Regional Seismic Wavefield Propagation

Through the examination of local and regional seismic waveform data the crust and upper mantle of southern California are investigated. Using local and regional seismic phases such as Pn, Sn, PL, and surface waves, seismic wave velocities of interesting tectonic structures are determined. These structures include the southern Sierra Nevada, San Bernardino Mountains, and the Salton Trough / Imperial Valley. Detailed studies of how seismic waves propagate at local and regional distances are also undertaken. Knowledge of the seismic wave propagation through these tectonic provinces provides for a robust determination of their characteristics. Further, complex source and site-related propagation are included through an investigation of the Kursk submarine explosion and basin-related site amplification

Reflections on Race-ing the Museum, Two Years Later

History of Art and Architecture professors and co-facilitators of the Race-ing the Museum workshop, Fozi and Savage offer insights into the workshop, its context, and its achievements

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/

Bayesian correlated clustering to integrate multiple datasets

Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct – but often complementary – information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured via parameters that describe the agreement among the datasets. Results: Using a set of 6 artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real S. cerevisiae datasets. In the 2-dataset case, we show that MDI’s performance is comparable to the present state of the art. We then move beyond the capabilities of current approaches and integrate gene expression, ChIP-chip and protein-protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques – as well as to non-integrative approaches – demonstrate that MDI is very competitive, while also providing information that would be difficult or impossible to extract using other methods

OjO Latino: A Photovoice Project in Recognition of the Latino Presence in Pittsburgh, PA

In recent years, the Latino population has increased rapidly in areas with traditionally low concentration of Latinos. In these emerging communities, Latinos often live scattered, confronting social isolation and social services not tailored to serve their cultural and linguistic needs. Latinos’ invisibility in Pittsburgh is evidenced by the absence of records of the Latino presence in the city’s museums and public archives. OjO Latino, a community engaged project, sought to advance the inclusion of the Latino community in Pittsburgh through Photovoice. This participatory expression methodology enables individuals to share their stories with the larger public through cultural and artistic expression. The intentional organization of the project as a group activity facilitated the transfer of power over the project to participants, creating solidarity and fomenting trust. During four meetings participants took part in a short photography training, discussed their photographs addressing the meaning of being Latino in Pittsburgh, and selected 34 photographs for exhibition organizing them in four themes: Work, Costumes, Family and Landscape and climate. OjO Latino held one exhibit in a community venue and another one at the university. In addition, the photographs are available in an electronic public repository. OjO Latino served a dual purpose of expanding the visibility of Latinos in and educating the larger community. The OjO Latino team got closer to the ways Latino immigrants see and experience the city. Their gaze challenged our own views and experiences and also spoke the saliency of nostalgia and social networks in their lives.  The open discussion of what it means to be Latino in an emerging community and the opportunity to produce a visual account of it, along with the acknowledgment of the presence of this diverse population promote human rights, ethnic identity as well as mental and social health

A Standardized Diagnostic Pathway for Suspected Appendicitis in Children Reduces Unnecessary Imaging

Introduction: Ultrasound (US) for the diagnosis of acute appendicitis is often nondiagnostic, and additional imaging is required. A standardized approach may reduce unnecessary imaging. Methods: We retrospectively analyzed all patients who had imaging for appendicitis in our emergency department in 2017 and evaluated patient characteristics associated with nondiagnostic US. Using these results, we developed a pediatric appendicitis score (PAS)-based imaging pathway and compared imaging trends prepathway and postpathway implementation. Results: A total of 971 patients received imaging for suspected appendicitis prepathway in 2017. Female sex, obesity, and low/intermediate PAS were significantly associated with nondiagnostic US, but not magnetic resonance imaging (MRI) (P \u3c 0.0001). Nearly one-third of patients received multiple imaging studies (US followed by MRI/computed tomography). As low/intermediate PAS was most strongly associated with a nondiagnostic US on multivariate analysis, we developed a PAS-based imaging stewardship pathway to eliminate imaging in low-PAS patients and reduce the number of patients with an intermediate PAS who received multiple imaging studies by obtaining an MRI as the first-line study. After implementation, only 22 low-PAS patients received imaging (compared with 238 preimplementation), and the proportion of intermediate-PAS patients receiving multiple imaging studies decreased from 31.4% to 13% (P \u3c 0.0001). The cost of imaging per 100 patients increased from $24,255 to$31,082. Conclusion: A PAS-based imaging stewardship pathway reduces unnecessary imaging for suspected appendicitis

The SunPy Project: Open Source Development and Status of the Version 1.0 Core Package

The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. This paper describes the first official stable release (version 1.0) of the core package, as well as the project organization and infrastructure. This paper concludes with a discussion of the future of the SunPy project

Constitutivism

A brief explanation and overview of constitutivism