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Article ; Online: Exploring the Employment Quality Evaluation Model of Application-Oriented University Graduates by Deep Learning.

He, Yiran / Zhang, Wanhong / Xu, Weiming / Sui, Xinru

Computational intelligence and neuroscience

2022 Volume 2022, Page(s) 2823614

Abstract: In view of the employment difficulties of college graduates, this paper analyzes the overflow of graduates in a particular period caused by the expansion of enrollment in various colleges and universities and the social phenomenon of social positions in ... ...

Abstract	In view of the employment difficulties of college graduates, this paper analyzes the overflow of graduates in a particular period caused by the expansion of enrollment in various colleges and universities and the social phenomenon of social positions in short supply. First, the employment status of application-oriented college students and the deficiencies of employment guidance courses are summarized. Then, deep learning technology is combined with the relevant employment concept to construct an employment training model to guide college students in employment. Besides, a questionnaire on learning effect and employment quality is designed from four perspectives: learning motivation, concentration, teaching process, and final results. The information collected through the questionnaire demonstrates that the employment quality and learning effect of male and female students are not significantly affected by gender differences. In addition, the
MeSH term(s)	Career Choice ; Deep Learning ; Employment ; Female ; Humans ; Male ; Students/psychology ; Universities
Language	English
Publishing date	2022-04-23
Publishing country	United States
Document type	Journal Article ; Retracted Publication
ZDB-ID	2388208-6
ISSN	1687-5273 ; 1687-5273
ISSN (online)	1687-5273
ISSN	1687-5273
DOI	10.1155/2022/2823614
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Book ; Online: Online Inference for Mixture Model of Streaming Graph Signals with Non-White Excitation

He, Yiran / Wai, Hoi-To

2022

Abstract: This paper considers a joint multi-graph inference and clustering problem for simultaneous inference of node centrality and association of graph signals with their graphs. We study a mixture model of filtered low pass graph signals with possibly non- ... ...

Abstract	This paper considers a joint multi-graph inference and clustering problem for simultaneous inference of node centrality and association of graph signals with their graphs. We study a mixture model of filtered low pass graph signals with possibly non-white and low-rank excitation. While the mixture model is motivated from practical scenarios, it presents significant challenges to prior graph learning methods. As a remedy, we consider an inference problem focusing on the node centrality of graphs. We design an expectation-maximization (EM) algorithm with a unique low-rank plus sparse prior derived from low pass signal property. We propose a novel online EM algorithm for inference from streaming data. As an example, we extend the online algorithm to detect if the signals are generated from an abnormal graph. We show that the proposed algorithms converge to a stationary point of the maximum-a-posterior (MAP) problem. Numerical experiments support our analysis.
Keywords	Statistics - Machine Learning ; Electrical Engineering and Systems Science - Signal Processing
Subject code	006
Publishing date	2022-07-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Detecting Low Pass Graph Signals via Spectral Pattern

Zhang, Chenyue / He, Yiran / Wai, Hoi-To

Sampling Complexity and Applications

2023

Abstract: This paper proposes a blind detection problem for low pass graph signals. Without assuming knowledge of the exact graph topology, we aim to detect if a set of graph signal observations are generated from a low pass graph filter. Our problem is motivated ... ...

Abstract	This paper proposes a blind detection problem for low pass graph signals. Without assuming knowledge of the exact graph topology, we aim to detect if a set of graph signal observations are generated from a low pass graph filter. Our problem is motivated by the widely adopted assumption of low pass (a.k.a.~smooth) signals required by many existing works in graph signal processing (GSP), as well as the longstanding problem of network dynamics identification. Focusing on detecting low pass graph signals on modular graphs whose cutoff frequency coincides with the number of clusters in the graph, we propose to leverage the unique spectral pattern exhibited by such low pass graph signals. We analyze the sample complexity of these detectors considering the effects of graph filter's properties, random delays, and other parameters. We show novel applications of the blind detector on robustifying graph learning, identifying antagonistic ties in opinion dynamics, and detecting anomalies in power systems. Numerical experiments validate our findings. Comment: 15 pages, 11 figures
Keywords	Electrical Engineering and Systems Science - Signal Processing
Subject code	511
Publishing date	2023-06-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Application of exosome engineering modification in targeted delivery of therapeutic drugs.

Wang, Yuanyuan / Jiang, Min / Zheng, Xuewen / He, Yiran / Ma, Xiaochuan / Li, Jiong / Pu, Kefeng

Biochemical pharmacology

2023 Volume 215, Page(s) 115691

Abstract: Cancer is the leading cause of premature death in humans. Scientists have developed several therapeutic drugs for cancer treatment. However, drug delivery faces many problems. First, traditional drugs do not target tumors and are prone to causing ... ...

Abstract	Cancer is the leading cause of premature death in humans. Scientists have developed several therapeutic drugs for cancer treatment. However, drug delivery faces many problems. First, traditional drugs do not target tumors and are prone to causing significant toxic side effects. Second, suitable drug carriers are essential for improving drug delivery to tumors or circulating cancer cells. Exosomes are natural extracellular vesicles with low immunogenicity and prolonged blood circulation in vivo. These characteristics render exosomes ideal drug carriers. This review highlights the properties of exosomes and mechanisms of exosome biogenesis. It also summarizes the engineering modification methods for enhancing exosome yield, targeting, and drug-loading capacity.
MeSH term(s)	Humans ; Exosomes ; Drug Delivery Systems/methods ; Drug Carriers ; Extracellular Vesicles/pathology ; Neoplasms/drug therapy ; Neoplasms/pathology
Chemical Substances	Drug Carriers
Language	English
Publishing date	2023-07-21
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	208787-x
ISSN	1873-2968 ; 0006-2952
ISSN (online)	1873-2968
ISSN	0006-2952
DOI	10.1016/j.bcp.2023.115691
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Article ; Online: A multi-omics study reveals the therapeutic effect of Linderae Radix water extract on irritable bowel syndrome (IBS-D).

Ouyang, Linqi / Liu, Tao / He, Yang / He, Yiran / Xu, Wenfeng / Deng, Guoyan / Deng, Guiming / Xiao, Xiaohe

Journal of ethnopharmacology

2024 Volume 328, Page(s) 118050

Abstract: Ethnopharmacological relevance: Linderae Radix (Lindera aggregata (Sims) Kosterm) is a traditional Chinese medicine known for its capability to regulate qi and relieve pain, particularly in the context of gastrointestinal disorders.: Aim of the study!# ...

Abstract	Ethnopharmacological relevance: Linderae Radix (Lindera aggregata (Sims) Kosterm) is a traditional Chinese medicine known for its capability to regulate qi and relieve pain, particularly in the context of gastrointestinal disorders. Aim of the study: While our previous research has demonstrated the efficacy of the Linderae Radix water extract (LRWE) in the treatment of diarrhea-predominant irritable bowel syndrome (IBS-D), the precise mechanisms remain elusive. This study aims to provide a comprehensive understanding of the therapeutic effects of LRWE on IBS-D through multi-omics techniques. Materials and methods: 16 S rRNA gene sequencing combined with LC-MS metabolomics was employed to investigate the effect of LRWE on the gut microbiota and metabolites of IBS-D rats. Spearman correlation analysis was performed on the gut microbiota and metabolites. Results: LRWE administration significantly ameliorated IBS-D rats' symptoms, including diarrhea, visceral hypersensitivity, and low-grade intestinal inflammation. Gut microbiota analysis revealed that LRWE influenced the diversity of the gut microbiota in IBS-D rats by significantly reducing the relative abundance of Patescibacteria and Candidatus Saccharimonas, while increasing the relative abundance of Jeotgalicoccus. Serum metabolomic analysis identified 16 differential metabolites, associated with LRWE's positive effects on IBS-D symptoms, focusing on glyoxylate and dicarboxylic acid metabolism, and cysteine and methionine metabolism. Spearman analysis demonstrated a strong correlation between cecal microbiota composition and serum metabolite levels. Conclusions: This study elucidates that LRWE plays a crucial role in the comprehensive therapeutic approach to IBS-D by restoring the relative abundance of gut microbiota and addressing the disturbed metabolism of endogenous biomarkers. The identified bacteria and metabolites present potential therapeutic targets for IBS-D.
MeSH term(s)	Rats ; Animals ; Irritable Bowel Syndrome/drug therapy ; Multiomics ; Diarrhea/drug therapy ; Diarrhea/microbiology ; Metabolomics/methods ; Biomarkers
Chemical Substances	Biomarkers
Language	English
Publishing date	2024-03-21
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	134511-4
ISSN	1872-7573 ; 0378-8741
ISSN (online)	1872-7573
ISSN	0378-8741
DOI	10.1016/j.jep.2024.118050
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Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

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Book ; Online: Detecting Central Nodes from Low-rank Excited Graph Signals via Structured Factor Analysis

He, Yiran / Wai, Hoi-To

2021

Abstract: This paper treats a blind detection problem to identify the central nodes in a graph from filtered graph signals. Unlike prior works which impose strong restrictions on the data model, we only require the underlying graph filter to satisfy a low pass ... ...

Abstract	This paper treats a blind detection problem to identify the central nodes in a graph from filtered graph signals. Unlike prior works which impose strong restrictions on the data model, we only require the underlying graph filter to satisfy a low pass property with a generic low-rank excitation model. We treat two cases depending on the low pass graph filter's strength. When the graph filter is strong low pass, i.e., it has a frequency response that drops sharply at the high frequencies, we show that the principal component analysis (PCA) method detects central nodes with high accuracy. For general low pass graph filter, we show that the graph signals can be described by a structured factor model featuring the product between a low-rank plus sparse factor and an unstructured factor. We propose a two-stage decomposition algorithm to learn the structured factor model via a judicious combination of the non-negative matrix factorization and robust PCA algorithms. We analyze the identifiability conditions for the model which lead to accurate central nodes detection. Numerical experiments on synthetic and real data are provided to support our findings. We demonstrate significant performance gains over prior works.
Keywords	Electrical Engineering and Systems Science - Signal Processing
Subject code	518
Publishing date	2021-09-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Insights into a novel nitrogen removal process based on simultaneous anammox and denitrification (SAD) following nitritation with in-situ NOB elimination

Chen, Jing / Zeng, Jia / He, Yiran / Sun, Shiquan / Wu, Haipeng / Zhou, Yaoyu / Chen, Zhenguo / Wang, Jianhui / Chen, Hong

Journal of environmental sciences (China). 2023 Mar., v. 125

2023

Abstract: Simultaneous anammox and denitrification (SAD) is an efficient approach to treat wastewater having a low C/N ratio; however, few studies have investigated a combination of SAD and partial nitritation (PN). In this study, a lab-scale up-flow blanket ... ...

Abstract	Simultaneous anammox and denitrification (SAD) is an efficient approach to treat wastewater having a low C/N ratio; however, few studies have investigated a combination of SAD and partial nitritation (PN). In this study, a lab-scale up-flow blanket filter (UBF) and zeolite sequence batch reactor (ZSBR) were continuously operated to implement SAD and PN advantages, respectively. The UBF achieved a high total nitrogen (TN) removal efficiency of over 70% during the start-up stage (days 1–50), and reached a TN removal efficiency of 96% in the following 90 days (days 51–140) at COD/NH⁺₄-N ratio of 2.5. The absolute abundance of anammox bateria increased to the highest value of 1.58 × 10⁷ copies/µL DNA; Comamonadaceae was predominant in the UBF at the optimal ratio. Meanwhile, ZSBR was initiated on day 115 as fast nitritation process to satisfy the influent requirement for the UBF. The combined process was started on day 140 and then lasted for 30 d, during the combined process, between the two reactors, the UBF was the main contributor for TN (66.5% ± 4.5%) and COD (71.8% ± 4.9%) removal. These results demonstrated that strong SAD occurred in the UBF when following a ZSBR with in-situ NOB elimination. This research presents insights into a novel biological nitrogen removal process for low C/N ratio wastewater treatment.
Keywords	Comamonadaceae ; DNA ; anaerobic ammonium oxidation ; batch systems ; denitrification ; nitrogen ; total nitrogen ; wastewater ; wastewater treatment ; zeolites ; China
Language	English
Dates of publication	2023-03
Size	p. 160-170.
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	1092300-7
ISSN	1878-7320 ; 1001-0742
ISSN (online)	1878-7320
ISSN	1001-0742
DOI	10.1016/j.jes.2022.01.019
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Product Graph Learning from Multi-attribute Graph Signals with Inter-layer Coupling

Zhang, Chenyue / He, Yiran / Wai, Hoi-To

2022

Abstract: This paper considers learning a product graph from multi-attribute graph signals. Our work is motivated by the widespread presence of multilayer networks that feature interactions within and across graph layers. Focusing on a product graph setting with ... ...

Abstract	This paper considers learning a product graph from multi-attribute graph signals. Our work is motivated by the widespread presence of multilayer networks that feature interactions within and across graph layers. Focusing on a product graph setting with homogeneous layers, we propose a bivariate polynomial graph filter model. We then consider the topology inference problems thru adapting existing spectral methods. We propose two solutions for the required spectral estimation step: a simplified solution via unfolding the multi-attribute data into matrices, and an exact solution via nearest Kronecker product decomposition (NKD). Interestingly, we show that strong inter-layer coupling can degrade the performance of the unfolding solution while the NKD solution is robust to inter-layer coupling effects. Numerical experiments show efficacy of our methods. Comment: 6 pages, 4 figures, submitted to ICASSP 2023
Keywords	Electrical Engineering and Systems Science - Signal Processing
Subject code	004
Publishing date	2022-11-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: HROM: Learning High-Resolution Representation and Object-Aware Masks for Visual Object Tracking.

Zhang, Dawei / Zheng, Zhonglong / Wang, Tianxiang / He, Yiran

Sensors (Basel, Switzerland)

2020 Volume 20, Issue 17

Abstract: Siamese network-based trackers consider tracking as features cross-correlation between the target template and the search region. Therefore, feature representation plays an important role for constructing a high-performance tracker. However, all existing ...

Abstract	Siamese network-based trackers consider tracking as features cross-correlation between the target template and the search region. Therefore, feature representation plays an important role for constructing a high-performance tracker. However, all existing Siamese networks extract the deep but low-resolution features of the entire patch, which is not robust enough to estimate the target bounding box accurately. In this work, to address this issue, we propose a novel high-resolution Siamese network, which connects the high-to-low resolution convolution streams in parallel as well as repeatedly exchanges the information across resolutions to maintain high-resolution representations. The resulting representation is semantically richer and spatially more precise by a simple yet effective multi-scale feature fusion strategy. Moreover, we exploit attention mechanisms to learn object-aware masks for adaptive feature refinement, and use deformable convolution to handle complex geometric transformations. This makes the target more discriminative against distractors and background. Without bells and whistles, extensive experiments on popular tracking benchmarks containing OTB100, UAV123, VOT2018 and LaSOT demonstrate that the proposed tracker achieves state-of-the-art performance and runs in real time, confirming its efficiency and effectiveness.
Language	English
Publishing date	2020-08-26
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s20174807
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Article ; Online: Collateral Impact of COVID-19 Prevention Measures on Re-Emergence of Scarlet Fever and Pertussis in Mainland China and Hong Kong China.

He, Yiran / Ma, Chenjin / Guo, Xiangyu / Pan, Jinren / Xu, Wangli / Liu, Shelan

International journal of environmental research and public health

2022 Volume 19, Issue 16

Abstract: The incidence of scarlet fever and pertussis has increased significantly in China in recent years. During the COVID-19 pandemic, stringent non-pharmaceutical intervention measures were widely adopted to contain the spread of the virus, which may also ... ...

Abstract	The incidence of scarlet fever and pertussis has increased significantly in China in recent years. During the COVID-19 pandemic, stringent non-pharmaceutical intervention measures were widely adopted to contain the spread of the virus, which may also have essential collateral impacts on other infectious diseases, such as scarlet fever and pertussis. We compared the incidence data of scarlet fever and pertussis in Mainland China and Hong Kong from 2004 to 2021 before and after the COVID-19 pandemic. The results show that the incidence of both diseases decreased significantly in 2020-2021 compared to the after-re-emergence stage in these two locations. Specifically, in 2020, scarlet fever decreased by 73.13% and pertussis by 76.63% in Mainland China, and 83.70% and 76.10%, respectively, in Hong Kong. In the absence of COVID-19, the predicted incidence of both diseases was much higher than the actual incidence in Mainland China and Hong Kong in 2020-2021. This study demonstrates that non-pharmaceutical measures implemented during the COVID-19 pandemic can partially reduce scarlet fever and pertussis re-emergence in Mainland China and Hong Kong.
MeSH term(s)	COVID-19/epidemiology ; COVID-19/prevention & control ; China/epidemiology ; Hong Kong/epidemiology ; Humans ; Pandemics/prevention & control ; Scarlet Fever/epidemiology ; Scarlet Fever/prevention & control ; Whooping Cough/epidemiology ; Whooping Cough/prevention & control
Language	English
Publishing date	2022-08-11
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph19169909
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