Cross-site Variability Research Articles

Combining Passive Sampling with Suspect and Nontarget Screening to Characterize Organic Micropollutants in Streams Draining Mixed-Use Watersheds.

Organic micropollutants (OMPs) represent an anthropogenic stressor on stream ecosystems. In this work, we combined passive sampling with suspect and nontarget screening enabled by liquid chromatography-high-resolution mass spectrometry to characterize complex mixtures of OMPs in streams draining mixed-use watersheds. Suspect screening identified 122 unique OMPs for target quantification in polar organic chemical integrative samplers (POCIS) and grab samples collected from 20 stream sites in upstate New York over two sampling seasons. Hierarchical clustering established the co-occurrence profiles of OMPs in connection with watershed attributes indicative of anthropogenic influences. Nontarget screening leveraging the time-integrative nature of POCIS and the cross-site variability in watershed attributes prioritized and confirmed 11 additional compounds that were ubiquitously present in monitored streams. Field sampling rates for 37 OMPs that simultaneously occurred in POCIS and grab samples spanned the range of 0.02 to 0.22 L/d with a median value of 0.07 L/d. Comparative analyses of the daily average loads, cumulative exposure-activity ratios, and multi-substance potentially affected fractions supported the feasibility of complementing grab sampling with POCIS for OMP load estimation and screening-level risk assessments. Overall, this work demonstrated a multi-watershed sampling and screening approach that can be adapted to assess OMP contamination in streams across landscapes.

Multisite reproducibility of quantitative susceptibility mapping and effective transverse relaxation rate in deep gray matter at 3 T using locally optimized sequences in 24 traveling heads

Iron concentration in the human brain plays a crucial role in several neurodegenerative diseases and can be monitored noninvasively using quantitative susceptibility mapping (QSM) and effective transverse relaxation rate (R2 *) mapping from multiecho T2 *-weighted images. Large population studies enable better understanding of pathologies and can benefit from pooling multisite data. However, reproducibility may be compromised between sites and studies using different hardware and sequence protocols. This work investigates QSM and R2 * reproducibility at 3T using locally optimized sequences from three centers and two vendors, and investigates possible reduction of cross-site variability through postprocessing approaches. Twenty-four healthy subjects traveled between three sites and were scanned twice at each site. Scan-rescan measurements from seven deep gray matter regions were used for assessing within-site and cross-site reproducibility using intraclass correlation coefficient (ICC) and within-subject standard deviation (SDw) measures. In addition, multiple QSM and R2 * postprocessing options were investigated with the aim to minimize cross-site sequence-related variations, including: mask generation approach, echo-timing selection, harmonizing spatial resolution, field map estimation, susceptibility inversion method, and linear field correction for magnitude images. The same-subject cross-site region of interest measurements for QSM and R2 * were highly correlated (R2 ≥ 0.94) and reproducible (mean ICC of 0.89 and 0.82 for QSM and R2 *, respectively). The mean cross-site SDw was 4.16 parts per billion (ppb) for QSM and 1.27 s-1 for R2 *. For within-site measurements of QSM and R2 *, the mean ICC was 0.97 and 0.87 and mean SDw was 2.36 ppb and 0.97 s-1 , respectively. The precision level is regionally dependent and is reduced in the frontal lobe, near brain edges, and in white matter regions. Cross-site QSM variability (mean SDw) was reduced up to 46% through postprocessing approaches, such as masking out less reliable regions, matching available echo timings and spatial resolution, avoiding the use of the nonconsistent magnitude contrast between scans in field estimation, and minimizing streaking artifacts.

Bayesian network based machine learning for design of pile foundations

Realistic estimation of the uncertainty associated with the bearing capacity, which is often represented by the uncertainty of a model bias factor, is important to reliability-based design of pile foundations. Due to the existence of cross-site variability, the statistics of a model bias factor may vary from one site to another. Also, as the number of site-specific load test data is often very limited, it is difficult to obtain the site-specific statistics of the model bias factor. This paper aims to establish a Bayesian network based machine learning method to develop site-specific statistics of the model bias factor utilizing information from both the regional and site-specific load test data, through which the resistance factor for design of the pile foundation can be calibrated. The suggested method has been verified using a comprehensive load test database for design of driven piles in Shanghai, China. It is found that a few site-specific pile load test data can significantly reduce the uncertainty associated with the model bias factor and hence increase the cost-effectiveness of the pile design. The method suggested in this paper lays a sound foundation for site-specific reliability-based design of pile foundations, and provides useful insight into the planning of site-specific load tests for design of pile foundations.

Differential responses of carbon and water vapor fluxes to climate among evergreen needleleaf forests in the USA

Understanding the differences in carbon and water vapor fluxes of spatially distributed evergreen needleleaf forests (ENFs) is crucial for accurately estimating regional or global carbon and water budgets and when predicting the responses of ENFs to current and future climate. We compared the fluxes of ten AmeriFlux ENF sites to investigate cross-site variability in net ecosystem exchange of carbon (NEE), gross primary production (GPP), and evapotranspiration (ET). We used wavelet cross-correlation analysis to examine responses of NEE and ET to common climatic drivers over multiple timescales and also determined optimum values of air temperature (T a) and vapor pressure deficit (VPD) for NEE and ET. We found larger differences in the NEE spectra than in the ET spectra across sites, demonstrating that spatial (site-to-site) variability was larger for NEE than for ET. The NEE and ET were decoupled differently across ENF sites because the wavelet cospectra between ET and climate variables were similar at all sites, while the wavelet cospectra between NEE and climate variables were higher (i.e., closer coupling between NEE and climatic drivers) in semi-arid and Mediterranean sites than in other sites. Ecosystem water use efficiency (EWUE) based on annual GPP/ET ranged from 1.3 ± 0.18 to 4.08 ± 0.62 g C mm−1 ET, while EWUE based on annual net ecosystem production (NEP)/ET ranged from 0.06 ± 0.04 to 1.02 ± 0.16 g C mm−1 ET) among ENFs. Responses of NEE and ET to T a varied across climatic zones. In particular, for ENF sites in semi-arid and Mediterranean climates, the maximum NEE and ET occurred at lower ranges of T a than in sites with warm and humid summers. The optimum T a and VPD values were higher for ET than for NEE, and ET was less sensitive to high values of T a and VPD. Large spatial variability in carbon and water vapor fluxes among ENFs and large variations in responses of NEE and ET to major climate variables among climatic zones necessitate sub-plant functional type parameterization based on climatic zones to better represent climate sensitivity of ENFs and to reduce uncertainty in model predictions.

Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests

Understanding the temporal patterns of leaf traits is critical in determining the seasonality and magnitude of terrestrial carbon, water, and energy fluxes. However, we lack robust and efficient ways to monitor the temporal dynamics of leaf traits. Here we assessed the potential of leaf spectroscopy to predict and monitor leaf traits across their entire life cycle at different forest sites and light environments (sunlit vs. shaded) using a weekly sampled dataset across the entire growing season at two temperate deciduous forests. The dataset includes field measured leaf-level directional-hemispherical reflectance/transmittance together with seven important leaf traits [total chlorophyll (chlorophyll a and b), carotenoids, mass-based nitrogen concentration (Nmass), mass-based carbon concentration (Cmass), and leaf mass per area (LMA)]. All leaf traits varied significantly throughout the growing season, and displayed trait-specific temporal patterns. We used a Partial Least Square Regression (PLSR) modeling approach to estimate leaf traits from spectra, and found that PLSR was able to capture the variability across time, sites, and light environments of all leaf traits investigated (R2 = 0.6–0.8 for temporal variability; R2 = 0.3–0.7 for cross-site variability; R2 = 0.4–0.8 for variability from light environments). We also tested alternative field sampling designs and found that for most leaf traits, biweekly leaf sampling throughout the growing season enabled accurate characterization of the seasonal patterns. Compared with the estimation of foliar pigments, the performance of Nmass, Cmass and LMA PLSR models improved more significantly with sampling frequency. Our results demonstrate that leaf spectra-trait relationships vary with time, and thus tracking the seasonality of leaf traits requires statistical models calibrated with data sampled throughout the growing season. Our results have broad implications for future research that use vegetation spectra to infer leaf traits at different growing stages.

Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium.

Standardization of immunophenotyping requires careful attention to reagents, sample handling, instrument setup, and data analysis, and is essential for successful cross-study and cross-center comparison of data. Experts developed five standardized, eight-color panels for identification of major immune cell subsets in peripheral blood. These were produced as pre-configured, lyophilized, reagents in 96-well plates. We present the results of a coordinated analysis of samples across nine laboratories using these panels with standardized operating procedures (SOPs). Manual gating was performed by each site and by a central site. Automated gating algorithms were developed and tested by the FlowCAP consortium. Centralized manual gating can reduce cross-center variability, and we sought to determine whether automated methods could streamline and standardize the analysis. Within-site variability was low in all experiments, but cross-site variability was lower when central analysis was performed in comparison with site-specific analysis. It was also lower for clearly defined cell subsets than those based on dim markers and for rare populations. Automated gating was able to match the performance of central manual analysis for all tested panels, exhibiting little to no bias and comparable variability. Standardized staining, data collection, and automated gating can increase power, reduce variability, and streamline analysis for immunophenotyping.

Lessons from Initiating the First Veterans Health Administration (VA) Women's Health Practice-based Research Network (WH-PBRN) Study.

The Veterans Health Administration (VA) Women's Health Practice-Based Research Network (WH-PBRN) was created to foster innovations for the health care of women veterans. The inaugural study by the WH-PBRN was designed to identify women veterans' own priorities and preferences for mental health services and to inform refinements to WH-PBRN operational procedures. Addressing the latter, this article reports lessons learned from the inaugural study. WH-PBRN site coordinators at the 4 participating sites convened weekly with the study coordinator and the WH-PBRN program manager to address logistical issues and identify lessons learned. Findings were categorized into a matrix of challenges and facilitators related to key study elements. Challenges to the conduct of PBRN-based research included tracking of regulatory documents; cross-site variability in some regulatory processes; and troubleshooting logistics of clinic-based recruitment. Facilitators included a central institutional review board, strong relationships between WH-PBRN research teams and women's health clinic teams, and the perception that women want to help other women veterans. Our experience with the inaugural WH-PBRN study demonstrated the feasibility of establishing productive relationships between local clinicians and researchers, and of recruiting a special population (women veterans) in diverse sites within an integrated health care system. This identified strengths of a PBRN approach.

Calibrating cross-site variability for reliability-based design of pile foundations

The cross-site variability (i.e., variability from site to site) makes the statistics of the bias factor of a design model vary from site to site. How to characterize the cross-site variability of the model bias factor is important for design of pile foundations based on site-specific load test data. In this study, a probabilistic model that allows for explicit modeling of the cross-site variability is suggested. An equation is derived based on Bayes’ theorem to calibrate the suggested model with load test data from different sites, which is applicable even when the number of load tests at each site is small. A procedure based on hybrid Markov Chain Monte Carlo simulation is employed to solve the Bayesian equation. How to update the statistics of the model bias factor, when applied to a future site, with site-specific load test data is also described. As an illustration, the probabilistic model is applied to the design of bored piles in Shanghai, China. It is found that, given a certain number of site-specific pile load tests, the effect of updating depends on the mean and the COV of the measured model bias factor. With the assistance of regional experience, a small number of load tests can significantly reduce the uncertainty associated with the design model, and further increase in the number of load tests may not change the site-specific statistics of the bias factor and hence the resistance factor substantially.

Availability of healthy snack foods and beverages in stores near high-income urban, low-income urban, and rural elementary and middle schools in Oregon.

Food stores near schools are an important source of snacks for children. However, few studies have assessed availability of healthy snacks in these settings. The aim of this study was to assess availability of healthy snack foods and beverages in stores near schools and examine how availability of healthy items varied by poverty level of the school and rural-urban location. Food stores were selected based on their proximity to elementary/middle schools in three categories: high-income urban, low-income urban, and rural. Audits were conducted within the stores to assess the presence or absence of 48 items in single-serving sizes, including healthy beverages, healthy snacks, fresh fruits, and fresh vegetables. Overall, availability of healthy snack foods and beverages was low in all stores. However, there was significant cross-site variability in availability of several snack and fruit items, with stores near high-income urban schools having higher availability, compared to stores near low-income urban and/or rural schools. Stores near rural schools generally had the lowest availability, although several fruits were found more often in rural stores than in urban stores. There were no significant differences in availability of healthy beverages and fresh vegetables across sites. Availability of healthy snack foods and beverages was limited in stores near schools, but these limitations were more severe in stores proximal to rural and low-income schools. Given that children frequent these stores to purchase snacks, efforts to increase the availability of healthy products, especially in stores near rural and low-income schools, should be a priority.

Perioperative compliance with VSGBI quality improvement framework for major limb amputations in a single vascular centre

Perioperative compliance with VSGBI quality improvement framework for major limb amputations in a single vascular centre

Exploring short-term leaf-litter decomposition dynamics in a Mediterranean ecosystem: dependence on litter type and site conditions

Plant litter decomposition plays an important role in the storage of soil organic matter in terrestrial ecosystems. Conversion of native vegetation to agricultural lands and subsequent land abandonment can lead to shifts in canopy structure, and consequently influence decomposition dynamics by alterations in soil temperature and moisture conditions, solar radiation exposure, and soil erosion patterns. This study was conducted to assess which parameters were more closely related to short-term decomposition dynamics of two predominant Mediterranean leaf litter types. Using the litterbag technique, we incubated leaf litter of Pinus halepensis and Rosmarinus officinalis in two Mediterranean land-uses with different degree of vegetation cover (open forest, abandoned agricultural field). Fresh local litter lost between 20 and 55% of its initial mass throughout the 20-month incubation period. Rosemary litter decomposed faster than pine litter, showing net N immobilization in the early stages of decomposition, in contrast to the net N release exhibited by pine litter. Parameters related to litter quality (N content or C:N) or land-use/site conditions (ash content, an index of soil deposition on litter) were found to explain the cross-site variability in mass loss rates for rosemary and Aleppo pine litter, respectively. The results from this study suggest that decomposition drivers may differ depending on litter type in this Mediterranean ecosystem. While rosemary litter was degraded mainly by microbial activity, decomposition of pine litter was likely driven primarily by abiotic processes like soil erosion.

Psychiatric Disorder, Comorbidity, and Suicidal Behavior in Juvenile Justice Youth

Results of collaborations with juvenile justice agencies nationwide were examined to provide generalizable estimates of psychiatric disorder and suicidality among justice system youth. Diagnostic assessments were aggregated from 57 sites ( N = 9,819) from an automated computer-assisted self-interview (Voice Diagnostic Interview Schedule for Children). Disorder was predicted from setting type (system intake, detention, corrections), adjusting for demographic and offense characteristics, and for cross-site variability within settings. Race by comorbid disorder interactions were examined in predicting substance use disorder (SUD). White youth, repeat offenders, and those with further justice system penetration reported higher rates of most disorders; girls reported higher rates of internalizing conditions only. Although presence of another disorder increased SUD for most groups, SUD was high in American Indians regardless of either affective disorder or recent suicide attempt. Findings highlight (a) varying mental health needs across settings, (b) prior justice contact relating strongly to need, (c) girls’ elevated rates of internalizing disorder, and (d) racial/ethnic differences in diagnostic profiles.

Healthy food availability in small urban food stores: a comparison of four US cities

Given that small food stores may be important retail food sources in low-income urban communities, our objective was to examine cross-city comparative data documenting healthy food availability within such facilities, particularly those located in low-income areas and nearby schools. Food stores in Baltimore, Maryland; Minneapolis/St. Paul, Minnesota; Oakland, California; and Philadelphia, Pennsylvania were selected for assessment based on proximity to low-income schools. Stores were defined as: (i) single-aisle (n 45); (ii) small (2-5 aisles; n 52); and (iii) large (> or = 6 aisles; n 8). Staff conducted in-store audits to assess the presence/absence of twenty-eight healthy items, organized within five categories: (i) fresh fruits/vegetables, (ii) processed fruits/vegetables, (iii) healthy beverages/low-fat dairy, (iv) healthy snacks and (v) other healthy staple foods. The availability of healthy food items was low, particularly in single-aisle and small stores, and there was significant cross-site variability in the availability of healthy snacks (P < 0.0001) and other healthy staple foods (P < 0.0001). No cross-site differences existed for fruits/vegetables or healthy beverages/low-fat dairy availability. Healthy food availability scores increased significantly with store size for nearly all food/beverage categories (P < 0.01). Overall, healthy food availability in these venues was limited. Region-specific factors may be important to consider in understanding factors influencing healthy food availability in small urban markets. Data suggest that efforts to promote healthy diets in low-income communities may be compromised by a lack of available healthy foods. Interventions targeting small stores need to be developed and tailored for use in urban areas across the USA.

Cost–threshold analyses of the National AIDS Demonstration Research HIV Prevention Interventions

The goal of the multisite National AIDS Demonstration Research (NADR) program was to reduce the sexual and drug injection-related HIV risks of out-of-treatment injection drug users and their sex partners. Previous analyses have established that the NADR interventions were effective at changing participants' risky behaviors. This study was to determine whether the NADR program also was cost-effective. Data from eight NADR study sites were included in the analysis. A mathematical model was used to translate reported sexual and injection-related behavior changes into an estimate of the number of infections prevented by the NADR interventions and then to calculate the corresponding savings in averted HIV/AIDS medical care costs and quality-adjusted years of life, assuming United States values for these parameters. Because cost data were not collected in the original NADR evaluation, the savings in averted medical care costs were compared with the cost of implementing a similar intervention program for injection drug users. The eight NADR interventions prevented approximately 129 infections among 6629 participants and their partners. Overall, the NADR program would be cost saving (i.e. provide net economic savings) if it cost less than US$2107 per person and would be cost-effective if it cost less than US$10,264 per person. Both of these estimates are considerably larger than the US$273 per person cost of the comparison intervention. There was substantial cross-site variability. The results of this analysis strongly suggest that the NADR interventions were cost-saving overall and were, at the very least, cost-effective at all eight sites. In the United States and other developed counties, investments in HIV-prevention interventions such as these have the potential to save substantial economic resources by averting HIV-related medical care expenses among injection drug users.