance. This review highlights the urgent need for further investigation into the aortic wall VSMC pathophysiology to identify novel therapeutic targets for AAA. These insights may pave the way for innovative treatment strategies in aortic disease management.

Mobile crowdsensing (MCS) utilizes the mobility of participating users and relies on the sensing ability of user devices to complete high-quality sensing tasks with limited cost. Designing an incentive mechanism that maximizes revenue for both service provider and users while ensuring the quality of sensing data and preserving users’ privacy remains a challenge in many scenarios. In this paper, we try to design an privacy-preserving incentive scheme based on DRL and Stackelberg game model which is dedicated to MCS. The proposed incentive mechanism is based on a two-stage Stackelberg game, in which the service provider is the leader and the user devices are the followers. We construct the relationship between user devices as a non-cooperative game and prove the existence and uniqueness of Nash equilibrium (NE) in this game. Considering the cost and quality of sensing data, we use the reputation constraint mechanism as the evaluation standard of data quality, and include sensing cost as indicator. Different from the traditional NE derivation method, we adopt deep reinforcement learning (DRL) approach (called PPO-DSIM) to derive NE and the optimal sensing strategy while protecting the user’s private information. Numerical simulation results show the convergence and effectiveness of the PPO-DSIM.

Background: Both sodium-glucose cotransporters (SGLTs) and Na+/H+ exchangers (NHEs) rely on a favorable Na-electrochemical gradient. Gastrin, through the cholecystokinin B receptor (CCKBR), can induce natriuresis and diuresis by inhibiting renal NHEs activity. The present study aims to unveil the role of renal CCKBR in diabetes through SGLT2-mediated glucose reabsorption.Methods: Renal tubule-specific Cckbr-knockout (CckbrCKO) mice and wild-type (WT) mice were utilized to investigate the effect of renal CCKBR on SGLT2 and systemic glucose homeostasis under normal diet, high-fat diet (HFD), and HFD with a subsequent injection of a low dose of streptozotocin. The regulation of SGLT2 expression by gastrin/CCKBR and the underlying mechanism was explored using human kidney (HK)-2 cells.Results: CCKBR was downregulated in kidneys of diabetic mice. Compared with WT mice, CckbrCKO mice exhibited a greater susceptibility to obesity and diabetes when subjected to HFD. In vitro experiments using HK-2 cells revealed an upregulation of glucose transporters after incubation with high glucose, a response that was significantly attenuated following gastrin intervention. The glucose uptake from the culture medium of cells was altered accordingly. Moreover, gastrin administration effectively mitigated hyperglycemia in WT diabetic mice by inhibition of SGLT2 mediated glucose reabsorption, but this effect was compromised in the absence of CCKBR, as seen in CckbrCKO mice. Mechanistically, gastrin/CCKBR substantially reduced SGLT2 expression in HK-2 cells exposed to high glucose, via modulating Erk/nuclear factor-kappa B (NF-κB) pathway.Conclusion: Our study underscores the crucial role of renal gastrin/CCKBR in SGLT2 regulation and glucose reabsorption, and renal gastrin/CCKBR can be a promising therapeutic target for diabetes.

Objectives Net water uptake (NWU) has been shown to have a linear relationship with brain edema. Based on an automated-Alberta Stroke Program Early Computed Tomography Score (ASPECTS) technique, we automatically derived NWU from baseline multimodal computed tomography (CT), namely ASPECTS-NWU. We aimed to determine if ASPECTS-NWU can predict the development of malignant cerebral edema (MCE). Methods One hundred and forty-six patients with large-vessel occlusion were retrospectively enrolled. Quantitative NWU based on automated-ASPECTS was measured both on nonenhanced CT (NECT) and CT angiography (CTA), namely NECT-ASPECT-NWU and CTA-ASPECTS-NWU. The correlation between ASPECTS-NWU and cerebral edema (CED) grades was calculated using Spearman rank correlation. Univariate logistic regression was used to assess the effect of radiological and clinical features on MCE, and a multivariable model with significant factors from the univariate regression analysis was built. Receiver operating characteristic (ROC) was obtained and area under curve (AUC) was compared. Results CTA-ASPECTS-NWU had a moderate positive correlation with CED grades ( r  = 0.62; 95% confidence interval [CI], 0.51–0.71; p  < 0.001). The CTA-ASPECTS-NWU performed better than the NECT-ASPECTS-NWU with AUC: 0.88 vs. 0.71 ( p  < 0.001). Multivariable logistic regression model integrating CTA-ASPECTS-NWU, collateral score, and age showed the CTA-ASPECTS-NWU was an independent predictor of MCE with an AUC of 0.94 (95% CI: 0.90–0.98; p  < 0.001). Conclusions This study demonstrates that ASPECTS-NWU is a quantitative predictor of MCE after large-vessel occlusion of the middle cerebral artery territory. The multivariable logistic regression model may enhance the identification of patients with MCE needing anti-edematous treatment. Key Points • The automated-ASPECTS technique can automatically detect the affected regions with early ischemic changes and NWU could be manually calculated. • The CTA-ASPECTS-NWU performs better than the NECT-ASPECTS-NWU on predicting the development of MCE. • The multivariable logistic regression model may enhance the identification of patients with MCE needing anti-edematous treatment.

Online education is popular for its flexibility and high accessibility. The transactions of educational multimedia data resources can effectively promote the development of educational informatization and solve the island situation of educational resources. However, educational resources may be facing severe illegal redistribution. And the copyright is not well protected. In most of existing transaction schemes for educational multimedia data, there is always a centralized third party, which may lead to dispute, distrust, or privacy issues. In this paper, we propose a fair and accountable trading scheme for educational multimedia data based on blockchain. We combine anti-collusion code named BIBD-ACC and asymmetric fingerprinting technology to achieve a relatively strong copyright protection. To realize a fair trading, we implement a smart contract with a reasonable pricing model. In addition, we leverage TEE to solve the privacy issues of public chain and IPFS to mitigate the storage cost of the blockchain. We implemented and evaluated the scheme in Ethereum. The results show that our scheme can achieve well copyright protection and preserve the users’ privacy. The overall overhead is reasonable.

In recent years, gradient boosting decision trees (GBDTs) has become a popular machine learning algorithm and there have been some studies on federated GBDT training to preserve clients’ privacy. However, existing schemes face some severe issues. For example, the integrity of the training process cannot be guaranteed. And most of the schemes ignore how to evaluate the performance gains from different clients’ datasets fairly. Developing a fair and secure contribution evaluation mechanism in federated learning to motivate clients to join federated learning remains a challenge. In this paper, we propose a fair and verifiable secure federated GBDT scheme that utilizes Trusted Execution Environments (TEEs) to ensure the integrity of the GBDT training process and quantify the contribution of different parties fairly. We propose a fair and verifiable contribution calculation mechanism based on TEE and the adaptive truncated Monte Carlo approximation Shapley value method. The mechanism can adapt to the limited resources of the device and avoid dishonest behaviors during the training process. In addition, as far as we all know, we attempted to implement the validation of contributions in the federated GBDT scheme for the first time. We implement a prototype of our scheme and evaluate it comprehensively. The results show that, compared with calculating the contribution of each party by the Shapley value method, our scheme can significantly improve the efficiency of contribution calculation in the case of more parties, and provide integrity and fairness guarantees for model and contribution calculations.