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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.

Radiation-induced brain injury (RIBI) is a significant challenge in radiotherapy for head and neck tumors, impacting patients' quality of life. In exploring potential treatments, this study focuses on memantine hydrochloride and hydrogen-rich water, hypothesized to mitigate RIBI through inhibiting the NLRP3/NLRC4/Caspase-1 pathway. In a controlled study involving 40 Sprague-Dawley rats, divided into five groups including a control and various treatment groups, we assessed the effects of these treatments on RIBI. Post-irradiation, all irradiated groups displayed symptoms like weight loss and salivation, with notable variations among different treatment approaches. Particularly, hydrogen-rich water showed a promising reduction in these symptoms. Histopathological analysis indicated substantial hippocampal damage in the radiation-only group, while the groups receiving memantine and/or hydrogen-rich water exhibited significant mitigation of such damage. Molecular studies, revealed a decrease in oxidative stress markers and an attenuated inflammatory response in the treatment groups. Immunohistochemistry further confirmed these molecular changes, suggesting the effectiveness of these agents. Echoing recent scientific inquiries into the protective roles of specific compounds against radiation-induced damages, our study adds to the growing body of evidence on the potential of memantine and hydrogen-rich water as novel therapeutic strategies for RIBI.

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.

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

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.

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 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.

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.

The identification of optimal landing sites is a critical first step for successful missions to the Moon and other extraterrestrial bodies, necessitating the integration of various environmental factors over large spatial scales. At the lunar south pole, site selection must balance engineering safety with areas of high scientific interest, requiring extensive analysis of potential locations. Although intelligent algorithms have been increasingly investigated for this purpose, the application of deep learning techniques in landing site selection remains unexplored. In this study, we employ one-dimensional convolutional neural networks (1D-CNNs) to quantitatively assess potential landing sites for exploration and lunar base construction, considering both scientific and engineering criteria. We also evaluate the influence of various factors on site selection using Shapley additive explanations (SHAP) values. The 1D-CNN model demonstrates robust performance across training, validation, and testing phases. Potential landing sites identified comprise less than 1% of the total study area, with factors such as visibility, volatile distribution, topography, and geological characteristics playing crucial roles. By applying operational constraints, we delineate sites suitable for direct landings and further refine this subset for base construction based on stringent requirements for resource utilization and energy sustainability. The combined use of CNN and SHAP enables more effective potential site screening and a deeper understanding of the factors influencing selection. Our findings offer a valuable framework for future lunar south pole expeditions, potentially minimizing manual survey efforts and enhancing the precision of landing site selection.

The emergence of semisupervised object detection (SSOD) techniques has greatly enhanced object detection performance. SSOD leverages a limited amount of labeled data along with a large quantity of unlabeled data. However, there exists a problem of sample inconsistency in remote sensing images, which manifests in two ways. First, remote sensing images are diverse and complex. Conventional random initialization methods for labeled data are insufficient for training teacher networks to generate high-quality pseudolabels. Finally, remote sensing images typically exhibit a long-tailed distribution, where some categories have a significant number of instances, while others have very few. This distribution poses significant challenges during model training. In this article, we propose the utilization of SSOD networks for remote sensing images characterized by a long-tailed distribution. To address the issue of sample inconsistency between labeled and unlabeled data, we employ a labeled data iterative selection strategy based on the active learning approach. We iteratively filter out high-value samples through the designed selection criteria. The selected samples are labeled and used as data for supervised training. This method filters out valuable labeled data, thereby improving the quality of pseudolabels. Inspired by transfer learning, we decouple the model training into the training of the backbone and the detector. We tackle the problem of sample inconsistency in long-tail distribution data by training the detector using balanced data across categories. Our approach exhibits an approximate 1% improvement over the current state-of-the-art models on both the DOTAv1.0 and DIOR datasets.

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.

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.

Fusobacterium vincentii brain abscesses are relatively rare. Here, we report our treatment of an anaerobic brain abscess caused by a mixed infection of Parvimonas micra, Streptococcus constellatus, Fusobacterium vincentii, and Bacteroides heparinolyticus diagnosed by metagenomic next-generation sequencing (mNGS). This is the first reported case of Fusobacterium vincentii in a brain abscess. This case highlights the possibility that oral anaerobic microbes can cause a brain abscess and demonstrates that mNGS has the potential to be deployed to provide rapid infection diagnosis and rationalize antimicrobial therapy for brain abscesses.

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.

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.

Objective: Ramucirumab is a VEGFR2 antagonist. The aim of this trial is to evaluate the efficacy and safety of ramucirumab combined with nab-paclitaxel, lobaplatin and S-1 in neoadjuvant and conversion therapy for advanced gastric cancer. Methods: and analysis: This study is a prospective single-center, randomized controlled and open label clinical study, enrolling a total of 140 patients with advanced gastric cancer distributed across two distinct cohorts (Cohort A n = 70; Cohort B n = 70). The central focus of the study lies in evaluating the pathological complete response (pCR) of the cancer post-neoadjuvant or conversion therapy. Secondary endpoints encompass the assessment of the R0 resection rate subsequent to the aforementioned therapies, the occurrence of adverse events (AE), progression-free survival (PFS), overall survival (OS), the objective response rate (ORR), the total response rate and its duration, the disease control rate (DCR), and the duration of overall response (DOR). Ethics: Ethics approval has been obtained from the Ethics Committee at the First Affiliated Hospital (Xijing Hospital) of Air force Military Medical University (KY20232220–F-1). Trial registration: This trial has been registered at the ClinicalTrials.gov: NCT06169410 (registration date: December 5, 2023).

When facing an unfamiliar environment, animals need to explore to gain new knowledge about which actions provide reward, but also put the newly acquired knowledge to use as quickly as possible. Optimal reinforcement learning strategies should therefore assess the uncertainties of these action-reward associations and utilise them to inform decision making. We propose a novel model whereby direct and indirect striatal pathways act together to estimate both the mean and variance of reward distributions, and mesolimbic dopaminergic neurons provide transient novelty signals, facilitating effective uncertainty-driven exploration. We utilised electrophysiological recording data to verify our model of the basal ganglia, and we fitted exploration strategies derived from the neural model to data from behavioural experiments. We also compared the performance of directed exploration strategies inspired by our basal ganglia model with other exploration algorithms including classic variants of upper confidence bound (UCB) strategy in simulation. The exploration strategies inspired by the basal ganglia model can achieve overall superior performance in simulation, and we found qualitatively similar results in fitting model to behavioural data compared with the fitting of more idealised normative models with less implementation level detail. Overall, our results suggest that transient dopamine levels in the basal ganglia that encode novelty could contribute to an uncertainty representation which efficiently drives exploration in reinforcement learning.

In recent years, although the air quality in the Liaoning region has improved, heavy pollution weather is still caused by external pollution transportation. In order to study the influence of multi-directional long-distance pollution on the heavily polluted weather in Liaoning, this paper analyzes three types of transport channel (northerly, northwesterly, and southwesterly transport corridors) by combining ground observations, airborne observations, AQI and the concentration data of atmospheric pollutants such as PM2.5, PM10 and so on. The results show that both northerly and southwesterly winds caused widespread fog and haze in the Liaoning region. The PM2.5 concentration reached the second-highest value on record (1479 μg/m3) during the pollution period caused by northwesterly wind transport. Wind and sand transported from the northwest mainly caused dusty weather in Liaoning, with PM10 concentration reaching up to 1200 μg/m3 during the pollution period. Northeasterly and northwesterly wind transport mainly led to the accumulation of pollutants in high places to high pollution values. Convergence of winds due to southwesterly wind transport led to prolonged accumulation of pollutants in the Liaoning region, resulting in severe pollution. Long-distance pollution transport is now one of the main sources of heavy pollution in the Liaoning region.

Abstract The Lachnospiraceae family holds promise as a source of next‐generation probiotics, yet a comprehensive delineation of its diversity is lacking, hampering the identification of suitable strains for future applications. To address this knowledge gap, we conducted an in‐depth genomic and functional analysis of 1868 high‐quality genomes, combining data from public databases with our new isolates. This data set represented 387 colonization‐selective species‐level clusters, of which eight genera represented multilineage clusters. Pan‐genome analysis, single‐nucleotide polymorphism (SNP) identification, and probiotic functional predictions revealed that species taxonomy, habitats, and geography together shape the functional diversity of Lachnospiraceae. Moreover, analyses of associations with atherosclerotic cardiovascular disease (ACVD) and inflammatory bowel disease (IBD) indicated that several strains of potentially novel Lachnospiraceae species possess the capacity to reduce the abundance of opportunistic pathogens, thereby imparting potential health benefits. Our findings shed light on the untapped potential of novel species enabling knowledge‐based selection of strains for the development of next‐generation probiotics holding promise for improving human health and disease management.

Background: In the central nervous system, glioma is the most common malignant tumor, and patients have a poor prognosis. Identification of novel marker genes and establishment of prognostic models are important for early diagnosis and prognosis determination. Methods: Download glioma data from the CGGA and TCG databases. Application of bioinformatics to analyze the impact of CYBB on the clinicopathological characteristics, immunological features and prognosis of gliomas. Using single-cell sequencing data from 7 glioblastoma patients in the CGGA database, the role of CYBB in the tumor microenvironment was analyzed. In addition, a prognostic model was constructed based on CYBB high and low differentially expressed genes and mitochondrial genes. Results: The expression of CYBB is closely related to various clinical features, immune cell infiltration level, immune checkpoint and survival time of patients. A 10-gene prediction model was constructed based on the differentially expressed genes of low and high CYBB and mitochondria-related genes. Glioma patients with higher risk scores had significantly lower survival probabilities. Receiver operating characteristic curves and nomograms were plotted over time to show the predictive accuracy and predictive value of the 10-gene prognostic model. Conclusions: Our study shows that CYBB is strongly correlated with clinical characteristics features and prognosis of glioma patients, and can be used as a potential therapeutic target. Prognostic models based on CYBB and mitochondrial genes have good performance in predicting prognosis of glioma patients.

ABSTRACTMosquitoes carry a large number of known and unknown viruses, some of which could cause serious diseases in humans or animals. Metagenomic sequencing for mosquito viromes is crucial for understanding the evolutionary history of viruses and preventing emerging mosquito-borne diseases. We collected 1,598 mosquitoes belonging to four species from five counties in Shandong Province, China in 2021. They were grouped by species and sampling locations and subjected to metagenomic next-generation sequencing for the analysis of the viromes. A total of 233,317,352 sequencing reads were classified into 30 viral families and an unclassified group. Comparative analysis showed that mosquitoes in Shandong Province generally possessed host-specific virome. We detected mosquito-borne viruses including Japanese encephalitis virus, Getah virus, and Kadipiro virus in Culex tritaeniorhynchus and Anopheles sinensis samples. Phylogenetic analysis showed that these pathogenic viruses may have existed in mosquitoes in Shandong Province for a long time. Meanwhile, we identified 22 novel viruses belonging to seven families and the genus Negevirus. Our study comprehensively described the viromes of several common mosquito species in Shandong Province, China, and demonstrated the major role of host species in shaping mosquito viromes. Furthermore, the metagenomic data provided valuable epidemiological information on multiple mosquito-borne viruses, highlighting the potential risk of infection transmission.IMPORTANCEMosquitoes are known as the source of various pathogens for humans and animals. Culex tritaeniorhynchus, Armigeres subalbatus, and Anopheles sinensis have been found to transmit the Getah virus, which has recently caused increasing infections in China. Cx. tritaeniorhynchus and Culex pipiens are the main vectors of Japanese encephalitis virus and have caused epidemics of Japanese encephalitis in China in past decades. These mosquitoes are widely present in Shandong Province, China, leading to a great threat to public health and the breeding industry. This study provided a comprehensive insight into the viromes of several common mosquito species in Shandong Province, China. The metagenomic sequencing data revealed the risks of multiple pathogenic mosquito-borne viruses, including Japanese encephalitis virus, Getah virus, and Kadipiro virus, which are of great importance for preventing emerging viral epidemics.

The prairie dog optimization (PDO) algorithm is a metaheuristic optimization algorithm that simulates the daily behavior of prairie dogs. The prairie dog groups have a unique mode of information exchange. They divide into several small groups to search for food based on special signals and build caves around the food sources. When encountering natural enemies, they emit different sound signals to remind their companions of the dangers. According to this unique information exchange mode, we propose a randomized audio signal factor to simulate the specific sounds of prairie dogs when encountering different foods or natural enemies. This strategy restores the prairie dog habitat and improves the algorithm's merit-seeking ability. In the initial stage of the algorithm, chaotic tent mapping is also added to initialize the population of prairie dogs and increase population diversity, even use lens opposition-based learning strategy to enhance the algorithm's global exploration ability. To verify the optimization performance of the modified prairie dog optimization algorithm, we applied it to 23 benchmark test functions, IEEE CEC2014 test functions, and six engineering design problems for testing. The experimental results illustrated that the modified prairie dog optimization algorithm has good optimization performance.

Abstract Current source is an indispensable component of magnetorheological (MR) systems. Though MR fluid has a phase change as fast as in 1 ms, the response of MR damper (MRD) to generate the damping force may be two orders of magnitude longer. Therefore, the rapid response of current source is a key to realize the real-time semi-active control of MR devices. This study proposes a programmable high-speed, low-cost current source exclusively for MR devices based on the synergy between supercapacitor and Buck converter (i.e., SSBC current source). SSBC current source features a strategy consisting of a lifting phase of supercapacitor and a following maintaining phase of Buck converter. Specifically, the high power density of supercapacitor contributes to rapidly lifting/raising the initial current, and then, like a “relay race”, the expected output is maintained through a Buck converter. Theoretical modeling and experiments are performed systematically. The response times (@ 95% of expected outputs) measured are 0.44, 0.84 and 1.88 ms for the outputs of 3, 6 and 9 A, respectively; these values are highlighted as the fastest level in this field. Besides, the response can be up to 24.6 and 43.7 times faster than the cases using supercapacitor and Buck converter to directly drive the MRD, respectively. SSBC current source is employed to generate a sequence of currents/magnetic inductions, only four variables of which need to be controlled programmatically: the order of lifting and maintaining phases, switching time of lifting phase, PWM duty cycle of Buck converter and duration of maintaining phase. The response time stability is verified by 100 cycles of on/off tests, showing a fluctuation of only 1.1%, which indicates a very reliable high-speed response. This study provides an exclusive power supply with a novel strategy for MR devices, which is believed to be an important promotion for MR technologies.

Abstract A novel 2‐D beam steering technology using a Fabry–Pérot antenna with a liquid‐based reconfigurable metasurface is presented. The antenna employs a reconfigurable partially reflecting surface to regulate phase distribution and adopts a microstrip antenna as feed to realise 2‐D beam steering. The antenna beam can be tilted in four different directions by injecting liquid metal into the specific area of the microfluidic channels embedded in the metasurface. Moreover, the antenna has a simple and compact structure with a low profile. A prototype is manufactured, and good agreement between simulated and experimental results verifies the correctness of the design. The measured results of the manufactured antenna prototype demonstrate that the main beam tilts to maximum values of ±15° and ±28° in the yoz and xoz planes, respectively, between 9.5 and 9.7 GHz.

DNA methylation plays an important role in the development and tissue differentiation of eukaryotes. In this study, bisulfite sequencing (BS-seq) technology was used to analyze the DNA methylation profiles of liver tissues taken from Rongchang pigs at three postnatal feeding stages, including newborn, suckling, and adult. The DNA methylation pattern across the genomes or genic region showed little difference between the three stages. We observed 419 differentially methylated regions (DMRs) in promoters, corresponding to 323 genes between newborn and suckling stages, in addition to 288 DMRs, corresponding to 134 genes, between suckling and adult stages and 351 DMRs, corresponding to 293 genes, between newborn and adult stages. These genes with DMRs were mainly enriched in metabolic, immune-related functional processes. Correlation analysis showed that the methylation level of gene promoters was significantly negatively correlated with gene expression. Further, we found that genes related to nutritional metabolism, e.g., carbohydrate metabolism (FAHD1 and GUSB) or fatty acid metabolism (LPIN1 and ACOX2), lost DNA methylation in their promoter, with mRNA expression increased in newborn pigs compared with those in the suckling stage. A few fatty acid metabolism-related genes (SLC27A5, ACOX2) were hypomethylated and highly expressed in the newborn stage, which might satisfy the nutritional requirements of Rongchang pigs with high neonatal birth rates. In the adult stage, HMGCS2—which is related to fatty acid β-oxidation—was hypomethylated and highly expressed, which explains that the characteristics of high energy utilization in adult Rongchang pigs and their immune-related genes (CD68, STAT2) may be related to the establishment of liver immunity. This study provides a comprehensive analysis of genome-wide DNA methylation patterns in pig liver postnatal development and growth. Our findings will serve as a valuable resource in hepatic metabolic studies and the agricultural food industry.

Fragrance is a valuable trait in rice varieties, with its aroma significantly influencing consumer preference. In this study, we conducted comprehensive metabolome and transcriptome analyses to elucidate the genetic and biochemical basis of fragrance in the Shangsixiangnuo (SSXN) variety, a fragrant indica rice cultivated in Guangxi, China. Through sensory evaluation and genetic analysis, we confirmed SSXN as strongly fragrant, with an 806 bp deletion in the BADH2 gene associated with fragrance production. In the metabolome analysis, a total of 238, 233, 105 and 60 metabolic compounds exhibited significant changes at the seedling (S), reproductive (R), filling (F), and maturation (M) stages, respectively. We identified four compounds that exhibited significant changes in SSXN across all four development stages. Our analyses revealed a significant upregulation of 2-acetyl-1-pyrroline (2AP), the well-studied aromatic compound, in SSXN compared to the non-fragrant variety. Additionally, correlation analysis identified several metabolites strongly associated with 2AP, including ethanone, 1-(1H-pyrrol-2-yl)-, 1H-pyrrole, and pyrrole. Furthermore, Weighted Gene Co-expression Network Analysis (WGCNA) analysis highlighted the magenta and yellow modules as particularly enriched in aroma-related metabolites, providing insights into the complex aromatic compounds underlying the fragrance of rice. In the transcriptome analysis, a total of 5582, 5506, 4965, and 4599 differential expressed genes (DEGs) were identified across the four developmental stages, with a notable enrichment of the common pathway amino sugar and nucleotide sugar metabolism in all stages. In our correlation analysis between metabolome and transcriptome data, the top three connected metabolites, phenol-, 3-amino-, and 2AP, along with ethanone, 1-(1H-pyrrol-2-yl)-, exhibited strong associations with transcripts, highlighting their potential roles in fragrance biosynthesis. Additionally, the downregulated expression of the P4H4 gene, encoding a procollagen-proline dioxygenase that specifically targets proline, in SSXN suggests its involvement in proline metabolism and potentially in aroma formation pathways. Overall, our study provides comprehensive insights into the genetic and biochemical mechanisms underlying fragrance production in rice, laying the foundation for further research aimed at enhancing fragrance quality in rice breeding programs.

This study investigated the influence of green manure planting on the spectroscopic properties of dissolved organic matter (DOM) in saline–alkali soil under freshwater leaching conditions at different soil depths. The UV254, UV253/UV203, α300, α355, SUVA254, SUVA260, and SR ultraviolet parameters indicated reductions in the content of large molecular substances, benzene ring substitution degree, colored dissolved organic matter, aromaticity, and hydrophobic components in the soil leachate DOM with an increasing soil depth. Compared with the non-green manure treatment control, green manure planting mitigated the leaching of dissolved organic matter in soil during saline irrigation, with rape green manure demonstrating superior effectiveness. Utilizing three-dimensional fluorescence combined with parallel factor analysis, this study analyzed three fluorescent components of soil leachate DOM: C1 (visible-light fulvic acid), C2 (humic acid), and C3 (tyrosine-like protein). The combined contribution of the two humic substance components (C1 + C2) was approximately 70%, indicating the dominance of humic substances in leachate DOM. The fluorescence parameters of soil leachate DOM included an average of the fluorescence index (FI) values between 1.4 and 1.9, low humification index (HIX) values consistently below 4, and biological index (BIX) values ranging from 0.8 to 1.0, suggesting a mixed source, low humification degree, poor stability, and moderate self-source characteristics. Compared with the non-green manure treatment control, both the green manure treatments exhibited a relatively higher proportion of biogenic sources and humification degree in soil leachate DOM. This suggests that planting green manure can reduce the relative DOM content under freshwater leaching conditions, increase the proportion of biogenic sources in soil leachate DOM, and enhance soil humification. Planting rapeseed green manure can diminish the leaching of DOM from land sources and augment soil humification.

Using the air quality monitoring data from the Department of Ecology and Environment of Liaoning Province for the period from 2017 to 2022, the temporal and spatial changes in the concentrations of various air pollutants in the Liaoning region for the periods from 2017 to 2019 and 2020 to 2022 were analyzed by using the Evaluation on the meteorological condition index of PM2.5 pollution (EMI) and the ArcGIS Kriging Interpolation Method, and the contributions of pollution reduction to the changes in the air quality of the Liaoning region were assessed. The results show that after the implementation of emission reduction measures, the quality of the atmospheric environment in the Liaoning region has significantly improved, and the mean concentrations of PM2.5, PM10, SO2, NO2, CO and O3 are all reduced by a certain magnitude in the period 2020 to 2022 compared with the period 2017 to 2019; Based on the EMI index calculation, the average EMI index in Liaoning during the period 2020 to 2022 is about 1.7% lower than the average value of the region during the period 2017 to 2019, and the atmospheric dispersion conditions are relatively good; From the perspective of daily changes in pollutant concentrations, the trend of PM2.5 and PM10 concentrations changed from double peaks and single valleys to single peaks and single valleys, and there were no significant changes in the types of valleys for CO, SO2, NO2, and O3, whereas the peaks of O3 concentrations during the daytime were basically the same as in previous years, and the concentrations during the nighttime were slightly higher than in previous years. Classification by topographic areas revealed that the mean pollutant concentration for the period from 2017 to 2019 was more significant than the mean value for the period from 2020 to 2022, except for O3, where the air quality in the mountainous areas of Liaodong and Liaoxi was better than that of the Liaohe Plain, and regional classification by coastal and inland, where the air quality in the coastal areas was better than that of the inland areas.

The intelligent transportation system (ITS) is committed to ensuring safe and effective next-generation traffic throughout a city. However, such efficient operation on urban traffic networks needs the support of big traffic data, especially Turning Movement Counts (TMC) at intersections. Generally, TMC data are more challenging to collect due to labor cost and accuracy problems. In this paper, we leverage the capabilities of Unmanned Aerial Vehicles (UAV) to collect real-time TMC data in a cost-efficient way. We proposed a real-time TMC data collection framework based on a live video stream. The vehicle tracking capability is boosted by multiple object tracking based on tracking by detection. In addition, a challenging case study was conducted, and our results demonstrate the feasibility and robustness of the proposed TMC data collection framework. Specifically, with a GTX 1650 graphics card, about 10 FPS can be achieved in real-time for the TMC data collection. The overall accuracy is 91.93%, and the best case is over 98% accurate. In the context of miscounting, the major reason is due to ID switching caused by background occlusion. The proposed framework is expected to provide real-time data for traffic capacity analysis and advanced traffic simulation such as digital twins.

Abstract Background After the standardization, recording and follow‐up of imatinib use that significantly prolongs survival of gastrointestinal stromal tumors (GISTs), a comprehensive reassessment of the prognosis of GISTs is necessary and more conductive to treatment options. Methods A total of 2185 GISTs between 2013 and 2016 were obtained from the Surveillance, Epidemiology, and End Results database and comprised our training (n = 1456) and internal validation cohorts (n = 729). The risk factors extracted from univariate and multivariate analyses were used to establish a predictive nomogram. The model was evaluated and tested in the validation cohort internally and in 159 patients with GIST diagnosed between January 2015 and June 2017 in Xijing Hospital externally. Results The median OS was 49 months (range, 0–83 months) in the training cohort and 51 months (0–83 months) in the validation cohort. The concordance index (C‐index) of the nomogram was 0.777 (95% CI, 0.752–0.802) and 0.7787 (0.7785, bootstrap corrected) in training and internal validation cohorts, respectively, and 0.7613 (0.7579, bootstrap corrected) in the external validation cohort. Receiver operating characteristic curves and calibration curves for 1‐, 3‐, and 5‐year overall survival (OS) showed a high degree of discrimination and calibration. The area under the curve showed that the new model performed better than the TNM staging system. In addition, the model could be dynamically visualized on a webpage. Conclusion We developed a comprehensive survival prediction model for assessing the 1‐, 3‐ and 5‐year OS of patients with GIST in the postimatinib era. This predictive model outperforms the traditional TNM staging system and sheds light on the improvement of the prognostic prediction and the selection of treatment strategies for GISTs.

Abstract Background The incidence of mobile phone addiction among adolescents in rural areas of China is increasing year by year, and has already exceeded that of some cities. And phone addiction increases the risk of anxiety and poor sleep. Therefore, this study used network analysis to investigate the relationship between mobile phone addiction and anxiety symptoms, and the predictability to sleep quality. Methods From September 2021 to March 2022, a total of 1920 rural adolescents in Xuzhou, China were included. The survey included information on phone addiction, anxiety symptoms, and sleep quality. Network analysis was used to estimate the network structure of adolescents’ mobile phone addiction and anxiety symptoms. LOWESS curve and linear regression were used to test the predictive ability of node-centrality on sleep quality. Results In the network of mobile phone addiction-anxiety symptoms, the most influential symptoms were Failure to cut down the time, Anxiety if not used for some time, and Alleviate loneliness. Irritability was the most prominent bridging symptom. Gender difference had no effect on network structure. Nodes in the network are not predictive of sleep quality. Conclusion Failure to cut down the time is the most important symptom, suggesting that measures should be taken to reduce the amount of time spent on mobile phones. For example, increase outdoor exercise, increase the real company of friends and family, in order to reduce the occurrence of mobile phone addiction and anxiety.

To overcome the time-consuming drawbacks of Computational Fluid Dynamics (CFD) numerical simulations, this paper proposes a hybrid model named PA-TLA (parallel architecture combining a TCN, LSTM, and an attention mechanism) based on the concept of intelligent aerodynamics and a parallel architecture. This model utilizes CFD data to drive efficient predictions of aircraft wake evolution at different initial altitudes during the approach phase. Initially, CFD simulations of continuous initial altitudes during the approach phase are used to generate aircraft wake evolution data, which are then validated against real-world LIDAR data to verify their reliability. The PA-TLA model is designed based on a parallel architecture, combining Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCNs), and a tensor concatenation module based on the attention mechanism, which ensures computational efficiency while fully leveraging the advantages of each component in a parallel processing framework. The study results show that the PA-TLA model outperforms both the LSTM and TCN models in predicting the three characteristic parameters of aircraft wake: vorticity, circulation, and Q-criterion. Compared to the serially structured TCN-LSTM, PA-TLA achieves an average reduction in mean squared error (MSE) of 6.80%, in mean absolute error (MAE) of 7.70%, and in root mean square error (RMSE) of 4.47%, with an average increase in the coefficient of determination (R2) of 0.36% and a 35% improvement in prediction efficiency. Lastly, this study combines numerical simulations and the PA-TLA deep learning architecture to analyze the near-ground wake vortex evolution. The results indicate that the ground effect increases air resistance and turbulence as vortices approach the ground, thereby slowing the decay rate of the wake vortex strength at lower altitudes. The ground effect also accelerates the dissipation and movement of vortex centers, causing more pronounced changes in vortex spacing at lower altitudes. Additionally, the vortex center height at lower altitudes initially decreases and then increases, unlike the continuous decrease observed at higher altitudes.

This study aimed to discuss the influence of specimen sizes on the compressive strength parameters of wood, specifically focusing on their compression strength, elastic modulus, and Poisson’s ratio. Therefore, three different-sized specimens (20 mm × 20 mm × 30 mm, 40 mm × 40 mm × 60 mm, 60 mm × 90 mm × 90 mm) were manufactured and tested in the longitudinal, radial, and tangential directions, following the standard testing method for acquiring the compressive strength of wood. Subsequently, based on the experimental results, compressive parameters, failure mechanisms, load–displacement curves, and stress–strain relationships were systematically analyzed for the three different-sized specimens. Meanwhile, the influence of specimen size on the compressive strength parameters of wood was also evaluated through finite element numerical simulations, utilizing the obtained mechanical parameters. The results revealed a significant correlation between compressive strength and specimen size, indicating a decrease in compressive strength with an increasing specimen size. Conversely, the elastic modulus and Poisson’s ratio exhibited less sensitivity to specimen size changes. Notably, the compressive strength parameters derived from small-sized specimens (20 mm × 20 mm × 30 mm) exhibited a lack of rationality, while those obtained from medium-sized (40 mm × 40 mm × 60 mm), and large-sized specimens (60 mm × 90 mm × 90 mm) demonstrated greater reliability, providing precise results in finite element numerical simulations.

Abstract Background The relationship between chronic hepatitis B (CHB) and Coronavirus disease 2019 (COVID-19) has been inconsistent in traditional observational studies. Methods We explored the total causal and direct causal associations between CHB and the three COVID-19 outcomes using univariate and multivariate Mendelian randomization (MR) analyses, respectively. Genome-wide association study datasets for CHB and COVID-19 were obtained from the Japan Biobank and the COVID-19 Host Genetics Initiative, respectively. Results Univariate MR analysis showed that CHB increased the risk of SARS-CoV-2 infection (OR = 1.04, 95% CI 1.01–1.07, P = 3.39E−03), hospitalized COVID-19 (OR = 1.10, 95% CI 1.06–1.13, P = 7.31E−08), and severe COVID-19 (OR = 1.16, 95%CI 1.08–1.26, P = 1.43E−04). A series of subsequent sensitivity analyses ensured the stability and reliability of these results. In multivariable MR analyses adjusting for type 2 diabetes, body mass index, basophil count, and smoking, genetically related CHB is still positively associated with increased risk of SARS-CoV-2 infection (OR = 1.06, 95% CI 1.02–1.11, P = 1.44E−03) and hospitalized COVID-19 (OR = 1.12, 95% CI 1.07–1.16, P = 5.13E−07). However, the causal link between CHB and severe COVID-19 was attenuated after adjustment for the above variables. In addition, the MR analysis did not support the causal effect of COVID-19 on CHB. Conclusions This study provides evidence that CHB increases COVID-19 susceptibility and severity among individuals of East Asian ancestry.

Abstract Objective To evaluate the effect of overall peripheral inflammatory levels on cognitive function, we explored the relationship between established biomarkers of peripheral inflammation (circulating C‐reactive protein [CRP], interleukin‐6 [IL‐6], and tumor necrosis factor‐α [TNF‐α]) and cognitive decline by performing a review of observational studies and creating an updated summary. Methods We included literatures exploring the relationship between peripheral levels of CRP, IL‐6, and TNF‐α and subsequent cognitive decline, published until July 2022, by searching the following databases: PubMed, Embase, Web of Science, the Cochrane Library, ClinicalTrials, CNKI, and VIP databases. We used random‐effects models to pool the odds ratios (ORs) for the risks of subsequent cognitive decline in older adults with high levels of peripheral inflammation. We initially screened out 501 literatures, of which only 17 were ultimately eligible. Overall, there were 19,516 older individuals included in our meta‐analysis, and 2134 of them experienced subsequent cognitive change. Results Individuals with high levels of peripheral inflammation may have 14% more chance to develop subsequent cognitive decline than those with low levels (OR = 1.14, 95% CI: 1.03–1.27; p

Seawater desalination is a new promising marine industry and an important way to supplement the shortage of land water resources and promote the efficient use of seawater resources. Currently, the global desalination industry is rapidly developing. In China’s new development plan, large-scale development of the seawater desalination industry is also an important strategic goal. However, compared to the technological development of the seawater desalination industry, its marketization is affected by various factors, and its development level is still relatively low. Therefore, based on real industry data, this paper constructs a tripartite evolutionary game model for seawater desalination enterprises, water supply enterprises, and the government, and identifies several entry barriers for seawater desalination in China. The results include: (1) For seawater desalination to enter the market in China, government supervision limit should be no greater than 400,000 tons per day, about 13.8% of total seawater desalination scale. (2) The entry cost should be no more than 10 million yuan per day for seawater desalination, which is equivalent to approximately 18.25 trillion yuan during a five-year period. Finally, (3) political relatedness should be at least 2.0 for seawater desalination to take place, and 3.0 for it to develop in full swing. The paper also discusses the division of state- and foreign-owned water companies and reveals that government subsidies are only effective if foreign-owned companies seek rents. Based on the findings of the study, we propose pertinent policy recommendations including top-level planning, desalinated water infrastructure development, subsidy policy implementation, and public engagement. These recommendations aim to aid the Chinese government in fostering the desalination sector.

Abstract Objective To uncover the effects of chrysosplenol D (CHD) on the progression of prostate cancer in vitro as well as in mice. Methods DU145 and PC‐3 cells were treated with increasing doses of CHD for 24, 48, or 72 h. Cell Counting Kit‐8 (CCK‐8) and colony formation assays were conducted to confirm the effects of CHD on cell viability. Flow cytometry (FCM) and immunostaining assays showed the effects of CHD on cell apoptosis and oxidative stress. Immunoblot was performed to detect the effects of CHD on autophagy. Besides, tumor growth assays were conducted to confirm the role of CHD in tumor growth in mice. GraphPad 6.0 was used for statistical analysis. All data were represented as mean ± SD. p

The long-term absence of organic drive management circuits has prevented organic integration from achieving full flexibility. The high off-state breakdown performance and its avalanche-like breakdown mechanism of copolymer Organic Semiconductor (OSC)-based Organic Field Effect Transistors (OFETs) have been revealed in recent researches. By employing diketopyrrolopyrrole-based conjugated copolymer OSC(DPPT-TT) as the semiconductor layer and Polymethyl methacrylate(PMMA) as the dielectric/passivation layer, the H-bridge circuit can be fabricated with four OFETs integrated on the same Corning glass substrate. The non-destructive avalanche-like breakdown mechanism provides the power OFET a 300 V high BV(Breakdown Voltage), whose critical electric field excesses 5 MV/cm. The fabricated devices also demonstrated the high current density more than 121 A/cm2 and field-effect mobility up to 0.22 cm2/( $\text{V}\cdot \text{s}$ ). The top-gate bottom-contact structure allows the fabricated OFETs a high on-state current density and good device stability without using package construction or other special isolation techniques. By connecting resistors and LEDs on the load side of the H-bridge circuit made of $63~\mu \text{m}$ channel devices, measurement results show that it has good driving effects and driving performance. These measured results show that organic power devices are a potential candidate for application in the power realm.

The risk of information leakage increases when the relay technology is introduced to extend the communication range and improve the quality of communication services of cell-edge users in visible light communication (VLC) systems. To evaluate these security risks, physical layer security (PLS) performance for decode-and-forward (DF) relay-aided VLC system is investigated in this paper. Specifically, the theoretical expressions of the upper and lower bounds of the security outage probability (SOP) for the DF relay-aided VLC system under the amplitude constraint are obtained based on a stochastic geometry analysis method. Monte Carlo simulation validates the correctness of the derived theoretical expressions of the upper and lower bounds of the SOP. Moreover, simulation results show that the security outage performance of the DF relay-aided VLC system is deteriorated compared with the non-relay VLC system. Furthermore, the influences of the eavesdroppers' distribution density, the radiation radius of LED source, the radiation radius and deployment height of the relay on the SOP have been investigated. Last but not least, the optimal deployment height of the relay can be determined for achieving the optimum security outage performance. These parameters can effectively guide the design of the secure relay-aided VLC system.

Aiming at the diversity of the entity mentions in the field of animal husbandry in Tibet, the reference resolution model Att-GRU-CNN based on multi-level attention mechanism is proposed. The model uses GRU network for global semantic feature learning and knowledge memory, and uses CNN network to further extract local high-level semantic features. A word-level attention layer is added on the GRU hidden layer, the feature vector of entity mention does dot product operation with the feature vector of each word, and the result is normalized and used as the weight value distribution of the words, thus, the prior knowledge that the mention is the most important in the context is given to the network; A sentence-level attention layer is added on the CNN convolution layer, the convolutional layer output and entity name library matrix do association operation, and the result is used as the weight value distribution of the sentence, so as to strengthen the relation between the sentence where the mention is located and the entity scientific name. At the same time, the data enhancement technology is used to improve the generalization ability of the model. Finally, the ablation experiment on Tibetan animal husbandry dataset verified the effectiveness of each component of the model and the superposition effect after combination; the performance comparison experiment is carried out on public datasets MUC, B3 and CEAF $\varphi _{4}$ . The experimental results show that this model has a significant improvement over other models in the accuracy rate, recall rate and F1 score.

Milk, known for its high content of short- and medium-chain fatty acids and unsaturated fatty acids, has attracted substantial attention due to its nutritional and health value. The regulation of fatty acid metabolism by non-coding RNAs has become a subject of growing attention, particularly in relation to fatty acid production at the transcriptional/epigenetic and post-transcriptional levels. This study established the circRNA-11228/miR-103/INSIG1 (insulin-inducible gene) regulatory network using methods such as qRT-PCR, dual luciferase reporting, and Western blot, with INSIG1 serving as the starting point. The experimental validation of circRNA-11228’s impact on cholesterol levels, lipid droplet secretion, and unsaturated fatty acid content was conducted using various assays, including triglycerides, cholesterol, oil red O, andEdU(5-ethynyl-2’-deoxyuridine) in bovine mammary epithelial cells (BMECs). Furthermore, the transfection of mimics and inhibitors synthesized from miR-103 into BMECs confirmed that miR-103 can promote cholesterol synthesis and lipid droplet secretion. Conversely, the INSIG1 gene was found to inhibit cholesterol synthesis and lipid droplet secretion. The “remediation” experiment validated the ability of miR-103 to alleviate the cellular effect of circRNA-11228. Taken together, our findings indicate that the binding of circRNA-11228 to miR-103 inhibits the expression of the target gene INSIG, thereby regulating milk fat production in BMECs. This study offers novel insights into producing high-quality milk and new ways to improve the dietary composition of residents.

Birinapant, an antagonist of the inhibitor of apoptosis proteins, upregulates MHCs in tumor cells and displays a better tumoricidal effect when used in combination with immune checkpoint inhibitors, indicating that Birinapant may affect the antigen presentation pathway; however, the mechanism remains elusive. Based on high-resolution mass spectrometry and in vitro and in vivo models, we adopted integrated genomics, proteomics, and immunopeptidomics strategies to study the mechanism underlying the regulation of tumor immunity by Birinapant from the perspective of antigen presentation. Firstly, in HT29 and MCF7 cells, Birinapant increased the number and abundance of immunopeptides and source proteins. Secondly, a greater number of cancer/testis antigen peptides with increased abundance and more neoantigens were identified following Birinapant treatment. Moreover, we demonstrate the existence and immunogenicity of a neoantigen derived from insertion/deletion mutation. Thirdly, in HT29 cell-derived xenograft models, Birinapant administration also reshaped the immunopeptidome, and the tumor exhibited better immunogenicity. These data suggest that Birinapant can reshape the tumor immunopeptidome with respect to quality and quantity, which improves the presentation of CTA peptides and neoantigens, thus enhancing the immunogenicity of tumor cells. Such changes may be vital to the effectiveness of combination therapy, which can be further transferred to the clinic or aid in the development of new immunotherapeutic strategies to improve the anti-tumor immune response.

In this work, a novel zinc–aluminum–magnesium (Zn-Al-Mg, ZM) coated steel was prepared using the hot-dip method. The microstructure and corrosion resistance of the ZM-coated steel were investigated. Compared to the conventional galvanized steel (GI), the ZM coating demonstrated a distinctive phase structure, consisting of Zn phase, binary eutectic (Zn/MgZn2), and ternary eutectic (Zn/Al/MgZn2). The corrosion resistance of the ZM-coated and GI-coated steels was evaluated by neutral salt spray test (NSST), polarization and electrochemical impedance spectroscopy (EIS). The results indicated that ZM-coated steel provided superior long-term corrosion protection in a NaCl environment compared to GI-coated steel. The scanning vibrating electrode technique (SVET) proved to be an effective method for investigating the evolution of the anodic and cathodic on the local coating surface. GI-coated steel exhibited a potential and current density distribution between the cathodic and anodic sites nearly three orders of magnitude higher than that of ZM-coated steel, suggesting a higher corrosion rate for GI-coated steel.

Infrared–visible image fusion is valuable across various applications due to the complementary information that it provides. However, the current fusion methods face challenges in achieving high-quality fused images. This paper identifies a limitation in the existing fusion framework that affects the fusion quality: modal differences between infrared and visible images are often overlooked, resulting in the poor fusion of the two modalities. This limitation implies that features from different sources may not be consistently fused, which can impact the quality of the fusion results. Therefore, we propose a framework that utilizes feature-based decomposition and domain normalization. This decomposition method separates infrared and visible images into common and unique regions. To reduce modal differences while retaining unique information from the source images, we apply domain normalization to the common regions within the unified feature space. This space can transform infrared features into a pseudo-visible domain, ensuring that all features are fused within the same domain and minimizing the impact of modal differences during the fusion process. Noise in the source images adversely affects the fused images, compromising the overall fusion performance. Thus, we propose the non-local Gaussian filter. This filter can learn the shape and parameters of its filtering kernel based on the image features, effectively removing noise while preserving details. Additionally, we propose a novel dense attention in the feature extraction module, enabling the network to understand and leverage inter-layer information. Our experiments demonstrate a marked improvement in fusion quality with our proposed method.

Graphene quantum dot (GQD) is a new type of carbon nanometer material. In addition to the excellent properties of graphene, it is superior due to the quantum limit effect and edge effect. Because of its advantages such as water solution, strong fluorescent, small size, and low biological toxicity, it has important application potential in various fields, especially in sensors and biomedical areas, which are mainly used as optical electrical sensors as well as in biological imaging and tumor therapy. In addition, GQDs have very important characteristics, such as optical and electrical properties. There are many preparation methods, divided into top-down and bottom-up methods, which have different advantages and disadvantages, respectively. In addition, the modification methods include heterogeneous doping, surface heterogeneity, etc. There are still many challenges in developing GQDs. For example, the synthesis steps are still hard to conduct, but as the inquiry continues to deepen, GQDs will be revolutionary materials in the future. In this work, the literature concerning research progress on GQDs has been reviewed and summarized, while the key challenges of their application have been pointed out, which may bring new insights to the application of GQDs.

Abstract Background Renal clear cell carcinoma (ccRCC) is the most prevalent tumors worldwide. Discovering effective biomarkers is essential to monitor the prognosis and provide alternative clinical options. SPTBN1 is implicated in various cancerous processes. However, its role in ccRCC remains unelucidated. This study intends to explore the biological function and mechanism of SPTBN1 in ccRCC. Methods Single-cell and bulk RNA-seq, tissue microarray, real-time quantitative PCR, and western blotting were applied to verify the expression and predictive value of SPTBN1 in ccRCC. Gain or loss of functional ccRCC cell line models were constructed, and in vitro and in vivo assays were performed to elucidate its tumorigenic phenotypes. Actinomycin D experiment, RNA immunoprecipitation (RIP), specific inhibitors, and rescue experiments were carried out to define the molecular mechanisms. Results SPTBN1 was down-regulated in ccRCC and knockdown of SPTBN1 displayed a remarkably oncogenic role both in vitro and in vivo; while overexpressing SPTBN1 reversed this effect. SPTBN1 mediated ccRCC progression via the pathway of glutamate pyruvate transaminase 2 (GPT2)-dependent glycolysis. The expression of GPT2 was significantly negatively correlated with that of SPTBN1. As an RNA binding protein SPTBN1, regulated the mRNA stability of GPT2. Conclusion Our research demonstrated that SPTBN1 is significantly down-regulated in ccRCC. SPTBN1 knockdown promotes ccRCC progression via activating GPT2-dependent glycolysis. SPTBN1 may serve as a therapeutic target for the treatment of ccRCC.

This paper addresses the numerical and experimental performance analysis of a windows heat recovery system made of heat pipes. For modelling, the heat pipe is considered as a pseudo solid material with high value of effective thermal conductivity. An experimental investigation using a window heat recovery prototype was carried out to predict the value of effective thermal conductivity of the heat pipes and to validate the numerical model. After validation, a parametric analysis was conducted to investigate the performance of the recovery system for different working conditions (mass flow rate and temperature difference between exhausted and supplied air). Based on the performance obtained in the parametric analysis, energy performance in building and the impact on velocity and pressure distributions are also evaluated with the support of CFD analysis. It is found that the effectiveness of window heat recovery made of heat pipes depends on ventilation rate and temperature difference between exhausted and supplied air. Increasing ventilation rates and temperature differences decrease the effectiveness. For ventilation rate between 10–60 m 3/h and temperature difference 10–30 °C, effectiveness between 65%–95% and pressure drop 4–80 Pa are obtained. For performance in building, the power consumption can be reduced between 3%–24% and the thermal comfort increased.

Dielectric elastomer actuators (DEAs) artificial muscle is a typical interdisciplinary research category, which has developed by leaps and bounds in the past 20 years, showing great application prospects in various fields. Upon external electrical stimulation, dielectric elastomers (DEs) display large deformation, high energy density and fast response, affording a promising material candidate for soft robotics. Herein, the working mechanisms, commonly used materials as well as the concepts for improving the performance of DEA materials are introduced. Various DEA driven soft robots, including soft grippers, bioinspired artificial arms, crawling/walking/underwater/flying/jumping soft robots and tunable lenses, are then described in detail. Finally, the main challenges of DEA driven soft robots are summarized, and some perspectives for promoting the practical application of DEAs are also proposed.

The underlying mechanism and influences of asymmetric surface texture on anti-friction & wear of a surface modified Al alloy under sliding friction were studied. Microstructure, hardness, wear resistance, texture of the composite modification surface were analyzed and evaluated. A flow field simulation model for wedge triangle texture was established with the Fluent finite element software. The results show that the counterclockwise wedge-shaped triangular texture has better friction and wear properties than the clockwise. The surface texture is beneficial to the storage of lubricating oil and abrasive particles, and the surface texture composite graphite coating can significantly improve the tribological properties of the substrate. The tribological mechanism of the asymmetric textured composite graphite coating is the antifriction effect of the lamellar structure of the graphite coating, and the storage of wear debris and the dynamic pressure lubrication effect of the micro-texture.