Stimulation of 5-HT2A receptors on astrocytes in primary culture opens voltage-independent Ca2+ channels

A two-step exocytosis/endocytosis protocol was used in rat pancreatic acini to study membrane trafficking events at the apical plasma membrane (APM) as a function of extracellular pH. Exocytosis, as measured by cholecystokinin (CCK)-8-induced release of amylase into the incubation medium, was relatively insensitive to changes in extracellular pH from 5.5 to 9.0. In contrast, endocytosis, as measured by temperature-dependent uptake of horseradish peroxidase (HRP), was robust at pH values between 6.5 and 8.3 but abolished at acidic pH values of 5.5 to 6.0. Energy metabolism and cell viability were maintained during pH 6-induced cessation of HRP uptake, and the vesicular block could be reversed upon raising the luminal pH to 7.4. Histochemical and morphometric studies of HRP uptake examined by electron microscopy indicated that extracellular pH regulates endocytosis at the apical plasma membrane. At pH 6.0 in prestimulated cells, HRP uptake at the APM was abolished, and acinar lumen membranes remained markedly dilated with decreased density of microvilli and "arrested" exocytic images. At pH 7.4, HRP was taken up into endolysosomal structures within the Golgi complex, and acinar lumen membranes were contracted. Cleavage of GP2, a glycosyl phosphatidylinositol-anchored protein, was associated with the pH-dependent activation of HRP uptake. These studies demonstrate that acinar lumen pH regulates endocytic but not exocytic activity at the APM and suggest that alkalinization of the acinar lumen by duct cells is required for retrieval of exocytic membranes into the acinar cell via vesicular uptake mechanisms. The role of acid-base interactions within the acinar lumen provides a novel basis for understanding the cellular and luminal defects observed within the exocrine pancreas in cystic fibrosis.     

Measurements and simulations of differential phase-tracking signals in optical disk data storage

The tracking-error signal generated in differential phase detection (DPD) is theoretically analyzed and numerically simulated. Experimental measurements of the DPD signal versus the tracking offset obtained on compact read-only and phase-change disks are also reported. The signal is sensitive to the geometry of the marks, intersymbol interference along the track, and cross-track cross talk. A characteristic parameter is introduced to relate the DPD signal to the reflectivities of the mark and the spacer. For read-only disks such as CD-ROM and DVD-ROM, the magnitude of the DPD signal does not seem to depend on the reflectivity of the disks, nor does it depend on the pit depth. As for the influence of the various aberrations on the DPD signal, coma in the cross-track direction is shown to give rise to significant tracking offset, whereas defocus and spherical aberrations reduce the magnitude of the DPD signal appreciably.     

Generation of droplets with different concentrations using gradient-microfluidic droplet generator

This study successfully uses the micro-mixers and flow-focusing devices, which are integrated into a gradient-microfluidic droplet generator, to generate the different sizes of the droplets with different concentrations simultaneously and applies these microcapsules for drug release. The sizes of these four types of droplet with different concentrations are uniformity with a coefficient of variation less than 5% and can be precisely controlled by adjusting the water phase flow rate and oil phase flow rate. Moreover, Ca-alginate microcapsules with different concentrations of the bovine serum albumin are used for uniform size drug release, and the Ca-alginate microcapsule size is from 60 to 105 μm in diameter. This developed microfluidic chip has the advantages of actively controlling the droplet diameter, simultaneously generating uniform-sized droplets with different concentrations, and having a simple process and a high throughput. This preparation approach for Ca-alginate microcapsules of four different concentrations will provide many potential applications for drug delivery and pharmaceutical area.     

Building strength models for high-performance concrete at different ages using genetic operation trees, nonlinear regression, and neural networks

Because the behavior of HPC at early age may be rather different at late age, this study proposed to establish the strength models of HPC at different ages, and to explore the difference between these models. A large number of experimental data were used to compare accuracy of the three model building techniques, nonlinear regression analysis (NLRA), back-propagation networks (BPN), and genetic operation trees (GOT). The results showed: (1) when NLRA was employed to establish the prediction model, the approach to establish HPC strength models based on the three separate data sets was more accurate than that used to establish HPC strength models for the total data set. (2) If an explicit formula is necessary, GOT is the best choice to build concrete strength models at medium and late ages (i.e., more than 14 days), while NLRA provides greater accuracy at early ages (i.e., less than 14 days); otherwise, BPN is the best choice.     

Cascade of genetic algorithm and decision tree for cancer classification on gene expression data

Abstract: Cancer classification, through gene expression data analysis, has produced remarkable results, and has indicated that gene expression assays could significantly aid in the development of efficient cancer diagnosis and classification platforms. However, cancer classification, based on DNA array data, remains a difficult problem. The main challenge is the overwhelming number of genes relative to the number of training samples, which implies that there are a large number of irrelevant genes to be dealt with. Another challenge is from the presence of noise inherent in the data set. It makes accurate classification of data more difficult when the sample size is small. We apply genetic algorithms (GAs) with an initial solution provided by t statistics, called t‐GA, for selecting a group of relevant genes from cancer microarray data. The decision‐tree‐based cancer classifier is built on the basis of these selected genes. The performance of this approach is evaluated by comparing it to other gene selection methods using publicly available gene expression data sets. Experimental results indicate that t‐GA has the best performance among the different gene selection methods. The Z‐score figure also shows that some genes are consistently preferentially chosen by t‐GA in each data set.     

A multiple-kernel support vector regression approach for stock market price forecasting

Support vector regression has been applied to stock market forecasting problems. However, it is usually needed to tune manually the hyperparameters of the kernel functions. Multiple-kernel learning was developed to deal with this problem, by which the kernel matrix weights and Lagrange multipliers can be simultaneously derived through semidefinite programming. However, the amount of time and space required is very demanding. We develop a two-stage multiple-kernel learning algorithm by incorporating sequential minimal optimization and the gradient projection method. By this algorithm, advantages from different hyperparameter settings can be combined and overall system performance can be improved. Besides, the user need not specify the hyperparameter settings in advance, and trial-and-error for determining appropriate hyperparameter settings can then be avoided. Experimental results, obtained by running on datasets taken from Taiwan Capitalization Weighted Stock Index, show that our method performs better than other methods.     

A study of maintenance policies for second-hand products

This paper proposes two maintenance schemes for reused products. In order to reduce the purchasing cost or conform to the concept of environmental protection, reuse is considered as one of the most efficient strategies. However, the initial quality of reused product is often inferior to the new one and then product failures will occur frequently during usage period. Therefore, two periodical preventive maintenance (PM) policies in this paper are developed to decrease the high failure rate of the second-hand products. When a second-hand product with Weibull life time distribution of known age is intended to be used for a pre-specified period of time, the optimal number of PM actions and the corresponding maintenance degree are derived such that the expected total maintenance cost is minimized. The impact of providing preventive maintenance is illustrated through numerical examples.     

Genetic algorithm-based heuristics for an open shop scheduling problem with setup,processing, and removal times separated

This report proposes a solution to the open shop scheduling problem with the objective of minimizing total job tardiness in the system. Some practical processing restrictions, such as independent setup and dependent removal times, are taken into account as well. The addressed problem is first described as a 0–1 integer programming model, and is then solved optimally. Subsequently, some hybrid genetic-based heuristics are proposed to solve the problem in an acceptable computation time. To demonstrate the adaptability of these heuristics, some performance comparisons are made with solutions provided by running either a mathematical programming model or certain classic meta-heuristics such as genetic algorithm, simulated annealing, and tabu search in various manufacturing scenarios. The experimental results show that the hybrid genetic-based heuristics perform well, especially the DGA. However, these heuristics require some more additional computations but are still acceptable.     

Enhanced sample concentration on a three-dimensional origami paper-based analytical device with non-uniform assay channel

Traditional microfluidic paper-based analytical devices (μPADs) consist of a flat straight channel printed on a paper substrate. Such devices provide a promising low-cost solution for a variety of biomedical assays. However, they have a relatively high sample consumption due to their use of external reservoirs. Moreover, in μPADs based on the ion concentration polarization (ICP) effect, controlling the cross-sectional area of the Nafion membrane relative to that of the hydrophilic channel is difficult. Accordingly, the present study utilizes an origami technique to create a μPAD with a three-dimensional (3D) structure. The μPAD features short channels and embedded reservoirs, and therefore reduces both the driving voltage requirement and the sample consumption. Moreover, the preconcentration effect is enhanced through the use of an additional hydrophilic area adjacent to the Nafion membrane. The existence of electroosmotic flow (EOF) within the proposed device is confirmed using a current-monitoring method. In addition, the occurrence of ICP is evaluated by measuring the current–voltage response of the device at external voltages ranging from 0 to 50 V. The experimental results obtained for a fluorescein sample with an initial concentration of 10^?5 M show that a 100-fold enhancement factor can be achieved given the use of a non-uniform-geometry design for the assay channel and an additional hydrophilic region with an area equal to approximately 10% of the channel cross-sectional area. Finally, a 100-fold factor can also be achieved for a fluorescein isothiocyanate sample with an initial concentration of 10^?6 M given an external driving voltage of 40 V.