Dynamics of Polymerization of Macromolecules with Multiple Binding Sites

In Nature, there are many examples of biological polymerizations in which the monomers possess multiple binding sites. Under certain circumstances, such branched polymerizations may produce a large macroparticle that constitutes a significant fraction of the monomers. In this paper, we show that the polymerizations of antibodies with antigens and the polymerization of fibrin are of this type. We then present the results of stochastic simulations for the time-evolutions of these processes, and characterize their gel transitions. Finally, we relate the innate fluctuations of these processes to the gel transition, and demonstrate the necessity of using a stochastic approach to quantify polymerization kinetics

Kinetics of random aggregation-fragmentation processes with multiple components

A computationally efficient algorithm is presented for exact simulation of the stochastic time evolution of spatially homogeneous aggregation-fragmentation processes featuring multiple components or conservation laws. The algorithm can predict the average size and composition distributions of aggregating particles as well as their fluctuations, regardless of the functional form (e.g., composition dependence) of the aggregation or fragmentation kernels. Furthermore, it accurately predicts the complete time evolutions of all moments of the size and composition distributions, even for systems that exhibit gel transitions. We demonstrate the robustness and utility of the algorithm in case studies of linear and branched polymerization processes, the last of which is a two-component process. These simulation results provide the stochastic description of these processes and give new insights into their gel transitions, fluctuations, and long-time behavior when deterministic approaches to aggregation kinetics may not be reliable

Field-to-farm gate greenhouse gas emissions from corn stover production in the Midwestern U.S.

Measured field data were used to compare two allocation methods on life cycle greenhouse gas emissions from corn (Zea mays L.) stover production in the Midwest U.S. We used publicly-available crop yield, nitrogen fertilizer, and direct soil nitrous oxide emissions data from the USDA-ARS Resilient Economic Agricultural Practices research program. Field data were aggregated from 9 locations across 26 site-years for 3 stover harvest rates (no removal; moderate removal e 3.1Mg ha-1; high removal e 7.2Mg ha-1) and 2 tillage practices (conventional; reduced/no-till). Net carbon uptake by crops was computed from measured plant carbon content. Monte Carlo simulations sampled input distributions to assess variability in farm-to-gate GHG emissions. The base analysis assumed no change in soil organic carbon stocks. In all cases, net CO2 uptake during crop growth and soil-respired CO2 dominated system emissions. Emissions were most sensitive to co-product accounting method, with system expansion emissions ~15% lower than mass allocation. Regardless of accounting method, lowest emissions occurred for a moderate removal rate under reduced/no-till management. The absence of correlations between N fertilization rate and stover removal rate or soil N2O emissions in this study challenges the use of such assumptions typically employed in life cycle assessments Storage of all carbon retained on the field as SOC could reduce emissions by an additional 15%. Our results highlight how variability in GHG emissions due to location and weather can overshadow the impact of farm management practices on field-to-farm gate emissions

An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays

<p>Abstract</p> <p>Background</p> <p>Although oligonucleotide microarray technology is ubiquitous in genomic research, reproducibility and standardization of expression measurements still concern many researchers. Cross-hybridization between microarray probes and non-target ssDNA has been implicated as a primary factor in sensitivity and selectivity loss. Since hybridization is a chemical process, it may be modeled at a population-level using a combination of material balance equations and thermodynamics. However, the hybridization reaction network may be exceptionally large for commercial arrays, which often possess at least one reporter per transcript. Quantification of the kinetics and equilibrium of exceptionally large chemical systems of this type is numerically infeasible with customary approaches.</p> <p>Results</p> <p>In this paper, we present a robust and computationally efficient algorithm for the simulation of hybridization processes underlying microarray assays. Our method may be utilized to identify the extent to which nucleic acid targets (e.g. cDNA) will cross-hybridize with probes, and by extension, characterize probe robustnessusing the information specified by MAGE-TAB. Using this algorithm, we characterize cross-hybridization in a modified commercial microarray assay.</p> <p>Conclusions</p> <p>By integrating stochastic simulation with thermodynamic prediction tools for DNA hybridization, one may robustly and rapidly characterize of the selectivity of a proposed microarray design at the probe and "system" levels. Our code is available at <url>http://www.laurenzi.net</url>.</p

Global variations in diabetes mellitus based on fasting glucose and haemogloblin A1c

Fasting plasma glucose (FPG) and haemoglobin A1c (HbA1c) are both used to diagnose diabetes, but may identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening had elevated FPG, HbA1c, or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardised proportion of diabetes that was previously undiagnosed, and detected in survey screening, ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the agestandardised proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global gap in diabetes diagnosis and surveillance.peer-reviewe

Life Cycle Greenhouse Gas Emissions and Freshwater Consumption of Marcellus Shale Gas

We present results of a life cycle assessment (LCA) of Marcellus shale gas used for power generation. The analysis employs the most extensive data set of any LCA of shale gas to date, encompassing data from actual gas production and power generation operations. Results indicate that a typical Marcellus gas life cycle yields 466 kg CO₂eq/MWh (80% confidence interval: 450–567 kg CO₂eq/MWh) of greenhouse gas (GHG) emissions and 224 gal/MWh (80% CI: 185–305 gal/MWh) of freshwater consumption. Operations associated with hydraulic fracturing constitute only 1.2% of the life cycle GHG emissions, and 6.2% of the life cycle freshwater consumption. These results are influenced most strongly by the estimated ultimate recovery (EUR) of the well and the power plant efficiency: increase in either quantity will reduce both life cycle freshwater consumption and GHG emissions relative to power generated at the plant. We conclude by comparing the life cycle impacts of Marcellus gas and U.S. coal: The carbon footprint of Marcellus gas is 53% (80% CI: 44–61%) lower than coal, and its freshwater consumption is about 50% of coal. We conclude that substantial GHG reductions and freshwater savings may result from the replacement of coal-fired power generation with gas-fired power generation

Field-to-farm gate greenhouse gas emissions from corn stover production in the Midwestern U.S.

Measured field data were used to compare two allocation methods on life cycle greenhouse gas emissions from corn (Zea mays L.) stover production in the Midwest U.S. We used publicly-available crop yield, nitrogen fertilizer, and direct soil nitrous oxide emissions data from the USDA-ARS Resilient Economic Agricultural Practices research program. Field data were aggregated from 9 locations across 26 site-years for 3 stover harvest rates (no removal; moderate removal e 3.1Mg ha-1; high removal e 7.2Mg ha-1) and 2 tillage practices (conventional; reduced/no-till). Net carbon uptake by crops was computed from measured plant carbon content. Monte Carlo simulations sampled input distributions to assess variability in farm-to-gate GHG emissions. The base analysis assumed no change in soil organic carbon stocks. In all cases, net CO2 uptake during crop growth and soil-respired CO2 dominated system emissions. Emissions were most sensitive to co-product accounting method, with system expansion emissions ~15% lower than mass allocation. Regardless of accounting method, lowest emissions occurred for a moderate removal rate under reduced/no-till management. The absence of correlations between N fertilization rate and stover removal rate or soil N2O emissions in this study challenges the use of such assumptions typically employed in life cycle assessments Storage of all carbon retained on the field as SOC could reduce emissions by an additional 15%. Our results highlight how variability in GHG emissions due to location and weather can overshadow the impact of farm management practices on field-to-farm gate emissions

Statistically Enhanced Model of In Situ Oil Sands Extraction Operations: An Evaluation of Variability in Greenhouse Gas Emissions

Greenhouse gas (GHG) emissions associated with extraction of bitumen from oil sands can vary from project to project and over time. However, the nature and magnitude of this variability have yet to be incorporated into life cycle studies. We present a statistically enhanced life cycle based model (GHOST-SE) for assessing variability of GHG emissions associated with the extraction of bitumen using in situ techniques in Alberta, Canada. It employs publicly available, company-reported operating data, facilitating assessment of inter- and intraproject variability as well as the time evolution of GHG emissions from commercial in situ oil sands projects. We estimate the median GHG emissions associated with bitumen production via cyclic steam stimulation (CSS) to be 77 kg CO₂eq/bbl bitumen (80% CI: 61–109 kg CO₂eq/bbl), and via steam assisted gravity drainage (SAGD) to be 68 kg CO₂eq/bbl bitumen (80% CI: 49–102 kg CO₂eq/bbl). We also show that the median emissions intensity of Alberta’s CSS and SAGD projects have been relatively stable from 2000 to 2013, despite greater than 6-fold growth in production. Variability between projects is the single largest source of variability (driven in part by reservoir characteristics) but intraproject variability (e.g., startups, interruptions), is also important and must be considered in order to inform research or policy priorities