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Excitation of high-Tc cuprates and certain organic superconductors with intense far-infrared optical pulses has been shown to create non-equilibrium states with optical properties that are consistent with transient high-temperature superconductivity. These non-equilibrium phases have been generated using femtosecond drives, and have been observed to disappear immediately after excitation, which is evidence of states that lack intrinsic rigidity. Here we make use of a new optical device to drive metallic K3C60 with mid-infrared pulses of tunable duration, ranging between one picosecond and one nanosecond. The same superconducting-like optical properties observed over short time windows for femtosecond excitation are shown here to become metastable under sustained optical driving, with lifetimes in excess of ten nanoseconds. Direct electrical probing, which becomes possible at these timescales, yields a vanishingly small resistance with the same relaxation time as that estimated by terahertz conductivity. We provide a theoretical description of the dynamics after excitation, and justify the observed slow relaxation by considering randomization of the order-parameter phase as the rate-limiting process that determines the decay of the light-induced superconductor.Evidence for light-induced superconductivity in K3C60 was limited to optical methods due to the short lifetime of the phase. Extending the lifetime from picoseconds to nanoseconds now allows measurement of its negligible electrical resistance.

Arrested replication forks, when restarted by homologous recombination, result in error-prone DNA syntheses and non-allelic homologous recombination. Fission yeast RTS1 is a model fork barrier used to probe mechanisms of recombination-dependent restart. RTS1 barrier activity is entirely dependent on the DNA binding protein Rtf1 and partially dependent on a second protein, Rtf2. Human RTF2 was recently implicated in fork restart, leading us to examine fission yeast Rtf2’s role in more detail. In agreement with previous studies, we observe reduced barrier activity upon rtf2 deletion. However, we identified Rtf2 to be physically associated with mRNA processing and splicing factors and rtf2 deletion to cause increased intron retention. One of the most affected introns resided in the rtf1 transcript. Using an intronless rtf1, we observed no reduction in RFB activity in the absence of Rtf2. Thus, Rtf2 is essential for correct rtf1 splicing to allow optimal RTS1 barrier activity.

Bullying in health workplaces has a negative impact on nurses, their families, multidisciplinary teams, patient care and the profession. This paper compares the experiences of Australian and UK baccalaureate nursing students in relation to bullying and harassment during clinical placement. A secondary analysis was conducted on two primary cross-sectional studies of bullying experiences of Australian and UK nursing students. Data were collected using the Student Experience of Bullying during Clinical Placement (SEBDCP) questionnaire and analysed using descriptive and inferential statistics. The total sample was 833 Australian and 561 UK students. Australian nursing students experienced a higher rate of bullying (50.1%) than UK students (35.5%). Students identified other nurses as the main perpetrators (Aust 53%, UK 68%), although patients were the main source of physical acts of bullying. Few bullied students chose to report the episode/s. The main reason for non-reporting was fear of being victimised. Sadly, some students felt bullying and harassment was ‘part of the job’. A culture of bullying in nursing persists internationally. Nursing students are vulnerable and can question their future in the ‘caring’ profession of nursing after experiencing and/or witnessing bullying during clinical placement. Bullying requires a zero tolerance approach. Education providers must develop clearer policies and implement procedures to protect students - the future nursing workforce. •Few studies have explored nursing students' experience of bullying on placement.•Up to one-half of Australian and UK respondents experienced bullying and harassment.•The source of bullying on placement is often nurses, managers and preceptors/mentors.•Cultural issues contribute significantly to the problem.•Changes to education, practice and policy are required to achieve the necessary cultural change.

Background Characterising programs of gene regulation by studying individual protein-DNA and protein-protein interactions would require a large volume of high-resolution proteomics data, and such data are not yet available. Instead, many gene regulatory network (GRN) techniques have been developed, which leverage the wealth of transcriptomic data generated by recent consortia to study indirect, gene-level relationships between transcriptional regulators. Despite the popularity of such methods, previous methods of GRN inference exhibit limitations that we highlight and address through the lens of information theory. Results We introduce new model-free and non-linear information theoretic measures for the inference of GRNs and other biological networks from continuous-valued data. Although previous tools have implemented mutual information as a means of inferring pairwise associations, they either introduce statistical bias through discretisation or are limited to modelling undirected relationships. Our approach overcomes both of these limitations, as demonstrated by a substantial improvement in empirical performance for a set of 160 GRNs of varying size and topology. Conclusions The information theoretic measures described in this study yield substantial improvements over previous approaches (e.g. ARACNE) and have been implemented in the latest release of NAIL (Network Analysis and Inference Library). However, despite the theoretical and empirical advantages of these new measures, they do not circumvent the fundamental limitation of indeterminacy exhibited across this class of biological networks. These methods have presently found value in computational neurobiology, and will likely gain traction for GRN analysis as the volume and quality of temporal transcriptomics data continues to improve.

Background Predictive gene expression modelling is an important tool in computational biology due to the volume of high-throughput sequencing data generated by recent consortia. However, the scope of previous studies has been restricted to a small set of cell-lines or experimental conditions due an inability to leverage distributed processing architectures for large, sharded data-sets. Results We present a distributed implementation of gene expression modelling using the MapReduce paradigm and prove that performance improves as a linear function of available processor cores. We then leverage the computational efficiency of this framework to explore the variability of epigenetic function across fifty histone modification data-sets from variety of cancerous and non-cancerous cell-lines. Conclusions We demonstrate that the genome-wide relationships between histone modifications and mRNA transcription are lineage, tissue and karyotype-invariant, and that models trained on matched -omics data from non-cancerous cell-lines are able to predict cancerous expression with equivalent genome-wide fidelity.

Computer vision plays a major role in most autonomous systems and is particularly fundamental within the robotics industry, where vision data are the main input to all navigation and high-level decision making. Although there is significant research into developing and optimising algorithms for feature detection and environment reconstruction, there is a comparative lack of emphasis on how best to map these abstract concepts onto an appropriate software architecture. In this study, we distinguish between functional and non-functional requirements of a computer vision system. Using a RoboCup humanoid robot system as a case study, we propose and develop a software architecture that fulfills the latter criteria. To demonstrate the modifiability of the proposed architecture, we detail a number of examples of feature detection algorithms that were modified to capture the rapidly evolving RoboCup requirements, with emphasis on which aspects of the underlying framework required modification to support their integration. To demonstrate portability, we port our vision system (designed for an application-specific DARwIn-OP humanoid robot) to a general-purpose, Raspberry Pi computer. We evaluate the processing time on both hardware platforms for several image streams under different conditions and compare relative to a vision system optimised for functional requirements only. The architecture and implementation presented in this study provide a highly generalisable framework for computer vision system design that is of particular benefit in research and development, competition and other environments in which rapid system evolution is necessary to adapt to domain-specific requirements.

Background With the continued exponential growth in data volume, large-scale data mining and machine learning experiments have become a necessity for many researchers without programming or statistics backgrounds. WEKA (Waikato Environment for Knowledge Analysis) is a gold standard framework that facilitates and simplifies this task by allowing specification of algorithms, hyper-parameters and test strategies from a streamlined Experimenter GUI. Despite its popularity, the WEKA Experimenter exhibits several limitations that we address in our new FlexDM software. Results FlexDM addresses four fundamental limitations with the WEKA Experimenter: reliance on a verbose and difficult-to-modify XML schema; inability to meta-optimise experiments over a large number of algorithm hyper-parameters; inability to recover from software or hardware failure during a large experiment; and failing to leverage modern multicore processor architectures. Direct comparisons between the FlexDM and default WEKA XML schemas demonstrate a 10-fold improvement in brevity for a specification that allows finer control of experimental procedures. The stability of FlexDM has been tested on a large biological dataset (approximately 450 k attributes by 150 samples), and automatic parallelisation of tasks yields a quasi-linear reduction in execution time when distributed across multiple processor cores. Conclusion FlexDM is a powerful and easy-to-use extension to the WEKA package, which better handles the increased volume and complexity of data that has emerged during the 20 years since WEKA’s original development. FlexDM has been tested on Windows, OSX and Linux operating systems and is provided as a pre-configured virtual reference environment for trivial usage and extensibility. This software can substantially improve the productivity of any research group conducting large-scale data mining or machine learning tasks, in addition to providing non-programmers with improved control over specific aspects of their data analysis pipeline via a succinct and simplified XML schema.

Background Transcription factors (TFs) and histone modifications (HMs) play critical roles in gene expression by regulating mRNA transcription. Modelling frameworks have been developed to integrate high-throughput omics data, with the aim of elucidating the regulatory logic that results from the interactions of DNA, TFs and HMs. These models have yielded an unexpected and poorly understood result: that TFs and HMs are statistically redundant in explaining mRNA transcript abundance at a genome-wide level. Results We constructed predictive models of gene expression by integrating RNA-sequencing, TF and HM chromatin immunoprecipitation sequencing and DNase I hypersensitivity data for two mammalian cell types. All models identified genome-wide statistical redundancy both within and between TFs and HMs, as previously reported. To investigate potential explanations, groups of genes were constructed for ontology-classified biological processes. Predictive models were constructed for each process to explore the distribution of statistical redundancy. We found significant variation in the predictive capacity of TFs and HMs across these processes and demonstrated the predictive power of HMs to be inversely proportional to process enrichment for housekeeping genes. Conclusions It is well established that the roles played by TFs and HMs are not functionally redundant. Instead, we attribute the statistical redundancy reported in this and previous genome-wide modelling studies to the heterogeneous distribution of HMs across chromatin domains. Furthermore, we conclude that statistical redundancy between individual TFs can be readily explained by nucleosome-mediated cooperative binding. This could possibly help the cell confer regulatory robustness by rejecting signalling noise and allowing control via multiple pathways.

Background Predictive modelling of gene expression is a powerful framework for the in silico exploration of transcriptional regulatory interactions through the integration of high-throughput -omics data. A major limitation of previous approaches is their inability to handle conditional interactions that emerge when genes are subject to different regulatory mechanisms. Although chromatin immunoprecipitation-based histone modification data are often used as proxies for chromatin accessibility, the association between these variables and expression often depends upon the presence of other epigenetic markers (e.g. DNA methylation or histone variants). These conditional interactions are poorly handled by previous predictive models and reduce the reliability of downstream biological inference. Results We have previously demonstrated that integrating both transcription factor and histone modification data within a single predictive model is rendered ineffective by their statistical redundancy. In this study, we evaluate four proposed methods for quantifying gene-level DNA methylation levels and demonstrate that inclusion of these data in predictive modelling frameworks is also subject to this critical limitation in data integration. Based on the hypothesis that statistical redundancy in epigenetic data is caused by conditional regulatory interactions within a dynamic chromatin context, we construct a new gene expression model which is the first to improve prediction accuracy by unsupervised identification of latent regulatory classes. We show that DNA methylation and H2A.Z histone variant data can be interpreted in this way to identify and explore the signatures of silenced and bivalent promoters, substantially improving genome-wide predictions of mRNA transcript abundance and downstream biological inference across multiple cell lines. Conclusions Previous models of gene expression have been applied successfully to several important problems in molecular biology, including the discovery of transcription factor roles, identification of regulatory elements responsible for differential expression patterns and comparative analysis of the transcriptome across distant species. Our analysis supports our hypothesis that statistical redundancy in epigenetic data is partially due to conditional relationships between these regulators and gene expression levels. This analysis provides insight into the heterogeneous roles of H3K4me3 and H3K27me3 in the presence of the H2A.Z histone variant (implicated in cancer progression) and how these signatures change during lineage commitment and carcinogenesis.

Cancer Australia has five messages that women need to know about ovarian cancer: * It is not a silent killer, women have symptoms even if they are vague, they still need to be followed up with their doctor. * There are currently no screening tests for ovarian cancer. * The disease occurs in women who don't have a family history. * If diagnosed, consult with a gynaecological oncologist. * Women know they own bodies and need to take notice of any changes in their body (Cancer Australia 2013a).