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Cell image and its smallest convex hull region. (a) Cell without a bud. The green line represents the convex hull, the red line is the outline of the cell image, and the pink line represents the major and minor axes of cell. (b) Cell with a bud. The green line represents the convex hull, the blue and yellow lines are the outline of the cell image, and the pink line represents the major axis of the whole budding cell. The red asterisks represent the touching points between the mother cell and its bud.
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The use of automated microscopes, combined with digital image analysis, is an increasingly important way of high-throughput phenotype analysis of biological systems. We have developed a new method of measuring the basic morphological features of budding yeast (Saccharomyces cerevisiae) cells. Using this method we have performed investigation on fou...
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Context 1
... we consider the cell to have a bud. Figure 4(a) shows that the cell region (red line) without a bud has nearly the same size as its convex polygon region (green line). Figure 4(b) shows a cell with a bud, and the cell region (blue and yellow line) is less than its convex polygon region (green line). ...
Context 2
... we consider the cell to have a bud. Figure 4(a) shows that the cell region (red line) without a bud has nearly the same size as its convex polygon region (green line). Figure 4(b) shows a cell with a bud, and the cell region (blue and yellow line) is less than its convex polygon region (green line). Its major axis (pink line) separates the cell into two parts (blue and yellow line). ...
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Citations
... We then applied quantitative analyses to evaluate cell area and cell roundness in the images acquired by both WSI and SEM. These two parameters are considered relevant to study yeast cell shapes (Lee et al., 2011;Liu et al., 2011;Wang and Lin, 2012;Bougen-Zhukov et al., 2017). In all cases, the data for cell morphometric assessment with WSI did not differ from the data acquired with SEM (P >0.90 for all), indicating that the resolution of WSI is accurate at this level and for this purpose. ...
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New strategies that enable fast and accurate visualization of Candida biofilms are necessary to better study their structure and response to antifungals agents. Here, we applied whole slide imaging (WSI) to study biofilm formation of Candida species. Three relevant biofilm-forming Candida species ( C. albicans ATCC 10231 , C. glabrata ATCC 2001, and C. tropicalis ATCC 750) were cultivated on glass coverslips both in presence and absence of widely used antifungals. Accumulated biofilms were stained with fluorescent markers and scanned in both bright-field and fluorescence modes using a WSI digital scanner. WSI enabled clear assessment of both size and structural features of Candida biofilms. Quantitative analyses readily detected reductions in biofilm-covered surface area upon antifungal exposure. Furthermore, we show that the overall biofilm growth can be adequately assessed across both bright-field and fluorescence modes. At the single-cell level, WSI proved adequate, as morphometric parameters evaluated with WSI did not differ significantly from those obtained with scanning electron microscopy, considered as golden standard at single-cell resolution. Thus, WSI allows for reliable visualization of Candida biofilms enabling both large-scale growth assessment and morphometric characterization of single-cell features, making it an important addition to the available microscopic toolset to image and analyze fungal biofilm growth.
... Two algorithms for computer analysis of microscopic images were developed for morphometric studies of single cells of budding yeast. The first was used in a robotic method for automated differentiation of the morphotypes of Saccharomyces cerevisiae strains with deleted genes (Liu et al., 2011). It was shown that the method allows statistically significant distinguishing of the studied strains by their morphological characteristics, even at different growth phases. ...
Modern ideas about the world of microorganisms have been developed mainly due to the studies at the population level. Such an approach was justified by the need to overcome the limitations in the sensitivity of the physicochemical, molecular biological, and genetic methods used. The potential differences between individual cells were therefore leveled, the properties of all cells were averaged, and the obtained data characterized the studied microbial populations as a whole. Over time, some analytical methods have been improved to such an extent that the prospect of quantitatively exploring many physicochemical and morphological properties of individual microbial cells has been opened up. This allowed taking a new approach to solving a number of problems, including the heterogeneity of microbial populations, the nature of the microorganisms unculturable in laboratory conditions and of persistent states, the development of biofilms, the interaction of microorganisms with plant and animal cells, the relationship of structure and function in metabolism, and a number of others. This review briefly describes the main methods of quantitative analysis of single bacterial and yeast cells at the cellular and subcellular levels and presents examples of their use according to the literature for the last fifteen years.
... Two single cell morphometry CDIA algorithms were designed for budding yeast studies by light microscopy imaging. The first was used in a robotic method to automatically obtain quantitative morphology features of Saccharomyces cerevisiae yeast cells of four deletant strains: YLR371w, YDR349c, YLR192c, and YDR414c [77]. It was demonstrated that the method provided an efficient means of getting statistically significant morphological differences between strains, and that these differences varied with growth stage. ...
This review is focused on using computer image analysis as a means of objective and quantitative characterizing optical images of the macroscopic (e.g. microbial colonies) and the microscopic (e.g. single cell) objects in the microbiological research. This is the way of making many visual inspection assays more objective and less time and labor consuming. Also, it can provide new visually inaccessible information on relation between some optical parameters and various biological features of the microbial cul-tures. Of special interest is application of image analysis in fluorescence microscopy as it opens new ways of using fluorescence based methodology for single microbial cell studies. Examples of using image analysis in the studies of both the macroscopic and the microscopic microbiological objects obtained by various imaging techniques are presented and discussed.
... Feature extraction and feature selection are widely used in biomedical data processing1112131415161718. In our research we use Student's t-test [19] feature selection method to detect the significant ADRs from thousands of medical events. ...
Adverse drug reactions (ADR) are widely concerning for public health issue.
In this study we propose an original approach to detect ADRs using a feature
matrix and feature selection. The experiments are carried out on the drug
Simvastatin. Major side effects for the drug are detected and better
performance is achieved compared to other computerized methods. Because
currently the detected ADRs are based solely on computerized methods, further
expert investigation is needed.
... Feature extraction and feature selection are widely used in biomedical data processing11121314151617. In our research we use Student's t-test [18] feature selection method to detect the significant ADRs from thousands of medical events. ...
Adverse drug reactions (ADRs) are big concern for public health. ADRs are one
of most common causes to withdraw some drugs from markets. Now two major
methods for detecting ADRs are spontaneous reporting system (SRS), and
prescription event monitoring (PEM). The World Health Organization (WHO)
defines a signal in pharmacovigilance as "any reported information on a
possible causal relationship between an adverse event and a drug, the
relationship being unknown or incompletely documented previously". For
spontaneous reporting systems, many machine learning methods are used to detect
ADRs, such as Bayesian confidence propagation neural network (BCPNN), decision
support methods, genetic algorithms, knowledge based approaches, etc. One
limitation is the reporting mechanism to submit ADR reports, which has serious
underreporting and is not able to accurately quantify the corresponding risk.
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database. First a feature matrix, which represents the medical events for the
patients before and after taking drugs, is created by linking patients'
prescriptions and corresponding medical events together. Then significant
features are selected based on feature selection methods, comparing the feature
matrix before patients take drugs with one after patients take drugs. Finally
the significant ADRs can be detected from thousands of medical events based on
corresponding features. Experiments are carried out on the drug Atorvastatin.
Good performance is achieved.
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In this study we propose a novel method to successfully detect the ADRs using
feature matrix and feature selection. A feature matrix, which characterizes the
medical events before patients take drugs or after patients take drugs, is
created from THIN database. The feature selection method of Student's t-test is
used to detect the significant features from thousands of medical events. The
significant ADRs, which are corresponding to significant features, are
detected. Experiments are performed on the drug Pioglitazone. Compared to other
computerized methods, our proposed method achieves good performance.
... Feature extraction and feature selection are widely used in biomedical data processing1112131415161718. In our research we use Student's t-test [19] feature selection method to detect the significant ADRs from thousands of medical events. ...
Adverse drug reaction (ADR) is widely concerned for public health issue. In
this study we propose an original approach to detect the ADRs using feature
matrix and feature selection. The experiments are carried out on the drug
Simvastatin. Major side effects for the drug are detected and better
performance is achieved compared to other computerized methods. The detected
ADRs are based on the computerized method, further investigation is needed.
... Feature extraction and feature selection are widely used in biomedical data processing [21][22][23][24][25][26][27]. In our research we use Student's t-test [28] feature selection method to detect the significant ADRs from thousands of medical events. ...
Adverse drug reaction (ADR) is widely concerned for public health issue. ADRs
are one of most common causes to withdraw some drugs from market. Prescription
event monitoring (PEM) is an important approach to detect the adverse drug
reactions. The main problem to deal with this method is how to automatically
extract the medical events or side effects from high-throughput medical data,
which are collected from day to day clinical practice. In this study we propose
an original approach to detect the ADRs using feature matrix and feature
selection. The experiments are carried out on the drug Pravastatin. Major side
effects for the drug are detected. The detected ADRs are based on computerized
method, further investigation is needed.
... Two single cell morphometry CDIA algorithms were designed for budding yeast studies by light microscopy imaging. The first was used in a robotic method to automatically obtain quantitative morphology features of Saccharomyces cerevisiae yeast cells of four deletant strains: YLR371w, YDR349c, YLR192c, and YDR414c [77]. It was demonstrated that the method provided an efficient means of getting statistically significant morphological differences between strains, and that these differences varied with growth stage. ...
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... Two single cell morphometry CDIA algorithms were designed for budding yeast stu- dies by light microscopy imaging. The first was used in a robotic method to automat- ically obtain quantitative morphology features of Saccharomyces cerevisiae yeast cells of four deletant strains: YLR371w, YDR349c, YLR192c, and YDR414c [77]. It was demonstrated that the method provided an efficient means of getting statistically sig- nificant morphological differences between strains, and that these differences varied with growth stage. ...
This review provides a concise description of the basic principles and options of computer-based image analysis for counting
and characterization of microbial colonies. Properties of several modern commercially available computer/automated colony
counters are described. A number of original works are used to exemplify the new methodological fields made possible by computer
image analysis, such as identification of microorganisms according to the external appearance of their colonies, as well as
characterization of physiological and biochemical properties and genomics of microbial cultures based on colony growth dynamics.
The conclusion is drawn that computer analysis of microbial colony images is a promising way to achieve a qualitatively new
level of performance in many conventional techniques and develop novel procedures and analytical systems.
Key wordscomputer-based image analysis-microbial colonies-computer-aided colony counter-identification of microorganisms-microbial physiology-microbial biochemistry-microbial genomics
