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  1. Article: Editorial: Targeting metabolism of cancer cells and host to overcome drug resistance: Preclinical and clinical studies.

    Wang, Lishun / Xu, Hanchen / Wu, Yadi

    Frontiers in oncology

    2023  Volume 13, Page(s) 1154661

    Language English
    Publishing date 2023-02-10
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2649216-7
    ISSN 2234-943X
    ISSN 2234-943X
    DOI 10.3389/fonc.2023.1154661
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: The crosstalk of intratumor bacteria and the tumor.

    Huang, Jiating / Mao, Yuqin / Wang, Lishun

    Frontiers in cellular and infection microbiology

    2024  Volume 13, Page(s) 1273254

    Abstract: The in-depth studies reveal the interaction between the host and commensal microbiomes. Symbiotic bacteria influence in tumor initiation, progression, and response to treatment. Recently, intratumor bacteria have been a burgeoning research field. The ... ...

    Abstract The in-depth studies reveal the interaction between the host and commensal microbiomes. Symbiotic bacteria influence in tumor initiation, progression, and response to treatment. Recently, intratumor bacteria have been a burgeoning research field. The tumor microenvironment is under vascular hyperplasia, aerobic glycolysis, hypoxia, and immunosuppression. It might be attractive for bacterial growth and proliferation. As a component of the tumor microenvironment, intratumor bacteria influence tumor growth and metastasis, as well as the efficacy of anti-tumor therapies. Therefore, understanding the intricate interplay of intratumoral bacteria and the host might contribute to better approaches to treat tumors. In this review, we summarize current evidence about roles of intratumor bacteria in tumor initiation and anti-tumor therapy, and what is remained to be solved in this field.
    MeSH term(s) Humans ; Neoplasms ; Immunosuppression Therapy ; Bacteria ; Tumor Microenvironment
    Language English
    Publishing date 2024-01-03
    Publishing country Switzerland
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 2619676-1
    ISSN 2235-2988 ; 2235-2988
    ISSN (online) 2235-2988
    ISSN 2235-2988
    DOI 10.3389/fcimb.2023.1273254
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Full-resolution and full-dynamic-range coded aperture compressive temporal imaging.

    Wang, Ping / Wang, Lishun / Qiao, Mu / Yuan, Xin

    Optics letters

    2023  Volume 48, Issue 18, Page(s) 4813–4816

    Abstract: Coded aperture compressive temporal imaging (CACTI) aims to capture a sequence of video frames in a single shot, using an off-the-shelf 2D sensor. This approach effectively increases the frame rate of the sensor while reducing data throughput ... ...

    Abstract Coded aperture compressive temporal imaging (CACTI) aims to capture a sequence of video frames in a single shot, using an off-the-shelf 2D sensor. This approach effectively increases the frame rate of the sensor while reducing data throughput requirements. However, previous CACTI systems have encountered challenges such as limited spatial resolution and a narrow dynamic range, primarily resulting from suboptimal optical modulation and sampling schemes. In this Letter, we present a highly efficient CACTI system that addresses these challenges by employing precise one-to-one pixel mapping between the sensor and modulator, while using structural gray scale masks instead of binary masks. Moreover, we develop a hybrid convolutional-Transformer deep network for accurate reconstruction of the captured frames. Both simulated and real data experiments demonstrate the superiority of our proposed system over previous approaches, exhibiting significant improvements in terms of spatial resolution and dynamic range.
    Language English
    Publishing date 2023-09-14
    Publishing country United States
    Document type Journal Article
    ISSN 1539-4794
    ISSN (online) 1539-4794
    DOI 10.1364/OL.499735
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Spatial-Temporal Transformer for Video Snapshot Compressive Imaging.

    Wang, Lishun / Cao, Miao / Zhong, Yong / Yuan, Xin

    IEEE transactions on pattern analysis and machine intelligence

    2023  Volume 45, Issue 7, Page(s) 9072–9089

    Abstract: Video snapshot compressive imaging (SCI) captures multiple sequential video frames by a single measurement using the idea of computational imaging. The underlying principle is to modulate high-speed frames through different masks and these modulated ... ...

    Abstract Video snapshot compressive imaging (SCI) captures multiple sequential video frames by a single measurement using the idea of computational imaging. The underlying principle is to modulate high-speed frames through different masks and these modulated frames are summed to a single measurement captured by a low-speed 2D sensor (dubbed optical encoder); following this, algorithms are employed to reconstruct the desired high-speed frames (dubbed software decoder) if needed. In this article, we consider the reconstruction algorithm in video SCI, i.e., recovering a series of video frames from a compressed measurement. Specifically, we propose a Spatial-Temporal transFormer (STFormer) to exploit the correlation in both spatial and temporal domains. STFormer network is composed of a token generation block, a video reconstruction block, and these two blocks are connected by a series of STFormer blocks. Each STFormer block consists of a spatial self-attention branch, a temporal self-attention branch and the outputs of these two branches are integrated by a fusion network. Extensive results on both simulated and real data demonstrate the state-of-the-art performance of STFormer. The code and models are publicly available at https://github.com/ucaswangls/STFormer.
    Language English
    Publishing date 2023-06-05
    Publishing country United States
    Document type Journal Article
    ISSN 1939-3539
    ISSN (online) 1939-3539
    DOI 10.1109/TPAMI.2022.3225382
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Structure and Crystallization of High-Calcium, CMAS Glass Ceramics Synthesized with a High Content of Slag.

    Chen, Lishun / Long, Yuting / Zhou, Mingkai / Wang, Huaide

    Materials (Basel, Switzerland)

    2022  Volume 15, Issue 2

    Abstract: In this work, more than 70 wt % of ferromanganese slag (containing 40 wt % CaO) was used to synthesize high-calcium, CaO-MgO- ... ...

    Abstract In this work, more than 70 wt % of ferromanganese slag (containing 40 wt % CaO) was used to synthesize high-calcium, CaO-MgO-Al
    Language English
    Publishing date 2022-01-16
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2487261-1
    ISSN 1996-1944
    ISSN 1996-1944
    DOI 10.3390/ma15020657
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: GLFormer

    Yilong He / Yong Zhong / Lishun Wang / Jiachen Dang

    Applied Sciences, Vol 12, Iss 8557, p

    Global and Local Context Aggregation Network for Temporal Action Detection

    2022  Volume 8557

    Abstract: As the core component of video analysis, Temporal Action Localization (TAL) has experienced remarkable success. However, some issues are not well addressed. First, most of the existing methods process the local context individually, without explicitly ... ...

    Abstract As the core component of video analysis, Temporal Action Localization (TAL) has experienced remarkable success. However, some issues are not well addressed. First, most of the existing methods process the local context individually, without explicitly exploiting the relations between features in an action instance as a whole. Second, the duration of different actions varies widely; thus, it is difficult to choose the proper temporal receptive field. To address these issues, this paper proposes a novel network, GLFormer, which can aggregate short, medium, and long temporal contexts. Our method consists of three independent branches with different ranges of attention, and these features are then concatenated along the temporal dimension to obtain richer features. One is multi-scale local convolution (MLC), which consists of multiple 1D convolutions with varying kernel sizes to capture the multi-scale context information. Another is window self-attention (WSA), which tries to explore the relationship between features within the window range. The last is global attention (GA), which is used to establish long-range dependencies across the full sequence. Moreover, we design a feature pyramid structure to be compatible with action instances of various durations. GLFormer achieves state-of-the-art performance on two challenging video benchmarks, THUMOS14 and ActivityNet 1.3. Our performance is 67.2% and 54.5% AP@0.5 on the datasets THUMOS14 and ActivityNet 1.3, respectively.
    Keywords temporal action detection ; computer vision ; deep learning ; artificial intelligence ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 006
    Language English
    Publishing date 2022-08-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Non-Local Temporal Difference Network for Temporal Action Detection.

    He, Yilong / Han, Xiao / Zhong, Yong / Wang, Lishun

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 21

    Abstract: As an important part of video understanding, temporal action detection (TAD) has wide application scenarios. It aims to simultaneously predict the boundary position and class label of every action instance in an untrimmed video. Most of the existing ... ...

    Abstract As an important part of video understanding, temporal action detection (TAD) has wide application scenarios. It aims to simultaneously predict the boundary position and class label of every action instance in an untrimmed video. Most of the existing temporal action detection methods adopt a stacked convolutional block strategy to model long temporal structures. However, most of the information between adjacent frames is redundant, and distant information is weakened after multiple convolution operations. In addition, the durations of action instances vary widely, making it difficult for single-scale modeling to fit complex video structures. To address this issue, we propose a non-local temporal difference network (NTD), including a chunk convolution (CC) module, a multiple temporal coordination (MTC) module, and a temporal difference (TD) module. The TD module adaptively enhances the motion information and boundary features with temporal attention weights. The CC module evenly divides the input sequence into N chunks, using multiple independent convolution blocks to simultaneously extract features from neighboring chunks. Therefore, it realizes the information delivered from distant frames while avoiding trapping into the local convolution. The MTC module designs a cascade residual architecture, which realizes the multiscale temporal feature aggregation without introducing additional parameters. The NTD achieves a state-of-the-art performance on two large-scale datasets, 36.2% mAP@avg and 71.6% mAP@0.5 on ActivityNet-v1.3 and THUMOS-14, respectively.
    MeSH term(s) Neural Networks, Computer ; Memory
    Language English
    Publishing date 2022-11-01
    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/s22218396
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: The association of serum serine levels with the risk of incident cancer: results from a nested case-control study.

    Liu, Tong / Liu, Chenan / Song, Mengmeng / Wei, Yaping / Song, Yun / Chen, Ping / Liu, Lishun / Wang, Binyan / Shi, Hanping

    Food & function

    2023  Volume 14, Issue 17, Page(s) 7969–7976

    Abstract: ... ...

    Abstract Background
    MeSH term(s) Male ; Adult ; Humans ; Risk Factors ; Chromatography, Liquid ; Case-Control Studies ; Tandem Mass Spectrometry ; Lung Neoplasms/epidemiology ; Lung Neoplasms/etiology ; Hypertension
    Language English
    Publishing date 2023-08-29
    Publishing country England
    Document type Journal Article
    ZDB-ID 2612033-1
    ISSN 2042-650X ; 2042-6496
    ISSN (online) 2042-650X
    ISSN 2042-6496
    DOI 10.1039/d3fo00808h
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Prevalence and related factors of sleep quality among Chinese undergraduates in Jiangsu Province: multiple models' analysis.

    Hu, Bin / Shen, Wen / Wang, Yun / Wu, Qi / Li, Jiali / Xu, Xiaozhou / Han, Yaohui / Xiao, Lishun / Yin, Dehui

    Frontiers in psychology

    2024  Volume 15, Page(s) 1343186

    Abstract: Background and aims: In China, a significant number of undergraduates are experiencing poor sleep quality. This study was designed to investigate the prevalence of poor sleep quality and identify associated factors among undergraduates in Jiangsu ... ...

    Abstract Background and aims: In China, a significant number of undergraduates are experiencing poor sleep quality. This study was designed to investigate the prevalence of poor sleep quality and identify associated factors among undergraduates in Jiangsu Province, China.
    Methods: A total of 8,457 participants were collected in 2022 using whole-group convenience sampling. The factors studied included basic demographics, family and social support, personal lifestyles, physical and mental health, mobile phone addiction index (MPAI), and the Connor-Davidson resilience scale (CD-RISC). The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. Four models, including weighted multiple linear regression, binary logistic regression, weighted linear mixed model, and logistic regression with random effects, were applied to identify associated factors for sleep quality.
    Results: Of the 8,457 participants analyzed, 26.64% (2,253) were classified into the poor sleep quality group with a PSQI score >7. No significant relationship was found between sleep quality and gender, native place, economic level of family, physical exercise, dormitory light, dormitory hygiene, and amativeness matter. Risk factors for sleep quality identified by the four models included lower CD-RISC, higher MPAI, fourth grade or above, smoking, drinking, greater academic pressure, greater employment pressure, roommate sleeping late, noisy dormitory, poorer physical health status, poorer mental health status, and psychological counseling.
    Conclusions: These findings provide valuable insights for university administrators, enabling them to better understand the risk factors associated with poor sleep quality in undergraduates. By identifying these factors, administrators can provide targeted intervention measures and counseling programs to improve students' sleep quality.
    Language English
    Publishing date 2024-04-10
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2563826-9
    ISSN 1664-1078
    ISSN 1664-1078
    DOI 10.3389/fpsyg.2024.1343186
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Book ; Online: EfficientSCI

    Wang, Lishun / Cao, Miao / Yuan, Xin

    Densely Connected Network with Space-time Factorization for Large-scale Video Snapshot Compressive Imaging

    2023  

    Abstract: Video snapshot compressive imaging (SCI) uses a two-dimensional detector to capture consecutive video frames during a single exposure time. Following this, an efficient reconstruction algorithm needs to be designed to reconstruct the desired video frames. ...

    Abstract Video snapshot compressive imaging (SCI) uses a two-dimensional detector to capture consecutive video frames during a single exposure time. Following this, an efficient reconstruction algorithm needs to be designed to reconstruct the desired video frames. Although recent deep learning-based state-of-the-art (SOTA) reconstruction algorithms have achieved good results in most tasks, they still face the following challenges due to excessive model complexity and GPU memory limitations: 1) these models need high computational cost, and 2) they are usually unable to reconstruct large-scale video frames at high compression ratios. To address these issues, we develop an efficient network for video SCI by using dense connections and space-time factorization mechanism within a single residual block, dubbed EfficientSCI. The EfficientSCI network can well establish spatial-temporal correlation by using convolution in the spatial domain and Transformer in the temporal domain, respectively. We are the first time to show that an UHD color video with high compression ratio can be reconstructed from a snapshot 2D measurement using a single end-to-end deep learning model with PSNR above 32 dB. Extensive results on both simulation and real data show that our method significantly outperforms all previous SOTA algorithms with better real-time performance. The code is at https://github.com/ucaswangls/EfficientSCI.git.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Computation and Language ; Electrical Engineering and Systems Science - Image and Video Processing
    Subject code 004 ; 006
    Publishing date 2023-05-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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