One-year delayed effect of fog on malaria transmission : a time-series analysis in the rain forest area of Mengla County, south-west China

Background: Malaria is a major public health burden in the tropics with the potential to significantly increase in response to climate change. Analyses of data from the recent past can elucidate how short-term variations in weather factors affect malaria transmission. This study explored the impact of climate variability on the transmission of malaria in the tropical rain forest area of Mengla County, south-west China. Methods: Ecological time-series analysis was performed on data collected between 1971 and 1999. Auto-regressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Results: At the time scale of months, the predictors for malaria incidence included: minimum temperature, maximum temperature, and fog day frequency. The effect of minimum temperature on malaria incidence was greater in the cool months than in the hot months. The fog day frequency in October had a positive effect on malaria incidence in May of the following year. At the time scale of years, the annual fog day frequency was the only weather predictor of the annual incidence of malaria. Conclusion: Fog day frequency was for the first time found to be a predictor of malaria incidence in a rain forest area. The one-year delayed effect of fog on malaria transmission may involve providing water input and maintaining aquatic breeding sites for mosquitoes in vulnerable times when there is little rainfall in the 6-month dry seasons. These findings should be considered in the prediction of future patterns of malaria for similar tropical rain forest areas worldwide.

Identifying programs to reduce road trauma to A.C.T. motorcyclists

A broad range of motorcycle safety programs and systems exist in Australia and New Zealand. These vary from statewide licensing and training systems run by government licensing and transport agencies to safety programs run in small communities and by individual rider groups. While the effectiveness of licensing and training has been reviewed and recommendations for improvement have been developed (e.g. Haworth & Mulvihill, 2005), little is known about many smaller or innovative programs, and their potential to improve motorcycle safety in the ACT.

Biotechnology learnings using ‘Claymation’ and ‘Slowmation’

This ‘Claymation’ and ‘Slowmation’ project incorporated content as well as skill development. The participants – 4 pre-service teachers and 4 secondary school students explored chromosome mapping and DNA replication. Through research, the writing, revising and editing of storyboards, two short videos were produced. Two of the pre-service teachers had prior experience with Claymation, however none of the participants had prior knowledge of chromosome mapping or DNA replication. This paper describes the learnings of the participants in terms of their self generated questions, the need for attention to detail, and argumentation / negotiation skills.

Impacts of periodic tillage on soil C stocks : a synthesis

Long-term loss of soil C stocks under conventional tillage and accrual of soil C following adoption of no-tillage have been well documented. No-tillage use is spreading, but it is common to occasionally till within a no-till regime or to regularly alternate between till and no-till practices within a rotation of different crops. Short-term studies indicate that substantial amounts of C can be lost from the soil immediately following a tillage event, but there are few field studies that have investigated the impact of infrequent tillage on soil C stocks. How much of the C sequestered under no-tillage is likely to be lost if the soil is tilled? What are the longer-term impacts of continued infrequent no-tillage? If producers are to be compensated for sequestering C in soil following adoption of conservation tillage practices, the impacts of infrequent tillage need to be quantified. A few studies have examined the short-term impacts of tillage on soil C and several have investigated the impacts of adoption of continuous no-tillage. We present: (1) results from a modeling study carried out to address these questions more broadly than the published literature allows, (2) a review of the literature examining the short-term impacts of tillage on soil C, (3) a review of published studies on the physical impacts of tillage and (4) a synthesis of these components to assess how infrequent tillage impacts soil C stocks and how changes in tillage frequency could impact soil C stocks and C sequestration. Results indicate that soil C declines significantly following even one tillage event (1-11 % of soil C lost). Longer-term losses increase as frequency of tillage increases. Model analyses indicate that cultivating and ripping are less disruptive than moldboard plowing, and soil C for those treatments average just 6% less than continuous NT compared to 27% less for CT. Most (80%) of the soil C gains of NT can be realized with NT coupled with biannual cultivating or ripping. (C) 2007 Elsevier B.V. All rights reserved.

Stabilization mechanisms of soil organic matter: Implications for C-saturation of soils

The relationship between soil structure and the ability of soil to stabilize soil organic matter (SOM) is a key element in soil C dynamics that has either been overlooked or treated in a cursory fashion when developing SOM models. The purpose of this paper is to review current knowledge of SOM dynamics within the framework of a newly proposed soil C saturation concept. Initially, we distinguish SOM that is protected against decomposition by various mechanisms from that which is not protected from decomposition. Methods of quantification and characteristics of three SOM pools defined as protected are discussed. Soil organic matter can be: (1) physically stabilized, or protected from decomposition, through microaggregation, or (2) intimate association with silt and clay particles, and (3) can be biochemically stabilized through the formation of recalcitrant SOM compounds. In addition to behavior of each SOM pool, we discuss implications of changes in land management on processes by which SOM compounds undergo protection and release. The characteristics and responses to changes in land use or land management are described for the light fraction (LF) and particulate organic matter (POM). We defined the LF and POM not occluded within microaggregates (53-250 mum sized aggregates as unprotected. Our conclusions are illustrated in a new conceptual SOM model that differs from most SOM models in that the model state variables are measurable SOM pools. We suggest that physicochemical characteristics inherent to soils define the maximum protective capacity of these pools, which limits increases in SOM (i.e. C sequestration) with increased organic residue inputs.

Spatial variability of soil organic carbon in grasslands: implications for detecting change at different scales

Extensive data used to quantify broad soil C changes (without information about causation), coupled with intensive data used for attribution of changes to specific management practices, could form the basis of an efficient national grassland soil C monitoring network. Based on variability of extensive (USDA/NRCS pedon database) and intensive field-level soil C data, we evaluated the efficacy of future sample collection to detect changes in soil C in grasslands. Potential soil C changes at a range of spatial scales related to changes in grassland management can be verified (alpha=0.1) after 5 years with collection of 34, 224, 501 samples at the county, state, or national scales, respectively. Farm-level analysis indicates that equivalent numbers of cores and distinct groups of cores (microplots) results in lowest soil C coefficients of variation for a variety of ecosystems. Our results suggest that grassland soil C changes can be precisely quantified using current technology at scales ranging from farms to the entire nation. (C) 2001 Elsevier Science Ltd. All rights reserved.

Incorporating weather into regionwide safety planning prediction models

Predicting safety on roadways is standard practice for road safety professionals and has a corresponding extensive literature. The majority of safety prediction models are estimated using roadway segment and intersection (microscale) data, while more recently efforts have been undertaken to predict safety at the planning level (macroscale). Safety prediction models typically include roadway, operations, and exposure variables—factors known to affect safety in fundamental ways. Environmental variables, in particular variables attempting to capture the effect of rain on road safety, are difficult to obtain and have rarely been considered. In the few cases weather variables have been included, historical averages rather than actual weather conditions during which crashes are observed have been used. Without the inclusion of weather related variables researchers have had difficulty explaining regional differences in the safety performance of various entities (e.g. intersections, road segments, highways, etc.) As part of the NCHRP 8-44 research effort, researchers developed PLANSAFE, or planning level safety prediction models. These models make use of socio-economic, demographic, and roadway variables for predicting planning level safety. Accounting for regional differences - similar to the experience for microscale safety models - has been problematic during the development of planning level safety prediction models. More specifically, without weather related variables there is an insufficient set of variables for explaining safety differences across regions and states. Furthermore, omitted variable bias resulting from excluding these important variables may adversely impact the coefficients of included variables, thus contributing to difficulty in model interpretation and accuracy. This paper summarizes the results of an effort to include weather related variables, particularly various measures of rainfall, into accident frequency prediction and the prediction of the frequency of fatal and/or injury degree of severity crash models. The purpose of the study was to determine whether these variables do in fact improve overall goodness of fit of the models, whether these variables may explain some or all of observed regional differences, and identifying the estimated effects of rainfall on safety. The models are based on Traffic Analysis Zone level datasets from Michigan, and Pima and Maricopa Counties in Arizona. Numerous rain-related variables were found to be statistically significant, selected rain related variables improved the overall goodness of fit, and inclusion of these variables reduced the portion of the model explained by the constant in the base models without weather variables. Rain tends to diminish safety, as expected, in fairly complex ways, depending on rain frequency and intensity.

Validation of the Chinese version of the Problem Areas in Diabetes (PAID-C) scale

OBJECTIVE To examine the psychometric properties of a Chinese version of the Problem Areas In Diabetes (PAID-C) scale. RESEARCH DESIGN AND METHODS The reliability and validity of the PAID-C were evaluated in a convenience sample of 205 outpatients with type 2 diabetes. Confirmatory factor analysis, Bland-Altman analysis, and Spearman's correlations facilitated the psychometric evaluation. RESULTS Confirmatory factor analysis confirmed a one-factor structure of the PAID-C (χ2/df ratio = 1.894, goodness-of-fit index = 0.901, comparative fit index = 0.905, root mean square error of approximation = 0.066). The PAID-C was associated with A1C (rs = 0.15; P < 0.05) and diabetes self-care behaviors in general diet (rs = −0.17; P < 0.05) and exercise (rs = −0.17; P < 0.05). The 4-week test-retest reliability demonstrated satisfactory stability (rs = 0.83; P < 0.01). CONCLUSIONS The PAID-C is a reliable and valid measure to determine diabetes-related emotional distress in Chinese people with type 2 diabetes.

Encyclopedia contributions: entries C-G in Oxford Companion to Australian Gardens

Caulfield, Harold William; p.131 Cowan, Alexander; p.164 Cowley, Ebenezer; p.164 East Talgai Station; p.193 Eaves, S.H.; p.193-194 Edgar, J.S.; p.196 Everist, Selwyn; p.206 Experimental Farms and Gardens; pp.207-208 Government Houses - Queensland; pp.267-268