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Sequence-based markers have added a new dimension in the efficiency of identifying alien introgressions in wheat. Expressed sequence tag-sequence tagged sites (EST-STS) markers have proved useful in tracing alien chromatin. In this study, we report the development of Thinopyrum bessarabicum- and Secale anatolicum-specific EST-STS markers and their application in tracing respective alien chromatin introgressions in wheat. The parental lines, Chinese Spring (CS), ISR991.1 (CS/Th. bessarabicum amphidiploid), and ISR1049.2 (CS/Secale anatolicum amphidiploid), were used as core experimental materials. Using comparative analysis of RNA-Seq data, 10 903 and 10 660 candidate sequences specific to Th. bessarabicum and S. anatolicum, respectively, were assembled and identified. To validate the genome specificity of these candidate sequences, 68 and 64 EST-STS markers were developed from randomly selected candidate sequences of Th. bessarabicum and S. anatolicum, respectively, and tested on sets of alien addition lines. Fifty-five and 53 markers for Th. bessarabicum and S. anatolicum chromatin, respectively, were assigned to chromosomal location(s), covering all seven chromosomes. Approximately 83% of S. anatolicum-specific markers were transferable to S. cereale. The genome-specific candidate sequences identified and the EST-STS markers developed will be valuable resources for exploitation of Th. bessarabicum and Secale species diversity in wheat and triticale breeding.

Polyandrous social insects such as the honey bee are prime candidates for parental manipulation of gene expression in offspring. Although there is good evidence for parent-of-origin effects in honey bees the epigenetic mechanisms that underlie these effects remain a mystery. Small RNA molecules such as miRNAs, piRNAs and siRNAs play important roles in transgenerational epigenetic inheritance and in the regulation of gene expression during development. Here we present the first characterisation of small RNAs present in honey bee reproductive tissues: ovaries, spermatheca, semen, fertilised and unfertilised eggs, and testes. We show that semen contains fewer piRNAs relative to eggs and ovaries, and that piRNAs and miRNAs which map antisense to genes involved in DNA regulation and developmental processes are differentially expressed between tissues. tRNA fragments are highly abundant in semen and have a similar profile to those seen in semen in other animals. Intriguingly we find abundant piRNAs that target the sex determination locus, suggesting that piRNAs may play a role in honey bee sex determination. We conclude that small RNAs play a fundamental role in honey bee gametogenesis and reproduction and provide a plausible mechanism for parent-of origin-effects on gene expression and reproductive physiology.

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation. Funding The European Commission FP7 NeurOmics project; CHDI Foundation; the Medical Research Council UK; the Brain Research Trust; and the Guarantors of Brain.

Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are neurodegenerative diseases that are considered to be on the same disease spectrum because of overlapping genetic, pathological and clinical traits. Changes in serum proteins in FTD and ALS are poorly understood, and currently no definitive biomarkers exist for diagnosing or monitoring disease progression for either disease. Here we applied quantitative discovery proteomics to analyze protein changes in FTD (N = 72) and ALS (N = 28) patient serum compared to controls (N = 22). Twenty three proteins were significantly altered in FTD compared to controls (increased—APOL1, C3, CTSH, EIF5A, MYH2, S100A8, SUSD5, WDR1; decreased—C1S, C7, CILP2, COMP, CRTAC1, EFEMP1, FBLN1, GSN, HSPG2, IGHV1, ITIH2, PROS1, SHBG, UMOD, VASN) and 14 proteins were significantly altered in ALS compared to controls (increased—APOL1, CKM, CTSH, IGHG1, IGKC, MYH2; decreased—C7, COMP, CRTAC1, EFEMP1, FBLN1, GSN, HSPG2, SHBG). There was substantial overlap in the proteins that were altered in FTD and ALS. These results were validated using western blotting. Gene ontology tools were used to assess functional pathways potentially dysregulated in the two diseases, and calcium ion binding and innate immunity pathways were altered in both diseases. When put together, these results suggest significant overlap in pathophysiological peripheral changes in FTD and ALS. This study represents the first proteomics side-by-side comparison of serum changes in FTD and ALS, providing new insights into under-recognized perturbed pathways and an avenue for biomarker development for FTD and ALS.

Sequence-based markers have added a new dimension in the efficiency of identifying alien introgressions in wheat. Expressed sequence tag-sequence tagged sites (EST-STS) markers have proved useful in tracing alien chromatin. In this study, we report the development of

Background High-throughput single-cell RNA-seq (scRNA-seq) is a powerful technology for studying gene expression variability in single cells; however, standard analysis approaches only consider the overall expression of each gene, masking additional heterogeneity that could exist through cell type-specific expression of alternative mRNA transcripts. Results Here we show that differential transcript usage (DTU) can be readily detected in data-sets generated from commonly used polyA-captured nanodroplet scRNA-seq technology. Our computational pipeline, Sierra, detects and quantifies polyadenylation sites in scRNA-seq data-sets, which are utilised to evaluate DTU between single-cell populations. We validate our approach by comparing cardiac cell populations derived from scRNA-seq to bulk RNA-seq of matched populations obtained through fluorescent activated cell sorting (FACS), demonstrating that we detect a significant overlap in cell-type DTU between the congruous data-sets. We further illustrate the utility of our method by detecting alternative transcript usage in human peripheral blood mononuclear cells (PBMCs), 3’UTR shortening in activated and proliferating cardiac fibroblasts from injured mouse hearts and finally by building an initial atlas of cell type-specific transcript usage across 12 mouse tissues. Conclusions We anticipate that Sierra will enable new avenues of transcriptional complexity and regulation to be explored in single-cell transcriptomic experiments. Sierra is available at https://github.com/VCCRI/Sierra .

Background Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global transcriptome profiling on the single cell level. Cell type identification from scRNA-seq data is a critical task in a variety of research such as developmental biology, cell reprogramming, and cancers. Typically, cell type identification relies on human inspection using a combination of prior biological knowledge (e.g. marker genes and morphology) and computational techniques (e.g. PCA and clustering). Due to the incompleteness of our current knowledge and the subjectivity involved in this process, a small amount of cells may be subject to mislabelling. Results Here, we propose a semi-supervised learning framework, named scReClassify, for ‘post hoc’ cell type identification from scRNA-seq datasets. Starting from an initial cell type annotation with potentially mislabelled cells, scReClassify first performs dimension reduction using PCA and next applies a semi-supervised learning method to learn and subsequently reclassify cells that are likely mislabelled initially to the most probable cell types. By using both simulated and real-world experimental datasets that profiled various tissues and biological systems, we demonstrate that scReClassify is able to accurately identify and reclassify misclassified cells to their correct cell types. Conclusions scReClassify can be used for scRNA-seq data as a post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure. It is implemented as an R package and is freely available from https://github.com/SydneyBioX/scReClassify

Background Differences in cell-type composition across subjects and conditions often carry biological significance. Recent advancements in single cell sequencing technologies enable cell-types to be identified at the single cell level, and as a result, cell-type composition of tissues can now be studied in exquisite detail. However, a number of challenges remain with cell-type composition analysis – none of the existing methods can identify cell-type perfectly and variability related to cell sampling exists in any single cell experiment. This necessitates the development of method for estimating uncertainty in cell-type composition. Results We developed a novel single cell differential composition (scDC) analysis method that performs differential cell-type composition analysis via bootstrap resampling. scDC captures the uncertainty associated with cell-type proportions of each subject via bias-corrected and accelerated bootstrap confidence intervals. We assessed the performance of our method using a number of simulated datasets and synthetic datasets curated from publicly available single cell datasets. In simulated datasets, scDC correctly recovered the true cell-type proportions. In synthetic datasets, the cell-type compositions returned by scDC were highly concordant with reference cell-type compositions from the original data. Since the majority of datasets tested in this study have only 2 to 5 subjects per condition, the addition of confidence intervals enabled better comparisons of compositional differences between subjects and across conditions. Conclusions scDC is a novel statistical method for performing differential cell-type composition analysis for scRNA-seq data. It uses bootstrap resampling to estimate the standard errors associated with cell-type proportion estimates and performs significance testing through GLM and GLMM models. We have made this method available to the scientific community as part of the scdney package (Single Cell Data Integrative Analysis) R package, available from https://github.com/SydneyBioX/scdney.

The Kinship Theory of Genomic Imprinting (KTGI) posits that, in species where females mate with multiple males, there is selection for a male to enhance the reproductive success of his offspring at the expense of other males and his mating partner. Reciprocal crosses between honey bee subspecies show parent-of-origin effects for reproductive traits, suggesting that males modify the expression of genes related to female function in their female offspring. This effect is likely to be greater in the Cape honey bee (Apis mellifera capensis), because a male's daughters have the unique ability to produce female offspring that can develop into reproductive workers or the next queen without mating. We generated reciprocal crosses between Capensis and another subspecies and used RNA-seq to identify transcripts that are over- or underexpressed in the embryos, depending on the parental origin of the gene. As predicted, 21 genes showed expression bias towards the Capensis father's allele in colonies with a Capensis father, with no such bias in the reciprocal cross. A further six genes showed a consistent bias towards expression of the father's allele across all eight colonies examined, regardless of the direction of the cross. Consistent with predictions of the KTGI, six of the 21 genes are associated with female reproduction. No gene consistently showed overexpression of the maternal allele.

Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with individual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors; scMerge can also enhance biological discovery through robust data integration, which we show through the inference of development trajectory in a liver dataset collection.

We report the results of a volume-limited survey using the Australia Telescope Compact Array to search for transient and quiescent radio emission from 15 southern hemisphere ultracool dwarfs. We detect radio emission from 2MASSW J0004348-404405 increasing the number of radio loud ultracool dwarfs to 22. We also observe radio emission from 2MASS J10481463-3956062 and 2MASSI J0339352-352544, two sources with previous radio detections. The radio emission from the three detected sources shows no variability or flare emission. Modelling this quiescent emission we find that it is consistent with optically thin gyrosynchrotron emission from a magnetosphere with an emitting region radius of (1 - 2)$R_*$, magnetic field inclination 20$^{\circ}$ - 80$^{\circ}$, field strength $\sim$10 - 200 G, and power-law electron density $\sim$10$^4$ - 10$^8$ cm$^{-3}$. Additionally, we place upper limits on four ultracool dwarfs with no previous radio observations. This increases the number of ultracool dwarfs studied at radio frequencies to 222. Analysing general trends of the radio emission for this sample of 15 sources, we find that the radio activity increases for later spectral types and more rapidly rotating objects. Furthermore, comparing the ratio of the radio to X-ray luminosities for these sources, we find 2MASS J10481463-3956062 and 2MASSI J0339352-352544 violate the Guedel-Benz relation by more than two orders of magnitude., 10 pages, 7 figures

The use of massively parallel sequencing of maternal cfDNA for non-invasive prenatal testing (NIPT) of aneuploidy is widely available. Recently, the scope of testing has increased to include selected subchromosomal abnormalities, but the number of samples reported has been small. We developed a calling pipeline based on a segmentation algorithm for the detection of these rearrangements in maternal plasma. The same read depth used in our standard pipeline for aneuploidy NIPT detected 15/18 (83%) samples with pathogenic rearrangements > 6 Mb but only 2/10 samples with rearrangements < 6 Mb, unless they were maternally inherited. There were two false-positive calls in 534 samples with no known subchromosomal abnormalities (specificity 99.6%). Using higher read depths, we detected 29/31 fetal subchromosomal abnormalities, including the three samples with maternally inherited microduplications. We conclude that test sensitivity is a function of the fetal fraction, read depth, and size of the fetal CNV and that at least one of the two false negatives is due to a low fetal fraction. The lack of an independent method for determining fetal fraction, especially for female fetuses, leads to uncertainty in test sensitivity, which currently has implications for this technique’s future as a clinical diagnostic test. Furthermore, to be effective, NIPT must be able to detect chromosomal rearrangements across the whole genome for a very low false-positive rate. Because standard NIPT can only detect the majority of larger (>6 Mb) chromosomal rearrangements and requires knowledge of fetal fraction, we consider that it is not yet ready for routine clinical implementation.

Concerted examination of multiple collections of single cell RNA-Seq (scRNA-Seq) data promises further biological insights that cannot be uncovered with individual datasets. However, such integrative analyses are challenging and require sophisticated methodologies. To enable effective interrogation of multiple scRNA-Seq datasets, we have developed a novel algorithm, named scMerge, that removes unwanted variation by combining stably expressed genes and utilizing pseudo-replicates across datasets. Analysis of large collections of publicly available datasets demonstrates that scMerge performs well in multiple scenarios and enhances biological discovery, including inferring cell developmental trajectories.

TDP-43 (encoded by the gene TARDBP) is an RNA binding protein central to the pathogenesis of amyotrophic lateral sclerosis (ALS). However, how TARDBP mutations trigger pathogenesis remains unknown. Here, we use novel mouse mutants carrying point mutations in endogenous Tardbp to dissect TDP-43 function at physiological levels both in vitro and in vivo. Interestingly, we find that mutations within the C-terminal domain of TDP-43 lead to a gain of splicing function. Using two different strains, we are able to separate TDP-43 loss- and gain-of-function effects. TDP-43 gain-of-function effects in these mice reveal a novel category of splicing events controlled by TDP-43, referred to as "skiptic" exons, in which skipping of constitutive exons causes changes in gene expression. In vivo, this gain-of-function mutation in endogenous Tardbp causes an adult-onset neuromuscular phenotype accompanied by motor neuron loss and neurodegenerative changes. Furthermore, we have validated the splicing gain-of-function and skiptic exons in ALS patient-derived cells. Our findings provide a novel pathogenic mechanism and highlight how TDP-43 gain of function and loss of function affect RNA processing differently, suggesting they may act at different disease stages. Keywords: ALS; cryptic exon; skiptic exon; splicing; TDP-43

Human genetic factors predispose to tuberculosis (TB). We studied 7.6 million genetic variants in 5,530 people with pulmonary TB and in 5,607 healthy controls. In the combined analysis of these subjects and the follow-up cohort (15,087 TB patients and controls altogether), we found an association between TB and variants located in introns of the ASAP1 gene on chromosome 8q24 (P = 2.6 × 10(-11) for rs4733781; P = 1.0 × 10(-10) for rs10956514). Dendritic cells (DCs) showed high ASAP1 expression that was reduced after Mycobacterium tuberculosis infection, and rs10956514 was associated with the level of reduction of ASAP1 expression. The ASAP1 protein is involved in actin and membrane remodeling and has been associated with podosomes. The ASAP1-depleted DCs showed impaired matrix degradation and migration. Therefore, genetically determined excessive reduction of ASAP1 expression in M. tuberculosis-infected DCs may lead to their impaired migration, suggesting a potential mechanism of predisposition to TB.

Devoy et al. develop the first mouse model to fully recapitulate human FUS-ALS, as defined by midlife-onset progressive degeneration of motor neurons with dominant inheritance. A toxic gain of function occurs in the absence of FUS protein aggregation, involving disturbance of ribosomes and mitochondria at the endoplasmic reticulum., Mutations in FUS are causative for amyotrophic lateral sclerosis with a dominant mode of inheritance. In trying to model FUS-amyotrophic lateral sclerosis (ALS) in mouse it is clear that FUS is dosage-sensitive and effects arise from overexpression per se in transgenic strains. Novel models are required that maintain physiological levels of FUS expression and that recapitulate the human disease—with progressive loss of motor neurons in heterozygous animals. Here, we describe a new humanized FUS-ALS mouse with a frameshift mutation, which fulfils both criteria: the FUS Delta14 mouse. Heterozygous animals express mutant humanized FUS protein at physiological levels and have adult onset progressive motor neuron loss and denervation of neuromuscular junctions. Additionally, we generated a novel antibody to the unique human frameshift peptide epitope, allowing specific identification of mutant FUS only. Using our new FUSDelta14 ALS mouse-antibody system we show that neurodegeneration occurs in the absence of FUS protein aggregation. FUS mislocalization increases as disease progresses, and mutant FUS accumulates at the rough endoplasmic reticulum. Further, transcriptomic analyses show progressive changes in ribosomal protein levels and mitochondrial function as early disease stages are initiated. Thus, our new physiological mouse model has provided novel insight into the early pathogenesis of FUS-ALS.

There is widespread transcriptional dysregulation in Huntington’s disease (HD) brain, but analysis is inevitably limited by advanced disease and postmortem changes. However, mutant HTT is ubiquitously expressed and acts systemically, meaning blood, which is readily available and contains cells that are dysfunctional in HD, could act as a surrogate for brain tissue. We conducted an RNA-Seq transcriptomic analysis using whole blood from two HD cohorts, and performed gene set enrichment analysis using public databases and weighted correlation network analysis modules from HD and control brain datasets. We identified dysregulated gene sets in blood that replicated in the independent cohorts, correlated with disease severity, corresponded to the most significantly dysregulated modules in the HD caudate, the most prominently affected brain region, and significantly overlapped with the transcriptional signature of HD myeloid cells. High-throughput sequencing technologies and use of gene sets likely surmounted the limitations of previously inconsistent HD blood expression studies. Our results suggest transcription is disrupted in peripheral cells in HD through mechanisms that parallel those in brain. Immune upregulation in HD overlapped with Alzheimer’s disease, suggesting a common pathogenic mechanism involving macrophage phagocytosis and microglial synaptic pruning, and raises the potential for shared therapeutic approaches.

We present 13 cm and 20 cm radio observations of the magnetic chemically peculiar star CU Virginis taken with the Australia Telescope Compact Array. We detect two circularly polarised radio pulses every rotation period which confirm previous detections. In the first pulse, the lower frequency emission arrives before the higher frequency emission and the ordering reverses in the second pulse. In order to explain the frequency dependence of the time between the two pulses, we construct a geometric model of the magnetosphere of CU Virginis, and consider various emission angles relative to the magnetic field lines. A simple electron cyclotron maser emission model, in which the emission is perpendicular to the magnetic field lines, is not consistent with our data. A model in which the emission is refracted through cold plasma in the magnetosphere is shown to have the correct pulse arrival time frequency dependence.

The VAST survey is a wide-field survey that observes with unprecedented instrument sensitivity (0.5 mJy or lower) and repeat cadence (a goal of 5 seconds) that will enable novel scientific discoveries related to known and unknown classes of radio transients and variables. Given the unprecedented observing characteristics of VAST, it is important to estimate source classification performance, and determine best practices prior to the launch of ASKAP's BETA in 2012. The goal of this study is to identify light-curve characterization and classification algorithms that are best suited for archival VAST light-curve classification. We perform our experiments on light-curve simulations of eight source types and achieve best-case performance of approximately 90% accuracy. We note that classification performance is most influenced by light-curve characterization rather than classifier algorithm.

Background Non-invasive prenatal testing (NIPT) for aneuploidies is now available through commercial companies in many countries, including through private practice in the United Kingdom (UK). Thorough evaluation of service delivery requirements are needed to facilitate NIPT being offered more widely within state funded healthcare systems such as the UK’s National Health Service (NHS). Successful implementation will require the development of laboratory standards, consideration of stakeholder views, an analysis of costs and development of patient and health professional educational materials. Methods/Design NIPT will be offered in an NHS setting as a contingent screening test. Pregnant woman will be recruited through six maternity units in England and Scotland. Women eligible for Down’s syndrome screening (DSS) will be informed about the study at the time of booking. Women that choose routine DSS will be offered NIPT if they have a screening risk ≥1:1000. NIPT results for trisomy 21, 18, 13 will be reported within 7–10 working days. Data on DSS, NIPT and invasive testing uptake, pregnancy outcomes and test efficacy will be collected. Additional data will be gathered though questionnaires to a) determine acceptability to patients and health professionals, b) evaluate patient and health professional education, c) assess informed choice in women accepting or declining testing and d) gauge family expenses. Qualitative interviews will also be conducted with a sub-set of participating women and health professionals. Discussion The results of this study will make a significant contribution to policy decisions around the implementation of NIPT for aneuploidies within the UK NHS. The laboratory standards for testing and reporting, education materials and counselling strategies developed as part of the study are likely to underpin the introduction of NIPT into NHS practice. NIHR Portfolio Number 13865

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the second \textit{XMM-Newton} serendipitous source catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10-fold cross validation accuracy of the training data is ${\sim}$97% on a seven-class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which, 2XMM J180658.7$-$500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ULX but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys., Accepted for ApJ

The advent of synoptic sky surveys has spurred the development of techniques for real-time classification of astronomical sources in order to ensure timely follow-up with appropriate instruments. Previous work has focused on algorithm selection or improved light curve representations, and naively convert light curves into structured feature sets without regard for the time span or phase of the light curves. In this paper, we highlight the violation of a fundamental machine learning assumption that occurs when archival light curves with long observational time spans are used to train classifiers that are applied to light curves with fewer observations. We propose two solutions to deal with the mismatch in the time spans of training and test light curves. The first is the use of classifier committees where each classifier is trained on light curves of different observational time spans. Only the committee member whose training set matches the test light curve time span is invoked for classification. The second solution uses hierarchical classifiers that are able to predict source types both individually and by sub-group, so that the user can trade-off an earlier, more robust classification with classification granularity. We test both methods using light curves from the MACHO survey, and demonstrate their usefulness in improving performance over similar methods that naively train on all available archival data., Comment: Astroinformatics workshop, IEEE International Conference on Data Mining 2013

The Australian Square Kilometre Array Pathfinder (ASKAP) will give us an unprecedented opportunity to investigate the transient sky at radio wavelengths. In this paper we present VAST, an ASKAP survey for Variables and Slow Transients. VAST will exploit the wide-field survey capabilities of ASKAP to enable the discovery and investigation of variable and transient phenomena from the local to the cosmological, including flare stars, intermittent pulsars, X-ray binaries, magnetars, extreme scattering events, interstellar scintillation, radio supernovae and orphan afterglows of gamma ray bursts. In addition, it will allow us to probe unexplored regions of parameter space where new classes of transient sources may be detected. In this paper we review the known radio transient and variable populations and the current results from blind radio surveys. We outline a comprehensive program based on a multi-tiered survey strategy to characterise the radio transient sky through detection and monitoring of transient and variable sources on the ASKAP imaging timescales of five seconds and greater. We also present an analysis of the expected source populations that we will be able to detect with VAST., 29 pages, 8 figures. Submitted for publication in Pub. Astron. Soc. Australia

Circulating tumor DNA (ctDNA) is becoming established as a tool to supplement conventional biopsies for molecular characterization and monitoring of solid cancers, especially for cancers where tumor tissue is difficult to obtain or is only available at limiting quantity. This requires reliable identification, in patient plasma, of tumor-specific DNA alterations that in some cases may be present as a small fraction of the total cell-free DNA molecules. To overcome these technical challenges, we have developed an enhanced platform for tagged-amplicon deep sequencing (TAm-Seq™). Using a combination of efficient library preparation and statistically-based analysis algorithms, this platform can be used to sequence, identify and quantify cancer mutations across a gene panel including both cancer hotspots, as well as entire coding regions of selected genes. This poster will present validated performance specifications of this multi-gene ctDNA sequencing assay. To perform analytical validation, we used reference standards and plasma DNA controls to demonstrate the sensitivity, specificity and quantitative accuracy of this ctDNA analysis platform. We found that our workflow, using 4 mL input plasma, yields very high sensitivity for variants that are present at allele fraction 0.25% or higher in plasma, and retains substantial sensitivity at allele fractions as low as 0.1%. Using dilution mixtures of well-characterised reference samples, we show that the assay accurately quantifies allele fractions with precision predominantly limited by stochastic sampling. Analysis of plasma samples from control individuals demonstrates a low false positive rate. The assay also detects DNA amplifications (including in ERBB2, MYC, KRAS, EGFR, MET, FGFR1, FGFR2) when the ctDNA are sufficiently high. Together, these data demonstrate the analytical validity and robustness of the TAm-Seq assay and support its use as a basis for clinical applications. Citation Format: Davina Gale, Vincent Plagnol, Andrew Lawson, Michelle Pugh, Sarah Smalley, Karen Howarth, Mikidache Madi, Bradley Durham, Vasudev Kumanduri, Kitty Lo, James Clark, Emma Green, Nitzan Rosenfeld, Tim Forshew. Analytical performance and validation of an enhanced TAm-Seq circulating tumor DNA sequencing assay. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3639.

Innate immune activation beyond the central nervous system is emerging as a vital component of the pathogenesis of neurodegeneration. Huntington’s disease (HD) is a fatal neurodegenerative disorder caused by a CAG repeat expansion in the huntingtin gene. The systemic innate immune system is thought to act as a modifier of disease progression; however, the molecular mechanisms remain only partially understood. Here we use RNA-sequencing to perform whole transcriptome analysis of primary monocytes from thirty manifest HD patients and thirty-three control subjects, cultured with and without a proinflammatory stimulus. In contrast with previous studies that have required stimulation to elicit phenotypic abnormalities, we demonstrate significant transcriptional differences in HD monocytes in their basal, unstimulated state. This includes previously undetected increased resting expression of genes encoding numerous proinflammatory cytokines, such as IL6. Further pathway analysis revealed widespread resting enrichment of proinflammatory functional gene sets, while upstream regulator analysis coupled with Western blotting suggests that abnormal basal activation of the NFOEB pathway plays a key role in mediating these transcriptional changes. That HD myeloid cells have a proinflammatory phenotype in the absence of stimulation is consistent with a priming effect of mutant huntingtin, whereby basal dysfunction leads to an exaggerated inflammatory response once a stimulus is encountered. These data advance our understanding of mutant huntingtin pathogenesis, establish resting myeloid cells as a key source of HD immune dysfunction, and further demonstrate the importance of systemic immunity in the potential treatment of HD and the wider study of neurodegeneration.

In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ~92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ~95%. Manual investigation of a random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.