The Cost of Unconventional Gas Extraction: A Hedonic Analysis

We focus on identification and estimation of potentially negative environmental impacts of unconventional natural gas extraction on property values in the United States and advance previous research by contributing new data and new identification strategies for isolating these potential impacts. Our study area consists of two counties in Pennsylvania that are home to large amounts of unconventional natural gas extraction but are otherwise isolated from other resource extraction industries or large urban areas. We deploy parametric, semi-parametric, and matching hedonic regression models that include recent quasi-experimental methods and, in contrast to previous research and much popular intuition, we fail to find robust significance that negative environmental externalities of natural gas extraction are manifested in nearby property values. While there may be plausible risks associated with unconventional natural gas extraction, we do not find consistent evidence to suggest that these risks significantly affect nearby property values

Assessment of Clostridiodes difficile infection risk and outcomes among patients diagnosed with cancer : a retrospective cohort study of the United States Veterans Healthcare System

Clostridioides difficile infection (CDI) is an urgent public health problem in the United States (U.S.). Nearly half a million patients suffer from and 29,000 patients die from CDI annually in the U.S. Importantly, prior studies have noted a disproportionate incidence of CDI among cancer patients. This may be due to a number of factors, including the underlying disease, immunosuppression, healthcare exposures, or medication exposures. Despite these trends, few studies have assessed cancer as an independent risk factor for CDI. Furthermore, limited data exist to describe the effect cancer has on CDI health outcomes or the most appropriate treatment approaches for cancer patients who develop CDI. The objectives of this study were to: 1) define the risk of CDI among cancer patients compared to non-cancer patients, 2) compare CDI clinical outcomes of patients with and without cancer, and 3) compare the effectiveness of CDI antibiotic therapy on CDI clinical outcomes among cancer patients. This was a retrospective study of CDI and non-CDI patients in the U.S. Veterans Health Administration. Data were obtained from the Veterans Affairs Informatics and Computing Infrastructure. A series of multivariable logistic regression models were conducted to determine the impact of cancer on CDI risk and health outcomes, including demographics, comorbidities, and healthcare and medication exposures as covariates. In aim 1, cancer (overall) was an independent risk factor for CDI (OR 1.41; 95% CI 1.35- 1.47). Metastatic disease was the strongest cancer predictor of CDI (OR 4.68; 95% CDI 4.02-5.45). In aim 2, cancer was associated with an increased risk for 30-day mortality following the CDI episode (OR 1.44; 95% CI 1.33-1.55). In aim 3, there was no statistically significant difference in 30-day mortality among CDI cancer patients who received metronidazole or oral vancomycin (OR 1.08; 95% CI 0.87-1.34). These data will be important for informing further local and public health initiatives for preventing CDI in high-risk groups, like cancer patients, and guiding treatment approaches.Pharmaceutical Science

Gibbs Sampling with Low-Power Spiking Digital Neurons

Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in a wide variety of applications including image classification and speech recognition. Inference and learning in these algorithms uses a Markov Chain Monte Carlo procedure called Gibbs sampling. A sigmoidal function forms the kernel of this sampler which can be realized from the firing statistics of noisy integrate-and-fire neurons on a neuromorphic VLSI substrate. This paper demonstrates such an implementation on an array of digital spiking neurons with stochastic leak and threshold properties for inference tasks and presents some key performance metrics for such a hardware-based sampler in both the generative and discriminative contexts.Comment: Accepted at ISCAS 201

RF Power Amplifier

Wireless devices are part of everyday life, cellphones and radio receivers impact more people than any piece of technology. Thus, the respective building blocks continue to advance and achieve better performance. A primary component of all wireless communication systems is the power amplifier that drives the antenna. The Institute of Electrical and Electronics Engineers (IEEE) holds the International Microwave Symposium (IMS) every year where teams compete internationally for the most efficient RF Power Amplifier (PA). Power amplifier technologies strive to maximize efficiency and linearity. Topologies to consider are D, E, F, F-1 and Doherty since they have a maximum theoretical efficiency of 100%. This project focuses on the design and simulation of a power amplifier in which design is optimized for Power Added Efficiency (PAE) at 3.5GHz using the class F topology and it will use IMS competition rules and performance metrics. Power conversion efficiency from DC to RF is referred to as PAE and linearity is the maximum spur to fundamental power ratio. An ideal class F amplifier creates a square output drain voltage and a half wave rectified sinusoidal current in order to maximize efficiency. For this project, a 3.5GHz GaN HEMT based power amplifier is designed and simulated in Keysight Advanced Design System (ADS) and Momentum [1]. Load-pull simulation, including the transistor model, sweeps the load impedance to find the optimal load for maximized efficiency or power output. An input and output microstrip stub network can be designed to match these ideal impedances to a 50Ω line. Momentum, an ADS integrated EM simulation software, is used to verify actual input and output network performance. Finally, in order to find overall system gain, power output, and efficiency, a harmonic balance simulation is performed. Project goals include class F amplifier topology harmonic balance and Momentum EM simulation to attain a minimum 80% PAE and 40dBm (10W) output power at 27dBm maximum input power (0.5W)

Impact of deposit recoat cycle length on hot corrosion of CMSX-4

Hot corrosion causes significant problems for both aerospace and power generation industries, where the combination of high temperature, corrosive gases, and contaminants severely limits component operating lifetimes in gas turbine hot gas streams. Multiple laboratory testing methodologies exist to study this hot corrosion, and these can be affected by a range of variables. This paper investigated the impact of varying deposit recoat cycle length when using the ‘deposit recoat’ testing method. CMSX-4 samples were exposed to simulated type II (pitting) hot corrosion conditions, with the same overall deposit load (averaged across the total exposure run), but different deposit recoat cycles. Post-exposure, samples underwent dimensional metrology analysis to compare metal loss resulting from different deposit recoat cycle lengths. Results for CMSX-4 suggest very small differences in corrosion losses, indicating CMSX-4 hot corrosion datasets obtained from deposit recoat experiments with different deposit recoat cycle lengths can be compared with confidence

Fecal Lipocalin 2, a Sensitive and Broadly Dynamic Non- Invasive Biomarker for Intestinal Inflammation

Inflammation has classically been defined histopathologically, especially by the presence of immune cell infiltrates. However, more recent studies suggest a role for low-grade inflammation in a variety of disorders ranging from metabolic syndrome to cancer, which is defined by modest elevations in pro-inflammatory gene expression. Consequently, there is a need for cost-effective, non-invasive biomarkers that, ideally, would have the sensitivity to detect low-grade inflammation and have a dynamic range broad enough to reflect classic robust intestinal inflammation. Herein, we report that, for assessment of intestinal inflammation, fecal lipocalin 2 (Lcn-2), measured by ELISA, serves this purpose. Specifically, using a well-characterized mouse model of DSS colitis, we observed that fecal Lcn-2 and intestinal expression of pro-inflammatory cytokines (IL-1b, CXCL1, TNFa) are modestly but significantly induced by very low concentrations of DSS (0.25 and 0.5%), and become markedly elevated at higher concentrations of DSS (1.0 and 4.0%). As expected, careful histopathologic analysis noted only modest immune infiltrates at low DSS concentration and robust colitis at higher DSS concentrations. In accordance, increased levels of the neutrophil product myeloperoxidase (MPO) was only detected in mice given 1.0 and 4.0% DSS. In addition, fecal Lcn-2 marks the severity of spontaneous colitis development in IL-10 deficient mice. Unlike histopathology, MPO, and q-RT-PCR, the assay of fecal Lcn-2 requires only a stool sample, permits measurement over time, and can detect inflammation as early as 1 day following DSS administration. Thus, assay of fecal Lcn-2 by ELISA can function as a non-invasive, sensitive, dynamic, stable and cost-effective means to monitor intestinal inflammation in mice