XdomView: protein domain and exon position visualization

SUMMARY: The relationship between intron distribution in the eukaryotic gene and protein structural elements is essential for understanding the origin and evolution of genes. XdomView is a web-based viewer mapping protein structural domains and intron positions in eukaryotic homologues to its tertiary structure. The association of sequence signals to 3D structure in XdomView provides a valuable visualization environment for eukaryotic gene organization, gene evolution, protein folding and protein structure classification. AVAILABILITY: Freely available from http://surya.bic.nus.edu.sg/xdom.     

Pattern recall in networks of chaotic neurons

This research investigates the potential utility of chaotic dynamics in neural information processing. A novel chaotic spiking neural network model is presented which is composed of non-linear dynamic state (NDS) neurons. The activity of each NDS neuron is driven by a set of non-linear equations coupled with a threshold based spike output mechanism. If time-delayed self-connections are enabled then the network stabilises to a periodic pattern of activation. Previous publications of this work have demonstrated that the chaotic dynamics which drive the network activity ensure that an extremely large number of such periodic patterns can be generated by this network. This paper presents a major extension to this model which enables the network to recall a pattern of activity from a selection of previously stabilised patterns.     

EMMPRIN Promotes the Expression of MMP-9 and Exacerbates Neurological Dysfunction in a Mouse Model of Intracerebral Hemorrhage

Neurochemical Research - Extracellular matrix metalloproteinase inducer (EMMPRIN) has been shown to be a vital inflammatory mediator in several neurological and neurodegenerative diseases. However,...     

Volumetric modulated arc therapy in prostate cancer patients with metallic hip prostheses in a UK centre

AimThis study aimed to investigate whether IMRT using VMAT is a viable and safe solution in dose escalated RT in these patients.BackgroundAn increasing number of prostate cancer patients are elderly and have hip prostheses. These implants pose challenges in radiotherapy treatment planning. Although intensity modulated radiotherapy (IMRT) is commonly used, there is a lack of clinical studies documenting its efficacy and toxicities in this subgroup of patients.Materials and methodsThe data from 23 patients with hip prostheses and non-metastatic prostate cancer treated with VMAT (volumetric modulated arc therapy) between 2009 and 2011, were retrospectively analyzed. Baseline characteristics, treatment details and outcome data were collected on all patients. The median follow up was 40.9 months. MRI-CT image fusion was performed and the treatment plans were created using RapidArc™ (RA) techniques utilizing 1 or 2 arcs and 10 MV photon beams.Results96% of patients were treated with a dose of 72 Gy/32 fractions over 44 days. 21/23 plans met the PTV targets. The mean homogeneity index was 1.07. 20/23 plans met all OAR constraints (rectum, bladder). Two plans deviated from rectal constraints, four from bladder constraints; all were classed as minor deviations. One patient experienced late grade 3 genitourinary toxicity. Three other patients experienced late grade 2 or lower gastrointestinal toxicity. One patient had biochemical failure and one had a non-prostate cancer related death.ConclusionsVMAT provides an elegant solution to deliver dose escalated RT in patients with unilateral and bilateral hip replacements with minimal acute and late toxicities.     

An integrated quantification method to increase the precision,robustness, and resolution of protein measurement in human plasma samples

Background

Current quantification methods for mass spectrometry (MS)-based proteomics either do not provide sufficient control of variability or are difficult to implement for routine clinical testing.

Results

We present here an integrated quantification (InteQuan) method that better controls pre-analytical and analytical variability than the popular quantification method using stable isotope-labeled standard peptides (SISQuan). We quantified 16 lung cancer biomarker candidates in human plasma samples in three assessment studies, using immunoaffinity depletion coupled with multiple reaction monitoring (MRM) MS. InteQuan outperformed SISQuan in precision in all three studies and tolerated a two-fold difference in sample loading. The three studies lasted over six months and encountered major changes in experimental settings. Nevertheless, plasma proteins in low ng/ml to low μg/ml concentrations were measured with a median technical coefficient of variation (CV) of 11.9% using InteQuan. The corresponding median CV using SISQuan was 15.3% after linear fitting. Furthermore, InteQuan surpassed SISQuan in measuring biological difference among clinical samples and in distinguishing benign versus cancer plasma samples.

Conclusions

We demonstrated that InteQuan is a simple yet robust quantification method for MS-based quantitative proteomics, especially for applications in biomarker research and in routine clinical testing.

Electronic supplementary material

The online version of this article (doi:10.1186/1559-0275-12-3) contains supplementary material, which is available to authorized users.     

Discrete Element Framework for Modelling Extracellular Matrix,Deformable Cells and Subcellular Components

This paper presents a framework for modelling biological tissues based on discrete particles. Cell components (e.g. cell membranes, cell cytoskeleton, cell nucleus) and extracellular matrix (e.g. collagen) are represented using collections of particles. Simple particle to particle interaction laws are used to simulate and control complex physical interaction types (e.g. cell-cell adhesion via cadherins, integrin basement membrane attachment, cytoskeletal mechanical properties). Particles may be given the capacity to change their properties and behaviours in response to changes in the cellular microenvironment (e.g., in response to cell-cell signalling or mechanical loadings). Each particle is in effect an ‘agent’, meaning that the agent can sense local environmental information and respond according to pre-determined or stochastic events. The behaviour of the proposed framework is exemplified through several biological problems of ongoing interest. These examples illustrate how the modelling framework allows enormous flexibility for representing the mechanical behaviour of different tissues, and we argue this is a more intuitive approach than perhaps offered by traditional continuum methods. Because of this flexibility, we believe the discrete modelling framework provides an avenue for biologists and bioengineers to explore the behaviour of tissue systems in a computational laboratory.