Identification and localization of polyketide synthase genes from cultures of diarrheic shellfish poisoning toxin producing dinoflagellates of the genus Prorocentrum

Aquatic toxins are responsible for a number of acute and chronic diseases in humans. Okadaic acid (OA) and other dinoflagellate derived polyketide toxins pose serious health risks on a global scale. Ingestion of OA contaminated shellfish causes diarrheic shellfish poisoning (DSP). Some evidence also suggests tumor promotion in the liver by OA. Microcystin-LR (MC-LR) is produced by cyanobacteria and is believed to be the most common freshwater toxin in the US. Humans may be exposed to this acute hepatotoxin through drinking or recreational use of contaminated waters. ^ OA producing dinoflagellates have not been cultured axenically. The presence of associated bacteria raises questions about the ultimate source of OA. Identification of the toxin-producing organism(s) is the first step in identifying the biosynthetic pathways involved in toxin production. Polyketide synthase (PKS) genes of toxic and non-toxic species were surveyed by construction of clonal libraries from PCR amplicons of various toxic and non-toxic species of Prorocentrum in an effort to identify genes, which may be part of the biosynthetic pathway of OA. Analysis of the PKS sequences revealed that toxic species shared identical PKS genes not present in non-toxic species. Interestingly, the same PKS genes were identified in a library constructed from associated bacteria. ^ Subsequent bacterial small subunit RNA (16S) clonal libraries identified several common bacterial species. The most frequent 16S sequences found were identified as species of the genus Roseobacter which has previously been implicated in the production of OA. Attempts to culture commonly occurring bacteria resulted in the isolation of Oceanicaulis alexandrii , a novel marine bacterium previously isolated from the dinoflagellate Alexandrium tamarense, from both P. lima, and P. hoffmanianum. ^ Metabolic studies of microcystin-LR, were conducted to probe the activity of the major human liver cytochromes (CYP) towards the toxin. CYPs may provide alternate routes of detoxification of toxins when the usual routes have been inhibited. For example, some research indicates that cyanobacterial xenobiotics, in particular, lipopolysaccharides may inhibit glutathione S-transferases allowing the toxin to persist long enough to be acted upon by other enzymes. These studies found that at least one human liver CYP was capable of metabolizing the toxin. ^

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Healthy Eating Index and Alternate Healthy Eating Index among Haitian Americans and African Americans with and without Type 2 Diabetes

Ethnicities within Black populations have not been distinguished in most nutrition studies. We sought to examine dietary differences between African Americans (AA) and Haitian Americans (HA) with and without type 2 diabetes using the Healthy Eating Index, 2005 (HEI-05), and the Alternate Healthy Eating Index (AHEI). The design was cross-sectional (225 AA, 246 HA) and recruitment was by community outreach. The eating indices were calculated from data collected with the Harvard food-frequency questionnaire. African Americans had lower HEI-05 scores (−8.67, 13.1); , than HA. Haitian American females and AA males had higher AHEI than AA females and HA males, respectively, () adjusting for age and education. Participants with diabetes had higher adherence to the HEI-05 (1.78, 6.01), , and lower adherence to the AHEI (16.3, −3.19), , , than participants without diabetes. The findings underscore the importance of disaggregating ethnicities and disease state when assessing diet.

Exodus: Alternate Documents [Press Release]

This is the press release for "Exodus: Alternate Documents" held from September 13th to October 31st, 2014.