ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications by A. Bellini, A. Bellini, A. Bellini, H. Diémoz, L. Di Liberto, G. P. Gobbi, A. Bracci, F. Pasqualini, F. Barnaba

“…Specific comparisons of the ALICENET products to independent measurements obtained with different techniques, such as particulate matter (PM) concentrations from in situ samplers and aerosol optical depth (AOD) from sun photometers, are also included here, revealing the good performances of the ALICENET algorithms. Overall, ALICENET represents a valuable resource to extend the current aerosol observational capabilities in Italy and in the Mediterranean area, and it contributes to bridging the gap between atmospheric science and its application to specific sectors, among which are air quality, solar energy, and aviation safety.…”
Get full text

Novel exosome-associated LncRNA model predicts colorectal cancer prognosis and drug response by Chi Zhou, Qian Qiu, Xinyu Liu, Tiantian Zhang, Leilei Liang, Yihang Yuan, Yufo Chen, Weijie Sun

“…Conclusion In this study, a variety of machine learning algorithms were used to construct the EALncRI based on ERG, which can effectively predict the prognosis and distinguish the immune landscape of CRC. …”
Get full text

Predicting biomass transportation costs: A machine learning approach for enhanced biofuel competitiveness by Ali Omidkar, Razieh Es’haghian, Hua Song

“…Consequently, this study explores the predictive capabilities of two alternative machine learning algorithms: random forests and artificial neural networks. …”
Get full text

Using Pleiades Satellite Imagery to Monitor Multi-Annual Coastal Dune Morphological Changes by Olivier Burvingt, Bruno Castelle, Vincent Marieu, Bertrand Lubac, Alexandre Nicolae Lerma, Nicolas Robin

“…However, ongoing improvements on the stereo matching algorithms and spatial resolution of the satellite sensors (e.g., Pleiades Neo) highlight the growing potential of Pleiades images as a cost-effective alternative to other mapping techniques of coastal dune topography.…”
Get full text

Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices? by Efrain Noa-Yarasca, Javier M. Osorio Leyton, Chad B. Hajda, Kabindra Adhikari, Douglas R. Smith

“…This study underscores the importance of spatial context in corn yield prediction and lays the foundation for future research across diverse agricultural settings, focusing on optimizing neighborhood size, integrating spatial and spectral data, and refining spatial dependencies through localized search algorithms.…”
Get full text

Using Video Cameras to Assess Physical Activity and Other Well-Being Behaviors in Urban Environments: Feasibility, Reliability, and Participant Reactivity Studies by Jack S Benton, James Evans, Jamie Anderson, David P French

“…It also provides a rigorous foundation for developing more scalable automated computer vision algorithms for assessing human behaviors.…”
Get full text

ATP6V0A4 as a novel prognostic biomarker and potential therapeutic target in oral squamous cell carcinoma by Xiaopu Gao, Jiamin Zhou, Yu Qiao, Chuyin Lin, Guanxiong Zhang, Qiuyu Wu, Zhikang Su, Qianji Zhang, Songkai Huang

“…A prognostic model was constructed using univariate Cox regression and LASSO regression, complemented by random forest algorithms to identify core genes. Subsequently, a multi-omics analysis strategy was employed, systematically conducting pan-cancer expression profiling, human protein atlas validation, GO/KEGG enrichment analysis, clinicopathological feature correlation analysis, and tumor immune microenvironment assessment. …”
Get full text

Machine Learning and Deep Learning Hybrid Approach Based on Muscle Imaging Features for Diagnosis of Esophageal Cancer by Yuan Hong, Hanlin Wang, Qi Zhang, Peng Zhang, Kang Cheng, Guodong Cao, Renquan Zhang, Bo Chen

“…Diagnostic models were developed using deep learning (2D and 3D neural networks) and traditional machine learning (11 algorithms with PyRadiomics-derived features). Multimodal features underwent Principal Component Analysis (PCA) for dimension reduction and were fused for final analysis. …”
Get full text

Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features by Jingna Tao, Zhongmian Zhang, Linghan Meng, Liju Zhang, Jiaqi Wang, Zhihong Li

“…We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. Receiver operating characteristic (ROC) curves were plotted using Python 3.12.4. …”
Get full text

LEVELS OF PATIENTS EXPOSURE AND A POTENTIAL FOR OPTIMIZATION OF THE PET DIAGNOSTICS IN THE RUSSIAN FEDERATION by L. A. Chipiga, I. A. Zvonova, D. V. Ryzhkova, M. A. Menkov, М. B. Dolgushin

“…Low dose computed tomography protocols, justification of diagnostic and multiphase computed tomography protocols, application of tube current modulation system and modern reconstruction algorithms, education and training of the staff in the field of radiation protection should be used for optimization of radiation protection of patient.…”
Get full text

Exploration of biomarkers for predicting the prognosis of patients with diffuse large B-cell lymphoma by machine-learning analysis by Shifen Wang, Hong Tao, Xingyun Zhao, Siwen Wu, Chunwei Yang, Yuanfei Shi, Zhenshu Xu, Dawei Cui

“…Moreover, four hub genes (CXCL9, CCL18, C1QA and CTSC) were significantly screened from the three datasets using RF algorithms. They were closely correlated with the overall survival of DLBCL patients. …”
Get full text

Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes by Guillermo Umpierrez, Vernon M Chinchilli, Erik B Lehman, Ariana Pichardo-Lowden, Matthew D Bolton, Christopher J DeFlitch, Paul M Haidet

“…We piloted an electronic medical record (EMR), evidence-based algorithmic CDS tool in an academic center to alert clinicians in real time about gaps in care related to inpatient glucose control and insulin utilization, and to provide management recommendations.Research design and methods The tool was designed to identify clinical situations in need for action: (1) severe or recurrent hyperglycemia in patients with diabetes: blood glucose (BG) ≥13.88 mmol/L (250 mg/dL) at least once or BG ≥10.0 mmol/L (180 mg/dL) at least twice, respectively; (2) recurrent hyperglycemia in patients with stress hyperglycemia: BG ≥10.0 mmol/L (180 mg/dL) at least twice; (3) impending or established hypoglycemia: BG 3.9–4.4 mmol/L (70–80 mg/dL) or ≤3.9 mmol/L (70 mg/dL); and (4) inappropriate sliding scale insulin (SSI) monotherapy in recurrent hyperglycemia, or anytime in patients with type 1 diabetes. …”
Get full text

Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease by Heather Mary-Kathleen Kosick, Chris McIntosh, Chinmay Bera, Mina Fakhriyehasl, Mohamed Shengir, Oyedele Adeyi, Leila Amiri, Giada Sebastiani, Kartik Jhaveri, Keyur Patel

“…We aimed to evaluate machine learning (ML) algorithms based on simple NIT and US for prediction of adverse liver-related outcomes in MASLD. …”
Get full text

Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis by Pang Z, Ou Y, Liang J, Huang S, Chen J, Huang S, Wei Q, Liu Y, Qin H, Chen Y

“…By combining logistic regression analysis with six machine learning algorithms, this study identified six predictors associated with PWH: subcutaneous lumbar spine index(SLSI), albumin, postoperative glucose, cerebrospinal fluid leakage(CSFL), neutrophil (NEU), and C-reactive protein(CRP). …”
Get full text

Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury by Chen Lin, Meng Zheng, Wensi Wu, Zhishan Wang, Guofeng Lu, Shaodan Feng, Xinlan Zhang

“…We assessed 113 combinations of 12 different algorithms to develop an internally and externally validated machine-learning model for diagnosing AKI. …”
Get full text

Validity of electronically administered Recent Physical Activity Questionnaire (RPAQ) in ten European countries. by Rajna Golubic, Anne M May, Kristin Benjaminsen Borch, Kim Overvad, Marie-Aline Charles, Maria Jose Tormo Diaz, Pilar Amiano, Domenico Palli, Elisavet Valanou, Matthaeus Vigl, Paul W Franks, Nicholas Wareham, Ulf Ekelund, Soren Brage

“…Revisiting occupational intensity assumptions in questionnaire estimation algorithms with occupational group-level empirical distributions reduced median PAEE-bias in manual (38.8 kJ/kg/day vs. 6.8 kJ/kg/day, p<0.001) and heavy manual workers (63.6 vs. -2.8 kJ/kg/day, p<0.001) in an independent hold-out sample [corrected].…”
Get full text

Benchmarking Federated Few-Shot Learning for Video-Based Action Recognition by Nguyen Anh Tu, Nartay Aikyn, Nursultan Makhanov, Assanali Abu, Kok-Seng Wong, Min-Ho Lee

“…Additionally, we explore three meta-learning paradigms and three FL algorithms to investigate their effectiveness and suggest the optimal choices for performance improvement. …”
Get full text

Remote sensing reveals the role of forage quality and quantity for summer habitat use in red deer by Thomas Rempfler, Christian Rossi, Jan Schweizer, Wibke Peters, Claudio Signer, Flurin Filli, Hannes Jenny, Klaus Hackländer, Sven Buchmann, Pia Anderwald

“…In particular, our approach employed random forest regression algorithms, integrating various remote sensing variables such as reflectance values, vegetation indices and optical traits derived from a radiative transfer model. …”
Get full text

Identification and validation of CKAP2 as a novel biomarker in the development and progression of rheumatoid arthritis by Qiongbing Zheng, Qiongbing Zheng, Youmian Lan, Jiexin Chen, Ling Lin, Ling Lin

“…Differentially expressed gene (DEG) analysis, functional enrichment analysis, and weighted gene co-expression network analysis (WGCNA) identified key gene modules in RA. Machine learning algorithms were used to identify hub genes, followed by immune infiltration analysis and gene set variation analysis (GSVA). …”
Get full text

Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models by Venkat U.C. Bodempudi, Guoming Li, J. Hunter Mason, Jeanna L. Wilson, Tianming Liu, Khaled M. Rasheed

“…The DLM framework included a bird detection model, data filtering algorithms based on mating duration, and logic frameworks for mating identification based on bird count changes. …”
Get full text