SD-SLAM: A Semantic SLAM Approach for Dynamic Scenes Based on LiDAR Point Clouds

Point cloud maps generated via LiDAR sensors using extensive remotely sensed data are commonly used by autonomous vehicles and robots for localization and navigation. However, dynamic objects contained in point cloud maps not only downgrade localization accuracy and navigation performance but also jeopardize the map quality. In response to this challenge, we propose in this paper a novel semantic SLAM approach for dynamic scenes based on LiDAR point clouds, referred to as SD-SLAM hereafter. The main contributions of this work are in three aspects: 1) introducing a semantic SLAM framework dedicatedly for dynamic scenes based on LiDAR point clouds, 2) Employing semantics and Kalman filtering to effectively differentiate between dynamic and semi-static landmarks, and 3) Making full use of semi-static and pure static landmarks with semantic information in the SD-SLAM process to improve localization and mapping performance. To evaluate the proposed SD-SLAM, tests were conducted using the widely adopted KITTI odometry dataset. Results demonstrate that the proposed SD-SLAM effectively mitigates the adverse effects of dynamic objects on SLAM, improving vehicle localization and mapping performance in dynamic scenes, and simultaneously constructing a static semantic map with multiple semantic classes for enhanced environment understanding

Identification and validation of NAD+ metabolism-related biomarkers in patients with diabetic peripheral neuropathy

BackgroundThe mechanism of Nicotinamide Adenine Dinucleotide (NAD+) metabolism-related genes (NMRGs) in diabetic peripheral neuropathy (DPN) is unclear. This study aimed to find new NMRGs biomarkers in DPN.MethodsDPN related datasets GSE95849 and GSE185011 were acquired from the Gene Expression Omnibus (GEO) database. 51 NMRGs were collected from a previous article. To explore NMRGs expression in DPN and control samples, differential expression analysis was completed in GSE95849 to obtain differentially expressed genes (DEGs), and the intersection of DEGs and NMRGs was regarded as DE-NMRGs. Next, a protein-protein interaction (PPI) network based on DE-NMRGs was constructed and biomarkers were screened by eight algorithms. Additionally, Gene Set Enrichment Analysis (GSEA) enrichment analysis was completed, biomarker-based column line graphs were constructed, lncRNA-miRNA-mRNA and competing endogenouse (ce) RNA networks were constructed, and drug prediction was completed. Finally, biomarkers expression validation was completed in GSE95849 and GSE185011.Results5217 DEGs were obtained from GSE95849 and 21 overlapping genes of DEGs and NMRGs were DE-NMRGs. Functional enrichment analysis revealed that DE-NMRGs were associated with glycosyl compound metabolic process. The PPI network contained 93 protein-interaction pairs and 21 nodes, with strong interactions between NMNAT1 and NAMPT, NADK and NMNAT3, ENPP3 and NUDT12 as biomarkers based on 8 algorithms. Expression validation suggested that ENPP3 and NUDT12 were upregulated in DPN samples (P < 0.05). Moreover, an alignment diagram with good diagnostic efficacy based on ENPP3 and NUDT12 were identified was constructed. GSEA suggested that ENPP3 was enriched in Toll like receptor (TLR) pathway, NUDT12 was enriched in maturity onset diabetes of the young and insulin pathway. Furthermore, 18 potential miRNAs and 36 Transcription factors (TFs) were predicted and the miRNA-mRNA-TF networks were constructed, suggesting that ENPP3 might regulate hsa-miR-34a-5p by affecting MYNN. The ceRNA network suggested that XLOC_013024 might regulate hsa-let-7b-5p by affecting NUDT12. 15 drugs were predicted, with 8 drugs affecting NUDT12 such as resveratrol, and 13 drugs affecting ENPP3 such as troglitazone.ConclusionENPP3 and NUDT12 might play key roles in DPN, which provides reference for further research on DPN

Reducing Redundancy in Maps without Lowering Accuracy: A Geometric Feature Fusion Approach for Simultaneous Localization and Mapping

Geometric map features, such as line segments and planes, are receiving increasing attention due to their advantages in simultaneous localization and mapping applications. However, large structures in different environments are very likely to appear repeatedly in several consecutive time steps, resulting in redundant features in the final map. These redundant features should be properly fused, in order to avoid ambiguity and reduce the computation load. In this paper, three criteria are proposed to evaluate the closeness between any two features extracted at two different times, in terms of their included angle, feature circle overlapping and relative distance. These criteria determine whether any two features should be fused in the mapping process. Using the three criteria, all features in the global map are categorized into different clusters with distinct labels, and a fused feature is then generated for each cluster by means of least squares fitting. Two competing methods are employed for comparative verification. The comparison results indicate that using the commonly used KITTI dataset and the commercial software PreScan, the proposed feature fusion method outperforms the competing methods in terms of conciseness and accuracy

N1-Guanyl-1,7-Diaminoheptane Sensitizes Estrogen Receptor Negative Breast Cancer Cells to Doxorubicin by Preventing Epithelial-Mesenchymal Transition through Inhibition of Eukaryotic Translation Initiation Factor 5A2 Activation

Background: Approximately 30% of breast cancer does not express the estrogen receptor (ER), which is necessary for endocrine-based therapy approaches. Many studies demonstrated that eukaryotic translation initiation factor 5A2 (eIF5A2) serves as a proliferation-related oncogene in tumorigenic processes. Methods: The present study used cell viability assays, EdU incorporation assays, western blot, and immunofluorescence to explore whether N1-guanyl-1,7-diaminoheptane (GC7), which inhibits eIF5A2 activation, exerts synergistic cytotoxicity with doxorubicin in breast cancer. Results: We found that GC7 enhanced doxorubicin cytotoxicity in ER-negative HCC1937 cells but had little effect in ER-positive MCF-7 and Bcap-37 cells. Administration of GC7 reversed the doxorubicin-induced epithelial-mesenchymal transition (EMT) in ER-negative breast cancer cells. Knockdown of eIF5A2 by siRNA inhibited the doxorubicin-induced EMT in ER-negative HCC1937 cells. Conclusion: These data demonstrated that GC7 combination therapy may enhance the therapeutic efficacy of doxorubicin in estrogen negative breast cancer cells by preventing EMT through inhibition of eIF5A2 activation

Biobased Composites Prepared Using an Environmentally Friendly Water-Slurry Methodology

To improve processability of benzoxazine monomer in preparation of composites, a water-slurry strategy was examined using several laboratory-scale instances. The water slurries were fabricated by mixing solid resin powder of 3-furfury-8-methoxy-3,4-dihydro-2<i>H</i>-1,3- benzoxazine with water and one type of filler particle, i.e., calcium carbonate, montmorillonite, or hollow glass beads. Experimental data show that approving liquidity can be achieved when more than 180 phr of water was mixed in the solid mixtures containing the resin powder and 100 phr of solid filler. The biobased composite prepared using the optimized condition exhibits outstanding mechanical properties and antifatigue performance as the composites prepared via solvent method. This water-slurry approach provides an environmentally friendly strategy to manuscript benzoxazine composites, offering benzoxazine with more promising applications in many industries such as building, wind energy, aircraft, and automobile

Therapeutic potential of the medicinal mushroom Ganoderma lucidum against Alzheimer's disease

Alzheimer’s disease (AD) is a high-incidence neurodegenerative disorder, characterized by cognitive impairment, memory loss, and psychiatric abnormalities. Ganoderma lucidum is a famous medicinal fungus with a long history of dietary intake, containing various bioactive components, and have been documented to exhibit antioxidant, anti-inflammatory, anti-tumor, anti-aging, and immunomodulatory effects, among others. Recent studies have shown that G. lucidum and its components have promising therapeutic potential against AD from various aspects, which can delay the progression of AD, improve cognitive function and quality of life. The underlying mechanisms mainly include inhibiting tau hyperphosphorylation, inhibiting Aβ formation, affecting activated microglia, regulating NF-κB/MAPK signalling pathway, inhibiting neuronal apoptosis, modulating immune system, and inhibiting acetylcholinesterase, etc. This paper systematically reviewed the relevant studies on the therapeutic potential of G. lucidum and its active components for treatment of AD, key points related with the mechanism studies and clinical trials have been discussed, and further perspectives have been proposed. Totally, as a natural medicinal mushroom, G. lucidum has the potential to be developed as effective adjuvant for AD treatment owing to its therapeutic efficacy against multiple pathogenesis of AD. Further mechanical investigation and clinical trials can help unlock the complete potential of G. lucidum as a therapeutic option for AD

Improved Synthesis of Cellulose Carbamates with Minimum Urea Based on an Easy Scale-up Method

Cellulose carbamates (CCs) were successfully prepared from cellulose/urea (CU) mixtures based on an easy scale-up method and minimum urea. Urea content and the reaction conditions on the nitrogen content of the reacted CU (RCU) mixtures and CCs were systematically investigated. RCU mixtures and CCs were characterized with elemental analysis, Fourier transform infrared spectroscopy, X-ray diffraction, NMR spectrometry and solubility testing. The result indicated that almost all of urea was involved in the derivatization reaction and cellulose was converted into CC with absence of byproducts. The nitrogen content of CCs increased with an increase of the urea content and the reaction temperature, as well as the reaction time. CCs retained the cellulose I crystalline, and the degree of polymerization hardly changed with the reaction conditions. CCs prepared from CU mixtures with the urea content of 3.4–4.6 wt % displayed good solubility in NaOH/ZnO aqueous solutions. Especially, RCU mixtures without washing could be also well dissolved in NaOH/ZnO solutions and its solubility could reach 97%. This work provided a simple, pollution-free and economic pathway for preparing CCs, which is expected to be useful for the CarbaCell process