Mining the Mind Research Network: A Novel Framework for Exploring Large Scale, Heterogeneous Translational Neuroscience Research Data Sources

PubMed Central

Bockholt, Henry J.; Scully, Mark; Courtney, William; Rachakonda, Srinivas; Scott, Adam; Caprihan, Arvind; Fries, Jill; Kalyanam, Ravi; Segall, Judith M.; de la Garza, Raul; Lane, Susan; Calhoun, Vince D.

2009-01-01

A neuroinformatics (NI) system is critical to brain imaging research in order to shorten the time between study conception and results. Such a NI system is required to scale well when large numbers of subjects are studied. Further, when multiple sites participate in research projects organizational issues become increasingly difficult. Optimized NI applications mitigate these problems. Additionally, NI software enables coordination across multiple studies, leveraging advantages potentially leading to exponential research discoveries. The web-based, Mind Research Network (MRN), database system has been designed and improved through our experience with 200 research studies and 250 researchers from seven different institutions. The MRN tools permit the collection, management, reporting and efficient use of large scale, heterogeneous data sources, e.g., multiple institutions, multiple principal investigators, multiple research programs and studies, and multimodal acquisitions. We have collected and analyzed data sets on thousands of research participants and have set up a framework to automatically analyze the data, thereby making efficient, practical data mining of this vast resource possible. This paper presents a comprehensive framework for capturing and analyzing heterogeneous neuroscience research data sources that has been fully optimized for end-users to perform novel data mining. PMID:20461147

Solar Data Mining at Georgia State University

NASA Astrophysics Data System (ADS)

Angryk, R.; Martens, P. C.; Schuh, M.; Aydin, B.; Kempton, D.; Banda, J.; Ma, R.; Naduvil-Vadukootu, S.; Akkineni, V.; Küçük, A.; Filali Boubrahimi, S.; Hamdi, S. M.

2016-12-01

In this talk we give an overview of research projects related to solar data analysis that are conducted at Georgia State University. We will provide update on multiple advances made by our research team on the analysis of image parameters, spatio-temporal patterns mining, temporal data analysis and our experiences with big, heterogeneous solar data visualization, analysis, processing and storage. We will talk about up-to-date data mining methodologies, and their importance for big data-driven solar physics research.

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

PubMed

Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

2015-01-01

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site. Copyright © 2014 Elsevier Ltd. All rights reserved.

Managing Heterogeneous Information Systems through Discovery and Retrieval of Generic Concepts.

ERIC Educational Resources Information Center

Srinivasan, Uma; Ngu, Anne H. H.; Gedeon, Tom

2000-01-01

Introduces a conceptual integration approach to heterogeneous databases or information systems that exploits the similarity in metalevel information and performs metadata mining on database objects to discover a set of concepts that serve as a domain abstraction and provide a conceptual layer above existing legacy systems. Presents results of…

Estimating correlation between multivariate longitudinal data in the presence of heterogeneity.

PubMed

Gao, Feng; Philip Miller, J; Xiong, Chengjie; Luo, Jingqin; Beiser, Julia A; Chen, Ling; Gordon, Mae O

2017-08-17

Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time. Bivariate linear mixed model (BLMM) provides a versatile tool with regard to assessing correlation. However, BLMMs often assume that all individuals are drawn from a single homogenous population where the individual trajectories are distributed smoothly around population average. Using longitudinal mean deviation (MD) and visual acuity (VA) from the Ocular Hypertension Treatment Study (OHTS), we demonstrated strategies to better understand the correlation between multivariate longitudinal data in the presence of potential heterogeneity. Conditional correlation (i.e., marginal correlation given random effects) was calculated to describe how the association between longitudinal outcomes evolved over time within specific subpopulation. The impact of heterogeneity on correlation was also assessed by simulated data. There was a significant positive correlation in both random intercepts (ρ = 0.278, 95% CI: 0.121-0.420) and random slopes (ρ = 0.579, 95% CI: 0.349-0.810) between longitudinal MD and VA, and the strength of correlation constantly increased over time. However, conditional correlation and simulation studies revealed that the correlation was induced primarily by participants with rapid deteriorating MD who only accounted for a small fraction of total samples. Conditional correlation given random effects provides a robust estimate to describe the correlation between multivariate longitudinal data in the presence of unobserved heterogeneity (NCT00000125).

Analysis of Mining Terrain Deformation Characteristics with Deformation Information System

NASA Astrophysics Data System (ADS)

Blachowski, Jan; Milczarek, Wojciech; Grzempowski, Piotr

2014-05-01

Mapping and prediction of mining related deformations of the earth surface is an important measure for minimising threat to surface infrastructure, human population, the environment and safety of the mining operation itself arising from underground extraction of useful minerals. The number of methods and techniques used for monitoring and analysis of mining terrain deformations is wide and increasing with the development of geographical information technologies. These include for example: terrestrial geodetic measurements, global positioning systems, remote sensing, spatial interpolation, finite element method modelling, GIS based modelling, geological modelling, empirical modelling using the Knothe theory, artificial neural networks, fuzzy logic calculations and other. The aim of this paper is to introduce the concept of an integrated Deformation Information System (DIS) developed in geographic information systems environment for analysis and modelling of various spatial data related to mining activity and demonstrate its applications for mapping and visualising, as well as identifying possible mining terrain deformation areas with various spatial modelling methods. The DIS concept is based on connected modules that include: the spatial database - the core of the system, the spatial data collection module formed by: terrestrial, satellite and remote sensing measurements of the ground changes, the spatial data mining module for data discovery and extraction, the geological modelling module, the spatial data modeling module with data processing algorithms for spatio-temporal analysis and mapping of mining deformations and their characteristics (e.g. deformation parameters: tilt, curvature and horizontal strain), the multivariate spatial data classification module and the visualization module allowing two-dimensional interactive and static mapping and three-dimensional visualizations of mining ground characteristics. The Systems's functionality has been presented on the case study of a coal mining region in SW Poland where it has been applied to study characteristics and map mining induced ground deformations in a city in the last two decades of underground coal extraction and in the first decade after the end of mining. The mining subsidence area and its deformation parameters (tilt and curvature) have been calculated and the latter classified and mapped according to the Polish regulations. In addition possible areas of ground deformation have been indicated based on multivariate spatial data analysis of geological and mining operation characteristics with the geographically weighted regression method.

Implications of Emerging Data Mining

NASA Astrophysics Data System (ADS)

Kulathuramaiyer, Narayanan; Maurer, Hermann

Data Mining describes a technology that discovers non-trivial hidden patterns in a large collection of data. Although this technology has a tremendous impact on our lives, the invaluable contributions of this invisible technology often go unnoticed. This paper discusses advances in data mining while focusing on the emerging data mining capability. Such data mining applications perform multidimensional mining on a wide variety of heterogeneous data sources, providing solutions to many unresolved problems. This paper also highlights the advantages and disadvantages arising from the ever-expanding scope of data mining. Data Mining augments human intelligence by equipping us with a wealth of knowledge and by empowering us to perform our daily tasks better. As the mining scope and capacity increases, users and organizations become more willing to compromise privacy. The huge data stores of the ‚master miners` allow them to gain deep insights into individual lifestyles and their social and behavioural patterns. Data integration and analysis capability of combining business and financial trends together with the ability to deterministically track market changes will drastically affect our lives.

Optimizing Functional Network Representation of Multivariate Time Series

NASA Astrophysics Data System (ADS)

Zanin, Massimiliano; Sousa, Pedro; Papo, David; Bajo, Ricardo; García-Prieto, Juan; Pozo, Francisco Del; Menasalvas, Ernestina; Boccaletti, Stefano

2012-09-01

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

Optimizing Functional Network Representation of Multivariate Time Series

PubMed Central

Zanin, Massimiliano; Sousa, Pedro; Papo, David; Bajo, Ricardo; García-Prieto, Juan; Pozo, Francisco del; Menasalvas, Ernestina; Boccaletti, Stefano

2012-01-01

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks. PMID:22953051

Reliable Early Classification on Multivariate Time Series with Numerical and Categorical Attributes

DTIC Science & Technology

2015-05-22

design a procedure of feature extraction in REACT named MEG (Mining Equivalence classes with shapelet Generators) based on the concept of...Equivalence Classes Mining [12, 15]. MEG can efficiently and effectively generate the discriminative features. In addition, several strategies are proposed...technique of parallel computing [4] to propose a process of pa- rallel MEG for substantially reducing the computational overhead of discovering shapelet

Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review.

PubMed

Maione, Camila; Barbosa, Rommel Melgaço

2018-01-24

Rice is one of the most important staple foods around the world. Authentication of rice is one of the most addressed concerns in the present literature, which includes recognition of its geographical origin and variety, certification of organic rice and many other issues. Good results have been achieved by multivariate data analysis and data mining techniques when combined with specific parameters for ascertaining authenticity and many other useful characteristics of rice, such as quality, yield and others. This paper brings a review of the recent research projects on discrimination and authentication of rice using multivariate data analysis and data mining techniques. We found that data obtained from image processing, molecular and atomic spectroscopy, elemental fingerprinting, genetic markers, molecular content and others are promising sources of information regarding geographical origin, variety and other aspects of rice, being widely used combined with multivariate data analysis techniques. Principal component analysis and linear discriminant analysis are the preferred methods, but several other data classification techniques such as support vector machines, artificial neural networks and others are also frequently present in some studies and show high performance for discrimination of rice.

Careflow Mining Techniques to Explore Type 2 Diabetes Evolution.

PubMed

Dagliati, Arianna; Tibollo, Valentina; Cogni, Giulia; Chiovato, Luca; Bellazzi, Riccardo; Sacchi, Lucia

2018-03-01

In this work we describe the application of a careflow mining algorithm to detect the most frequent patterns of care in a type 2 diabetes patients cohort. The applied method enriches the detected patterns with clinical data to define temporal phenotypes across the studied population. Novel phenotypes are discovered from heterogeneous data of 424 Italian patients, and compared in terms of metabolic control and complications. Results show that careflow mining can help to summarize the complex evolution of the disease into meaningful patterns, which are also significant from a clinical point of view.

Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis

ERIC Educational Resources Information Center

Ansari, Asim; Iyengar, Raghuram

2006-01-01

We develop semiparametric Bayesian Thurstonian models for analyzing repeated choice decisions involving multinomial, multivariate binary or multivariate ordinal data. Our modeling framework has multiple components that together yield considerable flexibility in modeling preference utilities, cross-sectional heterogeneity and parameter-driven…

Evaluation of the environmental contamination at an abandoned mining site using multivariate statistical techniques--the Rodalquilar (Southern Spain) mining district.

PubMed

Bagur, M G; Morales, S; López-Chicano, M

2009-11-15

Unsupervised and supervised pattern recognition techniques such as hierarchical cluster analysis, principal component analysis, factor analysis and linear discriminant analysis have been applied to water samples recollected in Rodalquilar mining district (Southern Spain) in order to identify different sources of environmental pollution caused by the abandoned mining industry. The effect of the mining activity on waters was monitored determining the concentration of eleven elements (Mn, Ba, Co, Cu, Zn, As, Cd, Sb, Hg, Au and Pb) by inductively coupled plasma mass spectrometry (ICP-MS). The Box-Cox transformation has been used to transform the data set in normal form in order to minimize the non-normal distribution of the geochemical data. The environmental impact is affected mainly by the mining activity developed in the zone, the acid drainage and finally by the chemical treatment used for the benefit of gold.

An Integrated Framework to Access and Mine Distributed Heterogeneous Data Streams with Uncertainty

DTIC Science & Technology

2015-05-13

Total Number: PERCENT_SUPPORTEDNAME FTE Equivalent: Total Number: Discipline Chris chance 0.20 CS Chris Cosey 0.20 CS Brittney Mack 0.20 CS Tuan Nguyen...Conference, New York, NY, Feb. 2014. 38. X. Zhu, S. Song, J. Wang, J. Sun , and P.S. Yu, "Matching Heterogeneous Events with Patterns", Proc. IEEE Intl. Conf

Time dependent neural network models for detecting changes of state in complex processes: applications in earth sciences and astronomy.

PubMed

Valdés, Julio J; Bonham-Carter, Graeme

2006-03-01

A computational intelligence approach is used to explore the problem of detecting internal state changes in time dependent processes; described by heterogeneous, multivariate time series with imprecise data and missing values. Such processes are approximated by collections of time dependent non-linear autoregressive models represented by a special kind of neuro-fuzzy neural network. Grid and high throughput computing model mining procedures based on neuro-fuzzy networks and genetic algorithms, generate: (i) collections of models composed of sets of time lag terms from the time series, and (ii) prediction functions represented by neuro-fuzzy networks. The composition of the models and their prediction capabilities, allows the identification of changes in the internal structure of the process. These changes are associated with the alternation of steady and transient states, zones with abnormal behavior, instability, and other situations. This approach is general, and its sensitivity for detecting subtle changes of state is revealed by simulation experiments. Its potential in the study of complex processes in earth sciences and astrophysics is illustrated with applications using paleoclimate and solar data.

On the data-driven inference of modulatory networks in climate science: an application to West African rainfall

NASA Astrophysics Data System (ADS)

González, D. L., II; Angus, M. P.; Tetteh, I. K.; Bello, G. A.; Padmanabhan, K.; Pendse, S. V.; Srinivas, S.; Yu, J.; Semazzi, F.; Kumar, V.; Samatova, N. F.

2014-04-01

Decades of hypothesis-driven and/or first-principles research have been applied towards the discovery and explanation of the mechanisms that drive climate phenomena, such as western African Sahel summer rainfall variability. Although connections between various climate factors have been theorized, not all of the key relationships are fully understood. We propose a data-driven approach to identify candidate players in this climate system, which can help explain underlying mechanisms and/or even suggest new relationships, to facilitate building a more comprehensive and predictive model of the modulatory relationships influencing a climate phenomenon of interest. We applied coupled heterogeneous association rule mining (CHARM), Lasso multivariate regression, and Dynamic Bayesian networks to find relationships within a complex system, and explored means with which to obtain a consensus result from the application of such varied methodologies. Using this fusion of approaches, we identified relationships among climate factors that modulate Sahel rainfall, including well-known associations from prior climate knowledge, as well as promising discoveries that invite further research by the climate science community.

Regional climate modulates the canopy mosaic of favourable and risky microclimates for insects.

PubMed

Pincebourde, Sylvain; Sinoquet, Herve; Combes, Didier; Casas, Jerome

2007-05-01

1. One major gap in our ability to predict the impacts of climate change is a quantitative analysis of temperatures experienced by organisms under natural conditions. We developed a framework to describe and quantify the impacts of local climate on the mosaic of microclimates and physiological states of insects within tree canopies. This approach was applied to a leaf mining moth feeding on apple leaf tissues. 2. Canopy geometry was explicitly considered by mapping the 3D position and orientation of more than 26 000 leaves in an apple tree. Four published models for canopy radiation interception, energy budget of leaves and mines, body temperature and developmental rate of the leaf miner were integrated. Model predictions were compared with actual microclimate temperatures. The biophysical model accurately predicted temperature within mines at different positions within the tree crown. 3. Field temperature measurements indicated that leaf and mine temperature patterns differ according to the regional climatic conditions (cloudy or sunny) and depending on their location within the canopy. Mines in the sun can be warmer than those in the shade by several degrees and the heterogeneity of mine temperature was incremented by 120%, compared with that of leaf temperature. 4. The integrated model was used to explore the impact of both warm and exceptionally hot climatic conditions recorded during a heat wave on the microclimate heterogeneity at canopy scale. During warm conditions, larvae in sunlight-exposed mines experienced nearly optimal growth conditions compared with those within shaded mines. The developmental rate was increased by almost 50% in the sunny microhabitat compared with the shaded location. Larvae, however, experienced optimal temperatures for their development inside shaded mines during extreme climatic conditions, whereas larvae in exposed mines were overheating, leading to major risks of mortality. 5. Tree canopies act as both magnifiers and reducers of the climatic regime experienced in open air outside canopies. Favourable and risky spots within the canopy do change as a function of the climatic conditions at the regional scale. The shifting nature of the mosaic of suitable and risky habitats may explain the observed uniform distribution of leaf miners within tree canopies.

Mining large heterogeneous data sets in drug discovery.

PubMed

Wild, David J

2009-10-01

Increasingly, effective drug discovery involves the searching and data mining of large volumes of information from many sources covering the domains of chemistry, biology and pharmacology amongst others. This has led to a proliferation of databases and data sources relevant to drug discovery. This paper provides a review of the publicly-available large-scale databases relevant to drug discovery, describes the kinds of data mining approaches that can be applied to them and discusses recent work in integrative data mining that looks for associations that pan multiple sources, including the use of Semantic Web techniques. The future of mining large data sets for drug discovery requires intelligent, semantic aggregation of information from all of the data sources described in this review, along with the application of advanced methods such as intelligent agents and inference engines in client applications.

LSST Astroinformatics And Astrostatistics: Data-oriented Astronomical Research

NASA Astrophysics Data System (ADS)

Borne, Kirk D.; Stassun, K.; Brunner, R. J.; Djorgovski, S. G.; Graham, M.; Hakkila, J.; Mahabal, A.; Paegert, M.; Pesenson, M.; Ptak, A.; Scargle, J.; Informatics, LSST; Statistics Team

2011-01-01

The LSST Informatics and Statistics Science Collaboration (ISSC) focuses on research and scientific discovery challenges posed by the very large and complex data collection that LSST will generate. Application areas include astroinformatics, machine learning, data mining, astrostatistics, visualization, scientific data semantics, time series analysis, and advanced signal processing. Research problems to be addressed with these methodologies include transient event characterization and classification, rare class discovery, correlation mining, outlier/anomaly/surprise detection, improved estimators (e.g., for photometric redshift or early onset supernova classification), exploration of highly dimensional (multivariate) data catalogs, and more. We present sample science results from these data-oriented approaches to large-data astronomical research. We present results from LSST ISSC team members, including the EB (Eclipsing Binary) Factory, the environmental variations in the fundamental plane of elliptical galaxies, and outlier detection in multivariate catalogs.

Multivariate Spatial Condition Mapping Using Subtractive Fuzzy Cluster Means

PubMed Central

Sabit, Hakilo; Al-Anbuky, Adnan

2014-01-01

Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining. PMID:25313495

Data-driven modeling of background and mine-related acidity and metals in river basins

USGS Publications Warehouse

Friedel, Michael J

2013-01-01

A novel application of self-organizing map (SOM) and multivariate statistical techniques is used to model the nonlinear interaction among basin mineral-resources, mining activity, and surface-water quality. First, the SOM is trained using sparse measurements from 228 sample sites in the Animas River Basin, Colorado. The model performance is validated by comparing stochastic predictions of basin-alteration assemblages and mining activity at 104 independent sites. The SOM correctly predicts (>98%) the predominant type of basin hydrothermal alteration and presence (or absence) of mining activity. Second, application of the Davies–Bouldin criteria to k-means clustering of SOM neurons identified ten unique environmental groups. Median statistics of these groups define a nonlinear water-quality response along the spatiotemporal hydrothermal alteration-mining gradient. These results reveal that it is possible to differentiate among the continuum between inputs of background and mine-related acidity and metals, and it provides a basis for future research and empirical model development.

An Extension of Multiple Correspondence Analysis for Identifying Heterogeneous Subgroups of Respondents

ERIC Educational Resources Information Center

Hwang, Heungsun; Montreal, Hec; Dillon, William R.; Takane, Yoshio

2006-01-01

An extension of multiple correspondence analysis is proposed that takes into account cluster-level heterogeneity in respondents' preferences/choices. The method involves combining multiple correspondence analysis and k-means in a unified framework. The former is used for uncovering a low-dimensional space of multivariate categorical variables…

Mining and Risk of Tuberculosis in Sub-Saharan Africa

PubMed Central

Basu, Sanjay; McKee, Martin; Lurie, Mark

2011-01-01

Objectives. We estimated the relationship between mining and tuberculosis (TB) among countries in sub-Saharan Africa. Methods. We used multivariate regression to estimate the contribution of mining activity to TB incidence, prevalence, and mortality, as well as rates of TB among people living with HIV, with control for economic, health system, and population confounders. Results. Mining production was associated with higher population TB incidence rates (adjusted b = 0.093; 95% confidence interval [CI] = 0.067, 0.120; with an increase of mining production of 1 SD corresponding to about 33% higher TB incidence or 760 000 more incident cases), after adjustment for economic and population controls. Similar results were observed for TB prevalence and mortality, as well as with alternative measures of mining activity. Independent of HIV, there were significant associations between mining production and TB incidence in countries with high HIV prevalence (≥ 4% antenatal HIV prevalence; HIV-adjusted B = 0.066; 95% CI = 0.050, 0.082) and between log gold mining production and TB incidence in all studied countries (HIV-adjusted B = 0.053; 95% CI = 0.032, 0.073). Conclusions. Mining is a significant determinant of countrywide variation in TB among sub-Saharan African nations. Comprehensive TB control strategies should explicitly address the role of mining activity and environments in the epidemic. PMID:20516372

Mining and risk of tuberculosis in sub-Saharan Africa.

PubMed

Stuckler, David; Basu, Sanjay; McKee, Martin; Lurie, Mark

2011-03-01

We estimated the relationship between mining and tuberculosis (TB) among countries in sub-Saharan Africa. We used multivariate regression to estimate the contribution of mining activity to TB incidence, prevalence, and mortality, as well as rates of TB among people living with HIV, with control for economic, health system, and population confounders. Mining production was associated with higher population TB incidence rates (adjusted b = 0.093; 95% confidence interval [CI] = 0.067, 0.120; with an increase of mining production of 1 SD corresponding to about 33% higher TB incidence or 760,000 more incident cases), after adjustment for economic and population controls. Similar results were observed for TB prevalence and mortality, as well as with alternative measures of mining activity. Independent of HIV, there were significant associations between mining production and TB incidence in countries with high HIV prevalence (≥ 4% antenatal HIV prevalence; HIV-adjusted B = 0.066; 95% CI = 0.050, 0.082) and between log gold mining production and TB incidence in all studied countries (HIV-adjusted B = 0.053; 95% CI = 0.032, 0.073). Mining is a significant determinant of countrywide variation in TB among sub-Saharan African nations. Comprehensive TB control strategies should explicitly address the role of mining activity and environments in the epidemic.

A case-based reasoning tool for breast cancer knowledge management with data mining concepts and techniques

NASA Astrophysics Data System (ADS)

Demigha, Souâd.

2016-03-01

The paper presents a Case-Based Reasoning Tool for Breast Cancer Knowledge Management to improve breast cancer screening. To develop this tool, we combine both concepts and techniques of Case-Based Reasoning (CBR) and Data Mining (DM). Physicians and radiologists ground their diagnosis on their expertise (past experience) based on clinical cases. Case-Based Reasoning is the process of solving new problems based on the solutions of similar past problems and structured as cases. CBR is suitable for medical use. On the other hand, existing traditional hospital information systems (HIS), Radiological Information Systems (RIS) and Picture Archiving Information Systems (PACS) don't allow managing efficiently medical information because of its complexity and heterogeneity. Data Mining is the process of mining information from a data set and transform it into an understandable structure for further use. Combining CBR to Data Mining techniques will facilitate diagnosis and decision-making of medical experts.

Semantic web for integrated network analysis in biomedicine.

PubMed

Chen, Huajun; Ding, Li; Wu, Zhaohui; Yu, Tong; Dhanapalan, Lavanya; Chen, Jake Y

2009-03-01

The Semantic Web technology enables integration of heterogeneous data on the World Wide Web by making the semantics of data explicit through formal ontologies. In this article, we survey the feasibility and state of the art of utilizing the Semantic Web technology to represent, integrate and analyze the knowledge in various biomedical networks. We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis. Through four case studies, we demonstrate how semantic graph mining can be applied to the analysis of disease-causal genes, Gene Ontology category cross-talks, drug efficacy analysis and herb-drug interactions analysis.

DebugIT for patient safety - improving the treatment with antibiotics through multimedia data mining of heterogeneous clinical data.

PubMed

Lovis, Christian; Colaert, Dirk; Stroetmann, Veli N

2008-01-01

The concepts and architecture underlying a large-scale integrating project funded within the 7th EU Framework Programme (FP7) are discussed. The main objective of the project is to build a tool that will have a significant impact for the monitoring and the control of infectious diseases and antimicrobial resistances in Europe; This will be realized by building a technical and semantic infrastructure able to share heterogeneous clinical data sets from different hospitals in different countries, with different languages and legislations; to analyze large amounts of this clinical data with advanced multimedia data mining and finally apply the obtained knowledge for clinical decisions and outcome monitoring. There are numerous challenges in this project at all levels, technical, semantical, legal and ethical that will have to be addressed.

Finding Groups Using Model-Based Cluster Analysis: Heterogeneous Emotional Self-Regulatory Processes and Heavy Alcohol Use Risk

ERIC Educational Resources Information Center

Mun, Eun Young; von Eye, Alexander; Bates, Marsha E.; Vaschillo, Evgeny G.

2008-01-01

Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of nonnested models using the Bayesian information criterion to compare multiple models and identify the…

Initiation, Combustion and Transition to Detonation in Homogeneous and Heterogeneous Reactive Mixtures: A Summary

DTIC Science & Technology

1981-09-01

undoubtedly associ- uid expanding-vapor explosions, or’ course of an explosion. In buildings ated with the manufacture, handling, BLEVEs , and unconfined...with its need for fossil fuel away, owing to atmospheric inhomo- energy, introduced coal mine explo- geneities, it is heard as a "boom." The blast wave...1947, when the fuels in enclosures compressed-air lines ship Grand Camp caught fire Combustion explosions of dusts In enclosures Coal mines , grain

The Effects of Sand Sediment Volume Heterogeneities on Sound Propagation and Scattering

DTIC Science & Technology

2012-09-30

modulus of a poroelastic medium,” J. Acoust . Soc. Am. 127, 3372–3384 (2010). 3. K. L. Williams, “An effective density fluid model for acoustic ...previously developed at APL- UW for the study of high-frequency acoustics . These models include perturbation models applied to scattering from the...scattering levels that may mask target detection. RELATED PROJECTS 1. “ Acoustic Color of mines and mine-like objects: Finite Element modeling (FEM

Improving machine operation management efficiency via improving the vehicle park structure and using the production operation information database

NASA Astrophysics Data System (ADS)

Koptev, V. Yu

2017-02-01

The work represents the results of studying basic interconnected criteria of separate equipment units of the transport network machines fleet, depending on production and mining factors to improve the transport systems management. Justifying the selection of a control system necessitates employing new methodologies and models, augmented with stability and transport flow criteria, accounting for mining work development dynamics on mining sites. A necessary condition is the accounting of technical and operating parameters related to vehicle operation. Modern open pit mining dispatching systems must include such kinds of the information database. An algorithm forming a machine fleet is presented based on multi-variation task solution in connection with defining reasonable operating features of a machine working as a part of a complex. Proposals cited in the work may apply to mining machines (drilling equipment, excavators) and construction equipment (bulldozers, cranes, pile-drivers), city transport and other types of production activities using machine fleet.

Is the co-seismic slip distribution fractal?

NASA Astrophysics Data System (ADS)

Milliner, Christopher; Sammis, Charles; Allam, Amir; Dolan, James

2015-04-01

Co-seismic along-strike slip heterogeneity is widely observed for many surface-rupturing earthquakes as revealed by field and high-resolution geodetic methods. However, this co-seismic slip variability is currently a poorly understood phenomenon. Key unanswered questions include: What are the characteristics and underlying causes of along-strike slip variability? Do the properties of slip variability change from fault-to-fault, along-strike or at different scales? We cross-correlate optical, pre- and post-event air photos using the program COSI-Corr to measure the near-field, surface deformation pattern of the 1992 Mw 7.3 Landers and 1999 Mw 7.1 Hector Mine earthquakes in high-resolution. We produce the co-seismic slip profiles of both events from over 1,000 displacement measurements and observe consistent along-strike slip variability. Although the observed slip heterogeneity seems apparently complex and disordered, a spectral analysis reveals that the slip distributions are indeed self-affine fractal i.e., slip exhibits a consistent degree of irregularity at all observable length scales, with a 'short-memory' and is not random. We find a fractal dimension of 1.58 and 1.75 for the Landers and Hector Mine earthquakes, respectively, indicating that slip is more heterogeneous for the Hector Mine event. Fractal slip is consistent with both dynamic and quasi-static numerical simulations that use non-planar faults, which in turn causes heterogeneous along-strike stress, and we attribute the observed fractal slip to fault surfaces of fractal roughness. As fault surfaces are known to smooth over geologic time due to abrasional wear and fracturing, we also test whether the fractal properties of slip distributions alters between earthquakes from immature to mature fault systems. We will present results that test this hypothesis by using the optical image correlation technique to measure historic, co-seismic slip distributions of earthquakes from structurally mature, large cumulative displacement faults and compare these slip distributions to those from immature fault systems. Our results have fundamental implications for an understanding of slip heterogeneity and the behavior of the rupture process.

Gravimetric surveys for assessing rock mass condition around a mine shaft

NASA Astrophysics Data System (ADS)

Madej, Janusz

2017-06-01

The fundamentals of use of vertical gravimetric surveying method in mine shafts are presented in the paper. The methods of gravimetric measurements and calculation of interval and complex density are discussed in detail. The density calculations are based on an original method accounting for the gravity influence of the mine shaft thus guaranteeing closeness of calculated and real values of density of rocks beyond the shaft lining. The results of many gravimetric surveys performed in shafts are presented and interpreted. As a result, information about the location of heterogeneous zones of work beyond the shaft lining is obtained. In many cases, these zones used to threaten the safe operation of machines and utilities in the shaft.

Radiometric surveying for the assessment of radiation dose and radon specific exhalation in underground environment

NASA Astrophysics Data System (ADS)

Bochiolo, M.; Verdoya, M.; Chiozzi, P.; Pasquale, V.

2012-08-01

We performed a radiometric survey for evaluating the natural radioactivity and the related potential hazard level both outdoor and indoor a mine tunnel. The mine is located in a zone of uranium enrichment in the Western Alps (Italy). At first, a γ-ray spectrometry survey of the area surrounding the mine was carried out to define the extent of the ore deposit. Then, spectrometric measurements were performed in the tunnel and rock samples were collected for laboratory analyses. The results point to significant heterogeneity in uranium concentration and consequently in the absorbed dose rate spatial distribution. Spectrometric results in situ and in the laboratory, together with radon air concentration measurements, were used to infer the radon specific exhalation and flow from the mine rocks. The specific exhalation is positively related to the activity concentration of uranium.

VISUAL DATA MINING IN ATMOSPHERIC SCIENCE DATA

EPA Science Inventory

This paper discusses the use of simple visual tools to explore multivariate spatially-referenced data. It describes interactive approaches such as linked brushing, and dynamic methods such as the grand tour. applied to studying the Comprehensive Ocean-Atmosphere Data Set (COADS)....

Using Fisher information to track stability in multivariate systems

EPA Science Inventory

With the current proliferation of data, the proficient use of statistical and mining techniques offer substantial benefits to capture useful information from any dataset. As numerous approaches make use of information theory concepts, here, we discuss how Fisher information (FI...

On the data-driven inference of modulatory networks in climate science: an application to West African rainfall

NASA Astrophysics Data System (ADS)

González, D. L., II; Angus, M. P.; Tetteh, I. K.; Bello, G. A.; Padmanabhan, K.; Pendse, S. V.; Srinivas, S.; Yu, J.; Semazzi, F.; Kumar, V.; Samatova, N. F.

2015-01-01

Decades of hypothesis-driven and/or first-principles research have been applied towards the discovery and explanation of the mechanisms that drive climate phenomena, such as western African Sahel summer rainfall~variability. Although connections between various climate factors have been theorized, not all of the key relationships are fully understood. We propose a data-driven approach to identify candidate players in this climate system, which can help explain underlying mechanisms and/or even suggest new relationships, to facilitate building a more comprehensive and predictive model of the modulatory relationships influencing a climate phenomenon of interest. We applied coupled heterogeneous association rule mining (CHARM), Lasso multivariate regression, and dynamic Bayesian networks to find relationships within a complex system, and explored means with which to obtain a consensus result from the application of such varied methodologies. Using this fusion of approaches, we identified relationships among climate factors that modulate Sahel rainfall. These relationships fall into two categories: well-known associations from prior climate knowledge, such as the relationship with the El Niño-Southern Oscillation (ENSO) and putative links, such as North Atlantic Oscillation, that invite further research.

On the data-driven inference of modulatory networks in climate science: An application to West African rainfall

DOE PAGES

Gonzalez, II, D. L.; Angus, M. P.; Tetteh, I. K.; ...

2015-01-13

Decades of hypothesis-driven and/or first-principles research have been applied towards the discovery and explanation of the mechanisms that drive climate phenomena, such as western African Sahel summer rainfall~variability. Although connections between various climate factors have been theorized, not all of the key relationships are fully understood. We propose a data-driven approach to identify candidate players in this climate system, which can help explain underlying mechanisms and/or even suggest new relationships, to facilitate building a more comprehensive and predictive model of the modulatory relationships influencing a climate phenomenon of interest. We applied coupled heterogeneous association rule mining (CHARM), Lasso multivariate regression,more » and dynamic Bayesian networks to find relationships within a complex system, and explored means with which to obtain a consensus result from the application of such varied methodologies. Using this fusion of approaches, we identified relationships among climate factors that modulate Sahel rainfall. As a result, these relationships fall into two categories: well-known associations from prior climate knowledge, such as the relationship with the El Niño–Southern Oscillation (ENSO) and putative links, such as North Atlantic Oscillation, that invite further research.« less

Natural decrease of dissolved arsenic in a small stream receiving drainages of abandoned silver mines in Guanajuato, Mexico.

PubMed

Arroyo, Yann Rene Ramos; Muñoz, Alma Hortensia Serafín; Barrientos, Eunice Yanez; Huerta, Irais Rodriguez; Wrobel, Kazimierz; Wrobel, Katarzyna

2013-11-01

Arsenic release from the abandoned mines and its fate in a local stream were studied. Physicochemical parameters, metals/metalloids and arsenic species were determined. One of the mine drainages was found as a point source of contamination with 309 μg L(-1) of dissolved arsenic; this concentration declined rapidly to 10.5 μg L(-1) about 2 km downstream. Data analysis confirmed that oxidation of As(III) released from the primary sulfide minerals was favored by the increase of pH and oxidation reduction potential; the results obtained in multivariate approach indicated that self-purification of water was due to association of As(V) with secondary solid phase containing Fe, Mn, Ca.

Multivariate Meta-Analysis of Preference-Based Quality of Life Values in Coronary Heart Disease.

PubMed

Stevanović, Jelena; Pechlivanoglou, Petros; Kampinga, Marthe A; Krabbe, Paul F M; Postma, Maarten J

2016-01-01

There are numerous health-related quality of life (HRQol) measurements used in coronary heart disease (CHD) in the literature. However, only values assessed with preference-based instruments can be directly applied in a cost-utility analysis (CUA). To summarize and synthesize instrument-specific preference-based values in CHD and the underlying disease-subgroups, stable angina and post-acute coronary syndrome (post-ACS), for developed countries, while accounting for study-level characteristics, and within- and between-study correlation. A systematic review was conducted to identify studies reporting preference-based values in CHD. A multivariate meta-analysis was applied to synthesize the HRQoL values. Meta-regression analyses examined the effect of study level covariates age, publication year, prevalence of diabetes and gender. A total of 40 studies providing preference-based values were detected. Synthesized estimates of HRQoL in post-ACS ranged from 0.64 (Quality of Well-Being) to 0.92 (EuroQol European"tariff"), while in stable angina they ranged from 0.64 (Short form 6D) to 0.89 (Standard Gamble). Similar findings were observed in estimates applying to general CHD. No significant improvement in model fit was found after adjusting for study-level covariates. Large between-study heterogeneity was observed in all the models investigated. The main finding of our study is the presence of large heterogeneity both within and between instrument-specific HRQoL values. Current economic models in CHD ignore this between-study heterogeneity. Multivariate meta-analysis can quantify this heterogeneity and offers the means for uncertainty around HRQoL values to be translated to uncertainty in CUAs.

Combined mining: discovering informative knowledge in complex data.

PubMed

Cao, Longbing; Zhang, Huaifeng; Zhao, Yanchang; Luo, Dan; Zhang, Chengqi

2011-06-01

Enterprise data mining applications often involve complex data such as multiple large heterogeneous data sources, user preferences, and business impact. In such situations, a single method or one-step mining is often limited in discovering informative knowledge. It would also be very time and space consuming, if not impossible, to join relevant large data sources for mining patterns consisting of multiple aspects of information. It is crucial to develop effective approaches for mining patterns combining necessary information from multiple relevant business lines, catering for real business settings and decision-making actions rather than just providing a single line of patterns. The recent years have seen increasing efforts on mining more informative patterns, e.g., integrating frequent pattern mining with classifications to generate frequent pattern-based classifiers. Rather than presenting a specific algorithm, this paper builds on our existing works and proposes combined mining as a general approach to mining for informative patterns combining components from either multiple data sets or multiple features or by multiple methods on demand. We summarize general frameworks, paradigms, and basic processes for multifeature combined mining, multisource combined mining, and multimethod combined mining. Novel types of combined patterns, such as incremental cluster patterns, can result from such frameworks, which cannot be directly produced by the existing methods. A set of real-world case studies has been conducted to test the frameworks, with some of them briefed in this paper. They identify combined patterns for informing government debt prevention and improving government service objectives, which show the flexibility and instantiation capability of combined mining in discovering informative knowledge in complex data.

Microbial and geochemical assessment of bauxitic un-mined and post-mined chronosequence soils from Mocho Mountains, Jamaica.

PubMed

Lewis, Dawn E; Chauhan, Ashvini; White, John R; Overholt, Will; Green, Stefan J; Jasrotia, Puja; Wafula, Denis; Jagoe, Charles

2012-10-01

Microorganisms are very sensitive to environmental change and can be used to gauge anthropogenic impacts and even predict restoration success of degraded environments. Here, we report assessment of bauxite mining activities on soil biogeochemistry and microbial community structure using un-mined and three post-mined sites in Jamaica. The post-mined soils represent a chronosequence, undergoing restoration since 1987, 1997, and 2007. Soils were collected during dry and wet seasons and analyzed for pH, organic matter (OM), total carbon (TC), nitrogen (TN), and phosphorus. The microbial community structure was assessed through quantitative PCR and massively parallel bacterial ribosomal RNA (rRNA) gene sequencing. Edaphic factors and microbial community composition were analyzed using multivariate statistical approaches and revealed a significant, negative impact of mining on soil that persisted even after greater than 20 years of restoration. Seasonal fluctuations contributed to variation in measured soil properties and community composition, but they were minor in comparison to long-term effects of mining. In both seasons, post-mined soils were higher in pH but OM, TC, and TN decreased. Bacterial rRNA gene analyses demonstrated a general decrease in diversity in post-mined soils and up to a 3-log decrease in rRNA gene abundance. Community composition analyses demonstrated that bacteria from the Proteobacteria (α, β, γ, δ), Acidobacteria, and Firmicutes were abundant in all soils. The abundance of Firmicutes was elevated in newer post-mined soils relative to the un-mined soil, and this contrasted a decrease, relative to un-mined soils, in proteobacterial and acidobacterial rRNA gene abundances. Our study indicates long-lasting impacts of mining activities to soil biogeochemical and microbial properties with impending loss in soil productivity.

Intratumor Heterogeneity of the Estrogen Receptor and the Long-term Risk of Fatal Breast Cancer.

PubMed

Lindström, Linda S; Yau, Christina; Czene, Kamila; Thompson, Carlie K; Hoadley, Katherine A; Van't Veer, Laura J; Balassanian, Ron; Bishop, John W; Carpenter, Philip M; Chen, Yunn-Yi; Datnow, Brian; Hasteh, Farnaz; Krings, Gregor; Lin, Fritz; Zhang, Yanhong; Nordenskjöld, Bo; Stål, Olle; Benz, Christopher C; Fornander, Tommy; Borowsky, Alexander D; Esserman, Laura J

2018-01-19

Breast cancer patients with estrogen receptor (ER)-positive disease have a continuous long-term risk for fatal breast cancer, but the biological factors influencing this risk are unknown. We aimed to determine whether high intratumor heterogeneity of ER predicts an increased long-term risk (25 years) of fatal breast cancer. The STO-3 trial enrolled 1780 postmenopausal lymph node-negative breast cancer patients randomly assigned to receive adjuvant tamoxifen vs not. The fraction of cancer cells for each ER intensity level was scored by breast cancer pathologists, and intratumor heterogeneity of ER was calculated using Rao's quadratic entropy and categorized into high and low heterogeneity using a predefined cutoff at the second tertile (67%). Long-term breast cancer-specific survival analyses by intra-tumor heterogeneity of ER were performed using Kaplan-Meier and multivariable Cox proportional hazard modeling adjusting for patient and tumor characteristics. A statistically significant difference in long-term survival by high vs low intratumor heterogeneity of ER was seen for all ER-positive patients (P < .001) and for patients with luminal A subtype tumors (P = .01). In multivariable analyses, patients with high intratumor heterogeneity of ER had a twofold increased long-term risk as compared with patients with low intratumor heterogeneity (ER-positive: hazard ratio [HR] = 1.98, 95% confidence interval [CI] = 1.31 to 3.00; luminal A subtype tumors: HR = 2.43, 95% CI = 1.18 to 4.99). Patients with high intratumor heterogeneity of ER had an increased long-term risk of fatal breast cancer. Interestingly, a similar long-term risk increase was seen in patients with luminal A subtype tumors. Our findings suggest that intratumor heterogeneity of ER is an independent long-term prognosticator with potential to change clinical management, especially for patients with luminal A tumors. © The Author(s) 2018. Published by Oxford University Press.

Natural forest expansion on reclaimed coal mines in Northern Spain: the role of native shrubs as suitable microsites.

PubMed

Alday, Josu G; Zaldívar, Pilar; Torroba-Balmori, Paloma; Fernández-Santos, Belén; Martínez-Ruiz, Carolina

2016-07-01

The characterization of suitable microsites for tree seedling establishment and growth is one of the most important tasks to achieve the restoration of native forest using natural processes in disturbed sites. For that, we assessed the natural Quercus petraea forest expansion in a 20-year-old reclaimed open-cast mine under sub-Mediterranean climate in northern Spain, monitoring seedling survival, growth, and recruitment during 5 years in three contrasting environments (undisturbed forest, mine edge, and mine center). Seedling density and proportion of dead branches decreased greatly from undisturbed forest towards the center of the mine. There was a positive effect of shrubs on Q. petraea seedling establishment in both mine environments, which increase as the environment undergoes more stress (from the mine edge to the center of the mine), and it was produced by different shrub structural features in each mine environment. Seedling survival reduction through time in three environments did not lead to a density reduction because there was a yearly recruitment of new seedlings. Seedling survival, annual growth, and height through time were greater in mine sites than in the undisturbed forest. The successful colonization patterns and positive neighbor effect of shrubs on natural seedlings establishment found in this study during the first years support the use of shrubs as ecosystem engineers to increase heterogeneity in micro-environmental conditions on reclaimed mine sites, which improves late-successional Quercus species establishment.

Mining and Integration of Environmental Data

NASA Astrophysics Data System (ADS)

Tran, V.; Hluchy, L.; Habala, O.; Ciglan, M.

2009-04-01

The project ADMIRE (Advanced Data Mining and Integration Research for Europe) is a 7th FP EU ICT project aims to deliver a consistent and easy-to-use technology for extracting information and knowledge. The project is motivated by the difficulty of extracting meaningful information by data mining combinations of data from multiple heterogeneous and distributed resources. It will also provide an abstract view of data mining and integration, which will give users and developers the power to cope with complexity and heterogeneity of services, data and processes. The data sets describing phenomena from domains like business, society, and environment often contain spatial and temporal dimensions. Integration of spatio-temporal data from different sources is a challenging task due to those dimensions. Different spatio-temporal data sets contain data at different resolutions (e.g. size of the spatial grid) and frequencies. This heterogeneity is the principal challenge of geo-spatial and temporal data sets integration - the integrated data set should hold homogeneous data of the same resolution and frequency. Thus, to integrate heterogeneous spatio-temporal data from distinct source, transformation of one or more data sets is necessary. Following transformation operation are required: • transformation to common spatial and temporal representation - (e.g. transformation to common coordinate system), • spatial and/or temporal aggregation - data from detailed data source are aggregated to match the resolution of other resources involved in the integration process, • spatial and/or temporal record decomposition - records from source with lower resolution data are decomposed to match the granularity of the other data source. This operation decreases data quality (e.g. transformation of data from 50km grid to 10 km grid) - data from lower resolution data set in the integrated schema are imprecise, but it allows us to preserve higher resolution data. We can decompose the spatio-temporal data integration to following phases: • pre-integration data processing - different data set can be physically stored in different formats (e.g. relational databases, text files); it might be necessary to pre-process the data sets to be integrated, • identification of transformation operations necessary to integrate data in spatio-temporal dimensions, • identification of transformation operations to be performed on non-spatio-temporal attributes and • output data schema and set generation - given prepared data and the set of transformation, operations, the final integrated schema is produces. Spatio-temporal dimension brings its specifics also to the problem of mining spatio-temporal data sets. Spatio-temporal relationships exist among records in (s-t) data sets and those relationships should be considered in mining operation. This means that when analyzing a record in spatio-temporal data set, the records in its spatial and/or temporal proximity should be taken into account. In addition, the relationships discovered in spatio-temporal data can be different when mining the same data on different scales (e.g. mining the same data sets on 50 km grid with daily data vs. 10 km grid with hourly data). To be able to do effective data mining, we first needed to gather a sufficient amount of environmental data covering similar area and time span. For this purpose we have engaged in cooperation with several organizations working in the environmental domain in Slovakia, some of which are also our partners from previous research efforts. The organizations which volunteered some of their data are the Slovak Hydro-meteorological Institute (SHMU), the Slovak Water Enterprise (SVP), the Soil Science and Conservation Institute (VUPOP), and the Institute of Hydrology of the Slovak Academy of Sciences (UHSAV). We have prepared scenarios from general meteorology, as well as specialized in hydrology and soil protection.

A methodology for multivariate phenotype-based genome-wide association studies to mine pleiotropic genes.

PubMed

Park, Sung Hee; Lee, Ji Young; Kim, Sangsoo

2011-01-01

Current Genome-Wide Association Studies (GWAS) are performed in a single trait framework without considering genetic correlations between important disease traits. Hence, the GWAS have limitations in discovering genetic risk factors affecting pleiotropic effects. This work reports a novel data mining approach to discover patterns of multiple phenotypic associations over 52 anthropometric and biochemical traits in KARE and a new analytical scheme for GWAS of multivariate phenotypes defined by the discovered patterns. This methodology applied to the GWAS for multivariate phenotype highLDLhighTG derived from the predicted patterns of the phenotypic associations. The patterns of the phenotypic associations were informative to draw relations between plasma lipid levels with bone mineral density and a cluster of common traits (Obesity, hypertension, insulin resistance) related to Metabolic Syndrome (MS). A total of 15 SNPs in six genes (PAK7, C20orf103, NRIP1, BCL2, TRPM3, and NAV1) were identified for significant associations with highLDLhighTG. Noteworthy findings were that the significant associations included a mis-sense mutation (PAK7:R335P), a frame shift mutation (C20orf103) and SNPs in splicing sites (TRPM3). The six genes corresponded to rat and mouse quantitative trait loci (QTLs) that had shown associations with the common traits such as the well characterized MS and even tumor susceptibility. Our findings suggest that the six genes may play important roles in the pleiotropic effects on lipid metabolism and the MS, which increase the risk of Type 2 Diabetes and cardiovascular disease. The use of the multivariate phenotypes can be advantageous in identifying genetic risk factors, accounting for the pleiotropic effects when the multivariate phenotypes have a common etiological pathway.

Fresh Biomass Estimation in Heterogeneous Grassland Using Hyperspectral Measurements and Multivariate Statistical Analysis

NASA Astrophysics Data System (ADS)

Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.

2014-12-01

Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.

Influence of surface flattening on biodiversity of terrestrial arthropods during early stages of brown coal spoil heap restoration.

PubMed

Moradi, Jabbar; Potocký, Pavel; Kočárek, Petr; Bartuška, Martin; Tajovský, Karel; Tichánek, Filip; Frouz, Jan; Tropek, Robert

2018-08-15

Heterogeneity of environmental conditions is the crucial factor supporting biodiversity in various habitats, including post-mining sites. The effects of micro-topographic heterogeneity on biodiversity and conservation potential of arthropod communities in post-industrial habitats had not been studied before now. At one of the largest European brown coal spoil heaps, we sampled eight groups of terrestrial arthropods with different life strategies (moths, spiders, ground beetles, ants, orthopteroids, centipedes, millipedes, and woodlice), in successionally young plots (5-18 y), with a heterogeneous wavy surface after heaping, and compared the communities with plots flattened by dozing. A combination of the standardized quantitative sampling, using two different methods, and a paired design of the plot selection enabled a robust analysis. Altogether, we recorded 380 species of the focal arthropods, 15 of them nationally threatened. We revealed the importance of the micro-topographic heterogeneity for the formation of the biodiversity of arthropods in their secondary refuges. The communities with higher biodiversity and conservation value were detected in the plots with heterogeneous surfaces; exceptions were ground beetles and millipedes. The surface flattening, often the first step of technical reclamation projects, thus suppress biodiversity of most terrestrial arthropods during the restoration of post-mining sites. Since the communities of both surface types differed, the proportional presence on both surfaces could be more efficient in supporting the local biodiversity. We suggest reducing the surface dozing for the cases with other concerns only, to achieve a proportional representation of both surface types. Such a combination of different restoration approaches would, thus, efficiently support high biodiversity of groups with various needs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Numerical and Experimental Investigation of Soil Heterogeneity around Landmines in Natural Soil

NASA Astrophysics Data System (ADS)

Wallen, B.; Smits, K. M.; Howington, S. E.

2015-12-01

The environment in which landmines are placed is oftentimes highly heterogeneous. These heterogeneities such as differences in soil type, packing and moisture, combined with changes in surface and climate conditions can oftentimes mask the presence of the mine. Understanding the impact of heterogeneity on heat and mass transfer behavior in the vicinity of landmines is paramount to properly identifying landmine locations for demining operations. This study investigates the impact of soil heterogeneity on soil moisture and temperature distributions around buried objects with the goal of increasing our ability to model and predict the environmental conditions that are most dynamic to mine detection performance. A ten-day field experiment was conducted in which two anti-personnel landmines at different depths and a limestone block of comparable size were buried. The site was instrumented with a series of sensors, monitoring atmospheric, surface and subsurface conditions to include measurements of soil moisture, soil and air temperature, relative humidity, vapor concentration, and meteorological conditions such as wind speed and net radiation. Infrared thermal imaging was used to provide continuous profiles of surface temperature conditions. The soil was well characterized in the laboratory to provide good understanding of field conditions for numerical modeling efforts. Experimental results demonstrate the strongest thermal contrast between shallow landmine emplacement and the surrounding soil occurring as the sun approaches its zenith and two hours after sunset until the sun directly impacts the soil above the landmine. A finite-element model of fluid flow and heat transport through porous media is compared against experimental observations, capturing the diurnal variation. A validated model, like this one, offers the opportunity to improve landmine detection probabilities and reduce false alarms caused by environmental variability.

Economics of Gypsum Production in Iran

NASA Astrophysics Data System (ADS)

Esmaeili, Abdoulkarim

The purpose of this research is to analyze the economics of gypsum production in Iran. The trend in production cost, selling price and profit are used to investigate economics of gypsum production. In addition, the multivariate time series method is used to determine factors affecting gypsum price in domestic market. The results indicated that due to increase in production and inflation, profitability of gypsum production has decreased during recent years. It is concluded that tariff and non-tariff barriers on mines machinery are among reasons for increasing production cost in Iranian gypsum mines. Decreasing such barriers could increase profitability of gypsum production in Iran.

An investigation into heterogeneity in a single vein-type uranium ore deposit: Implications for nuclear forensics.

PubMed

Keatley, A C; Scott, T B; Davis, S; Jones, C P; Turner, P

2015-12-01

Minor element composition and rare earth element (REE) concentrations in nuclear materials are important as they are used within the field of nuclear forensics as an indicator of sample origin. However recent studies into uranium ores and uranium ore concentrates (UOCs) have shown significant elemental and isotopic heterogeneity from a single mine site such that some sites have shown higher variation within the mine site than that seen between multiple sites. The elemental composition of both uranium and gangue minerals within ore samples taken along a single mineral vein in South West England have been measured and reported here. The analysis of the samples was undertaken to determine the extent of the localised variation in key elements. Energy Dispersive X-ray spectroscopy (EDS) was used to analyse the gangue mineralogy and measure major element composition. Minor element composition and rare earth element (REE) concentrations were measured by Electron Probe Microanalysis (EPMA). The results confirm that a number of key elements, REE concentrations and patterns used for origin location do show significant variation within mine. Furthermore significant variation is also visible on a meter scale. In addition three separate uranium phases were identified within the vein which indicates multiple uranium mineralisation events. In light of these localised elemental variations it is recommended that representative sampling for an area is undertaken prior to establishing the REE pattern that may be used to identify the originating mine for an unknown ore sample and prior to investigating impact of ore processing on any arising REE patterns. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prognostic value of the neutrophil to lymphocyte ratio in lung cancer: A meta-analysis.

PubMed

Yin, Yongmei; Wang, Jun; Wang, Xuedong; Gu, Lan; Pei, Hao; Kuai, Shougang; Zhang, Yingying; Shang, Zhongbo

2015-07-01

Recently, a series of studies explored the correlation between the neutrophil to lymphocyte ratio and the prognosis of lung cancer. However, the current opinion regarding the prognostic role of the neutrophil to lymphocyte ratio in lung cancer is inconsistent. We performed a meta-analysis of published articles to investigate the prognostic value of the neutrophil to lymphocyte ratio in lung cancer. The hazard ratio (HR) and its 95% confidence interval (CI) were calculated. An elevated neutrophil to lymphocyte ratio predicted worse overall survival, with a pooled HR of 1.243 (95%CI: 1.106-1.397; P(heterogeneity)=0.001) from multivariate studies and 1.867 (95%CI: 1.487-2.344; P(heterogeneity)=0.047) from univariate studies. Subgroup analysis showed that a high neutrophil to lymphocyte ratio yielded worse overall survival in non-small cell lung cancer (NSCLC) (HR=1.192, 95%CI: 1.061-1.399; P(heterogeneity)=0.003) as well as small cell lung cancer (SCLC) (HR=1.550, 95% CI: 1.156-2.077; P(heterogeneity)=0.625) in multivariate studies. The synthesized evidence from this meta-analysis of published articles demonstrated that an elevated neutrophil to lymphocyte ratio was a predictor of poor overall survival in patients with lung cancer.

Influence of gravel mining and other factors on detection probabilities of Coastal Plain fishes in the Mobile River Basin, Alabama

USGS Publications Warehouse

Hayer, C.-A.; Irwin, E.R.

2008-01-01

We used an information-theoretic approach to examine the variation in detection probabilities for 87 Piedmont and Coastal Plain fishes in relation to instream gravel mining in four Alabama streams of the Mobile River drainage. Biotic and abiotic variables were also included in candidate models. Detection probabilities were heterogeneous across species and varied with habitat type, stream, season, and water quality. Instream gravel mining influenced the variation in detection probabilities for 38% of the species collected, probably because it led to habitat loss and increased sedimentation. Higher detection probabilities were apparent at unmined sites than at mined sites for 78% of the species for which gravel mining was shown to influence detection probabilities, indicating potential negative impacts to these species. Physical and chemical attributes also explained the variation in detection probabilities for many species. These results indicate that anthropogenic impacts can affect detection probabilities for fishes, and such variation should be considered when developing monitoring programs or routine sampling protocols. ?? Copyright by the American Fisheries Society 2008.

Sources and fates of heavy metals in a mining-impacted stream: Temporal variability and the role of iron oxides

PubMed Central

Schaider, Laurel A.; Senn, David B.; Estes, Emily R.; Brabander, Daniel J.; Shine, James P.

2014-01-01

Heavy metal contamination of surface waters at mining sites often involves complex interactions of multiple sources and varying biogeochemical conditions. We compared surface and subsurface metal loading from mine waste pile runoff and mine drainage discharge and characterized the influence of iron oxides on metal fate along a 0.9-km stretch of Tar Creek (Oklahoma, USA), which drains an abandoned Zn/Pb mining area. The importance of each source varied by metal: mine waste pile runoff contributed 70% of Cd, while mine drainage contributed 90% of Pb, and both sources contributed similarly to Zn loading. Subsurface inputs accounted for 40% of flow and 40-70% of metal loading along this stretch. Streambed iron oxide aggregate material contained highly elevated Zn (up to 27,000 μg g−1), Pb (up to 550 μg g−1) and Cd (up to 200 μg g−1) and was characterized as a heterogeneous mixture of iron oxides, fine-grain mine waste, and organic material. Sequential extractions confirmed preferential sequestration of Pb by iron oxides, as well as substantial concentrations of Zn and Cd in iron oxide fractions, with additional accumulation of Zn, Pb, and Cd during downstream transport. Comparisons with historical data show that while metal concentrations in mine drainage have decreased by more than an order of magnitude in recent decades, the chemical composition of mine waste pile runoff has remained relatively constant, indicating less attenuation and increased relative importance of pile runoff. These results highlight the importance of monitoring temporal changes at contaminated sites associated with evolving speciation and simultaneously addressing surface and subsurface contamination from both mine waste piles and mine drainage. PMID:24867708

False alarm reduction by the And-ing of multiple multivariate Gaussian classifiers

NASA Astrophysics Data System (ADS)

Dobeck, Gerald J.; Cobb, J. Tory

2003-09-01

The high-resolution sonar is one of the principal sensors used by the Navy to detect and classify sea mines in minehunting operations. For such sonar systems, substantial effort has been devoted to the development of automated detection and classification (D/C) algorithms. These have been spurred by several factors including (1) aids for operators to reduce work overload, (2) more optimal use of all available data, and (3) the introduction of unmanned minehunting systems. The environments where sea mines are typically laid (harbor areas, shipping lanes, and the littorals) give rise to many false alarms caused by natural, biologic, and man-made clutter. The objective of the automated D/C algorithms is to eliminate most of these false alarms while still maintaining a very high probability of mine detection and classification (PdPc). In recent years, the benefits of fusing the outputs of multiple D/C algorithms have been studied. We refer to this as Algorithm Fusion. The results have been remarkable, including reliable robustness to new environments. This paper describes a method for training several multivariate Gaussian classifiers such that their And-ing dramatically reduces false alarms while maintaining a high probability of classification. This training approach is referred to as the Focused- Training method. This work extends our 2001-2002 work where the Focused-Training method was used with three other types of classifiers: the Attractor-based K-Nearest Neighbor Neural Network (a type of radial-basis, probabilistic neural network), the Optimal Discrimination Filter Classifier (based linear discrimination theory), and the Quadratic Penalty Function Support Vector Machine (QPFSVM). Although our experience has been gained in the area of sea mine detection and classification, the principles described herein are general and can be applied to a wide range of pattern recognition and automatic target recognition (ATR) problems.

Small-for-gestational age prevalence risk factors in central Appalachian states with mountain-top mining.

PubMed

Ferdosi, Hamid; Lamm, Steve H; Afari-Dwamena, Nana Ama; Dissen, Elisabeth; Chen, Rusan; Li, Ji; Feinleib, Manning

2018-01-01

To identify risk factors for small-for-gestational age (SGA) for counties in central Appalachian states (Kentucky (KY), Tennessee (TN), Virginia (VA), and West Virginia (WV)) with varied coal mining activities. Live birth certificate files (1990-2002) were used for obtaining SGA prevalence rates for mothers based on the coal mining activities of their counties of residence, mountain-top mining (MTM) activities, underground mining activities but no mountain-top mining activity (non-MTM), or having no mining activities (non-mining). Co-variable information, including maternal tobacco use, was also obtained from the live birth certificate. Adjusted odds ratios were obtained using multivariable logistic regression comparing SGA prevalence rates for counties with coal mining activities to those without coal mining activities and comparing SGA prevalence rates for counties with coal mining activities for those with and without mountain-top mining activities. Comparisons were also made among those who had reported tobacco use and those who had not. Both tobacco use prevalence and SGA prevalence were significantly greater for mining counties than for non-mining counties and for MTM counties than for non-MTM counties. Adjustment for tobacco use alone explained 50% of the increased SGA risk for mining counties and 75% of the risk for MTM counties, including demographic pre-natal care co-variables that explained 75% of the increased SGA risk for mining counties and 100% of the risk for MTM. The increased risk of SGA was limited to the third trimester births among tobacco users and independent of the mining activities of their counties of residence. This study demonstrates that the increased prevalence of SGA among residents of counties with mining activity was primarily explained by the differences in maternal tobacco use prevalence, an effect that itself was gestational-age dependent. Self-reported tobacco use marked the population at the increased risk for SGA in central Appalachian states. Int J Occup Med Environ Health 2018;31(1):11-23. This work is available in Open Access model and licensed under a CC BY-NC 3.0 PL license.

Data Mining: The Art of Automated Knowledge Extraction

NASA Astrophysics Data System (ADS)

Karimabadi, H.; Sipes, T.

2012-12-01

Data mining algorithms are used routinely in a wide variety of fields and they are gaining adoption in sciences. The realities of real world data analysis are that (a) data has flaws, and (b) the models and assumptions that we bring to the data are inevitably flawed, and/or biased and misspecified in some way. Data mining can improve data analysis by detecting anomalies in the data, check for consistency of the user model assumptions, and decipher complex patterns and relationships that would not be possible otherwise. The common form of data collected from in situ spacecraft measurements is multi-variate time series which represents one of the most challenging problems in data mining. We have successfully developed algorithms to deal with such data and have extended the algorithms to handle streaming data. In this talk, we illustrate the utility of our algorithms through several examples including automated detection of reconnection exhausts in the solar wind and flux ropes in the magnetotail. We also show examples from successful applications of our technique to analysis of 3D kinetic simulations. With an eye to the future, we provide an overview of our upcoming plans that include collaborative data mining, expert outsourcing data mining, computer vision for image analysis, among others. Finally, we discuss the integration of data mining algorithms with web-based services such as VxOs and other Heliophysics data centers and the resulting capabilities that it would enable.

The Wild West: Associations between mining and violence in Western Australia.

PubMed

Gilmore, William; Liang, Wenbin; Chikritzhs, Tanya

2016-04-01

To investigate the association between mining activity and police-reported assault offences across Western Australia. A cross-sectional multivariable negative binomial regression analysis at the local government area level. Local government areas in Western Australia. Victims of reported assault offences occurring in 2008-2009. Eight reported assault measures by gender of victim and type of assault. The analysis controlled for a range of potentially confounding variables, including numbers of licensed outlets and alcohol sales. Compared with females in other areas, females in mining regions had a 64% increased risk of assault, a 59% increased risk of non-domestic assault and a 136% increased risk of sexual assault. Risk of domestic assault was 64% higher for males in mining regions. Regions where mining is a major employer of people usually or temporarily residing in the area (i.e. usual residents or temporary fly-in fly-out residents) are associated with higher risk of assaults among females and domestic assaults among males, and these associations appear to be independent of licensed outlet numbers and alcohol sales. Mining communities appear to present a special case for the management and reduction of violence; public health and safety intervention needs to identify and address risk factors independent of alcohol use. © 2015 National Rural Health Alliance Inc.

Characterization and speciation of mercury-bearing mine wastes using X-ray absorption spectroscopy

USGS Publications Warehouse

Kim, C.S.; Brown, Gordon E.; Rytuba, J.J.

2000-01-01

Mining of mercury deposits located in the California Coast Range has resulted in the release of mercury to the local environment and water supplies. The solubility, transport, and potential bioavailability of mercury are controlled by its chemical speciation, which can be directly determined for samples with total mercury concentrations greater than 100 mg kg-1 (ppm) using X-ray absorption spectroscopy (XAS). This technique has the additional benefits of being non-destructive to the sample, element-specific, relatively sensitive at low concentrations, and requiring minimal sample preparation. In this study, Hg L(III)-edge extended X-ray absorption fine structure (EXAFS) spectra were collected for several mercury mine tailings (calcines) in the California Coast Range. Total mercury concentrations of samples analyzed ranged from 230 to 1060 ppm. Speciation data (mercury phases present and relative abundances) were obtained by comparing the spectra from heterogeneous, roasted (calcined) mine tailings samples with a spectral database of mercury minerals and sorbed mercury complexes. Speciation analyses were also conducted on known mixtures of pure mercury minerals in order to assess the quantitative accuracy of the technique. While some calcine samples were found to consist exclusively of mercuric sulfide, others contain additional, more soluble mercury phases, indicating a greater potential for the release of mercury into solution. Also, a correlation was observed between samples from hot-spring mercury deposits, in which chloride levels are elevated, and the presence of mercury-chloride species as detected by the speciation analysis. The speciation results demonstrate the ability of XAS to identify multiple mercury phases in a heterogeneous sample, with a quantitative accuracy of ??25% for the mercury-containing phases considered. Use of this technique, in conjunction with standard microanalytical techniques such as X-ray diffraction and electron probe microanalysis, is beneficial in the prioritization and remediation of mercury-contaminated mine sites. (C) 2000 Elsevier Science B.V.

Final Technical Report - Investigation into the Relationship between Heterogeneity and Heavy-Tailed Solute Transport

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weissmann, Gary S

2013-12-06

The objective of this project was to characterize the influence that naturally complex geologic media has on anomalous dispersion and to determine if the nature of dispersion can be estimated from the underlying heterogeneous media. The UNM portion of this project was to provide detailed representations of aquifer heterogeneity through producing highly-resolved models of outcrop analogs to aquifer materials. This project combined outcrop-scale heterogeneity characterization (conducted at the University of New Mexico), laboratory experiments (conducted at Sandia National Laboratory), and numerical simulations (conducted at Sandia National Laboratory and Colorado School of Mines). The study was designed to test whether establishedmore » dispersion theory accurately predicts the behavior of solute transport through heterogeneous media and to investigate the relationship between heterogeneity and the parameters that populate these models. The dispersion theory tested by this work was based upon the fractional advection-dispersion equation (fADE) model. Unlike most dispersion studies that develop a solute transport model by fitting the solute transport breakthrough curve, this project explored the nature of the heterogeneous media to better understand the connection between the model parameters and the aquifer heterogeneity. We also evaluated methods for simulating the heterogeneity to see whether these approaches (e.g., geostatistical) could reasonably replicate realistic heterogeneity. The UNM portion of this study focused on capturing realistic geologic heterogeneity of aquifer analogs using advanced outcrop mapping methods.« less

Improving Collaborative Learning in the Classroom: Text Mining Based Grouping and Representing

ERIC Educational Resources Information Center

Erkens, Melanie; Bodemer, Daniel; Hoppe, H. Ulrich

2016-01-01

Orchestrating collaborative learning in the classroom involves tasks such as forming learning groups with heterogeneous knowledge and making learners aware of the knowledge differences. However, gathering information on which the formation of appropriate groups and the creation of graphical knowledge representations can be based is very effortful…

Microbiological and Geochemical Characterization of Fluvially Deposited Sulfidic Mine Tailings

PubMed Central

Wielinga, Bruce; Lucy, Juliette K.; Moore, Johnnie N.; Seastone, October F.; Gannon, James E.

1999-01-01

The fluvial deposition of mine tailings generated from historic mining operations near Butte, Montana, has resulted in substantial surface and shallow groundwater contamination along Silver Bow Creek. Biogeochemical processes in the sediment and underlying hyporheic zone were studied in an attempt to characterize interactions consequential to heavy-metal contamination of shallow groundwater. Sediment cores were extracted and fractionated based on sediment stratification. Subsamples of each fraction were assayed for culturable heterotrophic microbiota, specific microbial guilds involved in metal redox transformations, and both aqueous- and solid-phase geochemistry. Populations of cultivable Fe(III)-reducing bacteria were most prominent in the anoxic, circumneutral pH regions associated with a ferricrete layer or in an oxic zone high in organic carbon and soluble iron. Sulfur- and iron-oxidizing bacteria were distributed in discrete zones throughout the tailings and were often recovered from sections at and below the anoxic groundwater interface. Sulfate-reducing bacteria were also widely distributed in the cores and often occurred in zones overlapping iron and sulfur oxidizers. Sulfate-reducing bacteria were consistently recovered from oxic zones that contained high concentrations of metals in the oxidizable fraction. Altogether, these results suggest a highly varied and complex microbial ecology within a very heterogeneous geochemical environment. Such physical and biological heterogeneity has often been overlooked when remediation strategies for metal contaminated environments are formulated. PMID:10103249

Integrating and analyzing medical and environmental data using ETL and Business Intelligence tools.

PubMed

Villar, Alejandro; Zarrabeitia, María T; Fdez-Arroyabe, Pablo; Santurtún, Ana

2018-06-01

Processing data that originates from different sources (such as environmental and medical data) can prove to be a difficult task, due to the heterogeneity of variables, storage systems, and file formats that can be used. Moreover, once the amount of data reaches a certain threshold, conventional mining methods (based on spreadsheets or statistical software) become cumbersome or even impossible to apply. Data Extract, Transform, and Load (ETL) solutions provide a framework to normalize and integrate heterogeneous data into a local data store. Additionally, the application of Online Analytical Processing (OLAP), a set of Business Intelligence (BI) methodologies and practices for multidimensional data analysis, can be an invaluable tool for its examination and mining. In this article, we describe a solution based on an ETL + OLAP tandem used for the on-the-fly analysis of tens of millions of individual medical, meteorological, and air quality observations from 16 provinces in Spain provided by 20 different national and regional entities in a diverse array for file types and formats, with the intention of evaluating the effect of several environmental variables on human health in future studies. Our work shows how a sizable amount of data, spread across a wide range of file formats and structures, and originating from a number of different sources belonging to various business domains, can be integrated in a single system that researchers can use for global data analysis and mining.

Integrating and analyzing medical and environmental data using ETL and Business Intelligence tools

NASA Astrophysics Data System (ADS)

Villar, Alejandro; Zarrabeitia, María T.; Fdez-Arroyabe, Pablo; Santurtún, Ana

2018-03-01

Processing data that originates from different sources (such as environmental and medical data) can prove to be a difficult task, due to the heterogeneity of variables, storage systems, and file formats that can be used. Moreover, once the amount of data reaches a certain threshold, conventional mining methods (based on spreadsheets or statistical software) become cumbersome or even impossible to apply. Data Extract, Transform, and Load (ETL) solutions provide a framework to normalize and integrate heterogeneous data into a local data store. Additionally, the application of Online Analytical Processing (OLAP), a set of Business Intelligence (BI) methodologies and practices for multidimensional data analysis, can be an invaluable tool for its examination and mining. In this article, we describe a solution based on an ETL + OLAP tandem used for the on-the-fly analysis of tens of millions of individual medical, meteorological, and air quality observations from 16 provinces in Spain provided by 20 different national and regional entities in a diverse array for file types and formats, with the intention of evaluating the effect of several environmental variables on human health in future studies. Our work shows how a sizable amount of data, spread across a wide range of file formats and structures, and originating from a number of different sources belonging to various business domains, can be integrated in a single system that researchers can use for global data analysis and mining.

Integrating and analyzing medical and environmental data using ETL and Business Intelligence tools

NASA Astrophysics Data System (ADS)

Villar, Alejandro; Zarrabeitia, María T.; Fdez-Arroyabe, Pablo; Santurtún, Ana

2018-06-01

Processing data that originates from different sources (such as environmental and medical data) can prove to be a difficult task, due to the heterogeneity of variables, storage systems, and file formats that can be used. Moreover, once the amount of data reaches a certain threshold, conventional mining methods (based on spreadsheets or statistical software) become cumbersome or even impossible to apply. Data Extract, Transform, and Load (ETL) solutions provide a framework to normalize and integrate heterogeneous data into a local data store. Additionally, the application of Online Analytical Processing (OLAP), a set of Business Intelligence (BI) methodologies and practices for multidimensional data analysis, can be an invaluable tool for its examination and mining. In this article, we describe a solution based on an ETL + OLAP tandem used for the on-the-fly analysis of tens of millions of individual medical, meteorological, and air quality observations from 16 provinces in Spain provided by 20 different national and regional entities in a diverse array for file types and formats, with the intention of evaluating the effect of several environmental variables on human health in future studies. Our work shows how a sizable amount of data, spread across a wide range of file formats and structures, and originating from a number of different sources belonging to various business domains, can be integrated in a single system that researchers can use for global data analysis and mining.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Solc, J.

The reclamation effort typically deals with consequences of mining activity instead of being planned well before the mining. Detailed assessment of principal hydro- and geochemical processes participating in pore and groundwater chemistry evolution was carried out at three surface mine localities in North Dakota-the Fritz mine, the Indian Head mine, and the Velva mine. The geochemical model MINTEQUA2 and advanced statistical analysis coupled with traditional interpretive techniques were used to determine site-specific environmental characteristics and to compare the differences between study sites. Multivariate statistical analysis indicates that sulfate, magnesium, calcium, the gypsum saturation index, and sodium contribute the most tomore » overall differences in groundwater chemistry between study sites. Soil paste extract pH and EC measurements performed on over 3700 samples document extremely acidic soils at the Fritz mine. The number of samples with pH <5.5 reaches 80%-90% of total samples from discrete depth near the top of the soil profile at the Fritz mine. Soil samples from Indian Head and Velva do not indicate the acidity below the pH of 5.5 limit. The percentage of samples with EC > 3 mS cm{sup -1} is between 20% and 40% at the Fritz mine and below 20% for samples from Indian Head and Velva. The results of geochemical modeling indicate an increased tendency for gypsum saturation within the vadose zone, particularly within the lands disturbed by mining activity. This trend is directly associated with increased concentrations of sulfate anions as a result of mineral oxidation. Geochemical modeling, statistical analysis, and soil extract pH and EC measurements proved to be reliable, fast, and relatively cost-effective tools for the assessment of soil acidity, the extent of the oxidation zone, and the potential for negative impact on pore and groundwater chemistry.« less

Query-Based Outlier Detection in Heterogeneous Information Networks.

PubMed

Kuck, Jonathan; Zhuang, Honglei; Yan, Xifeng; Cam, Hasan; Han, Jiawei

2015-03-01

Outlier or anomaly detection in large data sets is a fundamental task in data science, with broad applications. However, in real data sets with high-dimensional space, most outliers are hidden in certain dimensional combinations and are relative to a user's search space and interest. It is often more effective to give power to users and allow them to specify outlier queries flexibly, and the system will then process such mining queries efficiently. In this study, we introduce the concept of query-based outlier in heterogeneous information networks, design a query language to facilitate users to specify such queries flexibly, define a good outlier measure in heterogeneous networks, and study how to process outlier queries efficiently in large data sets. Our experiments on real data sets show that following such a methodology, interesting outliers can be defined and uncovered flexibly and effectively in large heterogeneous networks.

Query-Based Outlier Detection in Heterogeneous Information Networks

PubMed Central

Kuck, Jonathan; Zhuang, Honglei; Yan, Xifeng; Cam, Hasan; Han, Jiawei

2015-01-01

Outlier or anomaly detection in large data sets is a fundamental task in data science, with broad applications. However, in real data sets with high-dimensional space, most outliers are hidden in certain dimensional combinations and are relative to a user’s search space and interest. It is often more effective to give power to users and allow them to specify outlier queries flexibly, and the system will then process such mining queries efficiently. In this study, we introduce the concept of query-based outlier in heterogeneous information networks, design a query language to facilitate users to specify such queries flexibly, define a good outlier measure in heterogeneous networks, and study how to process outlier queries efficiently in large data sets. Our experiments on real data sets show that following such a methodology, interesting outliers can be defined and uncovered flexibly and effectively in large heterogeneous networks. PMID:27064397

Perfection Of Methods Of Mathematical Analysis For Increasing The Completeness Of Subsoil Development

NASA Astrophysics Data System (ADS)

Fokina, Mariya

2017-11-01

The economy of Russia is based around the mineral-raw material complex to the highest degree. The mining industry is a prioritized and important area. Given the high competitiveness of businesses in this sector, increasing the efficiency of completed work and manufactured products will become a central issue. Improvement of planning and management in this sector should be based on multivariant study and the optimization of planning decisions, the appraisal of their immediate and long-term results, taking the dynamic of economic development into account. All of this requires the use of economic mathematic models and methodsApplying an economic-mathematic model to determine optimal ore mine production capacity, we receive a figure of 4,712,000 tons. The production capacity of the Uchalinsky ore mine is 1560 thousand tons, and the Uzelginsky ore mine - 3650 thousand. Conducting a corresponding analysis of the production of OAO "Uchalinsky Gok", an optimal production plan was received: the optimal production of copper - 77961,4 rubles; the optimal production of zinc - 17975.66 rubles. The residual production volume of the two main ore mines of OAO "UGOK" is 160 million tons of ore.

Heavy metal pollution of coal mine-affected agricultural soils in the northern part of Bangladesh.

PubMed

Bhuiyan, Mohammad A H; Parvez, Lutfar; Islam, M A; Dampare, Samuel B; Suzuki, Shigeyuki

2010-01-15

Total concentrations of heavy metals in the soils of mine drainage and surrounding agricultural fields in the northern part of Bangladesh were determined to evaluate the level of contamination. The average concentrations of Ti, Mn, Zn, Pb, As, Fe, Rb, Sr, Nb and Zr exceeded the world normal averages and, in some cases, Mn, Zn, As and Pb exceeded the toxic limit of the respective metals. Soil pollution assessment was carried out using enrichment factor (EF), geoaccumulation index (I(geo)) and pollution load index (PLI). The soils show significant enrichment with Ti, Mn, Zn, Pb, As, Fe, Sr and Nb, indicating inputs from mining activities. The I(geo) values have revealed that Mn (1.24+/-0.38), Zn (1.49+/-0.58) and Pb (1.63+/-0.38) are significantly accumulated in the study area. The PLIs derived from contamination factors indicate that the distal part of the coal mine-affected area is the most polluted (PLI of 4.02). Multivariate statistical analyses, principal component and cluster analyses, suggest that Mn, Zn, Pb and Ti are derived from anthropogenic sources, particularly coal mining activities, and the extreme proximal and distal parts are heavily contaminated with maximum heavy metals.

Correlating Microbial Diversity Patterns with Geochemistry in an Extreme and Heterogeneous Environment of Mine Tailings

PubMed Central

Liu, Jun; Hua, Zheng-Shuang; Chen, Lin-Xing; Kuang, Jia-Liang; Li, Sheng-Jin; Shu, Wen-Sheng

2014-01-01

Recent molecular surveys have advanced our understanding of the forces shaping the large-scale ecological distribution of microbes in Earth's extreme habitats, such as hot springs and acid mine drainage. However, few investigations have attempted dense spatial analyses of specific sites to resolve the local diversity of these extraordinary organisms and how communities are shaped by the harsh environmental conditions found there. We have applied a 16S rRNA gene-targeted 454 pyrosequencing approach to explore the phylogenetic differentiation among 90 microbial communities from a massive copper tailing impoundment generating acidic drainage and coupled these variations in community composition with geochemical parameters to reveal ecological interactions in this extreme environment. Our data showed that the overall microbial diversity estimates and relative abundances of most of the dominant lineages were significantly correlated with pH, with the simplest assemblages occurring under extremely acidic conditions and more diverse assemblages associated with neutral pHs. The consistent shifts in community composition along the pH gradient indicated that different taxa were involved in the different acidification stages of the mine tailings. Moreover, the effect of pH in shaping phylogenetic structure within specific lineages was also clearly evident, although the phylogenetic differentiations within the Alphaproteobacteria, Deltaproteobacteria, and Firmicutes were attributed to variations in ferric and ferrous iron concentrations. Application of the microbial assemblage prediction model further supported pH as the major factor driving community structure and demonstrated that several of the major lineages are readily predictable. Together, these results suggest that pH is primarily responsible for structuring whole communities in the extreme and heterogeneous mine tailings, although the diverse microbial taxa may respond differently to various environmental conditions. PMID:24727268

Mining biomedical images towards valuable information retrieval in biomedical and life sciences

PubMed Central

Ahmed, Zeeshan; Zeeshan, Saman; Dandekar, Thomas

2016-01-01

Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries. PMID:27538578

Modeling Spatial Dependencies and Semantic Concepts in Data Mining

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vatsavai, Raju

Data mining is the process of discovering new patterns and relationships in large datasets. However, several studies have shown that general data mining techniques often fail to extract meaningful patterns and relationships from the spatial data owing to the violation of fundamental geospatial principles. In this tutorial, we introduce basic principles behind explicit modeling of spatial and semantic concepts in data mining. In particular, we focus on modeling these concepts in the widely used classification, clustering, and prediction algorithms. Classification is the process of learning a structure or model (from user given inputs) and applying the known model to themore » new data. Clustering is the process of discovering groups and structures in the data that are ``similar,'' without applying any known structures in the data. Prediction is the process of finding a function that models (explains) the data with least error. One common assumption among all these methods is that the data is independent and identically distributed. Such assumptions do not hold well in spatial data, where spatial dependency and spatial heterogeneity are a norm. In addition, spatial semantics are often ignored by the data mining algorithms. In this tutorial we cover recent advances in explicitly modeling of spatial dependencies and semantic concepts in data mining.« less

Mercury contamination in agricultural soils from abandoned metal mines classified by geology and mineralization.

PubMed

Kim, Han Sik; Jung, Myung Chae

2012-01-01

This survey aimed to compare mercury concentrations in soils related to geology and mineralization types of mines. A total of 16,386 surface soils (0~15 cm in depth) were taken from agricultural lands near 343 abandoned mines (within 2 km from each mine) and analyzed for Hg by AAS with a hydride-generation device. To meaningfully compare mercury levels in soils with geology and mineralization types, three subclassification criteria were adapted: (1) five mineralization types, (2) four valuable ore mineral types, and (3) four parent rock types. The average concentration of Hg in all soils was 0.204 mg kg(-1) with a range of 0.002-24.07 mg kg(-1). Based on the mineralization types, average Hg concentrations (mg kg(-1)) in the soils decreased in the order of pegmatite (0.250) > hydrothermal vein (0.208) > hydrothermal replacement (0.166) > skarn (0.121) > sedimentary deposits (0.045). In terms of the valuable ore mineral types, the concentrations decreased in the order of Au-Ag-base metal mines ≈ base metal mines > Au-Ag mines > Sn-W-Mo-Fe-Mn mines. For parent rock types, similar concentrations were found in the soils derived from sedimentary rocks and metamorphic rocks followed by heterogeneous rocks with igneous and metamorphic processes. Furthermore, farmland soils contained relatively higher Hg levels than paddy soils. Therefore, it can be concluded that soils in Au, Ag, and base metal mines derived from a hydrothermal vein type of metamorphic rocks and pegmatite deposits contained relatively higher concentrations of mercury in the surface environment.

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

PubMed

Snell, Kym I E; Hua, Harry; Debray, Thomas P A; Ensor, Joie; Look, Maxime P; Moons, Karel G M; Riley, Richard D

2016-01-01

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

NASA Astrophysics Data System (ADS)

Schwartz, Craig R.; Thelen, Brian J.; Kenton, Arthur C.

1995-06-01

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

A Temporal Pattern Mining Approach for Classifying Electronic Health Record Data

PubMed Central

Batal, Iyad; Valizadegan, Hamed; Cooper, Gregory F.; Hauskrecht, Milos

2013-01-01

We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the Minimal Predictive Temporal Patterns framework to generate a small set of predictive and non-spurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin induced thrombocytopenia. The results demonstrate the benefit of our approach in efficiently learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems. PMID:25309815

Windblown Dust Deposition Forecasting and Spread of Contamination around Mine Tailings.

PubMed

Stovern, Michael; Guzmán, Héctor; Rine, Kyle P; Felix, Omar; King, Matthew; Ela, Wendell P; Betterton, Eric A; Sáez, Avelino Eduardo

2016-02-01

Wind erosion, transport and deposition of windblown dust from anthropogenic sources, such as mine tailings impoundments, can have significant effects on the surrounding environment. The lack of vegetation and the vertical protrusion of the mine tailings above the neighboring terrain make the tailings susceptible to wind erosion. Modeling the erosion, transport and deposition of particulate matter from mine tailings is a challenge for many reasons, including heterogeneity of the soil surface, vegetative canopy coverage, dynamic meteorological conditions and topographic influences. In this work, a previously developed Deposition Forecasting Model (DFM) that is specifically designed to model the transport of particulate matter from mine tailings impoundments is verified using dust collection and topsoil measurements. The DFM is initialized using data from an operational Weather Research and Forecasting (WRF) model. The forecast deposition patterns are compared to dust collected by inverted-disc samplers and determined through gravimetric, chemical composition and lead isotopic analysis. The DFM is capable of predicting dust deposition patterns from the tailings impoundment to the surrounding area. The methodology and approach employed in this work can be generalized to other contaminated sites from which dust transport to the local environment can be assessed as a potential route for human exposure.

Windblown Dust Deposition Forecasting and Spread of Contamination around Mine Tailings

PubMed Central

Stovern, Michael; Guzmán, Héctor; Rine, Kyle P.; Felix, Omar; King, Matthew; Ela, Wendell P.; Betterton, Eric A.; Sáez, Avelino Eduardo

2017-01-01

Wind erosion, transport and deposition of windblown dust from anthropogenic sources, such as mine tailings impoundments, can have significant effects on the surrounding environment. The lack of vegetation and the vertical protrusion of the mine tailings above the neighboring terrain make the tailings susceptible to wind erosion. Modeling the erosion, transport and deposition of particulate matter from mine tailings is a challenge for many reasons, including heterogeneity of the soil surface, vegetative canopy coverage, dynamic meteorological conditions and topographic influences. In this work, a previously developed Deposition Forecasting Model (DFM) that is specifically designed to model the transport of particulate matter from mine tailings impoundments is verified using dust collection and topsoil measurements. The DFM is initialized using data from an operational Weather Research and Forecasting (WRF) model. The forecast deposition patterns are compared to dust collected by inverted-disc samplers and determined through gravimetric, chemical composition and lead isotopic analysis. The DFM is capable of predicting dust deposition patterns from the tailings impoundment to the surrounding area. The methodology and approach employed in this work can be generalized to other contaminated sites from which dust transport to the local environment can be assessed as a potential route for human exposure. PMID:29082035

Physics Mining of Multi-Source Data Sets

NASA Technical Reports Server (NTRS)

Helly, John; Karimabadi, Homa; Sipes, Tamara

2012-01-01

Powerful new parallel data mining algorithms can produce diagnostic and prognostic numerical models and analyses from observational data. These techniques yield higher-resolution measures than ever before of environmental parameters by fusing synoptic imagery and time-series measurements. These techniques are general and relevant to observational data, including raster, vector, and scalar, and can be applied in all Earth- and environmental science domains. Because they can be highly automated and are parallel, they scale to large spatial domains and are well suited to change and gap detection. This makes it possible to analyze spatial and temporal gaps in information, and facilitates within-mission replanning to optimize the allocation of observational resources. The basis of the innovation is the extension of a recently developed set of algorithms packaged into MineTool to multi-variate time-series data. MineTool is unique in that it automates the various steps of the data mining process, thus making it amenable to autonomous analysis of large data sets. Unlike techniques such as Artificial Neural Nets, which yield a blackbox solution, MineTool's outcome is always an analytical model in parametric form that expresses the output in terms of the input variables. This has the advantage that the derived equation can then be used to gain insight into the physical relevance and relative importance of the parameters and coefficients in the model. This is referred to as physics-mining of data. The capabilities of MineTool are extended to include both supervised and unsupervised algorithms, handle multi-type data sets, and parallelize it.

HC StratoMineR: A Web-Based Tool for the Rapid Analysis of High-Content Datasets.

PubMed

Omta, Wienand A; van Heesbeen, Roy G; Pagliero, Romina J; van der Velden, Lieke M; Lelieveld, Daphne; Nellen, Mehdi; Kramer, Maik; Yeong, Marley; Saeidi, Amir M; Medema, Rene H; Spruit, Marco; Brinkkemper, Sjaak; Klumperman, Judith; Egan, David A

2016-10-01

High-content screening (HCS) can generate large multidimensional datasets and when aligned with the appropriate data mining tools, it can yield valuable insights into the mechanism of action of bioactive molecules. However, easy-to-use data mining tools are not widely available, with the result that these datasets are frequently underutilized. Here, we present HC StratoMineR, a web-based tool for high-content data analysis. It is a decision-supportive platform that guides even non-expert users through a high-content data analysis workflow. HC StratoMineR is built by using My Structured Query Language for storage and querying, PHP: Hypertext Preprocessor as the main programming language, and jQuery for additional user interface functionality. R is used for statistical calculations, logic and data visualizations. Furthermore, C++ and graphical processor unit power is diffusely embedded in R by using the rcpp and rpud libraries for operations that are computationally highly intensive. We show that we can use HC StratoMineR for the analysis of multivariate data from a high-content siRNA knock-down screen and a small-molecule screen. It can be used to rapidly filter out undesirable data; to select relevant data; and to perform quality control, data reduction, data exploration, morphological hit picking, and data clustering. Our results demonstrate that HC StratoMineR can be used to functionally categorize HCS hits and, thus, provide valuable information for hit prioritization.

Effect of mine tailing on the spatial variability of soil nematodes from lead pollution in La Union (Spain).

PubMed

Rodríguez Martín, José Antonio; Gutiérrez, Carmen; Escuer, Miguel; García-González, Ma Teresa; Campos-Herrera, Raquel; Aguila, Nancy

2014-03-01

The Cartagena-La Union mining district, exploited since the end of the 3rd century BC, was one of the world's largest lead producers in the 19th century. Although activity ceased in 1991, today mining residues pose a huge pollution problem. This study characterises lead contents (total and DPTA) and other soil parameters (N, P, K, pH, SOM, CaCO3, granulometric fraction, etc.) using multivariate geostatistical methods in relation to nematode diversity. In this work, trophic groups and metabolic footprints of soil nematodes were measured using 193 samples from the mining, natural and agricultural areas in this district. We explored the relationship between soil health and nematode communities. High lead concentrations were quantified: mean 8,500 mg kg(-1) for total and 340 mg kg(-1) for DPTA in this mining area. Although nematode diversity was broad (81 taxa), their diversity, abundance and metabolic footprints significantly reduced in the mining area. Significant differences in the nematode community structure were observed, and the relative abundance of predators was sensitive to mine and agricultural activities, whilst omnivores reduced only in the agricultural area, and bacterial feeders exhibited a differential response to both anthropogenic disturbances. The total abundance of nematodes, trophic groups and c-p groups correlated negatively with soil Pb contents, and a positive relationship was found with SOM and N, P and K contents. Copyright © 2013 Elsevier B.V. All rights reserved.

Effects of coal mining, forestry, and road construction on southern Appalachian stream invertebrates and habitats.

PubMed

Gangloff, Michael M; Perkins, Michael; Blum, Peter W; Walker, Craig

2015-03-01

Coal has been extracted via surface and sub-surface mining for decades throughout the Appalachian Mountains. New interest in ridge-top mining has raised concerns about possible waterway impacts. We examined effects of forestry, mining, and road construction-based disturbance on physico-chemistry and macroinvertebrate communities in east-central Tennessee headwater streams. Although 11 of 30 sites failed Tennessee's biocriteria scoring system, invertebrate richness was moderately high and we did not find significant differences in any water chemistry or habitat parameters between sites with passing and failing scores. However, conductivity and dissolved solid concentrations appeared elevated in the majority of study streams. Principal components (PCs) analysis indicated that six PCs accounted for ~77 % of among-site habitat variability. One PC associated with dissolved oxygen and specific conductance explained the second highest proportion of among-site variability after catchment area. Specific conductance was not correlated with catchment area but was strongly correlated with mining activity. Composition and success of multivariate models using habitat PCs to predict macroinvertebrate metrics was highly variable. PC scores associated with water chemistry and substrate composition were most frequently included in significant models. These results suggest that impacts of historical and current coal mining remain a source of water quality and macroinvertebrate community impairment in this region, but effects are subtle. Our results suggest that surface mining may have chronic and system-wide effects on habitat conditions and invertebrate communities in Cumberland Plateau streams.

Resource potential for commodities in addition to Uranium in sandstone-hosted deposits: Chapter 13

USGS Publications Warehouse

Breit, George N.

2016-01-01

Sandstone-hosted deposits mined primarily for their uranium content also have been a source of vanadium and modest amounts of copper. Processing of these ores has also recovered small amounts of molybdenum, rhenium, rare earth elements, scandium, and selenium. These deposits share a generally common origin, but variations in the source of metals, composition of ore-forming solutions, and geologic history result in complex variability in deposit composition. This heterogeneity is evident regionally within the same host rock, as well as within districts. Future recovery of elements associated with uranium in these deposits will be strongly dependent on mining and ore-processing methods.

A Semi-automated Approach to Create Purposeful Mechanistic Datasets from Heterogeneous Data: Data Mining Towards the in silico Predictions for Oestrogen Receptor Modulation and Teratogenicity.

PubMed

Bashir Surfraz, M; Fowkes, Adrian; Plante, Jeffrey P

2017-08-01

The need to find an alternative to costly animal studies for developmental and reproductive toxicity testing has shifted the focus considerably to the assessment of in vitro developmental toxicology models and the exploitation of pharmacological data for relevant molecular initiating events. We hereby demonstrate how automation can be applied successfully to handle heterogeneous oestrogen receptor data from ChEMBL. Applying expert-derived thresholds to specific bioactivities allowed an activity call to be attributed to each data entry. Human intervention further improved this mechanistic dataset which was mined to develop structure-activity relationship alerts and an expert model covering 45 chemical classes for the prediction of oestrogen receptor modulation. The evaluation of the model using FDA EDKB and Tox21 data was quite encouraging. This model can also provide a teratogenicity prediction along with the additional information it provides relevant to the query compound, all of which will require careful assessment of potential risk by experts. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Methods for biological data integration: perspectives and challenges

PubMed Central

Gligorijević, Vladimir; Pržulj, Nataša

2015-01-01

Rapid technological advances have led to the production of different types of biological data and enabled construction of complex networks with various types of interactions between diverse biological entities. Standard network data analysis methods were shown to be limited in dealing with such heterogeneous networked data and consequently, new methods for integrative data analyses have been proposed. The integrative methods can collectively mine multiple types of biological data and produce more holistic, systems-level biological insights. We survey recent methods for collective mining (integration) of various types of networked biological data. We compare different state-of-the-art methods for data integration and highlight their advantages and disadvantages in addressing important biological problems. We identify the important computational challenges of these methods and provide a general guideline for which methods are suited for specific biological problems, or specific data types. Moreover, we propose that recent non-negative matrix factorization-based approaches may become the integration methodology of choice, as they are well suited and accurate in dealing with heterogeneous data and have many opportunities for further development. PMID:26490630

Confident Surgical Decision Making in Temporal Lobe Epilepsy by Heterogeneous Classifier Ensembles

PubMed Central

Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Jafari-Khouzani, Kourosh; Elisevich, Kost; Fotouhi, Farshad

2015-01-01

In medical domains with low tolerance for invalid predictions, classification confidence is highly important and traditional performance measures such as overall accuracy cannot provide adequate insight into classifications reliability. In this paper, a confident-prediction rate (CPR) which measures the upper limit of confident predictions has been proposed based on receiver operating characteristic (ROC) curves. It has been shown that heterogeneous ensemble of classifiers improves this measure. This ensemble approach has been applied to lateralization of focal epileptogenicity in temporal lobe epilepsy (TLE) and prediction of surgical outcomes. A goal of this study is to reduce extraoperative electrocorticography (eECoG) requirement which is the practice of using electrodes placed directly on the exposed surface of the brain. We have shown that such goal is achievable with application of data mining techniques. Furthermore, all TLE surgical operations do not result in complete relief from seizures and it is not always possible for human experts to identify such unsuccessful cases prior to surgery. This study demonstrates the capability of data mining techniques in prediction of undesirable outcome for a portion of such cases. PMID:26609547

Geochemistry and hydrology of perched groundwater springs: assessing elevated uranium concentrations at Pigeon Spring relative to nearby Pigeon Mine, Arizona (USA)

USGS Publications Warehouse

Beisner, Kimberly R.; Paretti, Nicholas; Tillman, Fred; Naftz, David L.; Bills, Donald; Walton-Day, Katie; Gallegos, Tanya J.

2017-01-01

The processes that affect water chemistry as the water flows from recharge areas through breccia-pipe uranium deposits in the Grand Canyon region of the southwestern United States are not well understood. Pigeon Spring had elevated uranium in 1982 (44 μg/L), compared to other perched springs (2.7–18 μg/L), prior to mining operations at the nearby Pigeon Mine. Perched groundwater springs in an area around the Pigeon Mine were sampled between 2009 and 2015 and compared with material from the Pigeon Mine to better understand the geochemistry and hydrology of the area. Two general groups of perched groundwater springs were identified from this study; one group is characterized by calcium sulfate type water, low uranium activity ratio 234U/238U (UAR) values, and a mixture of water with some component of modern water, and the other group by calcium-magnesium sulfate type water, higher UAR values, and radiocarbon ages indicating recharge on the order of several thousand years ago. Multivariate statistical principal components analysis of Pigeon Mine and spring samples indicate Cu, Pb, As, Mn, and Cd concentrations distinguished mining-related leachates from perched groundwater springs. The groundwater potentiometric surface indicates that perched groundwater at Pigeon Mine would likely flow toward the northwest away from Pigeon Spring. The geochemical analysis of the water, sediment and rock samples collected from the Snake Gulch area indicate that the elevated uranium at Pigeon Spring is likely related to a natural source of uranium upgradient from the spring and not likely related to the Pigeon Mine.

Mesothelioma in the Quebec chrysotile mining region

DOE Office of Scientific and Technical Information (OSTI.GOV)

Case, B.W.; Armstrong, B.; McDonald, J.C.

Previous studies of incidence of mesothelioma and lung tissue burden in workers and nonworkers in the Quebec chrysotile mining region showed that mesothelioma incidence is very slightly increased in the mining area, and that tremolite, or even commercial amphiboles, are responsible (and chrysotile is not). Recently, one of us (BC) noted an increase in the numbers of cases of mesothelioma coming to autopsy in the mining region. There were 19 cases, all confirmed histologically, since 1982, vs. 6 that we know of in the previous 10 y. Eighteen were occupationally exposed; one was the wife of chrysotile miner/miller. We examinedmore » lung tissue from 9 cases matched to controls of the same sex and age, dying in the same years in the same hospitals, without evidence of malignant disease. We found an excess in cases of typical asbestos bodies; tremolite fibers; and total amphiboles. In univariate linear relative risk analysis, both longer (>8 {mu}m) and shorter fibers are significant. Multivariate analysis indicates that while adjustment of chrysotile lung content for tremolite content eliminates any statistical effect of chrysotile, the reverse comparison retains significance.« less

Effects of soil and topographic factors on vegetation restoration in opencast coal mine dumps located in a loess area

NASA Astrophysics Data System (ADS)

Wang, Jinman; Wang, Hongdan; Cao, Yingui; Bai, Zhongke; Qin, Qian

2016-02-01

Vegetation plays an important role in improving and restoring fragile ecological environments. In the Antaibao opencast coal mine, located in a loess area, the eco-environment has been substantially disturbed by mining activities, and the relationship between the vegetation and environmental factors is not very clear. Therefore, it is crucial to understand the effects of soil and topographic factors on vegetation restoration to improve the fragile ecosystems of damaged land. An investigation of the soil, topography and vegetation in 50 reclamation sample plots in Shanxi Pingshuo Antaibao opencast coal mine dumps was performed. Statistical analyses in this study included one-way ANOVA and significance testing using SPSS 20.0, and multivariate techniques of detrended correspondence analysis (DCA) and redundancy analysis (RDA) using CANOCO 4.5. The RDA revealed the environmental factors that affected vegetation restoration. Various vegetation and soil variables were significantly correlated. The available K and rock content were good explanatory variables, and they were positively correlated with tree volume. The effects of the soil factors on vegetation restoration were higher than those of the topographic factors.

Effects of soil and topographic factors on vegetation restoration in opencast coal mine dumps located in a loess area

PubMed Central

Wang, Jinman; Wang, Hongdan; Cao, Yingui; Bai, Zhongke; Qin, Qian

2016-01-01

Vegetation plays an important role in improving and restoring fragile ecological environments. In the Antaibao opencast coal mine, located in a loess area, the eco-environment has been substantially disturbed by mining activities, and the relationship between the vegetation and environmental factors is not very clear. Therefore, it is crucial to understand the effects of soil and topographic factors on vegetation restoration to improve the fragile ecosystems of damaged land. An investigation of the soil, topography and vegetation in 50 reclamation sample plots in Shanxi Pingshuo Antaibao opencast coal mine dumps was performed. Statistical analyses in this study included one-way ANOVA and significance testing using SPSS 20.0, and multivariate techniques of detrended correspondence analysis (DCA) and redundancy analysis (RDA) using CANOCO 4.5. The RDA revealed the environmental factors that affected vegetation restoration. Various vegetation and soil variables were significantly correlated. The available K and rock content were good explanatory variables, and they were positively correlated with tree volume. The effects of the soil factors on vegetation restoration were higher than those of the topographic factors. PMID:26916152

Effects of soil and topographic factors on vegetation restoration in opencast coal mine dumps located in a loess area.

PubMed

Wang, Jinman; Wang, Hongdan; Cao, Yingui; Bai, Zhongke; Qin, Qian

2016-02-26

Vegetation plays an important role in improving and restoring fragile ecological environments. In the Antaibao opencast coal mine, located in a loess area, the eco-environment has been substantially disturbed by mining activities, and the relationship between the vegetation and environmental factors is not very clear. Therefore, it is crucial to understand the effects of soil and topographic factors on vegetation restoration to improve the fragile ecosystems of damaged land. An investigation of the soil, topography and vegetation in 50 reclamation sample plots in Shanxi Pingshuo Antaibao opencast coal mine dumps was performed. Statistical analyses in this study included one-way ANOVA and significance testing using SPSS 20.0, and multivariate techniques of detrended correspondence analysis (DCA) and redundancy analysis (RDA) using CANOCO 4.5. The RDA revealed the environmental factors that affected vegetation restoration. Various vegetation and soil variables were significantly correlated. The available K and rock content were good explanatory variables, and they were positively correlated with tree volume. The effects of the soil factors on vegetation restoration were higher than those of the topographic factors.

Mining biomedical images towards valuable information retrieval in biomedical and life sciences.

PubMed

Ahmed, Zeeshan; Zeeshan, Saman; Dandekar, Thomas

2016-01-01

Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries. © The Author(s) 2016. Published by Oxford University Press.

Multivariate study of trace element distribution in the geological record of Roñanzas Peat Bog (Asturias, N. Spain). Paleoenvironmental evolution and human activities over the last 8000 calyr BP.

PubMed

Gallego, José Luis R; Ortiz, José E; Sierra, Carlos; Torres, Trinidad; Llamas, J F

2013-06-01

Trace element concentrations in the Roñanzas peat bog record reveal a contribution of natural processes but the influence of anthropogenic factors predominates in the last two millenniums, particularly aerosol deposition linked to mining and industrial activities in northern Spain. We observed that the Roñanzas record can be considered a preserved environment, suitable to search for local (<50 km), regional (50-150 km) and/or long-distance human activity fingerprinting, specifically that related to the deposition of heavy metals such as Pb, Zn and Hg. We also carried out a multivariate statistical study in order to clarify the geochemical behavior of trace and major elements. Our study design represents a novel approach to assign natural vs. human contributions in peatlands. Therefore, synergies obtained by the simultaneous study of multivariate statistics and enrichment factors allow robust conclusions about paleoenvironmental evolution and human activities. Anthropogenic influence has also been reported in similar records in other parts of Europe, thereby suggesting large-scale sources for atmospheric pollution. However, here we revealed remarkable particularities, such as the association of Cd, Zn and Pb, mainly linked to regional and local factors (mining and more recently the metallurgical industry), whereas we propose that the occurrence of Hg is associated with a combination of regional factors and global atmospheric pollution. Copyright © 2013 Elsevier B.V. All rights reserved.

Appraising the Corporate Sustainability Reports - Text Mining and Multi-Discriminatory Analysis

NASA Astrophysics Data System (ADS)

Modapothala, J. R.; Issac, B.; Jayamani, E.

The voluntary disclosure of the sustainability reports by the companies attracts wider stakeholder groups. Diversity in these reports poses challenge to the users of information and regulators. This study appraises the corporate sustainability reports as per GRI (Global Reporting Initiative) guidelines (the most widely accepted and used) across all industrial sectors. Text mining is adopted to carry out the initial analysis with a large sample size of 2650 reports. Statistical analyses were performed for further investigation. The results indicate that the disclosures made by the companies differ across the industrial sectors. Multivariate Discriminant Analysis (MDA) shows that the environmental variable is a greater significant contributing factor towards explanation of sustainability report.

Automated information and control complex of hydro-gas endogenous mine processes

NASA Astrophysics Data System (ADS)

Davkaev, K. S.; Lyakhovets, M. V.; Gulevich, T. M.; Zolin, K. A.

2017-09-01

The automated information and control complex designed to prevent accidents, related to aerological situation in the underground workings, accounting of the received and handed over individual devices, transmission and display of measurement data, and the formation of preemptive solutions is considered. Examples for the automated workplace of an airgas control operator by individual means are given. The statistical characteristics of field data characterizing the aerological situation in the mine are obtained. The conducted studies of statistical characteristics confirm the feasibility of creating a subsystem of controlled gas distribution with an adaptive arrangement of points for gas control. The adaptive (multivariant) algorithm for processing measuring information of continuous multidimensional quantities and influencing factors has been developed.

Environmental exposure as an independent risk factor of chronic bronchitis in northwest Russia

PubMed Central

Nieminen, Pentti; Panychev, Dmitry; Lyalyushkin, Sergei; Komarov, German; Nikanov, Alexander; Borisenko, Mark; Kinnula, Vuokko L.; Toljamo, Tuula

2013-01-01

Background In some parts of the northwest Russia, Murmansk region, high exposures to heavy mining and refining industrial air pollution, especially sulphur dioxide, have been documented. Objective Our aim was to evaluate whether living in the mining area would be an independent risk factor of the respiratory symptoms. Design A cross-sectional survey of 200 Murmansk region adult citizens was performed. The main outcome variable was prolonged cough with sputum production that fulfilled the criteria of chronic bronchitis. Results Of the 200 participants, 53 (26.5%) stated that they had experienced chronic cough with phlegm during the last 2 years. The prevalence was higher among those subjects living in the mining area with its high pollution compared to those living outside this region (35% vs. 18%). Multivariable regression model confirmed that the risk for the chronic cough with sputum production was elevated in a statistical significant manner in the mining and refining area (adjusted OR 2.16, 95% CI 1.07–4.35) after adjustment for smoking status, age and sex. Conclusions The increased level of sulphur dioxide emitted during nickel mining and refining may explain these adverse health effects. This information is important for medical authorities when they make recommendations and issue guidelines regarding the relationship between environmental pollution and health outcomes. PMID:23440671

Surface water monitoring in the mercury mining district of Asturias (Spain).

PubMed

Loredo, Jorge; Petit-Domínguez, María Dolores; Ordóñez, Almudena; Galán, María Pilar; Fernández-Martínez, Rodolfo; Alvarez, Rodrigo; Rucandio, María Isabel

2010-04-15

Systematic monitoring of surface waters in the area of abandoned mine sites constitutes an essential step in the characterisation of pollution from historic mine sites. The analytical data collected throughout a hydrologic period can be used for hydrological modelling and also to select appropriate preventive and/or corrective measures in order to avoid pollution of watercourses. Caudal River drains the main abandoned Hg mine sites (located in Mieres and Pola de Lena districts) in Central Asturias (NW Spain). This paper describes a systematic monitoring of physical and chemical parameters in eighteen selected sampling points within the Caudal River catchment. At each sampling station, water flow, pH, specific conductance, dissolved oxygen, salinity, temperature, redox potential and turbidity were controlled "in situ" and major and trace elements were analysed in the laboratory. In the Hg-mineralised areas, As is present in the form of As-rich pyrite, realgar and occasionally arsenopyrite. Mine drainage and leachates from spoil heaps exhibit in some cases acidic conditions and high As contents, and they are incorporated to Caudal River tributaries. Multivariate statistical analysis aids to the interpretation of the spatial and temporary variations found in the sampled areas, as part of a methodology applicable to different environmental and geological studies. 2009 Elsevier B.V. All rights reserved.

Mass Casualty Incidents in the Underground Mining Industry: Applying the Haddon Matrix on an Integrative Literature Review.

PubMed

Engström, Karl Gunnar; Angrén, John; Björnstig, Ulf; Saveman, Britt-Inger

2018-02-01

Underground mining is associated with obvious risks that can lead to mass casualty incidents. Information about such incidents was analyzed in an integrated literature review. A literature search (1980-2015) identified 564 modern-era underground mining reports from countries sharing similar occupational health legislation. These reports were condensed to 31 reports after consideration of quality grading and appropriateness to the aim. The Haddon matrix was used for structure, separating human factors from technical and environmental details, and timing. Most of the reports were descriptive regarding injury-creating technical and environmental factors. The influence of rock characteristics was an important pre-event environmental factor. The organic nature of coal adds risks not shared in hard-rock mines. A sequence of mechanisms is commonly described, often initiated by a human factor in interaction with technology and step-wise escalation to involve environmental circumstances. Socioeconomic factors introduce heterogeneity. In the Haddon matrix, emergency medical services are mainly a post-event environmental issue, which were not well described in the available literature. The US Quecreek Coal Mine incident of 2002 stands out as a well-planned rescue mission. Evaluation of the preparedness to handle underground mining incidents deserves further scientific attention. Preparedness must include the medical aspects of rescue operations. (Disaster Med Public Health Preparedness. 2018;12:138-146).

Mine dewatering and impact assessment in an arid area: Case of Gulf region.

PubMed

Yihdego, Yohannes; Drury, Len

2016-11-01

Analytical and empirical solution coupled with water balance method were used to predict the ground water inflow to a mine pit excavated below the water table, final pit lake level/recovery and radius of influence, through long-term and time variant simulations. The solution considers the effect of decreased saturated thickness near the pit walls, distributed recharge to the water table and upward flow through the pit bottom. The approach is flexible to accommodate the anisotropy/heterogeneity of the real world. Final pit void water level was assessed through scenarios to know whether it will be consumed by evaporation and a shallow lake will form or not. The optimised radius of influence was estimated which is considered as crucial information in relation to the engineering aspects of mine planning and sustainable development of the mine area. Time-transient inflow over a period of time was estimated using solutions, including analytical element method (AEM). Their primary value is in providing estimates of pit inflow rates to be used in the mine dewatering. Inflow estimation and recovery helps whether there is water to supplement the demand and if there is any recovery issue to be dealt with in relation to surface and groundwater quality/eco-system, environmental evaluations and mitigation. Therefore, this method is good at informing decision makers in assessing the effects of mining operations and developing an appropriate water management strategy.

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Silicosis prevalence and risk factors in semi-precious stone mining in Brazil.

PubMed

Souza, Tamires P; Watte, Guilherme; Gusso, Alaíde M; Souza, Rafaela; Moreira, José da S; Knorst, Marli M

2017-06-01

Underground mining generates large amounts of dust and exposes workers to silica. This study aims to determine the prevalence and predictor factors for the development of silicosis among semi-precious-stone mineworkers in southern Brazil working in a self-administered cooperative. In a cross-sectional study of 348 current workers and retirees, demographic data, medical, and occupational history were collected through an interview performed by a nurse and medical record review. Risk factor associations were studied by Poisson multivariate regression. The overall prevalence of silicosis was 37%, while in current miners it was 28%. Several risk factors for silicosis were identified in the univariate analysis. Inadequate ventilation in the underground galleries combined with dry drilling, duration of silica exposure, and (inversely) education remained significant in the multivariate analysis (P < 0.05). This study is unusual in studying semi-precious stone mineworkers in a self-administered worker cooperative with limited resources. The prevalence of silicosis was very high. A number of recommendations are made-including technical support for worker cooperatives, surveillance of silica exposure and silicosis, exposure reduction measures, and benefits allowing impaired miners to leave the industry. © 2017 Wiley Periodicals, Inc.

Study of cyanotoxins presence from experimental cyanobacteria concentrations using a new data mining methodology based on multivariate adaptive regression splines in Trasona reservoir (Northern Spain).

PubMed

Garcia Nieto, P J; Sánchez Lasheras, F; de Cos Juez, F J; Alonso Fernández, J R

2011-11-15

There is an increasing need to describe cyanobacteria blooms since some cyanobacteria produce toxins, termed cyanotoxins. These latter can be toxic and dangerous to humans as well as other animals and life in general. It must be remarked that the cyanobacteria are reproduced explosively under certain conditions. This results in algae blooms, which can become harmful to other species if the cyanobacteria involved produce cyanotoxins. In this research work, the evolution of cyanotoxins in Trasona reservoir (Principality of Asturias, Northern Spain) was studied with success using the data mining methodology based on multivariate adaptive regression splines (MARS) technique. The results of the present study are two-fold. On one hand, the importance of the different kind of cyanobacteria over the presence of cyanotoxins in the reservoir is presented through the MARS model and on the other hand a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained. The agreement of the MARS model with experimental data confirmed the good performance of the same one. Finally, conclusions of this innovative research are exposed. Copyright © 2011 Elsevier B.V. All rights reserved.

Application-Specific Graph Sampling for Frequent Subgraph Mining and Community Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Purohit, Sumit; Choudhury, Sutanay; Holder, Lawrence B.

Graph mining is an important data analysis methodology, but struggles as the input graph size increases. The scalability and usability challenges posed by such large graphs make it imperative to sample the input graph and reduce its size. The critical challenge in sampling is to identify the appropriate algorithm to insure the resulting analysis does not suffer heavily from the data reduction. Predicting the expected performance degradation for a given graph and sampling algorithm is also useful. In this paper, we present different sampling approaches for graph mining applications such as Frequent Subgrpah Mining (FSM), and Community Detection (CD). Wemore » explore graph metrics such as PageRank, Triangles, and Diversity to sample a graph and conclude that for heterogeneous graphs Triangles and Diversity perform better than degree based metrics. We also present two new sampling variations for targeted graph mining applications. We present empirical results to show that knowledge of the target application, along with input graph properties can be used to select the best sampling algorithm. We also conclude that performance degradation is an abrupt, rather than gradual phenomena, as the sample size decreases. We present the empirical results to show that the performance degradation follows a logistic function.« less

PTEN Loss as Determined by Clinical-grade Immunohistochemistry Assay Is Associated with Worse Recurrence-free Survival in Prostate Cancer.

PubMed

Lotan, Tamara L; Wei, Wei; Morais, Carlos L; Hawley, Sarah T; Fazli, Ladan; Hurtado-Coll, Antonio; Troyer, Dean; McKenney, Jesse K; Simko, Jeffrey; Carroll, Peter R; Gleave, Martin; Lance, Raymond; Lin, Daniel W; Nelson, Peter S; Thompson, Ian M; True, Lawrence D; Feng, Ziding; Brooks, James D

2016-06-01

PTEN is the most commonly deleted tumor suppressor gene in primary prostate cancer (PCa) and its loss is associated with poor clinical outcomes and ERG gene rearrangement. We tested whether PTEN loss is associated with shorter recurrence-free survival (RFS) in surgically treated PCa patients with known ERG status. A genetically validated, automated PTEN immunohistochemistry (IHC) protocol was used for 1275 primary prostate tumors from the Canary Foundation retrospective PCa tissue microarray cohort to assess homogeneous (in all tumor tissue sampled) or heterogeneous (in a subset of tumor tissue sampled) PTEN loss. ERG status as determined by a genetically validated IHC assay was available for a subset of 938 tumors. Associations between PTEN and ERG status were assessed using Fisher's exact test. Kaplan-Meier and multivariate weighted Cox proportional models for RFS were constructed. When compared to intact PTEN, homogeneous (hazard ratio [HR] 1.66, p = 0.001) but not heterogeneous (HR 1.24, p = 0.14) PTEN loss was significantly associated with shorter RFS in multivariate models. Among ERG-positive tumors, homogeneous (HR 3.07, p < 0.0001) but not heterogeneous (HR 1.46, p = 0.10) PTEN loss was significantly associated with shorter RFS. Among ERG-negative tumors, PTEN did not reach significance for inclusion in the final multivariate models. The interaction term for PTEN and ERG status with respect to RFS did not reach statistical significance ( p = 0.11) for the current sample size. These data suggest that PTEN is a useful prognostic biomarker and that there is no statistically significant interaction between PTEN and ERG status for RFS. We found that loss of the PTEN tumor suppressor gene in prostate tumors as assessed by tissue staining is correlated with shorter time to prostate cancer recurrence after radical prostatectomy.

Ecosystem Health Assessment of Mining Cities Based on Landscape Pattern

NASA Astrophysics Data System (ADS)

Yu, W.; Liu, Y.; Lin, M.; Fang, F.; Xiao, R.

2017-09-01

Ecosystem health assessment (EHA) is one of the most important aspects in ecosystem management. Nowadays, ecological environment of mining cities is facing various problems. In this study, through ecosystem health theory and remote sensing images in 2005, 2009 and 2013, landscape pattern analysis and Vigor-Organization-Resilience (VOR) model were applied to set up an evaluation index system of ecosystem health of mining city to assess the healthy level of ecosystem in Panji District Huainan city. Results showed a temporal stable but high spatial heterogeneity landscape pattern during 2005-2013. According to the regional ecosystem health index, it experienced a rapid decline after a slight increase, and finally it maintained at an ordinary level. Among these areas, a significant distinction was presented in different towns. It indicates that the ecosystem health of Tianjijiedao town, the regional administrative centre, descended rapidly during the study period, and turned into the worst level in the study area. While the Hetuan Town, located in the northwestern suburb area of Panji District, stayed on a relatively better level than other towns. The impacts of coal mining collapse area, land reclamation on the landscape pattern and ecosystem health status of mining cities were also discussed. As a result of underground coal mining, land subsidence has become an inevitable problem in the study area. In addition, the coal mining subsidence area has brought about the destruction of the farmland, construction land and water bodies, which causing the change of the regional landscape pattern and making the evaluation of ecosystem health in mining area more difficult. Therefore, this study provided an ecosystem health approach for relevant departments to make scientific decisions.

Gold-rush in a forested El Dorado: deforestation leakages and the need for regional cooperation

NASA Astrophysics Data System (ADS)

Dezécache, Camille; Faure, Emmanuel; Gond, Valéry; Salles, Jean-Michel; Vieilledent, Ghislain; Hérault, Bruno

2017-03-01

Tropical forests of the Guiana Shield are the most affected by gold-mining in South America, experiencing an exponential increase in deforestation since the early 2000’s. Using yearly deforestation data encompassing Guyana, Suriname, French Guiana and the Brazilian State of Amapá, we demonstrated a strong relationship between deforestation due to gold-mining and gold-prices at the regional scale. In order to assess additional drivers of deforestation due to gold-mining, we focused on the national scale and highlighted the heterogeneity of the response to gold-prices under different political contexts. Deforestation due to gold-mining over the Guiana Shield occurs mainly in Guyana and Suriname. On the contrary, past and current repressive policies in Amapá and French Guiana likely contribute to the decorrelation of deforestation and gold prices. In this work, we finally present a case study focusing on French Guiana and Suriname, two neighbouring countries with very different levels of law enforcement against illegal gold-mining. We developed a modelling framework to estimate potential deforestation leakages from French Guiana to Suriname in the border areas. Based on our assumptions, we estimated a decrease in deforestation due to gold-mining of approx. 4300 hectares in French Guiana and an increase of approx. 12 100 hectares in Suriname in response to the active military repression of illegal gold-mining launched in French Guiana. Gold-mining in the Guiana Shield provides challenging questions regarding REDD+ implementation. These questions are discussed at the end of this study and are important to policy makers who need to provide sustainable alternative employment to local populations in order to ensure the effectiveness of environmental policies.

Anti-Müllerian hormone and risk of ovarian cancer in nine cohorts.

PubMed

Jung, Seungyoun; Allen, Naomi; Arslan, Alan A; Baglietto, Laura; Barricarte, Aurelio; Brinton, Louise A; Egleston, Brian L; Falk, Roni T; Fortner, Renée T; Helzlsouer, Kathy J; Gao, Yutang; Idahl, Annika; Kaaks, Rudolph; Krogh, Vittorio; Merritt, Melissa A; Lundin, Eva; Onland-Moret, N Charlotte; Rinaldi, Sabina; Schock, Helena; Shu, Xiao-Ou; Sluss, Patrick M; Staats, Paul N; Sacerdote, Carlotta; Travis, Ruth C; Tjønneland, Anne; Trichopoulou, Antonia; Tworoger, Shelley S; Visvanathan, Kala; Weiderpass, Elisabete; Zeleniuch-Jacquotte, Anne; Dorgan, Joanne F

2018-01-15

Animal and experimental data suggest that anti-Müllerian hormone (AMH) serves as a marker of ovarian reserve and inhibits the growth of ovarian tumors. However, few epidemiologic studies have examined the association between AMH and ovarian cancer risk. We conducted a nested case-control study of 302 ovarian cancer cases and 336 matched controls from nine cohorts. Prediagnostic blood samples of premenopausal women were assayed for AMH using a picoAMH enzyme-linked immunosorbent assay. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using multivariable-adjusted conditional logistic regression. AMH concentration was not associated with overall ovarian cancer risk. The multivariable-adjusted OR (95% CI), comparing the highest to the lowest quartile of AMH, was 0.99 (0.59-1.67) (P trend : 0.91). The association did not differ by age at blood draw or oral contraceptive use (all P heterogeneity : ≥0.26). There also was no evidence for heterogeneity of risk for tumors defined by histologic developmental pathway, stage, and grade, and by age at diagnosis and time between blood draw and diagnosis (all P heterogeneity : ≥0.39). In conclusion, this analysis of mostly late premenopausal women from nine cohorts does not support the hypothesized inverse association between prediagnostic circulating levels of AMH and risk of ovarian cancer. © 2017 UICC.

Unstable Malaria Transmission in the Southern Peruvian Amazon and Its Association with Gold Mining, Madre de Dios, 2001-2012.

PubMed

Sanchez, Juan F; Carnero, Andres M; Rivera, Esteban; Rosales, Luis A; Baldeviano, G Christian; Asencios, Jorge L; Edgel, Kimberly A; Vinetz, Joseph M; Lescano, Andres G

2017-02-08

The reemergence of malaria in the last decade in Madre de Dios, southern Peruvian Amazon basin, was accompanied by ecological, political, and socioeconomic changes related to the proliferation of illegal gold mining. We conducted a secondary analysis of passive malaria surveillance data reported by the health networks in Madre de Dios between 2001 and 2012. We calculated the number of cases of malaria by year, geographic location, intensity of illegal mining activities, and proximity of health facilities to the Peru-Brazil Interoceanic Highway. During 2001-2012, 203,773 febrile cases were identified in Madre de Dios, of which 30,811 (15.1%) were confirmed cases of malaria; all but 10 cases were due to Plasmodium vivax Cases of malaria rose rapidly between 2004 and 2007, reached 4,469 cases in 2005, and then declined after 2010 to pre-2004 levels. Health facilities located in areas of intense illegal gold mining reported 30-fold more cases than those in non-mining areas (ratio = 31.54, 95% confidence interval [CI] = 19.28, 51.60). Finally, health facilities located > 1 km from the Interoceanic Highway reported significantly more cases than health facilities within this distance (ratio = 16.20, 95% CI = 8.25, 31.80). Transmission of malaria in Madre de Dios is unstable, geographically heterogeneous, and strongly associated with illegal gold mining. These findings highlight the importance of spatially oriented interventions to control malaria in Madre de Dios, as well as the need for research on malaria transmission in illegal gold mining camps. © The American Society of Tropical Medicine and Hygiene.

Synchrotron-based X-Ray Spectroscopy Studies for Redox-based Remediation of Lead, Zinc, and Cadmium in Mine Waste Materials.

PubMed

Karna, Ranju R; Hettiarachchi, Ganga M; Newville, Matthew; Sun, ChengJun; Ma, Qing

2016-11-01

Several studies have examined the effect of submergence on the mobility of metals present in mine waste materials. This study examines the effect of organic carbon (OC) and sulfur (S) additions and submergence time on redox-induced biogeochemical transformations of lead (Pb), zinc (Zn), and cadmium (Cd) present in mine waste materials collected from the Tri-State mining district located in southeastern Kansas, southwestern Missouri, and northeastern Oklahoma. A completely randomized design, with a two-way treatment structure, was used for conducting a series of column experiments. Two replicates were used for each treatment combination. Effluent samples were collected at several time points, and soil samples were collected at the end of each column experiment. Because these samples are highly heterogeneous, we used a variety of synchrotron-based techniques to identify Pb, Zn, and Cd speciation at both micro- and bulk-scale. Spectroscopic analysis results from the study revealed that the addition of OC, with and without S, promoted metal-sulfide formation, whereas metal carbonates dominated in the nonamended flooded materials and in mine waste materials only amended with S. Therefore, the synergistic effect of OC and S may be more promising for managing mine waste materials disposed of in flooded subsidence mine pits instead of individual S or OC treatments. The mechanistic understanding gained in this study is also relevant for remediation of waste materials using natural or constructed wetland systems. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Assessment of risk to human health from simultaneous exposure to multiple contaminants in an artisanal gold mine in Serra Pelada, Pará, Brazil.

PubMed

de Souza, Edna Santos; Texeira, Renato Alves; da Costa, Hercília Samara Cardoso; Oliveira, Fábio Júnior; Melo, Leônidas Carrijo Azevedo; do Carmo Freitas Faial, Kelson; Fernandes, Antonio Rodrigues

2017-01-15

Contamination of soil, water and plants caused by gold mining is of great societal concern because of the risk of environmental pollution and risk to human health. The aim of the present study was to evaluate the risk to human health from ingestion of As, Ba, Co, Cu, Cd, Cr, Ni, Pb, Se and Ni present in soil, sterile and mineralized waste, and water and plants at a gold mine in Serra Pelada, Pará, Brazil. Samples of soil, sterile and mineralized waste, water and plants were collected around an artisanal gold mine located in Serra Pelada. The mean concentrations of potentially toxic elements in the soil were higher than the soil quality reference values as defined in the legislation, which may be attributeable to past mining activities. Water from the area close to the mine exhibited As, Ba and Pb concentrations exceeding the reference values established by the World Health Organization, deemed unfit for human consumption. Plants exhibited high Pb concentrations, representing a food safety risk to the population. The mean hazard index (HI) values were below the acceptable limit (1.0) established by the United States Environmental Protection Agency, although the highest HI values observed for adults and children were higher than the respective acceptable limits. Environmental contamination and risk to human health were heterogeneous in the surroundings of the mine. Mitigation strategies need to be adopted to decrease the risks of contamination to the environment and to the local population. Copyright © 2016 Elsevier B.V. All rights reserved.

Unstable Malaria Transmission in the Southern Peruvian Amazon and Its Association with Gold Mining, Madre de Dios, 2001–2012

PubMed Central

Sanchez, Juan F.; Carnero, Andres M.; Rivera, Esteban; Rosales, Luis A.; Baldeviano, G. Christian; Asencios, Jorge L.; Edgel, Kimberly A.; Vinetz, Joseph M.; Lescano, Andres G.

2017-01-01

The reemergence of malaria in the last decade in Madre de Dios, southern Peruvian Amazon basin, was accompanied by ecological, political, and socioeconomic changes related to the proliferation of illegal gold mining. We conducted a secondary analysis of passive malaria surveillance data reported by the health networks in Madre de Dios between 2001 and 2012. We calculated the number of cases of malaria by year, geographic location, intensity of illegal mining activities, and proximity of health facilities to the Peru–Brazil Interoceanic Highway. During 2001–2012, 203,773 febrile cases were identified in Madre de Dios, of which 30,811 (15.1%) were confirmed cases of malaria; all but 10 cases were due to Plasmodium vivax. Cases of malaria rose rapidly between 2004 and 2007, reached 4,469 cases in 2005, and then declined after 2010 to pre-2004 levels. Health facilities located in areas of intense illegal gold mining reported 30-fold more cases than those in non-mining areas (ratio = 31.54, 95% confidence interval [CI] = 19.28, 51.60). Finally, health facilities located > 1 km from the Interoceanic Highway reported significantly more cases than health facilities within this distance (ratio = 16.20, 95% CI = 8.25, 31.80). Transmission of malaria in Madre de Dios is unstable, geographically heterogeneous, and strongly associated with illegal gold mining. These findings highlight the importance of spatially oriented interventions to control malaria in Madre de Dios, as well as the need for research on malaria transmission in illegal gold mining camps. PMID:27879461

Analytical Fingerprint of Wolframite Ore Concentrates.

PubMed

Gäbler, Hans-Eike; Schink, Wilhelm; Goldmann, Simon; Bahr, Andreas; Gawronski, Timo

2017-07-01

Ongoing violent conflicts in Central Africa are fueled by illegal mining and trading of tantalum, tin, and tungsten ores. The credibility of document-based traceability systems can be improved by an analytical fingerprint applied as an independent method to confirm or doubt the documented origin of ore minerals. Wolframite (Fe,Mn)WO 4 is the most important ore mineral for tungsten and is subject to artisanal mining in Central Africa. Element concentrations of wolframite grains analyzed by laser ablation-inductively coupled plasma-mass spectrometry are used to establish the analytical fingerprint. The data from ore concentrate samples are multivariate, not normal or log-normal distributed. The samples cannot be regarded as representative aliquots of a population. Based on the Kolmogorov-Smirnov distance, a measure of similarity between a sample in question and reference samples from a database is determined. A decision criterion is deduced to recognize samples which do not originate from the declared mine site. © 2017 American Academy of Forensic Sciences.

Filling the gap between biology and computer science

PubMed Central

Aguilar-Ruiz, Jesús S; Moore, Jason H; Ritchie, Marylyn D

2008-01-01

This editorial introduces BioData Mining, a new journal which publishes research articles related to advances in computational methods and techniques for the extraction of useful knowledge from heterogeneous biological data. We outline the aims and scope of the journal, introduce the publishing model and describe the open peer review policy, which fosters interaction within the research community. PMID:18822148

A Comparison of Traditional, Step-Path, and Geostatistical Techniques in the Stability Analysis of a Large Open Pit

NASA Astrophysics Data System (ADS)

Mayer, J. M.; Stead, D.

2017-04-01

With the increased drive towards deeper and more complex mine designs, geotechnical engineers are often forced to reconsider traditional deterministic design techniques in favour of probabilistic methods. These alternative techniques allow for the direct quantification of uncertainties within a risk and/or decision analysis framework. However, conventional probabilistic practices typically discretize geological materials into discrete, homogeneous domains, with attributes defined by spatially constant random variables, despite the fact that geological media display inherent heterogeneous spatial characteristics. This research directly simulates this phenomenon using a geostatistical approach, known as sequential Gaussian simulation. The method utilizes the variogram which imposes a degree of controlled spatial heterogeneity on the system. Simulations are constrained using data from the Ok Tedi mine site in Papua New Guinea and designed to randomly vary the geological strength index and uniaxial compressive strength using Monte Carlo techniques. Results suggest that conventional probabilistic techniques have a fundamental limitation compared to geostatistical approaches, as they fail to account for the spatial dependencies inherent to geotechnical datasets. This can result in erroneous model predictions, which are overly conservative when compared to the geostatistical results.

Lithium, Vanadium and Chromium Uptake Ability of Brassica juncea from Lithium Mine Tailings.

PubMed

Elektorowicz, M; Keropian, Z

2015-01-01

The potential for phytoremediation and phytostabilization of lithium in lieu with vanadium and chromium on a formulated acidic heterogeneous growth media engineered around lithium mine tailings, was investigated in four phases: (1) overall efficiency of the removal of the three metals, (2) bioaccumulation ratios of the three metals, (3) overall relative growth rate, and (4) translocation index of the three metals in the physiology of the hyperaccumulator plant. A pot study was conducted to assess the suitability of Brassica juncea (Indian mustard) in a phytoremediation process whereby it was lingered for eighty-six days under homogeneous growth conditions and irrigated bidaily with organic fertilizer amended with LiCl. A post harvest data analysis was achieved through ashing and the implementation of cold digestion procedure in a concentrated hydrochloric acidic matrix. In physiological efficiency parameters, the hyperaccumulator plant was twice as able to phytostabilize chromium and four times was able to phytostabilize vanadium in comparison to lithium. Moreover, it was extremely efficient in translocating and accumulating lithium inside its upper physiological sites, more so than chromium and vanadium, thereby demonstrating Indian mustard, as a hyperaccumulator plant, for phytoextraction and phytostabilization in an acidic heterogeneous rhizosphere, with an extremely low relative growth rate.

[Rapid ecological assessment of tropical fish communities in a gold mine area of Costa Rica].

PubMed

Espinoza Mendiola, Mario

2008-12-01

Gold mining impacts have generated a great concern regarding aquatic systems and habitat fragmentation. Anthropogenic disturbances on the structure and heterogeneity of a system can have an important effect on aquatic community stability. Ecological rapid assessments (1996, 2002, and 2007) were employed to determine the structure, composition and distribution of tropical fish communities in several rivers and smaller creeks from a gold mining area in Cerro Crucitas, Costa Rica. In addition, species composition and relative abundance were related with habitat structure. A total of 35 species were registered, among which sardine Astyanax aeneus (Characidae) and livebearer Alfaro cultratus (Poeciliidae) were the most abundant fish (71%). The highest species richness was observed in Caño Crucitas (s=19) and Minas Creek (s=18). Significant differences in fish communities structure and composition from Infiernillo river and Minas creek were observed (lamda = 0.0, F(132, 66) = 2.24, p < 0.001). Presence and/or absence of certain species such as Dormitor gobiomorus, Rhamdia nicaraguensis, Parachromis loiseillei and Atractosteus tropicus explained most of the spatial variation among sites. Habitat structure also contributed to explain differences among sites (lamda = 0.004, F(60.183) = 5.52, p < 0.001). Substratum (soft and hard bottom types) and habitat attributes (elevation, width and depth) explained most of the variability observed in Infiernillo River, Caño Crucitas and Tamagá Creek. In addition, a significant association between fish species and habitat structure was observed. This study reveals a high complexity in tropical fish communities that inhabit a gold mine area. Furthermore, it highlights the importance of habitat heterogeneity in fish community dynamics. The loss and degradation of aquatic systems in Cerro Crucitas can have a strong negative effect on fish community structure and composition of local species. A better understanding of the use of specific habitats that serve as essential fish habitats can improve tropical fish conservation and management strategies, thus increasing local diversity, and thereby, the biological importance of the area.

Gender Differences in Conceptualizations of STEM Career Interest: Complementary Perspectives from Data Mining, Multivariate Data Analysis and Multidimensional Scaling

ERIC Educational Resources Information Center

Knezek, Gerald; Christensen, Rhonda; Tyler-Wood, Tandra; Gibson, David

2015-01-01

Data gathered from 325 middle school students in four U.S. states indicate that both male (p < 0.0005, RSQ = 0.33) and female (p < 0.0005, RSQ = 0.36) career aspirations for "being a scientist" are predictable based on knowledge of dispositions toward mathematics, science and engineering, plus self-reported creative tendencies. For…

Geological modeling of submeter scale heterogeneity and its influence on tracer transport in a fluvial aquifer

NASA Astrophysics Data System (ADS)

Ronayne, Michael J.; Gorelick, Steven M.; Zheng, Chunmiao

2010-10-01

We developed a new model of aquifer heterogeneity to analyze data from a single-well injection-withdrawal tracer test conducted at the Macrodispersion Experiment (MADE) site on the Columbus Air Force Base in Mississippi (USA). The physical heterogeneity model is a hybrid that combines 3-D lithofacies to represent submeter scale, highly connected channels within a background matrix based on a correlated multivariate Gaussian hydraulic conductivity field. The modeled aquifer architecture is informed by a variety of field data, including geologic core sampling. Geostatistical properties of this hybrid heterogeneity model are consistent with the statistics of the hydraulic conductivity data set based on extensive borehole flowmeter testing at the MADE site. The representation of detailed, small-scale geologic heterogeneity allows for explicit simulation of local preferential flow and slow advection, processes that explain the complex tracer response from the injection-withdrawal test. Based on the new heterogeneity model, advective-dispersive transport reproduces key characteristics of the observed tracer recovery curve, including a delayed concentration peak and a low-concentration tail. Importantly, our results suggest that intrafacies heterogeneity is responsible for local-scale mass transfer.

Ontology-based meta-analysis of global collections of high-throughput public data.

PubMed

Kupershmidt, Ilya; Su, Qiaojuan Jane; Grewal, Anoop; Sundaresh, Suman; Halperin, Inbal; Flynn, James; Shekar, Mamatha; Wang, Helen; Park, Jenny; Cui, Wenwu; Wall, Gregory D; Wisotzkey, Robert; Alag, Satnam; Akhtari, Saeid; Ronaghi, Mostafa

2010-09-29

The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today. We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets. Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.

Probabilistic, meso-scale flood loss modelling

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Botto, Anna; Schröter, Kai; Merz, Bruno

2016-04-01

Flood risk analyses are an important basis for decisions on flood risk management and adaptation. However, such analyses are associated with significant uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention during the last years, they are still not standard practice for flood risk assessments and even more for flood loss modelling. State of the art in flood loss modelling is still the use of simple, deterministic approaches like stage-damage functions. Novel probabilistic, multi-variate flood loss models have been developed and validated on the micro-scale using a data-mining approach, namely bagging decision trees (Merz et al. 2013). In this presentation we demonstrate and evaluate the upscaling of the approach to the meso-scale, namely on the basis of land-use units. The model is applied in 19 municipalities which were affected during the 2002 flood by the River Mulde in Saxony, Germany (Botto et al. submitted). The application of bagging decision tree based loss models provide a probability distribution of estimated loss per municipality. Validation is undertaken on the one hand via a comparison with eight deterministic loss models including stage-damage functions as well as multi-variate models. On the other hand the results are compared with official loss data provided by the Saxon Relief Bank (SAB). The results show, that uncertainties of loss estimation remain high. Thus, the significant advantage of this probabilistic flood loss estimation approach is that it inherently provides quantitative information about the uncertainty of the prediction. References: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64. Botto A, Kreibich H, Merz B, Schröter K (submitted) Probabilistic, multi-variable flood loss modelling on the meso-scale with BT-FLEMO. Risk Analysis.

Mean Comparison: Manifest Variable versus Latent Variable

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Bentler, Peter M.

2006-01-01

An extension of multiple correspondence analysis is proposed that takes into account cluster-level heterogeneity in respondents' preferences/choices. The method involves combining multiple correspondence analysis and k-means in a unified framework. The former is used for uncovering a low-dimensional space of multivariate categorical variables…

A link prediction method for heterogeneous networks based on BP neural network

NASA Astrophysics Data System (ADS)

Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu

2018-04-01

Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

Initial sediment transport model of the mining-affected Aries River Basin, Romania

USGS Publications Warehouse

Friedel, Michael J.; Linard, Joshua I.

2008-01-01

The Romanian government is interested in understanding the effects of existing and future mining activities on long-term dispersal, storage, and remobilization of sediment-associated metals. An initial Soil and Water Assessment Tool (SWAT) model was prepared using available data to evaluate hypothetical failure of the Valea Sesei tailings dam at the Rosia Poieni mine in the Aries River basin. Using the available data, the initial Aries River Basin SWAT model could not be manually calibrated to accurately reproduce monthly streamflow values observed at the Turda gage station. The poor simulation of the monthly streamflow is attributed to spatially limited soil and precipitation data, limited constraint information due to spatially and temporally limited streamflow measurements, and in ability to obtain optimal parameter values when using a manual calibration process. Suggestions to improve the Aries River basin sediment transport model include accounting for heterogeneity in model input, a two-tier nonlinear calibration strategy, and analysis of uncertainty in predictions.

PLAN2L: a web tool for integrated text mining and literature-based bioentity relation extraction.

PubMed

Krallinger, Martin; Rodriguez-Penagos, Carlos; Tendulkar, Ashish; Valencia, Alfonso

2009-07-01

There is an increasing interest in using literature mining techniques to complement information extracted from annotation databases or generated by bioinformatics applications. Here we present PLAN2L, a web-based online search system that integrates text mining and information extraction techniques to access systematically information useful for analyzing genetic, cellular and molecular aspects of the plant model organism Arabidopsis thaliana. Our system facilitates a more efficient retrieval of information relevant to heterogeneous biological topics, from implications in biological relationships at the level of protein interactions and gene regulation, to sub-cellular locations of gene products and associations to cellular and developmental processes, i.e. cell cycle, flowering, root, leaf and seed development. Beyond single entities, also predefined pairs of entities can be provided as queries for which literature-derived relations together with textual evidences are returned. PLAN2L does not require registration and is freely accessible at http://zope.bioinfo.cnio.es/plan2l.

On Design Mining: Coevolution and Surrogate Models.

PubMed

Preen, Richard J; Bull, Larry

2017-01-01

Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation. In this article, we focus upon the coevolutionary nature of the design process when it is decomposed into concurrent sub-design-threads due to the overall complexity of the task. Using an abstract, tunable model of coevolution, we consider strategies to sample subthread designs for whole-system testing and how best to construct and use surrogate models within the coevolutionary scenario. Drawing on our findings, we then describe the effective design of an array of six heterogeneous vertical-axis wind turbines.

Quantagenetics® analysis of laser-induced breakdown spectroscopic data: Rapid and accurate authentication of materials

NASA Astrophysics Data System (ADS)

McManus, Catherine E.; Dowe, James; McMillan, Nancy J.

2018-07-01

Many industrial and commercial issues involve authentication of such matters as the manufacturer or geographic source of a material, and quality control of materials, determining whether specific treatments have been properly applied, or if a material is authentic or fraudulent. Often, multiple analytical techniques and tests are used, resulting in expensive and time-consuming testing procedures. Laser-Induced Breakdown Spectroscopy (LIBS) is a rapid laser ablation spectroscopic analytical method. Each LIBS spectrum contains information about the concentration of every element, some isotopic ratios, and the molecular structure of the material, making it a unique and comprehensive signature of the material. Quantagenetics® is a multivariate statistical method based on Bayesian statistics that uses the Euclidian distance between LIBS spectra of materials to classify materials (US Patents 9,063,085 and 8,699,022). The fundamental idea behind Quantagenetics® is that LIBS spectra contain sufficient information to determine the origin and history of materials. This study presents two case studies that illustrate the method. LIBS spectra from 510 Colombian emeralds from 18 mines were classified by mine. Overall, 99.4% of the spectra were correctly classified; the success rate for individual mines ranges from 98.2% to 100%. Some of the mines are separated by distances as little as 200 m, indicating that the method uses the slight but consistent differences in composition to identify the mine of origin accurately. The second study used bars of 17-4 stainless steel from three manufacturers. Each of the three bars was cut into 90 coupons; 30 of each bar received no further treatment, another 30 from each bar received one tempering and hardening treatment, and the final 30 coupons from each bar received a different heat treatment. Using LIBS spectra taken from the coupons, the Quantagenetics® method classified the 270 coupons both by manufacturer (composition) and heat treatment (structure) with an overall success rate of 95.3%. Individual success rates range from 92.4% to 97.6%. These case studies were successful despite having no preconceived knowledge of the materials; artificial intelligence allows the materials to classify themselves without human intervention or bias. Multivariate analysis of LIBS spectra using the Quantagenetics® method has promise to improve quality control and authentication of a wide variety of materials in industrial enterprises.

Simultaneous Two-Way Clustering of Multiple Correspondence Analysis

ERIC Educational Resources Information Center

Hwang, Heungsun; Dillon, William R.

2010-01-01

A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which "k"-means is…

Spatial variability and temporal changes in the trace metal content of soils: implications for mine restoration plan.

PubMed

Chandra, Rachna; Prusty, B Anjan Kumar; Azeez, P A

2014-06-01

Trace metals in soils may be inherited from the parent materials or added to the system due to anthropogenic activities. In proposed mining areas, trace metals become an integral part of the soil system. Usually, researchers undertake experiments on plant species selection (for the restoration plan) only after the termination of mining activities, i.e. without any pre-mining information about the soil-plant interactions. Though not shown in studies, it is clear that several recovery plans remain unsuccessful while carrying out restoration experiments. Therefore, we hypothesize that to restore the area effectively, it is imperative to consider the pre-mining scenario of metal levels in parent material as well as the vegetation ecology of the region. With these specifics, we examined the concentrations of trace metals in parent soils at three proposed bauxite locations in the Eastern Ghats, India, and compared them at a spatio-temporal scale. Vegetation quantification and other basic soil parameters accounted for establishing the connection between soil and plants. The study recorded significant spatial heterogeneity in trace metal concentrations and the role of vegetation on metal availability. Oxidation reduction potential (ORP), pH and cation exchange capacity (CEC) directly influenced metal content, and Cu and Ni were lithogenic in origin. It implies that for effective restoration plant species varies for each geological location.

Geological disaster survey based on Curvelet transform with borehole Ground Penetrating Radar in Tonglushan old mine site.

PubMed

Tang, Xinjian; Sun, Tao; Tang, Zhijie; Zhou, Zenghui; Wei, Baoming

2011-06-01

Tonglushan old mine site located in Huangshi City, China, is very famous in the world. However, some of the ruins had suffered from geological disasters such as local deformation, surface cracking, in recent years. Structural abnormalities of rock-mass in deep underground were surveyed with borehole ground penetrating radar (GPR) to find out whether there were any mined galleries or mined-out areas below the ruins. With both the multiresolution analysis and sub-band directional of Curvelet transform, the feature information of targets' GPR signals were studied on Curvelet transform domain. Heterogeneity of geotechnical media and clutter jamming of complicated background of GPR signals could be conquered well, and the singularity characteristic information of typical rock mass signals could be extracted. Random noise had be removed by thresholding combined with Curvelet and the statistical characteristics of wanted signals and the noise, then direct wave suppression and the spatial distribution feature extraction could obtain a better result by making use of Curvelet transform directional. GprMax numerical modeling and analyzing of the sample data have verified the feasibility and effectiveness of our method. It is important and applicable for the analyzing of the geological structure and the disaster development about the Tonglushan old mine site. Copyright © 2011 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Sparse and Large-Scale Learning Models and Algorithms for Mining Heterogeneous Big Data

ERIC Educational Resources Information Center

Cai, Xiao

2013-01-01

With the development of PC, internet as well as mobile devices, we are facing a data exploding era. On one hand, more and more features can be collected to describe the data, making the size of the data descriptor larger and larger. On the other hand, the number of data itself explodes and can be collected from multiple resources. When the data…

Spatio-temporal patterns and source apportionment of pollution in Qiantang River (China) using neural-based modeling and multivariate statistical techniques

NASA Astrophysics Data System (ADS)

Su, Shiliang; Zhi, Junjun; Lou, Liping; Huang, Fang; Chen, Xia; Wu, Jiaping

Characterizing the spatio-temporal patterns and apportioning the pollution sources of water bodies are important for the management and protection of water resources. The main objective of this study is to describe the dynamics of water quality and provide references for improving river pollution control practices. Comprehensive application of neural-based modeling and different multivariate methods was used to evaluate the spatio-temporal patterns and source apportionment of pollution in Qiantang River, China. Measurement data were obtained and pretreated for 13 variables from 41 monitoring sites for the period of 2001-2004. A self-organizing map classified the 41 monitoring sites into three groups (Group A, B and C), representing different pollution characteristics. Four significant parameters (dissolved oxygen, biochemical oxygen demand, total phosphorus and total lead) were identified by discriminant analysis for distinguishing variations of different years, with about 80% correct assignment for temporal variation. Rotated principal component analysis (PCA) identified four potential pollution sources for Group A (domestic sewage and agricultural pollution, industrial wastewater pollution, mineral weathering, vehicle exhaust and sand mining), five for Group B (heavy metal pollution, agricultural runoff, vehicle exhaust and sand mining, mineral weathering, chemical plants discharge) and another five for Group C (vehicle exhaust and sand mining, chemical plants discharge, soil weathering, biochemical pollution, mineral weathering). The identified potential pollution sources explained 75.6% of the total variances for Group A, 75.0% for Group B and 80.0% for Group C, respectively. Receptor-based source apportionment was applied to further estimate source contributions for each pollution variable in the three groups, which facilitated and supported the PCA results. These results could assist managers to develop optimal strategies and determine priorities for river pollution control and effective water resources management.

Depth Discrimination Using Rg-to-Sg Spectral Amplitude Ratios for Seismic Events in Utah Recorded at Local Distances

DOE PAGES

Tibi, Rigobert; Koper, Keith D.; Pankow, Kristine L.; ...

2018-03-20

Most of the commonly used seismic discrimination approaches are designed for regional data. Relatively little attention has focused on discriminants for local distances (< 200 km), the range at which the smallest events are recorded. We take advantage of the variety of seismic sources and the existence of a dense regional seismic network in the Utah region to evaluate amplitude ratio seismic discrimination at local distances. First, we explored phase-amplitude Pg-to-Sg ratios for multiple frequency bands to classify events in a dataset that comprises populations of single-shot surface explosions, shallow and deep ripple-fired mining blasts, mining-induced events, and tectonic earthquakes.more » We achieved a limited success. Then, for the same dataset, we combined the Pg-to-Sg phase-amplitude ratios with Sg-to-Rg spectral amplitude ratios in a multivariate quadratic discriminant function (QDF) approach. For two-category, pairwise classification, seven out of ten population pairs show misclassification rates of about 20% or less, with five pairs showing rates of about 10% or less. The approach performs best for the pair involving the populations of single-shot explosions and mining-induced events. By combining both Pg-to-Sg and Rg-to-Sg ratios in the multivariate QDFs, we are able to achieve an average improvement of about 4–14% in misclassification rates compared to Pg-to-Sg ratios alone. When all five event populations are considered simultaneously, as expected, the potential for misclassification increases and our QDF approach using both Pg-to-Sg and Rg-to-Sg ratios achieves an average success rate of about 74%, compared to the rate of about 86% for two-category, pairwise classification.« less

Depth Discrimination Using Rg-to-Sg Spectral Amplitude Ratios for Seismic Events in Utah Recorded at Local Distances

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tibi, Rigobert; Koper, Keith D.; Pankow, Kristine L.

Most of the commonly used seismic discrimination approaches are designed for regional data. Relatively little attention has focused on discriminants for local distances (< 200 km), the range at which the smallest events are recorded. We take advantage of the variety of seismic sources and the existence of a dense regional seismic network in the Utah region to evaluate amplitude ratio seismic discrimination at local distances. First, we explored phase-amplitude Pg-to-Sg ratios for multiple frequency bands to classify events in a dataset that comprises populations of single-shot surface explosions, shallow and deep ripple-fired mining blasts, mining-induced events, and tectonic earthquakes.more » We achieved a limited success. Then, for the same dataset, we combined the Pg-to-Sg phase-amplitude ratios with Sg-to-Rg spectral amplitude ratios in a multivariate quadratic discriminant function (QDF) approach. For two-category, pairwise classification, seven out of ten population pairs show misclassification rates of about 20% or less, with five pairs showing rates of about 10% or less. The approach performs best for the pair involving the populations of single-shot explosions and mining-induced events. By combining both Pg-to-Sg and Rg-to-Sg ratios in the multivariate QDFs, we are able to achieve an average improvement of about 4–14% in misclassification rates compared to Pg-to-Sg ratios alone. When all five event populations are considered simultaneously, as expected, the potential for misclassification increases and our QDF approach using both Pg-to-Sg and Rg-to-Sg ratios achieves an average success rate of about 74%, compared to the rate of about 86% for two-category, pairwise classification.« less

Characterization, distribution, and risk assessment of heavy metals in agricultural soil and products around mining and smelting areas of Hezhang, China.

PubMed

Briki, Meryem; Ji, Hongbing; Li, Cai; Ding, Huaijian; Gao, Yang

2015-12-01

Mining and smelting have been releasing huge amount of toxic substances into the environment. In the present study, agricultural soil and different agricultural products (potato, Chinese cabbage, garlic bolt, corn) were analyzed to examine the source, spatial distribution, and risk of 12 elements (As, Be, Bi, Cd, Co, Cr, Cu, Hg, Ni, Pb, Sb, and Zn) in agricultural soil near mine fields, smelting fields, and mountain field around Hezhang County, west of Guizhou Province, China. Multivariate statistical analysis indicated that in mining area, As, Bi, Cd, Cu, Hg, Pb, Sb, and Zn were generated from anthropogenic sources; in smelting area, As, Be, Cd, Co, Cu, Pb, Sb, and Zn were derived from anthropogenic sources through zinc smelting ceased in 2004. The enrichment factors (EFs) and ecological risk index (RI) of soil in mining area are the most harmful, showing extremely high enrichment and very high ecological risk of As, Bi, Cd, Cu, Hg, Pb, Sb, and Zn. Zinc is the most significant enriched in the smelting area; however, mountain area has a moderate enrichment and ecological risk and do not present any ecological risk. According to spatial distribution, the concentrations depend on the nearby mining and smelting activities. Transfer factors (TFs) in the smelting area and mountain are high, implying a threat for human consumption. Therefore, further studies should be carried out taking into account the harm of those heavy metals and potential negative health effects from the consumption of agricultural products in these circumstances.

A multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration.

PubMed

Goovaerts, P; Albuquerque, Teresa; Antunes, Margarida

2016-11-01

This paper describes a multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration, with an application to an abandoned sedimentary gold mining region in Portugal. The main challenge was the existence of only a dozen gold measurements confined to the grounds of the old gold mines, which precluded the application of traditional interpolation techniques, such as cokriging. The analysis could, however, capitalize on 376 stream sediment samples that were analyzed for twenty two elements. Gold (Au) was first predicted at all 376 locations using linear regression (R 2 =0.798) and four metals (Fe, As, Sn and W), which are known to be mostly associated with the local gold's paragenesis. One hundred realizations of the spatial distribution of gold content were generated using sequential indicator simulation and a soft indicator coding of regression estimates, to supplement the hard indicator coding of gold measurements. Each simulated map then underwent a local cluster analysis to identify significant aggregates of low or high values. The one hundred classified maps were processed to derive the most likely classification of each simulated node and the associated probability of occurrence. Examining the distribution of the hot-spots and cold-spots reveals a clear enrichment in Au along the Erges River downstream from the old sedimentary mineralization.

Isotopically constrained lead sources in fugitive dust from unsurfaced roads in the southeast Missouri mining district.

PubMed

Witt, Emitt C; Pribil, Michael J; Hogan, John P; Wronkiewicz, David J

2016-09-01

The isotopic composition of lead (Pb) in fugitive dust suspended by a vehicle from 13 unsurfaced roads in Missouri was measured to identify the source of Pb within an established long-term mining area. A three end-member model using (207)Pb/(206)Pb and concentration as tracers resulted in fugitive dust samples plotting in the mixing field of well characterized heterogeneous end members. End members selected for this investigation include the (207)Pb/(206)Pb for 1) a Pb-mixture representing mine tailings, 2) aerosol Pb-impacted soils within close proximity to the Buick secondary recycling smelter, and 3) an average of soils, rock cores and drill cuttings representing the background conditions. Aqua regia total concentrations and (207)Pb/(206)Pb of mining area dust suggest that 35.4-84.3% of the source Pb in dust is associated with the mine tailings mixture, 9.1-52.7% is associated with the smelter mixture, and 0-21.6% is associated with background materials. Isotope ratios varied minimally within the operational phases of sequential extraction suggesting that mixing of all three Pb mixtures occurs throughout. Labile forms of Pb were attributed to all three end members. The extractable carbonate phase had as much as 96.6% of the total concentration associated with mine tailings, 51.8% associated with smelter deposition, and 34.2% with background. The next most labile geochemical phase (Fe + Mn Oxides) showed similar results with as much as 85.3% associated with mine tailings, 56.8% associated with smelter deposition, and 4.2% associated with the background soil. Published by Elsevier Ltd.

Isotopically constrained lead sources in fugitive dust from unsurfaced roads in the southeast Missouri mining district

USGS Publications Warehouse

Witt, Emitt C.; Pribil, Michael; Hogan, John P; Wronkiewicz, David

2016-01-01

The isotopic composition of lead (Pb) in fugitive dust suspended by a vehicle from 13 unsurfaced roads in Missouri was measured to identify the source of Pb within an established long-term mining area. A three end-member model using 207Pb/206Pb and concentration as tracers resulted in fugitive dust samples plotting in the mixing field of well characterized heterogeneous end members. End members selected for this investigation include the 207Pb/206Pb for 1) a Pb-mixture representing mine tailings, 2) aerosol Pb-impacted soils within close proximity to the Buick secondary recycling smelter, and 3) an average of soils, rock cores and drill cuttings representing the background conditions. Aqua regia total concentrations and 207Pb/206Pb of mining area dust suggest that 35.4–84.3% of the source Pb in dust is associated with the mine tailings mixture, 9.1–52.7% is associated with the smelter mixture, and 0–21.6% is associated with background materials. Isotope ratios varied minimally within the operational phases of sequential extraction suggesting that mixing of all three Pb mixtures occurs throughout. Labile forms of Pb were attributed to all three end members. The extractable carbonate phase had as much as 96.6% of the total concentration associated with mine tailings, 51.8% associated with smelter deposition, and 34.2% with background. The next most labile geochemical phase (Fe + Mn Oxides) showed similar results with as much as 85.3% associated with mine tailings, 56.8% associated with smelter deposition, and 4.2% associated with the background soil.

Multi-disease analysis of maternal antibody decay using non-linear mixed models accounting for censoring.

PubMed

Goeyvaerts, Nele; Leuridan, Elke; Faes, Christel; Van Damme, Pierre; Hens, Niel

2015-09-10

Biomedical studies often generate repeated measures of multiple outcomes on a set of subjects. It may be of interest to develop a biologically intuitive model for the joint evolution of these outcomes while assessing inter-subject heterogeneity. Even though it is common for biological processes to entail non-linear relationships, examples of multivariate non-linear mixed models (MNMMs) are still fairly rare. We contribute to this area by jointly analyzing the maternal antibody decay for measles, mumps, rubella, and varicella, allowing for a different non-linear decay model for each infectious disease. We present a general modeling framework to analyze multivariate non-linear longitudinal profiles subject to censoring, by combining multivariate random effects, non-linear growth and Tobit regression. We explore the hypothesis of a common infant-specific mechanism underlying maternal immunity using a pairwise correlated random-effects approach and evaluating different correlation matrix structures. The implied marginal correlation between maternal antibody levels is estimated using simulations. The mean duration of passive immunity was less than 4 months for all diseases with substantial heterogeneity between infants. The maternal antibody levels against rubella and varicella were found to be positively correlated, while little to no correlation could be inferred for the other disease pairs. For some pairs, computational issues occurred with increasing correlation matrix complexity, which underlines the importance of further developing estimation methods for MNMMs. Copyright © 2015 John Wiley & Sons, Ltd.

Optoelectronic instrumentation enhancement using data mining feedback for a 3D measurement system

NASA Astrophysics Data System (ADS)

Flores-Fuentes, Wendy; Sergiyenko, Oleg; Gonzalez-Navarro, Félix F.; Rivas-López, Moisés; Hernandez-Balbuena, Daniel; Rodríguez-Quiñonez, Julio C.; Tyrsa, Vera; Lindner, Lars

2016-12-01

3D measurement by a cyber-physical system based on optoelectronic scanning instrumentation has been enhanced by outliers and regression data mining feedback. The prototype has applications in (1) industrial manufacturing systems that include: robotic machinery, embedded vision, and motion control, (2) health care systems for measurement scanning, and (3) infrastructure by providing structural health monitoring. This paper presents new research performed in data processing of a 3D measurement vision sensing database. Outliers from multivariate data have been detected and removal to improve artificial intelligence regression algorithm results. Physical measurement error regression data has been used for 3D measurements error correction. Concluding, that the joint of physical phenomena, measurement and computation is an effectiveness action for feedback loops in the control of industrial, medical and civil tasks.

Heavy metal pollution caused by small-scale metal ore mining activities: A case study from a polymetallic mine in South China.

PubMed

Sun, Zehang; Xie, Xiande; Wang, Ping; Hu, Yuanan; Cheng, Hefa

2018-10-15

Although metal ore mining activities are well known as an important source of heavy metals, soil pollution caused by small-scale mining activities has long been overlooked. This study investigated the pollution of surface soils in an area surrounding a recently abandoned small-scale polymetallic mining district in Guangdong province of south China. A total of 13 tailing samples, 145 surface soil samples, and 29 water samples were collected, and the concentrations of major heavy metals, including Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Pb, and Se, were determined. The results show that the tailings contained high levels of heavy metals, with Cu, Zn, As, Cd, and Pb occurring in the ranges of 739-4.15 × 10 3 , 1.81 × 10 3 -5.00 × 10 3 , 118-1.26 × 10 3 , 8.14-57.7, and 1.23 × 10 3 -6.99 × 10 3  mg/kg, respectively. Heavy metals also occurred at high concentrations in the mine drainages (15.4-17.9 mg/L for Cu, 21.1-29.3 mg/L for Zn, 0.553-0.770 mg/L for Cd, and 1.17-2.57 mg/L for Pb), particularly those with pH below 3. The mean contents of Cu, Zn, As, Cd, and Pb in the surface soils of local farmlands were up to 7 times higher than the corresponding background values, and results of multivariate statistical analysis clearly indicate that Cu, Zn, Cd, and Pb were largely contributed by the mining activities. The surface soils from farmlands surrounding the mining district were moderately to seriously polluted, while the potential ecological risk of heavy metal pollution was extremely high. It was estimated that the input fluxes from the mining district to the surrounding farmlands were approximately 17.1, 59.2, 0.311, and 93.8 kg/ha/yr for Cu, Zn, Cd, and Pb, respectively, which probably occurred through transport of fine tailings by wind and runoff, and mine drainage as well. These findings indicate the significant need for proper containment of the mine tailings at small-scale metal ore mines. Copyright © 2018. Published by Elsevier B.V.

Igloo-Plot: a tool for visualization of multidimensional datasets.

PubMed

Kuntal, Bhusan K; Ghosh, Tarini Shankar; Mande, Sharmila S

2014-01-01

Advances in science and technology have resulted in an exponential growth of multivariate (or multi-dimensional) datasets which are being generated from various research areas especially in the domain of biological sciences. Visualization and analysis of such data (with the objective of uncovering the hidden patterns therein) is an important and challenging task. We present a tool, called Igloo-Plot, for efficient visualization of multidimensional datasets. The tool addresses some of the key limitations of contemporary multivariate visualization and analysis tools. The visualization layout, not only facilitates an easy identification of clusters of data-points having similar feature compositions, but also the 'marker features' specific to each of these clusters. The applicability of the various functionalities implemented herein is demonstrated using several well studied multi-dimensional datasets. Igloo-Plot is expected to be a valuable resource for researchers working in multivariate data mining studies. Igloo-Plot is available for download from: http://metagenomics.atc.tcs.com/IglooPlot/. Copyright © 2014 Elsevier Inc. All rights reserved.

Transient stress-coupling between the 1992 Landers and 1999 Hector Mine, California, earthquakes

USGS Publications Warehouse

Masterlark, Timothy; Wang, H.F.

2002-01-01

A three-dimensional finite-element model (FEM) of the Mojave block region in southern California is constructed to investigate transient stress-coupling between the 1992 Landers and 1999 Hector Mine earthquakes. The FEM simulates a poroelastic upper-crust layer coupled to a viscoelastic lower-crust layer, which is decoupled from the upper mantle. FEM predictions of the transient mechanical behavior of the crust are constrained by global positioning system (GPS) data, interferometric synthetic aperture radar (InSAR) images, fluid-pressure data from water wells, and the dislocation source of the 1999 Hector Mine earthquake. Two time-dependent parameters, hydraulic diffusivity of the upper crust and viscosity of the lower crust, are calibrated to 10–2 m2·sec–1 and 5 × 1018 Pa·sec respectively. The hydraulic diffusivity is relatively insensitive to heterogeneous fault-zone permeability specifications and fluid-flow boundary conditions along the elastic free-surface at the top of the problem domain. The calibrated FEM is used to predict the evolution of Coulomb stress during the interval separating the 1992 Landers and 1999 Hector Mine earthquakes. The predicted change in Coulomb stress near the hypocenter of the Hector Mine earthquake increases from 0.02 to 0.05 MPa during the 7-yr interval separating the two events. This increase is primarily attributed to the recovery of decreased excess fluid pressure from the 1992 Landers coseismic (undrained) strain field. Coulomb stress predictions are insensitive to small variations of fault-plane dip and hypocentral depth estimations of the Hector Mine rupture.

Intelligent On-Board Processing in the Sensor Web

NASA Astrophysics Data System (ADS)

Tanner, S.

2005-12-01

Most existing sensing systems are designed as passive, independent observers. They are rarely aware of the phenomena they observe, and are even less likely to be aware of what other sensors are observing within the same environment. Increasingly, intelligent processing of sensor data is taking place in real-time, using computing resources on-board the sensor or the platform itself. One can imagine a sensor network consisting of intelligent and autonomous space-borne, airborne, and ground-based sensors. These sensors will act independently of one another, yet each will be capable of both publishing and receiving sensor information, observations, and alerts among other sensors in the network. Furthermore, these sensors will be capable of acting upon this information, perhaps altering acquisition properties of their instruments, changing the location of their platform, or updating processing strategies for their own observations to provide responsive information or additional alerts. Such autonomous and intelligent sensor networking capabilities provide significant benefits for collections of heterogeneous sensors within any environment. They are crucial for multi-sensor observations and surveillance, where real-time communication with external components and users may be inhibited, and the environment may be hostile. In all environments, mission automation and communication capabilities among disparate sensors will enable quicker response to interesting, rare, or unexpected events. Additionally, an intelligent network of heterogeneous sensors provides the advantage that all of the sensors can benefit from the unique capabilities of each sensor in the network. The University of Alabama in Huntsville (UAH) is developing a unique approach to data processing, integration and mining through the use of the Adaptive On-Board Data Processing (AODP) framework. AODP is a key foundation technology for autonomous internetworking capabilities to support situational awareness by sensors and their on-board processes. The two primary research areas for this project are (1) the on-board processing and communications framework itself, and (2) data mining algorithms targeted to the needs and constraints of the on-board environment. The team is leveraging its experience in on-board processing, data mining, custom data processing, and sensor network design. Several unique UAH-developed technologies are employed in the AODP project, including EVE, an EnVironmEnt for on-board processing, and the data mining tools included in the Algorithm Development and Mining (ADaM) toolkit.

Probabilistic flood damage modelling at the meso-scale

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Botto, Anna; Schröter, Kai; Merz, Bruno

2014-05-01

Decisions on flood risk management and adaptation are usually based on risk analyses. Such analyses are associated with significant uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention during the last years, they are still not standard practice for flood risk assessments. Most damage models have in common that complex damaging processes are described by simple, deterministic approaches like stage-damage functions. Novel probabilistic, multi-variate flood damage models have been developed and validated on the micro-scale using a data-mining approach, namely bagging decision trees (Merz et al. 2013). In this presentation we show how the model BT-FLEMO (Bagging decision Tree based Flood Loss Estimation MOdel) can be applied on the meso-scale, namely on the basis of ATKIS land-use units. The model is applied in 19 municipalities which were affected during the 2002 flood by the River Mulde in Saxony, Germany. The application of BT-FLEMO provides a probability distribution of estimated damage to residential buildings per municipality. Validation is undertaken on the one hand via a comparison with eight other damage models including stage-damage functions as well as multi-variate models. On the other hand the results are compared with official damage data provided by the Saxon Relief Bank (SAB). The results show, that uncertainties of damage estimation remain high. Thus, the significant advantage of this probabilistic flood loss estimation model BT-FLEMO is that it inherently provides quantitative information about the uncertainty of the prediction. Reference: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64.

Unsupervised classification of multivariate geostatistical data: Two algorithms

NASA Astrophysics Data System (ADS)

Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

2015-12-01

With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

Differentiating the grades of thymic epithelial tumor malignancy using textural features of intratumoral heterogeneity via (18)F-FDG PET/CT.

PubMed

Lee, Hyo Sang; Oh, Jungsu S; Park, Young Soo; Jang, Se Jin; Choi, Ik Soo; Ryu, Jin-Sook

2016-05-01

We aimed to explore the ability of textural heterogeneity indices determined by (18)F-FDG PET/CT for grading the malignancy of thymic epithelial tumors (TETs). We retrospectively enrolled 47 patients with pathologically proven TETs who underwent pre-treatment (18)F-FDG PET/CT. TETs were classified by pathological results into three subgroups with increasing grades of malignancy: low-risk thymoma (LRT; WHO classification A, AB and B1), high-risk thymoma (B2 and B3), and thymic carcinoma (TC). Using (18)F-FDG PET/CT, we obtained conventional imaging indices including SUVmax and 20 intratumoral heterogeneity indices: i.e., four local-scale indices derived from the neighborhood gray-tone difference matrix (NGTDM), eight regional-scale indices from the gray-level run-length matrix (GLRLM), and eight regional-scale indices from the gray-level size zone matrix (GLSZM). Area under the receiver operating characteristic curve (AUC) was used to demonstrate the abilities of the imaging indices for differentiating subgroups. Multivariable logistic regression analysis was performed to show the independent significance of the textural indices. Combined criteria using optimal cutoff values of the SUVmax and a best-performing heterogeneity index were applied to investigate whether they improved differentiation between the subgroups. Most of the GLRLM and GLSZM indices and the SUVmax showed good or fair discrimination (AUC >0.7) with best performance for some of the GLRLM indices and the SUVmax, whereas the NGTDM indices showed relatively inferior performance. The discriminative ability of some of the GLSZM indices was independent from that of SUVmax in multivariate analysis. Combined use of the SUVmax and a GLSZM index improved positive predictive values for LRT and TC. Texture analysis of (18)F-FDG PET/CT scans has the potential to differentiate between TET tumor grades; regional-scale indices from GLRLM and GLSZM perform better than local-scale indices from the NGTDM. The SUVmax and heterogeneity indices may have complementary value in differentiating TET subgroups.

Microbial stratification in low pH oxic and suboxic macroscopic growths along an acid mine drainage

PubMed Central

Méndez-García, Celia; Mesa, Victoria; Sprenger, Richard R; Richter, Michael; Diez, María Suárez; Solano, Jennifer; Bargiela, Rafael; Golyshina, Olga V; Manteca, Ángel; Ramos, Juan Luis; Gallego, José R; Llorente, Irene; Martins dos Santos, Vitor AP; Jensen, Ole N; Peláez, Ana I; Sánchez, Jesús; Ferrer, Manuel

2014-01-01

Macroscopic growths at geographically separated acid mine drainages (AMDs) exhibit distinct populations. Yet, local heterogeneities are poorly understood. To gain novel mechanistic insights into this, we used OMICs tools to profile microbial populations coexisting in a single pyrite gallery AMD (pH ∼2) in three distinct compartments: two from a stratified streamer (uppermost oxic and lowermost anoxic sediment-attached strata) and one from a submerged anoxic non-stratified mat biofilm. The communities colonising pyrite and those in the mature formations appear to be populated by the greatest diversity of bacteria and archaea (including ‘ARMAN' (archaeal Richmond Mine acidophilic nano-organisms)-related), as compared with the known AMD, with ∼44.9% unclassified sequences. We propose that the thick polymeric matrix may provide a safety shield against the prevailing extreme condition and also a massive carbon source, enabling non-typical acidophiles to develop more easily. Only 1 of 39 species were shared, suggesting a high metabolic heterogeneity in local microenvironments, defined by the O2 concentration, spatial location and biofilm architecture. The suboxic mats, compositionally most similar to each other, are more diverse and active for S, CO2, CH4, fatty acid and lipopolysaccharide metabolism. The oxic stratum of the streamer, displaying a higher diversity of the so-called ‘ARMAN'-related Euryarchaeota, shows a higher expression level of proteins involved in signal transduction, cell growth and N, H2, Fe, aromatic amino acids, sphingolipid and peptidoglycan metabolism. Our study is the first to highlight profound taxonomic and functional shifts in single AMD formations, as well as new microbial species and the importance of H2 in acidic suboxic macroscopic growths. PMID:24430486

Annual research review: A meta-analysis of the worldwide prevalence of mental disorders in children and adolescents.

PubMed

Polanczyk, Guilherme V; Salum, Giovanni A; Sugaya, Luisa S; Caye, Arthur; Rohde, Luis A

2015-03-01

The literature on the prevalence of mental disorders affecting children and adolescents has expanded significantly over the last three decades around the world. Despite the field having matured significantly, there has been no meta-analysis to calculate a worldwide-pooled prevalence and to empirically assess the sources of heterogeneity of estimates. We conducted a systematic review of the literature searching in PubMed, PsycINFO, and EMBASE for prevalence studies of mental disorders investigating probabilistic community samples of children and adolescents with standardized assessments methods that derive diagnoses according to the DSM or ICD. Meta-analytical techniques were used to estimate the prevalence rates of any mental disorder and individual diagnostic groups. A meta-regression analysis was performed to estimate the effect of population and sample characteristics, study methods, assessment procedures, and case definition in determining the heterogeneity of estimates. We included 41 studies conducted in 27 countries from every world region. The worldwide-pooled prevalence of mental disorders was 13.4% (CI 95% 11.3-15.9). The worldwide prevalence of any anxiety disorder was 6.5% (CI 95% 4.7-9.1), any depressive disorder was 2.6% (CI 95% 1.7-3.9), attention-deficit hyperactivity disorder was 3.4% (CI 95% 2.6-4.5), and any disruptive disorder was 5.7% (CI 95% 4.0-8.1). Significant heterogeneity was detected for all pooled estimates. The multivariate metaregression analyses indicated that sample representativeness, sample frame, and diagnostic interview were significant moderators of prevalence estimates. Estimates did not vary as a function of geographic location of studies and year of data collection. The multivariate model explained 88.89% of prevalence heterogeneity, but residual heterogeneity was still significant. Additional meta-analysis detected significant pooled difference in prevalence rates according to requirement of funcional impairment for the diagnosis of mental disorders. Our findings suggest that mental disorders affect a significant number of children and adolescents worldwide. The pooled prevalence estimates and the identification of sources of heterogeneity have important implications to service, training, and research planning around the world. © 2015 Association for Child and Adolescent Mental Health.

Heterogeneous recurrence monitoring and control of nonlinear stochastic processes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Hui, E-mail: huiyang@usf.edu; Chen, Yun

Recurrence is one of the most common phenomena in natural and engineering systems. Process monitoring of dynamic transitions in nonlinear and nonstationary systems is more concerned with aperiodic recurrences and recurrence variations. However, little has been done to investigate the heterogeneous recurrence variations and link with the objectives of process monitoring and anomaly detection. Notably, nonlinear recurrence methodologies are based on homogeneous recurrences, which treat all recurrence states in the same way as black dots, and non-recurrence is white in recurrence plots. Heterogeneous recurrences are more concerned about the variations of recurrence states in terms of state properties (e.g., valuesmore » and relative locations) and the evolving dynamics (e.g., sequential state transitions). This paper presents a novel approach of heterogeneous recurrence analysis that utilizes a new fractal representation to delineate heterogeneous recurrence states in multiple scales, including the recurrences of both single states and multi-state sequences. Further, we developed a new set of heterogeneous recurrence quantifiers that are extracted from fractal representation in the transformed space. To that end, we integrated multivariate statistical control charts with heterogeneous recurrence analysis to simultaneously monitor two or more related quantifiers. Experimental results on nonlinear stochastic processes show that the proposed approach not only captures heterogeneous recurrence patterns in the fractal representation but also effectively monitors the changes in the dynamics of a complex system.« less

Palaeo-pollution from mining activities in the Vosges Mountains: 1000 years and still bioavailable.

PubMed

Mariet, Anne-Lise; de Vaufleury, Annette; Bégeot, Carole; Walter-Simonnet, Anne-Véronique; Gimbert, Frédéric

2016-07-01

Mining and smelting activities have contaminated the environment with trace metals (TMs) at a worldwide scale for at least two millennia. A combination of chemical approaches and active biomonitoring was performed to analyse the environmental availability and bioavailability of TM palaeo-pollution in a former PbAg mining district in the Vosges Mountains, France. Along a soil TM contamination gradient that covered eight stations, including two archaeological mining sites, the toxicokinetics of six TMs (Pb, Cd, As, Ag, Co, Sb) in the snail Cantareus aspersus revealed that palaeo-pollution from the studied sites remains bioavailable. This study provides the first data on the accumulation kinetics of Ag and Co for C. aspersus. The environmental availability of the TMs was estimated with three chemical extraction methods (aqua regia, EDTA 50 mM, CaCl2 10 mM). Univariate regression analyses showed that EDTA extraction is the best method for estimating the bioavailability of Pb, As, Ag, Co and Sb to snails. None of the three extractants was efficient for Cd. A multivariate analysis of bioaccumulation data revealed that TM bioavailability and transfer were modulated by exposure sources (soil, humus and vegetation) rather than by soil physico-chemical characteristics. Hence, although the deposition of mining wastes dates back several centuries, these wastes still represent a source of contamination that must be considered to develop relevant site management and environmental risk assessment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Community-level effects in edaphic fauna from an abandoned mining area: integration with chemical and toxicological lines of evidence.

PubMed

Antunes, Sara C; Castro, Bruno B; Moreira, Cláudia; Gonçalves, Fernando; Pereira, Ruth

2013-02-01

As a part of the Ecological Risk Assessment of a deactivated uranium mining area (Cunha Baixa), the aim of this study was to assess the drivers of litter arthropod community (ecological line of evidence) inhabiting soils with different degrees of contamination. Litter arthropods were collected in the mining area using a total of 70 pitfall traps, in the spring and autumn of 2004. Unlike information previously collected in the chemical and ecotoxicological lines of evidence, we found no clear evidence of impacts of soil contamination on the edaphic arthropod assemblage. Multivariate analyses were unable to extract relevant environmental gradients related to contamination, as most of the sites shared the same taxa overall. Given the consistency of the chemical and ecotoxicological lines of evidence, we must conclude that the litter arthropod assemblage underestimated the impacts of contamination in this abandoned mining area. In part, this could be due to the uncertainty caused by confounding factors that affect the litter arthropod community in the area. Nevertheless, despite the overall lack of responsiveness of the epigeic arthropod community data, a few taxa were negatively correlated with metal concentrations (Clubionidae and Staphylinidae), while Pseudoscorpionida were associated with the toxicological profile of the sites. These evidences suggest that community-level approaches with other animal and plant assemblages are necessary to reduce uncertainty relatively to the assessment of risks in higher evaluation tiers in the Cunha Baixa mine area. Copyright © 2012 Elsevier Inc. All rights reserved.

Heterogeneous selenite reduction by zero valent iron steel wool.

PubMed

Li, Ziyan; Huang, Donglin; McDonald, Louis M

2017-02-01

Mine drainage from the low-sulfur surface coal mines in southern West Virginia, USA, is circumneutral (pH > 6) but contains elevated selenium (Se) concentrations. Removal of selenite ions from aqueous solutions under anoxic condition at pH 6-8.5 by zero valent iron steel wool (ZVI-SW) was investigated in bench-scale kinetic experiments using wet chemical, microscopic and spectroscopic techniques (X-ray photoelectron spectroscopy). ZVI-SW could effectively and efficiently remove Se IV from solution with pH 6-8.5. A two-step removal mechanism was identified for Se IV reduction by ZVI-SW. The proposed mechanism was electrochemical reduction of Se IV by Fe 0 in an initial lag stage, followed by a faster heterogeneous reduction, mediated by an Fe II -bearing phase (hydroxide or green rust). Solution pH was a critical factor for the kinetic rate in the lag stage (0.33 h -1 for pH > 8 and 0.10 h -1 for pH 6-8). The length of lag stage was 20-30 min as determined by the time for dissolved Fe II concentration to reach 0.30 ± 0.04 mg L -1 which was critical for induction of the faster stage. About 65% of the initial Se IV was reduced to Se 0 , the primary reductive product in both stages.

A Complementary Measure of Heterogeneity on Mathematical Skills

ERIC Educational Resources Information Center

Fedriani, Eugenio M.; Moyano, Rafael

2012-01-01

Finding educational truths is an inherently multivariate problem. There are many factors affecting each student and their performances. Because of this, both measuring of skills and assessing students are always complex processes. This is a well-known problem, and a number of solutions have been proposed by specialists. One of its ramifications is…

Water quality assessment in the Bétaré-Oya gold mining area (East-Cameroon): Multivariate Statistical Analysis approach.

PubMed

Rakotondrabe, Felaniaina; Ndam Ngoupayou, Jules Remy; Mfonka, Zakari; Rasolomanana, Eddy Harilala; Nyangono Abolo, Alexis Jacob; Ako Ako, Andrew

2018-01-01

The influence of gold mining activities on the water quality in the Mari catchment in Bétaré-Oya (East Cameroon) was assessed in this study. Sampling was performed within the period of one hydrological year (2015 to 2016), with 22 sampling sites consisting of groundwater (06) and surface water (16). In addition to measuring the physicochemical parameters, such as pH, electrical conductivity, alkalinity, turbidity, suspended solids and CN - , eleven major elements (Na + , K + , Ca 2+ , Mg 2+ , NH 4 + , Cl - , NO 3 - , HCO 3 - , SO 4 2- , PO 4 3- and F - ) and eight heavy metals (Pb, Zn, Cd, Fe, Cu, As, Mn and Cr) were also analyzed using conventional hydrochemical methods, Multivariate Statistical Analysis and the Heavy metal Pollution Index (HPI). The results showed that the water from Mari catchment and Lom River was acidic to basic (5.4050mg NO 3 - /L. This water was found as two main types: calcium magnesium bicarbonate (CaMg-HCO 3 ), which was the most represented, and sodium bicarbonate potassium (NaK-HCO 3 ). As for trace elements in surface water, the contents of Pb, Cd, Mn, Cr and Fe were higher than recommended by the WHO guidelines, and therefore, the surface water was unsuitable for human consumption. Three phenomena were responsible for controlling the quality of the water in the study area: hydrolysis of silicate minerals of plutono-metamorphic rocks, which constitute the geological basement of this area; vegetation and soil leaching; and mining activities. The high concentrations of TSS and trace elements found in this basin were mainly due to gold mining activities (exploration and exploitation) as well as digging of rivers beds, excavation and gold amalgamation. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting exposure-response associations of ambient particulate matter with mortality in 73 Chinese cities.

PubMed

Madaniyazi, Lina; Guo, Yuming; Chen, Renjie; Kan, Haidong; Tong, Shilu

2016-01-01

Estimating the burden of mortality associated with particulates requires knowledge of exposure-response associations. However, the evidence on exposure-response associations is limited in many cities, especially in developing countries. In this study, we predicted associations of particulates smaller than 10 μm in aerodynamic diameter (PM10) with mortality in 73 Chinese cities. The meta-regression model was used to test and quantify which city-specific characteristics contributed significantly to the heterogeneity of PM10-mortality associations for 16 Chinese cities. Then, those city-specific characteristics with statistically significant regression coefficients were treated as independent variables to build multivariate meta-regression models. The model with the best fitness was used to predict PM10-mortality associations in 73 Chinese cities in 2010. Mean temperature, PM10 concentration and green space per capita could best explain the heterogeneity in PM10-mortality associations. Based on city-specific characteristics, we were able to develop multivariate meta-regression models to predict associations between air pollutants and health outcomes reasonably well. Copyright © 2015 Elsevier Ltd. All rights reserved.

Are residents of mountain-top mining counties more likely to have infants with birth defects? The West Virginia experience.

PubMed

Lamm, Steven H; Li, Ji; Robbins, Shayhan A; Dissen, Elisabeth; Chen, Rusan; Feinleib, Manning

2015-02-01

Pooled 1996 to 2003 birth certificate data for four central states in Appalachia indicated higher rates of infants with birth defects born to residents of counties with mountain-top mining (MTM) than born to residents of non-mining-counties (Ahern 2011). However, those analyses did not consider sources of uncertainty such as unbalanced distributions or quality of data. Quality issues have been a continuing problem with birth certificate analyses. We used 1990 to 2009 live birth certificate data for West Virginia to reassess this hypothesis. Forty-four hospitals contributed 98% of the MTM-county births and 95% of the non-mining-county births, of which six had more than 1000 births from both MTM and nonmining counties. Adjusted and stratified prevalence rate ratios (PRRs) were computed both by using Poisson regression and Mantel-Haenszel analysis. Unbalanced distribution of hospital births was observed by mining groups. The prevalence rate of infants with reported birth defects, higher in MTM-counties (0.021) than in non-mining-counties (0.015), yielded a significant crude PRR (cPRR = 1.43; 95% confidence interval [CI] = 1.36-1.52) but a nonsignificant hospital-adjusted PRR (adjPRR = 1.08; 95% CI = 0.97-1.20; p = 0.16) for the 44 hospitals. So did the six hospital data analysis ([cPRR = 2.39; 95% CI = 2.15-2.65] and [adjPRR = 1.01; 95% CI, 0.89-1.14; p = 0.87]). No increased risk of birth defects was observed for births from MTM-counties after adjustment for, or stratification by, hospital of birth. These results have consistently demonstrated that the reported association between birth defect rates and MTM coal mining was a consequence of data heterogeneity. The data do not demonstrate evidence of a "Mountain-top Mining" effect on the prevalence of infants with reported birth defects in WV. © 2014 Wiley Periodicals, Inc.

Geophysical exploration of historical mine dumps for the estimation of valuable residuals

NASA Astrophysics Data System (ADS)

Martin, Tina; Knieß, Rudolf; Noell, Ursula; Hupfer, Sarah; Kuhn, Kerstin; Günther, Thomas

2015-04-01

Within the project ROBEHA, funded by the German Federal Ministry of Education and Research (033R105) the economic potential of different abandoned dump sites for mine waste in the Harz Mountains was investigated. Two different mining dumps were geophysically and mineralogically analysed in order to characterize the mine dump structure and to estimate the volume of the potential recycling material. The geophysical methods comprised geoelectrics, radar, and spectral induced polarization (SIP). One about 100-year old mining dump containing residues from density separated Ag- and Sb-rich Pb (Zn)-gangue ores was investigated in detail. Like most small-scale mining waste disposal sites this investigated dump is very heterogeneously structured. Therefore, 27 geoelectrical profiles, more than 50 radar profiles, and several SIP profiles were measured and analysed. The results from the radar measurements, registered with the GSSI system and a shielded 200 MHz antenna, show the near surface boundary layer (down to 3-4 m beneath surface) of the waste residuals. These results can be used as pre-information for the inversion process of the geoelectrical data. The geoelectrical results reveal the mineral residues as layers with higher resistivities (> 300 Ohm*m) than the surrounding material. The SIP method found low phase signals (< 0.5°) for the residues. To estimate the volume of the potentially reusable material we analysed each geoelectrical profile and interpolated between the single profiles using the BERT algorithm. Taking into account the wooded areas of the mine dump and other parameters we get a first estimate for the volume of the residues but the economical viability and the environmental impact of the reworking of the dump still needs to be evaluated in detail. The results of the second mine dump, an abandoned Cu and Zn-rich slag heap, show that the slag residues are characterized by higher resistivities and higher phases. A localization of the slag residues which are covered by organic material could be realized applying these geophysical methods.

Assessment of the environmental effects of mining using SPOT-Vegetation NDVI

NASA Astrophysics Data System (ADS)

Tote, C.; Swinnen, E.; Goossens, M.; Reusen, I.; Delalieux, S.

2012-04-01

Within the ImpactMin project, funded by the Framework Programme 7 of the European Commission, new methods for the environmental impact monitoring of mining operations are being developed. The objective of this study is to analyze the impact of mining on soil properties through assessment of the vegetation status using time series analysis of low resolution Normalized Difference Vegetation Index (NDVI) images derived from SPOT-Vegetation. The study focuses on the surroundings of mining areas in the Orenburg region in the Russian Urals. Karabash has been a centre for mining and metal production for well over 3000 years, and environmental impact of (historical) mining in the area is extremely severe. The area was characterized as an 'ecological disaster zone', based on chemical analysis of soil samples in the area [1]. The mining activities were intensified in the early to mid-20th century, but the old smelter was modernized in the 1990s. A time series of 10-daily NDVI images from SPOT-Vegetation (S10 April/1998-December/2010 at 1km2 resolution, http://www.vgt.vito.be/) is analyzed. Different land cover types clearly show different phenology. To remove seasonal vegetation changes and thus to facilitate the interpretation through the historical record, a Standardized Difference Vegetation Index (SDVI) was calculated for each pixel and for each record of the time series. The first results of trend analyses indicate a strong recovery of open forests in the Karabash region in the last decade. To what extent this can be related to reduced mining impact or climate factors, still needs to be assessed. Further research will also focus on the spatial heterogeneity of phenological parameters, in relation to distance to and wind direction of the smelters and soil properties. [1] V. Nestersnko, "Urban associations of elements- environmental pollutants in Karabash city (Chelyabinsk oblast) as a reflection of ore-chemical descriptions of mineral raw material", Proceedings of the Chelyabinsk Scientific Center, vol. 3, pp. 58-62, 2006.

Attitude to health risk in the Canadian population: a cross-sectional survey

PubMed Central

Bansback, Nick; Harrison, Mark; Sadatsafavi, Mohsen; Stiggelbout, Anne; Whitehurst, David G.T.

2016-01-01

Background: Risk is a ubiquitous part of health care. Understanding how people respond to risks is important for predicting how populations make health decisions. Our objective was to seek preliminary descriptive insights into the attitude to health risk in the Canadian population and factors associated with heterogeneity in risk attitude. Methods: We used a large market-research panel to survey (in English and French) a representative sample of the Canadian general population that reflected the age, sex and geography of the population. The survey included the Health-Risk Attitude Scale, which predicts how a person resolves risky health decisions related to treatment, prevention of disease and health-related behaviour. In addition, we assessed participants' numeracy and risk understanding, as well as income band and level of education. We summarized the responses, and we explored the independent associations between demographics, numeracy, risk understanding and risk attitude in multivariable models. Results: Of 6780 respondents, 4949 (73.0%) were averse to health risks; however, but there was considerable heterogeneity in the magnitude of risk aversion. We found significant gradients of risk averse attitudes with increasing age and being female (p < 0.001) using the multivariable model. French-speaking participants appeared to be more risk averse than those who were English-speaking (p < 0.001), as were individuals scoring higher on the Subjective Numeracy Scale (p < 0.001). Interpreation: In general, Canadians were averse to health risks, but we found that a sizeable, identifiable group of risk takers exists. Heterogeneity in preferences for risk can explain variations in health care utilization in the context of patient-centred care. Understanding risk preference heterogeneity can help guide policy and assist in patient-physician decisions. PMID:27398375

Heterogeneous impact of alcohol consumption according to treatment method on survival in head and neck cancer: A prospective study.

PubMed

Sawabe, Michi; Ito, Hidemi; Oze, Isao; Hosono, Satoyo; Kawakita, Daisuke; Tanaka, Hideo; Hasegawa, Yasuhisa; Murakami, Shingo; Matsuo, Keitaro

2017-01-01

Alcohol consumption is an established risk factor, and also a potential prognostic factor, for squamous cell carcinoma of the head and neck (HNSCC). However, little is known about whether the prognostic impact of alcohol consumption differs by treatment method. We evaluated the association between alcohol drinking and survival by treatment method to the primary site in 427 patients with HNSCC treated between 2005 and 2013 at Aichi Cancer Center Central Hospital (Nagoya, Japan). The impact of alcohol on prognosis was measured by multivariable Cox regression analysis adjusted for established prognostic factors. Among all HNSCC patients, the overall survival rate was significantly poorer with increased levels of alcohol consumption in multivariable analysis (trend P = 0.038). Stratification by treatment method and primary site revealed that the impact of drinking was heterogeneous. Among laryngopharyngeal cancer (laryngeal, oropharyngeal, and hypopharyngeal cancer) patients receiving radiotherapy (n = 141), a significant dose-response relationship was observed (trend P = 0.034). In contrast, among laryngopharyngeal cancer patients treated with surgery (n = 80), no obvious impact of alcohol was observed. This heterogeneity in the impact of alcohol between surgery and radiotherapy was significant (for interaction, P = 0.048). Furthermore, among patients with oral cavity cancer treated by surgery, a significant impact of drinking on survival was seen with tongue cancer, but not with non-tongue oral cancer. We observed a significant inverse association between alcohol drinking and prognosis among HNSCC patients, and its impact was heterogeneous by treatment method and primary site. © 2016 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

Constructing Patient Specific Clinical Trajectories from Electronic Healthcare Reimbursement Claims using Sequential Pattern Mining

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pullum, Laura L; Hobson, Tanner C

We examine the use of electronic healthcare reimbursement claims (EHRC) for analyzing healthcare delivery and practice patterns across the United States (US). By analyzing over 1 billion EHRCs, we track patterns of clinical procedures administered to patients with heart disease (HD) using sequential pattern mining algorithms. Our analyses reveal that the clinical procedures performed on HD patients are highly varied leading up to and after the primary diagnosis. The discovered clinical procedure sequences reveal significant differences in the overall costs incurred across different parts of the US, indicating significant heterogeneity in treating HD patients. We show that a data-driven approachmore » to understand patient specific clinical trajectories constructed from EHRC can provide quantitative insights into how to better manage and treat patients.« less

An analysis toolbox to explore mesenchymal migration heterogeneity reveals adaptive switching between distinct modes

PubMed Central

Shafqat-Abbasi, Hamdah; Kowalewski, Jacob M; Kiss, Alexa; Gong, Xiaowei; Hernandez-Varas, Pablo; Berge, Ulrich; Jafari-Mamaghani, Mehrdad; Lock, John G; Strömblad, Staffan

2016-01-01

Mesenchymal (lamellipodial) migration is heterogeneous, although whether this reflects progressive variability or discrete, 'switchable' migration modalities, remains unclear. We present an analytical toolbox, based on quantitative single-cell imaging data, to interrogate this heterogeneity. Integrating supervised behavioral classification with multivariate analyses of cell motion, membrane dynamics, cell-matrix adhesion status and F-actin organization, this toolbox here enables the detection and characterization of two quantitatively distinct mesenchymal migration modes, termed 'Continuous' and 'Discontinuous'. Quantitative mode comparisons reveal differences in cell motion, spatiotemporal coordination of membrane protrusion/retraction, and how cells within each mode reorganize with changed cell speed. These modes thus represent distinctive migratory strategies. Additional analyses illuminate the macromolecular- and cellular-scale effects of molecular targeting (fibronectin, talin, ROCK), including 'adaptive switching' between Continuous (favored at high adhesion/full contraction) and Discontinuous (low adhesion/inhibited contraction) modes. Overall, this analytical toolbox now facilitates the exploration of both spontaneous and adaptive heterogeneity in mesenchymal migration. DOI: http://dx.doi.org/10.7554/eLife.11384.001 PMID:26821527

Comprehensive Fractal Description of Porosity of Coal of Different Ranks

PubMed Central

Ren, Jiangang; Zhang, Guocheng; Song, Zhimin; Liu, Gaofeng; Li, Bing

2014-01-01

We selected, as the objects of our research, lignite from the Beizao Mine, gas coal from the Caiyuan Mine, coking coal from the Xiqu Mine, and anthracite from the Guhanshan Mine. We used the mercury intrusion method and the low-temperature liquid nitrogen adsorption method to analyze the structure and shape of the coal pores and calculated the fractal dimensions of different aperture segments in the coal. The experimental results show that the fractal dimension of the aperture segment of lignite, gas coal, and coking coal with an aperture of greater than or equal to 10 nm, as well as the fractal dimension of the aperture segment of anthracite with an aperture of greater than or equal to 100 nm, can be calculated using the mercury intrusion method; the fractal dimension of the coal pore, with an aperture range between 2.03 nm and 361.14 nm, can be calculated using the liquid nitrogen adsorption method, of which the fractal dimensions bounded by apertures of 10 nm and 100 nm are different. Based on these findings, we defined and calculated the comprehensive fractal dimensions of the coal pores and achieved the unity of fractal dimensions for full apertures of coal pores, thereby facilitating, overall characterization for the heterogeneity of the coal pore structure. PMID:24955407

Source and fate of inorganic soil contamination around the abandoned Phillips sulfide mine, hudson Highlands, New York

USGS Publications Warehouse

Gilchrist, S.; Gates, A.; Elzinga, E.; Gorring, M.; Szabo, Z.

2011-01-01

The abandoned Phillips sulfide mine in the critical Highlands watershed in New York has been shown to produce strongly acidic mine drainage (AMD) with anomalous metal contaminants in first-order streams that exceeded local water standards by up to several orders of magnitude (Gilchrist et al., 2009). The metal-sulfide-rich tailings also produce contaminated soils with pH < 4, organic matter < 2.5% and trace metals sequestered in soil oxides. A geochemical transect to test worst-case soil contamination showed that Cr, Co and Ni correlated positively with Mn, (r = 0.72, r= 0.89, r = 0.80, respectively), suggesting Mn-oxide sequestration and that Cu and Pb correlated with Fe (r = 0.76, r = 0.83, respectively), suggesting sequestration in goethite. Ubiquitous, yellow coating on the mine wastes, including jarosite and goethite, is a carrier of the metals. Geochemical and μ-SXRF analyses determined Cu to be the major soil contaminant. μ-SXRF also demonstrated that the heterogeneous nature of the soil chemistry at the micro-meter scale is self-similar to those in the bulk soil samples. Generally metals decreased, with some fluctuations, rapidly downslope through suspension of fines and dissolution in AMD leaving the area of substantial contamination << 0.5 km from the source.

Rock Burst Monitoring by Integrated Microseismic and Electromagnetic Radiation Methods

NASA Astrophysics Data System (ADS)

Li, Xuelong; Wang, Enyuan; Li, Zhonghui; Liu, Zhentang; Song, Dazhao; Qiu, Liming

2016-11-01

For this study, microseismic (MS) and electromagnetic radiation (EMR) monitoring systems were installed in a coal mine to monitor rock bursts. The MS system monitors coal or rock mass ruptures in the whole mine, whereas the EMR equipment monitors the coal or rock stress in a small area. By analysing the MS energy, number of MS events, and EMR intensity with respect to rock bursts, it has been shown that the energy and number of MS events present a "quiet period" 1-3 days before the rock burst. The data also show that the EMR intensity reaches a peak before the rock burst and this EMR intensity peak generally corresponds to the MS "quiet period". There is a positive correlation between stress and EMR intensity. Buckling failure of coal or rock depends on the rheological properties and occurs after the peak stress in the high-stress concentration areas in deep mines. The MS "quiet period" before the rock burst is caused by the heterogeneity of the coal and rock structures, the transfer of high stress into internal areas, locked patches, and self-organized criticality near the stress peak. This study increases our understanding of coal and rock instability in deep mines. Combining MS and EMR to monitor rock burst could improve prediction accuracy.

PTEN Loss as Determined by Clinical-grade Immunohistochemistry Assay Is Associated with Worse Recurrence-free Survival in Prostate Cancer

PubMed Central

Lotan, Tamara L.; Wei, Wei; Morais, Carlos L.; Hawley, Sarah T.; Fazli, Ladan; Hurtado-Coll, Antonio; Troyer, Dean; McKenney, Jesse K.; Simko, Jeffrey; Carroll, Peter R.; Gleave, Martin; Lance, Raymond; Lin, Daniel W.; Nelson, Peter S.; Thompson, Ian M.; True, Lawrence D.; Feng, Ziding; Brooks, James D.

2015-01-01

Background PTEN is the most commonly deleted tumor suppressor gene in primary prostate cancer (PCa) and its loss is associated with poor clinical outcomes and ERG gene rearrangement. Objective We tested whether PTEN loss is associated with shorter recurrence-free survival (RFS) in surgically treated PCa patients with known ERG status. Design, setting, and participants A genetically validated, automated PTEN immunohistochemistry (IHC) protocol was used for 1275 primary prostate tumors from the Canary Foundation retrospective PCa tissue microarray cohort to assess homogeneous (in all tumor tissue sampled) or heterogeneous (in a subset of tumor tissue sampled) PTEN loss. ERG status as determined by a genetically validated IHC assay was available for a subset of 938 tumors. Outcome measurements and statistical analysis Associations between PTEN and ERG status were assessed using Fisher’s exact test. Kaplan-Meier and multivariate weighted Cox proportional models for RFS were constructed. Results and limitations When compared to intact PTEN, homogeneous (hazard ratio [HR] 1.66, p = 0.001) but not heterogeneous (HR 1.24, p = 0.14) PTEN loss was significantly associated with shorter RFS in multivariate models. Among ERG-positive tumors, homogeneous (HR 3.07, p < 0.0001) but not heterogeneous (HR 1.46, p = 0.10) PTEN loss was significantly associated with shorter RFS. Among ERG-negative tumors, PTEN did not reach significance for inclusion in the final multivariate models. The interaction term for PTEN and ERG status with respect to RFS did not reach statistical significance (p = 0.11) for the current sample size. Conclusions These data suggest that PTEN is a useful prognostic biomarker and that there is no statistically significant interaction between PTEN and ERG status for RFS. Patient summary We found that loss of the PTEN tumor suppressor gene in prostate tumors as assessed by tissue staining is correlated with shorter time to prostate cancer recurrence after radical prostatectomy. PMID:27617307

The Role of Economic Uncertainty on the Block Economic Value - a New Valuation Approach / Rola Czynnika Niepewności Przy Obliczaniu Wskaźnika Rentowności - Nowe Podejście

NASA Astrophysics Data System (ADS)

Dehghani, H.; Ataee-Pour, M.

2012-12-01

The block economic value (EV) is one of the most important parameters in mine evaluation. This parameter can affect significant factors such as mining sequence, final pit limit and net present value. Nowadays, the aim of open pit mine planning is to define optimum pit limits and an optimum life of mine production scheduling that maximizes the pit value under some technical and operational constraints. Therefore, it is necessary to calculate the block economic value at the first stage of the mine planning process, correctly. Unrealistic block economic value estimation may cause the mining project managers to make the wrong decision and thus may impose inexpiable losses to the project. The effective parameters such as metal price, operating cost, grade and so forth are always assumed certain in the conventional methods of EV calculation. While, obviously, these parameters have uncertain nature. Therefore, usually, the conventional methods results are far from reality. In order to solve this problem, a new technique is used base on an invented binomial tree which is developed in this research. This method can calculate the EV and project PV under economic uncertainty. In this paper, the EV and project PV were initially determined using Whittle formula based on certain economic parameters and a multivariate binomial tree based on the economic uncertainties such as the metal price and cost uncertainties. Finally the results were compared. It is concluded that applying the metal price and cost uncertainties causes the calculated block economic value and net present value to be more realistic than certain conditions.

JIM-13-0560 R1: Propensity scores for confounder adjustment when assessing the effects of medical interventions using non-experimental study designs

PubMed Central

Stürmer, Til; Wyss, Richard; Glynn, Robert J.; Brookhart, M. Alan

2014-01-01

Treatment effects, especially when comparing two or more therapeutic alternatives as in comparative effectiveness research, are likely to be heterogeneous across age, gender, co-morbidities, and co-medications. Propensity scores (PSs), an alternative to multivariable outcome models to control for measured confounding, have specific advantages in the presence of heterogeneous treatment effects. Implementing PSs using matching or weighting allows us to estimate different overall treatment effects in differently defined populations. Heterogeneous treatment effects can also be due to unmeasured confounding concentrated in those treated contrary to prediction. Sensitivity analyses based on PSs can help to assess such unmeasured confounding. PSs should be considered a primary or secondary analytic strategy in non-experimental medical research, including pharmacoepidemiology and non-experimental comparative effectiveness research. PMID:24520806

Diesel engine exhaust and lung cancer mortality: time-related factors in exposure and risk.

PubMed

Moolgavkar, Suresh H; Chang, Ellen T; Luebeck, Georg; Lau, Edmund C; Watson, Heather N; Crump, Kenny S; Boffetta, Paolo; McClellan, Roger

2015-04-01

To develop a quantitative exposure-response relationship between concentrations and durations of inhaled diesel engine exhaust (DEE) and increases in lung cancer risks, we examined the role of temporal factors in modifying the estimated effects of exposure to DEE on lung cancer mortality and characterized risk by mine type in the Diesel Exhaust in Miners Study (DEMS) cohort, which followed 12,315 workers through December 1997. We analyzed the data using parametric functions based on concepts of multistage carcinogenesis to directly estimate the hazard functions associated with estimated exposure to a surrogate marker of DEE, respirable elemental carbon (REC). The REC-associated risk of lung cancer mortality in DEMS is driven by increased risk in only one of four mine types (limestone), with statistically significant heterogeneity by mine type and no significant exposure-response relationship after removal of the limestone mine workers. Temporal factors, such as duration of exposure, play an important role in determining the risk of lung cancer mortality following exposure to REC, and the relative risk declines after exposure to REC stops. There is evidence of effect modification of risk by attained age. The modifying impact of temporal factors and effect modification by age should be addressed in any quantitative risk assessment (QRA) of DEE. Until there is a better understanding of why the risk appears to be confined to a single mine type, data from DEMS cannot reliably be used for QRA. © 2015 Society for Risk Analysis.

Spatial Temporal Analysis Of Mine-induced Seismicity

NASA Astrophysics Data System (ADS)

Fedotova, I. V.; Yunga, S. L.

The results of analysis of influence mine-induced seismicity on state of stress of a rock mass are represented. The spatial-temporal analysis of influence of mass explosions on rock massif deformation is carried out in the territory of a mine field Yukspor of a wing of the Joined Kirovsk mine JSC "Apatite". Estimation of influence of mass explosions on a massif were determined based firstly on the parameters of natural seismicic regime, and secondly taking into consideration change of seismic energy release. After long series of explosions variations in average number of seismic events was fixed. Is proved, that with increase of a volume of rocks, involved in a deforma- tion the released energy of seismic events, and characteristic intervals of time of their preparation are also varied. At the same time, the mechanism of destruction changes also: from destruction's, of a type shift - separation before destruction's, in a quasi- solid heterogeneous massif (in oxidized zones and zones of actuated faults). Analysis of a database seismicity of a massif from 1993 to 1999 years has confirmed, that the response of a massif on explosions is connected to stress-deformations state a mas- sif and parameters of a mining working. The analysis of spatial-temporal distribution of hypocenters of seismic events has allowed to allocate migration of fissile regions of destruction after mass explosions. The researches are executed at support of the Russian foundation for basic research, - projects 00-05-64758, 01-05-65340.

Recovery of Areas Degraded by Mining Within the Amazon Forest: Interaction of the Physical Condition of Soil and Biological Activity

NASA Astrophysics Data System (ADS)

Ribeiro, A. I.; Mello, G. F.; Longo, R. M.; Fengler, F. H.; Peche Filho, A., Sr.

2017-12-01

One of the greatest natural riches of Brazil is the Amazon rainforest. The Amazon region is known for its abundance of mineral resources, and may include topaz, oil, and especially cassiterite. In this scope, the mining sector in Brazil has great strategic importance because it accounts for approximately 30% of the country's exports with a mineral production of 40 billion dollars (Brazilian Mining Institute, 2015). In this scenario, as a consequence of mining, the Amazonian ecosystem has been undergoing a constant process of degradation. An important artifice in the exploitation of mineral resources is the rehabilitation and/or recovery of degraded areas. This recovery requires the establishment of degradation indicators and also the quality of the soil associated with its biota, since the Amazonian environment is dynamic, heterogeneous and complex in its physical, chemical and biological characteristics. In this way, this work presupposes that it is possible to characterize the different stages of recovery of tillage floor areas in deactivated cassiterite mines, within the Amazonian forest, in order to evaluate the interactions between the level of biological activity (Serrapilheira Height, Coefficient Metabolic, Basal Breath) and physical soil characteristics (aggregate DMG, Porosity, Total Soil Density, Moisture Content), through canonical correlation analysis. The results present correlations between the groups of indicators. Thus, from the use of the groups defined by canonical correlations, it was possible to identify the response of the set of physical and biological variables to the areas at different stages of recovery.

Feature Selection for Wheat Yield Prediction

NASA Astrophysics Data System (ADS)

Ruß, Georg; Kruse, Rudolf

Carrying out effective and sustainable agriculture has become an important issue in recent years. Agricultural production has to keep up with an everincreasing population by taking advantage of a field’s heterogeneity. Nowadays, modern technology such as the global positioning system (GPS) and a multitude of developed sensors enable farmers to better measure their fields’ heterogeneities. For this small-scale, precise treatment the term precision agriculture has been coined. However, the large amounts of data that are (literally) harvested during the growing season have to be analysed. In particular, the farmer is interested in knowing whether a newly developed heterogeneity sensor is potentially advantageous or not. Since the sensor data are readily available, this issue should be seen from an artificial intelligence perspective. There it can be treated as a feature selection problem. The additional task of yield prediction can be treated as a multi-dimensional regression problem. This article aims to present an approach towards solving these two practically important problems using artificial intelligence and data mining ideas and methodologies.

Identifying candidate drivers of drug response in heterogeneous cancer by mining high throughput genomics data.

PubMed

Nabavi, Sheida

2016-08-15

With advances in technologies, huge amounts of multiple types of high-throughput genomics data are available. These data have tremendous potential to identify new and clinically valuable biomarkers to guide the diagnosis, assessment of prognosis, and treatment of complex diseases, such as cancer. Integrating, analyzing, and interpreting big and noisy genomics data to obtain biologically meaningful results, however, remains highly challenging. Mining genomics datasets by utilizing advanced computational methods can help to address these issues. To facilitate the identification of a short list of biologically meaningful genes as candidate drivers of anti-cancer drug resistance from an enormous amount of heterogeneous data, we employed statistical machine-learning techniques and integrated genomics datasets. We developed a computational method that integrates gene expression, somatic mutation, and copy number aberration data of sensitive and resistant tumors. In this method, an integrative method based on module network analysis is applied to identify potential driver genes. This is followed by cross-validation and a comparison of the results of sensitive and resistance groups to obtain the final list of candidate biomarkers. We applied this method to the ovarian cancer data from the cancer genome atlas. The final result contains biologically relevant genes, such as COL11A1, which has been reported as a cis-platinum resistant biomarker for epithelial ovarian carcinoma in several recent studies. The described method yields a short list of aberrant genes that also control the expression of their co-regulated genes. The results suggest that the unbiased data driven computational method can identify biologically relevant candidate biomarkers. It can be utilized in a wide range of applications that compare two conditions with highly heterogeneous datasets.

Mixing-controlled uncertainty in long-term predictions of acid rock drainage from heterogeneous waste-rock piles

NASA Astrophysics Data System (ADS)

Pedretti, D.; Beckie, R. D.; Mayer, K. U.

2015-12-01

The chemistry of drainage from waste-rock piles at mine sites is difficult to predict because of a number of uncertainties including heterogeneous reactive mineral content, distribution of minerals, weathering rates and physical flow properties. In this presentation, we examine the effects of mixing on drainage chemistry over timescales of 100s of years. We use a 1-D streamtube conceptualization of flow in waste rocks and multicomponent reactive transport modeling. We simplify the reactive system to consist of acid-producing sulfide minerals and acid-neutralizing carbonate minerals and secondary sulfate and iron oxide minerals. We create multiple realizations of waste-rock piles with distinct distributions of reactive minerals along each flow path and examine the uncertainty of drainage geochemistry through time. The limited mixing of streamtubes that is characteristic of the vertical unsaturated flow in many waste-rock piles, allows individual flowpaths to sustain acid or neutral conditions to the base of the pile, where the streamtubes mix. Consequently, mixing and the acidity/alkalinity balance of the streamtube waters, and not the overall acid- and base-producing mineral contents, control the instantaneous discharge chemistry. Our results show that the limited mixing implied by preferential flow and the heterogeneous distribution of mineral contents lead to large uncertainty in drainage chemistry over short and medium time scales. However, over longer timescales when one of either the acid-producing or neutralizing primary phases is depleted, the drainage chemistry becomes less controlled by mixing and in turn less uncertain. A correct understanding of the temporal variability of uncertainty is key to make informed long-term decisions in mining settings regarding the management of waste material.

Improving Prediction Accuracy of “Central Line-Associated Blood Stream Infections” Using Data Mining Models

PubMed Central

Noaman, Amin Y.; Jamjoom, Arwa; Al-Abdullah, Nabeela; Nasir, Mahreen; Ali, Anser G.

2017-01-01

Prediction of nosocomial infections among patients is an important part of clinical surveillance programs to enable the related personnel to take preventive actions in advance. Designing a clinical surveillance program with capability of predicting nosocomial infections is a challenging task due to several reasons, including high dimensionality of medical data, heterogenous data representation, and special knowledge required to extract patterns for prediction. In this paper, we present details of six data mining methods implemented using cross industry standard process for data mining to predict central line-associated blood stream infections. For our study, we selected datasets of healthcare-associated infections from US National Healthcare Safety Network and consumer survey data from Hospital Consumer Assessment of Healthcare Providers and Systems. Our experiments show that central line-associated blood stream infections (CLABSIs) can be successfully predicted using AdaBoost method with an accuracy up to 89.7%. This will help in implementing effective clinical surveillance programs for infection control, as well as improving the accuracy detection of CLABSIs. Also, this reduces patients' hospital stay cost and maintains patients' safety. PMID:29085836

On mining complex sequential data by means of FCA and pattern structures

NASA Astrophysics Data System (ADS)

Buzmakov, Aleksey; Egho, Elias; Jay, Nicolas; Kuznetsov, Sergei O.; Napoli, Amedeo; Raïssi, Chedy

2016-02-01

Nowadays data-sets are available in very complex and heterogeneous ways. Mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of "complex" sequential data by means of interesting sequential patterns. We approach the problem using the elegant mathematical framework of formal concept analysis and its extension based on "pattern structures". Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences. We show how pattern structures along with projections (i.e. a data reduction of sequential structures) are able to enumerate more meaningful patterns and increase the computing efficiency of the approach. Finally, we show the applicability of the presented method for discovering and analysing interesting patient patterns from a French healthcare data-set on cancer. The quantitative and qualitative results (with annotations and analysis from a physician) are reported in this use-case which is the main motivation for this work.

Finding a Needle in the Haystack: Identification of Functionally Important Minority Phases in an Operating Battery.

PubMed

Zhang, Kai; Ren, Fang; Wang, Xuelong; Hu, Enyuan; Xu, Yahong; Yang, Xiao-Qing; Li, Hong; Chen, Liquan; Pianetta, Piero; Mehta, Apurva; Yu, Xiqian; Liu, Yijin

2017-12-13

The in-depth understanding of the minority phases' roles in functional materials, e.g., batteries, is critical for optimizing the system performance and the operational efficiency. Although the visualization of battery electrode under operating conditions has been demonstrated, the development of advanced data-mining approaches is still needed in order to identify minority phases and to understand their functionalities. The present study uses nanoscale X-ray spectromicroscopy to study a functional LiCoO 2 /Li battery pouch cell. The data-mining approaches developed herein were used to search through over 10 million X-ray absorption spectra that cover more than 100 active cathode particles. Two particles with unanticipated chemical fingerprints were identified and further analyzed, providing direct evidence and valuable insight into the undesired side reactions involving the cation dissolution and precipitation as well as the local overlithiation-caused subparticle domain deactivation. The data-mining approach described in this work is widely applicable to many other structurally complex and chemically heterogeneous systems, in which the secondary/minority phases could critically affect the overall performance of the system, well beyond battery research.

Molybdenum and zinc stable isotope variation in mining waste rock drainage and waste rock at the Antamina mine, Peru.

PubMed

Skierszkan, E K; Mayer, K U; Weis, D; Beckie, R D

2016-04-15

The stable isotope composition of molybdenum (Mo) and zinc (Zn) in mine wastes at the Antamina Copper-Zn-Mo mine, Peru, was characterized to investigate whether isotopic variation of these elements indicated metal attenuation processes in mine drainage. Waste rock and ore minerals were analyzed to identify the isotopic composition of Mo and Zn sources, namely molybdenites (MoS2) and sphalerites (ZnS). Molybdenum and Zn stable isotope ratios are reported relative to the NIST-SRM-3134 and PCIGR-1 Zn standards, respectively. δ(98)Mo among molybdenites ranged from -0.6 to +0.6‰ (n=9) while sphalerites showed no δ(66)Zn variations (0.11±0.01‰, 2 SD, n=5). Mine drainage samples from field waste rock weathering experiments were also analyzed to examine the extent of isotopic variability in the dissolved phase. Variations spanned 2.2‰ in δ(98)Mo (-0.1 to +2.1‰) and 0.7‰ in δ(66)Zn (-0.4 to +0.3‰) in mine drainage over a wide pH range (pH2.2-8.6). Lighter δ(66)Zn signatures were observed in alkaline pH conditions, which was consistent with Zn adsorption and/or hydrozincite (Zn5(OH)6(CO3)2) formation. However, in acidic mine drainage Zn isotopic compositions reflected the value of sphalerites. In addition, molybdenum isotope compositions in mine drainage were shifted towards heavier values (0.89±1.25‰, 2 SD, n=16), with some overlap, in comparison to molybdenites and waste rock (0.13±0.82‰, 2 SD, n=9). The cause of heavy Mo isotopic signatures in mine drainage was more difficult to resolve due to isotopic heterogeneity among ore minerals and a variety of possible overlapping processes including dissolution, adsorption and secondary mineral precipitation. This study shows that variation in metal isotope ratios are promising indicators of metal attenuation. Future characterization of isotopic fractionation associated to key environmental reactions will improve the power of Mo and Zn isotope ratios to track the fate of these elements in mine drainage. Copyright © 2015 Elsevier B.V. All rights reserved.

The Covariation of Antisocial Behavior and Substance Use in Adolescence: A Behavioral Genetic Perspective

ERIC Educational Resources Information Center

McAdams, Tom; Rowe, Richard; Rijsdijk, Fruhling; Maughan, Barbara; Eley, Thalia C.

2012-01-01

Multivariate genetic studies have revealed genetic correlations between antisocial behavior (ASB) and substance use (SU). However, ASB is heterogeneous, and it remains unclear whether all forms are similarly related to SU. The present study examines links between cannabis use, alcohol consumption, and aggressive and delinquent forms of ASB using a…

Integration of crosswell seismic data for simulating porosity in a heterogeneous carbonate aquifer

NASA Astrophysics Data System (ADS)

Emery, Xavier; Parra, Jorge

2013-11-01

A challenge for the geostatistical simulation of subsurface properties in mining, petroleum and groundwater applications is the integration of well logs and seismic measurements, which can provide information on geological heterogeneities at a wide range of scales. This paper presents a case study conducted at the Port Mayaca aquifer, located in western Martin County, Florida, in which it is of interest to simulate porosity, based on porosity logs at two wells and high-resolution crosswell seismic measurements of P-wave impedance. To this end, porosity and impedance are transformed into cross-correlated Gaussian random fields, using local transformations. The model parameters (transformation functions, mean values and correlation structure of the transformed fields) are inferred and checked against the data. Multiple realizations of porosity can then be constructed conditionally to the impedance information in the interwell region, which allow identifying one low-porosity structure and two to three flow units that connect the two wells, mapping heterogeneities within these units and visually assessing fluid paths in the aquifer. In particular, the results suggest that the paths in the lower flow units, formed by a network of heterogeneous conduits, are not as smooth as in the upper flow unit.

A multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration

PubMed Central

Goovaerts, P.; Albuquerque, Teresa; Antunes, Margarida

2015-01-01

This paper describes a multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration, with an application to an abandoned sedimentary gold mining region in Portugal. The main challenge was the existence of only a dozen gold measurements confined to the grounds of the old gold mines, which precluded the application of traditional interpolation techniques, such as cokriging. The analysis could, however, capitalize on 376 stream sediment samples that were analyzed for twenty two elements. Gold (Au) was first predicted at all 376 locations using linear regression (R2=0.798) and four metals (Fe, As, Sn and W), which are known to be mostly associated with the local gold’s paragenesis. One hundred realizations of the spatial distribution of gold content were generated using sequential indicator simulation and a soft indicator coding of regression estimates, to supplement the hard indicator coding of gold measurements. Each simulated map then underwent a local cluster analysis to identify significant aggregates of low or high values. The one hundred classified maps were processed to derive the most likely classification of each simulated node and the associated probability of occurrence. Examining the distribution of the hot-spots and cold-spots reveals a clear enrichment in Au along the Erges River downstream from the old sedimentary mineralization. PMID:27777638

Seasonal changes in antioxidative/oxidative profile of mining and non-mining populations of Syrian beancaper as determined by soil conditions.

PubMed

López-Orenes, Antonio; Bueso, María C; Conesa, Héctor M; Calderón, Antonio A; Ferrer, María A

2017-01-01

Soil pollution by heavy metals/metalloids (HMMs) is a problem worldwide. To prevent dispersion of contaminated particles by erosion, the maintenance of a vegetative cover is needed. Successful plant establishment in multi-polluted soils can be hampered not only by HMM toxicities, but also by soil nutrient deficiencies and the co-occurrence of abiotic stresses. Some plant species are able to thrive under these multi-stress scenarios often linked to marked fluctuations in environmental factors. This study aimed to investigate the metabolic adjustments involved in Zygophyllum fabago acclimative responses to conditions prevailing in HMM-enriched mine-tailings piles, during Mediterranean spring and summer. To this end, fully expanded leaves, and rhizosphere soil, of three contrasting mining and non-mining populations of Z. fabago grown spontaneously in south-eastern Spain were sampled in two consecutive years. Approximately 50 biochemical, physiological and edaphic parameters were examined, including leaf redox components, primary and secondary metabolites, endogenous levels of salicylic acid, and physicochemical properties of soil (fertility parameters and total concentration of HMMs). Multivariate data analysis showed a clear distinction in antioxidative/oxidative profiles between and within the populations studied. Levels of chlorophylls, proteins and proline characterized control plants whereas antioxidant capacity and C- and S-based antioxidant compounds were biomarkers of mining plants. Seasonal variations were characterized by higher levels of alkaloids and PAL and soluble peroxidase activities in summer, and by soluble sugars and hydroxycinnamic acids in spring irrespective of the population considered. Although the antioxidant systems are subjected to seasonal variations, the way and the intensity with which every population changes its antioxidative/oxidative profile seem to be determined by soil conditions. In short, Z. fabago displays a high physiological plasticity that allow it to successfully shift its metabolism to withstand the multiple stresses that plants must cope with in mine tailings piles under Mediterranean climatic conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

Fish assemblages and environmental variables associated with hard-rock mining in the Coeur d'Alene River basin, Idaho

USGS Publications Warehouse

Maret, Terry R.; MacCoy, Dorene E.

2002-01-01

As part of the U.S. Geological Survey's National Water Quality Assessment Program, fish assemblages, environmental variables, and associated mine densities were evaluated at 18 test and reference sites during the summer of 2000 in the Coeur d'Alene and St. Regis river basins in Idaho and Montana. Multimetric and multivariate analyses were used to examine patterns in fish assemblages and the associated environmental variables representing a gradient of mining intensity. The concentrations of cadmium (Cd), lead (Pb), and zinc (Zn) in water and streambed sediment found at test sites in watersheds where production mine densities were at least 0.2 mines/km2 (in a 500-m stream buffer) were significantly higher than the concentrations found at reference sites. Many of these metal concentrations exceeded Ambient Water Quality Criteria (AWQC) and the Canadian Probable Effect Level guidelines for streambed sediment. Regression analysis identified significant relationships between the production mine densities and the sum of Cd, Pb, and Zn concentrations in water and streambed sediment (r2 = 0.69 and 0.66, respectively; P < 0.01). Zinc was identified as the primary metal contaminant in both water and streambed sediment. Eighteen fish species in the families Salmonidae, Cottidae, Cyprinidae, Catostomidae, Centrarchidae, and Ictaluridae were collected. Principal components analysis of 11 fish metrics identified two distinct groups of sites corresponding to the reference and test sites, predominantly on the basis of the inverse relationship between percent cottids and percent salmonids (r = -0.64; P < 0.05). Streams located downstream from the areas of intensive hard-rock mining in the Coeur d'Alene River basin contained fewer native fish and lower abundances as a result of metal enrichment, not physical habitat degradation. Typically, salmonids were the predominant species at test sites where Zn concentrations exceeded the acute AWQC. Cottids were absent at these sites, which suggests that they are more severely affected by elevated metals than are salmonids.

Heavy metal pollution in soil associated with a large-scale cyanidation gold mining region in southeast of Jilin, China.

PubMed

Chen, Mo; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Jiang, Xue; Wu, Jichun

2017-01-01

Different gold mining and smelting processes can lead to distinctive heavy metal contamination patterns and results. This work examined heavy metal pollution from a large-scale cyanidation gold mining operation, which is distinguished from artisanal and small-scale amalgamation gold mining, in Jilin Province, China. A total of 20 samples including one background sample were collected from the surface of the mining area and the tailings pond in June 2013. These samples were analyzed for heavy metal concentrations and degree of pollution as well as sources of Cr, Cu, Zn, Pb, Ni, Cd, As, and Hg. The mean concentrations of Pb, Hg, and Cu (819.67, 0.12, and 46.92 mg kg -1 , respectively) in soil samples from the gold mine area exceeded local background values. The mean Hg content was less than the first-class standard of the Environmental Quality for Soils, which suggested that the cyanidation method is helpful for reducing Hg pollution. The geochemical accumulation index and enrichment factor results indicated clear signs that enrichment was present for Pb, Cu, and Hg, with the presence of serious Pb pollution and moderate presence to none of Hg and Cu pollution. Multivariate statistical analysis showed that there were three metal sources: (1) Pb, Cd, Cu, and As came from anthropogenic sources; (2) Cr and Zn were naturally occurring; whereas (3) Hg and Ni had a mix of anthropogenic and natural sources. Moreover, the tailings dam plays an important role in intercepting the tailings. Furthermore, the potential ecological risk assessment results showed that the study area poses a potentially strong risk to the ecological health. Furthermore, Pb and Hg (due to high concentration and high toxicity, respectively) are major pollutants on the risk index, and both Pb and Hg pollution should be of great concern at the Haigou gold mines in Jilin, China.

Environmental heterogeneity explains the genetic structure of Continental and Mediterranean populations of Fraxinus angustifolia Vahl.

PubMed

Temunović, Martina; Franjić, Jozo; Satovic, Zlatko; Grgurev, Marin; Frascaria-Lacoste, Nathalie; Fernández-Manjarrés, Juan F

2012-01-01

Tree species with wide distributions often exhibit different levels of genetic structuring correlated to their environment. However, understanding how environmental heterogeneity influences genetic variation is difficult because the effects of gene flow, drift and selection are confounded. We investigated the genetic variation and its ecological correlates in a wind-pollinated Mediterranean tree species, Fraxinus angustifolia Vahl, within a recognised glacial refugium in Croatia. We sampled 11 populations from environmentally divergent habitats within the Continental and Mediterranean biogeographical regions. We combined genetic data analyses based on nuclear microsatellite loci, multivariate statistics on environmental data and ecological niche modelling (ENM). We identified a geographic structure with a high genetic diversity and low differentiation in the Continental region, which contrasted with the significantly lower genetic diversity and higher population divergence in the Mediterranean region. The positive and significant correlation between environmental and genetic distances after controlling for geographic distance suggests an important influence of ecological divergence of the sites in shaping genetic variation. The ENM provided support for niche differentiation between the populations from the Continental and Mediterranean regions, suggesting that contemporary populations may represent two divergent ecotypes. Ecotype differentiation was also supported by multivariate environmental and genetic distance analyses. Our results suggest that despite extensive gene flow in continental areas, long-term stability of heterogeneous environments have likely promoted genetic divergence of ashes in this region and can explain the present-day genetic variation patterns of these ancient populations.

Environmental Heterogeneity Explains the Genetic Structure of Continental and Mediterranean Populations of Fraxinus angustifolia Vahl

PubMed Central

Temunović, Martina; Franjić, Jozo; Satovic, Zlatko; Grgurev, Marin; Frascaria-Lacoste, Nathalie; Fernández-Manjarrés, Juan F.

2012-01-01

Tree species with wide distributions often exhibit different levels of genetic structuring correlated to their environment. However, understanding how environmental heterogeneity influences genetic variation is difficult because the effects of gene flow, drift and selection are confounded. We investigated the genetic variation and its ecological correlates in a wind-pollinated Mediterranean tree species, Fraxinus angustifolia Vahl, within a recognised glacial refugium in Croatia. We sampled 11 populations from environmentally divergent habitats within the Continental and Mediterranean biogeographical regions. We combined genetic data analyses based on nuclear microsatellite loci, multivariate statistics on environmental data and ecological niche modelling (ENM). We identified a geographic structure with a high genetic diversity and low differentiation in the Continental region, which contrasted with the significantly lower genetic diversity and higher population divergence in the Mediterranean region. The positive and significant correlation between environmental and genetic distances after controlling for geographic distance suggests an important influence of ecological divergence of the sites in shaping genetic variation. The ENM provided support for niche differentiation between the populations from the Continental and Mediterranean regions, suggesting that contemporary populations may represent two divergent ecotypes. Ecotype differentiation was also supported by multivariate environmental and genetic distance analyses. Our results suggest that despite extensive gene flow in continental areas, long-term stability of heterogeneous environments have likely promoted genetic divergence of ashes in this region and can explain the present-day genetic variation patterns of these ancient populations. PMID:22905171

Spatio-temporal interpolation of precipitation during monsoon periods in Pakistan

NASA Astrophysics Data System (ADS)

Hussain, Ijaz; Spöck, Gunter; Pilz, Jürgen; Yu, Hwa-Lung

2010-08-01

Spatio-temporal estimation of precipitation over a region is essential to the modeling of hydrologic processes for water resources management. The changes of magnitude and space-time heterogeneity of rainfall observations make space-time estimation of precipitation a challenging task. In this paper we propose a Box-Cox transformed hierarchical Bayesian multivariate spatio-temporal interpolation method for the skewed response variable. The proposed method is applied to estimate space-time monthly precipitation in the monsoon periods during 1974-2000, and 27-year monthly average precipitation data are obtained from 51 stations in Pakistan. The results of transformed hierarchical Bayesian multivariate spatio-temporal interpolation are compared to those of non-transformed hierarchical Bayesian interpolation by using cross-validation. The software developed by [11] is used for Bayesian non-stationary multivariate space-time interpolation. It is observed that the transformed hierarchical Bayesian method provides more accuracy than the non-transformed hierarchical Bayesian method.

A way toward analyzing high-content bioimage data by means of semantic annotation and visual data mining

NASA Astrophysics Data System (ADS)

Herold, Julia; Abouna, Sylvie; Zhou, Luxian; Pelengaris, Stella; Epstein, David B. A.; Khan, Michael; Nattkemper, Tim W.

2009-02-01

In the last years, bioimaging has turned from qualitative measurements towards a high-throughput and highcontent modality, providing multiple variables for each biological sample analyzed. We present a system which combines machine learning based semantic image annotation and visual data mining to analyze such new multivariate bioimage data. Machine learning is employed for automatic semantic annotation of regions of interest. The annotation is the prerequisite for a biological object-oriented exploration of the feature space derived from the image variables. With the aid of visual data mining, the obtained data can be explored simultaneously in the image as well as in the feature domain. Especially when little is known of the underlying data, for example in the case of exploring the effects of a drug treatment, visual data mining can greatly aid the process of data evaluation. We demonstrate how our system is used for image evaluation to obtain information relevant to diabetes study and screening of new anti-diabetes treatments. Cells of the Islet of Langerhans and whole pancreas in pancreas tissue samples are annotated and object specific molecular features are extracted from aligned multichannel fluorescence images. These are interactively evaluated for cell type classification in order to determine the cell number and mass. Only few parameters need to be specified which makes it usable also for non computer experts and allows for high-throughput analysis.

Catastrophe theory—one of the basic components in the analysis of the seismic response of rock mass to explosions

NASA Astrophysics Data System (ADS)

Khachay, OA; Khachay, OYu

2018-03-01

It is shown that the dynamic process of mining can be controlled using the catastrophe theory. The control parameters can be values of blasting energy and locations of explosions relative to an area under study or operation. The kinematic and dynamic parameters of the deformation waves, as well as the structural features of rock mass through which these waves pass act as internal parameters. The use of the analysis methods for short-term and medium-term forecast of rock mass condition with the control parameters only is insufficient in the presence of sharp heterogeneity. However, the joint use of qualitative recommendations of the catastrophe theory and spatial–temporal data of changes in the internal parameters of rock mass will allow accident prevention in the course of mining.

Soil functional diversity analysis of a bauxite-mined restoration chronosequence.

PubMed

Lewis, Dawn E; White, John R; Wafula, Denis; Athar, Rana; Dickerson, Tamar; Williams, Henry N; Chauhan, Ashvini

2010-05-01

Soil microorganisms are sensitive to environmental perturbations such that changes in microbial community structure and function can provide early signs of anthropogenic disturbances and even predict restoration success. We evaluated the bacterial functional diversity of un-mined and three chronosequence sites at various stages of rehabilitation (0, 10, and 20 years old) located in the Mocho Mountains of Jamaica. Samples were collected during the dry and wet seasons and analyzed for metal concentrations, microbial biomass carbon, bacterial numbers, and functional responses of soil microbiota using community-level physiological profile (CLPP) assays. Regardless of the season, un-mined soils consisted of higher microbial biomass and numbers than any of the rehabilitated sites. Additionally, the number and rate of substrates utilized and substrate evenness (the distribution of color development between the substrates) were significantly greater in the un-mined soils with carbohydrates being preferentially utilized than amino acids, polymers, carboxylic acids, and esters. To some extent, functional responses varied with the seasons but the least physiological activity was shown by the site rehabilitated in 1987 indicating long-term perturbation to this ecosystem. Small subunit ribosomal DNA (SSUrDNA)-denaturing gradient-gel electrophoresis analyses on the microbiota collected from the most preferred CLPP substrates followed by taxonomic analyses showed Proteobacteria, specifically the gamma-proteobacteria, as the most functionally active phyla, indicating a propensity of this phyla to out-compete other groups under the prevailing conditions. Additionally, multivariate statistical analyses, Shannon's diversity, and evenness indices, principal component analysis, biplot and un-weighted-pair-group method with arithmetic averages dendrograms further confirmed that un-mined sites were distinctly different from the rehabilitated soils.

Distribution and Multivariate Pollution Risks Assessment of Heavy Metals and Natural Radionuclides Around Abandoned Iron-Ore Mines in North Central Nigeria

NASA Astrophysics Data System (ADS)

Isinkaye, Omoniyi Matthew

2018-02-01

The Itakpe abandoned iron-ore mines constitute the largest iron-ore deposits in Nigeria with an estimated reserve of about three million metric tons of ore. The present effort is a part of a comprehensive study to estimate the environmental and radiological health hazards associated with previous mining operations in the study area. In this regard, heavy metals (Fe, Zn, Cu, Cd, Cr, Mn, Pb, Ni, Co and As) and natural radionuclides (U, Th and K) were measured in rock, soil and water samples collected at different locations within the mining sites. Atomic absorption and gamma-ray spectrometry were utilized for the measurements. Fe, Mn, Zn, Cu, Ni, Cd, Cr, Co Pb and As were detected at varying concentrations in rock and soil samples. Cd, Cr, Pb and As were not detected in water samples. The concentrations of heavy metals vary according to the following pattern; rock ˃ soil ˃ water. The mean elemental concentrations of K, U and Th are 2.9%, 0.8 and 1.2 ppm and 1.3%, 0.7 and 1.7 ppm, respectively, for rock and soil samples. Pearson correlation analyses of the results indicate that the heavy metals are mostly negatively correlated with natural radionuclides in the study area. Cancer and non-cancer risks due to heavy metals and radiological hazards due to natural radionuclides to the population living within the vicinity of the abandoned mines are lower than acceptable limits. It can, therefore, be concluded that no significant environmental or radiological health hazard is envisaged.

The impact of covariance misspecification in multivariate Gaussian mixtures on estimation and inference: an application to longitudinal modeling.

PubMed

Heggeseth, Brianna C; Jewell, Nicholas P

2013-07-20

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence-that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.

Use of Multivariate Linkage Analysis for Dissection of a Complex Cognitive Trait

PubMed Central

Marlow, Angela J.; Fisher, Simon E.; Francks, Clyde; MacPhie, I. Laurence; Cherny, Stacey S.; Richardson, Alex J.; Talcott, Joel B.; Stein, John F.; Monaco, Anthony P.; Cardon, Lon R.

2003-01-01

Replication of linkage results for complex traits has been exceedingly difficult, owing in part to the inability to measure the precise underlying phenotype, small sample sizes, genetic heterogeneity, and statistical methods employed in analysis. Often, in any particular study, multiple correlated traits have been collected, yet these have been analyzed independently or, at most, in bivariate analyses. Theoretical arguments suggest that full multivariate analysis of all available traits should offer more power to detect linkage; however, this has not yet been evaluated on a genomewide scale. Here, we conduct multivariate genomewide analyses of quantitative-trait loci that influence reading- and language-related measures in families affected with developmental dyslexia. The results of these analyses are substantially clearer than those of previous univariate analyses of the same data set, helping to resolve a number of key issues. These outcomes highlight the relevance of multivariate analysis for complex disorders for dissection of linkage results in correlated traits. The approach employed here may aid positional cloning of susceptibility genes in a wide spectrum of complex traits. PMID:12587094

Risk Assessment and Source Identification of 17 Metals and Metalloids on Soils from the Half-Century Old Tungsten Mining Areas in Lianhuashan, Southern China.

PubMed

Guo, Li; Zhao, Weituo; Gu, Xiaowen; Zhao, Xinyun; Chen, Juan; Cheng, Shenggao

2017-11-29

Background: Mining activities always emit metal(loid)s into the surrounding environment, where their accumulation in the soil may pose risks and hazards to humans and ecosystems. Objective : This paper aims to determine of the type, source, chemical form, fate and transport, and accurate risk assessment of 17 metal(loid) contaminants including As, Cd, Cu, Ni, Pb, Zn, Cr, Ag, B, Bi, Co, Mo, Sb, Ti, V, W and Sn in the soils collected from an abandoned tungsten mining area, and to guide the implementing of appropriate remediation strategies. Methods : Contamination factors ( CFs ) and integrated pollution indexes ( IPIs ) and enrichment factors ( EFs ) were used to assess their ecological risk and the sources were identified by using multivariate statistics analysis, spatial distribution investigation and correlation matrix. Results : The IPI and EF values indicated the soils in the mine site and the closest downstream one were extremely disturbed by metal(loid)s such as As, Bi, W, B, Cu, Pb and Sn, which were emitted from the mining wastes and acid drainages and delivered by the runoff and human activities. Arsenic contamination was detected in nine sites with the highest CF values at 24.70 next to the mining site. The Cd contamination scattered in the paddy soils around the resident areas with higher fraction of bioavailable forms, primarily associated with intense application of phosphorus fertilizer. The lithogenic elements V, Ti, Ag, Ni, Sb, Mo exhibit low contamination in all sampling points and their distribution were depended on the soil texture and pedogenesis process. Conclusions : The long term historical mining activities have caused severe As contamination and higher enrichment of the other elements of orebody in the local soils. The appropriate remediation treatment approach should be proposed to reduce the bioavailability of Cd in the paddy soils and to immobilize As to reclaim the soils around the mining site. Furthermore, alternative fertilizing way and irrigating water sources are urgencies to reduce the input of Cd and As into the local soils effectively.

Risk Assessment and Source Identification of 17 Metals and Metalloids on Soils from the Half-Century Old Tungsten Mining Areas in Lianhuashan, Southern China

PubMed Central

Guo, Li; Zhao, Weituo; Gu, Xiaowen; Zhao, Xinyun; Chen, Juan; Cheng, Shenggao

2017-01-01

Background: Mining activities always emit metal(loid)s into the surrounding environment, where their accumulation in the soil may pose risks and hazards to humans and ecosystems. Objective: This paper aims to determine of the type, source, chemical form, fate and transport, and accurate risk assessment of 17 metal(loid) contaminants including As, Cd, Cu, Ni, Pb, Zn, Cr, Ag, B, Bi, Co, Mo, Sb, Ti, V, W and Sn in the soils collected from an abandoned tungsten mining area, and to guide the implementing of appropriate remediation strategies. Methods: Contamination factors (CFs) and integrated pollution indexes (IPIs) and enrichment factors (EFs) were used to assess their ecological risk and the sources were identified by using multivariate statistics analysis, spatial distribution investigation and correlation matrix. Results: The IPI and EF values indicated the soils in the mine site and the closest downstream one were extremely disturbed by metal(loid)s such as As, Bi, W, B, Cu, Pb and Sn, which were emitted from the mining wastes and acid drainages and delivered by the runoff and human activities. Arsenic contamination was detected in nine sites with the highest CF values at 24.70 next to the mining site. The Cd contamination scattered in the paddy soils around the resident areas with higher fraction of bioavailable forms, primarily associated with intense application of phosphorus fertilizer. The lithogenic elements V, Ti, Ag, Ni, Sb, Mo exhibit low contamination in all sampling points and their distribution were depended on the soil texture and pedogenesis process. Conclusions: The long term historical mining activities have caused severe As contamination and higher enrichment of the other elements of orebody in the local soils. The appropriate remediation treatment approach should be proposed to reduce the bioavailability of Cd in the paddy soils and to immobilize As to reclaim the soils around the mining site. Furthermore, alternative fertilizing way and irrigating water sources are urgencies to reduce the input of Cd and As into the local soils effectively. PMID:29186069

The Coastal Dynamics of Heterogeneous Sedimentary Environments: Numerical Modeling of Hydrodynamics and Mass Transport in Estuaries

DTIC Science & Technology

2010-08-12

environmental risk assessment using the example of areas contaminated due to mining activity." Applied Radiation and Isotopes 66(11): 1661-1665. Miles, A...Medina, et al. (2006). "The Prestige oil spill in Cantabria (Bay of Biscay). Part I: Operational forecasting system for quick response, risk assessment ...successfully applied to oil spill prediction using operational or near -operational models (Ko, Rowley et al. 2005; Castanedo, Medina et al. 2006

Evaluation of the mercaptobenzothiazole degradation by combined adsorption process and Fenton reaction using iron mining residue.

PubMed

Martins, Adriana Lau da Silva; Teixeira, Luís Alberto César; da Fonseca, Fabiana Valéria; Yokoyama, Lídia

2017-08-01

The present study investigated the degradation of mercaptobenzothiazole (MBT), evaluating homogeneous and heterogeneous systems. An iron mineral residue from the desliming step of iron mining was used as a source in the Fenton-like reaction (advanced oxidation process). A granulometric analysis of the residue was performed and yielded fractions with high hematite (Fe 2 O 3 ) and low quartz content in sieves from 74 to below 44 mm. In this particle size range, the hematite content from 58.9% to 67.4% and the Brunauer-Emmett-Teller area from 0.1345 to 1.3137 m 2  g -1 were obtained. The zeta potential curves as a function of pH were obtained for the residue, the MBT solution and mixtures thereof. The adsorption of MBT in the residue and its degradation through the Fenton-like reaction were investigated. Adsorption tests and the Fenton-like reaction were carried out, where the MBT species and the residue are oppositely charged, yielding, respectively, 10% MBT adsorption on the surface of the residue and 100% MBT degradation by the Fenton-like reaction at pH 3, hydrogen peroxide concentration of 25 mg L -1 , residue concentration of 3 g L -1 , 200 rpm and 25°C, from a 100 mg L -1 MBT solution. MBT degradation was found to occur mainly by the heterogeneous Fenton-like process.

Combination of complementary data mining methods for geographical characterization of extra virgin olive oils based on mineral composition.

PubMed

Sayago, Ana; González-Domínguez, Raúl; Beltrán, Rafael; Fernández-Recamales, Ángeles

2018-09-30

This work explores the potential of multi-element fingerprinting in combination with advanced data mining strategies to assess the geographical origin of extra virgin olive oil samples. For this purpose, the concentrations of 55 elements were determined in 125 oil samples from multiple Spanish geographic areas. Several unsupervised and supervised multivariate statistical techniques were used to build classification models and investigate the relationship between mineral composition of olive oils and their provenance. Results showed that Spanish extra virgin olive oils exhibit characteristic element profiles, which can be differentiated on the basis of their origin in accordance with three geographical areas: Atlantic coast (Huelva province), Mediterranean coast and inland regions. Furthermore, statistical modelling yielded high sensitivity and specificity, principally when random forest and support vector machines were employed, thus demonstrating the utility of these techniques in food traceability and authenticity research. Copyright © 2018 Elsevier Ltd. All rights reserved.

Speciation and distribution of copper in a mining soil using multiple synchrotron-based bulk and microscopic techniques.

PubMed

Yang, Jianjun; Liu, Jin; Dynes, James J; Peak, Derek; Regier, Tom; Wang, Jian; Zhu, Shenhai; Shi, Jiyan; Tse, John S

2014-02-01

Molecular-level understanding of soil Cu speciation and distribution assists in management of Cu contamination in mining sites. In this study, one soil sample, collected from a mining site contaminated since 1950s, was characterized complementarily by multiple synchrotron-based bulk and spatially resolved techniques for the speciation and distribution of Cu as well as other related elements (Fe, Ca, Mn, K, Al, and Si). Bulk X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectroscopy revealed that soil Cu was predominantly associated with Fe oxides instead of soil organic matter. This agreed with the closest association of Cu to Fe by microscopic X-ray fluorescence (U-XRF) and scanning transmission X-ray microscopy (STXM) nanoanalysis, along with the non-occurrence of photoreduction of soil Cu(II) by quick Cu L3,2-edge XANES spectroscopy (Q-XANES) which often occurs when Cu organic complexes are present. Furthermore, bulk-EXAFS and STXM-coupled Fe L3,2-edge nano-XANES analysis revealed soil Cu adsorbed primarily to Fe(III) oxides by inner-sphere complexation. Additionally, Cu K-edge μ-XANES, L3,2-edge bulk-XANES, and successive Q-XANES results identified the presence of Cu2S rather than radiation-damage artifacts dominant in certain microsites of the mining soil. This study demonstrates the great benefits in use of multiple combined synchrotron-based techniques for comprehensive understanding of Cu speciation in heterogeneous soil matrix, which facilitates our prediction of Cu reactivity and environmental fate in the mining site.

Impacts of detrital nano- and micro-scale particles (dNP) on contaminant dynamics in a coal mine AMD treatment system.

PubMed

Lefticariu, Liliana; Sutton, Stephen R; Bender, Kelly S; Lefticariu, Mihai; Pentrak, Martin; Stucki, Joseph W

2017-01-01

Pollutants in acid mine drainage (AMD) are usually sequestered in neoformed nano- and micro-scale particles (nNP) through precipitation, co-precipitation, and sorption. Subsequent biogeochemical processes may control nNP stability and thus long-term contaminant immobilization. Mineralogical, chemical, and microbiological data collected from sediments accumulated over a six-year period in a coal-mine AMD treatment system were used to identify the pathways of contaminant dynamics. We present evidence that detrital nano- and micron-scale particles (dNP), composed mostly of clay minerals originating from the partial weathering of coal-mine waste, mediated biogeochemical processes that catalyzed AMD contaminant (1) immobilization by facilitating heterogeneous nucleation and growth of nNP in oxic zones, and (2) remobilization by promoting phase transformation and reductive dissolution of nNP in anoxic zones. We found that dNP were relatively stable under acidic conditions and estimated a dNP content of ~0.1g/L in the influent AMD. In the AMD sediments, the initial nNP precipitates were schwertmannite and poorly crystalline goethite, which transformed to well-crystallized goethite, the primary nNP repository. Subsequent reductive dissolution of nNP resulted in the remobilization of up to 98% of S and 95% of Fe accompanied by the formation of a compact dNP layer. Effective treatment of pollutants could be enhanced by better understanding the complex, dynamic role dNP play in mediating biogeochemical processes and contaminant dynamics at coal-mine impacted sites. Copyright © 2016 Elsevier B.V. All rights reserved.

Zinc and lead poisoning in wild birds in the tri-state mining district (Oklahoma, Kansas, and Missouri).

PubMed

Beyer, W N; Dalgarn, J; Dudding, S; French, J B; Mateo, R; Miesner, J; Sileo, L; Spann, J

2005-01-01

The Tri-State Mining District (Oklahoma, Kansas, and Missouri) is contaminated with Pb, Cd, and Zn from mining, milling and smelting. Metals have been dispersed heterogeneously throughout the District in the form of milled mine waste ("chat"), as flotation tailings and from smelters as aerial deposition or slag. This study was conducted to determine if the habitat has been contaminated to the extent that the assessment populations of wild birds are exposed to toxic concentrations of metals. American robins (Turdus migratorius), northern cardinals (Cardinalis cardinalis), and waterfowl had increased Pb tissue concentrations (p < 0.05) compared with Pb tissue concentrations from reference birds, and the exposure of songbirds to Pb was comparable with that of birds observed at other sites severely contaminated with Pb. Mean activities of the Pb-sensitive enzyme delta-aminolevulinic acid dehydratase (ALAD) were decreased by >50% in red blood cells in these birds (p < 0.05). Several birds had tissue concentrations of Pb that have been associated with impaired biological functions and external signs of poisoning. Cadmium was increased in kidneys of songbirds (p < 0.05), but no proximal tubule cell necrosis associated with Cd poisoning was observed. Zinc concentrations in liver and kidney of waterfowl were significantly higher (p < 0.05) than reference values. The increased environmental concentrations of Zn associated with mining in the District accounted for the pancreatitis previously observed in five waterfowl from the District. The District is the first site at which free-flying wild birds have been found to be suffering severe effects of Zn poisoning.

The dendroanalysis of oak trees as a method of biomonitoring past and recent contamination in an area influenced by uranium mining.

PubMed

Märten, Arno; Berger, Dietrich; Köhler, Mirko; Merten, Dirk

2015-12-01

We reconstructed the contamination history of an area influenced by 40 years of uranium mining and subsequent remediation actions using dendroanalysis (i.e., the determination of the elemental content of tree rings). The uranium content in the tree rings of four individual oak trees (Quercus sp.) was determined by laser ablation with inductively coupled plasma mass spectrometry (LA-ICP-MS). This technique allows the investigation of trace metals in solid samples with a spatial resolution of 250 μm and a detection limit below 0.01 μg/g for uranium. The investigations show that in three of the four oaks sampled, there were temporally similar uranium concentrations. These were approximately 2 orders of magnitude higher (0.15 to 0.4 μg/g) than those from before the period of active mining (concentrations below 0.01 μg/g). After the mining was terminated and the area was restored, the uranium contents in the wood decreased by approximately 1 order of magnitude. The similar radial uranium distribution patterns of the three trees were confirmed by correlation analysis. In combination with the results of soil analyses, it was determined that there was a heterogeneous contamination in the forest investigated. This could be confirmed by pre-remediation soil uranium contents from literature. The uranium contents in the tree rings of the oaks investigated reflect the contamination history of the study area. This study demonstrates that the dendrochemical analysis of oak tree rings is a suitable technique for investigating past and recent uranium contamination in mining areas.

Multicausal Systems Ask for Multicausal Approaches: A Network Perspective on Subjective Well-Being in Individuals with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Deserno, Marie K.; Borsboom, Denny; Begeer, Sander; Geurts, Hilde M.

2017-01-01

Given the heterogeneity of autism spectrum disorder, an important limitation of much autism spectrum disorder research is that outcome measures are statistically modeled as separate dependent variables. Often, their multivariate structure is either ignored or treated as a nuisance. This study aims to lift this limitation by applying network…

Development and Sliding Wear Response of Epoxy Composites Filled with Coal Mine Overburden Material

NASA Astrophysics Data System (ADS)

Das, Prithika; Satapathy, Alok; Mishra, M. K.

2018-03-01

The paper reports on development and characterization of epoxy based composites filled with micro-sized mine overburden material. Coal mine overburden material is typically highly heterogeneous and is considered as waste material. For excavating each ton of coal, roughly 5 tons of overburden materials are removed and is dumped nearby occupying large space. Gainful utilization of this waste is a major challenge. In the present work, this material is used as filler materials in making a new class of epoxy matrix composites. Composites with different weight proportions of fillers (0, 10, 20, 30 and 40) wt. % are prepared by hand layup technique. Compression tests are performed as per corresponding ASTM standards to assess the compressive strength of these composites. Further, dry sliding tests are performed following ASTM G99 standards using a pin on disk machine. A design of experiment approach based on Taguchi’s L16 orthogonal arrays is adopted. Tests are performed at different sliding velocities for multiple sliding distances under varying normal loads. Specific wear rates of the composites under different test conditions are obtained. The analysis of the test results revealed that the filler content and the sliding velocity are the most predominant control factors affecting the wear rate. This work thus, opens up a new avenue for the value added utilization of coal mine overburden material.

Modeling meander morphodynamics over self-formed heterogeneous floodplains

NASA Astrophysics Data System (ADS)

Bogoni, Manuel; Putti, Mario; Lanzoni, Stefano

2017-06-01

This work addresses the signatures embedded in the planform geometry of meandering rivers consequent to the formation of floodplain heterogeneities as the river bends migrate. Two geomorphic features are specifically considered: scroll bars produced by lateral accretion of point bars at convex banks and oxbow lake fills consequent to neck cutoffs. The sedimentary architecture of these geomorphic units depends on the type and amount of sediment, and controls bank erodibility as the river impinges on them, favoring or contrasting the river migration. The geometry of numerically generated planforms obtained for different scenarios of floodplain heterogeneity is compared to that of natural meandering paths. Half meander metrics and spatial distribution of channel curvatures are used to disclose the complexity embedded in meandering geometry. Fourier Analysis, Principal Component Analysis, Singular Spectrum Analysis and Multivariate Singular Spectrum Analysis are used to emphasize the subtle but crucial differences which may emerge between apparently similar configurations. A closer similarity between observed and simulated planforms is attained when fully coupling flow and sediment dynamics (fully-coupled models) and when considering self-formed heterogeneities that are less erodible than the surrounding floodplain.

Screening Models of Aquifer Heterogeneity Using the Flow Dimension

NASA Astrophysics Data System (ADS)

Walker, D. D.; Cello, P. A.; Roberts, R. M.; Valocchi, A. J.

2007-12-01

Despite advances in test interpretation and modeling, typical groundwater modeling studies only indirectly use the parameters and information inferred from hydraulic tests. In particular, the Generalized Radial Flow approach to test interpretation infers the flow dimension, a parameter describing the geometry of the flow field during a hydraulic test. Noninteger values of the flow dimension often are inferred for tests in highly heterogeneous aquifers, yet subsequent modeling studies typically ignore the flow dimension. Monte Carlo analyses of detailed numerical models of aquifer tests examine the flow dimension for several stochastic models of heterogeneous transmissivity, T(x). These include multivariate lognormal, fractional Brownian motion, a site percolation network, and discrete linear features with lengths distributed as power-law. The behavior of the simulated flow dimensions are compared to the flow dimensions observed for multiple aquifer tests in a fractured dolomite aquifer in the Great Lakes region of North America. The combination of multiple hydraulic tests, observed fracture patterns, and the Monte Carlo results are used to screen models of heterogeneity and their parameters for subsequent groundwater flow modeling.

The Flow Dimension and Aquifer Heterogeneity: Field evidence and Numerical Analyses

NASA Astrophysics Data System (ADS)

Walker, D. D.; Cello, P. A.; Valocchi, A. J.; Roberts, R. M.; Loftis, B.

2008-12-01

The Generalized Radial Flow approach to hydraulic test interpretation infers the flow dimension to describe the geometry of the flow field during a hydraulic test. Noninteger values of the flow dimension often are inferred for tests in highly heterogeneous aquifers, yet subsequent modeling studies typically ignore the flow dimension. Monte Carlo analyses of detailed numerical models of aquifer tests examine the flow dimension for several stochastic models of heterogeneous transmissivity, T(x). These include multivariate lognormal, fractional Brownian motion, a site percolation network, and discrete linear features with lengths distributed as power-law. The behavior of the simulated flow dimensions are compared to the flow dimensions observed for multiple aquifer tests in a fractured dolomite aquifer in the Great Lakes region of North America. The combination of multiple hydraulic tests, observed fracture patterns, and the Monte Carlo results are used to screen models of heterogeneity and their parameters for subsequent groundwater flow modeling. The comparison shows that discrete linear features with lengths distributed as a power-law appear to be the most consistent with observations of the flow dimension in fractured dolomite aquifers.

On-Board Mining in the Sensor Web

NASA Astrophysics Data System (ADS)

Tanner, S.; Conover, H.; Graves, S.; Ramachandran, R.; Rushing, J.

2004-12-01

On-board data mining can contribute to many research and engineering applications, including natural hazard detection and prediction, intelligent sensor control, and the generation of customized data products for direct distribution to users. The ability to mine sensor data in real time can also be a critical component of autonomous operations, supporting deep space missions, unmanned aerial and ground-based vehicles (UAVs, UGVs), and a wide range of sensor meshes, webs and grids. On-board processing is expected to play a significant role in the next generation of NASA, Homeland Security, Department of Defense and civilian programs, providing for greater flexibility and versatility in measurements of physical systems. In addition, the use of UAV and UGV systems is increasing in military, emergency response and industrial applications. As research into the autonomy of these vehicles progresses, especially in fleet or web configurations, the applicability of on-board data mining is expected to increase significantly. Data mining in real time on board sensor platforms presents unique challenges. Most notably, the data to be mined is a continuous stream, rather than a fixed store such as a database. This means that the data mining algorithms must be modified to make only a single pass through the data. In addition, the on-board environment requires real time processing with limited computing resources, thus the algorithms must use fixed and relatively small amounts of processing time and memory. The University of Alabama in Huntsville is developing an innovative processing framework for the on-board data and information environment. The Environment for On-Board Processing (EVE) and the Adaptive On-board Data Processing (AODP) projects serve as proofs-of-concept of advanced information systems for remote sensing platforms. The EVE real-time processing infrastructure will upload, schedule and control the execution of processing plans on board remote sensors. These plans provide capabilities for autonomous data mining, classification and feature extraction using both streaming and buffered data sources. A ground-based testbed provides a heterogeneous, embedded hardware and software environment representing both space-based and ground-based sensor platforms, including wireless sensor mesh architectures. The AODP project explores the EVE concepts in the world of sensor-networks, including ad-hoc networks of small sensor platforms.

An ecosystem approach to evaluate restoration measures in the lignite mining district of Lusatia/Germany

NASA Astrophysics Data System (ADS)

Schaaf, Wolfgang

2015-04-01

Lignite mining in Lusatia has a history of over 100 years. Open-cast mining directly affected an area of 1000 km2. Since 20 years we established an ecosystem oriented approach to evaluate the development and site characteristics of post-mining areas mainly restored for agricultural and silvicultural land use. Water and element budgets of afforested sites were studied under different geochemical settings in a chronosequence approach (Schaaf 2001), as well as the effect of soil amendments like sewage sludge or compost in restoration (Schaaf & Hüttl 2006). Since 10 years we also study the development of natural site regeneration in the constructed catchment Chicken Creek at the watershed scale (Schaaf et al. 2011, 2013). One of the striking characteristics of post-mining sites is a very large small-scale soil heterogeneity that has to be taken into account with respect to soil forming processes and element cycling. Results from these studies in combination with smaller-scale process studies enable to evaluate the long-term effect of restoration measures and adapted land use options. In addition, it is crucial to compare these results with data from undisturbed, i.e. non-mined sites. Schaaf, W., 2001: What can element budgets of false-time series tell us about ecosystem development on post-lignite mining sites? Ecological Engineering 17, 241-252. Schaaf, W. and Hüttl, R. F., 2006: Direct and indirect effects of soil pollution by lignite mining. Water, Air and Soil Pollution - Focus 6, 253-264. Schaaf, W., Bens, O., Fischer, A., Gerke, H.H., Gerwin, W., Grünewald, U., Holländer, H.M., Kögel-Knabner, I., Mutz, M., Schloter, M., Schulin, R., Veste, M., Winter, S. & Hüttl, R.F., 2011: Patterns and processes of initial terrestrial-ecosystem development. Journal of Plant Nutrition and Soil Science, 174, 229-239. Schaaf, W., Elmer, M., Fischer, A., Gerwin, W., Nenov, R., Pretsch, H. and Zaplate, M.K., 2013: Feedbacks between vegetation, surface structures and hydrology during initial development of the artificial catchment `Chicken Creek'. Procedia Environmental Sciences 19, 86-95.

NDVI (Normalized Difference Vegetation Index) signatures of transient ecohydrological systems: The case of post-mining landscapes

NASA Astrophysics Data System (ADS)

Brück, Yasemine; Schulte Overberg, Philipp; Pohle, Ina; Hinz, Christoph

2017-04-01

Assessing ecohydrological systems that undergo state transitions due to environmental change is becoming increasingly important. One system that can be used to study severe disturbances are post-mining landscapes as they usually are associated with complete removal of vegetation and afterwards subsequent ecosystem restoration or spontaneous rehabilitation in line with natural succession. Within this context it is of interest, whether and how (fast) the land cover in these areas returns to conditions comparable to those in the undisturbed surrounding or those prior mining. Many aspects of mine site rehabilitation depend on climatic, geomorphic and ecological settings, which determine at which rate vegetation may be re-established. In order to identify general patterns of vegetation establishment, we propose to use NDVI (Normalized Difference Vegetation Index) time series for mine affected land to estimate rate of recovery across climate regions and ecoregions. In this study we analysed the MODIS Terra Satellite 8 day-composite NDVI for areas influenced by surface mining in different climates from 2001 to 2015. The locations have been chosen based on their extent and the data availability of mining and rehabilitation activities. We selected coal extraction as a case study as strip mining generates well-defined chronosequences of disturbance. The selected mining areas are located in equatorial, arid, warm temperate or snow climates with different precipitation and temperature conditions according to the Köppen-Geiger classification. We analysed the NDVI time series regarding significant characteristics of the re-vegetation phase. We applied hierarchical cluster analysis to capture the spatial heterogeneity between different pixels (ca. 250 * 250 m2 each) in and around each open cast mine. We disentangled seasonality, trend and residual components in the NDVI time series by Seasonal and Trend decomposition using LOESS. As expected the time of the removal of vegetation can be clearly identified from the NDVI time series and provides the starting point of disturbance. The cluster analysis allowed us to distinguish between the non-mining land, the mine and the restored land of different ages. Based on these clusters, the time series decomposition revealed the dominance of the trend of increasing NDVI in areas undergoing the restoration process as well as the prevailing seasonality of the oldest restored sites. The determined phase of a dominant trend component, lasting until the NDVI is in the range of the surrounding landscape or the pre-mining conditions, is in the scale of a decade. The impacts of different hydroclimatic regimes and different rehabilitation strategies on long term NDVI development are currently being investigated. Furthermore, coherence analysis will be applied to quantify short term influences of hydrometeorological variables on vegetation development.

Assessing anthropogenic levels, speciation, and potential mobility of rare earth elements (REEs) in ex-tin mining area.

PubMed

Khan, Aysha Masood; Yusoff, Ismail; Bakar, Nor Kartini Abu; Bakar, Ahmad Farid Abu; Alias, Yatimah

2016-12-01

A study was carried out to determine the level of rare earth elements (REEs) in water and sediment samples from ex-mining lakes and River in Kinta Valley, Perak, Malaysia. Surface water and sediments from an ex-mining lake and Kinta River water samples were analyzed for REEs by inductively coupled plasma mass spectrometry. The total concentration of REEs in the ex-mining lake water samples and sediments were found to be 3685 mg/l and 14159 mg/kg, respectively, while the total concentration of REEs in Kinta River water sample was found to be 1224 mg/l. REEs in mining lake water were found to be within 2.42 mg/l (Tb) to 46.50 mg/l (Ce), while for the Kinta River, it was 1.33 mg/l (Ho) to 29.95 mg/l (Ce). Sediment samples were also found with REEs from 9.81 mg/kg (Ho) to 765.84 mg/kg (Ce). Ce showed the highest average concentrations for mining lake (3.88 to 49.08 mg/l) and Kinta River (4.44 to 33.15 mg/l) water samples, while the concentration of La was the highest (11.59 to 771.61 mg/kg) in the mining lake sediment. Lu was shown to have the highest enrichment of REEs in ex-mining lake sediments (107.3). Multivariate statistical analyses such as factor analysis and principal component analysis indicated that REEs were associated and controlled by mixed origin, with similar contributions from anthropogenic and geogenic sources. The speciation study of REEs in ex-tin mining sediments using a modified five-stage sequential extraction procedure indicated that yttrium (Y), gadolinium (Gd), and lanthanum (La) were obtained at higher percentages from the adsorbed/exchanged/carbonate fraction. The average potential mobility of the REEs was arranged in a descending order: Yb > Gd > Y = Dy > Pr > Er > Tm > Eu > Nd > Tb > Sc > Lu > Ce > La, implying that under favorable conditions, these REEs could be released and subsequently pollute the environment.

Characterization of Interfacial Chemistry of Adhesive/Dentin Bond Using FTIR Chemical Imaging With Univariate and Multivariate Data Processing

PubMed Central

Wang, Yong; Yao, Xiaomei; Parthasarathy, Ranganathan

2008-01-01

Fourier transform infrared (FTIR) chemical imaging can be used to investigate molecular chemical features of the adhesive/dentin interfaces. However, the information is not straightforward, and is not easily extracted. The objective of this study was to use multivariate analysis methods, principal component analysis and fuzzy c-means clustering, to analyze spectral data in comparison with univariate analysis. The spectral imaging data collected from both the adhesive/healthy dentin and adhesive/caries-affected dentin specimens were used and compared. The univariate statistical methods such as mapping of intensities of specific functional group do not always accurately identify functional group locations and concentrations due to more or less band overlapping in adhesive and dentin. Apart from the ease with which information can be extracted, multivariate methods highlight subtle and often important changes in the spectra that are difficult to observe using univariate methods. The results showed that the multivariate methods gave more satisfactory, interpretable results than univariate methods and were conclusive in showing that they can discriminate and classify differences between healthy dentin and caries-affected dentin within the interfacial regions. It is demonstrated that the multivariate FTIR imaging approaches can be used in the rapid characterization of heterogeneous, complex structure. PMID:18980198

Mass Spectrometry Imaging for the Investigation of Intratumor Heterogeneity.

PubMed

Balluff, B; Hanselmann, M; Heeren, R M A

2017-01-01

One of the big clinical challenges in the treatment of cancer is the different behavior of cancer patients under guideline therapy. An important determinant for this phenomenon has been identified as inter- and intratumor heterogeneity. While intertumor heterogeneity refers to the differences in cancer characteristics between patients, intratumor heterogeneity refers to the clonal and nongenetic molecular diversity within a patient. The deciphering of intratumor heterogeneity is recognized as key to the development of novel therapeutics or treatment regimens. The investigation of intratumor heterogeneity is challenging since it requires an untargeted molecular analysis technique that accounts for the spatial and temporal dynamics of the tumor. So far, next-generation sequencing has contributed most to the understanding of clonal evolution within a cancer patient. However, it falls short in accounting for the spatial dimension. Mass spectrometry imaging (MSI) is a powerful tool for the untargeted but spatially resolved molecular analysis of biological tissues such as solid tumors. As it provides multidimensional datasets by the parallel acquisition of hundreds of mass channels, multivariate data analysis methods can be applied for the automated annotation of tissues. Moreover, it integrates the histology of the sample, which enables studying the molecular information in a histopathological context. This chapter will illustrate how MSI in combination with statistical methods and histology has been used for the description and discovery of intratumor heterogeneity in different cancers. This will give evidence that MSI constitutes a unique tool for the investigation of intratumor heterogeneity, and could hence become a key technology in cancer research. © 2017 Elsevier Inc. All rights reserved.

Crisp clustering of airborne geophysical data from the Alto Ligonha pegmatite field, northeastern Mozambique, to predict zones of increased rare earth element potential

NASA Astrophysics Data System (ADS)

Eberle, Detlef G.; Daudi, Elias X. F.; Muiuane, Elônio A.; Nyabeze, Peter; Pontavida, Alfredo M.

2012-01-01

The National Geology Directorate of Mozambique (DNG) and Maputo-based Eduardo-Mondlane University (UEM) entered a joint venture with the South African Council for Geoscience (CGS) to conduct a case study over the meso-Proterozoic Alto Ligonha pegmatite field in the Zambézia Province of northeastern Mozambique to support the local exploration and mining sectors. Rare-metal minerals, i.e. tantalum and niobium, as well as rare-earth minerals have been mined in the Alto Ligonha pegmatite field since decades, but due to the civil war (1977-1992) production nearly ceased. The Government now strives to promote mining in the region as contribution to poverty alleviation. This study was undertaken to facilitate the extraction of geological information from the high resolution airborne magnetic and radiometric data sets recently acquired through a World Bank funded survey and mapping project. The aim was to generate a value-added map from the airborne geophysical data that is easier to read and use by the exploration and mining industries than mere airborne geophysical grid data or maps. As a first step towards clustering, thorium (Th) and potassium (K) concentrations were determined from the airborne geophysical data as well as apparent magnetic susceptibility and first vertical magnetic gradient data. These four datasets were projected onto a 100 m spaced regular grid to assemble 850,000 four-element (multivariate) sample vectors over the study area. Classification of the sample vectors using crisp clustering based upon the Euclidian distance between sample and class centre provided a (pseudo-) geology map or value-added map, respectively, displaying the spatial distribution of six different classes in the study area. To learn the quality of sample allocation, the degree of membership of each sample vector was determined using a-posterior discriminant analysis. Geophysical ground truth control was essential to allocate geology/geophysical attributes to the six classes. The highest probability to meet pegmatite bodies is in close vicinity to (magnetic) amphibole schist occurring in areas where depletion of potassium as indication of metasomatic processes is evident from the airborne radiometric data. Clustering has proven to be a fast and effective method to compile value-added maps from multivariate geophysical datasets. Experience made in the Alto Ligonha pegmatite field encourages adopting this new methodology for mapping other parts of the Mozambique Fold Belt.

RAIN: RNA–protein Association and Interaction Networks

PubMed Central

Junge, Alexander; Refsgaard, Jan C.; Garde, Christian; Pan, Xiaoyong; Santos, Alberto; Alkan, Ferhat; Anthon, Christian; von Mering, Christian; Workman, Christopher T.; Jensen, Lars Juhl; Gorodkin, Jan

2017-01-01

Protein association networks can be inferred from a range of resources including experimental data, literature mining and computational predictions. These types of evidence are emerging for non-coding RNAs (ncRNAs) as well. However, integration of ncRNAs into protein association networks is challenging due to data heterogeneity. Here, we present a database of ncRNA–RNA and ncRNA–protein interactions and its integration with the STRING database of protein–protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data, interaction predictions and automatic literature mining. RAIN uses an integrative scoring scheme to assign a confidence score to each interaction. We demonstrate that RAIN outperforms the underlying microRNA-target predictions in inferring ncRNA interactions. RAIN can be operated through an easily accessible web interface and all interaction data can be downloaded. Database URL: http://rth.dk/resources/rain PMID:28077569

Improving risk-stratification of Diabetes complications using temporal data mining.

PubMed

Sacchi, Lucia; Dagliati, Arianna; Segagni, Daniele; Leporati, Paola; Chiovato, Luca; Bellazzi, Riccardo

2015-01-01

To understand which factor trigger worsened disease control is a crucial step in Type 2 Diabetes (T2D) patient management. The MOSAIC project, funded by the European Commission under the FP7 program, has been designed to integrate heterogeneous data sources and provide decision support in chronic T2D management through patients' continuous stratification. In this work we show how temporal data mining can be fruitfully exploited to improve risk stratification. In particular, we exploit administrative data on drug purchases to divide patients in meaningful groups. The detection of drug consumption patterns allows stratifying the population on the basis of subjects' purchasing attitude. Merging these findings with clinical values indicates the relevance of the applied methods while showing significant differences in the identified groups. This extensive approach emphasized the exploitation of administrative data to identify patterns able to explain clinical conditions.

Template for preparation of papers for IEEE sponsored conferences & symposia.

PubMed

Sacchi, L; Dagliati, A; Tibollo, V; Leporati, P; De Cata, P; Cerra, C; Chiovato, L; Bellazzi, R

2015-01-01

To improve the access to medical information is necessary to design and implement integrated informatics techniques aimed to gather data from different and heterogeneous sources. This paper describes the technologies used to integrate data coming from the electronic medical record of the IRCCS Fondazione Maugeri (FSM) hospital of Pavia, Italy, and combines them with administrative, pharmacy drugs purchase coming from the local healthcare agency (ASL) of the Pavia area and environmental open data of the same region. The integration process is focused on data coming from a cohort of one thousand patients diagnosed with Type 2 Diabetes Mellitus (T2DM). Data analysis and temporal data mining techniques have been integrated to enhance the initial dataset allowing the possibility to stratify patients using further information coming from the mined data like behavioral patterns of prescription-related drug purchases and other frequent clinical temporal patterns, through the use of an intuitive dashboard controlled system.

Massive problem reports mining and analysis based parallelism for similar search

NASA Astrophysics Data System (ADS)

Zhou, Ya; Hu, Cailin; Xiong, Han; Wei, Xiafei; Li, Ling

2017-05-01

Massive problem reports and solutions accumulated over time and continuously collected in XML Spreadsheet (XMLSS) format from enterprises and organizations, which record a series of comprehensive description about problems that can help technicians to trace problems and their solutions. It's a significant and challenging issue to effectively manage and analyze these massive semi-structured data to provide similar problem solutions, decisions of immediate problem and assisting product optimization for users during hardware and software maintenance. For this purpose, we build a data management system to manage, mine and analyze these data search results that can be categorized and organized into several categories for users to quickly find out where their interesting results locate. Experiment results demonstrate that this system is better than traditional centralized management system on the performance and the adaptive capability of heterogeneous data greatly. Besides, because of re-extracting topics, it enables each cluster to be described more precise and reasonable.

Daily Mean Temperature and Urolithiasis Presentation in Six Cities in Korea: Time-Series Analysis.

PubMed

Chi, Byung Hoon; Chang, In Ho; Choi, Se Young; Suh, Dong Churl; Chang, Chong Won; Choi, Yun Jung; Lee, Seo Yeon

2017-06-01

Seasonal variation in urinary stone presentation is well described in the literature. However, previous studies have some limitations. To explore overall cumulative exposure-response and the heterogeneity in the relationships between daily meteorological factors and urolithiasis incidence in 6 major Korean cities, we analyzed data on 687,833 urolithiasis patients from 2009 to 2013 for 6 large cities in Korea: Seoul, Incheon, Daejeon, Gwangju, Daegu, and Busan. Using a time-series design and distributing lag nonlinear methods, we estimated the relative risk (RR) of mean daily urolithiasis incidence (MDUI) associated with mean daily meteorological factors, including the cumulative RR for a 20-day period. The estimated location-specific associations were then pooled using multivariate meta-regression models. A positive association was confirmed between MDUI and mean daily temperature (MDT), and a negative association was shown between MDUI and mean daily relative humidity (MDRH) in all cities. The lag effect was within 5 days. The multivariate Cochran Q test for heterogeneity at MDT was 12.35 (P = 0.136), and the related I² statistic accounted for 35.2% of the variability. Additionally, the Cochran Q test for heterogeneity and I² statistic at MDHR were 26.73 (P value = 0.148) and 24.7% of variability in the total group. Association was confirmed between daily temperature, relative humidity and urolithiasis incidence, and the differences in urolithiasis incidence might have been partially attributable to the different frequencies and the ranges in temperature and humidity between cities in Korea. © 2017 The Korean Academy of Medical Sciences.

Finding a Needle in the Haystack: Identification of Functionally Important Minority Phases in an Operating Battery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Kai; Ren, Fang; Wang, Xuelong

The in-depth understanding of the minority phases’ roles in functional materials, e.g., batteries, is critical for optimizing the system performance and the operational efficiency. Although the visualization of battery electrode under operating conditions has been demonstrated, the development of advanced data-mining approaches is still needed in order to identify minority phases and to understand their functionalities. The present study uses nanoscale X-ray spectromicroscopy to study a functional LiCoO 2/Li battery pouch cell. The data-mining approaches developed herein were used to search through over 10 million X-ray absorption spectra that cover more than 100 active cathode particles. Two particles with unanticipatedmore » chemical fingerprints were identified and further analyzed, providing direct evidence and valuable insight into the undesired side reactions involving the cation dissolution and precipitation as well as the local overlithiation-caused subparticle domain deactivation. As a result, the data-mining approach described in this work is widely applicable to many other structurally complex and chemically heterogeneous systems, in which the secondary/minority phases could critically affect the overall performance of the system, well beyond battery research.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

S Gilchrist; A Gates; E Elzinga

The abandoned Phillips sulfide mine in the critical Highlands watershed in New York has been shown to produce strongly acidic mine drainage (AMD) with anomalous metal contaminants in first-order streams that exceeded local water standards by up to several orders of magnitude (Gilchrist et al., 2009). The metal-sulfide-rich tailings also produce contaminated soils with pH < 4, organic matter < 2.5% and trace metals sequestered in soil oxides. A geochemical transect to test worst-case soil contamination showed that Cr, Co and Ni correlated positively with Mn, (r = 0.72, r = 0.89, r = 0.80, respectively), suggesting Mn-oxide sequestration andmore » that Cu and Pb correlated with Fe (r = 0.76, r = 0.83, respectively), suggesting sequestration in goethite. Ubiquitous, yellow coating on the mine wastes, including jarosite and goethite, is a carrier of the metals. Geochemical and {mu}-SXRF analyses determined Cu to be the major soil contaminant, {mu}-SXRF also demonstrated that the heterogeneous nature of the soil chemistry at the micro-meter scale is self-similar to those in the bulk soil samples. Generally metals decreased, with some fluctuations, rapidly downslope through suspension of fines and dissolution in AMD leaving the area of substantial contamination << 0.5 km from the source.« less

Neural networks in astronomy.

PubMed

Tagliaferri, Roberto; Longo, Giuseppe; Milano, Leopoldo; Acernese, Fausto; Barone, Fabrizio; Ciaramella, Angelo; De Rosa, Rosario; Donalek, Ciro; Eleuteri, Antonio; Raiconi, Giancarlo; Sessa, Salvatore; Staiano, Antonino; Volpicelli, Alfredo

2003-01-01

In the last decade, the use of neural networks (NN) and of other soft computing methods has begun to spread also in the astronomical community which, due to the required accuracy of the measurements, is usually reluctant to use automatic tools to perform even the most common tasks of data reduction and data mining. The federation of heterogeneous large astronomical databases which is foreseen in the framework of the astrophysical virtual observatory and national virtual observatory projects, is, however, posing unprecedented data mining and visualization problems which will find a rather natural and user friendly answer in artificial intelligence tools based on NNs, fuzzy sets or genetic algorithms. This review is aimed to both astronomers (who often have little knowledge of the methodological background) and computer scientists (who often know little about potentially interesting applications), and therefore will be structured as follows: after giving a short introduction to the subject, we shall summarize the methodological background and focus our attention on some of the most interesting fields of application, namely: object extraction and classification, time series analysis, noise identification, and data mining. Most of the original work described in the paper has been performed in the framework of the AstroNeural collaboration (Napoli-Salerno).

Single-cell sequencing unveils the lifestyle and CRISPR-based population history of Hydrotalea sp. in acid mine drainage.

PubMed

Medeiros, J D; Leite, L R; Pylro, V S; Oliveira, F S; Almeida, V M; Fernandes, G R; Salim, A C M; Araújo, F M G; Volpini, A C; Oliveira, G; Cuadros-Orellana, S

2017-10-01

Acid mine drainage (AMD) is characterized by an acid and metal-rich run-off that originates from mining systems. Despite having been studied for many decades, much remains unknown about the microbial community dynamics in AMD sites, especially during their early development, when the acidity is moderate. Here, we describe draft genome assemblies from single cells retrieved from an early-stage AMD sample. These cells belong to the genus Hydrotalea and are closely related to Hydrotalea flava. The phylogeny and average nucleotide identity analysis suggest that all single amplified genomes (SAGs) form two clades that may represent different strains. These cells have the genomic potential for denitrification, copper and other metal resistance. Two coexisting CRISPR-Cas loci were recovered across SAGs, and we observed heterogeneity in the population with regard to the spacer sequences, together with the loss of trailer-end spacers. Our results suggest that the genomes of Hydrotalea sp. strains studied here are adjusting to a quickly changing selective pressure at the microhabitat scale, and an important form of this selective pressure is infection by foreign DNA. © 2017 John Wiley & Sons Ltd.

Finding a Needle in the Haystack: Identification of Functionally Important Minority Phases in an Operating Battery

DOE PAGES

Zhang, Kai; Ren, Fang; Wang, Xuelong; ...

2017-11-08

The in-depth understanding of the minority phases’ roles in functional materials, e.g., batteries, is critical for optimizing the system performance and the operational efficiency. Although the visualization of battery electrode under operating conditions has been demonstrated, the development of advanced data-mining approaches is still needed in order to identify minority phases and to understand their functionalities. The present study uses nanoscale X-ray spectromicroscopy to study a functional LiCoO 2/Li battery pouch cell. The data-mining approaches developed herein were used to search through over 10 million X-ray absorption spectra that cover more than 100 active cathode particles. Two particles with unanticipatedmore » chemical fingerprints were identified and further analyzed, providing direct evidence and valuable insight into the undesired side reactions involving the cation dissolution and precipitation as well as the local overlithiation-caused subparticle domain deactivation. As a result, the data-mining approach described in this work is widely applicable to many other structurally complex and chemically heterogeneous systems, in which the secondary/minority phases could critically affect the overall performance of the system, well beyond battery research.« less

A Dimensionally Reduced Clustering Methodology for Heterogeneous Occupational Medicine Data Mining.

PubMed

Saâdaoui, Foued; Bertrand, Pierre R; Boudet, Gil; Rouffiac, Karine; Dutheil, Frédéric; Chamoux, Alain

2015-10-01

Clustering is a set of techniques of the statistical learning aimed at finding structures of heterogeneous partitions grouping homogenous data called clusters. There are several fields in which clustering was successfully applied, such as medicine, biology, finance, economics, etc. In this paper, we introduce the notion of clustering in multifactorial data analysis problems. A case study is conducted for an occupational medicine problem with the purpose of analyzing patterns in a population of 813 individuals. To reduce the data set dimensionality, we base our approach on the Principal Component Analysis (PCA), which is the statistical tool most commonly used in factorial analysis. However, the problems in nature, especially in medicine, are often based on heterogeneous-type qualitative-quantitative measurements, whereas PCA only processes quantitative ones. Besides, qualitative data are originally unobservable quantitative responses that are usually binary-coded. Hence, we propose a new set of strategies allowing to simultaneously handle quantitative and qualitative data. The principle of this approach is to perform a projection of the qualitative variables on the subspaces spanned by quantitative ones. Subsequently, an optimal model is allocated to the resulting PCA-regressed subspaces.

Prognostic predictive value of preoperative intratumoral 2-deoxy-2-(18F)fluoro-D-glucose uptake heterogeneity in patients with high-grade serous ovarian cancer.

PubMed

Liu, Shuai; Feng, Zheng; Jiang, Zhaoxia; Wen, Hao; Xu, Junyan; Pan, Herong; Deng, Yu; Zhang, Lei; Ju, Xingzhu; Chen, Xiaojun; Wu, Xiaohua

2018-05-16

This study aimed to explore the clinical and prognostic significance of pretreatment positron-emission tomography/computed tomography (PET/CT) parameters, especially 2-deoxy-2-(F)fluoro-D-glucose-based heterogeneity, in high-grade serous ovarian cancer (HGSC). We retrospectively investigated 56 patients with HGSC who underwent PET/CT before primary surgery at our hospital between January 2010 and June 2015. None of these patients received neoadjuvant chemotherapy. PET/CT parameters, including maximum and mean standardized uptake value (SUVmax and SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and intratumoral heterogeneity index (HI), were measured for all patients. Differences of each PET/CT parameter between primary tumors (-P) and omental metastatic lesions (-M) were compared by paired t tests. Progression-free survival (PFS) and overall survival were analyzed by the Kaplan-Meier method and log-rank tests in univariate analyses. Cox regression analyses were used for multivariate analysis. SUVmean-P was higher than SUVmean-M (P=0.001). However, there were no statistical differences of SUVmax, MTV, TLG, or HI between primary and omental lesions. Chemosensitive patients tended to have higher levels of SUVmax-P (P=0.011), MTV-P (P=0.014), TLG-P (P=0.035), and HI-P (P=0.002), respectively. In univariate analyses, higher HI-P was associated with better PFS (P=0.007). However, in multivariate analysis, HI-P was not an independent predictor of PFS (P=0.581). Neither HI-P nor HI-M was the prognostic predictor for overall survival (P=0.078 and 0.063, respectively). 2-Deoxy-2-(F)fluoro-D-glucose-based heterogeneity appears to be a predictive and prognostic factor for patients with HGSC. Parameters of primary tumors have predominant value compared with omental metastatic lesions.

Competitive-Cooperative Automated Reasoning from Distributed and Multiple Source of Data

NASA Astrophysics Data System (ADS)

Fard, Amin Milani

Knowledge extraction from distributed database systems, have been investigated during past decade in order to analyze billions of information records. In this work a competitive deduction approach in a heterogeneous data grid environment is proposed using classic data mining and statistical methods. By applying a game theory concept in a multi-agent model, we tried to design a policy for hierarchical knowledge discovery and inference fusion. To show the system run, a sample multi-expert system has also been developed.

Impact of nickel mining in New Caledonia assessed by compositional data analysis of lichens.

PubMed

Pasquet, Camille; Le Monier, Pauline; Monna, Fabrice; Durlet, Christophe; Brigaud, Benjamin; Losno, Rémi; Chateau, Carmela; Laporte-Magoni, Christine; Gunkel-Grillon, Peggy

2016-01-01

The aim of this study is to explore the use of lichens as biomonitors of the impact of nickel mining and ore treatment on the atmosphere in the New Caledonian archipelago (South Pacific Ocean); both activities emitting also Co, Cr and possibly Fe. Metal contents were analysed in thirty-four epiphytic lichens, collected in the vicinity of the potential sources, and in places free from known historical mining. The highest Ni, Co, and Cr concentrations were, as expected, observed in lichens collected near ore deposits or treatment areas. The elemental composition in the lichens was explored by multivariate analysis, after appropriately transforming the variables (i.e. using compositional data analysis). The sample score of the first principal component (PC1) makes the largest (positive) multiplicative contribution to the log-ratios of metals originating from mining activities (Ni, Cr, Co) divided by Ti. The PC1 scores are used here as a surrogate of pollution levels related to mining and metallurgical activity. They can be viewed as synthetic indicators mapped to provide valuable information for the management and protection of ecosystems or, as a first step, to select locations where air filtration units could be installed, in the future, for air quality monitoring. However, as this approach drastically simplifies the problem, supplying a broadly efficient picture but little detail, recognizing the different sources of contamination may be difficult, more particularly when their chemical differences are subtle. It conveys only relative information: about ratios, not levels, and is therefore recommended as a preliminary step, in combination with close examination of raw concentration levels of lichens. Further validation using conventional air-monitoring by filter units should also prove beneficial.

Contribution of geophysical methods in the study of the floodplain structure (the Litavka River, the Czech Republic)

NASA Astrophysics Data System (ADS)

Kotková, Kristýna; Matys Grygar, Tomáš; Tůmová, Štěpánka; Elznicová, Jitka

2017-04-01

Mining and processing of polymetallic ores near the city of Příbram (the Czech Republic) have strongly impacted the fluvial system of the Litavka River. Beside of polymetallic mining during several hundred years with a peak between 1850 and 1950, the Litavka River was also influenced by uranium ore mining between 1948 and 1989. Severe contamination of the Litavka River system is known, but the alluvial architecture and specific distribution of contamination has not yet been satisfactorily described. However, such pieces of information are necessary for the predictions of the future behaviour of contaminants in the river system. We used geophysical methods for visualisation of subsurface layers of sediments and we have proved them very useful for the survey of the floodplain structure. It is especially advantageous when the surface topography of the floodplain does not reveal its internal structure, e.g. due to floodplain levelling by aggradation. Specifically, dipole electromagnetic profiling, also denoted electromagnetic induction sensing (DEMP) was used for quick detection of major heterogeneities in the floodplain structure. In addition, electrical resistivity tomography (ERT) was used for the exploration of lines across the heterogeneities shown by DEMP. This approach allows to choose the appropriate plan for the subsequent sampling in the floodplain to include all its structural (lithogenetic) units. Such rational strategy allows for reducing total amount of sampled sites without the risk of losing important information and production of false images. Both used geophysical tools and manual drill coring and the elemental analysis by handheld X-ray fluorescence spectrometry produced clear images of floodplain architecture and pollutant distribution. The internal structure of the Litavka River floodplain shows that lateral deposition and reworking of sediments played the main roles in the floodplain building. In the next centuries the lateral channel movement will rework contamination which is maximal in the current channel belt.

Molecular- and nm-scale Investigation of the Structure and Compositional Heterogeneity of Naturally Occurring Ferrihydrite

NASA Astrophysics Data System (ADS)

Cismasu, C.; Michel, F. M.; Stebbins, J. F.; Tcaciuc, A. P.; Brown, G. E.

2008-12-01

Ferrihydrite is a hydrated Fe(III) nano-oxide that forms in vast quantities in contaminated acid mine drainage environments. As a result of its high surface area, ferrihydrite is an important environmental sorbent, and plays an essential role in the geochemical cycling of pollutant metal(loid)s in these settings. Despite its environmental relevance, this nanomineral remains one of the least understood environmental solids in terms of its structure (bulk and surface), compositional variations, and the factors affecting its reactivity. Under natural aqueous conditions, ferrihydrite often precipitates in the presence of several inorganic compounds such as aluminum, silica, arsenic, etc., or in the presence of organic matter. These impurities can affect the molecular-level structure of naturally occurring ferrihydrite, thus modifying fundamental properties that are directly correlated with solid-phase stability and surface reactivity. Currently there exists a significant gap in our understanding of the structure of synthetic vs. natural ferrihydrites, due to the inherent difficulties associated to the investigation of these poorly crystalline nanophases. In this study, we combined synchrotron- and laboratory-based techniques to characterize naturally occurring ferrihydrite from an acid mine drainage system situated at the New Idria mercury mine in California. We used high-energy X-ray total scattering and pair distribution function analysis to elucidate quantitative structural details of these samples. We have additionally used scanning transmission X-ray microscopy high resolution imaging (30 nm) to evaluate the spatial relationship of major elements Si, Al, and C within ferrihydrite. Al, Si and C K-edge near- edge X-ray absorption fine structure spectroscopy and 27Al nuclear magnetic resonance spectroscopy were used to obtain short-range structural information. By combining these techniques we attain the highest level of resolution permitted by current analytical methods to study such naturally occurring nanomaterials, both at the molecular- and nm-scale. This work provides structural information at the short-, medium- and long- range, as well as evidence of compositional heterogeneity, and mineral/organic matter associations.

Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) with Raman Imaging Applied to Lunar Meteorites.

PubMed

Smith, Joseph P; Smith, Frank C; Booksh, Karl S

2018-03-01

Lunar meteorites provide a more random sampling of the surface of the Moon than do the returned lunar samples, and they provide valuable information to help estimate the chemical composition of the lunar crust, the lunar mantle, and the bulk Moon. As of July 2014, ∼96 lunar meteorites had been documented and ten of these are unbrecciated mare basalts. Using Raman imaging with multivariate curve resolution-alternating least squares (MCR-ALS), we investigated portions of polished thin sections of paired, unbrecciated, mare-basalt lunar meteorites that had been collected from the LaPaz Icefield (LAP) of Antarctica-LAP 02205 and LAP 04841. Polarized light microscopy displays that both meteorites are heterogeneous and consist of polydispersed sized and shaped particles of varying chemical composition. For two distinct probed areas within each meteorite, the individual chemical species and associated chemical maps were elucidated using MCR-ALS applied to Raman hyperspectral images. For LAP 02205, spatially and spectrally resolved clinopyroxene, ilmenite, substrate-adhesive epoxy, and diamond polish were observed within the probed areas. Similarly, for LAP 04841, spatially resolved chemical images with corresponding resolved Raman spectra of clinopyroxene, troilite, a high-temperature polymorph of anorthite, substrate-adhesive epoxy, and diamond polish were generated. In both LAP 02205 and LAP 04841, substrate-adhesive epoxy and diamond polish were more readily observed within fractures/veinlet features. Spectrally diverse clinopyroxenes were resolved in LAP 04841. Factors that allow these resolved clinopyroxenes to be differentiated include crystal orientation, spatially distinct chemical zoning of pyroxene crystals, and/or chemical and molecular composition. The minerals identified using this analytical methodology-clinopyroxene, anorthite, ilmenite, and troilite-are consistent with the results of previous studies of the two meteorites using electron microprobe analysis. To our knowledge, this is the first report of MCR-ALS with Raman imaging used for the investigation of both lunar and other types of meteorites. We have demonstrated the use of multivariate analysis methods, namely MCR-ALS, with Raman imaging to investigate heterogeneous lunar meteorites. Our analytical methodology can be used to elucidate the chemical, molecular, and structural characteristics of phases in a host of complex, heterogeneous geological, geochemical, and extraterrestrial materials.

Exploring the effects of acid mine drainage on diatom teratology using geometric morphometry.

PubMed

Olenici, Adriana; Blanco, Saúl; Borrego-Ramos, María; Momeu, Laura; Baciu, Călin

2017-10-01

Metal pollution of aquatic habitats is a major and persistent environmental problem. Acid mine drainage (AMD) affects lotic systems in numerous and interactive ways. In the present work, a mining area (Roșia Montană) was chosen as study site, and we focused on two aims: (i) to find the set of environmental predictors leading to the appearance of the abnormal diatom individuals in the study area and (ii) to assess the relationship between the degree of valve outline deformation and AMD-derived pollution. In this context, morphological differences between populations of Achnanthidium minutissimum and A. macrocephalum, including normal and abnormal individuals, were evidenced by means of valve shape analysis. Geometric morphometry managed to capture and discriminate normal and abnormal individuals. Multivariate analyses (NMDS, PLS) separated the four populations of the two species mentioned and revealed the main physico-chemical parameters that influenced valve deformation in this context, namely conductivity, Zn, and Cu. ANOSIM test evidenced the presence of statistically significant differences between normal and abnormal individuals within both chosen Achnanthidium taxa. In order to determine the relative contribution of each of the measured physico-chemical parameters in the observed valve outline deformations, a PLS was conducted, confirming the results of the NMDS. The presence of deformed individuals in the study area can be attributed to the fact that the diatom communities were strongly affected by AMD released from old mining works and waste rock deposits.

Coordinated role of soluble and cell wall bound phenols is a key feature of the metabolic adjustment in a mining woody fleabane (Dittrichia viscosa L.) population under semi-arid conditions.

PubMed

López-Orenes, Antonio; Bueso, María C; Párraga-Aguado, Isabel M; Calderón, Antonio A; Ferrer, María A

2018-03-15

Environmental contamination by hazardous heavy metals/metalloids (metal(loid)s) is growing worldwide. To restrict the migration of toxic contaminants, the establishment of a self-sustainable plant cover is required. Plant growth in multi-polluted soils is a challenging issue not only by metal(loid) toxicities, but also by the co-occurrence of other stressors. Dittrichia viscosa is a pioneer Mediterranean species able to thrive in metal(loid)-enriched tailings in semi-arid areas. The aim of the present work was to examine the metabolic adjustments involved in the acclimation responses of this plant to conditions prevailing in mine-tailings during Mediterranean spring and summer. For this purpose, fully-expanded leaves, and rhizosphere soil of both mining and non-mining populations of D. viscosa grown spontaneously in south-eastern Spain were sampled in two consecutive years. Quantitative analysis of >50 biochemical, physiological and edaphic parameters were performed, including nutrient status, metal(loid) contents, leaf redox components, primary and secondary metabolites, salicylic acid levels, and soil physicochemical properties. Results showed that mining plants exhibited high foliar Zn/Pb co-accumulation capacity, without substantially affecting their photosynthetic metabolism or nutritional status even in the driest summer period. The comparison of the antioxidative/oxidative profile between mining and non-mining D. viscosa populations revealed no major seasonal changes in the content of primary antioxidants (ascorbate and GSH), or in the levels of ROS. Multivariate analysis showed that phenylalanine ammonia-lyase (PAL) and peroxidase (PRX) activities and soluble and cell wall-bound phenols were potential biomarkers for discriminating between both populations. During the dry season, a marked enhancement in the activity of both PAL and soluble PRX resulted in both a drop in the accumulation of soluble phenols and an increase of the strong metal chelator caffeic acid in the cell-wall fraction, supporting the view that the plasticity of phenylpropanoid metabolism provide an effective way to counteract the effects of stress combinations. Copyright © 2017 Elsevier B.V. All rights reserved.

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

PubMed

Davatzikos, Christos

2016-10-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. Copyright © 2016. Published by Elsevier B.V.

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning

PubMed Central

Davatzikos, Christos

2017-01-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. PMID:27514582

Exploring the Links between Post-Industrial Landscape History and Ecology through Participatory Methods

PubMed Central

2015-01-01

There is increasing recognition of the importance for local biodiversity of post-mining sites, many of which lie near communities that have suffered significant social and economic deprivation as the result of mine closures. However, no studies to date have actively used the knowledge of local communities to relate the history and treatment of post-mining sites to their current ecological status. We report a study of two post-mining sites in the Yorkshire coalfield of the UK in which the local community were involved in developing site histories and assessing plant and invertebrate species composition. Site histories developed using participatory GIS revealed that the sites had a mixture of areas of spontaneous succession and technical reclamation, and identified that both planned management interventions and informal activities influenced habitat heterogeneity and ecological diversity. Two groups of informal activity were identified as being of particular importance. Firstly, there has been active protection by the community of flower-rich habitats of conservation value (e.g. calcareous grassland) and distinctive plant species (e.g. orchids) which has also provided important foraging resources for butterfly and bumblebee species. Secondly, disturbance by activities such as use of motorbikes, informal camping, and cutting of trees and shrubs for fuel, as well as planned management interventions such as spreading of brick rubble, has provided habitat for plant species of open waste ground and locally uncommon invertebrate species which require patches of bare ground. This study demonstrates the importance of informal, and often unrecorded, activities by the local community in providing diverse habitats and increased biodiversity within a post-mining site, and shows that active engagement with the local community and use of local knowledge can enhance ecological interpretation of such sites and provide a stronger basis for successful future management. PMID:26309041

Groundwater assessment and environmental impact in the abandoned mine of Kettara (Morocco).

PubMed

Moyé, Julien; Picard-Lesteven, Tanguy; Zouhri, Lahcen; El Amari, Khalid; Hibti, Mohamed; Benkaddour, Abdelfattah

2017-12-01

Many questions about the soil pollution due to mining activities have been analyzed by numerous methods which help to evaluate the dispersion of the Metallic Trace Elements (MTE) in the soil and stream sediments of the abandoned mine of Kettara (Morocco). The transport of these MTE could have an important role in the degradation of groundwater and the health of people who are living in the vicinity. The present paper aims to evaluate the groundwater samples from 15 hydrogeological wells. This evaluation concerns the hydrogeological parameters, pH, Electrical conductivity, temperature and the groundwater level, and the geochemical assessment of Mg, Ca, Ti, Cr, Mn, Fe, Co, Ni, Zn, Cu, As, Se, Cd, Sb, Tl and Pb. Furthermore, the Metallic Trace Elements are transported in the saturated zone via the fractures network. The groundwater flow is from the north-east to south-west. The spatial distribution of As, Fe, Zn and Mn is very heterogeneous, with high values observed in the north, upstream, of the mine site. This distribution is maybe related to: i) the existence of hydrogeological structures (dividing and drainage axes); ii) the individualization of the fractures network that affects the shaly lithostratigraphical formation; iii) the transport of the contaminants from the soil towards groundwater; and iv) interaction water/rocks. Some MTE anomalies are linked to the lithology and the fracturation system of the area. Therefore, the groundwater contamination by Arsenic is detected in the hydrogeological wells (E1 and E2). This pollution which is higher than guideline standards of Moroccan drinking water could affect the public health. The hydrogeological and geochemical investigations favor the geological origin (mafic rocks) of this contamination rather than mining activities. Copyright © 2017. Published by Elsevier Ltd.

Exploring the Links between Post-Industrial Landscape History and Ecology through Participatory Methods.

PubMed

Rich, Kevin J; Ridealgh, Michael; West, Sarah E; Cinderby, Steve; Ashmore, Mike

2015-01-01

There is increasing recognition of the importance for local biodiversity of post-mining sites, many of which lie near communities that have suffered significant social and economic deprivation as the result of mine closures. However, no studies to date have actively used the knowledge of local communities to relate the history and treatment of post-mining sites to their current ecological status. We report a study of two post-mining sites in the Yorkshire coalfield of the UK in which the local community were involved in developing site histories and assessing plant and invertebrate species composition. Site histories developed using participatory GIS revealed that the sites had a mixture of areas of spontaneous succession and technical reclamation, and identified that both planned management interventions and informal activities influenced habitat heterogeneity and ecological diversity. Two groups of informal activity were identified as being of particular importance. Firstly, there has been active protection by the community of flower-rich habitats of conservation value (e.g. calcareous grassland) and distinctive plant species (e.g. orchids) which has also provided important foraging resources for butterfly and bumblebee species. Secondly, disturbance by activities such as use of motorbikes, informal camping, and cutting of trees and shrubs for fuel, as well as planned management interventions such as spreading of brick rubble, has provided habitat for plant species of open waste ground and locally uncommon invertebrate species which require patches of bare ground. This study demonstrates the importance of informal, and often unrecorded, activities by the local community in providing diverse habitats and increased biodiversity within a post-mining site, and shows that active engagement with the local community and use of local knowledge can enhance ecological interpretation of such sites and provide a stronger basis for successful future management.

Visualizing frequent patterns in large multivariate time series

NASA Astrophysics Data System (ADS)

Hao, M.; Marwah, M.; Janetzko, H.; Sharma, R.; Keim, D. A.; Dayal, U.; Patnaik, D.; Ramakrishnan, N.

2011-01-01

The detection of previously unknown, frequently occurring patterns in time series, often called motifs, has been recognized as an important task. However, it is difficult to discover and visualize these motifs as their numbers increase, especially in large multivariate time series. To find frequent motifs, we use several temporal data mining and event encoding techniques to cluster and convert a multivariate time series to a sequence of events. Then we quantify the efficiency of the discovered motifs by linking them with a performance metric. To visualize frequent patterns in a large time series with potentially hundreds of nested motifs on a single display, we introduce three novel visual analytics methods: (1) motif layout, using colored rectangles for visualizing the occurrences and hierarchical relationships of motifs in a multivariate time series, (2) motif distortion, for enlarging or shrinking motifs as appropriate for easy analysis and (3) motif merging, to combine a number of identical adjacent motif instances without cluttering the display. Analysts can interactively optimize the degree of distortion and merging to get the best possible view. A specific motif (e.g., the most efficient or least efficient motif) can be quickly detected from a large time series for further investigation. We have applied these methods to two real-world data sets: data center cooling and oil well production. The results provide important new insights into the recurring patterns.

Amelioration of iron mine soils with biosolids: Effects on plant tissue metal content and earthworms.

PubMed

Cele, Emmanuel Nkosinathi; Maboeta, Mark

2016-11-01

The achievement of environmentally sound and economically feasible disposal strategies for biosolids is a major issue in the wastewater treatment industry around the world, including Swaziland. Currently, an iron ore mine site, which is located within a wildlife sanctuary, is being considered as a suitable place where controlled disposal of biosolids may be practiced. Therefore, this study was conducted to investigate the effects of urban biosolids on iron mine soils with regard to plant metal content and ecotoxicological effects on earthworms. This was done through chemical analysis of plants grown in biosolid-amended mine soil. Earthworm behaviour, reproduction and bioaccumulation tests were also conducted on biosolid-amended mine soil. According to the results obtained, the use of biosolids led to creation of soil conditions that were generally favourable to earthworms. However, plants were found to have accumulated Zn up to 346 mg kg -1 (in shoots) and 462 mg kg -1 (in roots). This was more than double the normal Zn content of plants. It was concluded that while biosolids can be beneficial to mine soils and earthworms, they can also lead to elevated metal content in plant tissues, which might be a concern to plant-dependant wildlife species. Nonetheless, it was not possible to satisfactorily estimate risks to forage quality since animal feeding tests with hyperaccumulator plants have not been reported. Quite possibly, there may be no cause for alarm since the uptake of metals from soil is greater in plants grown in pots in the greenhouse than from the same soil in the field since pot studies fail to mimic field conditions where the soil is heterogeneous and where the root system possesses a complex topology. It was thought that further field trials might assist in arriving at more satisfactory conclusions.

Zinc and lead poisoning in wild birds in the Tri-State Mining District (Oklahoma, Kansas, Missouri)

USGS Publications Warehouse

Beyer, W.N.; Dalgam, J.; Dudding, S.; French, J.B.; Mateo, R.; Miesner, J.; Sileo, L.; Spann, J.

2004-01-01

contaminated with Pb, Cd, and Zn from mining, milling and smelting. Metals have been dispersed heterogeneously throughout the District in the form of milled mine waste ('chat'), as flotation tailings and from smelters as aerial deposition or slag. This study was conducted to determine if the habitat has been contaminated to the extent that the assessment populations of wild birds are exposed to toxic concentrations of metals. American robins (Turdus migratorius), northern cardinals (Cardinalis cardinalis), and waterfowl had increased Pb tissue concentrations (p < 0.05) compared with Pb tissue concentrations from reference birds, and the exposure of songbirds to Pb was comparable with that of birds observed at other sites severely contaminated with Pb. Mean activities of the Pb-sensitive enzyme delta-aminolevulinic acid dehydratase (ALAD) were decreased by >50% in red blood cells in these birds (p < 0.05). Several birds had tissue concentrations of Pb that have been associated with impaired biological functions and external signs of poisoning. Cadmium was increased in kidneys of songbirds (p < 0.05), but no proximal tubule cell necrosis associated with Cd poisoning was observed. Zinc concentrations in liver and kidney of waterfowl were significantly higher (p < 0.05) than reference values. The increased environmental concentrations of Zn associated with mining in the District accounted for the pancreatitis previously observed in five waterfowl from the District. The District is the first site at which free-flying wild birds have been found to be suffering severe effects of Zn poisoning.

Effect of heterogeneity and anisotropy related to the construction method on transfer processes in waste rock piles.

PubMed

Lahmira, Belkacem; Lefebvre, René; Aubertin, Michel; Bussière, Bruno

2016-01-01

Waste rock piles producing acid mine drainage (AMD) are partially saturated systems involving multiphase (gas and liquid) flow and coupled transfer processes. Their internal structure and heterogeneous properties are inherited from their wide-ranging material grain sizes, their modes of deposition, and the underlying topography. This paper aims at assessing the effect of physical heterogeneity and anisotropy of waste rock piles on the physical processes involved in the generation of AMD. Generic waste rock pile conditions were represented with the numerical simulator TOUGH AMD based on those found at the Doyon mine waste rock pile (Canada). Models included four randomly distributed material types (coarse, intermediate, fine and very fine-grained). The term "randomly" as used in this study means that the vertical profile and spatial distribution of materials in waste rock piles (internal structure) defy stratigraphy principles applicable to natural sediments (superposition and continuity). The materials have different permeability and capillary properties, covering the typical range of materials found in waste rock piles. Anisotropy with a larger horizontal than vertical permeability was used to represent the effect of pile construction by benches, while the construction by end-dumping was presumed to induce a higher vertical than horizontal permeability. Results show that infiltrated precipitation preferentially flows in fine-grained materials, which remain almost saturated, whereas gas flows preferentially through the most permeable coarse materials, which have higher volumetric gas saturation. Anisotropy, which depends on pile construction methods, often controls global gas flow paths. Construction by benches favours lateral air entry close to the pile slope, whereas end-dumping leads to air entry from the surface to the interior of the pile by secondary gas convection cells. These results can be useful to construct and rehabilitate waste rock piles to minimize AMD, while controlling gas flow and oxygen supply. Copyright © 2015 Elsevier B.V. All rights reserved.

Tracking the Evolution of Non-Small-Cell Lung Cancer.

PubMed

Jamal-Hanjani, Mariam; Wilson, Gareth A; McGranahan, Nicholas; Birkbak, Nicolai J; Watkins, Thomas B K; Veeriah, Selvaraju; Shafi, Seema; Johnson, Diana H; Mitter, Richard; Rosenthal, Rachel; Salm, Max; Horswell, Stuart; Escudero, Mickael; Matthews, Nik; Rowan, Andrew; Chambers, Tim; Moore, David A; Turajlic, Samra; Xu, Hang; Lee, Siow-Ming; Forster, Martin D; Ahmad, Tanya; Hiley, Crispin T; Abbosh, Christopher; Falzon, Mary; Borg, Elaine; Marafioti, Teresa; Lawrence, David; Hayward, Martin; Kolvekar, Shyam; Panagiotopoulos, Nikolaos; Janes, Sam M; Thakrar, Ricky; Ahmed, Asia; Blackhall, Fiona; Summers, Yvonne; Shah, Rajesh; Joseph, Leena; Quinn, Anne M; Crosbie, Phil A; Naidu, Babu; Middleton, Gary; Langman, Gerald; Trotter, Simon; Nicolson, Marianne; Remmen, Hardy; Kerr, Keith; Chetty, Mahendran; Gomersall, Lesley; Fennell, Dean A; Nakas, Apostolos; Rathinam, Sridhar; Anand, Girija; Khan, Sajid; Russell, Peter; Ezhil, Veni; Ismail, Babikir; Irvin-Sellers, Melanie; Prakash, Vineet; Lester, Jason F; Kornaszewska, Malgorzata; Attanoos, Richard; Adams, Haydn; Davies, Helen; Dentro, Stefan; Taniere, Philippe; O'Sullivan, Brendan; Lowe, Helen L; Hartley, John A; Iles, Natasha; Bell, Harriet; Ngai, Yenting; Shaw, Jacqui A; Herrero, Javier; Szallasi, Zoltan; Schwarz, Roland F; Stewart, Aengus; Quezada, Sergio A; Le Quesne, John; Van Loo, Peter; Dive, Caroline; Hackshaw, Allan; Swanton, Charles

2017-06-01

Among patients with non-small-cell lung cancer (NSCLC), data on intratumor heterogeneity and cancer genome evolution have been limited to small retrospective cohorts. We wanted to prospectively investigate intratumor heterogeneity in relation to clinical outcome and to determine the clonal nature of driver events and evolutionary processes in early-stage NSCLC. In this prospective cohort study, we performed multiregion whole-exome sequencing on 100 early-stage NSCLC tumors that had been resected before systemic therapy. We sequenced and analyzed 327 tumor regions to define evolutionary histories, obtain a census of clonal and subclonal events, and assess the relationship between intratumor heterogeneity and recurrence-free survival. We observed widespread intratumor heterogeneity for both somatic copy-number alterations and mutations. Driver mutations in EGFR, MET, BRAF, and TP53 were almost always clonal. However, heterogeneous driver alterations that occurred later in evolution were found in more than 75% of the tumors and were common in PIK3CA and NF1 and in genes that are involved in chromatin modification and DNA damage response and repair. Genome doubling and ongoing dynamic chromosomal instability were associated with intratumor heterogeneity and resulted in parallel evolution of driver somatic copy-number alterations, including amplifications in CDK4, FOXA1, and BCL11A. Elevated copy-number heterogeneity was associated with an increased risk of recurrence or death (hazard ratio, 4.9; P=4.4×10 -4 ), which remained significant in multivariate analysis. Intratumor heterogeneity mediated through chromosome instability was associated with an increased risk of recurrence or death, a finding that supports the potential value of chromosome instability as a prognostic predictor. (Funded by Cancer Research UK and others; TRACERx ClinicalTrials.gov number, NCT01888601 .).

Seismic modeling of multidimensional heterogeneity scales of Mallik gas hydrate reservoirs, Northwest Territories of Canada

NASA Astrophysics Data System (ADS)

Huang, Jun-Wei; Bellefleur, Gilles; Milkereit, Bernd

2009-07-01

In hydrate-bearing sediments, the velocity and attenuation of compressional and shear waves depend primarily on the spatial distribution of hydrates in the pore space of the subsurface lithologies. Recent characterizations of gas hydrate accumulations based on seismic velocity and attenuation generally assume homogeneous sedimentary layers and neglect effects from large- and small-scale heterogeneities of hydrate-bearing sediments. We present an algorithm, based on stochastic medium theory, to construct heterogeneous multivariable models that mimic heterogeneities of hydrate-bearing sediments at the level of detail provided by borehole logging data. Using this algorithm, we model some key petrophysical properties of gas hydrates within heterogeneous sediments near the Mallik well site, Northwest Territories, Canada. The modeled density, and P and S wave velocities used in combination with a modified Biot-Gassmann theory provide a first-order estimate of the in situ volume of gas hydrate near the Mallik 5L-38 borehole. Our results suggest a range of 528 to 768 × 106 m3/km2 of natural gas trapped within hydrates, nearly an order of magnitude lower than earlier estimates which did not include effects of small-scale heterogeneities. Further, the petrophysical models are combined with a 3-D finite difference modeling algorithm to study seismic attenuation due to scattering and leaky mode propagation. Simulations of a near-offset vertical seismic profile and cross-borehole numerical surveys demonstrate that attenuation of seismic energy may not be directly related to the intrinsic attenuation of hydrate-bearing sediments but, instead, may be largely attributed to scattering from small-scale heterogeneities and highly attenuate leaky mode propagation of seismic waves through larger-scale heterogeneities in sediments.

Putting engineering back into protein engineering: bioinformatic approaches to catalyst design.

PubMed

Gustafsson, Claes; Govindarajan, Sridhar; Minshull, Jeremy

2003-08-01

Complex multivariate engineering problems are commonplace and not unique to protein engineering. Mathematical and data-mining tools developed in other fields of engineering have now been applied to analyze sequence-activity relationships of peptides and proteins and to assist in the design of proteins and peptides with specified properties. Decreasing costs of DNA sequencing in conjunction with methods to quickly synthesize statistically representative sets of proteins allow modern heuristic statistics to be applied to protein engineering. This provides an alternative approach to expensive assays or unreliable high-throughput surrogate screens.

Advanced Multivariate Inversion Techniques for High Resolution 3D Geophysical Modeling

DTIC Science & Technology

2010-09-01

crustal structures. But short periods are difficult to measure, especially in tectonically and geologically complex areas. On the other hand, gravity...East Africa Rift System Knowledge of crustal and upper mantle structure is of importance for understanding East Africa’s geodynamic evolution and for...area with less lateral heterogeneity but great tectonic complexity. To increase the effectiveness of the technique in this region, we explore gravity

Distribution of black-tailed jackrabbit habitat determined by GIS in southwestern Idaho

USGS Publications Warehouse

Knick, Steven T.; Dyer, D.L.

1997-01-01

We developed a multivariate description of black-tailed jackrabbit (Lepus californicus) habitat associations from Geographical Information Systems (GIS) signatures surrounding known jackrabbit locations in the Snake River Birds of Prey National Conservation Area (NCA), in southwestern Idaho. Habitat associations were determined for characteristics within a 1-km radius (approx home range size) of jackrabbits sighted on night spotlight surveys conducted from 1987 through 1995. Predictive habitat variables were number of shrub, agriculture, and hydrography cells, mean and standard deviation of shrub patch size, habitat richness, and a measure of spatial heterogeneity. In winter, jackrabbits used smaller and less variable sizes of shrub patches and areas of higher spatial heterogeneity when compared to summer observations (P 0.05), differed significantly between high and low population phase. We used the Mahalanobis distance statistic to rank all 50-m cells in a 440,000-ha region relative to the multivariate mean habitat vector. On verification surveys to test predicted models, we sighted jackrabbits in areas ranked close to the mean habitat vector. Areas burned by large-scale fires between 1980 and 1992 or in an area repeatedly burned by military training activities had greater Mahalanobis distances from the mean habitat vector than unburned areas and were less likely to contain habitats used by jackrabbits.

A Comprehensive Workflow of Mass Spectrometry-Based Untargeted Metabolomics in Cancer Metabolic Biomarker Discovery Using Human Plasma and Urine

PubMed Central

Zou, Wei; She, Jianwen; Tolstikov, Vladimir V.

2013-01-01

Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: hydrophilic interaction liquid chromatography (HILIC–LC), reversed-phase liquid chromatography (RP–LC), and gas chromatography (GC). All three techniques are coupled to a mass spectrometer (MS) in the full scan acquisition mode, and both unsupervised and supervised methods are used for data mining. The univariate and multivariate feature selection are used to determine subsets of potentially discriminative predictors. These predictors are further identified by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM) and data-dependent MS/MS and/or accurate mass MSn ion tree scans utilizing high resolution MS. A list combining all of the identified potential biomarkers generated from different platforms and algorithms is used for pathway analysis. Such a workflow combining comprehensive metabolic profiling and advanced data mining techniques may provide a powerful approach for metabolic pathway analysis and biomarker discovery in cancer research. Two case studies with previous published data are adapted and included in the context to elucidate the application of the workflow. PMID:24958150

An Energy-Efficient Underground Localization System Based on Heterogeneous Wireless Networks

PubMed Central

Yuan, Yazhou; Chen, Cailian; Guan, Xinping; Yang, Qiuling

2015-01-01

A precision positioning system with energy efficiency is of great necessity for guaranteeing personnel safety in underground mines. The location information of the miners' should be transmitted to the control center timely and reliably; therefore, a heterogeneous network with the backbone based on high speed Industrial Ethernet is deployed. Since the mobile wireless nodes are working in an irregular tunnel, a specific wireless propagation model cannot fit all situations. In this paper, an underground localization system is designed to enable the adaptation to kinds of harsh tunnel environments, but also to reduce the energy consumption and thus prolong the lifetime of the network. Three key techniques are developed and implemented to improve the system performance, including a step counting algorithm with accelerometers, a power control algorithm and an adaptive packets scheduling scheme. The simulation study and experimental results show the effectiveness of the proposed algorithms and the implementation. PMID:26016918

Semantic Framework of Internet of Things for Smart Cities: Case Studies.

PubMed

Zhang, Ningyu; Chen, Huajun; Chen, Xi; Chen, Jiaoyan

2016-09-14

In recent years, the advancement of sensor technology has led to the generation of heterogeneous Internet-of-Things (IoT) data by smart cities. Thus, the development and deployment of various aspects of IoT-based applications are necessary to mine the potential value of data to the benefit of people and their lives. However, the variety, volume, heterogeneity, and real-time nature of data obtained from smart cities pose considerable challenges. In this paper, we propose a semantic framework that integrates the IoT with machine learning for smart cities. The proposed framework retrieves and models urban data for certain kinds of IoT applications based on semantic and machine-learning technologies. Moreover, we propose two case studies: pollution detection from vehicles and traffic pattern detection. The experimental results show that our system is scalable and capable of accommodating a large number of urban regions with different types of IoT applications.

Semantic Framework of Internet of Things for Smart Cities: Case Studies

PubMed Central

Zhang, Ningyu; Chen, Huajun; Chen, Xi; Chen, Jiaoyan

2016-01-01

In recent years, the advancement of sensor technology has led to the generation of heterogeneous Internet-of-Things (IoT) data by smart cities. Thus, the development and deployment of various aspects of IoT-based applications are necessary to mine the potential value of data to the benefit of people and their lives. However, the variety, volume, heterogeneity, and real-time nature of data obtained from smart cities pose considerable challenges. In this paper, we propose a semantic framework that integrates the IoT with machine learning for smart cities. The proposed framework retrieves and models urban data for certain kinds of IoT applications based on semantic and machine-learning technologies. Moreover, we propose two case studies: pollution detection from vehicles and traffic pattern detection. The experimental results show that our system is scalable and capable of accommodating a large number of urban regions with different types of IoT applications. PMID:27649185

The Effects of Realistic Geological Heterogeneity on Seismic Modeling: Applications in Shear Wave Generation and Near-Surface Tunnel Detection

NASA Astrophysics Data System (ADS)

Sherman, Christopher Scott

Naturally occurring geologic heterogeneity is an important, but often overlooked, aspect of seismic wave propagation. This dissertation presents a strategy for modeling the effects of heterogeneity using a combination of geostatistics and Finite Difference simulation. In the first chapter, I discuss my motivations for studying geologic heterogeneity and seis- mic wave propagation. Models based upon fractal statistics are powerful tools in geophysics for modeling heterogeneity. The important features of these fractal models are illustrated using borehole log data from an oil well and geomorphological observations from a site in Death Valley, California. A large part of the computational work presented in this disserta- tion was completed using the Finite Difference Code E3D. I discuss the Python-based user interface for E3D and the computational strategies for working with heterogeneous models developed over the course of this research. The second chapter explores a phenomenon observed for wave propagation in heteroge- neous media - the generation of unexpected shear wave phases in the near-source region. In spite of their popularity amongst seismic researchers, approximate methods for modeling wave propagation in these media, such as the Born and Rytov methods or Radiative Trans- fer Theory, are incapable of explaining these shear waves. This is primarily due to these method's assumptions regarding the coupling of near-source terms with the heterogeneities and mode conversion. To determine the source of these shear waves, I generate a suite of 3D synthetic heterogeneous fractal geologic models and use E3D to simulate the wave propaga- tion for a vertical point force on the surface of the models. I also present a methodology for calculating the effective source radiation patterns from the models. The numerical results show that, due to a combination of mode conversion and coupling with near-source hetero- geneity, shear wave energy on the order of 10% of the compressional wave energy may be generated within the shear radiation node of the source. Interestingly, in some cases this shear wave may arise as a coherent pulse, which may be used to improve seismic imaging efforts. In the third and fourth chapters, I discuss the results of a numerical analysis and field study of seismic near-surface tunnel detection methods. Detecting unknown tunnels and voids, such as old mine workings or solution cavities in karst terrain, is a challenging prob- lem in geophysics and has implications for geotechnical design, public safety, and domestic security. Over the years, a number of different geophysical methods have been developed to locate these objects (microgravity, resistivity, seismic diffraction, etc.), each with varying results. One of the major challenges facing these methods is understanding the influence of geologic heterogeneity on their results, which makes this problem a natural extension of the modeling work discussed in previous chapters. In the third chapter, I present the results of a numerical study of surface-wave based tunnel detection methods. The results of this analysis show that these methods are capable of detecting a void buried within one wavelength of the surface, with size potentially much less than one wavelength. In addition, seismic surface- wave based detection methods are effective in media with moderate heterogeneity (epsilon < 5 %), and in fact, this heterogeneity may serve to increase the resolution of these methods. In the fourth chapter, I discuss the results of a field study of tunnel detection methods at a site within the Black Diamond Mines Regional Preserve, near Antioch California. I use a com- bination of surface wave backscattering, 1D surface wave attenuation, and 2D attenuation tomography to locate and determine the condition of two tunnels at this site. These results compliment the numerical study in chapter 3 and highlight their usefulness for detecting tunnels at other sites.

Coffee consumption modifies risk of estrogen-receptor negative breast cancer

PubMed Central

2011-01-01

Introduction Breast cancer is a complex disease and may be sub-divided into hormone-responsive (estrogen receptor (ER) positive) and non-hormone-responsive subtypes (ER-negative). Some evidence suggests that heterogeneity exists in the associations between coffee consumption and breast cancer risk, according to different estrogen receptor subtypes. We assessed the association between coffee consumption and postmenopausal breast cancer risk in a large population-based study (2,818 cases and 3,111 controls), overall, and stratified by ER tumour subtypes. Methods Odds ratios (OR) and corresponding 95% confidence intervals (CI) were estimated using the multivariate logistic regression models fitted to examine breast cancer risk in a stratified case-control analysis. Heterogeneity among ER subtypes was evaluated in a case-only analysis, by fitting binary logistic regression models, treating ER status as a dependent variable, with coffee consumption included as a covariate. Results In the Swedish study, coffee consumption was associated with a modest decrease in overall breast cancer risk in the age-adjusted model (OR> 5 cups/day compared to OR≤ 1 cup/day: 0.80, 95% CI: 0.64, 0.99, P trend = 0.028). In the stratified case-control analyses, a significant reduction in the risk of ER-negative breast cancer was observed in heavy coffee drinkers (OR> 5 cups/day compared to OR≤ 1 cup/day : 0.43, 95% CI: 0.25, 0.72, P trend = 0.0003) in a multivariate-adjusted model. The breast cancer risk reduction associated with higher coffee consumption was significantly higher for ER-negative compared to ER-positive tumours (P heterogeneity (age-adjusted) = 0.004). Conclusions A high daily intake of coffee was found to be associated with a statistically significant decrease in ER-negative breast cancer among postmenopausal women. PMID:21569535

Bone tissue heterogeneity is associated with fracture toughness: a polarization Raman spectroscopy study

NASA Astrophysics Data System (ADS)

Makowski, Alexander J.; Granke, Mathilde; Uppuganti, Sasidhar; Mahadevan-Jansen, Anita; Nyman, Jeffry S.

2015-02-01

Polarization Raman Spectroscopy has been used to demonstrate microstructural features and collagen fiber orientation in human and mouse bone, concurrently measuring both organization and composition; however, it is unclear as to what extent these measurements explain the mechanical quality of bone. In a cohort of age and gender matched cadaveric cortical bone samples (23-101 yr.), we show homogeneity of both composition and structure are associated with the age related decrease in fracture toughness. 64 samples were machined into uniform specimens and notched for mechanical fracture toughness testing and polished for Raman Spectroscopy. Fingerprint region spectra were acquired on wet bone prior to mechanical testing by sampling nine different microstructural features spaced in a 750x750 μm grid in the region of intended crack propagation. After ASTM E1820 single edge notched beam fracture toughness tests, the sample was dried in ethanol and the osteonal-interstitial border of one osteon was samples in a 32x32 grid of 2μm2 pixels for two orthogonal orientations relative to the long bone axis. Standard peak ratios from the 9 separate microstructures show heterogeneity between structures but do not sufficiently explain fracture toughness; however, peak ratios from mapping highlight both lamellar contrast (ν1Phos/Amide I) and osteon-interstitial contrast (ν1Phos/Proline). Combining registered orthogonal maps allowed for multivariate analysis of underlying biochemical signatures. Image entropy and homogeneity metrics of single principal components significantly explain resistance to crack initiation and propagation. Ultimately, a combination of polarization content and multivariate Raman signatures allowed for the association of microstructural tissue heterogeneity with fracture resistance.

Observational needs for estimating Alaskan soil carbon stocks under current and future climate

DOE PAGES

Vitharana, U. W. A.; Mishra, U.; Jastrow, J. D.; ...

2017-01-24

Representing land surface spatial heterogeneity when designing observation networks is a critical scientific challenge. Here we present a geospatial approach that utilizes the multivariate spatial heterogeneity of soil-forming factors—namely, climate, topography, land cover types, and surficial geology—to identify observation sites to improve soil organic carbon (SOC) stock estimates across the State of Alaska, USA. Standard deviations in existing SOC samples indicated that 657, 870, and 906 randomly distributed pedons would be required to quantify the average SOC stocks for 0–1 m, 0–2 m, and whole-profile depths, respectively, at a confidence interval of 5 kg C m -2. Using the spatialmore » correlation range of existing SOC samples, we identified that 309, 446, and 484 new observation sites are needed to estimate current SOC stocks to 1 m, 2 m, and whole-profile depths, respectively. We also investigated whether the identified sites might change under future climate by using eight decadal (2020–2099) projections of precipitation, temperature, and length of growing season for three representative concentration pathway (RCP 4.5, 6.0, and 8.5) scenarios of the Intergovernmental Panel on Climate Change. These analyses determined that 12 to 41 additional sites (906 + 12 to 41; depending upon the emission scenarios) would be needed to capture the impact of future climate on Alaskan whole-profile SOC stocks by 2100. The identified observation sites represent spatially distributed locations across Alaska that captures the multivariate heterogeneity of soil-forming factors under current and future climatic conditions. This information is needed for designing monitoring networks and benchmarking of Earth system model results.« less

Observational needs for estimating Alaskan soil carbon stocks under current and future climate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vitharana, U. W. A.; Mishra, U.; Jastrow, J. D.

Representing land surface spatial heterogeneity when designing observation networks is a critical scientific challenge. Here we present a geospatial approach that utilizes the multivariate spatial heterogeneity of soil-forming factors—namely, climate, topography, land cover types, and surficial geology—to identify observation sites to improve soil organic carbon (SOC) stock estimates across the State of Alaska, USA. Standard deviations in existing SOC samples indicated that 657, 870, and 906 randomly distributed pedons would be required to quantify the average SOC stocks for 0–1 m, 0–2 m, and whole-profile depths, respectively, at a confidence interval of 5 kg C m -2. Using the spatialmore » correlation range of existing SOC samples, we identified that 309, 446, and 484 new observation sites are needed to estimate current SOC stocks to 1 m, 2 m, and whole-profile depths, respectively. We also investigated whether the identified sites might change under future climate by using eight decadal (2020–2099) projections of precipitation, temperature, and length of growing season for three representative concentration pathway (RCP 4.5, 6.0, and 8.5) scenarios of the Intergovernmental Panel on Climate Change. These analyses determined that 12 to 41 additional sites (906 + 12 to 41; depending upon the emission scenarios) would be needed to capture the impact of future climate on Alaskan whole-profile SOC stocks by 2100. The identified observation sites represent spatially distributed locations across Alaska that captures the multivariate heterogeneity of soil-forming factors under current and future climatic conditions. This information is needed for designing monitoring networks and benchmarking of Earth system model results.« less

High Cigarette and Poly-Tobacco Use Among Workers in a Dusty Industry: New Jersey Quarry Workers.

PubMed

Graber, Judith M; Worthington, Karen; Almberg, Kirsten S; Meng, Qingyu; Rose, Cecile S; Cohen, Robert A

2016-04-01

Tobacco use is high among US extraction and construction workers, who can also incur occupational dust exposure. Information on different types of tobacco use among quarry/mine workers is sparse. During mandated training sessions, New Jersey quarry workers were surveyed about their tobacco use. Prevalence was calculated for single and multiple tobacco use by demographic and workplace characteristics; logistic regression was used to assess associations with smoking. Two hundred forty (97.1%) workers completed surveys. Among respondents, 41.7% [95% confidence interval (95% CI) 35.4 to 48.3] currently used any tobacco product of whom 28.1% smoked cigarettes. In multivariate analysis, positive associations with smoking included working as a contractor versus mine employee (odds ratio 2.32, 95% CI 1.01 to 5.36) and a usual job title of maintenance (odds ratio 2.02, 95% CI 0.87 to 4.94). Industry-specific information may be helpful in developing targeted tobacco-cessation programs.

Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method

NASA Astrophysics Data System (ADS)

Khandelwal, Manoj; Monjezi, M.

2013-03-01

Backbreak is an undesirable phenomenon in blasting operations. It can cause instability of mine walls, falling down of machinery, improper fragmentation, reduced efficiency of drilling, etc. The existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, the application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict backbreak in blasting operations of Soungun iron mine, Iran, incorporating rock properties and blast design parameters using the support vector machine (SVM) method. To investigate the suitability of this approach, the predictions by SVM have been compared with multivariate regression analysis (MVRA). The coefficient of determination (CoD) and the mean absolute error (MAE) were taken as performance measures. It was found that the CoD between measured and predicted backbreak was 0.987 and 0.89 by SVM and MVRA, respectively, whereas the MAE was 0.29 and 1.07 by SVM and MVRA, respectively.

Modeling genetic and nongenetic variation of feed efficiency and its partial relationships between component traits as a function of management and environmental factors.

PubMed

Lu, Y; Vandehaar, M J; Spurlock, D M; Weigel, K A; Armentano, L E; Staples, C R; Connor, E E; Wang, Z; Coffey, M; Veerkamp, R F; de Haas, Y; Tempelman, R J

2017-01-01

Feed efficiency (FE), characterized as the fraction of feed nutrients converted into salable milk or meat, is of increasing economic importance in the dairy industry. We conjecture that FE is a complex trait whose variation and relationships or partial efficiencies (PE) involving the conversion of dry matter intake to milk energy and metabolic body weight may be highly heterogeneous across environments or management scenarios. In this study, a hierarchical Bayesian multivariate mixed model was proposed to jointly infer upon such heterogeneity at both genetic and nongenetic levels on PE and variance components (VC). The heterogeneity was modeled by embedding mixed effects specifications on PE and VC in addition to those directly specified on the component traits. We validated the model by simulation and applied it to a joint analysis of a dairy FE consortium data set with 5,088 Holstein cows from 13 research stations in Canada, the Netherlands, the United Kingdom, and the United States. Although no differences were detected among research stations for PE at the genetic level, some evidence was found of heterogeneity in residual PE. Furthermore, substantial heterogeneity in VC across stations, parities, and ration was observed with heritability estimates of FE ranging from 0.16 to 0.46 across stations. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

SinCHet: a MATLAB toolbox for single cell heterogeneity analysis in cancer.

PubMed

Li, Jiannong; Smalley, Inna; Schell, Michael J; Smalley, Keiran S M; Chen, Y Ann

2017-09-15

Single-cell technologies allow characterization of transcriptomes and epigenomes for individual cells under different conditions and provide unprecedented resolution for researchers to investigate cellular heterogeneity in cancer. The SinCHet ( gle ell erogeneity) toolbox is developed in MATLAB and has a graphical user interface (GUI) for visualization and user interaction. It analyzes both continuous (e.g. mRNA expression) and binary omics data (e.g. discretized methylation data). The toolbox does not only quantify cellular heterogeneity using S hannon P rofile (SP) at different clonal resolutions but also detects heterogeneity differences using a D statistic between two populations. It is defined as the area under the P rofile of S hannon D ifference (PSD). This flexible tool provides a default clonal resolution using the change point of PSD detected by multivariate adaptive regression splines model; it also allows user-defined clonal resolutions for further investigation. This tool provides insights into emerging or disappearing clones between conditions, and enables the prioritization of biomarkers for follow-up experiments based on heterogeneity or marker differences between and/or within cell populations. The SinCHet software is freely available for non-profit academic use. The source code, example datasets, and the compiled package are available at http://labpages2.moffitt.org/chen/software/ . ann.chen@moffitt.org. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

MetaRanker 2.0: a web server for prioritization of genetic variation data

PubMed Central

Pers, Tune H.; Dworzyński, Piotr; Thomas, Cecilia Engel; Lage, Kasper; Brunak, Søren

2013-01-01

MetaRanker 2.0 is a web server for prioritization of common and rare frequency genetic variation data. Based on heterogeneous data sets including genetic association data, protein–protein interactions, large-scale text-mining data, copy number variation data and gene expression experiments, MetaRanker 2.0 prioritizes the protein-coding part of the human genome to shortlist candidate genes for targeted follow-up studies. MetaRanker 2.0 is made freely available at www.cbs.dtu.dk/services/MetaRanker-2.0. PMID:23703204

MetaRanker 2.0: a web server for prioritization of genetic variation data.

PubMed

Pers, Tune H; Dworzyński, Piotr; Thomas, Cecilia Engel; Lage, Kasper; Brunak, Søren

2013-07-01

MetaRanker 2.0 is a web server for prioritization of common and rare frequency genetic variation data. Based on heterogeneous data sets including genetic association data, protein-protein interactions, large-scale text-mining data, copy number variation data and gene expression experiments, MetaRanker 2.0 prioritizes the protein-coding part of the human genome to shortlist candidate genes for targeted follow-up studies. MetaRanker 2.0 is made freely available at www.cbs.dtu.dk/services/MetaRanker-2.0.

Saada: A Generator of Astronomical Database

NASA Astrophysics Data System (ADS)

Michel, L.

2011-11-01

Saada transforms a set of heterogeneous FITS files or VOtables of various categories (images, tables, spectra, etc.) in a powerful database deployed on the Web. Databases are located on your host and stay independent of any external server. This job doesn’t require writing code. Saada can mix data of various categories in multiple collections. Data collections can be linked each to others making relevant browsing paths and allowing data-mining oriented queries. Saada supports 4 VO services (Spectra, images, sources and TAP) . Data collections can be published immediately after the deployment of the Web interface.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morris, B.; Morgan, A.J.

The main purpose of the present study was to determine whether a positive Ca-Pb relationship exists in the tissues of L. terrestris. This species possesses well-developed Ca secretory/excretory glands and may thus be able to homeostatically regulate tissue (Ca). The worms were samples from six different stations across a heavily polluted disused Pb/Zn mine site, where the interstation (Ca) varied by as much as a factor of x 10. This heterogeneous site, therefore, offered a good opportunity to study additional aspects of Ca-Pb interactions in an earthworm population under field conditions.

Distribution of chemical elements in soils and stream sediments in the area of abandoned Sb-As-Tl Allchar mine, Republic of Macedonia.

PubMed

Bačeva, Katerina; Stafilov, Trajče; Šajn, Robert; Tănăselia, Claudiu; Makreski, Petre

2014-08-01

The aim of this study was to investigate the distribution of some toxic elements in topsoil and subsoil, focusing on the identification of natural and anthropogenic element sources in the small region of rare As-Sb-Tl mineralization outcrop and abandoned mine Allchar known for the highest natural concentration of Tl in soil worldwide. The samples of soil and sediments after total digestion were analyzed by inductively coupled plasma-mass spectrometry (ICP-MS) and inductively coupled plasma-atomic emission spectrometry (ICP-AES). Factor analysis (FA) was used to identify and characterize element associations. Six associations of elements were determined by the method of multivariate statistics: Rb-Ta-K-Nb-Ga-Sn-Ba-Bi-Li-Be-(La-Eu)-Hf-Zr-Zn-In-Pd-Ag-Pt-Mg; Tl-As-Sb-Hg; Te-S-Ag-Pt-Al-Sc-(Gd-Lu)-Y; Fe-Cu-V-Ge-Co-In; Pd-Zr-Hf-W-Be and Ni-Mn-Co-Cr-Mg. The purpose of the assessment was to determine the nature and extent of potential contamination as well as to broadly assess possible impacts to human health and the environment. The results from the analysis of the collected samples in the vicinity of the mine revealed that As and Tl elements have the highest median values. Higher median values for Sb are obviously as a result of the past mining activities and as a result of area surface phenomena in the past. Copyright © 2014 Elsevier Inc. All rights reserved.

Assessment of heavy metal pollution, spatial distribution and origin in agricultural soils along the Sinú River Basin, Colombia.

PubMed

Marrugo-Negrete, José; Pinedo-Hernández, José; Díez, Sergi

2017-04-01

The presence of metals in agricultural soils from anthropogenic activities such as mining and agricultural use of metals and metal-containing compounds is a potential threat for human health through the food chain. In this study, the concentration of heavy metals in 83 agricultural soils irrigated by the Sinú River, in northern Colombia, affected by mining areas upstream and inundated during seasonal floods events were determined to evaluate their sources and levels of pollution. The average concentrations of Cu, Ni, Pb, Cd, Hg and Zn were 1149, 661, 0.071, 0.040, 0.159 and 1365mg/kg respectively and exceeded the world normal averages, with the exception of Pb and Cd. Moreover, all values surpassed the background levels of soils in the same region. Soil pollution assessment was carried out using contamination factor (CF), enrichment factor (EF), geoaccumulation index (Igeo) and a risk assessment code (RAC). According to these indexes, the soils show a high degree of pollution of Ni and a moderate to high contamination of Zn and Cu; whereas, Pb, Cd and Hg present moderate pollution. However, based on the RAC index, a low environmental risk is found for all the analysed heavy metals. Multivariate statistical analyses, principal component and cluster analyses, suggest that soil contamination was mainly derived from agricultural practices, except for Hg, which was caused probably by atmospheric and river flow transport from upstream gold mining. Finally, high concentrations of Ni indicate a mixed pollution source from agricultural and ferronickel mining activities. Copyright © 2017 Elsevier Inc. All rights reserved.

Spatial distribution and sources of heavy metals in natural pasture soil around copper-molybdenum mine in Northeast China.

PubMed

Wang, Zhiqiang; Hong, Chen; Xing, Yi; Wang, Kang; Li, Yifei; Feng, Lihui; Ma, Silu

2018-06-15

The characterization of the content and source of heavy metals are essential to assess the potential threat of metals to human health. The present study collected 140 topsoil samples around a Cu-Mo mine (Wunugetushan, China) and investigated the concentrations and spatial distribution pattern of Cr, Ni, Zn, Cu, Mo and Cd in soil using multivariate and geostatistical analytical methods. Results indicated that the average concentrations of six heavy metals, especially Cu and Mo, were obviously higher than the local background values. Correlation analysis and principal component analysis divided these metals into three groups, including Cr and Ni, Cu and Mo, Zn and Cd. Meanwhile, the spatial distribution maps of heavy metals indicated that Cr and Ni in soil were no notable anthropogenic inputs and mainly controlled by natural factors because their spatial maps exhibited non-point source contamination. The concentrations of Cu and Mo gradually decreased with distance away from the mine area, suggesting that human mining activities may be crucial in the spreading of contaminants. Soil contamination of Zn were associated with livestock manure produced from grazing. In addition, the environmental risk of heavy metal pollution was assessed by geo-accumulation index. All the results revealed that the spatial distribution of heavy metals in soil were in agreement with the local human activities. Investigating and identifying the origin of heavy metals in pasture soil will lay the foundation for taking effective measures to preserve soil from the long-term accumulation of heavy metals. Copyright © 2018 Elsevier Inc. All rights reserved.

Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities.

PubMed

Quo, Chang F; Kaddi, Chanchala; Phan, John H; Zollanvari, Amin; Xu, Mingqing; Wang, May D; Alterovitz, Gil

2012-07-01

Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harben, P E; Harris, D; Myers, S

Seismic imaging and tracking methods have intelligence and monitoring applications. Current systems, however, do not adequately calibrate or model the unknown geological heterogeneity. Current systems are also not designed for rapid data acquisition and analysis in the field. This project seeks to build the core technological capabilities coupled with innovative deployment, processing, and analysis methodologies to allow seismic methods to be effectively utilized in the applications of seismic imaging and vehicle tracking where rapid (minutes to hours) and real-time analysis is required. The goal of this project is to build capabilities in acquisition system design, utilization and in full 3Dmore » finite difference modeling as well as statistical characterization of geological heterogeneity. Such capabilities coupled with a rapid field analysis methodology based on matched field processing are applied to problems associated with surveillance, battlefield management, finding hard and deeply buried targets, and portal monitoring. This project benefits the U.S. military and intelligence community in support of LLNL's national-security mission. FY03 was the final year of this project. In the 2.5 years this project has been active, numerous and varied developments and milestones have been accomplished. A wireless communication module for seismic data was developed to facilitate rapid seismic data acquisition and analysis. The E3D code was enhanced to include topographic effects. Codes were developed to implement the Karhunen-Loeve (K-L) statistical methodology for generating geological heterogeneity that can be utilized in E3D modeling. The matched field processing methodology applied to vehicle tracking and based on a field calibration to characterize geological heterogeneity was tested and successfully demonstrated in a tank tracking experiment at the Nevada Test Site. A 3-seismic-array vehicle tracking testbed was installed on-site at LLNL for testing real-time seismic tracking methods. A field experiment was conducted over a tunnel at the Nevada Site that quantified the tunnel reflection signal and, coupled with modeling, identified key needs and requirements in experimental layout of sensors. A large field experiment was conducted at the Lake Lynn Laboratory, a mine safety research facility in Pennsylvania, over a tunnel complex in realistic, difficult conditions. This experiment gathered the necessary data for a full 3D attempt to apply the methodology. The experiment also collected data to analyze the capabilities to detect and locate in-tunnel explosions for mine safety and other applications.« less

Cross-validation of a dementia screening test in a heterogeneous population.

PubMed

Ritchie, K A; Hallerman, E F

1989-09-01

Recognition of the increasing importance of early dementia screening for both research and clinical purposes has led to the development of numerous screening instruments. The most promising of these are based on neuropsychological measures which are able to focus on very specific cognitive functions. Of these tests the Iowa screening test is of particular interest to researchers and clinicians working with heterogenous populations or wishing to make cross-cultural comparisons as it is relatively culture-fair and does not assume literacy. A preliminary study of the performance of the Iowa in an Israeli sample of diverse ethnic origins and low education level suggests it to be a very sensitive measure even in such groups. The study also demonstrates the inadvisability of adopting item weights derived by multivariate statistical techniques from another population.

Tumor heterogeneity measured on F-18 fluorodeoxyglucose positron emission tomography/computed tomography combined with plasma Epstein-Barr Virus load predicts prognosis in patients with primary nasopharyngeal carcinoma.

PubMed

Chan, Sheng-Chieh; Chang, Kai-Ping; Fang, Yu-Hua Dean; Tsang, Ngan-Ming; Ng, Shu-Hang; Hsu, Cheng-Lung; Liao, Chun-Ta; Yen, Tzu-Chen

2017-01-01

Plasma Epstein-Barr virus (EBV) DNA concentrations predict prognosis in patients with nasopharyngeal carcinoma (NPC). Recent evidence also indicates that intratumor heterogeneity on F-18 fluorodeoxyglucose positron emission tomography ( 18 F-FDG PET) scans is predictive of treatment outcomes in different solid malignancies. Here, we sought to investigate the prognostic value of heterogeneity parameters in patients with primary NPC. Retrospective cohort study. We examined 101 patients with primary NPC who underwent pretreatment 18 F-FDG PET/computed tomography. Circulating levels of EBV DNA were measured in all participants. The following PET heterogeneity parameters were collected: histogram-based heterogeneity parameters, second-order texture features (uniformity, contrast, entropy, homogeneity, dissimilarity, inverse difference moment), and higher-order (coarseness, contrast, busyness, complexity, strength) texture features. The median follow-up time was 5.14 years. Total lesion glycolysis (TLG), tumor heterogeneity measured by histogram-based parameter skewness, and the majority of second-order or higher-order texture features were significantly associated with overall survival (OS) and/or recurrence-free survival (RFS). In multivariate analysis, age (P =.005), EBV DNA load (P = .0002), and uniformity (P = .001) independently predicted OS. Only skewness retained the independent prognostic significance for RFS. Tumor stage, standardized uptake value, or TLG did not show an independent association with survival endpoints. The combination of uniformity, EBV DNA load, and age resulted in a more reliable prognostic stratification (P < .001). Tumor heterogeneity is superior to traditional PET parameters for predicting outcomes in primary NPC. The combination of uniformity with EBV DNA load can improve prognostic stratification in this clinical entity. 4 Laryngoscope, 127:E22-E28, 2017. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

From engineering hydrology to Earth system science: milestones in the transformation of hydrologic science

NASA Astrophysics Data System (ADS)

Sivapalan, Murugesu

2018-03-01

Hydrology has undergone almost transformative changes over the past 50 years. Huge strides have been made in the transition from early empirical approaches to rigorous approaches based on the fluid mechanics of water movement on and below the land surface. However, progress has been hampered by problems posed by the presence of heterogeneity, including subsurface heterogeneity present at all scales. The inability to measure or map the heterogeneity everywhere prevented the development of balance equations and associated closure relations at the scales of interest, and has led to the virtual impasse we are presently in, in terms of development of physically based models needed for hydrologic predictions. An alternative to the mapping of heterogeneity everywhere is a new Earth system science view, which sees the heterogeneity as the end result of co-evolutionary hydrological, geomorphological, ecological, and pedological processes, each operating at a different rate, which help to shape the landscapes that we find in nature, including the heterogeneity that we do not readily see. The expectation is that instead of specifying exact details of the heterogeneity in our models, we can replace it (without loss of information) with the ecosystem function that they perform. Guided by this new Earth system science perspective, development of hydrologic science is now addressing new questions using novel holistic co-evolutionary approaches as opposed to the physical, fluid mechanics based reductionist approaches that we inherited from the recent past. In the emergent Anthropocene, the co-evolutionary view has expanded further to involve interactions and feedbacks with human-social processes as well. In this paper, I present my own perspective of key milestones in the transformation of hydrologic science from engineering hydrology to Earth system science, drawn from the work of several students and colleagues of mine, and discuss their implication for hydrologic observations, theory development, and predictions.

Smart Point Cloud: Definition and Remaining Challenges

NASA Astrophysics Data System (ADS)

Poux, F.; Hallot, P.; Neuville, R.; Billen, R.

2016-10-01

Dealing with coloured point cloud acquired from terrestrial laser scanner, this paper identifies remaining challenges for a new data structure: the smart point cloud. This concept arises with the statement that massive and discretized spatial information from active remote sensing technology is often underused due to data mining limitations. The generalisation of point cloud data associated with the heterogeneity and temporality of such datasets is the main issue regarding structure, segmentation, classification, and interaction for an immediate understanding. We propose to use both point cloud properties and human knowledge through machine learning to rapidly extract pertinent information, using user-centered information (smart data) rather than raw data. A review of feature detection, machine learning frameworks and database systems indexed both for mining queries and data visualisation is studied. Based on existing approaches, we propose a new 3-block flexible framework around device expertise, analytic expertise and domain base reflexion. This contribution serves as the first step for the realisation of a comprehensive smart point cloud data structure.

The Effect of Normalization in Violence Video Classification Performance

NASA Astrophysics Data System (ADS)

Ali, Ashikin; Senan, Norhalina

2017-08-01

Basically, data pre-processing is an important part of data mining. Normalization is a pre-processing stage for any type of problem statement, especially in video classification. Challenging problems that arises in video classification is because of the heterogeneous content, large variations in video quality and complex semantic meanings of the concepts involved. Therefore, to regularize this problem, it is thoughtful to ensure normalization or basically involvement of thorough pre-processing stage aids the robustness of classification performance. This process is to scale all the numeric variables into certain range to make it more meaningful for further phases in available data mining techniques. Thus, this paper attempts to examine the effect of 2 normalization techniques namely Min-max normalization and Z-score in violence video classifications towards the performance of classification rate using Multi-layer perceptron (MLP) classifier. Using Min-Max Normalization range of [0,1] the result shows almost 98% of accuracy, meanwhile Min-Max Normalization range of [-1,1] accuracy is 59% and for Z-score the accuracy is 50%.

Predicting crash frequency for multi-vehicle collision types using multivariate Poisson-lognormal spatial model: A comparative analysis.

PubMed

Hosseinpour, Mehdi; Sahebi, Sina; Zamzuri, Zamira Hasanah; Yahaya, Ahmad Shukri; Ismail, Noriszura

2018-06-01

According to crash configuration and pre-crash conditions, traffic crashes are classified into different collision types. Based on the literature, multi-vehicle crashes, such as head-on, rear-end, and angle crashes, are more frequent than single-vehicle crashes, and most often result in serious consequences. From a methodological point of view, the majority of prior studies focused on multivehicle collisions have employed univariate count models to estimate crash counts separately by collision type. However, univariate models fail to account for correlations which may exist between different collision types. Among others, multivariate Poisson lognormal (MVPLN) model with spatial correlation is a promising multivariate specification because it not only allows for unobserved heterogeneity (extra-Poisson variation) and dependencies between collision types, but also spatial correlation between adjacent sites. However, the MVPLN spatial model has rarely been applied in previous research for simultaneously modelling crash counts by collision type. Therefore, this study aims at utilizing a MVPLN spatial model to estimate crash counts for four different multi-vehicle collision types, including head-on, rear-end, angle, and sideswipe collisions. To investigate the performance of the MVPLN spatial model, a two-stage model and a univariate Poisson lognormal model (UNPLN) spatial model were also developed in this study. Detailed information on roadway characteristics, traffic volume, and crash history were collected on 407 homogeneous segments from Malaysian federal roads. The results indicate that the MVPLN spatial model outperforms the other comparing models in terms of goodness-of-fit measures. The results also show that the inclusion of spatial heterogeneity in the multivariate model significantly improves the model fit, as indicated by the Deviance Information Criterion (DIC). The correlation between crash types is high and positive, implying that the occurrence of a specific collision type is highly associated with the occurrence of other crash types on the same road segment. These results support the utilization of the MVPLN spatial model when predicting crash counts by collision manner. In terms of contributing factors, the results show that distinct crash types are attributed to different subsets of explanatory variables. Copyright © 2018 Elsevier Ltd. All rights reserved.

Multivariate Statistical Approach Applied to Sediment Source Tracking Through Quantification and Mineral Identification, Cheyenne River, South Dakota

NASA Astrophysics Data System (ADS)

Valder, J.; Kenner, S.; Long, A.

2008-12-01

Portions of the Cheyenne River are characterized as impaired by the U.S. Environmental Protection Agency because of water-quality exceedences. The Cheyenne River watershed includes the Black Hills National Forest and part of the Badlands National Park. Preliminary analysis indicates that the Badlands National Park is a major contributor to the exceedances of the water-quality constituents for total dissolved solids and total suspended solids. Water-quality data have been collected continuously since 2007, and in the second year of collection (2008), monthly grab and passive sediment samplers are being used to collect total suspended sediment and total dissolved solids in both base-flow and runoff-event conditions. In addition, sediment samples from the river channel, including bed, bank, and floodplain, have been collected. These samples are being analyzed at the South Dakota School of Mines and Technology's X-Ray Diffraction Lab to quantify the mineralogy of the sediments. A multivariate statistical approach (including principal components, least squares, and maximum likelihood techniques) is applied to the mineral percentages that were characterized for each site to identify the contributing source areas that are causing exceedances of sediment transport in the Cheyenne River watershed. Results of the multivariate analysis demonstrate the likely sources of solids found in the Cheyenne River samples. A further refinement of the methods is in progress that utilizes a conceptual model which, when applied with the multivariate statistical approach, provides a better estimate for sediment sources.

Bacterial, Archaeal, and Eukaryotic Diversity across Distinct Microhabitats in an Acid Mine Drainage

PubMed Central

Mesa, Victoria; Gallego, Jose L. R.; González-Gil, Ricardo; Lauga, Béatrice; Sánchez, Jesús; Méndez-García, Celia; Peláez, Ana I.

2017-01-01

Acid mine drainages are characterized by their low pH and the presence of dissolved toxic metallic species. Microorganisms survive in different microhabitats within the ecosystem, namely water, sediments, and biofilms. In this report, we surveyed the microbial diversity within all domains of life in the different microhabitats at Los Rueldos abandoned mercury underground mine (NW Spain), and predicted bacterial function based on community composition. Sediment samples contained higher proportions of soil bacteria (AD3, Acidobacteria), as well as Crenarchaeota and Methanomassiliicoccaceae archaea. Oxic and hypoxic biofilm samples were enriched in bacterial iron oxidizers from the genus Leptospirillum, order Acidithiobacillales, class Betaproteobacteria, and archaea from the class Thermoplasmata. Water samples were enriched in Cyanobacteria and Thermoplasmata archaea at a 3–98% of the sunlight influence, whilst Betaproteobacteria, Thermoplasmata archaea, and Micrarchaea dominated in acid water collected in total darkness. Stalactites hanging from the Fe-rich mine ceiling were dominated by the neutrophilic iron oxidizer Gallionella and other lineages that were absent in the rest of the microhabitats (e.g., Chlorobi, Chloroflexi). Eukaryotes were detected in biofilms and open-air water samples, and belonged mainly to clades SAR (Alveolata and Stramenopiles), and Opisthokonta (Fungi). Oxic and hypoxic biofilms displayed higher proportions of ciliates (Gonostomum, Oxytricha), whereas water samples were enriched in fungi (Paramicrosporidium and unknown microbial Helotiales). Predicted function through bacterial community composition suggested adaptive evolutive convergence of function in heterogeneous communities. Our study showcases a broad description of the microbial diversity across different microhabitats in the same environment and expands the knowledge on the diversity of microbial eukaryotes in AMD habitats. PMID:28955322

Geochemical processes controlling fate and transport of arsenic in acid mine drainage (AMD) and natural systems.

PubMed

Cheng, Hefa; Hu, Yuanan; Luo, Jian; Xu, Bin; Zhao, Jianfu

2009-06-15

Acid mine drainage (AMD) is often accompanied with elevated concentrations of arsenic, in the forms of arsenite, As(III), and/or arsenate, As(V), due to the high affinity of arsenic for sulfide mineral ores. This review summarizes the major geochemical processes controlling the release, speciation, fate, and distribution of inorganic arsenic in mine drainage and natural systems. Arsenic speciation depends highly on redox potential and pH of the solution, and arsenite can be oxidized to the less toxic arsenate form. Homogeneous oxidation of arsenite occurs rather slowly while its heterogeneous oxidation on mineral surfaces can greatly enhance the reaction rates. Little evidence suggests that precipitation reaction limits the concentrations of arsenic in natural water, while co-precipitation may lead to rapid arsenic removal when large amount of iron hydroxides precipitate out of the aqueous phase upon neutralization of the mine drainage. Both arsenate and arsenite adsorb on common metal oxides and clay minerals through formation of inner-sphere and/or outer-sphere complexes, controlling arsenic concentration in natural water bodies. Arsenite adsorbs less strongly than arsenate in the typical pH range of natural water and is more mobile. Part of the adsorbed arsenic species can be exchanged by common anions (e.g., PO(4)(3-) and SO(4)(2-)), especially phosphate, which leads to their re-mobilization. Understanding the geochemistry of arsenic is helpful for predicting its mobility and fate in AMD and natural systems, and for designing of cost-effective remediation/treatment strategies to reduce the occurrence and risk of arsenic contamination.

Fungal Biorecovery of Gold From E-waste.

PubMed

Bindschedler, Saskia; Vu Bouquet, Thi Quynh Trang; Job, Daniel; Joseph, Edith; Junier, Pilar

2017-01-01

Waste electric and electronic devices (e-waste) represent a source of valuable raw materials of great interest, and in the case of metals, e-waste might become a prized alternative source. Regarding gold, natural ores are difficult to mine due to their refractory nature and the richest ores have almost all been exploited. Additionally, some gold mining areas are present in geopolitically unstable regions. Finally, the gold mining industry produces toxic compounds, such as cyanides. As a result, the gold present in e-waste represents a nonnegligible resource (urban mining). Extraction methods of gold from natural ores (pyro- and hydrometallurgy) have been adapted to this particular type of matrix. However, to propose novel approaches with a lower environmental footprint, biotechnological methods using microorganisms are being developed (biometallurgy). These processes use the extensive metabolic potential of microbes (algae, bacteria, and fungi) to mobilize and immobilize gold from urban and industrial sources. In this review, we focus on the use of fungi for gold biomining. Fungi interact with gold by mobilizing it through mechanical attack as well as through biochemical leaching by the production of cyanides. Moreover, fungi are also able to release Au through the degradation of cyanide from aurocyanide complexes. Finally, fungi immobilize gold through biosorption, bioaccumulation, and biomineralization, in particular, as gold nanoparticles. Overall, the diversity of mechanisms of gold recycling using fungi combined with their filamentous lifestyle, which allows them to thrive in heterogeneous and solid environments such as e-waste, makes fungi an important bioresource to be harnessed for the biorecovery of gold. Copyright © 2017 Elsevier Inc. All rights reserved.

Robust tests for multivariate factorial designs under heteroscedasticity.

PubMed

Vallejo, Guillermo; Ato, Manuel

2012-06-01

The question of how to analyze several multivariate normal mean vectors when normality and covariance homogeneity assumptions are violated is considered in this article. For the two-way MANOVA layout, we address this problem adapting results presented by Brunner, Dette, and Munk (BDM; 1997) and Vallejo and Ato (modified Brown-Forsythe [MBF]; 2006) in the context of univariate factorial and split-plot designs and a multivariate version of the linear model (MLM) to accommodate heterogeneous data. Furthermore, we compare these procedures with the Welch-James (WJ) approximate degrees of freedom multivariate statistics based on ordinary least squares via Monte Carlo simulation. Our numerical studies show that of the methods evaluated, only the modified versions of the BDM and MBF procedures were robust to violations of underlying assumptions. The MLM approach was only occasionally liberal, and then by only a small amount, whereas the WJ procedure was often liberal if the interactive effects were involved in the design, particularly when the number of dependent variables increased and total sample size was small. On the other hand, it was also found that the MLM procedure was uniformly more powerful than its most direct competitors. The overall success rate was 22.4% for the BDM, 36.3% for the MBF, and 45.0% for the MLM.

Geochemical and mineralogical fingerprints to distinguish the exploited ferruginous mineralisations of Grotta della Monaca (Calabria, Italy)

NASA Astrophysics Data System (ADS)

Dimuccio, Luca Antonio; Rodrigues, Nelson; Larocca, Felice; Pratas, João; Amado, Ana Margarida; de Carvalho, Luís A. E. Batista

2017-02-01

This study examines the geochemical and mineralogical variations in the ferruginous mineralisations that crop out within Grotta della Monaca, which is considered to be the most striking and best known example of a prehistoric iron mine-cave from the southern Apennines (Calabria, Italy). Previous archaeological research identified three local and distinct ancient exploitation phases of these ferruginous mineralisations: (1) an Upper Palaeolithic phase; (2) a Late Neolithic phase; and (3) a post-Medieval phase. These materials, which have various forms of complex mineralogical admixtures and range in colour from yellow-orange to red and darker brown shades, mainly consist of iron oxides/hydroxides (essentially goethite and lepidocrocite), which are often mixed with subordinate and variable amounts of other matrix components (carbonates, sulphates, arsenates, silicates and organic matter). Such ferruginous mineralisations generally correspond to geochemically heterogeneous massive dyke/vein/mammillary/stratiform facies that are exposed within the local caves along open fractures and inclined bedding planes and that partially cover cave wall niches/notches/pockets and ceiling cupolas/holes. Selected samples/sub-samples are analysed through a multi-technique approach with a handheld portable X-ray Fluorescence, X-ray Diffraction, micro-Raman and Fourier Transform Infrared spectroscope (both conventional and attenuated total reflection), which is combined with subsequent multivariate statistical analysis of the elemental concentration data. The geochemical and mineralogical results are used to individualise similar compositional clusters. As expected, the identified groups, each of which has very specific geochemical-mineralogical ;fingerprints; and spatial distributions, enable us to identify the sampled ferruginous mineralisations. These specific mineral resources can be compared to similar raw materials that are found in other neighbouring archaeological sites, with obvious implications toward understanding local exploitation strategies through time and the exchanges and kinship networks of these materials.

A multivariate multilevel Gaussian model with a mixed effects structure in the mean and covariance part.

PubMed

Li, Baoyue; Bruyneel, Luk; Lesaffre, Emmanuel

2014-05-20

A traditional Gaussian hierarchical model assumes a nested multilevel structure for the mean and a constant variance at each level. We propose a Bayesian multivariate multilevel factor model that assumes a multilevel structure for both the mean and the covariance matrix. That is, in addition to a multilevel structure for the mean we also assume that the covariance matrix depends on covariates and random effects. This allows to explore whether the covariance structure depends on the values of the higher levels and as such models heterogeneity in the variances and correlation structure of the multivariate outcome across the higher level values. The approach is applied to the three-dimensional vector of burnout measurements collected on nurses in a large European study to answer the research question whether the covariance matrix of the outcomes depends on recorded system-level features in the organization of nursing care, but also on not-recorded factors that vary with countries, hospitals, and nursing units. Simulations illustrate the performance of our modeling approach. Copyright © 2013 John Wiley & Sons, Ltd.

Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.

PubMed

Wang, Longfei; Lee, Sungyoung; Gim, Jungsoo; Qiao, Dandi; Cho, Michael; Elston, Robert C; Silverman, Edwin K; Won, Sungho

2016-09-01

Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows. © 2016 WILEY PERIODICALS, INC.

Mercury Concentrations in Fish and Sediment within Streams are Influenced by Watershed and Landscape Variables including Historical Gold Mining in the Sierra Nevada, California

NASA Astrophysics Data System (ADS)

Alpers, C. N.; Yee, J. L.; Ackerman, J. T.; Orlando, J. L.; Slotton, D. G.; Marvin-DiPasquale, M. C.

2015-12-01

We compiled available data on total mercury (THg) and methylmercury (MeHg) concentrations in fish tissue and streambed sediment from stream sites in the Sierra Nevada, California, to assess whether spatial data, including information on historical mining, can be used to make robust predictions of fish fillet tissue THg concentrations. A total of 1,271 fish from five species collected at 103 sites during 1980-2012 were used for the modeling effort: 210 brown trout, 710 rainbow trout, 79 Sacramento pikeminnow, 93 Sacramento sucker, and 179 smallmouth bass. Sediment data were used from 73 sites, including 106 analyses of THg and 77 analyses of MeHg. The dataset included 391 fish (mostly rainbow trout) and 28 sediment samples collected explicitly for this study during 2011-12. Spatial data on historical mining included the USGS Mineral Resources Data System and publicly available maps and satellite photos showing the areas of hydraulic mine pits and other placer mines. Modeling was done using multivariate linear regression and multi-model inference using Akaike Information Criteria. Results indicate that fish THg, accounting for species and length, can be predicted using geospatial data on mining history together with other landscape characteristics including land use/land cover. A model requiring only geospatial data, with an R2 value of 0.61, predicted fish THg correctly with respect to over-or-under 0.2 μg/g wet weight (a California regulatory threshold) for 108 of 121 (89 %) size-species combinations tested. Data for THg in streambed sediment did not improve the geospatial-only model. However, data for sediment MeHg, loss on ignition (organic content), and percent of sediment less than 0.063 mm resulted in a slightly improved model, with an R2 value of 0.63. It is anticipated that these models will be useful to the State of California and others to predict areas where mercury concentrations in fish are likely to exceed regulatory criteria.

Evaluation of single- and dual-porosity models for reproducing the release of external and internal tracers from heterogeneous waste-rock piles.

PubMed

Blackmore, S; Pedretti, D; Mayer, K U; Smith, L; Beckie, R D

2018-05-30

Accurate predictions of solute release from waste-rock piles (WRPs) are paramount for decision making in mining-related environmental processes. Tracers provide information that can be used to estimate effective transport parameters and understand mechanisms controlling the hydraulic and geochemical behavior of WRPs. It is shown that internal tracers (i.e. initially present) together with external (i.e. applied) tracers provide complementary and quantitative information to identify transport mechanisms. The analysis focuses on two experimental WRPs, Piles 4 and Pile 5 at the Antamina Mine site (Peru), where both an internal chloride tracer and externally applied bromide tracer were monitored in discharge over three years. The results suggest that external tracers provide insight into transport associated with relatively fast flow regions that are activated during higher-rate recharge events. In contrast, internal tracers provide insight into mechanisms controlling solutes release from lower-permeability zones within the piles. Rate-limited diffusive processes, which can be mimicked by nonlocal mass-transfer models, affect both internal and external tracers. The sensitivity of the mass-transfer parameters to heterogeneity is higher for external tracers than for internal tracers, as indicated by the different mean residence times characterizing the flow paths associated with each tracer. The joint use of internal and external tracers provides a more comprehensive understanding of the transport mechanisms in WRPs. In particular, the tracer tests support the notion that a multi-porosity conceptualization of WRPs is more adequate for capturing key mechanisms than a dual-porosity conceptualization. Copyright © 2018 Elsevier B.V. All rights reserved.

[Statistical prediction methods in violence risk assessment and its application].

PubMed

Liu, Yuan-Yuan; Hu, Jun-Mei; Yang, Min; Li, Xiao-Song

2013-06-01

It is an urgent global problem how to improve the violence risk assessment. As a necessary part of risk assessment, statistical methods have remarkable impacts and effects. In this study, the predicted methods in violence risk assessment from the point of statistics are reviewed. The application of Logistic regression as the sample of multivariate statistical model, decision tree model as the sample of data mining technique, and neural networks model as the sample of artificial intelligence technology are all reviewed. This study provides data in order to contribute the further research of violence risk assessment.

Seasonal ionomic and metabolic changes in Aleppo pines growing on mine tailings under Mediterranean semi-arid climate.

PubMed

López-Orenes, Antonio; Bueso, María C; Conesa, Héctor; Calderón, Antonio A; Ferrer, María A

2018-05-11

Aleppo pine is the most abundant conifer species in Mediterranean basin. Knowledge of adaptive mechanisms to cope with different environmental stresses simultaneously is necessary to improve its resilience to the predicted climatic changes and anthropogenic stressors, such as heavy metal/metal(loid)s (HMMs) pollution. Here, one year-old needles and rhizosphere soil samples from five mining and non-mining (NM) populations of Aleppo pines grown spontaneously in SE Spain were sampled in two consecutive years during spring and summer. Quantitative determination of a wide suite of edaphic, biochemical, and physiological parameters was performed, including soil physicochemical properties, ionome profile, foliar redox components, primary and secondary metabolites. Mining rhizosphere soils were characterized by elevated contents of HMMs, particularly lead and zinc, and low carbon, nitrogen and potassium levels. Multivariate data analysis based on needle ionome and antioxidative/oxidative parameters revealed a clear distinction between seasons irrespective of the population considered. Spring needles were characterized by higher levels of HMMs, sulfur, glutathione (GSH), proanthocyanidins (PAs), and soluble phenols (TPC), whereas reduced chlorophylls and increased levels of carotenoids, relative water content and K + , Na + and Cl - typified summer needles. In general mining populations had higher levels of ascorbate, and TPC, and exhibited higher antioxidant activities than the NM population. This could contribute to prevent oxidative injury induced by HMMs. Taken together, results suggest that seasonal factors have a more marked effect on the metabolism of the Aleppo pine populations studied than that exerted by soil conditions. This effect could be mediated by water availability in surface soil layers. If this conclusion is right, predicted rainfall reduction and temperature increase in the Mediterranean basin associated to global climate change would lead to pine needle metabolism to express the summer pattern for more prolonged periods. This, in turn, could negatively affect the performance of Aleppo pine populations. Copyright © 2018 Elsevier B.V. All rights reserved.

Cytokine profile determined by data-mining analysis set into clusters of non-small-cell lung cancer patients according to prognosis.

PubMed

Barrera, L; Montes-Servín, E; Barrera, A; Ramírez-Tirado, L A; Salinas-Parra, F; Bañales-Méndez, J L; Sandoval-Ríos, M; Arrieta, Ó

2015-02-01

Immunoregulatory cytokines may play a fundamental role in tumor growth and metastases. Their effects are mediated through complex regulatory networks. Human cytokine profiles could define patient subgroups and represent new potential biomarkers. The aim of this study was to associate a cytokine profile obtained through data mining with the clinical characteristics of patients with advanced non-small-cell lung cancer (NSCLC). We conducted a prospective study of the plasma levels of 14 immunoregulatory cytokines by ELISA and a cytometric bead array assay in 110 NSCLC patients before chemotherapy and 25 control subjects. Cytokine levels and data-mining profiles were associated with clinical, quality of life and pathological outcomes. NSCLC patients had higher levels of interleukin (IL)-6, IL-8, IL-12p70, IL-17a and interferon (IFN)-γ, and lower levels of IL-33 and IL-29 compared with controls. The pro-inflammatory cytokines IL-1b, IL-6 and IL-8 were associated with lower hemoglobin levels, worse functional performance status (Eastern Cooperative Oncology Group, ECOG), fatigue and hyporexia. The anti-inflammatory cytokines IL-4, IL-10 and IL-33 were associated with anorexia and lower body mass index. We identified three clusters of patients according to data-mining analysis with different overall survival (OS; 25.4, 16.8 and 5.09 months, respectively, P = 0.0012). Multivariate analysis showed that ECOG performance status and data-mining clusters were significantly associated with OS (RR 3.59, [95% CI 1.9-6.7], P < 0.001 and 2.2, [1.2-3.8], P = 0.005). Our results provide evidence that complex cytokine networks may be used to identify patient subgroups with different prognoses in advanced NSCLC. These cytokines may represent potential biomarkers, particularly in the immunotherapy era in cancer research. © The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

PubMed

Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

2017-12-01

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA). Copyright © 2017 Elsevier Ltd. All rights reserved.

Advanced Multivariate Inversion Techniques for High Resolution 3D Geophysical Modeling

DTIC Science & Technology

2011-09-01

of seismic ambient noise – has been used to image crustal Vs variation with a lateral resolution upward of 100 km either on regional or on sub...to East Africa, we solve for velocity structure in an area with less lateral heterogeneity but great tectonic complexity. To increase the...demonstrate correlation with crustal geology. Figure 1 shows the 3D S-wave velocity model obtained from the joint inversion. The low-velocity anomaly

Intratumoral heterogeneity analysis reveals hidden associations between protein expression losses and patient survival in clear cell renal cell carcinoma

PubMed Central

Devarajan, Karthik; Parsons, Theodore; Wang, Qiong; O'Neill, Raymond; Solomides, Charalambos; Peiper, Stephen C.; Testa, Joseph R.; Uzzo, Robert; Yang, Haifeng

2017-01-01

Intratumoral heterogeneity (ITH) is a prominent feature of kidney cancer. It is not known whether it has utility in finding associations between protein expression and clinical parameters. We used ITH that is detected by immunohistochemistry (IHC) to aid the association analysis between the loss of SWI/SNF components and clinical parameters.160 ccRCC tumors (40 per tumor stage) were used to generate tissue microarray (TMA). Four foci from different regions of each tumor were selected. IHC was performed against PBRM1, ARID1A, SETD2, SMARCA4, and SMARCA2. Statistical analyses were performed to correlate biomarker losses with patho-clinical parameters. Categorical variables were compared between groups using Fisher's exact tests. Univariate and multivariable analyses were used to correlate biomarker changes and patient survivals. Multivariable analyses were performed by constructing decision trees using the classification and regression trees (CART) methodology. IHC detected widespread ITH in ccRCC tumors. The statistical analysis of the “Truncal loss” (root loss) found additional correlations between biomarker losses and tumor stages than the traditional “Loss in tumor (total)”. Losses of SMARCA4 or SMARCA2 significantly improved prognosis for overall survival (OS). Losses of PBRM1, ARID1A or SETD2 had the opposite effect. Thus “Truncal Loss” analysis revealed hidden links between protein losses and patient survival in ccRCC. PMID:28445125

Influence of habitat heterogeneity on the community structure of deep-sea harpacticoid communities from a canyon and an escarpment site on the continental rise off California

NASA Astrophysics Data System (ADS)

Thistle, David; Sedlacek, Linda; Carman, Kevin R.; Barry, James P.

2017-05-01

The sediment-covered deep-sea floor was initially thought to be environmentally homogeneous. Recent work has shown otherwise, and deep-sea ecologists have been searching for ecologically important environmental heterogeneities on different spatial and temporal scales, with particular interest in canyons. Here we report results for harpacticoid copepods from a site at 3262 m depth in the axis of Monterey Canyon and one on an escarpment 46 km away at 3090 m depth. Multivariate community analyses revealed significant differences between sites in community structure. Absolute abundance, the ratio of subadult copepodites to adults, species density, the proportion of the harpacticoid individuals that emerged, and the proportion that lived in tubes were significantly lower at the canyon site than at the escarpment site. The proportion of the harpacticoid individuals that belonged to the surface-dweller life-style group was significantly higher than at the escarpment site. These marked differences imply that ecologically important environmental heterogeneities exist. We speculate that differences between the sites in food conditions and sediment grain-size distributions are among them.

Association rule mining in the US Vaccine Adverse Event Reporting System (VAERS).

PubMed

Wei, Lai; Scott, John

2015-09-01

Spontaneous adverse event reporting systems are critical tools for monitoring the safety of licensed medical products. Commonly used signal detection algorithms identify disproportionate product-adverse event pairs and may not be sensitive to more complex potential signals. We sought to develop a computationally tractable multivariate data-mining approach to identify product-multiple adverse event associations. We describe an application of stepwise association rule mining (Step-ARM) to detect potential vaccine-symptom group associations in the US Vaccine Adverse Event Reporting System. Step-ARM identifies strong associations between one vaccine and one or more adverse events. To reduce the number of redundant association rules found by Step-ARM, we also propose a clustering method for the post-processing of association rules. In sample applications to a trivalent intradermal inactivated influenza virus vaccine and to measles, mumps, rubella, and varicella (MMRV) vaccine and in simulation studies, we find that Step-ARM can detect a variety of medically coherent potential vaccine-symptom group signals efficiently. In the MMRV example, Step-ARM appears to outperform univariate methods in detecting a known safety signal. Our approach is sensitive to potentially complex signals, which may be particularly important when monitoring novel medical countermeasure products such as pandemic influenza vaccines. The post-processing clustering algorithm improves the applicability of the approach as a screening method to identify patterns that may merit further investigation. Copyright © 2015 John Wiley & Sons, Ltd.

Soil quality assessment using GIS-based chemometric approach and pollution indices: Nakhlak mining district, Central Iran.

PubMed

Moore, Farid; Sheykhi, Vahideh; Salari, Mohammad; Bagheri, Adel

2016-04-01

This paper is a comprehensive assessment of the quality of soil in the Nakhlak mining district in Central Iran with special reference to potentially toxic metals. In this regard, an integrated approach involving geostatistical, correlation matrix, pollution indices, and chemical fractionation measurement is used to evaluate selected potentially toxic metals in soil samples. The fractionation of metals indicated a relatively high variability. Some metals (Mo, Ag, and Pb) showed important enrichment in the bioavailable fractions (i.e., exchangeable and carbonate), whereas the residual fraction mostly comprised Sb and Cr. The Cd, Zn, Co, Ni, Mo, Cu, and As were retained in Fe-Mn oxide and oxidizable fractions, suggesting that they may be released to the environment by changes in physicochemical conditions. The spatial variability patterns of 11 soil heavy metals (Ag, As, Cd, Co, Cr, Cu, Mo, Ni, Pb, Sb, and Zn) were identified and mapped. The results demonstrated that Ag, As, Cd, Mo, Cu, Pb, Sb, and Zn pollution are associated with mineralized veins and mining operations in this area. Further environmental monitoring and remedial actions are required for management of soil heavy metals in the study area. The present study not only enhanced our knowledge regarding soil pollution in the study area but also introduced a better technique to analyze pollution indices by multivariate geostatistical methods.

Evidence-based mapping of design heterogeneity prior to meta-analysis: a systematic review and evidence synthesis.

PubMed

Althuis, Michelle D; Weed, Douglas L; Frankenfeld, Cara L

2014-07-23

Assessment of design heterogeneity conducted prior to meta-analysis is infrequently reported; it is often presented post hoc to explain statistical heterogeneity. However, design heterogeneity determines the mix of included studies and how they are analyzed in a meta-analysis, which in turn can importantly influence the results. The goal of this work is to introduce ways to improve the assessment and reporting of design heterogeneity prior to statistical summarization of epidemiologic studies. In this paper, we use an assessment of sugar-sweetened beverages (SSB) and type 2 diabetes (T2D) as an example to show how a technique called 'evidence mapping' can be used to organize studies and evaluate design heterogeneity prior to meta-analysis.. Employing a systematic and reproducible approach, we evaluated the following elements across 11 selected cohort studies: variation in definitions of SSB, T2D, and co-variables, design features and population characteristics associated with specific definitions of SSB, and diversity in modeling strategies. Evidence mapping strategies effectively organized complex data and clearly depicted design heterogeneity. For example, across 11 studies of SSB and T2D, 7 measured diet only once (with 7 to 16 years of disease follow-up), 5 included primarily low SSB consumers, and 3 defined the study variable (SSB) as consumption of either sugar or artificially-sweetened beverages. This exercise also identified diversity in analysis strategies, such as adjustment for 11 to 17 co-variables and a large degree of fluctuation in SSB-T2D risk estimates depending on variables selected for multivariable models (2 to 95% change in the risk estimate from the age-adjusted model). Meta-analysis seeks to understand heterogeneity in addition to computing a summary risk estimate. This strategy effectively documents design heterogeneity, thus improving the practice of meta-analysis by aiding in: 1) protocol and analysis planning, 2) transparent reporting of differences in study designs, and 3) interpretation of pooled estimates. We recommend expanding the practice of meta-analysis reporting to include a table that summarizes design heterogeneity. This would provide readers with more evidence to interpret the summary risk estimates.

Evidence-based mapping of design heterogeneity prior to meta-analysis: a systematic review and evidence synthesis

PubMed Central

2014-01-01

Background Assessment of design heterogeneity conducted prior to meta-analysis is infrequently reported; it is often presented post hoc to explain statistical heterogeneity. However, design heterogeneity determines the mix of included studies and how they are analyzed in a meta-analysis, which in turn can importantly influence the results. The goal of this work is to introduce ways to improve the assessment and reporting of design heterogeneity prior to statistical summarization of epidemiologic studies. Methods In this paper, we use an assessment of sugar-sweetened beverages (SSB) and type 2 diabetes (T2D) as an example to show how a technique called ‘evidence mapping’ can be used to organize studies and evaluate design heterogeneity prior to meta-analysis.. Employing a systematic and reproducible approach, we evaluated the following elements across 11 selected cohort studies: variation in definitions of SSB, T2D, and co-variables, design features and population characteristics associated with specific definitions of SSB, and diversity in modeling strategies. Results Evidence mapping strategies effectively organized complex data and clearly depicted design heterogeneity. For example, across 11 studies of SSB and T2D, 7 measured diet only once (with 7 to 16 years of disease follow-up), 5 included primarily low SSB consumers, and 3 defined the study variable (SSB) as consumption of either sugar or artificially-sweetened beverages. This exercise also identified diversity in analysis strategies, such as adjustment for 11 to 17 co-variables and a large degree of fluctuation in SSB-T2D risk estimates depending on variables selected for multivariable models (2 to 95% change in the risk estimate from the age-adjusted model). Conclusions Meta-analysis seeks to understand heterogeneity in addition to computing a summary risk estimate. This strategy effectively documents design heterogeneity, thus improving the practice of meta-analysis by aiding in: 1) protocol and analysis planning, 2) transparent reporting of differences in study designs, and 3) interpretation of pooled estimates. We recommend expanding the practice of meta-analysis reporting to include a table that summarizes design heterogeneity. This would provide readers with more evidence to interpret the summary risk estimates. PMID:25055879

The Propagation of Seismic Waves in the Presence of Strong Elastic Property Contrasts

NASA Astrophysics Data System (ADS)

Saleh, R.; Jeyaraj, R.; Milkereit, B.; Liu, Q.; Valley, B.

2012-12-01

In an active underground mine there are many seismic activities taking place, such as seismic noises, blasts, tremors and microseismic events. In between the activities, the microseismic events are mainly used for monitoring purposes. The frequency content of microseismic events can be up to few KHz, which can result in wavelengths on the order of a few meters in hard rock environment. In an underground mine, considering the presence of both small wavelength and strong elastic contrasts, the simulation of seismic wave propagation is a challenge. With the recent availability of detailed 3D rock property models of mines, in addition to the development of efficient numerical techniques (such as Spectral Element Method (SEM)), and parallel computation facilities, a solution for such a problem is achievable. Most seismic wave scattering studies focus on large scales (>1 km) and weak elastic contrasts (velocity perturbations less than 10%). However, scattering in the presence of small-scale heterogeneities and large elastic contrasts is an area of ongoing research. In a mine environment, the presence of strong contrast discontinuities such as massive ore bodies, tunnels and infrastructure lead to discontinuities of displacement and/or stress tensor components, and have significant impact on the propagation of seismic waves. In order to obtain an accurate image of wave propagation in such a complex media, it is necessary to consider the presence of these discontinuities in numerical models. In this study, the effects of such a contrast are illustrated with 2D/3D modeling and compared with real broadband 3-component seismic data. The real broadband 3-component seismic data will be obtained in one of the Canadian underground mines in Ontario. One of the possible scenarios investigated in this study that may explain the observed complexity in seismic wavefield pattern in hard rock environments is the effect of near field displacements rather than far field. Considering the distribution of seismic sensors in a mine and the presence of seismic events within a mine, the recorded wavefield may represent a near-field displacement, which is not the case for most of seismic studies. The role of receiver characterization on the recorded event near the surface or around fault zones is also investigated. Using 2D/3D modeling, the effects of Vp/Vs variation on vertical and horizontal components of recorded amplitude has been shown.

Mechanization for Optimal Landscape Reclamation

NASA Astrophysics Data System (ADS)

Vondráčková, Terezie; Voštová, Věra; Kraus, Michal

2017-12-01

Reclamation is a method of ultimate utilization of land adversely affected by mining or other industrial activity. The paper explains the types of reclamation and the term “optimal reclamation”. Technological options of the long-lasting process of mine dumps reclamation starting with the removal of overlying rocks, transport and backfilling up to the follow-up remodelling of the mine dumps terrain. Technological units and equipment for stripping flow division. Stripping flow solution with respect to optimal reclamation. We recommend that the application of logistic chains and mining simulation with follow-up reclamation to open-pit mines be used for the implementation of optimal reclamation. In addition to a database of local heterogeneities of the stripped soil and reclaimed land, the flow of earths should be resolved in a manner allowing the most suitable soil substrate to be created for the restoration of agricultural and forest land on mine dumps. The methodology under development for the solution of a number of problems, including the geological survey of overlying rocks, extraction of stripping, their transport and backfilling in specified locations with the follow-up deployment of goal-directed reclamation. It will make possible to reduce the financial resources needed for the complex process chain by utilizing GIS, GPS and DGPS technologies, logistic tools and synergistic effects. When selecting machines for transport, moving and spreading of earths, various points of view and aspects must be taken into account. Among such aspects are e.g. the kind of earth to be operated by the respective construction machine, the kind of work activities to be performed, the machine’s capacity, the option to control the machine’s implement and economic aspects and clients’ requirements. All these points of view must be considered in the decision-making process so that the selected machine is capable of executing the required activity and that the use of an unsuitable machine is eliminated as it would result in a delay and increase in the project costs. Therefore, reclamation always includes extensive earth-moving work activities restoring the required relief of the land being reclaimed. Using the earth-moving machine capacity, the kind of soil in mine dumps, the kind of the work activity performed and the machine design, a SW application has been developed that allows the most suitable machine for the respective work technology to be selected with a view to preparing the land intended for reclamation.

The Detection and Characterization of Urbanization, Industrialization, and Longwall Mining Impacts on Forest Ecosystems Through the Use of GiS and Remote Sensing Techniques

NASA Astrophysics Data System (ADS)

Pfeil-McCullough, Erin Kathleen

Urbanization has far reaching and significant effects on forest ecosystems, directly through urban development and indirectly through supportive processes such as coal mining and agriculture. Urban processes modify the landscape leading to altered hillslope hydrology, increased disturbance, and the introduction of non-native forest pathogens. This dissertation addresses several challenges in our ability to detect these urbanization impacts on forests via geospatial analyses. The role of forests in urban hydrological processes has been extensively studied, but the impacts of urbanized hydrology on forests remain poorly examined. This dissertation documented impacts to hydrology and forests at variety of temporal and spatial scales: 1) A geospatial comparison of the historic and contemporary forests of Allegheny County, Pennsylvania revealed substantial shifts in tree species, but less change in the species soil moisture preference. These results document additional evidence that increased heterogeneity in urban soil moisture alters forest structure. 2) To examine soil moisture changes, impacts of longwall mine subsidence were assessed by using a Landsat based canopy moisture index and hot spot analysis tools at the forest patch scale. Declines in forest canopy moisture were detected over longwall mines as mining progressed through time, and results contradicted assumptions that the hydrological impacts overlying LMS recover within 4-5 years following subsidence of undermined land. 3) Utilizing a landslide susceptibility model (SINMAP), increases in landslide susceptibility were predicted in Pittsburgh, PA based on several scenarios of ash tree loss to the emerald ash borer (EAB), a bark beetle that rapidly kills ash trees. This model provides a tool to predict changes in landslide susceptibility following tree loss, increasing the understanding of urban forest function and its role in slope stability. Detecting how urbanized hydrology impacts forest health, function, and development is fundamental to sustaining the services forests provide. Results from this dissertation will ultimately allow improvements in the management and protection of both trees and water resources in urban systems and beyond.

Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD

PubMed Central

Fair, Damien A.; Bathula, Deepti; Nikolas, Molly A.; Nigg, Joel T.

2012-01-01

Research and clinical investigations in psychiatry largely rely on the de facto assumption that the diagnostic categories identified in the Diagnostic and Statistical Manual (DSM) represent homogeneous syndromes. However, the mechanistic heterogeneity that potentially underlies the existing classification scheme might limit discovery of etiology for most developmental psychiatric disorders. Another, perhaps less palpable, reality may also be interfering with progress—heterogeneity in typically developing populations. In this report we attempt to clarify neuropsychological heterogeneity in a large dataset of typically developing youth and youth with attention deficit/hyperactivity disorder (ADHD), using graph theory and community detection. We sought to determine whether data-driven neuropsychological subtypes could be discerned in children with and without the disorder. Because individual classification is the sine qua non for eventual clinical translation, we also apply support vector machine-based multivariate pattern analysis to identify how well ADHD status in individual children can be identified as defined by the community detection delineated subtypes. The analysis yielded several unique, but similar subtypes across both populations. Just as importantly, comparing typically developing children with ADHD children within each of these distinct subgroups increased diagnostic accuracy. Two important principles were identified that have the potential to advance our understanding of typical development and developmental neuropsychiatric disorders. The first tenet suggests that typically developing children can be classified into distinct neuropsychological subgroups with high precision. The second tenet proposes that some of the heterogeneity in individuals with ADHD might be “nested” in this normal variation. PMID:22474392

Heterogeneity of Glucose Metabolism in Esophageal Cancer Measured by Fractal Analysis of Fluorodeoxyglucose Positron Emission Tomography Image: Correlation between Metabolic Heterogeneity and Survival.

PubMed

Tochigi, Toru; Shuto, Kiyohiko; Kono, Tsuguaki; Ohira, Gaku; Tohma, Takayuki; Gunji, Hisashi; Hayano, Koichi; Narushima, Kazuo; Fujishiro, Takeshi; Hanaoka, Toshiharu; Akutsu, Yasunori; Okazumi, Shinichi; Matsubara, Hisahiro

2017-01-01

Intratumoral heterogeneity is a well-recognized characteristic feature of cancer. The purpose of this study is to assess the heterogeneity of the intratumoral glucose metabolism using fractal analysis, and evaluate its prognostic value in patients with esophageal squamous cell carcinoma (ESCC). 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) studies of 79 patients who received curative surgery were evaluated. FDG-PET images were analyzed using fractal analysis software, where differential box-counting method was employed to calculate the fractal dimension (FD) of the tumor lesion. Maximum standardized uptake value (SUVmax) and FD were compared with overall survival (OS). The median SUVmax and FD of ESCCs in this cohort were 13.8 and 1.95, respectively. In univariate analysis performed using Cox's proportional hazard model, T stage and FD showed significant associations with OS (p = 0.04, p < 0.0001, respectively), while SUVmax did not (p = 0.1). In Kaplan-Meier analysis, the low FD tumor (<1.95) showed a significant association with favorable OS (p < 0.0001). In wthe multivariate analysis among TNM staging, serum tumor markers, FD, and SUVmax, the FD was identified as the only independent prognostic factor for OS (p = 0.0006; hazards ratio 0.251, 95% CI 0.104-0.562). Metabolic heterogeneity measured by fractal analysis can be a novel imaging biomarker for survival in patients with ESCC. © 2016 S. Karger AG, Basel.

Chronic obstructive pulmonary disease in Welsh slate miners.

PubMed

Reynolds, C J; MacNeill, S J; Williams, J; Hodges, N G; Campbell, M J; Newman Taylor, A J; Cullinan, P

2017-01-01

Exposure to respirable crystalline silica (RCS) causes emphysema, airflow limitation and chronic obstructive pulmonary disease (COPD). Slate miners are exposed to slate dust containing RCS but their COPD risk has not previously been studied. To study the cumulative effect of mining on lung function and risk of COPD in a cohort of Welsh slate miners and whether these were independent of smoking and pneumoconiosis. The study was based on a secondary analysis of Medical Research Council (MRC) survey data. COPD was defined as forced expiratory volume in 1 s/forced vital capacity (FEV 1 /FVC) ratio <0.7. We created multivariable models to assess the association between mining and lung function after adjusting for age and smoking status. We used linear regression models for FEV 1 and FVC and logistic regression for COPD. In the original MRC study, 1255 men participated (726 slate miners, 529 unexposed non-miners). COPD was significantly more common in miners (n = 213, 33%) than non-miners (n = 120, 26%), P < 0.05. There was no statistically significant difference in risk of COPD between miners and non-miners when analysis was limited to non-smokers or those without radiographic evidence of pneumoconiosis. After adjustment for smoking, slate mining was associated with a reduction in %predicted FEV 1 [β coefficient = -3.97, 95% confidence interval (CI) -6.65, -1.29] and FVC (β coefficient = -2.32, 95% CI -4.31, -0.33) and increased risk of COPD (odds ratio: 1.38, 95% CI 1.06, 1.81). Slate mining may reduce lung function and increase the incidence of COPD independently of smoking and pneumoconiosis. © The Author 2016. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Heavy metals in the gold mine soil of the upstream area of a metropolitan drinking water source.

PubMed

Ding, Huaijian; Ji, Hongbing; Tang, Lei; Zhang, Aixing; Guo, Xinyue; Li, Cai; Gao, Yang; Briki, Mergem

2016-02-01

Pinggu District is adjacent to the county of Miyun, which contains the largest drinking water source of Beijing (Miyun Reservoir). The Wanzhuang gold field and tailing deposits are located in Pinggu, threatening Beijing's drinking water security. In this study, soil samples were collected from the surface of the mining area and the tailings piles and analyzed for physical and chemical properties, as well as heavy metal contents and particle size fraction to study the relationship between degree of pollution degree and particle size. Most metal concentrations in the gold mine soil samples exceeded the background levels in Beijing. The spatial distribution of As, Cd, Cu, Pb, and Zn was the same, while that of Cr and Ni was relatively similar. Trace element concentrations increased in larger particles, decreased in the 50-74 μm size fraction, and were lowest in the <2 μm size fraction. Multivariate analysis showed that Cu, Cd, Zn, and Pb originated from anthropogenic sources, while Cr, Ni, and Sc were of natural origin. The geo-accumulation index indicated serious Pb, As, and Cd pollution, but moderate to no Ni, Cr, and Hg pollution. The Tucker 3 model revealed three factors for particle fractions, metals, and samples. There were two factors in model A and three factors for both the metals and samples (models B and C, respectively). The potential ecological risk index shows that most of the study areas have very high potential ecological risk, a small portion has high potential ecological risk, and only a few sampling points on the perimeter have moderate ecological risk, with higher risk closer to the mining area.

Heavy metal pollution and spatial distribution in surface sediments of Mustafakemalpaşa stream located in the world's largest borate basin (Turkey).

PubMed

Omwene, Philip Isaac; Öncel, Mehmet Salim; Çelen, Meltem; Kobya, Mehmet

2018-06-07

Mining activities in addition to the geology of Mustafakemalpaşa catchment have for long been linked to its deteriorating water and sediment quality. This study assessed contamination levels of heavy metals and other major elements (Pb, As, B, Cd, Zn, Cr, Mo, Co, Ni, Cu, and Ag) in surface sediments of the area, and identified possible pollution sources. Sediment quality indicators, such as contamination factor (CF), enrichment factor (EF), geo-accumulation index (I geo ) and sediment quality guidelines were used, in addition to multivariate statistical technics; Pearson Correlation Matrix (PCM), Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). The highest contamination (annual average > 110 mg kg -1 ) was revealed by B, Cr, Ni, Zn and As. Moreover, As, Cd and Ni levels exceeded their respective probable effect concentrations (PEC), posing a potential negative impact to biota. The highest I geo values were recorded for Cr, B, Ni, As and Zn, and occurred near urban settlements and mining sites, particularly of coal and chromium. The present study also suggests use of site rank index (SRI) as an alternative to pollution load index (PLI), since the former is derived from the data of interest and eliminates arbitrary classifications. The sources of heavy metals in the sediments were attributed to fly ashes of coal-powered plants, urban waste leachate and weathering of sulfide ore minerals for Pb, Zn and Cu; urban-industrial wastes and mining wastes for Ni. Although Cr, As, Cd and B were ascribed to natural occurrence, their presences in river sediment is accelerated by mining. Copyright © 2018 Elsevier Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bačeva, Katerina; Stafilov, Trajče, E-mail: trajcest@pmf.ukim.mk; Šajn, Robert

The aim of this study was to investigate the distribution of some toxic elements in topsoil and subsoil, focusing on the identification of natural and anthropogenic element sources in the small region of rare As–Sb–Tl mineralization outcrop and abandoned mine Allchar known for the highest natural concentration of Tl in soil worldwide. The samples of soil and sediments after total digestion were analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and inductively coupled plasma–atomic emission spectrometry (ICP–AES). Factor analysis (FA) was used to identify and characterize element associations. Six associations of elements were determined by the method of multivariate statistics: Rb–Ta–K–Nb–Ga–Sn–Ba–Bi–Li–Be–(La–Eu)–Hf–Zr–Zn–In–Pd–Ag–Pt–Mg;more » Tl–As–Sb–Hg; Te–S–Ag–Pt–Al–Sc–(Gd–Lu)–Y; Fe–Cu–V–Ge–Co–In; Pd–Zr–Hf–W–Be and Ni–Mn–Co–Cr–Mg. The purpose of the assessment was to determine the nature and extent of potential contamination as well as to broadly assess possible impacts to human health and the environment. The results from the analysis of the collected samples in the vicinity of the mine revealed that As and Tl elements have the highest median values. Higher median values for Sb are obviously as a result of the past mining activities and as a result of area surface phenomena in the past. - Highlights: • Soil and river sediments were analyzed from Sb–As–Tl Allchar locality. • An increased content of certain toxic elements for environment was determined. • Highest As and Tl contents are obtained in the close vicinity of Allchar mine. • River sediments portray 160 times higher content of Sb than EU values. • The results classify Allchar as probably the highest natural Tl-deposit worldwide.« less

Microbial Reduction of Fe(III) and U(VI) in Aquifers: Simulations Exploring Coupled Effects of Heterogeneity and Fe(II) Sorption

NASA Astrophysics Data System (ADS)

Scheibe, T. D.; Fang, Y.; Roden, E. E.; Brooks, S. C.; Chien, Y.; Murray, C. J.

2004-05-01

Uranium is a significant groundwater contaminant at many former mining and processing sites. In its oxidized state, U(VI) is soluble and mobile, although strongly retarded by sorption to natural iron oxide surfaces. It has been demonstrated that commonly occurring subsurface microorganisms can reduce uranium and other metals when provided sufficient carbon as an electron donor. Reduced U(IV) precipitates as a solid phase; therefore biostimulation provides a potential strategy for in situ removal from contaminated groundwater. However, these biogeochemical reactions occur in the context of a complex heterogeneous environment in which flow and transport dynamics and abiotic reactions can have significant impacts. We have constructed a high-resolution numerical model of groundwater flow and multicomponent reactive transport that incorporates heterogeneity in hydraulic conductivity and initial Fe(III) distribution, microbial growth and transport dynamics, and effects of sorption or precipitation of biogenic Fe(II) on availability of Fe(III) as an electron acceptor. The biogeochemical reaction models and their parameters are based on laboratory experiments; the heterogeneous field-scale property distributions are based on interpretations of geophysical and other observations at a highly characterized field site. The model is being run in Monte Carlo mode to examine the controls that these factors exert on 1) the initial distribution of sorbed uranium in an oxic environment and 2) the reduction and immobilization of uranium upon introduction of a soluble electron donor.

Link-quality measurement and reporting in wireless sensor networks.

PubMed

Chehri, Abdellah; Jeon, Gwanggil; Choi, Byoungjo

2013-03-04

Wireless Sensor networks (WSNs) are created by small hardware devices that possess the necessary functionalities to measure and exchange a variety of environmental data in their deployment setting. In this paper, we discuss the experiments in deploying a testbed as a first step towards creating a fully functional heterogeneous wireless network-based underground monitoring system. The system is mainly composed of mobile and static ZigBee nodes, which are deployed on the underground mine galleries for measuring ambient temperature. In addition, we describe the measured results of link characteristics such as received signal strength, latency and throughput for different scenarios.

Link-Quality Measurement and Reporting in Wireless Sensor Networks

PubMed Central

Chehri, Abdellah; Jeon, Gwanggil; Choi, Byoungjo

2013-01-01

Wireless Sensor networks (WSNs) are created by small hardware devices that possess the necessary functionalities to measure and exchange a variety of environmental data in their deployment setting. In this paper, we discuss the experiments in deploying a testbed as a first step towards creating a fully functional heterogeneous wireless network-based underground monitoring system. The system is mainly composed of mobile and static ZigBee nodes, which are deployed on the underground mine galleries for measuring ambient temperature. In addition, we describe the measured results of link characteristics such as received signal strength, latency and throughput for different scenarios. PMID:23459389

Source apportionment of trace metals in river sediments: A comparison of three methods.

PubMed

Chen, Haiyang; Teng, Yanguo; Li, Jiao; Wu, Jin; Wang, Jinsheng

2016-04-01

Increasing trace metal pollution in river sediment poses a significant threat to watershed ecosystem health. Identifying potential sources of sediment metals and apportioning their contributions are of key importance for proposing prevention and control strategies of river pollution. In this study, three advanced multivariate receptor models, factor analysis with nonnegative constraints (FA-NNC), positive matrix factorization (PMF), and multivariate curve resolution weighted-alternating least-squares (MCR-WALS), were comparatively employed for source apportionment of trace metals in river sediments and applied to the Le'an River, a main tributary of Poyang Lake which is the largest freshwater lake in China. The pollution assessment with contamination factor and geoaccumulation index suggested that the river sediments in Le'an River were contaminated severely by trace metals due to human activities. With the three apportionment tools, similar source profiles of trace metals in sediments were extracted. Especially, the MCR-WALS and PMF models produced essentially the same results. Comparatively speaking, the weighted schemes might give better solutions than the unweighted FA-NNC because the uncertainty information of environmental data was considered by PMF and MCR-WALS. Anthropogenic sources were apportioned as the most important pollution sources influencing the sediment metals in Le'an River with contributions of about 90%. Among them, copper tailings occupied the largest contribution (38.4-42.2%), followed by mining wastewater (29.0-33.5%), and agricultural activities (18.2-18.7%). To protect the ecosystem of Le'an River and Poyang Lake, special attention should be paid to the discharges of mining wastewater and the leachates of copper tailing ponds in that region. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mining Input Data for Multivariate Probabilistic Modeling of Rainfall-Induced Landslide Hazard in the Lake ATITLÁN Watershed in Guatemala

NASA Astrophysics Data System (ADS)

Cobin, P. F.; Oommen, T.; Gierke, J. S.

2013-12-01

The Lake Atitlán watershed is home to approximately 200,000 people and is located in the western highlands of Guatemala. Steep slopes, highly susceptible to landslides during the rainy season, characterize the region. Typically these landslides occur during high-intensity precipitation events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. Different datasets of landslide and non-landslide points across the watershed were used to compare model success at a small scale and regional scale. This study used data from multiple attributes: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The open source software Weka was used for the data mining. Several attribute selection methods were applied to the data to predetermine the potential landslide causative influence. Different multivariate algorithms were then evaluated for their ability to predict landslide occurrence. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The attribute combinations of the most successful models were compared to the attribute evaluator results. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points for the regions selected in the watershed. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.

Application of Differential InSAR to Mining

NASA Astrophysics Data System (ADS)

Eneva, M.; Baker, E.; Xu, H.

2001-12-01

In a NASA funded project we are applying differential InSAR to measure surface deformation associated with mining at depth. Surface displacement can be caused by rockbursts associated with mine collapse or mining-induced stress released on nearby tectonic features. The latter type of rockbursts are similar to tectonic earthquakes, but generally occur at shallower depths than non-induced events of similar size. Thus significant co-seismic surface changes may accompany them. In addition, subsidence of a more gradual type may result from ongoing soft-rock (e.g., coal, potash, salt) mining. While such subsidence can accidentally occur above abandoned mines, it is most often planned as part of the ongoing ore extraction, especially in so-called long-wall mining. Predicting the amount and spatial extent of this subsidence is an aspect of mining engineering. It is important to compare these predictions with measurements of the actual deformation. Although mines use leveling and GPS measurements to monitor subsidence, these are generally performed with much smaller frequency (e.g., annually) and lower spatial resolution than repeat-pass differential InSAR can provide. We are using ERS-1/2 raw SAR data provided by ESA and Eurimage, and the Gamma software for their processing. At present we are focused on the processing and modeling of data from two representative sites. By the end of the project we will have analyzed several more sites of subsidence and M>4.5 rockbursts. As an example of mining subsidence, we are currently analyzing data from the site of a coal mine in Colorado (USA), operating in a relatively flat and arid area. Numerous adjacent long-wall panels of extraction are used, some exceeding 5 km in length. A 600 to 750-m length of panel may be extracted per month, with a maximum subsidence of 1.5 to 1.8 m expected over each panel. The surface deformation can be monitored especially well during the summers of 1995 and 1996, when nine good-quality ERS-1/2 SAR scenes were gathered. Two of these scenes form a tandem pair to be used for topography. We are also making use of a 30-m DEM from USGS, maps of extraction panels, leveling data and microearthquake locations. As an example of rockbursts, we are presently analyzing ERS-2 SAR data from the site of a M5.1 rockburst that occurred on April 22, 1999, in the gold fields of Welkom, South Africa. The event was induced on a fault transecting the mine and had a normal mechanism. Only two good-quality SAR scenes are available from this site, spanning about a year including the event. Thus the topography effect cannot be removed using interferometry. However, since flat surface and urban environment characterize this site, a clear fringe pattern is observed, apparently associated with the rockburst. This pattern suggests up to 9-cm subsidence. Its center is within 5 km from the seismically determined event location. Thus this rockburst represents an example of the capabilities of InSAR to provide ground truth locations for moderate shallow earthquakes. To model the seismic source, we are using the RNGCHN software (Feigl and Dupré, 1999) based on analytic solutions for a homogeneous half-space. In order to model deformation in realistically complex crust, including layered structure and lateral heterogeneities, we are also developing a 3D finite-difference method of estimating deformation in a volume due to displacement on a fault surface. This method will be also used for the modeling of mining subsidence.

Iron-mineral accretion from acid mine drainage and its application in passive treatment

PubMed Central

Florence, K.; Sapsford, D.J.; Johnson, D.B.; Kay, C.M.; Wolkersdorfer, C.

2016-01-01

ABSTRACT This study demonstrates substantial removal of iron (Fe) from acid mine drainage (pH ≈3) in a passive vertical flow reactor (VFR) with an equivalent footprint of 154 m2 per L/s mine water and residence times of >23 h. Average Fe removal rate was 67% with a high of 85% over the 10-month trial. The fraction of Fe passing a 0.22 µm filter (referred to here as Fe-filt) was seen to be removed in the VFR even when Fe(II) was absent, indicating that the contribution of microbial Fe(II) oxidation and precipitation was not the dominant removal mechanism in the VFR. Removal rates of Fe-filt in the VFR were up to 70% in residence times as low as 8 h compared with laboratory experiments where much smaller changes in Fe-filt were observed over 60 h. Centrifugation indicated that 80–90% of the influent Fe had particle sizes <35 nm. Together with analyses and geochemical modelling, this suggests that the Fe-filt fraction exists as either truly aqueous (but oversaturated) Fe(III) or nanoparticulate Fe(III) and that this metastability persists. When the water was contacted with VFR sludge, the Fe-filt fraction was destabilized, leading to an appreciably higher removal of this fraction. Heterogeneous precipitation and/or aggregation of nanoparticulate Fe(III) precipitates are considered predominant removal mechanisms. Microbial analyses of the mine water revealed the abundance of extracellular polymeric substance-generating Fe-oxidizing bacterium ‘Ferrovum myxofaciens’, which may aid the removal of iron and explain the unusual appearance and physical properties of the sludge. PMID:26675674

Geologic and tectonic characteristics of rockbursts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adushkin, V.V.; Charlamov, V.A.; Kondratyev, S.V.

1995-06-01

The modern mining enterprises have attained such scales of engineering activity that their direct influence to a rock massif and in series of cases to the region seismic regime doesn`t provoke any doubts. Excavation and removal of large volumes of rock mass, industrial explosions and other technological factors during long time can lead to the accumulation of man-made changes in rock massifs capable to cause catastrophic consequences. The stress state changes in considerable domains of massif create dangerous concentration of stresses at large geological heterogeneities - faults localized in the mining works zone. External influence can lead in that casemore » to such phenomena as tectonic rockbursts and man-made earthquakes. The rockbursts problem in world mining practice exists for more than two hundred years. So that its actuality not only doesn`t decrease but steadily mounts up as due to the mining works depth increase, enlargement of the useful minerals excavations volumes as due to the possibility of safe use of the rock massif potential energy for facilitating the mastering of the bowels of the Earth and for making that more cheap. The purpose of present work is to study the engineering activity influence to processes occurring in the upper part of Earth crust and in particular in a rock massif. The rock massif is treated in those studies as a geophysical medium - such approach takes into account the presence of block structure of medium and the continuous exchange of energy between parts of that structure. The idea ``geophysical medium`` is applied in geophysics sufficiently wide and stresses the difference of actual Earth crust and rock massifs from the continuous media models discussed in mechanics.« less

Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas.

PubMed

Karan, Shivesh Kishore; Samadder, Sukha Ranjan

2016-08-01

One objective of the present study was to evaluate the performance of support vector machine (SVM)-based image classification technique with the maximum likelihood classification (MLC) technique for a rapidly changing landscape of an open-cast mine. The other objective was to assess the change in land use pattern due to coal mining from 2006 to 2016. Assessing the change in land use pattern accurately is important for the development and monitoring of coalfields in conjunction with sustainable development. For the present study, Landsat 5 Thematic Mapper (TM) data of 2006 and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data of 2016 of a part of Jharia Coalfield, Dhanbad, India, were used. The SVM classification technique provided greater overall classification accuracy when compared to the MLC technique in classifying heterogeneous landscape with limited training dataset. SVM exceeded MLC in handling a difficult challenge of classifying features having near similar reflectance on the mean signature plot, an improvement of over 11 % was observed in classification of built-up area, and an improvement of 24 % was observed in classification of surface water using SVM; similarly, the SVM technique improved the overall land use classification accuracy by almost 6 and 3 % for Landsat 5 and Landsat 8 images, respectively. Results indicated that land degradation increased significantly from 2006 to 2016 in the study area. This study will help in quantifying the changes and can also serve as a basis for further decision support system studies aiding a variety of purposes such as planning and management of mines and environmental impact assessment.

Data-Enabled Quantification of Aluminum Microstructural Damage Under Tensile Loading

NASA Astrophysics Data System (ADS)

Wayne, Steven F.; Qi, G.; Zhang, L.

2016-08-01

The study of material failure with digital analytics is in its infancy and offers a new perspective to advance our understanding of damage initiation and evolution in metals. In this article, we study the failure of aluminum using data-enabled methods, statistics and data mining. Through the use of tension tests, we establish a multivariate acoustic-data matrix of random damage events, which typically are not visible and are very difficult to measure due to their variability, diversity and interactivity during damage processes. Aluminium alloy 6061-T651 and single crystal aluminium with a (111) orientation were evaluated by comparing the collection of acoustic signals from damage events caused primarily by slip in the single crystal and multimode fracture of the alloy. We found the resulting acoustic damage-event data to be large semi-structured volumes of Big Data with the potential to be mined for information that describes the materials damage state under strain. Our data-enabled analyses has allowed us to determine statistical distributions of multiscale random damage that provide a means to quantify the material damage state.

Data mining reveals a network of early-response genes as a consensus signature of drug-induced in vitro and in vivo toxicity.

PubMed

Zhang, J D; Berntenis, N; Roth, A; Ebeling, M

2014-06-01

Gene signatures of drug-induced toxicity are of broad interest, but they are often identified from small-scale, single-time point experiments, and are therefore of limited applicability. To address this issue, we performed multivariate analysis of gene expression, cell-based assays, and histopathological data in the TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation system) database. Data mining highlights four genes-EGR1, ATF3, GDF15 and FGF21-that are induced 2 h after drug administration in human and rat primary hepatocytes poised to eventually undergo cytotoxicity-induced cell death. Modelling and simulation reveals that these early stress-response genes form a functional network with evolutionarily conserved structure and intrinsic dynamics. This is underlined by the fact that early induction of this network in vivo predicts drug-induced liver and kidney pathology with high accuracy. Our findings demonstrate the value of early gene-expression signatures in predicting and understanding compound-induced toxicity. The identified network can empower first-line tests that reduce animal use and costs of safety evaluation.

Prediction of wastewater quality indicators at the inflow to the wastewater treatment plant using data mining methods

NASA Astrophysics Data System (ADS)

Szeląg, Bartosz; Barbusiński, Krzysztof; Studziński, Jan; Bartkiewicz, Lidia

2017-11-01

In the study, models developed using data mining methods are proposed for predicting wastewater quality indicators: biochemical and chemical oxygen demand, total suspended solids, total nitrogen and total phosphorus at the inflow to wastewater treatment plant (WWTP). The models are based on values measured in previous time steps and daily wastewater inflows. Also, independent prediction systems that can be used in case of monitoring devices malfunction are provided. Models of wastewater quality indicators were developed using MARS (multivariate adaptive regression spline) method, artificial neural networks (ANN) of the multilayer perceptron type combined with the classification model (SOM) and cascade neural networks (CNN). The lowest values of absolute and relative errors were obtained using ANN+SOM, whereas the MARS method produced the highest error values. It was shown that for the analysed WWTP it is possible to obtain continuous prediction of selected wastewater quality indicators using the two developed independent prediction systems. Such models can ensure reliable WWTP work when wastewater quality monitoring systems become inoperable, or are under maintenance.

Heavy metal contamination of agricultural soils affected by mining activities around the Ganxi River in Chenzhou, Southern China.

PubMed

Ma, Li; Sun, Jing; Yang, Zhaoguang; Wang, Lin

2015-12-01

Heavy metal contamination attracted a wide spread attention due to their strong toxicity and persistence. The Ganxi River, located in Chenzhou City, Southern China, has been severely polluted by lead/zinc ore mining activities. This work investigated the heavy metal pollution in agricultural soils around the Ganxi River. The total concentrations of heavy metals were determined by inductively coupled plasma-mass spectrometry. The potential risk associated with the heavy metals in soil was assessed by Nemerow comprehensive index and potential ecological risk index. In both methods, the study area was rated as very high risk. Multivariate statistical methods including Pearson's correlation analysis, hierarchical cluster analysis, and principal component analysis were employed to evaluate the relationships between heavy metals, as well as the correlation between heavy metals and pH, to identify the metal sources. Three distinct clusters have been observed by hierarchical cluster analysis. In principal component analysis, a total of two components were extracted to explain over 90% of the total variance, both of which were associated with anthropogenic sources.

Text mining factor analysis (TFA) in green tea patent data

NASA Astrophysics Data System (ADS)

Rahmawati, Sela; Suprijadi, Jadi; Zulhanif

2017-03-01

Factor analysis has become one of the most widely used multivariate statistical procedures in applied research endeavors across a multitude of domains. There are two main types of analyses based on factor analysis: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Both EFA and CFA aim to observed relationships among a group of indicators with a latent variable, but they differ fundamentally, a priori and restrictions made to the factor model. This method will be applied to patent data technology sector green tea to determine the development technology of green tea in the world. Patent analysis is useful in identifying the future technological trends in a specific field of technology. Database patent are obtained from agency European Patent Organization (EPO). In this paper, CFA model will be applied to the nominal data, which obtain from the presence absence matrix. While doing processing, analysis CFA for nominal data analysis was based on Tetrachoric matrix. Meanwhile, EFA model will be applied on a title from sector technology dominant. Title will be pre-processing first using text mining analysis.

Long-term passive restoration following fluvial deposition of sulphidic copper tailings: nature filters out the solutions.

PubMed

Nikolic, Nina; Böcker, Reinhard; Nikolic, Miroslav

2016-07-01

Despite the growing popularity of ecological restoration approach, data on primary succession on toxic post-mining substrates, under site environmental conditions which considerably differ from the surrounding environment, are still scarce. Here, we studied the spontaneous vegetation development on an unusual locality created by long-term and large-scale fluvial deposition of sulphidic tailings from a copper mine in a pronouncedly xerothermic, calcareous surrounding. We performed multivariate analyses of soil samples (20 physical and chemical parameters) and vegetation samples (floristic and structural parameters in three types of occurring forests), collected along the pollution gradients throughout the affected floodplain. The nature can cope with two types of imposed constraints: (a) excessive Cu concentrations and (b) very low pH, combined with nutrient deficiency. The former will still allow convergence to the original vegetation, while the latter will result in novel, depauperate assemblages of species typical for cooler and moister climate. Our results for the first time demonstrate that with the increasing severity of environmental filtering, the relative importance of the surrounding vegetation for primary succession strongly decreases.

Regression and Data Mining Methods for Analyses of Multiple Rare Variants in the Genetic Analysis Workshop 17 Mini-Exome Data

PubMed Central

Bailey-Wilson, Joan E.; Brennan, Jennifer S.; Bull, Shelley B; Culverhouse, Robert; Kim, Yoonhee; Jiang, Yuan; Jung, Jeesun; Li, Qing; Lamina, Claudia; Liu, Ying; Mägi, Reedik; Niu, Yue S.; Simpson, Claire L.; Wang, Libo; Yilmaz, Yildiz E.; Zhang, Heping; Zhang, Zhaogong

2012-01-01

Group 14 of Genetic Analysis Workshop 17 examined several issues related to analysis of complex traits using DNA sequence data. These issues included novel methods for analyzing rare genetic variants in an aggregated manner (often termed collapsing rare variants), evaluation of various study designs to increase power to detect effects of rare variants, and the use of machine learning approaches to model highly complex heterogeneous traits. Various published and novel methods for analyzing traits with extreme locus and allelic heterogeneity were applied to the simulated quantitative and disease phenotypes. Overall, we conclude that power is (as expected) dependent on locus-specific heritability or contribution to disease risk, large samples will be required to detect rare causal variants with small effect sizes, extreme phenotype sampling designs may increase power for smaller laboratory costs, methods that allow joint analysis of multiple variants per gene or pathway are more powerful in general than analyses of individual rare variants, population-specific analyses can be optimal when different subpopulations harbor private causal mutations, and machine learning methods may be useful for selecting subsets of predictors for follow-up in the presence of extreme locus heterogeneity and large numbers of potential predictors. PMID:22128066

A Ranking Approach on Large-Scale Graph With Multidimensional Heterogeneous Information.

PubMed

Wei, Wei; Gao, Bin; Liu, Tie-Yan; Wang, Taifeng; Li, Guohui; Li, Hang

2016-04-01

Graph-based ranking has been extensively studied and frequently applied in many applications, such as webpage ranking. It aims at mining potentially valuable information from the raw graph-structured data. Recently, with the proliferation of rich heterogeneous information (e.g., node/edge features and prior knowledge) available in many real-world graphs, how to effectively and efficiently leverage all information to improve the ranking performance becomes a new challenging problem. Previous methods only utilize part of such information and attempt to rank graph nodes according to link-based methods, of which the ranking performances are severely affected by several well-known issues, e.g., over-fitting or high computational complexity, especially when the scale of graph is very large. In this paper, we address the large-scale graph-based ranking problem and focus on how to effectively exploit rich heterogeneous information of the graph to improve the ranking performance. Specifically, we propose an innovative and effective semi-supervised PageRank (SSP) approach to parameterize the derived information within a unified semi-supervised learning framework (SSLF-GR), then simultaneously optimize the parameters and the ranking scores of graph nodes. Experiments on the real-world large-scale graphs demonstrate that our method significantly outperforms the algorithms that consider such graph information only partially.

Nearest neighbors by neighborhood counting.

PubMed

Wang, Hui

2006-06-01

Finding nearest neighbors is a general idea that underlies many artificial intelligence tasks, including machine learning, data mining, natural language understanding, and information retrieval. This idea is explicitly used in the k-nearest neighbors algorithm (kNN), a popular classification method. In this paper, this idea is adopted in the development of a general methodology, neighborhood counting, for devising similarity functions. We turn our focus from neighbors to neighborhoods, a region in the data space covering the data point in question. To measure the similarity between two data points, we consider all neighborhoods that cover both data points. We propose to use the number of such neighborhoods as a measure of similarity. Neighborhood can be defined for different types of data in different ways. Here, we consider one definition of neighborhood for multivariate data and derive a formula for such similarity, called neighborhood counting measure or NCM. NCM was tested experimentally in the framework of kNN. Experiments show that NCM is generally comparable to VDM and its variants, the state-of-the-art distance functions for multivariate data, and, at the same time, is consistently better for relatively large k values. Additionally, NCM consistently outperforms HEOM (a mixture of Euclidean and Hamming distances), the "standard" and most widely used distance function for multivariate data. NCM has a computational complexity in the same order as the standard Euclidean distance function and NCM is task independent and works for numerical and categorical data in a conceptually uniform way. The neighborhood counting methodology is proven sound for multivariate data experimentally. We hope it will work for other types of data.

Cross-country transferability of multi-variable damage models

NASA Astrophysics Data System (ADS)

Wagenaar, Dennis; Lüdtke, Stefan; Kreibich, Heidi; Bouwer, Laurens

2017-04-01

Flood damage assessment is often done with simple damage curves based only on flood water depth. Additionally, damage models are often transferred in space and time, e.g. from region to region or from one flood event to another. Validation has shown that depth-damage curve estimates are associated with high uncertainties, particularly when applied in regions outside the area where the data for curve development was collected. Recently, progress has been made with multi-variable damage models created with data-mining techniques, i.e. Bayesian Networks and random forest. However, it is still unknown to what extent and under which conditions model transfers are possible and reliable. Model validations in different countries will provide valuable insights into the transferability of multi-variable damage models. In this study we compare multi-variable models developed on basis of flood damage datasets from Germany as well as from The Netherlands. Data from several German floods was collected using computer aided telephone interviews. Data from the 1993 Meuse flood in the Netherlands is available, based on compensations paid by the government. The Bayesian network and random forest based models are applied and validated in both countries on basis of the individual datasets. A major challenge was the harmonization of the variables between both datasets due to factors like differences in variable definitions, and regional and temporal differences in flood hazard and exposure characteristics. Results of model validations and comparisons in both countries are discussed, particularly in respect to encountered challenges and possible solutions for an improvement of model transferability.

Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra.

PubMed

Martyna, Agnieszka; Zadora, Grzegorz; Neocleous, Tereza; Michalska, Aleksandra; Dean, Nema

2016-08-10

Many chemometric tools are invaluable and have proven effective in data mining and substantial dimensionality reduction of highly multivariate data. This becomes vital for interpreting various physicochemical data due to rapid development of advanced analytical techniques, delivering much information in a single measurement run. This concerns especially spectra, which are frequently used as the subject of comparative analysis in e.g. forensic sciences. In the presented study the microtraces collected from the scenarios of hit-and-run accidents were analysed. Plastic containers and automotive plastics (e.g. bumpers, headlamp lenses) were subjected to Fourier transform infrared spectrometry and car paints were analysed using Raman spectroscopy. In the forensic context analytical results must be interpreted and reported according to the standards of the interpretation schemes acknowledged in forensic sciences using the likelihood ratio approach. However, for proper construction of LR models for highly multivariate data, such as spectra, chemometric tools must be employed for substantial data compression. Conversion from classical feature representation to distance representation was proposed for revealing hidden data peculiarities and linear discriminant analysis was further applied for minimising the within-sample variability while maximising the between-sample variability. Both techniques enabled substantial reduction of data dimensionality. Univariate and multivariate likelihood ratio models were proposed for such data. It was shown that the combination of chemometric tools and the likelihood ratio approach is capable of solving the comparison problem of highly multivariate and correlated data after proper extraction of the most relevant features and variance information hidden in the data structure. Copyright © 2016 Elsevier B.V. All rights reserved.

Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study

PubMed Central

Neupane, Binod; Beyene, Joseph

2015-01-01

In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data in the endpoint are imputed with null effects and quite large variance. PMID:26196398

Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.

PubMed

Neupane, Binod; Beyene, Joseph

2015-01-01

In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data in the endpoint are imputed with null effects and quite large variance.

Mixed Responses to Systemic Therapy Revealed Potential Genetic Heterogeneity and Poor Survival in Patients with Non-Small Cell Lung Cancer.

PubMed

Dong, Zhong-Yi; Zhai, Hao-Ran; Hou, Qing-Yi; Su, Jian; Liu, Si-Yang; Yan, Hong-Hong; Li, Yang-Si; Chen, Zhi-Yong; Zhong, Wen-Zhao; Wu, Yi-Long

2017-01-01

A subset of patients with non-small cell lung cancer (NSCLC) fosters mixed responses (MRs) to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) or chemotherapy. However, little is known about the clinical and molecular features or the prognostic significance and potential mechanisms. The records of 246 consecutive patients with NSCLC receiving single-line chemotherapy or TKI treatment and who were assessed by baseline and interim positron emission tomography/computed tomography scans were collected retrospectively. The clinicopathological correlations of the MR were analyzed, and a multivariate analysis was performed to explore the prognostic significance of MR. The overall incidence of MR to systemic therapy was 21.5% (53/246) and predominated in patients with stage IIIB-IV, EGFR mutations and those who received TKI therapy (p < .05). Subgroup analyses based on MR classification (efficacious versus inefficacious) showed significant differences in subsequent treatment between the two groups (p < .001) and preferable progression-free survival (PFS) and overall survival (OS) in the efficacious MR group. Multivariate analyses demonstrated that the presence of MR was an independent unfavorable prognostic factor for PFS (hazard ratio [HR], 1.474; 95% confidence interval [CI], 1.018-2.134; p = .040) and OS (HR, 1.849; 95% CI, 1.190-2.871; p = .006) in patients with NSCLC. Induced by former systemic therapy, there were more T790M (18%), concomitant EGFR mutations (15%), and changes to EGFR wild type (19%) in the MR group among patients with EGFR mutations, which indicated higher incidence of genetic heterogeneity. MR was not a rare event in patients with NSCLC and tended to occur in those with advanced lung adenocarcinoma treated with a TKI. MR may result from genetic heterogeneity and is an unfavorable prognostic factor for survival. Further studies are imperative to explore subsequent treatment strategies. The Oncologist 2017;22:61-69Implications for Practice: Tumor heterogeneity tends to produce mixed responses (MR) to systemic therapy, including TKI and chemotherapy; however, the clinical significance and potential mechanisms are not fully understood, and the subsequent treatment after MR is also a clinical concern. The present study systemically assessed patients by PET/CT and differentiated MR and therapies. The study identified a relatively high incidence of MR in patients with advanced NSCLC, particularly those treated with targeted therapies. An MR may be an unfavorable prognostic factor and originate from genetic heterogeneity. Further studies are imperative to explore subsequent treatment strategies. © AlphaMed Press 2017.

Mixed Responses to Systemic Therapy Revealed Potential Genetic Heterogeneity and Poor Survival in Patients with Non‐Small Cell Lung Cancer

PubMed Central

Dong, Zhong‐Yi; Zhai, Hao‐Ran; Hou, Qing‐Yi; Su, Jian; Liu, Si‐Yang; Yan, Hong‐Hong; Li, Yang‐Si; Chen, Zhi‐Yong; Zhong, Wen‐Zhao

2017-01-01

Abstract Background. A subset of patients with non‐small cell lung cancer (NSCLC) fosters mixed responses (MRs) to epidermal growth factor receptor (EGFR)‐tyrosine kinase inhibitors (TKIs) or chemotherapy. However, little is known about the clinical and molecular features or the prognostic significance and potential mechanisms. Methods. The records of 246 consecutive patients with NSCLC receiving single‐line chemotherapy or TKI treatment and who were assessed by baseline and interim positron emission tomography/computed tomography scans were collected retrospectively. The clinicopathological correlations of the MR were analyzed, and a multivariate analysis was performed to explore the prognostic significance of MR. Results. The overall incidence of MR to systemic therapy was 21.5% (53/246) and predominated in patients with stage IIIB–IV, EGFR mutations and those who received TKI therapy (p < .05). Subgroup analyses based on MR classification (efficacious versus inefficacious) showed significant differences in subsequent treatment between the two groups (p < .001) and preferable progression‐free survival (PFS) and overall survival (OS) in the efficacious MR group. Multivariate analyses demonstrated that the presence of MR was an independent unfavorable prognostic factor for PFS (hazard ratio [HR], 1.474; 95% confidence interval [CI], 1.018–2.134; p = .040) and OS (HR, 1.849; 95% CI, 1.190–2.871; p = .006) in patients with NSCLC. Induced by former systemic therapy, there were more T790M (18%), concomitant EGFR mutations (15%), and changes to EGFR wild type (19%) in the MR group among patients with EGFR mutations, which indicated higher incidence of genetic heterogeneity. Conclusion. MR was not a rare event in patients with NSCLC and tended to occur in those with advanced lung adenocarcinoma treated with a TKI. MR may result from genetic heterogeneity and is an unfavorable prognostic factor for survival. Further studies are imperative to explore subsequent treatment strategies. Implications for Practice. Tumor heterogeneity tends to produce mixed responses (MR) to systemic therapy, including TKI and chemotherapy; however, the clinical significance and potential mechanisms are not fully understood, and the subsequent treatment after MR is also a clinical concern. The present study systemically assessed patients by PET/CT and differentiated MR and therapies. The study identified a relatively high incidence of MR in patients with advanced NSCLC, particularly those treated with targeted therapies. An MR may be an unfavorable prognostic factor and originate from genetic heterogeneity. Further studies are imperative to explore subsequent treatment strategies. PMID:28126915

Multivariate modelling of endophenotypes associated with the metabolic syndrome in Chinese twins.

PubMed

Pang, Z; Zhang, D; Li, S; Duan, H; Hjelmborg, J; Kruse, T A; Kyvik, K O; Christensen, K; Tan, Q

2010-12-01

The common genetic and environmental effects on endophenotypes related to the metabolic syndrome have been investigated using bivariate and multivariate twin models. This paper extends the pairwise analysis approach by introducing independent and common pathway models to Chinese twin data. The aim was to explore the common genetic architecture in the development of these phenotypes in the Chinese population. Three multivariate models including the full saturated Cholesky decomposition model, the common factor independent pathway model and the common factor common pathway model were fitted to 695 pairs of Chinese twins representing six phenotypes including BMI, total cholesterol, total triacylglycerol, fasting glucose, HDL and LDL. Performances of the nested models were compared with that of the full Cholesky model. Cross-phenotype correlation coefficients gave clear indication of common genetic or environmental backgrounds in the phenotypes. Decomposition of phenotypic correlation by the Cholesky model revealed that the observed phenotypic correlation among lipid phenotypes had genetic and unique environmental backgrounds. Both pathway models suggest a common genetic architecture for lipid phenotypes, which is distinct from that of the non-lipid phenotypes. The declining performance with model restriction indicates biological heterogeneity in development among some of these phenotypes. Our multivariate analyses revealed common genetic and environmental backgrounds for the studied lipid phenotypes in Chinese twins. Model performance showed that physiologically distinct endophenotypes may follow different genetic regulations.

Effects of reservoir heterogeneity on scaling of effective mass transfer coefficient for solute transport

NASA Astrophysics Data System (ADS)

Leung, Juliana Y.; Srinivasan, Sanjay

2016-09-01

Modeling transport process at large scale requires proper scale-up of subsurface heterogeneity and an understanding of its interaction with the underlying transport mechanisms. A technique based on volume averaging is applied to quantitatively assess the scaling characteristics of effective mass transfer coefficient in heterogeneous reservoir models. The effective mass transfer coefficient represents the combined contribution from diffusion and dispersion to the transport of non-reactive solute particles within a fluid phase. Although treatment of transport problems with the volume averaging technique has been published in the past, application to geological systems exhibiting realistic spatial variability remains a challenge. Previously, the authors developed a new procedure where results from a fine-scale numerical flow simulation reflecting the full physics of the transport process albeit over a sub-volume of the reservoir are integrated with the volume averaging technique to provide effective description of transport properties. The procedure is extended such that spatial averaging is performed at the local-heterogeneity scale. In this paper, the transport of a passive (non-reactive) solute is simulated on multiple reservoir models exhibiting different patterns of heterogeneities, and the scaling behavior of effective mass transfer coefficient (Keff) is examined and compared. One such set of models exhibit power-law (fractal) characteristics, and the variability of dispersion and Keff with scale is in good agreement with analytical expressions described in the literature. This work offers an insight into the impacts of heterogeneity on the scaling of effective transport parameters. A key finding is that spatial heterogeneity models with similar univariate and bivariate statistics may exhibit different scaling characteristics because of the influence of higher order statistics. More mixing is observed in the channelized models with higher-order continuity. It reinforces the notion that the flow response is influenced by the higher-order statistical description of heterogeneity. An important implication is that when scaling-up transport response from lab-scale results to the field scale, it is necessary to account for the scale-up of heterogeneity. Since the characteristics of higher-order multivariate distributions and large-scale heterogeneity are typically not captured in small-scale experiments, a reservoir modeling framework that captures the uncertainty in heterogeneity description should be adopted.

Ripening of salami: assessment of colour and aspect evolution using image analysis and multivariate image analysis.

PubMed

Fongaro, Lorenzo; Alamprese, Cristina; Casiraghi, Ernestina

2015-03-01

During ripening of salami, colour changes occur due to oxidation phenomena involving myoglobin. Moreover, shrinkage due to dehydration results in aspect modifications, mainly ascribable to fat aggregation. The aim of this work was the application of image analysis (IA) and multivariate image analysis (MIA) techniques to the study of colour and aspect changes occurring in salami during ripening. IA results showed that red, green, blue, and intensity parameters decreased due to the development of a global darker colour, while Heterogeneity increased due to fat aggregation. By applying MIA, different salami slice areas corresponding to fat and three different degrees of oxidised meat were identified and quantified. It was thus possible to study the trend of these different areas as a function of ripening, making objective an evaluation usually performed by subjective visual inspection. Copyright © 2014 Elsevier Ltd. All rights reserved.

Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients.

PubMed

Yue, Yong; Osipov, Arsen; Fraass, Benedick; Sandler, Howard; Zhang, Xiao; Nissen, Nicholas; Hendifar, Andrew; Tuli, Richard

2017-02-01

To stratify risks of pancreatic adenocarcinoma (PA) patients using pre- and post-radiotherapy (RT) PET/CT images, and to assess the prognostic value of texture variations in predicting therapy response of patients. Twenty-six PA patients treated with RT from 2011-2013 with pre- and post-treatment 18F-FDG-PET/CT scans were identified. Tumor locoregional texture was calculated using 3D kernel-based approach, and texture variations were identified by fitting discrepancies of texture maps of pre- and post-treatment images. A total of 48 texture and clinical variables were identified and evaluated for association with overall survival (OS). The prognostic heterogeneity features were selected using lasso/elastic net regression, and further were evaluated by multivariate Cox analysis. Median age was 69 y (range, 46-86 y). The texture map and temporal variations between pre- and post-treatment were well characterized by histograms and statistical fitting. The lasso analysis identified seven predictors (age, node stage, post-RT SUVmax, variations of homogeneity, variance, sum mean, and cluster tendency). The multivariate Cox analysis identified five significant variables: age, node stage, variations of homogeneity, variance, and cluster tendency (with P=0.020, 0.040, 0.065, 0.078, and 0.081, respectively). The patients were stratified into two groups based on the risk score of multivariate analysis with log-rank P=0.001: a low risk group (n=11) with a longer mean OS (29.3 months) and higher texture variation (>30%), and a high risk group (n=15) with a shorter mean OS (17.7 months) and lower texture variation (<15%). Locoregional metabolic texture response provides a feasible approach for evaluating and predicting clinical outcomes following treatment of PA with RT. The proposed method can be used to stratify patient risk and help select appropriate treatment strategies for individual patients toward implementing response-driven adaptive RT.

The analysis of the Tectonics - SSS - Seismicity System in the 3D-model of the Rasvumchorr Mine - Central Open Pit Natural and Technical System (Khibiny)

NASA Astrophysics Data System (ADS)

Zhirov, Dmitry; Klimov, Sergey; Zhirova, Anzhela; Panteleev, Alexey; Rybin, Vadim

2017-04-01

Main hazardous factors during the operation of deposits represent tectonics (structural dislocation), strain and stress state (SSS), and seismicity. The cause and effect relationships in the Fault Tectonics - SSS - Seismicity system were analyzed using a 3D geological and structural Rasvumchorr Mine - Central Open Pit model. This natural and technical system (NTS) has resulted from the development of the world-class apatite-nepheline deposits the Apatite Circus and Rasvumchorr Plateau. The 3D model integrates various spatial data on the earth's surface topography before and after mining, geometry of mines and dumps, SSS measurements and rock pressure, seismicity, fault tectonics and etc. The analysis of the 3D model has clearly demonstrated the localization of three main seismic emanation zones in the areas of maximum anthropogenic variation of the initial rock state, and namely: ore pass zone under the Southern edge of the Central open pit, collapse and joining zone of the Rasvumchorr Mine and NW edge of the open pit, and zone under the Apatite Circus plate - collapse console. And, on the contrary, in the area of a large dump under the underground mine, a perennial seismic minimum zone was identified. The relation of the seismicity and fault tectonics was revealed only in three local sectors near come certain echelon fissures of the Main Fault(MF). No confinement of increased seismicity areas to the MF and other numerous echelon fissures is observed. The same picture occurs towards manifestations of rock pressure. Only an insignificant part of echelon fissures (including low rank of hierarchy) controls hazardous manifestations of rock pressure (dumps, strong deformations of the mine contour, etc.). It is shown that the anthropogenic factor (explosive, geometry and arrangement of mined spaces and collapse console), as well as the time factor significantly change orientation and structure (contrast and heterogeneity) of the stress fields. Time series of natural geophysical field fluctuations were additionally analyzed in order to find relationships with the seismicity. A sustainable regular relationship between the seismicity and solar and lunar tides has been observed; though, medium (classes 3 to 6) and high (class 7 and above) energy values of the events reveal various symmetry towards the Lunar cycle phases. The relationship of seismicity with other geophysical fields, e.g., geomagnetic disturbances, is defined as weak to very weak. The anthropogenic (man-induced) factor mostly influences the seismicity in the NTS rock masses. A law for shifting of maximum seismicity zones following the advance of the mining front has been found. The 3D model integrates various spatial data on the earth's surface topography before and after mining, geometry of mines and dumps, SSS measurements, and rock pressure, seismicity, fault tectonics, and other manifestations. The study is made within R&D topic No. 0231-2015-0013. The collection, processing, and analysis of data for natural stress fields became possible due to the support from RSF grant 14-17-00751.

Prognostic value of stromal decorin expression in patients with breast cancer: a meta-analysis.

PubMed

Li, Shuang-Jiang; Chen, Da-Li; Zhang, Wen-Biao; Shen, Cheng; Che, Guo-Wei

2015-11-01

Numbers of studies have investigated the biological functions of decorin (DCN) in oncogenesis, tumor progression, angiogenesis and metastasis. Although many of them aim to highlight the prognostic value of stromal DCN expression in breast cancer, some controversial results still exist and a consensus has not been reached until now. Therefore, our meta-analysis aims to determine the prognostic significance of stromal DCN expression in breast cancer patients. PubMed, EMBASE, the Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for full-text literatures met out inclusion criteria. We applied the hazard ratio (HR) with 95% confidence interval (CI) as the appropriate summarized statistics. Q-test and I(2) statistic were employed to estimate the level of heterogeneity across the included studies. Sensitivity analysis was conducted to further identify the possible origins of heterogeneity. The publication bias was detected by Begg's test and Egger's test. There were three English literatures (involving 6 studies) included into our meta-analysis. On the one hand, both the summarized outcomes based on univariate analysis (HR: 0.513; 95% CI: 0.406-0.648; P<0.001) and multivariate analysis (HR: 0.544; 95% CI: 0.388-0.763; P<0.001) indicated that stromal DCN expression could promise the high cancer-specific survival (CSS) of breast cancer patients. On the other hand, both the summarized outcomes based on univariate analysis (HR: 0.504; 95% CI: 0.389-0.651; P<0.001) and multivariate analysis (HR: 0.568; 95% CI: 0.400-0.806; P=0.002) also indicated that stromal DCN expression was positively associated with high disease-free survival (DFS) of breast cancer patients. No significant heterogeneity or publication bias was observed within this meta-analysis. The present evidences indicate that high stromal DCN expression can significantly predict the good prognosis in patients with breast cancer. The discoveries from our meta-analysis have better be confirmed in the updated review pooling more relevant investigations in the future.

Photometric redshift estimation based on data mining with PhotoRApToR

NASA Astrophysics Data System (ADS)

Cavuoti, S.; Brescia, M.; De Stefano, V.; Longo, G.

2015-03-01

Photometric redshifts (photo-z) are crucial to the scientific exploitation of modern panchromatic digital surveys. In this paper we present PhotoRApToR (Photometric Research Application To Redshift): a Java/C ++ based desktop application capable to solve non-linear regression and multi-variate classification problems, in particular specialized for photo-z estimation. It embeds a machine learning algorithm, namely a multi-layer neural network trained by the Quasi Newton learning rule, and special tools dedicated to pre- and post-processing data. PhotoRApToR has been successfully tested on several scientific cases. The application is available for free download from the DAME Program web site.

Diamonds from Orapa Mine show a clear subduction signature in SIMS stable isotope data

NASA Astrophysics Data System (ADS)

Chinn, Ingrid L.; Perritt, Samantha H.; Stiefenhofer, Johann; Stern, Richard A.

2018-05-01

Spatially resolved analyses reveal considerable isotopic heterogeneity within and among diamonds ranging in size from 0.15 to 4.75 mm from the Orapa Mine, Botswana. The isotopic data are interpreted in conjunction with nitrogen aggregation state data and growth zone relationships from cathodoluminescence images. The integrated information confirms that a distinct diamond growth event (with low IaAB nitrogen aggregation states, moderately high nitrogen contents and δ13C and δ15N values compatible with average mantle values) is younger than the dominant population(s) of Type IaAB diamonds and cores of composite diamonds with more negative and positive δ13C and δ15N values, respectively. A significant proportion of the older diamond generation has high nitrogen contents, well outside the limit sector relationship, and these diamonds are likely to reflect derivation from subducted organic matter. Diamonds with low δ13C values combined with high nitrogen contents and positive δ15N values have not been previously widely recognised, even in studies of diamonds from Orapa. This may have been caused by prior analytical bias towards inclusion-bearing diamonds that are not necessarily representative of the entire range of diamond populations, and because of average measurements from heterogeneous diamonds measured by bulk combustion methods. Two distinct low nitrogen/Type II microdiamond populations were recognised that do not appear to continue into the macrodiamond sizes in the samples studied. Other populations, e.g. those containing residual singly-substituted nitrogen defects, range in size from small microdiamonds to large macrodiamonds. The total diamond content of the Orapa kimberlite thus reflects a complex assortment of multiple diamond populations.

Social Class, Family Formation, and Delinquency in Early Adulthood

PubMed Central

Kuhl, Danielle C.; Chavez, Jorge M.; Swisher, Raymond R.; Wilczak, Andrew

2015-01-01

Recent research suggests increasing heterogeneity in the transition from adolescence to early adulthood. This study considers how this heterogeneity may influence delinquency between these two developmental periods. We focus on the role of family transitions, educational attainment, and employment in predicting risk of nonviolent delinquency and substance use, as well as disparities in transitions across socioeconomic status subgroups. Data are from the National Longitudinal Study of Adolescent to Adult Health (Add Health). We find that family and neighborhood advantage are negatively associated with transitions into marriage, cohabitation, and parenthood, yet positively associated with educational attainment. In addition, adolescent family and neighborhood advantage are associated with a continuation of delinquent behavior and substance use during early adulthood. In multivariate analyses, accounting for family transitions in early adulthood largely attenuates the relationship between neighborhood advantage in adolescence and delinquency in early adulthood. We conclude by discussing the implications of our findings for developmental criminology. PMID:27418713

Dimensions of Experience: Exploring the Heterogeneity of the Wandering Mind.

PubMed

Wang, Hao-Ting; Poerio, Giulia; Murphy, Charlotte; Bzdok, Danilo; Jefferies, Elizabeth; Smallwood, Jonathan

2018-01-01

The tendency for the mind to wander to concerns other than the task at hand is a fundamental feature of human cognition, yet the consequences of variations in its experiential content for psychological functioning are not well understood. Here, we adopted multivariate pattern analysis to simultaneously decompose experience-sampling data and neural functional-connectivity data, which revealed dimensions that simultaneously describe individual variation in self-reported experience and default-mode-network connectivity. We identified dimensions corresponding to traits of positive-habitual thoughts and spontaneous task-unrelated thoughts. These dimensions were uniquely related to aspects of cognition, such as executive control and the ability to generate information in a creative fashion, and independently distinguished well-being measures. These data provide the most convincing evidence to date for an ontological view of the mind-wandering state as encompassing a broad range of different experiences and show that this heterogeneity underlies mind wandering's complex relationship to psychological functioning.

Discrete mixture modeling to address genetic heterogeneity in time-to-event regression

PubMed Central

Eng, Kevin H.; Hanlon, Bret M.

2014-01-01

Motivation: Time-to-event regression models are a critical tool for associating survival time outcomes with molecular data. Despite mounting evidence that genetic subgroups of the same clinical disease exist, little attention has been given to exploring how this heterogeneity affects time-to-event model building and how to accommodate it. Methods able to diagnose and model heterogeneity should be valuable additions to the biomarker discovery toolset. Results: We propose a mixture of survival functions that classifies subjects with similar relationships to a time-to-event response. This model incorporates multivariate regression and model selection and can be fit with an expectation maximization algorithm, we call Cox-assisted clustering. We illustrate a likely manifestation of genetic heterogeneity and demonstrate how it may affect survival models with little warning. An application to gene expression in ovarian cancer DNA repair pathways illustrates how the model may be used to learn new genetic subsets for risk stratification. We explore the implications of this model for censored observations and the effect on genomic predictors and diagnostic analysis. Availability and implementation: R implementation of CAC using standard packages is available at https://gist.github.com/programeng/8620b85146b14b6edf8f Data used in the analysis are publicly available. Contact: kevin.eng@roswellpark.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24532723

Speciation of adsorbates on surface of solids by infrared spectroscopy and chemometrics.

PubMed

Vilmin, Franck; Bazin, Philippe; Thibault-Starzyk, Frédéric; Travert, Arnaud

2015-09-03

Speciation, i.e. identification and quantification, of surface species on heterogeneous surfaces by infrared spectroscopy is important in many fields but remains a challenging task when facing strongly overlapped spectra of multiple adspecies. Here, we propose a new methodology, combining state of the art instrumental developments for quantitative infrared spectroscopy of adspecies and chemometrics tools, mainly a novel data processing algorithm, called SORB-MCR (SOft modeling by Recursive Based-Multivariate Curve Resolution) and multivariate calibration. After formal transposition of the general linear mixture model to adsorption spectral data, the main issues, i.e. validity of Beer-Lambert law and rank deficiency problems, are theoretically discussed. Then, the methodology is exposed through application to two case studies, each of them characterized by a specific type of rank deficiency: (i) speciation of physisorbed water species over a hydrated silica surface, and (ii) speciation (chemisorption and physisorption) of a silane probe molecule over a dehydrated silica surface. In both cases, we demonstrate the relevance of this approach which leads to a thorough surface speciation based on comprehensive and fully interpretable multivariate quantitative models. Limitations and drawbacks of the methodology are also underlined. Copyright © 2015 Elsevier B.V. All rights reserved.

Network structure of multivariate time series.

PubMed

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Multivariate analysis of the heterogeneous geochemical processes controlling arsenic enrichment in a shallow groundwater system.

PubMed

Huang, Shuangbing; Liu, Changrong; Wang, Yanxin; Zhan, Hongbin

2014-01-01

The effects of various geochemical processes on arsenic enrichment in a high-arsenic aquifer at Jianghan Plain in Central China were investigated using multivariate models developed from combined adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR). The results indicated that the optimum variable group for the AFNIS model consisted of bicarbonate, ammonium, phosphorus, iron, manganese, fluorescence index, pH, and siderite saturation. These data suggest that reductive dissolution of iron/manganese oxides, phosphate-competitive adsorption, pH-dependent desorption, and siderite precipitation could integrally affect arsenic concentration. Analysis of the MLR models indicated that reductive dissolution of iron(III) was primarily responsible for arsenic mobilization in groundwaters with low arsenic concentration. By contrast, for groundwaters with high arsenic concentration (i.e., > 170 μg/L), reductive dissolution of iron oxides approached a dynamic equilibrium. The desorption effects from phosphate-competitive adsorption and the increase in pH exhibited arsenic enrichment superior to that caused by iron(III) reductive dissolution as the groundwater chemistry evolved. The inhibition effect of siderite precipitation on arsenic mobilization was expected to exist in groundwater that was highly saturated with siderite. The results suggest an evolutionary dominance of specific geochemical process over other factors controlling arsenic concentration, which presented a heterogeneous distribution in aquifers. Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of Environmental Science and Health, Part A, to view the supplemental file.

Diagnostic performance of conventional MRI parameters and apparent diffusion coefficient values in differentiating between benign and malignant soft-tissue tumours.

PubMed

Song, Y; Yoon, Y C; Chong, Y; Seo, S W; Choi, Y-L; Sohn, I; Kim, M-J

2017-08-01

To compare the abilities of conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) in differentiating between benign and malignant soft-tissue tumours (STT). A total of 123 patients with STT who underwent 3 T MRI, including diffusion-weighted imaging (DWI), were retrospectively analysed using variate conventional MRI parameters, ADC mean and ADC min . For the all-STT group, the correlation between the malignant STT conventional MRI parameters, except deep compartment involvement, compared to those of benign STT were statistically significant with univariate analysis. Maximum diameter of the tumour (p=0.001; odds ratio [OR], 8.97) and ADC mean (p=0.020; OR, 4.30) were independent factors with multivariate analysis. For the non-myxoid non-haemosiderin STT group, signal heterogeneity on axial T1-weighted imaging (T1WI; p=0.017), ADC mean , and ADC min (p=0.001, p=0.001), showed significant differences with univariate analysis between malignancy and benignity. Signal heterogeneity in axial T1WI (p=0.025; OR, 12.64) and ADC mean (p=0.004; OR, 33.15) were independent factors with multivariate analysis. ADC values as well as conventional MRI parameters were useful in differentiating between benign and malignant STT. The ADC mean was the most powerful diagnostic parameter in non-myxoid non-haemosiderin STT. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

High coffee consumption and different brewing methods in relation to postmenopausal endometrial cancer risk in the Norwegian women and cancer study: a population-based prospective study.

PubMed

Gavrilyuk, Oxana; Braaten, Tonje; Skeie, Guri; Weiderpass, Elisabete; Dumeaux, Vanessa; Lund, Eiliv

2014-03-25

Coffee and its compounds have been proposed to inhibit endometrial carcinogenesis. Studies in the Norwegian population can be especially interesting due to the high coffee consumption and increasing incidence of endometrial cancer in the country. A total of 97 926 postmenopausal Norwegian women from the population-based prospective Norwegian Women and Cancer (NOWAC) Study, were included in the present analysis. We evaluated the general association between total coffee consumption and endometrial cancer risk as well as the possible impact of brewing method. Multivariate Cox regression analysis was used to estimate risks, and heterogeneity tests were performed to compare brewing methods. During an average of 10.9 years of follow-up, 462 incident endometrial cancer cases were identified. After multivariate adjustment, significant risk reduction was found among participants who drank ≥8 cups/day of coffee with a hazard ratio of 0.52 (95% confidence interval, CI 0.34-0.79). However, we did not observe a significant dose-response relationship. No significant heterogeneity in risk was found when comparing filtered and boiled coffee brewing methods. A reduction in endometrial cancer risk was observed in subgroup analyses among participants who drank ≥8 cups/day and had a body mass index ≥25 kg/m2, and in current smokers. These data suggest that in this population with high coffee consumption, endometrial cancer risk decreases in women consuming ≥8 cups/day, independent of brewing method.

Quarry identification of historical building materials by means of laser induced breakdown spectroscopy, X-ray fluorescence and chemometric analysis

NASA Astrophysics Data System (ADS)

Colao, F.; Fantoni, R.; Ortiz, P.; Vazquez, M. A.; Martin, J. M.; Ortiz, R.; Idris, N.

2010-08-01

To characterize historical building materials according to the geographic origin of the quarries from which they have been mined, the relative content of major and trace elements were determined by means of Laser Induced Breakdown Spectroscopy (LIBS) and X-ray Fluorescence (XRF) techniques. 48 different specimens were studied and the entire samples' set was divided in two different groups: the first, used as reference set, was composed by samples mined from eight different quarries located in Seville province; the second group was composed by specimens of unknown provenance collected in several historical buildings and churches in the city of Seville. Data reduction and analysis on laser induced breakdown spectroscopy and X-ray fluorescence measurements was performed using multivariate statistical approach, namely the Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA). A clear separation among reference sample materials mined from different quarries was observed in Principal Components (PC) score plots, then a supervised soft independent modeling of class analogy classification was trained and run, aiming to assess the provenance of unknown samples according to their elemental content. The obtained results were compared with the provenance assignments made on the basis of petrographical description. This work gives experimental evidence that laser induced breakdown spectroscopy measurements on a relatively small set of elements is a fast and effective method for the purpose of origin identification.

Associations of Individual-Related and Job-Related Risk Factors with Nonfatal Occupational Injury in the Coal Workers of Shanxi Province: A Cross-Sectional Study

PubMed Central

Qiao, Nan; Wang, Cong; Wang, Tong; Huang, Jian-Jun; Sun, Chen-Ming; Liang, Jie; Liu, Xiao-Meng

2015-01-01

Objectives To assess the relationships between the risk factors and the incidence of nonfatal occupational injury of coal mine workers of Shanxi Province. Methods A cross-sectional study was conducted from July 2013 to December 2013, and 4319 workers were recruited from more than 200,000 coal mine employees who are exposed to continuous potential risk of occupational injuries by using a two-stage stratified cluster sampling method. Trained interviewers having necessary medical knowledge conducted face-to-face interviews with the participants. Univariate and multivariable logistic regression models were used to estimate the odds ratio (OR) and the 95% confidence interval (CI). Results A total number of 3618 effective respondents were got from 4319 participants (83.77%) and the mean age of the participants was 41.5 years with the standard deviation of 8.65. Significant crude odds ratios were observed for all factors considered except for marital status, education, work duration, BMI, EPQ-RSC(P) scale and EPQ-RSC(L) scale. Results from multivariable logistic regression model showed significant adjusted odds ratios for risk factors including gender (female vs male 0.275, 0.094–0.800), age (≥55 vs ≤25yr 0.169, 0.032–0.900), work type (light physical labor vs heavy physical labor 0.504, 0.328–0.774), workplace (underground auxiliary vs underground front-line 0.595, 0.385–0.919), length of shiftwork experience (0~5yr vs no shift 2.075, 1.287–3.344 and ≥15yr vs no shift 2.076, 1.230–3.504) and EPQ-RSC(E) score (extraversion vs introversion 0.538, 0.334–0.867). Conclusions Several risk factors of nonfatal occupational injury were identified including male, age, heavy physical labor, underground front-line, length of shiftwork experience and introversion. The coal mining enterprises should pay attention to controlling the hazards associated with frontline physical work. Workers’ behaviors, life styles and personality traits should also be considered, so that the enterprises could set achievable targets for workers and lessen the exposed period to the risky underground workstation. PMID:26230266

Age-Related Differences and Heterogeneity in Executive Functions: Analysis of NAB Executive Functions Module Scores.

PubMed

Buczylowska, Dorota; Petermann, Franz

2016-05-01

Normative data from the German adaptation of the Neuropsychological Assessment Battery were used to examine age-related differences in 6 executive function tasks. A multivariate analysis of variance was employed to investigate the differences in performance in 484 participants aged 18-99 years. The coefficient of variation was calculated to compare the heterogeneity of scores between 10 age groups. Analyses showed an increase in the dispersion of scores with age, varying from 7% to 289%, in all subtests. Furthermore, age-dependent heterogeneity appeared to be associated with age-dependent decline because the subtests with the greatest increase in dispersion (i.e., Mazes, Planning, and Categories) also exhibited the greatest decrease in mean scores. In contrast, scores for the subtests Letter Fluency, Word Generation, and Judgment had the lowest increase in dispersion with the lowest decrease in mean scores. Consequently, the results presented here show a pattern of age-related differences in executive functioning that is consistent with the concept of crystallized and fluid intelligence. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas.

PubMed

Pyka, Thomas; Gempt, Jens; Hiob, Daniela; Ringel, Florian; Schlegel, Jürgen; Bette, Stefanie; Wester, Hans-Jürgen; Meyer, Bernhard; Förster, Stefan

2016-01-01

Amino acid positron emission tomography (PET) with [18F]-fluoroethyl-L-tyrosine (FET) is well established in the diagnostic work-up of malignant brain tumors. Analysis of FET-PET data using tumor-to-background ratios (TBR) has been shown to be highly valuable for the detection of viable hypermetabolic brain tumor tissue; however, it has not proven equally useful for tumor grading. Recently, textural features in 18-fluorodeoxyglucose-PET have been proposed as a method to quantify the heterogeneity of glucose metabolism in a variety of tumor entities. Herein we evaluate whether textural FET-PET features are of utility for grading and prognostication in patients with high-grade gliomas. One hundred thirteen patients (70 men, 43 women) with histologically proven high-grade gliomas were included in this retrospective study. All patients received static FET-PET scans prior to first-line therapy. TBR (max and mean), volumetric parameters and textural parameters based on gray-level neighborhood difference matrices were derived from static FET-PET images. Receiver operating characteristic (ROC) and discriminant function analyses were used to assess the value for tumor grading. Kaplan-Meier curves and univariate and multivariate Cox regression were employed for analysis of progression-free and overall survival. All FET-PET textural parameters showed the ability to differentiate between World Health Organization (WHO) grade III and IV tumors (p < 0.001; AUC 0.775). Further improvement in discriminatory power was possible through a combination of texture and metabolic tumor volume, classifying 85 % of tumors correctly (AUC 0.830). TBR and volumetric parameters alone were correlated with tumor grade, but showed lower AUC values (0.644 and 0.710, respectively). Furthermore, a correlation of FET-PET texture but not TBR was shown with patient PFS and OS, proving significant in multivariate analysis as well. Volumetric parameters were predictive for OS, but this correlation did not hold in multivariate analysis. Determination of uptake heterogeneity in pre-therapeutic FET-PET using textural features proved valuable for the (sub-)grading of high-grade glioma as well as prediction of tumor progression and patient survival, and showed improved performance compared to standard parameters such as TBR and tumor volume. Our results underscore the importance of intratumoral heterogeneity in the biology of high-grade glial cell tumors and may contribute to individual therapy planning in the future, although they must be confirmed in prospective studies before incorporation into clinical routine.

A New Predictive Model of Centerline Segregation in Continuous Cast Steel Slabs by Using Multivariate Adaptive Regression Splines Approach

PubMed Central

García Nieto, Paulino José; González Suárez, Victor Manuel; Álvarez Antón, Juan Carlos; Mayo Bayón, Ricardo; Sirgo Blanco, José Ángel; Díaz Fernández, Ana María

2015-01-01

The aim of this study was to obtain a predictive model able to perform an early detection of central segregation severity in continuous cast steel slabs. Segregation in steel cast products is an internal defect that can be very harmful when slabs are rolled in heavy plate mills. In this research work, the central segregation was studied with success using the data mining methodology based on multivariate adaptive regression splines (MARS) technique. For this purpose, the most important physical-chemical parameters are considered. The results of the present study are two-fold. In the first place, the significance of each physical-chemical variable on the segregation is presented through the model. Second, a model for forecasting segregation is obtained. Regression with optimal hyperparameters was performed and coefficients of determination equal to 0.93 for continuity factor estimation and 0.95 for average width were obtained when the MARS technique was applied to the experimental dataset, respectively. The agreement between experimental data and the model confirmed the good performance of the latter.

Future perspectives toward the early definition of a multivariate decision-support scheme employed in clinical decision making for senior citizens.

PubMed

Frantzidis, Christos A; Gilou, Sotiria; Billis, Antonis; Karagianni, Maria; Bratsas, Charalampos D; Bamidis, Panagiotis

2016-03-01

Recent neuroscientific studies focused on the identification of pathological neurophysiological patterns (emotions, geriatric depression, memory impairment and sleep disturbances) through computerised clinical decision-support systems. Almost all these research attempts employed either resting-state condition (e.g. eyes-closed) or event-related potentials extracted during a cognitive task known to be affected by the disease under consideration. This Letter reviews existing data mining techniques and aims to enhance their robustness by proposing a holistic decision framework dealing with comorbidities and early symptoms' identification, while it could be applied in realistic occasions. Multivariate features are elicited and fused in order to be compared with average activities characteristic of each neuropathology group. A proposed model of the specific cognitive function which may be based on previous findings (a priori information) and/or validated by current experimental data should be then formed. So, the proposed scheme facilitates the early identification and prevention of neurodegenerative phenomena. Neurophysiological semantic annotation is hypothesised to enhance the importance of the proposed framework in facilitating the personalised healthcare of the information society and medical informatics research community.

mESAdb: microRNA Expression and Sequence Analysis Database

PubMed Central

Kaya, Koray D.; Karakülah, Gökhan; Yakıcıer, Cengiz M.; Acar, Aybar C.; Konu, Özlen

2011-01-01

microRNA expression and sequence analysis database (http://konulab.fen.bilkent.edu.tr/mirna/) (mESAdb) is a regularly updated database for the multivariate analysis of sequences and expression of microRNAs from multiple taxa. mESAdb is modular and has a user interface implemented in PHP and JavaScript and coupled with statistical analysis and visualization packages written for the R language. The database primarily comprises mature microRNA sequences and their target data, along with selected human, mouse and zebrafish expression data sets. mESAdb analysis modules allow (i) mining of microRNA expression data sets for subsets of microRNAs selected manually or by motif; (ii) pair-wise multivariate analysis of expression data sets within and between taxa; and (iii) association of microRNA subsets with annotation databases, HUGE Navigator, KEGG and GO. The use of existing and customized R packages facilitates future addition of data sets and analysis tools. Furthermore, the ability to upload and analyze user-specified data sets makes mESAdb an interactive and expandable analysis tool for microRNA sequence and expression data. PMID:21177657

mESAdb: microRNA expression and sequence analysis database.

PubMed

Kaya, Koray D; Karakülah, Gökhan; Yakicier, Cengiz M; Acar, Aybar C; Konu, Ozlen

2011-01-01

microRNA expression and sequence analysis database (http://konulab.fen.bilkent.edu.tr/mirna/) (mESAdb) is a regularly updated database for the multivariate analysis of sequences and expression of microRNAs from multiple taxa. mESAdb is modular and has a user interface implemented in PHP and JavaScript and coupled with statistical analysis and visualization packages written for the R language. The database primarily comprises mature microRNA sequences and their target data, along with selected human, mouse and zebrafish expression data sets. mESAdb analysis modules allow (i) mining of microRNA expression data sets for subsets of microRNAs selected manually or by motif; (ii) pair-wise multivariate analysis of expression data sets within and between taxa; and (iii) association of microRNA subsets with annotation databases, HUGE Navigator, KEGG and GO. The use of existing and customized R packages facilitates future addition of data sets and analysis tools. Furthermore, the ability to upload and analyze user-specified data sets makes mESAdb an interactive and expandable analysis tool for microRNA sequence and expression data.

Intra-tumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the Cancer Genome Atlas.

PubMed

Mroz, Edmund A; Tward, Aaron D; Tward, Aaron M; Hammon, Rebecca J; Ren, Yin; Rocco, James W

2015-02-01

Although the involvement of intra-tumor genetic heterogeneity in tumor progression, treatment resistance, and metastasis is established, genetic heterogeneity is seldom examined in clinical trials or practice. Many studies of heterogeneity have had prespecified markers for tumor subpopulations, limiting their generalizability, or have involved massive efforts such as separate analysis of hundreds of individual cells, limiting their clinical use. We recently developed a general measure of intra-tumor genetic heterogeneity based on whole-exome sequencing (WES) of bulk tumor DNA, called mutant-allele tumor heterogeneity (MATH). Here, we examine data collected as part of a large, multi-institutional study to validate this measure and determine whether intra-tumor heterogeneity is itself related to mortality. Clinical and WES data were obtained from The Cancer Genome Atlas in October 2013 for 305 patients with head and neck squamous cell carcinoma (HNSCC), from 14 institutions. Initial pathologic diagnoses were between 1992 and 2011 (median, 2008). Median time to death for 131 deceased patients was 14 mo; median follow-up of living patients was 22 mo. Tumor MATH values were calculated from WES results. Despite the multiple head and neck tumor subsites and the variety of treatments, we found in this retrospective analysis a substantial relation of high MATH values to decreased overall survival (Cox proportional hazards analysis: hazard ratio for high/low heterogeneity, 2.2; 95% CI 1.4 to 3.3). This relation of intra-tumor heterogeneity to survival was not due to intra-tumor heterogeneity's associations with other clinical or molecular characteristics, including age, human papillomavirus status, tumor grade and TP53 mutation, and N classification. MATH improved prognostication over that provided by traditional clinical and molecular characteristics, maintained a significant relation to survival in multivariate analyses, and distinguished outcomes among patients having oral-cavity or laryngeal cancers even when standard disease staging was taken into account. Prospective studies, however, will be required before MATH can be used prognostically in clinical trials or practice. Such studies will need to examine homogeneously treated HNSCC at specific head and neck subsites, and determine the influence of cancer therapy on MATH values. Analysis of MATH and outcome in human-papillomavirus-positive oropharyngeal squamous cell carcinoma is particularly needed. To our knowledge this study is the first to combine data from hundreds of patients, treated at multiple institutions, to document a relation between intra-tumor heterogeneity and overall survival in any type of cancer. We suggest applying the simply calculated MATH metric of heterogeneity to prospective studies of HNSCC and other tumor types.

Spatial distribution of environmental risk associated to a uranium abandoned mine (Central Portugal)

NASA Astrophysics Data System (ADS)

Antunes, I. M.; Ribeiro, A. F.

2012-04-01

The abandoned uranium mine of Canto do Lagar is located at Arcozelo da Serra, central Portugal. The mine was exploited in an open pit and produced about 12430Kg of uranium oxide (U3O8), between 1987 and 1988. The dominant geological unit is the porphyritic coarse-grained two-mica granite, with biotite>muscovite. The uranium deposit consists of two gaps crushing, parallel to the coarse-grained porphyritic granite, with average direction N30°E, silicified, sericitized and reddish jasperized, with a width of approximately 10 meters. These gaps are accompanied by two thin veins of white quartz, 70°-80° WNW, ferruginous and jasperized with chalcedony, red jasper and opal. These veins are about 6 meters away from each other. They contain secondary U-phosphates phases such as autunite and torbernite. Rejected materials (1000000ton) were deposited on two dumps and a lake was formed in the open pit. To assess the environmental risk of the abandoned uranium mine of Canto do Lagar, were collected and analysed 70 samples on stream sediments, soils and mine tailings materials. The relation between samples composition were tested using the Principal Components Analysis (PCA) (multivariate analysis) and spatial distribution using Kriging Indicator. The spatial distribution of stream sediments shows that the probability of expression for principal component 1 (explaining Y, Zr, Nb, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Hf, Th and U contents), decreases along SE-NW direction. This component is explained by the samples located inside mine influence. The probability of expression for principal component 2 (explaining Be, Na, Al, Si, P, K, Ca, Ti, Mn, Fe, Co, Ni, Cu, As, Rb, Sr, Mo, Cs, Ba, Tl and Bi contents), increases to middle stream line. This component is explained by the samples located outside mine influence. The spatial distribution of soils, shows that the probability of expression for principal component 1 (explaining Mg, P, Ca, Ge, Sr, Y, Zr, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, W, Th and U contents) decreases along SE direction and increases along NE and SW directions. The probability of expression for principal component 2 (explaining pH, K, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Sr and Pb contents), decreases from central points (inside mine influence) to peripheral points (outside mine influence) and gradually increases along N and SW directions. The spatial distribution of tailing materials did not allowed a consistent spatial distribution. In general, the stream sediments are classified as unpolluted and not polluted or moderately polluted, according to geoaccumulation Müller index with exception of local samples, located inside mine influence. The soils cannot be used for public, private or residential uses according to the Canadian soil legislation. However, almost samples can be used for commercial or industrial occupation. According to the target values and intervention values for soils remediation, these soils need intervention. Tailing materials samples are much polluted in thorium (Th) and uranium (U) and they cannot be used for public, private or residential uses.

Macroscopic and microscopic observations of particle-facilitated mercury transport from New Idria and Sulphur Bank mercury mine tailings

USGS Publications Warehouse

Lowry, G.V.; Shaw, S.; Kim, C.S.; Rytuba, J.J.; Brown, Gordon E.

2004-01-01

Mercury (Hg) release from inoperative Hg mines in the California Coast Range has been documented, but little is known about the release and transport mechanisms. In this study, tailings from Hg mines located in different geologic settings-New Idria (NI), a Si-carbonate Hg deposit, and Sulphur Bank (SB), a hot-spring Hg deposit-were characterized, and particle release from these wastes was studied in column experiments to (1) investigate the mechanisms of Hg release from NI and SB mine wastes, (2) determine the speciation of particle-bound Hg released from the mine wastes, and (3) determine the effect of calcinations on Hg release processes. The physical and chemical properties of tailings and the colloids released from them were determined using chemical analyses, selective chemical extractions, XRD, SEM, TEM, and X-ray absorption spectroscopy techniques. The total Hg concentration in tailings increased with decreasing particle size in NI and SB calcines (roasted ore), but reached a maximum at an intermediate particle size in the SB waste rock (unroasted ore). Hg in the tailings exists predominantly as low-solubility HgS (cinnabar and metacinnabar), with NI calcines having >50% HgS, SB calcines having >89% HgS, and SB waste rock having ???100% HgS. Leaching experiments with a high-ionic-strength solution (0.1 M NaCl) resulted in a rapid but brief release of soluble and particulate Hg. Lowering the ionic strength of the leach solution (0.005 M NaCl) resulted in the release of colloidal Hg from two of the three mine wastes studied (NI calcines and SB waste rock). Colloid-associated Hg accounts for as much as 95% of the Hg released during episodic particle release. Colloids generated from the NI calcines are produced by a breakup and release mechanism and consist of hematite, jarosite/alunite, and Al-Si gel with particle sizes of 10-200 nm. ATEM and XAFS analyses indicate that the majority (???78%) of the mercury is present in the form of HgS. SB calcines also produced HgS colloids. The colloids generated from the SB waste rock were heterogeneous and varied in composition according to the column influent composition. ATEM and XAFS results indicate that Hg is entirely in the HgS form. Data from this study identify colloidal HgS as the dominant transported form of Hg from these mine waste materials.

Revealing the spatial region of a future fracture nucleation in heterogeneous materials at the initial deformation stage

NASA Astrophysics Data System (ADS)

Damaskinskaya, Ekaterina; Hilarov, Vladimir; Frolov, Dmitriy

2016-11-01

The energy distributions of acoustic emission (AE) signals have been analyzed on two scaling levels corresponding to deformation of granite samples and processes on a commercial mining enterprise. It is established that, in cases of localized fracture, the energy distribution of AE signals has shape described by a power law, while a dispersed fracture is characterized by an exponential energy distribution of the AE signals. Analysis of the functional form of the energy distribution performed at the initial stage of loading allows one to recognize a spatial region in the sample where localization of the defect formation will subsequently take place.

Unique Approach to Hydraulic Characterization at an Underground Lab

NASA Astrophysics Data System (ADS)

Jones, T. L.; Wang, J. S.

2009-12-01

The Sanford Underground Laboratory is the interim lab for the future federally funded DUSEL (Deep Underground Science and Engineering Lab). The Sanford Lab took over the abandoned Homestake mine in Lead, SD. Over three hundred miles of drift, extending 8,000 feet below the surface, are now being used to house experiments in disciplines including physics, geology, and biology. The lab is situated in Precambrian metamorphic rocks intersected by Tertiary dike swarms. Three relevant geologic units are defined within the Precambrian rock system; all of which are interpreted to be metamorphosed igneous and sedimentary deposits. The Sanford Lab provides a unique environment to study several aspects of hydrogeology and hydrology; including geochemistry, hydraulic systems in fractured aquifers, and fluvial activity within mine workings. Aquifer characteristics housing the mine workings’ is important to define for future and present research at the underground lab. Outlined here is a unique approach to defining the matrix porosity within the fractured aquifer system. The Homestake mine was abandoned and the pump system keeping the mine dry was turned off in 2003. Over the course of the next five years the water level rose 3470 feet. Oxidation of iron from the water left a red staining on the submerged rocks. Hydrological observations are conducted on different levels throughout the Homestake facility as the water levels are lowered. Isolated air pockets and long stretches of unstained areas along the roof of drifts have been observed, together with less frequent occurrences of seepages. These observations are documented to supplement hydrological monitoring and testing with sensors. The sizes and widths of the trapped air pockets are indications of low permeability values and can be used to estimate the degree of heterogeneity along drifts. It is noted that sections of long stretches of trapped air have more delayed drainages, consistent with low effective permeability values for the metamorphic rocks. The air pockets reveal a distinctive difference in size between the geologic units; the average size of the air pockets associated with different geologic units differs by an order of magnitude. The infrequent seepage observations are also consistent with the hydrological setting of this facility with low inflow rates.

Arsenic microdistribution and speciation in toenail clippings of children living in a historic gold mining area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pearce, Dora C.; Dowling, Kim; Gerson, Andrea R.

2010-05-04

Arsenic is naturally associated with gold mineralization and elevated in some soils and mine waste around historical gold mining activity in Victoria, Australia. To explore uptake, arsenic concentrations in children's toenail clippings and household soils were measured, and the microdistribution and speciation of arsenic in situ in toenail clipping thin sections investigated using synchrotron-based X-ray microprobe techniques. The ability to differentiate exogenous arsenic was explored by investigating surface contamination on cleaned clippings using depth profiling, and direct diffusion of arsenic into incubated clippings. Total arsenic concentrations ranged from 0.15 to 2.1 {micro}g/g (n = 29) in clipping samples and frommore » 3.3 to 130 {micro}g/g (n = 22) in household soils, with significant correlation between transformed arsenic concentrations (Pearson's r = 0.42, P = 0.023) when household soil was treated as independent. In clipping thin sections (n = 2), X-ray fluorescence (XRF) mapping showed discrete layering of arsenic consistent with nail structure, and irregular arsenic incorporation along the nail growth axis. Arsenic concentrations were heterogeneous at 10 x 10 {micro}m microprobe spot locations investigated (< 0.1 to 13.3 {micro}g/g). X-ray absorption near-edge structure (XANES) spectra suggested the presence of two distinct arsenic species: a lower oxidation state species, possibly with mixed sulphur and methyl coordination (denoted As{sub (-S, -ch3)}{sup {approx}III}); and a higher oxidation state species (denoted As{sub (-O)}{sup {approx}V}). Depth profiling suggested that surface contamination was unlikely (n = 4), and XRF and XANES analyses of thin sections of clippings incubated in dry or wet mine waste, or untreated, suggested direct diffusion of arsenic occurred under moist conditions. These findings suggest that arsenic in soil contributes to some systemic absorption associated with periodic exposures among children resident in areas of historic gold mining activity in Victoria, Australia. Future studies are required to ascertain if adverse health effects are associated with current levels of arsenic uptake.« less

Multivariate Prediction Equations for HbA1c Lowering, Weight Change, and Hypoglycemic Events Associated with Insulin Rescue Medication in Type 2 Diabetes Mellitus: Informing Economic Modeling.

PubMed

Willis, Michael; Asseburg, Christian; Nilsson, Andreas; Johnsson, Kristina; Kartman, Bernt

2017-03-01

Type 2 diabetes mellitus (T2DM) is chronic and progressive and the cost-effectiveness of new treatment interventions must be established over long time horizons. Given the limited durability of drugs, assumptions regarding downstream rescue medication can drive results. Especially for insulin, for which treatment effects and adverse events are known to depend on patient characteristics, this can be problematic for health economic evaluation involving modeling. To estimate parsimonious multivariate equations of treatment effects and hypoglycemic event risks for use in parameterizing insulin rescue therapy in model-based cost-effectiveness analysis. Clinical evidence for insulin use in T2DM was identified in PubMed and from published reviews and meta-analyses. Study and patient characteristics and treatment effects and adverse event rates were extracted and the data used to estimate parsimonious treatment effect and hypoglycemic event risk equations using multivariate regression analysis. Data from 91 studies featuring 171 usable study arms were identified, mostly for premix and basal insulin types. Multivariate prediction equations for glycated hemoglobin A 1c lowering and weight change were estimated separately for insulin-naive and insulin-experienced patients. Goodness of fit (R 2 ) for both outcomes were generally good, ranging from 0.44 to 0.84. Multivariate prediction equations for symptomatic, nocturnal, and severe hypoglycemic events were also estimated, though considerable heterogeneity in definitions limits their usefulness. Parsimonious and robust multivariate prediction equations were estimated for glycated hemoglobin A 1c and weight change, separately for insulin-naive and insulin-experienced patients. Using these in economic simulation modeling in T2DM can improve realism and flexibility in modeling insulin rescue medication. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

PubMed

Ma, Yan; Mazumdar, Madhu

2011-10-30

Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

Using Fisher information to track stability in multivariate ...

EPA Pesticide Factsheets

With the current proliferation of data, the proficient use of statistical and mining techniques offer substantial benefits to capture useful information from any dataset. As numerous approaches make use of information theory concepts, here, we discuss how Fisher information (FI) can be applied to sustainability science problems and used in data mining applications by analyzing patterns in data. FI was developed as a measure of information content in data, and it has been adapted to assess order in complex system behaviors. The main advantage of the approach is the ability to collapse multiple variables into an index that can be used to assess stability and track overall trends in a system, including its regimes and regime shifts. Here, we provide a brief overview of FI theory, followed by a simple step-by-step numerical example on how to compute FI. Furthermore, we introduce an open source Python library that can be freely downloaded from GitHub and we use it in a simple case study to evaluate the evolution of FI for the global-mean temperature from 1880 to 2015. Results indicate significant declines in FI starting in 1978, suggesting a possible regime shift. Demonstrate Fisher information as a useful method for assessing patterns in big data.

Knowledge management in secondary pharmaceutical manufacturing by mining of data historians-A proof-of-concept study.

PubMed

Meneghetti, Natascia; Facco, Pierantonio; Bezzo, Fabrizio; Himawan, Chrismono; Zomer, Simeone; Barolo, Massimiliano

2016-05-30

In this proof-of-concept study, a methodology is proposed to systematically analyze large data historians of secondary pharmaceutical manufacturing systems using data mining techniques. The objective is to develop an approach enabling to automatically retrieve operation-relevant information that can assist the management in the periodic review of a manufactory system. The proposed methodology allows one to automatically perform three tasks: the identification of single batches within the entire data-sequence of the historical dataset, the identification of distinct operating phases within each batch, and the characterization of a batch with respect to an assigned multivariate set of operating characteristics. The approach is tested on a six-month dataset of a commercial-scale granulation/drying system, where several millions of data entries are recorded. The quality of results and the generality of the approach indicate that there is a strong potential for extending the method to even larger historical datasets and to different operations, thus making it an advanced PAT tool that can assist the implementation of continual improvement paradigms within a quality-by-design framework. Copyright © 2016 Elsevier B.V. All rights reserved.

Flow cytometry and cell sorting of heterogeneous microbial populations: the importance of single-cell analyses.

PubMed Central

Davey, H M; Kell, D B

1996-01-01

The most fundamental questions such as whether a cell is alive, in the sense of being able to divide or to form a colony, may sometimes be very hard to answer, since even axenic microbial cultures are extremely heterogeneous. Analyses that seek to correlate such things as viability, which is a property of an individual cell, with macroscopic measurements of culture variables such as ATP content, respiratory activity, and so on, must inevitably fail. It is therefore necessary to make physiological measurements on individual cells. Flow cytometry is such a technique, which allows one to analyze cells rapidly and individually and permits the quantitative analysis of microbial heterogeneity. It therefore offers many advantages over conventional measurements for both routine and more exploratory analyses of microbial properties. While the technique has been widely applied to the study of mammalian cells, is use in microbiology has until recently been much more limited, largely because of the smaller size of microbes and the consequently smaller optical signals obtainable from them. Since these technical barriers no longer hold, flow cytometry with appropriate stains has been used for the rapid discrimination and identification of microbial cells, for the rapid assessment of viability and of the heterogeneous distributions of a wealth of other more detailed physiological properties, for the analysis of antimicrobial drug-cell interactions, and for the isolation of high-yielding strains of biotechnological interest. Flow cytometric analyses provide an abundance of multivariate data, and special methods have been devised to exploit these. Ongoing advances mean that modern flow cytometers may now be used by nonspecialists to effect a renaissance in our understanding of microbial heterogeneity. PMID:8987359

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

PubMed Central

Hu, Leland S.; Ning, Shuluo; Eschbacher, Jennifer M.; Baxter, Leslie C.; Gaw, Nathan; Ranjbar, Sara; Plasencia, Jonathan; Dueck, Amylou C.; Peng, Sen; Smith, Kris A.; Nakaji, Peter; Karis, John P.; Quarles, C. Chad; Wu, Teresa; Loftus, Joseph C.; Jenkins, Robert B.; Sicotte, Hugues; Kollmeyer, Thomas M.; O'Neill, Brian P.; Elmquist, William; Hoxworth, Joseph M.; Frakes, David; Sarkaria, Jann; Swanson, Kristin R.; Tran, Nhan L.; Li, Jing; Mitchell, J. Ross

2017-01-01

Background Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. Methods We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). Results We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). Conclusion MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology. PMID:27502248

Geographic Disparity in the Use of Hypofractionated Radiation Therapy Among Elderly Women Undergoing Breast Conservation for Invasive Breast Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gillespie, Erin F.; Matsuno, Rayna K.; Xu, Beibei

Purpose: To evaluate geographic heterogeneity in the delivery of hypofractionated radiation therapy (RT) for breast cancer among Medicare beneficiaries across the United States. Methods and Materials: We identified 190,193 patients from the Centers for Medicare and Medicaid Services Chronic Conditions Warehouse. The study included patients aged >65 years diagnosed with invasive breast cancer treated with breast conservation surgery followed by radiation diagnosed between 2000 and 2012. We analyzed data by hospital referral region based on patient residency ZIP code. The proportion of women who received hypofractionated RT within each region was analyzed over the study period. Multivariable logistic regression models identified predictors ofmore » hypofractionated RT. Results: Over the entire study period we found substantial geographic heterogeneity in the use of hypofractionated RT. The proportion of women receiving hypofractionated breast RT in individual hospital referral regions varied from 0% to 61%. We found no correlation between the use of hypofractionated RT and urban/rural setting or general geographic region. The proportion of hypofractionated RT increased in regions with higher density of radiation oncologists, as well as lower total Medicare reimbursements. Conclusions: This study demonstrates substantial geographic heterogeneity in the use of hypofractionated RT among elderly women with invasive breast cancer treated with lumpectomy in the United States. This heterogeneity persists despite clinical data from multiple randomized trials proving efficacy and safety compared with standard fractionation, and highlights possible inefficiency in health care delivery.« less

Drillers and mill operators in an open-pit gold mine are at risk for impaired lung function.

PubMed

Vinnikov, Denis

2016-01-01

Occupational studies of associations of exposures with impaired lung function in mining settings are built on exposure assessment and far less often on workplace approach, so the aim of this study was to identify vulnerable occupational groups for early lung function reduction in a cohort of healthy young miners. Data from annual screening lung function tests in gold mining company in Kyrgyzstan were linked to occupations. We compared per cent predicted forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and FEV1/FVC between occupational groups and tested selected occupations in multivariate regression adjusted for smoking and work duration for the following outcomes: FEV1 < 80 %, FEV1/FVC < 70 % and both. 1550 tests of permanent workers of 41 occupations (mean age 40.5 ± 9.2 years, 29.8 % never smokers) were included in the analysis. The mean overall VC was 103.0 ± 12.9 %; FVC 109.1 ± 13.0 % and FEV1 100.2 ± 25.9 %. Drillers and smoking food handlers had the lowest FEV1%. In non-smokers, the lowest FEV1 was in drillers (94.9 ± 11.3 % compared to 115.2 ± 17.7 % in engineers). Drillers (adjusted odds ratio (OR) 1.53 (95 % confidence interval (CI) 1.11-2.09)) and mill operators (OR 2.01 (1.13-3.57)) were at greater risk of obstructive ventilation pattern (FEV1/FVC < 70 %). Drilling and mill operations are the highest risk jobs in an open-pit mine for reduced lung function. Occupational medical clinic at site should follow-up workers in these occupations with depth and strongly recommend smoking cessation.

Chemomics-based marker compounds mining and mimetic processing for exploring chemical mechanisms in traditional processing of herbal medicines, a continuous study on Rehmanniae Radix.

PubMed

Zhou, Li; Xu, Jin-Di; Zhou, Shan-Shan; Shen, Hong; Mao, Qian; Kong, Ming; Zou, Ye-Ting; Xu, Ya-Yun; Xu, Jun; Li, Song-Lin

2017-12-29

Exploring processing chemistry, in particular the chemical transformation mechanisms involved, is a key step to elucidate the scientific basis in traditional processing of herbal medicines. Previously, taking Rehmanniae Radix (RR) as a case study, the holistic chemome (secondary metabolome and glycome) difference between raw and processed RR was revealed by integrating hyphenated chromatographic techniques-based targeted glycomics and untargeted metabolomics. Nevertheless, the complex chemical transformation mechanisms underpinning the holistic chemome variation in RR processing remain to be extensively clarified. As a continuous study, here a novel strategy by combining chemomics-based marker compounds mining and mimetic processing is proposed for further exploring the chemical mechanisms involved in herbal processing. First, the differential marker compounds between raw and processed herbs were rapidly discovered by untargeted chemomics-based mining approach through multivariate statistical analysis of the chemome data obtained by integrated metabolomics and glycomics analysis. Second, the marker compounds were mimetically processed under the simulated physicochemical conditions as in the herb processing, and the final reaction products were chemically characterized by targeted chemomics-based mining approach. Third, the main chemical transformation mechanisms involved were clarified by linking up the original marker compounds and their mimetic processing products. Using this strategy, a set of differential marker compounds including saccharides, glycosides and furfurals in raw and processed RR was rapidly found, and the major chemical mechanisms involved in RR processing were elucidated as stepwise transformations of saccharides (polysaccharides, oligosaccharides and monosaccharides) and glycosides (iridoid glycosides and phenethylalcohol glycosides) into furfurals (glycosylated/non-glycosylated hydroxymethylfurfurals) by deglycosylation and/or dehydration. The research deliverables indicated that the proposed strategy could advance the understanding of RR processing chemistry, and therefore may be considered a promising approach for delving into the scientific basis in traditional processing of herbal medicines. Copyright © 2017 Elsevier B.V. All rights reserved.

Assessment of heavy metal contamination in water and sediments of Trepça and Sitnica rivers, Kosovo, using pollution indicators and multivariate cluster analysis.

PubMed

Ferati, Flora; Kerolli-Mustafa, Mihone; Kraja-Ylli, Arjana

2015-06-01

The concentrations of As, Cd, Cr, Co, Cu, Ni, Pb, and Zn in water and sediment samples from Trepça and Sitnica rivers were determined to assess the level of contamination. Six water and sediment samples were collected during the period from April to July 2014. Most of the water samples was found within the European and Kosovo permissible limits. The highest concentration of As, Cd, Pb, and Zn originates primarily from anthropogenic sources such discharge of industrial water from mining flotation and from the mine waste eroded from the river banks. Sediment contamination assessment was carried out using the pollution indicators such as contamination factor (CF), degree of contamination (Cd), modified degree of contamination (mCd), pollution load index (PLI), and geo-accumulation index (Igeo). The CF values for the investigated metals indicated a high contaminated nature of sediments, while the Cd values indicated a very high contamination degree of sediments. The mCd values indicate a high degree of contamination of Sitnica river sediment to ultrahigh degree of contamination of Trepça river sediment. The PLI values ranged from 1.89 to 14.1 which indicate that the heavy metal concentration levels in all investigated sites exceeded the background values and sediment quality guidelines. The average values of Igeo revealed the following ranking of intensity of heavy metal contamination of the Trepça and Sitnica river sediments: Cd > As > Pb > Zn > Cu > Co > Cr > Ni. Cluster analysis suggests that As, Cd, Cr, Co, Cu, Ni, Pb, and Zn are derived from anthropogenic sources, particularly discharges from mining flotation and erosion form waste from a zinc mine plant. In order to protect the sediments from further contamination, the designing of a monitoring network and reducing the anthropogenic discharges are suggested.

Long-Term Impacts on Macroinvertebrates Downstream of Reclaimed Mountaintop Mining Valley Fills in Central Appalachia

NASA Astrophysics Data System (ADS)

Pond, Gregory J.; Passmore, Margaret E.; Pointon, Nancy D.; Felbinger, John K.; Walker, Craig A.; Krock, Kelly J. G.; Fulton, Jennifer B.; Nash, Whitney L.

2014-10-01

Recent studies have documented adverse effects to biological communities downstream of mountaintop coal mining and valley fills (VF), but few data exist on the longevity of these impacts. We sampled 15 headwater streams with VFs reclaimed 11-33 years prior to 2011 and sampled seven local reference sites that had no VFs. We collected chemical, habitat, and benthic macroinvertebrate data in April 2011; additional chemical samples were collected in September 2011. To assess ecological condition, we compared VF and reference abiotic and biotic data using: (1) ordination to detect multivariate differences, (2) benthic indices (a multimetric index and an observed/expected predictive model) calibrated to state reference conditions to detect impairment, and (3) correlation and regression analysis to detect relationships between biotic and abiotic data. Although VF sites had good instream habitat, nearly 90 % of these streams exhibited biological impairment. VF sites with higher index scores were co-located near unaffected tributaries; we suggest that these tributaries were sources of sensitive taxa as drifting colonists. There were clear losses of expected taxa across most VF sites and two functional feeding groups (% scrapers and %shredders) were significantly altered. Percent VF and forested area were related to biological quality but varied more than individual ions and specific conductance. Within the subset of VF sites, other descriptors (e.g., VF age, site distance from VF, the presence of impoundments, % forest) had no detectable relationships with biological condition. Although these VFs were constructed pursuant to permits and regulatory programs that have as their stated goals that (1) mined land be reclaimed and restored to its original use or a use of higher value, and (2) mining does not cause or contribute to violations of water quality standards, we found sustained ecological damage in headwaters streams draining VFs long after reclamation was completed.

Seismic imaging in hardrock environments: The role of heterogeneity?

NASA Astrophysics Data System (ADS)

Bongajum, Emmanuel; Milkereit, Bernd; Adam, Erick; Meng, Yijian

2012-10-01

We investigate the effect of petrophysical scale parameters and structural dips on wave propagation and imaging in heterogeneous media. Seismic wave propagation effects within the heterogeneous media are studied for different velocity models with scale lengths determined via stochastic analysis of petrophysical logs from the Matagami mine, Quebec, Canada. The elastic modeling study reveals that provided certain conditions of the velocity fluctuations are met, strong local distortions of amplitude and arrival times of propagating waves are observed as the degree of scale length anisotropy in the P-wave velocity increases. The location of these local amplitude anomalies is related to the dips characterizing the fabric of the host rocks. This result is different from the elliptical shape of direct waves often defined by effective anisotropic parameters used for layered media. Although estimates of anisotropic parameters suggest weak anisotropy in the investigated models, these effective anisotropic parameters often used in VTI/TTI do not sufficiently describe the effects of scale length anisotropy in heterogeneous media that show such local amplitude, travel time, and phase distortions in the wavefields. Numerical investigations on the implications for reverse time migration (RTM) routines corroborate that mean P-wave velocity of the host rocks produces reliable imaging results. Based on the RTM results, we postulate the following: weak anisotropy in hardrock environments is a sufficient assumption for processing seismic data; and seismic scattering effects due to velocity heterogeneity with a dip component is not sufficient to cause mislocation errors of target structures as observed in the discrepancy between the location of the strong seismic reflections associated to the Matagami sulfide orebody and its true location. Future work will investigate other factors that may provide plausible explanations for these mislocation problems, with the objective of providing a mitigation strategy for incorporation into the seismic data processing sequence when imaging in hardrock settings.

Application of multivariate probabilistic (Bayesian) networks to substance use disorder risk stratification and cost estimation.

PubMed

Weinstein, Lawrence; Radano, Todd A; Jack, Timothy; Kalina, Philip; Eberhardt, John S

2009-09-16

This paper explores the use of machine learning and Bayesian classification models to develop broadly applicable risk stratification models to guide disease management of health plan enrollees with substance use disorder (SUD). While the high costs and morbidities associated with SUD are understood by payers, who manage it through utilization review, acute interventions, coverage and cost limitations, and disease management, the literature shows mixed results for these modalities in improving patient outcomes and controlling cost. Our objective is to evaluate the potential of data mining methods to identify novel risk factors for chronic disease and stratification of enrollee utilization, which can be used to develop new methods for targeting disease management services to maximize benefits to both enrollees and payers. For our evaluation, we used DecisionQ machine learning algorithms to build Bayesian network models of a representative sample of data licensed from Thomson-Reuters' MarketScan consisting of 185,322 enrollees with three full-year claim records. Data sets were prepared, and a stepwise learning process was used to train a series of Bayesian belief networks (BBNs). The BBNs were validated using a 10 percent holdout set. The networks were highly predictive, with the risk-stratification BBNs producing area under the curve (AUC) for SUD positive of 0.948 (95 percent confidence interval [CI], 0.944-0.951) and 0.736 (95 percent CI, 0.721-0.752), respectively, and SUD negative of 0.951 (95 percent CI, 0.947-0.954) and 0.738 (95 percent CI, 0.727-0.750), respectively. The cost estimation models produced area under the curve ranging from 0.72 (95 percent CI, 0.708-0.731) to 0.961 (95 percent CI, 0.95-0.971). We were able to successfully model a large, heterogeneous population of commercial enrollees, applying state-of-the-art machine learning technology to develop complex and accurate multivariate models that support near-real-time scoring of novel payer populations based on historic claims and diagnostic data. Initial validation results indicate that we can stratify enrollees with SUD diagnoses into different cost categories with a high degree of sensitivity and specificity, and the most challenging issue becomes one of policy. Due to the social stigma associated with the disease and ethical issues pertaining to access to care and individual versus societal benefit, a thoughtful dialogue needs to occur about the appropriate way to implement these technologies.

High coffee consumption and different brewing methods in relation to postmenopausal endometrial cancer risk in the Norwegian Women and Cancer Study: a population-based prospective study

PubMed Central

2014-01-01

Background Coffee and its compounds have been proposed to inhibit endometrial carcinogenesis. Studies in the Norwegian population can be especially interesting due to the high coffee consumption and increasing incidence of endometrial cancer in the country. Methods A total of 97 926 postmenopausal Norwegian women from the population-based prospective Norwegian Women and Cancer (NOWAC) Study, were included in the present analysis. We evaluated the general association between total coffee consumption and endometrial cancer risk as well as the possible impact of brewing method. Multivariate Cox regression analysis was used to estimate risks, and heterogeneity tests were performed to compare brewing methods. Results During an average of 10.9 years of follow-up, 462 incident endometrial cancer cases were identified. After multivariate adjustment, significant risk reduction was found among participants who drank ≥8 cups/day of coffee with a hazard ratio of 0.52 (95% confidence interval, CI 0.34-0.79). However, we did not observe a significant dose-response relationship. No significant heterogeneity in risk was found when comparing filtered and boiled coffee brewing methods. A reduction in endometrial cancer risk was observed in subgroup analyses among participants who drank ≥8 cups/day and had a body mass index ≥25 kg/m2, and in current smokers. Conclusions These data suggest that in this population with high coffee consumption, endometrial cancer risk decreases in women consuming ≥8 cups/day, independent of brewing method. PMID:24666820

Plasma 25-hydroxyvitamin D and risk of breast cancer in the Nurses' Health Study II

PubMed Central

2011-01-01

Introduction Experimental evidence indicates vitamin D may play an important role in breast cancer etiology but epidemiologic evidence to date is inconsistent. Vitamin D comes from dietary intake and sun exposure and plasma levels of 25-hydroxyvitamin D (25(OH)D) are considered the best measure of vitamin D status. Methods We conducted a prospective nested case-control study within the Nurses' Health Study II (NHSII). Plasma samples collected in 1996 to 1999 were assayed for 25(OH)D in 613 cases, diagnosed after blood collection and before 1 June 2007, and in 1,218 matched controls. Multivariate relative risks (RR) and 95% confidence intervals (CI) were calculated by conditional logistic regression, adjusting for several breast cancer risk factors. Results No significant association was observed between plasma 25(OH)D levels and breast cancer risk (top vs. bottom quartile multivariate RR = 1.20, 95% CI (0.88 to 1.63), P-value, test for trend = 0.32). Results were similar when season-specific quartile cut points were used. Results did not change when restricted to women who were premenopausal at blood collection or premenopausal at diagnosis. Results were similar between estrogen receptor (ER)+/progesterone receptor (PR)+ and ER-/PR- tumors (P-value, test for heterogeneity = 0.51). The association did not vary by age at blood collection or season of blood collection, but did vary when stratified by body mass index (P-value, test for heterogeneity = 0.01). Conclusions Circulating 25(OH)D levels were not significantly associated with breast cancer risk in this predominantly premenopausal population. PMID:21569367

Initial Assessment of the Risk Assessment and Prediction Tool in a Heterogeneous Neurosurgical Patient Population.

PubMed

Piazza, Matthew; Sharma, Nikhil; Osiemo, Benjamin; McClintock, Scott; Missimer, Emily; Gardiner, Diana; Maloney, Eileen; Callahan, Danielle; Smith, J Lachlan; Welch, William; Schuster, James; Grady, M Sean; Malhotra, Neil R

2018-05-21

Bundled care payments are increasingly being explored for neurosurgical interventions. In this setting, skilled nursing facility (SNF) is less desirable from a cost perspective than discharge to home, underscoring the need for better preoperative prediction of postoperative disposition. To assess the capability of the Risk Assessment and Prediction Tool (RAPT) and other preoperative variables to determine expected disposition prior to surgery in a heterogeneous neurosurgical cohort, through observational study. Patients aged 50 yr or more undergoing elective neurosurgery were enrolled from June 2016 to February 2017 (n = 623). Logistic regression was used to identify preoperative characteristics predictive of discharge disposition. Results from multivariate analysis were used to create novel grading scales for the prediction of discharge disposition that were subsequently compared to the RAPT Score using Receiver Operating Characteristic analysis. Higher RAPT Score significantly predicted home disposition (P < .001). Age 65 and greater, dichotomized RAPT walk score, and spinal surgery below L2 were independent predictors of SNF discharge in multivariate analysis. A grading scale utilizing these variables had superior discriminatory power between SNF and home/rehab discharge when compared with RAPT score alone (P = .004). Our analysis identified age, lower lumbar/lumbosacral surgery, and RAPT walk score as independent predictors of discharge to SNF, and demonstrated superior predictive power compared with the total RAPT Score when combined in a novel grading scale. These tools may identify patients who may benefit from expedited discharge to subacute care facilities and decrease inpatient hospital resource utilization following surgery.

Polytopic vector analysis in igneous petrology: Application to lunar petrogenesis

NASA Technical Reports Server (NTRS)

Shervais, John W.; Ehrlich, R.

1993-01-01

Lunar samples represent a heterogeneous assemblage of rocks with complex inter-relationships that are difficult to decipher using standard petrogenetic approaches. These inter-relationships reflect several distinct petrogenetic trends as well as thermomechanical mixing of distinct components. Additional complications arise from the unequal quality of chemical analyses and from the fact that many samples (e.g., breccia clasts) are too small to be representative of the system from which they derived. Polytopic vector analysis (PVA) is a multi-variate procedure used as a tool for exploratory data analysis. PVA allows the analyst to classify samples and clarifies relationships among heterogenous samples with complex petrogenetic histories. It differs from orthogonal factor analysis in that it uses non-orthogonal multivariate sample vectors to extract sample endmember compositions. The output from a Q-mode (sample based) factor analysis is the initial step in PVA. The Q-mode analysis, using criteria established by Miesch and Klovan and Miesch, is used to determine the number of endmembers in the data system. The second step involves determination of endmembers and mixing proportions with all output expressed in the same geochemical variable as the input. The composition of endmembers is derived by analysis of the variability of the data set. Endmembers need not be present in the data set, nor is it necessary for their composition to be known a priori. A set of any endmembers defines a 'polytope' or classification figure (triangle for a three component system, tetrahedron for a four component system, a 'five-tope' in four dimensions for five component system, et cetera).

Characterizing Tumor Heterogeneity With Functional Imaging and Quantifying High-Risk Tumor Volume for Early Prediction of Treatment Outcome: Cervical Cancer as a Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayr, Nina A., E-mail: Nina.Mayr@osumc.edu; Huang Zhibin; Wang, Jian Z.

2012-07-01

Purpose: Treatment response in cancer has been monitored by measuring anatomic tumor volume (ATV) at various times without considering the inherent functional tumor heterogeneity known to critically influence ultimate treatment outcome: primary tumor control and survival. This study applied dynamic contrast-enhanced (DCE) functional MRI to characterize tumors' heterogeneous subregions with low DCE values, at risk for treatment failure, and to quantify the functional risk volume (FRV) for personalized early prediction of treatment outcome. Methods and Materials: DCE-MRI was performed in 102 stage IB{sub 2}-IVA cervical cancer patients to assess tumor perfusion heterogeneity before and during radiation/chemotherapy. FRV represents the totalmore » volume of tumor voxels with critically low DCE signal intensity (<2.1 compared with precontrast image, determined by previous receiver operator characteristic analysis). FRVs were correlated with treatment outcome (follow-up: 0.2-9.4, mean 6.8 years) and compared with ATVs (Mann-Whitney, Kaplan-Meier, and multivariate analyses). Results: Before and during therapy at 2-2.5 and 4-5 weeks of RT, FRVs >20, >13, and >5 cm{sup 3}, respectively, significantly predicted unfavorable 6-year primary tumor control (p = 0.003, 7.3 Multiplication-Sign 10{sup -8}, 2.0 Multiplication-Sign 10{sup -8}) and disease-specific survival (p = 1.9 Multiplication-Sign 10{sup -4}, 2.1 Multiplication-Sign 10{sup -6}, 2.5 Multiplication-Sign 10{sup -7}, respectively). The FRVs were superior to the ATVs as early predictors of outcome, and the differentiating power of FRVs increased during treatment. Discussion: Our preliminary results suggest that functional tumor heterogeneity can be characterized by DCE-MRI to quantify FRV for predicting ultimate long-term treatment outcome. FRV is a novel functional imaging heterogeneity parameter, superior to ATV, and can be clinically translated for personalized early outcome prediction before or as early as 2-5 weeks into treatment.« less

Visual Analytics for Heterogeneous Geoscience Data

NASA Astrophysics Data System (ADS)

Pan, Y.; Yu, L.; Zhu, F.; Rilee, M. L.; Kuo, K. S.; Jiang, H.; Yu, H.

2017-12-01

Geoscience data obtained from diverse sources have been routinely leveraged by scientists to study various phenomena. The principal data sources include observations and model simulation outputs. These data are characterized by spatiotemporal heterogeneity originated from different instrument design specifications and/or computational model requirements used in data generation processes. Such inherent heterogeneity poses several challenges in exploring and analyzing geoscience data. First, scientists often wish to identify features or patterns co-located among multiple data sources to derive and validate certain hypotheses. Heterogeneous data make it a tedious task to search such features in dissimilar datasets. Second, features of geoscience data are typically multivariate. It is challenging to tackle the high dimensionality of geoscience data and explore the relations among multiple variables in a scalable fashion. Third, there is a lack of transparency in traditional automated approaches, such as feature detection or clustering, in that scientists cannot intuitively interact with their analysis processes and interpret results. To address these issues, we present a new scalable approach that can assist scientists in analyzing voluminous and diverse geoscience data. We expose a high-level query interface that allows users to easily express their customized queries to search features of interest across multiple heterogeneous datasets. For identified features, we develop a visualization interface that enables interactive exploration and analytics in a linked-view manner. Specific visualization techniques such as scatter plots to parallel coordinates are employed in each view to allow users to explore various aspects of features. Different views are linked and refreshed according to user interactions in any individual view. In such a manner, a user can interactively and iteratively gain understanding into the data through a variety of visual analytics operations. We demonstrate with use cases how scientists can combine the query and visualization interfaces to enable a customized workflow facilitating studies using heterogeneous geoscience datasets.

Cancer in British vegetarians: updated analyses of 4998 incident cancers in a cohort of 32,491 meat eaters, 8612 fish eaters, 18,298 vegetarians, and 2246 vegans.

PubMed

Key, Timothy J; Appleby, Paul N; Crowe, Francesca L; Bradbury, Kathryn E; Schmidt, Julie A; Travis, Ruth C

2014-07-01

Vegetarian diets might affect the risk of cancer. The objective was to describe cancer incidence in vegetarians and nonvegetarians in a large sample in the United Kingdom. This was a pooled analysis of 2 prospective studies including 61,647 British men and women comprising 32,491 meat eaters, 8612 fish eaters, and 20,544 vegetarians (including 2246 vegans). Cancer incidence was followed through nationwide cancer registries. Cancer risk by vegetarian status was estimated by using multivariate Cox proportional hazards models. After an average follow-up of 14.9 y, there were 4998 incident cancers: 3275 in meat eaters (10.1%), 520 in fish eaters (6.0%), and 1203 in vegetarians (5.9%). There was significant heterogeneity between dietary groups in risks of the following cancers: stomach cancer [RRs (95% CIs) compared with meat eaters: 0.62 (0.27, 1.43) in fish eaters and 0.37 (0.19, 0.69) in vegetarians; P-heterogeneity = 0.006], colorectal cancer [RRs (95% CIs): 0.66 (0.48, 0.92) in fish eaters and 1.03 (0.84, 1.26) in vegetarians; P-heterogeneity = 0.033], cancers of the lymphatic and hematopoietic tissue [RRs (95% CIs): 0.96 (0.70, 1.32) in fish eaters and 0.64 (0.49, 0.84) in vegetarians; P-heterogeneity = 0.005], multiple myeloma [RRs (95% CIs): 0.77 (0.34, 1.76) in fish eaters and 0.23 (0.09, 0.59) in vegetarians; P-heterogeneity = 0.010], and all sites combined [RRs (95% CIs): 0.88 (0.80, 0.97) in fish eaters and 0.88 (0.82, 0.95) in vegetarians; P-heterogeneity = 0.0007]. In this British population, the risk of some cancers is lower in fish eaters and vegetarians than in meat eaters. © 2014 American Society for Nutrition.

Cancer in British vegetarians: updated analyses of 4998 incident cancers in a cohort of 32,491 meat eaters, 8612 fish eaters, 18,298 vegetarians, and 2246 vegans1234

PubMed Central

Key, Timothy J; Appleby, Paul N; Crowe, Francesca L; Bradbury, Kathryn E; Schmidt, Julie A; Travis, Ruth C

2014-01-01

Background: Vegetarian diets might affect the risk of cancer. Objective: The objective was to describe cancer incidence in vegetarians and nonvegetarians in a large sample in the United Kingdom. Design: This was a pooled analysis of 2 prospective studies including 61,647 British men and women comprising 32,491 meat eaters, 8612 fish eaters, and 20,544 vegetarians (including 2246 vegans). Cancer incidence was followed through nationwide cancer registries. Cancer risk by vegetarian status was estimated by using multivariate Cox proportional hazards models. Results: After an average follow-up of 14.9 y, there were 4998 incident cancers: 3275 in meat eaters (10.1%), 520 in fish eaters (6.0%), and 1203 in vegetarians (5.9%). There was significant heterogeneity between dietary groups in risks of the following cancers: stomach cancer [RRs (95% CIs) compared with meat eaters: 0.62 (0.27, 1.43) in fish eaters and 0.37 (0.19, 0.69) in vegetarians; P-heterogeneity = 0.006], colorectal cancer [RRs (95% CIs): 0.66 (0.48, 0.92) in fish eaters and 1.03 (0.84, 1.26) in vegetarians; P-heterogeneity = 0.033], cancers of the lymphatic and hematopoietic tissue [RRs (95% CIs): 0.96 (0.70, 1.32) in fish eaters and 0.64 (0.49, 0.84) in vegetarians; P-heterogeneity = 0.005], multiple myeloma [RRs (95% CIs): 0.77 (0.34, 1.76) in fish eaters and 0.23 (0.09, 0.59) in vegetarians; P-heterogeneity = 0.010], and all sites combined [RRs (95% CIs): 0.88 (0.80, 0.97) in fish eaters and 0.88 (0.82, 0.95) in vegetarians; P-heterogeneity = 0.0007]. Conclusion: In this British population, the risk of some cancers is lower in fish eaters and vegetarians than in meat eaters. PMID:24898235

Internal fracture heterogeneity in discrete fracture network modelling: Effect of correlation length and textures with connected and disconnected permeability field

NASA Astrophysics Data System (ADS)

Frampton, A.; Hyman, J.; Zou, L.

2017-12-01

Analysing flow and transport in sparsely fractured media is important for understanding how crystalline bedrock environments function as barriers to transport of contaminants, with important applications towards subsurface repositories for storage of spent nuclear fuel. Crystalline bedrocks are particularly favourable due to their geological stability, low advective flow and strong hydrogeochemical retention properties, which can delay transport of radionuclides, allowing decay to limit release to the biosphere. There are however many challenges involved in quantifying and modelling subsurface flow and transport in fractured media, largely due to geological complexity and heterogeneity, where the interplay between advective and dispersive flow strongly impacts both inert and reactive transport. A key to modelling transport in a Lagrangian framework involves quantifying pathway travel times and the hydrodynamic control of retention, and both these quantities strongly depend on heterogeneity of the fracture network at different scales. In this contribution, we present recent analysis of flow and transport considering fracture networks with single-fracture heterogeneity described by different multivariate normal distributions. A coherent triad of fields with identical correlation length and variance are created but which greatly differ in structure, corresponding to textures with well-connected low, medium and high permeability structures. Through numerical modelling of multiple scales in a stochastic setting we quantify the relative impact of texture type and correlation length against network topological measures, and identify key thresholds for cases where flow dispersion is controlled by single-fracture heterogeneity versus network-scale heterogeneity. This is achieved by using a recently developed novel numerical discrete fracture network model. Furthermore, we highlight enhanced flow channelling for cases where correlation structure continues across intersections in a network, and discuss application to realistic fracture networks using field data of sparsely fractured crystalline rock from the Swedish candidate repository site for spent nuclear fuel.

WE-E-17A-05: Complementary Prognostic Value of CT and 18F-FDG PET Non-Small Cell Lung Cancer Tumor Heterogeneity Features Quantified Through Texture Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Desseroit, M; Cheze Le Rest, C; Tixier, F

2014-06-15

Purpose: Previous studies have shown that CT or 18F-FDG PET intratumor heterogeneity features computed using texture analysis may have prognostic value in Non-Small Cell Lung Cancer (NSCLC), but have been mostly investigated separately. The purpose of this study was to evaluate the potential added value with respect to prognosis regarding the combination of non-enhanced CT and 18F-FDG PET heterogeneity textural features on primary NSCLC tumors. Methods: One hundred patients with non-metastatic NSCLC (stage I–III), treated with surgery and/or (chemo)radiotherapy, that underwent staging 18F-FDG PET/CT images, were retrospectively included. Morphological tumor volumes were semi-automatically delineated on non-enhanced CT using 3D SlicerTM.more » Metabolically active tumor volumes (MATV) were automatically delineated on PET using the Fuzzy Locally Adaptive Bayesian (FLAB) method. Intratumoral tissue density and FDG uptake heterogeneities were quantified using texture parameters calculated from co-occurrence, difference, and run-length matrices. In addition to these textural features, first order histogram-derived metrics were computed on the whole morphological CT tumor volume, as well as on sub-volumes corresponding to fine, medium or coarse textures determined through various levels of LoG-filtering. Association with survival regarding all extracted features was assessed using Cox regression for both univariate and multivariate analysis. Results: Several PET and CT heterogeneity features were prognostic factors of overall survival in the univariate analysis. CT histogram-derived kurtosis and uniformity, as well as Low Grey-level High Run Emphasis (LGHRE), and PET local entropy were independent prognostic factors. Combined with stage and MATV, they led to a powerful prognostic model (p<0.0001), with median survival of 49 vs. 12.6 months and a hazard ratio of 3.5. Conclusion: Intratumoral heterogeneity quantified through textural features extracted from both CT and FDG PET images have complementary and independent prognostic value in NSCLC.« less

Spectral Estimation Model Construction of Heavy Metals in Mining Reclamation Areas

PubMed Central

Dong, Jihong; Dai, Wenting; Xu, Jiren; Li, Songnian

2016-01-01

The study reported here examined, as the research subject, surface soils in the Liuxin mining area of Xuzhou, and explored the heavy metal content and spectral data by establishing quantitative models with Multivariable Linear Regression (MLR), Generalized Regression Neural Network (GRNN) and Sequential Minimal Optimization for Support Vector Machine (SMO-SVM) methods. The study results are as follows: (1) the estimations of the spectral inversion models established based on MLR, GRNN and SMO-SVM are satisfactory, and the MLR model provides the worst estimation, with R2 of more than 0.46. This result suggests that the stress sensitive bands of heavy metal pollution contain enough effective spectral information; (2) the GRNN model can simulate the data from small samples more effectively than the MLR model, and the R2 between the contents of the five heavy metals estimated by the GRNN model and the measured values are approximately 0.7; (3) the stability and accuracy of the spectral estimation using the SMO-SVM model are obviously better than that of the GRNN and MLR models. Among all five types of heavy metals, the estimation for cadmium (Cd) is the best when using the SMO-SVM model, and its R2 value reaches 0.8628; (4) using the optimal model to invert the Cd content in wheat that are planted on mine reclamation soil, the R2 and RMSE between the measured and the estimated values are 0.6683 and 0.0489, respectively. This result suggests that the method using the SMO-SVM model to estimate the contents of heavy metals in wheat samples is feasible. PMID:27367708

Spectral Estimation Model Construction of Heavy Metals in Mining Reclamation Areas.

PubMed

Dong, Jihong; Dai, Wenting; Xu, Jiren; Li, Songnian

2016-06-28

The study reported here examined, as the research subject, surface soils in the Liuxin mining area of Xuzhou, and explored the heavy metal content and spectral data by establishing quantitative models with Multivariable Linear Regression (MLR), Generalized Regression Neural Network (GRNN) and Sequential Minimal Optimization for Support Vector Machine (SMO-SVM) methods. The study results are as follows: (1) the estimations of the spectral inversion models established based on MLR, GRNN and SMO-SVM are satisfactory, and the MLR model provides the worst estimation, with R² of more than 0.46. This result suggests that the stress sensitive bands of heavy metal pollution contain enough effective spectral information; (2) the GRNN model can simulate the data from small samples more effectively than the MLR model, and the R² between the contents of the five heavy metals estimated by the GRNN model and the measured values are approximately 0.7; (3) the stability and accuracy of the spectral estimation using the SMO-SVM model are obviously better than that of the GRNN and MLR models. Among all five types of heavy metals, the estimation for cadmium (Cd) is the best when using the SMO-SVM model, and its R² value reaches 0.8628; (4) using the optimal model to invert the Cd content in wheat that are planted on mine reclamation soil, the R² and RMSE between the measured and the estimated values are 0.6683 and 0.0489, respectively. This result suggests that the method using the SMO-SVM model to estimate the contents of heavy metals in wheat samples is feasible.

Feasibility study on the use of soil washing to remediate the As-Hg contamination at an ancient mining and metallurgy area.

PubMed

Sierra, C; Menéndez-Aguado, J M; Afif, E; Carrero, M; Gallego, J R

2011-11-30

Soils in abandoned mining sites generally present high concentrations of trace elements, such as As and Hg. Here we assessed the feasibility of washing procedures to physically separate these toxic elements from soils affected by a considerable amount of mining and metallurgical waste ("La Soterraña", Asturias, NW Spain). After exhaustive soil sampling and subsequent particle-size separation via wet sieving, chemical and mineralogical analysis revealed that the finer fractions held very high concentrations of As (up to 32,500 ppm) and Hg (up to 1600 ppm). These elements were both associated mainly with Fe/Mn oxides and hydroxides. Textural and geochemical data were correlated with the geological substrate by means of a multivariate statistical analysis. In addition, the Hg liberation size (below 200 μm) was determined to be main factor conditioning the selection of suitable soil washing strategies. These studies were finally complemented with a specific-gravity study performed with a C800 Mozley separator together with a grindability test, both novel approaches in soil washing feasibility studies. The results highlighted the difficulties in treating "La Soterraña" soils. These difficulties are attributed to the presence of contaminants embedded in the soil and spoil heap aggregates, caused by the meteorization of gangue and ore minerals. As a result of these two characteristics, high concentrations of the contaminants accumulate in all grain-size fractions. Therefore, the soil washing approach proposed here includes the grinding of particles above 125 μm. Copyright © 2011 Elsevier B.V. All rights reserved.

B-mode sonographic criteria for differential diagnosis of cervicofacial lymphadenopathy in cat-scratch disease and toxoplasmosis.

PubMed

Ridder, Gerd Jürgen; Boedeker, Carsten Christof; Lee, Tao-Kwang Kevin; Sander, Anna

2003-04-01

Our purpose was to evaluate different sonographic parameters of cervicofacial lymphadenopathy caused by cat-scratch disease (CSD) and toxoplasmosis. By use of high-resolution B-mode sonography a total of 552 lymph nodes in the head and neck were detected between January 1997 and December 2001. There were 71 patients (422 lymph nodes) with CSD and 19 patients (130 lymph nodes) with toxoplasmosis. Sonographic variables, including 20 sonomorphologic features along with age and gender, were analyzed with multivariate logistic regression. Heterogenous lymph nodes were more often found in CSD (p =.003), and nonsharp nodal borders showed a significant association with CSD (p =.0005). Multivariate analysis identified sharpness of borders (p =.0001), S/L ratio (p =.0006), and type of lymphadenopathy (acute, abscessed, chronic) (p =.0006) as most significant for differentiating between CSD and toxoplasmosis. These results provide significant and useful criteria for ultrasonographic differentiation between CSD and toxoplasmosis. Copyright 2003 Wiley Periodicals, Inc.

Multivariate Analysis of Electron Detachment Dissociation and Infrared Multiphoton Dissociation Mass Spectra of Heparan Sulfate Tetrasaccharides Differing Only in Hexuronic acid Stereochemistry

NASA Astrophysics Data System (ADS)

Oh, Han Bin; Leach, Franklin E.; Arungundram, Sailaja; Al-Mafraji, Kanar; Venot, Andre; Boons, Geert-Jan; Amster, I. Jonathan

2011-03-01

The structural characterization of glycosaminoglycan (GAG) carbohydrates by mass spectrometry has been a long-standing analytical challenge due to the inherent heterogeneity of these biomolecules, specifically polydispersity, variability in sulfation, and hexuronic acid stereochemistry. Recent advances in tandem mass spectrometry methods employing threshold and electron-based ion activation have resulted in the ability to determine the location of the labile sulfate modification as well as assign the stereochemistry of hexuronic acid residues. To facilitate the analysis of complex electron detachment dissociation (EDD) spectra, principal component analysis (PCA) is employed to differentiate the hexuronic acid stereochemistry of four synthetic GAG epimers whose EDD spectra are nearly identical upon visual inspection. For comparison, PCA is also applied to infrared multiphoton dissociation spectra (IRMPD) of the examined epimers. To assess the applicability of multivariate methods in GAG mixture analysis, PCA is utilized to identify the relative content of two epimers in a binary mixture.

Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR-Cas9 gene perturbation screens.

PubMed

de Groot, Reinoud; Lüthi, Joel; Lindsay, Helen; Holtackers, René; Pelkmans, Lucas

2018-01-23

High-content imaging using automated microscopy and computer vision allows multivariate profiling of single-cell phenotypes. Here, we present methods for the application of the CISPR-Cas9 system in large-scale, image-based, gene perturbation experiments. We show that CRISPR-Cas9-mediated gene perturbation can be achieved in human tissue culture cells in a timeframe that is compatible with image-based phenotyping. We developed a pipeline to construct a large-scale arrayed library of 2,281 sequence-verified CRISPR-Cas9 targeting plasmids and profiled this library for genes affecting cellular morphology and the subcellular localization of components of the nuclear pore complex (NPC). We conceived a machine-learning method that harnesses genetic heterogeneity to score gene perturbations and identify phenotypically perturbed cells for in-depth characterization of gene perturbation effects. This approach enables genome-scale image-based multivariate gene perturbation profiling using CRISPR-Cas9. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.

Linking multimetric and multivariate approaches to assess the ecological condition of streams.

PubMed

Collier, Kevin J

2009-10-01

Few attempts have been made to combine multimetric and multivariate analyses for bioassessment despite recognition that an integrated method could yield powerful tools for bioassessment. An approach is described that integrates eight macroinvertebrate community metrics into a Principal Components Analysis to develop a Multivariate Condition Score (MCS) from a calibration dataset of 511 samples. The MCS is compared to an Index of Biotic Integrity (IBI) derived using the same metrics based on the ratio to the reference site mean. Both approaches were highly correlated although the MCS appeared to offer greater potential for discriminating a wider range of impaired conditions. Both the MCS and IBI displayed low temporal variability within reference sites, and were able to distinguish between reference conditions and low levels of catchment modification and local habitat degradation, although neither discriminated among three levels of low impact. Pseudosamples developed to test the response of the metric aggregation approaches to organic enrichment, urban, mining, pastoral and logging stressor scenarios ranked pressures in the same order, but the MCS provided a lower score for the urban scenario and a higher score for the pastoral scenario. The MCS was calculated for an independent test dataset of urban and reference sites, and yielded similar results to the IBI. Although both methods performed comparably, the MCS approach may have some advantages because it removes the subjectivity of assigning thresholds for scoring biological condition, and it appears to discriminate a wider range of degraded conditions.

Aluminum affects heterogeneous Fe(III) (Hydr)oxide nucleation, growth, and ostwald ripening.

PubMed

Hu, Yandi; Li, Qingyun; Lee, Byeongdu; Jun, Young-Shin

2014-01-01

Heterogeneous coprecipitation of iron and aluminum oxides is an important process for pollutant immobilization and removal in natural and engineered aqueous environments. Here, using a synchrotron-based small-angle X-ray scattering technique, we studied heterogeneous nucleation and growth of Fe(III) (hydr)oxide on quartz under conditions found in acid mine drainage (at pH = 3.7 ± 0.2, [Fe(3+)] = 10(-4) M) with different initial aqueous Al/Fe ratios (0:1, 1:1, and 5:1). Interestingly, although the atomic ratios of Al/Fe in the newly formed Fe(III) (hydr)oxide precipitates were less than 1%, the in situ particle size and volume evolutions of the precipitates on quartz were significantly influenced by aqueous Al/Fe ratios. At the end of the 3 h experiments, with aqueous Al/Fe ratios of 0:1, 1:1, and 5:1, the average radii of gyration of particles on quartz were 5.7 ± 0.3, 4.6 ± 0.1, and 3.7 ± 0.3 nm, respectively, and the ratio of total particle volumes on quartz was 1.7:3.4:1.0. The Fe(III) (hydr)oxide precipitates were poorly crystallized, and were positively charged in all solutions. In the presence of Al(3+), Al(3+) adsorption onto quartz changed the surface charge of quartz from negative to positive, which caused the slower heterogeneous growth of Fe(III) (hydr)oxide on quartz. Furthermore, Al affected the amount of water included in the Fe(III) (hydr)oxides, which can influence their adsorption capacity. This study yielded important information usable for pollutant removal not only in natural environments, but also in engineered water treatment processes.

Empirical Mining of Large Data Sets Already Helps to Solve Practical Ecological Problems; A Panoply of Working Examples (Invited)

NASA Astrophysics Data System (ADS)

Hargrove, W. W.; Hoffman, F. M.; Kumar, J.; Spruce, J.; Norman, S. P.

2013-12-01

Here we present diverse examples where empirical mining and statistical analysis of large data sets have already been shown to be useful for a wide variety of practical decision-making problems within the realm of large-scale ecology. Because a full understanding and appreciation of particular ecological phenomena are possible only after hypothesis-directed research regarding the existence and nature of that process, some ecologists may feel that purely empirical data harvesting may represent a less-than-satisfactory approach. Restricting ourselves exclusively to process-driven approaches, however, may actually slow progress, particularly for more complex or subtle ecological processes. We may not be able to afford the delays caused by such directed approaches. Rather than attempting to formulate and ask every relevant question correctly, empirical methods allow trends, relationships and associations to emerge freely from the data themselves, unencumbered by a priori theories, ideas and prejudices that have been imposed upon them. Although they cannot directly demonstrate causality, empirical methods can be extremely efficient at uncovering strong correlations with intermediate "linking" variables. In practice, these correlative structures and linking variables, once identified, may provide sufficient predictive power to be useful themselves. Such correlation "shadows" of causation can be harnessed by, e.g., Bayesian Belief Nets, which bias ecological management decisions, made with incomplete information, toward favorable outcomes. Empirical data-harvesting also generates a myriad of testable hypotheses regarding processes, some of which may even be correct. Quantitative statistical regionalizations based on quantitative multivariate similarity have lended insights into carbon eddy-flux direction and magnitude, wildfire biophysical conditions, phenological ecoregions useful for vegetation type mapping and monitoring, forest disease risk maps (e.g., sudden oak death), global aquatic ecoregion risk maps for aquatic invasives, and forest vertical structure ecoregions (e.g., using extensive LiDAR data sets). Multivariate Spatio-Temporal Clustering, which quantitatively places alternative future conditions on a common footing with present conditions, allows prediction of present and future shifts in tree species ranges, given alternative climatic change forecasts. ForWarn, a forest disturbance detection and monitoring system mining 12 years of national 8-day MODIS phenology data, has been operating since 2010, producing national maps every 8 days showing many kinds of potential forest disturbances. Forest resource managers can view disturbance maps via a web-based viewer, and alerts are issued when particular forest disturbances are seen. Regression-based decadal trend analysis showing long-term forest thrive and decline areas, and individual-based, brute-force supercomputing to map potential movement corridors and migration routes across landscapes will also be discussed. As significant ecological changes occur with increasing rapidity, such empirical data-mining approaches may be the most efficient means to help land managers find the best, most-actionable policies and decision strategies.

Geodynamic simulation of ore-bearing geological structural units by the example of the Strel'tsovka uranium ore field

NASA Astrophysics Data System (ADS)

Petrov, V. A.; Leksin, A. B.; Pogorelov, V. V.; Rebetsky, Yu. L.; San'kov, V. A.; Ashurkov, S. V.; Rasskazov, I. Yu.

2017-05-01

Information on designing a 3D integrated model of the deflected mode (DM) of rock massif near the Strel'tsovka uranium ore field (SUOF) in the southeastern Transbaikal region is presented in the paper. This information is based on the contemporary stresses estimated by geostructural and tectonophysical techniques and by studying the seismotectonic deformation of the Earth's surface using the data on earthquake source mechanisms and GPS geodesy focused on the recognition of active faults. A combination of the results of geostructural, geophysical, geotectonic, and petrophysical research, as well as original maps of faulting and the arrangement of seismic dislocations and seismotectonic regimes (stress tensors), allowed us to design models of the structure, properties, and rheological links of the medium and to determine the boundary conditions for numerical tectonophysical simulation using the method of terminal elements. The computed 2D and 3D models of the state of the rock massif have been integrated into 3D GIS created on the basis of the ArcGIS 10 platform with an ArcGIS 3D-Analyst module. The simulation results have been corroborated by in situ observations on a regional scale (the Klichka seismodislocation, active from the middle Pliocene to date) and on a local scale (heterogeneously strained rock massif at the Antei uranium deposit). The development of a regional geodynamic model of geological structural units makes it possible to carry out procedures to ensure the safety of mining operations under complex geomechanical conditions that can expose the operating mines and mines under construction, by the Argun Mining and Chemical Production Association (PAO PPGKhO) on a common methodical and geoinformational platform, to the hazards of explosions, as well as to use the simulation results aimed at finding new orebodies to assess the flanks and deep levels of the ore field.

Machine learning approaches to analysing textual injury surveillance data: a systematic review.

PubMed

Vallmuur, Kirsten

2015-06-01

To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Systematic review. The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Resource Service Model in the Industrial IoT System Based on Transparent Computing.

PubMed

Li, Weimin; Wang, Bin; Sheng, Jinfang; Dong, Ke; Li, Zitong; Hu, Yixiang

2018-03-26

The Internet of Things (IoT) has received a lot of attention, especially in industrial scenarios. One of the typical applications is the intelligent mine, which actually constructs the Six-Hedge underground systems with IoT platforms. Based on a case study of the Six Systems in the underground metal mine, this paper summarizes the main challenges of industrial IoT from the aspects of heterogeneity in devices and resources, security, reliability, deployment and maintenance costs. Then, a novel resource service model for the industrial IoT applications based on Transparent Computing (TC) is presented, which supports centralized management of all resources including operating system (OS), programs and data on the server-side for the IoT devices, thus offering an effective, reliable, secure and cross-OS IoT service and reducing the costs of IoT system deployment and maintenance. The model has five layers: sensing layer, aggregation layer, network layer, service and storage layer and interface and management layer. We also present a detailed analysis on the system architecture and key technologies of the model. Finally, the efficiency of the model is shown by an experiment prototype system.

Study on key technologies of optimization of big data for thermal power plant performance

NASA Astrophysics Data System (ADS)

Mao, Mingyang; Xiao, Hong

2018-06-01

Thermal power generation accounts for 70% of China's power generation, the pollutants accounted for 40% of the same kind of emissions, thermal power efficiency optimization needs to monitor and understand the whole process of coal combustion and pollutant migration, power system performance data show explosive growth trend, The purpose is to study the integration of numerical simulation of big data technology, the development of thermal power plant efficiency data optimization platform and nitrogen oxide emission reduction system for the thermal power plant to improve efficiency, energy saving and emission reduction to provide reliable technical support. The method is big data technology represented by "multi-source heterogeneous data integration", "large data distributed storage" and "high-performance real-time and off-line computing", can greatly enhance the energy consumption capacity of thermal power plants and the level of intelligent decision-making, and then use the data mining algorithm to establish the boiler combustion mathematical model, mining power plant boiler efficiency data, combined with numerical simulation technology to find the boiler combustion and pollutant generation rules and combustion parameters of boiler combustion and pollutant generation Influence. The result is to optimize the boiler combustion parameters, which can achieve energy saving.

Analysis of Landmine Fatalities and Injuries in the Kurdistan Region.

PubMed

Heshmati, Almas; Khayyat, Nabaz T

2015-09-01

This study analyzes landmine victim data in the Kurdistan Region during the period 1960 to 2005. A regression analysis is used to identify the determinants and impact of the probability of getting killed by mines and unexploded ordnances. The rates of killed/injured victims are explained using a set of socioeconomic variables. As the data are a repeated cross-section in which the individuals are observed when they are subjected to landmine incidents, and to account for the dynamic aspect of the process and heterogeneity by location as well as to control for unobserved location and time effects, a pseudo panel data are created where districts are observed over the entire time period forming a panel data. The results show that (a) males, children, and the elderly are more susceptible to a higher level of landmine risks; (b) landmine training and awareness programs do not reduce the rate of landmine mortality; and (c) the rate of incidents are declining over time. This result can be used in the planning, monitoring, and resource allocation for mine action, as well as labor market programs and rehabilitation activities. © The Author(s) 2014.

A Resource Service Model in the Industrial IoT System Based on Transparent Computing

PubMed Central

Wang, Bin; Sheng, Jinfang; Dong, Ke; Li, Zitong; Hu, Yixiang

2018-01-01

The Internet of Things (IoT) has received a lot of attention, especially in industrial scenarios. One of the typical applications is the intelligent mine, which actually constructs the Six-Hedge underground systems with IoT platforms. Based on a case study of the Six Systems in the underground metal mine, this paper summarizes the main challenges of industrial IoT from the aspects of heterogeneity in devices and resources, security, reliability, deployment and maintenance costs. Then, a novel resource service model for the industrial IoT applications based on Transparent Computing (TC) is presented, which supports centralized management of all resources including operating system (OS), programs and data on the server-side for the IoT devices, thus offering an effective, reliable, secure and cross-OS IoT service and reducing the costs of IoT system deployment and maintenance. The model has five layers: sensing layer, aggregation layer, network layer, service and storage layer and interface and management layer. We also present a detailed analysis on the system architecture and key technologies of the model. Finally, the efficiency of the model is shown by an experiment prototype system. PMID:29587450

Phylogenetic evidence of noteworthy microflora from the subsurface of the former Homestake gold mine, Lead, South Dakota

PubMed Central

Waddell, Evan J.; Elliott, Terran J.; Sani, Rajesh K.; Vahrenkamp, Jefferey M.; Roggenthen, William M.; Anderson, Cynthia M.; Bang, Sookie S.

2013-01-01

Molecular characterization of subsurface microbial communities in the former Homestake gold mine, South Dakota, was carried out by 16S rDNA sequence analysis using a water sample and a weathered soil–like sample. Geochemical analyses indicated that both samples were high in sulfur, rich in nitrogen and salt, but with significantly different metal concentrations. Microbial diversity comparisons unexpectedly revealed three distinct operational taxonomic units (OTUs) belonging to the archaeal phylum Thaumarchaeota typically identified from marine environments, and one OTU to a potentially novel phylum that falls sister to Thaumarchaeota. To our knowledge this is only the second report of Thaumarchaeota in a terrestrial environment. The majority of the clones from Archaea sequence libraries fell into two closely related OTUs and grouped most closely to an ammonia–oxidizing, carbon–fixing and halophilic thaumarchaeote genus, Nitrosopumilus. The two samples showed neither Euryarchaeota nor Crenarchaeota members that were often identified from other subsurface terrestrial ecosystems. Bacteria OTUs containing the highest percentage of sequences were related to sulfur-oxidizing bacteria of the orders Chromatiales and Thiotrichales. Community members of Bacteria from individual Homestake ecosystems were heterogeneous and distinctive to each community with unique phylotypes identified within each sample. PMID:20662386

Outbreak of fatal childhood lead poisoning related to artisanal gold mining in northwestern Nigeria, 2010.

PubMed

Dooyema, Carrie A; Neri, Antonio; Lo, Yi-Chun; Durant, James; Dargan, Paul I; Swarthout, Todd; Biya, Oladayo; Gidado, Saheed O; Haladu, Suleiman; Sani-Gwarzo, Nasir; Nguku, Patrick M; Akpan, Henry; Idris, Sa'ad; Bashir, Abdullahi M; Brown, Mary Jean

2012-04-01

In May 2010, a team of national and international organizations was assembled to investigate children's deaths due to lead poisoning in villages in northwestern Nigeria. Our goal was to determine the cause of the childhood lead poisoning outbreak, investigate risk factors for child mortality, and identify children < 5 years of age in need of emergency chelation therapy for lead poisoning. We administered a cross-sectional, door-to-door questionnaire in two affected villages, collected blood from children 2-59 months of age, and obtained soil samples from family compounds. Descriptive and bivariate analyses were performed with survey, blood lead, and environmental data. Multivariate logistic regression techniques were used to determine risk factors for childhood mortality. We surveyed 119 family compounds. Of 463 children < 5 years of age, 118 (25%) had died in the previous year. We tested 59% (204/345) of children < 5 years of age, and all were lead poisoned (≥ 10 µg/dL); 97% (198/204) of children had blood lead levels (BLLs) ≥ 45 µg/dL, the threshold for initiating chelation therapy. Gold ore was processed inside two-thirds of the family compounds surveyed. In multivariate modeling, significant risk factors for death in the previous year from suspected lead poisoning included the age of the child, the mother's work at ore-processing activities, community well as primary water source, and the soil lead concentration in the compound. The high levels of environmental contamination, percentage of children < 5 years of age with elevated BLLs (97%, > 45 µg/dL), and incidence of convulsions among children before death (82%) suggest that most of the recent childhood deaths in the two surveyed villages were caused by acute lead poisoning from gold ore-processing activities. Control measures included environmental remediation, chelation therapy, public health education, and control of mining activities.

Outbreak of Fatal Childhood Lead Poisoning Related to Artisanal Gold Mining in Northwestern Nigeria, 2010

PubMed Central

Neri, Antonio; Lo, Yi-Chun; Durant, James; Dargan, Paul I.; Swarthout, Todd; Biya, Oladayo; Gidado, Saheed O.; Haladu, Suleiman; Sani-Gwarzo, Nasir; Nguku, Patrick M.; Akpan, Henry; Idris, Sa’ad; Bashir, Abdullahi M.; Brown, Mary Jean

2011-01-01

Background: In May 2010, a team of national and international organizations was assembled to investigate children’s deaths due to lead poisoning in villages in northwestern Nigeria. Objectives: Our goal was to determine the cause of the childhood lead poisoning outbreak, investigate risk factors for child mortality, and identify children < 5 years of age in need of emergency chelation therapy for lead poisoning. Methods: We administered a cross-sectional, door-to-door questionnaire in two affected villages, collected blood from children 2–59 months of age, and obtained soil samples from family compounds. Descriptive and bivariate analyses were performed with survey, blood lead, and environmental data. Multivariate logistic regression techniques were used to determine risk factors for childhood mortality. Results: We surveyed 119 family compounds. Of 463 children < 5 years of age, 118 (25%) had died in the previous year. We tested 59% (204/345) of children < 5 years of age, and all were lead poisoned (≥ 10 µg/dL); 97% (198/204) of children had blood lead levels (BLLs) ≥ 45 µg/dL, the threshold for initiating chelation therapy. Gold ore was processed inside two-thirds of the family compounds surveyed. In multivariate modeling, significant risk factors for death in the previous year from suspected lead poisoning included the age of the child, the mother’s work at ore-processing activities, community well as primary water source, and the soil lead concentration in the compound. Conclusion: The high levels of environmental contamination, percentage of children < 5 years of age with elevated BLLs (97%, > 45 µg/dL), and incidence of convulsions among children before death (82%) suggest that most of the recent childhood deaths in the two surveyed villages were caused by acute lead poisoning from gold ore–processing activities. Control measures included environmental remediation, chelation therapy, public health education, and control of mining activities. PMID:22186192

TOUGHREACT: a new code of the TOUGH Family for Non-Isothermal multiphase reactive geochemical transport in variably saturated geologic media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Tianfu; Sonnenthal, Eric; Spycher, Nicolas

Coupled modeling of subsurface multiphase fluid and heat flow, solute transport and chemical reactions can be used for the assessment of acid mine drainage remediation, waste disposal sites, hydrothermal convection, contaminant transport, and groundwater quality. We have developed a comprehensive numerical simulator, TOUGHREACT, which considers non-isothermal multi-component chemical transport in both liquid and gas phases. A wide range of subsurface thermo-physical-chemical processes is considered under various thermohydrological and geochemical conditions of pressure, temperature, water saturation, and ionic strength. The code can be applied to one-, two- or three-dimensional porous and fractured media with physical and chemical heterogeneity.

Biosurveillance Using Clinical Diagnoses and Social Media Indicators in Military Populations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corley, Courtney D.; Volkova, Svitlana; Rounds, Jeremiah

U.S. military influenza surveillance uses electronic reporting of clinical diagnoses to monitor health of military personnel and detect naturally occurring and bioterrorism-related epidemics. While accurate, these systems lack in timeliness. More recently, researchers have used novel data sources to detect influenza in real time and capture nontraditional populations. With data-mining techniques, military social media users are identified and influenza-related discourse is integrated along with medical data into a comprehensive disease model. By leveraging heterogeneous data streams and developing dashboard biosurveillance analytics, the researchers hope to increase the speed at which outbreaks are detected and provide accurate disease forecasting among militarymore » personnel.« less

Biophysical, infrastructural and social heterogeneities explain spatial distribution of waterborne gastrointestinal disease burden in Mexico City

NASA Astrophysics Data System (ADS)

Baeza, Andrés; Estrada-Barón, Alejandra; Serrano-Candela, Fidel; Bojórquez, Luis A.; Eakin, Hallie; Escalante, Ana E.

2018-06-01

Due to unplanned growth, large extension and limited resources, most megacities in the developing world are vulnerable to hydrological hazards and infectious diseases caused by waterborne pathogens. Here we aim to elucidate the extent of the relation between the spatial heterogeneity of physical and socio-economic factors associated with hydrological hazards (flooding and scarcity) and the spatial distribution of gastrointestinal disease in Mexico City, a megacity with more than 8 million people. We applied spatial statistics and multivariate regression analyses to high resolution records of gastrointestinal diseases during two time frames (2007–2009 and 2010–2014). Results show a pattern of significant association between water flooding events and disease incidence in the city center (lowlands). We also found that in the periphery (highlands), higher incidence is generally associated with household infrastructure deficiency. Our findings suggest the need for integrated and spatially tailored interventions by public works and public health agencies, aimed to manage socio-hydrological vulnerability in Mexico City.

Impact of distributions on the archetypes and prototypes in heterogeneous nanoparticle ensembles.

PubMed

Fernandez, Michael; Wilson, Hugh F; Barnard, Amanda S

2017-01-05

The magnitude and complexity of the structural and functional data available on nanomaterials requires data analytics, statistical analysis and information technology to drive discovery. We demonstrate that multivariate statistical analysis can recognise the sets of truly significant nanostructures and their most relevant properties in heterogeneous ensembles with different probability distributions. The prototypical and archetypal nanostructures of five virtual ensembles of Si quantum dots (SiQDs) with Boltzmann, frequency, normal, Poisson and random distributions are identified using clustering and archetypal analysis, where we find that their diversity is defined by size and shape, regardless of the type of distribution. At the complex hull of the SiQD ensembles, simple configuration archetypes can efficiently describe a large number of SiQDs, whereas more complex shapes are needed to represent the average ordering of the ensembles. This approach provides a route towards the characterisation of computationally intractable virtual nanomaterial spaces, which can convert big data into smart data, and significantly reduce the workload to simulate experimentally relevant virtual samples.

Multiple-factor classification of a human-modified forest landscape in the Hsuehshan Mountain Range, Taiwan.

PubMed

Berg, Kevan J; Icyeh, Lahuy; Lin, Yih-Ren; Janz, Arnold; Newmaster, Steven G

2016-12-01

Human actions drive landscape heterogeneity, yet most ecosystem classifications omit the role of human influence. This study explores land use history to inform a classification of forestland of the Tayal Mrqwang indigenous people of Taiwan. Our objectives were to determine the extent to which human action drives landscape heterogeneity. We used interviews, field sampling, and multivariate analysis to relate vegetation patterns to environmental gradients and human modification across 76 sites. We identified eleven forest classes. In total, around 70 % of plots were at lower elevations and had a history of shifting cultivation, terrace farming, and settlement that resulted in alder, laurel, oak, pine, and bamboo stands. Higher elevation mixed conifer forests were least disturbed. Arboriculture and selective harvesting were drivers of other conspicuous forest patterns. The findings show that past land uses play a key role in shaping forests, which is important to consider when setting targets to guide forest management.

Determination of dominant sources of nitrate contamination in transboundary (Russian Federation/Ukraine) catchment with heterogeneous land use.

PubMed

Vystavna, Y; Diadin, D; Grynenko, V; Yakovlev, V; Vergeles, Y; Huneau, F; Rossi, P M; Hejzlar, J; Knöller, K

2017-09-18

Nitrate contamination of surface water and shallow groundwater was studied in transboundary (Russia/Ukraine) catchment with heterogeneous land use. Dominant sources of nitrate contamination were determined by applying a dual δ 15 N-NO 3 and δ 18 O-NO 3 isotope approach, multivariate statistics, and land use analysis. Nitrate concentration was highly variable from 0.25 to 22 mg L -1 in surface water and from 0.5 to 100 mg L -1 in groundwater. The applied method indicated that sewage to surface water and sewage and manure to groundwater were dominant sources of nitrate contamination. Nitrate/chloride molar ratio was added to support the dual isotope signature and indicated the contribution of fertilizers to the nitrate content in groundwater. Groundwater temperature was found to be an additional indicator of manure and sewerage leaks in the shallow aquifer which has limited protection and is vulnerable to groundwater pollution.

Prey Distribution, Physical Habitat Features, and Guild Traits Interact to Produce Contrasting Shorebird Assemblages among Foraging Patches

PubMed Central

VanDusen, Beth M.; Fegley, Stephen R.; Peterson, Charles H.

2012-01-01

Worldwide declines in shorebird populations, driven largely by habitat loss and degradation, motivate environmental managers to preserve and restore the critical coastal habitats on which these birds depend. Effective habitat management requires an understanding of the factors that determine habitat use and value to shorebirds, extending from individuals to the entire community. While investigating the factors that influenced shorebird foraging distributions among neighboring intertidal sand flats, we built upon species-level understandings of individual-based, small-scale foraging decisions to develop more comprehensive guild- and community-level insights. We found that densities and community composition of foraging shorebirds varied substantially among elevations within some tidal flats and among five flats despite their proximity (all located within a 400-m stretch of natural, unmodified inlet shoreline). Non-dimensional multivariate analyses revealed that the changing composition of the shorebird community among flats and tidal elevations correlated significantly (ρs = 0.56) with the spatial structure of the benthic invertebrate prey community. Sediment grain-sizes affected shorebird community spatial patterns indirectly by influencing benthic macroinvertebrate community compositions. Furthermore, combining sediment and macroinvertebrate information produced a 27% increase in correlation (ρs = 0.71) with shorebird assemblage patterns over the correlation of the bird community with the macroinvertebrate community alone. Beyond its indirect effects acting through prey distributions, granulometry of the flats influenced shorebird foraging directly by modifying prey availability. Our study highlights the importance of habitat heterogeneity, showing that no single patch type was ideal for the entire shorebird community. Generally, shorebird density and diversity were greatest at lower elevations on flats when they became exposed; these areas are at risk from human intervention by inlet sand mining, construction of groins and jetties that divert sediments from flats, and installation of seawalls on inlet shorelines that induce erosion of flats. PMID:23285153

Human alterations, dynamic equilibrium, and riparian ecosystem responses along selected rivers in Tuscany, Italy (Invited)

NASA Astrophysics Data System (ADS)

Hupp, C. R.; Rinaldi, M.

2010-12-01

Many, if not most, streams have been mildly to severely affected by human disturbance, which complicates efforts to understand riparian ecosystems. Mediterranean regions have a long history of human influences including: dams, stream channelization, mining of sediment, and levee /canal construction. Typically these alterations reduce the ecosystem services that functioning floodplains provide and may negatively impact the natural ecology of floodplains through reductions in suitable habitats, biodiversity, and nutrient cycling. Additionally, human alterations typically shift affected streams away from a state of natural dynamic equilibrium, where net sediment deposition is approximately in balance with net erosion. Lack of equilibrium typically affects the degree to which floodplain ecosystems are connected to streamflow regime. Low connectivity, usually from human- or climate-induced incision, may result in reduced flow on floodplains and lowered water tables. High connectivity may result in severe sediment deposition. Connectivity has a direct impact on vegetation communities. Riparian vegetation distribution patterns and diversity relative to various fluvial geomorphic channel patterns, landforms, and processes are described and interpreted for selected rivers of Tuscany, Central Italy; with emphasis on channel evolution following human impacts. Multivariate analysis reveals distinct quantitative vegetation patterns related to six fluvial geomorphic surfaces. Analysis of vegetation data also shows distinct associations of plants with adjustment processes related to the stage of channel evolution. Plant distribution patterns coincide with disturbance/landform/soil moisture gradients. Species richness increases from channel bed to terrace and on heterogeneous riparian areas, while species richness decreases from moderate to intense incision and from low to intense narrowing. As a feedback mechanism, woody vegetation in particular may facilitate geomorphic recovery of floodplains by affecting sedimentation dynamics. Identification and understanding of critical fluvial parameters related to floodplain connectivity (e.g. stream gradient, grain-size, and hydrography) and spatial and temporal sediment deposition/erosion process trajectories should facilitate management efforts to retain and/or regain important ecosystem services.

Using bioavailable soil fraction to assess the bioconcentration factor of plants in phytoremediation of mine soils

NASA Astrophysics Data System (ADS)

Roca, Núria; Rodríguez-Bocanegra, Javier; Bech, Jaume

2017-04-01

Polluted soils by heavy metals are characterized to present great concentrations of these pollutants. Ure wrote the following in 1996: "For understanding the chemistry of the heavy metals in their interaction with other soil components such as the clay minerals, organic matter and the soil solution, or to assess their mobility and retention as well as their availability to plants, the usual approach is to use selective chemical extraction". However, nowadays to assess the bioconcentration factor of plants in phytoremediation, the pseudototal or total concentration has been used. Strong mineral acids attack part of the silicate soil matrix and as consequence part of the heavy metals obtained are included in the structures of the mineral fraction. A different approach may, therefore, be more productive in the study of phytoremediation and the use of extractants, as EDTA or DTPA, can perhaps best be exploited by considering them in their role of bioconcentration factor. Moreover, EDTA and DTPA, which form strong complexes with many metals, can extract also organically complex metals. Properties of the soils collected in mining areas presented great variability, as they depend on materials where soils were developed, the complex mixture of heterogeneous wastes and the mining age. In the case of Caroline Mine in Hualgayoc (Perú), the mining is relatively modern and the available fraction of heavy metals of mine soils is low. The small available fraction concentration is due partly to both a few developed soil structure and low organic matter content. The only exception was the copper, with ranging from 1.2 to 36.2 % of total soil fraction. All plant species that were investigated in previous studies have a good ability to transport potential hazardous elements from the roots to the shoots and they have the ability to accumulate more than 1000 mg•kg-1 of heavy metals in the shoots. However, the bioconcentration factor was smaller than one for all the studied plants in every polluted site. The small bioconcentration values are due partly to both the large metal burdens of the mine soils and the fact that here the total concentration and not the extractable soil fraction concentration of the elements was used. When available fraction was used, the bioconcentration factor with DTPA was greater than one in all cases. The elevated Pb and Zn bioconcentration factor (>100) could be a good measure of the high capacity of these native plants to accumulate metals. The soils of the ancient Espinosa mine in Catalonia (Spain) presented great available concentrations of Cu, Pb and Zn and represent more than 50% of the total fraction in almost every polluted studied site. Therefore, the use of the bioconcentration factor doesn't show a relevant difference between total or extractable fraction because of the elevated extractable fraction of the total content. Therefore, the bioconcentration factor calculated with extractable fraction could be a good measure of plant capacity to accumulate metals.

Ancient Heavy Metal Contamination in Soils as a Driver of Tolerant Anthyllis vulneraria Rhizobial Communities

PubMed Central

Mohamad, Roba; Maynaud, Geraldine; Le Quéré, Antoine; Vidal, Céline; Klonowska, Agnieszka; Yashiro, Erika; Cleyet-Marel, Jean-Claude

2016-01-01

ABSTRACT Anthyllis vulneraria is a legume associated with nitrogen-fixing rhizobia that together offer an adapted biological material for mine-soil phytostabilization by limiting metal pollution. To find rhizobia associated with Anthyllis at a given site, we evaluated the genetic and phenotypic properties of a collection of 137 rhizobia recovered from soils presenting contrasting metal levels. Zn-Pb mine soils largely contained metal-tolerant rhizobia belonging to Mesorhizobium metallidurans or to another sister metal-tolerant species. All of the metal-tolerant isolates harbored the cadA marker gene (encoding a metal-efflux PIB-type ATPase transporter). In contrast, metal-sensitive strains were taxonomically distinct from metal-tolerant populations and consisted of new Mesorhizobium genospecies. Based on the symbiotic nodA marker, the populations comprise two symbiovar assemblages (potentially related to Anthyllis or Lotus host preferences) according to soil geographic locations but independently of metal content. Multivariate analysis showed that soil Pb and Cd concentrations differentially impacted the rhizobial communities and that a rhizobial community found in one geographically distant site was highly divergent from the others. In conclusion, heavy metal levels in soils drive the taxonomic composition of Anthyllis-associated rhizobial populations according to their metal-tolerance phenotype but not their symbiotic nodA diversity. In addition to heavy metals, local soil physicochemical and topoclimatic conditions also impact the rhizobial beta diversity. Mesorhizobium communities were locally adapted and site specific, and their use is recommended for the success of phytostabilization strategies based on Mesorhizobium-legume vegetation. IMPORTANCE Phytostabilization of toxic mine spoils limits heavy metal dispersion and environmental pollution by establishing a sustainable plant cover. This eco-friendly method is facilitated by the use of selected and adapted cover crop legumes living in symbiosis with rhizobia that can stimulate plant growth naturally through biological nitrogen fixation. We studied microsymbiont partners of a metal-tolerant legume, Anthyllis vulneraria, which is tolerant to very highly metal-polluted soils in mining and nonmining sites. Site-specific rhizobial communities were linked to taxonomic composition and metal tolerance capacity. The rhizobial species Mesorhizobium metallidurans was dominant in all Zn-Pb mines but one. It was not detected in unpolluted sites where other distinct Mesorhizobium species occur. Given the different soil conditions at the respective mining sites, including their heavy-metal contamination, revegetation strategies based on rhizobia adapting to local conditions are more likely to succeed over the long term compared to strategies based on introducing less-well-adapted strains. PMID:27793823

Large herbivores in novel ecosystems - Habitat selection by red deer (Cervus elaphus) in a former brown-coal mining area

PubMed Central

Bøcher, Peder Klith; Root-Bernstein, Meredith; Svenning, Jens-Christian

2017-01-01

After centuries of range contraction, many megafauna species are recolonizing parts of Europe. One example is the red deer (Cervus elaphus), which was able to expand its range and is now found in half the areas it inhabited in the beginning of the 19th century. Herbivores are important ecosystem engineers, influencing e.g. vegetation. Knowledge on their habitat selection and their influence on ecosystems might be crucial for future landscape management, especially for hybrid and novel ecosystems emerging in post-industrial landscapes. In this study, red deer habitat selection was studied in a former brown-coal mining area in Denmark. Here, natural settings were severely changed during the mining activity and its current landscape is in large parts managed by hunters as suitable deer habitat. We assessed red deer habitat preferences through feces presence and camera traps combined with land cover data from vegetation sampling, remote sensing and official geographic data. Red deer occurrence was negatively associated with human disturbance and positively associated with forage availability, tree cover and mean terrain height. Apparently, red deer are capable of recolonizing former industrial landscapes quite well if key conditions such as forage abundance and cover are appropriate. In the absence of carnivores, human disturbance, such as a hunting regime is a main reason why deer avoid certain areas. The resulting spatial heterogeneity red deer showed in their habitat use of the study area might be a tool to preserve mosaic landscapes of forest and open habitats and thus promote biodiversity in abandoned post-industrial landscapes. PMID:28505192

Acidification of Earth: An assessment across mechanisms and scales

USGS Publications Warehouse

Rice, Karen; Herman, Janet S.

2012-01-01

In this review article, anthropogenic activities that cause acidification of Earth’s air, waters, and soils are examined. Although there are many mechanisms of acidification, the focus is on the major ones, including emissions from combustion of fossil fuels and smelting of ores, mining of coal and metal ores, and application of nitrogen fertilizer to soils, by elucidating the underlying biogeochemical reactions as well as assessing the magnitude of the effects. These widespread activities have resulted in (1) increased CO2concentration in the atmosphere that acidifies the oceans; (2) acidic atmospheric deposition that acidifies soils and bodies of freshwater; (3) acid mine drainage that acidifies bodies of freshwater and groundwaters; and (4) nitrification that acidifies soils. Although natural geochemical reactions of mineral weathering and ion exchange work to buffer acidification, the slow reaction rates or the limited abundance of reactant phases are overwhelmed by the onslaught of anthropogenic acid loading. Relatively recent modifications of resource extraction and usage in some regions of the world have begun to ameliorate local acidification, but expanding use of resources in other regions is causing environmental acidification in previously unnoticed places. World maps of coal consumption, Cu mining and smelting, and N fertilizer application are presented to demonstrate the complex spatial heterogeneity of resource consumption as well as the overlap in acidifying potential derived from distinctly different phenomena. Projected population increase by country over the next four decades indicates areas with the highest potential for acidification, so enabling anticipation and planning to offset or mitigate the deleterious environmental effects associated with these global shifts in the consumption of energy, mineral, and food resources.

Large herbivores in novel ecosystems - Habitat selection by red deer (Cervus elaphus) in a former brown-coal mining area.

PubMed

Müller, Anke; Dahm, Maria; Bøcher, Peder Klith; Root-Bernstein, Meredith; Svenning, Jens-Christian

2017-01-01

After centuries of range contraction, many megafauna species are recolonizing parts of Europe. One example is the red deer (Cervus elaphus), which was able to expand its range and is now found in half the areas it inhabited in the beginning of the 19th century. Herbivores are important ecosystem engineers, influencing e.g. vegetation. Knowledge on their habitat selection and their influence on ecosystems might be crucial for future landscape management, especially for hybrid and novel ecosystems emerging in post-industrial landscapes. In this study, red deer habitat selection was studied in a former brown-coal mining area in Denmark. Here, natural settings were severely changed during the mining activity and its current landscape is in large parts managed by hunters as suitable deer habitat. We assessed red deer habitat preferences through feces presence and camera traps combined with land cover data from vegetation sampling, remote sensing and official geographic data. Red deer occurrence was negatively associated with human disturbance and positively associated with forage availability, tree cover and mean terrain height. Apparently, red deer are capable of recolonizing former industrial landscapes quite well if key conditions such as forage abundance and cover are appropriate. In the absence of carnivores, human disturbance, such as a hunting regime is a main reason why deer avoid certain areas. The resulting spatial heterogeneity red deer showed in their habitat use of the study area might be a tool to preserve mosaic landscapes of forest and open habitats and thus promote biodiversity in abandoned post-industrial landscapes.

Seismic imaging of gas hydrate reservoir heterogeneities

NASA Astrophysics Data System (ADS)

Huang, Jun-Wei

Natural gas hydrate, a type of inclusion compound or clathrate, are composed of gas molecules trapped within a cage of water molecules. The presence of gas hydrate has been confirmed by core samples recovered from boreholes. Interests in the distribution of natural gas hydrate stem from its potential as a future energy source, geohazard to drilling activities and their possible impact on climate change. However the current geophysical investigations of gas hydrate reservoirs are still too limited to fully resolve the location and the total amount of gas hydrate due to its complex nature of distribution. The goal of this thesis is twofold, i.e., to model (1) the heterogeneous gas hydrate reservoirs and (2) seismic wave propagation in the presence of heterogeneities in order to address the fundamental questions: where are the location and occurrence of gas hydrate and how much is stored in the sediments. Seismic scattering studies predict that certain heterogeneity scales and velocity contrasts will generate strong scattering and wave mode conversion. Vertical Seismic Profile (VSP) techniques can be used to calibrate seismic characterization of gas hydrate expressions on surface seismograms. To further explore the potential of VSP in detecting the heterogeneities, a wave equation based approach for P- and S-wave separation is developed. Tests on synthetic data as well as applications to field data suggest alternative acquisition geometries for VSP to enable wave mode separation. A new reservoir modeling technique based on random medium theory is developed to construct heterogeneous multi-variable models that mimic heterogeneities of hydrate-bearing sediments at the level of detail provided by borehole logging data. Using this new technique, I modeled the density, and P- and S-wave velocities in combination with a modified Biot-Gassmann theory and provided a first order estimate of the in situ volume of gas hydrate near the Mallik 5L-38 borehole. Our results suggest a range of 528 to 768x10 6 m3/km2 of natural gas trapped within hydrate, nearly an order of magnitude lower than earlier estimates which excluded effects of small-scale heterogeneities. Further, the petrophysical models are combined with a 3-D Finite Difference method to study seismic attenuation. Thus a framework is built to further tune the models of gas hydrate reservoirs with constraints from well logs other disciplinary data.

Intra-tumor Genetic Heterogeneity and Mortality in Head and Neck Cancer: Analysis of Data from The Cancer Genome Atlas

PubMed Central

Mroz, Edmund A.; Tward, Aaron M.; Hammon, Rebecca J.; Ren, Yin; Rocco, James W.

2015-01-01

Background Although the involvement of intra-tumor genetic heterogeneity in tumor progression, treatment resistance, and metastasis is established, genetic heterogeneity is seldom examined in clinical trials or practice. Many studies of heterogeneity have had prespecified markers for tumor subpopulations, limiting their generalizability, or have involved massive efforts such as separate analysis of hundreds of individual cells, limiting their clinical use. We recently developed a general measure of intra-tumor genetic heterogeneity based on whole-exome sequencing (WES) of bulk tumor DNA, called mutant-allele tumor heterogeneity (MATH). Here, we examine data collected as part of a large, multi-institutional study to validate this measure and determine whether intra-tumor heterogeneity is itself related to mortality. Methods and Findings Clinical and WES data were obtained from The Cancer Genome Atlas in October 2013 for 305 patients with head and neck squamous cell carcinoma (HNSCC), from 14 institutions. Initial pathologic diagnoses were between 1992 and 2011 (median, 2008). Median time to death for 131 deceased patients was 14 mo; median follow-up of living patients was 22 mo. Tumor MATH values were calculated from WES results. Despite the multiple head and neck tumor subsites and the variety of treatments, we found in this retrospective analysis a substantial relation of high MATH values to decreased overall survival (Cox proportional hazards analysis: hazard ratio for high/low heterogeneity, 2.2; 95% CI 1.4 to 3.3). This relation of intra-tumor heterogeneity to survival was not due to intra-tumor heterogeneity’s associations with other clinical or molecular characteristics, including age, human papillomavirus status, tumor grade and TP53 mutation, and N classification. MATH improved prognostication over that provided by traditional clinical and molecular characteristics, maintained a significant relation to survival in multivariate analyses, and distinguished outcomes among patients having oral-cavity or laryngeal cancers even when standard disease staging was taken into account. Prospective studies, however, will be required before MATH can be used prognostically in clinical trials or practice. Such studies will need to examine homogeneously treated HNSCC at specific head and neck subsites, and determine the influence of cancer therapy on MATH values. Analysis of MATH and outcome in human-papillomavirus-positive oropharyngeal squamous cell carcinoma is particularly needed. Conclusions To our knowledge this study is the first to combine data from hundreds of patients, treated at multiple institutions, to document a relation between intra-tumor heterogeneity and overall survival in any type of cancer. We suggest applying the simply calculated MATH metric of heterogeneity to prospective studies of HNSCC and other tumor types. PMID:25668320

Submicron-Scale Heterogeneities in Nickel Sorption of Various Cell-Mineral Aggregates Formed by Fe(II)-Oxidizing Bacteria.

PubMed

Schmid, Gregor; Zeitvogel, Fabian; Hao, Likai; Ingino, Pablo; Adaktylou, Irini; Eickhoff, Merle; Obst, Martin

2016-01-05

Fe(II)-oxidizing bacteria form biogenic cell-mineral aggregates (CMAs) composed of microbial cells, extracellular organic compounds, and ferric iron minerals. CMAs are capable of immobilizing large quantities of heavy metals, such as nickel, via sorption processes. CMAs play an important role for the fate of heavy metals in the environment, particularly in systems characterized by elevated concentrations of dissolved metals, such as mine drainage or contaminated sediments. We applied scanning transmission (soft) X-ray microscopy (STXM) spectrotomography for detailed 3D chemical mapping of nickel sorbed to CMAs on the submicron scale. We analyzed different CMAs produced by phototrophic or nitrate-reducing microbial Fe(II) oxidation and, in addition, a twisted stalk structure obtained from an environmental biofilm. Nickel showed a heterogeneous distribution and was found to be preferentially sorbed to biogenically precipitated iron minerals such as Fe(III)-(oxyhydr)oxides and, to a minor extent, associated with organic compounds. Some distinct nickel accumulations were identified on the surfaces of CMAs. Additional information obtained from scatter plots and angular distance maps, showing variations in the nickel-iron and nickel-organic carbon ratios, also revealed a general correlation between nickel and iron. Although a high correlation between nickel and iron was observed in 2D maps, 3D maps revealed this to be partly due to projection artifacts. In summary, by combining different approaches for data analysis, we unambiguously showed the heterogeneous sorption behavior of nickel to CMAs.

e-IQ and IQ knowledge mining for generalized LDA

NASA Astrophysics Data System (ADS)

Jenkins, Jeffrey; van Bergem, Rutger; Sweet, Charles; Vietsch, Eveline; Szu, Harold

2015-05-01

How can the human brain uncover patterns, associations and features in real-time, real-world data? There must be a general strategy used to transform raw signals into useful features, but representing this generalization in the context of our information extraction tool set is lacking. In contrast to Big Data (BD), Large Data Analysis (LDA) has become a reachable multi-disciplinary goal in recent years due in part to high performance computers and algorithm development, as well as the availability of large data sets. However, the experience of Machine Learning (ML) and information communities has not been generalized into an intuitive framework that is useful to researchers across disciplines. The data exploration phase of data mining is a prime example of this unspoken, ad-hoc nature of ML - the Computer Scientist works with a Subject Matter Expert (SME) to understand the data, and then build tools (i.e. classifiers, etc.) which can benefit the SME and the rest of the researchers in that field. We ask, why is there not a tool to represent information in a meaningful way to the researcher asking the question? Meaning is subjective and contextual across disciplines, so to ensure robustness, we draw examples from several disciplines and propose a generalized LDA framework for independent data understanding of heterogeneous sources which contribute to Knowledge Discovery in Databases (KDD). Then, we explore the concept of adaptive Information resolution through a 6W unsupervised learning methodology feedback system. In this paper, we will describe the general process of man-machine interaction in terms of an asymmetric directed graph theory (digging for embedded knowledge), and model the inverse machine-man feedback (digging for tacit knowledge) as an ANN unsupervised learning methodology. Finally, we propose a collective learning framework which utilizes a 6W semantic topology to organize heterogeneous knowledge and diffuse information to entities within a society in a personalized way.

Population genomic analysis of strain variation in Leptospirillum group II bacteria involved in acid mine drainage formation.

PubMed

Simmons, Sheri L; Dibartolo, Genevieve; Denef, Vincent J; Goltsman, Daniela S Aliaga; Thelen, Michael P; Banfield, Jillian F

2008-07-22

Deeply sampled community genomic (metagenomic) datasets enable comprehensive analysis of heterogeneity in natural microbial populations. In this study, we used sequence data obtained from the dominant member of a low-diversity natural chemoautotrophic microbial community to determine how coexisting closely related individuals differ from each other in terms of gene sequence and gene content, and to uncover evidence of evolutionary processes that occur over short timescales. DNA sequence obtained from an acid mine drainage biofilm was reconstructed, taking into account the effects of strain variation, to generate a nearly complete genome tiling path for a Leptospirillum group II species closely related to L. ferriphilum (sampling depth approximately 20x). The population is dominated by one sequence type, yet we detected evidence for relatively abundant variants (>99.5% sequence identity to the dominant type) at multiple loci, and a few rare variants. Blocks of other Leptospirillum group II types ( approximately 94% sequence identity) have recombined into one or more variants. Variant blocks of both types are more numerous near the origin of replication. Heterogeneity in genetic potential within the population arises from localized variation in gene content, typically focused in integrated plasmid/phage-like regions. Some laterally transferred gene blocks encode physiologically important genes, including quorum-sensing genes of the LuxIR system. Overall, results suggest inter- and intrapopulation genetic exchange involving distinct parental genome types and implicate gain and loss of phage and plasmid genes in recent evolution of this Leptospirillum group II population. Population genetic analyses of single nucleotide polymorphisms indicate variation between closely related strains is not maintained by positive selection, suggesting that these regions do not represent adaptive differences between strains. Thus, the most likely explanation for the observed patterns of polymorphism is divergence of ancestral strains due to geographic isolation, followed by mixing and subsequent recombination.

Population Genomic Analysis of Strain Variation in Leptospirillum Group II Bacteria Involved in Acid Mine Drainage Formation

PubMed Central

Denef, Vincent J; Goltsman, Daniela S. Aliaga; Thelen, Michael P; Banfield, Jillian F

2008-01-01

Deeply sampled community genomic (metagenomic) datasets enable comprehensive analysis of heterogeneity in natural microbial populations. In this study, we used sequence data obtained from the dominant member of a low-diversity natural chemoautotrophic microbial community to determine how coexisting closely related individuals differ from each other in terms of gene sequence and gene content, and to uncover evidence of evolutionary processes that occur over short timescales. DNA sequence obtained from an acid mine drainage biofilm was reconstructed, taking into account the effects of strain variation, to generate a nearly complete genome tiling path for a Leptospirillum group II species closely related to L. ferriphilum (sampling depth ∼20×). The population is dominated by one sequence type, yet we detected evidence for relatively abundant variants (>99.5% sequence identity to the dominant type) at multiple loci, and a few rare variants. Blocks of other Leptospirillum group II types (∼94% sequence identity) have recombined into one or more variants. Variant blocks of both types are more numerous near the origin of replication. Heterogeneity in genetic potential within the population arises from localized variation in gene content, typically focused in integrated plasmid/phage-like regions. Some laterally transferred gene blocks encode physiologically important genes, including quorum-sensing genes of the LuxIR system. Overall, results suggest inter- and intrapopulation genetic exchange involving distinct parental genome types and implicate gain and loss of phage and plasmid genes in recent evolution of this Leptospirillum group II population. Population genetic analyses of single nucleotide polymorphisms indicate variation between closely related strains is not maintained by positive selection, suggesting that these regions do not represent adaptive differences between strains. Thus, the most likely explanation for the observed patterns of polymorphism is divergence of ancestral strains due to geographic isolation, followed by mixing and subsequent recombination. PMID:18651792

Anthropogenic sources and environmentally relevant concentrations of heavy metals in surface water of a mining district in Ghana: a multivariate statistical approach.

PubMed

Armah, Frederick A; Obiri, Samuel; Yawson, David O; Onumah, Edward E; Yengoh, Genesis T; Afrifa, Ernest K A; Odoi, Justice O

2010-11-01

The levels of heavy metals in surface water and their potential origin (natural and anthropogenic) were respectively determined and analysed for the Obuasi mining area in Ghana. Using Hawth's tool an extension in ArcGIS 9.2 software, a total of 48 water sample points in Obuasi and its environs were randomly selected for study. The magnitude of As, Cu, Mn, Fe, Pb, Hg, Zn and Cd in surface water from the sampling sites were measured by flame Atomic Absorption Spectrophotometry (AAS). Water quality parameters including conductivity, pH, total dissolved solids and turbidity were also evaluated. Principal component analysis and cluster analysis, coupled with correlation coefficient analysis, were used to identify possible sources of these heavy metals. Pearson correlation coefficients among total metal concentrations and selected water properties showed a number of strong associations. The results indicate that apart from tap water, surface water in Obuasi has elevated heavy metal concentrations, especially Hg, Pb, As, Cu and Cd, which are above the Ghana Environmental Protection Agency (GEPA) and World Health Organisation (WHO) permissible levels; clearly demonstrating anthropogenic impact. The mean heavy metal concentrations in surface water divided by the corresponding background values of surface water in Obuasi decrease in the order of Cd > Cu > As > Pb > Hg > Zn > Mn > Fe. The results also showed that Cu, Mn, Cd and Fe are largely responsible for the variations in the data, explaining 72% of total variance; while Pb, As and Hg explain only 18.7% of total variance. Three main sources of these heavy metals were identified. As originates from nature (oxidation of sulphide minerals particularly arsenopyrite-FeAsS). Pb derives from water carrying drainage from towns and mine machinery maintenance yards. Cd, Zn, Fe and Mn mainly emanate from industry sources. Hg mainly originates from artisanal small-scale mining. It cannot be said that the difference in concentration of heavy metals might be attributed to difference in proximity to mining-related activities because this is inconsistent with the cluster analysis. Based on cluster analysis SN32, SN42 and SN43 all belong to group one and are spatially similar. But the maximum Cu concentration was found in SN32 while the minimum Cu concentration was found in SN42 and SN43.

Testing for qualitative heterogeneity: An application to composite endpoints in survival analysis.

PubMed

Oulhaj, Abderrahim; El Ghouch, Anouar; Holman, Rury R

2017-01-01

Composite endpoints are frequently used in clinical outcome trials to provide more endpoints, thereby increasing statistical power. A key requirement for a composite endpoint to be meaningful is the absence of the so-called qualitative heterogeneity to ensure a valid overall interpretation of any treatment effect identified. Qualitative heterogeneity occurs when individual components of a composite endpoint exhibit differences in the direction of a treatment effect. In this paper, we develop a general statistical method to test for qualitative heterogeneity, that is to test whether a given set of parameters share the same sign. This method is based on the intersection-union principle and, provided that the sample size is large, is valid whatever the model used for parameters estimation. We propose two versions of our testing procedure, one based on a random sampling from a Gaussian distribution and another version based on bootstrapping. Our work covers both the case of completely observed data and the case where some observations are censored which is an important issue in many clinical trials. We evaluated the size and power of our proposed tests by carrying out some extensive Monte Carlo simulations in the case of multivariate time to event data. The simulations were designed under a variety of conditions on dimensionality, censoring rate, sample size and correlation structure. Our testing procedure showed very good performances in terms of statistical power and type I error. The proposed test was applied to a data set from a single-center, randomized, double-blind controlled trial in the area of Alzheimer's disease.

Default mode network in childhood autism: posteromedial cortex heterogeneity and relationship with social deficits.

PubMed

Lynch, Charles J; Uddin, Lucina Q; Supekar, Kaustubh; Khouzam, Amirah; Phillips, Jennifer; Menon, Vinod

2013-08-01

The default mode network (DMN), a brain system anchored in the posteromedial cortex, has been identified as underconnected in adults with autism spectrum disorder (ASD). However, to date there have been no attempts to characterize this network and its involvement in mediating social deficits in children with ASD. Furthermore, the functionally heterogeneous profile of the posteromedial cortex raises questions regarding how altered connectivity manifests in specific functional modules within this brain region in children with ASD. Resting-state functional magnetic resonance imaging and an anatomically informed approach were used to investigate the functional connectivity of the DMN in 20 children with ASD and 19 age-, gender-, and IQ-matched typically developing (TD) children. Multivariate regression analyses were used to test whether altered patterns of connectivity are predictive of social impairment severity. Compared with TD children, children with ASD demonstrated hyperconnectivity of the posterior cingulate and retrosplenial cortices with predominately medial and anterolateral temporal cortex. In contrast, the precuneus in ASD children demonstrated hypoconnectivity with visual cortex, basal ganglia, and locally within the posteromedial cortex. Aberrant posterior cingulate cortex hyperconnectivity was linked with severity of social impairments in ASD, whereas precuneus hypoconnectivity was unrelated to social deficits. Consistent with previous work in healthy adults, a functionally heterogeneous profile of connectivity within the posteromedial cortex in both TD and ASD children was observed. This work links hyperconnectivity of DMN-related circuits to the core social deficits in young children with ASD and highlights fundamental aspects of posteromedial cortex heterogeneity. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Alpine Ecohydrology Across Scales: Propagating Fine-scale Heterogeneity to the Catchment and Beyond

NASA Astrophysics Data System (ADS)

Mastrotheodoros, T.; Pappas, C.; Molnar, P.; Burlando, P.; Hadjidoukas, P.; Fatichi, S.

2017-12-01

In mountainous ecosystems, complex topography and landscape heterogeneity govern ecohydrological states and fluxes. Here, we investigate topographic controls on water, energy and carbon fluxes across different climatic regimes and vegetation types representative of the European Alps. We use an ecohydrological model to perform fine-scale numerical experiments on a synthetic domain that comprises a symmetric mountain with eight catchments draining along the cardinal and intercardinal directions. Distributed meteorological model input variables are generated using observations from Switzerland. The model computes the incoming solar radiation based on the local topography. We implement a multivariate statistical framework to disentangle the impact of landscape heterogeneity (i.e., elevation, aspect, flow contributing area, vegetation type) on the simulated water, carbon, and energy dynamics. This allows us to identify the sensitivities of several ecohydrological variables (including leaf area index, evapotranspiration, snow-cover and net primary productivity) to topographic and meteorological inputs at different spatial and temporal scales. We also use an alpine catchment as a real case study to investigate how the natural variability of soil and land cover affects the idealized relationships that arise from the synthetic domain. In accordance with previous studies, our analysis shows a complex pattern of vegetation response to radiation. We find also different patterns of ecosystem sensitivity to topography-driven heterogeneity depending on the hydrological regime (i.e., wet vs. dry conditions). Our results suggest that topography-driven variability in ecohydrological variables (e.g. transpiration) at the fine spatial scale can exceed 50%, but it is substantially reduced ( 5%) when integrated at the catchment scale.

Understanding the trends in HIV and hepatitis C prevalence amongst injecting drug users in different settings--implications for intervention impact.

PubMed

Vickerman, Peter; Martin, Natasha K; Hickman, Matthew

2012-06-01

A recent systematic review observed that HIV prevalence amongst injectors is negligible (<1%) below a threshold HCV prevalence of 30%, but thereafter increases with HCV prevalence. We explore whether a model can reproduce these trends, what determines different epidemiological profiles and how this affects intervention impact. An HIV/HCV transmission model was developed. Univariate sensitivity analyses determined whether the model projected a HCV prevalence threshold below which HIV is negligible, and how different behavioural and epidemiological factors affect the threshold. Multivariate uncertainty analyses considered whether the model could reproduce the observed breadth of HIV/HCV epidemics, how specific behavioural patterns produce different epidemic profiles, and how this affects an intervention's impact (reduces injecting risk by 30%). The model projected a HCV prevalence threshold, which varied depending on the heterogeneity in risk, mixing, and injecting duration in a setting. Multivariate uncertainty analyses showed the model could produce the same range of observed HIV/HCV epidemics. Variability in injecting transmission risk, degree of heterogeneity and injecting duration mainly determined different epidemic profiles. The intervention resulted in 50%/28% reduction in HIV incidence/prevalence and 37%/10% reduction in HCV incidence/prevalence over five years. For either infection, greater impact occurred in settings with lower prevalence of that infection and higher prevalence of the other infection. There are threshold levels of HCV prevalence below which HIV risk is negligible but these thresholds are likely to vary by setting. A setting's HIV and HCV prevalence may give insights into IDU risk behaviour and intervention impact. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Detecting changes resulting from human pressure in a naturally quick-changing and heterogeneous environment: Spatial and temporal scales of variability in coastal lagoons

NASA Astrophysics Data System (ADS)

Pérez-Ruzafa, A.; Marcos, C.; Pérez-Ruzafa, I. M.; Barcala, E.; Hegazi, M. I.; Quispe, J.

2007-10-01

To detect changes in ecosystems due to human impact, experimental designs must include replicates at the appropriate scale to avoid pseudoreplication. Although coastal lagoons, with their highly variable environmental factors and biological assemblages, are relatively well-studied systems, very little is known about their natural scales of variation. In this study, we investigate the spatio-temporal scales of variability in the Mar Menor coastal lagoon (SE Spain) using structured hierarchical sampling designs, mixed and permutational multi-variate analyses of variance, and ordination multi-variate analyses applied to hydrographical parameters, nutrients, chlorophyll a and ichthyoplankton in the water column, and to macrophyte and fish benthic assemblages. Lagoon processes in the Mar Menor show heterogeneous patterns at different temporal and spatial scales. The water column characteristics (including nutrient concentration) showed small-scale spatio-temporal variability, from 10 0 to 10 1 km and from fortnightly to seasonally. Biological features (chlorophyll a concentration and ichthyoplankton assemblage descriptors) showed monthly changes and spatial patterns at the scale of 10 0 (chlorophyll a) - 10 1 km (ichthyoplankton). Benthic assemblages (macrophytes and fishes) showed significant differences between types of substrates in the same locality and between localities, according to horizontal gradients related with confinement in the lagoon, at the scale of 10 0-10 1 km. The vertical zonation of macrophyte assemblages (at scales of 10 1-10 2 cm) overlaps changes in substrata and horizontal gradients. Seasonal patterns in vegetation biomass were not significant, but the significant interaction between Locality and Season indicated that the seasons of maximum and minimum biomass depend on local environmental conditions. Benthic fish assemblages showed no significant patterns at the monthly scale but did show seasonal patterns.

Why do some studies find that CPR fraction is not a predictor of survival?

PubMed

Wik, Lars; Olsen, Jan-Aage; Persse, David; Sterz, Fritz; Lozano, Michael; Brouwer, Marc A; Westfall, Mark; Souders, Chris M; Travis, David T; Herken, Ulrich R; Lerner, E Brooke

2016-07-01

An 80% chest compression fraction (CCF) during resuscitation is recommended. However, heterogeneous results in CCF studies were found during the 2015 Consensus on Science (CoS), which may be because chest compressions are stopped for a wide variety of reasons including providing lifesaving care, provider distraction, fatigue, confusion, and inability to perform lifesaving skills efficiently. The effect of confounding variables on CCF to predict cardiac arrest survival. A secondary analysis of emergency medical services (EMS) treated out-of-hospital cardiac arrest (OHCA) patients who received manual compressions. CCF (percent of time patients received compressions) was determined from electronic defibrillator files. Two Sample Wilcoxon Rank Sum or regression determined a statistical association between CCF and age, gender, bystander CPR, public location, witnessed arrest, shockable rhythm, resuscitation duration, study site, and number of shocks. Univariate and multivariate logistic regressions were used to determine CCF effect on survival. Of 2132 patients with manual compressions 1997 had complete data. Shockable rhythm (p<0.001), public location (p<0.004), treatment duration (p<0.001), and number of shocks (p<0.001) were associated with lower CCF. Univariate logistic regression found that CCF was inversely associated with survival (OR 0.07; 95% CI 0.01-0.36). Multivariate regression controlling for factors associated with survival and/or CCF found that increasing CCF was associated with survival (OR 6.34; 95% CI 1.02-39.5). CCF cannot be looked at in isolation as a predictor of survival, but in the context of other resuscitation activities. When controlling for the effects of other resuscitation activities, a higher CCF is predictive of survival. This may explain the heterogeneity of findings during the CoS review. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Genetic differentiation in life history traits and thermal stress performance across a heterogeneous dune landscape in Arabidopsis lyrata.

PubMed

Wos, Guillaume; Willi, Yvonne

2018-05-26

Over very short spatial scales, the habitat of a species can differ in multiple abiotic and biotic factors. These factors may impose natural selection on several traits and can cause genetic differentiation within a population. We studied multivariate genetic differentiation in a plant species of a sand dune landscape by linking environmental variation with differences in genotypic trait values and gene expression levels to find traits and candidate genes of microgeographical adaptation. Maternal seed families of Arabidopsis lyrata were collected in Saugatuck Dunes State Park, Michigan, USA, and environmental parameters were recorded at each collection site. Offspring plants were raised in climate chambers and exposed to one of three temperature treatments: regular occurrence of frost, heat, or constant control conditions. Several traits were assessed: plant growth, time to flowering, and frost and heat resistance. The strongest trait-environment association was between a fast switch to sexual reproduction and weaker growth under frost, and growing in the open, away from trees. The second strongest association was between the trait combination of small plant size and early flowering under control conditions combined with large size under frost, and the combination of environmental conditions of growing close to trees, at low vegetation cover, on dune bottoms. Gene expression analysis by RNA-seq revealed candidate genes involved in multivariate trait differentiation. The results support the hypothesis that in natural populations, many environmental factors impose selection, and that they affect multiple traits, with the relative direction of trait change being complex. The results highlight that heterogeneity in the selection environment over small spatial scales is a main driver of the maintenance of adaptive genetic variation within populations.

Predicting the decisions of hospital based child protection teams to report to child protective services, police and community welfare services.

PubMed

Benbenishty, Rami; Jedwab, Merav; Chen, Wendy; Glasser, Saralee; Slutzky, Hanna; Siegal, Gil; Lavi-Sahar, Zohar; Lerner-Geva, Liat

2014-01-01

This study examines judgments made by hospital-based child protection teams (CPTs) when determining if there is reasonable suspicion that a child has been maltreated, and whether to report the case to a community welfare agency, to child protective services (CPS) and/or to the police. A prospective multi-center study of all 968 consecutive cases referred to CPTs during 2010-2011 in six medical centers in Israel. Centers were purposefully selected to represent the heterogeneity of medical centers in Israel in terms of size, geographical location and population characteristics. A structured questionnaire was designed to capture relevant information and judgments on each child referred to the team. Bivariate associations and multivariate multinomial logistic regressions were conducted to predict whether the decisions would be (a) to close the case, (b) to refer the case to community welfare services, or (c) to report it to CPS and/or the police. Bivariate and multivariate analyses identified a large number of case characteristics associated with higher probability of reporting to CPS/police or of referral to community welfare services. Case characteristics associated with the decisions include socio-demographic (e.g., ethnicity and financial status), parental functioning (e.g., mental health), previous contacts with authorities and hospital, current referral characteristics (e.g., parental referral vs. child referral), physical findings, and suspicious behaviors of child and parent. Most of the findings suggest that decisions of CPTs are based on indices that have strong support in the professional literature. Existing heterogeneity between cases, practitioners and medical centers had an impact on the overall predictability of the decision to report. Attending to collaboration between hospitals and community agencies is suggested to support learning and quality improvement. Copyright © 2013 Elsevier Ltd. All rights reserved.

Mapping the expansion of galamsey gold mines in the cocoa growing area of Ghana using optical remote sensing

NASA Astrophysics Data System (ADS)

Snapir, B.; Simms, D. M.; Waine, T. W.

2017-06-01

Artisanal gold mining (galamsey) and cocoa farming are essential sources of income for local populations in Ghana. Unfortunately the former poses serious threats to the environment and human health, and conflicts with cocoa farming and other livelihoods. Timely and spatially referenced information on the extent of galamsey is needed to understand and limit the negative impacts of mining. To address this, we use multi-date UK-DMC2 satellite images to map the extent and expansion of galamsey from 2011 to 2015. We map the total area of galamsey in 2013 over the cocoa growing area, using k-means clustering on a cloud-free 2013 image with strong spectral contrast between galamsey and the surrounding vegetation. We also process a pair of hazy images from 2011 and 2015 with Multivariate Alteration Detection to map the 2011-2015 galamsey expansion in a subset, labelled the change area. We use a set of visually interpreted random sample points to compute bias-corrected area estimates. We also delineate an indicative impact zone of pollution proportional to the density of galamsey, assuming a maximum radius of 10 km. In the cocoa growing area of Ghana, the estimated total area of galamsey in 2013 is 27,839 ha with an impact zone of 551,496 ha. In the change area, galamsey has more than tripled between 2011 and 2015, resulting in 603 ha of direct encroachment into protected forest reserves. Assuming the same growth rate for the rest of the cocoa growing area, the total area of galamsey in 2015 is estimated at 43,879 ha. Galamsey is developing along most of the river network (Offin, Ankobra, Birim, Anum, Tano), with downstream pollution affecting both land and water.

Understanding Human Motion Skill with Peak Timing Synergy

NASA Astrophysics Data System (ADS)

Ueno, Ken; Furukawa, Koichi

The careful observation of motion phenomena is important in understanding the skillful human motion. However, this is a difficult task due to the complexities in timing when dealing with the skilful control of anatomical structures. To investigate the dexterity of human motion, we decided to concentrate on timing with respect to motion, and we have proposed a method to extract the peak timing synergy from multivariate motion data. The peak timing synergy is defined as a frequent ordered graph with time stamps, which has nodes consisting of turning points in motion waveforms. A proposed algorithm, PRESTO automatically extracts the peak timing synergy. PRESTO comprises the following 3 processes: (1) detecting peak sequences with polygonal approximation; (2) generating peak-event sequences; and (3) finding frequent peak-event sequences using a sequential pattern mining method, generalized sequential patterns (GSP). Here, we measured right arm motion during the task of cello bowing and prepared a data set of the right shoulder and arm motion. We successfully extracted the peak timing synergy on cello bowing data set using the PRESTO algorithm, which consisted of common skills among cellists and personal skill differences. To evaluate the sequential pattern mining algorithm GSP in PRESTO, we compared the peak timing synergy by using GSP algorithm and the one by using filtering by reciprocal voting (FRV) algorithm as a non time-series method. We found that the support is 95 - 100% in GSP, while 83 - 96% in FRV and that the results by GSP are better than the one by FRV in the reproducibility of human motion. Therefore we show that sequential pattern mining approach is more effective to extract the peak timing synergy than non-time series analysis approach.

Using data mining to predict success in a weight loss trial.

PubMed

Batterham, M; Tapsell, L; Charlton, K; O'Shea, J; Thorne, R

2017-08-01

Traditional methods for predicting weight loss success use regression approaches, which make the assumption that the relationships between the independent and dependent (or logit of the dependent) variable are linear. The aim of the present study was to investigate the relationship between common demographic and early weight loss variables to predict weight loss success at 12 months without making this assumption. Data mining methods (decision trees, generalised additive models and multivariate adaptive regression splines), in addition to logistic regression, were employed to predict: (i) weight loss success (defined as ≥5%) at the end of a 12-month dietary intervention using demographic variables [body mass index (BMI), sex and age]; percentage weight loss at 1 month; and (iii) the difference between actual and predicted weight loss using an energy balance model. The methods were compared by assessing model parsimony and the area under the curve (AUC). The decision tree provided the most clinically useful model and had a good accuracy (AUC 0.720 95% confidence interval = 0.600-0.840). Percentage weight loss at 1 month (≥0.75%) was the strongest predictor for successful weight loss. Within those individuals losing ≥0.75%, individuals with a BMI (≥27 kg m -2 ) were more likely to be successful than those with a BMI between 25 and 27 kg m -2 . Data mining methods can provide a more accurate way of assessing relationships when conventional assumptions are not met. In the present study, a decision tree provided the most parsimonious model. Given that early weight loss cannot be predicted before randomisation, incorporating this information into a post randomisation trial design may give better weight loss results. © 2017 The British Dietetic Association Ltd.

Source apportionment by PMF on elemental concentrations obtained by PIXE analysis of PM10 samples collected at the vicinity of lignite power plants and mines in Megalopolis, Greece

NASA Astrophysics Data System (ADS)

Manousakas, M.; Diapouli, E.; Papaefthymiou, H.; Migliori, A.; Karydas, A. G.; Padilla-Alvarez, R.; Bogovac, M.; Kaiser, R. B.; Jaksic, M.; Bogdanovic-Radovic, I.; Eleftheriadis, K.

2015-04-01

Particulate matter (PM) is an important constituent of atmospheric pollution especially in areas under the influence of industrial emissions. Megalopolis is a small city of 10,000 inhabitants located in central Peloponnese in close proximity to three coal opencast mines and two lignite fired power plants. 50 PM10 samples were collected in Megalopolis during the years 2009-11 for elemental and multivariate analysis. For the elemental analysis PIXE was used as one of the most effective techniques in APM analytical characterization. Altogether, the concentrations of 22 elements (Z = 11-33), whereas Black Carbon was also determined for each sample using a reflectometer. Factorization software was used (EPA PMF 3.0) for source apportionment analysis. The analysis revealed that major emission sources were soil dust 33% (7.94 ± 0.27 μg/m3), biomass burning 19% (4.43 ± 0.27 μg/m3), road dust 15% (3.63 ± 0.37 μg/m3), power plant emissions 13% (3.01 ± 0.44 μg/m3), traffic 12% (2.82 ± 0.37 μg/m3), and sea spray 8% (1.99 ± 0.41 μg/m3). Wind trajectories have suggested that metals associated with emission from the power plants came mainly from west and were connected with the locations of the lignite mines located in this area. Soil resuspension, road dust and power plant emissions increased during the warm season of the year, while traffic/secondary, sea spray and biomass burning become dominant during the cold season.

Virtual Observatories, Data Mining, and Astroinformatics

NASA Astrophysics Data System (ADS)

Borne, Kirk

The historical, current, and future trends in knowledge discovery from data in astronomy are presented here. The story begins with a brief history of data gathering and data organization. A description of the development ofnew information science technologies for astronomical discovery is then presented. Among these are e-Science and the virtual observatory, with its data discovery, access, display, and integration protocols; astroinformatics and data mining for exploratory data analysis, information extraction, and knowledge discovery from distributed data collections; new sky surveys' databases, including rich multivariate observational parameter sets for large numbers of objects; and the emerging discipline of data-oriented astronomical research, called astroinformatics. Astroinformatics is described as the fourth paradigm of astronomical research, following the three traditional research methodologies: observation, theory, and computation/modeling. Astroinformatics research areas include machine learning, data mining, visualization, statistics, semantic science, and scientific data management.Each of these areas is now an active research discipline, with significantscience-enabling applications in astronomy. Research challenges and sample research scenarios are presented in these areas, in addition to sample algorithms for data-oriented research. These information science technologies enable scientific knowledge discovery from the increasingly large and complex data collections in astronomy. The education and training of the modern astronomy student must consequently include skill development in these areas, whose practitioners have traditionally been limited to applied mathematicians, computer scientists, and statisticians. Modern astronomical researchers must cross these traditional discipline boundaries, thereby borrowing the best of breed methodologies from multiple disciplines. In the era of large sky surveys and numerous large telescopes, the potential for astronomical discovery is equally large, and so the data-oriented research methods, algorithms, and techniques that are presented here will enable the greatest discovery potential from the ever-growing data and information resources in astronomy.

Recolonization patterns of ants in a rehabilitated lignite mine in central Italy: Potential for the use of Mediterranean ants as indicators of restoration processes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ottonetti, L.; Tucci, L.; Santini, G.

2006-03-15

Ant (Hymenoptera: Formicidae) assemblages were sampled with pitfall traps in three different habitats associated with a rehabilitated mine district and in undisturbed forests in Tuscany, Italy. The four habitats were (1) open fields (3-4 years old); (2) a middle-age mixed plantation (10 years); (3) an old-age mixed plantation (20 years); and (4) an oak woodland (40 years) not directly affected by mining activities. The aim of the study was to analyze ant recolonization patterns in order to provide insights on the use of Mediterranean ant fauna as indicators of restoration processes. Species richness and diversity were not significantly different amongmore » the four habitats. However, multivariate analyses showed that the assemblages in the different habitats were clearly differentiated, with similarity relationships reflecting a successional gradient among rehabilitated sites. The observed patterns of functional group changes along the gradient broadly accord with those of previous studies in other biogeographic regions. These were (1) a decrease of dominant Dolichoderinae and opportunists; (2) an increase in the proportion of cold-climate specialists; and (3) the appearance of the Cryptic species in the oldest plantations, with a maximum of abundance in the woodland. In conclusion, the results of our study supported the use of Mediterranean ants as a suitable tool for biomonitoring of restoration processes, and in particular, the functional group approach proved a valuable framework to better interpret local trends in terms of global ecological patterns. Further research is, however, needed in order to obtain a reliable classification of Mediterranean ant functional groups.« less

tmBioC: improving interoperability of text-mining tools with BioC.

PubMed

Khare, Ritu; Wei, Chih-Hsuan; Mao, Yuqing; Leaman, Robert; Lu, Zhiyong

2014-01-01

The lack of interoperability among biomedical text-mining tools is a major bottleneck in creating more complex applications. Despite the availability of numerous methods and techniques for various text-mining tasks, combining different tools requires substantial efforts and time owing to heterogeneity and variety in data formats. In response, BioC is a recent proposal that offers a minimalistic approach to tool interoperability by stipulating minimal changes to existing tools and applications. BioC is a family of XML formats that define how to present text documents and annotations, and also provides easy-to-use functions to read/write documents in the BioC format. In this study, we introduce our text-mining toolkit, which is designed to perform several challenging and significant tasks in the biomedical domain, and repackage the toolkit into BioC to enhance its interoperability. Our toolkit consists of six state-of-the-art tools for named-entity recognition, normalization and annotation (PubTator) of genes (GenNorm), diseases (DNorm), mutations (tmVar), species (SR4GN) and chemicals (tmChem). Although developed within the same group, each tool is designed to process input articles and output annotations in a different format. We modify these tools and enable them to read/write data in the proposed BioC format. We find that, using the BioC family of formats and functions, only minimal changes were required to build the newer versions of the tools. The resulting BioC wrapped toolkit, which we have named tmBioC, consists of our tools in BioC, an annotated full-text corpus in BioC, and a format detection and conversion tool. Furthermore, through participation in the 2013 BioCreative IV Interoperability Track, we empirically demonstrate that the tools in tmBioC can be more efficiently integrated with each other as well as with external tools: Our experimental results show that using BioC reduces >60% in lines of code for text-mining tool integration. The tmBioC toolkit is publicly available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/. Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

A hybrid computational approach for efficient Alzheimer's disease classification based on heterogeneous data.

PubMed

Ding, Xuemei; Bucholc, Magda; Wang, Haiying; Glass, David H; Wang, Hui; Clarke, Dave H; Bjourson, Anthony John; Dowey, Le Roy C; O'Kane, Maurice; Prasad, Girijesh; Maguire, Liam; Wong-Lin, KongFatt

2018-06-27

There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer's disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.

Application of spatial time domain reflectometry measurements in heterogeneous, rocky substrates

NASA Astrophysics Data System (ADS)

Gonzales, C.; Scheuermann, A.; Arnold, S.; Baumgartl, T.

2016-10-01

Measurement of soil moisture across depths using sensors is currently limited to point measurements or remote sensing technologies. Point measurements have limitations on spatial resolution, while the latter, although covering large areas may not represent real-time hydrologic processes, especially near the surface. The objective of the study was to determine the efficacy of elongated soil moisture probes—spatial time domain reflectometry (STDR)—and to describe transient soil moisture dynamics of unconsolidated mine waste rock materials. The probes were calibrated under controlled conditions in the glasshouse. Transient soil moisture content was measured using the gravimetric method and STDR. Volumetric soil moisture content derived from weighing was compared with values generated from a numerical model simulating the drying process. A calibration function was generated and applied to STDR field data sets. The use of elongated probes effectively assists in the real-time determination of the spatial distribution of soil moisture. It also allows hydrologic processes to be uncovered in the unsaturated zone, especially for water balance calculations that are commonly based on point measurements. The elongated soil moisture probes can potentially describe transient substrate processes and delineate heterogeneity in terms of the pore size distribution in a seasonally wet but otherwise arid environment.

Multivariate functions for predicting the sorption of 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-tricyclohexane (RDX) among taxonomically distinct soils.

PubMed

Katseanes, Chelsea K; Chappell, Mark A; Hopkins, Bryan G; Durham, Brian D; Price, Cynthia L; Porter, Beth E; Miller, Lesley F

2016-11-01

After nearly a century of use in numerous munition platforms, TNT and RDX contamination has turned up largely in the environment due to ammunition manufacturing or as part of releases from low-order detonations during training activities. Although the basic knowledge governing the environmental fate of TNT and RDX are known, accurate predictions of TNT and RDX persistence in soil remain elusive, particularly given the universal heterogeneity of pedomorphic soil types. In this work, we proposed a new solution for modeling the sorption and persistence of these munition constituents as multivariate mathematical functions correlating soil attribute data over a variety of taxonomically distinct soil types to contaminant behavior, instead of a single constant or parameter of a specific absolute value. To test this idea, we conducted experiments measuring the sorption of TNT and RDX on taxonomically different soil types that were extensively physical and chemically characterized. Statistical decomposition of the log-transformed, and auto-scaled soil characterization data using the dimension-reduction technique PCA (principal component analysis) revealed a strong latent structure based in the multiple pairwise correlations among the soil properties. TNT and RDX sorption partitioning coefficients (KD-TNT and KD-RDX) were regressed against this latent structure using partial least squares regression (PLSR), generating a 3-factor, multivariate linear functions. Here, PLSR models predicted KD-TNT and KD-RDX values based on attributes contributing to endogenous alkaline/calcareous and soil fertility criteria, respectively, exhibited among the different soil types: We hypothesized that the latent structure arising from the strong covariance of full multivariate geochemical matrix describing taxonomically distinguished soil types may provide the means for potentially predicting complex phenomena in soils. The development of predictive multivariate models tuned to a local soil's taxonomic designation would have direct benefit to military range managers seeking to anticipate the environmental risks of training activities on impact sites. Published by Elsevier Ltd.

From proteomics to systems biology: MAPA, MASS WESTERN, PROMEX, and COVAIN as a user-oriented platform.

PubMed

Weckwerth, Wolfram; Wienkoop, Stefanie; Hoehenwarter, Wolfgang; Egelhofer, Volker; Sun, Xiaoliang

2014-01-01

Genome sequencing and systems biology are revolutionizing life sciences. Proteomics emerged as a fundamental technique of this novel research area as it is the basis for gene function analysis and modeling of dynamic protein networks. Here a complete proteomics platform suited for functional genomics and systems biology is presented. The strategy includes MAPA (mass accuracy precursor alignment; http://www.univie.ac.at/mosys/software.html ) as a rapid exploratory analysis step; MASS WESTERN for targeted proteomics; COVAIN ( http://www.univie.ac.at/mosys/software.html ) for multivariate statistical analysis, data integration, and data mining; and PROMEX ( http://www.univie.ac.at/mosys/databases.html ) as a database module for proteogenomics and proteotypic peptides for targeted analysis. Moreover, the presented platform can also be utilized to integrate metabolomics and transcriptomics data for the analysis of metabolite-protein-transcript correlations and time course analysis using COVAIN. Examples for the integration of MAPA and MASS WESTERN data, proteogenomic and metabolic modeling approaches for functional genomics, phosphoproteomics by integration of MOAC (metal-oxide affinity chromatography) with MAPA, and the integration of metabolomics, transcriptomics, proteomics, and physiological data using this platform are presented. All software and step-by-step tutorials for data processing and data mining can be downloaded from http://www.univie.ac.at/mosys/software.html.

Spontaneous revegetation vs. forestry reclamation in post-mining sand pits.

PubMed

Šebelíková, Lenka; Řehounková, Klára; Prach, Karel

2016-07-01

Vegetation development of sites restored by two different methods, spontaneous revegetation and forestry reclamation, was compared in four sand pit mining complexes located in the southern part of the Czech Republic, central Europe. The space-for-time substitution method was applied to collect vegetation records in 13 differently aged and sufficiently large sites with known history. The restoration method, age (time since site abandonment/reclamation), groundwater table, slope, and aspect in all sampled plots were recorded in addition to the visual estimation of percentage cover of all present vascular plant species. Multivariate methods and GLM were used for the data elaboration. Restoration method was the major factor influencing species pattern. Both spontaneously revegetated and forestry reclaimed sites developed towards forest on a comparable timescale. Although the sites did not significantly differ in species richness (160 species in spontaneously revegetated vs. 111 in forestry reclaimed sites), spontaneously revegetated sites tended to be more diverse with more species of conservation potential (10 Red List species in spontaneous sites vs. 4 Red List species in forestry reclaimed sites). These results support the use of spontaneous revegetation as an effective and low-cost method of sand pit restoration and may contribute to implementation of this method in practice.

Heterogeneous rates for birth defects in Latin America: hints on causality.

PubMed

Lopez-Camelo, J S; Orioli, I M

1996-01-01

The aim of this work was to disclose risk factors associated with birth defects which were heterogeneously distributed in the different geographic regions sampled by the Latin American Collaborative Study of Congenital Malformations (ECLAMC). The material included 2,159,065 hospital births, delivered in the 1967-1989 period in 24 geographic regions of Latin America. Birth defect types with 50 case-control pairs or more were analyzed. A risk factor was defined as that available variable with differential geographic rates, correlated with those of a given birth defect type. Identified factors were tested by case-control multivariate logistic regression to confirm their role in the occurrence of the defect. Altitude and maternal acute illness during first trimester of pregnancy, named influenza, were risk factors for microtia. Prenatal drug exposure, mainly sex hormones, were connected with the occurrence of hypospadias in low frequency areas, while Native ancestry was a "protective" factor in the same regions. Acute (influenza), and chronic (epilepsy and syphilis) maternal illness during first trimester of pregnancy and gravidity higher than four were risk factors for cleft lip. The independence of these variables from maternal age suggested that low maternal socioeconomic level could explain the high birth defect order and, perhaps, syphilis in mothers. Postaxial polydactyly was associated with parental consanguinity, as well as Afro-American ancestry, suggesting genetic heterogeneity.

Multielement geochemistry identifies the spatial pattern of soil and sediment contamination in an urban parkland, Western Australia.

PubMed

Rate, Andrew W

2018-06-15

Urban environments are dynamic and highly heterogeneous, and multiple additions of potential contaminants are likely on timescales which are short relative to natural processes. The likely sources and location of soil or sediment contamination in urban environment should therefore be detectable using multielement geochemical composition combined with rigorously applied multivariate statistical techniques. Soil, wetland sediment, and street dust was sampled along intersecting transects in Robertson Park in metropolitan Perth, Western Australia. Samples were analysed for near-total concentrations of multiple elements (including Cd, Ce, Co, Cr, Cu, Fe, Gd, La, Mn, Nd, Ni, Pb, Y, and Zn), as well as pH, and electrical conductivity. Samples at some locations within Robertson Park had high concentrations of potentially toxic elements (Pb above Health Investigation Limits; As, Ba, Cu, Mn, Ni, Pb, V, and Zn above Ecological Investigation Limits). However, these concentrations carry low risk due to the main land use as recreational open space, the low proportion of samples exceeding guideline values, and a tendency for the highest concentrations to be located within the less accessible wetland basin. The different spatial distributions of different groups of contaminants was consistent with different inputs of contaminants related to changes in land use and technology over the history of the site. Multivariate statistical analyses reinforced the spatial information, with principal component analysis identifying geochemical associations of elements which were also spatially related. A multivariate linear discriminant model was able to discriminate samples into a-priori types, and could predict sample type with 84% accuracy based on multielement composition. The findings suggest substantial advantages of characterising a site using multielement and multivariate analyses, an approach which could benefit investigations of other sites of concern. Copyright © 2018 Elsevier B.V. All rights reserved.

Utility of Intermediate-Delay Washout CT Images for Differentiation of Malignant and Benign Adrenal Lesions: A Multivariate Analysis.

PubMed

Ng, Chaan S; Altinmakas, Emre; Wei, Wei; Ghosh, Payel; Li, Xiao; Grubbs, Elizabeth G; Perrier, Nancy D; Lee, Jeffrey E; Prieto, Victor G; Hobbs, Brian P

2018-06-27

The objective of this study was to identify features that impact the diagnostic performance of intermediate-delay washout CT for distinguishing malignant from benign adrenal lesions. This retrospective study evaluated 127 pathologically proven adrenal lesions (82 malignant, 45 benign) in 126 patients who had undergone portal venous phase and intermediate-delay washout CT (1-3 minutes after portal venous phase) with or without unenhanced images. Unenhanced images were available for 103 lesions. Quantitatively, lesion CT attenuation on unenhanced (UA) and delayed (DL) images, absolute and relative percentage of enhancement washout (APEW and RPEW, respectively), descriptive CT features (lesion size, margin characteristics, heterogeneity or homogeneity, fat, calcification), patient demographics, and medical history were evaluated for association with lesion status using multiple logistic regression with stepwise model selection. Area under the ROC curve (A z ) was calculated from both univariate and multivariate analyses. The predictive diagnostic performance of multivariate evaluations was ascertained through cross-validation. A z for DL, APEW, RPEW, and UA was 0.751, 0.795, 0.829, and 0.839, respectively. Multivariate analyses yielded the following significant CT quantitative features and associated A z when combined: RPEW and DL (A z = 0.861) when unenhanced images were not available and APEW and UA (A z = 0.889) when unenhanced images were available. Patient demographics and presence of a prior malignancy were additional significant factors, increasing A z to 0.903 and 0.927, respectively. The combined predictive classifier, without and with UA available, yielded 85.7% and 87.3% accuracies with cross-validation, respectively. When appropriately combined with other CT features, washout derived from intermediate-delay CT with or without additional clinical data has potential utility in differentiating malignant from benign adrenal lesions.

Investigation of As(V) removal from acid mine drainage by iron (hydr) oxide modified zeolite.

PubMed

Nekhunguni, Pfano Mathews; Tavengwa, Nikita Tawanda; Tutu, Hlanganani

2017-07-15

In this work, the synthesis of iron (hydr) oxide modified zeolite was achieved through precipitation of iron on the zeolite. The structure and surface morphology of iron (hydr) oxide modified zeolite (IHOMZ) was studied by scanning electron microscopy (SEM), coupled with an energy-dispersive X-ray spectroscopy (EDX), and Fourier transform infrared (FT-IR) spectra. The efficiency of IHOMZ was then investigated through batch technique for the extraction of As(V) from mine waste water. The optimum parameters for maximum As(V) adsorption were: an initial As(V) concentration (10 mg L -1 ), adsorbent dosage (3.0 g), contact time (90 min) and temperature (53 °C). The initial pH of the solution had no compelling effect on As(V) adsorption by IHOMZ. However, adsorption capacity was significantly affected by the solution temperature with 53 °C registering the maximum removal efficiency. The thermodynamic parameters: Entropy (ΔS° = 0.00815 kJ (K mol) -1 ), variation of the Gibbs free energy (ΔG°) and enthalpy (ΔH° = 9.392 kJ mol -1 ) of As(V) adsorption onto IHOMZ system signified a non-spontaneous and endothermic process. It was noted that Freundlich isotherm model exhibited a better fit to the equilibrium experimental data, implying that the adsorption process occurred on a heterogeneous surface. The kinetic data from As(V) adsorption experiments was depicted by the pseudo-second-order kinetic model (R 2  > 0.999), suggesting a chemisorption adsorption process. The experimental batch equilibrium results indicated that IHOMZ could be used as an effective sorbent for As(V) ion extraction from acid mine drainage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods.

PubMed

Patel, Tejal A; Puppala, Mamta; Ogunti, Richard O; Ensor, Joe E; He, Tiancheng; Shewale, Jitesh B; Ankerst, Donna P; Kaklamani, Virginia G; Rodriguez, Angel A; Wong, Stephen T C; Chang, Jenny C

2017-01-01

A key challenge to mining electronic health records for mammography research is the preponderance of unstructured narrative text, which strikingly limits usable output. The imaging characteristics of breast cancer subtypes have been described previously, but without standardization of parameters for data mining. The authors searched the enterprise-wide data warehouse at the Houston Methodist Hospital, the Methodist Environment for Translational Enhancement and Outcomes Research (METEOR), for patients with Breast Imaging Reporting and Data System (BI-RADS) category 5 mammogram readings performed between January 2006 and May 2015 and an available pathology report. The authors developed natural language processing (NLP) software algorithms to automatically extract mammographic and pathologic findings from free text mammogram and pathology reports. The correlation between mammographic imaging features and breast cancer subtype was analyzed using one-way analysis of variance and the Fisher exact test. The NLP algorithm was able to obtain key characteristics for 543 patients who met the inclusion criteria. Patients with estrogen receptor-positive tumors were more likely to have spiculated margins (P = .0008), and those with tumors that overexpressed human epidermal growth factor receptor 2 (HER2) were more likely to have heterogeneous and pleomorphic calcifications (P = .0078 and P = .0002, respectively). Mammographic imaging characteristics, obtained from an automated text search and the extraction of mammogram reports using NLP techniques, correlated with pathologic breast cancer subtype. The results of the current study validate previously reported trends assessed by manual data collection. Furthermore, NLP provides an automated means with which to scale up data extraction and analysis for clinical decision support. Cancer 2017;114-121. © 2016 American Cancer Society. © 2016 American Cancer Society.

Changing trends of rainfall and sediment fluxes in the Kinta River catchment, Malaysia

NASA Astrophysics Data System (ADS)

Ismail, W. R.; Hashim, M.

2015-03-01

The Kinta River, draining an area of 2566 km2, originates in the Korbu Mountain in Perak, Malaysia, and flows through heterogeneous, mixed land uses ranging from extensive forests to mining, rubber and oil palm plantations, and urban development. A land use change analysis of the Kinta River catchment was carried out together with assessment of the long-term trend in rainfall and sediment fluxes. The Mann-Kendall test was used to examine and assess the long-term trends in rainfall and its relationship with the sediment discharge trend. The land use analysis shows that forests, water bodies and mining land declined whilst built and agricultural land use increased significantly. This has influenced the sediment flux of the catchment. However, most of the rainfall stations and river gauging stations are experiencing an increasing trends, except at Kinta river at Tg. Rambutan. Sediment flux shows a net erosion for the period from 1961 to 1969. The total annual sediment discharge in the Kinta River catchment was low with an average rate of 1,757 t/km2/year. From 1970 to 1985, the annual sediment yield rose to an average rate of 4062 t/km2/year. Afterwards, from 1986 to 1993, the total annual sediment discharge decreased to an average rate of 1,306 t/km2/year and increased back during the period 1994 to 2000 to 2109 t/km2/year. From 2001 to 2006 the average sediment flux rate declined to 865 t/km2/year. The decline was almost 80% from the 1970s. High sediment flux in the early 1970s is partly associated with reduced tin mining activities in the area. This decreasing trend in sediment delivery leaving the Kinta River catchment is expected to continue dropping in the future.

QTLTableMiner++: semantic mining of QTL tables in scientific articles.

PubMed

Singh, Gurnoor; Kuzniar, Arnold; van Mulligen, Erik M; Gavai, Anand; Bachem, Christian W; Visser, Richard G F; Finkers, Richard

2018-05-25

A quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text rather than from tables. We present QTLTableMiner ++ (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature. QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file. The performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato (Solanum lycopersicum) and in potato (S. tuberosum). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall. QTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats.

Comparative genomic analysis of multiple strains of two unusual plant pathogens: Pseudomonas corrugata and Pseudomonas mediterranea

PubMed Central

Trantas, Emmanouil A.; Licciardello, Grazia; Almeida, Nalvo F.; Witek, Kamil; Strano, Cinzia P.; Duxbury, Zane; Ververidis, Filippos; Goumas, Dimitrios E.; Jones, Jonathan D. G.; Guttman, David S.; Catara, Vittoria; Sarris, Panagiotis F.

2015-01-01

The non-fluorescent pseudomonads, Pseudomonas corrugata (Pcor) and P. mediterranea (Pmed), are closely related species that cause pith necrosis, a disease of tomato that causes severe crop losses. However, they also show strong antagonistic effects against economically important pathogens, demonstrating their potential for utilization as biological control agents. In addition, their metabolic versatility makes them attractive for the production of commercial biomolecules and bioremediation. An extensive comparative genomics study is required to dissect the mechanisms that Pcor and Pmed employ to cause disease, prevent disease caused by other pathogens, and to mine their genomes for genes that encode proteins involved in commercially important chemical pathways. Here, we present the draft genomes of nine Pcor and Pmed strains from different geographical locations. This analysis covered significant genetic heterogeneity and allowed in-depth genomic comparison. All examined strains were able to trigger symptoms in tomato plants but not all induced a hypersensitive-like response in Nicotiana benthamiana. Genome-mining revealed the absence of type III secretion system and known type III effector-encoding genes from all examined Pcor and Pmed strains. The lack of a type III secretion system appears to be unique among the plant pathogenic pseudomonads. Several gene clusters coding for type VI secretion system were detected in all genomes. Genome-mining also revealed the presence of gene clusters for biosynthesis of siderophores, polyketides, non-ribosomal peptides, and hydrogen cyanide. A highly conserved quorum sensing system was detected in all strains, although species specific differences were observed. Our study provides the basis for in-depth investigations regarding the molecular mechanisms underlying virulence strategies in the battle between plants and microbes. PMID:26300874

Reservoir Identification: Parameter Characterization or Feature Classification

NASA Astrophysics Data System (ADS)

Cao, J.

2017-12-01

The ultimate goal of oil and gas exploration is to find the oil or gas reservoirs with industrial mining value. Therefore, the core task of modern oil and gas exploration is to identify oil or gas reservoirs on the seismic profiles. Traditionally, the reservoir is identify by seismic inversion of a series of physical parameters such as porosity, saturation, permeability, formation pressure, and so on. Due to the heterogeneity of the geological medium, the approximation of the inversion model and the incompleteness and noisy of the data, the inversion results are highly uncertain and must be calibrated or corrected with well data. In areas where there are few wells or no well, reservoir identification based on seismic inversion is high-risk. Reservoir identification is essentially a classification issue. In the identification process, the underground rocks are divided into reservoirs with industrial mining value and host rocks with non-industrial mining value. In addition to the traditional physical parameters classification, the classification may be achieved using one or a few comprehensive features. By introducing the concept of seismic-print, we have developed a new reservoir identification method based on seismic-print analysis. Furthermore, we explore the possibility to use deep leaning to discover the seismic-print characteristics of oil and gas reservoirs. Preliminary experiments have shown that the deep learning of seismic data could distinguish gas reservoirs from host rocks. The combination of both seismic-print analysis and seismic deep learning is expected to be a more robust reservoir identification method. The work was supported by NSFC under grant No. 41430323 and No. U1562219, and the National Key Research and Development Program under Grant No. 2016YFC0601

Combined DC resistivity and induced polarization (DC-IP) for mapping the internal composition of a mine waste rock pile in Nova Scotia, Canada

NASA Astrophysics Data System (ADS)

Power, Christopher; Tsourlos, Panagiotis; Ramasamy, Murugan; Nivorlis, Aristeidis; Mkandawire, Martin

2018-03-01

Mine waste rock piles (WRPs) can contain sulfidic minerals whose interaction with oxygen and water can generate acid mine drainage (AMD). Thus, WRPs can be a long-term source of environmental pollution. Since the generation of AMD and its release into the environment is dependent on the net volume and bulk composition of waste rock, effective characterization of WRPs is necessary for successful remedial design and monitoring. In this study, a combined DC resistivity and induced polarization (DC-IP) approach was employed to characterize an AMD-generating WRP in the Sydney Coalfield, Nova Scotia, Canada. Two-dimensional (2D) DC-IP imaging with 6 survey lines was performed to capture the full WRP landform. 2D DC results indicated a highly heterogeneous and moderately conductive waste rock underlain by a resistive bedrock containing numerous fractures. 2D IP (chargeability) results identified several highly-chargeable regions within the waste, with normalized chargeability delineating regions specific to waste mineralogy only. Three-dimensional (3D) DC-IP imaging, using 17 parallel lines on the plateau of the pile, was then used to focus on the composition of the waste rock. The full 3D inverted DC-IP distributions were used to identify coincident and continuous zones (isosurfaces) of low resistivity (<30 Ω-m) and high normalized chargeability (>0.4 mS/m) that were inferred as generated AMD (leachate) and stored AMD (sulfides), respectively. Integrated geological, hydrogeological and geochemical data increased confidence in the geoelectrical interpretations. Knowledge on the location of potentially more reactive waste material is extremely valuable for improved long-term AMD monitoring at the WRP.

Intratumoral heterogeneity: Role of differentiation in a potentially lethal phenotype of testicular cancer

PubMed Central

Bilen, Mehmet Asim; Hess, Kenneth R.; Broaddus, Russell R.; Kopetz, Scott; Wei, Chongjuan; Pagliaro, Lance C.; Karam, Jose A.; Ward, John F.; Wood, Christopher G.; Rao, Priya; Tu, Zachary H.; General, Rosale; Chen, Adrienne H.; Nieto, Yago L.; Yeung, Sai‐ching J.; Lin, Sue‐Hwa; Logothetis, Christopher J.; Pisters, Louis L.

2016-01-01

BACKGROUND Intratumoral heterogeneity presents a major obstacle to the widespread implementation of precision medicine. The authors assessed the origin of intratumoral heterogeneity in nonseminomatous germ cell tumor of the testis (NSGCT) and identified distinct tumor subtypes and a potentially lethal phenotype. METHODS In this retrospective study, all consecutive patients who had been diagnosed with an NSGCT between January 2000 and December 2010 were evaluated. The histologic makeup of primary tumors and the clinical course of disease were determined for each patient. A Fine and Gray proportional hazards regression analysis was used to determine the prognostic risk factors, and the Gray test was used to detect differences in the cumulative incidence of cancer death. In a separate prospective study, next‐generation sequencing was performed on tumor samples from 9 patients to identify any actionable mutations. RESULTS Six hundred fifteen patients were included in this study. Multivariate analysis revealed that the presence of yolk sac tumor in the primary tumor (P = .0003) was associated with an unfavorable prognosis. NSGCT could be divided into 5 subgroups. Patients in the yolk sac‐seminoma subgroup had the poorest clinical outcome (P = .0015). These tumors tended to undergo somatic transformation (P < .0001). Among the 9 NSGCTs that had a yolk sac tumor phenotype, no consistent gene mutation was detected. CONCLUSIONS The current data suggest that intratumoral heterogeneity is caused in part by differentiation of pluripotent progenitor cells. Integrated or multimodal therapy may be effective at addressing intratumoral heterogeneity and treating distinct subtypes as well as a potentially lethal phenotype of NSGCT. Cancer 2016;122:1836–43. © 2016 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. PMID:27018785

Uneven-aged silviculture can enhance within stand heterogeneity and beetle diversity.

PubMed

Joelsson, Klara; Hjältén, Joakim; Work, Timothy

2018-01-01

Uneven-aged silviculture may better maintain species assemblages associated with old-growth forests than clear felling in part due to habitat heterogeneity created by maintaining standing retention strips adjacent to harvest trails. Retention strips and harvest trails created at the time of tree removal will likely have different microclimate and may harbor different assemblages. In some cases, the resultant stand heterogeneity associated with uneven-aged silviculture may be similar to natural small-scale disturbances. For beetles, increased light and temperature as well as potential access to young vegetation and deadwood substrates present in harvset trails may harbor beetle assemblages similar to those found in natural gaps. We sampled saproxylic beetles using flight intercept traps placed in harvest corridors and retention strips in 9 replicated uneven-aged spruce stands in central Sweden. We compared abundance, species richness and composition between harvest corridors and retention strips using generalized linear models, rarefaction, permutational multivariate analysis of variance and indicator species analysis. Canopy openness doubled, mean temperature and variability in daily temperature increased and humidity decreased on harvest trails. Beetle richness and abundance were greater in harvests trails than in retention strips and the beetle species composition differed significantly between habitats. Twenty-five species were associated with harvest trails, including three old-growth specialists such as Agathidium discoideum (Erichson), currently red-listed. We observed only one species, Xylechinus pilosus (Ratzeburg) that strongly favored retention strips. Harvest trails foster both open habitat species and old-growth species while retention strips harbored forest interior specialists. The combination of closed canopy, stratified forest in the retention strips and gap-like conditions on the harvest trails thus increases overall species richness and maintains more diverse assemblages at the stand level than would otherwise be seen in less heterogeneous stand types. This suggests that uneven-aged silviculture may provide added conservation benefits for both open habitat and old-growth specialists than silvicultural approaches that reduce stand heterogeneity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Intratumoral heterogeneity: Role of differentiation in a potentially lethal phenotype of testicular cancer.

PubMed

Tu, Shi-Ming; Bilen, Mehmet Asim; Hess, Kenneth R; Broaddus, Russell R; Kopetz, Scott; Wei, Chongjuan; Pagliaro, Lance C; Karam, Jose A; Ward, John F; Wood, Christopher G; Rao, Priya; Tu, Zachary H; General, Rosale; Chen, Adrienne H; Nieto, Yago L; Yeung, Sai-Ching J; Lin, Sue-Hwa; Logothetis, Christopher J; Pisters, Louis L

2016-06-15

Intratumoral heterogeneity presents a major obstacle to the widespread implementation of precision medicine. The authors assessed the origin of intratumoral heterogeneity in nonseminomatous germ cell tumor of the testis (NSGCT) and identified distinct tumor subtypes and a potentially lethal phenotype. In this retrospective study, all consecutive patients who had been diagnosed with an NSGCT between January 2000 and December 2010 were evaluated. The histologic makeup of primary tumors and the clinical course of disease were determined for each patient. A Fine and Gray proportional hazards regression analysis was used to determine the prognostic risk factors, and the Gray test was used to detect differences in the cumulative incidence of cancer death. In a separate prospective study, next-generation sequencing was performed on tumor samples from 9 patients to identify any actionable mutations. Six hundred fifteen patients were included in this study. Multivariate analysis revealed that the presence of yolk sac tumor in the primary tumor (P = .0003) was associated with an unfavorable prognosis. NSGCT could be divided into 5 subgroups. Patients in the yolk sac-seminoma subgroup had the poorest clinical outcome (P = .0015). These tumors tended to undergo somatic transformation (P < .0001). Among the 9 NSGCTs that had a yolk sac tumor phenotype, no consistent gene mutation was detected. The current data suggest that intratumoral heterogeneity is caused in part by differentiation of pluripotent progenitor cells. Integrated or multimodal therapy may be effective at addressing intratumoral heterogeneity and treating distinct subtypes as well as a potentially lethal phenotype of NSGCT. Cancer 2016;122:1836-43. © 2016 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. © 2016 American Cancer Society.

An Improved Method of Heterogeneity Compensation for the Convolution / Superposition Algorithm

NASA Astrophysics Data System (ADS)

Jacques, Robert; McNutt, Todd

2014-03-01

Purpose: To improve the accuracy of convolution/superposition (C/S) in heterogeneous material by developing a new algorithm: heterogeneity compensated superposition (HCS). Methods: C/S has proven to be a good estimator of the dose deposited in a homogeneous volume. However, near heterogeneities electron disequilibrium occurs, leading to the faster fall-off and re-buildup of dose. We propose to filter the actual patient density in a position and direction sensitive manner, allowing the dose deposited near interfaces to be increased or decreased relative to C/S. We implemented the effective density function as a multivariate first-order recursive filter and incorporated it into GPU-accelerated, multi-energetic C/S implementation. We compared HCS against C/S using the ICCR 2000 Monte-Carlo accuracy benchmark, 23 similar accuracy benchmarks and 5 patient cases. Results: Multi-energetic HCS increased the dosimetric accuracy for the vast majority of voxels; in many cases near Monte-Carlo results were achieved. We defined the per-voxel error, %|mm, as the minimum of the distance to agreement in mm and the dosimetric percentage error relative to the maximum MC dose. HCS improved the average mean error by 0.79 %|mm for the patient volumes; reducing the average mean error from 1.93 %|mm to 1.14 %|mm. Very low densities (i.e. < 0.1 g / cm3) remained problematic, but may be solvable with a better filter function. Conclusions: HCS improved upon C/S's density scaled heterogeneity correction with a position and direction sensitive density filter. This method significantly improved the accuracy of the GPU based algorithm reaching the accuracy levels of Monte Carlo based methods with performance in a few tenths of seconds per beam. Acknowledgement: Funding for this research was provided by the NSF Cooperative Agreement EEC9731748, Elekta / IMPAC Medical Systems, Inc. and the Johns Hopkins University. James Satterthwaite provided the Monte Carlo benchmark simulations.

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma.

PubMed

Hu, Leland S; Ning, Shuluo; Eschbacher, Jennifer M; Baxter, Leslie C; Gaw, Nathan; Ranjbar, Sara; Plasencia, Jonathan; Dueck, Amylou C; Peng, Sen; Smith, Kris A; Nakaji, Peter; Karis, John P; Quarles, C Chad; Wu, Teresa; Loftus, Joseph C; Jenkins, Robert B; Sicotte, Hugues; Kollmeyer, Thomas M; O'Neill, Brian P; Elmquist, William; Hoxworth, Joseph M; Frakes, David; Sarkaria, Jann; Swanson, Kristin R; Tran, Nhan L; Li, Jing; Mitchell, J Ross

2017-01-01

Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology. © The Author(s) 2016. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Seismic Modeling Of Reservoir Heterogeneity Scales: An Application To Gas Hydrate Reservoirs

NASA Astrophysics Data System (ADS)

Huang, J.; Bellefleur, G.; Milkereit, B.

2008-12-01

Natural gas hydrates, a type of inclusion compound or clathrate, are composed of gas molecules trapped within a cage of water molecules. The occurrence of gas hydrates in permafrost regions has been confirmed by core samples recovered from the Mallik gas hydrate research wells located within Mackenzie Delta in Northwest Territories of Canada. Strong vertical variations of compressional and shear sonic velocities and weak surface seismic expressions of gas hydrates indicate that lithological heterogeneities control the distribution of hydrates. Seismic scattering studies predict that typical scales and strong physical contrasts due to gas hydrate concentration will generate strong forward scattering, leaving only weak energy captured by surface receivers. In order to understand the distribution of hydrates and the seismic scattering effects, an algorithm was developed to construct heterogeneous petrophysical reservoir models. The algorithm was based on well logs showing power law features and Gaussian or Non-Gaussian probability density distribution, and was designed to honor the whole statistical features of well logs such as the characteristic scales and the correlation among rock parameters. Multi-dimensional and multi-variable heterogeneous models representing the same statistical properties were constructed and applied to the heterogeneity analysis of gas hydrate reservoirs. The petrophysical models provide the platform to estimate rock physics properties as well as to study the impact of seismic scattering, wave mode conversion, and their integration on wave behavior in heterogeneous reservoirs. Using the Biot-Gassmann theory, the statistical parameters obtained from Mallik 5L-38, and the correlation length estimated from acoustic impedance inversion, gas hydrate volume fraction in Mallik area was estimated to be 1.8%, approximately 2x108 m3 natural gas stored in a hydrate bearing interval within 0.25 km2 lateral extension and between 889 m and 1115 m depth. With parallel 3-D viscoelastic Finite Difference (FD) software, we conducted a 3D numerical experiment of near offset Vertical Seismic Profile. The synthetic results implied that the strong attenuation observed in the field data might be caused by the scattering.

Mapping eQTL Networks with Mixed Graphical Markov Models

PubMed Central

Tur, Inma; Roverato, Alberto; Castelo, Robert

2014-01-01

Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect effects spread throughout genes as a result of genetic, molecular, and environmental perturbations. From a multivariate perspective one would like to adjust for the effect of all of these factors to end up with a network of direct associations connecting the path from genotype to phenotype. In this article we approach this challenge with mixed graphical Markov models, higher-order conditional independences, and q-order correlation graphs. These models show that additive genetic effects propagate through the network as function of gene–gene correlations. Our estimation of the eQTL network underlying a well-studied yeast data set leads to a sparse structure with more direct genetic and regulatory associations that enable a straightforward comparison of the genetic control of gene expression across chromosomes. Interestingly, it also reveals that eQTLs explain most of the expression variability of network hub genes. PMID:25271303

Climate Model Diagnostic Analyzer

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Pan, Lei; Zhai, Chengxing; Tang, Benyang; Kubar, Terry; Zhang, Zia; Wang, Wei

2015-01-01

The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation- and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multivariable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowd-sourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.

Multivariate analysis of correlation between electrophysiological and hemodynamic responses during cognitive processing

PubMed Central

Kujala, Jan; Sudre, Gustavo; Vartiainen, Johanna; Liljeström, Mia; Mitchell, Tom; Salmelin, Riitta

2014-01-01

Animal and human studies have frequently shown that in primary sensory and motor regions the BOLD signal correlates positively with high-frequency and negatively with low-frequency neuronal activity. However, recent evidence suggests that this relationship may also vary across cortical areas. Detailed knowledge of the possible spectral diversity between electrophysiological and hemodynamic responses across the human cortex would be essential for neural-level interpretation of fMRI data and for informative multimodal combination of electromagnetic and hemodynamic imaging data, especially in cognitive tasks. We applied multivariate partial least squares correlation analysis to MEG–fMRI data recorded in a reading paradigm to determine the correlation patterns between the data types, at once, across the cortex. Our results revealed heterogeneous patterns of high-frequency correlation between MEG and fMRI responses, with marked dissociation between lower and higher order cortical regions. The low-frequency range showed substantial variance, with negative and positive correlations manifesting at different frequencies across cortical regions. These findings demonstrate the complexity of the neurophysiological counterparts of hemodynamic fluctuations in cognitive processing. PMID:24518260

[Biases in the study of prognostic factors].

PubMed

Delgado-Rodríguez, M

1999-01-01

The main objective is to detail the main biases in the study of prognostic factors. Confounding bias is illustrated with social class, a prognostic factor still discussed. Within selection bias several cases are commented: response bias, specially frequent when the patients of a clinical trial are used; the shortcomings in the formation of an inception cohort; the fallacy of Neyman (bias due to the duration of disease) when the study begins with a cross-sectional study; the selection bias in the treatment of survivors for the different treatment opportunity of those living longer; the bias due to the inclusion of heterogeneous diagnostic groups; and the selection bias due to differential information losses and the use of statistical multivariate procedures. Within the biases during follow-up, an empiric rule to value the impact of the number of losses is given. In information bias the Will Rogers' phenomenon and the usefulness of clinical databases are discussed. Lastly, a recommendation against the use of cutoff points yielded by bivariate analyses to select the variable to be included in multivariate analysis is given.

Nano-metrology and terrain modelling - convergent practice in surface characterisation

USGS Publications Warehouse

Pike, R.J.

2000-01-01

The quantification of magnetic-tape and disk topography has a macro-scale counterpart in the Earth sciences - terrain modelling, the numerical representation of relief and pattern of the ground surface. The two practices arose independently and continue to function separately. This methodological paper introduces terrain modelling, discusses its similarities to and differences from industrial surface metrology, and raises the possibility of a unified discipline of quantitative surface characterisation. A brief discussion of an Earth-science problem, subdividing a heterogeneous terrain surface from a set of sample measurements, exemplifies a multivariate statistical procedure that may transfer to tribological applications of 3-D metrological height data.

An analysis of the influence of production conditions on the development of the microporous structure of the activated carbon fibres using the LBET method

NASA Astrophysics Data System (ADS)

Kwiatkowski, Mirosław

2017-12-01

The paper presents the results of the research on the application of the new analytical models of multilayer adsorption on heterogeneous surfaces with the unique fast multivariant identification procedure, together called LBET method, as a tool for analysing the microporous structure of the activated carbon fibres obtained from polyacrylonitrile by chemical activation using potassium and sodium hydroxides. The novel LBET method was employed particularly to evaluate the impact of the used activator and the hydroxide to polyacrylonitrile ratio on the obtained microporous structure of the activated carbon fibres.

Arsenic distribution and valence state variation studied by fast hierarchical length-scale morphological, compositional, and speciation imaging at the Nanoscopium, Synchrotron Soleil

NASA Astrophysics Data System (ADS)

Somogyi, Andrea; Medjoubi, Kadda; Sancho-Tomas, Maria; Visscher, P. T.; Baranton, Gil; Philippot, Pascal

2017-09-01

The understanding of real complex geological, environmental and geo-biological processes depends increasingly on in-depth non-invasive study of chemical composition and morphology. In this paper we used scanning hard X-ray nanoprobe techniques in order to study the elemental composition, morphology and As speciation in complex highly heterogeneous geological samples. Multivariate statistical analytical techniques, such as principal component analysis and clustering were used for data interpretation. These measurements revealed the quantitative and valance state inhomogeneity of As and its relation to the total compositional and morphological variation of the sample at sub-μm scales.

Preliminary results on variations of radon concentration associated with rock deformation in a uranium mine

NASA Astrophysics Data System (ADS)

Verdoya, Massimo; Bochiolo, Massimo; Chiozzi, Paolo; Pasquale, Vincenzo; Armadillo, Egidio; Rizzello, Daniele; Chiaberto, Enrico

2013-04-01

Time-series of radon concentration and environmental parameters were recently recorded in a uranium mine gallery, located in the Maritime Alps (NW Italy). The mine was bored in metarhyolites and porphyric schists mainly composed by quartz, feldspar, sericite and fluorite. U-bearing minerals are generally concentrated in veins heterogeneously spaced and made of crystals of metaautunite and metatorbernite. Radon air concentration monitoring was performed with an ionization chamber which was placed at the bottom of the gallery. Hourly mean values of temperature, pressure, and relative humidity were also measured. External data of atmospheric temperature, pressure and rainfall were also available from a meteorological station located nearby, at a similar altitude of the mine. The analysis of the time series recorded showed variation of radon concentration, of large amplitude, exhibiting daily and half-daily periods, which do not seem correlated with meteorological records. Searching for the origin of radon concentration changes and monitoring their amplitude as a function of time can provide important clues on the complex emanation process. During this process, radon reaches the air- and water-filled interstices by recoil and diffusion, where its migration is directed towards lower concentration regions, following the local gradient. The radon emanation from the rock matrix could also be controlled by stress changes acting on the rate of migration of radon into fissures, and fractures. This may yield emanation boosts due to rock extension and the consequent crack broadening, and emanation decrease when joints between cracks close. Thus, besides interaction and mass transfer with the external atmospheric environment, one possible explanation for the periodic changes in radon concentrations in the investigated mine, could be the variation of rock deformation related to lunar-solar tides. The large variation of concentration could be also due to the fact that the mine is located next to the Ligurian Sea coast. When the sea tides change the water level at the shore, this might produce additional pressure which increases the deformations (sea loading). This paper presents the preliminary results of an experiment, which is in progress in the uranium mine. During the experiment, several geophysical parameters are monitored together with radon concentration. After appropriate insulation in order to prevent radon escape through normal atmospheric circulation, the gallery was equipped with three radon detectors, four passive dosimeters, an array of unpolarisable electrodes for measurements of self-potential variations and a microgravimeter for monitoring of the tidal effect. We expect that changes in the mechanical state can be accompanied by changes in the electric potential. Since the latter variation can be related also to changes in the natural magnetic field, measurements with a three components fluxgate magnetometer are also being carried out. The recorded signals will be analysed according to standard procedures, such as spectral analysis and cross-correlation, aimed at discriminating the periodic components and the governing physical processes.

Unsupervised data mining in nanoscale x-ray spectro-microscopic study of NdFeB magnet

DOE Office of Scientific and Technical Information (OSTI.GOV)

Duan, Xiaoyue; Yang, Feifei; Antono, Erin

Novel developments in X-ray based spectro-microscopic characterization techniques have increased the rate of acquisition of spatially resolved spectroscopic data by several orders of magnitude over what was possible a few years ago. This accelerated data acquisition, with high spatial resolution at nanoscale and sensitivity to subtle differences in chemistry and atomic structure, provides a unique opportunity to investigate hierarchically complex and structurally heterogeneous systems found in functional devices and materials systems. However, handling and analyzing the large volume data generated poses significant challenges. Here we apply an unsupervised data-mining algorithm known as DBSCAN to study a rare-earth element based permanentmore » magnet material, Nd 2Fe 14B. We are able to reduce a large spectro-microscopic dataset of over 300,000 spectra to 3, preserving much of the underlying information. Scientists can easily and quickly analyze in detail three characteristic spectra. Our approach can rapidly provide a concise representation of a large and complex dataset to materials scientists and chemists. For instance, it shows that the surface of common Nd 2Fe 14B magnet is chemically and structurally very different from the bulk, suggesting a possible surface alteration effect possibly due to the corrosion, which could affect the material’s overall properties.« less

Novel drug target identification for the treatment of dementia using multi-relational association mining.

PubMed

Nguyen, Thanh-Phuong; Priami, Corrado; Caberlotto, Laura

2015-07-08

Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance. Despite the fact that the number of people with dementia worldwide is steadily increasing and regardless of the advances in the molecular characterization of the disease, current medical treatments for dementia are purely symptomatic and hardly effective. We present a novel multi-relational association mining method that integrates the huge amount of scientific data accumulated in recent years to predict potential novel targets for innovative therapeutic treatment of dementia. Owing to the ability of processing large volumes of heterogeneous data, our method achieves a high performance and predicts numerous drug targets including several serine threonine kinase and a G-protein coupled receptor. The predicted drug targets are mainly functionally related to metabolism, cell surface receptor signaling pathways, immune response, apoptosis, and long-term memory. Among the highly represented kinase family and among the G-protein coupled receptors, DLG4 (PSD-95), and the bradikynin receptor 2 are highlighted also for their proposed role in memory and cognition, as described in previous studies. These novel putative targets hold promises for the development of novel therapeutic approaches for the treatment of dementia.

Novel drug target identification for the treatment of dementia using multi-relational association mining

PubMed Central

Nguyen, Thanh-Phuong; Priami, Corrado; Caberlotto, Laura

2015-01-01

Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance. Despite the fact that the number of people with dementia worldwide is steadily increasing and regardless of the advances in the molecular characterization of the disease, current medical treatments for dementia are purely symptomatic and hardly effective. We present a novel multi-relational association mining method that integrates the huge amount of scientific data accumulated in recent years to predict potential novel targets for innovative therapeutic treatment of dementia. Owing to the ability of processing large volumes of heterogeneous data, our method achieves a high performance and predicts numerous drug targets including several serine threonine kinase and a G-protein coupled receptor. The predicted drug targets are mainly functionally related to metabolism, cell surface receptor signaling pathways, immune response, apoptosis, and long-term memory. Among the highly represented kinase family and among the G-protein coupled receptors, DLG4 (PSD-95), and the bradikynin receptor 2 are highlighted also for their proposed role in memory and cognition, as described in previous studies. These novel putative targets hold promises for the development of novel therapeutic approaches for the treatment of dementia. PMID:26154857

An Analysis Pipeline with Statistical and Visualization-Guided Knowledge Discovery for Michigan-Style Learning Classifier Systems

PubMed Central

Urbanowicz, Ryan J.; Granizo-Mackenzie, Ambrose; Moore, Jason H.

2014-01-01

Michigan-style learning classifier systems (M-LCSs) represent an adaptive and powerful class of evolutionary algorithms which distribute the learned solution over a sizable population of rules. However their application to complex real world data mining problems, such as genetic association studies, has been limited. Traditional knowledge discovery strategies for M-LCS rule populations involve sorting and manual rule inspection. While this approach may be sufficient for simpler problems, the confounding influence of noise and the need to discriminate between predictive and non-predictive attributes calls for additional strategies. Additionally, tests of significance must be adapted to M-LCS analyses in order to make them a viable option within fields that require such analyses to assess confidence. In this work we introduce an M-LCS analysis pipeline that combines uniquely applied visualizations with objective statistical evaluation for the identification of predictive attributes, and reliable rule generalizations in noisy single-step data mining problems. This work considers an alternative paradigm for knowledge discovery in M-LCSs, shifting the focus from individual rules to a global, population-wide perspective. We demonstrate the efficacy of this pipeline applied to the identification of epistasis (i.e., attribute interaction) and heterogeneity in noisy simulated genetic association data. PMID:25431544

Automated image processing and analysis of cartilage MRI: enabling technology for data mining applied to osteoarthritis

PubMed Central

Tameem, Hussain Z.; Sinha, Usha S.

2011-01-01

Osteoarthritis (OA) is a heterogeneous and multi-factorial disease characterized by the progressive loss of articular cartilage. Magnetic Resonance Imaging has been established as an accurate technique to assess cartilage damage through both cartilage morphology (volume and thickness) and cartilage water mobility (Spin-lattice relaxation, T2). The Osteoarthritis Initiative, OAI, is a large scale serial assessment of subjects at different stages of OA including those with pre-clinical symptoms. The electronic availability of the comprehensive data collected as part of the initiative provides an unprecedented opportunity to discover new relationships in complex diseases such as OA. However, imaging data, which provides the most accurate non-invasive assessment of OA, is not directly amenable for data mining. Changes in morphometry and relaxivity with OA disease are both complex and subtle, making manual methods extremely difficult. This chapter focuses on the image analysis techniques to automatically localize the differences in morphometry and relaxivity changes in different population sub-groups (normal and OA subjects segregated by age, gender, and race). The image analysis infrastructure will enable automatic extraction of cartilage features at the voxel level; the ultimate goal is to integrate this infrastructure to discover relationships between the image findings and other clinical features. PMID:21785520

Unsupervised data mining in nanoscale x-ray spectro-microscopic study of NdFeB magnet

DOE PAGES

Duan, Xiaoyue; Yang, Feifei; Antono, Erin; ...

2016-09-29

Novel developments in X-ray based spectro-microscopic characterization techniques have increased the rate of acquisition of spatially resolved spectroscopic data by several orders of magnitude over what was possible a few years ago. This accelerated data acquisition, with high spatial resolution at nanoscale and sensitivity to subtle differences in chemistry and atomic structure, provides a unique opportunity to investigate hierarchically complex and structurally heterogeneous systems found in functional devices and materials systems. However, handling and analyzing the large volume data generated poses significant challenges. Here we apply an unsupervised data-mining algorithm known as DBSCAN to study a rare-earth element based permanentmore » magnet material, Nd 2Fe 14B. We are able to reduce a large spectro-microscopic dataset of over 300,000 spectra to 3, preserving much of the underlying information. Scientists can easily and quickly analyze in detail three characteristic spectra. Our approach can rapidly provide a concise representation of a large and complex dataset to materials scientists and chemists. For instance, it shows that the surface of common Nd 2Fe 14B magnet is chemically and structurally very different from the bulk, suggesting a possible surface alteration effect possibly due to the corrosion, which could affect the material’s overall properties.« less

Automated image processing and analysis of cartilage MRI: enabling technology for data mining applied to osteoarthritis

NASA Astrophysics Data System (ADS)

Tameem, Hussain Z.; Sinha, Usha S.

2007-11-01

Osteoarthritis (OA) is a heterogeneous and multi-factorial disease characterized by the progressive loss of articular cartilage. Magnetic Resonance Imaging has been established as an accurate technique to assess cartilage damage through both cartilage morphology (volume and thickness) and cartilage water mobility (Spin-lattice relaxation, T2). The Osteoarthritis Initiative, OAI, is a large scale serial assessment of subjects at different stages of OA including those with pre-clinical symptoms. The electronic availability of the comprehensive data collected as part of the initiative provides an unprecedented opportunity to discover new relationships in complex diseases such as OA. However, imaging data, which provides the most accurate non-invasive assessment of OA, is not directly amenable for data mining. Changes in morphometry and relaxivity with OA disease are both complex and subtle, making manual methods extremely difficult. This chapter focuses on the image analysis techniques to automatically localize the differences in morphometry and relaxivity changes in different population sub-groups (normal and OA subjects segregated by age, gender, and race). The image analysis infrastructure will enable automatic extraction of cartilage features at the voxel level; the ultimate goal is to integrate this infrastructure to discover relationships between the image findings and other clinical features.

GEOGLE: context mining tool for the correlation between gene expression and the phenotypic distinction.

PubMed

Yu, Yao; Tu, Kang; Zheng, Siyuan; Li, Yun; Ding, Guohui; Ping, Jie; Hao, Pei; Li, Yixue

2009-08-25

In the post-genomic era, the development of high-throughput gene expression detection technology provides huge amounts of experimental data, which challenges the traditional pipelines for data processing and analyzing in scientific researches. In our work, we integrated gene expression information from Gene Expression Omnibus (GEO), biomedical ontology from Medical Subject Headings (MeSH) and signaling pathway knowledge from sigPathway entries to develop a context mining tool for gene expression analysis - GEOGLE. GEOGLE offers a rapid and convenient way for searching relevant experimental datasets, pathways and biological terms according to multiple types of queries: including biomedical vocabularies, GDS IDs, gene IDs, pathway names and signature list. Moreover, GEOGLE summarizes the signature genes from a subset of GDSes and estimates the correlation between gene expression and the phenotypic distinction with an integrated p value. This approach performing global searching of expression data may expand the traditional way of collecting heterogeneous gene expression experiment data. GEOGLE is a novel tool that provides researchers a quantitative way to understand the correlation between gene expression and phenotypic distinction through meta-analysis of gene expression datasets from different experiments, as well as the biological meaning behind. The web site and user guide of GEOGLE are available at: http://omics.biosino.org:14000/kweb/workflow.jsp?id=00020.

[Influencing factors for trauma-induced tibial infection in underground coal mine].

PubMed

Meng, W Z; Guo, Y J; Liu, Z K; Li, Y F; Wang, G Z

2016-07-20

Objective: To investigate the influencing factors for trauma-induced tibial infection in underground coal mine. Methods: A retrospective analysis was performed for the clinical data of 1 090 patients with tibial fracture complicated by bone infection who were injured in underground coal mine and admitted to our hospital from January 1995 to August 2015, including the type of trauma, injured parts, severity, and treatment outcome. The association between risk factors and infection was analyzed. Results: Among the 1 090 patients, 357 had the clinical manifestations of acute and chronic bone infection, 219 had red and swollen legs with heat pain, and 138 experienced skin necrosis, rupture, and discharge of pus. The incidence rates of tibial infection from 1995 to 2001, from 2002 to 2008, and from 2009 to 2015 were 31%, 26.9%, and 20.2%, respectively. The incidence rate of bone infection in the proximal segment of the tibia was significantly higher than that in the middle and distal segments (42.1% vs 18.9%/27.1%, P <0.01) . As for patients with different types of trauma (Gustilo typing) , the patients with type III fracture had a significantly higher incidence rate of bone infection than those with type I/II infection (52.8% vs 21.8%/24.6%, P <0.01) . The incidence rates of bone infection after bone traction, internal fixation with steel plates, fixation with external fixator, and fixation with intramedullary nail were 20.7%, 43.5%, 21.4%, and 26.1%, respectively, suggesting that internal fixation with steel plates had a significantly higher incidence rate of bone infection than other fixation methods ( P <0.01) . The multivariate logistic regression analysis showed that the position of tibial fracture and type of fracture were independent risk factors for bone infection. Conclusion: There is a high incidence rate of trauma-induced tibial infection in workers in underground coal mine. The position of tibial fracture and type of fracture are independent risk factors for bone infection. Vacuum sealing drainage and Ilizarov technique can achieve a satisfactory therapeutic effect.

Twelve years of succession on sandy substrates in a post-mining landscape: a Markov chain analysis.

PubMed

Baasch, Annett; Tischew, Sabine; Bruelheide, Helge

2010-06-01

Knowledge of succession rates and pathways is crucial for devising restoration strategies for highly disturbed ecosystems such as surface-mined land. As these processes have often only been described in qualitative terms, we used Markov models to quantify transitions between successional stages. However, Markov models are often considered not attractive for some reasons, such as model assumptions (e.g., stationarity in space and time, or the high expenditure of time required to estimate successional transitions in the field). Here we present a solution for converting multivariate ecological time series into transition matrices and demonstrate the applicability of this approach for a data set that resulted from monitoring the succession of sandy dry grassland in a post-mining landscape. We analyzed five transition matrices, four one-step matrices referring to specific periods of transition (1995-1998, 1998-2001, 2001-2004, 2004-2007), and one matrix for the whole study period (stationary model, 1995-2007). Finally, the stationary model was enhanced to a partly time-variable model. Applying the stationary and the time-variable models, we started a prediction well outside our calibration period, beginning with 100% bare soil in 1974 as the known start of the succession, and generated the coverage of 12 predefined vegetation types in three-year intervals. Transitions among vegetation types changed significantly in space and over time. While the probability of colonization was almost constant over time, the replacement rate tended to increase, indicating that the speed of succession accelerated with time or fluctuations became stronger. The predictions of both models agreed surprisingly well with the vegetation data observed more than two decades later. This shows that our dry grassland succession in a post-mining landscape can be adequately described by comparably simple types of Markov models, although some model assumptions have not been fulfilled and within-plot transitions have not been observed with point exactness. The major achievement of our proposed way to convert vegetation time series into transition matrices is the estimation of probability of events--a strength not provided by other frequently used statistical methods in vegetation science.

HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework.

PubMed

Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

2017-01-15

Multivariate pattern analysis techniques have been increasingly used over the past decade to derive highly sensitive and specific biomarkers of diseases on an individual basis. The driving assumption behind the vast majority of the existing methodologies is that a single imaging pattern can distinguish between healthy and diseased populations, or between two subgroups of patients (e.g., progressors vs. non-progressors). This assumption effectively ignores the ample evidence for the heterogeneous nature of brain diseases. Neurodegenerative, neuropsychiatric and neurodevelopmental disorders are largely characterized by high clinical heterogeneity, which likely stems in part from underlying neuroanatomical heterogeneity of various pathologies. Detecting and characterizing heterogeneity may deepen our understanding of disease mechanisms and lead to patient-specific treatments. However, few approaches tackle disease subtype discovery in a principled machine learning framework. To address this challenge, we present a novel non-linear learning algorithm for simultaneous binary classification and subtype identification, termed HYDRA (Heterogeneity through Discriminative Analysis). Neuroanatomical subtypes are effectively captured by multiple linear hyperplanes, which form a convex polytope that separates two groups (e.g., healthy controls from pathologic samples); each face of this polytope effectively defines a disease subtype. We validated HYDRA on simulated and clinical data. In the latter case, we applied the proposed method independently to the imaging and genetic datasets of the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) study. The imaging dataset consisted of T1-weighted volumetric magnetic resonance images of 123 AD patients and 177 controls. The genetic dataset consisted of single nucleotide polymorphism information of 103 AD patients and 139 controls. We identified 3 reproducible subtypes of atrophy in AD relative to controls: (1) diffuse and extensive atrophy, (2) precuneus and extensive temporal lobe atrophy, as well some prefrontal atrophy, (3) atrophy pattern very much confined to the hippocampus and the medial temporal lobe. The genetics dataset yielded two subtypes of AD characterized mainly by the presence/absence of the apolipoprotein E (APOE) ε4 genotype, but also involving differential presence of risk alleles of CD2AP, SPON1 and LOC39095 SNPs that were associated with differences in the respective patterns of brain atrophy, especially in the precuneus. The results demonstrate the potential of the proposed approach to map disease heterogeneity in neuroimaging and genetic studies. Copyright © 2016 Elsevier Inc. All rights reserved.

Applying robust design to study the effects of stratigraphic characteristics on brittle failure and bump potential in a coal mine

PubMed Central

Kim, Bo-Hyun; Larson, Mark K.; Lawson, Heather E.

2018-01-01

Bumps and other types of dynamic failure have been a persistent, worldwide problem in the underground coal mining industry, spanning decades. For example, in just five states in the U.S. from 1983 to 2014, there were 388 reportable bumps. Despite significant advances in mine design tools and mining practices, these events continue to occur. Many conditions have been associated with bump potential, such as the presence of stiff units in the local geology. The effect of a stiff sandstone unit on the potential for coal bumps depends on the location of the stiff unit in the stratigraphic column, the relative stiffness and strength of other structural members, and stress concentrations caused by mining. This study describes the results of a robust design to consider the impact of different lithologic risk factors impacting dynamic failure risk. Because the inherent variability of stratigraphic characteristics in sedimentary formations, such as thickness, engineering material properties, and location, is significant and the number of influential parameters in determining a parametric study is large, it is impractical to consider every simulation case by varying each parameter individually. Therefore, to save time and honor the statistical distributions of the parameters, it is necessary to develop a robust design to collect sufficient sample data and develop a statistical analysis method to draw accurate conclusions from the collected data. In this study, orthogonal arrays, which were developed using the robust design, are used to define the combination of the (a) thickness of a stiff sandstone inserted on the top and bottom of a coal seam in a massive shale mine roof and floor, (b) location of the stiff sandstone inserted on the top and bottom of the coal seam, and (c) material properties of the stiff sandstone and contacts as interfaces using the 3-dimensional numerical model, FLAC3D. After completion of the numerical experiments, statistical and multivariate analysis are performed using the calculated results from the orthogonal arrays to analyze the effect of these variables. As a consequence, the impact of each of the parameters on the potential for bumps is quantitatively classified in terms of a normalized intensity of plastic dissipated energy. By multiple regression, the intensity of plastic dissipated energy and migration of the risk from the roof to the floor via the pillars is predicted based on the value of the variables. The results demonstrate and suggest a possible capability to predict the bump potential in a given rock mass adjacent to the underground excavations and pillars. Assessing the risk of bumps is important to preventing fatalities and injuries resulting from bumps. PMID:29416902

From Process Models to Decision Making: The Use of Data Mining Techniques for Developing Effect Decision Support Systems

NASA Astrophysics Data System (ADS)

Conrads, P. A.; Roehl, E. A.

2010-12-01

Natural-resource managers face the difficult problem of controlling the interactions between hydrologic and man-made systems in ways that preserve resources while optimally meeting the needs of disparate stakeholders. Finding success depends on obtaining and employing detailed scientific knowledge about the cause-effect relations that govern the physics of these hydrologic systems. This knowledge is most credible when derived from large field-based datasets that encompass the wide range of variability in the parameters of interest. The means of converting data into knowledge of the hydrologic system often involves developing computer models that predict the consequences of alternative management practices to guide resource managers towards the best path forward. Complex hydrologic systems are typically modeled using computer programs that implement traditional, generalized, physical equations, which are calibrated to match the field data as closely as possible. This type of model commonly is limited in terms of demonstrable predictive accuracy, development time, and cost. The science of data mining presents a powerful complement to physics-based models. Data mining is a relatively new science that assists in converting large databases into knowledge and is uniquely able to leverage the real-time, multivariate data now being collected for hydrologic systems. In side-by-side comparisons with state-of-the-art physics-based hydrologic models, the authors have found data-mining solutions have been substantially more accurate, less time consuming to develop, and embeddable into spreadsheets and sophisticated decision support systems (DSS), making them easy to use by regulators and stakeholders. Three data-mining applications will be presented that demonstrate how data-mining techniques can be applied to existing environmental databases to address regional concerns of long-term consequences. In each case, data were transformed into information, and ultimately, into knowledge. In each case, DSSs were developed that facilitated the use of simulation models and analysis of model output to a broad range of end users with various technical abilities. When compared to other modeling projects of comparable scope and complexity, these DSSs were able to pass through needed technical reviews much more quickly. Unlike programs such as finite-element flow models, DSSs are by design open systems that are easy to use and readily disseminated directly to decision makers. The DSSs provide direct coupling of predictive models with the real-time databases that drive them, graphical user interfaces for point-and-click program control, and streaming displays of numerical and graphical results so that users can monitor the progress of long-term simulations. Customizations for specific problems include numerical optimization loops that invert predictive models; integrations with a three-dimensional finite-element flow model, GIS packages, and a plant ecology model; and color contouring of simulation output data.

CRC-113 gene expression signature for predicting prognosis in patients with colorectal cancer

PubMed Central

Nguyen, Dinh Truong; Kim, Jin-Hwan; Jo, Yong Hwa; Shahid, Muhammad; Akter, Salima; Aryal, Saurav Nath; Yoo, Ji Youn; Ahn, Yong-Joo; Cho, Kyoung Min; Lee, Ju-Seog; Choe, Wonchae; Kang, Insug; Ha, Joohun; Kim, Sung Soo

2015-01-01

Colorectal cancer (CRC) is the third leading cause of global cancer mortality. Recent studies have proposed several gene signatures to predict CRC prognosis, but none of those have proven reliable for predicting prognosis in clinical practice yet due to poor reproducibility and molecular heterogeneity. Here, we have established a prognostic signature of 113 probe sets (CRC-113) that include potential biomarkers and reflect the biological and clinical characteristics. Robustness and accuracy were significantly validated in external data sets from 19 centers in five countries. In multivariate analysis, CRC-113 gene signature showed a stronger prognostic value for survival and disease recurrence in CRC patients than current clinicopathological risk factors and molecular alterations. We also demonstrated that the CRC-113 gene signature reflected both genetic and epigenetic molecular heterogeneity in CRC patients. Furthermore, incorporation of the CRC-113 gene signature into a clinical context and molecular markers further refined the selection of the CRC patients who might benefit from postoperative chemotherapy. Conclusively, CRC-113 gene signature provides new possibilities for improving prognostic models and personalized therapeutic strategies. PMID:26397224