SCAD-penalized regression in high-dimensional partially linear models

We consider the problem of simultaneous variable selection and estimation in partially linear models with a divergent number of covariates in the linear part, under the assumption that the vector of regression coefficients is sparse. We apply the SCAD penalty to achieve sparsity in the linear part and use polynomial splines to estimate the nonparametric component. Under reasonable conditions, it is shown that consistency in terms of variable selection and estimation can be achieved simultaneously for the linear and nonparametric components. Furthermore, the SCAD-penalized estimators of the nonzero coefficients are shown to have the asymptotic oracle property, in the sense that it is asymptotically normal with the same means and covariances that they would have if the zero coefficients were known in advance. The finite sample behavior of the SCAD-penalized estimators is evaluated with simulation and illustrated with a data set.Comment: Published in at http://dx.doi.org/10.1214/07-AOS580 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Asymptotic oracle properties of SCAD-penalized least squares estimators

We study the asymptotic properties of the SCAD-penalized least squares estimator in sparse, high-dimensional, linear regression models when the number of covariates may increase with the sample size. We are particularly interested in the use of this estimator for simultaneous variable selection and estimation. We show that under appropriate conditions, the SCAD-penalized least squares estimator is consistent for variable selection and that the estimators of nonzero coefficients have the same asymptotic distribution as they would have if the zero coefficients were known in advance. Simulation studies indicate that this estimator performs well in terms of variable selection and estimation.Comment: Published at http://dx.doi.org/10.1214/074921707000000337 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

The effects of cardiac structure, valvular regurgitation, and left ventricular diastolic dysfunction on the diagnostic accuracy of Murray law–based quantitative flow ratio

ObjectiveThe study aimed to investigate the diagnostic accuracy of Murray law–based quantitative flow ratio (μQFR) from a single angiographic view in patients with abnormal cardiac structure, left ventricular diastolic dysfunction, and valvular regurgitation.BackgroundμQFR is a novel fluid dynamics method for deriving fractional flow reserve (FFR). In addition, current studies of μQFR mainly analyzed patients with normal cardiac structure and function. The accuracy of μQFR when patients had abnormal cardiac structure, left ventricular diastolic dysfunction, and valvular regurgitation has not been clear.MethodsThis study retrospectively analyzed 261 patients with 286 vessels that underwent both FFR and μQFR prior to intervention. The cardiac structure and function were measured using echocardiography. Pressure wire–derived FFR ≤0.80 was defined as hemodynamically significant coronary stenosis.ResultsμQFR had a moderate correlation with FFR (r = 0.73, p < 0.001), and the Bland–Altman plot presented no difference between the μQFR and FFR (0.006 ± 0.075, p = 0.192). With FFR as the standard, the diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of μQFR were 94.06% (90.65–96.50), 82.56% (72.87–89.90), 99.00% (96.44–99.88), 97.26 (89.91–99.30), and 92.96% (89.29–95.44), respectively. The concordance of μQFR/FFR was not associated with abnormal cardiac structure, valvular regurgitation (aortic valve, mitral valve, and tricuspid valve), and left ventricular diastolic function. Coronary hemodynamics showed no difference between normality and abnormality of cardiac structure and left ventricular diastolic function. Coronary hemodynamics demonstrated no difference among valvular regurgitation (none, mild, moderate, or severe).ConclusionμQFR showed an excellent agreement with FFR. The effect of abnormal cardiac structure, valvular regurgitation, and left ventricular diastolic function did not correlate with the diagnostic accuracy of μQFR. Coronary hemodynamics showed no difference in patients with abnormal cardiac structure, valvular regurgitation, and left ventricular diastolic function

Chk1 Inhibition Ameliorates Alzheimer's Disease Pathogenesis and Cognitive Dysfunction Through CIP2A/PP2A Signaling

Alzheimer's disease (AD) is the most common neurodegenerative disease with limited therapeutic strategies. Cell cycle checkpoint protein kinase 1 (Chk1) is a Ser/Thr protein kinase which is activated in response to DNA damage, the latter which is an early event in AD. However, whether DNA damage-induced Chk1 activation participates in the development of AD and Chk1 inhibition ameliorates AD-like pathogenesis remain unclarified. Here, we demonstrate that Chk1 activity and the levels of protein phosphatase 2A (PP2A) inhibitory protein CIP2A are elevated in AD human brains, APP/PS1 transgenic mice, and primary neurons with A beta treatment. Chk1 overexpression induces CIP2A upregulation, PP2A inhibition, tau and APP hyperphosphorylation, synaptic impairments, and cognitive memory deficit in mice. Moreover, Chk1 inhibitor (GDC0575) effectively increases PP2A activity, decreases tau phosphorylation, and inhibits A beta overproduction in AD cell models. GDC0575 also reverses AD-like cognitive deficits and prevents neuron loss and synaptic impairments in APP/PS1 mice. In conclusion, our study uncovers a mechanism by which DNA damage-induced Chk1 activation promotes CIP2A-mediated tau and APP hyperphosphorylation and cognitive dysfunction in Alzheimer's disease and highlights the therapeutic potential of Chk1 inhibitors in AD

On the issue of transparency and reproducibility in nanomedicine.

Following our call to join in the discussion over the suitability of implementing a reporting checklist for bio-nano papers, the community responds

SCAD-Penalized Regression in High-Dimensional Partially Linear Models

Summary. We consider the problem of simultaneous variable selection and estimation in partially linear models with a divergent number of covariates in the linear part, under the assumption that the vector of regression coefficients is sparse. We apply the SCAD penalty to achieve sparsity in the linear part and use polynomial splines to estimate the nonparametric component. Under reasonable conditions it is shown that consistency in terms of variable selection and estimation can be achieved simultaneously for the linear and nonparametric components. Furthermore, the SCAD-penalized estimators of the nonzero coefficients are shown to be asymptotically normal with the same means and covariances that they would have if the zero coefficients were known in advance. Simulation studies are conducted to evaluate the finite sample behavior of the SCAD-penalized estimators. Key Words and phrases. Asymptotic normality, high-dimensional data, oracle property, penalized estimation, semiparametric models, variable selection,. Short title. High-dimensional PLM AMS 2000 subject classification. Primary 62J05, 62G08; secondary 62E2

A general framework for parallel BDI agents in dynamic environments

The traditional BDI agent has 3 basic computational components that generate beliefs, generate intentions and execute intentions. They run in a sequential and cyclic manner. This may introduce several problems. Among them, the inability to watch the environment continuously in dynamic environments may be disastrous. There is also no support for goal and intention reconsideration and consideration of relationships between goals at the architecture level. A parallel BDI agent architecture was proposed in [15] and evaluated in [16]. Based on the work in [15] and [16], we propose in this paper, a general framework for the parallel BDI agent model. Under this general framework, parallel BDI agents with different configurations depending on the availability of physical resources may be built. These agents have a number of advantages over the sequential one: 1. changes in the agent's environment can be detected immediately; 2. emergencies will be dealt with immediately; 3. the support is provided at the architecture level for reconsideration of desires/intentions and the consideration of goal relationships when a new belief/desire is generated. We show some example parallel BDI agents with different configurations under the framework and their performance in a set of experiments.Published versio

Activity scheduling for a robotic caretaker agent for the elderly

A real-time robotic agent that takes care of an elderly person at home will need to schedule various tasks in real time. The deadlines of its tasks are generally soft (missing a deadline by a few minutes in most cases has no serious consequences). Another characteristic is that many tasks are preferably done close to some time points instead of as soon as possible. To support such time management behavior, we propose to enrich the BDI agent framework with an extension which consists of 2 processing components, a PCF (Priority Changing Function) Selector and a Priority Controller. The priorities of desires/intentions are represented by their PCFs. A PCF is a function of both time and the utility value of a desire/intention. So it represents both the urgency and the importance (beneficial value) of a desire/intention. We propose a method of constructing PCFs which model the change of priorities of tasks as time passes. Simulation experiments show that Sigmoid function can control the activities of an agent better than constant priorities with respect to getting tasks done with smaller Mean Earliness and smaller Mean Tardiness. A BDI agent built with this time management mechanism will try to complete its tasks at the right time. The order in which multiple goals and multiple intentions are handled will be flexible and time dependent.Accepted versio