Griffiths-like behavior and magnetocaloric properties of rare-earth silicide Tb2Co0.8Si3.2.

Novel rare-earth silicide, Tb2Co0.8Si3.2compound, crystallizes in Lu2CoGa3structure, a distorted substitution variant of theAlB2structure. The compound exhibits a complex magnetic state, with a ferromagnetic transition at 58 K, followed by successive antiferromagnetic transitions at 24 K and 8 K, respectively. Isothermal and magnetic hysteresis studies indicate the prominence of competing antiferro and ferromagnetic interactions in the compound. However, this does not lead to the formation of spin glass behavior, as confirmed by AC magnetic susceptibility and heat capacity studies. In the paramagnetic state, the short-range ferromagnetic ordering of cobalt creates a Griffiths-like anomaly that is suppressed at higher magnetic fields. Investigation of magnetocaloric and magnetoresistance properties identifies the compound as a conventional second-order magnetocaloric material with negative magnetoresistance. Furthermore, the determination of Landau coefficients and subsequent analysis indicate that the isothermal entropy change of the compound can be calculated from these coefficients.

Using the Chou's 5-steps rule to predict splice junctions with interpretable bidirectional long short-term memory networks.

Neural models have been able to obtain state-of-the-art performances on several genome sequence-based prediction tasks. Such models take only nucleotide sequences as input and learn relevant features on their own. However, extracting the interpretable motifs from the model remains a challenge. This work explores various existing visualization techniques in their ability to infer relevant sequence information learnt by a recurrent neural network (RNN) on the task of splice junction identification. The visualization techniques have been modulated to suit the genome sequences as input. The visualizations inspect genomic regions at the level of a single nucleotide as well as a span of consecutive nucleotides. This inspection is performed based on the modification of input sequences (perturbation based) or the embedding space (back-propagation based). We infer features pertaining to both canonical and non-canonical splicing from a single neural model. Results indicate that the visualization techniques produce comparable performances for branchpoint detection. However, in the case of canonical donor and acceptor junction motifs, perturbation based visualizations perform better than back-propagation based visualizations, and vice-versa for non-canonical motifs. The source code of our stand-alone SpliceVisuL tool is available at https://github.com/aaiitggrp/SpliceVisuL.