Ageing effect of plasma-treated wool

Atmospheric pressure plasma treatment of wool fabric, with a relatively short exposure time, effectively removed the covalently bonded lipid layer from the wool surface. The plasma-treated fabric showed increased wettability and the fibres showed greater roughness. X-ray photoelectron spectroscopy (XPS) analysis showed a much more hydrophilic surface with significant increases in oxygen and nitrogen concentrations and a decrease in carbon concentration. Adhesion, as measured by scanning probe microscopy (SPM) force volume analysis, also increased, consistent with the more hydrophilic surface leading to a greater meniscus force on the SPM probe. The ageing of fibres from the plasma-treated fabric was assessed over a period of 28 days. While no physical changes were observed, the chemical nature of the surface changed significantly. XPS showed a decrease in the hydrophilic nature of the surface with time, consistent with the measured decrease in wettability. This change is proposed to be due to the reorientation of proteolipid chains. SPM adhesion studies also showed the surface to be changing with time. After ageing for 28 days, the plasma-treated surface was relatively stable and still dramatically different from the untreated fibre, suggesting that the oxidation of the surface and modification or removal of the lipid layer were permanent.

Chimeric aptamers in cancer cell-targeted drug delivery

Aptamers are single-stranded structured oligonucleotides (DNA or RNA) that can bind to a wide range of targets ("apatopes") with high affinity and specificity. These nucleic acid ligands, generated from pools of random-sequence by an in vitro selection process referred to as systematic evolution of ligands by exponential enrichment (SELEX), have now been identified as excellent tools for chemical biology, therapeutic delivery, diagnosis, research, and monitoring therapy in real-time imaging. Today, aptamers represent an interesting class of modern pharmaceuticals which with their low immunogenic potential mimic extend many of the properties of monoclonal antibodies in diagnostics, research, and therapeutics. More recently, chimeric aptamer approach employing many different possible types of chimerization strategies has generated more stable and efficient chimeric aptamers with aptameraptamer, aptamernonaptamer biomacromolecules (siRNAs, proteins) and aptamernanoparticle chimeras. These chimeric aptamers when conjugated with various biomacromolecules like locked nucleic acid (LNA) to potentiate their stability, biodistribution, and targeting efficiency, have facilitated the accurate targeting in preclinical trials. We developed LNA-aptamer (anti-nucleolin and EpCAM) complexes which were loaded in iron-saturated bovine lactofeerin (Fe-blf)-coated dopamine modified surface of superparamagnetic iron oxide (Fe3O4) nanoparticles (SPIONs). This complex was used to deliver the specific aptamers in tumor cells in a co-culture model of normal and cancer cells. This review focuses on the chimeric aptamers, currently in development that are likely to find future practical applications in concert with other therapeutic molecules and modalities.

An application of machine learning techniques for the classification of glaucomatous progression

This paper presents an application of machine learning to the problem of classifying patients with glaucoma into one of two classes:stable and progressive glaucoma. The novelty of the work is the use of new features for the data analysis combined with machine learning techniques to classify the medical data. The paper describes the new features and the results of using decision trees to separate stable and progressive cases. Furthermore, we show the results of using an incremental learning algorithm for tracking stable and progressive cases over time. In both cases we used a dataset of progressive and stable glaucoma patients obtained from a glaucoma clinic.

An adaptive fuzzy min-max conflict-resolving classifier

This paper describes a novel adaptive network, which agglomerates a procedure based on the fuzzy min-max clustering method, a supervised ART (Adaptive Resonance Theory) neural network, and a constructive conflict-resolving algorithm, for pattern classification. The proposed classifier is a fusion of the ordering algorithm, Fuzzy ARTMAP (FAM) and the Dynamic Decay Adjustment (DDA) algorithm. The network, called Ordered FAMDDA, inherits the benefits of the trio, viz . an ability to identify a fixed order of training pattern presentation for good generalisation; stable and incrementally learning architecture; and dynamic width adjustment of the weights of hidden nodes of conflicting classes. Classification performance of the Ordered FAMDDA is assessed using two benchmark datasets. The performances are analysed and compared with those from FAM and Ordered FAM. The results indicate that the Ordered FAMDDA classifier performs at least as good as the mentioned networks. The proposed Ordered FAMDDA network is then applied to a condition monitoring problem in a power generation station. The process under scrutiny is the Circulating Water (CW) system, with prime attention to condition monitoring of the heat transfer efficiency of the condensers. The results and their implications are analysed and discussed.

Thermo-responsive PNIPAM nanofibres crosslinked by OpePOSS

Stable and re-usable thermo-responsive hydrogel nanofibres were roduced by electrospinning poly(Nisopropylacrylamide) (PNIPAM) in presence of a polyhedral oligomeric silsesquioxane (POSS) possessing eight epoxide groups, and of a 2-ethyl-4-methylimidazole (EMI) as a catalyst, followed by a heat curing treatment. The roles of the organic-base catalyst in the formation of crosslinked polymer network, fibre morphologies, and hydrogel properties were examined in this paper.

Lessons learned practices in the UAE construction industry

The failure of learning from our mistakes or those of others, has generated unnecessary waste of time and costs, in the construction industry, due to its project based, fragmented and unstable nature. Lessons Learned, as an important way of improving projects performance, is analyzed in this study, with the aim to explore the current practice of Lessons Learned in the UAE construction industry. A literature review has revealed what “Lessons Learned” is under different contexts, and focused on various factors influencing a Lessons Learned Programme. The research method of a series of structured interviews, followed by an on line questionnaire, is adopted in this study. It was found that although the concept of Lessons Learned is quite familiar by most of professionals in the project management in the UAE construction industry, Lessons Learned practice is mainly performed in an informal way (individually or ad hoc). As for barriers for Lessons Learned practice, Culture factors, such as “Afraid to be blamed for mistakes” and “lack of learning culture” (1st and 2nd rank) influence significantly in Lessons Learned practice. It is also found that a formal lessons learned programme does exist in some organizations. However, with the lack of a dedicated Lessons Learned repository and Lessons Learnt system, Lessons Learned has yet a long way to reach its potential.

IPA: Integrated predictive gene signature from gene expression based breast cancer patient samples

Background: Novel predictive markers are needed to accurately diagnose the breast cancer patients so they do not need to undergo any unnecessary aggressive therapies. Various gene expression studies based predictive gene signatureshave generated in the recent past to predict the binary estrogen-receptor subclass or to predict the therapy response subclass. However, the existing algorithms comes with many limitations, including low predictive performances over multiple cohorts of patients and non-significant or limited biological roles associated with thepredictive gene signatures. Therefore, the aim of this study is to develop novel predictive markers with improved performances.Methods: We propose a novel prediction algorithm called IPA to construct a predictive gene signature for performing multiple prediction tasks of predicting estrogen-receptor based binary subclass and predicting chemotherapy response (neoadjuvantly) based binary subclass. The constructed gene signature with considering multiple classification techniques was used to evaluate the algorithm performance on multiple cohorts of breast cancer patients.Results: The evaluation on multiple validation cohorts demonstrated that proposed algorithm achieved stable and high performance to perform prediction tasks, with consideration given to any classification techniques. We show that the predictive gene signature of our proposed algorithm reflects the mechanisms underlying the estrogen-receptors or response to therapy with significant greater biological interpretations, compared with the other existing algorithm.

Oxygen-doped boron nitride nanosheets with excellent performance in hydrogen storage

Hydrogen is considered one of the best energy sources. However, the lack of effective, stable, and safe storage materials has severely prevented its practical application. Strong effort has been made to try new nanostructured materials as new storage materials. In this study, oxygen-doped boron nitride (BN) nanosheets with 2-6 atomic layers, synthesized by a facile sol-gel method, show a storage capacity of 5.7wt% under 5MPa at room temperature, which is the highest hydrogen storage ever reported for any BN materials. Importantly, 89% of the stored hydrogen can be released when the hydrogen pressure is reduced to ambient conditions. Furthermore, the BN nanosheets exhibit an excellent storage cycling stability due to the stable two-dimensional nanostructure. The first principles calculations reveal that the high hydrogen storage mainly origins from the oxygen-doping of the BN nanosheets with increased adsorption energies of H2 on BN by 20-80% over pure BN sheets at the different coverage. © 2014 Elsevier Ltd.

Aggregation of Pi-based forecast to enhance prediction accuracy

In contrast to point forecast, prediction interval-based neural network offers itself as an effective tool to quantify the uncertainty and disturbances that associated with process data. However, single best neural network (NN) does not always guarantee to predict better quality of forecast for different data sets or a whole range of data set. Literature reported that ensemble of NNs using forecast combination produces stable and consistence forecast than single best NN. In this work, a NNs ensemble procedure is introduced to construct better quality of Pis. Weighted averaging forecasts combination mechanism is employed to combine the Pi-based forecast. As the key contribution of this paper, a new Pi-based cost function is proposed to optimize the individual weights for NN in combination process. An optimization algorithm, named simulated annealing (SA) is used to minimize the PI-based cost function. Finally, the proposed method is examined in two different case studies and compared the results with the individual best NNs and available simple averaging Pis aggregating method. Simulation results demonstrated that the proposed method improved the quality of Pis than individual best NNs and simple averaging ensemble method.

Synthesis of LiFePO4@carbon nanotube core-shell nanowires with a high-energy efficient method for superior lithium ion battery cathodes

A high-energy efficient method is developed for the synthesis of LiFePO4@CNT core-shell nanowire structures. The method consists of two steps: liquid deposition approach to prepare FePO4@CNT core-shell nanowires and solvothermal lithiation to obtain the LiFePO4@CNT core-shell nanowires at a low temperature. The solution phase method can be easily scaled up for commercial application. The performance of the materials produced by this method is evaluated in Li ion batteries. The one-dimensional LiFePO4@CNT nanowires offer a stable and efficient backbone for electron transport. The LiFePO4@CNT core-shell nanowires exhibit a high capacity of 132.8 mAh g^-1 at a rate of 0.2C, as well as high rate capability (64.4 mAh g^-1 at 20C) for Li ion storage.

Lubricin: a versatile, biological anti-adhesive with properties comparable to polyethylene glycol

Lubricin is a glycoprotein found in articular joints which has been recognized as being an important biological boundary lubricant molecule. Besides providing lubrication, we demonstrate, using a quartz crystal microbalance, that lubricin also exhibits anti-adhesive properties and is highly effective at preventing the non-specific adsorption of representative globular proteins and constituents of blood plasma. This impressive anti-adhesive property, combined with lubricin's ability to readily self-assemble to form dense, highly stable telechelic polymer brush layers on virtually any substrates, and its innate biocompatibility, makes it an attractive candidate for anti-adhesive and anti-fouling coatings. We show that coatings of lubricin protein are as effective as, or better than, self-assembled monolayers of polyethylene glycol over a wide range of pH and that this provides a simple, versatile, highly stable, and highly effective method of controlling unwanted adhesion to surfaces.

Hidden Markov models for cancer classification using gene expression profiles

This paper introduces an approach to cancer classification through gene expression profiles by designing supervised learning hidden Markov models (HMMs). Gene expression of each tumor type is modelled by an HMM, which maximizes the likelihood of the data. Prominent discriminant genes are selected by a novel method based on a modification of the analytic hierarchy process (AHP). Unlike conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The modified AHP aggregates ranking results of individual gene selection methods to form stable and robust gene subsets. Experimental results demonstrate the performance dominance of the HMM approach against six comparable classifiers. Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF. The modified AHP improves the classification performance not only of the HMM but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and HMM is a powerful tool for cancer classification and useful as a real clinical decision support system for medical practitioners.

The whiteness of David Bowie

During the 1980s, in particular, Bowie embodied particular notions of white masculinity that were on the one hand supportive of its idealized hegemony, and on the other subverted its normative power. I will take 1983 as the year when his whiteness is particularly visible and unstable. Bowie, as either the blonde dandy from Let’s Dance; the enigmatic character, Maj. Jack 'Strafer' Celliers from Merry Christmas Mr. Lawrence (1983); or the simmering vampire, John Blaylock from The Hunger (1983), crystlised the pure qualities of white masculinity while demonstrating its violent, queer and subversive nature. The chapter will suggest that Bowie has constantly operated along a white continuum, self-consciously embodying it, granting it carnal and ideological power, while drawing attention to its death-like instinct, its anti-reproductive progeny, its implicit queerness.I have chosen to read Bowie’s whiteness through this shortened window of temporality to enable me to draw into the analysis the historical and cultural issues of the period in question. 1983 registers as the year in which whiteness is acutely imagined to be under threat from the Asian tiger and transforming geo-political realities, its own languid anti-corporeality, the AIDS ‘epidemic’, and from the rise of racism in Europe and elsewhere - realities which require it to re-position its power relations with the sexual, and ethnic Other. The whiteness in/of David Bowie speaks particularly eloquently to this historical moment.

Rebound or resignation: developing a predictive model of return to subjective wellbeing set-point

Though subjective wellbeing (SWB) is generally stable and consistent over time, it can fall below its set-point in response to adverse life events. However, deviations from set-point levels are usually only temporary, as homeostatic processes operate to return SWB to its normal state. Given that income and close interpersonal relationships have been proposed as powerful external resources that are coincident with higher SWB, access to these resources may be an important predictor of whether or not a person is likely to recover their SWB following a departure from their set-point. Under the guiding framework of SWB Homeostasis Theory, this study considers whether access to a higher income and a committed partner can predict whether people who score lower than normal for SWB at baseline will return to normal set-point levels of SWB (rebound) or remain below the normal range (resigned) at follow-up. Participants were 733 people (53.3 % female) from the Australian Unity Longitudinal Wellbeing Study who ranged in age from 20 to 92 years (M = 59.65 years; SD = 13.15). Logistic regression analyses revealed that participants’ demographic characteristics were poor predictors of whether they rebounded or resigned. Consistent with homeostasis theory, the extent of departure from the proposed normal SWB set-point at baseline was significantly associated with rebound or resignation at time 2. These findings have implications for the way that SWB measures can be used in professional practice to identify people who are particularly vulnerable to depression and to guide the provision of appropriate and effective therapeutic interventions.

Molecular regulation of survivin targeted nano-therapy in advanced prostate cancer

 The increasing complexities of prostate cancer disease progression necessitates more stable and less toxic therapeutic strategies. The current study demonstrated for the first time, the survivin targeted anti-cancer therapeutic activity of the bio-molecular drugs such as SurR9-C84A and bovine lactoferrin in inducing prostate cancer specific apoptosis. Moreover, improved therapeutic efficacy was conferred to these bio-molecules either by their encapsulation in stem cell targeted bio-compatible nanoparticles, or by the synthesis of protein-cytotoxic drug conjugates. This study also highlighted the role played by miRNAs in the regulation of iron metabolism and apoptosis, mediated by the selective activation of p53 and PTEN pathways.