Genetic characterization of Yug Bogdanovac virus

We present pyrosequencing data and phylogenetic analysis for the full genome of Yug Bogdanovac virus (YBV), a member of the Vesicular stomatitis virus serogroup of the Rhabdoviridae isolated from a pool of Phlebotomus perfiliewi sandflies collected in Serbia in 1976. YBV shows very low nucleotide identities to other members of the Vesicular stomatitis virus serogroup and does not contain a reading frame for C′/C proteins

Hantavirus in Northern Short-tailed Shrew, United States

Phylogenetic analyses, based on partial medium- and large-segment sequences, support an ancient evolutionary origin of a genetically distinct hantavirus detected by reverse transcription–PCR in tissues of northern short-tailed shrews (Blarina brevicauda) captured in Minnesota in August 1998. To our knowledge, this is the first evidence of hantaviruses harbored by shrews in the Americas

Named entity recognition in electronic health records using transfer learning bootstrapped neural networks

Neural networks (NNs) have become the state of the art in many machine learning applications, such as image, sound (LeCun et al., 2015) and natural language processing (Young et al., 2017; Linggard et al., 2012). However, the success of NNs remains dependent on the availability of large labelled datasets, such as in the case of electronic health records (EHRs). With scarce data, NNs are unlikely to be able to extract this hidden information with practical accuracy. In this study, we develop an approach that solves these problems for named entity recognition, obtaining 94.6 F1 score in I2B2 2009 Medical Extraction Challenge (Uzuner et al., 2010), 4.3 above the architecture that won the competition. To achieve this, we bootstrap our NN models through transfer learning by pretraining word embeddings on a secondary task performed on a large pool of unannotated EHRs and using the output embeddings as a foundation of a range of NN architectures. Beyond the official I2B2 challenge, we further achieve 82.4 F1 on extracting relationships between medical terms using attention-based seq2seq models bootstrapped in the same manner