Low-Cost Particulate Detection in Bleed Air

Bleed air is brought into aircraft cabins in order to maintain the quality of the air for passenger and crew health and comfort. The bleed air can be contaminated by oil due to oil seal leaks in the compressor which have been reported randomly and generated significant public interest. Previous studies have measured the particulate size distribution in the bleed air entering the cabin, but never distinguished the type and material of the particulate matter (PM). The particulates could be potentially hazardous oil droplets from the oil seal leaks, water droplets due the presence of fog generated by the cooling system, and so on. In this study we propose a novel technique using light scattering technology to distinguish between contaminant types. This technique uses size and complex index of refraction as the measure. Since each material has a distinct index of refraction, by determining the index of refraction, our proposed low-cost detector could distinguish the compound in the aerosol as well as determine the particle size simultaneously.

Abstract 130: The Tufts PACE Clinical Predictive Model Registry: Update 1990 Through 2015

Background: Clinical Predictive Models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. The Tufts Predictive Analytics and Comparative Effectiveness (PACE) CPM Registry is a comprehensive database of cardiovascular (CVD) CPMs. The Registry was last updated in 2012 and there has been substantial growth in the number of CPMs that are available. Methods and Results: We updated a systematic review of CPMs for CVD to include articles published from January 1990 to March 2015. The Registry now includes prognostic (n=1047) and diagnostic (n = 27) CPMs. There was a 3-fold increase in the number of CPMs published between 2005 and 2014, when compared to 1995 and 2004 ( Figure) . There are 1074 models included in this database representing 68 distinct index/ outcome (I/O) pairings. 792 (72%) of the CPMs were derived from either North American (n = 448) or European (n = 344) populations. The database contains 265 CPMs predicting outcomes for patients with coronary artery disease, 187 CPMs for population samples at risk for incident CVD, and 158 models for patients with prior stroke. 697 (65%) CPMs report a c- statistic and overall the median reported c- statistic was 0.77 [IQR, 0.09]. Of the 10 most common index conditions, discrimination was highest for CPMs predicting outcomes following cardiac arrest (14/27 reporting, median c- statistic 0.83 [IQR, 0.08]). Discrimination was lowest for CPMs predicting outcomes for patients with other types of arrhythmias (16/22 reporting, median c -statistic 0.71 [IQR 0.07]). Of the CPMs included in this Registry only 422 (39%) report some measure of model calibration. Conclusions: There is continued growth and substantial redundancy of CPMs for a wide spectrum of CVD conditions. While the number of CPMs continues to increase, model performance is often inadequately reported and calibration is infrequently assessed. More work is needed to understand the potential impact of this literature.

A Genetic Method To Evaluate the Prevalence of Unique DNA Profiles between Sequential Ground Beef Batches

ABSTRACT The delineation of ground beef batches has implications for the management of product disposition policies in the event of Shiga toxin–producing Escherichia coli contamination. Analysis of individual contributor animal-specific DNA profiles can provide valuable empirical data for understanding the dynamics of ground meat production processes and can act as a surrogate for cross-contamination. A genetic method was developed for characterizing the source raw material flow and carryover between discrete batches of ground beef in a large-scale commercial beef grinding operation. The application developed involves the introduction of a genetically distinct source raw material batch into the grinding system and comprehensive sampling of that index batch and subsequent batches followed by single nucleotide polymorphism genotyping of random subsamples. Capture-mark-recapture statistical techniques were used to estimate (i) the number of carcass contributors and (ii) the associated level of carryover between batches. Carryover, expressed as a percentage of the total weight of the batch material (in pounds), was observed between the genetically distinct index batch and the next sequential batch at approximately 1%. The nondetection of additional carryover to subsequent batches, with a detection level of approximately 0.2%, supports a serial dilution model of same source raw material carryover, consistent with the recorded weight of beef trimmings used in each batch. For ground beef manufacturers, this method is a simple approach for validating the independence of finished batches of beef in their grind systems in support of product disposition policies.

Recurrence quantification analysis of denoised index returns via alpha-stable modeling of wavelet coefficients: detecting switching volatility regimes

In this paper we propose an enhancement of recurrence quantification analysis (RQA) performance in extracting the underlying non-linear dynamics of market index returns, under the assumption of data corrupted by additive white Gaussian noise. More specifically, first we show that the statistical distribution of wavelet decompositions of distinct index returns is best fitted using members of the alpha-stable distributions family. Then, an efficient maximum a posteriori (MAP) estimator is applied on pairs of wavelet coefficients at adjacent levels to suppress the noise effect, prior to performing RQA. Quantitative and qualitative results on 22 future indices indicate an improved interpretation capability of RQA when applied on denoised data using our proposed approach, as opposed to previous methods based solely on a Gaussian assumption for the underlying statistics, in terms of extracting the underlying dynamical structure of index returns generating processes. Furthermore, our results reveal an increased accuracy of the proposed method in detecting switching volatility regimes, which is important for estimating the risk associated with a financial instrument.

Abstract 174: Clinical Prediction Models for Cardiovascular Disease: The Tufts PACE CPM Database

Background: Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease (CVD) there are numerous CPMs available though the extent of this literature is not well described. Methods and Results: We conducted a systematic review for articles containing CPMs for CVD published between January 1990 through May 2012. CVD includes coronary artery disease (CAD), congestive heart failure (CHF), arrhythmias, stroke, venous thromboembolism (VTE) and peripheral vascular disease (PVD). We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. We included articles that describe newly developed CPMs that predict the risk of developing an outcome (prognostic models) or the probability of a specific diagnosis (diagnostic models). There are 796 models included in this database representing 31 distinct index conditions. 717 (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. There are 215 CPMs for patients with CAD, 168 CPMs for population samples at risk for incident CVD, and 79 models for patients with CHF (Figure). De novo CPMs predicting mortality were most commonly published for patients with known CAD (98 models) followed by HF (63 models) and stroke (24 models). There are 77 distinct index/ outcome (I/O) pairings and models are roughly evenly split between those predicting short term outcomes (< 3 months) and those predicting long term outcomes (< 6 months). There are 41 diagnostic CPMs included in this database, most commonly predicting diagnoses of CAD (11 models), VTE (10 models), and acute coronary syndrome (5 models). Of the de novo models in this database 450 (63%) report a c-statistic and 259 (36%) report either the Hosmer-Lemeshow statistic or show a calibration plot. Conclusions: There is an abundance of CPMs available for many CVD conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood.