A Human identification technique using images of the iris and wavelet transform

A new approach for recognizing the iris of the human eye is presented. Zero-crossings of the wavelet transform at various resolution levels are calculated over concentric circles on the iris, and the resulting one-dimensional (1-D) signals are compared with model features using different dissimilarity functions.

A security system based on human iris identification using wavelet transform

A security system based on the recognition of the iris of human eyes using the wavelet transform is presented. The zero-crossings of the wavelet transform are used to extract the unique features obtained from the grey-level profiles of the iris. The recognition process is performed in two stages. The first stage consists of building a one-dimensional representation of the grey-level profiles of the iris, followed by obtaining the wavelet transform zerocrossings of the resulting representation. The second stage is the matching procedure for iris recognition. The proposed approach uses only a few selected intermediate resolution levels for matching, thus making it computationally efficient as well as less sensitive to noise and quantisation errors. A normalisation process is implemented to compensate for size variations due to the possible changes in the camera-to-face distance. The technique has been tested on real images in both noise-free and noisy conditions. The technique is being investigated for real-time implementation, as a stand-alone system, for access control to high-security areas.

The reduced expression of the HADH2 protein causes X-linked mental retardation, choreoathetosis, and abnormal behavior

Recently, we defined a new syndromic form of X-linked mental retardation in a 4-generation family with a unique clinical phenotype characterized by mild mental retardation, choreoathetosis, and abnormal behavior (MRXS10). Linkage analysis in this family revealed a candidate region of 13.4 Mb between markers DXS1201 and DXS991 on Xp11; therefore, mutation analysis was performed by direct sequencing in most of the 135 annotated genes located in the region. The gene (HADH2) encoding L-3-hydroxyacyl-CoA dehydrogenase II displayed a sequence alteration (c.574 C-->A; p.R192R) in all patients and carrier females that was absent in unaffected male family members and could not be found in 2,500 control X chromosomes, including in those of 500 healthy males. The silent C-->A substitution is located in exon 5 and was shown by western blot to reduce the amount of HADH2 protein by 60%-70% in the patient. Quantitative in vivo and in vitro expression studies revealed a ratio of splicing transcript amounts different from those normally seen in controls. Apparently, the reduced expression of the wild-type fragment, which results in the decreased protein expression, rather than the increased amount of aberrant splicing fragments of the HADH2 gene, is pathogenic. Our data therefore strongly suggest that reduced expression of the HADH2 protein causes MRXS10, a phenotype different from that caused by 2-methyl-3-hydroxybutyryl-CoA dehydrogenase deficiency, which is a neurodegenerative disorder caused by missense mutations in this multifunctional protein.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

Molecular and cellular analysis of basement membrane invasion by human breast cancer cells in Matrigel-based in vitro assays

In vitro analyses of basement membrane invasiveness employing Matrigel (a murine tumor extract rich in basement membrane components) have been performed on human breast cancer model systems. Constitutive invasiveness of different human breast cancer (HBC) cell lines has been examined as well as regulation by steroid hormones, growth factors, and oncogenes. Carcinoma cells exhibiting a mesenchymal-like phenotype (vimentin expression, lack of cell border associated uvomorulin) show dramatically increased motility, invasiveness, and metastatic potential in nude mice. These findings support the hypothesis that epithelial to mesenchymal transition (EMT)-like events may be instrumental in the metastatic progression of human breast cancer. The MCF-7 subline MCF-7ADR appears to have undergone such a transition. The importance of such a transition may be reflected in the emergence of vimentin expression as an indicator of poor prognosis in HBC. Matrix degradation and laminin recognition are highlighted as potential targets for antimetastatic therapy, and analyses of laminin attachment and the matrix metalloproteinase (MMP) family in HBC cell lines are summarized. Matrigel-based assays have proved useful in the study of the molecular mechanisms of basement membrane invasiveness, their regulation in HBC cells, and their potential as targets for antimetastatic therapy.

Hepatocyte growth factor stimulates invasion across reconstituted basement membranes by a new human small intestinal cell line

Hepatocyte growth factor/scatter factor (HGF/SF) is a protein growth factor whose pleiotropic effects on epithelial cells include the stimulation of motility, mitosis and tubulogenesis. These responses are mediated by the cell surface tyrosine kinase receptor c-met. Because both the cytokine and receptor are found in the gastrointestinal tract, we have studied the effects of HGF/SF on transformed gut epithelial cells which express c-met. Here we describe the response of a new transformed human jejunal epithelioid cell line (HIE-7) to HGF/SF. Morphologically HIE-7 cells are immature. Their epithelial lineage was confirmed by reactivity with the epithelial specific antibodies AE1/AE3, Cam 5.2, Ber-EP4 and anti-EMA and is consistent with their expression of c-met mRNA and protein. In addition, electron microscopic analysis revealed the presence of primitive junctions and rudimentary microvilli, but features of polarization were absent. When grown on reconstituted basement membranes, HIE-7 cells formed closely associated multicellular cord-like structures adjacent to acellular spaces. However, the cells did not mature structurally, form lumen-like structures or express disaccharidase mRNA, even in the presence of recombinant HGF (rHGF). On the other hand, rHGF induced HIE-7 cells to scatter and stimulated their rapid migration in a modified wound assay. To determine whether the motogenic effect caused by rHGF is associated with HIE-7 cell invasiveness across reconstituted basement membranes, a Boyden chamber chemoinvasion assay was performed. rHGF stimulated a 10-fold increase in the number of HIE-7 cells that crossed the basement membrane barrier, while only stimulating a small increase in chemotaxis across a collagen IV matrix, suggesting that the cytokine activates matrix penetration by these cells. rHGF also stimulated the invasion of basement membranes by an undifferentiated rat intestinal cell line (IEC-6) and by two human colon cancer cell lines which are poorly differentiated (DLD-1 and SW 948). In contrast, two moderately well differentiated colon cancer cell lines (Caco-2 and HT-29) did not manifest an invasive response when exposed to rHGF. These results suggest that HGF/SF may play a significant role in the invasive behavior of anaplastic and poorly differentiated gut epithelial tumors.

Inpatient facial expression recognition with SMS alert

Facial expression recognition (FER) has been dramatically developed in recent years, thanks to the advancements in related fields, especially machine learning, image processing and human recognition. Accordingly, the impact and potential usage of automatic FER have been growing in a wide range of applications, including human-computer interaction, robot control and driver state surveillance. However, to date, robust recognition of facial expressions from images and videos is still a challenging task due to the difficulty in accurately extracting the useful emotional features. These features are often represented in different forms, such as static, dynamic, point-based geometric or region-based appearance. Facial movement features, which include feature position and shape changes, are generally caused by the movements of facial elements and muscles during the course of emotional expression. The facial elements, especially key elements, will constantly change their positions when subjects are expressing emotions. As a consequence, the same feature in different images usually has different positions. In some cases, the shape of the feature may also be distorted due to the subtle facial muscle movements. Therefore, for any feature representing a certain emotion, the geometric-based position and appearance-based shape normally changes from one image to another image in image databases, as well as in videos. This kind of movement features represents a rich pool of both static and dynamic characteristics of expressions, which playa critical role for FER. The vast majority of the past work on FER does not take the dynamics of facial expressions into account. Some efforts have been made on capturing and utilizing facial movement features, and almost all of them are static based. These efforts try to adopt either geometric features of the tracked facial points, or appearance difference between holistic facial regions in consequent frames or texture and motion changes in loca- facial regions. Although achieved promising results, these approaches often require accurate location and tracking of facial points, which remains problematic.

Parental influences on physical activity behavior in preschool children

Objective To evaluate a conceptual model linking parent physical activity (PA) orientations, parental support for PA, and PA behavior in preschool children. Methods Participants were 156 parent-child dyads from 13 child care centers in Queensland, Australia. Parents completed a questionnaire measuring parental PA, parental enjoyment of PA, perceived importance of PA, parental support for PA, parents' perceptions of competence, and child PA at home. MVPA while attending child care was measured via accelerometry. Data were collected between May and August of 2003. The relationships between the study variables and child PA were tested using observed variable path analysis. Results Parental PA and parents' perceptions of competence were positively associated with parental support for PA (β= 0.23 and 0.18, respectively, p<0.05). Parental support, in turn, was positively associated with child PA at home (β= 0.16, p<0.05), but not at child care (β= 0.01, p= 0.94). Parents' perceptions of competence was positively associated with both home-based and child care PA (β= 0.20 and 0.28, respectively, p<0.05). Conclusions Family-based interventions targeting preschoolers should include strategies to increase parental support for PA. Parents who perceive their child to have low physical competence should be encouraged to provide adequate support for PA. © 2009 Elsevier Inc.

Advances in population surveillance for physical activity and sedentary behavior : reliability and validity of time use surveys

Many countries conduct regular national time use surveys, some of which date back as far as the 1960s. Time use surveys potentially provide more detailed and accurate national estimates of the prevalence of sedentary and physical activity behavior than more traditional self-report surveillance systems. In this study, the authors determined the reliability and validity of time use surveys for assessing sedentary and physical activity behavior. In 2006 and 2007, participants (n = 134) were recruited from work sites in the Australian state of New South Wales. Participants completed a 2-day time use diary twice, 7 days apart, and wore an accelerometer. The 2 diaries were compared for test-retest reliability, and comparison with the accelerometer determined concurrent validity. Participants with similar activity patterns during the 2 diary periods showed reliability intraclass correlations of 0.74 and 0.73 for nonoccupational sedentary behavior and moderate/vigorous physical activity, respectively. Comparison of the diary with the accelerometer showed Spearman correlations of 0.57-0.59 and 0.45-0.69 for nonoccupational sedentary behavior and moderate/vigorous physical activity, respectively. Time use surveys appear to be more valid for population surveillance of nonoccupational sedentary behavior and health-enhancing physical activity than more traditional surveillance systems. National time use surveys could be used to retrospectively study nonoccupational sedentary and physical activity behavior over the past 5 decades.

Correlation of tumor- and stromal-derived MT1-MMP expression with progression of human ovarian tumors in SCID mice

Human ovarian carcinoma samples were orthotopically implanted into SCID mice to investigate the contribution of matrix metalloproteases (MMPs) to the spread of ovarian tumors. Mice were inoculated with patient tumor samples, and developed ovarian tumors over a 16-week period with metastasis occurring in some mice. Species-specific quantitative RT-PCR was used to identify the source of tumor-associated MMPs. Membrane-type (MT)1-MMP mRNA was significantly increased in high-grade tumors, tumors with evidence of serosal involvement, and tumors in which distant metastases were detected. The increase in MT1-MMP expression was predominantly from the human tumor cells, with a minor contribution from the mouse ovarian stroma. Neither human nor mouse MT2-MMP were correlated with tumor progression and MT3-MMP levels were negligible. While tumor cells did not produce significant amounts of MMP-2 or MMP-9, the presence of tumor was associated with increased levels of MMP-2 expression by mouse ovarian stroma. Stromal-derived MT1-MMP was greater in large tumors and was associated with stromal MMP-2 expression but neither was significantly linked with metastasis. These studies indicate that tumor-derived MT1-MMP, more so than other gelatinolytic MMPs, is strongly linked to aggressive tumor behavior. This orthotopic model of human ovarian carcinoma is appropriate for studying ovarian tumor progression, and will be valuable in the further investigation of the metastatic process.

The effect of prior visual information on recognition of speech and sounds

To identify and categorize complex stimuli such as familiar objects or speech, the human brain integrates information that is abstracted at multiple levels from its sensory inputs. Using cross-modal priming for spoken words and sounds, this functional magnetic resonance imaging study identified 3 distinct classes of visuoauditory incongruency effects: visuoauditory incongruency effects were selective for 1) spoken words in the left superior temporal sulcus (STS), 2) environmental sounds in the left angular gyrus (AG), and 3) both words and sounds in the lateral and medial prefrontal cortices (IFS/mPFC). From a cognitive perspective, these incongruency effects suggest that prior visual information influences the neural processes underlying speech and sound recognition at multiple levels, with the STS being involved in phonological, AG in semantic, and mPFC/IFS in higher conceptual processing. In terms of neural mechanisms, effective connectivity analyses (dynamic causal modeling) suggest that these incongruency effects may emerge via greater bottom-up effects from early auditory regions to intermediate multisensory integration areas (i.e., STS and AG). This is consistent with a predictive coding perspective on hierarchical Bayesian inference in the cortex where the domain of the prediction error (phonological vs. semantic) determines its regional expression (middle temporal gyrus/STS vs. AG/intraparietal sulcus).

Social water assessment protocol : a step towards connecting mining, water and human rights

The human right to water has recently been recognised by both the United Nations General Assembly and the Human Rights Council. As the mining industry interacts with water on multiple levels, it is important that these interactions respect the human right to water. Currently, a disconnect exists between mine site water management practices and the recognition of water from a human rights perspective. The Minerals Council of Australia (MCA) Water Accounting Framework (WAF) has previously been used to strengthen the connection between water management and human rights. This article extends this connection through the use of a Social Water Assessment Protocol (SWAP). The SWAP is scoping tool consisting of a set of questions classified into taxonomic themes under leading topics with suggested sources of data that enable mine sites to better understand the local water context in which they operate. Three of the themes contained in the SWAP – gender, Indigenous peoples and health – are discussed to demonstrate how the protocol may be useful in assisting mining companies to consider their impacts on the human right to water.

Human saliva as a tool to investigate intimate partner violence

Human saliva mirrors body’s health and well-being and many of the biomolecules present in blood or urine can also be found in salivary secretions. However, biomolecular concentrations in saliva are usually one tenth to one thousandth of the levels in blood (Pfaffe et al., 2011). Sensitive detection technology platforms are therefore required to detect biomolecules in saliva. Another road block to the advancement of salivary diagnostics is the lack of information related to healthy state saliva vs. a diseased saliva, baseline levels and reference ranges and diurnal variations. Saliva has numerous advantages over blood or urine as a diagnostic fluid: (a) the non-invasive nature of sample collection and the simple, safe, painless and cost-effective methods to collect it; (b) unskilled personnel can collect saliva samples at multiple time points; and (c) the total protein concentration is approximately a quarter of that is present in plasma, which makes it easier to investigate low abundance proteins (Pfaffe et al., 2011). Currently, saliva assays are routinely used to determine, diseases such as HIV, drugs and substances of abuse to provide information on exposure and give qualitative information on the type of illicit drug used (Kintz et al., 2009), cortisol levels for diagnosing Cushing’s syndrome (Doi et al., 2008), and use for biomonitoring of exposure to chemicals (Caporossi et al., 2010) to measure hormones (Gröschl, 2009)....

Machine learning for activity recognition : hip versus wrist data

Problem addressed Wrist-worn accelerometers are associated with greater compliance. However, validated algorithms for predicting activity type from wrist-worn accelerometer data are lacking. This study compared the activity recognition rates of an activity classifier trained on acceleration signal collected on the wrist and hip. Methodology 52 children and adolescents (mean age 13.7 +/- 3.1 year) completed 12 activity trials that were categorized into 7 activity classes: lying down, sitting, standing, walking, running, basketball, and dancing. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the right hip and the non-dominant wrist. Features were extracted from 10-s windows and inputted into a regularized logistic regression model using R (Glmnet + L1). Results Classification accuracy for the hip and wrist was 91.0% +/- 3.1% and 88.4% +/- 3.0%, respectively. The hip model exhibited excellent classification accuracy for sitting (91.3%), standing (95.8%), walking (95.8%), and running (96.8%); acceptable classification accuracy for lying down (88.3%) and basketball (81.9%); and modest accuracy for dance (64.1%). The wrist model exhibited excellent classification accuracy for sitting (93.0%), standing (91.7%), and walking (95.8%); acceptable classification accuracy for basketball (86.0%); and modest accuracy for running (78.8%), lying down (74.6%) and dance (69.4%). Potential Impact Both the hip and wrist algorithms achieved acceptable classification accuracy, allowing researchers to use either placement for activity recognition.

Visual place recognition for persistent robot navigation in changing environments

This thesis demonstrates that robots can learn about how the world changes, and can use this information to recognise where they are, even when the appearance of the environment has changed a great deal. The ability to localise in highly dynamic environments using vision only is a key tool for achieving long-term, autonomous navigation in unstructured outdoor environments. The proposed learning algorithms are designed to be unsupervised, and can be generated by the robot online in response to its observations of the world, without requiring information from a human operator or other external source.