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This study presents the Maize PeptideAtlas resource (www.peptideatlas.org/builds/maize) to help solve questions about the maize proteome. Publicly available raw tandem mass spectrometry (MS/MS) data for maize collected from ProteomeXchange were reanalyzed through a uniform processing and metadata annotation pipeline. These data are from a wide range of genetic backgrounds and many sample types and experimental conditions. The protein search space included different maize genome annotations for the B73 inbred line from MaizeGDB, UniProtKB, NCBI RefSeq, and for the W22 inbred line. 445 million MS/MS spectra were searched, of which 120 million were matched to 0.37 million distinct peptides. Peptides were matched to 66.2% of proteins in the most recent B73 nuclear genome annotation. Furthermore, most conserved plastid- and mitochondrial-encoded proteins (NCBI RefSeq annotations) were identified. Peptides and proteins identified in the other B73 genome annotations will improve maize genome annotation. We also illustrate the high-confidence detection of unique W22 proteins. N-terminal acetylation, phosphorylation, ubiquitination, and three lysine acylations (K-acetyl, K-malonyl, and K-hydroxyisobutyryl) were identified and can be inspected through a PTM viewer in PeptideAtlas. All matched MS/MS-derived peptide data are linked to spectral, technical, and biological metadata. This new PeptideAtlas is integrated in MaizeGDB with a peptide track in JBrowse.

Metal–organic frameworks (MOFs) have attained broad research attention in the areas of sensors, resistive memories, and optoelectronic synapses on the merits of their intriguing physical and chemical properties. In this review, recent progress on the synthesis of MOFs and their electronic applications is introduced and discussed. Initially, the crystal structures and properties of MOFs encompassing optical, electrical, and chemical properties are discussed in brief. Subsequently, advanced synthesis methods for MOFs are introduced, categorized into hydrothermal approach, microwave synthesis, mechanochemical synthesis, and electrochemical deposition. After that, the various roles of MOFs in widespread applications, including sensing, information storage, optoelectronic synapses, machine learning, and artificial intelligence, are discussed, highlighting their versatility and the innovative solutions they provide to long-standing challenges. Finally, an outlook on remaining challenges and a future perspective for MOFs are proposed.

With the advent of mobile networks, we are witnessing an unprecedented shift in the landscape of mobile network services, evolving from traditional voice calls to advanced artificial intelligence (AI) services. This paper delves into the intricacies of this evolution, particularly emphasizing the deep integration of AI agents into 6G networks. Despite recent researches in using large language model (LLM) and AI agent for network automation, the fundamental mobile AI agent use cases, their network requirements, potential network architecture and enabling technologies for supporting the pervasive AI agents in 6G era are largely unexplored. In this article, we present an in-depth analysis of typical mobile AI agent use cases in 6G, consisting of AI agent-based 6G network automation, handheld personalized agents, connected robotics and autonomous systems, and wearable AI agent. Then, we elucidate a novel system architecture that supports identified use cases. The article also addresses core aspects of enabling technologies, including 6G agent and application agent collaboration, efficient model and memory management, coordinated agent-to-agent communication and support of multi-modal data transmission. A proof of concept prototype is also presented to demonstrate 6G agent and application AI agent collaboration. Finally, three challenges and research directions: energy saving, security protection and AI agent tailored communication are discussed. This article lays a foundation for understanding the role of 6G in realizing the full potential of AI agents in various applications.

Perovskite nanocrystal (PNC) solids are promising materials for optoelectronic applications. Recent studies have shown that exciton diffusion in PNC solids occurs viaalternate exciton hopping (EH) and photon recycling (PR). The energy disorder induced by the size distribution is a common factor in PNC solids, and the impact of this energy disorder on the exciton diffusion remains unclear. Here, we investigated the exciton diffusion in CsPbBr3NC solids with a Gaussian size distribution of 11.2 ± 6.8 nm viasteady and time-resolved photoluminescence (PL) spectroscopy with multiple detection bands in transmission mode. Our results indicated that exciton diffusion was controlled by a downhill transfer among the different energy sites through the disordered energy landscape, as confirmed by the accompanying low-temperature PL analysis. A detailed examination revealed that the acceptor distribution in tandem with the reabsorption coefficient determined the contribution of EH and PR to exciton transfer between different energy sites. Consequently, the exciton diffusion mechanism varied in PNC solids of different thicknesses: in a thin solid with a thickness of several hundred nanometers, the exciton transfer was dominated by efficient EH and PR from the high-energy sites to the lower-energy sites; in a few-micrometer-thick solid, transfer from the medium-energy sites toward the lower-energy sites also became prominent and occurred mainly through PR. These findings enhance the understanding of the vital role that the acceptor distribution plays in the exciton diffusion process in PNC solids, providing important insights for optoelectronic applications based on PNC solids. Our work also exploits the use of commonly available tools for in-depth exciton diffusion studies, which reveals the interior diffusion information that is usually hidden in surface sensitive PL imaging methods.

Tropomyosin (TM) is the main allergen in shrimp (Litopenaeus vannamei). In this study, the effects of allergenicity and structure of TM by glycosylation (GOS-TM), phosphate treatment (SP-TM), and glycosylation combined with phosphate treatment (GOS-SP-TM) were investigated. Compared to GOS-TM and SP-TM, the IgG/IgE binding capacity of GOS-SP-TM was significantly decreased with 63.9 ± 2.0 and 49.7 ± 2.7%, respectively. Meanwhile, the α-helix content reduced, surface hydrophobicity increased, and 10 specific amino acids (K30, K38, S39, K48, K66, K74, K128, K161, S210, and K251) were modified by glycosylation on six IgE linear epitopes of GOS-SP-TM. In the BALB/c mice allergy model, GOS-SP-TM could significantly reduce the levels of specific IgE, IgG1, and CD4+IL-4+, while the levels of IgG2a, CD4+CD25+Foxp3+, and CD4+IFN-γ+were increased, which equilibrated Th1 and Th2 cells, thus alleviating allergic symptoms. These results indicated that glycosylation combined with phosphate treatment can provide a new insight into developing hypoallergenic shrimp food.

PtCo nanoalloys (NAs) deposited on carbon black are emerging as robust electrocatalysts for addressing the sluggish kinetic issue of oxygen reduction reaction (ORR). However, developing a simple and low-cost method to synthesize PtCo/C with excellent performance is still a great challenge. In this work, a one-pot method was used to successfully obtain the PtCo NAs on commercial carbon supports of acetylene black and Ketjenblack ECP600JD, respectively. Compared with those grown on Ketjenblack ECP600JD, the PtCo NAs grown on acetylene black exhibited higher electrochemical surface area (ECSA) and mass activity (MA), which may be attributed to the different particle sizes of PtCo NAs, distinct hydrophilicity, electroconductivity and charge distribution between the carbon supports and PtCo NAs. Our study provides valuable insights into the optimal design of carbon-supported ORR electrocatalysts with exceptional activity and durability.

This article proposes a decayed trust propagation method among multiple overlapping communities, and establishes a trust-driven consensus model for social network group decision making (SN-GDM). On the one hand, the use of overlapping nodes simplifies trust propagation by bridging complex connections among multiply overlapping communities. On the other hand, trust models' accuracy and realism are enhanced with the concept of trust decay, which accounts for the temporal dynamics of trust propagation. Thus, a first objective of this article is to develop a trust propagation operator based on trust decay among multiple overlapping communities by leveraging overlapping nodes. By incorporating overlapping nodes' diverse trust relationships and perspectives, this approach allows to achieve reliable sources for generating recommendations in SN-GDM. A second objective of this article is to design a decayed trust propagation induced consensus model to determine the optimal combination of overlapping nodes and feedback parameters, while balancing consensus efficiency and interaction willingness. The innovation of this approach is grounded in its ability to avoid excessive group adjustment to reach consensus. Numerical examples and comparative analysis demonstrate the model's performance in achieving efficient consensus under various representative recommendations.

In 2019, the EAT-Lancet Commission proposed a healthy dietary pattern that, along with reductions in food waste and improved agricultural practices, could feed the increasing global population sustainably. We developed a Planetary Health Diet Index (PHDI) to quantify adherence to the EAT-Lancet reference diet.

We derive a solution of a quantum corrected charged AdS black hole surrounded by perfect fluid dark matter (PFDM). The thermodynamic quantities and the modified first law of thermodynamics are obtained by considering the black hole mass as chemical enthalpy rather than energy. A first order phase transition (small to large black holes) is recognized from the swallowtail in the Gibbs free energy Gversus Hawking temperature THgraph. The black hole exhibits the characteristic “standard liquid/gas behavior” of van der Waals fluids on the P−Vgraph. By further studying the heat capacity of the black hole, we identify a second order phase transition at the event horizons x1and x2, respectively. Thus, we demonstrate that a quantum corrected charged AdS black hole surrounded by PFDM can be studied for its thermodynamic stability and phase transition from the perspective of black hole chemistry.

Recently, memristors have garnered widespread attention as neuromorphic devices that can simulate synaptic behavior, holding promise for future commercial applications in neuromorphic computing. In this paper, we present a memristor with an Au/Bi3.2La0.8Ti3O12(BLTO)/ITO structure, demonstrating a switching ratio of nearly 103over a duration of 104 s. It successfully simulates a range of synaptic behaviors, including long-term potentiation and depression, paired-pulse facilitation, spike-timing-dependent plasticity, spike-rate-dependent plasticity etc. Interestingly, we also employ it to simulate pain threshold, sensitization, and desensitization behaviors of pain-perceptual nociceptor (PPN). Lastly, by introducing memristor differential pairs (1T1R-1T1R), we train a neural network, effectively simplifying the learning process, reducing training time, and achieving a handwriting digit recognition accuracy of up to 97.19 %. Overall, the proposed device holds immense potential in the field of neuromorphic computing, offering possibilities for the next generation of high-performance neuromorphic computing chips.

Suboptimal diets may promote undesired weight gain in youths, with high ultraprocessed food (UPF) intake becoming a significant concern in the United States.

Today’s computing systems, to meet the enormous demands of information processing, have driven the development of brain-inspired neuromorphic systems. However, there are relatively few optoelectronic devices in most brain-inspired neuromorphic systems that can simultaneously regulate the conductivity through both optical and electrical signals. In this work, the Au/MXene/Y:HfO2/FTO ferroelectric memristor as an optoelectronic artificial synaptic device exhibited both digital and analog resistance switching (RS) behaviors under different voltages with a good switching ratio (>103). Under optoelectronic conditions, optimal weight update parameters and an enhanced algorithm achieved 97.1% recognition accuracy in convolutional neural networks. A new logic gate circuit specifically designed for optoelectronic inputs was established. Furthermore, the device integrates the impact of relative humidity to develop an innovative three-person voting mechanism with a veto power. These results provide a feasible approach for integrating optoelectronic artificial synapses with logic-based computing devices.

Purpose: Different external supply scenarios faced by the retailers will affect their choice of strategy when supply is disrupted and becomes far less than demand, urgently. This study focuses on analyzing both demand and supply side response strategies to meet customer demand and reduce the impact of the shortage during supply disruptions. Design/methodology/approach: According to the quantity of products that the external market can provide, the external supply scenarios were divided into sufficient-type external supply and learning-type external supply. A two-echelon perishable goods supply chain was analyzed, and three kinds of contingency strategy models for downstream retailers were investigated. First, in the sufficient external supply scenario, the optimal price and transshipment quantity to maximize retailer's profits is discussed. Second, in the scenario of learning-type external supply, this study analyzes the optimal decision in three mechanisms of the hybrid strategy and their application: price priority mechanism, quantity priority mechanism and price–quantity balance mechanism. Furthermore, the influence of penalty cost and supply on the priority orders of different mechanisms was studied. Findings: Results show that comparing the two pure strategies (pricing strategy and transshipment strategy)it was noted that the hybrid strategy produces the best results in sufficient-type external supply scenario. In the learning-type external supply scenario, a numerical study has shown the existence of three areas in case of penalty cost and supplier's capacity, and each areas has different priority orders of the three mechanisms. Under the situation of learning external supply, the retailer's optimal strategy is affected by parameters such as penalty cost and supply volume. Originality/value: The main innovation of the work lies in the following: First; the external supply situation was divided into sufficiency type and learning type, which improves the external situation faced by retailers after the outbreak of emergencies, helps retailers understand the external situation, conforms to the actual situation and has certain practical application value. Second; in the context of learning external supply, there are three coping strategies for retailers, including: Price priority mechanism, Quantity priority mechanism and Pricing and transshipment balance mechanism. This will help retailers make strategic choices, make more scientific management decisions and improve the supply chain emergency management theory. Third; the demand side response was managed through the change of external supply during supply side recovery period and supply disruption. The proposed model enables managing and analyzing supply disruption efficiently and effectively via handling uncertainty by considering all aspects of decision-making process. The proposed model can be applied in various fields such as vegetable and fruit, fresh food, etc. [ABSTRACT FROM AUTHOR]

In situvaccines (ISVs) utilize the localized delivery of chemotherapeutic agents or radiotherapy to stimulate the release of endogenous antigens from tumors, thereby eliciting systemic and persistent immune activation. Recently, a bioinspired ISV strategy has attracted tremendous attention due to its features such as an immune adjuvant effect and genetic plasticity. M13 bacteriophages are natural nanomaterials with intrinsic immunogenicity, genetic flexibility, and cost-effectiveness for large-scale production, demonstrating the potential for application in cancer vaccines. In this study, we propose an ISV based on the engineered M13 bacteriophage targeting CD40 (M13CD40) for dendritic cell (DC)-targeted immune stimulation, named H-GM-M13CD40. We induce immunogenic cell death and release tumor antigens through local delivery of (S)-10-hydroxycamptothecin (HCPT), followed by intratumoral injection of granulocyte-macrophage colony stimulating factor (GM-CSF) and M13CD40to enhance DC recruitment and activation. We demonstrate that this ISV strategy can result in significant accumulation and activation of DCs at the tumor site, reversing the immunosuppressive tumor microenvironment. In addition, H-GM-M13CD40can synergize with the PD-1 blockade and induce abscopal effects in cold tumor models. Overall, our study verifies the immunogenicity of the engineered M13CD40bacteriophage and provides a proof of concept that the engineered M13CD40phage can function as an adjuvant for ISVs.

The modified Claus Process stands out as the predominant method for sulfur recovery within natural gas and crude oil processing facilities. Over the past five decades, in a bid to mitigate the environmental consequences of SO2emissions, the efficiencies of sulfur recovery units have been enhanced through the integration of tail gas treating units (TGTU). Globally, it is imperative that this sulfur recovery efficiency attains a minimum of 99.95%. This paper delves into both bench- and pilot-scale evaluations of utilizing commercial 5 A molecular sieves to extract H2S from specific tail gas streams, as part of scrutinizing the efficacy of a novel tail gas treatment process, delineated in Patent No. US 10662061 B1 (May 26, 2020). The evaluation revealed that employing a 5 A molecular sieve to remove H2S from dry tail gas treating presents distinct advantages. Notably, the CO2adsorption capacity on 5 A molecular sieves, being several times that of H2S, did not substantially impair the physical stability and removal capacity of 99.95+% tail gas treating, even after 99 adsorption/desorption cycles. Furthermore, the evaluation demonstrated that extended regeneration at 300°C (as opposed to 250°C) following the 50th and 90th cycles could effectively reinstate the original removal capacity of the 5 A molecular sieves. Additionally, the gas emanating from the 5 A molecular sieve bed could be reused as regeneration gas for the second stage of the patented process. The H2S removal capacity of 5 A molecular sieves from dry tail gas, approximately 1.3 mmol/g, aligns with the reported capacities of other adsorbents, which range from 0.37 to 1.53 mmol/g. Given the cost-effectiveness of 5 A molecular sieves compared to AgY molecular sieves, and based on the evaluation outcomes, it is judicious to conclude that the use of 5 A molecular sieves will be a compelling option for future tail gas treating within sulfur recovery units.

In this paper, we study the effects of the rotation parameter a, the twist parameter n, the string cloud parameter b, the quintessence state parameter ωqand the charge parameter qon the horizons and ergosphere of rotating and twisting charged black holes with cloud of strings and quintessence, and obtain the equations of motion and effective potential of the particle on the equatorial plane of black hole. We find that a particle with the critical angular momentum L=LCfalling from infinity reaches the event horizon (ur= 0) and satisfies the circular orbit condition Veff=Veff′=0. We derive the expression of the center-of-mass (CM) energy of two particles with different masses from the equations of particle motion. We show that the CM energy can be arbitrarily large for extremal black holes when the particles reach the event horizon by adjusting the angular momentum of the incident particles. However, for non-extremal black holes, the CM energy of particles that reach the event horizon cannot diverge by adjusting the angular momentum of the incident particles.

Cancer progression and treatment-associated cellular stress impairs therapeutic outcome by inducing resistance. Endoplasmic reticulum (ER) stress is responsible for core events. Aberrant activation of stress sensors and their downstream components to disrupt homeostasis have emerged as vital regulators of tumor progression as well as response to cancer therapy. Here, an orchestrated nanophotoinducer (ERsNP) results in specific tumor ER-homing, induces hyperthermia and mounting oxidative stress associated reactive oxygen species (ROS), and provokes intense and lethal ER stress upon near-infrared laser irradiation. The strengthened “dying” of ER stress and ROS subsequently induce apoptosis for both primary and abscopal B16F10 and GL261 tumors, and promote damage-associated molecular patterns to evoke stress-dependent immunogenic cell death effects and release “self-antigens”. Thus, there is a cascade to activate maturation of dendritic cells, reprogram myeloid-derived suppressor cells to manipulate immunosuppression, and recruit cytotoxic T lymphocytes and effective antitumor response. The long-term protection against tumor recurrence is realized through cascaded combinatorial preoperative and postoperative photoimmunotherapy including the chemokine (C–C motif) receptor 2 antagonist, ERsNP upon laser irradiation, and an immune checkpoint inhibitor. The results highlight great promise of the orchestrated nanophotoinducer to exert potent immunogenic cell stress and death by reinforcing ER stress and oxidative stress to boost cancer photoimmunotherapy.

We aimed to compare outcomes of patients with first primary clinical T1a-bN0M0 NSCLC treated with surgery or stereotactic body radiation therapy (SBRT).

Inconsistent results have been reported regarding the association between low-to-moderate arsenic (As) exposure and diabetes. The effect of liver dysfunction on As-induced diabetes remains unclear. The cross-sectional study included 10,574 adults from 2017–2018 China National Human Biomonitoring. Urinary total As (TAs) levels were analyzed as markers of As exposure. Generalized linear mixed models and restricted cubic splines models were used to examine the relationships among TAs levels, serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) concentrations, and diabetes prevalence. Mediating analysis was performed to assess whether liver dysfunction mediated the association between TAs and diabetes. Overall, the OR (95% CI) of diabetes in participants in the second, third, and fourth quartiles of TAs were 1.08 (0.88, 1.33), 1.17 (0.94, 1.45), and 1.52 (1.22, 1.90), respectively, in the fully adjusted models compared with those in the lowest quartile. Serum ALT was positively associated with TAs and diabetes. Additionally, mediation analyses showed that ALT mediated 4.32% of the association between TAs and diabetes in the overall population and 8.86% in the population without alcohol consumption in the past year. This study suggested that alleviating the hepatotoxicity of As could have implications for both diabetes and liver disease.

Entering the 5G era, the mobile network operators (MNO) are facing greater challenges in providing services cost effectively than any other previous generations. The potential solutions to this are lying on the emerging trend of deep convergence of information technology (IT), communication technology (CT) and data technology (DT). In particular, the O-RAN technology, the representation of such ICDT convergence and proposed by the O-RAN ALLIANCE in 2018, is transforming Radio Access Networks towards a new paradigm featuring openness, cloudification and intelligence. O-RAN has gained huge attention from both industry and academia since its inception. In this paper, we presented the recent endeavors from China Mobile, including our deployment scenarios, various test results from open fronthaul, cloud platform to the intelligent controller. Our rich and comprehensive tests have demonstrated the viability and superiority of current O-RAN technologies. Furthermore, we also provide our deep thinking on the O-RAN future evolution in order to better serve the emerging applications such as Metaverse, cloud extended-reality (XR), extensive enterprise private 5G verticals and so on.

Deep learning frameworks or compilers optimize the operators in computation graph using fixed templates via significant engineering efforts, which may miss potential optimizations such as operator fusion. Therefore, automatically implementing and optimizing the emerging new combinations of operators on a specific hardware accelerator is of importance. In this article, we introduce GTCO, a tensor compilation system designed to accelerate transformer-based vision models’ inference on GPUs. GTCO tackles the operator fusion techniques in the transformer-based model using a novel dynamic programming algorithm and proposes a search policy with new sketch generation rules for the fused batch matrix multiplication and softmax operators. Tensor programs are sampled from an effective search space, and a hardware abstraction with hierarchical mapping from tensor computation to domain-specific accelerators (Tensor Cores) is formally defined. Finally, our framework can map and transform tensor expression into efficient CUDA kernels with hardware intrinsics on GPU. Our experimental results demonstrate that GTCO improves the end-to-end execution performance by up to $1.73\times $ relative to the cutting-edge deep learning library TensorRT on NVIDIA GPUs with Tensor Cores.

Protein intake plays an important role in maintaining the health status of older adults. However, few epidemiologic studies examined midlife protein intake in relation to healthy aging.

This laboratory experiment offers a research-style experience designed for senior undergraduate students in a multidisciplinary laboratory setting. The experiment focuses on the synthesis, characterization, and application of a cation-exchange membrane made of COF-SO3Na/PAN for ion separation. By participating in this practical session, students gain firsthand experience in both the fundamental procedures of membrane synthesis and the application of widely recognized scientific research methodologies. Furthermore, the experiment offers students hands-on opportunities to become acquainted with modern instrumental techniques extensively used in chemical research, such as Fourier-transform infrared spectroscopy (FT-IR), powder X-ray diffraction (PXRD), and scanning electron microscopy (SEM) analysis. The acquisition of these skills not only provides students with valuable practical experience but also equips them with the ability to effectively communicate their future research accomplishments to the broader scientific community. Moreover, the experiment demonstrates the significant potential of the synthesized membrane material for real-world applications, as evidenced by its effectiveness in osmotic energy extraction through ion separation using reverse electrodialysis (RED). This serves to underscore the practical utility of the research findings and reinforces students’ understanding of the broader implications of materials chemistry. It also enables instructors to contextualize the experiment within the broader field, encouraging students to explore the frontiers of knowledge and innovation.

The ongoing growth of data traffic from existing and new latency-sensitive applications poses a challenge to the data center network. Wavelength-routed optical circuit switching promises high bandwidth, low power and latency networking for data centers, but requires a wideband wavelength tunable source capable of nanosecond-scale hitless wavelength switching at every node. A tunable source based on DFB laser arrays is a potential candidate due to the high-speed dynamic property and high single-longitudinal-mode (SLM) stability of the DFB laser. However, the large-scale integration of the DFB laser and the high precision control of the lasing wavelength are still problems. In this work, we proposed and fabricated a monolithic integrated 16-channel DFB laser array with high SLM stability and 100-GHz channel spacing by using the reconstruction-equivalent-chirp (REC) technique. The maximum output power of each channel was above 6 mW. Based on the custom-designed fast switching drive board with a field-programmable gate array (FPGA), the switch-ON and OFF time between any pair of channel combinations were measured at less than 10 ns. Dynamic switching wavelengths remained stable and resided within the receiver bandwidth. Wavelength switching experiments for modulated light signals were done by driving the external modulator, achieving bit error performance at 1 × 10−9 for 10 Gb/s NRZ data.

The concept of augmented reality (AR) assistants has captured the human imagination for decades, becoming a staple of modern science fiction. To pursue this goal, it is necessary to develop artificial intelligence (AI)-based methods that simultaneously perceive the 3D environment, reason about physical tasks, and model the performer, all in real-time. Within this framework, a wide variety of sensors are needed to generate data across different modalities, such as audio, video, depth, speech, and time-of-flight. The required sensors are typically part of the AR headset, providing performer sensing and interaction through visual, audio, and haptic feedback. AI assistants not only record the performer as they perform activities, but also require machine learning (ML) models to understand and assist the performer as they interact with the physical world. Therefore, developing such assistants is a challenging task. We propose ARGUS, a visual analytics system to support the development of intelligent AR assistants. Our system was designed as part of a multi-year-long collaboration between visualization researchers and ML and AR experts. This co-design process has led to advances in the visualization of ML in AR. Our system allows for online visualization of object, action, and step detection as well as offline analysis of previously recorded AR sessions. It visualizes not only the multimodal sensor data streams but also the output of the ML models. This allows developers to gain insights into the performer activities as well as the ML models, helping them troubleshoot, improve, and fine-tune the components of the AR assistant.

Defects in the prelamin A processing enzyme caused by loss-of-function mutations in the ZMPSTE24gene are responsible for a spectrum of progeroid disorders characterized by the accumulation of farnesylated prelamin A. Here we report that defective prelamin A processing triggers nuclear RIPK1-dependent signalling that leads to necroptosis and inflammation. We show that accumulated prelamin A recruits RIPK1 to the nucleus to facilitate its activation upon tumour necrosis factor stimulation in ZMPSTE24-deficient cells. Kinase-activated RIPK1 then promotes RIPK3-mediated MLKL activation in the nucleus, leading to nuclear envelope disruption and necroptosis. This signalling relies on prelamin A farnesylation, which anchors prelamin A to nuclear envelope to serve as a nucleation platform for necroptosis. Genetic inactivation of necroptosis ameliorates the progeroid phenotypes in Zmpste24−/−mice. Our findings identify an unconventional nuclear necroptosis pathway resulting from ZMPSTE24 deficiency with pathogenic consequences in progeroid disorder and suggest RIPK1 as a feasible target for prelamin A-associated progeroid disorders.

Solid oxide electrolysis cells (SOECs) provide a promising way for converting renewable energy into chemical fuels. Traditionally, NiO/CGO (nickel-gadolinium doped ceria) cermet has shown its excellent properties in ionic and electronic conductivity under reducing conditions. Herein, we developed a novel 1D NiO/CGO cathode through a cerium metal-organic framework (MOF) derived process. The cathode’s 1D nanostructure integrated with a microchannel scaffold facilitates enhanced mass transport, providing vertically aligned pathways for CO2and H2O diffusion. Additionally, the 1D framework increases the number of interfacial sites and reduces ion diffusion distances, thereby simplifying electron/ion transport. Consequently, this advanced cathode achieved a significant breakthrough in SOEC performance, maintaining efficient CO2and H2O electrolysis at an extraordinary current density of 1.41 A/cm2at 1.5 V and excellent stability over 24 h at 850 °C. The enhanced performance of this newly developed cathode not only achieves a remarkable 100% improvement compared to those of NiO/CGO cathodes with varying geometrical configurations but also surpasses those of commercial NiO/CGO catalysts by an outstanding 40% when tested under identical conditions. The development of the 1D NiO/CGO enhances the efficiency and durability of ceramic cathodes for CO2and H2O co-electrolysis in SOECs and improves the scalability and effectiveness of SOECs in renewable energy applications.

The edge computing paradigm presents a promising solution for low-latency and high-throughput data processing, which is critical for future life-changing applications, such as autonomous driving and metaverse. Concurrently, the current radio access network (RAN) is evolving toward virtualization, intelligence, and flexibility, making it an ideal edge computing platform. However, the full potential of such a synergistic system has not yet been unleashed due to the lack of coordination between communication and computing. To better support diverse applications, this article proposes a communication-computing co-design approach for the next-generation RAN to embrace computing in a built-in manner. At a high level, we overview the features of communication and computing, identify the efforts and limitations of the current system, and introduce the designed architecture. From the perspective of communication and computing coordination, key technologies are introduced, including internetworking for computing terminals, traffic steering, and mobility management. A use case referring to mobility management is studied to verify the effectiveness of the built-in approach. We outline some critical challenges, thereby indicating future potential research directions in future RAN.

We investigated data from 180 consecutive patients with myelodysplastic/myeloproliferative neoplasms with SF3B1mutation and thrombocytosis (MDS/MPN-SF3B1-T) who were diagnosed according to the 2022 World Health Organization (WHO) classification of myeloid neoplasms to identify covariates associated with survival. At a median follow-up of 48 months (95% confidence interval [CI] 35–61 months), the median survival was 69 months (95% CI 59–79 months). Patients with bone marrow ring sideroblasts (RS) < 15% had shorter median overall survival (OS) than did those with bone marrow RS ≥ 15% (41 months [95% CI 32–50 months] versus76 months [95% CI 59–93 months]; P< 0.001). According to the univariable analyses of OS, age ≥ 65 years (P< 0.001), hemoglobin concentration (Hb) < 80 g/L (P= 0.090), platelet count (PLT) ≥ 800 × 10E + 9/L (P= 0.087), bone marrow RS < 15% (P< 0.001), the Revised International Prognostic Scoring System (IPSS-R) cytogenetic category intermediate/poor/very poor (P= 0.005), SETBP1mutation (P= 0.061) and SRSF2mutation (P< 0.001) were associated with poor survival. Based on variables selected from univariable analyses, two separate survival prediction models, a clinical survival model, and a clinical-molecular survival model, were developed using multivariable analyses with the minimum value of the Akaike information criterion (AIC) to specifically predict outcomes in patients with MDS/MPN-SF3B1-T according to the 2022 WHO classification.

Thermal vacuum test is an important procedure in the development of spacecraft and load. Due to the various layouts of heaters and temperature sensors for kinds of devices under test (DUTs), the temperature responses during heating process are not same. The thermal vacuum test is the universal experiment aimed at different devices, that the temperature control algorithm of test cannot be optimized for each test. These factors would cause temperature overshoot and multiple oscillations near the preset value during test, that may damage DUTs. This article proposes an anti-windup Smith fuzzy proportional-integral-derivative (PID) temperature controller based on residual error integral predictor. The Smith fuzzy PID (FPID) controller could automatically tune the controller’s coefficients and reduce the impact of time delay. On this basis, two steps are taken to suppress the overshoot in temperature control. First, the fuzzy rules are optimized to refine the tuning process of fuzzy logic at different error stages. Then, by predicting the error integral in the remaining heating time, the original integral term of the controller is replaced to resist the integral saturation. In addition, a miniaturized multichannel temperature acquisition device has been designed, which could be installed at the front end of the signal link, closer to the sensors, thereby reducing interference during signal transmission. Through extensive simulations, the proposed method outperforms traditional temperature control methods in the stages of heating and cooling. After verification by a real thermal vacuum test, the proposal can effectively suppress overshoot in temperature control under vacuum conditions, and also quickly stabilize the temperature near the set value, further reducing the impact of overshoot on the load.

5G is becoming more agile and inevitably more complicated for supporting various deployment scenarios and diversified applications. Artificial Intelligence (AI) is becoming one of the most essential driving forces for 5G evolution to handle the complexity and further improve the network spectrum efficiency, energy efficiency and user experience. In this article, we share the latest standardization progress of the industry on AI in 5G. Evolving directions for the intelligent radio access networks (RAN) are analyzed and further enhancements on both AI for RAN and RAN for AI are proposed based on a hierarchical and distributed intelligent RAN architecture. Case studies are presented along with field trial results. An innovative AI-empowered RAN and service-aware cross layer optimization framework is illustrated for the quality of experience (QoE) optimization of cloud virtual reality (VR) with converged communication and computing services provided by RAN. The extension of the solution to support the AI/ML services is also discussed to shed some light on how to evolve network for AI toward the 6G vision.

The research found that after doping with rare earth elements, a large number of electrons and holes will be produced on the surface of AlN, which makes the material have the characteristics of spontaneous polarization. A new type of ferroelectric material has made a new breakthrough in the application of nitride-materials in the field of integrated devices. In this paper, the application prospects and development trends of ferroelectric material ScAlN in memristors are reviewed. Firstly, various fabrication processes and structures of the current ScAlN thin films are described in detail to explore the implementation of their applications in synaptic devices. Secondly, a series of electrical properties of ScAlN films, such as the current switching ratio and long-term cycle durability, were tested to explore whether their electrical properties could meet the basic needs of memristor device materials. Finally, a series of summaries on the current research studies of ScAlN thin films in the synaptic simulation are made, and the working state of ScAlN thin films as a synaptic device is observed. The results show that the ScAlN ferroelectric material has high residual polarization, no wake-up function, excellent stability and obvious STDP behavior, which indicates that the modified material has wide application prospects in the research and development of memristors.

Due to the prevalence of electronic devices, intelligent sensors have attracted much interest for the detecting and providing alarms with respect to indoor electrical safety. Nonetheless, how to effectively identify various indoor electrical safety hazards remains a challenge. In this study, we fabricated single-walled carbon nanotube/poly(3-hexylthiophene-2,5-diyl) (SWCNT/P3HT) composites with exceptional bifunctional thermoelectric and photoelectric responses. Through synergy of the thermo-/photoelectric effects, the composites yielded greatly enhanced output voltages compared with the use of thermoelectric effects alone. Interestingly, modes of heat transfer can be effectively distinguished using the nominal Seebeck coefficients. Based on the remarkable output voltages and deviations in the nominal Seebeck coefficients, we developed indoor intelligent sensors capable of effectively identifying and monitoring diverse indoor electrical conditions, including electrical overheating, fire, and air conditioning flow. This pioneering investigation proposes a novel avenue for designing intelligent sensors that can recognize heat transfer modes and hence effectively monitor indoor electrical safety hazards.

AimsCurrently, the clinicopathological characteristics of gastric cancer (GC) with oncogenic NTRK alterations are not well known. Although NTRK fusion has been identified as prevalent in DNA mismatch repair protein deficient (dMMR) colorectal cancer (CRC), the relationship between NTRK alterations and dMMR protein expression in GC has not been previously explored.MethodsOur study comprised 51 cases of EBV(Epstein-barr virus)-associated gastric carcinomas, 94 cases of dMMR GC, 90 cases of gastric adenocarcinoma with hepatoid or enteroblastic differentiation (GAHED) and 256 cases of conventional GC. Furthermore, to investigate the connection between NTRK fusion and dMMR proteins, we collected dMMR tumours of various types, including 21 cases of duodenal adenocarcinomas, 46 endometrioid carcinomas and 82 CRCs. NTRK fusion and amplification were screened in GC and various types of dMMR tumours using fluorescence in situ hybridisation (FISH), while cases positive for FISH translocation underwent next-generation sequencing testing.ResultsOur findings revealed the existence of two cases each of NTRK fusions and NTRK amplifications, which were all enriched in case of GAHED. Additionally, following an analysis of several types of cancers, we discovered that NTRK gene alterations were only present in dMMR CRC.ConclusionsOur results indicate that NTRK gene alterations are not enriched in GC with dMMR but are specifically enriched in cases of GAHED.

The Radio Access Network (RAN) consists of numerous base stations that serve as access points for the end users to communicate with application services. Along with the increasingly powerful computing capability in base stations, and the stringent requirements from new services exemplified by augmented reality (AR) and virtual reality (VR), it is realistic and desirable to leverage the spare computing resource in the base stations to deploy such latency sensitive services. This concept, named Communication and Computing Integrated RAN (CCIR), is proposed in this article. CCIR is aimed to providing communication service and computing service in a holistic manner. This article presents design principles, logical architecture, and key enabling technologies of CCIR. Field trials on the computing resource migration among different base stations are conducted to demonstrate feasibility and effectiveness of CCIR. It is observed that the system performance is improved in terms of spectral efficiency and throughput due to the holistic orchestration and scheduling in CCIR.

Humans perceive the world by integrating multimodal sensory feedback, including visual and auditory stimuli, which holds true in virtual reality (VR) environments. Proper synchronization of these stimuli is crucial for perceiving a coherent and immersive VR experience. In this work, we focus on the interplay between audio and vision during localization tasks involving natural head-body rotations. We explore the impact of audio-visual offsets and rotation velocities on users' directional localization acuity for various viewing modes. Using psychometric functions, we model perceptual disparities between visual and auditory cues and determine offset detection thresholds. Our findings reveal that target localization accuracy is affected by perceptual audio-visual disparities during head-body rotations, but remains consistent in the absence of stimuli-head relative motion. We then showcase the effectiveness of our approach in predicting and enhancing users' localization accuracy within realistic VR gaming applications. To provide additional support for our findings, we implement a natural VR game wherein we apply a compensatory audio-visual offset derived from our measured psychometric functions. As a result, we demonstrate a substantial improvement of up to 40% in participants' target localization accuracy. We additionally provide guidelines for content creation to ensure coherent and seamless VR experiences.

Current cardiometabolic disease prevention guidelines recommend increasing dietary unsaturated fat intake while reducing saturated fats. Here we use lipidomics data from a randomized controlled dietary intervention trial to construct a multilipid score (MLS), summarizing the effects of replacing saturated fat with unsaturated fat on 45 lipid metabolite concentrations. In the EPIC-Potsdam cohort, a difference in the MLS, reflecting better dietary fat quality, was associated with a significant reduction in the incidence of cardiovascular disease (−32%; 95% confidence interval (95% CI): −21% to −42%) and type 2 diabetes (−26%; 95% CI: −15% to −35%). We built a closely correlated simplified score, reduced MLS (rMLS), and observed that beneficial rMLS changes, suggesting improved dietary fat quality over 10 years, were associated with lower diabetes risk (odds ratio per standard deviation of 0.76; 95% CI: 0.59 to 0.98) in the Nurses’ Health Study. Furthermore, in the PREDIMED trial, an olive oil-rich Mediterranean diet intervention primarily reduced diabetes incidence among participants with unfavorable preintervention rMLS levels, suggestive of disturbed lipid metabolism before intervention. Our findings indicate that the effects of dietary fat quality on the lipidome can contribute to a more precise understanding and possible prediction of the health outcomes of specific dietary fat modifications.

Network slicing (NS) can enable diverse communication services for vertical industries. In radio access network slicing, differential service level agreement (SLA) guaranteeing is an essential resource management task. Benefit from powerful data analysis capabilities, deep learning (DL) is suitable for intelligent resource management under the cases of complex constraints and time-varying states. Thus, DL has been used to manage resources for NS recently. However, the training of these DL-assisted methods is time-consuming and it is difficult to keep high SLA satisfaction rates dynamically. To address this problem, we propose a hierarchical intelligent NS resource configuration method via organically integrating NS preconfiguration models based on deep neural networks and NS reconfiguration models using multiarm bandits. A factory automation system is established to evaluate our proposed methods on different industrial services. Simulation and experimental results demonstrate that our proposed methods outperform benchmarks comprehensively.

Let p(·),q(·)and α(·)be variable exponents satisfying some Hölder continuous conditions. In this paper, the authors characterize the variable inhomogeneous Besov space Bp(·),q(·)α(·)(Rn)and the variable inhomogeneous Triebel-Lizorkin space Fp(·),q(·)α(·)(Rn)in terms of Hajłasz gradient, via establishing the boundedness of a kind of almost diagonal operator Aon the corresponding sequence spaces.

ABSTRACTA testing machine was developed to test the creep disturbance effect and explore the creep disturbance law of geopolymer cemented coal gangue-fly ash backfill (GCGFB). A creep disturbance constitutive model [...]

The resuspension of graphite dust is an important phenomenon in the release of radioactivity and the safety of nuclear reactors during severe accidents. In this study, a visualization experimental platform is constructed to study effects of particle size, flow velocity, and wall roughness on the resuspension characteristics of graphite particles. A statistical model of particle resuspension applicable to monolayer dispersed particles is developed based on the moment equilibrium of the particles and the flow field characteristics, as calculated by the large-eddy simulation framework. The results show that particle resuspension can be divided into short- and long-term resuspension stages. Most particle resuspension occurs during the short-term stage. With increases in flow velocity and particle diameter, the aerodynamic or adhesion force acting on the particles increases, and corresponding particle resuspension fraction increases. The influence of rough walls on particle resuspension is related to both the force on the particles and the arm ratio between the wall morphology and the particle diameter. A comparison with the experimental results demonstrates that the particle resuspension model developed in this study accurately predicts the impact of flow velocity, particle size, and wall roughness on particle resuspension.