Large seasonal and diurnal anthropogenic heat flux across four Australian cities

Anthropogenic heat release is a key component of the urban heat island. However, it is often excluded from studies of the urban heat island because reliable estimates are not available. This omission is important because anthropogenic heat can contribute up to 4ºC to the urban heat island, and increases heat stress to urban residents. The exclusion of anthropogenic heat means the urban heat island effect on temperatures may be under-estimated. Here we estimate anthropogenic heat for four Australian capital cities (Brisbane, Sydney, Melbourne and Adelaide) to inform the management of the urban heat island in a changing climate. Anthropogenic heat release was calculated using 2011 population census data and an inventory of hourly traffic volume, building electricity and gas use. Melbourne had the highest annual daily average anthropogenic heat emissions, which reached 376 W/m² in the city centre during the daytime, while Brisbane’s emissions were 261 W/m² and Sydney’s were 256 W/m² . Adelaide had the lowest emissions, with a daily average of 39 W/m² in the city centre. Emissions varied within and among the four cities and decreased rapidly with distance from the city centre, to < 5 W/m² at 20 km from the city in Brisbane, and 15 km in Adelaide. The highest emissions were found in the city centres during working hours. The peak emissions reached in the centre of Melbourne are similar to the peak emissions in London and Tokyo, where anthropogenic heat is a large component of the urban heat island. This indicates that anthropogenic heat could be an important contributor to the urban heat island in Australian capital cities, and needs to be considered in climate adaptation studies. This is an important problem because climate change, combined with an ageing population and urban growth, could double the deaths from heatwaves in Australian cities over the next 40 years

Community motivations to engage in conservation behaviour to conserve the Sumatran orangutan

Community-based conservation programs in developing countries often assume that heteronomous motivation (e.g. extrinsic incentives such as economic rewards and pressure or coercion to act) will motivate local communities to adopt conservation behaviors. However, this may not be as effective or sustainable as autonomous motivations (e.g. an intrinsic desire to act due to inherent enjoyment or self-identification with a behavior and through freedom of choice). This paper analyses the comparative effectiveness of heteronomous versus autonomous approaches to community-based conservation programs, using the example of Sumatran orangutan (Pongo abelii) conservation in Indonesia. Comparing three case study villages employing differing program designs, we found that heteronomous motivations (e.g. income from tourism) led to a change in self-reported behavior towards orangutan protection. However, they were ineffective in changing self reported behavior towards forest (i.e. orangutan habitat) protection. The most effective approach to creating self-reported behavior change throughout the community was with a combination of autonomous and heteronomous motivations. Individuals who were heteronomously motivated to protect the orangutan were found to be more likely to have changed attitudes than their self-reported behavior. These findings demonstrate that the current paradigm of motivating communities in developing countries to adopt conservation behaviors primarily through monetary incentives and rewards should also consider integrating autonomous motivational techniques which promote the intrinsic values of conservation. Such a combination will have a greater potential to achieve sustainable and cost-effective conservation outcomes. Our results highlight the importance of in-depth socio psychological analyses to assist the design and implementation of community-based conservation programs

A comparative evaluation of the effect of internet-based CME delivery format on satisfaction, knowledge and confidence

<p>Abstract</p> <p>Background</p> <p>Internet-based instruction in continuing medical education (CME) has been associated with favorable outcomes. However, more direct comparative studies of different Internet-based interventions, instructional methods, presentation formats, and approaches to implementation are needed. The purpose of this study was to conduct a comparative evaluation of two Internet-based CME delivery formats and the effect on satisfaction, knowledge and confidence outcomes.</p> <p>Methods</p> <p>Evaluative outcomes of two differing formats of an Internet-based CME course with identical subject matter were compared. A Scheduled Group Learning format involved case-based asynchronous discussions with peers and a facilitator over a scheduled 3-week delivery period. An eCME On Demand format did not include facilitated discussion and was not based on a schedule; participants could start and finish at any time. A retrospective, pre-post evaluation study design comparing identical satisfaction, knowledge and confidence outcome measures was conducted.</p> <p>Results</p> <p>Participants in the Scheduled Group Learning format reported significantly higher mean satisfaction ratings in some areas, performed significantly higher on a post-knowledge assessment and reported significantly higher post-confidence scores than participants in the eCME On Demand format that was not scheduled and did not include facilitated discussion activity.</p> <p>Conclusions</p> <p>The findings support the instructional benefits of a scheduled delivery format and facilitated asynchronous discussion in Internet-based CME.</p

Conceptual models for Mental Distress among HIV-infected and uninfected individuals: A contribution to clinical practice and research in primary-health-care centers in Zambia

<p>Abstract</p> <p>Background</p> <p>Mental distress is common in primary care and overrepresented among Human Immunodeficiency virus (HIV)-infected individuals, but access to effective treatment is limited, particularly in developing countries. Explanatory models (EM) are contextualised explanations of illnesses and treatments framed within a given society and are important in understanding an individual's perspective on the illness. Although individual variations are important in determining help-seeking and treatment behaviour patterns, the ability to cope with an illness and quality of life, the role of explanatory models in shaping treatment preferences is undervalued. The aim was to identify explanatory models employed by HIV-infected and uninfected individuals and to compare them with those employed by local health care providers. Furthermore, we aimed to build a theoretical model linking the perception of mental distress to treatment preferences and coping mechanisms.</p> <p>Methods</p> <p>Qualitative investigation nested in a cross-sectional validation study of 28 (male and female) attendees at four primary care clinics in Lusaka, Zambia, between December 2008 and May 2009. Consecutive clinic attendees were sampled on random days and conceptual models of mental distress were examined, using semi-structured interviews, in order to develop a taxonomic model in which each category was associated with a unique pattern of symptoms, treatment preferences and coping strategies.</p> <p>Results</p> <p>Mental distress was expressed primarily as somatic complaints including headaches, perturbed sleep and autonomic symptoms. Economic difficulties and interpersonal relationship problems were the most common causal models among uninfected individuals. Newly diagnosed HIV patients presented with a high degree of hopelessness and did not value seeking help for their symptoms. Patients not receiving anti-retroviral drugs (ARV) questioned their effectiveness and were equivocal about seeking help. Individuals receiving ARV were best adjusted to their status, expressed hope and valued counseling and support groups. Health care providers reported that 40% of mental distress cases were due to HIV infection.</p> <p>Conclusions</p> <p>Patient models concerning mental distress are critical to treatment-seeking decisions and coping mechanisms. Mental health interventions should be further researched and prioritized for HIV-infected individuals.</p

Restoring Coastal Plants to Improve Global Carbon Storage: Reaping What We Sow

Long-term carbon capture and storage (CCS) is currently considered a viable strategy for mitigating rising levels of atmospheric CO2 and associated impacts of global climate change. Until recently, the significant below-ground CCS capacity of coastal vegetation such as seagrasses, salt marshes, and mangroves has largely gone unrecognized in models of global carbon transfer. However, this reservoir of natural, free, and sustainable carbon storage potential is increasingly jeopardized by alarming trends in coastal habitat loss, totalling 30–50% of global abundance over the last century alone. Human intervention to restore lost habitats is a potentially powerful solution to improve natural rates of global CCS, but data suggest this approach is unlikely to substantially improve long-term CCS unless current restoration efforts are increased to an industrial scale. Failure to do so raises the question of whether resources currently used for expensive and time-consuming restoration projects would be more wisely invested in arresting further habitat loss and encouraging natural recovery

deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data

Gene fusions created by somatic genomic rearrangements are known to play an important role in the onset and development of some cancers, such as lymphomas and sarcomas. RNA-Seq (whole transcriptome shotgun sequencing) is proving to be a useful tool for the discovery of novel gene fusions in cancer transcriptomes. However, algorithmic methods for the discovery of gene fusions using RNA-Seq data remain underdeveloped. We have developed deFuse, a novel computational method for fusion discovery in tumor RNA-Seq data. Unlike existing methods that use only unique best-hit alignments and consider only fusion boundaries at the ends of known exons, deFuse considers all alignments and all possible locations for fusion boundaries. As a result, deFuse is able to identify fusion sequences with demonstrably better sensitivity than previous approaches. To increase the specificity of our approach, we curated a list of 60 true positive and 61 true negative fusion sequences (as confirmed by RT-PCR), and have trained an adaboost classifier on 11 novel features of the sequence data. The resulting classifier has an estimated value of 0.91 for the area under the ROC curve. We have used deFuse to discover gene fusions in 40 ovarian tumor samples, one ovarian cancer cell line, and three sarcoma samples. We report herein the first gene fusions discovered in ovarian cancer. We conclude that gene fusions are not infrequent events in ovarian cancer and that these events have the potential to substantially alter the expression patterns of the genes involved; gene fusions should therefore be considered in efforts to comprehensively characterize the mutational profiles of ovarian cancer transcriptomes

Estimating and Modelling Bias of the Hierarchical Partitioning Public-Domain Software: Implications in Environmental Management and Conservation

BACKGROUND: Hierarchical partitioning (HP) is an analytical method of multiple regression that identifies the most likely causal factors while alleviating multicollinearity problems. Its use is increasing in ecology and conservation by its usefulness for complementing multiple regression analysis. A public-domain software "hier.part package" has been developed for running HP in R software. Its authors highlight a "minor rounding error" for hierarchies constructed from >9 variables, however potential bias by using this module has not yet been examined. Knowing this bias is pivotal because, for example, the ranking obtained in HP is being used as a criterion for establishing priorities of conservation. METHODOLOGY/PRINCIPAL FINDINGS: Using numerical simulations and two real examples, we assessed the robustness of this HP module in relation to the order the variables have in the analysis. Results indicated a considerable effect of the variable order on the amount of independent variance explained by predictors for models with >9 explanatory variables. For these models the nominal ranking of importance of the predictors changed with variable order, i.e. predictors declared important by its contribution in explaining the response variable frequently changed to be either most or less important with other variable orders. The probability of changing position of a variable was best explained by the difference in independent explanatory power between that variable and the previous one in the nominal ranking of importance. The lesser is this difference, the more likely is the change of position. CONCLUSIONS/SIGNIFICANCE: HP should be applied with caution when more than 9 explanatory variables are used to know ranking of covariate importance. The explained variance is not a useful parameter to use in models with more than 9 independent variables. The inconsistency in the results obtained by HP should be considered in future studies as well as in those already published. Some recommendations to improve the analysis with this HP module are given

A review of elliptical and disc galaxy structure, and modern scaling laws

A century ago, in 1911 and 1913, Plummer and then Reynolds introduced their models to describe the radial distribution of stars in `nebulae'. This article reviews the progress since then, providing both an historical perspective and a contemporary review of the stellar structure of bulges, discs and elliptical galaxies. The quantification of galaxy nuclei, such as central mass deficits and excess nuclear light, plus the structure of dark matter halos and cD galaxy envelopes, are discussed. Issues pertaining to spiral galaxies including dust, bulge-to-disc ratios, bulgeless galaxies, bars and the identification of pseudobulges are also reviewed. An array of modern scaling relations involving sizes, luminosities, surface brightnesses and stellar concentrations are presented, many of which are shown to be curved. These 'redshift zero' relations not only quantify the behavior and nature of galaxies in the Universe today, but are the modern benchmark for evolutionary studies of galaxies, whether based on observations, N-body-simulations or semi-analytical modelling. For example, it is shown that some of the recently discovered compact elliptical galaxies at 1.5 < z < 2.5 may be the bulges of modern disc galaxies.Comment: Condensed version (due to Contract) of an invited review article to appear in "Planets, Stars and Stellar Systems"(www.springer.com/astronomy/book/978-90-481-8818-5). 500+ references incl. many somewhat forgotten, pioneer papers. Original submission to Springer: 07-June-201

Representative Landscapes in the Forested Area of Canada

Canada is a large nation with forested ecosystems that occupy over 60% of the national land base, and knowledge of the patterns of Canada’s land cover is important to proper environmental management of this vast resource. To this end, a circa 2000 Landsat-derived land cover map of the forested ecosystems of Canada has created a new window into understanding the composition and configuration of land cover patterns in forested Canada. Strategies for summarizing such large expanses of land cover are increasingly important, as land managers work to study and preserve distinctive areas, as well as to identify representative examples of current land-cover and land-use assemblages. Meanwhile, the development of extremely efficient clustering algorithms has become increasingly important in the world of computer science, in which billions of pieces of information on the internet are continually sifted for meaning for a vast variety of applications. One recently developed clustering algorithm quickly groups large numbers of items of any type in a given data set while simultaneously selecting a representative—or “exemplar”—from each cluster. In this context, the availability of both advanced data processing methods and a nationally available set of landscape metrics presents an opportunity to identify sets of representative landscapes to better understand landscape pattern, variation, and distribution across the forested area of Canada. In this research, we first identify and provide context for a small, interpretable set of exemplar landscapes that objectively represent land cover in each of Canada’s ten forested ecozones. Then, we demonstrate how this approach can be used to identify flagship and satellite long-term study areas inside and outside protected areas in the province of Ontario. These applications aid our understanding of Canada’s forest while augmenting its management toolbox, and may signal a broad range of applications for this versatile approach