Stable carbon isotopes of methane for real‐time process monitoring in anaerobic digesters

Efficient operation and stability of biogas plants requires continuous monitoring of the digester content. Traditional laboratory analysis of digester sludge is often complex and time‐consuming and shows a delayed response to disruptions within the fermentation process. As a new approach, we applied an online measurement technique (laser absorption spectroscopy) for real‐time monitoring of stable carbon isotopes of methane () in a pilot‐scale biogas digester (3500 L) regularly fed with maize silage. Generally, isotopic composition of methane gives information about specific substrate degradation, that is, methanogenic pathways that reflect the actual digester state. First results of a 2‐wk monitoring experiment show that stable carbon isotopes of methane respond promptly and highly dynamic to changes in the process state of the digester. In combination with other monitoring parameters (methane production rate, concentration of volatile fatty acids, and pH) the fluctuations in can be interpreted as a change in methanogenic pathways due to a high organic loading rate. In this context, might be used as a new parameter tool for monitoring and characterization of the process state of the digester.     

Effects of chronic elevated nitrate concentrations on the structure and function of river biofilms

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Different fates of deposited and in a temperate forest in northeast China: a 15N tracer study

Increasing atmospheric reactive nitrogen (N) deposition due to human activities could change N cycling in terrestrial ecosystems. However, the differences between the fates of deposited and are still not fully understood. Here, we investigated the fates of deposited and , respectively, via the application of ¹⁵NH₄NO₃ and NH₄¹⁵NO₃ in a temperate forest ecosystem. Results showed that at 410 days after tracer application, most was immobilized in litter layer (50 ± 2%), while a considerable amount of penetrated into 0–5 cm mineral soil (42 ± 2%), indicating that litter layer and 0–5 cm mineral soil were the major N sinks of and , respectively. Broad‐leaved trees assimilated more ¹⁵N under NH₄¹⁵NO₃ treatment compared to under ¹⁵NH₄NO₃ treatment, indicating their preference for –N. At 410 days after tracer application, 16 ± 4% added ¹⁵N was found in aboveground biomass under treatment, which was twice more than that under treatment (6 ± 1%). At the same time, approximately 80% added ¹⁵N was recovered in soil and plants under both treatments, which suggested that this forest had high potential for retention of deposited N. These results provided evidence that there were great differences between the fates of deposited and , which could help us better understand the mechanisms and capability of forest ecosystems as a sink of reactive nitrogen.     

speed‐ne: Software to simulate and estimate genetic effective population size (Ne) from linkage disequilibrium observed in single samples

The genetic effective population size, N_e, can be estimated from the average gametic disequilibrium () between pairs of loci, but such estimates require evaluation of assumptions and currently have few methods to estimate confidence intervals. speed‐ne is a suite of matlab computer code functions to estimate from with a graphical user interface and a rich set of outputs that aid in understanding data patterns and comparing multiple estimators. speed‐ne includes functions to either generate or input simulated genotype data to facilitate comparative studies of estimators under various population genetic scenarios. speed‐ne was validated with data simulated under both time‐forward and time‐backward coalescent models of genetic drift. Three classes of estimators were compared with simulated data to examine several general questions: what are the impacts of microsatellite null alleles on , how should missing data be treated, and does disequilibrium contributed by reduced recombination among some loci in a sample impact . Estimators differed greatly in precision in the scenarios examined, and a widely employed estimator exhibited the largest variances among replicate data sets. speed‐ne implements several jackknife approaches to estimate confidence intervals, and simulated data showed that jackknifing over loci and jackknifing over individuals provided ~95% confidence interval coverage for some estimators and should be useful for empirical studies. speed‐ne provides an open‐source extensible tool for estimation of from empirical genotype data and to conduct simulations of both microsatellite and single nucleotide polymorphism (SNP) data types to develop expectations and to compare estimators.     

Integrated process development of a reactive extraction concept for itaconic acid and application to a real fermentation broth

Itaconic acid (IA) has a high potential to be used as a bio‐based platform chemical and its biocatalytic production via fermentation has significantly improved within the last decade. Additionally downstream processing using reactive extraction (RE) was described, potentially enabling a more efficient sustainable bioprocess producing IA. The bottleneck to overcome is the connection of up‐ and downstream processing, caused by lack of biocompatibility of the RE systems and direct application to fermentation broth. Within this study, a biocompatible RE system for IA is defined (pH dependency, extraction mechanism) and used for direct application to a fermentation broth. By optimizing the biocatalyst, the production medium, and the extraction system in an integrated approach, it was possible to define critical parameters that enabled a tuning of the overall bioprocess. With an extraction yield of Y_IA = 0.80 ± 0.03, IA could be produced as sole carboxylic acid ( = 0.490 mol/kg^aq) using a RE system consisting of ethyl oleate as organic solvent and tri‐n‐octylamine as extractant ( = 0.6 mol/kg^org). This work is a proof of concept and demonstrates that by joint consideration of up‐ and downstream processing, optimized bioprocesses can be developed.     

Estimators for QST and coalescence times

Comparisons of to can provide insights into the evolutionary processes that lead to differentiation, or lack thereof, among the phenotypes of different groups (e.g., populations, species), and these comparisons have been performed on a variety of taxa, including humans. Here, I show that for neutrally evolving (i.e., by genetic drift, mutation, and gene flow alone) quantitative characters, the two commonly used estimators have somewhat different interpretations in terms of coalescence times, particularly when the number of groups that have been sampled is small. A similar situation occurs for estimators. Consequently, when observations come from only a small number of groups, which is not an unusual situation, it is important to match estimators appropriately when comparing to .     

Comparative effects of ammonium,nitrate and urea on growth and photosynthetic efficiency of three bloom‐forming cyanobacteria

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Explaining the doubling of N2O emissions under elevated CO2 in the Giessen FACE via in‐field 15N tracing

Rising atmospheric CO₂ concentrations are expected to increase nitrous oxide (N₂O) emissions from soils via changes in microbial nitrogen (N) transformations. Several studies have shown that N₂O emission increases under elevated atmospheric CO₂ (eCO₂), but the underlying processes are not yet fully understood. Here, we present results showing changes in soil N transformation dynamics from the Giessen Free Air CO₂ Enrichment (GiFACE): a permanent grassland that has been exposed to eCO₂, +20% relative to ambient concentrations (aCO₂), for 15 years. We applied in the field an ammonium‐nitrate fertilizer solution, in which either ammonium () or nitrate () was labelled with ¹⁵N. The simultaneous gross N transformation rates were analysed with a ¹⁵N tracing model and a solver method. The results confirmed that after 15 years of eCO₂ the N₂O emissions under eCO₂ were still more than twofold higher than under aCO₂. The tracing model results indicated that plant uptake of did not differ between treatments, but uptake of was significantly reduced under eCO₂. However, the and availability increased slightly under eCO₂. The N₂O isotopic signature indicated that under eCO₂ the sources of the additional emissions, 8,407 μg N₂O–N/m² during the first 58 days after labelling, were associated with reduction (+2.0%), oxidation (+11.1%) and organic N oxidation (+86.9%). We presume that increased plant growth and root exudation under eCO₂ provided an additional source of bioavailable supply of energy that triggered as a priming effect the stimulation of microbial soil organic matter (SOM) mineralization and fostered the activity of the bacterial nitrite reductase. The resulting increase in incomplete denitrification and therefore an increased N₂O:N₂ emission ratio, explains the doubling of N₂O emissions. If this occurs over a wide area of grasslands in the future, this positive feedback reaction may significantly accelerate climate change.     

Purging putative siblings from population genetic data sets: a cautionary view

Interest has surged recently in removing siblings from population genetic data sets before conducting downstream analyses. However, even if the pedigree is inferred correctly, this has the potential to do more harm than good. We used computer simulations and empirical samples of coho salmon to evaluate strategies for adjusting samples to account for family structure. We compared performance in full samples and sibling‐reduced samples of estimators of allele frequency (), population differentiation () and effective population size (). Results: (i) unless simulated samples included large family groups together with a component of unrelated individuals, removing siblings generally reduced precision of and ; (ii) based on the linkage disequilibrium method was largely unbiased using full random samples but became increasingly upwardly biased under aggressive purging of siblings. Under nonrandom sampling (some families over‐represented), using full samples was downwardly biased; removing just the right ‘Goldilocks’ fraction of siblings could produce an unbiased estimate, but this sweet spot varied widely among scenarios; (iii) weighting individuals based on the inferred pedigree (to produce a best linear unbiased estimator, BLUE) maximized precision of when the inferred pedigree was correct but performed poorly when the pedigree was wrong; (iv) a variant of sibling removal that leaves intact small sibling groups appears to be more robust to errors in inferences about family structure. Our results illustrate the complex challenges posed by presence of family structure, suggest that no single optimal solution exists and argue for caution in adjusting population genetic data sets for the presence of putative siblings without fully understanding the consequences.     

Nonlinear response surface in the study of interaction analysis of three combination drugs

Few articles have been written on analyzing three‐way interactions between drugs. It may seem to be quite straightforward to extend a statistical method from two‐drugs to three‐drugs. However, there may exist more complex nonlinear response surface of the interaction index () with more complex local synergy and/or local antagonism interspersed in different regions of drug combinations in a three‐drug study, compared in a two‐drug study. In addition, it is not possible to obtain a four‐dimensional (4D) response surface plot for a three‐drug study. We propose an analysis procedure to construct the dose combination regions of interest (say, the synergistic areas with ). First, use the model robust regression method (MRR), a semiparametric method, to fit the entire response surface of the , which allows to fit a complex response surface with local synergy/antagonism. Second, we run a modified genetic algorithm (MGA), a stochastic optimization method, many times with different random seeds, to allow to collect as many feasible points as possible that satisfy the estimated values of . Last, all these feasible points are used to construct the approximate dose regions of interest in a 3D. A case study with three anti‐cancer drugs in an in vitro experiment is employed to illustrate how to find the dose regions of interest.     

A practical approach in bioreactor scale‐up and process transfer using a combination of constant P/V and vvm as the criterion

Bioreactor scale‐up is a critical step in the production of therapeutic proteins such as monoclonal antibodies (MAbs). With the scale‐up criterion such as similar power input per volume or O₂ volumetric mass transfer coefficient ( ), adequate oxygen supply and cell growth can be largely achieved. However, CO₂ stripping in the growth phase is often inadequate. This could cascade down to increased base addition and osmolality, as well as residual lactate increase and compromised production and product quality. Here we describe a practical approach in bioreactor scale‐up and process transfer, where bioreactor information may be limited. We evaluated the sparger and (CO₂ volumetric mass transfer coefficient) from a range of bioreactor scales (3–2,000 L) with different spargers. Results demonstrated that for oxygen is not an issue when scaling from small‐scale to large‐scale bioreactors at the same gas flow rate per reactor volume (vvm). Results also showed that sparging CO₂ stripping, , is dominated by the gas throughput. As a result, a combination of a minimum constant vvm air or N₂ flow with a similar specific power was used as the general scale‐up criterion. An equation was developed to determine the minimum vvm required for removing CO₂ produced from cell respiration. We demonstrated the effectiveness of using such scale‐up criterion with five MAb projects exhibiting different cell growth and metabolic characteristics, scaled from 3 to 2,000 L bioreactors across four sites. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1146–1159, 2017     

Archaea S‐layer nanotube from a “black smoker” in complex with cyclo‐octasulfur (S8) rings

Elemental sulfur exists primarily as an ring and serves as terminal electron acceptor for a variety of sulfur‐fermenting bacteria. Hyperthermophilic archaea from black smoker vents are an exciting research tool to advance our knowledge of sulfur respiration under extreme conditions. Here, we use a hybrid method approach to demonstrate that the proteinaceous cavities of the S‐layer nanotube of the hyperthermophilic archaeon Staphylothermus marinus act as a storage reservoir for cyclo‐octasulfur . Fully atomistic molecular dynamics (MD) simulations were performed and the method of multiconfigurational thermodynamic integration was employed to compute the absolute free energy for transferring a ring of elemental sulfur from an aqueous bath into the largest hydrophobic cavity of a fragment of archaeal tetrabrachion. Comparisons with earlier MD studies of the free energy of hydration as a function of water occupancy in the same cavity of archaeal tetrabrachion show that the sulfur ring is energetically favored over water.     

Summary goodness‐of‐fit statistics for binary generalized linear models with noncanonical link functions

Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness‐of‐fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer–Lemeshow () and Pigeon–Heyse (J²) statistics can be applied directly. In a simulation study, , , and J² were used to evaluate the fit of probit, log–log, complementary log–log, and log models, all calculated with a common grouping method. The statistic consistently maintained Type I error rates, while those of and J² were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, had more power than or J².     

Contemporary effective population and metapopulation size (Ne and meta‐Ne): comparison among three salmonids inhabiting a fragmented system and differing in gene flow and its asymmetries

We estimated local and metapopulation effective sizes ( and meta‐) for three coexisting salmonid species (Salmo salar, Salvelinus fontinalis, Salvelinus alpinus) inhabiting a freshwater system comprising seven interconnected lakes. First, we hypothesized that might be inversely related to within‐species population divergence as reported in an earlier study (i.e., F_ST: S. salar> S. fontinalis> S. alpinus). Using the approximate Bayesian computation method implemented in ONeSAMP, we found significant differences in () between species, consistent with a hierarchy of adult population sizes (). Using another method based on a measure of linkage disequilibrium (LDNE: ), we found more finite values for S. salar than for the other two salmonids, in line with the results above that indicate that S. salar exhibits the lowest among the three species. Considering subpopulations as open to migration (i.e., removing putative immigrants) led to only marginal and non‐significant changes in , suggesting that migration may be at equilibrium between genetically similar sources. Second, we hypothesized that meta‐ might be significantly smaller than the sum of local s (null model) if gene flow is asymmetric, varies among subpopulations, and is driven by common landscape features such as waterfalls. One ‘bottom‐up’ or numerical approach that explicitly incorporates variable and asymmetric migration rates showed this very pattern, while a number of analytical models provided meta‐ estimates that were not significantly different from the null model or from each other. Our study of three species inhabiting a shared environment highlights the importance and utility of differentiating species‐specific and landscape effects, not only on dispersal but also in the demography of wild populations as assessed through local s and meta‐s and their relevance in ecology, evolution and conservation.     

pKa predictions for proteins,RNAs, and DNAs with the Gaussian dielectric function using DelPhi pKa

We developed a Poisson‐Boltzmann based approach to calculate the values of protein ionizable residues (Glu, Asp, His, Lys and Arg), nucleotides of RNA and single stranded DNA. Two novel features were utilized: the dielectric properties of the macromolecules and water phase were modeled via the smooth Gaussian‐based dielectric function in DelPhi and the corresponding electrostatic energies were calculated without defining the molecular surface. We tested the algorithm by calculating values for more than 300 residues from 32 proteins from the PPD dataset and achieved an overall RMSD of 0.77. Particularly, the RMSD of 0.55 was achieved for surface residues, while the RMSD of 1.1 for buried residues. The approach was also found capable of capturing the large shifts of various single point mutations in staphylococcal nuclease (SNase) from ‐cooperative dataset, resulting in an overall RMSD of 1.6 for this set of pKa's. Investigations showed that predictions for most of buried mutant residues of SNase could be improved by using higher dielectric constant values. Furthermore, an option to generate different hydrogen positions also improves predictions for buried carboxyl residues. Finally, the calculations on two RNAs demonstrated the capability of this approach for other types of biomolecules. Proteins 2015; 83:2186–2197. © 2015 Wiley Periodicals, Inc.     

High‐throughput identification of protein mutant stability computed from a double mutant fitness landscape

The effect of a mutation on protein stability is traditionally measured by genetic construction, expression, purification, and physical analysis using low‐throughput methods. This process is tedious and limits the number of mutants able to be examined in a single study. In contrast, functional fitness effects can be measured in a high‐throughput manner by various deep mutational scanning tools. Using protein GB 1, we have recently demonstrated the feasibility of estimating the mutational stability effect ( G) of single‐substitution based on the functional fitness profile of all double‐substitutions. The principle is to identify genetic backgrounds that have an exhausted stability margin. The functional effect of an additional substitution on these genetic backgrounds can then be used to compute the mutational G based on the biophysical relationship between functional fitness and thermodynamic stability. However, to identify such genetic backgrounds, the approach described in our previous study required a benchmark dataset, which is a set of known mutational G. In this study, a benchmark‐independent approach is developed. The genetic backgrounds of interest are identified using k‐means clustering with the integration of structural information. We further demonstrated that a reasonable approximation of G can also be obtained without taking structural information into account. In summary, this study describes a novel method for computing G from double‐substitution functional fitness profiles alone, without relying on any known mutational G as a benchmark.     

Effective size of density‐dependent populations in fluctuating environments

Reliable estimates of effective population size are of central importance in population genetics and evolutionary biology. For populations that fluctuate in size, harmonic mean population size is commonly used as a proxy for (multi‐) generational effective size. This assumes no effects of density dependence on the ratio between effective and actual population size, which limits its potential application. Here, we introduce density dependence on vital rates in a demographic model of variance effective size. We derive an expression for the ratio in a density‐regulated population in a fluctuating environment. We show by simulations that yearly genetic drift is accurately predicted by our model, and not proportional to as assumed by the harmonic mean model, where N is the total population size of mature individuals. We find a negative relationship between and N. For a given N, the ratio depends on variance in reproductive success and the degree of resource limitation acting on the population growth rate. Finally, our model indicate that environmental stochasticity may affect not only through fluctuations in N, but also for a given N at a given time. Our results show that estimates of effective population size must include effects of density dependence and environmental stochasticity.     

Deep soil carbon dynamics are driven more by soil type than by climate: a worldwide meta‐analysis of radiocarbon profiles

The response of soil carbon dynamics to climate and land‐use change will affect both the future climate and the quality of ecosystems. Deep soil carbon (>20 cm) is the primary component of the soil carbon pool, but the dynamics of deep soil carbon remain poorly understood. Therefore, radiocarbon activity (C), which is a function of the age of carbon, may help to understand the rates of soil carbon biodegradation and stabilization. We analyzed the published C contents in 122 profiles of mineral soil that were well distributed in most of the large world biomes, except for the boreal zone. With a multivariate extension of a linear mixed‐effects model whose inference was based on the parallel combination of two algorithms, the expectation–maximization (EM) and the Metropolis–Hasting algorithms, we expressed soil C profiles as a four‐parameter function of depth. The four‐parameter model produced insightful predictions of soil C as dependent on depth, soil type, climate, vegetation, land‐use and date of sampling (). Further analysis with the model showed that the age of topsoil carbon was primarily affected by climate and cultivation. By contrast, the age of deep soil carbon was affected more by soil taxa than by climate and thus illustrated the strong dependence of soil carbon dynamics on other pedologic traits such as clay content and mineralogy.     

Graphene oxide/silver nanohybrid: Optimization,antibacterial activity and its impregnation on bacterial cellulose as a potential wound dressing based on GO‐Ag nanocomposite‐coated BC

Recently, bacterial cellulose (BC) based wound dressing have raised significant interests in medical fields. However, to our best knowledge, it is apparent that the BC itself has no antibacterial activity. In this study, we optimized graphene oxide‐silver (GO‐Ag) nanohybrid synthesis using Response Surface Methodology and impregnate it to BC and carefully investigate their antibacterial activities against both the Gram‐negative bacteria Escherichia coli and the Gram‐positive bacteria Staphylococcus aureus. We discover that, compared to silver nanoparticles, GO‐Ag nanohybrid with an optimal GO suspension's pH and ratio is much more effective and shows synergistically enhanced, strong antibacterial activities at rather low dose. The GO‐Ag nanohybrid is more toxic to E. coli than that to S. aureus. The antibacterial and mechanical properties of BC/GO‐Ag composite are further investigated.