Community Seismic Network

The article describes the design of the Community Seismic Network, which is a dense open seismic network based on low cost sensors. The inputs are from sensors hosted by volunteers from the community by direct connection to their personal computers, or through sensors built into mobile devices. The server is cloud-based for robustness and to dynamically handle the load of impulsive earthquake events. The main product of the network is a map of peak acceleration, delivered within seconds of the ground shaking. The lateral variations in the level of shaking will be valuable to first responders, and the waveform information from a dense network will allow detailed mapping of the rupture process. Sensors in buildings may be useful for monitoring the state-of-health of the structure after major shaking

Early BCR-ABL1 Transcript Decline after 1 Month of Tyrosine Kinase Inhibitor Therapy as an Indicator for Treatment Response in Chronic Myeloid Leukemia

In chronic myeloid leukemia (CML), early treatment prediction is important to identify patients with inferior overall outcomes. We examined the feasibility of using reductions in BCR-ABL1 transcript levels after 1 month of tyrosine kinase inhibitor (TKI) treatment to predict therapy response. Fifty-two first-line TKI-treated CML patients were included (imatinib n = 26, dasatinib n = 21, nilotinib n = 5), and BCR-ABL1 transcript levels were measured at diagnosis (dg) and 1, 3, 6, 12, 18, 24, and 36 months. The fold change of the BCR-ABL1 transcripts at 1 month compared to initial BCR-ABL1 transcript levels was used to indicate early therapy response. In our cohort, 21% of patients had no decrease in BCR-ABL1 transcript levels after 1 month and were classified as poor responders. Surprisingly, these patients had lower BCR-ABL1 transcript levels at dg compared to responders (31% vs. 48%, p = 0.0083). Poor responders also significantly more often had enlarged spleen (55% vs. 15%; p<0.01) and a higher percentage of Ph+ CD34+CD38- cells in the bone marrow (91% vs. 75%, p<0.05). The major molecular response rates were inferior in the poor responders (at 12m 18% vs. 64%, p<0.01; 18m 27% vs. 75%, p<0.01; 24m 55% vs. 87%, p<0.01). In conclusion, early treatment response analysis defines a biologically distinct patient subgroup with inferior long-term outcomes.Peer reviewe

Evaluating the effectiveness of road mitigation measures.

The last 20 years have seen a dramatic increase in efforts to mitigate the negative effects of roads and traffic on wildlife, including fencing to prevent wildlife- vehicle collisions and wildlife crossing structures to facilitate landscape connectivity. While not necessarily explicitly articulated, the fundamental drivers behind road mitigation are human safety, animal welfare, and/or wildlife conservation. Concomitant with the increased effort to mitigate has been a focus on evaluating road mitigation. So far, research has mainly focussed on assessing the use of wildlife crossing structures, demonstrating that a broad range of species use them. However, this research has done little to address the question of the effectiveness of crossing structures, because use of a wildlife crossing structure does not necessarily equate to its effectiveness. The paucity of studies directly examining the effectiveness of crossing structures is exacerbated by the fact that such studies are often poorly designed, which limits the level of inference that can be made. Without well performed evaluations of the effectiveness of road mitigation measures, we may endanger the viability of wildlife populations and inefficiently use financial resources by installing structures that are not as effective as we think they are. In this paper we outline the essential elements of a good experimental design for such assessments and prioritize the parameters to be measured. The framework we propose will facilitate col- laboration between road agencies and scientists to undertake research programs that fully evaluate effectiveness of road mitigation measures. We discuss the added value of road mitigation evaluations for policy makers and transportation agencies and provide recom- mendations on how to incorporate such evaluations in road planning practices

Influences of Emergent Macrophytes on the Quality of Water Contaminated with Coal Ash Leachates

Coal combustion currently remains as one of the leading sources of energy in the world. The potential environmental contamination from the storage of coal ash has been highlighted in recent years by large spills from power plant retention ponds in Tennessee and North Carolina. These incidents, as well as permitted coal ash leachate discharge, make it important to understand the influences of coal ash on the ecology and biogeochemistry of aquatic ecosystems. Wetland ecosystems are particularly of interest, as they serve as reservoirs and transformers of pollutants in the landscape, and constructed wetland treatment systems (CWTS) have been used in the past to treat coal combustion waste. Emergent macrophytes play a pivotal role in CWTS and are often a defining feature of natural wetland habitats that can contribute to the removal of pollutants from the water column through a range of biological and chemical processes. This study explored the explored the effects of two emergent macrophyte species, Juncus effusus and Eliocharis quadrangulata, on the physicochemical properties and trace element concentrations of water contaminated with coal ash. A greenhouse study was performed with wetland microcosms dosed with leachates of fly ash derived from high sulfur and low sulfur coal sources. Microcosms were planted with J. effusus, E. quadrangulata or were unplanted to control for the presence of plants. Both types of leachate increased the electric conductivity (Ec) of microcosm water relative to controls received reverse osmosis water. High sulfur leachates increased water pH while low sulfur leachates decreased water pH. Both leachates significantly elevated boron and lithium concentrations in microcosm water and high sulfur leachates also elevated molybdenum significantly. The highest boron concentrations measured in the study exceeded several aquatic toxicity thresholds. The macrophytes did not display any signs of toxicity, but did appear to exert an influence on the water chemistry. The presence of either species reduced the Ec of microcosm water significantly more than when plants were not presence. Both species also appeared to increase the removal efficiency of trace elements from the water column compared to microcosms with no macrophytes. The findings of this study indicate that emergent macrophytes are tolerant to aquatic coal ash pollution and could potentially reduce associated perturbations in water quality. Their presence in aquatic ecosystems downstream from coal ash discharges could help maintain ecosystem integrity or they may be effectively utilized in CWTS for coal power plant wastewater. Further studies are needed to evaluate the influence of higher volumes of leachate contamination, bioaccumulation of trace elements in macrophytes and the speciation of trace elements from coal ash leachates

Leveraging Contextual Relationships Between Objects for Localization

Object localization is currently an active area of research in computer vision. The object localization task is to identify all locations of an object class within an image by drawing a bounding box around objects that are instances of that class. Object locations are typically found by computing a classification score over a small window at multiple locations in the image, based on some chosen criteria, and choosing the highest scoring windows as the object bounding-boxes. Localization methods vary widely, but there is a growing trend towards methods that are able to make localization more accurate and efficient through the use of context. In this thesis, I investigate whether contextual relationships between related objects can be leveraged to improve localization efficiency through a reduction in the number of windows considered for each localization task. I implement a context-driven localization model and evaluate it against two models that do not use context between objects for comparison. My model constrains the search spaces for the target object location and window size. I show that context-driven methods substantially reduce the mean number of windows necessary for localizing a target object versus the two models not using context. The results presented here suggest that contextual relationships between objects in an image can be leveraged to significantly improve localization efficiency by reducing the number of windows required to find the target object