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Background: Although biopsy is often entailed for managing patients with kidney allograft dysfunction, it is associated with potential complications of severe hemorrhage. Arterial spin labeling (ASL) is a non‐invasive technique that assesses tissue perfusion. Purpose: To assess the utility of ASL for the discrimination of patients with post‐transplant allograft dysfunction who do not need biopsy from those who need. Study Type: Prospective. Subjects: Forty‐six patients (34 males/12 females, aged 38.8 ± 9.5 years) with kidney allograft dysfunction, including 31 in which biopsy directly lead to changes in management (NECESSARY group) and 15 in which clinical management did not alter after biopsy (UNNECESSARY group). Field Strength/Sequence: 3.0 T and 3D fast‐spin echo sequence. Assessment: All patients underwent both ASL scan and biopsies. The serum creatinine, proteinuria, pathologic results, and cortical ASL readings were obtained and compared between the two groups. Statistical Analyses: Chi‐square test, independent student t‐test, Mann–Whitney U test, receiver‐operating characteristic curve. A two‐tailed P < 0.05 denoted statistical significance. Results: The NECESSARY group presented with significantly elevated serum creatinine as compared with the UNNECESSARY group (1.87 ± 0.56 mg/dL vs. 1.31 ± 0.37 mg/dL). The acute composite score was significantly higher in the NECESSARY group than that in the UNNECESSARY group (7 [4–8] vs. 1 [0–2]). Cortical ASL in the NECESSARY group was significantly decreased as compared with the UNNECESSARY group (108.06 [69.96–134.92] mL/min/100 g vs. 153.48 [113.19–160.37] mL/min/100 g). Serum creatinine differentiated UNNCESSARY group from the NECESSARY group with an area under the curve (AUC) and specificity of 0.79 and 54.84%, respectively. By comparison, the cortical ASL yielded an AUC of 0.75 and a specificity of 70.97%. Notably, the specificity was increased to 90.30% by combined use of serum creatinine and cortical ASL. Data Conclusion: The combined use of ASL and serum creatinine yielded a high specificity for selecting patients who may not need allograft biopsy. Level of Evidence: 2 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

Photocatalytic reactions for fuel generation are crucial for the world's energy needs. Covalent‐Organic‐Frameworks (COFs) have been extensively studied as promising designable photocatalysts for these reactions due to their efficient visible‐light absorption, suitable energy‐band structure, facilitated intramolecular charge separation, and fast mass transfer. However, the activities of pristine COFs remain unsatisfactory, due to intermolecular charge recombination. Recently, COF‐based heterostructures, which combine COFs with metal‐sulfides, metal‐oxides, carbon materials, or MOFs, have attracted increasing attention for enhancing solar‐to‐fuel conversion efficiency by facilitating interfacial photo‐generated carrier separation, sensitizing wide‐gap semiconductors, and promoting surface redox reactions. Thus, a review of the state‐of‐the‐art progress of COF‐based heterostructure photocatalysts in reactions such as H2 evolution, CO2 reduction, O2 evolution, H2O splitting and CO2 splitting is crucial for the design of new photocatalysts to promote solar‐to‐fuel conversion. In this review, the COF‐based heterostructures photocatalysts are highlighted based on their synthesis, properties, and reasons for enhanced activities. Moreover, design principles are raised for such photocatalysts for each fuel generation reaction, based on insights into related research. Finally, this review is concluded by proposing future trends for COF‐based heterostructures photocatalysts, with attention to the design of COFs and supports, analyzing the photocatalytic reaction dynamics, together with considering practical applications. [ABSTRACT FROM AUTHOR]

Background: Transplant renal artery stenosis (TRAS) has been shown to reduce kidney perfusion leading to post‐operative hypertension. We aimed to measure the perfusion and oxygenation changes in TRAS with arterial spin labeling (ASL) and blood oxygen level‐dependent (BOLD) imaging, respectively. Methods: In this single‐center prospective study, a total of seven patients with TRAS and seven age‐ and sex‐matched normal kidney transplant recipients underwent both ASL and BOLD imaging. Moreover, measurements of ASL and BOLD were also performed in five patients after successful angioplasty for TRAS. Results: Allograft cortical perfusion as measured by ASL in the TRAS group was significantly decreased as compared with normal control group (129.9 ± 46.6 ml/100 g vs. 202.4 ± 47.7 ml/100 g, P =.01). Interestingly, allograft oxygenation as indicated by R2* derived from BOLD in both the cortex (16.42 ± 1.90 Hz vs. 18.25 ± 4.34 Hz, P =.33) and the medulla (30.34 ± 2.35 Hz vs. 30.43 ± 6.85 Hz, P =.97) showed no statistical difference between the TRAS and normal control group. In addition, both cortical and medullary oxygenation remained unchanged despite significantly improved cortical perfusion in those undergone successful angioplasty. Conclusion: Cortical and medullary oxygenation were preserved in the presence of reduced allograft perfusion in clinically significant TRAS. Prospective larger studies are needed to conclusively establish perfusion and oxygenation changes in TRAS. [ABSTRACT FROM AUTHOR]

In this study, the neutral winds from meteor radars and the total electron content (TEC) from Beidou Geostationary (GEO) satellites were utilized to study the responses of the mesosphere and lower thermosphere and the ionosphere in the Asian‐Australian sector to the recent Sudden Stratospheric Warming (SSW) occurred in the Antarctic polar region during September 2019. It was demonstrated that the ionospheric TEC increased significantly, especially around the equatorial ionization anomaly (EIA) region during this SSW. A pronounced semidiurnal signature in the deviation TEC was observed near the EIA crest region, which was more significant in the southern hemisphere. Meanwhile, the enhancement period of the amplitudes of solar and lunar semidiurnal tides in winds in the mesosphere was coincided with that in TEC. The solar semidiurnal amplitudes increased from about 18 to 35 m/s in the zonal wind and from 15 to 25 m/s in the meridional wind at Mengcheng (33.4°N, 116.5°E, MLAT 27.4°N), and from about 15–38 m/s in the zonal wind at Kunming (25.6°N, 103.6°E, MLAT 19.3°N). The lunar semidiurnal amplitudes increased from 2 to 6 m/s in the zonal wind and from about 3–9 m/s in the meridional wind at Mengcheng, and from about 3–7 m/s in the meridional wind at Kunming, respectively. These indicated that the semidiurnal signature in TEC during the 2019 southern hemisphere SSW could be associated with the SSW induced by the atmospheric semidiurnal tide variations in the mesosphere. Key Points: Multiple observations were utilized to analyze the mesosphere and lower‐thermosphere (MLT) and ionospheric responses to the 2019 Sudden Stratospheric Warming (SSW) occurred in the Antarctic polar regionSemidiurnal signature in the ionosphere was the strongest at the equatorial ionization anomaly region and larger in the southern hemisphereIncreasing time of the solar and lunar semidiurnal tides in the MLT winds was coincided with that of total electron content during this SSW [ABSTRACT FROM AUTHOR]

Background: Although biopsy is essential for the diagnosis and management of kidney transplant recipients, it is invasive. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a noninvasive technique that can assess both capillary perfusion and tissue diffusion.Purpose: To evaluate the capability of IVIM-DWI as a differentiation of kidney transplant patients who need clinical intervention from those who need not.Study Type: Prospective.Subjects: In all, 33 kidney transplant patients who needed clinical intervention and 19 who need not.Field Strength/sequence: 3.0T; IVIM-DWI with a single-shot echo planar imaging sequence.Assessment: All patients underwent kidney transplant biopsy and IVIM-DWI scans. Patients were dichotomized into those who needed clinical intervention (CHANGE group) and those who need not (Non-CHANGE group) based on biopsy results. The values of total apparent diffusion coefficient (ADCT ), diffusion coefficient (D), and perfusion fraction (f) were acquired from renal cortex and medulla, respectively. The area under the curve (AUC) was calculated and compared.Statistical Tests: Independent Student's t-test, receiver-operating characteristic curve, and Spearman correlation analysis.Results: All the cortical and medullary DWI parameters in the CHANGE group were significantly lower than those in the Non-CHANGE group (all P ≤ 0.012). Except for medullary fp, all DWI parameters in both the cortex and the medulla were inversely correlated with both the chronic (ρ ranging from -0.33 to -0.54, all P ≤ 0.02) and acute (ρ ranging from -0.35 to -0.60, all P ≤ 0.01) composite scores. Cortical ADCT and D had the largest AUC and specificity of 0.84 and 75.8%, respectively. Combined use of cortical D and medullary fp at each optimal cutoff point yielded a specificity of 90.9%.Data Conclusion: DWI demonstrated potential as a noninvasive biomarker to allow the stratification of patients into categories in which kidney allograft biopsy results are or are not likely to change clinical management.Level Of Evidence: 1 TECHNICAL EFFICACY STAGE: 5 J. Magn. Reson. Imaging 2020;52:565-574. [ABSTRACT FROM AUTHOR]

The neutral winds in the mesospheric and lower thermospheric region obtained from a meteor radar chain within the latitudinal range of 18–53°N were decomposed to examine the latitudinal structures of the tides and mean winds during the sudden stratospheric warming (SSW) event in 2013. The zonal wind reversed from eastward to westward and the westward wind was larger at middle latitudes than that at low latitudes during the SSW. Meanwhile, a sharp increase in the northward wind was noticeable at all stations. The amplitudes of the diurnal and semidiurnal tides decreased from 5 to 13 January and increased subsequently. At the same time, the terdiurnal tides increased in amplitude, especially at Beijing, Wuhan, and Kunming, in response to the SSW. Moreover, the tidal phases showed a large shift that corresponded to a great increase in tidal wavelengths in the semidiurnal and terdiurnal components. The wavelet analysis further revealed that the quasi 6‐day wave oscillations dominated in the winds at Kunming and Sanya stations, as well as the quasi 16‐day wave dominated in the zonal wind, and the quasi 11‐day and 16‐day waves were prevailing in the meridional winds at Beijing and Wuhan. In the same vein, the quasi 16‐day and quasi 11‐day waves were prominent in the zonal and meridional winds during 1–18 January at Mohe. Therefore, the tide‐planetary wave interactions played a significant role in modulating the behaviors of the mesospheric and lower thermospheric dynamics during the SSW event. Key Points: Neutral wind observations from a meteor radar chain over China are utilized to study the MLT responses to the SSW in 2013Latitudinal structures of tides and winds are investigated during the SSWThe diurnal and semidiurnal tides of neutral winds show a large decrease followed by an increase during the SSW [ABSTRACT FROM AUTHOR]

Storm time development of equatorial plasma bubbles (EPBs) around the meridian 120°E/60°W during early September 2017, when the Bz component of interplanetary magnetic field (IMF) experienced two large southward excursions, producing a strong geomagnetic storm that included two main phase decreases, was investigated. The observations from networks of Global Navigation Satellite Systems total electron content receivers, very high frequency radars, and ionosondes operated around the meridian reveal that in the American and Asian sectors, intense EPB irregularities developed and extended to dip latitudes of ~30°N and 46°N, respectively, following rapid sunset F layer height rises during two episodes of strong southward IMF Bz excursions. The storm‐enhanced EPB irregularities, however, were not observed following the sunset terminator in the Pacific sector, where the sunset rise of F layer was not detected. More interestingly, the EPBs in the Asian sector were observed to drift toward the west, with velocity increasing from ~30 m/s at low latitude to ~95 m/s at middle latitude. The poleward increasing westward drifts drove the formation of west‐titled structure of irregularities. For the EPBs in the American sector, no apparent west‐tilted structure was detected. The results indicate that the prompt penetration undershielding electric fields (PPEF) of eastward polarity resulting from the two IMF Bz southward excursions dominated the generation of postsunset EPBs in the American and Asian sectors, respectively. The westward drifts of PPEF‐induced EPBs in the Asian sector could be attributed dominantly to disturbance westward wind, with a possible contribution to it arising from the PPEF. Plain Language Summary: The development and evolution of equatorial plasma bubbles (EPBs) exhibit complex global behavior during geomagnetic storms. In recent years, an international space weather meridian circle program, which aims to provide a global picture of unfolding space weather events by using diverse instruments along the approximate meridian 120°E/60°W, that is, the Asian and American longitude sectors, was launched. Considering the sunset interval (~12 hr) between the two longitudes, it is expected that the development of postsunset EPBs, if enhanced in one region by short‐lived prompt penetration electric fields (PPEF), would be inhibited in the other region under the delayed and long duration effect of disturbance dynamo electric fields. Here we report a unique case of significantly enhanced postsunset EPBs developments by PPEF in both the American and Asian sectors, but their total absence by disturbance dynamo electric fields in the Pacific sector during the September 2017 geomagnetic storm sequence. Moreover, the PPEF‐induced EPBs along the meridian show different characteristics, with apparent west‐tilted structure in the Asian sector but not in the American sector. This sort of study based on the international space weather meridian circle program observations will strengthen our understanding on the generation and evolution characteristics of EPBs during geomagnetic storms. Key Points: EPB development significantly enhanced along the meridian 120°E/60°W during the September 2017 geomagnetic stormThe enhanced EPBs along the meridian are mainly due to the PPEFs resulting from two large southward excursions of IMF BzThe westward drifts increasing with latitude/altitude produced west‐tilted structures of EPBs in the Asian sector [ABSTRACT FROM AUTHOR]

Abstract: We develop a Bayesian model to predict the maximum thickness of seasonally frozen ground (MTSFG) using historical air temperature and precipitation observations. We use the Stefan solution and meteorological data from 11 stations to estimate the MTSFG changes from 1961 to 2016 in the Yellow River source region of northwestern China. We employ an antecedent precipitation index model to estimate changes in the liquid soil water content. The marginal posterior probability distributions of the antecedent precipitation index parameters are estimated using Markov chain Monte Carlo sampling methods. We compare the results of our stochastic method with those obtained from the traditional deterministic method and find that they are consistent in general. The stochastic approach is effective for estimating the historical changes in the frozen ground depth (root‐mean‐square errors = 0.13–0.35 m), and it provides more information on model uncertainty regarding soil moisture variations. Additionally, simulation shows that the MTSFG has decreased by 0.31 cm per year over the last 56 years on the northeastern Qinghai‐Tibet Plateau. This decrease in frost depth accelerated in the 1990s and 2000s. Considering the lack of data on seasonally frozen soil monitoring, the Bayesian method provides a pragmatic approach to statistically model frozen ground changes using available meteorological data. [ABSTRACT FROM AUTHOR]

Abstract: In this study, multiple data sets from Beidou geostationary orbit satellites total electron contents (TECs), ionosonde, meteor radar, magnetometer, and model simulations have been used to investigate the ionospheric responses in the Asian‐Australian sector during the September 2017 geomagnetic storm. It was found that long‐duration daytime TEC enhancements that lasted from 7 to 12 September 2017 were observed by the Beidou geostationary orbit satellite constellation. This is a unique event as the prominent TEC enhancements persisted during the storm recovery phase when geomagnetic activity became quiet. The Thermosphere‐Ionosphere Electrodynamics Global Circulation Model predicted that the TEC enhancements on 7–9 September were associated with the geomagnetic activity, but it showed significant electron density depletions on 10 and 11 September in contrast to the observed TEC enhancements. Our results suggested that the observed long‐duration TEC enhancements from 7 to 12 September are mainly associated with the interplay of ionospheric dynamics and electrodynamics. Nevertheless, the root causes for the observed TEC enhancements seen in the storm recovery phase are unknown and require further observations and model studies. [ABSTRACT FROM AUTHOR]

Abstract: Neutral mesospheric densities at a low latitude have been derived during April 2011 to December 2014 using data from the Kunming meteor radar in China (25.6°N, 103.8°E). The daily mean density at 90 km was estimated using the ambipolar diffusion coefficients from the meteor radar and temperatures from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument. The seasonal variations of the meteor radar‐derived density are consistent with the density from the Mass Spectrometer and Incoherent Scatter (MSIS) model, show a dominant annual variation, with a maximum during winter, and a minimum during summer. A simple linear model was used to separate the effects of atmospheric density and the meteor velocity on the meteor radar peak detection height. We find that a 1 km/s difference in the vertical meteor velocity yields a change of approximately 0.42 km in peak height. The strong correlation between the meteor radar density and the velocity‐corrected peak height indicates that the meteor radar density estimates accurately reflect changes in neutral atmospheric density and that meteor peak detection heights, when adjusted for meteoroid velocity, can serve as a convenient tool for measuring density variations around the mesopause. A comparison of the ambipolar diffusion coefficient and peak height observed simultaneously by two co‐located meteor radars indicates that the relative errors of the daily mean ambipolar diffusion coefficient and peak height should be less than 5% and 6%, respectively, and that the absolute error of the peak height is less than 0.2 km. [ABSTRACT FROM AUTHOR]

We propose a flexible model for correlated medical cost data with several appealing features. First, the mean function is partially linear. Second, the distributional form for the response is not specified. Third, the covariance structure of correlated medical costs has a semiparametric form. We use extended generalized estimating equations to simultaneously estimate all parameters of interest. B-splines are used to estimate unknown functions, and a modification to Akaike information criterion is proposed for selecting knots in spline bases. We apply the model to correlated medical costs in the Medical Expenditure Panel Survey dataset. Simulation studies are conducted to assess the performance of our method. [ABSTRACT FROM AUTHOR]

Long-term prediction of groundwater dynamics is important for assessing water resources and their impacts on biogeochemical cycling. However, estimating future groundwater dynamics is challenging due to the wide range of spatiotemporal scales in hydrological processes and uncertainty in future climate conditions. In this study, we develop a Bayesian model to combine small-scale historical hydrological data with large-scale climate information to estimate groundwater dynamics at a floodplain site in Rifle, Colorado. Although we have only a few years of groundwater elevation measurements, we have 47 years of streamflow data from a gaging station approximately 43 km upstream and long-term climate prediction on the Upper Colorado River Basin. To estimate future daily groundwater dynamics, we first develop a time series model to downscale the monthly streamflow derived from climate information to daily streamflow, and then transform the daily streamflow to groundwater dynamics at the downstream floodplain site. We use Monte Carlo methods to estimate future groundwater dynamics at the site through sampling from the joint posterior probability distribution. The results suggest that although future groundwater levels are expected to be similar to the current levels, the timing of the high groundwater levels is predicted to occur about 1 month earlier. The developed framework is extendable to other sites to estimate future groundwater dynamics given disparate data sets and climate projections. Additionally, the obtained estimates are being used as input to a site-specific watershed reactive transport models to predict how climate-induced changes will influence future biogeochemical cycling relevant to a variety of ecosystem services. [ABSTRACT FROM AUTHOR]

In floodplain environments, a naturally reduced zone (NRZ) is considered to be a common biogeochemical hot spot, having distinct microbial and geochemical characteristics. Although important for understanding their role in mediating floodplain biogeochemical processes, mapping the subsurface distribution of NRZs over the dimensions of a floodplain is challenging, as conventional wellbore data are typically spatially limited and the distribution of NRZs is heterogeneous. In this study, we present an innovative methodology for the probabilistic mapping of NRZs within a three-dimensional (3-D) subsurface domain using induced polarization imaging, which is a noninvasive geophysical technique. Measurements consist of surface geophysical surveys and drilling-recovered sediments at the U.S. Department of Energy field site near Rifle, CO (USA). Inversion of surface time domain-induced polarization (TDIP) data yielded 3-D images of the complex electrical resistivity, in terms of magnitude and phase, which are associated with mineral precipitation and other lithological properties. By extracting the TDIP data values colocated with wellbore lithological logs, we found that the NRZs have a different distribution of resistivity and polarization from the other aquifer sediments. To estimate the spatial distribution of NRZs, we developed a Bayesian hierarchical model to integrate the geophysical and wellbore data. In addition, the resistivity images were used to estimate hydrostratigraphic interfaces under the floodplain. Validation results showed that the integration of electrical imaging and wellbore data using a Bayesian hierarchical model was capable of mapping spatially heterogeneous interfaces and NRZ distributions thereby providing a minimally invasive means to parameterize a hydrobiogeochemical model of the floodplain. [ABSTRACT FROM AUTHOR]

ABSTRACT An extension of a previously developed rock physics model is made that quantifies the relationship between the ductile fraction of a brittle/ductile binary mixture and the isotropic seismic reflection response. By making a weak scattering (Born) approximation and plane wave (eikonal) approximation, with a subsequent ordering according to the angles of incidence, singular value decomposition analyses are performed to understand the stack weightings, number of stacks, and the type of stacks that will optimally estimate two fundamental rock physics parameters - the ductile fraction and the compaction and/or diagenesis. It is concluded that the full PP stack, i.e., sum of all PP offset traces, and the 'full' PS stack, i.e., linear weighted sum of PS offset traces, are the two optimal stacks needed to estimate the two rock physics parameters. They dominate over both the second-order amplitude variation offset 'gradient' stack, which is a quadratically weighted sum of PP offset traces that is effectively the far offset traces minus the near offset traces, and the higher order fourth order PP stack (even at large angles of incidence). Using this result and model-based Bayesian inversion, the seismic detectability of the ductile fraction (shown by others to be the important rock property for the geomechanical response of unconventional reservoir fracking) is demonstrated on a model characteristic of the Marcellus shale play. [ABSTRACT FROM AUTHOR]

A stochastic model is developed to integrate multiscale geophysical and point data sets for characterizing coupled subsurface physiochemical properties over plume-relevant scales, which is desired for parameterizing reactive transport models. We utilize the concept of reactive facies, which is based on the hypothesis that subsurface units can be identified that have distinct reactive-transport-property distributions. To estimate and spatially distribute reactive facies and their associated properties over plume-relevant scales, we need to (1) document the physiochemical controls on plume behavior and the correspondence between geochemical, hydrogeological, and geophysical measurements; and (2) integrate multisource, multiscale data sets in a consistent manner. To tackle these cross-scale challenges, we develop a hierarchical Bayesian model to jointly invert various wellbore and geophysical data sets that have different resolutions and spatial coverage. We use Markov-chain Monte-Carlo sampling methods to draw many samples from the joint posterior distribution and subsequently estimate the marginal posterior distribution of reactive-facies field and their associated reactive transport properties. Synthetic studies demonstrate that our method can successfully integrate different types of data sets. We tested the framework using the data sets collected at the uranium-contaminated Savannah River Site F-Area, including wellbore lithology, cone penetrometer testing, and crosshole and surface seismic data. Results show that the method can estimate the spatial distribution of reactive facies and their associated reactive-transport properties along a 300 m plume centerline traverse with high resolution (1.2 m by 0.305 m). [ABSTRACT FROM AUTHOR]

Medical cost data are often skewed to the right and heteroscedastic, having a nonlinear relation with covariates. To tackle these issues, we consider an extension to generalized linear models by assuming nonlinear associations of covariates in the mean function and allowing the variance to be an unknown but smooth function of the mean. We make no further assumption on the distributional form. The unknown functions are described by penalized splines, and the estimation is carried out using nonparametric quasi-likelihood. Simulation studies show the flexibility and advantages of our approach. We apply the model to the annual medical costs of heart failure patients in the clinical data repository at the University of Virginia Hospital System. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Although mechanistic reaction networks have been developed to quantify the biogeochemical evolution of subsurface systems associated with bioremediation, it is difficult in practice to quantify the onset and distribution of these transitions at the field scale using commonly collected wellbore datasets. As an alternative approach to the mechanistic methods, we develop a data-driven, statistical model to identify biogeochemical transitions using various time-lapse aqueous geochemical data (e.g., Fe(II), sulfate, sulfide, acetate, and uranium concentrations) and induced polarization (IP) data. We assume that the biogeochemical transitions can be classified as several dominant states that correspond to redox transitions and test the method at a uranium-contaminated site. The relationships between the geophysical observations and geochemical time series vary depending upon the unknown underlying redox status, which is modeled as a hidden Markov random field. We estimate unknown parameters by maximizing the joint likelihood function using the maximization-expectation algorithm. The case study results show that when considered together aqueous geochemical data and IP imaginary conductivity provide a key diagnostic signature of biogeochemical stages. The developed method provides useful information for evaluating the effectiveness of bioremediation, such as the probability of being in specific redox stages following biostimulation where desirable pathways (e.g., uranium removal) are more highly favored. The use of geophysical data in the approach advances the possibility of using noninvasive methods to monitor critical biogeochemical system stages and transitions remotely and over field relevant scales (e.g., from square meters to several hectares). [ABSTRACT FROM AUTHOR]

As with any psychometric models, the validity of inferences from cognitive diagnosis models (CDMs) determines the extent to which these models can be useful. For inferences from CDMs to be valid, it is crucial that the fit of the model to the data is ascertained. Based on a simulation study, this study investigated the sensitivity of various fit statistics for absolute or relative fit under different CDM settings. The investigation covered various types of model-data misfit that can occur with the misspecifications of the Q-matrix, the CDM, or both. Six fit statistics were considered: -2 log likelihood (-2LL), Akaike's information criterion (AIC), Bayesian information criterion (BIC), and residuals based on the proportion correct of individual items (p), the correlations (r), and the log-odds ratio of item pairs (l). An empirical example involving real data was used to illustrate how the different fit statistics can be employed in conjunction with each other to identify different types of misspecifications. With these statistics and the saturated model serving as the basis, relative and absolute fit evaluation can be integrated to detect misspecification efficiently. [ABSTRACT FROM AUTHOR]

In this article, we implement a practical computational method for various semiparametric mixed effects models, estimating nonlinear functions by penalized splines. We approximate the integration of the penalized likelihood with respect to random effects with the use of adaptive Gaussian quadrature, which we can conveniently implement in SAS procedure NLMIXED. We carry out the selection of smoothing parameters through approximated generalized cross-validation scores. Our method has two advantages: (1) the estimation is more accurate than the current available quasi-likelihood method for sparse data, for example, binary data; and (2) it can be used in fitting more sophisticated models. We show the performance of our approach in simulation studies with longitudinal outcomes from three settings: binary, normal data after Box-Cox transformation, and count data with log-Gamma random effects. We also develop an estimation method for a longitudinal two-part nonparametric random effects model and apply it to analyze repeated measures of semicontinuous daily drinking records in a randomized controlled trial of topiramate. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background Various statistical methods have been used for data analysis in alcohol treatment studies. Trajectory analyses can better capture differences in treatment effects and may provide insight on the optimal duration of future clinical trials and grace periods. This improves on the limitation of commonly used parametric (e.g., linear) methods that cannot capture nonlinear temporal trends in the data. Methods We propose an exploratory approach, using more flexible smoothing mixed effects models, more accurately to characterize the temporal patterns of the drinking data. We estimated the trajectories of the treatment arms for data sets from 2 sources: a multisite topiramate study, and the Combined Pharmacotherapies (acamprosate and naltrexone) and Behavioral Interventions study. Results Our methods illustrate that drinking outcomes of both the topiramate and placebo arms declined over the entire course of the trial but with a greater rate of decline for the topiramate arm. By the point-wise confidence intervals, the heavy drinking probabilities for the topiramate arm might differ from those of the placebo arm as early as week 2. Furthermore, the heavy drinking probabilities of both arms seemed to stabilize at the end of the study. Overall, naltrexone was better than placebo in reducing drinking over time yet was not different from placebo for subjects receiving the combination of a brief medical management and an intensive combined behavioral intervention. Conclusions The estimated trajectory plots clearly showed nonlinear temporal trends of the treatment with different medications on drinking outcomes and offered more detailed interpretation of the results. This trajectory analysis approach is proposed as a valid exploratory method for evaluating efficacy in pharmacotherapy trials in alcoholism. [ABSTRACT FROM AUTHOR]

In this study, a 3Au1Pd alloy nanoparticles/graphene composite (3Au1Pd alloy NPs/GN) with carboxyl groups and hydroxyl groups was prepared facilely by co-reduction of graphene oxide (GO), HAuCl4, K2PdCl4, with an AuPd alloy molar ratio of 3 : 1. The composite modified glass carbon electrode (GCE) showed a good performance for detecting bisphenol A (BPA) due to the enhanced electron transfer kinetics and large active surface area. The effective enrichment of BPA is attributed to the carboxyl groups and hydroxyl groups on the composite. According to the results of differential pulse voltammetry (DPV), the BPA oxidation current is linear to its concentration in the range of 10 nM-5.0 µM ( R=0.998), and the detection limit is found to be 4.0 nM ( S/ N=3). [ABSTRACT FROM AUTHOR]

The impacts of global climatic change on belowground ecological processes of terrestrial ecosystems are still not clear. We therefore conducted an experiment in the subalpine coniferous forest ecosystem of the eastern edges of the Tibetan Plateau to study roots of Picea asperata seedlings and rhizosphere soil responses to soil warming and nitrogen availability from April 2007 to December 2008. The seedlings were subjected to two levels of temperature (ambient; infrared heater warming) and two nitrogen levels (0 or 25 g myear N). We used a free air temperature increase from an overhead infrared heater to raise both air and soil temperature by 2.1 and 2.6°C, respectively. The results showed that warming alone significantly increased total biomass, coarse root biomass and fine root biomass of P. asperata seedlings. Both total biomass and fine root biomass were increased, but coarse root biomass was significantly decreased by nitrogen fertilization and warming combined with nitrogen fertilization. Warming induced a prominent increase in soil organic carbon (SOC) and NO-N of rhizosphere soil, while nitrogen fertilization significantly decreased SOC and NH-N of rhizosphere soil. The warming, fertilization and warming × N fertilization interaction decreased soil microbial C significantly, but substantially increased soil microbial N. These results suggest that nitrogen deposition combined with warmer temperatures under future climatic change possibly will have no effect on fine root production of P. asperata seedlings, but could enhance the nitrification process of their rhizosphere soils in subalpine coniferous forests. [ABSTRACT FROM AUTHOR]

We develop a Bayesian model to invert surface seismic refraction data with depth constraints from boreholes for characterization of aquifer geometry and apply it to seismic and borehole data sets collected at the contaminated Oak Ridge National Laboratory site in Tennessee. Rather than the traditional approach of first inverting the seismic arrival times for seismic velocity and then using that information to aid in the spatial interpolation of wellbore data, we jointly invert seismic first arrival time data and wellbore-based information, such as depths of key lithological boundaries. We use a staggered-grid finite difference algorithm with second-order accuracy in time and fourth-order accuracy in space to model seismic full waveforms and use an automated method to pick the first arrival times. We use Markov Chain Monte Carlo methods to draw many samples from the joint posterior probability distribution, on which we can estimate the key interfaces and their associated uncertainty as a function of horizontal location and depth. We test the developed method on both synthetic and field case studies. The synthetic studies show that the developed method is effective at rigorous incorporation of multiscale data and the Bayesian inversion reduces uncertainty in estimates of aquifer zonation. Applications of the approach to field data, including two surface seismic profiles located 620 m apart from each other, reveal the presence of a low-velocity subsurface zone that is laterally persistent. This geophysically defined feature is aligned with the plume axis, suggesting it may serve as an important regional preferential flow pathway. [ABSTRACT FROM AUTHOR]

We develop a state-space Bayesian framework to combine time-lapse geophysical data with other types of information for quantitative estimation of biogeochemical parameters during bioremediation. We consider characteristics of end products of biogeochemical transformations as state vectors, which evolve under constraints of local environments through evolution equations, and consider time-lapse geophysical data as available observations, which could be linked to the state vectors through petrophysical models. We estimate the state vectors and their associated unknown parameters over time using Markov chain Monte Carlo sampling methods. To demonstrate the use of the state-space approach, we apply it to complex resistivity data collected during laboratory column biostimulation experiments that were poised to precipitate iron and zinc sulfides during sulfate reduction. We develop a petrophysical model based on sphere-shaped cells to link the sulfide precipitate properties to the time-lapse geophysical attributes and estimate volume fraction of the sulfide precipitates, fraction of the dispersed, sulfide-encrusted cells, mean radius of the aggregated clusters, and permeability over the course of the experiments. Results of the case study suggest that the developed state-space approach permits the use of geophysical data sets for providing quantitative estimates of end-product characteristics and hydrological feedbacks associated with biogeochemical transformations. Although tested here on laboratory column experiment data sets, the developed framework provides the foundation needed for quantitative field-scale estimation of biogeochemical parameters over space and time using direct, but often sparse wellbore data with indirect, but more spatially extensive geophysical data sets. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of uncertainty in rock-physics models on reservoir parameter estimation using seismic amplitude variation with angle and controlled-source electromagnetics data. The reservoir parameters are related to electrical resistivity by the Poupon model and to elastic moduli and density by the Xu-White model. To handle uncertainty in the rock-physics models, we consider their outputs to be random functions with modes or means given by the predictions of those rock-physics models and we consider the parameters of the rock-physics models to be random variables defined by specified probability distributions. Using a Bayesian framework and Markov Chain Monte Carlo sampling methods, we are able to obtain estimates of reservoir parameters and information on the uncertainty in the estimation. The developed method is applied to a synthetic case study based on a layered reservoir model and the results show that uncertainty in both rock-physics models and in their parameters may have significant effects on reservoir parameter estimation. When the biases in rock-physics models and in their associated parameters are unknown, conventional joint inversion approaches, which consider rock-physics models as deterministic functions and the model parameters as fixed values, may produce misleading results. The developed stochastic method in this study provides an integrated approach for quantifying how uncertainty and biases in rock-physics models and in their associated parameters affect the estimates of reservoir parameters and therefore is a more robust method for reservoir parameter estimation. [ABSTRACT FROM AUTHOR]

The study demonstrates the use of ground-penetrating radar (GPR) tomographic data for estimating sediment geochemical parameters using data collected at the Department of Energy South Oyster bacterial transport site in Virginia. By exploiting the site-specific mutual dependence of GPR attenuation and extractable Fe(II) and Fe(III) concentrations on lithofacies, we develop a statistical model in which lithofacies and Fe(II) and Fe(III) concentrations at each pixel between the boreholes are considered as random variables. The unknown variables are estimated by conditioning to the colocated GPR data and the lithofacies measurements along boreholes using a Markov Chain Monte Carlo method. Cross-validation results show that the geophysical data, constrained by lithofacies, have the potential for providing high-resolution, multidimensional information on extractable Fe(II) and Fe(III) concentrations at the South Oyster site. [ABSTRACT FROM AUTHOR]

A Bayesian model coupled with a fuzzy neural network (BFNN) is developed to enhance the use of geophysical data in lithofacies estimation. Prior estimates are inferred from borehole lithofacies measurements using indicator kriging, and posterior estimates are obtained by updating the prior using geophysical data. The novelty of this study lies in the use of the fuzzy neural network for the inference of the likelihood function. This allows spatial correlation of lithofacies as well as nonlinear cross correlation between lithofacies and geophysical attributes to be incorporated into lithofacies estimation. The effectiveness of BFNN is demonstrated using synthetic data emulating measurements at the Lawrence Livermore National Laboratory (LLNL) Site. [ABSTRACT FROM AUTHOR]

A multidisciplinary research team has conducted a field-scale bacterial transport study within an uncontaminated sandy Pleistocene aquifer near Oyster, Virginia. The overall goal of the project was to evaluate the importance of heterogeneities in controlling the field-scale transport of bacteria that are injected into the ground for remediation purposes. Geochemical, hydrological, geological, and geophysical data were collected to characterize the site prior to conducting chemical and bacterial injection experiments. In this paper we focus on results of a hydrogeological characterization effort using geophysical data collected across a range of spatial scales. The geophysical data employed include surface ground-penetrating radar, radar cross-hole tomography, seismic cross-hole tomography, cone penetrometer, and borehole electromagnetic flowmeter. These data were used to interpret the subregional and local stratigraphy, to provide high-resolution hydraulic conductivity estimates, and to provide information about the log conductivity spatial correlation function. The information from geophysical data was used to guide and assist the field operations and to constrain the numerical bacterial transport model. Although more field work of this nature is necessary to validate the usefulness and cost-effectiveness of including geophysical data in the characterization effort, qualitative and quantitative comparisons between tomographically obtained flow and transport parameter estimates with hydraulic well bore and bromide breakthrough measurements suggest that geophysical data can provide valuable, high-resolution information. This information, traditionally only partially obtainable by performing extensive and intrusive well bore sampling, may help to reduce the ambiguity associated with hydrogeological heterogeneity that is often encountered when interpreting field-scale bacterial transport data. [ABSTRACT FROM AUTHOR]

This study explores the use of ground penetrating radar (GPR) tomographic velocity, GPR tomographic attenuation, and seismic tomographic velocity for hydraulic conductivity estimation at the South Oyster Site, using a Bayesian framework. Since site-specific relations between hydraulic conductivity and geophysical properties are often nonlinear and subject to a large degree of uncertainty such as at this site, we developed a normal linear regression model that allows exploring these relationships systematically. Although the log-conductivity displays a small variation (σ2 = 0.30) and the geophysical data vary over only a small range, results indicate that the geophysical data improve the estimates of the hydraulic conductivity. The improvement is the most significant where prior information is limited. Among the geophysical data, GPR and seismic velocity are more useful than GPR attenuation. [ABSTRACT FROM AUTHOR]

A stochastic Bayesian approach for combining well logs and geophysical surveys for enhancing subsurface characterization is presented. The main challenge we face is in creating the bridge to link between ambiguously related geophysical surveys and well data. The second challenge is imposed by the disparity between the scale of the geophysical survey and the scale of the well logs. Our approach is intended to integrate and transform the well log data to a form where it can be updated by the geophysical survey, and this tends to be a convoluted process. Our approach starts with generating images of the lithology, conditional to well logs. Each lithology image is then used as the basis for generating a series of shaliness images, conditional to well log data. Shaliness images are converted to resistivity images using a site-specific petrophysical model relating between shaliness, resistivity, and lithology, to create the necessary interface with the cross-well resistivity survey. The lithology and resistivity images are then updated using cross-well electromagnetic resistivity surveys. We explored the limits of the approach through synthetic surveys of different resolutions and error levels, employing the relationships between the geophysical and hydrological attributes, which are weak, nonlinear, or both. The synthetic surveys closely mimic the conditions at the LLNL Superfund site. We show that the proposed stochastic Bayesian approach improves hydrogeological site characterization even when using low-resolution resistivity surveys. [ABSTRACT FROM AUTHOR]