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Advanced source apportionment of size-resolved trace elements at multiple sites in London during winter
Here, trace element measurements in PM 10–2.5 , PM 2.5–1.0 and PM 1.0–0.3 aerosol were performed with 2 h time resolution at kerbside, urban background and rural sites during the ClearfLo winter 2012 campaign in London. The environment-dependent variability of emissions was characterized using the Multilinear Engine implementation of the positive matrix factorization model, conducted on data sets comprising all three sites but segregated by size. Combining the sites enabled separation of sources with high temporal covariance but significant spatial variability. Separation of sizes improved source resolution by preventing sources occurring in only a single size fraction from having too small a contribution for the model to resolve. Anchor profiles were retrieved internally by analysing data subsets, and these profiles were used in the analyses of the complete data sets of all sites for enhanced source apportionment. A total of nine different factors were resolved (notable elements in brackets): in PM 10–2.5 , brake wear (Cu, Zr, Sb, Ba), other traffic-related (Fe), resuspended dust (Si, Ca), sea/road salt (Cl), aged sea salt (Na, Mg) and industrial (Cr, Ni); in PM 2.5–1.0 , brake wear, other traffic-related, resuspended dust, sea/road salt, aged sea salt and S-rich (S); and in PM 1.0–0.3 , traffic-related (Fe, Cu, Zr, Sb, Ba), resuspended dust, sea/road salt, aged sea salt, reacted Cl (Cl), S-rich and solid fuel (K, Pb). Human activities enhance the kerb-to-rural concentration gradients of coarse aged sea salt, typically considered to have a natural source, by 1.7–2.2. These site-dependent concentration differences reflect the effect of local resuspension processes in London. The anthropogenically influenced factors traffic (brake wear and other traffic-related processes), dust and sea/road salt provide further kerb-to-rural concentration enhancements by direct source emissions by a factor of 3.5–12.7. The traffic and dust factors are mainly emitted in PM 10–2.5 and show strong diurnal variations with concentrations up to 4 times higher during rush hour than during night-time. Regionally influenced S-rich and solid fuel factors, occurring primarily in PM 1.0–0.3 , have negligible resuspension influences, and concentrations are similar throughout the day and across the regions.
Dexterity and Worker Comfort in an Ergonomically Designed Glovebox Glove
Gloveboxes are prolifically used in nuclear facilities as the primary means of separation between the hazardous material and operators. Due to extended duration and high repetition of hand tasks in glovebox gloves, improperly fitting gloves can be uncomfortable and increase injury risks. Poor fit can increase time necessary to perform work tasks and stress the glove itself. The Plutonium Facility has the largest inventory of glovebox gloves at Los Alamos National Laboratory (LANL) with approximately 6000 gloves installed. The current glove mold dates back to the 1960's and is not based on the true anatomy of the hand. Design flaws (poor anatomical fit and biomechanics) are directly related to injuries of the elbow (lateral epicondylitis) and thumb (DeQuervain's tenosynovitis) injuries. The current design also contributes to 30% of unplanned glove openings. A new glovebox glove was developed and manufactured, incorporating over forty dimensions to produce the most comprehensive ergonomically sound design. The new design received a LANL patent (patent attorney docket No: LANS 36USD1 'Protective Glove'), one of 20 highest patents awarded by the Richard P. Feynman Center for Innovation. A SolidWorks model was given to the manufacturer for production of the glove mold.
CATMoS: Collaborative Acute Toxicity Modeling Suite
Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. Objectives: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different endpoints: LD50 value, U.S. Environmental Protection Agency hazard categories, Globally Harmonized System for Classification and Labelling hazard categories, very toxic chemicals (LD50 =50 mg/kg), and non-toxic chemicals (LD50 >2000 mg/kg). Methods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared to in vivo results. Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for over 800,000 chemicals have been made available via the NTP’s Integrated Chemical Environment. The models are also implemented in a free, standalone open-source tool, OPERA, which allows predictions of new and untested chemicals to be made.
Enhancing power plant safety through simulated cyber events - 341
There are gaps in understanding how a cyber-attack would manifest itself within power plants and what these events would look like within the control room from an operator's perspective. This is especially true for nuclear power plants where safety has much broader consequences than nonnuclear plants. The operating and emergency procedures that operators currently use are likely inadequate for targeted cyber-attacks. This research focuses on understanding how a cyber event would affect the operation of the plant, how an operator would perceive the event, and if the operator's actions would keep the plant in a safe condition. This research is part of Sandia's Laboratory Directed Research and Development program where a nuclear power plant cyber model of the control system digital architecture is coupled with a generic pressurized water reactor plant training simulator. Cyber event scenarios will be performed on the coupled system with plant operators. The scenarios simulate plant conditions that may exist during a cyber-attack, component failure, or insider sabotage, and provide an understanding of the displayed information and the actual plant conditions. These scenarios will determine if plant operators can 1) recognize that they are under cyber-attack and 2) take appropriate actions to keep the plant safe. This will also provide the opportunity to assess the operator cognitive workload during such events and identify where improvements might be made. Experiments with nuclear power plant operators will be carried out over FY 2018 and results of the research are expected by the end of FY 2018. (authors)
Spatiotemporal dynamics of ecosystem fires and biomass burning-induced carbon emissions in China over the past two decades
Fire is a major type of disturbance that has important influences on ecosystem dynamics and carbon cycles. Yet our understanding of ecosystem fires and their carbon cycle consequences is still limited, largely due to the difficulty of large-scale fire monitoring and the complex interactions between fire, vegetation, climate, and anthropogenic factors. Here, using data from satellite-derived fire observations and ecosystem model simulations, we performed a comprehensive investigation of the spatial and temporal dynamics of China's ecosystem fire disturbances and their carbon emissions over the past two decades (1997–2016). Satellite-derived results showed that on average about 3.47 - 4.53 x 10 4 km 2 of the land was burned annually during the past two decades, among which annual burned forest area was about 0.81 - 1.25 × 10 4 km 2 , accounting for 0.33-0.51% of the forest area in China. Biomass burning emitted about 23.02 TgC per year. Compared to satellite products, simulations from the Energy Exascale Earth System Land Model (ELM) strongly overestimated China's burned area and fire-induced carbon emissions. Annual burned area and fire-induced carbon emissions were high for boreal forest in Northeast China's Daxing'anling region and subtropical dry forest in South Yunnan, as revealed by both the satellite product and the model simulations. Our results suggest that climate and anthropogenic factors play critical roles in controlling the spatial and seasonal distribution of China's ecosystem fire disturbances. Our findings highlight the importance of multiple complementary approaches in assessing ecosystem fire disturbance and its carbon consequences. Further studies are required to improve the methods of observing and modelling China's ecosystem fire disturbances, which will provide valuable information for fire management and ecosystem sustainability in an era when both human activities and the natural environment are rapidly changing.
Defining blood hematology reference values in female pig-tailed macaques ( Macaca nemestrina ) using the Isolation Forest algorithm
Background: Pig-tailed macaques (PTMs) are commonly used as preclinical models to assess antiretroviral drugs for HIV prevention research. Drug toxicities and disease pathologies are often preceded by changes in blood hematology. To better assess the safety profile of pharmaceuticals, we defined normal ranges of hematological values in PTMs using an Isolation Forest (iForest) algorithm. Methods: Eighteen female PTMs were evaluated. Blood was collected 1–24 times per animal for a total of 159 samples. Complete blood counts were performed, and iForest was used to analyze the hematology data to detect outliers. Results: Median, IQR, and ranges were calculated for 13 hematology parameters. From all samples, 22 outliers were detected. These outliers were excluded from the reference index. Conclusions: Using iForest, we defined a normal range for hematology parameters in female PTMs. This reference index can be a valuable tool for future studies evaluating drug toxicities in PTMs.
Utilization of Unmanned Aircraft Systems for Environmental Purposes at the Savannah River Site – 26578
Born in the 1950s, the Savannah River Plant was constructed as a plutonium and tritium production plant. As the Cold War era came and went, a change of direction was signaled when the name of the facility changed from Savannah River Plant to Savannah River Site (1989) – the main mission at Savannah River shifted from “production” to “cleanup.” The Department of Energy is responsible for managing the 310 square miles of land that is the Savannah River Site and the cleanup/remediation activities that occur. The Savannah River National Laboratory utilizes unmanned aircraft systems to assist with some of those environmental monitoring and remediation activities. One function of unmanned aircraft systems at the Savannah River National Laboratory is conducting aerial photography and videography. Various infrastructure evaluations have been performed with unmanned aircraft – facility rooftop inspections, water tower lock verifications, earthen dam surveys, etc. An unmanned aircraft system has been used for progress footage of remediation projects – Dunbarton Bay remediation, Saltstone Disposal Unit construction, etc. Thermal cameras on an unmanned aircraft system are used to help identify cracks/leaks in structures from vantage points not readily accessible by personnel. Virtual tours of different waste units are conducted with unmanned aircraft systems for Site groups and federal/state regulators to save costs and travel time. Additionally, advanced remote sensing technologies are used on unmanned aircraft systems at the Savannah River Site. Light Detection and Ranging uses laser pulses to measure distances to the Earth's surface or other targets to create highly detailed topographic maps that accurately portray terrain (e.g., elevation changes, slopes, and contours). Data collection with a Light Detection and Ranging unit mounted on an unmanned aircraft system is quick and efficient – large-area surveys are conducted in shorter time frames. Because Light Detection and Ranging can penetrate through foliage and vegetation to ground level, it is being used in conjunction with a watershed model to study the General Separations Area of the Site to determine stormwater flow volume and direction, monitor stream stability, and forecast river flow behavior. A hyperspectral camera captures a wide spectrum of electromagnetic wavelengths across numerous narrow bands, beyond what the human eye can see. It provides detailed spectral information about the objects or surfaces in an image, which can be used to identify and analyze materials based on their spectral signatures. A hyperspectral camera mounted on an unmanned aircraft system has been used at the Savannah River Site for non-destructive evaluation of concrete and concrete structures. Unmanned aircraft systems at the Savannah River Site are also used for the application of herbicide on identified rooftops. P-Reactor and R-Reactor are entombed nuclear reactors at the Savannah River Site. Unwanted vegetation is often present on these rooftops – over time roots can penetrate seams, cracks, and joints of the general roof and concrete caps, leading to water ingress and deterioration of the structural components. For time and cost efficiencies as well as in the interest of personnel safety, an unmanned aircraft is used to dispense herbicide on rooftop areas where vegetation is noticed to help alleviate the issues/hazards.
Modeling for a Digital Twin-Based Remote Operation System Framework
New reactor designs and technologies are being developed to grow and advance the nuclear industry. Microreactors are one of the many new concepts for advancing the industry. Microreactors are very small reactors generally designed to have an operating power of 20 MWth or less. They are ideal for many applications in which it would not be feasible to have a large-scale reactor, such as powering remote communities, military bases, and mining sites. Many of these applications currently rely on diesel generators for power, and replacing those generators with the carbon-free energy of a microreactor is a major driving factor for microreactor development. However, most of the use cases for microreactors are in isolated locations where construction and labor costs are much higher. Microreactors will need to be comparable to other energy production methods for their deployment to be successful. Remotely operating the microreactor has a great potential to benefit the economics and make it more cost competitive. With remote operations, the operation facility location could be strategically chosen based on factors such as construction costs, workforce size, etc. The benefits of remote operations could be leveraged even more if the remote operation system is semi-autonomous. If the remote operation system is semi-autonomous, more microreactors could be operated and monitored from one remote operation facility. Additionally, with the system handling some tasks for the human operator, it could reduce the number of operating staff necessary for the microreactor. Digital twins can be used to introduce a level of automation to the remote operation system. Digital twins are capable of component monitoring, system operation and control, and predictive performance [1]. All of those features are important for a successful semi-autonomous remote operation system.
Status Update on IAEA MODARIA II Working Group 1 (Assessment and Decision Making) - 19391
During WM2018, the work being done under the International Atomic Energy Agency (IAEA)'s Modelling and Data for Radiological Impact Assessments (MODARIA) II Programme (https://wwwns.iaea.org/projects/modaria/modaria2.asp?s=8 and l=129) was introduced. The Programme comprises seven working groups covering a wide range of topics. Amongst them, Working Group 1 (WG1) is focused on dose assessment and decision aiding tools to address existing exposure situations due to past practices involving naturally occurring radioactive material (NORM) and nuclear legacy sites. Specifically, this covers the development of methods and toolsets for risk-informed decision-making for such complex sites, and includes practical demonstration of decision-making processes and tools through case studies. During 2018 WG1 participants met twice to discuss the status and plans of work being conducted by the WG. The group held its Second Interim Meeting from 7 to 11 May 2018 at the Jozef Stefan Institute (JSI) in Ljubljana, Slovenia. The objectives of the meeting were to: (1) Discuss the status of WG1 work; (2) Start preparation of the interim draft version of the MODARIA II WG1 Report (herein referred to as the 'Interim Report'); (3) Review the progress of planned data analyses, model-model comparisons, and model-data comparisons being conducted through case studies for the Tessenderlo Chemie Site in Belgium, the Zapadnoe Uranium Mill Tailings Site in Ukraine, and a Former Tin Processing Facility Site in Brazil; (4) Discuss calculations conducted for a NORM Site in China for potential addition to the WG1 case studies; (5) Report on progress on topical studies and other assignments performed to support the preparation of several sections of the Interim Report; and (6) Discuss plans for incorporating some preliminary case studies considered by MODARIA I WG1 and MODARIA I WG3. During the meeting in Slovenia, WG1 participants also visited the Zirovski vrh (RZV) Uranium Mine and Mill Tailings Site near Ljubljana. From 22 to 25 October 2018, WG1 members also participated in the MODARIA II Third Technical Meeting in Vienna, Austria. The meeting facilitated analysis of the progress made so far by the seven working groups, as well as exploration of the dissemination of the results of the MODARIA II Programme through publication in scientific journals dealing with the various subject areas covered by the Programme. WG1 participants focused on the preparation of its Interim Report, and engaged in technical exchanges with the IAEA Le Trench Project on characterization, assessment, remediation and management of buried waste at legacy trench sites, as well as the To Enhance uncertainties Reduction and stakeholders Involvement Towards integrated and graded Risk management of humans and wildlife In long-lasting radiological Exposure Situations (TERRITORIES) project (https://territories.eu/). Currently, WG1 is in the process of preparing a draft report to document the results of its various case studies and special topics, and is preparing for the final Interim Meeting in May 2019 and the final Technical Meeting in October 2019, before the conclusion of the MODARIA II Program at the end of 2019. This paper will provide a status update of these activities, along with preliminary information on the WG1 report, including potential recommendations for future activities. (authors)
Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction
Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing data, acquiring a sufficiently representative training set is a challenge due to both the inherent multi-modal variability of satellite or aerial imagery and the general high cost of labeling data. To address this challenge, we have developed ISOSCELES, an Iterative Self-Organizing SCEne LEvel Sampling method for hierarchical sampling of large image sets. Using affinity propagation, ISOSCELES automates the selection of highly representative training images. Compared to random sampling or using available reference data, the distribution of the training is principally data driven, reducing the chance of oversampling uninformative areas or undersampling informative ones. In comparison to manual sample selection by an analyst, ISOSCELES exploits descriptive features, spectral and/or textural, and eliminates human bias in sample selection. Using a hierarchical sampling approach, ISOSCELES can obtain a training set that reflects both between-scene variability, such as in viewing angle and time of day, and within-scene variability at the level of individual training samples. We verify the method by demonstrating its superiority to stratified random sampling in the challenging task of adapting a pre-trained model to a new image and spatial domain for country-scale building extraction. Using a pair of hand-labeled training sets comprising 1,987 sample image chips, a total of 496,000,000 individually labeled pixels, we show, across three distinct model architectures, an increase in accuracy, as measured by F1-score, of 2.2–4.2%.
An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring
The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.
Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources
We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos, and cosmic rays cover a wide range of wavelengths and timescales. Combining and processing these datasets that vary in volume, speed, and dimensionality requires new modes of instrument coordination, funding, and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal-processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets has powered the rise of artificial intelligence. This digital revolution now powers a multibillion dollar industry, with far-reaching implications in technology and society. In this chapter, we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in multi-messenger astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017 to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data.
Distinct Structural Features of the Peroxide Response Regulator from Group A Streptococcus Drive DNA Binding
Group A streptococcus (GAS, Streptococcus pyogenes) is a strict human pathogen that causes severe, invasive diseases. GAS does not produce catalase, but has an ability to resist killing by reactive oxygen species (ROS) through novel mechanisms. The peroxide response regulator (PerR), a member of ferric uptake regulator (Fur) family, plays a key role for GAS to cope with oxidative stress by regulating the expression of multiple genes. Our previous studies have found that expression of an iron-binding protein, Dpr, is under the direct control of PerR. To elucidate the molecular interactions of PerR with its cognate promoter, we have carried out structural studies on PerR and PerR-DNA complex. By combining crystallography and small-angle X-ray scattering (SAXS), we confirmed that the determined PerR crystal structure reflects its conformation in solution. Through mutagenesis and biochemical analysis, we have identified DNA-binding residues suggesting that PerR binds to the dpr promoter at the per box through a winged-helix motif. Furthermore, we have performed SAXS analysis and resolved the molecular architecture of PerR-DNA complex, in which two 30 bp DNA fragments wrap around two PerR homodimers by interacting with the adjacent positively-charged winged-helix motifs. Overall, we provide structural insights into molecular recognition of DNA by PerR and define the hollow structural arrangement of PerR-30bpDNA complex, which displays a unique topology distinct from currently proposed DNA-binding models for Fur family regulators.
Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data
As the number of phasor measurement units (PMUs) deployed in a power system increases, and their data volume streamed to the control canter intensifies, operators are facing challenges related to the analysis of such data, which need to be observed and responded to as the measurements are displayed in the Control Room. Humans are generally unable to process such large amount of data efficiently and rapidly. There is an apparent need for automated ways to analyze the data, extract actionable information about occurrence of specific events, and characterize the events quickly and cost effectively. This paper discusses the use of machine learning (ML) to facilitate such tasks by providing automated, highly computationally efficient, and cost-effective ways of extracting actionable information from synchrophasor big data in real-time. We developed Big Data Smart (BDSmart) ML-based prototype tool for the Control Room use that automatically analyses data properties from synchrophasor system measurements taken across the three grid Interconnections in the USA (Western, Eastern and ERCOT). The data collected from several hundreds of PMUs located across the Interconnections over a period of two years have been made available for our extensive study. As a result, we were able to identify a number of big data properties that influence how ML methodology is applied to select, develop, train and test the data models that can eventually be used for the tool implementation. The resulting set of candidate algorithms spans unsupervised, supervised, semi-supervised and transfer-learning approaches. Many ML techniques, such as decision trees, multinomial logistic regression, feed-forward neural networks, K-nearest neighbor, multiclass support vector machine, and single and multi-channel convolutional neural networks, are implemented, and their performance is examined. We offer the results from testing the data models. The novelty of our study is in the approaches for bad data detection and mitigation, selection of a simplified feature for event detection, and data label improvements. As a result, we came up with a list of recommendations for the utilities on how to improve the PMU recording practices to cater to the future ML applications aimed at automating the analysis of synchrophasor data.
Understanding collective human movement dynamics during large-scale events using big geosocial data analytics
Conventional approaches for modeling human mobility pattern often focus on human activity and movement dynamics in their regular daily lives and cannot capture changes in human movement dynamics in response to large-scale events. With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources (e.g., cell phone records, GPS trajectory data) from private data vendors to study human movement dynamics in response to large-scale natural or societal events. Big geosocial data such as georeferenced tweets are publicly available and dynamically evolving as real-world events are happening, making it more likely to capture the real-time sentiments and responses of populations. However, precisely-geolocated geosocial data is scarce and biased toward urban population centers. In this research, we developed a big geosocial data analytical framework for extracting human movement dynamics in response to large-scale events from publicly available georeferenced tweets. The framework includes a two-stage data collection module that collects data in a more targeted fashion in order to mitigate the data scarcity issue of georeferenced tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE) approach was adopted to fuse georeference information at different spatial scales, further augmenting the signals of human movement dynamics contained in georeferenced tweets. To correct for the sampling bias of georeferenced tweets, we adjusted the number of tweets for different spatial units (e.g., county, state) by population. To demonstrate the performance of the proposed analytic framework, we chose an astronomical event that occurred nationwide across the United States, i.e., the 2017 Great American Eclipse, as an example event and studied the human movement dynamics in response to this event. Finally, this analytic framework can easily be applied to other types of large-scale events such as hurricanes or earthquakes.
TU-FG-209-11: Validation of a Channelized Hotelling Observer to Optimize Chest Radiography Image Processing for Nodule Detection: A Human Observer Study
Purpose: To validate the use of a Channelized Hotelling Observer (CHO) model for guiding image processing parameter selection and enable improved nodule detection in digital chest radiography. Methods: In a previous study, an anthropomorphic chest phantom was imaged with and without PMMA simulated nodules using a GE Discovery XR656 digital radiography system. The impact of image processing parameters was then explored using a CHO with 10 Laguerre-Gauss channels. In this work, we validate the CHO’s trend in nodule detectability as a function of two processing parameters by conducting a signal-known-exactly, multi-reader-multi-case (MRMC) ROC observer study. Five naive readers scored confidence of nodule visualization in 384 images with 50% nodule prevalence. The image backgrounds were regions-of-interest extracted from 6 normal patient scans, and the digitally inserted simulated nodules were obtained from phantom data in previous work. Each patient image was processed with both a near-optimal and a worst-case parameter combination, as determined by the CHO for nodule detection. The same 192 ROIs were used for each image processing method, with 32 randomly selected lung ROIs per patient image. Finally, the MRMC data was analyzed using the freely available iMRMC software of Gallas et al. Results: The image processing parameters which were optimized for the CHO led to a statistically significant improvement (p=0.049) in human observer AUC from 0.78 to 0.86, relative to the image processing implementation which produced the lowest CHO performance. Conclusion: Differences in user-selectable image processing methods on a commercially available digital radiography system were shown to have a marked impact on performance of human observers in the task of lung nodule detection. Further, the effect of processing on humans was similar to the effect on CHO performance. Future work will expand this study to include a wider range of detection/classification tasks and more observers, including experienced chest radiologists.
Elemental mercury neurotoxicity and clinical recovery of function: A review of findings, and implications for occupational health
Highlights: • Substantial human studies, both acute and chronic, are available for evaluation. • Elemental mercury induced toxicity often displays substantial recovery. • Numerous examples fail to show full recovery from elemental mercury toxicity. • Recovery from elemental mercury toxicity is affected by age and endpoint. • Few animal studies have been directed to elemental mercury toxicity recovery. This paper assessed approximately 30 studies, mostly involving occupationally exposed subjects, concerning the extent to which those who developed elemental mercury (Hg)-induced central and/or peripheral neurotoxicities from chronic or acute exposures recover functionality and/or performance. While some recovery occurred in the vast majority of cases, the extent of such recoveries varied considerably by individual and endpoint. Factors accounting for the extensive inter-individual variation in toxicity and recovery were not specifically assessed such as age, gender, diet, environmental enrichment, chelation strategies and dose-rate. While the data indicate that psychomotor endpoints often show substantial and relatively rapid (i.e., 2–6 months) recovery and that neuropsychological endpoints display slower and less complete recovery, generalizations are difficult due to highly variable study designs, use of different endpoints measured between studies, different Hg exposures based on blood/urine concentrations and Hg dose-rates, the poor capacity for replicating findings due to the unpredictable/episodic nature of harmful exposures to elemental Hg, and the inconsistency of the initiation of studies after induced toxicities and the differing periods of follow up during recovery periods. Finally, there is strikingly limited animal model literature on the topic of recovery/reversibility of elemental Hg toxicity, a factor which significantly contributes to the overall marked uncertainties for predicting the rate and magnitude of recovery and the factors that affect it.