Hydrogen in plasma-nanofabrication : selective control of nanostructure heating and passivation

The possibility of independent control of the surface fluxes of energy and hydrogen-containing radicals, thus enabling selective control of the nanostructure heating and passivation, is demonstrated. In situ energy flux measurements reveal that even a small addition of H2 to low-pressure Ar plasmas leads to a dramatic increase in the energy deposition through H recombination on the surface. The heat release is quenched by a sequential addition of a hydrocarbon precursor while the surface passivation remains effective. Such selective control offers an effective mechanism for deterministic control of the growth shape, crystallinity, and density of nanostructures in plasma-aided nanofabrication. © 2010 American Institute of Physics.

Effect of input power and gas pressure on the roughening and selective etching of SiO2/Si surfaces in reactive plasmas

We report on the application low-temperature plasmas for roughening Si surfaces which is becoming increasingly important for a number of applications ranging from Si quantum dots to cell and protein attachment for devices such as "laboratory on a chip" and sensors. It is a requirement that Si surface roughening is scalable and is a single-step process. It is shown that the removal of naturally forming SiO2 can be used to assist in the roughening of the surface using a low-temperature plasma-based etching approach, similar to the commonly used in semiconductor micromanufacturing. It is demonstrated that the selectivity of SiO2 /Si etching can be easily controlled by tuning the plasma power, working gas pressure, and other discharge parameters. The achieved selectivity ranges from 0.4 to 25.2 thus providing an effective means for the control of surface roughness of Si during the oxide layer removal, which is required for many advance applications in bio- and nanotechnology.

Asymptotic minimax robust and misspecified Lorden quickest change detection for dependent stochastic processes

The quick detection of an abrupt unknown change in the conditional distribution of a dependent stochastic process has numerous applications. In this paper, we pose a minimax robust quickest change detection problem for cases where there is uncertainty about the post-change conditional distribution. Our minimax robust formulation is based on the popular Lorden criteria of optimal quickest change detection. Under a condition on the set of possible post-change distributions, we show that the widely known cumulative sum (CUSUM) rule is asymptotically minimax robust under our Lorden minimax robust formulation as a false alarm constraint becomes more strict. We also establish general asymptotic bounds on the detection delay of misspecified CUSUM rules (i.e. CUSUM rules that are designed with post- change distributions that differ from those of the observed sequence). We exploit these bounds to compare the delay performance of asymptotically minimax robust, asymptotically optimal, and other misspecified CUSUM rules. In simulation examples, we illustrate that asymptotically minimax robust CUSUM rules can provide better detection delay performance at greatly reduced computation effort compared to competing generalised likelihood ratio procedures.

Clinicopathological relevance of BRAF mutations in human cancer

BRAF represents one of the most frequently mutated protein kinase genes in human tumours. The mutation is commonly tested in pathology practice. BRAF mutation is seen in melanoma, papillary thyroid carcinoma (including papillary thyroid carcinoma arising from ovarian teratoma), ovarian serous tumours, colorectal carcinoma, gliomas, hepatobiliary carcinomas and hairy cell leukaemia. In these cancers, various genetic aberrations of the BRAF proto-oncogene, such as different point mutations and chromosomal rearrangements, have been reported. The most common mutation, BRAF V600E, can be detected by DNA sequencing and immunohistochemistry on formalin fixed, paraffin embedded tumour tissue. Detection of BRAF V600E mutation has the potential for clinical use as a diagnostic and prognostic marker. In addition, a great deal of research effort has been spent in strategies inhibiting its activity. Indeed, recent clinical trials involving BRAF selective inhibitors exhibited promising response rates in metastatic melanoma patients. Clinical trials are underway for other cancers. However, cutaneous side effects of treatment have been reported and therapeutic response to cancer is short-lived due to the emergence of several resistance mechanisms. In this review, we give an update on the clinical pathological relevance of BRAF mutation in cancer. It is hoped that the review will enhance the direction of future research and assist in more effective use of the knowledge of BRAF mutation in clinical practice.

Telomerase activity : detection and clinical implications in human cancer

Telomerase is an extremely important enzyme required for the immortalisation of tumour cells. Because the gene is activated in the vast majority of tumour tissues and remains unused in most somatic cells, it represents a marker with huge diagnostic, prognostic and treatment implications in cancer. This article summarises the basic structure and functions of telomerase and considers its clinical implications in colorectal and other cancers.

Detection of dynamic primary user with cooperative spectrum sensing

In this paper we demonstrate that existing cooperative spectrum sensing formulated for static primary users cannot accurately detect dynamic primary users regardless of the information fusion method. Performance error occurs as the sensing parameters calculated by the conventional detector result in sensing performance that violates the sensing requirements. Furthermore, the error is accumulated and compounded by the number of cooperating nodes. To address this limitation, we design and implement the duty cycle detection model for the context of cooperative spectrum sensing to accurately calculate the sensing parameters that satisfy the sensing requirements. We show that longer sensing duration is required to compensate for dynamic primary user traffic.

Modelling total duration of traffic incidents including incident detection and recovery time

Traffic incidents are key contributors to non-recurrent congestion, potentially generating significant delay. Factors that influence the duration of incidents are important to understand so that effective mitigation strategies can be implemented. To identify and quantify the effects of influential factors, a methodology for studying total incident duration based on historical data from an ‘integrated database’ is proposed. Incident duration models are developed using a selected freeway segment in the Southeast Queensland, Australia network. The models include incident detection and recovery time as components of incident duration. A hazard-based duration modelling approach is applied to model incident duration as a function of a variety of factors that influence traffic incident duration. Parametric accelerated failure time survival models are developed to capture heterogeneity as a function of explanatory variables, with both fixed and random parameters specifications. The analysis reveals that factors affecting incident duration include incident characteristics (severity, type, injury, medical requirements, etc.), infrastructure characteristics (roadway shoulder availability), time of day, and traffic characteristics. The results indicate that event type durations are uniquely different, thus requiring different responses to effectively clear them. Furthermore, the results highlight the presence of unobserved incident duration heterogeneity as captured by the random parameter models, suggesting that additional factors need to be considered in future modelling efforts.

Visible-light-induced selective photocatalytic oxidation of benzylamine into imine over supported Ag/AgI photocatalysts

Titanate nanotubes (TNT) supported AgI nanoparticles were prepared by a two-step method: the deposition of Ag2O on titanate nanotubes from AgNO3 solution and the subsequent I-adsorption process from NaI solution. It is found that the supported AgI samples exhibited excellent photoactivity for the selective oxidation of benzylamine to the corresponding imine under visible light illumination and the photocatalyst can be used for many times without apparent activity loss. X-ray diffraction studies, transmission electron microscopy, diffuse reflectance UV-Vis spectroscopy and nitrogen adsorption measurements were used for the characterization of the as-prepared and recycled AgI samples. It is found that under visible light irradiation, AgI partially decomposed to produce Ag/AgI nanostructure and thus stabilized. The photoactivity of supported Ag/AgI for the selective oxidation of benzylamine was studied in terms of the light intensity, wavelength, temperature and substituent. It is proposed that the formation of plasmonic Ag nanoparticles should be responsible for the high activity and selectivity.

Outbreak detection algorithms for seasonal disease data : a case study using Ross River virus disease

Background Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens. Methods We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms. Results The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs. Conclusions This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.

5-HT(2B) receptors are required for serotonin-selective antidepressant actions

The therapeutic effects induced by serotonin-selective reuptake inhibitor (SSRI) antidepressants are initially triggered by blocking the serotonin transporter and rely on long-term adaptations of pre- and post-synaptic receptors. We show here that long-term behavioral and neurogenic SSRI effects are abolished after either genetic or pharmacological inactivation of 5-HT(2B) receptors. Conversely, direct agonist stimulation of 5-HT(2B) receptors induces an SSRI-like response in behavioral and neurogenic assays. Moreover, the observation that (i) this receptor is expressed by raphe serotonergic neurons, (ii) the SSRI-induced increase in hippocampal extracellular serotonin concentration is strongly reduced in the absence of functional 5-HT(2B) receptors and (iii) a selective 5-HT(2B) agonist mimics SSRI responses, supports a positive regulation of serotonergic neurons by 5-HT(2B) receptors. The 5-HT(2B) receptor appears, therefore, to positively modulate serotonergic activity and to be required for the therapeutic actions of SSRIs. Consequently, the 5-HT(2B) receptor should be considered as a new tractable target in the combat against depression.

Topic dependent language modelling for spoken term detection

This paper investigates the effect of topic dependent language models (TDLM) on phonetic spoken term detection (STD) using dynamic match lattice spotting (DMLS). Phonetic STD consists of two steps: indexing and search. The accuracy of indexing audio segments into phone sequences using phone recognition methods directly affects the accuracy of the final STD system. If the topic of a document in known, recognizing the spoken words and indexing them to an intermediate representation is an easier task and consequently, detecting a search word in it will be more accurate and robust. In this paper, we propose the use of TDLMs in the indexing stage to improve the accuracy of STD in situations where the topic of the audio document is known in advance. It is shown that using TDLMs instead of the traditional general language model (GLM) improves STD performance according to figure of merit (FOM) criteria.

Unusual event detection in crowded scenes

Novel computer vision techniques have been developed to automatically detect unusual events in crowded scenes from video feeds of surveillance cameras. The research is useful in the design of the next generation intelligent video surveillance systems. Two major contributions are the construction of a novel machine learning model for multiple instance learning through compressive sensing, and the design of novel feature descriptors in the compressed video domain.

Detection of rain in acoustic recordings of the environment

Environmental monitoring has become increasingly important due to the significant impact of human activities and climate change on biodiversity. Environmental sound sources such as rain and insect vocalizations are a rich and underexploited source of information in environmental audio recordings. This paper is concerned with the classification of rain within acoustic sensor re-cordings. We present the novel application of a set of features for classifying environmental acoustics: acoustic entropy, the acoustic complexity index, spectral cover, and background noise. In order to improve the performance of the rain classification system we automatically classify segments of environmental recordings into the classes of heavy rain or non-rain. A decision tree classifier is experientially compared with other classifiers. The experimental results show that our system is effective in classifying segments of environmental audio recordings with an accuracy of 93% for the binary classification of heavy rain/non-rain.