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Epigenetic modifications are crucial for plant development. EFD (ExineFormationDefect) encodes a SAM-dependent methyltransferase that is essential for the pollen wall pattern formation and male fertility in Arabidopsis. In this study, we find that the expression of DRM2, a de novo DNA methyltransferase in plants, complements for the defects in efd, suggesting its potential de novo DNA methyltransferase activity. Genetic analysis indicates that EFD functions through HB21, as the knockout of HB21 fully restores fertility in efd mutants. DNA methylation and histone modification analyses reveal that EFD represses the transcription of HB21 through epigenetic mechanisms. Additionally, we demonstrate that HB21 directly represses the expression of genes crucial for pollen formation and anther dehiscence, including CalS5, RPG1/SWEET8, CYP703A2 and NST2. Collectively, our findings unveil a double negative regulatory cascade mediated by epigenetic modifications that coordinates anther development, offering insights into the epigenetic regulation of this process. Epigenetic modifications are crucial for plant development. Here, the authors find an epigenetically mediated double negative cascade for pollen formation and anther dehiscence, offering new insights into epigenetic regulation of anther development. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a disabling and highly prevalent condition affecting millions worldwide. Recently discovered, disulfidptosis represents a novel form of cell death induced by the excessive accumulation of cystine. Despite its significance, a systematic exploration of disulfidptosis-related genes (DRGs) in OA is lacking. Methods: This study utilized three OA-related datasets and DRGs. Differentially expressed (DE)-DRGs were derived by intersecting the differentially expressed genes (DEGs) from GSE114007 with DRGs. Feature genes underwent screening through three machine learning algorithms. High diagnostic value genes were identified using the receiver operating characteristic curve. Hub genes were confirmed through expression validation. These hub genes were then employed to construct a nomogram and conduct enrichment, immune, and correlation analyses. An additional validation of hub genes was performed through in vitro cell experiments. Results: SLC3A2 and PDLIM1 were designated as hub genes, displaying excellent diagnostic performance. PDLIM1 exhibited low expression in early chondrocyte differentiation, rising significantly in the late stage, while SLC3A2 showed high overall expression, declining in the late differentiation stage. Cellular experiments corroborated the correlation of SLC3A2 and PDLIM1 with chondrocyte inflammation. Conclusions: Two hub genes, SLC3A2 and PDLIM1, were identified in relation to disulfidptosis, providing potential directions for diagnosing and treating OA. [ABSTRACT FROM AUTHOR]

Purpose: Over the past 24 years, significant advancements have been made in applying artificial intelligence (AI) to musculoskeletal (MSK) diseases. However, there is a lack of analytical and descriptive investigations on the trajectory, essential research directions, current research scenario, pivotal focuses, and future perspectives. This research aims to provide a thorough update on the progress in AI for MSK diseases over the last 24 years. Methods: Data from the Web of Science database, covering January 1, 2000, to March 1, 2024, was analyzed. Using advanced analytical tools, we conducted comprehensive scientometric and visual analyses. Results: The findings highlight the predominant influence of the USA, which accounts for 28.53% of the total publications and plays a key role in shaping research in this field. Notable productivity was seen at institutions such as the University of California, San Francisco, Harvard Medical School, and Seoul National University. Valentina Pedoia is identified as the most prolific contributor. Scientific Reports had the highest number of publications in this area. The five most significant diseases are joint diseases, bone fractures, bone tumors, cartilage diseases, and spondylitis. Conclusion: This comprehensive scientometric assessment benefits both experienced researchers and newcomers, providing quick access to essential information and fostering the development of innovative concepts in this field. [ABSTRACT FROM AUTHOR]

During the past decade, mounting evidence has increasingly linked programmed cell death (PCD) to the progression and development of osteoarthritis (OA). There is a significant need for a thorough scientometric analysis that recapitulates the relationship between PCD and OA. This study aimed to collect articles and reviews focusing on PCD in OA, extracting data from January 1st, 2013, to October 31st, 2023, using the Web of Science. Various tools, including VOSviewer, CiteSpace, Pajek, Scimago Graphica, and the R package, were employed for scientometric and visualization analyses. Notably, China, the USA, and South Korea emerged as major contributors, collectively responsible for more than 85% of published papers and significantly influencing research in this field. Among different institutions, Shanghai Jiao Tong University, Xi'an Jiao Tong University, and Zhejiang University exhibited the highest productivity. Prolific authors included Wang Wei, Wang Jing, and Zhang Li. Osteoarthritis and Cartilage had the most publications in this area. Keywords related to PCD in OA prominently highlighted 'chondrocytes', 'inflammation', and 'oxidative stress', recognized as pivotal mechanisms contributing to PCD within OA. This study presents the first comprehensive scientometric analysis, offering a broad perspective on the knowledge framework and evolving patterns concerning PCD in relation to OA over the last decade. Such insights can aid researchers in comprehensively understanding this field and provide valuable directions for future explorations. [ABSTRACT FROM AUTHOR]

Insufficient antibacterial effects and over-fast degradation are the main limitations of magnesium (Mg)-based orthopedic implants. In this study, a sandwiched composite coating containing a triclosan (TCS)-loaded poly(lactic acid) (PLA) layer inside and brushite (DCPD) layer outside was prepared on the surface of the Mg–Nd–Zn–Zr (denoted as JDBM) implant. In vitro degradation tests revealed a remarkable improvement in the corrosion resistance and moderate degradation rate. The drug release profile demonstrated a controllable and sustained TCS release for at least two weeks in vitro. The antibacterial rates of the implant were all over 99.8% for S. aureus, S. epidermidis, and E. coli, demonstrating superior antibacterial effects. Additionally, this coated JDBM implant exhibited no cytotoxicity but improved cell adhesion and proliferation, indicating excellent cytocompatibility. In vivo assays were conducted by implant-related femur osteomyelitis and osseointegration models in rats. Few bacteria were attached to the implant surface and the surrounding bone tissue. Furthermore, the coated JDBM implant exhibited more new bone formation than other groups due to the synergistic biological effects of released TCS and Mg2+, revealing excellent osteogenic ability. In summary, the JDBM implant with the sandwiched composite coating could significantly enhance the antibacterial activities and osteogenic properties simultaneously by the controllable release of TCS and Mg2+, presenting great potential for clinical transformation. [ABSTRACT FROM AUTHOR]

Introduction/Aims: Many small‐sized, single‐center preclinical studies have investigated the benefits of introducing stem cells into the interior of nerve conduit. The aims of this meta‐analysis are to review and contrast the effects of various types of stem cells in in vivo models used to reconstruct peripheral nerve injuries (PNIs) and to assess the reliability and stability of the available evidence. Methods: A systematic search was conducted using Cochrane Library, Embase, PubMed, and Web of Science to identify studies conducted from January 1, 2000, to September 21, 2022, and investigate stem cell therapy in peripheral nerve reconstruction animal models. Studies that met the relevant criteria were deemed eligible for this meta‐analysis. Results: Fifty‐five preclinical studies with a total of 1234 animals were incorporated. Stem cells demonstrated a positive impact on peripheral nerve regeneration at different follow‐up times in the forest plots of five outcome indicators: compound muscle action potential (CMAP) amplitude, latency, muscle mass ratio, nerve conduction velocity, and sciatic functional index (SFI). In most comparisons, stem cell groups showed substantial differences compared with the control groups. The superior performance of adipose‐derived stem cells (ADSCs) in terms of SFI, CMAP amplitude, and latency (p <.001) was identified. Discussion: The findings consistently demonstrated a favorable outcome in the reconstruction process when utilizing different groups of stem cells, as opposed to control groups where stem cells were not employed. [ABSTRACT FROM AUTHOR]

Carbon trading is an effective way to achieve carbon neutrality. It is a market mechanism aimed at reducing global greenhouse gas emissions and carbon dioxide emissions. Blockchain technology can be applied to the carbon-trading scenario using characteristics that guarantee the security, decentralization, data immutability, and data traceability of the carbon-trading process. It would be difficult to implement carbon trading on blockchains for all enterprises and individuals, as the current performance of blockchains does not meet the requirements. There has been extensive research conducted on blockchain performance optimization, and the off-chain payment channel is one of the more mature solutions. This approach involves the transfer of transactions to off-chain transactions, thus avoiding high transaction fees. Existing research has addressed the problem of routing security and efficiency, with less emphasis on factors such as routing transaction costs, node reputation, and routing path considerations. This paper researches the optimization of payment routing in Payment Channel Networks (PCNs) and proposes the Multi-Factor Routing Payment Scheme (MFPS), which integrates factors such as the node reputation, transaction fee cost, and distance to select the route for payment transactions. In order to improve the success ratio of routing transactions, the transaction-splitting algorithm is proposed. To ensure the security and privacy of the transaction process, the Asymmetric Time-Lock Contract (ATLC) protocol is proposed. The results of extensive experimental simulations show that the MFPS proposed in this paper outperforms the ShortestPath, Cheapest, and SplitDistance algorithms. It achieves an approximately 13.8%∼49% improvement in the transaction success ratio and reduces the average transaction processing cost. The security and privacy measures can defend against wormhole and double-flower attacks and exhibit better performance in terms of computational verification and message overhead. [ABSTRACT FROM AUTHOR]

The emergence of computing power networks has improved the flexibility of resource scheduling. Considering the current trading scenario of computing power and network resources, most resources are no longer subject to change after being allocated to users until the end of the lease. However, this practice often leads to idle resources during resource usage. To optimize resource allocation, a trading mechanism is needed to encourage users to sell their idle resources. The Myerson auction mechanism precisely aims to maximize the seller's benefits. Therefore, we propose a resource allocation scheme based on the Myerson auction. In the scenario of the same user bidding distribution, we first combine the Myerson auction with Hyperledger Fabric by introducing a reserved price, which creates conditions for the application of blockchain in auction scenarios. Regarding different user bidding distributions, we propose a Myerson auction network model based on clustering algorithms, which makes the auction adaptable to more complex scenarios. The experimental findings show that the revenue generated by the auction model in both scenarios is significantly higher than that of the traditional sealed bid second-price auction, and can approach the expected revenue in the real Myerson auction scenario. [ABSTRACT FROM AUTHOR]

At present, some studies have combined federated learning with blockchain, so that participants can conduct federated learning tasks under decentralized conditions, sharing and aggregating model parameters. However, these schemes do not take into account the trusted supervision of federated learning and the case of malicious node attacks. This paper introduces the concept of a trusted computing sandbox to solve this problem. A federated learning multi-task scheduling mechanism based on a trusted computing sandbox is designed and a decentralized trusted computing sandbox composed of computing resources provided by each participant is constructed as a state channel. The training process of the model is carried out in the channel and the malicious behavior is supervised by the smart contract, ensuring the data privacy of the participant node and the reliability of the calculation during the training process. In addition, considering the resource heterogeneity of participant nodes, the deep reinforcement learning method was used in this paper to solve the resource scheduling optimization problem in the process of constructing the state channel. The proposed algorithm aims to minimize the completion time of the system and improve the efficiency of the system while meeting the requirements of tasks on service quality as much as possible. Experimental results show that the proposed algorithm has better performance than the traditional heuristic algorithm and meta-heuristic algorithm. [ABSTRACT FROM AUTHOR]

As a distributed machine learning paradigm, federated learning (FL) enables participating clients to share only model gradients instead of local data and achieves the secure sharing of private data. However, the lack of clients' willingness to participate in FL and the malicious influence of unreliable clients both seriously degrade the performance of FL. The current research on the incentive mechanism of FL lacks the accurate assessment of clients' truthfulness and reliability, and the incentive mechanism based on untruthful and unreliable clients is unreliable and inefficient. To solve this problem, we propose an incentive mechanism based on the reputation mechanism and reverse auction to achieve a more truthful, more reliable, and more efficient FL. First, we introduce the reputation mechanism to measure clients' truthfulness and reliability through multiple reputation evaluations and design a reliable client selection scheme. Then the reverse auction is introduced to select the optimal clients that maximize the social surplus while satisfying individual rationality, incentive compatibility, and weak budget balance. Extensive experimental results demonstrate that this incentive mechanism can motivate more clients with high-quality data and high reputations to participate in FL with less cost, which increases the FL tasks' economic benefit by 31% and improves the accuracy from 0.9356 to 0.9813, and then promote the efficient and stable development of the FL service trading market. [ABSTRACT FROM AUTHOR]

With the construction of power Internet of Things, the network scale is constantly expanding, the network structure is increasingly complex, and the number of terminals is increasing rapidly, resulting in huge pressure on network security protection, and it is urgent to build a credible operation environment to ensure that the Internet of Things runs safely and reliably. This paper proposes a power Internet of Things architecture combining trusted computing and blockchain. The architecture ensures the credibility and security of the terminal based on remote integrity verification and access control policy verification. A method for malicious node detection based on blockchain (MMNDB) is placed in acquisition layer. It is used to monitor the status of each acquisition devices in time, detect the malicious terminal in time, isolate the suspicious terminal with low credibility from the power Internet of Things, and ensure credibility of the acquisition terminal and the data collected. Under premise of ensuring the communication efficiency of the power communication network, this architecture can greatly ensure the stable working of the power Internet of Things. [ABSTRACT FROM AUTHOR]

A series of host-activated Tb3+ and Eu3+-co-doped CaNb2O6 (CNO) phosphors with tunable luminescence were synthesized via high-temperature solid-state method with the purpose of developing single-component phosphors for white light-emitting diode (LED) devices. Benefiting from blue broadband emission of CNO and multiple energy transfer of NbO43− → Tb3+ → Eu3+, white light-emitting CNO:Tb3+, Eu3+ phosphors were achieved by reasonably adjusting the doping content of Tb3+ and Eu3+ ions. Two typical white light-emitting phosphors, CNO:0.015Tb3+, 0.02Eu3+ with color coordinate (0.33, 0.33) and CNO:0.10Tb3+, 0.05Eu3+ with color coordinate (0.40, 0.39), could maintain 63% and 73% of the initial integral intensity at 150 °C and had good color stability. Subsequently, the representative CNO:0.015Tb3+, 0.02Eu3+ phosphors were successfully applied to a 275 nm ultraviolet chip to fabricate a white LED device with a low correlated color temperature of 3464 K and a high color rendering index of 86.0. The above results indicate that synthesized single-component white light-emitting CNO:Tb3+, Eu3+ phosphors have good application prospects in the field of white LED. Moreover, this strategy of using self-activated host and multiple energy transfer also provides guidance for the design of novel white light-emitting phosphors. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) is one of the important applications under the 5G massive machine type of communication (mMTC) scenario. To ensure the high reliability of IIoT services, it is necessary to apply an efficient resource allocation method under the dynamic and complex environment. In view of the absence of energy-efficient resource management architecture for the entire network, this article proposes an intelligent-driven green resource allocation mechanism for the IIoT under 5G heterogeneous networks. First, an intelligent end-to-end self-organizing resource allocation framework for IIoT service is given. Next, an energy-efficient resource allocation model within the framework is proposed. It is then solved by an intelligent mechanism with the asynchronous advantage actor critic driven deep reinforcement learning algorithm. Through the comparison analysis of different methods and rewards under IIoT scenarios with proper parameters setting, the proposed method can achieve better performance than other traditional deep learning (DL) methods and maintain service quality above accepted levels as well. [ABSTRACT FROM AUTHOR]