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author:("Ding, lima")
1.  Growth cone-specific functions of XMAP215 in restricting microtubule dynamics and promoting axonal outgrowth 
Neural Development  2013;8:22.
Background
Microtubule (MT) regulators play essential roles in multiple aspects of neural development. In vitro reconstitution assays have established that the XMAP215/Dis1/TOG family of MT regulators function as MT ‘plus-end-tracking proteins’ (+TIPs) that act as processive polymerases to drive MT growth in all eukaryotes, but few studies have examined their functions in vivo. In this study, we use quantitative analysis of high-resolution live imaging to examine the function of XMAP215 in embryonic Xenopus laevis neurons.
Results
Here, we show that XMAP215 is required for persistent axon outgrowth in vivo and ex vivo by preventing actomyosin-mediated axon retraction. Moreover, we discover that the effect of XMAP215 function on MT behavior depends on cell type and context. While partial knockdown leads to slower MT plus-end velocities in most cell types, it results in a surprising increase in MT plus-end velocities selective to growth cones. We investigate this further by using MT speckle microscopy to determine that differences in overall MT translocation are a major contributor of the velocity change within the growth cone. We also find that growth cone MT trajectories in the XMAP215 knockdown (KD) lack the constrained co-linearity that normally results from MT-F-actin interactions.
Conclusions
Collectively, our findings reveal unexpected functions for XMAP215 in axon outgrowth and growth cone MT dynamics. Not only does XMAP215 balance actomyosin-mediated axon retraction, but it also affects growth cone MT translocation rates and MT trajectory colinearity, all of which depend on regulated linkages to F-actin. Thus, our analysis suggests that XMAP215 functions as more than a simple MT polymerase, and that in both axon and growth cone, XMAP215 contributes to the coupling between MTs and F-actin. This indicates that the function and regulation of XMAP215 may be significantly more complicated than previously appreciated, and points to the importance of future investigations of XMAP215 function during MT and F-actin interactions.
doi:10.1186/1749-8104-8-22
PMCID: PMC3907036  PMID: 24289819
XMAP215; TOG; Microtubule dynamics; Growth cone; Quantitative imaging; Cytoskeleton; Actin
2.  Features versus Context: An approach for precise and detailed detection and delineation of faces and facial features 
The appearance-based approach to face detection has seen great advances in the last several years. In this approach, we learn the image statistics describing the texture pattern (appearance) of the object class we want to detect, e.g., the face. However, this approach has had a limited success in providing an accurate and detailed description of the internal facial features, i.e., eyes, brows, nose and mouth. In general, this is due to the limited information carried by the learned statistical model. While the face template is relatively rich in texture, facial features (e.g., eyes, nose and mouth) do not carry enough discriminative information to tell them apart from all possible background images. We resolve this problem by adding the context information of each facial feature in the design of the statistical model. In the proposed approach, the context information defines the image statistics most correlated with the surroundings of each facial component. This means that when we search for a face or facial feature we look for those locations which most resemble the feature yet are most dissimilar to its context. This dissimilarity with the context features forces the detector to gravitate toward an accurate estimate of the position of the facial feature. Learning to discriminate between feature and context templates is difficult however, because the context and the texture of the facial features vary widely under changing expression, pose and illumination, and may even resemble one another. We address this problem with the use of subclass divisions. We derive two algorithms to automatically divide the training samples of each facial feature into a set of subclasses, each representing a distinct construction of the same facial component (e.g., closed versus open eyes) or its context (e.g., different hairstyles). The first algorithm is based on a discriminant analysis formulation. The second algorithm is an extension of the AdaBoost approach. We provide extensive experimental results using still images and video sequences for a total of 3, 930 images. We show that the results are almost as good as those obtained with manual detection.
doi:10.1109/TPAMI.2010.28
PMCID: PMC3657115  PMID: 20847391
Face detection; facial feature detection; shape extraction; subclass learning; discriminant analysis; adaptive boosting; face recognition; American sign language; nonmanuals
3.  Clonal Production and Organization of Inhibitory Interneurons in the Neocortex 
Science (New York, N.y.)  2011;334(6055):480-486.
The neocortex contains excitatory neurons and inhibitory interneurons. Clones of neocortical excitatory neurons originating from the same progenitor cell are spatially organized and contribute to the formation of functional microcircuits. In contrast, relatively little is known about the production and organization of neocortical inhibitory interneurons. We found that neocortical inhibitory interneurons were produced as spatially organized clonal units in the developing ventral telencephalon. Furthermore, clonally related interneurons did not randomly disperse but formed spatially isolated clusters in the neocortex. Individual clonal clusters consisting of interneurons expressing the same or distinct neurochemical markers exhibited clear vertical or horizontal organization. These results suggest that the lineage relationship plays a pivotal role in the organization of inhibitory interneurons in the neocortex.
doi:10.1126/science.1208884
PMCID: PMC3304494  PMID: 22034427
4.  Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function 
Accurate assessment of cutaneous tissue oxygenation and vascular function is important for appropriate detection, staging, and treatment of many health disorders such as chronic wounds. We report the development of a dual-mode imaging system for non-invasive and non-contact imaging of cutaneous tissue oxygenation and vascular function. The imaging system integrated an infrared camera, a CCD camera, a liquid crystal tunable filter and a high intensity fiber light source. A Labview interface was programmed for equipment control, synchronization, image acquisition, processing, and visualization. Multispectral images captured by the CCD camera were used to reconstruct the tissue oxygenation map. Dynamic thermographic images captured by the infrared camera were used to reconstruct the vascular function map. Cutaneous tissue oxygenation and vascular function images were co-registered through fiduciary markers. The performance characteristics of the dual-mode image system were tested in humans.
doi:10.3791/2095
PMCID: PMC3159638  PMID: 21178967
5.  Modelling and Recognition of the Linguistic Components in American Sign Language 
Image and vision computing  2009;27(12):1826-1844.
The manual signs in sign languages are generated and interpreted using three basic building blocks: handshape, motion, and place of articulation. When combined, these three components (together with palm orientation) uniquely determine the meaning of the manual sign. This means that the use of pattern recognition techniques that only employ a subset of these components is inappropriate for interpreting the sign or to build automatic recognizers of the language. In this paper, we define an algorithm to model these three basic components form a single video sequence of two-dimensional pictures of a sign. Recognition of these three components are then combined to determine the class of the signs in the videos. Experiments are performed on a database of (isolated) American Sign Language (ASL) signs. The results demonstrate that, using semi-automatic detection, all three components can be reliably recovered from two-dimensional video sequences, allowing for an accurate representation and recognition of the signs.
doi:10.1016/j.imavis.2009.02.005
PMCID: PMC2757299  PMID: 20161003
American Sign Language; handshape; motion reconstruction; multiple cue recognition; computer vision
6.  Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia 
BMC Bioinformatics  2010;11(Suppl 9):S5.
Background
Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgVH) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgVH status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgVH mutational status which can accurately predict the survival outcome are yet to be discovered.
Results
In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO) and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgVH mutation status from the ZAP70 co-expression network.
Conclusions
We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgVH mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.
doi:10.1186/1471-2105-11-S9-S5
PMCID: PMC2967746  PMID: 21044363

Results 1-6 (6)