Simulation evaluation of Gibbs prior distributions for use in maximum a posteriori SPECT reconstructions

The effects of several of Gibbs prior distributions in terms of noise characteristics, edge sharpness, and overall quantitative accuracy of the final estimates obtained from an iterative maximum a posteriori (MAP) procedure applied to data from a realistic chest phantom are demonstrated. The effects of the adjustable parameters built into the prior distribution on these properties are examined. It is found that these parameter values influence the noise and edge characteristics of the final estimate and can generate reconstructions closer to the actual solution than maximum likelihood (ML). In addition, it is found that the choice of the shape of the prior distribution affects the noise characteristics and edge sharpness in the final estimate.     

Glutathione S-Transferase M3 Is Associated with Glycolysis in Intrinsic Temozolomide-Resistant Glioblastoma Multiforme Cells

Glioblastoma multiforme (GBM) is a malignant primary brain tumor. The 5-year relative survival rate of patients with GBM remains <30% on average despite aggressive treatments, and secondary therapy fails in 90% of patients. In chemotherapeutic failure, detoxification proteins are crucial to the activity of chemotherapy drugs. Usually, glutathione S-transferase (GST) superfamily members act as detoxification enzymes by activating xenobiotic metabolites through conjugation with glutathione in healthy cells. However, some overexpressed GSTs not only increase GST activity but also trigger chemotherapy resistance and tumorigenesis-related signaling transductions. Whether GSTM3 is involved in GBM chemoresistance remains unclear. In the current study, we found that T98G, a GBM cell line with pre-existing temozolomide (TMZ) resistance, has high glycolysis and GSTM3 expression. GSTM3 knockdown in T98G decreased glycolysis ability through lactate dehydrogenase A activity reduction. Moreover, it increased TMZ toxicity and decreased invasion ability. Furthermore, we provide next-generation sequencing–based identification of significantly changed messenger RNAs of T98G cells with GSTM3 knockdown for further research. GSTM3 was downregulated in intrinsic TMZ-resistant T98G with a change in the expression levels of some essential glycolysis-related genes. Thus, GSTM3 was associated with glycolysis in chemotherapeutic resistance in T98G cells. Our findings provide new insight into the GSTM3 mechanism in recurring GBM.     

Crystallization behavior of polylactide matrix under the influence of nano‐magnetite

Iron oxide nanoparticle (Fe₃O₄) has gained remarkable research interest for various applications, from environmental to biological, because of its superparamagnetic properties and good biocompatibility. In this work, the nucleation effect of Fe₃O₄ in a polylactide (PLA) matrix under an influence of an alternating magnetic field was studied. The nanocomposite films that is, containing different concentrations of Fe₃O₄ (~8 nm) which were uniformly dispersed in the PLA matrix, were prepared via a solution casting method. The amplitude and frequency of the magnetic field impose great effects on the morphology and nucleation rate of PLA crystallization. Thermogravimetric analysis was used to study the thermal stability of Fe₃O₄/PLA and it showed that the thermal stability of Fe₃O₄/PLA was affected by the Fe₃O₄ content. Fourier transform infrared spectroscopy and X‐ray diffractometry indicated that Fe₃O₄ shows impeding effect to the crystallization of PLA. Based on the differential scanning calorimetry results, composite with 1% of Fe₃O₄ content could promote the crystallization of PLA but it would become an obstacle when 3% of Fe₃O₄ was added. The result of polarized optical microscopy also showed a good agreement that the incorporation of Fe₃O₄ could act as an effective nucleation regent to the composite film. POLYM. ENG. SCI., 59:608–615, 2019. © 2018 Society of Plastics Engineers     

Evaluating p97 Inhibitor Analogues for Potency against p97–p37 and p97–Npl4–Ufd1 Complexes

We previously found that the p97 cofactor, p47, significantly decreased the potency of some ATP‐competitive p97 inhibitors such as ML240 [2‐(2‐amino‐1H‐benzo[d]imidazol‐1‐yl)‐N‐benzyl‐8‐methoxyquinazolin‐4‐amine] and ML241 [2‐(2H‐benzo[b][1,4]oxazin‐4(3H)‐yl)‐N‐benzyl‐5,6,7,8 tetrahydroquinazolin‐4‐amine]. In this study, we aimed to evaluate inhibitor potencies against two additional p97 cofactor complexes, p97–p37 and p97–Npl4–Ufd1. We focused on these two cofactor complexes, because the protein sequence of p37 is 50 % identical to that of p47, and the Npl4–Ufd1 heterodimer (NU) is the most‐studied p97 cofactor complex. We screened 200 p97 inhibitor analogues for their ability to inhibit the ATPase activity of p97 alone and of p97–p37 and p97–NU complexes. In contrast to the effect of p47, p37 and NU did not significantly change the potencies of most of the compounds. These results highlight differences among p97 cofactors in influencing p97 conformation and effects of inhibitors on p97 complexes, as compared to p97 alone. Continued efforts are needed to advance the development of complex‐specific p97 inhibitors.     

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.