Prediction of the Population at Risk of Atherothrombotic Disease: A Three Step Approach

The mainstay of the prevention of atherothrombotic disease (ATD, which is atherosclerotic disease, with emphasis on the thrombosis that so often precipitates the acute ATD event, such as acute myocardial infarction, acute cerebral infarction, aortic aneurysm, etc) is the prediction of the population at risk of ATD. There are many predictive tools, all of which use the same general risk factors, but the one favored by the author is the Bowling Green Study (BGS) graph.. This graph is based on the ATD risk factor constellations of 870 people in Bowling Green, Ohio, the county seat of Wood County, in northwest Ohio. (There is one other patient who has full lipid data and blood pressure data, but whose cigarette smoking status is not known.) The ordinate of the graph is the lipid arm and consists of the Cholesterol Retention Fraction (CRF, defined as [LDL-HDL]/LDL). HDL refers to high-density lipoprotein cholesterol and LDL refers to low-density lipoprotein cholesterol. The abscissa of the graph is the blood pressure arm, represented by the systolic blood pressure (SBP). This graph was initially developed in 1981 (using the LDL:HDL ratio) then modified in 1983 (using the CRF), and, by 1988, the author was able to generate a threshold line, which separated the main stream of ATD patients’ CRF-SBP plots from those of a few outliers. (The threshold line is not a regression line, but rather a divider, based on the principle of the fewest false negatives.) The 1988 threshold line was modified in 2000 to its present location at CRF-SBP loci (0.74, 100) and (0.49, 140). Many of the various ATD risk predictors are complex and difficult to use, whereas the graph is simple to use and based on the risk factor constellations of actual ATD patients, wherein lies its value.

Qualitative analysis of the response regimes and triggering mechanism of bistable NES

The main focus of this study is the development of an adapted complex variable method in the vicinity of equilibrium in bistable NES. A simplified chaos trigger model is established to describe the distance between the stable phase cycle and the pseudo-separatrix. An analytical expression can predict the excitation threshold for chaos occurrence. The relative positions between the chaos trigger threshold line and the Slow Invariant Manifold (SIM) structure can express the distribution of response regimes under growing harmonic excitation. This topological structure implies the alternation of the response regime and helps to classify the bistable NES. The experiment compares the analytical result of intra-well oscillation with the numerical result in the frequency domain. The experimental response regimes under different input energy levels and frequency domains have been observed and give ideas to guide the optimal design of a bistable NES. It is shown that the modest bistable NES possesses strong robustness to frequency perturbation.

Error Fusion of Hybrid Neural Networks for Mechanical Condition Dynamic Prediction

It is important for equipment to operate safely and reliably so that the working state of mechanical parts pushes forward an immense influence. Therefore, in order to enhance the dependability and security of mechanical equipment, to accurately predict the changing trend of mechanical components in advance plays a significant role. This paper introduces a novel condition prediction method, named error fusion of hybrid neural networks (EFHNN), by combining the error fusion of multiple sparse auto-encoders with convolutional neural networks for predicting the mechanical condition. First, to improve prediction accuracy, we can use the error fusion of multiple sparse auto-encoders to collect multi-feature information, and obtain a trend curve representing machine condition as well as a threshold line that can indicate the beginning of mechanical failure by computing the square prediction error (SPE). Then, convolutional neural networks predict the state of the machine according to the original data when the SPE value exceeds the threshold line. It can be seen from this result that the EFHNN method in the prediction of mechanical fault time series is available and superior.

A Threshold Line for Safe Geologic CO2 Storage Based on Field Measurement of Soil CO2 Flux

Carbon capture and storage (CCS) is an established and verified technology that can implement zero emissions on a large enough scale to limit temperature rise to below 2 °C, as stipulated in the Paris Agreement. However, leakage from CCS sites must be monitored to ensure containment performance. Surface monitoring of carbon dioxide (CO2) concentrations at onshore CCS sites is one method to locate and quantify CCS site leakage. Employing soil accumulation chambers, we have established baseline data for the natural flux of CO2 as a threshold alert to detect CO2 leakage flux to ensure the safety of onshore CCS sites. Within this context, we conducted on-site CO2 measurements at three different locations (A, B, and C) on the INAS test field at the Ito campus, Kyushu University (Japan). Furthermore, we developed a specific measurement system based on the closed-chamber method to continuously measure CO2 flux from soil and to investigate the correlation between CO2 flux from the soil surface and various parameters, including environmental factors and soil sample characteristics. In addition, gas permeability and the effect of different locations on soil CO2 flux are discussed in this study. Finally, we present an equation for estimating the soil CO2 flux used in the INAS field site that includes environmental factors and soil characteristics. This equation assists in defining the threshold line for an alert condition related to CO2 leakage at onshore CCS sites.

Abstract 2819: Cross-correlation Analysis of Myocardial Velocity is Superior to Conventional Time-to-Peak Parameters for Quantifying Left Ventricular Dyssynchrony

Background- A recent study showed that the most commonly used Tissue Doppler imaging (TDI) parameters to diagnose left ventricular dyssynchrony agree only 50% of the time. Most of these parameters require calculation of the ``time-to-peak” myocardial velocity. This ``time-to-peak” based analysis utilizes only one of >100 data points collected per heart cycle. Methods- We developed and tested a new dyssynchrony parameter, cross-correlation delay (XCD), that utilizes all velocity data points from 3 consecutive beats (~420 points). We hypothesized that XCD would be superior to existing methods at diagnosing dyssynchrony. We tested XCD on 11 members of a positive control group (echocardiographic responders to cardiac resynchronization therapy) and 12 members of a negative control group (normal echocardiogram and 12-lead ECG). We compared XCD to septal-to-lateral delay in time-to-peak (SLD), maximum difference in the basal 2- or 4-chamber times-to-peak (MaxDiff) and standard deviation of the 12 basal and mid-wall times-to-peak (Ts-SD). Results- An XCD threshold of 31ms discriminated between positive and negative controls with 100% sensitivity and specificity (Figure 1 ). SLD, MaxDiff and Ts-SD showed sensitivities of 36, 55 and 100% and specificities of 50, 42 and 50%, respectively. ROC analysis showed XCD and Ts-SD were superior to SLD and MaxDiff in discriminating between positive and negative controls (p<0.01). XCD was the only parameter which decreased after resynchronization in the positive controls (from 160±88ms to 69±61ms, p=0.003). Conclusion- XCD is superior to existing parameters at discriminating patients with LV dyssynchrony from those with normal function. Figure 1. XCD shows the greatest discrimination between positive and negative controls. Dyssynchrony values for each positive control are shown as x’s and values for each negative control are shown as circels. Different dyssynchrony parameters are shown in each subplot (A-D). Threshold values to diagnose dyssynchrony are plotted as horizontal lines in each figure. Note that x’s above the threshold line represent false positives while circles below the threshold line represent false negatives.