Mini-scale method for nuclear run-on transcription assay in plants

We present a mini-scale method for nuclear run-on transcription assay. In our method, all the centrifuge steps can be carried out by using micro-tubes for short time (5 min each) throughout the process, including isolation of transcriptionally active nuclei and purification of labeled RNA after synthesis of RNA in isolated nuclei. The assay can be performed using a small amount of plant tissue, which enables analysis of developmental changes in transcriptional status of given genes in a single individual plant. Successful results were obtained using the tissues of flower and leaf of petunia and embryo of pea, suggesting that the method is potentially applicable to a variety of plant tissues.

DUTCH : teaching method-based design

All design classes followed a systematic design approach, that, in an abstract way, can be characterized by figure 1. This approach is based on our design approach [1] that we labeled DUTCH (design for users and tasks, from concepts to handles).Consequently, each course starts with collecting, modeling, and analyzing an existing situation. The next step is the development of a vision on a future domain world where new technology and / or new representations have been implemented. This second step is the first tentative global design that will be represented in scenarios or prototypes and can be assessed. This second design model is based on both the client’s requirements and technological possibilities and challenges. In an iterative way multiple instantiations of detail design may follow, that each can be assessed and evaluated again...

Protocol : A highly sensitive RT-PCR method for detection and quantification of microRNAs

MicroRNAs (miRNAs) are a class of small non-coding RNAs with a critical role in development and environmental responses. Efficient and reliable detection of miRNAs is an essential step towards understanding their roles in specific cells and tissues. However, gel-based assays currently used to detect miRNAs are very limited in terms of throughput, sensitivity and specificity. Here we provide protocols for detection and quantification of miRNAs by RT-PCR. We describe an end-point and real-time looped RT-PCR procedure and demonstrate detection of miRNAs from as little as 20 pg of plant tissue total RNA and from total RNA isolated from as little as 0.1 l of phloem sap. In addition, we have developed an alternative real-time PCR assay that can further improve specificity when detecting low abundant miRNAs. Using this assay, we have demonstrated that miRNAs are differentially expressed in the phloem sap and the surrounding vascular tissue. This method enables fast, sensitive and specific miRNA expression profiling and is suitable for facilitation of high-throughput detection and quantification of miRNA expression.

The ranking based constrained document clustering method and its application to social event detection

With the growing size and variety of social media files on the web, it’s becoming critical to efficiently organize them into clusters for further processing. This paper presents a novel scalable constrained document clustering method that harnesses the power of search engines capable of dealing with large text data. Instead of calculating distance between the documents and all of the clusters’ centroids, a neighborhood of best cluster candidates is chosen using a document ranking scheme. To make the method faster and less memory dependable, the in-memory and in-database processing are combined in a semi-incremental manner. This method has been extensively tested in the social event detection application. Empirical analysis shows that the proposed method is efficient both in computation and memory usage while producing notable accuracy.

Physical activity recognition from accelerometer data using a multi-scale ensemble method

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.

Pressure state response-based method for evaluating social benefits from smart grid development

In this paper, the inherent mechanism of social benefits associated with smart grid development is examined based on the pressure state response (PSR) model from resource economics. The emerging types of technology brought up by smart grid development are regarded as pressures. The improvements of the performance and efficiency of power system operation, such as the enhanced capability of accommodating renewable energy generation, are regarded as states. The effects of smart grid development on society are regarded as responses. Then, a novel method for evaluating social benefits from smart grid development is presented. Finally, the social benefits from smart grid development in a province in northwest China are carried out by using the developed evaluation system, and reasonable evaluation results are attained.

A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter

Interior permanent-magnet synchronous motors (IPMSMs) become attractive candidates in modern hybrid electric vehicles and industrial applications. Usually, to obtain good control performance, the electric drives of this kind of motor require one position, one dc link, and at least two current sensors. Failure of any of these sensors might lead to degraded system performance or even instability. As such, sensor fault resilient control becomes a very important issue in modern drive systems. This paper proposes a novel sensor fault detection and isolation algorithm based on an extended Kalman filter. It is robust to system random noise and efficient in real-time implementation. Moreover, the proposed algorithm is compact and can detect and isolate all the sensor faults for IPMSM drives. Thorough theoretical analysis is provided, and the effectiveness of the proposed approach is proven by extensive experimental results.

Transformation in educational practices through STEM

The need for strong science, technology and innovation linkages between Higher Education Institutions (HEIs) and industries is a pivotal point for middle-income countries in their endeavor to enhance human capital in socioeconomic development. Currently, the University-Industry partnerships are at an infant stage in Sri Lankan higher education context. Technological maturity and effective communication skills are contributing factors for an efficient graduate profile. Also, expanding internship programs in particular for STEM disciplines provide work experience to students that would strengthen the relevance of higher education programs. This study reports historical overviews and current trends in STEM education in Sri Lanka. Emphasis will be drawn to recent technological and higher education curricular reforms. Data from the last 10 years were extracted from the higher education sector and Ministry of Higher Education Policy portfolios. Associations and trend analysis of the sector growth were compared with STEM existence, merger and predicted augmentations. Results were depicted and summarised based on STEM streams and disciplines. It was observed that the trend of STEM augmentation in the Sri Lankan Higher Education context is growing at a slow but steady pace. Further analysis with other sectors in particular, Industry information, would be useful and a worthwhile exercise.

Reduced excision repair cross-complementing 1 expression associates with enhanced papilloma formation and fibroblast transformation after genetic disruption of secreted protein acidic and rich in cysteine

SPARC (secreted protein acidic and rich in cysteine)/ osteonectin/BM-40 is a matricellular protein implicated in development, cell transformation and tumorigenesis. We have examined the role of SPARC in cell transformation induced chemically with 7,12-dimethylbenz[a]anthracene (DMBA) and 12- tetradecanoylphorbol-13-acetate (TPA) in embryonic fibroblasts and in the skin of mice. Embryonic fibroblasts from SPARCnull mice showed increases in cell proliferation, enhanced sensitivity to DMBA and a higher number of DMBA/TPA-induced transformation foci. The number of DMBA-DNA adducts was 9 times higher in SPARCnull fibroblasts and their stability was lower than wild-type fibroblasts, consistent with a reduction in excision repair cross-complementing 1 the nucleotide excision repair enzyme in these cells. The SPARCnull mice showed an increase in both the speed and number of papillomas forming after topical administration of DMBA/TPA to the skin. These papillomas showed reduced growth and reduced progression to a more malignant phenotype, indicating that the effect of SPARC on tumorigenesis depends upon the transformation stage and/or tissue context. These data reinforce a growing number of observations in which SPARC has shown opposite effects on different tumor types/stages.

DDT resistance and transformation by different microbial strains isolated from DDT-contaminated soils and compost materials

Bioremediation is a potential option to treat 1, 1, 1-trichloro-2, 2 bis (4-chlorophenyl) ethane (DDT) contaminated sites. In areas where suitable microbes are not present, the use of DDT resistant microbial inoculants may be necessary. It is vital that such inoculants do not produce recalcitrant breakdown products e.g. 1, 1-dichloro-2, 2-bis (4-chlorophenyl) ethylene (DDE). Therefore, this work aimed to screen DDT-contaminated soil and compost materials for the presence of DDT-resistant microbes for use as potential inoculants. Four compost amended soils, contaminated with different concentrations of DDT, were used to isolate DDT-resistant microbes in media containing 150 mg I -1 DDT at three temperatures (25, 37 and 55°C). In all soils, bacteria were more sensitive to DDT than actinomycetes and fungi. Bacteria isolated at 55°C from any source were the most DDT sensitive. However DDT-resistant bacterial strains showed more promise in degrading DDT than isolated fungal strains, as 1, 1-dichloro 2, 2-bis (4-chlorophenyl) ethane (DDD) was a major bacterial transformation product, while fungi tended to produce more DDE. Further studies on selected bacterial isolates found that the most promising bacterial strain (Bacillus sp. BHD-4) could remove 51% of DDT from liquid culture after 7 days growth. Of the amount transformed, 6% was found as DDD and 3% as DDE suggesting that further transformation of DDT and its metabolites occurred.

A novel identification method for ultra trace detection of biomolecules using functionalised Surface Enhanced Raman Spectroscopy (SERS)

This thesis developed a new method for measuring extremely low amounts of organic and biological molecules, using Surface enhanced Raman Spectroscopy. This method has many potential applications, e.g. medical diagnosis, public health, food provenance, antidoping, forensics and homeland security. The method development used caffeine as the small molecule example, and erythropoietin (EPO) as the large molecule. This method is much more sensitive and specific than currently used methods; rapid, simple and cost effective. The method can be used to detect target molecules in beverages and biological fluids without the usual preparation steps.

Modelling airport passenger group dynamics using an agent-based method

This thesis investigates the influence of passenger group dynamics on passengers' behaviour in an international airport. A simulation model is built to analyse passengers' behaviour during airport departure processes and during an emergency event. Results from the model showed that passengers' group dynamics have significant influences on the performance and utilisation of airport services. The agent-based model also provides a convenient way to investigate the effectiveness of space design and service allocations, which may contribute to the enhancement of passenger airport experiences.

Ultra sensitive label free surface enhanced Raman spectroscopy method for the detection of biomolecules

We present a proof of concept for a novel nanosensor for the detection of ultra-trace amounts of bio-active molecules in complex matrices. The nanosensor is comprised of gold nanoparticles with an ultra-thin silica shell and antibody surface attachment, which allows for the immobilization and direct detection of bio-active molecules by surface enhanced Raman spectroscopy (SERS) without requiring a Raman label. The ultra-thin passive layer (~1.3 nm thickness) prevents competing molecules from binding non-selectively to the gold surface without compromising the signal enhancement. The antibodies attached on the surface of the nanoparticles selectively bind to the target molecule with high affinity. The interaction between the nanosensor and the target analyte result in conformational rearrangements of the antibody binding sites, leading to significant changes in the surface enhanced Raman spectra of the nanoparticles when compared to the spectra of the un-reacted nanoparticles. Nanosensors of this design targeting the bio-active compounds erythropoietin and caffeine were able to detect ultra-trace amounts the analyte to the lower quantification limits of 3.5×10−13 M and 1×10−9 M, respectively.

Two dynamic discrete choice estimation problems and simulation method solutions

This paper considers two problems that frequently arise in dynamic discrete choice problems but have not received much attention with regard to simulation methods. The first problem is how to simulate unbiased simulators of probabilities conditional on past history. The second is simulating a discrete transition probability model when the underlying dependent variable is really continuous. Both methods work well relative to reasonable alternatives in the application discussed. However, in both cases, for this application, simpler methods also provide reasonably good results.