NetMets: software for quantifying and visualizing errors in biological network segmentation

One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization

Hybridization thermodynamics of NimbleGen Microarrays

Background While microarrays are the predominant method for gene expression profiling, probe signal variation is still an area of active research. Probe signal is sequence dependent and affected by probe-target binding strength and the competing formation of probe-probe dimers and secondary structures in probes and targets. Results We demonstrate the benefits of an improved model for microarray hybridization and assess the relative contributions of the probe-target binding strength and the different competing structures. Remarkably, specific and unspecific hybridization were apparently driven by different energetic contributions: For unspecific hybridization, the melting temperature Tm was the best predictor of signal variation. For specific hybridization, however, the effective interaction energy that fully considered competing structures was twice as powerful a predictor of probe signal variation. We show that this was largely due to the effects of secondary structures in the probe and target molecules. The predictive power of the strength of these intramolecular structures was already comparable to that of the melting temperature or the free energy of the probe-target duplex. Conclusions This analysis illustrates the importance of considering both the effects of probe-target binding strength and the different competing structures. For specific hybridization, the secondary structures of probe and target molecules turn out to be at least as important as the probe-target binding strength for an understanding of the observed microarray signal intensities. Besides their relevance for the design of new arrays, our results demonstrate the value of improving thermodynamic models for the read-out and interpretation of microarray signals

Differential Susceptibility of Interneurons Expressing Neuropeptide Y or Parvalbumin in the Aged Hippocampus to Acute Seizure Activity

Acute seizure (AS) activity in old age has an increased predisposition for evolving into temporal lobe epilepsy (TLE). Furthermore, spontaneous seizures and cognitive dysfunction after AS activity are often intense in the aged population than in young adults. This could be due to an increased vulnerability of inhibitory interneurons in the aged hippocampus to AS activity. We investigated this issue by comparing the survival of hippocampal GABA-ergic interneurons that contain the neuropeptide Y (NPY) or the calcium binding protein parvalbumin (PV) between young adult (5-months old) and aged (22-months old) F344 rats at 12 days after three-hours of AS activity. Graded intraperitoneal injections of the kainic acid (KA) induced AS activity and a diazepam injection at 3 hours after the onset terminated AS-activity. Measurement of interneuron numbers in different hippocampal subfields revealed that NPY+ interneurons were relatively resistant to AS activity in the aged hippocampus in comparison to the young adult hippocampus. Whereas, PV+ interneurons were highly susceptible to AS activity in both age groups. However, as aging alone substantially depleted these populations, the aged hippocampus after three-hours of AS activity exhibited 48% reductions in NPY+ interneurons and 70% reductions in PV+ interneurons, in comparison to the young hippocampus after similar AS activity. Thus, AS activity-induced TLE in old age is associated with far fewer hippocampal NPY+ and PV+ interneuron numbers than AS-induced TLE in the young adult age. This discrepancy likely underlies the severe spontaneous seizures and cognitive dysfunction observed in the aged people after AS activity