Interchangeability of Cross-Platform Orthophotographic and LiDAR Data in DeepLabV3+-Based Land Cover Classification Method by Shijun Pan, Keisuke Yoshida, Satoshi Nishiyama, Takashi Kojima, Yutaro Hashimoto

“…A unique and crucial aspect of this study is exploring the interchangeability of land cover classification models across different LiDAR platforms.…”
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Validation of Deep Learning–Based Automatic Retinal Layer Segmentation Algorithms for Age-Related Macular Degeneration with 2 Spectral-Domain OCT Devices by Souvick Mukherjee, PhD, Tharindu De Silva, PhD, Cameron Duic, BS, Gopal Jayakar, BS, Tiarnan D.L. Keenan, BM BCh, PhD, Alisa T. Thavikulwat, MD, Emily Chew, MD, Catherine Cukras, MD, PhD

“…This work validates deep learning image segmentation models across multiple OCT devices by testing robustness in generating clinically relevant metrics. …”
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Immunological landscape of children with Mycoplasma pneumoniae pneumonia in the post-COVID-19 era reveals distinctive severity indicators by Ran Jia, Haiyan Guo, Aizhen Lu, Caiyan Zhang, Yuanyuan Qi, Dingmei Wang, Wen He, Qing Wang, Zimei Cheng, Yajing Gao, Guoping Lu, Libo Wang, Xiaowen Zhai, Jin Xu, Xiaobo Zhang, Yi Wang, Yufeng Zhou

“…The proportion of naïve B cells was reduced, while functional B cells, including memory B cells and plasmablasts, increased in S-MPP. 12 out of 95 clinical laboratory indicators and 3 out of 48 cytokines significantly differed between S-MPP and NS-MPP. Finally, we performed logistic and LASSO regression analyses and developed a predictive model for S-MPP that incorporates naïve B cell percentage from flow cytometry, cholinesterase from clinical laboratory tests, and interleukin 18 from the cytokine assay. …”
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The m6A modification of LINC01133 suppresses ER+ breast cancer progression by modulating IGF2BP2 protein stability via a ubiquitination-dependent mechanism by Mai-dong Li, Mai-dong Li, Ben-jie Shan, Lei Wang, Shuang Gao, Li Hao, Hai-yang Yu, Yue-yin Pan, Yue-yin Pan

“…BackgroundBreast cancer is characterized as highly heterogenous and is a representative model to understand how molecular features of tumor biology determine therapeutic strategy. …”
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Detection of Sentence Boundaries in Polish Based on Acoustic Cues by Magdalena IGRAS, Bartosz ZIÓŁKO

“…Another significant outcome of the research is statistical models of acoustic cues correlated with punctuation in spoken Polish.…”
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Multiple Imputation Based on Conditional Quantile Estimation by Matteo Bottai, Huiling Zhen

“…The method does not require modeling a likelihood and has desirable features that may be useful in some practical settings. …”
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Experience analysis and forms of alienation by Søren Nagbøl

“…It outlines the characteristic features of the experience society; that is, the replacement of a society characterised by labour and a scarcity of resources with a society of abundance, defined by a psychophysical semantics. …”
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Challenges in Imputation of ICU Time-Series Data: A Comparison of Classical and Machine Learning Approaches by Favio Salinas, Marvin Agristean, Sobhan Moazemi, Steven Kessler, Bastian Dewitz, Hug Aubin, Artur Lichtenberg, Falko Schmid

“…TL was implemented by transforming COPRA to align with MIMIC-III, enabling fine-tuning of pre-trained models. TranSAITS achieved the best performance on the transformed dataset (M->C). …”
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Progression to fibrosis and hepatocellular carcinoma in DEN CCl4 liver mice, is associated with macrophage and striking regulatory T cells infiltration by Ananya Ajith, Jonathan Evraerts, Caroline Bouzin, Davide Brusa, Makram Merimi, Mehdi Najar, Mehdi Najar, Françoise Smets, Etienne Sokal, Mustapha Najimi

“…Regulatory T cells (Tregs) population exhibits an extraordinary 125-fold and 80-fold surge in the IM and NTT regions, respectively.ConclusionsThe DEN-CCl4 induced HCC mouse model replicates key immunosuppressive features of human HCC, notably increased Tregs and macrophages, which provides a robust platform for testing immunotherapies. …”
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Decoupling Urban Street Attractiveness: An Ensemble Learning Analysis of Color and Visual Element Contributions by Tao Wu, Zeyin Chen, Siying Li, Peixue Xing, Ruhang Wei, Xi Meng, Jingkai Zhao, Zhiqiang Wu, Renlu Qiao

“…SHAP assigns each feature a quantitative contribution to the model’s output, enabling transparent, post hoc explanations of how individual color metrics and visual elements drive VAPS. …”
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<i>Cirsium setosum</i> Extract-Loaded Hybrid Nanostructured Scaffolds Incorporating a Temperature-Sensitive Polymer for Mechanically Assisted Wound Healing by Xiaojing Jiang, Shaoxuan Zhu, Jinying Song, Xingwei Li, Chengbo Li, Guige Hou, Zhongfei Gao

“…In vitro studies confirmed the thistle extract’s potent antioxidant activity, while in vivo experiments revealed their effective hemostatic performance in a liver bleeding model when delivered via the composite nanofibers. …”
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From Labquakes to Megathrusts: Scaling Deep Learning Based Pickers Over 15 Orders of Magnitude by Peidong Shi, Men‐Andrin Meier, Linus Villiger, Katinka Tuinstra, Paul Antony Selvadurai, Federica Lanza, Sanyi Yuan, Anne Obermann, Maria Mesimeri, Jannes Münchmeyer, Patrick Bianchi, Stefan Wiemer

“…We propose four strategies—rescaling, model aggregation, shifting, and filtering—to enhance the performance of pre‐trained models on out‐of‐distribution data sets. …”
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Klasifikasi Sinyal Phonocardiogram Menggunakan Short Time Fourier Transform dan Convolutional Neural Network by Muhammad Alwi Adnan Amal, Dodi Zulherman, Rahmat Widadi

“…The system design test used a secondary dataset with 2,575 normal PCG records and 665 abnormal PCG records in wav format. Performance testing uses variations of Hamming, Hann and Blackman-Harris Window in the feature extraction section and variations in the number of convolution layers in the classification section. …”
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Machine Learning Applications for Predicting Longitudinal Cracking in Continuously Reinforced Concrete Pavement by Ali Alnaqbi, Ghazi G. Al-Khateeb, Waleed Zeiada

“…Conventional regression models provide insights into influential factors, but recognizing their limitations, the study extends to machine learning models. …”
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DCDGAN-STF: A Multiscale Deformable Convolution Distillation GAN for Remote Sensing Image Spatiotemporal Fusion by Yan Zhang, Rongbo Fan, PeiPei Duan, Jinfang Dong, Zhiyong Lei

“…Among the existing fusion algorithms, deep learning-based fusion models have demonstrated outstanding performance. …”
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Risk Factors and Clinical Predictors of Suicidal Behaviors and Non-Suicidal Self-Injury Among Pediatric Psychiatry Emergency Admissions Pre- and Post-Pandemic: A Retrospective Coho... by Roxana Șipoș, Tudor Văidean, Elena Predescu

“…The random forest model featured a slightly different order of the main predictors and highlighted the importance of additional predictors, such as prior suicide attempts, gender, and past non-suicidal self-injury. …”
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MetaTrans-FSTSF: A Transformer-Based Meta-Learning Framework for Few-Shot Time Series Forecasting in Flood Prediction by Jiange Jiang, Chen Chen, Anna Lackinger, Huimin Li, Wan Li, Qingqi Pei, Schahram Dustdar

“…While deep learning methods have demonstrated exceptional performance in time series prediction through advanced feature extraction and pattern recognition, they encounter significant limitations when applied to scenarios with sparse data, especially in flood forecasting. …”
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A Transcriptional Signature of Fatigue Derived from Patients with Primary Sjögren's Syndrome. by Katherine James, Shereen Al-Ali, Jessica Tarn, Simon J Cockell, Colin S Gillespie, Victoria Hindmarsh, James Locke, Sheryl Mitchell, Dennis Lendrem, Simon Bowman, Elizabeth Price, Colin T Pease, Paul Emery, Peter Lanyon, John A Hunter, Monica Gupta, Michele Bombardieri, Nurhan Sutcliffe, Costantino Pitzalis, John McLaren, Annie Cooper, Marian Regan, Ian Giles, David Isenberg, Vadivelu Saravanan, David Coady, Bhaskar Dasgupta, Neil McHugh, Steven Young-Min, Robert Moots, Nagui Gendi, Mohammed Akil, Bridget Griffiths, UK Primary Sjögren’s Syndrome registry, Anil Wipat, Julia Newton, David E Jones, John Isaacs, Jennifer Hallinan, Wan-Fai Ng

“…However, although fatigue is common in pSS, it does not manifest in all sufferers, providing an excellent model with which to explore the potential underpinning biological mechanisms.…”
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Estimation of Leaf, Spike, Stem and Total Biomass of Winter Wheat Under Water-Deficit Conditions Using UAV Multimodal Data and Machine Learning by Jinhang Liu, Wenying Zhang, Yongfeng Wu, Juncheng Ma, Yulin Zhang, Binhui Liu

“…Correlation analysis and Variance Inflation Factor (VIF) were employed for feature selection, and estimation models for leaf, spike, stem, and total AGB were constructed using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN) models. …”
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Predicting ROS1 and ALK fusions in NSCLC from H&E slides with a two-step vision transformer approach by Eghbal Amidi, Mohammadreza Ramzanpour, Ming Chen, Tommy Boucher, Mukund Varma, Timothy Samec, Brian Lamon, Nicolas Stransky, Mark R. Miglarese, Matthew Oberley, David Spetzler, George W. Sledge

“…A vision transformer model (MoCo-V3) was trained as a feature extractor, followed by training transformer-based models to predict the presence of ROS1 and ALK fusions. …”
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