Epidemiological and molecular features of dengue virus type-1 in New Caledonia, South Pacific, 2001–2013

Background The epidemiology of dengue in the South Pacific has been characterized by transmission of a single dominant serotype for 3–5 years, with subsequent replacement by another serotype. From 2001 to 2008 only DENV-1 was reported in the Pacific. In 2008, DENV-4 emerged and quickly displaced DENV-1 in the Pacific, except in New Caledonia (NC) where DENV-1 and DENV-4 co-circulated in 2008–2009. During 2012–2013, another DENV-1 outbreak occurred in NC, the third DENV-1 outbreak in a decade. Given that dengue is a serotype-specific immunizing infection, the recurrent outbreaks of a single serotype within a 10-year period was unexpected. Findings This study aimed to inform this phenomenon by examining the phylogenetic characteristics of the DENV-1 viruses in NC and other Pacific islands between 2001 and 2013. As a result, we have demonstrated that NC experienced introductions of viruses from both the Pacific (genotype IV) and South-east Asia (genotype I). Moreover, whereas genotype IV and I were co-circulating at the beginning of 2012, we observed that from the second half of 2012, i.e. during the major DENV-1 outbreak, all analyzed viruses were genotype I suggesting that a genotype switch occurred. Conclusions Repeated outbreaks of the same dengue serotype, as observed in NC, is uncommon in the Pacific islands. Why the earlier DENV-1 outbreaks did not induce sufficient herd immunity is unclear, and likely multifactorial, but the robust vector control program may have played a role by limiting transmission and thus maintaining a large susceptible pool in the population. Keywords: Dengue; Phylogeny; Genotype; Epidemics; New Caledonia

Features-pooling blind jpeg image steganalysis

In this research, we introduce a new blind steganalysis in detecting grayscale JPEG images. Features-pooling method is employed to extract the steganalytic features and the classification is done by using neural network. Three different steganographic models are tested and classification results are compared to the five state-of-the-art blind steganalysis.

Making YAWL and SmartPM interoperate : managing highly dynamic processes by exploiting automatic adaptation features

In the last years, the trade-o between exibility and sup- port has become a leading issue in work ow technology. In this paper we show how an imperative modeling approach used to de ne stable and well-understood processes can be complemented by a modeling ap- proach that enables automatic process adaptation and exploits planning techniques to deal with environmental changes and exceptions that may occur during process execution. To this end, we designed and imple- mented a Custom Service that allows the Yawl execution environment to delegate the execution of subprocesses and activities to the SmartPM execution environment, which is able to automatically adapt a process to deal with emerging changes and exceptions. We demonstrate the fea- sibility and validity of the approach by showing the design and execution of an emergency management process de ned for train derailments.

Chasing the great Australian dream : definition of "an ideal home" among baby boomers

In Australia, the idea of home ownership or The Great Australian Dream is still perceived as the main achievement of every Australian’s life. Perception of an ideal home is changing over the decades. Each generation has special requirements criteria which foster their dwelling space. This research identifies and compares three generations’ (Baby Boomers, Generation X and Generation Y) demographics, special requirements and perceptions regarding their ideal home. The examination of previous research and literature into the Queensland context reveals that the Baby Boomers population of people 65 and older is currently 11.8% of the state population and is expected to grow to almost one quarter of the population by 2051. This is the highest growth rate among these three generations. Further analysis of these three generations’ status and requirements shows that aging is the most critical issue for the housing systems. This is especially the case for Baby Boomers due to their demand for support services and health care in the home. The study reveals that ‘ageing in place’, is a preferred option for the aged. This raises questions as to how well the housing system and neighbourhood environments are able to support ageing in place, and what aging factors should be taken into consideration when designing Baby boomer’s home to facilitate health and wellbeing. Therefore, this research designed a qualitative approach to investigate Australian Baby Boomers homes around Queensland, predominantly in the Brisbane area, using semi-structured interviews and observations. It aims to find out the level of satisfaction of Australian Baby Boomers with their current home and their preferences and requirements in light of their ideal home. The findings contribute new knowledge in the light of ideal home mechanisms. A set of strategies has been developed from the findings that may help improve the level of comfort, safety and satisfaction that Baby Boomers experience in their current and future homes.

Computation of ECG signal features using MCMC modelling in software and FPGA reconfigurable hardware

Computational optimisation of clinically important electrocardiogram signal features, within a single heart beat, using a Markov-chain Monte Carlo (MCMC) method is undertaken. A detailed, efficient data-driven software implementation of an MCMC algorithm has been shown. Initially software parallelisation is explored and has been shown that despite the large amount of model parameter inter-dependency that parallelisation is possible. Also, an initial reconfigurable hardware approach is explored for future applicability to real-time computation on a portable ECG device, under continuous extended use.

Plasma-enabled, catalyst-free growth of carbon nanotubes on mechanically-written Si features with arbitrary shape

Simple, rapid, catalyst-free synthesis of complex patterns of long, vertically aligned multiwalled carbon nanotubes, strictly confined within mechanically-written features on a Si(1 0 0) surface is reported. It is shown that dense arrays of the nanotubes can nucleate and fully fill the features when the low-temperature microwave plasma is in a direct contact with the surface. This eliminates additional nanofabrication steps and inevitable contact losses in applications associated with carbon nanotube patterns. Using metal catalyst has long been considered essential for the nucleation and growth of surface-supported carbon nanotubes (CNTs) [1] and [2]. Only very recently, the possibility of CNT growth using non-metallic (e.g., oxide [3] and SiC [4]) catalysts or artificially created carbon-enriched surface layers [5] has been demonstrated. However, successful integration of carbon nanostructures into Si-based nanodevice platforms requires catalyst-free growth, as the catalyst nanoparticles introduce contact losses, and their catalytic activity is very difficult to control during the growth [6]. Furthermore, in many applications in microfluidics, biological and molecular filters, electronic, sensor, and energy conversion nanodevices, the CNTs need to be arranged in specific complex patterns [7] and [8]. These patterns need to contain the basic features (e.g., lines and dots) written using simple procedures and fully filled with dense arrays of high-quality, straight, yet separated nanotubes. In this paper, we report on a completely metal or oxide catalyst-free plasma-based approach for the direct and rapid growth of dense arrays of long vertically-aligned multi-walled carbon nanotubes arranged into complex patterns made of various combinations of basic features on a Si(1 0 0) surface written using simple mechanical techniques. The process was conducted in a plasma environment [9] and [10] produced by a microwave discharge which typically generates the low-temperature plasmas at the discharge power below 1 kW [11]. Our process starts from mechanical writing (scribing) a pattern of arbitrary features on pre-treated Si(1 0 0) wafers. Before and after the mechanical feature writing, the Si(1 0 0) substrates were cleaned in an aqueous solution of hydrofluoric acid for 2 min to remove any possible contaminations (such as oil traces which could decompose to free carbon at elevated temperatures) from the substrate surface. A piece of another silicon wafer cleaned in the same way as the substrate, or a diamond scriber were used to produce the growth patterns by a simple arbitrary mechanical writing, i.e., by making linear scratches or dot punctures on the Si wafer surface. The results were the same in both cases, i.e., when scratching the surface by Si or a diamond scriber. The procedure for preparation of the substrates did not involve any possibility of external metallic contaminations on the substrate surface. After the preparation, the substrates were loaded into an ASTeX model 5200 chemical vapour deposition (CVD) reactor, which was very carefully conditioned to remove any residue contamination. The samples were heated to at least 800 °C to remove any oxide that could have formed during the sample loading [12]. After loading the substrates into the reactor chamber, N2 gas was supplied into the chamber at the pressure of 7 Torr to ignite and sustain the discharge at the total power of 200 W. Then, a mixture of CH4 and 60% of N2 gases were supplied at 20 Torr, and the discharge power was increased to 700 W (power density of approximately 1.49 W/cm3). During the process, the microwave plasma was in a direct contact with the substrate. During the plasma exposure, no external heating source was used, and the substrate temperature (∼850 °C) was maintained merely due to the plasma heating. The features were exposed to a microwave plasma for 3–5 min. A photograph of the reactor and the plasma discharge is shown in Fig. 1a and b.

GAEC1 and colorectal cancer : a study of the relationships between a novel oncogene and clinicopathologic features

GAEC1 is a novel gene located at 7q22.1 that was detected in our previous work in esophageal cancer. The aims of the present study are to identify the copy number of GAEC1 in different colorectal tissues including carcinomas, adenomas, and nonneoplastic tissues and characterize any links to pathologic factors. The copy number of GAEC1 was studied by evaluating the quantitative amplification of GAEC1 DNA in 259 colorectal tissues (144 adenocarcinomas, 31 adenomas, and 84 nonneoplastic tissues) using real-time polymerase chain reaction. Copy number of GAEC1 DNA in colorectal adenocarcinomas was higher in comparison with nonneoplastic colorectum. Seventy-nine percent of the colorectal adenocarcinomas showed amplification and 15% showed deletion of GAEC1 (P < .0001). Of the adenomas, 90% showed deletion of GAEC1, with the remaining 10% showing normal copy number. The differences in GAEC1 copy number between colorectal adenocarcinoma, colorectal adenoma, and nonneoplastic colorectal tissue are significant (P < .0001). GAEC1 copy number was significantly higher in adenocarcinomas located in distal colorectum compared with proximal colon (P = .03). In conclusion, GAEC1 copy number was significantly different between colorectal adenocarcinomas, adenomas, and nonneoplastic colorectal tissues. The copy number was also related to the site of the cancer. These findings along with previous work in esophageal cancer imply that GAEC1 is commonly involved in the pathogenesis of colorectal adenocarcinoma.

Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.

An evaluation of crowd counting methods, features and regression models

Existing crowd counting algorithms rely on holistic, local or histogram based features to capture crowd properties. Regression is then employed to estimate the crowd size. Insufficient testing across multiple datasets has made it difficult to compare and contrast different methodologies. This paper presents an evaluation across multiple datasets to compare holistic, local and histogram based methods, and to compare various image features and regression models. A K-fold cross validation protocol is followed to evaluate the performance across five public datasets: UCSD, PETS 2009, Fudan, Mall and Grand Central datasets. Image features are categorised into five types: size, shape, edges, keypoints and textures. The regression models evaluated are: Gaussian process regression (GPR), linear regression, K nearest neighbours (KNN) and neural networks (NN). The results demonstrate that local features outperform equivalent holistic and histogram based features; optimal performance is observed using all image features except for textures; and that GPR outperforms linear, KNN and NN regression

Scene signatures : localised and point-less features for localisation

This paper is about localising across extreme lighting and weather conditions. We depart from the traditional point-feature-based approach as matching under dramatic appearance changes is a brittle and hard thing. Point feature detectors are fixed and rigid procedures which pass over an image examining small, low-level structure such as corners or blobs. They apply the same criteria applied all images of all places. This paper takes a contrary view and asks what is possible if instead we learn a bespoke detector for every place. Our localisation task then turns into curating a large bank of spatially indexed detectors and we show that this yields vastly superior performance in terms of robustness in exchange for a reduced but tolerable metric precision. We present an unsupervised system that produces broad-region detectors for distinctive visual elements, called scene signatures, which can be associated across almost all appearance changes. We show, using 21km of data collected over a period of 3 months, that our system is capable of producing metric localisation estimates from night-to-day or summer-to-winter conditions.

Speaker verification using I-vector features

This PhD research has provided novel solutions to three major challenges which have prevented the wide spread deployment of speaker recognition technology: (1) combating enrolment/ verification mismatch, (2) reducing the large amount of development and training data that is required and (3) reducing the duration of speech required to verify a speaker. A range of applications of speaker recognition technology from forensics in criminal investigations to secure access in banking will benefit from the research outcomes.

Multiscale features and information extraction of online strain for long-span bridges

The strain data acquired from structural health monitoring (SHM) systems play an important role in the state monitoring and damage identification of bridges. Due to the environmental complexity of civil structures, a better understanding of the actual strain data will help filling the gap between theoretical/laboratorial results and practical application. In the study, the multi-scale features of strain response are first revealed after abundant investigations on the actual data from two typical long-span bridges. Results show that, strain types at the three typical temporal scales of 10^5, 10^2 and 10^0 sec are caused by temperature change, trains and heavy trucks, and have their respective cut-off frequency in the order of 10^-2, 10^-1 and 10^0 Hz. Multi-resolution analysis and wavelet shrinkage are applied for separating and extracting these strain types. During the above process, two methods for determining thresholds are introduced. The excellent ability of wavelet transform on simultaneously time-frequency analysis leads to an effective information extraction. After extraction, the strain data will be compressed at an attractive ratio. This research may contribute to a further understanding of actual strain data of long-span bridges; also, the proposed extracting methodology is applicable on actual SHM systems.

An MRF based abnormal event detection approach using motion and appearance features

Abnormal event detection has attracted a lot of attention in the computer vision research community during recent years due to the increased focus on automated surveillance systems to improve security in public places. Due to the scarcity of training data and the definition of an abnormality being dependent on context, abnormal event detection is generally formulated as a data-driven approach where activities are modeled in an unsupervised fashion during the training phase. In this work, we use a Gaussian mixture model (GMM) to cluster the activities during the training phase, and propose a Gaussian mixture model based Markov random field (GMM-MRF) to estimate the likelihood scores of new videos in the testing phase. Further-more, we propose two new features: optical acceleration, and the histogram of optical flow gradients; to detect the presence of any abnormal objects and speed violations in the scene. We show that our proposed method outperforms other state of the art abnormal event detection algorithms on publicly available UCSD dataset.

Evaluation of features for leaf classification in challenging conditions

Fine-grained leaf classification has concentrated on the use of traditional shape and statistical features to classify ideal images. In this paper we evaluate the effectiveness of traditional hand-crafted features and propose the use of deep convolutional neural network (ConvNet) features. We introduce a range of condition variations to explore the robustness of these features, including: translation, scaling, rotation, shading and occlusion. Evaluations on the Flavia dataset demonstrate that in ideal imaging conditions, combining traditional and ConvNet features yields state-of-theart performance with an average accuracy of 97:3%�0:6% compared to traditional features which obtain an average accuracy of 91:2%�1:6%. Further experiments show that this combined classification approach consistently outperforms the best set of traditional features by an average of 5:7% for all of the evaluated condition variations.