Connective tissue growth factor [CTGF]/CCN2 stimulates mesangial cell migration through integrated dissolution of focal adhesion complexes and activation of cell polarization

Connective tissue growth factor [CTGF]/CCN2 is a prototypic member of the CCN family of regulatory proteins. CTGF expression is up-regulated in a number of fibrotic diseases, including diabetic nephropathy, where it is believed to act as a downstream mediator of TGF-beta function; however, the exact mechanisms whereby CTGF mediates its effects remain unclear. Here, we describe the role of CTGF in cell migration and actin disassembly in human mesangial cells, a primary target in the development of renal glomerulosclerosis. The addition of CTGF to primary mesangial cells induced cell migration and cytoskeletal rearrangement but had no effect on cell proliferation. Cytoskeletal rearrangement was associated with a loss of focal adhesions, involving tyrosine dephosphorylation of focal adhesion kinase and paxillin, increased activity of the protein tyrosine phosphatase SHP-2, with a concomitant decrease in RhoA and Rac1 activity. Conversely, Cdc42 activity was increased by CTGF. These functional responses were associated with the phosphorylation and translocation of protein kinase C-zeta to the leading edge of migrating cells. Inhibition of CTGF-induced protein kinase C-zeta activity with a myristolated PKC-zeta inhibitor prevented cell migration. Moreover, transient transfection of human mesangial cells with a PKC-zeta kinase inactive mutant (dominant negative) expression vector also led to a decrease in CTGF-induced migration compared with wild-type. Furthermore, CTGF stimulated phosphorylation and activation of GSK-3beta. These data highlight for the first time an integrated mechanism whereby CTGF regulates cell migration through facilitative actin cytoskeleton disassembly, which is mediated by dephosphorylation of focal adhesion kinase and paxillin, loss of RhoA activity, activation of Cdc42, and phosphorylation of PKC-zeta and GSK-3beta. These changes indicate that the initial stages of CTGF mediated mesangial cell migration are similar to those involved in the process of cell polarization. These findings begin to shed mechanistic light on the renal diabetic milieu, where increased CTGF expression in the glomerulus contributes to cellular dysfunction.

A sequential algorithm for sparse support vector classifiers

Support vector machines (SVMs), though accurate, are not preferred in applications requiring high classification speed or when deployed in systems of limited computational resources, due to the large number of support vectors involved in the model. To overcome this problem we have devised a primal SVM method with the following properties: (1) it solves for the SVM representation without the need to invoke the representer theorem, (2) forward and backward selections are combined to approach the final globally optimal solution, and (3) a criterion is introduced for identification of support vectors leading to a much reduced support vector set. In addition to introducing this method the paper analyzes the complexity of the algorithm and presents test results on three public benchmark problems and a human activity recognition application. These applications demonstrate the effectiveness and efficiency of the proposed algorithm.


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BIT-LEVEL SYSTOLIC ARRAY CIRCUIT FOR MATRIX VECTOR MULTIPLICATION.

A bit-level systolic array for computing matrix x vector products is described. The operation is carried out on bit parallel input data words and the basic circuit takes the form of a 1-bit slice. Several bit-slice components must be connected together to form the final result, and authors outline two different ways in which this can be done. The basic array also has considerable potential as a stand-alone device, and its use in computing the Walsh-Hadamard transform and discrete Fourier transform operations is briefly discussed.

Reduced-memory multirate vector quantisation

A new method is proposed which reduces the size of the memory needed to implement multirate vector quantizers. Investigations have shown that the performance of the coders implemented using this approach is comparable to that obtained from standard systems. The proposed method can therefore be used to reduce the hardware required to implement real-time speech coders.

Systolic array system for vector quantization using transformed sub-band coding.

The use of bit-level systolic arrays in the design of a vector quantized transformed subband coding system for speech signals is described. It is shown how the major components of this system can be decomposed into a small number of highly regular building blocks that interface directly to one another. These include circuits for the computation of the discrete cosine transform, the inverse discrete cosine transform, and vector quantization codebook search.

VLSI architectures for vector quantization

The real time implementation of an efficient signal compression technique, Vector Quantization (VQ), is of great importance to many digital signal coding applications. In this paper, we describe a new family of bit level systolic VLSI architectures which offer an attractive solution to this problem. These architectures are based on a bit serial, word parallel approach and high performance and efficiency can be achieved for VQ applications of a wide range of bandwidths. Compared with their bit parallel counterparts, these bit serial circuits provide better alternatives for VQ implementations in terms of performance and cost. © 1995 Kluwer Academic Publishers.

Systolic VLSI compiler (SVC) for high performance vector quantisation chips

An overview is given of a systolic VLSI compiler (SVC) tool currently under development for the automated design of high performance digital signal processing (DSP) chips. Attention is focused on the design of systolic vector quantization chips for use in both speech and image coding systems. The software in question consists of a cell library, silicon assemblers, simulators, test pattern generators, and a specially designed graphics shell interface which makes it expandable and user friendly. It allows very high performance digital coding systems to be rapidly designed in VLSI.

The biology of habitat dominance; can microbes behave as weeds?

Competition between microbial species is a product of, yet can lead to a reduction in, the microbial diversity of specific habitats. Microbial habitats can resemble ecological battlefields where microbial cells struggle to dominate and/or annihilate each other and we explore the hypothesis that (like plant weeds) some microbes are genetically hard-wired to behave in a vigorous and ecologically aggressive manner. These 'microbial weeds' are able to dominate the communities that develop in fertile but uncolonized - or at least partially vacant - habitats via traits enabling them to out-grow competitors; robust tolerances to habitat-relevant stress parameters and highly efficient energy-generation systems; avoidance of or resistance to viral infection, predation and grazers; potent antimicrobial systems; and exceptional abilities to sequester and store resources. In addition, those associated with nutritionally complex habitats are extraordinarily versatile in their utilization of diverse substrates. Weed species typically deploy multiple types of antimicrobial including toxins; volatile organic compounds that act as either hydrophobic or highly chaotropic stressors; biosurfactants; organic acids; and moderately chaotropic solutes that are produced in bulk quantities (e.g. acetone, ethanol). Whereas ability to dominate communities is habitat-specific we suggest that some microbial species are archetypal weeds including generalists such as: Pichia anomala, Acinetobacter spp. and Pseudomonas putida; specialists such as Dunaliella salina, Saccharomyces cerevisiae, Lactobacillus spp. and other lactic acid bacteria; freshwater autotrophs Gonyostomum semen and Microcystis aeruginosa; obligate anaerobes such as Clostridium acetobutylicum; facultative pathogens such as Rhodotorula mucilaginosa, Pantoea ananatis and Pseudomonas aeruginosa; and other extremotolerant and extremophilic microbes such as Aspergillus spp., Salinibacter ruber and Haloquadratum walsbyi. Some microbes, such as Escherichia coli, Mycobacterium smegmatis and Pseudoxylaria spp., exhibit characteristics of both weed and non-weed species. We propose that the concept of nonweeds represents a 'dustbin' group that includes species such as Synodropsis spp., Polypaecilum pisce, Metschnikowia orientalis, Salmonella spp., and Caulobacter crescentus. We show that microbial weeds are conceptually distinct from plant weeds, microbial copiotrophs, r-strategists, and other ecophysiological groups of microorganism. Microbial weed species are unlikely to emerge from stationary-phase or other types of closed communities; it is open habitats that select for weed phenotypes. Specific characteristics that are common to diverse types of open habitat are identified, and implications of weed biology and open-habitat ecology are discussed in the context of further studies needed in the fields of environmental and applied microbiology.

Infection systems biology:from reactive to proactive (P4) medicine

This short review establishes the conceptual bases and discusses the principal aspects of P4-shorthand for predictive, preventive, personalized and participatory medicine-medicine, in the framework of infectious diseases. P4 medicine is a new way to approach medical care; instead of acting when the patient is sick, physicians will be able to detect early warnings of disease to take early action. Furthermore, people might even be able to adjust their lifestyles to prevent disease. P4 medicine is fuelled by systems approaches to disease, including methods for personalized genome sequencing and new computational techniques for building dynamic disease predictive networks from massive amounts of data from a variety of OMICs. An excellent example of the effectiveness of the P4 medicine approach is the change in cancer treatments. Emphasis is placed on early detection, followed by genotyping of the patient to use the most adequate treatment according to the genetic background. Cardiovascular diseases and perhaps even neurodegenerative disorders will be the next targets for P4 medicine. The application of P4 medicine to infectious diseases is still in its infancy, but is a promising field that will provide much benefit to both the patients and the health-care system.

Coexpressing shRNA with fluorescence tags for quantification of cell migration studies

Understanding migration of cells has many implications in human physiology; some examples include developmental biology, healing, immune responses and tissue remodeling. On the other hand, invasive migration by tumor cells is pathological and is a major cause of mortality amongst cancer sufferers. Cell migration assays have been widely used to quantify potentially metastatic genes. In recent years, the use of RNAi has significantly increased the tools available in cell migration research due to its specific gene targeting for knockdown. The inability to ensure 100% transfection/transduction efficiency reduces the sensitivity of cell migration assays because cells not successfully transfected/transduced with the RNAi are also included in the calculations. This study introduces a different experimental setup mathematically expressed in our named normalized relative infected cell count (N-RICC) that analyses cell migration assays by co-expressing retrovirally transduced shRNA with fluorescence tags from a single vector. Vectors transduced into cells are visible under fluorescence, thus alleviating the problems involved with transduction efficiency by individually identifying cells with targeted genes. Designed shRNAs were targeted against a list of potentially metastatic genes in a highly migratory breast cancer cell line model, MDA-MB-231. We have successfully applied N-RICC analysis to show greater sensitivity of integrin alpha5 (ITGA5) and Ras homologue A (RhoA) in cell metastasis over conventional methods in scratch-wound assays and migration chambers assays.

Mean shift based gradient vector flow for image segmentation

In recent years, gradient vector flow (GVF) based algorithms have been successfully used to segment a variety of 2-D and 3-D imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods may lead to biased segmentation results. In this paper, we propose MSGVF, a mean shift based GVF segmentation algorithm that can successfully locate the correct borders. MSGVF is developed so that when the contour reaches equilibrium, the various forces resulting from the different energy terms are balanced. In addition, the smoothness constraint of image pixels is kept so that over- or under-segmentation can be reduced. Experimental results on publicly accessible datasets of dermoscopic and optic disc images demonstrate that the proposed method effectively detects the borders of the objects of interest.