e, previously unnoticed, effects of temperature in the unexposed offspring of heat-exposed parents in a global warming scenario. The role of local government units (LGUs) in disaster resilience is crucial for a hazard-prone country such as the Philippines. Although the country has its own institutional framework on disaster risk reduction, a number of issues limit LGUs' potential to perform its role. This study focused on building institutional resilience of LGUs towards building climate risk resilience in Aurora, Philippines by engaging key actors in the formulation of Local Climate Change Action Plans (LCCAP). The study adopted the shared learning process from the Climate Resilience Framework (CRF) to strengthen partnership and implement capacity building activities, aimed at developing the Climate and Disaster Risk Assessment (CDRA) and LCCAP beyond compliance. An institutional capacity assessment was administered through a survey involving 87 members of the Technical Working Group (TWG) from eight municipalities and provincial government. Institutional capacity was measured using 70 indicators representing access rights and entitlements, information flows, decision-making processes, application of new knowledge, capacity to anticipate risk, capacity to respond, as well as capacity to recover and change. Data were analyzed using descriptive statistics. Both Spearman Correlation and Cramer's V determined the interrelationship between socio-demographic variables and institutional characteristics. Results revealed that the LGUs performed better in risk response and management. A strong correlation between expertise and position vis-à-vis all resilient institution metrics was also observed, while gender is moderately correlated with all parameters except access rights and entitlements. Three key areas, not adequately articulated in current literature, need to be improved to enhance institutional resilience towards climate and disaster risks, namely staffing and human resource; access to financial support from other sources; and development of knowledge management systems. BACKGROUND No previous epidemiological study has investigated the combined association of long-term ambient nitrogen dioxide (NO2) and particulate matter of diameter size-2.5 (PM2.5) exposure with asthma outcomes among schoolchildren in Africa. OBJECTIVES This study investigated the independent and co-pollutant association of long-term exposures to ambient air pollutants on asthma-associated outcomes in a cohort of schoolchildren in the Western Cape Province of South Africa. METHODS A total of 590 grade-4 schoolchildren residing in four informal settlements were studied. Spirometry and fractional exhaled nitric-oxide (FeNO) measurements were conducted, including a standardized questionnaire administered to caregivers at baseline and 12-months follow-up. Annual NO2 and PM2.5 levels were estimated for each child's home using land-use regression modelling. Single- and two-pollutant models were constructed to assess the independent and co-pollutant association of both air pollutants (NO2 and PM2.5) on new cases of asthma-associated outcomes adjusting-for host characteristics, indoor exposures and study area. RESULTS The annual average concentration of PM2.5 and NO2 were 10.01μg/m3 and 16.62μg/m3 respectively, across the four study areas, and were below the local Standards of 20μg/m3 and 40μg/m3, for both pollutants, respectively. In the two-pollutant-adjusted models, an interquartile range (IQR) increase of 14.2μg/m3 in NO2 was associated with an increased risk of new onset of ocular-nasal symptoms (adjusted odds ratio-aOR 1.63, 95% CI 1.01-2.60), wheezing (aOR 3.57, 95% CI 1.18-10.92), more than two or more asthma symptom score (aOR 1.71, 95% CI 1.02-2.86), and airway inflammation defined as FeNO > 35 ppb (aOR 3.10, 95% CI 1.10-8.71), independent of PM2.5 exposures. https://www.selleckchem.com/products/17-AAG(Geldanamycin).html CONCLUSION This study provided evidence that ambient NO2 levels below local standards and international guidelines, independent of PM2.5 exposure, increases new cases of asthma-associated outcomes after 12-months. With the aim of obtaining information about the environmental fate and dynamics of the antibiotic doxycycline (DC) when it reaches soil, adsorption and desorption processes were studied for this compound in 20 agricultural soils, by means of batch-type and stirred-flow-chamber experiments. The results indicate that the studied soils adsorbed high amounts of DC, with adsorption percentages >91% in all cases. In addition, adsorption results were satisfactorily modeled, with good fittings to the Langmuir and Freundlich equations, with the values for Langmuir's maximum adsorption capacity (qmax) varying between 14,692 and 26,141 μmol kg-1 (average 17,816), and between 1,906 and 13,120 Ln μmol1-n kg-1 (average 6,969) for the Freundlich affinity coefficient, which are very high. The soil variables most related to the adsorption of the antibiotic were linked to organic matter (specifically, soil organic carbon-SOC- and soil nitrogen -N- contents), and to the clay fraction, as well as to cation exchange capacity, being the soils with a greater content in these variables those that presented a greater adsorption. FTIR results shown that DC adsorption mechanisms were based on interactions such as hydrogen bonds and π-π interactions between the antibiotic and soils. Desorption was very low, reaching values between 1 and 2% in batch experiments, and between 5 and 15% in stirred flow chamber experiments, which indicates a strong hysteresis affecting adsorption and desorption processes. This fact can be considered positively, as these soils could retain DC very strongly, thus reducing risks to human and ecological health. The developmental timing of exposures to toxic chemicals or combinations of chemicals may be as important as the dosage itself. This concept is called "critical windows of exposure." The time boundaries of such windows can be detected if exposure data are collected repeatedly in short time intervals. The development of tooth-matrix biomarkers which provide prenatal and postnatal exposure measures in repeated intervals can provide such data. Using teeth, we use reverse distributed lagged models (DLMs) to incorporate weekly prenatal and postnatal measures of exposures to estimate time-varying associations with developmental effects. The analysis of such data using lagged weighted quantile sum (WQS) regression as an extension to reverse DLMs for complex mixtures was first proposed by Bello et al. This prior algorithm was not operationally generalizable to large numbers of components (say, more than five or six). We propose a revised algorithm that may be useful for larger mixtures by combining time-specific WQS(t) indices in a reverse DLM.