Analysis and performance of a UPC implementation of a parallel longest common subsequence algorithm

An important problem in computational biology is finding the longest common subsequence (LCS) of two nucleotide sequences. This paper examines the correctness and performance of a recently proposed parallel LCS algorithm that uses successor tables and pruning rules to construct a list of sets from which an LCS can be easily reconstructed. Counterexamples are given for two pruning rules that were given with the original algorithm. Because of these errors, performance measurements originally reported cannot be validated. The work presented here shows that speedup can be reliably achieved by an implementation in Unified Parallel C that runs on an Infiniband cluster. This performance is partly facilitated by exploiting the software cache of the MuPC runtime system. In addition, this implementation achieved speedup without bulk memory copy operations and the associated programming complexity of message passing.

Estimation of scots pine defoliation by the common pine sawfly (Diprion pini L.) using multi-temporal radar data

In 1998-2001 Finland suffered the most severe insect outbreak ever recorded, over 500,000 hectares. The outbreak was caused by the common pine sawfly (Diprion pini L.). The outbreak has continued in the study area, Palokangas, ever since. To find a good method to monitor this type of outbreaks, the purpose of this study was to examine the efficacy of multi-temporal ERS-2 and ENVISAT SAR imagery for estimating Scots pine (Pinus sylvestris L.) defoliation. Three methods were tested: unsupervised k-means clustering, supervised linear discriminant analysis (LDA) and logistic regression. In addition, I assessed if harvested areas could be differentiated from the defoliated forest using the same methods. Two different speckle filters were used to determine the effect of filtering on the SAR imagery and subsequent results. The logistic regression performed best, producing a classification accuracy of 81.6% (kappa 0.62) with two classes (no defoliation, >20% defoliation). LDA accuracy was with two classes at best 77.7% (kappa 0.54) and k-means 72.8 (0.46). In general, the largest speckle filter, 5 x 5 image window, performed best. When additional classes were added the accuracy was usually degraded on a step-by-step basis. The results were good, but because of the restrictions in the study they should be confirmed with independent data, before full conclusions can be made that results are reliable. The restrictions include the small size field data and, thus, the problems with accuracy assessment (no separate testing data) as well as the lack of meteorological data from the imaging dates.

Comparison of common dredging equipment air emissions

The purpose of this study is to provide a procedure to include emissions to the atmosphere resulting from the combustion of diesel fuel during dredging operations into the decision-making process of dredging equipment selection. The proposed procedure is demonstrated for typical dredging methods and data from the Illinois Waterway as performed by the U.S. Army Corps of Engineers, Rock Island District. The equipment included in this study is a 16-inch cutterhead pipeline dredge and a mechanical bucket dredge used during the 2005 dredging season on the Illinois Waterway. Considerable effort has been put forth to identify and reduce environmental impacts from dredging operations. Though environmental impacts of dredging have been studied no efforts have been applied to the evaluation of air emissions from comparable types of dredging equipment, as in this study. By identifying the type of dredging equipment with the lowest air emissions, when cost, site conditions, and equipment availability are comparable, adverse environmental impacts can be minimized without compromising the dredging project. A total of 48 scenarios were developed by varying the dredged material quantity, transport distance, and production rates. This produced an “envelope” of results applicable to a broad range of site conditions. Total diesel fuel consumed was calculated using standard cost estimating practices as defined in the U.S. Army Corps of Engineers Construction Equipment Ownership and Operating Expense Schedule (USACE, 2005). The diesel fuel usage was estimated for all equipment used to mobilize and/or operate each dredging crew for every scenario. A Limited Life Cycle Assessment (LCA) was used to estimate the air emissions from two comparable dredging operations utilizing SimaPro LCA software. An Environmental Impact Single Score (EISS) was the SimaPro output selected for comparison with the cost per CY of dredging, potential production rates, and transport distances to identify possible decision points. The total dredging time was estimated for each dredging crew and scenario. An average hourly cost for both dredging crews was calculated based on Rock Island District 2005 dredging season records (Graham 2007/08). The results from this study confirm commonly used rules of thumb in the dredging industry by indicating that mechanical bucket dredges are better suited for long transport distances and have lower air emissions and cost per CY for smaller quantities of dredged material. In addition, the results show that a cutterhead pipeline dredge would be preferable for moderate and large volumes of dredged material when no additional booster pumps are required. Finally, the results indicate that production rates can be a significant factor when evaluating the air emissions from comparable dredging equipment.

Ultraviolet digital imaging of volcanic plumes : implementation and application to magmatic processes at basaltic volcanoes

Magmatic volatiles play a crucial role in volcanism, from magma production at depth to generation of seismic phenomena to control of eruption style. Accordingly, many models of volcano dynamics rely heavily on behavior of such volatiles. Yet measurements of emission rates of volcanic gases have historically been limited, which has restricted model verification to processes on the order of days or longer. UV cameras are a recent advancement in the field of remote sensing of volcanic SO2 emissions. They offer enhanced temporal and spatial resolution over previous measurement techniques, but need development before they can be widely adopted and achieve the promise of integration with other geophysical datasets. Large datasets require a means by which to quickly and efficiently use imagery to calculate emission rates. We present a suite of programs designed to semi-automatically determine emission rates of SO2 from series of UV images. Extraction of high temporal resolution SO2 emission rates via this software facilitates comparison of gas data to geophysical data for the purposes of evaluating models of volcanic activity and has already proven useful at several volcanoes. Integrated UV camera and seismic measurements recorded in January 2009 at Fuego volcano, Guatemala, provide new insight into the system’s shallow conduit processes. High temporal resolution SO2 data reveal patterns of SO2 emission rate relative to explosions and seismic tremor that indicate tremor and degassing share a common source process. Progressive decreases in emission rate appear to represent inhibition of gas loss from magma as a result of rheological stiffening in the upper conduit. Measurements of emission rate from two closely-spaced vents, made possible by the high spatial resolution of the camera, help constrain this model. UV camera measurements at Kilauea volcano, Hawaii, in May of 2010 captured two occurrences of lava filling and draining within the summit vent. Accompanying high lava stands were diminished SO2 emission rates, decreased seismic and infrasonic tremor, minor deflation, and slowed lava lake surface velocity. Incorporation of UV camera data into the multi-parameter dataset gives credence to the likelihood of shallow gas accumulation as the cause of such events.

INTEGRATED MANAGEMENT OF THE INVASIVE SPECIES CIRSIUM ARVENSE, CANADA THISTLE, AND PHRAGMITES AUSTRALIS, COMMON REED: USING ECOLOGICALLY BASED CONTROL MEASURES AT EACH STAGE OF THE INVASION PROCESS

Invasive plant species threaten natural areas by reducing biodiversity and altering ecosystem functions. They also impact agriculture by reducing crop and livestock productivity. Millions of dollars are spent on invasive species control each year, and traditionally, herbicides are used to manage invasive species. Herbicides have human and environmental health risks associated with them; therefore, it is essential that land managers and stakeholders attempt to reduce these risks by utilizing the principles of integrated weed management. Integrated weed management is a practice that incorporates a variety of measures and focuses on the ecology of the invasive plant to manage it. Roadways are high risk areas that have high incidence of invasive species. Roadways act as conduits for invasive species spread and are ideal harborages for population growth; therefore, roadways should be a primary target for invasive species control. There are four stages in the invasion process which an invasive species must overcome: transport, establishment, spread, and impact. The aim of this dissertation was to focus on these four stages and examine the mechanisms underlying the progression from one stage to the next, while also developing integrated weed management strategies. The target species were Phragmites australis, common reed, and Cisrium arvense, Canada thistle. The transport and establishment risks of P. australis can be reduced by removing rhizome fragments from soil when roadside maintenance is performed. The establishment and spread of C. arvense can be reduced by planting particular resistant species, e.g. Heterotheca villosa, especially those that can reduce light transmittance to the soil. Finally, the spread and impact of C. arvense can be mitigated on roadsides through the use of the herbicide aminopyralid. The risks associated with herbicide drift produced by application equipment can be reduced by using the Wet-Blade herbicide application system.

STATISTICAL METHODS FOR RARE AND COMMON VARIANT ASSOCIATION STUDIES

Complex human diseases are a major challenge for biological research. The goal of my research is to develop effective methods for biostatistics in order to create more opportunities for the prevention and cure of human diseases. This dissertation proposes statistical technologies that have the ability of being adapted to sequencing data in family-based designs, and that account for joint effects as well as gene-gene and gene-environment interactions in the GWA studies. The framework includes statistical methods for rare and common variant association studies. Although next-generation DNA sequencing technologies have made rare variant association studies feasible, the development of powerful statistical methods for rare variant association studies is still underway. Chapter 2 demonstrates two adaptive weighting methods for rare variant association studies based on family data for quantitative traits. The results show that both proposed methods are robust to population stratification, robust to the direction and magnitude of the effects of causal variants, and more powerful than the methods using weights suggested by Madsen and Browning [2009]. In Chapter 3, I extended the previously proposed test for Testing the effect of an Optimally Weighted combination of variants (TOW) [Sha et al., 2012] for unrelated individuals to TOW &ndash F, TOW for Family &ndash based design. Simulation results show that TOW &ndash F can control for population stratification in wide range of population structures including spatially structured populations, is robust to the directions of effect of causal variants, and is relatively robust to percentage of neutral variants. In GWA studies, this dissertation consists of a two &ndash locus joint effect analysis and a two-stage approach accounting for gene &ndash gene and gene &ndash environment interaction. Chapter 4 proposes a novel two &ndash stage approach, which is promising to identify joint effects, especially for monotonic models. The proposed approach outperforms a single &ndash marker method and a regular two &ndash stage analysis based on the two &ndash locus genotypic test. In Chapter 5, I proposed a gene &ndash based two &ndash stage approach to identify gene &ndash gene and gene &ndash environment interactions in GWA studies which can include rare variants. The two &ndash stage approach is applied to the GAW 17 dataset to identify the interaction between KDR gene and smoking status.