Changes in the Adhesive Properties of Spider Aggregate Glue During the Evolution of Cobwebs

We compare the prey capture glues produced by orb-weaving spiders (viscid glue) and their evolutionary descendents, the cobweb-weaving spiders (gumfoot glue). These glues are produced in homologous glands but exhibit contrasting structure, properties and response to changing humidity. Individual glue droplet stretching measurements indicate that the gumfoot glue behaves like a viscoelastic liquid in contrast to the viscid glue, which behaves like a viscoelastic solid. Moreover, the gumfoot glue is largely humidity-resistant – elasticity and adhesion are constant across variation in humidity and there is weak volume-dependence. Viscid glue, however, is highly humidity-sensitive. The glue expands an order of magnitude and demonstrates a monotonous reduction in elasticity under increased humidity, while glue adhesion optimizes at intermediate levels of humidity. We suggest that observed differences are due to different ‘tackifiers' used in these systems. These results shall inspire future efforts in fabricating stimuli-resistant and stimuli-sensitive materials

A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data

Batch effects are the systematic non-biological differences between batches (groups) of samples in microarray experiments due to various causes such as differences in sample preparation and hybridization protocols. Previous work focused mainly on the development of methods for effective batch effects removal. However, their impact on cross-batch prediction performance, which is one of the most important goals in microarray-based applications, has not been addressed. This paper uses a broad selection of data sets from the Microarray Quality Control Phase II (MAQC-II) effort, generated on three microarray platforms with different causes of batch effects to assess the efficacy of their removal. Two data sets from cross-tissue and cross-platform experiments are also included. Of the 120 cases studied using Support vector machines (SVM) and K nearest neighbors (KNN) as classifiers and Matthews correlation coefficient (MCC) as performance metric, we find that Ratio-G, Ratio-A, EJLR, mean-centering and standardization methods perform better or equivalent to no batch effect removal in 89, 85, 83, 79 and 75% of the cases, respectively, suggesting that the application of these methods is generally advisable and ratio-based methods are preferred