University –Industry Partnership: meeting the challenges of the 21st century

AtricleUniversities play a crucial role in society as producers and transmitters of knowledge. In recent years the discussion whether academia can encompass a third mission of enterprise development, in addition to research and teaching, has received greater attention (Mansfield,1995;Branscomb et al, 1999; Etzkowitz & Leydesdorff, 2000). Much of the current debate on university-industry links focuses on a narrow range of activities such as spin-offs and start-ups from universities and higher education institutes, and the licensing of intellectual property. However, as many authors have noted, university-industry links embrace a much broader spectrum of activities than commercialization of intellectual property rights ( Agrawal and Henderson, 2002; Mowery and Sampat, 2003; Cohen et al, 2002;Schartinger et al, 2001). In particular, Cohen et al (2002), using the data from the Carnegie Mellon Survey of R&D performing firms in the US, highlighted that for most industries, patents and licenses were of lower importance as channels for conveying public research to industry compared to publications, conferences, informal interactions and consulting. In addition, Schartinger et al (2001) and Roessner(1993) have shown that patenting and licensing account for a low proportion of university- industry interactions when compared to other formal arrangements such as contract research or joint research agreements. 2 In this paper the interface between universities and industry is studied as a way of responding to the economic needs of society in the twenty-first century through academic entrepreneurship i.e. the variety of ways in which universities take direct part in the commercialization of knowledge through the supply of creative research and inventions. This university-industry interaction will help the industry deal with financial pressure to reduce costs and increase efficiency; increased competition and rising customer expectations. It will also enhance the ability of universities to deal with global competition in the academic market place; pressure to diversify financing sources; and rising demands from students and society for quality and relevant curricula

A smooth test of goodness-of-fit for the baseline hazard function in recurrent event models

Conference paperIn this paper, we formulate a smooth test of goodness-of-fit for a simple hypothesis about the baseline hazard function in recurrent-event models. The formulation is an extension of Neyman' s goodness-of-fit approach, whose score tests are obtained by embedding the null hypothesis in a larger class of hazard rate functions. Since the application is in recurrent event models , the data is dynamic.A useful feature about this test is the parametric approach that makes inference about the hazard function more efficient. To examine the finite-sample properties of this test, we used simulated data . For validation, we applied the test to a real-life recurrent event data. Results show that the test possesses better power over wide range of alternatives, when compared with similar tests of the chi-square type in the literature.In this paper, we formulate a smooth test of goodness-of-fit for a simple hypothesis about the baseline hazard function in recurrent-event models. The formulation is an extension of Neyman' s goodness-of-fit approach, whose score tests are obtained by embedding the null hypothesis in a larger class of hazard rate functions. Since the application is in recurrent event models , the data is dynamic.A useful feature about this test is the parametric approach that makes inference about the hazard function more efficient. To examine the finite-sample properties of this test, we used simulated data . For validation, we applied the test to a real-life recurrent event data. Results show that the test possesses better power over wide range of alternatives, when compared with similar tests of the chi-square type in the literature

Mathematical model for pneumonia dynamics among children

The 2012 Southern Africa mathematical sciences association Conference (SAMSA 2012)26th -29th Nov 2012There are major advances which have been made to understand the epidemiology of infectious diseases. However, more than 2 million children in the developing countries still die from pneumonia each year. The eorts to promptly detect, eectively treat and control the spread of pneumonia is possible if its dynamics is understood. In this paper,we develop a mathematical model for pneumonia among children underve years of age. The model is analyzed using the theory of ordinary dierential equations and dynamical systems. We derive the basic reproduction number, R0, analyze the stability of equilibrium points and bifurcation analysis. The results of the analysis shows that there exist a locally stable disease free equilibrium point, Ef when R0 1.The analysis also shows that there is a possibility of a forward bifurcation

A copula-based approach to differential gene expression analysis

Conference paper presented in International Biometric Conference 2014Melanoma is a major public health concern in the developed world. Melanoma research has been enhanced by the introduction of microarray technology, whose main aim is to identify genes that are associated with outcomes of interest in melanoma biology and disease progression. Many statistical methods have been proposed for gene selection but so far none of them is regarded as the standard method. In addition, none of the proposed methods have applied copulas to identify genes that are associated with quantitative traits. In this study, we developed a copula-based approach to identify genes that are associated with quantitative traits in the systems biology of melanoma. To assess the statistical properties of model , we evaluated the power, the false-rejection rate and the true-rejection rate using simulated gene expression data . The model was then applied to a melanoma dataset for validation. Comparison of the copula approach with the Bayesian and other parametric approaches was performed, based on the false discovery rate (FOR) , the value of R-square and prognostic properties. It turned out that the copula model was more robust and better than the others in the selection of genes that were biologically and clinically significant.Melanoma is a major public health concern in the developed world. Melanoma research has been enhanced by the introduction of microarray technology, whose main aim is to identify genes that are associated with outcomes of interest in melanoma biology and disease progression. Many statistical methods have been proposed for gene selection but so far none of them is regarded as the standard method. In addition, none of the proposed methods have applied copulas to identify genes that are associated with quantitative traits. In this study, we developed a copula-based approach to identify genes that are associated with quantitative traits in the systems biology of melanoma. To assess the statistical properties of model , we evaluated the power, the false-rejection rate and the true-rejection rate using simulated gene expression data . The model was then applied to a melanoma dataset for validation. Comparison of the copula approach with the Bayesian and other parametric approaches was performed, based on the false discovery rate (FOR) , the value of R-square and prognostic properties. It turned out that the copula model was more robust and better than the others in the selection of genes that were biologically and clinically significant

Mathematical model for HIV and CD4+ cells dynamics in vivo

Published by International Electronic Journal of Pure and Applied Mathematics Volume 6 No. 2 2013, 83-103Mathematical models are used to provide insights into the mechanisms and dynamics of the progression of viral infection in vivo. Untangling the dynamics between HIV and CD4+ cellular populations and molecular interactions can be used to investigate the effective points of interventions in the HIV life cycle. With that in mind, we develop and analyze a stochastic model for In-Host HIV dynamics that includes combined therapeutic treatment and intracellular delay between the infection of a cell and the emission of viral particles. The unique feature is that both therapy and the intracellular delay are incorporated into the model. We show the usefulness of our stochastic approach towards modeling combined HIV treatment by obtaining probability generating function, the moment structures of the healthy CD4+ cell, and the virus particles at any time t and the probability of virus clearance. Our analysis show that, when it is assumed that the drug is not completely effective, as is the case of HIV in vivo, the predicted rate of decline in plasma HIV virus concentration depends on three factors: the initial viral load before therapeutic intervention, the efficacy of therapy and the length of the intracellular delay.Mathematical models are used to provide insights into the mechanisms and dynamics of the progression of viral infection in vivo. Untangling the dynamics between HIV and CD4+ cellular populations and molecular interactions can be used to investigate the effective points of interventions in the HIV life cycle. With that in mind, we develop and analyze a stochastic model for In-Host HIV dynamics that includes combined therapeutic treatment and intracellular delay between the infection of a cell and the emission of viral particles. The unique feature is that both therapy and the intracellular delay are incorporated into the model. We show the usefulness of our stochastic approach towards modeling combined HIV treatment by obtaining probability generating function, the moment structures of the healthy CD4+ cell, and the virus particles at any time t and the probability of virus clearance. Our analysis show that, when it is assumed that the drug is not completely effective, as is the case of HIV in vivo, the predicted rate of decline in plasma HIV virus concentration depends on three factors: the initial viral load before therapeutic intervention, the efficacy of therapy and the length of the intracellular delay

Semi-Markov model for evaluating the HIV patient treatment cost

ArticleThe aim of this study is to model the progression of HIV/AIDS disease and evaluate the cost of the anti-retroviral therapy for an HIV infected patient under ART follow-up using Non homogeneous semi-Markov processes. States of the Markov process are defined by the seriousness of the sickness based on the clinical scores. The five states considered are: Asymptomatic (CD$^{+}_{4}$ count > 500 cells/microliter); Symptomatic 1 (350 < CD$^{+}_{4}$ count ≤ 500 cells/microliter); Symptomatic 2 (200 < CD$^{+}_{4}$ count ≤ 350 cells/microliter); AIDS (CD$^{+}_{4}$ count ≤ 200 cells/microliter) and Death (Absorbing state). The first four states are named as good or alive states. The models formulated can be used to gain insights on the transition dynamics of the HIV patient given the follow-up time. The transition probability Model, when fitted with data will give insights on the conditional probability of a patient moving from one disease state to another, given the current state and the follow-up time. This model will also give the probability of survival for the HIV patient under treatment given the current state and follow-up time. The total Lifetime Treatment Cost model obtained, when applied to real data will give the cost of managing an HIV patient given the starting state, the treatment combination which incurs minimum cost and which treatment combination is most effective at each state. The treatment reward model also when applied to real data will give the state, which a patient should be maintained so that they remain healthy, noninfectious and productive to the society. Also the model will show the optimal/effective time to initiate treatment, which can be used to give advice on how to handle the HIV infecteds given their states

Stochastic model for In-Host HIV dynamics with therapeutic intervention

Conference paper presented at “The 2nd EAUMP Conference” on 22nd – 25th August 2012. Arusha - TanzaniaMathematical models are used to provide insights into the mechanisms the dynamics between HIV and CD4+ cellular populations and molecuar interactions can be used to investigate the effective points of interventions in the HIV life cycle. With that in mind, we develop and analyze a stochastic model for In-Host HIV dynamics that includes combined therapeutic treatment and intracellular delay between the infection of a cell and the emission of viral particles, which describes HIV infection of CD4+ T-cells during therapy. The unique feature is that both therapy and the intracellular delay are incorporated into the model. Models of HIV infection that include intracellular delays are more accurate representations of the biological data. We show the usefulness of our stochastic approach towards modeling combined HIV treatment by obtaining probability distribution, variance and co-variance structures of the healthy CD4+ cell, and the virus particles at any time t. Our analysis show that, when it is assumed that the drug is not completely effective, as is the case of HIV in vivo, the predicted rate of decline in plasma HIV virus concentration depends on three factors: the death rate of the virons, the ecacy of therapy and the length of the intracellular delay.Mathematical models are used to provide insights into the mechanisms the dynamics between HIV and CD4+ cellular populations and molecuar interactions can be used to investigate the eff ective points of interventions in the HIV life cycle. With that in mind, we develop and analyze a stochastic model for In-Host HIV dynamics that includes combined therapeutic treatment and intracellular delay between the infection of a cell and the emission of viral particles, which describes HIV infection of CD4+ T-cells during therapy. The unique feature is that both therapy and the intracellular delay are incorporated into the model. Models of HIV infection that include intracellular delays are more accurate representations of the biological data. We show the usefulness of our stochastic approach towards modeling combined HIV treatment by obtaining probability distribution, variance and co-variance structures of the healthy CD4+ cell, and the virus particles at any time t. Our analysis show that, when it is assumed that the drug is not completely eff ective, as is the case of HIV in vivo, the predicted rate of decline in plasma HIV virus concentration depends on three factors: the death rate of the virons, the e cacy of therapy and the length of the intracellular delay

Stochastic Model for Langerhans cells and HIV Dynamics in Vivo

Working paperMany aspects of the complex interaction between HIV and the human immune system remain elusive. Our objective is to study these inter-actions, focusing on the specic roles of langerhans cells (LCs) in HIV infection. In patients infected with HIV, a large amount of virus is as-sociated with LCs in lymphoid tissue. To assess the influence of LCs on HIV viral dynamics during anti-retroviral therapy, we present and analyse a stochastic model describing the dynamics of HIV, CD4+ T-cells, and LCs interactions under therapeutic intervention in vivo. We per-form sensitivity analyses on the model to determine which parameters and/or which interaction mechanisms strongly affect infection dynamics.Many aspects of the complex interaction between HIV and the human immune system remain elusive. Our objective is to study these inter-actions, focusing on the speci c roles of langerhans cells (LCs) in HIV infection. In patients infected with HIV, a large amount of virus is as-sociated with LCs in lymphoid tissue. To assess the influence of LCs on HIV viral dynamics during anti-retroviral therapy, we present and analyse a stochastic model describing the dynamics of HIV, CD4+ T-cells, and LCs interactions under therapeutic intervention in vivo. We per-form sensitivity analyses on the model to determine which parameters and/or which interaction mechanisms strongly affect infection dynamics

Array-based schemes for group screening with test errors which incorporate a concentration effect

Group screening is widely used as an efficient method for identifying samples or factors from a large population that are in some sense active. The focus in the present paper is on screening blood samples for infectious diseases when errors in testing are present. Specific attention is given to the introduction of a concentration effect, that is to settings in which the error in testing a group of blood samples depends on the number of samples in that group which are infected. Four array-based group screening schemes, the Dorfman, the and, the or and a modification of the and scheme, are considered and their performance appraised by deriving explicit formulae for the expected number of tests, the expected number of false negatives and the expected number of false positives. The results are illustrated by means of two examples. As an aside, relationships complementary to those derived in the context of blood screening are developed within the area of group factor screening.Group screening is widely used as an efficient method for identifying samples or factors from a large population that are in some sense active. The focus in the present paper is on screening blood samples for infectious diseases when errors in testing are present. Specific attention is given to the introduction of a concentration effect, that is to settings in which the error in testing a group of blood samples depends on the number of samples in that group which are infected. Four array-based group screening schemes, the Dorfman, the and, the or and a modification of the and scheme, are considered and their performance appraised by deriving explicit formulae for the expected number of tests, the expected number of false negatives and the expected number of false positives. The results are illustrated by means of two examples. As an aside, relationships complementary to those derived in the context of blood screening are developed within the area of group factor screening