Low serum sodium level during cardiopulmonary bypass predicts increased risk of postoperative stroke after coronary artery bypass graft surgery

ObjectiveRapid decreases in serum sodium levels are associated with altered mental status, seizures, and coma. During cardiac surgery, serum sodium levels decrease rapidly when cardiopulmonary bypass is initiated because cardiopulmonary bypass causes hemodilution. However, whether this decrease influences neurologic outcome after cardiac surgery remains unclear. We investigated whether the average serum sodium level during cardiopulmonary bypass is independently predictive of postoperative stroke or 30-day all-cause mortality in patients who undergo primary coronary artery bypass grafting.MethodsIn a single-institution, retrospective cohort of 2348 consecutive patients who underwent primary, isolated coronary artery bypass grafting, sequential multivariate logistic regression was performed to determine the threshold below which the average serum sodium level during cardiopulmonary bypass independently predicts postoperative stroke or early death. To further test the validity of this threshold and to control for selection bias, stepwise multivariate logistic regression was also performed on propensity score–matched patients (n = 924).ResultsAn average serum sodium level less than 130 mEq/L during cardiopulmonary bypass was independently predictive of stroke, both in the entire study cohort (1.44% vs 2.92%; odds ratio, 2.09; 95% confidence interval, 1.1-4.1; P = .03) and in the propensity-matched patients (0.9% vs 3.0%; odds ratio, 4.1; 95% confidence interval, 1.3-13.0; P = .02). The average serum sodium level during cardiopulmonary bypass was not independently associated with early death, regardless of what threshold value was used.ConclusionsAn average serum sodium level of less than 130 mEq/L during cardiopulmonary bypass is independently associated with an increased risk of postoperative stroke in patients who undergo primary coronary artery bypass grafting

Cost analysis of nondeterministic probabilistic programs

We consider the problem of expected cost analysis over nondeterministic probabilistic programs, which aims at automated methods for analyzing the resource-usage of such programs. Previous approaches for this problem could only handle nonnegative bounded costs. However, in many scenarios, such as queuing networks or analysis of cryptocurrency protocols, both positive and negative costs are necessary and the costs are unbounded as well. In this work, we present a sound and efficient approach to obtain polynomial bounds on the expected accumulated cost of nondeterministic probabilistic programs. Our approach can handle (a) general positive and negative costs with bounded updates in variables; and (b) nonnegative costs with general updates to variables. We show that several natural examples which could not be handled by previous approaches are captured in our framework. Moreover, our approach leads to an efficient polynomial-time algorithm, while no previous approach for cost analysis of probabilistic programs could guarantee polynomial runtime. Finally, we show the effectiveness of our approach using experimental results on a variety of programs for which we efficiently synthesize tight resource-usage bounds

Cost Analysis of Nondeterministic Probabilistic Programs

We consider the problem of expected cost analysis over nondeterministic probabilistic programs, which aims at automated methods for analyzing the resource-usage of such programs. Previous approaches for this problem could only handle nonnegative bounded costs. However, in many scenarios, such as queuing networks or analysis of cryptocurrency protocols, both positive and negative costs are necessary and the costs are unbounded as well. In this work, we present a sound and efficient approach to obtain polynomial bounds on the expected accumulated cost of nondeterministic probabilistic programs. Our approach can handle (a) general positive and negative costs with bounded updates in variables; and (b) nonnegative costs with general updates to variables. We show that several natural examples which could not be handled by previous approaches are captured in our framework. Moreover, our approach leads to an efficient polynomial-time algorithm, while no previous approach for cost analysis of probabilistic programs could guarantee polynomial runtime. Finally, we show the effectiveness of our approach by presenting experimental results on a variety of programs, motivated by real-world applications, for which we efficiently synthesize tight resource-usage bounds.Comment: A conference version will appear in the 40th ACM Conference on Programming Language Design and Implementation (PLDI 2019

Passive Acoustic Detection System

This Device is a passive acoustic detection system (P.A.D.S). This system is able to detect large objects (i.e. airplanes and tanks) at various distances within its hearing radius despite the visual conditions. These detection signals are transmitted wirelessly to a display unit, which can potentially be located out of sight of the sensor

Harmony and conflict prediction of space IQP teams using MBTI personality descriptors

This study looks at the relationship between cognitive diversity among team members, as measured by the MBTI and group performance. There were 44 WPI students formed into 14 IQP teams working concurrently on space-related topics. Group dynamics (leadership, conflict, division of labor and performance) were covered by a student questionnaire administered in the first third of the project and again toward the end. The results indicated that diversity was related to the student but not the advisor rating of how things went. The advisor ratings were most affected by whether there were certain "anchor" types of students on the team