Improved estimation of structure predictor quality

<p>Abstract</p> <p>Background</p> <p>Methods that can automatically assess the quality of computationally predicted protein structures are important, as they enable the selection of the most accurate structure from an ensemble of predictions. Assessment methods that determine the quality of a predicted structure by comparing it against the various structures predicted by different servers have been shown to outperform approaches that rely on the intrinsic characteristics of the structure itself.</p> <p>Results</p> <p>We examined techniques to estimate the quality of a predicted protein structure based on prediction consensus. LGA is used to align the structure in question to the structures for the same protein predicted by different servers. We examine both static (e.g. averaging) and dynamic (e.g. support vector machine) methods for aggregating these distances on two datasets.</p> <p>Conclusion</p> <p>We find that a constrained regression approach shows consistently good performance. Although it is not always the absolute best performing scheme, it is always performs on par with the best schemes across multiple datasets. The work presented here provides the basis for the construction of a regression model trained on data from existing structure prediction servers.</p

Controls on dissolved organic carbon quantity and chemical character in temperate rivers of North America

Understanding the processes controlling the transfer and chemical composition of dissolved organic carbon (DOC) in freshwater systems is crucial to understanding the carbon cycle and the effects of DOC on water quality. Previous studies have identified watershed‐scale controls on bulk DOC flux and concentration among small basins but fewer studies have explored controls among large basins or simultaneously considered the chemical composition of DOC. Because the chemical character of DOC drives riverine biogeochemical processes such as metabolism and photodegradation, accounting for chemical character in watershed‐scale studies will improve the way bulk DOC variability in rivers is interpreted. We analyzed DOC quantity and chemical character near the mouths of 17 large North American rivers, primarily between 2008 and 2010, and identified watershed characteristics that controlled variability. We quantified DOC chemical character using both specific ultraviolet absorbance at 254 nm (SUVA254) and XAD‐resin fractionation. Mean DOC concentration ranged from 2.1 to 47 mg C L−1 and mean SUVA254 ranged from 1.3 to 4.7 L mg C−1 m−1. We found a significant positive correlation between basin wetland cover and both bulk DOC concentration (R2 = 0.78; p \u3c 0.0001) and SUVA254 (R2 = 0.91; p \u3c 0.0001), while other land use characteristics were not correlated. The strong wetland relationship with bulk DOC concentration is similar to that found by others in small headwater catchments. However, two watersheds with extremely long surface water residence times, the Colorado and St. Lawrence, diverged from this wetland relationship. These results suggest that the role of riverine processes in altering the terrestrial DOC signal at the annual scale was minimal except in river systems with long surface water residence times. However, synoptic DOC sampling of both quantity and character throughout river networks will be needed to more rigorously test this finding. The inclusion of DOC chemical character will be vital to achieving a more complete understanding of bulk DOC dynamics in large river systems

The construction and evaluation of an instrument to determine the sports knowledge of boys from grades nine through twelve,

Thesis (M.A.)--Boston Universit

Quantitative global sensitivity analysis of a biologically based dose-response pregnancy model for the thyroid endocrine system

A deterministic biologically based dose-response model for the thyroidal system in a near-term pregnant woman and the fetus was recently developed to evaluate quantitatively thyroid hormone perturbations. The current work focuses on conducting a quantitative global sensitivity analysis on this complex model to identify and characterize the sources and contributions of uncertainties in the predicted model output. The workflow and methodologies suitable for computationally expensive models, such as the Morris screening method and Gaussian Emulation processes, were used for the implementation of the global sensitivity analysis. Sensitivity indices, such as main, total and interaction effects, were computed for a screened set of the total thyroidal system descriptive model input parameters. Furthermore, a narrower sub-set of the most influential parameters affecting the model output of maternal thyroid hormone levels were identified in addition to the characterization of their overall and pair-wise parameter interaction quotients. The characteristic trends of influence in model output for each of these individual model input parameters over their plausible ranges were elucidated using Gaussian Emulation processes. Through global sensitivity analysis we have gained a better understanding of the model behavior and performance beyond the domains of observation by the simultaneous variation in model inputs over their range of plausible uncertainties. The sensitivity analysis helped identify parameters that determine the driving mechanisms of the maternal and fetal iodide kinetics, thyroid function and their interactions, and contributed to an improved understanding of the system modeled. We have thus demonstrated the use and application of global sensitivity analysis for a biologically based dose-response model for sensitive life-stages such as pregnancy that provides richer information on the model and the thyroidal system modeled compared to local sensitivity analysis

Longitudinal analyses of immune responses to Plasmodium falciparum derived peptides corresponding to novel blood stage antigens in coastal Kenya.

We have recently described 95 predicted alpha-helical coiled-coil peptides derived from putative Plasmodium falciparum erythrocytic stage proteins. Seventy peptides recognized with the highest level of prevalence by sera from three endemic areas were selected for further studies. In this study, we sequentially examined antibody responses to these synthetic peptides in two cohorts of children at risk of clinical malaria in Kilifi district in coastal Kenya, in order to characterize the level of peptide recognition by age, and the role of anti-peptide antibodies in protection from clinical malaria. Antibody levels from 268 children in the first cohort (Chonyi) were assayed against 70 peptides. Thirty-nine peptides were selected for further study in a second cohort (Junju). The rationale for the second cohort was to confirm those peptides identified as protective in the first cohort. The Junju cohort comprised of children aged 1-6 years old (inclusive). Children were actively followed up to identify episodes of febrile malaria in both cohorts. Of the 70 peptides examined, 32 showed significantly (p<0.05) increased antibody recognition in older children and 40 showed significantly increased antibody recognition in parasitaemic children. Ten peptides were associated with a significantly reduced odds ratio (OR) for an episode of clinical malaria in the first cohort of children and two of these peptides (LR146 and AS202.11) were associated with a significantly reduced OR in both cohorts. LR146 is derived from hypothetical protein PFB0145c in PlasmoDB. Previous work has identified this protein as a target of antibodies effective in antibody dependent cellular inhibition (ADCI). The current study substantiates further the potential of protein PFB0145c and also identifies protein PF11_0424 as another likely target of protective antibodies against P. falciparum malaria

Detectable HIV Viral Load in Kenya: Data from a Population-Based Survey.

IntroductionAt the individual level, there is clear evidence that Human Immunodeficiency Virus (HIV) transmission can be substantially reduced by lowering viral load. However there are few data describing population-level HIV viremia especially in high-burden settings with substantial under-diagnosis of HIV infection. The 2nd Kenya AIDS Indicator Survey (KAIS 2012) provided a unique opportunity to evaluate the impact of antiretroviral therapy (ART) coverage on viremia and to examine the risks for failure to suppress viral replication. We report population-level HIV viral load suppression using data from KAIS 2012.MethodsBetween October 2012 to February 2013, KAIS 2012 surveyed household members, administered questionnaires and drew serum samples to test for HIV and, for those found to be infected with HIV, plasma viral load (PVL) was measured. Our principal outcome was unsuppressed HIV viremia, defined as a PVL ≥ 550 copies/mL. The exposure variables included current treatment with ART, prior history of an HIV diagnosis, and engagement in HIV care. All point estimates were adjusted to account for the KAIS 2012 cluster sampling design and survey non-response.ResultsOverall, 61·2% (95% CI: 56·4-66·1) of HIV-infected Kenyans aged 15-64 years had not achieved virological suppression. The base10 median (interquartile range [IQR]) and mean (95% CI) VL was 4,633 copies/mL (0-51,596) and 81,750 copies/mL (59,366-104,134), respectively. Among 266 persons taking ART, 26.1% (95% CI: 20.0-32.1) had detectable viremia. Non-ART use, younger age, and lack of awareness of HIV status were independently associated with significantly higher odds of detectable viral load. In multivariate analysis for the sub-sample of patients on ART, detectable viremia was independently associated with younger age and sub-optimal adherence to ART.DiscussionThis report adds to the limited data of nationally-representative surveys to report population- level virological suppression. We established heterogeneity across the ten administrative and HIV programmatic regions on levels of detectable viral load. Timely initiation of ART and retention in care are crucial for the elimination of transmission of HIV through sex, needle and syringe use or from mother to child. Further refinement of geospatial mapping of populations with highest risk of transmission is necessary

Effectiveness of Advanced Nitrogen-Removal Onsite Wastewater Treatment Systems in a New England Coastal Community

Wastewater is a major source of nitrogen (N) to groundwater and coastal waterbodies, threatening both environmental and public health. Advanced N-removal onsite wastewater treatment systems (OWTS) are used to reduce effluent N concentration; however, few studies have assessed their effectiveness. We evaluated the total N (TN) concentration of effluent from 50 advanced N-removal OWTS in Charlestown, Rhode Island, USA for 3 years. We quantified differences in effectiveness as a function of N-removal technology and home occupancy pattern (seasonal vs. year-round use), and examined the relationship between wastewater properties and TN concentration. RX30 systems produced the lowest median TN concentration (mg N/L) (13.2), followed by FAST (13.4), AX20 (14.9), and Norweco (33.8). Compliance with the state’s regulatory standard for effluent TN concentration (19 mg N/L) was highest for RX30 systems (78%), followed by AX20 (73%), FAST (67%), and Norweco (0%). Occupancy pattern did not affect effluent TN concentration. Variation in TN concentration was driven by ammonium and nitrate for all technologies, and also by temperature for FAST and pH for Norweco. Median daily (g N/day) and annual (kg N/yr) N loads were significantly higher for year-round (5.3 and 2.3) than for seasonal (3.7 and 0.41) systems, likely due to differences in volume of wastewater treated. Our results suggest that advanced N-removal OWTS vary in their compliance with the state regulatory standard for effluent TN and can withstand long periods of non-use without compromising effectiveness. Nevertheless, systems used year-round do produce a higher daily and annual N load than seasonally-used systems