Optical flow-based vascular respiratory motion compensation

This paper develops a new vascular respiratory motion compensation algorithm, Motion-Related Compensation (MRC), to conduct vascular respiratory motion compensation by extrapolating the correlation between invisible vascular and visible non-vascular. Robot-assisted vascular intervention can significantly reduce the radiation exposure of surgeons. In robot-assisted image-guided intervention, blood vessels are constantly moving/deforming due to respiration, and they are invisible in the X-ray images unless contrast agents are injected. The vascular respiratory motion compensation technique predicts 2D vascular roadmaps in live X-ray images. When blood vessels are visible after contrast agents injection, vascular respiratory motion compensation is conducted based on the sparse Lucas-Kanade feature tracker. An MRC model is trained to learn the correlation between vascular and non-vascular motions. During the intervention, the invisible blood vessels are predicted with visible tissues and the trained MRC model. Moreover, a Gaussian-based outlier filter is adopted for refinement. Experiments on in-vivo data sets show that the proposed method can yield vascular respiratory motion compensation in 0.032 sec, with an average error 1.086 mm. Our real-time and accurate vascular respiratory motion compensation approach contributes to modern vascular intervention and surgical robots.Comment: This manuscript has been accepted by IEEE Robotics and Automation Letter

Iterative PnP and its application in 3D-2D vascular image registration for robot navigation

This paper reports on a new real-time robot-centered 3D-2D vascular image alignment algorithm, which is robust to outliers and can align nonrigid shapes. Few works have managed to achieve both real-time and accurate performance for vascular intervention robots. This work bridges high-accuracy 3D-2D registration techniques and computational efficiency requirements in intervention robot applications. We categorize centerline-based vascular 3D-2D image registration problems as an iterative Perspective-n-Point (PnP) problem and propose to use the Levenberg-Marquardt solver on the Lie manifold. Then, the recently developed Reproducing Kernel Hilbert Space (RKHS) algorithm is introduced to overcome the ``big-to-small'' problem in typical robotic scenarios. Finally, an iterative reweighted least squares is applied to solve RKHS-based formulation efficiently. Experiments indicate that the proposed algorithm processes registration over 50 Hz (rigid) and 20 Hz (nonrigid) and obtains competing registration accuracy similar to other works. Results indicate that our Iterative PnP is suitable for future vascular intervention robot applications.Comment: Submitted to ICRA 202

Overview of Knowledge Graph Question Answering Enhanced by Large Language Models

Knowledge graph question answering (KGQA) is a technology that retrieves relevant answers from a knowledge graph by processing natural language questions posed by users. Early KGQA technologies were limited by the size of knowledge graphs, computational power, and natural language processing capabilities, resulting in lower accuracy. In recent years, with advancements in artificial intelligence, particularly the development of large language models (LLMs), KGQA technology has achieved significant improvements. LLMs such as GPT-3 have been widely applied to enhancing the performance of KGQA. To better study and learn the enhanced KGQA technologies, this paper summarizes various methods using LLMs for KGQA. Firstly, the relevant knowledge of LLMs and KGQA is summarized, including the technical principles and training methods of LLMs, as well as the basic concepts of knowledge graphs, question answering, and KGQA. Secondly, existing methods of enhancing KGQA with LLMs are reviewed from two dimensions: semantic parsing and information retrieval. The problems that these methods address and their limitations are analyzed. Additionally, related resources and evaluation methods for KGQA enhanced by LLMs are collected and organized, and the performance of existing methods is summarized. Finally, the limitations of current methods are analyzed, and future research directions are proposed

An Autofocus-Coupled UAMP Method for InISAR Imaging

Compressive sensing (CS) has been successfully applied to sparse aperture (SA) interferometric inverse synthetic aperture radar (InISAR) imaging, which outperforms traditional Fourier transform-based algorithms. However, existing CS-based algorithms generally assume that motion compensation has been completed perfectly, and do not consider the effect of residual phase error (RPE) in sparse imaging, which may cause degradation of 3D InISAR imaging performance in practical applications. To tackle this issue, an autofocus-coupled approximate message passing with unitary transformation (AFUAMP) algorithm for SA InISAR imaging is proposed in this paper, which can eliminate the effect of RPE on 3D imaging. Specifically, the phase error estimation process is introduced to the UAMP framework for autofocusing, which enables UAMP to simultaneously complete ISAR image reconstruction and autofocus, thereby leading to excellent performance of 3D InISAR imaging. In particular, we analyze the effect of RPE on the interferometric phase in InISAR imaging. Extensive simulation results demonstrate the effectiveness and superiority of the proposed algorithm compared to existing algorithms

Assessment ecological risk of heavy metal caused by high-intensity land reclamation in Bohai Bay, China.

The article examines the detailed spatial and temporal distributions of coastal reclamation in the northwest coast of Bohai Bay experiencing rapid coastal reclamation in China from 1974 to 2010 in annual intervals. Moreover, soil elements properties and spatial distribution in reclaimed area and inform the future coastal ecosystems management was also analyzed. The results shows that 910.7 km2 of coastal wetlands have been reclaimed and conversed to industrial land during the past 36 years. It covers intertidal beach, shallow sea and island with a percentage of 76.0%, 23.5% and 0.5%, respectively. The average concentration of Mn is 686.91mg/kg and the order of concentration of heavy metal are Cr>Zn>As>Ni>Cu>Pb>Cd>Hg. We used the "space for time substitution" method to test the soil properties changes after reclamation. The potential ecological risk of heavy metal is in low level and the risk of Cd and As is relatively higher. The ecosystem-based coastal protection and management are urgent to support sustainable coastal ecosystems in Bohai bay in the future

Microbial Fuels Cell-Based Biosensor for Toxicity Detection: A Review

With the unprecedented deterioration of environmental quality, rapid recognition of toxic compounds is paramount for performing in situ real-time monitoring. Although several analytical techniques based on electrochemistry or biosensors have been developed for the detection of toxic compounds, most of them are time-consuming, inaccurate, or cumbersome for practical applications. More recently, microbial fuel cell (MFC)-based biosensors have drawn increasing interest due to their sustainability and cost-effectiveness, with applications ranging from the monitoring of anaerobic digestion process parameters (VFA) to water quality detection (e.g., COD, BOD). When a MFC runs under correct conditions, the voltage generated is correlated with the amount of a given substrate. Based on this linear relationship, several studies have demonstrated that MFC-based biosensors could detect heavy metals such as copper, chromium, or zinc, as well as organic compounds, including p-nitrophenol (PNP), formaldehyde and levofloxacin. Both bacterial consortia and single strains can be used to develop MFC-based biosensors. Biosensors with single strains show several advantages over systems integrating bacterial consortia, such as selectivity and stability. One of the limitations of such sensors is that the detection range usually exceeds the actual pollution level. Therefore, improving their sensitivity is the most important for widespread application. Nonetheless, MFC-based biosensors represent a promising approach towards single pollutant detection

Integration of Whole-Genome Resequencing and Transcriptome Sequencing Reveals Candidate Genes in High Glossiness of Eggshell

Eggshell gloss is an important characteristic for the manifestation of eggshell appearance. However, no study has yet identified potential candidate genes for eggshell gloss between high-gloss (HG) and low-gloss (LG) chickens. The aim of this study was to perform a preliminary investigation into the formation mechanism of eggshell gloss and to identify potential genes. The eggshell gloss of 300-day-old Rhode Island Red hens was measured from three aspects. Uterine tissues of the selected HG and LG (n = 5) hens were collected for RNA-seq. Blood samples were also collected for whole-genome resequencing (WGRS). RNA-seq analysis showed that 150 differentially expressed genes (DEGs) were identified in the uterine tissues of HG and LG hens. These DEGs were mainly enriched in the calcium signaling pathway and the neuroactive ligand–receptor interaction pathway. Importantly, these two pathways were also significantly enriched in the WGRS analysis results. Further joint analysis of WGRS and RNA-seq data revealed that 5-hydroxytryptamine receptor 1F (HTR1F), zinc finger protein 536 (ZNF536), NEDD8 ubiquitin-like modifier (NEDD8), nerve growth factor (NGF) and calmodulin 1 (CALM1) are potential candidate genes for eggshell gloss. In summary, our research provides a reference for the study of eggshell gloss and lays a foundation for improving egg glossiness in layer breeding

Deep Learning Methods for Wood Composites Failure Predication

For glulam bonding performance assessment, the traditional method of manually measuring the wood failure percentage (WFP) is insufficient. In this paper, we developed a rapid assessment approach to predicate the WFP based on deep-learning (DL) techniques. bamboo/Larch laminated wood composites bonded with either phenolic resin (PF) or methylene diphenyl diisocyanate (MDI) were used for this sample analysis. Scanning of bamboo/larch laminated wood composites that have completed shear failure tests using an electronic scanner allows a digital image of the failure surface to be obtained, and this image is used in the training process of a deep convolutional neural networks (DCNNs).The result shows that the DL technique can predict the accurately localized failures of wood composites. The findings further indicate that the UNet model has the highest values of MIou, Accuracy, and F1 with 98.87%, 97.13%, and 94.88, respectively, compared to the values predicted by the PSPNet and DeepLab_v3+ models for wood composite failure predication. In addition, the test conditions of the materials, adhesives, and loadings affect the predication accuracy, and the optimal conditions were identified. The predicted value from training images assessed by DL techniques with the optimal conditions is 4.3%, which is the same as the experimental value measured through the traditional manual method. Overall, this advanced DL method could significantly facilitate the quality identification process of the wood composites, particularly in terms of measurement accuracy, speed, and stability, through the UNet model