2,357 results on '"CHEN Kang"'
Search Results
2. Advancing cloud security: Unveiling the protective potential of homomorphic secret sharing in secure cloud computing
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Sijjad Ali, Shuaib Ahmed Wadho, Aun Yichiet, Ming Lee Gan, and Chen Kang Lee
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Cloud computing ,Security ,Data protection ,Homomorphic encryption ,Undercover sharing techniques ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Cloud computing security and data protection are becoming increasingly critical as its use increases. The research we present demonstrates how undercover sharing techniques and homomorphic encryption can be combined to protect private information in cloud computing scenarios. We create a reliable, private, and confidential computation platform by utilizing this dual approach. Our strategy involves protecting data while dividing it among multiple servers. By using this distribution, the system is less likely to suffer from single points of failure and has a higher security level. To ensure information privacy and security, data encryption restricts access to authorized individuals only. As an additional feature, we employ homomorphic encryption to enable operations on encrypted data without direct access to the originals. By using this feature, sensitive data is protected from disclosure or misuse while being processed. Therefore, original data confidentiality can be preserved when computing on encrypted shares. Several performance tests were conducted to prove our strategy’s practicality and effectiveness. Our considerations extended beyond encryption and decryption time and processing overhead. In our research, we demonstrate that our method strikes the right balance between security and computational efficiency.
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- 2024
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3. Spontaneous unidirectional rotation of a symmetric gear driven by spherical active particles
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Wang Chao, Lian Wenchao, Li Huishu, Tian Wende, and Chen Kang
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ratchet effect ,spontaneous symmetry breaking ,active brownian particles ,ising model ,hysteresis ,Science ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Time reversal asymmetry and spatial anisotropy are considered two prerequisites for Brownian ratchet. An intriguing realization can be achieved by placing an asymmetric gear in the suspension of motile rod-like bacteria. Usually, alignment interactions caused by anisotropic collisions or hydrodynamics would boost the ratchet effect. Here, we are concerned with a perfectly isotropic system, i.e., symmetric gear immersed in a bath of spherical active Brownian particles. We find that, under certain conditions, kinetic symmetry-breaking arises spontaneously, i.e., the symmetric gear keeps rotating in one direction. Unexpectedly, such ratchet phenomenon does not rely on the direct many-particle interactions and moreover the introduction of alignment interaction would counterintuitively prevent it from happening! Further investigation reveals that such spontaneous symmetry-breaking phenomenon shares similarities with the equilibrium phase transition of the Ising model. Our results provide new insights and enhance our understanding of the fundamental aspects of active ratchet phenomena.
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- 2024
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4. Effect of SMAC Gene on Sensitivity of Lung Adenocarcinoma Cells to Paclitaxel and Cell Viability Based on caspase-3/Bcl-2/Bax Signaling Pathway
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CHEN Kang, CHEN Ying, NIU Zongxin, KANG Li, and ZULIPEYA·Aibaidula
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caspase-3/bcl-2/bax signaling pathway ,smac gene ,lung adenocarcinoma cells ,paclitaxel resistance sensitivity ,proliferation ,invasion ,apoptosis ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Objective To investigate the effect of the SMAC gene on paclitaxel sensitivity and cellular activity in lung adenocarcinoma cells based on the caspase-3/Bcl-2/Bax signaling pathway. Methods A paclitaxel-resistant cell line A549/Taxol was established for lung adenocarcinoma, and the cells were divided into four following groups: pcDNA-NC (transfected with pcDNA-NC blank vector), pcDNA-SMAC (transfected with pcDNA-SMAC vector), siRNA-NC (transfected with siRNA-NC empty virus vector), and siRNA-SMAC groups (transfected with siRNA-SMAC lentiviral vector). The SMAC mRNA expression in cells was detected by qRT-PCR; cell sensitivity was detected by MTT; cell proliferation ability was detected by cloning assay; cell invasion ability was detected by Transwell; apoptosis ability was detected by flow cytometry assay; and caspase-3, Bcl-2 and Bax protein expression in cells were detected by Western blot analysis. Results The SMAC mRNA expression was significantly lower in A549 cells compared with BEAS-2B cells (P < 0.05). The SMAC mRNA expression was significantly higher in the pcDNA-SMAC group than that in the pcDNA-NC group cells (P < 0.05). The SMAC mRNA expression was significantly lower in the cells of the siRNA-SMAC group (P < 0.05) than that in the siRNA-NC group. The SMAC mRNA expression was significantly lower in the cells of the siRNA-SMAC group (P < 0.05) than in the siRNA-NC group. Compared with the pcDNA-NC group, the cell IC50, cell clone number, cell invasion ability, and Bcl-2 protein and Bcl-2/Bax ratio were significantly lower in the pcDNA-SMAC group, the cell resistance index reversal was 2.51-fold, and the apoptosis ability and caspase-3, as well as Bax protein expression, were significantly higher (P < 0.05). Compared with the siRNA-NC group, cell IC50, cell clone number, cell invasion ability, and Bcl-2 protein and Bcl-2/Bax ratio were significantly higher in the siRNA-SMAC group, and apoptosis ability and caspase-3 and Bax protein expression were significantly lower (P < 0.05). Conclusion High expression of SMAC increases paclitaxel sensitivity, inhibits cell growth and invasion, promotes apoptosis in lung adenocarcinoma cells, and has a regulatory effect on the caspase-3/Bcl-2/Bax signaling pathway.
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- 2023
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5. Institutional Pension Insurance in Sustainable Development of Urban–Rural Intergenerational Support
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Chen Kang, Mingwang Cheng, and Xinyu Wei
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rural–urban relationship ,social pension ,downward transfers ,intergenerational support ,sustainable development ,Agriculture (General) ,S1-972 - Abstract
Parental downward support plays an important role in urban and rural sustainable development. It is of great significance to study parental downward transfers and their motivation. However, there is no consensus on the motivation behind parental downward transfers in China. This study examines the timing and monetary impacts of social pensions on parental downward transfers and assesses the motivations behind them. We found that pension insurance encouraged rural parents to provide time and monetary support to their children. Unlike rural parents, pension insurance increased the monetary support of urban parents but inhibited their time support. Because of the higher income level of urban parents and the better organization of the domestic service market, parents have the motivation and conditions to reduce their time support and increase monetary support. Our findings highlight the importance of parental downward transfers in urban and rural sustainable development.
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- 2024
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6. Neurofibromatosis TypeⅠComplicated with Gastric Cancer, Sigmoid Colon Cancer and Gastrointestinal Stromal Tumor: A Case Report
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WANG Zhou, CHEN Kang, WANG Wenjie, LI Jinzhou, and CHEN Xiao
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Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Published
- 2023
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7. Research progress of transcription factor AP-2 γ in malignant tumors
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CHEN Kang, CHENG Fan
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transcription factor ,ap-2γ ,embryonic development ,tumor ,Medicine - Abstract
Transcription factor AP-2γ regulates not only the genetic procedures that control the proliferation and differentiation of trophoblast cells during early embryonic development, but also is the key target in the development of breast cancer, lung cancer, testicular cancer, melanoma and pancreatic cancer, thus affecting the occurrence and development of tumors. Therefore, paying attention to the regulatory mechanisms of AP-2γ in the occurrence and development of malignant tumors may provide new ideas for the diagnosis and treatment of tumors.
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- 2022
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8. Development and Validation of Prognostic Nomogram Based on Negative Lymph Node Count for Patients with Gastric Signet Ring Cell Carcinoma
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LI Jinzhou, WANG Wenjie, YAO Yalong, MU Yanxi, CHEN Kang, SHEN Yimin, WANG Zhou, HUANG Zeping, and CHEN Xiao
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gastric signet ring cell carcinoma ,negative lymph node count ,prognosis ,nomogram ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Objective To explore the influence of negative lymph node count (NLNC) on the prognosis of patients with gastric signet ring cell carcinoma (GSRC) and develop a prognostic nomogram based on NLNC. Methods On the basis of the SEER database, 2 101 patients diagnosed with GSRC were collected and randomly divided into the modeling group and validation group to test the relationship between clinicopathological characteristics and the prognosis of GSRC. The multivariate Cox proportional hazard regression model was used to analyze the independent risk factors affecting overall survival and establish a prognostic prediction model. The consistency index (C-index), calibration curve, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to evaluate the accuracy and clinical applicability of the nomogram. Results All patients were divided according to the ratio of 7:3, with 1 473 in the modeling group and 628 in the validation group. NLNC > 10 (HR=0.578, 95%CI: 0.504-0.662, P < 0.001) was a protective factor for the prognosis of patients with GSRC, and the nomogram model was established based on multivariate Cox proportional hazards model. The C-index values of the nomogram were 0.737 (95%CI: 0.720-0.753) and 0.724 (95%CI: 0.699-0.749) in the modeling and validation groups, respectively, showing good discrimination. The calibration curves showed high consistency of the model. NRI=17.77%, continuous NRI=36.34%, and IDI=4.2% indicated that the model had positive returns compared with the traditional model. The DCA was far from the baseline, indicating that the model had good clinical applicability. Conclusion The increase in NLNC is a favorable factor for the prognosis of patients with GSRC, and a relatively accurate nomogram was established to predict the prognosis of patients with GSRC and help clinicians conduct individualized prognostic evaluations.
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- 2022
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9. Study on insulin resistance and ischemic cerebrovascular disease: A bibliometric analysis via CiteSpace
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Xue Zhou, Chen Kang, YuHong Hu, and XingChen Wang
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insulin resistance ,ischemic cerebrovascular disease ,association ,oxidative stress ,inflammation ,CiteSpace ,Public aspects of medicine ,RA1-1270 - Abstract
BackgroundIt is reported that insulin resistance widely exists in non-diabetic patients with a recent history of transient ischemic attack (TIA) or ischemic stroke. There is currently strong evidence to prove the bidirectional effect of glucose metabolism disorders and stroke events. Therefore, it is necessary to retrospectively tease out the current status, hotspots, and frontiers of insulin resistance and ischemic cerebrovascular disease through CiteSpace.Materials and methodsWe searched the Web of Science (WOS) for studies related to insulin resistance and ischemic cerebrovascular disease from 1999 to April 2022, then downloaded the data into CiteSpace to generate a knowledge visualization map.ResultsA total of 1,500 publications relevant to insulin resistance and ischemic cerebrovascular disease were retrieved. The USA had the most articles on this topic, followed by PEOPLES R CHINA and JAPAN. WALTER N KERNAN was the most prolific author, whose research mainly focused on insulin resistance intervention after stroke (IRIS) trial. The most common keywords were myocardial ischemia, metabolic syndrome, ischemic stroke, cerebral ischemia, association, oxidative stress, inflammation, and adipose tissue. Major ongoing research trends include three aspects: (1) the association between insulin resistance and ischemic cerebrovascular disease in non-diabetic patients, (2) the intrinsic pathological mechanism between insulin resistance and ischemic cerebrovascular disease, and (3) early intervention of insulin resistance to improve the prognosis of stroke.ConclusionThe results of this bibliometric study provide the current status and trends of clinical research publications in the field of insulin resistance and ischemic cerebrovascular disease. Insulin resistance is strongly associated with the occurrence of ischemic stroke, early neurological deterioration in stroke patients, post-stroke depression, and cerebral small vessel disease. Early treatment of insulin resistance can be an effective way to prevent the onset of ischemic stroke and improve stroke prognosis. This study may help researchers to identify hot topics and explore new research directions.
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- 2023
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10. Small molecule SWELL1 complex induction improves glycemic control and nonalcoholic fatty liver disease in murine Type 2 diabetes
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Susheel K. Gunasekar, Litao Xie, Ashutosh Kumar, Juan Hong, Pratik R. Chheda, Chen Kang, David M. Kern, Chau My-Ta, Joshua Maurer, John Heebink, Eva E. Gerber, Wojciech J. Grzesik, Macaulay Elliot-Hudson, Yanhui Zhang, Phillip Key, Chaitanya A. Kulkarni, Joseph W. Beals, Gordon I. Smith, Isaac Samuel, Jessica K. Smith, Peter Nau, Yumi Imai, Ryan D. Sheldon, Eric B. Taylor, Daniel J. Lerner, Andrew W. Norris, Samuel Klein, Stephen G. Brohawn, Robert Kerns, and Rajan Sah
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Science - Abstract
Type 2 diabetes is associated with insulin resistance, impaired insulin secretion and liver steatosis. Here the authors report a proof-of-concept study for small molecule SWELL1 modulators as a therapeutic approach to treat diabetes and associated liver steatosis by enhancing systemic insulin-sensitivity and insulin secretion in mice.
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- 2022
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11. Revealing Tissue Heterogeneity and Spatial Dark Genes from Spatially Resolved Transcriptomics by Multiview Graph Networks
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Ying Li, Yuejing Lu, Chen Kang, Peiluan Li, and Luonan Chen
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Science - Abstract
Spatially resolved transcriptomics (SRT) is capable of comprehensively characterizing gene expression patterns and providing an unbiased image of spatial composition. To fully understand the organizational complexity and tumor immune escape mechanism, we propose stMGATF, a multiview graph attention fusion model that integrates gene expression, histological images, spatial location, and gene association. To better extract information, stMGATF exploits SimCLRv2 for visual feature exaction and employs edge feature enhanced graph attention networks for the learning potential embedding of each view. A global attention mechanism is used to adaptively integrate 3 views to obtain low-dimensional representation. Applied to diverse SRT datasets, stMGATF is robust and outperforms other methods in detecting spatial domains and denoising data even with different resolutions and platforms. In particular, stMGATF contributes to the elucidation of tissue heterogeneity and extraction of 3-dimensional expression domains. Importantly, considering the associations between genes in tumors, stMGATF can identify the spatial dark genes ignored by traditional methods, which can be used to predict tumor-driving transcription factors and reveal tumor immune escape mechanisms, providing theoretical evidence for the development of new immunotherapeutic strategies.
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- 2023
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12. Open-vocabulary Multimodal Emotion Recognition: Dataset, Metric, and Benchmark
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Lian, Zheng, Sun, Haiyang, Sun, Licai, Chen, Lan, Chen, Haoyu, Gu, Hao, Wen, Zhuofan, Chen, Shun, Zhang, Siyuan, Yao, Hailiang, Xu, Mingyu, Chen, Kang, Liu, Bin, Liu, Rui, Liang, Shan, Li, Ya, Yi, Jiangyan, and Tao, Jianhua
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Computer Science - Human-Computer Interaction - Abstract
Multimodal Emotion Recognition (MER) is an important research topic. This paper advocates for a transformative paradigm in MER. The rationale behind our work is that current approaches often rely on a limited set of basic emotion labels, which do not adequately represent the rich spectrum of human emotions. These traditional and overly simplistic emotion categories fail to capture the inherent complexity and subtlety of human emotional experiences, leading to limited generalizability and practicality. Therefore, we propose a new MER paradigm called Open-vocabulary MER (OV-MER), which encompasses a broader range of emotion labels to reflect the richness of human emotions. This paradigm relaxes the label space, allowing for the prediction of arbitrary numbers and categories of emotions. To support this transition, we provide a comprehensive solution that includes a newly constructed database based on LLM and human collaborative annotations, along with corresponding metrics and a series of benchmarks. We hope this work advances emotion recognition from basic emotions to more nuanced emotions, contributing to the development of emotional AI.
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- 2024
13. WeatherFormer: Empowering Global Numerical Weather Forecasting with Space-Time Transformer
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Gong, Junchao, Han, Tao, Chen, Kang, and Bai, Lei
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Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Physics - Atmospheric and Oceanic Physics - Abstract
Numerical Weather Prediction (NWP) system is an infrastructure that exerts considerable impacts on modern society.Traditional NWP system, however, resolves it by solving complex partial differential equations with a huge computing cluster, resulting in tons of carbon emission. Exploring efficient and eco-friendly solutions for NWP attracts interest from Artificial Intelligence (AI) and earth science communities. To narrow the performance gap between the AI-based methods and physic predictor, this work proposes a new transformer-based NWP framework, termed as WeatherFormer, to model the complex spatio-temporal atmosphere dynamics and empowering the capability of data-driven NWP. WeatherFormer innovatively introduces the space-time factorized transformer blocks to decrease the parameters and memory consumption, in which Position-aware Adaptive Fourier Neural Operator (PAFNO) is proposed for location sensible token mixing. Besides, two data augmentation strategies are utilized to boost the performance and decrease training consumption. Extensive experiments on WeatherBench dataset show WeatherFormer achieves superior performance over existing deep learning methods and further approaches the most advanced physical model.
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- 2024
14. Self-supervised Anomaly Detection Pretraining Enhances Long-tail ECG Diagnosis
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Jiang, Aofan, Huang, Chaoqin, Cao, Qing, Xu, Yuchen, Zeng, Zi, Chen, Kang, Zhang, Ya, and Wang, Yanfeng
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Current computer-aided ECG diagnostic systems struggle with the underdetection of rare but critical cardiac anomalies due to the imbalanced nature of ECG datasets. This study introduces a novel approach using self-supervised anomaly detection pretraining to address this limitation. The anomaly detection model is specifically designed to detect and localize subtle deviations from normal cardiac patterns, capturing the nuanced details essential for accurate ECG interpretation. Validated on an extensive dataset of over one million ECG records from clinical practice, characterized by a long-tail distribution across 116 distinct categories, the anomaly detection-pretrained ECG diagnostic model has demonstrated a significant improvement in overall accuracy. Notably, our approach yielded a 94.7% AUROC, 92.2% sensitivity, and 92.5\% specificity for rare ECG types, significantly outperforming traditional methods and narrowing the performance gap with common ECG types. The integration of anomaly detection pretraining into ECG analysis represents a substantial contribution to the field, addressing the long-standing challenge of long-tail data distributions in clinical diagnostics. Furthermore, prospective validation in real-world clinical settings revealed that our AI-driven approach enhances diagnostic efficiency, precision, and completeness by 32%, 6.7%, and 11.8% respectively, when compared to standard practices. This advancement marks a pivotal step forward in the integration of AI within clinical cardiology, with particularly profound implications for emergency care, where rapid and accurate ECG interpretation is crucial. The contributions of this study not only push the boundaries of current ECG diagnostic capabilities but also lay the groundwork for more reliable and accessible cardiovascular care., Comment: arXiv admin note: text overlap with arXiv:2404.04935
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- 2024
15. An active filament on a cylindrical surface: morphologies and dynamics
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Shen, Chen, Qin, Chao-ran, Xu, Tian-liang, Chen, Kang, and Tian, Wen-de
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Condensed Matter - Soft Condensed Matter - Abstract
Structure and dynamics of an active polymer on a smooth cylindrical surface are studied by Brownian dynamics simulations. The effect of active force on the polymer adsorption behavior and the combined effect of chain mobility, length N, rigidity \k{appa}, and cylinder radius, R, on phase diagrams are systemically investigated. We find that complete adsorption is replaced by irregular alternative adsorption/desorption process at a large driving force. Three typical (spiral, helix-like, rod-like) conformations of the active polymer are observed, dependent on N, \k{appa}, and R. Dynamically, the polymer shows rotational motion in spiral state, snake-like motion in the intermediate state, and straight translational motion without turning back in the rod-like state. In the spiral state, we find that rotation velocity {\omega} and chain length follows a power-law relation {\omega}~N^(-0.42), consistent with the torque-balance theory of general Archimedean spirals. And the polymer shows super-diffusive behavior along the cylinder at long time in the helix-like and rod-like states. Our results highlight the mobility, rigidity, as well as curvature of surface can be used to regulate the polymer behavior.
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- 2024
16. Instability Mechanism of Osimertinib in Plasma and a Solving Strategy in the Pharmacokinetics Study
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Zheng Yuan, Xin Yu, Siyang Wu, Xiaonan Wu, Qiutao Wang, Wenhao Cheng, Weiyu Hu, Chen Kang, Wei Yang, Yingfei Li, and Xiao-Yang Zhou
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osimertinib ,plasma stability ,UPLC-MS/MS ,acetonitrile ,cysteine ,non-small-cell lung cancer ,Therapeutics. Pharmacology ,RM1-950 - Abstract
Osimertinib is a third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) and a star medication used to treat non-small-cell lung carcinomas (NSCLCs). It has caused broad public concern that osimertinib has relatively low stability in plasma. We explored why osimertinib and its primary metabolites AZ-5104 and AZ-7550 are unstable in rat plasma. Our results suggested that it is the main reason inducing their unstable phenomenon that the Michael addition reaction was putatively produced between the Michael acceptor of osimertinib and the cysteine in the plasma matrix. Consequently, we identified a method to stabilize osimertinib and its metabolite contents in plasma. The assay was observed to enhance the stability of osimertinib, AZ-5104, and AZ-7550 significantly. The validated method was subsequently applied to perform the pharmacokinetic study for osimertinib in rats with the newly established, elegant, and optimized ultra-performance liquid chromatography–tandem mass spectrometer (UPLC-MS/MS) strategy. The assay was assessed for accuracy, precision, matrix effects, recovery, and stability. This study can help understand the pharmacological effects of osimertinib and promote a solution for the similar problem of other Michael acceptor-contained third-generation EGFR-TKI.
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- 2022
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17. Integrated Physiochemical, Hormonal, and Transcriptomic Analysis Revealed the Underlying Mechanisms for Granulation in Huyou (Citrus changshanensis) Fruit
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Chen Kang, Anze Jiang, Han Yang, Guixia Zheng, Yue Wang, Jinping Cao, and Chongde Sun
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cell wall components ,citrus ,juice sac granulation ,phytohormones ,transcriptomic analysis ,Plant culture ,SB1-1110 - Abstract
Juice sac granulation is a common internal physiological disorder of citrus fruit. In the present study, we compared the physiochemical characteristics and transcriptome profiles of juice sacs in different granulation levels from Huyou fruit (Citrus changshanensis). The accumulation of cell wall components, including the water-soluble pectin, protopectin, cellulose, and lignin, were significantly correlated with the granulation process, resulting in the firmness increase of the juice sac. The in situ labeling of the cell wall components indicated the early accumulation of cellulose and high-methylesterified pectin in the outer layer cells, as well as the late accumulation of lignin in the inner layer cells of the juice sac. Several phytohormones, including auxins, abscisic acids, cytokinins, jasmonic acid, salicylic acid, and/or their metabolites, were positively correlated to the granulation level, indicating an active and complex phytohormones metabolism in the granulation process. Combining the trend analysis by the Mfuzz method and the module-trait correlation analysis by the Weighted Gene Co-expression Network Analysis method, a total of 2940 differentially expressed genes (DEGs) were found to be positively correlated with the granulation level. Gene Ontology (GO) enrichment indicated that the selected DEGs were mainly involved in the cell wall organization and biogenesis, cell wall macromolecule metabolic process, carbohydrate metabolic process, and polysaccharide metabolic process. Among these selected genes, those encoding β-1,4-xylosyltransferase IRX9, cellulose synthase, xyloglucan: xyloglucosyl transferase, xyloglucan galactosyltransferase MUR3, α-1,4-galacturonosyltransferase, expansin, polygalacturonase, pectinesterase, β-glucosidase, β-galactosidase, endo-1,3(4)-β-glucanase, endoglucanase and pectate lyase that required for the biosynthesis or structural modification of cell wall were identified. In addition, NAC, MYB, bHLH, and MADS were the top abundant transcription factors (TFs) families positively correlated with the granulation level, while the LOB was the top abundant TFs family negatively correlated with the granulation level.
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- 2022
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18. Pharmacokinetics, Tissue Distribution, and Excretion Characteristics of a Radix Polygoni Multiflori Extract in Rats
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Wenhao Cheng, Siyang Wu, Zheng Yuan, Weiyu Hu, Xin Yu, Nianxin Kang, Qiutao Wang, Mingying Zhu, Kexin Xia, Wei Yang, Chen Kang, Shuofeng Zhang, and Yingfei Li
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Radix Polygoni Multiflori ,pharmacokinetics ,tissue distribution ,excretion ,UPLC-MS/MS ,Therapeutics. Pharmacology ,RM1-950 - Abstract
Although progress has been achieved in the pharmacological activity and toxicity of Radix Polygoni Multiflori (RPM), the chemical basis of its toxicity is still unclear. Here, we performed a multicompound pharmacokinetic analysis and investigated the tissue distribution and excretion characteristics of RPM components after oral administration in rats. The findings demonstrated that the active ingredients of the RPM extract were quickly absorbed after oral administration, with high exposure levels of emodin, 2,3,5,4′-teterahydroxystilbene-2-O-β-D-glucoside (TSG), citreorosein, torachrysone-8-O-glucoside (TG), emodin-8-O-β-D-glucoside (EG), and physcion-8-O-β-D-glucoside (PG). The tissue distributions of emodin, TSG, TG, EG, and PG were high in the liver and kidney. These components were the key contributors to the effectiveness and toxicity of RPM on the liver and kidney. Most of the active ingredients were mainly excreted through feces and bile, while a few were converted into other products in the body and excreted through urine and feces.
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- 2022
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19. Escape of an Active Ring from an Attractive Surface: Behaving Like a Self-Propelled Brownian Particle
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Tang, Bin, Gao, Jin-cheng, Chen, Kang, Zhang, Tian Hui, and Tian, Wen-de
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Condensed Matter - Soft Condensed Matter ,Physics - Biological Physics ,Physics - Chemical Physics - Abstract
Escape of active agents from metastable states is of great interest in statistical and biological physics. In this study, we investigate the escape of a flexible active ring, composed of active Brownian particles, from a flat attractive surface using Brownian dynamics simulations. To systematically explore the effects of activity, persistence time, and the shape of attractive potentials, we calculate escape time and effective temperature. We observe two distinct escape mechanisms: Kramers-like thermal activation at small persistence times and the maximal force problem at large persistence time, where escape time is determined by persistence time. The escape time explicitly depends on the shape of the potential barrier at high activity and large persistence time. Moreover, when the propulsion force is biased along the ring's contour, escape becomes more difficult and is primarily driven by thermal noise. Our findings highlight that, despite its intricate configuration, the active ring can be effectively modeled as a self-propelled Brownian particle when studying its escape from a smooth surface.
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- 2024
20. Constrained motion of self-propelling eccentric disks linked by a spring
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Xu, Tian-liang, Qin, Chao-ran, Tang, Bin, Gao, Jin-cheng, Zhou, Jiankang, Chen, Kang, Zhang, Tian Hui, and Tian, Wen-de
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Condensed Matter - Soft Condensed Matter ,Physics - Biological Physics - Abstract
It has been supposed that the interplay of elasticity and activity plays a key role in triggering the non-equilibrium behaviors in biological systems. However, the experimental model system is missing to investigate the spatiotemporally dynamical phenomena. Here, a model system of an active chain, where active eccentric-disks are linked by a spring, is designed to study the interplay of activity, elasticity, and friction. Individual active chain exhibits longitudinal and transverse motion, however, it starts to self-rotate when pinning one end, and self-beats when clamping one end. Additionally, our eccentric-disk model can qualitatively reproduce such behaviors and explain the unusual self-rotation of the first disk around its geometric center. Further, the structure and dynamics of long chains were studied via simulations without steric interactions. It was found that hairpin conformation emerges in free motion, while in the constrained motions, the rotational and beating frequencies scale with the flexure number (the ratio of self-propelling force to bending rigidity), ~4/3. Scaling analysis suggests that it results from the balance between activity and energy dissipation. Our findings show that topological constraints play a vital role in non-equilibrium synergy behavior.
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- 2024
21. SpikeGS: 3D Gaussian Splatting from Spike Streams with High-Speed Camera Motion
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Zhang, Jiyuan, Chen, Kang, Chen, Shiyan, Zheng, Yajing, Huang, Tiejun, and Yu, Zhaofei
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Novel View Synthesis plays a crucial role by generating new 2D renderings from multi-view images of 3D scenes. However, capturing high-speed scenes with conventional cameras often leads to motion blur, hindering the effectiveness of 3D reconstruction. To address this challenge, high-frame-rate dense 3D reconstruction emerges as a vital technique, enabling detailed and accurate modeling of real-world objects or scenes in various fields, including Virtual Reality or embodied AI. Spike cameras, a novel type of neuromorphic sensor, continuously record scenes with an ultra-high temporal resolution, showing potential for accurate 3D reconstruction. Despite their promise, existing approaches, such as applying Neural Radiance Fields (NeRF) to spike cameras, encounter challenges due to the time-consuming rendering process. To address this issue, we make the first attempt to introduce the 3D Gaussian Splatting (3DGS) into spike cameras in high-speed capture, providing 3DGS as dense and continuous clues of views, then constructing SpikeGS. Specifically, to train SpikeGS, we establish computational equations between the rendering process of 3DGS and the processes of instantaneous imaging and exposing-like imaging of the continuous spike stream. Besides, we build a very lightweight but effective mapping process from spikes to instant images to support training. Furthermore, we introduced a new spike-based 3D rendering dataset for validation. Extensive experiments have demonstrated our method possesses the high quality of novel view rendering, proving the tremendous potential of spike cameras in modeling 3D scenes.
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- 2024
22. FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation
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Chen, Kun, Chen, Tao, Ye, Peng, Chen, Hao, Chen, Kang, Han, Tao, Ouyang, Wanli, and Bai, Lei
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Data assimilation is a vital component in modern global medium-range weather forecasting systems to obtain the best estimation of the atmospheric state by combining the short-term forecast and observations. Recently, AI-based data assimilation approaches have attracted increasing attention for their significant advantages over traditional techniques in terms of computational consumption. However, existing AI-based data assimilation methods can only handle observations with a specific resolution, lacking the compatibility and generalization ability to assimilate observations with other resolutions. Considering that complex real-world observations often have different resolutions, we propose the \textit{\textbf{Fourier Neural Processes}} (FNP) for \textit{arbitrary-resolution data assimilation} in this paper. Leveraging the efficiency of the designed modules and flexible structure of neural processes, FNP achieves state-of-the-art results in assimilating observations with varying resolutions, and also exhibits increasing advantages over the counterparts as the resolution and the amount of observations increase. Moreover, our FNP trained on a fixed resolution can directly handle the assimilation of observations with out-of-distribution resolutions and the observational information reconstruction task without additional fine-tuning, demonstrating its excellent generalization ability across data resolutions as well as across tasks.
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- 2024
23. SpikeMM: Flexi-Magnification of High-Speed Micro-Motions
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Zhang, Baoyue, Zheng, Yajing, Chen, Shiyan, Zhang, Jiyuan, Chen, Kang, Yu, Zhaofei, and Huang, Tiejun
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Computer Science - Computer Vision and Pattern Recognition - Abstract
The amplification of high-speed micro-motions holds significant promise, with applications spanning fault detection in fast-paced industrial environments to refining precision in medical procedures. However, conventional motion magnification algorithms often encounter challenges in high-speed scenarios due to low sampling rates or motion blur. In recent years, spike cameras have emerged as a superior alternative for visual tasks in such environments, owing to their unique capability to capture temporal and spatial frequency domains with exceptional fidelity. Unlike conventional cameras, which operate at fixed, low frequencies, spike cameras emulate the functionality of the retina, asynchronously capturing photon changes at each pixel position using spike streams. This innovative approach comprehensively records temporal and spatial visual information, rendering it particularly suitable for magnifying high-speed micro-motions.This paper introduces SpikeMM, a pioneering spike-based algorithm tailored specifically for high-speed motion magnification. SpikeMM integrates multi-level information extraction, spatial upsampling, and motion magnification modules, offering a self-supervised approach adaptable to a wide range of scenarios. Notably, SpikeMM facilitates seamless integration with high-performance super-resolution and motion magnification algorithms. We substantiate the efficacy of SpikeMM through rigorous validation using scenes captured by spike cameras, showcasing its capacity to magnify motions in real-world high-frequency settings.
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- 2024
24. LAGA: Layered 3D Avatar Generation and Customization via Gaussian Splatting
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Gong, Jia, Ji, Shenyu, Foo, Lin Geng, Chen, Kang, Rahmani, Hossein, and Liu, Jun
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Computer Science - Graphics ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Creating and customizing a 3D clothed avatar from textual descriptions is a critical and challenging task. Traditional methods often treat the human body and clothing as inseparable, limiting users' ability to freely mix and match garments. In response to this limitation, we present LAyered Gaussian Avatar (LAGA), a carefully designed framework enabling the creation of high-fidelity decomposable avatars with diverse garments. By decoupling garments from avatar, our framework empowers users to conviniently edit avatars at the garment level. Our approach begins by modeling the avatar using a set of Gaussian points organized in a layered structure, where each layer corresponds to a specific garment or the human body itself. To generate high-quality garments for each layer, we introduce a coarse-to-fine strategy for diverse garment generation and a novel dual-SDS loss function to maintain coherence between the generated garments and avatar components, including the human body and other garments. Moreover, we introduce three regularization losses to guide the movement of Gaussians for garment transfer, allowing garments to be freely transferred to various avatars. Extensive experimentation demonstrates that our approach surpasses existing methods in the generation of 3D clothed humans.
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- 2024
25. Enhancing Teaching and Learning for Pupils with Dyslexia: A Comprehensive Review of Technological and Non-Technological Interventions
- Author
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Salman Jav, Manoranjitham Muniandy, Chen Kang Lee, and Husniza Husni
- Abstract
Dyslexia is the most prevalent disorder in the world that causes difficulties with reading, writing, and spelling. Pupils with dyslexia show trouble with their cognitive skills. Various interventions were already introduced for their treatment but dyslexia is still a trending disorder. The available interventions utilized for these pupils' learning open the research area for the current state-of-art of learning interventions for pupils with dyslexia. The results of this Systematic Literature Review show the trending interventions, sensory approaches utilized, and difficulties for pupils with dyslexia learning. Papers published over a period of 5 years were reviewed and their data was collected using a rigid systematic process. Based on the gathered data, several analyses were conducted. The search shows that nowadays, technological-based interventions are trending specifically apps and games, in parallel haptics technology is in its very initial stage. The most predominant sensory approaches were visual and auditory, followed by kinesthetic and tactile, mainly intervening with non-technological and technological interventions. There are still many open issues and research opportunities in the field of learning interventions for pupils with dyslexia, as most researchers utilized the visual and auditory approaches for the feedback and guidance of these pupils, while they lack to utilize the kinesthetic and tactile.
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- 2024
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26. CleanSeq: A Pipeline for Contamination Detection, Cleanup, and Mutation Verifications from Microbial Genome Sequencing Data
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Caiyan Wang, Yang Xia, Yunfei Liu, Chen Kang, Nan Lu, Di Tian, Hui Lu, Fuhai Han, Jian Xu, and Tetsuya Yomo
- Subjects
contamination detection ,genome sequencing ,decontamination ,mutation verification ,experimental evolution ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
Contaminations frequently occur in bacterial cultures, which significantly affect the reproducibility and reliability of the results from whole-genome sequencing (WGS). Decontaminated WGS data with clean reads is the only desirable source for detecting possible variants correctly. Improvements in bioinformatics are essential to analyze the contaminated WGS dataset. Existing pipelines usually contain contamination detection, decontamination, and variant calling separately. The efficiency and results from existing pipelines fluctuate since distinctive computational models and parameters are applied. It is then promising to develop a bioinformatical tool containing functions to discriminate and remove contaminated reads and improve variant calling from clean reads. In this study, we established a Python-based pipeline named CleanSeq for automatic detection and removal of contaminating reads, analyzing possible genome variants with proper verifications via local re-alignments. The application and reproducibility are proven in either simulated, publicly available datasets or actual genome sequencing reads from our experimental evolution study in Escherichia coli. We successfully obtained decontaminated reads, called out all seven consistent mutations from the contaminated bacterial sample, and derived five colonies. Collectively, the results demonstrated that CleanSeq could effectively process the contaminated samples to achieve decontaminated reads, based on which reliable results (i.e., variant calling) could be obtained.
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- 2022
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27. Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning
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Jiang, Aofan, Huang, Chaoqin, Cao, Qing, Xu, Yuchen, Zeng, Zi, Chen, Kang, Zhang, Ya, and Wang, Yanfeng
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Computer Science - Computer Vision and Pattern Recognition - Abstract
The electrocardiogram (ECG) is an essential tool for diagnosing heart disease, with computer-aided systems improving diagnostic accuracy and reducing healthcare costs. Despite advancements, existing systems often miss rare cardiac anomalies that could be precursors to serious, life-threatening issues or alterations in the cardiac macro/microstructure. We address this gap by focusing on self-supervised anomaly detection (AD), training exclusively on normal ECGs to recognize deviations indicating anomalies. We introduce a novel self-supervised learning framework for ECG AD, utilizing a vast dataset of normal ECGs to autonomously detect and localize cardiac anomalies. It proposes a novel masking and restoration technique alongside a multi-scale cross-attention module, enhancing the model's ability to integrate global and local signal features. The framework emphasizes accurate localization of anomalies within ECG signals, ensuring the method's clinical relevance and reliability. To reduce the impact of individual variability, the approach further incorporates crucial patient-specific information from ECG reports, such as age and gender, thus enabling accurate identification of a broad spectrum of cardiac anomalies, including rare ones. Utilizing an extensive dataset of 478,803 ECG graphic reports from real-world clinical practice, our method has demonstrated exceptional effectiveness in AD across all tested conditions, regardless of their frequency of occurrence, significantly outperforming existing models. It achieved superior performance metrics, including an AUROC of 91.2%, an F1 score of 83.7%, a sensitivity rate of 84.2%, a specificity of 83.0%, and a precision of 75.6% with a fixed recall rate of 90%. It has also demonstrated robust localization capabilities, with an AUROC of 76.5% and a Dice coefficient of 65.3% for anomaly localization.
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- 2024
28. SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams
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Chen, Kang, Chen, Shiyan, Zhang, Jiyuan, Zhang, Baoyue, Zheng, Yajing, Huang, Tiejun, and Yu, Zhaofei
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Reconstructing a sequence of sharp images from the blurry input is crucial for enhancing our insights into the captured scene and poses a significant challenge due to the limited temporal features embedded in the image. Spike cameras, sampling at rates up to 40,000 Hz, have proven effective in capturing motion features and beneficial for solving this ill-posed problem. Nonetheless, existing methods fall into the supervised learning paradigm, which suffers from notable performance degradation when applied to real-world scenarios that diverge from the synthetic training data domain. Moreover, the quality of reconstructed images is capped by the generated images based on motion analysis interpolation, which inherently differs from the actual scene, affecting the generalization ability of these methods in real high-speed scenarios. To address these challenges, we propose the first self-supervised framework for the task of spike-guided motion deblurring. Our approach begins with the formulation of a spike-guided deblurring model that explores the theoretical relationships among spike streams, blurry images, and their corresponding sharp sequences. We subsequently develop a self-supervised cascaded framework to alleviate the issues of spike noise and spatial-resolution mismatching encountered in the deblurring model. With knowledge distillation and re-blurring loss, we further design a lightweight deblur network to generate high-quality sequences with brightness and texture consistency with the original input. Quantitative and qualitative experiments conducted on our real-world and synthetic datasets with spikes validate the superior generalization of the proposed framework. Our code, data and trained models will be available at \url{https://github.com/chenkang455/S-SDM}., Comment: 14 pages
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- 2024
29. Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark for Deception Reasoning
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Chen, Kang, Lian, Zheng, Sun, Haiyang, Liu, Rui, Yi, Jiangyan, Liu, Bin, and Tao, Jianhua
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Computer Science - Computation and Language - Abstract
Deception detection has attracted increasing attention due to its importance in real-world scenarios. Its main goal is to detect deceptive behaviors from multimodal clues such as gestures, facial expressions, prosody, etc. However, these bases are usually subjective and related to personal habits. Therefore, we extend deception detection to deception reasoning, further providing objective evidence to support subjective judgment. Specifically, we provide potential lies and basic facts and then analyze why this sentence may be a lie by combining factual inconsistencies and intent behind them. Compared with deception detection, this task is more applicable to real-world scenarios. For example, in interrogation, the police should judge whether a person is lying based on solid evidence. This paper presents our initial attempts at this task, including constructing a dataset and defining evaluation metrics. Meanwhile, this task can serve as a benchmark for evaluating the complex reasoning capability of large language models. Our code and data are provided in the supplementary material.
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- 2024
30. Global Tropical Cyclone Intensity Forecasting with Multi-modal Multi-scale Causal Autoregressive Model
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Wang, Xinyu, Chen, Kang, Liu, Lei, Han, Tao, Li, Bin, and Bai, Lei
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Physics - Atmospheric and Oceanic Physics ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Physics - Data Analysis, Statistics and Probability - Abstract
Accurate forecasting of Tropical cyclone (TC) intensity is crucial for formulating disaster risk reduction strategies. Current methods predominantly rely on limited spatiotemporal information from ERA5 data and neglect the causal relationships between these physical variables, failing to fully capture the spatial and temporal patterns required for intensity forecasting. To address this issue, we propose a Multi-modal multi-Scale Causal AutoRegressive model (MSCAR), which is the first model that combines causal relationships with large-scale multi-modal data for global TC intensity autoregressive forecasting. Furthermore, given the current absence of a TC dataset that offers a wide range of spatial variables, we present the Satellite and ERA5-based Tropical Cyclone Dataset (SETCD), which stands as the longest and most comprehensive global dataset related to TCs. Experiments on the dataset show that MSCAR outperforms the state-of-the-art methods, achieving maximum reductions in global and regional forecast errors of 9.52% and 6.74%, respectively. The code and dataset are publicly available at https://anonymous.4open.science/r/MSCAR.
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- 2024
31. ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast
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Xu, Wanghan, Chen, Kang, Han, Tao, Chen, Hao, Ouyang, Wanli, and Bai, Lei
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Data-driven weather forecast based on machine learning (ML) has experienced rapid development and demonstrated superior performance in the global medium-range forecast compared to traditional physics-based dynamical models. However, most of these ML models struggle with accurately predicting extreme weather, which is related to training loss and the uncertainty of weather systems. Through mathematical analysis, we prove that the use of symmetric losses, such as the Mean Squared Error (MSE), leads to biased predictions and underestimation of extreme values. To address this issue, we introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. Beyond the evolution in training loss, we introduce a training-free extreme value enhancement module named ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples, thereby increasing the hit rate of low-probability extreme events. Combined with an advanced global weather forecast model, extensive experiments show that our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
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- 2024
32. Microstructure Evolution and Tensile Properties of In Situ TiB2(p)/Al-Cu Composite Processed by Modified Strain-Induced Melt Activation Method with Equal Channel Angular Pressing Pre-deformation
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Yang, Sen, Wang, Kaikun, Sun, Zhiren, Li, Qipeng, and Chen, Kang
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- 2024
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33. Comprehensive evaluation of air quality: incense burning and night market emissions in Kaohsiung, Taiwan, using the ISCST3 air quality model
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Cheng, Pei-Cheng, Amesho, Kassian T. T., Huang, Yin-Cheng, Lin, Yuan-Chung, Chou, Feng-Chih, Wang, Tsu-Nai, Chen, Pei-Shih, Chen, Kang-Shin, Chang, Ken-Lin, and Lee, Chien-Hung
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- 2024
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34. The Application of Orychophragmus violaceus as a Green Manure Relieves Continuous Cropping Obstacles in Peanut Cultivation by Altering the Soil Microbial Community and Functional Gene Abundance
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Yu, Tianyi, Sun, Qiqi, Liu, Zhigang, Wang, Xuancang, Chen, Kang, Wu, Zhengfeng, Zhang, Jiancheng, and Sun, Xuewu
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- 2024
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35. MoSx nanowire networks derived from [Mo3S13]2− clusters for efficient electrocatalytic hydrogen evolution
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Yu, Haoxuan, Pan, Junan, Chen, Kang, Chao, Wang, Zhuang, Zechao, Feng, Sizhuo, Chen, Jianmei, Xie, Lingbin, Wang, Longlu, and Zhao, Qiang
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- 2024
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36. FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting
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Han, Tao, Guo, Song, Ling, Fenghua, Chen, Kang, Gong, Junchao, Luo, Jingjia, Gu, Junxia, Dai, Kan, Ouyang, Wanli, and Bai, Lei
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Physics - Atmospheric and Oceanic Physics - Abstract
Kilometer-scale modeling of global atmosphere dynamics enables fine-grained weather forecasting and decreases the risk of disastrous weather and climate activity. Therefore, building a kilometer-scale global forecast model is a persistent pursuit in the meteorology domain. Active international efforts have been made in past decades to improve the spatial resolution of numerical weather models. Nonetheless, developing the higher resolution numerical model remains a long-standing challenge due to the substantial consumption of computational resources. Recent advances in data-driven global weather forecasting models utilize reanalysis data for model training and have demonstrated comparable or even higher forecasting skills than numerical models. However, they are all limited by the resolution of reanalysis data and incapable of generating higher-resolution forecasts. This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$^{\circ}$ horizontal resolution. FengWu-GHR introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a pretrained low-resolution model. The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES. Furthermore, evaluations on station observations and case studies of extreme events support the competitive operational forecasting skill of FengWu-GHR at the high resolution., Comment: 19 pages
- Published
- 2024
37. Efficient Image Deblurring Networks based on Diffusion Models
- Author
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Chen, Kang and Liu, Yuanjie
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
This article presents a sliding window model for defocus deblurring, named Swintormer, which achieves the best performance to date with remarkably low memory usage. This method utilizes a diffusion model to generate latent prior features, aiding in the restoration of more detailed images. Additionally, by adapting the sliding window strategy, it incorporates specialized Transformer blocks to enhance inference efficiency. The adoption of this new approach has led to a substantial reduction in Multiply-Accumulate Operations (MACs) per iteration, drastically cutting down memory requirements. In comparison to the currently leading GRL method, our Swintormer model significantly reduces the computational load that must depend on memory capacity, from 140.35 GMACs to 8.02 GMACs, while improving the Peak Signal-to-Noise Ratio (PSNR) for defocus deblurring from 27.04 dB to 27.07 dB. This innovative technique enables the processing of higher resolution images on memory-limited devices, vastly broadening potential application scenarios. The article wraps up with an ablation study, offering a comprehensive examination of how each network module contributes to the final performance.The source code and model will be available at the following website: https://github.com/bnm6900030/swintormer.
- Published
- 2024
38. ITEACH-Net: Inverted Teacher-studEnt seArCH Network for Emotion Recognition in Conversation
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Sun, Haiyang, Lian, Zheng, Wang, Chenglong, Chen, Kang, Sun, Licai, Liu, Bin, and Tao, Jianhua
- Subjects
Computer Science - Multimedia - Abstract
There remain two critical challenges that hinder the development of ERC. Firstly, there is a lack of exploration into mining deeper insights from the data itself for conversational emotion tasks. Secondly, the systems exhibit vulnerability to random modality feature missing, which is a common occurrence in realistic settings. Focusing on these two key challenges, we propose a novel framework for incomplete multimodal learning in ERC, called "Inverted Teacher-studEnt seArCH Network (ITEACH-Net)." ITEACH-Net comprises two novel components: the Emotion Context Changing Encoder (ECCE) and the Inverted Teacher-Student (ITS) framework. Specifically, leveraging the tendency for emotional states to exhibit local stability within conversational contexts, ECCE captures these patterns and further perceives their evolution over time. Recognizing the varying challenges of handling incomplete versus complete data, ITS employs a teacher-student framework to decouple the respective computations. Subsequently, through Neural Architecture Search, the student model develops enhanced computational capabilities for handling incomplete data compared to the teacher model. During testing, we design a novel evaluation method, testing the model's performance under different missing rate conditions without altering the model weights. We conduct experiments on three benchmark ERC datasets, and the results demonstrate that our ITEACH-Net outperforms existing methods in incomplete multimodal ERC. We believe ITEACH-Net can inspire relevant research on the intrinsic nature of emotions within conversation scenarios and pave a more robust route for incomplete learning techniques. Codes will be made available.
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- 2023
39. Towards an end-to-end artificial intelligence driven global weather forecasting system
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Chen, Kun, Bai, Lei, Ling, Fenghua, Ye, Peng, Chen, Tao, Luo, Jing-Jia, Chen, Hao, Xiao, Yi, Chen, Kang, Han, Tao, and Ouyang, Wanli
- Subjects
Physics - Atmospheric and Oceanic Physics ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
The weather forecasting system is important for science and society, and significant achievements have been made in applying artificial intelligence (AI) to medium-range weather forecasting. However, existing AI-based weather forecasting models rely on analysis or reanalysis products from traditional numerical weather prediction (NWP) systems as initial conditions for making predictions. Initial states are typically generated by traditional data assimilation components, which are computational expensive and time-consuming. Here we present an AI-based data assimilation model, i.e., Adas, for global weather variables. By introducing the confidence matrix, Adas employs gated convolution to handle sparse observations and gated cross-attention for capturing the interactions between the background and observations. Further, we combine Adas with the advanced AI-based forecasting model (i.e., FengWu) to construct the first end-to-end AI-based global weather forecasting system: FengWu-Adas. We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term. Moreover, we are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential. We have also achieved the forecasts based on the analyses generated by AI with a skillful forecast lead time exceeding that of the IFS for the first time.
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- 2023
40. FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation
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Xiao, Yi, Bai, Lei, Xue, Wei, Chen, Kang, Han, Tao, and Ouyang, Wanli
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Physics - Atmospheric and Oceanic Physics ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Weather forecasting is a crucial yet highly challenging task. With the maturity of Artificial Intelligence (AI), the emergence of data-driven weather forecasting models has opened up a new paradigm for the development of weather forecasting systems. Despite the significant successes that have been achieved (e.g., surpassing advanced traditional physical models for global medium-range forecasting), existing data-driven weather forecasting models still rely on the analysis fields generated by the traditional assimilation and forecasting system, which hampers the significance of data-driven weather forecasting models regarding both computational cost and forecasting accuracy. In this work, we explore the possibility of coupling the data-driven weather forecasting model with data assimilation by integrating the global AI weather forecasting model, FengWu, with one of the most popular assimilation algorithms, Four-Dimensional Variational (4DVar) assimilation, and develop an AI-based cyclic weather forecasting system, FengWu-4DVar. FengWu-4DVar can incorporate observational data into the data-driven weather forecasting model and consider the temporal evolution of atmospheric dynamics to obtain accurate analysis fields for making predictions in a cycling manner without the help of physical models. Owning to the auto-differentiation ability of deep learning models, FengWu-4DVar eliminates the need of developing the cumbersome adjoint model, which is usually required in the traditional implementation of the 4DVar algorithm. Experiments on the simulated observational dataset demonstrate that FengWu-4DVar is capable of generating reasonable analysis fields for making accurate and efficient iterative predictions., Comment: 15 pages, 8 figures
- Published
- 2023
41. Textual Prompt Guided Image Restoration
- Author
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Yan, Qiuhai, Jiang, Aiwen, Chen, Kang, Peng, Long, Yi, Qiaosi, and Zhang, Chunjie
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Image restoration has always been a cutting-edge topic in the academic and industrial fields of computer vision. Since degradation signals are often random and diverse, "all-in-one" models that can do blind image restoration have been concerned in recent years. Early works require training specialized headers and tails to handle each degradation of concern, which are manually cumbersome. Recent works focus on learning visual prompts from data distribution to identify degradation type. However, the prompts employed in most of models are non-text, lacking sufficient emphasis on the importance of human-in-the-loop. In this paper, an effective textual prompt guided image restoration model has been proposed. In this model, task-specific BERT is fine-tuned to accurately understand user's instructions and generating textual prompt guidance. Depth-wise multi-head transposed attentions and gated convolution modules are designed to bridge the gap between textual prompts and visual features. The proposed model has innovatively introduced semantic prompts into low-level visual domain. It highlights the potential to provide a natural, precise, and controllable way to perform image restoration tasks. Extensive experiments have been done on public denoising, dehazing and deraining datasets. The experiment results demonstrate that, compared with popular state-of-the-art methods, the proposed model can obtain much more superior performance, achieving accurate recognition and removal of degradation without increasing model's complexity. Related source codes and data will be publicly available on github site https://github.com/MoTong-AI-studio/TextPromptIR., Comment: 12 pages, 10figures
- Published
- 2023
42. GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition
- Author
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Lian, Zheng, Sun, Licai, Sun, Haiyang, Chen, Kang, Wen, Zhuofan, Gu, Hao, Liu, Bin, and Tao, Jianhua
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Multimedia - Abstract
Recently, GPT-4 with Vision (GPT-4V) has demonstrated remarkable visual capabilities across various tasks, but its performance in emotion recognition has not been fully evaluated. To bridge this gap, we present the quantitative evaluation results of GPT-4V on 21 benchmark datasets covering 6 tasks: visual sentiment analysis, tweet sentiment analysis, micro-expression recognition, facial emotion recognition, dynamic facial emotion recognition, and multimodal emotion recognition. This paper collectively refers to these tasks as ``Generalized Emotion Recognition (GER)''. Through experimental analysis, we observe that GPT-4V exhibits strong visual understanding capabilities in GER tasks. Meanwhile, GPT-4V shows the ability to integrate multimodal clues and exploit temporal information, which is also critical for emotion recognition. However, it's worth noting that GPT-4V is primarily designed for general domains and cannot recognize micro-expressions that require specialized knowledge. To the best of our knowledge, this paper provides the first quantitative assessment of GPT-4V for GER tasks. We have open-sourced the code and encourage subsequent researchers to broaden the evaluation scope by including more tasks and datasets. Our code and evaluation results are available at: https://github.com/zeroQiaoba/gpt4v-emotion.
- Published
- 2023
43. Metal-based nanowires in electrical biosensing
- Author
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Zhong, Shen-Jie, Chen, Kang-Yu, Wang, Shao-Lei, Manshaii, Farid, Jing, Nan, Wang, Kai-Dong, Liu, Shi-Chang, and Zhou, Yun-Lei
- Published
- 2024
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44. Microstructure Evolution and Corrosion Performance of Additively Manufactured GH4099 Superalloy Produced by Selective Laser Melting
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Lu, Zhen, Zhang, Chengcai, Huang, Yilin, Zhang, Hongbin, Chen, Kang, Zhou, Haiping, Wang, Zhongwei, Deng, Nana, and Gu, Lianwang
- Published
- 2024
- Full Text
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45. Precise impact time and angle guidance strategy under time-varying velocity
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Wang, Yiwei, Li, Ruichen, Wu, Zihao, Chen, Kang, and Yu, Dengxiu
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- 2024
- Full Text
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46. Enhancing teaching and learning for pupils with dyslexia: A comprehensive review of technological and non-technological interventions
- Author
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Javed, Salman, Muniandy, Manoranjitham, Lee, Chen Kang, and Husni, Husniza
- Published
- 2024
- Full Text
- View/download PDF
47. Assessment of monoclonal antibody glycosylation: a comparative study using HRMS, NMR, and HILIC-FLD
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Shipman, Joshua, Karfunkle, Michael, Zhu, Hongbin, Zhuo, You, Chen, Kang, Patabandige, Milani, Wu, Di, Oyugi, Mercy, Kerr, Richard, Yang, Kui, and Rogstad, Sarah
- Published
- 2024
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48. Pivotal role for long noncoding RNAs in zygotic genome activation in mice
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Chen, Kang, Liu, Wenju, Zhu, Jiang, Kou, Xiaochen, Zhao, Yanhong, Wang, Hong, Jiang, Cizhong, Gao, Shaorong, and Kang, Lan
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- 2024
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49. Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection
- Author
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Jiang, Aofan, Huang, Chaoqin, Cao, Qing, Wu, Shuang, Zeng, Zi, Chen, Kang, Zhang, Ya, and Wang, Yanfeng
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Electrocardiogram (ECG) is a widely used diagnostic tool for detecting heart conditions. Rare cardiac diseases may be underdiagnosed using traditional ECG analysis, considering that no training dataset can exhaust all possible cardiac disorders. This paper proposes using anomaly detection to identify any unhealthy status, with normal ECGs solely for training. However, detecting anomalies in ECG can be challenging due to significant inter-individual differences and anomalies present in both global rhythm and local morphology. To address this challenge, this paper introduces a novel multi-scale cross-restoration framework for ECG anomaly detection and localization that considers both local and global ECG characteristics. The proposed framework employs a two-branch autoencoder to facilitate multi-scale feature learning through a masking and restoration process, with one branch focusing on global features from the entire ECG and the other on local features from heartbeat-level details, mimicking the diagnostic process of cardiologists. Anomalies are identified by their high restoration errors. To evaluate the performance on a large number of individuals, this paper introduces a new challenging benchmark with signal point-level ground truths annotated by experienced cardiologists. The proposed method demonstrates state-of-the-art performance on this benchmark and two other well-known ECG datasets. The benchmark dataset and source code are available at: \url{https://github.com/MediaBrain-SJTU/ECGAD}, Comment: MICCAI 2023 Early Accept
- Published
- 2023
50. The divergence of DHN-derived melanin pathways in Metarhizium robertsii
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Xie, Linan, Liu, Yang, Zhang, Yujie, Chen, Kang, Yue, Qun, Wang, Chen, Dun, Baoqing, Xu, Yuquan, and Zhang, Liwen
- Published
- 2024
- Full Text
- View/download PDF
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