Hubble space telescope STIS spectroscopy of the peculiar nova-like variables BK Lyn, V751 Cygni, and V380 Oph

We obtained Hubble STIS spectra of three nova-like variables: V751 Cygni, V380 Oph, and—the only confirmed nova-like variable known to be below the period gap—BK Lyn. In all three systems, the spectra were taken during high optical brightness state, and a luminous accretion disk dominates their far-ultraviolet (FUV) light. We assessed a lower limit of the distances by applying the infrared photometric method of Knigge. Within the limitations imposed by the poorly known system parameters (such as the inclination, white dwarf mass, and the applicability of steady state accretion disks) we obtained satisfactory fits to BK Lyn using optically thick accretion disk models with an accretion rate of for a white dwarf mass of Mwd = 1.2M and for Mwd = 0.4M. However, for the VY Scl-type nova-like variable V751 Cygni and for the SW Sex star V380 Oph, we are unable to obtain satisfactory synthetic spectral fits to the high state FUV spectra using optically thick steady state accretion disk models. The lack of FUV spectra information down to the Lyman limit hinders the extraction of information about the accreting white dwarf during the high states of these nova-like systems

Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies

BackgroundAutomated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies.ResultsUsing a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs.ConclusionThe study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.<br /

The fractured landscape of RNA-seq alignment: the default in our STARs

Many tools are available for RNA-seq alignment and expression quantification, with comparative value being hard to establish. Benchmarking assessments often highlight methods' good performance, but are focused on either model data or fail to explain variation in performance. This leaves us to ask, what is the most meaningful way to assess different alignment choices? And importantly, where is there room for progress? In this work, we explore the answers to these two questions by performing an exhaustive assessment of the STAR aligner. We assess STAR's performance across a range of alignment parameters using common metrics, and then on biologically focused tasks. We find technical metrics such as fraction mapping or expression profile correlation to be uninformative, capturing properties unlikely to have any role in biological discovery. Surprisingly, we find that changes in alignment parameters within a wide range have little impact on both technical and biological performance. Yet, when performance finally does break, it happens in difficult regions, such as X-Y paralogs and MHC genes. We believe improved reporting by developers will help establish where results are likely to be robust or fragile, providing a better baseline to establish where methodological progress can still occur

Identification of novel therapeutics for complex diseases from genome-wide association data

Background: Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials. Methods. We previously used Gentrepid (http://www.gentrepid.org) as a platform to predict 1,497 candidate genes for the seven complex diseases considered in the Wellcome Trust Case-Control Consortium genome-wide association study; namely Type 2 Diabetes, Bipolar Disorder, Crohn's Disease, Hypertension, Type 1 Diabetes, Coronary Artery Disease and Rheumatoid Arthritis. Here, we adopted a simple approach to integrate drug data from three publicly available drug databases: the Therapeutic Target Database, the Pharmacogenomics Knowledgebase and DrugBank; with candidate gene predictions from Gentrepid at the systems level. Results: Using the publicly available drug databases as sources of drug-target association data, we identified a total of 428 candidate genes as novel therapeutic targets for the seven phenotypes of interest, and 2,130 drugs feasible for repositioning against the predicted novel targets. Conclusions: By integrating genetic, bioinformatic and drug data, we have demonstrated that currently available drugs may be repositioned as novel therapeutics for the seven diseases studied here, quickly taking advantage of prior work in pharmaceutics to translate ground-breaking results in genetics to clinical treatments. © 2014 Grover et al.; licensee BioMed Central Ltd

Novel therapeutics for coronary artery disease from genome-wide association study data

BACKGROUND: Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been remarkably successful in identifying genetic loci contributing to CAD. Modern in silico platforms, such as candidate gene prediction tools, permit a systematic analysis of GWAS data to identify candidate genes for complex diseases like CAD. Subsequent integration of drug-target data from drug databases with the predicted candidate genes can potentially identify novel therapeutics suitable for repositioning towards treatment of CAD. METHODS: Previously, we were able to predict 264 candidate genes and 104 potential therapeutic targets for CAD using Gentrepid (http://www.gentrepid.org), a candidate gene prediction platform with two bioinformatic modules to reanalyze Wellcome Trust Case-Control Consortium GWAS data. In an expanded study, using five bioinformatic modules on the same data, Gentrepid predicted 647 candidate genes and successfully replicated 55% of the candidate genes identified by the more powerful CARDIoGRAMplusC4D consortium meta-analysis. Hence, Gentrepid was capable of enhancing lower quality genotype-phenotype data, using an independent knowledgebase of existing biological data. Here, we used our methodology to integrate drug data from three drug databases: the Therapeutic Target Database, PharmGKB and Drug Bank, with the 647 candidate gene predictions from Gentrepid. We utilized known CAD targets, the scientific literature, existing drug data and the CARDIoGRAMplusC4D meta-analysis study as benchmarks to validate Gentrepid predictions for CAD. RESULTS: Our analysis identified a total of 184 predicted candidate genes as novel therapeutic targets for CAD, and 981 novel therapeutics feasible for repositioning in clinical trials towards treatment of CAD. The benchmarks based on known CAD targets and the scientific literature showed that our results were significant (p < 0.05). CONCLUSIONS: We have demonstrated that available drugs may potentially be repositioned as novel therapeutics for the treatment of CAD. Drug repositioning can save valuable time and money spent on preclinical and phase I clinical studies

Global Patterns of Recent Mass Movement on Asteroid (101955) Bennu

The exploration of near‐Earth asteroids has revealed dynamic surfaces characterized by mobile, unconsolidated material that responds to local geophysical gradients, resulting in distinct morphologies and boulder distributions. The OSIRIS‐REx (Origins, Spectral Interpretation, Resource Identification, and Security‐Regolith Explorer) mission confirmed that asteroid (101955) Bennu is a rubble pile with an unconsolidated surface dominated by boulders. In this work, we documented morphologies indicative of mass movement on Bennu and assessed the relationship to slope and other geologic features on the surface. We found globally distributed morphologic evidence of mass movement on Bennu up to ~70° latitude and on spatial scales ranging from individual boulders (meter scale) to a single debris flow ~100 m long and several meters thick. The apparent direction of mass movement is consistent with the local downslope direction and dominantly moves from the midlatitudes toward the equator. Mass movement appears to have altered the surface expression of large (≥30m diameter) boulders, excavating them in the midlatitudes and burying them in the equatorial region. Up to a 10 ± 1 m depth of material may have been transported away from the midlatitudes, which would have deposited a layer ~5 ± 1 m thick in the equatorial region assuming a stagnated flow model. This mass movement could explain the observed paucity of small (\u3c50‐m diameter) craters and may have contributed material to Bennu\u27s equatorial ridge. Models of changes in slope suggest that the midlatitude mass movement occurred in the past several hundred thousand years in regions that became steeper by several degrees

Heterogenous humoral and cellular immune responses with distinct trajectories post-SARS-CoV-2 infection in a population-based cohort.

To better understand the development of SARS-CoV-2-specific immunity over time, a detailed evaluation of humoral and cellular responses is required. Here, we characterize anti-Spike (S) IgA and IgG in a representative population-based cohort of 431 SARS-CoV-2-infected individuals up to 217 days after diagnosis, demonstrating that 85% develop and maintain anti-S responses. In a subsample of 64 participants, we further assess anti-Nucleocapsid (N) IgG, neutralizing antibody activity, and T cell responses to Membrane (M), N, and S proteins. In contrast to S-specific antibody responses, anti-N IgG levels decline substantially over time and neutralizing activity toward Delta and Omicron variants is low to non-existent within just weeks of Wildtype SARS-CoV-2 infection. Virus-specific T cells are detectable in most participants, albeit more variable than antibody responses. Cluster analyses of the co-evolution of antibody and T cell responses within individuals identify five distinct trajectories characterized by specific immune patterns and clinical factors. These findings demonstrate the relevant heterogeneity in humoral and cellular immunity to SARS-CoV-2 while also identifying consistent patterns where antibody and T cell responses may work in a compensatory manner to provide protection

The Unseen Population of F to K-type Companions to Hot Subdwarf Stars

We present a method to select hot subdwarf stars with A to M-type companions using photometric selection criteria. We cover a wide range in wavelength by combining GALEX ultraviolet data, optical photometry from the SDSS and the Carlsberg Meridian telescope, near-infrared data from 2MASS and UKIDSS. We construct two complimentary samples, one by matching GALEX, CMC and 2MASS, as well as a smaller, but deeper, sample using GALEX, SDSS and UKIDSS. In both cases, a large number of composite subdwarf plus main-sequence star candidates were found. We fit their spectral energy distributions with a composite model in order to estimate the subdwarf and companion star effective temperatures along with the distance to each system. The distribution of subdwarf effective temperature was found to primarily lie in the 20,000 - 30,000 K regime, but we also find cooler subdwarf candidates, making up ~5-10 per cent. The most prevalent companion spectral types were seen to be main-sequence stars between F0 and K0, while subdwarfs with M-type companions appear much rarer. This is clear observational confirmation that a very efficient first stable Roche-lobe overflow channel appears to produce a large number of subdwarfs with F to K-type companions. Our samples thus support the importance of binary evolution for subdwarf formation.Comment: 30 pages, 10 figures, 11 tables. Accepted for publication in MNRA

Nova-like Cataclysmic Variables in the Infrared

Novalike cataclysmic variables have persistently high mass transfer rates and prominent steady state accretion disks. We present an analysis of infrared observations of twelve novalikes obtained from the Two Micron All Sky Survey, the Spitzer Space Telescope, and the Wide-field Infrared Survey Explorer All Sky Survey. The presence of an infrared excess at >3-5 microns over the expectation of a theoretical steady state accretion disk is ubiquitous in our sample. The strength of the infrared excess is not correlated with orbital period, but shows a statistically significant correlation (but shallow trend) with system inclination that might be partially (but not completely) linked to the increasing view of the cooler outer accretion disk and disk rim at higher inclinations. We discuss the possible origin of the infrared excess in terms of emission from bremsstrahlung or circumbinary dust, with either mechanism facilitated by the mass outflows (e.g., disk wind/corona, accretion stream overflow, and so on) present in novalikes. Our comparison of the relative advantages and disadvantages of either mechanism for explaining the observations suggests that the situation is rather ambiguous, largely circumstantial, and in need of stricter observational constraints.Peer reviewe