Response to comment on "solid recovered fuel: Materials flow analysis and fuel property development during the mechanical processing of biodried waste"

Laner and Cencic1 comment on Velis et al. (2013)2 clarifying certain points on the use of the material flow analysis (MFA) software STAN3. We welcome the correspondence and the opportunity this exchange provides to discuss optimal approaches to using STAN. In keeping with Velis et al.2 these physically impossible, and otherwise insignificant, negative flows have enabled improvements to STAN. Here, we elaborate on the practicalities of using STAN in our research and on the correctness and validation of our results, notwithstanding the inclusion of negative flows. We explain the contribution of our approach to solid waste management and resource recovery

Concentrations of Polychlorinated Biphenyls (PCB’s), Chlorinated Pesticides, and Heavy Metals and Other Elements in Tissues of Belugas, Delphinapterus leucas, from Cook Inlet

Tissues from Cook Inlet beluga whales, Delphinapterus leucas, that were collected as part of the Alaska Marine Mammal Tissue Archival Project were analyzed for polychlorinated biphenyls (PCB’s), chlorinated pesticides, and heavy metals and other elements. Concentrations of total PCB’s (ΣPCB’s), total DDT (ΣDDT), chlordane compounds, hexachlorobenzene (HCB), dieldrin, mirex, toxaphene, and hexachlorocyclohexane (HCH) measured in Cook Inlet beluga blubber were compared with those reported for belugas from two Arctic Alaska locations (Point Hope and Point Lay), Greenland, Arctic Canada, and the highly contaminated stock from the St. Lawrence estuary in eastern Canada. The Arctic and Cook Inlet belugas had much lower concentrations (ΣPCB’s and ΣDDT were an order of magnitude lower) than those found in animals from the St. Lawrence estuary. The Cook Inlet belugas had the lowest concentrations of all (ΣPCB’s aver-aged 1.49 ± 0.70 and 0.79 ± 0.56 mg/kg wet mass, and ΣDDT averaged 1.35 ± 0.73 and 0.59 ± 0.45 mg/kg in males and females, respectively). Concentrations in the blubber of the Cook Inlet males were significantly lower than those found in the males of the Arctic Alaska belugas (ΣPCB’s and ΣDDT were about half). The lower levels in the Cook Inlet animals might be due to differences in contaminant sources, food web differences, or different age distributions among the animals sampled. Cook Inlet males had higher mean and median concentrations than did females, a result attributable to the transfer of these compounds from mother to calf during pregnancy and during lactation. Liver concentrations of cadmium and mercury were lower in the Cook Inlet belugas (most cadmium values were <1 mg/kg and mercury values were 0.704–11.42 mg/kg wet mass), but copper levels were significantly higher in the Cook Inlet animals (3.97–123.8 mg/kg wet mass) than in Arctic Alaska animals and similar to those reported for belugas from Hudson Bay. Although total mercury levels were the lowest in the Cook Inlet population, methylmercury concentrations were similar among all three groups of the Alaska animals examined (0.34–2.11 mg/kg wet mass). As has been reported for the Point Hope and Point Lay belugas, hepatic concentrations of silver were r

TDT-HET: A new transmission disequilibrium test that incorporates locus heterogeneity into the analysis of family-based association data

<p>Abstract</p> <p>Background</p> <p>Locus heterogeneity is one of the most documented phenomena in genetics. To date, relatively little work had been done on the development of methods to address locus heterogeneity in genetic association analysis. Motivated by Zhou and Pan's work, we present a mixture model of linked and unlinked trios and develop a statistical method to estimate the probability that a heterozygous parent transmits the disease allele at a di-allelic locus, and the probability that any trio is in the linked group. The purpose here is the development of a test that extends the classic transmission disequilibrium test (<it>TDT</it>) to one that accounts for locus heterogeneity.</p> <p>Results</p> <p>Our simulations suggest that, for sufficiently large sample size (1000 trios) our method has good power to detect association even the proportion of unlinked trios is high (75%). While the median difference (<it>TDT-HET </it>empirical power - <it>TDT </it>empirical power) is approximately 0 for all MOI, there are parameter settings for which the power difference can be substantial. Our multi-locus simulations suggest that our method has good power to detect association as long as the markers are reasonably well-correlated and the genotype relative risk are larger. Results of both single-locus and multi-locus simulations suggest our method maintains the correct type I error rate.</p> <p>Finally, the <it>TDT-HET </it>statistic shows highly significant p-values for most of the idiopathic scoliosis candidate loci, and for some loci, the estimated proportion of unlinked trios approaches or exceeds 50%, suggesting the presence of locus heterogeneity.</p> <p>Conclusions</p> <p>We have developed an extension of the <it>TDT </it>statistic (<it>TDT-HET</it>) that allows for locus heterogeneity among coded trios. Benefits of our method include: estimates of parameters in the presence of heterogeneity, and reasonable power even when the proportion of linked trios is small. Also, we have extended multi-locus methods to <it>TDT-HET </it>and have demonstrated that the empirical power may be high to detect linkage. Last, given that we obtain PPBs, we conjecture that the <it>TDT-HET </it>may be a useful method for correctly identifying linked trios. We anticipate that researchers will find this property increasingly useful as they apply next-generation sequencing data in family based studies.</p

Quenched Chiral Perturbation Theory for Heavy-light Mesons

We formulate quenched chiral perturbation theory for heavy-light mesons coupled to pions, and calculate the one-loop chiral logarithmic corrections to $f_B$, $f_{B_{s}}$, $B_B$ and $B_{B_{s}}$. We also calculate these corrections for ``partially quenched'' theories. In both theories, the chiral logarithms diverge in the chiral limit, indicating that (partially) quenched theories should not be used to study this limit. Comparing the chiral logarithms to those in QCD, we estimate the errors caused by (partial) quenching. By forming suitable ratios, we can reduce the uncertainties in our estimates.Comment: 22 pages, revtex format, 5 Postscript figure

A Simplified Score to Quantify Comorbidity in COPD

Importance Comorbidities are common in COPD, but quantifying their burden is difficult. Currently there is a COPD-specific comorbidity index to predict mortality and another to predict general quality of life. We sought to develop and validate a COPD-specific comorbidity score that reflects comorbidity burden on patient-centered outcomes. Materials and Methods Using the COPDGene study (GOLD II-IV COPD), we developed comorbidity scores to describe patient-centered outcomes employing three techniques: 1) simple count, 2) weighted score, and 3) weighted score based upon statistical selection procedure. We tested associations, area under the Curve (AUC) and calibration statistics to validate scores internally with outcomes of respiratory disease-specific quality of life (St. George's Respiratory Questionnaire, SGRQ), six minute walk distance (6MWD), modified Medical Research Council (mMRC) dyspnea score and exacerbation risk, ultimately choosing one score for external validation in SPIROMICS. Results Associations between comorbidities and all outcomes were comparable across the three scores. All scores added predictive ability to models including age, gender, race, current smoking status, pack-years smoked and FEV1 (p<0.001 for all comparisons). Area under the curve (AUC) was similar between all three scores across outcomes: SGRQ (range 0·7624–0·7676), MMRC (0·7590–0·7644), 6MWD (0·7531–0·7560) and exacerbation risk (0·6831–0·6919). Because of similar performance, the comorbidity count was used for external validation. In the SPIROMICS cohort, the comorbidity count performed well to predict SGRQ (AUC 0·7891), MMRC (AUC 0·7611), 6MWD (AUC 0·7086), and exacerbation risk (AUC 0·7341). Conclusions Quantifying comorbidity provides a more thorough understanding of the risk for patient-centered outcomes in COPD. A comorbidity count performs well to quantify comorbidity in a diverse population with COPD

Paracetamol reduces influenza-induced immunopathology in a mouse model of infection without compromising virus clearance or the generation of protective immunity

Background: Seasonal influenza A infection affects a significant cohort of the global population annually, resulting in considerable morbidity and mortality. Therapeutic strategies are of limited efficacy, and during a pandemic outbreak would only be available to a minority of the global population. Over-the-counter medicines are routinely taken by individuals suffering from influenza, but few studies have been conducted to determine their effectiveness in reducing pulmonary immunopathology or the influence they exert upon the generation of protective immunity. Methods: A mouse model of influenza infection was utilised to assess the efficacy of paracetamol (acetaminophen) in reducing influenza-induced pathology and to examine whether paracetamol affects generation of protective immunity. Results: Administration (intraperitoneal) of paracetamol significantly decreased the infiltration of inflammatory cells into the airway spaces, reduced pulmonary immunopathology associated with acute infection and improved the overall lung function of mice, without adversely affecting the induction of virus-specific adaptive responses. Mice treated with paracetamol exhibited an ability to resist a second infection with heterologous virus comparable with that of untreated mice. Conclusions: Our results demonstrate that paracetamol dramatically reduces the morbidity associated with influenza but does not compromise the development of adaptive immune responses. Overall, these data support the utility of paracetamol for reducing the clinical symptoms associated with influenza virus infection

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online