[
Neurotoxicology,
2008]
Manganese (Mn) is a transition metal that is essential for normal cell growth and development, but is toxic at high concentrations. While Mn deficiency is uncommon in humans, Mn toxicity is known to be readily prevalent due to occupational overexposure in miners, smelters and possibly welders. Excessive exposure to Mn can cause Parkinson''s disease-like syndrome; patients typically exhibit extrapyramidal symptoms that include tremor, rigidity and hypokinesia [Calne DB, Chu NS, Huang CC, Lu CS, Olanow W. Manganism and idiopathic parkinsonism: similarities and differences. Neurology 1994;44(9):1583-6; Dobson AW, Erikson KM, Aschner M. Manganese neurotoxicity. Ann NY Acad Sci 2004;1012:115-28]. Mn-induced motor neuron diseases have been the subjects of numerous studies; however, this review is not intended to discuss its neurotoxic potential or its role in the etiology of motor neuron disorders. Rather, it will focus on Mn uptake and transport via the orthologues of the divalent metal transporter (DMT1) and its possible implications to Mn toxicity in various categories of eukaryotic systems, such as in vitro cell lines, in vivo rodents, the fruitfly, Drosophila melanogaster, the honeybee, Apis mellifera L., the nematode, Caenorhabditis elegans and the baker''s yeast, Saccharomyces cerevisiae.
[
Biochim Biophys Acta,
2017]
The development of new microscopy techniques for super-resolved, long-term monitoring of cellular and subcellular dynamics in living organisms is revealing new fundamental aspects of tissue development and repair. However, new microscopy approaches present several challenges. In addition to unprecedented requirements for data storage, the analysis of high resolution, time-lapse images is too complex to be done manually. Machine learning techniques are ideally suited for the (semi-)automated analysis of multidimensional image data. In particular, support vector machines (SVMs), have emerged as an efficient method to analyze microscopy images obtained from animals. Here, we discuss the use of SVMs to analyze in vivo microscopy data. We introduce the mathematical framework behind SVMs, and we describe the metrics used by SVMs and other machine learning approaches to classify image data. We discuss the influence of different SVM parameters in the context of an algorithm for cell segmentation and tracking. Finally, we describe how the application of SVMs has been critical to study protein localization in yeast screens, for lineage tracing in C. elegans, or to determine the developmental stage of Drosophila embryos to investigate gene expression dynamics. We propose that SVMs will become central tools in the analysis of the complex image data that novel microscopy modalities have made possible. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.