Multivariate image analysis in biomedicine

The influence of the microwave oven on the production of solid culture medium and quality of microbial growth

Numerous cultivation media currently exist, whether selective, non-selective, enrichment or identification. However, they all have a common goal, which is the growth of microorganisms; the constitution and quality of the culture medium must favor it. For this reason, an important factor that directly affects the quality of a culture medium is its production. Thus, this article investigated the use of a microwave oven in the production of Sabouraud dextrose agar (SDA), and the microbial inactivation compared to the autoclave in a microbiology laboratory. The quality of the medium, time exposure, and sterilization potential were performed using fungal strains of Candida spp., Cryptococcus spp., Microsporum spp., and Aspergillus spp. The results showed that the advantages of the use of a microwave oven for the preparation of SDA are practicality, speed, lower energy expense, pH, and constituents preservation of the culture medium, resulting in a richer growth compared to autoclaved SDA. The multivariate analysis of digital images allowed the detection of melanoidins (brownish tone of medium), which are responsible for the negative influence on the microorganisms growth. This research shows the use of the microwave oven as an efficient alternative for the production of the culture medium and maintaining their best quality.

Analysis of multivariate images in fluorescence microscopy

A multivariate image is an image stack in which each pixel contains several variables. Such images are common in many fields (medicine, imaging microscopy, satellite imaging...) and their analysis requires adapted multivariate statistical methods. In fluorescence imaging microscopy, different probes or different measurements such as intensity, fluorescence lifetime or spectral information can be observed from one view. However, this is not yet analysed as multivariate images. Here, we are presenting a full approach of multivariate analysis of fluorescence microscopy images and we are proposing a free R package (multifluo) to conduct it.

Quantification of cell infection caused by Listeria monocytogenes invasion

Listeria monocytogenes causes a life-threatening food-borne disease known as Listeriosis. Elderly,immunocompromised, and pregnant women are primarily the victims of this facultative intracellular Gram-positive pathogen. Since the bacteria survive intracellularly within the human host cells they are protected against the immune system and poorly accessed by many antibiotics. In order to screen pharmaceutical substances for their ability to interfere with the infection, persistence and release of L. monocytogenes a high content as say is required. We established a high content screen (HCS) using the RAW 264.7 mouse macrophage cell line seeded into 96-well glass bottom microplates. Cells were infected with GFP-expressing L. monocytogenes and stained thereafter with Hoechst 33342.Automated image acquisition was carried out by the Scan(R) screening station. We have developed an algorithm that automatically grades cells in microscopy images of fluorescent-tagged Listeria for the severity of infection. The grading accuracy of this newly developed algorithm is 97.1% as compared to a 74.3%grading accuracy we obtained using the commercial Olympus Scan(R) software.

Multiclass detection of cells in multicontrast composite images

In this paper, we describe a framework for multiclass cell detection in composite images consisting of images obtained with three different contrast methods for transmitted light illumination (referred to as multicontrast composite images). Compared to previous multiclass cell detection results [1], the use of multicontrast composite images was found to improve the detection accuracy by introducing more discriminatory information into the system. Preprocessing multicontrast composite images with Kernel PCA was found to be superior to traditional linear PCA preprocessing, especially in difficult classification scenarios where high-order nonlinear correlations are expected to be important. Systematic study of our approach under different overlap conditions suggests that it possesses sufficient speed and accuracy for use in some practical systems.

Evaluation of Glyph-based Multivariate Scalar Volume Visualization Techniques

We present a user study quantifying the effectiveness of Scaled Data-Driven Spheres (SDDS), a multivariate three-dimensional data set visualization technique. The user study compares SDDS, which uses separate sets of colored sphere glyphs to depict variable values, to superquadric glyphs, an alternative technique that maps all variable values to a single glyph. User study participants performed tasks designed to measure their ability to estimate values of particular variables and identify relationships among variables. Results from the study show that users were significantly more accurate and faster for both tasks under the SDDS condition.

The neurological basis of occupation

The purpose of the present paper was to survey the literature about the neurological basis of human activity and its relationship to occupation and health. Activities related to neurological function were organized into three categories: those that activate the brain's reward system; those that promote the relaxation response; and those that preserve cognitive function into old age. The results from the literature review correlating neurological evidence and activities showed that purposeful and meaningful activities could counter the effects of stress-related diseases and reduce the risk for dementia. Specifically, it was found that music, drawing, meditation, reading, arts and crafts, and home repairs, for example, can stimulate the neurogical system and enhance health and well-being, Prospective research studies are needed to examine the effects of purposeful activities on reducing stress and slowing the rate of cognitive decline.

Remote detection of volatile organic compounds by passive multispectral infrared imaging measurements

Automated pattern recognition methodology is described for the detection of signatures of volatile organic compounds from passive multispectral infrared imaging data collected from an aircraft platform. Data are acquired in an across-track scanning mode with a downward-looking line scanner based on 8 to 16 spectral channels in the 8-14 and 3-5 microm spectral ranges. Two controlled release experiments are performed in which plumes of ethanol are generated and detected from aircraft overflights at altitudes of 2200 to 2800 ft (671 to 853 m). In addition, a methanol release from a chemical manufacturing facility is monitored. Automated classifiers are developed by application of piecewise linear discriminant analysis to the calibrated, registered, and preprocessed radiance data acquired by the line scanner. Preprocessing steps evaluated include contrast enhancement, temperature-emissivity separation, feature selection, and feature extraction/noise reduction by the minimum noise fraction (MNF) transform. Successful classifiers are developed for both compounds and are tested with data not used in the classifier development. Separation of temperature and emissivity by use of the alpha residual calculation is found to reduce false positive detections to a negligible level, and the MNF transform is shown to enhance detection sensitivity.