Modification of Hydrophilic and Hydrophobic Surfaces Using an Ionic-Complementary Peptide

Ionic-complementary peptides are novel nano-biomaterials with a variety of biomedical applications including potential biosurface engineering. This study presents evidence that a model ionic-complementary peptide EAK16-II is capable of assembling/coating on hydrophilic mica as well as hydrophobic highly ordered pyrolytic graphite (HOPG) surfaces with different nano-patterns. EAK16-II forms randomly oriented nanofibers or nanofiber networks on mica, while ordered nanofibers parallel or oriented 60° or 120° to each other on HOPG, reflecting the crystallographic symmetry of graphite (0001). The density of coated nanofibers on both surfaces can be controlled by adjusting the peptide concentration and the contact time of the peptide solution with the surface. The coated EAK16-II nanofibers alter the wettability of the two surfaces differently: the water contact angle of bare mica surface is measured to be <10°, while it increases to 20.3±2.9° upon 2 h modification of the surface using a 29 µM EAK16-II solution. In contrast, the water contact angle decreases significantly from 71.2±11.1° to 39.4±4.3° after the HOPG surface is coated with a 29 µM peptide solution for 2 h. The stability of the EAK16-II nanofibers on both surfaces is further evaluated by immersing the surface into acidic and basic solutions and analyzing the changes in the nanofiber surface coverage. The EAK16-II nanofibers on mica remain stable in acidic solution but not in alkaline solution, while they are stable on the HOPG surface regardless of the solution pH. This work demonstrates the possibility of using self-assembling peptides for surface modification applications

The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

In the last five years there have been a large number of new time series classification algorithms proposed in the literature. These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series classification archive. The archive has recently been expanded to 85 data sets, over half of which have been donated by researchers at the University of East Anglia. Aspects of previous evaluations have made comparisons between algorithms difficult. For example, several different programming languages have been used, experiments involved a single train/test split and some used normalised data whilst others did not. The relaunch of the archive provides a timely opportunity to thoroughly evaluate algorithms on a larger number of datasets. We have implemented 18 recently proposed algorithms in a common Java framework and compared them against two standard benchmark classifiers (and each other) by performing 100 resampling experiments on each of the 85 datasets. We use these results to test several hypotheses relating to whether the algorithms are significantly more accurate than the benchmarks and each other. Our results indicate that only 9 of these algorithms are significantly more accurate than both benchmarks and that one classifier, the Collective of Transformation Ensembles, is significantly more accurate than all of the others. All of our experiments and results are reproducible: we release all of our code, results and experimental details and we hope these experiments form the basis for more rigorous testing of new algorithms in the future

Pheromone-sensing neurons regulate peripheral lipid metabolism in <i>Caenorhabditis elegans</i>

It is now established that the central nervous system plays an important role in regulating whole body metabolism and energy balance. However, the extent to which sensory systems relay environmental information to modulate metabolic events in peripheral tissues has remained poorly understood. In addition, it has been challenging to map the molecular mechanisms underlying discrete sensory modalities with respect to their role in lipid metabolism. In previous work our lab has identified instructive roles for serotonin signaling as a surrogate for food availability, as well as oxygen sensing, in the control of whole body metabolism. In this study, we now identify a role for a pair of pheromone-sensing neurons in regulating fat metabolism in C. elegans, which has emerged as a tractable and highly informative model to study the neurobiology of metabolism. A genetic screen revealed that GPA-3, a member of the Gα family of G proteins, regulates body fat content in the intestine, the major metabolic organ for C. elegans. Genetic and reconstitution studies revealed that the potent body fat phenotype of gpa-3 null mutants is controlled from a pair of neurons called ADL(L/R). We show that cAMP functions as the second messenger in the ADL neurons, and regulates body fat stores via the neurotransmitter acetylcholine, from downstream neurons. We find that the pheromone ascr#3, which is detected by the ADL neurons, regulates body fat stores in a GPA-3-dependent manner. We define here a third sensory modality, pheromone sensing, as a major regulator of body fat metabolism. The pheromone ascr#3 is an indicator of population density, thus we hypothesize that pheromone sensing provides a salient 'denominator' to evaluate the amount of food available within a population and to accordingly adjust metabolic rate and body fat levels