On translating technology research into industrial applications

Using personal cases he previously experienced, Prof Wong will share with colleagues the influence of environments and market forces that drive the applied technology research into practice. The speech will be delivered in an informal, interactive format, rather than didactic

Affective Computing for Late-Life Mood and Cognitive Disorders

Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer\u27s disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed

Bioluminescence Imaging of Heme Oxygenase-1 Upregulation in the Gua Sha Procedure

Gua Sha is a traditional Chinese folk therapy that employs skin scraping to cause subcutaneous microvascular blood extravasation and bruises. The protocol for bioluminescent optical imaging of HO-1-luciferase transgenic mice reported in this manuscript provides a rapid in vivo assay of the upregulation of the heme oxygenase-1 (HO-1) gene expression in response to the Gua Sha procedure. HO-1 has long been known to provide cytoprotection against oxidative stress. The upregulation of HO-1, assessed by the bioluminescence output, is thought to represent an antioxidative response to circulating hemoglobin products released by Gua Sha. Gua Sha was administered by repeated strokes of a smooth spoon edge over lubricated skin on the back or other targeted body part of the transgenic mouse until petechiae (splinter hemorrhages) or ecchymosis (bruises) indicative of extravasation of blood from subcutaneous capillaries was observed. After Gua Sha, bioluminescence imaging sessions were carried out daily for several days to follow the dynamics of HO-1 expression in multiple internal organs

A Screen for Morphological Complexity Identifies Regulators of Switch-like Transitions between Discrete Cell Shapes

The way in which cells adopt different morphologies is not fully understood. Cell shape could be a continuous variable or restricted to a set of discrete forms. We developed quantitative methods to describe cell shape and show that Drosophila hemocytes in culture are a heterogeneous mixture of five discrete morphologies. In an RNAi screen of genes affecting the morphological complexity of heterogeneous populations, we found that most genes regulate the transition between discrete shapes rather than generating new morphologies. In particular, we identified a subset of genes, including the tumour suppressor PTEN, that decrease the heterogeneity of the population leading to populations enriched in rounded or elongated forms. We show that these genes have a highly conserved function as regulators of cell shape in both mouse and human metastatic melanoma cells

Automatic Segmentation Of High-Throughput Rnai Fluorescent Cellular Images

High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes. The large number of images produced in each study make manual analysis intractable; hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. Cells are then extracted by modeling the interaction between them as well as combining both gradient and region information in the Actin and Rac channels. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. Promising experimental results demonstrate that automatic segmentation of high-throughput genome-wide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems. © 2008 IEEE

Robust feature extraction and reduction of mass spectrometry data for cancer classification

Application of proteomics coupled with pattern classification techniques to discover novel biomarkers that can be used for the predictive diagnoses of several cancer diseases. However, for effective classification, the extraction of good features that can represent the identities of different classes plays the frontal critical factor for any classification problems. In addition, another major problem associated with pattern recognition is how to effectively handle a large number of features. This paper address these two frontal issues for mass spectrometry (MS) classification. We apply the theory of linear predictive coding to extract features and vector quantization to reduce the storage of the large feature space of MS data. The proposed methodology was tested using two MS-based cancer datasets and the results are promising

Segmentation, recognition and tracing analysis for high-content cell-cycle screening

We present in this paper some new and efficient algorithms for segmentation, recognition and tracing analysis of cell phases for high-content screening. The conceptual frameworks are based on the morphological structures of cells where a series of morphological structural points are established. Furthermore, we address the issue of touching cells and then propose morphological techniques for cell separation, reconstruction and tracing analysis. The new segmentation method can resolve the question of over-segmentation. The tracing analysis of cell phases is based on cell shape, geometrical features and difference information of corresponding neighbor frames. Experiment results test the efficiency of the new metho

Integrated algorithms for image analysis and classification of nuclear division for high-content cell-cycle screening

Advances in fluorescent probing and microscopic imaging technology provide important tools for biomedical research in studying the structures and functions of cells and molecules. Such studies require the processing and analysis of huge amounts of image data, and manual image analysis is very time consuming, thus costly, and also potentially inaccurate and poor reproducibility. In this paper, we present and combine several advanced computational, probabilistic, and fuzzy-set methods for the computerized classification of cell nuclei in different mitotic phases. We tested our proposed methods with real image sequences recorded over a period of twenty-four hours at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective for the task of classification

Classification of cell phases in time-lapse images by vector quantization and markov models

Advances in fluorescent probing and microscopic imaging technology provide important tools for biology and medicine research in studying the structures and functions of cells and molecules. Such studies require the processing and analysis of huge amounts of image data, and manual image analysis is very time consuming, thus costly, and also potentially inaccurate and poorly reproducible. Stages of an automated cellular imaging analysis consist of segmentation, feature extraction, classification, and\ud tracking of individual cells in a dynamic cellular population. Image classification of cell phases in a fully automatic manner presents the most difficult task of such analysis. We are interested in applying several advanced computational, probabilistic, and fuzzy-set methods for the computerized classification of cell nuclei in different mitotic phases. We tested several proposed computational procedures with real image sequences recorded over a period of twenty-four hours at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective and has potential for higher performance with better cellular feature extraction strategy