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The unresolved struggle of 16S rRNA amplicon sequencing: a benchmarking analysis of clustering and denoising methods

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Environmental microbiome 2025年第1期20卷 51页

作者： Mohamed Fares Engy K Tharwat Ilse Cleenwerck Pieter Monsieurs Rob Van Houdt Peter Vandamme Mohamed El-Hadidi Mohamed Mysara Bioinformatics Group Center for Informatics Science School of Information Technology and Computer Science Nile University Giza Egypt. Veterinary Research Institute National Research Centre Giza Egypt. Laboratory of Microbiology Department of Biochemistry and Microbiology Faculty of Sciences BCCM/LMG Bacteria Collection Ghent University Ghent Belgium. Department of Biomedical Sciences Institute of Tropical Medicine Antwerp Antwerp Belgium. Microbiology Unit Nuclear Medical Applications Belgian Nuclear Research Centre (SCK CEN) Mol Belgium. Laboratory for Microbiology Department of Biochemistry and Microbiology Ghent University Ghent Belgium. Bioinformatics Group Center for Informatics Science School of Information Technology and Computer Science Nile University Giza Egypt. MMaysara@nu.edu.eg.

BACKGROUND:Although 16S rRNA gene amplicon sequencing has become an indispensable method for microbiome studies, this analysis is not error-free, and remains prone to several biases and errors. Numerous algorithms have been developed to eliminate these errors and consolidate the output into distance-based Operational Taxonomic Units (OTUs) or denoising-based Amplicon Sequence Variants (ASVs). An objective comparison between them has been obscured by various experimental setups and parameters. In the present study, we conducted a comprehensive benchmarking analysis of the error rates, microbial composition, over-merging/over-splitting of reference sequences, and diversity analyses using the most complex mock community, comprising 227 bacterial strains and the Mockrobiota database. Using unified preprocessing steps, we were able to compare DADA2, Deblur, MED, UNOISE3, UPARSE, DGC (Distance-based Greedy Clustering), AN (Average Neighborhood), and Opticlust objectively. RESULTS:ASV algorithms-led by DADA2- resulted in having a consistent output, yet suffered from over-splitting, while OTU algorithms-led by UPARSE-achieved clusters with lower errors, yet with more over-merging. Notably, UPARSE and DADA2 showed the closest resemblance to the intended microbial community, especially when considering measures for alpha and beta diversity. CONCLUSION:Our unbiased comparative evaluation examined the performance of eight algorithms dedicated to the analysis of 16S rRNA amplicon sequences with a wide range of mock datasets. Our analysis shed light on the pros and cons of each algorithm and the accuracy of the produced OTUs or ASVs. The utilization of the most complex mock community and the benchmarking comparison presented here offer a framework for the comparison between OTU/ASV algorithms and an objective method for the assessment of new tools and algorithms.

关键词： 16S rRNA amplicon sequencing Amplicon sequence variants (ASVs) Denoising. Operational taxonomical units (OTUs)

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ECgMLP: A novel gated MLP model for enhanced endometrial cancer diagnosis

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computer Methods and Programs in Biomedicine Update 2025年 7卷

作者： Sheakh, Md. Alif Azam, Sami Tahosin, Mst. Sazia Karim, Asif Montaha, Sidratul Fahim, Kayes Uddin Shafiabady, Niusha Jonkman, Mirjam De Boer, Friso Health Informatics Research Laboratory (HIRL) Department of Computer Science and Engineering Daffodil International University Dhaka 1341 Bangladesh Faculty of Science and Technology Charles Darwin University Darwin 0909 NT Australia Department of Computer Science and Engineering University of Calgary Calgary Canada Department of Information Technology Australian Catholic University North Sydney 2060 NSW Australia

Endometrial cancеr is the fourth fastеst-growing cancеr among women worldwide, affecting the uterus's lining. This research proposes a novel approach called ECgMLP for the automated diagnosis of endometrial cancer by analyzing histopathological images. Several preprocessing techniques are employed to increase the quality of the images, including normalization, Non-Local Means denoising, and alpha-beta enhancement. Effective segmentation is achieved through a combination of Otsu thresholding, morphological operations, distance transformations, and the watershed approach to identify major regions of interest. Through a sequence of blocks, the ECgMLP architecture processes input images to remove unimportant patterns. Model hyperparameters are improved via ablation research. The evaluations show a maximum accuracy of 99.26 % for identifying multi-class histopathological categories of endometrial tissue, which is higher than the previous best technique. The proposed model offers an automated, correct diagnosis, enhancing clinical processes. This proposition could be added to the current tools for finding endometrial cancer early, leading to better patient outcomes. © 2025

关键词： Deep learning Endometrial cancer gMLP Histopathological Watershed

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Robust Attacks Detection Model for Internet of Flying Things based on Generative Adversarial Network (GAN) and Adversarial Training

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IEEE Internet of Things Journal 2025年

作者： Gaber, Tarek Ali, Tarek Nicho, Mathew Torky, Mohamed Engineering and Environment University of Salford School of Science Manchester United Kingdom Suez Canal University Faculty of Computers and Informatics Ismailia Egypt Manchester Metropolitan University Department of Computing and Mathematics Manchester United Kingdom Rabdan Academy Research and Innovation Centre Abu Dhabi United Arab Emirates University of Queensland School of Electrical Engineering and Computer Science Australia Egyptian Russian University-Badr City Faculty of Artificial Intelligence Cairo Egypt

The Internet of Flying Things (IoFT) holds significant promise in fields like disaster management and surveillance. However, it is increasingly vulnerable to cyberattacks that can compromise the confidentiality, integrity, and availability (CIA) of sensitive data. Despite the growing interest in proposing Intrusion Detection Systems (IDSs) for IoFT networks, current literature faces key limitations, particularly the shortage of publicly available IoFT datasets with diverse attacks, and the fact that existing IDSs lack robustness against sophisticated adversarial machine learning attacks. This paper is the first study to address these limitations by proposing a more resilient and accurate IDS tailored for IoFT networks (RIDS-IoFT). We introduce a novel IDS that leverages Generative Adversarial Networks (GANs) to generate a hybrid dataset that combines real IoFT traffic data with GAN-generated adversarial attacks, addressing the dataset diversity issue. Additionally, we introduce an innovative adversarial training method to enhance the system’s defense against evolving threats, such as Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), and Carlini & Wagner (C&W) attacks. The proposed RIDS-IoFT was evaluated using four machine learning models, Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR), on two datasets: ECU-IoFT and CICIDS2018. The IDS’s performance was assessed based on its ability to detect both traditional and adversarial attacks. The results show that the Random Forest model achieved the highest detection accuracy, up to 96.5%, demonstrating superior performance across both real and hybrid datasets. The proposed RIDS-IoFT not only enhances detection accuracy but also strengthens resilience against adversarial threats, making it suitable for resource-constrained IoFT environments. In conclusion, this study presents a comprehensive approach to securing IoFT networks by combining real and synthetic d

关键词： Generative adversarial networks

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Neuro-Mimetic Developmental Architecture for Continual Learning through Self-Organizing Multimodal Perception Coordination

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IEEE Access 2025年

作者： Dawood, Farhan Masuyama, Naoki Loo, Chu Kiong Iqra University Department of Computing & Technology Islamabad Pakistan Osaka Metropolitan University Graduate School of Informatics 1-1 Gakuen-cho Naka-ku Sakai-Shi Osaka599-8531 Japan University of Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Kuala Lumpur50603 Malaysia

Developing cognitive capabilities in autonomous agents stands as a challenging yet pivotal task. In order to construct cohesive representations and extract meaningful insights from the environment, the brain utilizes patterns from a variety of sensory inputs, including vision and sound. Furthermore, related sensory inputs can activate each other’s representations, highlighting the brain’s ability to associate and integrate information across different sensory channels – key to cognitive development and adaptive learning. Current learning methods often mirror the developmental process of infants, who enhance their cognition through guidance and exploration. These methods often struggle with issues such as catastrophic forgetting and stability-plasticity trade-offs. This study presents a novel brain-inspired hierarchical autonomous framework, Cognitive Deep Self-Organizing Neural Network (CDSN), designed to enable autonomous agents to acquire object concepts dynamically. The architecture includes dual parallel audio-visual information pathways, incorporating three layers based on a Topological Kernel CIM-based Adaptive Resonance Neural Network (TC-ART). The first layer, referred to as the receptive layer, learns and organizes visual attributes and object names autonomously in an unsupervised manner. Subsequently, the second layer, the concept layer, distills clustered results from the corresponding receptive layer to create succinct symbol representation. In a synchronized manner, visual and auditory concepts are combined concurrently in the third layer, the associative layer, to establish real-time associative connections between the modalities. Furthermore, this layer introduces a top-down response approach, allowing agents to independently retrieve associated modalities and adapt acquired knowledge in a hierarchical manner. Experimental evaluations conducted on object datasets demonstrate the proposed architecture’s efficacy in online learning and the association o

关键词： Multilayer neural networks

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Proposed Framework for Detection of Breast Tumors

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computers, Materials & Continua 2023年第2期74卷 2927-2944页

作者： Mostafa Elbaz Haitham Elwahsh Ibrahim Mahmoud El-Henawy Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt

computer vision is one of the significant trends in computer *** plays as a vital role in many applications,especially in the medical *** detection and segmentation of different tumors is a big challenge in the medical *** proposed framework uses ultrasound images from Kaggle,applying five diverse models to denoise the images,using the best possible noise-free image as input to the U-Net model for segmentation of the tumor,and then using the Convolution Neural Network(CNN)model to classify whether the tumor is benign,malignant,or *** main challenge faced by the framework in the segmentation is the speckle ***’s is a multiplicative and negative issue in breast ultrasound imaging,because of this noise,the image resolution and contrast become reduced,which affects the diagnostic value of this imaging *** result,speckle noise reduction is very vital for the segmentation *** framework uses five models such as Generative Adversarial Denoising Network(DGAN-Net),Denoising U-Shaped Net(D-U-NET),Batch Renormalization U-Net(Br-UNET),Generative Adversarial Network(GAN),and Nonlocal Neutrosophic ofWiener Filtering(NLNWF)for reducing the speckle noise from the breast ultrasound images then choose the best image according to peak signal to noise ratio(PSNR)for each level of *** five used methods have been compared with classical filters such as Bilateral,Frost,Kuan,and Lee and they proved their efficiency according to PSNR in different levels of *** five diverse models are achieved PSNR results for speckle noise at level(0.1,0.25,0.5,0.75),(33.354,29.415,27.218,24.115),(31.424,28.353,27.246,24.244),(32.243,28.42,27.744,24.893),(31.234,28.212,26.983,23.234)and(33.013,29.491,28.556,25.011)forDGAN,Br-U-NET,D-U-NET,GANand NLNWF *** to the value of PSNR and level of speckle noise,the best image passed for segmentation using U-Net and classification usingCNNto detect tumor *** experiments proved

关键词： Breast tumor speckle noise GAN model U-Net model neutrosophic

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Expectation maximization—vector approximate message passing based generalized linear model for channel estimation in intelligent reflecting surface-assisted millimeter multi-user multiple-input multiple-output systems

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PeerJ computer science 2025年 11卷 e2582-e2582页

作者： Ali, Shoukath K. Philip, Sajan P. Khan, Arfat Ahmad Moses, Leeban Cengiz, Korhan Akleylek, Sedat Ivković, Nikola Department of Electronics and Communication Engineering Presidency University Karnataka Bengaluru India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Department of Electrical-Electronics Engineering Istinye University Istanbul Turkey Institute of Computer Science University of Tartu Tartu Estonia Faculty of Organization and Informatics University of Zagreb Pavlinska Varaždin Croatia

Channel estimation poses a main challenge in intelligent reflecting surface (IRS)assisted millimeter wave (mmWave) multi-user multiple-input multiple-output (MIMO) systems due to the substantial number of antennas at the base station (BS) and the passive reflective elements within the IRS lacking sufficient signal processing capabilities. This article addresses this challenge by proposing a channel estimation technique for IRS-assisted mmWave MIMO systems. The problem of channel estimation is normally taken as a compressed sensing (CS) problem, typically addressed through algorithms such as Orthogonal Matching Pursuit (OMP), Generalized Approximate Message Passing (GAMP), and Vector Approximate Message Passing with Expectation-Maximization (EM-VAMP). EM-VAMP demonstrates better performance only when a Gaussian mixture (GM) distribution is chosen as the prior for the sparse channel, especially at high signal-to-noise ratios (SNRs). To address this, the article introduces the application of generalized linear models (GLMs), extensions of standard linear models, providing increased flexibility in modeling data that deviates from Gaussian distribution. Numerical results unveil that the proposed Its EM-VAMP-GLM is much more robust to the existing OMP, GAMP and EM-LAMP algorithms. Copyright 2025 K et al. Distributed under Creative Commons CC-BY 4.0

关键词： Gaussian distribution

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ON FREE SUBGROUPS IN LEAVITT PATH ALGEBRAS

arXiv

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arXiv 2025年

作者： Hai, Bui Xuan Khanh, Huynh Viet Faculty of Mathematics and Computer Science University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Department of Mathematics and Informatics HCMC University of Education 280 An Duong Vuong Str. Dist. 5 Ho Chi Minh City Viet Nam

Let E be a graph and K a field. In this paper we prove that the multiplicative group of a unital noncommutative Leavitt path algebra LK(E) contains non-cyclic free subgroups provided K is of characteristic 0. Further, we provide a description of the generators of such free subgroups in term of the graph *** Codes 16S88, 20H20 © 2025, CC BY.

关键词： Algebra

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Generalized saturation game

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Discrete Applied Mathematics 2025年 374卷 33-49页

作者： Balázs Patkós Miloš Stojaković Jelena Stratijev Máté Vizer HUN-REN Alfréd Rényi Institute of Mathematics Budapest 1053 Reáltanoda utca 13-15 Hungary Department of Mathematics and Informatics Faculty of Sciences University of Novi Sad 21000 Novi Sad Serbia Department of Fundamental Sciences Faculty of Technical Sciences University of Novi Sad 21000 Novi Sad Serbia Department of Computer Science and Information Theory Budapest University of Technology and Economics Budapest Hungary

We study the following game version of the generalized graph Turán problem. For two fixed graphs F and H , two players, Max and Mini, alternately claim unclaimed edges of the complete graph K n such that the graph G of the claimed edges must remain F -free throughout the game. The game ends when no further edges can be claimed, i.e. when G becomes F -saturated. The H -score of the game is the number of copies of H in G . Max aims to maximize the H -score, while Mini wants to minimize it. The H -score of the game when both players play optimally is denoted by s 1 ( n , # H , F ) when Max starts, and by s 2 ( n , # H , F ) when Mini starts. We study these values for several natural choices of F and H .

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Enhanced Global Optimization Using a Novel Hybrid Sine Cosine-Gazelle Algorithm with Brownian Motion and Lévy Flight Mechanisms

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International Journal of Computational Intelligence Systems 2025年第1期18卷 1-55页

作者： Singh, Gyan Biswas, Saptadeep Ezugwu, Absalom El-Shamir Simic, Vladimir Bera, Uttam Kumar Saleem, Kashif Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade11010 Serbia Department of Industrial Engineering and Management College of Engineering Yuan Ze University Taoyuan City320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of Department of Computer Science and Engineering College of Applied Studies and Community Service King Saud University Riyadh11362 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan

Metaheuristic algorithms are crucial for solving intricate optimization problems in diverse fields. The Sine Cosine Algorithm (SCA), known for its efficiency and simplicity in global search, sometimes struggles with premature convergence and inadequate exploitation. To address these challenges, this study introduces a novel hybrid SCA-Gazelle Optimisation Algorithm (HSCAGOA) by integrating the Gazelle Optimisation Algorithm’s (GOA) exploitation strategy. Inspired by gazelle behaviour, GOA enhances local search capabilities, improving the balance between the exploration and exploitation phases. Additionally, HSCAGOA incorporates Brownian motion and Lévy flight mechanisms to further enhance exploration capabilities. This research rigorously evaluates HSCAGOA through extensive computational experiments on 33 benchmark test problems and six engineering design challenges. Comparisons with classical SCA and various state-of-the-art optimisation algorithms show that HSCAGOA consistently achieves faster convergence and higher solution quality across diverse optimisation landscapes. To validate these results, ranking analysis is conducted using the Wilcoxon rank-sum test and the Wilcoxon signed-rank test, confirming the efficacy of HSCAGOA. Furthermore, employing the Combined Compromise Solution (CoCoSo) method for multi-criteria decision-making enables systematic ranking and comparison of HSCAGOA’s performance against other algorithms. Additionally, a comparative analysis was conducted against renowned CEC competition winners, including LSHADEcnEpSin, LSHADESPACMA, and CMA-ES. HSCAGOA is evaluated through extensive computational experiments involving CEC 2017 benchmark test functions. Moreover, sensitivity analysis is performed to assess the robustness of HSCAGOA under varying configurations, including different population sizes and maximum iteration counts on CEC 2022 benchmark test functions. The findings highlight the algorithm’s adaptability and reliability in addressin

关键词： Design for testability

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Integration of NGS Genomic Metadata Analysis with Open-Source Genomic Databases for Single Cell Tissue Samples

Integration of NGS Genomic Metadata Analysis with Open-Sourc...

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International Conference on Computational Cybernetics (ICCC)

作者： Marianna Dimitrova Kucarov Béla Molnár Miklos Kozlovszky 3DHistech Ltd. Doctoral School of Applied Informatics and Applied Mathematics BioTech Óbuda University Budapest Hungary 3DHistech Ltd. Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Hun Medical Device Research Group LPDS Inst. for Computer Science and Control (SZTAKI) Hungarian Research Network (HUN-REN)

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

In this paper, we present the development and use of an automated platform that integrates the evaluation of genomic metadata from single-cells in colorectal tissues with the open source KEGG genomic database via API using the Biopython package. The system collects and fuses the information into its own PostgreSQL database, from which the results of the analysis are transferred to a graphical user interface using the NpgSql extension. Thus, the implemented application allows the identification of molecular pathways and gene-specific drug targets associated with the mutated genes per cell, providing the combined visualization of tumor diagnostics and gene therapy opportunities. Using the application, 9 predefined gene panels, containing 2800 genes, were analyzed based on which the molecular pathway and drug target coverage of the genes, as well as the correlation between the pathways and drug target values were evaluated.

关键词： Drugs Correlation Databases Genomics Metadata Gene therapy Time factors Bioinformatics Tumors Testing

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