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Saccharomyces cerevisiae IA3 is a 68 amino acid peptide inhibitor of yeast proteinase A (YPRA) characterized as a random coil when in solution, folding into an N-terminal amphipathic alpha helix for residues 2-32 when bound to YPRA, with residues 33-68 unresolved in the crystal complex. Circular dichroism (CD) spectroscopy results show that amino acid substitutions that remove hydrogen-bonding interactions observed within the hydrophilic face of the NTD of IA3-YPRA crystal complex reduce the 2,2,2-trifluoroethanol (TFE)-induced helical transition in solution. Although nearly all substitutions decreased TFE-induced helicity compared to wild-type (WT), each construct did retain helical character in the presence of 30% (v/v) TFE and retained disorder in the absence of TFE. The NTDs of 8 different Saccharomyces species have nearly identical amino acid sequences, indicating that the NTD of IA3 may be highly evolved to adopt a helical fold when bound to YPRA and in the presence of TFE but remain unstructured in solution. Only one natural amino acid substitution explored within the solvent exposed face of the NTD of IA3 induced TFE-helicity greater than the WT sequence. However, chemical modification of a cysteine by a nitroxide spin label that contains an acetamide side chain did enhance TFE-induced helicity. This finding suggests that non-natural amino acids that can increase hydrogen bonding or alter hydration through side chain interactions may be important to consider when rationally designing IDPs with varied biotechnological applications. 

Many developers of biometric systems start with modest samples before general deployment. However, they are interested in how their systems will work with much larger samples. To assist them, we evaluated the effect of gallery size on biometric performance. Identification rates describe the performance of biometric identification, whereas ROC-based measures describe the performance of biometric authentication (verification). Therefore, we examined how increases in gallery size affected identification rates (i.e., Rank-1 Identification Rate, or Rank-1 IR) and ROC-based measures such as equal error rate (EER). We studied these phenomena with synthetic data as well as real data from a face recognition study. It is well known that the Rank-1 IR declines with increasing gallery size, and that the relationship is linear against log(gallery size). We have confirmed this with synthetic and real data. We have shown that this decline can be counteracted with the inclusion of additional information (features) for larger gallery sizes. We have also described the curves which can be used to predict how much additional information would be required to stabilize the Rank-1 IR as a function of gallery size. These equations are also linear in log(gallery size). We have also shown that the entire ROC-curve was not systematically affected by gallery size, and so ROC-based scalar performance metrics such as EER are also stable across gallery size. Unsurprisingly, as additional uncorrelated features are added to the model, EER decreases. We were interested in determining the impact of adding more features on the median, spread and shape of similarity score distributions. We present evidence that these decreases in EER are driven primarily by decreases in the spread of the impostor similarity score distribution. 

Typically, the position error of an eye-tracking device is measured as the distance of the eye-position from the target position in two-dimensional space (angular offset).  Accuracy is the mean angular offset.  The mean is a highly interpretable measure of central tendency if the underlying error distribution is unimodal and normal. However, in the context of an underlying multimodal distribution, the mean is less interpretable. We will present evidence that the majority of such distributions are multimodal.  Only 14.7% of fixation angular offset distributions  were  unimodal, and  of  these,  only  11.5%  were normally distributed.  (Of the entire dataset, 1.7% were unimodal and normal.)  This multimodality is true even if there is only a single, continuous tracking fixation segment per trial. We present several approaches to measure accuracy in the face of multimodality. We also address the role of fixation drift in partially explaining multimodality. 

It is generally accepted that relatively more permanent (i.e., more temporally persistent) traits are more valuable for biometric performance than less permanent traits. Although this finding is intuitive, there is no current work identifying exactly where in the biometric analysis temporal persistence makes a difference. In this paper, we answer this question. In a recent report, we introduced the intraclass correlation coefficient (ICC) as an index of temporal persistence for such features. Here, we present a novel approach using synthetic features to study which aspects of a biometric identification study are influenced by the temporal persistence of features. What we show is that using more temporally persistent features produces effects on the similarity score distributions that explain why this quality is so key to biometric performance. The results identified with the synthetic data are largely reinforced by an analysis of two datasets, one based on eye-movements and one based on gait. There was one difference between the synthetic and real data, related to the intercorrelation of features in real data. Removing these intercorrelations for real datasets with a decorrelation step produced results which were very similar to that obtained with synthetic features.