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| 1 | Non-small cell lung carcinoma(NSCLC):Implications on molecular pathology and advances in early diagnostics and therapeutics显示文摘Continuous revision of the histologic and stage-wise classification of lung cancer by the World Health Organization (WHO) provides the foundation for therapeutic advances by promoting molecular targeted and immunotherapies and ensuring accurate diagnosis. Cancer epidemiologic data provide helpful information for cancer prevention, diagnosis, and management, supporting health-care interventions. Global cancer mortality projections from 2016 to 2060 show that cancer will overtake ischemic heart diseases (IHD) as the leading cause of death (18.9 million) immediately after 2030, surpassing non-small cell lung cancer (NSCLC), which accounts for 85 percent of lung cancers. The clinical stage at the diagnosis is the main prognostic factor in NSCLC therapies. Advanced early diagnostic methods are essential as the initial stages of cancer show reduced mortality compared to the advanced stages. Sophisticated approaches to proper histological classification and NSCLC management have improved clinical efficiency. Although immune checkpoint inhibitors (ICIs) and targeted molecular therapies have refined the therapeutic management of late-stage NSCLC, the specificity and sensitivity of cancer biomarkers should be improved by focusing on prospective studies, followed by their use as therapeutic tools. The liquid biopsy candidates such as circulating tumor cells (CTCs), circulating cell-free tumor DNA (cfDNA), tumor educated platelets (TEP), and extracellular vesicles (EVs) possess cancer-derived biomolecules and aid in tracing: driver mutations leading to cancer, acquired resistance caused by various generations of therapeutic agents, refractory disease, prognosis, and surveillance. | Hafiza Padinharayil Jinsu Varghese Mithun Chacko John Golgodu Krishnamurthy Rajanikant Cornelia M.Wilson Minnatallah Al-Yozbaki Kaviyarasi Renu Saikat Dewanjee Rupa Sanyal Abhijit Dey Anirban Goutam Mukherjee Uddesh Ramesh Wanjari Abilash Valsala Gopalakrishnan Alex George | 2023 | Genes & Diseases2023，10，3： | 1 |
| 2 | Capacity Optimization Using Augmented Lagrange Method in Intelligent Reflecting Surface-Based MIMO Communication Systems显示文摘In this paper,we present an algorithm for capacity optimization in intelligent reflecting surface(IRS)-based multiple-input multiple-output(MIMO)communication systems.To maximize the capacity of elements in IRS,we use augmented Lagrange method with the equivalent transformations on the covariance matrix and reflection matrix constraints.This results an adjustable phase shift on the incident signal.Furthermore,we reshape the complex-valued covariance matrix and reflection matrix to a vector for the ease of calculating partial derivatives to find the search direction.Then,the quasi-Newton updates and modified Broyden-Fletcher-Goldfarb-Shano(BFGS)method in the complex domain form are used to find the local minimum.Finally,numerical simulation results demonstrate that our proposed IRS-aided system using the algorithm performs better than the state-of-the-art and the conventional communication systems. | Daina Chang Hao Jiang Jie Zhou Hongming Zhang Mithun Mukherjee | 2020 | China Communications2020，17，12： | 0 |
| 3 | Dense Spatial-Temporal Graph Convolutional Network Based on Lightweight OpenPose for Detecting Falls显示文摘Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy.To solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose.Lightweight OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the network.The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in parallel.The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection accuracy.Two representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation benchmarks.The results show that the proposed model is superior to the current fall detection models.The accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively. | Xiaorui Zhang Qijian Xie Wei Sun Yongjun Ren Mithun Mukherjee | 2023 | Computers, Materials & Continua2023，77，10： | 0 |
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