Characterization of the C-terminal domain of a potassium channel from Streptomyces lividans (KcsA).

KcsA, a potassium channel from Streptomyces lividans, is a good model for probing the general working mechanism of potassium channels. To date, the physiological activator of KcsA is still unknown, but in vitro studies showed that it could be opened by lowering the pH of the cytoplasmic compartment to 4. The C-terminal domain (CTD, residues 112-160) was proposed to be the modulator for this pH-responsive event. Here, we support this proposal by examining the pH profiles of: (a) thermal stability of KcsA with and without its CTD and (b) aggregation properties of a recombinant fragment of CTD. We found that the presence of the CTD weakened and enhanced the stability of KcsA at acidic and basic pH values, respectively. In addition, the CTD fragment oligomerized at basic pH values with a transition profile close to that of channel opening. Our results are consistent with the CTD being a pH modulator. We propose herein a mechanism on how this domain may contribute to the pH-dependent opening of KcsA.

Validating a biometric authentication system: sample size requirements.

Authentication systems based on biometric features (e.g., fingerprint impressions, iris scans, human face images, etc.) are increasingly gaining widespread use and popularity. Often, vendors and owners of these commercial biometric systems claim impressive performance that is estimated based on some proprietary data. In such situations, there is a need to independently validate the claimed performance levels. System performance is typically evaluated by collecting biometric templates from n different subjects, and for convenience, acquiring multiple instances of the biometric for each of the n subjects. Very little work has been done in 1) constructing confidence regions based on the ROC curve for validating the claimed performance levels and 2) determining the required number of biometric samples needed to establish confidence regions of prespecified width for the ROC curve. To simplify the analysis that address these two problems, several previous studies have assumed that multiple acquisitions of the biometric entity are statistically independent. This assumption is too restrictive and is generally not valid. We have developed a validation technique based on multivariate copula models for correlated biometric acquisitions. Based on the same model, we also determine the minimum number of samples required to achieve confidence bands of desired width for the ROC curve. We illustrate the estimation of the confidence bands as well as the required number of biometric samples using a fingerprint matching system that is applied on samples collected from a small population.