9,385 research outputs found
Portable Bacterial Identification System Based on Elastic Light Scatter Patterns
Background
Conventional diagnosis and identification of bacteria requires shipment of samples to a laboratory for genetic and biochemical analysis. This process can take days and imposes significant delay to action in situations where timely intervention can save lives and reduce associated costs. To enable faster response to an outbreak, a low-cost, small-footprint, portable microbial-identification instrument using forward scatterometry has been developed. Results
This device, weighing 9 lb and measuring 12 × 6 × 10.5 in., utilizes elastic light scatter (ELS) patterns to accurately capture bacterial colony characteristics and delivers the classification results via wireless access. The overall system consists of two CCD cameras, one rotational and one translational stage, and a 635-nm laser diode. Various software algorithms such as Hough transform, 2-D geometric moments, and the traveling salesman problem (TSP) have been implemented to provide colony count and circularity, centering process, and minimized travel time among colonies. Conclusions
Experiments were conducted with four bacteria genera using pure and mixed plate and as proof of principle a field test was conducted in four different locations where the average classification rate ranged between 95 and 100%
Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning
We present a computational live bacteria detection system that periodically
captures coherent microscopy images of bacterial growth inside a 60 mm diameter
agar-plate and analyzes these time-lapsed holograms using deep neural networks
for rapid detection of bacterial growth and classification of the corresponding
species. The performance of our system was demonstrated by rapid detection of
Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and
Klebsiella pneumoniae subsp. pneumoniae) in water samples. These results were
confirmed against gold-standard culture-based results, shortening the detection
time of bacterial growth by >12 h as compared to the Environmental Protection
Agency (EPA)-approved analytical methods. Our experiments further confirmed
that this method successfully detects 90% of bacterial colonies within 7-10 h
(and >95% within 12 h) with a precision of 99.2-100%, and correctly identifies
their species in 7.6-12 h with 80% accuracy. Using pre-incubation of samples in
growth media, our system achieved a limit of detection (LOD) of ~1 colony
forming unit (CFU)/L within 9 h of total test time. This computational bacteria
detection and classification platform is highly cost-effective (~$0.6 per test)
and high-throughput with a scanning speed of 24 cm2/min over the entire plate
surface, making it highly suitable for integration with the existing analytical
methods currently used for bacteria detection on agar plates. Powered by deep
learning, this automated and cost-effective live bacteria detection platform
can be transformative for a wide range of applications in microbiology by
significantly reducing the detection time, also automating the identification
of colonies, without labeling or the need for an expert.Comment: 24 pages, 6 figure
Intelligent computational system for colony-forming-unit enumeration and differentiation
Accurate quantitative analysis of microorganisms is recognized as an essential tool for gauging safety and quality in a wide range of fields. The enumeration processes of viable microorganisms via traditional culturing techniques are methodically convenient and cost-effective, conferring high applicability worldwide. However, manual counting can be time-consuming, laborious and imprecise. Furthermore, particular pathologies require an urgent and accurate response for the therapy to be effective. To reduce time limitations and perhaps discrepancies, this work introduces an intelligent image processing software capable of automatically quantifying the number of Colony Forming Units (CFUs) in Petri-plates. This rapid enumeration enables the technician to provide an expeditious assessment of the microbial load. Moreover, an auxiliary system is able to differentiate among colony images of Echerichia coli, Pseudomonas aeruginosa and Staphylococcus aureus via Machine Learning, based on a Convolutional Neural Network in a process of cross-validation. For testing and validation of the system, the three bacterial groups were cultured, and a significant labeled database was created, exercising suited microbiological laboratory methodologies and subsequent image acquisition. The system demonstrated acceptable accuracy measures; the mean values of precision, recall and F-measure were 95%, 95% and 0.95, for E. coli, 91%, 91% and 0.90 for P. aeruginosa, and 84%, 86% and 0.85 for S. aureus. The adopted deep learning approach accomplished satisfactory results, manifesting 90.31% of accuracy. Ultimately, evidence related to the time-saving potential of the system was achieved; the time spent on the quantification of plates with a high number of colonies might be reduced to a half and occasionally to a third.A análise quantitativa de microrganismos é uma ferramenta essencial na aferição da segurança e qualidade numa ampla variedade de áreas. O processo de enumeração de microrganismos viáveis através das técnicas de cultura tradicionais é económica e metodologicamente adequado, conferindo lhe alta aplicabilidade a nÃvel mundial. Contudo, a contagem manual pode ser morosa, laboriosa e imprecisa. Em adição, certas patologias requerem uma urgente e precisa resposta de modo a que a terapia seja eficaz. De forma a reduzir limitações e discrepâncias, este trabalho apresenta um software inteligente de processamento de imagem capaz de quantificar automaticamente o número de Unidades Formadoras de Colónias (UFCs) em placas de Petri. Esta rápida enumeração, possibilita ao técnico uma expedita avaliação da carga microbiana. Adicionalmente, um sistema auxiliar tem a capacidade de diferenciar imagens de colónias de Echerichia coli, Pseudomonas aeruginosa e Staphylococcus aureus recorrendo a Machine Learning, através de uma Rede Neuronal Convolucional num processo de validação cruzada. Para testar e validar o sistema, os três grupos bacterianos foram cultivados e uma significativa base de dados foi criada, recorrendo à s adequadas metodologias microbiológicas laboratoriais e subsequente aquisição de imagens. O sistema demonstrou medidas de precisão aceitáveis; os valores médios de precisão, recall e F-measure, foram 95%, 95% e 0.95, para E. coli, 91%, 91% e 0.90 para P. aeruginosa, e 84%, 86% e 0.85 para S. aureus. A abordagem deep learning obteve resultados satisfatórios, manifestando 90.31% de precisão. O sistema revelou potencial em economizar tempo; a duração de tarefas afetas à quantificação de placas com elevado número de colónias poderá ser reduzida para metade e ocasionalmente para um terço
Reconstructing the Forest of Lineage Trees of Diverse Bacterial Communities Using Bio-inspired Image Analysis
Cell segmentation and tracking allow us to extract a plethora of cell
attributes from bacterial time-lapse cell movies, thus promoting computational
modeling and simulation of biological processes down to the single-cell level.
However, to analyze successfully complex cell movies, imaging multiple
interacting bacterial clones as they grow and merge to generate overcrowded
bacterial communities with thousands of cells in the field of view,
segmentation results should be near perfect to warrant good tracking results.
We introduce here a fully automated closed-loop bio-inspired computational
strategy that exploits prior knowledge about the expected structure of a
colony's lineage tree to locate and correct segmentation errors in analyzed
movie frames. We show that this correction strategy is effective, resulting in
improved cell tracking and consequently trustworthy deep colony lineage trees.
Our image analysis approach has the unique capability to keep tracking cells
even after clonal subpopulations merge in the movie. This enables the
reconstruction of the complete Forest of Lineage Trees (FLT) representation of
evolving multi-clonal bacterial communities. Moreover, the percentage of valid
cell trajectories extracted from the image analysis almost doubles after
segmentation correction. This plethora of trustworthy data extracted from a
complex cell movie analysis enables single-cell analytics as a tool for
addressing compelling questions for human health, such as understanding the
role of single-cell stochasticity in antibiotics resistance without losing site
of the inter-cellular interactions and microenvironment effects that may shape
it
Equine or porcine synovial fluid as a novel ex vivo model for the study of bacterial free-floating biofilms that form in human joint infections
Bacterial invasion of synovial joints, as in infectious or septic arthritis, can be difficult to treat in both veterinary and human clinical practice. Biofilms, in the form of free-floating clumps or aggregates, are involved with the pathogenesis of infectious arthritis and periprosthetic joint infection (PJI). Infection of a joint containing an orthopedic implant can additionally complicate these infections due to the presence of adherent biofilms. Because of these biofilm phenotypes, bacteria within these infected joints show increased antimicrobial tolerance even at high antibiotic concentrations. To date, animal models of PJI or infectious arthritis have been limited to small animals such as rodents or rabbits. Small animal models, however, yield limited quantities of synovial fluid making them impractical for in vitro research. Herein, we describe the use of ex vivo equine and porcine models for the study of synovial fluid induced biofilm aggregate formation and antimicrobial tolerance. We observed Staphylococcus aureus and other bacterial pathogens adapt the same biofilm aggregate phenotype with significant antimicrobial tolerance in both equine and porcine synovial fluid, analogous to human synovial fluid. We also demonstrate that enzymatic dispersal of synovial fluid aggregates restores the activity of antimicrobials. Future studies investigating the interaction of bacterial cell surface proteins with host synovial fluid proteins can be readily carried out in equine or porcine ex vivo models to identify novel drug targets for treatment of prevention of these difficult to treat infectious diseases
An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images
This paper proposes a novel pixel interval down-sampling network (PID-Net)
for dense tiny objects (yeast cells) counting tasks with higher accuracy. The
PID-Net is an end-to-end CNN model with encoder to decoder architecture. The
pixel interval down-sampling operations are concatenated with max-pooling
operations to combine the sparse and dense features. It addresses the
limitation of contour conglutination of dense objects while counting.
Evaluation was done using classical segmentation metrics (Dice, Jaccard,
Hausdorff distance) as well as counting metrics. Experimental result shows that
the proposed PID-Net has the best performance and potential for dense tiny
objects counting tasks, which achieves 96.97% counting accuracy on the dataset
with 2448 yeast cell images. By comparing with the state-of-the-art approaches
like Attention U-Net, Swin U-Net and Trans U-Net, the proposed PID-Net can
segment the dense tiny objects with clearer boundaries and fewer incorrect
debris, which shows the great potential of PID-Net in the task of accurate
counting tasks
Definition of a near real time microbiological monitor for space vehicles
Efforts to identify the ideal candidate to serve as the biological monitor on the space station Freedom are discussed. The literature review, the evaluation scheme, descriptions of candidate monitors, experimental studies, test beds, and culture techniques are discussed. Particular attention is given to descriptions of five candidate monitors or monitoring techniques: laser light scattering, primary fluorescence, secondary fluorescence, the volatile product detector, and the surface acoustic wave detector
Principal component-based image segmentation: a new approach to outline in vitro cell colonies
publishedVersio
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