Mechanically activated rupture of single covalent bonds: evidence of force induced bond hydrolysis.

We have used temperature-dependent single molecule force spectroscopy to stretch covalently anchored carboxymethylated amylose (CMA) polymers attached to an amino-functionalized AFM cantilever. Using an Arrhenius kinetics model based on a Morse potential as a one-dimensional representation of covalent bonds, we have extracted kinetic and structural parameters of the bond rupture process. With 35.5 kJ mol−1, we found a significantly smaller dissociation energy and with 9.0 × 102 s−1 to 3.6 × 103 s−1 also smaller Arrhenius pre-factors than expected for homolytic bond scission. One possible explanation for the severely reduced dissociation energy and Arrhenius pre-factors is the mechanically activated hydrolysis of covalent bonds. Both the carboxylic acid amide and the siloxane bond in the amino-silane surface linker are in principle prone to bond hydrolysis. Scattering, slope and curvature of the scattered data plots indicate that in fact two competing rupture mechanisms are observed

Broad geographic sampling reveals the shared basis and environmental correlates of seasonal adaptation in Drosophila.

To advance our understanding of adaptation to temporally varying selection pressures, we identified signatures of seasonal adaptation occurring in parallel among Drosophila melanogaster populations. Specifically, we estimated allele frequencies genome-wide from flies sampled early and late in the growing season from 20 widely dispersed populations. We identified parallel seasonal allele frequency shifts across North America and Europe, demonstrating that seasonal adaptation is a general phenomenon of temperate fly populations. Seasonally fluctuating polymorphisms are enriched in large chromosomal inversions, and we find a broad concordance between seasonal and spatial allele frequency change. The direction of allele frequency change at seasonally variable polymorphisms can be predicted by weather conditions in the weeks prior to sampling, linking the environment and the genomic response to selection. Our results suggest that fluctuating selection is an important evolutionary force affecting patterns of genetic variation in Drosophila

Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models

Recent years have seen a growing interest within the natural language processing (NLP)community in evaluating the ability of semantic models to capture human meaning representation in the brain. Existing research has mainly focused on applying semantic models to de-code brain activity patterns associated with the meaning of individual words, and, more recently, this approach has been extended to sentences and larger text fragments. Our work is the first to investigate metaphor process-ing in the brain in this context. We evaluate a range of semantic models (word embeddings, compositional, and visual models) in their ability to decode brain activity associated with reading of both literal and metaphoric sentences. Our results suggest that compositional models and word embeddings are able to capture differences in the processing of literal and metaphoric sentences, providing sup-port for the idea that the literal meaning is not fully accessible during familiar metaphor comprehension