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In this study, a hybrid bipolar arc plasma stimulation (BAPS) and dual pulse electroporation (DPEP) technique was used to form microchannels, and the effect of highly efficient transformation drug delivery (TDD) on the skin was analyzed. The BAPS technique was applied to pig skin to form a microchannel as a biological sample to measure and observe the plasma as a micropore. During the BAPS, an output voltage of 1 kVpp at 40 kHz was applied at duty rates of 20%, 40%, and 60%. The BAPS technique was applied to the keratin skin layer, and microchannels with an average size of $30\,\,\mu \text{m}\,\,\times 30\,\,\mu \text{m}$ and up to $77\,\,\mu \text{m}\,\,\times 55\,\,\mu \text{m}$ were formed only in the keratin layer without any damage to the dermal layer. Next, DPEP was stimulated to increase the permeability of the endothelial membrane in the structure with the microchannels. The proposed hybrid technique effectively introduces TDD deeper into the employed 20 female subjects skin test with average age of 44 years, and the results in the absorption rate increase about 418%.

Recently, active matrix organic light-emitting diode (AMOLED) displays have widely been used for user-interface applications on smartphones. Since frequent interactions between a user and a display which come from such applications tend to incur big display power consumption, the display power efficiency should be managed exactly and fast. For this purpose, a high-accurate and fast power model for AMOLED displays is greatly required for efficient power management. In this paper, we propose a novel high-accurate and fast power model for AMOLED displays, which is developed using a multiple regression technique to account for the dependency between R, G, and B channels and is polished using various performance optimization techniques. Then, the proposed model is applied to five AMOLED display panels of a CHIMEI panel and Galaxy S1, 3, 5, and 7 panels from SAMSUMG. The average error rates are obtained on these panels for four well-known image datasets. The proposed model shows the lowest error rate of 1.29% on Galaxy S7, while the simple model and Park’s model show the error rates of 6.91% and 6.57%, respectively. In addition, in order to guarantee the fast execution time of the proposed power model on smartphones, we implement performance optimizations to the model. For runtime application, the proposed power model is implemented at the operating system level of the Android smartphone equipped with Exynos 4412. The optimized power model shows an average execution time of approximately 7.77 milli-seconds per image displayed on the panel with 720x1280 resolution. Consequently, the proposed power model is shown to be very fast and accurate, and thus it is highly useful in smartphones with AMOLED displays.

In this paper, we describe applying techniques for accelerating neural network algorithms on mobile embedded systems. MNIST handwriting artificial neural networks that identify numerical images are accelerated and compared using OpenCL, OpenMP, NEON, and other performance optimization techniques.

With recent advances in high throughput sequencing platforms, it is possible to sequence RNA obtained from biological samples more cost-effectively and comprehensively. Due to the ubiquity of the technology, massive volumes of RNA sequence data are now being generated, and as a result the need for more efficient analysis software has become an urgent challenge.

Quantile normalization (QN) is a technique for microarray data processing and is the default normalization method in the Robust Multi-array Average (RMA) procedure, which was primarily designed for analysing gene expression data from Affymetrix arrays. Given the abundance of Affymetrix microarrays and the popularity of the RMA method, it is crucially important that the normalization procedure is applied appropriately. In this study we carried out simulation experiments and also analysed real microarray data to investigate the suitability of RMA when it is applied to dataset with different groups of biological samples. From our experiments, we showed that RMA with QN does not preserve the biological signal included in each group, but rather it would mix the signals between the groups. We also showed that the Median Polish method in the summarization step of RMA has similar mixing effect. RMA is one of the most widely used methods in microarray data processing and has been applied to a vast volume of data in biomedical research. The problematic behaviour of this method suggests that previous studies employing RMA could have been misadvised or adversely affected. Therefore we think it is crucially important that the research community recognizes the issue and starts to address it. The two core elements of the RMA method, quantile normalization and Median Polish, both have the undesirable effects of mixing biological signals between different sample groups, which can be detrimental to drawing valid biological conclusions and to any subsequent analyses. Based on the evidence presented here and that in the literature, we recommend exercising caution when using RMA as a method of processing microarray gene expression data, particularly in situations where there are likely to be unknown subgroups of samples.

One of the major challenges in systems biology is to understand the complex responses of a biological system to external perturbations or internal signalling depending on its biological conditions. Genome-wide transcriptomic profiling of cellular systems under various chemical perturbations allows the manifestation of certain features of the chemicals through their transcriptomic expression profiles. The insights obtained may help to establish the connections between human diseases, associated genes and therapeutic drugs. The main objective of this study was to systematically analyse cellular gene expression data under various drug treatments to elucidate drug-feature specific transcriptomic signatures. We first extracted drug-related information (drug features) from the collected textual description of DrugBank entries using text-mining techniques. A novel statistical method employing orthogonal least square learning was proposed to obtain drug-feature-specific signatures by integrating gene expression with DrugBank data. To obtain robust signatures from noisy input datasets, a stringent ensemble approach was applied with the combination of three techniques: resampling, leave-one-out cross validation, and aggregation. The validation experiments showed that the proposed method has the capacity of extracting biologically meaningful drug-feature-specific gene expression signatures. It was also shown that most of signature genes are connected with common hub genes by regulatory network analysis. The common hub genes were further shown to be related to general drug metabolism by Gene Ontology analysis. Each set of genes has relatively few interactions with other sets, indicating the modular nature of each signature and its drug-feature-specificity. Based on Gene Ontology analysis, we also found that each set of drug feature (DF)-specific genes were indeed enriched in biological processes related to the drug feature. The results of these experiments demonstrated the potential of the method for predicting certain features of new drugs using their transcriptomic profiles, providing a useful methodological framework and a valuable resource for drug development and characterization.

A novel packaging method for high power modules based on one-step sintering process using a proper jig and nano silver paste is described. First, using Ansoft Q3D Extractor, electromagnetic simulation is carried out to design the best chip array and DC bus connection, which giving the lowest stray inductance. A die-bonder is used for a precise die attachment, which provides high placement accuracy (< 25 µm). One step sintering process between Si chip to DBC and DBC to baseplate was established under a low temperature (< 260 °C) and low pressure (< 10 MPa). In addition, the relation of the porosity and pressure on the adhesion of sintered silver layers was investigated. Finally, thermal performance of the proposed package and cooling is then evaluated with both FEA (finite element analysis) simulation and experiments. The simulation and experimental results, which show the lowest value (

A PRAM includes 8 b embedded RISC to generate the optimized internal timing and voltage parameters to control the variations of the cell resistances. The PRAM blocks with small margin window of cell resistances are detected, analyzed and controlled by processor-based built-in self-optimizer (BISO). A 4 Mb test PRAM is fabricated in a 90 nm 3-metal diode-switch PRAM cell technology. Measured margin increases by up to 221%.

We present an efficient algorithm for reverse engineering gene regulatory networks from microarray datasets using linear system of ordinary differential equations to deal with issues of underdetermined and ill-conditioned datasets. Our method was evaluated in in silico experiments. It was shown that the method can be readily applied to reconstruct the sparse network structure for a linear system with relatively small number of measurements. The algorithm can be also used to reconstruct partial network structure with extremely small number of measurements. The method was successfully applied to predict networks and to interpret yeast cell cycle gene expression data.