Colonyzer: automated quantification of micro-organism growth characteristics on solid agar

<p>Abstract</p> <p>Background</p> <p>High-throughput screens comparing growth rates of arrays of distinct micro-organism cultures on solid agar are useful, rapid methods of quantifying genetic interactions. Growth rate is an informative phenotype which can be estimated by measuring cell densities at one or more times after inoculation. Precise estimates can be made by inoculating cultures onto agar and capturing cell density frequently by plate-scanning or photography, especially throughout the exponential growth phase, and summarising growth with a simple dynamic model (e.g. the logistic growth model). In order to parametrize such a model, a robust image analysis tool capable of capturing a wide range of cell densities from plate photographs is required.</p> <p>Results</p> <p>Colonyzer is a collection of image analysis algorithms for automatic quantification of the size, granularity, colour and location of micro-organism cultures grown on solid agar. Colonyzer is uniquely sensitive to extremely low cell densities photographed after dilute liquid culture inoculation (spotting) due to image segmentation using a mixed Gaussian model for plate-wide thresholding based on pixel intensity. Colonyzer is robust to slight experimental imperfections and corrects for lighting gradients which would otherwise introduce spatial bias to cell density estimates without the need for imaging dummy plates. Colonyzer is general enough to quantify cultures growing in any rectangular array format, either growing after pinning with a dense inoculum or growing with the irregular morphology characteristic of spotted cultures. Colonyzer was developed using the open source packages: Python, RPy and the Python Imaging Library and its source code and documentation are available on SourceForge under GNU General Public License. Colonyzer is adaptable to suit specific requirements: e.g. automatic detection of cultures at irregular locations on streaked plates for robotic picking, or decreasing analysis time by disabling components such as lighting correction or colour measures.</p> <p>Conclusion</p> <p>Colonyzer can automatically quantify culture growth from large batches of captured images of microbial cultures grown during genome-wide scans over the wide range of cell densities observable after highly dilute liquid spot inoculation, as well as after more concentrated pinning inoculation. Colonyzer is open-source, allowing users to assess it, adapt it to particular research requirements and to contribute to its development.</p

FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis

Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ

Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing C. elegans embryo

<p>Abstract</p> <p>Background</p> <p>Image analysis is an essential component in many biological experiments that study gene expression, cell cycle progression, and protein localization. A protocol for tracking the expression of individual <it>C. elegans </it>genes was developed that collects image samples of a developing embryo by 3-D time lapse microscopy. In this protocol, a program called StarryNite performs the automatic recognition of fluorescently labeled cells and traces their lineage. However, due to the amount of noise present in the data and due to the challenges introduced by increasing number of cells in later stages of development, this program is not error free. In the current version, the error correction (<it>i.e</it>., editing) is performed manually using a graphical interface tool named AceTree, which is specifically developed for this task. For a single experiment, this manual annotation task takes several hours.</p> <p>Results</p> <p>In this paper, we reduce the time required to correct errors made by StarryNite. We target one of the most frequent error types (movements annotated as divisions) and train a support vector machine (SVM) classifier to decide whether a division call made by StarryNite is correct or not. We show, via cross-validation experiments on several benchmark data sets, that the SVM successfully identifies this type of error significantly. A new version of StarryNite that includes the trained SVM classifier is available at <url>http://starrynite.sourceforge.net</url>.</p> <p>Conclusions</p> <p>We demonstrate the utility of a machine learning approach to error annotation for StarryNite. In the process, we also provide some general methodologies for developing and validating a classifier with respect to a given pattern recognition task.</p

Does Diabetes Accelerate the Progression of Aortic Stenosis through Enhanced Inflammatory Response within Aortic valves?

Diabetes predisposes to aortic stenosis (AS). We aimed to investigate if diabetes affects the expression of selected coagulation proteins and inflammatory markers in AS valves. Twenty patients with severe AS and concomitant type 2 diabetes mellitus (DM) and 40 well-matched patients without DM scheduled for valve replacement were recruited. Valvular tissue factor (TF), TF pathway inhibitor (TFPI), prothrombin, C-reactive protein (CRP) expression were evaluated by immunostaining and TF, prothrombin, and CRP transcripts were analyzed by real-time PCR. DM patients had elevated plasma CRP (9.2 [0.74–51.9] mg/l vs. 4.7 [0.59–23.14] mg/l, p = 0.009) and TF (293.06 [192.32–386.12] pg/ml vs. 140 [104.17–177.76] pg/ml, p = 0.003) compared to non-DM patients. In DM group, TF−, TFPI−, and prothrombin expression within valves was not related to demographics, body mass index, and concomitant diseases, whereas increased expression related to DM was found for CRP on both protein (2.87 [0.5–9]% vs. 0.94 [0–4]%, p = 0.01) and transcript levels (1.3 ± 0.61 vs. 0.22 ± 0.43, p = 0.009). CRP-positive areas were positively correlated with mRNA TF (r = 0.84, p = 0.036). Diabetes mellitus is associated with enhanced inflammation within AS valves, measured by CRP expression, which may contribute to faster AS progression

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Diffusion MRI of Structural Brain Plasticity Induced by a Learning and Memory Task

Background: Activity-induced structural remodeling of dendritic spines and glial cells was recently proposed as an important factor in neuroplasticity and suggested to accompany the induction of long-term potentiation (LTP). Although T1 and diffusion MRI have been used to study structural changes resulting from long-term training, the cellular basis of the findings obtained and their relationship to neuroplasticity are poorly understood. Methodology/Principal Finding: Here we used diffusion tensor imaging (DTI) to examine the microstructural manifestations of neuroplasticity in rats that performed a spatial navigation task. We found that DTI can be used to define the selective localization of neuroplasticity induced by different tasks and that this process is age-dependent in cingulate cortex and corpus callosum and age-independent in the dentate gyrus. Conclusion/Significance: We relate the observed DTI changes to the structural plasticity that occurs in astrocytes and discuss the potential of MRI for probing structural neuroplasticity and hence indirectly localizing LTP

Similar Neural Activity during Fear and Disgust in the Rat Basolateral Amygdala

Much research has focused on how the amygdala processes individual affects, yet little is known about how multiple types of positive and negative affects are encoded relative to one another at the single-cell level. In particular, it is unclear whether different negative affects, such as fear and disgust, are encoded more similarly than negative and positive affects, such as fear and pleasure. Here we test the hypothesis that the basolateral nucleus of the amygdala (BLA), a region known to be important for learned fear and other affects, encodes affective valence by comparing neuronal activity in the BLA during a conditioned fear stimulus (fear CS) with activity during intraoral delivery of an aversive fluid that induces a disgust response and a rewarding fluid that induces a hedonic response. Consistent with the hypothesis, neuronal activity during the fear CS and aversive fluid infusion, but not during the fear CS and rewarding fluid infusion, was more similar than expected by chance. We also found that the greater similarity in activity during the fear- and disgust-eliciting stimuli was specific to a subpopulation of cells and a limited window of time. Our results suggest that a subpopulation of BLA neurons encodes affective valence during learned fear, and furthermore, within this subpopulation, different negative affects are encoded more similarly than negative and positive affects in a time-specific manner

The Four types of Tregs in malignant lymphomas

Regulatory T cells (Tregs) are a specialized subpopulation of CD4+ T cells, which act to suppress the activation of other immune cells. Tregs represent important modulators for the interaction between lymphomas and host microenvironment. Lymphomas are a group of serious and frequently fatal malignant diseases of lymphocytes. Recent studies revealed that some lymphoma T cells might adopt a Treg profile. Assessment of Treg phenotypes and genotypes in patients may offer prediction of outcome in many types of lymphomas including diffuse large B-cell lymphoma, follicular lymphoma, cutaneous T cell lymphoma, and Hodgkin's lymphoma. Based on characterized roles of Tregs in lymphomas, we can categorize the various roles into four groups: (a) suppressor Tregs; (b) malignant Tregs; (c) direct tumor-killing Tregs; and (d) incompetent Tregs. The classification into four groups is significant in predicting prognosis and designing Tregs-based immunotherapies for treating lymphomas. In patients with lymphomas where Tregs serve either as suppressor Tregs or malignant Tregs, anti-tumor cytotoxicity is suppressed thus decreased numbers of Tregs are associated with a good prognosis. In contrast, in patients with lymphomas where Tregs serve as tumor-killing Tregs and incompetent Tregs, anti-tumor cytotoxicity is enhanced or anti-autoimmune Tregs activities are weakened thus increased numbers of Tregs are associated with a good prognosis and reduced numbers of Tregs are associated with a poor prognosis. However, the mechanisms underlying the various roles of Tregs in patients with lymphomas remain unknown. Therefore, further research is needed in this regard as well as the utility of Tregs as prognostic factors and therapy strategies in different lymphomas