Cysteine (Cys) is a critically important amino acid, serving a variety of functions within proteins including structural roles, catalysis, and regulation of function through post-translational modifications. Predictin...
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Cysteine (Cys) is a critically important amino acid, serving a variety of functions within proteins including structural roles, catalysis, and regulation of function through post-translational modifications. Predicting which Cys residues are likely to be reactive is a very sought after feature. Few methods are currently available for the task, either based on evaluation of physicochemical features (e.g., pKa and exposure) or based on similarity with known instances. In this study, we developed an algorithm (named HAL-Cy) which blends previous work with novel implementations to identify reactive Cys from nonreactive. HAL-Cy present two major components: (i) an energy based part, rooted on the evaluation of H-bond network contributions and (ii) a knowledge based part, composed of different profiling approaches (including a newly developed weighting matrix for sequence profiling). In our evaluations, HAL-Cy provided significantly improved performances, as tested in comparisons with existing approaches. We implemented our algorithm in a web service (Cy-preds), the ultimate product of our work;we provided it with a variety of additional features, tools, and options: Cy-preds is capable of performing fully automated calculations for a thorough analysis of Cys reactivity in proteins, ranging from reactivity predictions (e.g., with HAL-Cy) to functional characterization. We believe it represents an original, effective, and very useful addition to the current array of tools available to scientists involved in redox biology, Cys biochemistry, and structural bioinformatics.
Background: Patients hospitalized for suspected acute coronary syndrome (ACS) are at risk for transient myocardial ischemia. During the "rule-out" phase, continuous ECG ST-segment monitoring can identify tra...
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Background: Patients hospitalized for suspected acute coronary syndrome (ACS) are at risk for transient myocardial ischemia. During the "rule-out" phase, continuous ECG ST-segment monitoring can identify transient myocardial ischemia, even when asymptomatic. However, current ST-segment monitoring software is vastly underutilized due to false positive alarms, with resultant alarm fatigue. Current ST algorithms may contribute to alarm fatigue because;(I) they are not designed with a delay (minutes), rather alarm to brief spikes (i.e., turning, heart rate changes), and (2) alarm to changes in a single ECG lead, rather than contiguous leads. Purpose: This study was designed to determine sensitivity, and specificity, of ST algorithms when accounting for;ST magnitude (100 mu V vs 200 mu V), duration, and changes in contiguous ECG leads (i.e., aVL, I, aVR, II, aVF, III;V1, V2, V3, V4, V5, V6, V6, I). Methods: This was a secondary analysis from the COMPARE Study, which assessed occurrence rates for transient myocardial ischemia in hospitalized patients with suspected ACS using 12-lead Holter. Transient myocardial ischemia was identified from Holter using >100 mu V ST-segment up arrow or down arrow, in >1 ECG lead, > I min. algorithms tested against Holter transient myocardial ischemia were done using the University of California San Francisco (UCSF) ECG algorithm and included: (1)100 mu V vs 200 mu V any lead during a 5-min ST average;(2)100 mu V vs 200 mu V any lead >5 min, (3) 100 mu V vs 200 mu V any lead during a 5-min ST average in contiguous leads, and (4) 100 mu V vs 200 mu V > 5 min in contiguous leads (Table below). Results: In 361 patients;mean age 63 + 12 years, 63% male, 56% prior CAD, 43 (11%) had transient myocardial ischemia. Of the 43 patients with transient myocardial ischemia, 17 (40%) had ST-segment elevation events, and 26 (60%) ST-segment depression events. A higher proportion of patients with ST segment depression has missed ischemic events. [GRAPHICS]
An automatic programming system using object-oriented programming techniques is described. This system is intended for use as a design tool for signal processing software. The system starts with a graphical descriptio...
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An automatic programming system using object-oriented programming techniques is described. This system is intended for use as a design tool for signal processing software. The system starts with a graphical description of an algorithm, supports interactive editing and performance analysis, and produces as output source code which implements the algorithm.
The editorial introduces JMI Issue 3 Volume 11, looks ahead to SPIE Medical Imaging, and highlights the journal’s policy on conference article submission.
The editorial introduces JMI Issue 3 Volume 11, looks ahead to SPIE Medical Imaging, and highlights the journal’s policy on conference article submission.
Objective: In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving ...
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Objective: In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving Study. Background: Driver distraction has been a leading cause of motor vehicle crashes, but the relationship between distractions and crash risk lacks detailed quantification. Method: The authors compared 24 algorithms that varied according to how they incorporated three potential contributors to distraction-glance duration, glance history, and glance location-on how well the algorithms predicted crash risk. Results: Distraction estimated from driver eye-glance patterns was positively associated with crash risk. The algorithms incorporating ongoing off-road glance duration predicted crash risk better than did the algorithms incorporating glance history. Augmenting glance duration with other elements of glance behavior-1.5th power of duration and duration weighted by glance location-produced similar prediction performance as glance duration alone. Conclusions: The distraction level estimated by the algorithms that include current glance duration provides the most sensitive indicator of crash risk. Application: The results inform the design of algorithms to monitor driver state that support realtime distraction mitigation systems.
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