HENG ZHANG^1,#, WENJING CHENG^2,#, HAIBO ZHAO², WEIDONG CHEN², QIUJIE ZHANG^2,*, QING-QING YU^2,*

Oncology Research, Vol.32, No.2, pp. 297-308, 2024, DOI:10.32604/or.2023.043138 - 28 December 2023

Abstract Background: Colorectal cancer (CRC) belongs to the class of significantly malignant tumors found in humans. Recently, dysregulated fatty acid metabolism (FAM) has been a topic of attention due to its modulation in cancer, specifically CRC. However, the regulatory FAM pathways in CRC require comprehensive elucidation. Methods: The clinical and gene expression data of 175 fatty acid metabolic genes (FAMGs) linked with colon adenocarcinoma (COAD) and normal cornerstone genes were gathered through The Cancer Genome Atlas (TCGA)-COAD corroborating with the Molecular Signature Database v7.2 (MSigDB). Initially, crucial prognostic genes were selected by uni- and multi-variate Cox… More >

ZEPENG DONG, CHENYE ZHAO, SHIBO HU, KUI YANG, JUNHUI YU^*, XUEJUN SUN, JIANBAO ZHENG^*

BIOCELL, Vol.47, No.12, pp. 2735-2745, 2023, DOI:10.32604/biocell.2023.043422 - 27 December 2023

Abstract Objectives: Angiogenesis plays a significant role in the occurrence and development of inflammatory bowel disease (IBD). The aim of this study is to explore potential angiogenesis related genes (ARGs) in IBD through bioinformatics analysis and in vivo experiments. Methods: GSE57945, GSE87466, and GSE36807 were obtained from the Gene Expression Omnibus database. GSE57945 was used as the training set, while GSE87466 and GSE36807 were used as the validation set. The key ARGs associated with IBD were identified using the least absolute shrinkage and selection operator (LASSO) and random forest methods. These identified ARGs were then utilized to construct… More >

Yaping Luo¹, Na Guo^1,*, Dong Liu², Shuming Peng³, Xinchen Wang⁴, Jie Wu³

Revue Internationale de Géomatique, Vol.32, pp. 39-52, 2023, DOI:10.32604/RIG.2023.046014 - 20 December 2023

Abstract Using hyperspectral data collected in January and June 2022 from the Sha River, the concentrations of total nitrogen (TN) and total phosphorus (TP) were estimated using the differential method. The results indicate that the optimal bands for estimation vary monthly due to temperature fluctuations. In the TN model, the power function model at 586 nm in January exhibited the strongest fit, yielding a fit coefficient (R²) of 0.95 and F-value of 164.57 at a significance level (p) of less than 0.01. Conversely, the exponential model at 477 nm in June provided the best fit, with R² = More > Graphic Abstract

AMGAD M. RABIE^*

BIOCELL, Vol.47, No.10, pp. 2133-2139, 2023, DOI:10.32604/biocell.2023.030057 - 08 November 2023

Abstract Despite the global decline in the severity of the coronavirus disease 2019 (COVID-19) cases, the disease still represents a major concern to the relevant scientific and medical communities. The primary concern of drug scientists, virologists, and other concerned specialists in this respect is to find ready-to-use suitable and potent anticoronaviral therapies that are broadly effective against the different species/strains of the coronaviruses in general, not only against the current and previous coronaviruses (e.g., the recently-appeared severe acute respiratory syndrome coronavirus 2 “SARS-CoV-2”), i.e., effective antiviral agents for treatment and/or prophylaxis of any coronaviral infections, including More > Graphic Abstract

Rufus Gikera^1,*, Jonathan Mwaura², Elizaphan Muuro³, Shadrack Mambo³

Journal on Artificial Intelligence, Vol.5, pp. 75-112, 2023, DOI:10.32604/jai.2023.043229 - 26 October 2023

Abstract k-means is a popular clustering algorithm because of its simplicity and scalability to handle large datasets. However, one of its setbacks is the challenge of identifying the correct k-hyperparameter value. Tuning this value correctly is critical for building effective k-means models. The use of the traditional elbow method to help identify this value has a long-standing literature. However, when using this method with certain datasets, smooth curves may appear, making it challenging to identify the k-value due to its unclear nature. On the other hand, various internal validation indexes, which are proposed as a solution to this… More >

Ana María Peco Chacón, Isaac Segovia Ramírez, Fausto Pedro García Márquez^*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2595-2608, 2023, DOI:10.32604/iasc.2023.037277 - 11 September 2023

Abstract Maintenance operations have a critical influence on power generation by wind turbines (WT). Advanced algorithms must analyze large volume of data from condition monitoring systems (CMS) to determine the actual working conditions and avoid false alarms. This paper proposes different support vector machine (SVM) algorithms for the prediction and detection of false alarms. K-Fold cross-validation (CV) is applied to evaluate the classification reliability of these algorithms. Supervisory Control and Data Acquisition (SCADA) data from an operating WT are applied to test the proposed approach. The results from the quadratic SVM showed an accuracy rate of More >

Minh-Thanh Vo¹, Trang Nguyen², Tuong Le^3,4,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1555-1568, 2023, DOI:10.32604/cmc.2023.039334 - 30 August 2023

Abstract The use of machine learning to predict student employability is important in order to analyse a student’s capability to get a job. Based on the results of this type of analysis, university managers can improve the employability of their students, which can help in attracting students in the future. In addition, learners can focus on the essential skills identified through this analysis during their studies, to increase their employability. An effective method called OPT-BAG (OPTimisation of BAGging classifiers) was therefore developed to model the problem of predicting the employability of students. This model can help… More >

Jaimon Dennis Quadros¹, Sher Afghan Khan², Abdul Aabid^3,*, Muneer Baig³

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2331-2352, 2023, DOI:10.32604/cmes.2023.028925 - 03 August 2023

Abstract The application of abruptly enlarged flows to adjust the drag of aerodynamic vehicles using machine learning models has not been investigated previously. The process variables (Mach number (M), nozzle pressure ratio (η), area ratio (α), and length to diameter ratio (γ )) were numerically explored to address several aspects of this process, namely base pressure (β) and base pressure with cavity (β_cav). In this work, the optimal base pressure is determined using the PCA-BAS-ENN based algorithm to modify the base pressure presetting accuracy, thereby regulating the base drag required for smooth flow of aerodynamic vehicles. Based… More > Graphic Abstract

LIN QIU^1,#, ANQI TAO^1,#, XIAOQIAN SUN^4,5, FEI LIU¹, XIANPENG GE^2,3,*, CUIYING LI^1,*

Oncology Research, Vol.31, No.5, pp. 715-752, 2023, DOI:10.32604/or.2023.029443 - 21 July 2023

Abstract We analyzed RNA-sequencing (RNA-seq) and clinical data from head and neck squamous cell carcinoma (HNSCC) patients in The Cancer Genome Atlas (TCGA) Genomic Data Commons (GDC) portal to investigate the prognostic value of anoikis-related genes (ARGs) in HNSCC and develop new targeted drugs. Differentially expressed ARGs were screened using bioinformatics methods; subsequently, a prognostic model including three ARGs (CDKN2A, BIRC5, and PLAU) was constructed. Our results showed that the model-based risk score was a good prognostic indicator, and the potential of the three ARGs in HNSCC prognosis was validated by the TISCH database, the model’s… More >

Xiaokui Huang¹, Yingbin Xue¹, Aaqil Khan¹, Hanqiao Hu¹, Naijie Feng^1,2,*, Dianfeng Zheng^1,2,*

Phyton-International Journal of Experimental Botany, Vol.92, No.8, pp. 2369-2382, 2023, DOI:10.32604/phyton.2023.027457 - 25 June 2023

Abstract For the purpose of functional validation, the mung bean (Vigna radiata) VrPR gene was cloned and overexpressed in Arabidopsis thaliana. The findings revealed that the ORF of VrPR contained 1200 bp, in which 399 amino acids were encoded. Bioinformatics analysis showed that the VrPR protein belonged to the NADB Rossmann superfamily, which was one of the non-transmembrane hydrophilic proteins. VrPR was assumed to have 44 amino acid phosphorylation sites and be contained in chloroplasts. The VrPR secondary structure comprised of random coil, α helix, β angle, and extended chain, all of which were quite compatible with the anticipated tertiary structure. More >