TMAX – a Database System Integrating Tissue Biomarker & Genomic Data

Background: Popular approaches in human tissue-based biomarker discovery include tissue microarrays (TMAs) and DNA Microarrays (DMAs) for protein and gene expression profiling respectively. The data generated by these analytic platforms, together with associated image, clinical and pathological data currently reside on widely different information platforms, making searching and cross-platform analysis difficult. Consequently, there is a strong need to develop a single coherent database capable of correlating all available data types.

Method: This study presents TMAX, a database system to facilitate biomarker discovery tasks. TMAX organises a variety of biomarker discovery-related data into the database. Both TMA and DMA experimental data are integrated in TMAX and connected through common DNA/protein biomarkers. Patient clinical data (including tissue pathological data), computer assisted tissue image and associated analytic data are also included in TMAX to enable the truly high throughput processing of ultra-large digital slides for both TMAs and whole slide tissue digital slides. A comprehensive web front-end was built with embedded XML parser software and predefined SQL queries to enable rapid data exchange in the form of standard XML files.

Results & Conclusion: TMAX represents one of the first attempts to integrate TMA data with public gene expression experiment data. Experiments suggest that TMAX is robust in managing large quantities of data from different sources (clinical, TMA, DMA and image analysis). Its web front-end is user friendly, easy to use, and most importantly allows the rapid and easy data exchange of biomarker discovery related data. In conclusion, TMAX is a robust biomarker discovery data repository and research tool, which opens up the opportunities for biomarker discovery and further integromics research.

REMA: A computer-based mapping tool for analysis of restriction sites in multiple DNA sequences

REMA is an interactive web-based program which predicts endonuclease cut sites in DNA sequences. It analyses Multiple sequences simultaneously and predicts the number and size of fragments as well as provides restriction maps. The users can select single or paired combinations of all commercially available enzymes. Additionally, REMA permits prediction of multiple sequence terminal fragment sizes and suggests suitable restriction enzymes for maximally discriminatory results. REMA is an easy to use, web based program which will have a wide application in molecular biology research. Availability: REMA is written in Perl and is freely available for non-commercial use. Detailed information on installation can be obtained from Jan Szubert (jan.szubert@gmail.com) and the web based application is accessible on the internet at the URL http://www.macaulay.ac.uk/rema. Contact: b.singh@macaulay.ac.uk. (C) 2007 Elsevier B.V. All rights reserved.

Common-sense knowledge for a computer vision system for human action recognition

This work presents a novel approach for human action recognition based on the combination of computer vision techniques and common-sense knowledge and reasoning capabilities. The emphasis of this work is on how common sense has to be leveraged to a vision-based human action recognition so that nonsensical errors can be amended at the understanding stage. The proposed framework is to be deployed in a realistic environment in which humans behave rationally, that is, motivated by an aim or a reason. © 2012 Springer-Verlag.

Hormone profiling in a novel enteroendocrine cell line pGIP/neo:STC-1

GIP is a peptide hormone of therapeutic interest in type 2 diabetes and obesity. This study evaluated pGIP/neo STC-1 as a potential K-cell model for studying GIP secretion.

Profiling the ionome of rice and its use in discriminating geographical origins at the regional scale, China

Element profile was investigated for their use to trace the geographical origin of rice (Oryza sativa L.) samples. The concentrations of 13 elements (calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), boron (B), manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), arsenic (As), selenium (Se), molybdenum (Mo), and cadmium (Cd)) were determined in the rice samples by inductively coupled plasma optical emission and mass spectrometry. Most of the essential elements for human health in rice were within normal ranges except for Mo and Se. Mo concentrations were twice as high as those in rice from Vietnam and Spain. Meanwhile, Se concentrations were three times lower in the whole province compared to the Chinese average level of 0.088 mg/kg. About 12% of the rice samples failed the Chinese national food safety standard of 0.2 mg/kg for Cd. Combined with the multi-elemental profile in rice, the principal component analysis (PCA), discriminant function analysis (DFA) and Fibonacci index analysis (FIA) were applied to discriminate geographical origins of the samples. Results indicated that the FIA method could achieve a more effective geographical origin classification compared with PCA and DFA, due to its efficiency in making the grouping even when the elemental variability was so high that PCA and DFA showed little discriminatory power. Furthermore, some elements were identified as the most powerful indicators of geographical origin: Ca, Ni, Fe and Cd. This suggests that the newly established methodology of FIA based on the ionome profile can be applied to determine the geographical origin of rice.