Radon, From the Ground into Our Schools: Parent/Guardian Awareness of Radon Levels in Vermont Schools

Introduction. Radon is the leading cause of lung cancer among non-smokers. Ex- posure to radon in schools may be harmful to schoolchildren, faculty, and staff, but there is currently no legislation mandating testing or mitigation of radon levels in Vermont schools. Objectives. The goal of our study was to assess Vermont parents’ awareness of radon’s harmful effects, as well as awareness of and support for testing and mitigation of radon levels in their children’s schools. Methods. We distributed paper and online surveys to Vermont parents of children grades K-12. 126 surveys were received and quantitatively analyzed. We held a focus group of two Vermont parents to gather qualitative data. Results. Most surveyed parents demonstrated general knowledge of radon, but only 51% believed that radon affects the lungs. 8% were confident that their children’s schools had informed them about radon levels. 91.2% believe their children’s schools should take action to address elevated radon levels and 87% would support mandated mitigation. There is some concern and lack of knowledge about the financial implications of radon mitigation. Conclusions. Most Vermont parents of children grades K-12 are unaware that radon is a lung carcinogen and do not know their children’s school’s radon levels or mitigation status. However, most are in favor of legislation that would require testing and dis- closure of schools’ high radon levels. Educating parents about school radon levels and their association with lung cancer could be a foundation for community support of legislation that mandates testing and mitigation of radon in Vermont schools.https://scholarworks.uvm.edu/comphp_gallery/1252/thumbnail.jp

Perceived vs. Actual: Bridging the gap in the understanding of psychology majors’ skills

Abstract Introduction Psychology undergraduates gain various knowledge, skills, and abilities (KSAs) from their curriculum as outlined in the American Psychological Association’s (2023) “Guidelines For the Undergraduate Psychology Major”, but what is preventing them from understanding and expressing these KSAs to employers? These KSAs acquired in the psychology curriculum are some of the most sought-after characteristics employers are looking for in candidates (National Association of Colleges and Employers, 2016). This study will help illuminate the paradox of how psychology is one of the most popular fields of study among undergraduate students while yielding one of the highest underemployment rates in the country by exploring the potential disconnect between the perceived and actual competencies undergraduate psychology students gain over their college career. Methods To identify the specific areas in which this disconnect potentially occurs, this study will seek input from undergraduate psychology majors at Appalachian State University regarding their perceptions of career readiness. To collect this information, a self-administered online questionnaire employing Qualtrics will be utilized. The questionnaire will be designed to clarify the potential gap between a student’s perceived KSAs and the KSAs they actually obtain from their undergraduate curriculum. The main focus of the questions will be to identify students’ perceived KSAs followed by supplemental questions focused on measuring potential contributing factors such as students’ awareness of career development opportunities, awareness of careers in psychology outside of graduate school, and students’ commitment to their prospective career path. Expected Results & Implications The findings of this research will serve as a valuable resource for addressing and narrowing the perceived-versus-actual skills gap prevalent among psychology undergraduates. Once this gap is understood, psychology undergraduate students will be able to better understand and communicate their actual KSAs leading to more accurate marketability of skills and therefore more professionally fulfilling employment opportunities. These findings will also help employers to better understand the capabilities of psychology undergraduates. Additionally, universities and their professors can use these findings to curate their curriculums to better prepare psychology majors for the workforce

The Lantern Vol. 68, No. 1, Fall 2000

• In Attempting to Imitate J. Agard (III) • Headstones • Calligraphy Grace • Fifty Years • Morning • The Millstone • Quick Stop-Off • Jesus Wept (SuperBuick Bodybag) • Just a God • Amy • Silver Doubloons • Ogbanje • Left Behind • Asymmetrical Smile • Sundays • Pie in the Sky • No Surprises • Bill Gooden\u27s Son • Downcast Eyes Meet Tablecloth • Wetlands • Desperate Actions • Receiving End • A Pack of Matches • Coffeehttps://digitalcommons.ursinus.edu/lantern/1157/thumbnail.jp

The Lantern Vol. 68, No. 1, Fall 2000

• In Attempting to Imitate J. Agard (III) • Headstones • Calligraphy Grace • Fifty Years • Morning • The Millstone • Quick Stop-Off • Jesus Wept (SuperBuick Bodybag) • Just a God • Amy • Silver Doubloons • Ogbanje • Left Behind • Asymmetrical Smile • Sundays • Pie in the Sky • No Surprises • Bill Gooden\u27s Son • Downcast Eyes Meet Tablecloth • Wetlands • Desperate Actions • Receiving End • A Pack of Matches • Coffeehttps://digitalcommons.ursinus.edu/lantern/1157/thumbnail.jp

Post-fire comparisons of forest floor and soil carbon, nitrogen, and mercury pools with fire severity indices

Forest fires are important contributors of C, N, and Hg to the atmosphere. In the fall of 2011, a large wildfire occurred in northern Minnesota and we were able to quickly access the area to sample the forest floor and mineral soil for C, N, and Hg pools. When compared with unburned reference soils, the mean loss of C resulting from fire in the forest floor and the upper 20 cm of mineral soil was 19.3 Mg ha−1, for N the mean loss was 0.17 Mg ha−1, and for Hg the mean loss was 9.3 g ha−1. To assess the influence of fire severity on the forest floor and mineral soils, we used an established method that included a soil burn severity index and a tree burn severity index with a gradient of severity classes. It was apparent that the unburned reference class had greater forest floor C, N, and Hg pools and higher C/N ratios than the burned classes. The C/N ratios of the 0- to 10- and 10- to 20-cm mineral soils in the unburned reference class were also greater than in the burned classes, indicating that a small amount of C was lost and/or N was gained, potentially through leaching unburned forest floor material. However, with a couple of exceptions, the severity classes were unable to differentiate the forest floor and mineral soil impacts among soil burn and tree burn severity indices. Developing burn severity indices that are reflective of soil elemental impacts is an important first step in scaling ecosystem impacts both within and across fire events

The Grizzly, October 11, 2000

University Students Disappointed by Rally • Ruhe\u27s \u27Athens\u27 with Ursinus Faces is a Work of Art • Homecoming 2000: Alumni Remember Collegeville Days • Food Critics Speak up at Dining Services Meeting • New Prof. has Students all Shook up...Over Shakespeare?! • Brodbeck Residents Take it to Extreme • French Officials Approve Morning-After Pill • Should Patients\u27 Drug Use be Confidential? • Nearing Fall Break, Freshmen High on UC Experience • The Wrong-Way Geese • Best Buddies: Offering Friendship, Making a Difference • Opinions: New Breed of Grizzly at Ursinus College; Abortion Pill Provides Pause for Debate; Pro-Life Sends Wrong Message; Is Bioengineering Ethical?; Ursinus Students React to Israeli-PLO Clashes; Presidential Debate Shows Just how Mediocre Politics can be; Defending Al Gore • Battle of the Bands Rocks in Reimert • Harpoon Louie\u27s a World Away from Wismer • Poetry Slam on Campus in November • Bears Maul Blue Jays • Women\u27s Rugby Roughed Up by Hawks • Binge Drinking Growing Problem on College Campuses • Roofies: Date Rape Drug More Popular, Dangerous Than Ever • Men\u27s Soccer Downs Aggies • New Coaches Bring Promise to Programs • Matty Earns McIntyre Award • Lowell\u27s Lone Goal Leads Bears to OT win Over Davidson Coll. • Lady Bears Struggle to go on Offensive • Volleyball Stomps the Sciences; Drops two CC Matches • Annual Alumni Lacrosse Match Ends in tie • Leadership in Adventure: ESS Class Molds Leaders Through Sporthttps://digitalcommons.ursinus.edu/grizzlynews/1475/thumbnail.jp

On the cutting edge of glioblastoma surgery:where neurosurgeons agree and disagree on surgical decisions

OBJECTIVE: The aim of glioblastoma surgery is to maximize the extent of resection while preserving functional integrity. Standards are lacking for surgical decision-making, and previous studies indicate treatment variations. These shortcomings reflect the need to evaluate larger populations from different care teams. In this study, the authors used probability maps to quantify and compare surgical decision-making throughout the brain by 12 neurosurgical teams for patients with glioblastoma. METHODS: The study included all adult patients who underwent first-time glioblastoma surgery in 2012-2013 and were treated by 1 of the 12 participating neurosurgical teams. Voxel-wise probability maps of tumor location, biopsy, and resection were constructed for each team to identify and compare patient treatment variations. Brain regions with different biopsy and resection results between teams were identified and analyzed for patient functional outcome and survival. RESULTS: The study cohort consisted of 1087 patients, of whom 363 underwent a biopsy and 724 a resection. Biopsy and resection decisions were generally comparable between teams, providing benchmarks for probability maps of resections and biopsies for glioblastoma. Differences in biopsy rates were identified for the right superior frontal gyrus and indicated variation in biopsy decisions. Differences in resection rates were identified for the left superior parietal lobule, indicating variations in resection decisions. CONCLUSIONS: Probability maps of glioblastoma surgery enabled capture of clinical practice decisions and indicated that teams generally agreed on which region to biopsy or to resect. However, treatment variations reflecting clinical dilemmas were observed and pinpointed by using the probability maps, which could therefore be useful for quality-of-care discussions between surgical teams for patients with glioblastoma

Quantifying eloquent locations for glioblastoma surgery using resection probability maps

OBJECTIVE Decisions in glioblastoma surgery are often guided by presumed eloquence of the tumor location. The authors introduce the "expected residual tumor volume" (eRV) and the "expected resectability index" (eRI) based on previous decisions aggregated in resection probability maps. The diagnostic accuracy of eRV and eRI to predict biopsy decisions, resectability, functional outcome, and survival was determined. METHODS Consecutive patients with first-time glioblastoma surgery in 2012-2013 were included from 12 hospitals. The eRV was calculated from the preoperative MR images of each patient using a resection probability map, and the eRI was derived from the tumor volume. As reference, Sawaya's tumor location eloquence grades (EGs) were classified. Resectability was measured as observed extent of resection (EOR) and residual volume, and functional outcome as change in Karnofsky Performance Scale score. Receiver operating characteristic curves and multivariable logistic regression were applied. RESULTS Of 915 patients, 674 (74%) underwent a resection with a median EOR of 97%, functional improvement in 71 (8%), functional decline in 78 (9%), and median survival of 12.8 months. The eRI and eRV identified biopsies and EORs of at least 80%, 90%, or 98% better than EG. The eRV and eRI predicted observed residual volumes under 10, 5, and 1 ml better than EG. The eRV, eRI, and EG had low diagnostic accuracy for functional outcome changes. Higher eRV and lower eRI were strongly associated with shorter survival, independent of known prognostic factors. CONCLUSIONS The eRV and eRI predict biopsy decisions, resectability, and survival better than eloquence grading and may be useful preoperative indices to support surgical decisions

Preoperative Brain Tumor Imaging:Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16–54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5–15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.publishedVersio