Structural Design, Fabrication and Evaluation of Resorbable Fiber-Based Tissue Engineering Scaffolds

The use of tissue engineering to regenerate viable tissue relies on selecting the appropriate cell line, developing a resorbable scaffold and optimizing the culture conditions including the use of biomolecular cues and sometimes mechanical stimulation. This review of the literature focuses on the required scaffold properties, including the polymer material, the structural design, the total porosity, pore size distribution, mechanical performance, physical integrity in multiphase structures as well as surface morphology, rate of resorption and biocompatibility. The chapter will explain the unique advantages of using textile technologies for tissue engineering scaffold fabrication, and will delineate the differences in design, fabrication and performance of woven, warp and weft knitted, braided, nonwoven and electrospun scaffolds. In addition, it will explain how different types of tissues can be regenerated by each textile technology for a particular clinical application. The use of different synthetic and natural resorbable polymer fibers will be discussed, as well as the need for specialized finishing techniques such as heat setting, cross linking, coating and impregnation, depending on the tissue engineering application

Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation

Common measures of brain functional connectivity (FC) including covariance and correlation matrices are semi-positive definite (SPD) matrices residing on a cone-shape Riemannian manifold. Despite its remarkable success for Euclidean-valued data generation, use of standard generative adversarial networks (GANs) to generate manifold-valued FC data neglects its inherent SPD structure and hence the inter-relatedness of edges in real FC. We propose a novel graph-regularized manifold-aware conditional Wasserstein GAN (GR-SPD-GAN) for FC data generation on the SPD manifold that can preserve the global FC structure. Specifically, we optimize a generalized Wasserstein distance between the real and generated SPD data under an adversarial training, conditioned on the class labels. The resulting generator can synthesize new SPD-valued FC matrices associated with different classes of brain networks, e.g., brain disorder or healthy control. Furthermore, we introduce additional population graph-based regularization terms on both the SPD manifold and its tangent space to encourage the generator to respect the inter-subject similarity of FC patterns in the real data. This also helps in avoiding mode collapse and produces more stable GAN training. Evaluated on resting-state functional magnetic resonance imaging (fMRI) data of major depressive disorder (MDD), qualitative and quantitative results show that the proposed GR-SPD-GAN clearly outperforms several state-of-the-art GANs in generating more realistic fMRI-based FC samples. When applied to FC data augmentation for MDD identification, classification models trained on augmented data generated by our approach achieved the largest margin of improvement in classification accuracy among the competing GANs over baselines without data augmentation.Comment: 10 pages, 4 figure

CSNL: A cost-sensitive non-linear decision tree algorithm

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification. The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes. The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes

A survey of cost-sensitive decision tree induction algorithms

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field

Entanglement sudden birth of two trapped ions interacting with a time-dependent laser field

We explore and develop the mathematics of the two multi-level ions. In particular, we describe some new features of quantum entanglement in two three-level trapped ions confined in a one-dimensional harmonic potential, allowing the instantaneous position of the center-of-mass motion of the ions to be explicitly time-dependent. By solving the exact dynamics of the system, we show how survivability of the quantum entanglement is determined by a specific choice of the initial state settings.Comment: 13 pages, 4 figure

Development of a Health-Protective Drinking Water Level for Perchlorate

We evaluated animal and human toxicity data for perchlorate and identified reduction of thyroidal iodide uptake as the critical end point in the development of a health-protective drinking water level [also known as the public health goal (PHG)] for the chemical. This work was performed under the drinking water program of the Office of Environmental Health Hazard Assessment of the California Environmental Protection Agency. For dose–response characterization, we applied benchmark-dose modeling to human data and determined a point of departure (the 95% lower confidence limit for 5% inhibition of iodide uptake) of 0.0037 mg/kg/day. A PHG of 6 ppb was calculated by using an uncertainty factor of 10, a relative source contribution of 60%, and exposure assumptions specific to pregnant women. The California Department of Health Services will use the PHG, together with other considerations such as economic impact and engineering feasibility, to develop a California maximum contaminant level for perchlorate. We consider the PHG to be adequately protective of sensitive subpopulations, including pregnant women, their fetuses, infants, and people with hypothyroidism

Inducing safer oblique trees without costs

Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification. Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety. This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming

The Most Obscured AGNs in the XMM-SERVS Fields

We perform X-ray spectral analyses to derive the characteristics (e.g., column density, X-ray luminosity) of ≈10,200 active galactic nuclei (AGNs) in the XMM-Spitzer Extragalactic Representative Volume Survey, which was designed to investigate the growth of supermassive black holes across a wide dynamic range of cosmic environments. Using physical torus models (e.g., Borus02) and a Bayesian approach, we uncover 22 representative Compton-thick (CT; NH > 1.5 × 1024 cm−2) AGN candidates with good signal-to-noise ratios as well as a large sample of 136 heavily obscured AGNs. We also find an increasing CT fraction (fCT) from low (z 0.75) redshift. Our CT candidates tend to show hard X-ray spectral shapes and dust extinction in their spectral energy distribution fits, which may shed light on the connection between AGN obscuration and host-galaxy evolution

The Most Obscured AGNs in the XMM-SERVS Fields

We perform X-ray spectral analyses to derive characteristics (e.g., column density, X-ray luminosity) of $\approx$10,200 active galactic nuclei (AGNs) in the XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS), which was designed to investigate the growth of supermassive black holes across a wide dynamic range of cosmic environments. Using physical torus models (e.g., Borus02) and a Bayesian approach, we uncover 22 representative Compton-thick (CT; $N_{\rm H} \;>\; 1.5\times10^{24}\; \rm cm^{-2}$) AGN candidates with good signal-to-noise ratios as well as a large sample of 136 heavily obscured AGNs. We also find an increasing CT fraction (\fct ) from low ($z<0.75$) to high ($z>0.75$) redshift. Our CT candidates tend to show hard X-ray spectral shapes and dust extinction in their SED fits, which may shed light on the connection between AGN obscuration and host-galaxy evolution.Comment: 12 pages, 9 figures, accepted for publication in Ap