Viewing the efficiency of chaos control

This paper aims to cast some new light on controlling chaos using the OGY- and the Zero-Spectral-Radius methods. In deriving those methods we use a generalized procedure differing from the usual ones. This procedure allows us to conveniently treat maps to be controlled bringing the orbit to both various saddles and to sources with both real and complex eigenvalues. We demonstrate the procedure and the subsequent control on a variety of maps. We evaluate the control by examining the basins of attraction of the relevant controlled systems graphically and in some cases analytically

Modeling Single Electron Transfer in Si:P Double Quantum Dots

Solid-state systems such as P donors in Si have considerable potential for realization of scalable quantum computation. Recent experimental work in this area has focused on implanted Si:P double quantum dots (DQDs) that represent a preliminary step towards the realization of single donor charge-based qubits. This paper focuses on the techniques involved in analyzing the charge transfer within such DQD devices and understanding the impact of fabrication parameters on this process. We show that misalignment between the buried dots and surface gates affects the charge transfer behavior and identify some of the challenges posed by reducing the size of the metallic dot to the few donor regime.Comment: 11 pages, 7 figures, submitted to Nanotechnolog

Replica theory for learning curves for Gaussian processes on random graphs

Statistical physics approaches can be used to derive accurate predictions for the performance of inference methods learning from potentially noisy data, as quantified by the learning curve defined as the average error versus number of training examples. We analyse a challenging problem in the area of non-parametric inference where an effectively infinite number of parameters has to be learned, specifically Gaussian process regression. When the inputs are vertices on a random graph and the outputs noisy function values, we show that replica techniques can be used to obtain exact performance predictions in the limit of large graphs. The covariance of the Gaussian process prior is defined by a random walk kernel, the discrete analogue of squared exponential kernels on continuous spaces. Conventionally this kernel is normalised only globally, so that the prior variance can differ between vertices; as a more principled alternative we consider local normalisation, where the prior variance is uniform

Characterization of the “frequent exacerbator phenotype” in bronchiectasis

Rationale: Exacerbations are key events in the natural history of bronchiectasis, but clinical predictors and outcomes of patients with frequently exacerbating disease are not well described. Objectives: To establish if there is a \u201cfrequent exacerbator phenotype\u201d in bronchiectasis and the impact of exacerbations on long-term clinical outcomes. Methods: We studied patients with bronchiectasis enrolled from 10 clinical centers in Europe and Israel, with up to 5 years of follow-up. Patients were categorized by baseline exacerbation frequency (zero, one, two, or three or more per year). The repeatability of exacerbation status was assessed, as well as the independent impact of exacerbation history on hospitalizations, quality of life, and mortality. Measurements and Main Results: A total of 2,572 patients were included. Frequent exacerbations were the strongest predictor of future exacerbation frequency, suggesting a consistent phenotype.The incident rate ratios for future exacerbations were 1.73 (95% confidence interval [CI], 1.47-2.02; P, 0.0001) for one exacerbation per year, 3.14 (95% CI, 2.70-3.66; P, 0.0001) for two exacerbations, and 5.97 (95% CI, 5.27-6.78; P, 0.0001) for patients with three or more exacerbations per year at baseline. Additional independent predictors of future exacerbation frequency were Haemophilus influenzae and Pseudomonas aeruginosa infection, FEV1, radiological severity of disease, and coexisting chronic obstructive pulmonary disease. Patients with frequently exacerbating disease had worse quality of life and were more likely to be hospitalized during followup. Mortality over up to 5 years of follow-up increased with increasing exacerbation frequency. Conclusions: The frequent exacerbator phenotype in bronchiectasis is consistent over time and shows high disease severity, poor quality of life, and increased mortality during follow-up

Disorder Predictors Also Predict Backbone Dynamics for a Family of Disordered Proteins

Several algorithms have been developed that use amino acid sequences to predict whether or not a protein or a region of a protein is disordered. These algorithms make accurate predictions for disordered regions that are 30 amino acids or longer, but it is unclear whether the predictions can be directly related to the backbone dynamics of individual amino acid residues. The nuclear Overhauser effect between the amide nitrogen and hydrogen (NHNOE) provides an unambiguous measure of backbone dynamics at single residue resolution and is an excellent tool for characterizing the dynamic behavior of disordered proteins. In this report, we show that the NHNOE values for several members of a family of disordered proteins are highly correlated with the output from three popular algorithms used to predict disordered regions from amino acid sequence. This is the first test between an experimental measure of residue specific backbone dynamics and disorder predictions. The results suggest that some disorder predictors can accurately estimate the backbone dynamics of individual amino acids in a long disordered region

Role of A Novel Angiogenesis FKBPL-CD44 Pathway in Preeclampsia Risk Stratification and Mesenchymal Stem Cell Treatment.

ContextPreeclampsia is a leading cardiovascular complication in pregnancy lacking effective diagnostic and treatment strategies.ObjectiveTo investigate the diagnostic and therapeutic target potential of the angiogenesis proteins, FK506-binding protein like (FKBPL) and CD44.Design and interventionFKBPL and CD44 plasma concentration or placental expression were determined in women pre- or postdiagnosis of preeclampsia. Trophoblast and endothelial cell function was assessed following mesenchymal stem cell (MSC) treatment and in the context of FKBPL signaling.Settings and participantsHuman samples prediagnosis (15 and 20 weeks of gestation; n ≥ 57), or postdiagnosis (n = 18 for plasma; n = 4 for placenta) of preeclampsia were used to determine FKBPL and CD44 levels, compared to healthy controls. Trophoblast or endothelial cells were exposed to low/high oxygen, and treated with MSC-conditioned media (MSC-CM) or a FKBPL overexpression plasmid.Main outcome measuresPreeclampsia risk stratification and diagnostic potential of FKBPL and CD44 were investigated. MSC treatment effects and FKBPL-CD44 signaling in trophoblast and endothelial cells were assessed.ResultsThe CD44/FKBPL ratio was reduced in placenta and plasma following clinical diagnosis of preeclampsia. At 20 weeks of gestation, a high plasma CD44/FKBPL ratio was independently associated with the 2.3-fold increased risk of preeclampsia (odds ratio = 2.3, 95% confidence interval [CI] 1.03-5.23, P = 0.04). In combination with high mean arterial blood pressure (>82.5 mmHg), the risk further increased to 3.9-fold (95% CI 1.30-11.84, P = 0.016). Both hypoxia and MSC-based therapy inhibited FKBPL-CD44 signaling, enhancing cell angiogenesis.ConclusionsThe FKBPL-CD44 pathway appears to have a central role in the pathogenesis of preeclampsia, showing promising utilities for early diagnostic and therapeutic purposes

Learning pair-wise gene functional similarity by multiplex gene expression maps

Abstract Background The relationships between the gene functional similarity and gene expression profile, and between gene function annotation and gene sequence have been studied extensively. However, not much work has considered the connection between gene functions and location of a gene's expression in the mammalian tissues. On the other hand, although unsupervised learning methods have been commonly used in functional genomics, supervised learning cannot be directly applied to a set of normal genes without having a target (class) attribute. Results Here, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps that provide information about the location of gene expression. The features are extracted from expression maps and the labels denote the functional similarities of pairs of genes. We make use of wavelet features, original expression values, difference and average values of neighboring voxels and other features to perform boosting analysis. The experimental results show that with increasing similarities of gene expression maps, the functional similarities are increased too. The model predicts the functional similarities between genes to a certain degree. The weights of the features in the model indicate the features that are more significant for this prediction. Conclusions By considering pairs of genes, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps. We also explore the relationship between similarities of gene maps and gene functions. By using AdaBoost coupled with our proposed weak classifier we analyze a large-scale gene expression dataset and predict gene functional similarities. We also detect the most significant single voxels and pairs of neighboring voxels and visualize them in the expression map image of a mouse brain. This work is very important for predicting functions of unknown genes. It also has broader applicability since the methodology can be applied to analyze any large-scale dataset without a target attribute and is not restricted to gene expressions