Process to Develop Rewarding Natural Resources Internships

A successful natural resources internship experience is a result of good planning, execution, and follow-up. The internship can be beneficial to the student involved, the employing entity, and the sponsoring university or college. This presentation will focus on a process designed to produce rewarding internship experiences for students in natural resources disciplines. The College of Natural Resources (CNR) at Utah State University is made up of three departments: Department of Environment and Society with majors in Environmental Studies, Human Geography, Geography Teaching, and Recreation Resources Management. Department of Watershed Sciences with majors in Fisheries and Aquatic Sciences, Physical Geography, and Watershed and Earth Systems. Department of Wildland Resources with majors in Conservation and Restoration Ecology, Forestry, Rangeland Resources, and Wildlife Science. Each of these disciplines lends itself to producing a valuable, hands-on learning, internship experience. Actual internship opportunities in several of these disciplines will be used to illustrate the process developed in the CNR. The process involves developing objectives for the internship experience based on, for example, the job description provided by the employer, deciding on the number of university credit hours to be awarded for the internship, the monitoring system to be used to ensure the students is doing the work agreed upon or changes in the duties do fit the internship expectations, and deciding upon the product of the internship

Extracting moods from songs and BBC programs based on emotional context

The increasing amounts of media becoming available in converged digital broadcast and mobile broadband networks will require intelligent interfaces capable of personalizing the selection of content. Aiming to capture the mood in the content, we construct a semantic space based on tags, frequently used to describe emotions associated with music in the last.fm social network. Implementing latent semantic analysis (LSA), we model the affective context of songs based on their lyrics, and apply a similar approach to extract moods from BBC synopsis descriptions of TV episodes using TV-Anytime atmosphere terms. Based on our early results, we propose that LSA could be implemented as machinelearning method to extract emotional context and model affective user preferences

Determination of Dose From Light Charged Ions Relevant to Hadron Therapy Using the Particle and Heavy Ion Transport System (PHITS)

In conventional radiotherapy for tumor treatment, photons are used to impart an energetic dose inside a tumor with the goal of killing the cancerous cells. This process is intrinsically inefficient due to the fact that photons lose their energy exponentially with depth causing the highest dose to occur in overlying healthy tissue. However, charged particles with a mass of 1 amu or greater lose their energy in a manner that allows for a high dose to be localized at significant depth. The area of high dose localization is known as the Bragg Peak. Exploitation of the Bragg Peak could lead to more efficient non-invasive treatment plans by reducing the dose in healthy tissues. Using the Particle and Heavy Ion Transport System (PHITS), the dose and fragmentation particles from ions of 1H, 4He, 7Li, 12C, 16O, and 20Ne were found at varying depths in a water phantom. A water filled cylindrical phantom with a radius of 10 cm was used to mimic a human body. The energy of each ion was selected so that the Bragg Peak would occur approximately 10 cm into the depth of the water phantom where a 1 cm radius water sphere was placed to simulate a solid tumor. Dose equivalent localization rates within the tumor were found to be 14.5, 36.5, 45.7, 49.5, 41.3, and 34.1 percent for 1H, 4He, 7Li, 12C, 16O, and 20Ne, respectively. The percentage of dose within the tumor increased with increasing atomic number up to 12C, decreasing thereafter. The total dose distal from the tumor ranged from 0.1, 0.9, 2.8, 0.9, 0.5, and 0.6 percent for the ions ordered by their masses. Complementing its high dose in the tumor, carbon was seen to experience the lowest amount of dose escaping due to fragmentation and scattering, on a dose normalized basis

Utah\u27s Great Outdoor Open Space Project

As our populations increase, and more and more development takes place, critical lands and waters are threatened or even lost in the ensuing rush for economic progress

Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton Therapy

Purpose: To assess the performance of a proton-specific knowledge based planning (KBPP) model in creation of robustly optimized intensity-modulated proton therapy (IMPT) plans for treatment of patients with prostate cancer. Materials and Methods: Forty-five patients with localized prostate cancer, who had previously been treated with volumetric modulated arc therapy, were selected and replanned with robustly optimized IMPT. A KBPP model was generated from the results of 30 of the patients, and the remaining 15 patient results were used for validation. The KBPP model quality and accuracy were evaluated with the model-provided organ-at-risk regression plots and metrics. The KBPP quality was also assessed through comparison of expert and KBPP-generated IMPT plans for target coverage and organ-at-risk sparing. Results: The resulting R (2) (mean ± SD, 0.87 ± 0.07) between dosimetric and geometric features, as well as the χ(2) test (1.17 ± 0.07) between the original and estimated data, showed the model had good quality. All the KBPP plans were clinically acceptable. Compared with the expert plans, the KBPP plans had marginally higher dose-volume indices for the rectum V65Gy (0.8% ± 2.94%), but delivered a lower dose to the bladder (-1.06% ± 2.9% for bladder V65Gy). In addition, KBPP plans achieved lower hotspot (-0.67Gy ± 2.17Gy) and lower integral dose (-0.09Gy ± 0.3Gy) than the expert plans did. Moreover, the KBPP generated better plans that demonstrated slightly greater clinical target volume V95 (0.1% ± 0.68%) and lower homogeneity index (-1.13 ± 2.34). Conclusions: The results demonstrated that robustly optimized IMPT plans created by the KBPP model are of high quality and are comparable to expert plans. Furthermore, the KBPP model can generate more-robust and more-homogenous plans compared with those of expert plans. More studies need to be done for the validation of the proton KBPP model at more-complicated treatment sites

Alumoxane/ferroxane nanoparticles for the removal of viral pathogens: the importance of surface functionality to nanoparticle activity

A bi-functional nano-composite coating has been created on a porous Nomex fabric support as a trap for aspirated virus contaminated water. Nomex fabric was successively dip-coated in solutions containing cysteic acid functionalized alumina (alumoxane) nanoparticles and cysteic acid functionalized iron oxide (ferroxane) nanoparticles to form a nanoparticle coated Nomex (NPN) fabric. From SEM and EDX the nanoparticle coating of the Nomex fibers is uniform, continuous, and conformal. The NPN was used as a filter for aspirated bacteriophage MS2 viruses using end-on filtration. All measurements were repeated to give statistical reliability. The NPN fabrics show a large decrease as compared to Nomex alone or alumoxane coated Nomex . An increase in the ferroxane content results in an equivalent increase in virus retention. This suggests that it is the ferroxane that has an active role in deactivating and/or binding the virus. Heating the NPN to 160 C results in the loss of cysteic acid functional groups (without loss of the iron nanoparticleﾒs core structure) and the resulting fabric behaves similar to that of untreated Nomex , showing that the surface functionalization of the nanoparticles is vital for the surface collapse of aspirated water droplets and the absorption and immobilization of the MS2 viruses. Thus, for virus immobilization, it is not sufficient to have iron oxide nanoparticles per se, but the surface functionality of a nanoparticle is vitally important in ensuring efficacy

Financing U.S. Graduate Medical Education: A Policy Position Paper of the Alliance for Academic Internal Medicine and the American College of Physicians

In this position paper, the Alliance for Academic Internal Medicine and the American College of Physicians examine the state of graduate medical education (GME) financing in the United States and recent proposals to reform GME funding. They make a series of recommendations to reform the current funding system to better align GME with the needs of the nation's health care workforce. These recommendations include using Medicare GME funds to meet policy goals and to ensure an adequate supply of physicians, a proper specialty mix, and appropriate training sites; spreading the costs of financing GME across the health care system; evaluating the true cost of training a resident and establishing a single per-resident amount; increasing transparency and innovation; and ensuring that primary care residents receive training in well-functioning ambulatory settings that are financially supported for their training roles