A discourse of ‘abnormality’: Exploring discussions of people living in Australia with a deafness or hearing loss

Adopting a social constructionist framework, the authors conducted a synthetic discourse analysis to explore how people living in Australia with deafness construct their experience of deafness. An online forum facilitated access and communication between the lead author and 24 widely dispersed and linguistically diverse forum contributors. The authors discuss the productive and restrictive effects of the emergent discourse of deafness as abnormal and the rhetorical strategies mobilized in people’s accounts: fitting in, acceptance as permission to be different, and the need to prove normality. Using these strategies was productive in that the forum respondents were enabled to reposition deafness as a positive, socially valued identity position. However, the need to manage deafness was reproduced as an individual concern, disallowing any exploration of how deafness could be reconstructed as socially valued. The article concludes with a discussion of the implications of the deafness as abnormal discourse.

Tumour islet Foxp3+ T-cell infiltration predicts poor outcome in nonsmall cell lung cancer

The impact of host immunity on outcome in nonsmall cell lung cancer (NSCLC) is controversial. We examined the relationship between lymphoid infiltration patterns in NSCLC and prognosis. Tumour- and stroma-infiltrating CD3+, CD8+ and forkhead box P3 (Foxp3)+ T-lymphocytes were identified using immunohistochemistry and a novel image analysis algorithm to assess total, cytotoxic and regulatory T-lymphocyte counts, respectively, in 196 NSCLC cases. The median cell count was selected as a cut-point to define patient subgroups and the ratio of the corresponding tumour islet:stroma (TI/S) counts was determined. There was a positive association between overall survival and increased CD8+ TI/S ratio (hazard ratio (HR) for death 0.44, p<0.001) but an inverse relationship between Foxp3+ TI/S ratio and overall survival (HR 4.86, p<0.001). Patients with high CD8+ islet (HR 0.48, p<0.001) and Foxp3+ stromal (HR 0.23, p<0.001) counts had better survival, whereas high CD3+ and CD8+ stromal counts and high Foxp3+ islet infiltration conferred a worse survival (HR 1.55, 2.19 and 3.14, respectively). By multivariate analysis, a high CD8+ TI/S ratio conferred an improved survival (HR 0.48, p=0.002) but a high Foxp3+ TI/S ratio was associated with worse survival (HR 3.91, p<0.001). Microlocalisation of infiltrating T-lymphocytes is a powerful predictor of outcome in resected NSCLC.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

The fluctuations in slag foam under dynamic conditions

In the present article, slag foaming phenomenon under dynamic conditions is critically analyzed on the basis of the results of high-temperature X-ray image analysis experiments. The results indicate that the mismatch between the gas generation rate and gas escape rate has a serious impact on the foam height. This mismatch is attributed to the chemical reaction rate, which has to be considered in modeling slag foaming under dynamic conditions. The results further imply that a critical ratio of bubble size/crucible size exists, where wall effects are likely to become prominent.

Introduction of high-content screening method for studying drug-induced mitochondrial dysfunction in hepatocytes

Drug induced liver injury is one of the frequent reasons for the drug removal from the market. During the recent years there has been a pressure to develop more cost efficient, faster and easier ways to investigate drug-induced toxicity in order to recognize hepatotoxic drugs in the earlier phases of drug development. High Content Screening (HCS) instrument is an automated microscope equipped with image analysis software. It makes the image analysis faster and decreases the risk for an error caused by a person by analyzing the images always in the same way. Because the amount of drug and time needed in the analysis are smaller and multiple parameters can be analyzed from the same cells, the method should be more sensitive, effective and cheaper than the conventional assays in cytotoxicity testing. Liver cells are rich in mitochondria and many drugs target their toxicity to hepatocyte mitochondria. Mitochondria produce the majority of the ATP in the cell through oxidative phosphorylation. They maintain biochemical homeostasis in the cell and participate in cell death. Mitochondria is divided into two compartments by inner and outer mitochondrial membranes. The oxidative phosphorylation happens in the inner mitochondrial membrane. A part of the respiratory chain, a protein called cytochrome c, activates caspase cascades when released. This leads to apoptosis. The aim of this study was to implement, optimize and compare mitochondrial toxicity HCS assays in live cells and fixed cells in two cellular models: human HepG2 hepatoma cell line and rat primary hepatocytes. Three different hepato- and mitochondriatoxic drugs (staurosporine, rotenone and tolcapone) were used. Cells were treated with the drugs, incubated with the fluorescent probes and then the images were analyzed using Cellomics ArrayScan VTI reader. Finally the results obtained after optimizing methods were compared to each other and to the results of the conventional cytotoxicity assays, ATP and LDH measurements. After optimization the live cell method and rat primary hepatocytes were selected to be used in the experiments. Staurosporine was the most toxic of the three drugs and caused most damage to the cells most quickly. Rotenone was not that toxic, but the results were more reproducible and thus it would serve as a good positive control in the screening. Tolcapone was the least toxic. So far the conventional analysis of cytotoxicity worked better than the HCS methods. More optimization needs to be done to get the HCS method more sensitive. This was not possible in this study due to time limit.

Change detection and controlling forest information using multi-temporal Landsat TM imagery.

A method was developed for relative radiometric calibration of single multitemporal Landsat TM image, several multitemporal images covering each others, and several multitemporal images covering different geographic locations. The radiometricly calibrated difference images were used for detecting rapid changes on forest stands. The nonparametric Kernel method was applied for change detection. The accuracy of the change detection was estimated by inspecting the image analysis results in field. The change classification was applied for controlling the quality of the continuously updated forest stand information. The aim was to ensure that all the manmade changes and any forest damages were correctly updated including the attribute and stand delineation information. The image analysis results were compared with the registered treatments and the stand information base. The stands with discrepancies between these two information sources were recommended to be field inspected.

The effect of mischmetal addition on the structure and mechanical properties of a cast Al-7Si-0.3Mg alloy containing excess iron (up to 0.6 Pct)

The effect of Fe content (0.2 to 0.6 pct) on the microstructure and mechanical properties of a cast Al-7Si-0.3Mg (LM 25/356) alloy has been investigated. Further, 1 pct mischmetal (MM) additions (a mixture of rare-earth (RE) elements) were made to these alloys, and their mechanical properties at room and at elevated temperatures (up to 200 degreesC) were evaluated. A structure-property correlation on this alloy was attempted using optical microstructure analysis, fractographs, X-ray diffraction, energy-dispersive analysis of X-rays (EDX), and quantitative metallography by image analysis. An increase in Fe content increased the volume percentage of Fe-bearing intermetallic compounds (beta and pi phases), contributing to the lower yield strength (YS), ultimate tensile strength (UTS), percentage elongation, and higher hardness. An addition of 1 pct MM to the alloys containing 0.2 and 0.6 pct Fe was found to refine the microstructure; modify the eutectic silicon and La, Ce, and Nd present in the MM; form different intermetallic compounds with Al, Si, Fe, and Mg; and improve the mechanical properties of the alloys both at room and elevated temperatures.

Grain Floatation During Equiaxed Solidification of an Al-Cu Alloy in a Side-Cooled Cavity: Part I-Experimental Studies

Experimental studies were performed to investigate the role and influence of grain movement on macrosegregation and microstructure evolution during equiaxed solidification. Casting experiments were performed with a grain-refined Al-Cu alloy in a rectangular sand mold. For the aluminum alloy studied, the equiaxed grains are lighter than the bulk melt and thus float up. Experiments were designed to investigate floatation phenomena of equiaxed grains in the presence of thermosolutal convection. Cooling curves were recorded at key locations in both the casting and the chill. Quantitative image analysis and spatial chemical analysis were performed on the solidified casting to observe the chemical and microstructural inhomogeneity created by the melt convection and solid floatation. Several notable features that can be attributed to grain movement were observed in temperature histories, macrosegregation patterns, and microstructures. In our experiments, the floatation of grains influences the thermal conditions and the overall flow direction in the casting cavity. In some cases, the induced flow resulting from the grain movement caused a flow reversal. This in turn influences the solidification direction, microstructure evolution, and the overall macrosegregation behavior.

Experimental Studies on High-Pressure Spray Structure of Biofuels

The high-pressure spray characteristics of biofuels, specifically, Pongamia oil and its blends with diesel are studied for various gas pressures. Two single-hole solenoid injectors with nozzle diameters of 200 and 260 mu m are used along with a high-pressure common-rail direct-injection system to inject fuel into a high-pressure spray visualization chamber. The spray structure is characterized using a high-speed laser-based shadowgraphy technique. The spray structure of Pongamia oil revealed the presence of an intact liquid core at low gas pressure. At high gas pressures, the spray atomization of the Pongamia oil showed marked improvement. The spray tip penetration of Pongamia oil and its blends with diesel is higher compared to that of diesel for all test conditions. The spray cone angle of Pongamia oil and 50% Pongamia oil blend with diesel is lower as compared to that of diesel. Both these observations are attributed to the presence of large droplets carrying higher momentum in oil and blend. The droplet size is measured at an injection pressure of 1000 bar and gas pressure of 30 bar at 25 mm below the nozzle tip using the particle/droplet image.analysis (PDIA) method. The droplet size measurements have shown that the Sauter mean diameter (SMD) in the spray core of Pongamia oil is more than twice that of diesel. The spray tip penetration of the 20% blend of Pongamia with diesel (P20) is similar to that of diesel but the SMD is 50% higher. Based on experimental data, appropriate spray tip penetration correlation is proposed for the vegetable oil fuels such as Pongamia.

Deformation field in indentation of a granular ensemble

An experimental study has been made of the flow field in indentation of a model granular material. A granular ensemble composed of spherical sand particles with average size of 0.4 mm is indented with a flat ended punch under plane-strain conditions. The region around the indenter is imaged in situ using a high-speed charge-coupled device (CCD) imaging system. By applying a hybrid image analysis technique to image sequences of the indentation, flow parameters such as velocity, velocity gradient, and strain rate are measured at high resolution. The measurements have enabled characterization of the main features of the flow such as dead material zones, velocity jumps, localization of deformation, and regions of highly rotational flow resembling vortices. Implications for validation of theoretical analyses and applications are discussed.

Spray characterization of gasoline-ethanol blends from a multi-hole port fuel injector

This work reports the measured spray structure and droplet size distributions of ethanol-gasoline blends for a low-pressure, multi-hole, port fuel injector (PFI). This study presents previously unavailable data for this class of injectors which are widely used in automotive applications. Specifically, gasoline, ethanol, and gasoline-ethanol blends containing 10%, 20% and 50% ethanol were studied using laser backlight imaging, and particle/droplet image analysis (PDIA) techniques. The fuel mass injected, spray structure and tip penetrations, droplet size distributions, and Sauter mean diameter were determined for the blends, at two different injection pressures. Results indicate that the gasoline and ethanol sprays have similar characteristics in terms of spray progression and droplet sizes in spite of the large difference in viscosity. It appears that the complex mode of atomization utilized in these injectors involving interaction of multiple fuel jets is fairly insensitive to the fuel viscosity over a range of values. This result has interesting ramifications for existing gasoline fuel systems which need to handle blends and even pure ethanol, which is one of the renewable fuels of the future. (C) 2012 Elsevier Ltd. All rights reserved.

HIGH-RESOLUTION IMAGING OF THE ATLBS REGIONS: THE RADIO SOURCE COUNTS

The Australia Telescope Low-brightness Survey (ATLBS) regions have been mosaic imaged at a radio frequency of 1.4 GHz with 6 `' angular resolution and 72 mu Jy beam(-1) rms noise. The images (centered at R. A. 00(h)35(m)00(s), decl. -67 degrees 00'00 `' and R. A. 00(h)59(m)17(s), decl. -67.00'00 `', J2000 epoch) cover 8.42 deg(2) sky area and have no artifacts or imaging errors above the image thermal noise. Multi-resolution radio and optical r-band images (made using the 4 m CTIO Blanco telescope) were used to recognize multi-component sources and prepare a source list; the detection threshold was 0.38 mJy in a low-resolution radio image made with beam FWHM of 50 `'. Radio source counts in the flux density range 0.4-8.7 mJy are estimated, with corrections applied for noise bias, effective area correction, and resolution bias. The resolution bias is mitigated using low-resolution radio images, while effects of source confusion are removed by using high-resolution images for identifying blended sources. Below 1 mJy the ATLBS counts are systematically lower than the previous estimates. Showing no evidence for an upturn down to 0.4 mJy, they do not require any changes in the radio source population down to the limit of the survey. The work suggests that automated image analysis for counts may be dependent on the ability of the imaging to reproduce connecting emission with low surface brightness and on the ability of the algorithm to recognize sources, which may require that source finding algorithms effectively work with multi-resolution and multi-wavelength data. The work underscores the importance of using source lists-as opposed to component lists-and correcting for the noise bias in order to precisely estimate counts close to the image noise and determine the upturn at sub-mJy flux density.

A UNIFIED APPROACH FOR OPTIMIZATION OF SNAKUSCULES AND OVUSCULES

Automated image segmentation techniques are useful tools in biological image analysis and are an essential step in tracking applications. Typically, snakes or active contours are used for segmentation and they evolve under the influence of certain internal and external forces. Recently, a new class of shape-specific active contours have been introduced, which are known as Snakuscules and Ovuscules. These contours are based on a pair of concentric circles and ellipses as the shape templates, and the optimization is carried out by maximizing a contrast function between the outer and inner templates. In this paper, we present a unified approach to the formulation and optimization of Snakuscules and Ovuscules by considering a specific form of affine transformations acting on a pair of concentric circles. We show how the parameters of the affine transformation may be optimized for, to generate either Snakuscules or Ovuscules. Our approach allows for a unified formulation and relies only on generic regularization terms and not shape-specific regularization functions. We show how the calculations of the partial derivatives may be made efficient thanks to the Green's theorem. Results on synthesized as well as real data are presented.

A Huber-loss-driven clustering technique and its application to robust cell detection in confocal microscopy images

We address the problem of detecting cells in biological images. The problem is important in many automated image analysis applications. We identify the problem as one of clustering and formulate it within the framework of robust estimation using loss functions. We show how suitable loss functions may be chosen based on a priori knowledge of the noise distribution. Specifically, in the context of biological images, since the measurement noise is not Gaussian, quadratic loss functions yield suboptimal results. We show that by incorporating the Huber loss function, cells can be detected robustly and accurately. To initialize the algorithm, we also propose a seed selection approach. Simulation results show that Huber loss exhibits better performance compared with some standard loss functions. We also provide experimental results on confocal images of yeast cells. The proposed technique exhibits good detection performance even when the signal-to-noise ratio is low.

Hierarchical artificial immune system for crop stage classification

This paper presents a new hierarchical clustering algorithm for crop stage classification using hyperspectral satellite image. Amongst the multiple benefits and uses of remote sensing, one of the important application is to solve the problem of crop stage classification. Modern commercial imaging satellites, owing to their large volume of satellite imagery, offer greater opportunities for automated image analysis. Hence, we propose a unsupervised algorithm namely Hierarchical Artificial Immune System (HAIS) of two steps: splitting the cluster centers and merging them. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The classification results have been compared with K-means and Artificial Immune System algorithms. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is accurate.