Multiple-site Data Research Articles

Approaches to reducing rotational ambiguity in receptor modeling of ambient particulate matter

Rotational ambiguity is a major problem in the application of factor analysis methods to the mixture resolution problem. However, self-modeling curve resolution of spectrochemical data is often able to achieve good separations given fixed spectral characteristics including true zero absorbance values and typically smaller measurement uncertainties. For air pollution mixture problems such as the identification and quantification of sources of ambient particulate matter (PM), there are problems of the lack of true zero values in either the source profiles or their contributions to the ambient concentrations, variability in the source profiles, and typically much larger measurement uncertainties. Recent work has provided several approaches to reducing rotational ambiguity and providing improved resolution including preprocessing the data to remove the meteorologically induced covariance, applying constraints based on a priori knowledge, and analysis of multiple site data. Examples of these applications to airborne PM have shown improved results.

Solar Irradiance Forecasting Based on Deep Learning Methodologies and Multi-Site Data

The ever-growing interest in and requirement for green energy have led to an increased focus on research related to forecasting solar irradiance recently. This study aims to develop forecast models based on deep learning (DL) methodologies and multiple-site data to predict the daily solar irradiance in two locations of India based on the daily solar radiation data obtained from NASA’s POWER project repository over 36 years (1983–2019). The forecast modeling of solar irradiance data is performed for extracting and learning the symmetry latent in data patterns and relationships by the machine learning models and utilizing it to predict future solar data. The goodness of fit and model performance are compared with rolling window evaluation using mean squared error, root-mean-square error and coefficient of determination (R2) for evaluation. The contributions of this study can be summarized as follows: (i) time series models based on deep learning methodologies were implemented to forecast the daily solar irradiance of two locations in India in consideration of the historical data collected by NASA; (ii) the models were developed on the basis of single-location univariate data as well as multiple-location data; (iii) the accuracy, performance and reliability of the model were investigated on the basis of standard performance evaluation metrics and rolling window evaluation; (iv) the feature importance of the nearby locations with respect to forecasting target location solar irradiance was analyzed and compared based on the solar irradiance data obtained from NASA over 36 years. The results indicate that the bidirectional long short-term memory (LSTM) and attention-based LSTM models can be used for forecasting daily solar irradiance data. According to the findings, the multiple-site data with solar irradiance historical data improve upon the forecast performance of single-location univariate solar data.

Likelihood Ratio Test Method for Multiple Medical Devices Comparison Using Multiple-Site Data with Continuous Outcomes.

With a record number of medical devices approved or cleared, it is important to understand the performance of devices once they are on the market. Using data from multiple medical devices and multiple sites, the problem of interest in this article is to detect if a device is a signal; that is, if a device performs significantly different from other devices of the same class, when the outcome of interest is a continuous variable. We develop a normal likelihood ratio test (LRT) method, henceforth referred to as normal-LRT, by incorporating sample size information into the methodological framework, to detect device signals using multi-site and multi-device data. It is shown via extensive simulation that the proposed method controls the type-I error and false discovery rate (FDR), while having good power and sensitivity. This method is applied to a hypothetical case study, in which 6 medical devices of the same class are compared. The normal-LRT method can be considered as a tool for device signal detection using data from multi-site and multi-device when the outcome of interest is a continuous measurement.

Evaluation of Altered Functional Connections in Male Children With Autism Spectrum Disorders on Multiple-Site Data Optimized With Machine Learning.

No univocal and reliable brain-based biomarkers have been detected to date in Autism Spectrum Disorders (ASD). Neuroimaging studies have consistently revealed alterations in brain structure and function of individuals with ASD; however, it remains difficult to ascertain the extent and localization of affected brain networks. In this context, the application of Machine Learning (ML) classification methods to neuroimaging data has the potential to contribute to a better distinction between subjects with ASD and typical development controls (TD). This study is focused on the analysis of resting-state fMRI data of individuals with ASD and matched TD, available within the ABIDE collection. To reduce the multiple sources of heterogeneity that impact on understanding the neural underpinnings of autistic condition, we selected a subgroup of 190 subjects (102 with ASD and 88 TD) according to the following criteria: male children (age range: 6.5–13 years); rs-fMRI data acquired with open eyes; data from the University sites that provided the largest number of scans (KKI, NYU, UCLA, UM). Connectivity values were evaluated as the linear correlation between pairs of time series of brain areas; then, a Linear kernel Support Vector Machine (L-SVM) classification, with an inter-site cross-validation scheme, was carried out. A permutation test was conducted to identify over-connectivity and under-connectivity alterations in the ASD group. The mean L-SVM classification performance, in terms of the area under the ROC curve (AUC), was 0.75 ± 0.05. The highest performance was obtained using data from KKI, NYU and UCLA sites in training and data from UM as testing set (AUC = 0.83). Specifically, stronger functional connectivity (FC) in ASD with respect to TD involve (p < 0.001) the angular gyrus with the precuneus in the right (R) hemisphere, and the R frontal operculum cortex with the pars opercularis of the left (L) inferior frontal gyrus. Weaker connections in ASD group with respect to TD are the intra-hemispheric R temporal fusiform cortex with the R hippocampus, and the L supramarginal gyrus with L planum polare. The results indicate that both under- and over-FC occurred in a selected cohort of ASD children relative to TD controls, and that these functional alterations are spread in different brain networks.

Identification of biased sectors in emission data using a combination of chemical transport model and receptor model

This study presented a comparison approach with multiple source apportionment methods to identify which sectors of emission data have large biases. The source apportionment methods for the comparison approach included both receptor and chemical transport models, which are widely used to quantify the impacts of emission sources on fine particulate matter of less than 2.5 μm in diameter (PM2.5). We used daily chemical component concentration data in the year 2013, including data for water-soluble ions, elements, and carbonaceous species of PM2.5 at 11 sites in the Kinki–Tokai district in Japan in order to apply the Positive Matrix Factorization (PMF) model for the source apportionment. Seven PMF factors of PM2.5 were identified with the temporal and spatial variation patterns and also retained features of the sites. These factors comprised two types of secondary sulfate, road transportation, heavy oil combustion by ships, biomass burning, secondary nitrate, and soil and industrial dust, accounting for 46%, 17%, 7%, 14%, 13%, and 3% of the PM2.5, respectively. The multiple-site data enabled a comprehensive identification of the PM2.5 sources. For the same period, source contributions were estimated by air quality simulations using the Community Multiscale Air Quality model (CMAQ) with the brute-force method (BFM) for four source categories. Both models provided consistent results for the following three of the four source categories: secondary sulfates, road transportation, and heavy oil combustion sources. For these three target categories, the models’ agreement was supported by the small differences and high correlations between the CMAQ/BFM- and PMF-estimated source contributions to the concentrations of PM2.5, SO42−, and EC. In contrast, contributions of the biomass burning sources apportioned by CMAQ/BFM were much lower than and little correlated with those captured by the PMF model, indicating large uncertainties in the biomass burning emissions used in the CMAQ simulations. Thus, this comparison approach using the two antithetical models enables us to identify which sectors of emission data have large biases for improvement of future air quality simulations.

Characterization of non-methane hydrocarbons and their sources in an industrialized coastal city, Yangtze River Delta, China

Ningbo is a highly industrialized city in the coastal area of the Yangtze River Delta (YRD), China. Large emissions and transport of non-methane hydrocarbons (NMHCs) may contribute to regional ozone (O3) and particulate matter (PM) pollution; however, the concentrations and sources of ambient NMHCs have not yet been investigated in Ningbo. In this study, ambient NMHCs were measured at two residential (SZ and CX) and two industrial (ZH and BL) sites and one suburban (XS) site over ten consecutive days in each season (10–20 December 2012 in winter, 14–23 April 2013 in spring, 15–24 July 2013 in summer, 22–31 October 2013, in autumn). A positive matrix factorization (PMF) model using multiple site data was deployed to explore the source contributions and their spatial and seasonal characteristics. The measurement results showed obvious seasonal variations in ambient NMHC concentrations (ranging from 17.89–28.48ppbv); chemical compositions were similar among the five sampling sites. PMF analysis showed that the petrochemical industry was the largest contributor (an average of 35.64%) to ambient NMHCs, while contributions of smaller sources (i.e., chemical and paint industries [14.34%], fuel and tank evaporation [16.02%], and residential solvent usage [7.24%]) showed spatial variations. Liquefied petroleum gas and fuel and tank evaporation contributed more in summer and autumn, while the contribution of the chemical and paint industries was greater in spring and winter. An evaluation of the ozone formation potential and secondary organic aerosol potential suggested that petrochemical and solvent-related sources were key parameters in mitigation of secondary pollutant formation. Seasonal variations in source contributions should be considered when formulating an effective NMHC abatement strategy.

Diffusion magnetic resonance imaging study of schizophrenia in the context of abnormal neurodevelopment using multiple site data in a Chinese Han population.

Schizophrenia has increasingly been considered a neurodevelopmental disorder, and the advancement of neuroimaging techniques and associated computational methods has enabled quantitative re-examination of this important theory on the pathogenesis of the disease. Inspired by previous findings from neonatal brains, we proposed that an increase in diffusion magnetic resonance imaging (dMRI) mean diffusivity (MD) should be observed in the cerebral cortex of schizophrenia patients compared with healthy controls, corresponding to lower tissue complexity and potentially a failure to reach cortical maturation. We tested this hypothesis using dMRI data from a Chinese Han population comprising patients from four different hospital sites. Utilizing data-driven methods based on the state-of-the-art tensor-based registration algorithm, significantly increased MD measurements were consistently observed in the cortex of schizophrenia patients across all four sites, despite differences in psychopathology, exposure to antipsychotic medication and scanners used for image acquisition. Specifically, we found increased MD in the limbic system of the schizophrenic brain, mainly involving the bilateral insular and prefrontal cortices. In light of the existing literature, we speculate that this may represent a neuroanatomical signature of the disorder, reflecting microstructural deficits due to developmental abnormalities. Our findings not only provide strong support to the abnormal neurodevelopment theory of schizophrenia, but also highlight an important neuroimaging endophenotype for monitoring the developmental trajectory of high-risk subjects of the disease, thereby facilitating early detection and prevention.

Characterization of PM Using Multiple Site Data in a Heavily Industrialized Region of Turkey

Source apportionment has most often been applied to a time series of data collected at a single site. However, in a complex airshed where there are multiple sources, it may be helpful to collect samples from multiple sites to ensure that some of them have low contributions from specific sources such that edges can be properly defined. In this study, samples were collected at multiple sites in the Aliaga region (38°40′–38°54′N and 26°50′–27°03′E) located in western Turkey on the coast of the Aegean Sea. This area contains a number of significant air pollution sources including five scrap iron-steel processing plants with electric arc furnaces (EAFs), several steel rolling mills, a petroleum refinery, a petrochemical complex, a natural gas-fired power plant, a fertilizer plant, ship breaking yards, coal storage and packaging, scrap storage and classification sites, large slag and scrap piles, heavy road traffic, very intense transportation activities including ferrous scrap trucks and busy ports used for product and raw material transportation. A total of 456 samples of PM10 at six sampling sites and 88 samples of PM2.5 at one site were collected for four seasons and the elemental composition was determined for 43 elements. The newest version of EPA PMF (V5.0) that has the capability of handling multiple site data was used for source apportionment. Eight factors were identified as iron-steel production from scrap (23.4%), re-suspended and road dust (23.3%), crustal (20.5%), marine aerosol (14.4%), biomass and wood combustion (7.2%), salvage activities (4.7%), coal combustion (3.7%) and residual oil combustion (2.8%). The pattern of source contributions and conditional probability function analysis were consistent with the locations of the known sources. Thus, the multiple site data allowed for a comprehensive identification of the primary sources of PM in this region.

Spatial and seasonal variation and source apportionment of volatile organic compounds (VOCs) in a heavily industrialized region

Source apportionment is generally applied to a time series of pollutant concentrations measured at a single site. However, in a complex airshed having multiple pollutant sources, it may be helpful to collect samples from several sites to ensure that some of them have low contributions from specific sources. Ambient air samples (n = 160) were collected by passive sampling during four seasons in 2009 and 2010 at forty different sites in Aliaga, Turkey to determine the spatial, seasonal variations and possible sources of volatile organic compounds (VOCs). Fifty-eight VOCs (Σ58VOC) were detected. Σ58VOC concentrations ranged between 0.1 and 1770 μg m−3 (avg ± SD, 67 ± 193 μg m−3). Aliphatic hydrocarbons were generally predominant with a high percentage of contribution (31%–88%) at all sites. Aromatic VOCs were the second highest group (8–50%), followed by halogenated VOCs (1–24%) and oxygenated VOCs (0.04–5.9%). Highly variable spatial distribution of ambient VOC concentrations suggested that the major sources in this region were industrial plants. Generally, VOC concentrations were higher in summer than in winter probably due to increased volatilization from their sources at higher ambient temperatures. However, high atmospheric VOC concentrations were also observed in winter and fall near the petroleum refinery and petrochemical complex, probably due to the calm conditions and high atmospheric stability that is commonly encountered during the winter months in the area, restricting the dilution of pollutants. The newest version of EPA PMF (V5.0) (Positive Matrix Factorization) having the capability of handling multiple site data was used for source apportionment. Refinery and petroleum products, petrochemical industry, solvent use and industrial processes, and vehicle exhaust were the identified VOC sources in the study area, contributing 56%, 22%, 12%, and 10%, respectively to the Σ58VOC concentrations. Carcinogenic risks due to lifetime exposure to seven VOCs were also estimated. Estimated risks were the highest for 1,2-dichloroethane, followed by benzene, chloroform, and carbon tetrachloride. Carcinogenic risks for trichloroethene, 1,1,2-trichloroethane, and bromoform were lower than the general acceptable risk level of 1.0 × 10−6. However, risks for 1,2-dichloroethane, benzene, chloroform, and carbon tetrachloride were substantially higher than the acceptable level. It was concluded that carcinogenic risks may reach considerably high levels for a significant portion of the population living in the study area.

Source apportionment of polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) in ambient air of an industrial region in Turkey

Source apportionment is generally applied to a time series of data collected at a single site. However, in a complex airshed containing several different sources, it may be helpful to collect samples from multiple sites to ensure that some of them have low contributions from specific sources, thus the boundaries can be properly defined. Ambient air polycyclic aromatic hydrocarbon (PAH) and polychlorinated biphenyls (PCB) samples (n = 160) were collected at 40 sites during four seasons (summer, fall, winter, and spring) between July 2009 and April 2010 in the heavily industrialized Aliaga region in Turkey to investigate their spatial, seasonal variations and to identify possible PAH and PCB sources. The newest version of EPA PMF (V5.0) having the capability of handling multiple site data was used for source apportionment. Five PAH sources were identified as biomass and coal combustion, iron–steel production, unburned crude oil and petroleum products, and diesel and gasoline exhaust emissions with contributions of 40, 27, 27, 3, and 3%, respectively. The sources of PCBs were identified as iron–steel production (consisting of steel-making and ship breaking activities), coal and wood combustion, and evaporative emissions from technical PCB mixtures with contributions of 57, 31, and 12%, respectively.

Source apportionment of the ambient PM2.5 across St. Louis using constrained positive matrix factorization

In most cases, receptor models are applied to data from a single monitoring site even if there are multiple sampling locations in a given urban area. When it can be reasonably expected that the sites are affected by the same set of sources, it is possible to use the spatial variability of the source contributions to enhance the source apportionment. With the framework of positive matrix factorization, it is possible to enhance the results through an effective use of multiple site data. There have been several previous studies of the sources of ambient PM2.5 in St Louis, MO based on data from the US EPA chemical speciation network and the St Louis-Midwest Supersite. However, these different analyses identified different sets of sources including the omission of known major emission sources. A re-examination of the previous studies was undertaken using knowledge of the existing sources based on independent data and the resulting profiles were used to constrain the solution. These new solutions provide more realistic results in which the source impacts of all of the major sources could be assessed at each site. © 2011 Elsevier Ltd.

PCN137 ONCOLOGY PATIENT-REPORTED CLAIMS: MAXIMISING THE CHANCE FOR SUCCESS

Objectives/purpose: To review Patient Reported Outcome (PRO) labelling claims achieved in oncology in Europe and in the United States and consider the benefits, and challenges faced. Methods: PROLabels database was searched to identify oncology products with PRO labelling approved in Europe since 1995 or in the United States since 1998. The US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) websites and guidance documents were reviewed. PUBMED was searched for articles on PRO claims in oncology. Results: Among all oncology products approved, 22 were identified with PRO claims; 10 in the United States, 7 in Europe, and 5 in both. The language used in the labelling was limited to benefit (e.g. “…resulted in symptom benefits by significantly prolonging time to deterioration in cough, dyspnoea, and pain, versus placebo”) and equivalence (e.g. “no statistical differences were observed between treatment groups for global QoL”). Seven products used a validated HRQoL tool; two used symptom tools; two used both; seven used single-item symptom measures (one was unknown). The following emerged as likely reasons for success: ensuring systematic PRO data collection; clear rationale for pre-specified endpoints; adequately powered trials to detect differences and clinically significant changes; adjusting for multiplicity; developing an a priori statistical analysis plan including primary and subgroup analyses, dealing with missing data, pooling multiple-site data; establishing clinical versus statistical significance; interpreting failure to detect change. End-stage patient drop-out rates and cessation of trials due to exceptional therapeutic benefit pose significant challenges to demonstrating treatment PRO improvement. Conclusions: PRO labelling claims demonstrate treatment impact and the trade-off between efficacy and side effects ultimately facilitating product differentiation. Reliable and valid instruments specific to the desired language, claim, and target population are required. Practical considerations include rationale for study endpoints, transparency in assumptions, and attention to subtle variations in data.

Convergent Validity of Revealed and Stated Recreation Behavior with Quality Change: A Comparison of Multiple and Single Site Demands

We consider the convergent validity of several demand models using beach recreation data. Two models employ multiple site data: a count data demand system model and the Kuhn–Tucker demand system model. We explore the role of existing variation in beach width in explaining trip choices, and analyze a hypothetical 100 foot increase in beach width. We compare these models to a single equation model where we jointly estimate revealed and stated preference trip data, and focus on a hypothetical scenario considering a 100 foot increase in beach width. In each case we develop estimates of the change in beach visits and the welfare impacts from the increase in width. The trip change estimates from two of the three models are similar and convergent valid, though the willingness to pay estimates differ in magnitude.

Spatial distribution of source locations for particulate nitrate and sulfate in the upper-midwestern United States

Two back-trajectory analysis methods designed to be used with multiple site data, simplified quantitative transport bias analysis (SQTBA) and residence time weighted concentration (RTWC), were applied to nitrate and sulfate concentration data from two rural sites (the Mammoth Cave National Park and the Great Smoky Mountain National Park) and five urban sites (Chicago, Cleveland, Detroit, Indianapolis, and St. Louis) for an intensive investigation on the spatial patterns of origins for these two species in the upper-midwestern area. The study was made by dividing the data into five categories: all sites and all seasons, rural sites in summer, rural sites in winter, urban sites in summer, and urban sites in winter. A general conclusion was that the origins of the nitrate in these seven sites were mainly in the upper-midwestern areas, while the sulfate in these seven sites were mainly from the Ohio and Tennessee River Valley areas. The upper-midwestern areas are regions of high ammonia emissions rather than high NO x emissions. In the winter, metropolitan areas showed the highest nitrate emission potential suggesting the importance of local NO x emissions. In the summer, ammonia emissions from fertilizer application in the lower midwestern area made a significant contribution to nitrate in the rural sites of this study. The impact of the wind direction prevalence on the source spatial patterns was observed by comparing the urban and rural patterns of the summer. The differences between the results of two methods are discussed and suggestions for applying these methods are also provided.

Comparison of two trajectory based models for locating particle sources for two rural New York sites

Two back trajectory-based statistical models, simplified quantitative transport bias analysis and residence-time weighted concentrations (RTWC) have been compared for their capabilities of identifying likely locations of source emissions contributing to observed particle concentrations at Potsdam and Stockton, New York. Quantitative transport bias analysis (QTBA) attempts to take into account the distribution of concentrations around the directions of the back trajectories. In full QTBA approach, deposition processes (wet and dry) are also considered. Simplified QTBA omits the consideration of deposition. It is best used with multiple site data. Similarly the RTWC approach uses concentrations measured at different sites along with the back trajectories to distribute the concentration contributions across the spatial domain of the trajectories. In this study, these models are used in combination with the source contribution values obtained by the previous positive matrix factorization analysis of particle composition data from Potsdam and Stockton. The six common sources for the two sites, sulfate, soil, zinc smelter, nitrate, wood smoke and copper smelter were analyzed. The results of the two methods are consistent and locate large and clearly defined sources well. RTWC approach can find more minor sources but may also give unrealistic estimations of the source locations.

Usefulness of multiple-site venous sampling in the treatment of adrenocorticotropic hormone-producing pituitary adenomas

Background While venous sampling for adrenocorticotropic hormone (ACTH) may be the most accurate way to establish a diagnosis of Cushing’s disease, its usefulness for lateralization of adenomas is limited even in combination with simultaneous bilateral sampling methods. Therefore, to increase accuracy in predicting the lateralization of adenomas, we performed a trial in which we carried out simultaneous venous sampling from multiple sites of the cavernous sinus (CS) and inferior petrosal sinus (IPS). Here we report on the usefulness of our method in the correct tumor lateralization in patients with Cushing’s disease. Methods Eighteen patients with Cushing’s disease underwent simultaneous bilateral ACTH sampling. The samples were obtained from the anterior, middle, and posterior CS and the IPS. CS sampling after stimulation with corticotropin-releasing hormone (CRH) was also performed. The central-to-peripheral ACTH ratio (c/p ratio) was calculated using sampling data from each site; the lateralizing gradients (right versus left) were calculated using the c/p ratio. Instead of the conventional method where the lateralization gradient is based on sampling data from a single site, we used the distribution of the c/p ratio determined from multiple-site data. Results There was no significant difference in the mean lateralization gradient obtained from each set of sampling data ( p > 0.05). With the conventional method, correct tumor lateralization was obtained in 50% of tumors in the IPS; 72.2 to 77.8% of tumors in the CS; and 77.8% of tumors in the middle CS after CRH. Our method using ACTH contour analysis based on multiple sites produced correct results in all 18 of the patients examined. The difference in correct calls between the conventional method and our method was significant ( p < 0.05). Conclusions Multiple-site sampling of ACTH is valuable for lateralizing the adenoma in patients with Cushing’s disease; it produces more correct results than does single venous sampling.

A RULEBASE MODEL FOR DATA AND KNOWLEDGE INTEGRATION IN MULTIPLE SYSTEMS ENVIRONMENTS

There are three aspects of integration that a computerized enterprise using multiple systems must address: (1) data integration (among, e.g., product design databases, shop floor control databases, and production planning databases), (2) knowledge integration (across various knowledge-based systems that share common enterprise facts, operating rules, or decision logic), and (3) data and knowledge integration (as as to, at least, calibrate databases with factbases and data models with knowledge models). A key issue in this integration problem is the global representation of knowledge (including data semantic constraints and application processing logic) in a form compatible with that of data resources. This paper develops a rulebase model to address this need. In particular, the model combines production rule and relation into a structure that allows (1) full factbase management, (2) inclusion of complete logic (both condition and action), and (3) efficient rulebase processing. It contributes directly to the problem of trigger management (or more generally, rule and event management) in both single-site and multiple-site data and knowledge systems. The model is implemented as a part of the Metadatabase system at Rensselaer.

Limitations of Augustinsson plots.

The Augustinsson plot transforms the Michaelis-Menten equation by graphing velocity, v, as a function of velocity/substrate, v/s. The plot is linear with a negative slope for a single class of saturable sites and curvilinear for multiple classes of sites. Statistically, Augustinsson plots suffer from the limitation that there is no true independent variable. Experimental errors form a Gaussian distribution that rests neither parallel to the v axis nor v/s axis, but rather along a line emanating from the origin. Widening the range of s values produces increasingly large errors in the calculated Km and Vm values, especially when multiple sites are present. Exact data containing no error produces accurate constants using either the Michaelis-Menten or Augustinsson equation. With Km and Vm constants previously derived by fitting data with the statistically valid Michaelis-Menten equation, the Augustinsson plot may be used accurately to display multiple site data.