The Story of Sound Off: A Community Writing/ Community Listening Experiment

This provocation reflects on Sound Off, a community writing project with listening as its central activity and storytelling as a key component. Understanding Sound Off as an experimental site for community listening, I highlight the need for listening in localized contexts, while exploring how we might design community writing projects as listening spaces. Perhaps most provocatively, I identify challenges that teachers, scholars, and activists need to address for community writing to become fully multimodal and reflect reciprocity among participants

Reading and Judging: Russian Literature on Trial

This dissertation explores the ethical and aesthetic stakes of readers’ judgments by analyzing mock trials of literary characters that were performed in Soviet Russia and abroad in the 1920s and 1930s. Literary trials were part of a larger craze for public mock trials in the decades after the Russian Revolution. Mock trials functioned as a participatory and educational form of entertainment. Fictional defendants included Lenin, invented characters accused of drunkenness and hooliganism, and the Bible. At the same time as increasingly propagandistic mock trials were being performed, intellectuals staged trials of characters from nineteenth-century and contemporary Russian literature. In émigré communities such as Berlin, Paris, and Prague, literary trials were popular as entertainment and fundraisers through the 1920s and 1930s. My analysis focuses on mock trials of characters from works by Dostoevsky and Tolstoy, whose novels proved especially popular for mock trial adaptations in the 1920 and 1930s. I also consider Nabokov’s participation in a mock trial based on The Kreutzer Sonata as a bridge between Tolstoy’s novella and Nabokov’s later novel Lolita. I read back and forth between the literary works and their mock trial adaptations in order to explore both how trial participants interpreted the texts and how the texts respond to the kinds of judgment at work in the trials. The challenges that Dostoevsky and Tolstoy’s fiction pose to readers became the central questions of mock trial adaptations: What is the relationship between interpretation and truth? Do we have the right to judge others? Does narrative have the power to redeem? I argue that while Soviet and émigré literary trials offer selective, politically motivated readings of the original works, they also enter into dialogue with the works’ major ethical questions and offer new ways of thinking about how truth, judgment, and redemption operate in them. As a result, the mock trials bring together two approaches to literature: a reader-centric approach that interprets the text in order to reveal something about the reader’s current reality, and a text-centric approach that aims to uncover the original meaning. While some of the literary trial interpretations and judgments appear to be misreadings, or bad readings, of the original works, I argue that this kind of reading, which closely attends to textual details while asking the text to speak to the readers’ present, offers a model for an ethically engaged approach to literature

Reviews

500 Computing Tips for Teachers and Lecturers by Phil Race and Steve McDowell, London: Kogan Page, 1996. ISBN: 0–7494–1931–8. 135 pages, paperback. £15.99

Affinity and dose of TCR engagement yield proportional enhancer and gene activity in CD4+ T cells.

Affinity and dose of T cell receptor (TCR) interaction with antigens govern the magnitude of CD4+ T cell responses, but questions remain regarding the quantitative translation of TCR engagement into downstream signals. We find that while the response of mouse CD4+ T cells to antigenic stimulation is bimodal, activated cells exhibit analog responses proportional to signal strength. Gene expression output reflects TCR signal strength, providing a signature of T cell activation. Expression changes rely on a pre-established enhancer landscape and quantitative acetylation at AP-1 binding sites. Finally, we show that graded expression of activation genes depends on ERK pathway activation, suggesting that an ERK-AP-1 axis plays an important role in translating TCR signal strength into proportional activation of enhancers and genes essential for T cell function

The TRENDS High-Contrast Imaging Survey. VII. Discovery of a Nearby Sirius-like White Dwarf System (HD 169889)

Monitoring the long-term radial velocity (RV) and acceleration of nearby stars has proven an effective method for directly detecting binary and substellar companions. Some fraction of nearby RV trend systems are expected to be comprised of compact objects that likewise induce a systemic Doppler signal. In this paper, we report the discovery of a white dwarf companion found to orbit the nearby ($\pi = 28.297 \pm 0.066$ mas) G9 V star HD 169889. High-contrast imaging observations using NIRC2 at Keck and LMIRCam at the LBT uncover the ($\Delta H = 9.76 \pm 0.16$, $\Delta L' = 9.60 \pm 0.03$) companion at an angular separation of 0.8'' (28 au). Thirteen years of precise Doppler observations reveal a steep linear acceleration in RV time series and place a dynamical constraint on the companion mass of $M \geq 0.369 \pm 0.010 M_{\odot}$. This "Sirius-like" system adds to the census of white dwarf companions suspected to be missing in the solar neighborhood.Comment: Accepted to Ap

Choreographic Process and Performance

“Nerves” (choreographed by Kendra Fox) is an original dance composition created for five dancers that explores musicality, partnering and the artistry of each dancer. Created for an open space, the work can be performed on a stage or in a site-specific location. The piece formed as an investigation of space, shape and time, and was propelled forward to performance with the support of the Baroni Family Dance Entrepreneurship Grant. “Nerves” was first showcased at the American College Dance Association’s regional festival at Boston University in February 2018 as a culmination and synthesis of movement principles and concepts in choreographic form. Through continued investigation and exploration of physicality and relationships between the dancers, the piece has taken on its’ current form. The newest version of the work will be performed under a new title at the Spring 2018 Contemporary Dance Ensemble with PSU Choirs Collaborative Showcase

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P<0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP