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119 results on '"Huang, Jiaxuan"'

1. Topological inverse band theory in waveguide quantum electrodynamics

Author

Ke, Yongguan, Huang, Jiaxuan, Liu, Wenjie, Kivshar, Yuri, and Lee, Chaohong

Subjects
Physics - Optics, Condensed Matter - Quantum Gases, Quantum Physics
Abstract

Topological phases play a crucial role in the fundamental physics of light-matter interaction and emerging applications of quantum technologies. However, the topological band theory of waveguide QED systems is known to break down, because the energy bands become disconnected. Here, we introduce a concept of the inverse energy band and explore analytically topological scattering in a waveguide with an array of quantum emitters. We uncover a rich structure of topological phase transitions, symmetric scale-free localization, completely flat bands, and the corresponding dark Wannier states. Although bulk-edge correspondence is partially broken because of radiative decay, we prove analytically that the scale-free localized states are distributed in a single inverse energy band in the topological phase and in two inverse bands in the trivial phase. Surprisingly, the winding number of the scattering textures depends on both the topological phase of inverse subradiant band and the odevity of the cell number. Our work uncovers the field of the topological inverse bands, and it brings a novel vision to topological phases in light-matter interactions., Comment: Accepted for publication in Phys. Rev. Lett

Published
2023
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5. Research on Anomaly Detection Model for Power Consumption Data Based on Time-Series Reconstruction.

Author

Mao, Zhenghui, Zhou, Bijun, Huang, Jiaxuan, Liu, Dandan, and Yang, Qiangqiang

Subjects
RANDOM forest algorithms, ENERGY consumption, OFFICE buildings, DEEP learning, TIME series analysis
Abstract

The power consumption data in buildings can be viewed as a time series, where outliers indicate unreasonable energy usage patterns. Accurately detecting these outliers and improving energy management methods based on the findings can lead to energy savings. To detect outliers, an anomaly detection model based on time-series reconstruction, AF-GS-RandomForest, is proposed. This model comprises two modules: prediction and detection. The prediction module uses the Autoformer algorithm to build an accurate and robust predictive model for unstable nonlinear sequences, and calculates the model residuals based on the prediction results. Points with large residuals are considered outliers, as they significantly differ from the normal pattern. The detection module employs a random forest algorithm optimized by grid search to detect residuals and ultimately identify outliers. The algorithm's accuracy and robustness were tested on public datasets, and it was applied to a power consumption dataset of an office building. Compared with commonly used algorithms, the proposed algorithm improved precision by 2.2%, recall by 12.1%, and F1 score by 7.7%, outperforming conventional anomaly detection algorithms. [ABSTRACT FROM AUTHOR]

Published
2024
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6. A Secure and Efficient Authentication Scheme for Large-Scale IoT Devices Based on Zero-Knowledge Proof.

Author

Su, Ziyi, Wang, Shiwei, Cai, Hongliu, Huang, Jiaxuan, Chen, Yourong, Zhang, Xudong, and Alam, Muhammad

Subjects
ENERGY industries, TRUST, SECURITY systems, INTERNET of things, ANONYMITY, PUBLIC key cryptography
Abstract

Current authentication schemes based on zero-knowledge proof (ZKP) still face issues such as high computation costs, low efficiency, and security assurance difficulty. Therefore, we propose a secure and efficient authentication scheme (SEAS) for large-scale IoT devices based on ZKP. In the initialization phase, the trusted authority creates prerequisites for device traceability and system security. Then, we propose a new registration method to ensure device anonymity. In the identity tracing and revocation phase, we revoke the real identity of abnormal devices by decrypting and updating group public keys, avoiding their access and reducing revocation costs. In the authentication phase, we check the arithmetic relationship between blind certificates, proofs, and other random data. We propose a new anonymous batch authentication method to effectively reduce computation costs, enhance authentication efficiency, and guarantee device authentication security. Security analysis and experimental results show that an SEAS can ensure security and effectively reduce verification time and energy costs. Its security and performance exceed existing schemes. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Nominal Types for Erlang

Author

Huang, Jiaxuan and Huang, Jiaxuan

Abstract

Nominal types are types that are identified by user-defined names. Nominal type systems utilize type names for type-checking, in contrast to structural type systems, where relations among types are solely determined by the structure of types. This paper proposes a nominal type system for the functional programming language Erlang, in which all existing type systems are structural. Nominal type-checking is implemented in the static analysis tool Dialyzer, expanding its current capacities. Experiments and user feedback have both shown that nominal types will be a useful addition for Erlang.

Published
2024

19. Modeling and Balancing the Resource Economy in Gustav

Author

Huang, Jiaxuan and Huang, Jiaxuan

Abstract

In-game resource balancing is a crucial part of game design. By providing an appropriate amount of various kinds of resources to players, the game ensures that players are presented with meaningful challenges. Players are able to interact with the resource economy in their own way, and therefore crafting their unique experience in a role-playing game. This project proposes a toolchain combining a constraint programming model, statistical methods, and a user survey for in-game resource balancing. By reviewing in-game supply and demand of resources from a global perspective, the project provides scientific and procedural methods with projectable results.

Published
2023

20. Artifact Detection and Restoration in Histology Images With Stain-Style and Structural Preservation

Author

Ke, Jing, Liu, Kai, Sun, Yuxiang, Xue, Yuying, Huang, Jiaxuan, Lu, Yizhou, Dai, Jun, Chen, Yaobing, Han, Xiaodan, Shen, Yiqing, and Shen, Dinggang

Abstract

The artifacts in histology images may encumber the accurate interpretation of medical information and cause misdiagnosis. Accordingly, prepending manual quality control of artifacts considerably decreases the degree of automation. To close this gap, we propose a methodical pre-processing framework to detect and restore artifacts, which minimizes their impact on downstream AI diagnostic tasks. First, the artifact recognition network AR-Classifier first differentiates common artifacts from normal tissues, e.g., tissue folds, marking dye, tattoo pigment, spot, and out-of-focus, and also catalogs artifact patches by their restorability. Then, the succeeding artifact restoration network AR-CycleGAN performs de-artifact processing where stain styles and tissue structures can be maximally retained. We construct a benchmark for performance evaluation, curated from both clinically collected WSIs and public datasets of colorectal and breast cancer. The functional structures are compared with state-of-the-art methods, and also comprehensively evaluated by multiple metrics across multiple tasks, including artifact classification, artifact restoration, downstream diagnostic tasks of tumor classification and nuclei segmentation. The proposed system allows full automation of deep learning based histology image analysis without human intervention. Moreover, the structure-independent characteristic enables its processing with various artifact subtypes. The source code and data in this research are available at https://github.com/yunboer/AR-classifier-and-AR-CycleGAN.

Published
2023
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29. DNAzymes-Embedded Framework Nucleic Acids (FNAzymes) for Metal Ions Imaging in Living Cells.

Author

Zhu, Dan, Huang, Jiaxuan, Xia, Yanting, Su, Shao, Zuo, Xiaolei, Li, Qian, and Wang, Lianhui

Subjects
NUCLEIC acids, METAL ions, CELL imaging, FLUORESCENCE quenching, FLOW cytometry
Abstract

Simultaneous and non-destructive quantitative detection of intracellular metal ions holds great promise for improving the accuracy of diagnosis and biological research. Herein, novel multicolor DNAzymes-embedded framework nucleic acids (FNAzymes) were presented, which can easily enter cells and achieve simultaneous and quantitative detection of intracellular physiologically related Cu2+ and Zn2+. Two types of DNAzymes, specific to Cu2+ and Zn2+, were encoded in the framework nucleic acids (FNAs) via self-assembly. With the formation of a well-ordered FNAzyme nanostructure, the fluorophore and the quencher were close to each other; therefore, the fluorescence was quenched. In the presence of Cu2+ and Zn2+, the integrated FNAzymes would be specifically cleaved, resulting in the release of fluorophores in cells. Consequently, the fluorescence in living cells could be observed by a confocal microscope and semi-quantitatively analyzed by flow cytometry with low-nanomolar sensitivity for both metal ions. The FNAzymes have high uniformity and structural accuracy, which are beneficial for intracellular detection with excellent reproducibility. This proposed method offers new opportunities for non-destructive, semi-quantitative, multi-target detection in living cells. [ABSTRACT FROM AUTHOR]

Published
2023
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35. Dynamic susceptibility mapping of slow-moving landslides using PSInSAR

Author

Peter M. Atkinson, Huang Jiaxuan, and Xie Mowen

Subjects
010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, General Earth and Planetary Sciences, Landslide, 02 engineering and technology, Landslide susceptibility, 01 natural sciences, Geomorphology, Geology, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Dynamic susceptibility
Abstract

A landslide susceptibility map (LSM) is a valuable tool for landslide assessment and land use management. This research proposes a landslide susceptibility dynamic map (DLSM) to increase LSM utilit...

Published
2020

42. Enantiomeric profiling of a chiral benzothiazole necroptosis inhibitor

Author

Hao Zhang, Chunlin Zhuang, Huang Jiaxuan, Wannian Zhang, Xu Lijuan, Qu Zhuo, Jing Zhu, and Jian-Qiang Yu

Subjects
Models, Molecular, Programmed cell death, Stereochemistry, Necroptosis, Clinical Biochemistry, Pharmaceutical Science, 01 natural sciences, Biochemistry, chemistry.chemical_compound, RIPK1, Structure-Activity Relationship, Drug Discovery, Humans, Benzothiazoles, Molecular Biology, Dose-Response Relationship, Drug, Molecular Structure, 010405 organic chemistry, Kinase, Organic Chemistry, Rational design, Stereoisomerism, 0104 chemical sciences, 010404 medicinal & biomolecular chemistry, Benzothiazole, chemistry, Molecular Medicine, Phosphorylation, Enantiomer
Abstract

Necroptosis is a form of programmed cell death that contributes to the pathophysiology of multiple diseases. Development of small-molecule anti-necroptosis agents has great promising clinical therapeutic relevance. The benzothiazole compounds were discovered by our group from an in-house fluorine-containing compound library as potent necroptosis inhibitors. Herein, a chiral dimethylcyclopropyl benzothiazole necroptosis inhibitor was developed and the enantiomeric profiling resulted that the (S) form was generally more potent than the (R) counterpart in 2 ~ 4-fold toward cell necroptosis, receptor-interacting protein (RIP) kinases 1 and 3. The chiral compounds could significantly inhibit the expression of the phosphorylation of RIPK1, RIPK3 and MLKL in necroptotic cells. The molecular modelling studies predicted the binding modes of the enantiomers with RIP and explained their activity differences, guiding further rational design of the chiral necroptosis inhibitors.

Published
2021

44. Discovery of bardoxolone derivatives as novel orally active necroptosis inhibitors

Author

Hao Ma, Zhibin Wang, Wannian Zhang, Jian-Qiang Yu, Yuanyuan Wang, Lichao Zhang, Huang Jiaxuan, Zhengguang Yao, and Chunlin Zhuang

Subjects
Male, Programmed cell death, Cell Survival, Necroptosis, Administration, Oral, Pharmacology, 01 natural sciences, Rats, Sprague-Dawley, 03 medical and health sciences, RIPK1, Mice, Structure-Activity Relationship, Drug Discovery, Structure–activity relationship, Animals, Humans, Oleanolic Acid, Cells, Cultured, 030304 developmental biology, 0303 health sciences, biology, Dose-Response Relationship, Drug, Molecular Structure, 010405 organic chemistry, Chemistry, Drug discovery, Organic Chemistry, Infarction, Middle Cerebral Artery, General Medicine, Hsp90, Systemic Inflammatory Response Syndrome, 0104 chemical sciences, Rats, Mice, Inbred C57BL, biology.protein, Phosphorylation, Female, Bardoxolone
Abstract

Necroptosis is a form of programmed cell death that contributes to the pathophysiology of cerebral ischemia/reperfusion (I/R) injury. In this study, bardoxolone (CDDO, 7) was an inhibitor of necroptosis identified from an in-house natural product library. Further optimization led to identify a more potent analogue 20. Compound 20 could effectively protect against necroptosis in human and mouse cells. The antinecroptotic effect could also be synergized with other necroptosis inhibitors. It blocked necrosome formation by targeting Hsp90 to inhibit the phosphorylation of RIPK1 and RIPK3 in necroptotic cells. In vivo, this compound was orally active to alleviate TNF-induced systemic inflammatory response syndrome (SIRS) and cerebral I/R injury. Our results suggested that 20 could be a lead compound for discovering necroptosis inhibitors in I/R treatment.

Published
2020

47. Direct inhibition of Keap1-Nrf2 Protein-Protein interaction as a potential therapeutic strategy for Alzheimer's disease

Author

Hao Shang, Chengguo Xing, Huang Jiaxuan, Chen Yufei, Wannian Zhang, Jian-Qiang Yu, Chunlin Zhuang, Sun Yi, and He Ling

Subjects
Male, NF-E2-Related Factor 2, Amyloid beta, Hippocampus, Pharmacology, medicine.disease_cause, 01 natural sciences, Biochemistry, Neuroprotection, Superoxide dismutase, Mice, Western blot, Alzheimer Disease, Drug Discovery, medicine, Animals, Humans, Protein Interaction Domains and Motifs, Molecular Biology, Kelch-Like ECH-Associated Protein 1, biology, medicine.diagnostic_test, 010405 organic chemistry, Chemistry, Organic Chemistry, KEAP1, 0104 chemical sciences, Disease Models, Animal, 010404 medicinal & biomolecular chemistry, medicine.anatomical_structure, biology.protein, Neuron, Oxidative stress
Abstract

The Kelch-like ECH-associated protein 1 (Keap1)-nuclear factor erythroid 2-related factor 2 (Nrf2) pathway works as the key regulator against oxidative stress damage in many cells and organs. It has been a widely proposed therapeutic target for neurodegenerative diseases, including Alzheimer's disease (AD). This study aimed at determining the neuroprotective activity of 9 (NXPZ-2), a small-molecule compound that directly inhibits the Keap1-Nrf2 protein-protein interaction, in an amyloid beta 1-42 (Aβ1-42) oligomer intracerebroventricularly (i.c.v.) injected mouse model. Behavioral tests showed that NXPZ-2 treatment dose-relatedly ameliorated learning and memory dysfunction in Aβ1-42-treated mice. HE and Nissl staining showed that NXPZ-2 improved brain tissue pathological changes in AD mice by increasing neuron quantity and function. Western blot analysis of the hippocampus and cortex showed up-regulated Nrf2 in whole cell lysate, with increased nuclear translocation to increase Nrf2-targeted antioxidant enzymes (HO-1, NQO-1) and decreased p-Tau in NXPZ-2-treated mice. ELISA results showed that NXPZ-2 treatment increased serum Nrf2 and significantly decreased serum Aβ1-42 levels in AD mice. Furthermore, hippocampal and cortical superoxide dismutase (SOD) and glutathione (GSH) levels increased, while malondialdehyde (MDA) levels decreased. No obvious toxicity was observed in primary cultured mouse cortical neurons and organs with NXPZ-2 treatment. No ameliorative effect was observed of NXPZ-2 in Nrf2 knockout AD mice. Collectively, our findings demonstrated that NXPZ-2 could be a promising therapeutic agent against AD, and provided the first set of experimental evidence, in a mouse model, to support Keap1-Nrf2 interaction as a validated target for the Nrf2 reactivation in AD.

Published
2020

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