Novel Techniques and Their Applications to Health Foods, Agricultural and Medical Biotechnology: Functional Genomics and Basic Epigenetic Controls in Plant and Animal Cells

Selected applications of novel techniques for analyzing Health Food formulations, as well as for advanced investigations in Agricultural and Medical Biotechnology aimed at defining the multiple connections between functional genomics and epigenomic, fundamental control mechanisms in both animal and plant cells are being reviewed with the aim of unraveling future developments and policy changes that are likely to open new niches for Biotechnology and prevent the shrinking or closing of existing markets. Amongst the selected novel techniques with applications in both Agricultural and Medical Biotechnology are: immobilized bacterial cells and enzymes, microencapsulation and liposome production, genetic manipulation of microorganisms, development of novel vaccines from plants, epigenomics of mammalian cells and organisms, and biocomputational tools for molecular modeling related to disease and Bioinformatics. Both fundamental and applied aspects of the emerging new techniques are being discussed in relation to their anticipated, marked impact on future markets and present policy changes that are needed for success in either Agricultural or Medical Biotechnology. The novel techniques are illustrated with figures presenting the most important features of representative and powerful tools which are currently being developed for both immediate and long term applications in Agriculture, Health Food formulation and production, pharmaceuticals and Medicine. The research aspects are naturally emphasized in our review as they are key to further developments in Biotechnology; however, the course adopted for the implementation of biotechnological applications, and the policies associated with biotechnological applications are clearly the determining factors for future Biotechnology successes, be they pharmaceutical, medical or agricultural

Uptake of the human papillomavirus vaccine in Kenya : testing the health belief model through pathway modeling on cohort data

Background: Many studies investigate HPV vaccine acceptability, applying health behavior theories to identify determinants; few include real uptake, the final variable of interest. This study investigated the utility of the Health Belief Model (HBM) in predicting HPV vaccine uptake in Kenya, focusing on the importance of promotion, probing willingness to vaccinate as precursor of uptake and exploring the added value of personal characteristics. Methods: Longitudinal data were collected before and after a pilot HPV vaccination program in Eldoret among mothers of eligible girls (N = 255). Through pathway modeling, associations between vaccine uptake and the HBM constructs, willingness to vaccinate and adequate promotion were examined. Adequate promotion was defined as a personal evaluation of promotional information received. Finally, baseline cervical cancer awareness and socio-demographic variables were added to the model verifying their direct, mediating or moderating effects on the predictive value of the HBM. Results: Perceiving yourself as adequately informed at follow-up was the strongest determinant of vaccine uptake. HBM constructs (susceptibility, self-efficacy and foreseeing father's refusal as barrier) only influenced willingness to vaccinate, which was not correlated with vaccination. Baseline awareness of cervical cancer predicted uptake. Conclusions: The association between adequate promotion and vaccination reveals the importance of triggers beyond personal control. Adoption of new health behaviors might be more determined by organizational variables, such as promotion, than by prior personal beliefs. Assessing users' and non-users' perspectives during and after implementing a vaccination program can help identifying stronger determinants of vaccination behavior

Emerging Challenges and Opportunities in Infectious Disease Epidemiology.

Much of the intellectual tradition of modern epidemiology stems from efforts to understand and combat chronic diseases persisting through the 20th century epidemiologic transition of countries such as the United States and United Kingdom. After decades of relative obscurity, infectious disease epidemiology has undergone an intellectual rebirth in recent years amid increasing recognition of the threat posed by both new and familiar pathogens. Here, we review the emerging coalescence of infectious disease epidemiology around a core set of study designs and statistical methods bearing little resemblance to the chronic disease epidemiology toolkit. We offer our outlook on challenges and opportunities facing the field, including the integration of novel molecular and digital information sources into disease surveillance, the assimilation of such data into models of pathogen spread, and the increasing contribution of models to public health practice. We next consider emerging paradigms in causal inference for infectious diseases, ranging from approaches to evaluating vaccines and antimicrobial therapies to the task of ascribing clinical syndromes to etiologic microorganisms, an age-old problem transformed by our increasing ability to characterize human-associated microbiota. These areas represent an increasingly important component of epidemiology training programs for future generations of researchers and practitioners

VO: Vaccine Ontology

Vaccine research, as well as the development, testing, clinical trials, and commercial uses of vaccines involve complex processes with various biological data that include gene and protein expression, analysis of molecular and cellular interactions, study of tissue and whole body responses, and extensive epidemiological modeling. Although many data resources are available to meet different aspects of vaccine needs, it remains a challenge how we are to standardize vaccine annotation, integrate data about varied vaccine types and resources, and support advanced vaccine data analysis and inference. To address these problems, the community-based Vaccine Ontology (VO, "http://www.violinet.org/vaccineontology":http://www.violinet.org/vaccineontology) has been developed through collaboration with vaccine researchers and many national and international centers and programs, including the National Center for Biomedical Ontology (NCBO), the Infectious Disease Ontology (IDO) Initiative, and the Ontology for Biomedical Investigations (OBI). VO utilizes the Basic Formal Ontology (BFO) as the top ontology and the Relation Ontology (RO) for definition of term relationships. VO is represented in the Web Ontology Language (OWL) and edited using the Protégé-OWL. Currently VO contains more than 2000 terms and relationships. VO emphasizes on classification of vaccines and vaccine components, vaccine quality and phenotypes, and host immune response to vaccines. These reflect different aspects of vaccine composition and biology and can thus be used to model individual vaccines. More than 200 licensed vaccines and many vaccine candidates in research or clinical trials have been modeled in VO. VO is being used for vaccine literature mining through collaboration with the National Center for Integrative Biomedical Informatics (NCIBI). Multiple VO applications will be presented.
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Ebola Model and Optimal Control with Vaccination Constraints

The Ebola virus disease is a severe viral haemorrhagic fever syndrome caused by Ebola virus. This disease is transmitted by direct contact with the body fluids of an infected person and objects contaminated with virus or infected animals, with a death rate close to 90% in humans. Recently, some mathematical models have been presented to analyse the spread of the 2014 Ebola outbreak in West Africa. In this paper, we introduce vaccination of the susceptible population with the aim of controlling the spread of the disease and analyse two optimal control problems related with the transmission of Ebola disease with vaccination. Firstly, we consider the case where the total number of available vaccines in a fixed period of time is limited. Secondly, we analyse the situation where there is a limited supply of vaccines at each instant of time for a fixed interval of time. The optimal control problems have been solved analytically. Finally, we have performed a number of numerical simulations in order to compare the models with vaccination and the model without vaccination, which has recently been shown to fit the real data. Three vaccination scenarios have been considered for our numerical simulations, namely: unlimited supply of vaccines; limited total number of vaccines; and limited supply of vaccines at each instant of time.Comment: This is a preprint of a paper whose final and definite form is with 'Journal of Industrial and Management Optimization' (JIMO), ISSN 1547-5816 (print), ISSN 1553-166X (online). Submitted February 2016; revised November 2016; accepted for publication March 201