Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE by Nan Wei, René Michael Mathy, De-Hua Chang, Philipp Mayer, Jakob Liermann, Christoph Springfeld, Michael T Dill, Thomas Longerich, Georg Lurje, Hans-Ulrich Kauczor, Mark O. Wielpütz, Osman Öcal

“…This study aims to develop and validate a predictive model that integrates deep learning and machine learning algorithms on longitudinal contrast-enhanced MRI (CE-MRI) to predict treatment response in HCC patients undergoing DEB-TACE. …”
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Evaluating Leaf Water Potential of Maize Through Multi-Cultivar Dehydration Experiments and Segmentation Thresholding by Shuanghui Zhao, Yanqun Zhang, Pancen Feng, Xinlong Hu, Yan Mo, Hao Li, Jiusheng Li

“…The analysis screened published VIs highly correlated with <i>Ψ_leaf</i> and constructed a model for <i>Ψ_leaf</i> estimation based on three algorithms—partial least squares regression (PLSR), random forest (RF), and multiple linear stepwise regression (MLR)—for each cultivar and all three cultivars. …”
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Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning by Chenbo Yang, Chenbo Yang, Meichen Feng, Juan Bai, Hui Sun, Rutian Bi, Lifang Song, Chao Wang, Yu Zhao, Wude Yang, Lujie Xiao, Meijun Zhang, Xiaoyan Song

“…Hyperspectral monitoring models for winter wheat ChD were constructed using 8 machine learning algorithms, including partial least squares regression, support vector regression, multi-layer perceptron regression, random forest regression, extra-trees regression (ETsR), decision tree regression, K-nearest neighbors regression, and gaussian process regression, based on the full spectrum band and the band selected by competitive adaptive reweighted sampling (CARS). …”
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Performance Evaluation of Robotic Harvester with Integrated Real-Time Perception and Path Planning for Dwarf Hedge-Planted Apple Orchard by Tantan Jin, Xiongzhe Han, Pingan Wang, Yang Lyu, Eunha Chang, Haetnim Jeong, Lirong Xiang

“…A custom control system integrating all components was validated in partially controlled orchards, where obstacle clearance and thinning were conducted to ensure operation safety. …”
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Prevention and Management of Spinal Cord Ischemia After Aortic Surgery: An Umbrella Review by Alexandros G. Brotis, Adamantios Kalogeras, Metaxia Bareka, Eleni Arnaoutoglou, Kostas Spanos, Miltiadis Matsagkas, Kostas N. Fountas

“…Several preventive strategies have been partially investigated, including cerebrospinal fluid drainage, hypothermia, and distal aortic perfusion. …”
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A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus by Csaba Dávid, Kristóf Giber, Katalin Kerti-Szigeti, Mihály Köllő, Zoltan Nusser, Laszlo Acsady

“…Unsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. …”
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Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion by Xiaoshuo Cui, Xiaoxue Sun, Shuxin Xuan, Jinyu Liu, Dongfang Zhang, Jun Zhang, Xiaofei Fan, Xuesong Suo

“…The physicochemical parameters and spectral characteristics of broccoli are important indicators partially reflecting its shelf life. However, few studies have used spectral image information to predict and evaluate the shelf life of broccoli. …”
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Application of Deep Learning Techniques in Uranium Microparticle Fission Track Detection by ZHAO Xiong, REN Fangda, SHEN Yan

“…The aim of this paper was to achieve automatic recognition and detection of uranium microparticle fission tracks, thereby significantly reducing manual labor costs and partially replacing traditional manual operations. …”
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Unveiling lipoprotein subfractions signature in high-FNPO PCOS: implications for PCOM diagnosis and risk assessment using advanced machine learning models by Xueqi Yan, Ziyi Yang, Hui Zhao, Gengchen Feng, Shumin Li, Yimeng Li, Yu Sun, Jinlong Ma, Han Zhao, Xueying Gao, Shigang Zhao

“…Ten machine learning algorithms and recursive feature elimination with logistic regression were used to construct the effective model to predict PCOM based on the new guideline. …”
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Development of an explainable prediction model for portal vein system thrombosis post-splenectomy in patients with cirrhosis by Caixia Dong, Guodong Li, Rui Zhou, Dou Qu, Duwei Dai, Junxia Zhao, Lingbo An, Xiaojie Song, Jiazhen Zhu, Zong Fang Li

“…The five machine learning algorithms (logistic regression, Gaussian Naive Bayes, decision tree, random forest and AdaBoost) were employed to train the predictive models for assessing risks of PVST, which were validated using five fold cross-validation. …”
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Functional Connectivity Changes in Primary Motor Cortex Subregions of Patients With Obstructive Sleep Apnea by Lifeng Li, Qimeng Shi, Bowen Fang, Yuting Liu, Xiang Liu, Yongqiang Shu, Yingke Deng, Yumeng Liu, Haijun Li, Junjie Zhou, Dechang Peng

“…Additionally, we employed three machine learning algorithms—support vector machine (SVM), random forest (RF), and logistic regression (LR)—to distinguish patients with OSA from HC based on FC features. …”
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STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG by Raquel Fernández-Martín, Alfonso Gijón, Odile Feys, Elodie Juvené, Alec Aeby, Charline Urbain, Xavier De Tiège, Vincent Wens

“…Clinical MEG analysis in epileptic patients traditionally relies on the visual identification of IEDs, which is time consuming and partially subjective. Automatic, data-driven detection methods exist but show limited performance. …”
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Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned by Marketa Ciharova, Khadicha Amarti, Ward van Breda, Ward van Breda, Martin J. Gevonden, Sina Ghassemi, Annet Kleiboer, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Sophia Trofimova, Alexander C. Cooper, Xianhua Peng, Xianhua Peng, Mieke Schulte, Mieke Schulte, Eirini Karyotaki, Eirini Karyotaki, Eirini Karyotaki, Pim Cuijpers, Pim Cuijpers, Pim Cuijpers, Heleen Riper, Heleen Riper

“…We aimed to detect laboratory-induced stress using multimodal data and identify challenges researchers may encounter when conducting a similar study.MethodsWe conducted a preliminary exploration of performance of a machine-learning algorithm trained on multimodal data, namely visual, acoustic, verbal, and physiological features, in its ability to detect stress severity following a partially automated online version of the Trier Social Stress Test. …”
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Cardiac Magnetic Resonance Imaging with Myocardial Strain Assessment Correlates with Cardiopulmonary Exercise Testing in Patients with Pectus Excavatum by André Lollert, Tariq Abu-Tair, Tilman Emrich, Karl-Friedrich Kreitner, Alexander Sterlin, Christoph Kampmann, Gundula Staatz

“…In addition to cardiac volumetry, we assessed the strain rates of both ventricles using a feature-tracking algorithm of a piece of commercially available post-processing software. …”
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Bioinformatics analysis and experimental studies reveal KPNA2 as a novel biomarker of hepatocellular carcinoma progression and telomere maintenance by Ke Ding, Lei Liu, Wang Yong, Beicheng Sun, Wenjie Zhang

“…Telomerase inhibition partially alleviated the inhibitory effect of KPNA2 overexpression on cell proliferation and migration. …”
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Research on short-term power load forecasting based on deep reinforcement learning with multiple intelligences by Tianyun Luo, Dunlin Zhu, Jinming Liu, Sheng Yang, Jinglong He, Yuan Fu

“…In this paper, we analyze the multi-intelligence application architecture in power load forecasting, and analyze the function of each intelligent unit applied to short-term power load forecasting; based on clarifying the interaction relationship of each intelligent unit in short-term power load forecasting, we model short-term power load forecasting as a distributed and partially observable Markov decision-making process, which is suitable for multi-intelligence deep reinforcement learning; based on the MATD3 algorithm, a centralized training-distributed execution framework is used to train multiple intelligences within the model to achieve short-term power load forecasting. …”
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The risk factors of cardiovascular disorders in children with chronic bronchopulmonary diseases by O. Ye. Pashkova, G. O. Lezhenko

“…We examined 144 patients aged 3–16 years (mean age was 11.3 ± 1.2 years) with chronic bronchopulmonary pathology (60 patients with cystic fibrosis with pancreatic insufficiency and 84 patients with heavily treated or partially treated persistent bronchial asthma) and 68 conditionally healthy children who made up the control group. …”
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Optimization of treatment for patients with combined unstable pelvic and thoracic injuries by О.А. Бур’янов, В.П. Кваша, В.М. Дьомін, Д.В. Мясніков, В.М. Лянскорунський

“…For type B pelvic injuries, which are characterized by anterior, rotational and partially posterior instability, stabilization of the anterior part is sufficient. …”
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Low-Carbon Slag Concrete Design Optimization Method Considering the Coupled Effects of Formwork Stripping, Strength Progress, and Carbonation Durability by Li-Na Zhang, Seung-Jun Kwon, Xiao-Yong Wang

“…Partially substituting cement with slag is an efficient approach to lowering the carbon footprint of concrete. …”
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Submeridional boundary zone in Asia: seismicity, lithosphere structure, and the distribution of convective flows in the upper mantle by N. A. Bushenkova, O. A. Kuchay, V. V. Chervov

“…A similar approach was applied in [Koulakov, Bushenkova, 2010 for the territory of Siberia; however, that model only partially covered the submeridional transregional boundary zone and was based on fewer ISC data (until 2001). …”
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