Rapid detection and typing of Staphylococcus aureus based on nanopore Cas9-targeted sequencing by PAN Shufan, YANG Donglei, WANG Pengfei

“…Based on the comparison results, the presence of Staphylococcus aureus, MRSA or not, and spa and SCCmec types were determined.Results·Two sets of crRNAs were designed. Based on grayscale analysis of electrophoresis results, the set with higher cleavage efficiency was selected for further experiments. …”
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autoMEA: machine learning-based burst detection for multi-electrode array datasets by Vinicius Hernandes, Anouk M. Heuvelmans, Anouk M. Heuvelmans, Valentina Gualtieri, Dimphna H. Meijer, Geeske M. van Woerden, Geeske M. van Woerden, Geeske M. van Woerden, Eliska Greplova

“…At present, the key challenge in the utilization of MEA data is the sheer complexity of the measured datasets. …”
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multiDGD: A versatile deep generative model for multi-omics data by Viktoria Schuster, Emma Dann, Anders Krogh, Sarah A. Teichmann

“…Consequently, the complexity of multi-omics data sets is increasing massively. Existing models for multi-modal data are typically limited in functionality or scalability, making data integration and downstream analysis cumbersome. …”
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A novel test for gene-ancestry interactions in genome-wide association data. by Joanna L Davies, Jean-Baptiste Cazier, Malcolm G Dunlop, Richard S Houlston, Ian P Tomlinson, Chris C Holmes

“…The association replicated in two additional, independently-collected data sets. Our method can be used to detect associations between genetic variants and disease that have been obscured by population genetic heterogeneity. …”
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Context-aware data augmentation for enhanced speech command recognition in industrial environments by Giuseppe De Simone, Antonio Greco, Francesco Rosa, Alessia Saggese, Mario Vento

“…To improve reliability in noisy environments, a Keyword Spotting module is introduced, activating the recognition system only when a predefined keyword is detected. The proposed system was evaluated using real-world samples collected in a noisy industrial setting. …”
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AIDFL: An Information-Driven Anomaly Detector for Data Poisoning in Decentralized Federated Learning by Xiao Chen, Chao Feng, Shaohua Wang

“…Existing defense mechanisms face significantly reduced effectiveness under non-IID data distributions. To address these challenges, AIDFL is proposed to utilize conditional entropy and mutual information, which are independent of data distribution to detect and mitigate data poisoning attacks in DFL environments. …”
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Modal Regression Estimation by Local Linear Approach in High-Dimensional Data Case by Fatimah A. Almulhim, Mohammed B. Alamari, Ali Laksaci, Zoulikha Kaid

“…This paper introduces a new nonparametric estimator for detecting the conditional mode in the functional input variable setting. …”
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Hepatic Hemangioma in Celiac Patients: Data from a Large Consecutive Series by Sara Massironi, Federica Branchi, Roberta Elisa Rossi, Mirella Fraquelli, Luca Elli, Maria Teresa Bardella, Federica Cavalcoli, Dario Conte

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Unsupervised Coherent Noise Removal From Seismological Distributed Acoustic Sensing Data by Sebastian Konietzny, Voon Hui Lai, Meghan S. Miller, John Townend, Stefan Harmeling

“…Evaluations on real‐world DAS data further confirm the robustness of our method, positioning it as a valuable tool for analyzing large‐scale DAS data sets in various geoscientific contexts. …”
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Unified Calibration-Based Failure Prediction Quantization for Automatic Target Recognition by Sihang Dang, Yunlong Zhang, Zhaoqiang Xia, Xiaoyue Jiang, Shuliang Gui, Xiaoyi Feng

“…As new unknown samples are captured, the recognition model faces a dual challenge: it must accurately identify preexisting known classes and detect new unknown classes. However, models trained on limited data often struggle with this task, leading to inevitable prediction failures. …”
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USING REMOTE SENSING AND GIS-TECHNIQUES IN SOUTH EAST CASPIAN COASTAL CHANGES DETECTION by S. R. Mousavi, K. Solaimani

“…SPOT-Pan data were georeferenced with respect to 1 : 50 000 topographic maps using a Universal Transverse Mercator (UTM) projection, then all the needed data sets were registered to the SPOT-Pan image. …”
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Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion by Rocío Aznar-Gimeno, Jose Luis Perez-Lasierra, Pablo Pérez-Lázaro, Irene Bosque-López, Marina Azpíroz-Puente, Pilar Salvo-Ibáñez, Martin Morita-Hernandez, Ana Caren Hernández-Ruiz, Antonio Gómez-Bernal, María de la Vega Rodrigalvarez-Chamarro, José-Víctor Alfaro-Santafé, Rafael del Hoyo-Alonso, Javier Alfaro-Santafé

“…The multimodal approach enhances model accuracy, potentially supporting early disease detection and intervention in home settings.…”
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Leveraging large language models for automated detection of velopharyngeal dysfunction in patients with cleft palate by Myranda Uselton Shirk, Catherine Dang, Jaewoo Cho, Hanlin Chen, Lily Hofstetter, Jack Bijur, Claiborne Lucas, Andrew James, Ricardo-Torres Guzman, Andrea Hiller, Noah Alter, Amy Stone, Maria Powell, Matthew E. Pontell, Matthew E. Pontell

“…Whisper demonstrated robust performance across diverse recording conditions and required minimal training data, showcasing its scalability and efficiency for hypernasality detection.ConclusionThis study demonstrates the effectiveness of the Whisper-based model for hypernasality detection. …”
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Vibration-Based Anomaly Detection in Industrial Machines: A Comparison of Autoencoders and Latent Spaces by Luca Radicioni, Francesco Morgan Bono, Simone Cinquemani

“…This paper focuses on predictive maintenance through vibration analysis, utilizing data-driven models. This study explores the application of unsupervised learning methods, particularly Convolutional Autoencoders (CAEs) and variational Autoencoders (VAEs), for anomaly detection (AD) in vibration signals. …”
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Dual-Aspect Active Learning with Domain-Adversarial Training for Low-Resource Misinformation Detection by Luyao Hu, Guangpu Han, Shichang Liu, Yuqing Ren, Xu Wang, Zhengyi Yang, Feng Jiang

“…Although deep learning-based detection methods have achieved promising results, their effectiveness heavily relies on large amounts of labeled data, limiting their applicability in low-resource scenarios. …”
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Scaling convolutional neural networks achieves expert level seizure detection in neonatal EEG by Robert Hogan, Sean R. Mathieson, Aurel Luca, Soraia Ventura, Sean Griffin, Geraldine B. Boylan, John M. O’Toole

“…We have developed and validated a seizure detection model using retrospective EEG data from 332 neonates. …”
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Impact of occupancy behavior on building energy efficiency: What’s next in detection and monitoring technologies? by Wenjie Song, John Calautit

“…Personalization and adaptability emerge as key themes, particularly in multi-occupant contexts, while multi-sensor data fusion promises to enhance detection stability and reduce false positives. …”
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Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks by Marcos Luengo Viñuela, Jesús-Ángel Román Gallego

“…Despite progress, challenges persist, such as limited training data and IoT device processing constraints with large data volumes. …”
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A Representation-Learning-Based Graph and Generative Network for Hyperspectral Small Target Detection by Yunsong Li, Jiaping Zhong, Weiying Xie, Paolo Gamba

“…These results demonstrate the accuracy of the model in different evaluation metrics, with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>AU</mi><msub><mi mathvariant="normal">C</mi><mfenced separators="" open="(" close=")"><mrow><mi mathvariant="normal">D</mi><mo>,</mo><mi mathvariant="normal">F</mi></mrow></mfenced></msub></mrow></semantics></math></inline-formula> achieving the highest score, indicating strong detection performance across varying thresholds. Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.…”
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Comprehensive Outlier Detection in Wireless Sensor Network with Fast Optimization Algorithm of Classification Model by Haiqing Yao, Heng Cao, Jin Li

“…To reduce the complexity of optimization algorithm for QSSVM model in existing techniques, a fast optimization algorithm based on average Euclidean distance has been developed and employed to the comprehensive outlier detection method. Evaluated by real and synthetic WSN data sets, our methods have shown an excellent outlier detection performance, and they have been proved to meet the requirements of online adaptive outlier detection in the case of nonstationary detection tasks of WSN.…”
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