Dynamically reconfigurable architectures for video coding and hyperspectral imaging systems

Programa de Doctorado: Ingeniería de Telecomunicación Avanzada.

Novel algorithms for electronic structure based molecular dynamics with linear system-size scaling

Die vorliegende Arbeit behandelt die Entwicklung und Verbesserung von linear skalierenden Algorithmen für Elektronenstruktur basierte Molekulardynamik. Molekulardynamik ist eine Methode zur Computersimulation des komplexen Zusammenspiels zwischen Atomen und Molekülen bei endlicher Temperatur. Ein entscheidender Vorteil dieser Methode ist ihre hohe Genauigkeit und Vorhersagekraft. Allerdings verhindert der Rechenaufwand, welcher grundsätzlich kubisch mit der Anzahl der Atome skaliert, die Anwendung auf große Systeme und lange Zeitskalen. Ausgehend von einem neuen Formalismus, basierend auf dem großkanonischen Potential und einer Faktorisierung der Dichtematrix, wird die Diagonalisierung der entsprechenden Hamiltonmatrix vermieden. Dieser nutzt aus, dass die Hamilton- und die Dichtematrix aufgrund von Lokalisierung dünn besetzt sind. Das reduziert den Rechenaufwand so, dass er linear mit der Systemgröße skaliert. Um seine Effizienz zu demonstrieren, wird der daraus entstehende Algorithmus auf ein System mit flüssigem Methan angewandt, das extremem Druck (etwa 100 GPa) und extremer Temperatur (2000 - 8000 K) ausgesetzt ist. In der Simulation dissoziiert Methan bei Temperaturen oberhalb von 4000 K. Die Bildung von sp²-gebundenem polymerischen Kohlenstoff wird beobachtet. Die Simulationen liefern keinen Hinweis auf die Entstehung von Diamant und wirken sich daher auf die bisherigen Planetenmodelle von Neptun und Uranus aus. Da das Umgehen der Diagonalisierung der Hamiltonmatrix die Inversion von Matrizen mit sich bringt, wird zusätzlich das Problem behandelt, eine (inverse) p-te Wurzel einer gegebenen Matrix zu berechnen. Dies resultiert in einer neuen Formel für symmetrisch positiv definite Matrizen. Sie verallgemeinert die Newton-Schulz Iteration, Altmans Formel für beschränkte und nicht singuläre Operatoren und Newtons Methode zur Berechnung von Nullstellen von Funktionen. Der Nachweis wird erbracht, dass die Konvergenzordnung immer mindestens quadratisch ist und adaptives Anpassen eines Parameters q in allen Fällen zu besseren Ergebnissen führt.

Bloodstream infections in pediatric ECLS: usefulness of daily blood culture monitoring and predictive value of biological markers. The British Columbia experience

INTRODUCTION: The incidence of bloodstream infection (BSI) in extracorporeal life support (ECLS) is reported between 0.9 and 19.5%. In January 2006, the Extracorporeal Life Support Organization (ELSO) reported an overall incidence of 8.78% distributed as follows: respiratory: 6.5% (neonatal), 20.8% (pediatric); cardiac: 8.2% (neonatal) and 12.6% (pediatric). METHOD: At BC Children's Hospital (BCCH) daily surveillance blood cultures (BC) are performed and antibiotic prophylaxis is not routinely recommended. Positive BC (BC+) were reviewed, including resistance profiles, collection time of BC+, time to positivity and mortality. White blood cell count, absolute neutrophile count, immature/total ratio, platelet count, fibrinogen and lactate were analyzed 48, 24 and 0 h prior to BSI. A univariate linear regression analysis was performed. RESULTS: From 1999 to 2005, 89 patients underwent ECLS. After exclusion, 84 patients were reviewed. The attack rate was 22.6% (19 BSI) and 13.1% after exclusion of coagulase-negative staphylococci (n = 8). BSI patients were significantly longer on ECLS (157 h) compared to the no-BSI group (127 h, 95% CI: 106-148). Six BSI patients died on ECLS (35%; 4 congenital diaphragmatic hernias, 1 hypoplastic left heart syndrome and 1 after a tetralogy repair). BCCH survival on ECLS was 71 and 58% at discharge, which is comparable to previous reports. No patient died primarily because of BSI. No BSI predictor was identified, although lactate may show a decreasing trend before BSI (P = 0.102). CONCLUSION: Compared with ELSO, the studied BSI incidence was higher with a comparable mortality. We speculate that our BSI rate is explained by underreporting of "contaminants" in the literature, the use of broad-spectrum antibiotic prophylaxis and a higher yield with daily monitoring BC. We support daily surveillance blood cultures as an alternative to antibiotic prophylaxis in the management of patients on ECLS.

DEVELOPMENT OF A PREDICTIVE COMBUSTION MODEL OF A SPARK IGNITED ENGINE WITH GASOLINE DIRECT INJECTION, VARIABLE VALVE TIMING, DURATION AND LIFT TECHNOLOGIES

There is a need by engine manufactures for computationally efficient and accurate predictive combustion modeling tools for integration in engine simulation software for the assessment of combustion system hardware designs and early development of engine calibrations. This thesis discusses the process for the development and validation of a combustion modeling tool for Gasoline Direct Injected Spark Ignited Engine with variable valve timing, lift and duration valvetrain hardware from experimental data. Data was correlated and regressed from accepted methods for calculating the turbulent flow and flame propagation characteristics for an internal combustion engine. A non-linear regression modeling method was utilized to develop a combustion model to determine the fuel mass burn rate at multiple points during the combustion process. The computational fluid dynamic software Converge ©, was used to simulate and correlate the 3-D combustion system, port and piston geometry to the turbulent flow development within the cylinder to properly predict the experimental data turbulent flow parameters through the intake, compression and expansion processes. The engine simulation software GT-Power © is then used to determine the 1-D flow characteristics of the engine hardware being tested to correlate the regressed combustion modeling tool to experimental data to determine accuracy. The results of the combustion modeling tool show accurate trends capturing the combustion sensitivities to turbulent flow, thermodynamic and internal residual effects with changes in intake and exhaust valve timing, lift and duration.

Is a change in patient-reported dysphagia after induction chemotherapy in locally advanced esophageal cancer a predictive factor for pathological response to neoadjuvant chemoradiation?

GOALS OF WORK: In patients with locally advanced esophageal cancer, only those responding to the treatment ultimately benefit from preoperative chemoradiation. We investigated whether changes in subjective dysphagia or eating restrictions after two cycles of induction chemotherapy can predict histopathological tumor response observed after chemoradiation. In addition, we examined general long-term quality of life (QoL) and, in particular, eating restrictions after esophagectomy. MATERIALS AND METHODS: Patients with resectable, locally advanced squamous cell- or adenocarcinoma of the esophagus were treated with two cycles of chemotherapy followed by chemoradiation and surgery. They were asked to complete the EORTC oesophageal-specific QoL module (EORTC QLQ-OES24), and linear analogue self-assessment QoL indicators, before and during neoadjuvant therapy and quarterly until 1 year postoperatively. A median change of at least eight points was considered as clinically meaningful. MAIN RESULTS: Clinically meaningful improvements in the median scores for dysphagia and eating restrictions were found during induction chemotherapy. These improvements were not associated with a histopathological response observed after chemoradiation, but enhanced treatment compliance. Postoperatively, dysphagia scores remained low at 1 year, while eating restrictions persisted more frequently in patients with extended transthoracic resection compared to those with limited transhiatal resection. CONCLUSIONS: The improvement of dysphagia and eating restrictions after induction chemotherapy did not predict tumor response observed after chemoradiation. One year after esophagectomy, dysphagia was a minor problem, and global QoL was rather good. Eating restrictions persisted depending on the surgical technique used.

Predictive Margins and Marginal Effects in Stata

Tables of estimated regression coefficients, usually accompanied by additional information such as standard errors, t-statistics, p-values, confidence intervals or significance stars, have long been the preferred way of communicating results from statistical models. In recent years, however, the limits of this form of exposition have been increasingly recognized. For example, interpretation of regression tables can be very challenging in the presence of complications such as interaction effects, categorical variables, or nonlinear functional forms. Furthermore, while these issues might still be manageable in the case of linear regression, interpretational difficulties can be overwhelming in nonlinear models such as logistic regression. To facilitate sensible interpretation of such models it is often necessary to compute additional results such as marginal effects, predictive margins, or contrasts. Moreover, smart graphical displays of results can be very valuable in making complex relations accessible. A number of helpful commands geared at supporting these tasks have been recently introduced in Stata, making elaborate interpretation and communication of regression results possible without much extra effort. Examples of such commands are -margins-, -contrasts-, and -marginsplot-. In my talk, I will discuss the capabilities of these commands and present a range of examples illustrating their use.

A comparison of Markov and generalized linear models of hospital mortality

This paper reports a comparison of three modeling strategies for the analysis of hospital mortality in a sample of general medicine inpatients in a Department of Veterans Affairs medical center. Logistic regression, a Markov chain model, and longitudinal logistic regression were evaluated on predictive performance as measured by the c-index and on accuracy of expected numbers of deaths compared to observed. The logistic regression used patient information collected at admission; the Markov model was comprised of two absorbing states for discharge and death and three transient states reflecting increasing severity of illness as measured by laboratory data collected during the hospital stay; longitudinal regression employed Generalized Estimating Equations (GEE) to model covariance structure for the repeated binary outcome. Results showed that the logistic regression predicted hospital mortality as well as the alternative methods but was limited in scope of application. The Markov chain provides insights into how day to day changes of illness severity lead to discharge or death. The longitudinal logistic regression showed that increasing illness trajectory is associated with hospital mortality. The conclusion is reached that for standard applications in modeling hospital mortality, logistic regression is adequate, but for new challenges facing health services research today, alternative methods are equally predictive, practical, and can provide new insights. ^

PREDICTIVE VARIABLES FOR WEIGHT CHANGE 2 YEARS FOLLOWING PARTICIPATION IN A WEIGHT LOSS INTERVENTION PROGRAM

A historical prospective study was designed to assess the man weight status of subjects who participated in a behavioral weight reduction program in 1983 and to determine whether there was an association between the dependent variable weight change and any of 31 independent variables after a 2 year follow-up period. Data was obtained by abstracting the subjects records and from a follow-up questionnaire administered 2 years following program participation. Five hundred nine subjects (386 females and 123 males) of 1460 subjects who participated in the program, completed and returned the questionnaire. Results showed that mean weight was significantly different (p < 0.001) between the measurement at baseline and after a 2 year follow-up period. The mean weight loss of the group was 5.8 pounds, 10.7 pounds for males and 4.2 pounds for females after a 2 year follow-up period. A total of 63.9% of the group, 69.9% of males and 61.9% of females were still below their initial weight after the 2 year follow-up period. Sixteen of the 31 variables assessed utilizing bivariate analyses were found to be significantly (p (LESSTHEQ) 0.05) associated with weight change after a 2 year follow-up period. These variables were then entered into a multivariate linear regression model. A total of 37.9% of the variance of the dependent variable, weight change, was accounted for by all 16 variables. Eight of these variables were found to be significantly (p (LESSTHEQ) 0.05) predictive of weight change in the stepwise multivariate process accounting for 37.1% of the variance. These variables included: Two baseline variables (percent over ideal body weight at enrollment and occupation) and six follow-up variables (feeling in control of eating habits, percent of body weight lost during treatment, frequency of weight measurement, physical activity, eating in response to emotions, and number of pounds of weight gain needed to resume a diet). It was concluded that a greater amount of emphasis should be placed on the six follow-up variables by clinicians involved in the treatment of obesity, and by the subjects themselves to enhance their chances of success at long-term weight loss. ^

Diagnostic Accuracy of Full-Body Linear X-Ray Scanning in Multiple Trauma Patients in Comparison to Computed Tomography

Purpose: The purpose of this study was to evaluate the diagnostic accuracy of full-body linear X-ray scanning (LS) in multiple trauma patients in comparison to 128-multislice computed tomography (MSCT). Materials and Methods: 106 multiple trauma patients (female: 33; male: 73) were retrospectively included in this study. All patients underwent LS of the whole body, including extremities, and MSCT covering the neck, thorax, abdomen, and pelvis. The diagnostic accuracy of LS for the detection of fractures of the truncal skeleton and pneumothoraces was evaluated in comparison to MSCT by two observers in consensus. Extremity fractures detected by LS were documented. Results: The overall sensitivity of LS was 49.2 %, the specificity was 93.3 %, the positive predictive value was 91 %, and the negative predictive value was 57.5 %. The overall sensitivity for vertebral fractures was 16.7 %, and the specificity was 100 %. The sensitivity was 48.7 % and the specificity 98.2 % for all other fractures. Pneumothoraces were detected in 12 patients by CT, but not by LS. 40 extremity fractures were detected by LS, of which 4 fractures were dislocated, and 2 were fully covered by MSCT. Conclusion: The diagnostic accuracy of LS is limited in the evaluation of acute trauma of the truncal skeleton. LS allows fast whole-body X-ray imaging, and may be valuable for detecting extremity fractures in trauma patients in addition to MSCT. Key Points: • The overall sensitivity of LS for truncal skeleton injuries in multiple-trauma patients was < 50 %.• The diagnostic reference standard MSCT is the preferred and reliable imaging modality.• LS may be valuable for quick detection of extremity fractures. Citation Format: • Jöres APW., Heverhagen JT, Bonél H et al. Diagnostic Accuracy of Full-Body Linear X-Ray Scanning in Multiple Trauma Patients in Comparison to Computed Tomography. Fortschr Röntgenstr 2016; 188: 163 - 171.

Complex promoter and coding region β2-adrenergic receptor haplotypes alter receptor expression and predict in vivo responsiveness

The human β2-adrenergic receptor gene has multiple single-nucleotide polymorphisms (SNPs), but the relevance of chromosomally phased SNPs (haplotypes) is not known. The phylogeny and the in vitro and in vivo consequences of variations in the 5′ upstream and ORF were delineated in a multiethnic reference population and an asthmatic cohort. Thirteen SNPs were found organized into 12 haplotypes out of the theoretically possible 8,192 combinations. Deep divergence in the distribution of some haplotypes was noted in Caucasian, African-American, Asian, and Hispanic-Latino ethnic groups with >20-fold differences among the frequencies of the four major haplotypes. The relevance of the five most common β2-adrenergic receptor haplotype pairs was determined in vivo by assessing the bronchodilator response to β agonist in asthmatics. Mean responses by haplotype pair varied by >2-fold, and response was significantly related to the haplotype pair (P = 0.007) but not to individual SNPs. Expression vectors representing two of the haplotypes differing at eight of the SNP loci and associated with divergent in vivo responsiveness to agonist were used to transfect HEK293 cells. β2-adrenergic receptor mRNA levels and receptor density in cells transfected with the haplotype associated with the greater physiologic response were ≈50% greater than those transfected with the lower response haplotype. The results indicate that the unique interactions of multiple SNPs within a haplotype ultimately can affect biologic and therapeutic phenotype and that individual SNPs may have poor predictive power as pharmacogenetic loci.

Impact of artifact correction methods on R-R interbeat signals to quantifying heart rate variability (HRV) according to linear and nonlinear methods.

In the analysis of heart rate variability (HRV) are used temporal series that contains the distances between successive heartbeats in order to assess autonomic regulation of the cardiovascular system. These series are obtained from the electrocardiogram (ECG) signal analysis, which can be affected by different types of artifacts leading to incorrect interpretations in the analysis of the HRV signals. Classic approach to deal with these artifacts implies the use of correction methods, some of them based on interpolation, substitution or statistical techniques. However, there are few studies that shows the accuracy and performance of these correction methods on real HRV signals. This study aims to determine the performance of some linear and non-linear correction methods on HRV signals with induced artefacts by quantification of its linear and nonlinear HRV parameters. As part of the methodology, ECG signals of rats measured using the technique of telemetry were used to generate real heart rate variability signals without any error. In these series were simulated missing points (beats) in different quantities in order to emulate a real experimental situation as accurately as possible. In order to compare recovering efficiency, deletion (DEL), linear interpolation (LI), cubic spline interpolation (CI), moving average window (MAW) and nonlinear predictive interpolation (NPI) were used as correction methods for the series with induced artifacts. The accuracy of each correction method was known through the results obtained after the measurement of the mean value of the series (AVNN), standard deviation (SDNN), root mean square error of the differences between successive heartbeats (RMSSD), Lomb\'s periodogram (LSP), Detrended Fluctuation Analysis (DFA), multiscale entropy (MSE) and symbolic dynamics (SD) on each HRV signal with and without artifacts. The results show that, at low levels of missing points the performance of all correction techniques are very similar with very close values for each HRV parameter. However, at higher levels of losses only the NPI method allows to obtain HRV parameters with low error values and low quantity of significant differences in comparison to the values calculated for the same signals without the presence of missing points.

Inverse forecasting:a new approach for predictive modelling

In the last two decades there have been substantial developments in the mathematical theory of inverse optimization problems, and their applications have expanded greatly. In parallel, time series analysis and forecasting have become increasingly important in various fields of research such as data mining, economics, business, engineering, medicine, politics, and many others. Despite the large uses of linear programming in forecasting models there is no a single application of inverse optimization reported in the forecasting literature when the time series data is available. Thus the goal of this paper is to introduce inverse optimization into forecasting field, and to provide a streamlined approach to time series analysis and forecasting using inverse linear programming. An application has been used to demonstrate the use of inverse forecasting developed in this study. © 2007 Elsevier Ltd. All rights reserved.

Added luminance ramp alters perceived edge blur and contrast:A critical test for derivative-based models of edge coding

In many models of edge analysis in biological vision, the initial stage is a linear 2nd derivative operation. Such models predict that adding a linear luminance ramp to an edge will have no effect on the edge's appearance, since the ramp has no effect on the 2nd derivative. Our experiments did not support this prediction: adding a negative-going ramp to a positive-going edge (or vice-versa) greatly reduced the perceived blur and contrast of the edge. The effects on a fairly sharp edge were accurately predicted by a nonlinear multi-scale model of edge processing [Georgeson, M. A., May, K. A., Freeman, T. C. A., & Hesse, G. S. (in press). From filters to features: Scale-space analysis of edge and blur coding in human vision. Journal of Vision], in which a half-wave rectifier comes after the 1st derivative filter. But we also found that the ramp affected perceived blur more profoundly when the edge blur was large, and this greater effect was not predicted by the existing model. The model's fit to these data was much improved when the simple half-wave rectifier was replaced by a threshold-like transducer [May, K. A. & Georgeson, M. A. (2007). Blurred edges look faint, and faint edges look sharp: The effect of a gradient threshold in a multi-scale edge coding model. Vision Research, 47, 1705-1720.]. This modified model correctly predicted that the interaction between ramp gradient and edge scale would be much larger for blur perception than for contrast perception. In our model, the ramp narrows an internal representation of the gradient profile, leading to a reduction in perceived blur. This in turn reduces perceived contrast because estimated blur plays a role in the model's estimation of contrast. Interestingly, the model predicts that analogous effects should occur when the width of the window containing the edge is made narrower. This has already been confirmed for blur perception; here, we further support the model by showing a similar effect for contrast perception. © 2007 Elsevier Ltd. All rights reserved.

Statnote 20: non-linear regression: fitting a general polynomial curve

In some circumstances, there may be no scientific model of the relationship between X and Y that can be specified in advance and indeed the objective of the investigation may be to provide a ‘curve of best fit’ for predictive purposes. In such an example, the fitting of successive polynomials may be the best approach. There are various strategies to decide on the polynomial of best fit depending on the objectives of the investigation.

Data methods in optometry. Part 10: non-linear regression analysis

1. The techniques associated with regression, whether linear or non-linear, are some of the most useful statistical procedures that can be applied in clinical studies in optometry. 2. In some cases, there may be no scientific model of the relationship between X and Y that can be specified in advance and the objective may be to provide a ‘curve of best fit’ for predictive purposes. In such cases, the fitting of a general polynomial type curve may be the best approach. 3. An investigator may have a specific model in mind that relates Y to X and the data may provide a test of this hypothesis. Some of these curves can be reduced to a linear regression by transformation, e.g., the exponential and negative exponential decay curves. 4. In some circumstances, e.g., the asymptotic curve or logistic growth law, a more complex process of curve fitting involving non-linear estimation will be required.