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Abstract Osteoarthritis (OA) is a chronic degenerative joint condition characterised by cartilage deterioration and changes in bone morphology, resulting in pain and impaired joint mobility. Investigation into the pathophysiological mechanisms underlying OA has highlighted the significance of mitochondrial dysfunction in its progression. Mitochondria, which are cellular organelles, play a crucial role in regulating energy metabolism, generating reactive oxygen species, and facilitating essential biological processes including apoptosis. In recent years, the utilisation of exogenous drugs and MT to improve mitochondrial function in chondrocytes has shown great promise in OA treatment. Numerous studies have investigated the potential of stem cells and extracellular vesicles in mitochondrial transfer. This review aims to explore the underlying mechanisms of mitochondrial dysfunction in OA and assess the progress in utilising mitochondrial transfer as a therapeutic approach for this disease.

Tiger puffer is a commercially important fish cultured in high-density environments, and its accurate detection is indispensable for determining growth conditions and realizing accurate feeding. However, the detection precision and recall of farmed tiger puffer are low due to target blurring and occlusion in real farming environments. The farmed tiger puffer detection model, called knowledge aggregation YOLO (KAYOLO), fuses prior knowledge with improved YOLOv5 and was proposed to solve this problem. To alleviate feature loss caused by target blurring, we drew on the human practice of using prior knowledge for reasoning when recognizing blurred targets and used prior knowledge to strengthen the tiger puffer's features and improve detection precision. To address missed detection caused by mutual occlusion in high-density farming environments, a prediction box aggregation method, aggregating prediction boxes of the same object, was proposed to reduce the influence among different objects to improve detection recall. To validate the effectiveness of the proposed methods, ablation experiments, model performance experiments, and model robustness experiments were designed. The experimental results showed that KAYOLO's detection precision and recall results reached 94.92% and 92.21%, respectively. The two indices were improved by 1.29% and 1.35%, respectively, compared to those of YOLOv5. Compared with the recent state-of-the-art underwater object detection models, such as SWIPENet, RoIMix, FERNet, and SK-YOLOv5, KAYOLO achieved 2.09%, 1.63%, 1.13% and 0.85% higher precision and 1.2%, 0.18%, 1.74% and 0.39% higher recall, respectively. Experiments were conducted on different datasets to verify the model's robustness, and the precision and recall of KAYOLO were improved by approximately 1.3% compared to those of YOLOv5. The study showed that KAYOLO effectively enhanced farmed tiger puffer detection by reducing blurring and occlusion effects. Additionally, the model had a strong generalization ability on different datasets, indicating that the model can be adapted to different environments, and it has strong robustness.

Abstract Osteoarthritis (OA) is the most common form of arthritic disease, and phenotypic modification of chondrocytes is an important mechanism that contributes to the loss of cartilage homeostasis. This study identified that Fascin actin-bundling protein 1 (FSCN1) plays a pivotal role in regulating chondrocytes phenotype and maintaining cartilage homeostasis. Proteome-wide screening revealed markedly upregulated FSCN1 protein expression in human OA cartilage. FSCN1 accumulation was confirmed in the superficial layer of OA cartilage from humans and mice, primarily in dedifferentiated-like chondrocytes, associated with enhanced actin stress fiber formation and upregulated type I and III collagens. FSCN1-inducible knockout mice exhibited delayed cartilage degeneration following experimental OA surgery. Mechanistically, FSCN1 promoted actin polymerization and disrupted the inhibition of Decorin on TGF-β1, leading to excessive TGF-β1 production and ALK1/Smad1/5 signaling activation, thus, accelerated chondrocyte dedifferentiation. Intra-articular injection of FSCN1-overexpressing adeno-associated virus exacerbated OA progression in mice, which was mitigated by an ALK1 inhibitor. Moreover, FSCN1 inhibitor NP-G2-044 effectively reduced extracellular matrix degradation in OA mice, cultured human OA chondrocytes, and cartilage explants by suppressing ALK1/Smad1/5 signaling. These findings suggest that targeting FSCN1 represents a promising therapeutic approach for OA.

The artificial breeding of carp existed in China 8000 years ago. The breeding of carp played an important role in ancient Chinese history, and it still played a crucial role in modern aquaculture. The output of carp in China is very high, accounting for 8.42 % of the total output of freshwater products in 2023, ranking fifth in the total output of freshwater aquaculture fish. At the same time, carp is also the fish species with the highest number of new varieties bred in China. With the advent of the genomics era, breeding technologies have also made progress. This paper reviews the history and current status of carp farming, the germplasm resources of carp, and the application of fish genetic breeding technology in the germplasm innovation of carp.

Abstract The traceability of groundwater nitrate pollution is crucial for controlling and managing polluted groundwater. This study integrates hydrochemistry, nitrate isotope (δ15N-NO3 − and δ18O-NO3 −), and self-organizing map (SOM) and end-member mixing (EMMTE) models to identify the sources and quantify the contributions of nitrate pollution to groundwater in an intensive agricultural region in the Sha River Basin in southwestern Henan Province. The results indicate that the NO3 −-N concentration in 74% (n = 39) of the groundwater samples exceeded the WHO standard of 10 mg/L. According to the results of EMMTE modeling, soil nitrogen (68.4%) was the main source of nitrate in Cluster-1, followed by manure and sewage (16.5%), chemical fertilizer (11.9%) and atmospheric deposition (3.3%). In Cluster-2, soil nitrogen (60.1%) was the main source of nitrate, with a significant increase in the contribution of manure and sewage (35.5%). The considerable contributions of soil nitrogen may be attributed to the high nitrogen fertilizer usage that accumulated in the soil in this traditional agricultural area. Moreover, it is apparent that most Cluster-2 sampling sites with high contributions of manure and sewage are located around residential land. Therefore, the arbitrary discharge and leaching of domestic sewage may be responsible for these results. Therefore, this study provides useful assistance for the continuous management and pollution control of groundwater in the Sha River Basin.

Abstract Off-line leachate collection from agricultural landscapes cannot guarantee precise evaluation of agricultural non-point source (ANPS) due to geospatial variations, time, and transportation from the field to the laboratory. Implementing an in-situ nitrogen and phosphorous monitoring system with a robust photochemical flow analysis is imperative for precision agriculture, enabling real-time intervention to minimize non-point source pollution and overcome the limitations posed by conventional analysis in laboratory. A reliable, robust and in-situ approach was proposed to monitor nitrogen and phosphorous for determining ANPS pollution. In this study, a home-made porous ceramic probe and the frequency domain reflectometer (FDR) based water content sensors were strategically placed at different soil depths to facilitate the collection of leachates. These solutions were subsequently analyzed by in-situ photochemical flow analysis monitoring system built across the field to estimate the concentrations of phosphorus and nitrogen. After applying both natural and artificial irrigation to the agricultural landscape, at least 10 mL of soil leachates was consistently collected using the porous ceramic probe within 20 min, regardless of the depth of the soil layers when the volumetric soil water contents are greater than 19%. The experimental results showed that under different weather conditions and irrigation conditions, the soil water content of 50 cm and 90 cm below the soil surface was 19.58% and 26.08%, respectively. The average concentrations of NH4 +-N, NO3 −-N, PO4 3− are 0.584 mg/L, 15.7 mg/L, 0.844 mg/L, and 0.562 mg/L, 16.828 mg/L and 0.878 mg/L at depths of 50 cm and 90 cm below the soil surface, respectively. Moreover, the comparison with conventional laboratory spectroscopic analysis confirmed R2 values of 0.9951, 0.9943, 0.9947 average concentration ranges of NH4 +-N, NO3 −-N, and PO4 3−, showcasing the accuracy and reliability of robust photochemical flow analysis in-situ monitoring system. The suggested monitoring system can be helpful in the assessment of soil nutrition for precision agriculture.

Abstract Background Phytobacter diazotrophicus (P. diazotrophicus) is an opportunistic pathogen that causes nosocomial outbreaks and sepsis. However, there are no reports of P. diazotrophicus isolated from human blood in China. Case presentation A 27-day-old female infant was admitted to our hospital with fever and high bilirubin levels. The clinical features included jaundice, abnormal coagulation, cholestasis, fever, convulsions, weak muscle tension, sucking weakness, ascites, abnormal tyrosine metabolism, cerebral oedema, abnormal liver function, clavicle fracture, and haemolytic anaemia. The strain isolated from the patient’s blood was identified as P. diazotrophicus by whole-genome sequencing (WGS). Galactosemia type 1 (GALAC1) was diagnosed using whole-exome sequencing (WES). Based on drug sensitivity results, 10 days of anti-infective treatment with meropenem combined with lactose-free milk powder improved symptoms. Conclusion P. diazotrophicus was successfully identified in a patient with neonatal sepsis combined with galactosemia. Galactosemia may be an important factor in neonatal sepsis. This case further expands our understanding of the clinical characteristics of GALAC1.

ABSTRACT: Marek's disease (MD), an immunosuppression disease induced by Marek's disease virus (MDV), is one of the significant diseases affecting the health and productive performance of poultry. The roles of circular RNAs (circRNAs) in MD development were poorly understood. In this study, we found a circRNA derived from exon 6 of RUNX family transcription factor 2 (RUNX2) gene, named circRUNX2.2, was highly expressed in chicken tumorous spleens (TS) induced by MDV. Through fluorescence in situ hybridization and nuclear-cytoplasmic separation assay, we determined circRUNX2.2 was mainly located in the nucleus. Knockout experiments confirmed that the flanking complementary sequences (RCMs) mediated its circularization. Gain of function assay and dual luciferase reporter gene assay revealed that circRUNX2.2 could promote the expression of RUNX2 via binding with its promoter region. RNA antisense purification assay and mass spectrometry assay showed circRUNX2.2 could recruit proteins such as CHD9 protein. Knocking down CHD9 expression decreased the expression of RUNX2 gene, which confirmed the positive regulation that circRUNX2.2 on RUNX2 expression was probably facilitated via recruiting CHD9 protein. Functional experiments showed that circRUNX2.2 promoted the proliferation of the MD lymphoma-derived chicken cell line, MDCC-MSB1, which confirmed the potential oncogenic role of circRNX2.2 in tumor development. In conclusion, we found that the RUNX2-derived circRUNX2.2 can positively regulate the transcription of the parental gene RUNX2 in a cis-acting manner. The high expression of circRUNX2.2 in MD tumor tissues indicated that it might mediate MD lymphoma progression.

Abstract Background The cardiometabolic index (CMI) is a new metric derived from the triglyceride-glucose index and body mass index and is considered a potential marker for cardiovascular risk assessment. This study aimed to examine the correlation between the CMI and the presence and severity of arteriosclerosis in patients with type 2 diabetes mellitus (T2DM). Methods This study involved 2243 patients with T2DM. The CMI was derived by dividing the triglyceride level (mmol/L) by the high-density lipoprotein level (mmol/L) and then multiplying the quotient by the waist-to-height ratio. Multivariate logistic regression was used to analyze the correlations between the CMI and BMI blood biomarkers, blood pressure, and brachial-ankle pulse wave velocity (baPWV). Results Patients were categorized into three groups based on their CMI: Group C1 (CMI 1.355; n = 750). Increased BMI, fasting glucose, insulin (at 120 min), total cholesterol (TC), and baPWV values were observed in Groups C2 and C3, with statistically significant trends (all trends P

With the development of Earth observation technology, it becomes easier and easier to acquire multimodal image data at the same time. To improve the performance of a multimodal remote-sensing detection algorithm, a new fusion feature optimization detection network is proposed. The method is designed to solve the problem of performance degradation caused by the unreliability of single-modal data in multimodal remote-sensing data. The key to obtain high-quality fusion features from multimodal data with interference is to suppress single-modal redundant features and fully integrate multimodal features. The proposed method mainly includes two improvements. First, a novel joint expression optimization module is designed to enhance the target features and suppress the redundant and interference features that affect the fusion effect. In addition, we propose a novel specific information enhancement module to further enhance the discriminative feature information of targets within each modal image. Experiments on the DroneVehicle dataset show that our proposed method is state of the art on this dataset.

The use of wireless sensors has expanded across various industries, highlighting the need for accurate positioning research. In irregular network environments, conventional distance vector jump algorithms result in poor localization performance. To solve this problem, an improved distance vector jump algorithm is proposed in this study. The algorithm introduces geometric constraint mechanism and optimized weighted DV-HOP localization algorithm. The proposed method applies an optimal weight function to determine the hop count from unknown nodes to anchor nodes and calculates the unknown node coordinates using the least squares method. Additionally, geometric constraint mechanisms are used to eliminate positioning errors caused by outliers. The experimental results demonstrate that the positioning error is 0.37% in C-type network environment, 0.38% in F-type network environment, 0.29% in L-type network environment and 0.30% in H-type network environment. These results verify the effectiveness of the proposed algorithm in reducing positioning errors, which can meet the accuracy requirements of practical applications in complex network environments.

With the global population growth and increasing demand for high-quality protein, aquaculture has experienced rapid development. Fish culture management and feed supply are crucial components of aquaculture. Traditional baiting management relies on experiential judgment and regular observation, which often leads to inefficient baiting practices and wastage. To address these issues, intelligent bait casting decisions have emerged. Leveraging advanced artificial intelligence algorithms, intelligent bait casting decisions can overcome most drawbacks of traditional bait management and enhance breeding efficiency. However, most of the current intelligent baiting decisions are focused on using methods such as image processing and target detection to identify different feeding actions and patterns. These methods do not discuss based on video streams and do not consider the changes in fish behavior during the baiting process. Therefore, we proposed a real-time analysis method based on the density distribution of fish feeding behavior (FishFeed). Firstly, this method upgrades the input mechanism, not only handling static images but also capable of real-time video stream analysis. Secondly, by evaluating the fish school density distribution through a new intelligent baiting strategy, this method can monitor the feeding behavior of fish school during the baiting process in real time. Finally, we constructed a dataset for fish school density analysis (DlouFishDensity) that includes a wealth of video and image frames, providing a valuable resource for research. Experimental results indicate that our algorithm outperforms MCNN, improving MAE by 1.63 and 1.35, MSE by 1.92 and 1.58, and reducing prediction time by 2.56 seconds on the same dataset. By implementing real-time analysis of fish feeding behavior density distribution, our method offers a more efficient and effective approach to baiting management in aquaculture, contributing to improved breeding efficiency and resource utilization.

Adversarial examples have been shown to easily mislead neural networks, and many strategies have been proposed to defend them. To address the problem that most transformation-based defense strategies will degrade the accuracy of clean images, we proposed an Enhanced Image Transformation Generative Adversarial Network (EITGAN). Positive perturbations were employed in the EITGAN to counteract adversarial effects while enhancing the classified performance of the samples. We also used the image super-resolution method to mitigate the effect of adversarial perturbations. The proposed method does not require modification or retraining of the classifier. Extensive experiments demonstrated that the enhanced samples generated by the EITGAN effectively defended against adversarial attacks without compromising human visual recognition, and their classification performance was superior to that of clean images.

Objective Aiming at the problem that the assembly quality of interference fit components is difficult to control, a method for monitoring the assembly quality of such components based on digital twin technology is proposed.Methods Combined with digital twin technology, the framework of an assembly quality monitoring system for interference fit components is constructed; the actual assembly process is comprehensively analyzed and a real-time monitoring process of the assembly process based on the press-fitting force is designed; and a state prediction model of the press-fitting force based on the Markov model is established, and a comprehensive judgment of the assembly quality of the interference fit components is realized by the comprehensive comparison of theoretical press-fitting force, actual press-fitting force and press-fitting force prediction state.Results The interference fit between the output shaft and sun gear of a typical marine planetary reducer is used as an example for verification. The results show that this method can realize the real-time collection and monitoring of the press-fitting force at key points in the assembly process and predict the future quality characteristics of the point. At the same time, the experimental results of the physical assembly platform are compared with the model prediction results, and it is found that the two are consistent, which proves that the proposed method of monitoring the assembly quality of interference fit components based on digital twin technology is effective and feasible.ConclusionThe results of this study have important reference significance for the realization of quality monitoring in the process of the intelligent assembly of mechanical products.

Plants have evolved intricate signaling pathways, which operate as networks governed by feedback to deal with stressors. Nevertheless, the sophisticated molecular mechanisms underlying these routes still need to be comprehended, and experimental validation poses significant challenges and expenses. Consequently, computational hypothesis evaluation gains prominence in understanding plant signaling dynamics. Biosensors are genetically modified to emit light when exposed to a particular hormone, such as abscisic acid (ABA), enabling quantification. We developed computational models to simulate the relationship between ABA concentrations and bioluminescent sensors utilizing the Hill equation and ordinary differential equations (ODEs), aiding better hypothesis development regarding plant signaling. Based on simulation results, the luminescence intensity was recorded for a concentration of 47.646 RLUs for 1.5 μmol, given the specified parameters and model assumptions. This method enhances our understanding of plant signaling pathways at the cellular level, offering significant benefits to the scientific community in a cost-effective manner. The alignment of these computational predictions with experimental results emphasizes the robustness of our approach, providing a cost-effective means to validate mathematical models empirically. The research intended to correlate the bioluminescence of biosensors with plant signaling and its mathematical models for quantified detection of specific plant hormone ABA.

In large-scale satellite constellations, the efficiency of inter-satellite communication is paramount. Traditional topology control strategies, such as the Manhattan configuration, provide stable links but can result in indirect communication paths, affecting the efficiency of information transfer. This paper addresses this issue by proposing an innovative “3 + 1” dynamic topology control scheme. The scheme retains three static links determined by the relative angular velocity and acceleration while introducing a dynamic link based on distance and angular velocity constraints to optimize the link duration and overall network communication efficiency. To address the complexity of matching dynamic links, this paper introduces an elite strategy-based maximum weighted matching algorithm for general graphs. Compared to traditional greedy algorithms, our proposed algorithm significantly improves the link duration and topological stability. Through simulation experiments comparing communication delays between Xiamen and Los Angeles, our results show that the proposed dynamic link scheme substantially reduces the average delay, enhancing the efficiency and flexibility of inter-satellite communication. This research not only extends the duration of inter-satellite links but also provides new perspectives and methodologies for further studies on inter-satellite topology control strategies.

To address the limitations of inadequate real-time performance and robustness encountered in estimating the pose of non-cooperative spacecraft during on-orbit missions, a novel method of feature point distribution selection learning is proposed. This approach utilizes a non-coplanar key point selection network with uncertainty prediction, pioneering in its capability to accurately estimate the pose of non-cooperative spacecraft, thereby representing a significant advancement in the field. Initially, the feasibility of designing a non-coplanar key point selection network was analyzed based on the influence of sensor layout on the pose measurement. Subsequently, the key point selection network was designed and trained, leveraging images extracted from the spacecraft detection network. The network detected 11 pre-selected key points with distinctive features and was able to accurately predict their uncertainties and relative positional relationships. Upon selection of the key points exhibiting low uncertainty and non-coplanar relative positions, we utilized the EPnP algorithm to achieve accurate pose estimation of the target spacecraft. Our experimental evaluation on the SPEED dataset, which comes from the International Satellite Attitude Estimation Competition, validates the effectiveness of our key point selection network, significantly enhancing estimation accuracy and timeliness compared to other monocular spacecraft attitude estimation methods. This advancement provides robust technological support for spacecraft guidance, control, and proximity operations in orbital service missions.

Cell mechanics is essential to cell development and function, and its dynamics evolution reflects the physiological state of cells. Here, we investigate the dynamical mechanical properties of single cells under various drug conditions, and present two mathematical approaches to quantitatively characterizing the cell physiological state. It is demonstrated that the cellular mechanical properties upon the drug action increase over time and tend to saturate, and can be mathematically characterized by a linear time-invariant dynamical model. It is shown that the transition matrices of dynamical cell systems significantly improve the classification accuracies of the cells under different drug actions. Furthermore, it is revealed that there exists a positive linear correlation between the cytoskeleton density and the cellular mechanical properties, and the physiological state of a cell in terms of its cytoskeleton density can be predicted from its mechanical properties by a linear regression model. This study builds a relationship between the cellular mechanical properties and the cellular physiological state, adding information for evaluating drug efficacy.

Fish segmentation in underwater videos provides basic data for fish measurements, which is vital information that supports fish habitat monitoring and fishery resources survey. However, because of water turbidity and insufficient lighting, fish segmentation in underwater videos has low accuracy and poor robustness. Most previous work has utilized static fish appearance information while ignoring fish motion in underwater videos. Considering that motion contains more detail, this paper proposes a method that simultaneously combines appearance and motion information to guide fish segmentation in underwater videos. First, underwater videos are preprocessed to highlight fish in motion, and obtain high-quality underwater optical flow. Then, a multi-source guidance network (MSGNet) is presented to segment fish in complex underwater videos with degraded visual features. To enhance both fish appearance and motion information, a non-local-based multiple co-attention guidance module (M-CAGM) is applied in the encoder stage, in which the appearance and motion features from the intra-frame salient fish and the moving fish in video sequences are reciprocally enhanced. In addition, a feature adaptive fusion module (FAFM) is introduced in the decoder stage to avoid errors accumulated in the video sequences due to blurred fish or inaccurate optical flow. Experiments based on three publicly available datasets were designed to test the performance of the proposed model. The mean pixel accuracy (mPA) and mean intersection over union (mIoU) of MSGNet were 91.89% and 88.91% respectively with the mixed dataset. Compared with those of the advanced underwater fish segmentation and video object segmentation models, the mPA and mIoU of the proposed model significantly improved. The results showed that MSGNet achieves excellent segmentation performance in complex underwater videos and can provide an effective segmentation solution for fisheries resource assessment and ocean observation. The proposed model and code are exposed via Github1.

Abstract Background The centrosome is one of the most important non-membranous organelles regulating microtubule organization and progression of cell mitosis. The coiled-coil alpha-helical rod protein 1 (CCHCR1, also known as HCR) gene is considered to be a psoriasis susceptibility gene, and the protein is suggested to be localized to the P-bodies and centrosomes in mammalian cells. However, the exact cellular function of HCR and its potential regulatory role in the centrosomes remain unexplored. Results We found that HCR interacts directly with astrin, a key factor in centrosome maturation and mitosis. Immunoprecipitation assays showed that the coiled-coil region present in the C-terminus of HCR and astrin respectively mediated the interaction between them. Astrin not only recruits HCR to the centrosome, but also protects HCR from ubiquitin-proteasome-mediated degradation. In addition, depletion of either HCR or astrin significantly reduced centrosome localization of CEP72 and subsequent MCPH proteins, including CEP152, CDK5RAP2, and CEP63. The absence of HCR also caused centriole duplication defects and mitotic errors, resulting in multipolar spindle formation, genomic instability, and DNA damage. Conclusion We conclude that HCR is localized and stabilized at the centrosome by directly binding to astrin. HCR are required for the centrosomal recruitment of MCPH proteins and centriolar duplication. Both HCR and astrin play key roles in keeping normal microtubule assembly and maintaining genomic stability.

Interior tomography of X-ray computed tomography (CT) has many advantages, such as a lower radiation dose and lower detector hardware cost compared to traditional CT. However, this imaging technique only uses the projection data passing through the region of interest (ROI) for imaging; accordingly, the projection data are truncated at both ends of the detector, so the traditional analytical reconstruction algorithm cannot satisfy the demand of clinical diagnosis. To solve the above limitations, in this paper we propose a high-quality statistical iterative reconstruction algorithm that uses the zeroth-order image moment as novel prior knowledge; the zeroth-order image moment can be estimated in the projection domain using the Helgason–Ludwig consistency condition. Then, the L1norm of sparse representation, in terms of dictionary learning, and the zeroth-order image moment constraints are incorporated into the statistical iterative reconstruction framework to construct an objective function. Finally, the objective function is minimized using an alternating minimization iterative algorithm. The chest CT image simulated and CT real data experimental results demonstrate that the proposed approach can remove shift artifacts effectively and has superior performance in removing noise and persevering fine structures than the total variation (TV)-based approach.

Objective: By screening the core genes in lung adenocarcinoma (LUAD) with bioinformatics, our study evaluated its prognosis value and role in infiltration process of immune cells. Methods: Using GEO database, we screened 5 gene chips, including GSE11072, GSE32863, GSE43458, GSE115002, and GSE116959. Then, we obtained the corresponding differentially expressed genes by analyzed 5 gene chips online by GEO2R (P 1). Then, through DAVID online platform, Cytoscape 3.6.1 software and PPI network analysis, the network was visualized and obtain the final core genes. Next, we plan to use the GEPIA, UALCAN, Kaplan–Meier plotter and Time 2.0 database for corresponding analysis. The GEPIA database was used to verify the expression of core genes in LUAD and normal lung tissues, and survival analysis was used to evaluate the value of core genes in the prognosis of LUAD patients. UALCAN was used to verify the expression of the LUAD core gene and promoter methylation status, and the predictive value of core genes was evaluated in LUAD patients by the Kaplan–Meier plotter online tool. Then, we used the Time 2.0 database to identify the relationship to immune infiltration in LUAD. Finally, we used the human protein atlas (HPA) database for online immunohistochemical analysis of the expressed proteins. Results: The expression of CCNB2 and CDC20 in LUAD were higher than those in normal lung tissues, their increased expression was negatively correlated with the overall survival rate of LUAD, and they were involved in cell cycle signal transduction, oocyte meiosis signal transduction as well as the infiltration process of immune cells in LUAD. The expression proteins of CCNB2 and CDC20 were also different in lung cancer tissue and normal lung tissue. Therefore, CCNB2 and CDC20 were identified as the vital core genes. Conclusion: CCNB2 and CDC20 are essential genes that may constitute prognostic biomarkers in LUAD, they also participate the immune infiltration process and protein expression process of LUAD, and might provides basis for clinical anti-tumor drug research.

The process mineralogy of a low-grade refractory phosphate ore in Sichuan Province was studied by means of chemical analysis, microscopic identification, X-ray diffraction and infrared spectrometry analysis. The ore minerals are mainly composed of colloidal apatite, fine-grained dolomite and quartz, and contain a small amount of pyrite, limonite, hydromica, carbon and so on. The contents of P2O5, MgO and SiO2 in the samples are 17.54%, 6.83% and 18.36%. The results of the study show that the phosphate concentrate cannot be obtained by single reverse flotation process, and high-quality phosphate concentrate can be obtained by either forward-reverse flotation or x-ray separation.

For the past 20 years, the majority of cell culture studies reported that increasing cholesterol level increases amyloid-β (Aβ) production. Conversely, other studies and genetic evidences support that cellular cholesterol loss leads to Aβ generation. As a highly controversial issue in Alzheimer’s disease pathogenesis, the apparent contradiction prompted us to again explore the role of cellular cholesterol in Aβ production. Here, we adopted new neuronal and astrocytic cell models induced by 3β-hydroxysterol-Δ24 reductase (DHCR24), which obviously differ from the widely used cell models with overexpressing amyloid precursor protein (APP) in the majority of previous studies. In neuronal and astrocytic cell model, we found that deficiency of cellular cholesterol by DHCR24 knockdown obviously increased intracellular and extracellular Aβ generation. Importantly, in cell models with overexpressing APP, we found that APP overexpression could disrupt cellular cholesterol homeostasis and affect function of cells, coupled with the increase of APP β-cleavage product, 99-residue transmembrane C-terminal domain. Therefore, we suppose the results derived from the APP knockin models will need to be re-evaluated. One rational explanation for the discrepancy between our outcomes and the previous studies could be attributed to the two different cell models. Mechanistically, we showed that cellular cholesterol loss obviously altered APP intracellular localization by affecting cholesterol-related trafficking protein of APP. Therefore, our outcomes strongly support cellular cholesterol loss by DHCR24 knockdown leads to Aβ production.

Abstract Six chest leads are the standardized clinical devices of diagnosing cardiac diseases. Emerging epidermal electronics technology shift the dangling wires and bulky devices to imperceptible wearing, achieving both comfortable experience and high-fidelity measuring. Extending small areas of current epidermal electronics to the chest scale requires eliminating interference from long epidermal interconnects and rendering the data acquisition (DAQ) portable. Herein, we developed a chest-scale epidermal electronic system (EES) for standard precordial-lead ECG and hydration monitoring, including the only μm-thick substrate-free epidermal sensing module and the soft wireless DAQ module. An electrical compensation strategy using double channels within the DAQ module and epidermal compensated branches (ECB) is proposed to eliminate unwanted signals from the long epidermal interconnects and to achieve the desired ECG. In this way, the EES works stably and precisely under different levels of exercise. Patients with sinus arrhythmias have been tested, demonstrating the prospect of EES in cardiac diseases.

Abstract Previous studies show that 3β-hydroxysterol-Δ24 reductase (DHCR24) has a remarked decline in the brain of AD patients. In brain cholesterol synthetic metabolism, DHCR24 is known as the heavily key synthetase in cholesterol synthesis. Moreover, mutations of DHCR24 gene result in inhibition of the enzymatic activity of DHCR24, causing brain cholesterol deficiency and desmosterol accumulation. Furthermore, in vitro studies also demonstrated that DHCR24 knockdown lead to the inhibition of cholesterol synthesis, and the decrease of plasma membrane cholesterol and intracellular cholesterol level. Obviously, DHCR24 could play a crucial role in maintaining cholesterol homeostasis via the control of cholesterol synthesis. Over the past two decades, accumulating data suggests that DHCR24 activity is downregulated by major risk factors for AD, suggesting a potential link between DHCR24 downregulation and AD pathogenesis. Thus, the brain cholesterol loss seems to be induced by the major risk factors for AD, suggesting a possible causative link between brain cholesterol loss and AD. According to previous data and our study, we further found that the reduced cholesterol level in plasma membrane and intracellular compartments by the deficiency of DHCR24 activity obviously was involved in β-amyloid generation, tau hyperphosphorylation, apoptosis. Importantly, increasing evidences reveal that the brain cholesterol loss and lipid raft disorganization are obviously linked to neuropathological impairments which are associated with AD pathogenesis. Therefore, based on previous data and research on DHCR24, we suppose that the brain cholesterol deficiency/loss might be involved in the pathogenesis of AD.

Accurate fish individual recognition is one of the critical technologies for large-scale fishery farming when trying to achieve accurate, green farming and sustainable development. It is an essential link for aquaculture to move toward automation and intelligence. However, existing fish individual data collection methods cannot cope with the interference of light, blur, and pose in the natural underwater environment, which makes the captured fish individual images of poor quality. These low-quality images can cause significant interference with the training of recognition networks. In order to solve the above problems, this paper proposes an underwater fish individual recognition method (FishFace) that combines data quality assessment and loss weighting. First, we introduce the Gem pooing and quality evaluation module, which is based on EfficientNet. This module is an improved fish recognition network that can evaluate the quality of fish images well, and it does not need additional labels; second, we propose a new loss function, FishFace Loss, which will weigh the loss according to the quality of the image so that the model focuses more on recognizable fish images, and less on images that are difficult to recognize. Finally, we collect a dataset for fish individual recognition (WideFish), which contains and annotates 5000 images of 300 fish. The experimental results show that, compared with the state-of-the-art individual recognition methods, Rank1 accuracy is improved by 2.60% and 3.12% on the public dataset DlouFish and the proposed WideFish dataset, respectively.

We have established a novel method for quadrature signal construction in a semiconductor laser diode self-mixing interferometry system using two photodiodes and a beam splitter with a liquid crystal phase shifter (LCPS). This method entails placing an LCPS between the photodiode and the beam splitter so that another phase shift self-mixing signal can be obtained. Then, an arctangent phase algorithm can be used to demodulate the pair of quadrature signals to reconstruct the vibration information of the target object. This method simplifies the self-mixing signal demodulation process and the reconstruction of vibration information. Our experimental results demonstrate the feasibility of using self-mixing phase shifter detection for self-mixing optical measurements. The work illustrates a sort of efficient and referable novel design guidance model which supports the quadrature signals construction in a self-mixing interferometer based on a semiconductor laser diode.

As one of weakest link of harmonic reducer，flexspline has always been the object of concern in the reliability research of harmonic reducer. The CSG-32-80 harmonic reducer is taken as the object，and the transient analysis of the flexspline is carried out by using Ansys Workbench. The distribution of stress and strain of the flexspline is obtained. The results of finite element analysis is imported into Ncode Designlife fatigue analysis module，and the fatigue life of flexspline is obtained by combining S-N Stress-Life curve of flexspline material. The relationship between the maximum stress and the length，wall thickness and tooth width of the flexspline are explored by orthogonal experiment. It provides a theoretical reference for the optimal design of flexspline structure.

Automatic detection of subsequence anomalies (i.e., an abnormal waveform denoted by a sequence of data points) in time series is critical in a wide variety of domains. However, most existing methods for subsequence anomaly detection often require knowing the length and the total number of anomalies in time series. Some methods fail to capture recurrent subsequence anomalies due to using only local or neighborhood information for anomaly detection. To address these limitations, in this paper, we propose a novel graph-represented time series (GraphTS) method for discovering subsequence anomalies. In GraphTS, we provide a new concept of time series graph representation model, which represents both recurrent and rare patterns in a time series. Particularly, in GraphTS, we develop a new 2D time series visualization (2Dviz) method, which compacts all 1D time series patterns into a 2D spatial temporal space. The 2Dviz method transfers time series patterns into a higher-resolution plot for easier sequence anomaly recognition (or detecting subsequence anomalies). Then, a Graph is constructed based on the 2D spatial temporal space of time series to capture recurrent and rare subsequence patterns effectively. The represented Graph also can be used to discover single and recurrent subsequence anomalies with arbitrary lengths. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and efficiency.

CTLA4 deficiency and LRBA deficiency are a group disorders of immune dysregulation that affect CTLA4 pathway. The patients mainly present with autoimmunity, antibody deficiency and recurrent infections. Here we reported three Chinese patients with LRBA and CTLA4 mutations. They all presented with chronic diarrhea, hypokalemia, organomegaly, recurrent infections, and hypogammaglobulinemia. Reduced Treg cells and increased percentage of circulating follicular helper T (cTfh) cells were revealed in these patients. Although steroid and immunoglobulin therapy were given, the enteropathy was persistent. Therefore, abatacept treatment was provided to these patients. They showed a marked improvement of enteropathy and gastrointestinal endoscopy showed alleviated inflammatory lesion and follicular hyperplasia. Furthermore, the frequency of cTfh cells was reduced after abatacept therapy. Taken together, targeted therapy with abatacept is a promising treatment modality for patients with LRBA and CTLA4 deficiency. The findings also suggest that the frequency of cTfh cells could serve as a marker for tracking disease activity and the response to abatacept therapy.

This paper investigates the distance-based formation and cooperative path-following control problems for multiple fixed-wing unmanned aerial vehicles (UAVs). In this study, we design the distance-based formation control structure to achieve the virtual leader and followers pre-defined rigid formation pattern, ensuring simultaneously relative localization. A path-following control strategy based on adaptive dynamic surface and neural network control technology is proposed to approximate the uncertain disturbances of the environment and unmodeled dynamics. And the longitudinal and lateral subsystems’ adaptive fault-tolerant controllers are designed, respectively, to achieve the fault-tolerant control of UAVs’ formation in three-dimensional environments. Furthermore, the adaptive sliding mode controller with an auxiliary controller is designed to realize the UAVs path following with limited input saturation. Finally, simulation examples are given to clarify and verify the effectiveness of the theoretical results.

Deep-learning-based SAR ship classification has become a research hotspot in the military and civilian fields and achieved remarkable performance. However, the volume of available SAR ship classification data is relatively small, meaning that previous deep-learning-based methods have usually struggled with overfitting problems. Moreover, due to the limitation of the SAR imaging mechanism, the large intraclass diversity and small interclass similarity further degrade the classification performance. To address these issues, we propose a label smoothing auxiliary classifier generative adversarial network with triplet loss (LST-ACGAN) for SAR ship classification. In our method, an ACGAN is introduced to generate SAR ship samples with category labels. To address the model collapse problem in the ACGAN, the smooth category labels are assigned to generated samples. Moreover, triplet loss is integrated into the ACGAN for discriminative feature learning to enhance the margin of different classes. Extensive experiments on the OpenSARShip dataset demonstrate the superior performance of our method compared to the previous methods.

Ensuring the privacy and secrecy of digital medical images has become a pressing issue as a result of the quick development of smart medical technology and the exponential growth in the quantity of medical images transmitted and stored in networks. The lightweight multiple-image encryption approach for medical images that is suggested in this research can encrypt/decrypt any number of medical photos of varied sizes with just one encryption operation and has a computational cost that is similar to encrypting a single image. The plaintext images with different sizes are filled at the right and bottom of the image to ensure that the size of all plaintext images is uniform; then, all the filled images are stacked to obtain a superimposed image. The initial key, which is generated using the SHA-256 technique, is then used as the starting value of the linear congruence algorithm to create the encryption key sequence. The cipher picture is then created by encrypting the superimposed image with the encryption key and DNA encoding. The algorithm can be made even more secure by implementing a decryption mechanism that decrypts the image independently in order to reduce the possibility of information leaking during the decryption process. The outcomes of the simulation experiment demonstrate the algorithm’s strong security and resistance to interference such as noise pollution and lost image content.

Abstract In inertial confinement fusion, quantitative and high-spatial resolution ( $$< 10\,\upmu $$ < 10 μ m) measurements of the X-rays self-emitted by the hotspot are critical for studying the physical processes of the implosion stagnation stage. Herein, the 8 ± 0.39-keV monochromatic X-ray distribution from the entire hotspot is quantitatively observed in 5- $$\upmu $$ μ m spatial resolution using a Kirkpatrick–Baez microscope, with impacts from the responses of the diagnosis system removed, for the first time, in implosion experiments at the 100 kJ laser facility in China. Two-dimensional calculations along with 2.5% P2 drive asymmetry and 0.3 ablator self-emission are congruent with the experimental results, especially for the photon number distribution, hotspot profile, and neutron yield. Theoretical calculations enabled a better understanding of the experimental results. Furthermore, the origins of the 17.81% contour profile of the deuterium-deuterium hotspot and the accurate Gaussian source approximation of the core emission area in the implosion capsule are clarified in detail. This work is significant for quantitatively exploring the physical conditions of the hotspot and updating the theoretical model of capsule implosion.

Abstract Probiotics when applied in complex evolving (micro-)ecosystems, might be selectively beneficial or detrimental to pathogens when their prophylactic efficacies are prone to ambient interactions. Here, we document a counter-intuitive phenomenon that probiotic-treated zebrafish (Danio rerio) were respectively healthy at higher but succumbed at lower level of challenge with a pathogenic Vibrio isolate. This was confirmed by prominent dissimilarities in fish survival and histology. Based upon the profiling of the zebrafish microbiome, and the probiotic and the pathogen shared gene orthogroups (genetic niche overlaps in genomes), this consequently might have modified the probiotic metabolome as well as the virulence of the pathogen. Although it did not reshuffle the architecture of the commensal microbiome of the vertebrate host, it might have altered the probiotic-pathogen inter-genus and intra-species communications. Such in-depth analyses are needed to avoid counteractive phenomena of probiotics and to optimise their efficacies to magnify human and animal well-being. Moreover, such studies will be valuable to improve the relevant guidelines published by organisations such as FAO, OIE and WHO.

Recently, unsupervised domain adaptation (UDA) has attracted increasing attention to address the domain shift problem in the semantic segmentation task. Although previous UDA methods have achieved promising performance, they still suffer from the distribution gaps between source and target domains, especially the resolution discrepancy in the remote sensing images. To address this problem, this study designs a novel end-to-end semantic segmentation network, namely, Super-Resolution Domain Adaptation Network (SRDA-Net). SRDA-Net can simultaneously achieve the super-resolution task and the domain adaptation task, thus satisfying the requirement of semantic segmentation for remote sensing images, which usually involve various resolution images. The proposed SRDA-Net includes three parts: a super-resolution and segmentation (SRS) model, which focuses on recovering high-resolution image and predicting segmentation map, a pixel-level domain classifier (PDC) for determining which domain the pixel belongs to, and an output-space domain classifier (ODC) for distinguishing which domain the pixel contribution is from. By jointly optimizing SRS with two classifiers, the proposed method can not only eliminate the resolution difference between source and target domains but also improve the performance of the semantic segmentation task. Experimental results on two remote sensing datasets with different resolutions demonstrate that SRDA-Net performs favorably against some state-of-the-art methods in terms of accuracy and visual quality. Code and models are available at https://github.com/tangzhenjie/SRDA-Net.

Abstract Excessive expression of matrix metalloproteinase 9 (MMP-9) impedes healing of diabetic chronic wounds, thus wound dressing that could effectively inhibit the expression of MMP-9 offers significant clinical translation for diabetic wound healing. Herein, a hybrid hydrogel dressing was developed for localized and sustained delivery of MMP-9 siRNA (siMMP-9). siMMP-9 was complexed with Gly-TETA (GT), the GT/siMMP9 complex was then loaded into a thermosensitive hydrogel based on Pluronic F-127 (PF) and methylcellulose (MC). In vitro, this hybrid hydrogel dressing exhibited negligible cytotoxicity, prolonged the release of GT/siMMP-9 for up to 7 days, and significantly reduced MMP-9 expression. In vivo assessment in diabetic rats demonstrated that hydrogel provided localized and sustained delivery via the thermosensitive controlled release of entrapped GT/siMMP-9 into wound tissues for 7 days, resulting in dramatic MMP-9 silencing which significantly improved diabetic wound closure. This hybrid hydrogel dressing exhibited excellent biocompatibility, with no observed systemic toxicity in rats. Taken together, the hybrid hydrogel dressing may constitute an effective and biocompatible means of enhancing diabetic wound healing through effective silencing of the MMP-9 gene, and this hydrogel delivery system also offers a platform for in vivo delivery of siRNA for the treatment of other diseases.

To perform indicator selection and verification for the on-orbit fault reconstruction of a giant satellite swarm, a hybrid multi-objective fault reconstruction algorithm is proposed and then verified by Monte Carlo analysis. First, according to the on-orbit failure analysis of the satellite swarm, several optimization indicators, such as the health state of the satellite swarm, the total energy consumption of reconstruction, and the balance of fuel consumption, are proposed. Then, a hybrid multi-objective fitness function is constructed, and a hybrid multi-objective genetic algorithm is used to optimize the objective function to obtain the optimal reconstruction strategy. Finally, the algorithm is statistically verified by Monte Carlo analysis. The simulation results not only show the algorithm’s validity but also reveal the relationship between the number of satellite faults and the health of the satellite swarm. From this, the maximum number of faulty satellites allowed in the giant satellite swarm is calculated, which is significant for assessing the swarm’s health.

The utilisation of miniature robots has become essential in the domain of minimally invasive surgery and long-distance delivery of nanomedicine. Among these, the miniature magnetic continuum robot (MCR) stands out because of its simple structure and dexterity, which allow it to penetrate small cavities, transport specialised tools such as a laser, and deliver medications to support surgical treatment. Nevertheless, because of their soft bodies with a single stiffness, conventional MCRs have limited controllability when navigating through intricate cavities. To address this limitation, we propose a novel concentric magnetic continuum robot (C-MCR) comprising a concentric magnetic catheter with a guidewire having varying stiffness. The C-MCR allows substantial curvature bending owing to its difference in stiffness, and its detachable nature allows it to have four working modes to adapt to specific application requirements with improved stiffness controllability. Experiments demonstrate the ability of the C-MCR to navigate complex pathways and deliver nanomedicines over long distances to specific areas via its internal channels using a large homemade eight-coil electromagnetic system. The C-MCR offers promising application prospects for the long-distance delivery of tailored nanomedicines because of its simple operation, reduced risks, and larger attainable workspace.

Deep learning models have enabled significant performance improvements to remote sensing image processing. Usually, a large number of training samples is required for detection models. In this study, a dynamic simulation training strategy is designed to generate samples in real time during training. The few adversarial examples are not only directly involved in the training but are also used to fit the distribution model of adversarial noise, helping the real-time generated samples to be similar to adversarial examples. The noise of the training samples is randomly generated according to the distribution model, and the random variation of training inputs reduces the overfitting phenomenon. To enhance the detectability of adversarial noise, the input model is no longer a normalized image but a JPEG error image. Experiments show that with the proposed dynamic simulation training strategy, common classification models such as ResNet and DenseNet can effectively detect adversarial examples.

Aiming at the problems of low efficiency and imperfect test scheme of the existing accelerated life test of the harmonic reducer， a double stress step-down accelerated life test method is proposed. Taking the harmonic reducer for industrial robots as the research object， through comparing the acceleration schemes and analyzing the failure mechanism of accelerated life， a double stress step-down test load spectrum is formulated. Based on the iterative relationship between the step-down load stresses of each segment， the optimal parameters of Weibull distribution of harmonic driver are calculated by genetic algorithm. The results show that the acceleration factor for this double stress step-down life test is 12.5. The test efficiency is accurately and effectively improved by this scheme， and a reference for optimizing the design of the harmonic reducer and shortening the test cycle of the accelerated life test of the harmonic reducer are provided.

To ensure the safety and stability of spacecrafts of which thousands of telemetry parameters are monitored, fast and accurate response to anomalies or potential hazards is very important and challenging. This task becomes more difficult when the obtained telemetry data are sampled at irregular intervals. Long Short-Term Memory networks (LSTM), as time series prediction models, have been applied to satellite anomaly detection and show a promising prospect. However, the anomaly detection method merely based on LSTM does not show a stable performance: when the prediction performance of LSTM is not satisfying, the performance of subsequent anomaly detection will be affected, and the impact is augmented when the telemetry data are of irregular intervals. In order to solve these problems, time intervals are introduced into the LSTM model directly. Besides that, a novel anomaly detection method, Detecting Anomalies using LSTM and Ensembled One-Class Support Vector Machines (DALEO) is proposed to further improve the performance of anomaly detection. In DALEO, multiple One-Class Support Vector Machines are used to obtain the ensemble outputs of high precision and high recall respectively. These ensemble outputs are integrated into the two stages of the anomaly detection method with LSTM in a novel way. Extensive empirical studies on real-world datasets of satellites and space shuttles demonstrate that DALEO improves the performance of anomaly detection significantly when dealing with telemetry data with irregular intervals.

A demodulation method for tunable diode laser absorption spectroscopy (TDLAS) second harmonic based on the Hilbert transform is proposed in this paper. The second harmonic of the TDLAS signal can be easily obtained without a reference signal. The TDLAS signal is firstly processed by band-pass filtering, then the envelope of the processed signal is obtained with Hilbert transform. And finally, the second harmonic is extracted from the 1f component of the envelope. The validity of the proposed method is confirmed by simulation and verified by experiment, and the result shows that the error is acceptable under the cases of weak absorbance, which indicates that the proposed method is applicable to practical trace gas detection.

A demodulation method for tunable diode laser absorption spectroscopy (TDLAS) second harmonic based on the Hilbert transform is proposed in this paper. The second harmonic of the TDLAS signal can be easily obtained without a reference signal. The TDLAS signal is firstly processed by band-pass filtering, then the envelope of the processed signal is obtained with Hilbert transform. And finally, the second harmonic is extracted from the 1f component of the envelope. The validity of the proposed method is confirmed by simulation and verified by experiment, and the result shows that the error is acceptable under the cases of weak absorbance, which indicates that the proposed method is applicable to practical trace gas detection.

Nitrate nitrogen (NO3- -N) in the soil is one of the important nutrients for growing crops. During the period of precipitation or irrigation, an excessive NO3- -N readily causes its leaching or runoff from the soil surface to rivers due to inaccurate fertilization and water management, leading to non-point source pollution. In general, the measurement of the NO3--N relies upon the laboratory-based absorbance, which is often time-consuming, therefore not suitable for the rapid measurements in the field directly. Ion-selective electrodes (ISEs) support the possibility of NO3--N measurement by measuring the nitrate (NO3-) ions in soil quickly and accurately due to the high water solubility and mobility of NO3- ions. However, such a method suffers from a complicated calibration to remove the influences caused by both temperature and other ions in the measured solution, thus limiting field use. In this study, a kind of all-solid ISE system combined with a temperature sensor and a pH electrode is proposed to automatically measure the concentrations of the NO3--N. In this study, a soil water content calibration function was established, which significantly reduces a relative error (RE) by 13.09%. The experimental results showed that the stabilization time of this electrode system was less than 15 s with a slope of −51.63 mV/decade in the linear range of 10–5–10–2.2 mol/L. Both the limit of detection of 0.5 ppm of the NO3--N and a relative SD of less than 3% were obtained together with the recovery rate of 90–110%. Compared with the UV-Vis spectroscopy method, a correlation coefficient (R2) of 0.9952 was obtained. The performances of this all-solid ISE system are satisfied for measuring the NO3--N in the field.

BackgroundThe T cell receptor (TCR) diversity is essential for effective T cell immunity. Previous studies showed that TCR diversity in Wiskott–Aldrich Syndrome (WAS) patients was severely impaired, especially in the memory T cell populations. Whether this defect was caused by intrinsic WASp deficiency or extrinsic reasons is still unclear.MethodsWe sorted different T cell subsets from the bone marrow chimeric mice model using both magnetic beads and flow cytometry. TCR repertoires of memory T cells, especially CD4+ effector memory T (TEM) cells and CD8+ central memory T (TCM) cells, were analyzed using the UMI quantitative high-throughput sequencing (HTS).ResultsAn average of 5.51 million sequencing reads of 32 samples was obtained from the Illumina sequencing platform. Bioinformatic analyses showed that compared with wild type (WT), WAS knock out (KO)-CD4+ TEM cells exhibited increased Simpson index and decreased D50 index (P

Considering the issue of noise reduction associated with Laser Doppler Interference (LDI) signal, the paper presented a correlation coefficient local capping robust empirical mode decomposition (REMD) filter algorithm for LDI laser sensor that enables more robust reconstruction of the displacement information from an LDI signal. The performance of the algorithm is studied, and it is shown that the algorithm is capable of removing high-frequency noise. Useful information can be extracted more easily by this method, and the Hilbert phase unwrapping displacement reconstructions method based on this algorithm has been experimentally validated. The experimental results show that the proposed method can improve the frequency separation performance in experiments, and is robust against noise interference.

Considering the issue of noise reduction associated with Laser Doppler Interference (LDI) signal, the paper presented a correlation coefficient local capping robust empirical mode decomposition (REMD) filter algorithm for LDI laser sensor that enables more robust reconstruction of the displacement information from an LDI signal. The performance of the algorithm is studied, and it is shown that the algorithm is capable of removing high-frequency noise. Useful information can be extracted more easily by this method, and the Hilbert phase unwrapping displacement reconstructions method based on this algorithm has been experimentally validated. The experimental results show that the proposed method can improve the frequency separation performance in experiments, and is robust against noise interference.