CcpA-mediated regulation of cellular energy metabolism in the ruminal bacterium Streptococcus bovis by Yunan Weng, Yaqian Jin, Wenze Zhang, Lihong Wang, Hongrong Wang

“…Orthogonal projections to latent structures discriminant analysis (OPLS-DA) further distinguished between each pair of groups. KEGG prediction indicated that the abundance and enrichment of metabolites were primarily involved in carbon metabolism, amino acid metabolism, and nucleotide metabolomics. …”
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Comparing and Combining Artificial Intelligence and Spectral/Statistical Approaches for Elevating Prostate Cancer Assessment in a Biparametric MRI: A Pilot Study by Rulon Mayer, Yuan Yuan, Jayaram Udupa, Baris Turkbey, Peter Choyke, Dong Han, Haibo Lin, Charles B. Simone

“…Combining Z-SSMNet with spectral/statistical approaches significantly enhanced tumor grade prediction, possibly providing an alternative to current prostate tumor assessment.…”
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Biomarkers of microvascularture by ultra Micro-angiography (UMA) assist to identify papillary thyroid carcinoma (PTC) with atypia of undetermined significance by Qingsong Wang, Zhewei Li, Jie Zhang, Sijie Zhang, Lijun Wang, Hongjian Yao, Hong Zhang, Jing Li, Shuo Wang, Jinglai Sun, Wenhui Zhang, Hui Yu

“…Conclusions This study demonstrated the potential value of microvasculature in the prediction of PTC in AUS Cases and improved the performance of ultrasound examination. …”
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Using Calibration Transfer Strategy to Update Hyperspectral Model for Quantitating Soluble Solid Content of Blueberry Across Different Batches by Biao Chen, Xuhuang Huang, Shenwen Tan, Guangjun Qiu, Huaiyin Lin, Xuejun Yue, Junzhi Chen, Wenshan Zhong, Xuantian Li, Le Zhang

“…This high-performance model could achieve a high determination coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi mathvariant="normal">R</mi><mi mathvariant="normal">P</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula>) of 0.8965 and a low root mean square error of prediction (RMSEP) of 0.3707 °Brix. Using the sample dataset for the year 2025, the hyperspectral model was updated by the semi-supervised parameter-free calibration enhancement (SS-PFCE) algorithm. …”
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An Optimal Approach for Assessing Weibull Parameters and Wind Power Potential for Six Coastal Cities in Pakistan by Ghulam Abbas, Arshad Ali, Faizan Rashid, Naveed Ashraf, Zohaib Mushtaq, Muhammad Zubair

“…In this research, we have ameliorated the performance of the recently-introduced novel energy pattern factor method (NEPFM) via a direct search algorithm, i.e., simplex search algorithm (SSA). We designate the resulting algorithm as NEPFM-SSA as it took NEPFM&#x2019;s Weibull distribution parameters as an initial guess and retuned them with the help of the simplex search algorithm to get updated Weibull distribution parameters, which ensure better fitting characteristics. …”
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A Novel Approach to Pine Nut Classification: Combining Near-Infrared Spectroscopy and Image Shape Features with Soft Voting-Based Ensemble Learning by Yueyun Yu, Xin Huang, Danjv Lv, Benjamin K. Ng, Chan-Tong Lam

“…This study proposes a novel near-infrared (NIR) spectral feature selection algorithm, termed the improved binary equilibrium optimizer with selection probability (IBiEO-SP), which incorporates a dynamic probability adjustment mechanism to achieve efficient feature dimensionality reduction. …”
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Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine by Debendra Muduli, Rani Kumari, Adnan Akhunzada, Korhan Cengiz, Santosh Kumar Sharma, Rakesh Ranjan Kumar, Dinesh Kumar Sah

“…In this MOD-POA+ELM algorithm, the modified pelican optimization algorithm (MOD-POA) has been utilized to optimize the parameters of ELM’s hidden neurons. …”
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Comparison of four different assays to evaluate cellular-mediated immunity against cytomegalovirus in solid organ transplantation by Angela Casas-Parra, Angela Casas-Parra, Hendrik Veltman, Alba Romero-Caballero, Alba Romero-Caballero, Rosana Gelpi-Remiro, Rosana Gelpi-Remiro, Marc Boigues-Pons, Imán Allalou, Ian Linares-Pardo, Anna Vila-Santandreu, Anna Vila-Santandreu, Eva Martínez-Cáceres, Eva Martínez-Cáceres, Eva Martínez-Cáceres, María Iglesias-Escudero, María Iglesias-Escudero, María Iglesias-Escudero

“…In this context, measuring CMV-specific cell-mediated immunity (CMI) has proven to be a valuable tool, with high negative predictive value (NPV) for the absence of CMV viremia in patients with positive tests. …”
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The value of a combined model based on ultra-radiomics and multi-modal ultrasound in the benign-malignant differentiation of C-TIRADS 4A thyroid nodules: a prospective multicenter... by Shuai Cui, Qifan Liu, Hailong Wang, Husha Li, Wei Li, Chenlong Li, Leilei Bi, Yang Mu, Wenjing Guo, Jundong Yao, Zhoulong Zhang

“…The predictive efficiency and accuracy of these models were evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA). …”
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Application of artificial intelligence-assisted Traditional Chinese medicine formula granules in bone and joint degenerative diseases in the field of holistic health by Xian-Quan Zhang, Xian-Quan Zhang, Xian-Quan Zhang, Xing-De Wei, Zhuo-Xu Gu, Guang-Hui Zhou, Chu-Jia Huang, Ming-Hui Luo, Ling-Feng Zeng, Ling-Feng Zeng, Ling-Feng Zeng, Wei-Yi Yang, Wei-Yi Yang

“…It analyzes the application models of AI technology in optimizing TCM formula granules, predicting drug efficacy, and personalized treatment. …”
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Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer by Rui Wang, Rui Wang, Guan-Hua Qin, Guan-Hua Qin, Yifei Jiang, Fu-Xiang Chen, Fu-Xiang Chen, Zi-Han Wang, Zi-Han Wang, Lin-Ling Ju, Lin Chen, Da Fu, En-Yu Liu, Su-Qing Zhang, Wei-Hua Cai

“…A 7-gene mCAF-associated risk model was constructed using advanced machine learning algorithms, and the biological significance of PHLDA1 was validated through co-culture experiments and pan-cancer analyses.ResultsOur multiomics analysis revealed that the novel 7-gene model (comprising USP36, KLF5, MT2A, KDM6B, PHLDA1, REL, and DDIT4) accurately predicts patient survival, immunotherapy response, and TME status. …”
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Metabolic profiles in laryngeal cancer defined two distinct molecular subtypes with divergent prognoses by Dan Zheng, Dan Zheng, Xuan Pu, Xuan Pu, XuHui Deng, XuHui Deng, Cui Liu, Cui Liu, SiJun Li, SiJun Li

“…Furthermore, we explored the potentials of several key tumor markers for both diagnosis and prognosis prediction.…”
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Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score by Matthew M. Churpek, MD, MPH, PhD, ATSF, Kyle A. Carey, MPH, Ashley Snyder, MPH, Christopher J Winslow, MD, Emily Gilbert, MD, Nirav S Shah, MD, MPH, Brian W. Patterson, MD, MPH, Majid Afshar, MD, MSCR, Alan Weiss, MD, MBA, Devendra N. Amin, MD, Deborah J. Rhodes, MD, Dana P. Edelson, MD, MS

“…All adult patients admitted to the inpatient medical-surgical wards and at 21 hospitals from three health systems for retrospective (2009–2023) and prospective (2023–2024) external validation. PREDICTION MODEL:. Predictor variables (demographics, vital signs, documentation, and laboratory values) were used in a gradient-boosted trees algorithm to predict ICU transfer or death in the next 24 hours. …”
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Transdimensional Inference for Gravitational-wave Astronomy with Bilby by Hui Tong, Nir Guttman, Teagan A. Clarke, Paul D. Lasky, Eric Thrane, Ethan Payne, Rowina Nathan, Ben Farr, Maya Fishbach, Gregory Ashton, Valentina Di Marco

“…The tBilby package allows users to set up transdimensional inference calculations using the existing Bilby architecture with off-the-shelf nested samplers and/or Markov Chain Monte Carlo algorithms. Transdimensional models are particularly helpful when seeking to test theoretically uncertain predictions described by phenomenological models. …”
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Development and evaluation of a soft pneumatic muscle for elbow joint rehabilitation by Mostafa Orban, Mostafa Orban, Mostafa Orban, Kai Guo, Kai Guo, Caijun Luo, Caijun Luo, Hongbo Yang, Hongbo Yang, Karim Badr, Karim Badr, Mahmoud Elsamanty, Mahmoud Elsamanty

“…The algorithm achieved a high predictive accuracy of 99.8% in spatial coordination tracking, indicating the precision of the system in monitoring and controlling the actuator’s motion.…”
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GRAVITY ANOMALIES OF THE CRUST AND UPPER MANTLE FOR CENTRAL AND SOUTH ASIA by V. N. Senachin, A. A. Baranov

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Identification of a PANoptosis-related long noncoding rna risk signature for prognosis and immunology in colon adenocarcinoma by Yuekai Cui, Jie Mei, Shengsheng Zhao, Bingzi Zhu, Jianhua Lu, Hongzheng Li, Binglong Bai, Weijian Sun, Wenyu Jin, Xueqiong Zhu, Shangrui Rao, Yongdong Yi

“…Long noncoding RNAs (lncRNAs) play crucial roles in PCD. However, the predictive value of PANoptosis-related lncRNAs (PRlncRNAs) for colon adenocarcinoma (COAD) has not been established. …”
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A validated heart-specific model for splice-disrupting variants in childhood heart disease by Robert Lesurf, Jeroen Breckpot, Jade Bouwmeester, Nour Hanafi, Anjali Jain, Yijing Liang, Tanya Papaz, Jane Lougheed, Tapas Mondal, Mahmoud Alsalehi, Luis Altamirano-Diaz, Erwin Oechslin, Enrique Audain, Gregor Dombrowsky, Alex V. Postma, Odilia I. Woudstra, Berto J. Bouma, Marc-Phillip Hitz, Connie R. Bezzina, Gillian M. Blue, David S. Winlaw, Seema Mital

“…Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. …”
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Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformation by Junsong Jin, Fangtao Chai, Jinchuan Long, Chang Gao, Shaolei Wang, Pan Zeng, Xuefeng Tang, Pan Gong, Mao Zhang, Lei Deng, Xinyun Wang

“…The developed hot processing map can precisely predict microstructure evolution. The optimal pre-deformation amount is determined to be 2 %, with a recommended hot processing window defined as 300–400 °C and 0.07–7 s−1.…”
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Whole genome sequencing improves tissue-of-origin diagnosis and treatment options for cancer of unknown primary by Richard J. Rebello, Atara Posner, Ruining Dong, Owen W. J. Prall, Tharani Sivakumaran, Camilla B. Mitchell, Aidan Flynn, Alex Caneborg, Catherine Mitchell, Sehrish Kanwal, Clare Fedele, Samantha Webb, Krista Fisher, Hui-Li Wong, Shiva Balachander, Wenying Zhu, Shannon Nicolson, Voula Dimitriadis, Nicholas Wilcken, Anna DeFazio, Bo Gao, Madhu Singh, Ian M. Collins, Christopher Steer, Mark Warren, Narayan Karanth, Huiling Xu, Andrew Fellowes, Rodney J. Hicks, Kym Pham Stewart, Charles Shale, Peter Priestley, Sarah-Jane Dawson, Joseph H. A. Vissers, Stephen B. Fox, Penelope Schofield, David Bowtell, Oliver Hofmann, Sean M. Grimmond, Linda Mileshkin, Richard W. Tothill

“…Curated WGTS features and a CUP prediction algorithm (CUPPA) trained on WGTS data of known cancer types informs TOO in 71% of cases otherwise undiagnosed by clinicopathology review. …”
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