Continual learning with a Bayesian approach for evolving the baselines of a leagile project portfolio

This article introduces a Bayesian learning approach for planning continuously evolving leagile project and portfolio baselines. Unlike the traditional project management approach, which uses static project baselines, the approach proposed in this study suggests learning from immediately prior experience to establish an evolving baseline for performance estimation. The principle of Pasteur’s quadrant is used to realize a highly practical solution, which extends the existing wisdom on leagile continuous planning. This study compares the accuracy of the proposed Bayesian approach with the traditional approach using real data. The results suggest that the evolving Bayesian baselines can generate a more realistic measure of performance than traditional baselines, enabling leagile projects and portfolios to be better managed in the continuously changing environments of today

Total Cerebral Small Vessel Disease Score and Cerebral Bleeding Risk in Patients With Acute Stroke Treated With Intravenous Thrombolysis

OBJECTIVE: The aim of this study was to investigate the association of total cerebral small vessel disease (cSVD) score with the risk of intracerebral hemorrhage (ICH) in patients with acute ischemic stroke who received intravenous thrombolysis (IVT) using recombinant tissue-plasminogen activator (rt-PA). METHODS: We retrospectively reviewed clinical data from two stroke registries of patients with acute ischemic stroke treated with IVT. We assessed the baseline magnetic resonance (MR) visible cSVD markers and total cSVD score (ranging from 0 to 4) between patients with and without ICH after IVT. Logistic regression analysis was used to determine the association of total cSVD score with the risk of ICH after IVT, adjusted for cofounders selected by least absolute shrinkage and selection operator (LASSO). We additionally performed an E-value analysis to fully explain away a specific exposure-outcome association. Receiver operating characteristic (ROC) curve analysis was used to quantify the predictive potential of the total cSVD score for any ICH after IVT. RESULTS: Among 271 eligible patients, 55 (20.3%) patients experienced any ICH, 16 (5.9%) patients experienced a symptomatic ICH (sICH), and 5 (1.85%) patients had remote intracranial parenchymal hemorrhage (rPH). Logistic regression analysis showed that the risk of any ICH increased with increasing cSVD score [per unit increase, adjusted odds ratio (OR) 2.03, 95% CI 1.22–3.41, P = 0.007]. Sensitivity analyses using E-value revealed that it would need moderately robust unobserved confounding to render the exposure-outcome (cSVD-any ICH) association null. ROC analysis showed that compared with the National Institutes of Health Stroke Scale (NIHSS) score alone, a combination of cSVD and NIHSS score had a larger area under the curve for any ICH (0.811, 95% CI 0.756–0.866 vs. 0.784, 95% CI 0.723–0.846, P = 0.0004). CONCLUSION: The total cSVD score is associated with an increased risk of any ICH after IVT and improves prediction for any ICH compared with NIHSS alone

FERONIA interacts with ABI2-type phosphatases to facilitate signaling cross-talk between abscisic acid and RALF peptide in Arabidopsis

[EN] Receptor-like kinase FERONIA (FER) plays a crucial role in plant response to small molecule hormones [e.g., auxin and abscisic acid (ABA)] and peptide signals [e.g., rapid alkalinization factor (RALF)]. It remains unknown how FER integrates these different signaling events in the control of cell growth and stress responses. Under stress conditions, increased levels of ABA will inhibit cell elongation in the roots. In our previous work, we have shown that FER, through activation of the guanine nucleotide exchange factor 1 (GEF1)/4/10-Rho of Plant 11 (ROP11) pathway, enhances the activity of the phosphatase ABA Insensitive 2 (ABI2), a negative regulator of ABA signaling, thereby inhibiting ABA response. In this study, we found that both RALF and ABA activated FER by increasing the phosphorylation level of FER. The FER loss-of-function mutant displayed strong hypersensitivity to both ABA and abiotic stresses such as salt and cold conditions, indicating that FER plays a key role in ABA and stress responses. We further showed that ABI2 directly interacted with and dephosphorylated FER, leading to inhibition of FER activity. Several other ABI2-like phosphatases also function in this pathway, and ABA-dependent FER activation required PYRABACTIN RESISTANCE (PYR)/PYR1-LIKE (PYL)/REGULATORY COMPONENTS OF ABA RECEPTORS (RCAR)-A-type protein phosphatase type 2C (PP2CA) modules. Furthermore, suppression of RALF1 gene expression, similar to disruption of the FER gene, rendered plants hypersensitive to ABA. These results formulated a mechanism for ABA activation of FER and for cross-talk between ABA and peptide hormone RALF in the control of plant growth and responses to stress signals.We thank Dr. Alice Cheung, Dr. Daniel Moura, Grossniklaus Ueli, Dr. Jigang Li, and Dr. Nieng Yan for providing plant, ABI1 antibody, or plasmid materials, and Dr. Legong Li for assistance in laser confocal microscopy. This work was supported by grants from National Natural Science Foundation of China (NSFC-31400232, 31571444), the State Key Laboratory of Molecular Developmental Biology (2015-MDB-KF-12), the Fundamental Research Funds for the Central Universities of China, and a grant from the National Science Foundation.Chen, J.; Yu, F.; Liu, Y.; Du, C.; Li, X.; Zhu, S.; Wang, X.... (2016). FERONIA interacts with ABI2-type phosphatases to facilitate signaling cross-talk between abscisic acid and RALF peptide in Arabidopsis. Proceedings of the National Academy of Sciences. 113(37):E5519-E5527. https://doi.org/10.1073/pnas.1608449113SE5519E55271133

Physical-Statistical Modeling and Optimization of Cardiovascular Systems

Heart disease remains the No.1 leading cause of death in U.S. and in the world. To improve cardiac care services, there is an urgent need of developing early diagnosis of heart diseases and optimal intervention strategies. As such, it calls upon a better understanding of the pathology of heart diseases. Computer simulation and modeling have been widely applied to overcome many practical and ethical limitations in in-vivo, ex-vivo, and whole-animal experiments. Computer experiments provide physiologists and cardiologists an indispensable tool to characterize, model and analyze cardiac function both in healthy and in diseased heart. Most importantly, simulation modeling empowers the analysis of causal relationships of cardiac dysfunction from ion channels to the whole heart, which physical experiments alone cannot achieve. Growing evidences show that aberrant glycosylation have dramatic influence on cardiac and neuronal function. Variable but modest reduction in glycosylation among congenital disorders of glycosylation (CDG) subtypes has multi-system effects leading to a high infant mortality rate. In addition, CDG in all young patients tends to cause Atrial Fibrillation (AF), i.e., the most common sustained cardiac arrhythmia. The mortality rate from AF has been increasing in the past two decades. Due to the increasing healthcare burden of AF, studying the AF mechanisms and developing optimal ablation strategies are now urgently needed. Very little is known about how glycosylation modulates cardiac electrical signaling. It is also a significant challenge to experimentally connect the changes at one organizational level (e.g.,electrical conduction among cardiac tissue) to measured changes at another organizational level (e.g., ion channels). In this study, we integrate the data from in vitro experiments with in-silico models to simulate the effects of reduced glycosylation on the gating kinetics of cardiac ion channel, i.e., hERG channels, Na+ channels, K+ channels, and to predict the glycosylation modulation dynamics in individual cardiac cells and tissues. The complex gating kinetics of Na+ channels is modeled with a 9-state Markov model that have voltage-dependent transition rates of exponential forms. The model calibration is quite a challenge as the Markov model is non-linear, non-convex, ill-posed, and has a large parametric space. We developed a new metamodel-based simulation optimization approach for calibrating the model with the in-vitro experimental data. This proposed algorithm is shown to be efficient in learning the Markov model of Na+ model. Moreover, it can be easily transformed and applied to many other optimization problems in computer modeling. In addition, the understanding of AF initiation and maintenance has remained sketchy at best. One salient problem is the inability to interpret intracardiac recordings, which prevents us from reconstructing the rhythmic mechanisms for AF, due to multiple wavelets\u27 circulating, clashing and continuously changing direction in the atria. We are designing computer experiments to simulate the single/multiple activations on atrial tissues and the corresponding intra-cardiac signals. This research will create a novel computer-aided decision support tool to optimize AF ablation procedures

Stochastic Modeling and Dynamic Analysis of the Cardiovascular System with Rotary Left Ventricular Assist Devices

Left ventricular assist devices (LVADs) have been used for end-stage heart failure patients as a therapeutic option. The aortic valve plays a critical role in heart failure and its treatment with a LVAD. The cardiovascular-LVAD model is often used to investigate the physiological demands required by patients and predict the hemodynamic of the native heart supported with a LVAD. As it is a “bridge-to-recovery” treatment, it is important to maintain appropriate and active dynamics of the aortic valve and the cardiac output of the native heart, which requires that the LVAD pump be adjusted so that a proper balance between the blood contributed through the aortic valve and the pump is maintained. In this paper, we investigate how the pump power of the LVAD pump can affect the dynamic behaviors of the aortic valve for different levels of activity and different severities of heart failure. Our objective is to identify a critical value of the pump power (i.e., breakpoint) to ensure that the LVAD pump does not take over the pumping function in the cardiovascular-pump system and share the ejected blood with the left ventricle to help the heart to recover. In addition, the hemodynamic often involves variability due to patients’ heterogeneity and the stochastic nature of the cardiovascular system. The variability poses significant challenges to understanding dynamic behaviors of the aortic valve and cardiac output. A generalized polynomial chaos (gPC) expansion is used in this work to develop a stochastic cardiovascular-pump model for efficient uncertainty propagation, from which it is possible to rapidly calculate the variance in the aortic valve opening duration and the cardiac output in the presence of variability. The simulation results show that the gPC-based cardiovascular-pump model is a reliable platform that can provide useful information to understand the effect of the LVAD pump on the hemodynamic of the heart

A new analytical framework for missing data imputation and classification with uncertainty: Missing data imputation and heart failure readmission prediction.

BackgroundThe wide adoption of electronic health records (EHR) system has provided vast opportunities to advance health care services. However, the prevalence of missing values in EHR system poses a great challenge on data analysis to support clinical decision-making. The objective of this study is to develop a new methodological framework that can address the missing data challenge and provide a reliable tool to predict the hospital readmission among Heart Failure patients.MethodsWe used Gaussian Process Latent Variable Model (GPLVM) to impute the missing values. Specifically, a lower dimensional embedding was learned from a small complete dataset and then used to impute the missing values in the incomplete dataset. The GPLVM-based missing data imputation can provide both the mean estimate and the uncertainty associated with the mean estimate. To incorporate the uncertainty in prediction, a constrained support vector machine (cSVM) was developed to obtain robust predictions. We first sampled multiple datasets from the distributions of input uncertainty and trained a support vector machine for each dataset. Then an optimal classifier was identified by selecting the support vectors that maximize the separation margin of a newly sampled dataset and minimize the similarity with the pre-trained support vectors.ResultsThe proposed model was derived and validated using Physionet MIMIC-III clinical database. The GPLVM imputation provided normalized mean absolute errors of 0.11 and 0.12 respectively when 20% and 30% of instances contained missing values, and the confidence bounds of the estimations captures 97% of the true values. The cSVM model provided an average Area Under Curve of 0.68, which improves the prediction accuracy by 7% as compared to some existing classifiers.ConclusionsThe proposed method provides accurate imputation of missing values and has a better prediction performance as compared to existing models that can only deal with deterministic inputs

An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis

Intracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose significant challenges in data analysis. This study aims to introduce a statistical approach to account for the data uncertainty while analyzing EGMs for abnormal electrical impulse identification. The activation order of catheter sensors was modeled with a multinomial distribution, and maximum likelihood estimations were done to track the electrical wave conduction path in the presence of uncertainty. Robust optimization was performed to locate the electrical impulses based on the local conduction velocity and the geodesic distances between catheter sensors. The proposed algorithm can identify the focal sources when the electrical conduction is initiated by irregular electrical impulses and involves wave collisions, breakups, and spiral waves. The statistical modeling framework can efficiently deal with data uncertainties and provide a reliable estimation of the focal source locations. This shows the great potential of a statistical approach for the quantitative analysis of the stochastic activity of electrical waves in cardiac disorders and suggests future investigations integrating statistical methods with a deterministic geometry-based method to achieve advanced diagnostic performance