Search

The rapid accumulation of molecular data motivates development of innovative approaches to computationally characterize sequences, structures and functions of biological and chemical molecules in an efficient, accessible and accurate manner. Notwithstanding several computational tools that characterize protein or nucleic acids data, there are no one-stop computational toolkits that comprehensively characterize a wide range of biomolecules. We address this vital need by developing a holistic platform that generates features from sequence and structural data for a diverse collection of molecule types. Our freely available and easy-to-use iFeatureOmega platform generates, analyzes and visualizes 189 representations for biological sequences, structures and ligands. To the best of our knowledge, iFeatureOmega provides the largest scope when directly compared to the current solutions, in terms of the number of feature extraction and analysis approaches and coverage of different molecules. We release three versions of iFeatureOmega including a webserver, command line interface and graphical interface to satisfy needs of experienced bioinformaticians and less computer-savvy biologists and biochemists. With the assistance of iFeatureOmega, users can encode their molecular data into representations that facilitate construction of predictive models and analytical studies. We highlight benefits of iFeatureOmega based on three research applications, demonstrating how it can be used to accelerate and streamline research in bioinformatics, computational biology, and cheminformatics areas. The iFeatureOmega webserver is freely available at http://ifeatureomega.erc.monash.edu and the standalone versions can be downloaded from https://github.com/Superzchen/iFeatureOmega-GUI/ and https://github.com/Superzchen/iFeatureOmega-CLI/.

Background: Healthcare is among the leading industries which drives the use of personal devices for work purposes (BYOD). However, allowing BYOD for healthcare workers comes at a cost, as it puts sensitive information assets such as patient data residing on personal devices at risk of potential data breaches. Objective: Previous review of the literature has highlighted the dearth of empirical studies in hospital settings regarding BYOD usage. As such, this paper aims to report BYOD usage trends in Australian hospitals through a national survey, first of its kind in Australia. Methods: An anonymous survey was conducted online among health IT personnel, asking them about their experiences about BYOD usage in their hospitals. 28 responses were collected based on public Australian hospitals, which included 21 hospital groups and 7 standalone hospitals, likely to represent more than 100 hospitals in total. Survey responses were quantitatively analysed through descriptive statistical analysis and cross tabulation. Results: BYOD is allowed in majority of the hospitals, and among all major staff groups, with doctors being the leading group. Participants ranked reasons for allowing BYOD, and most of them were related to improvement in clinical productivity, efficiency and mobility for clinical staff. Challenges were generally related to data security such as patient data breaches and compliance with data security laws, according to them. More than two thirds of hospitals had a cybersecurity officer employed, and CIOs were the most dominant group who held responsibility for managing BYOD within the hospital. Conclusion: This paper provides a starting point for better understanding of BYOD usage in a complex healthcare environment based on empirical evidence, one which highlights the security-usability conundrum, confirming previous literature themes.

Epilepsy is the most common neurological disorder. The diagnosis commonly requires manual visual electroencephalogram (EEG) analysis which is time-consuming. Deep learning has shown promising performance in detecting interictal epileptiform discharges (IED) and may improve the quality of epilepsy monitoring. However, most of the datasets in the literature are small (n≤100) and collected from single clinical centre, limiting the generalization across different devices and settings. To better automate IED detection, we cross-evaluated a Resnet architecture on 2 sets of routine EEG recordings from patients with idiopathic generalized epilepsy collected at the Alfred Health Hospital and Royal Melbourne Hospital (RMH). We split these EEG recordings into 2s windows with or without IED and evaluated different model variants in terms of how well they classified these windows. The results from our experiment showed that the architecture generalized well across different datasets with an AUC score of 0.894 (95% CI, 0.881–0.907) when trained on Alfred’s dataset and tested on RMH’s dataset, and 0.857 (95% CI, 0.847–0.867) vice versa. In addition, we compared our best model variant with Persyst and observed that the model was comparable.