Face mask sampling for the detection of Mycobacterium tuberculosis in expelled aerosols. by Caroline M L Williams, Eddy S G Cheah, Joanne Malkin, Hemu Patel, Jacob Otu, Kodjovi Mlaga, Jayne S Sutherland, Martin Antonio, Nelun Perera, Gerrit Woltmann, Pranabashis Haldar, Natalie J Garton, Michael R Barer

“…Variation was seen in mask positivity and the level of contamination detected in multiple samples from the same patient. …”
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Enhancing Federated Intrusion Detection with Class-Specific Dynamic Sampling by Sungkwan Youm, Taeyoon Kim

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Detection of Sarcocystis parasites in environmental samples from Lithuanian farms by Agnė Baranauskaitė, Petras Prakas, Modestas Petrauskas, Selene Rubiola, Elena Servienė, Živilė Strazdaitė-Žielienė

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Detection of Haemophilus ducreyi from environmental and animal samples in Cameroon. by Philippe Ndzomo, Serges Tchatchouang, Onana Boyomo, Tania Crucitti, Michael Marks, Sara Eyangoh

“…<h4>Results</h4>HD was not detected in any of the environmental samples but it was on both clothing (13.3%) and in flies (27%). …”
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An Acoustofluidic Device for Sample Preparation and Detection of Small Extracellular Vesicles by Jessica F. Liu, Jianping Xia, Joseph Rich, Shuaiguo Zhao, Kaichun Yang, Brandon Lu, Ying Chen, Tiffany Wen Ye, Tony Jun Huang

“…In this study, we introduce a novel sharp-edge acoustofluidic platform designed for rapid and effective sample preparation, coupled with sensitive detection of specific sEV populations based on their surface markers. …”
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Metric-based learning approach to botnet detection with small samples by Honggang LIN, Junjing ZHU, Lin CHEN

“…Botnets pose a great threat to the Internet, and early detection is crucial for maintaining cybersecurity.However, in the early stages of botnet discovery, obtaining a small number of labeled samples restricts the training of current detection models based on deep learning, leading to poor detection results.To address this issue, a botnet detection method called BT-RN, based on metric learning, was proposed for small sample backgrounds.The task-based meta-learning training strategy was used to optimize the model.The verification set was introduced into the task and the similarity between the verification sample and the training sample feature representation was measured to quickly accumulate experience, thereby reducing the model’s dependence on the labeled sample space.The feature-level attention mechanism was introduced.By calculating the attention coefficients of each dimension in the feature, the feature representation was re-integrated and the importance attention was assigned to optimize the feature representation, thereby reducing the feature sparseness of the deep neural network in small samples.The residual network design pattern was introduced, and the skip link was used to avoid the risk of model degradation and gradient disappearance caused by the deeper network after increasing the feature-level attention mechanism module.…”
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A Pipette for High-Resolution Sampling and Delivery of pL Bio-Samples by Ziyang Han, Pengfei Gong, Hengxiang Su, Zehang Gao, Shilun Feng, Jianlong Zhao

Subjects: “…sampling pipette…”
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An Algorithm for Smoker Detection by Hiba Youni, Saleem Omar, Mafaz Alwahhab, Enas Mqdad

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Evaluation of Soil Samples Preparation Techniques for Detecting Selenium by Using Atomic Fluorescence Spectroscopy-Based Detection by Lin Zhu, Zheng-he Hu, Xiu-long Chen, Surat Hongsibsong

“…In order to optimize the experimental process for the sensitive detection of Se in samples by atomic fluorescence spectrometry (AFS), an excellent experimental technique was selected to provide a reference for Se detection. …”
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Intelligent detection method on network malicious traffic based on sample enhancement by Tieming CHEN, Chengqiang JIN, Mingqi LYU, Tiantian ZHU

Subjects: “…sample enhancement;anomaly detection;traffic detection;machine learning…”
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A novel PCR assay and sampling techniques for the detection of Raillietiella orientalis by Jenna N. Palmisano, Corinna M. Hazelrig, Jack A. Gazil, Jennifer K. Hanco, Terence M. Farrell, James E. Bogan, Nicole M. Nemeth, Nicole M. Nemeth, Anna E. Savage

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Use of traffic sampling in anomaly detection for high-throughput network links by Marek Bolanowski, Andrzej Paszkiewicz, Hubert Mazur

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An interpretable approach for trustworthy intrusion detection systems against evasion samples by Ngoc Tai Nguyen, Hien Do Hoang, The Duy Phan, Van-Hau Pham

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Microbial occurrence and symbiont detection in a global sample of lichen metagenomes by Gulnara Tagirdzhanova, Paul Saary, Ellen S. Cameron, Carmen C. G. Allen, Arkadiy I. Garber, David Díaz Escandón, Andrew T. Cook, Spencer Goyette, Veera Tuovinen Nogerius, Alfredo Passo, Helmut Mayrhofer, Håkon Holien, Tor Tønsberg, Lisa Y. Stein, Robert D. Finn, Toby Spribille

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Isolation and molecular detection of Cyclospora from water samples in Mosul city by Senaa Abdullah Ali Al-jarjary, Manal H. Hasan, Omar Hashim Sheet

“…Furthermore, the PCR as revealed that Cyclospora was detected in 3.13% (1 of 32) of the water samples collected from Mosul. …”
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Detection of ADB-4en-PINACA metabolite in the authentic urine samples by Ching Yee Fong, Hooi Yan Moy

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Multi-factor consideration sample assignment for oriented tiny object detection by Jun Miao, Peng Liu, Yuanhua Qiao

Subjects: “…Tiny object detection…”
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An interpretable approach for trustworthy intrusion detection systems against evasion samples by Ngoc Tai Nguyen, Hien Do Hoang, The Duy Phan, Van-Hau Pham

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Optimization of the viability PCR for accurate detection of Staphylococcus aureus in food samples. by Mai Dinh Thanh, Gemma Agustí, Anneluise Mader, Francesc Codony

“…For artificially contaminated food samples with such a high dead cell count, complete PCR signal reduction was observed in ground pepper, - oregano, and infant milk powder, while ground paprika, - allspice, and - pork exhibited PCR signals close to the detection limit. …”
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Sample Inflation Interpolation for Consistency Regularization in Remote Sensing Change Detection by Zuo Jiang, Haobo Chen, Yi Tang

“…This approach increases both the quantity and diversity of change samples in the training set, effectively compensating for potential information loss and reducing missed detections. …”
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