Visual Analytics of Surveillance Data on Foodborne Vibriosis, United States, 1973–2010

Foodborne illnesses caused by microbial and chemical contaminants in food are a substantial health burden worldwide. In 2007, human vibriosis (non-cholera Vibrio infections) became a notifiable disease in the United States. In addition, Vibrio species are among the 31 major known pathogens transmitted through food in the United States. Diverse surveillance systems for foodborne pathogens also track outbreaks, illnesses, hospitalization and deaths due to non-cholera vibrios. Considering the recognition of vibriosis as a notifiable disease in the United States and the availability of diverse surveillance systems, there is a need for the development of easily deployed visualization and analysis approaches that can combine diverse data sources in an interactive manner. Current efforts to address this need are still limited. Visual analytics is an iterative process conducted via visual interfaces that involves collecting information, data preprocessing, knowledge representation, interaction, and decision making. We have utilized public domain outbreak and surveillance data sources covering 1973 to 2010, as well as visual analytics software to demonstrate integrated and interactive visualizations of data on foodborne outbreaks and surveillance of Vibrio species. Through the data visualization, we were able to identify unique patterns and/or novel relationships within and across datasets regarding (i) causative agent; (ii) foodborne outbreaks and illness per state; (iii) location of infection; (iv) vehicle (food) of infection; (v) anatomical site of isolation of Vibrio species; (vi) patients and complications of vibriosis; (vii) incidence of laboratory-confirmed vibriosis and V. parahaemolyticus outbreaks. The additional use of emerging visual analytics approaches for interaction with data on vibriosis, including non-foodborne related disease, can guide disease control and prevention as well as ongoing outbreak investigations

Effects of Extraction Process Parameters on the Quality Characteristics of Parinari Polyandra B. Seed Oil

Extraction process parameters can influence the quality of oil obtained from seeds during extraction process. The effect of extraction process parameters on the quality of parinari seed oil, a potential renewable industrial raw material, is yet to be reported. This research was aimed at investigating the effects of some extraction process parameters on some quality characteristics of parinari oil (acid, iodine and saponification values). This work reports for the first time the effects of extraction process parameters on the properties of parinari seed oil. The parinari seeds were harvested and oil was obtained from the seeds by solvent extraction method based on the experimental design. The experimental design followed central composite design (CCD). Effects of extraction parameters on the quality characteristics were investigated. The study parameters were time (2-6 h), temperature (60-70 oC), solid- solvent ratio (0.03 - 0.08 g/cm3) and solvent types (n-hexane and petroleum ether). The responses were acid, saponification and iodine values. The results indicated that time and temperature significantly influenced the physicochemical properties of parinari oil. Response Surface Methodology (RSM) based analysis of variance indicated that the models obtained were all significant (p < 0.0001). The set of conditions for the optimal quality was obtained at temperature (60 oC), time (2 h), solid to solvent ratio (0.07 g/cm3) and n-hexane solvent with 89.7% desirability. The effects of the extraction process parameters on parinari oil quality were obtained. The information provided from this work will be useful for potential scaling up of the parinari oil extraction process