Beliefs about the Potential Impacts of Exploiting Non-Timber Forest Products Predict Voluntary Participation in Monitoring.

PubMed

Dantas Brites, Alice; Morsello, Carla

2017-06-01

Harvesting and trading non-timber forest products is advocated as a win-win strategy for conservation and development, yet it can produce negative ecological and socioeconomic impacts. Hence, monitoring exploitation outcomes is essential, and participatory monitoring has been suggested to be the most suitable approach. Among possible approaches, participatory monitoring is preferred because it is likely to increase people's awareness and beliefs regarding impacts or potential impacts, thus inducing behavioral changes, although the evidence in this regard is contradictory. We therefore evaluated whether people's beliefs about the potential ecological and socioeconomic impacts of non-timber forest product exploitation increased their likelihood of volunteering to monitor. We studied a community of forest inhabitants in the Brazilian Amazon who harvested and traded a commercially important non-timber forest product. Two methods of data gathering were employed: (i) a survey of 166 adults (51 households) to evaluate people's beliefs and their stated intention to engage in four different monitoring tasks and (ii) four pilot monitoring tasks to evaluate who actually participated. Based on mixed-effects regressions, the results indicated that beliefs regarding both types of impacts could predict participation in certain tasks, although gender, age and schooling were occasionally stronger predictors. On average, people had stronger beliefs about potential socioeconomic impacts than about potential ecological impacts, with the former also predicting participation in ecological data gathering. This finding reinforces the importance of monitoring both types of impacts to help achieve the win-win outcomes originally proposed by non-timber forest product trade initiatives.

Survey of ocular irritation predictive capacity using Chorioallantoic Membrane Vascular Assay (CAMVA) and Bovine Corneal Opacity and Permeability (BCOP) test historical data for 319 personal care products over fourteen years.

PubMed

Donahue, D A; Kaufman, L E; Avalos, J; Simion, F A; Cerven, D R

2011-03-01

The Chorioallantoic Membrane Vascular Assay (CAMVA) and Bovine Corneal Opacity and Permeability (BCOP) test are widely used to predict ocular irritation potential for consumer-use products. These in vitro assays do not require live animals, produce reliable predictive data for defined applicability domains compared to the Draize rabbit eye test, and are rapid and inexpensive. Data from 304 CAMVA and/or BCOP studies (319 formulations) were surveyed to determine the feasibility of predicting ocular irritation potential for various formulations. Hair shampoos, skin cleansers, and ethanol-based hair styling sprays were repeatedly predicted to be ocular irritants (accuracy rate=0.90-1.00), with skin cleanser and hair shampoo irritation largely dependent on surfactant species and concentration. Conversely, skin lotions/moisturizers and hair styling gels/lotions were repeatedly predicted to be non-irritants (accuracy rate=0.92 and 0.82, respectively). For hair shampoos, ethanol-based hair stylers, skin cleansers, and skin lotions/moisturizers, future ocular irritation testing (i.e., CAMVA/BCOP) can be nearly eliminated if new formulations are systematically compared to those previously tested using a defined decision tree. For other tested product categories, new formulations should continue to be evaluated in CAMVA/BCOP for ocular irritation potential because either the historical data exhibit significant variability (hair conditioners and mousses) or the historical sample size is too small to permit definitive conclusions (deodorants, make-up removers, massage oils, facial masks, body sprays, and other hair styling products). All decision tree conclusions should be made within a conservative weight-of-evidence context, considering the reported limitations of the BCOP test for alcohols, ketones, and solids. Copyright © 2010 Elsevier Ltd. All rights reserved.

PRISM 3: expanded prediction of natural product chemical structures from microbial genomes

PubMed Central

Skinnider, Michael A.; Merwin, Nishanth J.; Johnston, Chad W.

2017-01-01

Abstract Microbial natural products represent a rich resource of pharmaceutically and industrially important compounds. Genome sequencing has revealed that the majority of natural products remain undiscovered, and computational methods to connect biosynthetic gene clusters to their corresponding natural products therefore have the potential to revitalize natural product discovery. Previously, we described PRediction Informatics for Secondary Metabolomes (PRISM), a combinatorial approach to chemical structure prediction for genetically encoded nonribosomal peptides and type I and II polyketides. Here, we present a ground-up rewrite of the PRISM structure prediction algorithm to derive prediction of natural products arising from non-modular biosynthetic paradigms. Within this new version, PRISM 3, natural product scaffolds are modeled as chemical graphs, permitting structure prediction for aminocoumarins, antimetabolites, bisindoles and phosphonate natural products, and building upon the addition of ribosomally synthesized and post-translationally modified peptides. Further, with the addition of cluster detection for 11 new cluster types, PRISM 3 expands to detect 22 distinct natural product cluster types. Other major modifications to PRISM include improved sequence input and ORF detection, user-friendliness and output. Distribution of PRISM 3 over a 300-core server grid improves the speed and capacity of the web application. PRISM 3 is available at http://magarveylab.ca/prism/. PMID:28460067

An automated Genomes-to-Natural Products platform (GNP) for the discovery of modular natural products.

PubMed

Johnston, Chad W; Skinnider, Michael A; Wyatt, Morgan A; Li, Xiang; Ranieri, Michael R M; Yang, Lian; Zechel, David L; Ma, Bin; Magarvey, Nathan A

2015-09-28

Bacterial natural products are a diverse and valuable group of small molecules, and genome sequencing indicates that the vast majority remain undiscovered. The prediction of natural product structures from biosynthetic assembly lines can facilitate their discovery, but highly automated, accurate, and integrated systems are required to mine the broad spectrum of sequenced bacterial genomes. Here we present a genome-guided natural products discovery tool to automatically predict, combinatorialize and identify polyketides and nonribosomal peptides from biosynthetic assembly lines using LC-MS/MS data of crude extracts in a high-throughput manner. We detail the directed identification and isolation of six genetically predicted polyketides and nonribosomal peptides using our Genome-to-Natural Products platform. This highly automated, user-friendly programme provides a means of realizing the potential of genetically encoded natural products.

Characterization of silver nanoparticles in selected consumer products and its relevance for predicting children's potential exposures.

PubMed

Tulve, Nicolle S; Stefaniak, Aleksandr B; Vance, Marina E; Rogers, Kim; Mwilu, Samuel; LeBouf, Ryan F; Schwegler-Berry, Diane; Willis, Robert; Thomas, Treye A; Marr, Linsey C

2015-05-01

Due to their antifungal, antibacterial, antiviral, and antimicrobial properties, silver nanoparticles (AgNPs) are used in consumer products intended for use by children or in the home. Children may be especially affected by the normal use of consumer products because of their physiological functions, developmental stage, and activities and behaviors. Despite much research to date, children's potential exposures to AgNPs are not well characterized. Our objectives were to characterize selected consumer products containing AgNPs and to use the data to estimate a child's potential non-dietary ingestion exposure. We identified and cataloged 165 consumer products claiming to contain AgNPs that may be used by or near children or found in the home. Nineteen products (textile, liquid, plastic) were selected for further analysis. We developed a tiered analytical approach to determine silver content, form (particulate or ionic), size, morphology, agglomeration state, and composition. Silver was detected in all products except one sippy cup body. Among products in a given category, silver mass contributions were highly variable and not always uniformly distributed within products, highlighting the need to sample multiple areas of a product. Electron microscopy confirmed the presence of AgNPs. Using this data, a child's potential non-dietary ingestion exposure to AgNPs when drinking milk formula from a sippy cup is 1.53 μg Ag/kg. Additional research is needed to understand the number and types of consumer products containing silver and the concentrations of silver in these products in order to more accurately predict children's potential aggregate and cumulative exposures to AgNPs. Published by Elsevier GmbH.

Characterization of silver nanoparticles in selected consumer products and its relevance for predicting children’s potential exposures

PubMed Central

Tulve, Nicolle S.; Stefaniak, Aleksandr B.; Vance, Marina E.; Rogers, Kim; Mwilu, Samuel; LeBouf, Ryan F.; Schwegler-Berry, Diane; Willis, Robert; Thomas, Treye A.; Marr, Linsey C.

2015-01-01

Due to their antifungal, antibacterial, antiviral, and antimicrobial properties, silver nanoparticles (AgNPs) are used in consumer products intended for use by children or in the home. Children may be especially affected by the normal use of consumer products because of their physiological functions, developmental stage, and activities and behaviors. Despite much research to date, children’s potential exposures to AgNPs are not well characterized. Our objectives were to characterize selected consumer products containing AgNPs and to use the data to estimate a child’s potential non-dietary ingestion exposure. We identified and cataloged 165 consumer products claiming to contain AgNPs that may be used by or near children or found in the home. Nineteen products (textile, liquid, plastic) were selected for further analysis. We developed a tiered analytical approach to determine silver content, form (particulate or ionic), size, morphology, agglomeration state, and composition. Silver was detected in all products except one sippy cup body. Among products in a given category, silver mass contributions were highly variable and not always uniformly distributed within products, highlighting the need to sample multiple areas of a product. Electron microscopy confirmed the presence of AgNPs. Using this data, a child’s potential non-dietary ingestion exposure to AgNPs when drinking milk formula from a sippy cup is 1.53 μg Ag/kg. Additional research is needed to understand the number and types of consumer products containing silver and the concentrations of silver in these products in order to more accurately predict children’s potential aggregate and cumulative exposures to AgNPs. PMID:25747543

Measurement and prediction of post-fire erosion at the hillslope scale, Colorado Front Range

Treesearch

Juan de Dios Benavides-Solorio; Lee H. MacDonald

2005-01-01

Post-fire soil erosion is of considerable concern because of the potential decline in site productivity and adverse effects on downstream resources. For the Colorado Front Range there is a paucity of post-fire erosion data and a corresponding lack of predictive models. This study measured hillslope-scale sediment production rates and site characteristics for three wild...

Potential Utility of the Real-Time TMPA-RT Precipitation Estimates in Streamflow Prediction

NASA Technical Reports Server (NTRS)

Su, Fengge; Gao, Huilin; Huffman, George J.; Lettenmaier, Dennis P.

2010-01-01

We investigate the potential utility of the real-time Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA-RT) data for streamflow prediction, both through direct comparisons of TMPA-RT estimates with a gridded gauge product, and through evaluation of streamflow simulations over four tributaries of La Plata Basin (LPB) in South America using the two precipitation products. Our assessments indicate that the relative accuracy and the hydrologic performance of TMPA-RT-based streamflow simulations generally improved after February 2005. The improvements in TMPA-RT since 2005 are closely related to upgrades in the TMPA-RT algorithm in early February, 2005 which include use of additional microwave sensors (AMSR-E and AMSU-B) and implementation of different calibration schemes. Our work suggests considerable potential for hydrologic prediction using purely satellite-derived precipitation estimates (no adjustments by in situ gauges) in parts of the globe where in situ observations are sparse.

Assessment of potential biomass energy production in China towards 2030 and 2050

NASA Astrophysics Data System (ADS)

Zhao, Guangling

2018-01-01

The objective of this paper is to provide a more detailed picture of potential biomass energy production in the Chinese energy system towards 2030 and 2050. Biomass for bioenergy feedstocks comes from five sources, which are agricultural crop residues, forest residues and industrial wood waste, energy crops and woody crops, animal manure, and municipal solid waste. The potential biomass production is predicted based on the resource availability. In the process of identifying biomass resources production, assumptions are made regarding arable land, marginal land, crops yields, forest growth rate, and meat consumption and waste production. Four scenarios were designed to describe the potential biomass energy production to elaborate the role of biomass energy in the Chinese energy system in 2030. The assessment shows that under certain restrictions on land availability, the maximum potential biomass energy productions are estimated to be 18,833 and 24,901 PJ in 2030 and 2050.

Consumer preference models: fuzzy theory approach

NASA Astrophysics Data System (ADS)

Turksen, I. B.; Wilson, I. A.

1993-12-01

Consumer preference models are widely used in new product design, marketing management, pricing and market segmentation. The purpose of this article is to develop and test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation) and how much to make (market share prediction).

Metabotypes with properly functioning mitochondria and anti-inflammation predict extended productive life span in dairy cows

PubMed Central

Huber, K.; Dänicke, S.; Rehage, J.; Sauerwein, H.; Otto, W.; Rolle-Kampczyk, U.; von Bergen, M.

2016-01-01

The failure to adapt metabolism to the homeorhetic demands of lactation is considered as a main factor in reducing the productive life span of dairy cows. The so far defined markers of production performance and metabolic health in dairy cows do not predict the length of productive life span satisfyingly. This study aimed to identify novel pathways and biomarkers related to productive life in dairy cows by means of (targeted) metabolomics. In a longitudinal study from 42 days before up to 100 days after parturition, we identified metabolites such as long-chain acylcarnitines and biogenic amines associated with extended productive life spans. These metabolites are mainly secreted by the liver and depend on the functionality of hepatic mitochondria. The concentrations of biogenic amines and some acylcarnitines differed already before the onset of lactation thus indicating their predictive potential for continuation or early ending of productive life. PMID:27089826

Metabotypes with properly functioning mitochondria and anti-inflammation predict extended productive life span in dairy cows.

PubMed

Huber, K; Dänicke, S; Rehage, J; Sauerwein, H; Otto, W; Rolle-Kampczyk, U; von Bergen, M

2016-04-19

The failure to adapt metabolism to the homeorhetic demands of lactation is considered as a main factor in reducing the productive life span of dairy cows. The so far defined markers of production performance and metabolic health in dairy cows do not predict the length of productive life span satisfyingly. This study aimed to identify novel pathways and biomarkers related to productive life in dairy cows by means of (targeted) metabolomics. In a longitudinal study from 42 days before up to 100 days after parturition, we identified metabolites such as long-chain acylcarnitines and biogenic amines associated with extended productive life spans. These metabolites are mainly secreted by the liver and depend on the functionality of hepatic mitochondria. The concentrations of biogenic amines and some acylcarnitines differed already before the onset of lactation thus indicating their predictive potential for continuation or early ending of productive life.

Nature is the best source of anti-inflammatory drugs: indexing natural products for their anti-inflammatory bioactivity.

PubMed

Aswad, Miran; Rayan, Mahmoud; Abu-Lafi, Saleh; Falah, Mizied; Raiyn, Jamal; Abdallah, Ziyad; Rayan, Anwar

2018-01-01

The aim was to index natural products for less expensive preventive or curative anti-inflammatory therapeutic drugs. A set of 441 anti-inflammatory drugs representing the active domain and 2892 natural products representing the inactive domain was used to construct a predictive model for bioactivity-indexing purposes. The model for indexing the natural products for potential anti-inflammatory activity was constructed using the iterative stochastic elimination algorithm (ISE). ISE is capable of differentiating between active and inactive anti-inflammatory molecules. By applying the prediction model to a mix set of (active/inactive) substances, we managed to capture 38% of the anti-inflammatory drugs in the top 1% of the screened set of chemicals, yielding enrichment factor of 38. Ten natural products that scored highly as potential anti-inflammatory drug candidates are disclosed. Searching the PubMed revealed that only three molecules (Moupinamide, Capsaicin, and Hypaphorine) out of the ten were tested and reported as anti-inflammatory. The other seven phytochemicals await evaluation for their anti-inflammatory activity in wet lab. The proposed anti-inflammatory model can be utilized for the virtual screening of large chemical databases and for indexing natural products for potential anti-inflammatory activity.

PRISM 3: expanded prediction of natural product chemical structures from microbial genomes.

PubMed

Skinnider, Michael A; Merwin, Nishanth J; Johnston, Chad W; Magarvey, Nathan A

2017-07-03

Microbial natural products represent a rich resource of pharmaceutically and industrially important compounds. Genome sequencing has revealed that the majority of natural products remain undiscovered, and computational methods to connect biosynthetic gene clusters to their corresponding natural products therefore have the potential to revitalize natural product discovery. Previously, we described PRediction Informatics for Secondary Metabolomes (PRISM), a combinatorial approach to chemical structure prediction for genetically encoded nonribosomal peptides and type I and II polyketides. Here, we present a ground-up rewrite of the PRISM structure prediction algorithm to derive prediction of natural products arising from non-modular biosynthetic paradigms. Within this new version, PRISM 3, natural product scaffolds are modeled as chemical graphs, permitting structure prediction for aminocoumarins, antimetabolites, bisindoles and phosphonate natural products, and building upon the addition of ribosomally synthesized and post-translationally modified peptides. Further, with the addition of cluster detection for 11 new cluster types, PRISM 3 expands to detect 22 distinct natural product cluster types. Other major modifications to PRISM include improved sequence input and ORF detection, user-friendliness and output. Distribution of PRISM 3 over a 300-core server grid improves the speed and capacity of the web application. PRISM 3 is available at http://magarveylab.ca/prism/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

In Silico Identification of Bioremediation Potential: Carbamazepine and Other Recalcitrant Personal Care Products.

PubMed

Aukema, Kelly G; Escalante, Diego E; Maltby, Meghan M; Bera, Asim K; Aksan, Alptekin; Wackett, Lawrence P

2017-01-17

Emerging contaminants are principally personal care products not readily removed by conventional wastewater treatment and, with an increasing reliance on water recycling, become disseminated in drinking water supplies. Carbamazepine, a widely used neuroactive pharmaceutical, increasingly escapes wastewater treatment and is found in potable water. In this study, a mechanism is proposed by which carbamazepine resists biodegradation, and a previously unknown microbial biodegradation was predicted computationally. The prediction identified biphenyl dioxygenase from Paraburkholderia xenovorans LB400 as the best candidate enzyme for metabolizing carbamazepine. The rate of degradation described here is 40 times greater than the best reported rates. The metabolites cis-10,11-dihydroxy-10,11-dihydrocarbamazepine and cis-2,3-dihydroxy-2,3-dihydrocarbamazepine were demonstrated with the native organism and a recombinant host. The metabolites are considered nonharmful and mitigate the generation of carcinogenic acridine products known to form when advanced oxidation methods are used in water treatment. Other recalcitrant personal care products were subjected to prediction by the Pathway Prediction System and tested experimentally with P. xenovorans LB400. It was shown to biodegrade structurally diverse compounds. Predictions indicated hydrolase or oxygenase enzymes catalyzed the initial reactions. This study highlights the potential for using the growing body of enzyme-structural and genomic information with computational methods to rapidly identify enzymes and microorganisms that biodegrade emerging contaminants.

Linking climate, gross primary productivity, and site index across forests of the western United States

Treesearch

Aaron R. Weiskittel; Nicholas L. Crookston; Philip J. Radtke

2011-01-01

Assessing forest productivity is important for developing effective management regimes and predicting future growth. Despite some important limitations, the most common means for quantifying forest stand-level potential productivity is site index (SI). Another measure of productivity is gross primary production (GPP). In this paper, SI is compared with GPP estimates...

Site relationships and black walnut height growth in natural stands in eastern Kansas

Treesearch

Wayne A. Geyer; Felix, Jr. Ponder

2004-01-01

Prediction of forestland productivity is needed for proper species selection in tree planting. By relating site quality to site and soil characteristics, potential productivity can be estimated for non-forested areas. Our study measured the growth potential of black walnut in natural stands in southeastern Kansas. We looked at over 200 stands on unglaciated soils....

Species Diversity and Functional Prediction of Surface Bacterial Communities on Aging Flue-Cured Tobaccos.

PubMed

Wang, Fan; Zhao, Hongwei; Xiang, Haiying; Wu, Lijun; Men, Xiao; Qi, Chang; Chen, Guoqiang; Zhang, Haibo; Wang, Yi; Xian, Mo

2018-06-05

Microbes on aging flue-cured tobaccos (ATFs) improve the aroma and other qualities desirable in products. Understanding the relevant organisms would picture microbial community diversity, metabolic potential, and their applications. However, limited efforts have been made on characterizing the microbial quality and functional profiling. Herein, we present our investigation of the bacterial diversity and predicted potential genetic capability of the bacteria from two AFTs using 16S rRNA gene sequences and phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) software. The results show that dominant bacteria from AFT surfaces were classified into 48 genera, 36 families, and 7 phyla. In addition, Bacillus spp. was found prevalent on both ATFs. Furthermore, PICRUSt predictions of bacterial community functions revealed many attractive metabolic capacities in the AFT microbiota, including several involved in the biosynthesis of flavors and fragrances and the degradation of harmful compounds, such as nicotine and nitrite. These results provide insights into the importance of AFT bacteria in determining product qualities and indicate specific microbial species with predicted enzymatic capabilities for the production of high-efficiency flavors, the degradation of undesirable compounds, and the provision of nicotine and nitrite tolerance which suggest fruitful areas of investigation into the manipulation of AFT microbiota for AFT and other product improvements.

Potential Seasonal Predictability of Water Cycle in Observations and Reanalysis

NASA Astrophysics Data System (ADS)

Feng, X.; Houser, P.

2012-12-01

Identification of predictability of water cycle variability is crucial for climate prediction, water resources availability, ecosystem management and hazard mitigation. An analysis that can assess the potential skill in seasonal prediction was proposed by the authors, named as analysis of covariance (ANOCOVA). This method tests whether interannual variability of seasonal means exceeds that due to weather noise under the null hypothesis that seasonal means are identical every year. It has the advantage of taking into account autocorrelation structure in the daily time series but also accounting for the uncertainty of the estimated parameters in the significance test. During the past several years, multiple reanalysis datasets have become available for studying climate variability and understanding climate system. We are motivated to compare the potential predictability of water cycle variation from different reanalysis datasets against observations using the newly proposed ANOCOVA method. The selected eight reanalyses include the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP/NCAR) 40-year Reanalysis Project (NNRP), the National Centers for Environmental Prediction-Department of Energy (NCEP/DOE) Reanalysis Project (NDRP), the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-year Reanalysis, The Japan Meteorological Agency 25-year Reanalysis Project (JRA25), the ECMWF) Interim Reanalysis (ERAINT), the NCEP Climate Forecast System Reanalysis (CFSR), the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA), and the National Oceanic and Atmospheric Administration-Cooperative Institute for Research in Environmental Sciences (NOAA/CIRES) 20th Century Reanalysis Version 2 (20CR). For key water cycle components, precipitation and evaporation, all reanalyses consistently show high fraction of predictable variance in the tropics, low predictability over the extratropics, more potential predictability over the ocean than land, and a stronger seasonal variation in potential predictability over land than ocean. The substantial differences are observed especially over the extropical areas where boundary-forced signal is not as significant as in tropics. We further evaluate the accuracy of reanalysis in estimating seasonal predictability over several selected regions, where rain gauge measurement or land surface data assimilation product is available and accurate, to gain insight on the strength and weakness of reanalysis products.

Characterization of silver nanoparticles in selected consumer products and its relevance for predicting children's potential exposures

EPA Science Inventory

Due to their antifungal, antibacterial, antiviral, and antimicrobial properties, silver nanoparticles (AgNPs) are used in consumer products intended for use by children or in the home. Children may be especially affected by the normal use of consumer products because of their phy...

Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics, and epigenetics data

PubMed Central

Nguyen, Quan H; Tellam, Ross L; Naval-Sanchez, Marina; Porto-Neto, Laercio R; Barendse, William; Reverter, Antonio; Hayes, Benjamin; Kijas, James; Dalrymple, Brian P

2018-01-01

Abstract Genome sequences for hundreds of mammalian species are available, but an understanding of their genomic regulatory regions, which control gene expression, is only beginning. A comprehensive prediction of potential active regulatory regions is necessary to functionally study the roles of the majority of genomic variants in evolution, domestication, and animal production. We developed a computational method to predict regulatory DNA sequences (promoters, enhancers, and transcription factor binding sites) in production animals (cows and pigs) and extended its broad applicability to other mammals. The method utilizes human regulatory features identified from thousands of tissues, cell lines, and experimental assays to find homologous regions that are conserved in sequences and genome organization and are enriched for regulatory elements in the genome sequences of other mammalian species. Importantly, we developed a filtering strategy, including a machine learning classification method, to utilize a very small number of species-specific experimental datasets available to select for the likely active regulatory regions. The method finds the optimal combination of sensitivity and accuracy to unbiasedly predict regulatory regions in mammalian species. Furthermore, we demonstrated the utility of the predicted regulatory datasets in cattle for prioritizing variants associated with multiple production and climate change adaptation traits and identifying potential genome editing targets. PMID:29618048

Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics, and epigenetics data.

PubMed

Nguyen, Quan H; Tellam, Ross L; Naval-Sanchez, Marina; Porto-Neto, Laercio R; Barendse, William; Reverter, Antonio; Hayes, Benjamin; Kijas, James; Dalrymple, Brian P

2018-03-01

Genome sequences for hundreds of mammalian species are available, but an understanding of their genomic regulatory regions, which control gene expression, is only beginning. A comprehensive prediction of potential active regulatory regions is necessary to functionally study the roles of the majority of genomic variants in evolution, domestication, and animal production. We developed a computational method to predict regulatory DNA sequences (promoters, enhancers, and transcription factor binding sites) in production animals (cows and pigs) and extended its broad applicability to other mammals. The method utilizes human regulatory features identified from thousands of tissues, cell lines, and experimental assays to find homologous regions that are conserved in sequences and genome organization and are enriched for regulatory elements in the genome sequences of other mammalian species. Importantly, we developed a filtering strategy, including a machine learning classification method, to utilize a very small number of species-specific experimental datasets available to select for the likely active regulatory regions. The method finds the optimal combination of sensitivity and accuracy to unbiasedly predict regulatory regions in mammalian species. Furthermore, we demonstrated the utility of the predicted regulatory datasets in cattle for prioritizing variants associated with multiple production and climate change adaptation traits and identifying potential genome editing targets.

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

DOE PAGES

Boer, George J.; Smith, Douglas M.; Cassou, Christophe; ...

2016-01-01

The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Predictionmore » (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Furthermore, groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.« less

Deep-water kelp refugia as potential hotspots of tropical marine diversity and productivity.

PubMed

Graham, Michael H; Kinlan, Brian P; Druehl, Louis D; Garske, Lauren E; Banks, Stuart

2007-10-16

Classic marine ecological paradigms view kelp forests as inherently temperate-boreal phenomena replaced by coral reefs in tropical waters. These paradigms hinge on the notion that tropical surface waters are too warm and nutrient-depleted to support kelp productivity and survival. We present a synthetic oceanographic and ecophysiological model that accurately identifies all known kelp populations and, by using the same criteria, predicts the existence of >23,500 km(2) unexplored submerged (30- to 200-m depth) tropical kelp habitats. Predicted tropical kelp habitats were most probable in regions where bathymetry and upwelling resulted in mixed-layer shoaling above the depth of minimum annual irradiance dose for kelp survival. Using model predictions, we discovered extensive new deep-water Eisenia galapagensis populations in the Galápagos that increased in abundance with increasing depth to >60 m, complete with cold-water flora and fauna of temperate affinities. The predictability of deep-water kelp habitat and the discovery of expansive deep-water Galápagos kelp forests validate the extent of deep-water tropical kelp refugia, with potential implications for regional productivity and biodiversity, tropical food web ecology, and understanding of the resilience of tropical marine systems to climate change.

Potential predictability of Northern America surface temperature in AGCMs and CGCMs

NASA Astrophysics Data System (ADS)

Tang, Youmin; Chen, Dake; Yan, Xiaoqin

2015-07-01

In this study, the potential predictability of the Northern America (NA) surface air temperature (SAT) was explored using an information-based predictability framework and two multiple model ensemble products: a one-tier prediction by coupled models (T1), and a two-tier prediction by atmospheric models only (T2). Furthermore, the potential predictability was optimally decomposed into different modes for both T1 and T2, by extracting the most predictable structures. Emphasis was placed on the comparison of the predictability between T1 and T2. It was found that the potential predictability of the NA SAT is seasonal and spatially dependent in both T1 and T2. Higher predictability occurs in spring and winter and over the southeastern US and northwestern Canada. There is no significant difference of potential predictability between T1 and T2 for most areas of NA, although T1 has higher potential predictability than T2 in the southeastern US. Both T1 and T2 display similar most predictable components (PrCs) for the NA SAT, characterized by the inter-annual variability mode and the long-term trend mode. The first one is inherent to the tropical Pacific sea surface temperature forcing, such as the El Nino-Southern Oscillation, whereas the second one is closely associated with global warming. In general, the PrC modes can better characterize the predictability in T1 than in T2, in particular for the inter-annual variability mode in the fall. The prediction skill against observations is better measured by the PrC analysis than by principal component analysis for all seasons, indicating the stronger capability of PrCA in extracting prediction targets.

Use of Natural Products as Chemical Library for Drug Discovery and Network Pharmacology

PubMed Central

Gu, Jiangyong; Gui, Yuanshen; Chen, Lirong; Yuan, Gu; Lu, Hui-Zhe; Xu, Xiaojie

2013-01-01

Background Natural products have been an important source of lead compounds for drug discovery. How to find and evaluate bioactive natural products is critical to the achievement of drug/lead discovery from natural products. Methodology We collected 19,7201 natural products structures, reported biological activities and virtual screening results. Principal component analysis was employed to explore the chemical space, and we found that there was a large portion of overlap between natural products and FDA-approved drugs in the chemical space, which indicated that natural products had large quantity of potential lead compounds. We also explored the network properties of natural product-target networks and found that polypharmacology was greatly enriched to those compounds with large degree and high betweenness centrality. In order to make up for a lack of experimental data, high throughput virtual screening was employed. All natural products were docked to 332 target proteins of FDA-approved drugs. The most potential natural products for drug discovery and their indications were predicted based on a docking score-weighted prediction model. Conclusions Analysis of molecular descriptors, distribution in chemical space and biological activities of natural products was conducted in this article. Natural products have vast chemical diversity, good drug-like properties and can interact with multiple cellular target proteins. PMID:23638153

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boer, George J.; Smith, Douglas M.; Cassou, Christophe

The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Predictionmore » (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Furthermore, groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.« less

Association of comorbid mental health symptoms and physical health conditions with employee productivity.

PubMed

Parker, Kristin M; Wilson, Mark G; Vandenberg, Robert J; DeJoy, David M; Orpinas, Pamela

2009-10-01

This study tests the hypothesis that employees with comorbid physical health conditions and mental health symptoms are less productive than other employees. Self-reported health status and productivity measures were collected from 1723 employees of a national retail organization. chi2, analysis of variance, and linear contrast analyses were conducted to evaluate whether health status groups differed on productivity measures. Multivariate linear regression and multinomial logistic regression analyses were conducted to analyze how predictive health status was of productivity. Those with comorbidities were significantly less productive on all productivity measures compared with all other health status groups and those with only physical health conditions or mental health symptoms. Health status also significantly predicted levels of employee productivity. These findings provide evidence for the relationship between health statuses and productivity, which has potential programmatic implications.

PROXIMAL: a method for Prediction of Xenobiotic Metabolism.

PubMed

Yousofshahi, Mona; Manteiga, Sara; Wu, Charmian; Lee, Kyongbum; Hassoun, Soha

2015-12-22

Contamination of the environment with bioactive chemicals has emerged as a potential public health risk. These substances that may cause distress or disease in humans can be found in air, water and food supplies. An open question is whether these chemicals transform into potentially more active or toxic derivatives via xenobiotic metabolizing enzymes expressed in the body. We present a new prediction tool, which we call PROXIMAL (Prediction of Xenobiotic Metabolism) for identifying possible transformation products of xenobiotic chemicals in the liver. Using reaction data from DrugBank and KEGG, PROXIMAL builds look-up tables that catalog the sites and types of structural modifications performed by Phase I and Phase II enzymes. Given a compound of interest, PROXIMAL searches for substructures that match the sites cataloged in the look-up tables, applies the corresponding modifications to generate a panel of possible transformation products, and ranks the products based on the activity and abundance of the enzymes involved. PROXIMAL generates transformations that are specific for the chemical of interest by analyzing the chemical's substructures. We evaluate the accuracy of PROXIMAL's predictions through case studies on two environmental chemicals with suspected endocrine disrupting activity, bisphenol A (BPA) and 4-chlorobiphenyl (PCB3). Comparisons with published reports confirm 5 out of 7 and 17 out of 26 of the predicted derivatives for BPA and PCB3, respectively. We also compare biotransformation predictions generated by PROXIMAL with those generated by METEOR and Metaprint2D-react, two other prediction tools. PROXIMAL can predict transformations of chemicals that contain substructures recognizable by human liver enzymes. It also has the ability to rank the predicted metabolites based on the activity and abundance of enzymes involved in xenobiotic transformation.

Machine Learning Estimates of Natural Product Conformational Energies

PubMed Central

Rupp, Matthias; Bauer, Matthias R.; Wilcken, Rainer; Lange, Andreas; Reutlinger, Michael; Boeckler, Frank M.; Schneider, Gisbert

2014-01-01

Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures. PMID:24453952

Prediction of gas production using well logs, Cretaceous of north-central Montana

USGS Publications Warehouse

Hester, T.C.

1999-01-01

Cretaceous gas sands underlie much of east-central Alberta and southern Saskatchewan, eastern Montana, western North Dakota, and parts of South Dakota and Wyoming. Estimates of recoverable biogenic methane from these rocks in the United States are as high as 91 TCF. In northern Montana, current production is localized around a few major structural features, while vast areas in between these structures are not being exploited. Although the potential for production exists, the lack of commercial development is due to three major factors: 1) the lack of pipeline infrastructure; 2) the lack of predictable and reliable rates of production; and 3) the difficulty in recognizing and selecting potentially productive gas-charged intervals. Unconventional (tight), continuous-type reservoirs, such as those in the Cretaceous of the northern Great Plains, are not well suited for conventional methods of formation evaluation. Pay zones frequently consist only of thinly laminated intervals of sandstone, silt, shale stringers, and disseminated clay. Potential producing intervals are commonly unrecognizable on well logs, and thus are overlooked. To aid in the identification and selection of potential producing intervals, a calibration system is developed here that empirically links the 'gas effect' to gas production. The calibration system combines the effects of porosity, water saturation, and clay content into a single 'gas-production index' (GPI) that relates the in-situ rock with production potential. The fundamental method for isolating the gas effect for calibration is a crossplot of neutron porosity minus density porosity vs gamma-ray intensity. Well-log and gas-production data used for this study consist of 242 perforated intervals from 53 gas-producing wells. Interval depths range from about 250 to 2400 ft. Gas volumes in the peak calendar year of production range from about 4 to 136 MMCF. Nine producing formations are represented. Producing-interval data show that porosity and gas production are closely linked to clay volume. Highest porosities and maximum gas production occur together at an intermediate clay content of about 12% (60 API). As clay volume exceeds 35% (130 API), minimum porosity required for production increases rapidly, and the number of potential producing intervals declines. Gas production from intervals where clay volume exceeds 50% is rare. Effective porosities of less than about 8% are probably inadequate for commercial gas production in these rocks regardless of clay content.

Competition between relatives and the evolution of dispersal in a parasitoid wasp

PubMed Central

INNOCENT, T. M.; ABE, J.; WEST, S. A.; REECE, S. E.

2014-01-01

Evolutionary theory predicts that levels of dispersal vary in response to the extent of local competition for resources and the relatedness between potential competitors. Here, we test these predictions by making use of a female dispersal dimorphism in the parasitoid wasp Melittobia australica. We show that there are two distinct female morphs, which differ in morphology, pattern of egg production, and dispersal behaviour. As predicted by theory, we found that greater competition for resources resulted in increased production of dispersing females. In contrast, we did not find support for the prediction that high relatedness between competitors increases the production of dispersing females in Melittobia. Finally, we exploit the close links between the evolutionary processes leading to selection for dispersal and for biased sex ratios to examine whether the pattern of dispersal can help distinguish between competing hypotheses for the lack of sex ratio adjustment in Melittobia. PMID:20492084

Physics, chemistry and pulmonary sequelae of thermodegradation events in long-mission space flight

NASA Technical Reports Server (NTRS)

Todd, Paul; Sklar, Michael; Ramirez, W. Fred; Smith, Gerald J.; Morgenthaler, George W.; Oberdoerster, Guenter

1993-01-01

An event in which electronic insulation consisting of polytetrafluoroethylene undergoes thermodegradation on the Space Station Freedom is considered experimentally and theoretically from the initial chemistry and convective transport through pulmonary deposition in humans. The low-gravity enviroment impacts various stages of event simulation. Vapor-phase and particulate thermodegradation products were considered as potential spacecraft contaminants. A potential pathway for the production of ultrafine particles was identified. Different approaches to the simulation and prediction of contaminant transport were studied and used to predict the distribution of generic vapor-phase products in a Space Station model. A lung transport model was used to assess the pulmonary distribution of inhaled particles, and, finally, the impact of adaptation to low gravity on the human response to this inhalation risk was explored on the basis of known physiological modifications of the immune, endocrine, musculoskeletal and pulmonary systems that accompany space flight.

Shelf Life of Food Products: From Open Labeling to Real-Time Measurements.

PubMed

Corradini, Maria G

2018-03-25

The labels currently used on food and beverage products only provide consumers with a rough guide to their expected shelf lives because they assume that a product only experiences a limited range of predefined handling and storage conditions. These static labels do not take into consideration conditions that might shorten a product's shelf life (such as temperature abuse), which can lead to problems associated with food safety and waste. Advances in shelf-life estimation have the potential to improve the safety, reliability, and sustainability of the food supply. Selection of appropriate kinetic models and data-analysis techniques is essential to predict shelf life, to account for variability in environmental conditions, and to allow real-time monitoring. Novel analytical tools to determine safety and quality attributes in situ coupled with modern tracking technologies and appropriate predictive tools have the potential to provide accurate estimations of the remaining shelf life of a food product in real time. This review summarizes the necessary steps to attain a transition from open labeling to real-time shelf-life measurements.

Accurate classical short-range forces for the study of collision cascades in Fe–Ni–Cr

DOE PAGES

Béland, Laurent Karim; Tamm, Artur; Mu, Sai; ...

2017-05-10

The predictive power of a classical molecular dynamics simulation is largely determined by the physical validity of its underlying empirical potential. In the case of high-energy collision cascades, it was recently shown that correctly modeling interactions at short distances is necessary to accurately predict primary damage production. An ab initio based framework is introduced for modifying an existing embedded-atom method FeNiCr potential to handle these short-range interactions. Density functional theory is used to calculate the energetics of two atoms approaching each other, embedded in the alloy, and to calculate the equation of state of the alloy as it is compressed.more » The pairwise terms and the embedding terms of the potential are modi ed in accordance with the ab initio results. Using this reparametrized potential, collision cascades are performed in Ni 50Fe 50, Ni 80Cr 20 and Ni 33Fe 33Cr 33. The simulations reveal that alloying Ni and NiCr to Fe reduces primary damage production, in agreement with some previous calculations. Alloying Ni and NiFe to Cr does not reduce primary damage production, in contradiction with previous calculations.« less

Role of surface and subsurface processes in scaling N2O emissions along riverine networks

PubMed Central

Marzadri, Alessandra; Dee, Martha M.; Tonina, Daniele; Bellin, Alberto; Tank, Jennifer L.

2017-01-01

Riverine environments, such as streams and rivers, have been reported as sources of the potent greenhouse gas nitrous oxide (N2O) to the atmosphere mainly via microbially mediated denitrification. Our limited understanding of the relative roles of the near-surface streambed sediment (hyporheic zone), benthic, and water column zones in controlling N2O production precludes predictions of N2O emissions along riverine networks. Here, we analyze N2O emissions from streams and rivers worldwide of different sizes, morphology, land cover, biomes, and climatic conditions. We show that the primary source of N2O emissions varies with stream and river size and shifts from the hyporheic–benthic zone in headwater streams to the benthic–water column zone in rivers. This analysis reveals that N2O production is bounded between two N2O emission potentials: the upper N2O emission potential results from production within the benthic–hyporheic zone, and the lower N2O emission potential reflects the production within the benthic–water column zone. By understanding the scaling nature of N2O production along riverine networks, our framework facilitates predictions of riverine N2O emissions globally using widely accessible chemical and hydromorphological datasets and thus, quantifies the effect of human activity and natural processes on N2O production. PMID:28400514

Simulating the impact of no-till systems on field water fluxes and maize productivity under semi-arid conditions

NASA Astrophysics Data System (ADS)

Mupangwa, W.; Jewitt, G. P. W.

Crop output from the smallholder farming sector in sub-Saharan Africa is trailing population growth leading to widespread household food insecurity. It is therefore imperative that crop production in semi-arid areas be improved in order to meet the food demand of the ever increasing human population. No-till farming practices have the potential to increase crop productivity in smallholder production systems of sub-Saharan Africa, but rarely do because of the constraints experienced by these farmers. One of the most significant of these is the consumption of mulch by livestock. In the absence of long term on-farm assessment of the no-till system under smallholder conditions, simulation modelling is a tool that provides an insight into the potential benefits and can highlight shortcomings of the system under existing soil, climatic and socio-economic conditions. Thus, this study was designed to better understand the long term impact of no-till system without mulch cover on field water fluxes and maize productivity under a highly variable rainfall pattern typical of semi-arid South Africa. The simulated on-farm experiment consisted of two tillage treatments namely oxen-drawn conventional ploughing (CT) and ripping (NT). The APSIM model was applied for a 95 year period after first being calibrated and validated using measured runoff and maize yield data. The predicted results showed significantly higher surface runoff from the conventional system compared to the no-till system. Predicted deep drainage losses were higher from the NT system compared to the CT system regardless of the rainfall pattern. However, the APSIM model predicted 62% of the annual rainfall being lost through soil evaporation from both tillage systems. The predicted yields from the two systems were within 50 kg ha -1 difference in 74% of the years used in the simulation. In only 9% of the years, the model predicted higher grain yield in the NT system compared to the CT system. It is suggested that NT systems may have great potential for reducing surface runoff from smallholder fields and that the NT systems may have potential to recharge groundwater resources through increased deep drainage. However, it was also noted that the APSIM model has major shortcomings in simulating the water balance at this level of detail and that the findings need to be confirmed by further field based and modelling studies. Nevertheless, it is clear that without mulch or a cover crop, the continued high soil evaporation and correspondingly low crop yields suggest that there is little benefit to farmers adopting NT systems in semiarid environments, despite potential water resources benefits downstream. In such cases, the potential for payment for ecosystem services should be explored.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2 O emissions.

PubMed

Ehrhardt, Fiona; Soussana, Jean-François; Bellocchi, Gianni; Grace, Peter; McAuliffe, Russel; Recous, Sylvie; Sándor, Renáta; Smith, Pete; Snow, Val; de Antoni Migliorati, Massimiliano; Basso, Bruno; Bhatia, Arti; Brilli, Lorenzo; Doltra, Jordi; Dorich, Christopher D; Doro, Luca; Fitton, Nuala; Giacomini, Sandro J; Grant, Brian; Harrison, Matthew T; Jones, Stephanie K; Kirschbaum, Miko U F; Klumpp, Katja; Laville, Patricia; Léonard, Joël; Liebig, Mark; Lieffering, Mark; Martin, Raphaël; Massad, Raia S; Meier, Elizabeth; Merbold, Lutz; Moore, Andrew D; Myrgiotis, Vasileios; Newton, Paul; Pattey, Elizabeth; Rolinski, Susanne; Sharp, Joanna; Smith, Ward N; Wu, Lianhai; Zhang, Qing

2018-02-01

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N 2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N 2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N 2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N 2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N 2 O emissions. Yield-scaled N 2 O emissions (N 2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N 2 O emissions at field scale is discussed. © 2017 John Wiley & Sons Ltd.

Quantitative structure-property relationships for chemical functional use and weight fractions in consumer articles

EPA Science Inventory

Chemical functional use -- the functional role a chemical plays in processes or products -- may be a useful heuristic for predicting human exposure potential in that it comprises information about the compound's likely physical properties and the product formulations or articles ...

Predicting the reactivity of adhesive starting materials

Treesearch

Anthony H. Conner

1999-01-01

Phenolic compounds are important in the production of bonded-wood products. Phenolic compounds in addition to phenol and resorcinol are potential alternative feedstocks for producing adhesives. The reactivity of a wide variety of phenolic compounds with formaldehyde was investigated using semi-empirical and ab initio computational chemistry methods...

A systematic study of chemogenomics of carbohydrates.

PubMed

Gu, Jiangyong; Luo, Fang; Chen, Lirong; Yuan, Gu; Xu, Xiaojie

2014-03-04

Chemogenomics focuses on the interactions between biologically active molecules and protein targets for drug discovery. Carbohydrates are the most abundant compounds in natural products. Compared with other drugs, the carbohydrate drugs show weaker side effects. Searching for multi-target carbohydrate drugs can be regarded as a solution to improve therapeutic efficacy and safety. In this work, we collected 60 344 carbohydrates from the Universal Natural Products Database (UNPD) and explored the chemical space of carbohydrates by principal component analysis. We found that there is a large quantity of potential lead compounds among carbohydrates. Then we explored the potential of carbohydrates in drug discovery by using a network-based multi-target computational approach. All carbohydrates were docked to 2389 target proteins. The most potential carbohydrates for drug discovery and their indications were predicted based on a docking score-weighted prediction model. We also explored the interactions between carbohydrates and target proteins to find the pathological networks, potential drug candidates and new indications.

Information theory-based algorithm for in silico prediction of PCR products with whole genomic sequences as templates.

PubMed

Cao, Youfang; Wang, Lianjie; Xu, Kexue; Kou, Chunhai; Zhang, Yulei; Wei, Guifang; He, Junjian; Wang, Yunfang; Zhao, Liping

2005-07-26

A new algorithm for assessing similarity between primer and template has been developed based on the hypothesis that annealing of primer to template is an information transfer process. Primer sequence is converted to a vector of the full potential hydrogen numbers (3 for G or C, 2 for A or T), while template sequence is converted to a vector of the actual hydrogen bond numbers formed after primer annealing. The former is considered as source information and the latter destination information. An information coefficient is calculated as a measure for fidelity of this information transfer process and thus a measure of similarity between primer and potential annealing site on template. Successful prediction of PCR products from whole genomic sequences with a computer program based on the algorithm demonstrated the potential of this new algorithm in areas like in silico PCR and gene finding.

Is past academic productivity predictive of radiology resident academic productivity?

PubMed

Patterson, Stephanie K; Fitzgerald, James T; Boyse, Tedric D; Cohan, Richard H

2002-02-01

The authors performed this study to determine whether academic productivity in college and medical school is predictive of the number of publications produced during radiology residency. The authors reviewed the records of 73 radiology residents who completed their residency from 1990 to 2000. Academic productivity during college, medical school, and radiology residency, other postgraduate degrees, and past careers other than radiology were tabulated. The personal essay attached to the residency application was reviewed for any stated academic interest. Residents were classified as being either previously productive or previously unproductive. Publication rates during residency and immediately after residency were compared for the two groups. For the productive residents, a correlation analysis was used to examine the relationship between past frequency of publication and type of previous activity. Least-squares regression analysis was used to investigate the relationship between preresidency academic productivity, advanced degrees, stated interest in academics, and other careers and radiology residency publications. There was no statistically significant difference in the number of articles published by those residents who were active and those who were not active before residency (P = .21). Only authorship of papers as an undergraduate was weakly predictive of residency publication. These selected measures of academic productivity as an undergraduate and during medical school are not helpful for predicting publication during residency. There was no difference in publication potential between those residents who were academically productive in the past and those who were not.

The impact of estimation errors on evaluations of timber production opportunities.

Treesearch

Dennis L. Schweitzer

1970-01-01

Errors in estimating costs and return, the timing of harvests, and the cost of using funds can greatly affect the apparent desirability of investments in timber production. Partial derivatives are used to measure the impact of these errors on the predicted present net worth of potential investments in timber production. Graphs that illustrate the impact of each type...

Estimating Subglottal Pressure from Neck-Surface Acceleration during Normal Voice Production

ERIC Educational Resources Information Center

Fryd, Amanda S.; Van Stan, Jarrad H.; Hillman, Robert E.; Mehta, Daryush D.

2016-01-01

Purpose: The purpose of this study was to evaluate the potential for estimating subglottal air pressure using a neck-surface accelerometer and to compare the accuracy of predicting subglottal air pressure relative to predicting acoustic sound pressure level (SPL). Method: Indirect estimates of subglottal pressure (P[subscript sg]') were obtained…

The equation of state of predominant detonation products

NASA Astrophysics Data System (ADS)

Zaug, Joseph; Crowhurst, Jonathan; Bastea, Sorin; Fried, Laurence

2009-06-01

The equation of state of detonation products, when incorporated into an experimentally grounded thermochemical reaction algorithm can be used to predict the performance of explosives. Here we report laser based Impulsive Stimulated Light Scattering measurements of the speed of sound from a variety of polar and nonpolar detonation product supercritical fluids and mixtures. The speed of sound data are used to improve the exponential-six potentials employed within the Cheetah thermochemical code. We will discuss the improvements made to Cheetah in terms of predictions vs. measured performance data for common polymer blended explosives. Accurately computing the chemistry that occurs from reacted binder materials is one important step forward in our efforts.

The Use of Statistical Downscaling to Project Regional Climate Changes as they Relate to Future Energy Production

NASA Astrophysics Data System (ADS)

Werth, D. W.; O'Steen, L.; Chen, K.; Altinakar, M. S.; Garrett, A.; Aleman, S.; Ramalingam, V.

2010-12-01

Global climate change has the potential for profound impacts on society, and poses significant challenges to government and industry in the areas of energy security and sustainability. Given that the ability to exploit energy resources often depends on the climate, the possibility of climate change means we cannot simply assume that the untapped potential of today will still exist in the future. Predictions of future climate are generally based on global climate models (GCMs) which, due to computational limitations, are run at spatial resolutions of hundreds of kilometers. While the results from these models can predict climatic trends averaged over large spatial and temporal scales, their ability to describe the effects of atmospheric phenomena that affect weather on regional to local scales is inadequate. We propose the use of several optimized statistical downscaling techniques that can infer climate change at the local scale from coarse resolution GCM predictions, and apply the results to assess future sustainability for two sources of energy production dependent on adequate water resources: nuclear power (through the dissipation of waste heat from cooling towers, ponds, etc.) and hydroelectric power. All methods will be trained with 20th century data, and applied to data from the years 2040-2049 to get the local-scale changes. Models of cooling tower operation and hydropower potential will then use the downscaled data to predict the possible changes in energy production, and the implications of climate change on plant siting, design, and contribution to the future energy grid can then be examined.

PCR Amplicon Prediction from Multiplex Degenerate Primer and Probe Sets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gardner, S. N.

2013-08-08

Assessing primer specificity and predicting both desired and off-target amplification products is an essential step for robust PCR assay design. Code is described to predict potential polymerase chain reaction (PCR) amplicons in a large sequence database such as NCBI nt from either singleplex or a large multiplexed set of primers, allowing degenerate primer and probe bases, with target mismatch annotates amplicons with gene information automatically downloaded from NCBI, and optionally it can predict whether there are also TaqMan/Luminex probe matches within predicted amplicons.

Predicting enteric methane emission of dairy cows with milk Fourier-transform infrared spectra and gas chromatography-based milk fatty acid profiles.

PubMed

van Gastelen, S; Mollenhorst, H; Antunes-Fernandes, E C; Hettinga, K A; van Burgsteden, G G; Dijkstra, J; Rademaker, J L W

2018-06-01

The objective of the present study was to compare the prediction potential of milk Fourier-transform infrared spectroscopy (FTIR) for CH 4 emissions of dairy cows with that of gas chromatography (GC)-based milk fatty acids (MFA). Data from 9 experiments with lactating Holstein-Friesian cows, with a total of 30 dietary treatments and 218 observations, were used. Methane emissions were measured for 3 consecutive days in climate respiration chambers and expressed as production (g/d), yield (g/kg of dry matter intake; DMI), and intensity (g/kg of fat- and protein-corrected milk; FPCM). Dry matter intake was 16.3 ± 2.18 kg/d (mean ± standard deviation), FPCM yield was 25.9 ± 5.06 kg/d, CH 4 production was 366 ± 53.9 g/d, CH 4 yield was 22.5 ± 2.10 g/kg of DMI, and CH 4 intensity was 14.4 ± 2.58 g/kg of FPCM. Milk was sampled during the same days and analyzed by GC and by FTIR. Multivariate GC-determined MFA-based and FTIR-based CH 4 prediction models were developed, and subsequently, the final CH 4 prediction models were evaluated with root mean squared error of prediction and concordance correlation coefficient analysis. Further, we performed a random 10-fold cross validation to calculate the performance parameters of the models (e.g., the coefficient of determination of cross validation). The final GC-determined MFA-based CH 4 prediction models estimate CH 4 production, yield, and intensity with a root mean squared error of prediction of 35.7 g/d, 1.6 g/kg of DMI, and 1.6 g/kg of FPCM and with a concordance correlation coefficient of 0.72, 0.59, and 0.77, respectively. The final FTIR-based CH 4 prediction models estimate CH 4 production, yield, and intensity with a root mean squared error of prediction of 43.2 g/d, 1.9 g/kg of DMI, and 1.7 g/kg of FPCM and with a concordance correlation coefficient of 0.52, 0.40, and 0.72, respectively. The GC-determined MFA-based prediction models described a greater part of the observed variation in CH 4 emission than did the FTIR-based models. The cross validation results indicate that all CH 4 prediction models (both GC-determined MFA-based and FTIR-based models) are robust; the difference between the coefficient of determination and the coefficient of determination of cross validation ranged from 0.01 to 0.07. The results indicate that GC-determined MFA have a greater potential than FTIR spectra to estimate CH 4 production, yield, and intensity. Both techniques hold potential but may not yet be ready to predict CH 4 emission of dairy cows in practice. Additional CH 4 measurements are needed to improve the accuracy and robustness of GC-determined MFA and FTIR spectra for CH 4 prediction. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Comparison of methane production potential, biodegradability, and kinetics of different organic substrates.

PubMed

Li, Yeqing; Zhang, Ruihong; Liu, Guangqing; Chen, Chang; He, Yanfeng; Liu, Xiaoying

2013-12-01

The methane production potential, biodegradability, and kinetics of a wide range of organic substrates were determined using a unified and simple method. Results showed that feedstocks that contained high energy density and easily degradable substrates exhibited high methane production potential and biodegradability. Lignocellulosic biomass with high content of fibrous compositions had low methane yield and biodegradability. Feedstocks with high lignin content (≥ 15%, on a TS basis) had low first-order rate constant (0.05-0.06 1/d) compared to others. A negative linear correlation between lignin content and experimental methane yield (or biodegradability) was found for lignocellulosic and manure wastes. This could be used as a fast method to predict the methane production potential and biodegradability of fiber-rich substrates. The findings of this study provided a database for the conversion efficiency of different organic substrates and might be useful for applications of biomethane potential assay and anaerobic digestion in the future. Copyright © 2013 Elsevier Ltd. All rights reserved.

Snowmelt-Driven Trade-Offs Between Early and Late Season Productivity Negatively Impact Forest Carbon Uptake During Drought

NASA Astrophysics Data System (ADS)

Knowles, John F.; Molotch, Noah P.; Trujillo, Ernesto; Litvak, Marcy E.

2018-04-01

Future projections of declining snowpack and increasing potential evaporation are predicted to advance the timing of snowmelt in mountain ecosystems globally with unknown implications for snowmelt-driven forest productivity. Accordingly, this study combined satellite- and tower-based observations to investigate the forest productivity response to snowpack and potential evaporation variability between 1989 and 2012 throughout the Southern Rocky Mountain ecoregion, United States. Our results show that early and late season productivity were significantly and inversely related and that future shifts toward earlier and/or reduced snowmelt could decrease snowmelt water use efficiency and thus restrict productivity despite a longer growing season. This was explained by increasing snow aridity, which incorporated evaporative demand and snow water supply, and was modified by summer precipitation to determine total annual productivity. The combination of low snow accumulation and record high potential evaporation in 2012 resulted in the 34 year minimum ecosystem productivity that could be indicative of future conditions.

A NEW LOG EVALUATION METHOD TO APPRAISE MESAVERDE RE-COMPLETION OPPORTUNITIES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albert Greer

2003-09-11

Artificial intelligence tools, fuzzy logic and neural networks were used to evaluate the potential of the behind pipe Mesaverde formation in BMG's Mancos formation wells. A fractal geostatistical mapping algorithm was also used to predict Mesaverde production. Additionally, a conventional geological study was conducted. To date one Mesaverde completion has been performed. The Janet No.3 Mesaverde completion was non-economic. Both the AI method and the geostatistical methods predicted the failure of the Janet No.3. The Gavilan No.1 in the Mesaverde was completed during the course of the study and was an extremely good well. This well was not included inmore » the statistical dataset. The AI method predicted very good production while the fractal map predicted a poor producer.« less

On the Process and Consequences of Job Rationing in Oregon's Declining Wood Products Industry. WRDC Discussion Paper No. 4.

ERIC Educational Resources Information Center

Stevens, Joe B.; And Others

The study of the mobility of the wood products labor force was made in response to a predicted decline in manpower needs for the wood products industry in western Oregon and western Washington. Variables affecting workers' employability and mobility were analyzed to determine the potential in Oregon for mobility within and out of the industry, the…

Study of the geothermal production potential in the Williston Basin, North Dakota

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chu, Min H.

1991-09-10

Preliminary studies of geothermal production potential for the North Dakota portion of the Williston Basin have been carried out. Reservoir data such as formation depth, subsurface temperatures, and water quality were reviewed for geothermal brine production predictions. This study, in addition, provides important information about net pay thickness, porosity, volume of geothermal water available, and productivity index for future geothermal direct-use development. Preliminary results show that the Inyan Kara Formation of the Dakota Group is the most favorable geothermal resource in terms of water quality and productivity. The Madison, Duperow, and Red River Formations are deeper formations but because ofmore » their low permeability and great depth, the potential flow rates from these three formations are considerably less than those of the Inyan Kara Formation. Also, poor water quality and low porosity will make those formations less favorable for geothermal direct-use development.« less

Effects of shifting seasonal rainfall patterns on net primary productivity and carbon storage in tropical seasonally dry ecosystems

NASA Astrophysics Data System (ADS)

Rohr, T.; Manzoni, S.; Feng, X.; Menezes, R.; Porporato, A. M.

2013-12-01

Although seasonally dry ecosystems (SDEs), identified by prolonged drought followed by a short, but intense, rainy season, cover large regions of the tropics, their biogeochemical response to seasonal rainfall and soil carbon (C) sequestration potential are not well characterized. Both productivity and soil respiration are positively affected by seasonal soil moisture availability, creating a delicate balance between C deposition through litterfall and C losses through heterotrophic respiration. As climate change projections for the tropics predict decreased annual rainfall and increased dry season length, it is critical to understand how variations in seasonal rainfall distributions control this balance. To address this question, we develop a minimal model linking the seasonal behavior of the ensemble soil moisture, plant productivity, the related soil C inputs through litterfall, and soil C dynamics. The model is parameterized for a case study from a drought-deciduous caatinga ecosystem in northeastern Brazil. Results indicate that when altering the seasonal rainfall patterns for a fixed annual rainfall, both plant productivity and soil C sequestration potential are largely, and nonlinearly, dependent on wet season duration. Moreover, total annual rainfall plays a dominant role in describing this relationship, leading at times to the emergence of distinct optima in both primary production and C sequestration. Examining these results in the context of climate-driven changes to wet season duration and mean annual precipitation indicate that the initial hydroclimatic regime of a particular ecosystem is an important factor to predict both the magnitude and direction of the effects of shifting seasonal distributions on productivity and C storage. Although highly productive ecosystems will likely experience declining C storage with predicted climate shifts, those currently operating well below peak production can potentially see improved C stocks with the onset of declining rainfall due to reduced soil respiration. a) Annual average net primary productivity and b) the temporally averaged ensemble soil carbon concentration <(C_yr )> are plotted against the length of the wet season T_W, for six annual rainfall rates (m yr-1).

Comparative genomic analysis of the multispecies probiotic-marketed product VSL#3.

PubMed

Douillard, François P; Mora, Diego; Eijlander, Robyn T; Wels, Michiel; de Vos, Willem M

2018-01-01

Several probiotic-marketed formulations available for the consumers contain live lactic acid bacteria and/or bifidobacteria. The multispecies product commercialized as VSL#3 has been used for treating various gastro-intestinal disorders. However, like many other products, the bacterial strains present in VSL#3 have only been characterized to a limited extent and their efficacy as well as their predicted mode of action remain unclear, preventing further applications or comparative studies. In this work, the genomes of all eight bacterial strains present in VSL#3 were sequenced and characterized, to advance insights into the possible mode of action of this product and also to serve as a basis for future work and trials. Phylogenetic and genomic data analysis allowed us to identify the 7 species present in the VSL#3 product as specified by the manufacturer. The 8 strains present belong to the species Streptococcus thermophilus, Lactobacillus acidophilus, Lactobacillus paracasei, Lactobacillus plantarum, Lactobacillus helveticus, Bifidobacterium breve and B. animalis subsp. lactis (two distinct strains). Comparative genomics revealed that the draft genomes of the S. thermophilus and L. helveticus strains were predicted to encode most of the defence systems such as restriction modification and CRISPR-Cas systems. Genes associated with a variety of potential probiotic functions were also identified. Thus, in the three Bifidobacterium spp., gene clusters were predicted to encode tight adherence pili, known to promote bacteria-host interaction and intestinal barrier integrity, and to impact host cell development. Various repertoires of putative signalling proteins were predicted to be encoded by the genomes of the Lactobacillus spp., i.e. surface layer proteins, LPXTG-containing proteins, or sortase-dependent pili that may interact with the intestinal mucosa and dendritic cells. Taken altogether, the individual genomic characterization of the strains present in the VSL#3 product confirmed the product specifications, determined its coding capacity as well as identified potential probiotic functions.

Comparative genomic analysis of the multispecies probiotic-marketed product VSL#3

PubMed Central

Mora, Diego; Eijlander, Robyn T.; Wels, Michiel; de Vos, Willem M.

2018-01-01

Several probiotic-marketed formulations available for the consumers contain live lactic acid bacteria and/or bifidobacteria. The multispecies product commercialized as VSL#3 has been used for treating various gastro-intestinal disorders. However, like many other products, the bacterial strains present in VSL#3 have only been characterized to a limited extent and their efficacy as well as their predicted mode of action remain unclear, preventing further applications or comparative studies. In this work, the genomes of all eight bacterial strains present in VSL#3 were sequenced and characterized, to advance insights into the possible mode of action of this product and also to serve as a basis for future work and trials. Phylogenetic and genomic data analysis allowed us to identify the 7 species present in the VSL#3 product as specified by the manufacturer. The 8 strains present belong to the species Streptococcus thermophilus, Lactobacillus acidophilus, Lactobacillus paracasei, Lactobacillus plantarum, Lactobacillus helveticus, Bifidobacterium breve and B. animalis subsp. lactis (two distinct strains). Comparative genomics revealed that the draft genomes of the S. thermophilus and L. helveticus strains were predicted to encode most of the defence systems such as restriction modification and CRISPR-Cas systems. Genes associated with a variety of potential probiotic functions were also identified. Thus, in the three Bifidobacterium spp., gene clusters were predicted to encode tight adherence pili, known to promote bacteria-host interaction and intestinal barrier integrity, and to impact host cell development. Various repertoires of putative signalling proteins were predicted to be encoded by the genomes of the Lactobacillus spp., i.e. surface layer proteins, LPXTG-containing proteins, or sortase-dependent pili that may interact with the intestinal mucosa and dendritic cells. Taken altogether, the individual genomic characterization of the strains present in the VSL#3 product confirmed the product specifications, determined its coding capacity as well as identified potential probiotic functions. PMID:29451876

Predicting the safety and efficacy of buffer therapy to raise tumour pHe: an integrative modelling study.

PubMed

Martin, N K; Robey, I F; Gaffney, E A; Gillies, R J; Gatenby, R A; Maini, P K

2012-03-27

Clinical positron emission tomography imaging has demonstrated the vast majority of human cancers exhibit significantly increased glucose metabolism when compared with adjacent normal tissue, resulting in an acidic tumour microenvironment. Recent studies demonstrated reducing this acidity through systemic buffers significantly inhibits development and growth of metastases in mouse xenografts. We apply and extend a previously developed mathematical model of blood and tumour buffering to examine the impact of oral administration of bicarbonate buffer in mice, and the potential impact in humans. We recapitulate the experimentally observed tumour pHe effect of buffer therapy, testing a model prediction in vivo in mice. We parameterise the model to humans to determine the translational safety and efficacy, and predict patient subgroups who could have enhanced treatment response, and the most promising combination or alternative buffer therapies. The model predicts a previously unseen potentially dangerous elevation in blood pHe resulting from bicarbonate therapy in mice, which is confirmed by our in vivo experiments. Simulations predict limited efficacy of bicarbonate, especially in humans with more aggressive cancers. We predict buffer therapy would be most effectual: in elderly patients or individuals with renal impairments; in combination with proton production inhibitors (such as dichloroacetate), renal glomular filtration rate inhibitors (such as non-steroidal anti-inflammatory drugs and angiotensin-converting enzyme inhibitors), or with an alternative buffer reagent possessing an optimal pK of 7.1-7.2. Our mathematical model confirms bicarbonate acts as an effective agent to raise tumour pHe, but potentially induces metabolic alkalosis at the high doses necessary for tumour pHe normalisation. We predict use in elderly patients or in combination with proton production inhibitors or buffers with a pK of 7.1-7.2 is most promising.

Predicting plankton net community production in the Atlantic Ocean

NASA Astrophysics Data System (ADS)

Serret, Pablo; Robinson, Carol; Fernández, Emilio; Teira, Eva; Tilstone, Gavin; Pérez, Valesca

2009-07-01

We present, test and implement two contrasting models to predict euphotic zone net community production (NCP), which are based on 14C primary production (PO 14CP) to NCP relationships over two latitudinal (ca. 30°S-45°N) transects traversing highly productive and oligotrophic provinces of the Atlantic Ocean (NADR, CNRY, BENG, NAST-E, ETRA and SATL, Longhurst et al., 1995 [An estimation of global primary production in the ocean from satellite radiometer data. Journal of Plankton Research 17, 1245-1271]). The two models include similar ranges of PO 14CP and community structure, but differ in the relative influence of allochthonous organic matter in the oligotrophic provinces. Both models were used to predict NCP from PO 14CP measurements obtained during 11 local and three seasonal studies in the Atlantic, Pacific and Indian Oceans, and from satellite-derived estimates of PO 14CP. Comparison of these NCP predictions with concurrent in situ measurements and geochemical estimates of NCP showed that geographic and annual patterns of NCP can only be predicted when the relative trophic importance of local vs. distant processes is similar in both modeled and predicted ecosystems. The system-dependent ability of our models to predict NCP seasonality suggests that trophic-level dynamics are stronger than differences in hydrodynamic regime, taxonomic composition and phytoplankton growth. The regional differences in the predictive power of both models confirm the existence of biogeographic differences in the scale of trophic dynamics, which impede the use of a single generalized equation to estimate global marine plankton NCP. This paper shows the potential of a systematic empirical approach to predict plankton NCP from local and satellite-derived P estimates.

Using soil temperature and moisture to predict forest soil nitrogen mineralization

Treesearch

Jennifer D. Knoepp; Wayne T. Swank

2002-01-01

Due to the importance of N in forest productivity ecosystem and nutrient cycling research often includes measurement of soil N transformation rates as indices of potential availability and ecosystem losses of N. We examined the feasibility of using soil temperature and moisture content to predict soil N mineralization rates (Nmin) at the Coweeta Hydrologic Laboratory...

Global Potential Net Prmary Production Predicted from Vegetation Class, Precipitation, and Temperature

USDA-ARS?s Scientific Manuscript database

Net Primary Production (NPP), the difference between CO2 fixed by photosynthesis and CO2 lost to autotrophic respiration, is one of the most important components of the carbon cycle. Our goal was to develop a simple regression model to estimate global NPP using climate and land cover data. Approxima...

A Quality Classification System for Young Hardwood Trees - The First Step in Predicting Future Products

Treesearch

David L. Sonderman; Robert L. Brisbin

1978-01-01

Forest managers have no objective way to determine the relative value of culturally treated forest stands in terms of product potential. This paper describes the first step in the development of a quality classification system based on the measurement of individual tree characteristics for young hardwood stands.

Seasonal Storminess in the North Pacific, Bering Sea, and Alaskan Regions

NASA Astrophysics Data System (ADS)

Shippee, N. J.; Atkinson, D. E.; Walsh, J. E.; Partain, J.; Gottschalck, J.; Marra, J.

2012-12-01

Annually, extra-tropical cyclones present a high impact natural hazard to the North Pacific, Bering Sea, and Alaskan regions. In these regions, extensive subsistence and commercial fishing, new oil and gas field development, tourism, growing interest in and exploitation of new commercial shipping potential, and increasing military and Coast Guard activity, all represent potential parties impacted by storms in these waters. It is of interest to many parties to begin developing capacity to provide some indication of storm activity at a monthly- to seasonal-outlook (30 to 90 days) timeframe. Using storm track data from NOAA's Climate Prediction Center for the North Pacific and Alaskan region, an experimental seasonal storminess outlook product, using eigen-based methods similar to the operational seasonal temperature and precipitation products currently produced at NOAA CPC, has been created and tested in hindcast mode using predicted states of ENSO, the Pacific Decadal Oscillation (PDO), the Pacific-North American Pattern (PNA), and the Arctic Oscillation (AO). A sample of the seasonal storminess outlook product will be shown along with a discussion of the utility of individual teleconnection patterns in the generation of the product.

Validation databases for simulation models: aboveground biomass and net primary productive, (NPP) estimation using eastwide FIA data

Treesearch

Jennifer C. Jenkins; Richard A. Birdsey

2000-01-01

As interest grows in the role of forest growth in the carbon cycle, and as simulation models are applied to predict future forest productivity at large spatial scales, the need for reliable and field-based data for evaluation of model estimates is clear. We created estimates of potential forest biomass and annual aboveground production for the Chesapeake Bay watershed...

Neural networks for dimensionality reduction of fluorescence spectra and prediction of drinking water disinfection by-products.

PubMed

Peleato, Nicolas M; Legge, Raymond L; Andrews, Robert C

2018-06-01

The use of fluorescence data coupled with neural networks for improved predictability of drinking water disinfection by-products (DBPs) was investigated. Novel application of autoencoders to process high-dimensional fluorescence data was related to common dimensionality reduction techniques of parallel factors analysis (PARAFAC) and principal component analysis (PCA). The proposed method was assessed based on component interpretability as well as for prediction of organic matter reactivity to formation of DBPs. Optimal prediction accuracies on a validation dataset were observed with an autoencoder-neural network approach or by utilizing the full spectrum without pre-processing. Latent representation by an autoencoder appeared to mitigate overfitting when compared to other methods. Although DBP prediction error was minimized by other pre-processing techniques, PARAFAC yielded interpretable components which resemble fluorescence expected from individual organic fluorophores. Through analysis of the network weights, fluorescence regions associated with DBP formation can be identified, representing a potential method to distinguish reactivity between fluorophore groupings. However, distinct results due to the applied dimensionality reduction approaches were observed, dictating a need for considering the role of data pre-processing in the interpretability of the results. In comparison to common organic measures currently used for DBP formation prediction, fluorescence was shown to improve prediction accuracies, with improvements to DBP prediction best realized when appropriate pre-processing and regression techniques were applied. The results of this study show promise for the potential application of neural networks to best utilize fluorescence EEM data for prediction of organic matter reactivity. Copyright © 2018 Elsevier Ltd. All rights reserved.

Allergenic potential of novel proteins - What can we learn from animal production?

PubMed

Ekmay, Ricardo D; Coon, Craig N; Ladics, Gregory S; Herman, Rod A

2017-10-01

Currently, risk assessment of the allergenic potential of novel proteins relies heavily on evaluating protein digestibility under normal conditions based on the theory that allergens are more resistant to gastrointestinal digestion than non-allergens. There is also proposed guidance for expanded in vitro digestibility assay conditions to include vulnerable sub-populations. One of the underlying rationales for the expanded guidance is that current in vitro assays do not accurately replicate the range of physiological conditions. Animal scientists have long sought to predict protein and amino acid digestibility for precision nutrition. Monogastric production animals, especially swine, have gastrointestinal systems similar to humans, and evaluating potential allergen digestibility in this context may be beneficial. Currently, there is no compelling evidence that the mechanisms sometimes postulated to be associated with allergenic sensitization, e.g. antacid modification of stomach pH, are valid among production animals. Furthermore, examples are provided where non-biologically representative assays are better at predicting protein and amino acid digestibility compared with those designed to mimic in vivo conditions. Greater emphasis should be made to align in vitro assessments with in vivo data. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Designing optimal cell factories: integer programming couples elementary mode analysis with regulation

PubMed Central

2012-01-01

Background Elementary mode (EM) analysis is ideally suited for metabolic engineering as it allows for an unbiased decomposition of metabolic networks in biologically meaningful pathways. Recently, constrained minimal cut sets (cMCS) have been introduced to derive optimal design strategies for strain improvement by using the full potential of EM analysis. However, this approach does not allow for the inclusion of regulatory information. Results Here we present an alternative, novel and simple method for the prediction of cMCS, which allows to account for boolean transcriptional regulation. We use binary linear programming and show that the design of a regulated, optimal metabolic network of minimal functionality can be formulated as a standard optimization problem, where EM and regulation show up as constraints. We validated our tool by optimizing ethanol production in E. coli. Our study showed that up to 70% of the predicted cMCS contained non-enzymatic, non-annotated reactions, which are difficult to engineer. These cMCS are automatically excluded by our approach utilizing simple weight functions. Finally, due to efficient preprocessing, the binary program remains computationally feasible. Conclusions We used integer programming to predict efficient deletion strategies to metabolically engineer a production organism. Our formulation utilizes the full potential of cMCS but adds additional flexibility to the design process. In particular our method allows to integrate regulatory information into the metabolic design process and explicitly favors experimentally feasible deletions. Our method remains manageable even if millions or potentially billions of EM enter the analysis. We demonstrated that our approach is able to correctly predict the most efficient designs for ethanol production in E. coli. PMID:22898474

Using Mid Infrared Spectroscopy to Predict the Decomposability of Soil Organic Matter Stored in Arctic Tundra Soils

NASA Astrophysics Data System (ADS)

Matamala, R.; Fan, Z.; Jastrow, J. D.; Liang, C.; Calderon, F.; Michaelson, G.; Ping, C. L.; Mishra, U.; Hofmann, S. M.

2016-12-01

The large amounts of organic matter stored in permafrost-region soils are preserved in a relatively undecomposed state by the cold and wet environmental conditions limiting decomposer activity. With pending climate changes and the potential for warming of Arctic soils, there is a need to better understand the amount and potential susceptibility to mineralization of the carbon stored in the soils of this region. Studies have suggested that soil C:N ratio or other indicators based on the molecular composition of soil organic matter could be good predictors of potential decomposability. In this study, we investigated the capability of Fourier-transform mid infrared spectroscopy (MidIR) spectroscopy to predict the evolution of carbon dioxide (CO2) produced by Arctic tundra soils during a 60-day laboratory incubation. Soils collected from four tundra sites on the Coastal Plain, and Arctic Foothills of the North Slope of Alaska were separated into active-layer organic, active-layer mineral, and upper permafrost and incubated at 1, 4, 8 and 16 °C. Carbon dioxide production was measured throughout the incubations. Total soil organic carbon (SOC) and total nitrogen (TN) concentrations, salt (0.5 M K2SO4) extractable organic matter (SEOM), and MidIR spectra of the soils were measured before and after incubation. Multivariate partial least squares (PLS) modeling was used to predict cumulative CO2 production, decay rates, and the other measurements. MidIR reliably estimated SOC and TN and SEOM concentrations. The MidIR prediction models of CO2 production were very good for active-layer mineral and upper permafrost soils and good for the active-layer organic soils. SEOM was also a very good predictor of CO2 produced during the incubations. Analysis of the standardized beta coefficients from the PLS models of CO2 production for the three soil layers indicated a small number (9) of influential spectral bands. Of these, bands associated with O-H and N-H stretch, carbonates, and ester C-O appeared to be most important for predicting CO2 production for both active-layer mineral and upper permafrost soils. Further analysis of these influential bands and their relationships to SEOM in soil will be explored. Our results show that the MidIR spectra contains valuable information that can be related to decomposability of soils.

Assessing the skill of seasonal meteorological forecast products for predicting droughts and water scarcity in highly regulated basins

NASA Astrophysics Data System (ADS)

Squeri, Marika; Giuliani, Matteo; Castelletti, Andrea; Pulido-Velazquez, Manuel; Marcos-Garcia, Patricia; Macian-Sorribes, Hector

2017-04-01

Drought and water scarcity are important issues in Southern Europe and many predictions suggest that their frequency and severity will increase over the next years, potentially leading to negative environmental and socio-economic impacts. This work focuses on the Jucar river basin, located in the hinterland of Valencia (Eastern Spain), which is historically affected by long and severe dry periods that negatively impact several economic sectors, with irrigated agriculture representing the main consumptive demand in the basin (79%). Monitoring drought and water scarcity is crucial to activate timely drought management strategies in the basin. However, most traditional drought indexes fail in detecting critical events due to the large presence of human regulation supporting the irrigated agriculture. Over the last 20 years, a sophisticated drought monitoring system has been set up to properly capture the status of the catchment by means of the state index, a weighted linear combination of twelve indicators that depends on observations of precipitation, streamflow, reservoirs' storages and groundwater levels in representative locations at the basin. In this work, we explore the possibility of predicting the state index, which is currently used only as a monitoring tool, in order to prompt anticipatory actions before the drought/water scarcity event starts. In particular, we test the forecasting skill of retrospective seasonal meteorological predictions from the European Centre for Medium-range Weather Forecasts (ECMWF) System 4. The 7-months lead time of these products allows predicting in February the values of the state index until September, thus covering the entire agricultural season. Preliminary results suggest that the Sys4-ECMWF products are skillful in predicting the state index, potentially supporting the design of anticipatory drought management actions.

Prediction of in vivo neutral detergent fiber digestibility and digestion rate of potentially digestible neutral detergent fiber: comparison of models.

PubMed

Huhtanen, P; Seppälä, A; Ahvenjärvi, S; Rinne, M

2008-10-01

Eleven 1-pool, seven 2-pool, and three 3-pool models were compared in fitting gas production data and predicting in vivo NDF digestibility and effective first-order digestion rate of potentially digestible NDF (pdNDF). Isolated NDF from 15 grass silages harvested at different stages of maturity was incubated in triplicate in rumen fluid-buffer solution for 72 h to estimate the digestion kinetics from cumulative gas production profiles. In vivo digestibility was estimated by the total fecal collection method in sheep fed at a maintenance level of feeding. The concentration of pdNDF was estimated by a 12-d in situ incubation. The parameter values from gas production profiles and pdNDF were used in a 2-compartment rumen model to predict pdNDF digestibility using 50 h of rumen residence time distributed in a ratio of 0.4:0.6 between the non-escapable and escapable pools. The effective first-order digestion rate was computed both from observed in vivo and model-predicted pdNDF digestibility assuming the passage kinetic model described above. There were marked differences between the models in fitting the gas production data. The fit improved with increasing number of pools, suggesting that silage pdNDF is not a homogenous substrate. Generally, the models predicted in vivo NDF digestibility and digestion rate accurately. However, a good fit of gas production data was not necessarily translated into improved predictions of the in vivo data. The models overestimating the asymptotic gas volumes tended to underestimate the in vivo digestibility. Investigating the time-related residuals during the later phases of fermentation is important when the data are used to estimate the first-order digestion rate of pdNDF. Relatively simple models such as the France model or even a single exponential model with discrete lag period satisfied the minimum criteria for a good model. Further, the comparison of feedstuffs on the basis of parameter values is more unequivocal than in the case of multiple-pool models.

The genome editing toolbox: a spectrum of approaches for targeted modification.

PubMed

Cheng, Joseph K; Alper, Hal S

2014-12-01

The increase in quality, quantity, and complexity of recombinant products heavily drives the need to predictably engineer model and complex (mammalian) cell systems. However, until recently, limited tools offered the ability to precisely manipulate their genomes, thus impeding the full potential of rational cell line development processes. Targeted genome editing can combine the advances in synthetic and systems biology with current cellular hosts to further push productivity and expand the product repertoire. This review highlights recent advances in targeted genome editing techniques, discussing some of their capabilities and limitations and their potential to aid advances in pharmaceutical biotechnology. Copyright © 2014 Elsevier Ltd. All rights reserved.

Potential functional variants associated with age at puberty in a validation population of swine

USDA-ARS?s Scientific Manuscript database

Puberty in pigs is defined as age at first estrus and gilts that have an earlier age at puberty are more likely to have greater sow lifetime productivity. Because age at puberty is predictive for sow longevity and lifetime productivity, but not routinely measured in commercial herds, it would be ben...

Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

DOE Office of Scientific and Technical Information (OSTI.GOV)

Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov; Cross, Kevin P.

Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describemore » the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive performance.« less

The production of transuranium elements by the r-process nucleosynthesis

NASA Astrophysics Data System (ADS)

Goriely, S.; Martínez Pinedo, G.

2015-12-01

The production of super-heavy transuranium elements by stellar nucleosynthesis processes remains an open question. The most promising process that could potentially give rise to the formation of such elements is the so-called rapid neutron-capture process, or r-process, known to be at the origin of approximately half of the A > 60 stable nuclei observed in nature. However, despite important efforts, the astrophysical site of the r-process remains unidentified. Here, we study the r-process nucleosynthesis in material that is dynamically ejected by tidal and pressure forces during the merging of binary neutron stars. Neutron star mergers could potentially be the dominant r-process site in the Galaxy, but also due to the extreme neutron richness found in such environment, could potentially synthesise super-heavy elements. R-process nucleosynthesis during the decompression is known to be largely insensitive to the detailed astrophysical conditions because of efficient fission recycling, producing a composition that closely follows the solar r-abundance distribution for nuclei with mass numbers A > 140. During the neutron irradiation, nuclei up to charge numbers Z ≃ 110 and mass number A ≃ 340 are produced, with a major peak production at the N = 184 shell closure, i.e. around A ≃ 280. Super-heavy nuclei with Z > 110 can hardly be produced due to the efficient fission taking place along those isotopic chains. Long-lived transuranium nuclei are inevitably produced by the r-process. The predictions concerning the production of transuranium nuclei remain however very sensitive to the predictions of fission barrier heights for such super-heavy nuclei. More nuclear predictions within different microscopic approaches are needed.

Rabbit Feces as Feed for Ruminants and as an Energy Source.

PubMed

Peiretti, Pier Giorgio; Tassone, Sonia; Gai, Francesco; Gasco, Laura; Masoero, Giorgio

2014-12-05

There are prospects for using novel feeds from various sources to provide ruminants with alternative sources of protein and energy such as by-products, and animal wastes. Rabbit feces are a concentrated source of fiber and could have commercial potential both as input biomass in anaerobic processes for biogas production, as well as a fibrous source for ruminal degradation. The aims of this work were to assess the potential as ruminant feeding and as biogas production of rabbit feces, in comparison with 12 crops. The chemical composition and the potential and experimental in vitro true digestibility (IVTD) and neutral detergent fiber digestibility (NDFD) of 148 feces samples were determined by using chemical methods, Daisy system digestibility and/or NIRS predictions. The average biomethane potential (BMP) was 286 ± 10 lCH4/kg SV with -4% vs. the crops average. Milk forage unit (milk FU), IVTD and NDFD of feces were 0.54 ± 0.06 milk FU/kg DM, 74% ± 3% and 50% ± 5%, respectively, with comparisons of -19%, -11% and -24% vs. the crops average. Reconstruction of the potential values based on the chemical constituents but using the crop partial least square model well agreed with the NIRS calibrations and cross-validation. In a global NIRS calibration of the feces and crops the relative predicted deviation for IVTD, NDFD and milk FU were 3.1, 2.9 and 2.6, respectively, and only 1.5 for BMP. Running the Daisy system for rabbit feces in rumen fluid gave some inconsistencies, weakened the functional relationships, and appeared not to be correlated with the potential values of IVTD and NDFD. Nevertheless, the energetic potential of feces appears to be similar to some conventional crops at different degrees of maturity. Thus we conclude that rabbit feces has potential value as a ruminant feed and for biogas production.

Anaerobic biodegradation of aircraft deicing fluid in UASB reactors.

PubMed

Tham, P T Pham thi; Kennedy, K J Kevin J

2004-05-01

A central composite design was employed to methodically investigate anaerobic treatment of aircraft deicing fluid (ADF) in bench-scale Upflow Anaerobic Sludge Blanket (UASB) reactors. A total of 23 runs at 17 different operating conditions (0.8% 1.6% ADF (6000-12,000mg/L COD), 12-56h HRT, and 18-36gVSS/L) were conducted in continuous mode. The development of four empirical models describing process responses (i.e. COD removal efficiency, biomass-specific acetoclastic activity, methane production rate, and methane production potential) as functions of ADF concentration, hydraulic retention time, and biomass concentration is presented. Model verification indicated that predicted responses (COD removal efficiencies, biomass-specific acetoclastic activity, and methane production rates and potential) were in good agreement with experimental results. Biomass-specific acetoclastic activity was improved two-fold from 0.23gCOD/gVSS/d for inoculum to a maximum of 0.55gCOD/gVSS/d during ADF treatment in UASB reactors. For the design window, COD removal efficiencies were higher than 90%. The predicted methane production potentials were close to theoretical values, and methane production rates increased as the organic loading rate is increased. ADF toxicity effects were evident for 1.6% ADF at medium organic loadings (SOLR above 0.5gCOD/gVSS/d). In contrast, good reactor stability and excellent COD removal efficiencies were achieved at 1.2% ADF for reactor loadings approaching that of highly loaded systems (0.73gCOD/gVSS/d).

Limnology of nine small lakes, Matanuska-Susitna Borough, Alaska, and the survival and growth rates of rainbow trout

USGS Publications Warehouse

Woods, P.F.

1985-01-01

The survival and growth rates of rainbow trout (Salmo gairdnieri) were concurrently measured with selected limnological characteristics in nine small (surface area < 25 sq hectometers) lakes in the Matanuska-Susitna Borough. The project goal was to develop empirical models for predicting rainbow trout growth rates from the following variables: total phosphorus concentration, chlorophyll a concentration, Secchi disc transparency, or the morphoedaphic index--a means of characterizing potential biological productivity. No suitable model could be developed from the data collected during 1982 and 1983. The lack of significant correlation was attributed in part to the wide variation in survival of rainbow trout. Winterkills, caused by severe depletion of dissolved oxygen, were suspected in four of the lakes. Varied levels of fishing pressure and competition with threespine stickleback (Gasterosteus aculeatus) also influenced survival of rainbow trout but their effects were overshadowed by winterkill. Predictive capability was also reduced because of inconsistencies in rankings generated by each of the four limnological variables chosen as indicators of potential biological productivity. A lake ranked low in productivity by one variable was commonly ranked high in productivity by another variable. The survivability of rainbow trout stocked in lakes such as these nine may be a more important indicator of potential biomass production than are indicators of lake fertility. Assessments of a lake 's susceptibility to winterkill and the degree of competition with threespine stickleback are suggested as important topics for additional research. (Author 's abstract)

Prospective Predictors of Novel Tobacco and Nicotine Product Use in Emerging Adulthood

PubMed Central

Hampson, Sarah E.; Andrews, Judy A.; Severson, Herbert H.; Barckley, Maureen

2015-01-01

Objective To investigate whether risk factors for cigarette smoking assessed in adolescence predict the use of novel tobacco and nicotine products (hookah, little cigars, and e-cigarettes) in early emerging adulthood. Methods In a longitudinal study (N = 862), risk factors were measured in middle and high school and novel product use was measured in emerging adulthood (mean age 22.4 years). Structural equation modelling was used to test a model predicting lifetime use of any of hookah, little cigars, and e-cigarettes in early emerging adulthood from distal predictors (gender, maternal smoking through Grade 8, already tried alcohol, cigarettes, or marijuana by Grade 8, and sensation seeking at Grade 8), and potential mediators (intentions to smoke cigarettes, drink alcohol or smoke marijuana at Grade 9, and smoking trajectory across high school). Results The most prevalent novel tobacco product was hookah (21.7%), followed by little cigars (16.8%), and e-cigarettes (6.6%). Maternal smoking, having already tried substances, and sensation seeking each predicted the use of at least one of these products via an indirect path through intentions to use substances and membership in a high school smoking trajectory. Conclusions Risk factors for cigarette smoking were found to predict novel tobacco use, suggesting that interventions to prevent cigarette smoking could be extended to include common novel tobacco products. PMID:26206439

Statistical Method Based on Confidence and Prediction Regions for Analysis of Volatile Organic Compounds in Human Breath Gas

NASA Astrophysics Data System (ADS)

Wimmer, G.

2008-01-01

In this paper we introduce two confidence and two prediction regions for statistical characterization of concentration measurements of product ions in order to discriminate various groups of persons for prospective better detection of primary lung cancer. Two MATLAB algorithms have been created for more adequate description of concentration measurements of volatile organic compounds in human breath gas for potential detection of primary lung cancer and for evaluation of the appropriate confidence and prediction regions.

A fuzzy set preference model for market share analysis

NASA Technical Reports Server (NTRS)

Turksen, I. B.; Willson, Ian A.

1992-01-01

Consumer preference models are widely used in new product design, marketing management, pricing, and market segmentation. The success of new products depends on accurate market share prediction and design decisions based on consumer preferences. The vague linguistic nature of consumer preferences and product attributes, combined with the substantial differences between individuals, creates a formidable challenge to marketing models. The most widely used methodology is conjoint analysis. Conjoint models, as currently implemented, represent linguistic preferences as ratio or interval-scaled numbers, use only numeric product attributes, and require aggregation of individuals for estimation purposes. It is not surprising that these models are costly to implement, are inflexible, and have a predictive validity that is not substantially better than chance. This affects the accuracy of market share estimates. A fuzzy set preference model can easily represent linguistic variables either in consumer preferences or product attributes with minimal measurement requirements (ordinal scales), while still estimating overall preferences suitable for market share prediction. This approach results in flexible individual-level conjoint models which can provide more accurate market share estimates from a smaller number of more meaningful consumer ratings. Fuzzy sets can be incorporated within existing preference model structures, such as a linear combination, using the techniques developed for conjoint analysis and market share estimation. The purpose of this article is to develop and fully test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation), and how much to make (market share prediction).

Predicted facies, sedimentary structures and potential resources of Jurassic petroleum complex in S-E sWestern Siberia (based on well logging data)

NASA Astrophysics Data System (ADS)

Prakojo, F.; Lobova, G.; Abramova, R.

2015-11-01

This paper is devoted to the current problem in petroleum geology and geophysics- prediction of facies sediments for further evaluation of productive layers. Applying the acoustic method and the characterizing sedimentary structure for each coastal-marine-delta type was determined. The summary of sedimentary structure characteristics and reservoir properties (porosity and permeability) of typical facies were described. Logging models SP, EL and GR (configuration, curve range) in interpreting geophysical data for each litho-facies were identified. According to geophysical characteristics these sediments can be classified as coastal-marine-delta. Prediction models for potential Jurassic oil-gas bearing complexes (horizon J11) in one S-E Western Siberian deposit were conducted. Comparing forecasting to actual testing data of layer J11 showed that the prediction is about 85%.

Potential predictability of a Colombian river flow

NASA Astrophysics Data System (ADS)

Córdoba-Machado, Samir; Palomino-Lemus, Reiner; Quishpe-Vásquez, César; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2017-04-01

In this study the predictability of an important Colombian river (Cauca) has been analysed based on the use of climatic variables as potential predictors. Cauca River is considered one of the most important rivers of Colombia because its basin supports important productive activities related with the agriculture, such as the production of coffee or sugar. Potential relationships between the Cauca River seasonal streamflow anomalies and different climatic variables such as sea surface temperature (SST), precipitation (Pt), temperature over land (Tm) and soil water (Sw) have been analysed for the period 1949-2009. For this end, moving correlation analysis of 30 years have been carried out for lags from one to four seasons for the global SST, and from one to two seasons for South America Pt, Tm and Sw. Also, the stability of the significant correlations have been also studied, identifying the regions used as potential predictors of streamflow. Finally, in order to establish a prediction scheme based on the previous stable correlations, a Principal Component Analysis (PCA) applied on the potential predictor regions has been carried out in order to obtain a representative time series for each predictor field. Significant and stable correlations between the seasonal streamflow and the tropical Pacific SST (El Niño region) are found for lags from one to four (one-year) season. Additionally, some regions in the Indian and Atlantic Oceans also show significant and stable correlations at different lags, highlighting the importance that exerts the Atlantic SST on the hydrology of Colombia. Also significant and stable correlations are found with the Pt, Tm and Sw for some regions over South America, at lags of one and two seasons. The prediction of Cauca seasonal streamflow based on this scheme shows an acceptable skill and represents a relative improvement compared with the predictability obtained using the teleconnection indices associated with El Niño. Keywords: Streamflow, predictability, Cauca, Colombia. Acknowledgements: This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

Modeling homeorhetic trajectories of milk component yields, body composition and dry-matter intake in dairy cows: Influence of parity, milk production potential and breed.

PubMed

Daniel, J B; Friggens, N C; van Laar, H; Ingvartsen, K L; Sauvant, D

2018-06-01

The control of nutrient partitioning is complex and affected by many factors, among them physiological state and production potential. Therefore, the current model aims to provide for dairy cows a dynamic framework to predict a consistent set of reference performance patterns (milk component yields, body composition change, dry-matter intake) sensitive to physiological status across a range of milk production potentials (within and between breeds). Flows and partition of net energy toward maintenance, growth, gestation, body reserves and milk components are described in the model. The structure of the model is characterized by two sub-models, a regulating sub-model of homeorhetic control which sets dynamic partitioning rules along the lactation, and an operating sub-model that translates this into animal performance. The regulating sub-model describes lactation as the result of three driving forces: (1) use of previously acquired resources through mobilization, (2) acquisition of new resources with a priority of partition towards milk and (3) subsequent use of resources towards body reserves gain. The dynamics of these three driving forces were adjusted separately for fat (milk and body), protein (milk and body) and lactose (milk). Milk yield is predicted from lactose and protein yields with an empirical equation developed from literature data. The model predicts desired dry-matter intake as an outcome of net energy requirements for a given dietary net energy content. The parameters controlling milk component yields and body composition changes were calibrated using two data sets in which the diet was the same for all animals. Weekly data from Holstein dairy cows was used to calibrate the model within-breed across milk production potentials. A second data set was used to evaluate the model and to calibrate it for breed differences (Holstein, Danish Red and Jersey) on the mobilization/reconstitution of body composition and on the yield of individual milk components. These calibrations showed that the model framework was able to adequately simulate milk yield, milk component yields, body composition changes and dry-matter intake throughout lactation for primiparous and multiparous cows differing in their production level.

Crop biometric maps: the key to prediction.

PubMed

Rovira-Más, Francisco; Sáiz-Rubio, Verónica

2013-09-23

The sustainability of agricultural production in the twenty-first century, both in industrialized and developing countries, benefits from the integration of farm management with information technology such that individual plants, rows, or subfields may be endowed with a singular "identity." This approach approximates the nature of agricultural processes to the engineering of industrial processes. In order to cope with the vast variability of nature and the uncertainties of agricultural production, the concept of crop biometrics is defined as the scientific analysis of agricultural observations confined to spaces of reduced dimensions and known position with the purpose of building prediction models. This article develops the idea of crop biometrics by setting its principles, discussing the selection and quantization of biometric traits, and analyzing the mathematical relationships among measured and predicted traits. Crop biometric maps were applied to the case of a wine-production vineyard, in which vegetation amount, relative altitude in the field, soil compaction, berry size, grape yield, juice pH, and grape sugar content were selected as biometric traits. The enological potential of grapes was assessed with a quality-index map defined as a combination of titratable acidity, sugar content, and must pH. Prediction models for yield and quality were developed for high and low resolution maps, showing the great potential of crop biometric maps as a strategic tool for vineyard growers as well as for crop managers in general, due to the wide versatility of the methodology proposed.

Crop Biometric Maps: The Key to Prediction

PubMed Central

Rovira-Más, Francisco; Sáiz-Rubio, Verónica

2013-01-01

The sustainability of agricultural production in the twenty-first century, both in industrialized and developing countries, benefits from the integration of farm management with information technology such that individual plants, rows, or subfields may be endowed with a singular “identity.” This approach approximates the nature of agricultural processes to the engineering of industrial processes. In order to cope with the vast variability of nature and the uncertainties of agricultural production, the concept of crop biometrics is defined as the scientific analysis of agricultural observations confined to spaces of reduced dimensions and known position with the purpose of building prediction models. This article develops the idea of crop biometrics by setting its principles, discussing the selection and quantization of biometric traits, and analyzing the mathematical relationships among measured and predicted traits. Crop biometric maps were applied to the case of a wine-production vineyard, in which vegetation amount, relative altitude in the field, soil compaction, berry size, grape yield, juice pH, and grape sugar content were selected as biometric traits. The enological potential of grapes was assessed with a quality-index map defined as a combination of titratable acidity, sugar content, and must pH. Prediction models for yield and quality were developed for high and low resolution maps, showing the great potential of crop biometric maps as a strategic tool for vineyard growers as well as for crop managers in general, due to the wide versatility of the methodology proposed. PMID:24064605

GIS-Based Multi-Criteria Analysis for Arabica Coffee Expansion in Rwanda

PubMed Central

Nzeyimana, Innocent; Hartemink, Alfred E.; Geissen, Violette

2014-01-01

The Government of Rwanda is implementing policies to increase the area of Arabica coffee production. Information on the suitable areas for sustainably growing Arabica coffee is still scarce. This study aimed to analyze suitable areas for Arabica coffee production. We analyzed the spatial distribution of actual and potential production zones for Arabica coffee, their productivity levels and predicted potential yields. We used a geographic information system (GIS) for a weighted overlay analysis to assess the major production zones of Arabica coffee and their qualitative productivity indices. Actual coffee yields were measured in the field and were used to assess potential productivity zones and yields using ordinary kriging with ArcGIS software. The production of coffee covers about 32 000 ha, or 2.3% of all cultivated land in the country. The major zones of production are the Kivu Lake Borders, Central Plateau, Eastern Plateau, and Mayaga agro-ecological zones, where coffee is mainly cultivated on moderate slopes. In the highlands, coffee is grown on steep slopes that can exceed 55%. About 21% percent of the country has a moderate yield potential, ranging between 1.0 and 1.6 t coffee ha−1, and 70% has a low yield potential (<1.0 t coffee ha−1). Only 9% of the country has a high yield potential of 1.6–2.4 t coffee ha−1. Those areas are found near Lake Kivu where the dominant soil Orders are Inceptisols and Ultisols. Moderate yield potential is found in the Birunga (volcano), Congo-Nile watershed Divide, Impala and Imbo zones. Low-yield regions (<1 t ha−1) occur in the eastern semi-dry lowlands, Central Plateau, Eastern Plateau, Buberuka Highlands, and Mayaga zones. The weighted overlay analysis and ordinary kriging indicated a large spatial variability of potential productivity indices. Increasing the area and productivity of coffee in Rwanda thus has considerable potential. PMID:25299459

Transferring site information for black walnut from native woodlands in southeastern Kansas USA to identify sites for agroforestry practices

Treesearch

Wayne A. Geyer; Felix Ponder

2013-01-01

Black walnut (Juglans nigra) is an important tree species for temperate agroforestry in the United States for timber, nuts, wildlife, and abrasives. Predictions of forestland productivity are needed for proper species selection in tree planting. Potential productivity can be estimated for nonforested areas and agricultural croplands by relating site...

Theoretical characterization of the potential energy surface for NH + NO

NASA Technical Reports Server (NTRS)

Walch, Stephen P.

1992-01-01

The potential energy surface (PES) for NH + NO was characterized using complete active space self-consistent field (CASSCF) gradient calculations to determine the stationary point geometries and frequencies followed by CASSCF/internally contracted configuration interaction (CCI) calculations to refine the energetics. The present results are in qualitative accord with the BAC-MP4 calculations, but there are differences as large as 8 kcal/mol in the detailed energetics. Addition of NH to NO on a (2)A' surface, which correlated with N2 + OH or H + N2O products, involves barriers of 3.2 kcal/mol (trans) and 6.3 kcal/mol (cis). Experimental evidence for these barriers is found in earlier works. The (2)A' surface has no barrier to addition, but does not correlate with products. Surface crossings between the barrierless (2)A' surface and the (2)A' surface may be important. Production of N2 + OH products is predicted to occur via a planar saddle point of (2)A' symmetry. This is in accord with the preferential formation of II(A') lambda doublet levels of OH in earlier experiments. Addition of NH (1)delta to NO is found to occur on an excited state surface and is predicted to lead to N2O product as observed in earlier works.

Regional prediction of long-term landfill gas to energy potential.

PubMed

Amini, Hamid R; Reinhart, Debra R

2011-01-01

Quantifying landfill gas to energy (LFGTE) potential as a source of renewable energy is difficult due to the challenges involved in modeling landfill gas (LFG) generation. In this paper a methodology is presented to estimate LFGTE potential on a regional scale over a 25-year timeframe with consideration of modeling uncertainties. The methodology was demonstrated for the US state of Florida, as a case study, and showed that Florida could increase the annual LFGTE production by more than threefold by 2035 through installation of LFGTE facilities at all landfills. The estimated electricity production potential from Florida LFG is equivalent to removing some 70 million vehicles from highways or replacing over 800 million barrels of oil consumption during the 2010-2035 timeframe. Diverting food waste could significantly reduce fugitive LFG emissions, while having minimal effect on the LFGTE potential; whereas, achieving high diversion goals through increased recycling will result in reduced uncollected LFG and significant loss of energy production potential which may be offset by energy savings from material recovery and reuse. Estimates showed that the power density for Florida LFGTE production could reach as high as 10 Wm(-2) with optimized landfill operation and energy production practices. The environmental benefits from increased lifetime LFG collection efficiencies magnify the value of LFGTE projects. Copyright © 2011 Elsevier Ltd. All rights reserved.

A Modelling Approach to Estimate the Impact of Sodium Reduction in Soups on Cardiovascular Health in the Netherlands

PubMed Central

Bruins, Maaike J.; Dötsch-Klerk, Mariska; Matthee, Joep; Kearney, Mary; van Elk, Kathelijn; Weber, Peter; Eggersdorfer, Manfred

2015-01-01

Hypertension is a major modifiable risk factor for cardiovascular disease and mortality, which could be lowered by reducing dietary sodium. The potential health impact of a product reformulation in the Netherlands was modelled, selecting packaged soups containing on average 25% less sodium as an example of an achievable product reformulation when implemented gradually. First, the blood pressure lowering resulting from sodium intake reduction was modelled. Second, the predicted blood pressure lowering was translated into potentially preventable incidence and mortality cases from stroke, acute myocardial infarction (AMI), angina pectoris, and heart failure (HF) implementing one year salt reduction. Finally, the potentially preventable subsequent lifetime Disability-Adjusted Life Years (DALYs) were calculated. The sodium reduction in soups might potentially reduce the incidence and mortality of stroke by approximately 0.5%, AMI and angina by 0.3%, and HF by 0.2%. The related burden of disease could be reduced by approximately 800 lifetime DALYs. This modelling approach can be used to provide insight into the potential public health impact of sodium reduction in specific food products. The data demonstrate that an achievable food product reformulation to reduce sodium can potentially benefit public health, albeit modest. When implemented across multiple product categories and countries, a significant health impact could be achieved. PMID:26393647

Testing for the 'predictability' of dynamically triggered earthquakes in The Geysers geothermal field

NASA Astrophysics Data System (ADS)

Aiken, Chastity; Meng, Xiaofeng; Hardebeck, Jeanne

2018-03-01

The Geysers geothermal field is well known for being susceptible to dynamic triggering of earthquakes by large distant earthquakes, owing to the introduction of fluids for energy production. Yet, it is unknown if dynamic triggering of earthquakes is 'predictable' or whether dynamic triggering could lead to a potential hazard for energy production. In this paper, our goal is to investigate the characteristics of triggering and the physical conditions that promote triggering to determine whether or not triggering is in anyway foreseeable. We find that, at present, triggering in The Geysers is not easily 'predictable' in terms of when and where based on observable physical conditions. However, triggered earthquake magnitude positively correlates with peak imparted dynamic stress, and larger dynamic stresses tend to trigger sequences similar to mainshock-aftershock sequences. Thus, we may be able to 'predict' what size earthquakes to expect at The Geysers following a large distant earthquake.

Vegetative biomass predicts inflorescence production along a CO2 concentration gradient in mesic grassland

NASA Astrophysics Data System (ADS)

Fay, P. A.; Collins, H.; Polley, W.

2016-12-01

Atmospheric CO2 concentration will likely exceed 500 µL L-1 by 2050, often increasing plant community productivity in part by increasing abundance of species favored by increased CA . Whether increased abundance translates to increased inflorescence production is poorly understood, and is important because it indicates the potential effects of CO2 enrichment on genetic variability and the potential for evolutionary change in future generations. We examined whether the responses of inflorescence production to CO2 enrichment in four C4 grasses and a C3 forb were predicted their vegetative biomass, and by soil moisture, soil nitrogen, or light availability. Inflorescence production was studied in a long-term CO2 concentration gradient spanning pre-industrial to anticipated mid-21st century values (250 - 500 µL L-1) maintained on clay, silty clay and sandy loam soils common in the U.S. Southern Plains. We expected that CO2 enrichment would increase inflorescence production, and more so with higher water, nitrogen, or light availability. However, structural equation modeling revealed that vegetative biomass was the single consistent direct predictor of flowering for all species (p < 0.001). Vegetative biomass increased, decreased, or did not respond to CO2 enrichment depending on the species. For the increasing species Sorghastrum nutans (C4 grass) and Solidago canadensis (C3 forb), direct CO2 effects on flowering were only weakly mediated by indirect effects of soil water content and soil NO3-N availability. For the decreasing species (Bouteloua curtipendula, C4 grass), the negative CO2-flowering relationship was cancelled (p = 0.39) by indirect effects of increased SWC and NO3-N on clay and silty clay soils. For the species with no CO2 response, inflorescence production was predicted only by direct water content (p < 0.0001, Schizachyrium scoparius, C4 grass) or vegetative biomass (p = 0.0009, Tridens albescens, C4 grass) effects. Light availability was unrelated to inflorescence production. Changes in inflorescence production are thus closely tied to direct and indirect effects of CO2 enrichment on vegetative biomass, and may either increase, decrease, or leave unchanged the potential for genetic variability and evolutionary change in future generations in response to global change drivers.

Application of the KeratinoSens™ assay for assessing the skin sensitization potential of agrochemical active ingredients and formulations.

PubMed

Settivari, Raja S; Gehen, Sean C; Amado, Ricardo Acosta; Visconti, Nicolo R; Boverhof, Darrell R; Carney, Edward W

2015-07-01

Assessment of skin sensitization potential is an important component of the safety evaluation process for agrochemical products. Recently, non-animal approaches including the KeratinoSens™ assay have been developed for predicting skin sensitization potential. Assessing the utility of the KeratinoSens™ assay for use with multi-component mixtures such as agrochemical formulations has not been previously evaluated and is a significant need. This study was undertaken to evaluate the KeratinoSens™ assay prediction potential for agrochemical formulations. The assay was conducted for 8 agrochemical active ingredients (AIs) including 3 sensitizers (acetochlor, meptyldinocap, triclopyr), 5 non-sensitizers (aminopyralid, clopyralid, florasulam, methoxyfenozide, oxyfluorfen) and 10 formulations for which in vivo sensitization data were available. The KeratinoSens™ correctly predicted the sensitization potential of all the AIs. For agrochemical formulations it was necessary to modify the standard assay procedure whereby the formulation was assumed to have a common molecular weight. The resultant approach correctly predicted the sensitization potential for 3 of 4 sensitizing formulations and all 6 non-sensitizing formulations when compared to in vivo data. Only the meptyldinocap-containing formulation was misclassified, as a result of high cytotoxicity. These results demonstrate the promising utility of the KeratinoSens™ assay for evaluating the skin sensitization potential of agrochemical AIs and formulations. Copyright © 2015 Elsevier Inc. All rights reserved.

Prediction of thermodynamic properties of coal derivatives. Progress report, September 1, 1981-August 31, 1982

DOE Office of Scientific and Technical Information (OSTI.GOV)

Donohue, M.D.

It is the purpose of this research program to develop a model to predict the thermodynamic properties of coal derivatives. Unlike natural gas and petroleum, coal and its gasification and liquefaction products are predominantly aromatic and have substantial quadrupole moments. Because of these quadrupole forces, the numerous correlational techniques that have been developed for petroleum products cannot be used to predict the thermodynamic properties of coal derivatives. We are presently developing a correlation that will be useful in predicting the thermodynamic properties of coal derivatives. This theory is based on the Perturbed-Hard-Chain theory, but is different from PHCT in twomore » respects. First, PHCT uses a square-well to describe the intermolecular potential energy between two molecules. In our new theory, the Lennard-Jones potential energy function is used. The second difference is that we take into account the effect of quadrupole forces on the intermolecular potential energy. In PHCT these forces were ignored. In PHCT the contributions to the partition function (or equation of state) that arise from the attractive forces between molecules (regardless of whether these forces are treated as a square-well or by Lennard-Jones) are calculated by assuming that they are perturbations on a hard sphere. In calculating the contributions to the partition function that arise from the quadrupole-quadrupole interactions, we use a second order perturbation about the Lennard-Jones. For aromatic molecules, the effect of this additional perturbation is significant.« less

BioFuelDB: a database and prediction server of enzymes involved in biofuels production.

PubMed

Chaudhary, Nikhil; Gupta, Ankit; Gupta, Sudheer; Sharma, Vineet K

2017-01-01

In light of the rapid decrease in fossils fuel reserves and an increasing demand for energy, novel methods are required to explore alternative biofuel production processes to alleviate these pressures. A wide variety of molecules which can either be used as biofuels or as biofuel precursors are produced using microbial enzymes. However, the common challenges in the industrial implementation of enzyme catalysis for biofuel production are the unavailability of a comprehensive biofuel enzyme resource, low efficiency of known enzymes, and limited availability of enzymes which can function under extreme conditions in the industrial processes. We have developed a comprehensive database of known enzymes with proven or potential applications in biofuel production through text mining of PubMed abstracts and other publicly available information. A total of 131 enzymes with a role in biofuel production were identified and classified into six enzyme classes and four broad application categories namely 'Alcohol production', 'Biodiesel production', 'Fuel Cell' and 'Alternate biofuels'. A prediction tool 'Benz' was developed to identify and classify novel homologues of the known biofuel enzyme sequences from sequenced genomes and metagenomes. 'Benz' employs a hybrid approach incorporating HMMER 3.0 and RAPSearch2 programs to provide high accuracy and high speed for prediction. Using the Benz tool, 153,754 novel homologues of biofuel enzymes were identified from 23 diverse metagenomic sources. The comprehensive data of curated biofuel enzymes, their novel homologs identified from diverse metagenomes, and the hybrid prediction tool Benz are presented as a web server which can be used for the prediction of biofuel enzymes from genomic and metagenomic datasets. The database and the Benz tool is publicly available at http://metabiosys.iiserb.ac.in/biofueldb& http://metagenomics.iiserb.ac.in/biofueldb.

Toward production from gas hydrates: Current status, assessment of resources, and simulation-based evaluation of technology and potential

USGS Publications Warehouse

Moridis, G.J.; Collett, T.S.; Boswell, R.; Kurihara, M.; Reagan, M.T.; Koh, C.; Sloan, E.D.

2009-01-01

Gas hydrates (GHs) are a vast energy resource with global distribution in the permafrost and in the oceans. Even if conservative estimates are considered and only a small fraction is recoverable, the sheer size of the resource is so large that it demands evaluation as a potential energy source. In this review paper, we discuss the distribution of natural GH accumulations, the status of the primary international research and development (R&D) programs, and the remaining science and technological challenges facing the commercialization of production. After a brief examination of GH accumulations that are well characterized and appear to be models for future development and gas production, we analyze the role of numerical simulation in the assessment of the hydrate-production potential, identify the data needs for reliable predictions, evaluate the status of knowledge with regard to these needs, discuss knowledge gaps and their impact, and reach the conclusion that the numerical-simulation capabilities are quite advanced and that the related gaps either are not significant or are being addressed. We review the current body of literature relevant to potential productivity from different types of GH deposits and determine that there are consistent indications of a large production potential at high rates across long periods from a wide variety of hydrate deposits. Finally, we identify (a) features, conditions, geology and techniques that are desirable in potential production targets; (b) methods to maximize production; and (c) some of the conditions and characteristics that render certain GH deposits undesirable for production. Copyright ?? 2009 Society of Petroleum Engineers.

Toward production from gas hydrates: Current status, assessment of resources, and simulation-based evaluation of technology and potential

USGS Publications Warehouse

Moridis, G.J.; Collett, T.S.; Boswell, R.; Kurihara, M.; Reagan, M.T.; Koh, C.; Sloan, E.D.

2008-01-01

Gas hydrates are a vast energy resource with global distribution in the permafrost and in the oceans. Even if conservative estimates are considered and only a small fraction is recoverable, the sheer size of the resource is so large that it demands evaluation as a potential energy source. In this review paper, we discuss the distribution of natural gas hydrate accumulations, the status of the primary international R&D programs, and the remaining science and technological challenges facing commercialization of production. After a brief examination of gas hydrate accumulations that are well characterized and appear to be models for future development and gas production, we analyze the role of numerical simulation in the assessment of the hydrate production potential, identify the data needs for reliable predictions, evaluate the status of knowledge with regard to these needs, discuss knowledge gaps and their impact, and reach the conclusion that the numerical simulation capabilities are quite advanced and that the related gaps are either not significant or are being addressed. We review the current body of literature relevant to potential productivity from different types of gas hydrate deposits, and determine that there are consistent indications of a large production potential at high rates over long periods from a wide variety of hydrate deposits. Finally, we identify (a) features, conditions, geology and techniques that are desirable in potential production targets, (b) methods to maximize production, and (c) some of the conditions and characteristics that render certain gas hydrate deposits undesirable for production. Copyright 2008, Society of Petroleum Engineers.

Application of predictive modelling techniques in industry: from food design up to risk assessment.

PubMed

Membré, Jeanne-Marie; Lambert, Ronald J W

2008-11-30

In this communication, examples of applications of predictive microbiology in industrial contexts (i.e. Nestlé and Unilever) are presented which cover a range of applications in food safety from formulation and process design to consumer safety risk assessment. A tailor-made, private expert system, developed to support safe product/process design assessment is introduced as an example of how predictive models can be deployed for use by non-experts. Its use in conjunction with other tools and software available in the public domain is discussed. Specific applications of predictive microbiology techniques are presented relating to investigations of either growth or limits to growth with respect to product formulation or process conditions. An example of a probabilistic exposure assessment model for chilled food application is provided and its potential added value as a food safety management tool in an industrial context is weighed against its disadvantages. The role of predictive microbiology in the suite of tools available to food industry and some of its advantages and constraints are discussed.

Solar radiation and functional traits explain the decline of forest primary productivity along a tropical elevation gradient.

PubMed

Fyllas, Nikolaos M; Bentley, Lisa Patrick; Shenkin, Alexander; Asner, Gregory P; Atkin, Owen K; Díaz, Sandra; Enquist, Brian J; Farfan-Rios, William; Gloor, Emanuel; Guerrieri, Rossella; Huasco, Walter Huaraca; Ishida, Yoko; Martin, Roberta E; Meir, Patrick; Phillips, Oliver; Salinas, Norma; Silman, Miles; Weerasinghe, Lasantha K; Zaragoza-Castells, Joana; Malhi, Yadvinder

2017-06-01

One of the major challenges in ecology is to understand how ecosystems respond to changes in environmental conditions, and how taxonomic and functional diversity mediate these changes. In this study, we use a trait-spectra and individual-based model, to analyse variation in forest primary productivity along a 3.3 km elevation gradient in the Amazon-Andes. The model accurately predicted the magnitude and trends in forest productivity with elevation, with solar radiation and plant functional traits (leaf dry mass per area, leaf nitrogen and phosphorus concentration, and wood density) collectively accounting for productivity variation. Remarkably, explicit representation of temperature variation with elevation was not required to achieve accurate predictions of forest productivity, as trait variation driven by species turnover appears to capture the effect of temperature. Our semi-mechanistic model suggests that spatial variation in traits can potentially be used to estimate spatial variation in productivity at the landscape scale. © 2017 John Wiley & Sons Ltd/CNRS.

Exubera. Inhale therapeutic systems.

PubMed

Bindra, Sanjit; Cefalu, William T

2002-05-01

Inhale, in colaboration with Pfizer and Aventis Pharma (formerly Hoechst Marion Roussel; HMR), is developing an insulin formulation utilizing its pulmonary delivery technology for macromolecules for the potential treatment of type I and II diabetes. By July 2001, the phase III program had been completed and the companies had begun to assemble data for MAA and NDA filings; however, it was already clear at this time that additional data might be required for filing. By December 2001, it had been decided that the NDA should include an increased level of controlled, long-term pulmonary safety data in diabetic patients and a major study was planned to be completed in 2002, with the NDA filed thereafter (during 2002). US-05997848 was issued to Inhale Therapeutic Systems in December 1999, and corresponds to WO-09524183, filed in February 1995. Equivalent applications have appeared to date in Australia, Brazil, Canada, China, Czech Republic, Europe, Finland, Hungary, Japan, Norway, New Zealand, Poland and South Africa. This family of applications is specific to pulmonary delivery of insulin. In February 1999, Lehman Brothers gave this inhaled insulin a 60% probability of reaching market, with a possible launch date of 2001. The analysts estimated peak sales at $3 billion in 2011. In May 2000, Aventis predicted that estimated peak sales would be in excess of $1 billion. In February 2000, Merrill Lynch expected product launch in 2002 and predicted that it would be a multibillion-dollar product. Analysts Merril Lynch predicted, in September and November 2000, that the product would be launched by 2002, with sales in that year of e75 million, rising to euro 500 million in 2004. In April 2001, Merrill Lynch predicted that filing for this drug would occur in 2001. Following the report of the potential delay in regulatory filing, issued in July 2001, Deutsche Banc Alex Brown predicted a filing would take place in the fourth quarter of 2002 and launch would take place in the first quarter of 2003. In August 2001, Lehman Brothers predicted that launch would take place in the first half of 2002 and that the product would make sales of $475 million in 2003, rising to $875 million in 2004. In the same month, Deutsche Bank predicted that there would be worldwide sales of $50 million in 2003, rising to $400 million in 2005. At this time, analysts at Credit Suisse predicted a launch of the product in 2003, with sales of $70 million in that year, rising to $550 million in 2005. By October 2001, Deutsche Bank predicted sales of $50 million in 2004 and $250 million in 2005. In September 2001, Morgan Stanley predicted sales of $500 million in 2002, rising to $1250 million in 2006.

Predicting properties of gas and solid streams by intrinsic kinetics of fast pyrolysis of wood

DOE PAGES

Klinger, Jordan; Bar-Ziv, Ezra; Shonnard, David; ...

2015-12-12

Pyrolysis has the potential to create a biocrude oil from biomass sources that can be used as fuel or as feedstock for subsequent upgrading to hydrocarbon fuels or other chemicals. The product distribution/composition, however, is linked to the biomass source. This work investigates the products formed from pyrolysis of woody biomass with a previously developed chemical kinetics model. Different woody feedstocks reported in prior literature are placed on a common basis (moisture, ash, fixed carbon free) and normalized by initial elemental composition through ultimate analysis. Observed product distributions over the full devolatilization range are explored, reconstructed by the model, andmore » verified with independent experimental data collected with a microwave-assisted pyrolysis system. These trends include production of permanent gas (CO, CO 2), char, and condensable (oil, water) species. Elementary compositions of these streams are also investigated. As a result, close agreement between literature data, model predictions, and independent experimental data indicate that the proposed model/method is able to predict the ideal distribution from fast pyrolysis given reaction temperature, residence time, and feedstock composition.« less

Predicting the safety and efficacy of buffer therapy to raise tumour pHe: an integrative modelling study

PubMed Central

Martin, N K; Robey, I F; Gaffney, E A; Gillies, R J; Gatenby, R A; Maini, P K

2012-01-01

Background: Clinical positron emission tomography imaging has demonstrated the vast majority of human cancers exhibit significantly increased glucose metabolism when compared with adjacent normal tissue, resulting in an acidic tumour microenvironment. Recent studies demonstrated reducing this acidity through systemic buffers significantly inhibits development and growth of metastases in mouse xenografts. Methods: We apply and extend a previously developed mathematical model of blood and tumour buffering to examine the impact of oral administration of bicarbonate buffer in mice, and the potential impact in humans. We recapitulate the experimentally observed tumour pHe effect of buffer therapy, testing a model prediction in vivo in mice. We parameterise the model to humans to determine the translational safety and efficacy, and predict patient subgroups who could have enhanced treatment response, and the most promising combination or alternative buffer therapies. Results: The model predicts a previously unseen potentially dangerous elevation in blood pHe resulting from bicarbonate therapy in mice, which is confirmed by our in vivo experiments. Simulations predict limited efficacy of bicarbonate, especially in humans with more aggressive cancers. We predict buffer therapy would be most effectual: in elderly patients or individuals with renal impairments; in combination with proton production inhibitors (such as dichloroacetate), renal glomular filtration rate inhibitors (such as non-steroidal anti-inflammatory drugs and angiotensin-converting enzyme inhibitors), or with an alternative buffer reagent possessing an optimal pK of 7.1–7.2. Conclusion: Our mathematical model confirms bicarbonate acts as an effective agent to raise tumour pHe, but potentially induces metabolic alkalosis at the high doses necessary for tumour pHe normalisation. We predict use in elderly patients or in combination with proton production inhibitors or buffers with a pK of 7.1–7.2 is most promising. PMID:22382688

Earlier Snowmelt Changes the Ratio Between Early and Late Season Forest Productivity

NASA Astrophysics Data System (ADS)

Knowles, J. F.; Molotch, N. P.; Trujillo, E.; Litvak, M. E.

2017-12-01

Future projections of declining snowpack and increasing potential evaporation associated with climate warming are predicted to advance the timing of snowmelt in mountain ecosystems globally. This scenario has direct implications for snowmelt-driven forest productivity, but the net effect of temporally shifting moisture dynamics is unknown with respect to the annual carbon balance. Accordingly, this study uses both satellite- and tower-based observations to document the forest productivity response to snowpack and potential evaporation variability between 1989 and 2012 throughout the southern Rocky Mountain ecoregion, USA. These results show that a combination of low snow accumulation and record high potential evaporation in 2012 resulted in the 34-year minimum ecosystem productivity that could be indicative of future conditions. Moreover, early and late season productivity were significantly and inversely related, suggesting that future shifts toward earlier or reduced snowmelt could increase late-season moisture stress to vegetation and thus restrict productivity despite a longer growing season. This relationship was further subject to modification by summer precipitation, and the controls on the early/late season productivity ratio are explored within the context of ecosystem carbon storage in the future. Any perturbation to the carbon cycle at this scale represents a potential feedback to climate change since snow-covered forests represent an important global carbon sink.

Global scale analysis and evaluation of an improved mechanistic representation of plant nitrogen and carbon dynamics in the Community Land Model (CLM)

NASA Astrophysics Data System (ADS)

Ghimire, B.; Riley, W. J.; Koven, C. D.; Randerson, J. T.; Mu, M.; Kattge, J.; Rogers, A.; Reich, P. B.

2014-12-01

In many ecosystems, nitrogen is the most limiting nutrient for plant growth and productivity. However mechanistic representation of nitrogen uptake linked to root traits, and functional nitrogen allocation among different leaf enzymes involved in respiration and photosynthesis is currently lacking in Earth System models. The linkage between nitrogen availability and plant productivity is simplistically represented by potential photosynthesis rates, and is subsequently downregulated depending on nitrogen supply and other nitrogen consumers in the model (e.g., nitrification). This type of potential photosynthesis rate calculation is problematic for several reasons. Firstly, plants do not photosynthesize at potential rates and then downregulate. Secondly, there is considerable subjectivity on the meaning of potential photosynthesis rates. Thirdly, there exists lack of understanding on modeling these potential photosynthesis rates in a changing climate. In addition to model structural issues in representing photosynthesis rates, the role of plant roots in nutrient acquisition have been largely ignored in Earth System models. For example, in CLM4.5, nitrogen uptake is linked to leaf level processes (e.g., primarily productivity) rather than root scale process involved in nitrogen uptake. We present a new plant model for CLM with an improved mechanistic presentation of plant nitrogen uptake based on root scale Michaelis Menten kinetics, and stronger linkages between leaf nitrogen and plant productivity by inferring relationships observed in global databases of plant traits (including the TRY database and several individual studies). We also incorporate improved representation of plant nitrogen leaf allocation, especially in tropical regions where significant over-prediction of plant growth and productivity in CLM4.5 simulations exist. We evaluate our improved global model simulations using the International Land Model Benchmarking (ILAMB) framework. We conclude that mechanistic representation of leaf-level nitrogen allocation and a theoretically consistent treatment of competition with belowground consumers leads to overall improvements in CLM4.5's global carbon cycling predictions.

An integrated model of environmental effects on growth, carbohydrate balance, and mortality of Pinus ponderosa forests in the southern Rocky Mountains.

PubMed

Tague, Christina L; McDowell, Nathan G; Allen, Craig D

2013-01-01

Climate-induced tree mortality is an increasing concern for forest managers around the world. We used a coupled hydrologic and ecosystem carbon cycling model to assess temperature and precipitation impacts on productivity and survival of ponderosa pine (Pinus ponderosa). Model predictions were evaluated using observations of productivity and survival for three ponderosa pine stands located across an 800 m elevation gradient in the southern Rocky Mountains, USA, during a 10-year period that ended in a severe drought and extensive tree mortality at the lowest elevation site. We demonstrate the utility of a relatively simple representation of declines in non-structural carbohydrate (NSC) as an approach for estimating patterns of ponderosa pine vulnerability to drought and the likelihood of survival along an elevation gradient. We assess the sensitivity of simulated net primary production, NSC storage dynamics, and mortality to site climate and soil characteristics as well as uncertainty in the allocation of carbon to the NSC pool. For a fairly wide set of assumptions, the model estimates captured elevational gradients and temporal patterns in growth and biomass. Model results that best predict mortality risk also yield productivity, leaf area, and biomass estimates that are qualitatively consistent with observations across the sites. Using this constrained set of parameters, we found that productivity and likelihood of survival were equally dependent on elevation-driven variation in temperature and precipitation. Our results demonstrate the potential for a coupled hydrology-ecosystem carbon cycling model that includes a simple model of NSC dynamics to predict drought-related mortality. Given that increases in temperature and in the frequency and severity of drought are predicted for a broad range of ponderosa pine and other western North America conifer forest habitats, the model potentially has broad utility for assessing ecosystem vulnerabilities.

An integrated model of environmental effects on growth, carbohydrate balance, and mortality of Pinus ponderosa forests in the southern Rocky Mountains

USGS Publications Warehouse

Tague, Christina L.; McDowell, Nathan G.; Allen, Craig D.

2013-01-01

Climate-induced tree mortality is an increasing concern for forest managers around the world. We used a coupled hydrologic and ecosystem carbon cycling model to assess temperature and precipitation impacts on productivity and survival of ponderosa pine (Pinus ponderosa). Model predictions were evaluated using observations of productivity and survival for three ponderosa pine stands located across an 800 m elevation gradient in the southern Rocky Mountains, USA, during a 10-year period that ended in a severe drought and extensive tree mortality at the lowest elevation site. We demonstrate the utility of a relatively simple representation of declines in non-structural carbohydrate (NSC) as an approach for estimating patterns of ponderosa pine vulnerability to drought and the likelihood of survival along an elevation gradient. We assess the sensitivity of simulated net primary production, NSC storage dynamics, and mortality to site climate and soil characteristics as well as uncertainty in the allocation of carbon to the NSC pool. For a fairly wide set of assumptions, the model estimates captured elevational gradients and temporal patterns in growth and biomass. Model results that best predict mortality risk also yield productivity, leaf area, and biomass estimates that are qualitatively consistent with observations across the sites. Using this constrained set of parameters, we found that productivity and likelihood of survival were equally dependent on elevation-driven variation in temperature and precipitation. Our results demonstrate the potential for a coupled hydrology-ecosystem carbon cycling model that includes a simple model of NSC dynamics to predict drought-related mortality. Given that increases in temperature and in the frequency and severity of drought are predicted for a broad range of ponderosa pine and other western North America conifer forest habitats, the model potentially has broad utility for assessing ecosystem vulnerabilities.

An Integrated Model of Environmental Effects on Growth, Carbohydrate Balance, and Mortality of Pinus ponderosa Forests in the Southern Rocky Mountains

PubMed Central

Tague, Christina L.; McDowell, Nathan G.; Allen, Craig D.

2013-01-01

Climate-induced tree mortality is an increasing concern for forest managers around the world. We used a coupled hydrologic and ecosystem carbon cycling model to assess temperature and precipitation impacts on productivity and survival of ponderosa pine (Pinus ponderosa). Model predictions were evaluated using observations of productivity and survival for three ponderosa pine stands located across an 800 m elevation gradient in the southern Rocky Mountains, USA, during a 10-year period that ended in a severe drought and extensive tree mortality at the lowest elevation site. We demonstrate the utility of a relatively simple representation of declines in non-structural carbohydrate (NSC) as an approach for estimating patterns of ponderosa pine vulnerability to drought and the likelihood of survival along an elevation gradient. We assess the sensitivity of simulated net primary production, NSC storage dynamics, and mortality to site climate and soil characteristics as well as uncertainty in the allocation of carbon to the NSC pool. For a fairly wide set of assumptions, the model estimates captured elevational gradients and temporal patterns in growth and biomass. Model results that best predict mortality risk also yield productivity, leaf area, and biomass estimates that are qualitatively consistent with observations across the sites. Using this constrained set of parameters, we found that productivity and likelihood of survival were equally dependent on elevation-driven variation in temperature and precipitation. Our results demonstrate the potential for a coupled hydrology-ecosystem carbon cycling model that includes a simple model of NSC dynamics to predict drought-related mortality. Given that increases in temperature and in the frequency and severity of drought are predicted for a broad range of ponderosa pine and other western North America conifer forest habitats, the model potentially has broad utility for assessing ecosystem vulnerabilities. PMID:24282532

'Fishing' for alternatives to mountaintop mining in southern West Virginia.

PubMed

McGarvey, Daniel J; Johnston, John M

2013-04-01

Mountaintop removal mining (MTR) is a major industry in southern West Virginia with many detrimental effects for small to mid-sized streams, and interest in alternative, sustainable industries is on the rise. As a first step in a larger effort to assess the value of sport fisheries in southern West Virginia, we estimate the potential abundances of two popular sport fishes-smallmouth bass (Micropterus dolomieu) and brook trout (Salvelinus fontinalis)-in the Coal River Basin (CRB). A self-thinning model that incorporates net primary production and terrestrial insect subsidies is first used to predict potential densities of adult (age 1+) smallmouth bass and brook trout. Predicted densities (fish ha(-1)) are then multiplied by the surface area of the CRB stream network (ha) to estimate regional abundance. Median predicted abundances of bass and trout are 38 806 and 118 094 fish (total abundances with the CRB), respectively. However, when streams that intersect permitted MTR areas in the CRB are removed from the dataset, predicted abundances of bass and trout decrease by ~12-14 %. We conclude that significant potential exists in the CRB to capitalize on sport fisheries, but MTR may be undermining this potential.

A new simplex chemometric approach to identify olive oil blends with potentially high traceability.

PubMed

Semmar, N; Laroussi-Mezghani, S; Grati-Kamoun, N; Hammami, M; Artaud, J

2016-10-01

Olive oil blends (OOBs) are complex matrices combining different cultivars at variable proportions. Although qualitative determinations of OOBs have been subjected to several chemometric works, quantitative evaluations of their contents remain poorly developed because of traceability difficulties concerning co-occurring cultivars. Around this question, we recently published an original simplex approach helping to develop predictive models of the proportions of co-occurring cultivars from chemical profiles of resulting blends (Semmar & Artaud, 2015). Beyond predictive model construction and validation, this paper presents an extension based on prediction errors' analysis to statistically define the blends with the highest predictability among all the possible ones that can be made by mixing cultivars at different proportions. This provides an interesting way to identify a priori labeled commercial products with potentially high traceability taking into account the natural chemical variability of different constitutive cultivars. Copyright © 2016 Elsevier Ltd. All rights reserved.

Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

PubMed Central

Draper, John; Enot, David P; Parker, David; Beckmann, Manfred; Snowdon, Stuart; Lin, Wanchang; Zubair, Hassan

2009-01-01

Background Metabolomics experiments using Mass Spectrometry (MS) technology measure the mass to charge ratio (m/z) and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform routinely with a mass accuracy of < 5 ppm (parts per million) thus providing potentially a direct method for signal putative annotation using databases containing metabolite mass information. Most database interfaces support only simple queries with the default assumption that molecules either gain or lose a single proton when ionised. In reality the annotation process is confounded by the fact that many ionisation products will be not only molecular isotopes but also salt/solvent adducts and neutral loss fragments of original metabolites. This report describes an annotation strategy that will allow searching based on all potential ionisation products predicted to form during electrospray ionisation (ESI). Results Metabolite 'structures' harvested from publicly accessible databases were converted into a common format to generate a comprehensive archive in MZedDB. 'Rules' were derived from chemical information that allowed MZedDB to generate a list of adducts and neutral loss fragments putatively able to form for each structure and calculate, on the fly, the exact molecular weight of every potential ionisation product to provide targets for annotation searches based on accurate mass. We demonstrate that data matrices representing populations of ionisation products generated from different biological matrices contain a large proportion (sometimes > 50%) of molecular isotopes, salt adducts and neutral loss fragments. Correlation analysis of ESI-MS data features confirmed the predicted relationships of m/z signals. An integrated isotope enumerator in MZedDB allowed verification of exact isotopic pattern distributions to corroborate experimental data. Conclusion We conclude that although ultra-high accurate mass instruments provide major insight into the chemical diversity of biological extracts, the facile annotation of a large proportion of signals is not possible by simple, automated query of current databases using computed molecular formulae. Parameterising MZedDB to take into account predicted ionisation behaviour and the biological source of any sample improves greatly both the frequency and accuracy of potential annotation 'hits' in ESI-MS data. PMID:19622150

Where’s the beef? Predicting the effects of climate change on cattle production in western U.S. rangelands

Treesearch

Sue Miller; Matt Reeves; Karen Bagne; John Tanaka

2017-01-01

Cattle production capacity on western rangelands is potentially vulnerable to climate change through impacts on the amount of forage, changes in vegetation type, heat stress, and year-to-year forage variability. The researchers in this study projected climate change effects to rangelands through 2100 and compared them to a present-day baseline to estimate vulnerability...

Effects of climate change and shifts in forest composition on forest net primary production

Treesearch

Jyh-Min Chiang; Louts [Louis] R. Iverson; Anantha Prasad; Kim J. Brown

2008-01-01

Forests are dynamic in both structure and species composition, and these dynamics are strongly influenced by climate. However, the net effects of future tree species composition on net primary production (NPP) are not well understood. The objective of this work was to model the potential range shifts of tree species (DISTRIB Model) and predict their impacts on NPP (...

Prospective Predictors of Novel Tobacco and Nicotine Product Use in Emerging Adulthood.

PubMed

Hampson, Sarah E; Andrews, Judy A; Severson, Herbert H; Barckley, Maureen

2015-08-01

The purpose of this study was to investigate whether risk factors for cigarette smoking assessed in adolescence predict the use of novel tobacco and nicotine products (hookah, little cigars, and e-cigarettes) in early emerging adulthood. In a longitudinal study (N = 862), risk factors were measured in middle and high school, and novel product use was measured in emerging adulthood (mean age 22.4 years). Structural equation modeling was used to test a model predicting lifetime use of any of hookah, little cigars, and e-cigarettes in early emerging adulthood from distal predictors (gender, maternal smoking through Grade 8; already tried alcohol, cigarettes, or marijuana by Grade 8; and sensation seeking at Grade 8) and potential mediators (intentions to smoke cigarettes, drink alcohol or smoke marijuana at Grade 9, and smoking trajectory across high school). The most prevalent novel tobacco product was hookah (21.7%), followed by little cigars (16.8%) and e-cigarettes (6.6%). Maternal smoking, having already tried substances, and sensation seeking each predicted the use of at least one of these products via an indirect path through intentions to use substances and membership in a high-school smoking trajectory. Risk factors for cigarette smoking were found to predict novel tobacco use, suggesting that interventions to prevent cigarette smoking could be extended to include common novel tobacco products. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Multivariable model predictive control design of reactive distillation column for Dimethyl Ether production

NASA Astrophysics Data System (ADS)

Wahid, A.; Putra, I. G. E. P.

2018-03-01

Dimethyl ether (DME) as an alternative clean energy has attracted a growing attention in the recent years. DME production via reactive distillation has potential for capital cost and energy requirement savings. However, combination of reaction and distillation on a single column makes reactive distillation process a very complex multivariable system with high non-linearity of process and strong interaction between process variables. This study investigates a multivariable model predictive control (MPC) based on two-point temperature control strategy for the DME reactive distillation column to maintain the purities of both product streams. The process model is estimated by a first order plus dead time model. The DME and water purity is maintained by controlling a stage temperature in rectifying and stripping section, respectively. The result shows that the model predictive controller performed faster responses compared to conventional PI controller that are showed by the smaller ISE values. In addition, the MPC controller is able to handle the loop interactions well.

Rapid biochemical methane potential prediction of urban organic waste with near-infrared reflectance spectroscopy.

PubMed

Fitamo, T; Triolo, J M; Boldrin, A; Scheutz, C

2017-08-01

The anaerobic digestibility of various biomass feedstocks in biogas plants is determined with biochemical methane potential (BMP) assays. However, experimental BMP analysis is time-consuming, costly and challenging to optimise stock management and feeding to achieve improved biogas production. The aim of the present study is to develop a fast and reliable model based on near-infrared reflectance spectroscopy (NIRS) for the BMP prediction of urban organic waste (UOW). The model comprised 87 UOW samples. Additionally, 88 plant biomass samples were included, to develop a combined model predicting BMP. The coefficient of determination (R 2 ) and root mean square error in prediction (RMSE P ) of the UOW model were 0.88 and 44 mL CH 4 /g VS, while the combined model was 0.89 and 50 mL CH 4 /g VS. Improved model performance was obtained for the two individual models compared to the combined version. The BMP prediction with NIRS was satisfactory and moderately successful. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computational Approaches to Predict Indices of Cyanobacteria Toxicity.

EPA Science Inventory

As nutrient inputs increase, productivity increases and lakes transition from low trophic state (e.g., oligotrophic) to higher trophic states (e.g., eutrophic). These broad trophic state classifications are good predictors of ecosystem health and the potential for ecosystem serv...

Computational Approaches to Predict Indices of Cyanobacteria Toxicity

EPA Science Inventory

As nutrient inputs increase, productivity increases and lakes transition from low trophic state (e.g. oligotrophic) to higher trophic states (e.g. eutrophic). These broad trophic state classifications are good predictors of ecosystem health and the potential for ecosystem servic...

Power quality analysis based on spatial correlation

NASA Astrophysics Data System (ADS)

Li, Jiangtao; Zhao, Gang; Liu, Haibo; Li, Fenghou; Liu, Xiaoli

2018-03-01

With the industrialization and urbanization, the status of electricity in the production and life is getting higher and higher. So the prediction of power quality is the more potential significance. Traditional power quality analysis methods include: power quality data compression, disturbance event pattern classification, disturbance parameter calculation. Under certain conditions, these methods can predict power quality. This paper analyses the temporal variation of power quality of one provincial power grid in China from time angle. The distribution of power quality was analyzed based on spatial autocorrelation. This paper tries to prove that the research idea of geography is effective for mining the potential information of power quality.

Constructing vegetation productivity equations by employing undisturbed soils data: An Oliver County, North Dakota case study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burley, J.B.; Polakowski, K.J.; Fowler, G.

Surface mine reclamation specialists have been searching for predictive methods to assess the capability of disturbed soils to support vegetation growth. We conducted a study to develop a vegetation productivity equation for reclaiming surface mines in Oliver County, North Dakota, thereby allowing investigators to quantitatively determine the plant growth potential of a reclaimed soil. The study examined the predictive modeling potential for both agronomic crops and woody plants, including: wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), oat (Avena sativa L.), corn (Zea mays L.), grass and legume mixtures, Eastern red cedar (Juniperus virginiana L.), Black Hills spruce (Picea glaucamore » var. densata Bailey), Colorado spruce (Picea pungens Engelm.), ponderosa pine (Pinus ponderosa var. scope Engelm.), green ash (Fraxinus pennsylvanica Marsh.), Eastern cottonwood Populus deltoides (Bart. ex Marsh.), Siberian elm (Ulmus pumila L.), Siberian peashrub (Caragana arborescens Lam), American plum (Prunus americans Marsh.), and chokecherry ( Prunus virginiana L.). An equation was developed which is highly significant (p<0.0001), explaining 81.08% of the variance (coefficient of multiple determination=0.8108), with all regressors significant (p{le}0.048, Type II Sums of Squares). The measurement of seven soil parameters are required to predict soil vegetation productivity: percent slope, available water holding capacity, percent rock fragments, topographic position, electrical conductivity, pH, and percent organic matter. While the equation was developed from data on undisturbed soils, the equation`s predictions were positively correlated (0.71424, p{le}0.0203) with a small data set (n=10) from reclaimed soils.« less

Comparative analysis of remotely-sensed data products via ecological niche modeling of avian influenza case occurrences in Middle Eastern poultry.

PubMed

Bodbyl-Roels, Sarah; Peterson, A Townsend; Xiao, Xiangming

2011-03-28

Ecological niche modeling integrates known sites of occurrence of species or phenomena with data on environmental variation across landscapes to infer environmental spaces potentially inhabited (i.e., the ecological niche) to generate predictive maps of potential distributions in geographic space. Key inputs to this process include raster data layers characterizing spatial variation in environmental parameters, such as vegetation indices from remotely sensed satellite imagery. The extent to which ecological niche models reflect real-world distributions depends on a number of factors, but an obvious concern is the quality and content of the environmental data layers. We assessed ecological niche model predictions of H5N1 avian flu presence quantitatively within and among four geographic regions, based on models incorporating two means of summarizing three vegetation indices derived from the MODIS satellite. We evaluated our models for predictive ability using partial ROC analysis and GLM ANOVA to compare performance among indices and regions. We found correlations between vegetation indices to be high, such that they contain information that overlaps broadly. Neither the type of vegetation index used nor method of summary affected model performance significantly. However, the degree to which model predictions had to be transferred (i.e., projected onto landscapes and conditions not represented on the landscape of training) impacted predictive strength greatly (within-region model predictions far out-performed models projected among regions). Our results provide the first quantitative tests of most appropriate uses of different remotely sensed data sets in ecological niche modeling applications. While our testing did not result in a decisive "best" index product or means of summarizing indices, it emphasizes the need for careful evaluation of products used in modeling (e.g. matching temporal dimensions and spatial resolution) for optimum performance, instead of simple reliance on large numbers of data layers.

Atmospheric hydrocarbon emissions and concentrations in the barnett shale natural gas production region.

PubMed

Zavala-Araiza, Daniel; Sullivan, David W; Allen, David T

2014-05-06

Hourly ambient hydrocarbon concentration data were collected, in the Barnett Shale Natural Gas Production Region, using automated gas chromatography (auto-GC), for the period from April 2010 to December 2011. Data for three sites were compared: a site in the geographical center of the natural gas production region (Eagle Mountain Lake (EML)); a rural/suburban site at the periphery of the production region (Flower Mound Shiloh), and an urban site (Hinton). The dominant hydrocarbon species observed in the Barnett Shale region were light alkanes. Analyses of daily, monthly, and hourly patterns showed little variation in relative composition. Observed concentrations were compared to concentrations predicted using a dispersion model (AERMOD) and a spatially resolved inventory of volatile organic compounds (VOC) emissions from natural gas production (Barnett Shale Special Emissions Inventory) prepared by the Texas Commission on Environmental Quality (TCEQ), and other emissions information. The predicted concentrations of VOC due to natural gas production were 0-40% lower than background corrected measurements, after accounting for potential under-estimation of certain emission categories. Hourly and daily variations in observed, background corrected concentrations were primarily explained by variability in meteorology, suggesting that episodic emission events had little impact on hourly averaged concentrations. Total emissions for VOC from natural gas production sources are estimated to be approximately 25,300 tons/yr, when accounting for potential under-estimation of certain emission categories. This region produced, in 2011, approximately 5 bcf/d of natural gas (100 Gg/d) for a VOC to natural gas production ratio (mass basis) of 0.0006.

Biogeographic affinity helps explain productivity-richness relationships at regional and local scales

USGS Publications Warehouse

Harrison, S.; Grace, J.B.

2007-01-01

The unresolved question of what causes the observed positive relationship between large-scale productivity and species richness has long interested ecologists and evolutionists. Here we examine a potential explanation that we call the biogeographic affinity hypothesis, which proposes that the productivity-richness relationship is a function of species' climatic tolerances that in turn are shaped by the earth's climatic history combined with evolutionary niche conservatism. Using botanical data from regions and sites across California, we find support for a key prediction of this hypothesis, namely, that the productivity-species richness relationship differs strongly and predictably among groups of higher taxa on the basis of their biogeographic affinities (i.e., between families or genera primarily associated with north-temperate, semiarid, or desert zones). We also show that a consideration of biogeographic affinity can yield new insights on how productivity-richness patterns at large geographic scales filter down to affect patterns of species richness and composition within local communities. ?? 2007 by The University of Chicago. All rights reserved.

Comparison of the methane production potential and biodegradability of kitchen waste from different sources under mesophilic and thermophilic conditions.

PubMed

Yang, Ziyi; Wang, Wen; Zhang, Shuyu; Ma, Zonghu; Anwar, Naveed; Liu, Guangqing; Zhang, Ruihong

2017-04-01

The methane production potential of kitchen waste (KW) obtained from different sources was compared through mesophilic and thermophilic anaerobic digestion. The methane yields (MYs) obtained with the same KW sample under different temperatures were similar, whereas the MYs obtained with different samples differed significantly. The highest MY obtained in S7 was 54%-60% higher than the lowest MY in S3. The modified Gompertz model was utilized to simulate the methane production process. The maximum production rate of methane under thermophilic conditions was 2%-86% higher than that under mesophilic conditions. The characteristics of different KW samples were studied. In the distribution of total chemical oxygen demand, the diversity of organic compounds of KW was the most dominant factor that affected the potential MYs of KW. The effect of the C/N and C/P ratios or the concentration of metal ions was insignificant. Two typical methods to calculate the theoretical MY (TMY) were compared, the organic composition method can simulate methane production more precisely than the elemental analysis method. Significant linear correlations were found between TMY org and MYs under mesophilic and thermophilic conditions. The organic composition method can thus be utilized as a fast technique to predict the methane production potential of KW.

Road to the future of systems biotechnology: CRISPR-Cas-mediated metabolic engineering for recombinant protein production.

PubMed

Roointan, Amir; Morowvat, Mohammad Hossein

The rising potential for CRISPR-Cas-mediated genome editing has revolutionized our strategies in basic and practical bioengineering research. It provides a predictable and precise method for genome modification in a robust and reproducible fashion. Emergence of systems biotechnology and synthetic biology approaches coupled with CRISPR-Cas technology could change the future of cell factories to possess some new features which have not been found naturally. We have discussed the possibility and versatile potentials of CRISPR-Cas technology for metabolic engineering of a recombinant host for heterologous protein production. We describe the mechanisms involved in this metabolic engineering approach and present the diverse features of its application in biotechnology and protein production.

Warranty optimisation based on the prediction of costs to the manufacturer using neural network model and Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Stamenkovic, Dragan D.; Popovic, Vladimir M.

2015-02-01

Warranty is a powerful marketing tool, but it always involves additional costs to the manufacturer. In order to reduce these costs and make use of warranty's marketing potential, the manufacturer needs to master the techniques for warranty cost prediction according to the reliability characteristics of the product. In this paper a combination free replacement and pro rata warranty policy is analysed as warranty model for one type of light bulbs. Since operating conditions have a great impact on product reliability, they need to be considered in such analysis. A neural network model is used to predict light bulb reliability characteristics based on the data from the tests of light bulbs in various operating conditions. Compared with a linear regression model used in the literature for similar tasks, the neural network model proved to be a more accurate method for such prediction. Reliability parameters obtained in this way are later used in Monte Carlo simulation for the prediction of times to failure needed for warranty cost calculation. The results of the analysis make possible for the manufacturer to choose the optimal warranty policy based on expected product operating conditions. In such a way, the manufacturer can lower the costs and increase the profit.

RobOKoD: microbial strain design for (over)production of target compounds.

PubMed

Stanford, Natalie J; Millard, Pierre; Swainston, Neil

2015-01-01

Sustainable production of target compounds such as biofuels and high-value chemicals for pharmaceutical, agrochemical, and chemical industries is becoming an increasing priority given their current dependency upon diminishing petrochemical resources. Designing these strains is difficult, with current methods focusing primarily on knocking-out genes, dismissing other vital steps of strain design including the overexpression and dampening of genes. The design predictions from current methods also do not translate well-into successful strains in the laboratory. Here, we introduce RobOKoD (Robust, Overexpression, Knockout and Dampening), a method for predicting strain designs for overproduction of targets. The method uses flux variability analysis to profile each reaction within the system under differing production percentages of target-compound and biomass. Using these profiles, reactions are identified as potential knockout, overexpression, or dampening targets. The identified reactions are ranked according to their suitability, providing flexibility in strain design for users. The software was tested by designing a butanol-producing Escherichia coli strain, and was compared against the popular OptKnock and RobustKnock methods. RobOKoD shows favorable design predictions, when predictions from these methods are compared to a successful butanol-producing experimentally-validated strain. Overall RobOKoD provides users with rankings of predicted beneficial genetic interventions with which to support optimized strain design.

RobOKoD: microbial strain design for (over)production of target compounds

PubMed Central

Stanford, Natalie J.; Millard, Pierre; Swainston, Neil

2015-01-01

Sustainable production of target compounds such as biofuels and high-value chemicals for pharmaceutical, agrochemical, and chemical industries is becoming an increasing priority given their current dependency upon diminishing petrochemical resources. Designing these strains is difficult, with current methods focusing primarily on knocking-out genes, dismissing other vital steps of strain design including the overexpression and dampening of genes. The design predictions from current methods also do not translate well-into successful strains in the laboratory. Here, we introduce RobOKoD (Robust, Overexpression, Knockout and Dampening), a method for predicting strain designs for overproduction of targets. The method uses flux variability analysis to profile each reaction within the system under differing production percentages of target-compound and biomass. Using these profiles, reactions are identified as potential knockout, overexpression, or dampening targets. The identified reactions are ranked according to their suitability, providing flexibility in strain design for users. The software was tested by designing a butanol-producing Escherichia coli strain, and was compared against the popular OptKnock and RobustKnock methods. RobOKoD shows favorable design predictions, when predictions from these methods are compared to a successful butanol-producing experimentally-validated strain. Overall RobOKoD provides users with rankings of predicted beneficial genetic interventions with which to support optimized strain design. PMID:25853130

Prioritizing human pharmaceuticals for ecological risks in the freshwater environment of Korea.

PubMed

Ji, Kyunghee; Han, Eun Jeong; Back, Sunhyoung; Park, Jeongim; Ryu, Jisung; Choi, Kyungho

2016-04-01

Pharmaceutical residues are potential threats to aquatic ecosystems. Because more than 3000 active pharmaceutical ingredients (APIs) are in use, identifying high-priority pharmaceuticals is important for developing appropriate management options. Priority pharmaceuticals may vary by geographical region, because their occurrence levels can be influenced by demographic, societal, and regional characteristics. In the present study, the authors prioritized human pharmaceuticals of potential ecological risk in the Korean water environment, based on amount of use, biological activity, and regional hydrologic characteristics. For this purpose, the authors estimated the amounts of annual production of 695 human APIs in Korea. Then derived predicted environmental concentrations, using 2 approaches, to develop an initial candidate list of target pharmaceuticals. Major antineoplastic drugs and hormones were added in the initial candidate list regardless of their production amount because of their high biological activity potential. The predicted no effect concentrations were derived for those pharmaceuticals based on ecotoxicity information available in the literature or by model prediction. Priority lists of human pharmaceuticals were developed based on ecological risks and availability of relevant information. Those priority APIs identified include acetaminophen, clarithromycin, ciprofloxacin, ofloxacin, metformin, and norethisterone. Many of these pharmaceuticals have been neither adequately monitored nor assessed for risks in Korea. Further efforts are needed to improve these lists and to develop management decisions for these compounds in Korean water. © 2015 SETAC.

Under which climate and soil conditions the plant productivity-precipitation relationship is linear or nonlinear?

PubMed

Ye, Jian-Sheng; Pei, Jiu-Ying; Fang, Chao

2018-03-01

Understanding under which climate and soil conditions the plant productivity-precipitation relationship is linear or nonlinear is useful for accurately predicting the response of ecosystem function to global environmental change. Using long-term (2000-2016) net primary productivity (NPP)-precipitation datasets derived from satellite observations, we identify >5600pixels in the North Hemisphere landmass that fit either linear or nonlinear temporal NPP-precipitation relationships. Differences in climate (precipitation, radiation, ratio of actual to potential evapotranspiration, temperature) and soil factors (nitrogen, phosphorous, organic carbon, field capacity) between the linear and nonlinear types are evaluated. Our analysis shows that both linear and nonlinear types exhibit similar interannual precipitation variabilities and occurrences of extreme precipitation. Permutational multivariate analysis of variance suggests that linear and nonlinear types differ significantly regarding to radiation, ratio of actual to potential evapotranspiration, and soil factors. The nonlinear type possesses lower radiation and/or less soil nutrients than the linear type, thereby suggesting that nonlinear type features higher degree of limitation from resources other than precipitation. This study suggests several factors limiting the responses of plant productivity to changes in precipitation, thus causing nonlinear NPP-precipitation pattern. Precipitation manipulation and modeling experiments should combine with changes in other climate and soil factors to better predict the response of plant productivity under future climate. Copyright © 2017 Elsevier B.V. All rights reserved.

Preschool speech articulation and nonword repetition abilities may help predict eventual recovery or persistence of stuttering.

PubMed

Spencer, Caroline; Weber-Fox, Christine

2014-09-01

In preschool children, we investigated whether expressive and receptive language, phonological, articulatory, and/or verbal working memory proficiencies aid in predicting eventual recovery or persistence of stuttering. Participants included 65 children, including 25 children who do not stutter (CWNS) and 40 who stutter (CWS) recruited at age 3;9-5;8. At initial testing, participants were administered the Test of Auditory Comprehension of Language, 3rd edition (TACL-3), Structured Photographic Expressive Language Test, 3rd edition (SPELT-3), Bankson-Bernthal Test of Phonology-Consonant Inventory subtest (BBTOP-CI), Nonword Repetition Test (NRT; Dollaghan & Campbell, 1998), and Test of Auditory Perceptual Skills-Revised (TAPS-R) auditory number memory and auditory word memory subtests. Stuttering behaviors of CWS were assessed in subsequent years, forming groups whose stuttering eventually persisted (CWS-Per; n=19) or recovered (CWS-Rec; n=21). Proficiency scores in morphosyntactic skills, consonant production, verbal working memory for known words, and phonological working memory and speech production for novel nonwords obtained at the initial testing were analyzed for each group. CWS-Per were less proficient than CWNS and CWS-Rec in measures of consonant production (BBTOP-CI) and repetition of novel phonological sequences (NRT). In contrast, receptive language, expressive language, and verbal working memory abilities did not distinguish CWS-Rec from CWS-Per. Binary logistic regression analysis indicated that preschool BBTOP-CI scores and overall NRT proficiency significantly predicted future recovery status. Results suggest that phonological and speech articulation abilities in the preschool years should be considered with other predictive factors as part of a comprehensive risk assessment for the development of chronic stuttering. At the end of this activity the reader will be able to: (1) describe the current status of nonlinguistic and linguistic predictors for recovery and persistence of stuttering; (2) summarize current evidence regarding the potential value of consonant cluster articulation and nonword repetition abilities in helping to predict stuttering outcome in preschool children; (3) discuss the current findings in relation to potential implications for theories of developmental stuttering; (4) discuss the current findings in relation to potential considerations for the evaluation and treatment of developmental stuttering. Copyright © 2014 Elsevier Inc. All rights reserved.

Predicting the global warming potential of agro-ecosystems

NASA Astrophysics Data System (ADS)

Lehuger, S.; Gabrielle, B.; Larmanou, E.; Laville, P.; Cellier, P.; Loubet, B.

2007-04-01

Nitrous oxide, carbon dioxide and methane are the main biogenic greenhouse gases (GHG) contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate thus requires a capacity to predict the net exchanges of these gases in an integrated manner, as related to environmental conditions and crop management. Here, we used two year-round data sets from two intensively-monitored cropping systems in northern France to test the ability of the biophysical crop model CERES-EGC to simulate GHG exchanges at the plot-scale. The experiments involved maize and rapeseed crops on a loam and rendzina soils, respectively. The model was subsequently extrapolated to predict CO2 and N2O fluxes over an entire crop rotation. Indirect emissions (IE) arising from the production of agricultural inputs and from cropping operations were also added to the final GWP. One experimental site (involving a wheat-maize-barley rotation on a loamy soil) was a net source of GHG with a GWP of 350 kg CO2-C eq ha-1 yr-1, of which 75% were due to IE and 25% to direct N2O emissions. The other site (involving an oilseed rape-wheat-barley rotation on a rendzina) was a net sink of GHG for -250 kg CO2-C eq ha-1 yr-1, mainly due to a higher predicted C sequestration potential and C return from crops. Such modelling approach makes it possible to test various agronomic management scenarios, in order to design productive agro-ecosystems with low global warming impact.

Application of Dempster-Shafer theory of evidence model to geoelectric and hydraulic parameters for groundwater potential zonation

NASA Astrophysics Data System (ADS)

Mogaji, Kehinde Anthony; Lim, Hwee San

2018-06-01

The application of a GIS - based Dempster - Shafer data driven model named as evidential belief function EBF- methodology to groundwater potential conditioning factors (GPCFs) derived from geophysical and hydrogeological data sets for assessing groundwater potentiality was presented in this study. The proposed method's efficacy in managing degree of uncertainty in spatial predictive models motivated this research. The method procedural approaches entail firstly, the database containing groundwater data records (bore wells location inventory, hydrogeological data record, etc.) and geophysical measurement data construction. From the database, different influencing groundwater occurrence factors, namely aquifer layer thickness, aquifer layer resistivity, overburden material resistivity, overburden material thickness, aquifer hydraulic conductivity and aquifer transmissivity were extracted and prepared. Further, the bore well location inventories were partitioned randomly into a ratio of 70% (19 wells) for model training and 30% (9 wells) for model testing. The synthesized of the GPCFs via applying the DS - EBF model algorithms produced the groundwater productivity potential index (GPPI) map which demarcated the area into low - medium, medium, medium - high and high potential zones. The analyzed percentage degree of uncertainty for the predicted lows potential zones classes and mediums/highs potential zones classes are >10% and <10%, respectively. The DS theory model-based GPPI map's validation through ROC approach established prediction rate accuracy of 88.8%. Successively, the determined transverse resistance (TR) values in the range of 1280 and 30,000 Ω my for the area geoelectrically delineated aquifer units of the predicted potential zones through Dar - Zarrouk Parameter analysis quantitatively confirm the DS theory modeling prediction results. This research results have expand the capability of DS - EBF model in predictive modeling by effective uncertainty management. Thus, the produced map could form part of decision support system reliable to be used by local authorities for groundwater exploitation and management in the area.

Potential occupational risk of amines in carbon capture for power generation.

PubMed

Gentry, P Robinan; House-Knight, Tamara; Harris, Angela; Greene, Tracy; Campleman, Sharan

2014-08-01

While CO2 capture and storage (CCS) technology has been well studied in terms of its efficacy and cost of implementation, there is limited available data concerning the potential for occupational exposure to amines, mixtures of amines, or degradation of by-products from the CCS process. This paper is a critical review of the available data concerning the potential effects of amines and CCS-degradation by-products. A comprehensive review of the occupational health and safety issues associated with exposure to amines and amine by-products at CCS facilities was performed, along with a review of the regulatory status and guidelines of amines, by-products, and CCS process vapor mixtures. There are no specific guidelines or regulations regarding permissible levels of exposure via air for amines and degradation products that could form atmospheric oxidation of amines released from post-combustion CO2 capture plants. While there has been a worldwide effort to develop legal and regulatory frameworks for CCS, none are directly related to occupational exposures. By-products of alkanolamine degradation may pose the most significant health hazard to workers in CCS facilities, with several aldehydes, amides, nitramines, and nitrosamines classified as either known or potential/possible human carcinogens. The absence of large-scale CCS facilities; absence and unreliability of reported data in the literature from pilot facilities; and proprietary amine blends make it difficult to estimate potential amine exposures and predict formation and exposure to degradation products.

Predicting future US water yield and ecosystem productivity by linking an ecohydrological model to WRF dynamically downscaled climate projections

Treesearch

S. Sun; Ge Sun; Erika Cohen Mack; Steve McNulty; Peter Caldwell; K. Duan; Y. Zhang

2015-01-01

Quantifying the potential impacts of climate change on water yield and ecosystem productivity (i.e., carbon balances) is essential to developing sound watershed restoration plans, and climate change adaptation and mitigation strategies. This study links an ecohydrological model (Water Supply and Stress Index, WaSSI) with WRF (Weather Research and Forecasting Model)...

Egg production patterns of two invertebrate species in rocky subtidal areas under different fishing regimes along the coast of central Chile

PubMed Central

Ospina-Álvarez, Andres; González, Catherine; Fernández, Miriam

2017-01-01

Fishing is a major source of human impact, reducing density and size of a wide range of exploited species in comparison to areas exhibiting strong regulations (no-take and partially protected areas, including Territorial Use Rights for Fisheries, TURFs). Since size and density might have important consequences on reproduction, and therefore natural re-seeding, we monitored adult size, density and potential fecundity of the keyhole limpet (Fissurella latimarginata) and the red sea urchin (Loxechinus albus) in areas under two fishing regimes (TURFs and Open Access Areas, OAAs). Analyzing the distribution of suitable habitats, we predict spatial patterns of potential egg production, to identify reproductive hotspots along the central coast of Chile. The current system of TURFs in central Chile showed higher potential egg production of F. latimarginata and of L. albus than expected under a complete OAAs scenario (67 and 52% respectively). Potential egg production showed more than a twofold reduction when the complete TURFs scenario was compared against complete OAAs condition in both species. Individual size and density explained between 60% and 100% of the variability in potential egg production, suggesting the importance of the enhancement of both biological variables in TURFs in Chile. Potential egg production for both species in the northern part of the studied domain was higher due to the combined effect of (a) suitable habitat and (b) concentration of TURFs. Our results suggest that partially protected areas, such as TURFs can significantly enhance the production of propagules that could seed exploited areas. PMID:28481886

Rain use efficiency across a precipitation gradient on the Tibetan Plateau

USDA-ARS?s Scientific Manuscript database

Rain use efficiency (RUE), commonly described as the ratio of aboveground net primary production (ANPP) to mean annual precipitation (MAP), is a critical indicator for predicting potential responses of grassland ecosystems to changing precipitation regimes. However, current understanding on patterns...

A Game Theoretical Approach to Hacktivism: Is Attack Likelihood a Product of Risks and Payoffs?

PubMed

Bodford, Jessica E; Kwan, Virginia S Y

2018-02-01

The current study examines hacktivism (i.e., hacking to convey a moral, ethical, or social justice message) through a general game theoretic framework-that is, as a product of costs and benefits. Given the inherent risk of carrying out a hacktivist attack (e.g., legal action, imprisonment), it would be rational for the user to weigh these risks against perceived benefits of carrying out the attack. As such, we examined computer science students' estimations of risks, payoffs, and attack likelihood through a game theoretic design. Furthermore, this study aims at constructing a descriptive profile of potential hacktivists, exploring two predicted covariates of attack decision making, namely, peer prevalence of hacking and sex differences. Contrary to expectations, results suggest that participants' estimations of attack likelihood stemmed solely from expected payoffs, rather than subjective risks. Peer prevalence significantly predicted increased payoffs and attack likelihood, suggesting an underlying descriptive norm in social networks. Notably, we observed no sex differences in the decision to attack, nor in the factors predicting attack likelihood. Implications for policymakers and the understanding and prevention of hacktivism are discussed, as are the possible ramifications of widely communicated payoffs over potential risks in hacking communities.

Rational selection of alternative, environmentally compatible surfactants for biotechnological production of pharmaceuticals--a step toward green biotechnology.

PubMed

Straub, Jürg Oliver; Shearer, Russel; Studer, Martin

2014-09-01

The biotechnological production of pharmaceutical active substances needs ancillary substances. Surfactants are used at the end of the cell culture as a protection against potential viral or bacterial contamination and to lyse the producing cells for isolation and purification of the products. To find a replacement for a surfactant that had raised environmental concern, environmentally relevant data for potential alternatives were searched for in the literature. Significant data gaps were filled with additional tests: biodegradability, algal growth inhibition, acute daphnid immobilization and chronic daphnid reproduction toxicity, acute fish toxicity, and activated sludge respiration inhibition. The results were used to model removal in the wastewater treatment plants (WWTPs) serving 3 biotechnological production sites in the Roche Group. Predicted environmental concentrations (PECs) were calculated using realistic amounts of surfactants and site-specific wastewater fluxes, modeled removals for the WWTPs and dilution factors by the respective receiving waters. Predicted no-effect concentrations (PNECs) were derived for WWTPs and for both fresh and marine receiving waters as the treated wastewater of 1 production site is discharged into a coastal water. This resulted in a spreadsheet showing PECs, PNECs, and PEC ÷ PNEC risk characterization ratios for the WWTPs and receiving waters for all investigated surfactants and all 3 sites. This spreadsheet now serves as a selection support for the biotechnological developers. This risk-based prioritization of surfactants is a step toward green biotechnological production. © 2014 SETAC.

Risk Factors and Biomarkers of Age-Related Macular Degeneration

PubMed Central

Lambert, Nathan G.; Singh, Malkit K.; ElShelmani, Hanan; Mansergh, Fiona C.; Wride, Michael A.; Padilla, Maximilian; Keegan, David; Hogg, Ruth E.; Ambati, Balamurali K.

2016-01-01

A biomarker can be a substance or structure measured in body parts, fluids or products that can affect or predict disease incidence. As age-related macular degeneration (AMD) is the leading cause of blindness in the developed world, much research and effort has been invested in the identification of different biomarkers to predict disease incidence, identify at risk individuals, elucidate causative pathophysiological etiologies, guide screening, monitoring and treatment parameters, and predict disease outcomes. To date, a host of genetic, environmental, proteomic, and cellular targets have been identified as both risk factors and potential biomarkers for AMD. Despite this, their use has been confined to research settings and has not yet crossed into the clinical arena. A greater understanding of these factors and their use as potential biomarkers for AMD can guide future research and clinical practice. This article will discuss known risk factors and novel, potential biomarkers of AMD in addition to their application in both academic and clinical settings. PMID:27156982

Field measurements of the ambient ozone formation potential in Beijing during winter

NASA Astrophysics Data System (ADS)

Crilley, Leigh; Kramer, Louisa; Thomson, Steven; Lee, James; Squires, Freya; Bloss, William

2017-04-01

The air quality issues in Beijing have been well-documented, and the severe air pollution levels result in a unique chemical mix in the urban boundary layer, both in terms of concentration and composition. As many of the atmospheric chemical process are non-linear and interlinked, this makes predictions difficult for species formed in atmosphere, such as ozone, requiring field measurements to understand these processes in order to guide mitigation efforts. To investigate the ozone formation potential of ambient air, we employed a custom built instrument to measure in near real time the potential for in situ ozone production, using an artificial light source. Our results are thus indicative of the ozone formation potential for the sampled ambient air mixture. Measurements were performed as part of the Air Pollution and Human Health (APHH) field campaign in November / December 2016 at a suburban site in central Beijing. We also conducted experiments to examine the ozone production sensitivity to NOx. We will present preliminarily results from ambient sampling and NOx experiments demonstrating changes in the ozone production potential during clean and haze periods in Beijing.

Toward Biopredictive Dissolution for Enteric Coated Dosage Forms.

PubMed

Al-Gousous, J; Amidon, G L; Langguth, P

2016-06-06

The aim of this work was to develop a phosphate buffer based dissolution method for enteric-coated formulations with improved biopredictivity for fasted conditions. Two commercially available enteric-coated aspirin products were used as model formulations (Aspirin Protect 300 mg, and Walgreens Aspirin 325 mg). The disintegration performance of these products in a physiological 8 mM pH 6.5 bicarbonate buffer (representing the conditions in the proximal small intestine) was used as a standard to optimize the employed phosphate buffer molarity. To account for the fact that a pH and buffer molarity gradient exists along the small intestine, the introduction of such a gradient was proposed for products with prolonged lag times (when it leads to a release lower than 75% in the first hour post acid stage) in the proposed buffer. This would allow the method also to predict the performance of later-disintegrating products. Dissolution performance using the accordingly developed method was compared to that observed when using two well-established dissolution methods: the United States Pharmacopeia (USP) method and blank fasted state simulated intestinal fluid (FaSSIF). The resulting dissolution profiles were convoluted using GastroPlus software to obtain predicted pharmacokinetic profiles. A pharmacokinetic study on healthy human volunteers was performed to evaluate the predictions made by the different dissolution setups. The novel method provided the best prediction, by a relatively wide margin, for the difference between the lag times of the two tested formulations, indicating its being able to predict the post gastric emptying onset of drug release with reasonable accuracy. Both the new and the blank FaSSIF methods showed potential for establishing in vitro-in vivo correlation (IVIVC) concerning the prediction of Cmax and AUC0-24 (prediction errors not more than 20%). However, these predictions are strongly affected by the highly variable first pass metabolism necessitating the evaluation of an absorption rate metric that is more independent of the first-pass effect. The Cmax/AUC0-24 ratio was selected for this purpose. Regarding this metric's predictions, the new method provided very good prediction of the two products' performances relative to each other (only 1.05% prediction error in this regard), while its predictions for the individual products' values in absolute terms were borderline, narrowly missing the regulatory 20% prediction error limits (21.51% for Aspirin Protect and 22.58% for Walgreens Aspirin). The blank FaSSIF-based method provided good Cmax/AUC0-24 ratio prediction, in absolute terms, for Aspirin Protect (9.05% prediction error), but its prediction for Walgreens Aspirin (33.97% prediction error) was overwhelmingly poor. Thus it gave practically the same average but much higher maximum prediction errors compared to the new method, and it was strongly overdiscriminating as for predicting their performances relative to one another. The USP method, despite not being overdiscriminating, provided poor predictions of the individual products' Cmax/AUC0-24 ratios. This indicates that, overall, the new method is of improved biopredictivity compared to established methods.

Humidity-corrected Arrhenius equation: The reference condition approach.

PubMed

Naveršnik, Klemen; Jurečič, Rok

2016-03-16

Accelerated and stress stability data is often used to predict shelf life of pharmaceuticals. Temperature, combined with humidity accelerates chemical decomposition and the Arrhenius equation is used to extrapolate accelerated stability results to long-term stability. Statistical estimation of the humidity-corrected Arrhenius equation is not straightforward due to its non-linearity. A two stage nonlinear fitting approach is used in practice, followed by a prediction stage. We developed a single-stage statistical procedure, called the reference condition approach, which has better statistical properties (less collinearity, direct estimation of uncertainty, narrower prediction interval) and is significantly easier to use, compared to the existing approaches. Our statistical model was populated with data from a 35-day stress stability study on a laboratory batch of vitamin tablets and required mere 30 laboratory assay determinations. The stability prediction agreed well with the actual 24-month long term stability of the product. The approach has high potential to assist product formulation, specification setting and stability statements. Copyright © 2016 Elsevier B.V. All rights reserved.

Statistical study of free magnetic energy and flare productivity of solar active regions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Su, J. T.; Jing, J.; Wang, S.

Photospheric vector magnetograms from the Helioseismic and Magnetic Imager on board the Solar Dynamic Observatory are utilized as the boundary conditions to extrapolate both nonlinear force-free and potential magnetic fields in solar corona. Based on the extrapolations, we are able to determine the free magnetic energy (FME) stored in active regions (ARs). Over 3000 vector magnetograms in 61 ARs were analyzed. We compare FME with the ARs' flare index (FI) and find that there is a weak correlation (<60%) between FME and FI. FME shows slightly improved flare predictability relative to the total unsigned magnetic flux of ARs in themore » following two aspects: (1) the flare productivity predicted by FME is higher than that predicted by magnetic flux and (2) the correlation between FI and FME is higher than that between FI and magnetic flux. However, this improvement is not significant enough to make a substantial difference in time-accumulated FI, rather than individual flare, predictions.« less

Identification of potentially hazardous human gene products in GMO risk assessment.

PubMed

Bergmans, Hans; Logie, Colin; Van Maanen, Kees; Hermsen, Harm; Meredyth, Michelle; Van Der Vlugt, Cécile

2008-01-01

Genetically modified organisms (GMOs), e.g. viral vectors, could threaten the environment if by their release they spread hazardous gene products. Even in contained use, to prevent adverse consequences, viral vectors carrying genes from mammals or humans should be especially scrutinized as to whether gene products that they synthesize could be hazardous in their new context. Examples of such potentially hazardous gene products (PHGPs) are: protein toxins, products of dominant alleles that have a role in hereditary diseases, gene products and sequences involved in genome rearrangements, gene products involved in immunomodulation or with an endocrine function, gene products involved in apoptosis, activated proto-oncogenes. For contained use of a GMO that carries a construct encoding a PHGP, the precautionary principle dictates that safety measures should be applied on a "worst case" basis, until the risks of the specific case have been assessed. The potential hazard of cloned genes can be estimated before empirical data on the actual GMO become available. Preliminary data may be used to focus hazard identification and risk assessment. Both predictive and empirical data may also help to identify what further information is needed to assess the risk of the GMO. A two-step approach, whereby a PHGP is evaluated for its conceptual dangers, then checked by data bank searches, is delineated here.

Evaluating models of climate and forest vegetation

NASA Technical Reports Server (NTRS)

Clark, James S.

1992-01-01

Understanding how the biosphere may respond to increasing trace gas concentrations in the atmosphere requires models that contain vegetation responses to regional climate. Most of the processes ecologists study in forests, including trophic interactions, nutrient cycling, and disturbance regimes, and vital components of the world economy, such as forest products and agriculture, will be influenced in potentially unexpected ways by changing climate. These vegetation changes affect climate in the following ways: changing C, N, and S pools; trace gases; albedo; and water balance. The complexity of the indirect interactions among variables that depend on climate, together with the range of different space/time scales that best describe these processes, make the problems of modeling and prediction enormously difficult. These problems of predicting vegetation response to climate warming and potential ways of testing model predictions are the subjects of this chapter.

Cost Models for MMC Manufacturing Processes

NASA Technical Reports Server (NTRS)

Elzey, Dana M.; Wadley, Haydn N. G.

1996-01-01

Processes for the manufacture of advanced metal matrix composites are rapidly approaching maturity in the research laboratory and there is growing interest in their transition to industrial production. However, research conducted to date has almost exclusively focused on overcoming the technical barriers to producing high-quality material and little attention has been given to the economical feasibility of these laboratory approaches and process cost issues. A quantitative cost modeling (QCM) approach was developed to address these issues. QCM are cost analysis tools based on predictive process models relating process conditions to the attributes of the final product. An important attribute, of the QCM approach is the ability to predict the sensitivity of material production costs to product quality and to quantitatively explore trade-offs between cost and quality. Applications of the cost models allow more efficient direction of future MMC process technology development and a more accurate assessment of MMC market potential. Cost models were developed for two state-of-the art metal matrix composite (MMC) manufacturing processes: tape casting and plasma spray deposition. Quality and Cost models are presented for both processes and the resulting predicted quality-cost curves are presented and discussed.

Ocean modelling for aquaculture and fisheries in Irish waters

NASA Astrophysics Data System (ADS)

Dabrowski, T.; Lyons, K.; Cusack, C.; Casal, G.; Berry, A.; Nolan, G. D.

2016-01-01

The Marine Institute, Ireland, runs a suite of operational regional and coastal ocean models. Recent developments include several tailored products that focus on the key needs of the Irish aquaculture sector. In this article, an overview of the products and services derived from the models are presented. The authors give an overview of a shellfish model developed in-house and that was designed to predict the growth, the physiological interactions with the ecosystem, and the level of coliform contamination of the blue mussel. As such, this model is applicable in studies on the carrying capacity of embayments, assessment of the impacts of pollution on aquaculture grounds, and the determination of shellfish water classes. Further services include the assimilation of the model-predicted shelf water movement into a new harmful algal bloom alert system used to inform end users of potential toxic shellfish events and high biomass blooms that include fish-killing species. Models are also used to identify potential sites for offshore aquaculture, to inform studies of potential cross-contamination in farms from the dispersal of planktonic sea lice larvae and other pathogens that can infect finfish, and to provide modelled products that underpin the assessment and advisory services on the sustainable exploitation of the resources of marine fisheries. This paper demonstrates that ocean models can provide an invaluable contribution to the sustainable blue growth of aquaculture and fisheries.

Lead-contaminated imported tamarind candy and children's blood lead levels.

PubMed

Lynch, R A; Boatright, D T; Moss, S K

2000-01-01

In 1999, an investigation implicated tamarind candy as the potential source of lead exposure for a child with a significantly elevated blood lead level (BLL). The Oklahoma City-County Health Department tested two types of tamarind suckers and their packaging for lead content. More than 50% of the tested suckers exceeded the US Food and Drug Administration (FDA) Level of Concern for lead in this type of product. The authors calculated that a child consuming one-quarter to one-half of either of the two types of suckers in a day would exceed the maximum FDA Provis onal Tolerable Intake for lead. High lead concentrations in the two types of wrappers suggested leaching as a potential source of contamination. The authors used the Environmental Protection Agency's Integrated Exposure Uptake Biokinetic (IEUBK) model to predict the effects of consumption of contaminated tamarind suckers on populat on BLLs. The IEUBK model predicted that consumption of either type of sucker at a rate of one per day would result in dramatic increases in mean BLLs for children ages 6-84 months in Oklahoma and in the percentage of children wth elevated BLLs (> or =10 micrograms per deciliter [microg/dL]). The authors conclude that consumption of these products represents a potential public health threat. In addition, a history of lead contamination in imported tamarind products suggests that import control measures may not be completely effective in preventing additional lead exposure.

Quantitative Microbial Risk Assessment for Escherichia coli O157:H7 in Fresh-Cut Lettuce.

PubMed

Pang, Hao; Lambertini, Elisabetta; Buchanan, Robert L; Schaffner, Donald W; Pradhan, Abani K

2017-02-01

Leafy green vegetables, including lettuce, are recognized as potential vehicles for foodborne pathogens such as Escherichia coli O157:H7. Fresh-cut lettuce is potentially at high risk of causing foodborne illnesses, as it is generally consumed without cooking. Quantitative microbial risk assessments (QMRAs) are gaining more attention as an effective tool to assess and control potential risks associated with foodborne pathogens. This study developed a QMRA model for E. coli O157:H7 in fresh-cut lettuce and evaluated the effects of different potential intervention strategies on the reduction of public health risks. The fresh-cut lettuce production and supply chain was modeled from field production, with both irrigation water and soil as initial contamination sources, to consumption at home. The baseline model (with no interventions) predicted a mean probability of 1 illness per 10 million servings and a mean of 2,160 illness cases per year in the United States. All intervention strategies evaluated (chlorine, ultrasound and organic acid, irradiation, bacteriophage, and consumer washing) significantly reduced the estimated mean number of illness cases when compared with the baseline model prediction (from 11.4- to 17.9-fold reduction). Sensitivity analyses indicated that retail and home storage temperature were the most important factors affecting the predicted number of illness cases. The developed QMRA model provided a framework for estimating risk associated with consumption of E. coli O157:H7-contaminated fresh-cut lettuce and can guide the evaluation and development of intervention strategies aimed at reducing such risk.

Importance of Watershed Land Use in Predicting Benthic Invertebrate Condition in the Virginian Biogeographic Province, USA.

EPA Science Inventory

Estuaries are dynamic transition zones linking freshwater and oceanic habitats. These productive ecosystems are threatened by a variety of stressors including human modification of coastal watersheds. In this study we examined potential linkages between estuarine condition and...

Supergravity inflation free from harmful relics

NASA Astrophysics Data System (ADS)

Greene, Patrick B.; Kadota, Kenji; Murayama, Hitoshi

2003-08-01

We present a realistic supergravity inflation model that is free from the overproduction of potentially dangerous relics in cosmology, namely, moduli and gravitinos, which can lead to inconsistencies with the predictions of baryon asymmetry and nucleosynthesis. The radiative correction turns out to play a crucial role in our analysis, raising the mass of the supersymmetry breaking field to an intermediate scale. We pay particular attention to the nonthermal production of gravitinos using the nonminimal Kähler potential we obtained from loop correction. This nonthermal gravitino production is diminished, however, because of the relatively small scale of the inflaton mass and the small amplitudes of the hidden sector fields.

Economic costs of protistan and metazoan parasites to global mariculture.

PubMed

Shinn, A P; Pratoomyot, J; Bron, J E; Paladini, G; Brooker, E E; Brooker, A J

2015-01-01

Parasites have a major impact on global finfish and shellfish aquaculture, having significant effects on farm production, sustainability and economic viability. Parasite infections and impacts can, according to pathogen and context, be considered to be either unpredictable/sporadic or predictable/regular. Although both types of infection may result in the loss of stock and incur costs associated with the control and management of infection, predictable infections can also lead to costs associated with prophylaxis and related activities. The estimation of the economic cost of a parasite event is frequently complicated by the complex interplay of numerous factors associated with a specific incident, which may range from direct production losses to downstream socio-economic impacts on livelihoods and satellite industries associated with the primary producer. In this study, we examine the world's major marine and brackish water aquaculture production industries and provide estimates of the potential economic costs attributable to a range of key parasite pathogens using 498 specific events for the purposes of illustration and estimation of costs. This study provides a baseline resource for risk assessment and the development of more robust biosecurity practices, which can in turn help mitigate against and/or minimise the potential impacts of parasite-mediated disease in aquaculture.

Modelling of the production of gaseous by-products in anaerobic digestion.

PubMed

Strik, D P; Domnanovich, A M; Pfeiffer, B; Karlovitz, M; Zani, L; Braun, R; Holubar, P

2003-01-01

Goal of the EU-Project AMONCO (Advanced Prediction, Monitoring and Controlling of Anaerobic Digestion Processes Behaviour towards Biogas Usage in Fuel Cells) is demonstration of the practical use of biogas in fuel cells. The right precondition is a biogas quality which fits into the fuel cells tolerances. Therefore the mission of the workgroup Environmental biotechnology is to control anaerobic digestion in a way that production of potential harmful by-products for fuel cells is reduced. A good understanding of the production of these by products is essential for an applicable decision support tool. This poster presents the modelling of hydrogen sulfide by means of hierarchical neural networks and a classical mathematical method.

Using conjoint analysis to measure the acceptability of rectal microbicides among men who have sex with men in four South American cities.

PubMed

Kinsler, Janni J; Cunningham, William E; Nureña, César R; Nadjat-Haiem, Carsten; Grinsztejn, Beatriz; Casapia, Martin; Montoya-Herrera, Orlando; Sánchez, Jorge; Galea, Jerome T

2012-08-01

Conjoint Analysis (CJA), a statistical market-based technique that assesses the value consumers place on product characteristics, may be used to predict acceptability of hypothetical products. Rectal Microbicides (RM)-substances that would prevent HIV infection during receptive anal intercourse-will require acceptability data from potential users in multiple settings to inform the development process by providing valuable information on desirable product characteristics and issues surrounding potential barriers to product use. This study applied CJA to explore the acceptability of eight different hypothetical RM among 128 MSM in Lima and Iquitos, Peru; Guayaquil, Ecuador; and Rio de Janeiro, Brazil. Overall RM acceptability was highest in Guayaquil and lowest in Rio. Product effectiveness had the greatest impact on acceptability in all four cities, but the impact of other product characteristics varied by city. This study demonstrates that MSM from the same region but from different cities place different values on RM characteristics that could impact uptake of an actual RM. Understanding specific consumer preferences is crucial during RM product development, clinical trials and eventual product dissemination.

Predicting emissions from oil and gas operations in the Uinta Basin, Utah.

PubMed

Wilkey, Jonathan; Kelly, Kerry; Jaramillo, Isabel Cristina; Spinti, Jennifer; Ring, Terry; Hogue, Michael; Pasqualini, Donatella

2016-05-01

In this study, emissions of ozone precursors from oil and gas operations in Utah's Uinta Basin are predicted (with uncertainty estimates) from 2015-2019 using a Monte-Carlo model of (a) drilling and production activity, and (b) emission factors. Cross-validation tests against actual drilling and production data from 2010-2014 show that the model can accurately predict both types of activities, returning median results that are within 5% of actual values for drilling, 0.1% for oil production, and 4% for gas production. A variety of one-time (drilling) and ongoing (oil and gas production) emission factors for greenhouse gases, methane, and volatile organic compounds (VOCs) are applied to the predicted oil and gas operations. Based on the range of emission factor values reported in the literature, emissions from well completions are the most significant source of emissions, followed by gas transmission and production. We estimate that the annual average VOC emissions rate for the oil and gas industry over the 2010-2015 time period was 44.2E+06 (mean) ± 12.8E+06 (standard deviation) kg VOCs per year (with all applicable emissions reductions). On the same basis, over the 2015-2019 period annual average VOC emissions from oil and gas operations are expected to drop 45% to 24.2E+06 ± 3.43E+06 kg VOCs per year, due to decreases in drilling activity and tighter emission standards. This study improves upon previous methods for estimating emissions of ozone precursors from oil and gas operations in Utah's Uinta Basin by tracking one-time and ongoing emission events on a well-by-well basis. The proposed method has proven highly accurate at predicting drilling and production activity and includes uncertainty estimates to describe the range of potential emissions inventory outcomes. If similar input data are available in other oil and gas producing regions, then the method developed here could be applied to those regions as well.

Modeling the yield potential of dryland canola under current and future climates in California

NASA Astrophysics Data System (ADS)

George, N.; Kaffka, S.; Beeck, C.; Bucaram, S.; Zhang, J.

2012-12-01

Models predict that the climate of California will become hotter, drier and more variable under future climate change scenarios. This will lead to both increased irrigation demand and reduced irrigation water availability. In addition, it is predicted that most common Californian crops will suffer a concomitant decline in productivity. To remain productive and economically viable, future agricultural systems will need to have greater water use efficiency, tolerance of high temperatures, and tolerance of more erratic temperature and rainfall patterns. Canola (Brassica napus) is the third most important oilseed globally, supporting large and well-established agricultural industries in Canada, Europe and Australia. It is an agronomically useful and economically valuable crop, with multiple end markets, that can be grown in California as a dryland winter rotation with little to no irrigation demand. This gives canola great potential as a new crop for Californian farmers both now and as the climate changes. Given practical and financial limitations it is not always possible to immediately or widely evaluate a crop in a new region. Crop production models are therefore valuable tools for assessing the potential of new crops, better targeting further field research, and refining research questions. APSIM is a modular modeling framework developed by the Agricultural Production Systems Research Unit in Australia, it combines biophysical and management modules to simulate cropping systems. This study was undertaken to examine the yield potential of Australian canola varieties having different water requirements and maturity classes in California using APSIM. The objective of the work was to identify the agricultural regions of California most ideally suited to the production of Australian cultivars of canola and to simulate the production of canola in these regions to estimate yield-potential. This will establish whether the introduction and in-field evaluation of better-adapted canola varieties can be justified, and the potential value of a California canola industry both now and in the future. Winter annual crops like canola use rainfall in a Mediterranean climate like California more efficiently than spring or summer crops. Our results suggest that under current production costs and seed prices, dry farmed canola will have good potential in certain areas of the California. Canola yields decline with annual winter precipitation, however economically viable yields are still achieved at relatively precipitation levels (200 mm). Results from simulation, combined with related economic modeling (reported elsewhere) suggest that canola will be viable in a variety of production systems in the northern Sacramento Valley and some coastal locations, even under drier future climate scenarios. The in-field evaluation of Australian canola varieties should contribute to maintain or improving resource use efficiency and farm profitability.

Litter P content drives consumer production in detritus-based streams spanning an experimental N:P gradient.

PubMed

Demi, Lee M; Benstead, Jonathan P; Rosemond, Amy D; Maerz, John C

2018-02-01

Ecological stoichiometry theory (EST) is a key framework for predicting how variation in N:P supply ratios influences biological processes, at molecular to ecosystem scales, by altering the availability of C, N, and P relative to organismal requirements. We tested EST predictions by fertilizing five forest streams at different dissolved molar N:P ratios (2, 8, 16, 32, 128) for two years and tracking responses of macroinvertebrate consumers to the resulting steep experimental gradient in basal resource stoichiometry (leaf litter %N, %P, and N:P). Nitrogen and P content of leaf litter, the dominant basal resource, increased in all five streams following enrichment, with steepest responses in litter %P and N:P ratio. Additionally, increases in primary consumer biomass and production occurred in all five streams following N and P enrichment (averages across all streams: biomass by 1.2×, production by 1.6×). Patterns of both biomass and production were best predicted by leaf litter N:P and %P and were unrelated to leaf litter %N. Primary consumer production increased most in streams where decreases in leaf litter N:P were largest. Macroinvertebrate predator biomass and production were also strongly positively related to litter %P, providing robust experimental evidence for the primacy of P limitation at multiple trophic levels in these ecosystems. However, production of predatory macroinvertebrates was not related directly to primary consumer production, suggesting the importance of additional controls for macroinvertebrates at upper trophic positions. Our results reveal potential drivers of animal production in detritus-based ecosystems, including the relative importance of resource quality vs. quantity. Our study also sheds light on the more general impacts of variation in N:P supply ratio on nutrient-poor ecosystems, providing strong empirical support for predictions that nutrient enrichment increases food web productivity whenever large elemental imbalances between basal resources and consumer demand are reduced. © 2017 by the Ecological Society of America.

Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Valerio, Luis G.; Arvidson, Kirk B.; Chanderbhan, Ronald F.

2007-07-01

Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest ismore » MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200 chemicals, comprised primarily of pharmaceutical, industrial and some natural products developed under an FDA-MDL cooperative research and development agreement (CRADA). The predictive performance for this group of dietary natural products and the control group was 97% sensitivity and 80% concordance. Specificity was marginal at 53%. This study finds that the in silico QSAR analysis employing this software's rodent carcinogenicity database is capable of identifying the rodent carcinogenic potential of naturally occurring organic molecules found in the human diet with a high degree of sensitivity. It is the first study to demonstrate successful QSAR predictive modeling of naturally occurring carcinogens found in the human diet using an external validation test. Further test validation of this software and expansion of the training data set for dietary chemicals will help to support the future use of such QSAR methods for screening and prioritizing the risk of dietary chemicals when actual animal data are inadequate, equivocal, or absent.« less

Disinfection by-products (DBPs) in drinking water and predictive models for their occurrence: a review.

PubMed

Sadiq, Rehan; Rodriguez, Manuel J

2004-04-05

Disinfection for drinking water reduces the risk of pathogenic infection but may pose chemical threat to human health due to disinfection residues and their by-products (DBPs) when the organic and inorganic precursors are present in water. More than 250 DBPs have been identified, but the behavioural profile of only approximately 20 DBPs are adequately known. In the last 2 decades, many modelling attempts have been made to predict the occurrence of DBPs in drinking water. Models have been developed based on data generated in laboratory-scaled and field-scaled investigations. The objective of this paper is to review DBPs predictive models, identify their advantages and limitations, and examine their potential applications as decision-making tools for water treatment analysis, epidemiological studies and regulatory concerns. The paper concludes with a discussion about the future research needs in this area.

Plasma processes in inert gas thrusters

NASA Technical Reports Server (NTRS)

Kaufman, H. R.; Robinson, R. S.

1979-01-01

Inert gas thrusters, particularly with large diameters, have continued to be of interest for space propulsion applications. Two plasma processes are treated in this study: electron diffusion across magnetic fields and double ion production in inert-gas thrusters. A model is developed to describe electron diffusion across a magnetic field that is driven by both density and potential gradients, with Bohm diffusion used to predict the diffusion rate. This model has applications to conduction across magnetic fields inside a discharge chamber, as well as through a magnetic baffle region used to isolate a hollow cathode from the main chamber. A theory for double ion production is presented, which is not as complete as the electron diffusion theory described, but it should be a useful tool for predicting double ion sputter erosion. Correlations are developed that may be used, without experimental data, to predict double ion densities for the design of new and especially larger ion thrusters.

Prediction of thyroid C-cell carcinogenicity after chronic administration of GLP1-R agonists in rodents.

PubMed

van den Brink, Willem; Emerenciana, Annette; Bellanti, Francesco; Della Pasqua, Oscar; van der Laan, Jan Willem

2017-04-01

Increased incidence of C-cell carcinogenicity has been observed for glucagon-like-protein-1 receptor (GLP-1r) agonists in rodents. It is suggested that the duration of exposure is an indicator of carcinogenic potential in rodents of the different products on the market. Furthermore, the role of GLP-1-related mechanisms in the induction of C-cell carcinogenicity has gained increased attention by regulatory agencies. This study proposes an integrative pharmacokinetic/pharmacodynamic (PKPD) framework to identify explanatory factors and characterize differences in carcinogenic potential of the GLP-1r agonist products. PK models for four products (exenatide QW (once weekly), exenatide BID (twice daily), liraglutide and lixisenatide) were developed using nonlinear mixed effects modelling. Predicted exposure was subsequently linked to GLP-1r stimulation using in vitro GLP-1r potency data. A logistic regression model was then applied to exenatide QW and liraglutide data to assess the relationship between GLP-1r stimulation and thyroid C-cell hyperplasia incidence as pre-neoplastic predictor of a carcinogenic response. The model showed a significant association between predicted GLP-1r stimulation and C-cell hyperplasia after 2years of treatment. The predictive performance of the model was evaluated using lixisenatide, for which hyperplasia data were accurately described during the validation step. The use of a model-based approach provided insight into the relationship between C-cell hyperplasia and GLP-1r stimulation for all four products, which is not possible with traditional data analysis methods. It can be concluded that both pharmacokinetics (exposure) and pharmacodynamics (potency for GLP-1r) factors determine C-cell hyperplasia incidence in rodents. Our work highlights the pharmacological basis for GLP-1r agonist-induced C-cell carcinogenicity. The concept is promising for application to other drug classes. Copyright © 2017 Elsevier Inc. All rights reserved.

External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics.

PubMed

Devillers, J; Pandard, P; Richard, B

2013-01-01

Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure-biodegradation relationship (SBR) and quantitative structure-biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite™ includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency.

The OH-initiated atmospheric oxidation of cyclopentene: A coupled-cluster study of the potential energy surface

NASA Astrophysics Data System (ADS)

Zhang, Weichao; Du, Benni

2013-07-01

We performed the first theoretical potential energy surface investigation on the mechanism and products of the reaction of OH+ cyclopentene in the absence and presence of O2 by using high-level quantum chemical methods CCSD(T)/6-311++G(d,p)//BH&HLYP/6-311++G(d,p)+ZPE × 0.9335. Energies for several species are also refined at the CCSD(T)/cc-pVTZ levels of theory. The calculations indicate that the major products are cyclopentanone, 1-cyclopenten-1-ol, and 2-cyclopenten-1-ol in the absence of O2, which are in qualitative accordance with the available experimental observations. In the presence of O2, the dominant products are predicted to be glutaraldehyde and 1,2-epoxycyclopentanol.

Total energy expenditure in burned children using the doubly labeled water technique

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goran, M.I.; Peters, E.J.; Herndon, D.N.

Total energy expenditure (TEE) was measured in 15 burned children with the doubly labeled water technique. Application of the technique in burned children required evaluation of potential errors resulting from nutritional intake altering background enrichments during studies and from the high rate of water turnover relative to CO2 production. Five studies were discarded because of these potential problems. TEE was 1.33 +/- 0.27 times predicted basal energy expenditure (BEE), and in studies where resting energy expenditure (REE) was simultaneously measured, TEE was 1.18 +/- 0.17 times REE, which in turn was 1.16 +/- 0.10 times predicted BEE. TEE was significantlymore » correlated with measured REE (r2 = 0.92) but not with predicted BEE. These studies substantiate the advantage of measuring REE to predict TEE in severely burned patients as opposed to relying on standardized equations. Therefore we recommend that optimal nutritional support will be achieved in convalescent burned children by multiplying REE by an activity factor of 1.2.« less

Food Allergens: Is There a Correlation between Stability to Digestion and Allergenicity?

PubMed

Bøgh, Katrine Lindholm; Madsen, Charlotte Bernhard

2016-07-03

Food allergy is a major health problem in the Western countries, affecting 3-8% of the population. It has not yet been established what makes a dietary protein a food allergen. Several characteristics have been proposed to be shared by food allergens. One of these is resistance to digestion. This paper reviews data from digestibility studies on purified food allergens and evaluates the predictive value of digestibility tests on the allergenic potential. We point out that food allergens do not necessarily resist digestion. We discuss how the choice of in vitro digestibility assay condition and the method used for detection of residual intact protein as well as fragments hereof may greatly influence the outcome as well as the interpretation of results. The finding that digests from food allergens may retain allergenicity, stresses the importance of using immunological assays for evaluating the allergenic potential of food allergen digestion products. Studies assessing the allergenicity of digestion products, by either IgE-binding, elicitation or sensitizing capacity, shows that digestion may abolish, decrease, have no effect, or even increase the allergenicity of food allergens. Therefore, the predictive value of the pepsin resistance test for assessing the allergenic potential of novel proteins can be questioned.

Breeding goals for the Kenya dual purpose goat. I. Model development and application to smallholder production systems.

PubMed

Bett, R C; Kosgey, I S; Bebe, B O; Kahi, A K

2007-10-01

A deterministic model was developed and applied to evaluate biological and economic variables that characterize smallholder production systems utilizing the Kenya Dual Purpose goat (KDPG) in Kenya. The systems were defined as: smallholder low-potential (SLP), smallholder medium-potential (SMP) and smallholder high-potential (SHP). The model was able to predict revenues and costs to the system. Revenues were from sale of milk, surplus yearlings and cull-forage animals, while costs included those incurred for feeds, husbandry, marketing and fixed asset (fixed costs). Of the total outputs, revenue from meat and milk accounted for about 55% and 45%, respectively, in SMP and 39% and 61% in SHP. Total costs comprised mainly variable costs (98%), with husbandry costs being the highest in both SMP and SLP. The total profit per doe per year was KSh 315.48 in SMP, KSh -1352.75 in SLP and KSh -80.22 in SHP. Results suggest that the utilization of the KDPG goat in Kenya is more profitable in the smallholder medium-potential production system. The implication for the application of the model to smallholder production systems in Kenya is discussed.

Complete nucleotide sequence of the freshwater unicellular cyanobacterium Synechococcus elongatus PCC 6301 chromosome: gene content and organization.

PubMed

Sugita, Chieko; Ogata, Koretsugu; Shikata, Masamitsu; Jikuya, Hiroyuki; Takano, Jun; Furumichi, Miho; Kanehisa, Minoru; Omata, Tatsuo; Sugiura, Masahiro; Sugita, Mamoru

2007-01-01

The entire genome of the unicellular cyanobacterium Synechococcus elongatus PCC 6301 (formerly Anacystis nidulans Berkeley strain 6301) was sequenced. The genome consisted of a circular chromosome 2,696,255 bp long. A total of 2,525 potential protein-coding genes, two sets of rRNA genes, 45 tRNA genes representing 42 tRNA species, and several genes for small stable RNAs were assigned to the chromosome by similarity searches and computer predictions. The translated products of 56% of the potential protein-coding genes showed sequence similarities to experimentally identified and predicted proteins of known function, and the products of 35% of the genes showed sequence similarities to the translated products of hypothetical genes. The remaining 9% of genes lacked significant similarities to genes for predicted proteins in the public DNA databases. Some 139 genes coding for photosynthesis-related components were identified. Thirty-seven genes for two-component signal transduction systems were also identified. This is the smallest number of such genes identified in cyanobacteria, except for marine cyanobacteria, suggesting that only simple signal transduction systems are found in this strain. The gene arrangement and nucleotide sequence of Synechococcus elongatus PCC 6301 were nearly identical to those of a closely related strain Synechococcus elongatus PCC 7942, except for the presence of a 188.6 kb inversion. The sequences as well as the gene information shown in this paper are available in the Web database, CYORF (http://www.cyano.genome.jp/).

Androgen receptor mediated compensation of estradiol in response to aromatase inhibition: a mathematical model

EPA Science Inventory

Chemicals in the environment have the potential to cause reproductive toxicity by acting on the hypothalamus-pituitary-gonadal (HPG) axis. We have developed a mathematical model to predict chemical impacts on reproductive hormone production in the highly conserved HPG axis using...

Challenges to Predicting Productivity of Grazing Ruminants. Where to now?

USDA-ARS?s Scientific Manuscript database

The Fourth Grazing Livestock Nutrition Conference was convened at Estes Park July 9 and 10.There were over 28 poster presentations and 12 conference papers presented. The papers were organized in 6 topical sessions ranging from microbiology to supplementation. The first session covered the potential...

GREENER CHEMICAL PROCESS DESIGN ALTERNATIVES ARE REVEALED USING THE WASTE REDUCTION DECISION SUPPORT SYSTEM (WAR DSS)

EPA Science Inventory

The Waste Reduction Decision Support System (WAR DSS) is a Java-based software product providing comprehensive modeling of potential adverse environmental impacts (PEI) predicted to result from newly designed or redesigned chemical manufacturing processes. The purpose of this so...

Alaska North Slope regional gas hydrate production modeling forecasts

USGS Publications Warehouse

Wilson, S.J.; Hunter, R.B.; Collett, T.S.; Hancock, S.; Boswell, R.; Anderson, B.J.

2011-01-01

A series of gas hydrate development scenarios were created to assess the range of outcomes predicted for the possible development of the "Eileen" gas hydrate accumulation, North Slope, Alaska. Production forecasts for the "reference case" were built using the 2002 Mallik production tests, mechanistic simulation, and geologic studies conducted by the US Geological Survey. Three additional scenarios were considered: A "downside-scenario" which fails to identify viable production, an "upside-scenario" describes results that are better than expected. To capture the full range of possible outcomes and balance the downside case, an "extreme upside scenario" assumes each well is exceptionally productive.Starting with a representative type-well simulation forecasts, field development timing is applied and the sum of individual well forecasts creating the field-wide production forecast. This technique is commonly used to schedule large-scale resource plays where drilling schedules are complex and production forecasts must account for many changing parameters. The complementary forecasts of rig count, capital investment, and cash flow can be used in a pre-appraisal assessment of potential commercial viability.Since no significant gas sales are currently possible on the North Slope of Alaska, typical parameters were used to create downside, reference, and upside case forecasts that predict from 0 to 71??BM3 (2.5??tcf) of gas may be produced in 20 years and nearly 283??BM3 (10??tcf) ultimate recovery after 100 years.Outlining a range of possible outcomes enables decision makers to visualize the pace and milestones that will be required to evaluate gas hydrate resource development in the Eileen accumulation. Critical values of peak production rate, time to meaningful production volumes, and investments required to rule out a downside case are provided. Upside cases identify potential if both depressurization and thermal stimulation yield positive results. An "extreme upside" case captures the full potential of unconstrained development with widely spaced wells. The results of this study indicate that recoverable gas hydrate resources may exist in the Eileen accumulation and that it represents a good opportunity for continued research. ?? 2010 Elsevier Ltd.

Primary productivity and the prospects for biofuels in the United Kingdom

NASA Astrophysics Data System (ADS)

Lawson, G. J.; Callaghan, T. V.

1983-09-01

Estimates of land use and plant productivity are combined to predict total annual primary production in the UK as 252 million tonnes dry matter (10.5 t ha-1yr-1). Annual above ground production is predicted to be 165 Mt (6.9 t ha-1yr-1). Within these totals, intensive agriculture contributes 60%, productive woodland 8%, natural vegetation 26% and urban vegetation 5%. However, only 25% of total plant production is cropped by man and animals, and most of this is subsequently discarded as wastes and residues. 2112 PJ of organic material is available for fuel without reducing food or fibre production, but since much of this could not be economically collected, 859 PJ is calculated as a more realistic biofuel contribution by the year 2000. After deducting 50% conversion losses, this could save P1 billion (1979 prices) in oil imports. Short rotation energy plantations, forest residues, coppice woodlands, animal and crop wastes, industrial and domestic wastes, catch crops, natural vegetation and urban vegetation all have immediate or short term potential as biofuel sources. Sensitive planning is required to reduce environmental impact, but in some cases more diverse wildlife habitats may be created.

Global warming and hepatotoxin production by cyanobacteria: what can we learn from experiments?

PubMed

El-Shehawy, Rehab; Gorokhova, Elena; Fernández-Piñas, Francisca; del Campo, Francisca F

2012-04-01

Global temperature is expected to rise throughout this century, and blooms of cyanobacteria in lakes and estuaries are predicted to increase with the current level of global warming. The potential environmental, economic and sanitation repercussions of these blooms have attracted considerable attention among the world's scientific communities, water management agencies and general public. Of particular concern is the worldwide occurrence of hepatotoxic cyanobacteria posing a serious threat to global public health. Here, we highlight plausible effects of global warming on physiological and molecular changes in these cyanobacteria and resulting effects on hepatotoxin production. We also emphasize the importance of understanding the natural biological function(s) of hepatotoxins, various mechanisms governing their synthesis, and climate-driven changes in food-web interactions, if we are to predict consequences of the current and projected levels of global warming for production and accumulation of hepatotoxins in aquatic ecosystems. Copyright © 2011 Elsevier Ltd. All rights reserved.

Analysis of Hydrogen Generation through Thermochemical Gasification of Coconut Shell Using Thermodynamic Equilibrium Model Considering Char and Tar

PubMed Central

Rupesh, Shanmughom; Muraleedharan, Chandrasekharan; Arun, Palatel

2014-01-01

This work investigates the potential of coconut shell for air-steam gasification using thermodynamic equilibrium model. A thermodynamic equilibrium model considering tar and realistic char conversion was developed using MATLAB software to predict the product gas composition. After comparing it with experimental results the prediction capability of the model is enhanced by multiplying equilibrium constants with suitable coefficients. The modified model is used to study the effect of key process parameters like temperature, steam to biomass ratio, and equivalence ratio on product gas yield, composition, and heating value of syngas along with gasification efficiency. For a steam to biomass ratio of unity, the maximum mole fraction of hydrogen in the product gas is found to be 36.14% with a lower heating value of 7.49 MJ/Nm3 at a gasification temperature of 1500 K and equivalence ratio of 0.15. PMID:27433487

Analysis of Hydrogen Generation through Thermochemical Gasification of Coconut Shell Using Thermodynamic Equilibrium Model Considering Char and Tar.

PubMed

Rupesh, Shanmughom; Muraleedharan, Chandrasekharan; Arun, Palatel

2014-01-01

This work investigates the potential of coconut shell for air-steam gasification using thermodynamic equilibrium model. A thermodynamic equilibrium model considering tar and realistic char conversion was developed using MATLAB software to predict the product gas composition. After comparing it with experimental results the prediction capability of the model is enhanced by multiplying equilibrium constants with suitable coefficients. The modified model is used to study the effect of key process parameters like temperature, steam to biomass ratio, and equivalence ratio on product gas yield, composition, and heating value of syngas along with gasification efficiency. For a steam to biomass ratio of unity, the maximum mole fraction of hydrogen in the product gas is found to be 36.14% with a lower heating value of 7.49 MJ/Nm(3) at a gasification temperature of 1500 K and equivalence ratio of 0.15.

Design Principles for Covalent Organic Frameworks as Efficient Electrocatalysts in Clean Energy Conversion and Green Oxidizer Production.

PubMed

Lin, Chun-Yu; Zhang, Lipeng; Zhao, Zhenghang; Xia, Zhenhai

2017-05-01

Covalent organic frameworks (COFs), an emerging class of framework materials linked by covalent bonds, hold potential for various applications such as efficient electrocatalysts, photovoltaics, and sensors. To rationally design COF-based electrocatalysts for oxygen reduction and evolution reactions in fuel cells and metal-air batteries, activity descriptors, derived from orbital energy and bonding structures, are identified with the first-principle calculations for the COFs, which correlate COF structures with their catalytic activities. The calculations also predict that alkaline-earth metal-porphyrin COFs could catalyze the direct production of H 2 O 2 , a green oxidizer and an energy carrier. These predictions are supported by experimental data, and the design principles derived from the descriptors provide an approach for rational design of new electrocatalysts for both clean energy conversion and green oxidizer production. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A review of dark fermentative hydrogen production from biodegradable municipal waste fractions.

PubMed

De Gioannis, G; Muntoni, A; Polettini, A; Pomi, R

2013-06-01

Hydrogen is believed to play a potentially key role in the implementation of sustainable energy production, particularly when it is produced from renewable sources and low energy-demanding processes. In the present paper an attempt was made at critically reviewing more than 80 recent publications, in order to harmonize and compare the available results from different studies on hydrogen production from FW and OFMSW through dark fermentation, and derive reliable information about process yield and stability in view of building related predictive models. The review was focused on the effect of factors, recognized as potentially affecting process evolution (including type of substrate and co-substrate and relative ratio, type of inoculum, food/microorganisms [F/M] ratio, applied pre-treatment, reactor configuration, temperature and pH), on the fermentation yield and kinetics. Statistical analysis of literature data from batch experiments was also conducted, showing that the variables affecting the H2 production yield were ranked in the order: type of co-substrate, type of pre-treatment, operating pH, control of initial pH and fermentation temperature. However, due to the dispersion of data observed in some instances, the ambiguity about the presence of additional hidden variables cannot be resolved. The results from the analysis thus suggest that, for reliable predictive models of fermentative hydrogen production to be derived, a high level of consistency between data is strictly required, claiming for more systematic and comprehensive studies on the subject. Copyright © 2013 Elsevier Ltd. All rights reserved.

Limitations on gas exchange recovery following natural drought in Californian oak woodlands.

NASA Astrophysics Data System (ADS)

Ackerly, D.; Skelton, R. P.; Dawson, T.; Thompson, S.; Feng, X.; Weitz, A.; McLaughlin, B.

2017-12-01

Abstract Background/Question/Methods Drought can cause major damage to plant communities, but species damage thresholds and post-drought recovery of forest productivity are not yet predictable. We asked the question how should forest net primary productivity recover following exposure to severe drought? We used a natural drought period to investigate whether drought responses and post-drought recovery of canopy health could be predicted by properties of the water transport system. We aimed to test the hypothesis that recovery of gas exchange and canopy health would be most severely limited by xylem embolism in stems. To do this we monitored leaf level gas exchange and water status for multiple individuals of two deciduous and two evergreen species for four years spanning a severe drought event and following subsequent rehydration. Results/Discussion Severe drought caused major declines in leaf water potential, reduced stomatal conductance and assimilation rates and increased canopy bareness in our four canopy species. Water potential surpassed levels associated with incipient embolism in leaves of most trees. In contrast, due to hydraulic segmentation, water potential only rarely surpassed critical thresholds in the stems of the study trees. Individuals that surpassed critical thresholds of embolism in the stem displayed significant canopy dieback and mortality. Thus, recovery of plant gas exchange and canopy health was predicted by xylem safety margin in stems, but not leaves, providing strong support for stem cavitation vulnerability as an index of damage under natural drought conditions.

Peak oil and health in low- and middle-income countries: impacts and potential responses.

PubMed

Winch, Peter; Stepnitz, Rebecca

2011-09-01

Peak oil refers to the predicted peak and subsequent decline in global production of petroleum products over the coming decades. We describe how peak oil will affect health, nutrition, and health systems in low- and middle-income countries along 5 pathways. The negative effects of peak oil on health and nutrition will be felt most acutely in the 58 low-income countries experiencing minimal or negative economic growth because of their patterns of sociopolitical, geographic, and economic vulnerability. The global health community needs to take additional steps to build resilience among the residents of low- and middle-income countries and maintain access to maternal and other health services in the face of predicted changes in availability and price of fossil fuels.

Overcoming substrate limitations for improved production of ethylene in E. coli

DOE PAGES

Lynch, Sean; Eckert, Carrie; Yu, Jianping; ...

2016-01-04

Ethylene is an important industrial compound for the production of a wide variety of plastics and chemicals. At present, ethylene production involves steam cracking of a fossil-based feedstock, representing the highest CO 2-emitting process in the chemical industry. Biological ethylene production can be achieved via expression of a single protein, the ethylene-forming enzyme (EFE), found in some bacteria and fungi; it has the potential to provide a sustainable alternative to steam cracking, provided that significant increases in productivity can be achieved. A key barrier is determining factors that influence the availability of substrates for the EFE reaction in potential microbialmore » hosts. In the presence of O 2, EFE catalyzes ethylene formation from the substrates α-ketoglutarate (AKG) and arginine. The concentrations of AKG, a key TCA cycle intermediate, and arginine are tightly controlled by an intricate regulatory system that coordinates carbon and nitrogen metabolism. Thus, reliably predicting which genetic changes will ultimately lead to increased AKG and arginine availability is challenging.« less

Optimizing production of asperolide A, a potential anti-tumor tetranorditerpenoid originally produced by the algal-derived endophytic fungus Aspergillus wentii EN-48

NASA Astrophysics Data System (ADS)

Xu, Rui; Li, Xiaoming; Xu, Gangming; Wang, Bingui

2017-05-01

The marine algal-derived endophytic fungus Aspergillus wentii EN-48 produces the potential anti-tumor agent asperolide A, a tetranorlabdane diterpenoid active against lung cancer. However, the fermentation yield of asperolide A was very low and only produced in static cultures. Static fermentation conditions of A. wentii EN-48 were optimized employing response surface methodology to enhance the production of asperolide A. The optimized conditions resulted in a 13.9-fold yield enhancement, which matched the predicted value, and the optimized conditions were successfully used in scale-up fermentation for the production of asperolide A. Exogenous addition of plant hormones (especially 10 μmol/L methyl jasmonate) stimulated asperolide A production. To our knowledge, this is first optimized production of an asperolide by a marine-derived fungus. The optimization is Effective and valuable to supply material for further anti-tumor mechanism studies and preclinical evaluation of asperolide A and other norditerpenoids.

Potential health hazards associated with exposures to asbestos-containing drywall accessory products: A state-of-the-science assessment.

PubMed

Phelka, Amanda D; Finley, Brent L

2012-01-01

Until the late 1970s, chrysotile asbestos was an ingredient in most industrial and consumer drywall accessory products manufactured in the US. In 1977, the Consumer Product Safety Commission (CPSC) issued a ban of consumer patching compounds containing "respirable, free-form asbestos" based on their prediction of exceptionally high rates of asbestos-related diseases among individuals using patching compounds for as little as a few days. Although hundreds of thousands of workers and homeowners handling these products may have experienced exposure to asbestos prior to the ban, there has been no systematic effort to summarize and interpret the information relevant to the potential health effects of such exposures. In this analysis, we provide a comprehensive review and analysis of the scientific studies assessing fiber type and dimension, toxicological and epidemiological endpoints, and airborne fiber concentrations associated with joint compound use. We conclude that: 1) asbestos in drywall accessory products was primarily short fiber (< 5 µm) chrysotile, 2) asbestos in inhaled joint compound particulate is probably not biopersistent in the lung, 3) estimated cumulative chrysotile exposures experienced by workers and homeowners are below levels known to be associated with respiratory disease, and 4) mortality studies of drywall installers have not demonstrated a significantly increased incidence of death attributable to any asbestos-related disease. Consequently, contrary to the predictions of the CPSC, the current weight of evidence does not indicate any clear health risks associated with the use of asbestos-containing drywall accessory products. We also describe information gaps and suggest possible areas of future research.

A modeling approach to direct interspecies electron transfer process in anaerobic transformation of ethanol to methane.

PubMed

Liu, Yiwen; Zhang, Yaobin; Zhao, Zhiqiang; Ngo, Huu Hao; Guo, Wenshan; Zhou, Junliang; Peng, Lai; Ni, Bing-Jie

2017-01-01

Recent studies have shown that direct interspecies electron transfer (DIET) plays an important part in contributing to methane production from anaerobic digestion. However, so far anaerobic digestion models that have been proposed only consider two pathways for methane production, namely, acetoclastic methanogenesis and hydrogenotrophic methanogenesis, via indirect interspecies hydrogen transfer, which lacks an effective way for incorporating DIET into this paradigm. In this work, a new mathematical model is specifically developed to describe DIET process in anaerobic digestion through introducing extracellular electron transfer as a new pathway for methane production, taking anaerobic transformation of ethanol to methane as an example. The developed model was able to successfully predict experimental data on methane dynamics under different experimental conditions, supporting the validity of the developed model. Modeling predictions clearly demonstrated that DIET plays an important role in contributing to overall methane production (up to 33 %) and conductive material (i.e., carbon cloth) addition would significantly promote DIET through increasing ethanol conversion rate and methane production rate. The model developed in this work will potentially enhance our current understanding on syntrophic metabolism via DIET.

Predictors of Incomes. AIR Forum 1981 Paper.

ERIC Educational Resources Information Center

Witmer, David R.

Income predictions that provide some indication of the potential value of attending college are considered. Standard multiple regression analysis of data describing the income experiences of men 25 years old and older were used to determine differences in incomes of high school and college graduates. Information on the gross national product was…

Flexible stocking as a strategy for enhancing ranch profitability in the face of a changing and variable climate

USDA-ARS?s Scientific Manuscript database

Predicted climate change impacts include increased weather variability and increased occurrences of extreme events such as drought. Such climate changes potentially affect cattle performance as well as pasture and range productivity. These climate induced risks are often coupled with variable market...

Diversity Matters: Parent Input Predicts Toddler Verb Production

ERIC Educational Resources Information Center

Hsu, Ning; Hadley, Pamela A.; Rispoli, Matthew

2017-01-01

The contribution of parent input to children's subsequent expressive verb diversity was explored in twenty typically developing toddlers with small verb lexicons. Child developmental factors and parent input measures (i.e. verb quantity, verb diversity, and verb-related structural cues) at age 1;9 were examined as potential predictors of…

Computational prediction of dermal diffusivity for large number of chemicals – challenges and applications

EPA Science Inventory

The assessment of risk from dermal exposure for thousands of chemicals, such as consumer products, due to their potential to enter the environment as contaminants is a daunting task. A strategy has been developed to integrate high-throughput technologies with toxicity, known as ...

The predictive effect of inflammatory markers and lipid accumulation product index on clinical symptoms associated with polycystic ovary syndrome in nonobese adolescents and younger aged women.

PubMed

Tola, Esra Nur; Yalcin, Serenat Eris; Dugan, Nadiye

2017-07-01

The aim of our study is to analyse the inflammatory markers and lipid accumulation product (LAP) index in nonobese adolescents and younger aged women with polycystic ovary syndrome (PCOS) compared with age and body mass index (BMI)-matched healthy controls and to determine whether the investigated parameters are potential markers for the etiopathogenesis of PCOS. We also aim to determine whether these inflammatory markers are predictive for developing some clinical implications, such as cardiovascular disease (CVD) and insulin resistance (IR), associated with PCOS. A total of 34 adolescents and younger aged females with PCOS, and 33 age and BMI-matched healthy controls were recruited for our study. All participants were nonobese (BMI<25). Neopterin (NEO), C-reactive protein (CRP) levels and complete blood parameters were assessed. LAP index and homeostasis model assessment of IR (HOMA-IR) were calculated; anthropometric, clinical and biochemical parameters were also recorded. Serum NEO, CRP levels and LAP index were significantly increased in nonobese adolescents and younger aged females with PCOS compared to healthy controls. We could not found any predictive effect of investigated inflammatory markers and LAP index on CVD risk among PCOS patients after adjustment for abdominal obesity. We also found a positive predictive effect of WBC and a negative predictive effect of lymphocytes on IR in PCOS patients after adjustment for abdominal obesity. We did not find any predictor effect of NEO on IR, but it was a positive predictive marker for an elevated HOMA-IR index. Elevated NEO, CRP levels and LAP index could have potential roles in the etiopathogenesis of PCOS in nonobese adolescents and younger aged females,NEO could be a predictive marker for elevated HOMA-IR index, and WBC and lymphocytes could be predictive for the development of IR among nonobese adolescents and younger aged females with PCOS. Copyright © 2017 Elsevier B.V. All rights reserved.

Development of an in vitro skin sensitization test based on ROS production in THP-1 cells.

PubMed

Saito, Kazutoshi; Miyazawa, Masaaki; Nukada, Yuko; Sakaguchi, Hitoshi; Nishiyama, Naohiro

2013-03-01

Recently, it has been reported that reactive oxygen species (ROS) produced by contact allergens can affect dendritic cell migration and contact hypersensitivity. The aim of the present study was to develop a new in vitro assay that could predict the skin sensitizing potential of chemicals by measuring ROS production in THP-1 (human monocytic leukemia cell line) cells. THP-1 cells were pre-loaded with a ROS sensitive fluorescent dye, 5-(and 6-)-chloromethyl-2', 7'-dichlorodihydrofluorescein diacetate, acetyl ester (CM-H2DCFDA), for 15min, then incubated with test chemicals for 30min. The fluorescence intensity was measured by flow cytometry. For the skin sensitizers, 25 out of 30 induced over a 2-fold ROS production at more than 90% of cell viability. In contrast, increases were only seen in 4 out of 20 non-sensitizers. The overall accuracy for the local lymph node assay (LLNA) was 82% for 50 chemicals tested. A correlation was found between the estimated concentration showing 2-fold ROS production in the ROS assay and the EC3 values (estimated concentration required to induce positive response) of the LLNA. These results indicated that the THP-1 cell-based ROS assay was a rapid and highly sensitive detection system able to predict skin sensitizing potentials and potency of chemicals. Copyright © 2012 Elsevier Ltd. All rights reserved.

Levels of cystathionine gamma lyase production by Geotrichum candidum in synthetic media and correlation with the presence of sulphur flavours in cheese.

PubMed

Gente, Stéphanie; La Carbona, Stéphanie; Guéguen, Micheline

2007-03-10

Geotrichum candidum is a cheese-ripening agent with the potential to produce sulphur flavour compounds in soft cheeses. We aimed to develop an alternative test for predicting the aromatic (sulphur flavours) potential of G. candidum strains in soft cheese. Twelve strains of G. candidum with different levels of demethiolase activity (determined by a chemical method) in YEL-met (yeast extract, lactate methionine) medium were studied. We investigated cgl (cystathionine gamma lyase) gene expression after culture in three media - YEL-met, casamino acid and curd media - and then carried out sensory analysis on a Camembert cheese matrix. We found no correlation between demethiolase activity in vitro and cgl gene expression. Sensory analysis (detection of sulphur flavours) identified different aromatic profiles linked to cgl expression, but not to demethiolase activity. The RT-PCR technique described here is potentially useful for predicting the tendency of a given strain of G. candidum to develop sulphur flavours in cheese matrix. This is the first demonstration that an in vitro molecular approach could be used as a predictive test for evaluating the potential of G. candidum strains to generate sulphur compounds in situ (Camembert cheese matrix).

The carbon footprint of dairy production systems through partial life cycle assessment.

PubMed

Rotz, C A; Montes, F; Chianese, D S

2010-03-01

Greenhouse gas (GHG) emissions and their potential effect on the environment has become an important national and international issue. Dairy production, along with all other types of animal agriculture, is a recognized source of GHG emissions, but little information exists on the net emissions from dairy farms. Component models for predicting all important sources and sinks of CH(4), N(2)O, and CO(2) from primary and secondary sources in dairy production were integrated in a software tool called the Dairy Greenhouse Gas model, or DairyGHG. This tool calculates the carbon footprint of a dairy production system as the net exchange of all GHG in CO(2) equivalent units per unit of energy-corrected milk produced. Primary emission sources include enteric fermentation, manure, cropland used in feed production, and the combustion of fuel in machinery used to produce feed and handle manure. Secondary emissions are those occurring during the production of resources used on the farm, which can include fuel, electricity, machinery, fertilizer, pesticides, plastic, and purchased replacement animals. A long-term C balance is assumed for the production system, which does not account for potential depletion or sequestration of soil carbon. An evaluation of dairy farms of various sizes and production strategies gave carbon footprints of 0.37 to 0.69kg of CO(2) equivalent units/kg of energy-corrected milk, depending upon milk production level and the feeding and manure handling strategies used. In a comparison with previous studies, DairyGHG predicted C footprints similar to those reported when similar assumptions were made for feeding strategy, milk production, allocation method between milk and animal coproducts, and sources of CO(2) and secondary emissions. DairyGHG provides a relatively simple tool for evaluating management effects on net GHG emissions and the overall carbon footprint of dairy production systems.

Forecasting Impacts of Climate Change on Indicators of British Columbia's Biodiversity

NASA Astrophysics Data System (ADS)

Holmes, Keith Richard

Understanding the relationships between biodiversity and climate is essential for predicting the impact of climate change on broad-scale landscape processes. Utilizing indirect indicators of biodiversity derived from remotely sensed imagery, we present an approach to forecast shifts in the spatial distribution of biodiversity. Indirect indicators, such as remotely sensed plant productivity metrics, representing landscape seasonality, minimum growth, and total greenness have been linked to species richness over broad spatial scales, providing unique capacity for biodiversity modeling. Our goal is to map future spatial distributions of plant productivity metrics based on expected climate change and to quantify anticipated change to park habitat in British Columbia. Using an archival dataset sourced from the Advanced Very High Resolution Radiometer (AVHRR) satellite from the years 1987 to 2007 at 1km spatial resolution, corresponding historical climate data, and regression tree modeling, we developed regional models of the relationships between climate and annual productivity growth. Historical interconnections between climate and annual productivity were coupled with three climate change scenarios modeled by the Canadian Centre for Climate Modeling and Analysis (CCCma) to predict and map productivity components to the year 2065. Results indicate we can expect a warmer and wetter environment, which may lead to increased productivity in the north and higher elevations. Overall, seasonality is expected to decrease and greenness productivity metrics are expected to increase. The Coastal Mountains and high elevation edge habitats across British Columbia are forecasted to experience the greatest amount of change. In the future, protected areas may have potential higher greenness and lower seasonality as represented by indirect biodiversity indicators. The predictive model highlights potential gaps in protection along the central interior and Rocky Mountains. Protected areas are expected to experience the greatest change with indirect indicators located along mountainous elevations of British Columbia. Our indirect indicator approach to predict change in biodiversity provides resource managers with information to mitigate and adapt to future habitat dynamics. Spatially specific recommendations from our dataset provide information necessary for management. For instance, knowing there is a projected depletion of habitat representation in the East Rocky Mountains, sensitive species in the threatened Mountain Hemlock ecozone, or preservation of rare habitats in the decreasing greenness of the southern interior region is essential information for managers tasked with long term biodiversity conservation. Forecasting productivity levels, linked to the distribution of species richness, presents a novel approach for understanding the future implications of climate change on broad scale biodiversity.

A new unsteady mixing model to predict NO(x) production during rapid mixing in a dual-stage combustor

NASA Technical Reports Server (NTRS)

Menon, Suresh

1992-01-01

An advanced gas turbine engine to power supersonic transport aircraft is currently under study. In addition to high combustion efficiency requirements, environmental concerns have placed stringent restrictions on the pollutant emissions from these engines. A combustor design with the potential for minimizing pollutants such as NO(x) emissions is undergoing experimental evaluation. A major technical issue in the design of this combustor is how to rapidly mix the hot, fuel-rich primary zone product with the secondary diluent air to obtain a fuel-lean mixture for combustion in the second stage. Numerical predictions using steady-state methods cannot account for the unsteady phenomena in the mixing region. Therefore, to evaluate the effect of unsteady mixing and combustion processes, a novel unsteady mixing model is demonstrated here. This model has been used to study multispecies mixing as well as propane-air and hydrogen-air jet nonpremixed flames, and has been used to predict NO(x) production in the mixing region. Comparison with available experimental data show good agreement, thereby providing validation of the mixing model. With this demonstration, this mixing model is ready to be implemented in conjunction with steady-state prediction methods and provide an improved engineering design analysis tool.

Analysis of Genetic Algorithm for Rule-Set Production (GARP) modeling approach for predicting distributions of fleas implicated as vectors of plague, Yersinia pestis, in California.

PubMed

Adjemian, Jennifer C Z; Girvetz, Evan H; Beckett, Laurel; Foley, Janet E

2006-01-01

More than 20 species of fleas in California are implicated as potential vectors of Yersinia pestis. Extremely limited spatial data exist for plague vectors-a key component to understanding where the greatest risks for human, domestic animal, and wildlife health exist. This study increases the spatial data available for 13 potential plague vectors by using the ecological niche modeling system Genetic Algorithm for Rule-Set Production (GARP) to predict their respective distributions. Because the available sample sizes in our data set varied greatly from one species to another, we also performed an analysis of the robustness of GARP by using the data available for flea Oropsylla montana (Baker) to quantify the effects that sample size and the chosen explanatory variables have on the final species distribution map. GARP effectively modeled the distributions of 13 vector species. Furthermore, our analyses show that all of these modeled ranges are robust, with a sample size of six fleas or greater not significantly impacting the percentage of the in-state area where the flea was predicted to be found, or the testing accuracy of the model. The results of this study will help guide the sampling efforts of future studies focusing on plague vectors.

Biological methane production under putative Enceladus-like conditions.

PubMed

Taubner, Ruth-Sophie; Pappenreiter, Patricia; Zwicker, Jennifer; Smrzka, Daniel; Pruckner, Christian; Kolar, Philipp; Bernacchi, Sébastien; Seifert, Arne H; Krajete, Alexander; Bach, Wolfgang; Peckmann, Jörn; Paulik, Christian; Firneis, Maria G; Schleper, Christa; Rittmann, Simon K-M R

2018-02-27

The detection of silica-rich dust particles, as an indication for ongoing hydrothermal activity, and the presence of water and organic molecules in the plume of Enceladus, have made Saturn's icy moon a hot spot in the search for potential extraterrestrial life. Methanogenic archaea are among the organisms that could potentially thrive under the predicted conditions on Enceladus, considering that both molecular hydrogen (H 2 ) and methane (CH 4 ) have been detected in the plume. Here we show that a methanogenic archaeon, Methanothermococcus okinawensis, can produce CH 4 under physicochemical conditions extrapolated for Enceladus. Up to 72% carbon dioxide to CH 4 conversion is reached at 50 bar in the presence of potential inhibitors. Furthermore, kinetic and thermodynamic computations of low-temperature serpentinization indicate that there may be sufficient H 2 gas production to serve as a substrate for CH 4 production on Enceladus. We conclude that some of the CH 4 detected in the plume of Enceladus might, in principle, be produced by methanogens.

Lead-contaminated imported tamarind candy and children's blood lead levels.

PubMed Central

Lynch, R A; Boatright, D T; Moss, S K

2000-01-01

In 1999, an investigation implicated tamarind candy as the potential source of lead exposure for a child with a significantly elevated blood lead level (BLL). The Oklahoma City-County Health Department tested two types of tamarind suckers and their packaging for lead content. More than 50% of the tested suckers exceeded the US Food and Drug Administration (FDA) Level of Concern for lead in this type of product. The authors calculated that a child consuming one-quarter to one-half of either of the two types of suckers in a day would exceed the maximum FDA Provis onal Tolerable Intake for lead. High lead concentrations in the two types of wrappers suggested leaching as a potential source of contamination. The authors used the Environmental Protection Agency's Integrated Exposure Uptake Biokinetic (IEUBK) model to predict the effects of consumption of contaminated tamarind suckers on populat on BLLs. The IEUBK model predicted that consumption of either type of sucker at a rate of one per day would result in dramatic increases in mean BLLs for children ages 6-84 months in Oklahoma and in the percentage of children wth elevated BLLs (> or =10 micrograms per deciliter [microg/dL]). The authors conclude that consumption of these products represents a potential public health threat. In addition, a history of lead contamination in imported tamarind products suggests that import control measures may not be completely effective in preventing additional lead exposure. PMID:11354337

Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US

USGS Publications Warehouse

Kumar, S.; Spaulding, S.A.; Stohlgren, T.J.; Hermann, K.A.; Schmidt, T.S.; Bahls, L.L.

2009-01-01

The diatom Didymosphenia geminata is a single-celled alga found in lakes, streams, and rivers. Nuisance blooms of D geminata affect the diversity, abundance, and productivity of other aquatic organisms. Because D geminata can be transported by humans on waders and other gear, accurate spatial prediction of habitat suitability is urgently needed for early detection and rapid response, as well as for evaluation of monitoring and control programs. We compared four modeling methods to predict D geminata's habitat distribution; two methods use presence-absence data (logistic regression and classification and regression tree [CART]), and two involve presence data (maximum entropy model [Maxent] and genetic algorithm for rule-set production [GARP]). Using these methods, we evaluated spatially explicit, bioclimatic and environmental variables as predictors of diatom distribution. The Maxent model provided the most accurate predictions, followed by logistic regression, CART, and GARP. The most suitable habitats were predicted to occur in the western US, in relatively cool sites, and at high elevations with a high base-flow index. The results provide insights into the factors that affect the distribution of D geminata and a spatial basis for the prediction of nuisance blooms. ?? The Ecological Society of America.

A Market-Basket Approach to Predict the Acute Aquatic Toxicity of Munitions and Energetic Materials.

PubMed

Burgoon, Lyle D

2016-06-01

An ongoing challenge in chemical production, including the production of insensitive munitions and energetics, is the ability to make predictions about potential environmental hazards early in the process. To address this challenge, a quantitative structure activity relationship model was developed to predict acute fathead minnow toxicity of insensitive munitions and energetic materials. Computational predictive toxicology models like this one may be used to identify and prioritize environmentally safer materials early in their development. The developed model is based on the Apriori market-basket/frequent itemset mining approach to identify probabilistic prediction rules using chemical atom-pairs and the lethality data for 57 compounds from a fathead minnow acute toxicity assay. Lethality data were discretized into four categories based on the Globally Harmonized System of Classification and Labelling of Chemicals. Apriori identified toxicophores for categories two and three. The model classified 32 of the 57 compounds correctly, with a fivefold cross-validation classification rate of 74 %. A structure-based surrogate approach classified the remaining 25 chemicals correctly at 48 %. This result is unsurprising as these 25 chemicals were fairly unique within the larger set.

Climate variability and the European agricultural production

NASA Astrophysics Data System (ADS)

Guimarães Nobre, Gabriela; Hunink, Johannes E.; Baruth, Bettina; Aerts, Jeroen C. J. H.; Ward, Philip J.

2017-04-01

By 2050, the global demand for maize, wheat and other major crops is expected to grow sharply. To meet this challenge, agricultural systems have to increase substantially their production. However, the expanding world population, coupled with a decline of arable land per person, and the variability in global climate, are obstacles to achieving the increasing demand. Creating a resilient agriculture system requires the incorporation of preparedness measures against weather-related events, which can trigger disruptive risks such as droughts. This study examines the influence of large-scale climate variability on agriculture production applying a robust decision-making tool named fast-and-frugal trees (FFT). We created FFTs using a dataset of crop production and indices of climate variability: the El Niño Southern Oscillation (SOI) and the North Atlantic Oscillation (NAO). Our main goal is to predict the occurrence of below-average crop production, using these two indices at different lead times. Initial results indicated that SOI and NAO have strong links with European low sugar beet production. For some areas, the FFTs were able to detect below-average productivity events six months before harvesting with hit rate and predictive positive value higher than 70%. We found that shorter lead times, such as three months before harvesting, have the highest predictive skill. Additionally, we observed that the responses of low production events to the phases of the NAO and SOI vary spatially and seasonally. Through the comprehension of the relationship between large scale climate variability and European drought related agricultural impact, this study reflects on how this information could potentially improve the management of the agricultural sector by coupling the findings with seasonal forecasting system of crop production.

Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions.

PubMed

Truong, Tuyet T A; Hardy, Giles E St J; Andrew, Margaret E

2017-01-01

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam's lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species.

Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions

PubMed Central

Truong, Tuyet T. A.; Hardy, Giles E. St. J.; Andrew, Margaret E.

2017-01-01

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam’s lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species. PMID:28555147

Making Online Products More Tangible: The Effect of Product Presentation Formats on Product Evaluations.

PubMed

Verhagen, Tibert; Vonkeman, Charlotte; van Dolen, Willemijn

2016-07-01

Although several studies have looked at the effects of online product presentations on consumer decision making, no study thus far has considered a potential key factor in online product evaluations: tangibility. The present study aims at filling this gap by developing and testing a model that relates different online product presentation formats to the three-dimensional concept of product tangibility. We test how the three tangibility dimensions influence perceived diagnosticity and, eventually, online purchase intentions. A between-subjects lab experiment (n = 366) was used to test the hypothesized effects of three common online product presentation formats (pictures vs. 360 spin rotation vs. virtual mirror). The results showed that out of these formats, virtual mirrors were superior in providing a sense of product tangibility, followed by the 360-spin rotation format and static pictures. Furthermore, in terms of predictive validity, two of the three tangibility dimensions significantly increased perceived diagnosticity, which, in turn, positively and strongly affected purchase intentions. Overall, our results add to previous works studying the relationships between online product presentation formats and consumer decision making. Also, they hold value for online practitioners by highlighting the potential benefits of applying technologically advanced product presentation formats such as the virtual mirror.

How important is drinking water exposure for the risks of engineered nanoparticles to consumers?

PubMed

Tiede, Karen; Hanssen, Steffen Foss; Westerhoff, Paul; Fern, Gordon J; Hankin, Steven M; Aitken, Robert J; Chaudhry, Qasim; Boxall, Alistair B A

2016-01-01

This study explored the potential for engineered nanoparticles (ENPs) to contaminate the UK drinking water supplies and established the significance of the drinking water exposure route compared to other routes of human exposure. A review of the occurrence and quantities of ENPs in different product types on the UK market as well as release scenarios, their possible fate and behaviour in raw water and during drinking water treatment was performed. Based on the available data, all the ENPs which are likely to reach water sources were identified and categorized. Worst case concentrations of ENPs in raw water and treated drinking water, using a simple exposure model, were estimated and then qualitatively compared to available estimates for human exposure through other routes. A range of metal, metal oxide and organic-based ENPs were identified that have the potential to contaminate drinking waters. Worst case predicted concentrations in drinking waters were in the low- to sub-µg/l range and more realistic estimates were tens of ng/l or less. For the majority of product types, human exposure via drinking water was predicted to be less important than exposure via other routes. The exceptions were some clothing materials, paints and coatings and cleaning products containing Ag, Al, TiO2, Fe2O3 ENPs and carbon-based materials.

Identification of Linkages between EDCs in Personal Care Products and Breast Cancer through Data Integration Combined with Gene Network Analysis.

PubMed

Jeong, Hyeri; Kim, Jongwoon; Kim, Youngjun

2017-09-30

Approximately 1000 chemicals have been reported to possibly have endocrine disrupting effects, some of which are used in consumer products, such as personal care products (PCPs) and cosmetics. We conducted data integration combined with gene network analysis to: (i) identify causal molecular mechanisms between endocrine disrupting chemicals (EDCs) used in PCPs and breast cancer; and (ii) screen candidate EDCs associated with breast cancer. Among EDCs used in PCPs, four EDCs having correlation with breast cancer were selected, and we curated 27 common interacting genes between those EDCs and breast cancer to perform the gene network analysis. Based on the gene network analysis, ESR1, TP53, NCOA1, AKT1, and BCL6 were found to be key genes to demonstrate the molecular mechanisms of EDCs in the development of breast cancer. Using GeneMANIA, we additionally predicted 20 genes which could interact with the 27 common genes. In total, 47 genes combining the common and predicted genes were functionally grouped with the gene ontology and KEGG pathway terms. With those genes, we finally screened candidate EDCs for their potential to increase breast cancer risk. This study highlights that our approach can provide insights to understand mechanisms of breast cancer and identify potential EDCs which are in association with breast cancer.

Herb-drug interactions: challenges and opportunities for improved predictions.

PubMed

Brantley, Scott J; Argikar, Aneesh A; Lin, Yvonne S; Nagar, Swati; Paine, Mary F

2014-03-01

Supported by a usage history that predates written records and the perception that "natural" ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb-drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb-drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb-drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb-drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens.

Herb–Drug Interactions: Challenges and Opportunities for Improved Predictions

PubMed Central

Brantley, Scott J.; Argikar, Aneesh A.; Lin, Yvonne S.; Nagar, Swati

2014-01-01

Supported by a usage history that predates written records and the perception that “natural” ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb–drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb–drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb–drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb–drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens. PMID:24335390

Cleaning and asthma: A systematic review and approach for effective safety assessment.

PubMed

Vincent, Melissa J; Parker, Ann; Maier, Andrew

2017-11-01

Research indicates a correlative relationship between asthma and use of consumer cleaning products. We conduct a systematic review of epidemiological literature on persons who use or are exposed to cleaning products, both in occupational and domestic settings, and risk of asthma or asthma-like symptoms to improve understanding of the causal relationship between exposure and asthma. A scoring method for assessing study reliability is presented. Although research indicates an association between asthma and the use of cleaning products, no study robustly investigates exposure to cleaning products or ingredients along with asthma risk. This limits determination of causal relationships between asthma and specific products or ingredients in chemical safety assessment. These limitations, and a lack of robust animal models for toxicological assessment of asthma, create the need for a weight-of-evidence (WoE) approach to examine an ingredient or product's asthmatic potential. This proposed WoE method organizes diverse lines of data (i.e., asthma, sensitization, and irritation information) through a systematic, hierarchical framework that provides qualitatively categorized conclusions using hazard bands to predict a specific product or ingredient's potential for asthma induction. This work provides a method for prioritizing chemicals as a first step for quantitative and scenario-specific safety assessments based on their potential for inducing asthmatic effects. Acetic acid is used as a case study to test this framework. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Biogeochemical modeling of CO2 and CH4 production in anoxic Arctic soil microcosms

NASA Astrophysics Data System (ADS)

Tang, Guoping; Zheng, Jianqiu; Xu, Xiaofeng; Yang, Ziming; Graham, David E.; Gu, Baohua; Painter, Scott L.; Thornton, Peter E.

2016-09-01

Soil organic carbon turnover to CO2 and CH4 is sensitive to soil redox potential and pH conditions. However, land surface models do not consider redox and pH in the aqueous phase explicitly, thereby limiting their use for making predictions in anoxic environments. Using recent data from incubations of Arctic soils, we extend the Community Land Model with coupled carbon and nitrogen (CLM-CN) decomposition cascade to include simple organic substrate turnover, fermentation, Fe(III) reduction, and methanogenesis reactions, and assess the efficacy of various temperature and pH response functions. Incorporating the Windermere Humic Aqueous Model (WHAM) enables us to approximately describe the observed pH evolution without additional parameterization. Although Fe(III) reduction is normally assumed to compete with methanogenesis, the model predicts that Fe(III) reduction raises the pH from acidic to neutral, thereby reducing environmental stress to methanogens and accelerating methane production when substrates are not limiting. The equilibrium speciation predicts a substantial increase in CO2 solubility as pH increases, and taking into account CO2 adsorption to surface sites of metal oxides further decreases the predicted headspace gas-phase fraction at low pH. Without adequate representation of these speciation reactions, as well as the impacts of pH, temperature, and pressure, the CO2 production from closed microcosms can be substantially underestimated based on headspace CO2 measurements only. Our results demonstrate the efficacy of geochemical models for simulating soil biogeochemistry and provide predictive understanding and mechanistic representations that can be incorporated into land surface models to improve climate predictions.

Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghimire, Bardan; Riley, William J.; Koven, Charles D.

In many ecosystems, nitrogen is the most limiting nutrient for plant growth and productivity. However, current Earth System Models (ESMs) do not mechanistically represent functional nitrogen allocation for photosynthesis or the linkage between nitrogen uptake and root traits. The current version of CLM (4.5) links nitrogen availability and plant productivity via (1) an instantaneous downregulation of potential photosynthesis rates based on soil mineral nitrogen availability, and (2) apportionment of soil nitrogen between plants and competing nitrogen consumers assumed to be proportional to their relative N demands. However, plants do not photosynthesize at potential rates and then downregulate; instead photosynthesis ratesmore » are governed by nitrogen that has been allocated to the physiological processes underpinning photosynthesis. Furthermore, the role of plant roots in nutrient acquisition has also been largely ignored in ESMs. We therefore present a new plant nitrogen model for CLM4.5 with (1) improved representations of linkages between leaf nitrogen and plant productivity based on observed relationships in a global plant trait database and (2) plant nitrogen uptake based on root-scale Michaelis-Menten uptake kinetics. Our model improvements led to a global bias reduction in GPP, LAI, and biomass of 70%, 11%, and 49%, respectively. Furthermore, water use efficiency predictions were improved conceptually, qualitatively, and in magnitude. The new model's GPP responses to nitrogen deposition, CO 2 fertilization, and climate also differed from the baseline model. The mechanistic representation of leaf-level nitrogen allocation and a theoretically consistent treatment of competition with belowground consumers led to overall improvements in global carbon cycling predictions.« less

Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions

DOE PAGES

Ghimire, Bardan; Riley, William J.; Koven, Charles D.; ...

2016-05-01

In many ecosystems, nitrogen is the most limiting nutrient for plant growth and productivity. However, current Earth System Models (ESMs) do not mechanistically represent functional nitrogen allocation for photosynthesis or the linkage between nitrogen uptake and root traits. The current version of CLM (4.5) links nitrogen availability and plant productivity via (1) an instantaneous downregulation of potential photosynthesis rates based on soil mineral nitrogen availability, and (2) apportionment of soil nitrogen between plants and competing nitrogen consumers assumed to be proportional to their relative N demands. However, plants do not photosynthesize at potential rates and then downregulate; instead photosynthesis ratesmore » are governed by nitrogen that has been allocated to the physiological processes underpinning photosynthesis. Furthermore, the role of plant roots in nutrient acquisition has also been largely ignored in ESMs. We therefore present a new plant nitrogen model for CLM4.5 with (1) improved representations of linkages between leaf nitrogen and plant productivity based on observed relationships in a global plant trait database and (2) plant nitrogen uptake based on root-scale Michaelis-Menten uptake kinetics. Our model improvements led to a global bias reduction in GPP, LAI, and biomass of 70%, 11%, and 49%, respectively. Furthermore, water use efficiency predictions were improved conceptually, qualitatively, and in magnitude. The new model's GPP responses to nitrogen deposition, CO 2 fertilization, and climate also differed from the baseline model. The mechanistic representation of leaf-level nitrogen allocation and a theoretically consistent treatment of competition with belowground consumers led to overall improvements in global carbon cycling predictions.« less

Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions

NASA Astrophysics Data System (ADS)

Ghimire, Bardan; Riley, William J.; Koven, Charles D.; Mu, Mingquan; Randerson, James T.

2016-06-01

In many ecosystems, nitrogen is the most limiting nutrient for plant growth and productivity. However, current Earth System Models (ESMs) do not mechanistically represent functional nitrogen allocation for photosynthesis or the linkage between nitrogen uptake and root traits. The current version of CLM (4.5) links nitrogen availability and plant productivity via (1) an instantaneous downregulation of potential photosynthesis rates based on soil mineral nitrogen availability, and (2) apportionment of soil nitrogen between plants and competing nitrogen consumers assumed to be proportional to their relative N demands. However, plants do not photosynthesize at potential rates and then downregulate; instead photosynthesis rates are governed by nitrogen that has been allocated to the physiological processes underpinning photosynthesis. Furthermore, the role of plant roots in nutrient acquisition has also been largely ignored in ESMs. We therefore present a new plant nitrogen model for CLM4.5 with (1) improved representations of linkages between leaf nitrogen and plant productivity based on observed relationships in a global plant trait database and (2) plant nitrogen uptake based on root-scale Michaelis-Menten uptake kinetics. Our model improvements led to a global bias reduction in GPP, LAI, and biomass of 70%, 11%, and 49%, respectively. Furthermore, water use efficiency predictions were improved conceptually, qualitatively, and in magnitude. The new model's GPP responses to nitrogen deposition, CO2 fertilization, and climate also differed from the baseline model. The mechanistic representation of leaf-level nitrogen allocation and a theoretically consistent treatment of competition with belowground consumers led to overall improvements in global carbon cycling predictions.

Antecedent Moisture and Biological Inertia as Predictors of Plant and Ecosystem Productivity in Arid and Semiarid Systems

NASA Astrophysics Data System (ADS)

Ogle, K.

2011-12-01

Many plant and ecosystem processes in arid and semiarid systems may be affected by antecedent environmental conditions (e.g., precipitation patterns, soil water availability, temperature) that integrate over past days, weeks, months, seasons, or years. However, the importance of such antecedent exogenous effects relative to conditions occurring at the time of the observed process is relatively unexplored. Even less is known about the potential importance of antecedent endogenous effects that describe the influence of past ecosystem states on the current ecosystem state; e.g., how is current ecosystem productivity related to past productivity patterns? We hypothesize that incorporation of antecedent exogenous and endogenous factors can improve our predictive understanding of many plant and ecosystem processes, especially in arid and semiarid ecosystems. Furthermore, the common approach to quantifying the effects of antecedent (exogenous) variables relies on arbitrary, deterministic definitions of antecedent variables that (1) may not accurately describe the role of antecedent conditions and (2) ignore uncertainty associated with applying deterministic definitions. In this study, we employ a stochastic framework for (1) computing the antecedent variables that estimates the relative importance of conditions experienced each time unit into the past, also providing insight into potential lag responses, and (2) estimating the effect of antecedent factors on the response variable of interest. We employ this approach to explore the potential roles of antecedent exogenous and endogenous influences in three settings that illustrate the: (1) importance of antecedent precipitation for net primary productivity in the shortgrass steppe in northern Colorado, (2) dependency of tree growth on antecedent precipitation and past growth states for pinyon growing in western Colorado, and (3) influence of antecedent soil water and prior root status on observed root growth in the Mojave Desert FACE experiment. All three examples suggest that antecedent conditions are critical to predicting different indices of productivity such that the incorporation of antecedent effects explained an additional 20-40% of the variation in the productivity responses. Antecedent endogenous factors were important for understanding tree and root growth, suggesting a potential biological inertia effect that is likely linked to labile carbon storage and allocation strategies. The role of antecedent exogenous (water) variables suggests a lag response whose duration and timing differs according to the time scale of the response variable. In summary, antecedent water availability and past endogenous states appear critical to understanding plant and ecosystem productivity in arid and semiarid systems, and this study describes a stochastic framework for quantifying the potential influence of such antecedent conditions.

Biodiversity and productivity

Treesearch

M.R. Willig

2011-01-01

Researchers predict that human activities especially landscape modification and climate change will have a considerable impact on the distribution and abundance of species at local, regional, and global scales in the 21st century ( 1, 2). This is a concern for a number of reasons, including the potential loss of goods and services that biodiversity provides to people...

Chemical-gene interaction networks and causal reasoning for biological effects prediction and prioritization of contaminants for environmental monitoring and surveillance (poster)

EPA Science Inventory

Product Description:Evaluation of the potential effects of complex mixtures of chemicals in the environment is challenged by the lack of extensive toxicity data for many chemicals. However, there are growing sources of online information that curate and compile literature reports...

Too many chemicals, too little time: Rapid in silico methods to characterize and predict ADME properties for chemical toxicity and exposure potential

EPA Science Inventory

Evaluating proposed alternative chemical structures to support the design of safer chemicals and products is an important component of EPA's Green Chemistry and Design for the Environment (DfE) Programs. As such, science-based alternatives assessment is essential to support EPA's...

Psychosocial Predictors of Women's Physical Health in Middle Adulthood.

ERIC Educational Resources Information Center

Thomas, Sandra P.

Although health is a key element in one's experience of middle adulthood as a time of productivity and personal fulfillment, research on psychosocial factors predictive of mid-life health is sparse, especially for women. Psychosocial variables are not only highly salient to health, but also are potentially modifiable by women themselves. This…

Ensiling of fish industry waste for biogas production: a lab scale evaluation of biochemical methane potential (BMP) and kinetics.

PubMed

Kafle, Gopi Krishna; Kim, Sang Hun; Sung, Kyung Ill

2013-01-01

Fish waste (FW) obtained from a fish processor was ensiled for biogas production. The FW silages were prepared by mixing FW with bread waste (BW) and brewery grain waste (BGW), and the quality of the prepared silages were evaluated. The biogas potentials of BW, BGW, three different types of FW, and FW silages were measured. A first-order kinetic model and the modified Gompertz model were also used to predict methane yield. The biogas and methane yield for FW silages after 96 days was calculated to be 671-763 mL/g VS and 441-482 mL/g VS, respectively. There were smaller differences between measured and predicted methane yield for FW silages when using a modified Gompertz model (1.1-4.3%) than when using a first-order kinetic model (22.5-32.4%). The critical HRTs and technical digestion times (T(80-90)) for the FW silages were calculated to be 21.0-23.8 days and 40.5-52.8 days, respectively. Copyright © 2012 Elsevier Ltd. All rights reserved.

Predicting the Reasons of Customer Complaints: A First Step Toward Anticipating Quality Issues of In Vitro Diagnostics Assays with Machine Learning.

PubMed

Aris-Brosou, Stephane; Kim, James; Li, Li; Liu, Hui

2018-05-15

Vendors in the health care industry produce diagnostic systems that, through a secured connection, allow them to monitor performance almost in real time. However, challenges exist in analyzing and interpreting large volumes of noisy quality control (QC) data. As a result, some QC shifts may not be detected early enough by the vendor, but lead a customer to complain. The aim of this study was to hypothesize that a more proactive response could be designed by utilizing the collected QC data more efficiently. Our aim is therefore to help prevent customer complaints by predicting them based on the QC data collected by in vitro diagnostic systems. QC data from five select in vitro diagnostic assays were combined with the corresponding database of customer complaints over a period of 90 days. A subset of these data over the last 45 days was also analyzed to assess how the length of the training period affects predictions. We defined a set of features used to train two classifiers, one based on decision trees and the other based on adaptive boosting, and assessed model performance by cross-validation. The cross-validations showed classification error rates close to zero for some assays with adaptive boosting when predicting the potential cause of customer complaints. Performance was improved by shortening the training period when the volume of complaints increased. Denoising filters that reduced the number of categories to predict further improved performance, as their application simplified the prediction problem. This novel approach to predicting customer complaints based on QC data may allow the diagnostic industry, the expected end user of our approach, to proactively identify potential product quality issues and fix these before receiving customer complaints. This represents a new step in the direction of using big data toward product quality improvement. ©Stephane Aris-Brosou, James Kim, Li Li, Hui Liu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 15.05.2018.

Predicting the Reasons of Customer Complaints: A First Step Toward Anticipating Quality Issues of In Vitro Diagnostics Assays with Machine Learning

PubMed Central

Kim, James; Li, Li; Liu, Hui

2018-01-01

Background Vendors in the health care industry produce diagnostic systems that, through a secured connection, allow them to monitor performance almost in real time. However, challenges exist in analyzing and interpreting large volumes of noisy quality control (QC) data. As a result, some QC shifts may not be detected early enough by the vendor, but lead a customer to complain. Objective The aim of this study was to hypothesize that a more proactive response could be designed by utilizing the collected QC data more efficiently. Our aim is therefore to help prevent customer complaints by predicting them based on the QC data collected by in vitro diagnostic systems. Methods QC data from five select in vitro diagnostic assays were combined with the corresponding database of customer complaints over a period of 90 days. A subset of these data over the last 45 days was also analyzed to assess how the length of the training period affects predictions. We defined a set of features used to train two classifiers, one based on decision trees and the other based on adaptive boosting, and assessed model performance by cross-validation. Results The cross-validations showed classification error rates close to zero for some assays with adaptive boosting when predicting the potential cause of customer complaints. Performance was improved by shortening the training period when the volume of complaints increased. Denoising filters that reduced the number of categories to predict further improved performance, as their application simplified the prediction problem. Conclusions This novel approach to predicting customer complaints based on QC data may allow the diagnostic industry, the expected end user of our approach, to proactively identify potential product quality issues and fix these before receiving customer complaints. This represents a new step in the direction of using big data toward product quality improvement. PMID:29764796

Agriculture in the climate change negotiations; ensuring that food production is not threatened.

PubMed

Muldowney, J; Mounsey, J; Kinsella, L

2013-06-01

With the human population predicted to reach nine billion by 2050, demand for food is predicted to more than double over this time period, a trend which will lead to increased greenhouse gas (GHG) emissions from agriculture. Furthermore, expansion in food production is predicted to occur primarily in the developing world, where adaptation to climate change may be more difficult and opportunities to mitigate emissions limited. In the establishment of the United Nations Framework Convention on Climate Change (UNFCCC), 'ensuring that food production is not threatened' is explicitly mentioned in the objective of the Convention. However, the focus of negotiations under the Convention has largely been on reducing GHG emissions from energy, and industrial activities and realizing the potential of forestry as a carbon sink. There has been little attention by the UNFCCC to address the challenges and opportunities for the agriculture sector. Since 2006, concerted efforts have been made to raise the prominence of agriculture within the negotiations. The most recent The Intergovernmental Panel on Climate Change report and 'The Emissions Gap Report' by the UNEP highlighted the significant mitigation potential of agriculture, which can help contribute towards keeping global temperature rises below the 2°C limit agreed in Cancun. Agriculture has to be a part of the solution to address climate change, but this will also require a focus on how agriculture systems can adapt to climate change in order to continue to increase food output. However, to effectively realize this potential, systematic and dedicated discussion and decisions within the UNFCCC are needed. UNFCCC discussions on a specific agriculture agenda item started in 2012, but are currently inconclusive. However, Parties are generally in agreement on the importance of agriculture in contributing to food security and employment as well as the need to improve understanding of agriculture and how it can contribute to realizing climate objectives. Discussions on agriculture are continuing with a view to finding an acceptable approach to address the climate change related challenges faced by agriculture worldwide and to ensure that 'food production is not threatened'.

Comparison of in vitro eye irritation potential by bovine corneal opacity and permeability (BCOP) assay to erythema scores in human eye sting test of surfactant-based formulations.

PubMed

Cater, Kathleen C; Harbell, John W

2008-01-01

The bovine corneal opacity and permeability (BCOP) assay can be used to predict relative eye irritation potential of surfactant-based personal care formulations relative to a corporate benchmark. The human eye sting test is typically used to evaluate product claims of no tears/no stinging for children's bath products. A preliminary investigation was conducted to test a hypothesis that the BCOP assay could be used as a prediction model for relative ranking of human eye irritation responses under conditions of a standard human eye sting test to surfactant-based formulations. BCOP assays and human eye sting tests were conducted on 4 commercial and 1 prototype body wash (BW) developed specifically for children or as mild bath products. In the human eye sting test, 10 mul of a 10% dosing solution is instilled into one eye of each panelist (n = 20), and the contralateral eye is dosed with sterile water as a control. Bulbar conjunctival erythema responses of each eye are graded at 30 seconds by an ophthalmologist. The BCOP assay permeability values (optical density at 490 nm [OD(490)]) for the 5 BWs ranged from 0.438 to 1.252 (i.e., least to most irritating). By comparison, the number of panelists exhibiting erythema responses (mild to moderately pink) ranged from 3 of 20 panelists for the least irritating BW to 10 of 20 panelists for the most irritating BW tested. The relative ranking of eye irritation potential of the 5 BWs in the BCOP assay compares favorably with the relative ranking of the BWs in the human eye sting test. Based on these findings, the permeability endpoint of the BCOP assay, as described for surfactant-based formulations, showed promise as a prediction model for relative ranking of conjunctival erythema responses in the human eye. Consequently, screening of prototype formulations in the BCOP assay would allow for formula optimization of mild bath products prior to investment in a human eye sting test.

Short communication: Genetic study of methane production predicted from milk fat composition in dairy cows.

PubMed

van Engelen, S; Bovenhuis, H; Dijkstra, J; van Arendonk, J A M; Visker, M H P W

2015-11-01

Dairy cows produce enteric methane, a greenhouse gas with 25 times the global warming potential of CO2. Breeding could make a permanent, cumulative, and long-term contribution to methane reduction. Due to a lack of accurate, repeatable, individual methane measurements needed for breeding, indicators of methane production based on milk fatty acids have been proposed. The aim of the present study was to quantify the genetic variation for predicted methane yields. The milk fat composition of 1,905 first-lactation Dutch Holstein-Friesian cows was used to investigate 3 different predicted methane yields (g/kg of DMI): Methane1, Methane2, and Methane3. Methane1 was based on the milk fat proportions of C17:0anteiso, C18:1 rans-10+11, C18:1 cis-11, and C18:1 cis-13 (R(2)=0.73). Methane2 was based on C4:0, C18:0, C18:1 trans-10+11, and C18:1 cis-11 (R(2)=0.70). Methane3 was based on C4:0, C6:0, and C18:1 trans-10+11 (R(2)=0.63). Predicted methane yields were demonstrated to be heritable traits, with heritabilities between 0.12 and 0.44. Breeding can, thus, be used to decrease methane production predicted based on milk fatty acids. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Cost benefits of ergonomic intervention in a hospital: a preliminary study using Oxenburgh's productivity model.

PubMed

Busse, M; Bridger, B

1997-09-01

This case study of absenteeism amongst nurses was carried out using the productivity model of Oxenburgh (1991). Data on absenteeism amongst nurses were collected from one private hospital. Areas of high risk of injury were identified and the presence of ergonomic risk factors determined. The productivity model was used to calculate the costs of absenteeism in terms of actual rates of pay and loss of productivity. Potential benefits resulting from ergonomic improvements to the work environment were estimated using the productivity model. The model predicted that even modest reductions in injury would justify the additional expenditure in a relatively short period of time. Further investigations of injuries to nurses in both State and Private Sector Hospitals seem to be justified.

A novel two-dimensional liquid-chromatography method for online prediction of the toxicity of transformation products of benzophenones after water chlorination.

PubMed

Li, Jian; Ma, Li-Yun; Xu, Li; Shi, Zhi-Guo

2015-08-01

Benzophenone-type UV filters (BPs) are ubiquitous in the environment. Transformation products (TPs) of BPs with suspected toxicity are likely to be produced during disinfection of water by chlorination. To quickly predict the toxicity of TPs, in this study, a novel two-dimensional liquid-chromatography (2D-LC) method was established in which the objective of the first dimension was to separate the multiple components of the BPs sample after chlorination, using a reversed-phase liquid-chromatography mode. A biochromatographic system, i.e. bio-partitioning micellar chromatography with the polyoxyethylene (23) lauryl ether aqueous solution as the mobile phase, served as the second dimension to predict the toxicity of the fraction from the first dimension on the basis of the quantitative retention-activity relationships (QRARs) model. Six BPs, namely 2,4-dihydroxybenzophenone, oxybenzone, 4-hydroxybenzophenone, 2-hydroxy-4-methoxybenzophenone-5-sulfonic acid, 2,2'-dihydroxy-4,4'-dimethoxybenzophenone and 2,2'-dihydroxy-4-methoxybenzophenone, were the target analytes subjected to chlorination. The products of these BPs after chlorination were directly injected to the 2D-LC system for analysis. The results indicated that most TPs may be less toxic than their parent chemicals, but some may be more toxic, and that intestinal toxicity of TPs may be more obvious than blood toxicity. The proposed method is time-saving, high-throughput, and reliable, and has great potential for predicting toxicity or bioactivity of unknown and/or known components in a complex sample. Graphical Abstract The scheme for the 2D-LC online prediction of toxicity of the transformation products of benzophenone-type UV filters after chlorination.

Predicted national productivity implications of calorie and sodium reductions in the American diet.

PubMed

Dall, Timothy M; Fulgoni, Victor L; Zhang, Yiduo; Reimers, Kristin J; Packard, Patricia T; Astwood, James D

2009-01-01

To model the potential long-term national productivity benefits from reduced daily intake of calories and sodium. Simulation based on secondary data analysis; quantitative research. Measures include absenteeism, presenteeism, disability, and premature mortality under various hypothetical dietary changes. United States. Two hundred twenty-five million adults. Findings come from a Nutrition Impact Model that combines information from national surveys, peer-reviewed studies, and government reports. We compare current estimates of national productivity loss associated with overweight, obesity, and hypertension to estimates for hypothetical scenarios in which national prevalence of these risk factors is lower. Using the simulation model, we illustrate how modest dietary change can achieve lower national prevalence of excess weight and hypertension. We estimate that permanent 100-kcal reductions in daily intake among the overweight/obese would eliminate approximately 71.2 million cases of overweight/obesity. In the long term, this could increase national productivity by $45.7 billion annually. Long-term sodium reductions of 400 mg in those with uncontrolled hypertension would eliminate about 1.5 million cases, potentially increasing productivity by $2.5 billion annually. More aggressive diet changes of 500 kcal and 1100 mg of sodium reductions yield potential productivity benefits of $133.3 and $5.8 billion, respectively. The potential long-term benefit of reduced calories and sodium, combining medical cost savings with productivity increases, ranges from $108.5 billion for moderate reductions to $255.6 billion for aggressive reductions. These findings help inform public health policy and the business case for improving diet. (AmJ Health Promot 2009;23[6]:423-430.)

Using an agent-based model to evaluate the effect of producer specialization on the epidemiological resilience of livestock production networks.

PubMed

Wiltshire, Serge W

2018-01-01

An agent-based computer model that builds representative regional U.S. hog production networks was developed and employed to assess the potential impact of the ongoing trend towards increased producer specialization upon network-level resilience to catastrophic disease outbreaks. Empirical analyses suggest that the spatial distribution and connectivity patterns of contact networks often predict epidemic spreading dynamics. Our model heuristically generates realistic systems composed of hog producer, feed mill, and slaughter plant agents. Network edges are added during each run as agents exchange livestock and feed. The heuristics governing agents' contact patterns account for factors including their industry roles, physical proximities, and the age of their livestock. In each run, an infection is introduced, and may spread according to probabilities associated with the various modes of contact. For each of three treatments-defined by one-phase, two-phase, and three-phase production systems-a parameter variation experiment examines the impact of the spatial density of producer agents in the system upon the length and size of disease outbreaks. Resulting data show phase transitions whereby, above some density threshold, systemic outbreaks become possible, echoing findings from percolation theory. Data analysis reveals that multi-phase production systems are vulnerable to catastrophic outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outbreak scales and durations. Key differences in network-level metrics shed light on these results, suggesting that the absence of potentially-bridging producer-producer edges may be largely responsible for the superior disease resilience of single-phase "farrow to finish" production systems.

Impacts of Climate Change on the Timing of the Production Season of Maple Syrup in Eastern Canada

PubMed Central

Côté, Benoît; Logan, Travis; Power, Hugues; Charron, Isabelle; Duchesne, Louis

2015-01-01

Maple syrup production is an important economic activity in north-eastern North-America. The beginning and length of the production season is linked to daily variation in temperature. There are increasing concerns about the potential impact of climatic change on this industry. Here, we used weekly data of syrup yield for the 1999–2011 period from 121 maple stands in 11 regions of Québec (Canada) to predict how the period of production may be impacted by climate warming. The date at which the production begins is highly variable between years with an average range of 36 days among the regions. However, the average start date for a given region, which ranged from Julian day 65 to 83, was highly predictable (r2 = 0.88) using the average temperature from January to April (TJ-A). A logistic model predicting the weekly presence or absence of production was also developed. Using the inputs of 77 future climate scenarios issued from global models, projections of future production timing were made based on average TJ-A and on the logistic model. The projections of both approaches were in very good agreement and suggest that the sap season will be displaced to occur 15–19 days earlier on average in the 2080–2100 period. The data also show that the displacement in time will not be accompanied by a greater between years variability in the beginning of the season. However, in the southern part of Québec, very short periods of syrup production due to unfavourable conditions in the spring will occur more frequently in the future although their absolute frequencies will remain low. PMID:26682889

Impacts of Climate Change on the Timing of the Production Season of Maple Syrup in Eastern Canada.

PubMed

Houle, Daniel; Paquette, Alain; Côté, Benoît; Logan, Travis; Power, Hugues; Charron, Isabelle; Duchesne, Louis

2015-01-01

Maple syrup production is an important economic activity in north-eastern North-America. The beginning and length of the production season is linked to daily variation in temperature. There are increasing concerns about the potential impact of climatic change on this industry. Here, we used weekly data of syrup yield for the 1999-2011 period from 121 maple stands in 11 regions of Québec (Canada) to predict how the period of production may be impacted by climate warming. The date at which the production begins is highly variable between years with an average range of 36 days among the regions. However, the average start date for a given region, which ranged from Julian day 65 to 83, was highly predictable (r2 = 0.88) using the average temperature from January to April (TJ-A). A logistic model predicting the weekly presence or absence of production was also developed. Using the inputs of 77 future climate scenarios issued from global models, projections of future production timing were made based on average TJ-A and on the logistic model. The projections of both approaches were in very good agreement and suggest that the sap season will be displaced to occur 15-19 days earlier on average in the 2080-2100 period. The data also show that the displacement in time will not be accompanied by a greater between years variability in the beginning of the season. However, in the southern part of Québec, very short periods of syrup production due to unfavourable conditions in the spring will occur more frequently in the future although their absolute frequencies will remain low.

Evaluation and prediction of solar radiation for energy management based on neural networks

NASA Astrophysics Data System (ADS)

Aldoshina, O. V.; Van Tai, Dinh

2017-08-01

Currently, there is a high rate of distribution of renewable energy sources and distributed power generation based on intelligent networks; therefore, meteorological forecasts are particularly useful for planning and managing the energy system in order to increase its overall efficiency and productivity. The application of artificial neural networks (ANN) in the field of photovoltaic energy is presented in this article. Implemented in this study, two periodically repeating dynamic ANS, that are the concentration of the time delay of a neural network (CTDNN) and the non-linear autoregression of a network with exogenous inputs of the NAEI, are used in the development of a model for estimating and daily forecasting of solar radiation. ANN show good productivity, as reliable and accurate models of daily solar radiation are obtained. This allows to successfully predict the photovoltaic output power for this installation. The potential of the proposed method for controlling the energy of the electrical network is shown using the example of the application of the NAEI network for predicting the electric load.

Metabolomics for organic food authentication: Results from a long-term field study in carrots.

PubMed

Cubero-Leon, Elena; De Rudder, Olivier; Maquet, Alain

2018-01-15

Increasing demand for organic products and their premium prices make them an attractive target for fraudulent malpractices. In this study, a large-scale comparative metabolomics approach was applied to investigate the effect of the agronomic production system on the metabolite composition of carrots and to build statistical models for prediction purposes. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) was applied successfully to predict the origin of the agricultural system of the harvested carrots on the basis of features determined by liquid chromatography-mass spectrometry. When the training set used to build the OPLS-DA models contained samples representative of each harvest year, the models were able to classify unknown samples correctly (100% correct classification). If a harvest year was left out of the training sets and used for predictions, the correct classification rates achieved ranged from 76% to 100%. The results therefore highlight the potential of metabolomic fingerprinting for organic food authentication purposes. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

The application of electrochemistry to pharmaceutical stability testing--comparison with in silico prediction and chemical forced degradation approaches.

PubMed

Torres, Susana; Brown, Roland; Szucs, Roman; Hawkins, Joel M; Zelesky, Todd; Scrivens, Garry; Pettman, Alan; Taylor, Mark R

2015-11-10

The aim of this study was to evaluate the use of electrochemistry to generate oxidative degradation products of a model pharmaceutical compound. The compound was oxidized at different potentials using an electrochemical flow-cell fitted with a glassy carbon working electrode, a Pd/H2 reference electrode and a titanium auxiliary electrode. The oxidative products formed were identified and structurally characterized by LC-ESI-MS/MS using a high resolution Q-TOF mass spectrometer. Results from electrochemical oxidation using electrolytes of different pH were compared to those from chemical oxidation and from accelerated stability studies. Additionally, oxidative degradation products predicted using an in silico commercially available software were compared to those obtained from the various experimental methods. The electrochemical approach proved to be useful as an oxidative stress test as all of the final oxidation products observed under accelerated stability studies could be generated; previously reported reactive intermediate species were not observed most likely because the electrochemical mechanism differs from the oxidative pathway followed under accelerated stability conditions. In comparison to chemical degradation tests electrochemical degradation has the advantage of being much faster and does not require the use of strong oxidizing agents. Moreover, it enables the study of different operating parameters in short periods of time and optimisation of the reaction conditions (pH and applied potential) to achieve different oxidative products mixtures. This technique may prove useful as a stress test condition for the generation of oxidative degradation products and may help accelerate structure elucidation and development of stability indicating analytical methods. Copyright © 2015 Elsevier B.V. All rights reserved.

Unintended consequences: organizational practices and their impact on workplace safety and productivity.

PubMed

Kaminski, M

2001-04-01

Managers often implement new organizational practices to improve firm performance while neglecting possible side effects. This study examines the relationship between 6 organizational practices and both productivity and injury rates in 86 small manufacturing firms. The use of performance-based pay was associated with higher injury rates and lower productivity (on 1 of 2 measures). The opposite pattern held for training: Training hours were negatively related to the injury rate and positively related to 1 measure of productivity. Surprisingly, higher hours worked per week was associated with a lower injury rate and also with lower productivity. The use of teams was associated with a lower injury rate but was unrelated to productivity. The potential interaction between hazard control measures and organizational practices in predicting injury rates is also discussed.

Comparison of Measured and Predicted Bioconcentration Estimates of Pharmaceuticals in Fish Plasma and Prediction of Chronic Risk.

PubMed

Nallani, Gopinath; Venables, Barney; Constantine, Lisa; Huggett, Duane

2016-05-01

Evaluation of the environmental risk of human pharmaceuticals is now a mandatory component in all new drug applications submitted for approval in EU. With >3000 drugs currently in use, it is not feasible to test each active ingredient, so prioritization is key. A recent review has listed nine prioritization approaches including the fish plasma model (FPM). The present paper focuses on comparison of measured and predicted fish plasma bioconcentration factors (BCFs) of four common over-the-counter/prescribed pharmaceuticals: norethindrone (NET), ibuprofen (IBU), verapamil (VER) and clozapine (CLZ). The measured data were obtained from the earlier published fish BCF studies. The measured BCF estimates of NET, IBU, VER and CLZ were 13.4, 1.4, 0.7 and 31.2, while the corresponding predicted BCFs (based log Kow at pH 7) were 19, 1.0, 7.6 and 30, respectively. These results indicate that the predicted BCFs matched well the measured values. The BCF estimates were used to calculate the human: fish plasma concentration ratios of each drug to predict potential risk to fish. The plasma ratio results show the following order of risk potential for fish: NET > CLZ > VER > IBU. The FPM has value in prioritizing pharmaceutical products for ecotoxicological assessments.

Predicting acute and chronic effects of wood preservative products in Daphnia magna and Pseudokirchneriella subcapitata based on the concept of concentration addition.

PubMed

Coors, Anja; Weisbrod, Barbara; Schoknecht, Ute; Sacher, Frank; Kehrer, Anja

2014-02-01

The current European legislation requires that combined effects of the active substances and any substance of concern contained in biocidal products are taken into account in environmental risk assessment. The hypothesis whether the consideration of active substances together with all formulation additives that are labeled as presenting an environmental hazard is sufficient for a reliable environmental risk assessment was tested in the present study by investigating 3 wood preservative products. Relevant single substances in the products, some of their generic mixtures, the biocidal products themselves, and aqueous eluates prepared from the products (representing potential environmental mixtures) were tested for effects on algal growth and Daphnia acute immobilization as well as reproduction. Predictions for the products and the eluates were based on the concept of concentration addition and were mostly found to provide reliable or at least protective estimates for the observed acute and chronic toxicity of the mixtures. The mixture toxicity considerations also indicated that the toxicity of each product was dominated by just 1 of the components, and that assessments based only on the dominating substance would be similarly protective as a full-mixture risk assessment. Yet, there remained uncertainty in some cases that could be related to the toxicity of transformation products, the impact of unidentified formulation additives, or synergistic interaction between active substances and formulation additives. © 2013 SETAC.

Study on biomethane production and biodegradability of different leafy vegetables in anaerobic digestion.

PubMed

Yan, Hu; Zhao, Chen; Zhang, Jiafu; Zhang, Ruihong; Xue, Chunyu; Liu, Guangqing; Chen, Chang

2017-12-01

Enormous amounts of vegetable residues are wasted annually, causing many environmental problems due to their high moisture and organic contents. In this study, the methane production potential of 20 kinds of typical leafy vegetable residues in China were explored using a unified method. A connection between the biochemical components and the methane yields of these vegetables was well established which could be used to predict biogas performance in practice. A high volatile solid/total solid (VS/TS) ratio and hemicellulose content exhibited a positive impact on the biogas yield while lignin had a negative impact. In addition, three kinetic models were used to describe the methane production process of these agro-wastes. The systematic comparison of the methane production potentials of these leafy vegetables shown in this study will not only serve as a reference for basic research on anaerobic digestion but also provide useful data and information for agro-industrial applications of vegetable residues in future work.

Allergen source materials: state-of-the-art.

PubMed

Esch, Robert E

2009-01-01

A variety of positive outcomes can be realized from validation and risk management activities (see Table 4). They are dependent on the participation of multiple functional groups including the quality unit, regulatory and legal affairs, engineering and production operations, research and development, and sales and marketing. Quality risk management is receiving increased attention in the area of public health, pharmacovigilance, and pharmaceutical manufacturing. Recent examples of its regulatory use in our industry include the assessment of the potential risks of transmissible spongiform encephalopathies (TSE) agents through contaminated products], the risks of precipitates in allergenic extracts, and the revision of the potency limits for standardized dust mite and grass allergen vaccines. Its application to allergen source material process validation activities allowed for a practical strategy, especially in a complex manufacturing environment involving hundreds of products with multiple intended uses. In addition, the use of tools such as FMEA was useful in evaluating proposed changes made to manufacturing procedures and product specifications, new regulatory actions, and customer feedback or complaints. The success of such a quality assurance programs will ultimately be reflected in the elimination or reduction of product failures, improvement in the detection and prediction of potential product failures, and increased confidence in product quality.

Ranking of Texas reservoirs for application of carbon dioxide miscible displacement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ham, J

Of the 431 reservoirs screened, 211 projected revenue that exceeded cost, ie, were profitable. Only the top 154 reservoirs, however, showed a profit greater than 30%. The top 10 reservoirs predicted a profit of at least 80%. Six of the top ten were Gulf Coast sandstones. The reservoirs are representative of the most productive discoveries in Texas; they account for about 72% of the recorded 52 billion barrels oil production in the State. Preliminary evaluation in this study implied that potential production from CO{sub 2}-EOR could be as much as 4 billion barrels. In order to enhance the chances ofmore » achieving this, DOE should consider a targeted outreach program to the specific independent operators controlling the leases. Development of ownership/technical potential maps and an outreach program should be initiated to aid this identification.« less

Single well productivity prediction of carbonate reservoir

NASA Astrophysics Data System (ADS)

Le, Xu

2018-06-01

It is very important to predict the single-well productivity for the development of oilfields. The fracture structure of carbonate fractured-cavity reservoirs is complex, and the change of single-well productivity is inconsistent with that of sandstone reservoir. Therefore, the establishment of carbonate oil well productivity It is very important. Based on reservoir reality, three different methods for predicting the productivity of carbonate reservoirs have been established based on different types of reservoirs. (1) To qualitatively analyze the single-well capacity relations corresponding to different reservoir types, predict the production capacity according to the different wells encountered by single well; (2) Predict the productivity of carbonate reservoir wells by using numerical simulation technology; (3) According to the historical production data of oil well, fit the relevant capacity formula and make single-well productivity prediction; (4) Predict the production capacity by using oil well productivity formula of carbonate reservoir.

Predicting pathogen growth during short-term temperature abuse of raw pork, beef, and poultry products: use of an isothermal-based predictive tool.

PubMed

Ingham, Steven C; Fanslau, Melody A; Burnham, Greg M; Ingham, Barbara H; Norback, John P; Schaffner, Donald W

2007-06-01

A computer-based tool (available at: www.wisc.edu/foodsafety/meatresearch) was developed for predicting pathogen growth in raw pork, beef, and poultry meat. The tool, THERM (temperature history evaluation for raw meats), predicts the growth of pathogens in pork and beef (Escherichia coli O157:H7, Salmonella serovars, and Staphylococcus aureus) and on poultry (Salmonella serovars and S. aureus) during short-term temperature abuse. The model was developed as follows: 25-g samples of raw ground pork, beef, and turkey were inoculated with a five-strain cocktail of the target pathogen(s) and held at isothermal temperatures from 10 to 43.3 degrees C. Log CFU per sample data were obtained for each pathogen and used to determine lag-phase duration (LPD) and growth rate (GR) by DMFit software. The LPD and GR were used to develop the THERM predictive tool, into which chronological time and temperature data for raw meat processing and storage are entered. The THERM tool then predicts a delta log CFU value for the desired pathogen-product combination. The accuracy of THERM was tested in 20 different inoculation experiments that involved multiple products (coarse-ground beef, skinless chicken breast meat, turkey scapula meat, and ground turkey) and temperature-abuse scenarios. With the time-temperature data from each experiment, THERM accurately predicted the pathogen growth and no growth (with growth defined as delta log CFU > 0.3) in 67, 85, and 95% of the experiments with E. coli 0157:H7, Salmonella serovars, and S. aureus, respectively, and yielded fail-safe predictions in the remaining experiments. We conclude that THERM is a useful tool for qualitatively predicting pathogen behavior (growth and no growth) in raw meats. Potential applications include evaluating process deviations and critical limits under the HACCP (hazard analysis critical control point) system.

Synthetic biology: tools to design microbes for the production of chemicals and fuels.

PubMed

Seo, Sang Woo; Yang, Jina; Min, Byung Eun; Jang, Sungho; Lim, Jae Hyung; Lim, Hyun Gyu; Kim, Seong Cheol; Kim, Se Yeon; Jeong, Jun Hong; Jung, Gyoo Yeol

2013-11-01

The engineering of biological systems to achieve specific purposes requires design tools that function in a predictable and quantitative manner. Recent advances in the field of synthetic biology, particularly in the programmable control of gene expression at multiple levels of regulation, have increased our ability to efficiently design and optimize biological systems to perform designed tasks. Furthermore, implementation of these designs in biological systems highlights the potential of using these tools to build microbial cell factories for the production of chemicals and fuels. In this paper, we review current developments in the design of tools for controlling gene expression at transcriptional, post-transcriptional and post-translational levels, and consider potential applications of these tools. Copyright © 2013 Elsevier Inc. All rights reserved.

Mapping Global Potential Risk of Mango Sudden Decline Disease Caused by Ceratocystis fimbriata.

PubMed

Galdino, Tarcísio Visintin da Silva; Kumar, Sunil; Oliveira, Leonardo S S; Alfenas, Acelino C; Neven, Lisa G; Al-Sadi, Abdullah M; Picanço, Marcelo C

2016-01-01

The Mango Sudden Decline (MSD), also referred to as Mango Wilt, is an important disease of mango in Brazil, Oman and Pakistan. This fungus is mainly disseminated by the mango bark beetle, Hypocryphalus mangiferae (Stebbing), by infected plant material, and the infested soils where it is able to survive for long periods. The best way to avoid losses due to MSD is to prevent its establishment in mango production areas. Our objectives in this study were to: (1) predict the global potential distribution of MSD, (2) identify the mango growing areas that are under potential risk of MSD establishment, and (3) identify climatic factors associated with MSD distribution. Occurrence records were collected from Brazil, Oman and Pakistan where the disease is currently known to occur in mango. We used the correlative maximum entropy based model (MaxEnt) algorithm to assess the global potential distribution of MSD. The MaxEnt model predicted suitable areas in countries where the disease does not already occur in mango, but where mango is grown. Among these areas are the largest mango producers in the world including India, China, Thailand, Indonesia, and Mexico. The mean annual temperature, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest month variables contributed most to the potential distribution of MSD disease. The mango bark beetle vector is known to occur beyond the locations where MSD currently exists and where the model predicted suitable areas, thus showing a high likelihood for disease establishment in areas predicted by our model. Our study is the first to map the potential risk of MSD establishment on a global scale. This information can be used in designing strategies to prevent introduction and establishment of MSD disease, and in preparation of efficient pest risk assessments and monitoring programs.

Mapping Global Potential Risk of Mango Sudden Decline Disease Caused by Ceratocystis fimbriata

PubMed Central

Oliveira, Leonardo S. S.; Alfenas, Acelino C.; Neven, Lisa G.; Al-Sadi, Abdullah M.

2016-01-01

The Mango Sudden Decline (MSD), also referred to as Mango Wilt, is an important disease of mango in Brazil, Oman and Pakistan. This fungus is mainly disseminated by the mango bark beetle, Hypocryphalus mangiferae (Stebbing), by infected plant material, and the infested soils where it is able to survive for long periods. The best way to avoid losses due to MSD is to prevent its establishment in mango production areas. Our objectives in this study were to: (1) predict the global potential distribution of MSD, (2) identify the mango growing areas that are under potential risk of MSD establishment, and (3) identify climatic factors associated with MSD distribution. Occurrence records were collected from Brazil, Oman and Pakistan where the disease is currently known to occur in mango. We used the correlative maximum entropy based model (MaxEnt) algorithm to assess the global potential distribution of MSD. The MaxEnt model predicted suitable areas in countries where the disease does not already occur in mango, but where mango is grown. Among these areas are the largest mango producers in the world including India, China, Thailand, Indonesia, and Mexico. The mean annual temperature, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest month variables contributed most to the potential distribution of MSD disease. The mango bark beetle vector is known to occur beyond the locations where MSD currently exists and where the model predicted suitable areas, thus showing a high likelihood for disease establishment in areas predicted by our model. Our study is the first to map the potential risk of MSD establishment on a global scale. This information can be used in designing strategies to prevent introduction and establishment of MSD disease, and in preparation of efficient pest risk assessments and monitoring programs. PMID:27415625

Prediction of thyroid C-cell carcinogenicity after chronic administration of GLP1-R agonists in rodents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brink, Willem van den; Emerenciana, Annette

Increased incidence of C-cell carcinogenicity has been observed for glucagon-like-protein-1 receptor (GLP-1r) agonists in rodents. It is suggested that the duration of exposure is an indicator of carcinogenic potential in rodents of the different products on the market. Furthermore, the role of GLP-1-related mechanisms in the induction of C-cell carcinogenicity has gained increased attention by regulatory agencies. This study proposes an integrative pharmacokinetic/pharmacodynamic (PKPD) framework to identify explanatory factors and characterize differences in carcinogenic potential of the GLP-1r agonist products. PK models for four products (exenatide QW (once weekly), exenatide BID (twice daily), liraglutide and lixisenatide) were developed using nonlinearmore » mixed effects modelling. Predicted exposure was subsequently linked to GLP-1r stimulation using in vitro GLP-1r potency data. A logistic regression model was then applied to exenatide QW and liraglutide data to assess the relationship between GLP-1r stimulation and thyroid C-cell hyperplasia incidence as pre-neoplastic predictor of a carcinogenic response. The model showed a significant association between predicted GLP-1r stimulation and C-cell hyperplasia after 2 years of treatment. The predictive performance of the model was evaluated using lixisenatide, for which hyperplasia data were accurately described during the validation step. The use of a model-based approach provided insight into the relationship between C-cell hyperplasia and GLP-1r stimulation for all four products, which is not possible with traditional data analysis methods. It can be concluded that both pharmacokinetics (exposure) and pharmacodynamics (potency for GLP-1r) factors determine C-cell hyperplasia incidence in rodents. Our work highlights the pharmacological basis for GLP-1r agonist-induced C-cell carcinogenicity. The concept is promising for application to other drug classes. - Highlights: • An integrative PKPD model is applied to study GLP-1r agonist carcinogenicity. • C-cell carcinogenicity is impacted by both pharmacokinetics and pharmacodynamics. • The relation of GLP-1r stimulation and C-cell hyperplasia appears drug-independent. • Understanding carcinogenic risk needs a pharmacological basis.« less

Hazard assessment of nitrosamine and nitramine by-products of amine-based CCS: alternative approaches.

PubMed

Buist, H E; Devito, S; Goldbohm, R A; Stierum, R H; Venhorst, J; Kroese, E D

2015-04-01

Carbon capture and storage (CCS) technologies are considered vital and economic elements for achieving global CO2 reduction targets, and is currently introduced worldwide (for more information on CCS, consult for example the websites of the International Energy Agency (http://www.iea.org/topics/ccs/) and the Global CCS Institute (http://www.globalccsinstitute.com/)). One prominent CCS technology, the amine-based post-combustion process, may generate nitrosamines and their related nitramines as by-products, the former well known for their potential mutagenic and carcinogenic properties. In order to efficiently assess the carcinogenic potency of any of these by-products this paper reviews and discusses novel prediction approaches consuming less time, money and animals than the traditionally applied 2-year rodent assay. For this, available animal carcinogenicity studies with N-nitroso compounds and nitramines have been used to derive carcinogenic potency values, that were subsequently used to assess the predictive performance of alternative prediction approaches for these chemicals. Promising cancer prediction models are the QSARs developed by the Helguera group, in vitro transformation assays, and the in vivo initiation-promotion, and transgenic animal assays. All these models, however, have not been adequately explored for this purpose, as the number of N-nitroso compounds investigated is yet too limited, and therefore further testing with relevant N-nitroso compounds is needed. Copyright © 2015. Published by Elsevier Inc.

Constraining the optical potential in the search for η-mesic 4He

NASA Astrophysics Data System (ADS)

Skurzok, M.; Moskal, P.; Kelkar, N. G.; Hirenzaki, S.; Nagahiro, H.; Ikeno, N.

2018-07-01

A consistent description of the dd →4Heη and dd → (4Heη)bound→ X cross sections was recently proposed with a broad range of real (V0) and imaginary (W0), η-4He optical potential parameters leading to a good agreement with the dd →4Heη data. Here we compare the predictions of the model below the η production threshold, with the WASA-at-COSY excitation functions for the dd →3HeNπ reactions to put stronger constraints on (V0 ,W0). The allowed parameter space (with |V0 | < ∼ 60 MeV and |W0 | < ∼ 7 MeV estimated at 90% CL) excludes most optical model predictions of η-4He nuclei except for some loosely bound narrow states.

Use of the University of Minnesota Biocatalysis/Biodegradation Database for study of microbial degradation

PubMed Central

2012-01-01

Microorganisms are ubiquitous on earth and have diverse metabolic transformative capabilities important for environmental biodegradation of chemicals that helps maintain ecosystem and human health. Microbial biodegradative metabolism is the main focus of the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD). UM-BBD data has also been used to develop a computational metabolic pathway prediction system that can be applied to chemicals for which biodegradation data is currently lacking. The UM-Pathway Prediction System (UM-PPS) relies on metabolic rules that are based on organic functional groups and predicts plausible biodegradative metabolism. The predictions are useful to environmental chemists that look for metabolic intermediates, for regulators looking for potential toxic products, for microbiologists seeking to understand microbial biodegradation, and others with a wide-range of interests. PMID:22587916

Potential Improvements in Space Weather Forecasting using New Products Developed for the Upcoming DSCOVR Solar Wind Mission

NASA Astrophysics Data System (ADS)

Cash, M. D.; Biesecker, D. A.; Reinard, A. A.

2013-05-01

The Deep Space Climate Observatory (DSCOVR) mission, which is scheduled for launch in late 2014, will provide real-time solar wind thermal plasma and magnetic measurements to ensure continuous monitoring for space weather forecasting. DSCOVR will be located at the L1 Lagrangian point and will include a Faraday cup to measure the proton and alpha components of the solar wind and a triaxial fluxgate magnetometer to measure the magnetic field in three dimensions. The real-time data provided by DSCOVR will be used to generate space weather applications and products that have been demonstrated to be highly accurate and provide actionable information for customers. We present several future space weather products currently under evaluation for development. New potential space weather products for use with DSCOVR real-time data include: automated shock detection, more accurate L1 to Earth delay time, automatic solar wind regime identification, and prediction of rotations in solar wind Bz within magnetic clouds. Additional ideas from the community on future space weather products are encouraged.

Progesterone potentially degrades to potent androgens in surface waters.

PubMed

Ojoghoro, Jasper O; Chaudhary, Abdul J; Campo, Pablo; Sumpter, John P; Scrimshaw, Mark D

2017-02-01

Progesterone is a natural hormone, excreted in higher concentrations than estrogens, and has been detected in the aqueous environment. As with other compounds, it is transformed during wastewater treatment processes and in the environment. However, minor modifications to the structure may result in transformation products which still exhibit biological activity, so understanding what transformation products are formed is of importance. The current study was undertaken to identify putative transformation products resulting from spiking river water with progesterone in a laboratory-based degradation study and hence to follow the metabolic breakdown pathways. On the basis of literature reports and predictions from the EAWAG Biocatalysis/biodegradation database, target putative transformation products were initially monitored under unit resolution mass spectrometry. The identity of these transformation products was confirmed by using accurate-mass quadrupole time-of-flight. The study results highlight that transformation of progesterone can potentially create other classes of steroids, some of which may still be potent, and possess other types of biological activity. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Graphical user interface for yield and dose estimations for cyclotron-produced technetium

NASA Astrophysics Data System (ADS)

Hou, X.; Vuckovic, M.; Buckley, K.; Bénard, F.; Schaffer, P.; Ruth, T.; Celler, A.

2014-07-01

The cyclotron-based 100Mo(p,2n)99mTc reaction has been proposed as an alternative method for solving the shortage of 99mTc. With this production method, however, even if highly enriched molybdenum is used, various radioactive and stable isotopes will be produced simultaneously with 99mTc. In order to optimize reaction parameters and estimate potential patient doses from radiotracers labeled with cyclotron produced 99mTc, the yields for all reaction products must be estimated. Such calculations, however, are extremely complex and time consuming. Therefore, the objective of this study was to design a graphical user interface (GUI) that would automate these calculations, facilitate analysis of the experimental data, and predict dosimetry. The resulting GUI, named Cyclotron production Yields and Dosimetry (CYD), is based on Matlab®. It has three parts providing (a) reaction yield calculations, (b) predictions of gamma emissions and (c) dosimetry estimations. The paper presents the outline of the GUI, lists the parameters that must be provided by the user, discusses the details of calculations and provides examples of the results. Our initial experience shows that the proposed GUI allows the user to very efficiently calculate the yields of reaction products and analyze gamma spectroscopy data. However, it is expected that the main advantage of this GUI will be at the later clinical stage when entering reaction parameters will allow the user to predict production yields and estimate radiation doses to patients for each particular cyclotron run.

Graphical user interface for yield and dose estimations for cyclotron-produced technetium.

PubMed

Hou, X; Vuckovic, M; Buckley, K; Bénard, F; Schaffer, P; Ruth, T; Celler, A

2014-07-07

The cyclotron-based (100)Mo(p,2n)(99m)Tc reaction has been proposed as an alternative method for solving the shortage of (99m)Tc. With this production method, however, even if highly enriched molybdenum is used, various radioactive and stable isotopes will be produced simultaneously with (99m)Tc. In order to optimize reaction parameters and estimate potential patient doses from radiotracers labeled with cyclotron produced (99m)Tc, the yields for all reaction products must be estimated. Such calculations, however, are extremely complex and time consuming. Therefore, the objective of this study was to design a graphical user interface (GUI) that would automate these calculations, facilitate analysis of the experimental data, and predict dosimetry. The resulting GUI, named Cyclotron production Yields and Dosimetry (CYD), is based on Matlab®. It has three parts providing (a) reaction yield calculations, (b) predictions of gamma emissions and (c) dosimetry estimations. The paper presents the outline of the GUI, lists the parameters that must be provided by the user, discusses the details of calculations and provides examples of the results. Our initial experience shows that the proposed GUI allows the user to very efficiently calculate the yields of reaction products and analyze gamma spectroscopy data. However, it is expected that the main advantage of this GUI will be at the later clinical stage when entering reaction parameters will allow the user to predict production yields and estimate radiation doses to patients for each particular cyclotron run.

Predicting Consumer Biomass, Size-Structure, Production, Catch Potential, Responses to Fishing and Associated Uncertainties in the World's Marine Ecosystems.

PubMed

Jennings, Simon; Collingridge, Kate

2015-01-01

Existing estimates of fish and consumer biomass in the world's oceans are disparate. This creates uncertainty about the roles of fish and other consumers in biogeochemical cycles and ecosystem processes, the extent of human and environmental impacts and fishery potential. We develop and use a size-based macroecological model to assess the effects of parameter uncertainty on predicted consumer biomass, production and distribution. Resulting uncertainty is large (e.g. median global biomass 4.9 billion tonnes for consumers weighing 1 g to 1000 kg; 50% uncertainty intervals of 2 to 10.4 billion tonnes; 90% uncertainty intervals of 0.3 to 26.1 billion tonnes) and driven primarily by uncertainty in trophic transfer efficiency and its relationship with predator-prey body mass ratios. Even the upper uncertainty intervals for global predictions of consumer biomass demonstrate the remarkable scarcity of marine consumers, with less than one part in 30 million by volume of the global oceans comprising tissue of macroscopic animals. Thus the apparently high densities of marine life seen in surface and coastal waters and frequently visited abundance hotspots will likely give many in society a false impression of the abundance of marine animals. Unexploited baseline biomass predictions from the simple macroecological model were used to calibrate a more complex size- and trait-based model to estimate fisheries yield and impacts. Yields are highly dependent on baseline biomass and fisheries selectivity. Predicted global sustainable fisheries yield increases ≈4 fold when smaller individuals (< 20 cm from species of maximum mass < 1 kg) are targeted in all oceans, but the predicted yields would rarely be accessible in practice and this fishing strategy leads to the collapse of larger species if fishing mortality rates on different size classes cannot be decoupled. Our analyses show that models with minimal parameter demands that are based on a few established ecological principles can support equitable analysis and comparison of diverse ecosystems. The analyses provide insights into the effects of parameter uncertainty on global biomass and production estimates, which have yet to be achieved with complex models, and will therefore help to highlight priorities for future research and data collection. However, the focus on simple model structures and global processes means that non-phytoplankton primary production and several groups, structures and processes of ecological and conservation interest are not represented. Consequently, our simple models become increasingly less useful than more complex alternatives when addressing questions about food web structure and function, biodiversity, resilience and human impacts at smaller scales and for areas closer to coasts.

Mapping microbial ecosystems and spoilage-gene flow in breweries highlights patterns of contamination and resistance

PubMed Central

Bokulich, Nicholas A; Bergsveinson, Jordyn; Ziola, Barry; Mills, David A

2015-01-01

Distinct microbial ecosystems have evolved to meet the challenges of indoor environments, shaping the microbial communities that interact most with modern human activities. Microbial transmission in food-processing facilities has an enormous impact on the qualities and healthfulness of foods, beneficially or detrimentally interacting with food products. To explore modes of microbial transmission and spoilage-gene frequency in a commercial food-production scenario, we profiled hop-resistance gene frequencies and bacterial and fungal communities in a brewery. We employed a Bayesian approach for predicting routes of contamination, revealing critical control points for microbial management. Physically mapping microbial populations over time illustrates patterns of dispersal and identifies potential contaminant reservoirs within this environment. Habitual exposure to beer is associated with increased abundance of spoilage genes, predicting greater contamination risk. Elucidating the genetic landscapes of indoor environments poses important practical implications for food-production systems and these concepts are translatable to other built environments. DOI: http://dx.doi.org/10.7554/eLife.04634.001 PMID:25756611

Potential impact of a transatlantic trade and Investment partnership on the global forest sector

Treesearch

Joseph Buongiorno; Paul Rougieux; Ahmed Barkaoui; Shushuai Zhu; Patrice Harou

2014-01-01

The effects of a transatlantic trade agreement on the global forest sector were assessed with the Global Forest Products Model, conditional on previous macroeconomic impacts predicted with a general equilibrium model. Comprehensive tariff elimination per se had little effect on the forest sector. However, with deeper reforms and integration consumption would increase...

Responses of plant growth and metabolism to environmental variables predicted from laboratory measurements

Treesearch

Lee D. Hansen; Bruce N. Smith; Richard S. Criddle; J. N. Church

2001-01-01

The Arrhenius activation energies, and therefore temperature coefficients, for rates of catabolic production of ATP and for anabolic use of ATP differ. Because the intracellular concentration of ATP and the phosphorylation potential must be controlled within a narrow range for cell survival, a mechanism must exist to balance these rates during temperature variation in...

Impairment of speech production predicted by lesion load of the left arcuate fasciculus.

PubMed

Marchina, Sarah; Zhu, Lin L; Norton, Andrea; Zipse, Lauryn; Wan, Catherine Y; Schlaug, Gottfried

2011-08-01

Previous studies have suggested that patients' potential for poststroke language recovery is related to lesion size; however, lesion location may also be of importance, particularly when fiber tracts that are critical to the sensorimotor mapping of sounds for articulation (eg, the arcuate fasciculus) have been damaged. In this study, we tested the hypothesis that lesion loads of the arcuate fasciculus (ie, volume of arcuate fasciculus that is affected by a patient's lesion) and of 2 other tracts involved in language processing (the extreme capsule and the uncinate fasciculus) are inversely related to the severity of speech production impairments in patients with stroke with aphasia. Thirty patients with chronic stroke with residual impairments in speech production underwent high-resolution anatomic MRI and a battery of cognitive and language tests. Impairment was assessed using 3 functional measures of spontaneous speech (eg, rate, informativeness, and overall efficiency) as well as naming ability. To quantitatively analyze the relationship between impairment scores and lesion load along the 3 fiber tracts, we calculated tract-lesion overlap volumes for each patient using probabilistic maps of the tracts derived from diffusion tensor images of 10 age-matched healthy subjects. Regression analyses showed that arcuate fasciculus lesion load, but not extreme capsule or uncinate fasciculus lesion load or overall lesion size, significantly predicted rate, informativeness, and overall efficiency of speech as well as naming ability. A new variable, arcuate fasciculus lesion load, complements established voxel-based lesion mapping techniques and, in the future, may potentially be used to estimate impairment and recovery potential after stroke and refine inclusion criteria for experimental rehabilitation programs.

Quantitative and Systems Pharmacology 3. Network-Based Identification of New Targets for Natural Products Enables Potential Uses in Aging-Associated Disorders.

PubMed

Fang, Jiansong; Gao, Li; Ma, Huili; Wu, Qihui; Wu, Tian; Wu, Jun; Wang, Qi; Cheng, Feixiong

2017-01-01

Aging that refers the accumulation of genetic and physiology changes in cells and tissues over a lifetime has been shown a high risk of developing various complex diseases, such as neurodegenerative disease, cardiovascular disease and cancer. Over the past several decades, natural products have been demonstrated as anti-aging interveners via extending lifespan and preventing aging-associated disorders. In this study, we developed an integrated systems pharmacology infrastructure to uncover new indications for aging-associated disorders by natural products. Specifically, we incorporated 411 high-quality aging-associated human genes or human-orthologous genes from mus musculus (MM), saccharomyces cerevisiae (SC), c aenorhabditis elegans (CE), and drosophila melanogaster (DM). We constructed a global drug-target network of natural products by integrating both experimental and computationally predicted drug-target interactions (DTI). We further built the statistical network models for identification of new anti-aging indications of natural products through integration of the curated aging-associated genes and drug-target network of natural products. High accuracy was achieved on the network models. We showcased several network-predicted anti-aging indications of four typical natural products (caffeic acid, metformin, myricetin, and resveratrol) with new mechanism-of-actions. In summary, this study offers a powerful systems pharmacology infrastructure to identify natural products for treatment of aging-associated disorders.

Quantitative and Systems Pharmacology 3. Network-Based Identification of New Targets for Natural Products Enables Potential Uses in Aging-Associated Disorders

PubMed Central

Fang, Jiansong; Gao, Li; Ma, Huili; Wu, Qihui; Wu, Tian; Wu, Jun; Wang, Qi; Cheng, Feixiong

2017-01-01

Aging that refers the accumulation of genetic and physiology changes in cells and tissues over a lifetime has been shown a high risk of developing various complex diseases, such as neurodegenerative disease, cardiovascular disease and cancer. Over the past several decades, natural products have been demonstrated as anti-aging interveners via extending lifespan and preventing aging-associated disorders. In this study, we developed an integrated systems pharmacology infrastructure to uncover new indications for aging-associated disorders by natural products. Specifically, we incorporated 411 high-quality aging-associated human genes or human-orthologous genes from mus musculus (MM), saccharomyces cerevisiae (SC), caenorhabditis elegans (CE), and drosophila melanogaster (DM). We constructed a global drug-target network of natural products by integrating both experimental and computationally predicted drug-target interactions (DTI). We further built the statistical network models for identification of new anti-aging indications of natural products through integration of the curated aging-associated genes and drug-target network of natural products. High accuracy was achieved on the network models. We showcased several network-predicted anti-aging indications of four typical natural products (caffeic acid, metformin, myricetin, and resveratrol) with new mechanism-of-actions. In summary, this study offers a powerful systems pharmacology infrastructure to identify natural products for treatment of aging-associated disorders. PMID:29093681

The Earth Science Vision

NASA Technical Reports Server (NTRS)

Schoeberl, Mark; Rychekewkitsch, Michael; Andrucyk, Dennis; McConaughy, Gail; Meeson, Blanche; Hildebrand, Peter; Einaudi, Franco (Technical Monitor)

2000-01-01

NASA's Earth Science Enterprise's long range vision is to enable the development of a national proactive environmental predictive capability through targeted scientific research and technological innovation. Proactive environmental prediction means the prediction of environmental events and their secondary consequences. These consequences range from disasters and disease outbreak to improved food production and reduced transportation, energy and insurance costs. The economic advantage of this predictive capability will greatly outweigh the cost of development. Developing this predictive capability requires a greatly improved understanding of the earth system and the interaction of the various components of that system. It also requires a change in our approach to gathering data about the earth and a change in our current methodology in processing that data including its delivery to the customers. And, most importantly, it requires a renewed partnership between NASA and its sister agencies. We identify six application themes that summarize the potential of proactive environmental prediction. We also identify four technology themes that articulate our approach to implementing proactive environmental prediction.

Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification.

PubMed

Wang, Hsin-Wei; Lin, Ya-Chi; Pai, Tun-Wen; Chang, Hao-Teng

2011-01-01

Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%).

A Practical Framework Toward Prediction of Breaking Force and Disintegration of Tablet Formulations Using Machine Learning Tools.

PubMed

Akseli, Ilgaz; Xie, Jingjin; Schultz, Leon; Ladyzhynsky, Nadia; Bramante, Tommasina; He, Xiaorong; Deanne, Rich; Horspool, Keith R; Schwabe, Robert

2017-01-01

Enabling the paradigm of quality by design requires the ability to quantitatively correlate material properties and process variables to measureable product performance attributes. Conventional, quality-by-test methods for determining tablet breaking force and disintegration time usually involve destructive tests, which consume significant amount of time and labor and provide limited information. Recent advances in material characterization, statistical analysis, and machine learning have provided multiple tools that have the potential to develop nondestructive, fast, and accurate approaches in drug product development. In this work, a methodology to predict the breaking force and disintegration time of tablet formulations using nondestructive ultrasonics and machine learning tools was developed. The input variables to the model include intrinsic properties of formulation and extrinsic process variables influencing the tablet during manufacturing. The model has been applied to predict breaking force and disintegration time using small quantities of active pharmaceutical ingredient and prototype formulation designs. The novel approach presented is a step forward toward rational design of a robust drug product based on insight into the performance of common materials during formulation and process development. It may also help expedite drug product development timeline and reduce active pharmaceutical ingredient usage while improving efficiency of the overall process. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Microscale to Manufacturing Scale-up of Cell-Free Cytokine Production—A New Approach for Shortening Protein Production Development Timelines

PubMed Central

Zawada, James F; Yin, Gang; Steiner, Alexander R; Yang, Junhao; Naresh, Alpana; Roy, Sushmita M; Gold, Daniel S; Heinsohn, Henry G; Murray, Christopher J

2011-01-01

Engineering robust protein production and purification of correctly folded biotherapeutic proteins in cell-based systems is often challenging due to the requirements for maintaining complex cellular networks for cell viability and the need to develop associated downstream processes that reproducibly yield biopharmaceutical products with high product quality. Here, we present an alternative Escherichia coli-based open cell-free synthesis (OCFS) system that is optimized for predictable high-yield protein synthesis and folding at any scale with straightforward downstream purification processes. We describe how the linear scalability of OCFS allows rapid process optimization of parameters affecting extract activation, gene sequence optimization, and redox folding conditions for disulfide bond formation at microliter scales. Efficient and predictable high-level protein production can then be achieved using batch processes in standard bioreactors. We show how a fully bioactive protein produced by OCFS from optimized frozen extract can be purified directly using a streamlined purification process that yields a biologically active cytokine, human granulocyte-macrophage colony-stimulating factor, produced at titers of 700 mg/L in 10 h. These results represent a milestone for in vitro protein synthesis, with potential for the cGMP production of disulfide-bonded biotherapeutic proteins. Biotechnol. Bioeng. 2011; 108:1570–1578. © 2011 Wiley Periodicals, Inc. PMID:21337337

Skagit River coho salmon life history model—Users’ guide

USGS Publications Warehouse

Woodward, Andrea; Kirby, Grant; Morris, Scott

2017-09-29

Natural resource management is conducted in the context of multiple anthropogenic stressors and is further challenged owing to changing climate. Experiments to determine the effects of climate change on complex ecological systems are nearly impossible. However, using a simulation model to synthesize current understanding of key ecological processes through the life cycle of a fish population can provide a platform for exploring potential effects of and management responses to changing conditions. Potential climate-change scenarios can be imposed, responses can be observed, and the effectiveness of potential actions can be evaluated. This approach is limited owing to future conditions likely deviating in range and timing from conditions used to create the model so that the model is expected to become obsolete. In the meantime, however, the modeling process explicitly states assumptions, clarifies information gaps, and provides a means to better understand which relationships are robust and which are vulnerable to changing climate by observing whether and why model output diverges from actual observations through time. The purpose of the model described herein is to provide such a decision-support tool regarding coho (Oncorhynchus kisutch) salmon for the Sauk-Suiattle Indian Tribe of Washington State.The Skagit coho salmon model is implemented in a system dynamics format and has three primary stocks—(1) predicted smolts, (2) realized smolts, and (3) escapement. “Predicted smolts” are the number of smolts expected based on the number of spawners in any year and the Ricker production curve. Pink salmon (Oncorhynchus gorbuscha) return to the Skagit River in odd years, and when they overlap with juvenile rearing coho salmon, coho smolt production is substantially higher than in non-pink years. Therefore, the model uses alternative Ricker equations to predict smolts depending on whether their juvenile year was a pink or non-pink year. The stock “realized smolts” is calculated based on the expected effect of streamflow conditions to alter the productivity predicted by the Ricker curve. Adverse conditions include scouring flow events that occur when redds are present; high-flow events during winter on juveniles, which can cause fish displacement and adverse water turbidity; and extremely low flows in summer. The stock “escapement” represents the fish remaining after accounting for ocean mortality and harvest. Ocean mortality has been linked with indices of ocean conditions, which are related to ocean biological productivity. Ocean survival also may have a density-dependent component such that lower survival is associated with higher numbers of smolts. The model allows the user to change certain model parameters and inputs, and choose among alternative predictors for certain modeled relations.

Using Reanalysis Data for the Prediction of Seasonal Wind Turbine Power Losses Due to Icing

NASA Astrophysics Data System (ADS)

Burtch, D.; Mullendore, G. L.; Delene, D. J.; Storm, B.

2013-12-01

The Northern Plains region of the United States is home to a significant amount of potential wind energy. However, in winter months capturing this potential power is severely impacted by the meteorological conditions, in the form of icing. Predicting the expected loss in power production due to icing is a valuable parameter that can be used in wind turbine operations, determination of wind turbine site locations and long-term energy estimates which are used for financing purposes. Currently, losses due to icing must be estimated when developing predictions for turbine feasibility and financing studies, while icing maps, a tool commonly used in Europe, are lacking in the United States. This study uses the Modern-Era Retrospective Analysis for Research and Applications (MERRA) dataset in conjunction with turbine production data to investigate various methods of predicting seasonal losses (October-March) due to icing at two wind turbine sites located 121 km apart in North Dakota. The prediction of icing losses is based on temperature and relative humidity thresholds and is accomplished using three methods. For each of the three methods, the required atmospheric variables are determined in one of two ways: using industry-specific software to correlate anemometer data in conjunction with the MERRA dataset and using only the MERRA dataset for all variables. For each season, a percentage of the total expected generated power lost due to icing is determined and compared to observed losses from the production data. An optimization is performed in order to determine the relative humidity threshold that minimizes the difference between the predicted and observed values. Eight seasons of data are used to determine an optimal relative humidity threshold, and a further three seasons of data are used to test this threshold. Preliminary results have shown that the optimized relative humidity threshold for the northern turbine is higher than the southern turbine for all methods. For the three test seasons, the optimized thresholds tend to under-predict the icing losses. However, the threshold determined using boundary layer similarity theory most closely predicts the power losses due to icing versus the other methods. For the northern turbine, the average predicted power loss over the three seasons is 4.65 % while the observed power loss is 6.22 % (average difference of 1.57 %). For the southern turbine, the average predicted power loss and observed power loss over the same time period are 4.43 % and 6.16 %, respectively (average difference of 1.73 %). The three-year average, however, does not clearly capture the variability that exists season-to-season. On examination of each of the test seasons individually, the optimized relative humidity threshold methodology performs better than fixed power loss estimates commonly used in the wind energy industry.

Using VAPEPS for noise control on Space Station Freedom

NASA Technical Reports Server (NTRS)

Badilla, Gloria; Bergen, Thomas; Scharton, Terry

1991-01-01

Noise environmental control is an important design consideration for Space Station Freedom (SSF), both for crew safety and productivity. Acoustic noise requirements are established to eliminate fatigue and potential hearing loss by crew members from long-term exposure and to facilitate speech communication. VAPEPS (VibroAcoustic Payload Environment Prediction System) is currently being applied to SSF for prediction of the on-orbit noise and vibration environments induced in the 50 to 10,000 Hz frequency range. Various sources such as fans, pumps, centrifuges, exercise equipment, and other mechanical devices are used in the analysis. The predictions will be used in design tradeoff studies and to provide confidence that requirements will be met. Preliminary predictions show that the required levels will be exceeded unless substantial noise control measures are incorporated in the SSF design. Predicted levels for an SSF design without acoustic control treatments exceed requirements by 25 dB in some one-third octave frequency bands.

Leachate properties as indicators of methane production process in MSW anaerobic digestion bioreactor landfill

NASA Astrophysics Data System (ADS)

Zeng, Yunmin; Wang, Li'ao; Xu, Tengtun; Li, Jiaxiang; Song, Xue; Hu, Chaochao

2018-03-01

In this paper, bioreactor was used to simulate the municipal solid waste (MSW) biodegradation process of landfill, tracing and testing trash methanogenic process and characteristics of leachate during anaerobic digestion, exploring the relationship between the two processes, aiming to screen out the indicators that can predict the methane production process of anaerobic digestion, which provides the support for real-time adjustment of technological parameters of MSW anaerobic digestion system and ensures the efficient operation of bioreactor landfill. The results showed that MSW digestion gas production rate constant is 0.0259 1/d, biogas production potential is 61.93 L/kg. The concentration of TN in leachate continued to increase, showing the trend of nitrogen accumulation. "Ammonia poisoning" was an important factor inhibiting waste anaerobic digestion gas production. In the anaerobic digestion system, although pH values of leachate can indicate methane production process to some degree, there are obvious lagging behind, so it cannot be used as indicator alone. The TOC/TN value of leachate has a certain indication on the stability of the methane production system. When TOC/TN value was larger than12, anaerobic digestion system was stable along with normal production of biogas. However, when TOC/TN value was lower than 12, the digestive system is unstable and the gas production is small. In the process of anaerobic digestion, the synthesis and transformation of valeric acid is more active. HAc/HVa changed greatly and had obvious inflection points, from which methane production period can be predicted.

Biogeochemical modeling of CO 2 and CH 4 production in anoxic Arctic soil microcosms

DOE PAGES

Tang, Guoping; Zheng, Jianqiu; Xu, Xiaofeng; ...

2016-09-12

Soil organic carbon turnover to CO 2 and CH 4 is sensitive to soil redox potential and pH conditions. But, land surface models do not consider redox and pH in the aqueous phase explicitly, thereby limiting their use for making predictions in anoxic environments. Using recent data from incubations of Arctic soils, we extend the Community Land Model with coupled carbon and nitrogen (CLM-CN) decomposition cascade to include simple organic substrate turnover, fermentation, Fe(III) reduction, and methanogenesis reactions, and assess the efficacy of various temperature and pH response functions. Incorporating the Windermere Humic Aqueous Model (WHAM) enables us to approximatelymore » describe the observed pH evolution without additional parameterization. Though Fe(III) reduction is normally assumed to compete with methanogenesis, the model predicts that Fe(III) reduction raises the pH from acidic to neutral, thereby reducing environmental stress to methanogens and accelerating methane production when substrates are not limiting. Furthermore, the equilibrium speciation predicts a substantial increase in CO 2 solubility as pH increases, and taking into account CO 2 adsorption to surface sites of metal oxides further decreases the predicted headspace gas-phase fraction at low pH. Without adequate representation of these speciation reactions, as well as the impacts of pH, temperature, and pressure, the CO 2 production from closed microcosms can be substantially underestimated based on headspace CO 2 measurements only. Our results demonstrate the efficacy of geochemical models for simulating soil biogeochemistry and provide predictive understanding and mechanistic representations that can be incorporated into land surface models to improve climate predictions.« less

Allergic sensitization: screening methods

PubMed Central

2014-01-01

Experimental in silico, in vitro, and rodent models for screening and predicting protein sensitizing potential are discussed, including whether there is evidence of new sensitizations and allergies since the introduction of genetically modified crops in 1996, the importance of linear versus conformational epitopes, and protein families that become allergens. Some common challenges for predicting protein sensitization are addressed: (a) exposure routes; (b) frequency and dose of exposure; (c) dose-response relationships; (d) role of digestion, food processing, and the food matrix; (e) role of infection; (f) role of the gut microbiota; (g) influence of the structure and physicochemical properties of the protein; and (h) the genetic background and physiology of consumers. The consensus view is that sensitization screening models are not yet validated to definitively predict the de novo sensitizing potential of a novel protein. However, they would be extremely useful in the discovery and research phases of understanding the mechanisms of food allergy development, and may prove fruitful to provide information regarding potential allergenicity risk assessment of future products on a case by case basis. These data and findings were presented at a 2012 international symposium in Prague organized by the Protein Allergenicity Technical Committee of the International Life Sciences Institute’s Health and Environmental Sciences Institute. PMID:24739743

Electron transport chain-dependent and -independent mechanisms of mitochondrial H2O2 emission during long-chain fatty acid oxidation.

PubMed

Seifert, Erin L; Estey, Carmen; Xuan, Jian Y; Harper, Mary-Ellen

2010-02-19

Oxidative stress in skeletal muscle is a hallmark of various pathophysiologic states that also feature increased reliance on long-chain fatty acid (LCFA) substrate, such as insulin resistance and exercise. However, little is known about the mechanistic basis of the LCFA-induced reactive oxygen species (ROS) burden in intact mitochondria, and elucidation of this mechanistic basis was the goal of this study. Specific aims were to determine the extent to which LCFA catabolism is associated with ROS production and to gain mechanistic insights into the associated ROS production. Because intermediates and by-products of LCFA catabolism may interfere with antioxidant mechanisms, we predicted that ROS formation during LCFA catabolism reflects a complex process involving multiple sites of ROS production as well as modified mitochondrial function. Thus, we utilized several complementary approaches to probe the underlying mechanism(s). Using skeletal muscle mitochondria, our findings indicate that even a low supply of LCFA is associated with ROS formation in excess of that generated by NADH-linked substrates. Moreover, ROS production was evident across the physiologic range of membrane potential and was relatively insensitive to membrane potential changes. Determinations of topology and membrane potential as well as use of inhibitors revealed complex III and the electron transfer flavoprotein (ETF) and ETF-oxidoreductase, as likely sites of ROS production. Finally, ROS production was sensitive to matrix levels of LCFA catabolic intermediates, indicating that mitochondrial export of LCFA catabolic intermediates can play a role in determining ROS levels.

Overcoming substrate limitations for improved production of ethylene in E. coli.

PubMed

Lynch, Sean; Eckert, Carrie; Yu, Jianping; Gill, Ryan; Maness, Pin-Ching

2016-01-01

Ethylene is an important industrial compound for the production of a wide variety of plastics and chemicals. At present, ethylene production involves steam cracking of a fossil-based feedstock, representing the highest CO2-emitting process in the chemical industry. Biological ethylene production can be achieved via expression of a single protein, the ethylene-forming enzyme (EFE), found in some bacteria and fungi; it has the potential to provide a sustainable alternative to steam cracking, provided that significant increases in productivity can be achieved. A key barrier is determining factors that influence the availability of substrates for the EFE reaction in potential microbial hosts. In the presence of O2, EFE catalyzes ethylene formation from the substrates α-ketoglutarate (AKG) and arginine. The concentrations of AKG, a key TCA cycle intermediate, and arginine are tightly controlled by an intricate regulatory system that coordinates carbon and nitrogen metabolism. Therefore, reliably predicting which genetic changes will ultimately lead to increased AKG and arginine availability is challenging. We systematically explored the effects of media composition (rich versus defined), gene copy number, and the addition of exogenous substrates and other metabolites on the formation of ethylene in Escherichia coli expressing EFE. Guided by these results, we tested a number of genetic modifications predicted to improve substrate supply and ethylene production, including knockout of competing pathways and overexpression of key enzymes. Several such modifications led to higher AKG levels and higher ethylene productivity, with the best performing strain more than doubling ethylene productivity (from 81 ± 3 to 188 ± 13 nmol/OD600/mL). Both EFE activity and substrate supply can be limiting factors in ethylene production. Targeted modifications in central carbon metabolism, such as overexpression of isocitrate dehydrogenase, and deletion of glutamate synthase or the transcription regulator ArgR, can effectively enhance substrate supply and ethylene productivity. These results not only provide insight into the intricate regulatory network of the TCA cycle, but also guide future pathway and genome-scale engineering efforts to further boost ethylene productivity.

Learning to Predict Chemical Reactions

PubMed Central

Kayala, Matthew A.; Azencott, Chloé-Agathe; Chen, Jonathan H.

2011-01-01

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles respectively are not high-throughput, are not generalizable or scalable, or lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry dataset consisting of 1630 full multi-step reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval, problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of non-productive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal (http://cdb.ics.uci.edu) under the Toolkits section. PMID:21819139

Potential ecological and economic consequences of climate-driven agricultural and silvicultural transformations in central Siberia

NASA Astrophysics Data System (ADS)

Tchebakova, Nadezhda M.; Zander, Evgeniya V.; Pyzhev, Anton I.; Parfenova, Elena I.; Soja, Amber J.

2014-05-01

Increased warming predicted from general circulation models (GCMs) by the end of the century is expected to dramatically impact Siberian forests. Both natural climate-change-caused disturbance (weather, wildfire, infestation) and anthropogenic disturbance (legal/illegal logging) has increased, and their impact on Siberian boreal forest has been mounting over the last three decades. The Siberian BioClimatic Model (SiBCliM) was used to simulate Siberian forests, and the resultant maps show a severely decreased forest that has shifted northwards and a changed composition. Predicted dryer climates would enhance the risks of high fire danger and thawing permafrost, both of which challenge contemporary ecosystems. Our current goal is to evaluate the ecological and economic consequences of climate warming, to optimise economic loss/gain effects in forestry versus agriculture, to question the relative economic value of supporting forestry, agriculture or a mixed agro-forestry at the southern forest border in central Siberia predicted to undergo the most noticeable landcover and landuse changes. We developed and used forest and agricultural bioclimatic models to predict forest shifts; novel tree species and their climatypes are introduced in a warmer climate and/or potential novel agriculture are introduced with a potential variety of crops by the end of the century. We applied two strategies to estimate climate change effects, motivated by forest disturbance. One is a genetic means of assisting trees and forests to be harmonized with a changing climate by developing management strategies for seed transfer to locations that are best ecologically suited to the genotypes in future climates. The second strategy is the establishment of agricultural lands in new forest-steppe and steppe habitats, because the forests would retreat northwards. Currently, food, forage, and biofuel crops primarily reside in the steppe and forest-steppe zones which are known to have favorable climatic and soil resources. During this century, traditional Siberian crops are predicted to gradually shift northwards and new crops, which are currently non-existent but potentially important in a warmer climate, could be introduced in the extreme south. In a future warmer climate, the economic effect of climate change impacts on agriculture was estimated based on a production function approach and the Ricardian model. The production function estimated climate impacts of temperature, precipitation and carbon dioxide levels. The Ricardian model examined climate impacts on the net rent or value of farmland at various regions. The models produced the optimal distribution of agricultural lands between crop, livestock, and forestry sectors to compensate economic losses in forestry in potential landuse areas depending on climatic change.

Effect of quantity and composition of waste on the prediction of annual methane potential from landfills.

PubMed

Cho, Han Sang; Moon, Hee Sun; Kim, Jae Young

2012-04-01

A study was conducted to investigate the effect of waste composition change on the methane production in landfills. An empirical equation for the methane potential of the mixed waste is derived based on the methane potential values of individual waste components and the compositional ratio of waste components. A correction factor was introduced in the equation and was determined from the BMP and lysimeter tests. The equation and LandGEM were applied for a full size landfill and the annual methane potential was estimated. Results showed that the changes in quantity of waste affected the annual methane potential from the landfill more than the changes of waste composition. Copyright © 2012 Elsevier Ltd. All rights reserved.

Enhancing the fathead minnow fish embryo toxicity test: Optimizing embryo production and assessing the utility of additional test endpoints.

PubMed

Roush, Kyle S; Krzykwa, Julie C; Malmquist, Jacob A; Stephens, Dane A; Sellin Jeffries, Marlo K

2018-05-30

The fathead minnow fish embryo toxicity (FET) test has been identified as a potential alternative to toxicity test methods that utilize older fish. However, several challenges have been identified with the fathead minnow FET test, including: 1) difficulties in obtaining appropriately-staged embryos for FET test initiation, 2) a paucity of data comparing fathead minnow FET test performance to the fathead minnow larval growth and survival (LGS) test and 3) a lack of sublethal endpoints that could be used to estimate chronic toxicity and/or predict adverse effects. These challenges were addressed through three study objectives. The first objective was to optimize embryo production by assessing the effect of breeding group composition (number of males and females) on egg production. Results showed that groups containing one male and four females produced the largest clutches, enhancing the likelihood of procuring sufficient numbers of embryos for FET test initiation. The second study objective was to compare the performance of the FET test to that of the fathead minnow LGS test using three reference toxicants. The FET and LGS tests were similar in their ability to predict the acute toxicity of sodium chloride and ethanol, but the FET test was found to be more sensitive than the LGS test for sodium dodecyl sulfate. The last objective of the study was to evaluate the utility and practicality of several sublethal metrics (i.e., growth, developmental abnormalities and growth- and stress-related gene expression) as FET test endpoints. Developmental abnormalities, including pericardial edema and hatch success, were found to offer the most promise as additional FET test endpoints, given their responsiveness, potential for predicting adverse effects, ease of assessment and low cost of measurement. Copyright © 2018 Elsevier Inc. All rights reserved.

Using changes in agricultural utility to quantify future climate-induced risk to conservation.

PubMed

Estes, Lyndon D; Paroz, Lydie-Line; Bradley, Bethany A; Green, Jonathan M H; Hole, David G; Holness, Stephen; Ziv, Guy; Oppenheimer, Michael G; Wilcove, David S

2014-04-01

Much of the biodiversity-related climate change impacts research has focused on the direct effects to species and ecosystems. Far less attention has been paid to the potential ecological consequences of human efforts to address the effects of climate change, which may equal or exceed the direct effects of climate change on biodiversity. One of the most significant human responses is likely to be mediated through changes in the agricultural utility of land. As farmers adapt their practices to changing climates, they may increase pressure on some areas that are important to conserve (conservation lands) whereas lessening it on others. We quantified how the agricultural utility of South African conservation lands may be altered by climate change. We assumed that the probability of an area being farmed is linked to the economic benefits of doing so, using land productivity values to represent production benefit and topographic ruggedness as a proxy for costs associated with mechanical workability. We computed current and future values of maize and wheat production in key conservation lands using the DSSAT4.5 model and 36 crop-climate response scenarios. Most conservation lands had, and were predicted to continue to have, low agricultural utility because of their location in rugged terrain. However, several areas were predicted to maintain or gain high agricultural utility and may therefore be at risk of near-term or future conversion to cropland. Conversely, some areas were predicted to decrease in agricultural utility and may therefore prove easier to protect from conversion. Our study provides an approximate but readily transferable method for incorporating potential human responses to climate change into conservation planning. © 2013 Society for Conservation Biology.

Fungi regulate response of N2O production to warming and grazing in a Tibetan grassland

NASA Astrophysics Data System (ADS)

Zhong, Lei; Wang, Shiping; Xu, Xingliang; Wang, Yanfen; Rui, Yichao; Zhou, Xiaoqi; Shen, Qinhua; Wang, Jinzhi; Jiang, Lili; Luo, Caiyun; Gu, Tianbao; Ma, Wenchao; Chen, Guanyi

2018-03-01

Lack of understanding of the effects of warming and winter grazing on soil fungal contribution to nitrous oxide (N2O) production has limited our ability to predict N2O fluxes under changes in climate and land use management, because soil fungi play an important role in driving terrestrial N cycling. Here, we examined the effects of 10 years' warming and winter grazing on soil N2O emissions potential in an alpine meadow. Our results showed that soil bacteria and fungi contributed 46 % and 54 % to nitrification, and 37 % and 63 % to denitrification, respectively. Neither warming nor winter grazing affected the activity of enzymes responsible for overall nitrification and denitrification. However, warming significantly increased the enzyme activity of bacterial nitrification and denitrification to 53 % and 55 %, respectively. Warming significantly decreased enzyme activity of fungal nitrification and denitrification to 47 % and 45 %, respectively, while winter grazing had no such effect. We conclude that soil fungi could be the main source for N2O production potential in the Tibetan alpine grasslands. Warming and winter grazing may not affect the potential for soil N2O production potential, but climate warming can alter biotic pathways responsible for N2O production. These findings indicate that characterizing how fungal nitrification/denitrification contributes to N2O production, as well as how it responds to environmental and land use changes, can advance our understanding of N cycling. Therefore, our results provide some new insights about ecological controls on N2O production and lead to refine greenhouse gas flux models.

Impacts of marine renewable energy scheme operation on the eutrophication potential of the Severn Estuary, UK

NASA Astrophysics Data System (ADS)

Kadiri, Margaret; Kay, David; Ahmadian, Reza; Bockelmann-Evans, Bettina; Falconer, Roger; Bray, Michaela

2013-04-01

In recent years there has being growing global interest in the generation of electricity from renewable resources. Amongst these, marine energy resource is now being considered to form a significant part of the energy mix, with plans for the implementation of several marine renewable energy schemes such as barrages and tidal stream turbines around the UK in the near future. Although marine energy presents a great potential for future electricity generation, there are major concerns over its potential impacts, particularly barrages, on the hydro-environment. Previous studies have shown that a barrage could significantly alter the hydrodynamic regime and tidal flow characteristics of an estuary, with changes to sediment transport (Kadiri et al., 2012). However, changes to nutrients have been overlooked to date. Hence, considerable uncertainty remains as to how a barrage would affect the trophic status of an estuary. This is particularly important because eutrophication can lead to algal toxin production and increased mortality of aquatic invertebrates and fish populations. Therefore, this study examines the impacts of the two different modes of operation of a barrage (i.e. ebb generation and flood-ebb generation) on the eutrophication potential of the Severn Estuary using a simplified model developed by the UK's Comprehensive Studies Task Team (CSTT). The model uses a set of equations and site-specific input data to predict equilibrium dissolved nutrient concentrations, phytoplankton biomass, light-controlled phytoplankton growth rate and primary production which are compared against CSTT set standards for assessing the eutrophic status of estuaries and coastal waters. The estuary volume and tidal flushing time under the two operating modes were estimated using a hydrodynamic model and field surveys were conducted to obtain dissolved nitrate and phosphate concentrations which served as input data. The predicted equilibrium dissolved nitrate and phosphate concentrations were slightly greater under ebb generation compared to flood-ebb generation. However, the concentrations did not exceed the CSTT standard indicating that hypernutrification is not likely to occur. Similarly, the predicted phytoplankton biomass and light-controlled growth rate under both ebb and flood-ebb generation were less than the CSTT standards suggesting no likelihood of eutrophication. The predicted phytoplankton production, however, was significantly greater under ebb generation compared to flood-ebb generation due to restricted tidal flushing decreasing nutrient dispersion and increasing the residence time of nutrient in the region upstream of the barrage. This study also examines the wider positive ecological implications of these findings for the Severn Estuary. Reference Kadiri, M., Ahmadian, R., Bockelmann-Evans, B., Rauen, W., and Falconer, R., 2012. A review of the potential water quality impacts of tidal renewable energy systems. Renewable and Sustainable Energy Reviews, 16: 329- 341.

PERF - A new approach to the experimental study of radiative aerodynamic heating and radiative blockage by ablation products

NASA Technical Reports Server (NTRS)

Walberg, G.

1974-01-01

The present work describes a facility designed to validate the various aspects of radiative flow field theory, including the absorption of shock layer radiation by ablation products. The facility is capable of producing radiation with a spectrum similar to that of an entry vehicle shock layer and is designed to allow measurements at vacuum ultraviolet wavelengths where the most significant absorption by ablation products is predicted to occur. The design concept of the facility is presented along with results of theoretical analyses carried out to assess its research potential. Experimental data obtained during tests that simulated earth and Venusian entry and in which simulated ablation products were injected into the stagnation region flow field are discussed.

Bulk tank milk prevalence and production losses, spatial analysis, and predictive risk mapping of Ostertagia ostertagi infections in Mexican cattle herds.

PubMed

Villa-Mancera, Abel; Pastelín-Rojas, César; Olivares-Pérez, Jaime; Córdova-Izquierdo, Alejandro; Reynoso-Palomar, Alejandro

2018-05-01

This study investigated the prevalence, production losses, spatial clustering, and predictive risk mapping in different climate zones in five states of Mexico. The bulk tank milk samples obtained between January and April 2015 were analyzed for antibodies against Ostertagia ostertagi using the Svanovir ELISA. A total of 1204 farm owners or managers answered the questionnaire. The overall herd prevalence and mean optical density ratio (ODR) of parasite were 61.96% and 0.55, respectively. Overall, the production loss was approximately 0.542 kg of milk per parasited cow per day (mean ODR = 0.92, 142 farms, 11.79%). The spatial disease cluster analysis using SatScan software indicated that two high-risk clusters were observed. In the multivariable analysis, three models were tested for potential association with the ELISA results supported by climatic, environmental, and management factors. The final logistic regression model based on both climatic/environmental and management variables included the factors rainfall, elevation, land surface temperature (LST) day, and parasite control program that were significantly associated with an increased risk of infection. Geostatistical kriging was applied to generate a risk map for the presence of parasite in dairy cattle herds in Mexico. The results indicate that climatic and meteorological factors had a higher potential impact on the spatial distribution of O. ostertagi than the management factors.

Identification of Linkages between EDCs in Personal Care Products and Breast Cancer through Data Integration Combined with Gene Network Analysis

PubMed Central

Kim, Jongwoon

2017-01-01

Approximately 1000 chemicals have been reported to possibly have endocrine disrupting effects, some of which are used in consumer products, such as personal care products (PCPs) and cosmetics. We conducted data integration combined with gene network analysis to: (i) identify causal molecular mechanisms between endocrine disrupting chemicals (EDCs) used in PCPs and breast cancer; and (ii) screen candidate EDCs associated with breast cancer. Among EDCs used in PCPs, four EDCs having correlation with breast cancer were selected, and we curated 27 common interacting genes between those EDCs and breast cancer to perform the gene network analysis. Based on the gene network analysis, ESR1, TP53, NCOA1, AKT1, and BCL6 were found to be key genes to demonstrate the molecular mechanisms of EDCs in the development of breast cancer. Using GeneMANIA, we additionally predicted 20 genes which could interact with the 27 common genes. In total, 47 genes combining the common and predicted genes were functionally grouped with the gene ontology and KEGG pathway terms. With those genes, we finally screened candidate EDCs for their potential to increase breast cancer risk. This study highlights that our approach can provide insights to understand mechanisms of breast cancer and identify potential EDCs which are in association with breast cancer. PMID:28973975

Early pleural fluid dynamics following video-assisted thoracoscopic lobectomy has limited clinical value

PubMed Central

Holbek, Bo Laksáfoss; Petersen, René Horsleben; Kehlet, Henrik

2017-01-01

The objective of this study was to evaluate the potential of predicting the pleural fluid output in patients after video-assisted thoracoscopic lobectomy of the lung. Detailed measurements of continuous fluid output were obtained prospectively using an electronic thoracic drainage device (Thopaz+™, Medela AG, Switzerland). Patients were divided into high (≥500 mL) and low (<500 mL) 24-hour fluid output, and detailed flow curves were plotted graphically to identify arithmetic patterns predicting fluid output in the early (≤24 hours) and later (24–48 hours) post-operative phase. Furthermore, multiple logistic regression analysis was used to predict high 24-hour fluid output using baseline data. Data were obtained from 50 patients, where 52% had a fluid output of <500 mL/24 hours. From visual assessment of flow curves, patients were grouped according to fluid output 6 hours postoperatively. An output ≥200 mL/6 hours was predictive of ‘high 24-hour fluid output’ (P<0.0001). However, 33% of patients with <200 mL/6 hours ended with a ‘high 24-hour fluid output’. Baseline data showed no predictive value of fluid production, and 24-hour fluid output had no predictive value of fluid output between 24 and 48 hours. Assessment of initial fluid production may predict high 24-hour fluid output (≥500 mL) but seems to lack clinical value in drain removal criteria. PMID:28840021

Use of in vitro methods to rank surfactants for irritation potential in support of new product development.

PubMed

Casterton, P L; Potts, L F; Klein, B D

1994-08-01

11 surfactant raw materials with potential applications in light-duty liquid cleaning products were evaluated in vitro using a human skin analogue (ATS SKIN(2) Model ZK1100) for predicting cytotoxicity (MTT reduction) and inflammation [prostaglandin E(2) (PGE(2)) release]. Two of the 11 raw materials, both in the same compound family, were selected to be individually combined with each of the other nine in a 90:10 (raw:selected raw) mixture. Selection criteria were based on desired performance characteristics and low irritation potential as suggested from the individual surfactant assay data. To determine whether irritation potential was mitigated, MTT and PGE(2) scores were again determined for each of the 18 combinations with the resulting data being compared with the untreated raw material data. A plot of the data indicated that one of two selected materials may have an 'anti-irritant' effect. For raw materials with intrinsic MTT scores of less than 50 mug/ml and with the original data corrected for possible dilution effects, a statistical comparison between individual raw materials and the two sets of combinations was done using a one-sample analysis. Both cytotoxicity (MTT) and inflammation (PGE(2)) were significantly decreased by the milder of the two selected raw materials. By factoring the data into future new product decisions, this methodology has become a useful and practical tool for Amway product development.

Expanding the biomass resource: sustainable oil production via fast pyrolysis of low input high diversity biomass and the potential integration of thermochemical and biological conversion routes.

PubMed

Corton, J; Donnison, I S; Patel, M; Bühle, L; Hodgson, E; Wachendorf, M; Bridgwater, A; Allison, G; Fraser, M D

2016-09-01

Waste biomass is generated during the conservation management of semi-natural habitats, and represents an unused resource and potential bioenergy feedstock that does not compete with food production. Thermogravimetric analysis was used to characterise a representative range of biomass generated during conservation management in Wales. Of the biomass types assessed, those dominated by rush ( Juncus effuses ) and bracken ( Pteridium aquilinum ) exhibited the highest and lowest volatile compositions respectively and were selected for bench scale conversion via fast pyrolysis. Each biomass type was ensiled and a sub-sample of silage was washed and pressed. Demineralization of conservation biomass through washing and pressing was associated with higher oil yields following fast pyrolysis. The oil yields were within the published range established for the dedicated energy crops miscanthus and willow. In order to examine the potential a multiple output energy system was developed with gross power production estimates following valorisation of the press fluid, char and oil. If used in multi fuel industrial burners the char and oil alone would displace 3.9 × 10 5  tonnes per year of No. 2 light oil using Welsh biomass from conservation management. Bioenergy and product development using these feedstocks could simultaneously support biodiversity management and displace fossil fuels, thereby reducing GHG emissions. Gross power generation predictions show good potential.

Coupling of Bayesian Networks with GIS for wildfire risk assessment on natural and agricultural areas of the Mediterranean

NASA Astrophysics Data System (ADS)

Scherb, Anke; Papakosta, Panagiota; Straub, Daniel

2014-05-01

Wildfires cause severe damages to ecosystems, socio-economic assets, and human lives in the Mediterranean. To facilitate coping with wildfire risks, an understanding of the factors influencing wildfire occurrence and behavior (e.g. human activity, weather conditions, topography, fuel loads) and their interaction is of importance, as is the implementation of this knowledge in improved wildfire hazard and risk prediction systems. In this project, a probabilistic wildfire risk prediction model is developed, with integrated fire occurrence and fire propagation probability and potential impact prediction on natural and cultivated areas. Bayesian Networks (BNs) are used to facilitate the probabilistic modeling. The final BN model is a spatial-temporal prediction system at the meso scale (1 km2 spatial and 1 day temporal resolution). The modeled consequences account for potential restoration costs and production losses referred to forests, agriculture, and (semi-) natural areas. BNs and a geographic information system (GIS) are coupled within this project to support a semi-automated BN model parameter learning and the spatial-temporal risk prediction. The coupling also enables the visualization of prediction results by means of daily maps. The BN parameters are learnt for Cyprus with data from 2006-2009. Data from 2010 is used as validation data set. A special focus is put on the performance evaluation of the BN for fire occurrence, which is modeled as binary classifier and thus, could be validated by means of Receiver Operator Characteristic (ROC) curves. With the final best models, AUC values of more than 70% for validation could be achieved, which indicates potential for reliable prediction performance via BN. Maps of selected days in 2010 are shown to illustrate final prediction results. The resulting system can be easily expanded to predict additional expected damages in the mesoscale (e.g. building and infrastructure damages). The system can support planning of preventive measures (e.g. state resources allocation for wildfire prevention and preparedness) and assist recuperation plans of damaged areas.

Arginine deiminase pathway provides ATP and boosts growth of the gas-fermenting acetogen Clostridium autoethanogenum.

PubMed

Valgepea, Kaspar; Loi, Kim Q; Behrendorff, James B; Lemgruber, Renato de S P; Plan, Manuel; Hodson, Mark P; Köpke, Michael; Nielsen, Lars K; Marcellin, Esteban

2017-05-01

Acetogens are attractive organisms for the production of chemicals and fuels from inexpensive and non-food feedstocks such as syngas (CO, CO 2 and H 2 ). Expanding their product spectrum beyond native compounds is dictated by energetics, particularly ATP availability. Acetogens have evolved sophisticated strategies to conserve energy from reduction potential differences between major redox couples, however, this coupling is sensitive to small changes in thermodynamic equilibria. To accelerate the development of strains for energy-intensive products from gases, we used a genome-scale metabolic model (GEM) to explore alternative ATP-generating pathways in the gas-fermenting acetogen Clostridium autoethanogenum. Shadow price analysis revealed a preference of C. autoethanogenum for nine amino acids. This prediction was experimentally confirmed under heterotrophic conditions. Subsequent in silico simulations identified arginine (ARG) as a key enhancer for growth. Predictions were experimentally validated, and faster growth was measured in media containing ARG (t D ~4h) compared to growth on yeast extract (t D ~9h). The growth-boosting effect of ARG was confirmed during autotrophic growth. Metabolic modelling and experiments showed that acetate production is nearly abolished and fast growth is realised by a three-fold increase in ATP production through the arginine deiminase (ADI) pathway. The involvement of the ADI pathway was confirmed by metabolomics and RNA-sequencing which revealed a ~500-fold up-regulation of the ADI pathway with an unexpected down-regulation of the Wood-Ljungdahl pathway. The data presented here offer a potential route for supplying cells with ATP, while demonstrating the usefulness of metabolic modelling for the discovery of native pathways for stimulating growth or enhancing energy availability. Copyright © 2017 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Using an agent-based model to evaluate the effect of producer specialization on the epidemiological resilience of livestock production networks

PubMed Central

2018-01-01

An agent-based computer model that builds representative regional U.S. hog production networks was developed and employed to assess the potential impact of the ongoing trend towards increased producer specialization upon network-level resilience to catastrophic disease outbreaks. Empirical analyses suggest that the spatial distribution and connectivity patterns of contact networks often predict epidemic spreading dynamics. Our model heuristically generates realistic systems composed of hog producer, feed mill, and slaughter plant agents. Network edges are added during each run as agents exchange livestock and feed. The heuristics governing agents’ contact patterns account for factors including their industry roles, physical proximities, and the age of their livestock. In each run, an infection is introduced, and may spread according to probabilities associated with the various modes of contact. For each of three treatments—defined by one-phase, two-phase, and three-phase production systems—a parameter variation experiment examines the impact of the spatial density of producer agents in the system upon the length and size of disease outbreaks. Resulting data show phase transitions whereby, above some density threshold, systemic outbreaks become possible, echoing findings from percolation theory. Data analysis reveals that multi-phase production systems are vulnerable to catastrophic outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outbreak scales and durations. Key differences in network-level metrics shed light on these results, suggesting that the absence of potentially-bridging producer–producer edges may be largely responsible for the superior disease resilience of single-phase “farrow to finish” production systems. PMID:29522574

Gas exchange recovery following natural drought is rapid unless limited by loss of leaf hydraulic conductance: evidence from an evergreen woodland.

PubMed

Skelton, Robert P; Brodribb, Timothy J; McAdam, Scott A M; Mitchell, Patrick J

2017-09-01

Drought can cause major damage to plant communities, but species damage thresholds and postdrought recovery of forest productivity are not yet predictable. We used an El Niño drought event as a natural experiment to test whether postdrought recovery of gas exchange could be predicted by properties of the water transport system, or if metabolism, primarily high abscisic acid concentration, might delay recovery. We monitored detailed physiological responses, including shoot sapflow, leaf gas exchange, leaf water potential and foliar abscisic acid (ABA), during drought and through the subsequent rehydration period for a sample of eight canopy and understory species. Severe drought caused major declines in leaf water potential, elevated foliar ABA concentrations and reduced stomatal conductance and assimilation rates in our eight sample species. Leaf water potential surpassed levels associated with incipient loss of leaf hydraulic conductance in four species. Following heavy rainfall gas exchange in all species, except those trees predicted to have suffered hydraulic impairment, recovered to prestressed rates within 1 d. Recovery of plant gas exchange was rapid and could be predicted by the hydraulic safety margin, providing strong support for leaf vulnerability to water deficit as an index of damage under natural drought conditions. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

The Impact of a Potential Shale Gas Development in Germany and the United Kingdom on Local and Regional Air Quality

NASA Astrophysics Data System (ADS)

Weger, L.; Lupascu, A.; Cremonese, L.; Butler, T. M.

2017-12-01

Numerous countries in Europe that possess domestic shale gas reserves are considering exploiting this unconventional gas resource as part of their energy transition agenda. While natural gas generates less CO2 emissions upon combustion compared to coal or oil, making it attractive as a bridge in the transition from fossil fuels to renewables, production of shale gas leads to emissions of CH4 and air pollutants such as NOx, VOCs and PM. These gases in turn influence the climate as well as air quality. In this study, we investigate the impact of a potential shale gas development in Germany and the United Kingdom on local and regional air quality. This work builds on our previous study in which we constructed emissions scenarios based on shale gas utilization in these counties. In order to explore the influence of shale gas production on air quality, we investigate emissions predicted from our shale gas scenarios with the Weather Research and Forecasting model with chemistry (WRF-Chem) model. In order to do this, we first design a model set-up over Europe and evaluate its performance for the meteorological and chemical parameters. Subsequently we add shale gas emissions fluxes based on the scenarios over the area of the grid in which the shale gas activities are predicted to occur. Finally, we model these emissions and analyze the impact on air quality on both a local and regional scale. The aims of this work are to predict the range of adverse effects on air quality, highlight the importance of emissions control strategies in reducing air pollution, to promote further discussion, and to provide policy makers with information for decision making on a potential shale gas development in the two study countries.

Model-based analysis of N-glycosylation in Chinese hamster ovary cells

PubMed Central

Krambeck, Frederick J.; Bennun, Sandra V.; Betenbaugh, Michael J.

2017-01-01

The Chinese hamster ovary (CHO) cell is the gold standard for manufacturing of glycosylated recombinant proteins for production of biotherapeutics. The similarity of its glycosylation patterns to the human versions enable the products of this cell line favorable pharmacokinetic properties and lower likelihood of causing immunogenic responses. Because glycan structures are the product of the concerted action of intracellular enzymes, it is difficult to predict a priori how the effects of genetic manipulations alter glycan structures of cells and therapeutic properties. For that reason, quantitative models able to predict glycosylation have emerged as promising tools to deal with the complexity of glycosylation processing. For example, an earlier version of the same model used in this study was used by others to successfully predict changes in enzyme activities that could produce a desired change in glycan structure. In this study we utilize an updated version of this model to provide a comprehensive analysis of N-glycosylation in ten Chinese hamster ovary (CHO) cell lines that include a wild type parent and nine mutants of CHO, through interpretation of previously published mass spectrometry data. The updated N-glycosylation mathematical model contains up to 50,605 glycan structures. Adjusting the enzyme activities in this model to match N-glycan mass spectra produces detailed predictions of the glycosylation process, enzyme activity profiles and complete glycosylation profiles of each of the cell lines. These profiles are consistent with biochemical and genetic data reported previously. The model-based results also predict glycosylation features of the cell lines not previously published, indicating more complex changes in glycosylation enzyme activities than just those resulting directly from gene mutations. The model predicts that the CHO cell lines possess regulatory mechanisms that allow them to adjust glycosylation enzyme activities to mitigate side effects of the primary loss or gain of glycosylation function known to exist in these mutant cell lines. Quantitative models of CHO cell glycosylation have the potential for predicting how glycoengineering manipulations might affect glycoform distributions to improve the therapeutic performance of glycoprotein products. PMID:28486471

Thinking ahead: The role and roots of prediction in language comprehension

PubMed Central

Federmeier, Kara D.

2009-01-01

Reviewed are studies using event-related potentials to examine when and how sentence context information is used during language comprehension. Results suggest that, when it can, the brain uses context to predict features of likely upcoming items. However, although prediction seems important for comprehension, it also appears susceptible to age-related deterioration and can be associated with processing costs. The brain may address this trade-off by employing multiple processing strategies, distributed across the two cerebral hemispheres. In particular, left hemisphere language processing seems to be oriented toward prediction and the use of top-down cues, whereas right hemisphere comprehension is more bottom-up, biased toward the veridical maintenance of information. Such asymmetries may arise, in turn, because language comprehension mechanisms are integrated with language production mechanisms only in the left hemisphere (the PARLO framework). PMID:17521377

Rotational Parameters from Vibronic Eigenfunctions of Jahn-Teller Active Molecules

NASA Astrophysics Data System (ADS)

Garner, Scott M.; Miller, Terry A.

2017-06-01

The structure in rotational spectra of many free radical molecules is complicated by Jahn-Teller distortions. Understanding the magnitudes of these distortions is vital to determining the equilibrium geometric structure and details of potential energy surfaces predicted from electronic structure calculations. For example, in the recently studied {\\widetilde{A}^2E^{''} } state of the NO_3 radical, the magnitudes of distortions are yet to be well understood as results from experimental spectroscopic studies of its vibrational and rotational structure disagree with results from electronic structure calculations of the potential energy surface. By fitting either vibrationally resolved spectra or vibronic levels determined by a calculated potential energy surface, we obtain vibronic eigenfunctions for the system as linear combinations of basis functions from products of harmonic oscillators and the degenerate components of the electronic state. Using these vibronic eigenfunctions we are able to predict parameters in the rotational Hamiltonian such as the Watson Jahn-Teller distortion term, h_1, and compare with the results from the analysis of rotational experiments.

An in silico algal toxicity model with a wide applicability potential for industrial chemicals and pharmaceuticals.

PubMed

Önlü, Serli; Saçan, Melek Türker

2017-04-01

The authors modeled the 72-h algal toxicity data of hundreds of chemicals with different modes of action as a function of chemical structures. They developed mode of action-based local quantitative structure-toxicity relationship (QSTR) models for nonpolar and polar narcotics as well as a global QSTR model with a wide applicability potential for industrial chemicals and pharmaceuticals. The present study rigorously evaluated the generated models, meeting the Organisation for Economic Co-operation and Development principles of robustness, validity, and transparency. The proposed global model had a broad structural coverage for the toxicity prediction of diverse chemicals (some of which are high-production volume chemicals) with no experimental toxicity data. The global model is potentially useful for endpoint predictions, the evaluation of algal toxicity screening, and the prioritization of chemicals, as well as for the decision of further testing and the development of risk-management measures in a scientific and regulatory frame. Environ Toxicol Chem 2017;36:1012-1019. © 2016 SETAC. © 2016 SETAC.

Evaluation of an operational real-time irrigation scheduling scheme for drip irrigated citrus fields in Picassent, Spain

NASA Astrophysics Data System (ADS)

Li, Dazhi; Hendricks-Franssen, Harrie-Jan; Han, Xujun; Jiménez Bello, Miguel Angel; Martínez Alzamora, Fernando; Vereecken, Harry

2017-04-01

Irrigated agriculture accounts worldwide for 40% of food production and 70% of fresh water withdrawals. Irrigation scheduling aims to minimize water use while maintaining the agricultural production. In this study we were concerned with the real-time automatic control of irrigation, which calculates daily water allocation by combining information from soil moisture sensors and a land surface model. The combination of soil moisture measurements and predictions by the Community Land Model (CLM) using sequential data assimilation (DA) is a promising alternative to improve the estimate of soil and plant water status. The LETKF (Local Ensemble Transform Kalman Filter) was chosen to assimilate soil water content measured by FDR (Frequency Domain Reflectometry) into CLM and improve the initial (soil moisture) conditions for the next model run. In addition, predictions by the GFS (Global Forecast System) atmospheric simulation model were used as atmospheric input data for CLM to predict an ensemble of possible soil moisture evolutions for the next days. The difference between predicted and target soil water content is defined as the water deficit, and the irrigation amount was calculated by the integrated water deficit over the root zone. The corresponding irrigation time to apply the required water was introduced in SCADA (supervisory control and data acquisition system) for each citrus field. In total 6 fields were irrigated according our optimization approach including data assimilation (CLM-DA) and there were also 2 fields following the FAO (Food and Agriculture Organization) water balance method and 4 fields controlled by farmers as reference. During the real-time irrigation campaign in Valencia from July to October in 2015 and June to October in 2016, the applied irrigation amount, stem water potential and soil moisture content were recorded. The data indicated that 5% 20% less irrigation water was needed for the CLM-DA scheduled fields than for the other fields following the FAO or farmers' method. Stem water potential data indicated that the CLM-DA fields were not suffering from water stress during most of the irrigation period. Even though the CLM-DA fields received the least irrigation water, the orange production was not suppressed either. Our results show the water saving potential of the CLM-DA method compared to other traditional irrigation methods.

Landfill gas control at military installations. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shafer, R.A.; Renta-Babb, A.; Bandy, J.T.

1984-01-01

This report provides information useful to Army personnel responsible for recognizing and solving potential problems from gas generated by landfills. Information is provided on recognizing and gauging the magnitude of landfill gas problems; selecting appropriate gas control strategies, procedures, and equipment; use of computer modeling to predict gas production and migration and the success of gas control devices; and safety considerations.

Simulated effects of converting pasture to energy cane for bioenergy with the daycent model: predicting changes to greenhouse gas emissions and soil carbon

USDA-ARS?s Scientific Manuscript database

Bioenergy related land use change will likely alter biogeochemical cycles and global greenhouse gas budgets. Energy cane (Saccharum officinarum L.) is a sugarcane variety and an emerging biofuel feedstock for cellulosic bio-ethanol production. It has a potential for high yields and can be grown on f...

Bioluminescence for determining energy state of plants

NASA Technical Reports Server (NTRS)

Ching, T. M.

1975-01-01

Bioluminescence produced by the luciferin-luciferase system is a very sensitive assay for ATP content in extracts of plant materials. The ATP test for seed and pollen viability and vigor is presented, along with prediction of high growth potential and productivity in new crosses and selections of breeding materials. ATP as an indicator for environmental quality, stresses, and metabolic regulation is also considered.

Review of nitrogen fate models applicable to forest landscapes in the Southern U.S.

Treesearch

D. M. Amatya; C. G. Rossi; A. Saleh; Z. Dai; M. A. Youssef; R. G. Williams; D. D. Bosch; G. M. Chescheir; G. Sun; R. W. Skaggs; C. C. Trettin; E. D. Vance; J. E. Nettles; S. Tian

2013-01-01

Assessing the environmental impacts of fertilizer nitrogen (N) used to increase productivity in managed forests is complex due to a wide range of abiotic and biotic factors affecting its forms and movement. Models developed to predict fertilizer N fate (e.g., cycling processes) and water quality impacts vary widely in their design, scope, and potential application. We...

Preliminary stochastic model for managing Vibrio parahaemolyticus and total viable bacterial counts in a Pacific oyster (Crassostrea gigas) supply chain.

PubMed

Fernandez-Piquer, Judith; Bowman, John P; Ross, Tom; Estrada-Flores, Silvia; Tamplin, Mark L

2013-07-01

Vibrio parahaemolyticus can accumulate and grow in oysters stored without refrigeration, representing a potential food safety risk. High temperatures during oyster storage can lead to an increase in total viable bacteria counts, decreasing product shelf life. Therefore, a predictive tool that allows the estimation of both V. parahaemolyticus populations and total viable bacteria counts in parallel is needed. A stochastic model was developed to quantitatively assess the populations of V. parahaemolyticus and total viable bacteria in Pacific oysters for six different supply chain scenarios. The stochastic model encompassed operations from oyster farms through consumers and was built using risk analysis software. Probabilistic distributions and predictions for the percentage of Pacific oysters containing V. parahaemolyticus and high levels of viable bacteria at the point of consumption were generated for each simulated scenario. This tool can provide valuable information about V. parahaemolyticus exposure and potential control measures and can help oyster companies and regulatory agencies evaluate the impact of product quality and safety during cold chain management. If coupled with suitable monitoring systems, such models could enable preemptive action to be taken to counteract unfavorable supply chain conditions.

Continuous high-solids corn liquefaction and fermentation with stripping of ethanol.

PubMed

Taylor, Frank; Marquez, Marco A; Johnston, David B; Goldberg, Neil M; Hicks, Kevin B

2010-06-01

Removal of ethanol from the fermentor during fermentation can increase productivity and reduce the costs for dewatering the product and coproduct. One approach is to recycle the fermentor contents through a stripping column, where a non-condensable gas removes ethanol to a condenser. Previous research showed that this approach is feasible. Savings of $0.03 per gallon were predicted at 34% corn dry solids. Greater savings were predicted at higher concentration. Now the feasibility has been demonstrated at over 40% corn dry solids, using a continuous corn liquefaction system. A pilot plant, that continuously fed corn meal at more than one bushel (25 kg) per day, was operated for 60 consecutive days, continuously converting 95% of starch and producing 88% of the maximum theoretical yield of ethanol. A computer simulation was used to analyze the results. The fermentation and stripping systems were not significantly affected when the CO(2) stripping gas was partially replaced by nitrogen or air, potentially lowering costs associated with the gas recycle loop. It was concluded that previous estimates of potential cost savings are still valid. (c) 2010. Published by Elsevier Ltd. All rights reserved.

Microwave pretreatment of switchgrass for bioethanol production

NASA Astrophysics Data System (ADS)

Keshwani, Deepak Radhakrishin

Lignocellulosic materials are promising alternative feedstocks for bioethanol production. These materials include agricultural residues, cellulosic waste such as newsprint and office paper, logging residues, and herbaceous and woody crops. However, the recalcitrant nature of lignocellulosic biomass necessitates a pretreatment step to improve the yield of fermentable sugars. The overall goal of this dissertation is to expand the current state of knowledge on microwave-based pretreatment of lignocellulosic biomass. Existing research on bioenergy and value-added applications of switchgrass is reviewed in Chapter 2. Switchgrass is an herbaceous energy crop native to North America and has high biomass productivity, potentially low requirements for agricultural inputs and positive environmental impacts. Based on results from test plots, yields in excess of 20 Mg/ha have been reported. Environmental benefits associated with switchgrass include the potential for carbon sequestration, nutrient recovery from run-off, soil remediation and provision of habitats for grassland birds. Published research on pretreatment of switchgrass reported glucose yields ranging from 70-90% and xylose yields ranging from 70-100% after hydrolysis and ethanol yields ranging from 72-92% after fermentation. Other potential value-added uses of switchgrass include gasification, bio-oil production, newsprint production and fiber reinforcement in thermoplastic composites. Research on microwave-based pretreatment of switchgrass and coastal bermudagrass is presented in Chapter 3. Pretreatments were carried out by immersing the biomass in dilute chemical reagents and exposing the slurry to microwave radiation at 250 watts for residence times ranging from 5 to 20 minutes. Preliminary experiments identified alkalis as suitable chemical reagents for microwave-based pretreatment. An evaluation of different alkalis identified sodium hydroxide as the most effective alkali reagent. Under optimum pretreatment conditions, 82% glucose and 63% xylose yields were achieved for switchgrass, and 87% glucose and 59% xylose yields were achieved for coastal bermudagrass following enzymatic hydrolysis of the pretreated biomass. The optimum enzyme loadings were 15 FPU/g and 20 CBU/g for switchgrass and 10 FPU/g and 20 CBU/g for coastal bermudagrass. Dielectric properties for dilute sodium hydroxide solutions were measured and compared to solid loss, lignin reduction and reducing sugar levels in hydrolyzates. Results indicate that the dielectric loss tangent of alkali solutions is a potential indicator of the severity of microwave-based pretreatments. Modeling of pretreatment processes can be a valuable tool in process simulations of bioethanol production from lignocellulosic biomass. Chapter 4 discusses three different approaches that were used to model delignification and carbohydrate loss during microwave-based pretreatment of switchgrass: statistical linear regression modeling, kinetic modeling using a time-dependent rate coefficient, and a Mamdani-type fuzzy inference system. The dielectric loss tangent of the alkali reagent and pretreatment time were used as predictors in all models. The statistical linear regression model for delignification gave comparable root mean square error (RMSE) values for training and testing data and predictions were approximately within 1% of experimental values. The kinetic model for delignification and xylan loss gave comparable RMSE values for training and testing data sets and predictions were approximately within 2% of experimental values. The kinetic model for cellulose loss was not as effective and predictions were only within 5-7% of experimental values. The time-dependent rate coefficients of the kinetic models calculated from experimental data were consistent with the heterogeneity (or lack thereof) of individual biomass components. The Mamdani-type fuzzy inference system was shown to be an effective means to model pretreatment processes and gave the most accurate predictions (<3%) for cellulose loss.

Prediction of Chemical Function: Model Development and ...

EPA Pesticide Factsheets

The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

Prediction is Production: The missing link between language production and comprehension.

PubMed

Martin, Clara D; Branzi, Francesca M; Bar, Moshe

2018-01-18

Language comprehension often involves the generation of predictions. It has been hypothesized that such prediction-for-comprehension entails actual language production. Recent studies provided evidence that the production system is recruited during language comprehension, but the link between production and prediction during comprehension remains hypothetical. Here, we tested this hypothesis by comparing prediction during sentence comprehension (primary task) in participants having the production system either available or not (non-verbal versus verbal secondary task). In the primary task, sentences containing an expected or unexpected target noun-phrase were presented during electroencephalography recording. Prediction, measured as the magnitude of the N400 effect elicited by the article (expected versus unexpected), was hindered only when the production system was taxed during sentence context reading. The present study provides the first direct evidence that the availability of the speech production system is necessary for generating lexical prediction during sentence comprehension. Furthermore, these important results provide an explanation for the recruitment of language production during comprehension.

Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Winkler, David A., E-mail: dave.winkler@csiro.au

2016-05-15

Nanomaterials research is one of the fastest growing contemporary research areas. The unprecedented properties of these materials have meant that they are being incorporated into products very quickly. Regulatory agencies are concerned they cannot assess the potential hazards of these materials adequately, as data on the biological properties of nanomaterials are still relatively limited and expensive to acquire. Computational modelling methods have much to offer in helping understand the mechanisms by which toxicity may occur, and in predicting the likelihood of adverse biological impacts of materials not yet tested experimentally. This paper reviews the progress these methods, particularly those QSAR-based,more » have made in understanding and predicting potentially adverse biological effects of nanomaterials, and also the limitations and pitfalls of these methods. - Highlights: • Nanomaterials regulators need good information to make good decisions. • Nanomaterials and their interactions with biology are very complex. • Computational methods use existing data to predict properties of new nanomaterials. • Statistical, data driven modelling methods have been successfully applied to this task. • Much more must be learnt before robust toolkits will be widely usable by regulators.« less

Predicted avian responses to bioenergy development scenarios in an intensive agricultural landscape

USGS Publications Warehouse

Uden, Daniel R.; Allen, Craig R.; Mitchell, Rob B.; McCoy, Tim D.; Guan, Qingfeng

2015-01-01

Conversion of native prairie to agriculture has increased food and bioenergy production but decreased wildlife habitat. However, enrollment of highly erodible cropland in conservation programs has compensated for some grassland loss. In the future, climate change and production of second-generation perennial biofuel crops could further transform agricultural landscapes and increase or decrease grassland area. Switchgrass (Panicum virgatum) is an alternative biofuel feedstock that may be economically and environmentally superior to maize (Zea mays) grain for ethanol production on marginally productive lands. Switchgrass could benefit farmers economically and increase grassland area, but there is uncertainty as to how conversions between rowcrops, switchgrass monocultures and conservation grasslands might occur and affect wildlife. To explore potential impacts on grassland birds, we developed four agricultural land-use change scenarios for an intensively cultivated landscape, each driven by potential future climatic changes and ensuing irrigation limitations, ethanol demand, commodity prices, and continuation of a conservation program. For each scenario, we calculated changes in area for landcover classes and predicted changes in grassland bird abundances. Overall, birds responded positively to the replacement of rowcrops with switchgrass and negatively to the conversion of conservation grasslands to switchgrass or rowcrops. Landscape context and interactions between climate, crop water use, and irrigation availability could influence future land-use, and subsequently, avian habitat quality and quantity. Switchgrass is likely to provide higher quality avian habitat than rowcrops but lower quality habitat than conservation grasslands, and therefore, may most benefit birds in heavily cultivated, irrigation dependent landscapes under warmer and drier conditions, where economic profitability may also encourage conversions to drought tolerant bioenergy feedstocks.

The use of life-cycle assessment to evaluate the environmental impacts of growing genetically modified, nitrogen use-efficient canola.

PubMed

Strange, Alison; Park, Julian; Bennett, Richard; Phipps, Richard

2008-05-01

Agriculture, particularly intensive crop production, makes a significant contribution to environmental pollution. A variety of canola (Brassica napus) has been genetically modified to enhance nitrogen use efficiency, effectively reducing the amount of fertilizer required for crop production. A partial life-cycle assessment adapted to crop production was used to assess the potential environmental impacts of growing genetically modified, nitrogen use-efficient (GMNUE) canola in North Dakota and Minnesota compared with a conventionally bred control variety. The analysis took into account the entire production system used to produce 1 tonne of canola. This comprised raw material extraction, processing and transportation, as well as all agricultural field operations. All emissions associated with the production of 1 tonne of canola were listed, aggregated and weighted in order to calculate the level of environmental impact. The findings show that there are a range of potential environmental benefits associated with growing GMNUE canola. These include reduced impacts on global warming, freshwater ecotoxicity, eutrophication and acidification. Given the large areas of canola grown in North America and, in particular, Canada, as well as the wide acceptance of genetically modified varieties in this area, there is the potential for GMNUE canola to reduce pollution from agriculture, with the largest reductions predicted to be in greenhouse gases and diffuse water pollution.

State of the Science Review: Potential for Beneficial Use of ...

EPA Pesticide Factsheets

Metal and metalloid contamination of soil and sediment is a widespread problem both in urban and rural areas throughout the United States (U.S. EPA, 2014). Beneficial use of waste by-products as amendments to remediate metal-contaminated soils and sediments can provide major economic and environmental advantages on both a site-specific and national scale. These waste by-products can also reduce our need to mine virgin materials or produce synthetic materials for amendments. Waste by-products must not be hazardous or pose unacceptable risk to human health and the environment, and should be a suitable replacement for virgin and synthetic materials. This review serves to present the state of science on in-situ remediation of metal-contaminated soil and sediment and the potential for beneficial usage of waste by-product materials. Not all unintended consequences can be fully understood or predicted prior to implementing a treatment option, however some realized, and potentially unrealized, benefits and unintended consequences are explored. The objectives of this review article are to: (1) summarize the current state of the science on in-situ treatment of metal-contaminated soils and sediments; (2) review the more recent use of non-municipal and non-hazardous waste by-products for use as soil and sediment amendments; and (3) identify physical and chemical properties that are indicative of the success or effectiveness of using a specific amendment to treat metal

Prior Publication Productivity, Grant Percentile Ranking, and Topic-Normalized Citation Impact of NHLBI Cardiovascular R01 Grants

PubMed Central

Kaltman, Jonathan R.; Evans, Frank; Danthi, Narasimhan; Wu, Colin O.; DiMichele, Donna; Lauer, Michael S.

2014-01-01

Rationale We previously demonstrated absence of association between peer-review derived percentile ranking and raw citation impact in a large cohort of NHLBI cardiovascular R01 grants, but we did not consider pre-grant investigator publication productivity. We also did not normalize citation counts for scientific field, type of paper, and year of publication. Objective Determine whether measures of investigator prior productivity predict a grant’s subsequent scientific impact as measured by normalized citation metrics. Methods and Results We identified 1492 investigator-initiated de novo NHLBI R01 grant applications funded between 2001 and 2008 and linked the publications from these grants to their “InCites™” (Thompson Reuters) citation record. InCites™ provides a normalized citation count for each publication stratifying by year of publication, type of publication, and field of science. The co-primary endpoints for this analysis were the normalized citation impact per million dollars allocated and the number of publications per grant that have normalized citation rate in the top decile per million dollars allocated (“top-10% papers”). Prior productivity measures included the number of NHLBI-supported publications each principal investigator published in the 5 years before grant review and the corresponding prior normalized citation impact score. After accounting for potential confounders, there was no association between peer-review percentile ranking and bibliometric endpoints (all adjusted P > 0.5). However, prior productivity was predictive (P<0.0001). Conclusion Even after normalizing citation counts, we confirmed a lack of association between peer-review grant percentile ranking and grant citation impact. However, prior investigator publication productivity was predictive of grant-specific citation impact. PMID:25214575

Prior publication productivity, grant percentile ranking, and topic-normalized citation impact of NHLBI cardiovascular R01 grants.

PubMed

Kaltman, Jonathan R; Evans, Frank J; Danthi, Narasimhan S; Wu, Colin O; DiMichele, Donna M; Lauer, Michael S

2014-09-12

We previously demonstrated absence of association between peer-review-derived percentile ranking and raw citation impact in a large cohort of National Heart, Lung, and Blood Institute cardiovascular R01 grants, but we did not consider pregrant investigator publication productivity. We also did not normalize citation counts for scientific field, type of article, and year of publication. To determine whether measures of investigator prior productivity predict a grant's subsequent scientific impact as measured by normalized citation metrics. We identified 1492 investigator-initiated de novo National Heart, Lung, and Blood Institute R01 grant applications funded between 2001 and 2008 and linked the publications from these grants to their InCites (Thompson Reuters) citation record. InCites provides a normalized citation count for each publication stratifying by year of publication, type of publication, and field of science. The coprimary end points for this analysis were the normalized citation impact per million dollars allocated and the number of publications per grant that has normalized citation rate in the top decile per million dollars allocated (top 10% articles). Prior productivity measures included the number of National Heart, Lung, and Blood Institute-supported publications each principal investigator published in the 5 years before grant review and the corresponding prior normalized citation impact score. After accounting for potential confounders, there was no association between peer-review percentile ranking and bibliometric end points (all adjusted P>0.5). However, prior productivity was predictive (P<0.0001). Even after normalizing citation counts, we confirmed a lack of association between peer-review grant percentile ranking and grant citation impact. However, prior investigator publication productivity was predictive of grant-specific citation impact. © 2014 American Heart Association, Inc.

New approach to predict photoallergic potentials of chemicals based on murine local lymph node assay.

PubMed

Maeda, Yosuke; Hirosaki, Haruka; Yamanaka, Hidenori; Takeyoshi, Masahiro

2018-05-23

Photoallergic dermatitis, caused by pharmaceuticals and other consumer products, is a very important issue in human health. However, S10 guidelines of the International Conference on Harmonization do not recommend the existing prediction methods for photoallergy because of their low predictability in human cases. We applied local lymph node assay (LLNA), a reliable, quantitative skin sensitization prediction test, to develop a new photoallergy prediction method. This method involves a three-step approach: (1) ultraviolet (UV) absorption analysis; (2) determination of no observed adverse effect level for skin phototoxicity based on LLNA; and (3) photoallergy evaluation based on LLNA. Photoallergic potential of chemicals was evaluated by comparing lymph node cell proliferation among groups treated with chemicals with minimal effect levels of skin sensitization and skin phototoxicity under UV irradiation (UV+) or non-UV irradiation (UV-). A case showing significant difference (P < .05) in lymph node cell proliferation rates between UV- and UV+ groups was considered positive for photoallergic reaction. After testing 13 chemicals, seven human photoallergens tested positive and the other six, with no evidence of causing photoallergic dermatitis or UV absorption, tested negative. Among these chemicals, both doxycycline hydrochloride and minocycline hydrochloride were tetracycline antibiotics with different photoallergic properties, and the new method clearly distinguished between the photoallergic properties of these chemicals. These findings suggested high predictability of our method; therefore, it is promising and effective in predicting human photoallergens. Copyright © 2018 John Wiley & Sons, Ltd.

The integrated simulation and assessment of the impacts of process change in biotherapeutic antibody production.

PubMed

Chhatre, Sunil; Jones, Carl; Francis, Richard; O'Donovan, Kieran; Titchener-Hooker, Nigel; Newcombe, Anthony; Keshavarz-Moore, Eli

2006-01-01

Growing commercial pressures in the pharmaceutical industry are establishing a need for robust computer simulations of whole bioprocesses to allow rapid prediction of the effects of changes made to manufacturing operations. This paper presents an integrated process simulation that models the cGMP manufacture of the FDA-approved biotherapeutic CroFab, an IgG fragment used to treat rattlesnake envenomation (Protherics U.K. Limited, Blaenwaun, Ffostrasol, Llandysul, Wales, U.K.). Initially, the product is isolated from ovine serum by precipitation and centrifugation, before enzymatic digestion of the IgG to produce FAB and FC fragments. These are purified by ion exchange and affinity chromatography to remove the FC and non-specific FAB fragments from the final venom-specific FAB product. The model was constructed in a discrete event simulation environment and used to determine the potential impact of a series of changes to the process, such as increasing the step efficiencies or volumes of chromatographic matrices, upon product yields and process times. The study indicated that the overall FAB yield was particularly sensitive to changes in the digestive and affinity chromatographic step efficiencies, which have a predicted 30% greater impact on process FAB yield than do the precipitation or centrifugation stages. The study showed that increasing the volume of affinity matrix has a negligible impact upon total process time. Although results such as these would require experimental verification within the physical constraints of the process and the facility, the model predictions are still useful in allowing rapid "what-if" scenario analysis of the likely impacts of process changes within such an integrated production process.

Moderators of the relationship between frequent family demands and inflammation among adolescents.

PubMed

Levine, Cynthia S; Hoffer, Lauren C; Chen, Edith

2017-05-01

Frequent demands from others in relationships are associated with worse physiological and health outcomes. The present research investigated 2 potential moderators of the relationship between frequency of demands from one's family and inflammatory profiles among adolescents: (a) closeness of adolescents' relationships with their families, and (b) the frequency with which adolescents provided help to their families. Two hundred thirty-four adolescents, ages 13-16 (Mage = 14.53; 47.83% male), completed a daily dairy in which they reported on the frequency of demands made by family members. They were also interviewed about the closeness of their family relationships and reported in the daily diary on how frequently they provided help to their families. Adolescents also underwent a blood draw to assess low-grade inflammation and proinflammatory cytokine production in response to bacterial stimulation. More frequent demands from family predicted higher levels of low-grade inflammation and cytokine production in response to bacterial stimulation in adolescents. Family closeness moderated the relationship between frequent demands and stimulated cytokine production such that more frequent demands predicted higher cytokine production among adolescents who were closer to their families. Furthermore, frequency of providing help moderated the relationship between frequent demands and both low-grade inflammation and stimulated cytokine production, such that more frequent demands predicted worse inflammatory profiles among adolescents who provided more help to their families. These findings build on previous work on family demands and health to show under what circumstances family demands might have a physiological cost. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A simplified model for predicting malaria entomologic inoculation rates based on entomologic and parasitologic parameters relevant to control.

PubMed

Killeen, G F; McKenzie, F E; Foy, B D; Schieffelin, C; Billingsley, P F; Beier, J C

2000-05-01

Malaria transmission intensity is modeled from the starting perspective of individual vector mosquitoes and is expressed directly as the entomologic inoculation rate (EIR). The potential of individual mosquitoes to transmit malaria during their lifetime is presented graphically as a function of their feeding cycle length and survival, human biting preferences, and the parasite sporogonic incubation period. The EIR is then calculated as the product of 1) the potential of individual vectors to transmit malaria during their lifetime, 2) vector emergence rate relative to human population size, and 3) the infectiousness of the human population to vectors. Thus, impacts on more than one of these parameters will amplify each other's effects. The EIRs transmitted by the dominant vector species at four malaria-endemic sites from Papua New Guinea, Tanzania, and Nigeria were predicted using field measurements of these characteristics together with human biting rate and human reservoir infectiousness. This model predicted EIRs (+/- SD) that are 1.13 +/- 0.37 (range = 0.84-1.59) times those measured in the field. For these four sites, mosquito emergence rate and lifetime transmission potential were more important determinants of the EIR than human reservoir infectiousness. This model and the input parameters from the four sites allow the potential impacts of various control measures on malaria transmission intensity to be tested under a range of endemic conditions. The model has potential applications for the development and implementation of transmission control measures and for public health education.

Qualification Testing Versus Quantitative Reliability Testing of PV - Gaining Confidence in a Rapidly Changing Technology: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kurtz, Sarah; Repins, Ingrid L; Hacke, Peter L

Continued growth of PV system deployment would be enhanced by quantitative, low-uncertainty predictions of the degradation and failure rates of PV modules and systems. The intended product lifetime (decades) far exceeds the product development cycle (months), limiting our ability to reduce the uncertainty of the predictions for this rapidly changing technology. Yet, business decisions (setting insurance rates, analyzing return on investment, etc.) require quantitative risk assessment. Moving toward more quantitative assessments requires consideration of many factors, including the intended application, consequence of a possible failure, variability in the manufacturing, installation, and operation, as well as uncertainty in the measured accelerationmore » factors, which provide the basis for predictions based on accelerated tests. As the industry matures, it is useful to periodically assess the overall strategy for standards development and prioritization of research to provide a technical basis both for the standards and the analysis related to the application of those. To this end, this paper suggests a tiered approach to creating risk assessments. Recent and planned potential improvements in international standards are also summarized.« less

Displacement damage and predicted non-ionizing energy loss in GaAs

NASA Astrophysics Data System (ADS)

Gao, Fei; Chen, Nanjun; Hernandez-Rivera, Efrain; Huang, Danhong; LeVan, Paul D.

2017-03-01

Large-scale molecular dynamics (MD) simulations, along with bond-order interatomic potentials, have been applied to study the defect production for lattice atom recoil energies from 500 eV to 20 keV in gallium arsenide (GaAs). At low energies, the most surviving defects are single interstitials and vacancies, and only 20% of the interstitial population is contained in clusters. However, a direct-impact amorphization in GaAs occurs with a high degree of probability during the cascade lifetime for Ga PKAs (primary knock-on atoms) with energies larger than 2 keV. The results reveal a non-linear defect production that increases with the PKA energy. The damage density within a cascade core is evaluated, and used to develop a model that describes a new energy partition function. Based on the MD results, we have developed a model to determine the non-ionizing energy loss (NIEL) in GaAs, which can be used to predict the displacement damage degradation induced by space radiation on electronic components. The calculated NIEL predictions are compared with the available data, thus validating the NIEL model developed in this study.

Preliminary investigation of parasitic radioisotope production using the LANL IPF secondary neutron flux

NASA Astrophysics Data System (ADS)

Engle, J. W.; Kelsey, C. T.; Bach, H.; Ballard, B. D.; Fassbender, M. E.; John, K. D.; Birnbaum, E. R.; Nortier, F. M.

2012-12-01

In order to ascertain the potential for radioisotope production and material science studies using the Isotope Production Facility at Los Alamos National Lab, a two-pronged investigation has been initiated. The Monte Carlo for Neutral Particles eXtended (MCNPX) code has been used in conjunction with the CINDER 90 burnup code to predict neutron flux energy distributions as a result of routine irradiations and to estimate yields of radioisotopes of interest for hypothetical irradiation conditions. A threshold foil activation experiment is planned to study the neutron flux using measured yields of radioisotopes, quantified by HPGe gamma spectroscopy, from representative nuclear reactions with known thresholds up to 50 MeV.

Development of simple-to-apply biogas kinetic models for the co-digestion of food waste and maize husk.

PubMed

Owamah, H I; Izinyon, O C

2015-10-01

Biogas kinetic models are often used to characterize substrate degradation and prediction of biogas production potential. Most of these existing models are however difficult to apply to substrates they were not developed for since their applications are usually substrate specific. Biodegradability kinetic (BIK) model and maximum biogas production potential and stability assessment (MBPPSA) model were therefore developed in this study for better understanding of the anaerobic co-digestion of food waste and maize husk for biogas production. Biodegradability constant (k) was estimated as 0.11 d(-1) using the BIK model. The results of maximum biogas production potential (A) obtained using the MBPPSA model were found to be in good correspondence, both in value and trend with the results obtained using the popular but complex modified Gompertz model for digesters B-1, B-2, B-3, B-4, and B-5. The (If) value of MBPPSA model also showed that digesters B-3, B-4, and B-5 were stable, while B-1 and B-2 were inhibited/unstable. Similar stability observation was also obtained using the modified Gompertz model. The MBPPSA model can therefore be used as an alternative model for anaerobic digestion feasibility studies and plant design. Copyright © 2015 Elsevier Ltd. All rights reserved.

Vibrational self-consistent field theory using optimized curvilinear coordinates.

PubMed

Bulik, Ireneusz W; Frisch, Michael J; Vaccaro, Patrick H

2017-07-28

A vibrational SCF model is presented in which the functions forming the single-mode functions in the product wavefunction are expressed in terms of internal coordinates and the coordinates used for each mode are optimized variationally. This model involves no approximations to the kinetic energy operator and does not require a Taylor-series expansion of the potential. The non-linear optimization of coordinates is found to give much better product wavefunctions than the limited variations considered in most previous applications of SCF methods to vibrational problems. The approach is tested using published potential energy surfaces for water, ammonia, and formaldehyde. Variational flexibility allowed in the current ansätze results in excellent zero-point energies expressed through single-product states and accurate fundamental transition frequencies realized by short configuration-interaction expansions. Fully variational optimization of single-product states for excited vibrational levels also is discussed. The highlighted methodology constitutes an excellent starting point for more sophisticated treatments, as the bulk characteristics of many-mode coupling are accounted for efficiently in terms of compact wavefunctions (as evident from the accurate prediction of transition frequencies).

Oxidation of danofloxacin by free chlorine-kinetic study, structural identification of by-products by LC-MS/MS and potential toxicity of by-products using in silico test.

PubMed

Yassine, Montaha; Rifai, Ahmad; Doumyati, Samah; Trivella, Aurélien; Mazellier, Patrick; Budzinski, Hélène; Al Iskandarani, Mohamad

2017-03-01

In this study, we aimed to investigate the kinetics and the mechanism of reaction of the fluoroquinolone antibacterial danofloxacin (DANO) by free available chlorine (FAC) during water chlorination process. Kinetic study was thus performed at pH 7.2, 20 °C in the presence of an excess of total chlorine. Under these experimental conditions, a second-order reaction rate constant (first-order relative to DANO concentration and first-order relative to FAC concentration) was evaluated to k~1446 M -1  s -1 . Five degradation products were identified at different reaction times. Their structures were investigated by using fragmentations obtained at different CID collision energies in MS/MS experiments. Moreover, the toxicity of the proposed structures was predicted by using T.E.S.T. The results indicated that all by-products may have a developmental toxicity. The oral rat LD 50 concentration was predicted to be lower than that of DANO. Furthermore, two degradation compounds presented a concentration level for fathead minnow LC 50 (96 h) lower than that of DANO and presented toxicity for the marine animals.

Downscaling Coarse Scale Microwave Soil Moisture Product using Machine Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, P.; Moradkhani, H.; Yan, H.

2016-12-01

Soil moisture (SM) is a key variable in partitioning and examining the global water-energy cycle, agricultural planning, and water resource management. It is also strongly coupled with climate change, playing an important role in weather forecasting and drought monitoring and prediction, flood modeling and irrigation management. Although satellite retrievals can provide an unprecedented information of soil moisture at a global-scale, the products might be inadequate for basin scale study or regional assessment. To improve the spatial resolution of SM, this work presents a novel approach based on Machine Learning (ML) technique that allows for downscaling of the satellite soil moisture to fine resolution. For this purpose, the SMAP L-band radiometer SM products were used and conditioned on the Variable Infiltration Capacity (VIC) model prediction to describe the relationship between the coarse and fine scale soil moisture data. The proposed downscaling approach was applied to a western US basin and the products were compared against the available SM data from in-situ gauge stations. The obtained results indicated a great potential of the machine learning technique to derive the fine resolution soil moisture information that is currently used for land data assimilation applications.

Production of a solvent, detergent, and thermotolerant lipase by a newly isolated Acinetobacter sp. in submerged and solid-state fermentations.

PubMed

Khoramnia, Anahita; Ebrahimpour, Afshin; Beh, Boon Kee; Lai, Oi Ming

2011-01-01

The lipase production ability of a newly isolated Acinetobacter sp. in submerged (SmF) and solid-state (SSF) fermentations was evaluated. The results demonstrated this strain as one of the rare bacterium, which is able to grow and produce lipase in SSF even more than SmF. Coconut oil cake as a cheap agroindustrial residue was employed as the solid substrate. The lipase production was optimized in both media using artificial neural network. Multilayer normal and full feed forward backpropagation networks were selected to build predictive models to optimize the culture parameters for lipase production in SmF and SSF systems, respectively. The produced models for both systems showed high predictive accuracy where the obtained conditions were close together. The produced enzyme was characterized as a thermotolerant lipase, although the organism was mesophile. The optimum temperature for the enzyme activity was 45°C where 63% of its activity remained at 70°C after 2 h. This lipase remained active after 24 h in a broad range of pH (6-11). The lipase demonstrated strong solvent and detergent tolerance potentials. Therefore, this inexpensive lipase production for such a potent and industrially valuable lipase is promising and of considerable commercial interest for biotechnological applications.

Genome-scale metabolic model of Pichia pastoris with native and humanized glycosylation of recombinant proteins.

PubMed

Irani, Zahra Azimzadeh; Kerkhoven, Eduard J; Shojaosadati, Seyed Abbas; Nielsen, Jens

2016-05-01

Pichia pastoris is used for commercial production of human therapeutic proteins, and genome-scale models of P. pastoris metabolism have been generated in the past to study the metabolism and associated protein production by this yeast. A major challenge with clinical usage of recombinant proteins produced by P. pastoris is the difference in N-glycosylation of proteins produced by humans and this yeast. However, through metabolic engineering, a P. pastoris strain capable of producing humanized N-glycosylated proteins was constructed. The current genome-scale models of P. pastoris do not address native nor humanized N-glycosylation, and we therefore developed ihGlycopastoris, an extension to the iLC915 model with both native and humanized N-glycosylation for recombinant protein production, but also an estimation of N-glycosylation of P. pastoris native proteins. This new model gives a better prediction of protein yield, demonstrates the effect of the different types of N-glycosylation of protein yield, and can be used to predict potential targets for strain improvement. The model represents a step towards a more complete description of protein production in P. pastoris, which is required for using these models to understand and optimize protein production processes. © 2015 Wiley Periodicals, Inc.

Evolution of increased phenotypic diversity enhances population performance by reducing sexual harassment in damselflies.

PubMed

Takahashi, Yuma; Kagawa, Kotaro; Svensson, Erik I; Kawata, Masakado

2014-07-18

The effect of evolutionary changes in traits and phenotypic/genetic diversity on ecological dynamics has received much theoretical attention; however, the mechanisms and ecological consequences are usually unknown. Female-limited colour polymorphism in damselflies is a counter-adaptation to male mating harassment, and thus, is expected to alter population dynamics through relaxing sexual conflict. Here we show the side effect of the evolution of female morph diversity on population performance (for example, population productivity and sustainability) in damselflies. Our theoretical model incorporating key features of the sexual interaction predicts that the evolution of increased phenotypic diversity will reduce overall fitness costs to females from sexual conflict, which in turn will increase productivity, density and stability of a population. Field data and mesocosm experiments support these model predictions. Our study suggests that increased phenotypic diversity can enhance population performance that can potentially reduce extinction rates and thereby influence macroevolutionary processes.

High salt intake causes leptin resistance and obesity in mice by stimulating endogenous fructose production and metabolism.

PubMed

Lanaspa, Miguel A; Kuwabara, Masanari; Andres-Hernando, Ana; Li, Nancy; Cicerchi, Christina; Jensen, Thomas; Orlicky, David J; Roncal-Jimenez, Carlos A; Ishimoto, Takuji; Nakagawa, Takahiko; Rodriguez-Iturbe, Bernardo; MacLean, Paul S; Johnson, Richard J

2018-03-20

Dietary guidelines for obesity typically focus on three food groups (carbohydrates, fat, and protein) and caloric restriction. Intake of noncaloric nutrients, such as salt, are rarely discussed. However, recently high salt intake has been reported to predict the development of obesity and insulin resistance. The mechanism for this effect is unknown. Here we show that high intake of salt activates the aldose reductase-fructokinase pathway in the liver and hypothalamus, leading to endogenous fructose production with the development of leptin resistance and hyperphagia that cause obesity, insulin resistance, and fatty liver. A high-salt diet was also found to predict the development of diabetes and nonalcoholic fatty liver disease in a healthy population. These studies provide insights into the pathogenesis of obesity and diabetes and raise the potential for reduction in salt intake as an additional interventional approach for reducing the risk for developing obesity and metabolic syndrome.

Anthropogenic climate change has altered primary productivity in Lake Superior

PubMed Central

O'Beirne, M. D.; Werne, J. P.; Hecky, R. E.; Johnson, T. C.; Katsev, S.; Reavie, E. D.

2017-01-01

Anthropogenic climate change has the potential to alter many facets of Earth's freshwater resources, especially lacustrine ecosystems. The effects of anthropogenic changes in Lake Superior, which is Earth's largest freshwater lake by area, are not well documented (spatially or temporally) and predicted future states in response to climate change vary. Here we show that Lake Superior experienced a slow, steady increase in production throughout the Holocene using (paleo)productivity proxies in lacustrine sediments to reconstruct past changes in primary production. Furthermore, data from the last century indicate a rapid increase in primary production, which we attribute to increasing surface water temperatures and longer seasonal stratification related to longer ice-free periods in Lake Superior due to anthropogenic climate warming. These observations demonstrate that anthropogenic effects have become a prominent influence on one of Earth's largest, most pristine lacustrine ecosystems. PMID:28598413

Anthropogenic climate change has altered primary productivity in Lake Superior.

PubMed

O'Beirne, M D; Werne, J P; Hecky, R E; Johnson, T C; Katsev, S; Reavie, E D

2017-06-09

Anthropogenic climate change has the potential to alter many facets of Earth's freshwater resources, especially lacustrine ecosystems. The effects of anthropogenic changes in Lake Superior, which is Earth's largest freshwater lake by area, are not well documented (spatially or temporally) and predicted future states in response to climate change vary. Here we show that Lake Superior experienced a slow, steady increase in production throughout the Holocene using (paleo)productivity proxies in lacustrine sediments to reconstruct past changes in primary production. Furthermore, data from the last century indicate a rapid increase in primary production, which we attribute to increasing surface water temperatures and longer seasonal stratification related to longer ice-free periods in Lake Superior due to anthropogenic climate warming. These observations demonstrate that anthropogenic effects have become a prominent influence on one of Earth's largest, most pristine lacustrine ecosystems.

Simple agrometeorological models for estimating Guineagrass yield in Southeast Brazil.

PubMed

Pezzopane, José Ricardo Macedo; da Cruz, Pedro Gomes; Santos, Patricia Menezes; Bosi, Cristiam; de Araujo, Leandro Coelho

2014-09-01

The objective of this work was to develop and evaluate agrometeorological models to simulate the production of Guineagrass. For this purpose, we used forage yield from 54 growing periods between December 2004-January 2007 and April 2010-March 2012 in irrigated and non-irrigated pastures in São Carlos, São Paulo state, Brazil (latitude 21°57'42″ S, longitude 47°50'28″ W and altitude 860 m). Initially we performed linear regressions between the agrometeorological variables and the average dry matter accumulation rate for irrigated conditions. Then we determined the effect of soil water availability on the relative forage yield considering irrigated and non-irrigated pastures, by means of segmented linear regression among water balance and relative production variables (dry matter accumulation rates with and without irrigation). The models generated were evaluated with independent data related to 21 growing periods without irrigation in the same location, from eight growing periods in 2000 and 13 growing periods between December 2004-January 2007 and April 2010-March 2012. The results obtained show the satisfactory predictive capacity of the agrometeorological models under irrigated conditions based on univariate regression (mean temperature, minimum temperature and potential evapotranspiration or degreedays) or multivariate regression. The response of irrigation on production was well correlated with the climatological water balance variables (ratio between actual and potential evapotranspiration or between actual and maximum soil water storage). The models that performed best for estimating Guineagrass yield without irrigation were based on minimum temperature corrected by relative soil water storage, determined by the ratio between the actual soil water storage and the soil water holding capacity.irrigation in the same location, in 2000, 2010 and 2011. The results obtained show the satisfactory predictive capacity of the agrometeorological models under irrigated conditions based on univariate regression (mean temperature, potential evapotranspiration or degree-days) or multivariate regression. The response of irrigation on production was well correlated with the climatological water balance variables (ratio between actual and potential evapotranspiration or between actual and maximum soil water storage). The models that performed best for estimating Guineagrass yield without irrigation were based on degree-days corrected by the water deficit factor.

A positive relationship between spring temperature and productivity in 20 songbird species in the boreal zone.

PubMed

Meller, Kalle; Piha, Markus; Vähätalo, Anssi V; Lehikoinen, Aleksi

2018-03-01

Anthropogenic climate warming has already affected the population dynamics of numerous species and is predicted to do so also in the future. To predict the effects of climate change, it is important to know whether productivity is linked to temperature, and whether species' traits affect responses to climate change. To address these objectives, we analysed monitoring data from the Finnish constant effort site ringing scheme collected in 1987-2013 for 20 common songbird species together with climatic data. Warm spring temperature had a positive linear relationship with productivity across the community of 20 species independent of species' traits (realized thermal niche or migration behaviour), suggesting that even the warmest spring temperatures remained below the thermal optimum for reproduction, possibly due to our boreal study area being closer to the cold edge of all study species' distributions. The result also suggests a lack of mismatch between the timing of breeding and peak availability of invertebrate food of the study species. Productivity was positively related to annual growth rates in long-distance migrants, but not in short-distance migrants. Across the 27-year study period, temporal trends in productivity were mostly absent. The population sizes of species with colder thermal niches had decreasing trends, which were not related to temperature responses or temporal trends in productivity. The positive connection between spring temperature and productivity suggests that climate warming has potential to increase the productivity in bird species in the boreal zone, at least in the short term.

Rainmakers: why bad weather means good productivity.

PubMed

Lee, Jooa Julia; Gino, Francesca; Staats, Bradley R

2014-05-01

People believe that weather conditions influence their everyday work life, but to date, little is known about how weather affects individual productivity. Contrary to conventional wisdom, we predict and find that bad weather increases individual productivity and that it does so by eliminating potential cognitive distractions resulting from good weather. When the weather is bad, individuals appear to focus more on their work than on alternate outdoor activities. We investigate the proposed relationship between worse weather and higher productivity through 4 studies: (a) field data on employees' productivity from a bank in Japan, (b) 2 studies from an online labor market in the United States, and (c) a laboratory experiment. Our findings suggest that worker productivity is higher on bad-, rather than good-, weather days and that cognitive distractions associated with good weather may explain the relationship. We discuss the theoretical and practical implications of our research. (c) 2014 APA, all rights reserved.

Consistent ozone-induced decreases in pasture forage quality across several grassland types and consequences for UK lamb production.

PubMed

Hayes, Felicity; Mills, Gina; Jones, Laurence; Abbott, John; Ashmore, Mike; Barnes, Jeremy; Neil Cape, J; Coyle, Mhairi; Peacock, Simon; Rintoul, Naomi; Toet, Sylvia; Wedlich, Kerstin; Wyness, Kirsten

2016-02-01

In this study we have demonstrated that rising background ozone has the potential to reduce grassland forage quality and explored the implications for livestock production. We analysed pasture samples from seven ozone exposure experiments comprising mesotrophic, calcareous, haymeadow and sanddune unimproved grasslands conducted in open-top chambers, solardomes and a field release system. Across all grassland types, there were significant increases in acid detergent fibre, crude fibre and lignin content with increasing ozone concentration, resulting in decreased pasture quality in terms of the metabolisable energy content of the vegetation. We derived a dose-response function for metabolisable energy of the grassland with ozone concentration, applicable to a range of grassland types, and used this to predict effects on pasture quality of UK vegetation at 1 km resolution using modelled ozone data for 2007 and for predicted higher average ozone concentrations in 2020. This showed a potential total reduction in lamb production in the UK of approximately 4% in 2020 compared to 2007. The largest impacts were in geographical areas of modest ozone increases between the two years, but where large numbers of lambs were present. For an individual farmer working to a very small cost margin this could represent a large reduction in profit, both in regions where the impacts per lamb and those where the impacts per km(2) of grazing land are largest. In the short term farmers could adapt their lamb management in response to changed forage quality by additional supplementary feed of high metabolisable energy content. Nationally this increase in annual additional feed in 2020 compared to 2007 would be 2,166 tonnes (an increase of 0.7%). Of added concern are the longer-term consequences of continual deterioration of pasture quality and the implications for changes in farming practices to compensate for potential reductions in livestock production capacity. Copyright © 2015 Elsevier B.V. All rights reserved.

A Systematic Approach to Evaluate Herb-Drug Interaction Mechanisms: Investigation of Milk Thistle Extracts and Eight Isolated Constituents as CYP3A Inhibitors

PubMed Central

Brantley, Scott J.; Graf, Tyler N.; Oberlies, Nicholas H.

2013-01-01

Despite increasing recognition of potential untoward interactions between herbal products and conventional medications, a standard system for prospective assessment of these interactions remains elusive. This information gap was addressed by evaluating the drug interaction liability of the model herbal product milk thistle (Silybum marianum) with the CYP3A probe substrate midazolam. The inhibitory effects of commercially available milk thistle extracts and isolated constituents on midazolam 1′-hydroxylation were screened using human liver and intestinal microsomes. Relative to vehicle, the extract silymarin and constituents silybin A, isosilybin A, isosilybin B, and silychristin at 100 μM demonstrated >50% inhibition of CYP3A activity with at least one microsomal preparation, prompting IC50 determination. The IC50s for isosilybin B and silychristin were ∼60 and 90 μM, respectively, whereas those for the remaining constituents were >100 μM. Extracts and constituents that contained the 1,4-dioxane moiety demonstrated a >1.5-fold shift in IC50 when tested as potential mechanism-based inhibitors. The semipurified extract, silibinin, and the two associated constituents (silybin A and silybin B) demonstrated mechanism-based inhibition of recombinant CYP3A4 (KI, ∼100 μM; kinact, ∼0.20 min−1) but not microsomal CYP3A activity. The maximum predicted increases in midazolam area under the curve using the static mechanistic equation and recombinant CYP3A4 data were 1.75-fold, which may necessitate clinical assessment. Evaluation of the interaction liability of single herbal product constituents, in addition to commercially available extracts, will enable elucidation of mechanisms underlying potential clinically significant herb-drug interactions. Application of this framework to other herbal products would permit predictions of herb-drug interactions and assist in prioritizing clinical evaluation. PMID:23801821

A systematic approach to evaluate herb-drug interaction mechanisms: investigation of milk thistle extracts and eight isolated constituents as CYP3A inhibitors.

PubMed

Brantley, Scott J; Graf, Tyler N; Oberlies, Nicholas H; Paine, Mary F

2013-09-01

Despite increasing recognition of potential untoward interactions between herbal products and conventional medications, a standard system for prospective assessment of these interactions remains elusive. This information gap was addressed by evaluating the drug interaction liability of the model herbal product milk thistle (Silybum marianum) with the CYP3A probe substrate midazolam. The inhibitory effects of commercially available milk thistle extracts and isolated constituents on midazolam 1'-hydroxylation were screened using human liver and intestinal microsomes. Relative to vehicle, the extract silymarin and constituents silybin A, isosilybin A, isosilybin B, and silychristin at 100 μM demonstrated >50% inhibition of CYP3A activity with at least one microsomal preparation, prompting IC50 determination. The IC50s for isosilybin B and silychristin were ∼60 and 90 μM, respectively, whereas those for the remaining constituents were >100 μM. Extracts and constituents that contained the 1,4-dioxane moiety demonstrated a >1.5-fold shift in IC50 when tested as potential mechanism-based inhibitors. The semipurified extract, silibinin, and the two associated constituents (silybin A and silybin B) demonstrated mechanism-based inhibition of recombinant CYP3A4 (KI, ∼100 μM; kinact, ∼0.20 min(-1)) but not microsomal CYP3A activity. The maximum predicted increases in midazolam area under the curve using the static mechanistic equation and recombinant CYP3A4 data were 1.75-fold, which may necessitate clinical assessment. Evaluation of the interaction liability of single herbal product constituents, in addition to commercially available extracts, will enable elucidation of mechanisms underlying potential clinically significant herb-drug interactions. Application of this framework to other herbal products would permit predictions of herb-drug interactions and assist in prioritizing clinical evaluation.

Biomass, Bioenergy and the Sustainability of Soils and Climate: What Role for Biochar?

NASA Astrophysics Data System (ADS)

Sohi, Saran

2013-04-01

Biochar is the solid, carbon rich product of heating biomass with the exclusion of air (pyrolysis). Whereas charcoal is derived from wood, biochar is a co-product of energy capture and can derive from waste or non-waste, virgin or non-virgin biomass resources. But also, biochar is not a fuel - rather it is intended for the beneficial amendment of soil in agriculture, forestry and horticulture. This results in long-term storage of plant-derived carbon that could improve yield or efficiency of crop production, and/or mitigate trace gas emissions from the land. Life cycle analysis (LCA) shows that pyrolysis bioenergy with biochar production should offer considerably more carbon abatement than combustion, or gasification of the same feedstock. This has potential to link climate change mitigation to bioenergy and sustainable use of soil. But, in economic terms, the opportunity cost of producing biochar (reflecting the calorific value of its stored carbon) is inflated by bioenergy subsidies. This, combined with a lack of clear regulatory position and no mature pyrolysis technologies at large scale, means that pyrolysis-biochar systems (PBS) remain largely conceptual at the current time. Precise understanding of its function and an ability to predict its impact on different soils and crops with certainty, biochar should acquire a monetary value. Combining such knowledge with a system that monetizes climate change mitigation potential (such as carbon markets), could see schemes for producing and using biochar escalate - including a context for its deployment in biomass crops, or through pyrolysis of residues from other bioenergy processes. This talk explores the opportunity, challenges and risks in pursuing biochar production in various bioenergy contexts including enhanced sustainability of soil use in biomass crop production, improving the carbon balance and value chain in biofuel production, and using organic waste streams more effectively (including the processing of clean agricultural residues). Research knowledge that has emerged since 2005, when the term "biochar" was coined will be summarized (and currently resulting in over 200 research papers per year), highlighting the limits of predictability and certainty for biochar function and the extent to which these may ultimately be addressed. The policy context will be highlighted, including some recommendations and priorities for potential next steps.

Projected climate change threatens pollinators and crop production in Brazil

PubMed Central

Costa, Wilian França; Cordeiro, Guaraci Duran; Imperatriz-Fonseca, Vera Lucia; Saraiva, Antonio Mauro; Biesmeijer, Jacobus; Garibaldi, Lucas Alejandro

2017-01-01

Animal pollination can impact food security since many crops depend on pollinators to produce fruits and seeds. However, the effects of projected climate change on crop pollinators and therefore on crop production are still unclear, especially for wild pollinators and aggregate community responses. Using species distributional modeling, we assessed the effects of climate change on the geographic distribution of 95 pollinator species of 13 Brazilian crops, and we estimated their relative impacts on crop production. We described these effects at the municipality level, and we assessed the crops that were grown, the gross production volume of these crops, the total crop production value, and the number of inhabitants. Overall, considering all crop species, we found that the projected climate change will reduce the probability of pollinator occurrence by almost 0.13 by 2050. Our models predict that almost 90% of the municipalities analyzed will face species loss. Decreases in the pollinator occurrence probability varied from 0.08 (persimmon) to 0.25 (tomato) and will potentially affect 9% (mandarin) to 100% (sunflower) of the municipalities that produce each crop. Municipalities in central and southern Brazil will potentially face relatively large impacts on crop production due to pollinator loss. In contrast, some municipalities in northern Brazil, particularly in the northwestern Amazon, could potentially benefit from climate change because pollinators of some crops may increase. The decline in the probability of pollinator occurrence is found in a large number of municipalities with the lowest GDP and will also likely affect some places where crop production is high (20% to 90% of the GDP) and where the number of inhabitants is also high (more than 6 million people). Our study highlights key municipalities where crops are economically important and where pollinators will potentially face the worst conditions due to climate change. However, pollinators may be able to find new suitable areas that have the potential to improve crop production. The results shown here could guide policy decisions for adapting to climate change and for preventing the loss of pollinator species and crop production. PMID:28792956

Projected climate change threatens pollinators and crop production in Brazil.

PubMed

Giannini, Tereza Cristina; Costa, Wilian França; Cordeiro, Guaraci Duran; Imperatriz-Fonseca, Vera Lucia; Saraiva, Antonio Mauro; Biesmeijer, Jacobus; Garibaldi, Lucas Alejandro

2017-01-01

Animal pollination can impact food security since many crops depend on pollinators to produce fruits and seeds. However, the effects of projected climate change on crop pollinators and therefore on crop production are still unclear, especially for wild pollinators and aggregate community responses. Using species distributional modeling, we assessed the effects of climate change on the geographic distribution of 95 pollinator species of 13 Brazilian crops, and we estimated their relative impacts on crop production. We described these effects at the municipality level, and we assessed the crops that were grown, the gross production volume of these crops, the total crop production value, and the number of inhabitants. Overall, considering all crop species, we found that the projected climate change will reduce the probability of pollinator occurrence by almost 0.13 by 2050. Our models predict that almost 90% of the municipalities analyzed will face species loss. Decreases in the pollinator occurrence probability varied from 0.08 (persimmon) to 0.25 (tomato) and will potentially affect 9% (mandarin) to 100% (sunflower) of the municipalities that produce each crop. Municipalities in central and southern Brazil will potentially face relatively large impacts on crop production due to pollinator loss. In contrast, some municipalities in northern Brazil, particularly in the northwestern Amazon, could potentially benefit from climate change because pollinators of some crops may increase. The decline in the probability of pollinator occurrence is found in a large number of municipalities with the lowest GDP and will also likely affect some places where crop production is high (20% to 90% of the GDP) and where the number of inhabitants is also high (more than 6 million people). Our study highlights key municipalities where crops are economically important and where pollinators will potentially face the worst conditions due to climate change. However, pollinators may be able to find new suitable areas that have the potential to improve crop production. The results shown here could guide policy decisions for adapting to climate change and for preventing the loss of pollinator species and crop production.

Placing microalgae on the biofuels priority list: a review of the technological challenges

PubMed Central

Greenwell, H. C.; Laurens, L. M. L.; Shields, R. J.; Lovitt, R. W.; Flynn, K. J.

2010-01-01

Microalgae provide various potential advantages for biofuel production when compared with ‘traditional’ crops. Specifically, large-scale microalgal culture need not compete for arable land, while in theory their productivity is greater. In consequence, there has been resurgence in interest and a proliferation of algae fuel projects. However, while on a theoretical basis, microalgae may produce between 10- and 100-fold more oil per acre, such capacities have not been validated on a commercial scale. We critically review current designs of algal culture facilities, including photobioreactors and open ponds, with regards to photosynthetic productivity and associated biomass and oil production and include an analysis of alternative approaches using models, balancing space needs, productivity and biomass concentrations, together with nutrient requirements. In the light of the current interest in synthetic genomics and genetic modifications, we also evaluate the options for potential metabolic engineering of the lipid biosynthesis pathways of microalgae. We conclude that although significant literature exists on microalgal growth and biochemistry, significantly more work needs to be undertaken to understand and potentially manipulate algal lipid metabolism. Furthermore, with regards to chemical upgrading of algal lipids and biomass, we describe alternative fuel synthesis routes, and discuss and evaluate the application of catalysts traditionally used for plant oils. Simulations that incorporate financial elements, along with fluid dynamics and algae growth models, are likely to be increasingly useful for predicting reactor design efficiency and life cycle analysis to determine the viability of the various options for large-scale culture. The greatest potential for cost reduction and increased yields most probably lies within closed or hybrid closed–open production systems. PMID:20031983

Reconstituted human corneal epithelium: a new alternative to the Draize eye test for the assessment of the eye irritation potential of chemicals and cosmetic products.

PubMed

Doucet, O; Lanvin, M; Thillou, C; Linossier, C; Pupat, C; Merlin, B; Zastrow, L

2006-06-01

The aim of this study was to evaluate the interest of a new three-dimensional epithelial model cultivated from human corneal cells to replace animal testing in the assessment of eye tolerance. To this end, 65 formulated cosmetic products and 36 chemicals were tested by means of this in vitro model using a simplified toxicokinetic approach. The chemicals were selected from the ECETOC data bank and the EC/HO International validation study list. Very satisfactory results were obtained in terms of concordance with the Draize test data for the formulated cosmetic products. Moreover, the response of the corneal model appeared predictive of human ocular response clinically observed by ophthalmologists. The in vitro scores for the chemicals tested strongly correlated with their respective scores in vivo. For all the compounds tested, the response of the corneal model to irritants was similar regardless of their chemical structure, suggesting a good robustness of the prediction model proposed. We concluded that this new three-dimensional epithelial model, developed from human corneal cells, could be promising for the prediction of eye irritation induced by chemicals and complex formulated products, and that these two types of materials should be tested using a similar protocol. A simple shortening of the exposure period was required for the chemicals assumed to be more aggressively irritant to the epithelial tissues than the cosmetic formulae.

Uncertainties in Decadal Model Evaluation due to the Choice of Different Reanalysis Products

NASA Astrophysics Data System (ADS)

Illing, Sebastian; Kadow, Christopher; Kunst, Oliver; Cubasch, Ulrich

2014-05-01

In recent years decadal predictions have become very popular in the climate science community. A major task is the evaluation and validation of a decadal prediction system. Therefore hindcast experiments are performed and evaluated against observation based or reanalysis data-sets. That is, various metrics and skill scores like the anomaly correlation or the mean squared error skill score (MSSS) are calculated to estimate potential prediction skill of the model system. Our results will mostly feature the Baseline 1 hindcast experiments from the MiKlip decadal prediction system. MiKlip (www.fona-miklip.de) is a project for medium-term climate prediction funded by the Federal Ministry of Education and Research in Germany (BMBF) and has the aim to create a model system that can provide reliable decadal forecasts on climate and weather. There are various reanalysis and observation based products covering at least the last forty years which can be used for model evaluation, for instance the 20th Century Reanalysis from NOAA-CIRES, the Climate Forecast System Reanalysis from NCEP or the Interim Reanalysis from ECMWF. Each of them is based on different climate models and observations. We will show that the choice of the reanalysis product has a huge impact on the value of various skill metrics. In some cases this may actually lead to a change in the interpretation of the results, e.g. when one tries to compare two model versions and the anomaly correlation difference changes its sign for two different reanalysis products. We will also show first results of our studies investigating the influence and effect of this source of uncertainty for decadal model evaluation. Furthermore we point out regions which are most affected by this uncertainty and where one has to cautious interpreting skill scores. In addition we introduce some strategies to overcome or at least reduce this source of uncertainty.

Electron Transport Chain-dependent and -independent Mechanisms of Mitochondrial H2O2 Emission during Long-chain Fatty Acid Oxidation*

PubMed Central

Seifert, Erin L.; Estey, Carmen; Xuan, Jian Y.; Harper, Mary-Ellen

2010-01-01

Oxidative stress in skeletal muscle is a hallmark of various pathophysiologic states that also feature increased reliance on long-chain fatty acid (LCFA) substrate, such as insulin resistance and exercise. However, little is known about the mechanistic basis of the LCFA-induced reactive oxygen species (ROS) burden in intact mitochondria, and elucidation of this mechanistic basis was the goal of this study. Specific aims were to determine the extent to which LCFA catabolism is associated with ROS production and to gain mechanistic insights into the associated ROS production. Because intermediates and by-products of LCFA catabolism may interfere with antioxidant mechanisms, we predicted that ROS formation during LCFA catabolism reflects a complex process involving multiple sites of ROS production as well as modified mitochondrial function. Thus, we utilized several complementary approaches to probe the underlying mechanism(s). Using skeletal muscle mitochondria, our findings indicate that even a low supply of LCFA is associated with ROS formation in excess of that generated by NADH-linked substrates. Moreover, ROS production was evident across the physiologic range of membrane potential and was relatively insensitive to membrane potential changes. Determinations of topology and membrane potential as well as use of inhibitors revealed complex III and the electron transfer flavoprotein (ETF) and ETF-oxidoreductase, as likely sites of ROS production. Finally, ROS production was sensitive to matrix levels of LCFA catabolic intermediates, indicating that mitochondrial export of LCFA catabolic intermediates can play a role in determining ROS levels. PMID:20032466

Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites.

PubMed

Covington, Brett C; McLean, John A; Bachmann, Brian O

2017-01-04

Covering: 2000 to 2016The labor-intensive process of microbial natural product discovery is contingent upon identifying discrete secondary metabolites of interest within complex biological extracts, which contain inventories of all extractable small molecules produced by an organism or consortium. Historically, compound isolation prioritization has been driven by observed biological activity and/or relative metabolite abundance and followed by dereplication via accurate mass analysis. Decades of discovery using variants of these methods has generated the natural pharmacopeia but also contributes to recent high rediscovery rates. However, genomic sequencing reveals substantial untapped potential in previously mined organisms, and can provide useful prescience of potentially new secondary metabolites that ultimately enables isolation. Recently, advances in comparative metabolomics analyses have been coupled to secondary metabolic predictions to accelerate bioactivity and abundance-independent discovery work flows. In this review we will discuss the various analytical and computational techniques that enable MS-based metabolomic applications to natural product discovery and discuss the future prospects for comparative metabolomics in natural product discovery.

Biodiesel production and Environmental CO2 cleanup using Oleaginous Microorganisms from Al-Hassa area in Saudi Arabia

NASA Astrophysics Data System (ADS)

El-Sinawi, Abdulaziz; Shathele, Mohammad

2014-12-01

Biodiesel production is rapidly moving towards the mainstream as an alternative source of energy. Algae oil is one of the viable feed stocks among others to produce Biodiesel. However the difficulties in efficient biodiesel production from algae lie not in the extraction of the oil, but in finding an algal strain with a high lipid content and fast growth rate. This paper presents an experimental work performed to study the production of biodiesel from local algae strains in Al-Hassa territory of the eastern province in Saudi Arabia which was found to contain high lipid contents and show rapid growth. The collected results predict that those types of desert algae are promising and are considered to be a potential feedstock for biofuels.

Comparison of fission product release predictions using PARFUME with results from the AGR-1 safety tests

DOE Office of Scientific and Technical Information (OSTI.GOV)

Collin, Blaise P.; Petti, David A.; Demkowicz, Paul A.

Safety tests were conducted on fuel compacts from AGR-1, the first irradiation experiment of the Advanced Gas Reactor (AGR) Fuel Development and Qualification program, at temperatures ranging from 1600 to 1800 °C to determine fission product release at temperatures that bound reactor accident conditions. The PARFUME (PARticle FUel ModEl) code was used to predict the release of fission products silver, cesium, strontium, and krypton from fuel compacts containing tristructural isotropic (TRISO) coated particles during 15 of these safety tests. Comparisons between PARFUME predictions and post-irradiation examination results of the safety tests were conducted on two types of AGR-1 compacts: compactsmore » containing only intact particles and compacts containing one or more particles whose SiC layers failed during safety testing. In both cases, PARFUME globally over-predicted the experimental release fractions by several orders of magnitude: more than three (intact) and two (failed SiC) orders of magnitude for silver, more than three and up to two orders of magnitude for strontium, and up to two and more than one orders of magnitude for krypton. The release of cesium from intact particles was also largely over-predicted (by up to five orders of magnitude) but its release from particles with failed SiC was only over-predicted by a factor of about 3. These over-predictions can be largely attributed to an over-estimation of the diffusivities used in the modeling of fission product transport in TRISO-coated particles. The integral release nature of the data makes it difficult to estimate the individual over-estimations in the kernel or each coating layer. Nevertheless, a tentative assessment of correction factors to these diffusivities was performed to enable a better match between the modeling predictions and the safety testing results. The method could only be successfully applied to silver and cesium. In the case of strontium, correction factors could not be assessed because potential release during the safety tests could not be distinguished from matrix content released during irradiation. Furthermore, in the case of krypton, all the coating layers are partly retentive and the available data did not allow the level of retention in individual layers to be determined, hence preventing derivation of any correction factors.« less

Comparison of fission product release predictions using PARFUME with results from the AGR-1 safety tests

DOE PAGES

Collin, Blaise P.; Petti, David A.; Demkowicz, Paul A.; ...

2016-04-07

Safety tests were conducted on fuel compacts from AGR-1, the first irradiation experiment of the Advanced Gas Reactor (AGR) Fuel Development and Qualification program, at temperatures ranging from 1600 to 1800 °C to determine fission product release at temperatures that bound reactor accident conditions. The PARFUME (PARticle FUel ModEl) code was used to predict the release of fission products silver, cesium, strontium, and krypton from fuel compacts containing tristructural isotropic (TRISO) coated particles during 15 of these safety tests. Comparisons between PARFUME predictions and post-irradiation examination results of the safety tests were conducted on two types of AGR-1 compacts: compactsmore » containing only intact particles and compacts containing one or more particles whose SiC layers failed during safety testing. In both cases, PARFUME globally over-predicted the experimental release fractions by several orders of magnitude: more than three (intact) and two (failed SiC) orders of magnitude for silver, more than three and up to two orders of magnitude for strontium, and up to two and more than one orders of magnitude for krypton. The release of cesium from intact particles was also largely over-predicted (by up to five orders of magnitude) but its release from particles with failed SiC was only over-predicted by a factor of about 3. These over-predictions can be largely attributed to an over-estimation of the diffusivities used in the modeling of fission product transport in TRISO-coated particles. The integral release nature of the data makes it difficult to estimate the individual over-estimations in the kernel or each coating layer. Nevertheless, a tentative assessment of correction factors to these diffusivities was performed to enable a better match between the modeling predictions and the safety testing results. The method could only be successfully applied to silver and cesium. In the case of strontium, correction factors could not be assessed because potential release during the safety tests could not be distinguished from matrix content released during irradiation. Furthermore, in the case of krypton, all the coating layers are partly retentive and the available data did not allow the level of retention in individual layers to be determined, hence preventing derivation of any correction factors.« less

Assessing exposure to transformation products of soil-applied organic contaminants in surface water: comparison of model predictions and field data.

PubMed

Kern, Susanne; Singer, Heinz; Hollender, Juliane; Schwarzenbach, René P; Fenner, Kathrin

2011-04-01

Transformation products (TPs) of chemicals released to soil, for example, pesticides, are regularly detected in surface and groundwater with some TPs even dominating observed pesticide levels. Given the large number of TPs potentially formed in the environment, straightforward prioritization methods based on available data and simple, evaluative models are required to identify TPs with a high aquatic exposure potential. While different such methods exist, none of them has so far been systematically evaluated against field data. Using a dynamic multimedia, multispecies model for TP prioritization, we compared the predicted relative surface water exposure potential of pesticides and their TPs with experimental data for 16 pesticides and 46 TPs measured in a small river draining a Swiss agricultural catchment. Twenty TPs were determined quantitatively using solid-phase extraction liquid chromatography mass spectrometry (SPE-LC-MS/MS), whereas the remaining 26 TPs could only be detected qualitatively because of the lack of analytical reference standards. Accordingly, the two sets of TPs were used for quantitative and qualitative model evaluation, respectively. Quantitative comparison of predicted with measured surface water exposure ratios for 20 pairs of TPs and parent pesticides indicated agreement within a factor of 10, except for chloridazon-desphenyl and chloridazon-methyl-desphenyl. The latter two TPs were found to be present in elevated concentrations during baseflow conditions and in groundwater samples across Switzerland, pointing toward high concentrations in exfiltrating groundwater. A simple leaching relationship was shown to qualitatively agree with the observed baseflow concentrations and to thus be useful in identifying TPs for which the simple prioritization model might underestimate actual surface water concentrations. Application of the model to the 26 qualitatively analyzed TPs showed that most of those TPs categorized as exhibiting a high aquatic exposure potential could be confirmed to be present in the majority of water samples investigated. On the basis of these results, we propose a generally applicable, model-based approach to identify those TPs of soil-applied organic contaminants that exhibit a high aquatic exposure potential to prioritize them for higher-tier, experimental investigations.

Predicting Consumer Biomass, Size-Structure, Production, Catch Potential, Responses to Fishing and Associated Uncertainties in the World’s Marine Ecosystems

PubMed Central

Jennings, Simon; Collingridge, Kate

2015-01-01

Existing estimates of fish and consumer biomass in the world’s oceans are disparate. This creates uncertainty about the roles of fish and other consumers in biogeochemical cycles and ecosystem processes, the extent of human and environmental impacts and fishery potential. We develop and use a size-based macroecological model to assess the effects of parameter uncertainty on predicted consumer biomass, production and distribution. Resulting uncertainty is large (e.g. median global biomass 4.9 billion tonnes for consumers weighing 1 g to 1000 kg; 50% uncertainty intervals of 2 to 10.4 billion tonnes; 90% uncertainty intervals of 0.3 to 26.1 billion tonnes) and driven primarily by uncertainty in trophic transfer efficiency and its relationship with predator-prey body mass ratios. Even the upper uncertainty intervals for global predictions of consumer biomass demonstrate the remarkable scarcity of marine consumers, with less than one part in 30 million by volume of the global oceans comprising tissue of macroscopic animals. Thus the apparently high densities of marine life seen in surface and coastal waters and frequently visited abundance hotspots will likely give many in society a false impression of the abundance of marine animals. Unexploited baseline biomass predictions from the simple macroecological model were used to calibrate a more complex size- and trait-based model to estimate fisheries yield and impacts. Yields are highly dependent on baseline biomass and fisheries selectivity. Predicted global sustainable fisheries yield increases ≈4 fold when smaller individuals (< 20 cm from species of maximum mass < 1kg) are targeted in all oceans, but the predicted yields would rarely be accessible in practice and this fishing strategy leads to the collapse of larger species if fishing mortality rates on different size classes cannot be decoupled. Our analyses show that models with minimal parameter demands that are based on a few established ecological principles can support equitable analysis and comparison of diverse ecosystems. The analyses provide insights into the effects of parameter uncertainty on global biomass and production estimates, which have yet to be achieved with complex models, and will therefore help to highlight priorities for future research and data collection. However, the focus on simple model structures and global processes means that non-phytoplankton primary production and several groups, structures and processes of ecological and conservation interest are not represented. Consequently, our simple models become increasingly less useful than more complex alternatives when addressing questions about food web structure and function, biodiversity, resilience and human impacts at smaller scales and for areas closer to coasts. PMID:26226590

Simulating secondary organic aerosol in a regional air quality model using the statistical oxidation model - Part 1: Assessing the influence of constrained multi-generational ageing

NASA Astrophysics Data System (ADS)

Jathar, S. H.; Cappa, C. D.; Wexler, A. S.; Seinfeld, J. H.; Kleeman, M. J.

2015-09-01

Multi-generational oxidation of volatile organic compound (VOC) oxidation products can significantly alter the mass, chemical composition and properties of secondary organic aerosol (SOA) compared to calculations that consider only the first few generations of oxidation reactions. However, the most commonly used state-of-the-science schemes in 3-D regional or global models that account for multi-generational oxidation (1) consider only functionalization reactions but do not consider fragmentation reactions, (2) have not been constrained to experimental data; and (3) are added on top of existing parameterizations. The incomplete description of multi-generational oxidation in these models has the potential to bias source apportionment and control calculations for SOA. In this work, we used the Statistical Oxidation Model (SOM) of Cappa and Wilson (2012), constrained by experimental laboratory chamber data, to evaluate the regional implications of multi-generational oxidation considering both functionalization and fragmentation reactions. SOM was implemented into the regional UCD/CIT air quality model and applied to air quality episodes in California and the eastern US. The mass, composition and properties of SOA predicted using SOM are compared to SOA predictions generated by a traditional "two-product" model to fully investigate the impact of explicit and self-consistent accounting of multi-generational oxidation. Results show that SOA mass concentrations predicted by the UCD/CIT-SOM model are very similar to those predicted by a two-product model when both models use parameters that are derived from the same chamber data. Since the two-product model does not explicitly resolve multi-generational oxidation reactions, this finding suggests that the chamber data used to parameterize the models captures the majority of the SOA mass formation from multi-generational oxidation under the conditions tested. Consequently, the use of low and high NOx yields perturbs SOA concentrations by a factor of two and are probably a much stronger determinant in 3-D models than constrained multi-generational oxidation. While total predicted SOA mass is similar for the SOM and two-product models, the SOM model predicts increased SOA contributions from anthropogenic (alkane, aromatic) and sesquiterpenes and decreased SOA contributions from isoprene and monoterpene relative to the two-product model calculations. The SOA predicted by SOM has a much lower volatility than that predicted by the traditional model resulting in better qualitative agreement with volatility measurements of ambient OA. On account of its lower-volatility, the SOA mass produced by SOM does not appear to be as strongly influenced by the inclusion of oligomerization reactions, whereas the two-product model relies heavily on oligomerization to form low volatility SOA products. Finally, an unconstrained contemporary hybrid scheme to model multi-generational oxidation within the framework of a two-product model in which "ageing" reactions are added on top of the existing two-product parameterization is considered. This hybrid scheme formed at least three times more SOA than the SOM during regional simulations as a result of excessive transformation of semi-volatile vapors into lower volatility material that strongly partitions to the particle phase. This finding suggests that these "hybrid" multi-generational schemes should be used with great caution in regional models.

Simulating secondary organic aerosol in a regional air quality model using the statistical oxidation model - Part 1: Assessing the influence of constrained multi-generational ageing

NASA Astrophysics Data System (ADS)

Jathar, S. H.; Cappa, C. D.; Wexler, A. S.; Seinfeld, J. H.; Kleeman, M. J.

2016-02-01

Multi-generational oxidation of volatile organic compound (VOC) oxidation products can significantly alter the mass, chemical composition and properties of secondary organic aerosol (SOA) compared to calculations that consider only the first few generations of oxidation reactions. However, the most commonly used state-of-the-science schemes in 3-D regional or global models that account for multi-generational oxidation (1) consider only functionalization reactions but do not consider fragmentation reactions, (2) have not been constrained to experimental data and (3) are added on top of existing parameterizations. The incomplete description of multi-generational oxidation in these models has the potential to bias source apportionment and control calculations for SOA. In this work, we used the statistical oxidation model (SOM) of Cappa and Wilson (2012), constrained by experimental laboratory chamber data, to evaluate the regional implications of multi-generational oxidation considering both functionalization and fragmentation reactions. SOM was implemented into the regional University of California at Davis / California Institute of Technology (UCD/CIT) air quality model and applied to air quality episodes in California and the eastern USA. The mass, composition and properties of SOA predicted using SOM were compared to SOA predictions generated by a traditional two-product model to fully investigate the impact of explicit and self-consistent accounting of multi-generational oxidation.Results show that SOA mass concentrations predicted by the UCD/CIT-SOM model are very similar to those predicted by a two-product model when both models use parameters that are derived from the same chamber data. Since the two-product model does not explicitly resolve multi-generational oxidation reactions, this finding suggests that the chamber data used to parameterize the models captures the majority of the SOA mass formation from multi-generational oxidation under the conditions tested. Consequently, the use of low and high NOx yields perturbs SOA concentrations by a factor of two and are probably a much stronger determinant in 3-D models than multi-generational oxidation. While total predicted SOA mass is similar for the SOM and two-product models, the SOM model predicts increased SOA contributions from anthropogenic (alkane, aromatic) and sesquiterpenes and decreased SOA contributions from isoprene and monoterpene relative to the two-product model calculations. The SOA predicted by SOM has a much lower volatility than that predicted by the traditional model, resulting in better qualitative agreement with volatility measurements of ambient OA. On account of its lower-volatility, the SOA mass produced by SOM does not appear to be as strongly influenced by the inclusion of oligomerization reactions, whereas the two-product model relies heavily on oligomerization to form low-volatility SOA products. Finally, an unconstrained contemporary hybrid scheme to model multi-generational oxidation within the framework of a two-product model in which ageing reactions are added on top of the existing two-product parameterization is considered. This hybrid scheme formed at least 3 times more SOA than the SOM during regional simulations as a result of excessive transformation of semi-volatile vapors into lower volatility material that strongly partitions to the particle phase. This finding suggests that these hybrid multi-generational schemes should be used with great caution in regional models.

Identifying new persistent and bioaccumulative organics among chemicals in commerce. III: byproducts, impurities, and transformation products.

PubMed

Howard, Philip H; Muir, Derek C G

2013-05-21

The goal of this series of studies was to identify commercial chemicals that might be persistent and bioaccumulative (PB) and that were not being considered in current wastewater and aquatic environmental measurement programs. In this study, we focus on chemicals that are not on commercial chemical lists such as U.S. EPA's Inventory Update Rule but may be found as byproducts or impurities in commercial chemicals or are likely transformation products from commercial chemical use. We evaluated the 610 chemicals from our earlier publication as well as high production volume chemicals and identified 320 chemicals (39 byproducts and impurities, and 281 transformation products) that could be potential PB chemicals. Four examples are discussed in detail; these chemicals had a fair amount of information on the commercial synthesis and byproducts and impurities that might be found in the commercial product. Unfortunately for many of the 610 chemicals, as well as the transformation products, little or no information was available. Use of computer-aided software to predict the transformation pathways in combination with the biodegradation rules of thumb and some basic organic chemistry has allowed 281 potential PB transformation products to be suggested for some of the 610 commercial chemicals; more PB transformation products were not selected since microbial degradation often results in less persistent and less bioaccumulative metabolites.

Antibiotics in Animal Products

NASA Astrophysics Data System (ADS)

Falcão, Amílcar C.

The administration of antibiotics to animals to prevent or treat diseases led us to be concerned about the impact of these antibiotics on human health. In fact, animal products could be a potential vehicle to transfer drugs to humans. Using appropri ated mathematical and statistical models, one can predict the kinetic profile of drugs and their metabolites and, consequently, develop preventive procedures regarding drug transmission (i.e., determination of appropriate withdrawal periods). Nevertheless, in the present chapter the mathematical and statistical concepts for data interpretation are strictly given to allow understanding of some basic pharma-cokinetic principles and to illustrate the determination of withdrawal periods

Characterization of ROS1 cDNA from a human glioblastoma cell line

DOE Office of Scientific and Technical Information (OSTI.GOV)

Birchmeier, C.; O'Neill, K.; Riggs, M.

1990-06-01

The authors have isolated and characterized a human ROS1 cDNA from the glioblastoma cell line SW-1088. The cDNA, 8.3 kilobases long, has the potential to encode a transmembrane tyrosine-specific protein kinase with a predicted molecular mass of 259 kDa. The putative extracellular domain of ROS1 is homologous to the extracellular domain of the sevenless gene product from Drosophila. No comparable similarities in the extracellular domains were found between ROS1 and other receptor-type tyrosine kinases. Together, ROS1 and sevenless gene products define a distinct subclass of transmember tyrosine kinases.

Complete genome sequence of Defluviimonas alba cai42T, a microbial exopolysaccharides producer.

PubMed

Zhao, Jie-Yu; Geng, Shuang; Xu, Lian; Hu, Bing; Sun, Ji-Quan; Nie, Yong; Tang, Yue-Qin; Wu, Xiao-Lei

2016-12-10

Defluviimonas alba cai42 T , isolated from the oil-production water in Xinjiang Oilfield in China, has a strong ability to produce exopolysaccharides (EPS). We hereby present its complete genome sequence information which consists of a circular chromosome and three plasmids. The strain characteristically contains various genes encoding for enzymes involved in EPS biosynthesis, modification, and export. According to the genomic and physiochemical data, it is predicted that the strain has the potential to be utilized in industrial production of microbial EPS. Copyright © 2016 Elsevier B.V. All rights reserved.

Evaluation and Application of Gridded Snow Water Equivalent Products for Improving Snowmelt Flood Predictions in the Red River Basin of the North

NASA Astrophysics Data System (ADS)

Schroeder, R.; Jacobs, J. M.; Vuyovich, C.; Cho, E.; Tuttle, S. E.

2017-12-01

Each spring the Red River basin (RRB) of the North, located between the states of Minnesota and North Dakota and southern Manitoba, is vulnerable to dangerous spring snowmelt floods. Flat terrain, low permeability soils and a lack of satisfactory ground observations of snow pack conditions make accurate predictions of the onset and magnitude of major spring flood events in the RRB very challenging. This study investigated the potential benefit of using gridded snow water equivalent (SWE) products from passive microwave satellite missions and model output simulations to improve snowmelt flood predictions in the RRB using NOAA's operational Community Hydrologic Prediction System (CHPS). Level-3 satellite SWE products from AMSR-E, AMSR2 and SSM/I, as well as SWE computed from Level-2 brightness temperatures (Tb) measurements, including model output simulations of SWE from SNODAS and GlobSnow-2 were chosen to support the snowmelt modeling exercises. SWE observations were aggregated spatially (i.e. to the NOAA North Central River Forecast Center forecast basins) and temporally (i.e. by obtaining daily screened and weekly unscreened maximum SWE composites) to assess the value of daily satellite SWE observations relative to weekly maximums. Data screening methods removed the impacts of snow melt and cloud contamination on SWE and consisted of diurnal SWE differences and a temperature-insensitive polarization difference ratio, respectively. We examined the ability of the satellite and model output simulations to capture peak SWE and investigated temporal accuracies of screened and unscreened satellite and model output SWE. The resulting SWE observations were employed to update the SNOW-17 snow accumulation and ablation model of CHPS to assess the benefit of using temporally and spatially consistent SWE observations for snow melt predictions in two test basins in the RRB.

Draft Genome Sequence of Bacillus licheniformis Strain YNP1-TSU Isolated from Whiterock Springs in Yellowstone National Park

PubMed Central

O'Hair, Joshua A.; Li, Hui; Thapa, Santosh; Scholz, Matthew B.

2017-01-01

ABSTRACT Novel cellulolytic microorganisms can potentially influence second-generation biofuel production. This paper reports the draft genome sequence of Bacillus licheniformis strain YNP1-TSU, isolated from hydrothermal-vegetative microbiomes inside Yellowstone National Park. The assembled sequence contigs predicted 4,230 coding genes, 66 tRNAs, and 10 rRNAs through automated annotation. PMID:28254968

The secondary drying and the fate of organic solvents for spray dried dispersion drug product.

PubMed

Hsieh, Daniel S; Yue, Hongfei; Nicholson, Sarah J; Roberts, Daniel; Schild, Richard; Gamble, John F; Lindrud, Mark

2015-05-01

To understand the mechanisms of secondary drying of spray-dried dispersion (SDD) drug product and establish a model to describe the fate of organic solvents in such a product. The experimental approach includes characterization of the SDD particles, drying studies of SDD using an integrated weighing balance and mass spectrometer, and the subsequent generation of the drying curve. The theoretical approach includes the establishment of a Fickian diffusion model. The kinetics of solvent removal during secondary drying from the lab scale to a bench scale follows Fickian diffusion model. Excellent agreement is obtained between the experimental data and the prediction from the modeling. The diffusion process is dependent upon temperature. The key to a successful scale up of the secondary drying is to control the drying temperature. The fate of primary solvents including methanol and acetone, and their potential impurity such as benzene can be described by the Fickian diffusion model. A mathematical relationship based upon the ratio of diffusion coefficient was established to predict the benzene concentration from the fate of the primary solvent during the secondary drying process.

Coupling sensing to crop models for closed-loop plant production in advanced life support systems

NASA Astrophysics Data System (ADS)

Cavazzoni, James; Ling, Peter P.

1999-01-01

We present a conceptual framework for coupling sensing to crop models for closed-loop analysis of plant production for NASA's program in advanced life support. Crop status may be monitored through non-destructive observations, while models may be independently applied to crop production planning and decision support. To achieve coupling, environmental variables and observations are linked to mode inputs and outputs, and monitoring results compared with model predictions of plant growth and development. The information thus provided may be useful in diagnosing problems with the plant growth system, or as a feedback to the model for evaluation of plant scheduling and potential yield. In this paper, we demonstrate this coupling using machine vision sensing of canopy height and top projected canopy area, and the CROPGRO crop growth model. Model simulations and scenarios are used for illustration. We also compare model predictions of the machine vision variables with data from soybean experiments conducted at New Jersey Agriculture Experiment Station Horticulture Greenhouse Facility, Rutgers University. Model simulations produce reasonable agreement with the available data, supporting our illustration.

Exponential 6 parameterization for the JCZ3-EOS

DOE Office of Scientific and Technical Information (OSTI.GOV)

McGee, B.C.; Hobbs, M.L.; Baer, M.R.

1998-07-01

A database has been created for use with the Jacobs-Cowperthwaite-Zwisler-3 equation-of-state (JCZ3-EOS) to determine thermochemical equilibrium for detonation and expansion states of energetic materials. The JCZ3-EOS uses the exponential 6 intermolecular potential function to describe interactions between molecules. All product species are characterized by r*, the radius of the minimum pair potential energy, and {var_epsilon}/k, the well depth energy normalized by Boltzmann`s constant. These parameters constitute the JCZS (S for Sandia) EOS database describing 750 gases (including all the gases in the JANNAF tables), and have been obtained by using Lennard-Jones potential parameters, a corresponding states theory, pure liquid shockmore » Hugoniot data, and fit values using an empirical EOS. This database can be used with the CHEETAH 1.40 or CHEETAH 2.0 interface to the TIGER computer program that predicts the equilibrium state of gas- and condensed-phase product species. The large JCZS-EOS database permits intermolecular potential based equilibrium calculations of energetic materials with complex elemental composition.« less

Learning to predict chemical reactions.

PubMed

Kayala, Matthew A; Azencott, Chloé-Agathe; Chen, Jonathan H; Baldi, Pierre

2011-09-26

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles, respectively, are not high throughput, are not generalizable or scalable, and lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry data set consisting of 1630 full multistep reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top-ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of nonproductive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal ( http://cdb.ics.uci.edu) under the Toolkits section.

Comparison of predicted binders in Rhipicephalus (Boophilus) microplus intestine protein variants Bm86 Campo Grande strain, Bm86 and Bm95.

PubMed

Andreotti, Renato; Pedroso, Marisela S; Caetano, Alexandre R; Martins, Natália F

2008-01-01

This paper reports the sequence analysis of Bm86 Campo Grande strain comparing it with Bm86 and Bm95 antigens from the preparations TickGardPLUS and Gavac, respectively. The PCR product was cloned into pMOSBlue and sequenced. The secondary structure prediction tool PSIPRED was used to calculate alpha helices and beta strand contents of the predicted polypeptide. The hydrophobicity profile was calculated using the algorithms from the Hopp and Woods method, in addition to identification of potential MHC class-I binding regions in the antigens. Pair-wise alignment revealed that the similarity between Bm86 Campo Grande strain and Bm86 is 0.2% higher than that between Bm86 Campo Grande strain and Bm95 antigens. The identities were 96.5% and 96.3% respectively. Major suggestive differences in hydrophobicity were predicted among the sequences in two specific regions.

A large-eddy simulation based power estimation capability for wind farms over complex terrain

NASA Astrophysics Data System (ADS)

Senocak, I.; Sandusky, M.; Deleon, R.

2017-12-01

There has been an increasing interest in predicting wind fields over complex terrain at the micro-scale for resource assessment, turbine siting, and power forecasting. These capabilities are made possible by advancements in computational speed from a new generation of computing hardware, numerical methods and physics modelling. The micro-scale wind prediction model presented in this work is based on the large-eddy simulation paradigm with surface-stress parameterization. The complex terrain is represented using an immersed-boundary method that takes into account the parameterization of the surface stresses. Governing equations of incompressible fluid flow are solved using a projection method with second-order accurate schemes in space and time. We use actuator disk models with rotation to simulate the influence of turbines on the wind field. Data regarding power production from individual turbines are mostly restricted because of proprietary nature of the wind energy business. Most studies report percentage drop of power relative to power from the first row. There have been different approaches to predict power production. Some studies simply report available wind power in the upstream, some studies estimate power production using power curves available from turbine manufacturers, and some studies estimate power as torque multiplied by rotational speed. In the present work, we propose a black-box approach that considers a control volume around a turbine and estimate the power extracted from the turbine based on the conservation of energy principle. We applied our wind power prediction capability to wind farms over flat terrain such as the wind farm over Mower County, Minnesota and the Horns Rev offshore wind farm in Denmark. The results from these simulations are in good agreement with published data. We also estimate power production from a hypothetical wind farm in complex terrain region and identify potential zones suitable for wind power production.

Modeling the cadmium balance in Australian agricultural systems in view of potential impacts on food and water quality.

PubMed

de Vries, W; McLaughlin, M J

2013-09-01

The historical build up and future cadmium (Cd) concentrations in top soils and in crops of four Australian agricultural systems are predicted with a mass balance model, focusing on the period 1900-2100. The systems include a rotation of dryland cereals, a rotation of sugarcane and peanuts/soybean, intensive dairy production and intensive horticulture. The input of Cd to soil is calculated from fertilizer application and atmospheric deposition and also examines options including biosolid and animal manure application in the sugarcane rotation and dryland cereal production systems. Cadmium output from the soil is calculated from leaching to deeper horizons and removal with the harvested crop or with livestock products. Parameter values for all Cd fluxes were based on a number of measurements on Australian soil-plant systems. In the period 1900-2000, soil Cd concentrations were predicted to increase on average between 0.21 mg kg(-1) in dryland cereals, 0.42 mg kg(-1) in intensive agriculture and 0.68 mg kg(-1) in dairy production, which are within the range of measured increases in soils in these systems. Predicted soil concentrations exceed critical soil Cd concentrations, based on food quality criteria for Cd in crops during the simulation period in clay-rich soils under dairy production and intensive horticulture. Predicted dissolved Cd concentrations in soil pore water exceed a ground water quality criterion of 2 μg l(-1) in light textured soils, except for the sugarcane rotation due to large water leaching fluxes. Results suggest that the present fertilizer Cd inputs in Australia are in excess of the long-term critical loads in heavy-textured soils for dryland cereals and that all other systems are at low risk. Calculated critical Cd/P ratios in P fertilizers vary from <50 to >1000 mg Cd kg P(-1) for the different soil, crop and environmental conditions applied. Copyright © 2013 Elsevier B.V. All rights reserved.

Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada.

PubMed

Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang

2016-09-01

As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for different period of year ecologists might focus on.

Cytoprophet: a Cytoscape plug-in for protein and domain interaction networks inference.

PubMed

Morcos, Faruck; Lamanna, Charles; Sikora, Marcin; Izaguirre, Jesús

2008-10-01

Cytoprophet is a software tool that allows prediction and visualization of protein and domain interaction networks. It is implemented as a plug-in of Cytoscape, an open source software framework for analysis and visualization of molecular networks. Cytoprophet implements three algorithms that predict new potential physical interactions using the domain composition of proteins and experimental assays. The algorithms for protein and domain interaction inference include maximum likelihood estimation (MLE) using expectation maximization (EM); the set cover approach maximum specificity set cover (MSSC) and the sum-product algorithm (SPA). After accepting an input set of proteins with Uniprot ID/Accession numbers and a selected prediction algorithm, Cytoprophet draws a network of potential interactions with probability scores and GO distances as edge attributes. A network of domain interactions between the domains of the initial protein list can also be generated. Cytoprophet was designed to take advantage of the visual capabilities of Cytoscape and be simple to use. An example of inference in a signaling network of myxobacterium Myxococcus xanthus is presented and available at Cytoprophet's website. http://cytoprophet.cse.nd.edu.

First Application of Newly Developed FT-NIR Spectroscopic Methodology to Predict Authenticity of Extra Virgin Olive Oil Retail Products in the USA.

PubMed

Mossoba, Magdi M; Azizian, Hormoz; Fardin-Kia, Ali Reza; Karunathilaka, Sanjeewa R; Kramer, John K G

2017-05-01

Economically motivated adulteration (EMA) of extra virgin olive oils (EVOO) has been a worldwide problem and a concern for government regulators for a long time. The US Food and Drug Administration (FDA) is mandated to protect the US public against intentional adulteration of foods and has jurisdiction over deceptive label declarations. To detect EMA of olive oil and address food safety vulnerabilities, we used a previously developed rapid screening methodology to authenticate EVOO. For the first time, a recently developed FT-NIR spectroscopic methodology in conjunction with partial least squares analysis was applied to commercial products labeled EVOO purchased in College Park, MD, USA to rapidly predict whether they are authentic, potentially mixed with refined olive oil (RO) or other vegetable oil(s), or are of lower quality. Of the 88 commercial products labeled EVOO that were assessed according to published specified ranges, 33 (37.5%) satisfied the three published FT-NIR requirements identified for authentic EVOO products which included the purity test. This test was based on limits established for the contents of three potential adulterants, oils high in linoleic acid (OH-LNA), oils high in oleic acid (OH-OLA), palm olein (PO), and/or RO. The remaining 55 samples (62.5%) did not meet one or more of the criteria established for authentic EVOO. The breakdown of the 55 products was EVOO potentially mixed with OH-LNA (25.5%), OH-OLA (10.9%), PO (5.4%), RO (25.5%), or a combination of any of these four (32.7%). If assessments had been based strictly on whether the fatty acid composition was within the established ranges set by the International Olive Council (IOC), less than 10% would have been identified as non-EVOO. These findings are significant not only because they were consistent with previously published data based on the results of two sensory panels that were accredited by IOC but more importantly each measurement/analysis was accomplished in less than 5 min.

High energy γ-ray production from Be, C, and Al targets with 65 MeV 3He bombardment

NASA Astrophysics Data System (ADS)

Hosaka, M.; Ishii, K.; Ohura, M.; Terakawa, A.; Miyamoto, S.; Guan, Z.; Orihara, H.; Kasagi, J.

1996-11-01

High-energy γ rays from targets of Be, C, and Al bombarded with 65 MeV 3He ions have been measured by the use of a γ-ray detector system consisting of seven BaF2 scintillators. The energy spectra were obtained up to the maximum energy kinematically permitted in each collision at detection angles of 35°-144°. The experimental cross sections are compared with calculations of the potential bremsstrahlung on which the theory has been developed by Nakayama and Bertsch. It is shown that the prediction of potential bremsstrahlung can well reproduce the production cross sections of γ rays of energy near the kinematical maximum energy in collisions, while this result is contrary to the previous one of Tam et al. in α and d bombardments.

Cosmic acceleration from M theory on twisted spaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Neupane, Ishwaree P.; Wiltshire, David L.

2005-10-15

In a recent paper [I. P. Neupane and D. L. Wiltshire, Phys. Lett. B 619, 201 (2005).] we have found a new class of accelerating cosmologies arising from a time-dependent compactification of classical supergravity on product spaces that include one or more geometric twists along with nontrivial curved internal spaces. With such effects, a scalar potential can have a local minimum with positive vacuum energy. The existence of such a minimum generically predicts a period of accelerated expansion in the four-dimensional Einstein conformal frame. Here we extend our knowledge of these cosmological solutions by presenting new examples and discuss themore » properties of the solutions in a more general setting. We also relate the known (asymptotic) solutions for multiscalar fields with exponential potentials to the accelerating solutions arising from simple (or twisted) product spaces for internal manifolds.« less

Test evaluation of potential heat shield contamination of an Outer Planet Probe's atmospheric sampling system

NASA Technical Reports Server (NTRS)

Kessler, W. C.; Woeller, F. H.; Wilkins, M. E.

1975-01-01

An Outer Planets Probe which retains the charred heatshield during atmospheric descent must deploy a sampling tube through the heatshield to extract atmospheric samples for analysis. Once the sampling tube is deployed, the atmospheric samples ingested must be free of contaminant gases generated by the heatshield. Outgassing products such as methane and water vapor are present in planetary atmospheres and hence, ingestion of such species would result in gas analyzer measurement uncertainties. This paper evaluates the potential for, and design impact of, the extracted atmospheric samples being contaminated by heatshield outgassing products. Flight trajectory data for Jupiter, Saturn and Uranus entries are analyzed to define the conditions resulting in the greatest potential for outgassing products being ingested into the probe's sampling system. An experimental program is defined and described which simulates the key flow field features for a planetary flight in a ground-based test facility. The primary parameters varied in the test include: sampling tube length, injectant mass flow rate and angle of attack. Measured contaminant levels predict the critical sampling tube length for contamination avoidance. Thus, the study demonstrates the compatibility of a retained heatshield concept and high quality atmospheric trace species measurements.

Biophoton detection and low-intensity light therapy: a potential clinical partnership.

PubMed

Tafur, Joseph; Van Wijk, Eduard P A; Van Wijk, Roeland; Mills, Paul J

2010-02-01

Low-intensity light therapy (LILT) is showing promise in the treatment of a wide variety of medical conditions. Concurrently, our knowledge of LILT mechanisms continues to expand. We are now aware of LILT's potential to induce cellular effects through, for example, accelerated ATP production and the mitigation of oxidative stress. In clinical use, however, it is often difficult to predict patient response to LILT. It appears that cellular reduction/oxidation (redox) state may play a central role in determining sensitivity to LILT and may help explain variability in patient responsiveness. In LILT, conditions associated with elevated reactive oxygen species (ROS) production, e.g. diabetic hyperglycemia, demonstrate increased sensitivity to LILT. Consequently, assessment of tissue redox conditions in vivo may prove helpful in identifying responsive tissues. A noninvasive redox measure may be useful in advancing investigation in LILT and may one day be helpful in better identifying responsive patients. The detection of biophotons, the production of which is associated with cellular redox state and the generation of ROS, represents just such an opportunity. In this review, we will present the case for pursuing further investigation into the potential clinical partnership between biophoton detection and LILT.

Biophoton Detection and Low-Intensity Light Therapy: A Potential Clinical Partnership

PubMed Central

Van Wijk, Eduard P.A.; Van Wijk, Roeland; Mills, Paul J.

2010-01-01

Abstract Low-intensity light therapy (LILT) is showing promise in the treatment of a wide variety of medical conditions. Concurrently, our knowledge of LILT mechanisms continues to expand. We are now aware of LILT's potential to induce cellular effects through, for example, accelerated ATP production and the mitigation of oxidative stress. In clinical use, however, it is often difficult to predict patient response to LILT. It appears that cellular reduction/oxidation (redox) state may play a central role in determining sensitivity to LILT and may help explain variability in patient responsiveness. In LILT, conditions associated with elevated reactive oxygen species (ROS) production, e.g. diabetic hyperglycemia, demonstrate increased sensitivity to LILT. Consequently, assessment of tissue redox conditions in vivo may prove helpful in identifying responsive tissues. A noninvasive redox measure may be useful in advancing investigation in LILT and may one day be helpful in better identifying responsive patients. The detection of biophotons, the production of which is associated with cellular redox state and the generation of ROS, represents just such an opportunity. In this review, we will present the case for pursuing further investigation into the potential clinical partnership between biophoton detection and LILT. PMID:19754267

COBRA: A Computational Brewing Application for Predicting the Molecular Composition of Organic Aerosols

PubMed Central

Fooshee, David R.; Nguyen, Tran B.; Nizkorodov, Sergey A.; Laskin, Julia; Laskin, Alexander; Baldi, Pierre

2012-01-01

Atmospheric organic aerosols (OA) represent a significant fraction of airborne particulate matter and can impact climate, visibility, and human health. These mixtures are difficult to characterize experimentally due to their complex and dynamic chemical composition. We introduce a novel Computational Brewing Application (COBRA) and apply it to modeling oligomerization chemistry stemming from condensation and addition reactions in OA formed by photooxidation of isoprene. COBRA uses two lists as input: a list of chemical structures comprising the molecular starting pool, and a list of rules defining potential reactions between molecules. Reactions are performed iteratively, with products of all previous iterations serving as reactants for the next. The simulation generated thousands of structures in the mass range of 120–500 Da, and correctly predicted ~70% of the individual OA constituents observed by high-resolution mass spectrometry. Select predicted structures were confirmed with tandem mass spectrometry. Esterification was shown to play the most significant role in oligomer formation, with hemiacetal formation less important, and aldol condensation insignificant. COBRA is not limited to atmospheric aerosol chemistry; it should be applicable to the prediction of reaction products in other complex mixtures for which reasonable reaction mechanisms and seed molecules can be supplied by experimental or theoretical methods. PMID:22568707

Prediction of purification of biopharmeceuticals with molecular dynamics

NASA Astrophysics Data System (ADS)

Ustach, Vincent; Faller, Roland

Purification of biopharmeceuticals remains the most expensive part of protein-based drug production. In ion exchange chromatography (IEX), prediction of the elution ionic strength of host cell and target proteins has the potential to reduce the parameter space for scale-up of protein production. The complex shape and charge distribution of proteins and pores complicates predictions of the interactions in these systems. All-atom molecular dynamics methods are beyond the scope of computational limits for mass transport regimes. We present a coarse-grained model for proteins for prediction of elution pH and ionic strength. By extending the raspberry model for colloid particles to surface shapes and charge distributions of proteins, we can reproduce the behavior of proteins in IEX. The average charge states of titratatable amino acid residues at relevant pH values are determined by extrapolation from all-atom molecular dynamics at pH 7. The pH specific all-atom electrostatic field is then mapped onto the coarse-grained surface beads of the raspberry particle. The hydrodynamics are reproduced with the lattice-Boltzmann scheme. This combination of methods allows very long simulation times. The model is being validated for known elution procedures by comparing the data with experiments. Defense Threat Reduction Agency (Grant Number HDTRA1-15-1-0054).

On the performance of satellite precipitation products in riverine flood modeling: A review

NASA Astrophysics Data System (ADS)

Maggioni, Viviana; Massari, Christian

2018-03-01

This work is meant to summarize lessons learned on using satellite precipitation products for riverine flood modeling and to propose future directions in this field of research. Firstly, the most common satellite precipitation products (SPPs) during the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) eras are reviewed. Secondly, we discuss the main errors and uncertainty sources in these datasets that have the potential to affect streamflow and runoff model simulations. Thirdly, past studies that focused on using SPPs for predicting streamflow and runoff are analyzed. As the impact of floods depends not only on the characteristics of the flood itself, but also on the characteristics of the region (population density, land use, geophysical and climatic factors), a regional analysis is required to assess the performance of hydrologic models in monitoring and predicting floods. The performance of SPP-forced hydrological models was shown to largely depend on several factors, including precipitation type, seasonality, hydrological model formulation, topography. Across several basins around the world, the bias in SPPs was recognized as a major issue and bias correction methods of different complexity were shown to significantly reduce streamflow errors. Model re-calibration was also raised as a viable option to improve SPP-forced streamflow simulations, but caution is necessary when recalibrating models with SPP, which may result in unrealistic parameter values. From a general standpoint, there is significant potential for using satellite observations in flood forecasting, but the performance of SPP in hydrological modeling is still inadequate for operational purposes.

Aspirin may promote mitochondrial biogenesis via the production of hydrogen peroxide and the induction of Sirtuin1/PGC-1α genes

PubMed Central

Kamble, Pratibha; Selvarajan, Krithika; Narasimhulu, Chandrakala Aluganti; Nandave, Mukesh; Parthasarathy, Sampath

2013-01-01

Based on the rapid hydrolysis of acetyl salicylic acid (ASA, Aspirin) to salicylic acid (SA), the ability of SA to form dihydroxy benzoic acid (DBA), and the latter’s redox reactions to yield hydrogen peroxide (H2O2), we predicted that ASA may have the potential to induce Sirtuin1 (Sirt1) and its downstream effects. We observed that treatment of cultured liver cells with ASA resulted in the induction of Sirt1, peroxisome proliferator-activated receptor-gamma co-activator-1α (PGC-1α), and NAD(P)H quinone oxidoreductase 1 (Nqo1) genes. Paraoxonase 1 (PON1) and Aryl hydrocarbon receptor (AhR) siRNA transfections inhibited the induction of gene expressions by ASA suggesting the need for the acetyl ester hydrolysis and hydroxylation to DHBA. The latter also induced Sirt1, confirming the proposed pathway. As predicted, ASA and SA treatment resulted in the production of H2O2, a known inducer of Sirt1 and confirmed in the current studies. More importantly, ASA treatment resulted in an increase in mitochondria as seen by tracking dyes. We suggest that DHBA, generated from ASA, via its oxidation/reduction reactions mediated by Nqo1 might be involved in the production of O2-. and H2O2. As Sirt1 and PGC-1α profoundly affect mitochondrial metabolism and energy utilization, ASA may have therapeutic potential beyond its ability to inhibit cyclooxygenases. PMID:23228932

Structure-based prediction and identification of 4-epimerization activity of phosphate sugars in class II aldolases.

PubMed

Lee, Seon-Hwa; Hong, Seung-Hye; An, Jung-Ung; Kim, Kyoung-Rok; Kim, Dong-Eun; Kang, Lin-Woo; Oh, Deok-Kun

2017-05-16

Sugar 4-epimerization reactions are important for the production of rare sugars and their derivatives, which have various potential industrial applications. For example, the production of tagatose, a functional sweetener, from fructose by sugar 4-epimerization is currently constrained because a fructose 4-epimerase does not exist in nature. We found that class II D-fructose-1,6-bisphosphate aldolase (FbaA) catalyzed the 4-epimerization of D-fructose-6-phosphate (F6P) to D-tagatose-6-phosphate (T6P) based on the prediction via structural comparisons with epimerase and molecular docking and the identification of the condensed products of C3 sugars. In vivo, the 4-epimerization activity of FbaA is normally repressed. This can be explained by our results showing the catalytic efficiency of D-fructose-6-phosphate kinase for F6P phosphorylation was significantly higher than that of FbaA for F6P epimerization. Here, we identified the epimerization reactions and the responsible catalytic residues through observation of the reactions of FbaA and L-rhamnulose-1-phosphate aldolases (RhaD) variants with substituted catalytic residues using different substrates. Moreover, we obtained detailed potential epimerization reaction mechanism of FbaA and a general epimerization mechanism of the class II aldolases L-fuculose-1-phosphate aldolase, RhaD, and FbaA. Thus, class II aldolases can be used as 4-epimerases for the stereo-selective synthesis of valuable carbohydrates.

Discussion of comparison study of hydraulic fracturing models -- Test case: GRI Staged Field Experiment No. 3

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cleary, M.P.

This paper provides comments to a companion journal paper on predictive modeling of hydraulic fracturing patterns (N.R. Warpinski et. al., 1994). The former paper was designed to compare various modeling methods to demonstrate the most accurate methods under various geologic constraints. The comments of this paper are centered around potential deficiencies in the former authors paper which include: limited actual comparisons offered between models, the issues of matching predictive data with that from related field operations was lacking or undocumented, and the relevance/impact of accurate modeling on the overall hydraulic fracturing cost and production.

Chemical potential dependence of particle ratios within a unified thermal approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bashir, I., E-mail: inamhep@gmail.com; Nanda, H.; Uddin, S.

2016-06-15

A unified statistical thermal freeze-out model (USTFM) is used to study the chemical potential dependence of identified particle ratios at mid-rapidity in heavy-ion collisions. We successfully reproduce the experimental data ranging from SPS energies to LHC energies, suggesting the statistical nature of the particle production in these collisions and hence the validity of our approach. The behavior of the freeze-out temperature is studied with respect to chemical potential. The freeze-out temperature is found to be universal at the RHIC and LHC and is close to the QCD predicted phase transition temperature, suggesting that the chemical freeze-out occurs soon after themore » hadronization takes place.« less

Probing CP violation in e + e − production of the Higgs boson and toponia

DOE PAGES

Hagiwara, Kaoru; Ma, Kai; Yokoya, Hiroshi

2016-06-01

We study the CP violation in the Higgs boson and toponia production process at the ILC where the toponia are produced near the threshold. With the approximation that the production vertex of the Higgs boson and toponia is contact, and neglecting the P-wave toponia, we analytically calculated the density matrix for the production and decay of the toponia. Under these assumptions, the production spectrum of the toponia is solely determined by the spin quantum number, therefore the toponia can be either singlet or triplet. We find that the production rate of the singlet toponium is highly suppressed, and behaves justmore » like the production of a P-wave toponia. In the case of the triplet toponium, three completely independent CP observables, namely azimuthal angles of lepton and antilepton in the toponium rest-frame as well as their sum, are predicted based on our analytical results, and checked by using the tree-level event generator. The non-trivial correlations come from the longitudinal-transverse interferences for the azimuthal angles of leptons, and the transverse-transverse interference for their sum. These three observables are well defined at the ILC, where the rest frame of the toponium can be reconstructed directly. Furthermore, the QCD-strong corrections, which are important near the threshold region, are also studied with the approximation of spin-independent QCD-Coulomb potential. While the total cross section is enhanced, the spin correlations predicted in this paper are not affected.« less

Probing CP violation in e +e - production of the Higgs boson and toponia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hagiwara, Kaoru; Ma, Kai; Yokoya, Hiroshi

We study the CP violation in the Higgs boson and toponia production process at the ILC where the toponia are produced near the threshold. With the approximation that the production vertex of the Higgs boson and toponia is contact, and neglecting the P-wave toponia, we analytically calculated the density matrix for the production and decay of the toponia. Under these assumptions, the production spectrum of the toponia is solely determined by the spin quantum number, therefore the toponia can be either singlet or triplet. We find that the production rate of the singlet toponium is highly suppressed, and behaves justmore » like the production of a P-wave toponia. In the case of the triplet toponium, three completely independent CP observables, namely azimuthal angles of lepton and antilepton in the toponium rest-frame as well as their sum, are predicted based on our analytical results, and checked by using the tree-level event generator. The non-trivial correlations come from the longitudinal-transverse interferences for the azimuthal angles of leptons, and the transverse-transverse interference for their sum. These three observables are well defined at the ILC, where the rest frame of the toponium can be reconstructed directly. Furthermore, the QCD-strong corrections, which are important near the threshold region, are also studied with the approximation of spin-independent QCD-Coulomb potential. While the total cross section is enhanced, the spin correlations predicted in this paper are not affected.« less

Probing CP violation in e +e - production of the Higgs boson and toponia

DOE PAGES

Hagiwara, Kaoru; Ma, Kai; Yokoya, Hiroshi

2016-06-08

We study the CP violation in the Higgs boson and toponia production process at the ILC where the toponia are produced near the threshold. With the approximation that the production vertex of the Higgs boson and toponia is contact, and neglecting the P-wave toponia, we analytically calculated the density matrix for the production and decay of the toponia. Under these assumptions, the production spectrum of the toponia is solely determined by the spin quantum number, therefore the toponia can be either singlet or triplet. We find that the production rate of the singlet toponium is highly suppressed, and behaves justmore » like the production of a P-wave toponia. In the case of the triplet toponium, three completely independent CP observables, namely azimuthal angles of lepton and antilepton in the toponium rest-frame as well as their sum, are predicted based on our analytical results, and checked by using the tree-level event generator. The non-trivial correlations come from the longitudinal-transverse interferences for the azimuthal angles of leptons, and the transverse-transverse interference for their sum. These three observables are well defined at the ILC, where the rest frame of the toponium can be reconstructed directly. Furthermore, the QCD-strong corrections, which are important near the threshold region, are also studied with the approximation of spin-independent QCD-Coulomb potential. While the total cross section is enhanced, the spin correlations predicted in this paper are not affected.« less

Classification of baseline toxicants for QSAR predictions to replace fish acute toxicity studies.

PubMed

Nendza, Monika; Müller, Martin; Wenzel, Andrea

2017-03-22

Fish acute toxicity studies are required for environmental hazard and risk assessment of chemicals by national and international legislations such as REACH, the regulations of plant protection products and biocidal products, or the GHS (globally harmonised system) for classification and labelling of chemicals. Alternative methods like QSARs (quantitative structure-activity relationships) can replace many ecotoxicity tests. However, complete substitution of in vivo animal tests by in silico methods may not be realistic. For the so-called baseline toxicants, it is possible to predict the fish acute toxicity with sufficient accuracy from log K ow and, hence, valid QSARs can replace in vivo testing. In contrast, excess toxicants and chemicals not reliably classified as baseline toxicants require further in silico, in vitro or in vivo assessments. Thus, the critical task is to discriminate between baseline and excess toxicants. For fish acute toxicity, we derived a scheme based on structural alerts and physicochemical property thresholds to classify chemicals as either baseline toxicants (=predictable by QSARs) or as potential excess toxicants (=not predictable by baseline QSARs). The step-wise approach identifies baseline toxicants (true negatives) in a precautionary way to avoid false negative predictions. Therefore, a certain fraction of false positives can be tolerated, i.e. baseline toxicants without specific effects that may be tested instead of predicted. Application of the classification scheme to a new heterogeneous dataset for diverse fish species results in 40% baseline toxicants, 24% excess toxicants and 36% compounds not classified. Thus, we can conclude that replacing about half of the fish acute toxicity tests by QSAR predictions is realistic to be achieved in the short-term. The long-term goals are classification criteria also for further groups of toxicants and to replace as many in vivo fish acute toxicity tests as possible with valid QSAR predictions.

Predicting potential global distributions of two Miscanthus grasses: implications for horticulture, biofuel production, and biological invasions.

PubMed

Hager, Heather A; Sinasac, Sarah E; Gedalof, Ze'ev; Newman, Jonathan A

2014-01-01

In many regions, large proportions of the naturalized and invasive non-native floras were originally introduced deliberately by humans. Pest risk assessments are now used in many jurisdictions to regulate the importation of species and usually include an estimation of the potential distribution in the import area. Two species of Asian grass (Miscanthus sacchariflorus and M. sinensis) that were originally introduced to North America as ornamental plants have since escaped cultivation. These species and their hybrid offspring are now receiving attention for large-scale production as biofuel crops in North America and elsewhere. We evaluated their potential global climate suitability for cultivation and potential invasion using the niche model CLIMEX and evaluated the models' sensitivity to the parameter values. We then compared the sensitivity of projections of future climatically suitable area under two climate models and two emissions scenarios. The models indicate that the species have been introduced to most of the potential global climatically suitable areas in the northern but not the southern hemisphere. The more narrowly distributed species (M. sacchariflorus) is more sensitive to changes in model parameters, which could have implications for modelling species of conservation concern. Climate projections indicate likely contractions in potential range in the south, but expansions in the north, particularly in introduced areas where biomass production trials are under way. Climate sensitivity analysis shows that projections differ more between the selected climate change models than between the selected emissions scenarios. Local-scale assessments are required to overlay suitable habitat with climate projections to estimate areas of cultivation potential and invasion risk.

Polymer adhesion predictions for oral dosage forms to enhance drug administration safety. Part 2: In vitro approach using mechanical force methods.

PubMed

Drumond, Nélio; Stegemann, Sven

2018-06-01

Predicting the potential for unintended adhesion of solid oral dosage forms (SODF) to mucosal tissue is an important aspect that should be considered during drug product development. Previous investigations into low strength mucoadhesion based on particle interactions methods provided evidence that rheological measurements could be used to obtain valid predictions for the development of SODF coatings that can be safely swallowed. The aim of this second work was to estimate the low mucoadhesive strength properties of different polymers using in vitro methods based on mechanical forces and to identify which methods are more precise when measuring reduced mucoadhesion. Another aim was to compare the obtained results to the ones achieved with in vitro particle interaction methods in order to evaluate which methodology can provide stronger predictions. The combined results correlate between particle interaction methods and mechanical force measurements. The polyethylene glycol grades (PEG) and carnauba wax showed the lowest adhesive potential and are predicted to support safe swallowing. Hydroxypropyl methylcellulose (HPMC) along with high molecular grades of polyvinylpyrrolidone (PVP) and polyvinyl alcohol (PVA) exhibited strong in vitro mucoadhesive strength. The combination of rheological and force tensiometer measurements should be considered when assessing the reduced mucoadhesion of polymer coatings to support safe swallowing of SODF. Copyright © 2018 Elsevier B.V. All rights reserved.

Dynamic predictive model for the growth of Salmonella spp. in liquid whole egg.

PubMed

Singh, Aikansh; Korasapati, Nageswara R; Juneja, Vijay K; Subbiah, Jeyamkondan; Froning, Glenn; Thippareddi, Harshavardhan

2011-04-01

A dynamic model for the growth of Salmonella spp. in liquid whole egg (LWE) (approximately pH 7.8) under continuously varying temperature was developed. The model was validated using 2 (5 to 15 °C; 600 h and 10 to 40 °C; 52 h) sinusoidal, continuously varying temperature profiles. LWE adjusted to pH 7.8 was inoculated with approximately 2.5-3.0 log CFU/mL of Salmonella spp., and the growth data at several isothermal conditions (5, 7, 10, 15, 20, 25, 30, 35, 37, 39, 41, 43, 45, and 47 °C) was collected. A primary model (Baranyi model) was fitted for each temperature growth data and corresponding maximum growth rates were estimated. Pseudo-R2 values were greater than 0.97 for primary models. Modified Ratkowsky model was used to fit the secondary model. The pseudo-R2 and root mean square error were 0.99 and 0.06 log CFU/mL, respectively, for the secondary model. A dynamic model for the prediction of Salmonella spp. growth under varying temperature conditions was developed using 4th-order Runge-Kutta method. The developed dynamic model was validated for 2 sinusoidal temperature profiles, 5 to 15 °C (for 600 h) and 10 to 40 °C (for 52 h) with corresponding root mean squared error values of 0.28 and 0.23 log CFU/mL, respectively, between predicted and observed Salmonella spp. populations. The developed dynamic model can be used to predict the growth of Salmonella spp. in LWE under varying temperature conditions.   Liquid egg and egg products are widely used in food processing and in restaurant operations. These products can be contaminated with Salmonella spp. during breaking and other unit operations during processing. The raw, liquid egg products are stored under refrigeration prior to pasteurization. However, process deviations can occur such as refrigeration failure, leading to temperature fluctuations above the required temperatures as specified in the critical limits within hazard analysis and critical control point plans for the operations. The processors are required to evaluate the potential growth of Salmonella spp. in such products before the product can be used, or further processed. Dynamic predictive models are excellent tools for regulators as well as the processing plant personnel to evaluate the microbiological safety of the product under such conditions.

The Future of Satellite-based Lightning Detection

NASA Technical Reports Server (NTRS)

Bocippio, Dennis J.; Christian, Hugh J.; Arnold, James E. (Technical Monitor)

2001-01-01

The future of satellite-based optical lightning detection, beyond the end of the current TRMM mission, is discussed. Opportunities for new low-earth orbit missions are reviewed. The potential for geostationary observations is significant; such observations provide order-of-magnitude gains in sampling and data efficiency over existing satellite convective observations. The feasibility and performance (resolution, sensitivity) of geostationary measurements using current technology is discussed. In addition to direct and continuous hemispheric observation of lighting, geostationary measurements have the potential (through data assimilation) to dramatically improve short and medium range forecasts, offering benefits to prediction of NOx productions and/or vertical transport.

Monitoring of substrate and product concentrations in acetic fermentation processes for onion vinegar production by NIR spectroscopy: value addition to worthless onions.

PubMed

González-Sáiz, J M; Esteban-Díez, I; Sánchez-Gallardo, C; Pizarro, C

2008-08-01

Wastes and by-products of the onion-processing industry pose an increasing disposal and environmental problem and represent a loss of valuable sources of nutrients. The present study focused on the production of vinegar from worthless onions as a potential valorisation route which could provide a viable solution to multiple disposal and environmental problems, simultaneously offering the possibility of converting waste materials into a useful food-grade product and of exploiting the unique properties and health benefits of onions. This study deals specifically with the second and definitive step of the onion vinegar production process: the efficient production of vinegar from onion waste by transforming onion ethanol, previously produced by alcoholic fermentation, into acetic acid via acetic fermentation. Near-infrared spectroscopy (NIRS), coupled with multivariate calibration methods, has been used to monitor the concentrations of both substrates and products in acetic fermentation. Separate partial least squares (PLS) regression models, correlating NIR spectral data of fermentation samples with each kinetic parameter studied, were developed. Wavelength selection was also performed applying the iterative predictor weighting-PLS (IPW-PLS) method in order to only consider significant spectral features in each model development to improve the quality of the final models constructed. Biomass, substrate (ethanol) and product (acetic acid) concentration were predicted in the acetic fermentation of onion alcohol with high accuracy using IPW-PLS models with a root-mean-square error of the residuals in external prediction (RMSEP) lower than 2.5% for both ethanol and acetic acid, and an RMSEP of 6.1% for total biomass concentration (a very satisfactory result considering the relatively low precision and accuracy associated with the reference method used for determining the latter). Thus, the simple and reliable calibration models proposed in this study suggest that they could be implemented in routine applications to monitor and predict the key species involved in the acetic fermentation of onion alcohol, allowing the onion vinegar production process to be controlled in real time.

Spatial patterns of primary productivity derived from the Dynamic Habitat Indices predict patterns of species richness and distributions in the tropics

NASA Astrophysics Data System (ADS)

Suttidate, Naparat

Humans are changing the Earth's ecosystems, which has profound consequences for biodiversity. To understand how species respond to these changes, biodiversity science requires accurate assessments of biodiversity. However, biodiversity assessments are still limited in tropical regions. The Dynamic Habitat Indices (DHIs), derived from satellite data, summarize dynamic patterns of annual primary productivity: (a) cumulative annual productivity, (b) minimum annual productivity, and (c) seasonal variation in productivity. The DHIs have been successfully used in temperate regions, but not yet in the tropics. My goal was to evaluate the importance of primary productivity measured via the DHIs for assessing patterns of species richness and distributions in Thailand. First, I assessed the relationships between the DHIs and tropical bird species richness. I also evaluated the complementarity of the DHIs and topography, climate, latitudinal gradients, habitat heterogeneity, and habitat area in explaining bird species richness. I found that among three DHIs, cumulative annual productivity was the most important factor in explaining bird species richness and that the DHIs outperformed other environmental variables. Second, I developed texture measures derive from DHI cumulative annual productivity, and compared them to habitat composition and fragmentation as predictors of tropical forest bird distributions. I found that adding texture measures to habitat composition and fragmentation models improved the prediction of tropical bird distributions, especially area- and edge-sensitive tropical forest bird species. Third, I predicted the effects of trophic interactions between primary productivity, prey, and predators in relation to habitat connectivity for Indochinese tigers (Panthera tigris). I found that including trophic interactions improved habitat suitability models for tigers. However, tiger habitat is highly fragmented with few dispersal corridors. I also identified potential habitat patches and corridors that could serve as target sites for conservation. In summary, my dissertation reveals the relationship between species diversity and dynamic patterns of primary productivity. The DHIs are effective measures to identify assess broad-scale patterns of biodiversity in tropical ecosystems, and assist conservation planning and resource management. My dissertation research contributes substantially to biodiversity science, and has broad societal relevance, in striving to protect biodiversity and the ecosystem services given rapid environmental changes.

Transient traceability analysis of land carbon storage dynamics: procedures and its application to two forest ecosystems

NASA Astrophysics Data System (ADS)

Jiang, L.; Shi, Z.; Xia, J.; Liang, J.; Lu, X.; Wang, Y.; Luo, Y.

2017-12-01

Uptake of anthropogenically emitted carbon (C) dioxide by terrestrial ecosystem is critical for determining future climate. However, Earth system models project large uncertainties in future C storage. To help identify sources of uncertainties in model predictions, this study develops a transient traceability framework to trace components of C storage dynamics. Transient C storage (X) can be decomposed into two components, C storage capacity (Xc) and C storage potential (Xp). Xc is the maximum C amount that an ecosystem can potentially store and Xp represents the internal capacity of an ecosystem to equilibrate C input and output for a network of pools. Xc is co-determined by net primary production (NPP) and residence time (𝜏N), with the latter being determined by allocation coefficients, transfer coefficients, environmental scalar, and exit rate. Xp is the product of redistribution matrix (𝜏ch) and net ecosystem exchange. We applied this framework to two contrasting ecosystems, Duke Forest and Harvard Forest with an ecosystem model. This framework helps identify the mechanisms underlying the responses of carbon cycling in the two forests to climate change. The temporal trajectories of X are similar between the two ecosystems. Using this framework, we found that two different mechanisms leading to the similar trajectory. This framework has potential to reveal mechanisms behind transient C storage in response to various global change factors. It can also identify sources of uncertainties in predicted transient C storage across models and can therefore be useful for model intercomparison.

Potential effects of sulfur pollutants on grape production in New York State

DOE Office of Scientific and Technical Information (OSTI.GOV)

Knudson, D.A.; Viessman, S.

1983-01-01

This paper presents the results of a prototype analysis of sulfur pollutants on graph production in New York State. Principal grape production areas for the state are defined and predictions of sulfur dioxide concentrations associated with present and projected sources are computed. Sulfur dioxide concentrations are based on the results of a multi-source dispersion model, whereas concentrations for other pollutants are derived from observations. This information is used in conjunction with results from experiments conducted to identify threshold levels of damage and/or injury to a variety of grape species to pollutants. Determination is then made whether the subject crop ismore » at risk from present and projected concentrations of pollutants.« less

Applications of systems biology towards microbial fuel production.

PubMed

Gowen, Christopher M; Fong, Stephen S

2011-10-01

Harnessing the immense natural diversity of biological functions for economical production of fuel has enormous potential benefits. Inevitably, however, the native capabilities for any given organism must be modified to increase the productivity or efficiency of a biofuel bioprocess. From a broad perspective, the challenge is to sufficiently understand the details of cellular functionality to be able to prospectively predict and modify the cellular function of a microorganism. Recent advances in experimental and computational systems biology approaches can be used to better understand cellular level function and guide future experiments. With pressure to quickly develop viable, renewable biofuel processes a balance must be maintained between obtaining depth of biological knowledge and applying that knowledge. Copyright © 2011 Elsevier Ltd. All rights reserved.

Lysine and Glutamic Acids as the End Products of Multi-response of Optimized Fermented Medium by Mucor mucedo KP736529.

PubMed

El-Hersh, Mohammed S; Saber, WesamEldin I A; El-Fadaly, Husain A; Mahmoud, Mohammed K

Amino acids are important for living organisms, they acting as crucial for metabolic activities and energy generation, wherein the deficiency in these amino acids cause various physiological defects. The aim of this study is to investigate the effect of some nutritional factors on the amino acids production by Mucor mucedo KP736529 during fermentation intervals. Mucor mucedo KP736529 was selected according to proteolytic activity. Corn steep liquor and olive cake were used in the fermented medium during Placket-Burman and central composite design to maximize the production of lysine and glutamic acids. During the screening by Plackett-Burman design, olive cake and Corn Steep Liquor (CSL) had potential importance for the higher production of amino acids. The individual fractionation of total amino acids showed both lysine and glutamic as the major amino acids associated with the fermentation process. Moreover, the Central Composite Design (CCD) has been adopted to explain the interaction between olive cake and CSL on the production of lysine and glutamic acids. The model recorded significant F-value, with high values of R 2, adjusted R 2 and predicted R 2 for both lysine and glutamic, indicating the validity of the data. Solving equation for maximum production of lysine recorded theoretical levels of olive cake and CSL, being 2.58 and 1.83 g L -1, respectively, with predicting value of lysine at 1.470 μg mL -1, whereas the predicting value of glutamic acid reached 0.805 mg mL -1 at levels of 2.49 and 1.93 g L -1 from olive cake and CSL, respectively. The desirability function (D) showed the actual responses being 1.473±0.009 and 0.801±0.004 μg mL -1 for lysine and glutamic acids, respectively. The model showed adequate validity to be applied in a large-scale production of both lysine and glutamic acids.

Thermal decomposition of ethanol. 4. Ab initio chemical kinetics for reactions of H atoms with CH3CH2O and CH3CHOH radicals.

PubMed

Xu, Z F; Xu, Kun; Lin, M C

2011-04-21

The potential energy surfaces of H-atom reactions with CH(3)CH(2)O and CH(3)CHOH, two major radicals in the decomposition and oxidation of ethanol, have been studied at the CCSD(T)/6-311+G(3df,2p) level of theory with geometric optimization carried out at the BH&HLYP/6-311+G(3df,2p) level. The direct hydrogen abstraction channels and the indirect association/decomposition channels from the chemically activated ethanol molecule have been considered for both reactions. The rate constants for both reactions have been calculated at 100-3000 K and 10(-4) Torr to 10(3) atm Ar pressure by microcanonical VTST/RRKM theory with master equation solution for all accessible product channels. The results show that the major product channel of the CH(3)CH(2)O + H reaction is CH(3) + CH(2)OH under atmospheric pressure conditions. Only at high pressure and low temperature, the rate constant for CH(3)CH(2)OH formation by collisonal deactivation becomes dominant. For CH(3)CHOH + H, there are three major product channels; at high temperatures, CH(3)+CH(2)OH production predominates at low pressures (P < 100 Torr), while the formation of CH(3)CH(2)OH by collisional deactivation becomes competitive at high pressures and low temperatures (T < 500 K). At high temperatures, the direct hydrogen abstraction reaction producing CH(2)CHOH + H(2) becomes dominant. Rate constants for all accessible product channels in both systems have been predicted and tabulated for modeling applications. The predicted value for CH(3)CHOH + H at 295 K and 1 Torr pressure agrees closely with available experimental data. For practical modeling applications, the rate constants for the thermal unimolecular decomposition of ethanol giving key accessible products have been predicted; those for the two major product channels taking place by dehydration and C-C breaking agree closely with available literature data.

Comparative genomics of Roseobacter clade bacteria isolated from the accessory nidamental gland of Euprymna scolopes

PubMed Central

Collins, Andrew J.; Fullmer, Matthew S.; Gogarten, Johann P.; Nyholm, Spencer V.

2015-01-01

The accessory nidamental gland (ANG) of the female Hawaiian bobtail squid, Euprymna scolopes, houses a consortium of bacteria including members of the Flavobacteriales, Rhizobiales, and Verrucomicrobia but is dominated by members of the Roseobacter clade (Rhodobacterales) within the Alphaproteobacteria. These bacteria are deposited into the jelly coat of the squid’s eggs, however, the function of the ANG and its bacterial symbionts has yet to be elucidated. In order to gain insight into this consortium and its potential role in host reproduction, we cultured 12 Rhodobacterales isolates from ANGs of sexually mature female squid and sequenced their genomes with Illumina sequencing technology. For taxonomic analyses, the ribosomal proteins of 79 genomes representing both roseobacters and non-roseobacters along with a separate MLSA analysis of 33 housekeeping genes from Roseobacter organisms placed all 12 isolates from the ANG within two groups of a single Roseobacter clade. Average nucelotide identity analysis suggests the ANG isolates represent three genera (Leisingera, Ruegeria, and Tateyamaria) comprised of seven putative species groups. All but one of the isolates contains a predicted Type VI secretion system, which has been shown to be important in secreting signaling and/or effector molecules in host–microbe associations and in bacteria–bacteria interactions. All sequenced genomes also show potential for secondary metabolite production, and are predicted to be involved with the production of acyl homoserine lactones (AHLs) and/or siderophores. An AHL bioassay confirmed AHL production in three tested isolates and from whole ANG homogenates. The dominant symbiont, Leisingera sp. ANG1, showed greater viability in iron-limiting conditions compared to other roseobacters, possibly due to higher levels of siderophore production. Future comparisons will try to elucidate novel metabolic pathways of the ANG symbionts to understand their putative role in host development. PMID:25755651

Visualization of the Invisible, Explanation of the Unknown, Ruggedization of the Unstable: Sensitivity Analysis, Virtual Tryout and Robust Design through Systematic Stochastic Simulation

NASA Astrophysics Data System (ADS)

Zwickl, Titus; Carleer, Bart; Kubli, Waldemar

2005-08-01

In the past decade, sheet metal forming simulation became a well established tool to predict the formability of parts. In the automotive industry, this has enabled significant reduction in the cost and time for vehicle design and development, and has helped to improve the quality and performance of vehicle parts. However, production stoppages for troubleshooting and unplanned die maintenance, as well as production quality fluctuations continue to plague manufacturing cost and time. The focus therefore has shifted in recent times beyond mere feasibility to robustness of the product and process being engineered. Ensuring robustness is the next big challenge for the virtual tryout / simulation technology. We introduce new methods, based on systematic stochastic simulations, to visualize the behavior of the part during the whole forming process — in simulation as well as in production. Sensitivity analysis explains the response of the part to changes in influencing parameters. Virtual tryout allows quick exploration of changed designs and conditions. Robust design and manufacturing guarantees quality and process capability for the production process. While conventional simulations helped to reduce development time and cost by ensuring feasible processes, robustness engineering tools have the potential for far greater cost and time savings. Through examples we illustrate how expected and unexpected behavior of deep drawing parts may be tracked down, identified and assigned to the influential parameters. With this knowledge, defects can be eliminated or springback can be compensated e.g.; the response of the part to uncontrollable noise can be predicted and minimized. The newly introduced methods enable more reliable and predictable stamping processes in general.

Resource competition model predicts zonation and increasing nutrient use efficiency along a wetland salinity gradient

USGS Publications Warehouse

Schoolmaster, Donald; Stagg, Camille L.

2018-01-01

A trade-off between competitive ability and stress tolerance has been hypothesized and empirically supported to explain the zonation of species across stress gradients for a number of systems. Since stress often reduces plant productivity, one might expect a pattern of decreasing productivity across the zones of the stress gradient. However, this pattern is often not observed in coastal wetlands that show patterns of zonation along a salinity gradient. To address the potentially complex relationship between stress, zonation, and productivity in coastal wetlands, we developed a model of plant biomass as a function of resource competition and salinity stress. Analysis of the model confirms the conventional wisdom that a trade-off between competitive ability and stress tolerance is a necessary condition for zonation. It also suggests that a negative relationship between salinity and production can be overcome if (1) the supply of the limiting resource increases with greater salinity stress or (2) nutrient use efficiency increases with increasing salinity. We fit the equilibrium solution of the dynamic model to data from Louisiana coastal wetlands to test its ability to explain patterns of production across the landscape gradient and derive predictions that could be tested with independent data. We found support for a number of the model predictions, including patterns of decreasing competitive ability and increasing nutrient use efficiency across a gradient from freshwater to saline wetlands. In addition to providing a quantitative framework to support the mechanistic hypotheses of zonation, these results suggest that this simple model is a useful platform to further build upon, simulate and test mechanistic hypotheses of more complex patterns and phenomena in coastal wetlands.

Prevalence and challenge tests of Listeria monocytogenes in Belgian produced and retailed mayonnaise-based deli-salads, cooked meat products and smoked fish between 2005 and 2007.

PubMed

Uyttendaele, M; Busschaert, P; Valero, A; Geeraerd, A H; Vermeulen, A; Jacxsens, L; Goh, K K; De Loy, A; Van Impe, J F; Devlieghere, F

2009-07-31

Processed ready-to-eat (RTE) foods with a prolonged shelf-life under refrigeration are at risk products for listeriosis. This manuscript provides an overview of prevalence data (n=1974) and challenge tests (n=299) related to Listeria monocytogenes for three categories of RTE food i) mayonnaise-based deli-salads (1187 presence/absence tests and 182 challenge tests), ii) cooked meat products (639 presence/absence tests and 92 challenge tests), and iii) smoked fish (90 presence/absence tests and 25 challenge tests), based on data records obtained from various food business operators in Belgium in the frame of the validation and verification of their HACCP plans over the period 2005-2007. Overall, the prevalence of L. monocytogenes in these RTE foods in the present study was lower compared to former studies in Belgium. For mayonnaise-based deli-salads, in 80 out of 1187 samples (6.7%) the pathogen was detected in 25 g. L. monocytogenes positive samples were often associated with smoked fish deli-salads. Cooked meat products showed a 1.1% (n=639) prevalence of the pathogen. For both food categories, numbers per gram never exceeded 100 CFU. L. monocytogenes was detected in 27.8% (25/90) smoked fish samples, while 4/25 positive samples failed to comply to the 100 CFU/g limit set out in EU Regulation 2073/2005. Challenge testing showed growth potential in 18/182 (9.9%) deli-salads and 61/92 (66%) cooked meat products. Nevertheless, both for deli-salads and cooked meat products, appropriate product formulation and storage conditions based upon hurdle technology could guarantee no growth of L. monocytogenes throughout the shelf-life as specified by the food business operator. Challenge testing of smoked fish showed growth of L. monocytogenes in 12/25 samples stored for 3-4 weeks at 4 degrees C. Of 45 (non-inoculated) smoked fish samples (13 of which were initially positive in 25 g) which were subjected to shelf-life testing, numbers exceeded 100 CFU/g in only one sample after storage until the end of shelf-life. Predictive models, dedicated to and validated for a particular food category, taking into account the inhibitory effect of various factors in hurdle technology, provided predictions of growth potential of L. monocytogenes corresponding to observed growth in challenge testing. Based on the combined prevalence data and growth potential, mayonnaise-based deli-salads and cooked meat products can be classified as intermediate risk foods, smoked fish as a high risk food.

Dissolved organic carbon concentration controls benthic primary production: results from in situ chambers in north-temperate lakes

USGS Publications Warehouse

Godwin, Sean C.; Jones, Stuart E.; Weidel, Brian C.; Solomon, Christopher T.

2014-01-01

We evaluated several potential drivers of primary production by benthic algae (periphyton) in north-temperate lakes. We used continuous dissolved oxygen measurements from in situ benthic chambers to quantify primary production by periphyton at multiple depths across 11 lakes encompassing a broad range of dissolved organic carbon (DOC) and total phosphorous (TP) concentrations. Light-use efficiency (primary production per unit incident light) was inversely related to average light availability (% of surface light) in 7 of the 11 study lakes, indicating that benthic algal assemblages exhibit photoadaptation, likely through physiological or compositional changes. DOC alone explained 86% of the variability in log-transformed whole-lake benthic production rates. TP was not an important driver of benthic production via its effects on nutrient and light availability. This result is contrary to studies in other systems, but may be common in relatively pristine north-temperate lakes. Our simple empirical model may allow for the prediction of whole-lake benthic primary production from easily obtained measurements of DOC concentration.

Bayesian modeling of Clostridium perfringens growth in beef-in-sauce products.

PubMed

Jaloustre, S; Cornu, M; Morelli, E; Noël, V; Delignette-Muller, M L

2011-04-01

Models on Clostridium perfringens growth which have been published to date have all been deterministic. A probabilistic model describing growth under non-isothermal conditions was thus proposed for predicting C. perfringens growth in beef-in-sauce products cooked and distributed in a French hospital. Model parameters were estimated from different types of data from various studies. A Bayesian approach was proposed to model the overall uncertainty regarding parameters and potential variability on the 'work to be done' (h(0)) during the germination, outgrowth and lag phase. Three models which differed according to their description of this parameter h(0) were tested. The model with inter-curve variability on h(0) was found to be the best one, on the basis of goodness-of-fit assessment and validation with literature data on results obtained under non-isothermal conditions. This model was used in two-dimensional Monte Carlo simulations to predict C. perfringens growth throughout the preparation of beef-in-sauce products, using temperature profiles recorded in a hospital kitchen. The median predicted growth was 7.8×10(-2) log(10) cfu·g(-1) (95% credibility interval [2.4×10(-2), 0.8]) despite the fact that for more than 50% of the registered temperature profiles cooling steps were longer than those required by French regulations. Copyright © 2010 Elsevier Ltd. All rights reserved.

Understanding the Radioactive Ingrowth and Decay of Naturally Occurring Radioactive Materials in the Environment: An Analysis of Produced Fluids from the Marcellus Shale.

PubMed

Nelson, Andrew W; Eitrheim, Eric S; Knight, Andrew W; May, Dustin; Mehrhoff, Marinea A; Shannon, Robert; Litman, Robert; Burnett, William C; Forbes, Tori Z; Schultz, Michael K

2015-07-01

The economic value of unconventional natural gas resources has stimulated rapid globalization of horizontal drilling and hydraulic fracturing. However, natural radioactivity found in the large volumes of "produced fluids" generated by these technologies is emerging as an international environmental health concern. Current assessments of the radioactivity concentration in liquid wastes focus on a single element-radium. However, the use of radium alone to predict radioactivity concentrations can greatly underestimate total levels. We investigated the contribution to radioactivity concentrations from naturally occurring radioactive materials (NORM), including uranium, thorium, actinium, radium, lead, bismuth, and polonium isotopes, to the total radioactivity of hydraulic fracturing wastes. For this study we used established methods and developed new methods designed to quantitate NORM of public health concern that may be enriched in complex brines from hydraulic fracturing wastes. Specifically, we examined the use of high-purity germanium gamma spectrometry and isotope dilution alpha spectrometry to quantitate NORM. We observed that radium decay products were initially absent from produced fluids due to differences in solubility. However, in systems closed to the release of gaseous radon, our model predicted that decay products will begin to ingrow immediately and (under these closed-system conditions) can contribute to an increase in the total radioactivity for more than 100 years. Accurate predictions of radioactivity concentrations are critical for estimating doses to potentially exposed individuals and the surrounding environment. These predictions must include an understanding of the geochemistry, decay properties, and ingrowth kinetics of radium and its decay product radionuclides.

The bitterness intensity of clarithromycin evaluated by a taste sensor.

PubMed

Tanigake, Atsu; Miyanaga, Yohko; Nakamura, Tomoko; Tsuji, Eriko; Matsuyama, Kenji; Kunitomo, Masaru; Uchida, Takahiro

2003-11-01

The purpose of this study was to evaluate the ability of a quantitative prediction method using a taste sensor to determine the bitterness of clarithromycin powder suspensions of various concentrations and of a commercial clarithromycin dry syrup product (Clarith dry syrup, Taisho Pharmaceutical Co., Ltd., Tokyo) containing aminoalkyl methacrylate polymer as a taste-masker. The bitterness of the clarithromycin dry syrup product dissolved in various beverages was also evaluated in gustatory sensation tests and using the taste sensor. In the sensor measurements, three variables were used to predict bitterness in single and multiple regression analysis: relative sensor output (R), the change of membrane potential caused by adsorption (CPA), and CPA/R ratio. The CPA values for channel 3 of the sensor predicted well the bitterness of clarithromycin powder suspensions and their filtered solutions. For Clarith dry syrup, the sensor output was small, suggesting that aminoalkyl methacrylate polymer was successful in almost complete masking of the bitter taste of the dry syrup product. When the bitterness intensities of mixtures of 1 g of Clarith dry syrup with 25 ml of water, coffee, tea, green tea, cocoa, milk, and a sports drink were examined, a good correlation was obtained between the results from human taste tests and the predicted values calculated on the basis of multiple regression analysis using CPA data from channel 4, and the CPA/R ratio from channel 3 of the taste sensor (r(2)=0.963, p<0.005). Co-administration of 1 g of Clarith dry syrup with an acidic sports drink was found to be the most bitter using either method.

Updated numerical model with uncertainty assessment of 1950-56 drought conditions on brackish-water movement within the Edwards aquifer, San Antonio, Texas

USGS Publications Warehouse

Brakefield, Linzy K.; White, Jeremy T.; Houston, Natalie A.; Thomas, Jonathan V.

2015-01-01

Predictive results of total spring discharge during the 7-year period, as well as head predictions at Bexar County index well J-17, were much different than the dissolved-solids concentration change results at the production wells. These upper bounds are an order of magnitude larger than the actual prediction which implies that (1) the predictions of total spring discharge at Comal and San Marcos Springs and head at Bexar County index well J-17 made with this model are not reliable, and (2) parameters that control these predictions are not informed well by the observation dataset during historymatching, even though the history-matching process yielded parameters to reproduce spring discharges and heads at these locations during the history-matching period. Furthermore, because spring discharges at these two springs and heads at Bexar County index well J-17 represent more of a cumulative effect of upstream conditions over a larger distance (and longer time), many more parameters (with their own uncertainties) are potentially controlling these predictions than the prediction of dissolved-solids concentration change at the prediction wells, and therefore contributing to a large posterior uncertainty.

A SIMPLIFIED MODEL FOR PREDICTING MALARIA ENTOMOLOGIC INOCULATION RATES BASED ON ENTOMOLOGIC AND PARASITOLOGIC PARAMETERS RELEVANT TO CONTROL

PubMed Central

KILLEEN, GERRY F.; McKENZIE, F. ELLIS; FOY, BRIAN D.; SCHIEFFELIN, CATHERINE; BILLINGSLEY, PETER F.; BEIER, JOHN C.

2008-01-01

Malaria transmission intensity is modeled from the starting perspective of individual vector mosquitoes and is expressed directly as the entomologic inoculation rate (EIR). The potential of individual mosquitoes to transmit malaria during their lifetime is presented graphically as a function of their feeding cycle length and survival, human biting preferences, and the parasite sporogonic incubation period. The EIR is then calculated as the product of 1) the potential of individual vectors to transmit malaria during their lifetime, 2) vector emergence rate relative to human population size, and 3) the infectiousness of the human population to vectors. Thus, impacts on more than one of these parameters will amplify each other’s effects. The EIRs transmitted by the dominant vector species at four malaria-endemic sites from Papua New Guinea, Tanzania, and Nigeria were predicted using field measurements of these characteristics together with human biting rate and human reservoir infectiousness. This model predicted EIRs (± SD) that are 1.13 ± 0.37 (range = 0.84–1.59) times those measured in the field. For these four sites, mosquito emergence rate and lifetime transmission potential were more important determinants of the EIR than human reservoir infectiousness. This model and the input parameters from the four sites allow the potential impacts of various control measures on malaria transmission intensity to be tested under a range of endemic conditions. The model has potential applications for the development and implementation of transmission control measures and for public health education. PMID:11289661

When is genetic modification socially acceptable? When used to advance human health through avenues other than food.

PubMed

Olynk Widmar, Nicole J; Dominick, S R; Tyner, Wallace E; Ruple, Audrey

2017-01-01

Given the potential for genetic modification (GM) to impact human health, via food and health mechanisms, a greater understanding of the social acceptance of GM is necessary to facilitate improved health outcomes. This analysis sought to quantify U.S. residents' acceptance of GM across five potential uses (grain production, fruit or vegetable production, livestock production, human medicine, and human health, i.e. disease vector control) and provides an in-depth analysis of a timely case study-the Zika virus (ZIKV). The two categories with the highest levels of acceptance for GM use were human medicine (62% acceptance) and human health (68% acceptance); the proportions agreeing with the use of GM for these two categories were statistically different from all other categories. Acceptance of GM in food uses revealed 44% of the sample accepted the use of GM in livestock production while grain production and fruit and vegetable production showed similar levels of agreement with 49% and 48% of responses, respectively. Two variables were significant in all five models predicting GM acceptance; namely, being male and GM awareness. Being male was significant and positive for all models; respondents who reported being male were more likely (than those who reported female) to agree with all five of the uses of GM studied. Those who were reportedly aware of GM mosquito technology were also more likely to agree with all uses of GM technology investigated. The potential relationship between awareness of GM technology uses and acceptance of other uses could help inform rates of acceptance of new technologies by various population segments.

When is genetic modification socially acceptable? When used to advance human health through avenues other than food

PubMed Central

Olynk Widmar, Nicole J.; Tyner, Wallace E.; Ruple, Audrey

2017-01-01

Given the potential for genetic modification (GM) to impact human health, via food and health mechanisms, a greater understanding of the social acceptance of GM is necessary to facilitate improved health outcomes. This analysis sought to quantify U.S. residents’ acceptance of GM across five potential uses (grain production, fruit or vegetable production, livestock production, human medicine, and human health, i.e. disease vector control) and provides an in-depth analysis of a timely case study–the Zika virus (ZIKV). The two categories with the highest levels of acceptance for GM use were human medicine (62% acceptance) and human health (68% acceptance); the proportions agreeing with the use of GM for these two categories were statistically different from all other categories. Acceptance of GM in food uses revealed 44% of the sample accepted the use of GM in livestock production while grain production and fruit and vegetable production showed similar levels of agreement with 49% and 48% of responses, respectively. Two variables were significant in all five models predicting GM acceptance; namely, being male and GM awareness. Being male was significant and positive for all models; respondents who reported being male were more likely (than those who reported female) to agree with all five of the uses of GM studied. Those who were reportedly aware of GM mosquito technology were also more likely to agree with all uses of GM technology investigated. The potential relationship between awareness of GM technology uses and acceptance of other uses could help inform rates of acceptance of new technologies by various population segments. PMID:28591218

Biodegradability and ecotoxicitiy of tramadol, ranitidine, and their photoderivatives in the aquatic environment.

PubMed

Bergheim, Marlies; Gieré, Reto; Kümmerer, Klaus

2012-01-01

This study was designed to assess the fate and the overall potential impacts of the widely prescribed drugs ranitidine and tramadol after their introduction into the aquatic environment. The probability to detect these two drugs in the aquatic environment was studied by analyzing their abiotic and biotic degradation properties. For this purpose, samples were irradiated with different light sources, and three widely used biodegradability tests from the OECD series, the closed bottle test (OECD 301 D), the manometric respirometry test (OECD 301 F) and the Zahn-Wellens test (OECD 302 B), were conducted. The ecotoxicity of the photolytically formed transformation products was assessed by performing the bacterial growth inhibition test (EN ISO 10712). Furthermore, quantitative structure-activity relationship analysis and a risk analysis based on the calculation of the predicted environmental concentrations have also been conducted to assess the environmental risk potential of the transformation products. The possible formation of stable products by microbial or photolytical transformation has been investigated with DOC and LC-MS analytics. In the present study, neither ranitidine, nor tramadol, nor their photoderivatives were found to be readily or inherently biodegradable according to test guidelines. The photolytic transformation was faster under a UV lamp compared to the reaction under an Xe lamp with a spectrum that mimics sunlight. No chronic toxicity against bacteria was found for ranitidine or its photolytic decomposition products, but a low toxicity was detected for the resulting mixture of the photolytic transformation products of tramadol. The study demonstrates that transformation products may have a higher environmental risk potential than the respective parent compounds.

Predicting Greenhouse Gas Emissions and Soil Carbon from Changing Pasture to an Energy Crop

PubMed Central

Duval, Benjamin D.; Anderson-Teixeira, Kristina J.; Davis, Sarah C.; Keogh, Cindy; Long, Stephen P.; Parton, William J.; DeLucia, Evan H.

2013-01-01

Bioenergy related land use change would likely alter biogeochemical cycles and global greenhouse gas budgets. Energy cane (Saccharum officinarum L.) is a sugarcane variety and an emerging biofuel feedstock for cellulosic bio-ethanol production. It has potential for high yields and can be grown on marginal land, which minimizes competition with grain and vegetable production. The DayCent biogeochemical model was parameterized to infer potential yields of energy cane and how changing land from grazed pasture to energy cane would affect greenhouse gas (CO2, CH4 and N2O) fluxes and soil C pools. The model was used to simulate energy cane production on two soil types in central Florida, nutrient poor Spodosols and organic Histosols. Energy cane was productive on both soil types (yielding 46–76 Mg dry mass⋅ha−1). Yields were maintained through three annual cropping cycles on Histosols but declined with each harvest on Spodosols. Overall, converting pasture to energy cane created a sink for GHGs on Spodosols and reduced the size of the GHG source on Histosols. This change was driven on both soil types by eliminating CH4 emissions from cattle and by the large increase in C uptake by greater biomass production in energy cane relative to pasture. However, the change from pasture to energy cane caused Histosols to lose 4493 g CO2 eq⋅m−2 over 15 years of energy cane production. Cultivation of energy cane on former pasture on Spodosol soils in the southeast US has the potential for high biomass yield and the mitigation of GHG emissions. PMID:23991028

Reservoir management in a hydrodynamic environment, Iagifu-Hedinia area, Southern Highlands, Papua New Guinea

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eisenberg, L.I.; Langston, M.V.; Fitzmorris, R.E.

Northwest to southeast regional scale flow in the Toro Sandstone parallels the Papuan Fold and Thrust Belt for a distance of 115 km, passing through Iagifu/Hedinia oil field along the way. This has had a profound effect on oil distribution in the Toro there, having swept the northwest side free of movable oil. A structurally controlled flow restriction causes a local, rapid drop in hydraulic potential, tilting local oil/water contacts up to six degrees and causing the three sandstone members of the Toro to locally behave as separate reservoirs, each with its own hydrocarbon/water contact. Reservoir simulations of Iagifu/Hedinia whichmore » include a flowing aquifer are able to match observed production history. Without a flowing aquifer, simulation predicts greater and earlier water production, and a greater pressure drop in the oil leg than has been observed. Reservoir modeling using a flowing aquifer has allowed downhole, structural targeting of later infill wells to be much closer to the OWC than would otherwise have been thought prudent, and has raised questions as to the potential effectiveness of a downdip water injection scheme. Production results from a small satellite field upstream of the main Iagifu/Hedinia field have shown a sudden increase in water production and reservoir pressure after a long period of pressure decline and no water production. This behavior appears to be due to an influx of higher hydraulic potential from a separate reservoir sand, the influx being brought about by pressure draw down during production and consequent breakdown of fault seal.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Long, Philip E.; Wurstner, Signe K.; Sullivan, E. C.

Ice coverage of the Arctic Ocean is predicted to become thinner and to cover less area with time. The combination of more ice-free waters for exploration and navigation, along with increasing demand for hydrocarbons and improvements in technologies for the discovery and exploitation of new hydrocarbon resources have focused attention on the hydrocarbon potential of the Arctic Basin and its margins. The purpose of this document is to 1) summarize results of a review of published hydrocarbon resources in the Arctic, including both conventional oil and gas and methane hydrates and 2) develop a set of digital maps of themore » hydrocarbon potential of the Arctic Ocean. These maps can be combined with predictions of ice-free areas to enable estimates of the likely regions and sequence of hydrocarbon production development in the Arctic. In this report, conventional oil and gas resources are explicitly linked with potential gas hydrate resources. This has not been attempted previously and is particularly powerful as the likelihood of gas production from marine gas hydrates increases. Available or planned infrastructure, such as pipelines, combined with the geospatial distribution of hydrocarbons is a very strong determinant of the temporal-spatial development of Arctic hydrocarbon resources. Significant unknowns decrease the certainty of predictions for development of hydrocarbon resources. These include: 1) Areas in the Russian Arctic that are poorly mapped, 2) Disputed ownership: primarily the Lomonosov Ridge, 3) Lack of detailed information on gas hydrate distribution, and 4) Technical risk associated with the ability to extract methane gas from gas hydrates. Logistics may control areas of exploration more than hydrocarbon potential. Accessibility, established ownership, and leasing of exploration blocks may trump quality of source rock, reservoir, and size of target. With this in mind, the main areas that are likely to be explored first are the Bering Strait and Chukchi Sea, in spite of the fact that these areas do not have highest potential for future hydrocarbon reserves. Opportunities for improving the mapping and assessment of Arctic hydrocarbon resources include: 1) Refining hydrocarbon potential on a basin-by-basin basis, 2) Developing more realistic and detailed distribution of gas hydrate, and 3) Assessing the likely future scenarios for development of infrastructure and their interaction with hydrocarbon potential. It would also be useful to develop a more sophisticated approach to merging conventional and gas hydrate resource potential that considers the technical uncertainty associated with exploitation of gas hydrate resources. Taken together, additional work in these areas could significantly improve our understanding of the exploitation of Arctic hydrocarbons as ice-free areas increase in the future.« less

Diamond Tool Specific Wear Rate Assessment in Granite Machining by Means of Knoop Micro-Hardness and Process Parameters

NASA Astrophysics Data System (ADS)

Goktan, R. M.; Gunes Yılmaz, N.

2017-09-01

The present study was undertaken to investigate the potential usability of Knoop micro-hardness, both as a single parameter and in combination with operational parameters, for sawblade specific wear rate (SWR) assessment in the machining of ornamental granites. The sawing tests were performed on different commercially available granite varieties by using a fully instrumented side-cutting machine. During the sawing tests, two fundamental productivity parameters, namely the workpiece feed rate and cutting depth, were varied at different levels. The good correspondence observed between the measured Knoop hardness and SWR values for different operational conditions indicates that it has the potential to be used as a rock material property that can be employed in preliminary wear estimations of diamond sawblades. Also, a multiple regression model directed to SWR prediction was developed which takes into account the Knoop hardness, cutting depth and workpiece feed rate. The relative contribution of each independent variable in the prediction of SWR was determined by using test statistics. The prediction accuracy of the established model was checked against new observations. The strong prediction performance of the model suggests that its framework may be applied to other granites and operational conditions for quantifying or differentiating the relative wear performance of diamond sawblades.

Summer drought predictability over Europe: empirical versus dynamical forecasts

NASA Astrophysics Data System (ADS)

Turco, Marco; Ceglar, Andrej; Prodhomme, Chloé; Soret, Albert; Toreti, Andrea; Doblas-Reyes Francisco, J.

2017-08-01

Seasonal climate forecasts could be an important planning tool for farmers, government and insurance companies that can lead to better and timely management of seasonal climate risks. However, climate seasonal forecasts are often under-used, because potential users are not well aware of the capabilities and limitations of these products. This study aims at assessing the merits and caveats of a statistical empirical method, the ensemble streamflow prediction system (ESP, an ensemble based on reordering historical data) and an operational dynamical forecast system, the European Centre for Medium-Range Weather Forecasts—System 4 (S4) in predicting summer drought in Europe. Droughts are defined using the Standardized Precipitation Evapotranspiration Index for the month of August integrated over 6 months. Both systems show useful and mostly comparable deterministic skill. We argue that this source of predictability is mostly attributable to the observed initial conditions. S4 shows only higher skill in terms of ability to probabilistically identify drought occurrence. Thus, currently, both approaches provide useful information and ESP represents a computationally fast alternative to dynamical prediction applications for drought prediction.

A stress wave based approach to NDE of logs for assessing potential veneer quality: Part I—small-diameter ponderosa pine.

Treesearch

Robert J. Ross; Susan W. Willits; William Von Segen; Terry Black; Brian K. Brashaw; Roy F. Pellerin

1999-01-01

Longitudinal stress wave nondestructive evaluation (NDE) techniques have been used in a variety of applications in the forest products industry. Recently, it has been shown that they can significantly aid in the assessment of log quality, particularly when they are used to predict performance of structural lumber obtained from a log. The purpose of the research...

Potential economic impact of limiting the international trade of timber as a phytosanitary measure

Treesearch

Ruhong Li; J. Buongiorno; S. Zhu; J.A. Turner; J. Prestemon

2007-01-01

We assessed the impact on the world forest sector of reducing the risk of exotic pest spread by curtailing the roundwood trade. The analysis compared predictions from 2006 to 2015, with and without a gradual ban of roundwood exports between 2006 and 2010. With a ban on roundwood trade, world consumer expenditures for wood products and producer revenues would rise by 2...

Establishing a baseline of estuarine submerged aquatic vegetation resources across salinity zones within coastal areas of the northern Gulf of Mexico

USGS Publications Warehouse

Hillmann, Eva R.; DeMarco, Kristin; LaPeyre, Megan K.

2016-01-01

Coastal ecosystems are dynamic and productive areas that are vulnerable to effects of global climate change. Despite their potentially limited spatial extent, submerged aquatic vegetation (SAV) beds function in coastal ecosystems as foundation species, and perform important ecological services. However, limited understanding of the factors controlling SAV distribution and abundance across multiple salinity zones (fresh, intermediate, brackish, and saline) in the northern Gulf of Mexico restricts the ability of models to accurately predict resource availability. We sampled 384 potential coastal SAV sites across the northern Gulf of Mexico in 2013 and 2014, and examined community and species-specific SAV distribution and biomass in relation to year, salinity, turbidity, and water depth. After two years of sampling, 14 species of SAV were documented, with three species (coontail [Ceratophyllum demersum], Eurasian watermilfoil [Myriophyllum spicatum], and widgeon grass [Ruppia maritima]) accounting for 54% of above-ground biomass collected. Salinity and water depth were dominant drivers of species assemblages but had little effect on SAV biomass. Predicted changes in salinity and water depths along the northern Gulf of Mexico coast will likely alter SAV production and species assemblages, shifting to more saline and depth-tolerant assemblages, which in turn may affect habitat and food resources for associated faunal species.

1,4-Benzoquinone reductase from Phanerochaete chrysosporium: cDNA cloning and regulation of expression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Akileswaran, L.; Brock, B.J.; Cereghino, J.L.

1999-02-01

A cDNA clone encoding a quinone reductase (QR) from the white rot basidiomycete Phanerochaete chrysosporium was isolated and sequenced. The cDNA consisted of 1,007 nucleotides and a poly(A) tail and encoded a deduced protein containing 271 amino acids. The experimentally determined eight-amino-acid N-germinal sequence of the purified QR protein from P. chrysosporium matched amino acids 72 to 79 of the predicted translation product of the cDNA. The M{sub r} of the predicted translation product, beginning with Pro-72, was essentially identical to the experimentally determined M{sub r} of one monomer of the QR dimer, and this finding suggested that QR ismore » synthesized as a proenzyme. The results of in vitro transcription-translation experiments suggested that QR is synthesized as a proenzyme with a 71-amino-acid leader sequence. This leader sequence contains two potential KEX2 cleavage sites and numerous potential cleavage sites for dipeptidyl aminopeptidase. The QR activity in cultures of P. chrysosporium increased following the addition of 2-dimethoxybenzoquinone, vanillic acid, or several other aromatic compounds. An immunoblot analysis indicated that induction resulted in an increase in the amount of QR protein, and a Northern blot analysis indicated that this regulation occurs at the level of the qr mRNA.« less

The acute and chronic effects of wastes associated with offshore oil and gas production on temperate and tropical marine ecological processes.

PubMed

Holdway, Douglas A

2002-03-01

A review of the acute and chronic effects of produced formation water (PFW), drilling fluids (muds) including oil-based cutting muds, water-based cutting muds, ester-based cutting muds and chemical additives, and crude oils associated with offshore oil and gas production was undertaken in relation to both temperate and tropical marine ecological processes. The main environmental effects are summarized, often in tabular form. Generally, the temporal and spatial scales of these studies, along with the large levels of inherent variation in natural environments, have precluded our ability to predict the potential long-term environmental impacts of the offshore oil and gas production industry. A series of critical questions regarding the environmental effects of the offshore oil and gas production industry that still remain unanswered are provided for future consideration.

Production of ethyl levulinate by direct conversion of wheat straw in ethanol media.

PubMed

Chang, Chun; Xu, Guizhuan; Jiang, Xiaoxian

2012-10-01

The production of ethyl levulinate from wheat straw by direct conversion in ethanol media was investigated. Response surface methodology (RSM) was applied to optimize the effects of processing parameters, and the regression analysis was performed on the data obtained. A close agreement between the experimental results and the model predictions was achieved. The optimal conditions for ethyl levulinate production from wheat straw were acid concentration 2.5%, reaction temperature 183°C, mass ratio of liquid to solid 19.8 and reaction time 36 min. Under the optimum conditions, the yield of ethyl levulinate 17.91% was obtained, representing a theoretical yield of 51.0%. The results suggest that wheat straw can be used as potential raw materials for the production of ethyl levulinate by direct conversion in ethanol media. Copyright © 2012 Elsevier Ltd. All rights reserved.

Application of theoretical methods to increase succinate production in engineered strains.

PubMed

Valderrama-Gomez, M A; Kreitmayer, D; Wolf, S; Marin-Sanguino, A; Kremling, A

2017-04-01

Computational methods have enabled the discovery of non-intuitive strategies to enhance the production of a variety of target molecules. In the case of succinate production, reviews covering the topic have not yet analyzed the impact and future potential that such methods may have. In this work, we review the application of computational methods to the production of succinic acid. We found that while a total of 26 theoretical studies were published between 2002 and 2016, only 10 studies reported the successful experimental implementation of any kind of theoretical knowledge. None of the experimental studies reported an exact application of the computational predictions. However, the combination of computational analysis with complementary strategies, such as directed evolution and comparative genome analysis, serves as a proof of concept and demonstrates that successful metabolic engineering can be guided by rational computational methods.

Are Agrofuels a conservation threat or opportunity for grassland birds in the United States?

USGS Publications Warehouse

Robertson, Bruce A.; Rice, Robert A.; Ribic, Christine; Babcock, Bruce A.; Landis, Douglas A.; Herkert, James R.; Fletcher, Robert J.; Fontaine, Joseph J; Doran, Patrick J.; Schemske, Douglas W.

2012-01-01

In the United States, government-mandated growth in the production of crops dedicated to biofuel (agrofuels) is predicted to increase the demands on existing agricultural lands, potentially threatening the persistence of populations of grassland birds they support. We review recently published literature and datasets to (1) examine the ability of alternative agrofuel crops and their management regimes to provide habitat for grassland birds, (2) determine how crop placement in agricultural landscapes and agrofuel-related land-use change will affect grassland birds, and (3) identify critical research and policy-development needs associated with agrofuel production. We find that native perennial plants proposed as feedstock for agrofuel (switchgrass, Panicum virgatum, and mixed grass—forb prairie) have considerable potential to provide new habitat to a wide range of grassland birds, including rare and threatened species. However, industrialization of agrofuel production that maximizes biomass, homogenizes vegetation structure, and results in the cultivation of small fields within largely forested landscapes is likely to reduce species richness and/or abundance of grassland-dependent birds. Realizing the potential benefits of agrofuel production for grassland birds' conservation will require the development of new policies that encourage agricultural practices specifically targeting the needs of grassland specialists. The broad array of grower-incentive programs in existence may deliver new agrofuel policies effectively but will require coordination at a spatial scale broader than currently practiced, preferably within an adaptive-management framework.

High resolution crop growth simulation for identification of potential adaptation strategies under climate change

NASA Astrophysics Data System (ADS)

Kim, K. S.; Yoo, B. H.

2016-12-01

Impact assessment of climate change on crop production would facilitate planning of adaptation strategies. Because socio-environmental conditions would differ by local areas, it would be advantageous to assess potential adaptation measures at a specific area. The objectives of this study was to develop a crop growth simulation system at a very high spatial resolution, e.g., 30 m, and to assess different adaptation options including shift of planting date and use of different cultivars. The Decision Support System for Agrotechnology Transfer (DSSAT) model was used to predict yields of soybean and maize in Korea. Gridded data for climate and soil were used to prepare input data for the DSSAT model. Weather input data were prepared at the resolution of 30 m using bilinear interpolation from gridded climate scenario data. Those climate data were obtained from Korean Meteorology Administration. Spatial resolution of temperature and precipitation was 1 km whereas that of solar radiation was 12.5 km. Soil series data at the 30 m resolution were obtained from the soil database operated by Rural Development Administration, Korea. The SOL file, which is a soil input file for the DSSAT model was prepared using physical and chemical properties of a given soil series, which were available from the soil database. Crop yields were predicted by potential adaptation options based on planting date and cultivar. For example, 10 planting dates and three cultivars were used to identify ideal management options for climate change adaptation. In prediction of maize yield, combination of 20 planting dates and two cultivars was used as management options. Predicted crop yields differed by site even within a relatively small region. For example, the maximum of average yields for 2001-2010 seasons differed by sites In a county of which areas is 520 km2 (Fig. 1). There was also spatial variation in the ideal management option in the region (Fig. 2). These results suggested that local assessment of climate change impact on crop production would be useful for planning adaptation options.

Present and Potential Future Distribution of Common Vampire Bats in the Americas and the Associated Risk to Cattle

PubMed Central

Lee, Dana N.; Papeş, Monica; Van Den Bussche, Ronald A.

2012-01-01

Success of the cattle industry in Latin America is impeded by the common vampire bat, Desmodus rotundus, through decreases in milk production and mass gain and increased risk of secondary infection and rabies. We used ecological niche modeling to predict the current potential distribution of D. rotundus and the future distribution of the species for the years 2030, 2050, and 2080 based on the A2, A1B, and B1 climate scenarios from the Intergovernmental Panel on Climate Change. We then combined the present day potential distribution with cattle density estimates to identify areas where cattle are at higher risk for the negative impacts due to D. rotundus. We evaluated our risk prediction by plotting 17 documented outbreaks of cattle rabies. Our results indicated highly suitable habitat for D. rotundus occurs throughout most of Mexico and Central America as well as portions of Venezuela, Guyana, the Brazilian highlands, western Ecuador, northern Argentina, and east of the Andes in Peru, Bolivia, and Paraguay. With future climate projections suitable habitat for D. rotundus is predicted in these same areas and additional areas in French Guyana, Suriname, Venezuela and Columbia; however D. rotundus are not likely to expand into the U.S. because of inadequate ‘temperature seasonality.’ Areas with large portions of cattle at risk include Mexico, Central America, Paraguay, and Brazil. Twelve of 17 documented cattle rabies outbreaks were represented in regions predicted at risk. Our present day and future predictions can help authorities focus rabies prevention efforts and inform cattle ranchers which areas are at an increased risk of cattle rabies because it has suitable habitat for D. rotundus. PMID:22900023

Present and potential future distribution of common vampire bats in the Americas and the associated risk to cattle.

PubMed

Lee, Dana N; Papeş, Monica; Van den Bussche, Ronald A

2012-01-01

Success of the cattle industry in Latin America is impeded by the common vampire bat, Desmodus rotundus, through decreases in milk production and mass gain and increased risk of secondary infection and rabies. We used ecological niche modeling to predict the current potential distribution of D. rotundus and the future distribution of the species for the years 2030, 2050, and 2080 based on the A2, A1B, and B1 climate scenarios from the Intergovernmental Panel on Climate Change. We then combined the present day potential distribution with cattle density estimates to identify areas where cattle are at higher risk for the negative impacts due to D. rotundus. We evaluated our risk prediction by plotting 17 documented outbreaks of cattle rabies. Our results indicated highly suitable habitat for D. rotundus occurs throughout most of Mexico and Central America as well as portions of Venezuela, Guyana, the Brazilian highlands, western Ecuador, northern Argentina, and east of the Andes in Peru, Bolivia, and Paraguay. With future climate projections suitable habitat for D. rotundus is predicted in these same areas and additional areas in French Guyana, Suriname, Venezuela and Columbia; however D. rotundus are not likely to expand into the U.S. because of inadequate 'temperature seasonality.' Areas with large portions of cattle at risk include Mexico, Central America, Paraguay, and Brazil. Twelve of 17 documented cattle rabies outbreaks were represented in regions predicted at risk. Our present day and future predictions can help authorities focus rabies prevention efforts and inform cattle ranchers which areas are at an increased risk of cattle rabies because it has suitable habitat for D. rotundus.

Comparing Time-Dependent Geomagnetic and Atmospheric Effects on Cosmogenic Nuclide Production Rate Scaling

NASA Astrophysics Data System (ADS)

Lifton, N. A.

2014-12-01

A recently published cosmogenic nuclide production rate scaling model based on analytical fits to Monte Carlo simulations of atmospheric cosmic ray flux spectra (both of which agree well with measured spectra) (Lifton et al., 2014, Earth Planet. Sci. Lett. 386, 149-160: termed the LSD model) provides two main advantages over previous scaling models: identification and quantification of potential sources of bias in the earlier models, and the ability to generate nuclide-specific scaling factors easily for a wide range of input parameters. The new model also provides a flexible framework for exploring the implications of advances in model inputs. In this work, the scaling implications of two recent time-dependent spherical harmonic geomagnetic models spanning the Holocene will be explored. Korte and Constable (2011, Phys. Earth Planet. Int. 188, 247-259) and Korte et al. (2011, Earth Planet. Sci. Lett. 312, 497-505) recently updated earlier spherical harmonic paleomagnetic models used by Lifton et al. (2014) with paleomagnetic measurements from sediment cores in addition to archeomagnetic and volcanic data. These updated models offer improved accuracy over the previous versions, in part to due to increased temporal and spatial data coverage. With the new models as input, trajectory-traced estimates of effective vertical cutoff rigidity (RC- the standard method for ordering cosmic ray data) yield significantly different time-integrated scaling predictions when compared to the earlier models. These results will be compared to scaling predictions using another recent time-dependent spherical harmonic model of the Holocene geomagnetic field by Pavón-Carrasco et al. (2014, Earth Planet. Sci. Lett. 388, 98-109), based solely on archeomagnetic and volcanic paleomagnetic data, but extending to 14 ka. In addition, the potential effects of time-dependent atmospheric models on LSD scaling predictions will be presented. Given the typical dominance of altitudinal over latitudinal scaling effects on cosmogenic nuclide production, incorporating transient global simulations of atmospheric structure (e.g., Liu et al., 2009, Science 325, 310-314) into scaling frameworks may contribute to improved understanding of long-term production rate variations.

Bioactive Natural Products Prioritization Using Massive Multi-informational Molecular Networks.

PubMed

Olivon, Florent; Allard, Pierre-Marie; Koval, Alexey; Righi, Davide; Genta-Jouve, Gregory; Neyts, Johan; Apel, Cécile; Pannecouque, Christophe; Nothias, Louis-Félix; Cachet, Xavier; Marcourt, Laurence; Roussi, Fanny; Katanaev, Vladimir L; Touboul, David; Wolfender, Jean-Luc; Litaudon, Marc

2017-10-20

Natural products represent an inexhaustible source of novel therapeutic agents. Their complex and constrained three-dimensional structures endow these molecules with exceptional biological properties, thereby giving them a major role in drug discovery programs. However, the search for new bioactive metabolites is hampered by the chemical complexity of the biological matrices in which they are found. The purification of single constituents from such matrices requires such a significant amount of work that it should be ideally performed only on molecules of high potential value (i.e., chemical novelty and biological activity). Recent bioinformatics approaches based on mass spectrometry metabolite profiling methods are beginning to address the complex task of compound identification within complex mixtures. However, in parallel to these developments, methods providing information on the bioactivity potential of natural products prior to their isolation are still lacking and are of key interest to target the isolation of valuable natural products only. In the present investigation, we propose an integrated analysis strategy for bioactive natural products prioritization. Our approach uses massive molecular networks embedding various informational layers (bioactivity and taxonomical data) to highlight potentially bioactive scaffolds within the chemical diversity of crude extracts collections. We exemplify this workflow by targeting the isolation of predicted active and nonactive metabolites from two botanical sources (Bocquillonia nervosa and Neoguillauminia cleopatra) against two biological targets (Wnt signaling pathway and chikungunya virus replication). Eventually, the detection and isolation processes of a daphnane diterpene orthoester and four 12-deoxyphorbols inhibiting the Wnt signaling pathway and exhibiting potent antiviral activities against the CHIKV virus are detailed. Combined with efficient metabolite annotation tools, this bioactive natural products prioritization pipeline proves to be efficient. Implementation of this approach in drug discovery programs based on natural extract screening should speed up and rationalize the isolation of bioactive natural products.

Prediction in complex systems: The case of the international trade network

NASA Astrophysics Data System (ADS)

Vidmer, Alexandre; Zeng, An; Medo, Matúš; Zhang, Yi-Cheng

2015-10-01

Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can be represented as a complex network where links connect countries with the products that they export. Link prediction techniques based on heat and mass diffusion processes are employed to obtain predictions for products exported in the future. These baseline predictions are improved using a recent metric of country fitness and product similarity. The overall best results are achieved with a newly developed metric of product similarity which takes advantage of causality in the network evolution.

Livestock and food security: vulnerability to population growth and climate change

PubMed Central

Godber, Olivia F; Wall, Richard

2014-01-01

Livestock production is an important contributor to sustainable food security for many nations, particularly in low-income areas and marginal habitats that are unsuitable for crop production. Animal products account for approximately one-third of global human protein consumption. Here, a range of indicators, derived from FAOSTAT and World Bank statistics, are used to model the relative vulnerability of nations at the global scale to predicted climate and population changes, which are likely to impact on their use of grazing livestock for food. Vulnerability analysis has been widely used in global change science to predict impacts on food security and famine. It is a tool that is useful to inform policy decision making and direct the targeting of interventions. The model developed shows that nations within sub-Saharan Africa, particularly in the Sahel region, and some Asian nations are likely to be the most vulnerable. Livestock-based food security is already compromised in many areas on these continents and suffers constraints from current climate in addition to the lack of economic and technical support allowing mitigation of predicted climate change impacts. Governance is shown to be a highly influential factor and, paradoxically, it is suggested that current self-sufficiency may increase future potential vulnerability because trade networks are poorly developed. This may be relieved through freer trade of food products, which is also associated with improved governance. Policy decisions, support and interventions will need to be targeted at the most vulnerable nations, but given the strong influence of governance, to be effective, any implementation will require considerable care in the management of underlying structural reform. PMID:24692268

Diffusion-controlled reference material for VOC emissions testing: proof of concept.

PubMed

Cox, S S; Liu, Z; Little, J C; Howard-Reed, C; Nabinger, S J; Persily, A

2010-10-01

Because of concerns about indoor air quality, there is growing awareness of the need to reduce the rate at which indoor materials and products emit volatile organic compounds (VOCs). To meet consumer demand for low emitting products, manufacturers are increasingly submitting materials to independent laboratories for emissions testing. However, the same product tested by different laboratories can result in very different emissions profiles because of a general lack of test validation procedures. There is a need for a reference material that can be used as a known emissions source and that will have the same emission rate when tested by different laboratories under the same conditions. A reference material was created by loading toluene into a polymethyl pentene film. A fundamental emissions model was used to predict the toluene emissions profile. Measured VOC emissions profiles using small-chamber emissions tests compared reasonably well to the emissions profile predicted using the emissions model, demonstrating the feasibility of the proposed approach to create a diffusion-controlled reference material. To calibrate emissions test chambers and improve the reproducibility of VOC emission measurements among different laboratories, a reference material has been created using a polymer film loaded with a representative VOC. Initial results show that the film's VOC emission profile measured in a conventional test chamber compares well to predictions based on independently determined material/chemical properties and a fundamental emissions model. The use of such reference materials has the potential to build consensus and confidence in emissions testing as well as 'level the playing field' for product testing laboratories and manufacturers.

Physical stability and recrystallization kinetics of amorphous ibipinabant drug product by fourier transform raman spectroscopy.

PubMed

Sinclair, Wayne; Leane, Michael; Clarke, Graham; Dennis, Andrew; Tobyn, Mike; Timmins, Peter

2011-11-01

The solid-state physical stability and recrystallization kinetics during storage stability are described for an amorphous solid dispersed drug substance, ibipinabant, at a low concentration (1.0%, w/w) in a solid oral dosage form (tablet). The recrystallization behavior of the amorphous ibipinabant-polyvinylpyrrolidone solid dispersion in the tablet product was characterized by Fourier transform (FT) Raman spectroscopy. A partial least-square analysis used for multivariate calibration based on Raman spectra was developed and validated to detect less than 5% (w/w) of the crystalline form (equivalent to less than 0.05% of the total mass of the tablet). The method provided reliable and highly accurate predictive crystallinity assessments after exposure to a variety of stability storage conditions. It was determined that exposure to moisture had a significant impact on the crystallinity of amorphous ibipinabant. The information provided by the method has potential utility for predictive physical stability assessments. Dissolution testing demonstrated that the predicted crystallinity had a direct correlation with this physical property of the drug product. Recrystallization kinetics was measured using FT Raman spectroscopy for the solid dispersion from the tablet product stored at controlled temperature and relative humidity. The measurements were evaluated by application of the Johnson-Mehl-Avrami (JMA) kinetic model to determine recrystallization rate constants and Avrami exponent (n = 2). The analysis showed that the JMA equation could describe the process very well, and indicated that the recrystallization kinetics observed was a two-step process with an induction period (nucleation) followed by rod-like crystal growth. Copyright © 2011 Wiley-Liss, Inc.

Seasonal sea ice predictions for the Arctic based on assimilation of remotely sensed observations

NASA Astrophysics Data System (ADS)

Kauker, F.; Kaminski, T.; Ricker, R.; Toudal-Pedersen, L.; Dybkjaer, G.; Melsheimer, C.; Eastwood, S.; Sumata, H.; Karcher, M.; Gerdes, R.

2015-10-01

The recent thinning and shrinking of the Arctic sea ice cover has increased the interest in seasonal sea ice forecasts. Typical tools for such forecasts are numerical models of the coupled ocean sea ice system such as the North Atlantic/Arctic Ocean Sea Ice Model (NAOSIM). The model uses as input the initial state of the system and the atmospheric boundary condition over the forecasting period. This study investigates the potential of remotely sensed ice thickness observations in constraining the initial model state. For this purpose it employs a variational assimilation system around NAOSIM and the Alfred Wegener Institute's CryoSat-2 ice thickness product in conjunction with the University of Bremen's snow depth product and the OSI SAF ice concentration and sea surface temperature products. We investigate the skill of predictions of the summer ice conditions starting in March for three different years. Straightforward assimilation of the above combination of data streams results in slight improvements over some regions (especially in the Beaufort Sea) but degrades the over-all fit to independent observations. A considerable enhancement of forecast skill is demonstrated for a bias correction scheme for the CryoSat-2 ice thickness product that uses a spatially varying scaling factor.

Enhancing microbial production of biofuels by expanding microbial metabolic pathways.

PubMed

Yu, Ping; Chen, Xingge; Li, Peng

2017-09-01

Fatty acid, isoprenoid, and alcohol pathways have been successfully engineered to produce biofuels. By introducing three genes, atfA, adhE, and pdc, into Escherichia coli to expand fatty acid pathway, up to 1.28 g/L of fatty acid ethyl esters can be achieved. The isoprenoid pathway can be expanded to produce bisabolene with a high titer of 900 mg/L in Saccharomyces cerevisiae. Short- and long-chain alcohols can also be effectively biosynthesized by extending the carbon chain of ketoacids with an engineered "+1" alcohol pathway. Thus, it can be concluded that expanding microbial metabolic pathways has enormous potential for enhancing microbial production of biofuels for future industrial applications. However, some major challenges for microbial production of biofuels should be overcome to compete with traditional fossil fuels: lowering production costs, reducing the time required to construct genetic elements and to increase their predictability and reliability, and creating reusable parts with useful and predictable behavior. To address these challenges, several aspects should be further considered in future: mining and transformation of genetic elements related to metabolic pathways, assembling biofuel elements and coordinating their functions, enhancing the tolerance of host cells to biofuels, and creating modular subpathways that can be easily interconnected. © 2016 International Union of Biochemistry and Molecular Biology, Inc.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, Guoping; Zheng, Jianqiu; Xu, Xiaofeng

Soil organic carbon turnover to CO 2 and CH 4 is sensitive to soil redox potential and pH conditions. But, land surface models do not consider redox and pH in the aqueous phase explicitly, thereby limiting their use for making predictions in anoxic environments. Using recent data from incubations of Arctic soils, we extend the Community Land Model with coupled carbon and nitrogen (CLM-CN) decomposition cascade to include simple organic substrate turnover, fermentation, Fe(III) reduction, and methanogenesis reactions, and assess the efficacy of various temperature and pH response functions. Incorporating the Windermere Humic Aqueous Model (WHAM) enables us to approximatelymore » describe the observed pH evolution without additional parameterization. Though Fe(III) reduction is normally assumed to compete with methanogenesis, the model predicts that Fe(III) reduction raises the pH from acidic to neutral, thereby reducing environmental stress to methanogens and accelerating methane production when substrates are not limiting. Furthermore, the equilibrium speciation predicts a substantial increase in CO 2 solubility as pH increases, and taking into account CO 2 adsorption to surface sites of metal oxides further decreases the predicted headspace gas-phase fraction at low pH. Without adequate representation of these speciation reactions, as well as the impacts of pH, temperature, and pressure, the CO 2 production from closed microcosms can be substantially underestimated based on headspace CO 2 measurements only. Our results demonstrate the efficacy of geochemical models for simulating soil biogeochemistry and provide predictive understanding and mechanistic representations that can be incorporated into land surface models to improve climate predictions.« less

Theoretical predictions for α -decay chains of 118 290 -298Og isotopes using a finite-range nucleon-nucleon interaction

NASA Astrophysics Data System (ADS)

Ismail, M.; Adel, A.

2018-04-01

The α -decay half-lives of the recently synthesized superheavy nuclei (SHN) are investigated by employing the density dependent cluster model. A realistic nucleon-nucleon (NN ) interaction with a finite-range exchange part is used to calculate the microscopic α -nucleus potential in the well-established double-folding model. The calculated potential is then implemented to find both the assault frequency and the penetration probability of the α particle by means of the Wentzel-Kramers-Brillouin (WKB) approximation in combination with the Bohr-Sommerfeld quantization condition. The calculated values of α -decay half-lives of the recently synthesized Og isotopes and its decay products are in good agreement with the experimental data. Moreover, the calculated values of α -decay half-lives have been compared with those values evaluated using other theoretical models, and it was found that our theoretical values match well with their counterparts. The competition between α decay and spontaneous fission is investigated and predictions for possible decay modes for the unknown nuclei 118 290 -298Og are presented. We studied the behavior of the α -decay half-lives of Og isotopes and their decay products as a function of the mass number of the parent nuclei. We found that the behavior of the curves is governed by proton and neutron magic numbers found from previous studies. The proton numbers Z =114 , 116, 108, 106 and the neutron numbers N =172 , 164, 162, 158 show some magic character. We hope that the theoretical prediction of α -decay chains provides a new perspective to experimentalists.

Review of the NURE assessment of the U.S. Gulf Coast Uranium Province

USGS Publications Warehouse

Hall, Susan M.

2013-01-01

Historic exploration and development were used to evaluate the reliability of domestic uranium reserves and potential resources estimated by the U.S. Department of Energy national uranium resource evaluation (NURE) program in the U.S. Gulf Coast Uranium Province. NURE estimated 87 million pounds of reserves in the $30/lb U3O8 cost category in the Coast Plain uranium resource region, most in the Gulf Coast Uranium Province. Since NURE, 40 million pounds of reserves have been mined, and 38 million pounds are estimated to remain in place as of 2012, accounting for all but 9 million pounds of U3O8 in the reserve or production categories in the NURE estimate. Considering the complexities and uncertainties of the analysis, this study indicates that the NURE reserve estimates for the province were accurate. An unconditional potential resource of 1.4 billion pounds of U3O8, 600 million pounds of U3O8 in the forward cost category of $30/lb U3O8 (1980 prices), was estimated in 106 favorable areas by the NURE program in the province. Removing potential resources from the non-productive Houston embayment, and those reserves estimated below historic and current mining depths reduces the unconditional potential resource 33% to about 930 million pounds of U3O8, and that in the $30/lb cost category 34% to 399 million pounds of U3O8. Based on production records and reserve estimates tabulated for the region, most of the production since 1980 is likely from the reserves identified by NURE. The potential resource predicted by NURE has not been developed, likely due to a variety of factors related to the low uranium prices that have prevailed since 1980.

Food allergy animal models: an overview.

PubMed

Helm, Ricki M

2002-05-01

Specific food allergy is characterized by sensitization to innocuous food proteins with production of allergen-specific IgE that binds to receptors on basophils and mast cells. Upon recurrent exposure to the same allergen, an allergic response is induced by mediator release following cross-linking of cell-bound allergen-specific IgE. The determination of what makes an innocuous food protein an allergen in predisposed individuals is unknown; however, mechanistic and protein allergen predictive models are being actively investigated in a number of animal models. Currently, there is no animal model that will actively profile known food allergens, predict the allergic potential of novel food proteins, or demonstrate clinically the human food allergic sensitization/allergic response. Animal models under investigation include mice, rats, the guinea pig, atopic dog, and neonatal swine. These models are being assessed for production of IgE, clinical responses to re-exposure, and a ranking of food allergens (based on potency) including a nonfood allergen protein source. A selection of animal models actively being investigated that will contribute to our understanding of what makes a protein an allergen and future predictive models for assessing the allergenicity of novel proteins is presented in this review.

A Mathematical Model of Neutral Lipid Content in terms of Initial Nitrogen Concentration and Validation in Coelastrum sp. HA-1 and Application in Chlorella sorokiniana

PubMed Central

Zhao, Yue; Liu, Zhiyong; Liu, Chenfeng; Hu, Zhipeng

2017-01-01

Microalgae are considered to be a potential major biomass feedstock for biofuel due to their high lipid content. However, no correlation equations as a function of initial nitrogen concentration for lipid accumulation have been developed for simplicity to predict lipid production and optimize the lipid production process. In this study, a lipid accumulation model was developed with simple parameters based on the assumption protein synthesis shift to lipid synthesis by a linear function of nitrogen quota. The model predictions fitted well for the growth, lipid content, and nitrogen consumption of Coelastrum sp. HA-1 under various initial nitrogen concentrations. Then the model was applied successfully in Chlorella sorokiniana to predict the lipid content with different light intensities. The quantitative relationship between initial nitrogen concentrations and the final lipid content with sensitivity analysis of the model were also discussed. Based on the model results, the conversion efficiency from protein synthesis to lipid synthesis is higher and higher in microalgae metabolism process as nitrogen decreases; however, the carbohydrate composition content remains basically unchanged neither in HA-1 nor in C. sorokiniana. PMID:28194424

An interspecies correlation model to predict acute dermal toxicity of plant protection products to terrestrial life stages of amphibians using fish acute toxicity and bioconcentration data.

PubMed

Weltje, Lennart; Janz, Philipp; Sowig, Peter

2017-12-01

This paper presents a model to predict acute dermal toxicity of plant protection products (PPPs) to terrestrial amphibian life stages from (regulatory) fish data. By combining existing concepts, including interspecies correlation estimation (ICE), allometric relations, lethal body burden (LBB) and bioconcentration modelling, an equation was derived that predicts the amphibian median lethal dermal dose (LD 50 ) from standard acute toxicity values (96-h LC 50 ) for fish and bioconcentration factors (BCF) in fish. Where possible, fish BCF values were corrected to 5% lipid, and to parent compound. Then, BCF values were adjusted to an exposure duration of 96 h, in case steady state took longer to be achieved. The derived correlation equation is based on 32 LD 50 values from acute dermal toxicity experiments with 15 different species of anuran amphibians, comprising 15 different PPPs. The developed ICE model can be used in a screening approach to estimate the acute risk to amphibian terrestrial life stages from dermal exposures to PPPs with organic active substances. This has the potential to reduce unnecessary testing of vertebrates. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effect of rainfall seasonality on carbon storage in tropical dry ecosystems

NASA Astrophysics Data System (ADS)

Rohr, Tyler; Manzoni, Stefano; Feng, Xue; Menezes, Rômulo S. C.; Porporato, Amilcare

2013-07-01

seasonally dry conditions are typical of large areas of the tropics, their biogeochemical responses to seasonal rainfall and soil carbon (C) sequestration potential are not well characterized. Seasonal moisture availability positively affects both productivity and soil respiration, resulting in a delicate balance between C deposition as litterfall and C loss through heterotrophic respiration. To understand how rainfall seasonality (i.e., duration of the wet season and rainfall distribution) affects this balance and to provide estimates of long-term C sequestration, we develop a minimal model linking the seasonal behavior of the ensemble soil moisture, plant productivity, related C inputs through litterfall, and soil C dynamics. A drought-deciduous caatinga ecosystem in northeastern Brazil is used as a case study to parameterize the model. When extended to different patterns of rainfall seasonality, the results indicate that for fixed annual rainfall, both plant productivity and soil C sequestration potential are largely, and nonlinearly, dependent on wet season duration. Moreover, total annual rainfall is a critical driver of this relationship, leading at times to distinct optima in both production and C storage. These theoretical predictions are discussed in the context of parameter uncertainties and possible changes in rainfall regimes in tropical dry ecosystems.

Cell based advanced therapeutic medicinal products for bone repair: Keep it simple?

PubMed

Leijten, J; Chai, Y C; Papantoniou, I; Geris, L; Schrooten, J; Luyten, F P

2015-04-01

The development of cell based advanced therapeutic medicinal products (ATMPs) for bone repair has been expected to revolutionize the health care system for the clinical treatment of bone defects. Despite this great promise, the clinical outcomes of the few cell based ATMPs that have been translated into clinical treatments have been far from impressive. In part, the clinical outcomes have been hampered because of the simplicity of the first wave of products. In response the field has set-out and amassed a plethora of complexities to alleviate the simplicity induced limitations. Many of these potential second wave products have remained "stuck" in the development pipeline. This is due to a number of reasons including the lack of a regulatory framework that has been evolving in the last years and the shortage of enabling technologies for industrial manufacturing to deal with these novel complexities. In this review, we reflect on the current ATMPs and give special attention to novel approaches that are able to provide complexity to ATMPs in a straightforward manner. Moreover, we discuss the potential tools able to produce or predict 'goldilocks' ATMPs, which are neither too simple nor too complex. Copyright © 2014 Elsevier B.V. All rights reserved.

Potential economic value of drought information to support early warning in Africa

NASA Astrophysics Data System (ADS)

Quiroga, S.; Iglesias, A.; Diz, A.; Garrote, L.

2012-04-01

We present a methodology to estimate the economic value of advanced climate information for food production in Africa under climate change scenarios. The results aim to facilitate better choices in water resources management. The methodology includes 4 sequential steps. First two contrasting management strategies (with and without early warning) are defined. Second, the associated impacts of the management actions are estimated by calculating the effect of drought in crop productivity under climate change scenarios. Third, the optimal management option is calculated as a function of the drought information and risk aversion of potential information users. Finally we use these optimal management simulations to compute the economic value of enhanced water allocation rules to support stable food production in Africa. Our results show how a timely response to climate variations can help reduce loses in food production. The proposed framework is developed within the Dewfora project (Early warning and forecasting systems to predict climate related drought vulnerability and risk in Africa) that aims to improve the knowledge on drought forecasting, warning and mitigation, and advance the understanding of climate related vulnerability to drought and to develop a prototype operational forecasting.

Metagenomic identification of active methanogens and methanotrophs in serpentinite springs of the Voltri Massif, Italy.

PubMed

Brazelton, William J; Thornton, Christopher N; Hyer, Alex; Twing, Katrina I; Longino, August A; Lang, Susan Q; Lilley, Marvin D; Früh-Green, Gretchen L; Schrenk, Matthew O

2017-01-01

The production of hydrogen and methane by geochemical reactions associated with the serpentinization of ultramafic rocks can potentially support subsurface microbial ecosystems independent of the photosynthetic biosphere. Methanogenic and methanotrophic microorganisms are abundant in marine hydrothermal systems heavily influenced by serpentinization, but evidence for methane-cycling archaea and bacteria in continental serpentinite springs has been limited. This report provides metagenomic and experimental evidence for active methanogenesis and methanotrophy by microbial communities in serpentinite springs of the Voltri Massif, Italy. Methanogens belonging to family Methanobacteriaceae and methanotrophic bacteria belonging to family Methylococcaceae were heavily enriched in three ultrabasic springs (pH 12). Metagenomic data also suggest the potential for hydrogen oxidation, hydrogen production, carbon fixation, fermentation, and organic acid metabolism in the ultrabasic springs. The predicted metabolic capabilities are consistent with an active subsurface ecosystem supported by energy and carbon liberated by geochemical reactions within the serpentinite rocks of the Voltri Massif.

Modeling the competition between antenna size mutant and wild type microalgae in outdoor mass culture.

PubMed

de Mooij, Tim; Schediwy, Kira; Wijffels, René H; Janssen, Marcel

2016-12-20

Under high light conditions, microalgae are oversaturated with light which significantly reduces the light use efficiency. Microalgae with a reduced pigment content, antenna size mutants, have been proposed as a potential solution to increase the light use efficiency. The goal of this study was to investigate the competition between antenna size mutants and wild type microalgae in mass cultures. Using a kinetic model and literature-derived experimental data from wild type Chlorella sorokiniana, the productivity and competition of wild type cells and antenna size mutants were simulated. Cultivation was simulated in an outdoor microalgal raceway pond production system which was assumed to be limited by light only. Light conditions were based on a Mediterranean location (Tunisia) and a more temperate location (the Netherlands). Several wild type contamination levels were simulated in each mutant culture separately to predict the effect on the productivity over the cultivation time of a hypothetical summer season of 100days. The simulations demonstrate a good potential of antenna size reduction to increase the biomass productivity of microalgal cultures. However, it was also found that after a contamination with wild type cells the mutant cultures will be rapidly overgrown resulting in productivity loss. Copyright © 2016 Elsevier B.V. All rights reserved.

An Energy Balance Model to Predict Chemical Partitioning in a Photosynthetic Microbial Mat

NASA Technical Reports Server (NTRS)

Hoehler, Tori M.; Albert, Daniel B.; DesMarais, David J.

2006-01-01

Studies of biosignature formation in photosynthetic microbial mat communities offer potentially useful insights with regards to both solar and extrasolar astrobiology. Biosignature formation in such systems results from the chemical transformation of photosynthetically fixed carbon by accessory microorganisms. This fixed carbon represents a source not only of reducing power, but also energy, to these organisms, so that chemical and energy budgets should be coupled. We tested this hypothesis by applying an energy balance model to predict the fate of photosynthetic productivity under dark, anoxic conditions. Fermentation of photosynthetically fixed carbon is taken to be the only source of energy available to cyanobacteria in the absence of light and oxygen, and nitrogen fixation is the principal energy demand. The alternate fate for fixed carbon is to build cyanobacterial biomass with Redfield C:N ratio. The model predicts that, under completely nitrogen-limited conditions, growth is optimized when 78% of fixed carbon stores are directed into fermentative energy generation, with the remainder allocated to growth. These predictions were compared to measurements made on microbial mats that are known to be both nitrogen-limited and populated by actively nitrogen-fixing cyanobacteria. In these mats, under dark, anoxic conditions, 82% of fixed carbon stores were diverted into fermentation. The close agreement between these independent approaches suggests that energy balance models may provide a quantitative means of predicting chemical partitioning within such systems - an important step towards understanding how biological productivity is ultimately partitioned into biosignature compounds.

Application of a Hybrid Forest Growth Model to Evaluate Climate Change Impacts on Productivity, Nutrient Cycling and Mortality in a Montane Forest Ecosystem.

PubMed

Seely, Brad; Welham, Clive; Scoullar, Kim

2015-01-01

Climate change introduces considerable uncertainty in forest management planning and outcomes, potentially undermining efforts at achieving sustainable practices. Here, we describe the development and application of the FORECAST Climate model. Constructed using a hybrid simulation approach, the model includes an explicit representation of the effect of temperature and moisture availability on tree growth and survival, litter decomposition, and nutrient cycling. The model also includes a representation of the impact of increasing atmospheric CO2 on water use efficiency, but no direct CO2 fertilization effect. FORECAST Climate was evaluated for its ability to reproduce the effects of historical climate on Douglas-fir and lodgepole pine growth in a montane forest in southern British Columbia, Canada, as measured using tree ring analysis. The model was subsequently used to project the long-term impacts of alternative future climate change scenarios on forest productivity in young and established stands. There was a close association between predicted sapwood production and measured tree ring chronologies, providing confidence that model is able to predict the relative impact of annual climate variability on tree productivity. Simulations of future climate change suggest a modest increase in productivity in young stands of both species related to an increase in growing season length. In contrast, results showed a negative impact on stemwood biomass production (particularly in the case of lodgepole pine) for established stands due to increased moisture stress mortality.

Application of a Hybrid Forest Growth Model to Evaluate Climate Change Impacts on Productivity, Nutrient Cycling and Mortality in a Montane Forest Ecosystem

PubMed Central

Seely, Brad; Welham, Clive; Scoullar, Kim

2015-01-01

Climate change introduces considerable uncertainty in forest management planning and outcomes, potentially undermining efforts at achieving sustainable practices. Here, we describe the development and application of the FORECAST Climate model. Constructed using a hybrid simulation approach, the model includes an explicit representation of the effect of temperature and moisture availability on tree growth and survival, litter decomposition, and nutrient cycling. The model also includes a representation of the impact of increasing atmospheric CO2 on water use efficiency, but no direct CO2 fertilization effect. FORECAST Climate was evaluated for its ability to reproduce the effects of historical climate on Douglas-fir and lodgepole pine growth in a montane forest in southern British Columbia, Canada, as measured using tree ring analysis. The model was subsequently used to project the long-term impacts of alternative future climate change scenarios on forest productivity in young and established stands. There was a close association between predicted sapwood production and measured tree ring chronologies, providing confidence that model is able to predict the relative impact of annual climate variability on tree productivity. Simulations of future climate change suggest a modest increase in productivity in young stands of both species related to an increase in growing season length. In contrast, results showed a negative impact on stemwood biomass production (particularly in the case of lodgepole pine) for established stands due to increased moisture stress mortality. PMID:26267446

The North Alabama Lightning Warning Product

NASA Technical Reports Server (NTRS)

Buechler, Dennis E.; Blakeslee, R. J.; Stano, G. T.

2009-01-01

The North Alabama Lightning Mapping Array NALMA has been collecting total lightning data on storms in the Tennessee Valley region since 2001. Forecasters from nearby National Weather Service (NWS) offices have been ingesting this data for display with other AWIPS products. The current lightning product used by the offices is the lightning source density plot. The new product provides a probabalistic, short-term, graphical forecast of the probability of lightning activity occurring at 5 min intervals over the next 30 minutes . One of the uses of the current lightning source density product by the Huntsville National Weather Service Office is to identify areas of potential for cloud-to-ground flashes based on where LMA total lightning is occurring. This product quantifies that observation. The Lightning Warning Product is derived from total lightning observations from the Washington, D.C. (DCLMA) and North Alabama Lightning Mapping Arrays and cloud-to-ground lightning flashes detected by the National Lightning Detection Network (NLDN). Probability predictions are provided for both intracloud and cloud-to-ground flashes. The gridded product can be displayed on AWIPS workstations in a manner similar to that of the lightning source density product.

Nanomaterial Toxicity Testing in the 21st Century: Use of a Predictive Toxicological Approach and High Throughput Screening

PubMed Central

NEL, ANDRE; XIA, TIAN; MENG, HUAN; WANG, XIANG; LIN, SIJIE; JI, ZHAOXIA; ZHANG, HAIYUAN

2014-01-01

Conspectus The production of engineered nanomaterials (ENMs) is a scientific breakthrough in material design and the development of new consumer products. While the successful implementation of nanotechnology is important for the growth of the global economy, we also need to consider the possible environmental health and safety (EHS) impact as a result of the novel physicochemical properties that could generate hazardous biological outcomes. In order to assess ENM hazard, reliable and reproducible screening approaches are needed to test the basic materials as well as nano-enabled products. A platform is required to investigate the potentially endless number of bio-physicochemical interactions at the nano/bio interface, in response to which we have developed a predictive toxicological approach. We define a predictive toxicological approach as the use of mechanisms-based high throughput screening in vitro to make predictions about the physicochemical properties of ENMs that may lead to the generation of pathology or disease outcomes in vivo. The in vivo results are used to validate and improve the in vitro high throughput screening (HTS) and to establish structure-activity relationships (SARs) that allow hazard ranking and modeling by an appropriate combination of in vitro and in vivo testing. This notion is in agreement with the landmark 2007 report from the US National Academy of Sciences, “Toxicity Testing in the 21st Century: A Vision and a Strategy” (http://www.nap.edu/catalog.php?record_id=11970), which advocates increased efficiency of toxicity testing by transitioning from qualitative, descriptive animal testing to quantitative, mechanistic and pathway-based toxicity testing in human cells or cell lines using high throughput approaches. Accordingly, we have implemented HTS approaches to screen compositional and combinatorial ENM libraries to develop hazard ranking and structure-activity relationships that can be used for predicting in vivo injury outcomes. This predictive approach allows the bulk of the screening analysis and high volume data generation to be carried out in vitro, following which limited, but critical, validation studies are carried out in animals or whole organisms. Risk reduction in the exposed human or environmental populations can then focus on limiting or avoiding exposures that trigger these toxicological responses as well as implementing safer design of potentially hazardous ENMs. In this communication, we review the tools required for establishing predictive toxicology paradigms to assess inhalation and environmental toxicological scenarios through the use of compositional and combinatorial ENM libraries, mechanism-based HTS assays, hazard ranking and development of nano-SARs. We will discuss the major injury paradigms that have emerged based on specific ENM properties, as well as describing the safer design of ZnO nanoparticles based on characterization of dissolution chemistry as a major predictor of toxicity. PMID:22676423

Interstellar Ices and Radiation-induced Oxidations of Alcohols

NASA Astrophysics Data System (ADS)

Hudson, R. L.; Moore, M. H.

2018-04-01

Infrared spectra of ices containing alcohols that are known or potential interstellar molecules are examined before and after irradiation with 1 MeV protons at ∼20 K. The low-temperature oxidation (hydrogen loss) of six alcohols is followed, and conclusions are drawn based on the results. The formation of reaction products is discussed in terms of the literature on the radiation chemistry of alcohols and a systematic variation in their structures. The results from these new laboratory measurements are then applied to a recent study of propargyl alcohol. Connections are drawn between known interstellar molecules, and several new reaction products in interstellar ices are predicted.

Beauty production at HERA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yagues, A.

Beauty quark production in ep collisions is being studied at HERA. The latest results in deep inelastic scattering (DIS) and photoproduction (PHP) regime performed by the ZEUS and HI experiments are presented here. The first measurement exploits the potential of the ZEUS mi-crovertex detector to identify beauty in PHP dijet events in an inclusive analysis. In the second measurement, beauty quarks were identified through their decays into muons. Finally, two measurements of the beauty contribution to the proton structure function, F{sub 2}{sup b???b}, in DIS are presented. The four measurements are consistent with previous results and are reasonably well describedmore » by QCD predictions.« less

Genetic engineering applied to agriculture has a long row to hoe.

PubMed

Miller, Henry I

2018-01-02

In spite of the lack of scientific justification for skepticism about crops modified with molecular techniques of genetic engineering, they have been the most scrutinized agricultural products in human history. The assumption that "genetically engineered" or "genetically modified" is a meaningful - and dangerous - classification has led to excessive and dilatory regulation. The modern molecular techniques are an extension, or refinement, of older, less precise, less predictable methods of genetic modification, but as long as today's activists and regulators remain convinced that so called "GMOs" represent a distinct and dangerous category of research and products, genetic engineering will fall short of its potential.

Solar Glare Hazard Analysis Tool v. 4.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ho, Clifford L.; Sims, Cianan

SGHAT predicts the occurrence and intensity of glare caused by a user-specified solar panel array when viewed from one or more observation points. An interactive mapping interface is used to determine the latitude, longitude and elevation of the array and observation points. The presence and intensity of glare is then calculated along a given time interval throughout the year, based on the position of the sun. The potential ocular hazard is also reported. The maximum energy production of the solar array is also estimated so that alternative designs can be compared to determine the design that yields the most energymore » production while mitigating glare.« less

Dephosphorization of Levitated Silicon-Iron Droplets for Production of Solar-Grade Silicon

NASA Astrophysics Data System (ADS)

Le, Katherine; Yang, Yindong; Barati, Mansoor; McLean, Alexander

2018-05-01

The treatment of relatively inexpensive silicon-iron alloys is a potential refining route in order to generate solar-grade silicon. Phosphorus is one of the more difficult impurity elements to remove by conventional processing. In this study, electromagnetic levitation was used to investigate phosphorus behavior in silicon-iron alloy droplets exposed to H2-Ar gas mixtures under various experimental conditions including, refining time, temperature (1723 K to 1993 K), gas flow rate, iron content, and initial phosphorus concentration in the alloy. Thermodynamic modeling of the dephosphorization reaction permitted prediction of the various gaseous products and indicated that diatomic phosphorus is the dominant species formed.

Solar Glaze Hazard Analysis Tool v. 3.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ho, Clifford K.; Sims, Cianan A.

SGHAT predicts the occurrence and intensity of glare caused by a user-specified solar panel array when viewed from one or more observation points. An interactive mapping interface is used to determine the latitude, longitude and elevation of the array and observation points. The presence and intensity of glare is then calculated along a given time interval throughout the year, based on the position of the sun. The potential ocular hazard is also reported. The maximum energy production of the solar array is also estimated so that alternative designs can be compared to determine the design that yields the most energymore » production while mitigating glare.« less

Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy.

PubMed

Fang, Jiansong; Wu, Zengrui; Cai, Chuipu; Wang, Qi; Tang, Yun; Cheng, Feixiong

2017-11-27

Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

Molecular dynamics simulation of thermal transport in UO 2 containing uranium, oxygen, and fission-product defects

DOE PAGES

Liu, Xiang -Yang; Cooper, Michael William D.; McClellan, Kenneth James; ...

2016-10-25

Uranium dioxide (UO 2) is the most commonly used fuel in light-water nuclear reactors and thermal conductivity controls the removal of heat produced by fission, thereby governing fuel temperature during normal and accident conditions. The use of fuel performance codes by the industry to predict operational behavior is widespread. A primary source of uncertainty in these codes is thermal conductivity, and optimized fuel utilization may be possible if existing empirical models are replaced with models that incorporate explicit thermal-conductivity-degradation mechanisms during fuel burn up. This approach is able to represent the degradation of thermal conductivity due to each individual defectmore » type, rather than the overall burn-up measure typically used, which is not an accurate representation of the chemical or microstructure state of the fuel that actually governs thermal conductivity and other properties. To generate a mechanistic thermal conductivity model, molecular dynamics (MD) simulations of UO 2 thermal conductivity including representative uranium and oxygen defects and fission products are carried out. These calculations employ a standard Buckingham-type interatomic potential and a potential that combines the many-body embedded-atom-method potential with Morse-Buckingham pair potentials. Potential parameters for UO 2+x and ZrO 2 are developed for the latter potential. Physical insights from the resonant phonon-spin-scattering mechanism due to spins on the magnetic uranium ions are introduced into the treatment of the MD results, with the corresponding relaxation time derived from existing experimental data. High defect scattering is predicted for Xe atoms compared to that of La and Zr ions. Uranium defects reduce the thermal conductivity more than oxygen defects. For each defect and fission product, scattering parameters are derived for application in both a Callaway model and the corresponding high-temperature model typically used in fuel-performance codes. The model is validated by comparison to low-temperature experimental measurements on single-crystal hyperstoichiometric UO 2+x samples and high-temperature literature data. Furthermore, this work will enable more accurate fuel-performance simulations and will extend to new fuel types and operating conditions, all of which improve the fuel economics of nuclear energy and maintain high fuel reliability and safety.« less

Molecular dynamics simulation of thermal transport in UO 2 containing uranium, oxygen, and fission-product defects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Xiang -Yang; Cooper, Michael William D.; McClellan, Kenneth James

Uranium dioxide (UO 2) is the most commonly used fuel in light-water nuclear reactors and thermal conductivity controls the removal of heat produced by fission, thereby governing fuel temperature during normal and accident conditions. The use of fuel performance codes by the industry to predict operational behavior is widespread. A primary source of uncertainty in these codes is thermal conductivity, and optimized fuel utilization may be possible if existing empirical models are replaced with models that incorporate explicit thermal-conductivity-degradation mechanisms during fuel burn up. This approach is able to represent the degradation of thermal conductivity due to each individual defectmore » type, rather than the overall burn-up measure typically used, which is not an accurate representation of the chemical or microstructure state of the fuel that actually governs thermal conductivity and other properties. To generate a mechanistic thermal conductivity model, molecular dynamics (MD) simulations of UO 2 thermal conductivity including representative uranium and oxygen defects and fission products are carried out. These calculations employ a standard Buckingham-type interatomic potential and a potential that combines the many-body embedded-atom-method potential with Morse-Buckingham pair potentials. Potential parameters for UO 2+x and ZrO 2 are developed for the latter potential. Physical insights from the resonant phonon-spin-scattering mechanism due to spins on the magnetic uranium ions are introduced into the treatment of the MD results, with the corresponding relaxation time derived from existing experimental data. High defect scattering is predicted for Xe atoms compared to that of La and Zr ions. Uranium defects reduce the thermal conductivity more than oxygen defects. For each defect and fission product, scattering parameters are derived for application in both a Callaway model and the corresponding high-temperature model typically used in fuel-performance codes. The model is validated by comparison to low-temperature experimental measurements on single-crystal hyperstoichiometric UO 2+x samples and high-temperature literature data. Furthermore, this work will enable more accurate fuel-performance simulations and will extend to new fuel types and operating conditions, all of which improve the fuel economics of nuclear energy and maintain high fuel reliability and safety.« less

Evaluation and sensitivity testing of a coupled Landsat-MODIS downscaling method for land surface temperature and vegetation indices in semi-arid regions

NASA Astrophysics Data System (ADS)

Kim, Jongyoun; Hogue, Terri S.

2012-01-01

The current study investigates a method to provide land surface parameters [i.e., land surface temperature (LST) and normalized difference vegetation index (NDVI)] at a high spatial (˜30 and 60 m) and temporal (daily and 8-day) resolution by combining advantages from Landsat and moderate-resolution imaging spectroradiometer (MODIS) satellites. We adopt a previously developed subtraction method that merges the spatial detail of higher-resolution imagery (Landsat) with the temporal change observed in coarser or moderate-resolution imagery (MODIS). Applying the temporal difference between MODIS images observed at two different dates to a higher-resolution Landsat image allows prediction of a combined or fused image (Landsat+MODIS) at a future date. Evaluation of the resultant merged products is undertaken within the Southeastern Arizona region where data is available from a range of flux tower sites. The Landsat+MODIS fused products capture the raw Landsat values and also reflect the MODIS temporal variation. The predicted Landsat+MODIS LST improves mean absolute error around 5°C at the more heterogeneous sites compared to the original satellite products. The fused Landsat+MODIS NDVI product also shows good correlation to ground-based data and is relatively consistent except during the acute (monsoon) growing season. The sensitivity of the fused product relative to temporal gaps in Landsat data appears to be more affected by uncertainty associated with regional precipitation and green-up, than the length of the gap associated with Landsat viewing, suggesting the potential to use a minimal number of original Landsat images during relatively stable land surface and climate conditions. Our extensive validation yields insight on the ability of the proposed method to integrate multiscale platforms and the potential for reducing costs associated with high-resolution satellite systems (e.g., SPOT, QuickBird, IKONOS).

Development and Validation of Remote Sensing-Based Surface Inundation Products for Vector-Borne Disease Risk in East Africa

NASA Astrophysics Data System (ADS)

Jensen, K.; McDonald, K. C.; Ceccato, P.; Schroeder, R.; Podest, E.

2014-12-01

The potential impact of climate variability and change on the spread of infectious disease is of increasingly critical concern to public health. Newly-available remote sensing datasets may be combined with predictive modeling to develop new capabilities to mitigate risks of vector-borne diseases such as malaria, leishmaniasis, and rift valley fever. We have developed improved remote sensing-based products for monitoring water bodies and inundation dynamics that have potential utility for improving risk forecasts of vector-borne disease epidemics. These products include daily and seasonal surface inundation based on the global mappings of inundated area fraction derived at the 25-km scale from active and passive microwave instruments ERS, QuikSCAT, ASCAT, and SSM/I data - the Satellite Water Microwave Product Series (SWAMPS). Focusing on the East African region, we present validation of this product using multi-temporal classification of inundated areas in this region derived from high resolution PALSAR (100m) and Landsat (30m) observations. We assess historical occurrence of malaria in the east African country of Eritrea with respect to the time series SWAMPS datasets, and we aim to construct a framework for use of these new datasets to improve prediction of future malaria risk in this region. This work is supported through funding from the NASA Applied Sciences Program, the NASA Terrestrial Ecology Program, and the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. This study is also supported and monitored by National Oceanic and Atmospheric Administration (NOAA) under Grant - CREST Grant # NA11SEC4810004. The statements contained within the manuscript/research article are not the opinions of the funding agency or the U.S. government, but reflect the authors' opinions. This work was conducted in part under the framework of the ALOS Kyoto and Carbon Initiative. ALOS PALSAR data were provided by JAXA EORC.

Milk Thistle Constituents Inhibit Raloxifene Intestinal Glucuronidation: A Potential Clinically Relevant Natural Product-Drug Interaction.

PubMed

Gufford, Brandon T; Chen, Gang; Vergara, Ana G; Lazarus, Philip; Oberlies, Nicholas H; Paine, Mary F

2015-09-01

Women at high risk of developing breast cancer are prescribed selective estrogen response modulators, including raloxifene, as chemoprevention. Patients often seek complementary and alternative treatment modalities, including herbal products, to supplement prescribed medications. Milk thistle preparations, including silibinin and silymarin, are top-selling herbal products that may be consumed by women taking raloxifene, which undergoes extensive first-pass glucuronidation in the intestine. Key constituents in milk thistle, flavonolignans, were previously shown to be potent inhibitors of intestinal UDP-glucuronosyl transferases (UGTs), with IC50s ≤ 10 μM. Taken together, milk thistle preparations may perpetrate unwanted interactions with raloxifene. The objective of this work was to evaluate the inhibitory effects of individual milk thistle constituents on the intestinal glucuronidation of raloxifene using human intestinal microsomes and human embryonic kidney cell lysates overexpressing UGT1A1, UGT1A8, and UGT1A10, isoforms highly expressed in the intestine that are critical to raloxifene clearance. The flavonolignans silybin A and silybin B were potent inhibitors of both raloxifene 4'- and 6-glucuronidation in all enzyme systems. The Kis (human intestinal microsomes, 27-66 µM; UGT1A1, 3.2-8.3 µM; UGT1A8, 19-73 µM; and UGT1A10, 65-120 µM) encompassed reported intestinal tissue concentrations (20-310 µM), prompting prediction of clinical interaction risk using a mechanistic static model. Silibinin and silymarin were predicted to increase raloxifene systemic exposure by 4- to 5-fold, indicating high interaction risk that merits further evaluation. This systematic investigation of the potential interaction between a widely used herbal product and chemopreventive agent underscores the importance of understanding natural product-drug interactions in the context of cancer prevention. Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.

Connecting marine productivity to sea-spray via microscale biological processes: phytoplancton demise and viral infection

NASA Astrophysics Data System (ADS)

Facchini, C.; O'Dowd, C. D. D.; Danovaro, R.

2015-12-01

The processes that link phytoplankton biomass and productivity to the organic matter enrichment in sea spray aerosol are far from being elucidated and modelling predictions remain highly uncertain at the moment. While some studies have asserted that the enrichment of OM in sea spray aerosol is independent on marine productivity, others, have shown significant correlation with phytoplankton biomass and productivity (Chl-a retrieved by satellites). We present here new results illustrating a clear link between OM mass-fraction enrichment in sea spray (OMss) and both phytoplankton-biomass and Net Primary Productivity (NPP). We suggest that the OM enrichment of sea spray through the demise of the bloom, driven by nanoscale biological processes (such as viral infections), which determine the release of celldebris, exudates and other colloidal material. This OM, through processes, leads to enrichment in sea-spray, thus demonstrating an important coupling between biologically-drive plankton bloom termination, marine productivity and sea-spraymodification with potentially significant climate impacts.

Evaluation of Electromagnetic Near-Field Measurement Technique as Non-Destructive Testing for Composite Structures

NASA Astrophysics Data System (ADS)

Raad Hussein, Alaa; Badri Albarody, Thar M.; Megat Yusoff, Puteri Sri Melor Bt

2018-05-01

Nowadays there is no viable non-destructive method that could detect flaws in complex composite products. Such a method could provide unique tools to allow engineers to minimize time consumption and cost during the evaluation of various product parameters without disturbing production. The latest research and development on propagation waves introduce micro, radio and millimetre waves as new potential non-destructive test methods for evaluation of mechanical flaws and prediction of failure in a product during production. This paper focuses on recent developments, usage, classification of electromagnetic waves under the range of radio frequency, millimetre and micro-waves. In addition, this paper reviews the application of propagation wave and proposed a new health monitoring technique based on Doppler Effect for vibration measurement in complex composite structures. Doppler Effect is influenced by dynamic behaviour of the composite structures and both are effect by flaws occurred inside the structure. Composite manufacturers, especially Aerospace industry are demanding these methods comprehensively inspect and evaluate the damages and defects in their products.

Effects of feather wear and temperature on prediction of food intake and residual food consumption.

PubMed

Herremans, M; Decuypere, E; Siau, O

1989-03-01

Heat production, which accounts for 0.6 of gross energy intake, is insufficiently represented in predictions of food intake. Especially when heat production is elevated (for example by lower temperature or poor feathering) the classical predictions based on body weight, body-weight change and egg mass are inadequate. Heat production was reliably estimated as [35.5-environmental temperature (degree C)] x [Defeathering (=%IBPW) + 21]. Including this term (PHP: predicted heat production) in equations predicting food intake significantly increased accuracy of prediction, especially under suboptimal conditions. Within the range of body weights tested (from 1.6 kg in brown layers to 2.8 kg in dwarf broiler breeders), body weight as an independent variable contributed little to the prediction of food intake; especially within strains its effect was better included in the intercept. Significantly reduced absolute values of residual food consumption were obtained over a wide range of conditions by using predictions of food intake based on body-weight change, egg mass, predicted heat production (PHP) and an intercept, instead of body weight, body-weight change, egg mass and an intercept.

Osanetant Sanofi-Synthélabo.

PubMed

Kamali, F

2001-07-01

Osanetant is a neurokinin (NK3) receptor antagonist under development by Sanofi-Synthélabo (formerly Sanofi) as a potential treatment for schizophrenia [328910]. Sanofi was originally investigating its potential use as a treatment for psychosis and anxiety [169511]. Following phase IIa clinical trials [307656], [328910], [359231], osanetant entered phase IIb development in February 2001 [409432]. Osanetant was the first potent and selective non-peptide antagonist described for the NK3 tachykinin receptor [176305]. It has a higher affinity for human and guinea pig NK3 receptors than for rat NK3 receptors [176305]. In October 1999, Lehman Brothers predicted that the probability of the product reaching the market was 10%, with a possible launch in 2003 and potential peak sales of US $200 million in 2011 [346267].

A roadmap for research on crassulacean acid metabolism (CAM) to enhance sustainable food and bioenergy production in a hotter, drier world.

PubMed

Yang, Xiaohan; Cushman, John C; Borland, Anne M; Edwards, Erika J; Wullschleger, Stan D; Tuskan, Gerald A; Owen, Nick A; Griffiths, Howard; Smith, J Andrew C; De Paoli, Henrique C; Weston, David J; Cottingham, Robert; Hartwell, James; Davis, Sarah C; Silvera, Katia; Ming, Ray; Schlauch, Karen; Abraham, Paul; Stewart, J Ryan; Guo, Hao-Bo; Albion, Rebecca; Ha, Jungmin; Lim, Sung Don; Wone, Bernard W M; Yim, Won Cheol; Garcia, Travis; Mayer, Jesse A; Petereit, Juli; Nair, Sujithkumar S; Casey, Erin; Hettich, Robert L; Ceusters, Johan; Ranjan, Priya; Palla, Kaitlin J; Yin, Hengfu; Reyes-García, Casandra; Andrade, José Luis; Freschi, Luciano; Beltrán, Juan D; Dever, Louisa V; Boxall, Susanna F; Waller, Jade; Davies, Jack; Bupphada, Phaitun; Kadu, Nirja; Winter, Klaus; Sage, Rowan F; Aguilar, Cristobal N; Schmutz, Jeremy; Jenkins, Jerry; Holtum, Joseph A M

2015-08-01

Crassulacean acid metabolism (CAM) is a specialized mode of photosynthesis that features nocturnal CO2 uptake, facilitates increased water-use efficiency (WUE), and enables CAM plants to inhabit water-limited environments such as semi-arid deserts or seasonally dry forests. Human population growth and global climate change now present challenges for agricultural production systems to increase food, feed, forage, fiber, and fuel production. One approach to meet these challenges is to increase reliance on CAM crops, such as Agave and Opuntia, for biomass production on semi-arid, abandoned, marginal, or degraded agricultural lands. Major research efforts are now underway to assess the productivity of CAM crop species and to harness the WUE of CAM by engineering this pathway into existing food, feed, and bioenergy crops. An improved understanding of CAM has potential for high returns on research investment. To exploit the potential of CAM crops and CAM bioengineering, it will be necessary to elucidate the evolution, genomic features, and regulatory mechanisms of CAM. Field trials and predictive models will be required to assess the productivity of CAM crops, while new synthetic biology approaches need to be developed for CAM engineering. Infrastructure will be needed for CAM model systems, field trials, mutant collections, and data management. © 2015 ORNL/UT-Battelle New Phytologist © 2015 New Phytologist Trust.

Maximum Entropy Production As a Framework for Understanding How Living Systems Evolve, Organize and Function

NASA Astrophysics Data System (ADS)

Vallino, J. J.; Algar, C. K.; Huber, J. A.; Fernandez-Gonzalez, N.

2014-12-01

The maximum entropy production (MEP) principle holds that non equilibrium systems with sufficient degrees of freedom will likely be found in a state that maximizes entropy production or, analogously, maximizes potential energy destruction rate. The theory does not distinguish between abiotic or biotic systems; however, we will show that systems that can coordinate function over time and/or space can potentially dissipate more free energy than purely Markovian processes (such as fire or a rock rolling down a hill) that only maximize instantaneous entropy production. Biological systems have the ability to store useful information acquired via evolution and curated by natural selection in genomic sequences that allow them to execute temporal strategies and coordinate function over space. For example, circadian rhythms allow phototrophs to "predict" that sun light will return and can orchestrate metabolic machinery appropriately before sunrise, which not only gives them a competitive advantage, but also increases the total entropy production rate compared to systems that lack such anticipatory control. Similarly, coordination over space, such a quorum sensing in microbial biofilms, can increase acquisition of spatially distributed resources and free energy and thereby enhance entropy production. In this talk we will develop a modeling framework to describe microbial biogeochemistry based on the MEP conjecture constrained by information and resource availability. Results from model simulations will be compared to laboratory experiments to demonstrate the usefulness of the MEP approach.

Unintended allergens in precautionary labelled and unlabelled products pose significant risks to UK allergic consumers.

PubMed

Remington, B C; Baumert, J L; Blom, W M; Houben, G F; Taylor, S L; Kruizinga, A G

2015-07-01

Allergens in food may pose a risk to allergic consumers. While there is EU regulation for allergens present as an ingredient, this is not the case for unintended allergen presence (UAP). Food companies use precautionary allergen labels to inform allergic individuals of a potential risk from UAPs. This study investigates the risk of an allergic reaction within the milk-, wheat-, hazelnut- and peanut-allergic populations when ingesting UK foods across multiple product categories with and without precautionary allergen labelling. Allergen risk assessment using probabilistic techniques enables the estimation of the residual risk after the consumption of a product that unintentionally contains an allergen. Within this selection of UK products, the majority that tested positive for an allergen contained a concentration of allergen predicted to cause a reaction in >1% of the allergic population. The concentrations of allergens measured were greater than the VITAL(®) 2.0 action levels and would trigger precautionary allergen labelling. This was found for products both with and without precautionary allergen labelling. The results highlight the need for the food industry and regulators to adopt a transparent, risk-based approach for the communication of the risk associated with potential cross-contact that could occur in the processing facility or production chain. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Potential climate change impacts on temperate forest ecosystem processes

USGS Publications Warehouse

Peters, Emily B.; Wythers, Kirk R.; Zhang, Shuxia; Bradford, John B.; Reich, Peter B.

2013-01-01

Large changes in atmospheric CO2, temperature and precipitation are predicted by 2100, yet the long-term consequences for carbon, water, and nitrogen cycling in forests are poorly understood. We applied the PnET-CN ecosystem model to compare the long-term effects of changing climate and atmospheric CO2 on productivity, evapotranspiration, runoff, and net nitrogen mineralization in current Great Lakes forest types. We used two statistically downscaled climate projections, PCM B1 (warmer and wetter) and GFDL A1FI (hotter and drier), to represent two potential future climate and atmospheric CO2 scenarios. To separate the effects of climate and CO2, we ran PnET-CN including and excluding the CO2 routine. Our results suggest that, with rising CO2 and without changes in forest type, average regional productivity could increase from 67% to 142%, changes in evapotranspiration could range from –3% to +6%, runoff could increase from 2% to 22%, and net N mineralization could increase 10% to 12%. Ecosystem responses varied geographically and by forest type. Increased productivity was almost entirely driven by CO2 fertilization effects, rather than by temperature or precipitation (model runs holding CO2 constant showed stable or declining productivity). The relative importance of edaphic and climatic spatial drivers of productivity varied over time, suggesting that productivity in Great Lakes forests may switch from being temperature to water limited by the end of the century.

Predictive acute toxicity tests with pesticides.

PubMed

Brown, V K

1983-01-01

By definition pesticides are biocidal products and this implies a probability that pesticides may be acutely toxic to species other than the designated target species. The ways in which pesticides are manufactured, formulated, packaged, distributed and used necessitates a potential for the exposure of non-target species although the technology exists to minimize adventitious exposure. The occurrence of deliberate exposure of non-target species due to the misuse of pesticides is known to happen. The array of predictive acute toxicity tests carried out on pesticides and involving the use of laboratory animals can be justified as providing data on which hazard assessment can be based. This paper addresses the justification and rationale of this statement.

Web-based decision support system to predict risk level of long term rice production

NASA Astrophysics Data System (ADS)

Mukhlash, Imam; Maulidiyah, Ratna; Sutikno; Setiyono, Budi

2017-09-01

Appropriate decision making in risk management of rice production is very important in agricultural planning, especially for Indonesia which is an agricultural country. Good decision would be obtained if the supporting data required are satisfied and using appropriate methods. This study aims to develop a Decision Support System that can be used to predict the risk level of rice production in some districts which are central of rice production in East Java. Web-based decision support system is constructed so that the information can be easily accessed and understood. Components of the system are data management, model management, and user interface. This research uses regression models of OLS and Copula. OLS model used to predict rainfall while Copula model used to predict harvested area. Experimental results show that the models used are successfully predict the harvested area of rice production in some districts which are central of rice production in East Java at any given time based on the conditions and climate of a region. Furthermore, it can predict the amount of rice production with the level of risk. System generates prediction of production risk level in the long term for some districts that can be used as a decision support for the authorities.

Input dynamics of pesticide transformation products into surface water

NASA Astrophysics Data System (ADS)

Kern, Susanne; Singer, Heinz; Hollender, Juliane; Schwarzenbach, René P.; Fenner, Kathrin

2010-05-01

Some pesticide transformation products have been observed to occur in higher concentrations and more frequently than the parent active pesticide in surface water and groundwater. These products are often more mobile and sometimes more stable than the parent pesticide. If they also represent the major product into which the parent substance is transformed, these transformation products may dominate observed pesticide occurrences in surface water and groundwater. Their potential contribution to the overall risk to the aquatic environment caused by the use of the parent pesticide should therefore not be neglected in chemical risk and water quality assessments. The same is true for transformation products of other compound classes that might reach the soil environment, such as veterinary pharmaceuticals. However, the fate and input pathways of transformation products of soil-applied chemicals into surface water are not yet well understood, which largely prevents their appropriate inclusion into chemical risk and water quality assessments. Here, we studied whether prioritization methods based on available environmental fate data from pesticide registration dossiers in combination with basic fate models could help identify transformation products which can be found in relevant concentrations in surface and groundwater and which should therefore be included into monitoring programs. A three-box steady state model containing air, soil, and surface water compartments was used to predict relative inputs of pesticide transformation products into surface waters based on their physico-chemical and environmental fate properties. The model predictions were compared to monitoring data from a small Swiss river located in an intensely agricultural catchment (90 km2) which was flow-proportionally sampled from May to October 2008 and screened for 74 pesticides as well as 50 corresponding transformation products. Sampling mainly occurred during high discharge, but additional samples during baseflow conditions were also taken. The analytical measurements included solid phase extraction, liquid chromatography and high resolution mass spectrometry (SPE-LC-HR-MS/MS). Quantification was achieved using reference standards and internal standards. Besides the well-known transformation products of triazine and chloroacetanilide herbicides, transformation products of other compound classes such as azoxystrobin acid (from azoxystrobin, strobilurin fungicide), chloridazon-desphenyl and chloridazon-methyl-desphenyl (from chloridazon, pyridazinone herbicide), and metamitron-desamino (from metamitron, triazinone herbicide) were analyzed in surface water. For a selection of widely used pesticides in the catchment, modelled ratios of transformation product versus parent pesticide concentrations were compared to the measured concentration ratios in the river for the application period and for two 2-month periods following application. Concentration ratios agreed within a factor of 10 for all pairs of parent pesticides and transformation products, and for all seasons, with a single exception. The ratio of chloridazon-desphenyl to chloridazon was under-predicted by a factor of approximately 20. The data revealed that chloridazon-desphenyl was also found in elevated concentrations in all baseflow samples, indicating its presence in the groundwater component of the catchment. The same was true for other transformation products (e.g., metamitron-desamino, chloridazon-methly-desphenyl, metolachlor-ESA), but to a lesser degree. Based on baseflow separation of the hydrograph, the concentration ratio estimation model was supplemented with an additional baseflow component. The concentrations in the baseflow component were estimated with a simple leaching relationship that was compared against measured baseflow concentrations and groundwater findings in Switzerland. The final model yielded good agreement for all compounds and is therefore deemed suitable for prioritization of transformation products with a relevant exposure potential. It also clearly indicated the contribution of groundwater to the overall occurrence of pesticides and their transformation products in Swiss surface waters.

High-Resolution Analysis Products to Support Severe Weather and Cloud-to-Ground Lightning Threat Assessments over Florida

NASA Technical Reports Server (NTRS)

Case, Jonathan; Spratt, Scott; Sharp, David

2006-01-01

The Applied Meteorology Unit (AMU) located at the Kennedy Space Center (KSC)/Cape Canaveral Air Force Station (CCAFS) implemented an operational configuration of the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS), as well as the ARPS numerical weather prediction (NWP) model. Operational, high-resolution ADAS analyses have been produced from this configuration at the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) over the past several years. Since that time, ADAS fields have become an integral part of forecast operations at both NWS MLB and SMG. To continue providing additional utility, the AMU has been tasked to implement visualization products to assess the potential for supercell thunderstorms and significant tornadoes, and to improve assessments of short-term cloud-to-ground (CG) lightning potential. This paper and presentation focuses on the visualization products developed by the AMU for the operational high-resolution ADAS and AR.PS at the NWS MLB and SMG. The two severe weather threat graphics implemented within ADAS/ARPS are the Supercell Composite Parameter (SCP) and Significant Tornado Parameter (SIP). The SCP was designed to identify areas with supercell thunderstorm potential through a combination of several instability and shear parameters. The SIP was designed to identify areas that favor supercells producing significant tornadoes (F2 or greater intensity) versus non-tornadic supercells. Both indices were developed by the NOAAINWS Storm Prediction Center (SPC) and were normalized by key threshold values based on previous studies. The indices apply only to discrete storms, not other convective modes. In a post-analysis mode, the AMU calculated SCP and SIP for graphical output using an ADAS configuration similar to the operational set-ups at NWS MLB and SMG. Graphical images from ADAS were generated every 15 minutes for 13 August 2004, the day that Hurricane Charley approached and made landfall on the Florida peninsula. Several tornadoes struck the interior of the Florida peninsula in advance of Hurricane Charley's landfall during the daylight hours of 13 August. Since SPC had previously examined this case using SCP and SIP graphics generated from output of the Rapid Update Cycle (RUC) model, this day served as a good benchmark to compare and validate the high-resolution ADAS graphics against the smoother RUC analyses, which serves as background fields to the ADAS analyses. The ADAS-generated SCP and STP graphics have been integrated into the suite of products examined operationally by NWS MLB forecasters and are used to provide additional guidance for assessment of the near-storm environment during convective situations.

Geographic information system (GIS) simulation of emergency power production from disaster debris in a combined heat and power (CHP) system

NASA Astrophysics Data System (ADS)

Ryals, Christopher Shannon

The objective of this study is to determine a predicted energy capacity of disaster debris for the production of emergency power using a combined heat and power (CHP) unit. A prediction simulation using geographic information systems (GIS) will use data from past storms to calculate an estimated amount of debris along with an estimated energy potential of said debris. Rather than the expense and burden of transporting woody debris such as downed trees and wood framing materials offsite, they can be processed (sorting and chipping) to provide an onsite energy source to provide power to emergency management facilities such as shelters in schools and hospitals. A CHP unit can simultaneously produce heat, cooling effects and electrical power using various biomass sources. This study surveys the quantity and composition of debris produced for a given classification of disaster and location. A comparison of power efficiency estimates for various disasters is conducted.

Challenges Facing Crop Production And (Some) Potential Solutions

NASA Astrophysics Data System (ADS)

Schnable, P. S.

2017-12-01

To overcome some of the myriad challenges facing sustainable crop production we are seeking to develop statistical models that will predict crop performance in diverse agronomic environments. Crop phenotypes such as yield and drought tolerance are controlled by genotype, environment (considered broadly) and their interaction (GxE). As a consequence of the next generation sequencing revolution genotyping data are now available for a wide diversity of accessions in each of the major crops. The necessary volumes of phenotypic data, however, remain limiting and our understanding of molecular basis of GxE is minimal. To address this limitation, we are collaborating with engineers to construct new sensors and robots to automatically collect large volumes of phenotypic data. Two types of high-throughput, high-resolution, field-based phenotyping systems and new sensors will be described. Some of these technologies will be introduced within the context of the Genomes to Fields Initiative. Progress towards developing predictive models will be briefly summarized. An administrative structure that fosters transdisciplinary collaborations will be briefly described.

Shifts in community size structure drive temperature invariance of secondary production in a stream-warming experiment.

PubMed

Nelson, Daniel; Benstead, Jonathan P; Huryn, Alexander D; Cross, Wyatt F; Hood, James M; Johnson, Philip W; Junker, James R; Gíslason, Gísli M; Ólafsson, Jón S

2017-07-01

A central question at the interface of food-web and climate change research is how secondary production, or the formation of heterotroph biomass over time, will respond to rising temperatures. The metabolic theory of ecology (MTE) hypothesizes the temperature-invariance of secondary production, driven by matched and opposed forces that reduce biomass of heterotrophs while increasing their biomass turnover rate (production : biomass, or P:B) with warming. To test this prediction at the whole community level, we used a geothermal heat exchanger to experimentally warm a stream in southwest Iceland by 3.8°C for two years. We quantified invertebrate community biomass, production, and P : B in the experimental stream and a reference stream for one year prior to warming and two years during warming. As predicted, warming had a neutral effect on community production, but this result was not driven by opposing effects on community biomass and P:B. Instead, warming had a positive effect on both the biomass and production of larger-bodied, slower-growing taxa (e.g., larval black flies, dipteran predators, snails) and a negative effect on small-bodied taxa with relatively high growth rates (e.g., ostracods, larval chironomids). We attribute these divergent responses to differences in thermal preference between small- vs. large-bodied taxa. Although metabolic demand vs. resource supply must ultimately constrain community production, our results highlight the potential for idiosyncratic community responses to warming, driven by variation in thermal preference and body size within regional species pools. © 2017 by the Ecological Society of America.

Integrating Plant Science and Crop Modeling: Assessment of the Impact of Climate Change on Soybean and Maize Production.

PubMed

Fodor, Nándor; Challinor, Andrew; Droutsas, Ioannis; Ramirez-Villegas, Julian; Zabel, Florian; Koehler, Ann-Kristin; Foyer, Christine H

2017-11-01

Increasing global CO2 emissions have profound consequences for plant biology, not least because of direct influences on carbon gain. However, much remains uncertain regarding how our major crops will respond to a future high CO2 world. Crop model inter-comparison studies have identified large uncertainties and biases associated with climate change. The need to quantify uncertainty has drawn the fields of plant molecular physiology, crop breeding and biology, and climate change modeling closer together. Comparing data from different models that have been used to assess the potential climate change impacts on soybean and maize production, future yield losses have been predicted for both major crops. When CO2 fertilization effects are taken into account significant yield gains are predicted for soybean, together with a shift in global production from the Southern to the Northern hemisphere. Maize production is also forecast to shift northwards. However, unless plant breeders are able to produce new hybrids with improved traits, the forecasted yield losses for maize will only be mitigated by agro-management adaptations. In addition, the increasing demands of a growing world population will require larger areas of marginal land to be used for maize and soybean production. We summarize the outputs of crop models, together with mitigation options for decreasing the negative impacts of climate on the global maize and soybean production, providing an overview of projected land-use change as a major determining factor for future global crop production. © The Author 2017. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.

Prediction of plant lncRNA by ensemble machine learning classifiers.

PubMed

Simopoulos, Caitlin M A; Weretilnyk, Elizabeth A; Golding, G Brian

2018-05-02

In plants, long non-protein coding RNAs are believed to have essential roles in development and stress responses. However, relative to advances on discerning biological roles for long non-protein coding RNAs in animal systems, this RNA class in plants is largely understudied. With comparatively few validated plant long non-coding RNAs, research on this potentially critical class of RNA is hindered by a lack of appropriate prediction tools and databases. Supervised learning models trained on data sets of mostly non-validated, non-coding transcripts have been previously used to identify this enigmatic RNA class with applications largely focused on animal systems. Our approach uses a training set comprised only of empirically validated long non-protein coding RNAs from plant, animal, and viral sources to predict and rank candidate long non-protein coding gene products for future functional validation. Individual stochastic gradient boosting and random forest classifiers trained on only empirically validated long non-protein coding RNAs were constructed. In order to use the strengths of multiple classifiers, we combined multiple models into a single stacking meta-learner. This ensemble approach benefits from the diversity of several learners to effectively identify putative plant long non-coding RNAs from transcript sequence features. When the predicted genes identified by the ensemble classifier were compared to those listed in GreeNC, an established plant long non-coding RNA database, overlap for predicted genes from Arabidopsis thaliana, Oryza sativa and Eutrema salsugineum ranged from 51 to 83% with the highest agreement in Eutrema salsugineum. Most of the highest ranking predictions from Arabidopsis thaliana were annotated as potential natural antisense genes, pseudogenes, transposable elements, or simply computationally predicted hypothetical protein. Due to the nature of this tool, the model can be updated as new long non-protein coding transcripts are identified and functionally verified. This ensemble classifier is an accurate tool that can be used to rank long non-protein coding RNA predictions for use in conjunction with gene expression studies. Selection of plant transcripts with a high potential for regulatory roles as long non-protein coding RNAs will advance research in the elucidation of long non-protein coding RNA function.

Global potential distribution of Drosophila suzukii (Diptera, Drosophilidae)

PubMed Central

dos Santos, Luana A.; Mendes, Mayara F.; Krüger, Alexandra P.; Blauth, Monica L.; Gottschalk, Marco S.

2017-01-01

Drosophila suzukii (Matsumura) is a species native to Western Asia that is able to pierce intact fruit during egg laying, causing it to be considered a fruit crop pest in many countries. Drosophila suzukii have a rapid expansion worldwide; occurrences were recorded in North America and Europe in 2008, and South America in 2013. Due to this rapid expansion, we modeled the potential distribution of this species using the Maximum Entropy Modeling (MaxEnt) algorithm and the Genetic Algorithm for Ruleset Production (GARP) using 407 sites with known occurrences worldwide and 11 predictor variables. After 1000 replicates, the value of the average area under the curve (AUC) of the model predictions with 1000 replicates was 0.97 for MaxEnt and 0.87 for GARP, indicating that both models had optimal performances. The environmental variables that most influenced the prediction of the MaxEnt model were the annual mean temperature, the maximum temperature of the warmest month, the mean temperature of the coldest quarter and the annual precipitation. The models indicated high environmental suitability, mainly in temperate and subtropical areas in the continents of Asia, Europe and North and South America, where the species has already been recorded. The potential for further invasions of the African and Australian continents is predicted due to the environmental suitability of these areas for this species. PMID:28323903

[Cytotoxicity of chemicals used in household products: estimation of eye irritating potency of 25 chemicals tested during 1991-1996].

PubMed

Ikarashi, Y; Tsuchiya, T; Nakamura, A

1997-01-01

Cytotoxicity potential of chemicals was evaluated by determining the concentrations inducing 50% reduction of neutral red (NR) uptake into Chinese hamster fibroblast V79 cells compared with control culture (IC50). The results of cytotoxicity test for surfactants with the data produced by the in vivo Draize eye and skin irritation test were compared. There was a good correlation between cytotoxicity and eye irritation score obtained from the Draize test. In contrast, no correlation was observed between Draize skin irritation score and cytotoxic potential of chemicals. Therefore, the NR cytotoxicity test was regarded as a possible in vitro model for predicting eye irritation. Based on the IC50 values in the NR cytotoxicity test, the eye irritation classification (weak, moderate and strong) for each chemical used in household products has been established. We evaluated the cytotoxicity of 25 chemicals used for antimicrobial, rubber accelerator, rubber antioxidant, ultraviolet absorber etc. in household products, and estimated the eye irritating potency of these test chemicals according to the criterion.

Formulation Predictive Dissolution (fPD) Testing to Advance Oral Drug Product Development: an Introduction to the US FDA Funded '21st Century BA/BE' Project.

PubMed

Hens, Bart; Sinko, Patrick; Job, Nicholas; Dean, Meagan; Al-Gousous, Jozef; Salehi, Niloufar; Ziff, Robert M; Tsume, Yasuhiro; Bermejo, Marival; Paixão, Paulo; Brasseur, James G; Yu, Alex; Talattof, Arjang; Benninghoff, Gail; Langguth, Peter; Lennernäs, Hans; Hasler, William L; Marciani, Luca; Dickens, Joseph; Shedden, Kerby; Sun, Duxin; Amidon, Gregory E; Amidon, Gordon L

2018-06-23

Over the past decade, formulation predictive dissolution (fPD) testing has gained increasing attention. Another mindset is pushed forward where scientists in our field are more confident to explore the in vivo behavior of an oral drug product by performing predictive in vitro dissolution studies. Similarly, there is an increasing interest in the application of modern computational fluid dynamics (CFD) frameworks and high-performance computing platforms to study the local processes underlying absorption within the gastrointestinal (GI) tract. In that way, CFD and computing platforms both can inform future PBPK-based in silico frameworks and determine the GI-motility-driven hydrodynamic impacts that should be incorporated into in vitro dissolution methods for in vivo relevance. Current compendial dissolution methods are not always reliable to predict the in vivo behavior, especially not for biopharmaceutics classification system (BCS) class 2/4 compounds suffering from a low aqueous solubility. Developing a predictive dissolution test will be more reliable, cost-effective and less time-consuming as long as the predictive power of the test is sufficiently strong. There is a need to develop a biorelevant, predictive dissolution method that can be applied by pharmaceutical drug companies to facilitate marketing access for generic and novel drug products. In 2014, Prof. Gordon L. Amidon and his team initiated a far-ranging research program designed to integrate (1) in vivo studies in humans in order to further improve the understanding of the intraluminal processing of oral dosage forms and dissolved drug along the gastrointestinal (GI) tract, (2) advancement of in vitro methodologies that incorporates higher levels of in vivo relevance and (3) computational experiments to study the local processes underlying dissolution, transport and absorption within the intestines performed with a new unique CFD based framework. Of particular importance is revealing the physiological variables determining the variability in in vivo dissolution and GI absorption from person to person in order to address (potential) in vivo BE failures. This paper provides an introduction to this multidisciplinary project, informs the reader about current achievements and outlines future directions. Copyright © 2018. Published by Elsevier B.V.

Effects of chronic N additions on tissue chemistry, photosynthetic capacity, and carbon sequestration potential of a red pine (Pinus resinosa Ait.) stand in the NE United States

Treesearch

G.A. Bauer; F.A. Bazzaz; R. Minocha; S. Long; A. Magill; J. Aber; G.M. Berntson

2004-01-01

Temperate forests are predicted to play a key role as important sinks for atmospheric carbon dioxide, which could be enhanced by nitrogen (N) deposition. However, experimental evidence suggests that the impact of N deposition on temperate forest productivity may not be as great as originally assumed. We investigated how chronic N addition affects needle morphology,...

Re-assessment of plant carbon dynamics at the Duke free-air CO2 enrichment site: interactions of atmospheric [CO2] with nitrogen and water availability over stand development

Treesearch

Heather R. McCarthy; Ram Oren; Kurt H Johnsen; Anne Gallet-Budynek; Seth G. Pritchard; Charles W Cook; Shannon L. LaDeau; Robert B. Jackson; Adrien C. Finzi

2010-01-01

The potential for elevated [CO2]-induced changes to plant carbon (C) storage, through modifications in plant production and allocation of C among plant pools, is an important source of uncertainty when predicting future forest function. Utilizing 10 yr of data from the Duke free-air CO2 enrichment site, we evaluated the...

A validated model to predict microalgae growth in outdoor pond cultures subjected to fluctuating light intensities and water temperatures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huesemann, Michael H.; Crowe, Braden J.; Waller, Peter

Here, a microalgae biomass growth model was developed for screening novel strains for their potential to exhibit high biomass productivities under nutrient-replete conditions in outdoor ponds subjected to fluctuating light intensities and water temperatures. Growth is modeled by first estimating the light attenuation by biomass according to a scatter-corrected Beer-Lambert Law, and then calculating the specific growth rate in discretized culture volume slices that receive declining light intensities due to attenuation. The model requires the following experimentally determined strain-specific input parameters: specific growth rate as a function of light intensity and temperature, biomass loss rate in the dark as amore » function of temperature and average light intensity during the preceding light period, and the scatter-corrected biomass light absorption coefficient. The model was successful in predicting the growth performance and biomass productivity of three different microalgae species (Chlorella sorokiniana, Nannochloropsis salina, and Picochlorum sp.) in raceway pond cultures (batch and semi-continuous) subjected to diurnal sunlight intensity and water temperature variations. Model predictions were moderately sensitive to minor deviations in input parameters. To increase the predictive power of this and other microalgae biomass growth models, a better understanding of the effects of mixing-induced rapid light dark cycles on photo-inhibition and short-term biomass losses due to dark respiration in the aphotic zone of the pond is needed.« less

A validated model to predict microalgae growth in outdoor pond cultures subjected to fluctuating light intensities and water temperatures

DOE PAGES

Huesemann, Michael H.; Crowe, Braden J.; Waller, Peter; ...

2015-12-11

Here, a microalgae biomass growth model was developed for screening novel strains for their potential to exhibit high biomass productivities under nutrient-replete conditions in outdoor ponds subjected to fluctuating light intensities and water temperatures. Growth is modeled by first estimating the light attenuation by biomass according to a scatter-corrected Beer-Lambert Law, and then calculating the specific growth rate in discretized culture volume slices that receive declining light intensities due to attenuation. The model requires the following experimentally determined strain-specific input parameters: specific growth rate as a function of light intensity and temperature, biomass loss rate in the dark as amore » function of temperature and average light intensity during the preceding light period, and the scatter-corrected biomass light absorption coefficient. The model was successful in predicting the growth performance and biomass productivity of three different microalgae species (Chlorella sorokiniana, Nannochloropsis salina, and Picochlorum sp.) in raceway pond cultures (batch and semi-continuous) subjected to diurnal sunlight intensity and water temperature variations. Model predictions were moderately sensitive to minor deviations in input parameters. To increase the predictive power of this and other microalgae biomass growth models, a better understanding of the effects of mixing-induced rapid light dark cycles on photo-inhibition and short-term biomass losses due to dark respiration in the aphotic zone of the pond is needed.« less

In silico peptide prediction for antibody generation to recognize 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) in genetically modified organisms.

PubMed

Marani, Mariela M; Costa, Joana; Mafra, Isabel; Oliveira, Maria Beatriz P P; Camperi, Silvia A; Leite, José Roberto de Souza Almeida

2015-03-01

For the prospective immunorecognition of 5-enolpyruvylshikimate-3-phosphate synthase (CP4-EPSPS) as a biomarker protein expressed by transgenic soybean, an extensive in silico evaluation of the referred protein was performed. The main objective of this study was the selection of a set of peptides that could function as potential immunogens for the production of novel antibodies against CP4-EPSPS protein. For this purpose, the protein was in silico cleaved with trypsin/chymotrypsin and the resultant peptides were extensively analyzed for further selection of the best candidates for antibody production. The analysis enabled the successful proposal of four peptides with potential immunogenicity for their future use as screening biomarkers of genetically modified organisms. To our knowledge, this is the first attempt to select and define potential linear epitopes for the immunization of animals and, subsequently, to generate adequate antibodies for CP4-EPSPS recognition. The present work will be followed by the synthesis of the candidate peptides to be incubated in animals for antibody generation and potential applicability for the development of an immunosensor for CP4-EPSPS detection. © 2015 Wiley Periodicals, Inc.

Legionella shows a diverse secondary metabolism dependent on a broad spectrum Sfp-type phosphopantetheinyl transferase.

PubMed

Tobias, Nicholas J; Ahrendt, Tilman; Schell, Ursula; Miltenberger, Melissa; Hilbi, Hubert; Bode, Helge B

2016-01-01

Several members of the genus Legionella cause Legionnaires' disease, a potentially debilitating form of pneumonia. Studies frequently focus on the abundant number of virulence factors present in this genus. However, what is often overlooked is the role of secondary metabolites from Legionella . Following whole genome sequencing, we assembled and annotated the Legionella parisiensis DSM 19216 genome. Together with 14 other members of the Legionella , we performed comparative genomics and analysed the secondary metabolite potential of each strain. We found that Legionella contains a huge variety of biosynthetic gene clusters (BGCs) that are potentially making a significant number of novel natural products with undefined function. Surprisingly, only a single Sfp-like phosphopantetheinyl transferase is found in all Legionella strains analyzed that might be responsible for the activation of all carrier proteins in primary (fatty acid biosynthesis) and secondary metabolism (polyketide and non-ribosomal peptide synthesis). Using conserved active site motifs, we predict some novel compounds that are probably involved in cell-cell communication, differing to known communication systems. We identify several gene clusters, which may represent novel signaling mechanisms and demonstrate the natural product potential of Legionella .

Comprehensive annotation of secondary metabolite biosynthetic genes and gene clusters of Aspergillus nidulans, A. fumigatus, A. niger and A. oryzae

PubMed Central

2013-01-01

Background Secondary metabolite production, a hallmark of filamentous fungi, is an expanding area of research for the Aspergilli. These compounds are potent chemicals, ranging from deadly toxins to therapeutic antibiotics to potential anti-cancer drugs. The genome sequences for multiple Aspergilli have been determined, and provide a wealth of predictive information about secondary metabolite production. Sequence analysis and gene overexpression strategies have enabled the discovery of novel secondary metabolites and the genes involved in their biosynthesis. The Aspergillus Genome Database (AspGD) provides a central repository for gene annotation and protein information for Aspergillus species. These annotations include Gene Ontology (GO) terms, phenotype data, gene names and descriptions and they are crucial for interpreting both small- and large-scale data and for aiding in the design of new experiments that further Aspergillus research. Results We have manually curated Biological Process GO annotations for all genes in AspGD with recorded functions in secondary metabolite production, adding new GO terms that specifically describe each secondary metabolite. We then leveraged these new annotations to predict roles in secondary metabolism for genes lacking experimental characterization. As a starting point for manually annotating Aspergillus secondary metabolite gene clusters, we used antiSMASH (antibiotics and Secondary Metabolite Analysis SHell) and SMURF (Secondary Metabolite Unknown Regions Finder) algorithms to identify potential clusters in A. nidulans, A. fumigatus, A. niger and A. oryzae, which we subsequently refined through manual curation. Conclusions This set of 266 manually curated secondary metabolite gene clusters will facilitate the investigation of novel Aspergillus secondary metabolites. PMID:23617571