Outcomes-based Funding and Responsibility Center Management: Combining the Best of State and Institutional Budget Models to Achieve Shared Goals

State governments serve as a key funding source for public higher education. An alternative to historically based state subsidies or enrollment-based formulas, outcomes-based funding allows states to convey goals for higher education by allocating state tax dollars based on measures of outcomes. Within higher education institutions, the Responsibility Center Management model engages deans and other mid-level managers in the responsibility and accountability for revenue generation as well as expense management. Policymakers will benefit from understanding this approach and how it could be used in concert with outcomes-based funding to support the development and delivery of new academic paradigms, expand access to underrepresented students, and, ultimately, increase educational attainment for a greater number of people. This article describes the potential alignment between incentives created by the Responsibility Center Management model and goals of outcomesbased funding. With an integration of the two models, there is a greater assurance of achieving the goals of both—fiscal sustainability and student success. By using Responsibility Center Management, college and university administrators are better able to marshal resources to help students complete their degrees and other credentials while also reaping the benefits of an outcomes-based funding system that directs public funding toward institutions that are doing just that

Naloxone-precipitated morphine withdrawal behavior and brain IL-1beta expression: Comparison of different mouse strains

The development of opioid dependence involves classical neuronal opioid receptor activation and is due in part to engagement of glia causing a proinflammatory response. Such opioid-induced glial activation occurs, at least in part, through a non-classical opioid mechanism involving Toll-like-receptor 4 (TLR4). Among the immune factors released following the opioid-glia-TLR4 interaction, interleukin-1β (IL-1β) plays a prominent role. Previous animal behavioral studies have demonstrated significant heterogeneity of chronic morphine-induced tolerance and dependence between different mouse strains. The aim of this study was to investigate whether the heterogeneity of chronic opioid-induced IL-1β expression contributes to differences in opioid tolerance and withdrawal behaviors. Chronic morphine-induced tolerance and dependence were assessed in 3 inbred wild-type mouse strains (Balb/c, CBA, and C57BL/6) and 2 knockout strains (TLR4 and MyD88). Analysis of brain nuclei (medial prefrontal cortex, cortex, brain stem, hippocampus, and midbrain and diencephalon regions combined) revealed that, of inbred wild-type mice, there are significant main effects of morphine treatment on IL-1β expression in the brain regions analyzed (p<0.02 for all regions analyzed). A significant increase in hippocampal IL-1β expression was found in C57BL/6 mice after morphine treatment, whilst, a significant decrease was found in the mPFC region of wild-type Balb/c mice. Furthermore, the results of wild-type inbred strains demonstrated that the elevated hippocampal IL-1β expression is associated with withdrawal jumping behavior. Interestingly, knockout of TLR4, but not MyD88 protected against the development of analgesic tolerance. Gene sequence differences of IL - 1β and TLR4 genes alone did not explain the heterogeneity of dependence behavior between mouse strains. Together, these data further support the involvement of opioid-induced CNS immune signaling in dependence development. Moreover, this study demonstrated the advantages of utilizing multiple mouse strains and indicates that appropriate choice of mouse strains could enhance future research outcomes.Liang Liu, Janet K. Coller, Linda R. Watkins, Andrew A. Somogyi, Mark R. Hutchinso

Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation.

Improving nitrogen use efficiency (NUE) at both the individual cow and the herd level has become a key target in dairy production systems, for both environmental and economic reasons. Cost-effective and large-scale phenotyping methods are required to improve NUE through genetic selection and by feeding and management strategies. The aim of this study was to evaluate the possibility of using mid-infrared (MIR) spectra of milk to predict individual dairy cow NUE during early lactation. Data were collected from 129 Holstein cows, from calving until 50 d in milk, in 3 research herds (Denmark, Ireland, and the UK). In 2 of the herds, diets were designed to challenge cows metabolically, whereas a diet reflecting local management practices was offered in the third herd. Nitrogen intake (kg/d) and nitrogen excreted in milk (kg/d) were calculated daily. Nitrogen use efficiency was calculated as the ratio between nitrogen in milk and nitrogen intake, and expressed as a percentage. Individual daily values for NUE ranged from 9.7 to 81.7%, with an average of 36.9% and standard deviation of 10.4%. Milk MIR spectra were recorded twice weekly and were standardized into a common format to avoid bias between apparatus or sampling periods. Regression models predicting NUE using milk MIR spectra were developed on 1,034 observations using partial least squares or support vector machines regression methods. The models were then evaluated through (1) a cross-validation using 10 subsets, (2) a cow validation excluding 25% of the cows to be used as a validation set, and (3) a diet validation excluding each of the diets one by one to be used as validation sets. The best statistical performances were obtained when using the support vector machines method. Inclusion of milk yield and lactation number as predictors, in combination with the spectra, also improved the calibration. In cross-validation, the best model predicted NUE with a coefficient of determination of cross-validation of 0.74 and a relative error of 14%, which is suitable to discriminate between low- and high-NUE cows. When performing the cow validation, the relative error remained at 14%, and during the diet validation the relative error ranged from 12 to 34%. In the diet validation, the models showed a lack of robustness, demonstrating difficulties in predicting NUE for diets and for samples that were not represented in the calibration data set. Hence, a need exists to integrate more data in the models to cover a maximum of variability regarding breeds, diets, lactation stages, management practices, seasons, MIR instruments, and geographic regions. Although the model needs to be validated and improved for use in routine conditions, these preliminary results showed that it was possible to obtain information on NUE through milk MIR spectra. This could potentially allow large-scale predictions to aid both further genetic and genomic studies, and the development of farm management tools