Client–server architecture for collaborative remote experimentation

Experience in teaching engineering related subjects has shown that a complementary approach combining theoretical and practical exercises is vital for effective learning. Increasingly, teaching institutions are offering remote access to distant laboratories as part of an overall e-learning strategy. However, the majority of remote access laboratories developed to date have suffered from a major deficiency, namely the provision of a web-based environment that accurately recreates the group working and tutor driven experience of traditional on-campus based laboratories. This paper addresses this issue and presents a client–server architecture, based on Web services and .NET remoting services, for an integrated learning environment for remote experimentation that allows students in disparate locations to simultaneously and collaboratively complete complex experimental exercises.     

Effect of project role,age and gender differences on the formation and revision of project decision judgements

In this study, the authors examine how demographic variables such as project role, age and gender moderate the formation and revision of explicit decision judgements in a project by practitioners over the lifecycle of that project. Understanding the impact of these moderating factors on decisions made by project management practitioners can contribute to more effective managerial decision making; for example when decisions are being taken on whether or not to abandon a project. Empirical data are obtained from a quantitative survey of 1313 project management practitioners across seven countries. Data analysis is undertaken using log-linear modelling in SAS9.2. The results show that while project role and age of practitioners served as influencing factors when forming or revising decision judgements at any stage in the lifecycle, gender was not found to show any significant effect.¹     

Sensitive assay for mycoplasma detection in mammalian cell culture

Mycoplasma contamination in mammalian cell cultures is often overlooked yet is a serious issue which can induce a myriad of cellular changes leading to false interpretation of experimental results. Here, we present a simple and sensitive assay to monitor mycoplasma contamination (mycosensor) based on degradation of the Gaussia luciferase reporter in the conditioned medium of cells. This assay proved to be more sensitive as compared to a commercially available bioluminescent assay in detecting mycoplasma contamination in seven different cell lines. The Gaussia luciferase mycosensor assay provides an easy tool to monitor mammalian cell contaminants in a high-throughput fashion.     

Interaction of beta-amyloid interactions with peptide functionalized gold nanoparticles

The physicochemical properties of gold nanoparticles (GNPs) functionalized with peptides and N-methylated peptides were studied with respect to their interaction with beta-amyloid (1-42). Peptides with sequences of CGGIGLMVG and CGGGGGIGLMVG linked with GNPs of an average diameter of 13 nm were employed for this study. The peptide-GNPs were found to be soluble and dispersed at pH 7.4 in a sodium phosphate aqueous buffer solution. The resonance spectra of each peptide coated GNP was measured in the absence and presence of beta-amyloid (1-42). The difference in the intensity of the lambda(max) of the resonance absorption bands was attributed to the interaction of the functionalized GNPs with the protein. Particles bearing the CGGGGGIGLMVG sequence exhibited the largest change in lambda(max) intensity; the prevention of fibril formation and inhibition of cytotoxicity was also examined.     

All-optical sampling utilising two-photon absorption in semiconductor microcavity

A highly-efficient optical sampling system based on two-photon absorption in a semiconductor microcavity is presented. The sensitivity of the sampling system is calculated to be 0.1 mW/sup 2/ with a temporal resolution of 2 ps.     

Data Interoperability of Whole Exome Sequencing (WES) Based Mutational Burden Estimates from Different Laboratories

Immune checkpoint inhibitors, which unleash a patient’s own T cells to kill tumors, are revolutionizing cancer treatment. Several independent studies suggest that higher non-synonymous mutational burden assessed by whole exome sequencing (WES) in tumors is associated with improved objective response, durable clinical benefit, and progression-free survival in immune checkpoint inhibitors treatment. Next-generation sequencing (NGS) is a promising technology being used in the clinic to direct patient treatment. Cancer genome WES poses a unique challenge due to tumor heterogeneity and sequencing artifacts introduced by formalin-fixed, paraffin-embedded (FFPE) tissue. In order to evaluate the data interoperability of WES data from different sources to survey tumor mutational landscape, we compared WES data of several tumor/normal matched samples from five commercial vendors. A large data discrepancy was observed from vendors’ self-reported data. Independent data analysis from vendors’ raw NGS data shows that whole exome sequencing data from qualified vendors can be combined and analyzed uniformly to derive comparable quantitative estimates of tumor mutational burden.     

Chromatic Dispersion Monitoring of 80-Gb/s OTDM Data Signal via Two-Photon Absorption in a Semiconductor Microcavity

In this letter, a novel method of chromatic dispersion monitoring via two-photon absorption (TPA) is investigated. A specially designed semiconductor microcavity is employed as a TPA detector for monitoring data signals operating at rates up to 80Gb/s. As the microcavity has a wavelength-dependent response, a single device can be used to monitor multiple channels in a multiwavelength optical telecommunication system     

Heat dissipation in wearable computers aided by thermal coupling with the user

Wearable computers and PDA's are physically close to, or are in contact with, the user during most of the day. This proximity would seemingly limit the amount of heat such a device may generate, conflicting with user demands for increasing processor speeds and wireless capabilities. However, this paper explores significantly increasing the heat dissipation capability per unit surface area of a mobile computer by thermally coupling it to the user. In particular, a heat dissipation model of a forearmmounted wearable computer is developed, and the model is verified experimentally. In the process, this paper also provides tools and novel suggestions for heat dissipation that may influence the design of a wearable computer.     

Analysis of membrane microdomain-associated proteins in the insular cortex of post-mortem human brain

Membrane microdomains (MM) are membrane rafts within the cell membrane enriched in cholesterol and glycosphingolipids that have been implicated in the trafficking and sorting of membrane proteins, secretory and endocytotic pathways, and signal transduction. To date, MM have not been characterised in the human brain. We reason that by identifying MM in the normal human cortex, we may better understand the molecular mechanisms of human brain dysfunction. To characterize the protein composition of MM in the human brain, we have carried out a comprehensive proteomic analysis of detergent resistant membranes (DRMs) associated proteins derived from human postmortem insular cortex using 1-DE separation prior to LC coupled to MS/MS or GeLC-MS/MS. Eighty five proteins were identified including 57 unique to human brain cortex DRMs (by comparison with DRM proteins reported in other cell types). High levels of signal transduction, cell adhesion, cell transport and cell trafficking proteins were identified including synaptic proteins such as synapsin II and synaptic vesicle membrane protein, mitochondrial proteins such as ATPase subunits and metabolic enzymes such as malate dehydrogenase. This data will facilitate our understanding of protein expression changes within membranes in candidate brain regions in human brain diseases such as schizophrenia, bipolar disorder and other psychiatric and neurodegenerative disorders.     

Statistical and computational intelligence techniques for inferential model development: a comparative evaluation and a novel proposition for fusion

In industry today many products are sold for their efficacy rather than their chemical composition. Many variables (dependent variables), which characterize the quality of the final product in a manufacturing process, can be difficult to measure in real-time. Measurement difficulties can be due to a variety of reasons, including: (1) Reliability of on-line sensors, (2) Lack of appropriate on-line instrumentation. It is often the case that off-line laboratory tests are the only means of determining product quality measurements. However such laboratory analyses introduce delays in the measurement of key performance indicators. This can result in a significant economic loss if the analysed product fails the quality control test. In such situations an improved monitoring system is therefore required to determine product quality online and minimise commercial wastage. To facilitate this, advanced monitoring and control or optimisation techniques require inferred measurements, generated with correlations from readily available process variables (independent variables). Although inferential models are widely used in industry, only a few techniques for inferential model development are discussed in the open literature. This paper therefore will present a comparative evaluation study of the current inferential measurement techniques. An improved systematic approach for the development of inferential models using intelligent and soft computing systems is also highlighted. The proposed approach is designed to address some of the problems that currently exist in the area of inferential modelling through the fusion of statistical and computational intelligence models. A novel method of fusion is also proposed and an industrial case study is then presented to demonstrate the methodology by inferring the ‘Anchorage’ of polymeric-coated substrates (i.e. Tyvek or paper) in the coating industry. The application on which this methodology is demonstrated is unique. No such work in the literature to date has presented any inferential modelling strategies in the area of the coating industry. This strategy developed through the fusion of statistical and artificial modelling to generate a hybrid inferential measurement system has the potential to significantly improve the quality control monitoring system and reduce the economic loss encountered through the production of off-spec material.