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Early detection of feline chronic kidney disease via 3-hydroxykynurenine and machine learning

SCIENTIFIC REPORTS 2025年第1期15卷 1-18页

作者： Vanden Broecke, Ellen Van Mulders, Laurens De Paepe, Ellen Paepe, Dominique Daminet, Sylvie Vanhaecke, Lynn Univ Ghent Fac Vet Med Dept Translat Physiol Infectiol & Publ Hlth Lab Integrat Metabol LIMET Salisburylaan 133 B-9820 Merelbeke Belgium Univ Ghent Fac Vet Med Small Anim Dept Salisburylaan 133 B-9820 Merelbeke Belgium Queens Univ Belfast Inst Global Food Secur Sch Biol Sci Chlorine Gardens 19 Belfast BT9 5DL North Ireland

Feline chronic kidney disease (CKD) is one of the most frequently encountered diseases in veterinary practice, and the leading cause of mortality in cats over five years of age. While diagnosing advanced CKD is straightforward, current routine tests fail to diagnose early CKD. Therefore, this study aimed to identify early metabolic biomarkers. First, cats were retrospectively divided into two populations to conduct a case-control study, comparing the urinary and serum metabolome of healthy (n = 61) and CKD IRIS stage 2 cats (CKD2, n = 63). Subsequently, longitudinal validation was conducted in an independent population comprising healthy cats that remained healthy (n = 26) and cats that developed CKD2 (n = 22) within one year. Univariate, multivariate, and machine learning-based (ML) approaches were compared. The serum-to-urine ratio of 3-hydroxykynurenine was identified as a single biomarker candidate, yielding a high AUC (0.844) and accuracy (0.804), while linear support vector machine-based modelling employing metabolites and clinical parameters enhanced AUC (0.929) and accuracy (0.862) six months before traditional diagnosis. Furthermore, analysis of variable importance indicated consistent key serum metabolites, namely creatinine, SDMA, 2-hydroxyethanesulfonate, and aconitic acid. By enabling accurate diagnosis at least six months earlier, the highlighted metabolites may pave the way for improved diagnostics, ultimately contributing to timely disease management.

关键词： Diagnostic panel Renal disease Targeted metabolomics Kynurenines machine learning algorithms Multivariate models

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An Online Algorithm for Optimizing Network Transmission Cost of Federated learning in the Cloud

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第3期12卷 1457-1469页

作者： Yan, Haotian Pan, Li Liu, Shijun Wu, Dong Shandong Univ Sch Software Jinan 250101 Peoples R China Shandong Univ Shandong Key Lab Fdn Software Jinan 250101 Peoples R China Jinan Inspur Data Technol Co Ltd Jinan 250101 Peoples R China

Data privacy concerns and related regulations such as the General Data Protection Regulation in machine learning have fostered a boom in federated learning (FL). However, the costly infrastructure and time-consuming deployments pose significant barriers to the widespread adoption of FL in real-world scenarios. To increase the user-friendliness of federated learning while reducing deployment costs and improving its scalability, service providers are beginning to offer federated learning as a service (FLaaS) in the cloud. Due to the distributed nature of FL, communication overhead imposes significant network costs on FLaaS providers. In mainstream cloud platforms, there are two main types of billing methods for networking products, which are on-demand and reserved. How to optimally combine these two billing models to optimize communication cost in the face of time-varying demands of federated learning in the cloud poses a challenge to FLaaS providers. To address this problem, we propose OnlineNS, an online algorithm for optimally making networking product selection decisions without prior knowledge of future demand sequences. Our algorithm can achieve better cost performance compared to online algorithms that are widely used in practice. The theoretical analysis and simulations based on real-world traces as well as synthetic datasets validate the effectiveness of our online algorithm and demonstrate that it can achieve better cost performance compared to benchmarks with the same communication performance.

关键词： Costs Cloud computing Training Federated learning Data models Complexity theory Relays Optimization machine learning algorithms Distributed databases Networking federated learning as a service (FLaaS) cost optimization online algorithm

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A Hybrid Deep learning Model for UAV Path Planning in Dynamic Environments

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IEEE ACCESS 2025年 13卷 67459-67475页

作者： Zhang, Junchi Xian, Yanning Zhu, Xun Deng, Hongtao Jianghan Univ Sch Artificial Intelligence Wuhan 430056 Hubei Peoples R China

Integrating Unmanned Aerial Vehicles (UAVs) into military operations has become increasingly significant in recent years. In practical missions, UAVs are usually operated in unpredictable conditions, requiring robust planning to adapt to real-time changes. Thus, an effective path-planning algorithm is essential for navigating UAVs through complex terrains. However, the commonly used Rapidly-exploring Random Tree (RRT) algorithm for UAV path planning often generates suboptimal paths that require extensive post-processing to improve. While its variants can converge to the optimal solution, they suffer from more memory and computation consumption. On the other hand, purely learning-based methods could result in planning failures or excessively long paths generated for obstacle avoidance. In this work, we introduce a UAV path planning model that integrates diverse sensor data through a neural network architecture featuring CNN, LSTM, and an attention mechanism. Then, the deep learning-enhanced RRT algorithm combines the neural module with the informed sampling methods for adaptive and efficient pathfinding in dynamic environments. After carefully considering the trade-offs among path optimality, computational efficiency, and fewer turning angles, our approach exhibits superior overall performance compared to Informed RRT*, Batch Informed Trees (BIT*) algorithms, and the state-of-the-art Layered RQN.

关键词： Heuristic algorithms Autonomous aerial vehicles Path planning Vehicle dynamics Navigation machine learning algorithms Long short term memory Turning Real-time systems Planning hybrid neural network RRT UAV dynamic environment

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Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma

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BMC CANCER 2025年第1期25卷 1-16页

作者： Shen, Tao Dong, Tingting Wang, Haiyang Ding, Yi Zhang, Jianuo Zhu, Xinyi Ding, Yeping Cai, Wen Wei, Yalan Wang, Qiao Wang, Sufen Jiang, Feiyun Tang, Bin Anhui Normal Univ Coll Life Sci Anhui Prov Engn Res Ctr Mol Detect & Diagnost Anhui Prov Key Lab Mol Enzymol & Mech Major Dis Wuhu Peoples R China East China Normal Univ Peoples Hosp Wuhu City 2 Dept Gynecol Wuhu Affiliated Hosp Wuhu 241000 Peoples R China East China Normal Univ Peoples Hosp Wuhu City 2 Dept Pathol Wuhu Affiliated Hosp Wuhu 241000 Peoples R China Wannan Med Coll Yijishan Hosp Dept Pathol Affiliated Hosp 1 Wuhu Peoples R China

ObjectiveNeuroendocrine cervical carcinoma (NECC) is a rare but highly aggressive tumor. The clinical management of NECC follows neuroendocrine neoplasms and cervical cancer in general. However, the diagnosis and prognosis of NECC remain dismal. The aim of this study was to identify a specific protein signature for the diagnosis of *** and gene expression data for NECC and other cervical cancers were retrieved or downloaded from self-collected samples or public resources. Eleven machine-learning algorithms were packaged into 66 combinations, of which we selected the optimal algorithm, including randomForest, SVM-RFE, and LASSO, to select key NECC specific dysregulated proteins (kNsDEPs). The diagnostic effect of kNsDEPs was validated by a set of predictive models and immunohistochemical staining method. The dysregulation patterns of kNsDEPs were further investigated in other neuroendocrine *** results showed that NECC displays distinctive biological characteristics, such as HPV18 infection, and exhibits unique molecular features, particularly an enrichment in cytoskeleton-related functions. Furthermore, secretagogin (SCGN), adenylyl cyclase-associated protein 2 (CAP2), and calcyclin-binding protein (CACYBP) were identified as kNsDEPs. These kNsDEPs play a central role in cytoskeleton protein binding and showcase robust diagnostic ability and specificity for NECC. Moreover, the concurrent upregulation of SCGN and CACYBP, along with the downregulation of CAP2, represents a unique feature of NECC, distinguishing it from other neuroendocrine *** study uncovers the significance of kNsDEPs and elucidates their regulated networks in the context of NECC. It highlights the pivotal role of kNsDEPs in NECC diagnosis, thus offering promising prospects for the development of diagnostic biomarkers for NECC.

关键词： Neuroendocrine cervical carcinoma Cervical cancer Proteomics machine learning algorithms Predictive model

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Deep learning-Augmented Evolutionary Strategies for Intelligent Global Optimization

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IEEE ACCESS 2025年 13卷 70138-70178页

作者： Ezugwu, Absalom El-Shamir Oyelade, Olaide Nathaniel Agushaka, Jeffrey Ovre Saha, Apu Kumar North West Univ Unit Data Sci & Comp ZA-2520 Potchefstroom South Africa Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT9 5BN North Ireland Fed Univ Lafia Dept Comp Sci Lafia 950101 Nigeria Natl Inst Technol Agartala Dept Math Agartala 799046 Tripura India

This paper introduces the Susceptible-Infected-Removed Optimizer (SIRO), a novel learned heuristic inspired by biological systems and deep learning techniques. SIRO models its search process after the SIR epidemiological compartmental model, predicting the susceptibility, infection, and recovery dynamics of solutions. SIRO integrates deep learning into its initialization and parameter setting to enhance its efficiency, enabling intelligent and adaptive behavior. This hybridization improves solution quality, accelerates convergence, enhances robustness, and reduces computational costs. The algorithm's performance was evaluated using CEC 2017 benchmark functions, demonstrating superior results in hybrid functions (C1-C28) despite moderate performance on traditional CEC1-CEC14 functions. Friedman's test ranked SIRO 4th overall, with SSA as the top-performing algorithm. Additionally, SIRO was tested on real-world optimization problems, including mechanical engineering design, hyperparameter tuning, and feature selection for medical image classification. In the classification task, SIRO-enhanced CNN achieved an accuracy of 0.86 at the 5th epoch, outperforming CNN (0.66), CNN-GA (0.76), and CNN-WOA (0.75). Furthermore, SIRO reported a precision of 0.96, recall of 1.0, and F1-score of 0.98, highlighting its effectiveness. These results validate the benefits of integrating a learning mechanism into SIRO, yielding superior precision, computational efficiency, and performance over conventional optimization approaches.

关键词： Metaheuristics Optimization machine learning algorithms Search problems Heuristic algorithms Deep learning Prediction algorithms MIMICs Genetic algorithms Gravity Susceptible-infected-removed SIR-model optimization algorithms engineering design optimization metaheuristics machine learning deep learning

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Identification of Acacia invasive species in protected areas of Spain using PlanetScope high-resolution satellite images and machine learning models in time series: an important action for protective management of forests

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FOREST ECOLOGY AND MANAGEMENT 2025年 586卷

作者： Eskandari, Saeedeh Acuna-Alonso, Carolina Alvarez, Xana Agr Res Educ & Extens Org AREEO Res Inst Forests & Rangelands RIFR Forest Res Div Tehran Iran Univ Vigo Sch Forestry Engn Hydroforestry Geomodeling Res Grp Pontevedra 36005 Spain

Natural protected areas are important ecosystems in the world which provide valuable habitats for many unique fauna and flora. However, they have been destroyed by many natural and anthropogenic factors in recent years. One of the main destructive factors of forests in these ecosystems is the establishment of Invasive Plant Species (IPS). This study aims to map the forest area and Acacia sp. distribution (as the main IPS) in Cies Islands in northwestern Spain using high resolution satellite images, and machine learning methods in time series. For this purpose, PlanetScope satellite images of the study area were obtained for June of 2016, 2020, and 2024. In addition, some data of location of tree species in forest cover and distribution of Acacia sp. were collected during fieldworks to use them as ground truth data. Four machine learning algorithms (RF, SVM, DT, and ANN) were used to separate the forest cover in time series. Results showed that Random Forest (RF) algorithm was the most accurate method to classify forest cover in the study area (for 2016, OA: 89.16 %, k: 0.86;for 2020, OA: 93.33 %, k: 0.91;for 2024, OA: 95.83 %, k: 0.93). In addition, results demonstrated that the forest area increased in Cies Islands during 8 years based on RF algorithm (2016: 129.99 ha, 2020: 134.05 ha, 2024: 135.45 ha). Results showed that the area of Acacia sp. increased about 28.01 % in these islands over 4 years (2016-2020) and then decreased about 21.31 % in following 4 years (2020-2024). Validation of Acacia sp. distribution maps showed that RF has effectively classified Acacia sp. in Cies Islands in different years (for 2016, OA: 86.66 %, k: 0.82;for 2020, OA: 90, k: 0.87;for 2024, OA: 95 %, k: 0.91). This study presented an innovative methodology that combined satellite imagery and machine learning models to provide the practical tools for forest managers against invasive plant species. The maps produced in this study facilitate the conservation of biodiversity inside protec

关键词： High-resolution satellite images Invasive Plant Species (IPS) machine learning algorithms Forest management Cies Islands Square plot

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Lithologic Mapping in the Karamaili Ophiolite-Mélange Belt in Xinjiang, China, with machine learning and Integration of SDGSAT-1 TIS, Landsat-8 OLI and ASTER-GDEM

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NATURAL RESOURCES RESEARCH 2025年第3期34卷 1437-1465页

作者： Zhang, Zhao Yin, Fang Zhu, Yunqiang Liu, Lei Changan Univ Sch Earth Sci & Resources Xian 710054 Peoples R China Minist Nat Resources New Energy Minerals & Resources Informat Engn Tech Xian 710054 Peoples R China Changan Univ Sch Land Engn Shaanxi Key Lab Land Consolidat Xian 710054 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China

Lithological mapping is an effective tool for geological surveys and mineral exploration. However, it faces challenges in identifying complex rock types and improving classification accuracy. We mapped lithological units in the Karamaili ophiolite-m & eacute;lange belt of Xinjiang using integrated machine learning algorithms, including artificial neural network (ANN), Mahalanobis distance (MD), support vector machine (SVM), and random forest (RF). These algorithms were utilized to process remote sensing datasets acquired by the Sustainable Development Science Satellite 1 Thermal Infrared Spectrometer (SDGSAT-1 TIS), Landsat-8 Operational Land Imager (OLI), and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER-GDEM). The results indicated that the overall accuracies of ANN, MD, SVM, and RF were 68.87%, 78.98%, 93.4%, and 98.36%, respectively. The SVM and RF effectively mapped the lithological units. The SDGSAT-1 TIS data helped to identify mafic-ultramafic and feldspar-rich rocks, while Landsat-8 OLI helped to successfully delineate granitoid and complex lithologies. The ASTER-GDEM data helped improve mapping accuracy by providing detailed topographic information. Thus, this study confirmed the efficacy of the implemented approaches to delineate mineralization zones and to discriminate lithological units. This study provides detailed geological data for lithological mapping and serves as a significant reference for geological surveys and environmental monitoring.

关键词： SDGSAT-1 TIS Rock-forming minerals Karamaili ophiolitem & eacute lange belt Lithology mapping machine learning algorithms

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Academic Performance Prediction Using machine learning Approaches: A Survey

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IEEE TRANSACTIONS ON learning TECHNOLOGIES 2025年 18卷 351-368页

作者： Pan, Jialun Zhao, Zhanzhan Han, Dongkun Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China Chinese Univ Hong Kong Shenzhen Sch Humanities & Social Sci Shenzhen Peoples R China

Properly predicting students'academic performance is crucial for elevating educational outcomes in various disciplines. Through precise performance prediction, schools can quickly pinpoint students facing challenges and provide customized educational materials suited to their specific learning needs. The reliance on teachers' experience to predict students' academic performance has proven to be less accurate and efficient than desired. Consequently, the past decade has witnessed a marked surge in employing machine learning and data mining techniques to forecast students' performance. However, the academic community has yet to agree on the most effective algorithm for predicting academic outcomes. Nonetheless, conducting an analysis and comparison of the existing algorithms in this field remains meaningful. Furthermore, recommendations for selecting an appropriate algorithm will be provided to interested researchers and educators based on their specific requirements. This article reviews the state-of-the-art literature on academic performance predictions using machine learning approaches in recent years. It details the variables analyzed, the algorithms implemented, the datasets utilized, and the evaluation metrics applied to assess model efficacy. What makes this work different is that relevant surveys in the past 10 years are also analyzed and compared, highlighting their contributions and review methods. In addition, we compared the accuracy of various machine learning models using popular open-access datasets and determined the best-performing algorithms among them. Our dataset and source codes are released for future algorithm comparisons and evaluations in this community.

关键词： Reviews Prediction algorithms Surveys machine learning Data mining machine learning algorithms Random forests Artificial intelligence Accuracy Logistic regression Artificial intelligence (AI) in education learning analytics machine learning neural networks (NNs) performance prediction students' performance supervised learning survey

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Identification and Validation of Circadian Rhythm-Related Genes Involved in Intervertebral Disc Degeneration and Analysis of Immune Cell Infiltration via machine learning

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JOR SPINE 2025年第2期8卷 e70066页

作者： Zhang, Yongbo Chen, Liuyang Yang, Sheng Dai, Rui Sun, Hua Zhang, Liang Yangzhou Univ Northern Jiangsu Peoples Hosp Dept Orthoped Yangzhou Peoples R China Dalian Med Univ Yangzhou Sch Clin Med Dept Orthoped Yangzhou Peoples R China Northern Jiangsu Peoples Hosp Dept Orthoped Yangzhou Peoples R China

BackgroundLow back pain is a significant burden worldwide, and intervertebral disc degeneration (IVDD) is identified as the primary cause. Recent research has emphasized the significant role of circadian rhythms (CRs) and immunity in affecting intervertebral discs (IVD). However, the influence of circadian rhythms and immunity on the mechanism of IVDD remains unclear. This study aimed to identify and validate key rhythm-related genes in IVDD and analyze their correlation with immune cell *** gene expression profiles related to IVDD and rhythm-related genes were obtained from the Gene Expression Omnibus and GeneCards databases to identify differentially expressed rhythm-related genes (DERGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) were conducted to explore the biological functions of these genes. LASSO regression and SVM algorithms were employed to identify hub genes. We subsequently investigated the correlation between hub rhythm-related genes and immune cell infiltration. Finally, nucleus pulposus-derived mesenchymal stem cells (NPMSCs) were isolated from normal and degenerative human IVD tissues. Hub rhythm-related genes expression in NPMSCs was confirmed by real-time quantitative PCR (RT-qPCR).ResultsSix hub genes related to CRs (CCND1, FOXO1, FRMD8, NTRK2, PRRT1, and TFPI) were screened out. Immune infiltration analysis revealed that the IVDD group had significantly more M0 macrophages and significantly fewer follicular helper T cells than those of the control group. Specifically, M0 macrophages were significantly associated with FRMD8, PRRT1, and TFPI. T follicular helper cells were significantly associated with FRDM8, FOXO1, and CCND1. We further confirmed that CCND1, FRMD8, NTRK2, and TFPI were dysrhythmic within NPMSCs from degenerated IVD in *** genes (CCND1, FOXO1, FRMD8, NTRK2, PRRT1 and TFPI) linked to circadian rhythms associated with IVDD progression

关键词： bioinformatics circadian rhythms immune cell infiltration intervertebral disc degeneration machine learning algorithms mesenchymal stem cell

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Advanced radiomic prediction of osteoporosis in primary hyperparathyroidism: a machine learning-based analysis of CT images

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RADIOLOGIA MEDICA 2025年 1-8页

作者： Adarve-Castro, Antonio Soria-Utrilla, Virginia Castro-Garcia, Jose Miguel Dominguez-Pinos, Maria Dolores Sendra-Portero, Francisco Ruiz-Gomez, Miguel J. Algarra-Garcia, Jose Univ Malaga Fac Med Dept Radiol & Med Fis Bulevar Louis Pasteur 32 Malaga 29010 Spain Univ Hosp Virgen Victoria Malaga Spain Reg Univ Hosp Malaga Malaga Spain Gen Univ Hosp Alicante Alicante Spain

This study aims to assess the proficiency of supervised machine learning techniques in discriminating between normal and abnormal bone mineral density (BMD) by leveraging clinical features and texture analysis of spinal bone tissue in patients diagnosed with primary hyperparathyroidism (PHP). From a total of 219 patients diagnosed with PHP, the 58 who had undergone both DXA and abdominal CT scan were included in this study. BMD was assessed by quantifying the Hounsfield units (HU) and performing texture analysis on every CT scan. The first lumbar vertebral body texture features were extracted by using LifeX 7.3.0 software. Initial classification into normal or abnormal BMD was performed with different machine learning techniques by training a model with the variables obtained from the texture analysis. Differentiating osteopenia from osteoporosis was evaluated by creating two models, one including the variables obtained from the texture analysis and HU and another one which only included the HU. Their performance was evaluated in the validation and test groups by calculating the accuracy, precision, recall, F1 score, and AUC. Bayes demonstrated higher performance for discerning individuals with normal and abnormal BMD, with an AUC of 0.916. The results from the second analysis showed a better performance for the model including the variables obtained from the texture analysis compared to the model that was solely trained with the HU (AUC in the training group of 0.77 vs. 0.65 in the test groups, respectively). In conclusion, analysis of BMD obtained from abdominal CT scans including texture analysis provide a better classification of normal density, osteopenia and osteoporosis in patients with PHP.

关键词： Osteoporosis Bone mineral density Hyperparathyroidism Radiomic textures machine learning algorithms

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