Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. This is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality Mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data.

Analysis of residues of prometryne and acetochlor in soil-water system by solid-phase extraction and gas chromatography/mass spectrometry

A highly sensitive and simple analytical method was developed for analyzing the binary mixed pesticides of prometryne and acetochlor in soil–water system by gas chromatography/mass spectrometry (GC/MS). The sample solution was first purified by C18 solid-phase extraction column, which was leached by acetone. The leachate was enriched to 1.0 mL by pressure blowing concentrator and then analyzed by GC/MS. The linear calibration curves were showed in the range of 1–15 μg/mL with a correlation coefficient of 0.9991. The average recoveries (n = 5) were between 95.3 and 115.7%, with relative standard deviations ranged from 1.71 and 7.95%. The limits of detection of Prometryne/Acetochlor were up to 0.06 and 0.17 μg/mL, respectively. This method provides a reliable approach to examine and evaluate the residues of prometryne and acetochlor in the soil–water system.

Ammonia nitrogen removal from aqueous solution using functionalized zeolite columns

In this study, a functionalized zeolites column was developed to remove ammonia nitrogen with a low concentration (50 mg/L) from aqueous solution. The absorption properties and regeneration capacity were investigated. Through breakthrough and elution curve for dynamic adsorption, we found the wastewater with 50 mg/L ammonia nitrogen took 7 h to flow 10 g modified zeolites column with diameters of 24 to 64 meshes at a flow rate of 2 mL/min. The saturated extent of adsorption was up to 7.95 mg/g, and the saturated adsorption time was 22 h. The process of dynamic adsorption could be fitted by the Thomas Model. The regeneration ability was optimized by 0.1 M Na2CO3 as a regenerant. With excellent absorption ability for removing ammonia nitrogen with a low concentration, the functionalized zeolites could be potentially used a high-performance adsorbent for removing ammonia nitrogen.

Blind extraction using fractional lower-order statistics

In traditional method to blindly extract interesting source signals sequentially, the second-order or higher-order statistics of signals are often utilized. However, for impulsive sources, both of the second-order and higher-order statistics may degenerate. Therefore, it is necessary to exploit new method for the blind extraction of impulsive sources. Based on the best compression-reconstruction principle, a novel model is proposed in this work, together with the corresponding algorithm. The proposed method can be used for blind extraction of sources which are distributed from alpha stable process. Simulations are given to illustrate availability and robustness of our algorithm.

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.