Progressive induction of left ventricular pressure overload in a large animal model elicits myocardial remodeling and a unique matrix signature

ObjectivePatients with severe left ventricular pressure overload secondary to aortic stenosis can present with signs and symptoms of heart failure despite normal left ventricular ejection fraction. This process occurs, at least in part, as a result of left ventricular pressure overload–induced extracellular matrix remodeling that promulgates increased left ventricular stiffness and impaired diastolic function. However, the determinants that drive extracellular matrix remodeling in this form of left ventricular pressure overload remain to be fully defined.MethodsLeft ventricular pressure overload was induced in mature pigs (n = 15) by progressive ascending aortic cuff inflation (once per week for 4 weeks), whereby left ventricular mass, left ventricular ejection fraction, and regional myocardial stiffness (rKm) were compared with referent controls (n = 12). Determinants of extracellular matrix remodeling were assessed by measuring levels of mRNA expression for fibrillar collagens, matrix metalloproteinases, and tissue inhibitors of matrix metalloproteinase 1 and 4.ResultsWith left ventricular pressure overload, left ventricular mass and rKm increased by 2- and 3-fold, respectively, compared with control, with no change in left ventricular ejection fraction. Left ventricular myocardial collagen increased approximately 2-fold, which was accompanied by reduced solubility (ie, increased cross-linking) with left ventricular pressure overload, but mRNA expression for fibrillar collagen and matrix metalloproteinases remained relatively unchanged. In contrast, a robust increase in mRNA expression for tissue inhibitors of matrix metalloproteinase-1 and 4 occurred with left ventricular pressure overload.ConclusionsIn a progressive model of left ventricular pressure overload, which recapitulates the phenotype of aortic stenosis, increased extracellular matrix accumulation and subsequently increased myocardial stiffness were not due to increased fibrillar collagen expression but rather to determinants of post-translational control that included increased collagen stability (thereby resistant to matrix metalloproteinase degradation) and increased endogenous matrix metalloproteinase inhibition. Targeting these extracellular matrix post-translational events with left ventricular pressure overload may hold both diagnostic and therapeutic relevance

NuSTAR Spectroscopy of Multi-Component X-ray Reflection from NGC 1068

We report on observations of NGC1068 with NuSTAR, which provide the best constraints to date on its $>10$~keV spectral shape. We find no strong variability over the past two decades, consistent with its Compton-thick AGN classification. The combined NuSTAR, Chandra, XMM-Newton, and Swift-BAT spectral dataset offers new insights into the complex reflected emission. The critical combination of the high signal-to-noise NuSTAR data and a spatial decomposition with Chandra allow us to break several model degeneracies and greatly aid physical interpretation. When modeled as a monolithic (i.e., a single N_H) reflector, none of the common Compton-reflection models are able to match the neutral fluorescence lines and broad spectral shape of the Compton reflection. A multi-component reflector with three distinct column densities (e.g., N_H~1.5e23, 5e24, and 1e25 cm^{-2}) provides a more reasonable fit to the spectral lines and Compton hump, with near-solar Fe abundances. In this model, the higher N_H components provide the bulk of the Compton hump flux while the lower N_H component produces much of the line emission, effectively decoupling two key features of Compton reflection. We note that ~30% of the neutral Fe Kalpha line flux arises from >2" (~140 pc), implying that a significant fraction of the <10 keV reflected component arises from regions well outside of a parsec-scale torus. These results likely have ramifications for the interpretation of poorer signal-to-noise observations and/or more distant objects [Abridged].Comment: Submitted to ApJ; 23 pages (ApJ format); 11 figures and 3 tables; Comments welcomed

NuSTAR discovery of a luminosity dependent cyclotron line energy in Vela X-1

We present NuSTAR observations of Vela X-1, a persistent, yet highly variable, neutron star high-mass X-ray binary (HMXB). Two observations were taken at similar orbital phases but separated by nearly a year. They show very different 3–79 keV flux levels as well as strong variability during each observation, covering almost one order of magnitude in flux. These observations allow, for the first time ever, investigations on kilo-second time-scales of how the centroid energies of cyclotron resonant scattering features (CRSFs) depend on flux for a persistent HMXB. We find that the line energy of the harmonic CRSF is correlated with flux, as expected in the sub-critical accretion regime. We argue that Vela X-1 has a very narrow accretion column with a radius of around 0.4 km that sustains a Coulomb interaction dominated shock at the observed luminosities of L_x ~ 3 × 10^36 erg s^−1. Besides the prominent harmonic line at 55 keV the fundamental line around 25 keV is clearly detected. We find that the strengths of the two CRSFs are anti-correlated, which we explain by photon spawning. This anti-correlation is a possible explanation for the debate about the existence of the fundamental line. The ratio of the line energies is variable with time and deviates significantly from 2.0, also a possible consequence of photon spawning, which changes the shape of the line. During the second observation, Vela X-1 showed a short off-state in which the power-law softened and a cut-off was no longer measurable. It is likely that the source switched to a different accretion regime at these low mass accretion rates, explaining the drastic change in spectral shape

Impacts of herbivory by ecological replacements on an island ecosystem

The use of ecological replacements (analogue species to replace extinct taxa) to restore ecosystem functioning is a promising conservation tool. However, this approach is controversial, in part due to a paucity of data on interactions between analogue species and established taxa in the ecosystem. We conducted ecological surveys, comprehensively DNA barcoded an ecosystem's flora and inferred the diet of the introduced Aldabra giant tortoise, acting as an ecological replacement, to understand how it might have modified island plant communities on a Mauritian islet. Through further dietary analyses, we investigated consequential effects on the threatened endemic Telfair's skink. Dietary overlap between tortoises and skinks was greater than expected by chance. However, there was a negative correlation between tortoise and skink preferences in herbivory and minimal overlap in the plants most frequently consumed by the reptiles. Changes in the plant community associated with 7 years of tortoise grazing were characterised by a decrease in the percentage cover of native herbs and creepers, and an increase in the cover of an invasive herb when compared to areas without tortoises. However, tortoise dietary preferences themselves did not directly drive changes in the plant community. Tortoises successfully dispersed the seeds of an endemic palm, which in time may increase the extent of unique palm-rich habitat. We found no evidence that tortoises have increased the extent of plant species hypothesised to be part of a lost Mauritian tortoise grazed community. Synthesis and applications. Due to a negative correlation in tortoise and skink dietary preferences and minimal overlap in the most frequently consumed taxa, the presence of tortoises is unlikely to have detrimental impacts on Telfair's skinks. Tortoise presence is likely to be beneficial to skinks in the long term by increasing the extent of palm-rich habitat. Although tortoises are likely to play a role in controlling invasive plants, they are not a panacea for this challenge. After 7 years, tortoises have not resurrected a lost tortoise grazed community that we hypothesise might have existed in limited areas on the islet, indicating that further interventions may be required to restore this plant community

Metagenomics reveals sediment microbial community response to Deepwater Horizon oil spill

The Deepwater Horizon (DWH) oil spill in the spring of 2010 resulted in an input of ∼4.1 million barrels of oil to the Gulf of Mexico; >22% of this oil is unaccounted for, with unknown environmental consequences. Here we investigated the impact of oil deposition on microbial communities in surface sediments collected at 64 sites by targeted sequencing of 16S rRNA genes, shotgun metagenomic sequencing of 14 of these samples and mineralization experiments using (14)C-labeled model substrates. The 16S rRNA gene data indicated that the most heavily oil-impacted sediments were enriched in an uncultured Gammaproteobacterium and a Colwellia species, both of which were highly similar to sequences in the DWH deep-sea hydrocarbon plume. The primary drivers in structuring the microbial community were nitrogen and hydrocarbons. Annotation of unassembled metagenomic data revealed the most abundant hydrocarbon degradation pathway encoded genes involved in degrading aliphatic and simple aromatics via butane monooxygenase. The activity of key hydrocarbon degradation pathways by sediment microbes was confirmed by determining the mineralization of (14)C-labeled model substrates in the following order: propylene glycol, dodecane, toluene and phenanthrene. Further, analysis of metagenomic sequence data revealed an increase in abundance of genes involved in denitrification pathways in samples that exceeded the Environmental Protection Agency (EPA)'s benchmarks for polycyclic aromatic hydrocarbons (PAHs) compared with those that did not. Importantly, these data demonstrate that the indigenous sediment microbiota contributed an important ecosystem service for remediation of oil in the Gulf. However, PAHs were more recalcitrant to degradation, and their persistence could have deleterious impacts on the sediment ecosystem

New models and online calculator for predicting non-sentinel lymph node status in sentinel lymph node positive breast cancer patients

<p>Abstract</p> <p>Background</p> <p>Current practice is to perform a completion axillary lymph node dissection (ALND) for breast cancer patients with tumor-involved sentinel lymph nodes (SLNs), although fewer than half will have non-sentinel node (NSLN) metastasis. Our goal was to develop new models to quantify the risk of NSLN metastasis in SLN-positive patients and to compare predictive capabilities to another widely used model.</p> <p>Methods</p> <p>We constructed three models to predict NSLN status: recursive partitioning with receiver operating characteristic curves (RP-ROC), boosted Classification and Regression Trees (CART), and multivariate logistic regression (MLR) informed by CART. Data were compiled from a multicenter Northern California and Oregon database of 784 patients who prospectively underwent SLN biopsy and completion ALND. We compared the predictive abilities of our best model and the Memorial Sloan-Kettering Breast Cancer Nomogram (Nomogram) in our dataset and an independent dataset from Northwestern University.</p> <p>Results</p> <p>285 patients had positive SLNs, of which 213 had known angiolymphatic invasion status and 171 had complete pathologic data including hormone receptor status. 264 (93%) patients had limited SLN disease (micrometastasis, 70%, or isolated tumor cells, 23%). 101 (35%) of all SLN-positive patients had tumor-involved NSLNs. Three variables (tumor size, angiolymphatic invasion, and SLN metastasis size) predicted risk in all our models. RP-ROC and boosted CART stratified patients into four risk levels. MLR informed by CART was most accurate. Using two composite predictors calculated from three variables, MLR informed by CART was more accurate than the Nomogram computed using eight predictors. In our dataset, area under ROC curve (AUC) was 0.83/0.85 for MLR (n = 213/n = 171) and 0.77 for Nomogram (n = 171). When applied to an independent dataset (n = 77), AUC was 0.74 for our model and 0.62 for Nomogram. The composite predictors in our model were the product of angiolymphatic invasion and size of SLN metastasis, and the product of tumor size and square of SLN metastasis size.</p> <p>Conclusion</p> <p>We present a new model developed from a community-based SLN database that uses only three rather than eight variables to achieve higher accuracy than the Nomogram for predicting NSLN status in two different datasets. </p