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Radiation-Based Multi-Modal Therapy Combining with Immunotherapy to Develop a Vaccine-Like Effective Treatment for Triple-Negative Breast Cancer

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Breast cancer (Dove Medical Press) 2025年 17卷 483-496页

作者： Mengyan Dai Zuhong Tian Fanyuan Xu Bang Yao Hongxia Liang Dongyan Li Jiangang Wang Junyan Rong Tianshuai Liu Haili Tang Hongbing Lu Wenli Zhang The College of Life Sciences Northwest University Xi'an City 710069 People's Republic of China. School of Biomedical Engineering Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception The Fourth Military Medical University Xi'an Shaanxi 710032 People's Republic of China. State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and Xijing Hospital of Digestive Diseases Fourth Military Medical University Xi'an 710032 People's Republic of China. School of Materials Science and Engineering Shaanxi University of Science and Technology Xi'an Shaanxi 710021 People's Republic of China. Department of General Surgery The Second Affiliated Hospital of the Fourth Military Medical University Xi'an 710038 People's Republic of China.

Background:Triple-negative breast cancer (TNBC) is an aggressive malignancy with high metastasis and recurrence rates. Current treatments like chemotherapy and immunotherapy face challenges due to chemotherapy side effects, limited immunotherapy applicability, and TNBC's immunosuppressive microenvironment. Purpose:To achieve a more effective treatment for TNBC, a novel therapeutic strategy has been developed, which uses X-ray excited photodynamic therapy (X-PDT) to activate the tumor immune microenvironment following with the immunotherapy of Anti-CTLA4. Methods:Base on the 4T1 tumor mouse model, this study initially investigated the regulatory effects of X-PDT on the tumor immune microenvironment. Subsequently, the therapeutic efficacy of combining X-PDT with Anti-CTLA4 was evaluated for its inhibitory effects on primary, metastatic, and recurrent tumors. The underlying mechanisms were further elucidated through comprehensive techniques including flow cytometry, ELISA, and immunofluorescence assays. Results:The synergistic strategy can effectively ablate the primary tumor while inhibiting metastasis and preventing recurrence like a vaccine. It enhances intratumoural dendritic cells (DCs) maturation (from 25.7% to 58.3%, < 0.05) and immune T cell infiltration activating a strong anti-tumor immune response. The anti-tumor efficacy of synergistic therapy is enhanced by 2.5 times comparing with immunotherapy alone, while the tumor metastasis has been inhibited significantly. The maturation level of mature dendritic cells was increased from 26.7% to 86.3% ( < 0.01). The intratumoural CD8/CD4 T cells were increased from 0.51% and 1.54% to 15.4% and 23.1% ( < 0.0001), respectively. The synergistic therapy exerts a powerful vaccine-like long-term immune memory function to prevent tumor recurrence with the elevated level of effector memory T (Tem) cells (from 12.8% to 33.3%, < 0.05). Conclusion:Based on the 4T1 mouse model, developed an effective vaccine-like therapeutic

关键词： X-ray excited photodynamic therapy cancer vaccine combination therapies immunotherapy nanodrugs

Temporal Correlation Meets Embedding: Towards a 2nd Generation of JDE-based Real-Time Multi-Object Tracking

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arXiv 2024年

作者： Zhang, Yunfei Liang, Chao Gao, Jin Zhang, Zhipeng Hu, Weiming Maybank, Stephen Zhou, Xue Li, Liang School of Information Science and Technology Shanghaitech University Shanghai201210 China State Key Laboratory of Multimodal Artificial Intelligence System Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China KargoBot China Department of Computer Science and Information Systems Birkbeck College LondonWC1E 7HX United Kingdom School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China Beijing Institute of Basic Medical Sciences Beijing100850 China

Joint Detection and Embedding (JDE) trackers have demonstrated excellent performance in Multi-Object Tracking (MOT) tasks by incorporating the extraction of appearance features as auxiliary tasks through embedding Re-Identification task (ReID) into the detector, achieving a balance between inference speed and tracking performance. However, solving the competition between the detector and the feature extractor has always been a challenge. Meanwhile, the issue of directly embedding the ReID task into MOT has remained unresolved. The lack of high discriminability in appearance features results in their limited utility. In this paper, a new learning approach using cross-correlation to capture temporal information of objects is proposed. The feature extraction network is no longer trained solely on appearance features from each frame but learns richer motion features by utilizing feature heatmaps from consecutive frames, which addresses the challenge of inter-class feature similarity. Furthermore, our learning approach is applied to a more lightweight feature extraction network, and treat the feature matching scores as strong cues rather than auxiliary cues, with an appropriate weight calculation to reflect the compatibility between our obtained features and the MOT task. Our tracker, named TCBTrack, achieves state-of-the-art performance on multiple public benchmarks, i.e., MOT17, MOT20, and DanceTrack datasets. Specifically, on the DanceTrack test set, we achieve 56.8 HOTA, 58.1 IDF1 and 92.5 MOTA, making it the best online tracker capable of achieving real-time performance. Comparative evaluations with other trackers prove that our tracker achieves the best balance between speed, robustness and accuracy. Code is available at https://***/yfzhang1214/TCBTrack. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

Programmable Switch Aided Content Popularity Prediction and Caching Strategy 3

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Programmable Switch Aided Content Popularity Prediction and ...

3rd International Conference on Hot Information-Centric Networking, HotICN 2020

作者： He, Wenji Yao, Haipeng Mai, Tianle Guizani, Mohsen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Qatar University Computer Science and Engineering Department Doha Qatar

ISBN: (纸本)9781728192161

Content distribution is the most critical task for the current Internet, (e.g., the estimated video traffic will reach 82 percent of the Internet traffic by 2022). With the fast increase of load of the network, traditional host-centric based network paradigm (i.e., TCP/IP) faces great challenges in terms of efficiency, security, and privacy. To solve the problems confronting the current Internet, the Information-Centric Network (ICN) becomes a promising solution, where the focal point is identified content rather than specific host addresses. This paradigm brings many benefits, e.g., network traffic reduction, low retrieval latency. Besides, benefiting from the advance of programmable network hardware, the operator can reconfigure the network hardware' behavior, thus providing hardware support to describe the ICN instances. However, ICN also poses new challenges to cache management. The cache redundancy and unequal resource allocation will seriously affect the performance of the network. In this paper, we propose a distributed variational Bayes aided content popularity prediction algorithm. The extensive and indepth simulations are performed to evaluate our proposed algorithm in comparison to the other state-of-the-art schemes. © 2020 IEEE.

关键词： Barium compounds

Efficiency of microbial fermentation on microbial shifts, enzymatic activity, and transcriptions in black soldier fly larvae during the sugarcane waste conversion

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Environmental Pollution 2025年 381卷 126588页

作者： Memon, Fareed Uddin Li, ChiHo Ahmad, Sheraz Cui, Ying Feng, Xingbao Zeng, Peng Nabi, Farhan Huang, Zhijun Tettamanti, Gianluca Chen, Ting Tian, Ling Guangdong Provincial Key Lab of Agro-animal Genomics and Molecular Breeding College of Animal Science South China Agricultural University Guangzhou510642 China Guangxi Key Laboratory of Sericulture Ecology and Applied Intelligent Technology/ Guangxi Collaborative Innovation Center of Modern Sericulture and Silk Hechi University Hechi546300 China Yam Limited Co. Ltd. Hong Kong College of Natural Resources and Environment South China Agricultural University Guangzhou510642 China Department of Biotechnology and Life Sciences University of Insubria Varese21100 Italy University of Napoli Federico II Portici 80055 Italy Guangdong Province Key Laboratory of Animal Nutritional Regulation National Engineering Research Center for Breeding Swine Industry State Key Laboratory of Livestock and Poultry Breeding South China Agricultural University Guangdong 510642 China

Sugarcane trash, consisting mainly of dried leaves, tops and cans, is often burned in the field, posing serious risks to human health, contributing to environmental pollution, and depleting soil nutrients. Black soldier fly larvae (BSFL) offer a promising solution for organic waste management by converting it into insect proteins. This study aimed to develop a microbial fermentation method to utilize sugarcane waste as feed for BSFL and investigate underlying mechanisms. Our results revealed that all fermented groups exhibited enhanced growth and developmental performance, with the combination of Bacillus subtilis, Enterococcus faecalis, and Saccharomyces cerevisiae leading to the highest increases in larval body weight, survival rate, substrate conversion efficiency, and protein content compared to the unfermented group. Metagenomic analysis revealed a notable increase in the phylum Firmicutes, along with its beneficial strains such as Bacillus licheniformis, B. safensis, B. pumilus, Cytobacillus kochii, and Lysinibacillus fusiformis, in the gut of BSFL reared on fermented sugarcane waste, leading to improved gut homeostasis and reduced pathogenic loads. Additionally, the BSFL fed with fermented sugarcane waste exhibited high abundances of carbohydrate-active enzymes (CAZymes) involved in cellulose, hemicellulose, and starch degradation at both class and family levels. Transcriptome analysis further indicated the upregulation of key genes associated with key physiological processes, such as carbohydrate metabolism, chitin biosynthesis, and defense mechanisms. Collectively, these findings demonstrate the synergistic potential of microbial fermentation and BSFL for sustainable sugarcane waste management while advancing the understanding of host-microbe-diet interactions in insect-based bioconversion systems. © 2025 Elsevier Ltd

关键词： Waste treatment

ECG-DPM: Electrocardiogram Generation via a Spectrogram-based Diffusion Probabilistic Model

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ECG-DPM: Electrocardiogram Generation via a Spectrogram-base...

Smart World Congress (SWC), IEEE

作者： Lujundong Li Tong Xia Haojie Zhang Dongchen He Kun Qian Bin Hu Yoshiharu Yamamoto Björn W. Schuller Cecilia Mascolo Key Laboratory of Brain Health Intelligent Evaluation and Intervention Ministry of Education Beijing Institute of Technology Beijing China School of Medical Technology Beijing Institute of Technology Beijing China Info Hub AI Thrust the Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Technology University of Cambridge UK School of Life Sciences Peking University Beijing China Guaduate School of Frontier Sciences Computational Biology and Medical Sciences The University of Tokyo Japan Educational Physiology Laboratory Graduate School of Education The University of Tokyo Japan GLAM – the Group on Language Audio & Music Imperial College London UK CHI – Chair of Health Informatics MRI Technical University of Munich Germany

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

An electrocardiogram (ECG) records the electrical signals from the heart to assess various cardiovascular conditions. Deep learning methods have been proposed to model ECGs, but the insufficient availability of ECG data and annotations often hinders their performance. To address this challenge, this paper explores the latest data synthesis technique, i.e., diffusion probabilistic models (DPMs), to enable the generation of an unlimited number of ECGs representing various cardiovascular conditions. In contrast to previous approaches that treat ECGs as time series data or convert them into power spectrograms, we introduce a novel multi-channel spectrogram-based diffusion framework. In our method, the diffusion model enhances generation diversity, while the multi-channel spectrogram preserves both magnitude and phase information, ensuring high fidelity in the reconstructed ECGs. Extensive experiments conducted on real-world ECG data demonstrate the superiority of our approach. Notably, our ECG-DPM outperforms the best baseline by a margin ranging from ${1 2. 5 \%}$ to ${6 2. 5 \%}$ when generating ECGs for 30 seconds.

关键词： Training Heart Biological system modeling Time series analysis Electrocardiography Diffusion models Brain modeling Data models Distance measurement Spectrogram

Detecting adversarial examples from sensitivity inconsistency of spatial-transform domain

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arXiv 2021年

作者： Tian, Jinyu Zhou, Jiantao Li, Yuanman Duan, Jia State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University China

Deep neural networks (DNNs) have been shown to be vulnerable against adversarial examples (AEs), which are maliciously designed to cause dramatic model output errors. In this work, we reveal that normal examples (NEs) are insensitive to the fluctuations occurring at the highly-curved region of the decision boundary, while AEs typically designed over one single domain (mostly spatial domain) exhibit exorbitant sensitivity on such fluctuations. This phenomenon motivates us to design another classifier (called dual classifier) with transformed decision boundary, which can be collaboratively used with the original classifier (called primal classifier) to detect AEs, by virtue of the sensitivity inconsistency. When comparing with the state-of-the-art algorithms based on Local Intrinsic Dimensionality (LID), Mahalanobis Distance (MD), and Feature Squeezing (FS), our proposed Sensitivity Inconsistency Detector (SID) achieves improved AE detection performance and superior generalization capabilities, especially in the challenging cases where the adversarial perturbation levels are small. Intensive experimental results on ResNet and VGG validate the superiority of the proposed SID. © 2021, CC BY-NC-ND.

关键词： Deep neural networks

Random resistive memory-based deep extreme point learning machine for unified visual processing

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arXiv 2023年

作者： Wang, Shaocong Gao, Yizhao Li, Yi Zhang, Woyu Yu, Yifei Wang, Bo Lin, Ning Chen, Hegan Zhang, Yue Jiang, Yang Wang, Dingchen Chen, Jia Dai, Peng Jiang, Hao Lin, Peng Zhang, Xumeng Qi, Xiaojuan Xu, Xiaoxin So, Hayden Wang, Zhongrui Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China ACCESS - AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong University of Chinese Academy of Sciences Beijing100049 China Frontier Institute of Chip and System Fudan University Shanghai200433 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Visual sensors, including 3D LiDAR, neuromorphic DVS sensors, and conventional frame cameras, are increasingly integrated into edge-side intelligent machines. Realizing intensive multi-sensory data analysis directly on edge intelligent machines is crucial for numerous emerging edge applications, such as augmented and virtual reality and unmanned aerial vehicles, which necessitates unified data representation, unprecedented hardware energy efficiency and rapid model training. However, multi-sensory data are intrinsically heterogeneous, causing significant complexity in the system development for edge-side intelligent machines. In addition, the performance of conventional digital hardware is limited by the physically separated processing and memory units, known as the von Neumann bottleneck, and the physical limit of transistor scaling, which contributes to the slowdown of Moore's law. These limitations are further intensified by the tedious training of models with ever-increasing sizes. In this study, we propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM), that offers efficient unified point set analysis. Data-wise, the multi-sensory data are unified as point sets and can be processed universally. Software-wise, most weights of deep extreme point learning machines are exempted from training, which significantly reduce training complexity. Hardware-wise, nanoscale resistive memory not only enables collocation of memory and processing, mitigating the von Neumann bottleneck and the slowdown of Moore's law, but also leverages the inherent programming stochasticity for generating the random and sparse weights of the DEPLM, which also lessens the impact of read noise. We demonstrate the system's versatility across various data modalities and two different learning tasks. Compared to a conventional digital hardware-based system, our co-design system achieves 5.90×, 21.04×, and 15.79× energy efficiency improv

关键词： Cost reduction

Pre-Chirp-Domain Index Modulation for Full-Diversity Affine Frequency Division Multiplexing towards 6G

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IEEE Transactions on Wireless Communications 2025年

作者： Liu, Guangyao Mao, Tianqi Xiao, Zhenyu Wen, Miaowen Liu, Ruiqi Zhao, Jingjing Basar, Ertugrul Wang, Zhaocheng Chen, Sheng Beihang University School of Electronic and Information Engineering State Key Laboratory of CNS/ATM Beijing100191 China Greater Bay Area Innovation Research Institute of BIT Zhuhai519000 China Zhuhai519088 China South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China ZTE Corporation Wireless and Computing Research Institute Beijing100029 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China Tampere University Department of Electrical Engineering Tampere33720 Finland Koc University Department of Electrical and Electronics Engineering Sariyer Istanbul34450 Turkey Tsinghua University Department of Electronic Engineering Beijing100084 China University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

As a superior multicarrier technique utilizing chirp signals for high-mobility communications, affine frequency division multiplexing (AFDM) is envisioned to be a promising candidate for sixth-generation (6G) wireless networks. AFDM is based on the discrete affine Fourier transform (DAFT) with two adjustable parameters of the chirp signals, termed the pre-chirp and post-chirp parameters, respectively. Whilst the post-chirp parameter complies with stringent constraints to combat the time-frequency doubly selective channel fading, we show that the pre-chirp counterpart can be flexibly manipulated for an additional degree of freedom. Therefore, this paper proposes a novel AFDM scheme with the pre-chirp index modulation (PIM) philosophy (AFDM-PIM), which can implicitly convey extra information bits through dynamic pre-chirp parameter assignment, thus enhancing both spectral and energy efficiency. Specifically, we first demonstrate that the subcarrier orthogonality is still maintained by applying distinct pre-chirp parameters to various subcarriers in the AFDM modulation process. Inspired by this property, we allow each AFDM subcarrier to carry a unique pre-chirp signal according to the incoming bits. By such an arrangement, extra bits can be embedded into the index patterns of pre-chirp parameter assignment without additional energy consumption. We derive asymptotically tight upper bounds on the average bit error probability (BEP) of the proposed schemes with the maximum-likelihood detection, and validate that the proposed AFDM-PIM can achieve full diversity under doubly dispersive channels. Based on the derived result, we further propose an optimal pre-chirp alphabet design to enhance the bit error rate (BER) performance via intelligent optimization algorithms. Simulation results demonstrate that the proposed AFDM-PIM outperforms the classical benchmarks. © 2002-2012 IEEE.

关键词： Chirp modulation

TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing systems

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arXiv 2024年

作者： Yang, Jing Zhao, Yu Yang, Linyao Wang, Xiao Chen, Long Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha410000 China Zhejiang Lab Hangzhou311121 China School of Artificial Intelligence Anhui University Hefei266114 China Department of Computer and Information Science University of Macau 999078 China Faculty of Innovation Engineering Macau University of Science and Technology 999078 China

Temporal relation extraction (TRE) aims to grasp the evolution of events or actions, and thus shape the workflow of associated tasks, so it holds promise in helping understand task requests initiated by requesters in crowdsourcing systems. However, existing methods still struggle with limited and unevenly distributed annotated data. Therefore, inspired by the abundant global knowledge stored within pre-trained language models (PLMs), we propose a multi-task prompt learning framework for TRE (TemPrompt), incorporating prompt tuning and contrastive learning to tackle these issues. To elicit more effective prompts for PLMs, we introduce a task-oriented prompt construction approach that thoroughly takes the myriad factors of TRE into consideration for automatic prompt generation. In addition, we design temporal event reasoning in the form of masked language modeling as auxiliary tasks to bolster the model’s focus on events and temporal cues. The experimental results demonstrate that TemPrompt outperforms all compared baselines across the majority of metrics under both standard and few-shot settings. A case study on designing and manufacturing printed circuit boards is provided to validate its effectiveness in crowdsourcing scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning