A Lazy Approach for Supporting Nested Transactions

Transactional memory (TM) is a compelling alternative to traditional synchronization, and implementing TM primitives directly in hardware offers a potential performance advantage over software-based methods. In this paper, we demonstrate that many of the actions associated with transaction abort and commit may be performed lazily -- that is, incrementally, and on demand. This technique is ideal for hardware, since it requires little space or work; in addition, it can improve performance by sparing accesses to committing or aborting locations from having to stall until the commit or abort completes. We further show that our lazy abort and commit technique supports open nesting and orElse, two language-level proposals which rely on transactional nesting. We also provide design notes for supporting lazy abort and commit on our own hardware TM system, based on VTM

Identifying Clusters in Bayesian Disease Mapping

Disease mapping is the field of spatial epidemiology interested in estimating the spatial pattern in disease risk across $n$ areal units. One aim is to identify units exhibiting elevated disease risks, so that public health interventions can be made. Bayesian hierarchical models with a spatially smooth conditional autoregressive prior are used for this purpose, but they cannot identify the spatial extent of high-risk clusters. Therefore we propose a two stage solution to this problem, with the first stage being a spatially adjusted hierarchical agglomerative clustering algorithm. This algorithm is applied to data prior to the study period, and produces $n$ potential cluster structures for the disease data. The second stage fits a separate Poisson log-linear model to the study data for each cluster structure, which allows for step-changes in risk where two clusters meet. The most appropriate cluster structure is chosen by model comparison techniques, specifically by minimising the Deviance Information Criterion. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland

Spatial clustering of average risks and risk trends in Bayesian disease mapping

Spatiotemporal disease mapping focuses on estimating the spatial pattern in disease risk across a set of nonoverlapping areal units over a fixed period of time. The key aim of such research is to identify areas that have a high average level of disease risk or where disease risk is increasing over time, thus allowing public health interventions to be focused on these areas. Such aims are well suited to the statistical approach of clustering, and while much research has been done in this area in a purely spatial setting, only a handful of approaches have focused on spatiotemporal clustering of disease risk. Therefore, this paper outlines a new modeling approach for clustering spatiotemporal disease risk data, by clustering areas based on both their mean risk levels and the behavior of their temporal trends. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland

Evidence of global-scale aeolian dispersal and endemism in isolated geothermal microbial communities of Antarctica

New evidence in aerobiology challenges the assumption that geographical isolation is an effective barrier to microbial transport. However, given the uncertainty with which aerobiological organisms are recruited into existing communities, the ultimate impact of microbial dispersal is difficult to assess. To evaluate the ecological significance of global-scale microbial dispersal, molecular genetic approaches were used to examine microbial communities inhabiting fumarolic soils on Mt. Erebus, the southernmost geothermal site on Earth. There, hot, fumarolic soils provide an effective environmental filter to test the viability of organisms that have been distributed via aeolian transport over geological time. We find that cosmopolitan thermophiles dominate the surface, whereas endemic Archaea and members of poorly understood Bacterial candidate divisions dominate the immediate subsurface. These results imply that aeolian processes readily disperse viable organisms globally, where they are incorporated into pre-existing complex communities of endemic and cosmopolitan taxa

AJAE Appendix: Nonlinear Dynamics and Structural Change in the U.S. Hog-Corn Ratio: A Time-Varying Star Approach

The material contained herein is supplementary to the article named in the title and published in the American Journal of Agricultural Economics, Volume 88, Number 1, February 2006.Agribusiness,

The ecological dichotomy of ammonia-oxidizing archaea and bacteria in the hyper-arid soils of the Antarctic Dry Valleys

The McMurdo Dry Valleys of Antarctica are considered to be one of the most physically and chemically extreme terrestrial environments on the Earth. However, little is known about the organisms involved in nitrogen transformations in these environments. In this study, we investigated the diversity and abundance of ammonia-oxidizing archaea (AOA) and bacteria (AOB) in four McMurdo Dry Valleys with highly variable soil geochemical properties and climatic conditions: Miers Valley, Upper Wright Valley, Beacon Valley and Battleship Promontory. The bacterial communities of these four Dry Valleys have been examined previously, and the results suggested that the extremely localized bacterial diversities are likely driven by the disparate physicochemical conditions associated with these locations. Here we showed that AOB and AOA amoA gene diversity was generally low; only four AOA and three AOB operational taxonomic units (OTUs) were identified from a total of 420 AOA and AOB amoA clones. Quantitative PCR analysis of amoA genes revealed clear differences in the relative abundances of AOA and AOB amoA genes among samples from the four dry valleys. Although AOB amoA gene dominated the ammonia-oxidizing community in soils from Miers Valley and Battleship Promontory, AOA amoA gene were more abundant in samples from Upper Wright and Beacon Valleys, where the environmental conditions are considerably harsher (e.g., extremely low soil C/N ratios and much higher soil electrical conductivity). Correlations between environmental variables and amoA genes copy numbers, as examined by redundancy analysis (RDA), revealed that higher AOA/AOB ratios were closely related to soils with high salts and Cu contents and low pH. Our findings hint at a dichotomized distribution of AOA and AOB within the Dry Valleys, potentially driven by environmental constraints

Benthic microbial communities of coastal terrestrial and ice shelf Antarctic meltwater ponds.

The numerous perennial meltwater ponds distributed throughout Antarctica represent diverse and productive ecosystems central to the ecological functioning of the surrounding ultra oligotrophic environment. The dominant taxa in the pond benthic communities have been well described however, little is known regarding their regional dispersal and local drivers to community structure. The benthic microbial communities of 12 meltwater ponds in the McMurdo Sound of Antarctica were investigated to examine variation between pond microbial communities and their biogeography. Geochemically comparable but geomorphologically distinct ponds were selected from Bratina Island (ice shelf) and Miers Valley (terrestrial) (<40 km between study sites), and community structure within ponds was compared using DNA fingerprinting and pyrosequencing of 16S rRNA gene amplicons. More than 85% of total sequence reads were shared between pooled benthic communities at different locations (OTU0.05), which in combination with favorable prevailing winds suggests aeolian regional distribution. Consistent with previous findings Proteobacteria and Bacteroidetes were the dominant phyla representing over 50% of total sequences; however, a large number of other phyla (21) were also detected in this ecosystem. Although dominant Bacteria were ubiquitous between ponds, site and local selection resulted in heterogeneous community structures and with more than 45% of diversity being pond specific. Potassium was identified as the most significant contributing factor to the cosmopolitan community structure and aluminum to the location unique community based on a BEST analysis (Spearman's correlation coefficient of 0.632 and 0.806, respectively). These results indicate that the microbial communities in meltwater ponds are easily dispersed regionally and that the local geochemical environment drives the ponds community structure