The occurrence of large, high‐intensity wildfires requires plant biomass, or fuel, that is sufficiently dry to burn. This poses the question, what is “sufficiently dry”? Until recently, the ability ...to address this question has been constrained by the spatiotemporal scale of available methods to monitor the moisture contents of both dead and live fuels. Here we take advantage of recent developments in macroscale monitoring of fuel moisture through a combination of remote sensing and climatic modeling. We show there are clear thresholds of fuel moisture content associated with the occurrence of wildfires in forests and woodlands. Furthermore, we show that transformations in fuel moisture conditions across these thresholds can occur rapidly, within a month. Both the approach presented here, and our findings, can be immediately applied and may greatly improve fire risk assessments in forests and woodlands globally.
Key Points
We apply recently developed models for monitoring of live and dead fuel moisture contents
We show that the relationship between fuel moisture and wildfire exhibits threshold behavior
Changes in fuel moisture sufficient to cross critical thresholds can occur over weeks to months
Drought monitoring is important to analyse the influence of rainfall deficiency patterns on bushfire behaviour. Remote sensing provides tools for spatially explicit monitoring of drought across large ...areas. The objective of this study was to assess the performance of MODIS-based reflectance spectral indices to monitor drought across forest and woodland vegetation types in the fire prone Sydney Basin Bioregion, NSW, Australia. A time series of eight spectral indices were created from 2000 to 2009 to monitor inter-annual changes in drought and were compared to the Standardized Precipitation Index (SPI), a precipitation deficit/surplus indicator. A pixel-to-weather station paired correlation approach was used to assess the relationship between SPI and the MODIS-based spectral indices at different time scales. Results show that the Normalised Difference Infrared Index—band 6 (NDIIb6) provided the most suitable indicator of drought for the high biomass vegetation types considered. The NDIIb6 had the highest sensitivity to drought intensity and was highly correlated with SPI at all time scales analysed (i.e., 1, 3 and 6-month SPI) suggesting that variations in precipitation patterns have a stronger influence on vegetation water content than vegetation greenness properties. Spatial similarities were also found between patterns of NDIIb6-based drought maps and SPI values distribution. NDIIb6 outperformed the spectral index currently in use for operational drought monitoring systems in the region (Normalised Difference Vegetation Index, NDVI) and its implementation in existing drought-monitoring systems is recommended.
► We monitored drought of forested areas using eight MODIS-based spectral indices. ► The Standardized Precipitation Index (SPI) was used as drought indicator. ► NDIIb6 showed the strongest correlation with SPI. ► NDIIb6 discriminated among droughts of different intensity. ► NDIIb6-based drought maps showed good spatial agreement with SPI distribution.
Abstract Background Insulin resistance (IR), which can be quantified by HOMA index (fasting glucose X fasting insulin/22.5), is considered the “primum movens” for the development of Metabolic ...Syndrome. Many authors have suggested that insulin resistance could raise both incidence and mortality of coronary heart disease (CHD). IR is also associated with important predictors of cardiovascular disease, as increased concentration of LDL or triglyceride, decreased concentration of HDL, high systolic blood pressure, visceral obesity. There is accumulating evidence that chronic sub-clinical inflammation, as measured by inflammatory markers as C-reactive protein (CRP) and fibrinogen, is related with insulin resistance. Aim of the study To clarify if insulin resistance would predict cardiovascular disease independently of the other risk factors, such as hypertension, visceral obesity or dyslipidemia, by focusing our attention on the relation between Acute Coronary Syndrome (ACS) and high HOMA index. Methods We evaluated glucose and insulin levels at baseline and post-prandial phase, in order to estimate HOMA index in both the conditions; we related the data obtained with the incidence of cardiovascular events, also investigating traditional cardiovascular risk factors. The cohort included 118 patients with a clinical diagnosis of ACS and excluded those with type 1 diabetes, acute inflammatory diseases, hepatic or renal failure, disreactive disorders, autoimmunity and cancer. Subjects Subjects were followed-up for a period of 1 year, being subdivided in three groups: (1) subjects at elevated HOMA (HOMA ≥6); (2) subjects at intermediate HOMA (HOMA <6 and ≥2); (3) subjects at low HOMA (HOMA ≤2). We considered as end points new cardiovascular events, cerebrovascular events (both TIA and stroke), procedures of revascularization with angioplasty or surgery, cardiovascular death, sudden death. Results Patients with elevated HOMA have a higher incidence of previous cardio- and cerebrovascular events ( p = 0.03), myocardial infarction without ST elevation ( p = 0.005), unstable angina ( p = 0.01), asymptomatic carotid plaques ( p = 0.05), depressed systolic function ( p = 0.05); we found, also, a significant statistic correlation between HOMA index and high levels of CRP, fibrinogen, serum creatinine and TnI. Cardiovascular and cerebrovascular events were registered in 61% of patients with elevated HOMA during the follow up, despite of 25% registered in the control group: so we could consider HOMA index as a negative prognostic variable, also in virtue by the statistic correlation with the inflammatory markers, whose power of prediction is already known. Conclusions Beyond traditional cardiovascular risk factors, insulin resistance quantified by HOMA index seems to significantly have an important prognostic role, both in primary and secondary prevention in patients with Acute Coronary Syndrome.
Summary
Nucleos(t)ide analogues (NAs) and peginterferon have complementary effects in chronic hepatitis B, but it is unclear whether combination therapy improves responses in genotype D‐infected ...patients. We conducted an open‐label study of peginterferon alfa‐2a 180 μg/wk added to ongoing NA therapy in hepatitis B e antigen (HBeAg)‐negative, genotype D‐infected patients with hepatitis B virus DNA <20 IU/mL. The primary endpoint was proportion of patients with ≥50% decline in serum HBsAg by the end of the 48‐week add‐on phase. Seventy patients received treatment, 11 were withdrawn at week 24 for no decrease in HBsAg, and 14 withdrew for other reasons. Response rate (per‐protocol population) was 67.4% (29/43) at week 48 (95% confidence interval CI: 51, 81) and 50.9% (28/55) at week 96 (95% CI: 38, 66). Median serum HBsAg decreased throughout peginterferon alfa‐2a treatment and was significantly lower than baseline at weeks 48, 72 and 96 (P < 0.001). Decreases in HBsAg of ≥0.5‐log10 and ≥1‐log10 were documented in 19 (44.2%) and 6 (14.0%) patients at week 48 and 6 (10.9%) and 17 (30.9%) patients at week 96. The proportion of patients with HBsAg <1000, <500, <100 and <10 IU/mL at ≥1 timepoint during treatment was 78.6% (n = 44), 57.1% (n = 32), 21.4% (n = 12) and 7.1% (n = 4). Interferon gamma‐induced protein 10 increased from baseline up to week 48, with week 12 levels significantly associated with response at week 48. Addition of peginterferon alfa‐2a to ongoing NA therapy significantly decreased HBsAg levels in HBeAg‐negative patients with genotype D infection (ClinicalTrials.gov NCT01706575).
In this open‐label study in patients with genotype D, HBeAg‐negative chronic hepatitis B receiving nucleos(t)ide analogue therapy and with HBV DNA <20 IU/mL, peginterferon alfa‐2a add‐on therapy reduced HBsAg levels, with 29/43 (67.4%) patients having a >50% decline in HBsAg at week 48. An increase in Interferon gamma‐induced protein 10 from baseline up to week 48 was significantly associated with response.
Spatial connectivity of areas of dry fuels is considered a significant influence on the incidence of large fires. Precipitation patterns can dynamically affect fuel connectivity through controls on ...the distribution of dry fuels. Spatio‐temporal monitoring of precipitation‐driven variations in dry fuel connectivity patterns could therefore offer the potential to monitor fire danger. In this paper we present an innovative graph theoretic‐based approach to monitor fire danger using remotely sensed patterns of dry fuel connectivity. We analysed the temporal evolution of dry fuel connectivity in south‐eastern Australia during recent fire seasons. The analysis showed that rapid changes in the connectivity of dry fuels determine the pre‐conditions for major fires. We found that large fires affected highly connected dry portions of the landscape, confirming the potential of this approach to monitor the temporal evolution of fire danger.
Key Points
Variations in dry fuel connectivity can be highly dynamic
Remotely sensed patterns of fuel connectivity can be used to monitor fire danger
Patterns of area burnt and dry fuel connectivity closely matched
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