Article
Oncology
Bin Pan, Xiangbo Bu, Menghan Cao, Xin Zhang, Tianqun Huo, Ziang Li, Xiao Gao, Li Jing, Xuanxiang Luo, Hu Feng, Feng Yuan, Kaijin Guo
Summary: Our study identified 153 DEGs in ES, with 82 upregulated and 71 downregulated. Bioinformatics analysis revealed that ICAM1 is the key gene leading to ES invasion and metastasis. Cell and molecular biology experiments confirmed that inactivation of ICAM1 inhibits ES metastasis. Survival and correlation analysis indicated that ICAM1 is a risk factor in ES patients and its expression is correlated with EWSR and FLI1.
JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY
(2021)
Article
Genetics & Heredity
Yangfan Zhou, Bin Xu, Shusheng Wu, Yulian Liu
Summary: The study investigated the immune microenvironment in Ewing's sarcoma patients and identified 10 immune-related, independent prognostic genes, providing a new approach for predicting and treating Ewing's sarcoma.
FRONTIERS IN GENETICS
(2021)
Article
Cell Biology
Giorgia Giordano, Alessandra Merlini, Giulio Ferrero, Giulia Mesiano, Erika Fiorino, Silvia Brusco, Maria Laura Centomo, Valeria Leuci, Lorenzo D'Ambrosio, Massimo Aglietta, Dario Sangiolo, Giovanni Grignani, Ymera Pignochino
Summary: The study focused on the expression of tyrosine kinase EphA2 in bone sarcomas and found it to be a potential therapeutic target. EPHA2 was observed to be expressed at higher levels in bone sarcoma cell lines and tumor tissues, correlating with different histological types and clinical outcomes.
Article
Oncology
Sarah Consalvo, Florian Hinterwimmer, Norbert Harrasser, Ulrich Lenze, Georg Matziolis, Rudiger von Eisenhart-Rothe, Carolin Knebel
Summary: This study analyzed the role of C-reactive protein (CRP) as a prognostic factor in Ewing's sarcoma. The results showed that serum CRP levels were significantly different in patients with a poorer prognosis and in patients who presented distant metastasis, indicating it as a sensitive prognostic risk factor in Ewing's sarcoma cases.
Article
Oncology
Gaston Barbero, Maria Victoria Castro, Maria Josefina Quezada, Pablo Lopez-Bergami
Summary: In this study, bioinformatics analysis was used to identify genes, biological processes, and signaling pathways involved in melanoma progression. The differential expression of these genes in primary and metastatic melanoma was found to be associated with tumor thickness and survival.
Article
Multidisciplinary Sciences
LiangHong Chen, Xin Qi, JingYu Wang, JiaLi Yin, PeiHong Sun, Yan Sun, Yan Wu, Li Zhang, XingHua Gao
Summary: Using bioinformatics, this study identified differentially expressed genes (DEGs) and predicted miRNAs between patients with atopic dermatitis (AD) and healthy controls. The results provide initial insights into dysfunctional inflammatory and immune responses associated with AD, and offer the potential for developing novel therapeutic targets for preventing and treating the disease.
SCIENTIFIC REPORTS
(2022)
Article
Multidisciplinary Sciences
Yike Zhu, Dan Huang, Zhongyan Zhao, Chuansen Lu
Summary: Epilepsy is a common brain disorder that is difficult to identify and treat. This study analyzed gene expression datasets to identify genes related to the progression and prognosis of epilepsy, highlighting 16 hub genes through functional enrichment and PPI network analysis. These findings provide potential insights for the diagnosis and treatment of epilepsy, but further research is needed to understand the functional roles of these genes.
Article
Oncology
Zhaoyu Fu, Bo Yu, Mingxi Liu, Bo Wu, Yuanyuan Hou, Hongyu Wang, Yuting Jiang, Dong Zhu
Summary: By analyzing the expression profiles of metabolism-related genes and clinical information, the study aimed to predict the prognosis of Ewing's sarcoma patients and provide a basis for personalized treatment. The results demonstrated that a prognostic model based on metabolism-related DEGs can effectively differentiate high-risk and low-risk patients, with the risk score index being an independent prognostic factor for Ewing's sarcoma.
TRANSLATIONAL ONCOLOGY
(2021)
Article
Medicine, General & Internal
Wenle Li, Qian Zhou, Wencai Liu, Chan Xu, Zhi-Ri Tang, Shengtao Dong, Haosheng Wang, Wanying Li, Kai Zhang, Rong Li, Wenshi Zhang, Zhaohui Hu, Su Shibin, Qiang Liu, Sirui Kuang, Chengliang Yin
Summary: A risk prediction model for lymph node metastasis (LNM) of Ewing's sarcoma (ES) was developed based on machine learning algorithms. The model showed good performance in identifying LNM in ES patients, with the help of clinicopathological data.
FRONTIERS IN MEDICINE
(2022)
Article
Immunology
Jingwei Zhang, Junchao Huang, Wenjun Liu, Liang Ding, Dongdong Cheng, Haijun Xiao
Summary: This study aimed to identify common oncogenic genes and pathways in osteosarcoma and Ewing's sarcoma. Through analysis of gene expression data and protein-protein interaction networks, it revealed the significant role of genes such as FN1, COL1A1, and COL1A2 in the development of both types of tumors.
JOURNAL OF IMMUNOLOGY RESEARCH
(2022)
Article
Biochemistry & Molecular Biology
Jose Maria Medina, Muhammad Nadeem Abbas, Chaima Bensaoud, Michael Hackenberg, Michail Kotsyfakis
Summary: This study provides an exhaustive analysis of long non-coding RNAs (lncRNAs) in Ixodes ricinus ticks, revealing their stable expression and diverse biological roles related to tick-host interaction. The findings highlight the importance of incorporating data from different sources to generate a solid reference set of lncRNAs and suggest the possibility of lncRNAs acting as host miRNA sponges in tick-host interaction.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Immunology
Xiqin Tong, Fuling Zhou
Summary: This study analyzed the mutation status of 31 mitochondrial metabolism-related genes in AML patients and constructed a prognosis model based on five genes, which accurately distinguished high-risk and low-risk patients. It was also found that high-risk patients had more immune-cell infiltration and poor immunotherapy response.
FRONTIERS IN IMMUNOLOGY
(2023)
Review
Dentistry, Oral Surgery & Medicine
Monica Hermann Spiguel, Lauren Frenzel Schuch, Luan Nathiel Kovalski, Julia Turra Ribeiro, Bruna Barcelos So, Felipe Martins Silveira, Pablo Agustin Vargas, Marco Antonio Trevizani Martins, Virgilio Gonzales Zanella, Pedro Bandeira Aleixo, Vivian Petersen Wagner, Manoela Domingues Martins
Summary: This study conducted a systematic review of head and neck Ewing sarcoma, with a focus on demographic and clinical features, histopathological findings, treatment, follow-up, and survival rate. A total of 227 cases were described in 186 studies, with an average age of 22.7 years and a slightly higher prevalence in males. The respiratory tract was the most common site, followed by the jawbones. Multimodal treatment regimens were used, and the overall survival rate was found to be lower in older patients with distant metastasis (p < 0.05).
Article
Public, Environmental & Occupational Health
Jinkui Wang, Chenghao Zhanghuang, Xiaojun Tan, Tao Mi, Jiayan Liu, Liming Jin, Mujie Li, Zhaoxia Zhang, Dawei He
Summary: A new nomogram has been developed to predict the cancer-specific survival (CSS) of childhood osteosarcoma (OSC) and Ewing's sarcoma (EWS). This predictive model shows good accuracy and reliability, and can assist doctors and patients in developing clinical strategies.
FRONTIERS IN PUBLIC HEALTH
(2022)
Article
Medicine, General & Internal
Ya-Chih Liu, Ting-Chi Yeh, Pao-Su Wu, Jin-Cherng Sheu, Hung-Chang Lee, Chun-Yan Yeung, Chuen-Bin Jiang, Hsi-Che Liu, Jen-Yin Hou, Wai-Tao Chan
Summary: This article reports a case of rare pancreatic extraosseous Ewing's sarcoma and emphasizes the importance of early recognition of this disease. The patient underwent chemotherapy, surgery, and radiotherapy with no recurrence or complications.
Article
Computer Science, Artificial Intelligence
Zhenzhi Wu, Zhihong Zhang, Huanhuan Gao, Jun Qin, Rongzhen Zhao, Guangshe Zhao, Guoqi Li
Summary: Bio-inspired methods are being introduced into artificial neural networks for efficient processing of spatio-temporal tasks, with the Leaky Integrate and Fire (LIF) model being the most prominent. The introduction of electrical synapses is shown to be an important factor in achieving high accuracy on realistic spatio-temporal tasks, with the proposed network showing significant improvement over traditional LIF models on five datasets. Through modeling electrical synapses in artificial LIF neurons and training deep networks using the ECLIF model, high accuracy has been achieved.
Article
Multidisciplinary Sciences
Yujie Wu, Rong Zhao, Jun Zhu, Feng Chen, Mingkun Xu, Guoqi Li, Sen Song, Lei Deng, Guanrui Wang, Hao Zheng, Songchen Ma, Jing Pei, Youhui Zhang, Mingguo Zhao, Luping Shi
Summary: This article introduces a neuromorphic global-local synergic learning model that combines brain-inspired metalearning and differentiable spiking models, allowing for multiscale learning and achieving significant advantages in various tasks.
NATURE COMMUNICATIONS
(2022)
Article
Automation & Control Systems
Yue Yang, Jiangshuai Huang, Xiaojie Su, Kai Wang, Guoqi Li
Summary: This article discusses the adaptive control for a class of strict-feedback nonlinear systems with uncertainties under injection and deception attacks. It proposes an adaptive control scheme to deal with the attacks while ensuring that regulation errors can be made arbitrarily small by adjusting control parameters.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Hongkai Zhang, Chunyan Shi, Lin Yang, Nan Zhang, Guoqi Li, Zhen Zhou, Yifeng Gao, Dongting Liu, Lei Xu, Zhanming Fan
Summary: In this study, early DMIF changes in a T1DM mouse model were evaluated using MRI T1 mapping, with ECV identified as an accurate imaging marker for quantitatively assessing DMIF changes over time. The findings suggest that ECV has the potential to accurately detect DMIF in the early stage, making it a useful imaging tool for assessing the need for early intervention in T1DM mice.
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2023)
Article
Chemistry, Physical
Guoqi Li, Jihao Zhang, Lin Li, Chunze Yuan, Tsu-Chien Weng
Summary: In this study, a new type of NiFe double-layer hydroxide (NiFe-LDH) catalyst was synthesized using the hydrothermal method, mixed with different equivalent terephthalic acid (TPA). The catalyst with one equivalent of TPA showed the best performance in terms of oxygen evolution reaction (OER), with low overpotential and high current density, as well as excellent stability.
Article
Automation & Control Systems
Yihan Lin, Jiawei Sun, Guoqi Li, Gaoxi Xiao, Changyun Wen, Lei Deng, H. Eugene Stanley
Summary: The number of control sources is a limiting factor in many network control tasks, but exploiting the temporal variation of network topology can overcome this limitation. The proposed spatiotemporal input control strategy reduces the required number of sources to 2, which is significant for complex network control problems.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Computer Science, Artificial Intelligence
Ying Ma, Dayuan Chen, Tian Wang, Guoqi Li, Ming Yan
Summary: Partial label learning is a weakly supervised learning method that predicts one label from a candidate label set. However, there may be noisy labels in the training data due to the assignment of candidate labels. It is also challenging to improve accuracy due to the combination of partial label learning and semi-supervised learning. Existing methods in semi-supervised partial label learning neglect the noisy labels, leading to contaminated data. We propose a method called SeePLL that addresses the label contamination issue through reliable label propagation.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jibin Wu, Yansong Chua, Malu Zhang, Guoqi Li, Haizhou Li, Kay Chen Tan
Summary: Spiking neural networks (SNNs) are a prominent biologically inspired computing model but are not directly applicable to standard error backpropagation algorithm due to the nondifferentiable nature of spiking neuronal functions. In this work, a tandem learning framework consisting of an SNN and an artificial neural network (ANN) is proposed to train the SNN at the spike-train level. The proposed tandem learning rule demonstrates competitive pattern recognition and regression capabilities with reduced inference time and total synaptic operations.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ling Liang, Xing Hu, Lei Deng, Yujie Wu, Guoqi Li, Yufei Ding, Peng Li, Yuan Xie
Summary: This study investigates the adversarial attack against spiking neural networks (SNNs) and identifies challenges distinct from artificial neural networks (ANNs) attack. Two approaches are proposed to address the gradient-input incompatibility and gradient vanishing issues, contributing to the development of an adversarial attack methodology for SNNs. Experimental results validate the effectiveness of the proposed methods and provide comparisons with ANN under different attack methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Dingheng Wang, Bijiao Wu, Guangshe Zhao, Man Yao, Hengnu Chen, Lei Deng, Tianyi Yan, Guoqi Li
Summary: This article introduces a method for compressing recurrent neural networks (RNNs) based on Kronecker CANDECOMP/PARAFAC (KCP) decomposition. Experimental results demonstrate that KCP-RNNs achieve comparable accuracy, high compression ratios, and efficiency in both space and computation complexity compared to other tensor decomposition methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Lei Deng, Yujie Wu, Yifan Hu, Ling Liang, Guoqi Li, Xing Hu, Yufei Ding, Peng Li, Yuan Xie
Summary: This paper investigates the compression of spiking neural networks (SNNs) and presents a comprehensive approach to achieve SNN compression. The study shows that the running efficiency of SNNs can be improved through methods such as connection pruning, weight quantization, and activity regularization, while accuracy loss can be minimized through spatiotemporal backpropagation and alternating direction method of multipliers.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Qizheng Pan, Ming Yan, Guoqi Li, Jianmin Li, Ying Ma
Summary: This paper introduces a new method called SKDTEA, which improves the performance of multi-label classification through self-knowledge distillation. The method addresses the issue of output bias induced by overfitting by removing the latent space alignment in TEA-based solutions. Experimental results demonstrate significant superiority of the proposed method in multi-label classification.
2022 IEEE INTERNATIONAL CONFERENCE ON KNOWLEDGE GRAPH (ICKG)
(2022)
Article
Engineering, Electrical & Electronic
Xin Liu, Mingyu Yan, Lei Deng, Yujie Wu, De Han, Guoqi Li, Xiaochun Ye, Dongrui Fan
Summary: This paper proposes a general neuromorphic framework called mm-SNN, which utilizes spiking neural networks to process mmWave radar data, overcoming noise and sparsity issues, and achieving considerable performance in trajectory estimation task.
NEUROMORPHIC COMPUTING AND ENGINEERING
(2022)
Article
Automation & Control Systems
Yang Wu, Ding-Heng Wang, Xiao-Tong Lu, Fan Yang, Man Yao, Wei-Sheng Dong, Jian-Bo Shi, Guo-Qi Li
Summary: Visual recognition is a key research area in computer vision, pattern recognition, and artificial intelligence. While accuracy is important, efficiency is also crucial for both academic research and industrial applications. This survey reviews recent advances and proposes new directions for improving the efficiency of visual recognition approaches.
MACHINE INTELLIGENCE RESEARCH
(2022)
Proceedings Paper
Automation & Control Systems
Ling Liang, Zhaodong Chen, Lei Deng, Fengbin Tu, Guoqi Li, Yuan Xie
Summary: Spiking neural networks have the potential to achieve brain-like intelligence, but suffer from low accuracy and training efficiency on GPUs. This work presents a framework to solve the inefficiency of training SNNs on GPUs, which achieves significant speedup and reduced memory consumption compared to vanilla Pytorch implementation.
PROCEEDINGS OF THE 2022 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2022)
(2022)
