4DRVO-Net: Deep 4D Radar-Visual Odometry Using Multi-Modal and Multi-Scale Adaptive Fusion

Four-dimensional (4D) radar--visual odometry (4DRVO) integrates complementary information from 4D radar and cameras, making it an attractive solution for achieving accurate and robust pose estimation. However, 4DRVO may exhibit significant tracking errors owing to three main factors: 1) sparsity of 4D radar point clouds; 2) inaccurate data association and insufficient feature interaction between the 4D radar and camera; and 3) disturbances caused by dynamic objects in the environment, affecting odometry estimation. In this paper, we present 4DRVO-Net, which is a method for 4D radar--visual odometry. This method leverages the feature pyramid, pose warping, and cost volume (PWC) network architecture to progressively estimate and refine poses. Specifically, we propose a multi-scale feature extraction network called Radar-PointNet++ that fully considers rich 4D radar point information, enabling fine-grained learning for sparse 4D radar point clouds. To effectively integrate the two modalities, we design an adaptive 4D radar--camera fusion module (A-RCFM) that automatically selects image features based on 4D radar point features, facilitating multi-scale cross-modal feature interaction and adaptive multi-modal feature fusion. In addition, we introduce a velocity-guided point-confidence estimation module to measure local motion patterns, reduce the influence of dynamic objects and outliers, and provide continuous updates during pose refinement. We demonstrate the excellent performance of our method and the effectiveness of each module design on both the VoD and in-house datasets. Our method outperforms all learning-based and geometry-based methods for most sequences in the VoD dataset. Furthermore, it has exhibited promising performance that closely approaches that of the 64-line LiDAR odometry results of A-LOAM without mapping optimization.Comment: 14 pages,12 figure

Modeling spectrum access strategies in cognitive radio networks using Colored Petri Nets

The spectrum access strategy is one of the important design aspects for better system capacity in cognitive radio networks (CRN), which is characterized as complex and concurrent access processing of multiple users. The common approach modeling spectrum access is Markov Chain (MC), which is prone to state space explosion with the increasing of the number of users. In this paper, an executable hierarchical Colored Petri Nets (CPN) model for the spectrum access in CRN is investigated to overcome the explicit limitation using MC. After the verification that the CPN model is isomorphic to MC in the case of arriving with Poisson distribution, the advantage of CPN on computation complexity is analyzed. Finally, a spectrum access strategy with queuing for the secondary users is proposed and modeled by CPN, which is demonstrated more flexible and workable than MC by the results

Semi-Direct Monocular SLAM With Three Levels of Parallel Optimizations

In practical applications, how to use the complementary strengths of the direct and the feature-based methods for effective fusion may be the main challenge of simultaneous localization and mapping (SLAM). To solve this challenge, we propose the DO-SLAM, a novel fast and accurate semi-direct visual SLAM framework, which can maintain the direct method’s fast performance and the high precision and loop closure capability of the feature-based method. The direct method is used as the first half of the DO-SLAM to track the camera pose rapidly and robustly. The feature-based method is used as the second half of the DO-SLAM to refine the keyframe poses, perform loop closures, and build a globally consistent, long-term, sparse feature map that can be reused. The proposed pipeline fuses direct odometry and feature-based SLAM to perform three levels of parallel optimizations: (1) In the direct method module, the keyframe poses are estimated by minimizing the photometric error, (2) In the feature-based module, using the poses calculated by the inter-frame matching to correct and fuse the poses calculated by the direct method module as the initial poses, and the initial poses are optimized by the motion-only bundle adjustment, and (3) A pose graph optimization is used to achieve global map consistency in the presence of loop closures. Experimental evaluation on two benchmark datasets demonstrates that the proposed approach achieves higher accuracy and robustness on motion estimation compared to the other state-of-the-art methods

Stop the spread of zebra mussels: dreissena polymorpha: an invasive mussel

BACKGROUND:The epidemic tendency of hemorrhagic fever with renal syndrome (HFRS) is on the rise in recent years in Guangzhou. This study aimed to explore the associations between meteorological factors and HFRS epidemic risk in Guangzhou for the period from 2006-2015. METHODS:We obtained data of HFRS cases in Guangzhou from the National Notifiable Disease Report System (NNDRS) during the period of 2006-2015. Meteorological data were obtained from the Guangzhou Meteorological Bureau. A negative binomial multivariable regression was used to explore the relationship between meteorological variables and HFRS. RESULTS:The annual average incidence was 0.92 per 100000, with the annual incidence ranging from 0.64/100000 in 2009 to 1.05/100000 in 2012. The monthly number of HFRS cases decreased by 5.543% (95%CI -5.564% to -5.523%) each time the temperature was increased by 1°C and the number of cases decreased by 0.075% (95%CI -0.076% to -0.074%) each time the aggregate rainfall was increased by 1 mm. We found that average temperature with a one-month lag was significantly associated with HFRS transmission. CONCLUSIONS:Meteorological factors had significant association with occurrence of HFRS in Guangzhou, Southern China. This study provides preliminary information for further studies on epidemiological prediction of HFRS and for developing an early warning system

Transcriptome Sequencing and Analysis of Leaf Tissue of <i>Avicennia marina</i> Using the Illumina Platform

<div><p><i>Avicennia marina</i> is a widely distributed mangrove species that thrives in high-salinity habitats. It plays a significant role in supporting coastal ecosystem and holds unique potential for studying molecular mechanisms underlying ecological adaptation. Despite and sometimes because of its numerous merits, this species is facing increasing pressure of exploitation and deforestation. Both study on adaptation mechanisms and conservation efforts necessitate more genomic resources for <i>A. marina</i>. In this study, we used Illumina sequencing of an <i>A. marina</i> foliar cDNA library to generate a transcriptome dataset for gene and marker discovery. We obtained 40 million high-quality reads and assembled them into 91,125 unigenes with a mean length of 463 bp. These unigenes covered most of the publicly available <i>A. marina</i> Sanger ESTs and greatly extended the repertoire of transcripts for this species. A total of 54,497 and 32,637 unigenes were annotated based on homology to sequences in the NCBI non-redundant and the Swiss-prot protein databases, respectively. Both Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed some transcriptomic signatures of stress adaptation for this halophytic species. We also detected an extraordinary amount of transcripts derived from fungal endophytes and demonstrated the utility of transcriptome sequencing in surveying endophyte diversity without isolating them out of plant tissues. Additionally, we identified 3,423 candidate simple sequence repeats (SSRs) from 3,141 unigenes with a density of one SSR locus every 8.25 kb sequence. Our transcriptomic data will provide valuable resources for ecological, genetic and evolutionary studies in <i>A. marina</i>.</p></div

Identification of a Novel Cuproptosis-Related Gene Signature for Prognostic Implication in Head and Neck Squamous Carcinomas

Head and neck squamous carcinoma (HNSC) is a frequent and deadly malignancy that is challenging to manage. The existing treatment options have considerable efficacy limitations. Hence, the identification of new therapeutic targets and the development of efficacious treatments are urgent needs. Cuproptosis, a non-apoptotic programmed cell death caused by excess copper, has only very recently been discovered. The present study investigated the prognostic importance of genes involved in cuproptosis through the mRNA expression data and related clinical information of HNSC patients downloaded from public databases. Our results revealed that many cuproptosis-related genes were differentially expressed between normal and HNSC tissues in the TCGA cohort. Moreover, 39 differentially expressed genes were associated with the prognosis of HNSC patients. Then, a 24-gene signature was identified in the TCGA cohort utilizing the LASSO Cox regression model. HNSC expression data used for validation were obtained from the GEO database. Consequently, we divided patients into high- and low-risk groups based on the 24-gene signature. Furthermore, we demonstrated that the high-risk group had a worse prognosis when compared to the low-risk group. Additionally, significant differences were found between the two groups in metabolic pathways, immune microenvironment, etc. In conclusion, we found a cuproptosis-related gene signature that can be used effectively to predict OS in HNSC patients. Thus, targeting cuproptosis might be an alternative and promising strategy for HNSC patients

Residue dynamics and risk assessment of dimethoate in sweet potato, purple flowering stalk, Chinese kale, celery, and soil

<p>Residue dynamics and risk assessment of the insecticide dimethoate applied to sweet potato, purple flowering stalk, Chinese kale, celery were investigated under the climatic conditions of China. The dissipation experiments indicated that the half-lives of dimethoate in purple flowering stalk, Chinese kale, celery, and soil were 5.9–6.5, 3.8–5.1, 3.5–5.4, 3.4–3.6 d, respectively. The terminal residues of dimethoate and omethoate in the vegetables and soil ranged from 0.008 to 1.73 mg kg⁻¹ at preharvest intervals of 3, 5, and 7 d. The results showed risk quotient (RQ) of <1 for sweet potato, Chinese kale, and celery, and of >1 for purple flowering stalk when under the age of 18, indicating that spraying dimethoate on sweet potato, Chinese kale, and celery at the recommended dosage is safe for human consumption, whereas spraying it on purple flowering stalk is associated with some risks to human health.</p

Gene Ontology classifications of plant-derived and fungus-derived unigenes.

<p>Gene Ontology classifications of plant-derived and fungus-derived unigenes.</p