University of Vermont

About: University of Vermont is a education organization based out in Burlington, Vermont, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 17592 authors who have published 38251 publications receiving 1609874 citations. The organization is also known as: UVM & University of Vermont and State Agricultural College.

Analysis of a consumer survey on plug-in hybrid electric vehicles

Abstract: Plug-in Hybrid Electric Vehicles (PHEVs) show potential to reduce greenhouse gas (GHG) emissions, increase fuel efficiency, and offer driving ranges that are not limited by battery capacity. However, these benefits will not be realized if consumers do not adopt this new technology. Several agent-based models have been developed to model potential market penetration of PHEVs, but gaps in the available data limit the usefulness of these models. To address this, we administered a survey to 1000 stated US residents, using Amazon Mechanical Turk, to better understand factors influencing the potential for PHEV market penetration. Our analysis of the survey results reveals quantitative patterns and correlations that extend the existing literature. For example, respondents who felt most strongly about reducing US transportation energy consumption and cutting greenhouse gas emissions had, respectively, 71 and 44 times greater odds of saying they would consider purchasing a compact PHEV than those who felt least strongly about these issues. However, even the most inclined to consider a compact PHEV were not generally willing to pay more than a few thousand US dollars extra for the sticker price. Consistent with prior research, we found that financial and battery-related concerns remain major obstacles to widespread PHEV market penetration. We discuss how our results help to inform agent-based models of PHEV market penetration, governmental policies, and manufacturer pricing and marketing strategies to promote consumer adoption of PHEVs.

A Phase II Trial of Neoadjuvant Chemoradiation and Local Excision for T2N0 Rectal Cancer: Preliminary Results of the ACOSOG Z6041 Trial

Abstract: We designed American College of Surgeons Oncology Group (ACOSOG) Z6041, a prospective, multicenter, single-arm, phase II trial to assess the efficacy and safety of neoadjuvant chemoradiation (CRT) and local excision (LE) for T2N0 rectal cancer. Here, we report tumor response, CRT-related toxicity, and perioperative complications (PCs). Clinically staged T2N0 rectal cancer patients were treated with capecitabine and oxaliplatin during radiation followed by LE. Because of toxicity, capecitabine and radiation doses were reduced. LE was performed 6 weeks after CRT. Patients were evaluated for clinical and pathologic response. CRT-related complications and PCs were recorded. Ninety patients were accrued; 6 received nonprotocol treatment. The remaining 84 were 65% male; median age 63 years; 83% Eastern Cooperative Oncology Group performance score 0; 92% white; mean tumor size 2.9 cm; and average distance from anal verge 5.1 cm. Five patients were considered ineligible. Therapy was completed per protocol in 79 patients, but two patients did not undergo LE. Among 77 eligible patients who underwent LE, 34 patients achieved a pathologic complete response (44%) and 49 (64%) tumors were downstaged (ypT0–1), but 4 patients (5%) had ypT3 tumors. Five LE specimens contained lymph nodes; one T3 tumor had a positive node. All but one patient had negative margins. Thirty-three (39%) of 84 patients developed CRT-related grade ≥3 complications. Rectal pain was the most common PC. CRT before LE for T2N0 tumors results in a high pathologic complete response rate and negative resection margins. However, complications during CRT and after LE are high. The true efficacy of this approach will ultimately be assessed by the long-term oncologic outcomes.

Forecasting the onset and course of mental illness with Twitter data

Abstract: We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N = 279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners’ average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (Nusers = 174, Ntweets = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness.