Co-expression of CD147 (EMMPRIN), CD44v3-10, MDR1 and monocarboxylate transporters is associated with prostate cancer drug resistance and progression

Background: The aim of this study is to seek an association between markers of metastatic potential, drug resistance-related protein and monocarboxylate transporters in prostate cancer (CaP). Methods: We evaluated the expression of invasive markers (CD147, CD44v3-10), drug-resistance protein (MDR1) and monocarboxylate transporters (MCT1 and MCT4) in CaP metastatic cell lines and CaP tissue microarrays (n=140) by immunostaining. The co-expression of CD147 and CD44v3-10 with that of MDR1, MCT1 and MCT4 in CaP cell lines was evaluated using confocal microscopy. The relationship between the expression of CD147 and CD44v3-10 and the sensitivity (IC50) to docetaxel in CaP cell lines was assessed using MTT assay. The relationship between expression of CD44v3-10, MDR1 and MCT4 and various clinicopathological CaP progression parameters was examined. Results: CD147 and CD44v3-10 were co-expressed with MDR1, MCT1 and MCT4 in primary and metastatic CaP cells. Both CD147 and CD44v3-10 expression levels were inversely related to docetaxel sensitivity (IC50) in metastatic CaP cell lines. Overexpression of CD44v3-10, MDR1 and MCT4 was found in most primary CaP tissues, and was significantly associated with CaP progression. Conclusions: Our results suggest that the overexpression of CD147, CD44v3-10, MDR1 and MCT4 is associated with CaP progression. Expression of both CD147 and CD44v3-10 is correlated with drug resistance during CaP metastasis and could be a useful potential therapeutic target in advanced disease.

Expression of PSA-RP2, an alternatively spliced variant from the PSA gene, is increased in prostate cancer tissues but the protein is not secreted from prostate cancer cells

PSA-RP2 is a variant transcript expressed from the PSA gene that is conserved in gorillas, chimpanzees and humans suggesting a particular relevance for this transcript in these primates. We demonstrated by qRT-PCR that PSA-RP2 is upregulated in prostate cancer compared with benign prostatic hyperplasia tissues. The PSA-RP2 protein was not detected in seminal fluid and was cytoplasmically localised but not secreted from LNCaP or transfected PC3 prostate cells, despite secretion from transfected Cos-7 and HEK293 kidney cell lines. PSA-RP2-transfected PC3 cells showed slightly decreased proliferation and increased migration towards PC3-conditioned medium that could suggest a functional role in prostate cancer.

Bayesian methodology for genetics of complex diseases

Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.

Schizophrenia and the progression of emotional expression in relation to others

Gaining an improved understanding of people diagnosed with schizophrenia has the potential to influence priorities for therapy. Psychosis is commonly understood through the perspective of the medical model. However, the experience of social context surrounding psychosis is not well understood. In this research project we used a phenomenological methodology with a longitudinal design to interview 7 participants across a 12-month period to understand the social experiences surrounding psychosis. Eleven themes were explicated and divided into two phases of the illness experience: (a) transition into emotional shutdown included the experiences of not being acknowledged, relational confusion, not being expressive, detachment, reliving the past, and having no sense of direction; and (b) recovery from emotional shutdown included the experiences of being acknowledged, expression, resolution, independence, and a sense of direction. The experiential themes provide clinicians with new insights to better assess vulnerability, and have the potential to inform goals for therapy.

Facial expression recognition using facial movement features

Facial expression is an important channel for human communication and can be applied in many real applications. One critical step for facial expression recognition (FER) is to accurately extract emotional features. Current approaches on FER in static images have not fully considered and utilized the features of facial element and muscle movements, which represent static and dynamic, as well as geometric and appearance characteristics of facial expressions. This paper proposes an approach to solve this limitation using ‘salient’ distance features, which are obtained by extracting patch-based 3D Gabor features, selecting the ‘salient’ patches, and performing patch matching operations. The experimental results demonstrate high correct recognition rate (CRR), significant performance improvements due to the consideration of facial element and muscle movements, promising results under face registration errors, and fast processing time. The comparison with the state-of-the-art performance confirms that the proposed approach achieves the highest CRR on the JAFFE database and is among the top performers on the Cohn-Kanade (CK) database.

Improving the performance of facial expression recognition using dynamic, subtle and regional features

Human facial expression is a complex process characterized of dynamic, subtle and regional emotional features. State-of-the-art approaches on facial expression recognition (FER) have not fully utilized this kind of features to improve the recognition performance. This paper proposes an approach to overcome this limitation using patch-based ‘salient’ Gabor features. A set of 3D patches are extracted to represent the subtle and regional features, and then inputted into patch matching operations for capturing the dynamic features. Experimental results show a significant performance improvement of the proposed approach due to the use of the dynamic features. Performance comparison with pervious work also confirms that the proposed approach achieves the highest CRR reported to date on the JAFFE database and a top-level performance on the Cohn-Kanade (CK) database.

Closing the gap between bandit and full-information online optimization : high-probability regret bound

We demonstrate a modification of the algorithm of Dani et al for the online linear optimization problem in the bandit setting, which allows us to achieve an O( \sqrt{T ln T} ) regret bound in high probability against an adaptive adversary, as opposed to the in expectation result against an oblivious adversary of Dani et al. We obtain the same dependence on the dimension as that exhibited by Dani et al. The results of this paper rest firmly on those of Dani et al and the remarkable technique of Auer et al for obtaining high-probability bounds via optimistic estimates. This paper answers an open question: it eliminates the gap between the high-probability bounds obtained in the full-information vs bandit settings.

Evaluation of texture and geometry for dimensional facial expression recognition

Facial expression recognition (FER) algorithms mainly focus on classification into a small discrete set of emotions or representation of emotions using facial action units (AUs). Dimensional representation of emotions as continuous values in an arousal-valence space is relatively less investigated. It is not fully known whether fusion of geometric and texture features will result in better dimensional representation of spontaneous emotions. Moreover, the performance of many previously proposed approaches to dimensional representation has not been evaluated thoroughly on publicly available databases. To address these limitations, this paper presents an evaluation framework for dimensional representation of spontaneous facial expressions using texture and geometric features. SIFT, Gabor and LBP features are extracted around facial fiducial points and fused with FAP distance features. The CFS algorithm is adopted for discriminative texture feature selection. Experimental results evaluated on the publicly accessible NVIE database demonstrate that fusion of texture and geometry does not lead to a much better performance than using texture alone, but does result in a significant performance improvement over geometry alone. LBP features perform the best when fused with geometric features. Distributions of arousal and valence for different emotions obtained via the feature extraction process are compared with those obtained from subjective ground truth values assigned by viewers. Predicted valence is found to have a more similar distribution to ground truth than arousal in terms of covariance or Bhattacharya distance, but it shows a greater distance between the means.