Rapid and more sensitive methods for the detection and quantification of viable Legionella cells have been developed. In this paper, a comparative analysis of environmental water samples using the ScanVIT-Legionella™ method and the traditional "gold standard" method of culturing is realised indicating the usefulness of the ScanVIT method. The ScanVIT-Legionella™ method was performed on environmental water samples from different locations of Huesca region (Spain). Legionella micro-colonies should appear green colour and Legionella pneumophila micro-colonies appear red. Twenty-one environmental water samples analysed by standard culture plus five control samples (Two sterile water samples with Legionella as positive controls and three sterile water samples as negative controls). All of them were used to apply ScanVIT-Legionella™ method. From of 21 environmental samples eleven were positive, six negative with both methods and four samples were negative for culture method and positive for ScanVIT-Legionella™ method. The positive control samples were positive and the negative were negative for both methods. A comparative analysis of the results obtained with two methods showed a strong positive determination coefficient (R(2) = 0.99753). The results demonstrate the usefulness of the ScanVIT-Legionella™ method as a rapid diagnostic tool in order to provide a diagnosis as quick as possible. ScanVIT-Legionella™ method offers a series of advantages such as quickly diagnosis, higher sensitivity and the possibility to identify Legionella spp. and L. pneumophila simultaneously.
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http://dx.doi.org/10.1007/s12088-013-0363-6 | DOI Listing |
Sci Rep
December 2024
KAUST Center of Excellence for Smart Health (KCSH), King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia.
Analyzing microbial samples remains computationally challenging due to their diversity and complexity. The lack of robust de novo protein function prediction methods exacerbates the difficulty in deriving functional insights from these samples. Traditional prediction methods, dependent on homology and sequence similarity, often fail to predict functions for novel proteins and proteins without known homologs.
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December 2024
School of Mechanical Engineering, Liaoning Engineering Vocational College, Tieling, 112008, Liaoning, People's Republic of China.
The paper proposes a multi-rigid-body system state identification method based on self-healing model in order to improve the accuracy and reliability of CNC machine tools. Firstly, considering the influence of the joint surface, the Lagrange method is used to establish the mechanical model of the multi-rigid-body system. We input acceleration information and use the second-order modulation function to complete the online real-time identification of the joint surface parameters, thereby establishing the self-healing mechanical model of the multi-rigid-body system.
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December 2024
Department of Electrical and Electronics Engineering, Engineering Faculty, Düzce University, Düzce, Turkey.
The study suggests a better multi-objective optimization method called 2-Archive Multi-Objective Cuckoo Search (MOCS2arc). It is then used to improve eight classical truss structures and six ZDT test functions. The optimization aims to minimize both mass and compliance simultaneously.
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December 2024
School of Electrical Engineering, Vellore Institute of Technology, Chennai, 600127, India.
Spherical tanks have been predominantly used in process industries due to their large storage capability. The fundamental challenges in process industries require a very efficient controller to control the various process parameters owing to their nonlinear behavior. The current research work in this paper aims to propose the Approximate Generalized Time Moments (AGTM) optimization technique for designing Fractional-Order PI (FOPI) and Fractional-Order PID (FOPID) controllers for the nonlinear Single Spherical Tank Liquid Level System (SSTLLS).
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December 2024
College of Information Engineering, SuQian University, SuQian, 223800, China.
The safety and reliability of rotating machinery hinge significantly on the proper functioning of rolling bearings. In the last few years, there have been significant advances in the algorithms for intelligent fault diagnosis of bearings. However, the vibration signals collected by machines are inevitably affected by irrelevant noise because of the complex working environments of bearings.
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