Automated Mycoplasma genitalium molecular macrolide resistance detection and nucleic acid target semi-quantitation: patient demographic considerations by Josephine Moore, Trinity Krueger, Amanda Zapp, Stephen C. Lavey, Kimber L. Munson, Irene A. Stafford, Michael E. Newcomb, Brian Mustanski, Erik Munson

“…Within the university cohort, MRM-LDT detection rate was increased in symptomatic patients (P = 0.005). …”
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Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection by Md. Najmul Mowla, Davood Asadi, Shamsul Masum, Khaled Rabie

“…Effective detection and classification of forest fire imagery are critical for timely and efficient wildfire management. …”
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Enhancing Cloud Detection in Polar Regions Using Combined Spectral and Textural Features for Landsat 8/9 OLI Imagery by Xinran Shen, Teng Li, Chong Liu, Shaoyin Wang, Lei Zheng, Qi Liang, Xiao Cheng, Jiaqi Yao

“…Initially, the algorithm employs function of mask (Fmask) for preliminary cloud detection, followed by a block processing strategy that integrates the gray-level co-occurrence matrix to extract local texture features for secondary discrimination, helping to eliminate snow misidentified as clouds. …”
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Automated Detection of High Frequency Oscillations in Intracranial EEG Using the Combination of Short-Time Energy and Convolutional Neural Networks by Dakun Lai, Xinyue Zhang, Kefei Ma, Zichu Chen, Wenjing Chen, Heng Zhang, Han Yuan, Lei Ding

“…The proposed methodology presents the following significant advantages: 1) compared with the recently reported HFOs detector based on the CNN using only the 1D temporal EEG signal, the proposed method achieves a higher accuracy using the deep CNN classifier on 2D time–frequency map of HFOs, of which the evaluated sensitivity and false discovery rate (FDR) for identifying ripples are 88.16% and 12.58%, respectively, and the corresponding sensitivity and FDR are 93.37% and 8.1% for detecting fast ripples, respectively; 2) it is capable of automatically extracting the shared features of HFOs events of different patients and would be much robust, unlike other automated methodologies proposed in the literature where the characteristics of HFOs were extracted manually on the basis of researchers’ knowledge, which, probably, is prone to observer bias; and 3) with the proposed STE estimation, all suspicious ripples and fast ripples could be initially found out and transformed into time–frequency map for subsequently CNN-based classification, rather than transforming and classifying the raw data, thus requiring a lower computational resource. …”
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Utilizing plant-based biogenic volatile organic compounds (bVOCs) to detect aflatoxin in peanut plants, pods, and kernels by Daniel E. Sabo, Justin J. Pitts, Olga Kemenova, Christopher A. Heist, Benjamin Joffe, Xiaojuan (Judy) Song, William M. Hammond

“…Aflatoxins are responsible for approximately 25 % of liver cancer cases worldwide, while also costing millions of dollars in lost revenue on an annual basis. Current detection techniques based on high pressure liquid chromatography (HPLC) are time, labor and cost intensive. …”
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DETECTION AND SYSTEMATIZATION OF SIGNS AND MARKERS OF MODIFICATIONS IN MEDIA CONTENT FOR THE DEVELOPMENT OF A METHODOLOGY TO ENHANCING CRITICAL THINKING IN THE ERA OF DEEPFAKES by Ганна Чемерис

“…The paper focuses on creating a comprehensive framework for detecting manipulated content and identifying indicators such as pixelization, unnatural expressions, etc. …”
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Application value of nucleic acid MALDI-TOF MS in mycobacterial species identification and drug resistance detection in Mycobacterium tuberculosis by Xiaofang Liu, Honghong Niu, Donglin Guo, Huixia Gao, Lihong Wu, Jingyang Liu, Chunfeng Bai, Yuxi Li, Peilong Wang, Zhengfeng Zhou, Yuling Wang, Jianqin Liang, Wenping Gong

“…The positive detection rate of nucleic acid MALDI-TOF MS for mycobacterium was 84.96%, which was significantly higher than that of AFB staining (29.32%). …”
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Deep learning-based underwater metal object detection using input image data and corrosion protection of mild steel used in underwater study: A case study: Part A: Deep learning-ba... by Thankappan Sasilatha, Doss Hebciba Mary Suvakeen Amala, Rajendran Susai Santhammal, Časlav Lačnjevac, Gurmeet Singh

“…In first input image is initially preprocessed and that images is given to the KFCM-Segmentation. …”
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A comparative study of 68Gallium-prostate specific membrane antigen positron emission tomography-computed tomography and magnetic resonance imaging for lymph node staging in high r... by Manoj Gupta, Partha Choudhury, Dibyamohan Hazarika, Sudhir Rawal

“…In ROC analysis, area under the curve was also significantly high for lymph node detectability by 68Ga-PSMA PET-CT. Our initial experience shows that 68GaPSMA PET-CT is a very promising tracer for N staging in the initial workup of prostate cancer. …”
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Advanced driver assistance system (ADAS) and machine learning (ML): The dynamic duo revolutionizing the automotive industry by Harsh SHAH, Karan SHAH, Kushagra DARJI, Adit SHAH, Manan SHAH

“…Subsequently, the critical algorithms used in ADAS technology, which include face detection, head-pose estimation, gaze estimation, and link detection are discussed. …”
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Automatic Bone Fracture Reduction Technique with Section Registration by Qinhui YUAN, Mengxing LIU, Chu GUO, Yukun AN, Ping ZHOU

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Coordinated surveillance system under the One Health approach for cross‐border pathogens that threaten the Union – options for sustainable surveillance strategies for priority path... by European Food Safety Authority (EFSA), John Berezowski, Katinka De Balogh, Fernanda C Dórea, Simon Ruegg, Alessandro Broglia, Gabriele Zancanaro, Andrea Gervelmeyer

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HMG Box-containing Protein 1 (HBP1) Protects Against Pancreatic Injury in Acute Pancreatitis but Promotes Neoplastic ProgressionSummary by Seung-Won Lee, Taelor Ekstrom, Elise C. Manalo, Shangyuan Ye, Mark Berry, Dmytro Grygoryev, Malwina Szczepaniak, Jinho Lee, Carlos Origel Marmolejo, Syber Haverlack, Juyoung Lee, Vidhi M. Shah, Dove Keith, John L. Muschler, Koei Chin, Rosalie C. Sears, Stuart P. Weisberg, Terry Morgan, Jungsun Kim

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A Satellite Full-Waveform Laser Decomposition Method for Forested Areas Based on Hidden Peak Detection and Adaptive Genetic Optimization by Fangxv Zhang, Xiao Wang, Leiguang Wang, Fan Mo, Liping Zhao, Xiaomeng Yang, Xin Lv, Junfeng Xie

“…This method uses hidden peak detection algorithms to improve the accurate extraction of the Gaussian components from the original waveform and provides the initial parameters. …”
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Early Detection and Longitudinal Follow‐Up of Non‐Invasive Biomarkers for Laryngeal Squamous Cell Carcinoma: First Results of the BEAL‐Study by Ann‐Christin Winkelmann, Olaf Wendler, Johannes Schuster, Abbas Agaimy, Marlen Haderlein, Benjamin Frey, Sarina Katrin Mueller

“…For this reason, this study is still ongoing, and only initial results are presented here. This study's objective is to examine the potential of Nov and uPAR as serum biomarkers for early detection and follow‐up of LSCC. …”
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A Two-Phase Feature Selection Framework for Intrusion Detection System: Balancing Relevance and Computational Efficiency (2P-FSID) by C. Rajathi, Rukmani Panjanathan

“…This study proposes the Two-Phase Feature Selection framework for Intrusion Detection (2P-FSID) to enhance model performance and interpretability. …”
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A Comparative Analysis of the Usability of Consumer Graphics Cards for Deep Learning in the Aspects of Inland Navigational Signs Detection for Vision Systems by Pawel Adamski, Jacek Lubczonek

“…The main objective is to assess the feasibility of using consumer GPUs for deep learning tasks involving navigational sign detection, a critical component for ensuring safe and efficient inland transportation. …”
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A deep learning based intrusion detection system for CAN vehicle based on combination of triple attention mechanism and GGO algorithm by Hongwei Yang, Mehdi Effatparvar

“…The purpose of TAN here is to identify intrusion within 3 steps. Within the initial phase, the major features have been extracted, and TAN functions as a descriptor of feature. …”
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Ensuring Food Security and Biodiversity: A Novel Convolutional Neural Network (CNN) for Early Detection of Plant Disease in Precision Agriculture by Md Jiabul Hoque, Md. Saiful Islam, Mahadi Hassan, Mohammad Minhazul Islam

“…Conventional disease detection methods in agriculture are constrained by the presence of personal opinions and the amount of work required, which hinder broad-scale disease monitoring. …”
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Quantification of H3.1-nucleosomes using a chemiluminescent immunoassay: A reliable method for neutrophil extracellular trap detection. by Marion Wargnies, Guillaume Rommelaere, Julie Candiracci, Dorian Pamart, Robin Varsebroucq, Florian Jibassia, Finley Serneo, Virginie Laloux, Olivia Thiry, Fanny Lambert, Alison Lobbens, Priscilla Van den Ackerveken, Marielle Herzog

“…Since NETs consist of long strains of decondensed chromatin, with nucleosomes as their basic units, we propose circulating H3.1-nucleosomes as biomarkers for NETs detection in clinical plasma samples. In the initial phase of our study, we confirmed the presence of H3.1-nucleosomes by immunofluorescence and immunoprecipitation experiments in two in vitro NET models: neutrophil-like cells differentiated from the HL-60 cell line, and primary neutrophils isolated from whole blood, both treated with either phorbol 12-myristate 13-acetate or calcium ionophore A23187 to induce NETs formation. …”
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