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Bimetallic CuCo catalysts with different Cu to Co ratios on N-doped porous carbon materials (N-C) were achieved using impregnation method and applied in the hydrogenation of furfural (FAL) to furfuryl alcohol (FOL). The high hydrogenation activity of FAL over Cu1Co1/N-C was originated from the synergistic interactions of Cu and Co species, where Co0and Cu0simultaneously adsorb and activate H2, and Cu+served as Lewis acid sites to activate CO. Meanwhile, electrons transfer from Cu to Co promoted the formation of Cu+. In situFourier transform infrared spectroscopy analysis indicated that Cu1Co1/N-C adsorbed FAL with a tilted η1-(O) configuration. The superior Cu1Co1/N-C showed excellent adsorbed ability towards H2and FAL, but weak adsorption for FOL. Therefore, Cu1Co1/N-C possessed 93.1% FAL conversion and 99.0% FOL selectivity after 5 h reaction, which also exhibited satisfactory reusability in FAL hydrogenation for five cycles.

Advancements in artificial intelligence have significantly improved the monitoring of tool wear in machining processes, thereby enhancing the overall quality of machining. However, the scarcity of tool wear samples poses a challenge to the enhancement of model precision. This necessitates the exploration of monitoring techniques that are effective even with small sample sizes. A method involving a triplet long short-term memory (LSTM) neural network is introduced, which offers the potential for superior accuracy even with limited training data. During the machining process, spindle vibrations are captured using a triaxial accelerometer. The raw data is processed by a triplet network, which uses an LSTM as the base model, thereby facilitating the aggregation within classes and separation between classes. A soft-max classification layer is subsequently integrated into the model, which enables the precise determination of tool wear states. The base model is optimized using a Genetic Algorithm to ensure model efficiency and accuracy before it is expanded into a triplet network. Experimental results from a vertical machining center confirm that the triplet LSTM network offers superior accuracy compared to a standard LSTM network, even when the sample size is small.

Acoustic keyword spotting (KWS) plays a pivotal role in the voice-activated systems of artificial intelligence (AI), allowing for hands-free interactions between humans and smart devices through information retrieval of the voice commands. The cloud computing technology integrated with the artificial neural networks has been employed to execute the KWS tasks, which however suffers from propagation delay and the risk of privacy breach. Here, we report a single-node reservoir computing (RC) system based on the CuInP2S6(CIPS)/graphene heterostructure planar device for implementing the KWS task with low computation cost. Through deliberately tuning the Schottky barrier height at the ferroelectric CIPS interfaces for the thermionic injection and transport of the electrons, the typical nonlinear current response and fading memory characteristics are achieved in the device. Additionally, the device exhibits diverse synaptic plasticity with an excellent separation capability of the temporal information. We construct a RC system through employing the ferroelectric device as the physical node to spot the acoustic keywords, i.e., the natural numbers from 1 to 9 based on simulation, in which the system demonstrates outstanding performance with high accuracy rate (>94.6%) and recall rate (>92.0%). Our work promises physical RC in single-node configuration as a prospective computing platform to process the acoustic keywords, promoting its applications in the artificial auditory system at the edge.

Interferon-alpha (IFN-α) is a first-line drug for treating chronic hepatitis B (CHB). Guanylate-binding protein 1 (GBP1) is one of the interferon-stimulating factors, which participates in the innate immunity of the host and plays an antiviral and antibacterial role. In this study, we explored how GBP1 is involved in IFN-α antiviral activity against HBV. Before being gathered, HepG2-NTCP and HepG2 2.15 cells were transfected with the wild-type hGBP1 plasmid or si-GBP1, respectively, and followed by stimulation with Peg-IFNα-2b. We systematically explored the role of GBP1 in regulating HBV infection in cell models. Additionally, we also examined GBP1 levels in CHB patients. GBP1 activity increased, and its half-life was prolonged after HBV infection. Overexpression of GBP1 inhibited the production of HBsAg and HBeAg, as well as HBs protein and HBV total RNA levels, whereas silencing of GBP1 inhibited its ability to block viral infections. Interestingly, overexpressing GBP1 co-treatment with Peg-IFNα-2b further increased the antiviral effect of IFN-α, while GBP1 silencing co-treatment with Peg-IFNα-2b partly restored its inhibitory effect on HBV. Mechanistically, GBP1 mediates the anti-HBV response of Peg-IFNα-2b by targeting HBs. Analysis of clinical samples revealed that GBP1 was elevated in CHB patients and increased with Peg-IFNα-2b treatment, while GBP1 showed good stability in the interferon response group. Our study demonstrates that GBP1 inhibits HBV replication and promotes HBsAg clearance. It is possible to achieve antiviral effects through the regulation of IFN-α induced immune responses in response to HBV.

The integration of tailorable mechanical properties, dimensional stability, and reprocessability is of significance in the design of sustainable polymer materials. Herein, side‐chain engineering is employed to fabricate cross‐linked supramolecular polymers with customizable mechanical properties. Three kinds of side chains, including methyl, 1‐ethyl pentyl, and 1‐hexyl nonyl, are used to modify the supramonomers. Through the copolymerization of low‐content supramonomers and covalent monomers, cross‐linked supramolecular polyureas with a wide range of mechanical properties spanning from rigid plastics to elastic materials are successfully constructed. Specifically, the Young's modulus can be adjusted from 525 to 128 MPa by tuning the side chain of supramonomers from methyl to 1‐hexyl nonyl. Meanwhile, the materials still retain exceptional recyclability and solvent resistance. Even after seven generations of recycling processes, the reprocessed cross‐linked supramolecular polyureas maintain over 95% of their original mechanical properties. It is anticipated that side‐chain engineering is a facile method for designing customized polymer materials to achieve both tailored mechanical properties and desirable functions.

We examined the results of a large-scale national survey of online secondary education in China. The online survey of 33,194 high school students and 5,667 teachers has provided comprehensive and representative data regarding the quality of online education and its implementation during the pandemic.Questionnaire surveys administered to students and teachers in different grades and comparative analysis of different cohorts revealed group heterogeneity of the online learning effect.The findings have elucidated the emergency teaching situation in China during the pandemic, yielding targeted suggestions for school education following the resumption of classes.

With the development of information technology, massive and heterogeneous consumer electronic products can access the network. These products may engage third-party servers for federated learning and generating more accurate models, where they can monitor, collect and aggregate various data from households almost in real-time. Even though federated learning can update participant parameter data without collecting their raw data, prior research revealed that the shared gradients still retain sensitive information from the training set. Meanwhile, malicious third-party aggregation servers may return forged aggregated gradients, and lightweight execution of the entire solution needs to be ensured during the aggregation process. This paper demonstrates a verifiable federated learning scheme supporting secure data aggregation without using bilinear groups (FLVA) to address these issues. Particularly, to solve the issue of private key leakage in the gradient aggregation process on electronic product data, a three-party key negotiation protocol is developed. The private gradients are uploaded and aggregated in ciphertext format, which ensures the privacy of the electronic product gradient. Security analysis showed that our FLVA system can effectively protect the security and privacy of the private gradients. Finally, the experimental results showed that compared with existing solutions, the proposed scheme is more efficient and practical.

Argonaute (Ago) proteins mediate RNA- or DNA-guided inhibition of nucleic acids1,2. Although the mechanisms used by eukaryotic Ago proteins and long prokaryotic Ago proteins (pAgos) are known, that used by short pAgos remains elusive. Here we determined the cryo-electron microscopy structures of a short pAgo and the associated TIR-APAZ proteins (SPARTA) from Crenotalea thermophila(Crt): a free-state Crt-SPARTA; a guide RNA–target DNA-loaded Crt-SPARTA; two Crt-SPARTA dimers with distinct TIR organization; and a Crt-SPARTA tetramer. These structures reveal that Crt-SPARTA is composed of a bilobal-fold Ago lobe that connects with a TIR lobe. Whereas the Crt-Ago contains a MID and a PIWI domain, Crt-TIR-APAZ has a TIR domain, an N-like domain, a linker domain and a trigger domain. The bound RNA–DNA duplex adopts a B-form conformation that is recognized by base-specific contacts. Nucleic acid binding causes conformational changes because the trigger domain acts as a ‘roadblock’ that prevents the guide RNA 5′ ends and the target DNA 3′ ends from reaching their canonical pockets; this disorders the MID domain and promotes Crt-SPARTA dimerization. Two RNA–DNA-loaded Crt-SPARTA dimers form a tetramer through their TIR domains. Four Crt-TIR domains assemble into two parallel head-to-tail-organized TIR dimers, indicating an NADase-active conformation, which is supported by our mutagenesis study. Our results reveal the structural basis of short-pAgo-mediated defence against invading nucleic acids, and provide insights for optimizing the detection of SPARTA-based programmable DNA sequences.

Tropical cloud clusters (TCCs) can potentially develop into tropical cyclones (TCs), leading to significant casualties and economic losses. Accurate prediction of tropical cyclogenesis (TCG) is crucial for early warnings. Most traditional deep learning methods applied to TCG prediction rely on predictors from a single time point, neglect the ocean-atmosphere interactions, and exhibit low model interpretability. This study proposes the Tropical Cyclogenesis Prediction-Net (TCGP-Net) based on the Swin Transformer, which leverages convolutional operations and attention mechanisms to encode spatiotemporal features and capture the temporal evolution of predictors. This model incorporates the coupled ocean-atmosphere interactions, including multiple variables such as sea surface temperature. Additionally, causal inference and integrated gradients are employed to validate the effectiveness of the predictors and provide an interpretability analysis of the model’s decision-making process. The model is trained using GridSat satellite data and ERA5 reanalysis datasets. Experimental results demonstrate that TCGP-Net achieves high accuracy and stability, with a detection rate of 97.9% and a false alarm rate of 2.2% for predicting TCG 24 hours in advance, significantly outperforming existing models. This indicates that TCGP-Net is a reliable tool for tropical cyclogenesis prediction.

As a critical infrastructure for contemporary information technology industry, industrial internet of things (IIoT) contains a vast amount of sensitive data, making it a key requirement to ensure data security. As the use of wireless networks as a means of communication between nodes is becoming more and more common, in order to prevent malicious attacks from compromising the system, a zero-trust authentication system is necessary. In this article, we propose a comprehensive implementation framework for zero-trust verification of IIoT wireless transmission nodes, which utilizes federated learning to achieve zero-trust rule training and terminal model training, while employing blockchain technology for on-chain aggregation and cloud backup of the models. This approach enhances the accuracy and availability of the zero-trust rules while safeguarding the security of IIoT nodes. The constructed zero-trust framework incorporates a self-incremental learning function, and experiments show that it achieves a high level of accuracy at recognising attacks. Finally, we discuss the challenges of utilizing federated learning in zero-trust for IIoT and several potential solutions to address these challenges.

Cardiovascular diseases (CVDs) are a leading factor driving mortality worldwide. Iron, an essential trace mineral, is important in numerous biological processes, and its role in CVDs has raised broad discussion for decades. Iron-mediated cell death, namely ferroptosis, has attracted much attention due to its critical role in cardiomyocyte damage and CVDs. Furthermore, ferritinophagy is the upstream mechanism that induces ferroptosis, and is closely related to CVDs. This review aims to delineate the processes and mechanisms of ferroptosis and ferritinophagy, and the regulatory pathways and molecular targets involved in ferritinophagy, and to determine their roles in CVDs. Furthermore, we discuss the possibility of targeting ferritinophagy-induced ferroptosis modulators for treating CVDs. Collectively, this review offers some new insights into the pathology of CVDs and identifies possible therapeutic targets.

In actual physiological environments, bacteria can activate the immune system and release lactic acid. However, the detailed contribution of lactic acid to the passivation behavior of titanium (Ti) alloys is still unclear. The current work investigated the in vitro passivation behavior of Ti–6Al–4V (TC4) alloys fabricated by laser powder bed fusion in Hank's solution with and without adding lactic acid. Electrochemical methods, inductively coupled plasma atomic emission spectrometer, and X-ray photoelectron spectroscopy were jointly used. Adding lactic acid decreases the corrosion resistance of samples by degrading the formed passive film. The film formed in the (lactic acid)-containing solution exhibits a higher level of oxygen vacancies and a lower thickness, attributed to the suppressed formation of Ti4+transformed from Ti3+and Ti2+. Moreover, the presence of lactic acid would increase the open circuit potential, relieve the ions release, and hinder the deposition of calcium phosphates within 24 h immersion.

Due to the rapid development of demand response management and distributed energy resources, prosumers are becoming more proactive, which also promotes the emergence of peer-to-peer (P2P) energy trading mechanisms. However, we find that it is quite difficult to simultaneously achieve high computational efficiency, decentralized operations and solution optimality in a P2P energy trading mechanism, which is called the “P2P energy trading trilemma”. In this paper, we first propose a novel negotiation mechanism for P2P energy trading that can maximize social welfare in a decentralized manner and respect physical network constraints. Then, the local optimization problem is transformed into a closed-form alternating update algorithm (AUA), so that the computational efficiency can be greatly improved. Therefore, our proposed mechanism solves the “P2P energy trading trilemma” to a certain extent. Another challenge is that the locational marginal price (LMP)-based market mechanism cannot satisfy the incentive-compatible property. To encourage prosumers to cooperate in a lack-of-trust environment, a novel incentive-compatible mechanism is proposed for P2P energy trading using consensus mechanism inspired by proof of solution (PoSo) and smart contract (SC). Finally, we simulate the functionality of the mechanism in terms of convergence performance, reliability, scalability, computational efficiency, and SC operations.

Deep learning networks can be applied to the field of intelligent prediction of part surface roughness. However, the surface roughness samples of parts have the problems of high collection cost, unbalanced categories, and complicated data distribution, which inevitably limit the application of deep learning network models in the field of intelligent prediction of part surface roughness. To solve these problems, this article proposes a novel data augmentation method based on CoralGAN for prediction of part surface roughness, which introduces the domain adaptive method deep coral function to help optimize the network parameters of the generator of generative adversarial network (GAN). Specifically, the vibration signal collected during processing is converted into frequency spectrum data and input into CoralGAN. The training of the generator is guided by coral loss, that is, the distance between the covariances of the real samples and generated samples features, not just the statistical consistency of the traditional GAN. Experiments have been carried out on the three-axis vertical machining center. Research shows that the proposed method can improve the prediction accuracy of part surface roughness to 95.5%.

This paper presents the reflections of one of the authors, a junior faculty member, on teaching a master’s level, English-medium instruction (EMI) course. The course was titled, International Research on Teacher Education, and it took place in a Chinese research-intensive university. It was, in part, a response to the need for internationalisation in higher education. The authors explore seven contradictions embedded in the EMI course: (1) teacher-centred vs. student-centred educational beliefs, (2) authoritarian vs. democratic classroom atmosphere, (3) traditional printed materials vs. multi-modal learning resources, (4) direct instruction vs. self-regulated learning, (5) classroom-based teaching vs. place-based learning, (6) individual learning vs. group-based work, and (7) summative assessment vs. formative assessment. The authors apply Engeström’s cultural historical activity theory to extrapolate these contradictions. Using student narratives to demonstrate, the titular question of whether the course was a bane or a boon is answered. An autoethnographic or three-dimensional narrative approach was employed to analyse the data in the context of temporality, sociality, and place. Additionally, the authors discuss the dilemmatic space and implications associated with implementing the EMI course in China.

With hot water extraction and ethanol precipitation, three high molecular weight polysaccharides, LZLs (LZL-1, LZL-2 and LZL-3), were successfully purified from the edible bulbs of Lanzhou lily by DEAE-52 cellulose and sephadex G-200 column. The structure features of LZLs were characterized by gas chromatography, scanning electron microscope, high performance gel-permeation chromatography, IR and NMR, and the antioxidant activities of LZLs were assayed. The results showed that LZL-1, LZL-2 and LZL-3 were composed of glucose and mannose with the molar ratios of 2.4:2.5, 2.6:2.5 and 2.2:2.5, while their molecular weights were 2.33 × 106, 1.92 × 106and 1.26 × 106, respectively. LZLs were mainly α-pyranose configurations with lamellar structure and triple helix conformation except LZL-3. LZLs had similar glycosidic linkage patterns with main substitution at O-6 of glucose or mannose but the different substitution positions at O-3 of them. Vitro bioassays showed that LZLs had effective scavenging abilities to free radicals, especially to DPPH and hydroxyl radicals. When the concentration reached 4 mg/mL, the scavenging rate of LZLs to DPPH and hydroxyl radicals were up to 60% and 70%, respectively. These findings provided more references for potential applications of Lanzhou lily polysaccharides as natural antioxidant agents in food and pharmaceuticals.

Motion control is critical in mobile robot systems, which determines the reliability and accuracy of a robot. Due to model uncertainties and widespread external disturbances, a simple control strategy cannot match tracking accuracy with disturbance immunity, while a complex controller will consume excessive energy. For precise motion control with disturbance immunity and low energy consumption, a control method based on an enhanced reduced-order extended state observer (ERESOBC) is proposed to control the motor-wheels dynamic model of a differential driven mobile robot (DDMR). In this method, only unknown state error and negative disturbance are estimated by the enhanced reduced-order extended state observer (ERESO), which reduces the required energy of the observer. In addition, a simple state-feedback–feedforward controller is used to track the reference signal and compensate for negative disturbance. Through numerical simulation and application example, the tracking performance and disturbance rejection performance of DDMR are compared with the traditional control method based on enhanced extended state observer (EESOBC), and the results show the superiority of the ERESOBC method.

As location-based applications flourishing, we will witness soon the transferring of a prodigious amount of data from the local to a public cloud. The rising demand for outsourced data is moving toward a wider geographical area with arbitrary distribution (i.e., dense or sparse) and query scope (i.e., limited or vast). The outsourced individual data should be preserved when being queried as facing cloud risks, especially for location information. Geometric range queries are one of the most fundamental search functions. However, the existed works of secure geometric queries are far from practical usage on efficiency and security simultaneously. In this paper, we propose a novel scheme, LuxGeo. Our scheme reaches a constant navigation and a linear sweep, which is tailored for secure and efficient location-lookup. Our experiments over three real-world spatial datasets have shown its practical efficiency. For example, it only takes $\mathbf {10.01s}$10.01s with $\mathbf {728}$728 tuples retrieved over $\mathbf {63,369}$63,369 ciphertext dataset for a single query. LuxGeo has better performance than the existed solutions for a GSE problem on efficiency and security.

Capsule network (CapsNet) acts as a promising alternative to the typical convolutional neural network, which is the dominant network to develop the remaining useful life (RUL) estimation models for mechanical equipment. Although CapsNet comes with an impressive ability to represent entities’ hierarchical relationships through a high-dimensional vector embedding, it fails to capture the long-term temporal correlation of run-to-failure time series measured from degraded mechanical equipment. On the other hand, the slow-varying dynamics, which reveals the low-frequency information hidden in mechanical dynamical behavior, is overlooked in the existing RUL estimation models (including CapsNet), limiting the utmost ability of advanced networks. To address the aforementioned concerns, we propose a slow-varying dynamics-assisted temporal CapsNet (SD-TemCapsNet) to simultaneously learn the slow-varying dynamics and temporal dynamics from measurements for accurate RUL estimation. First, in light of the sensitivity of fault evolution, slow-varying features are decomposed from normal raw data to convey the low-frequency components corresponding to the system dynamics. Next, the long short-term memory (LSTM) mechanism is introduced into CapsNet to capture the temporal correlation of time series. To this end, experiments conducted on an aircraft engine and a milling machine verify that the proposed SD-TemCapsNet outperforms the mainstream methods. In comparison with CapsNet, the estimation accuracy of the aircraft engine with four different scenarios has been improved by 10.17%, 24.97%, 3.25%, and 13.03% about the index root mean squared error, respectively. Similarly, the estimation accuracy of the milling machine has been improved by 23.57% compared to LSTM and 19.54% compared to CapsNet.

Extreme wildfire events and cyclones are on the rise across tropical regions in response to climate change. Despite assumptions about their impact on phytoplankton through nutrient supplies, field evidence is lacking, and their combined effects remain unclear. In an on‐site microcosm experiment conducted in the Xisha Islands, South China Sea (SCS) after Typhoon Noru, we observed enhanced phytoplankton growth in response to exposure to total suspended particulates (TSP) from wildfires (2 mg/L and 6 mg/L) under wind‐driven upwelling conditions. Upwelled nutrients had a limited effect on Chl‐a concentration due to phosphate depletion, by contrast, wildfire TSP contributed nutrients enriched in nitrogen and phosphate, resulting in a 3.30–5.61‐fold increase in Chl‐a. However, upwelled nutrients increased the diatom‐to‐dinoflagellate ratio from the initial 11.0 to 12.7, TSP at low and high levels reduced the ratio to 0.3–0.8 and significantly altered the communities, with 61.8% of species, including two dominant diatoms, negatively correlated with N and/or P supplies. Species diversity declined significantly at high TSP levels. These findings suggest that enhanced primary productivity by wildfires may come at the cost of an altered phytoplankton community. This field study improves understanding of the effects of simultaneous occurrences of multiple extreme climate events on marine ecosystems. Increasing wildfires and typhoons in tropical regions due to global warming are believed to affect algae growth by altering nutrient inputs from atmospheric deposition and upwelling. Tropical coral reefs rely on algae to sustain high biomass in the food web. However, there is limited evidence of how wildfires impact algae under upwelling conditions. This study in the coral reef regions of the SCS found that wildfires provide nutrients including nitrogen and phosphorus, which enhance algae growth more effectively under upwelling compared to upwelling alone. However, when typhoon and wildfire particles are combined, they tend to alter the types of algae present. Some algae become less competitive, leading to reduced species diversity overall. Given the potential variations in carbon export efficiency among species, wildfires may influence carbon sequestration by algae. This field study suggests that the simultaneous occurrence of wildfires and tropical cyclones may enhance productivity but pose potential risks to coral reef ecosystems. Wildfire particulates (2–6 mg/L) alleviated P limitation and extended the promotion of phytoplankton growth under wind‐driven upwellingWhen combined with upwelling, wildfire particulates altered the phytoplankton community, lowering the diatom‐to‐dinoflagellate ratioNearly 60% of algal species displayed a negative relationship with N and/or P provided by wildfire particulates Wildfire particulates (2–6 mg/L) alleviated P limitation and extended the promotion of phytoplankton growth under wind‐driven upwelling When combined with upwelling, wildfire particulates altered the phytoplankton community, lowering the diatom‐to‐dinoflagellate ratio Nearly 60% of algal species displayed a negative relationship with N and/or P provided by wildfire particulates

With the continuously increasing application of photovoltaic (PV) panels, how to effectively manage these valuable facilities has become an issue of concern. To date, some methods have been developed to meet this purpose. However, to date, a satisfactory solution has not been achieved for managing large-scale solar PV power plants. To address this issue, a new PV panel condition monitoring and fault diagnosis technique is developed in this paper. The new technique uses a U-Net neural network and a classifier in combination to intelligently analyse the PV panel’s infrared thermal images taken by drones or other kinds of remote operating systems. The novelties of this research include: (1) a U-net neural network is developed and trained to carry out image segmentation, thereby significantly improving the efficiency of image processing; (2) the condition monitoring and fault diagnosis is based on the contour features in the ‘mask’ of true colour infrared images. As compared to the true colour images, their mask images have little interference information, the reliability and accuracy of condition monitoring and fault diagnosis based on mask images can be guaranteed to a large extent. In the research, 295 infrared images were taken first from the PV panels in different health states, and then their ‘masks’ were manually created using the software LabelMe. Secondly, enlarge the number of image samples using three image expansion methods (i.e. mirroring left and right, flipping up and down, and cropping and then zooming in) to establish the image sample database for training and testing the U-net neural network. Thirdly, use 1852 infrared images and their mask images stored in the database to train a U-Net neural network until the trained U-net neural network can create mask images as accurately as the software LabelMe does. The trained neural network will guarantee that the achieved segmentation accuracy can be as high as 95.2%. Finally, four potential criteria (i.e., Contour area, Perimeter, Aspect ratio and Ratio of contour area to the area of contour outer rectangle) are proposed to characterise the contour of mask images and then based on the calculation results of these four criteria, different types of PV panel faults are diagnosed with the aid of three kinds of classifiers. The classifiers are Decision tree, K-Nearest Neighbours algorithm (KNN), and Support-vector machine (SVM). The research results have shown that the combined use of a well-trained U-Net neural network and Decision tree can diagnose the PV panel faults with 99.8% accuracy. Therefore, it may arguably provide a promising intelligent tool for condition monitoring the PV panels.

Hierarchical ZSM-5 with loose nanocrystallites was synthesized by a traditional hydrothermal procedure. Factors controlling the formation of hierarchical zeolite were investigated in detail. Despite having comparable acid properties, the increased external surfaces offer the hierarchical ZSM-5nan excellent catalytic performance. After reaction for 414 h, the hierarchical ZSM-5ncatalyst still keeps a 88.5% conversion of isopropylbenzene, while the ZSM-5μ and ZSM-5mcatalysts only show 37.5% (25.8 h), 28.2% (11 h) conversions, respectively; the initial conversion of n-octane over ZSM-5nis 94.3 wt %, obviously higher than 82.8 wt % on ZSM-5μ, the half-life period related to the catalytic activity on ZSM-5nand ZSM-5μ are 271 h, and 22 h, respectively. The prolonged catalysis life for hierarchical ZSM-5nduring the catalytic cracking of n-octane is ascribed to the shortened diffusion path length and an “excretory system”, carbon nanotubes (CNTs) self-generating during the cracking processing. The shortened microporous channels attribute to weakening adsorption energy barrier and promoting the coke precursors rapid diffusing toward the pore windows. Moreover, the autogenetic CNTs help the carbon precursors escaping from the pore windows and keep them far away from the zeolite crystals in the catalysts. That avoids the carbon deposit in the micropores as well as in the pore windows and on the external surface of the catalyst.

Inhibition of nucleic acid targets is mediated by Argonaute (Ago) proteins guided by RNA or DNA. Although the mechanisms underpinning the functions of eukaryotic and “long” prokaryotic Ago proteins (pAgos) are well understood, those for short pAgos remain enigmatic. Here, we determine two cryoelectron microscopy structures of short pAgos in association with the NADase-domain-containing protein Sir2-APAZ from Geobacter sulfurreducens(GsSir2/Ago): the guide RNA-target DNA-loaded GsSir2/Ago quaternary complex (2.58 Å) and the dimer of the quaternary complex (2.93Å). These structures show that the nucleic acid binding causes profound conformational changes that result in disorder or partial dissociation of the Sir2 domain, suggesting that it adopts a NADase-active conformation. Subsequently, two RNA-/DNA-loaded GsSir2/Ago complexes form a dimer through their MID domains, further enhancing NADase activity through synergistic effects. The findings provide a structural basis for short-pAgo-mediated defense against invading nucleic acids.

ABSTRACTGlycogen synthesis kinase-3β (GSK-3β) is a kinase shown to regulate esophageal cancer (EC) progression. However, the significance of GSK-3β in phosphorylation of small kinetochore-associated protein (SKAP) has not been fully characterized. GSK-3β/SKAP expression was analyzed in EC tissues by RT-qPCR. The association between GSK-3β expression and the overall survival was analyzed using the Kaplan–Meier method. Transwell and wound healing assays were performed to assess the effects of GSK-3β/SKAP knockdown on EC cell migration and invasion. By in vitrokinase assay, the SKAP T294 site was identified as a phosphorylated target of GSK-3β. Moreover, we established two cell lines expressing either T294D (phosphor-mimic) or T294A (phosphor-deficiency) SKAP to analyze the effect of SKAP phosphorylation on EC cell invasion, migration, and epithelial–mesenchymal transition (EMT) process. GSK-3β was overexpressed and positively correlated with SKAP levels in EC tissues. Increased GSK-3β expression was associated with EC poor prognosis. Both of GSK-3β knockdown and silencing SKAP decreased EC cell migration and invasion. GSK-3β phosphorylated SKAP protein at Thr294 site. Additionally, a T294D mutant SKAP enhanced cell migration, invasion, and EMT process. Conversely, a T294A mutant SKAP inhibited EC cell malignancy. Meanwhile, cell invasion and migration abilities were inhibited after silencing GSK-3β in EC109-WT, EC109-T294A and EC109-T294D cells. Phosphorylation of SKAP induced by GSK-3β promoted EC cell migration and invasion.

Supramolecular polymers, originating from the interplay between polymer science and supramolecular chemistry, have attracted increasing interest in the scientific and industrial communities. To date, most supramolecular polymers are constructed in homogeneous solutions. Supramolecular polymerization normally takes place spontaneously in solutions, thus creating challenges in fabricating supramolecular polymers in a controlled manner. By combining supramolecular polymerization and interfacial polymerization, supramolecular polymerization can be transferred from homogeneous solutions to interfaces, which allows for the controlled production of supramolecular polymers. In this Perspective, we will summarize recent progress and the advantages in supramolecular polymerization at solid–liquid and liquid–liquid interfaces. Meanwhile, current challenges and opportunities in the field of supramolecular polymerization at interfaces are proposed and discussed. It is anticipated that this Perspective will inspire supramolecular polymerization at interfaces and facilitate the construction of supramolecular polymeric materials with diverse architectures and tailor-made functions.

The emergence of SARS-CoV-2 variants of concern and repeated outbreaks of coronavirus epidemics in the past two decades emphasize the need for next-generation pan-coronaviral therapeutics. Drugging the multi-functional papain-like protease (PLpro) domain of the viral nsp3 holds promise. However, none of the known coronavirus PLpro inhibitors has been shown to be in vivoactive. Herein, we screened a structurally diverse library of 50,080 compounds for potential coronavirus PLpro inhibitors and identified a noncovalent lead inhibitor F0213 that has broad-spectrum anti-coronaviral activity, including against the Sarbecoviruses(SARS-CoV-1 and SARS-CoV-2), Merbecovirus(MERS-CoV), as well as the Alphacoronavirus(hCoV-229E and hCoV-OC43). Importantly, F0213 confers protection in both SARS-CoV-2-infected hamsters and MERS-CoV-infected human DPP4-knockin mice. F0213 possesses a dual therapeutic functionality that suppresses coronavirus replication via blocking viral polyprotein cleavage, as well as promoting antiviral immunity by antagonizing the PLpro deubiquitinase activity. Despite the significant difference of substrate recognition, mode of inhibition studies suggest that F0213 is a competitive inhibitor against SARS2-PLpro via binding with the 157K amino acid residue, whereas an allosteric inhibitor of MERS-PLpro interacting with its 271E position. Our proof-of-concept findings demonstrated that PLpro is a valid target for the development of broad-spectrum anti-coronavirus agents. The orally administered F0213 may serve as a promising lead compound for combating the ongoing COVID-19 pandemic and future coronavirus outbreaks.

This paper develops a modular modeling and efficient formulation of launch dynamics with marching fire (LDMF) using a mixed formulation of the transfer matrix method for multibody systems (MSTMM) and Newton-Euler formulation. Taking a ground-borne multiple launch rocket systems (MLRS), the focus is on the launching subsystem comprising the rocket, flexible tube, and tube tail. The launching subsystem is treated as a coupled rigid-flexible multibody system, where the rocket and tube tail are treated as rigid bodies while the flexible tube as a beam with large motion. Firstly, the tube and tube tail can be elegantly handled by the MSTMM, a computationally efficient order-N formulation. Then, the equation of motion of the in-bore rocket with relative kinematics w.r.t. the tube using the Newton-Euler method is derived. Finally, the rocket, tube, and tube tail dynamics are coupled, yielding the equation of motion of the launching subsystem that can be regarded as a building block and further integrated with other subsystems. The deduced dynamics equation of the launching subsystem is not limited to ground-borne MLRS but also fits for tanks, self-propelled artilleries, and other air-borne and naval-borne weapons undergoing large motion. Numerical simulation results of LDMF are given and partially verified by the experiment.

As the power source of an aircraft, aero-engine tends to meet many rigorous requirements for high thrust-weight ratio and reliability with the continuous improvement of aero-engine performance. In this paper, based on the order statistics and stochastic process theory, an improved dynamic load-strength interference (LSI) model was proposed for the reliability analysis of aero-engine rotor blade system, with strength degradation and catastrophic failure involved. In presented model, the “unconventional active” characteristic of rotor blade system, changeable functioning relationships and system-component configurations, was fully considered, which is necessary for both theoretical analysis and engineering application. In addition, to reduce the computation cost, a simplified form of the improved LSI model was also built for convenience of engineering application. To verify the effectiveness of the improved model, reliability of turbojet 7 engine rotor blade system was calculated by the improved LSI model based on the results of static finite element analysis. Compared with the traditional LSI model, the result showed that there were significant differences between the calculation results of the two models, in which the improved model was more appropriate to the practical condition.

Colorectal cancer (CRC) cells have low or absent tumor cell PD-L1 expression that we previously demonstrated can confer chemotherapy resistance. Here, we demonstrate that PD-L1depletion enhances JNK activity resulting in increased BimThr116phosphorylation and its sequestration by MCL-1 and BCL-2. Activated JNK signaling in PD-L1-depeted cells was due to reduced mRNA stability of the CYLD deubiquitinase. PD-L1 was found to compete with the ribonuclease EXOSC10 for binding to CYLDmRNA. Thus, loss of PD-L1promoted binding and degradation of CYLDmRNA by EXOSC10 which enhanced JNK activity. An irreversible JNK inhibitor (JNK-IN-8) reduced BimThr116phosphorylation and unsequestered Bim from MCL-1 and BCL-2 to promote apoptosis. In cells lacking PD-L1, treatment with JNK-IN-8, an MCL-1 antagonist (AZD5991), or their combination promoted apoptosis and reduced long-term clonogenic survival by anticancer drugs. Similar effects of the JNK inhibitor on cell viability were observed in CRC organoids with suppression of PD-L1. These data indicate that JNK inhibition may represent a promising strategy to overcome drug resistance in CRC cells with low or absent PD-L1expression.

The shieldin complex functions as the downstream effector of 53BP1–RIF1 to promote DNA double-strand break end-joining by restricting end resection. The SHLD2 subunit binds to single-stranded DNA ends and blocks end resection through OB-fold domains. Besides blocking end resection, it is unclear how the shieldin complex processes SHLD2-bound single-stranded DNA and promotes non-homologous end-joining. Here, we identify a downstream effector of the shieldin complex, ASTE1, as a structure-specific DNA endonuclease that specifically cleaves single-stranded DNA and 3′ overhang DNA. ASTE1 localizes to DNA damage sites in a shieldin-dependent manner. Loss of ASTE1 impairs non-homologous end-joining, leads to hyper-resection and causes defective immunoglobulin class switch recombination. ASTE1 deficiency also causes resistance to poly(ADP-ribose) polymerase inhibitors in BRCA1-deficient cells owing to restoration of homologous recombination. These findings suggest that ASTE1-mediated 3′ single-stranded DNA end cleavage contributes to the control of DSB repair choice by 53BP1, RIF1 and shieldin.

IMPORTANCE: Obesity disproportionately affects Black women, who also have a higher risk of death after a breast cancer diagnosis compared with women of other racial/ethnic groups. However, few studies have evaluated the association of measures of adiposity with mortality among Black breast cancer survivors. OBJECTIVE: To assess the association of measures of adiposity with survival after a breast cancer diagnosis among Black women. DESIGN, SETTING, AND PARTICIPANTS: This prospective population-based cohort study comprised 1891 women with stage 0 to IV breast cancer who self-identified as African American or Black and were ages 20 to 75 years. The New Jersey State Cancer Registry was used to identify women living in 10 counties in New Jersey who were recruited from March 1, 2006, to February 29, 2020, and followed up until September 2, 2020. EXPOSURES: Measures of adiposity, including body mass index, body fat distribution (waist circumference and waist-to-hip ratio), and body composition (percent body fat and fat mass index), were collected during in-person interviews at approximately 10 months after breast cancer diagnosis. MAIN OUTCOMES AND MEASURES: All-cause and breast cancer–specific mortality. RESULTS: Among 1891 women, the mean (SD) age at breast cancer diagnosis was 54.5 (10.8) years. During a median follow-up of 5.9 years (range, 0.5-14.8 years), 286 deaths were identified; of those, 175 deaths (61.2%) were associated with breast cancer. A total of 1060 women (56.1%) had obesity, and 1291 women (68.3%) had central obesity. Higher adiposity, particularly higher waist-to-hip ratio, was associated with worse survival. Women in the highest quartile of waist-to-hip ratio had a 61% increased risk of dying from any cause (hazard ratio [HR], 1.61; 95% CI, 1.12-2.33) and a 68% increased risk of breast cancer death (HR, 1.68; 95% CI, 1.04-2.71) compared with women in the lowest quartile. The risks of all-cause and breast cancer–specific death were similarly high among women in the highest quartile for waist circumference (HR, 1.74 [95% CI, 1.26-2.41] and 1.64 [95% CI, 1.08-2.48], respectively), percent body fat (HR, 1.53 [95% CI, 1.09-2.15] and 1.81 [95% CI, 1.17-2.80]), and fat mass index (HR, 1.57 [95% CI, 1.11-2.22] and 1.74 [95% CI, 1.10-2.75]); however, the risk was less substantial for body mass index (HR, 1.26 [95% CI, 0.89-1.79] and 1.33 [95% CI, 0.84-2.10]). In analyses stratified by estrogen receptor status, menopausal status, and age, a higher waist-to-hip ratio was associated with a higher risk of all-cause death among women who had estrogen receptor–negative tumors (HR, 2.24; 95% CI, 1.14-4.41), women who were postmenopausal (HR, 2.15; 95% CI, 1.28-3.61), and women who were 60 years or older at diagnosis (HR per 0.10-U increase, 1.76; 95% CI, 1.37-2.26). CONCLUSIONS AND RELEVANCE: In this population-based cohort study, central obesity and higher adiposity were associated with higher all-cause and breast cancer–specific mortality among Black breast cancer survivors. Simple measures of body fat distribution and body composition were found to be useful tools for identifying Black women with a higher risk of death after a breast cancer diagnosis.

The authors present the case of a 35-year-old male who was diagnosed with acute retinal necrosis syndrome that evolved into retinal detachment (RD), even after immediate treatment with systemic antiviral medications, as well as intravitreal injection and laser photocoagulation. Pars plana vitrectomy plus silicone oil tamponade was performed, but silicone oil emulsified 22 months later. Due to the widespread necrosis lesions and defects of the peripheral retina, RD was highly likely to reoccur after silicone oil removal; thus, foldable capsular vitreous body was implanted to support the retina. No recurrent RD was observed afterward.[[Ophthalmic Surg Lasers Imaging Retina.2020;51:653–657.]

Ionic conductors that combine transparency, elasticity, and underwater self-healing capability are highly desirable because of their applications in biosensors, touch panels, marine ships, and so forth. Polymer materials based on ion–dipole interactions can meet these requirements. However, a key trade-off is their relatively weak mechanical properties because of the plasticizing effect. Here, we designed and synthesized a new ionic liquid building block to enable a new design of multivalent ion–dipole interaction. The resultant ion–dipole polymer complex not only enhances the Young’s modulus by more than 3 times but also possesses much better elasticity, without any sacrifice on the ionic conductivity or self-healing capability.

Programmed death ligand-1 (PD-L1 or B7-H1) is well known for its role in immune checkpoint regulation, but its function inside the tumor cells has rarely been explored. Here we report that nuclear PD-L1 is important for cancer cell sister chromatid cohesion. We found that depletion of PD-L1 suppresses cancer cell proliferation, colony formation in vitro, and tumor growth in vivo in immune-deficient NSG mice independent of its role in immune checkpoint. Specifically, PD-L1 functions as a subunit of the cohesin complex, and its deficiency leads to formation of multinucleated cells and causes a defect in sister chromatid cohesion. Mechanistically, PD-L1 compensates for the loss of Sororin, whose expression is suppressed in cancer cells overexpressing PD-L1. PD-L1 competes with Wing Apart-Like (WAPL) for binding to PDS5B, and secures proper sister chromatid cohesion and segregation. Our findings suggest an important role for nuclear PD-L1 in cancer cells independent of its function in immune checkpoint.