RNA hybrid-capture next-generation sequencing has high sensitivity in identifying known and less characterized oncogenic and likely oncogenic NTRK fusions in a real-world standard-... by Zachary D. Wallen, Marni Tierno, Erica Schnettler, Alison Roos, Michelle Green, Kobina Amoah, Rebecca A. Previs, Stephanie Hastings, Sarabjot Pabla, Taylor J. Jensen, Brian Caveney, Marcia Eisenberg, Pratheesh Sathyan, Shakti H. Ramkissoon, Shakti H. Ramkissoon, Eric A. Severson

“…Routine testing for NTRK fusions and treatment with TRK inhibitors has been recommended in multiple tumor types; however, differences between testing technologies used for detecting NTRK fusions can result in variable likelihoods of identification.MethodsTo assess the prevalence of NTRK fusions in a real-world standard-of-care setting, we analyzed data from 19,591 FFPE samples encompassing 35 solid tumor types submitted for comprehensive genomic profiling (CGP) as part of routine clinical care. …”
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Trait-focused low-cost and stereo-based 3D plant modeling for phenotyping via deep neural detection by M. Wattad, S. Filin

“…Notwithstanding, the generation of such data is challenged by the requirement to acquire a large set of images or the use of active sensors, which exhibit sensitivity to illumination and require lengthy acquisition campaigns. …”
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Construction of a model for the rapid detection of acids in strong-flavor original Baijiu based on mid-infrared spectroscopy technology by PENG Houbo, LIAO Li, GAN Qiao, XU Jia, JING Xiong, QIAN Yu, ZOU Yongfang

“…The results showed that the coefficients of determination R² of the quantitative models for total acid, caproic acid, acetic acid and butyric acid were more than 0.90, the root-mean-square error (RMSE) of both the calibration set and the prediction set was less than 0.041 g/L, the optimal numbers of principal component were 9, 19, 8 and 9, respectively, and the relative errors of the quantitative models were less than 10% for total acid, caproic acid and acetic acid and less than 51% for butyric acid. …”
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Optimized Error Detection and Correction in ATM Networks Using the Enhanced Berlekamp Massey Algorithm With Hybrid Automatic Repeat Request by Mahmut Aykac

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Gas Leak Detection and Leakage Rate Identification in Underground Utility Tunnels Using a Convolutional Recurrent Neural Network by Ziyang Jiang, Canghai Zhang, Zhao Xu, Wenbin Song

“…Via infrared thermal imaging gas experiments, data were acquired and a dataset established. To address the low-resolution problem of existing imaging devices, video super-resolution (VSR) was used to improve the data quality. …”
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Modelling radiological features fusion and explainable AI in pneumonia detection: A graph- based deep learning and transformer approach by Pratham Kaushik, Eshika Jain, Vinay Kukreja, Shanmugasundaram Hariharan, Murugaperumal Krishnamoorthy, Vandana Ahuja, Abhishek Bhattacherjee, Rajesh Kumar Kaushal, Shih-Yu Chen

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Predicting infectivity: comparing four PCR‐based assays to detect culturable SARS‐CoV‐2 in clinical samples by Emily A Bruce, Margaret G Mills, Reigran Sampoleo, Garrett A Perchetti, Meei‐Li Huang, Hannah W Despres, Madaline M Schmidt, Pavitra Roychoudhury, David J Shirley, Keith R Jerome, Alexander L Greninger, Jason W Botten

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A comprehensive case study of deep learning on the detection of alpha thalassemia and beta thalassemia using public and private datasets by Muhammad Umar Nasir, Muhammad Tahir Naseem, Taher M. Ghazal, Muhammad Zubair, Oualid Ali, Sagheer Abbas, Munir Ahmad, Khan Muhammad Adnan

“…Public datasets were sourced from medical databases, while private datasets were collected from clinical records, offering a more comprehensive feature set and larger sample sizes. After data preprocessing and splitting, model performance was evaluated. …”
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Detection of power theft in sensitive stations based on generalized robust distance metric and multi-classification support vector machine by Wei Zhang, Qiong Cao, Shuai Yang, Hao Guo

“…The model selects current, voltage, power factor and electric quantity as key detection indexes, and analyzes them by collecting electricity consumption data. …”
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Critical Fluctuations as an Early Warning Signal of Sports Injuries? A Proof of Concept Using Football Monitoring Data by Niklas D. Neumann, Jur J. Brauers, Nico W. van Yperen, Mees van der Linde, Koen A. P. M. Lemmink, Michel S. Brink, Fred Hasselman, Ruud J. R. den Hartigh

“…Yet, the relatively high false positive rate on the entire data set, including periods without injuries, suggests critical fluctuations may also precede transitions to other (e.g., stronger) states. …”
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Modeling serum M‐protein response for early detection of biochemical relapse in myeloma patients treated with bortezomib, lenalidomide and dexamethasone by Yuki Otani, Yunqi Zhao, Guanyu Wang, Richard Labotka, Mark Rogge, Neeraj Gupta, Majid Vakilynejad, Dean Bottino, Yusuke Tanigawara

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Outlier Detection and Removal in Multivariate Time Series for a More Robust Machine Learning–based Solar Flare Prediction by Junzhi Wen, Azim Ahmadzadeh, Manolis K. Georgoulis, Viacheslav M. Sadykov, Rafal A. Angryk

“…Although various machine learning algorithms have been employed to improve solar flare prediction, there has been limited focus on improving performance using outlier detection. In this study, we propose the use of a tree-based outlier detection algorithm, Isolation Forest (iForest), to identify multivariate time-series instances within the flare-forecasting benchmark data set, Space Weather Analytics for Solar Flares (SWAN-SF). …”
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Evaluating the Influence of Clinical Data on Inter-Observer Variability in Optic Disc Analysis for AI-Assisted Glaucoma Screening by Pourjavan S, Bourguignon GH, Marinescu C, Otjacques L, Boschi A

“…For AI to effectively assist in glaucoma detection, it must be trained on comprehensive datasets that include more than just fundus images.These findings emphasize the importance of using a broad range of clinical data when training AI models for glaucoma screening to improve accuracy and reliability in real-world settings.Keywords: artificial intelligence, diagnostic imaging, glaucoma screening, clinical decision support, multimodal diagnostic…”
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Probabilistic Noise Detection and Weighted Non-Negative Matrix Factorization-Based Noise Reduction Methods for Snapping Shrimp Noise by Suhyeon Park, Jongwon Seok, Jungpyo Hong

“…In addition, Continuous Wave and Linear Frequency Modulation signals were set as target signals and combined with the SAVEX-15 data for evaluation of noise reduction performance. …”
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Exploring the potential of tick transcriptomes for virus screening: A data reuse approach for tick-borne virus surveillance. by Koray Ergunay, Brian P Bourke, Yvonne-Marie Linton

“…<h4>Background</h4>We set out to investigate the utility of publicly available tick transcriptomic data to identify and characterize known and recently described tick-borne viruses, using de novo assembly and subsequent protein database alignment and taxonomical binning.…”
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Comparative analysis of hybrid-SNP microarray and nanopore sequencing for detection of large-sized copy number variants in the human genome by Catarina Silva, José Ferrão, Bárbara Marques, Sónia Pedro, Hildeberto Correia, Ana Valente, António Sebastião Rodrigues, Luís Vieira

“…From a total of 48 high confidence variants (truth set), variant calling detected 79% of the truth set variants, increasing to 86% for interstitial CNV. …”
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Video WeAther RecoGnition (VARG): An Intensity-Labeled Video Weather Recognition Dataset by Himanshu Gupta, Oleksandr Kotlyar, Henrik Andreasson, Achim J. Lilienthal

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Comparison of Deep Learning and Clinician Performance for Detecting Referable Glaucoma from Fundus Photographs in a Safety Net Population by Van Nguyen, MD, Sreenidhi Iyengar, Haroon Rasheed, MD, Galo Apolo, Zhiwei Li, Aniket Kumar, Hong Nguyen, Austin Bohner, MD, Kyle Bolo, MD, Rahul Dhodapkar, MD, Jiun Do, MD, PhD, Andrew T. Duong, MD, Jeffrey Gluckstein, MD, Kendra Hong, MD, Lucas L. Humayun, Alanna James, MD, Junhui Lee, MD, Kent Nguyen, OD, Brandon J. Wong, MD, Jose-Luis Ambite, PhD, Carl Kesselman, PhD, Lauren P. Daskivich, MD, Michael Pazzani, PhD, Benjamin Y. Xu, MD, PhD

“…Algorithm performance (AUROC = 0.93) also matched or exceeded the sensitivity (range, 0.78–1.00) and specificity (range, 0.32–0.87) of 6 certified LAC DHS optometrists in the subsets of the test data set they graded. Conclusions: A DL algorithm for detecting referable glaucoma trained using patient-level data provided by certified LAC DHS optometrists approximates or exceeds performance by ophthalmologists and optometrists, who exhibit variable sensitivity and specificity unrelated to experience level. …”
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SARB-DF: A Continual Learning Aided Framework for Deepfake Video Detection Using Self-Attention Residual Block by P.G Prathibha, P. S. Tamizharasan, Alavikunhu Panthakkan, Wathiq Mansoor, Hussain Al Ahmad

“…The framework uses weight regularization and a dynamic sample set to continuously learn and adapt to new synthetic data. …”
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A novel cross-validated machine learning based Alertix-Cancer Risk Index for early detection of canine malignancies by Hanan Sharif, Hanan Sharif, Reza Arabi Belaghi, Kiran Kumar Jagarlamudi, Sara Saellström, Liya Wang, Henrik Rönnberg, Staffan Eriksson, Staffan Eriksson

“…Serum TK1 protein levels were measured using TK1-ELISA and cCRP levels by a quantitative ELISA. The whole data set was divided as training (70%) and validation (30%). …”
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