In JoVE (1)
Articles by Mostafa Ghannad-Rezaie in JoVE
Using Microfluidics Chips for Live Imaging and Study of Injury Responses in Drosophila Larvae Bibhudatta Mishra1, Mostafa Ghannad-Rezaie2, Jiaxing Li1, Xin Wang1, Yan Hao1, Bing Ye3,4, Nikos Chronis2,5, Catherine A. Collins1 1Department of Molecular, Cellular and Developmental Biology, University of Michigan, 2Department of Biomedical Engineering, University of Michigan, 3Life Sciences Institute, University of Michigan, 4Department of Cell and Developmental Biology, University of Michigan, 5Department of Mechanical Engineering, University of Michigan Drosophila larvae are an attractive model system for live imaging due to their translucent cuticle and powerful genetics. This protocol describes how to utilize a single-layer PDMS device, called the 'larva chip' for live imaging of cellular processes within neurons of 3rd instar Drosophila larvae.
Other articles by Mostafa Ghannad-Rezaie on PubMed
Selection-Fusion Approach for Classification of Datasets with Missing Values Pattern Recognition. Jun, 2010 | Pubmed ID: 20212921 This paper proposes a new approach based on missing value pattern discovery for classifying incomplete data. This approach is particularly designed for classification of datasets with a small number of samples and a high percentage of missing values where available missing value treatment approaches do not usually work well. Based on the pattern of the missing values, the proposed approach finds subsets of samples for which most of the features are available and trains a classifier for each subset. Then, it combines the outputs of the classifiers. Subset selection is translated into a clustering problem, allowing derivation of a mathematical framework for it. A trade off is established between the computational complexity (number of subsets) and the accuracy of the overall classifier. To deal with this trade off, a numerical criterion is proposed for the prediction of the overall performance. The proposed method is applied to seven datasets from the popular University of California, Irvine data mining archive and an epilepsy dataset from Henry Ford Hospital, Detroit, Michigan (total of eight datasets). Experimental results show that classification accuracy of the proposed method is superior to those of the widely used multiple imputations method and four other methods. They also show that the level of superiority depends on the pattern and percentage of missing values.
Microfluidic Chips for in Vivo Imaging of Cellular Responses to Neural Injury in Drosophila Larvae PloS One. 2012 | Pubmed ID: 22291895 With powerful genetics and a translucent cuticle, the Drosophila larva is an ideal model system for live imaging studies of neuronal cell biology and function. Here, we present an easy-to-use approach for high resolution live imaging in Drosophila using microfluidic chips. Two different designs allow for non-invasive and chemical-free immobilization of 3(rd) instar larvae over short (up to 1 hour) and long (up to 10 hours) time periods. We utilized these 'larva chips' to characterize several sub-cellular responses to axotomy which occur over a range of time scales in intact, unanaesthetized animals. These include waves of calcium which are induced within seconds of axotomy, and the intracellular transport of vesicles whose rate and flux within axons changes dramatically within 3 hours of axotomy. Axonal transport halts throughout the entire distal stump, but increases in the proximal stump. These responses precede the degeneration of the distal stump and regenerative sprouting of the proximal stump, which is initiated after a 7 hour period of dormancy and is associated with a dramatic increase in F-actin dynamics. In addition to allowing for the study of axonal regeneration in vivo, the larva chips can be utilized for a wide variety of in vivo imaging applications in Drosophila.