Abstract: Gravitational-wave observations of neutron star mergers can probe the nuclear equation of state by measuring the imprint of the neutron star’s tidal deformability on the signal. We investigate the ability of future gravitational-wave observations to produce a precise measurement of the equation of state from binary neutron star inspirals. Because measurability of the tidal effect depends on the equation of state, we explore several equations of state that span current observational constraints. We generate a population of binary neutron stars as seen by a simulated Advanced LIGO–Virgo network, as well as by a planned Cosmic Explorer observatory. We perform Bayesian inference to measure the parameters of each signal, and we combine measurements across each population to determine R 1.4, the radius of a 1.4 M ⊙ neutron star. We find that, with 321 signals, the LIGO–Virgo network is able to measure R 1.4 to better than 2% precision for all equations of state we consider; however, we also find that achieving this precision could take decades of observation, depending on the equation of state and the merger rate. On the other hand, we find that with one year of observation, Cosmic Explorer will measure R 1.4 to better than 0.6% precision. In both cases, we find that systematic biases, such as from an incorrect mass prior, can significantly impact measurement accuracy, and efforts will be required to mitigate these effects.

Purpose

Despite American College of Rheumatology recommendations, appropriate and timely initiation of biologic therapies does not always occur. This study examined openness to and preference for attributes of biologic therapies among patients with rheumatoid arthritis (RA), differences in patients' and rheumatologists' perceptions, and discussions around biologic therapy initiation.

Patients and methods

A self-administered online survey was completed by 243 adult patients with RA in the US who were taking disease-modifying antirheumatic drugs (DMARDs) and had never taken, but had discussed biologic therapy with a rheumatologist. Patients were recruited from a consumer panel (n=142) and patient advocacy organization (n=101). A separate survey was completed by 103 rheumatologists who treated at least 25 patients with RA per month with biologic therapy. Descriptive and bivariate analyses were conducted separately for patients and rheumatologists. Attributes of biologic therapy included route of administration (intravenous infusion or subcutaneous injection), frequency of injections/infusions, and duration of infusion.

Results

Over half of patients (53.1%) were open to both intravenous infusion and subcutaneous injection, whereas rheumatologists reported 40.7% of patients would be open to both. Only 26.3% of patients strongly preferred subcutaneous injection, whereas rheumatologists reported 35.2%. Discrepancies were even more pronounced among specific patient types (eg, older vs younger patients and Medicare recipients). Among patients, 23% reported initiating discussion about biologics and 54% reported their rheumatologist initiated the discussion. A majority of rheumatologists reported discussing in detail several key aspects of biologics, whereas a minority of patients reported the same.

Conclusion

Preferences differed among patients with RA from rheumatologists' perceptions of these preferences for biologic therapy, including greater openness to intravenous infusion among patients than assumed by rheumatologists and relative lack of discussion about key aspects of biologic therapy perceived by patients. There is a need for more open communication about treatment options, which may encourage more appropriate, timely transition to biologic therapy.

We report the discovery of the optical afterglow of the γ-ray burst (GRB) 130702A, identified upon searching 71 deg2 surrounding the Fermi Gamma-ray Burst Monitor (GBM) localization. Discovered and characterized by the intermediate Palomar Transient Factory, iPTF13bxl is the first afterglow discovered solely based on a GBM localization. Real-time image subtraction, machine learning, human vetting, and rapid response multi-wavelength follow-up enabled us to quickly narrow a list of 27,004 optical transient candidates to a single afterglow-like source. Detection of a new, fading X-ray source by Swift and a radio counterpart by CARMA and the Very Large Array confirmed the association between iPTF13bxl and GRB 130702A. Spectroscopy with the Magellan and Palomar 200 inch telescopes showed the afterglow to be at a redshift of z = 0.145, placing GRB 130702A among the lowest redshift GRBs detected to date. The prompt γ-ray energy release and afterglow luminosity are intermediate between typical cosmological GRBs and nearby sub-luminous events such as GRB 980425 and GRB 060218. The bright afterglow and emerging supernova offer an opportunity for extensive panchromatic follow-up. Our discovery of iPTF13bxl demonstrates the first observational proof-of-principle for ∼10 Fermi-iPTF localizations annually. Furthermore, it represents an important step toward overcoming the challenges inherent in uncovering faint optical counterparts to comparably localized gravitational wave events in the Advanced LIGO and Virgo era. © 2013. The American Astronomical Society. All rights reserved.

Background

A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging.

Results

We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2.

Conclusions

The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.