A biologically inspired appearance model for robust visual tracking

In this work, we propose a biologically inspired appearance model for robust visual tracking. Motivated in part by the success of the hierarchical organization of the primary visual cortex (area V1), we establish an architecture consisting of five layers: whitening, rectification, normalization, coding and polling. The first three layers stem from the models developed for object recognition. In this paper, our attention focuses on the coding and pooling layers. In particular, we use a discriminative sparse coding method in the coding layer along with spatial pyramid representation in the pooling layer, which makes it easier to distinguish the target to be tracked from its background in the presence of appearance variations. An extensive experimental study shows that the proposed method has higher tracking accuracy than several state-of-the-art trackers.

A Hybrid Swarm Based Approach in Addressing University Timetabling Problems

This paper is concerned with the application of an automated hybrid approach in addressing the university timetabling problem. The approach described is based on the nature-inspired artificial bee colony (ABC) algorithm. An ABC algorithm is a biologically-inspired optimization approach, which has been widely implemented in solving a range of optimization problems in recent years such as job shop scheduling and machine timetabling problems. Although the approach has proven to be robust across a range of problems, it is acknowledged within the literature that there currently exist a number of inefficiencies regarding the exploration and exploitation abilities. These inefficiencies can often lead to a slow convergence speed within the search process. Hence, this paper introduces a variant of the algorithm which utilizes a global best model inspired from particle swarm optimization to enhance the global exploration ability while hybridizing with the great deluge (GD) algorithm in order to improve the local exploitation ability. Using this approach, an effective balance between exploration and exploitation is attained. In addition, a traditional local search approach is incorporated within the GD algorithm with the aim of further enhancing the performance of the overall hybrid method. To evaluate the performance of the proposed approach, two diverse university timetabling datasets are investigated, i.e., Carter's examination timetabling and Socha course timetabling datasets. It should be noted that both problems have differing complexity and different solution landscapes. Experimental results demonstrate that the proposed method is capable of producing high quality solutions across both these benchmark problems, showing a good degree of generality in the approach. Moreover, the proposed method produces best results on some instances as compared with other approaches presented in the literature.

Identification of Determinants of Glucose-Dependent Insulinotropic Polypeptide Receptor That Interact with N-Terminal Biologically Active Region of the Natural Ligand

Glucose-dependent insulinotropic polypeptide receptor (GIPR), a member of family B of the G-protein coupled receptors, is a potential therapeutic target for which discovery of nonpeptide ligands is highly desirable. Structure-activity relationship studies indicated that the N-terminal part of glucose-dependent insulinotropic polypeptide (GIP) is crucial for biological activity. Here, we aimed at identification of residues in the GIPR involved in functional interaction with N-terminal moiety of GIP. A homology model of the transmembrane core of GIPR was constructed, whereas a three-dimensional model of the complex formed between GIP and the N-terminal extracellular domain of GIPR was taken from the crystal structure. The latter complex was docked to the transmembrane domains of GIPR, allowing in silico identification of putative residues of the agonist binding/activation site. All mutants were expressed at the surface of human embryonic kidney 293 cells as indicated by flow cytometry and confocal microscopy analysis of fluorescent GIP binding. Mutation of residues Arg183, Arg190, Arg300, and Phe357 caused shifts of 76-, 71-, 42-, and 16-fold in the potency to induce cAMP formation, respectively. Further characterization of these mutants, including tests with alanine-substituted GIP analogs, were in agreement with interaction of Glu3 in GIP with Arg183 in GIPR. Furthermore, they strongly supported a binding mode of GIP to GIPR in which the N-terminal moiety of GIP was sited within transmembrane helices (TMH) 2, 3, 5, and 6 with biologically crucial Tyr1 interacting with Gln224 (TMH3), Arg300 (TMH5), and Phe357 (TMH6). These data represent an important step toward understanding activation of GIPR by GIP, which should facilitate the rational design of therapeutic agents.

The Colorectal cancer disease-specific transcriptome may facilitate the discovery of more biologically and clinically relevant information

BACKGROUND: To date, there are no clinically reliable predictive markers of response to the current treatment regimens for advanced colorectal cancer. The aim of the current study was to compare and assess the power of transcriptional profiling using a generic microarray and a disease-specific transcriptome-based microarray. We also examined the biological and clinical relevance of the disease-specific transcriptome.

METHODS: DNA microarray profiling was carried out on isogenic sensitive and 5-FU-resistant HCT116 colorectal cancer cell lines using the Affymetrix HG-U133 Plus2.0 array and the Almac Diagnostics Colorectal cancer disease specific Research tool. In addition, DNA microarray profiling was also carried out on pre-treatment metastatic colorectal cancer biopsies using the colorectal cancer disease specific Research tool. The two microarray platforms were compared based on detection of probesets and biological information.

RESULTS: The results demonstrated that the disease-specific transcriptome-based microarray was able to out-perform the generic genomic-based microarray on a number of levels including detection of transcripts and pathway analysis. In addition, the disease-specific microarray contains a high percentage of antisense transcripts and further analysis demonstrated that a number of these exist in sense:antisense pairs. Comparison between cell line models and metastatic CRC patient biopsies further demonstrated that a number of the identified sense:antisense pairs were also detected in CRC patient biopsies, suggesting potential clinical relevance.

CONCLUSIONS: Analysis from our in vitro and clinical experiments has demonstrated that many transcripts exist in sense:antisense pairs including IGF2BP2, which may have a direct regulatory function in the context of colorectal cancer. While the functional relevance of the antisense transcripts has been established by many studies, their functional role is currently unclear; however, the numbers that have been detected by the disease-specific microarray would suggest that they may be important regulatory transcripts. This study has demonstrated the power of a disease-specific transcriptome-based approach and highlighted the potential novel biologically and clinically relevant information that is gained when using such a methodology.

Bioinformatics inspired algorithm for stereo correspondence

In this paper, we exploit the analogy between protein sequence alignment and image pair correspondence to design a bioinformatics-inspired framework for stereo matching based on dynamic programming. This approach also led to the creation of a meaningfulness graph, which helps to predict matching validity according to image overlap and pixel similarity. Finally, we propose an automatic procedure to estimate automatically all matching parameters. This work is evaluated qualitatively and quantitatively using a standard benchmarking dataset and by conducting stereo matching experiments between images captured at different resolutions. Results confirm the validity of the computer vision/bioinformatics analogy to develop a versatile and accurate low complexity stereo matching algorithm.

Predicting IQ of biologically "at risk" children from age 3 to school entry:sensitivity and specificity of the Stanford-Binet Intelligence Scale IV

Predictive validity of the Stanford-Binet Intelligence Scale Fourth Edition (S-B IV) from age 3 years to ages 4-5 years was evaluated with biologically "at risk" children without major sensory or motor impairments (n = 236). Using the standard scoring, children with full scale IQ <or = 84 on the Wechsler Preschool and Primary Scale of Intelligence at age 4-5 years were poorly identified (sensitivity 54%) from the composite S-B IV score at age 3. However, sensitivity improved greatly to 78% by including as a predictor the number of subtests the child was actually able to perform at age 3 years. Measures from the Home Screening Questionnaire and ratings of mother-child interaction further improved sensitivity to 83%. The standard method for calculating the composite score on the S-B IV excludes subtests with a raw score of 0, which overestimates cognitive functioning in young biologically high risk children. Accuracy of early identification was improved significantly by considering the number of subtests the child did not perform at age 3 years.