Select Synch and Co-Synch protocols using a CIDR yield similar pregnancy rates after a fixed-time insemination in suckled Bos indicus × Bos taurus cows.

Primiparous and multiparous suckled Bos indicus × Bos taurus cows were used to compare a Select Synch + controlled internal drug release (CIDR) and timed artificial insemination (AI) protocol (SSC) to a modified CO-Synch + CIDR protocol (COS). Five separate groups of cows were utilized (n = 659) and within a group, cows were allotted to two treatments on the basis of body condition score (BCS) taken 10 days before and days postpartum at CIDR insertion (Day 0). Blood samples were collected on Day -10 and 0 for assessment of circulating concentrations of progesterone to determine estrous cycling status. Cows in both treatments received intramuscular (im) GnRH (100 µg) and a CIDR inserted on Day 0. On Day 7, the SSC treatment had CIDR removed and received im PGF2α (25 mg), whereas COS treatment had CIDR removed and received im PGF2α (25 mg) on Day 7.5. In the SSC treatment, estrus was detected daily at 0700 and 1700 hours for 72 hours after PGF2α administration, and cows were artificially inseminated 8 to 12 hours after a detected estrus. Cows failing to exhibit estrus were subjected to timed-AI and received im GnRH (100 µg) 76 ± 4 hours after PGF2α administration. In the COS treatment, cows received im GnRH (100 µg) and were fixed-time artificially inseminated at 64 ± 4 hours after PGF2α administration. Although group differences (P < 0.05) existed for synchronized AI pregnancy rates, there were no significant interactions of group with any variable analyzed; hence, groups were pooled for further evaluation. Within the SSC treatment, the 72-hour estrous response was 50.6% (168 of 332). Conception rate of cows in estrus was 66.1% (111 of 168), and timed-AI pregnancy rate for nonresponders was 32.3% (53 of 164). Overall synchronized AI pregnancy rates were similar (P > 0.05) for SSC (49.4%; n = 164 of 332) and COS (47.1%; n = 154 of 327). Estrous cycling status and days postpartum did not (P > 0.05) influence synchronized AI pregnancy rates but BCS did (P < 0.05). Cows with a BCS greater than or equal to 5 had synchronized AI pregnancy rates of 50% compared to 39% for cows with BCS lesser than 5.0. In summary, the modified COS protocol yielded similar synchronized AI pregnancy rates compared to the SSC protocol, and it appears that the modified COS protocol has the potential to be an effective timed-AI protocol in suckled Bos indicus × Bos taurus cows.

Assessment of medical reserve corps volunteers' emergency response willingness using a threat- and efficacy-based model.

The goal of this study was to investigate the willingness of Medical Reserve Corps (MRC) volunteers to participate in public health emergency-related activities by assessing their attitudes and beliefs. MRC volunteers responded to an online survey organized around the Extended Parallel Process Model (EPPM). Respondents reported agreement with attitude/belief statements representing perceived threat, perceived efficacy, and personal/organizational preparedness in 4 scenarios: a weather-related disaster, a pandemic influenza emergency, a radiological ("dirty bomb") emergency, and an inhalational anthrax bioterrorism emergency. Logistic regression analyses were used to evaluate predictors of volunteer response willingness. In 2 response contexts (if asked and regardless of severity), self-reported willingness to respond was higher among those with a high perceived self-efficacy than among those with low perceived self-efficacy. Analyses of the association between attitude/belief statements and the EPPM profiles indicated that, under all 4 scenarios and with few exceptions, those with a perceived high threat/high efficacy EPPM profile had statistically higher odds of agreement with the attitude/belief statements than those with a perceived low threat/low efficacy EPPM profile. The radiological emergency consistently received the lowest agreement rates for the attitude/belief statements and response willingness across scenarios. The findings suggest that enrollment with an MRC unit is not automatically predictive of willingness to respond in these types of scenarios. While MRC volunteers' self-reported willingness to respond was found to differ across scenarios and among different attitude and belief statements, the identification of self-efficacy as the primary predictor of willingness to respond regardless of severity and if asked highlights the critical role of efficacy in an organized volunteer response context.

A subregional-scale method to assess aquifer vulnerability to pesticides.

A method to predict aquifer vulnerability to pesticide contamination at the subregional scale was developed. The assessment method was designed to incorporate relevant hydrologic and pesticide-transport information and to use generally available data. The method assumes steady-state advection of pesticides in the vadose zone, including sorption and biological decay. The solution is presented as a vulnerability index (VI) that increases as the aquifer vulnerability increases. The hydrologic input data for the VI model are obtained from the soil survey geographic database. Pesticides were grouped into three leachability classes using a leachability ratio (half-life divided by organic carbon partition coefficient). Pesticide transformation is assumed to occur in the surface layer. The influence of vertical transport in the remainder of the vadose zone has been incorporated by applying a multiplying factor to the VI that varies with depth to ground water. Hydraulic conductivity is used as a surrogate for soil-water velocity for practical purposes. The performance of the VI model is evaluated using ground water data from Weld County, Colorado. The model is demonstrated to be successful at predicting relative vulnerability, defined as the magnitude of pesticide concentration and percent of wells in a unit that exhibit a pesticide detection. Areas of low, medium, and high vulnerability are assigned. Results indicate that the vulnerability classifications are most dependent on the leachability ratio, hydraulic conductivity, and organic carbon content.