A Neural Network Classifier for the COI Barcode Gene

Mitochondrial Cytochrome C Oxidase subunit I (CO I – to be read as “see – oh one”) is a 658 base pair region in the gene encoding that is proposed as standard barcode for animals. Meaning, the CO I is a special region found in animal DNA that is studied to identify the species of the animal. Currently, there is an implementation of an algorithm called ARBitrator which identifies and extracts these CO I sequences from enormous genes database called GenBank. The ARBitrator is good at extracting the CO I sequences that have better specificity and accuracy as compared to other existing algorithms for CO I sequence identification[1][2]. Now, this project aims at training a neural network to learn the features of the CO I sequences extracted by ARBitrator, so that this neural network can be used in future to further recognize CO I sequences. Effectively, we are aiming to successfully design, train, and use a deep learning neural network to learn to recognize CO I sequences in a supervised way. This is the first time that a neural network is explored and used for this purpose

Tuning the caloric response of BaTiO3_3 by tensile epitaxial strain

We investigate the effect of epitaxial strain on the electrocaloric effect (ECE) in BaTiO$_3$ by means of ab initio based molecular dynamics simulations. We show that tensile strain can be used to optimize the operation range for ferroic cooling. Strain in the range of $\leq 1$ % can be used to shift the operation temperature by several hundreds of Kelvin both to higher and lower temperatures, depending on the direction of the external field. In addition, the transformation between multi-domain and mono-domain states, induced by an in-plane electric field, results in an additional peak of the adiabatic temperature change at lower temperatures, and a broad temperature interval where the caloric response scales linearly with the applied field strength, even up to very high fields.Comment: 6 pages, 4 figure