Optimization strategy for encapsulation efficiency and size of drug loaded silica xerogel/polymer core‐shell composite nanoparticles prepared by gelation‐emulsion method

It has been commonly discovered that reducing particle size always accompanies with undesirable deterioration of drug encapsulation efficiency in double emulsion based techniques. However, a clear optimization strategy for process variables to minimize this negative impact has been rarely reported. To fill this gap, we have successfully developed an optimization strategy for silica xerogel/polymer composite nanoparticles prepared by our recently developed gelation‐emulsion method. To develop this strategy, interactive effects of multiple process variables were investigated through a four‐factor three‐level experimental design by considering all screened dominant process variables influencing particle size and encapsulation efficiency, including sonication time of second emulsion (t₂), sonication power of the second emulsion (P₂), total volume of the second emulsion (V₂) and volume ratio of aqueous phase and primary emulsion (r). The optimization strategy for fabricating the target particle size with optimal encapsulation efficiency was designed by adjusting the process variables in the order of r, V₂, t₂, and P₂ With this strategy, conspicuous enhancement of the encapsulation efficiency (e.g., from 27 to 63% for a particle size of 211 nm) and significant increment of the feasible size range through our novel fabrication method from 192–569 nm to 90–914 nm have been achieved in this study. POLYM. ENG. SCI., 2017. © 2017 Society of Plastics Engineers     

Autonomy-oriented computing (AOC): formulating computational systems with autonomous components

Autonomous multientity systems are plentiful in natural and artificial worlds. Many systems have been studied in depth and some models of them have been built as computational systems for problem solving. Central to these computational systems is the notion of autonomy. This article surveys research work done along this direction and presents autonomy-oriented computing (AOC) as a paradigm to describe systems for solving hard computational problems and for characterizing the behaviors of a complex system. AOC differs from major complex-system-related studies such as artificial life, simulated evolution, and multiagent systems in that AOC is not just intended to replicate complex behavior, emulate evolution, or coordinate the functioning of many interacting agents. AOC emphasizes the modeling of autonomy in the entities of a complex system and the self-organization of them in achieving a specific goal. Through implemented applications, we describe three main approaches to AOC, as well as an AOC framework with formal definitions of essential constructs and their interrelationships, including the notions of emergent autonomy, self-organization, and the interactions among entities and environment.     

On the order reduction of linear function observers

This note analyzes a new algorithm presented in [1] for designing a linear function observer with a minimum number of arbitrary poles. It shows that the maximum order of the observer of [1] isnu + ... + nu_{p} - pforp leq qandn - qforp geq q, instead ofp(nu_{1} - 1)as suggested in [1], wherenu{i}, i = 1top, are the descending ordered observability indexes of system (A, C) andn,p, andqare the order of the system, the number of the functions, and the number of the system outputs, respectively. This note also shows the significance of this result. For presentational purposes, only a special case of [1] is considered here. However, the technical properties as proved in this note are general.     

A two-stage empirical Bayes method for identifying differentially expressed genes

Due to the high dimensionality of microarray gene expression data and complicated correlations in gene expression levels, statistical methods for analyzing these data often are computationally intensive, requiring special expertise for their implementation. Biologists without such expertise will benefit from a computationally efficient and easy-to-implement analytic method. In this article, we develop such a method: a two-stage empirical Bayes method for identifying differentially expressed genes. We use a special technique to reduce the dimension of the parameter space in the preliminary stage, and construct a Bayesian model in the second stage. The computation of our method is efficient and requires little calibration for real microarray gene expression data. Specifically, we employ statistical tools, including the empirical Bayes estimation and a distribution approximation approach, to speed up computation and at the same time to preserve precision. We develop a score for evaluating the magnitude of the overall differential gene expression levels based on our Bayesian model, and declare differential expression according to the posterior probabilities that their scores exceed some threshold value. The number of declarations is determined by a false discovery rate procedure.     

Volatility dynamics of the US business cycle: A multivariate asymmetric GARCH approach

Most empirical investigations of the business cycles in the United States have excluded the dimension of asymmetric conditional volatility. This paper analyses the volatility dynamics of the US business cycle by comparing the performance of various multivariate generalised autoregressive conditional heteroskedasticity (GARCH) models. In particular, we propose two bivariate GARCH models to examine the evidence of volatility asymmetry and time-varying correlations concurrently, and then apply the proposed models to five sectors of Industrial Production of the United States. Our findings provide strong evidence of asymmetric conditional volatility in all sectors, and some support of time-varying correlations in various sectoral pairs. This has important policy implications for government to consider the effective countercyclical measures during recessions.     

Geometry of single axis motions using conic fitting

Previous algorithms for recovering 3D geometry from an uncalibrated image sequence of a single axis motion of unknown rotation angles are mainly based on the computation of two-view fundamental matrices and three-view trifocal tensors. We propose three new methods that are based on fitting a conic locus to corresponding image points over multiple views. The main advantage is that determining only five parameters of a conic from one corresponding point over at least five views is simpler and more robust than determining a fundamental matrix from two views or a trifocal tensor from three views. It is shown that the geometry of single axis motion can be recovered either by computing one conic locus and one fundamental matrix or by computing at least two conic loci. A maximum likelihood solution based on this parametrization of the single axis motion is also described for optimal estimation using three or more loci. The experiments on real image sequences demonstrate the simplicity, accuracy, and robustness of the new methods.     

A complete analytical solution to the equationTA - FT = LCand its applications

In this note, a complete, analytical, and restriction-free solution with complete and explicit freedom of the matrix equationTA - FT = LCis proposed. Here(A, C)is given and is observable, andFis in the Jordan form with arbitrary given eigenvalues. This solution appears to be new because it can be applied directly to obtain significantly better solutions to the following three basic design problems: 1) 2-D system eigenvalue assignment; 2) function observer design; and 3) state feedback eigenstructure design, as shown in this note.     

Comparison of 3D OS-EM and 4D MAP-RBI-EM reconstruction algorithms for cardiac motion abnormality classification using a motion observer

Using a heart motion observer, we compared the performance of two image reconstruction techniques, a 3D OS-EM algorithm with post Butterworth spatial filtering and a 4D MAP-RBI-EM algorithm. The task was to classify gated myocardial perfusion (GMP) SPECT images of beating hearts with or without regional motion abnormalities. Noise-free simulated GMP SPECT projection data was generated from two 4D NCAT beating heart phantom models, one with normal motion and the other with a 50% motion defect in a pie-shaped wedge region-of-interest (ROI) in the anterior-lateral left ventricular wall. The projection data were scaled to clinical GMP SPECT count level before Poisson noise was simulated to generate 40 noise realizations. The noise-free and noisy projection data were reconstructed using the two reconstruction algorithms, parameters chosen to optimize the tradeoff between image bias and noise. As a motion observer, a 3D motion estimation method previously developed was applied to estimate the radial motion on the ROI from two adjacent gates. The receiver operating characteristic (ROC) curves were computed for radial motion magnitudes corresponding to each reconstruction technique. The area under the ROC curve (AUC) was calculated as an index for classification of regional motion. The reconstructed images with better bias and noise tradeoff were found to offer better classification for hearts with or without regional motion defects. The 3D cardiac motion estimation algorithm, serving as a heart motion observer, was better able to distinguish the abnormal from the normal regional motion in GMP SPECT images obtained from the 4D MAP-RBI-EM algorithm than from the 3D OS-EM algorithm with post Butterworth spatial filtering.     

Gravity is an important but secondary determinant of regional pulmonary blood flow in upright primates

Original studies leading to the gravitational model of pulmonary blood flow and contemporary studies showing gravity-independent perfusion differ in the recent use of laboratory animals instead of humans. We explored the distribution of pulmonary blood flow in baboons because their anatomy, serial distribution of vascular resistances, and hemodynamic responses to hypoxia are similar to those of humans. Four baboons were anesthetized with ketamine, intubated, and mechanically ventilated. Different colors of fluorescent microspheres were given intravenously while the animals were in the supine, prone, upright (repeated), and head-down (repeated) postures. The animals were killed, and their lungs were excised, dried, and diced into approximately 2-cm3 pieces with the spatial coordinates recorded for each piece. Regional blood flow was determined for each posture from the fluorescent signals of each piece. Perfusion heterogeneity was greatest in the upright posture and least when prone. Using multiple-stepwise regression, we estimate that 7, 5, and 25% of perfusion heterogeneity is due to gravity in the supine, prone, and upright postures, respectively. Although important, gravity is not the predominant determinant of pulmonary perfusion heterogeneity in upright primates. Because of anatomic similarities, the same may be true for humans.