Integrated RNA extraction and RT-PCR for semi-quantitative gene expression studies on a microfluidic device

This paper describes the development of a microfluidic methodology, using RNA extraction and reverse transcription PCR, for investigating expression levels of cytochrome P450 genes. Cytochrome P450 enzymes are involved in the metabolism of xenobiotics, including many commonly prescribed drugs, therefore information on their expression is useful in both pharmaceutical and clinical settings. RNA extraction, from rat liver tissue or primary rat hepatocytes, was performed using a silica-based solid-phase extraction technique. Following elution of the purified RNA, amplification of target sequences for the housekeeping gene, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and the cytochrome P450 gene CYP1A2, was carried out using a one-step reverse transcription PCR. Once the microfluidic methodology had been optimized, analysis of control and 3-methylcholanthrene-induced primary rat hepatocytes were used to evaluate the system. As expected, GAPDH was consistently expressed, whereas CYP1A2 levels were found to be raised in the drug-treated samples. The proposed system offers an initial platform for development of both rapid throughput analyzers for pharmaceutical drug screening and point-of-care diagnostic tests to aid provision of drug regimens, which can be tailor-made to the individual patient.

Integrated DNA extraction and amplification using electrokinetic pumping in a microfluidic device

An integrated system employing anion exchange for the extraction of DNA from biological samples prior to polymerase chain reaction DNA amplification has been developed, based on microfluidic methodology utilising electrokinetic pumping. In this system, the biological samples were added directly to chitosan-coated silica beads to facilitate DNA immobilisation. The purified, pre-concentrated DNA was then eluted using a combination of electro-osmotic flow enhanced with electrophoretic mobility, which enable DNA to be transported by both mechanisms into the DNA amplification chamber. Through optimisation of the DNA elution conditions, average DNA extraction efficiencies of 69.1% were achievable. Subsequent DNA amplification performed on the microfluidic system demonstrated not only the ability to use electrokinetic movement to integrate the two processes on a single device, but also that the quality and quantity of DNA eluted was suitable for downstream analysis. This work offers an attractive real-world to chip interface and a route to simpler Lab-on-a-Chip technology which eliminates the need for moving parts.

Solvent extraction and purification of sugars from hemicellulose hydrolysates using boronic acid carriers

Research was performed to determine whether it was technically feasible to use boronic acid extractants to purify and concentrate the sugars present in hemicellulose hydrolysates. Initially, five types of boronic acids (phenylboronic acid, 3,5-dimethylphenylboronic acid, 4-tert-butylphenylboronic acid, trans-β-styreneboronic acid or naphthalene-2-boronic acid) dissolved in an organic diluent (Shellsol® 2046 or Exxal® 10) containing the quaternary amine Aliquat® 336 were tested for their ability to extract sugars (fructose, glucose, sucrose and xylose) from a buffered, immiscible aqueous solution. Naphthalene- 2-boronic acid was found to give the greatest extraction of xylose regardless of which diluent was used. Trials were then conducted to extract xylose and glucose from solutions derived from the dilute acid hydrolysis of sugar cane bagasse and to then strip the loaded organic solutions using an aqueous solution containing hydrochloric acid. This produced a strip solution in which the xylose concentration had been increased over 7× that of the original hydrolysate while reducing the concentration of the undesirable acid-soluble lignin by over 90%. Hence, this process can be exploited to produce high concentration xylose solutions suitable for direct fermentation.

Patient admission prediction using a pruned fuzzy min–max neural network with rule extraction

A useful patient admission prediction model that helps the emergency department of a hospital admit patients efficiently is of great importance. It not only improves the care quality provided by the emergency department but also reduces waiting time of patients. This paper proposes an automatic prediction method for patient admission based on a fuzzy min–max neural network (FMM) with rules extraction. The FMM neural network forms a set of hyperboxes by learning through data samples, and the learned knowledge is used for prediction. In addition to providing predictions, decision rules are extracted from the FMM hyperboxes to provide an explanation for each prediction. In order to simplify the structure of FMM and the decision rules, an optimization method that simultaneously maximizes prediction accuracy and minimizes the number of FMM hyperboxes is proposed. Specifically, a genetic algorithm is formulated to find the optimal configuration of the decision rules. The experimental results using a large data set consisting of 450740 real patient records reveal that the proposed method achieves comparable or even better prediction accuracy than state-of-the-art classifiers with the additional ability to extract a set of explanatory rules to justify its predictions.

Blind extraction of cyclostationary signal from convolutional mixtures

Extracting a signal of interest from available measurements is a challenging problem. One property which can be utilized to extract the signal is cyclostationarity, which exists in many signals. Various blind source separation methods based on cyclostationarity have been reported in the literature but they assume that the mixing system is instantaneous. In this paper, we propose a method for blind extraction of cyclostationary signal from convolutional mixtures. Given that the signal of interest has a unique cyclostationary frequency and the sensors are placed close to the concerned signal, we show that the signal of interest can be estimated from the measured data. Simulations results show the effectiveness of our method.

Optimization of zeaxanthin and β-carotene extraction from Chlorella saccharophila isolated from New Zealand marine waters

Zeaxanthin is an important natural pigment which has found commercial application in food and nutritional supplements. Its potential widespread use requires an easy and effective extraction methodology for obtaining higher yields. Extraction from Chlorella sp. under optimized conditions demonstrated a marked reduction in extraction time (13.48min) compared with a control experiment (30min). The extraction conditions such as solvent/cell dry weight (CDW) ratio, power, pulse, time and their combinations were optimized using response surface methodology (RSM). Almost all the variables were shown significantly (p-value <0.05) affect the carotenoid yield. Significant interaction (p-value <0.05) was observed with a substantial effect on zeaxanthin yield for solvent/CDW ratio and power, as well as power and time, whereas the β-carotene control exhibited significant interaction between solvent/CDW ratio and pulse, as well as between pulse and time. The R 2-value approached unity in both models, demonstrating their accuracy. Data obtained from these interactions were used to construct 3D response plots. Solvent/CDW ratio of 67.38μlmg-1, power 27.82% (total power 500W), pulse length of 19.7s and time 13.48min were found to be the optimized conditions for zeaxanthin (11.2mgg-1) and β-carotene (4.98mgg-1) extraction.

Motor imagery data classification for BCI application using wavelet packet feature extraction

The noninvasive brain imaging modalities have provided us an extraordinary means for monitoring the working brain. Among these modalities, Electroencephalography (EEG) is the most widely used technique for measuring the brain signals under different tasks, due to its mobility, low cost, and high temporal resolution. In this paper we investigate the use of EEG signals in brain-computer interface (BCI) systems.

Bayesian nonparametric extraction of hidden contexts from pervasive honest signals

Hidden patterns and contexts play an important part in intelligent pervasive systems. Most of the existing works have focused on simple forms of contexts derived directly from raw signals. High-level constructs and patterns have been largely neglected or remained under-explored in pervasive computing, mainly due to the growing complexity over time and the lack of efficient principal methods to extract them. Traditional parametric modeling approaches from machine learning find it difficult to discover new, unseen patterns and contexts arising from continuous growth of data streams due to its practice of training-then-prediction paradigm. In this work, we propose to apply Bayesian nonparametric models as a systematic and rigorous paradigm to continuously learn hidden patterns and contexts from raw social signals to provide basic building blocks for context-aware applications. Bayesian nonparametric models allow the model complexity to grow with data, fitting naturally to several problems encountered in pervasive computing. Under this framework, we use nonparametric prior distributions to model the data generative process, which helps towards learning the number of latent patterns automatically, adapting to changes in data and discovering never-seen-before patterns, contexts and activities. The proposed methods are agnostic to data types, however our work shall demonstrate to two types of signals: accelerometer activity data and Bluetooth proximal data. © 2014 IEEE.

Understanding response surface optimisation to the modeling of Astaxanthin extraction from a novel strain Thraustochytrium sp. S7

A fast growing, highly orange color pigmented strain of Thraustochytrids was isolated from New Zealand marine waters. This strain showed efficient utilization of glycerol as carbon source and produced significant amount of cell dry biomass (2.08gL-1), TFA (30.15% of dry cell weight), DHA (27.83% of TFA) and astaxanthin (131.56μgg-1). Astaxanthin is the dominant constituent in the carotenoid profile of Thraustochytrium sp. S7 and is an important antioxidant. Different cell disruption methods were applied for efficient astaxanthin extraction. Mechanical disruption of cells via ultrasonication resulted in the highest astaxanthin yield, from 26.78±1.25μgg-1 to 156.07±4.16μgg-1. Optimization of ultrasonication process using response surface methodology resulted into significant decrease in lysis time from 30min to 10min. This strain can be used for concurrent production of lipids and high value co-products such as DHA and astaxanthin.

Comparison of cell disruption methods for improving lipid extraction from thraustochytrid strains

Lipid extraction is an integral part of biodiesel production, as it facilitates the release of fatty acids from algal cells. To utilise thraustochytrids as a potential source for lipid production. We evaluated the extraction efficiency of various solvents and solvent combinations for lipid extraction from Schizochytrium sp. S31 and Thraustochytrium sp. AMCQS5-5. The maximum lipid extraction yield was 22% using a chloroform:methanol ratio of 2:1. We compared various cell disruption methods to improve lipid extraction yields, including grinding with liquid nitrogen, bead vortexing, osmotic shock, water bath, sonication and shake mill. The highest lipid extraction yields were obtained using osmotic shock and 48.7% from Schizochytrium sp. S31 and 29.1% from Thraustochytrium sp. AMCQS5-5. Saturated and monounsaturated fatty acid contents were more than 60% in Schizochytrium sp. S31 which suggests their suitability for biodiesel production.

Quantitative secondary electron imaging for work function extraction at atomic level and layer identification of graphene

Two-dimensional (2D) materials usually have a layer-dependent work function, which require fast and accurate detection for the evaluation of their device performance. A detection technique with high throughput and high spatial resolution has not yet been explored. Using a scanning electron microscope, we have developed and implemented a quantitative analytical technique which allows effective extraction of the work function of graphene. This technique uses the secondary electron contrast and has nanometre-resolved layer information. The measurement of few-layer graphene flakes shows the variation of work function between graphene layers with a precision of less than 10 meV. It is expected that this technique will prove extremely useful for researchers in a broad range of fields due to its revolutionary throughput and accuracy.

SECC: simultaneous extraction of context and community from pervasive signals

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain the human dynamics or behaviors and then use them as the way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows nested structure to be built to explain data at multiple levels. We demonstrate our framework on three public datasets where the advantages of the proposed approach are validated.