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Recently, an affine-projection-like M-estimate (APLM) algorithm has gained popularity for its ability to effectively handle impulsive background disturbances. Nevertheless, the APLM algorithm’s performance is negatively affected by steady-state misalignment. To address this issue while maintaining equivalent computational complexity, a robust cost function based on the Andrew’s sine estimator (ASE) is introduced and a corresponding affine-projection Andrew’s sine estimator (APASE) algorithm is proposed in this paper. To further enhance the tracking capability and accelerate the convergence rate, we develop the combined-step-size APASE (CSS-APASE) algorithm using a combination of two different step sizes. A series of simulation studies are conducted in system identification and echo cancellation scenarios, which confirms that the proposed algorithms can attain reduced misalignment compared to other currently available algorithms in cases of impulsive noise. Meanwhile, we also establish a bound on the learning rate to ensure the stability of the proposed algorithms.

Phyllite bimrocks are widely distributed in the eastern margin of the Tibetan Plateau, and it is the main geomaterial for landslides, slopes, dam basement and subgrades in this area. However, the flow behavior of phyllite bimrocks is unknown, especially the flow behavior of phyllite bimrocks with the orientation of blocks. This paper reports the coupling characteristics of flow and orientation of blocks in phyllite bimrocks. The flow behavior of phyllite bimrocks with different block percentages and block sizes was studied by a series of permeability experiments. A large-scale permeability apparatus was designed, and specimens with varying percentages of block and block sizes were produced by the same dip angle of blocks and compaction degree. Based on the Reynolds number analysis, it was found that the flow in phyllite bimrocks becomes laminar to turbulent under lower hydraulic gradient, and the flow behavior of phyllite bimrocks does not obey Darcy’s law. Furthermore, the Forchheimer equation is better at analyzing the flow behavior of phyllite bimrocks compared with Izbash equation. In addition, based on the coefficients a in the Forchheimer equation, the hydraulic conductivity of phyllite bimrocks can be calculated. The calculation result shows that when the percentage of blocks is 25%, the hydraulic conductivity reaches the minimum. Besides, the hydraulic conductivity increases approximately linear with the block size increase. On the basis of previous studies, coefficients A and B of the Forchheimer equation are detected by the normalized objective function analysis. The results would provide a valuable reference for risk assessment and prevention of phyllite bimrock slope.

Vibration energy capture devices can convert the mechanical energy from ambient source into electrical energy. The captured electrical energy can provide energy for low-power devices such as microelectromechanical systems(MEMS) as a supplement to the power system. Vibration energy has been widely concerned by researchers because of the characteristics of easy access and green. The conversion of mechanical vibration energy into electrical energy can be achieved by electromagnetic, electrostatic, piezoelectric, magnetostrictive, dielectric elastomer and emerging friction nano-types. This paper have discussioned some parts of the vibration energy harvesting: collection principle, collection method and the energy storage circuit. At present, the research and design of mechanical vibration energy harvesting structures focus on three aspects: broadening the collection frequency band, collecting dimensions and improving efficiency. Finally, the future direction of energy harvesting research is predicted.

ABSTRACT Purpose Previous studies have shown higher cardiovascular mortality risk with higher monocyte–lymphocyte ratio levels in general population. However, the levels of oxidative stress in individuals with type 2 diabetes are higher than those in the general population, which may affect the link between monocyte‐to‐lymphocyte ratio and cardiovascular disease deaths. And the association between the monocyte‐to‐lymphocyte ratio and mortality risk in people with type 2 diabetes still be unknown. This study aimed to investigate the prognostic significance of monocyte‐to‐lymphocyte ratio in type 2 diabetes. Methods This analysis involved 2,954 individuals with type 2 diabetes from the National Health and Nutrition Examination Survey 1999–2010. The National Death Index records through December 31, 2019, was used to determine all‐cause and cardiovascular mortality. The prognostic roles were determined using Cox regression models, restricted cubic spline analysis, and time‐dependent receiver operating characteristic curve analysis. Results During an average follow‐up period of 12.4 years, a total of 1,007 deaths occurred, while 252 were due to cardiovascular disease. An elevated monocyte‐to‐lymphocyte ratio level exhibited a significant dose–response relationship with an increased risk of all‐cause mortality (1.34 [95% CI 1.12, 1.60] for all‐cause mortality [P trend = 0.001]). The multivariable‐adjusted HR was 1.81 (95% CI 1.25, 2.63) (P trend = 0.001) for cardiovascular mortality indicating a U‐shaped relationship (P nonlinear = 0.013). Conclusions The results of this study indicate a U‐shaped relationship between the monocyte‐to‐lymphocyte ratio and cardiovascular mortality in individuals with diabetes. Both very low and high monocyte‐to‐lymphocyte ratio monocyte‐to‐lymphocyte ratio values were found to be associated with increased cardiovascular mortality risk.

Deep learning has been successfully applied to solve the synthetic aperture radar (SAR) imaging problem, which shows superior imaging performance to compressive sensing (CS)-based methods under sparse sampling conditions. However, due to the computation of the large-scale matrix, the optimal searching in an iterative manner will involve tremendous computational complexity with slow convergence, which will prevent deep learning algorithms from being efficiently applied for SAR imaging. To address this problem, an efficient network is proposed for SAR imaging under sparse sampling conditions, which can be designed by an improved conjugate gradient (CG) optimization strategy. First, the large-scale matrix in the CG algorithm can be approximately decomposed by introducing a matched filtering (MF)-based operator, which will facilitate the gradient computation with high efficiency during the optimization process. Second, the strategy utilizes the advantages of CG optimization to precisely eliminate the error component with conjugate searching in each iteration to achieve fast convergence. By incorporating the improved CG into the convolutional neural network (CNN), the network can be developed to automatically learn the prior information and parameters from the training data, based on which the efficiency of the designed network can be increased dramatically to achieve high imaging performance for SAR applications, especially in the cases of wide-scene and high-resolution imaging. Experimental results show that the proposed network exhibits excellent imaging performance and high computational efficiency in SAR imaging of point targets, surface targets, and different types of real scenes.

Abstract Background This study aims to examine the temporal changes in the incidence, prevalence, and disability-adjusted life years (DALYs) of depressive disorders as well as its association with age, period, and birth cohort among Chinese from 1990 to 2021, and forecast the future trends of incidence rates and numbers from 2022 to 2030. Methods Data for analysis were obtained from the Global Burden of Disease (GBD) 2021. Joinpoint analysis was used to calculate the annual percentage change (APC) and average annual percent change (AAPC) to describe the rates of depressive disorders. Age, period, and cohort model was utilized to disentangle age, period, and birth cohort effects on rates of depressive disorders. Bayesian age-period-cohort (BAPC) analysis was capitalized to forecast the incidence rates and numbers for different sexes and age groups from 2022 to 2030. Results The age-standardized incidence (ASIR, AAPC: -0.35 [95%CI: -0.65, -0.04]), prevalence (ASPR, AAPC: -0.20 [95%CI: -0.24, -0.16]), and DAYLs (AAPC: -0.28 [95%CI: -0.51, -0.05]) rates of both sexes showed a downward trend from 1990 to 2021, despite a volatility growth in recent years (APC in 2019–2021 of ASPR: 0.96 [95%CI: 0.70, 1.23]). Females exhibited a higher burden of depressive disorders compared to males but experienced a more rapid rate of reduction changes. The burden of depressive disorders was most owing to the age effect and period effect in recent years. The ASIR was predicted to decrease in the whole population (males in 2030: 1,546.3 per 100,000 people; females in 2030: 2,465.8 per 100,000 people), but in children, adolescents, and the elderly demonstrate unfavorable trends in the future. Conclusion The burden of depressive disorders decreased in China from 1990 to 2021 in terms of age-standardized rates, but increased in recent years. Children, adolescents, and the elderly are the risk groups for future depressive disorders. Considering the large population, the increasing fewer children, and the aging trend, as well as the possible long-term effects of COVID-19 on human psychological burden, more sex-age-sensitive social healthcare programs should be considered in the future to minimize the burden of depressive disorders in China.

Abstract A novel broadband water‐based Fabry–Pérot (FP) antenna with beam‐steerable technology is proposed. The antenna consists of a water‐based reconfigurable partially reflective surface (RPRS) and a source antenna. The proposed water‐based RPRS has 13 columns of microfluidic channels inside. The water‐based RPRS achieved 1‐D beam steering by regulating phase distribution through the injection of water into its microfluidic channels. The antenna has exhibited a 10‐dB impedance bandwidth from 5.07 to 6.46 GHz (a fractional bandwidth of 24.1%). This design achieves a beam steering from −11° to +11° in the elevation plane, with measured realised gains over 10 dBi. To verify the correctness of the design, an antenna prototype was fabricated. The measured results are in good agreement with the simulated results, confirming the accuracy of the design principle. The measured results demonstrate that the main beam of the antenna prototype tilts to −12° in the broadside direction.

Abstract This study leverages China’s staggered implementation of the intellectual property pilot and demonstration city (IPPDC) policy as a quasi-natural experiment to evaluate the impact of intellectual property protection (IPP) on enterprise supply chain resilience (SCR). The empirical analysis reveals that the establishment of the IPPDC significantly enhances SCR by promoting innovation, improving technology diffusion capabilities, and fostering a cooperative culture within enterprises. Additionally, when enterprise innovation and technology diffusion are treated as threshold variables, the positive effects of IPP on SCR intensify, exhibiting a nonlinear and progressively increasing spillover effect. In contrast, the influence of IPP on SCR through enterprise cooperation follows a U-shaped trajectory, with an initial decline followed by a subsequent rise. These findings emphasize the importance of interactions between policy-makers and enterprises in strengthening supply chain responsibility. Heterogeneity analysis indicates that IPPDC policies have a stronger effect on enterprises with robust SCR, with more significant improvements in the creation of SCR than in resistance and recovery abilities. This effect is consistently observed across primary, secondary, and tertiary industries, with particularly substantial impacts in western China. Moreover, private enterprises benefit more from IPP policies than do state-owned enterprises. These findings provide valuable insights for policy-makers by demonstrating the practical implications of IPP policies beyond their theoretical constructs. This study underscores the critical role of IPP in fortifying SCR, ultimately strengthening firms’ global competitiveness and resilience amid rising geopolitical risk and global uncertainty.

This study investigates the effects of orientation and strain rate on the hydrogen-enhanced localized plasticity mechanism during shear deformation of interstitial free steel. The stress-orientation correlation of hydrogen embrittlement is related to atomic-scale mechanisms, as evidenced through in situ electron backscatter diffraction, slow-strain-rate shear testing, electron microscopic characterization, and molecular dynamics simulations. The dislocation density increment after hydrogen charging was 59.25% higher along the [100] crystallographic zone axis than along the [11‾1‾] crystallographic zone axis, which is mainly attributed to the combined effect of strain-induced transport and interstitial diffusion of hydrogen atoms. The strain rate correlation of hydrogen embrittlement is also affected by the interstitial diffusion during the strain-induced transport of H. Interstitial diffused hydrogen induce dislocation forest emissions that promote local plasticity, which ultimately leads to the orientation and strain rate correlation of hydrogen embrittlement.

Abstract Background This study aimed to understand the vision health status of adolescents in the Western Pacific Region (WPR) using Global Burden of Disease (GBD) data from 1990 to 2019. Methods We conducted a comprehensive analysis of blindness and vision loss using GBD data from 1990 to 2019, analyzed trends in the prevalence and burden of blindness and vision loss over time using joinpoint regression, and analyzed their independent effects on blindness and vision loss in three dimensions using age-period-cohort (APC) modeling. Results The prevalence of blindness and vision loss among adolescents in the WPR showed an increasing trend between 1990 and 2019 (AAPC: 0.56%) and a slight increase in YLD (AAPC: 0.11%). The joinpoint regression showed a decreasing trend after 2017 (AAPC: -2.45%). The prevalence and burden trended downward in most Western Pacific countries, with only China rising significantly (AAPC prevalence: 0.57%). Refractive disorders had the highest prevalence among the subgroups. The risk of blindness and vision loss in adolescents reduced with age but grew later in time and at later birth. In addition, prevalence and burden were generally higher in females than males. Conclusions In the context of a declining global trend in the prevalence and burden of blindness and vision loss among adolescents, there is an increasing trend among adolescents in WPR, with the most pronounced rise in China. This finding has important implications for WPR, suggesting that more attention should be given to adolescent eye health in the region.

Visible light communication (VLC) is a promising technology for sixth-generation (6G) networks, offering a broad, license-free spectrum, enhanced security, and protection from radio frequency (RF) interference. This makes VLC an excellent complement to traditional RF communication. VLC is mainly used indoors, typically covering distances ranging between 2 and 5 meters. A major challenge in VLC is increasing data rates, especially when obstacles block the direct line of sight (LoS). To overcome such obstacles, reconfigurable intelligent surfaces (RISs) have been effectively used in wireless networks. Meta-surface-based simultaneous transmission and reflection reconfigurable intelligent surfaces (STAR RISs) have emerged to address LoS blockage and provide 360° coverage in radio-frequency wireless systems. The latest advancement is the proposal of optical simultaneous transmission and reflection reconfigurable intelligent surfaces for VLC systems. Liquid crystal (LC) RISs can electronically tune their physicochemical properties, such as the refractive index, by altering the orientation of the LC molecules using an external electric field. This fine-tuning capability enables LC RISs to precisely direct incident light beams, ensuring that the refracted beams perfectly align with the photodetector’s field of view. This paper introduces a VLC downlink communication system based on indoor power domain non-orthogonal multiple access (PD-NOMA) technology. The system is designed and optimized to include a STAR RIS in the channel and LC RIS-based receivers with co-assisted composition. By utilizing the energy splitting (ES) protocol, STAR RIS is deployed in the transmission channel, while LC RIS-based VLC receivers are employed at the receiver side. Both components significantly enhance the optical signal, improving overall system performance. The system performance is evaluated by solving a sum-rate maximization problem that accounts for practical scenarios such as the presence of nonuser-obscured LoS paths between the transmitter and the receiver. A low-complexity algorithm using fractional programming and step-by-step optimization is recommended for managing perfect channel state information. Owing to the nonconvexity of the objective function, it is first transformed into a multinomial fractional planning problem using the Lagrangian dual transform, making it more tractable. Following this, a step-by-step optimization scheme combined with the CVX tool is used to obtain a feasible solution. This paper also evaluates how the number of light-emitting diodes (LEDs), the optical signal wavelength, and the LC refractive index affect the performance of indoor VLC systems. The final numerical simulation results show that the sum rate of the indoor VLC system incorporating both STAR RIS and LC RIS is significantly higher compared to systems using only reflective RISs and no LC RISs, especially under the ES operation protocol. Additionally, system performance is influenced by the number of LEDs, incident light wavelength, and LC refractive index. These findings provide an important reference for the design and optimization of indoor VLC systems.

Abstract Our study examines the relationship between intellectual property rights protection (IPRP) and carbon emissions (CE) using an unbalanced panel data set from 116 countries spanning 2008–2020. The analysis reveals a positive relationship between IPRP and CE. Mechanism tests and nonlinear analysis further elucidate this relationship. The mediation effect results indicated that IPRP indirectly fostered CE growth by inhibiting energy structure’s improvement, demonstrating that IPRP exacerbated the dependence on traditional high-carbon energy sources, hindering the transition to cleaner, more efficient energy systems. The analysis also revealed that IPRP indirectly promoted the increase of CE by promoting technological progress and economic growth. The moderation effect results suggest that political stability could mitigate the positive impact of IPRP on CE. Furthermore, the analysis reveals an inverted U-shaped relationship between IPRP and CE, indicating that while IPRP promotes an increase in CE in the initial stages, its intensification suppresses CE growth beyond a turning point. Hence, our study provides new theoretical and empirical evidence to understand the complex relationship between IPRP and CE. It also offers policy recommendations for effectively integrating global carbon reduction efforts with intellectual property policies.

Postoperative cognitive dysfunction (POCD) is a prevalent complication that significantly affects the quality of life. Notably, patients who have experienced ischemic stroke are at an increased risk of developing POCD. Exploring the underlying mechanisms of POCD is crucial for its management. Numerous studies have established neuroinflammation as an independent risk factor in POCD pathogenesis, with TREM2 emerging as a key neuroprotective factor that modulates neuroinflammatory responses through the PI3K/Akt signaling pathway. In this study, we aimed to investigate the effect of TREM2 on POCD in a mouse model of ischemic stroke, with a focus on the mechanisms involving TREM2 and the PI3K/Akt signaling pathway. Our findings indicated that mice with ischemic stroke exhibited severe cognitive impairment after surgical trauma. However, we observed that an enriched environment (EE) could ameliorate this cognitive impairment by upregulating microglia TREM2 expression in the hippocampus and suppressing neuroinflammation. Additionally, the PI3K/AKT signaling pathway was activated in the hippocampal tissue of the mice housed in EE. Importantly, the beneficial neuroprotective and anti-inflammatory effects of EE were abolished when TREM2 was knocked down, underscoring the essential role of TREM2 in mediating the effects of EE on neuroinflammation and cognitive function after ischemic stroke and surgical trauma. In general, our study has confirmed a potential molecular mechanism that led to the occurrence of POCD in individuals with ischemic stroke and provided new strategies to treat POCD.

ABSTRACTRoad extraction from high-resolution remote sensing images (HRSI) is confronted with the challenge that roads are occluded by other objects, including opaque obstructions and similarly colored areas. This paper proposes a dual convolutional network based on hypergraph and multilevel feature fusion (DHM) for road extraction to address these challenges. The DHM consists of two branch networks (HGNN branch and CNN branch) and a bimodal feature fusion module (BFFM). In the HGNN branch, an algorithm is developed to exploit the shape features of roads and construct hypergraphs on the HRSI. Then, hypergraph neural networks are employed for the first time to capture the long-range context of roads to enhance road connectivity. In the CNN branch, a bottleneck fusion module integrated with an encoder-decoder network structure is built to aggregate multiscale local features. In BFFM, the long-range context from the HGNN branch and the local features from the CNN branch are fused through the designed position converter and enhanced graph reasoning module to achieve the complementary advantages of the dual-branch network. Extensive experiments on three datasets show that DHM outperforms other state-of-the-art methods, especially on the GS-Mountain road dataset. Furthermore, DHM significantly improves road extraction in occluded and similar road regions.

Abstract In esophageal squamous cell carcinoma (ESCC), lymph node (LN) metastasis is associated with poor survival. Emerging evidence has demonstrated elevated CD8+ T‐cell levels in metastatic LNs following immunotherapy and increased chemoresistance. However, the underlying regulatory mechanisms of CD8+ T cells in chemoresistant/metastatic patients have not been elucidated. Given that metastasis is linked to aberrant splicing patterns, transcripts with alternative splicing forms also play a critical role. With spatial transcriptomics (ST), spatial isoform transcriptomics (SiT), single‐cell RNA sequencing (scRNA‐seq), and T‐cell receptor (TCR) sequencing, the spatial isoforms are analyzed quantitatively in human solid tumors and LNs. These isoforms are classified according to expression and spatial distribution patterns and proposed that the temporal heterogeneity in isoforms is attributed to isoform biogenesis dynamics. C1QC+ tumor‐associated macrophages (TAMs) contribute to the formation of metastases in the context of both immunotherapy and chemotherapy. Additionally, CD74 isoforms can be targeted by mRNA drugs, such as antisense oligonucleotides (ASOs) and RNA interference (RNAi), to prevent chemoresistance and metastasis. Overall, this work suggests that C1QC+ TAMs interact with CD8+ CXCL13+ Tex cells via enrichment with the CD74 isoform in the ESCC ‘s metastatic lymph node.

In this paper, a novel simplified hybrid model is developed to simulate the transient thermal behaviors of capillary-box heat exchangers (CBHEs) buried in the seabed, serving as the front-end heat exchangers in seawater source heat pump system (SWHPs). The thermal resistance and capacity (TRC) approach is applied for derivation of the governing equations inside and outside the heat exchangers. Also, an analytical solution is integrated to model heat transfer in the seabed base. The effects of seawater seepage on the thermal performance of CBHEs are taken into account in the present model. The state-space representation is used to solve the governing equations. The model is verified against experimental data, achieving very good agreement with the mean bias error (MBE) of 7.2 %. A comparison with a three-dimensional computational fluid dynamic (CFD) model indicates that the TRC model's maximum relative error and MBE are 0.7 % and 2.0 % lower than those of the CFD model. Additionally, the ratio of the time required by the CFD and TRC models for a 31-day run was 138. These results demonstrate that the TRC model is sufficiently accurate and fast in the thermal simulation of CBHEs. Furthermore, the thermal properties of CBHEs are examined using the present model. The model in this study provides practical implications for heat transfer analysis and design improvement of CBHEs utilized in SWHPs.

Abstract Parkinson’s disease (PD) is a progressive neurodegenerative disease characterized by mitochondrial dysfunction and accumulation of alpha-synuclein (α-Syn)-containing protein aggregates known as Lewy bodies (LB). Here, we investigated the entry of α-Syn into mitochondria to cause mitochondrial dysfunction and loss of cellular fitness in vivo. We show that α-Syn expressed in yeast and human cells is constitutively imported into mitochondria. In a transgenic mouse model, the level of endogenous α-Syn accumulation in mitochondria of dopaminergic neurons and microglia increases with age. The imported α-Syn is degraded by conserved mitochondrial proteases, most notably NLN and PITRM1 (Prd1 and Cym1 in yeast, respectively). α-Syn in the mitochondrial matrix that is not degraded interacts with respiratory chain complexes, leading to loss of mitochondrial DNA (mtDNA), mitochondrial membrane potential and cellular fitness decline. Importantly, enhancing mitochondrial proteolysis by increasing levels of specific proteases alleviated these defects in yeast, human cells, and a PD model of mouse primary neurons. Together, our results provide a direct link between α-synuclein-mediated cellular toxicity and its import into mitochondria and reveal potential therapeutic targets for the treatment of α-synucleinopathies.

Land rocky desertification (RD) is one of the most serious environmental disasters in karst landforms. Identifying the rocky desertification severity level (RDSL) is a key task in the prevention and control projects of rocky desertification in karst areas. How to efficiently and accurately identify the RDSL is an urgent issue. It requires higher accuracy and more advanced techniques. Currently, machine learning-based remote sensing technology (RST) faces challenges in identifying the RDSL, including insufficient dataset features, low accuracy of identification models, and incomplete exploration of rocky desertification driving factors. To address these issues, this study leverages multi-source remote sensing satellite data and related product data to construct a multidimensional dataset with feature factors. By combining convolutional neural networks (CNN) and graph neural networks (GNN), a graph convolutional network segmentation model based on deep learning image segmentation is proposed for the automatic identification of RDSL. In addition, the study has investigated the spatiotemporal changes of RD in Guizhou Province in recent years and explored the impacts of various natural driving factors on the RDSLs. The experimental results indicate that the multidimensional feature dataset (Dataset-2) contributes to enhancing the identification accuracy of the model. The proposed model has capabilities such as composite representation in non-Euclidean space, deep extraction of image semantics, and multiscale segmentation and fusion. The model achieves an Mean Intersection over Union (MIoU) of 84.724, which outperforms other mainstream image segmentation methods. Although rocky desertification from 2015 to 2022 in Guizhou Province is significantly distributed, there is a trend toward mitigation. This study provides effective technical tools and data support for exploring the evolution process of desertification in subtropical karst areas, as well as for the implementation of projects related to environmental protection, afforestation, soil and water conservation, and land monitoring.

Objective In this study, we investigated the cause of the AngII dose elevation in aqueous humor of primary open-angle glaucoma (POAG) patients. Methods Enzyme-linked immunosorbent assay (ELISA), western blotting were used to detect concentration of Angiotensin Converting Enzyme 2 (ACE2) and Prolylcarboxypeptidase (PRCP). AngII and AngII + Recombinant PRCP were injected into anterior chamber of mouse eye. Mouse Intraocular pressure (IOP) was measured every week, mouse eye sections were conducted Hematoxylin-and-Eosin (H&E) staining, Masson’ staining and Immunofluorescence staining. Western blotting and Immunofluorescence staining assays to detected fibrosis of trabecular meshwork cells. Mass spectrometry was used to identify proteins of aqueous humor. Results PRCP dose are decreased in aqueous humor of POAG patients. There is a negative correlation between PRCP and AngII levels in aqueous humor and between PRCP levels and the IOP. PRCP treatment reverses fibrosis of trabecular meshwork (TM) and prevents IOP elevation induced by AngII. Exogenous PRCP rescues fibrosis induced by AngII in HTMCs. Proteome profiling detected 502 differentially expressed proteins. Conclusion Our study found PRCP dose was decreased in POAG patients’ aqueous humor, and it might cause high level of AngII. Restoration of PRCP rescued fibrosis of TM cells and ameliorated IOP in AngII treatment mouse.

Background: Butein, a natural chalcone found in plants, exhibits promising anti-tumor effects across cancers, yet its role in Colorectal cancer (CRC) remain largely unclear. Methods: We utilized pharmacological, molecular, and in vivo models to assess its impact on CRC cell proliferation, apoptosis, migration, and invasion. Molecular docking and network pharmacology analyses predicted potential target proteins and pathways regulated by Butein. Experimental validations included western blotting, immunohistochemistry, and functional assays. Results: Butein significantly inhibited CRC cell proliferation, induced apoptosis, and disrupted the epithelial-mesenchymal transition (EMT). Its repression of cell migration and invasion capabilities, suggesting potential anti-metastatic properties. The activation of the p38 signaling pathway emerged as a key mechanism mediating Butein’s anti-tumor effects, supported by network pharmacology predictions and experimental evidence. Conclusion: This study highlights the multifaceted anti-cancer potential of Butein in CRC, emphasizing its promising application and the novel role of the p38 pathway.

Abstract Space-air-ground integrated networks comprise a multi-level heterogeneous integrated network that combines satellite-based, aerial, and terrestrial networks. With the increasing human exploration of space and growing demands for internet applications, space-air-ground integrated networks have gradually emerged as the direction for communication network development. These networks face various challenges such as extensive coverage, diverse communication node types, low-quality communication links, and simultaneous operation of multiple network protocols. However, the rapid development and widespread application of artificial intelligence and machine learning technologies in recent years have offered new perspectives and solutions for the communication architecture and routing algorithm research within space-air-ground integrated networks. In these networks, not all nodes can typically communicate directly with satellites; instead, a specific set of specialized communication nodes facilitates data communication between aerial and satellite networks due to their superior communication capabilities. Consequently, in contrast to traditional communication architectures, space-air-ground integrated networks, particularly in the terrestrial layer, often need to address challenges related to the diversity of communication node types and low-quality communication links. A well-designed routing approach becomes crucial in addressing these issues. Therefore, this paper proposes an AODV routing network protocol based on an improved ant colony algorithm (AC-AODV), specifically designed for the terrestrial layer within the space-air-ground integrated networks. By integrating information such as the type, energy, and location of communication nodes, this protocol aims to facilitate network communication. The objective is to guide information flow through nodes that are more suitable for communication, either by relaying communication or by connecting with satellites through specialized nodes. This approach alleviates the burden on ordinary nodes within the terrestrial communication network, thereby enhancing the overall network performance. In this protocol, specialized nodes hold a higher forwarding priority than regular nodes. When a source node needs to transmit data, it enters the route discovery phase, utilizing its own type, location, and energy information as heuristic data to calculate forwarding probabilities. Subsequently, it broadcasts route request (RREQ) messages to find the path. Upon receiving the RREQ message, the destination node sends an RREP message for updating information elements and selects the optimal path based on these information elements. Compared to AODV, AC-AODV shows significant improvements in performance metrics such as transmission latency, throughput, energy conversion rate, and packet loss rate.

As the proliferation and development of automated container terminal continue, the issues of efficiency and safety become increasingly significant. The container yard is one of the most crucial cargo distribution centers in a terminal. Automated Guided Vehicles (AGVs) that carry materials of varying hazard levels through these yards without compromising on the safe transportation of hazardous materials, while also maximizing efficiency, is a complex challenge. This research introduces an algorithm that integrates Long Short-Term Memory (LSTM) neural network with reinforcement learning techniques, specifically Deep Q-Network (DQN), for routing an AGV carrying hazardous materials within a container yard. The objective is to ensure that the AGV carrying hazardous materials efficiently reaches its destination while effectively avoiding AGVs carrying non-hazardous materials. Utilizing real data from the Meishan Port in Ningbo, Zhejiang, China, the actual yard is first abstracted into an undirected graph. Since LSTM neural network can efficiently conveys and represents information in long time sequences and do not causes useful information before long time to be ignored, a two-layer LSTM neural network with 64 neurons per layer was constructed for predicting the motion trajectory of AGVs carrying non-hazardous materials, which are incorporated into the map as background AGVs. Subsequently, DQN is employed to plan the route for an AGV transporting hazardous materials, aiming to reach its destination swiftly while avoiding encounters with other AGVs. Experimental tests have shown that the route planning algorithm proposed in this study improves the level of avoidance of hazardous material AGV in relation to non-hazardous material AGVs. Compared to the method where hazardous material AGV follow the shortest path to their destination, the avoidance efficiency was enhanced by 3.11%. This improvement demonstrates potential strategies for balancing efficiency and safety in automated terminals. Additionally, it provides insights for designing avoidance schemes for autonomous driving AGVs, offering solutions for complex operational environments where safety and efficient navigation are paramount.

Abstract Background Sugarcane (Saccharum spp.) holds exceptional global significance as a vital crop, serving as a primary source of sucrose, bioenergy, and various by-products. The optimization of sugarcane breeding by fine-tuning essential traits has become crucial for enhancing crop productivity and stress resilience. Leucine-rich repeat receptor-like kinases (LRR-RLK) genes present promising targets for this purpose, as they are involved in various aspects of plant development and defense processes. Results Here, we present a detailed overview of phylogeny and expression of 288 (495 alleles) and 312 (1365 alleles) LRR-RLK genes from two founding Saccharum species, respectively. Phylogenetic analysis categorized these genes into 15 subfamilies, revealing considerable expansion or reduction in certain LRR-type subfamilies. Compared to other plant species, both Saccharum species had more significant LRR-RLK genes. Examination of cis-acting elements demonstrated that SsLRR-RLK and SoLRR-RLK genes exhibited no significant difference in the types of elements included, primarily involved in four physiological processes. This suggests a broad conservation of LRR-RLK gene function during Saccharum evolution. Synteny analysis indicated that all LRR-RLK genes in both Saccharum species underwent gene duplication, primarily through whole-genome duplication (WGD) or segmental duplication. We identified 28 LRR-RLK genes exhibiting novel expression patterns in response to different tissues, gradient development leaves, and circadian rhythm in the two Saccharum species. Additionally, SoLRR-RLK104, SoLRR-RLK7, SoLRR-RLK113, and SsLRR-RLK134 were identified as candidate genes for sugarcane disease defense response regulators through transcriptome data analysis of two disease stresses. This suggests LRR-RLK genes of sugarcane involvement in regulating various biological processes, including leaf development, plant morphology, photosynthesis, maintenance of circadian rhythm stability, and defense against sugarcane diseases. Conclusions This investigation into gene duplication, functional conservation, and divergence of LRR-RLK genes in two founding Saccharum species lays the groundwork for a comprehensive genomic analysis of the entire LRR-RLK gene family in Saccharum. The results reveal LRR-RLK gene played a critical role in Saccharum adaptation to diverse conditions, offering valuable insights for targeted breeding and precise phenotypic adjustments.

Small- and medium-sized reservoirs significantly alter natural flood processes, making it essential to understand their impact on runoff for effective water resource management. However, the lack of measured data for most small reservoirs poses challenges for accurately simulating their behavior. This study proposes a novel method that utilizes readily available satellite observation data, integrating hydraulic, hydrological, and mathematical formulas to derive outflow coefficients. Based on the Grid-XinAnJiang (GXAJ) model, the enhanced GXAJ-R model accounts for the storage and release effects of ungauged reservoirs and is applied to the Tunxi watershed. Results show that the original GXAJ model achieved a stable performance with an average NSE of 0.88 during calibration, while the NSE values of the GXAJ and GXAJ-R models during validation ranged from 0.78 to 0.97 and 0.85 to 0.99, respectively, with an average improvement of 0.03 in the GXAJ-R model. This enhanced model significantly improves peak flow simulation accuracy, reduces relative flood peak error by approximately 10%, and replicates the flood flow process with higher fidelity. Additionally, the area–volume model derived from classified small-scale data demonstrates high accuracy and reliability, with correlation coefficients above 0.8, making it applicable to other ungauged reservoirs. The OTSU-NDWI method, which improves the NDWI, effectively enhances the accuracy of water body extraction from remote sensing, achieving overall accuracy and kappa coefficient values exceeding 0.8 and 0.6, respectively. This study highlights the potential of integrating satellite data with hydrological models to enhance the understanding of reservoir behavior in data-scarce regions. It also suggests the possibility of broader applications in similarly ungauged basins, providing valuable tools for flood management and risk assessment.

The latest-generation geostationary meteorological satellite, Fengyun-4B (FY-4B), equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), offers high-spatiotemporal-resolution three-dimensional temperature structures. Its deployment serves as a critical complement to atmospheric temperature profile (ATP) observation in the Tibetan Plateau (TP). Based on radiosonde observation (RAOB) and the fifth-generation ECMWF global climate atmospheric reanalysis (ERA5), this study validates the availability and representativeness of FY-4B/GIIRS ATP products in the eastern TP region. Due to the issue of satellite zenith, this study focuses solely on examining the eastern TP region. Under a clear sky, FY-4B/GIIRS ATP exhibits good consistency with RAOB compared to cloudy conditions, with an average root mean square error (RMSE) of 2.57 K. FY-4B/GIIRS tends to underestimate temperatures in the lower layers while overestimating temperatures in the upper layers. The bias varies across seasons. Except for summer, the horizontal and vertical bias distribution patterns are similar, though there are slight differences in values. Despite the presence of bias, FY-4B/GIIRS ATP maintains a good consistency with observations and reanalysis data, indicating commendable product quality. These results demonstrate that it can play a vital role in augmenting the ATP observation network limited by sparse radiosonde stations in the eastern TP, offering crucial data support for numerical weather prediction, weather monitoring, and related meteorological research in this region.

The equitable utilization of marine resources and the maintenance of healthy and sustainable marine ecosystems are essential to advancing the United Nations Sustainable Development Goals (SDGs) and promoting the well-being of all humanity. In this paper, we propose a Blue Equity Assessment Framework, which is based on a systematic literature review approach and takes into account the characteristics of Social-Ocean Systems (SOSs). The framework consists of Distributional Equity, Procedural Equity, Recognitional Equity, and Contextual Equity. The aim of this framework is to conduct equity analyses of policies or behaviors within SOSs in order to assess whether blue equity is explicitly implemented and practiced in these societal norms and policy mechanisms. The assessment framework reveals that blue equity has a positive synergy for the majority of the SDGs. This synergy is analyzed in terms of its influence on the assessment dimensions of distributional justice, procedural justice, recognitional justice, and contextual justice. The findings aim to enhance societal understanding of blue equity issues, thereby guiding policymakers in the prioritization of blue equity in the development and utilization of marine resources and in the transformation of global ocean governance.

The surface electromyographic (sEMG) signals reflect human motor intention and can be utilized for human-machine interfaces (HMI). Comparing to the sparse multi-channel (SMC) electrodes, the high-density (HD) electrodes have a large number of electrodes and compact space between electrodes, which can achieve more sEMG information and have the potential to achieve higher performance in myocontrol. However, when the HD electrodes grid shift or damage, it will affect gesture recognition and reduce recognition accuracy. To minimize the impact resulting from the electrodes shift and damage, we proposed an attention deep fast convolutional neural network (attention-DFCNN) model by utilizing the temporary and spatial characteristics of high-density surface electromyography (HD-sEMG) signals. Contrary to the previous methods, which are mostly base on sEMG temporal features, the attention-DFCNN model can improve the robustness and stability by combining the spatial and temporary features. The performance of the proposed model was compared with other classical method and deep learning methods. We used the dataset provided by The University Medical Center Göttingen. Seven able-bodied subjects and one amputee involved in this work. Each subject executed nine gestures under the electrodes shift (10 mm) and damage (6 channels). As for the electrodes shift 10 mm in four directions (inwards; onwards; upwards; downwards) on seven able-bodied subjects, without any pre-training, the average accuracy of attention-DFCNN (0.942 ± 0.04) is significantly higher than LSDA (0.910 ± 0.04, p

Hexafluorocyclotriphosphazene (HFPN) is a promising flame retardant to be applied in lithium-ion batteries (LIBs) to decrease the fire risk during the treatment of electrolyte. In this manuscript, the influence of HFPN on the combustion characteristics of LIB electrolyte was investigated, the combustion behaviors and flame retardancy of the electrolyte with or without HFPN were systematically studied by using cone calorimeter, flash point meter and a series of standard equipment, as well as self-made water spray fire extinguishing platform. The flash points of 3 kinds of control group as base solution and 3 kinds of flame-retarded electrolyte were measured and obtained by SCKB3000 closed cup automatic flash point tester. The combustion characteristics of 6 types of electrolytes were analyzed by a cone calorimeter. The heat release rate (HRR), fire growth index (FGI), fire magnitude index (FMI) and other important combustion parameters of the electrolytes were studied. Based on the self-made water mist fire extinguishing platform, the fire extinguishing effect on 6 kinds of electrolytes under the same water mist condition was compared, and the influence of flame retardants in the fire extinguishing process was analyzed. The results showed that the change of flash point was obviously related to the composition of electrolyte. Flame retardant could reduce the peak HRR (pHRR) during electrolyte fire to a large extent, while it could also prolong the combustion duration of electrolyte pool fire. Under the action of fine water mist, the electrolyte pool fire would quickly extinguish, but there would remain a lot of smoke.

Considerable progress has recently been made in cancer immunotherapy, including immune checkpoint blockade, cancer vaccine, and adoptive T cell methods. The lack of effective targets is a major cause of the low immunotherapy response rate in colorectal cancer (CRC). Here, we used a proteogenomic strategy comprising immunopeptidomics, whole exome sequencing, and 16 S ribosomal DNA sequencing analyses of 8 patients with CRC to identify neoantigens and bacterial peptides that can serve as antitumor targets. This study directly identified several personalized neoantigens and bacterial immunopeptides. Immunoassays showed that all neoantigens and 5 of 8 bacterial immunopeptides could be recognized by autologous T cells. Additionally, T cell receptor (TCR) αβ sequencing revealed the TCR repertoire of epitope-reactive CD8+ T cells. Functional studies showed that T cell receptor-T (TCR-T) could be activated by epitope pulsed lymphoblastoid cells. Overall, this study comprehensively profiled the CRC immunopeptidome, revealing several neoantigens and bacterial peptides with potential to serve as immunotherapy targets in CRC.

Mitochondria are the cellular energy hub and central target of metabolic regulation. Mitochondria also facilitate proteostasis through pathways such as the ‘mitochondria as guardian in cytosol’ (MAGIC) whereby cytosolic misfolded proteins (MPs) are imported into and degraded inside mitochondria. In this study, a genome-wide screen in Saccharomyces cerevisiae uncovered that Snf1, the yeast AMP-activated protein kinase (AMPK), inhibits the import of MPs into mitochondria while promoting mitochondrial biogenesis under glucose starvation. We show that this inhibition requires a downstream transcription factor regulating mitochondrial gene expression and is likely to be conferred through substrate competition and mitochondrial import channel selectivity. We further show that Snf1/AMPK activation protects mitochondrial fitness in yeast and human cells under stress induced by MPs such as those associated with neurodegenerative diseases.