Rigidity of SU(2,2|2)-symmetric solutions in Type IIB

We investigate the existence of half-BPS solutions in Type IIB supergravity which are invariant under the superalgebra SU(2,2|2) realized on either AdS_5 x S^2 x S^1 or AdS_5 x S^3 warped over a Riemann surface \Sigma with boundary. We prove that, in both cases, the only solution is AdS_5 x S^5 itself. We argue that this result provides evidence for the non-existence of fully back-reacted intersecting D3/D7 branes with either AdS_5 x S^2 x S^1 x \Sigma or AdS_5 x S^3 x \Sigma near-horizon limits.Comment: 55 page

New Synthetic Thrombin Inhibitors: Molecular Design and Experimental Verification

BACKGROUND: The development of new anticoagulants is an important goal for the improvement of thromboses treatments. OBJECTIVES: The design, synthesis and experimental testing of new safe and effective small molecule direct thrombin inhibitors for intravenous administration. METHODS: Computer-aided molecular design of new thrombin inhibitors was performed using our original docking program SOL, which is based on the genetic algorithm of global energy minimization in the framework of a Merck Molecular Force Field. This program takes into account the effects of solvent. The designed molecules with the best scoring functions (calculated binding energies) were synthesized and their thrombin inhibitory activity evaluated experimentally in vitro using a chromogenic substrate in a buffer system and using a thrombin generation test in isolated plasma and in vivo using the newly developed model of hemodilution-induced hypercoagulation in rats. The acute toxicities of the most promising new thrombin inhibitors were evaluated in mice, and their stabilities in aqueous solutions were measured. RESULTS: New compounds that are both effective direct thrombin inhibitors (the best K(I) was <1 nM) and strong anticoagulants in plasma (an IC(50) in the thrombin generation assay of approximately 100 nM) were discovered. These compounds contain one of the following new residues as the basic fragment: isothiuronium, 4-aminopyridinium, or 2-aminothiazolinium. LD(50) values for the best new inhibitors ranged from 166.7 to >1111.1 mg/kg. A plasma-substituting solution supplemented with one of the new inhibitors prevented hypercoagulation in the rat model of hemodilution-induced hypercoagulation. Activities of the best new inhibitors in physiological saline (1 µM solutions) were stable after sterilization by autoclaving, and the inhibitors remained stable at long-term storage over more than 1.5 years at room temperature and at 4°C. CONCLUSIONS: The high efficacy, stability and low acute toxicity reveal that the inhibitors that were developed may be promising for potential medical applications

Fast empirical Bayesian LASSO for multiple quantitative trait locus mapping

<p>Abstract</p> <p>Background</p> <p>The Bayesian shrinkage technique has been applied to multiple quantitative trait loci (QTLs) mapping to estimate the genetic effects of QTLs on quantitative traits from a very large set of possible effects including the main and epistatic effects of QTLs. Although the recently developed empirical Bayes (EB) method significantly reduced computation comparing with the fully Bayesian approach, its speed and accuracy are limited by the fact that numerical optimization is required to estimate the variance components in the QTL model.</p> <p>Results</p> <p>We developed a fast empirical Bayesian LASSO (EBLASSO) method for multiple QTL mapping. The fact that the EBLASSO can estimate the variance components in a closed form along with other algorithmic techniques render the EBLASSO method more efficient and accurate. Comparing with the EB method, our simulation study demonstrated that the EBLASSO method could substantially improve the computational speed and detect more QTL effects without increasing the false positive rate. Particularly, the EBLASSO algorithm running on a personal computer could easily handle a linear QTL model with more than 100,000 variables in our simulation study. Real data analysis also demonstrated that the EBLASSO method detected more reasonable effects than the EB method. Comparing with the LASSO, our simulation showed that the current version of the EBLASSO implemented in Matlab had similar speed as the LASSO implemented in Fortran, and that the EBLASSO detected the same number of true effects as the LASSO but a much smaller number of false positive effects.</p> <p>Conclusions</p> <p>The EBLASSO method can handle a large number of effects possibly including both the main and epistatic QTL effects, environmental effects and the effects of gene-environment interactions. It will be a very useful tool for multiple QTL mapping.</p

The time-profile of cell growth in fission yeast: model selection criteria favoring bilinear models over exponential ones

BACKGROUND: There is considerable controversy concerning the exact growth profile of size parameters during the cell cycle. Linear, exponential and bilinear models are commonly considered, and the same model may not apply for all species. Selection of the most adequate model to describe a given data-set requires the use of quantitative model selection criteria, such as the partial (sequential) F-test, the Akaike information criterion and the Schwarz Bayesian information criterion, which are suitable for comparing differently parameterized models in terms of the quality and robustness of the fit but have not yet been used in cell growth-profile studies. RESULTS: Length increase data from representative individual fission yeast (Schizosaccharomyces pombe) cells measured on time-lapse films have been reanalyzed using these model selection criteria. To fit the data, an extended version of a recently introduced linearized biexponential (LinBiExp) model was developed, which makes possible a smooth, continuously differentiable transition between two linear segments and, hence, allows fully parametrized bilinear fittings. Despite relatively small differences, essentially all the quantitative selection criteria considered here indicated that the bilinear model was somewhat more adequate than the exponential model for fitting these fission yeast data. CONCLUSION: A general quantitative framework was introduced to judge the adequacy of bilinear versus exponential models in the description of growth time-profiles. For single cell growth, because of the relatively limited data-range, the statistical evidence is not strong enough to favor one model clearly over the other and to settle the bilinear versus exponential dispute. Nevertheless, for the present individual cell growth data for fission yeast, the bilinear model seems more adequate according to all metrics, especially in the case of wee1Δ cells

Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

BACKGROUND: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the existing non-linear methods have a couple of critical problems, the model selection problem and the model parameter tuning problem, that remain unsolved or even untouched. In general, a unified framework that allows model parameters of both linear and non-linear models to be easily tuned is always preferred in real-world applications. Kernel-induced learning methods form a class of approaches that show promising potentials to achieve this goal. RESULTS: A hierarchical statistical model named kernel-imbedded Gaussian process (KIGP) is developed under a unified Bayesian framework for binary disease classification problems using microarray gene expression data. In particular, based on a probit regression setting, an adaptive algorithm with a cascading structure is designed to find the appropriate kernel, to discover the potentially significant genes, and to make the optimal class prediction accordingly. A Gibbs sampler is built as the core of the algorithm to make Bayesian inferences. Simulation studies showed that, even without any knowledge of the underlying generative model, the KIGP performed very close to the theoretical Bayesian bound not only in the case with a linear Bayesian classifier but also in the case with a very non-linear Bayesian classifier. This sheds light on its broader usability to microarray data analysis problems, especially to those that linear methods work awkwardly. The KIGP was also applied to four published microarray datasets, and the results showed that the KIGP performed better than or at least as well as any of the referred state-of-the-art methods did in all of these cases. CONCLUSION: Mathematically built on the kernel-induced feature space concept under a Bayesian framework, the KIGP method presented in this paper provides a unified machine learning approach to explore both the linear and the possibly non-linear underlying relationship between the target features of a given binary disease classification problem and the related explanatory gene expression data. More importantly, it incorporates the model parameter tuning into the framework. The model selection problem is addressed in the form of selecting a proper kernel type. The KIGP method also gives Bayesian probabilistic predictions for disease classification. These properties and features are beneficial to most real-world applications. The algorithm is naturally robust in numerical computation. The simulation studies and the published data studies demonstrated that the proposed KIGP performs satisfactorily and consistently

Myocardial ultrasonic tissue characterization in patients with thyroid dysfunction

<p>Abstract</p> <p>Background</p> <p>Structural myocardial abnormalities have been extensively documented in hypothyroidism. Experimental studies in animal models have also shown involvement of thyroid hormones in gene expression of myocardial collagen. This study was planned to investigate the ability of ultrasonic tissue characterization, as evaluated by integrated backscatter (IBS), to early identify myocardial involvement in thyroid dysfunction.</p> <p>Patients and Methods</p> <p>We studied 15 patients with hyperthyroidism (HYPER), 8 patients with hypothyroidism (HYPO), 14 patients with subclinical hypothyroidism (SCH) and 19 normal (N) subjects, who had normal LV systolic function. After treatment, 10 HYPER, 6 HYPO, and 8 SCH patients were reevaluated. IBS images were obtained and analyzed in parasternal short axis (papillary muscle level) view, at left ventricular (LV) posterior wall. The following IBS variables were analyzed: 1) the corrected coefficient (CC) of IBS, obtained by dividing IBS intensity by IBS intensity measured in a rubber phantom, using the same equipment adjustments, at the same depth; 2) cardiac cyclic variation (CV) of IBS - peak-to-peak difference between maximal and minimal values of IBS during cardiac cycle; 3) cardiac cyclic variation index (CVI) of IBS - percentual relationship between the cyclic variation (CV) and the mean value of IBS intensity.</p> <p>Results</p> <p>CC of IBS was significantly larger (p < 0.05) in HYPER (1.57 ± 0.6) and HYPO (1.53 ± 0.3) as compared to SCH (1.32 ± 0.3) or N (1.15 ± 0.27). The CV (dB) (HYPO: 7.5 ± 2.4; SCH: 8.2 ± 3.1; HYPER: 8.2 ± 2.0) and the CVI (HYPO: 35.6 ± 19.7%; SCH: 34.7 ± 17.5%; HYPER: 37.8 ± 11.6%) were not significantly different in patients with thyroid dysfunction as compared to N (7.0 ± 2.0 and 44.5 ± 15.1%).</p> <p>Conclusions</p> <p>CC of IBS was able to differentiate cardiac involvement in patients with overt HYPO and HYPER who had normal LV systolic function. These early myocardial structural abnormalities were partially reversed by drug therapy in HYPER group. On the other hand, although mean IBS intensity tended to be slightly larger in patients with SCH as compared to N, this difference was not statistical significant.</p

Morphological correlates to cognitive dysfunction in schizophrenia as studied with Bayesian regression

BACKGROUND: Relationships between cognitive deficits and brain morphological changes observed in schizophrenia are alternately explained by less gray matter in the brain cerebral cortex, by alterations in neural circuitry involving the basal ganglia, and by alteration in cerebellar structures and related neural circuitry. This work explored a model encompassing all of these possibilities to identify the strongest morphological relationships to cognitive skill in schizophrenia. METHODS: Seventy-one patients with schizophrenia and sixty-five healthy control subjects were characterized by neuropsychological tests covering six functional domains. Measures of sixteen brain morphological structures were taken using semi-automatic and fully manual tracing of MRI images, with the full set of measures completed on thirty of the patients and twenty controls. Group differences were calculated. A Bayesian decision-theoretic method identified those morphological features, which best explained neuropsychological test scores in the context of a multivariate response linear model with interactions. RESULTS: Patients performed significantly worse on all neuropsychological tests except some regarding executive function. The most prominent morphological observations were enlarged ventricles, reduced posterior superior vermis gray matter volumes, and increased putamen gray matter volumes in the patients. The Bayesian method associated putamen volumes with verbal learning, vigilance, and (to a lesser extent) executive function, while caudate volumes were associated with working memory. Vermis regions were associated with vigilance, executive function, and, less strongly, visuo-motor speed. Ventricular volume was strongly associated with visuo-motor speed, vocabulary, and executive function. Those neuropsychological tests, which were strongly associated to ventricular volume, showed only weak association to diagnosis, possibly because ventricular volume was regarded a proxy for diagnosis. Diagnosis was strongly associated with the other neuropsychological tests, implying that the morphological associations for these tasks reflected morphological effects and not merely group volumetric differences. Interaction effects were rarely associated, indicating that volumetric relationships to neuropsychological performance were similar for both patients and controls. CONCLUSION: The association of subcortical and cerebellar structures to verbal learning, vigilance, and working memory supports the importance of neural connectivity to these functions. The finding that a morphological indicator of diagnosis (ventricular volume) provided more explanatory power than diagnosis itself for visuo-motor speed, vocabulary, and executive function suggests that volumetric abnormalities in the disease are more important for cognition than non-morphological features

Recursive regularization for inferring gene networks from time-course gene expression profiles

<p>Abstract</p> <p>Background</p> <p>Inferring gene networks from time-course microarray experiments with vector autoregressive (VAR) model is the process of identifying functional associations between genes through multivariate time series. This problem can be cast as a variable selection problem in Statistics. One of the promising methods for variable selection is the elastic net proposed by Zou and Hastie (2005). However, VAR modeling with the elastic net succeeds in increasing the number of true positives while it also results in increasing the number of false positives.</p> <p>Results</p> <p>By incorporating relative importance of the VAR coefficients into the elastic net, we propose a new class of regularization, called recursive elastic net, to increase the capability of the elastic net and estimate gene networks based on the VAR model. The recursive elastic net can reduce the number of false positives gradually by updating the importance. Numerical simulations and comparisons demonstrate that the proposed method succeeds in reducing the number of false positives drastically while keeping the high number of true positives in the network inference and achieves two or more times higher true discovery rate (the proportion of true positives among the selected edges) than the competing methods even when the number of time points is small. We also compared our method with various reverse-engineering algorithms on experimental data of MCF-7 breast cancer cells stimulated with two ErbB ligands, EGF and HRG.</p> <p>Conclusion</p> <p>The recursive elastic net is a powerful tool for inferring gene networks from time-course gene expression profiles.</p