An extension to the rank constraint for improving the reliability of stereo matching algorithms using the rank transform

The rank transform is one non-parametric transform which has been applied to the stereo matching problem The advantages of this transform include its invariance to radio metric distortion and its amenability to hardware implementation. This paper describes the derivation of the rank constraint for matching using the rank transform Previous work has shown that this constraint was capable of resolving ambiguous matches thereby improving match reliability A new matching algorithm incorporating this constraint was also proposed. This paper extends on this previous work by proposing a matching algorithm which uses a dimensional match surface in which the match score is computed for every possible template and match window combination. The principal advantage of this algorithm is that the use of the match surface enforces the left�right consistency and uniqueness constraints thus improving the algorithms ability to remove invalid matches Experimental results for a number of test stereo pairs show that the new algorithm is capable of identifying and removing a large number of in incorrect matches particularly in the case of occlusions

An accurate and reliable stereo matching algorithm incorporating the rank constraint

A fundamental problem faced by stereo vision algorithms is that of determining correspondences between two images which comprise a stereo pair. This paper presents work towards the development of a new matching algorithm, based on the rank transform. This algorithm makes use of both area-based and edge-based information, and is therefore referred to as a hybrid algorithm. In addition, this algorithm uses a number of matching constraints,including the novel rank constraint. Results obtained using a number of test pairs show that the matching algorithm is capable of removing a significant proportion of invalid matches. The accuracy of matching in the vicinity of edges is also improved.

A hybrid stereo matching algorithm incorporating the rank constraint

A fundamental problem faced by stereo vision algorithms is that of determining correspondences between two images which comprise a stereo pair. This paper presents work towards the development of a new matching algorithm, based on the rank transform. This algorithm makes use of both area-based and edge-based information, and is therefore referred to as a hybrid algorithm. In addition, this algorithm uses a number of matching constraints, including the novel rank constraint. Results obtained using a number of test pairs show that the matching algorithm is capable of removing most invalid matches. The accuracy of matching in the vicinity of edges is also improved.

A constraint to improve the reliability of stereo matching using the rank transform

The rank transform is a non-parametric technique which has been recently proposed for the stereo matching problem. The motivation behind its application to the matching problem is its invariance to certain types of image distortion and noise, as well as its amenability to real-time implementation. This paper derives an analytic expression for the process of matching using the rank transform, and then goes on to derive one constraint which must be satisfied for a correct match. This has been dubbed the rank order constraint or simply the rank constraint. Experimental work has shown that this constraint is capable of resolving ambiguous matches, thereby improving matching reliability. This constraint was incorporated into a new algorithm for matching using the rank transform. This modified algorithm resulted in an increased proportion of correct matches, for all test imagery used.

Evaluation of new and existing confidence measures for stereo matching

This paper outlines existing matching diagnostics, which may be used for identifying invalid matches and estimating the probability of a correct match. In addition, it proposes a new diagnostic for error prediction which can be used with the rank and census transforms. Both the existing and the new diagnostics have been evaluated and compared for a number of test images. In each case, a confidence estimate was computed for every location of the disparity map, and disparities having a low confidence estimate removed from the disparity map. Collectively, these confidence estimates may be termed a confidence map. Such information would be useful for potential applications of stereo vision such as automation and navigation.

Fast and robust stereo matching algorithms for mining automation

The mining environment, being complex, irregular, and time-varying, presents a challenging prospect for stereo vision. For this application, speed, reliability, and the ability to produce a dense depth map are of foremost importance. This paper evaluates a number of matching techniques for possible use in a stereo vision sensor for mining automation applications. Area-based techniques have been investigated because they have the potential to yield dense maps, are amenable to fast hardware implementation, and are suited to textured scenes. In addition, two nonparametric transforms, namely, rank and census, have been investigated. Matching algorithms using these transforms were found to have a number of clear advantages, including reliability in the presence of radiometric distortion, low computational complexity, and amenability to hardware implementation.

Suitability of non-parametric stereo matching algorithms for mining automation

The mining environment, being complex, irregular and time varying, presents a challenging prospect for stereo vision. For this application, speed, reliability, and the ability to produce a dense depth map are of foremost importance. This paper assesses the suitability of a number of matching techniques for use in a stereo vision sensor for close range scenes consisting primarily of rocks. These include traditional area-based matching metrics, and non-parametric transforms, in particular, the rank and census transforms. Experimental results show that the rank and census transforms exhibit a number of clear advantages over area-based matching metrics, including their low computational complexity, and robustness to certain types of distortion.

The impact of litigation risk on corporate prospective disclosure : a review of the empirical literature

We review the literature on the impact of litigation risk (a form of external governance) on corporate prospective disclosure decisions as reflected in management earnings forecasts. From this analysis we identify four key areas for future research. First, litigation risk warrants more attention from researchers; currently it tends to be treated as a secondary factor impacting MEF decisions. Second, it would be informative from a governance perspective for researchers to explore why litigation risk has a differential impact on MEF decisions across countries. Third, understanding the interaction between litigation risk and forecast/firm-specific characteristics is important from management, investor and regulatory perspectives but is currently under-explored Last, research on the litigation risk and MEF attributes link is piecemeal and incomplete, requiring more integrated and expanded analysis.

mCOPA: analysis of heterogeneous features in cancer expression data

Background Cancer outlier profile analysis (COPA) has proven to be an effective approach to analyzing cancer expression data, leading to the discovery of the TMPRSS2 and ETS family gene fusion events in prostate cancer. However, the original COPA algorithm did not identify down-regulated outliers, and the currently available R package implementing the method is similarly restricted to the analysis of over-expressed outliers. Here we present a modified outlier detection method, mCOPA, which contains refinements to the outlier-detection algorithm, identifies both over- and under-expressed outliers, is freely available, and can be applied to any expression dataset. Results We compare our method to other feature-selection approaches, and demonstrate that mCOPA frequently selects more-informative features than do differential expression or variance-based feature selection approaches, and is able to recover observed clinical subtypes more consistently. We demonstrate the application of mCOPA to prostate cancer expression data, and explore the use of outliers in clustering, pathway analysis, and the identification of tumour suppressors. We analyse the under-expressed outliers to identify known and novel prostate cancer tumour suppressor genes, validating these against data in Oncomine and the Cancer Gene Index. We also demonstrate how a combination of outlier analysis and pathway analysis can identify molecular mechanisms disrupted in individual tumours. Conclusions We demonstrate that mCOPA offers advantages, compared to differential expression or variance, in selecting outlier features, and that the features so selected are better able to assign samples to clinically annotated subtypes. Further, we show that the biology explored by outlier analysis differs from that uncovered in differential expression or variance analysis. mCOPA is an important new tool for the exploration of cancer datasets and the discovery of new cancer subtypes, and can be combined with pathway and functional analysis approaches to discover mechanisms underpinning heterogeneity in cancers

IGF2 increases de novo steroidogenesis in prostate cancer cells

Androgen-dependent pathways regulate maintenance and growth of normal and malignant prostate tissues. Androgen deprivation therapy (ADT) exploits this dependence and is used to treat metastatic prostate cancer; however, regression initially seen with ADT gives way to development of incurable castration-resistant prostate cancer (CRPC). Although ADT generates a therapeutic response, it is also associated with a pattern of metabolic alterations consistent with metabolic syndrome including elevated circulating insulin. Because CRPC cells are capable of synthesizing androgens de novo, we hypothesized that insulin may also influence steroidogenesis in CRPC. In this study, we examined this hypothesis by evaluating the effect of insulin on steroid synthesis in prostate cancer cell lines. Treatment with 10 nmol/L insulin increased mRNA and protein expression of steroidogenesis enzymes and upregulated the insulin receptor substrate insulin receptor substrate 2 (IRS-2). Similarly, insulin treatment upregulated intracellular testosterone levels and secreted androgens, with the concentrations of steroids observed similar to the levels reported in prostate cancer patients. With similar potency to dihydrotestosterone, insulin treatment resulted in increased mRNA expression of prostate-specific antigen. CRPC progression also correlated with increased expression of IRS-2 and insulin receptor in vivo. Taken together, our findings support the hypothesis that the elevated insulin levels associated with therapeutic castration may exacerbate progression of prostate cancer to incurable CRPC in part by enhancing steroidogenesis.

Cloning of a novel insulin-regulated ghrelin transcript in prostate cancer

Ghrelin is a multifunctional hormone, with roles in stimulating appetite and regulating energy balance, insulin secretion and glucose homeostasis. The ghrelin gene locus (GHRL) is highly complex and gives rise to a range of novel transcripts derived from alternative first exons and internally spliced exons. The wild-type transcript encodes a 117 amino acid preprohormone that is processed to yield the 28 amino acid peptide ghrelin. Here, we identified insulin-responsive transcription corresponding to cryptic exons in intron 2 of the human ghrelin gene. A transcript, termed in2c-ghrelin (intron 2-cryptic), was cloned from the testis and the LNCaP prostate cancer cell line. This transcript may encode an 83 AA preproghrelin isoform that codes for the ghrelin, but not obestatin. It is expressed in a limited number of normal tissues and in tumours of the prostate, testis, breast and ovary. Finally, we confirmed that in2c-ghrelin transcript expression, as well as the recently described in1-ghrelin transcript, is significantly upregulated by insulin in cultured prostate cancer cells. Metabolic syndrome and hyperinsulinaemia has been associated with prostate cancer risk and progression. This may be particularly significant after androgen deprivation therapy for prostate cancer, which induces hyperinsulinaemia, and this could contribute to castrate resistant prostate cancer growth. We have previously demonstrated that ghrelin stimulates prostate cancer cell line proliferation in vitro. This study is the first description of insulin regulation of a ghrelin transcript in cancer, and should provide further impetus for studies into the expression, regulation and function of ghrelin gene products.

Transferring a successful strategy for supporting accelerated nursing students from a small to a large cohort

In 2009 and 2010, withdrawal rates from a Pharmacology unit of accelerated QUT nursing students in the first year of their degree, were higher than for continuing students. The cohort of 216 accelerated students in 2011 had university or non-university qualification or equivalent experience and included domestic and international students. A previously tested intervention was introduced in 2011 to improve retention rates and support all Pharmacology students in their first year of nursing. The intervention involved a community website, on-line tutors and an “O week” workshop comprising information about library resources, effective learning strategies and learning tips from a previous student as well as review anatomy, physiology and microbiology lectures. Withdrawal rates for accelerated students in the Pharmacology unit improved and all students found the workshop and review lectures to be informative and valuable. The intervention was therefore successfully transferred to a large, diverse cohort of accelerated nursing students.

FOXC2 expression links epithelial-mesenchymal transition and stem cell properties in breast cancer

Resistance to chemotherapy and metastases are the major causes of breast cancer-related mortality. Moreover, cancer stem cells (CSC) play critical roles in cancer progression and treatment resistance. Previously, it was found that CSC-like cells can be generated by aberrant activation of epithelial–mesenchymal transition (EMT), thereby making anti-EMT strategies a novel therapeutic option for treatment of aggressive breast cancers. Here, we report that the transcription factor FOXC2 induced in response to multiple EMT signaling pathways as well as elevated in stem cell-enriched factions is a critical determinant of mesenchymal and stem cell properties, in cells induced to undergo EMT- and CSC-enriched breast cancer cell lines. More specifically, attenuation of FOXC2 expression using lentiviral short hairpin RNA led to inhibition of the mesenchymal phenotype and associated invasive and stem cell properties, which included reduced mammosphere-forming ability and tumor initiation. Whereas, overexpression of FOXC2 was sufficient to induce CSC properties and spontaneous metastasis in transformed human mammary epithelial cells. Furthermore, a FOXC2-induced gene expression signature was enriched in the claudin-low/basal B breast tumor subtype that contains EMT and CSC features. Having identified PDGFR-β to be regulated by FOXC2, we show that the U.S. Food and Drug Administration-approved PDGFR inhibitor, sunitinib, targets FOXC2-expressing tumor cells leading to reduced CSC and metastatic properties. Thus, FOXC2 or its associated gene expression program may provide an effective target for anti-EMT-based therapies for the treatment of claudin-low/basal B breast tumors or other EMT-/CSC-enriched tumors.