Constraints on core-collapse supernova progenitors from explosion site integral field spectroscopy

Observationally, supernovae (SNe) are divided into subclasses pertaining to their distinct characteristics. This diversity reflects the diversity in the progenitor stars. It is not entirely clear how different evolutionary paths leading massive stars to become a SN are governed by fundamental parameters such as progenitor initial mass and metallicity. This paper places constraints on progenitor initial mass and metallicity in distinct core-collapse SN subclasses, through a study of the parent stellar populations at the explosion sites. Integral field spectroscopy (IFS) of 83 nearby SN explosion sites with a median distance of 18 Mpc has been collected and analysed, enabling detection and spectral extraction of the parent stellar population of SN progenitors. From the parent stellar population spectrum, the initial mass and metallicity of the coeval progenitor are derived by means of comparison to simple stellar population models and strong-line methods. Additionally, near-infrared IFS was employed to characterise the star formation history at the explosion sites. No significant metallicity differences are observed among distinct SN types. The typical progenitor mass is found to be highest for SN Ic, followed by type Ib, then types IIb and II. SN IIn is the least associated with young stellar populations and thus massive progenitors. However, statistically significant differences in progenitor initial mass are observed only when comparing SNe IIn with other subclasses. Stripped-envelope SN progenitors with initial mass estimate lower than 25~$M_\odot$ are found; these are thought to be the result of binary progenitors. Confirming previous studies, these results support the notion that core-collapse SN progenitors cannot arise from single-star channel only, and both single and binary channels are at play in the production of core-collapse SNe. [ABRIDGED]Comment: 18 pages, 10 figures, accepted to A&

Slow-Speed Supernovae from the Palomar Transient Factory: Two Channels

Since the discovery of the unusual prototype SN 2002cx, the eponymous class of low-velocity, hydrogen-poor supernovae has grown to include at most another two dozen members identified from several heterogeneous surveys, in some cases ambiguously. Here we present the results of a systematic study of 1077 hydrogen-poor supernovae discovered by the Palomar Transient Factory, leading to nine new members of this peculiar class. Moreover we find there are two distinct subclasses based on their spectroscopic, photometric, and host galaxy properties: The "SN 2002cx-like" supernovae tend to be in later-type or more irregular hosts, have more varied and generally dimmer luminosities, have longer rise times, and lack a Ti II trough when compared to the "SN 2002es-like" supernovae. None of our objects show helium, and we counter a previous claim of two such events. We also find that these transients comprise 5.6+17-3.7% (90% confidence) of all SNe Ia, lower compared to earlier estimates. Combining our objects with the literature sample, we propose that these subclasses have two distinct physical origins.Comment: 49 pages, 36 figures, submitted to Ap

A common theme in interaction of bacterial immunoglobulin-binding proteins with immunoglobulins illustrated in the equine system

The M protein of Streptococcus equi subsp. equi known as fibrinogen-binding protein (FgBP) is a cell wall-associated protein with antiphagocytic activity that binds IgG. Recombinant versions of the seven equine IgG subclasses were used to investigate the subclass specificity of FgBP. FgBP bound predominantly to equine IgG4 and IgG7, with little or no binding to the other subclasses. Competitive binding experiments revealed that FgBP could inhibit the binding of staphylococcal protein A and streptococcal protein G to both IgG4 and IgG7, implicating the Fc interdomain region in binding to FgBP. To identify which of the two IgG Fc domains contributed to the interaction with FgBP, we tested two human IgG1/IgA1 domain swap mutants and found that both domains are required for full binding, with the CH3 domain playing a critical role. The binding site for FgBP was further localized using recombinant equine IgG7 antibodies with single or double point mutations to residues lying at the CH2-CH3 interface. We found that interaction of FgBP with equine IgG4 and IgG7 was able to disrupt C1q binding and antibody-mediated activation of the classical complement pathway, demonstrating an effective means by which S. equi may evade the immune response. The mode of interaction of FgBP with IgG fits a common theme for bacterial Ig-binding proteins. Remarkably, for those interactions studied in detail, it emerges that all the Ig-binding proteins target the CH2-CH3 domain interface, regardless of specificity for IgG or IgA, streptococcal or staphylococcal origin, or host species (equine or human)

The Marker State Space (MSS) Method for Classifying Clinical Samples

The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications. © 2013 Fallon et al