Limitations of the widely used GAM42a and BET42a probes targeting bacteria in the Gammaproteobacteria radiation.

PubMed

Yeates, Christine; Saunders, Aaron M; Crocetti, Gregory R; Blackall, Linda L

2003-05-01

The 23S rRNA-targeted probes GAM42a and BET42a provided equivocal results with the uncultured gammaproteobacterium 'Candidatus Competibacter phosphatis' where some cells bound GAM42a and other cells bound BET42a in fluorescence in situ hybridization (FISH) experiments. Probes GAM42a and BET42a span positions 1027-1043 in the 23S rRNA and differ from each other by one nucleotide at position 1033. Clone libraries were prepared from PCR products spanning the 16S rRNA genes, intergenic spacer region and 23S rRNA genes from two mixed cultures enriched in 'Candidatus C. phosphatis'. With individual clone inserts, the 16S rDNA portion was used to confirm the source organism as 'Candidatus C. phosphatis' and the 23S rDNA portion was used to determine the sequence of the GAM42a/BET42a probe target region. Of the 19 clones sequenced, 8 had the GAM42a probe target (T at position 1033) and 11 had G at position 1033, the only mismatch with GAM42a. However, none of the clones had the BET42a probe target (A at 1033). Non-canonical base-pairing between the 23S rRNA of 'Candidatus C. phosphatis' with G at position 1033 and GAM42a (G-A) or BET42a (G-T) is likely to explain the probing anomalies. A probe (GAM42_C1033) was optimized for use in FISH, targeting cells with G at position 1033, and was found to highlight not only some 'Candidatus C. phosphatis' cells, but also other bacteria. This demonstrates that there are bacteria in addition to 'Candidatus C. phosphatis' with the GAM42_C1033 probe target and not the BET42a or GAM42a probe target.

A habitat suitability model for Chinese sturgeon determined using the generalized additive method

NASA Astrophysics Data System (ADS)

Yi, Yujun; Sun, Jie; Zhang, Shanghong

2016-03-01

The Chinese sturgeon is a type of large anadromous fish that migrates between the ocean and rivers. Because of the construction of dams, this sturgeon's migration path has been cut off, and this species currently is on the verge of extinction. Simulating suitable environmental conditions for spawning followed by repairing or rebuilding its spawning grounds are effective ways to protect this species. Various habitat suitability models based on expert knowledge have been used to evaluate the suitability of spawning habitat. In this study, a two-dimensional hydraulic simulation is used to inform a habitat suitability model based on the generalized additive method (GAM). The GAM is based on real data. The values of water depth and velocity are calculated first via the hydrodynamic model and later applied in the GAM. The final habitat suitability model is validated using the catch per unit effort (CPUEd) data of 1999 and 2003. The model results show that a velocity of 1.06-1.56 m/s and a depth of 13.33-20.33 m are highly suitable ranges for the Chinese sturgeon to spawn. The hydraulic habitat suitability indexes (HHSI) for seven discharges (4000; 9000; 12,000; 16,000; 20,000; 30,000; and 40,000 m3/s) are calculated to evaluate integrated habitat suitability. The results show that the integrated habitat suitability reaches its highest value at a discharge of 16,000 m3/s. This study is the first to apply a GAM to evaluate the suitability of spawning grounds for the Chinese sturgeon. The study provides a reference for the identification of potential spawning grounds in the entire basin.

Modeling the flux of metabolites in the juvenile hormone biosynthesis pathway using generalized additive models and ordinary differential equations.

PubMed

Martínez-Rincón, Raúl O; Rivera-Pérez, Crisalejandra; Diambra, Luis; Noriega, Fernando G

2017-01-01

Juvenile hormone (JH) regulates development and reproductive maturation in insects. The corpora allata (CA) from female adult mosquitoes synthesize fluctuating levels of JH, which have been linked to the ovarian development and are influenced by nutritional signals. The rate of JH biosynthesis is controlled by the rate of flux of isoprenoids in the pathway, which is the outcome of a complex interplay of changes in precursor pools and enzyme levels. A comprehensive study of the changes in enzymatic activities and precursor pool sizes have been previously reported for the mosquito Aedes aegypti JH biosynthesis pathway. In the present studies, we used two different quantitative approaches to describe and predict how changes in the individual metabolic reactions in the pathway affect JH synthesis. First, we constructed generalized additive models (GAMs) that described the association between changes in specific metabolite concentrations with changes in enzymatic activities and substrate concentrations. Changes in substrate concentrations explained 50% or more of the model deviances in 7 of the 13 metabolic steps analyzed. Addition of information on enzymatic activities almost always improved the fitness of GAMs built solely based on substrate concentrations. GAMs were validated using experimental data that were not included when the model was built. In addition, a system of ordinary differential equations (ODE) was developed to describe the instantaneous changes in metabolites as a function of the levels of enzymatic catalytic activities. The results demonstrated the ability of the models to predict changes in the flux of metabolites in the JH pathway, and can be used in the future to design and validate experimental manipulations of JH synthesis.

“Skill of Generalized Additive Model to Detect PM2.5 Health ...

EPA Pesticide Factsheets

Summary. Measures of health outcomes are collinear with meteorology and air quality, making analysis of connections between human health and air quality difficult. The purpose of this analysis was to determine time scales and periods shared by the variables of interest (and by implication scales and periods that are not shared). Hospital admissions, meteorology (temperature and relative humidity), and air quality (PM2.5 and daily maximum ozone) for New York City during the period 2000-2006 were decomposed into temporal scales ranging from 2 days to greater than two years using a complex wavelet transform. Health effects were modeled as functions of the wavelet components of meteorology and air quality using the generalized additive model (GAM) framework. This simulation study showed that GAM is extremely successful at extracting and estimating a health effect embedded in a dataset. It also shows that, if the objective in mind is to estimate the health signal but not to fully explain this signal, a simple GAM model with a single confounder (calendar time) whose smooth representation includes a sufficient number of constraints is as good as a more complex model.Introduction. In the context of wavelet regression, confounding occurs when two or more independent variables interact with the dependent variable at the same frequency. Confounding also acts on a variety of time scales, changing the PM2.5 coefficient (magnitude and sign) and its significance ac

Geodesic acoustic mode (GAM) like oscillations and RMP effect in the STOR-M tokamak

NASA Astrophysics Data System (ADS)

Basu, Debjyoti; Nakajima, Masaru; Melnikov, A. V.; McColl, David; Rohollahi, Akbar; Elgriw, Sayf; Xiao, Chijin; Hirose, Akira

2018-02-01

A new kind of quasi-coherent mode was observed in ohmic plasma in the STOR-M tokamak. It is featured with a clear solitary peak around 30-35 kHz in the power spectra of the ion saturation current (I_sat) of Langmuir probe as well as poloidal and toroidal mode numbers (m  =  1,n  =  0) as per the prediction of conventional geodesic acoustic mode (GAM) theory. The dispersion relation of the mode is also similar to GAM and it also shows collisional damping. In contrast to conventional GAM, the floating potential ϕ of the observed GAM-like mode does not show similar symmetric poloidal and toroidal mode numbers (m  =  0,n  =  0), but has (m  =  1,n  =  1). The GAM-like mode has also a pronounced magnetic component with mixed poloidal modes (m=3~and~m=5; n=1 ), as observed by Mirnov coils. This mode is suppressed by the application of resonance magnetic perturbations.

GamTest: Psychometric Evaluation and the Role of Emotions in an Online Self-Test for Gambling Behavior.

PubMed

Jonsson, Jakob; Munck, Ingrid; Volberg, Rachel; Carlbring, Per

2017-06-01

Recent increases in the number of online gambling sites have made gambling more available, which may contribute to an increase in gambling problems. At the same time, online gambling provides opportunities to introduce measures intended to prevent problem gambling. GamTest is an online test of gambling behavior that provides information that can be used to give players individualized feedback and recommendations for action. The aim of this study is to explore the dimensionality of GamTest and validate it against the Problem Gambling Severity Index (PGSI) and the gambler's own perceived problems. A recent psychometric approach, exploratory structural equation modeling (ESEM) is used. Well-defined constructs are identified in a two-step procedure fitting a traditional exploratory factor analysis model as well as a so-called bifactor model. Using data collected at four Nordic gambling sites in the autumn of 2009 (n = 10,402), the GamTest ESEM analyses indicate high correspondence with the players' own understanding of their problems and with the PGSI, a validated measure of problem gambling. We conclude that GamTest captures five dimensions of problematic gambling (i.e., overconsumption of money and time, and monetary, social and emotional negative consequences) with high reliability, and that the bifactor approach, composed of a general factor and specific residual factors, reproduces all these factors except one, the negative consequences emotional factor, which contributes to the dominant part of the general factor. The results underscore the importance of tailoring feedback and support to online gamblers with a particular focus on how to handle emotions in relation to their gambling behavior.

A combined approach of generalized additive model and bootstrap with small sample sets for fault diagnosis in fermentation process of glutamate.

PubMed

Liu, Chunbo; Pan, Feng; Li, Yun

2016-07-29

Glutamate is of great importance in food and pharmaceutical industries. There is still lack of effective statistical approaches for fault diagnosis in the fermentation process of glutamate. To date, the statistical approach based on generalized additive model (GAM) and bootstrap has not been used for fault diagnosis in fermentation processes, much less the fermentation process of glutamate with small samples sets. A combined approach of GAM and bootstrap was developed for the online fault diagnosis in the fermentation process of glutamate with small sample sets. GAM was first used to model the relationship between glutamate production and different fermentation parameters using online data from four normal fermentation experiments of glutamate. The fitted GAM with fermentation time, dissolved oxygen, oxygen uptake rate and carbon dioxide evolution rate captured 99.6 % variance of glutamate production during fermentation process. Bootstrap was then used to quantify the uncertainty of the estimated production of glutamate from the fitted GAM using 95 % confidence interval. The proposed approach was then used for the online fault diagnosis in the abnormal fermentation processes of glutamate, and a fault was defined as the estimated production of glutamate fell outside the 95 % confidence interval. The online fault diagnosis based on the proposed approach identified not only the start of the fault in the fermentation process, but also the end of the fault when the fermentation conditions were back to normal. The proposed approach only used a small sample sets from normal fermentations excitements to establish the approach, and then only required online recorded data on fermentation parameters for fault diagnosis in the fermentation process of glutamate. The proposed approach based on GAM and bootstrap provides a new and effective way for the fault diagnosis in the fermentation process of glutamate with small sample sets.

Interaction between the Sbcc Gene of Escherichia Coli and the Gam Gene of Phage λ

PubMed Central

Kulkarni, S. K.; Stahl, F. W.

1989-01-01

gam mutants of phage λ carrying long palindromes fail to form plaques on wild-type Escherichia coli but do grow on strains that are mutant in the sbcC gene. gam(+) λ carrying the same palindrome grow on both hosts and on a host deleted for the recB, C and D genes. These results suggest that the Gam protein of λ, known to interact also with E. coli's recBCD protein, can interact with the product of the sbcC gene. PMID:2531105

Assessment selection in human-automation interaction studies: The Failure-GAM2E and review of assessment methods for highly automated driving.

PubMed

Grane, Camilla

2018-01-01

Highly automated driving will change driver's behavioural patterns. Traditional methods used for assessing manual driving will only be applicable for the parts of human-automation interaction where the driver intervenes such as in hand-over and take-over situations. Therefore, driver behaviour assessment will need to adapt to the new driving scenarios. This paper aims at simplifying the process of selecting appropriate assessment methods. Thirty-five papers were reviewed to examine potential and relevant methods. The review showed that many studies still relies on traditional driving assessment methods. A new method, the Failure-GAM 2 E model, with purpose to aid assessment selection when planning a study, is proposed and exemplified in the paper. Failure-GAM 2 E includes a systematic step-by-step procedure defining the situation, failures (Failure), goals (G), actions (A), subjective methods (M), objective methods (M) and equipment (E). The use of Failure-GAM 2 E in a study example resulted in a well-reasoned assessment plan, a new way of measuring trust through feet movements and a proposed Optimal Risk Management Model. Failure-GAM 2 E and the Optimal Risk Management Model are believed to support the planning process for research studies in the field of human-automation interaction. Copyright © 2017 Elsevier Ltd. All rights reserved.

Generalized additive models and Lucilia sericata growth: assessing confidence intervals and error rates in forensic entomology.

PubMed

Tarone, Aaron M; Foran, David R

2008-07-01

Forensic entomologists use blow fly development to estimate a postmortem interval. Although accurate, fly age estimates can be imprecise for older developmental stages and no standard means of assigning confidence intervals exists. Presented here is a method for modeling growth of the forensically important blow fly Lucilia sericata, using generalized additive models (GAMs). Eighteen GAMs were created to predict the extent of juvenile fly development, encompassing developmental stage, length, weight, strain, and temperature data, collected from 2559 individuals. All measures were informative, explaining up to 92.6% of the deviance in the data, though strain and temperature exerted negligible influences. Predictions made with an independent data set allowed for a subsequent examination of error. Estimates using length and developmental stage were within 5% of true development percent during the feeding portion of the larval life cycle, while predictions for postfeeding third instars were less precise, but within expected error.

gamAID: Greedy CP tensor decomposition for supervised EHR-based disease trajectory differentiation.

PubMed

Henderson, Jette; Ho, Joyce; Ghosh, Joydeep

2017-07-01

We propose gamAID, an exploratory, supervised nonnegative tensor factorization method that iteratively extracts phenotypes from tensors constructed from medical count data. Using data from diabetic patients who later on get diagnosed with chronic kidney disorder (CKD) as well as diabetic patients who do not receive a CKD diagnosis, we demonstrate the potential of gamAID to discover phenotypes that characterize patients who are at risk for developing a disease.

The Geocybernetic Assessment Matrix (GAM) — A new assessment tool for evaluating the level and nature of sustainability or unsustainability

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

Phillips, Jason, E-mail: jp1@tiscali.co.uk

Evaluating sustainability from EIA-based assessments has been problematic at best. This is due to the use of reductionist and qualitative approaches which is dependent upon the perspective of the assessor(s). Therefore, a more rigorous and holistic approach is required to evaluate sustainability in a more consistent way. In this paper, a matrix-based methodology in order to assess the indicated level and nature of sustainability for any project, policy, indicators, legislation, regulation, or other framework is described. The Geocybernetic Assessment Matrix (GAM) is designed to evaluate the level and nature of sustainability or unsustainability occurring in respect the fundamental and complexmore » geocybernetic paradigms. The GAM method is described in detail in respect to the theory behind it and the methodology. The GAM is then demonstrated using an appropriate case study — Part 1 of the UK Climate Change Act (2008) concerning carbon budgets and targets. The results indicate that the Part 1 of Act may not achieve the desired goals in contributing towards sustainable development through the stated mechanisms for carbon budgets and targets. The paper then discusses the broader context of the GAM with respect to the core themes evident in the development and application of the GAM of: sustainability science; sustainability assessment; application value of the GAM; and future research and development. - Highlights: • A new assessment tool called the Geocybernetic Assessment Matrix (GAM) described. • GAM evaluates the level and nature of sustainability or unsustainability. • GAM demonstrated by application to Part 1 of the UK Climate Change Act (CCA). • Part 1 of CCA has significant flaws in achieving a sustainable pathway. • GAM offers a potentially useful tool for quantitatively evaluating sustainability.« less

Comparison of the up-conversion photoluminescence for GAP, GAG and GAM phosphors

NASA Astrophysics Data System (ADS)

Deng, Taoli; Jiang, Xianbang

2018-04-01

GdAlO3:Er3+/Yb3+, Gd3Al5O12:Er3+/Yb3+ and Gd4Al2O9:Er3+/Yb3+ phosphors were prepared by co-precipitation. The effects for Gd2O3-Al2O3 composite oxides as the host materials with different crystal structures such as GdAlO3, Gd3Al5O12 and Gd4Al2O9 were investigated. It was found that the perovskite structured GdAlO3:Er3+/Yb3+ (GAP phosphor) could be obtained from the precursor when the calcination temperature was 1000 °C, while the garnet structured Gd3Al5O12:Er3+/Yb3+ (GAG phosphor) could be formed when the calcination temperature was 1300 °C, but the monoclinic-structured Gd4Al2O9:Er3+/Yb3+ (GAM phosphor) could be formed only when the calcination temperature was raised up to 1500 °C. The difference of the up-conversion photoluminescence (UCPL) spectra under 980 nm between the GAP, GAG and GAM phosphors was studied. The result showed that the UCPL intensity of the GAP phosphor was close to that of the GAM phosphor with much higher red-to-green intensity ratio than that of GAP phosphor. The UCPL intensity of GAG phosphor was the weakest among them. Finally, the factors which influenced on the UCPL of the GAP, GAG and GAM phosphors were discussed.

Efficacy of a DNA vaccine carrying Eimeria maxima Gam56 antigen gene against coccidiosis in chickens.

PubMed

Xu, Jinjun; Zhang, Yan; Tao, Jianping

2013-04-01

To control coccidiosis without using prophylactic medications, a DNA vaccine targeting the gametophyte antigen Gam56 from Eimeria maxima in chickens was constructed, and the immunogenicity and protective effects were evaluated. The ORF of Gam56 gene was cloned into an eukaryotic expression vector pcDNA3.1(zeo)+. Expression of Gam56 protein in COS-7 cells transfected with recombinant plasmid pcDNA-Gam56 was confirmed by indirect immunofluorescence assay. The DNA vaccine was injected intramuscularly to yellow feathered broilers of 1-week old at 3 dosages (25, 50, and 100 µg/chick). Injection was repeated once 1 week later. One week after the second injection, birds were challenged orally with 5×10(4) sporulated oocysts of E. maxima, then weighed and killed at day 8 post challenge. Blood samples were collected and examined for specific peripheral blood lymphocyte proliferation activity and serum antibody levels. Compared with control groups, the administration of pcDNA-Gam56 vaccine markedly increased the lymphocyte proliferation activity (P<0.05) at day 7 and 14 after the first immunization. The level of lymphocyte proliferation started to decrease on day 21 after the first immunization. A similar trend was seen in specific antibody levels. Among the 3 pcDNA-Gam56 immunized groups, the median dosage group displayed the highest lymphocyte proliferation and antibody levels (P<0.05). The median dosage group had the greatest relative body weight gain (89.7%), and the greatest oocyst shedding reduction (53.7%). These results indicate that median dosage of DNA vaccine had good immunogenicity and immune protection effects, and may be used in field applications for coccidiosis control.

Efficacy of a DNA Vaccine Carrying Eimeria maxima Gam56 Antigen Gene against Coccidiosis in Chickens

PubMed Central

Xu, Jinjun; Zhang, Yan

2013-01-01

To control coccidiosis without using prophylactic medications, a DNA vaccine targeting the gametophyte antigen Gam56 from Eimeria maxima in chickens was constructed, and the immunogenicity and protective effects were evaluated. The ORF of Gam56 gene was cloned into an eukaryotic expression vector pcDNA3.1(zeo)+. Expression of Gam56 protein in COS-7 cells transfected with recombinant plasmid pcDNA-Gam56 was confirmed by indirect immunofluorescence assay. The DNA vaccine was injected intramuscularly to yellow feathered broilers of 1-week old at 3 dosages (25, 50, and 100 µg/chick). Injection was repeated once 1 week later. One week after the second injection, birds were challenged orally with 5×104 sporulated oocysts of E. maxima, then weighed and killed at day 8 post challenge. Blood samples were collected and examined for specific peripheral blood lymphocyte proliferation activity and serum antibody levels. Compared with control groups, the administration of pcDNA-Gam56 vaccine markedly increased the lymphocyte proliferation activity (P<0.05) at day 7 and 14 after the first immunization. The level of lymphocyte proliferation started to decrease on day 21 after the first immunization. A similar trend was seen in specific antibody levels. Among the 3 pcDNA-Gam56 immunized groups, the median dosage group displayed the highest lymphocyte proliferation and antibody levels (P<0.05). The median dosage group had the greatest relative body weight gain (89.7%), and the greatest oocyst shedding reduction (53.7%). These results indicate that median dosage of DNA vaccine had good immunogenicity and immune protection effects, and may be used in field applications for coccidiosis control. PMID:23710081

Eimeria maxima recombinant Gam82 gametocyte antigen vaccine protects against coccidiosis and augments humoral and cell-mediated immunity.

PubMed

Jang, Seung I; Lillehoj, Hyun S; Lee, Sung Hyen; Lee, Kyung Woo; Park, Myeong Seon; Cha, Sung-Rok; Lillehoj, Erik P; Subramanian, B Mohana; Sriraman, R; Srinivasan, V A

2010-04-09

Intestinal infection with Eimeria, the etiologic agent of avian coccidiosis, stimulates protective immunity to subsequent colonization by the homologous parasite, while cross-protection against heterologous species is poor. As a first step toward the development of a broad specificity Eimeria vaccine, this study was designed to assess a purified recombinant protein from Eimeria maxima gametocytes (Gam82) in stimulating immunity against experimental infection with live parasites. Following Gam82 intramuscular immunization and oral parasite challenge, body weight gain, fecal oocyst output, lesion scores, serum antibody response, and cytokine production were assessed to evaluate vaccination efficacy. Animals vaccinated with Gam82 and challenged with E. maxima showed lower oocyst shedding and reduced intestinal pathology compared with non-vaccinated and parasite-challenged animals. Gam82 vaccination also stimulated the production of antigen-specific serum antibodies and induced greater levels of IL-2 and IL-15 mRNAs compared with non-vaccinated controls. These results demonstrate that the Gam82 recombinant protein protects against E. maxima and augments humoral and cell-mediated immunity. Published by Elsevier Ltd.

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting

PubMed Central

2010-01-01

Background A common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics. Results This research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases. Conclusions The GAM permutation testing methods

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting.

PubMed

Young, Robin L; Weinberg, Janice; Vieira, Verónica; Ozonoff, Al; Webster, Thomas F

2010-07-19

A common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics. This research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases. The GAM permutation testing methods provide a regression

Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques

NASA Astrophysics Data System (ADS)

Chen, Wei; Pourghasemi, Hamid Reza; Panahi, Mahdi; Kornejady, Aiding; Wang, Jiale; Xie, Xiaoshen; Cao, Shubo

2017-11-01

The spatial prediction of landslide susceptibility is an important prerequisite for the analysis of landslide hazards and risks in any area. This research uses three data mining techniques, such as an adaptive neuro-fuzzy inference system combined with frequency ratio (ANFIS-FR), a generalized additive model (GAM), and a support vector machine (SVM), for landslide susceptibility mapping in Hanyuan County, China. In the first step, in accordance with a review of the previous literature, twelve conditioning factors, including slope aspect, altitude, slope angle, topographic wetness index (TWI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), and lithology, were selected. In the second step, a collinearity test and correlation analysis between the conditioning factors and landslides were applied. In the third step, we used three advanced methods, namely, ANFIS-FR, GAM, and SVM, for landslide susceptibility modeling. Subsequently, the results of their accuracy were validated using a receiver operating characteristic curve. The results showed that all three models have good prediction capabilities, while the SVM model has the highest prediction rate of 0.875, followed by the ANFIS-FR and GAM models with prediction rates of 0.851 and 0.846, respectively. Thus, the landslide susceptibility maps produced in the study area can be applied for management of hazards and risks in landslide-prone Hanyuan County.

Near-road air pollutant concentrations of CO and PM 2.5: A comparison of MOBILE6.2/CALINE4 and generalized additive models

NASA Astrophysics Data System (ADS)

Zhang, Kai; Batterman, Stuart

2010-05-01

The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM 2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM 2.5 showed that GAM emission estimates were much higher (by 4-5 times) than the dispersion model results, and that the traffic-PM 2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM 2.5 concentrations, a likely result of underestimating PM 2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality.

Collaborative Autonomy with Group Autonomy for Mobile Systems (GAMS)

DTIC Science & Technology

2014-11-18

Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. Any opinions...tutorials, doxygen) Intro MADARA GAMS Conclusion 9 James Edmondson © 2014 Carnegie Mellon University How MADARA helps researchers and developers...architecture portability to prevent vendor lock-in and shorten transition timeframe • Open source. Free. Extensible. • Allows reseachers to focus

Treatment of nonhealing diabetic foot ulcers with a platelet-derived growth factor gene-activated matrix (GAM501): results of a phase 1/2 trial.

PubMed

Mulder, Gerit; Tallis, Arthur J; Marshall, V Tracy; Mozingo, David; Phillips, Laurie; Pierce, Glenn F; Chandler, Lois A; Sosnowski, Barbara K

2009-01-01

The results from a Phase 1/2 study of a replication-defective adenovirus encoding human platelet-derived growth factor (PDGF)-B formulated in a bovine collagen (Ad-5PDGF-B; 2.6% collagen; GAM501) gel for nonhealing neuropathic diabetic foot ulcers is reported. The primary objectives of the study were to evaluate the safety, maximum-tolerated dose, and preliminary biological activity of GAM501. Fifteen patients enrolled into the study with chronic, nonhealing ulcers received either a single administration of GAM501 at one of three dose levels, or up to four administrations of GAM501 at 1-week intervals. All patients received standard of care treatment including debridement and were required to wear an off-loading shoe. GAM501 was found to be safe and well tolerated with no evidence of systemic or local toxicity at all doses so no maximum-tolerated dose was reached. Serum antibody titers to platelet-derived growth factor-B homodimer and collagen were negative and adenoviral DNA was not detected in the blood. In the 12 patients that completed the study, ulcer closure was observed by Month 3 in 10 patients, seven of whom received a single application of GAM501. In conclusion, GAM501 did not appear to have any toxicity at doses that showed biological activity. GAM501 holds promise as a potentially effective treatment for nonhealing diabetic foot ulcers.

Functional genomics of gam56: characterisation of the role of a 56 kilodalton sexual stage antigen in oocyst wall formation in Eimeria maxima.

PubMed

Belli, Sabina I; Witcombe, David; Wallach, Michael G; Smith, Nicholas C

2002-12-19

Gam56 (M(r) 56,000) is an antigen found in the sexual (macrogametocyte) stage of the intestinal parasite Eimeria maxima that is implicated in protective immunity. The gene (gam56) encoding this protein was cloned and sequenced. It is a single-copy, intronless gene, that localises to a 1,754 bp transcript, and is first detected at 120 h p.i. The gene predicts two distinct protein domains; a tyrosine-serine rich region, composed of amino acids implicated in oocyst wall formation in Eimeria spp., and a proline-methionine rich region often detected in extensins, protein components of plant cell walls. The tyrosine-serine rich region predicts a secondary structure commonly seen in the structural protein fibroin, a component of the cocoon of the caterpillar Bombyx mori. The inference that gam56 is a structural component of the oocyst wall was confirmed when a specific antibody to gam56 recognised the wall forming bodies in macrogametocytes, and the walls of oocysts and sporocysts. Together, these data identify a developmentally regulated, sexual stage gene in E. maxima that shares primary and secondary structure features in common with intrinsic structural proteins in other parasites such as Schistosoma mansoni and Fasciola hepatica, and other organisms across different phyla, including the caterpillar Bombyx mori. In addition, these findings provide evidence for the molecular mechanisms underlying oocyst wall formation in Eimeria and the role of gametocyte antigens in this process.

Mixtures of GAMs for habitat suitability analysis with overdispersed presence / absence data

PubMed Central

Pleydell, David R.J.; Chrétien, Stéphane

2009-01-01

A new approach to species distribution modelling based on unsupervised classification via a finite mixture of GAMs incorporating habitat suitability curves is proposed. A tailored EM algorithm is outlined for computing maximum likelihood estimates. Several submodels incorporating various parameter constraints are explored. Simulation studies confirm, that under certain constraints, the habitat suitability curves are recovered with good precision. The method is also applied to a set of real data concerning presence/absence of observable small mammal indices collected on the Tibetan plateau. The resulting classification was found to correspond to species-level differences in habitat preference described in previous ecological work. PMID:20401331

Changing spousal roles and their effect on recovery in gamblers anonymous: GamAnon, social support, wives and husbands.

PubMed

Ferentzy, Peter; Skinner, Wayne; Antze, Paul

2010-09-01

This paper examines changing spousal roles and their effects upon recovery in Gamblers Anonymous (GA). It is based upon a qualitative study designed to gage uniformity as well as variations in approaches to recovery in GA. Interviews were conducted with 39 GA members (26 men, 13 women; mean age 56.5 years). Though the study was based in the Toronto area, only 13 interviews involved participants from that region. Phone interviews were conducted with GA members from various regions of both Canada and the US. GamAnon, GA's sister fellowship, has been designed for anyone affected seriously by someone's gambling problem. In practice, GamAnon comprises mostly women--spouses of male GA members--who traditionally have taken a keen interest in the ways in which their husbands achieve and maintain abstinence from gambling. Changing spousal roles have led to fewer women joining GamAnon, as many opt instead to part with troubled spouses. As well, more women are attending GA than in the past, typically with husbands who are disinclined to join GamAnon. All of this has drastically altered how GA members pursue recovery. These changes and their implications are discussed.

Creativity in context; the courage in Therivel's GAM/DP.

PubMed

Gruber, Craig W

2013-03-01

The desire to quantify and categorize creativity so that we can easily identify the individual characteristics which serve as its precursors is one which authors have been attempting to successfully complete for some time. In this light, William Therivel's High Creativity Unmasked, discusses how his GAM/DP theory accounts for highly creative people from history as well as a rationale for the rise and fall of creativity in selected cultures throughout history. By examining the latest published work on how Genetic Endowment, Assistances, Misfortunes and Divisions of Power, and how they relate to creativity, Therivel proposes unique rationales for the rises and declines of cultures. This paper examines how creativity and courage work together and in some cases side by side, to explain more fully the role of creativity in culture. This article examines some of Therivel's examples and provides alternate explanations for the phenomena he ascribes to the decline of creativity. In addition, the complexity of both creativity and courage are discussed, both in the current context, and in the next steps for research and theoretical discussion.

Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods

USGS Publications Warehouse

Moisen, Gretchen G.; Freeman, E.A.; Blackard, J.A.; Frescino, T.S.; Zimmermann, N.E.; Edwards, T.C.

2006-01-01

Many efforts are underway to produce broad-scale forest attribute maps by modelling forest class and structure variables collected in forest inventories as functions of satellite-based and biophysical information. Typically, variants of classification and regression trees implemented in Rulequest's?? See5 and Cubist (for binary and continuous responses, respectively) are the tools of choice in many of these applications. These tools are widely used in large remote sensing applications, but are not easily interpretable, do not have ties with survey estimation methods, and use proprietary unpublished algorithms. Consequently, three alternative modelling techniques were compared for mapping presence and basal area of 13 species located in the mountain ranges of Utah, USA. The modelling techniques compared included the widely used See5/Cubist, generalized additive models (GAMs), and stochastic gradient boosting (SGB). Model performance was evaluated using independent test data sets. Evaluation criteria for mapping species presence included specificity, sensitivity, Kappa, and area under the curve (AUC). Evaluation criteria for the continuous basal area variables included correlation and relative mean squared error. For predicting species presence (setting thresholds to maximize Kappa), SGB had higher values for the majority of the species for specificity and Kappa, while GAMs had higher values for the majority of the species for sensitivity. In evaluating resultant AUC values, GAM and/or SGB models had significantly better results than the See5 models where significant differences could be detected between models. For nine out of 13 species, basal area prediction results for all modelling techniques were poor (correlations less than 0.5 and relative mean squared errors greater than 0.8), but SGB provided the most stable predictions in these instances. SGB and Cubist performed equally well for modelling basal area for three species with moderate prediction success

Analysis of habitat characteristics of small pelagic fish based on generalized additive models in Kepulauan Seribu Waters

NASA Astrophysics Data System (ADS)

Rivai, A. A.; Siregar, V. P.; Agus, S. B.; Yasuma, H.

2018-03-01

One of the required information for sustainable fisheries management is about the habitat characteristics of a fish species. This information can be used to map the distribution of fish and map the potential fishing ground. This study aimed to analyze the habitat characteristics of small pelagic fishes (anchovy, squid, sardine and scads) which were mainly caught by lift net in Kepulauan Seribu waters. Research on habitat characteristics had been widely done, but the use of total suspended solid (TSS) parameters in this analysis is still lacking. TSS parameter which was extracted from Landsat 8 along with five other oceanographic parameters, CPUE data and location of fishing ground data from lift net fisheries in Kepulauan Seribu were included in this analysis. This analysis used Generalized Additive Models (GAMs) to evaluate the relationship between CPUE and oceanographic parameters. The results of the analysis showed that each fish species had different habitat characteristics. TSS and sea surface height had a great influence on the value of CPUE from each species. All the oceanographic parameters affected the CPUE of each species. This study demonstrated the effective use of GAMs to identify the essential habitat of a fish species.

Using generalized additive modeling to empirically identify thresholds within the ITERS in relation to toddlers' cognitive development.

PubMed

Setodji, Claude Messan; Le, Vi-Nhuan; Schaack, Diana

2013-04-01

Research linking high-quality child care programs and children's cognitive development has contributed to the growing popularity of child care quality benchmarking efforts such as quality rating and improvement systems (QRIS). Consequently, there has been an increased interest in and a need for approaches to identifying thresholds, or cutpoints, in the child care quality measures used in these benchmarking efforts that differentiate between different levels of children's cognitive functioning. To date, research has provided little guidance to policymakers as to where these thresholds should be set. Using the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B) data set, this study explores the use of generalized additive modeling (GAM) as a method of identifying thresholds on the Infant/Toddler Environment Rating Scale (ITERS) in relation to toddlers' performance on the Mental Development subscale of the Bayley Scales of Infant Development (the Bayley Mental Development Scale Short Form-Research Edition, or BMDSF-R). The present findings suggest that simple linear models do not always correctly depict the relationships between ITERS scores and BMDSF-R scores and that GAM-derived thresholds were more effective at differentiating among children's performance levels on the BMDSF-R. Additionally, the present findings suggest that there is a minimum threshold on the ITERS that must be exceeded before significant improvements in children's cognitive development can be expected. There may also be a ceiling threshold on the ITERS, such that beyond a certain level, only marginal increases in children's BMDSF-R scores are observed. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Gamming Chairs and Gimballed Beds: Seafaring Women on Board Nineteenth-Century Ships

NASA Astrophysics Data System (ADS)

Seaborn, Laurel

2017-04-01

During the nineteenth century, many captains' wives from New England took up residence on the ships their husbands commanded. This article focuses on how those women at sea attempted to use material culture to domesticate their voyaging space. While writing in their journals, they referred to not only the small personal things such as books and knitting needles that they brought in their trunks, but also large items, built for and used by women, such as gamming chairs, deckhouses, parlor organs, sewing machines, and gimballed beds. Mary Brewster attempted to retreat from the ship's officers in her small deckhouse, Annie Brassey slept in the gimballed bed, and Lucy Lord Howes disembarked in a gamming chair when captured by Confederates during the Civil War. Evidence of these artifacts found during shipwreck archaeology could be used to further what is known of the culture aboard ships on which women lived. Analysis of the material culture reveals how a captain's wife domesticated space, altered her environment, and made a home on the ship for her family.

Low dose radiation risks for women surviving the a-bombs in Japan: generalized additive model.

PubMed

Dropkin, Greg

2016-11-24

Analyses of cancer mortality and incidence in Japanese A-bomb survivors have been used to estimate radiation risks, which are generally higher for women. Relative Risk (RR) is usually modelled as a linear function of dose. Extrapolation from data including high doses predicts small risks at low doses. Generalized Additive Models (GAMs) are flexible methods for modelling non-linear behaviour. GAMs are applied to cancer incidence in female low dose subcohorts, using anonymous public data for the 1958 - 1998 Life Span Study, to test for linearity, explore interactions, adjust for the skewed dose distribution, examine significance below 100 mGy, and estimate risks at 10 mGy. For all solid cancer incidence, RR estimated from 0 - 100 mGy and 0 - 20 mGy subcohorts is significantly raised. The response tapers above 150 mGy. At low doses, RR increases with age-at-exposure and decreases with time-since-exposure, the preferred covariate. Using the empirical cumulative distribution of dose improves model fit, and capacity to detect non-linear responses. RR is elevated over wide ranges of covariate values. Results are stable under simulation, or when removing exceptional data cells, or adjusting neutron RBE. Estimates of Excess RR at 10 mGy using the cumulative dose distribution are 10 - 45 times higher than extrapolations from a linear model fitted to the full cohort. Below 100 mGy, quasipoisson models find significant effects for all solid, squamous, uterus, corpus, and thyroid cancers, and for respiratory cancers when age-at-exposure > 35 yrs. Results for the thyroid are compatible with studies of children treated for tinea capitis, and Chernobyl survivors. Results for the uterus are compatible with studies of UK nuclear workers and the Techa River cohort. Non-linear models find large, significant cancer risks for Japanese women exposed to low dose radiation from the atomic bombings. The risks should be reflected in protection standards.

Use of generalised additive models to categorise continuous variables in clinical prediction

PubMed Central

2013-01-01

Background In medical practice many, essentially continuous, clinical parameters tend to be categorised by physicians for ease of decision-making. Indeed, categorisation is a common practice both in medical research and in the development of clinical prediction rules, particularly where the ensuing models are to be applied in daily clinical practice to support clinicians in the decision-making process. Since the number of categories into which a continuous predictor must be categorised depends partly on the relationship between the predictor and the outcome, the need for more than two categories must be borne in mind. Methods We propose a categorisation methodology for clinical-prediction models, using Generalised Additive Models (GAMs) with P-spline smoothers to determine the relationship between the continuous predictor and the outcome. The proposed method consists of creating at least one average-risk category along with high- and low-risk categories based on the GAM smooth function. We applied this methodology to a prospective cohort of patients with exacerbated chronic obstructive pulmonary disease. The predictors selected were respiratory rate and partial pressure of carbon dioxide in the blood (PCO2), and the response variable was poor evolution. An additive logistic regression model was used to show the relationship between the covariates and the dichotomous response variable. The proposed categorisation was compared to the continuous predictor as the best option, using the AIC and AUC evaluation parameters. The sample was divided into a derivation (60%) and validation (40%) samples. The first was used to obtain the cut points while the second was used to validate the proposed methodology. Results The three-category proposal for the respiratory rate was ≤ 20;(20,24];> 24, for which the following values were obtained: AIC=314.5 and AUC=0.638. The respective values for the continuous predictor were AIC=317.1 and AUC=0.634, with no statistically

Identification and analysis of Eimeria nieschulzi gametocyte genes reveal splicing events of gam genes and conserved motifs in the wall-forming proteins within the genus Eimeria (Coccidia, Apicomplexa)

PubMed Central

Wiedmer, Stefanie; Erdbeer, Alexander; Volke, Beate; Randel, Stephanie; Kapplusch, Franz; Hanig, Sacha; Kurth, Michael

2017-01-01

The genus Eimeria (Apicomplexa, Coccidia) provides a wide range of different species with different hosts to study common and variable features within the genus and its species. A common characteristic of all known Eimeria species is the oocyst, the infectious stage where its life cycle starts and ends. In our study, we utilized Eimeria nieschulzi as a model organism. This rat-specific parasite has complex oocyst morphology and can be transfected and even cultivated in vitro up to the oocyst stage. We wanted to elucidate how the known oocyst wall-forming proteins are preserved in this rodent Eimeria species compared to other Eimeria. In newly obtained genomics data, we were able to identify different gametocyte genes that are orthologous to already known gam genes involved in the oocyst wall formation of avian Eimeria species. These genes appeared putatively as single exon genes, but cDNA analysis showed alternative splicing events in the transcripts. The analysis of the translated sequence revealed different conserved motifs but also dissimilar regions in GAM proteins, as well as polymorphic regions. The occurrence of an underrepresented gam56 gene version suggests the existence of a second distinct E. nieschulzi genotype within the E. nieschulzi Landers isolate that we maintain. PMID:29210668

Spatial Assessment of Model Errors from Four Regression Techniques

Treesearch

Lianjun Zhang; Jeffrey H. Gove; Jeffrey H. Gove

2005-01-01

Fomst modelers have attempted to account for the spatial autocorrelations among trees in growth and yield models by applying alternative regression techniques such as linear mixed models (LMM), generalized additive models (GAM), and geographicalIy weighted regression (GWR). However, the model errors are commonly assessed using average errors across the entire study...

A penalized framework for distributed lag non-linear models.

PubMed

Gasparrini, Antonio; Scheipl, Fabian; Armstrong, Ben; Kenward, Michael G

2017-09-01

Distributed lag non-linear models (DLNMs) are a modelling tool for describing potentially non-linear and delayed dependencies. Here, we illustrate an extension of the DLNM framework through the use of penalized splines within generalized additive models (GAM). This extension offers built-in model selection procedures and the possibility of accommodating assumptions on the shape of the lag structure through specific penalties. In addition, this framework includes, as special cases, simpler models previously proposed for linear relationships (DLMs). Alternative versions of penalized DLNMs are compared with each other and with the standard unpenalized version in a simulation study. Results show that this penalized extension to the DLNM class provides greater flexibility and improved inferential properties. The framework exploits recent theoretical developments of GAMs and is implemented using efficient routines within freely available software. Real-data applications are illustrated through two reproducible examples in time series and survival analysis. © 2017 The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.

Using the General Algebraic Modeling System on Peregrine | High-Performance

Science.gov Websites

directory, type the following: module load gams cp /nopt/nrel/apps/gams/example/trnsport.gms . gams trnsport file. For example, if your model input uses LP procedure and you want to use Gurobi solver to solve it directory that you run GAMS. For example, for the Gurobi solver, its option file is "gurobi.opt"

Shape of the BMI-mortality association by cause of death, using generalized additive models: NHIS 1986-2006.

PubMed

Zajacova, Anna; Burgard, Sarah A

2012-03-01

Numerous studies have examined the association between body mass index (BMI) and mortality. The precise shape of their association, however, has not been established. We use nonparametric methods to determine the relationship between BMI and mortality. Data from the National Health Interview Survey-Linked Mortality Files 1986-2006 for adults aged 50 to 80 are analyzed using a Poisson approach to survival modeling within the generalized additive model (GAM) framework. The BMI-mortality association is more V shaped than U shaped, with the odds of dying rising steeply from the lowest risk point at BMIs of 23 to 26. The association varies considerably by time since interview and cause of death. For instance, the association has an inverted J shape for respiratory causes but is monotonically increasing for diabetes deaths. Our findings have implications for interpreting results from BMI-mortality studies and suggest caution in translating the findings into public health messages.

A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping

NASA Astrophysics Data System (ADS)

Naghibi, Seyed Amir; Moghaddam, Davood Davoodi; Kalantar, Bahareh; Pradhan, Biswajeet; Kisi, Ozgur

2017-05-01

In recent years, application of ensemble models has been increased tremendously in various types of natural hazard assessment such as landslides and floods. However, application of this kind of robust models in groundwater potential mapping is relatively new. This study applied four data mining algorithms including AdaBoost, Bagging, generalized additive model (GAM), and Naive Bayes (NB) models to map groundwater potential. Then, a novel frequency ratio data mining ensemble model (FREM) was introduced and evaluated. For this purpose, eleven groundwater conditioning factors (GCFs), including altitude, slope aspect, slope angle, plan curvature, stream power index (SPI), river density, distance from rivers, topographic wetness index (TWI), land use, normalized difference vegetation index (NDVI), and lithology were mapped. About 281 well locations with high potential were selected. Wells were randomly partitioned into two classes for training the models (70% or 197) and validating them (30% or 84). AdaBoost, Bagging, GAM, and NB algorithms were employed to get groundwater potential maps (GPMs). The GPMs were categorized into potential classes using natural break method of classification scheme. In the next stage, frequency ratio (FR) value was calculated for the output of the four aforementioned models and were summed, and finally a GPM was produced using FREM. For validating the models, area under receiver operating characteristics (ROC) curve was calculated. The ROC curve for prediction dataset was 94.8, 93.5, 92.6, 92.0, and 84.4% for FREM, Bagging, AdaBoost, GAM, and NB models, respectively. The results indicated that FREM had the best performance among all the models. The better performance of the FREM model could be related to reduction of over fitting and possible errors. Other models such as AdaBoost, Bagging, GAM, and NB also produced acceptable performance in groundwater modelling. The GPMs produced in the current study may facilitate groundwater exploitation

How many sightings to model rare marine species distributions

PubMed Central

Authier, Matthieu; Monestiez, Pascal; Ridoux, Vincent

2018-01-01

Despite large efforts, datasets with few sightings are often available for rare species of marine megafauna that typically live at low densities. This paucity of data makes modelling the habitat of these taxa particularly challenging. We tested the predictive performance of different types of species distribution models fitted to decreasing numbers of sightings. Generalised additive models (GAMs) with three different residual distributions and the presence only model MaxEnt were tested on two megafauna case studies differing in both the number of sightings and ecological niches. From a dolphin (277 sightings) and an auk (1,455 sightings) datasets, we simulated rarity with a sighting thinning protocol by random sampling (without replacement) of a decreasing fraction of sightings. Better prediction of the distribution of a rarely sighted species occupying a narrow habitat (auk dataset) was expected compared to the distribution of a rarely sighted species occupying a broad habitat (dolphin dataset). We used the original datasets to set up a baseline model and fitted additional models on fewer sightings but keeping effort constant. Model predictive performance was assessed with mean squared error and area under the curve. Predictions provided by the models fitted to the thinned-out datasets were better than a homogeneous spatial distribution down to a threshold of approximately 30 sightings for a GAM with a Tweedie distribution and approximately 130 sightings for the other models. Thinning the sighting data for the taxon with narrower habitats seemed to be less detrimental to model predictive performance than for the broader habitat taxon. To generate reliable habitat modelling predictions for rarely sighted marine predators, our results suggest (1) using GAMs with a Tweedie distribution with presence-absence data and (2) implementing, as a conservative empirical measure, at least 50 sightings in the models. PMID:29529097

General Algebraic Modeling System Tutorial | High-Performance Computing |

Science.gov Websites

power generation from two different fuels. The goal is to minimize the cost for one of the fuels while Here's a basic tutorial for modeling optimization problems with the General Algebraic Modeling System (GAMS). Overview The GAMS (General Algebraic Modeling System) package is essentially a compiler for a

Predictive modeling of deep-sea fish distribution in the Azores

NASA Astrophysics Data System (ADS)

Parra, Hugo E.; Pham, Christopher K.; Menezes, Gui M.; Rosa, Alexandra; Tempera, Fernando; Morato, Telmo

2017-11-01

Understanding the link between fish and their habitat is essential for an ecosystem approach to fisheries management. However, determining such relationship is challenging, especially for deep-sea species. In this study, we applied generalized additive models (GAMs) to relate presence-absence and relative abundance data of eight economically-important fish species to environmental variables (depth, slope, aspect, substrate type, bottom temperature, salinity and oxygen saturation). We combined 13 years of catch data collected from systematic longline surveys performed across the region. Overall, presence-absence GAMs performed better than abundance models and predictions made for the observed data successfully predicted the occurrence of the eight deep-sea fish species. Depth was the most influential predictor of all fish species occurrence and abundance distributions, whereas other factors were found to be significant for some species but did not show such a clear influence. Our results predicted that despite the extensive Azores EEZ, the habitats available for the studied deep-sea fish species are highly limited and patchy, restricted to seamounts slopes and summits, offshore banks and island slopes. Despite some identified limitations, our GAMs provide an improved knowledge of the spatial distribution of these commercially important fish species in the region.

The General Aggression Model.

PubMed

Allen, Johnie J; Anderson, Craig A; Bushman, Brad J

2018-02-01

The General Aggression Model (GAM) is a comprehensive, integrative, framework for understanding aggression. It considers the role of social, cognitive, personality, developmental, and biological factors on aggression. Proximate processes of GAM detail how person and situation factors influence cognitions, feelings, and arousal, which in turn affect appraisal and decision processes, which in turn influence aggressive or nonaggressive behavioral outcomes. Each cycle of the proximate processes serves as a learning trial that affects the development and accessibility of aggressive knowledge structures. Distal processes of GAM detail how biological and persistent environmental factors can influence personality through changes in knowledge structures. GAM has been applied to understand aggression in many contexts including media violence effects, domestic violence, intergroup violence, temperature effects, pain effects, and the effects of global climate change. Copyright © 2017 Elsevier Ltd. All rights reserved.

GamR, the LysR-Type Galactose Metabolism Regulator, Regulates hrp Gene Expression via Transcriptional Activation of Two Key hrp Regulators, HrpG and HrpX, in Xanthomonas oryzae pv. oryzae.

PubMed

Rashid, M Mamunur; Ikawa, Yumi; Tsuge, Seiji

2016-07-01

Xanthomonas oryzae pv. oryzae is the causal agent of bacterial leaf blight of rice. For the virulence of the bacterium, the hrp genes, encoding components of the type III secretion system, are indispensable. The expression of hrp genes is regulated by two key hrp regulators, HrpG and HrpX: HrpG regulates hrpX, and HrpX regulates other hrp genes. Several other regulators have been shown to be involved in the regulation of hrp genes. Here, we found that a LysR-type transcriptional regulator that we named GamR, encoded by XOO_2767 of X. oryzae pv. oryzae strain MAFF311018, positively regulated the transcription of both hrpG and hrpX, which are adjacent to each other but have opposite orientations, with an intergenic upstream region in common. In a gel electrophoresis mobility shift assay, GamR bound directly to the middle of the upstream region common to hrpG and hrpX The loss of either GamR or its binding sites decreased hrpG and hrpX expression. Also, GamR bound to the upstream region of either a galactose metabolism-related gene (XOO_2768) or a galactose metabolism-related operon (XOO_2768 to XOO_2771) located next to gamR itself and positively regulated the genes. The deletion of the regulator gene resulted in less bacterial growth in a synthetic medium with galactose as a sole sugar source. Interestingly, induction of the galactose metabolism-related gene was dependent on galactose, while that of the hrp regulator genes was galactose independent. Our results indicate that the LysR-type transcriptional regulator that regulates the galactose metabolism-related gene(s) also acts in positive regulation of two key hrp regulators and the following hrp genes in X. oryzae pv. oryzae. The expression of hrp genes encoding components of the type III secretion system is essential for the virulence of many plant-pathogenic bacteria, including Xanthomonas oryzae pv. oryzae. It is specifically induced during infection. Research has revealed that in this bacterium, hrp gene

"Urban geosites" as an alternative geotourism destination - evidence from Belgrade

NASA Astrophysics Data System (ADS)

Petrović, Marko D.; Lukić, Dobrila M.; Radovanović, Milan; Vujko, Aleksandra; Gajić, Tamara; Vuković, Darko

2017-10-01

The research aimed at testing the combination of GAM/M-GAM models (Geosite Assessment Model/Modified Geosite Assessment Model) on selected geoheritage sites (geosites) of great scientific significance and geotourism potential. Testing was done on eight sites in the City of Belgrade (Serbian capital), an area which has significant potential for geotourism development in typical urban conditions. For this purpose, an assessment scale was used to highlight differences and similarities between main and additional values of the observed geosites. The modification of the original GAM model is based on the inclusion of visitors' opinions regarding the importance of indicators in the assessment process. The assessment was done by using both GAM/M-GAM and its results were analyzed and compared afterwards. The analysis has successfully identified locations and features of geosites that require action for maintaining or increasing their overall value and function. Moreover, the principal aim of the paper was to analyze the relevance of each sub-indicator for the assessment process by introducing the importance factor in the modified model. The authors were able to point out those values of principal importance for geosite visitors, as well as to attach a different relevance to sub-indicators, which can influence the position of the geosites in the GAM/M-GAM matrices.

Linking livestock snow disaster mortality and environmental stressors in the Qinghai-Tibetan Plateau: Quantification based on generalized additive models.

PubMed

Li, Yijia; Ye, Tao; Liu, Weihang; Gao, Yu

2018-06-01

Livestock snow disaster occurs widely in Central-to-Eastern Asian temperate and alpine grasslands. The effects of snow disaster on livestock involve a complex interaction between precipitation, vegetation, livestock, and herder communities. Quantifying the relationship among livestock mortality, snow hazard intensity, and seasonal environmental stressors is of great importance for snow disaster early warning, risk assessments, and adaptation strategies. Using a wide-spatial extent, long-time series, and event-based livestock snow disaster dataset, this study quantified those relationships and established a quantitative model of livestock mortality for prediction purpose for the Qinghai-Tibet Plateau region. Estimations using generalized additive models (GAMs) were shown to accurately predict livestock mortality and mortality rate due to snow disaster, with adjusted-R 2 up to 0.794 and 0.666, respectively. These results showed that a longer snow disaster duration, lower temperatures during the disaster, and a drier summer with less vegetation all contribute significantly and non-linearly to higher mortality (rate), after controlling for elevation and socioeconomic conditions. These results can be readily applied to risk assessment and risk-based adaptation actions. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings.

PubMed

Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D

2017-09-11

Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.

Improved base excision repair inhibition and bacteriophage Mu Gam protein yields C:G-to-T:A base editors with higher efficiency and product purity

PubMed Central

Komor, Alexis C.; Zhao, Kevin T.; Packer, Michael S.; Gaudelli, Nicole M.; Waterbury, Amanda L.; Koblan, Luke W.; Kim, Y. Bill; Badran, Ahmed H.; Liu, David R.

2017-01-01

We recently developed base editing, the programmable conversion of target C:G base pairs to T:A without inducing double-stranded DNA breaks (DSBs) or requiring homology-directed repair using engineered fusions of Cas9 variants and cytidine deaminases. Over the past year, the third-generation base editor (BE3) and related technologies have been successfully used by many researchers in a wide range of organisms. The product distribution of base editing—the frequency with which the target C:G is converted to mixtures of undesired by-products, along with the desired T:A product—varies in a target site–dependent manner. We characterize determinants of base editing outcomes in human cells and establish that the formation of undesired products is dependent on uracil N-glycosylase (UNG) and is more likely to occur at target sites containing only a single C within the base editing activity window. We engineered CDA1-BE3 and AID-BE3, which use cytidine deaminase homologs that increase base editing efficiency for some sequences. On the basis of these observations, we engineered fourth-generation base editors (BE4 and SaBE4) that increase the efficiency of C:G to T:A base editing by approximately 50%, while halving the frequency of undesired by-products compared to BE3. Fusing BE3, BE4, SaBE3, or SaBE4 to Gam, a bacteriophage Mu protein that binds DSBs greatly reduces indel formation during base editing, in most cases to below 1.5%, and further improves product purity. BE4, SaBE4, BE4-Gam, and SaBE4-Gam represent the state of the art in C:G-to-T:A base editing, and we recommend their use in future efforts. PMID:28875174

Statistical Modeling of Fire Occurrence Using Data from the Tōhoku, Japan Earthquake and Tsunami.

PubMed

Anderson, Dana; Davidson, Rachel A; Himoto, Keisuke; Scawthorn, Charles

2016-02-01

In this article, we develop statistical models to predict the number and geographic distribution of fires caused by earthquake ground motion and tsunami inundation in Japan. Using new, uniquely large, and consistent data sets from the 2011 Tōhoku earthquake and tsunami, we fitted three types of models-generalized linear models (GLMs), generalized additive models (GAMs), and boosted regression trees (BRTs). This is the first time the latter two have been used in this application. A simple conceptual framework guided identification of candidate covariates. Models were then compared based on their out-of-sample predictive power, goodness of fit to the data, ease of implementation, and relative importance of the framework concepts. For the ground motion data set, we recommend a Poisson GAM; for the tsunami data set, a negative binomial (NB) GLM or NB GAM. The best models generate out-of-sample predictions of the total number of ignitions in the region within one or two. Prefecture-level prediction errors average approximately three. All models demonstrate predictive power far superior to four from the literature that were also tested. A nonlinear relationship is apparent between ignitions and ground motion, so for GLMs, which assume a linear response-covariate relationship, instrumental intensity was the preferred ground motion covariate because it captures part of that nonlinearity. Measures of commercial exposure were preferred over measures of residential exposure for both ground motion and tsunami ignition models. This may vary in other regions, but nevertheless highlights the value of testing alternative measures for each concept. Models with the best predictive power included two or three covariates. © 2015 Society for Risk Analysis.

Use of a Generalized Additive Model to Investigate Key Abiotic Factors Affecting Microcystin Cellular Quotas in Heavy Bloom Areas of Lake Taihu

PubMed Central

Tao, Min; Xie, Ping; Chen, Jun; Qin, Boqiang; Zhang, Dawen; Niu, Yuan; Zhang, Meng; Wang, Qing; Wu, Laiyan

2012-01-01

Lake Taihu is the third largest freshwater lake in China and is suffering from serious cyanobacterial blooms with the associated drinking water contamination by microcystin (MC) for millions of citizens. So far, most studies on MCs have been limited to two small bays, while systematic research on the whole lake is lacking. To explain the variations in MC concentrations during cyanobacterial bloom, a large-scale survey at 30 sites across the lake was conducted monthly in 2008. The health risks of MC exposure were high, especially in the northern area. Both Microcystis abundance and MC cellular quotas presented positive correlations with MC concentration in the bloom seasons, suggesting that the toxic risks during Microcystis proliferations were affected by variations in both Microcystis density and MC production per Microcystis cell. Use of a powerful predictive modeling tool named generalized additive model (GAM) helped visualize significant effects of abiotic factors related to carbon fixation and proliferation of Microcystis (conductivity, dissolved inorganic carbon (DIC), water temperature and pH) on MC cellular quotas from recruitment period of Microcystis to the bloom seasons, suggesting the possible use of these factors, in addition to Microcystis abundance, as warning signs to predict toxic events in the future. The interesting relationship between macrophytes and MC cellular quotas of Microcystis (i.e., high MC cellular quotas in the presence of macrophytes) needs further investigation. PMID:22384128

Applying multibeam sonar and mathematical modeling for mapping seabed substrate and biota of offshore shallows

NASA Astrophysics Data System (ADS)

Herkül, Kristjan; Peterson, Anneliis; Paekivi, Sander

2017-06-01

Both basic science and marine spatial planning are in a need of high resolution spatially continuous data on seabed habitats and biota. As conventional point-wise sampling is unable to cover large spatial extents in high detail, it must be supplemented with remote sensing and modeling in order to fulfill the scientific and management needs. The combined use of in situ sampling, sonar scanning, and mathematical modeling is becoming the main method for mapping both abiotic and biotic seabed features. Further development and testing of the methods in varying locations and environmental settings is essential for moving towards unified and generally accepted methodology. To fill the relevant research gap in the Baltic Sea, we used multibeam sonar and mathematical modeling methods - generalized additive models (GAM) and random forest (RF) - together with underwater video to map seabed substrate and epibenthos of offshore shallows. In addition to testing the general applicability of the proposed complex of techniques, the predictive power of different sonar-based variables and modeling algorithms were tested. Mean depth, followed by mean backscatter, were the most influential variables in most of the models. Generally, mean values of sonar-based variables had higher predictive power than their standard deviations. The predictive accuracy of RF was higher than that of GAM. To conclude, we found the method to be feasible and with predictive accuracy similar to previous studies of sonar-based mapping.

Seasonal prediction of winter haze days in the north central North China Plain

NASA Astrophysics Data System (ADS)

Yin, Zhicong; Wang, Huijun

2016-11-01

Recently, the winter (December-February) haze pollution over the north central North China Plain (NCP) has become severe. By treating the year-to-year increment as the predictand, two new statistical schemes were established using the multiple linear regression (MLR) and the generalized additive model (GAM). By analyzing the associated increment of atmospheric circulation, seven leading predictors were selected to predict the upcoming winter haze days over the NCP (WHDNCP). After cross validation, the root mean square error and explained variance of the MLR (GAM) prediction model was 3.39 (3.38) and 53 % (54 %), respectively. For the final predicted WHDNCP, both of these models could capture the interannual and interdecadal trends and the extremums successfully. Independent prediction tests for 2014 and 2015 also confirmed the good predictive skill of the new schemes. The predicted bias of the MLR (GAM) prediction model in 2014 and 2015 was 0.09 (-0.07) and -3.33 (-1.01), respectively. Compared to the MLR model, the GAM model had a higher predictive skill in reproducing the rapid and continuous increase of WHDNCP after 2010.

A climatological model of North Indian Ocean tropical cyclone genesis, tracks and landfall

NASA Astrophysics Data System (ADS)

Wahiduzzaman, Mohammad; Oliver, Eric C. J.; Wotherspoon, Simon J.; Holbrook, Neil J.

2017-10-01

Extensive damage and loss of life can be caused by tropical cyclones (TCs) that make landfall. Modelling of TC landfall probability is beneficial to insurance/re-insurance companies, decision makers, government policy and planning, and residents in coastal areas. In this study, we develop a climatological model of tropical cyclone genesis, tracks and landfall for North Indian Ocean (NIO) rim countries based on kernel density estimation, a generalised additive model (GAM) including an Euler integration step, and landfall detection using a country mask approach. Using a 35-year record (1979-2013) of tropical cyclone track observations from the Joint Typhoon Warning Centre (part of the International Best Track Archive Climate Stewardship Version 6), the GAM is fitted to the observed cyclone track velocities as a smooth function of location in each season. The distribution of cyclone genesis points is approximated by kernel density estimation. The model simulated TCs are randomly selected from the fitted kernel (TC genesis), and the cyclone paths (TC tracks), represented by the GAM together with the application of stochastic innovations at each step, are simulated to generate a suite of NIO rim landfall statistics. Three hindcast validation methods are applied to evaluate the integrity of the model. First, leave-one-out cross validation is applied whereby the country of landfall is determined by the majority vote (considering the location by only highest percentage of landfall) from the simulated tracks. Second, the probability distribution of simulated landfall is evaluated against the observed landfall. Third, the distances between the point of observed landfall and simulated landfall are compared and quantified. Overall, the model shows very good cross-validated hindcast skill of modelled landfalling cyclones against observations in each of the NIO tropical cyclone seasons and for most NIO rim countries, with only a relatively small difference in the percentage of

Modeling acute respiratory illness during the 2007 San Diego wildland fires using a coupled emissions-transport system and generalized additive modeling.

PubMed

Thelen, Brian; French, Nancy H F; Koziol, Benjamin W; Billmire, Michael; Owen, Robert Chris; Johnson, Jeffrey; Ginsberg, Michele; Loboda, Tatiana; Wu, Shiliang

2013-11-05

A study of the impacts on respiratory health of the 2007 wildland fires in and around San Diego County, California is presented. This study helps to address the impact of fire emissions on human health by modeling the exposure potential of proximate populations to atmospheric particulate matter (PM) from vegetation fires. Currently, there is no standard methodology to model and forecast the potential respiratory health effects of PM plumes from wildland fires, and in part this is due to a lack of methodology for rigorously relating the two. The contribution in this research specifically targets that absence by modeling explicitly the emission, transmission, and distribution of PM following a wildland fire in both space and time. Coupled empirical and deterministic models describing particulate matter (PM) emissions and atmospheric dispersion were linked to spatially explicit syndromic surveillance health data records collected through the San Diego Aberration Detection and Incident Characterization (SDADIC) system using a Generalized Additive Modeling (GAM) statistical approach. Two levels of geographic aggregation were modeled, a county-wide regional level and division of the county into six sub regions. Selected health syndromes within SDADIC from 16 emergency departments within San Diego County relevant for respiratory health were identified for inclusion in the model. The model captured the variability in emergency department visits due to several factors by including nine ancillary variables in addition to wildfire PM concentration. The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case). The sub-regional analyses show that demographic variables also influence respiratory health outcomes from smoke. The model developed in this study allows a

Modeling acute respiratory illness during the 2007 San Diego wildland fires using a coupled emissions-transport system and generalized additive modeling

PubMed Central

2013-01-01

Background A study of the impacts on respiratory health of the 2007 wildland fires in and around San Diego County, California is presented. This study helps to address the impact of fire emissions on human health by modeling the exposure potential of proximate populations to atmospheric particulate matter (PM) from vegetation fires. Currently, there is no standard methodology to model and forecast the potential respiratory health effects of PM plumes from wildland fires, and in part this is due to a lack of methodology for rigorously relating the two. The contribution in this research specifically targets that absence by modeling explicitly the emission, transmission, and distribution of PM following a wildland fire in both space and time. Methods Coupled empirical and deterministic models describing particulate matter (PM) emissions and atmospheric dispersion were linked to spatially explicit syndromic surveillance health data records collected through the San Diego Aberration Detection and Incident Characterization (SDADIC) system using a Generalized Additive Modeling (GAM) statistical approach. Two levels of geographic aggregation were modeled, a county-wide regional level and division of the county into six sub regions. Selected health syndromes within SDADIC from 16 emergency departments within San Diego County relevant for respiratory health were identified for inclusion in the model. Results The model captured the variability in emergency department visits due to several factors by including nine ancillary variables in addition to wildfire PM concentration. The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case). The sub-regional analyses show that demographic variables also influence respiratory health outcomes from smoke. Conclusions The

Evaluation of Intercomparisons of Four Different Types of Model Simulating TWP-ICE

NASA Technical Reports Server (NTRS)

Petch, Jon; Hill, Adrian; Davies, Laura; Fridlind, Ann; Jakob, Christian; Lin, Yanluan; Xie, Shaoecheng; Zhu, Ping

2013-01-01

Four model intercomparisons were run and evaluated using the TWP-ICE field campaign, each involving different types of atmospheric model. Here we highlight what can be learnt from having single-column model (SCM), cloud-resolving model (CRM), global atmosphere model (GAM) and limited-area model (LAM) intercomparisons all based around the same field campaign. We also make recommendations for anyone planning further large multi-model intercomparisons to ensure they are of maximum value to the model development community. CRMs tended to match observations better than other model types, although there were exceptions such as outgoing long-wave radiation. All SCMs grew large temperature and moisture biases and performed worse than other model types for many diagnostics. The GAMs produced a delayed and significantly reduced peak in domain-average rain rate when compared to the observations. While it was shown that this was in part due to the analysis used to drive these models, the LAMs were also driven by this analysis and did not have the problem to the same extent. Based on differences between the models with parametrized convection (SCMs and GAMs) and those without (CRMs and LAMs), we speculate that that having explicit convection helps to constrain liquid water whereas the ice contents are controlled more by the representation of the microphysics.

Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape

PubMed Central

Coupé, Christophe

2018-01-01

As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for ‘difficult’ variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS

Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape.

PubMed

Coupé, Christophe

2018-01-01

As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for 'difficult' variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we

Comparative polyphenolic content and antioxidant activities of Genista tinctoria L. and Genistella sagittalis (L.) Gams (Fabaceae).

PubMed

Hanganu, Daniela; Olah, Neli Kinga; Benedec, Daniela; Mocan, Andrei; Crisan, Gianina; Vlase, Laurian; Popica, Iulia; Oniga, Ilioara

2016-01-01

The aim of this study was focused on the polyphenolic composition and antioxidant capacity of Genista tinctoria L. and Genistella sagittalis (L.) Gams. A qualitative and quantitative characterization of the main phenolic compounds from the extracts were carried out using a HPLC-MS method. The total polyphenolic and flavonoid content was spectrophotometrically determined. The antioxidant activity towards various radicals generated in different systems was evaluated usingDPPH bleaching method, Trolox equivalent antioxidant capacity assay (TEAC) and Oxygen radical absorbance capacity (ORAC), and all indicated that G. tinctoria extract was more antioxidant than G. sagittalis extract.That was in good agreement with the total polyphenolic and flavonoidic content.Chlorogenic acid, p-coumaric acid, isoquercitrin and apigenin were identified in bothspecies. Caffeic acid, ferulic acid, hyperoside, rutin, quercitrin and luteolin were found only in G. tinctoria, while quercetin was determined in G. sagittalis.

A statistical model for monitoring shell disease in inshore lobster fisheries: A case study in Long Island Sound

PubMed Central

Chen, Yong

2017-01-01

The expansion of shell disease is an emerging threat to the inshore lobster fisheries in the northeastern United States. The development of models to improve the efficiency and precision of existing monitoring programs is advocated as an important step in mitigating its harmful effects. The objective of this study is to construct a statistical model that could enhance the existing monitoring effort through (1) identification of potential disease-associated abiotic and biotic factors, and (2) estimation of spatial variation in disease prevalence in the lobster fishery. A delta-generalized additive modeling (GAM) approach was applied using bottom trawl survey data collected from 2001–2013 in Long Island Sound, a tidal estuary between New York and Connecticut states. Spatial distribution of shell disease prevalence was found to be strongly influenced by the interactive effects of latitude and longitude, possibly indicative of a geographic origin of shell disease. Bottom temperature, bottom salinity, and depth were also important factors affecting the spatial variability in shell disease prevalence. The delta-GAM projected high disease prevalence in non-surveyed locations. Additionally, a potential spatial discrepancy was found between modeled disease hotspots and survey-based gravity centers of disease prevalence. This study provides a modeling framework to enhance research, monitoring and management of emerging and continuing marine disease threats. PMID:28196150

Spatio-temporal modelling of dengue fever incidence in Malaysia

NASA Astrophysics Data System (ADS)

Che-Him, Norziha; Ghazali Kamardan, M.; Saifullah Rusiman, Mohd; Sufahani, Suliadi; Mohamad, Mahathir; @ Kamariah Kamaruddin, Nafisah

2018-04-01

Previous studies reported significant relationship between dengue incidence rate (DIR) and both climatic and non-climatic factors. Therefore, this study proposes a generalised additive model (GAM) framework for dengue risk in Malaysia by using both climatic and non-climatic factors. The data used is monthly DIR for 12 states of Malaysia from 2001 to 2009. In this study, we considered an annual trend, seasonal effects, population, population density and lagged DIR, rainfall, temperature, number of rainy days and El Niño-Southern Oscillation (ENSO). The population density is found to be positively related to monthly DIR. There are generally weak relationships between monthly DIR and climate variables. A negative binomial GAM shows that there are statistically significant relationships between DIR with climatic and non-climatic factors. These include mean rainfall and temperature, the number of rainy days, sea surface temperature and the interaction between mean temperature (lag 1 month) and sea surface temperature (lag 6 months). These also apply to DIR (lag 3 months) and population density.

Model Developments for Development of Improved Emissions Scenarios: Developing Purchasing-Power Parity Models, Analyzing Uncertainty, and Developing Data Sets for Gridded Integrated Assessment Models

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

Yang, Zili; Nordhaus, William

2009-03-19

In the duration of this project, we finished the main tasks set up in the initial proposal. These tasks include: setting up the basic platform in GAMS language for the new RICE 2007 model; testing various model structure of RICE 2007; incorporating PPP data set in the new RICE model; developing gridded data set for IA modeling.

Modeling Monthly Spatial Distribution of Ommastrephes bartramii CPUE in the Northwest Pacific and Its Spatially Nonstationary Relationships with the Marine Environment

NASA Astrophysics Data System (ADS)

Feng, Yongjiu; Liu, Yang; Chen, Xinjun

2018-06-01

There are substantial spatial variations in the relationships between catch-per-unit-effort (CPUE) and oceanographic conditions with respect to pelagic species. This study examines the monthly spatiotemporal distribution of CPUE of the neon flying squid, Ommastrephes bartramii, in the Northwest Pacific from July to November during 2004-2013, and analyzes the relationships with oceanographic conditions using a generalized additive model (GAM) and geographically weighted regression (GWR) model. The results show that most of the squids were harvested in waters with sea surface temperature (SST) between 7.6 and 24.6°C, chlorophyll- a (Chl- a) concentration below 1.0 mg m-3, sea surface salinity (SSS) between 32.7 and 34.6, and sea surface height (SSH) between -12.8 and 28.4 cm. The monthly spatial distribution patterns of O. bartramii predicted using GAM and GWR models are similar to observed patterns for all months. There are notable variations in the local coefficients of GWR, indicating the presence of spatial non-stationarity in the relationship between O. bartramii CPUE and oceanographic conditions. The statistical results show that there were nearly equal positive and negative coefficients for Chl- a, more positive than negative coefficients for SST, and more negative than positive coefficients for SSS and SSH. The overall accuracies of the hot spots predicted by GWR exceed 60% (except for October), indicating a good performance of this model and its improvement over GAM. Our study provides a better understanding of the ecological dynamics of O. bartramii CPUE and makes it possible to use GWR to study the spatially nonstationary characteristics of other pelagic species.

Examining spatiotemporal distribution and CPUE-environment relationships for the jumbo flying squid Dosidicus gigas offshore Peru based on spatial autoregressive model

NASA Astrophysics Data System (ADS)

Feng, Yongjiu; Chen, Xinjun; Liu, Yang

2017-09-01

The spatiotemporal distribution and relationship between nominal catch-per-unit-effort (CPUE) and environment for the jumbo flying squid (Dosidicus gigas) were examined in offshore Peruvian waters during 2009-2013. Three typical oceanographic factors affecting the squid habitat were investigated in this research, including sea surface temperature (SST), sea surface salinity (SSS) and sea surface height (SSH). We studied the CPUE-environment relationships for D. gigas using a spatially-lagged version of spatial autoregressive (SAR) model and a generalized additive model (GAM), with the latter for auxiliary and comparative purposes. The annual fishery centroids were distributed broadly in an area bounded by 79.5°-82.7°W and 11.9°-17.1°S, while the monthly fishery centroids were spatially close and lay in a smaller area bounded by 81.0°-81.2°W and 14.3°-15.4°S. Our results show that the preferred environmental ranges for D. gigas offshore Peru were 20.9°-21.9°C for SST, 35.16-35.32 for SSS and 27.2-31.5 cm for SSH in the areas bounded by 78°-80°W/82-84°W and 15°-18°S. Monthly spatial distributions during October to December were predicted using the calibrated GAM and SAR models and general similarities were found between the observed and predicted patterns for the nominal CPUE of D. gigas. The overall accuracies for the hotspots generated by the SAR model were much higher than those produced by the GAM model for all three months. Our results contribute to a better understanding of the spatiotemporal distributions of D. gigas offshore Peru, and offer a new SAR modeling method for advancing fishery science.

Plankton Biomass Models Based on GIS and Remote Sensing Technique for Predicting Marine Megafauna Hotspots in the Solor Waters

NASA Astrophysics Data System (ADS)

Putra, MIH; Lewis, SA; Kurniasih, EM; Prabuning, D.; Faiqoh, E.

2016-11-01

Geographic information system and remote sensing techniques can be used to assist with distribution modelling; a useful tool that helps with strategic design and management plans for MPAs. This study built a pilot model of plankton biomass and distribution in the waters off Solor and Lembata, and is the first study to identify marine megafauna foraging areas in the region. Forty-three samples of zooplankton were collected every 4 km according to the range time and station of aqua MODIS. Generalized additive model (GAM) we used to modelling zooplankton biomass response from environmental properties.Thirty one samples were used to build a model of inverse distance weighting (IDW) (cell size 0.01°) and 12 samples were used as a control to verify the models accuracy. Furthermore, Getis-Ord Gi was used to identify the significance of the hotspot and cold-spot for foraging area. The GAM models was explain 88.1% response of zooplankton biomass and percent to full moon, phytopankton biomassbeing strong predictors. The sampling design was essential in order to build highly accurate models. Our models 96% accurate for phytoplankton and 88% accurate for zooplankton. The foraging behaviour was significantly related to plankton biomass hotspots, which were two times higher compared to plankton cold-spots. In addition, extremely steep slopes of the Lamakera strait support strong upwelling with highly productive waters that affect the presence of marine megafauna. This study detects that the Lamakera strait provides the planktonic requirements for marine megafauna foraging, helping to explain why this region supports such high diversity and abundance of marine megafauna.

Topsoil organic carbon content of Europe, a new map based on a generalised additive model

NASA Astrophysics Data System (ADS)

de Brogniez, Delphine; Ballabio, Cristiano; Stevens, Antoine; Jones, Robert J. A.; Montanarella, Luca; van Wesemael, Bas

2014-05-01

There is an increasing demand for up-to-date spatially continuous organic carbon (OC) data for global environment and climatic modeling. Whilst the current map of topsoil organic carbon content for Europe (Jones et al., 2005) was produced by applying expert-knowledge based pedo-transfer rules on large soil mapping units, the aim of this study was to replace it by applying digital soil mapping techniques on the first European harmonised geo-referenced topsoil (0-20 cm) database, which arises from the LUCAS (land use/cover area frame statistical survey) survey. A generalized additive model (GAM) was calibrated on 85% of the dataset (ca. 17 000 soil samples) and a backward stepwise approach selected slope, land cover, temperature, net primary productivity, latitude and longitude as environmental covariates (500 m resolution). The validation of the model (applied on 15% of the dataset), gave an R2 of 0.27. We observed that most organic soils were under-predicted by the model and that soils of Scandinavia were also poorly predicted. The model showed an RMSE of 42 g kg-1 for mineral soils and of 287 g kg-1 for organic soils. The map of predicted OC content showed the lowest values in Mediterranean countries and in croplands across Europe, whereas highest OC content were predicted in wetlands, woodlands and in mountainous areas. The map of standard error of the OC model predictions showed high values in northern latitudes, wetlands, moors and heathlands, whereas low uncertainty was mostly found in croplands. A comparison of our results with the map of Jones et al. (2005) showed a general agreement on the prediction of mineral soils' OC content, most probably because the models use some common covariates, namely land cover and temperature. Our model however failed to predict values of OC content greater than 200 g kg-1, which we explain by the imposed unimodal distribution of our model, whose mean is tilted towards the majority of soils, which are mineral. Finally, average

Functional Additive Mixed Models

PubMed Central

Scheipl, Fabian; Staicu, Ana-Maria; Greven, Sonja

2014-01-01

We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects with flexible correlation structures for, e.g., spatial, temporal, or longitudinal functional data. Additionally, our framework includes linear and nonlinear effects of functional and scalar covariates that may vary smoothly over the index of the functional response. It accommodates densely or sparsely observed functional responses and predictors which may be observed with additional error and includes both spline-based and functional principal component-based terms. Estimation and inference in this framework is based on standard additive mixed models, allowing us to take advantage of established methods and robust, flexible algorithms. We provide easy-to-use open source software in the pffr() function for the R-package refund. Simulations show that the proposed method recovers relevant effects reliably, handles small sample sizes well and also scales to larger data sets. Applications with spatially and longitudinally observed functional data demonstrate the flexibility in modeling and interpretability of results of our approach. PMID:26347592

Functional Additive Mixed Models.

PubMed

Scheipl, Fabian; Staicu, Ana-Maria; Greven, Sonja

2015-04-01

We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects with flexible correlation structures for, e.g., spatial, temporal, or longitudinal functional data. Additionally, our framework includes linear and nonlinear effects of functional and scalar covariates that may vary smoothly over the index of the functional response. It accommodates densely or sparsely observed functional responses and predictors which may be observed with additional error and includes both spline-based and functional principal component-based terms. Estimation and inference in this framework is based on standard additive mixed models, allowing us to take advantage of established methods and robust, flexible algorithms. We provide easy-to-use open source software in the pffr() function for the R-package refund. Simulations show that the proposed method recovers relevant effects reliably, handles small sample sizes well and also scales to larger data sets. Applications with spatially and longitudinally observed functional data demonstrate the flexibility in modeling and interpretability of results of our approach.

The relation between air pollution and respiratory deaths in Tehran, Iran- using generalized additive models.

PubMed

Dehghan, Azizallah; Khanjani, Narges; Bahrampour, Abbas; Goudarzi, Gholamreza; Yunesian, Masoud

2018-03-20

Some epidemiological evidence has shown a relation between ambient air pollution and adverse health outcomes. The aim of this study was to investigate the effect of air pollution on mortality from respiratory diseases in Tehran, Iran. In this ecological study, air pollution data was inquired from the Tehran Province Environmental Protection Agency and the Tehran Air Quality Control Company. Meteorological data was collected from the Tehran Meteorology Organization and mortality data from the Tehran Cemetery Mortality Registration. Generalized Additive Models (GAM) was used for data analysis with different lags, up to 15 days. A 10-unit increase in all pollutants except CO (1-unit) was used to compute the Relative Risk of deaths. During 2005 until 2014, 37,967 respiratory deaths occurred in Tehran in which 21,913 (57.7%) were male. The strongest relationship between NO 2 and PM 10 and respiratory death was seen on the same day (lag 0), and was respectively (RR = 1.04, 95% CI: 1.02-1.07) and (RR = 1.03, 95% CI: 1.02-1.04). O 3 and PM 2.5 had the strongest relationship with respiratory deaths on lag 2 and 1 respectively, and the RR was equal to 1.03, 95% CI: 1.01-1.05 and 1.06, 95% CI: 1.02-1.10 respectively. NO 2 , O 3 , PM 10 and PM 2.5 also showed significant relations with respiratory deaths in the older age groups. The findings of this study showed that O 3 , NO 2 , PM 10 and PM 2.5 air pollutants were related to respiratory deaths in Tehran. Reducing ambient air pollution can save lives in Tehran.

Functional Generalized Additive Models.

PubMed

McLean, Mathew W; Hooker, Giles; Staicu, Ana-Maria; Scheipl, Fabian; Ruppert, David

2014-01-01

We introduce the functional generalized additive model (FGAM), a novel regression model for association studies between a scalar response and a functional predictor. We model the link-transformed mean response as the integral with respect to t of F { X ( t ), t } where F (·,·) is an unknown regression function and X ( t ) is a functional covariate. Rather than having an additive model in a finite number of principal components as in Müller and Yao (2008), our model incorporates the functional predictor directly and thus our model can be viewed as the natural functional extension of generalized additive models. We estimate F (·,·) using tensor-product B-splines with roughness penalties. A pointwise quantile transformation of the functional predictor is also considered to ensure each tensor-product B-spline has observed data on its support. The methods are evaluated using simulated data and their predictive performance is compared with other competing scalar-on-function regression alternatives. We illustrate the usefulness of our approach through an application to brain tractography, where X ( t ) is a signal from diffusion tensor imaging at position, t , along a tract in the brain. In one example, the response is disease-status (case or control) and in a second example, it is the score on a cognitive test. R code for performing the simulations and fitting the FGAM can be found in supplemental materials available online.

Competing pathways in drug metabolism. I. Effect of input concentration on the conjugation of gentisamide in the once-through in situ perfused rat liver preparation

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

Morris, M.E.; Yuen, V.; Tang, B.K.

1988-05-01

Sulfation and glucuronidation are two parallel pathways for the metabolism of phenolic substrates. Gentisamide (GAM) was used as a model compound to examine the effects of parallel competing pathways on drug disappearance and metabolite formation in the once-through perfused rat liver preparation. GAM was found to form one glucuronide (GAM-5G) and two sulfate (GAM-2S and GAM-5S) conjugates. These GAM conjugates were biosynthesized in recirculating rat liver preparations, and were isolated by preparative high-performance liquid chromatography. Specific incorporation of 35S-sodium sulfate and (14C)glucose into GAM sulfate and glucuronide conjugates revealed corresponding elution patterns as labeled GAM metabolites. Their identities were characterizedmore » by enzymatic and acid hydrolyses and by NMR spectroscopy. Gentisamide-5-sulfate (GAM-5S) and gentisamide-5-glucuronide (GAM-5G) are major metabolites, and gentisamide-2-sulfate (GAM-2S) is a minor metabolite. Single-pass rat liver perfusions were used to examine the effect of stepwise increases/decreases of input GAM concentration (CIn) on the extraction ratio (E) of GAM and formation of metabolites. The E of GAM remained constant (about 0.89) at input concentrations from 0.9 to 120 microM and decreased at CIn greater than 120 microM. Metabolite patterns, however, changed with GAM CIn, even when E was constant at CIn up to 120 microM. GAM-5S was present as the major metabolite of GAM at all GAM CInS in most liver preparations but the proportions of GAM-5S and GAM-2S decreased at increasing CIn; the proportion of GAM-5G, a minor metabolite at low CIn, increased with increasing CIn. Biliary excretion rates at steady state accounted for 5.3 +/- 2.7% (mean +/- S.D.) of the input rate: GAM-5G was the predominant metabolite found.« less

[The use of interviews in participative intervention and research: the GAM tool as a collective interview].

PubMed

Sade, Christian; de Barros, Leticia Maria Renault; Melo, Jorge José Maciel; Passos, Eduardo

2013-10-01

This paper seeks to assess a way of conducting interviews in line with the ideology of Brazilian Psychiatric Reform. In the methodology of participative intervention and research in mental health, the interview is less a data collection than a data harvesting procedure. It is designed to apply the principles of psychosocial care, autonomy as the basis for treatment, the predominance of the users and of their social networks and civic participation. Inspired by the Explicitation Interview technique, the contention is that the handling of the interview presupposes an open attitude able to promote and embrace different viewpoints. This attitude makes the interview a collective experience of sharing and belonging, allowing participants to reposition themselves subjectively in treatment with the emergence of groupality. As an example of using the interview as a methodological tool in mental health research, we examine research into adaptation of the tool of Autonomous Medication Management (GAM). It is an interventionist approach guided by principles that foster autonomy and the protagonist status of users of psychotropic medication, their quality of life, their rights and recognition of the multiple significances of medication, understood here as a collective interview technique.

INTERANNUAL VARIATION IN METEOROLOGICALLY ADJUSTED OZONE LEVELS IN THE EASTERN UNITED STATES: A COMPARISON OF TWO APPROACHED

EPA Science Inventory

Assessing the influence of abatement efforts and other human activities on ozone levels is complicated by the atmosphere's changeable nature. Two statistical methods, the dynamic linear model(DLM) and the generalized additive model (GAM), are used to estimate ozone trends in the...

Computational Process Modeling for Additive Manufacturing

NASA Technical Reports Server (NTRS)

Bagg, Stacey; Zhang, Wei

2014-01-01

Computational Process and Material Modeling of Powder Bed additive manufacturing of IN 718. Optimize material build parameters with reduced time and cost through modeling. Increase understanding of build properties. Increase reliability of builds. Decrease time to adoption of process for critical hardware. Potential to decrease post-build heat treatments. Conduct single-track and coupon builds at various build parameters. Record build parameter information and QM Meltpool data. Refine Applied Optimization powder bed AM process model using data. Report thermal modeling results. Conduct metallography of build samples. Calibrate STK models using metallography findings. Run STK models using AO thermal profiles and report STK modeling results. Validate modeling with additional build. Photodiode Intensity measurements highly linear with power input. Melt Pool Intensity highly correlated to Melt Pool Size. Melt Pool size and intensity increase with power. Applied Optimization will use data to develop powder bed additive manufacturing process model.

Strategic Mobility 21: Modeling, Simulation, and Analysis

DTIC Science & Technology

2010-04-14

using AnyLogic , which is a Java programmed, multi-method simulation modeling tool developed by XJ Technologies. The last section examines the academic... simulation model from an Arena platform to an AnyLogic based Web Service. MATLAB is useful for small problems with few nodes, but GAMS/CPLEX is better... Transportation Modeling Studio TM . The SCASN modeling and simulation program was designed to be generic in nature to allow for use by both commercial and

Modelling the ecological consequences of whole tree harvest for bioenergy production

NASA Astrophysics Data System (ADS)

Skår, Silje; Lange, Holger; Sogn, Trine

2013-04-01

There is an increasing demand for energy from biomass as a substitute to fossil fuels worldwide, and the Norwegian government plans to double the production of bioenergy to 9% of the national energy production or to 28 TWh per year by 2020. A large part of this increase may come from forests, which have a great potential with respect to biomass supply as forest growth increasingly has exceeded harvest in the last decades. One feasible option is the utilization of forest residues (needles, twigs and branches) in addition to stems, known as Whole Tree Harvest (WTH). As opposed to WTH, the residues are traditionally left in the forest with Conventional Timber Harvesting (CH). However, the residues contain a large share of the treés nutrients, indicating that WTH may possibly alter the supply of nutrients and organic matter to the soil and the forest ecosystem. This may potentially lead to reduced tree growth. Other implications can be nutrient imbalance, loss of carbon from the soil and changes in species composition and diversity. This study aims to identify key factors and appropriate strategies for ecologically sustainable WTH in Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) forest stands in Norway. We focus on identifying key factors driving soil organic matter, nutrients, biomass, biodiversity etc. Simulations of the effect on the carbon and nitrogen budget with the two harvesting methods will also be conducted. Data from field trials and long-term manipulation experiments are used to obtain a first overview of key variables. The relationships between the variables are hitherto unknown, but it is by no means obvious that they could be assumed as linear; thus, an ordinary multiple linear regression approach is expected to be insufficient. Here we apply two advanced and highly flexible modelling frameworks which hardly have been used in the context of tree growth, nutrient balances and biomass removal so far: Generalized Additive Models (GAMs) and

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

PubMed Central

Song, Yong-Ze; Yang, Hong-Lei; Peng, Jun-Huan; Song, Yi-Rong; Sun, Qian; Li, Yuan

2015-01-01

Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi’an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5. PMID:26540446

An Interactive Tool For Semi-automated Statistical Prediction Using Earth Observations and Models

NASA Astrophysics Data System (ADS)

Zaitchik, B. F.; Berhane, F.; Tadesse, T.

2015-12-01

We developed a semi-automated statistical prediction tool applicable to concurrent analysis or seasonal prediction of any time series variable in any geographic location. The tool was developed using Shiny, JavaScript, HTML and CSS. A user can extract a predictand by drawing a polygon over a region of interest on the provided user interface (global map). The user can select the Climatic Research Unit (CRU) precipitation or Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as predictand. They can also upload their own predictand time series. Predictors can be extracted from sea surface temperature, sea level pressure, winds at different pressure levels, air temperature at various pressure levels, and geopotential height at different pressure levels. By default, reanalysis fields are applied as predictors, but the user can also upload their own predictors, including a wide range of compatible satellite-derived datasets. The package generates correlations of the variables selected with the predictand. The user also has the option to generate composites of the variables based on the predictand. Next, the user can extract predictors by drawing polygons over the regions that show strong correlations (composites). Then, the user can select some or all of the statistical prediction models provided. Provided models include Linear Regression models (GLM, SGLM), Tree-based models (bagging, random forest, boosting), Artificial Neural Network, and other non-linear models such as Generalized Additive Model (GAM) and Multivariate Adaptive Regression Splines (MARS). Finally, the user can download the analysis steps they used, such as the region they selected, the time period they specified, the predictand and predictors they chose and preprocessing options they used, and the model results in PDF or HTML format. Key words: Semi-automated prediction, Shiny, R, GLM, ANN, RF, GAM, MARS

VARIABLE SELECTION IN NONPARAMETRIC ADDITIVE MODELS

PubMed Central

Huang, Jian; Horowitz, Joel L.; Wei, Fengrong

2010-01-01

We consider a nonparametric additive model of a conditional mean function in which the number of variables and additive components may be larger than the sample size but the number of nonzero additive components is “small” relative to the sample size. The statistical problem is to determine which additive components are nonzero. The additive components are approximated by truncated series expansions with B-spline bases. With this approximation, the problem of component selection becomes that of selecting the groups of coefficients in the expansion. We apply the adaptive group Lasso to select nonzero components, using the group Lasso to obtain an initial estimator and reduce the dimension of the problem. We give conditions under which the group Lasso selects a model whose number of components is comparable with the underlying model, and the adaptive group Lasso selects the nonzero components correctly with probability approaching one as the sample size increases and achieves the optimal rate of convergence. The results of Monte Carlo experiments show that the adaptive group Lasso procedure works well with samples of moderate size. A data example is used to illustrate the application of the proposed method. PMID:21127739

Time Scale Effects in Acute Association between Air-Pollution and Mortality

EPA Science Inventory

We used wavelet analysis and generalized additive models (GAM) to study timescale effects in the acute association between mortality and air-pollution. Daily averages of measured NO₂ concentrations in the metropolitan Paris area are used as indicators of human exposure...

Further advances in predicting species distributions

Treesearch

Gretchen G. Moisen; Thomas C. Edwards; Patrick E. Osborne

2006-01-01

In 2001, a workshop focused on the use of generalized linear models (GLM: McCullagh and Nelder, 1989) and generalized additive models (GAM: Hastie and Tibshirani, 1986, 1990) for predicting species distributions was held in Riederalp, Switzerland. This topic led to the publication of special issues in Ecological Modelling (Guisan et al., 2002) and Biodiversity and...

Modelling increased landslide susceptibility near highways in the Andes of southern Ecuador

NASA Astrophysics Data System (ADS)

Brenning, Alexander; Muenchow, Jannes

2016-04-01

Modelling increased landslide susceptibility near highways in the Andes of southern Ecuador A. Brenning (1), J. Muenchow (1) (1) Department of Geography, Friedrich Schiller University Jena, Loebdergraben 32, 07743 Jena, Germany Mountain roads are affected by and also affect themselves landslide suceptibility. Especially in developing countries, inadequate drainage systems and mechanical destabilization of hillslopes by undercutting and overloading are known processes through which road construction and maintenance can enhance landslide activity within the immediate surroundings of road infrastructure. In the Andes of southern Ecuador, strong precipitation gradients as well as lithological differences provide an excellent study site in which the relationship between highways and landslide susceptibility and its regional differentiation can be studied. This study uses Generalized Additive Models (GAM) to investigate patterns of landslide susceptibility along two paved interurban highways in the tropical Andes of southern Ecuador. The relationship of landslides to distance from road is modeled while accounting for topographic, climatic and lithological predictors as possible confounders and modifiers, focusing on the odds ratio of landslide occurrence at 25 m versus 200 m distance from the highway. Spatial attention is given to uncertainties in estimated odds ratios of landslide occurrence using spatial block bootstrap techniques. The GAM is able to represent nonlinear additive terms as well as bivariate smooth interaction terms, providing a good tradeoff between model complexity and interpretability. The estimated odds of landslide occurrence were 18-21 times higher near the highway than at 200 m distance, based on different analyses, with lower 95% confidence limits always >13. (Semi-) parametric estimates confirmed this general range of values but suggests slightly higher odds ratios (95% confidence interval: 15.5-25.3). Highway-related effects were observed to

Modeling mountain pine beetle habitat suitability within Sequoia National Park

NASA Astrophysics Data System (ADS)

Nguyen, Andrew

Understanding significant changes in climate and their effects on timber resources can help forest managers make better decisions regarding the preservation of natural resources and land management. These changes may to alter natural ecosystems dependent on historical and current climate conditions. Increasing mountain pine beetle (MBP) outbreaks within the southern Sierra Nevada are the result of these alterations. This study better understands MPB behavior within Sequoia National Park (SNP) and model its current and future habitat distribution. Variables contributing to MPB spread are vegetation stress, soil moisture, temperature, precipitation, disturbance, and presence of Ponderosa (Pinus ponderosa) and Lodgepole (Pinus contorta) pine trees. These variables were obtained using various modeled, insitu, and remotely sensed sources. The generalized additive model (GAM) was used to calculate the statistical significance of each variable contributing to MPB spread and also created maps identifying habitat suitability. Results indicate vegetation stress and forest disturbance to be variables most indicative of MPB spread. Additionally, the model was able to detect habitat suitability of MPB with a 45% accuracy concluding that a geospatial driven modeling approach can be used to delineate potential MPB spread within SNP.

Online Statistical Modeling (Regression Analysis) for Independent Responses

NASA Astrophysics Data System (ADS)

Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus

2017-06-01

Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.

Spatial prediction of Soil Organic Carbon contents in croplands, grasslands and forests using environmental covariates and Generalized Additive Models (Southern Belgium)

NASA Astrophysics Data System (ADS)

Chartin, Caroline; Stevens, Antoine; van Wesemael, Bas

2015-04-01

Providing spatially continuous Soil Organic Carbon data (SOC) is needed to support decisions regarding soil management, and inform the political debate with quantified estimates of the status and change of the soil resource. Digital Soil Mapping techniques are based on relations existing between a soil parameter (measured at different locations in space at a defined period) and relevant covariates (spatially continuous data) that are factors controlling soil formation and explaining the spatial variability of the target variable. This study aimed at apply DSM techniques to recent SOC content measurements (2005-2013) in three different landuses, i.e. cropland, grassland, and forest, in the Walloon region (Southern Belgium). For this purpose, SOC databases of two regional Soil Monitoring Networks (CARBOSOL for croplands and grasslands, and IPRFW for forests) were first harmonized, totalising about 1,220 observations. Median values of SOC content for croplands, grasslands, and forests, are respectively of 12.8, 29.0, and 43.1 g C kg-1. Then, a set of spatial layers were prepared with a resolution of 40 meters and with the same grid topology, containing environmental covariates such as, landuses, Digital Elevation Model and its derivatives, soil texture, C factor, carbon inputs by manure, and climate. Here, in addition to the three classical texture classes (clays, silt, and sand), we tested the use of clays + fine silt content (particles < 20 µm and related to stable carbon fraction) as soil covariate explaining SOC variations. For each of the three land uses (cropland, grassland and forest), a Generalized Additive Model (GAM) was calibrated on two thirds of respective dataset. The remaining samples were assigned to a test set to assess model performance. A backward stepwise procedure was followed to select the relevant environmental covariates using their approximate p-values (the level of significance was set at p < 0.05). Standard errors were estimated for each of

Vertical Launch System Loadout Planner

DTIC Science & Technology

2015-03-01

United States Navy USS United States’ Ship VBA Visual Basic for Applications VLP VLS Loadout Planner VLS Vertical Launch System...with 32 gigabytes of random access memory and eight processors, General Algebraic Modeling System (GAMS) CPLEX version 24 (GAMS, 2015) solves this...problem in ten minutes to an integer tolerance of 10%. The GAMS interpreter and CPLEX solver require 75 Megabytes of random access memory for this

Geodesic acoustic modes in noncircular cross section tokamaks

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

Sorokina, E. A., E-mail: sorokina.ekaterina@gmail.com; Lakhin, V. P.; Konovaltseva, L. V.

2017-03-15

The influence of the shape of the plasma cross section on the continuous spectrum of geodesic acoustic modes (GAMs) in a tokamak is analyzed in the framework of the MHD model. An expression for the frequency of a local GAM for a model noncircular cross section plasma equilibrium is derived. Amendments to the oscillation frequency due to the plasma elongation and triangularity and finite tokamak aspect ratio are calculated. It is shown that the main factor affecting the GAM spectrum is the plasma elongation, resulting in a significant decrease in the mode frequency.

Hyperspectral Mapping of the Invasive Species Pepperweed and the Development of a Habitat Suitability Model

NASA Technical Reports Server (NTRS)

Nguyen, Andrew; Gole, Alexander; Randall, Jarom; Dlott, Glade; Zhang, Sylvia; Alfaro, Brian; Schmidt, Cindy; Skiles, J. W.

2011-01-01

Mapping and predicting the spatial distribution of invasive plant species is central to habitat management, however difficult to implement at landscape and regional scales. Remote sensing techniques can reduce the impact field campaigns have on these ecologically sensitive areas and can provide a regional and multi-temporal view of invasive species spread. Invasive perennial pepperweed (Lepidium latifolium) is now widespread in fragmented estuaries of the South San Francisco Bay, and is shown to degrade native vegetation in estuaries and adjacent habitats, thereby reducing forage and shelter for wildlife. The purpose of this study is to map the present distribution of pepperweed in estuarine areas of the South San Francisco Bay Salt Pond Restoration Project (Alviso, CA), and create a habitat suitability model to predict future spread. Pepperweed reflectance data were collected in-situ with a GER 1500 spectroradiometer along with 88 corresponding pepperweed presence and absence points used for building the statistical models. The spectral angle mapper (SAM) classification algorithm was used to distinguish the reflectance spectrum of pepperweed and map its distribution using an image from EO-1 Hyperion. To map pepperweed, we performed a supervised classification on an ASTER image with a resulting classification accuracy of 71.8%. We generated a weighted overlay analysis model within a geographic information system (GIS) framework to predict areas in the study site most susceptible to pepperweed colonization. Variables for the model included propensity for disturbance, status of pond restoration, proximity to water channels, and terrain curvature. A Generalized Additive Model (GAM) was also used to generate a probability map and investigate the statistical probability that each variable contributed to predict pepperweed spread. Results from the GAM revealed distance to channels, distance to ponds and curvature were statistically significant (p < 0.01) in determining

Modeling the effects of AADT on predicting multiple-vehicle crashes at urban and suburban signalized intersections.

PubMed

Chen, Chen; Xie, Yuanchang

2016-06-01

Annual Average Daily Traffic (AADT) is often considered as a main covariate for predicting crash frequencies at urban and suburban intersections. A linear functional form is typically assumed for the Safety Performance Function (SPF) to describe the relationship between the natural logarithm of expected crash frequency and covariates derived from AADTs. Such a linearity assumption has been questioned by many researchers. This study applies Generalized Additive Models (GAMs) and Piecewise Linear Negative Binomial (PLNB) regression models to fit intersection crash data. Various covariates derived from minor-and major-approach AADTs are considered. Three different dependent variables are modeled, which are total multiple-vehicle crashes, rear-end crashes, and angle crashes. The modeling results suggest that a nonlinear functional form may be more appropriate. Also, the results show that it is important to take into consideration the joint safety effects of multiple covariates. Additionally, it is found that the ratio of minor to major-approach AADT has a varying impact on intersection safety and deserves further investigations. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Use of Amino Sugars by Bacillus subtilis: Presence of a Unique Operon for the Catabolism of Glucosamine

PubMed Central

Gaugué, Isabelle; Oberto, Jacques; Putzer, Harald; Plumbridge, Jacqueline

2013-01-01

B. subtilis grows more rapidly using the amino sugar glucosamine as carbon source, than with N-acetylglucosamine. Genes for the transport and metabolism of N-acetylglucosamine (nagP and nagAB) are found in all the sequenced Bacilli (except Anoxybacillus flavithermus). In B. subtilis there is an additional operon (gamAP) encoding second copies of genes for the transport and catabolism of glucosamine. We have developed a method to make multiple deletion mutations in B. subtilis employing an excisable spectinomycin resistance cassette. Using this method we have analysed the contribution of the different genes of the nag and gam operons for their role in utilization of glucosamine and N-acetylglucosamine. Faster growth on glucosamine is due to the presence of the gamAP operon, which is strongly induced by glucosamine. Although the gamA and nagB genes encode isozymes of GlcN6P deaminase, catabolism of N-acetylglucosamine relies mostly upon the gamA gene product. The genes for use of N-acetylglucosamine, nagAB and nagP, are repressed by YvoA (NagR), a GntR family regulator, whose gene is part of the nagAB yvoA(nagR) operon. The gamAP operon is repressed by YbgA, another GntR family repressor, whose gene is expressed divergently from gamAP. The nagAB yvoA synton is found throughout the Bacilli and most firmicutes. On the other hand the ybgA-gamAP synton, which includes the ybgB gene for a small protein of unknown provenance, is only found in B. subtilis (and a few very close relatives). The origin of ybgBA-gamAP grouping is unknown but synteny analysis suggests lateral transfer from an unidentified donor. The presence of gamAP has enabled B. subtilis to efficiently use glucosamine as carbon source. PMID:23667565

Description and Evaluation of Numerical Groundwater Flow Models for the Edwards Aquifer, South-Central Texas

USGS Publications Warehouse

Lindgren, Richard J.; Taylor, Charles J.; Houston, Natalie A.

2009-01-01

A substantial number of public water system wells in south-central Texas withdraw groundwater from the karstic, highly productive Edwards aquifer. However, the use of numerical groundwater flow models to aid in the delineation of contributing areas for public water system wells in the Edwards aquifer is problematic because of the complex hydrogeologic framework and the presence of conduit-dominated flow paths in the aquifer. The U.S. Geological Survey, in cooperation with the Texas Commission on Environmental Quality, evaluated six published numerical groundwater flow models (all deterministic) that have been developed for the Edwards aquifer San Antonio segment or Barton Springs segment, or both. This report describes the models developed and evaluates each with respect to accessibility and ease of use, range of conditions simulated, accuracy of simulations, agreement with dye-tracer tests, and limitations of the models. These models are (1) GWSIM model of the San Antonio segment, a FORTRAN computer-model code that pre-dates the development of MODFLOW; (2) MODFLOW conduit-flow model of San Antonio and Barton Springs segments; (3) MODFLOW diffuse-flow model of San Antonio and Barton Springs segments; (4) MODFLOW Groundwater Availability Modeling [GAM] model of the Barton Springs segment; (5) MODFLOW recalibrated GAM model of the Barton Springs segment; and (6) MODFLOW-DCM (dual conductivity model) conduit model of the Barton Springs segment. The GWSIM model code is not commercially available, is limited in its application to the San Antonio segment of the Edwards aquifer, and lacks the ability of MODFLOW to easily incorporate newly developed processes and packages to better simulate hydrologic processes. MODFLOW is a widely used and tested code for numerical modeling of groundwater flow, is well documented, and is in the public domain. These attributes make MODFLOW a preferred code with regard to accessibility and ease of use. The MODFLOW conduit-flow model

Cost-of-illness studies based on massive data: a prevalence-based, top-down regression approach.

PubMed

Stollenwerk, Björn; Welchowski, Thomas; Vogl, Matthias; Stock, Stephanie

2016-04-01

Despite the increasing availability of routine data, no analysis method has yet been presented for cost-of-illness (COI) studies based on massive data. We aim, first, to present such a method and, second, to assess the relevance of the associated gain in numerical efficiency. We propose a prevalence-based, top-down regression approach consisting of five steps: aggregating the data; fitting a generalized additive model (GAM); predicting costs via the fitted GAM; comparing predicted costs between prevalent and non-prevalent subjects; and quantifying the stochastic uncertainty via error propagation. To demonstrate the method, it was applied to aggregated data in the context of chronic lung disease to German sickness funds data (from 1999), covering over 7.3 million insured. To assess the gain in numerical efficiency, the computational time of the innovative approach has been compared with corresponding GAMs applied to simulated individual-level data. Furthermore, the probability of model failure was modeled via logistic regression. Applying the innovative method was reasonably fast (19 min). In contrast, regarding patient-level data, computational time increased disproportionately by sample size. Furthermore, using patient-level data was accompanied by a substantial risk of model failure (about 80 % for 6 million subjects). The gain in computational efficiency of the innovative COI method seems to be of practical relevance. Furthermore, it may yield more precise cost estimates.

Ridge, Lasso and Bayesian additive-dominance genomic models.

PubMed

Azevedo, Camila Ferreira; de Resende, Marcos Deon Vilela; E Silva, Fabyano Fonseca; Viana, José Marcelo Soriano; Valente, Magno Sávio Ferreira; Resende, Márcio Fernando Ribeiro; Muñoz, Patricio

2015-08-25

A complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes). G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close. Amongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (-2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models.

Assessing non-additive effects in GBLUP model.

PubMed

Vieira, I C; Dos Santos, J P R; Pires, L P M; Lima, B M; Gonçalves, F M A; Balestre, M

2017-05-10

Understanding non-additive effects in the expression of quantitative traits is very important in genotype selection, especially in species where the commercial products are clones or hybrids. The use of molecular markers has allowed the study of non-additive genetic effects on a genomic level, in addition to a better understanding of its importance in quantitative traits. Thus, the purpose of this study was to evaluate the behavior of the GBLUP model in different genetic models and relationship matrices and their influence on the estimates of genetic parameters. We used real data of the circumference at breast height in Eucalyptus spp and simulated data from a population of F 2 . Three commonly reported kinship structures in the literature were adopted. The simulation results showed that the inclusion of epistatic kinship improved prediction estimates of genomic breeding values. However, the non-additive effects were not accurately recovered. The Fisher information matrix for real dataset showed high collinearity in estimates of additive, dominant, and epistatic variance, causing no gain in the prediction of the unobserved data and convergence problems. Estimates presented differences of genetic parameters and correlations considering the different kinship structures. Our results show that the inclusion of non-additive effects can improve the predictive ability or even the prediction of additive effects. However, the high distortions observed in the variance estimates when the Hardy-Weinberg equilibrium assumption is violated due to the presence of selection or inbreeding can converge at zero gains in models that consider epistasis in genomic kinship.

Using the Gurobi Solvers on the Peregrine System | High-Performance

Science.gov Websites

Peregrine System Gurobi Optimizer is a suite of solvers for mathematical programming. It is licensed for ('GRB_MATLAB_PATH') >> path(path,grb) Gurobi and GAMS GAMS is a high-level modeling system for mathematical

Combined proportional and additive residual error models in population pharmacokinetic modelling.

PubMed

Proost, Johannes H

2017-11-15

In pharmacokinetic modelling, a combined proportional and additive residual error model is often preferred over a proportional or additive residual error model. Different approaches have been proposed, but a comparison between approaches is still lacking. The theoretical background of the methods is described. Method VAR assumes that the variance of the residual error is the sum of the statistically independent proportional and additive components; this method can be coded in three ways. Method SD assumes that the standard deviation of the residual error is the sum of the proportional and additive components. Using datasets from literature and simulations based on these datasets, the methods are compared using NONMEM. The different coding of methods VAR yield identical results. Using method SD, the values of the parameters describing residual error are lower than for method VAR, but the values of the structural parameters and their inter-individual variability are hardly affected by the choice of the method. Both methods are valid approaches in combined proportional and additive residual error modelling, and selection may be based on OFV. When the result of an analysis is used for simulation purposes, it is essential that the simulation tool uses the same method as used during analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

Numerical modelling of geodesic acoustic mode relaxation in a tokamak edge

DOE PAGES

Dorf, M. A.; Cohen, R. H.; Dorr, M.; ...

2013-05-08

Here, the edge of a tokamak in a high confinement (H mode) regime is characterized by steep density gradients and a large radial electric field. Recent analytical studies demonstrated that the presence of a strong radial electric field consistent with a subsonic pedestal equilibrium modifies the conventional results of the neoclassical formalism developed for the core region. In the present work we make use of the recently developed gyrokinetic code COGENT to numerically investigate neoclassical transport in a tokamak edge including the effects of a strong radial electric field. The results of numerical simulations are found to be in goodmore » qualitative agreement with the theoretical predictions and the quantitative discrepancy is discussed. In addition, the present work investigates the effects of a strong radial electric field on the relaxation of geodesic acoustic modes (GAMs) in a tokamak edge. Numerical simulations demonstrate that the presence of a strong radial electric field characteristic of a tokamak pedestal can enhance the GAM decay rate, and heuristic arguments elucidating this finding are provided.« less

Does transport time help explain the high trauma mortality rates in rural areas? New and traditional predictors assessed by new and traditional statistical methods

PubMed Central

Røislien, Jo; Lossius, Hans Morten; Kristiansen, Thomas

2015-01-01

Background Trauma is a leading global cause of death. Trauma mortality rates are higher in rural areas, constituting a challenge for quality and equality in trauma care. The aim of the study was to explore population density and transport time to hospital care as possible predictors of geographical differences in mortality rates, and to what extent choice of statistical method might affect the analytical results and accompanying clinical conclusions. Methods Using data from the Norwegian Cause of Death registry, deaths from external causes 1998–2007 were analysed. Norway consists of 434 municipalities, and municipality population density and travel time to hospital care were entered as predictors of municipality mortality rates in univariate and multiple regression models of increasing model complexity. We fitted linear regression models with continuous and categorised predictors, as well as piecewise linear and generalised additive models (GAMs). Models were compared using Akaike's information criterion (AIC). Results Population density was an independent predictor of trauma mortality rates, while the contribution of transport time to hospital care was highly dependent on choice of statistical model. A multiple GAM or piecewise linear model was superior, and similar, in terms of AIC. However, while transport time was statistically significant in multiple models with piecewise linear or categorised predictors, it was not in GAM or standard linear regression. Conclusions Population density is an independent predictor of trauma mortality rates. The added explanatory value of transport time to hospital care is marginal and model-dependent, highlighting the importance of exploring several statistical models when studying complex associations in observational data. PMID:25972600

Spatial modelling of disease using data- and knowledge-driven approaches.

PubMed

Stevens, Kim B; Pfeiffer, Dirk U

2011-09-01

The purpose of spatial modelling in animal and public health is three-fold: describing existing spatial patterns of risk, attempting to understand the biological mechanisms that lead to disease occurrence and predicting what will happen in the medium to long-term future (temporal prediction) or in different geographical areas (spatial prediction). Traditional methods for temporal and spatial predictions include general and generalized linear models (GLM), generalized additive models (GAM) and Bayesian estimation methods. However, such models require both disease presence and absence data which are not always easy to obtain. Novel spatial modelling methods such as maximum entropy (MAXENT) and the genetic algorithm for rule set production (GARP) require only disease presence data and have been used extensively in the fields of ecology and conservation, to model species distribution and habitat suitability. Other methods, such as multicriteria decision analysis (MCDA), use knowledge of the causal factors of disease occurrence to identify areas potentially suitable for disease. In addition to their less restrictive data requirements, some of these novel methods have been shown to outperform traditional statistical methods in predictive ability (Elith et al., 2006). This review paper provides details of some of these novel methods for mapping disease distribution, highlights their advantages and limitations, and identifies studies which have used the methods to model various aspects of disease distribution. Copyright © 2011. Published by Elsevier Ltd.

Additive mixed effect model for recurrent gap time data.

PubMed

Ding, Jieli; Sun, Liuquan

2017-04-01

Gap times between recurrent events are often of primary interest in medical and observational studies. The additive hazards model, focusing on risk differences rather than risk ratios, has been widely used in practice. However, the marginal additive hazards model does not take the dependence among gap times into account. In this paper, we propose an additive mixed effect model to analyze gap time data, and the proposed model includes a subject-specific random effect to account for the dependence among the gap times. Estimating equation approaches are developed for parameter estimation, and the asymptotic properties of the resulting estimators are established. In addition, some graphical and numerical procedures are presented for model checking. The finite sample behavior of the proposed methods is evaluated through simulation studies, and an application to a data set from a clinic study on chronic granulomatous disease is provided.

An optimization model to agroindustrial sector in antioquia (Colombia, South America)

NASA Astrophysics Data System (ADS)

Fernandez, J.

2015-06-01

This paper develops a proposal of a general optimization model for the flower industry, which is defined by using discrete simulation and nonlinear optimization, whose mathematical models have been solved by using ProModel simulation tools and Gams optimization. It defines the operations that constitute the production and marketing of the sector, statistically validated data taken directly from each operation through field work, the discrete simulation model of the operations and the linear optimization model of the entire industry chain are raised. The model is solved with the tools described above and presents the results validated in a case study.

Validation analysis of probabilistic models of dietary exposure to food additives.

PubMed

Gilsenan, M B; Thompson, R L; Lambe, J; Gibney, M J

2003-10-01

The validity of a range of simple conceptual models designed specifically for the estimation of food additive intakes using probabilistic analysis was assessed. Modelled intake estimates that fell below traditional conservative point estimates of intake and above 'true' additive intakes (calculated from a reference database at brand level) were considered to be in a valid region. Models were developed for 10 food additives by combining food intake data, the probability of an additive being present in a food group and additive concentration data. Food intake and additive concentration data were entered as raw data or as a lognormal distribution, and the probability of an additive being present was entered based on the per cent brands or the per cent eating occasions within a food group that contained an additive. Since the three model components assumed two possible modes of input, the validity of eight (2(3)) model combinations was assessed. All model inputs were derived from the reference database. An iterative approach was employed in which the validity of individual model components was assessed first, followed by validation of full conceptual models. While the distribution of intake estimates from models fell below conservative intakes, which assume that the additive is present at maximum permitted levels (MPLs) in all foods in which it is permitted, intake estimates were not consistently above 'true' intakes. These analyses indicate the need for more complex models for the estimation of food additive intakes using probabilistic analysis. Such models should incorporate information on market share and/or brand loyalty.

Using generalized additive (mixed) models to analyze single case designs.

PubMed

Shadish, William R; Zuur, Alain F; Sullivan, Kristynn J

2014-04-01

This article shows how to apply generalized additive models and generalized additive mixed models to single-case design data. These models excel at detecting the functional form between two variables (often called trend), that is, whether trend exists, and if it does, what its shape is (e.g., linear and nonlinear). In many respects, however, these models are also an ideal vehicle for analyzing single-case designs because they can consider level, trend, variability, overlap, immediacy of effect, and phase consistency that single-case design researchers examine when interpreting a functional relation. We show how these models can be implemented in a wide variety of ways to test whether treatment is effective, whether cases differ from each other, whether treatment effects vary over cases, and whether trend varies over cases. We illustrate diagnostic statistics and graphs, and we discuss overdispersion of data in detail, with examples of quasibinomial models for overdispersed data, including how to compute dispersion and quasi-AIC fit indices in generalized additive models. We show how generalized additive mixed models can be used to estimate autoregressive models and random effects and discuss the limitations of the mixed models compared to generalized additive models. We provide extensive annotated syntax for doing all these analyses in the free computer program R. Copyright © 2013 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

A field test of three LQAS designs to assess the prevalence of acute malnutrition.

PubMed

Deitchler, Megan; Valadez, Joseph J; Egge, Kari; Fernandez, Soledad; Hennigan, Mary

2007-08-01

The conventional method for assessing the prevalence of Global Acute Malnutrition (GAM) in emergency settings is the 30 x 30 cluster-survey. This study describes alternative approaches: three Lot Quality Assurance Sampling (LQAS) designs to assess GAM. The LQAS designs were field-tested and their results compared with those from a 30 x 30 cluster-survey. Computer simulations confirmed that small clusters instead of a simple random sample could be used for LQAS assessments of GAM. Three LQAS designs were developed (33 x 6, 67 x 3, Sequential design) to assess GAM thresholds of 10, 15 and 20%. The designs were field-tested simultaneously with a 30 x 30 cluster-survey in Siraro, Ethiopia during June 2003. Using a nested study design, anthropometric, morbidity and vaccination data were collected on all children 6-59 months in sampled households. Hypothesis tests about GAM thresholds were conducted for each LQAS design. Point estimates were obtained for the 30 x 30 cluster-survey and the 33 x 6 and 67 x 3 LQAS designs. Hypothesis tests showed GAM as <10% for the 33 x 6 design and GAM as > or =10% for the 67 x 3 and Sequential designs. Point estimates for the 33 x 6 and 67 x 3 designs were similar to those of the 30 x 30 cluster-survey for GAM (6.7%, CI = 3.2-10.2%; 8.2%, CI = 4.3-12.1%, 7.4%, CI = 4.8-9.9%) and all other indicators. The CIs for the LQAS designs were only slightly wider than the CIs for the 30 x 30 cluster-survey; yet the LQAS designs required substantially less time to administer. The LQAS designs provide statistically appropriate alternatives to the more time-consuming 30 x 30 cluster-survey. However, additional field-testing is needed using independent samples rather than a nested study design.

Versatility of Cooperative Transcriptional Activation: A Thermodynamical Modeling Analysis for Greater-Than-Additive and Less-Than-Additive Effects

PubMed Central

Frank, Till D.; Carmody, Aimée M.; Kholodenko, Boris N.

2012-01-01

We derive a statistical model of transcriptional activation using equilibrium thermodynamics of chemical reactions. We examine to what extent this statistical model predicts synergy effects of cooperative activation of gene expression. We determine parameter domains in which greater-than-additive and less-than-additive effects are predicted for cooperative regulation by two activators. We show that the statistical approach can be used to identify different causes of synergistic greater-than-additive effects: nonlinearities of the thermostatistical transcriptional machinery and three-body interactions between RNA polymerase and two activators. In particular, our model-based analysis suggests that at low transcription factor concentrations cooperative activation cannot yield synergistic greater-than-additive effects, i.e., DNA transcription can only exhibit less-than-additive effects. Accordingly, transcriptional activity turns from synergistic greater-than-additive responses at relatively high transcription factor concentrations into less-than-additive responses at relatively low concentrations. In addition, two types of re-entrant phenomena are predicted. First, our analysis predicts that under particular circumstances transcriptional activity will feature a sequence of less-than-additive, greater-than-additive, and eventually less-than-additive effects when for fixed activator concentrations the regulatory impact of activators on the binding of RNA polymerase to the promoter increases from weak, to moderate, to strong. Second, for appropriate promoter conditions when activator concentrations are increased then the aforementioned re-entrant sequence of less-than-additive, greater-than-additive, and less-than-additive effects is predicted as well. Finally, our model-based analysis suggests that even for weak activators that individually induce only negligible increases in promoter activity, promoter activity can exhibit greater-than-additive responses when

Effects of additional food in a delayed predator-prey model.

PubMed

Sahoo, Banshidhar; Poria, Swarup

2015-03-01

We examine the effects of supplying additional food to predator in a gestation delay induced predator-prey system with habitat complexity. Additional food works in favor of predator growth in our model. Presence of additional food reduces the predatory attack rate to prey in the model. Supplying additional food we can control predator population. Taking time delay as bifurcation parameter the stability of the coexisting equilibrium point is analyzed. Hopf bifurcation analysis is done with respect to time delay in presence of additional food. The direction of Hopf bifurcations and the stability of bifurcated periodic solutions are determined by applying the normal form theory and the center manifold theorem. The qualitative dynamical behavior of the model is simulated using experimental parameter values. It is observed that fluctuations of the population size can be controlled either by supplying additional food suitably or by increasing the degree of habitat complexity. It is pointed out that Hopf bifurcation occurs in the system when the delay crosses some critical value. This critical value of delay strongly depends on quality and quantity of supplied additional food. Therefore, the variation of predator population significantly effects the dynamics of the model. Model results are compared with experimental results and biological implications of the analytical findings are discussed in the conclusion section. Copyright © 2015 Elsevier Inc. All rights reserved.

Comprehensive European dietary exposure model (CEDEM) for food additives.

PubMed

Tennant, David R

2016-05-01

European methods for assessing dietary exposures to nutrients, additives and other substances in food are limited by the availability of detailed food consumption data for all member states. A proposed comprehensive European dietary exposure model (CEDEM) applies summary data published by the European Food Safety Authority (EFSA) in a deterministic model based on an algorithm from the EFSA intake method for food additives. The proposed approach can predict estimates of food additive exposure provided in previous EFSA scientific opinions that were based on the full European food consumption database.

Dynamics of kinetic geodesic-acoustic modes and the radial electric field in tokamak neoclassical plasmas

NASA Astrophysics Data System (ADS)

Xu, X. Q.; Belli, E.; Bodi, K.; Candy, J.; Chang, C. S.; Cohen, R. H.; Colella, P.; Dimits, A. M.; Dorr, M. R.; Gao, Z.; Hittinger, J. A.; Ko, S.; Krasheninnikov, S.; McKee, G. R.; Nevins, W. M.; Rognlien, T. D.; Snyder, P. B.; Suh, J.; Umansky, M. V.

2009-06-01

We present edge gyrokinetic simulations of tokamak plasmas using the fully non-linear (full-f) continuum code TEMPEST. A non-linear Boltzmann model is used for the electrons. The electric field is obtained by solving the 2D gyrokinetic Poisson equation. We demonstrate the following. (1) High harmonic resonances (n > 2) significantly enhance geodesic-acoustic mode (GAM) damping at high q (tokamak safety factor), and are necessary to explain the damping observed in our TEMPEST q-scans and consistent with the experimental measurements of the scaling of the GAM amplitude with edge q95 in the absence of obvious evidence that there is a strong q-dependence of the turbulent drive and damping of the GAM. (2) The kinetic GAM exists in the edge for steep density and temperature gradients in the form of outgoing waves, its radial scale is set by the ion temperature profile, and ion temperature inhomogeneity is necessary for GAM radial propagation. (3) The development of the neoclassical electric field evolves through different phases of relaxation, including GAMs, their radial propagation and their long-time collisional decay. (4) Natural consequences of orbits in the pedestal and scrape-off layer region in divertor geometry are substantial non-Maxwellian ion distributions and parallel flow characteristics qualitatively like those observed in experiments.

Fully Nonlinear Edge Gyrokinetic Simulations of Kinetic Geodesic-Acoustic Modes and Boundary Flows

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

Xu, X Q; Belli, E; Bodi, K

We present edge gyrokinetic neoclassical simulations of tokamak plasmas using the fully nonlinear (full-f) continuum code TEMPEST. A nonlinear Boltzmann model is used for the electrons. The electric field is obtained by solving the 2D gyrokinetic Poisson Equation. We demonstrate the following: (1) High harmonic resonances (n > 2) significantly enhance geodesic-acoustic mode (GAM) damping at high-q (tokamak safety factor), and are necessary to explain both the damping observed in our TEMPEST q-scans and experimental measurements of the scaling of the GAM amplitude with edge q{sub 95} in the absence of obvious evidence that there is a strong q dependencemore » of the turbulent drive and damping of the GAM. (2) The kinetic GAM exists in the edge for steep density and temperature gradients in the form of outgoing waves, its radial scale is set by the ion temperature profile, and ion temperature inhomogeneity is necessary for GAM radial propagation. (3) The development of the neoclassical electric field evolves through different phases of relaxation, including GAMs, their radial propagation, and their long-time collisional decay. (4) Natural consequences of orbits in the pedestal and scrape-off layer region in divertor geometry are substantial non-Maxwellian ion distributions and flow characteristics qualitatively like those observed in experiments.« less

An Exploratory Study of Pre-Admission Predictors of Hardiness and Retention for United States Military Academy Cadets Using Regression Modeling

DTIC Science & Technology

2013-06-01

Character in Sports Index CV Cross Validation FAS Faculty Appraisal Score FFM Five-Factor Model, also known as the “Big Five” GAM... FFM ). USMA does not allow personality testing as a selection tool. However, perhaps we may discover whether pre-admission information can predict...characteristic, and personality factors as described by the Five Factor Model ( FFM ) to determine their effect on one’s academic performance at USMA (Clark

Multicriteria Cost Assessment and Logistics Modeling for Military Humanitarian Assistance and Disaster Relief Aerial Delivery Operations

DTIC Science & Technology

2015-03-01

vulnerable people will have access to this airdropped consumable aid (since nobody 1 is necessarily coordinating the distribution on the ground... VBA ) platforms (see Appendix B). In particular, we used GAMS v.23.9.3 with IBM ILOG CPLEX 12.4.0.1 to solve the stochastic, mixed-integer weighted...goal programming model, and we used Excel/ VBA to create an auto- matic, user-friendly interface with the decision maker for model input and analysis of

Using stable isotopes of albacore tuna and predictive models to characterize bioregions and examine ecological change in the SW Pacific Ocean

NASA Astrophysics Data System (ADS)

Pethybridge, Heidi R.; Young, Jock W.; Kuhnert, Petra M.; Farley, Jessica H.

2015-05-01

Broad-scale food web inferences of 534 albacore tuna, Thunnus alalunga, in the south-west Pacific Ocean in 2009 and 2010 were made using bulk nitrogen (δ15N) and carbon (δ13C) stable isotopes. Condition was also examined for the same fish using C:N ratios. A Generalized Additive Modeling (GAM) approach was used to analyze spatio-temporal, biological and environmental drivers that impact the distribution of tuna isotopes and to create oceanographic maps. Based on model formulations, five bioregions with distinct isotopic signatures were identified and were related to known biological, nutrient cycling and oceanographic (temperature, primary productivity and eddy) features associated with the East Australian Current. δ15N values showed a large-scale, uniform latitudinal gradient decreasing from the south to north, in a region encompassing oligotrophic waters in the Coral Sea. In contrast, δ13C values were lower in the nutrient rich Tasman Sea waters and offshore East Australia. C:N ratios suggested that albacore occupying southern and offshore waters were in better condition. Ontogenetic trends in all three biochemical parameters were identified and related to differences in size distribution. Regional-specific temporal variations were detected including similar monthly changes for both isotopes and significantly less enriched δ13C (by 1.9‰) than in previous work undertaken in 2006, potentially signifying a substantial shift in the carbon cycle that supports food webs off central east Australia. Our results showed that isotopic measurements in tuna and the GAM framework provide powerful tools to assess ecosystem functioning and change by linking sources of nutrients and organic matter to local food web assembly. Such knowledge is vital to support an ecosystem based approach to fisheries management including biogeochemical whole-of-ecosystem models and monitoring programs at regional and landscape-scales.

Modelling the behaviour of additives in gun barrels

NASA Astrophysics Data System (ADS)

Rhodes, N.; Ludwig, J. C.

1986-01-01

A mathematical model which predicts the flow and heat transfer in a gun barrel is described. The model is transient, two-dimensional and equations are solved for velocities and enthalpies of a gas phase, which arises from the combustion of propellant and cartridge case, for particle additives which are released from the case; volume fractions of the gas and particles. Closure of the equations is obtained using a two-equation turbulence model. Preliminary calculations are described in which the proportions of particle additives in the cartridge case was altered. The model gives a good prediction of the ballistic performance and the gas to wall heat transfer. However, the expected magnitude of reduction in heat transfer when particles are present is not predicted. The predictions of gas flow invalidate some of the assumptions made regarding case and propellant behavior during combustion and further work is required to investigate these effects and other possible interactions, both chemical and physical, between gas and particles.

Modelling the standing timber volume of Baden-Württemberg-A large-scale approach using a fusion of Landsat, airborne LiDAR and National Forest Inventory data

NASA Astrophysics Data System (ADS)

Maack, Joachim; Lingenfelder, Marcus; Weinacker, Holger; Koch, Barbara

2016-07-01

Remote sensing-based timber volume estimation is key for modelling the regional potential, accessibility and price of lignocellulosic raw material for an emerging bioeconomy. We used a unique wall-to-wall airborne LiDAR dataset and Landsat 7 satellite images in combination with terrestrial inventory data derived from the National Forest Inventory (NFI), and applied generalized additive models (GAM) to estimate spatially explicit timber distribution and volume in forested areas. Since the NFI data showed an underlying structure regarding size and ownership, we additionally constructed a socio-economic predictor to enhance the accuracy of the analysis. Furthermore, we balanced the training dataset with a bootstrap method to achieve unbiased regression weights for interpolating timber volume. Finally, we compared and discussed the model performance of the original approach (r2 = 0.56, NRMSE = 9.65%), the approach with balanced training data (r2 = 0.69, NRMSE = 12.43%) and the final approach with balanced training data and the additional socio-economic predictor (r2 = 0.72, NRMSE = 12.17%). The results demonstrate the usefulness of remote sensing techniques for mapping timber volume for a future lignocellulose-based bioeconomy.

Genetic control of enhanced mutability of mitochondrial DNA and gamma-ray sensitivity in Saccharomyces cerevisiae.

PubMed Central

Foury, F; Goffeau, A

1979-01-01

Five nuclear mutants enhancing the spontaneous mutation rate of mtDNA have been isolated in Saccharomyces cerevisiae. These mutators fall into five complementation groups and are located at five genetic loci different from rad50 to rad57 loci. Three mutants (gam1, gam2, and gam4), insensitive or weakly sensitive to gamma-rays, exhibit increased frequency of spontaneous production of mutants with large deletions of the mtDNA (p-) and of all tested mitochondrial drug-resistant mutants. Two other mutants (gam3 and gam5), highly sensitive to gamma-rays, increase only the mutation rate of particular alleles of the mtDNA. The mutant gam5 enhances only the production of p- and erythromycin-resistant clones. The mutant gam3 exhibits an enhanced rate of oligomycin-resistant clones as well as a collateral increase of nuclear mutability. The existence of gam3 and gam5 mutants indicates that at least two common steps control both nuclear DNA repair and the mutability of particular alleles of the mtDNA. However, the general spontaneous mutability of the mtDNA includes at least three steps not involved in the repair of nuclear DNA, as revealed by the gam1, gam2, and gam4 mutations. PMID:392521

Genomic Model with Correlation Between Additive and Dominance Effects.

PubMed

Xiang, Tao; Christensen, Ole Fredslund; Vitezica, Zulma Gladis; Legarra, Andres

2018-05-09

Dominance genetic effects are rarely included in pedigree-based genetic evaluation. With the availability of single nucleotide polymorphism markers and the development of genomic evaluation, estimates of dominance genetic effects have become feasible using genomic best linear unbiased prediction (GBLUP). Usually, studies involving additive and dominance genetic effects ignore possible relationships between them. It has been often suggested that the magnitude of functional additive and dominance effects at the quantitative trait loci are related, but there is no existing GBLUP-like approach accounting for such correlation. Wellmann and Bennewitz showed two ways of considering directional relationships between additive and dominance effects, which they estimated in a Bayesian framework. However, these relationships cannot be fitted at the level of individuals instead of loci in a mixed model and are not compatible with standard animal or plant breeding software. This comes from a fundamental ambiguity in assigning the reference allele at a given locus. We show that, if there has been selection, assigning the most frequent as the reference allele orients the correlation between functional additive and dominance effects. As a consequence, the most frequent reference allele is expected to have a positive value. We also demonstrate that selection creates negative covariance between genotypic additive and dominance genetic values. For parameter estimation, it is possible to use a combined additive and dominance relationship matrix computed from marker genotypes, and to use standard restricted maximum likelihood (REML) algorithms based on an equivalent model. Through a simulation study, we show that such correlations can easily be estimated by mixed model software and accuracy of prediction for genetic values is slightly improved if such correlations are used in GBLUP. However, a model assuming uncorrelated effects and fitting orthogonal breeding values and dominant

Risk Map of Cholera Infection for Vaccine Deployment: The Eastern Kolkata Case

PubMed Central

You, Young Ae; Ali, Mohammad; Kanungo, Suman; Sah, Binod; Manna, Byomkesh; Puri, Mahesh; Nair, G. Balakrish; Bhattacharya, Sujit Kumar; Convertino, Matteo; Deen, Jacqueline L.; Lopez, Anna Lena; Wierzba, Thomas F.; Clemens, John; Sur, Dipika

2013-01-01

Background Despite advancement of our knowledge, cholera remains a public health concern. During March-April 2010, a large cholera outbreak afflicted the eastern part of Kolkata, India. The quantification of importance of socio-environmental factors in the risk of cholera, and the calculation of the risk is fundamental for deploying vaccination strategies. Here we investigate socio-environmental characteristics between high and low risk areas as well as the potential impact of vaccination on the spatial occurrence of the disease. Methods and Findings The study area comprised three wards of Kolkata Municipal Corporation. A mass cholera vaccination campaign was conducted in mid-2006 as the part of a clinical trial. Cholera cases and data of the trial to identify high risk areas for cholera were analyzed. We used a generalized additive model (GAM) to detect risk areas, and to evaluate the importance of socio-environmental characteristics between high and low risk areas. During the one-year pre-vaccination and two-year post-vaccination periods, 95 and 183 cholera cases were detected in 111,882 and 121,827 study participants, respectively. The GAM model predicts that high risk areas in the west part of the study area where the outbreak largely occurred. High risk areas in both periods were characterized by poor people, use of unsafe water, and proximity to canals used as the main drainage for rain and waste water. Cholera vaccine uptake was significantly lower in the high risk areas compared to low risk areas. Conclusion The study shows that even a parsimonious model like GAM predicts high risk areas where cholera outbreaks largely occurred. This is useful for indicating where interventions would be effective in controlling the disease risk. Data showed that vaccination decreased the risk of infection. Overall, the GAM-based risk map is useful for policymakers, especially those from countries where cholera remains to be endemic with periodic outbreaks. PMID:23936491

An electrical circuit model for additive-modified SnO2 ceramics

NASA Astrophysics Data System (ADS)

Karami Horastani, Zahra; Alaei, Reza; Karami, Amirhossein

2018-05-01

In this paper an electrical circuit model for additive-modified metal oxide ceramics based on their physical structures and electrical resistivities is presented. The model predicts resistance of the sample at different additive concentrations and different temperatures. To evaluate the model two types of composite ceramics, SWCNT/SnO2 with SWCNT concentrations of 0.3, 0.6, 1.2, 2.4 and 3.8%wt, and Ag/SnO2 with Ag concentrations of 0.3, 0.5, 0.8 and 1.5%wt, were prepared and their electrical resistances versus temperature were experimentally measured. It is shown that the experimental data are in good agreement with the results obtained from the model. The proposed model can be used in the design process of ceramic-based gas sensors, and it also clarifies the role of additive in gas sensing process of additive-modified metal oxide gas sensors. Furthermore the model can be used in the system level modeling of designs in which these sensors are also present.

Modeling Errors in Daily Precipitation Measurements: Additive or Multiplicative?

NASA Technical Reports Server (NTRS)

Tian, Yudong; Huffman, George J.; Adler, Robert F.; Tang, Ling; Sapiano, Matthew; Maggioni, Viviana; Wu, Huan

2013-01-01

The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application.In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as non constant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice.

Exploration of the "larval pool": development and ground-truthing of a larval transport model off leeward Hawai'i.

PubMed

Wren, Johanna L K; Kobayashi, Donald R

2016-01-01

Most adult reef fish show site fidelity thus dispersal is limited to the mobile larval stage of the fish, and effective management of such species requires an understanding of the patterns of larval dispersal. In this study, we assess larval reef fish distributions in the waters west of the Big Island of Hawai'i using both in situ and model data. Catches from Cobb midwater trawls off west Hawai'i show that reef fish larvae are most numerous in offshore waters deeper than 3,000 m and consist largely of pre-settlement Pomacanthids, Acanthurids and Chaetodontids. Utilizing a Lagrangian larval dispersal model, we were able to replicate the observed shore fish distributions from the trawl data and we identified the 100 m depth strata as the most likely depth of occupancy. Additionally, our model showed that for larval shore fish with a pelagic larval duration longer than 40 days there was no significant change in settlement success in our model. By creating a general additive model (GAM) incorporating lunar phase and angle we were able to explain 67.5% of the variance between modeled and in situ Acanthurid abundances. We took steps towards creating a predictive larval distribution model that will greatly aid in understanding the spatiotemporal nature of the larval pool in west Hawai'i, and the dispersal of larvae throughout the Hawaiian archipelago.

An investigation of the mechanism underlying teacher aggression: Testing I3 theory and the General Aggression Model.

PubMed

Montuoro, Paul; Mainhard, Tim

2017-12-01

Considerable research has investigated the deleterious effects of teachers responding aggressively to students who misbehave, but the mechanism underlying this dysfunctional behaviour remains unknown. This study investigated whether the mechanism underlying teacher aggression follows I 3 theory or General Aggression Model (GAM) metatheory of human aggression. I 3 theory explains exceptional, catastrophic events of human aggression, whereas the GAM explains common human aggression behaviours. A total of 249 Australian teachers participated in this study, including 142 primary school teachers (Mdn [age] = 35-39 years; Mdn [years teaching] = 10-14 years; 84% female) and 107 secondary school teachers (Mdn [age] = 45-49 years; Mdn [years teaching] = 15-19 years; 65% female). Participants completed four online self-report questionnaires, which assessed caregiving responsiveness, trait self-control, misbehaviour provocation, and teacher aggression. Analyses revealed that the GAM most accurately captures the mechanism underlying teacher aggression, with lower caregiving responsiveness appearing to indirectly lead to teacher aggression via higher misbehaviour provocation and lower trait self-control in serial, controlling for gender, age, years teaching, and current role (primary, secondary). This study indicates that teacher aggression proceeds from 'the person in the situation'. Specifically, lower caregiving responsiveness appears to negatively shape a teacher's affective, cognitive, and arousal states, which influence how they perceive and interpret student misbehaviour. These internal states, in turn, appear to negatively influence appraisal and decision processes, leading to immediate appraisal and impulsive actions. These results raise the possibility that teacher aggression is a form of countertransference. © 2017 The British Psychological Society.

Modeling zoonotic cutaneous leishmaniasis incidence in central Tunisia from 2009-2015: Forecasting models using climate variables as predictors.

PubMed

Talmoudi, Khouloud; Bellali, Hedia; Ben-Alaya, Nissaf; Saez, Marc; Malouche, Dhafer; Chahed, Mohamed Kouni

2017-08-01

Transmission of zoonotic cutaneous leishmaniasis (ZCL) depends on the presence, density and distribution of Leishmania major rodent reservoir and the development of these rodents is known to have a significant dependence on environmental and climate factors. ZCL in Tunisia is one of the most common forms of leishmaniasis. The aim of this paper was to build a regression model of ZCL cases to identify the relationship between ZCL occurrence and possible risk factors, and to develop a predicting model for ZCL's control and prevention purposes. Monthly reported ZCL cases, environmental and bioclimatic data were collected over 6 years (2009-2015). Three rural areas in the governorate of Sidi Bouzid were selected as the study area. Cross-correlation analysis was used to identify the relevant lagged effects of possible risk factors, associated with ZCL cases. Non-parametric modeling techniques known as generalized additive model (GAM) and generalized additive mixed models (GAMM) were applied in this work. These techniques have the ability to approximate the relationship between the predictors (inputs) and the response variable (output), and express the relationship mathematically. The goodness-of-fit of the constructed model was determined by Generalized cross-validation (GCV) score and residual test. There were a total of 1019 notified ZCL cases from July 2009 to June 2015. The results showed seasonal distribution of reported ZCL cases from August to January. The model highlighted that rodent density, average temperature, cumulative rainfall and average relative humidity, with different time lags, all play role in sustaining and increasing the ZCL incidence. The GAMM model could be applied to predict the occurrence of ZCL in central Tunisia and could help for the establishment of an early warning system to control and prevent ZCL in central Tunisia.

Modeling zoonotic cutaneous leishmaniasis incidence in central Tunisia from 2009-2015: Forecasting models using climate variables as predictors

PubMed Central

Bellali, Hedia; Ben-Alaya, Nissaf; Saez, Marc; Malouche, Dhafer; Chahed, Mohamed Kouni

2017-01-01

Transmission of zoonotic cutaneous leishmaniasis (ZCL) depends on the presence, density and distribution of Leishmania major rodent reservoir and the development of these rodents is known to have a significant dependence on environmental and climate factors. ZCL in Tunisia is one of the most common forms of leishmaniasis. The aim of this paper was to build a regression model of ZCL cases to identify the relationship between ZCL occurrence and possible risk factors, and to develop a predicting model for ZCL's control and prevention purposes. Monthly reported ZCL cases, environmental and bioclimatic data were collected over 6 years (2009–2015). Three rural areas in the governorate of Sidi Bouzid were selected as the study area. Cross-correlation analysis was used to identify the relevant lagged effects of possible risk factors, associated with ZCL cases. Non-parametric modeling techniques known as generalized additive model (GAM) and generalized additive mixed models (GAMM) were applied in this work. These techniques have the ability to approximate the relationship between the predictors (inputs) and the response variable (output), and express the relationship mathematically. The goodness-of-fit of the constructed model was determined by Generalized cross-validation (GCV) score and residual test. There were a total of 1019 notified ZCL cases from July 2009 to June 2015. The results showed seasonal distribution of reported ZCL cases from August to January. The model highlighted that rodent density, average temperature, cumulative rainfall and average relative humidity, with different time lags, all play role in sustaining and increasing the ZCL incidence. The GAMM model could be applied to predict the occurrence of ZCL in central Tunisia and could help for the establishment of an early warning system to control and prevent ZCL in central Tunisia. PMID:28841642

The Development of Web-based Graphical User Interface for Unified Modeling Data with Multi (Correlated) Responses

NASA Astrophysics Data System (ADS)

Made Tirta, I.; Anggraeni, Dian

2018-04-01

Statistical models have been developed rapidly into various directions to accommodate various types of data. Data collected from longitudinal, repeated measured, clustered data (either continuous, binary, count, or ordinal), are more likely to be correlated. Therefore statistical model for independent responses, such as Generalized Linear Model (GLM), Generalized Additive Model (GAM) are not appropriate. There are several models available to apply for correlated responses including GEEs (Generalized Estimating Equations), for marginal model and various mixed effect model such as GLMM (Generalized Linear Mixed Models) and HGLM (Hierarchical Generalized Linear Models) for subject spesific models. These models are available on free open source software R, but they can only be accessed through command line interface (using scrit). On the othe hand, most practical researchers very much rely on menu based or Graphical User Interface (GUI). We develop, using Shiny framework, standard pull down menu Web-GUI that unifies most models for correlated responses. The Web-GUI has accomodated almost all needed features. It enables users to do and compare various modeling for repeated measure data (GEE, GLMM, HGLM, GEE for nominal responses) much more easily trough online menus. This paper discusses the features of the Web-GUI and illustrates the use of them. In General we find that GEE, GLMM, HGLM gave very closed results.

Influence of Coastal Submarine Groundwater Discharges on Seagrass Communities in a Subtropical Karstic Environment.

PubMed

Kantún-Manzano, C A; Herrera-Silveira, J A; Arcega-Cabrera, F

2018-01-01

The influence of coastal submarine groundwater discharges (SGD) on the distribution and abundance of seagrass meadows was investigated. In 2012, hydrological variability, nutrient variability in sediments and the biotic characteristics of two seagrass beds, one with SGD present and one without, were studied. Findings showed that SGD inputs were related with one dominant seagrass species. To further understand this, a generalized additive model (GAM) was used to explore the relationship between seagrass biomass and environment conditions (water and sediment variables). Salinity range (21-35.5 PSU) was the most influential variable (85%), explaining why H. wrightii was the sole plant species present at the SGD site. At the site without SGD, GAM could not be performed since environmental variables could not explain a total variance of > 60%. This research shows the relevance of monitoring SGD inputs in coastal karstic areas since they significantly affect biotic characteristics of seagrass beds.

Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications

PubMed Central

Bouvet, J-M; Makouanzi, G; Cros, D; Vigneron, Ph

2016-01-01

Hybrids are broadly used in plant breeding and accurate estimation of variance components is crucial for optimizing genetic gain. Genome-wide information may be used to explore models designed to assess the extent of additive and non-additive variance and test their prediction accuracy for the genomic selection. Ten linear mixed models, involving pedigree- and marker-based relationship matrices among parents, were developed to estimate additive (A), dominance (D) and epistatic (AA, AD and DD) effects. Five complementary models, involving the gametic phase to estimate marker-based relationships among hybrid progenies, were developed to assess the same effects. The models were compared using tree height and 3303 single-nucleotide polymorphism markers from 1130 cloned individuals obtained via controlled crosses of 13 Eucalyptus urophylla females with 9 Eucalyptus grandis males. Akaike information criterion (AIC), variance ratios, asymptotic correlation matrices of estimates, goodness-of-fit, prediction accuracy and mean square error (MSE) were used for the comparisons. The variance components and variance ratios differed according to the model. Models with a parent marker-based relationship matrix performed better than those that were pedigree-based, that is, an absence of singularities, lower AIC, higher goodness-of-fit and accuracy and smaller MSE. However, AD and DD variances were estimated with high s.es. Using the same criteria, progeny gametic phase-based models performed better in fitting the observations and predicting genetic values. However, DD variance could not be separated from the dominance variance and null estimates were obtained for AA and AD effects. This study highlighted the advantages of progeny models using genome-wide information. PMID:26328760

Complex multi-enhancer contacts captured by genome architecture mapping.

PubMed

Beagrie, Robert A; Scialdone, Antonio; Schueler, Markus; Kraemer, Dorothee C A; Chotalia, Mita; Xie, Sheila Q; Barbieri, Mariano; de Santiago, Inês; Lavitas, Liron-Mark; Branco, Miguel R; Fraser, James; Dostie, Josée; Game, Laurence; Dillon, Niall; Edwards, Paul A W; Nicodemi, Mario; Pombo, Ana

2017-03-23

The organization of the genome in the nucleus and the interactions of genes with their regulatory elements are key features of transcriptional control and their disruption can cause disease. Here we report a genome-wide method, genome architecture mapping (GAM), for measuring chromatin contacts and other features of three-dimensional chromatin topology on the basis of sequencing DNA from a large collection of thin nuclear sections. We apply GAM to mouse embryonic stem cells and identify enrichment for specific interactions between active genes and enhancers across very large genomic distances using a mathematical model termed SLICE (statistical inference of co-segregation). GAM also reveals an abundance of three-way contacts across the genome, especially between regions that are highly transcribed or contain super-enhancers, providing a level of insight into genome architecture that, owing to the technical limitations of current technologies, has previously remained unattainable. Furthermore, GAM highlights a role for gene-expression-specific contacts in organizing the genome in mammalian nuclei.

MILP model for integrated balancing and sequencing mixed-model two-sided assembly line with variable launching interval and assignment restrictions

NASA Astrophysics Data System (ADS)

Azmi, N. I. L. Mohd; Ahmad, R.; Zainuddin, Z. M.

2017-09-01

This research explores the Mixed-Model Two-Sided Assembly Line (MMTSAL). There are two interrelated problems in MMTSAL which are line balancing and model sequencing. In previous studies, many researchers considered these problems separately and only few studied them simultaneously for one-sided line. However in this study, these two problems are solved simultaneously to obtain more efficient solution. The Mixed Integer Linear Programming (MILP) model with objectives of minimizing total utility work and idle time is generated by considering variable launching interval and assignment restriction constraint. The problem is analysed using small-size test cases to validate the integrated model. Throughout this paper, numerical experiment was conducted by using General Algebraic Modelling System (GAMS) with the solver CPLEX. Experimental results indicate that integrating the problems of model sequencing and line balancing help to minimise the proposed objectives function.

Environmental drivers of mesozooplankton biomass variability in the North Pacific Subtropical Gyre

NASA Astrophysics Data System (ADS)

Valencia, Bellineth; Landry, Michael R.; Décima, Moira; Hannides, Cecelia C. S.

2016-12-01

The environmental drivers of zooplankton variability are poorly explored for the central subtropical Pacific, where a direct bottom-up food-web connection is suggested by increasing trends in primary production and mesozooplankton biomass at station ALOHA (A Long-term Oligotrophic Habitat Assessment) over the past 20 years (1994-2013). Here we use generalized additive models (GAMs) to investigate how these trends relate to the major modes of North Pacific climate variability. A GAM based on monthly mean data explains 43% of the temporal variability in mesozooplankton biomass with significant influences from primary productivity (PP), sea surface temperature (SST), North Pacific Gyre Oscillation (NPGO), and El Niño. This result mainly reflects the seasonal plankton cycle at station ALOHA, in which increasing light and SST lead to enhanced nitrogen fixation, productivity, and zooplankton biomass during summertime. Based on annual mean data, GAMs for two variables suggest that PP and 3-4 year lagged NPGO individually account for 40% of zooplankton variability. The full annual mean GAM explains 70% of variability of zooplankton biomass with significant influences from PP, 4 year lagged NPGO, and 4 year lagged Pacific Decadal Oscillation (PDO). The NPGO affects wind stress, sea surface height, and subtropical gyre circulation and has been linked to mideuphotic zone anomalies in salinity and PP at station ALOHA. Our study broadens the known impact of this climate mode on plankton dynamics in the North Pacific. While lagged transport effects are also evident for subtropical waters, our study highlights a strong coupling between zooplankton fluctuations and PP, which differs from the transport-dominated climate influences that have been found for North Pacific boundary currents.

Improving the Taiwan Military’s Disaster Relief Response to Typhoons

DTIC Science & Technology

2015-06-01

circulation, are mostly westbound. When they reach the vicinity of Taiwan or the Philippines , which are always at the edge of the Pacific subtropical high...files from the POM base case model, one set for each design point. To automate the process of running all the GAMS files, a Windows batch file ( BAT ...is used to call on GAMS to solve each version of the model. The BAT file creates a new directory for each run to hold output, and one of the outputs

Ozone Observations by the Gas and Aerosol Measurement Sensor during SOLVE II

NASA Technical Reports Server (NTRS)

Pitts, M. C.; Thomason, L. W.; Zawodny, J. M.; Wenny, B. N.; Livingston, J. M.; Russell, P. B.; Yee, J.-H.; Swartz, W. H.; Shetter, R. E.

2006-01-01

The Gas and Aerosol Measurement Sensor (GAMS) was deployed aboard the NASA DC-8 aircraft during the second SAGE III Ozone Loss and Validation Experiment (SOLVE II). GAMS acquired line-of-sight (LOS) direct solar irradiance spectra during the sunlit portions of ten science flights of the DC-8 between 12 January and 4 February 2003. Differential line-of-sight (DLOS) optical depth spectra are produced from the GAMS raw solar irradiance spectra. Then, DLOS ozone number densities are retrieved from the GAMS spectra using a multiple linear regression spectral fitting technique. Both the DLOS optical depth spectra and retrieved ozone data are compared with coincident measurements from two other solar instruments aboard the DC-8 platform to demonstrate the robustness and stability of the GAMS data. The GAMS ozone measurements are then utilized to evaluate the quality of the Wulf band ozone cross sections, a critical component of the SAGE III aerosol, water vapor, and temperature/pressure retrievals. Results suggest the ozone cross section compilation of Shettle and Anderson currently used operationally in SAGE III data processing may be in error by as much as 10-20% in theWulf bands, and their lack of reported temperature dependence is a significant deficiency. A second, more recent, cross section database compiled for the SCIAMACHY satellite mission appears to be of much better quality in the Wulf bands, but still may have errors as large as 5% near the Wulf band absorption peaks, which is slightly larger than their stated uncertainty. Additional laboratory measurements of the Wulf band cross sections should be pursued to further reduce their uncertainty and better quantify their temperature dependence.

Optimization Research of Generation Investment Based on Linear Programming Model

NASA Astrophysics Data System (ADS)

Wu, Juan; Ge, Xueqian

Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.

Corrections of stratified tropospheric delays in SAR interferometry: Validation with global atmospheric models

NASA Astrophysics Data System (ADS)

Doin, Marie-Pierre; Lasserre, Cécile; Peltzer, Gilles; Cavalié, Olivier; Doubre, Cécile

2010-05-01

The main limiting factor on the accuracy of Interferometric SAR measurements (InSAR) comes from phase propagation delays through the troposphere. The delay can be divided into a stratified component, which correlates with the topography and often dominates the tropospheric signal, and a turbulent component. We use Global Atmospheric Models (GAM) to estimate the stratified phase delay and delay-elevation ratio at epochs of SAR acquisitions, and compare them to observed phase delay derived from SAR interferograms. Three test areas are selected with different geographic and climatic environments and with large SAR archive available. The Lake Mead, Nevada, USA is covered by 79 ERS1/2 and ENVISAT acquisitions, the Haiyuan Fault area, Gansu, China, by 24 ERS1/2 acquisitions, and the Afar region, Republic of Djibouti, by 91 Radarsat acquisitions. The hydrostatic and wet stratified delays are computed from GAM as a function of atmospheric pressure P, temperature T, and water vapor partial pressure e vertical profiles. The hydrostatic delay, which depends on ratio P/T, varies significantly at low elevation and cannot be neglected. The wet component of the delay depends mostly on the near surface specific humidity. GAM predicted delay-elevation ratios are in good agreement with the ratios derived from InSAR data away from deforming zones. Both estimations of the delay-elevation ratio can thus be used to perform a first order correction of the observed interferometric phase to retrieve a ground motion signal of low amplitude. We also demonstrate that aliasing of daily and seasonal variations in the stratified delay due to uneven sampling of SAR data significantly bias InSAR data stacks or time series produced after temporal smoothing. In all three test cases, the InSAR data stacks or smoothed time series present a residual stratified delay of the order of the expected deformation signal. In all cases, correcting interferograms from the stratified delay removes all these

Corrections of stratified tropospheric delays in SAR interferometry: Validation with global atmospheric models

NASA Astrophysics Data System (ADS)

Doin, M.-P.; Lasserre, C.; Peltzer, G.; Cavalié, O.; Doubre, C.

2009-09-01

The main limiting factor on the accuracy of Interferometric SAR measurements (InSAR) comes from phase propagation delays through the troposphere. The delay can be divided into a stratified component, which correlates with the topography and often dominates the tropospheric signal, and a turbulent component. We use Global Atmospheric Models (GAM) to estimate the stratified phase delay and delay-elevation ratio at epochs of SAR acquisitions, and compare them to observed phase delay derived from SAR interferograms. Three test areas are selected with different geographic and climatic environments and with large SAR archive available. The Lake Mead, Nevada, USA is covered by 79 ERS1/2 and ENVISAT acquisitions, the Haiyuan Fault area, Gansu, China, by 24 ERS1/2 acquisitions, and the Afar region, Republic of Djibouti, by 91 Radarsat acquisitions. The hydrostatic and wet stratified delays are computed from GAM as a function of atmospheric pressure P, temperature T, and water vapor partial pressure e vertical profiles. The hydrostatic delay, which depends on ratio P/ T, varies significantly at low elevation and cannot be neglected. The wet component of the delay depends mostly on the near surface specific humidity. GAM predicted delay-elevation ratios are in good agreement with the ratios derived from InSAR data away from deforming zones. Both estimations of the delay-elevation ratio can thus be used to perform a first order correction of the observed interferometric phase to retrieve a ground motion signal of low amplitude. We also demonstrate that aliasing of daily and seasonal variations in the stratified delay due to uneven sampling of SAR data significantly bias InSAR data stacks or time series produced after temporal smoothing. In all three test cases, the InSAR data stacks or smoothed time series present a residual stratified delay of the order of the expected deformation signal. In all cases, correcting interferograms from the stratified delay removes all these

Modeling the cardiovascular system using a nonlinear additive autoregressive model with exogenous input

NASA Astrophysics Data System (ADS)

Riedl, M.; Suhrbier, A.; Malberg, H.; Penzel, T.; Bretthauer, G.; Kurths, J.; Wessel, N.

2008-07-01

The parameters of heart rate variability and blood pressure variability have proved to be useful analytical tools in cardiovascular physics and medicine. Model-based analysis of these variabilities additionally leads to new prognostic information about mechanisms behind regulations in the cardiovascular system. In this paper, we analyze the complex interaction between heart rate, systolic blood pressure, and respiration by nonparametric fitted nonlinear additive autoregressive models with external inputs. Therefore, we consider measurements of healthy persons and patients suffering from obstructive sleep apnea syndrome (OSAS), with and without hypertension. It is shown that the proposed nonlinear models are capable of describing short-term fluctuations in heart rate as well as systolic blood pressure significantly better than similar linear ones, which confirms the assumption of nonlinear controlled heart rate and blood pressure. Furthermore, the comparison of the nonlinear and linear approaches reveals that the heart rate and blood pressure variability in healthy subjects is caused by a higher level of noise as well as nonlinearity than in patients suffering from OSAS. The residue analysis points at a further source of heart rate and blood pressure variability in healthy subjects, in addition to heart rate, systolic blood pressure, and respiration. Comparison of the nonlinear models within and among the different groups of subjects suggests the ability to discriminate the cohorts that could lead to a stratification of hypertension risk in OSAS patients.

Comparing GWAS Results of Complex Traits Using Full Genetic Model and Additive Models for Revealing Genetic Architecture

PubMed Central

Monir, Md. Mamun; Zhu, Jun

2017-01-01

Most of the genome-wide association studies (GWASs) for human complex diseases have ignored dominance, epistasis and ethnic interactions. We conducted comparative GWASs for total cholesterol using full model and additive models, which illustrate the impacts of the ignoring genetic variants on analysis results and demonstrate how genetic effects of multiple loci could differ across different ethnic groups. There were 15 quantitative trait loci with 13 individual loci and 3 pairs of epistasis loci identified by full model, whereas only 14 loci (9 common loci and 5 different loci) identified by multi-loci additive model. Again, 4 full model detected loci were not detected using multi-loci additive model. PLINK-analysis identified two loci and GCTA-analysis detected only one locus with genome-wide significance. Full model identified three previously reported genes as well as several new genes. Bioinformatics analysis showed some new genes are related with cholesterol related chemicals and/or diseases. Analyses of cholesterol data and simulation studies revealed that the full model performs were better than the additive-model performs in terms of detecting power and unbiased estimations of genetic variants of complex traits. PMID:28079101

Additions to Mars Global Reference Atmospheric Model (MARS-GRAM)

NASA Technical Reports Server (NTRS)

Justus, C. G.; James, Bonnie

1992-01-01

Three major additions or modifications were made to the Mars Global Reference Atmospheric Model (Mars-GRAM): (1) in addition to the interactive version, a new batch version is available, which uses NAMELIST input, and is completely modular, so that the main driver program can easily be replaced by any calling program, such as a trajectory simulation program; (2) both the interactive and batch versions now have an option for treating local-scale dust storm effects, rather than just the global-scale dust storms in the original Mars-GRAM; and (3) the Zurek wave perturbation model was added, to simulate the effects of tidal perturbations, in addition to the random (mountain wave) perturbation model of the original Mars-GRAM. A minor modification was also made which allows heights to go 'below' local terrain height and return 'realistic' pressure, density, and temperature, and not the surface values, as returned by the original Mars-GRAM. This feature will allow simulations of Mars rover paths which might go into local 'valley' areas which lie below the average height of the present, rather coarse-resolution, terrain height data used by Mars-GRAM. Sample input and output of both the interactive and batch versions of Mars-GRAM are presented.

Additions to Mars Global Reference Atmospheric Model (Mars-GRAM)

NASA Technical Reports Server (NTRS)

Justus, C. G.

1991-01-01

Three major additions or modifications were made to the Mars Global Reference Atmospheric Model (Mars-GRAM): (1) in addition to the interactive version, a new batch version is available, which uses NAMELIST input, and is completely modular, so that the main driver program can easily be replaced by any calling program, such as a trajectory simulation program; (2) both the interactive and batch versions now have an option for treating local-scale dust storm effects, rather than just the global-scale dust storms in the original Mars-GRAM; and (3) the Zurek wave perturbation model was added, to simulate the effects of tidal perturbations, in addition to the random (mountain wave) perturbation model of the original Mars-GRAM. A minor modification has also been made which allows heights to go below local terrain height and return realistic pressure, density, and temperature (not the surface values) as returned by the original Mars-GRAM. This feature will allow simulations of Mars rover paths which might go into local valley areas which lie below the average height of the present, rather coarse-resolution, terrain height data used by Mars-GRAM. Sample input and output of both the interactive and batch version of Mars-GRAM are presented.

Ultrasound-responsive gene-activated matrices for osteogenic gene therapy using matrix-assisted sonoporation.

PubMed

Nomikou, N; Feichtinger, G A; Saha, S; Nuernberger, S; Heimel, P; Redl, H; McHale, A P

2018-01-01

Gene-activated matrix (GAM)-based therapeutics for tissue regeneration are limited by efficacy, the lack of spatiotemporal control and availability of target cells, all of which impact negatively on their translation to the clinic. Here, an advanced ultrasound-responsive GAM is described containing target cells that facilitates matrix-assisted sonoporation (MAS) to induce osteogenic differentiation. Ultrasound-responsive GAMs consisting of fibrin/collagen hybrid-matrices containing microbubbles, bone morphogenetic protein BMP2/7 coexpression plasmids together with C2C12 cells were treated with ultrasound either in vitro or following parenteral intramuscular implantation in vivo. Using direct measurement for alkaline phosphatase activity, von Kossa staining and immunohistochemical analysis for osteocalcin expression, MAS-stimulated osteogenic differentiation was confirmed in the GAMs in vitro 7 days after treatment with ultrasound. At day 30 post-treatment with ultrasound, ectopic osteogenic differentiation was confirmed in vivo using X-ray microcomputed tomography and histological analysis. Osteogenic differentiation was indicated by the presence of ectopic bone structures in all animals treated with MAS. In addition, bone volumes in this group were statistically greater than those in the control groups. This novel approach of incorporating a MAS capability into GAMs could be exploited to facilitate ex vivo gene transfer with subsequent surgical implantation or alternatively provide a minimally invasive means of stimulating in situ transgene delivery for osteoinductive gene-based therapies. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Acute childhood asthma in Galway city from 1985-2005: relationship to air pollution and climate.

PubMed

Loftus, A; Loftus, B G; Muircheartaigh, I O; Newell, J; Scarrott, C; Jennings, S

2014-01-01

We examine the relationship of air pollution and climatic variables to asthma admission rates of children in Galway city over a 21 year period. Paediatric asthma admissions were recorded from 1985-2005, and admission rates per thousand calculated for pre-school (1-4 years), school aged (5-14 years) and all children (1-14 years) on a monthly and annual basis. These data were compared to average monthly and annual climatic variables (rainfall, humidity, sunshine, wind speed and temperature) and black smoke levels for the city. Simple correlation and Poisson Generalized Additive Models (GAM) were used. Admission rates each month are significantly correlated with smoke levels (p = 0.007). Poisson GAM also shows a relationship between admissions and pollution (p = 0.07). Annual smoke levels impact more on admission rates of preschoolers (p = 0.04) than school age children (p = 0.10). These data suggest that air pollution is an important factor in the epidemiology of acute childhood asthma.

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

Zhang, Xuesong; Izaurralde, Roberto C.; Manowitz, David H.

Accurate quantification and clear understanding of regional scale cropland carbon (C) cycling is critical for designing effective policies and management practices that can contribute toward stabilizing atmospheric CO2 concentrations. However, extrapolating site-scale observations to regional scales represents a major challenge confronting the agricultural modeling community. This study introduces a novel geospatial agricultural modeling system (GAMS) exploring the integration of the mechanistic Environmental Policy Integrated Climate model, spatially-resolved data, surveyed management data, and supercomputing functions for cropland C budgets estimates. This modeling system creates spatially-explicit modeling units at a spatial resolution consistent with remotely-sensed crop identification and assigns cropping systems tomore » each of them by geo-referencing surveyed crop management information at the county or state level. A parallel computing algorithm was also developed to facilitate the computationally intensive model runs and output post-processing and visualization. We evaluated GAMS against National Agricultural Statistics Service (NASS) reported crop yields and inventory estimated county-scale cropland C budgets averaged over 2000–2008. We observed good overall agreement, with spatial correlation of 0.89, 0.90, 0.41, and 0.87, for crop yields, Net Primary Production (NPP), Soil Organic C (SOC) change, and Net Ecosystem Exchange (NEE), respectively. However, we also detected notable differences in the magnitude of NPP and NEE, as well as in the spatial pattern of SOC change. By performing crop-specific annual comparisons, we discuss possible explanations for the discrepancies between GAMS and the inventory method, such as data requirements, representation of agroecosystem processes, completeness and accuracy of crop management data, and accuracy of crop area representation. Based on these analyses, we further discuss strategies to improve GAMS by

The linear relationship between the Vulnerable Elders Survey-13 score and mortality in an Asian population of community-dwelling older persons.

PubMed

Wang, Jye; Lin, Wender; Chang, Ling-Hui

2018-01-01

The Vulnerable Elders Survey-13 (VES-13) has been used as a screening tool to identify vulnerable community-dwelling older persons for more in-depth assessment and targeted interventions. Although many studies supported its use in different populations, few have addressed Asian populations. The optimal scaling system for the VES-13 in predicting health outcomes also has not been adequately tested. This study (1) assesses the applicability of the VES-13 to predict the mortality of community-dwelling older persons in Taiwan, (2) identifies the best scaling system for the VES-13 in predicting mortality using generalized additive models (GAMs), and (3) determines whether including covariates, such as socio-demographic factors and common geriatric syndromes, improves model fitting. This retrospective longitudinal cohort study analyzed the data of 2184 community-dwelling persons 65 years old or older from the 2003 wave of the national-wide Taiwan Longitudinal Study on Aging. Cox proportional hazards models and Generalized Additive Models (GAMs) were used. The VES-13 significantly predicted the mortality of Taiwan's community-dwelling elders. A one-point increase in the VES-13 score raised the risk of death by 26% (hazard ratio, 1.26; 95% confidence interval, 1.21-1.32). The hazard ratio of death increased linearly with each additional VES-13 score point, suggesting that using a continuous scale is appropriate. Inclusion of socio-demographic factors and geriatric syndromes improved the model-fitting. The VES-13 is appropriate for an Asian population. VES-13 scores linearly predict the mortality of this population. Adjusting the weighting of the physical activity items may improve the performance of the VES-13. Copyright © 2017 Elsevier B.V. All rights reserved.

PID feedback controller used as a tactical asset allocation technique: The G.A.M. model

NASA Astrophysics Data System (ADS)

Gandolfi, G.; Sabatini, A.; Rossolini, M.

2007-09-01

The objective of this paper is to illustrate a tactical asset allocation technique utilizing the PID controller. The proportional-integral-derivative (PID) controller is widely applied in most industrial processes; it has been successfully used for over 50 years and it is used by more than 95% of the plants processes. It is a robust and easily understood algorithm that can provide excellent control performance in spite of the diverse dynamic characteristics of the process plant. In finance, the process plant, controlled by the PID controller, can be represented by financial market assets forming a portfolio. More specifically, in the present work, the plant is represented by a risk-adjusted return variable. Money and portfolio managers’ main target is to achieve a relevant risk-adjusted return in their managing activities. In literature and in the financial industry business, numerous kinds of return/risk ratios are commonly studied and used. The aim of this work is to perform a tactical asset allocation technique consisting in the optimization of risk adjusted return by means of asset allocation methodologies based on the PID model-free feedback control modeling procedure. The process plant does not need to be mathematically modeled: the PID control action lies in altering the portfolio asset weights, according to the PID algorithm and its parameters, Ziegler-and-Nichols-tuned, in order to approach the desired portfolio risk-adjusted return efficiently.

Statistical modelling of variability in sediment-water nutrient and oxygen fluxes

NASA Astrophysics Data System (ADS)

Serpetti, Natalia; Witte, Ursula; Heath, Michael

2016-06-01

Organic detritus entering, or produced, in the marine environment is re-mineralised to inorganic nutrient in the seafloor sediments. The flux of dissolved inorganic nutrient between the sediment and overlying water column is a key process in the marine ecosystem, which binds the biogeochemical sub-system to the living food web. These fluxes are potentially affected by a wide range of physical and biological factors and disentangling these is a significant challenge. Here we develop a set of General Additive Models (GAM) of nitrate, nitrite, ammonia, phosphate, silicate and oxygen fluxes, based on a year-long campaign of field measurements off the north-east coast of Scotland. We show that sediment grain size, turbidity due to sediment re-suspension, temperature, and biogenic matter content were the key factors affecting oxygen consumption, ammonia and silicate fluxes. However, phosphate fluxes were only related to suspended sediment concentrations, whilst nitrate fluxes showed no clear relationship to any of the expected drivers of change, probably due to the effects of denitrification. Our analyses show that the stoichiometry of nutrient regeneration in the ecosystem is not necessarily constant and may be affected by combinations of processes. We anticipate that our statistical modelling results will form the basis for testing the functionality of process-based mathematical models of whole-sediment biogeochemistry.

Winter air pollution and infant bronchiolitis in Paris.

PubMed

Ségala, Claire; Poizeau, David; Mesbah, Mounir; Willems, Sylvie; Maidenberg, Manuel

2008-01-01

Respiratory syncytial virus (RSV) is one of the most common respiratory pathogens in infants and young children. It is not known why some previously healthy infants, when in contact with RSV, develop bronchiolitis whereas others have only mild symptoms. Our study aimed to evaluate the possible association between emergency hospital visits for bronchiolitis and air pollution in the Paris region during four winter seasons. We included children under the age of 3 years who attended emergency room services for bronchiolitis (following standardized definition) during the period 1997-2001. Two series of data from 34 hospitals, the daily number of emergency hospital consultations (n=50857) and the daily number of hospitalizations (n=16588) for bronchiolitis, were analyzed using alternative statistical methods; these were the generalized additive model (GAM) and case-crossover models. After adjustments for public holidays, holidays and meteorological variables the case-crossover model showed that PM10, BS, SO2 and NO2 were positively associated with both consultations and hospitalizations. GAM models, adjusting for long-term trend, seasonality, holiday, public holiday, weekday and meteorological variables, gave similar results for SO2 and PM10. This study shows that air pollution may act as a trigger for the occurrence of acute severe bronchiolitis cases.

Electroacoustics modeling of piezoelectric welders for ultrasonic additive manufacturing processes

NASA Astrophysics Data System (ADS)

Hehr, Adam; Dapino, Marcelo J.

2016-04-01

Ultrasonic additive manufacturing (UAM) is a recent 3D metal printing technology which utilizes ultrasonic vibrations from high power piezoelectric transducers to additively weld similar and dissimilar metal foils. CNC machining is used intermittent of welding to create internal channels, embed temperature sensitive components, sensors, and materials, and for net shaping parts. Structural dynamics of the welder and work piece influence the performance of the welder and part quality. To understand the impact of structural dynamics on UAM, a linear time-invariant model is used to relate system shear force and electric current inputs to the system outputs of welder velocity and voltage. Frequency response measurements are combined with in-situ operating measurements of the welder to identify model parameters and to verify model assumptions. The proposed LTI model can enhance process consistency, performance, and guide the development of improved quality monitoring and control strategies.

Modeling process-structure-property relationships for additive manufacturing

NASA Astrophysics Data System (ADS)

Yan, Wentao; Lin, Stephen; Kafka, Orion L.; Yu, Cheng; Liu, Zeliang; Lian, Yanping; Wolff, Sarah; Cao, Jian; Wagner, Gregory J.; Liu, Wing Kam

2018-02-01

This paper presents our latest work on comprehensive modeling of process-structure-property relationships for additive manufacturing (AM) materials, including using data-mining techniques to close the cycle of design-predict-optimize. To illustrate the processstructure relationship, the multi-scale multi-physics process modeling starts from the micro-scale to establish a mechanistic heat source model, to the meso-scale models of individual powder particle evolution, and finally to the macro-scale model to simulate the fabrication process of a complex product. To link structure and properties, a highefficiency mechanistic model, self-consistent clustering analyses, is developed to capture a variety of material response. The model incorporates factors such as voids, phase composition, inclusions, and grain structures, which are the differentiating features of AM metals. Furthermore, we propose data-mining as an effective solution for novel rapid design and optimization, which is motivated by the numerous influencing factors in the AM process. We believe this paper will provide a roadmap to advance AM fundamental understanding and guide the monitoring and advanced diagnostics of AM processing.

Modeling of Ti-W Solidification Microstructures Under Additive Manufacturing Conditions

NASA Astrophysics Data System (ADS)

Rolchigo, Matthew R.; Mendoza, Michael Y.; Samimi, Peyman; Brice, David A.; Martin, Brian; Collins, Peter C.; LeSar, Richard

2017-07-01

Additive manufacturing (AM) processes have many benefits for the fabrication of alloy parts, including the potential for greater microstructural control and targeted properties than traditional metallurgy processes. To accelerate utilization of this process to produce such parts, an effective computational modeling approach to identify the relationships between material and process parameters, microstructure, and part properties is essential. Development of such a model requires accounting for the many factors in play during this process, including laser absorption, material addition and melting, fluid flow, various modes of heat transport, and solidification. In this paper, we start with a more modest goal, to create a multiscale model for a specific AM process, Laser Engineered Net Shaping (LENS™), which couples a continuum-level description of a simplified beam melting problem (coupling heat absorption, heat transport, and fluid flow) with a Lattice Boltzmann-cellular automata (LB-CA) microscale model of combined fluid flow, solute transport, and solidification. We apply this model to a binary Ti-5.5 wt pct W alloy and compare calculated quantities, such as dendrite arm spacing, with experimental results reported in a companion paper.

Materials Testing and Cost Modeling for Composite Parts Through Additive Manufacturing

DTIC Science & Technology

2016-04-30

FDM include plastic jet printing (PJP), fused filament modeling ( FFM ), and fused filament fabrication (FFF). FFF was coined by the RepRap project to...additive manufacturing processes? • Fused deposition modeling (FDM) trademarked by Stratasys • Fused filament modeling ( FFM ) and fused filament

Effect of climate on incidence of respiratory syncytial virus infections in a refugee camp in Kenya: A non-Gaussian time-series analysis

PubMed Central

Omony, Jimmy; Mwalili, Samuel M.; Achia, Thomas N. O.; Gichangi, Anthony; Mwambi, Henry

2017-01-01

Respiratory syncytial virus (RSV) is one of the major causes of acute lower respiratory tract infections (ALRTI) in children. Children younger than 1 year are the most susceptible to RSV infection. RSV infections occur seasonally in temperate climate regions. Based on RSV surveillance and climatic data, we developed statistical models that were assessed and compared to predict the relationship between weather and RSV incidence among refugee children younger than 5 years in Dadaab refugee camp in Kenya. Most time-series analyses rely on the assumption of Gaussian-distributed data. However, surveillance data often do not have a Gaussian distribution. We used a generalized linear model (GLM) with a sinusoidal component over time to account for seasonal variation and extended it to a generalized additive model (GAM) with smoothing cubic splines. Climatic factors were included as covariates in the models before and after timescale decompositions, and the results were compared. Models with decomposed covariates fit RSV incidence data better than those without. The Poisson GAM with decomposed covariates of climatic factors fit the data well and had a higher explanatory and predictive power than GLM. The best model predicted the relationship between atmospheric conditions and RSV infection incidence among children younger than 5 years. This knowledge helps public health officials to prepare for, and respond more effectively to increasing RSV incidence in low-resource regions or communities. PMID:28570627

Graphene Aerogel Templated Fabrication of Phase Change Microspheres as Thermal Buffers in Microelectronic Devices.

PubMed

Wang, Xuchun; Li, Guangyong; Hong, Guo; Guo, Qiang; Zhang, Xuetong

2017-11-29

Phase change materials, changing from solid to liquid and vice versa, are capable of storing and releasing a large amount of thermal energy during the phase change, and thus hold promise for numerous applications including thermal protection of electronic devices. Shaping these materials into microspheres for additional fascinating properties is efficient but challenging. In this regard, a novel phase change microsphere with the design for electrical-regulation and thermal storage/release properties was fabricated via the combination of monodispersed graphene aerogel microsphere (GAM) and phase change paraffin. A programmable method, i.e., coupling ink jetting-liquid marbling-supercritical drying (ILS) techniques, was demonstrated to produce monodispersed graphene aerogel microspheres (GAMs) with precise size-control. The resulting GAMs showed ultralow density, low electrical resistance, and high specific surface area with only ca. 5% diameter variation coefficient, and exhibited promising performance in smart switches. The phase change microspheres were obtained by capillary filling of phase change paraffin inside the GAMs and exhibited excellent properties, such as low electrical resistance, high latent heat, well sphericity, and thermal buffering. Assembling the phase change microsphere into the microcircuit, we found that this tiny device was quite sensitive and could respond to heat as low as 0.027 J.

Flood loss model transfer: on the value of additional data

NASA Astrophysics Data System (ADS)

Schröter, Kai; Lüdtke, Stefan; Vogel, Kristin; Kreibich, Heidi; Thieken, Annegret; Merz, Bruno

2017-04-01

The transfer of models across geographical regions and flood events is a key challenge in flood loss estimation. Variations in local characteristics and continuous system changes require regional adjustments and continuous updating with current evidence. However, acquiring data on damage influencing factors is expensive and therefore assessing the value of additional data in terms of model reliability and performance improvement is of high relevance. The present study utilizes empirical flood loss data on direct damage to residential buildings available from computer aided telephone interviews that were carried out after the floods in 2002, 2005, 2006, 2010, 2011 and 2013 mainly in the Elbe and Danube catchments in Germany. Flood loss model performance is assessed for incrementally increased numbers of loss data which are differentiated according to region and flood event. Two flood loss modeling approaches are considered: (i) a multi-variable flood loss model approach using Random Forests and (ii) a uni-variable stage damage function. Both model approaches are embedded in a bootstrapping process which allows evaluating the uncertainty of model predictions. Predictive performance of both models is evaluated with regard to mean bias, mean absolute and mean squared errors, as well as hit rate and sharpness. Mean bias and mean absolute error give information about the accuracy of model predictions; mean squared error and sharpness about precision and hit rate is an indicator for model reliability. The results of incremental, regional and temporal updating demonstrate the usefulness of additional data to improve model predictive performance and increase model reliability, particularly in a spatial-temporal transfer setting.

TEMPEST Simulations of Collisionless Damping of Geodesic-Acoustic Mode in Edge Plasma Pedestal

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

Xu, X Q; Xiong, Z; Nevins, W M

The fully nonlinear (full-f) 4D TEMPEST gyrokinetic continuum code produces frequency, collisionless damping of GAM and zonal flow with fully nonlinear Boltzmann electrons for the inverse aspect ratio {epsilon}-scan and the tokamak safety factor q-scan in homogeneous plasmas. The TEMPEST simulation shows that GAM exists in edge plasma pedestal for steep density and temperature gradients, and an initial GAM relaxes to the standard neoclassical residual, rather than Rosenbluth-Hinton residual due to the presence of ion-ion collisions. The enhanced GAM damping explains experimental BES measurements on the edge q scaling of the GAM amplitude.

TEMPEST Simulations of Collisionless Damping of Geodesic-Acoustic Mode in Edge Plasma Pedestal

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

Xu, X; Xiong, Z; Nevins, W

The fully nonlinear 4D TEMPEST gyrokinetic continuum code produces frequency, collisionless damping of geodesic-acoustic mode (GAM) and zonal flow with fully nonlinear Boltzmann electrons for the inverse aspect ratio {epsilon}-scan and the tokamak safety factor q-scan in homogeneous plasmas. The TEMPEST simulation shows that GAM exists in edge plasma pedestal for steep density and temperature gradients, and an initial GAM relaxes to the standard neoclassical residual, rather than Rosenbluth-Hinton residual due to the presence of ion-ion collisions. The enhanced GAM damping explains experimental BES measurements on the edge q scaling of the GAM amplitude.

Using Set Model for Learning Addition of Integers

ERIC Educational Resources Information Center

Lestari, Umi Puji; Putri, Ratu Ilma Indra; Hartono, Yusuf

2015-01-01

This study aims to investigate how set model can help students' understanding of addition of integers in fourth grade. The study has been carried out to 23 students and a teacher of IVC SD Iba Palembang in January 2015. This study is a design research that also promotes PMRI as the underlying design context and activity. Results showed that the…

Aggregating the response in time series regression models, applied to weather-related cardiovascular mortality.

PubMed

Masselot, Pierre; Chebana, Fateh; Bélanger, Diane; St-Hilaire, André; Abdous, Belkacem; Gosselin, Pierre; Ouarda, Taha B M J

2018-07-01

In environmental epidemiology studies, health response data (e.g. hospitalization or mortality) are often noisy because of hospital organization and other social factors. The noise in the data can hide the true signal related to the exposure. The signal can be unveiled by performing a temporal aggregation on health data and then using it as the response in regression analysis. From aggregated series, a general methodology is introduced to account for the particularities of an aggregated response in a regression setting. This methodology can be used with usually applied regression models in weather-related health studies, such as generalized additive models (GAM) and distributed lag nonlinear models (DLNM). In particular, the residuals are modelled using an autoregressive-moving average (ARMA) model to account for the temporal dependence. The proposed methodology is illustrated by modelling the influence of temperature on cardiovascular mortality in Canada. A comparison with classical DLNMs is provided and several aggregation methods are compared. Results show that there is an increase in the fit quality when the response is aggregated, and that the estimated relationship focuses more on the outcome over several days than the classical DLNM. More precisely, among various investigated aggregation schemes, it was found that an aggregation with an asymmetric Epanechnikov kernel is more suited for studying the temperature-mortality relationship. Copyright © 2018. Published by Elsevier B.V.

Aggregating the response in time series regression models, applied to weather-related cardiovascular mortality

NASA Astrophysics Data System (ADS)

Masselot, Pierre; Chebana, Fateh; Bélanger, Diane; St-Hilaire, André; Abdous, Belkacem; Gosselin, Pierre; Ouarda, Taha B. M. J.

2018-07-01

In environmental epidemiology studies, health response data (e.g. hospitalization or mortality) are often noisy because of hospital organization and other social factors. The noise in the data can hide the true signal related to the exposure. The signal can be unveiled by performing a temporal aggregation on health data and then using it as the response in regression analysis. From aggregated series, a general methodology is introduced to account for the particularities of an aggregated response in a regression setting. This methodology can be used with usually applied regression models in weather-related health studies, such as generalized additive models (GAM) and distributed lag nonlinear models (DLNM). In particular, the residuals are modelled using an autoregressive-moving average (ARMA) model to account for the temporal dependence. The proposed methodology is illustrated by modelling the influence of temperature on cardiovascular mortality in Canada. A comparison with classical DLNMs is provided and several aggregation methods are compared. Results show that there is an increase in the fit quality when the response is aggregated, and that the estimated relationship focuses more on the outcome over several days than the classical DLNM. More precisely, among various investigated aggregation schemes, it was found that an aggregation with an asymmetric Epanechnikov kernel is more suited for studying the temperature-mortality relationship.

Concentration Addition, Independent Action and Generalized Concentration Addition Models for Mixture Effect Prediction of Sex Hormone Synthesis In Vitro

PubMed Central

Hadrup, Niels; Taxvig, Camilla; Pedersen, Mikael; Nellemann, Christine; Hass, Ulla; Vinggaard, Anne Marie

2013-01-01

Humans are concomitantly exposed to numerous chemicals. An infinite number of combinations and doses thereof can be imagined. For toxicological risk assessment the mathematical prediction of mixture effects, using knowledge on single chemicals, is therefore desirable. We investigated pros and cons of the concentration addition (CA), independent action (IA) and generalized concentration addition (GCA) models. First we measured effects of single chemicals and mixtures thereof on steroid synthesis in H295R cells. Then single chemical data were applied to the models; predictions of mixture effects were calculated and compared to the experimental mixture data. Mixture 1 contained environmental chemicals adjusted in ratio according to human exposure levels. Mixture 2 was a potency adjusted mixture containing five pesticides. Prediction of testosterone effects coincided with the experimental Mixture 1 data. In contrast, antagonism was observed for effects of Mixture 2 on this hormone. The mixtures contained chemicals exerting only limited maximal effects. This hampered prediction by the CA and IA models, whereas the GCA model could be used to predict a full dose response curve. Regarding effects on progesterone and estradiol, some chemicals were having stimulatory effects whereas others had inhibitory effects. The three models were not applicable in this situation and no predictions could be performed. Finally, the expected contributions of single chemicals to the mixture effects were calculated. Prochloraz was the predominant but not sole driver of the mixtures, suggesting that one chemical alone was not responsible for the mixture effects. In conclusion, the GCA model seemed to be superior to the CA and IA models for the prediction of testosterone effects. A situation with chemicals exerting opposing effects, for which the models could not be applied, was identified. In addition, the data indicate that in non-potency adjusted mixtures the effects cannot always be

Modelling of additive manufacturing processes: a review and classification

NASA Astrophysics Data System (ADS)

Stavropoulos, Panagiotis; Foteinopoulos, Panagis

2018-03-01

Additive manufacturing (AM) is a very promising technology; however, there are a number of open issues related to the different AM processes. The literature on modelling the existing AM processes is reviewed and classified. A categorization of the different AM processes in process groups, according to the process mechanism, has been conducted and the most important issues are stated. Suggestions are made as to which approach is more appropriate according to the key performance indicator desired to be modelled and a discussion is included as to the way that future modelling work can better contribute to improving today's AM process understanding.

CREATION OF THE MODEL ADDITIONAL PROTOCOL

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

Houck, F.; Rosenthal, M.; Wulf, N.

In 1991, the international nuclear nonproliferation community was dismayed to discover that the implementation of safeguards by the International Atomic Energy Agency (IAEA) under its NPT INFCIRC/153 safeguards agreement with Iraq had failed to detect Iraq's nuclear weapon program. It was now clear that ensuring that states were fulfilling their obligations under the NPT would require not just detecting diversion but also the ability to detect undeclared materials and activities. To achieve this, the IAEA initiated what would turn out to be a five-year effort to reappraise the NPT safeguards system. The effort engaged the IAEA and its Member Statesmore » and led to agreement in 1997 on a new safeguards agreement, the Model Protocol Additional to the Agreement(s) between States and the International Atomic Energy Agency for the Application of Safeguards. The Model Protocol makes explicit that one IAEA goal is to provide assurance of the absence of undeclared nuclear material and activities. The Model Protocol requires an expanded declaration that identifies a State's nuclear potential, empowers the IAEA to raise questions about the correctness and completeness of the State's declaration, and, if needed, allows IAEA access to locations. The information required and the locations available for access are much broader than those provided for under INFCIRC/153. The negotiation was completed in quite a short time because it started with a relatively complete draft of an agreement prepared by the IAEA Secretariat. This paper describes how the Model Protocol was constructed and reviews key decisions that were made both during the five-year period and in the actual negotiation.« less

An original traffic additional emission model and numerical simulation on a signalized road

NASA Astrophysics Data System (ADS)

Zhu, Wen-Xing; Zhang, Jing-Yu

2017-02-01

Based on VSP (Vehicle Specific Power) model traffic real emissions were theoretically classified into two parts: basic emission and additional emission. An original additional emission model was presented to calculate the vehicle's emission due to the signal control effects. Car-following model was developed and used to describe the traffic behavior including cruising, accelerating, decelerating and idling at a signalized intersection. Simulations were conducted under two situations: single intersection and two adjacent intersections with their respective control policy. Results are in good agreement with the theoretical analysis. It is also proved that additional emission model may be used to design the signal control policy in our modern traffic system to solve the serious environmental problems.

Comprehensive comparisons of geodesic acoustic mode characteristics and dynamics between Tore Supra experiments and gyrokinetic simulations

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

Storelli, A., E-mail: alexandre.storelli@lpp.polytechnique.fr; Vermare, L.; Hennequin, P.

2015-06-15

In a dedicated collisionality scan in Tore Supra, the geodesic acoustic mode (GAM) is detected and identified with the Doppler backscattering technique. Observations are compared to the results of a simulation with the gyrokinetic code GYSELA. We found that the GAM frequency in experiments is lower than predicted by simulation and theory. Moreover, the disagreement is higher in the low collisionality scenario. Bursts of non harmonic GAM oscillations have been characterized with filtering techniques, such as the Hilbert-Huang transform. When comparing this dynamical behaviour between experiments and simulation, the probability density function of GAM amplitude and the burst autocorrelation timemore » are found to be remarkably similar. In the simulation, where the radial profile of GAM frequency is continuous, we observed a phenomenon of radial phase mixing of the GAM oscillations, which could influence the burst autocorrelation time.« less

Research on Capacity Addition using Market Model with Transmission Congestion under Competitive Environment

NASA Astrophysics Data System (ADS)

Katsura, Yasufumi; Attaviriyanupap, Pathom; Kataoka, Yoshihiko

In this research, the fundamental premises for deregulation of the electric power industry are reevaluated. The authors develop a simple model to represent wholesale electricity market with highly congested network. The model is developed by simplifying the power system and market in New York ISO based on available data of New York ISO in 2004 with some estimation. Based on the developed model and construction cost data from the past, the economic impact of transmission line addition on market participants and the impact of deregulation on power plant additions under market with transmission congestion are studied. Simulation results show that the market signals may fail to facilitate proper capacity additions and results in the undesirable over-construction and insufficient-construction cycle of capacity addition.

Computational Process Modeling for Additive Manufacturing (OSU)

NASA Technical Reports Server (NTRS)

Bagg, Stacey; Zhang, Wei

2015-01-01

Powder-Bed Additive Manufacturing (AM) through Direct Metal Laser Sintering (DMLS) or Selective Laser Melting (SLM) is being used by NASA and the Aerospace industry to "print" parts that traditionally are very complex, high cost, or long schedule lead items. The process spreads a thin layer of metal powder over a build platform, then melts the powder in a series of welds in a desired shape. The next layer of powder is applied, and the process is repeated until layer-by-layer, a very complex part can be built. This reduces cost and schedule by eliminating very complex tooling and processes traditionally used in aerospace component manufacturing. To use the process to print end-use items, NASA seeks to understand SLM material well enough to develop a method of qualifying parts for space flight operation. Traditionally, a new material process takes many years and high investment to generate statistical databases and experiential knowledge, but computational modeling can truncate the schedule and cost -many experiments can be run quickly in a model, which would take years and a high material cost to run empirically. This project seeks to optimize material build parameters with reduced time and cost through modeling.

On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway Analysis.

PubMed

Li, Bing; Chun, Hyonho; Zhao, Hongyu

2014-09-01

We introduce a nonparametric method for estimating non-gaussian graphical models based on a new statistical relation called additive conditional independence, which is a three-way relation among random vectors that resembles the logical structure of conditional independence. Additive conditional independence allows us to use one-dimensional kernel regardless of the dimension of the graph, which not only avoids the curse of dimensionality but also simplifies computation. It also gives rise to a parallel structure to the gaussian graphical model that replaces the precision matrix by an additive precision operator. The estimators derived from additive conditional independence cover the recently introduced nonparanormal graphical model as a special case, but outperform it when the gaussian copula assumption is violated. We compare the new method with existing ones by simulations and in genetic pathway analysis.

Additive Manufacturing Modeling and Simulation A Literature Review for Electron Beam Free Form Fabrication

NASA Technical Reports Server (NTRS)

Seufzer, William J.

2014-01-01

Additive manufacturing is coming into industrial use and has several desirable attributes. Control of the deposition remains a complex challenge, and so this literature review was initiated to capture current modeling efforts in the field of additive manufacturing. This paper summarizes about 10 years of modeling and simulation related to both welding and additive manufacturing. The goals were to learn who is doing what in modeling and simulation, to summarize various approaches taken to create models, and to identify research gaps. Later sections in the report summarize implications for closed-loop-control of the process, implications for local research efforts, and implications for local modeling efforts.

On an additive partial correlation operator and nonparametric estimation of graphical models.

PubMed

Lee, Kuang-Yao; Li, Bing; Zhao, Hongyu

2016-09-01

We introduce an additive partial correlation operator as an extension of partial correlation to the nonlinear setting, and use it to develop a new estimator for nonparametric graphical models. Our graphical models are based on additive conditional independence, a statistical relation that captures the spirit of conditional independence without having to resort to high-dimensional kernels for its estimation. The additive partial correlation operator completely characterizes additive conditional independence, and has the additional advantage of putting marginal variation on appropriate scales when evaluating interdependence, which leads to more accurate statistical inference. We establish the consistency of the proposed estimator. Through simulation experiments and analysis of the DREAM4 Challenge dataset, we demonstrate that our method performs better than existing methods in cases where the Gaussian or copula Gaussian assumption does not hold, and that a more appropriate scaling for our method further enhances its performance.

On an additive partial correlation operator and nonparametric estimation of graphical models

PubMed Central

Li, Bing; Zhao, Hongyu

2016-01-01

Abstract We introduce an additive partial correlation operator as an extension of partial correlation to the nonlinear setting, and use it to develop a new estimator for nonparametric graphical models. Our graphical models are based on additive conditional independence, a statistical relation that captures the spirit of conditional independence without having to resort to high-dimensional kernels for its estimation. The additive partial correlation operator completely characterizes additive conditional independence, and has the additional advantage of putting marginal variation on appropriate scales when evaluating interdependence, which leads to more accurate statistical inference. We establish the consistency of the proposed estimator. Through simulation experiments and analysis of the DREAM4 Challenge dataset, we demonstrate that our method performs better than existing methods in cases where the Gaussian or copula Gaussian assumption does not hold, and that a more appropriate scaling for our method further enhances its performance. PMID:29422689

Parametrically Guided Generalized Additive Models with Application to Mergers and Acquisitions Data.

PubMed

Fan, Jianqing; Maity, Arnab; Wang, Yihui; Wu, Yichao

2013-01-01

Generalized nonparametric additive models present a flexible way to evaluate the effects of several covariates on a general outcome of interest via a link function. In this modeling framework, one assumes that the effect of each of the covariates is nonparametric and additive. However, in practice, often there is prior information available about the shape of the regression functions, possibly from pilot studies or exploratory analysis. In this paper, we consider such situations and propose an estimation procedure where the prior information is used as a parametric guide to fit the additive model. Specifically, we first posit a parametric family for each of the regression functions using the prior information (parametric guides). After removing these parametric trends, we then estimate the remainder of the nonparametric functions using a nonparametric generalized additive model, and form the final estimates by adding back the parametric trend. We investigate the asymptotic properties of the estimates and show that when a good guide is chosen, the asymptotic variance of the estimates can be reduced significantly while keeping the asymptotic variance same as the unguided estimator. We observe the performance of our method via a simulation study and demonstrate our method by applying to a real data set on mergers and acquisitions.

Parametrically Guided Generalized Additive Models with Application to Mergers and Acquisitions Data

PubMed Central

Fan, Jianqing; Maity, Arnab; Wang, Yihui; Wu, Yichao

2012-01-01

Generalized nonparametric additive models present a flexible way to evaluate the effects of several covariates on a general outcome of interest via a link function. In this modeling framework, one assumes that the effect of each of the covariates is nonparametric and additive. However, in practice, often there is prior information available about the shape of the regression functions, possibly from pilot studies or exploratory analysis. In this paper, we consider such situations and propose an estimation procedure where the prior information is used as a parametric guide to fit the additive model. Specifically, we first posit a parametric family for each of the regression functions using the prior information (parametric guides). After removing these parametric trends, we then estimate the remainder of the nonparametric functions using a nonparametric generalized additive model, and form the final estimates by adding back the parametric trend. We investigate the asymptotic properties of the estimates and show that when a good guide is chosen, the asymptotic variance of the estimates can be reduced significantly while keeping the asymptotic variance same as the unguided estimator. We observe the performance of our method via a simulation study and demonstrate our method by applying to a real data set on mergers and acquisitions. PMID:23645976

Distributed Energy Resources Customer Adoption Model - Graphical User Interface, Version 2.1.8

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

Ewald, Friedrich; Stadler, Michael; Cardoso, Goncalo F

The DER-CAM Graphical User Interface has been redesigned to consist of a dynamic tree structure on the left side of the application window to allow users to quickly navigate between different data categories and views. Views can either be tables with model parameters and input data, the optimization results, or a graphical interface to draw circuit topology and visualize investment results. The model parameters and input data consist of tables where values are assigned to specific keys. The aggregation of all model parameters and input data amounts to the data required to build a DER-CAM model, and is passed tomore » the GAMS solver when users initiate the DER-CAM optimization process. Passing data to the GAMS solver relies on the use of a Java server that handles DER-CAM requests, queuing, and results delivery. This component of the DER-CAM GUI can be deployed either locally or remotely, and constitutes an intermediate step between the user data input and manipulation, and the execution of a DER-CAM optimization in the GAMS engine. The results view shows the results of the DER-CAM optimization and distinguishes between a single and a multi-objective process. The single optimization runs the DER-CAM optimization once and presents the results as a combination of summary charts and hourly dispatch profiles. The multi-objective optimization process consists of a sequence of runs initiated by the GUI, including: 1) CO2 minimization, 2) cost minimization, 3) a user defined number of points in-between objectives 1) and 2). The multi-objective results view includes both access to the detailed results of each point generated by the process as well as the generation of a Pareto Frontier graph to illustrate the trade-off between objectives. DER-CAM GUI 2.1.8 also introduces the ability to graphically generate circuit topologies, enabling support to DER-CAM 5.0.0. This feature consists of: 1) The drawing area, where users can manually create nodes and define their

Predicting fish community properties within estuaries: Influence of habitat type and other environmental features

NASA Astrophysics Data System (ADS)

França, Susana; Vasconcelos, Rita P.; Fonseca, Vanessa F.; Tanner, Susanne E.; Reis-Santos, Patrick; Costa, Maria José; Cabral, Henrique N.

2012-07-01

Statistical models predicting species distributions are essential not only to increase knowledge on species but for their application in conservation and ecologically-based management. The variation of fish species richness and abundance in the most representative habitats (saltmarsh, mudflat and subtidal) in five estuaries along the Portuguese coast was analysed through seasonal sampling surveys in 2009. Generalized additive models (GAM) were developed to describe the variation of species richness and abundances with a set of geomorphologic, hydrologic and environmental characteristics from the sampled estuaries and habitats. GAM were chosen as the complex interactions dominating these ecosystems and species distribution are non-linear. Final models built for each estuary and for all estuaries together performed well during the calibration phase and also during the validation phase, where an unused data sub-set from each estuary was used. There was not a similar combination of variables retained by the models for the studied estuaries but factors such as the area of the habitat, the distance to estuary mouth, percentage of mud in the sediment and depth were commonly retained. The partial effect of these predictor variables on the variation of species richness and abundance in the estuaries varied markedly and the importance of preserving the heterogeneity of habitats within estuaries was highlighted. Models for each individual estuary performed better than models for estuaries combined. Predictive models could be useful as a preliminary tool to prepare long-term conservation plans at different scales.

Voxel-wise prostate cell density prediction using multiparametric magnetic resonance imaging and machine learning.

PubMed

Sun, Yu; Reynolds, Hayley M; Wraith, Darren; Williams, Scott; Finnegan, Mary E; Mitchell, Catherine; Murphy, Declan; Haworth, Annette

2018-04-26

There are currently no methods to estimate cell density in the prostate. This study aimed to develop predictive models to estimate prostate cell density from multiparametric magnetic resonance imaging (mpMRI) data at a voxel level using machine learning techniques. In vivo mpMRI data were collected from 30 patients before radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced imaging. Ground truth cell density maps were computed from histology and co-registered with mpMRI. Feature extraction and selection were performed on mpMRI data. Final models were fitted using three regression algorithms including multivariate adaptive regression spline (MARS), polynomial regression (PR) and generalised additive model (GAM). Model parameters were optimised using leave-one-out cross-validation on the training data and model performance was evaluated on test data using root mean square error (RMSE) measurements. Predictive models to estimate voxel-wise prostate cell density were successfully trained and tested using the three algorithms. The best model (GAM) achieved a RMSE of 1.06 (± 0.06) × 10 3 cells/mm 2 and a relative deviation of 13.3 ± 0.8%. Prostate cell density can be quantitatively estimated non-invasively from mpMRI data using high-quality co-registered data at a voxel level. These cell density predictions could be used for tissue classification, treatment response evaluation and personalised radiotherapy.

Generalized DSS shell for developing simulation and optimization hydro-economic models of complex water resources systems

NASA Astrophysics Data System (ADS)

Pulido-Velazquez, Manuel; Lopez-Nicolas, Antonio; Harou, Julien J.; Andreu, Joaquin

2013-04-01

Hydrologic-economic models allow integrated analysis of water supply, demand and infrastructure management at the river basin scale. These models simultaneously analyze engineering, hydrology and economic aspects of water resources management. Two new tools have been designed to develop models within this approach: a simulation tool (SIM_GAMS), for models in which water is allocated each month based on supply priorities to competing uses and system operating rules, and an optimization tool (OPT_GAMS), in which water resources are allocated optimally following economic criteria. The characterization of the water resource network system requires a connectivity matrix representing the topology of the elements, generated using HydroPlatform. HydroPlatform, an open-source software platform for network (node-link) models, allows to store, display and export all information needed to characterize the system. Two generic non-linear models have been programmed in GAMS to use the inputs from HydroPlatform in simulation and optimization models. The simulation model allocates water resources on a monthly basis, according to different targets (demands, storage, environmental flows, hydropower production, etc.), priorities and other system operating rules (such as reservoir operating rules). The optimization model's objective function is designed so that the system meets operational targets (ranked according to priorities) each month while following system operating rules. This function is analogous to the one used in the simulation module of the DSS AQUATOOL. Each element of the system has its own contribution to the objective function through unit cost coefficients that preserve the relative priority rank and the system operating rules. The model incorporates groundwater and stream-aquifer interaction (allowing conjunctive use simulation) with a wide range of modeling options, from lumped and analytical approaches to parameter-distributed models (eigenvalue approach). Such

Predicting the occurrence of wildfires with binary structured additive regression models.

PubMed

Ríos-Pena, Laura; Kneib, Thomas; Cadarso-Suárez, Carmen; Marey-Pérez, Manuel

2017-02-01

Wildfires are one of the main environmental problems facing societies today, and in the case of Galicia (north-west Spain), they are the main cause of forest destruction. This paper used binary structured additive regression (STAR) for modelling the occurrence of wildfires in Galicia. Binary STAR models are a recent contribution to the classical logistic regression and binary generalized additive models. Their main advantage lies in their flexibility for modelling non-linear effects, while simultaneously incorporating spatial and temporal variables directly, thereby making it possible to reveal possible relationships among the variables considered. The results showed that the occurrence of wildfires depends on many covariates which display variable behaviour across space and time, and which largely determine the likelihood of ignition of a fire. The joint possibility of working on spatial scales with a resolution of 1 × 1 km cells and mapping predictions in a colour range makes STAR models a useful tool for plotting and predicting wildfire occurrence. Lastly, it will facilitate the development of fire behaviour models, which can be invaluable when it comes to drawing up fire-prevention and firefighting plans. Copyright © 2016 Elsevier Ltd. All rights reserved.

Grain-Size Based Additivity Models for Scaling Multi-rate Uranyl Surface Complexation in Subsurface Sediments

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

Zhang, Xiaoying; Liu, Chongxuan; Hu, Bill X.

This study statistically analyzed a grain-size based additivity model that has been proposed to scale reaction rates and parameters from laboratory to field. The additivity model assumed that reaction properties in a sediment including surface area, reactive site concentration, reaction rate, and extent can be predicted from field-scale grain size distribution by linearly adding reaction properties for individual grain size fractions. This study focused on the statistical analysis of the additivity model with respect to reaction rate constants using multi-rate uranyl (U(VI)) surface complexation reactions in a contaminated sediment as an example. Experimental data of rate-limited U(VI) desorption in amore » stirred flow-cell reactor were used to estimate the statistical properties of multi-rate parameters for individual grain size fractions. The statistical properties of the rate constants for the individual grain size fractions were then used to analyze the statistical properties of the additivity model to predict rate-limited U(VI) desorption in the composite sediment, and to evaluate the relative importance of individual grain size fractions to the overall U(VI) desorption. The result indicated that the additivity model provided a good prediction of the U(VI) desorption in the composite sediment. However, the rate constants were not directly scalable using the additivity model, and U(VI) desorption in individual grain size fractions have to be simulated in order to apply the additivity model. An approximate additivity model for directly scaling rate constants was subsequently proposed and evaluated. The result found that the approximate model provided a good prediction of the experimental results within statistical uncertainty. This study also found that a gravel size fraction (2-8mm), which is often ignored in modeling U(VI) sorption and desorption, is statistically significant to the U(VI) desorption in the sediment.« less

Formation and reduction of carcinogenic furan in various model systems containing food additives.

PubMed

Kim, Jin-Sil; Her, Jae-Young; Lee, Kwang-Geun

2015-12-15

The aim of this study was to analyse and reduce furan in various model systems. Furan model systems consisting of monosaccharides (0.5M glucose and ribose), amino acids (0.5M alanine and serine) and/or 1.0M ascorbic acid were heated at 121°C for 25 min. The effects of food additives (each 0.1M) such as metal ions (iron sulphate, magnesium sulphate, zinc sulphate and calcium sulphate), antioxidants (BHT and BHA), and sodium sulphite on the formation of furan were measured. The level of furan formed in the model systems was 6.8-527.3 ng/ml. The level of furan in the model systems of glucose/serine and glucose/alanine increased 7-674% when food additives were added. In contrast, the level of furan decreased by 18-51% in the Maillard reaction model systems that included ribose and alanine/serine with food additives except zinc sulphate. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modeled distribution and abundance of a pelagic seabird reveal trends in relation to fisheries

USGS Publications Warehouse

Renner, Martin; Parrish, Julia K.; Piatt, John F.; Kuletz, Kathy J.; Edwards, Ann E.; Hunt, George L.

2013-01-01

The northern fulmar Fulmarus glacialis is one of the most visible and widespread seabirds in the eastern Bering Sea and Aleutian Islands. However, relatively little is known about its abundance, trends, or the factors that shape its distribution. We used a long-term pelagic dataset to model changes in fulmar at-sea distribution and abundance since the mid-1970s. We used an ensemble model, based on a weighted average of generalized additive model (GAM), multivariate adaptive regression splines (MARS), and random forest models to estimate the pelagic distribution and density of fulmars in the waters of the Aleutian Archipelago and Bering Sea. The most important predictor variables were colony effect, sea surface temperature, distribution of fisheries, location, and primary productivity. We calculated a time series from the ratio of observed to predicted values and found that fulmar at-sea abundance declined from the 1970s to the 2000s at a rate of 0.83% (± 0.39% SE) per annum. Interpolating fulmar densities on a spatial grid through time, we found that the center of fulmar distribution in the Bering Sea has shifted north, coinciding with a northward shift in fish catches and a warming ocean. Our study shows that fisheries are an important, but not the only factor, shaping fulmar distribution and abundance trends in the eastern Bering Sea and Aleutian Islands.

Nonlinear Full-f Edge Gyrokinetic Turbulence Simulations

NASA Astrophysics Data System (ADS)

Xu, X. Q.; Dimits, A. M.; Umansky, M. V.

2008-11-01

TEMPEST is a nonlinear full-f 5D electrostatic gyrokinetic code for simulations of neoclassical and turbulent transport for tokamak plasmas. Given an initial density perturbation, 4D TEMPEST simulations show that the kinetic GAM exists in the edge in the form of outgoing waves [1], its radial scale is set by plasma profiles, and the ion temperature inhomogeneity is necessary for GAM radial propagation. From an initial Maxwellian distribution with uniform poloidal profiles on flux surfaces, the 5D TEMPEST simulations in a flux coordinates with Boltzmann electron model in a circular geometry show the development of neoclassical equilibrium, the generation of the neoclassical electric field due to neoclassical polarization, and followed by a growth of instability due to the spatial gradients. 5D TEMPEST simulations of kinetic GAM turbulent generation, radial propagation, and its impact on transport will be reported. [1] X. Q. Xu, Phys. Rev. E., 78 (2008).

Experimental evidence of turbulence regulation by time-varying E  ×  B flows

NASA Astrophysics Data System (ADS)

Silva, C.; Henriques, R.; Hidalgo, C.; Fernandes, H.

2018-02-01

The interaction between zonal flows and turbulence is a self-regulating mechanism. Understanding this interaction is crucial to control plasma confinement. Results presented in this paper aim at understanding the conditions under which geodesic acoustic modes (GAMs) influence turbulent transport. This is achieved by performing perturbative experiments where GAMs are stimulated and externally controlled. Experiments on ISTTOK revealed that increasing the GAM shear rate over its natural value leads to a reduction of the turbulent transport and to an enhancement in particle confinement. Taking advantage of our unique experimental tools, it is shown that GAMs play an important role in the regulation of the fluctuations, constituting further evidence that the GAM shear rate is enough to regulate the ambient fluctuations without totally suppressing the turbulence.

Doubly Robust Additive Hazards Models to Estimate Effects of a Continuous Exposure on Survival.

PubMed

Wang, Yan; Lee, Mihye; Liu, Pengfei; Shi, Liuhua; Yu, Zhi; Abu Awad, Yara; Zanobetti, Antonella; Schwartz, Joel D

2017-11-01

The effect of an exposure on survival can be biased when the regression model is misspecified. Hazard difference is easier to use in risk assessment than hazard ratio and has a clearer interpretation in the assessment of effect modifications. We proposed two doubly robust additive hazards models to estimate the causal hazard difference of a continuous exposure on survival. The first model is an inverse probability-weighted additive hazards regression. The second model is an extension of the doubly robust estimator for binary exposures by categorizing the continuous exposure. We compared these with the marginal structural model and outcome regression with correct and incorrect model specifications using simulations. We applied doubly robust additive hazard models to the estimation of hazard difference of long-term exposure to PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 microns) on survival using a large cohort of 13 million older adults residing in seven states of the Southeastern United States. We showed that the proposed approaches are doubly robust. We found that each 1 μg m increase in annual PM2.5 exposure was associated with a causal hazard difference in mortality of 8.0 × 10 (95% confidence interval 7.4 × 10, 8.7 × 10), which was modified by age, medical history, socioeconomic status, and urbanicity. The overall hazard difference translates to approximately 5.5 (5.1, 6.0) thousand deaths per year in the study population. The proposed approaches improve the robustness of the additive hazards model and produce a novel additive causal estimate of PM2.5 on survival and several additive effect modifications, including social inequality.

Multiscale and Multiphysics Modeling of Additive Manufacturing of Advanced Materials

NASA Technical Reports Server (NTRS)

Liou, Frank; Newkirk, Joseph; Fan, Zhiqiang; Sparks, Todd; Chen, Xueyang; Fletcher, Kenneth; Zhang, Jingwei; Zhang, Yunlu; Kumar, Kannan Suresh; Karnati, Sreekar

2015-01-01

The objective of this proposed project is to research and develop a prediction tool for advanced additive manufacturing (AAM) processes for advanced materials and develop experimental methods to provide fundamental properties and establish validation data. Aircraft structures and engines demand materials that are stronger, useable at much higher temperatures, provide less acoustic transmission, and enable more aeroelastic tailoring than those currently used. Significant improvements in properties can only be achieved by processing the materials under nonequilibrium conditions, such as AAM processes. AAM processes encompass a class of processes that use a focused heat source to create a melt pool on a substrate. Examples include Electron Beam Freeform Fabrication and Direct Metal Deposition. These types of additive processes enable fabrication of parts directly from CAD drawings. To achieve the desired material properties and geometries of the final structure, assessing the impact of process parameters and predicting optimized conditions with numerical modeling as an effective prediction tool is necessary. The targets for the processing are multiple and at different spatial scales, and the physical phenomena associated occur in multiphysics and multiscale. In this project, the research work has been developed to model AAM processes in a multiscale and multiphysics approach. A macroscale model was developed to investigate the residual stresses and distortion in AAM processes. A sequentially coupled, thermomechanical, finite element model was developed and validated experimentally. The results showed the temperature distribution, residual stress, and deformation within the formed deposits and substrates. A mesoscale model was developed to include heat transfer, phase change with mushy zone, incompressible free surface flow, solute redistribution, and surface tension. Because of excessive computing time needed, a parallel computing approach was also tested. In addition

Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids.

PubMed

Bonnafous, Fanny; Fievet, Ghislain; Blanchet, Nicolas; Boniface, Marie-Claude; Carrère, Sébastien; Gouzy, Jérôme; Legrand, Ludovic; Marage, Gwenola; Bret-Mestries, Emmanuelle; Munos, Stéphane; Pouilly, Nicolas; Vincourt, Patrick; Langlade, Nicolas; Mangin, Brigitte

2018-02-01

This study compares five models of GWAS, to show the added value of non-additive modeling of allelic effects to identify genomic regions controlling flowering time of sunflower hybrids. Genome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids.

Evaluation of land use regression models for NO2 in El Paso, Texas, USA

PubMed Central

Gonzales, Melissa; Myers, Orrin; Smith, Luther; Olvera, Hector A.; Mukerjee, Shaibal; Li, Wen-Whai; Pingitore, Nicholas; Amaya, Maria; Burchiel, Scott; Berwick, Marianne

2012-01-01

Developing suitable exposure estimates for air pollution health studies is problematic due to spatial and temporal variation in concentrations and often limited monitoring data. Though land use regression models (LURs) are often used for this purpose, their applicability to later periods of time, larger geographic areas, and seasonal variation is largely untested. We evaluate a series of mixed model LURs to describe the spatial-temporal gradients of NO2 across El Paso County, Texas based on measurements collected during cool and warm seasons in 2006–2007 (2006–7). We also evaluated performance of a general additive model (GAM) developed for central El Paso in 1999 to assess spatial gradients across the County in 2006–7. Five LURs were developed iteratively from the study data and their predictions were averaged to provide robust nitrogen dioxide (NO2) concentration gradients across the county. Despite differences in sampling time frame, model covariates and model estimation methods, predicted NO2 concentration gradients were similar in the current study as compared to the 1999 study. Through a comprehensive LUR modeling campaign, it was shown that the nature of the most influential predictive variables remained the same for El Paso between the 1999 and 2006–7. The similar LUR results obtained here demonstrate that, at least for El Paso, LURs developed from prior years may still be applicable to assess exposure conditions in subsequent years and in different seasons when seasonal variation is taken into consideration. PMID:22728301

Involvement of Receptor-like Protein Tyrosine Phosphatase ζ/RPTPβ and Its Ligand Pleiotrophin/Heparin-binding Growth-associated Molecule (HB-GAM) in Neuronal Migration

PubMed Central

Maeda, Nobuaki; Noda, Masaharu

1998-01-01

Pleiotrophin/heparin-binding growth-associated molecule (HB-GAM) is a specific ligand of protein tyrosine phosphatase ζ (PTPζ)/receptor-like protein tyrosine phosphatase β (RPTPβ) expressed in the brain as a chondroitin sulfate proteoglycan. Pleiotrophin and PTPζ isoforms are localized along the radial glial fibers, a scaffold for neuronal migration, suggesting that these molecules are involved in migratory processes of neurons during brain development. In this study, we examined the roles of pleiotrophin-PTPζ interaction in the neuronal migration using cell migration assay systems with glass fibers and Boyden chambers. Pleiotrophin and poly-l-lysine coated on the substratums stimulated cell migration of cortical neurons, while laminin, fibronectin, and tenascin exerted almost no effect. Pleiotrophin-induced and poly-l-lysine–induced neuronal migrations showed significant differences in sensitivity to various molecules and reagents. Polyclonal antibodies against the extracellular domain of PTPζ, PTPζ-S, an extracellular secreted form of PTPζ, and sodium vanadate, a protein tyrosine phosphatase inhibitor, added into the culture medium strongly suppressed specifically the pleiotrophin-induced neuronal migration. Furthermore, chondroitin sulfate C but not chondroitin sulfate A inhibited pleiotrophin-induced neuronal migration, in good accordance with our previous findings that chondroitin sulfate constitutes a part of the pleiotrophin-binding site of PTPζ, and PTPζ-pleiotrophin binding is inhibited by chondroitin sulfate C but not by chondroitin sulfate A. Immunocytochemical analysis indicated that the transmembrane forms of PTPζ are expressed on the migrating neurons especially at the lamellipodia along the leading processes. These results suggest that PTPζ is involved in the neuronal migration as a neuronal receptor of pleiotrophin distributed along radial glial fibers. PMID:9660874

Spatial variation in mortality risk for haematological malignancies near a petrochemical refinery: a population-based case-control study

PubMed Central

Di Salvo, Francesca; Meneghini, Elisabetta; Vieira, Veronica; Baili, Paolo; Mariottini, Mauro; Baldini, Marco; Micheli, Andrea; Sant, Milena

2015-01-01

Introduction The study investigated the geographic variation of mortality risk for hematological malignancies (HMs) in order to identify potential high-risk areas near an Italian petrochemical refinery. Material and methods A population-based case-control study was conducted and residential histories for 171 cases and 338 sex- and age-matched controls were collected. Confounding factors were obtained from interviews with consenting relatives for 109 HM deaths and 267 controls. To produce risk mortality maps, two different approaches were applied. We mapped (1) adptive kernel density relative risk estimation (KDE) for case-control studies which estimates a spatial relative risk function using the ratio between cases and controls’ densities, and (2) estimated odds ratios for case-control study data using generalized additive models (GAMs) to smooth the effect of location, a proxy for exposure, while adjusting for confounding variables. Results No high-risk areas for HM mortality were identified among all subjects (men and women combined), by applying both approaches. Using the adaptive KDE approach, we found a significant increase in death risk only among women in a large area 2–6 km southeast of the refinery and the application of GAMs also identified a similarly-located significant high-risk area among women only (global p-value<0.025). Potential confounding risk factors we considered in the GAM did not alter the results. Conclusion Both approaches identified a high-risk area close to the refinery among women only. Those spatial methods are useful tools for public policy management to determine priority areas for intervention. Our findings suggest several directions for further research in order to identify other potential environmental exposures that may be assessed in forthcoming studies based on detailed exposure modeling. PMID:26073202

Tempest Simulations of Collisionless Damping of the Geodesic-Acoustic Mode in Edge-Plasma Pedestals

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

Xu, X. Q.; Xiong, Z.; Nevins, W. M.

The fully nonlinear (full-f) four-dimensional TEMPEST gyrokinetic continuum code correctly produces the frequency and collisionless damping of geodesic-acoustic modes (GAMs) and zonal flow, with fully nonlinear Boltzmann electrons for the inverse aspect ratio {epsilon} scan and the tokamak safety factor q scan in homogeneous plasmas. TEMPEST simulations show that the GAMs exist in the edge pedestal for steep density and temperature gradients in the form of outgoing waves. The enhanced GAM damping may explain experimental beam emission spectroscopy measurements on the edge q scaling of the GAM amplitude.

Tempest Simulations of Collisionless Damping of the Geodesic-Acoustic Mode in Edge-Plasma Pedestals

NASA Astrophysics Data System (ADS)

Xu, X. Q.; Xiong, Z.; Gao, Z.; Nevins, W. M.; McKee, G. R.

2008-05-01

The fully nonlinear (full-f) four-dimensional TEMPEST gyrokinetic continuum code correctly produces the frequency and collisionless damping of geodesic-acoustic modes (GAMs) and zonal flow, with fully nonlinear Boltzmann electrons for the inverse aspect ratio γ scan and the tokamak safety factor q scan in homogeneous plasmas. TEMPEST simulations show that the GAMs exist in the edge pedestal for steep density and temperature gradients in the form of outgoing waves. The enhanced GAM damping may explain experimental beam emission spectroscopy measurements on the edge q scaling of the GAM amplitude.

TEMPEST simulations of collisionless damping of the geodesic-acoustic mode in edge-plasma pedestals.

PubMed

Xu, X Q; Xiong, Z; Gao, Z; Nevins, W M; McKee, G R

2008-05-30

The fully nonlinear (full-f) four-dimensional TEMPEST gyrokinetic continuum code correctly produces the frequency and collisionless damping of geodesic-acoustic modes (GAMs) and zonal flow, with fully nonlinear Boltzmann electrons for the inverse aspect ratio scan and the tokamak safety factor q scan in homogeneous plasmas. TEMPEST simulations show that the GAMs exist in the edge pedestal for steep density and temperature gradients in the form of outgoing waves. The enhanced GAM damping may explain experimental beam emission spectroscopy measurements on the edge q scaling of the GAM amplitude.

Quantile regression via vector generalized additive models.

PubMed

Yee, Thomas W

2004-07-30

One of the most popular methods for quantile regression is the LMS method of Cole and Green. The method naturally falls within a penalized likelihood framework, and consequently allows for considerable flexible because all three parameters may be modelled by cubic smoothing splines. The model is also very understandable: for a given value of the covariate, the LMS method applies a Box-Cox transformation to the response in order to transform it to standard normality; to obtain the quantiles, an inverse Box-Cox transformation is applied to the quantiles of the standard normal distribution. The purposes of this article are three-fold. Firstly, LMS quantile regression is presented within the framework of the class of vector generalized additive models. This confers a number of advantages such as a unifying theory and estimation process. Secondly, a new LMS method based on the Yeo-Johnson transformation is proposed, which has the advantage that the response is not restricted to be positive. Lastly, this paper describes a software implementation of three LMS quantile regression methods in the S language. This includes the LMS-Yeo-Johnson method, which is estimated efficiently by a new numerical integration scheme. The LMS-Yeo-Johnson method is illustrated by way of a large cross-sectional data set from a New Zealand working population. Copyright 2004 John Wiley & Sons, Ltd.

Network Reconstruction Using Nonparametric Additive ODE Models

PubMed Central

Henderson, James; Michailidis, George

2014-01-01

Network representations of biological systems are widespread and reconstructing unknown networks from data is a focal problem for computational biologists. For example, the series of biochemical reactions in a metabolic pathway can be represented as a network, with nodes corresponding to metabolites and edges linking reactants to products. In a different context, regulatory relationships among genes are commonly represented as directed networks with edges pointing from influential genes to their targets. Reconstructing such networks from data is a challenging problem receiving much attention in the literature. There is a particular need for approaches tailored to time-series data and not reliant on direct intervention experiments, as the former are often more readily available. In this paper, we introduce an approach to reconstructing directed networks based on dynamic systems models. Our approach generalizes commonly used ODE models based on linear or nonlinear dynamics by extending the functional class for the functions involved from parametric to nonparametric models. Concomitantly we limit the complexity by imposing an additive structure on the estimated slope functions. Thus the submodel associated with each node is a sum of univariate functions. These univariate component functions form the basis for a novel coupling metric that we define in order to quantify the strength of proposed relationships and hence rank potential edges. We show the utility of the method by reconstructing networks using simulated data from computational models for the glycolytic pathway of Lactocaccus Lactis and a gene network regulating the pluripotency of mouse embryonic stem cells. For purposes of comparison, we also assess reconstruction performance using gene networks from the DREAM challenges. We compare our method to those that similarly rely on dynamic systems models and use the results to attempt to disentangle the distinct roles of linearity, sparsity, and derivative

Fine-mapping additive and dominant SNP effects using group-LASSO and Fractional Resample Model Averaging

PubMed Central

Sabourin, Jeremy; Nobel, Andrew B.; Valdar, William

2014-01-01

Genomewide association studies sometimes identify loci at which both the number and identities of the underlying causal variants are ambiguous. In such cases, statistical methods that model effects of multiple SNPs simultaneously can help disentangle the observed patterns of association and provide information about how those SNPs could be prioritized for follow-up studies. Current multi-SNP methods, however, tend to assume that SNP effects are well captured by additive genetics; yet when genetic dominance is present, this assumption translates to reduced power and faulty prioritizations. We describe a statistical procedure for prioritizing SNPs at GWAS loci that efficiently models both additive and dominance effects. Our method, LLARRMA-dawg, combines a group LASSO procedure for sparse modeling of multiple SNP effects with a resampling procedure based on fractional observation weights; it estimates for each SNP the robustness of association with the phenotype both to sampling variation and to competing explanations from other SNPs. In producing a SNP prioritization that best identifies underlying true signals, we show that: our method easily outperforms a single marker analysis; when additive-only signals are present, our joint model for additive and dominance is equivalent to or only slightly less powerful than modeling additive-only effects; and, when dominance signals are present, even in combination with substantial additive effects, our joint model is unequivocally more powerful than a model assuming additivity. We also describe how performance can be improved through calibrated randomized penalization, and discuss how dominance in ungenotyped SNPs can be incorporated through either heterozygote dosage or multiple imputation. PMID:25417853

Games for health for children—Current status and needed research

USDA-ARS?s Scientific Manuscript database

Videogames for health (G4H) offer exciting, innovative, potentially highly effective methods for increasing knowledge, delivering persuasive messages, changing behaviors, and influencing health outcomes. Although early outcome results are promising, additional research is needed to determine the gam...

Interactions across Multiple Stimulus Dimensions in Primary Auditory Cortex.

PubMed

Sloas, David C; Zhuo, Ran; Xue, Hongbo; Chambers, Anna R; Kolaczyk, Eric; Polley, Daniel B; Sen, Kamal

2016-01-01

Although sensory cortex is thought to be important for the perception of complex objects, its specific role in representing complex stimuli remains unknown. Complex objects are rich in information along multiple stimulus dimensions. The position of cortex in the sensory hierarchy suggests that cortical neurons may integrate across these dimensions to form a more gestalt representation of auditory objects. Yet, studies of cortical neurons typically explore single or few dimensions due to the difficulty of determining optimal stimuli in a high dimensional stimulus space. Evolutionary algorithms (EAs) provide a potentially powerful approach for exploring multidimensional stimulus spaces based on real-time spike feedback, but two important issues arise in their application. First, it is unclear whether it is necessary to characterize cortical responses to multidimensional stimuli or whether it suffices to characterize cortical responses to a single dimension at a time. Second, quantitative methods for analyzing complex multidimensional data from an EA are lacking. Here, we apply a statistical method for nonlinear regression, the generalized additive model (GAM), to address these issues. The GAM quantitatively describes the dependence between neural response and all stimulus dimensions. We find that auditory cortical neurons in mice are sensitive to interactions across dimensions. These interactions are diverse across the population, indicating significant integration across stimulus dimensions in auditory cortex. This result strongly motivates using multidimensional stimuli in auditory cortex. Together, the EA and the GAM provide a novel quantitative paradigm for investigating neural coding of complex multidimensional stimuli in auditory and other sensory cortices.

Landslide susceptibility near highways is increased by one order of magnitude in the Andes of southern Ecuador, Loja province

NASA Astrophysics Data System (ADS)

Brenning, A.; Schwinn, M.; Ruiz-Páez, A. P.; Muenchow, J.

2014-03-01

Mountain roads in developing countries are known to increase landslide occurrence due to often inadequate drainage systems and mechanical destabilization of hillslopes by undercutting and overloading. This study empirically investigates landslide initiation frequency along two paved interurban highways in the tropical Andes of southern Ecuador across different climatic regimes. Generalized additive models (GAM) and generalized linear models (GLM) were used to analyze the relationship between mapped landslide initiation points and distance to highway while accounting for topographic, climatic and geological predictors as possible confounders. A spatial block bootstrap was used to obtain non-parametric confidence intervals for the odds ratio of landslide occurrence near the highways (25 m distance) compared to a 200 m distance. The estimated odds ratio was 18-21 with lower 95% confidence bounds > 13 in all analyses. Spatial bootstrap estimation using the GAM supports the higher odds ratio estimate of 21.2 (95% confidence interval: 15.5-25.3). The highway-related effects were observed to fade at about 150 m distance. Road effects appear to be enhanced in geological units characterized by Holocene gravels and Laramide andesite/basalt. Overall, landslide susceptibility was found to be more than one order of magnitude higher in close proximity to paved interurban highways in the Andes of southern Ecuador.

Landslide susceptibility near highways is increased by 1 order of magnitude in the Andes of southern Ecuador, Loja province

NASA Astrophysics Data System (ADS)

Brenning, A.; Schwinn, M.; Ruiz-Páez, A. P.; Muenchow, J.

2015-01-01

Mountain roads in developing countries are known to increase landslide occurrence due to often inadequate drainage systems and mechanical destabilization of hillslopes by undercutting and overloading. This study empirically investigates landslide initiation frequency along two paved interurban highways in the tropical Andes of southern Ecuador across different climatic regimes. Generalized additive models (GAM) and generalized linear models (GLM) were used to analyze the relationship between mapped landslide initiation points and distance to highway while accounting for topographic, climatic, and geological predictors as possible confounders. A spatial block bootstrap was used to obtain nonparametric confidence intervals for the odds ratio of landslide occurrence near the highways (25 m distance) compared to a 200 m distance. The estimated odds ratio was 18-21, with lower 95% confidence bounds >13 in all analyses. Spatial bootstrap estimation using the GAM supports the higher odds ratio estimate of 21.2 (95% confidence interval: 15.5-25.3). The highway-related effects were observed to fade at about 150 m distance. Road effects appear to be enhanced in geological units characterized by Holocene gravels and Laramide andesite/basalt. Overall, landslide susceptibility was found to be more than 1 order of magnitude higher in close proximity to paved interurban highways in the Andes of southern Ecuador.

Spatio-temporal dynamics of turbulence trapped in geodesic acoustic modes

NASA Astrophysics Data System (ADS)

Sasaki, M.; Kobayashi, T.; Itoh, K.; Kasuya, N.; Kosuga, Y.; Fujisawa, A.; Itoh, S.-I.

2018-01-01

The spatio-temporal dynamics of turbulence with the interaction of geodesic acoustic modes (GAMs) are investigated, focusing on the phase-space structure of turbulence, where the phase-space consists of real-space and wavenumber-space. Based on the wave-kinetic framework, the coupling equation between the GAM and the turbulence is numerically solved. The turbulence trapped by the GAM velocity field is obtained. Due to the trapping effect, the turbulence intensity increases where the second derivative of the GAM velocity (curvature of the GAM) is negative. While, in the positive-curvature region, the turbulence is suppressed. Since the trapped turbulence propagates with the GAMs, this relationship is sustained spatially and temporally. The dynamics of the turbulence in the wavenumber spectrum are converted in the evolution of the frequency spectrum, and the simulation result is compared with the experimental observation in JFT-2M tokamak, where the similar patterns are obtained. The turbulence trapping effect is a key to understand the spatial structure of the turbulence in the presence of sheared flows.

A Single-Boundary Accumulator Model of Response Times in an Addition Verification Task

PubMed Central

Faulkenberry, Thomas J.

2017-01-01

Current theories of mathematical cognition offer competing accounts of the interplay between encoding and calculation in mental arithmetic. Additive models propose that manipulations of problem format do not interact with the cognitive processes used in calculation. Alternatively, interactive models suppose that format manipulations have a direct effect on calculation processes. In the present study, we tested these competing models by fitting participants' RT distributions in an arithmetic verification task with a single-boundary accumulator model (the shifted Wald distribution). We found that in addition to providing a more complete description of RT distributions, the accumulator model afforded a potentially more sensitive test of format effects. Specifically, we found that format affected drift rate, which implies that problem format has a direct impact on calculation processes. These data give further support for an interactive model of mental arithmetic. PMID:28769853

Increased Expression of Stress Inducible Protein 1 in Glioma-Associated Microglia/Macrophages

PubMed Central

da Fonseca, Anna Carolina Carvalho; Wang, Huaqing; Fan, Haitao; Chen, Xuebo; Zhang, Ian; Zhang, Leying; Lima, Flavia Regina Souza; Badie, Behnam

2014-01-01

Factors released by glioma-associated microglia/macrophages (GAMs) play an important role in the growth and infiltration of tumors. We have previously demonstrated that the co-chaperone stress-inducible protein 1 (STI1) secreted by microglia promotes proliferation and migration of human glioblastoma (GBM) cell lines in vitro. In the present study, in order to investigate the role of STI1 in a physiological context, we used a glioma model to evaluate STI1 expression in vivo. Here, we demonstrate that STI1 expression in both the tumor and in the infiltrating GAMs and lymphocytes significantly increased with tumor progression. Interestingly, high expression of STI1 was observed in macrophages and lymphocytes that infiltrated brain tumors, whereas STI1 expression in the circulating blood monocytes and lymphocytes remained unchanged. Our results correlate, for the first time, the expression of STI1 and glioma progression, and suggest that STI1 expression in GAMs and infiltrating lymphocytes is modulated by the brain tumor microenvironment. PMID:25042352

Effects of Weapons on Aggressive Thoughts, Angry Feelings, Hostile Appraisals, and Aggressive Behavior: A Meta-Analytic Review of the Weapons Effect Literature.

PubMed

Benjamin, Arlin J; Kepes, Sven; Bushman, Brad J

2017-09-01

Guns are associated with aggression. A landmark 1967 study showed that simply seeing a gun can increase aggression-called the "weapons effect." This meta-analysis integrates the findings of weapons effect studies conducted from 1967 to 2017. It includes 162 effect-size estimates from 78 independent studies involving 7,668 participants. The theoretical framework used to explain the weapons effect was the General Aggression Model (GAM), which proposes three routes to aggression-cognitive, affective, and arousal. The GAM also proposes that hostile appraisals can facilitate aggression. As predicted by the GAM, the mere presence of weapons increased aggressive thoughts, hostile appraisals, and aggression, suggesting a cognitive route from weapons to aggression. Weapons did not significantly increase angry feelings. Only one study tested the effects of weapons on arousal. These findings also contribute to the debate about social priming by showing that incidental exposure to a stimulus (weapon) can affect subsequent related behavior (aggression).

Genetic analysis of the roles of agaA, agaI, and agaS genes in the N-acetyl-D-galactosamine and D-galactosamine catabolic pathways in Escherichia coli strains O157:H7 and C

PubMed Central

2013-01-01

Background The catabolic pathways of N-acetyl-D-galactosamine (Aga) and D-galactosamine (Gam) in E. coli were proposed from bioinformatic analysis of the aga/gam regulon in E. coli K-12 and later from studies using E. coli C. Of the thirteen genes in this cluster, the roles of agaA, agaI, and agaS predicted to code for Aga-6-P-deacetylase, Gam-6-P deaminase/isomerase, and ketose-aldolase isomerase, respectively, have not been experimentally tested. Here we study their roles in Aga and Gam utilization in E. coli O157:H7 and in E. coli C. Results Knockout mutants in agaA, agaI, and agaS were constructed to test their roles in Aga and Gam utilization. Knockout mutants in the N-acetylglucosamine (GlcNAc) pathway genes nagA and nagB coding for GlcNAc-6-P deacetylase and glucosamine-6-P deaminase/isomerase, respectively, and double knockout mutants ΔagaA ΔnagA and ∆agaI ∆nagB were also constructed to investigate if there is any interplay of these enzymes between the Aga/Gam and the GlcNAc pathways. It is shown that Aga utilization was unaffected in ΔagaA mutants but ΔagaA ΔnagA mutants were blocked in Aga and GlcNAc utilization. E. coli C ΔnagA could not grow on GlcNAc but could grow when the aga/gam regulon was constitutively expressed. Complementation of ΔagaA ΔnagA mutants with either agaA or nagA resulted in growth on both Aga and GlcNAc. It was also found that ΔagaI, ΔnagB, and ∆agaI ΔnagB mutants were unaffected in utilization of Aga and Gam. Importantly, ΔagaS mutants were blocked in Aga and Gam utilization. Expression analysis of relevant genes in these strains with different genetic backgrounds by real time RT-PCR supported these observations. Conclusions Aga utilization was not affected in ΔagaA mutants because nagA was expressed and substituted for agaA. Complementation of ΔagaA ΔnagA mutants with either agaA or nagA also showed that both agaA and nagA can substitute for each other. The ∆agaI, ∆nagB, and ∆agaI ∆nagB mutants were

Leveraging Past and Current Measurements to Probabilistically Nowcast Low Visibility Procedures at an Airport

NASA Astrophysics Data System (ADS)

Mayr, G. J.; Kneringer, P.; Dietz, S. J.; Zeileis, A.

2016-12-01

Low visibility or low cloud ceiling reduce the capacity of airports by requiring special low visibility procedures (LVP) for incoming/departing aircraft. Probabilistic forecasts when such procedures will become necessary help to mitigate delays and economic losses.We compare the performance of probabilistic nowcasts with two statistical methods: ordered logistic regression, and trees and random forests. These models harness historic and current meteorological measurements in the vicinity of the airport and LVP states, and incorporate diurnal and seasonal climatological information via generalized additive models (GAM). The methods are applied at Vienna International Airport (Austria). The performance is benchmarked against climatology, persistence and human forecasters.

Metal Big Area Additive Manufacturing: Process Modeling and Validation

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

Simunovic, Srdjan; Nycz, Andrzej; Noakes, Mark W

Metal Big Area Additive Manufacturing (mBAAM) is a new additive manufacturing (AM) technology for printing large-scale 3D objects. mBAAM is based on the gas metal arc welding process and uses a continuous feed of welding wire to manufacture an object. An electric arc forms between the wire and the substrate, which melts the wire and deposits a bead of molten metal along the predetermined path. In general, the welding process parameters and local conditions determine the shape of the deposited bead. The sequence of the bead deposition and the corresponding thermal history of the manufactured object determine the long rangemore » effects, such as thermal-induced distortions and residual stresses. Therefore, the resulting performance or final properties of the manufactured object are dependent on its geometry and the deposition path, in addition to depending on the basic welding process parameters. Physical testing is critical for gaining the necessary knowledge for quality prints, but traversing the process parameter space in order to develop an optimized build strategy for each new design is impractical by pure experimental means. Computational modeling and optimization may accelerate development of a build process strategy and saves time and resources. Because computational modeling provides these opportunities, we have developed a physics-based Finite Element Method (FEM) simulation framework and numerical models to support the mBAAM process s development and design. In this paper, we performed a sequentially coupled heat transfer and stress analysis for predicting the final deformation of a small rectangular structure printed using the mild steel welding wire. Using the new simulation technologies, material was progressively added into the FEM simulation as the arc weld traversed the build path. In the sequentially coupled heat transfer and stress analysis, the heat transfer was performed to calculate the temperature evolution, which was used in a stress

Grain-Size Based Additivity Models for Scaling Multi-rate Uranyl Surface Complexation in Subsurface Sediments

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

Zhang, Xiaoying; Liu, Chongxuan; Hu, Bill X.

The additivity model assumed that field-scale reaction properties in a sediment including surface area, reactive site concentration, and reaction rate can be predicted from field-scale grain-size distribution by linearly adding reaction properties estimated in laboratory for individual grain-size fractions. This study evaluated the additivity model in scaling mass transfer-limited, multi-rate uranyl (U(VI)) surface complexation reactions in a contaminated sediment. Experimental data of rate-limited U(VI) desorption in a stirred flow-cell reactor were used to estimate the statistical properties of the rate constants for individual grain-size fractions, which were then used to predict rate-limited U(VI) desorption in the composite sediment. The resultmore » indicated that the additivity model with respect to the rate of U(VI) desorption provided a good prediction of U(VI) desorption in the composite sediment. However, the rate constants were not directly scalable using the additivity model. An approximate additivity model for directly scaling rate constants was subsequently proposed and evaluated. The result found that the approximate model provided a good prediction of the experimental results within statistical uncertainty. This study also found that a gravel-size fraction (2 to 8 mm), which is often ignored in modeling U(VI) sorption and desorption, is statistically significant to the U(VI) desorption in the sediment.« less

Predicting bottlenose dolphin distribution along Liguria coast (northwestern Mediterranean Sea) through different modeling techniques and indirect predictors.

PubMed

Marini, C; Fossa, F; Paoli, C; Bellingeri, M; Gnone, G; Vassallo, P

2015-03-01

Habitat modeling is an important tool to investigate the quality of the habitat for a species within a certain area, to predict species distribution and to understand the ecological processes behind it. Many species have been investigated by means of habitat modeling techniques mainly to address effective management and protection policies and cetaceans play an important role in this context. The bottlenose dolphin (Tursiops truncatus) has been investigated with habitat modeling techniques since 1997. The objectives of this work were to predict the distribution of bottlenose dolphin in a coastal area through the use of static morphological features and to compare the prediction performances of three different modeling techniques: Generalized Linear Model (GLM), Generalized Additive Model (GAM) and Random Forest (RF). Four static variables were tested: depth, bottom slope, distance from 100 m bathymetric contour and distance from coast. RF revealed itself both the most accurate and the most precise modeling technique with very high distribution probabilities predicted in presence cells (90.4% of mean predicted probabilities) and with 66.7% of presence cells with a predicted probability comprised between 90% and 100%. The bottlenose distribution obtained with RF allowed the identification of specific areas with particularly high presence probability along the coastal zone; the recognition of these core areas may be the starting point to develop effective management practices to improve T. truncatus protection. Copyright © 2014 Elsevier Ltd. All rights reserved.

Antimicrobial combinations: Bliss independence and Loewe additivity derived from mechanistic multi-hit models

PubMed Central

Yu, Guozhi; Hozé, Nathanaël; Rolff, Jens

2016-01-01

Antimicrobial peptides (AMPs) and antibiotics reduce the net growth rate of bacterial populations they target. It is relevant to understand if effects of multiple antimicrobials are synergistic or antagonistic, in particular for AMP responses, because naturally occurring responses involve multiple AMPs. There are several competing proposals describing how multiple types of antimicrobials add up when applied in combination, such as Loewe additivity or Bliss independence. These additivity terms are defined ad hoc from abstract principles explaining the supposed interaction between the antimicrobials. Here, we link these ad hoc combination terms to a mathematical model that represents the dynamics of antimicrobial molecules hitting targets on bacterial cells. In this multi-hit model, bacteria are killed when a certain number of targets are hit by antimicrobials. Using this bottom-up approach reveals that Bliss independence should be the model of choice if no interaction between antimicrobial molecules is expected. Loewe additivity, on the other hand, describes scenarios in which antimicrobials affect the same components of the cell, i.e. are not acting independently. While our approach idealizes the dynamics of antimicrobials, it provides a conceptual underpinning of the additivity terms. The choice of the additivity term is essential to determine synergy or antagonism of antimicrobials. This article is part of the themed issue ‘Evolutionary ecology of arthropod antimicrobial peptides’. PMID:27160596

Stochastic modeling of sunshine number data

NASA Astrophysics Data System (ADS)

Brabec, Marek; Paulescu, Marius; Badescu, Viorel

2013-11-01

In this paper, we will present a unified statistical modeling framework for estimation and forecasting sunshine number (SSN) data. Sunshine number has been proposed earlier to describe sunshine time series in qualitative terms (Theor Appl Climatol 72 (2002) 127-136) and since then, it was shown to be useful not only for theoretical purposes but also for practical considerations, e.g. those related to the development of photovoltaic energy production. Statistical modeling and prediction of SSN as a binary time series has been challenging problem, however. Our statistical model for SSN time series is based on an underlying stochastic process formulation of Markov chain type. We will show how its transition probabilities can be efficiently estimated within logistic regression framework. In fact, our logistic Markovian model can be relatively easily fitted via maximum likelihood approach. This is optimal in many respects and it also enables us to use formalized statistical inference theory to obtain not only the point estimates of transition probabilities and their functions of interest, but also related uncertainties, as well as to test of various hypotheses of practical interest, etc. It is straightforward to deal with non-homogeneous transition probabilities in this framework. Very importantly from both physical and practical points of view, logistic Markov model class allows us to test hypotheses about how SSN dependents on various external covariates (e.g. elevation angle, solar time, etc.) and about details of the dynamic model (order and functional shape of the Markov kernel, etc.). Therefore, using generalized additive model approach (GAM), we can fit and compare models of various complexity which insist on keeping physical interpretation of the statistical model and its parts. After introducing the Markovian model and general approach for identification of its parameters, we will illustrate its use and performance on high resolution SSN data from the Solar

Stochastic modeling of sunshine number data

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

Brabec, Marek, E-mail: mbrabec@cs.cas.cz; Paulescu, Marius; Badescu, Viorel

2013-11-13

In this paper, we will present a unified statistical modeling framework for estimation and forecasting sunshine number (SSN) data. Sunshine number has been proposed earlier to describe sunshine time series in qualitative terms (Theor Appl Climatol 72 (2002) 127-136) and since then, it was shown to be useful not only for theoretical purposes but also for practical considerations, e.g. those related to the development of photovoltaic energy production. Statistical modeling and prediction of SSN as a binary time series has been challenging problem, however. Our statistical model for SSN time series is based on an underlying stochastic process formulation ofmore » Markov chain type. We will show how its transition probabilities can be efficiently estimated within logistic regression framework. In fact, our logistic Markovian model can be relatively easily fitted via maximum likelihood approach. This is optimal in many respects and it also enables us to use formalized statistical inference theory to obtain not only the point estimates of transition probabilities and their functions of interest, but also related uncertainties, as well as to test of various hypotheses of practical interest, etc. It is straightforward to deal with non-homogeneous transition probabilities in this framework. Very importantly from both physical and practical points of view, logistic Markov model class allows us to test hypotheses about how SSN dependents on various external covariates (e.g. elevation angle, solar time, etc.) and about details of the dynamic model (order and functional shape of the Markov kernel, etc.). Therefore, using generalized additive model approach (GAM), we can fit and compare models of various complexity which insist on keeping physical interpretation of the statistical model and its parts. After introducing the Markovian model and general approach for identification of its parameters, we will illustrate its use and performance on high resolution SSN data from the

Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling.

PubMed

Wu, Hulin; Lu, Tao; Xue, Hongqi; Liang, Hua

2014-04-02

The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. High-dimensional linear ODE models have been proposed to identify GRNs, but with a limitation of the linear regulation effect assumption. In this article, we propose a sparse additive ODE (SA-ODE) model, coupled with ODE estimation methods and adaptive group LASSO techniques, to model dynamic GRNs that could flexibly deal with nonlinear regulation effects. The asymptotic properties of the proposed method are established and simulation studies are performed to validate the proposed approach. An application example for identifying the nonlinear dynamic GRN of T-cell activation is used to illustrate the usefulness of the proposed method.

Geo-additive modelling of malaria in Burundi

PubMed Central

2011-01-01

Background Malaria is a major public health issue in Burundi in terms of both morbidity and mortality, with around 2.5 million clinical cases and more than 15,000 deaths each year. It is still the single main cause of mortality in pregnant women and children below five years of age. Because of the severe health and economic burden of malaria, there is still a growing need for methods that will help to understand the influencing factors. Several studies/researches have been done on the subject yielding different results as which factors are most responsible for the increase in malaria transmission. This paper considers the modelling of the dependence of malaria cases on spatial determinants and climatic covariates including rainfall, temperature and humidity in Burundi. Methods The analysis carried out in this work exploits real monthly data collected in the area of Burundi over 12 years (1996-2007). Semi-parametric regression models are used. The spatial analysis is based on a geo-additive model using provinces as the geographic units of study. The spatial effect is split into structured (correlated) and unstructured (uncorrelated) components. Inference is fully Bayesian and uses Markov chain Monte Carlo techniques. The effects of the continuous covariates are modelled by cubic p-splines with 20 equidistant knots and second order random walk penalty. For the spatially correlated effect, Markov random field prior is chosen. The spatially uncorrelated effects are assumed to be i.i.d. Gaussian. The effects of climatic covariates and the effects of other spatial determinants are estimated simultaneously in a unified regression framework. Results The results obtained from the proposed model suggest that although malaria incidence in a given month is strongly positively associated with the minimum temperature of the previous months, regional patterns of malaria that are related to factors other than climatic variables have been identified, without being able to explain

Geometric Modeling of Cellular Materials for Additive Manufacturing in Biomedical Field: A Review.

PubMed

Savio, Gianpaolo; Rosso, Stefano; Meneghello, Roberto; Concheri, Gianmaria

2018-01-01

Advances in additive manufacturing technologies facilitate the fabrication of cellular materials that have tailored functional characteristics. The application of solid freeform fabrication techniques is especially exploited in designing scaffolds for tissue engineering. In this review, firstly, a classification of cellular materials from a geometric point of view is proposed; then, the main approaches on geometric modeling of cellular materials are discussed. Finally, an investigation on porous scaffolds fabricated by additive manufacturing technologies is pointed out. Perspectives in geometric modeling of scaffolds for tissue engineering are also proposed.

Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)

NASA Astrophysics Data System (ADS)

Saleh Ahmar, Ansari; Guritno, Suryo; Abdurakhman; Rahman, Abdul; Awi; Alimuddin; Minggi, Ilham; Arif Tiro, M.; Kasim Aidid, M.; Annas, Suwardi; Utami Sutiksno, Dian; Ahmar, Dewi S.; Ahmar, Kurniawan H.; Abqary Ahmar, A.; Zaki, Ahmad; Abdullah, Dahlan; Rahim, Robbi; Nurdiyanto, Heri; Hidayat, Rahmat; Napitupulu, Darmawan; Simarmata, Janner; Kurniasih, Nuning; Andretti Abdillah, Leon; Pranolo, Andri; Haviluddin; Albra, Wahyudin; Arifin, A. Nurani M.

2018-01-01

The aim this study is discussed on the detection and correction of data containing the additive outlier (AO) on the model ARIMA (p, d, q). The process of detection and correction of data using an iterative procedure popularized by Box, Jenkins, and Reinsel (1994). By using this method we obtained an ARIMA models were fit to the data containing AO, this model is added to the original model of ARIMA coefficients obtained from the iteration process using regression methods. In the simulation data is obtained that the data contained AO initial models are ARIMA (2,0,0) with MSE = 36,780, after the detection and correction of data obtained by the iteration of the model ARIMA (2,0,0) with the coefficients obtained from the regression Zt = 0,106+0,204Z t-1+0,401Z t-2-329X 1(t)+115X 2(t)+35,9X 3(t) and MSE = 19,365. This shows that there is an improvement of forecasting error rate data.

Development of a core Clostridium thermocellum kinetic metabolic model consistent with multiple genetic perturbations

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

Dash, Satyakam; Khodayari, Ali; Zhou, Jilai

Background. Clostridium thermocellum is a Gram-positive anaerobe with the ability to hydrolyze and metabolize cellulose into biofuels such as ethanol, making it an attractive candidate for consolidated bioprocessing (CBP). At present, metabolic engineering in C. thermocellum is hindered due to the incomplete description of its metabolic repertoire and regulation within a predictive metabolic model. Genome-scale metabolic (GSM) models augmented with kinetic models of metabolism have been shown to be effective at recapitulating perturbed metabolic phenotypes. Results. In this effort, we first update a second-generation genome-scale metabolic model (iCth446) for C. thermocellum by correcting cofactor dependencies, restoring elemental and charge balances,more » and updating GAM and NGAM values to improve phenotype predictions. The iCth446 model is next used as a scaffold to develop a core kinetic model (k-ctherm118) of the C. thermocellum central metabolism using the Ensemble Modeling (EM) paradigm. Model parameterization is carried out by simultaneously imposing fermentation yield data in lactate, malate, acetate, and hydrogen production pathways for 19 measured metabolites spanning a library of 19 distinct single and multiple gene knockout mutants along with 18 intracellular metabolite concentration data for a Δgldh mutant and ten experimentally measured Michaelis–Menten kinetic parameters. Conclusions. The k-ctherm118 model captures significant metabolic changes caused by (1) nitrogen limitation leading to increased yields for lactate, pyruvate, and amino acids, and (2) ethanol stress causing an increase in intracellular sugar phosphate concentrations (~1.5-fold) due to upregulation of cofactor pools. Robustness analysis of k-ctherm118 alludes to the presence of a secondary activity of ketol-acid reductoisomerase and possible regulation by valine and/or leucine pool levels. In addition, cross-validation and robustness analysis allude to missing elements in k

Development of a core Clostridium thermocellum kinetic metabolic model consistent with multiple genetic perturbations

DOE PAGES

Dash, Satyakam; Khodayari, Ali; Zhou, Jilai; ...

2017-05-02

Background. Clostridium thermocellum is a Gram-positive anaerobe with the ability to hydrolyze and metabolize cellulose into biofuels such as ethanol, making it an attractive candidate for consolidated bioprocessing (CBP). At present, metabolic engineering in C. thermocellum is hindered due to the incomplete description of its metabolic repertoire and regulation within a predictive metabolic model. Genome-scale metabolic (GSM) models augmented with kinetic models of metabolism have been shown to be effective at recapitulating perturbed metabolic phenotypes. Results. In this effort, we first update a second-generation genome-scale metabolic model (iCth446) for C. thermocellum by correcting cofactor dependencies, restoring elemental and charge balances,more » and updating GAM and NGAM values to improve phenotype predictions. The iCth446 model is next used as a scaffold to develop a core kinetic model (k-ctherm118) of the C. thermocellum central metabolism using the Ensemble Modeling (EM) paradigm. Model parameterization is carried out by simultaneously imposing fermentation yield data in lactate, malate, acetate, and hydrogen production pathways for 19 measured metabolites spanning a library of 19 distinct single and multiple gene knockout mutants along with 18 intracellular metabolite concentration data for a Δgldh mutant and ten experimentally measured Michaelis–Menten kinetic parameters. Conclusions. The k-ctherm118 model captures significant metabolic changes caused by (1) nitrogen limitation leading to increased yields for lactate, pyruvate, and amino acids, and (2) ethanol stress causing an increase in intracellular sugar phosphate concentrations (~1.5-fold) due to upregulation of cofactor pools. Robustness analysis of k-ctherm118 alludes to the presence of a secondary activity of ketol-acid reductoisomerase and possible regulation by valine and/or leucine pool levels. In addition, cross-validation and robustness analysis allude to missing elements in k

Process Modeling and Validation for Metal Big Area Additive Manufacturing

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

Simunovic, Srdjan; Nycz, Andrzej; Noakes, Mark W.

Metal Big Area Additive Manufacturing (mBAAM) is a new additive manufacturing (AM) technology based on the metal arc welding. A continuously fed metal wire is melted by an electric arc that forms between the wire and the substrate, and deposited in the form of a bead of molten metal along the predetermined path. Objects are manufactured one layer at a time starting from the base plate. The final properties of the manufactured object are dependent on its geometry and the metal deposition path, in addition to depending on the basic welding process parameters. Computational modeling can be used to acceleratemore » the development of the mBAAM technology as well as a design and optimization tool for the actual manufacturing process. We have developed a finite element method simulation framework for mBAAM using the new features of software ABAQUS. The computational simulation of material deposition with heat transfer is performed first, followed by the structural analysis based on the temperature history for predicting the final deformation and stress state. In this formulation, we assume that two physics phenomena are coupled in only one direction, i.e. the temperatures are driving the deformation and internal stresses, but their feedback on the temperatures is negligible. The experiment instrumentation (measurement types, sensor types, sensor locations, sensor placements, measurement intervals) and the measurements are presented. The temperatures and distortions from the simulations show good correlation with experimental measurements. Ongoing modeling work is also briefly discussed.« less

A simulations approach for meta-analysis of genetic association studies based on additive genetic model.

PubMed

John, Majnu; Lencz, Todd; Malhotra, Anil K; Correll, Christoph U; Zhang, Jian-Ping

2018-06-01

Meta-analysis of genetic association studies is being increasingly used to assess phenotypic differences between genotype groups. When the underlying genetic model is assumed to be dominant or recessive, assessing the phenotype differences based on summary statistics, reported for individual studies in a meta-analysis, is a valid strategy. However, when the genetic model is additive, a similar strategy based on summary statistics will lead to biased results. This fact about the additive model is one of the things that we establish in this paper, using simulations. The main goal of this paper is to present an alternate strategy for the additive model based on simulating data for the individual studies. We show that the alternate strategy is far superior to the strategy based on summary statistics.

A Quantitative Proxy for Sea-Ice Based on Diatoms: A Cautionary Tale.

NASA Astrophysics Data System (ADS)

Nesterovich, A.; Caissie, B.

2016-12-01

Sea ice in the Polar Regions supports unique and productive ecosystems, but the current decline in the Arctic sea ice extent prompts questions about previous sea ice declines and the response of ice related ecosystems. Since satellite data only extend back to 1978, the study of sea ice before this time requires a proxy. Being one of the most productive, diatom-dominated regions in the world and having a wide range of sea ice concentrations, the Bering and Chukchi seas are a perfect place to find a relationship between the presence of sea ice and diatom community composition. The aim of this work is to develop a diatom-based proxy for the sea ice extent. A total of 473 species have been identified in 104 sediment samples, most of which were collected on board the US Coast Guard Cutter Healy ice breaker (2006, 2007) and the Norseman II (2008). The study also included some of the archived diatom smear slides made from sediments collected in 1969. The assemblages were compared to satellite-derived sea ice extent data averaged over the 10 years preceding the sampling. Previous studies in the Arctic and Antarctic regions demonstrated that the Generalized Additive Model (GAM) is one of the best choices for proxy construction. It has the advantage of using only several species instead of the whole assemblage, thus including only sea ice-associated species and minimizing the noise created by species responding to other environmental factors. Our GAM on three species (Connia compita, Fragilariopsis reginae-jahniae, and Neodenticula seminae) has low standard deviation, high level of explained variation, and holds under the ten-fold cross-validation; the standard residual analysis is acceptable. However, a spatial residual analysis revealed that the model consistently over predicts in the Chukchi Sea and under predicts in the Bering Sea. Including a spatial model into the GAM didn't improve the situation. This has led us to test other methods, including a non-parametric model

Geometric Modeling of Cellular Materials for Additive Manufacturing in Biomedical Field: A Review

PubMed Central

Rosso, Stefano; Meneghello, Roberto; Concheri, Gianmaria

2018-01-01

Advances in additive manufacturing technologies facilitate the fabrication of cellular materials that have tailored functional characteristics. The application of solid freeform fabrication techniques is especially exploited in designing scaffolds for tissue engineering. In this review, firstly, a classification of cellular materials from a geometric point of view is proposed; then, the main approaches on geometric modeling of cellular materials are discussed. Finally, an investigation on porous scaffolds fabricated by additive manufacturing technologies is pointed out. Perspectives in geometric modeling of scaffolds for tissue engineering are also proposed. PMID:29487626

Study of abrasive resistance of foundries models obtained with use of additive technology

NASA Astrophysics Data System (ADS)

Ol'khovik, Evgeniy

2017-10-01

A problem of determination of resistance of the foundry models and patterns from ABS (PLA) plastic, obtained by the method of 3D printing with using FDM additive technology, to abrasive wear and resistance in the environment of foundry sand mould is considered in the present study. The description of a technique and equipment for tests of castings models and patterns for wear is provided in the article. The manufacturing techniques of models with the use of the 3D printer (additive technology) are described. The scheme with vibration load was applied to samples tests. For the most qualitative research of influence of sandy mix on plastic, models in real conditions of abrasive wear have been organized. The results also examined the application of acrylic paintwork to the plastic model and a two-component coating. The practical offers and recommendation on production of master models with the use of FDM technology allowing one to reach indicators of durability, exceeding 2000 cycles of moulding in foundry sand mix, are described.

Comparison of prosthetic models produced by traditional and additive manufacturing methods.

PubMed

Park, Jin-Young; Kim, Hae-Young; Kim, Ji-Hwan; Kim, Jae-Hong; Kim, Woong-Chul

2015-08-01

The purpose of this study was to verify the clinical-feasibility of additive manufacturing by comparing the accuracy of four different manufacturing methods for metal coping: the conventional lost wax technique (CLWT); subtractive methods with wax blank milling (WBM); and two additive methods, multi jet modeling (MJM), and micro-stereolithography (Micro-SLA). Thirty study models were created using an acrylic model with the maxillary upper right canine, first premolar, and first molar teeth. Based on the scan files from a non-contact blue light scanner (Identica; Medit Co. Ltd., Seoul, Korea), thirty cores were produced using the WBM, MJM, and Micro-SLA methods, respectively, and another thirty frameworks were produced using the CLWT method. To measure the marginal and internal gap, the silicone replica method was adopted, and the silicone images obtained were evaluated using a digital microscope (KH-7700; Hirox, Tokyo, Japan) at 140X magnification. Analyses were performed using two-way analysis of variance (ANOVA) and Tukey post hoc test (α=.05). The mean marginal gaps and internal gaps showed significant differences according to tooth type (P<.001 and P<.001, respectively) and manufacturing method (P<.037 and P<.001, respectively). Micro-SLA did not show any significant difference from CLWT regarding mean marginal gap compared to the WBM and MJM methods. The mean values of gaps resulting from the four different manufacturing methods were within a clinically allowable range, and, thus, the clinical use of additive manufacturing methods is acceptable as an alternative to the traditional lost wax-technique and subtractive manufacturing.

Escherichia coli attachment to model particulates: The effects of bacterial cell characteristics and particulate properties.

PubMed

Liang, Xiao; Liao, Chunyu; Soupir, Michelle L; Jarboe, Laura R; Thompson, Michael L; Dixon, Philip M

2017-01-01

E. coli bacteria move in streams freely in a planktonic state or attached to suspended particulates. Attachment is a dynamic process, and the fraction of attached microorganisms is thought to be affected by both bacterial characteristics and particulate properties. In this study, we investigated how the properties of cell surfaces and stream particulates influence attachment. Attachment assays were conducted for 77 E. coli strains and three model particulates (ferrihydrite, Ca-montmorillonite, or corn stover) under environmentally relevant conditions. Surface area, particle size distribution, and total carbon content were determined for each type of particulate. Among the three particulates, attachment fractions to corn stover were significantly larger than the attachments to 2-line ferrihydrite (p-value = 0.0036) and Ca-montmorillonite (p-value = 0.022). Furthermore, attachment to Ca-montmorillonite and corn stover was successfully modeled by a Generalized Additive Model (GAM) using cell characteristics as predictor variables. The natural logarithm of the net charge on the bacterial surface had a significant, positive, and linear impact on the attachment of E. coli bacteria to Ca-montmorillonite (p-value = 0.013), but it did not significantly impact the attachment to corn stover (p-value = 0.36). The large diversities in cell characteristics among 77 E. coli strains, particulate properties, and attachment fractions clearly demonstrated the inadequacy of using a static parameter or linear coefficient to predict the attachment behavior of E. coli in stream water quality models.

A regularized variable selection procedure in additive hazards model with stratified case-cohort design.

PubMed

Ni, Ai; Cai, Jianwen

2018-07-01

Case-cohort designs are commonly used in large epidemiological studies to reduce the cost associated with covariate measurement. In many such studies the number of covariates is very large. An efficient variable selection method is needed for case-cohort studies where the covariates are only observed in a subset of the sample. Current literature on this topic has been focused on the proportional hazards model. However, in many studies the additive hazards model is preferred over the proportional hazards model either because the proportional hazards assumption is violated or the additive hazards model provides more relevent information to the research question. Motivated by one such study, the Atherosclerosis Risk in Communities (ARIC) study, we investigate the properties of a regularized variable selection procedure in stratified case-cohort design under an additive hazards model with a diverging number of parameters. We establish the consistency and asymptotic normality of the penalized estimator and prove its oracle property. Simulation studies are conducted to assess the finite sample performance of the proposed method with a modified cross-validation tuning parameter selection methods. We apply the variable selection procedure to the ARIC study to demonstrate its practical use.

Developing a dengue forecast model using machine learning: A case study in China.

PubMed

Guo, Pi; Liu, Tao; Zhang, Qin; Wang, Li; Xiao, Jianpeng; Zhang, Qingying; Luo, Ganfeng; Li, Zhihao; He, Jianfeng; Zhang, Yonghui; Ma, Wenjun

2017-10-01

In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue. Weekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011-2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China. The proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings can help the government and community respond early to dengue epidemics.

Developing a dengue forecast model using machine learning: A case study in China

PubMed Central

Zhang, Qin; Wang, Li; Xiao, Jianpeng; Zhang, Qingying; Luo, Ganfeng; Li, Zhihao; He, Jianfeng; Zhang, Yonghui; Ma, Wenjun

2017-01-01

Background In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue. Methodology/Principal findings Weekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011–2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China. Conclusion and significance The proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings

Hyperspectral-based predictive modelling of grapevine water status in the Portuguese Douro wine region

NASA Astrophysics Data System (ADS)

Pôças, Isabel; Gonçalves, João; Costa, Patrícia Malva; Gonçalves, Igor; Pereira, Luís S.; Cunha, Mario

2017-06-01

In this study, hyperspectral reflectance (HySR) data derived from a handheld spectroradiometer were used to assess the water status of three grapevine cultivars in two sub-regions of Douro wine region during two consecutive years. A large set of potential predictors derived from the HySR data were considered for modelling/predicting the predawn leaf water potential (Ψpd) through different statistical and machine learning techniques. Three HySR vegetation indices were selected as final predictors for the computation of the models and the in-season time trend was removed from data by using a time predictor. The vegetation indices selected were the Normalized Reflectance Index for the wavelengths 554 nm and 561 nm (NRI554;561), the water index (WI) for the wavelengths 900 nm and 970 nm, and the D1 index which is associated with the rate of reflectance increase in the wavelengths of 706 nm and 730 nm. These vegetation indices covered the green, red edge and the near infrared domains of the electromagnetic spectrum. A large set of state-of-the-art analysis and statistical and machine-learning modelling techniques were tested. Predictive modelling techniques based on generalized boosted model (GBM), bagged multivariate adaptive regression splines (B-MARS), generalized additive model (GAM), and Bayesian regularized neural networks (BRNN) showed the best performance for predicting Ψpd, with an average determination coefficient (R2) ranging between 0.78 and 0.80 and RMSE varying between 0.11 and 0.12 MPa. When cultivar Touriga Nacional was used for training the models and the cultivars Touriga Franca and Tinta Barroca for testing (independent validation), the models performance was good, particularly for GBM (R2 = 0.85; RMSE = 0.09 MPa). Additionally, the comparison of Ψpd observed and predicted showed an equitable dispersion of data from the various cultivars. The results achieved show a good potential of these predictive models based on vegetation indices to support

Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

NASA Astrophysics Data System (ADS)

Salimi, F.; Ristovski, Z.; Mazaheri, M.; Laiman, R.; Crilley, L. R.; He, C.; Clifford, S.; Morawska, L.

2014-06-01

Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods which have been recently employed to analyse PNSD data, however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K-means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and silhouette width validation values and the K-means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K-means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectra to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help

Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

NASA Astrophysics Data System (ADS)

Salimi, F.; Ristovski, Z.; Mazaheri, M.; Laiman, R.; Crilley, L. R.; He, C.; Clifford, S.; Morawska, L.

2014-11-01

Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods that have been recently employed to analyse PNSD data; however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and Silhouette width validation values and the K means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectrum to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help

Gallium Maltolate Disrupts Tumor Iron Metabolism and Retards the Growth of Glioblastoma by Inhibiting Mitochondrial Function and Ribonucleotide Reductase.

PubMed

Chitambar, Christopher R; Al-Gizawiy, Mona M; Alhajala, Hisham S; Pechman, Kimberly R; Wereley, Janine P; Wujek, Robert; Clark, Paul A; Kuo, John S; Antholine, William E; Schmainda, Kathleen M

2018-06-01

Gallium, a metal with antineoplastic activity, binds transferrin (Tf) and enters tumor cells via Tf receptor1 (TfR1); it disrupts iron homeostasis leading to cell death. We hypothesized that TfR1 on brain microvascular endothelial cells (BMEC) would facilitate Tf-Ga transport into the brain enabling it to target TfR-bearing glioblastoma. We show that U-87 MG and D54 glioblastoma cell lines and multiple glioblastoma stem cell (GSC) lines express TfRs, and that their growth is inhibited by gallium maltolate (GaM) in vitro After 24 hours of incubation with GaM, cells displayed a loss of mitochondrial reserve capacity followed by a dose-dependent decrease in oxygen consumption and a decrease in the activity of the iron-dependent M2 subunit of ribonucleotide reductase (RRM2). IHC staining of rat and human tumor-bearing brains showed that glioblastoma, but not normal glial cells, expressed TfR1 and RRM2, and that glioblastoma expressed greater levels of H- and L-ferritin than normal brain. In an orthotopic U-87 MG glioblastoma xenograft rat model, GaM retarded the growth of brain tumors relative to untreated control ( P = 0.0159) and reduced tumor mitotic figures ( P = 0.045). Tumors in GaM-treated animals displayed an upregulation of TfR1 expression relative to control animals, thus indicating that gallium produced tumor iron deprivation. GaM also inhibited iron uptake and upregulated TfR1 expression in U-87 MG and D54 cells in vitro We conclude that GaM enters the brain via TfR1 on BMECs and targets iron metabolism in glioblastoma in vivo, thus inhibiting tumor growth. Further development of novel gallium compounds for brain tumor treatment is warranted. Mol Cancer Ther; 17(6); 1240-50. ©2018 AACR . ©2018 American Association for Cancer Research.

Applying Additive Hazards Models for Analyzing Survival in Patients with Colorectal Cancer in Fars Province, Southern Iran

PubMed

Madadizadeh, Farzan; Ghanbarnejad, Amin; Ghavami, Vahid; Zare Bandamiri, Mohammad; Mohammadianpanah, Mohammad

2017-04-01

Introduction: Colorectal cancer (CRC) is a commonly fatal cancer that ranks as third worldwide and third and the fifth in Iranian women and men, respectively. There are several methods for analyzing time to event data. Additive hazards regression models take priority over the popular Cox proportional hazards model if the absolute hazard (risk) change instead of hazard ratio is of primary concern, or a proportionality assumption is not made. Methods: This study used data gathered from medical records of 561 colorectal cancer patients who were admitted to Namazi Hospital, Shiraz, Iran, during 2005 to 2010 and followed until December 2015. The nonparametric Aalen’s additive hazards model, semiparametric Lin and Ying’s additive hazards model and Cox proportional hazards model were applied for data analysis. The proportionality assumption for the Cox model was evaluated with a test based on the Schoenfeld residuals and for test goodness of fit in additive models, Cox-Snell residual plots were used. Analyses were performed with SAS 9.2 and R3.2 software. Results: The median follow-up time was 49 months. The five-year survival rate and the mean survival time after cancer diagnosis were 59.6% and 68.1±1.4 months, respectively. Multivariate analyses using Lin and Ying’s additive model and the Cox proportional model indicated that the age of diagnosis, site of tumor, stage, and proportion of positive lymph nodes, lymphovascular invasion and type of treatment were factors affecting survival of the CRC patients. Conclusion: Additive models are suitable alternatives to the Cox proportionality model if there is interest in evaluation of absolute hazard change, or no proportionality assumption is made. Creative Commons Attribution License

Do walleye pollock exhibit flexibility in where or when they spawn based on variability in water temperature?

NASA Astrophysics Data System (ADS)

Bacheler, Nathan M.; Ciannelli, Lorenzo; Bailey, Kevin M.; Bartolino, Valerio

2012-06-01

Environmental variability is increasingly recognized as a primary determinant of year-class strength of marine fishes by directly or indirectly influencing egg and larval development, growth, and survival. Here we examined the role of annual water temperature variability in determining when and where walleye pollock (Theragra chalcogramma) spawn in the eastern Bering Sea. Walleye pollock spawning was examined using both long-term ichthyoplankton data (N=19 years), as well as with historical spatially explicit, foreign-reported, commercial catch data occurring during the primary walleye pollock spawning season (February-May) each year (N=22 years in total). We constructed variable-coefficient generalized additive models (GAMs) to relate the spatially explicit egg or adult catch-per-unit-effort (CPUE) to predictor variables including spawning stock biomass, season, position, and water temperature. The adjusted R2 value was 63.1% for the egg CPUE model and 35.5% for the adult CPUE model. Both egg and adult GAMs suggest that spawning progresses seasonally from Bogoslof Island in February and March to Outer Domain waters between the Pribilof and Unimak Islands by May. Most importantly, walleye pollock egg and adult CPUE was predicted to generally increase throughout the study area as mean annual water temperature increased. These results suggest low interannual variability in the spatial and temporal dynamics of walleye pollock spawning regardless of changes in environmental conditions, at least at the spatial scale examined in this study and within the time frame of decades.

Increased expression of stress inducible protein 1 in glioma-associated microglia/macrophages.

PubMed

Carvalho da Fonseca, Anna Carolina; Wang, Huaqing; Fan, Haitao; Chen, Xuebo; Zhang, Ian; Zhang, Leying; Lima, Flavia Regina Souza; Badie, Behnam

2014-09-15

Factors released by glioma-associated microglia/macrophages (GAMs) play an important role in the growth and infiltration of tumors. We have previously demonstrated that the co-chaperone stress-inducible protein 1 (STI1) secreted by microglia promotes proliferation and migration of human glioblastoma (GBM) cell lines in vitro. In the present study, in order to investigate the role of STI1 in a physiological context, we used a glioma model to evaluate STI1 expression in vivo. Here, we demonstrate that STI1 expression in both the tumor and in the infiltrating GAMs and lymphocytes significantly increased with tumor progression. Interestingly, high expression of STI1 was observed in macrophages and lymphocytes that infiltrated brain tumors, whereas STI1 expression in the circulating blood monocytes and lymphocytes remained unchanged. Our results correlate, for the first time, the expression of STI1 and glioma progression, and suggest that STI1 expression in GAMs and infiltrating lymphocytes is modulated by the brain tumor microenvironment. Copyright © 2014 Elsevier B.V. All rights reserved.

Comparison of the hydrological excitation functions HAM of polar motion for the period 1980.0-2007.0

NASA Astrophysics Data System (ADS)

Nastula, J.; Pasnicka, M.; Kolaczek, B.

2011-10-01

In this study we compared contributions of polar motion excitation determined from hydrological models and harmonic coefficients of the Earth gravity field obtained from Gravity Recovery and Climate Experiment (GRACE). Hydrological excitation function (hydrological angular momentum - HAM) has been estimated from models of global hydrology, based on the observed distribution of surface water, snow, ice and soil moisture. All of them were compared with observed Geodetic Angular Momentum (GAM), excitations of polar motion. The spectra of these excitation functions of polar motion and residual geodetic excitation function G-A-O obtained from GAM by elimination of atmospheric and oceanic excitation functions were computed too. Phasor diagrams of the seasonal components of the polar motion excitation functions of all HAM excitation functions as well as of two GRACE solutions: CSR, CNES were determined and discussed.

Serum and tissue concentrations of gallium after oral administration of gallium nitrate and gallium maltolate to neonatal calves.

PubMed

Monk, Caroline S; Sweeney, Raymond W; Bernstein, Lawrence R; Fecteau, Marie-Eve

2016-02-01

To determine serum and tissue concentrations of gallium (Ga) after oral administration of gallium nitrate (GaN) and gallium maltolate (GaM) to neonatal calves. 8 healthy neonatal calves. Calves were assigned to 1 of 2 groups (4 calves/group). Gallium (50 mg/kg) was administered as GaN or GaM (equivalent to 13.15 mg of Ga/kg for GaN and 7.85 mg of Ga/kg for GaM) by oral gavage once daily for 5 days. Blood samples were collected 0, 0.25, 0.5, 1, 2, 4, 8, 12, and 24 hours after Ga administration on day 1; 4 and 24 hours after Ga administration on days 2, 3, and 4; and 4, 12, and 24 hours after Ga administration on day 5. On day 6, calves were euthanized and tissue samples were obtained. Serum and tissue Ga concentrations were measured by use of mass spectrometry. Data were adjusted for total Ga dose, and comparisons were made between the 2 groups. Calves receiving GaM had a significantly higher dose-adjusted area under the curve and dose-adjusted maximum serum Ga concentration than did calves receiving GaN. Despite receiving less Ga per dose, calves receiving GaM had tissue Ga concentrations similar to those for calves receiving GaN. In this study, calves receiving GaM had significantly higher Ga absorption than did calves receiving GaN. These findings suggested that GaM might be useful as a prophylactic agent against Mycobacterium avium subsp paratuberculosis infection in neonatal calves.

Additive Partial Least Squares for efficient modelling of independent variance sources demonstrated on practical case studies.

PubMed

Luoma, Pekka; Natschläger, Thomas; Malli, Birgit; Pawliczek, Marcin; Brandstetter, Markus

2018-05-12

A model recalibration method based on additive Partial Least Squares (PLS) regression is generalized for multi-adjustment scenarios of independent variance sources (referred to as additive PLS - aPLS). aPLS allows for effortless model readjustment under changing measurement conditions and the combination of independent variance sources with the initial model by means of additive modelling. We demonstrate these distinguishing features on two NIR spectroscopic case-studies. In case study 1 aPLS was used as a readjustment method for an emerging offset. The achieved RMS error of prediction (1.91 a.u.) was of similar level as before the offset occurred (2.11 a.u.). In case-study 2 a calibration combining different variance sources was conducted. The achieved performance was of sufficient level with an absolute error being better than 0.8% of the mean concentration, therefore being able to compensate negative effects of two independent variance sources. The presented results show the applicability of the aPLS approach. The main advantages of the method are that the original model stays unadjusted and that the modelling is conducted on concrete changes in the spectra thus supporting efficient (in most cases straightforward) modelling. Additionally, the method is put into context of existing machine learning algorithms. Copyright © 2018 Elsevier B.V. All rights reserved.

Generalized neurofuzzy network modeling algorithms using Bézier-Bernstein polynomial functions and additive decomposition.

PubMed

Hong, X; Harris, C J

2000-01-01

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bézier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bézier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bézier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bézier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

Does the model of additive effect in placebo research still hold true? A narrative review

PubMed Central

Berger, Bettina; Weger, Ulrich; Heusser, Peter

2017-01-01

Personalised and contextualised care has been turned into a major demand by people involved in healthcare suggesting to move toward person-centred medicine. The assessment of person-centred medicine can be most effectively achieved if treatments are investigated using ‘with versus without’ person-centredness or integrative study designs. However, this assumes that the components of an integrative or person-centred intervention have an additive relationship to produce the total effect. Beecher’s model of additivity assumes an additive relation between placebo and drug effects and is thus presenting an arithmetic summation. So far, no review has been carried out assessing the validity of the additive model, which is to be questioned and more closely investigated in this review. Initial searches for primary studies were undertaken in July 2016 using Pubmed and Google Scholar. In order to find matching publications of similar magnitude for the comparison part of this review, corresponding matches for all included reviews were sought. A total of 22 reviews and 3 clinical and experimental studies fulfilled the inclusion criteria. The results pointed to the following factors actively questioning the additive model: interactions of various effects, trial design, conditioning, context effects and factors, neurobiological factors, mechanism of action, statistical factors, intervention-specific factors (alcohol, caffeine), side-effects and type of intervention. All but one of the closely assessed publications was questioning the additive model. A closer examination of study design is necessary. An attempt in a more systematic approach geared towards solutions could be a suggestion for future research in this field. PMID:28321318

Does the model of additive effect in placebo research still hold true? A narrative review.

PubMed

Boehm, Katja; Berger, Bettina; Weger, Ulrich; Heusser, Peter

2017-03-01

Personalised and contextualised care has been turned into a major demand by people involved in healthcare suggesting to move toward person-centred medicine. The assessment of person-centred medicine can be most effectively achieved if treatments are investigated using 'with versus without' person-centredness or integrative study designs. However, this assumes that the components of an integrative or person-centred intervention have an additive relationship to produce the total effect. Beecher's model of additivity assumes an additive relation between placebo and drug effects and is thus presenting an arithmetic summation. So far, no review has been carried out assessing the validity of the additive model, which is to be questioned and more closely investigated in this review. Initial searches for primary studies were undertaken in July 2016 using Pubmed and Google Scholar. In order to find matching publications of similar magnitude for the comparison part of this review, corresponding matches for all included reviews were sought. A total of 22 reviews and 3 clinical and experimental studies fulfilled the inclusion criteria. The results pointed to the following factors actively questioning the additive model: interactions of various effects, trial design, conditioning, context effects and factors, neurobiological factors, mechanism of action, statistical factors, intervention-specific factors (alcohol, caffeine), side-effects and type of intervention. All but one of the closely assessed publications was questioning the additive model. A closer examination of study design is necessary. An attempt in a more systematic approach geared towards solutions could be a suggestion for future research in this field.

Comparison of the antimicrobial activities of gallium nitrate and gallium maltolate against Mycobacterium avium subsp. paratuberculosis in vitro.

PubMed

Fecteau, Marie-Eve; Aceto, Helen W; Bernstein, Lawrence R; Sweeney, Raymond W

2014-10-01

Johne's disease (JD) is an enteric infection of cattle and other ruminants caused by Mycobacterium avium subsp. paratuberculosis (MAP). This study compared the antimicrobial activities of gallium nitrate (GaN) and gallium maltolate (GaM) against two field MAP isolates by use of broth culture. The concentrations that resulted in 99% growth inhibition of isolates 1 and 2 were, respectively, 636 µM and 183 µM for GaN, and 251 µM and 142 µM for GaM. For both isolates, time to detection was significantly higher for GaM than GaN. These results suggest that GaM is more efficient than GaN in inhibiting MAP growth in vitro. Copyright © 2014 Elsevier Ltd. All rights reserved.

Mean and oscillating plasma flows and turbulence interactions across the L-H confinement transition.

PubMed

Conway, G D; Angioni, C; Ryter, F; Sauter, P; Vicente, J

2011-02-11

A complex interaction between turbulence driven E × B zonal flow oscillations, i.e., geodesic acoustic modes (GAMs), the turbulence, and mean equilibrium flows is observed during the low to high (L-H) plasma confinement mode transition in the ASDEX Upgrade tokamak. Below the L-H threshold at low densities a limit-cycle oscillation forms with competition between the turbulence level and the GAM flow shearing. At higher densities the cycle is diminished, while in the H mode the cycle duration becomes too short to sustain the GAM, which is replaced by large amplitude broadband flow perturbations. Initially GAM amplitude increases as the H-mode transition is approached, but is then suppressed in the H mode by enhanced mean flow shear.

Testing a Gender Additive Model: The Role of Body Image in Adolescent Depression

ERIC Educational Resources Information Center

Bearman, Sarah Kate; Stice, Eric

2008-01-01

Despite consistent evidence that adolescent girls are at greater risk of developing depression than adolescent boys, risk factor models that account for this difference have been elusive. The objective of this research was to examine risk factors proposed by the "gender additive" model of depression that attempts to partially explain the increased…

Shell model for drag reduction with polymer additives in homogeneous turbulence.

PubMed

Benzi, Roberto; De Angelis, Elisabetta; Govindarajan, Rama; Procaccia, Itamar

2003-07-01

Recent direct numerical simulations of the finite-extensibility nonlinear elastic dumbbell model with the Peterlin approximation of non-Newtonian hydrodynamics revealed that the phenomenon of drag reduction by polymer additives exists (albeit in reduced form) also in homogeneous turbulence. We use here a simple shell model for homogeneous viscoelastic flows, which recaptures the essential observations of the full simulations. The simplicity of the shell model allows us to offer a transparent explanation of the main observations. It is shown that the mechanism for drag reduction operates mainly on large scales. Understanding the mechanism allows us to predict how the amount of drag reduction depends on the various parameters in the model. The main conclusion is that drag reduction is not a universal phenomenon; it peaks in a window of parameters such as the Reynolds number and the relaxation rate of the polymer.

Snow model design for operational purposes

NASA Astrophysics Data System (ADS)

Kolberg, Sjur

2017-04-01

A parsimonious distributed energy balance snow model intended for operational use is evaluated using discharge, snow covered area and grain size; the latter two as observed from the MODIS sensor. The snow model is an improvement of the existing GamSnow model, which is a part of the Enki modelling framework. Core requirements for the new version have been: 1. Reduction of calibration freedom, motivated by previous experience of non-identifiable parameters in the existing version 2. Improvement of process representation based on recent advances in physically based snow modelling 3. Limiting the sensitivity to forcing data which are poorly known over the spatial domain of interest (often in mountainous areas) 4. Preference for observable states, and the ability to improve from updates. The albedo calculation is completely revised, now based on grain size through an emulation of the SNICAR model (Flanner and Zender, 2006; Gardener and Sharp, 2010). The number of calibration parameters in the albedo model is reduced from 6 to 2. The wind function governing turbulent energy fluxes has been reduced from 2 to 1 parameter. Following Raleigh et al (2011), snow surface radiant temperature is split from the top layer thermodynamic temperature, using bias-corrected wet-bulb temperature to model the former. Analyses are ongoing, and the poster will bring evaluation results from 16 years of MODIS observations and more than 25 catchments in southern Norway.

Hydrological signal in polar motion excitation from a combination of geophysical and gravimetric series

NASA Astrophysics Data System (ADS)

Nastula, Jolanta; Winska, Malgorzata; Salstein, David A.

2015-08-01

One can estimate the hydrological signal in polar motion excitation as a residual, namely the difference between observed geodetic excitation functions (Geodetic Angular Momentum, GAM) and the sum of Atmospheric Angular Momentum (AAM) and Oceanic Angular Momentum (OAM).The aim of this study is to find the optimal model and results for hydrological excitation functions in terms of their agreement with the computed difference between GAM and atmospheric and oceanic signals.The atmospheric and oceanic model-based data that we use in this study are the geophysical excitation functions of AAM, OAM available from the Special Bureaus for the Atmosphere and Oceans of the Geophysical Global Fluids Center (GGFC) of the International Earth Rotation and Reference Systems Service (IERS). For the atmosphere and ocean, these functions are based on the mass and motion fields of the fluids.Global models of land hydrology are used to estimate hydrological excitation functions of polar motion (Hydrological Angular Momentum - HAM). These HAM series are the mass of water substance determined from the various types of land-based hydrological reservoirs. In addition the HAM are estimated from spherical harmonic coefficients of the Earth’s gravity field. We use several sets of degree-2, order-1 harmonics of the Earth’s gravity field, derived from the Gravity Recovery and Climate Experiment (GRACE), Satellite Laser Ranging (SLR), and Global Navigation Satellite Systems (GNSS) data.Finally, these several different HAM series are used to determine the best model of hydrological excitation of polar motion. The model is found by looking for the combination of these series that fits the geodetic residuals using the least-square method.In addition, we will access model results from the Coupled Model Intercomparison Project, fifth experiment (CMIP-5) to examine atmospheric excitations from the twentieth century and estimates for the twenty-first century to see the possible signals and trends

In vitro differential diagnosis of clavus and verruca by a predictive model generated from electrical impedance.

PubMed

Hung, Chien-Ya; Sun, Pei-Lun; Chiang, Shu-Jen; Jaw, Fu-Shan

2014-01-01

Similar clinical appearances prevent accurate diagnosis of two common skin diseases, clavus and verruca. In this study, electrical impedance is employed as a novel tool to generate a predictive model for differentiating these two diseases. We used 29 clavus and 28 verruca lesions. To obtain impedance parameters, a LCR-meter system was applied to measure capacitance (C), resistance (Re), impedance magnitude (Z), and phase angle (θ). These values were combined with lesion thickness (d) to characterize the tissue specimens. The results from clavus and verruca were then fitted to a univariate logistic regression model with the generalized estimating equations (GEE) method. In model generation, log ZSD and θSD were formulated as predictors by fitting a multiple logistic regression model with the same GEE method. The potential nonlinear effects of covariates were detected by fitting generalized additive models (GAM). Moreover, the model was validated by the goodness-of-fit (GOF) assessments. Significant mean differences of the index d, Re, Z, and θ are found between clavus and verruca (p<0.001). A final predictive model is established with Z and θ indices. The model fits the observed data quite well. In GOF evaluation, the area under the receiver operating characteristics (ROC) curve is 0.875 (>0.7), the adjusted generalized R2 is 0.512 (>0.3), and the p value of the Hosmer-Lemeshow GOF test is 0.350 (>0.05). This technique promises to provide an approved model for differential diagnosis of clavus and verruca. It could provide a rapid, relatively low-cost, safe and non-invasive screening tool in clinic use.

Incorporating additional tree and environmental variables in a lodgepole pine stem profile model

Treesearch

John C. Byrne

1993-01-01

A new variable-form segmented stem profile model is developed for lodgepole pine (Pinus contorta) trees from the northern Rocky Mountains of the United States. I improved estimates of stem diameter by predicting two of the model coefficients with linear equations using a measure of tree form, defined as a ratio of dbh and total height. Additional improvements were...

Managing global accounts.

PubMed

Yip, George S; Bink, Audrey J M

2007-09-01

Global account management--which treats a multinational customer's operations as one integrated account, with coherent terms for pricing, product specifications, and service--has proliferated over the past decade. Yet according to the authors' research, only about a third of the suppliers that have offered GAM are pleased with the results. The unhappy majority may be suffering from confusion about when, how, and to whom to provide it. Yip, the director of research and innovation at Capgemini, and Bink, the head of marketing communications at Uxbridge College, have found that GAM can improve customer satisfaction by 20% or more and can raise both profits and revenues by at least 15% within just a few years of its introduction. They provide guidelines to help companies achieve similar results. The first steps are determining whether your products or services are appropriate for GAM, whether your customers want such a program, whether those customers are crucial to your strategy, and how GAM might affect your competitive advantage. If moving forward makes sense, the authors' exhibit, "A Scorecard for Selecting Global Accounts," can help you target the right customers. The final step is deciding which of three basic forms to offer: coordination GAM (in which national operations remain relatively strong), control GAM (in which the global operation and the national operations are fairly balanced), and separate GAM (in which a new business unit has total responsibility for global accounts). Given the difficulty and expense of providing multiple varieties, the vast majority of companies should initially customize just one---and they should be careful not to start with a choice that is too ambitious for either themselves or their customers to handle.

A parallelized three-dimensional cellular automaton model for grain growth during additive manufacturing

NASA Astrophysics Data System (ADS)

Lian, Yanping; Lin, Stephen; Yan, Wentao; Liu, Wing Kam; Wagner, Gregory J.

2018-05-01

In this paper, a parallelized 3D cellular automaton computational model is developed to predict grain morphology for solidification of metal during the additive manufacturing process. Solidification phenomena are characterized by highly localized events, such as the nucleation and growth of multiple grains. As a result, parallelization requires careful treatment of load balancing between processors as well as interprocess communication in order to maintain a high parallel efficiency. We give a detailed summary of the formulation of the model, as well as a description of the communication strategies implemented to ensure parallel efficiency. Scaling tests on a representative problem with about half a billion cells demonstrate parallel efficiency of more than 80% on 8 processors and around 50% on 64; loss of efficiency is attributable to load imbalance due to near-surface grain nucleation in this test problem. The model is further demonstrated through an additive manufacturing simulation with resulting grain structures showing reasonable agreement with those observed in experiments.

A parallelized three-dimensional cellular automaton model for grain growth during additive manufacturing

NASA Astrophysics Data System (ADS)

Lian, Yanping; Lin, Stephen; Yan, Wentao; Liu, Wing Kam; Wagner, Gregory J.

2018-01-01

In this paper, a parallelized 3D cellular automaton computational model is developed to predict grain morphology for solidification of metal during the additive manufacturing process. Solidification phenomena are characterized by highly localized events, such as the nucleation and growth of multiple grains. As a result, parallelization requires careful treatment of load balancing between processors as well as interprocess communication in order to maintain a high parallel efficiency. We give a detailed summary of the formulation of the model, as well as a description of the communication strategies implemented to ensure parallel efficiency. Scaling tests on a representative problem with about half a billion cells demonstrate parallel efficiency of more than 80% on 8 processors and around 50% on 64; loss of efficiency is attributable to load imbalance due to near-surface grain nucleation in this test problem. The model is further demonstrated through an additive manufacturing simulation with resulting grain structures showing reasonable agreement with those observed in experiments.

NB-PLC channel modelling with cyclostationary noise addition & OFDM implementation for smart grid

NASA Astrophysics Data System (ADS)

Thomas, Togis; Gupta, K. K.

2016-03-01

Power line communication (PLC) technology can be a viable solution for the future ubiquitous networks because it provides a cheaper alternative to other wired technology currently being used for communication. In smart grid Power Line Communication (PLC) is used to support communication with low rate on low voltage (LV) distribution network. In this paper, we propose the channel modelling of narrowband (NB) PLC in the frequency range 5 KHz to 500 KHz by using ABCD parameter with cyclostationary noise addition. Behaviour of the channel was studied by the addition of 11KV/230V transformer, by varying load location and load. Bit error rate (BER) Vs signal to noise ratio SNR) was plotted for the proposed model by employing OFDM. Our simulation results based on the proposed channel model show an acceptable performance in terms of bit error rate versus signal to noise ratio, which enables communication required for smart grid applications.

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection

PubMed Central

Li, Tingting; Cheng, Zhengguo; Zhang, Le

2017-01-01

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency. PMID:29194393

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection.

PubMed

Li, Tingting; Cheng, Zhengguo; Zhang, Le

2017-12-01

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency.

Association of circulating branched-chain amino acids with cardiometabolic traits differs between adults and the oldest-old.

PubMed

Sun, Liang; Hu, Caiyou; Yang, Ruiyue; Lv, Yuan; Yuan, Huiping; Liang, Qinghua; He, Benjin; Pang, Guofang; Jiang, Menghua; Dong, Jun; Yang, Ze

2017-10-24

Branched-chain amino acids (BCAAs) are promising for their potential anti-aging effects. However, findings in adults suggest that circulating BCAAs are associated with cardiometabolic risk. Moreover, little information is available about how BCAAs influence clustered cardiometabolic traits in the oldest-old (>85 years), which are the fastest-growing segment of the population in developed countries. Here, we applied a targeted metabolomics approach to measure serum BCAAs in Chinese participants (aged 21-110 years) based on a longevity cohort. The differences of quantitative and dichotomous cardiometabolic traits were compared across BCAAs tertiles. A generalized additive model (GAM) was used to explore the dose-response relationship between BCAAs and the risk of metabolic syndrome (MetS). Overall, BCAAs were correlated with most of the examined cardiometabolic traits. The odds ratios for MetS across the increasing BCAA tertiles were 3.22 (1.70 - 6.12) and 5.27 (2.88 - 9.94, referenced to tertile 1) after adjusting for age and gender ( P trend < 0.001). The association still existed after further controlling for lifestyle factors and inflammation factors. However, the correlations between circulating BCAAs and quantitative traits were weakened in the oldest-old, except for lipids, the levels of which were distinctly different from those in adults. The stratified analysis also suggested that the risky BCAAs-MetS association was more pronounced in adults than in the oldest-old. Moreover, generalized additive model (GAM)-based curve-fitting suggested that only when BCAAs exceeded a threshold (approximately 450 μmol/L) was the BCAAs-MetS association significant. The relationship might be aging-dependent and was more pronounced in adults than in the oldest-old.

Model Scramjet Inlet Unstart Induced by Mass Addition and Heat Release

NASA Astrophysics Data System (ADS)

Im, Seong-Kyun; Baccarella, Damiano; McGann, Brendan; Liu, Qili; Wermer, Lydiy; Do, Hyungrok

2015-11-01

The inlet unstart phenomena in a model scramjet are investigated at an arc-heated hypersonic wind tunnel. The unstart induced by nitrogen or ethylene jets at low or high enthalpy Mach 4.5 freestream flow conditions are compared. The jet injection pressurizes the downstream flow by mass addition and flow blockage. In case of the ethylene jet injection, heat release from combustion increases the backpressure further. Time-resolved schlieren imaging is performed at the jet and the lip of the model inlet to visualize the flow features during unstart. High frequency pressure measurements are used to provide information on pressure fluctuation at the scramjet wall. In both of the mass and heat release driven unstart cases, it is observed that there are similar flow transient and quasi-steady behaviors of unstart shockwave system during the unstart processes. Combustion driven unstart induces severe oscillatory flow motions of the jet and the unstart shock at the lip of the scramjet inlet after the completion of the unstart process, while the unstarted flow induced by solely mass addition remains relatively steady. The discrepancies between the processes of mass and heat release driven unstart are explained by flow choking mechanism.

Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models.

PubMed

Baquero, Oswaldo Santos; Santana, Lidia Maria Reis; Chiaravalloti-Neto, Francisco

2018-01-01

Globally, the number of dengue cases has been on the increase since 1990 and this trend has also been found in Brazil and its most populated city-São Paulo. Surveillance systems based on predictions allow for timely decision making processes, and in turn, timely and efficient interventions to reduce the burden of the disease. We conducted a comparative study of dengue predictions in São Paulo city to test the performance of trained seasonal autoregressive integrated moving average models, generalized additive models and artificial neural networks. We also used a naïve model as a benchmark. A generalized additive model with lags of the number of cases and meteorological variables had the best performance, predicted epidemics of unprecedented magnitude and its performance was 3.16 times higher than the benchmark and 1.47 higher that the next best performing model. The predictive models captured the seasonal patterns but differed in their capacity to anticipate large epidemics and all outperformed the benchmark. In addition to be able to predict epidemics of unprecedented magnitude, the best model had computational advantages, since its training and tuning was straightforward and required seconds or at most few minutes. These are desired characteristics to provide timely results for decision makers. However, it should be noted that predictions are made just one month ahead and this is a limitation that future studies could try to reduce.

3D model of filler melting with micro-beam plasma arc based on additive manufacturing technology

NASA Astrophysics Data System (ADS)

Chen, Weilin; Yang, Tao; Yang, Ruixin

2017-07-01

Additive manufacturing technology is a systematic process based on discrete-accumulation principle, which is derived by the dimension of parts. Aiming at the dimension mathematical model and slicing problems in additive manufacturing process, the constitutive relations between micro-beam plasma welding parameters and the dimension of part were investigated. The slicing algorithm and slicing were also studied based on the dimension characteristics. By using the direct slicing algorithm according to the geometric characteristics of model, a hollow thin-wall spherical part was fabricated by 3D additive manufacturing technology using micro-beam plasma.

In-Situ monitoring and modeling of metal additive manufacturing powder bed fusion

NASA Astrophysics Data System (ADS)

Alldredge, Jacob; Slotwinski, John; Storck, Steven; Kim, Sam; Goldberg, Arnold; Montalbano, Timothy

2018-04-01

One of the major challenges in metal additive manufacturing is developing in-situ sensing and feedback control capabilities to eliminate build errors and allow qualified part creation without the need for costly and destructive external testing. Previously, many groups have focused on high fidelity numerical modeling and true temperature thermal imaging systems. These approaches require large computational resources or costly hardware that requires complex calibration and are difficult to integrate into commercial systems. In addition, due to the rapid change in the state of the material as well as its surface properties, getting true temperature is complicated and difficult. Here, we describe a different approach where we implement a low cost thermal imaging solution allowing for relative temperature measurements sufficient for detecting unwanted process variability. We match this with a faster than real time qualitative model that allows the process to be rapidly modeled during the build. The hope is to combine these two, allowing for the detection of anomalies in real time, enabling corrective action to potentially be taken, or parts to be stopped immediately after the error, saving material and time. Here we describe our sensor setup, its costs and abilities. We also show the ability to detect in real time unwanted process deviations. We also show that the output of our high speed model agrees qualitatively with experimental results. These results lay the groundwork for our vision of an integrated feedback and control scheme that combines low cost, easy to use sensors and fast modeling for process deviation monitoring.

Solar radiation increases suicide rate after adjusting for other climate factors in South Korea.

PubMed

Jee, Hee-Jung; Cho, Chul-Hyun; Lee, Yu Jin; Choi, Nari; An, Hyonggin; Lee, Heon-Jeong

2017-03-01

Previous studies have indicated that suicide rates have significant seasonal variations. There is seasonal discordance between temperature and solar radiation due to the monsoon season in South Korea. We investigated the seasonality of suicide and assessed its association with climate variables in South Korea. Suicide rates were obtained from the National Statistical Office of South Korea, and climatic data were obtained from the Korea Meteorological Administration for the period of 1992-2010. We conducted analyses using a generalized additive model (GAM). First, we explored the seasonality of suicide and climate variables such as mean temperature, daily temperature range, solar radiation, and relative humidity. Next, we identified confounding climate variables associated with suicide rate. To estimate the adjusted effect of solar radiation on the suicide rate, we investigated the confounding variables using a multivariable GAM. Suicide rate showed seasonality with a pattern similar to that of solar radiation. We found that the suicide rate increased 1.008 times when solar radiation increased by 1 MJ/m 2 after adjusting for other confounding climate factors (P < 0.001). Solar radiation has a significant linear relationship with suicide after adjusting for region, other climate variables, and time trends. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Effects of oceanographic factors on spatial distribution of Whale Shark in Cendrawasih Bay National Park, West Papua

NASA Astrophysics Data System (ADS)

Ranintyari, Maulida; Sunarto; Syamsuddin, Mega L.; Astuty, Sri

2018-05-01

Whale sharks are a leading species in Cendrawasih Bay due to its benign nature and its regular appearance. Recently, whale sharks are vulnerable to scarcity and even extinction. One of the efforts to maintain the existence of the whale shark population is by knowing its spatial distribution. This study aims to analyze how the oceanographic factors affect the spatial distribution of whale sharks in Cendrawasih Bay National Park. The method used in this research is descriptive with the quantitative approach using the Generalized Additive Model (GAM) analysis. The data consisted of the whale shark monitoring data in TNTC taken by WWF-Indonesia, and image data of sea surface temperature (SST) and chlorophyll-a concentration of Aqua-MODIS, and also sea surface current from Aviso. Analyses were conducted for the period of January 2012 until March 2015. The GAM result indicated that sea surface current was better than the other environment (SST and chlorophyll-a concentration) as an oceanographic predictor of whale shark appearance. High probabilities of the whale shark’s to appear on the surface were observed in sea surface current velocities between 0.30-0.60 m/s, for SST ranged from 30.50-31.80 °C, and for chlorophyll-a concentration ranged from 0.20-0.40 mg/m3.

Insulin resistance index (HOMA-IR) levels in a general adult population: curves percentile by gender and age. The EPIRCE study.

PubMed

Gayoso-Diz, Pilar; Otero-Gonzalez, Alfonso; Rodriguez-Alvarez, María Xosé; Gude, Francisco; Cadarso-Suarez, Carmen; García, Fernando; De Francisco, Angel

2011-10-01

To describe the distribution of HOMA-IR levels in a general nondiabetic population and its relationships with metabolic and lifestyles characteristics. Cross-sectional study. Data from 2246 nondiabetic adults in a random Spanish population sample, stratified by age and gender, were analyzed. Assessments included a structured interview, physical examination, and blood sampling. Generalized additive models (GAMs) were used to assess the effect of lifestyle habits and clinical and demographic measurements on HOMA-IR. Multivariate GAMs and quantile regression analyses of HOMA-IR were carried out separately in men and women. This study shows refined estimations of HOMA-IR levels by age, body mass index, and waist circumference in men and women. HOMA-IR levels were higher in men (2.06) than women (1.95) (P=0.047). In women, but not men, HOMA-IR and age showed a significant nonlinear association (P=0.006), with increased levels above fifty years of age. We estimated HOMA-IR curves percentile in men and women. Age- and gender-adjusted HOMA-IR levels are reported in a representative Spanish adult non-diabetic population. There are gender-specific differences, with increased levels in women over fifty years of age that may be related with changes in body fat distribution after menopause. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Impact of communities, health, and emotional-related factors on smoking use: comparison of joint modeling of mean and dispersion and Bayes' hierarchical models on add health survey.

PubMed

Pu, Jie; Fang, Di; Wilson, Jeffrey R

2017-02-03

The analysis of correlated binary data is commonly addressed through the use of conditional models with random effects included in the systematic component as opposed to generalized estimating equations (GEE) models that addressed the random component. Since the joint distribution of the observations is usually unknown, the conditional distribution is a natural approach. Our objective was to compare the fit of different binary models for correlated data in Tabaco use. We advocate that the joint modeling of the mean and dispersion may be at times just as adequate. We assessed the ability of these models to account for the intraclass correlation. In so doing, we concentrated on fitting logistic regression models to address smoking behaviors. Frequentist and Bayes' hierarchical models were used to predict conditional probabilities, and the joint modeling (GLM and GAM) models were used to predict marginal probabilities. These models were fitted to National Longitudinal Study of Adolescent to Adult Health (Add Health) data for Tabaco use. We found that people were less likely to smoke if they had higher income, high school or higher education and religious. Individuals were more likely to smoke if they had abused drug or alcohol, spent more time on TV and video games, and been arrested. Moreover, individuals who drank alcohol early in life were more likely to be a regular smoker. Children who experienced mistreatment from their parents were more likely to use Tabaco regularly. The joint modeling of the mean and dispersion models offered a flexible and meaningful method of addressing the intraclass correlation. They do not require one to identify random effects nor distinguish from one level of the hierarchy to the other. Moreover, once one can identify the significant random effects, one can obtain similar results to the random coefficient models. We found that the set of marginal models accounting for extravariation through the additional dispersion submodel produced

Addressing Hydro-economic Modeling Limitations - A Limited Foresight Sacramento Valley Model and an Open-source Modeling Platform

NASA Astrophysics Data System (ADS)

Harou, J. J.; Hansen, K. M.

2008-12-01

Increased scarcity of world water resources is inevitable given the limited supply and increased human pressures. The idea that "some scarcity is optimal" must be accepted for rational resource use and infrastructure management decisions to be made. Hydro-economic systems models are unique at representing the overlap of economic drivers, socio-political forces and distributed water resource systems. They demonstrate the tangible benefits of cooperation and integrated flexible system management. Further improvement of models, quality control practices and software will be needed for these academic policy tools to become accepted into mainstream water resource practice. Promising features include: calibration methods, limited foresight optimization formulations, linked simulation-optimization approaches (e.g. embedding pre-existing calibrated simulation models), spatial groundwater models, stream-aquifer interactions and stream routing, etc.. Conventional user-friendly decision support systems helped spread simulation models on a massive scale. Hydro-economic models must also find a means to facilitate construction, distribution and use. Some of these issues and model features are illustrated with a hydro-economic optimization model of the Sacramento Valley. Carry-over storage value functions are used to limit hydrologic foresight of the multi- period optimization model. Pumping costs are included in the formulation by tracking regional piezometric head of groundwater sub-basins. To help build and maintain this type of network model, an open-source water management modeling software platform is described and initial project work is discussed. The objective is to generically facilitate the connection of models, such as those developed in a modeling environment (GAMS, MatLab, Octave, "), to a geographic user interface (drag and drop node-link network) and a database (topology, parameters and time series). These features aim to incrementally move hydro- economic models

A family of triaxial modified Hubble mass models: Effects of the additional radial functions

NASA Astrophysics Data System (ADS)

Das, Mousumi; Thakur, Parijat; Ann, H. B.

2005-03-01

The projected properties of triaxial generalization of the modified Hubble mass models are studied. These models are constructed by adding the additional radial functions, each multiplied by a low-order spherical harmonic, to the models of [Chakraborty, D.K., Thakur, P., 2000. MNRAS 318, 1273]. The projected surface density of mass models can be calculated analytically which allows us to derive the analytic expressions of axial ratio and position angle of major axis of constant density elliptical contours at asymptotic radii. The models are more general than those studied earlier in the sense that the inclusions of additional terms in density distribution, allow one to produce varieties of the radial profile of axial ratio and position angle, in particular, their small scale variations at inner radii. Strong correlations are found to exist between the observed axial ratio evaluated at 0.25Re and at 4Re which occupy well-separated regions in the parameter space for different choices of the intrinsic axial ratios. These correlations can be exploited to predict the intrinsic shape of the mass model, independent of the viewing angles. Using Bayesian statistics, the result of a test case launched for an estimation of the shape of a model galaxy is found to be satisfactory.

Risk Based Framework for Geotechnical Asset Management

DOT National Transportation Integrated Search

2017-12-28

This report presents the outcome from a multi-year research study to incorporate a risk management framework for the Alaska Department of Transportation & Public Facilities Geotechnical Asset Management (GAM) Plan. The GAM Plan was developed by Paul ...

Rain water transport and storage in a model sandy soil with hydrogel particle additives.

PubMed

Wei, Y; Durian, D J

2014-10-01

We study rain water infiltration and drainage in a dry model sandy soil with superabsorbent hydrogel particle additives by measuring the mass of retained water for non-ponding rainfall using a self-built 3D laboratory set-up. In the pure model sandy soil, the retained water curve measurements indicate that instead of a stable horizontal wetting front that grows downward uniformly, a narrow fingered flow forms under the top layer of water-saturated soil. This rain water channelization phenomenon not only further reduces the available rain water in the plant root zone, but also affects the efficiency of soil additives, such as superabsorbent hydrogel particles. Our studies show that the shape of the retained water curve for a soil packing with hydrogel particle additives strongly depends on the location and the concentration of the hydrogel particles in the model sandy soil. By carefully choosing the particle size and distribution methods, we may use the swollen hydrogel particles to modify the soil pore structure, to clog or extend the water channels in sandy soils, or to build water reservoirs in the plant root zone.

Marginal regression approach for additive hazards models with clustered current status data.

PubMed

Su, Pei-Fang; Chi, Yunchan

2014-01-15

Current status data arise naturally from tumorigenicity experiments, epidemiology studies, biomedicine, econometrics and demographic and sociology studies. Moreover, clustered current status data may occur with animals from the same litter in tumorigenicity experiments or with subjects from the same family in epidemiology studies. Because the only information extracted from current status data is whether the survival times are before or after the monitoring or censoring times, the nonparametric maximum likelihood estimator of survival function converges at a rate of n(1/3) to a complicated limiting distribution. Hence, semiparametric regression models such as the additive hazards model have been extended for independent current status data to derive the test statistics, whose distributions converge at a rate of n(1/2) , for testing the regression parameters. However, a straightforward application of these statistical methods to clustered current status data is not appropriate because intracluster correlation needs to be taken into account. Therefore, this paper proposes two estimating functions for estimating the parameters in the additive hazards model for clustered current status data. The comparative results from simulation studies are presented, and the application of the proposed estimating functions to one real data set is illustrated. Copyright © 2013 John Wiley & Sons, Ltd.

Enhanced release and drug delivery of celecoxib into physiological environment by the different types of nanoscale vehicles

NASA Astrophysics Data System (ADS)

Khazraei, Avideh; Tarlani, Aliakbar; Naderi, Nima; Muzart, Jacques; Abdulhameed (Kaabi), Zahra; Eslami-Moghadam, Mahbube

2017-11-01

Celecoxib (CEL) as the very low water soluble drug was loaded 16 and 50% (w/w) through an impregnation method on varieties of alumina nanostructures such as synthetic sol-gel γ-alumina (Gam-Al), functionalized sol-gel γ-alumina (Gam-Al-NH2), organized nano porous alumina (Onp-Al) and then the results compared with commercial alumina (Com-Al) and SBA-15 (SBA). Analyses of the samples were carried out by FT-IR, X-ray diffraction (XRD) and N2-sorption. in vitro studies were accomplished in simulated body fluid (SBF), simulated gastric fluid (SGF) and simulated intestinal fluid (SIF). In vivo study was carried out on male wistar rats under standard conditions. The N2-sorption revealed the initial pore characteristics of the nanocarriers. XRD patterns showed that the 50% loaded samples contain bulk celecoxib and its solubility in body fluids is lower than that of 16% loaded samples. In the case of 16% loaded samples, the drug solubility in three simulated body fluids drug was found to decrease in the following order: Gam-Al-CEL > Onp-Al-CEL > Com-Al-CEL > SBA-CEL. Gam-Al-CEL showed the highest release (96%) in SBF after 60 min in vivo study showed significant decrease in pain score in rats for Gam-Al-NH2-CEL-16% and Gam-Al-CEL-50%. It could be concluded that the synthetic aluminas have a developing future potential compared to the formal SBA and commercial alumina.

QSAR prediction of additive and non-additive mixture toxicities of antibiotics and pesticide.

PubMed

Qin, Li-Tang; Chen, Yu-Han; Zhang, Xin; Mo, Ling-Yun; Zeng, Hong-Hu; Liang, Yan-Peng

2018-05-01

Antibiotics and pesticides may exist as a mixture in real environment. The combined effect of mixture can either be additive or non-additive (synergism and antagonism). However, no effective predictive approach exists on predicting the synergistic and antagonistic toxicities of mixtures. In this study, we developed a quantitative structure-activity relationship (QSAR) model for the toxicities (half effect concentration, EC 50 ) of 45 binary and multi-component mixtures composed of two antibiotics and four pesticides. The acute toxicities of single compound and mixtures toward Aliivibrio fischeri were tested. A genetic algorithm was used to obtain the optimized model with three theoretical descriptors. Various internal and external validation techniques indicated that the coefficient of determination of 0.9366 and root mean square error of 0.1345 for the QSAR model predicted that 45 mixture toxicities presented additive, synergistic, and antagonistic effects. Compared with the traditional concentration additive and independent action models, the QSAR model exhibited an advantage in predicting mixture toxicity. Thus, the presented approach may be able to fill the gaps in predicting non-additive toxicities of binary and multi-component mixtures. Copyright © 2018 Elsevier Ltd. All rights reserved.

Interannual and seasonal variability of winter-spring cohort of neon flying squid abundance in the Northwest Pacific Ocean during 1995-2011

NASA Astrophysics Data System (ADS)

Yu, Wei; Chen, Xinjun; Yi, Qian

2016-06-01

The neon flying squid, Ommastrephes bartramii, is a species of economically important cephalopod in the Northwest Pacific Ocean. Its short lifespan increases the susceptibility of the distribution and abundance to the direct impact of the environmental conditions. Based on the generalized linear model (GLM) and generalized additive model (GAM), the commercial fishery data from the Chinese squid-jigging fleets during 1995 to 2011 were used to examine the interannual and seasonal variability in the abundance of O. bartramii, and to evaluate the influences of variables on the abundance (catch per unit effort, CPUE). The results from GLM suggested that year, month, latitude, sea surface temperature (SST), mixed layer depth (MLD), and the interaction term ( SST×MLD) were significant factors. The optimal model based on GAM included all the six significant variables and could explain 42.43% of the variance in nominal CPUE. The importance of the six variables was ranked by decreasing magnitude: year, month, latitude, SST, MLD and SST×MLD. The squid was mainly distributed in the waters between 40°N and 44°N in the Northwest Pacific Ocean. The optimal ranges of SST and MLD were from 14 to 20°C and from 10 to 30 m, respectively. The squid abundance greatly fluctuated from 1995 to 2011. The CPUE was low during 1995-2002 and high during 2003-2008. Furthermore, the squid abundance was typically high in August. The interannual and seasonal variabilities in the squid abundance were associated with the variations of marine environmental conditions and the life history characteristics of squid.

A systematic review of methodology: time series regression analysis for environmental factors and infectious diseases.

PubMed

Imai, Chisato; Hashizume, Masahiro

2015-03-01

Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM. Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted. Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes (i.e. daily data). These concerns may be attributable to features specific to infectious diseases, such as transmission among individuals and complicated causal mechanisms. The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Future studies should pay careful attention to details and examine alternative models or methods that improve studies using time series regression analysis for environmental determinants of infectious diseases.

Recreation-induced changes in boreal bird communities in protected areas.

PubMed

Kangas, K; Luoto, M; Ihantola, A; Tomppo, E; Siikamäki, P

2010-09-01

The impacts of human-induced disturbance on birds have been studied in growing extent, but there are relatively few studies about the effects of recreation on forest bird communities in protected areas. In this paper, the relative importance of recreation as well as environmental variables on bird communities in Oulanka National Park, in northeastern Finland, was investigated using general additive models (GAM). Bird data collected using the line transect method along hiking trails and in undisturbed control areas were related to number of visits, area of tourism infrastructure, and habitat variables. We further examined the impact of spatial autocorrelation by calculating an autocovariate term for GAMs. Our results indicate that number of visits affects the occurrence and composition of bird communities, but it had no impact on total species richness. Open-cup nesters breeding on the ground showed strongest negative response to visitor pressure, whereas the open-cup nesters nesting in trees and shrubs were more tolerant. For cavity-nesting species, recreation had no significant impact. The contribution of the number of visits was generally low also in models in which it was selected, and the occurrence of birds was mainly determined by habitat characteristics of the area. However, our results show that the recreation-induced disturbance with relatively low visitor pressure can have negative impacts on some bird species and groups of species and should be considered in management of protected areas with recreational activities.

Thermal Stability of Nanocrystalline Alloys by Solute Additions and A Thermodynamic Modeling

NASA Astrophysics Data System (ADS)

Saber, Mostafa

and alpha → gamma phase transformation in Fe-Ni-Zr alloys. In addition to the experimental study of thermal stabilization of nanocrystalline Fe-Cr-Zr or Fe-Ni-Zr alloys, the thesis presented here developed a new predictive model, applicable to strongly segregating solutes, for thermodynamic stabilization of binary alloys. This model can serve as a benchmark for selecting solute and evaluating the possible contribution of stabilization. Following a regular solution model, both the chemical and elastic strain energy contributions are combined to obtain the mixing enthalpy. The total Gibbs free energy of mixing is then minimized with respect to simultaneous variations in the grain boundary volume fraction and the solute concentration in the grain boundary and the grain interior. The Lagrange multiplier method was used to obtained numerical solutions. Application are given for the temperature dependence of the grain size and the grain boundary solute excess for selected binary system where experimental results imply that thermodynamic stabilization could be operative. This thesis also extends the binary model to a new model for thermodynamic stabilization of ternary nanocrystalline alloys. It is applicable to strongly segregating size-misfit solutes and uses input data available in the literature. In a same manner as the binary model, this model is based on a regular solution approach such that the chemical and elastic strain energy contributions are incorporated into the mixing enthalpy DeltaHmix, and the mixing entropy DeltaSmix is obtained using the ideal solution approximation. The Gibbs mixing free energy Delta Gmix is then minimized with respect to simultaneous variations in grain growth and solute segregation parameters. The Lagrange multiplier method is similarly used to obtain numerical solutions for the minimum Delta Gmix. The temperature dependence of the nanocrystalline grain size and interfacial solute excess can be obtained for selected ternary systems. As

Boosted structured additive regression for Escherichia coli fed-batch fermentation modeling.

PubMed

Melcher, Michael; Scharl, Theresa; Luchner, Markus; Striedner, Gerald; Leisch, Friedrich

2017-02-01

The quality of biopharmaceuticals and patients' safety are of highest priority and there are tremendous efforts to replace empirical production process designs by knowledge-based approaches. Main challenge in this context is that real-time access to process variables related to product quality and quantity is severely limited. To date comprehensive on- and offline monitoring platforms are used to generate process data sets that allow for development of mechanistic and/or data driven models for real-time prediction of these important quantities. Ultimate goal is to implement model based feed-back control loops that facilitate online control of product quality. In this contribution, we explore structured additive regression (STAR) models in combination with boosting as a variable selection tool for modeling the cell dry mass, product concentration, and optical density on the basis of online available process variables and two-dimensional fluorescence spectroscopic data. STAR models are powerful extensions of linear models allowing for inclusion of smooth effects or interactions between predictors. Boosting constructs the final model in a stepwise manner and provides a variable importance measure via predictor selection frequencies. Our results show that the cell dry mass can be modeled with a relative error of about ±3%, the optical density with ±6%, the soluble protein with ±16%, and the insoluble product with an accuracy of ±12%. Biotechnol. Bioeng. 2017;114: 321-334. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers

PubMed Central

Su, Guosheng; Christensen, Ole F.; Ostersen, Tage; Henryon, Mark; Lund, Mogens S.

2012-01-01

Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP) markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1) a simple additive genetic model (MA), 2) a model including both additive and additive by additive epistatic genetic effects (MAE), 3) a model including both additive and dominance genetic effects (MAD), and 4) a full model including all three genetic components (MAED). Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions. PMID:23028912

Can ligand addition to soil enhance Cd phytoextraction? A mechanistic model study.

PubMed

Lin, Zhongbing; Schneider, André; Nguyen, Christophe; Sterckeman, Thibault

2014-11-01

Phytoextraction is a potential method for cleaning Cd-polluted soils. Ligand addition to soil is expected to enhance Cd phytoextraction. However, experimental results show that this addition has contradictory effects on plant Cd uptake. A mechanistic model simulating the reaction kinetics (adsorption on solid phase, complexation in solution), transport (convection, diffusion) and root absorption (symplastic, apoplastic) of Cd and its complexes in soil was developed. This was used to calculate plant Cd uptake with and without ligand addition in a great number of combinations of soil, ligand and plant characteristics, varying the parameters within defined domains. Ligand addition generally strongly reduced hydrated Cd (Cd(2+)) concentration in soil solution through Cd complexation. Dissociation of Cd complex ([Formula: see text]) could not compensate for this reduction, which greatly lowered Cd(2+) symplastic uptake by roots. The apoplastic uptake of [Formula: see text] was not sufficient to compensate for the decrease in symplastic uptake. This explained why in the majority of the cases, ligand addition resulted in the reduction of the simulated Cd phytoextraction. A few results showed an enhanced phytoextraction in very particular conditions (strong plant transpiration with high apoplastic Cd uptake capacity), but this enhancement was very limited, making chelant-enhanced phytoextraction poorly efficient for Cd.

Registration of ‘Puma’ soft white winter wheat

USDA-ARS?s Scientific Manuscript database

Resistance to strawbreaker foot rot (caused by Oculimacula yallundae Crous & W. Gams and O. acuformis Crous & W. Gams), stripe rust (caused by Puccinia striiformis Westend. f. sp. tritici Eriks.), and Cephalosporium stripe (caused by Cephalosporium gramineum Nisikado and Ikata) are important traits ...

Conservation of proteins involved in oocyst wall formation in Eimeria maxima, Eimeria tenella and Eimeria acervulina

PubMed Central

Belli, Sabina I.; Ferguson, David J.P.; Katrib, Marilyn; Slapetova, Iveta; Mai, Kelly; Slapeta, Jan; Flowers, Sarah A.; Miska, Kate B.; Tomley, Fiona M.; Shirley, Martin W.; Wallach, Michael G.; Smith, Nicholas C.

2009-01-01

Vaccination with proteins from gametocytes of Eimeria maxima protects chickens, via transfer of maternal antibodies, against infection with several species of Eimeria. Antibodies to E. maxima gametocyte proteins recognise proteins in the wall forming bodies of macrogametocytes and oocyst walls of E. maxima, Eimeria tenella and Eimeria acervulina. Homologous genes for two major gametocyte proteins – GAM56 and GAM82 – were found in E. maxima, E. tenella and E. acervulina. Alignment of the predicted protein sequences of these genes reveals that, as well as sharing regions of tyrosine richness, strong homology exists in their amino-terminal regions, where protective antibodies bind. This study confirms the conservation of the roles of GAM56 and GAM82 in oocyst wall formation and shows that antibodies to gametocyte antigens of E. maxima cross-react with homologous proteins in other species, helping to explain cross-species maternal immunity. PMID:19477178

Forecast of severe fever with thrombocytopenia syndrome incidence with meteorological factors.

PubMed

Sun, Ji-Min; Lu, Liang; Liu, Ke-Ke; Yang, Jun; Wu, Hai-Xia; Liu, Qi-Yong

2018-06-01

Severe fever with thrombocytopenia syndrome (SFTS) is emerging and some studies reported that SFTS incidence was associated with meteorological factors, while no report on SFTS forecast models was reported up to date. In this study, we constructed and compared three forecast models using autoregressive integrated moving average (ARIMA) model, negative binomial regression model (NBM), and quasi-Poisson generalized additive model (GAM). The dataset from 2011 to 2015 were used for model construction and the dataset in 2016 were used for external validity assessment. All the three models fitted the SFTS cases reasonably well during the training process and forecast process, while the NBM model forecasted better than other two models. Moreover, we demonstrated that temperature and relative humidity played key roles in explaining the temporal dynamics of SFTS occurrence. Our study contributes to better understanding of SFTS dynamics and provides predictive tools for the control and prevention of SFTS. Copyright © 2018 Elsevier B.V. All rights reserved.

Spatial Evaluation and Modeling of Dengue Seroprevalence and Vector Density in Rio de Janeiro, Brazil

PubMed Central

Honório, Nildimar Alves; Nogueira, Rita Maria Ribeiro; Codeço, Cláudia Torres; Carvalho, Marilia Sá; Cruz, Oswaldo Gonçalves; de Avelar Figueiredo Mafra Magalhães, Mônica; de Araújo, Josélio Maria Galvão; de Araújo, Eliane Saraiva Machado; Gomes, Marcelo Quintela; Pinheiro, Luciane Silva; da Silva Pinel, Célio; Lourenço-de-Oliveira, Ricardo

2009-01-01

Background Rio de Janeiro, Brazil, experienced a severe dengue fever epidemic in 2008. This was the worst epidemic ever, characterized by a sharp increase in case-fatality rate, mainly among younger individuals. A combination of factors, such as climate, mosquito abundance, buildup of the susceptible population, or viral evolution, could explain the severity of this epidemic. The main objective of this study is to model the spatial patterns of dengue seroprevalence in three neighborhoods with different socioeconomic profiles in Rio de Janeiro. As blood sampling coincided with the peak of dengue transmission, we were also able to identify recent dengue infections and visually relate them to Aedes aegypti spatial distribution abundance. We analyzed individual and spatial factors associated with seroprevalence using Generalized Additive Model (GAM). Methodology/Principal Findings Three neighborhoods were investigated: a central urban neighborhood, and two isolated areas characterized as a slum and a suburban area. Weekly mosquito collections started in September 2006 and continued until March 2008. In each study area, 40 adult traps and 40 egg traps were installed in a random sample of premises, and two infestation indexes calculated: mean adult density and mean egg density. Sera from individuals living in the three neighborhoods were collected before the 2008 epidemic (July through November 2007) and during the epidemic (February through April 2008). Sera were tested for DENV-reactive IgM, IgG, Nested RT-PCR, and Real Time RT-PCR. From the before–after epidemics paired data, we described seroprevalence, recent dengue infections (asymptomatic or not), and seroconversion. Recent dengue infection varied from 1.3% to 14.1% among study areas. The highest IgM seropositivity occurred in the slum, where mosquito abundance was the lowest, but household conditions were the best for promoting contact between hosts and vectors. By fitting spatial GAM we found dengue

The potential application of European market research data in dietary exposure modelling of food additives.

PubMed

Tennant, David Robin; Bruyninckx, Chris

2018-03-01

Consumer exposure assessments for food additives are incomplete without information about the proportions of foods in each authorised category that contain the additive. Such information has been difficult to obtain but the Mintel Global New Products Database (GNPD) provides information about product launches across Europe over the past 20 years. These data can be searched to identify products with specific additives listed on product labels and the numbers compared with total product launches for food and drink categories in the same database to determine the frequency of occurrence. There are uncertainties associated with the data but these can be managed by adopting a cautious and conservative approach. GNPD data can be mapped with authorised food categories and with food descriptions used in the EFSA Comprehensive European Food Consumption Surveys Database for exposure modelling. The data, when presented as percent occurrence, could be incorporated into the EFSA ANS Panel's 'brand-loyal/non-brand loyal exposure model in a quantitative way. Case studies of preservative, antioxidant, colour and sweetener additives showed that the impact of including occurrence data is greatest in the non-brand loyal scenario. Recommendations for future research include identifying occurrence data for alcoholic beverages, linking regulatory food codes, FoodEx and GNPD product descriptions, developing the use of occurrence data for carry-over foods and improving understanding of brand loyalty in consumer exposure models.

Bayesian structured additive regression modeling of epidemic data: application to cholera

PubMed Central

2012-01-01

Background A significant interest in spatial epidemiology lies in identifying associated risk factors which enhances the risk of infection. Most studies, however, make no, or limited use of the spatial structure of the data, as well as possible nonlinear effects of the risk factors. Methods We develop a Bayesian Structured Additive Regression model for cholera epidemic data. Model estimation and inference is based on fully Bayesian approach via Markov Chain Monte Carlo (MCMC) simulations. The model is applied to cholera epidemic data in the Kumasi Metropolis, Ghana. Proximity to refuse dumps, density of refuse dumps, and proximity to potential cholera reservoirs were modeled as continuous functions; presence of slum settlers and population density were modeled as fixed effects, whereas spatial references to the communities were modeled as structured and unstructured spatial effects. Results We observe that the risk of cholera is associated with slum settlements and high population density. The risk of cholera is equal and lower for communities with fewer refuse dumps, but variable and higher for communities with more refuse dumps. The risk is also lower for communities distant from refuse dumps and potential cholera reservoirs. The results also indicate distinct spatial variation in the risk of cholera infection. Conclusion The study highlights the usefulness of Bayesian semi-parametric regression model analyzing public health data. These findings could serve as novel information to help health planners and policy makers in making effective decisions to control or prevent cholera epidemics. PMID:22866662

Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series.

PubMed

Weber, Juliane; Zachow, Christopher; Witthaut, Dirk

2018-03-01

Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.

Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series

NASA Astrophysics Data System (ADS)

Weber, Juliane; Zachow, Christopher; Witthaut, Dirk

2018-03-01

Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.

pH regulation of recombinant glucoamylase production in Fusarium venenatum JeRS 325, a transformant with a Fusarium oxysporum alkaline (trypsin-like) protease promoter.

PubMed

Wiebe, M G; Robson, G D; Shuster, J R; Trinci, A P

1999-08-05

Fusarium venenatum (formerly Fusarium graminearum) JeRS 325 produces heterologous glucoamylase (GAM) under the regulation of a Fusarium oxysporum alkaline (trypsin-like) protease promoter. The glucoamylase gene was used as a reporter gene to study the effects of ammonium and pH on GAM production under the control of the alkaline protease promoter. Between pH 4.0 and 5.8, GAM production in glucose-limited chemostat cultures of JeRS 325 grown at a dilution rate of 0.10 h-1 (doubling time, 6.9 h) on (NH4)2SO4 medium increased in a linear manner with increase in pH. However, at pH 4.0 and below GAM production was almost completely repressed in glucose-limited chemostat cultures grown on (NH4)2SO4 or NaNO3 medium. Thus GAM production in JeRS 325 is regulated by culture pH, not by the nature of the nitrogen source in the medium. The difficulty of using unbuffered medium when investigating putative ammonium repression is also shown. The study demonstrates the potential for use of the alkaline protease promoter in F. graminearum for the production of recombinant proteins in a pH dependent man ner. Copyright 1999 John Wiley & Sons, Inc.

Axisymmetric electrostatic magnetohydrodynamic oscillations in tokamaks with general cross-sections and toroidal flow

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

Chu, M. S.; Guo, Wenfeng

2016-06-15

The frequency spectrum and mode structure of axisymmetric electrostatic oscillations [the zonal flow (ZF), sound waves (SW), geodesic acoustic modes (GAM), and electrostatic mean flows (EMF)] in tokamaks with general cross-sections and toroidal flows are studied analytically using the electrostatic approximation for magnetohydrodynamic modes. These modes constitute the “electrostatic continua.” Starting from the energy principle for a tokamak plasma with toroidal rotation, we showed that these modes are completely stable. The ZF, the SW, and the EMF could all be viewed as special cases of the general GAM. The Euler equations for the general GAM are obtained and are solvedmore » analytically for both the low and high range of Mach numbers. The solution consists of the usual countable infinite set of eigen-modes with discrete eigen-frequencies, and two modes with lower frequencies. The countable infinite set is identified with the regular GAM. The lower frequency mode, which is also divergence free as the plasma rotation tends to zero, is identified as the ZF. The other lower (zero) frequency mode is a pure geodesic E×B flow and not divergence free is identified as the EMF. The frequency of the EMF is shown to be exactly 0 independent of plasma cross-section or its flow Mach number. We also show that in general, sound waves with no geodesic components are (almost) completely lost in tokamaks with a general cross-sectional shape. The exception is the special case of strict up-down symmetry. In this case, half of the GAMs would have no geodesic displacements. They are identified as the SW. Present day tokamaks, although not strictly up-down symmetric, usually are only slightly up-down asymmetric. They are expected to share the property with the up-down symmetric tokamak in that half of the GAMs would be more sound wave-like, i.e., have much weaker coupling to the geodesic components than the other half of non-sound-wave-like modes with stronger coupling to the

78 FR 12271 - Wireline Competition Bureau Seeks Additional Comment In Connect America Cost Model Virtual Workshop

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-22

... Competition Bureau seeks public input on additional questions relating to modeling voice capability and Annual... submitting comments and additional information on the rulemaking process, see the SUPPLEMENTARY INFORMATION section of this document. FOR FURTHER INFORMATION CONTACT: Katie King, Wireline Competition Bureau at (202...

Potential uncertainty reduction in model-averaged benchmark dose estimates informed by an additional dose study.

PubMed

Shao, Kan; Small, Mitchell J

2011-10-01

A methodology is presented for assessing the information value of an additional dosage experiment in existing bioassay studies. The analysis demonstrates the potential reduction in the uncertainty of toxicity metrics derived from expanded studies, providing insights for future studies. Bayesian methods are used to fit alternative dose-response models using Markov chain Monte Carlo (MCMC) simulation for parameter estimation and Bayesian model averaging (BMA) is used to compare and combine the alternative models. BMA predictions for benchmark dose (BMD) are developed, with uncertainty in these predictions used to derive the lower bound BMDL. The MCMC and BMA results provide a basis for a subsequent Monte Carlo analysis that backcasts the dosage where an additional test group would have been most beneficial in reducing the uncertainty in the BMD prediction, along with the magnitude of the expected uncertainty reduction. Uncertainty reductions are measured in terms of reduced interval widths of predicted BMD values and increases in BMDL values that occur as a result of this reduced uncertainty. The methodology is illustrated using two existing data sets for TCDD carcinogenicity, fitted with two alternative dose-response models (logistic and quantal-linear). The example shows that an additional dose at a relatively high value would have been most effective for reducing the uncertainty in BMA BMD estimates, with predicted reductions in the widths of uncertainty intervals of approximately 30%, and expected increases in BMDL values of 5-10%. The results demonstrate that dose selection for studies that subsequently inform dose-response models can benefit from consideration of how these models will be fit, combined, and interpreted. © 2011 Society for Risk Analysis.

Weather and air pollutants have an impact on patients with respiratory diseases and breathing difficulties in Munich, Germany

NASA Astrophysics Data System (ADS)

Wanka, E. R.; Bayerstadler, A.; Heumann, C.; Nowak, D.; Jörres, R. A.; Fischer, R.

2014-03-01

This study determined the influence of various meteorological variables and air pollutants on airway disorders in general, and asthma and/or chronic obstructive pulmonary disease in particular, in Munich, Bavaria, during 2006 and 2007. This was achieved through an evaluation of the daily frequency of calls to medical and emergency call centres, ambulatory medical care visits at general practitioners, and prescriptions of antibiotics for respiratory diseases. Meteorological parameters were extracted from data supplied by the European Centre for Medium Range Weather Forecast. Data on air pollutant levels were extracted from the air quality database of the European Environmental Agency for different measurement sites. In addition to descriptive analyses, a backward elimination procedure was performed to identify variables associated with medical outcome variables. Afterwards, generalised additive models (GAM) were used to verify whether the selected variables had a linear or nonlinear impact on the medical outcomes. The analyses demonstrated associations between environmental parameters and daily frequencies of different medical outcomes, such as visits at GPs and air pressure (-27 % per 10 hPa change) or ozone (-24 % per 10 μg/m3 change). The results of the GAM indicated that the effects of some covariates, such as carbon monoxide on consultations at GPs, or humidity on medical calls in general, were nonlinear, while the type of association varied between medical outcomes. These data suggest that the multiple, complex effect of environmental factors on medical outcomes should not be assumed homogeneous or linear a priori and that different settings might be associated with different types of associations.

Time-series analysis of the barriers for admission into a spinal rehabilitation unit.

PubMed

New, P W; Akram, M

2016-02-01

This is a prospective open-cohort case series. The objective of this study was to assess changes over time in the duration of key acute hospital process barriers for patients with spinal cord damage (SCD) from admission until transfer into spinal rehabilitation unit (SRU) or other destinations. The study was conducted in Acute hospitals, Victoria, Australia (2006-2013). Duration of the following discrete sequential processes was measured: acute hospital admission until referral to SRU, referral until SRU assessment, SRU assessment until ready for SRU transfer and ready for transfer until SRU admission. Time-series analysis was performed using a generalised additive model (GAM). Seasonality of non-traumatic spinal cord dysfunction (SCDys) was examined. GAM analysis shows that the waiting time for admission into SRU was significantly (P<0.001) longer for patients who were female, who had tetraplegia, who were motor complete, had a pelvic pressure ulcer and who were referred from another health network. Age had a non-linear effect on the duration of waiting for transfer from acute hospital to SRU and both the acute hospital and SRU length of stay (LOS). The duration patients spent waiting for SRU admission increased over the study period. There was an increase in the number of referrals over the study period and an increase in the number of patients accepted but not admitted into the SRU. There was no notable seasonal influence on the referral of patients with SCDys. Time-series analysis provides additional insights into changes in the waiting times for SRU admission and the LOS in hospital for patients with SCD.

Addition Table of Colours: Additive and Subtractive Mixtures Described Using a Single Reasoning Model

ERIC Educational Resources Information Center

Mota, A. R.; Lopes dos Santos, J. M. B.

2014-01-01

Students' misconceptions concerning colour phenomena and the apparent complexity of the underlying concepts--due to the different domains of knowledge involved--make its teaching very difficult. We have developed and tested a teaching device, the addition table of colours (ATC), that encompasses additive and subtractive mixtures in a single…

Bridging the gap between research and practice: review of a targeted hospital inpatient fall prevention programme.

PubMed

Barker, A; Kamar, J; Morton, A; Berlowitz, D

2009-12-01

Falls among older inpatients are frequent and have negative consequences. In this study, the effectiveness of a fall prevention programme in reducing falls and fall injuries in an acute hospital was studied. Retrospective audit. The Northern Hospital, an acute, metropolitan, hospital in Australia. A multi-factorial fall prevention programme that included establishment of a multidisciplinary committee, risk assessment of all patients on "high-risk" wards and targeted interventions for patients identified as high risk. Fall and fall injury rates per 1000 occupied bed-days were analysed using generalised additive models (GAM) and, because of the presence of autocorrelation, generalised additive mixed models (GAMM), respectively. During the 9-year observation of 271 095 patients, there were 2910 falls and 843 fall injuries. The GAM predicted rate of falls was stable in the 3 years after the programme was implemented, increased in 2006, then decreased between October 2006 and December 2007 from 4.13 (95% CI 3.65 to 4.67) to 2.83 (95% CI 2.24 to 3.59; p = 0.005). The GAMM predicted rate of fall injuries reduced from 1.66 (95% CI 1.24 to 2.21) to 0.61 (95% CI 0.43 to 0.88) after programme implementation (p<0.001). The falls rate varied throughout the observation period, and no significant change in the rate from preprogramme to postprogramme implementation was observed. The finding of no reduction in falls during the observation period may be explained by improved reporting throughout the observation period. The reduction in fall injuries was substantial and sustained. Identification of a local problem, use of a fall risk assessment to guide the delivery of simple interventions, integration of processes into daily clinical practice and creating systems that demand accountability of staff are factors that appear to have contributed to the programme's success.

Genetic variation maintained in multilocus models of additive quantitative traits under stabilizing selection.

PubMed Central

Bürger, R; Gimelfarb, A

1999-01-01

Stabilizing selection for an intermediate optimum is generally considered to deplete genetic variation in quantitative traits. However, conflicting results from various types of models have been obtained. While classical analyses assuming a large number of independent additive loci with individually small effects indicated that no genetic variation is preserved under stabilizing selection, several analyses of two-locus models showed the contrary. We perform a complete analysis of a generalization of Wright's two-locus quadratic-optimum model and investigate numerically the ability of quadratic stabilizing selection to maintain genetic variation in additive quantitative traits controlled by up to five loci. A statistical approach is employed by choosing randomly 4000 parameter sets (allelic effects, recombination rates, and strength of selection) for a given number of loci. For each parameter set we iterate the recursion equations that describe the dynamics of gamete frequencies starting from 20 randomly chosen initial conditions until an equilibrium is reached, record the quantities of interest, and calculate their corresponding mean values. As the number of loci increases from two to five, the fraction of the genome expected to be polymorphic declines surprisingly rapidly, and the loci that are polymorphic increasingly are those with small effects on the trait. As a result, the genetic variance expected to be maintained under stabilizing selection decreases very rapidly with increased number of loci. The equilibrium structure expected under stabilizing selection on an additive trait differs markedly from that expected under selection with no constraints on genotypic fitness values. The expected genetic variance, the expected polymorphic fraction of the genome, as well as other quantities of interest, are only weakly dependent on the selection intensity and the level of recombination. PMID:10353920

Developmental times and life table statistics of Aulacorthum solani (Hemiptera: Aphididae) at six constant temperatures, with recommendations on the application of temperature-dependent development models

USDA-ARS?s Scientific Manuscript database

Developmental rates and age-specific life tables were determined for Aulacorthum solani (Kaltenbach) (known as foxglove aphid or glasshouse potato aphid) at 6 constant temperatures feeding on pansy (Viola × wittrockiana) (Gams.). Previously, there were no complete life table studies of this species...

Ozone in the southeastern United States: An observation-based model using measurements from the SEARCH network

NASA Astrophysics Data System (ADS)

Blanchard, C. L.; Hidy, G. M.; Tanenbaum, S.

2014-05-01

A generalized additive model (GAM) is used to examine the influence of meteorological factors, nitrogen oxides (NOx = NO + NO2), and non-methane hydrocarbons (NMOC) on daily peak 8-h ozone (O3) concentrations. Application to 2002-2011 monitoring data from the Southeastern Aerosol Research and Characterization (SEARCH) program showed sensitivity of peak 8-h O3 to morning concentrations of nitric oxide (NO) and nitrogen dioxide (NO2) and to afternoon concentrations of NO2 reaction products (NOz). Peak O3 decreased with increasing NO and increased with increasing NO2 concentrations, consistent with reactions involving O3, NO, and NO2. Ozone production efficiency (OPE), estimated from the modeled relation between peak 8-h O3 and afternoon NOz, was ˜40-100 percent higher at rural compared to urban sites. OPE was nonlinear at all sites, decreasing with increasing NOz concentration. The mean ratio of NOz/NOy showed a two-fold increase from urban to rural sites, associated with chemical aging in stagnant air masses from one day (urban sites) to two or more days (non-urban sites). Peak 8-h O3 concentrations in Atlanta were sensitive to concentrations of both non-biogenic NMOC and NOz. Non-urban Yorkville, Georgia, peak 8-h O3 concentrations were sensitive to NOz but not to non-biogenic NMOC concentrations. The results are consistent with expected NMOC and NOx sensitivity in urban and non-urban locales.

Mixed Model Methods for Genomic Prediction and Variance Component Estimation of Additive and Dominance Effects Using SNP Markers

PubMed Central

Da, Yang; Wang, Chunkao; Wang, Shengwen; Hu, Guo

2014-01-01

We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using pedigree information. Based on this genomic model, two sets of computationally complementary but mathematically identical mixed model methods were developed for genomic best linear unbiased prediction (GBLUP) and genomic restricted maximum likelihood estimation (GREML) of additive and dominance effects using SNP markers. These two sets are referred to as the CE and QM sets, where the CE set was designed for large numbers of markers and the QM set was designed for large numbers of individuals. GBLUP and associated accuracy formulations for individuals in training and validation data sets were derived for breeding values, dominance deviations and genotypic values. Simulation study showed that GREML and GBLUP generally were able to capture small additive and dominance effects that each accounted for 0.00005–0.0003 of the phenotypic variance and GREML was able to differentiate true additive and dominance heritability levels. GBLUP of the total genetic value as the summation of additive and dominance effects had higher prediction accuracy than either additive or dominance GBLUP, causal variants had the highest accuracy of GREML and GBLUP, and predicted accuracies were in agreement with observed accuracies. Genomic additive and dominance relationship matrices using SNP markers were consistent with theoretical expectations. The GREML and GBLUP methods can be an effective tool for assessing the type and magnitude of genetic effects affecting a phenotype and for predicting the total genetic value at the whole genome level. PMID:24498162

Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.

PubMed

Da, Yang; Wang, Chunkao; Wang, Shengwen; Hu, Guo

2014-01-01

We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using pedigree information. Based on this genomic model, two sets of computationally complementary but mathematically identical mixed model methods were developed for genomic best linear unbiased prediction (GBLUP) and genomic restricted maximum likelihood estimation (GREML) of additive and dominance effects using SNP markers. These two sets are referred to as the CE and QM sets, where the CE set was designed for large numbers of markers and the QM set was designed for large numbers of individuals. GBLUP and associated accuracy formulations for individuals in training and validation data sets were derived for breeding values, dominance deviations and genotypic values. Simulation study showed that GREML and GBLUP generally were able to capture small additive and dominance effects that each accounted for 0.00005-0.0003 of the phenotypic variance and GREML was able to differentiate true additive and dominance heritability levels. GBLUP of the total genetic value as the summation of additive and dominance effects had higher prediction accuracy than either additive or dominance GBLUP, causal variants had the highest accuracy of GREML and GBLUP, and predicted accuracies were in agreement with observed accuracies. Genomic additive and dominance relationship matrices using SNP markers were consistent with theoretical expectations. The GREML and GBLUP methods can be an effective tool for assessing the type and magnitude of genetic effects affecting a phenotype and for predicting the total genetic value at the whole genome level.

Development of a QTL-environment-based predictive model for node addition rate in common bean.

PubMed

Zhang, Li; Gezan, Salvador A; Eduardo Vallejos, C; Jones, James W; Boote, Kenneth J; Clavijo-Michelangeli, Jose A; Bhakta, Mehul; Osorno, Juan M; Rao, Idupulapati; Beebe, Stephen; Roman-Paoli, Elvin; Gonzalez, Abiezer; Beaver, James; Ricaurte, Jaumer; Colbert, Raphael; Correll, Melanie J

2017-05-01

This work reports the effects of the genetic makeup, the environment and the genotype by environment interactions for node addition rate in an RIL population of common bean. This information was used to build a predictive model for node addition rate. To select a plant genotype that will thrive in targeted environments it is critical to understand the genotype by environment interaction (GEI). In this study, multi-environment QTL analysis was used to characterize node addition rate (NAR, node day - 1 ) on the main stem of the common bean (Phaseolus vulgaris L). This analysis was carried out with field data of 171 recombinant inbred lines that were grown at five sites (Florida, Puerto Rico, 2 sites in Colombia, and North Dakota). Four QTLs (Nar1, Nar2, Nar3 and Nar4) were identified, one of which had significant QTL by environment interactions (QEI), that is, Nar2 with temperature. Temperature was identified as the main environmental factor affecting NAR while day length and solar radiation played a minor role. Integration of sites as covariates into a QTL mixed site-effect model, and further replacing the site component with explanatory environmental covariates (i.e., temperature, day length and solar radiation) yielded a model that explained 73% of the phenotypic variation for NAR with root mean square error of 16.25% of the mean. The QTL consistency and stability was examined through a tenfold cross validation with different sets of genotypes and these four QTLs were always detected with 50-90% probability. The final model was evaluated using leave-one-site-out method to assess the influence of site on node addition rate. These analyses provided a quantitative measure of the effects on NAR of common beans exerted by the genetic makeup, the environment and their interactions.

Violent video games: The effects of narrative context and reward structure on in-game and postgame aggression.

PubMed

Sauer, James D; Drummond, Aaron; Nova, Natalie

2015-09-01

The potential influence of video game violence on real-world aggression has generated considerable public and scientific interest. Some previous research suggests that playing violent video games can increase postgame aggression. The generalized aggression model (GAM) attributes this to the generalized activation of aggressive schemata. However, it is unclear whether game mechanics that contextualize and encourage or inhibit in-game violence moderate this relationship. Thus, we examined the effects of reward structures and narrative context in a violent video game on in-game and postgame aggression. Contrary to GAM-based predictions, our manipulations differentially affected in-game and postgame aggression. Reward structures selectively affected in-game aggression, whereas narrative context selectively affected postgame aggression. Players who enacted in-game violence through a heroic character exhibited less postgame aggression than players who enacted comparable levels of in-game violence through an antiheroic character. Effects were not attributable to self-activation or character-identification mechanisms, but were consistent with social-cognitive context effects on the interpretation of behavior. These results contradict the GAM's assertion that violent video games affect aggression through a generalized activation mechanism. From an applied perspective, consumer choices may be aided by considering not just game content, but the context in which content is portrayed. (c) 2015 APA, all rights reserved).

Can interface features affect aggression resulting from violent video game play? An examination of realistic controller and large screen size.

PubMed

Kim, Ki Joon; Sundar, S Shyam

2013-05-01

Aggressiveness attributed to violent video game play is typically studied as a function of the content features of the game. However, can interface features of the game also affect aggression? Guided by the General Aggression Model (GAM), we examine the controller type (gun replica vs. mouse) and screen size (large vs. small) as key technological aspects that may affect the state aggression of gamers, with spatial presence and arousal as potential mediators. Results from a between-subjects experiment showed that a realistic controller and a large screen display induced greater aggression, presence, and arousal than a conventional mouse and a small screen display, respectively, and confirmed that trait aggression was a significant predictor of gamers' state aggression. Contrary to GAM, however, arousal showed no effects on aggression; instead, presence emerged as a significant mediator.

Summary of hydrogeology and simulation of ground-water flow and land-surface subsidence in the northern part of the Gulf Coast aquifer system, Texas

USGS Publications Warehouse

Kasmarek, Mark C.; Robinson, James L.

2004-01-01

The northern part of the Gulf Coast aquifer system in Texas, which includes the Chicot, Evangeline, and Jasper aquifers, supplies most of the water used for industrial, municipal, agricultural, and commercial purposes for an approximately 25,000- square-mile (mi2) area that includes the Beaumont and Houston metropolitan areas. The area has an abundant amount of potable ground water, but withdrawals of large quantities of ground water have resulted in potentiometric-surface declines in the Chicot, Evangeline, and Jasper aquifers and land-surface subsidence from depressurization and compaction of clay layers interbedded in the aquifer sediments. This fact sheet summarizes a study done in cooperation with the Texas Water Development Board (TWDB) and the Harris-Galveston Coastal Subsidence District (HGCSD) as a part of the TWDB Ground-Water Availability Modeling (or Model) (GAM) program. The study was designed to develop and test a ground-water-flow model of the northern part of the Gulf Coast aquifer system in the GAM area (fig. 1) that waterresource managers can use as a tool to address future groundwater- availability issues.

Game theoretic power allocation and waveform selection for satellite communications

NASA Astrophysics Data System (ADS)

Shu, Zhihui; Wang, Gang; Tian, Xin; Shen, Dan; Pham, Khanh; Blasch, Erik; Chen, Genshe

2015-05-01

Game theory is a useful method to model interactions between agents with conflicting interests. In this paper, we set up a Game Theoretic Model for Satellite Communications (SATCOM) to solve the interaction between the transmission pair (blue side) and the jammer (red side) to reach a Nash Equilibrium (NE). First, the IFT Game Application Model (iGAM) for SATCOM is formulated to improve the utility of the transmission pair while considering the interference from a jammer. Specifically, in our framework, the frame error rate performance of different modulation and coding schemes is used in the game theoretic solution. Next, the game theoretic analysis shows that the transmission pair can choose the optimal waveform and power given the received power from the jammer. We also describe how the jammer chooses the optimal power given the waveform and power allocation from the transmission pair. Finally, simulations are implemented for the iGAM and the simulation results show the effectiveness of the SATCOM power allocation, waveform selection scheme, and jamming mitigation.

Regression analysis of informative current status data with the additive hazards model.

PubMed

Zhao, Shishun; Hu, Tao; Ma, Ling; Wang, Peijie; Sun, Jianguo

2015-04-01

This paper discusses regression analysis of current status failure time data arising from the additive hazards model in the presence of informative censoring. Many methods have been developed for regression analysis of current status data under various regression models if the censoring is noninformative, and also there exists a large literature on parametric analysis of informative current status data in the context of tumorgenicity experiments. In this paper, a semiparametric maximum likelihood estimation procedure is presented and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring time. Furthermore, I-splines are used to approximate the nonparametric functions involved and the asymptotic consistency and normality of the proposed estimators are established. A simulation study is conducted and indicates that the proposed approach works well for practical situations. An illustrative example is also provided.

Inter-annual variability of North Sea plaice spawning habitat

NASA Astrophysics Data System (ADS)

Loots, C.; Vaz, S.; Koubbi, P.; Planque, B.; Coppin, F.; Verin, Y.

2010-11-01

Potential spawning habitat is defined as the area where environmental conditions are suitable for spawning to occur. Spawning adult data from the first quarter (January-March) of the International Bottom Trawl Survey have been used to study the inter-annual variability of the potential spawning habitat of North Sea plaice from 1980 to 2007. Generalised additive models (GAM) were used to create a model that related five environmental variables (depth, bottom temperature and salinity, seabed stress and sediment type) to presence-absence and abundance of spawning adults. Then, the habitat model was applied each year from 1970 to 2007 to predict inter-annual variability of the potential spawning habitat. Predicted responses obtained by GAM for each year were mapped using kriging. A hierarchical classification associated with a correspondence analysis was performed to cluster spawning suitable areas and to determine how they evolved across years. The potential spawning habitat was consistent with historical spawning ground locations described in the literature from eggs surveys. It was also found that the potential spawning habitat varied across years. Suitable areas were located in the southern part of the North Sea and along the eastern coast of England and Scotland in the eighties; they expanded further north from the nineties. Annual survey distributions did not show such northward expansion and remained located in the southern North Sea. This suggests that this species' actual spatial distribution remains stable against changing environmental conditions, and that the potential spawning habitat is not fully occupied. Changes in environmental conditions appear to remain within plaice environmental ranges, meaning that other factors may control the spatial distribution of plaice spawning habitat.

Optimizing simulated fertilizer additions using a genetic algorithm with a nutrient uptake model

Treesearch

Wendell P. Cropper; N.B. Comerford

2005-01-01

Intensive management of pine plantations in the southeastern coastal plain typically involves weed and pest control, and the addition of fertilizer to meet the high nutrient demand of rapidly growing pines. In this study we coupled a mechanistic nutrient uptake model (SSAND, soil supply and nutrient demand) with a genetic algorithm (GA) in order to estimate the minimum...

Guarana Provides Additional Stimulation over Caffeine Alone in the Planarian Model

PubMed Central

Moustakas, Dimitrios; Mezzio, Michael; Rodriguez, Branden R.; Constable, Mic Andre; Mulligan, Margaret E.; Voura, Evelyn B.

2015-01-01

The stimulant effect of energy drinks is primarily attributed to the caffeine they contain. Many energy drinks also contain other ingredients that might enhance the tonic effects of these caffeinated beverages. One of these additives is guarana. Guarana is a climbing plant native to the Amazon whose seeds contain approximately four times the amount of caffeine found in coffee beans. The mix of other natural chemicals contained in guarana seeds is thought to heighten the stimulant effects of guarana over caffeine alone. Yet, despite the growing use of guarana as an additive in energy drinks, and a burgeoning market for it as a nutritional supplement, the science examining guarana and how it affects other dietary ingredients is lacking. To appreciate the stimulant effects of guarana and other natural products, a straightforward model to investigate their physiological properties is needed. The planarian provides such a system. The locomotor activity and convulsive response of planarians with substance exposure has been shown to provide an excellent system to measure the effects of drug stimulation, addiction and withdrawal. To gauge the stimulant effects of guarana we studied how it altered the locomotor activity of the planarian species Dugesia tigrina. We report evidence that guarana seeds provide additional stimulation over caffeine alone, and document the changes to this stimulation in the context of both caffeine and glucose. PMID:25880065

A heterogeneous fleet vehicle routing model for solving the LPG distribution problem: A case study

NASA Astrophysics Data System (ADS)

Onut, S.; Kamber, M. R.; Altay, G.

2014-03-01

Vehicle Routing Problem (VRP) is an important management problem in the field of distribution and logistics. In VRPs, routes from a distribution point to geographically distributed points are designed with minimum cost and considering customer demands. All points should be visited only once and by one vehicle in one route. Total demand in one route should not exceed the capacity of the vehicle that assigned to that route. VRPs are varied due to real life constraints related to vehicle types, number of depots, transportation conditions and time periods, etc. Heterogeneous fleet vehicle routing problem is a kind of VRP that vehicles have different capacity and costs. There are two types of vehicles in our problem. In this study, it is used the real world data and obtained from a company that operates in LPG sector in Turkey. An optimization model is established for planning daily routes and assigned vehicles. The model is solved by GAMS and optimal solution is found in a reasonable time.

AA9int: SNP Interaction Pattern Search Using Non-Hierarchical Additive Model Set.

PubMed

Lin, Hui-Yi; Huang, Po-Yu; Chen, Dung-Tsa; Tung, Heng-Yuan; Sellers, Thomas A; Pow-Sang, Julio; Eeles, Rosalind; Easton, Doug; Kote-Jarai, Zsofia; Amin Al Olama, Ali; Benlloch, Sara; Muir, Kenneth; Giles, Graham G; Wiklund, Fredrik; Gronberg, Henrik; Haiman, Christopher A; Schleutker, Johanna; Nordestgaard, Børge G; Travis, Ruth C; Hamdy, Freddie; Neal, David E; Pashayan, Nora; Khaw, Kay-Tee; Stanford, Janet L; Blot, William J; Thibodeau, Stephen N; Maier, Christiane; Kibel, Adam S; Cybulski, Cezary; Cannon-Albright, Lisa; Brenner, Hermann; Kaneva, Radka; Batra, Jyotsna; Teixeira, Manuel R; Pandha, Hardev; Lu, Yong-Jie; Park, Jong Y

2018-06-07

The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions. We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies. The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/. hlin1@lsuhsc.edu. Supplementary data are available at Bioinformatics online.

A Systematic Review of Methodology: Time Series Regression Analysis for Environmental Factors and Infectious Diseases

PubMed Central

Imai, Chisato; Hashizume, Masahiro

2015-01-01

Background: Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM. Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted. Findings: Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes (i.e. daily data). These concerns may be attributable to features specific to infectious diseases, such as transmission among individuals and complicated causal mechanisms. Conclusion: The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Future studies should pay careful attention to details and examine alternative models or methods that improve studies using time series regression analysis for environmental determinants of infectious diseases. PMID:25859149

Spatial, Temporal, and Habitat-Related Variation in Abundance of Pelagic Fishes in the Gulf of Mexico: Potential Implications of the Deepwater Horizon Oil Spill

PubMed Central

Rooker, Jay R.; Kitchens, Larissa L.; Dance, Michael A.; Wells, R. J. David; Falterman, Brett; Cornic, Maëlle

2013-01-01

Time-series data collected over a four-year period were used to characterize patterns of abundance for pelagic fishes in the northern Gulf of Mexico (GoM) before (2007–2009) and after (2010) the Deepwater Horizon oil spill. Four numerically dominant pelagic species (blackfin tuna, blue marlin, dolphinfish, and sailfish) were included in our assessment, and larval density of each species was lower in 2010 than any of the three years prior to the oil spill, although larval abundance in 2010 was often statistically similar to other years surveyed. To assess potential overlap between suitable habitat of pelagic fish larvae and surface oil, generalized additive models (GAMs) were developed to evaluate the influence of ocean conditions on the abundance of larvae from 2007–2009. Explanatory variables from GAMs were then linked to environmental data from 2010 to predict the probability of occurrence for each species. The spatial extent of surface oil overlapped with early life habitat of each species, possibly indicating that the availability of high quality habitat was affected by the DH oil spill. Shifts in the distribution of spawning adults is another factor known to influence the abundance of larvae, and the spatial occurrence of a model pelagic predator (blue marlin) was characterized over the same four-year period using electronic tags. The spatial extent of oil coincided with areas used by adult blue marlin from 2007–2009, and the occurrence of blue marlin in areas impacted by the DH oil spill was lower in 2010 relative to pre-spill years. PMID:24130759

Climate Factors as Important Determinants of Dengue Incidence in Curaçao.

PubMed

Limper, M; Thai, K T D; Gerstenbluth, I; Osterhaus, A D M E; Duits, A J; van Gorp, E C M

2016-03-01

Macro- and microclimates may have variable impact on dengue incidence in different settings. We estimated the short-term impact and delayed effects of climate variables on dengue morbidity in Curaçao. Monthly dengue incidence data from 1999 to 2009 were included to estimate the short-term influences of climate variables by employing wavelet analysis, generalized additive models (GAM) and distributed lag nonlinear models (DLNM) on rainfall, temperature and relative humidity in relation to dengue incidence. Dengue incidence showed a significant irregular 4-year multi-annual cycle associated with climate variables. Based on GAM, temperature showed a U-shape, while humidity and rainfall exhibited a dome-shaped association, suggesting that deviation from mean temperature increases and deviation from mean humidity and rainfall decreases dengue incidence, respectively. Rainfall was associated with an immediate increase in dengue incidence of 4.1% (95% CI: 2.2-8.1%) after a 10-mm increase, with a maximum increase of 6.5% (95% CI: 3.2-10.0%) after 1.5 month lag. A 1 °C decrease of mean temperature was associated with a RR of 17.4% (95% CI: 11.2-27.0%); the effect was inversed for a 1°C increase of mean temperature (RR= 0.457, 95% CI: 0.278-0.752). Climate variables are important determinants of dengue incidence and provide insight into its short-term effects. An increase in mean temperature was associated with lower dengue incidence, whereas lower temperatures were associated with higher dengue incidence. © 2015 Blackwell Verlag GmbH.

A novel model for through-silicon via (TSV) filling process simulation considering three additives and current density effect

NASA Astrophysics Data System (ADS)

Wang, Fuliang; Zhao, Zhipeng; Wang, Feng; Wang, Yan; Nie, Nantian

2017-12-01

Through-silicon via (TSV) filling by electrochemical deposition is still a challenge for 3D IC packaging, and three-component additive systems (accelerator, suppressor, and leveler) were commonly used in the industry to achieve void-free filling. However, models considering three additive systems and the current density effect have not been fully studied. In this paper, a novel three-component model was developed to study the TSV filling mechanism and process, where the interaction behavior of the three additives (accelerator, suppressor, and leveler) were considered, and the adsorption, desorption, and consumption coefficient of the three additives were changed with the current density. Based on this new model, the three filling types (seam void, ‘V’ shape, and key hole) were simulated under different current density conditions, and the filling results were verified by experiments. The effect of the current density on the copper ion concentration, additives surface coverage, and local current density distribution during the TSV filling process were obtained. Based on the simulation and experimental results, the diffusion-adsorption-desorption-consumption competition behavior between the suppressor, the accelerator, and the leveler were discussed. The filling mechanisms under different current densities were also analyzed.

Aggregation of gluten proteins in model dough after fibre polysaccharide addition.

PubMed

Nawrocka, Agnieszka; Szymańska-Chargot, Monika; Miś, Antoni; Wilczewska, Agnieszka Z; Markiewicz, Karolina H

2017-09-15

FT-Raman spectroscopy, thermogravimetry and differential scanning calorimetry were used to study changes in structure of gluten proteins and their thermal properties influenced by four dietary fibre polysaccharides (microcrystalline cellulose, inulin, apple pectin and citrus pectin) during development of a model dough. The flour reconstituted from wheat starch and wheat gluten was mixed with the polysaccharides in five concentrations: 3%, 6%, 9%, 12% and 18%. The obtained results showed that all polysaccharides induced similar changes in secondary structure of gluten proteins concerning formation of aggregates (1604cm -1 ), H-bonded parallel- and antiparallel-β-sheets (1690cm -1 ) and H-bonded β-turns (1664cm -1 ). These changes concerned mainly glutenins since β-structures are characteristic for them. The observed structural changes confirmed hypothesis about partial dehydration of gluten network after polysaccharides addition. The gluten aggregation and dehydration processes were also reflected in the DSC results, while the TGA ones showed that gluten network remained thermally stable after polysaccharides addition. Copyright © 2017 Elsevier Ltd. All rights reserved.

Nitrogen deposition outweighs climatic variability in driving annual growth rate of canopy beech trees: Evidence from long-term growth reconstruction across a geographic gradient.

PubMed

Gentilesca, Tiziana; Rita, Angelo; Brunetti, Michele; Giammarchi, Francesco; Leonardi, Stefano; Magnani, Federico; van Noije, Twan; Tonon, Giustino; Borghetti, Marco

2018-07-01

In this study, we investigated the role of climatic variability and atmospheric nitrogen deposition in driving long-term tree growth in canopy beech trees along a geographic gradient in the montane belt of the Italian peninsula, from the Alps to the southern Apennines. We sampled dominant trees at different developmental stages (from young to mature tree cohorts, with tree ages spanning from 35 to 160 years) and used stem analysis to infer historic reconstruction of tree volume and dominant height. Annual growth volume (G V ) and height (G H ) variability were related to annual variability in model simulated atmospheric nitrogen deposition and site-specific climatic variables, (i.e. mean annual temperature, total annual precipitation, mean growing period temperature, total growing period precipitation, and standard precipitation evapotranspiration index) and atmospheric CO 2 concentration, including tree cambial age among growth predictors. Generalized additive models (GAM), linear mixed-effects models (LMM), and Bayesian regression models (BRM) were independently employed to assess explanatory variables. The main results from our study were as follows: (i) tree age was the main explanatory variable for long-term growth variability; (ii) GAM, LMM, and BRM results consistently indicated climatic variables and CO 2 effects on G V and G H were weak, therefore evidence of recent climatic variability influence on beech annual growth rates was limited in the montane belt of the Italian peninsula; (iii) instead, significant positive nitrogen deposition (N dep ) effects were repeatedly observed in G V and G H ; the positive effects of N dep on canopy height growth rates, which tended to level off at N dep values greater than approximately 1.0 g m -2  y -1 , were interpreted as positive impacts on forest stand above-ground net productivity at the selected study sites. © 2018 John Wiley & Sons Ltd.

Parapenaeus longirostris (Lucas, 1846) an early warning indicator species of global warming in the central Mediterranean Sea

NASA Astrophysics Data System (ADS)

Colloca, Francesco; Mastrantonio, Gianluca; Lasinio, Giovanna Jona; Ligas, Alessandro; Sartor, Paolo

2014-10-01

The effect of temperature increase on the stock of the deep-sea pink shrimp (Parapenaeus longirostris) was analysed along the western coasts of Italy (North Tyrrhenian-Ligurian Sea: Geographical Sub-Area 9). This crustacean is currently one of the most important commercial species of the trawl fisheries in the Mediterranean Sea. Landings of the species in the North Tyrrhenian-Ligurian Sea have grown consistently during the last years following a rapid increase in the stock size. Since the deep-sea pink shrimp stock is exploited on the same fishing ground of other heavily overexploited stocks in a full mixed and poorly selective fishery, its condition seems to be largely independent of the current fishing exploitation pattern suggesting a positive role of climate change on the dynamic of the stock. To test this hypothesis we investigated the effect of sea surface temperature (SST) on density and distribution of P. longirostris by means of general additive models (GAMs). Two different models were developed for the whole stock and for the recruits (CL < 20 mm) using time series of MEDITS (International bottom trawl survey in the Mediterranean) survey density indices (n km- 2) covering the period 1995-2010. Predictors included were geographical coordinates, quarterly averaged minimum SST, sampling depth and year. Spawners density was included as predictor into the GAM for recruits. The best GAM for the whole stock explained 67.1% of the total deviance, showing a clear increase in density in concomitance with the expansion of the stock northward. We found a significant positive effect of the min SST of all seasons, as expected considering that P. longirostris spawn all year round, with the highest influence played by summer min SST, either in the same or previous year. The best model for recruits explained 64.9% of the total deviance. Recruitment increased linearly with the density of spawners showing a positive temporal trend and an expansion northward. The observed

Influence of the variation of geometrical and topological traits on light interception efficiency of apple trees: sensitivity analysis and metamodelling for ideotype definition

PubMed Central

Da Silva, David; Han, Liqi; Faivre, Robert; Costes, Evelyne

2014-01-01

Background and Aims The impact of a fruit tree's architecture on its performance is still under debate, especially with regard to the definition of varietal ideotypes and the selection of architectural traits in breeding programmes. This study aimed at providing proof that a modelling approach can contribute to this debate, by using in silico exploration of different combinations of traits and their consequences on light interception, here considered as one of the key parameters to optimize fruit tree production. Methods The variability of organ geometrical traits, previously described in a bi-parental population, was used to simulate 1- to 5-year-old apple trees (Malus × domestica). Branching sequences along trunks observed during the first year of growth of the same hybrid trees were used to initiate the simulations, and hidden semi-Markov chains previously parameterized were used in subsequent years. Tree total leaf area (TLA) and silhouette to total area ratio (STAR) values were estimated, and a sensitivity analysis was performed, based on a metamodelling approach and a generalized additive model (GAM), to analyse the relative impact of organ geometry and lateral shoot types on STAR. Key Results A larger increase over years in TLA mean and variance was generated by varying branching along trunks than by varying organ geometry, whereas the inverse was observed for STAR, where mean values stabilized from year 3 to year 5. The internode length and leaf area had the highest impact on STAR, whereas long sylleptic shoots had a more significant effect than proleptic shoots. Although the GAM did not account for interactions, the additive effects of the geometrical factors explained >90% of STAR variation, but much less in the case of branching factors. Conclusions This study demonstrates that the proposed modelling approach could contribute to screening architectural traits and their relative impact on tree performance, here viewed through light interception. Even

Sexual Arousal Patterns of Autogynephilic Male Cross-Dressers.

PubMed

Hsu, Kevin J; Rosenthal, A M; Miller, David I; Bailey, J Michael

2017-01-01

Men's sexual arousal patterns have been an important window into the nature of their erotic interests. Autogynephilia is a natal male's paraphilic tendency to be sexually aroused by the thought or image of being a woman. Autogynephilic arousal per se is difficult to assess objectively, because it is inwardly focused. However, assessing sexual arousal patterns of autogynephilic males in response to external stimuli is also potentially useful. For example, there is substantial association between autogynephilia and gynandromorphophilia (GAMP), or sexual attraction to gynandromorphs (GAMs), colloquially "she-males." GAMP men's sexual arousal patterns in response to GAM, female, and male stimuli have recently been characterized. In the present study, we extended this understanding by comparing the sexual arousal patterns of autogynephilic male cross-dressers, GAMP men, heterosexual men, and homosexual men. Erotic stimuli included sexually explicit videos of men, women, and GAMs. Autogynephilic men were much more similar in their arousal patterns to heterosexual and GAMP men than to homosexual men. However, similar to GAMP men, autogynephilic men showed increased arousal by GAM stimuli relative to female stimuli compared with heterosexual men.

Bodotria jejuensis sp. nov. (Crustacea, Cumacea, Bodotriidae), a new Korean cumacean.

PubMed

Lee, Chang-Mok; Kim, Sung-Hyun; Kim, Young-Hyo

2016-09-06

A new species of Cumacea belonging to the genus Bodotria Goodsir, 1843 was collected from the Jeju-do Island, Korea. The new speices, Bodotria jejuensis sp. nov. is similar to B. similis Calman, 1907, B. rugosa Gamô, 1963, B. carinata Gamô, 1964, and B. biplicata Gamô, 1964 in having a pitted carapace, prominent dorso-lateral carina, and unarticulated endopod of uropod. However, longish elliptical shape of carapace, well-developed dorso-lateral carina and distinct lateral ridge are the major characteristics which serve to distinguish the new species from its congeners. The new species is fully illustrated and extensively compared with related species. A key to the Korean Bodotria species is also provided.

Trends in malnutrition and mortality in Darfur, Sudan, between 2004 and 2008: a meta-analysis of publicly available surveys.

PubMed

Nielsen, Jens; Prudhon, Claudine; de Radigues, Xavier

2011-08-01

The humanitarian response to the crisis in Darfur is the largest humanitarian operation in the world. To investigate the evolution of the conditions of the affected population, we analysed trends in malnutrition and mortality, the most widely accepted indicators for assessing the degree of severity of a crisis. We did a meta-analysis of 164 publicly available surveys taking into account changes in the contextual situation and humanitarian aid; type of population [residents and internally displaced persons (IDPs)]; and seasonal variations. Data on global acute malnutrition (GAM), severe acute malnutrition (SAM), crude death rate (CDR) and under-five death rate (U5DR) were analysed using a random effect model. GAM and SAM decreased by 16% and 28%, respectively, in 2004-05, whereas CDR dropped by 44-75% per year depending on state and type of population and U5DR decreased by an overall 50% yearly. Both security and the humanitarian contexts became increasingly complex after 2005, but levels of malnutrition stabilized in North and South Darfur. In West Darfur, GAM remained stable but SAM tended to increase for IDPs, although mortality rates remained constant. Mortality increased slightly for residents in South Darfur after 2005, even though nutritional status was stable. GAM, SAM, CDR and U5DR fluctuated markedly with seasons. A meta-analysis of myriads of surveys permitted us to draw an overall picture of the situation in Darfur and to identify some of its influencing factors. The large humanitarian operation, which gained momentum through 2004-05, was able to contain the crisis despite huge difficulties, but did not compensate for seasonal variations. The situation has remained fragile with some negative patterns tending to emerge. It is crucial that the humanitarian situation continues to be closely monitored.

Patient-specific in vitro models for hemodynamic analysis of congenital heart disease - Additive manufacturing approach.

PubMed

Medero, Rafael; García-Rodríguez, Sylvana; François, Christopher J; Roldán-Alzate, Alejandro

2017-03-21

Non-invasive hemodynamic assessment of total cavopulmonary connection (TCPC) is challenging due to the complex anatomy. Additive manufacturing (AM) is a suitable alternative for creating patient-specific in vitro models for flow measurements using four-dimensional (4D) Flow MRI. These in vitro systems have the potential to serve as validation for computational fluid dynamics (CFD), simulating different physiological conditions. This study investigated three different AM technologies, stereolithography (SLA), selective laser sintering (SLS) and fused deposition modeling (FDM), to determine differences in hemodynamics when measuring flow using 4D Flow MRI. The models were created using patient-specific MRI data from an extracardiac TCPC. These models were connected to a perfusion pump circulating water at three different flow rates. Data was processed for visualization and quantification of velocity, flow distribution, vorticity and kinetic energy. These results were compared between each model. In addition, the flow distribution obtained in vitro was compared to in vivo. The results showed significant difference in velocities measured at the outlets of the models that required internal support material when printing. Furthermore, an ultrasound flow sensor was used to validate flow measurements at the inlets and outlets of the in vitro models. These results were highly correlated to those measured with 4D Flow MRI. This study showed that commercially available AM technologies can be used to create patient-specific vascular models for in vitro hemodynamic studies at reasonable costs. However, technologies that do not require internal supports during manufacturing allow smoother internal surfaces, which makes them better suited for flow analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.

The effect of tailor-made additives on crystal growth of methyl paraben: Experiments and modelling

NASA Astrophysics Data System (ADS)

Cai, Zhihui; Liu, Yong; Song, Yang; Guan, Guoqiang; Jiang, Yanbin

2017-03-01

In this study, methyl paraben (MP) was selected as the model component, and acetaminophen (APAP), p-methyl acetanilide (PMAA) and acetanilide (ACET), which share the similar molecular structure as MP, were selected as the three tailor-made additives to study the effect of tailor-made additives on the crystal growth of MP. HPLC results indicated that the MP crystals induced by the three additives contained MP only. Photographs of the single crystals prepared indicated that the morphology of the MP crystals was greatly changed by the additives, but PXRD and single crystal diffraction results illustrated that the MP crystals were the same polymorph only with different crystal habits, and no new crystal form was found compared with other references. To investigate the effect of the additives on the crystal growth, the interaction between additives and facets was discussed in detail using the DFT methods and MD simulations. The results showed that APAP, PMAA and ACET would be selectively adsorbed on the growth surfaces of the crystal facets, which induced the change in MP crystal habits.

The development, evaluation, and application of O3 flux and flux-response models for additional agricultural crops

Treesearch

L. D. Emberson; W. J. Massman; P. Buker; G. Soja; I. Van De Sand; G. Mills; C. Jacobs

2006-01-01

Currently, stomatal O3 flux and flux-response models only exist for wheat and potato (LRTAP Convention, 2004), as such there is a need to extend these models to include additional crop types. The possibility of establishing robust stomatal flux models for five agricultural crops (tomato, grapevine, sugar beet, maize and sunflower) was investigated. These crops were...

Monitoring hand, foot and mouth disease by combining search engine query data and meteorological factors.

PubMed

Huang, Da-Cang; Wang, Jin-Feng

2018-01-15

Hand, foot and mouth disease (HFMD) has been recognized as a significant public health threat and poses a tremendous challenge to disease control departments. To date, the relationship between meteorological factors and HFMD has been documented, and public interest of disease has been proven to be trackable from the Internet. However, no study has explored the combination of these two factors in the monitoring of HFMD. Therefore, the main aim of this study was to develop an effective monitoring model of HFMD in Guangzhou, China by utilizing historical HFMD cases, Internet-based search engine query data and meteorological factors. To this end, a case study was conducted in Guangzhou, using a network-based generalized additive model (GAM) including all factors related to HFMD. Three other models were also constructed using some of the variables for comparison. The results suggested that the model showed the best estimating ability when considering all of the related factors. Copyright © 2017 Elsevier B.V. All rights reserved.

The Impact and the Implications of Policy Regarding the Organisation of Support for Pupils with Dyslexia in Irish Primary Mainstream Schools

ERIC Educational Resources Information Center

Tiernan, Bairbre; Casserly, Ann Marie

2018-01-01

There have been vast changes in relation to the way educational teaching support provision is organised for pupils with dyslexia in Ireland. A qualitative research approach was utilised to examine the impact and implications of the General Allocation Model (GAM) regarding the organisation of support for pupils with dyslexia in Irish Primary…

Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study

PubMed Central

Agogo, George O.; van der Voet, Hilko; Veer, Pieter van’t; Ferrari, Pietro; Leenders, Max; Muller, David C.; Sánchez-Cantalejo, Emilio; Bamia, Christina; Braaten, Tonje; Knüppel, Sven; Johansson, Ingegerd; van Eeuwijk, Fred A.; Boshuizen, Hendriek

2014-01-01

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model. PMID:25402487

Estimating interaction on an additive scale between continuous determinants in a logistic regression model.

PubMed

Knol, Mirjam J; van der Tweel, Ingeborg; Grobbee, Diederick E; Numans, Mattijs E; Geerlings, Mirjam I

2007-10-01

To determine the presence of interaction in epidemiologic research, typically a product term is added to the regression model. In linear regression, the regression coefficient of the product term reflects interaction as departure from additivity. However, in logistic regression it refers to interaction as departure from multiplicativity. Rothman has argued that interaction estimated as departure from additivity better reflects biologic interaction. So far, literature on estimating interaction on an additive scale using logistic regression only focused on dichotomous determinants. The objective of the present study was to provide the methods to estimate interaction between continuous determinants and to illustrate these methods with a clinical example. and results From the existing literature we derived the formulas to quantify interaction as departure from additivity between one continuous and one dichotomous determinant and between two continuous determinants using logistic regression. Bootstrapping was used to calculate the corresponding confidence intervals. To illustrate the theory with an empirical example, data from the Utrecht Health Project were used, with age and body mass index as risk factors for elevated diastolic blood pressure. The methods and formulas presented in this article are intended to assist epidemiologists to calculate interaction on an additive scale between two variables on a certain outcome. The proposed methods are included in a spreadsheet which is freely available at: http://www.juliuscenter.nl/additive-interaction.xls.

GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems.

PubMed

Sadowski, Krzysztof L; Thierens, Dirk; Bosman, Peter A N

2018-01-01

Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Algorithms (EAs) are very efficient at learning structure in both the discrete, and in the continuous domain. In this article, discrete and continuous model-building mechanisms are integrated for the Mixed-Integer (MI) domain, comprising discrete and continuous variables. We revisit a recently introduced model-based evolutionary algorithm for the MI domain, the Genetic Algorithm for Model-Based mixed-Integer opTimization (GAMBIT). We extend GAMBIT with a parameterless scheme that allows for practical use of the algorithm without the need to explicitly specify any parameters. We furthermore contrast GAMBIT with other model-based alternatives. The ultimate goal of processing mixed dependences explicitly in GAMBIT is also addressed by introducing a new mechanism for the explicit exploitation of mixed dependences. We find that processing mixed dependences with this novel mechanism allows for more efficient optimization. We further contrast the parameterless GAMBIT with Mixed-Integer Evolution Strategies (MIES) and other state-of-the-art MI optimization algorithms from the General Algebraic Modeling System (GAMS) commercial algorithm suite on problems with and without constraints, and show that GAMBIT is capable of solving problems where variable dependences prevent many algorithms from successfully optimizing them.

Comparison of modeling methods to predict the spatial distribution of deep-sea coral and sponge in the Gulf of Alaska

NASA Astrophysics Data System (ADS)

Rooper, Christopher N.; Zimmermann, Mark; Prescott, Megan M.

2017-08-01

Deep-sea coral and sponge ecosystems are widespread throughout most of Alaska's marine waters, and are associated with many different species of fishes and invertebrates. These ecosystems are vulnerable to the effects of commercial fishing activities and climate change. We compared four commonly used species distribution models (general linear models, generalized additive models, boosted regression trees and random forest models) and an ensemble model to predict the presence or absence and abundance of six groups of benthic invertebrate taxa in the Gulf of Alaska. All four model types performed adequately on training data for predicting presence and absence, with regression forest models having the best overall performance measured by the area under the receiver-operating-curve (AUC). The models also performed well on the test data for presence and absence with average AUCs ranging from 0.66 to 0.82. For the test data, ensemble models performed the best. For abundance data, there was an obvious demarcation in performance between the two regression-based methods (general linear models and generalized additive models), and the tree-based models. The boosted regression tree and random forest models out-performed the other models by a wide margin on both the training and testing data. However, there was a significant drop-off in performance for all models of invertebrate abundance ( 50%) when moving from the training data to the testing data. Ensemble model performance was between the tree-based and regression-based methods. The maps of predictions from the models for both presence and abundance agreed very well across model types, with an increase in variability in predictions for the abundance data. We conclude that where data conforms well to the modeled distribution (such as the presence-absence data and binomial distribution in this study), the four types of models will provide similar results, although the regression-type models may be more consistent with

Additive effect of mesenchymal stem cells and defibrotide in an arterial rat thrombosis model.

PubMed

Dilli, Dilek; Kılıç, Emine; Yumuşak, Nihat; Beken, Serdar; Uçkan Çetinkaya, Duygu; Karabulut, Ramazan; Zenciroğlu, Ayşegu L

2017-06-01

In this study, we aimed to investigate the additive effect of mesenchymal stem cells (MSC) and defibrotide (DFT) in a rat model of femoral arterial thrombosis. Thirty Sprague Dawley rats were included. An arterial thrombosis model by ferric chloride (FeCl3) was developed in the left femoral artery. The rats were equally assigned to 5 groups: Group 1-Sham-operated (without arterial injury); Group 2-Phosphate buffered saline (PBS) injected; Group 3-MSC; Group 4-DFT; Group 5-MSC + DFT. All had two intraperitoneal injections of 0.5 ml: the 1st injection was 4 h after the procedure and the 2nd one 48 h after the 1st injection. The rats were sacrificed 7 days after the 2nd injection. Although the use of human bone marrow-derived (hBM) hBM-MSC or DFT alone enabled partial resolution of the thrombus, combining them resulted in near-complete resolution. Neovascularization was two-fold better in hBM-MSC + DFT treated rats (11.6 ± 2.4 channels) compared with the hBM-MSC (3.8 ± 2.7 channels) and DFT groups (5.5 ± 1.8 channels) (P < 0.0001 and P= 0.002, respectively). The combined use of hBM-MSC and DFT in a rat model of arterial thrombosis showed additive effect resulting in near-complete resolution of the thrombus.

Functional Resilience and Response to a Dietary Additive (Kefir) in Models of Foregut and Hindgut Microbial Fermentation In Vitro

PubMed Central

de la Fuente, Gabriel; Jones, Eleanor; Jones, Shann; Newbold, Charles J.

2017-01-01

Stability in gut ecosystems is an important area of study that impacts on the use of additives and is related with several pathologies. Kefir is a fermented milk drink made with a consortium of yeast and bacteria as a fermentation starter, of which the use as additive in companion and livestock animals has increased in the last few years. To investigate the effect of kefir milk on foregut and hindgut digestive systems, an in vitro approach was followed. Either rumen fluid or horse fecal contents were used as a microbial inoculate and the inclusion of kefir (fresh, autoclaved, or pasteurized) was tested. Gas production over 72 h of incubation was recorded and pH, volatile fatty acids (VFAs), lactate and ammonia concentration as well as lactic acid (LAB) and acetic acid bacteria, and yeast total numbers were also measured. Both direct and indirect (by subtracting their respective blanks) effects were analyzed and a multivariate analysis was performed to compare foregut and hindgut fermentation models. Addition of kefir boosted the fermentation by increasing molar concentration of VFAs and ammonia and shifting the Acetate to Propionate ratio in both models but heat processing techniques like pasteurization or autoclaving influenced the way the kefir is fermented and reacts with the present microbiota. In terms of comparison between both models, the foregut model seems to be less affected by the inclusion of Kefir than the hindgut model. In terms of variability in the response, the hindgut model appeared to be more variable than the foregut model in the way that it reacted indirectly to the addition of different types of kefir. PMID:28702019

Functional Resilience and Response to a Dietary Additive (Kefir) in Models of Foregut and Hindgut Microbial Fermentation In Vitro.

PubMed

de la Fuente, Gabriel; Jones, Eleanor; Jones, Shann; Newbold, Charles J

2017-01-01

Stability in gut ecosystems is an important area of study that impacts on the use of additives and is related with several pathologies. Kefir is a fermented milk drink made with a consortium of yeast and bacteria as a fermentation starter, of which the use as additive in companion and livestock animals has increased in the last few years. To investigate the effect of kefir milk on foregut and hindgut digestive systems, an in vitro approach was followed. Either rumen fluid or horse fecal contents were used as a microbial inoculate and the inclusion of kefir (fresh, autoclaved, or pasteurized) was tested. Gas production over 72 h of incubation was recorded and pH, volatile fatty acids (VFAs), lactate and ammonia concentration as well as lactic acid (LAB) and acetic acid bacteria, and yeast total numbers were also measured. Both direct and indirect (by subtracting their respective blanks) effects were analyzed and a multivariate analysis was performed to compare foregut and hindgut fermentation models. Addition of kefir boosted the fermentation by increasing molar concentration of VFAs and ammonia and shifting the Acetate to Propionate ratio in both models but heat processing techniques like pasteurization or autoclaving influenced the way the kefir is fermented and reacts with the present microbiota. In terms of comparison between both models, the foregut model seems to be less affected by the inclusion of Kefir than the hindgut model. In terms of variability in the response, the hindgut model appeared to be more variable than the foregut model in the way that it reacted indirectly to the addition of different types of kefir.

Urban-hazard risk analysis: mapping of heat-related risks in the elderly in major Italian cities.

PubMed

Morabito, Marco; Crisci, Alfonso; Gioli, Beniamino; Gualtieri, Giovanni; Toscano, Piero; Di Stefano, Valentina; Orlandini, Simone; Gensini, Gian Franco

2015-01-01

Short-term impacts of high temperatures on the elderly are well known. Even though Italy has the highest proportion of elderly citizens in Europe, there is a lack of information on spatial heat-related elderly risks. Development of high-resolution, heat-related urban risk maps regarding the elderly population (≥ 65). A long time-series (2001-2013) of remote sensing MODIS data, averaged over the summer period for eleven major Italian cities, were downscaled to obtain high spatial resolution (100 m) daytime and night-time land surface temperatures (LST). LST was estimated pixel-wise by applying two statistical model approaches: 1) the Linear Regression Model (LRM); 2) the Generalized Additive Model (GAM). Total and elderly population density data were extracted from the Joint Research Centre population grid (100 m) from the 2001 census (Eurostat source), and processed together using "Crichton's Risk Triangle" hazard-risk methodology for obtaining a Heat-related Elderly Risk Index (HERI). The GAM procedure allowed for improved daytime and night-time LST estimations compared to the LRM approach. High-resolution maps of daytime and night-time HERI levels were developed for inland and coastal cities. Urban areas with the hazardous HERI level (very high risk) were not necessarily characterized by the highest temperatures. The hazardous HERI level was generally localized to encompass the city-centre in inland cities and the inner area in coastal cities. The two most dangerous HERI levels were greater in the coastal rather than inland cities. This study shows the great potential of combining geospatial technologies and spatial demographic characteristics within a simple and flexible framework in order to provide high-resolution urban mapping of daytime and night-time HERI. In this way, potential areas for intervention are immediately identified with up-to-street level details. This information could support public health operators and facilitate coordination for heat

Effect of Additional Incentives for Aviation Biofuels: Results from the Biomass Scenario Model

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

Vimmerstedt, Laura J; Newes, Emily K

2017-12-05

The National Renewable Energy Laboratory supported the Department of Energy, Bioenergy Technologies Office, with analysis of alternative jet fuels in collaboration with the U.S. Department of Transportation, Federal Aviation Administration. Airlines for America requested additional exploratory scenarios within FAA analytic framework. Airlines for America requested additional analysis using the same analytic framework, the Biomass Scenario Model. The results were presented at a public working meeting of the California Air Resources Board on including alternative jet fuel in the Low Carbon Fuel Standard on March 17, 2017 (https://www.arb.ca.gov/fuels/lcfs/lcfs_meetings/lcfs_meetings.htm). This presentation clarifies and annotates the slides from the public working meeting, andmore » provides a link to the full data set. NREL does not advocate for or against the policies analyzed in this study.« less

Effect of Additional Incentives for Aviation Biofuels: Results from the Biomass Scenario Model

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

Vimmerstedt, Laura J; Newes, Emily K

The National Renewable Energy Laboratory supported the Department of Energy, Bioenergy Technologies Office, with analysis of alternative jet fuels in collaboration with the U.S. Department of Transportation, Federal Aviation Administration. Airlines for America requested additional exploratory scenarios within FAA analytic framework. Airlines for America requested additional analysis using the same analytic framework, the Biomass Scenario Model. The results were presented at a public working meeting of the California Air Resources Board on including alternative jet fuel in the Low Carbon Fuel Standard on March 17, 2017 (https://www.arb.ca.gov/fuels/lcfs/lcfs_meetings/lcfs_meetings.htm). This presentation clarifies and annotates the slides from the public working meeting, andmore » provides a link to the full data set. NREL does not advocate for or against the policies analyzed in this study.« less

[Critical of the additive model of the randomized controlled trial].

PubMed

Boussageon, Rémy; Gueyffier, François; Bejan-Angoulvant, Theodora; Felden-Dominiak, Géraldine

2008-01-01

Randomized, double-blind, placebo-controlled clinical trials are currently the best way to demonstrate the clinical effectiveness of drugs. Its methodology relies on the method of difference (John Stuart Mill), through which the observed difference between two groups (drug vs placebo) can be attributed to the pharmacological effect of the drug being tested. However, this additive model can be questioned in the event of statistical interactions between the pharmacological and the placebo effects. Evidence in different domains has shown that the placebo effect can influence the effect of the active principle. This article evaluates the methodological, clinical and epistemological consequences of this phenomenon. Topics treated include extrapolating results, accounting for heterogeneous results, demonstrating the existence of several factors in the placebo effect, the necessity to take these factors into account for given symptoms or pathologies, as well as the problem of the "specific" effect.

Reduction of carcinogenic 4(5)-methylimidazole in a caramel model system: influence of food additives.

PubMed

Seo, Seulgi; Ka, Mi-Hyun; Lee, Kwang-Geun

2014-07-09

The effect of various food additives on the formation of carcinogenic 4(5)-methylimidazole (4-MI) in a caramel model system was investigated. The relationship between the levels of 4-MI and various pyrazines was studied. When glucose and ammonium hydroxide were heated, the amount of 4-MI was 556 ± 1.3 μg/mL, which increased to 583 ± 2.6 μg/mL by the addition of 0.1 M of sodium sulfite. When various food additives, such as 0.1 M of iron sulfate, magnesium sulfate, zinc sulfate, tryptophan, and cysteine were added, the amount of 4-MI was reduced to 110 ± 0.7, 483 ± 2.0, 460 ± 2.0, 409 ± 4.4, and 397 ± 1.7 μg/mL, respectively. The greatest reduction, 80%, occurred with the addition of iron sulfate. Among the 12 pyrazines, 2-ethyl-6-methylpyrazine with 4-MI showed the highest correlation (r = -0.8239).

Spontaneous collective synchronization in the Kuramoto model with additional non-local interactions

NASA Astrophysics Data System (ADS)

Gupta, Shamik

2017-10-01

In the context of the celebrated Kuramoto model of globally-coupled phase oscillators of distributed natural frequencies, which serves as a paradigm to investigate spontaneous collective synchronization in many-body interacting systems, we report on a very rich phase diagram in presence of thermal noise and an additional non-local interaction on a one-dimensional periodic lattice. Remarkably, the phase diagram involves both equilibrium and non-equilibrium phase transitions. In two contrasting limits of the dynamics, we obtain exact analytical results for the phase transitions. These two limits correspond to (i) the absence of thermal noise, when the dynamics reduces to that of a non-linear dynamical system, and (ii) the oscillators having the same natural frequency, when the dynamics becomes that of a statistical system in contact with a heat bath and relaxing to a statistical equilibrium state. In the former case, our exact analysis is based on the use of the so-called Ott-Antonsen ansatz to derive a reduced set of nonlinear partial differential equations for the macroscopic evolution of the system. Our results for the case of statistical equilibrium are on the other hand obtained by extending the well-known transfer matrix approach for nearest-neighbor Ising model to consider non-local interactions. The work offers a case study of exact analysis in many-body interacting systems. The results obtained underline the crucial role of additional non-local interactions in either destroying or enhancing the possibility of observing synchrony in mean-field systems exhibiting spontaneous synchronization.

Nutrition surveillance using a small open cohort: experience from Burkina Faso.

PubMed

Altmann, Mathias; Fermanian, Christophe; Jiao, Boshen; Altare, Chiara; Loada, Martin; Myatt, Mark

2016-01-01

Nutritional surveillance remains generally weak and early warning systems are needed in areas with high burden of acute under-nutrition. In order to enhance insight into nutritional surveillance, a community-based sentinel sites approach, known as the Listening Posts (LP) Project, was piloted in Burkina Faso by Action Contre la Faim (ACF). This paper presents ACF's experience with the LP approach and investigates potential selection and observational biases. Six primary sampling units (PSUs) were selected in each livelihood zone using the centric systematic area sampling methodology. In each PSU, 22 children aged between 6 and 24 months were selected by proximity sampling. The prevalence of GAM for each month from January 2011 to December 2013 was estimated using a Bayesian normal-normal conjugate analysis followed by PROBIT estimation. To validate the LP approach in detecting changes over time, the time trends of MUAC from LP and from five cross-sectional surveys were modelled using polynomial regression and compared by using a Wald test. The differences between prevalence estimates from the two data sources were used to assess selection and observational biases. The 95 % credible interval around GAM prevalence estimates using LP approach ranged between +6.5 %/-6.0 % on a prevalence of 36.1 % and +3.5 %/-2.9 % on a prevalence of 10.8 %. LP and cross-sectional surveys time trend models were well correlated (p = 0.6337). Although LP showed a slight but significant trend for GAM to decrease over time at a rate of -0.26 %/visit, the prevalence estimates from the two data sources showed good agreement over a 3-year period. The LP methodology has proved to be valid in following trends of GAM prevalence for a period of 3 years without selection bias. However, a slight observational bias was observed, requiring a periodical reselection of the sentinel sites. This kind of surveillance project is suited to use in areas with high burden of acute under

Spider Communities and Biological Control in Native Habitats Surrounding Greenhouses.

PubMed

Cotes, Belén; González, Mónica; Benítez, Emilio; De Mas, Eva; Clemente-Orta, Gemma; Campos, Mercedes; Rodríguez, Estefanía

2018-03-14

The promotion of native vegetation as a habitat for natural enemies, which could increase their abundance and fitness, is especially useful in highly simplified settings such as Mediterranean greenhouse landscapes. Spiders as generalist predators may also be involved in intra-guild predation. However, the niche complementarity provided by spiders as a group means that increased spider diversity may facilitate complementary control actions. In this study, the interactions between spiders, the two major horticultural pests, Bemisia tabaci and Frankliniella occidentalis , and their naturally occurring predators and parasitoids were evaluated in a mix of 21 newly planted shrubs selected for habitat management in a highly disturbed horticultural system. The effects of all factors were evaluated using redundancy analysis (RDA) and the generalized additive model (GAM) to assess the statistical significance of abundance of spiders and pests. The GAM showed that the abundance of both pests had a significant effect on hunter spider's abundance, whereas the abundance of B. tabaci , but not F. occidentalis , affected web-weavers' abundance. Ordination analysis showed that spider abundance closely correlated with that of B. tabaci but not with that of F. occidentalis , suggesting that complementarity occurs, and thereby probability of biocontrol, with respect to the targeted pest B. tabaci , although the temporal patterns of the spiders differed from those of F. occidentalis . Conservation strategies involving the establishment of these native plants around greenhouses could be an effective way to reduce pest populations outdoors.

Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya.

PubMed

Sewe, Maquins Odhiambo; Tozan, Yesim; Ahlm, Clas; Rocklöv, Joacim

2017-06-01

Malaria surveillance data provide opportunity to develop forecasting models. Seasonal variability in environmental factors correlate with malaria transmission, thus the identification of transmission patterns is useful in developing prediction models. However, with changing seasonal transmission patterns, either due to interventions or shifting weather seasons, traditional modelling approaches may not yield adequate predictive skill. Two statistical models,a general additive model (GAM) and GAMBOOST model with boosted regression were contrasted by assessing their predictive accuracy in forecasting malaria admissions at lead times of one to three months. Monthly admission data for children under five years with confirmed malaria at the Siaya district hospital in Western Kenya for the period 2003 to 2013 were used together with satellite derived data on rainfall, average temperature and evapotranspiration(ET). There was a total of 8,476 confirmed malaria admissions. The peak of malaria season changed and malaria admissions reduced overtime. The GAMBOOST model at 1-month lead time had the highest predictive skill during both the training and test periods and thus can be utilized in a malaria early warning system.

Optimal location of emergency stations in underground mine networks using a multiobjective mathematical model.

PubMed

Lotfian, Reza; Najafi, Mehdi

2018-02-26

Background Every year, many mining accidents occur in underground mines all over the world resulting in the death and maiming of many miners and heavy financial losses to mining companies. Underground mining accounts for an increasing share of these events due to their special circumstances and the risks of working therein. Thus, the optimal location of emergency stations within the network of an underground mine in order to provide medical first aid and transport injured people at the right time, plays an essential role in reducing deaths and disabilities caused by accidents Objective The main objective of this study is to determine the location of emergency stations (ES) within the network of an underground coal mine in order to minimize the outreach time for the injured. Methods A three-objective mathematical model is presented for placement of ES facility location selection and allocation of facilities to the injured in various stopes. Results Taking into account the radius of influence for each ES, the proposed model is capable to reduce the maximum time for provision of emergency services in the event of accident for each stope. In addition, the coverage or lack of coverage of each stope by any of the emergency facility is determined by means of Floyd-Warshall algorithm and graph. To solve the problem, a global criterion method using GAMS software is used to evaluate the accuracy and efficiency of the model. Conclusions 7 locations were selected from among 46 candidates for the establishment of emergency facilities in Tabas underground coal mine. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Model for Assembly Line Re-Balancing Considering Additional Capacity and Outsourcing to Face Demand Fluctuations

NASA Astrophysics Data System (ADS)

Samadhi, TMAA; Sumihartati, Atin

2016-02-01

The most critical stage in a garment industry is sewing process, because generally, it consists of a number of operations and a large number of sewing machines for each operation. Therefore, it requires a balancing method that can assign task to work station with balance workloads. Many studies on assembly line balancing assume a new assembly line, but in reality, due to demand fluctuation and demand increased a re-balancing is needed. To cope with those fluctuating demand changes, additional capacity can be carried out by investing in spare sewing machine and paying for sewing service through outsourcing. This study develops an assembly line balancing (ALB) model on existing line to cope with fluctuating demand change. Capacity redesign is decided if the fluctuation demand exceeds the available capacity through a combination of making investment on new machines and outsourcing while considering for minimizing the cost of idle capacity in the future. The objective of the model is to minimize the total cost of the line assembly that consists of operating costs, machine cost, adding capacity cost, losses cost due to idle capacity and outsourcing costs. The model develop is based on an integer programming model. The model is tested for a set of data of one year demand with the existing number of sewing machines of 41 units. The result shows that additional maximum capacity up to 76 units of machine required when there is an increase of 60% of the average demand, at the equal cost parameters..

Effects of additional data on Bayesian clustering.

PubMed

Yamazaki, Keisuke

2017-10-01

Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional information will improve the accuracy of the estimation of the latent variable. Many proposed learning methods are able to use additional data; these include semi-supervised learning and transfer learning. However, from a statistical point of view, a complex probabilistic model that encompasses both the initial and additional data might be less accurate due to having a higher-dimensional parameter. The present paper presents a theoretical analysis of the accuracy of such a model and clarifies which factor has the greatest effect on its accuracy, the advantages of obtaining additional data, and the disadvantages of increasing the complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Colorectal cancer risk and nitrate exposure through drinking water and diet.

PubMed

Espejo-Herrera, Nadia; Gràcia-Lavedan, Esther; Boldo, Elena; Aragonés, Nuria; Pérez-Gómez, Beatriz; Pollán, Marina; Molina, Antonio J; Fernández, Tania; Martín, Vicente; La Vecchia, Carlo; Bosetti, Cristina; Tavani, Alessandra; Polesel, Jerry; Serraino, Diego; Gómez Acebo, Inés; Altzibar, Jone M; Ardanaz, Eva; Burgui, Rosana; Pisa, Federica; Fernández-Tardón, Guillermo; Tardón, Adonina; Peiró, Rosana; Navarro, Carmen; Castaño-Vinyals, Gemma; Moreno, Victor; Righi, Elena; Aggazzotti, Gabriella; Basagaña, Xavier; Nieuwenhuijsen, Mark; Kogevinas, Manolis; Villanueva, Cristina M

2016-07-15

Ingested nitrate leads to the endogenous synthesis of N-nitroso compounds (NOCs), animal carcinogens with limited human evidence. We aimed to evaluate the risk of colorectal cancer (CRC) associated with nitrate exposure in drinking water and diet. A case-control study in Spain and Italy during 2008-2013 was conducted. Hospital-based incident cases and population-based (Spain) or hospital-based (Italy) controls were interviewed on residential history, water consumption since age 18, and dietary information. Long-term waterborne ingested nitrate was derived from routine monitoring records, linked to subjects' residential histories and water consumption habits. Dietary nitrate intake was estimated from food frequency questionnaires and published food composition databases. Odd ratios (OR) were calculated using mixed models with area as random effect, adjusted for CRC risk factors and other covariables. Generalized additive models (GAMs) were used to analyze exposure-response relationships. Interaction with endogenous nitrosation factors and other covariables was also evaluated. In total 1,869 cases and 3,530 controls were analyzed. Average waterborne ingested nitrate ranged from 3.4 to 19.7 mg/day, among areas. OR (95% CIs) of CRC was 1.49 (1.24, 1.78) for >10 versus ≤5 mg/day, overall. Associations were larger among men versus women, and among subjects with high red meat intake. GAMs showed increasing exposure-response relationship among men. Animal-derived dietary nitrate was associated with rectal, but not with colon cancer risk. In conclusion, a positive association between CRC risk and waterborne ingested nitrate is suggested, mainly among subgroups with other risk factors. Heterogeneous effects of nitrate from different sources (water, animal and vegetables) warrant further research. © 2016 UICC.

Strengthen forensic entomology in court--the need for data exploration and the validation of a generalised additive mixed model.

PubMed

Baqué, Michèle; Amendt, Jens

2013-01-01

Developmental data of juvenile blow flies (Diptera: Calliphoridae) are typically used to calculate the age of immature stages found on or around a corpse and thus to estimate a minimum post-mortem interval (PMI(min)). However, many of those data sets don't take into account that immature blow flies grow in a non-linear fashion. Linear models do not supply a sufficient reliability on age estimates and may even lead to an erroneous determination of the PMI(min). According to the Daubert standard and the need for improvements in forensic science, new statistic tools like smoothing methods and mixed models allow the modelling of non-linear relationships and expand the field of statistical analyses. The present study introduces into the background and application of these statistical techniques by analysing a model which describes the development of the forensically important blow fly Calliphora vicina at different temperatures. The comparison of three statistical methods (linear regression, generalised additive modelling and generalised additive mixed modelling) clearly demonstrates that only the latter provided regression parameters that reflect the data adequately. We focus explicitly on both the exploration of the data--to assure their quality and to show the importance of checking it carefully prior to conducting the statistical tests--and the validation of the resulting models. Hence, we present a common method for evaluating and testing forensic entomological data sets by using for the first time generalised additive mixed models.

Toward Obtaining Reliable Particulate Air Quality Information from Satellites

NASA Astrophysics Data System (ADS)

Strawa, A. W.; Chatfield, R. B.; Legg, M.; Esswein, R.; Justice, E.

2009-12-01

Air quality agencies use ground sites to monitor air quality, providing accurate information at particular points. Using measurements from satellite imagery has the potential to provide air quality information in a timely manner with better spatial resolution and at a lower cost that can also useful for model validation. While previous studies show acceptable correlations between Aerosol Optical Depth (AOD) derived from MODIS and surface Particulate Matter (PM) measurements on the eastern US, the data do not correlate well in the western US (Al-Saadi et al., 2005; Engle-Cox et al., 2004) . This paper seeks to improve the AOD-PM correlations by using advanced statistical analysis techniques. Our study area is the San Joaquin Valley in California because air quality in this region has failed to meet state and federal attainment standards for PM for the past several years. A previous investigation found good correlation of the AOD values between MODIS, MISR and AERONET, but poor correlations (R2 ~ 0.02) between satellite-based AOD and surface PM2.5 measurements. PM2.5 measurements correlated somewhat better (R2 ~ 0.18) with MODIS-derived AOD using the Deep Blue surface reflectance algorithm (Hsu et al., 2006) rather than the standard MODIS algorithm. This level of correlation is not adequate for reliable air quality measurements. Pelletier et al. (2007) used generalized additive models (GAMs) and meteorological data to improve the correlation between PM and AERONET AOD in western Europe. Additive models are more flexible than linear models and the functional relationships can be plotted to give a sense of the relationship between the predictor and the response. In this paper we use GAMs to improve surface PM2.5 to MODIS-AOD correlations. For example, we achieve an R2 ~ 0.44 using a GAM that includes the Deep Blue AOD, and day of year as parameters. Including NOx observations, improves the R2 ~ 0.64. Surprisingly Ångström exponent did not prove to be a significant

Interactive short-term effects of equivalent temperature and air pollution on human mortality in Berlin and Lisbon.

PubMed

Burkart, Katrin; Canário, Paulo; Breitner, Susanne; Schneider, Alexandra; Scherber, Katharina; Andrade, Henrique; Alcoforado, Maria João; Endlicher, Wilfried

2013-12-01

There is substantial evidence that both temperature and air pollution are predictors of mortality. Thus far, few studies have focused on the potential interactive effects between the thermal environment and different measures of air pollution. Such interactions, however, are biologically plausible, as (extreme) temperature or increased air pollution might make individuals more susceptible to the effects of each respective predictor. This study investigated the interactive effects between equivalent temperature and air pollution (ozone and particulate matter) in Berlin (Germany) and Lisbon (Portugal) using different types of Poisson regression models. The findings suggest that interactive effects exist between air pollutants and equivalent temperature. Bivariate response surface models and generalised additive models (GAMs) including interaction terms showed an increased risk of mortality during periods of elevated equivalent temperatures and air pollution. Cold effects were mostly unaffected by air pollution. The study underscores the importance of air pollution control in mitigating heat effects. Copyright © 2013 Elsevier Ltd. All rights reserved.

Nonseparable exchange–correlation functional for molecules, including homogeneous catalysis involving transition metals

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

Yu, Haoyu S.; Zhang, Wenjing; Verma, Pragya

2015-01-01

The goal of this work is to develop a gradient approximation to the exchange–correlation functional of Kohn–Sham density functional theory for treating molecular problems with a special emphasis on the prediction of quantities important for homogeneous catalysis and other molecular energetics. Our training and validation of exchange–correlation functionals is organized in terms of databases and subdatabases. The key properties required for homogeneous catalysis are main group bond energies (database MGBE137), transition metal bond energies (database TMBE32), reaction barrier heights (database BH76), and molecular structures (database MS10). We also consider 26 other databases, most of which are subdatabases of a newlymore » extended broad database called Database 2015, which is presented in the present article and in its ESI. Based on the mathematical form of a nonseparable gradient approximation (NGA), as first employed in the N12 functional, we design a new functional by using Database 2015 and by adding smoothness constraints to the optimization of the functional. The resulting functional is called the gradient approximation for molecules, or GAM. The GAM functional gives better results for MGBE137, TMBE32, and BH76 than any available generalized gradient approximation (GGA) or than N12. The GAM functional also gives reasonable results for MS10 with an MUE of 0.018 Å. The GAM functional provides good results both within the training sets and outside the training sets. The convergence tests and the smooth curves of exchange–correlation enhancement factor as a function of the reduced density gradient show that the GAM functional is a smooth functional that should not lead to extra expense or instability in optimizations. NGAs, like GGAs, have the advantage over meta-GGAs and hybrid GGAs of respectively smaller grid-size requirements for integrations and lower costs for extended systems. These computational advantages combined with the relatively high

Homogeneous studies of transiting extrasolar planets - III. Additional planets and stellar models

NASA Astrophysics Data System (ADS)

Southworth, John

2010-11-01

I derive the physical properties of 30 transiting extrasolar planetary systems using a homogeneous analysis of published data. The light curves are modelled with the JKTEBOP code, with special attention paid to the treatment of limb darkening, orbital eccentricity and error analysis. The light from some systems is contaminated by faint nearby stars, which if ignored will systematically bias the results. I show that it is not realistically possible to account for this using only transit light curves: light-curve solutions must be constrained by measurements of the amount of contaminating light. A contamination of 5 per cent is enough to make the measurement of a planetary radius 2 per cent too low. The physical properties of the 30 transiting systems are obtained by interpolating in tabulated predictions from theoretical stellar models to find the best match to the light-curve parameters and the measured stellar velocity amplitude, temperature and metal abundance. Statistical errors are propagated by a perturbation analysis which constructs complete error budgets for each output parameter. These error budgets are used to compile a list of systems which would benefit from additional photometric or spectroscopic measurements. The systematic errors arising from the inclusion of stellar models are assessed by using five independent sets of theoretical predictions for low-mass stars. This model dependence sets a lower limit on the accuracy of measurements of the physical properties of the systems, ranging from 1 per cent for the stellar mass to 0.6 per cent for the mass of the planet and 0.3 per cent for other quantities. The stellar density and the planetary surface gravity and equilibrium temperature are not affected by this model dependence. An external test on these systematic errors is performed by comparing the two discovery papers of the WASP-11/HAT-P-10 system: these two studies differ in their assessment of the ratio of the radii of the components and the

Lumped mass model of a 1D metastructure for vibration suppression with no additional mass

NASA Astrophysics Data System (ADS)

Reichl, Katherine K.; Inman, Daniel J.

2017-09-01

The article examines the effectiveness of metastructures for vibration suppression from a weight standpoint. Metastructures, a metamaterial inspired concept, are structures with distributed vibration absorbers. In automotive and aerospace industries, it is critical to have low levels of vibrations while also using lightweight materials. Previous work has shown that metastructures are effective at mitigating vibrations, but do not consider the effects of mass. This work takes mass into consideration by comparing a structure with vibration absorbers to a structure of equal mass with no absorbers. These structures are modeled as one-dimensional lumped mass models, chosen for simplicity. Results compare both the steady-state and the transient responses. As a quantitative performance measure, the H2 norm, which is related to the area under the frequency response function, is calculated and compared for both the metastructure and the baseline structure. These results show that it is possible to obtain a favorable vibration response without adding additional mass to the structure. Additionally, the performance measure is utilized to optimize the geometry of the structure, determine the optimal ratio of mass in the absorber to mass of the host structure, and determine the frequencies of the absorbers. The dynamic response of this model is verified using a finite element analysis.

Global two-fluid simulations of geodesic acoustic modes in strongly shaped tight aspect ratio tokamak plasmas

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

Robinson, J. R.; Hnat, B.; Thyagaraja, A.

2013-05-15

Following recent observations suggesting the presence of the geodesic acoustic mode (GAM) in ohmically heated discharges in the Mega Amp Spherical Tokamak (MAST) [J. R. Robinson et al., Plasma Phys. Controlled Fusion 54, 105007 (2012)], the behaviour of the GAM is studied numerically using the two fluid, global code CENTORI [P. J. Knight et al. Comput. Phys. Commun. 183, 2346 (2012)]. We examine mode localisation and effects of magnetic geometry, given by aspect ratio, elongation, and safety factor, on the observed frequency of the mode. An excellent agreement between simulations and experimental data is found for simulation plasma parameters matchedmore » to those of MAST. Increasing aspect ratio yields good agreement between the GAM frequency found in the simulations and an analytical result obtained for elongated large aspect ratio plasmas.« less

Perfect absorption in 1D photonic crystal nanobeam embedded with graphene/Al2O3 multilayer stack

NASA Astrophysics Data System (ADS)

Liu, Hanqing; Zha, Song; Liu, Peiguo; Zhou, Xiaotian; Bian, Li-an

2018-05-01

We exploit the concept of critical coupling to graphene based chip-integrated applications and numerically demonstrate that a perfect absorption (PA) absorber in the near-infrared can be obtained by graphene/Al2O3 multilayer stack (GAMS) critical coupling with a resonant cavity in the 1D photonic crystal nanobeam (PCN). The key point is dynamically matching the coupling rate of incident light wave to the cavity with the absorbing rate of GAMS via electrically modulating the chemical potential of graphene. Simulation results show that the radius of GAMS as well as the thickness of Al2O3 layer are closely connected with the performance of perfect absorption. These results may provide potential applications in the high-density integrated optical devices, photolectric transducers, and laser pulse limiters.

Spatial transferability of habitat suitability models of Nephrops norvegicus among fished areas in the Northeast Atlantic: sufficiently stable for marine resource conservation?

PubMed

Lauria, Valentina; Power, Anne Marie; Lordan, Colm; Weetman, Adrian; Johnson, Mark P

2015-01-01

Knowledge of the spatial distribution and habitat associations of species in relation to the environment is essential for their management and conservation. Habitat suitability models are useful in quantifying species-environment relationships and predicting species distribution patterns. Little is known, however, about the stability and performance of habitat suitability models when projected into new areas (spatial transferability) and how this can inform resource management. The aims of this study were to model habitat suitability of Norway lobster (Nephrops norvegicus) in five fished areas of the Northeast Atlantic (Aran ground, Irish Sea, Celtic Sea, Scotland Inshore and Fladen ground), and to test for spatial transferability of habitat models among multiple regions. Nephrops burrow density was modelled using generalised additive models (GAMs) with predictors selected from four environmental variables (depth, slope, sediment and rugosity). Models were evaluated and tested for spatial transferability among areas. The optimum models (lowest AICc) for different areas always included depth and sediment as predictors. Burrow densities were generally greater at depth and in finer sediments, but relationships for individual areas were sometimes more complex. Aside from an inclusion of depth and sediment, the optimum models differed between fished areas. When it came to tests of spatial transferability, however, most of the models were able to predict Nephrops density in other areas. Furthermore, transferability was not dependent on use of the optimum models since competing models were also able to achieve a similar level of transferability to new areas. A degree of decoupling between model 'fitting' performance and spatial transferability supports the use of simpler models when extrapolating habitat suitability maps to different areas. Differences in the form and performance of models from different areas may supply further information on the processes shaping

Group additivity calculations of the thermodynamic properties of unfolded proteins in aqueous solution: a critical comparison of peptide-based and HKF models.

PubMed

Hakin, A W; Hedwig, G R

2001-02-15

A recent paper in this journal [Amend and Helgeson, Biophys. Chem. 84 (2000) 105] presented a new group additivity model to calculate various thermodynamic properties of unfolded proteins in aqueous solution. The parameters given for the revised Helgeson-Kirkham-Flowers (HKF) equations of state for all the constituent groups of unfolded proteins can be used, in principle, to calculate the partial molar heat capacity, C(o)p.2, and volume, V2(0), at infinite dilution of any polypeptide. Calculations of the values of C(o)p.2 and V2(0) for several polypeptides have been carried out to test the predictive utility of the HKF group additivity model. The results obtained are in very poor agreement with experimental data, and also with results calculated using a peptide-based group additivity model. A critical assessment of these two additivity models is presented.

A Bayesian additive model for understanding public transport usage in special events.

PubMed

Rodrigues, Filipe; Borysov, Stanislav; Ribeiro, Bernardete; Pereira, Francisco

2016-12-02

Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise. Although these are usually planned well in advance, their impact is difficult to predict, even when organisers and transportation operators coordinate. The problem highly increases when several events happen concurrently. To solve these problems, costly processes, heavily reliant on manual search and personal experience, are usual practice in large cities like Singapore, London or Tokyo. This paper presents a Bayesian additive model with Gaussian process components that combines smart card records from public transport with context information about events that is continuously mined from the Web. We develop an efficient approximate inference algorithm using expectation propagation, which allows us to predict the total number of public transportation trips to the special event areas, thereby contributing to a more adaptive transportation system. Furthermore, for multiple concurrent event scenarios, the proposed algorithm is able to disaggregate gross trip counts into their most likely components related to specific events and routine behavior. Using real data from Singapore, we show that the presented model outperforms the best baseline model by up to 26% in R2 and also has explanatory power for its individual components.

Tree Biomass Allocation and Its Model Additivity for Casuarina equisetifolia in a Tropical Forest of Hainan Island, China.

PubMed

Xue, Yang; Yang, Zhongyang; Wang, Xiaoyan; Lin, Zhipan; Li, Dunxi; Su, Shaofeng

2016-01-01

Casuarina equisetifolia is commonly planted and used in the construction of coastal shelterbelt protection in Hainan Island. Thus, it is critical to accurately estimate the tree biomass of Casuarina equisetifolia L. for forest managers to evaluate the biomass stock in Hainan. The data for this work consisted of 72 trees, which were divided into three age groups: young forest, middle-aged forest, and mature forest. The proportion of biomass from the trunk significantly increased with age (P<0.05). However, the biomass of the branch and leaf decreased, and the biomass of the root did not change. To test whether the crown radius (CR) can improve biomass estimates of C. equisetifolia, we introduced CR into the biomass models. Here, six models were used to estimate the biomass of each component, including the trunk, the branch, the leaf, and the root. In each group, we selected one model among these six models for each component. The results showed that including the CR greatly improved the model performance and reduced the error, especially for the young and mature forests. In addition, to ensure biomass additivity, the selected equation for each component was fitted as a system of equations using seemingly unrelated regression (SUR). The SUR method not only gave efficient and accurate estimates but also achieved the logical additivity. The results in this study provide a robust estimation of tree biomass components and total biomass over three groups of C. equisetifolia.

Tree Biomass Allocation and Its Model Additivity for Casuarina equisetifolia in a Tropical Forest of Hainan Island, China

PubMed Central

Xue, Yang; Yang, Zhongyang; Wang, Xiaoyan; Lin, Zhipan; Li, Dunxi; Su, Shaofeng

2016-01-01

Casuarina equisetifolia is commonly planted and used in the construction of coastal shelterbelt protection in Hainan Island. Thus, it is critical to accurately estimate the tree biomass of Casuarina equisetifolia L. for forest managers to evaluate the biomass stock in Hainan. The data for this work consisted of 72 trees, which were divided into three age groups: young forest, middle-aged forest, and mature forest. The proportion of biomass from the trunk significantly increased with age (P<0.05). However, the biomass of the branch and leaf decreased, and the biomass of the root did not change. To test whether the crown radius (CR) can improve biomass estimates of C. equisetifolia, we introduced CR into the biomass models. Here, six models were used to estimate the biomass of each component, including the trunk, the branch, the leaf, and the root. In each group, we selected one model among these six models for each component. The results showed that including the CR greatly improved the model performance and reduced the error, especially for the young and mature forests. In addition, to ensure biomass additivity, the selected equation for each component was fitted as a system of equations using seemingly unrelated regression (SUR). The SUR method not only gave efficient and accurate estimates but also achieved the logical additivity. The results in this study provide a robust estimation of tree biomass components and total biomass over three groups of C. equisetifolia. PMID:27002822

Evaluation of 3D Additively Manufactured Canine Brain Models for Teaching Veterinary Neuroanatomy.

PubMed

Schoenfeld-Tacher, Regina M; Horn, Timothy J; Scheviak, Tyler A; Royal, Kenneth D; Hudson, Lola C

Physical specimens are essential to the teaching of veterinary anatomy. While fresh and fixed cadavers have long been the medium of choice, plastinated specimens have gained widespread acceptance as adjuncts to dissection materials. Even though the plastination process increases the durability of specimens, these are still derived from animal tissues and require periodic replacement if used by students on a regular basis. This study investigated the use of three-dimensional additively manufactured (3D AM) models (colloquially referred to as 3D-printed models) of the canine brain as a replacement for plastinated or formalin-fixed brains. The models investigated were built based on a micro-MRI of a single canine brain and have numerous practical advantages, such as durability, lower cost over time, and reduction of animal use. The effectiveness of the models was assessed by comparing performance among students who were instructed using either plastinated brains or 3D AM models. This study used propensity score matching to generate similar pairs of students. Pairings were based on gender and initial anatomy performance across two consecutive classes of first-year veterinary students. Students' performance on a practical neuroanatomy exam was compared, and no significant differences were found in scores based on the type of material (3D AM models or plastinated specimens) used for instruction. Students in both groups were equally able to identify neuroanatomical structures on cadaveric material, as well as respond to questions involving application of neuroanatomy knowledge. Therefore, we postulate that 3D AM canine brain models are an acceptable alternative to plastinated specimens in teaching veterinary neuroanatomy.

Modeling a Change in Flowrate through Detention or Additional Pavement on the Receiving Stream : Final Report

DOT National Transportation Integrated Search

2017-11-01

The addition or removal of flow from a stream affects the water surface downstream and possibly upstream. The extent of such effects is generally determined by modeling the receiving stream. Guidance that concisely describes how far up/downstream a h...

Meta-analysis of high-latitude nitrogen-addition and warming studies implies ecological mechanisms overlooked by land models

NASA Astrophysics Data System (ADS)

Bouskill, N. J.; Riley, W. J.; Tang, J. Y.

2014-12-01

Accurate representation of ecosystem processes in land models is crucial for reducing predictive uncertainty in energy and greenhouse gas feedbacks with the climate. Here we describe an observational and modeling meta-analysis approach to benchmark land models, and apply the method to the land model CLM4.5 with two versions of belowground biogeochemistry. We focused our analysis on the aboveground and belowground responses to warming and nitrogen addition in high-latitude ecosystems, and identified absent or poorly parameterized mechanisms in CLM4.5. While the two model versions predicted similar soil carbon stock trajectories following both warming and nitrogen addition, other predicted variables (e.g., belowground respiration) differed from observations in both magnitude and direction, indicating that CLM4.5 has inadequate underlying mechanisms for representing high-latitude ecosystems. On the basis of observational synthesis, we attribute the model-observation differences to missing representations of microbial dynamics, aboveground and belowground coupling, and nutrient cycling, and we use the observational meta-analysis to discuss potential approaches to improving the current models. However, we also urge caution concerning the selection of data sets and experiments for meta-analysis. For example, the concentrations of nitrogen applied in the synthesized field experiments (average = 72 kg ha-1 yr-1) are many times higher than projected soil nitrogen concentrations (from nitrogen deposition and release during mineralization), which precludes a rigorous evaluation of the model responses to likely nitrogen perturbations. Overall, we demonstrate that elucidating ecological mechanisms via meta-analysis can identify deficiencies in ecosystem models and empirical experiments.

Comparing physically-based and statistical landslide susceptibility model outputs - a case study from Lower Austria

NASA Astrophysics Data System (ADS)

Canli, Ekrem; Thiebes, Benni; Petschko, Helene; Glade, Thomas

2015-04-01

By now there is a broad consensus that due to human-induced global change the frequency and magnitude of heavy precipitation events is expected to increase in certain parts of the world. Given the fact, that rainfall serves as the most common triggering agent for landslide initiation, also an increased landside activity can be expected there. Landslide occurrence is a globally spread phenomenon that clearly needs to be handled. The present and well known problems in modelling landslide susceptibility and hazard give uncertain results in the prediction. This includes the lack of a universal applicable modelling solution for adequately assessing landslide susceptibility (which can be seen as the relative indication of the spatial probability of landslide initiation). Generally speaking, there are three major approaches for performing landslide susceptibility analysis: heuristic, statistical and deterministic models, all with different assumptions, its distinctive data requirements and differently interpretable outcomes. Still, detailed comparison of resulting landslide susceptibility maps are rare. In this presentation, the susceptibility modelling outputs of a deterministic model (Stability INdex MAPping - SINMAP) and a statistical modelling approach (generalized additive model - GAM) are compared. SINMAP is an infinite slope stability model which requires parameterization of soil mechanical parameters. Modelling with the generalized additive model, which represents a non-linear extension of a generalized linear model, requires a high quality landslide inventory that serves as the dependent variable in the statistical approach. Both methods rely on topographical data derived from the DTM. The comparison has been carried out in a study area located in the district of Waidhofen/Ybbs in Lower Austria. For the whole district (ca. 132 km²), 1063 landslides have been mapped and partially used within the analysis and the validation of the model outputs. The respective

78 FR 32224 - Availability of Version 3.1.2 of the Connect America Fund Phase II Cost Model; Additional...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-29

... Version 3.1.2 of the Connect America Fund Phase II Cost Model; Additional Discussion Topics in Connect America Cost Model Virtual Workshop AGENCY: Federal Communications Commission. ACTION: Proposed rule... America Cost Model (CAM v3.1.2), which allows Commission staff and interested parties to calculate costs...

An Open Software Platform for Sharing Water Resource Models, Code and Data

NASA Astrophysics Data System (ADS)

Knox, Stephen; Meier, Philipp; Mohamed, Khaled; Korteling, Brett; Matrosov, Evgenii; Huskova, Ivana; Harou, Julien; Rosenberg, David; Tilmant, Amaury; Medellin-Azuara, Josue; Wicks, Jon

2016-04-01

The modelling of managed water resource systems requires new approaches in the face of increasing future uncertainty. Water resources management models, even if applied to diverse problem areas, use common approaches such as representing the problem as a network of nodes and links. We propose a data management software platform, called Hydra, that uses this commonality to allow multiple models using a node-link structure to be managed and run using a single software system. Hydra's user interface allows users to manage network topology and associated data. Hydra feeds this data directly into a model, importing from and exporting to different file formats using Apps. An App connects Hydra to a custom model, a modelling system such as GAMS or MATLAB or to different file formats such as MS Excel, CSV and ESRI Shapefiles. Hydra allows users to manage their data in a single, consistent place. Apps can be used to run domain-specific models and allow users to work with their own required file formats. The Hydra App Store offers a collaborative space where model developers can publish, review and comment on Apps, models and data. Example Apps and open-source libraries are available in a variety of languages (Python, Java and .NET). The App Store can act as a hub for water resource modellers to view and share Apps, models and data easily. This encourages an ecosystem of development using a shared platform, resulting in more model integration and potentially greater unity within resource modelling communities. www.hydraplatform.org www.hydraappstore.com

Meta-analysis of high-latitude nitrogen-addition and warming studies implies ecological mechanisms overlooked by land models

DOE PAGES

Bouskill, N. J.; Riley, W. J.; Tang, J. Y.

2014-12-11

Accurate representation of ecosystem processes in land models is crucial for reducing predictive uncertainty in energy and greenhouse gas feedbacks with the climate. Here we describe an observational and modeling meta-analysis approach to benchmark land models, and apply the method to the land model CLM4.5 with two versions of belowground biogeochemistry. We focused our analysis on the aboveground and belowground responses to warming and nitrogen addition in high-latitude ecosystems, and identified absent or poorly parameterized mechanisms in CLM4.5. While the two model versions predicted similar soil carbon stock trajectories following both warming and nitrogen addition, other predicted variables (e.g., belowgroundmore » respiration) differed from observations in both magnitude and direction, indicating that CLM4.5 has inadequate underlying mechanisms for representing high-latitude ecosystems. On the basis of observational synthesis, we attribute the model–observation differences to missing representations of microbial dynamics, aboveground and belowground coupling, and nutrient cycling, and we use the observational meta-analysis to discuss potential approaches to improving the current models. However, we also urge caution concerning the selection of data sets and experiments for meta-analysis. For example, the concentrations of nitrogen applied in the synthesized field experiments (average = 72 kg ha -1 yr -1) are many times higher than projected soil nitrogen concentrations (from nitrogen deposition and release during mineralization), which precludes a rigorous evaluation of the model responses to likely nitrogen perturbations. Overall, we demonstrate that elucidating ecological mechanisms via meta-analysis can identify deficiencies in ecosystem models and empirical experiments.« less

Representational Flexibility and Problem-Solving Ability in Fraction and Decimal Number Addition: A Structural Model

ERIC Educational Resources Information Center

Deliyianni, Eleni; Gagatsis, Athanasios; Elia, Iliada; Panaoura, Areti

2016-01-01

The aim of this study was to propose and validate a structural model in fraction and decimal number addition, which is founded primarily on a synthesis of major theoretical approaches in the field of representations in Mathematics and also on previous research on the learning of fractions and decimals. The study was conducted among 1,701 primary…

Atmospheric corrections in interferometric synthetic aperture radar surface deformation - a case study of the city of Mendoza, Argentina

NASA Astrophysics Data System (ADS)

Balbarani, S.; Euillades, P. A.; Euillades, L. D.; Casu, F.; Riveros, N. C.

2013-09-01

Differential interferometry is a remote sensing technique that allows studying crustal deformation produced by several phenomena like earthquakes, landslides, land subsidence and volcanic eruptions. Advanced techniques, like small baseline subsets (SBAS), exploit series of images acquired by synthetic aperture radar (SAR) sensors during a given time span. Phase propagation delay in the atmosphere is the main systematic error of interferometric SAR measurements. It affects differently images acquired at different days or even at different hours of the same day. So, datasets acquired during the same time span from different sensors (or sensor configuration) often give diverging results. Here we processed two datasets acquired from June 2010 to December 2011 by COSMO-SkyMed satellites. One of them is HH-polarized, and the other one is VV-polarized and acquired on different days. As expected, time series computed from these datasets show differences. We attributed them to non-compensated atmospheric artifacts and tried to correct them by using ERA-Interim global atmospheric model (GAM) data. With this method, we were able to correct less than 50% of the scenes, considering an area where no phase unwrapping errors were detected. We conclude that GAM-based corrections are not enough for explaining differences in computed time series, at least in the processed area of interest. We remark that no direct meteorological data for the GAM-based corrections were employed. Further research is needed in order to understand under what conditions this kind of data can be used.

Supra-additive effects of tramadol and acetaminophen in a human pain model.

PubMed

Filitz, Jörg; Ihmsen, Harald; Günther, Werner; Tröster, Andreas; Schwilden, Helmut; Schüttler, Jürgen; Koppert, Wolfgang

2008-06-01

The combination of analgesic drugs with different pharmacological properties may show better efficacy with less side effects. Aim of this study was to examine the analgesic and antihyperalgesic properties of the weak opioid tramadol and the non-opioid acetaminophen, alone as well as in combination, in an experimental pain model in humans. After approval of the local Ethics Committee, 17 healthy volunteers were enrolled in this double-blind and placebo-controlled study in a cross-over design. Transcutaneous electrical stimulation at high current densities (29.6+/-16.2 mA) induced spontaneous acute pain (NRS=6 of 10) and distinct areas of hyperalgesia for painful mechanical stimuli (pinprick-hyperalgesia). Pain intensities as well as the extent of the areas of hyperalgesia were assessed before, during and 150 min after a 15 min lasting intravenous infusion of acetaminophen (650 mg), tramadol (75 mg), a combination of both (325 mg acetaminophen and 37.5mg tramadol), or saline 0.9%. Tramadol led to a maximum pain reduction of 11.7+/-4.2% with negligible antihyperalgesic properties. In contrast, acetaminophen led to a similar pain reduction (9.8+/-4.4%), but a sustained antihyperalgesic effect (34.5+/-14.0% reduction of hyperalgesic area). The combination of both analgesics at half doses led to a supra-additive pain reduction of 15.2+/-5.7% and an enhanced antihyperalgesic effect (41.1+/-14.3% reduction of hyperalgesic areas) as compared to single administration of acetaminophen. Our study provides first results on interactions of tramadol and acetaminophen on experimental pain and hyperalgesia in humans. Pharmacodynamic modeling combined with the isobolographic technique showed supra-additive effects of the combination of acetaminophen and tramadol concerning both, analgesia and antihyperalgesia. The results might act as a rationale for combining both analgesics.

Evaluation of the environmental epidemiologic data and methodology for the air quality standard in Beijing

NASA Astrophysics Data System (ADS)

Li, Xu; Jiang, Yanfeng; Yin, Ling; Liu, Bo; Du, Pengfei; Hassan, Mujtaba; Wang, Shigong; Li, Tanshi

2017-09-01

To evaluate the relationship between exposure to air pollutants and respiratory emergency room visits, a generalized additive model (GAM) was used to analyze the exposure-effect relationship between air pollutants and respiratory emergency room visits. The results showed that NO2, SO2, and PM10 have positive relationships with respiratory disease. Concentration increases of 10 μg/m3 in NO2, SO2, and PM10 corresponded to 3.90% (95%CI 3.56-4.25), 0.81% (95%CI -0.09-1.72), and 0.64% (95%CI 0.55-0.74) increases in respiratory emergency room visits. In addition, there is a strong synergic effect of PM10 and NO2 on respiratory diseases. The threshold values of the national standard grade II limits used in Beijing should be adjusted. An appropriate standard could effectively promote a significant decline in respiratory room visits and would eventually be beneficial to air quality management in residential areas.

Model studies of hydrogen atom addition and abstraction processes involving ortho-, meta-, and para-benzynes.

PubMed

Clark, A E; Davidson, E R

2001-10-31

H-atom addition and abstraction processes involving ortho-, meta-, and para-benzyne have been investigated by multiconfigurational self-consistent field methods. The H(A) + H(B)...H(C) reaction (where r(BC) is adjusted to mimic the appropriate singlet-triplet energy gap) is shown to effectively model H-atom addition to benzyne. The doublet multiconfiguration wave functions are shown to mix the "singlet" and "triplet" valence bond structures of H(B)...H(C) along the reaction coordinate; however, the extent of mixing is dependent on the singlet-triplet energy gap (DeltaE(ST)) of the H(B)...H(C) diradical. Early in the reaction, the ground-state wave function is essentially the "singlet" VB function, yet it gains significant "triplet" VB character along the reaction coordinate that allows H(A)-H(B) bond formation. Conversely, the wave function of the first excited state is predominantly the "triplet" VB configuration early in the reaction coordinate, but gains "singlet" VB character when the H-atom is close to a radical center. As a result, the potential energy surface (PES) for H-atom addition to triplet H(B)...H(C) diradical is repulsive! The H3 model predicts, in agreement with the actual calculations on benzyne, that the singlet diradical electrons are not coupled strongly enough to give rise to an activation barrier associated with C-H bond formation. Moreover, this model predicts that the PES for H-atom addition to triplet benzyne will be characterized by a repulsive curve early in the reaction coordinate, followed by a potential avoided crossing with the (pi)1(sigma*)1 state of the phenyl radical. In contrast to H-atom addition, large activation barriers characterize the abstraction process in both the singlet ground state and first triplet state. In the ground state, this barrier results from the weakly avoided crossing of the dominant VB configurations in the ground-state singlet (S0) and first excited singlet (S1) because of the large energy gap between S0

Modeling of Processing-Induced Pore Morphology in an Additively-Manufactured Ti-6Al-4V Alloy

PubMed Central

Kabir, Mohammad Rizviul; Richter, Henning

2017-01-01

A selective laser melting (SLM)-based, additively-manufactured Ti-6Al-4V alloy is prone to the accumulation of undesirable defects during layer-by-layer material build-up. Defects in the form of complex-shaped pores are one of the critical issues that need to be considered during the processing of this alloy. Depending on the process parameters, pores with concave or convex boundaries may occur. To exploit the full potential of additively-manufactured Ti-6Al-4V, the interdependency between the process parameters, pore morphology, and resultant mechanical properties, needs to be understood. By incorporating morphological details into numerical models for micromechanical analyses, an in-depth understanding of how these pores interact with the Ti-6Al-4V microstructure can be gained. However, available models for pore analysis lack a realistic description of both the Ti-6Al-4V grain microstructure, and the pore geometry. To overcome this, we propose a comprehensive approach for modeling and discretizing pores with complex geometry, situated in a polycrystalline microstructure. In this approach, the polycrystalline microstructure is modeled by means of Voronoi tessellations, and the complex pore geometry is approximated by strategically combining overlapping spheres of varied sizes. The proposed approach provides an elegant way to model the microstructure of SLM-processed Ti-6Al-4V containing pores or crack-like voids, and makes it possible to investigate the relationship between process parameters, pore morphology, and resultant mechanical properties in a finite-element-based simulation framework. PMID:28772504

Modeling of Processing-Induced Pore Morphology in an Additively-Manufactured Ti-6Al-4V Alloy.

PubMed

Kabir, Mohammad Rizviul; Richter, Henning

2017-02-08

A selective laser melting (SLM)-based, additively-manufactured Ti-6Al-4V alloy is prone to the accumulation of undesirable defects during layer-by-layer material build-up. Defects in the form of complex-shaped pores are one of the critical issues that need to be considered during the processing of this alloy. Depending on the process parameters, pores with concave or convex boundaries may occur. To exploit the full potential of additively-manufactured Ti-6Al-4V, the interdependency between the process parameters, pore morphology, and resultant mechanical properties, needs to be understood. By incorporating morphological details into numerical models for micromechanical analyses, an in-depth understanding of how these pores interact with the Ti-6Al-4V microstructure can be gained. However, available models for pore analysis lack a realistic description of both the Ti-6Al-4V grain microstructure, and the pore geometry. To overcome this, we propose a comprehensive approach for modeling and discretizing pores with complex geometry, situated in a polycrystalline microstructure. In this approach, the polycrystalline microstructure is modeled by means of Voronoi tessellations, and the complex pore geometry is approximated by strategically combining overlapping spheres of varied sizes. The proposed approach provides an elegant way to model the microstructure of SLM-processed Ti-6Al-4V containing pores or crack-like voids, and makes it possible to investigate the relationship between process parameters, pore morphology, and resultant mechanical properties in a finite-element-based simulation framework.

Dynamic modeling for rigid rotor bearing systems with a localized defect considering additional deformations at the sharp edges

NASA Astrophysics Data System (ADS)

Liu, Jing; Shao, Yimin

2017-06-01

Rotor bearing systems (RBSs) play a very valuable role for wind turbine gearboxes, aero-engines, high speed spindles, and other rotational machinery. An in-depth understanding of vibrations of the RBSs is very useful for condition monitoring and diagnosis applications of these machines. A new twelve-degree-of-freedom dynamic model for rigid RBSs with a localized defect (LOD) is proposed. This model can formulate the housing support stiffness, interfacial frictional moments including load dependent and load independent components, time-varying displacement excitation caused by a LOD, additional deformations at the sharp edges of the LOD, and lubricating oil film. The time-varying displacement model is determined by a half-sine function. A new method for calculating the additional deformations at the sharp edges of the LOD is analytical derived based on an elastic quarter-space method presented in the literature. The proposed dynamic model is utilized to analyze the influences of the housing support stiffness and LOD sizes on the vibration characteristics of the rigid RBS, which cannot be predicted by the previous dynamic models in the literature. The results show that the presented method can give a new dynamic modeling method for vibration formulation for a rigid RBS with and without the LOD on the races.

An Analysis of Kindergarten and First Grade Children's Addition and Subtraction Problem Solving Modeling and Accuracy.

ERIC Educational Resources Information Center

Shores, Jay H.; Underhill, Robert G.

A study was undertaken of the effects of formal education and conservation of numerousness on addition and subtraction problem types. Thirty-six kindergarten and 36 first-grade subjects randomly selected from one area of a school district were administered measures of conservation, problem-solving success, and modeling ability. Following factor…

Nonlinear feedback in a six-dimensional Lorenz Model: impact of an additional heating term

NASA Astrophysics Data System (ADS)

Shen, B.-W.

2015-03-01

In this study, a six-dimensional Lorenz model (6DLM) is derived, based on a recent study using a five-dimensional (5-D) Lorenz model (LM), in order to examine the impact of an additional mode and its accompanying heating term on solution stability. The new mode added to improve the representation of the steamfunction is referred to as a secondary streamfunction mode, while the two additional modes, that appear in both the 6DLM and 5DLM but not in the original LM, are referred to as secondary temperature modes. Two energy conservation relationships of the 6DLM are first derived in the dissipationless limit. The impact of three additional modes on solution stability is examined by comparing numerical solutions and ensemble Lyapunov exponents of the 6DLM and 5DLM as well as the original LM. For the onset of chaos, the critical value of the normalized Rayleigh number (rc) is determined to be 41.1. The critical value is larger than that in the 3DLM (rc ~ 24.74), but slightly smaller than the one in the 5DLM (rc ~ 42.9). A stability analysis and numerical experiments obtained using generalized LMs, with or without simplifications, suggest the following: (1) negative nonlinear feedback in association with the secondary temperature modes, as first identified using the 5DLM, plays a dominant role in providing feedback for improving the solution's stability of the 6DLM, (2) the additional heating term in association with the secondary streamfunction mode may destabilize the solution, and (3) overall feedback due to the secondary streamfunction mode is much smaller than the feedback due to the secondary temperature modes; therefore, the critical Rayleigh number of the 6DLM is comparable to that of the 5DLM. The 5DLM and 6DLM collectively suggest different roles for small-scale processes (i.e., stabilization vs. destabilization), consistent with the following statement by Lorenz (1972): If the flap of a butterfly's wings can be instrumental in generating a tornado, it can

Nonlinear feedback in a six-dimensional Lorenz model: impact of an additional heating term

NASA Astrophysics Data System (ADS)

Shen, B.-W.

2015-12-01

In this study, a six-dimensional Lorenz model (6DLM) is derived, based on a recent study using a five-dimensional (5-D) Lorenz model (LM), in order to examine the impact of an additional mode and its accompanying heating term on solution stability. The new mode added to improve the representation of the streamfunction is referred to as a secondary streamfunction mode, while the two additional modes, which appear in both the 6DLM and 5DLM but not in the original LM, are referred to as secondary temperature modes. Two energy conservation relationships of the 6DLM are first derived in the dissipationless limit. The impact of three additional modes on solution stability is examined by comparing numerical solutions and ensemble Lyapunov exponents of the 6DLM and 5DLM as well as the original LM. For the onset of chaos, the critical value of the normalized Rayleigh number (rc) is determined to be 41.1. The critical value is larger than that in the 3DLM (rc ~ 24.74), but slightly smaller than the one in the 5DLM (rc ~ 42.9). A stability analysis and numerical experiments obtained using generalized LMs, with or without simplifications, suggest the following: (1) negative nonlinear feedback in association with the secondary temperature modes, as first identified using the 5DLM, plays a dominant role in providing feedback for improving the solution's stability of the 6DLM, (2) the additional heating term in association with the secondary streamfunction mode may destabilize the solution, and (3) overall feedback due to the secondary streamfunction mode is much smaller than the feedback due to the secondary temperature modes; therefore, the critical Rayleigh number of the 6DLM is comparable to that of the 5DLM. The 5DLM and 6DLM collectively suggest different roles for small-scale processes (i.e., stabilization vs. destabilization), consistent with the following statement by Lorenz (1972): "If the flap of a butterfly's wings can be instrumental in generating a tornado, it can

A low-altitude mountain range as an important refugium for two narrow endemics in the Southwest Australian Floristic Region biodiversity hotspot.

PubMed

Keppel, Gunnar; Robinson, Todd P; Wardell-Johnson, Grant W; Yates, Colin J; Van Niel, Kimberly P; Byrne, Margaret; Schut, Antonius G T

2017-01-01

Low-altitude mountains constitute important centres of diversity in landscapes with little topographic variation, such as the Southwest Australian Floristic Region (SWAFR). They also provide unique climatic and edaphic conditions that may allow them to function as refugia. We investigate whether the Porongurups (altitude 655 m) in the SWAFR will provide a refugium for the endemic Ornduffia calthifolia and O. marchantii under forecast climate change. We used species distribution modelling based on WorldClim climatic data, 30-m elevation data and a 2-m-resolution LiDAR-derived digital elevation model (DEM) to predict current and future distributions of the Ornduffia species at local and regional scales based on 605 field-based abundance estimates. Future distributions were forecast using RCP2.6 and RCP4.5 projections. To determine whether local edaphic and biotic factors impact these forecasts, we tested whether soil depth and vegetation height were significant predictors of abundance using generalized additive models (GAMs). Species distribution modelling revealed the importance of elevation and topographic variables at the local scale for determining distributions of both species, which also preferred shadier locations and higher slopes. However, O. calthifolia occurred at higher (cooler) elevations with rugged, concave topography, while O. marchantii occurred in disturbed sites at lower locations with less rugged, convex topography. Under future climates both species are likely to severely contract under the milder RCP2.6 projection (approx. 2 °C of global warming), but are unlikely to persist if warming is more severe (RCP4.5). GAMs showed that soil depth and vegetation height are important predictors of O. calthifolia and O. marchantii distributions, respectively. The Porongurups constitute an important refugium for O. calthifolia and O. marchantii, but limits to this capacity may be reached if global warming exceeds 2 °C. This capacity is moderated at

A low-altitude mountain range as an important refugium for two narrow endemics in the Southwest Australian Floristic Region biodiversity hotspot

PubMed Central

Robinson, Todd P.; Wardell-Johnson, Grant W.; Yates, Colin J.; Van Niel, Kimberly P.; Byrne, Margaret; Schut, Antonius G. T.

2017-01-01

Background and Aims Low-altitude mountains constitute important centres of diversity in landscapes with little topographic variation, such as the Southwest Australian Floristic Region (SWAFR). They also provide unique climatic and edaphic conditions that may allow them to function as refugia. We investigate whether the Porongurups (altitude 655 m) in the SWAFR will provide a refugium for the endemic Ornduffia calthifolia and O. marchantii under forecast climate change. Methods We used species distribution modelling based on WorldClim climatic data, 30-m elevation data and a 2-m-resolution LiDAR-derived digital elevation model (DEM) to predict current and future distributions of the Ornduffia species at local and regional scales based on 605 field-based abundance estimates. Future distributions were forecast using RCP2.6 and RCP4.5 projections. To determine whether local edaphic and biotic factors impact these forecasts, we tested whether soil depth and vegetation height were significant predictors of abundance using generalized additive models (GAMs). Key Results Species distribution modelling revealed the importance of elevation and topographic variables at the local scale for determining distributions of both species, which also preferred shadier locations and higher slopes. However, O. calthifolia occurred at higher (cooler) elevations with rugged, concave topography, while O. marchantii occurred in disturbed sites at lower locations with less rugged, convex topography. Under future climates both species are likely to severely contract under the milder RCP2.6 projection (approx. 2 °C of global warming), but are unlikely to persist if warming is more severe (RCP4.5). GAMs showed that soil depth and vegetation height are important predictors of O. calthifolia and O. marchantii distributions, respectively. Conclusions The Porongurups constitute an important refugium for O. calthifolia and O. marchantii, but limits to this capacity may be reached if global

Polylactides in additive biomanufacturing.

PubMed

Poh, Patrina S P; Chhaya, Mohit P; Wunner, Felix M; De-Juan-Pardo, Elena M; Schilling, Arndt F; Schantz, Jan-Thorsten; van Griensven, Martijn; Hutmacher, Dietmar W

2016-12-15

New advanced manufacturing technologies under the alias of additive biomanufacturing allow the design and fabrication of a range of products from pre-operative models, cutting guides and medical devices to scaffolds. The process of printing in 3 dimensions of cells, extracellular matrix (ECM) and biomaterials (bioinks, powders, etc.) to generate in vitro and/or in vivo tissue analogue structures has been termed bioprinting. To further advance in additive biomanufacturing, there are many aspects that we can learn from the wider additive manufacturing (AM) industry, which have progressed tremendously since its introduction into the manufacturing sector. First, this review gives an overview of additive manufacturing and both industry and academia efforts in addressing specific challenges in the AM technologies to drive toward AM-enabled industrial revolution. After which, considerations of poly(lactides) as a biomaterial in additive biomanufacturing are discussed. Challenges in wider additive biomanufacturing field are discussed in terms of (a) biomaterials; (b) computer-aided design, engineering and manufacturing; (c) AM and additive biomanufacturing printers hardware; and (d) system integration. Finally, the outlook for additive biomanufacturing was discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

Dike Strength Analysis on a Regional Scale Based On a Stochastic Subsoil Model

NASA Astrophysics Data System (ADS)

Koelewijn, A. R.; Vastenburg, E. W.

2013-12-01

About two-third of the Netherlands is protected against flooding by dikes and levees. The subsoil can be characterized by fluvial and marine sediments. Maintaining the safety of these dikes and levees is of vital importance. Insufficient safety is not permissible, but excessive safety would imply a waste of money and other resources. Therefore safety assessments are carried out on a regular basis. Over the past decades, a practice has grown to calculate a limited number of cross-sections, roughly one every 500 to 1000 meters. For this purpose, a representative cross-section is selected as an estimate of the most vulnerable surface geometry and the subsoil conditions determined from boreholes and cone penetration tests, for which slope stability and piping analyses are carried out. This is a time-consuming procedure which is not only expensive, but also neglects geological knowledge. A method to incorporate geological knowledge of an area, including updating on the basis of additional investigations, has been described in Koelewijn et al. [2011]. In addition, various groups have worked to incorporate geotechnical stability models and detailed Lidar-measurements of the surface into a more efficient and rational calculation process [Knoeff et al. 2011, Lam et al. 2013, van den Ham & Mastbergen, 2013]. Combining this experience with the 3D subsoil model opens possibilities for cost-effective additional soil investigations for those locations where ruling out unfavorable conditions really influences the decisions to be made regarding rejection and improvement, see the figure for examples of different subsoil profiles along a dike. The resulting system has been applied for semi-automated calculations of dikes in various parts of the Netherlands, totalling over 4000 km by now, and a part of the Mississippi levee system. [van den Ham & Mastbergen, 2013] G.A. van den Ham & D.R. Mastbergen, A semi-probabilistic assessment method for flow slides. AGU Fall meeting, 2013

The Job Demands-Resources Model: An Analysis of Additive and Joint Effects of Demands and Resources

ERIC Educational Resources Information Center

Hu, Qiao; Schaufeli, Wilmar B.; Taris, Toon W.

2011-01-01

The present study investigated the additive, synergistic, and moderating effects of job demands and job resources on well-being (burnout and work engagement) and organizational outcomes, as specified by the Job Demands-Resources (JD-R) model. A survey was conducted among two Chinese samples: 625 blue collar workers and 761 health professionals. A…

Does the high–tech industry consistently reduce CO{sub 2} emissions? Results from nonparametric additive regression model

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

Xu, Bin; Research Center of Applied Statistics, Jiangxi University of Finance and Economics, Nanchang, Jiangxi 330013; Lin, Boqiang, E-mail: bqlin@xmu.edu.cn

China is currently the world's largest carbon dioxide (CO{sub 2}) emitter. Moreover, total energy consumption and CO{sub 2} emissions in China will continue to increase due to the rapid growth of industrialization and urbanization. Therefore, vigorously developing the high–tech industry becomes an inevitable choice to reduce CO{sub 2} emissions at the moment or in the future. However, ignoring the existing nonlinear links between economic variables, most scholars use traditional linear models to explore the impact of the high–tech industry on CO{sub 2} emissions from an aggregate perspective. Few studies have focused on nonlinear relationships and regional differences in China. Basedmore » on panel data of 1998–2014, this study uses the nonparametric additive regression model to explore the nonlinear effect of the high–tech industry from a regional perspective. The estimated results show that the residual sum of squares (SSR) of the nonparametric additive regression model in the eastern, central and western regions are 0.693, 0.054 and 0.085 respectively, which are much less those that of the traditional linear regression model (3.158, 4.227 and 7.196). This verifies that the nonparametric additive regression model has a better fitting effect. Specifically, the high–tech industry produces an inverted “U–shaped” nonlinear impact on CO{sub 2} emissions in the eastern region, but a positive “U–shaped” nonlinear effect in the central and western regions. Therefore, the nonlinear impact of the high–tech industry on CO{sub 2} emissions in the three regions should be given adequate attention in developing effective abatement policies. - Highlights: • The nonlinear effect of the high–tech industry on CO{sub 2} emissions was investigated. • The high–tech industry yields an inverted “U–shaped” effect in the eastern region. • The high–tech industry has a positive “U–shaped” nonlinear effect in other regions. • The linear

The next mesothelioma wave: mortality trends and forecast to 2030 in Brazil.

PubMed

Algranti, Eduardo; Saito, Cézar Akiyoshi; Carneiro, Ana Paula Scalia; Moreira, Bruno; Mendonça, Elizabete Medina Coeli; Bussacos, Marco Antonio

2015-10-01

There are limited data on mesothelioma mortality in industrializing countries, where, at present, most of the asbestos consumption occurs. To analyze temporal trends and to calculate mortality rates from mesothelioma and cancer of the pleura in Brazil from 2000 to 2012 and to estimate future mortality rates. We retrieved records of deaths from mesothelioma (ICD-10C45) and cancer of the pleura (ICD-10C38.4) from 2000 to 2012 in adults aged 30 years and over. Crude and age-standardized mortality rates (ASMR) were calculated. Rate ratios of mean crude mortality for selected municipalities were compared to the Brazilian rate. A regression was carried out of the annual number of deaths against asbestos consumption using a Generalized Additive Model (GAM). The best model was chosen to estimate the future burden and peak period of deaths. There were 929C45 and 1379 C38.4 deaths. The ratio of men to women for C45 was 1.4. A positive trend in C45 numbers was observed in Brazil (p=0.0012), particularly in São Paulo (p=0.0004) where ASMRs presented an increasing linear trend (p=0.0344). Selected municipalities harboring asbestos manipulation presented 3.7-11 fold rate ratios of C45 compared to Brazil. GAM presented best fits for latencies of 34 years or more. It is estimated that the peak incidence of C45 mortality will occur between 2021 and 2026. The observed ASMRs and the gender ratio close to 1 suggest underreporting. Even so, deaths are increasing and mesothelioma clusters were identified. Compared to industrialized countries Brazil displays a 15-20 year lag in estimated peak mesothelioma mortality which is consistent with the lag of asbestos peak consumption in the country. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cluster designs to assess the prevalence of acute malnutrition by lot quality assurance sampling: a validation study by computer simulation

PubMed Central

Olives, Casey; Pagano, Marcello; Deitchler, Megan; Hedt, Bethany L; Egge, Kari; Valadez, Joseph J

2009-01-01

Traditional lot quality assurance sampling (LQAS) methods require simple random sampling to guarantee valid results. However, cluster sampling has been proposed to reduce the number of random starting points. This study uses simulations to examine the classification error of two such designs, a 67×3 (67 clusters of three observations) and a 33×6 (33 clusters of six observations) sampling scheme to assess the prevalence of global acute malnutrition (GAM). Further, we explore the use of a 67×3 sequential sampling scheme for LQAS classification of GAM prevalence. Results indicate that, for independent clusters with moderate intracluster correlation for the GAM outcome, the three sampling designs maintain approximate validity for LQAS analysis. Sequential sampling can substantially reduce the average sample size that is required for data collection. The presence of intercluster correlation can impact dramatically the classification error that is associated with LQAS analysis. PMID:20011037

Cluster designs to assess the prevalence of acute malnutrition by lot quality assurance sampling: a validation study by computer simulation.

PubMed

Olives, Casey; Pagano, Marcello; Deitchler, Megan; Hedt, Bethany L; Egge, Kari; Valadez, Joseph J

2009-04-01

Traditional lot quality assurance sampling (LQAS) methods require simple random sampling to guarantee valid results. However, cluster sampling has been proposed to reduce the number of random starting points. This study uses simulations to examine the classification error of two such designs, a 67x3 (67 clusters of three observations) and a 33x6 (33 clusters of six observations) sampling scheme to assess the prevalence of global acute malnutrition (GAM). Further, we explore the use of a 67x3 sequential sampling scheme for LQAS classification of GAM prevalence. Results indicate that, for independent clusters with moderate intracluster correlation for the GAM outcome, the three sampling designs maintain approximate validity for LQAS analysis. Sequential sampling can substantially reduce the average sample size that is required for data collection. The presence of intercluster correlation can impact dramatically the classification error that is associated with LQAS analysis.

Use of Gifu Anaerobic Medium for culturing 32 dominant species of human gut microbes and its evaluation based on short-chain fatty acids fermentation profiles.

PubMed

Gotoh, Aina; Nara, Misaki; Sugiyama, Yuta; Sakanaka, Mikiyasu; Yachi, Hiroyuki; Kitakata, Aya; Nakagawa, Akira; Minami, Hiromichi; Okuda, Shujiro; Katoh, Toshihiko; Katayama, Takane; Kurihara, Shin

2017-10-01

Recently, a "human gut microbial gene catalogue," which ranks the dominance of microbe genus/species in human fecal samples, was published. Most of the bacteria ranked in the catalog are currently publicly available; however, the growth media recommended by the distributors vary among species, hampering physiological comparisons among the bacteria. To address this problem, we evaluated Gifu anaerobic medium (GAM) as a standard medium. Forty-four publicly available species of the top 56 species listed in the "human gut microbial gene catalogue" were cultured in GAM, and out of these, 32 (72%) were successfully cultured. Short-chain fatty acids from the bacterial culture supernatants were then quantified, and bacterial metabolic pathways were predicted based on in silico genomic sequence analysis. Our system provides a useful platform for assessing growth properties and analyzing metabolites of dominant human gut bacteria grown in GAM and supplemented with compounds of interest.

Using 3D Printing (Additive Manufacturing) to Produce Low-Cost Simulation Models for Medical Training.

PubMed

Lichtenberger, John P; Tatum, Peter S; Gada, Satyen; Wyn, Mark; Ho, Vincent B; Liacouras, Peter

2018-03-01

This work describes customized, task-specific simulation models derived from 3D printing in clinical settings and medical professional training programs. Simulation models/task trainers have an array of purposes and desired achievements for the trainee, defining that these are the first step in the production process. After this purpose is defined, computer-aided design and 3D printing (additive manufacturing) are used to create a customized anatomical model. Simulation models then undergo initial in-house testing by medical specialists followed by a larger scale beta testing. Feedback is acquired, via surveys, to validate effectiveness and to guide or determine if any future modifications and/or improvements are necessary. Numerous custom simulation models have been successfully completed with resulting task trainers designed for procedures, including removal of ocular foreign bodies, ultrasound-guided joint injections, nerve block injections, and various suturing and reconstruction procedures. These task trainers have been frequently utilized in the delivery of simulation-based training with increasing demand. 3D printing has been integral to the production of limited-quantity, low-cost simulation models across a variety of medical specialties. In general, production cost is a small fraction of a commercial, generic simulation model, if available. These simulation and training models are customized to the educational need and serve an integral role in the education of our military health professionals.

Effect of Hydrogen Addition on Methane HCCI Engine Ignition Timing and Emissions Using a Multi-zone Model

NASA Astrophysics Data System (ADS)

Wang, Zi-han; Wang, Chun-mei; Tang, Hua-xin; Zuo, Cheng-ji; Xu, Hong-ming

2009-06-01

Ignition timing control is of great importance in homogeneous charge compression ignition engines. The effect of hydrogen addition on methane combustion was investigated using a CHEMKIN multi-zone model. Results show that hydrogen addition advances ignition timing and enhances peak pressure and temperature. A brief analysis of chemical kinetics of methane blending hydrogen is also performed in order to investigate the scope of its application, and the analysis suggests that OH radical plays an important role in the oxidation. Hydrogen addition increases NOx while decreasing HC and CO emissions. Exhaust gas recirculation (EGR) also advances ignition timing; however, its effects on emissions are generally the opposite. By adjusting the hydrogen addition and EGR rate, the ignition timing can be regulated with a low emission level. Investigation into zones suggests that NOx is mostly formed in core zones while HC and CO mostly originate in the crevice and the quench layer.

The effect of atmospheric thermal conditions and urban thermal pollution on all-cause and cardiovascular mortality in Bangladesh.

PubMed

Burkart, Katrin; Schneider, Alexandra; Breitner, Susanne; Khan, Mobarak Hossain; Krämer, Alexander; Endlicher, Wilfried

2011-01-01

This study assessed the effect of temperature and thermal atmospheric conditions on all-cause and cardiovascular mortality in Bangladesh. In particular, differences in the response to elevated temperatures between urban and rural areas were investigated. Generalized additive models (GAMs) for daily death counts, adjusted for trend, season, day of the month and age were separately fitted for urban and rural areas. Breakpoint models were applied for determining the increase in mortality above and below a threshold (equivalent) temperature. Generally, a 'V'-shaped (equivalent) temperature-mortality curve with increasing mortality at low and high temperatures was observed. Particularly, urban areas suffered from heat-related mortality with a steep increase above a specific threshold. This adverse heat effect may well increase with ongoing urbanization and the intensification of the urban heat island due to the densification of building structures. Moreover, rising temperatures due to climate change could aggravate thermal stress. Copyright © 2011 Elsevier Ltd. All rights reserved.

Color Addition and Subtraction Apps

ERIC Educational Resources Information Center

Ruiz, Frances; Ruiz, Michael J.

2015-01-01

Color addition and subtraction apps in HTML5 have been developed for students as an online hands-on experience so that they can more easily master principles introduced through traditional classroom demonstrations. The evolution of the additive RGB color model is traced through the early IBM color adapters so that students can proceed step by step…

Statistical inference for the additive hazards model under outcome-dependent sampling.

PubMed

Yu, Jichang; Liu, Yanyan; Sandler, Dale P; Zhou, Haibo

2015-09-01

Cost-effective study design and proper inference procedures for data from such designs are always of particular interests to study investigators. In this article, we propose a biased sampling scheme, an outcome-dependent sampling (ODS) design for survival data with right censoring under the additive hazards model. We develop a weighted pseudo-score estimator for the regression parameters for the proposed design and derive the asymptotic properties of the proposed estimator. We also provide some suggestions for using the proposed method by evaluating the relative efficiency of the proposed method against simple random sampling design and derive the optimal allocation of the subsamples for the proposed design. Simulation studies show that the proposed ODS design is more powerful than other existing designs and the proposed estimator is more efficient than other estimators. We apply our method to analyze a cancer study conducted at NIEHS, the Cancer Incidence and Mortality of Uranium Miners Study, to study the risk of radon exposure to cancer.

Statistical inference for the additive hazards model under outcome-dependent sampling

PubMed Central

Yu, Jichang; Liu, Yanyan; Sandler, Dale P.; Zhou, Haibo

2015-01-01

Cost-effective study design and proper inference procedures for data from such designs are always of particular interests to study investigators. In this article, we propose a biased sampling scheme, an outcome-dependent sampling (ODS) design for survival data with right censoring under the additive hazards model. We develop a weighted pseudo-score estimator for the regression parameters for the proposed design and derive the asymptotic properties of the proposed estimator. We also provide some suggestions for using the proposed method by evaluating the relative efficiency of the proposed method against simple random sampling design and derive the optimal allocation of the subsamples for the proposed design. Simulation studies show that the proposed ODS design is more powerful than other existing designs and the proposed estimator is more efficient than other estimators. We apply our method to analyze a cancer study conducted at NIEHS, the Cancer Incidence and Mortality of Uranium Miners Study, to study the risk of radon exposure to cancer. PMID:26379363

Modeling the Effect of Geomorphic Change Triggered by Large Wood Addition on Salmon Habitat in a Forested Coastal Watershed

NASA Astrophysics Data System (ADS)

Bair, R.; Segura, C.; Lorion, C.

2015-12-01

Large wood (LW) additions are often part of fish habitat restorations in the PNW where historic forest clear-cutting limited natural wood recruitment. These efforts' relative successes are rarely reported in terms of ecological significance to different life stages of fish. Understanding the effectiveness of LW additions will contribute to successfully managing forest land. In this study we quantify the geomorphic change of a restoration project involving LW additions to three alluvial reaches in Mill Creek, OR. The reaches are 110-130m in plane-bed morphology and drain 2-16km2. We quantify the change in available habitat to different life stages of coho salmon in terms of velocity (v), shear stress (t), flow depth, and grain size distributions (GSD) considering existing thresholds in the literature for acceptable habitat. Flow conditions before and after LW additions are assessed using a 2D hydrodynamic model (FaSTMECH). Model inputs include detailed channel topography, discharge, and surface GSD. The spatial-temporal variability of sediment transport was also quantified based the modeled t distributions and the GSD to document changes in the overall geomorphic regime. Initial modeling results for pre wood conditions show mean t and v values ranging between 0 and 26N/m2 and between 0 and 2.4m/s, respectively for up to bankfull flow (Qbf). The distributions of both t and v become progressively wider and peak at higher values as flow increases with the notable exception at Qbf for which the area of low velocity increases noticeably. The spatial distributions of velocity results indicates that the extent of suitable habitat for adult coho decreased by 18% between flows 30 and 55% of BF. However the area of suitable habitat increased by 15% between 0.55Qbf and Qbf as the flow spreads from the channel into the floodplain. We expect the LW will enhance floodplain connectivity and thus available habitat by creating additional areas of low v during winter flows.

Unraveling additive from nonadditive effects using genomic relationship matrices.

PubMed

Muñoz, Patricio R; Resende, Marcio F R; Gezan, Salvador A; Resende, Marcos Deon Vilela; de Los Campos, Gustavo; Kirst, Matias; Huber, Dudley; Peter, Gary F

2014-12-01

The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies. Copyright © 2014 by the Genetics Society of America.