2008-2009 Master Class - Jeffrey Biegel (Piano)

https://spiral.lynn.edu/conservatory_masterclasses/1105/thumbnail.jp

2007-2008 Master Class - Jeffrey Biegel (Piano)

https://spiral.lynn.edu/conservatory_masterclasses/1126/thumbnail.jp

Medulloblastoma outcome is adversely associated with overexpression of EEF1D, RPL30, and RPS20 on the long arm of chromosome 8

BACKGROUND: Medulloblastoma is the most common malignant brain tumor of childhood. Improvements in clinical outcome require a better understanding of the genetic alterations to identify clinically significant biological factors and to stratify patients accordingly. In the present study, we applied cytogenetic characterization to guide the identification of biologically significant genes from gene expression microarray profiles of medulloblastoma. METHODS: We analyzed 71 primary medulloblastomas for chromosomal copy number aberrations (CNAs) using comparative genomic hybridization (CGH). Among 64 tumors that we previously analyzed by gene expression microarrays, 27 were included in our CGH series. We analyzed clinical outcome with respect to CNAs and microarray results. We filtered microarray data using specific CNAs to detect differentially expressed candidate genes associated with survival. RESULTS: The most frequent lesions detected in our series involved chromosome 17; loss of 16q, 10q, or 8p; and gain of 7q or 2p. Recurrent amplifications at 2p23-p24, 2q14, 7q34, and 12p13 were also observed. Gain of 8q is associated with worse overall survival (p = 0.0141), which is not entirely attributable to MYC amplification or overexpression. By applying CGH results to gene expression analysis of medulloblastoma, we identified three 8q-mapped genes that are associated with overall survival in the larger group of 64 patients (p < 0.05): eukaryotic translation elongation factor 1D (EEF1D), ribosomal protein L30 (RPL30), and ribosomal protein S20 (RPS20). CONCLUSION: The complementary use of CGH and expression profiles can facilitate the identification of clinically significant candidate genes involved in medulloblastoma growth. We demonstrate that gain of 8q and expression levels of three 8q-mapped candidate genes (EEF1D, RPL30, RPS20) are associated with adverse outcome in medulloblastoma

The Effects of Experimentally Induced Rumination, Positive Reappraisal, Acceptance, and Distancing When Thinking About a Stressful Event on Affect States in Adolescents

The current study compares the effects of experimentally induced rumination, positive reappraisal, distancing, and acceptance on affect states in adolescents aged 13–18. Participants (N = 160) were instructed to think about a recent stressful event. Next, they received specific instructions on how to think about that event in each condition. Manipulation checks revealed that the manipulations were successful, except for acceptance. The two most reported events were “a fight” and “death of loved one”. Results showed that positive reappraisal (i.e., thinking about the benefits and personal growth) caused a significantly larger increase in positive affect and decrease in negative affect compared to rumination, distancing, and acceptance. Current findings implicate that positive reappraisal seems an adequate coping strategy in the short-term, and therefore could be applied in interventions for youth experiencing difficulties managing negative affect. Future research should focus on long-term effects of these cognitive strategies and on more intensive training of acceptance

Disrupting LIN28 in atypical teratoid rhabdoid tumors reveals the importance of the mitogen activated protein kinase pathway as a therapeutic target

Atypical teratoid rhabdoid tumor (AT/RT) is among the most fatal of all pediatric brain tumors. Aside from loss of function mutations in the SMARCB1 (BAF47/INI1/SNF5) chromatin remodeling gene, little is known of other molecular drivers of AT/RT. LIN28A and LIN28B are stem cell factors that regulate thousands of RNAs and are expressed in aggressive cancers. We identified high-levels of LIN28A and LIN28B in AT/RT primary tumors and cell lines, with corresponding low levels of the LIN28-regulated microRNAs of the let-7 family. Knockdown of LIN28A by lentiviral shRNA in the AT/RT cell lines CHLA-06-ATRT and BT37 inhibited growth, cell proliferation and colony formation and induced apoptosis. Suppression of LIN28A in orthotopic xenograft models led to a more than doubling of median survival compared to empty vector controls (48 vs 115 days). LIN28A knockdown led to increased expression of let-7b and let-7g microRNAs and a down-regulation of KRAS mRNA. AT/RT primary tumors expressed increased mitogen activated protein (MAP) kinase pathway activity, and the MEK inhibitor selumetinib (AZD6244) decreased AT/RT growth and increased apoptosis. These data implicate LIN28/RAS/MAP kinase as key drivers of AT/RT tumorigenesis and indicate that targeting this pathway may be a therapeutic option in this aggressive pediatric malignancy

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages