Intelligent multimedia indexing and retrieval through multi-source information extraction and merging

This paper reports work on automated meta-data\ud creation for multimedia content. The approach results\ud in the generation of a conceptual index of\ud the content which may then be searched via semantic\ud categories instead of keywords. The novelty\ud of the work is to exploit multiple sources of\ud information relating to video content (in this case\ud the rich range of sources covering important sports\ud events). News, commentaries and web reports covering\ud international football games in multiple languages\ud and multiple modalities is analysed and the\ud resultant data merged. This merging process leads\ud to increased accuracy relative to individual sources

Diabetes reversal via gene transfer: building on successes in animal models

Type 1 diabetes (T1D) is caused by the autoimmune destruction of the insulin-producing pancreatic β-cells. People with T1D manage their hyperglycemia using daily insulin injections; however, this does not prevent the development of long-term diabetic complications such as retinopathy, nephropathy, neuropathy, and various macrovascular disorders. Currently, the only "cure" for T1D is pancreas transplantation or islet-cell transplantation; however, this is hampered by the limited number of donors and the requirement for life-long immunosuppression. As a result, the need for alternative therapies is vital. One of the strategies employed to correct T1D is the use of gene transfer to generate the production of an “artificial” β-cell that is capable of secreting insulin in response to fluctuating glucose concentrations that normally occurs in people without T1D. The treatment of many diseases using cell and gene therapy is generating significant attention in the T1D research community; however, for a cell therapy to enter clinical trials, success and safety must first be shown in an appropriate animal model. Animal models have been used in diabetes research for over a century, have improved our understanding of the pathophysiology of diabetes, and have led to the discovery of useful drugs for the treatment of the disease. Currently, the nonobese diabetic mouse is the animal model of choice for the study of T1D as it most closely reflects disease development in humans. The aim of this review is to evaluate the success of cell and gene therapy to reverse T1D in animal models for future clinical application

Testing linear hypotheses in high-dimensional regressions

For a multivariate linear model, Wilk's likelihood ratio test (LRT) constitutes one of the cornerstone tools. However, the computation of its quantiles under the null or the alternative requires complex analytic approximations and more importantly, these distributional approximations are feasible only for moderate dimension of the dependent variable, say $p\le 20$. On the other hand, assuming that the data dimension $p$ as well as the number $q$ of regression variables are fixed while the sample size $n$ grows, several asymptotic approximations are proposed in the literature for Wilk's \bLa including the widely used chi-square approximation. In this paper, we consider necessary modifications to Wilk's test in a high-dimensional context, specifically assuming a high data dimension $p$ and a large sample size $n$. Based on recent random matrix theory, the correction we propose to Wilk's test is asymptotically Gaussian under the null and simulations demonstrate that the corrected LRT has very satisfactory size and power, surely in the large $p$ and large $n$ context, but also for moderately large data dimensions like $p=30$ or $p=50$. As a byproduct, we give a reason explaining why the standard chi-square approximation fails for high-dimensional data. We also introduce a new procedure for the classical multiple sample significance test in MANOVA which is valid for high-dimensional data.Comment: Accepted 02/2012 for publication in "Statistics". 20 pages, 2 pages and 2 table

The use of β-cell transcription factors in engineering artificial β cells from non-pancreatic tissue

Type 1 diabetes results from the autoimmune destruction of the insulin-producing pancreatic beta (β) cells. Patients with type 1 diabetes control their blood glucose levels using several daily injections of exogenous insulin; however, this does not eliminate the long-term complications of hyperglycaemia. Currently, the only clinically viable treatments for type 1 diabetes are whole pancreas and islet transplantation. As a result, there is an urgent need to develop alternative therapies. Recently, cell and gene therapy have shown promise as a potential cure for type 1 diabetes through the genetic engineering of 'artificial' β cells to regulate blood glucose levels without adverse side effects and the need for immunosuppression. This review compares putative target cells and the use of pancreatic transcription factors for gene modification, with the ultimate goal of engineering a glucose-responsive 'artificial' β cell that mimics the function of pancreatic β cells, while avoiding autoimmune destruction

On the Largest Singular Values of Random Matrices with Independent Cauchy Entries

We apply the method of determinants to study the distribution of the largest singular values of large $m \times n$ real rectangular random matrices with independent Cauchy entries. We show that statistical properties of the (rescaled by a factor of \frac{1}{m^2\*n^2})largest singular values agree in the limit with the statistics of the inhomogeneous Poisson random point process with the intensity $\frac{1}{\pi} x^{-3/2}$ and, therefore, are different from the Tracy-Widom law. Among other corollaries of our method we show an interesting connection between the mathematical expectations of the determinants of complex rectangular $m \times n$ standard Wishart ensemble and real rectangular $2m \times 2n$ standard Wishart ensemble.Comment: We have shown in the revised version that the statistics of the largest eigenavlues of a sample covariance random matrix with i.i.d. Cauchy entries agree in the limit with the statistics of the inhomogeneous Poisson random point process with the intensity $\frac{1}{\pi} x^{-3/2}.

Luciferase-based reporting of suicide gene activity in murine mesenchymal stem cells

© 2019 Gerace et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Due to their ease of isolation, gene modification and tumor-homing properties, mesenchymal stem cells (MSCs) are an attractive cellular vehicle for the delivery of toxic suicide genes to a variety of cancers in pre-clinical models. In addition, the incorporation of suicide genes in stem cell-derived cell replacement therapies improves their safety profile by permitting graft destruction in the event of unexpected tumorigeneses or unwanted differentiation. Due to the functional requirement of ATP for the Firefly luciferase gene Luc2 to produce light, luciferase-based reporting of cytotoxicity can be engineered into potential cell therapies. Consequently, we nucleofected mammalian expression plasmids containing both the Luc2 and the yeast fusion cytosine deaminase uracil phosphoribosyltransferase (CDUPRT) genes for expression in murine MSCs to assess luciferase as a reporter of suicide gene cytotoxicity, and MSC as vehicles of suicide gene therapy. In vitro bioluminescence imaging (BLI) showed that following the addition of the non-toxic prodrug fluorocytosine (5-FC), CDUPRT-expressing MSCs displayed enhanced cytotoxicity in comparison to Luc2 reporter MSC controls. This study demonstrates the utility of luciferase as a reporter of CDUPRT-mediated cytotoxicity in murine MSC using BLI