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Agboka KM, Peter E, Bwambale E, Sokame BM. Biological reinforcement learning simulation for natural enemy -host behavior: Exploring deep learning algorithms for population dynamics. MethodsX 2024; 13:102845. [PMID: 39092273 PMCID: PMC11292350 DOI: 10.1016/j.mex.2024.102845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 07/02/2024] [Indexed: 08/04/2024] Open
Abstract
This study introduces a simulation of biological reinforcement learning to explore the behavior of natural enemies in the presence of host pests, aiming to analyze the population dynamics between natural enemies and insect pests within an ecological context. The simulation leverages on Q-learning, a reinforcement learning algorithm, to model the decision-making processes of both parasitoids/predators and pests, thereby assessing the impact of varying parasitism and predation rates on pest population growth. Simulation parameters, such as episode count, duration in months, steps, learning rate, and discount factor, were set arbitrarily. Environmental and reward matrices, representing climatic conditions, crop availability, and the rewards for different actions, were established for each month. Initial Q-tables for parasitoids/predators and pests, along with population arrays, were used to track population dynamics.•The simulation, illustrated through the Aphid-Ladybird beetle interaction case study over multiple episodes, includes a sensitivity analysis to evaluate the effects of different predation rates.•Findings reveal detailed population dynamics, phase relationships between predator and pest populations, and the significant influence of predation rates.•These insights contribute to a deeper understanding of ecological systems and inform potential pest management strategies.
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Affiliation(s)
- Komi Mensah Agboka
- International Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772 00100, Nairobi, Kenya
| | - Emmanuel Peter
- International Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772 00100, Nairobi, Kenya
- Department of Agronomy, Faculty of Agriculture, Federal University Gashua, P.M.B 1005, Yobe, Nigeria
| | - Erion Bwambale
- Department of Agricultural and Biosystems Engineering, Makerere University, P. O. Box 7062, Kampala, Uganda
| | - Bonoukpoè Mawuko Sokame
- International Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772 00100, Nairobi, Kenya
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Zeeshan, Bahrami S, Park S, Cho S. Antibody functionalized capacitance sensor for label-free and real-time detection of bacteria and antibiotic susceptibility. Talanta 2024; 272:125831. [PMID: 38428133 DOI: 10.1016/j.talanta.2024.125831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 02/09/2024] [Accepted: 02/23/2024] [Indexed: 03/03/2024]
Abstract
The effective management of infectious diseases and the growing concern of antibiotic resistance necessitates accurate and targeted therapies, highlighting the importance of antibiotic susceptibility testing. This study aimed to develop a real-time impedimetric biosensor for identifying and monitoring bacterial growth and antibiotic susceptibility. The biosensor employed a gold 8-channel disk-shaped microelectrode array with specific antibodies as bio-recognition elements. This setup was allowed for the analysis of bacterial samples, including Staphylococcus aureus, Bacillus cereus, and Micrococcus luteus. These microorganisms were successfully cultured and detected within 1 h of incubation even with a minimal bacterial concentration of 10 CFU/ml. Overall, the developed biosensor array exhibits promising capabilities for monitoring S. aureus, B. cereus and M. luteus, showcasing an excellent linear response ranging from 10 to 104 CFU/ml with a detection limit of 0.95, 1.22 and 1.04 CFU/mL respectively. Moreover, real-time monitoring of antibiotic susceptibility was facilitated by changes in capacitance, which dropped when bacteria were exposed to antibiotic doses higher than their minimum inhibitory concentration (MIC), indicating suppressed bacterial growth. The capacitance measurements enabled determination of half-maximal cytotoxic concentrations (CC50) values for each bacteria-antibiotic pair. As a proof-of-concept application, the developed sensor array was employed as a sensing platform for the real time detection of bacteria in milk samples, which ensured the reliability of the sensor for in-field detection of foodborne pathogens and rapid antimicrobial susceptibility tests (ASTs).
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Affiliation(s)
- Zeeshan
- Department of Electronic Engineering, Gachon University, 1342 Seongnamdaero, Seongnam-si, Gyeonggi-do, 13120, South Korea.
| | - Sadra Bahrami
- School of Mechanical Engineering, Sungkyunkwan University (SKKU), Seobu-ro 2066, Jangan-gu, Suwon, 16419, South Korea.
| | - Sungsu Park
- School of Mechanical Engineering, Sungkyunkwan University (SKKU), Seobu-ro 2066, Jangan-gu, Suwon, 16419, South Korea.
| | - Sungbo Cho
- Department of Electronic Engineering, Gachon University, 1342 Seongnamdaero, Seongnam-si, Gyeonggi-do, 13120, South Korea; Department of Health Science and Technology, GAIHST, Gachon University, Incheon, 21999, South Korea.
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Luo X, Zhang J, Tan H, Jiang J, Li J, Wen W. Real-Time 3D Tracking of Multi-Particle in the Wide-Field Illumination Based on Deep Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:2583. [PMID: 38676200 PMCID: PMC11054292 DOI: 10.3390/s24082583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/09/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024]
Abstract
In diverse realms of research, such as holographic optical tweezer mechanical measurements, colloidal particle motion state examinations, cell tracking, and drug delivery, the localization and analysis of particle motion command paramount significance. Algorithms ranging from conventional numerical methods to advanced deep-learning networks mark substantial strides in the sphere of particle orientation analysis. However, the need for datasets has hindered the application of deep learning in particle tracking. In this work, we elucidated an efficacious methodology pivoted toward generating synthetic datasets conducive to this domain that resonates with robustness and precision when applied to real-world data of tracking 3D particles. We developed a 3D real-time particle positioning network based on the CenterNet network. After conducting experiments, our network has achieved a horizontal positioning error of 0.0478 μm and a z-axis positioning error of 0.1990 μm. It shows the capability to handle real-time tracking of particles, diverse in dimensions, near the focal plane with high precision. In addition, we have rendered all datasets cultivated during this investigation accessible.
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Affiliation(s)
- Xiao Luo
- Department of Physics, The Hong Kong University of Science and Technology, Hong Kong 999077, China;
| | - Jie Zhang
- Advanced Materials Thrust, The Hong Kong University of Science and Technology, Guangzhou 511400, China; (J.Z.); (J.J.); (J.L.)
| | - Handong Tan
- Department of Individualized Interdisciplinary Program (Advanced Materials), The Hong Kong University of Science and Technology, Hong Kong 999077, China;
| | - Jiahao Jiang
- Advanced Materials Thrust, The Hong Kong University of Science and Technology, Guangzhou 511400, China; (J.Z.); (J.J.); (J.L.)
| | - Junda Li
- Advanced Materials Thrust, The Hong Kong University of Science and Technology, Guangzhou 511400, China; (J.Z.); (J.J.); (J.L.)
| | - Weijia Wen
- Department of Physics, The Hong Kong University of Science and Technology, Hong Kong 999077, China;
- Advanced Materials Thrust, The Hong Kong University of Science and Technology, Guangzhou 511400, China; (J.Z.); (J.J.); (J.L.)
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Baddal B, Taner F, Uzun Ozsahin D. Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review. Diagnostics (Basel) 2024; 14:484. [PMID: 38472956 DOI: 10.3390/diagnostics14050484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/23/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.
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Affiliation(s)
- Buket Baddal
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Ferdiye Taner
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
- Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
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Cao L, Zeng L, Wang Y, Cao J, Han Z, Chen Y, Wang Y, Zhong G, Qiao S. U 2-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting. Microorganisms 2024; 12:201. [PMID: 38258027 PMCID: PMC10820204 DOI: 10.3390/microorganisms12010201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
In this paper, an automatic colony counting system based on an improved image preprocessing algorithm and convolutional neural network (CNN)-assisted automatic counting method was developed. Firstly, we assembled an LED backlighting illumination platform as an image capturing system to obtain photographs of laboratory cultures. Consequently, a dataset was introduced consisting of 390 photos of agar plate cultures, which included 8 microorganisms. Secondly, we implemented a new algorithm for image preprocessing based on light intensity correction, which facilitated clearer differentiation between colony and media areas. Thirdly, a U2-Net was used to predict the probability distribution of the edge of the Petri dish in images to locate region of interest (ROI), and then threshold segmentation was applied to separate it. This U2-Net achieved an F1 score of 99.5% and a mean absolute error (MAE) of 0.0033 on the validation set. Then, another U2-Net was used to separate the colony region within the ROI. This U2-Net achieved an F1 score of 96.5% and an MAE of 0.005 on the validation set. After that, the colony area was segmented into multiple components containing single or adhesive colonies. Finally, the colony components (CC) were innovatively rotated and the image crops were resized as the input (with 14,921 image crops in the training set and 4281 image crops in the validation set) for the ResNet50 network to automatically count the number of colonies. Our method achieved an overall recovery of 97.82% for colony counting and exhibited excellent performance in adhesion classification. To the best of our knowledge, the proposed "light intensity correction-based image preprocessing→U2-Net segmentation for Petri dish edge→U2-Net segmentation for colony region→ResNet50-based counting" scheme represents a new attempt and demonstrates a high degree of automation and accuracy in recognizing and counting single-colony and multi-colony targets.
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Affiliation(s)
- Libo Cao
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
| | - Liping Zeng
- Department of Pathogen Biology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing 211166, China;
| | - Yaoxuan Wang
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
| | - Jiayi Cao
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
| | - Ziyu Han
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
| | - Yang Chen
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
| | - Yuxi Wang
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
| | - Guowei Zhong
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
| | - Shanlei Qiao
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
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