Properties of low-threshold motor axons in the human median nerve

This study investigated the excitability and accommodative properties of low-threshold human motor axons to test whether these motor axons have greater expression of the persistent Na(+) conductance, I(NaP). Computer-controlled threshold tracking was used to study 22 single motor units and the data were compared with compound motor potentials of various amplitudes recorded in the same experimental session. Detailed comparisons were made between the single units and compound potentials that were 40% or 5% of maximal amplitude, the former because this is the compound potential size used in most threshold tracking studies of axonal excitability, the latter because this is the compound potential most likely to be composed entirely of motor axons with low thresholds to electrical recruitment. Measurements were made of the strength-duration relationship, threshold electrotonus, current-voltage relationship, recovery cycle and latent addition. The findings did not support a difference in I(NaP). Instead they pointed to greater activity of the hyperpolarization-activated inwardly rectifying current (I(h)) as the basis for low threshold to electrical recruitment in human motor axons. Computer modelling confirmed this finding, with a doubling of the hyperpolarization-activated conductance proving the best single parameter adjustment to fit the experimental data. We suggest that the hyperpolarization-activated cyclic nucleotide-gated (HCN) channel(s) expressed on human motor axons may be active at rest and contribute to resting membrane potential.

Treatment outcome of 42 replanted permanent incisors with a median follow-up of 2.8 years

To evaluate the treatment outcome of avulsed and replanted permanent incisors.

A Flow Trace Generator using Graph-based Traffic Classification Techniques

We propose a novel methodology to generate realistic network flow traces to enable systematic evaluation of network monitoring systems in various traffic conditions. Our technique uses a graph-based approach to model the communication structure observed in real-world traces and to extract traffic templates. By combining extracted and user-defined traffic templates, realistic network flow traces that comprise normal traffic and customized conditions are generated in a scalable manner. A proof-of-concept implementation demonstrates the utility and simplicity of our method to produce a variety of evaluation scenarios. We show that the extraction of templates from real-world traffic leads to a manageable number of templates that still enable accurate re-creation of the original communication properties on the network flow level.