Full-loop operation and cathodic acidification of a microbial fuel cell operated on domestic wastewater

The present study emphasizes the importance of overcoming proton limitation in a microbial fuel cell operated on domestic wastewater. When the anode-treated effluent was allowed to trickle into the cathodic compartment (full-loop operation), high COD and suspended solids removal efficiencies over 75% and 84%, respectively, were achieved while ensuring substantial and sustainable power generation. Lower removal efficiencies resulted in decreased cell electromotive force caused by excess substrate crossover. By decreasing the pH in the cathodic compartment to values below 2, we were able to further increase the maximum power generation by 180% in batch mode and 380% in continuous mode as compared to a negative control (tap water of pH 7.6). Under the optimized conditions, the internal resistance and electromotive force were 11 Ω and 0.6 V, respectively, which correspond to the state of the art.     

Comparative study of the effect of ultrasonication on the anaerobic biodegradability of food waste in single and two-stage systems

Five different mesophilic systems were evaluated in this study for the anaerobic treatment of food waste. Systems A and B were one stage methane with unsonicated and sonicated feeds, respectively, while, systems C and D were two-stage hydrogen and methane with unsonicated and sonicated feeds, respectively. System E comprised a novel sonicated biological hydrogen reactor (SBHR) followed by methane reactor. The results showed that sonication inside the reactor in the first stage (system E) showed superior results compared to all other systems. Overall VSS removal efficiencies of 67%, 59%, 51%, 44%, and 36% were achieved in systems E, D, C, B, and A, respectively. Volumetric hydrogen production rates of 4.8, 3.3, and 2.6 L H₂/L_reactor d were achieved in the SBHR, CSTR with and without sonicated feed, respectively, while, methane production rates of 1.6, 2.1, 2.3, 2.6, and 3.2 L CH₄/L_reactor d were achieved in systems A-E, respectively.     

A Review and Evaluation of Plant Protection Product Ranking Tools Used in Agriculture

Pesticides used in the agricultural sector could potentially negatively impact the ecosystem, and consequently human and animal health. At the European level, legislation such as European Directive 67/548EEC stipulates that prior to the use of plant protection products, a risk assessment has to be performed. Due to the large number of chemicals used worldwide, it is practically impossible to implement a full quantitative risk assessment for all chemicals. Therefore, chemical ranking systems can be used as an initial screening of pesticides with the view to identifying those requiring further analysis. Nineteen commonly used pesticide ranking tools were evaluated according to: scale of intervention; environmental compartments; human and ecosystem effects; and stage of development and functionality. The tools were assigned a numerical score (maximum of 15) based on their fulfilment of these criteria. This facilitated identification of tools that could be used for a given set of input data and user requirements. The results showed great diversity with total scores between 6 and 13.5. The advantages and disadvantages of each individual model are described. Care needs to be taken in selecting a pesticide ranking tool that fulfills the required criteria in terms of scale, effects, functionality, and environmental compartments analyzed.     

Sexual dimorphism in human cranial trait scores: Effects of population,age, and body size

Sex estimation from the skull is commonly performed by physical and forensic anthropologists using a five‐trait scoring system developed by Walker. Despite the popularity of this method, validation studies evaluating its accuracy across a variety of samples are lacking. Furthermore, it remains unclear what other intrinsic or extrinsic variables are related to the expression of these traits. In this study, cranial trait scores and postcranial measurements were collected from four diverse population groups (U.S. Whites, U.S. Blacks, medieval Nubians, and Arikara Native Americans) following Walker's protocols (total n = 499). Univariate and multivariate analyses were utilized to evaluate the accuracy of these traits in sex estimation, and to test for the effects of population, age, and body size on trait expressions. Results revealed significant effects of population on all trait scores. Sample‐specific correct sex classification rates ranged from 74% to 94%, with an overall accuracy of 85% for the pooled sample. Classification performance varied among the traits (best for glabella and mastoid scores and worst for nuchal scores). Furthermore, correlations between traits were weak or nonsignificant, suggesting that different factors may influence individual traits. Some traits displayed correlations with age and/or postcranial size that were significant but weak, and within‐population analyses did not reveal any consistent relationships between these traits across all groups. These results indicate that neither age nor body size plays a large role in trait expression, and thus does not need to be incorporated into sex estimation methods. Am J Phys Anthropol 154:259–269, 2014. © 2014 Wiley Periodicals, Inc.     

Summarizing techniques that combine three non-parametric scores to detect disease-associated 2-way SNP-SNP interactions

Identifying susceptibility genes that influence complex diseases is extremely difficult because loci often influence the disease state through genetic interactions. Numerous approaches to detect disease-associated SNP-SNP interactions have been developed, but none consistently generates high-quality results under different disease scenarios. Using summarizing techniques to combine a number of existing methods may provide a solution to this problem. Here we used three popular non-parametric methods—Gini, absolute probability difference (APD), and entropy—to develop two novel summary scores, namely principle component score (PCS) and Z-sum score (ZSS), with which to predict disease-associated genetic interactions. We used a simulation study to compare performance of the non-parametric scores, the summary scores, the scaled-sum score (SSS; used in polymorphism interaction analysis (PIA)), and the multifactor dimensionality reduction (MDR). The non-parametric methods achieved high power, but no non-parametric method outperformed all others under a variety of epistatic scenarios. PCS and ZSS, however, outperformed MDR. PCS, ZSS and SSS displayed controlled type-I-errors (< 0.05) compared to GS, APDS, ES (> 0.05). A real data study using the genetic-analysis-workshop 16 (GAW 16) rheumatoid arthritis dataset identified a number of interesting SNP-SNP interactions.     

A Closed-form Jackknife Solution for the Behrens-Fisher Problem

A simple, closed-form jackknife estimate of the actual variance of the Mann-Whitney-Wilcoxon statistic, as opposed to the standard permutational variance under the test's null hypothesis has been derived which permits avoiding anticonservative performance in the presence of heterosce-dasticity. The formulation given allows modifications of the exponential scores test, of censored data tests by Gehan (1965), Peto & Peto (1977) and Prentice (1978), of tests for monotonic τ association by Kendall (1962) and for tests of ordered k-sample hypotheses. A Monte Carlo study supports recommendations for the jackknife procedures, but also shows their limited advantages in exponential scores and censored data versions. Thus, the paper extends results by Fligner & Policello (1981).     

Universal Pattern and Diverse Strengths of Successive Synonymous Codon Bias in Three Domains of Life, Particularly Among Prokaryotic Genomes

There has been significant progress in understanding the process of protein translation in recent years. One of the best examples is the discovery of usage bias in successive synonymous codons and its role in eukaryotic translation efficiency. We observed here a similar type of bias in the other two life domains, bacteria and archaea, although the bias strength was much smaller than in eukaryotes. Among 136 prokaryotic genomes, 98 were found to have significant bias from random use of successive synonymous codons with Z scores larger than three. Furthermore, significantly different bias strengths were found between prokaryotes grouped by various genomic or biochemical characteristics. Interestingly, the bias strength measured by a general Z score could be fitted well (R = 0.83, P < 10⁻¹⁵) by three genomic variables: genome size, G + C content, and tRNA gene number based on multiple linear regression. A different distribution of synonymous codon pairs between protein-coding genes and intergenic sequences suggests that bias is caused by translation selection. The present results indicate that protein translation is tuned by codon (pair) usage, and the intensity of the regulation is associated with genome size, tRNA gene number, and G + C content.     

PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data

Protein function prediction is a central problem in bioinformatics, increasing in importance recently due to the rapid accumulation of biological data awaiting interpretation. Sequence data represents the bulk of this new stock and is the obvious target for consideration as input, as newly sequenced organisms often lack any other type of biological characterization. We have previously introduced PFP (Protein Function Prediction) as our sequence-based predictor of Gene Ontology (GO) functional terms. PFP interprets the results of a PSI-BLAST search by extracting and scoring individual functional attributes, searching a wide range of E-value sequence matches, and utilizing conventional data mining techniques to fill in missing information. We have shown it to be effective in predicting both specific and low-resolution functional attributes when sufficient data is unavailable. Here we describe (1) significant improvements to the PFP infrastructure, including the addition of prediction significance and confidence scores, (2) a thorough benchmark of performance and comparisons to other related prediction methods, and (3) applications of PFP predictions to genome-scale data. We applied PFP predictions to uncharacterized protein sequences from 15 organisms. Among these sequences, 60-90% could be annotated with a GO molecular function term at high confidence (>or=80%). We also applied our predictions to the protein-protein interaction network of the Malaria plasmodium (Plasmodium falciparum). High confidence GO biological process predictions (>or=90%) from PFP increased the number of fully enriched interactions in this dataset from 23% of interactions to 94%. Our benchmark comparison shows significant performance improvement of PFP relative to GOtcha, InterProScan, and PSI-BLAST predictions. This is consistent with the performance of PFP as the overall best predictor in both the AFP-SIG '05 and CASP7 function (FN) assessments. PFP is available as a web service at http://dragon.bio.purdue.edu/pfp/.     

A comparison of methods for estimating the causal effect of a treatment in randomized clinical trials subject to noncompliance

Summary .  We consider the analysis of clinical trials that involve randomization to an active treatment ( T  = 1) or a control treatment ( T  = 0), when the active treatment is subject to all-or-nothing compliance. We compare three approaches to estimating treatment efficacy in this situation: as-treated analysis, per-protocol analysis, and instrumental variable (IV) estimation, where the treatment effect is estimated using the randomization indicator as an IV. Both model- and method-of-moment based IV estimators are considered. The assumptions underlying these estimators are assessed, standard errors and mean squared errors of the estimates are compared, and design implications of the three methods are examined. Extensions of the methods to include observed covariates are then discussed, emphasizing the role of compliance propensity methods and the contrasting role of covariates in these extensions. Methods are illustrated on data from the Women Take Pride study, an assessment of behavioral treatments for women with heart disease.