Quality time [QoS in 802.11e]

Wireless local area networks (WLANs) based on the IEEE 802.11 standard are now widespread. Most are used to provide access for mobile devices to a conventional wired infrastructure, and some are used where wires are not possible, forming an ad hoc network of their own. There are several varieties at the physical or radio layer (802.11, 802.11a, 802.11b, 802.11g), with each featuring different data rates, modulation schemes and transmission frequencies. However, all of them share a common medium access control (MAC) layer. As this is largely based on a contention approach, it does not allow prioritising of traffic or stations, so it cannot easily provide the quality of service (QoS) required by time-sensitive applications, such as voice or video transmission. In order to address this shortfall of the technology, the IEEE set up a task group that is aiming to enhance the MAC layer protocol so that it can provide QoS. The latest draft at the time of writing is Draft 11, dated October 2004. The article describes the yet-to-be-ratified 802.11e standard and is based on that draft.

FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.