Assessing variability and long-term trends in burned area by merging multiple satellite fire products

Long term, high quality estimates of burned area are needed for improving both prognostic and diagnostic fire emissions models and for assessing feedbacks between fire and the climate system. We developed global, monthly burned area estimates aggregated to 0.5° spatial resolution for the time period July 1996 through mid-2009 using four satellite data sets. From 2001ĝ€ "2009, our primary data source was 500-m burned area maps produced using Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance imagery; more than 90% of the global area burned during this time period was mapped in this fashion. During times when the 500-m MODIS data were not available, we used a combination of local regression and regional regression trees developed over periods when burned area and Terra MODIS active fire data were available to indirectly estimate burned area. Cross-calibration with fire observations from the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS) and the Along-Track Scanning Radiometer (ATSR) allowed the data set to be extended prior to the MODIS era. With our data set we estimated that the global annual area burned for the years 1997ĝ€ "2008 varied between 330 and 431 Mha, with the maximum occurring in 1998. We compared our data set to the recent GFED2, L3JRC, GLOBCARBON, and MODIS MCD45A1 global burned area products and found substantial differences in many regions. Lastly, we assessed the interannual variability and long-term trends in global burned area over the past 13 years. This burned area time series serves as the basis for the third version of the Global Fire Emissions Database (GFED3) estimates of trace gas and aerosol emissions.

Augmentation of cardiac contractility mediated by the human beta(3)-adrenergic receptor overexpressed in the hearts of transgenic mice.

BACKGROUND: Stimulation of beta(1)- and beta(2)-adrenergic receptors (ARs) in the heart results in positive inotropy. In contrast, it has been reported that the beta(3)AR is also expressed in the human heart and that its stimulation leads to negative inotropic effects. METHODS AND RESULTS: To better understand the role of beta(3)ARs in cardiac function, we generated transgenic mice with cardiac-specific overexpression of 330 fmol/mg protein of the human beta(3)AR (TGbeta(3) mice). Hemodynamic characterization was performed by cardiac catheterization in closed-chest anesthetized mice, by pressure-volume-loop analysis, and by echocardiography in conscious mice. After propranolol blockade of endogenous beta(1)- and beta(2)ARs, isoproterenol resulted in an increase in contractility in the TGbeta(3) mice (30%), with no effect in wild-type mice. Similarly, stimulation with the selective human beta(3)AR agonist L-755,507 significantly increased contractility in the TGbeta(3) mice (160%), with no effect in wild-type mice, as determined by hemodynamic measurements and by end-systolic pressure-volume relations. The underlying mechanism of the positive inotropy incurred with L-755,507 in the TGbeta(3) mice was investigated in terms of beta(3)AR-G-protein coupling and adenylyl cyclase activation. Stimulation of cardiac membranes from TGbeta(3) mice with L-755,507 resulted in a pertussis toxin-insensitive 1.33-fold increase in [(35)S]GTPgammaS loading and a 1.6-fold increase in adenylyl cyclase activity. CONCLUSIONS: Cardiac overexpression of human beta(3)ARs results in positive inotropy only on stimulation with a beta(3)AR agonist. Overexpressed beta(3)ARs couple to G(s) and activate adenylyl cyclase on agonist stimulation.

Validation of ICDPIC software injury severity scores using a large regional trauma registry.

BACKGROUND: Administrative or quality improvement registries may or may not contain the elements needed for investigations by trauma researchers. International Classification of Diseases Program for Injury Categorisation (ICDPIC), a statistical program available through Stata, is a powerful tool that can extract injury severity scores from ICD-9-CM codes. We conducted a validation study for use of the ICDPIC in trauma research. METHODS: We conducted a retrospective cohort validation study of 40,418 patients with injury using a large regional trauma registry. ICDPIC-generated AIS scores for each body region were compared with trauma registry AIS scores (gold standard) in adult and paediatric populations. A separate analysis was conducted among patients with traumatic brain injury (TBI) comparing the ICDPIC tool with ICD-9-CM embedded severity codes. Performance in characterising overall injury severity, by the ISS, was also assessed. RESULTS: The ICDPIC tool generated substantial correlations in thoracic and abdominal trauma (weighted κ 0.87-0.92), and in head and neck trauma (weighted κ 0.76-0.83). The ICDPIC tool captured TBI severity better than ICD-9-CM code embedded severity and offered the advantage of generating a severity value for every patient (rather than having missing data). Its ability to produce an accurate severity score was consistent within each body region as well as overall. CONCLUSIONS: The ICDPIC tool performs well in classifying injury severity and is superior to ICD-9-CM embedded severity for TBI. Use of ICDPIC demonstrates substantial efficiency and may be a preferred tool in determining injury severity for large trauma datasets, provided researchers understand its limitations and take caution when examining smaller trauma datasets.

Biomarkers of PTA

© Springer Science+Business Media New York 2015.Prognostic biomarkers may indicate the likelihood of disease development and speed of progression or may serve as predictive indicators of responsiveness to treatment. Joint injuries, particularly severe injuries, may result in post-traumatic osteoarthritis (PTOA), and pre- and post-injury prognostic biomarkers are needed to enhance primary and secondary prevention approaches for PTOA. Several macromolecules from joint structures found in serum, urine, and synovial fluid are promising biochemical markers for monitoring joint metabolism and health before and after joint injury. The use of metabolic profiling (analysis of small molecules) as a predictive tool for osteoarthritis (OA) has increased in the past decade. Although there is some question as to whether PTOA and idiopathic OA are comparable conditions, there is some evidence to suggest that components of their pathogenesis are similar. Potentially, biomarkers important to the high-risk PTOA profile translate to idiopathic OA. Further work is needed to confirm the utility of macromolecules and metabolites as biomarkers for PTOA, particularly focusing on those strongly correlated to clinical efficacy measures important to the patient (e.g., symptoms, physical function, and quality of life) and the causal pathway of PTOA.