Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating.
The shear frame allows testing of thin weak snowpack layers that are often critical for slab avalanche release. A shear metal frame with an area of 0.01–0.05 m2 is used to grip the snow a few mm above a buried weak snowpack layer. Using a force gauge, the frame is pulled until a fracture occurs in the weak layer within 1 s. The strength is calculated from the maximum force divided by the area of the frame. Finite-element studies show that the shear stress in the weak layer is concentrated below the cross-members that subdivide the frame and where the weak layer is notched at the front and back of the frame. Placing the bottom of the frame in the weak layer increases the stress concentrations, and results in significantly lower strength measurements than placing the bottom of the frame a few mm above the weak layer. Based on over 800 sets of 7–12 tests in western Canada, coefficients of variation average 14% and 18% from level study plots and avalanche start zones, respectively. Consequently,sets of 12 tests typically yield a precision of the mean of 10% with 95% confidence, which is sufficient for monitoring of strength change of weak layers over time in study plots. With consistent technique, there is no significant difference in mean strength measurements obtained by different experienced shear frame operators using the same approximate loading rate and technique for placing the frame. Although fracture surfaces are usually planar, only one of eleven shapes of non-planar fracture surfaces showed significantly different strength compared to planar fracture surfaces. For weak layers thick enough for density measurements, the shear strength is plotted against density and grain form. From these data, empirical equations are determined to estimate the shear strength of weak snowpack layers.
Stability indices for skier-triggering of slab avalanches are discussed in terms of an adjustment to the skier-induced stress to allow for ski penetration and an adjustment to shear strength (measured with a shear frame) to allow for normal load due to the slab overlying the weak layer. The proposed adjustment to shear strength depends on the microstructure of the weak layer. These adjustments are incorporated into a refinement of the previously established Swiss stability index for skier-triggering. The percentage of correct predictions for the Swiss and refined indices are evaluated using data from 115 weak layers on skier-tested avalanche slopes, 83 of which were classified as persistent weak layers of surface hoar, faceted crystals or depth hoar and 32 as non-persistent weak layers. The refined index reduces the number of incorrectly predicted slab avalanches for the persistent weak layers.
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