Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVNI classifier gives promising results.

Identification of genes associated with tumorigenesis of meibomian cell carcinoma by microarray analysis

Meibomian cell carcinoma (MCC) is a malignant tumor of the meibomian glands located in the eyelids. No information exists on the cytogenctic and genetic aspects of MCC. There is no report on the gene expression profile of MCC. Thus there is a need, for both scientific and clinical reasons, to identify genes and pathways that are involved in the development and progression of MCC. We analyzed the gene expression profile of MCC by the microarray technique. Forty-four genes were upregulated and 149 genes were downregulated in MCC. Differential expression data were confirmed for 5 genes by semiquantitative RT-PCR in MCC tumors: GTF2H4, RBM12, UBE2D3, DDX17, and LZTS1. We found dysregulation of two major pathways in MCC: MAPK and JAK/STAT. Clusters of genes on chromosomes 1, 12, and 19 were dysregUlated in MCC. The data presented here will facilitate the identification of specific markers and therapeutic targets for the treatment of MCC patients. (c) 2007 Elsevier Inc. All rights reserved.

Defining the pathway to insulin-like growth factor system targeting in cancer

The insulin-like growth factors (IGEs; IGF-1 and IGF-2) play central roles in cell growth, differentiation, survival, transformation and metastasis. The biologic effects of the IGFs are mediated by the IGF-1 receptor (IGF-1R), a receptor tyrosine kinase with homology to the insulin receptor (IR). Dysregulation of the ICE system is well recognized as a key contributor to the progression of multiple cancers, with IGF-1R activation increasing the tumorigenic potential of breast, prostate, lung, colon and head and neck squamous cell carcinoma (HNSCC). Despite this relationship, targeting the IGF-1R has only recently undergone development as a molecular cancer therapeutic. As it has taken hold, we are witnessing a robust increase and interest in targeting the inhibition of IGF-1R signaling. This is accentuated by the list of over 30 drugs, including monoclonal antibodies (mAbs) and tyrosine kinase inhibitors (TKIs) that are under evaluation as single agents or in combination therapies 1]. The ICE-binding proteins (IGFBPs) represent the third component of the ICE system consisting of a class of six soluble secretory proteins. They represent a unique class of naturally occurring ICE-antagonists that bind to and sequester IGF-1 and IGF-2, inhibiting their access to the IGF-1R. Due to their dual targeting of the IGFs without affecting insulin action, the IGFBPs are an untapped ``third'' class of IGF-1R inhibitors. in this commentary, we highlight some of the significant aspects of and prospects for targeting the IGF-1R and describe what the future may hold. (C) 2010 Elsevier Inc. All rights reserved.