Neonatal brain injury influences structural connectivity and childhood functional outcomes. by Alice Ramirez, Shabnam Peyvandi, Stephany Cox, Dawn Gano, Duan Xu, Olga Tymofiyeva, Patrick S McQuillen

“…Maturation of whole-brain neural networks can be quantified during development using diffusion magnetic resonance imaging (dMRI) in combination with graph theory metrics. …”
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Progression risk of adolescent idiopathic scoliosis based on SHAP-Explained machine learning models: a multicenter retrospective study by Xinyi Fang, Ting Weng, Zhehao Zhang, Wanfeng Gong, Yu Zhang, Mei Wang, Jianhua Wang, Zhongxiang Ding, Can Lai

“…Abstract Objective To develop an interpretable machine learning model, explained using SHAP, based on imaging features of adolescent idiopathic scoliosis extracted by convolutional neural networks (CNNs), in order to predict the risk of curve progression and identify the most accurate predictive model. …”
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Interest of Chest CT to Assess the Prognosis of SARS-CoV-2 Pneumonia: An In-Hospital-Based Experience in Sub-Saharan Africa by Serge Emmanuel Obe -A- Ndzem Holenn, Tacite Kpanya Mazoba, Désiré Yaya Mukanga, Tyna Bongosepe Zokere, Djo Lungela, Jean-Robert Makulo, Steve Ahuka, Angèle Tanzia Mbongo, Antoine Aundu Molua

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Construction of a sugar and acid content estimation model for Miliang-1 kiwifruit during storage by LIU Li, YANG Tianyi, DONG Congying, SHI Caiyun, SI Peng, WEI Zhifeng, GAO Dengtao

“…During the research, a hyperspectral imaging system was used to collect hyperspectral reflectance data of these kiwifruits at different storage times. …”
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Deep dive into deep learning methods for cervical cancer detection and classification by Pooja Patre, Dipti Verma

“…We explore various deep learning architectures, particularly convolutional neural networks (CNNs), and their applications in the segmentation and classification of cervical cytology images. …”
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HEROIC: a 5-year observational cohort study aimed at identifying novel factors that drive diabetic kidney disease: rationale and study protocol by Neil Ashman, Ben Caplin, David Wheeler, Tahseen Ahmad Chowdhury, Kieran Mccafferty, Sinead Knight, Paul Hockings, Stanley L Fan, Johannes Hulthe, Robert Kleta, Vasilios Papastefanou, Hemal Mehta, Alan Salama, Sinela Hadzovic, Lisa Jarl, Robert Unwin, Benjamin Challis, Anna K Sundgren, Muhammad Magdi Yaqoob

“…Anonymised imaging data will be stored in a ISO27001-certificated data warehouse.Results will be reported through peer-reviewed manuscripts and conferences and disseminated to participants, patients and the public using web-based and social media engagement tools as well as through public events.…”
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Current Topics on the Integration of Artificial Intelligence in the Histopathological and Molecular Diagnosis of Uveal Melanoma by Serena Salzano, Giuseppe Broggi, Andrea Russo, Teresio Avitabile, Antonio Longo, Rosario Caltabiano, Manuel Mazzucchelli

“…<b>Methods:</b> This work delves into the application of AI technologies such as machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs) in various diagnostic procedures, molecular profiling, and predictive analysis. …”
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Exploring the capabilities of hyperspectral remote sensing for soil texture evaluation by Mohammad Hosseinpour-Zarnaq, Mahmoud Omid, Fereydoon Sarmadian, Hassan Ghasemi-Mobtaker, Reza Alimardani, Pouya Bohlol

“…Additionally, we compared the performance of random forest (RF) algorithms with partial least squares regression (PLSR), multiple linear regression (MLR), support vector machine regression (SVR), decision trees (DTs), and multilayer perceptron (MLP) neural networks, addressing the effects of feature selection and irregular soil data on the modeling procedure. …”
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Plant leaf classification using the multiscale entropy of curvature and feature aggregation by Raphael G. Pinheiro, José G.F. Lopes, Marcelo M.S. Souza, Fátima N.S. Medeiros

“…Moreover, the handcrafted descriptors achieved better results with 40 features than LeNet’s 50 features. The qualitative analysis of the multidimensional data visualization results prove that combining different shape features and texture details improved the description of the leaf images, as it provided better intraclass compactness and interclass separation in these datasets.…”
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ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases by Sheng Yu, Li Xie, Liang Dai

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Deep Learning Methods and UAV Technologies for Crop Disease Detection by S. G. Mudarisov, I. R. Miftakhov

“…The paper also addresses challenges associated with the use of unmanned aerial vehicles, such as data quality limitations, the complexity of processing large volumes of images, and the need for the development of more advanced models. …”
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Deciphering Immunometabolic Landscape in Rheumatoid Arthritis: Integrative Multiomics, Explainable Machine Learning and Experimental Validation by Dong Q, Wu J, Zhang H, Chen X, Xu X, Chen J, Shi C, Luo L, Zhang D

“…Explainable machine learning, PPI network analysis, and qPCR jointly identified four hub genes (PFN1, SRP14, S100A11, and SAP18). …”
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Mechanisms of sorafenib-induced cardiotoxicity: ER stress induces upregulation of ATF3, leading to downregulation of NDUFS1 expression and mitochondrial dysfunction by Ming Yan, Ming Yan, Ming Yan, Cheng Peng, Cheng Peng, Xueyan Lang, Xueyan Lang, Yilan Li, Yilan Li, Yao Zhang, Yao Zhang

“…These outcomes were supported by Western blot analysis and microscopic imaging, and were significantly mitigated following treatment with the ER stress inhibitor GSK2606414. …”
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Biological heart and brain ageing in subjects with cardiovascular diseases by Elizabeth Mcavoy, Elizabeth Mcavoy, Elizabeth Mcavoy, Matthias Wilms, Matthias Wilms, Matthias Wilms, Matthias Wilms, Matthias Wilms, Nils D. Forkert, Nils D. Forkert, Nils D. Forkert, Nils D. Forkert

“…Therefore, this study aims to explore how cardiovascular diseases affect biological ageing of the brain and heart by quantifying the brain age gap (BAG) and the heart age gap (HAG) and relating the two to each other.MethodsThis study used data from UK Biobank participants with available T1-weighted brain magnetic resonance imaging (MRI) scans, cardiac MRI-derived features, as well as pulse wave analysis cardiac measurements. …”
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Differentiating biomarker features and familial characteristics of B-SNIP psychosis Biotypes by David A. Parker, Rebekah L. Trotti, Jennifer E. McDowell, Sarah K. Keedy, Matcheri S. Keshavan, Godfrey D. Pearlson, Elliot S. Gershon, Elena I. Ivleva, Ling-Yu Huang, Kodiak Sauer, S. Kristian Hill, John A. Sweeney, Carol A. Tamminga, Brett A. Clementz

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Digital marketing and customer orientation as predictors of sustainability in tourism SMES by Nancy Tupac Yupanqui Lima, Karla Liliana Haro-Zea, Robin Alexander Díaz Saavedra

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Rapid literature mapping on the recent use of machine learning for wildlife imagery by Nakagawa, Shinichi, Lagisz, Malgorzata, Francis, Roxane, Tam, Jessica, Li, Xun, Elphinstone, Andrew, Jordan, Neil R., O'Brien, Justine K., Pitcher, Benjamin J., Van Sluys, Monique, Sowmya, Arcot, Kingsford, Richard T.

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Links between sea surface temperature anomalies and seismic activity in the Red Sea across multiple temporal scales by Ahmed M. El Kenawy, Emad K. Mohamed, Mohamed M. Abdelaal, Mohamed El Alfy, Azza Ragab, Hassan Aboelkhair

“…We utilized the Egyptian National Seismic Network (ENSN) and ISC-GEM Global Instrumental Earthquake Catalogue (version 9.0) provided by the Global Earthquake Risk Model (GEM) Organizations and the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite SST data. …”
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Dexmedetomidine and the glymphatic system: a new perspective in managing postoperative cognitive dysfunction by Sha-Sha Zhang, Fan-Geng Meng, Yuan-Yuan Rong, Yi-Wen Zhang, Hua-Qin Liu

“…The review emphasizes the need for future studies to incorporate multimodal imaging, integrated mechanistic analysis, and identification of high-risk patient subgroups, in order to facilitate the clinical translation of Dex as a glymphatic-targeted neuroprotective agent.…”
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Targeting macrophages and ion homeostasis in T2D: new genes and therapeutic pathways identified by Lisha Mou, Lisha Mou, Tony Bowei Wang, Ying Lu, Ying Lu, Zijing Wu, Zijing Wu, Yuxian Chen, Ziqi Luo, Xinyu Wang, Zuhui Pu, Zuhui Pu

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