DEVELOPMENT OF A VALIDATED MODEL FOR USE IN MINIMIZING NOx EMISSIONS AND MAXIMIZING CARBON UTILIZATION WHEN CO-FIRING BIOMASS WITH COAL

In full-scale boilers, the effect of biomass cofiring on NO{sub x} and unburned carbon (UBC) emissions has been found to be site-specific. Few sets of field data are comparable and no consistent database of information exists upon which cofiring fuel choice or injection system design can be based to assure that NOX emissions will be minimized and UBC be reduced. This report presents the results of a comprehensive project that generated an extensive set of pilot-scale test data that were used to validate a new predictive model for the cofiring of biomass and coal. All testing was performed at the 3.6 MMBtu/hr (1.75 MW{sub t}) Southern Company Services/Southern Research Institute Combustion Research Facility where a variety of burner configurations, coals, biomasses, and biomass injection schemes were utilized to generate a database of consistent, scalable, experimental results (422 separate test conditions). This database was then used to validate a new model for predicting NO{sub x} and UBC emissions from the cofiring of biomass and coal. This model is based on an Advanced Post-Processing (APP) technique that generates an equivalent network of idealized reactor elements from a conventional CFD simulation. The APP reactor network is a computational environment that allows for the incorporation of all relevant chemical reaction mechanisms and provides a new tool to quantify NOx and UBC emissions for any cofired combination of coal and biomass

Bacteriological Water Quality Indicators in Natural Waters

Abstract A number of natural waters were analyzed for the presence of somatic coliphages, total and fecal coliforms (TC and FC), Escherichia coli (Ec), heterotrophic plate count (HPC) and fecal streptococci (FS). Sources sampled include permanent and intermittent streams, irrigation canals, potable water treatment plant influents and sewage treatment plant influents and effluents and receiving waters above and below those effluents. Earlier studies in Puerto Rico have suggested that coliphages are only detected in natural waters contaminated with sewage(1). In this study most sources had coliphages most of the time. Coliphage densities are compared with the bacteriological indicators analyzed. Standard Methods(2) includes formulae for the estimation of total and fecal coliform densities from coliphage results. For this study coefficients of empirical formulae to estimate TC and FC densities from coliphage occurrence are given and extended to E. coli densities. Coliphages have proven to be reliable indicators of the occurrence of TC and FC, though not Ec, in these samples and the ease, reliability and precision of the method suggest that it may with confidence be substituted for other methods for natural water monitoring. Key Words: coliphage, natural water quality. Methods & Materials Samples were collected in clean, sterile polypropylene 0.5 or 1 L bottles. A dechlorinating agent was not used for samples from non-chlorinated sources. All samples were transported immediately to the laboratory and refrigerated. All analyses were completed within thirty hours of sample collection. Analyses for coliphage, total coliform(TC), fecal coliform(FC) and fecal streptococci(FS) were in accordance with Standard Methods(2). Heterotrophic plate counts were made on R2A medium, by spread plate techniques and were incubated in the dark for 168 hours at ambient temperature. All coliphage determinations were made utilizing the host culture, Escherichia coli C, ATCC 13706 and following the technique in Standard Methods. Plaques were counted at 6 hours. Presumptive E. coli, (Ec) determinations were made utilizing MPN methods with media containing MUG. MUG-positive cultures (cultures which fluoresce when exposed to long-wave UV -approximately 340 nm) are presumed positive for the presence of E. coli. Samples were collected from 4 sites on an irrigation canal system (canal samples), 5 sites on Río Guanajibo (river samples), a sewage treatment plant influent and effluent and a potable water treatment plant influent (collected at the plant, piped from a small reservoir). Two of the canal sample sites are the influent and effluent of a large wetland, consisting of approximately 350 acres with an average depth of water of 1.5 -2 feet (1.8 billion gallons, 648,000 m 3 )with an unknown residence time. Statistical analyses were performed utilizing PC software, SPSS and SYSTAT, both from SPSS, Inc, Chicago, IL. Estimates of TC and FC identified as "calculated" (TCcalc, e.g.) were according to formulae 1 and 3 from Standard Methods

Merging Augmented Reality and Anatomically Correct 3D Models in the Development of a Training Tool for Endotracheal Intubation

Augmented reality is often used for medical training systems in which the user visualizes 3D information superimposed on the real world. In this context, we introduce a augmented reality tool to train the medical practitioner hand-eye coordination in performing critical procedures such as endotracheal intubation

Application of Augmented Reality to Visualizing Anatomical Airways

Visualizing information in three dimensions provides an increased understanding of the data presented. Furthermore, the ability to manipulate or interact with data visualized in three dimensions is superior. Within the medical community, augmented reality is being used for interactive, three-dimensional (3D) visualization. This type of visualization, which enhances the real world with computer generated information, requires a display device, a computer to generate the 3D data, and a system to track the user. In addition to these requirements, however, the hardware must be properly integrated to insure correct visualization. To this end, we present components of an integrated augmented reality system consisting of a novel head-mounted projective display, a Linux-based PC, and a commercially available optical tracking system. We demonstrate the system with the visualization of anatomical airways superimposed on a human patient simulator

Reconstructing terrestrial nutrient cycling using stable nitrogen isotopes in wood

Although recent anthropogenic effects on the global nitrogen (N) cycle have been significant, the consequences of increased anthropogenic N on terrestrial ecosystems are unclear. Studies of the impact of increased reactive N on forest ecosystems—impacts on hydrologic and gaseous loss pathways, retention capacity, and even net primary productivity— have been particularly limited by a lack of long-term baseline biogeochemical data. Stable nitrogen isotope analysis (ratio of ¹⁵N to ¹⁴N, termed δ¹⁵N) of wood chronologies offers the potential to address changes in ecosystem N cycling on millennial timescales and across broad geographic regions. Currently, nearly 50 studies have been published utilizing wood δ¹⁵N records; however, there are significant differences in study design and data interpretation. Here, we identify four categories of wood δ¹⁵N studies, summarize the common themes and primary findings of each category, identify gaps in the spatial and temporal scope of current wood δ¹⁵N chronologies, and synthesize methodological frameworks for future research by presenting eight suggestions for common methodological approaches and enhanced integration across studies. Wood δ¹⁵N records have the potential to provide valuable information for interpreting modern biogeochemical cycling. This review serves to advance the utility of this technique for long-term biogeochemical reconstructions

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

[Comment] Redefine statistical significance

The lack of reproducibility of scientific studies has caused growing concern over the credibility of claims of new discoveries based on “statistically significant” findings. There has been much progress toward documenting and addressing several causes of this lack of reproducibility (e.g., multiple testing, P-hacking, publication bias, and under-powered studies). However, we believe that a leading cause of non-reproducibility has not yet been adequately addressed: Statistical standards of evidence for claiming discoveries in many fields of science are simply too low. Associating “statistically significant” findings with P < 0.05 results in a high rate of false positives even in the absence of other experimental, procedural and reporting problems. For fields where the threshold for defining statistical significance is P<0.05, we propose a change to P<0.005. This simple step would immediately improve the reproducibility of scientific research in many fields. Results that would currently be called “significant” but do not meet the new threshold should instead be called “suggestive.” While statisticians have known the relative weakness of using P≈0.05 as a threshold for discovery and the proposal to lower it to 0.005 is not new (1, 2), a critical mass of researchers now endorse this change. We restrict our recommendation to claims of discovery of new effects. We do not address the appropriate threshold for confirmatory or contradictory replications of existing claims. We also do not advocate changes to discovery thresholds in fields that have already adopted more stringent standards (e.g., genomics and high-energy physics research; see Potential Objections below). We also restrict our recommendation to studies that conduct null hypothesis significance tests. We have diverse views about how best to improve reproducibility, and many of us believe that other ways of summarizing the data, such as Bayes factors or other posterior summaries based on clearly articulated model assumptions, are preferable to P-values. However, changing the P-value threshold is simple and might quickly achieve broad acceptance

CropPol: a dynamic, open and global database on crop pollination

Seventy five percent of the world's food crops benefit from insect pollination. Hence, there has been increased interest in how global change drivers impact this critical ecosystem service. Because standardized data on crop pollination are rarely available, we are limited in our capacity to understand the variation in pollination benefits to crop yield, as well as to anticipate changes in this service, develop predictions, and inform management actions. Here, we present CropPol, a dynamic, open and global database on crop pollination. It contains measurements recorded from 202 crop studies, covering 3,394 field observations, 2,552 yield measurements (i.e. berry weight, number of fruits and kg per hectare, among others), and 47,752 insect records from 48 commercial crops distributed around the globe. CropPol comprises 32 of the 87 leading global crops and commodities that are pollinator dependent. Malus domestica is the most represented crop (32 studies), followed by Brassica napus (22 studies), Vaccinium corymbosum (13 studies), and Citrullus lanatus (12 studies). The most abundant pollinator guilds recorded are honey bees (34.22% counts), bumblebees (19.19%), flies other than Syrphidae and Bombyliidae (13.18%), other wild bees (13.13%), beetles (10.97%), Syrphidae (4.87%), and Bombyliidae (0.05%). Locations comprise 34 countries distributed among Europe (76 studies), Northern America (60), Latin America and the Caribbean (29), Asia (20), Oceania (10), and Africa (7). Sampling spans three decades and is concentrated on 2001-05 (21 studies), 2006-10 (40), 2011-15 (88), and 2016-20 (50). This is the most comprehensive open global data set on measurements of crop flower visitors, crop pollinators and pollination to date, and we encourage researchers to add more datasets to this database in the future. This data set is released for non-commercial use only. Credits should be given to this paper (i.e., proper citation), and the products generated with this database should be shared under the same license terms (CC BY-NC-SA). This article is protected by copyright. All rights reserved

CCNE1 and survival of patients with tubo-ovarian high-grade serous carcinoma: An Ovarian Tumor Tissue Analysis consortium study

BACKGROUND: Cyclin E1 (CCNE1) is a potential predictive marker and therapeutic target in tubo-ovarian high-grade serous carcinoma (HGSC). Smaller studies have revealed unfavorable associations for CCNE1 amplification and CCNE1 overexpression with survival, but to date no large-scale, histotype-specific validation has been performed. The hypothesis was that high-level amplification of CCNE1 and CCNE1 overexpression, as well as a combination of the two, are linked to shorter overall survival in HGSC. METHODS: Within the Ovarian Tumor Tissue Analysis consortium, amplification status and protein level in 3029 HGSC cases and mRNA expression in 2419 samples were investigated. RESULTS: High-level amplification (>8 copies by chromogenic in situ hybridization) was found in 8.6% of HGSC and overexpression (>60% with at least 5% demonstrating strong intensity by immunohistochemistry) was found in 22.4%. CCNE1 high-level amplification and overexpression both were linked to shorter overall survival in multivariate survival analysis adjusted for age and stage, with hazard stratification by study (hazard ratio [HR], 1.26; 95% CI, 1.08-1.47, p = .034, and HR, 1.18; 95% CI, 1.05-1.32, p = .015, respectively). This was also true for cases with combined high-level amplification/overexpression (HR, 1.26; 95% CI, 1.09-1.47, p = .033). CCNE1 mRNA expression was not associated with overall survival (HR, 1.00 per 1-SD increase; 95% CI, 0.94-1.06; p = .58). CCNE1 high-level amplification is mutually exclusive with the presence of germline BRCA1/2 pathogenic variants and shows an inverse association to RB1 loss. CONCLUSION: This study provides large-scale validation that CCNE1 high-level amplification is associated with shorter survival, supporting its utility as a prognostic biomarker in HGSC

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University