Developing a Sleep Algxorithm to Support a Digital Medicine System: Noninterventional, Observational Sleep Study by Jeffrey M Cochran

“…Patch-acquired ACC and ECG data were compared against PSG data to build machine learning classification models to distinguish periods of wake from sleep. …”
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Perilesional dominance: radiomics of multiparametric MRI enhances differentiation of IgG4-Related ophthalmic disease and orbital MALT lymphoma by Jie Li, Chenran Zhou, Xiaoxia Qu, Lianze Du, Qinghai Yuan, Qinghe Han, Junfang Xian

“…DCA demonstrated a net benefit of 0.18 at a high-risk threshold of 30%, equivalent to avoiding 18 unnecessary biopsies per 100 cases. …”
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Medicinal plants with neuroprotective potential in East African countries: A systematic review by Ibe Michael Usman, Ekom Monday Etukudo, Elna Owembabazi, Wusa Makena, Augustine Oviosun, Makeri Danladi, Vivian Ojiakor, Patrick Maduabuchi Aja, Emeka Anyanwu, Yanurkis Duranones Rosales, Mario Edgar Fernandez Diaz, Josiah Ifie, Ismahil Adekunle Adeniyi, Ilemobayo Victor Fasogbon, Victor Bassey Archibong

“…Behavioral outcomes aligned with these biochemical changes, showing marked improvements in memory and learning, motor activity, seizure threshold, and anxiety-like behavior. …”
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Zero-shot building footprint extraction and regularization based on Segment Anything model with Mesh Model by J. Zhong, Y. Zhang, Y. Zhang, X. Liu, X. Liu, J. Zhang, L. Fei, W. Xia, B. Zhang, W. Fan, D. Yue

“…Traditional manual contouring methods are time-consuming and laborious, while deep learning-based building extraction methods often require a large amount of labeled data and have limited generalization ability. …”
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Nasal pathobiont abundance is a moderate feedlot-dependent indicator of bovine respiratory disease in beef cattle by Ruth Eunice Centeno-Delphia, Natalie Glidden, Erica Long, Audrey Ellis, Sarah Hoffman, Kara Mosier, Noelmi Ulloa, Johnnie Junior Cheng, Josiah Levi Davidson, Suraj Mohan, Mohamed Kamel, Josh I. Szasz, Jon Schoonmaker, Jennifer Koziol, Jacquelyn P. Boerman, Aaron Ault, Mohit S. Verma, Timothy A. Johnson

“…Utilizing the abundance of these pathobionts and analyzing their combined abundance with machine learning models resulted in an accuracy of approximately 63% for sample classification into disease status. …”
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Deconvolving X-Ray Galaxy Cluster Spectra Using a Recurrent Inference Machine by Carter Lee Rhea, Julie Hlavacek-Larrondo, Alexandre Adam, Ralph Kraft, Ákos Bogdán, Laurence Perreault-Levasseur, Marine Prunier

“…Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new frontiers. …”
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Coupling HEC-RAS and AI for River Morphodynamics Assessment Under Changing Flow Regimes: Enhancing Disaster Preparedness for the Ottawa River by Mohammad Uzair Anwar Qureshi, Afshin Amiri, Isa Ebtehaj, Silvio José Guimere, Juraj Cunderlik, Hossein Bonakdari

“…Despite significant advancements in flood forecasting using machine learning (ML) algorithms, recent events have revealed hydrological behaviors deviating from historical model development trends. …”
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Can Peritumoral Radiomics Based on MRI Predict the Microvascular Invasion Status of Combined Hepatocellular Carcinoma and Cholangiocarcinoma Before Surgery? by Guo L, Huang C, Hao P, Jia N, Zhang L

“…The DCA shows that when the threshold is approximately 11.08%– 66.47%, the net return of the fusion model is higher than that of the clinical and radiomics models under the same conditions.Conclusion: Radiomics with a 1cm extension around the tumor can improve the performance of machine-learning models in predicting MVI labels.Keywords: peritumoral radiomics, microvascular invasion, combined hepatocellular carcinoma and cholangiocarcinoma, Shapley additive explans…”
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Associations of greenhouse gases, air pollutants and dynamics of scrub typhus incidence in China: a nationwide time-series study by Haoyue Cao, Jianqiang Han, Weiming Hou, Juxiang Yuan

“…First, an early warning method was applied to estimate the epidemic threshold and the grading intensity threshold. Second, four statistical methods were used to assess the correlation and lag effects across different age groups and epidemic periods. …”
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Finding New Hot Subdwarf Stars in SDSS Images by Jiangchuan Zhang, Yude Bu, Huili Wu, Yuhang Zhang, Shanshan Li, Zhenxin Lei, Zhenping Yi, Xiaoming Kong, Meng Liu

“…Many studies apply machine learning classification to search for hot subdwarf stars based on high quality spectra. …”
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π-π2max: Bridging molecular characteristics to crystal packing in nitro-containing two-dimensional energetic materials by Xiaokai He, Chao Chen, Zhixiang Zhang, Linyuan Wen, Yiding Ma, Yilin Cao, Yingzhe Liu

“…Furthermore, the proposed model shows superior classification predictive performance compared to typical machine learning methods, such as random forest, on the external validation samples. …”
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Clinical and cost-effectiveness of parenting intervention for mothers experiencing psychosocial stress: insights from the early closure of the Mellow Babies RCT by Lucy Thompson, Jessica Tanner, Matthew Breckons, Naomi Young, Laura Ternent, Thenmalar Vadiveloo, Philip Wilson, Danny Wight, Louise Marryat, Iain McGowan, Graeme MacLennan, Angus MacBeth, James McTaggart, Tim Allison, John Norrie

“…Baseline, follow-up and process evaluation data were analysed to allow optimal learning from the study. Direct communication (letter) combined with health visitor referral was a better means of recruitment. …”
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Prediction of solid pseudopapillary tumor invasiveness of the pancreas based on multiphase contrast-enhanced CT radiomics nomogram by Dabin Ren, Liqiu Liu, Aiyun Sun, Yuguo Wei, Tingfan Wu, Yongtao Wang, Xiaxia He, Zishan Liu, Jie Zhu, Guoyu Wang

“…Radiomics features were extracted from the contrast-enhanced CT images, and logistic regression analysis was employed to establish a machine learning model, including an unenhanced model (model U), an arterial phase model (model A), a venous phase model (model V), and a combined radiomics model (model U+A+V). …”
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Analysis of influencing factors for primary membranous nephropathy complicated with atherosclerotic cardiovascular disease and establishment of a nomogram prediction model by Mirixiatijiang· Maimaiti, Zhu Guo-qiang, Su Ming-jie, Halinuer· Shadekejiang, Zhang Xue-qin, Lu Chen

“…ObjectiveTo investigate the risk factors for atherosclerotic cardiovascular disease（ASCVD） in primary membranous nephropathy（PMN） patients by machine deep learning， and to construct a nomogram.MethodsThis was a retrospective study including 620 patients with membranous nephropathy diagnosed by renal puncture in the First Affiliated Hospital of Xinjiang Medical University from September 1， 2017 to May 31， 2023. …”
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Diagnostic Value of F-FDG PET/CT Radiomics in Lymphoma: A Systematic Review and Meta-Analysis by Chaoying Liu MD, Jun Zhao PhD, Heng Zhang PhD, Xinye Ni PhD

“…Introduction Various machine learning models and features have been proposed for lymphoma diagnosis using 18 F-fluorodeoxyglucose ( 18 F-FDG) PET/CT radiomics. …”
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Alternative splicing analysis in a Spanish ASD (Autism Spectrum Disorders) cohort: in silico prediction and characterization by S. Dominguez-Alonso, M. Tubío-Fungueiriño, J. González-Peñas, M. Fernández-Prieto, M. Parellada, C. Arango, A. Carracedo, C. Rodriguez-Fontenla

“…We utilized SpliceAI, a newly developed machine-learning tool, to identify high-confidence splicing variants in the Spanish ASD cohort and applied a stringent threshold (Δ ≥ 0.8) to ensure robust confidence in the predictions. …”
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POS Tagging Bahasa Madura dengan Menggunakan Algoritma Brill Tagger by Nindian Puspa Dewi, Ubaidi Ubaidi

“…One reason is sense of pride and also quite difficult to learn Bahasa Madura because it has a variety of dialects and language levels. …”
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Classification-based point cloud denoising and 3D reconstruction of roadways by Denghong CHEN, Ning PANG, Wen NIE, Juqiang FENG, Jiliang KAN, Jinjing ZHANG

“…Accordingly, this study designed a deep-learning implicit surface reconstruction method based on signed distance functions (SDFs). …”
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Development and validation of a postoperative prognostic model for hormone receptor positive early stage breast cancer recurrence by Ruixin Pan, Haoting Shi, Yiqing Shen, Xue Wang, Shi Zhao, Nan Zhang, Xueyan Zhang, Shuwen Dong, Chao Hu, Jiayi Wu, Weimin Chai, Xiaosong Chen, Kunwei Shen

“…The model performance was evaluated using C-index for the overall population and subgroups. Threshold for selecting 5-year recurrence risk > 10% was determined. …”
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Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens by Paul English, Muir J. Morrison, Blaine Mathison, Elizabeth Enrico, Ryan Shean, Brendan O'Fallon, Deven Rupp, Katie Knight, Alexandra Rangel, Jeffrey Gilivary, Amanda Vance, Haleina Hatch, Leo Lin, David P. Ng, Salika M. Shakir

“…Following acid-fast staining, whole slide images (WSI) were digitized, and AFB organisms were manually annotated. A machine learning computer vision model was trained using 11,411 annotated organisms across 109 WSI. …”
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