Validation of a data-driven motion-compensated PET brain image reconstruction algorithm in clinical patients using four radiotracers by Ole L. Munk, Anders B. Rodell, Patricia B. Danielsen, Josefine R. Madsen, Mie T. Sørensen, Niels Okkels, Jacob Horsager, Katrine B. Andersen, Per Borghammer, Joel Aanerud, Judson Jones, Inki Hong, Sven Zuehlsdorff

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Pathological and radiological assessment of benign breast lesions with BIRADS IVc/V subtypes. should we repeat the biopsy? by Wesam Rjoop, Anwar Rjoop, Alia Almohtaseb, Lama Bataineh, Zeina Nser Joubi, Maha Gharaibeh, Abdalrahman Al-Qwabah, Yousef Alasheh, Ismail Matalka

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Integration of ground-based and remote sensing data with deep learning algorithms for mapping habitats in Natura 2000 protected oak forests by Lucia Čahojová, Ivan Jarolímek, Barbora Klímová, Michal Kollár, Michaela Michalková, Karol Mikula, Aneta A. Ožvat, Denisa Slabejová, Mária Šibíková

“…Oak-dominated forest habitats were differentiated solely through multispectral data obtained from Sentinel-2 satellites. A dataset was selected for the training of a deep learning algorithm called the Natural Numerical Network on the basis of the analysis results. …”
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Metagenome-assembled-genomes recovered from the Arctic drift expedition MOSAiC by William Boulton, Asaf Salamov, Igor V. Grigoriev, Sara Calhoun, Kurt LaButti, Robert Riley, Kerrie Barry, Allison A. Fong, Clara J. M. Hoppe, Katja Metfies, Kersten Oetjen, Sarah Lena Eggers, Oliver Müller, Jessie Gardner, Mats A. Granskog, Anders Torstensson, Marc Oggier, Aud Larsen, Gunnar Bratbak, Andrew Toseland, Richard M. Leggett, Vincent Moulton, Thomas Mock

“…The generation of metagenome-assembled-genomes (MAGs) from metagenomes are a starting point for genome-resolved analyses. This dataset presents a catalogue of MAGs recovered from a set of 73 samples from MOSAiC, including 2407 prokaryotic and 56 eukaryotic MAGs, as well as annotations of a near complete eukaryotic MAG using the Joint Genome Institute (JGI) annotation pipeline. …”
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Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease by Ziyi Yuan, Zhaodi Huang, Chaojun Li, Shengrong Li, Qingguo Ren, Xiaona Xia, Qingjun Jiang, Daoqiang Zhang, Qi Zhu, Xiangshui Meng, Xiangshui Meng

“…ObjectivesTo propose a multimodal functional brain network (FBN) and structural brain network (SBN) topological feature fusion technique based on resting-state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), 3D-T1-weighted imaging (3D-T1WI), and demographic characteristics to diagnose mild cognitive impairment (MCI) in patients with unilateral middle cerebral artery (MCA) steno-occlusive disease.MethodsThe performances of different algorithms on the MCI dataset were evaluated using 5-fold cross-validation. …”
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Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019 by Jenberu Mekurianew Kelkay, Deje Sendek Anteneh, Henok Dessie Wubneh, Abraham Dessie Gessesse, Gebeyehu Fassil Gebeyehu, Kalkidan Kassahun Aweke, Mikiyas Birhanu Ejigu, Mathias Amare Sendeku, Kirubel Adrissie Barkneh, Hasset Girma Demissie, Wubshet D. Negash, Birku Getie Mihret

“…Data have been extracted and processed with Stata version 17. The dataset was then imported into a Jupyter notebook for further detailed analysis and visualization. …”
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DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI by Sergio Morell-Ortega, Marina Ruiz-Perez, Marien Gadea, Roberto Vivo-Hernando, Gregorio Rubio, Fernando Aparici, Maria de la Iglesia-Vaya, Gwenaelle Catheline, Boris Mansencal, Pierrick Coupé, José V. Manjón

“…Unlike current tools that operate at standard resolution (1 mm3) or using mono-modal data, the proposed method improves cerebellum lobule segmentation through the use of a multimodal and ultra-high resolution (0.125 mm3) training dataset. To develop the method, first, a database of semi-automatically labelled cerebellum lobules was created to train the proposed method with ultra-high resolution T1 and T2 MR images. …”
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Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity by Xiaochen Tang, Yanyan Wei, Jiaoyan Pang, Lihua Xu, Huiru Cui, Xu Liu, Yegang Hu, Mingliang Ju, Yingying Tang, Bin Long, Wei Liu, Min Su, Tianhong Zhang, Jijun Wang

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Multi-omics reveals that ST6GAL1 promotes colorectal cancer progression through LGALS3BP sialylation by Yuanchao Shi, Zhenzhong Pan, Jingwei Duan, Zexing Wang, Yiliang Fang, Bo Tang, Quanlin Guan

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Novel models based on machine learning to predict the prognosis of metaplastic breast cancer by Yinghui Zhang, Wenxin An, Cong Wang, Xiaolei Liu, Qihong Zhang, Yue Zhang, Shaoqiang Cheng

“…Furthermore, the CatBoost model maintains strong performance in an external independent dataset, with AUC values of 0.937 for 1-year survival, 0.907 for 3-year survival, and 0.890 for 5-year survival, respectively. …”
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Penerapan Algoritma Deep Learning Convolutional Neural Network Dalam Menentukan Kematangan Buah Jeruk Manis Berdasarkan Citra Red Green Blue (RGB) by Budi Yanto, Erni Rouza, Luth Fimawahib, B.Herawan Hayadi, Rinanda Rizki Pratama

“…Sistem ini dirancang dan dibangun menggunakan bahasa pemrograman matlab versi R2018a, dengan 100 dataset gambar jeruk manis menunjukan tingkat akurasi sebesar 96% untuk training 92% untuk testing yang dinilai telah mampu melakukan klasifikasi kelayakan buah jeruk manis dengan sangat baik. …”
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Application Of ArtifiCial Intelligence in E-Governance: A Comparative Study of Supervised Machine Learning and Ensemble Learning Algorithms on Crime Prediction. by Niyonzima, Ivan, Muhaise, Hussein, Akankwasa, Aureri

“…The performance of ensemble learning algorithms using the original datasets includes 58.58%, 59.81%, 55.23% and 55.74% for AD, GBM, RF and ET respectively. …”
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Identification of Potential Diagnostic Biomarkers of Carotid Atherosclerosis in Obese Populations by Wu X, Pan J, Pan X, Kang J, Ren J, Huang Y, Gong L, Li Y

“…Xize Wu,1,* Jiaxiang Pan,2,* Xue Pan,1,3,* Jian Kang,1 Jiaqi Ren,1 Yuxi Huang,1 Lihong Gong,2,4 Yue Li2,4 1Graduate School, Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning, 110847, People’s Republic of China; 2Department of Cardiology, Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning, 110032, People’s Republic of China; 3College of Traditional Chinese Medicine, Dazhou Vocational College of Chinese Medicine, Dazhou, Sichuan, 635000, People’s Republic of China; 4Liaoning Provincial Key Laboratory of TCM Geriatric Cardio-Cerebrovascular Diseases, Shenyang, Liaoning, 110032, People’s Republic of China*These authors contributed equally to this workCorrespondence: Lihong Gong; Yue Li, Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, No. 33, Beiling Street, Huanggu District, Shenyang, Liaoning, 110032, People’s Republic of China, Tel +86 024-82961105, Email [email protected]; [email protected]: This study aimed to investigate the potential mechanisms and biomarkers between Obesity (OB) and carotid atherosclerosis (CAS).Methods: The GSE12828, GSE125771, GSE43292, and GSE100927 datasets were combined and normalized to obtain CAS-related differentially expressed genes (DEGs), and OB-related DEGs were obtained from the GSE151839 dataset and the GeneCards database. …”
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Environmental, socioeconomic, and sociocultural drivers of monkeypox transmission in the Democratic Republic of the Congo: a One Health perspective by Guangyu Lu, Zeyin Chong, Enyu Xu, Ce Na, Kaixuan Liu, Liying Chai, Pengpeng Xia, Kai Yang, Guoqiang Zhu, Jinkou Zhao, Olaf Müller

“…Methods Epidemiological, environmental, socioeconomic, and sociocultural data from the DRC from 2000 to 2015 were obtained from publicly available dataset. Using these data, we applied negative binomial regression model, least absolute shrinkage and selection operator regression model, and principal component analysis (PCA) to identify key environmental, socioeconomic, and sociocultural factors contributing to mpox transmission. …”
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Nicotine dependence is associated with an increased risk of developing chronic, non-communicable inflammatory disease: a large-scale retrospective cohort study by Khalaf Kridin, Khalaf Kridin, Khalaf Kridin, Cristian Papara, Katja Bieber, David A. De Luca, Jan Philipp Klein, Marlene A. Ludwig, Philip Curman, Philip Curman, Philip Curman, Philip Curman, Artem Vorobyev, Astrid Dempfle, Ralf J. Ludwig

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Analisis Kredit Pembayaran Biaya Kuliah Dengan Pendekatan Pembelajaran Mesin by Arliyanti Nurdin, Rizqa Amelia Zunaidi, Muhammad Arkan Fauzan Wicaksono, Agi Lobita Japtara Martadinata

“…Pada penelitian ini, algoritma machine learning digunakan untuk analisis kredit pembayaran biaya kuliah pada perguruan tinggi. Dataset yang digunakan adalah data riwayat tagihan, transaksi pembayaran, dan data pengajuan kredit/ angsuran. …”
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Plasma myeloperoxidase interactions with cadmium, lead, arsenic, and selenium and their impact on chronic kidney disease by Hsi-Hsien Chen, Ya-Li Huang, Chih-Yin Wu, Mei-Chieh Chen, Horng-Sheng Shiue, Sheng-Lun Hsu, Ying-Chin Lin, Yu-Mei Hsueh

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Summer Research Internship Curriculum to Promote Self-Efficacy, Researcher Identity, and Peer-to-Peer Learning: Retrospective Cohort Study by Yulia A Levites Strekalova, Rachel Liu-Galvin, Samuel Border, Sara Midence, Mishal Khan, Maya VanZanten, John Tomaszewski, Sanjay Jain, Pinaki Sarder

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Clinicopathological relationship between PM2.5 exposure and acute exacerbations of chronic obstructive pulmonary disease by Anfeng Cui, Yingying Shao, Yang Wang, Haodong Wang, Jiayi Song, Caiji Liu, Ningning Shen, Zhiqing Yang, Wangliang Zhang, Tiane Luo, Chen Wang

“…This data was paired with Shanxi's average monthly PM2.5 data from the Tracking Air Pollution in China (TAP) dataset. Correlations between PM2.5 levels and AECOPD clinicopathological features were assessed using ANOVA and Spearman’s correlation. …”
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Framework for smartphone-based grape detection and vineyard management using UAV-trained AI by Sergio Vélez, Mar Ariza-Sentís, Mario Triviño, Antonio Carlos Cob-Parro, Miquel Mila, João Valente

“…To this end, using a dataset of UAV videos recorded during early growth stages in July (BBCH77-BBCH79), the X-Decoder segments vegetation in the front of the frames from their background and surroundings. …”
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