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Book ; Online: Guessing What, Noise or Codeword?

Ma, Xiao

2024

Abstract: In this paper, we distinguish two guessing algorithms for decoding binary linear codes. One is the guessing noise decoding (GND) algorithm, and the other is the guessing codeword decoding (GCD) algorithm. We prove that the GCD is a maximum likelihood (ML) ...

Abstract	In this paper, we distinguish two guessing algorithms for decoding binary linear codes. One is the guessing noise decoding (GND) algorithm, and the other is the guessing codeword decoding (GCD) algorithm. We prove that the GCD is a maximum likelihood (ML) decoding algorithm and that the GCD is more efficient than GND for most practical applications. We also introduce several variants of ordered statistic decoding (OSD) to trade off the complexity of the Gaussian elimination (GE) and that of the guessing, which may find applications in decoding short block codes in the high signal-to-noise ratio (SNR) region. Comment: 6 pages, 4 figures
Keywords	Computer Science - Information Theory
Publishing date	2024-01-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An Occasional Itching Yellow-Red Nodule on the Left Vulva.

Ma, Xiao / Ma, Dong-Lai

Indian journal of dermatology

2024 Volume 69, Issue 1, Page(s) 107

Language	English
Publishing date	2024-02-27
Publishing country	India
Document type	Journal Article
ZDB-ID	416069-1
ISSN	1998-3611 ; 0019-5154
ISSN (online)	1998-3611
ISSN	0019-5154
DOI	10.4103/ijd.ijd_207_23
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Article ; Online: Finger vein recognition method based on ant colony optimization and improved EfficientNetV2.

Ma, Xiao / Luo, Xuemei

Mathematical biosciences and engineering : MBE

2023 Volume 20, Issue 6, Page(s) 11081–11100

Abstract: Deep learning is an important technology in the field of image recognition. Finger vein recognition based on deep learning is one of the research hotspots in the field of image recognition and has attracted a lot of attention. Among them, CNN is the most ...

Abstract	Deep learning is an important technology in the field of image recognition. Finger vein recognition based on deep learning is one of the research hotspots in the field of image recognition and has attracted a lot of attention. Among them, CNN is the most core part, which can be trained to get a model that can extract finger vein image features. In the existing research, some studies have used methods such as combination of multiple CNN models and joint loss function to improve the accuracy and robustness of finger vein recognition. However, in practical applications, finger vein recognition still faces some challenges, such as how to solve the interference and noise in finger vein images, how to improve the robustness of the model, and how to solve the cross-domain problem. In this paper, we propose a finger vein recognition method based on ant colony optimization and improved EfficientNetV2, using ACO to participate in ROI extraction, fusing dual attention fusion network (DANet) with EfficientNetV2, and conducting experiments on two publicly available databases, and the results show that the recognition rate using the proposed method on the FV-USM dataset reaches The results show that the proposed method achieves a recognition rate of 98.96% on the FV-USM dataset, which is better than other algorithmic models, proving that the method has good recognition rate and application prospects for finger vein recognition.
MeSH term(s)	Algorithms ; Fingers/blood supply ; Veins/diagnostic imaging ; Databases, Factual
Language	English
Publishing date	2023-06-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	2265126-3
ISSN	1551-0018 ; 1551-0018
ISSN (online)	1551-0018
ISSN	1551-0018
DOI	10.3934/mbe.2023490
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Article ; Online: Cooperative activated hopping dynamics in binary glass-forming liquids: effects of the size ratio, composition, and interparticle interactions.

Ma, Xiao-Juan / Zhang, Rui

Soft matter

2023 Volume 19, Issue 25, Page(s) 4746–4771

Abstract: Slow dynamics in supercooled and glassy liquids is an important research topic in soft matter physics. Compared to the traditionally focused one-component systems, glassy dynamics in mixture systems adds in a rich set of new complexities, which are ... ...

Abstract	Slow dynamics in supercooled and glassy liquids is an important research topic in soft matter physics. Compared to the traditionally focused one-component systems, glassy dynamics in mixture systems adds in a rich set of new complexities, which are fundamentally interesting and also relevant for many technological applications. In this paper, we apply the recently developed self-consistent cooperative hopping theory (SCCHT) to systematically investigate the effects of the size ratio, composition and interparticle interactions on the cooperative activated hopping dynamics of matrix (in larger size) and penetrant (in smaller size) particles in varied binary sphere mixture model systems, with a specific focus on ultrahigh mixture packing fractions that mimic the deeply supercooled glass transition conditions for molecular/polymeric mixture materials. Analysis shows that in these high activation barrier cases, the long-range elastic distortion associated with a matrix particle hopping over its cage confinement always generates an elastic barrier of a nonnegligible magnitude, although the ratio between the elastic barrier and local barrier contribution is sensitively dependent on all three mixture-specific system factors considered in this work. SCCHT predicts two general scenarios of penetrant-matrix cooperative activated hopping dynamics: matrix/penetrant co-hopping (regime 1) or the penetrant mean barrier hopping time shorter than that of the matrix (regime 2). Increasing the penetrant-to-matrix size ratio or the penetrant-matrix cross-attraction strength is found to universally enlarge the composition window of regime 1. Diverse dynamical properties characterising different aspects of the cooperative activated hopping process, including the penetrant and matrix transient localization lengths, penetrant and matrix hopping jump distances, different types of local and elastic activated barriers, and matrix long-time diffusivity, relaxation time and dynamic fragility are quantitatively studied against a wide range of variations over the three system factors. Of particular interest is the universal "anti-plasticization" phenomenon achievable for sufficiently strong cross-attractive interactions. The prospects this work opens for the exploration of a wide variety of polymer-based mixture materials are briefly discussed at the end.
Language	English
Publishing date	2023-06-28
Publishing country	England
Document type	Journal Article
ZDB-ID	2191476-X
ISSN	1744-6848 ; 1744-683X
ISSN (online)	1744-6848
ISSN	1744-683X
DOI	10.1039/d3sm00312d
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Article ; Online: An 81-Year-Old Woman With Erosions and Pustules Affecting Her Scalp.

Ma, Xiao / Ma, Dong-Lai

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America

2023 Volume 77, Issue 3, Page(s) 460–463

MeSH term(s)	Humans ; Female ; Aged, 80 and over ; Scalp ; Exanthema ; Blister ; Communicable Diseases
Language	English
Publishing date	2023-08-14
Publishing country	United States
Document type	Case Reports ; Journal Article
ZDB-ID	1099781-7
ISSN	1537-6591 ; 1058-4838
ISSN (online)	1537-6591
ISSN	1058-4838
DOI	10.1093/cid/ciac906
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Article ; Online: A Comprehensive Review on Biotransformation, Interaction, and Health of Gut Microbiota and Bioactive Components.

Zhang, Lin / Ma, Xiao-Gen

Combinatorial chemistry & high throughput screening

2023

Abstract: Background: The relationship between gut microbiota and bioactive components has become the research focus in the world. We attempted to clarify the relationship between biotransformation and metabolites of gut microbiota and bioactive components, and ... ...

Abstract	Background: The relationship between gut microbiota and bioactive components has become the research focus in the world. We attempted to clarify the relationship between biotransformation and metabolites of gut microbiota and bioactive components, and explore the metabolic pathway and mechanism of bioactive ingredients in vivo, which will provide an important theoretical basis for the clinical research of bioactive ingredients and rationality of drugs, and also provide an important reference for the development of new drugs with high bioavailability. Methods: The related references of this review on microbiota and bioactive components were collected from both online and offline databases, such as ScienceDirect, PubMed, Elsevier, Willy, SciFinder, Google Scholar, Web of Science, Baidu Scholar, SciHub, Scopus, and CNKI. Results: This review summarized the biotransformation of bioactive components under the action of gut microbiota, including flavonoids, terpenoids, phenylpropanoids, alkaloids, steroids, and other compounds. The interaction of bioactive components and gut microbiota is a key link for drug efficacy. Relevant research is crucial to clarify bioactive components and their mechanisms, which involve the complex interaction among bioactive components, gut microbiota, and intestinal epithelial cells. This review also summarized the individualized, precise, and targeted intervention of gut microbiota in the field of intestinal microorganisms from the aspects of dietary fiber, microecological agents, fecal microbiota transplantation, and postbiotics. It will provide an important reference for intestinal microecology in the field of nutrition and health for people. Conclusion: To sum up, the importance of human gut microbiota in the research of bioactive components metabolism and transformation has attracted the attention of scholars all over the world. It is believed that with the deepening of research, human gut microbiota will be more widely used in the pharmacodynamic basis, drug toxicity relationship, new drug discovery, drug absorption mechanism, and drug transport mechanism in the future.
Language	English
Publishing date	2023-10-24
Publishing country	United Arab Emirates
Document type	Journal Article
ZDB-ID	2064785-2
ISSN	1875-5402 ; 1386-2073
ISSN (online)	1875-5402
ISSN	1386-2073
DOI	10.2174/0113862073257733231011072004
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Article ; Online: The roles of gut microbiome and metabolites associated with skin photoaging in mice by intestinal flora sequencing and metabolomics.

Qu, Liping / Ma, Xiao / Wang, Feifei

Life sciences

2024 Volume 341, Page(s) 122487

Abstract: Photoaging of skin, a chronic disease, can produce the appearance changes and cancer lesions of skin. Therefore, it is of great significance to investigate the mechanisms and explore effective methods to treat the disorder. Gut microbiota and intestinal ... ...

Abstract	Photoaging of skin, a chronic disease, can produce the appearance changes and cancer lesions of skin. Therefore, it is of great significance to investigate the mechanisms and explore effective methods to treat the disorder. Gut microbiota and intestinal metabolisms have critical roles in a variety of diseases. However, their roles on photoaging of skin were not well tested. In the present work, the results showed that compared with control group, the levels of MDA, SOD and CAT associated with oxidative stress, the levels of COL I, CER, and HA associated with skin function, and the mRNA levels of IL-1β, IL-6, TNF-α associated with inflammation after long-term exposure to ultraviolet radiation in mice were significantly changed. Skin pathological tissue was also seriously damaged. The protein levels of AQP3 and FLG were significantly decreased. Ultraviolet exposure also promoted skin photoaging by activating TNFR1/TRAF2-mediated MAPK pathway, in which the protein levels of P38/P-P38, c-FOS/P-c-FOS, MMP1, TNFR1 and TRAF2 were significantly increased in model mice compared with control group. In fecal microbiota transplantation (FMT) experiment, we found that the intestinal microbiome of control mice alleviated skin photoaging via adjusting the protein levels of P38/P-P38, c-FOS/P-c-FOS, MMP1, TNFR1 and TRAF2. 16S rRNA sequencing found that 1639 intestinal bacteria were found, in which 15 bacteria including norank_f_Ruminococcaceae, Lachnospirac -eae_NK4A136_group, Lachnoclostridium, etc., were significantly different at the genus level. Untargeted GC-TOF/MS and UHPLC-MS/MS metabolomics showed 72 and 188 metabolites including taurine, ornithine, L-arginine, L-histidine, sucrose with significant differences compared with control group. Then, amino acid targeting assay showed 10 amino acids including L-ornithine, L-arginine and L-citrulline with higher levels in control group compared with model group. In addition, we also found that the variation of Lachnoclostridium abundance may regulate L-arginine metabolism to affect skin photoaging. Some intestinal bacteria and metabolites including amino acids may be closely related to skin photoaging, which should provide new methods to treat skin photoaging in the future.
MeSH term(s)	Animals ; Mice ; Skin Aging ; Gastrointestinal Microbiome ; Matrix Metalloproteinase 1 ; RNA, Ribosomal, 16S/genetics ; Receptors, Tumor Necrosis Factor, Type I ; TNF Receptor-Associated Factor 2 ; Tandem Mass Spectrometry ; Ultraviolet Rays/adverse effects ; Metabolomics ; Arginine ; Citrulline ; Ornithine
Chemical Substances	Matrix Metalloproteinase 1 (EC 3.4.24.7) ; RNA, Ribosomal, 16S ; Receptors, Tumor Necrosis Factor, Type I ; TNF Receptor-Associated Factor 2 ; Arginine (94ZLA3W45F) ; Citrulline (29VT07BGDA) ; Ornithine (E524N2IXA3)
Language	English
Publishing date	2024-02-03
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	3378-9
ISSN	1879-0631 ; 0024-3205
ISSN (online)	1879-0631
ISSN	0024-3205
DOI	10.1016/j.lfs.2024.122487
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Article ; Online: Hierarchical Reinforcement Learning for UAV-PE Game With Alternative Delay Update Method.

Ma, Xiao / Yuan, Yuan / Guo, Lei

IEEE transactions on neural networks and learning systems

2024 Volume PP

Abstract: This article proposes a novel hierarchical reinforcement learning (HRL) algorithm for unmanned aerial vehicle pursuit-evasion (UAV-PE) game systems with an alternative delay update (ADU) method. In the proposed algorithm, the approximate solutions of the ...

Abstract	This article proposes a novel hierarchical reinforcement learning (HRL) algorithm for unmanned aerial vehicle pursuit-evasion (UAV-PE) game systems with an alternative delay update (ADU) method. In the proposed algorithm, the approximate solutions of the UAV-PE game problem are derived from a hierarchical learning process, which relies on a zero-sum game process of kinematics and a corresponding optimal process of dynamics. In this case, deep neural networks (NNs) are used to approximate the policy and value functions of UAV-PE game systems in kinematics and dynamics level. Furthermore, the ADU method is adopted to improve the training efficiency of deep NN by fixing one player of the UAV-PE game systems to form a stable environment. The goal of this article is to develop an HRL algorithm with an ADU method for obtaining approximate Nash equilibrium (NE) solutions of the considered UAV-PE game systems which are subjected to the coupling of kinematics and dynamics. Subsequently, sufficient conditions are provided for analyzing the convergence and optimality of the proposed HRL algorithm. Moreover, the inequalities of overload are obtained to guarantee that the state of dynamics tracks with the control input of kinematics in UAV-PE game systems. Finally, simulation examples are provided to demonstrate the feasibility and usefulness of the proposed HRL algorithm and ADU method.
Language	English
Publishing date	2024-02-21
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2024.3362969
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Article ; Online: Alleviation of Rheumatoid Arthritis by Inducing IDO Expression with

Ma, Xiao / Liu, Dongming / Yu, Wenhao / Han, Caixia

Journal of immunology research

2024 Volume 2024, Page(s) 8816919

Abstract: Rheumatoid arthritis (RA) represents the autoimmune disorder that shows aggressive arthritis as the main symptom. It is difficult to treat and can lead to joint deformation and function loss. At present, ...

Abstract	Rheumatoid arthritis (RA) represents the autoimmune disorder that shows aggressive arthritis as the main symptom. It is difficult to treat and can lead to joint deformation and function loss. At present,
MeSH term(s)	Mice ; Animals ; Cattle ; Trichinella spiralis ; Arthritis, Rheumatoid ; Autoimmune Diseases ; Apoptosis ; Cell Proliferation ; Disease Models, Animal ; Mice, Inbred BALB C
Language	English
Publishing date	2024-01-17
Publishing country	Egypt
Document type	Journal Article
ZDB-ID	2817541-4
ISSN	2314-7156 ; 2314-7156
ISSN (online)	2314-7156
ISSN	2314-7156
DOI	10.1155/2024/8816919
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Article ; Online: Wavelength selection method for near-infrared spectroscopy based on Max-Relevance Min-Redundancy.

Ma, Xiao-Hui / Chen, Zheng-Guang / Liu, Jin-Ming

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

2024 Volume 310, Page(s) 123933

Abstract: Near-infrared spectroscopy (NIRS) is a rapid, nondestructive analytical technique utilized in various fields. However, the NIR data, which consists of hundreds of dimensions, may exhibit considerable duplication in the spectrum information. This ... ...

Abstract	Near-infrared spectroscopy (NIRS) is a rapid, nondestructive analytical technique utilized in various fields. However, the NIR data, which consists of hundreds of dimensions, may exhibit considerable duplication in the spectrum information. This redundancy might impair modeling effectiveness. As a result, feature selection on the spectral data becomes critical. The Max-Relevance Min-Redundancy (mRMR) method stands out among the different feature selection techniques for dimensional reduction. The approach depends on mutual information (MI) between random variables as the basis for feature selection and is unaffected by modeling methods. However, it is necessary to clarify the benefits of the maximum correlation minimal redundancy algorithm in the context of near-infrared spectral feature selection, as well as its adaptability to various modeling methods. This research focuses on the NIR spectral dataset of maize germination rate, and the mRMR method is utilized to select spectral features. Based on the preceding foundation, we create models for Support Vector Regression, Gaussian Process Regression, Random Forest, and Neural Networks. The experimental findings demonstrate that, among the feature selection methods employed in this paper, the Max-Relevance Min-Redundancy algorithm outperforms others regarding the corn germination rate dataset.
Language	English
Publishing date	2024-01-22
Publishing country	England
Document type	Journal Article
ZDB-ID	210413-1
ISSN	1873-3557 ; 0370-8322 ; 0584-8539 ; 1386-1425
ISSN (online)	1873-3557
ISSN	0370-8322 ; 0584-8539 ; 1386-1425
DOI	10.1016/j.saa.2024.123933
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