Unique Transcriptional Profile of Sustained Ligand-Activated Preconditioning in Pre- and Post-Ischemic Myocardium

BACKGROUND: Opioidergic SLP (sustained ligand-activated preconditioning) induced by 3–5 days of opioid receptor (OR) agonism induces persistent protection against ischemia-reperfusion (I-R) injury in young and aged hearts, and is mechanistically distinct from conventional preconditioning responses. We thus applied unbiased gene-array interrogation to identify molecular effects of SLP in pre- and post-ischemic myocardium. METHODOLOGY/PRINCIPAL FINDINGS: Male C57Bl/6 mice were implanted with 75 mg morphine or placebo pellets for 5 days. Resultant SLP did not modify cardiac function, and markedly reduced dysfunction and injury in perfused hearts subjected to 25 min ischemia/45 min reperfusion. Microarray analysis identified 14 up- and 86 down-regulated genes in normoxic hearts from SLP mice (≥1.3-fold change, FDR≤5%). Induced genes encoded sarcomeric/contractile proteins (Myh7, Mybpc3,Myom2,Des), natriuretic peptides (Nppa,Nppb) and stress-signaling elements (Csda,Ptgds). Highly repressed genes primarily encoded chemokines (Ccl2,Ccl4,Ccl7,Ccl9,Ccl13,Ccl3l3,Cxcl3), cytokines (Il1b,Il6,Tnf) and other proteins involved in inflammation/immunity (C3,Cd74,Cd83, Cd86,Hla-dbq1,Hla-drb1,Saa1,Selp,Serpina3), together with endoplasmic stress proteins (known: Dnajb1,Herpud1,Socs3; putative: Il6, Gadd45g,Rcan1) and transcriptional controllers (Egr2,Egr3, Fos,Hmox1,Nfkbid). Biological themes modified thus related to inflammation/immunity, together with cellular/cardiovascular movement and development. SLP also modified the transcriptional response to I-R (46 genes uniquely altered post-ischemia), which may influence later infarction/remodeling. This included up-regulated determinants of cellular resistance to oxidant (Mgst3,Gstm1,Gstm2) and other forms of stress (Xirp1,Ankrd1,Clu), and repression of stress-response genes (Hspa1a,Hspd1,Hsp90aa,Hsph1,Serpinh1) and Txnip. CONCLUSIONS: Protection via SLP is associated with transcriptional repression of inflammation/immunity, up-regulation of sarcomeric elements and natriuretic peptides, and modulation of cell stress, growth and development, while conventional protective molecules are unaltered

Regression modeling of longitudinal binary outcomes with outcome-dependent observation times

Conventional longitudinal data analysis methods assume that outcomes are independent of the data-collection schedule. However, the independence assumption may be violated, for example, when adverse events trigger additional physician visits in between prescheduled follow-ups. Observation times may therefore be associated with outcome values, which may introduce bias when estimating the eect of covariates on outcomes using standard longitudinal regression methods. Existing semi-parametric methods that accommodate outcome-dependent observation times are limited to the analysis of continuous outcomes. We develop new methods for the analysis of binary outcomes, while retaining the exibility of semi-parametric models. Our methods are based on counting process approaches, rather than relying on possibly intractable likelihood-based or pseudo-likelihood-based approaches, and provide marginal, population-level inference. In simulations, we evaluate the statistical properties of our proposed methods. Comparisons are made to \u27naive\u27 GEE approaches that either do not account for outcome-dependent observation times or incorporate weights based on the observation-time process. We illustrate the utility of our proposed methods using data from a randomized controlled trial of interventions designed to improve adherence to warfarin therapy. We show that our method performs well in the presence of outcome-dependent observation times, and provide identical inference to \u27naive\u27 approaches when observation times are not associated with outcomes

Spatial factor analysis: a new tool for estimating joint species distributions and correlations in species range

1. Predicting and explaining the distribution and density of species is one of the oldest concerns in ecology. Species distributions can be estimated using geostatistical methods, which estimate a latent spatial variable explaining observed variation in densities, but geostatistical methods may be imprecise for species with low densities or few observations. Additionally, simple geostatistical methods fail to account for correlations in distribution among species and generally estimate such cross-correlations as a post hoc exercise. 2. We therefore present spatial factor analysis (SFA), a spatial model for estimating a low-rank approximation to multivariate data, and use it to jointly estimate the distribution of multiple species simultaneously. We also derive an analytic estimate of cross-correlations among species from SFA parameters. 3. As a first example, we show that distributions for 10 bird species in the breeding bird survey in 2012 can be parsimoniously represented using only five spatial factors. As a second case study, we show that forward prediction of catches for 20 rockfishes (Sebastes spp.) off the U.S. West Coast is more accurate using SFA than analysing each species individually. Finally, we show that single-species models give a different picture of cross-correlations than joint estimation using SFA. 4. Spatial factor analysis complements a growing list of tools for jointly modelling the distribution of multiple species and provides a parsimonious summary of cross-correlation without requiring explicit declaration of habitat variables. We conclude by proposing future research that would model species cross-correlations using dissimilarity of species' traits, and the development of spatial dynamic factor analysis for a low-rank approximation to spatial time-series data

National CO\u3csub\u3e2\u3c/sub\u3e budgets (2015-2020) inferred from atmospheric CO\u3csub\u3e2\u3c/sub\u3e observations in support of the global stocktake

Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries\u27 carbon budgets. These estimates are based on top-down NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-Averaged dry-Air mole fraction (XCO2) retrievals (ACOS v10), in situ CO2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with bottom-up estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015-2020) as both a global 1gg×g1g gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites\u27 (CEOS) website: 10.48588/npf6-sw92 . Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29-4.58gPgCO2yr-1 (0.90-1.25gPgCyr-1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems