Effects of super-enhancers in cancer metastasis: mechanisms and therapeutic targets.

Metastasis remains the principal cause of cancer-related lethality despite advancements in cancer treatment. Dysfunctional epigenetic alterations are crucial in the metastatic cascade. Among these, super-enhancers (SEs), emerging as new epigenetic regulators, consist of large clusters of regulatory elements that drive the high-level expression of genes essential for the oncogenic process, upon which cancer cells develop a profound dependency. These SE-driven oncogenes play an important role in regulating various facets of metastasis, including the promotion of tumor proliferation in primary and distal metastatic organs, facilitating cellular migration and invasion into the vasculature, triggering epithelial-mesenchymal transition, enhancing cancer stem cell-like properties, circumventing immune detection, and adapting to the heterogeneity of metastatic niches. This heavy reliance on SE-mediated transcription delineates a vulnerable target for therapeutic intervention in cancer cells. In this article, we review current insights into the characteristics, identification methodologies, formation, and activation mechanisms of SEs. We also elaborate the oncogenic roles and regulatory functions of SEs in the context of cancer metastasis. Ultimately, we discuss the potential of SEs as novel therapeutic targets and their implications in clinical oncology, offering insights into future directions for innovative cancer treatment strategies.

Multidimensional Refinement Graph Convolutional Network With Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition.

Graph convolutional networks (GCNs) have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the similarity of interclass data. Moreover, the noisy data from pose extraction increase the challenge of fine-grained recognition. In this work, we propose a flexible attention block called channel-variable spatial-temporal attention (CVSTA) to enhance the discriminative power of spatial-temporal joints and obtain a more compact intraclass feature distribution. Based on CVSTA, we construct a multidimensional refinement GCN (MDR-GCN) that can improve the discrimination among channel-, joint-, and frame-level features for fine-grained actions. Furthermore, we propose a robust decouple loss (RDL) that significantly boosts the effect of the CVSTA and reduces the impact of noise. The proposed method combining MDR-GCN with RDL outperforms the known state-of-the-art skeleton-based approaches on fine-grained datasets, FineGym99 and FSD-10, and also on the coarse NTU-RGB + D 120 dataset and NTU-RGB + D X-view version. Our code is publicly available at https://github.com/dingyn-Reno/MDR-GCN.

Bioactivities and Mechanisms of Action of Sinomenine and Its Derivatives: A Comprehensive Review.

Sinomenine, an isoquinoline alkaloid extracted from the roots and stems of Sinomenium acutum, has been extensively studied for its derivatives as bioactive agents. This review concentrates on the research advancements in the biological activities and action mechanisms of sinomenine-related compounds until November 2023. The findings indicate a broad spectrum of pharmacological effects, including antitumor, anti-inflammation, neuroprotection, and immunosuppressive properties. These compounds are notably effective against breast, lung, liver, and prostate cancers, exhibiting IC50 values of approximately 121.4 nM against PC-3 and DU-145 cells, primarily through the PI3K/Akt/mTOR, NF-κB, MAPK, and JAK/STAT signaling pathways. Additionally, they manifest anti-inflammatory and analgesic effects predominantly via the NF-κB, MAPK, and Nrf2 signaling pathways. Utilized in treating rheumatic arthritis, these alkaloids also play a significant role in cardiovascular and cerebrovascular protection, as well as organ protection through the NF-κB, Nrf2, MAPK, and PI3K/Akt/mTOR signaling pathways. This review concludes with perspectives and insights on this topic, highlighting the potential of sinomenine-related compounds in clinical applications and the development of medications derived from natural products.

Bioactivities and Mechanisms of Action of Diphyllin and Its Derivatives: A Comprehensive Systematic Review.

Natural products are treasure houses for modern drug discovery. Diphyllin is a natural arylnaphthalene lignan lactone isolated from the leaf of Astilboides tabularis. Studies have found that it possesses plenty of bioactivity characteristics. In this paper, we reviewed the structure, bioactivity, and mechanism of action of diphyllin and its derivatives. The references were obtained from PubMed, Web of Science, and Science Direct databases up to August 2023. Papers without a bio-evaluation were excluded. Diphyllin and its derivatives have demonstrated V-ATPase inhibition, anti-tumor, anti-virus, anti-biofilm, anti-inflammatory, and anti-oxidant activities. The most studied activities of diphyllin and its derivatives are V-ATPase inhibition, anti-tumor activities, and anti-virus activities. Furthermore, V-ATPase inhibition activity is the mechanism of many bioactivities, including anti-tumor, anti-virus, and anti-inflammatory activities. We also found that the galactosylated modification of diphyllin is a common phenomenon in plants, and therefore, galactosylated modification is applied by researchers in the laboratory to obtain more excellent diphyllin derivatives. This review will provide useful information for the development of diphyllin-based anti-tumor and anti-virus compounds.

Identifying the potential miRNA biomarkers based on multi-view networks and reinforcement learning for diseases.

MicroRNAs (miRNAs) play important roles in the occurrence and development of diseases. However, it is still challenging to identify the effective miRNA biomarkers for improving the disease diagnosis and prognosis. In this study, we proposed the miRNA data analysis method based on multi-view miRNA networks and reinforcement learning, miRMarker, to define the potential miRNA disease biomarkers. miRMarker constructs the cooperative regulation network and functional similarity network based on the expression data and known miRNA-disease relations, respectively. The cooperative regulation of miRNAs was evaluated by measuring the changes of relative expression. Natural language processing was introduced for calculating the miRNA functional similarity. Then, miRMarker integrates the multi-view miRNA networks and defines the informative miRNA modules through a reinforcement learning strategy. We compared miRMarker with eight efficient data analysis methods on nine transcriptomics datasets to show its superiority in disease sample discrimination. The comparison results suggested that miRMarker outperformed other data analysis methods in receiver operating characteristic analysis. Furthermore, the defined miRNA modules of miRMarker on colorectal cancer data not only show the excellent performance of cancer sample discrimination but also play significant roles in the cancer-related pathway disturbances. The experimental results indicate that miRMarker can build the robust miRNA interaction network by integrating the multi-view networks. Besides, exploring the miRNA interaction network using reinforcement learning favors defining the important miRNA modules. In summary, miRMarker can be a hopeful tool in biomarker identification for human diseases.

Multi-view emotional expressions dataset using 2D pose estimation.

Human body expressions convey emotional shifts and intentions of action and, in some cases, are even more effective than other emotion models. Despite many datasets of body expressions incorporating motion capture available, there is a lack of more widely distributed datasets regarding naturalized body expressions based on the 2D video. In this paper, therefore, we report the multi-view emotional expressions dataset (MEED) using 2D pose estimation. Twenty-two actors presented six emotional (anger, disgust, fear, happiness, sadness, surprise) and neutral body movements from three viewpoints (left, front, right). A total of 4102 videos were captured. The MEED consists of the corresponding pose estimation results (i.e., 397,809 PNG files and 397,809 JSON files). The size of MEED exceeds 150 GB. We believe this dataset will benefit the research in various fields, including affective computing, human-computer interaction, social neuroscience, and psychiatry.

Pharmacological activity and mechanism of pyrazines.

Heterocycles are common in the structure of drugs used clinically to deal with diseases. Such drugs usually contain nitrogen, oxygen and sulfur, which possess electron-accepting capacity and can form hydrogen bonds. These properties often bring enhanced target binding ability to these compounds when compared to alkanes. Pyrazine is a nitrogen-containing six-membered heterocyclic ring and many of its derivatives are identified as bioactive molecules. We review here the most active pyrazine compounds in terms of their structure, activity in vitro and in vivo (mainly antitumor activity) and the reported mechanisms of action. References have been downloaded through Web of Science, PubMed, Science Direct, Google Scholar and SciFinder Scholar. Publications reporting only the chemistry of pyrazine derivatives are beyond the scope of this review and have not been included. We found that compounds in which a pyrazine ring was fused into other heterocycles especially pyrrole or imidazole were the highly studied pyrazine derivatives, whose antineoplastic activity had been widely investigated. To the best of our knowledge, this is the first review of pyrazine derivatives and their bioactivity, especially their antitumor activity. This review should be useful for those engaged in development of medications based on heterocyclic compounds especially those based on pyrazine.

Identification of hub genes and pathways in lung metastatic colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is one of the most prevalent types of malignant tumours. Metastasis is the leading cause of cancer-related mortality, with lung metastases accounting for 32.9% of all metastatic CRCs. However, since the biological mechanism of lung metastatic CRC is poorly understood, limited therapeutic targets are available. In the present study, we aimed to identify the key genes and molecular processes involved in CRC lung metastasis. METHODS: The differentially expressed genes (DEGs) between primary and lung metastatic CRC patients were obtained from the Gene Expression Omnibus (GEO) database via the GEO2R tool. The enriched biological processes and pathways modulated by the DEGs were determined with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome Gene Sets analyses. The search tool Retrieval of Interacting Genes (STRING) and Cytoscape were used to construct a protein-protein interaction (PPI) network among DEGs. RESULTS: The DEGs were enriched in surfactant metabolism, cell-cell communication and chemokine signaling pathways. The defined hub genes were included CLU, SFTPD, CCL18, SPP1, APOE, BGN and MMP3. Among them, CLU, SFTPD and CCL18 might be associated with the specific lung tropism metastasis in CRC. In addition, the expression and prognostic values of the hub genes in CRC patients were verified in database of The Cancer Genome Atlas (TCGA) and GEO. Moreover, the protein levels of the hub genes were detected in primary and lung metastatic CRC cells, serum or tissues. Furthermore, SFTPD was confirmed to facilitate cellular proliferation and lung metastasis in CRC. CONCLUSION: This bioinformatics study may provide a better understanding of the candidate therapeutic targets and molecular mechanisms for CRC lung metastasis.

Discovery of novel benzohydroxamate-based histone deacetylase 6 (HDAC6) inhibitors with the ability to potentiate anti-PD-L1 immunotherapy in melanoma.

In this study, a novel series of histone deacetylases 6 (HDAC6) inhibitors containing polycyclic aromatic rings were discovered and evaluated for their pharmacological activities. The most potent compound 10c exhibited high HDAC6 inhibitory activity (IC50 = 261 nM) and excellent HDAC6 selectivity (SI = 109 for HDAC6 over HDAC3). 10c also showed decent antiproliferative activity in vitro with IC50 of 7.37-21.84 µM against four cancer cell lines, comparable to that of tubastatin A (average IC50 = 6.10 µM). Further mechanism studies revealed that 10c efficiently induced apoptosis and S-phase arrest in B16-F10 cells. In addition, 10c markedly increased the expression of acetylated-α-tubulin both in vitro and in vivo, without affecting the levels of acetylated-H3 (marker of HDAC1 inhibition). Furthermore, 10c (80 mg/kg) exhibited moderate antitumor efficacy in a melanoma tumour model with a tumour growth inhibition (TGI) of 32.9%, comparable to that (TGI = 31.3%) of tubastatin A. Importantly, the combination of 10c with NP19 (a small molecule PD-L1 inhibitor discovered by us before) decreased tumour burden substantially (TGI% = 60.1%) as compared to monotherapy groups. Moreover, the combination of 10c with NP19 enhanced the anti-tumour immune response, mediated by a decrease of PD-L1 expression levels and increased infiltration of anti-tumour CD8+ T cells in tumour tissues. Collectively, 10c represents a novel HDAC6 inhibitor deserving further investigation as a potential anti-cancer agent.

Targeting CDK12 obviates the malignant phenotypes of colorectal cancer through the Wnt/ß-catenin signaling pathway.

Colorectal cancer (CRC) is the second most common cause of cancer-related mortality and lies third in terms of morbidity due to the limited number of effective druggable targets. Since cancer stem cells (CSCs) are considered to be one of the roots of tumorigenesis, outgrowth and metastasis, targeting CSCs may be a promising strategy to reverse the malignant phenotypes of CRC. Cyclin-dependent kinase 12 (CDK12) has been reported to be involved in the self-renewal of CSCs in various cancers, rendering it an attractive potential target against CSCs to consequently limit the malignant phenotypes in CRC. In the present study, we aimed to investigate whether CDK12 can be a potential therapeutic target for patients with CRC and clarify its underlying mechanism. We found that CDK12, but not CDK13 is required for CRC survival. CDK12 was found to drive tumor initiation according to the colitis-associated colorectal cancer mouse model. In addition, CDK12 promoted CRC outgrowth and hepatic metastasis in the subcutaneous allograft and liver metastasis mouse models, respectively. In particular, CDK12 was able to induce the self-renewal of CRC CSCs. Mechanistically, the activation of Wnt/ß-catenin signaling mediated by CDK12 was implicated in stemness regulation and malignant phenotype maintenance. These findings indicate that CDK12 is a candidate druggable target in CRC. Therefore, the CDK12 inhibitor SR-4835 warrants clinical trial testing in patients with CRC.

Synthesis, biological activity and mechanism of action of novel allosecurinine derivatives as potential antitumor agents.

Cancer with low survival rates is the second main cause of death among all diseases in the world and consequently, effective antineoplastic agents are urgently needed. Allosecurinine is a plant-derived indolicidine securinega alkaloid shown bioactivity. The object of this study is to investigate synthetic allosecurinine derivatives with considerable anticancer capacity against nine human cancer cell lines as well as mechanism of action. We synthesized twenty-three novel allosecurinine derivatives and evaluated their antitumor activity against nine cancer cell lines for 72 h by MTT and CCK8 assays. FCM was applied to analyze the apoptosis, mitochondrial membrane potential, DNA content, ROS production, CD11b expression. Western blot was selected to analyze the protein expression. Structure-activity relationships were established and potential anticancer lead BA-3 which induced differentiation of leukemia cells towards granulocytosis at low concentration and apoptosis at high concentration was identified. Mechanism studies showed that mitochondrial pathway mediated apoptosis within cancer cells with cell cycle blocking was induced by BA-3. In addition, western blot assays revealed that BA-3 induced expression of the proapoptotic factor Bax, p21 and reduced the levels of antiapoptotic protein such as Bcl-2, XIAP, YAP1, PARP, STAT3, p-STAT3, and c-Myc. Collectively, BA-3 was a lead compound for oncotherapy at least in part, through the STAT3 pathway. These results were an important step in further studies on allosecurinine-based antitumor agent development.

Heat-activated peracetic acid for degradation of diclofenac: kinetics, influencing factors and mechanism.

ABSTRACTHeat-activated peracetic acid (PAA) was used to degrade diclofenac (DCF) in this study. Electron paramagnetic resonance and radical scavenging experiments proved that organic radicals (i.e. CH3C(=O)O• and CH3C(=O)OO•) were the primary active species for DCF removal in the heat/PAA process. The degradation efficiency of DCF increased with the increase of temperature or initial PAA concentration in the heat/PAA process, and the optimal reaction pH for DCF removal was neutral. The presence of NO3- or SO42- insignificantly affected DCF degradation, while Cl- was favourable for DCF removal in this process. In contrast, an obvious inhibition on the removal of DCF was observed with the addition of natural organic matter, which might be responsible for the lower DCF removal in real waters. Finally, dechlorination, formylation, dehydrogenation and hydroxylation were proposed to be four degradation pathways of DCF in the heat/PAA system based on the five detected transformation products.

Construction and validation of the Dalian emotional movement open-source set (DEMOS).

Human body movements are important for emotion recognition and social communication and have received extensive attention from researchers. In this field, emotional biological motion stimuli, as depicted by point-light displays, are widely used. However, the number of stimuli in the existing material library is small, and there is a lack of standardized indicators, which subsequently limits experimental design and conduction. Therefore, based on our prior kinematic dataset, we constructed the Dalian Emotional Movement Open-source Set (DEMOS) using computational modeling. The DEMOS has three views (i.e., frontal 0°, left 45°, and left 90°) and in total comprises 2664 high-quality videos of emotional biological motion, each displaying happiness, sadness, anger, fear, disgust, and neutral. All stimuli were validated in terms of recognition accuracy, emotional intensity, and subjective movement. The objective movement for each expression was also calculated. The DEMOS can be downloaded for free from https://osf.io/83fst/ . To our knowledge, this is the largest multi-view emotional biological motion set based on the whole body. The DEMOS can be applied in many fields, including affective computing, social cognition, and psychiatry.

Hierarchical Neighbors Embedding.

Manifold learning now plays an important role in machine learning and many relevant applications. In spite of the superior performance of manifold learning techniques in dealing with nonlinear data distribution, their performance would drop when facing the problem of data sparsity. It is hard to obtain satisfactory embeddings when sparsely sampled high-dimensional data are mapped into the observation space. To address this issue, in this article, we propose hierarchical neighbors embedding (HNE), which enhances the local connections through hierarchical combination of neighbors. And three different HNE-based implementations are derived by further analyzing the topological connection and reconstruction performance. The experimental results on both the synthetic and real-world datasets illustrate that our HNE-based methods could obtain more faithful embeddings with better topological and geometrical properties. From the view of embedding quality, HNE develops the outstanding advantages in dealing with data of general distributions. Furthermore, comparing with other state-of-the-art manifold learning methods, HNE shows its superiority in dealing with sparsely sampled data and weak-connected manifolds.

CDK12 orchestrates super-enhancer-associated CCDC137 transcription to direct hepatic metastasis in colorectal cancer.

BACKGROUND: Hepatic metastasis is the primary and direct cause of death in individuals with colorectal cancer (CRC) attribute to lack of effective therapeutic targets. The present study aimed to identify potential druggable candidate targets for patients with liver metastatic CRC. METHODS: The transcriptional profiles of super-enhancers (SEs) in primary and liver metastatic CRC were evaluated in publicly accessible CRC datasets. Immunohistochemistry of human CRC tissues was conducted to determine the expression level of CDK12. Cellular proliferation, survival and stemness were examined upon CDK12 inhibition by shCDK12 or a selective CDK12 inhibitor named SR-4835 with multiple in vitro and in vivo assays. RNA sequencing and bioinformatics analyses were carried out to investigate the mechanisms of CDK12 inhibition in CRC cells. RESULTS: We identified CDK12 as a driver gene for direct hepatic metastasis in CRC. Suppression of CDK12 led to robust inhibition of proliferation, survival and stemness. Mechanistically, CDK12 intervention preferentially repressed the transcription of SE-associated genes. Integration of the SE landscape and RNA sequencing, BCL2L1 and CCDC137 were identified as SE-associated oncogenic genes to strengthen the abilities of cellular survival, proliferation and stemness, eventually increasing liver metastasis of CRC. CONCLUSIONS: Our data highlight the potential of CDK12 and SE-associated oncogenic transcripts as therapeutic targets for patients with liver metastatic CRC.

Rapid degradation of sulfamethoxazole by permanganate combined with bisulfite: efficiency, influence factors and mechanism.

In this study, permanganate combined with bisulfite (PM/BS), a novel advanced oxidation process, was used for rapidly removing sulfamethoxazole (SMX) from contaminated water. The results showed that 80% SMX was removed within 10 s in the PM/BS system, while no obvious SMX degradation was observed in the PM or BS alone system within 300 s. Reactive manganese species (RMnS, Mn(III), Mn(V) and Mn(VI)), sulfate radical (SO4•-) and hydroxyl radical (HO•) formed in the PM/BS system all played a role in accelerated degradation of SMX. Due to the loss of RMnS, SMX degradation was significantly inhibited with the increase in pH. The best [BS]:[PM] ratio for SMX removal was 7.5:1-10:1. The presence of Cl-, HCO3- or natural organic matter (NOM) significantly inhibited the degradation of SMX, while SO42- and NO3- had little impact on SMX removal. Based on the detected transformation products, two degradation pathways of SMX by PM/BS, namely N-S bond cleavage and amino oxidation, were proposed.

Activated peracetic acid by Mn3O4 for sulfamethoxazole degradation: A novel heterogeneous advanced oxidation process.

In this study, a novel peracetic acid (PAA)-based advanced oxidation process using Mn3O4 as a catalyst was proposed. A thorough sulfamethoxazole (SMX) removal could be achieved within 12 min in Mn3O4/PAA system at neutral pH. The characterization results of fresh and used Mn3O4 suggested that ≡Mn(II), ≡Mn(III) and ≡Mn(IV) on Mn3O4 were the Mn species for PAA activation, constituting the redox cycles of ≡Mn(II)/≡Mn(III) and ≡Mn(III)/≡Mn(IV) simultaneously. Organic radicals (i.e., CH3C(O)O• and CH3C(O)OO•) were verified to be the dominant reactive species responsible for SMX degradation in Mn3O4/PAA system by radical scavenging experiments. The neutral condition was the most favorable pH for SMX removal in Mn3O4/PAA system and the increase of PAA or Mn3O4 dosage could enhance SMX degradation. Presence of HCO3- and natural organic matter (NOM) could inhibit SMX degradation, while Cl-, NO3- and SO42- had a negligible effect on SMX removal. The thorough SMX removal in successive experiments and characterization results of used Mn3O4 suggested the good reusability and stability of Mn3O4 for PAA activation. Based on six detected transformation products of SMX, hydroxylation, nitration, bond cleavage and coupling reaction were proposed to be its degradation pathways in Mn3O4/PAA system.

Factors Related to Diabetes Self-Management Among Patients with Type 2 Diabetes: A Chinese Cross-Sectional Survey Based on Self-Determination Theory and Social Support Theory.

Purpose: Self-management behavior (SMB) plays a significant role in glycemic control. This study aimed to explore the factors related to SMB among patients with type 2 diabetes and how these factors interacted with each other. Patients and Methods: Patients diagnosed with type 2 diabetes were recruited from 18 community healthcare stations (CHSs) from six community healthcare centers (CHCs) in Beijing, China from April to May in 2017. Motivation, competence, autonomy support, social support, self-management skills, adherence to self-monitoring of blood glucose (SMBG) and haemoglobin A1c (HbA1c) measurement were tested by questionnaire. Correlation analysis and path analysis were performed so as to identify the factors associated with patients' SMB. Results: A total of 532 participants completed this study. Participants who have good compliance to SMBG got higher scores in social support (F = 7.68, p = 0.01), competence (F = 10.47, p = 0.01), and skills (F = 12.34, p < 0.01). Higher competence (ß = 0.03, P < 0.001), higher social support (ß = 0.01, P < 0.001), better skills(ß = 0.01, P < 0.001) directly led to better adherence to SMBG. Social support had a positive effect on autonomy support (ß = 0.69, P < 0.001), motivation (ß = 0.45, P < 0.001), competence (ß = 0.28, P < 0.001), skills (ß = 0.14, P < 0.001), which was also indirectly linked to better adherence to SMBG. Better self-management skills directly led to better adherence to HbA1c measurement (ß = 0.03, P < 0.001). Social support had a positive effect on autonomy support (ß = 0.69, P < 0.001), motivation (ß = 0.45, P < 0.001), competence (ß = 0.28, P < 0.001), skills (ß = 0.14, P < 0.001), which was also indirectly linked to better adherence to HbA1c measurement. Conclusion: Self-determination theory and social support theory were practical in explaining SMB in Chinese population. Competence, motivation and social support played an important role in diabetic self-management. Paying attention to the promotion of individual's intrinsic motivation and self-efficacy may be able to help patients maintain self-management behavior in the long-term routine.

DHX9 contributes to the malignant phenotypes of colorectal cancer via activating NF-κB signaling pathway.

Colorectal cancer (CRC) is the leading cause of cancer-related mortality worldwide, which makes it urgent to identify novel therapeutic targets for CRC treatment. In this study, DHX9 was filtered out as the prominent proliferation promoters of CRC by siRNA screening. Moreover, DHX9 was overexpressed in CRC cell lines, clinical CRC tissues and colitis-associated colorectal cancer (CAC) mouse model. The upregulation of DHX9 was positively correlated with poor prognosis in patients with CRC. Through gain- and loss-of function experiments, we found that DHX9 promoted CRC cell proliferation, colony formation, apoptosis resistance, migration and invasion in vitro. Furthermore, a xenograft mouse model and a hepatic metastasis mouse model were utilized to confirm that forced overexpression of DHX9 enhanced CRC outgrowth and metastasis in vivo, while DHX9 ablation produced the opposite effect. Mechanistically, from one aspect, DHX9 enhances p65 phosphorylation, promotes p65 nuclear translocation to facilitate NF-κB-mediated transcriptional activity. From another aspect, DHX9 interacts with p65 and RNA polymerase II (RNA Pol II) to enhance the downstream targets of NF-κB (e.g., Survivin, Snail) expression to potentiate the malignant phenotypes of CRC. Together, our results suggest that DHX9 may be a potential therapeutic target for prevention and treatment of CRC patients.

Real-Time 2-D Lidar Odometry Based on ICP.

This study presents a 2-D lidar odometry based on an ICP (iterative closest point) variant used in a simple and straightforward platform that achieves real-time and low-drift performance. With a designated multi-scale feature extraction procedure, the lidar cloud information can be utilized at multiple levels and the speed of data association can be accelerated according to the multi-scale data structure, thereby achieving robust feature extraction and fast scan-matching algorithms. First, on a large scale, the lidar point cloud data are classified according to the curvature into two parts: smooth collection and rough collection. Then, on a small scale, noise and unstable points in the smooth or rough collection are filtered, and edge points and corner points are extracted. Then, the proposed tangent-vector-pairs based on edge and corner points are applied to evaluate the rotation term, which is significant for producing a stable solution in motion estimation. We compare our performance with two excellent open-source SLAM algorithms, Cartographer and Hector SLAM, using collected and open-access datasets in structured indoor environments. The results indicate that our method can achieve better accuracy.