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Characterization of prognosis and immune infiltration by a novel glutamine metabolism-related model in cutaneous melanoma

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BIOCELL 2023年第9期47卷 1931-1945页

作者： MENGQIN ZHU TIANYI XU HAN ZHANG XIN FAN YULAN WANG JIAJIA ZHANG FEI YU Shanghai Clinical College Anhui Medical UniversityShanghai200040China The Fifth Clinical Medical College Anhui Medical UniversityHefei230032China Department of Nuclear Medicine Shanghai Tenth People’s HospitalTongji University School of MedicineShanghai200040China Institute of Nuclear Medicine Tongji University School of MedicineShanghai200040China National Genomics Data Center and CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China Department of Biomedical Engineering Nanjing University of Aeronautics and AstronauticsNanjing210000China

Glutamine metabolism(GM)plays an important role in tumor growth and *** cutaneous melanoma(SKCM)is a glutamine-dependent ***,the molecular characteristics and action mechanism of GM on SKCM remain ***,we aimed to explore the effects of GM-related genes on survival,clinicopathological characteristics,and the tumor microenvironment in *** this study,682 SKCM samples were obtained from the Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)*** clustering was used to classify SKCM samples into distinct subtypes based on 41 GM-related *** in survival,immune infiltration,clinical characteristics,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways as well as differentially expressed genes(DEGs)between subgroups were evaluated.A prognostic model was constructed according to prognostic *** analyses in survival,immune infiltration,tumor microenvironment(TME),tumor mutation burden(TMB),stemness,and drug sensitivity between risk groups were *** identified two distinct GM-related subtypes on SKCM and found that GM-related gene alterations were associated with survival probability,clinical features,biological function,and immune *** a risk model based on six DEGs(IL18,SEMA6A,PAEP,TNFRSF17,AIM2,and CXCL10)was constructed and validated for predicting overall survival in SKCM *** results showed that the risk score was negatively correlated with CD8+T cells,activated CD4+memory T cells,M1 macrophages,andγδT *** group with a low-risk score was accompanied by a better survival rate with higher TME scores and lower stemness ***,the group with high-and low-risk score had a significant difference with the sensitivity of 75 drugs(p<0.001).Overall,distinct subtypes in SKCM patients based on GM-related genes were identified and the risk model was constructed,which might contribute to prognosis prediction,guide clinical therapy,and develop novel

关键词： Glutamine metabolism Cutaneous melanoma Immune infiltration Overall survival

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Comparisons of corneal biomechanical and tomographic parameters among thin normal cornea,forme fruste keratoconus,and mild keratoconus

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Eye and Vision 2021年第1期8卷 443-453页

作者： Lei Tian Di Zhang Lili Guo Xiao Qin Hui Zhang Haixia Zhang Ying Jie Lin Li Beijing Institute of Ophthalmology Beijing Tongren Eye CenterBeijing Ophthalmology&Visual Sciences Key LaboratoryBeijing Tongren HospitalCapital Medical UniversityBeijing 100730China Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical UniversityBeijing 100069China School of Biomedical Engineering Capital Medical UniversityBeijing 100069China Beijing Advanced Innovation Center for Big Data‑Based Precision Medicine Beihang University&Capital Medical UniversityBeijing Tongren HospitalBeijing 100730China

Background:To compare the dynamic corneal response(DCR)and tomographic parameters of thin normal cornea(TNC)with thinnest corneal thickness(TCT)(≤500μm),forme fruste keratoconus(FFKC)and mild keratoconus(MKC)had their central corneal thickness(CCT)matched by Scheimpflug imaging(Pentacam)and corneal visualization Scheimpflug technology(Corvis ST).Methods:CCT were matched in 50 eyes with FFKC,50 eyes with MKC,and 53 TNC eyes with TCT≤500μ*** differences in DCR and tomographic parameters among the three groups were *** receiver operating characteristic(ROC)curve was used to analyze the diagnostic significance of these *** propagation(BP)neural network was used to establish the keratoconus diagnosis ***:Fifty CCT-matched FFKC eyes,50 MKC eyes and 50 TNC eyes were *** age and biomechanically corrected intraocular pressure(bIOP)did not differ significantly among the three groups(all P>0.05).The index of height asymmetry(IHA)and height decentration(IHD)differed significantly among the three groups(all P<0.05).IHD also had sufficient strength(area under the ROC curves(AUC)>0.80)to differentiate FFKC and MKC from TNC *** DCR parameters showed significant differences between the MKC and TNC groups,and the deflection amplitude of the first applanation(A1DA)showed a good potential to differentiate(AUC>0.70)FFKC and MKC from TNC *** model by BP neural network showed an accurate diagnostic efficiency of about 91%.Conclusions:The majority of the tomographic and DCR parameters differed among the three *** IHD and partial DCR parameters assessed by Corvis ST distinguished FFKC and MKC from TNC when controlled for CCT.

关键词： Thin normal cornea Forme fruste keratoconus Mild keratoconus Corneal biomechanical parameters

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Multiple-Mode Orthogonal Frequency Division Multiplexing with Cascade Index Modulation

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IEEE Transactions on Vehicular Technology 2025年

作者： Hu, Zeng Liu, Yun Cui, Yinming Wan, Dehuan Wang, Jing Wu, Ting Zhongkai University of Agriculture and Engineering College of Information Science and Technology Guangzhou510225 China South China University of Technology Guangdong Provincial Key Laboratory of Short-Range Wireless Detection and Communication Guangzhou510640 China Guangdong University of Finance Center for Data Science and Artificial Intelligence Guangzhou510521 China South China University of Technology School of Electronic and Information Engineering Guangzhou510641 China Guangzhou Medical University Qingyuan People's Hospital Qingyuan Hospital Guangdong Province Qingyuan511518 China Guangzhou Medical University School of Biomedical Engineering Guangdong Province Guangzhou511436 China

Multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) is a promising multicarrier transmission technique proposed recently, which focus on exploiting the index domain of the multiple distinguishable modes (constellations) and uses full permutations of the indices of multiple symbol modes to transmit information bits implicitly. Compared to conventional OFDM-IM, a factorial increase of the IM patterns in MM-OFDM-IM prohibits its application in realistic communication systems due to exponentially increased modulation and demodulation complexity when the number of symbol modes is large. In this paper, we propose a novel and efficient MM-OFDM with cascade IM (MM-OFDM-CIM) scheme, which first selects partial symbol modes for multiple mode index modulation (MM-IM) of the first subblock in the cluster and then performs MM-IM based on the rest symbol modes for the second subblock. The proposed CIM method divides the IM process into two steps and achieves considerably low computational complexity in detection. Furthermore, physical layer security can be realized by adjusting the size of subblocks within the cluster due to the flexible CIM designs. Computer simulations show that the proposed MM-OFDM-CIM has potential to achieve near the same bit error rate performance with considerably low computational complexity and achieves excellent secure communication performance between the legitimate users. © 1967-2012 IEEE.

关键词： Orthogonal frequency division multiplexing

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Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution

arXiv

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arXiv 2024年

作者： Xu, Tianyi Zhou, Yijie Hu, Xiaotao Zhang, Kai Zhang, Anran Qiu, Xingye Xu, Jun School of Statistics and Data Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China School of Intelligence Science and Technology Nanjing University Suzhou China Tencent Data Platform Beijing China Zhejiang University Hangzhou China Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China

Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary scale upsampler. These feature extractors often use fixed network architectures to address different ASSR inference tasks, each of which is characterized by an input image and an upsampling scale. However, this overlooks the difficulty variance of super-resolution on different inference scenarios, where simple images or small SR scales could be resolved with less computational effort than difficult images or large SR scales. To tackle this difficulty variability, in this paper, we propose a Task-Aware Dynamic Transformer (TADT) as an input-adaptive feature extractor for efficient image ASSR. Our TADT consists of a multi-scale feature extraction backbone built upon groups of Multi-Scale Transformer Blocks (MSTBs) and a Task-Aware Routing Controller (TARC). The TARC predicts the inference paths within feature extraction backbone, specifically selecting MSTBs based on the input images and SR scales. The prediction of inference path is guided by a new loss function to trade-off the SR accuracy and efficiency. Experiments demonstrate that, when working with three popular arbitrary-scale upsamplers, our TADT achieves state-of-the-art ASSR performance when compared with mainstream feature extractors, but with relatively fewer computational costs. The code will be publicly released. © 2024, CC BY.

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Detecting Adversarial data by Probing Multiple Perturbations Using Expected Perturbation Score

arXiv

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arXiv 2023年

作者： Zhang, Shuhai Liu, Feng Yang, Jiahao Yang, Yifan Li, Changsheng Han, Bo Tan, Mingkui School of Software Engineering South China University of Technology China Pazhou Laboratory China The University of Melbourne Australia Beijing Institute of Technology Beijing China Department of Computer Science Hong Kong Baptist University Hong Kong Key Laboratory of Big Data and Intelligent Robot Ministry of Education China

Adversarial detection aims to determine whether a given sample is an adversarial one based on the discrepancy between natural and adversarial distributions. Unfortunately, estimating or comparing two data distributions is extremely difficult, especially in high-dimension spaces. Recently, the gradient of log probability density (a.k.a., score) w.r.t. the sample is used as an alternative statistic to compute. However, we find that the score is sensitive in identifying adversarial samples due to insufficient information with one sample only. In this paper, we propose a new statistic called expected perturbation score (EPS), which is essentially the expected score of a sample after various perturbations. Specifically, to obtain adequate information regarding one sample, we perturb it by adding various noises to capture its multi-view observations. We theoretically prove that EPS is a proper statistic to compute the discrepancy between two samples under mild conditions. In practice, we can use a pre-trained diffusion model to estimate EPS for each sample. Last, we propose an EPS-based adversarial detection (EPSAD) method, in which we develop EPS-based maximum mean discrepancy (MMD) as a metric to measure the discrepancy between the test sample and natural samples. We also prove that the EPS-based MMD between natural and adversarial samples is larger than that among natural samples. Extensive experiments show the superior adversarial detection performance of our EPS-AD. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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A Promising Novel Red-Emitting Phorphor Sr3canb2o9: Eu3+ for Near-Uv Excited Wleds

SSRN

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SSRN 2022年

作者： Chen, Qian Zhao, Rongli Gong, Xinyong Deng, Chaoyong Zhang, Jun Cui, Ruirui Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guiyang550025 China School of Electronics and Information Engineering Guiyang University Guiyang550005 China College of Computer and Information Engineering Guizhou University of Commerce Guiyang550014 China

A traditional high-temperature solid-phase reaction technique was used to successfully create a series of Sr3CaNb2O9: xEu3+ phosphors with a double perovskite structure. These phosphors' thermal stability and international committee color coordinates have both been carefully examined, along with their microstructure, absorption spectra, and photoluminescence characteristics. The results show that there are no miscellaneous terms in the prepared samples;the microscopic morphology is uniform and full. Under the excitation at 394 nm ( 7F0 → 5L6 ), the emission peaks of the luminous material are most obvious at 593 nm ( 5D0 → 7F1 ) and 611 nm ( 5D0 → 7F2 ). It is noteworthy that the ratio between 611 nm and 593 nm exceeds 1 when the x is greater than 0.2, due to the shift of the main occupancy sites of Eu3+ from the Ca2+ sites with inversion symmetry to the Sr2+ sites, thus providing an environment with inversion asymmetry. Moreover, the optimal doping of Eu3+ is x = 0.30. The concentration quenching mechanism is the closest to the ion interaction. The luminescence intensity percentage of Sr3CaNb2O9: 0.3Eu3+ is 72.5% at 420 K and It's color purity is 98.49%. The aforementioned findings suggest that Sr3CaNb2O9 activated by Eu3+ can be employed as a red light-emitting component for white light-emitting diodes that are driven by near-UV radiation. © 2022, The Authors. All rights reserved.

关键词： Rare earths

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Multi-scale Efficient Graph-Transformer for Whole Slide Image Classification

arXiv

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arXiv 2023年

作者： Ding, Saisai Li, Juncheng Wang, Jun Ying, Shihui Shi, Jun The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University China The Department of Mathematics School of Science Shanghai University China

The multi-scale information among the whole slide images (WSIs) is essential for cancer diagnosis. Although the existing multi-scale vision Transformer has shown its effectiveness for learning multi-scale image representation, it still cannot work well on the gigapixel WSIs due to their extremely large image sizes. To this end, we propose a novel Multi-scale Efficient Graph-Transformer (MEGT) framework for WSI classification. The key idea of MEGT is to adopt two independent Efficient Graph-based Transformer (EGT) branches to process the low-resolution and high-resolution patch embeddings (i.e., tokens in a Transformer) of WSIs, respectively, and then fuse these tokens via a multi-scale feature fusion module (MFFM). Specifically, we design an EGT to efficiently learn the local-global information of patch tokens, which integrates the graph representation into Transformer to capture spatial-related information of WSIs. Meanwhile, we propose a novel MFFM to alleviate the semantic gap among different resolution patches during feature fusion, which creates a non-patch token for each branch as an agent to exchange information with another branch by cross-attention. In addition, to expedite network training, a novel token pruning module is developed in EGT to reduce the redundant tokens. Extensive experiments on TCGA-RCC and CAMELYON16 datasets demonstrate the effectiveness of the proposed MEGT. © 2023, CC BY.

关键词： Image classification

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Robust optimization for quantum reinforcement learning control using partial observations

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Physical Review A 2022年第6期105卷 062443-062443页

作者： Chen Jiang Yu Pan Zheng-Guang Wu Qing Gao Daoyi Dong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou 310027 China The School of Automation Science and Electrical Engineering and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China The School of Engineering and Information Technology University of New South Wales Canberra Australian Capital Territory 2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of the quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of the quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and the quantum approximate optimization algorithm (QAOA). It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for a QAOA with a noisy control Hamiltonian. This robust control optimization model can be trained to compensate for the uncertainties in practical quantum computing.

关键词： Machine learning Quantum control Quantum engineering

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Multi-Band Compatible Camouflage Enabled by Phase Transition Modulation of Flexible Gst Films

SSRN

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SSRN 2024年

作者： Wang, Jian Wu, Zuoxu Sun, Xiaoyu Tang, Zunqian Wang, Chong Yu, Fangyuan Zhao, Zirui Mao, Jun Zhang, Qian Cao, Feng School of Science Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen518055 China School of Materials Science and Engineering Institute of Materials Genome & Big Data Harbin Institute of Technology Shenzhen518055 China State Key Laboratory of Advanced Welding and Joining China Fuzhou University China

With the rapid development of detection technology, there is an urgent need for multi-band compatible camouflage technology, particularly in the visible-infrared band. However, the conflicting requirements between camouflage across different bands and surface thermal management pose significant challenges in achieving synchronized control for multiple bands in diverse application environments and targets. Herein, we propose a spectrally selective emitter leveraging phase-change modulation of Ge2Sb2Te5, capable of achieving effective camouflage in both the visible band and two atmospheric windows over a wide temperature range. The designed emitters exhibit colorful appearances, compatible with various background environments, thereby ensuring the visible camouflage. Meanwhile, the emitters demonstrate low emissivity values of 0.1 in the MWIR band and 0.08 in the LWIR band, effectively reducing the infrared emission through atmospheric *** radiative temperatures of the optimized emitters in the MWIR and LWIR bands are reduced by 105 °C and 140 °C relative to the target surface (200 °C), respectively, providing excellent radiative suppression compared to similar studies. Remarkably, the designed emitter still demonstrates robust flexibility, making it suitable for a variety of targets with complex curved surfaces. This work paves the way for designing the flexible visible-infrared compatible camouflage coatings with integrated thermal management functionality. © 2024, The Authors. All rights reserved.

关键词： Germanium alloys

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Sampling-Based Methods for Multi-Block Optimization Problems over Transport Polytopes

arXiv

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arXiv 2023年

作者： Hu, Yukuan Li, Mengyu Liu, Xin Meng, Cheng State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Institute of Statistics and Big Data Renmin University of China Beijing China Center for Applied Statistics Institute of Statistics and Big Data Renmin University of China Beijing China

This paper focuses on multi-block optimization problems over transport polytopes, which underlie various applications including strongly correlated quantum physics and machine learning. Conventional block coordinate descent-type methods for the general multi-block problems store and operate on the matrix variables directly, resulting in formidable expenditure for large-scale settings. On the other hand, optimal transport problems, as a special case, have attracted extensive attention and numerical techniques that waive the use of the full matrices have recently emerged. However, it remains nontrivial to apply these techniques to the multi-block, possibly nonconvex problems with theoretical guarantees. In this work, we leverage the benefits of both sides and develop novel sampling-based block coordinate descent-type methods, which are equipped with either entropy regularization or Kullback-Leibler divergence. Each iteration of these methods solves subproblems restricted on the sampled degrees of freedom. Consequently, they involve only sparse matrices, which amounts to considerable complexity reductions. We explicitly characterize the sampling-induced errors and establish convergence and asymptotic properties for the methods equipped with the entropy regularization. Numerical experiments on typical strongly correlated electron systems corroborate their superior scalability over the methods utilizing full matrices. The advantage also enables the first visualization of approximate optimal transport maps between electron positions in three-dimensional *** Codes 52-08, 65C05, 65F50, 81V05, 90C30 © 2023, CC BY.

关键词： Entropy

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