Utilizing Multi-omics analysis to elucidate the role of mitochondrial gene defects in Gastric cancer progression. by Jie Chu, Hanying Song, Kemin Fu, Wei Xiao, Jiudong Jiang, Qixin Gan, Bo Deng

“…Additionally, both the ssGSEA algorithm and the CIBERSORT algorithm were utilized to evaluate changes and effects in immunological characteristics during gastric cancer pathogenesis.…”
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Exploring the potential of cell-free RNA and Pyramid Scene Parsing Network for early preeclampsia screening by Zhuo Zhao, Xiaoxu Liu, Yonghui Guan, Chunfang Li, Zheng Wang

“…Based on the differences in cfRNA expression profiles, the Calculated Ground Truth values of the NP and PE groups in the sequencing data were acquired (Calculated PRI). The differential algorithm was embedded in the PSPNet neural network and the network was then trained using the generated dataset. …”
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Study on water quality analysis and early-warning technology based on rough set and evidence theory by ZHU Qiong-yao, ZHANG Guang-xin, FENG Tian-heng, HUANG Ping-jie, HOU Di-bo

“…As it is known to all, there are lots of monitoring parameters in water quality detection, monitoring and information management system, and the evolution principles and changing trends of water quality are difficult to be obtained. …”
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Quantifying Uncertainties in Solar Wind Forecasting due to Incomplete Solar Magnetic Field Information by Stephan G. Heinemann, Jens Pomoell, Ronald M. Caplan, Mathew J. Owens, Shaela Jones, Lisa Upton, Bibhuti Kumar Jha, Charles N. Arge

“…Further comparison with the thermodynamic Magnetohydrodynamic Algorithm outside a Sphere model indicates that uncertainties in the different models can lead to even larger variations in solar wind forecasts compared to those within a single model. …”
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Characteristic, relationship and impact of thermokarst lakes and retrogressive thaw slumps over the Qinghai-Tibetan plateau by Wenwen Li, Denghua Yan, Yu Lou, Baisha Weng, Lin Zhu, Yuequn Lai, Yunzhe Wang

“…We further employed the eXtreme gradient boosting algorithm and ICESat-2 ATL08 laser altimetry data to quantify changes in water storage due to TLs. …”
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An analysis of spatial changes in the manufacturing industry in china’s three major urban clusters from 2015 to 2019 using POI data by Chenxi Jin, Chenjing Fan, Yiwen Gong, Xinran Huang, Shiqi Li, Runhan Liu, Chunwei Guo, Yuxin Liu

“…The evolution of the manufacturing industry at the scale of 451 districts and counties in the urban clusters and the factors driving new entrants in the manufacturing industry were studied. …”
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Quantum DeepONet: Neural operators accelerated by quantum computing by Pengpeng Xiao, Muqing Zheng, Anran Jiao, Xiu Yang, Lu Lu

“…However, classical DeepONet entails quadratic complexity concerning input dimensions during evaluation. Given the progress in quantum algorithms and hardware, here we propose to utilize quantum computing to accelerate DeepONet evaluations, yielding complexity that is linear in input dimensions. …”
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Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud by Chiranjit Singha, Kishore Chandra Swain, Satiprasad Sahoo, Ayad M. Fadhil Al-Quraishi, Joseph Omeiza Alao, Hussein Almohamad, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo

“…The scatterplot analysis revealed a positive correlation between forest biomass and factors, such as forest canopy height, elevation, normalized differential vegetation index, normalized differential moisture index, and the ratio of VV/VH after the monsoon season. …”
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Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis by Hang Chen, Biao Wu, Biao Wu, Kunyu Guan, Liang Chen, Kangjie Chai, Maoji Ying, Dazhi Li, Weicheng Zhao

“…The ssGSEA technique was first utilized to assess lipid metabolism scores in samples affected by atherosclerosis, thereby aiding in the discovery of important regulatory genes linked to lipid metabolism via WGCNA. Following this, differential expression analysis and functional evaluations were carried out, after which various machine learning approaches were employed to determine significant diagnostic genes for atherosclerosis. …”
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Evaluation and optimization of carbon emission for federal edge intelligence network by Peng ZHANG, Yong XIAO, Jiwei HU, Liang LIAO, Jianxin LYU, Zegang BAI

“…In recent years, the continuous evolution of communication technology has led to a significant increase in energy consumption.With the widespread application and deep deployment of artificial intelligence (AI) technology and algorithms in telecommunication networks, the network architecture and technological evolution of network intelligent will pose even more severe challenges to the energy efficiency and emission reduction of future 6G.Federated edge intelligence (FEI), based on edge computing and distributed federated machine learning, has been widely acknowledged as one of the key pathway for implementing network native intelligence.However, evaluating and optimizing the comprehensive carbon emissions of federated edge intelligence networks remains a significant challenge.To address this issue, a framework and a method for assessing the carbon emissions of federated edge intelligence networks were proposed.Subsequently, three carbon emission optimization schemes for FEI networks were presented, including dynamic energy trading (DET), dynamic task allocation (DTA), and dynamic energy trading and task allocation (DETA).Finally, by utilizing a simulation network built on real hardware and employing real-world carbon intensity datasets, FEI networks lifecycle carbon emission experiments were conducted.The experimental results demonstrate that all three optimization schemes significantly reduce the carbon emissions of FEI networks under different scenarios and constraints.This provides a basis for the sustainable development of next-generation intelligent communication networks and the realization of low-carbon 6G networks.…”
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Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition by Libang Chen, Jinyan Lin, Qihang Bian, Yikun Liu, Jianying Zhou

“…Maximum Histogram Equalization (MHE) is used to achieve high contrast while maintaining fidelity to the original content. We evaluated our algorithm using data collected from both a fog chamber and outdoor environments and performed comparative analyses with existing methods. …”
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Targeted inhibition of Gus-expressing Enterococcus faecalis to promote intestinal stem cell and epithelial renovation contributes to the relief of irinotecan chemotoxicity by dehyd... by Ruiyang Gao, Bei Yue, Cheng Lv, Xiaolong Geng, Zhilun Yu, Hao Wang, Beibei Zhang, Fangbin Ai, Ziyi Wang, Donghui Liu, Zhengtao Wang, Kaixian Chen, Wei Dou

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Construction of a feature gene and machine prediction model for inflammatory bowel disease based on multichip joint analysis by Yan Chaosheng, Sun Haowen, Rao Jingjing, Dai Yuanyuan, Duan Wenhui, Sheng Yingyue, Xue Yuzheng

“…To select genetic features, we utilized three machine learning algorithms, namely, least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF), to identify differentially expressed genes. …”
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A single-snapshot inverse solver for two-species graph model of tau pathology spreading in human Alzheimer’s disease by Zheyu Wen, Ali Ghafouri, George Biros, the Alzheimer’s Disease Neuroimaging Initiative (ADNI)

“…This optimization problem is solved with a projection-based quasi-Newton algorithm. We evaluate the performance of our method on both synthetic and clinical data. …”
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Azithromycin in the complex therapy of COVID-19. Advantages and effectiveness of the use of the dispersed form in outpatient settings by D. S. Sukhanov, V. S. Maryushkina, D. Yu. Azovtsev, S. V. Okovity

“…The pathogenetic mechanisms of COVID-19 predisposing to the development of community-acquired pneumonia and their differential diagnostic criteria are considered. The necessity of reasonable prescribing of antibiotics is emphasized. …”
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Automated extracellular volume fraction measurement for diagnosis and prognostication in patients with light-chain cardiac amyloidosis. by In-Chang Hwang, Eun Ju Chun, Pan Ki Kim, Myeongju Kim, Jiesuck Park, Hong-Mi Choi, Yeonyee E Yoon, Goo-Yeong Cho, Byoung Wook Choi

“…A semi-automated deep learning algorithm (Myomics-Q) was used for the analysis of the CMR images. …”
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Exploring the impact of deubiquitination on melanoma prognosis through single-cell RNA sequencing by Su Peng, Jiaheng Xie, Xiaohu He

“…Cells were categorized into DUB_high and DUB_low groups based on AUCell scores, followed by differential expression analysis. Importantly, we constructed a robust prognostic model utilizing various genes, which was evaluated in the TCGA cohort and an external validation cohort.ResultsOur prognostic model, developed using Random Survival Forest (RSF) and Ridge Regression methods, demonstrated excellent predictive performance, evidenced by high C-index and AUC values across multiple cohorts. …”
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Impact of ITH on PRAD patients and feasibility analysis of the positive correlation gene MYLK2 applied to PRAD treatment by Chuanyu Ma, Chuanyu Ma, Guandu Li, Xiaohan Song, Xiaochen Qi, Tao Jiang

“…The ITH-score of PRAD samples was evaluated using the DEPTH algorithm. The optimal cut-off value of RiskScore was calculated based on the difference in survival curves, and PRAD patients were classified into high ITH and low ITH groups based on the optimal cut-off value. …”
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A prior information-based multi-population multi-objective optimization for estimating 18F-FDG PET/CT pharmacokinetics of hepatocellular carcinoma by Yiwei Xiong, Siming Li, Jianfeng He, Shaobo Wang

“…The single-individual Levenberg–Marquardt (LM) algorithm, single-population algorithms (Particle Swarm Optimization (PSO), Differential Evolution (DE), and Genetic Algorithm (GA)) and p-MPMO optimization algorithms (p-MPMOPSO, p-MPMODE, and p-MPMOGA) were used to estimate the parameters. …”
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Towards complexity in de Sitter space from the doubled-scaled Sachdev-Ye-Kitaev model by Sergio E. Aguilar-Gutierrez

“…We interpret spread complexity in terms of a time difference between antipodal observers in SdS3 space, and a boundary time difference of the dual LdS2 CFTs. …”
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