Fertilizer nitrogen, soil chemical properties, and their determinacy on rice yield: Evidence from 92 paddy fields of a large-scale farm in the Kanto Region of Japan

NASA Astrophysics Data System (ADS)

Li, D.; Nanseki, T.; Chomei, Y.; Yokota, S.

2017-07-01

Rice, a staple crop in Japan, is at risk of decreasing production and its yield highly depends on soil fertility. This study aimed to investigate determinants of rice yield, from the perspectives of fertilizer nitrogen and soil chemical properties. The data were sampled in 2014 and 2015 from 92 peat soil paddy fields on a large-scale farm located in the Kanto Region of Japan. The rice variety used was the most widely planted Koshihikari in Japan. Regression analysis indicated that fertilizer nitrogen significantly affected the yield, with a significant sustained effect to the subsequent year. Twelve soil chemical properties, including pH, cation exchange capacity, content of pyridine base elements, phosphoric acid, and silicic acid, were estimated. In addition to silicic acid, magnesia, in forms of its exchangeable content, saturation, and ratios to potassium and lime, positively affected the yield, while phosphoric acid negatively affected the yield. We assessed the soil chemical properties by soil quality index and principal component analysis. Positive effects were identified for both approaches, with the former performing better in explaining the rice yield. For soil quality index, the individual standardized soil properties and margins for improvement were indicated for each paddy field. Finally, multivariate regression on the principal components identified the most significant properties.

Estimating the impact of mineral aerosols on crop yields in food insecure regions using statistical crop models

NASA Astrophysics Data System (ADS)

Hoffman, A.; Forest, C. E.; Kemanian, A.

2016-12-01

A significant number of food-insecure nations exist in regions of the world where dust plays a large role in the climate system. While the impacts of common climate variables (e.g. temperature, precipitation, ozone, and carbon dioxide) on crop yields are relatively well understood, the impact of mineral aerosols on yields have not yet been thoroughly investigated. This research aims to develop the data and tools to progress our understanding of mineral aerosol impacts on crop yields. Suspended dust affects crop yields by altering the amount and type of radiation reaching the plant, modifying local temperature and precipitation. While dust events (i.e. dust storms) affect crop yields by depleting the soil of nutrients or by defoliation via particle abrasion. The impact of dust on yields is modeled statistically because we are uncertain which impacts will dominate the response on national and regional scales considered in this study. Multiple linear regression is used in a number of large-scale statistical crop modeling studies to estimate yield responses to various climate variables. In alignment with previous work, we develop linear crop models, but build upon this simple method of regression with machine-learning techniques (e.g. random forests) to identify important statistical predictors and isolate how dust affects yields on the scales of interest. To perform this analysis, we develop a crop-climate dataset for maize, soybean, groundnut, sorghum, rice, and wheat for the regions of West Africa, East Africa, South Africa, and the Sahel. Random forest regression models consistently model historic crop yields better than the linear models. In several instances, the random forest models accurately capture the temperature and precipitation threshold behavior in crops. Additionally, improving agricultural technology has caused a well-documented positive trend that dominates time series of global and regional yields. This trend is often removed before regression with traditional crop models, but likely at the cost of removing climate information. Our random forest models consistently discover the positive trend without removing any additional data. The application of random forests as a statistical crop model provides insight into understanding the impact of dust on yields in marginal food producing regions.

What Is Wrong with ANOVA and Multiple Regression? Analyzing Sentence Reading Times with Hierarchical Linear Models

ERIC Educational Resources Information Center

Richter, Tobias

2006-01-01

Most reading time studies using naturalistic texts yield data sets characterized by a multilevel structure: Sentences (sentence level) are nested within persons (person level). In contrast to analysis of variance and multiple regression techniques, hierarchical linear models take the multilevel structure of reading time data into account. They…

In Spite of Indeterminacy Many Common Factor Score Estimates Yield an Identical Reproduced Covariance Matrix

ERIC Educational Resources Information Center

Beauducel, Andre

2007-01-01

It was investigated whether commonly used factor score estimates lead to the same reproduced covariance matrix of observed variables. This was achieved by means of Schonemann and Steiger's (1976) regression component analysis, since it is possible to compute the reproduced covariance matrices of the regression components corresponding to different…

Establishing a Mathematical Equations and Improving the Production of L-tert-Leucine by Uniform Design and Regression Analysis.

PubMed

Jiang, Wei; Xu, Chao-Zhen; Jiang, Si-Zhi; Zhang, Tang-Duo; Wang, Shi-Zhen; Fang, Bai-Shan

2017-04-01

L-tert-Leucine (L-Tle) and its derivatives are extensively used as crucial building blocks for chiral auxiliaries, pharmaceutically active ingredients, and ligands. Combining with formate dehydrogenase (FDH) for regenerating the expensive coenzyme NADH, leucine dehydrogenase (LeuDH) is continually used for synthesizing L-Tle from α-keto acid. A multilevel factorial experimental design was executed for research of this system. In this work, an efficient optimization method for improving the productivity of L-Tle was developed. And the mathematical model between different fermentation conditions and L-Tle yield was also determined in the form of the equation by using uniform design and regression analysis. The multivariate regression equation was conveniently implemented in water, with a space time yield of 505.9 g L -1  day -1 and an enantiomeric excess value of >99 %. These results demonstrated that this method might become an ideal protocol for industrial production of chiral compounds and unnatural amino acids such as chiral drug intermediates.

Genetic parameters for body condition score, body weight, milk yield, and fertility estimated using random regression models.

PubMed

Berry, D P; Buckley, F; Dillon, P; Evans, R D; Rath, M; Veerkamp, R F

2003-11-01

Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields from 8725 multiparous Holstein-Friesian cows. A cubic random regression was sufficient to model the changing genetic variances for BCS, BW, and milk across different days in milk. The genetic correlations between BCS and fertility changed little over the lactation; genetic correlations between BCS and interval to first service and between BCS and pregnancy rate to first service varied from -0.47 to -0.31, and from 0.15 to 0.38, respectively. This suggests that maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in midlactation when the genetic variance for BCS is largest. Selection for increased BW resulted in shorter intervals to first service, but more services and poorer pregnancy rates; genetic correlations between BW and pregnancy rate to first service varied from -0.52 to -0.45. Genetic selection for higher lactation milk yield alone through selection on increased milk yield in early lactation is likely to have a more deleterious effect on genetic merit for fertility than selection on higher milk yield in late lactation.

Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA's-AVHRR.

PubMed

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha(-1). Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly.

Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR

PubMed Central

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha−1. Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly. PMID:23967112

Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials

PubMed Central

2018-01-01

Objective The objective of this study was to estimate genetic parameters of milk, fat, and protein yields within and across lactations in Tunisian Holsteins using a random regression test-day (TD) model. Methods A random regression multiple trait multiple lactation TD model was used to estimate genetic parameters in the Tunisian dairy cattle population. Data were TD yields of milk, fat, and protein from the first three lactations. Random regressions were modeled with third-order Legendre polynomials for the additive genetic, and permanent environment effects. Heritabilities, and genetic correlations were estimated by Bayesian techniques using the Gibbs sampler. Results All variance components tended to be high in the beginning and the end of lactations. Additive genetic variances for milk, fat, and protein yields were the lowest and were the least variable compared to permanent variances. Heritability values tended to increase with parity. Estimates of heritabilities for 305-d yield-traits were low to moderate, 0.14 to 0.2, 0.12 to 0.17, and 0.13 to 0.18 for milk, fat, and protein yields, respectively. Within-parity, genetic correlations among traits were up to 0.74. Genetic correlations among lactations for the yield traits were relatively high and ranged from 0.78±0.01 to 0.82±0.03, between the first and second parities, from 0.73±0.03 to 0.8±0.04 between the first and third parities, and from 0.82±0.02 to 0.84±0.04 between the second and third parities. Conclusion These results are comparable to previously reported estimates on the same population, indicating that the adoption of a random regression TD model as the official genetic evaluation for production traits in Tunisia, as developed by most Interbull countries, is possible in the Tunisian Holsteins. PMID:28823122

Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials.

PubMed

Ben Zaabza, Hafedh; Ben Gara, Abderrahmen; Rekik, Boulbaba

2018-05-01

The objective of this study was to estimate genetic parameters of milk, fat, and protein yields within and across lactations in Tunisian Holsteins using a random regression test-day (TD) model. A random regression multiple trait multiple lactation TD model was used to estimate genetic parameters in the Tunisian dairy cattle population. Data were TD yields of milk, fat, and protein from the first three lactations. Random regressions were modeled with third-order Legendre polynomials for the additive genetic, and permanent environment effects. Heritabilities, and genetic correlations were estimated by Bayesian techniques using the Gibbs sampler. All variance components tended to be high in the beginning and the end of lactations. Additive genetic variances for milk, fat, and protein yields were the lowest and were the least variable compared to permanent variances. Heritability values tended to increase with parity. Estimates of heritabilities for 305-d yield-traits were low to moderate, 0.14 to 0.2, 0.12 to 0.17, and 0.13 to 0.18 for milk, fat, and protein yields, respectively. Within-parity, genetic correlations among traits were up to 0.74. Genetic correlations among lactations for the yield traits were relatively high and ranged from 0.78±0.01 to 0.82±0.03, between the first and second parities, from 0.73±0.03 to 0.8±0.04 between the first and third parities, and from 0.82±0.02 to 0.84±0.04 between the second and third parities. These results are comparable to previously reported estimates on the same population, indicating that the adoption of a random regression TD model as the official genetic evaluation for production traits in Tunisia, as developed by most Interbull countries, is possible in the Tunisian Holsteins.

Economic benefits of reducing fire-related sediment in southwestern fire-prone ecosystems

Treesearch

John Loomis; Pete Wohlgemuth; Armando González-Cabán; Don English

2003-01-01

A multiple regression analysis of fire interval and resulting sediment yield (controlling for relief ratio, rainfall, etc.) indicates that reducing the fire interval from the current average 22 years to a prescribed fire interval of 5 years would reduce sediment yield by 2 million cubic meters in the 86.2 square kilometer southern California watershed adjacent to and...

Comparison of methods for the analysis of relatively simple mediation models.

PubMed

Rijnhart, Judith J M; Twisk, Jos W R; Chinapaw, Mai J M; de Boer, Michiel R; Heymans, Martijn W

2017-09-01

Statistical mediation analysis is an often used method in trials, to unravel the pathways underlying the effect of an intervention on a particular outcome variable. Throughout the years, several methods have been proposed, such as ordinary least square (OLS) regression, structural equation modeling (SEM), and the potential outcomes framework. Most applied researchers do not know that these methods are mathematically equivalent when applied to mediation models with a continuous mediator and outcome variable. Therefore, the aim of this paper was to demonstrate the similarities between OLS regression, SEM, and the potential outcomes framework in three mediation models: 1) a crude model, 2) a confounder-adjusted model, and 3) a model with an interaction term for exposure-mediator interaction. Secondary data analysis of a randomized controlled trial that included 546 schoolchildren. In our data example, the mediator and outcome variable were both continuous. We compared the estimates of the total, direct and indirect effects, proportion mediated, and 95% confidence intervals (CIs) for the indirect effect across OLS regression, SEM, and the potential outcomes framework. OLS regression, SEM, and the potential outcomes framework yielded the same effect estimates in the crude mediation model, the confounder-adjusted mediation model, and the mediation model with an interaction term for exposure-mediator interaction. Since OLS regression, SEM, and the potential outcomes framework yield the same results in three mediation models with a continuous mediator and outcome variable, researchers can continue using the method that is most convenient to them.

[Winter wheat yield gap between field blocks based on comparative performance analysis].

PubMed

Chen, Jian; Wang, Zhong-Yi; Li, Liang-Tao; Zhang, Ke-Feng; Yu, Zhen-Rong

2008-09-01

Based on a two-year household survey data, the yield gap of winter wheat in Quzhou County of Hebei Province, China in 2003-2004 was studied through comparative performance analysis (CPA). The results showed that there was a greater yield gap (from 4.2 to 7.9 t x hm(-2)) between field blocks, with a variation coefficient of 0.14. Through stepwise forward linear multiple regression, it was found that the yield model with 8 selected variables could explain 63% variability of winter wheat yield. Among the variables selected, soil salinity, soil fertility, and irrigation water quality were the most important limiting factors, accounting for 52% of the total yield gap. Crop variety was another important limiting factor, accounting for 14%; while planting date, fertilizer type, disease and pest, and water press accounted for 7%, 14%, 10%, and 3%, respectively. Therefore, besides soil and climate conditions, management practices occupied the majority of yield variability in Quzhou County, suggesting that the yield gap could be reduced significantly through optimum field management.

Statistical modelling for precision agriculture: A case study in optimal environmental schedules for Agaricus Bisporus production via variable domain functional regression.

PubMed

Panayi, Efstathios; Peters, Gareth W; Kyriakides, George

2017-01-01

Quantifying the effects of environmental factors over the duration of the growing process on Agaricus Bisporus (button mushroom) yields has been difficult, as common functional data analysis approaches require fixed length functional data. The data available from commercial growers, however, is of variable duration, due to commercial considerations. We employ a recently proposed regression technique termed Variable-Domain Functional Regression in order to be able to accommodate these irregular-length datasets. In this way, we are able to quantify the contribution of covariates such as temperature, humidity and water spraying volumes across the growing process, and for different lengths of growing processes. Our results indicate that optimal oxygen and temperature levels vary across the growing cycle and we propose environmental schedules for these covariates to optimise overall yields.

Statistical modelling for precision agriculture: A case study in optimal environmental schedules for Agaricus Bisporus production via variable domain functional regression

PubMed Central

Panayi, Efstathios; Kyriakides, George

2017-01-01

Quantifying the effects of environmental factors over the duration of the growing process on Agaricus Bisporus (button mushroom) yields has been difficult, as common functional data analysis approaches require fixed length functional data. The data available from commercial growers, however, is of variable duration, due to commercial considerations. We employ a recently proposed regression technique termed Variable-Domain Functional Regression in order to be able to accommodate these irregular-length datasets. In this way, we are able to quantify the contribution of covariates such as temperature, humidity and water spraying volumes across the growing process, and for different lengths of growing processes. Our results indicate that optimal oxygen and temperature levels vary across the growing cycle and we propose environmental schedules for these covariates to optimise overall yields. PMID:28961254

A Systematic Review and Meta-Regression Analysis of Lung Cancer Risk and Inorganic Arsenic in Drinking Water.

PubMed

Lamm, Steven H; Ferdosi, Hamid; Dissen, Elisabeth K; Li, Ji; Ahn, Jaeil

2015-12-07

High levels (> 200 µg/L) of inorganic arsenic in drinking water are known to be a cause of human lung cancer, but the evidence at lower levels is uncertain. We have sought the epidemiological studies that have examined the dose-response relationship between arsenic levels in drinking water and the risk of lung cancer over a range that includes both high and low levels of arsenic. Regression analysis, based on six studies identified from an electronic search, examined the relationship between the log of the relative risk and the log of the arsenic exposure over a range of 1-1000 µg/L. The best-fitting continuous meta-regression model was sought and found to be a no-constant linear-quadratic analysis where both the risk and the exposure had been logarithmically transformed. This yielded both a statistically significant positive coefficient for the quadratic term and a statistically significant negative coefficient for the linear term. Sub-analyses by study design yielded results that were similar for both ecological studies and non-ecological studies. Statistically significant X-intercepts consistently found no increased level of risk at approximately 100-150 µg/L arsenic.

A Systematic Review and Meta-Regression Analysis of Lung Cancer Risk and Inorganic Arsenic in Drinking Water

PubMed Central

Lamm, Steven H.; Ferdosi, Hamid; Dissen, Elisabeth K.; Li, Ji; Ahn, Jaeil

2015-01-01

High levels (> 200 µg/L) of inorganic arsenic in drinking water are known to be a cause of human lung cancer, but the evidence at lower levels is uncertain. We have sought the epidemiological studies that have examined the dose-response relationship between arsenic levels in drinking water and the risk of lung cancer over a range that includes both high and low levels of arsenic. Regression analysis, based on six studies identified from an electronic search, examined the relationship between the log of the relative risk and the log of the arsenic exposure over a range of 1–1000 µg/L. The best-fitting continuous meta-regression model was sought and found to be a no-constant linear-quadratic analysis where both the risk and the exposure had been logarithmically transformed. This yielded both a statistically significant positive coefficient for the quadratic term and a statistically significant negative coefficient for the linear term. Sub-analyses by study design yielded results that were similar for both ecological studies and non-ecological studies. Statistically significant X-intercepts consistently found no increased level of risk at approximately 100–150 µg/L arsenic. PMID:26690190

Evaluation of the Williams-type model for barley yields in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Barnett, T. L. (Principal Investigator)

1981-01-01

The Williams-type yield model is based on multiple regression analysis of historial time series data at CRD level pooled to regional level (groups of similar CRDs). Basic variables considered in the analysis include USDA yield, monthly mean temperature, monthly precipitation, soil texture and topographic information, and variables derived from these. Technologic trend is represented by piecewise linear and/or quadratic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-1979) demonstrate that biases are small and performance based on root mean square appears to be acceptable for the intended AgRISTARS large area applications. The model is objective, adequate, timely, simple, and not costly. It consideres scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

Phytotoxicity and accumulation of chromium in carrot plants and the derivation of soil thresholds for Chinese soils.

PubMed

Ding, Changfeng; Li, Xiaogang; Zhang, Taolin; Ma, Yibing; Wang, Xingxiang

2014-10-01

Soil environmental quality standards in respect of heavy metals for farmlands should be established considering both their effects on crop yield and their accumulation in the edible part. A greenhouse experiment was conducted to investigate the effects of chromium (Cr) on biomass production and Cr accumulation in carrot plants grown in a wide range of soils. The results revealed that carrot yield significantly decreased in 18 of the total 20 soils with Cr addition being the soil environmental quality standard of China. The Cr content of carrot grown in the five soils with pH>8.0 exceeded the maximum allowable level (0.5mgkg(-1)) according to the Chinese General Standard for Contaminants in Foods. The relationship between carrot Cr concentration and soil pH could be well fitted (R(2)=0.70, P<0.0001) by a linear-linear segmented regression model. The addition of Cr to soil influenced carrot yield firstly rather than the food quality. The major soil factors controlling Cr phytotoxicity and the prediction models were further identified and developed using path analysis and stepwise multiple linear regression analysis. Soil Cr thresholds for phytotoxicity meanwhile ensuring food safety were then derived on the condition of 10 percent yield reduction. Copyright © 2014 Elsevier Inc. All rights reserved.

Prediction of random-regression coefficient for daily milk yield after 305 days in milk by using the regression-coefficient estimates from the first 305 days.

PubMed

Yamazaki, Takeshi; Takeda, Hisato; Hagiya, Koichi; Yamaguchi, Satoshi; Sasaki, Osamu

2018-03-13

Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a random regression model. We analyzed test-day milk records from 85690 Holstein cows in their first lactations and 131727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. The first-order Legendre polynomials were practical covariates of random regression for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery.

PubMed

Tan, Kok Chooi; Lim, Hwee San; Matjafri, Mohd Zubir; Abdullah, Khiruddin

2012-06-01

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.

A novel approach to identify genes that determine grain protein deviation in cereals.

PubMed

Mosleth, Ellen F; Wan, Yongfang; Lysenko, Artem; Chope, Gemma A; Penson, Simon P; Shewry, Peter R; Hawkesford, Malcolm J

2015-06-01

Grain yield and protein content were determined for six wheat cultivars grown over 3 years at multiple sites and at multiple nitrogen (N) fertilizer inputs. Although grain protein content was negatively correlated with yield, some grain samples had higher protein contents than expected based on their yields, a trait referred to as grain protein deviation (GPD). We used novel statistical approaches to identify gene transcripts significantly related to GPD across environments. The yield and protein content were initially adjusted for nitrogen fertilizer inputs and then adjusted for yield (to remove the negative correlation with protein content), resulting in a parameter termed corrected GPD. Significant genetic variation in corrected GPD was observed for six cultivars grown over a range of environmental conditions (a total of 584 samples). Gene transcript profiles were determined in a subset of 161 samples of developing grain to identify transcripts contributing to GPD. Principal component analysis (PCA), analysis of variance (ANOVA) and means of scores regression (MSR) were used to identify individual principal components (PCs) correlating with GPD alone. Scores of the selected PCs, which were significantly related to GPD and protein content but not to the yield and significantly affected by cultivar, were identified as reflecting a multivariate pattern of gene expression related to genetic variation in GPD. Transcripts with consistent variation along the selected PCs were identified by an approach hereby called one-block means of scores regression (one-block MSR). © 2014 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

Ozone and sulfur dioxide effects on three tall fescue cultivars

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

Flagler, R.B.; Youngner, V.B.

Although many reports have been published concerning differential susceptibility of various crops and/or cultivars to air pollutants, most have used foliar injury instead of the marketable yield as the factor that determined susceptibility for the crop. In an examination of screening in terms of marketable yield, three cultivars of tall fescue (Festuca arundinacea Schreb.), 'Alta,' 'Fawn,' and 'Kentucky 31,' were exposed to 0-0.40 ppm O/sub 3/ or 0-0.50 ppm SO/sub 2/ 6 h/d, once a week, for 7 and 9 weeks, respectively. Experimental design was a randomized complete block with three replications. Statistical analysis was by standard analysis of variancemore » and regression techniques. Three variables were analyzed: top dry weight (yield), tiller number, and weight per tiller. Ozone had a significant effect on all three variables. Significant linear decreases in yield and weight per tiller occurred with increasing O/sub 3/ concentrations. Linear regressions of these variables on O/sub 3/ concentration produced significantly different regression coefficients. The coefficient for Kentucky 31 was significantly greater than Alta or Fawn, which did not differ from each other. This indicated that Kentucky 31 was more susceptible to O/sub 3/ than either of the other cultivars. Percent reductions in dry weight for the three cultivars at highest O/sub 3/ level were 35, 44, and 53%, respectively, for Fawn, Alta, and Kentucky 31. For weight per tiller, Kentucky 31 had a higher percent reduction than the other cultivars (59 vs. 46 and 44%). Tiller number was generally increased by O/sub 3/, but this variable was not useful for determining differential susceptibility to the pollutant. Sulfur dioxide treatments produced no significant effects on any of the variables analyzed.« less

Estimation of monthly water yields and flows for 1951-2012 for the United States portion of the Great Lakes Basin with AFINCH

USGS Publications Warehouse

Luukkonen, Carol L.; Holtschlag, David J.; Reeves, Howard W.; Hoard, Christopher J.; Fuller, Lori M.

2015-01-01

Monthly water yields from 105,829 catchments and corresponding flows in 107,691 stream segments were estimated for water years 1951–2012 in the Great Lakes Basin in the United States. Both sets of estimates were computed by using the Analysis of Flows In Networks of CHannels (AFINCH) application within the NHDPlus geospatial data framework. AFINCH provides an environment to develop constrained regression models to integrate monthly streamflow and water-use data with monthly climatic data and fixed basin characteristics data available within NHDPlus or supplied by the user. For this study, the U.S. Great Lakes Basin was partitioned into seven study areas by grouping selected hydrologic subregions and adjoining cataloguing units. This report documents the regression models and data used to estimate monthly water yields and flows in each study area. Estimates of monthly water yields and flows are presented in a Web-based mapper application. Monthly flow time series for individual stream segments can be retrieved from the Web application and used to approximate monthly flow-duration characteristics and to identify possible trends.

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows.

PubMed

Bignardi, A B; El Faro, L; Torres Júnior, R A A; Cardoso, V L; Machado, P F; Albuquerque, L G

2011-10-31

We analyzed 152,145 test-day records from 7317 first lactations of Holstein cows recorded from 1995 to 2003. Our objective was to model variations in test-day milk yield during the first lactation of Holstein cows by random regression model (RRM), using various functions in order to obtain adequate and parsimonious models for the estimation of genetic parameters. Test-day milk yields were grouped into weekly classes of days in milk, ranging from 1 to 44 weeks. The contemporary groups were defined as herd-test-day. The analyses were performed using a single-trait RRM, including the direct additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. The mean trend of milk yield was modeled with a fourth-order orthogonal Legendre polynomial. The additive genetic and permanent environmental covariance functions were estimated by random regression on two parametric functions, Ali and Schaeffer and Wilmink, and on B-spline functions of days in milk. The covariance components and the genetic parameters were estimated by the restricted maximum likelihood method. Results from RRM parametric and B-spline functions were compared to RRM on Legendre polynomials and with a multi-trait analysis, using the same data set. Heritability estimates presented similar trends during mid-lactation (13 to 31 weeks) and between week 37 and the end of lactation, for all RRM. Heritabilities obtained by multi-trait analysis were of a lower magnitude than those estimated by RRM. The RRMs with a higher number of parameters were more useful to describe the genetic variation of test-day milk yield throughout the lactation. RRM using B-spline and Legendre polynomials as base functions appears to be the most adequate to describe the covariance structure of the data.

Analysis of Chi-square Automatic Interaction Detection (CHAID) and Classification and Regression Tree (CRT) for Classification of Corn Production

NASA Astrophysics Data System (ADS)

Susanti, Yuliana; Zukhronah, Etik; Pratiwi, Hasih; Respatiwulan; Sri Sulistijowati, H.

2017-11-01

To achieve food resilience in Indonesia, food diversification by exploring potentials of local food is required. Corn is one of alternating staple food of Javanese society. For that reason, corn production needs to be improved by considering the influencing factors. CHAID and CRT are methods of data mining which can be used to classify the influencing variables. The present study seeks to dig up information on the potentials of local food availability of corn in regencies and cities in Java Island. CHAID analysis yields four classifications with accuracy of 78.8%, while CRT analysis yields seven classifications with accuracy of 79.6%.

A note on variance estimation in random effects meta-regression.

PubMed

Sidik, Kurex; Jonkman, Jeffrey N

2005-01-01

For random effects meta-regression inference, variance estimation for the parameter estimates is discussed. Because estimated weights are used for meta-regression analysis in practice, the assumed or estimated covariance matrix used in meta-regression is not strictly correct, due to possible errors in estimating the weights. Therefore, this note investigates the use of a robust variance estimation approach for obtaining variances of the parameter estimates in random effects meta-regression inference. This method treats the assumed covariance matrix of the effect measure variables as a working covariance matrix. Using an example of meta-analysis data from clinical trials of a vaccine, the robust variance estimation approach is illustrated in comparison with two other methods of variance estimation. A simulation study is presented, comparing the three methods of variance estimation in terms of bias and coverage probability. We find that, despite the seeming suitability of the robust estimator for random effects meta-regression, the improved variance estimator of Knapp and Hartung (2003) yields the best performance among the three estimators, and thus may provide the best protection against errors in the estimated weights.

Improving Crop Yield and Nutrient Use Efficiency via Biofertilization—A Global Meta-analysis

PubMed Central

Schütz, Lukas; Gattinger, Andreas; Meier, Matthias; Müller, Adrian; Boller, Thomas; Mäder, Paul; Mathimaran, Natarajan

2018-01-01

The application of microbial inoculants (biofertilizers) is a promising technology for future sustainable farming systems in view of rapidly decreasing phosphorus stocks and the need to more efficiently use available nitrogen (N). Various microbial taxa are currently used as biofertilizers, based on their capacity to access nutrients from fertilizers and soil stocks, to fix atmospheric nitrogen, to improve water uptake or to act as biocontrol agents. Despite the existence of a considerable knowledge on effects of specific taxa of biofertilizers, a comprehensive quantitative assessment of the performance of biofertilizers with different traits such as phosphorus solubilization and N fixation applied to various crops at a global scale is missing. We conducted a meta-analysis to quantify benefits of biofertilizers in terms of yield increase, nitrogen and phosphorus use efficiency, based on 171 peer reviewed publications that met eligibility criteria. Major findings are: (i) the superiority of biofertilizer performance in dry climates over other climatic regions (yield response: dry climate +20.0 ± 1.7%, tropical climate +14.9 ± 1.2%, oceanic climate +10.0 ± 3.7%, continental climate +8.5 ± 2.4%); (ii) meta-regression analyses revealed that yield response due to biofertilizer application was generally small at low soil P levels; efficacy increased along higher soil P levels in the order arbuscular mycorrhizal fungi (AMF), P solubilizers, and N fixers; (iii) meta-regressions showed that the success of inoculation with AMF was greater at low organic matter content and at neutral pH. Our comprehensive analysis provides a basis and guidance for proper choice and application of biofertilizers. PMID:29375594

Improving Crop Yield and Nutrient Use Efficiency via Biofertilization-A Global Meta-analysis.

PubMed

Schütz, Lukas; Gattinger, Andreas; Meier, Matthias; Müller, Adrian; Boller, Thomas; Mäder, Paul; Mathimaran, Natarajan

2017-01-01

The application of microbial inoculants (biofertilizers) is a promising technology for future sustainable farming systems in view of rapidly decreasing phosphorus stocks and the need to more efficiently use available nitrogen (N). Various microbial taxa are currently used as biofertilizers, based on their capacity to access nutrients from fertilizers and soil stocks, to fix atmospheric nitrogen, to improve water uptake or to act as biocontrol agents. Despite the existence of a considerable knowledge on effects of specific taxa of biofertilizers, a comprehensive quantitative assessment of the performance of biofertilizers with different traits such as phosphorus solubilization and N fixation applied to various crops at a global scale is missing. We conducted a meta-analysis to quantify benefits of biofertilizers in terms of yield increase, nitrogen and phosphorus use efficiency, based on 171 peer reviewed publications that met eligibility criteria. Major findings are: (i) the superiority of biofertilizer performance in dry climates over other climatic regions (yield response: dry climate +20.0 ± 1.7%, tropical climate +14.9 ± 1.2%, oceanic climate +10.0 ± 3.7%, continental climate +8.5 ± 2.4%); (ii) meta-regression analyses revealed that yield response due to biofertilizer application was generally small at low soil P levels; efficacy increased along higher soil P levels in the order arbuscular mycorrhizal fungi (AMF), P solubilizers, and N fixers; (iii) meta-regressions showed that the success of inoculation with AMF was greater at low organic matter content and at neutral pH. Our comprehensive analysis provides a basis and guidance for proper choice and application of biofertilizers.

Regression method for estimating long-term mean annual ground-water recharge rates from base flow in Pennsylvania

USGS Publications Warehouse

Risser, Dennis W.; Thompson, Ronald E.; Stuckey, Marla H.

2008-01-01

A method was developed for making estimates of long-term, mean annual ground-water recharge from streamflow data at 80 streamflow-gaging stations in Pennsylvania. The method relates mean annual base-flow yield derived from the streamflow data (as a proxy for recharge) to the climatic, geologic, hydrologic, and physiographic characteristics of the basins (basin characteristics) by use of a regression equation. Base-flow yield is the base flow of a stream divided by the drainage area of the basin, expressed in inches of water basinwide. Mean annual base-flow yield was computed for the period of available streamflow record at continuous streamflow-gaging stations by use of the computer program PART, which separates base flow from direct runoff on the streamflow hydrograph. Base flow provides a reasonable estimate of recharge for basins where streamflow is mostly unaffected by upstream regulation, diversion, or mining. Twenty-eight basin characteristics were included in the exploratory regression analysis as possible predictors of base-flow yield. Basin characteristics found to be statistically significant predictors of mean annual base-flow yield during 1971-2000 at the 95-percent confidence level were (1) mean annual precipitation, (2) average maximum daily temperature, (3) percentage of sand in the soil, (4) percentage of carbonate bedrock in the basin, and (5) stream channel slope. The equation for predicting recharge was developed using ordinary least-squares regression. The standard error of prediction for the equation on log-transformed data was 9.7 percent, and the coefficient of determination was 0.80. The equation can be used to predict long-term, mean annual recharge rates for ungaged basins, providing that the explanatory basin characteristics can be determined and that the underlying assumption is accepted that base-flow yield derived from PART is a reasonable estimate of ground-water recharge rates. For example, application of the equation for 370 hydrologic units in Pennsylvania predicted a range of ground-water recharge from about 6.0 to 22 inches per year. A map of the predicted recharge illustrates the general magnitude and variability of recharge throughout Pennsylvania.

Computer Simulation of Human Behavior: Assessment of Creativity.

ERIC Educational Resources Information Center

Greene, John F.

The major purpose of this study is to further the development of procedures which minimize current limitations of creativity instruments, thus yielding a reliable and functional means for assessing creativity. Computerized content analysis and multiple regression are employed to simulate the creativity ratings of trained judges. The computerized…

Wheat yield dynamics: a structural econometric analysis.

PubMed

Sahin, Afsin; Akdi, Yilmaz; Arslan, Fahrettin

2007-10-15

In this study we initially have tried to explore the wheat situation in Turkey, which has a small-open economy and in the member countries of European Union (EU). We have observed that increasing the wheat yield is fundamental to obtain comparative advantage among countries by depressing domestic prices. Also the changing structure of supporting schemes in Turkey makes it necessary to increase its wheat yield level. For this purpose, we have used available data to determine the dynamics of wheat yield by Ordinary Least Square Regression methods. In order to find out whether there is a linear relationship among these series we have checked each series whether they are integrated at the same order or not. Consequently, we have pointed out that fertilizer usage and precipitation level are substantial inputs for producing high wheat yield. Furthermore, in respect for our model, fertilizer usage affects wheat yield more than precipitation level.

Estimating milk yield and value losses from increased somatic cell count on US dairy farms.

PubMed

Hadrich, J C; Wolf, C A; Lombard, J; Dolak, T M

2018-04-01

Milk loss due to increased somatic cell counts (SCC) results in economic losses for dairy producers. This research uses 10 mo of consecutive dairy herd improvement data from 2013 and 2014 to estimate milk yield loss using SCC as a proxy for clinical and subclinical mastitis. A fixed effects regression was used to examine factors that affected milk yield while controlling for herd-level management. Breed, milking frequency, days in milk, seasonality, SCC, cumulative months with SCC greater than 100,000 cells/mL, lactation, and herd size were variables included in the regression analysis. The cumulative months with SCC above a threshold was included as a proxy for chronic mastitis. Milk yield loss increased as the number of test days with SCC ≥100,000 cells/mL increased. Results from the regression were used to estimate a monetary value of milk loss related to SCC as a function of cow and operation related explanatory variables for a representative dairy cow. The largest losses occurred from increased cumulative test days with a SCC ≥100,000 cells/mL, with daily losses of $1.20/cow per day in the first month to $2.06/cow per day in mo 10. Results demonstrate the importance of including the duration of months above a threshold SCC when estimating milk yield losses. Cows with chronic mastitis, measured by increased consecutive test days with SCC ≥100,000 cells/mL, resulted in higher milk losses than cows with a new infection. This provides farm managers with a method to evaluate the trade-off between treatment and culling decisions as it relates to mastitis control and early detection. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Mapping intra-field yield variation using high resolution satellite imagery to integrate bioenergy and environmental stewardship in an agricultural watershed

DOE PAGES

Hamada, Yuki; Ssegane, Herbert; Negri, Maria Cristina

2015-07-31

Biofuels are important alternatives for meeting our future energy needs. Successful bioenergy crop production requires maintaining environmental sustainability and minimum impacts on current net annual food, feed, and fiber production. The objectives of this study were to: (1) determine under-productive areas within an agricultural field in a watershed using a single date; high resolution remote sensing and (2) examine impacts of growing bioenergy crops in the under-productive areas using hydrologic modeling in order to facilitate sustainable landscape design. Normalized difference indices (NDIs) were computed based on the ratio of all possible two-band combinations using the RapidEye and the National Agriculturalmore » Imagery Program images collected in summer 2011. A multiple regression analysis was performed using 10 NDIs and five RapidEye spectral bands. The regression analysis suggested that the red and near infrared bands and NDI using red-edge and near infrared that is known as the red-edge normalized difference vegetation index (RENDVI) had the highest correlation (R 2 = 0.524) with the reference yield. Although predictive yield map showed striking similarity to the reference yield map, the model had modest correlation; thus, further research is needed to improve predictive capability for absolute yields. Forecasted impact using the Soil and Water Assessment Tool model of growing switchgrass ( Panicum virgatum) on under-productive areas based on corn yield thresholds of 3.1, 4.7, and 6.3 Mg·ha -1 showed reduction of tile NO 3-N and sediment exports by 15.9%–25.9% and 25%–39%, respectively. Corresponding reductions in water yields ranged from 0.9% to 2.5%. While further research is warranted, the study demonstrated the integration of remote sensing and hydrologic modeling to quantify the multifunctional value of projected future landscape patterns in a context of sustainable bioenergy crop production.« less

Mapping intra-field yield variation using high resolution satellite imagery to integrate bioenergy and environmental stewardship in an agricultural watershed

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

Hamada, Yuki; Ssegane, Herbert; Negri, Maria Cristina

Biofuels are important alternatives for meeting our future energy needs. Successful bioenergy crop production requires maintaining environmental sustainability and minimum impacts on current net annual food, feed, and fiber production. The objectives of this study were to: (1) determine under-productive areas within an agricultural field in a watershed using a single date; high resolution remote sensing and (2) examine impacts of growing bioenergy crops in the under-productive areas using hydrologic modeling in order to facilitate sustainable landscape design. Normalized difference indices (NDIs) were computed based on the ratio of all possible two-band combinations using the RapidEye and the National Agriculturalmore » Imagery Program images collected in summer 2011. A multiple regression analysis was performed using 10 NDIs and five RapidEye spectral bands. The regression analysis suggested that the red and near infrared bands and NDI using red-edge and near infrared that is known as the red-edge normalized difference vegetation index (RENDVI) had the highest correlation (R 2 = 0.524) with the reference yield. Although predictive yield map showed striking similarity to the reference yield map, the model had modest correlation; thus, further research is needed to improve predictive capability for absolute yields. Forecasted impact using the Soil and Water Assessment Tool model of growing switchgrass ( Panicum virgatum) on under-productive areas based on corn yield thresholds of 3.1, 4.7, and 6.3 Mg·ha -1 showed reduction of tile NO 3-N and sediment exports by 15.9%–25.9% and 25%–39%, respectively. Corresponding reductions in water yields ranged from 0.9% to 2.5%. While further research is warranted, the study demonstrated the integration of remote sensing and hydrologic modeling to quantify the multifunctional value of projected future landscape patterns in a context of sustainable bioenergy crop production.« less

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

Blackleg (Leptosphaeria maculans) Severity and Yield Loss in Canola in Alberta, Canada

PubMed Central

Hwang, Sheau-Fang; Strelkov, Stephen E.; Peng, Gary; Ahmed, Hafiz; Zhou, Qixing; Turnbull, George

2016-01-01

Blackleg, caused by Leptosphaeria maculans, is an important disease of oilseed rape (Brassica napus L.) in Canada and throughout the world. Severe epidemics of blackleg can result in significant yield losses. Understanding disease-yield relationships is a prerequisite for measuring the agronomic efficacy and economic benefits of control methods. Field experiments were conducted in 2013, 2014, and 2015 to determine the relationship between blackleg disease severity and yield in a susceptible cultivar and in moderately resistant to resistant canola hybrids. Disease severity was lower, and seed yield was 120%–128% greater, in the moderately resistant to resistant hybrids compared with the susceptible cultivar. Regression analysis showed that pod number and seed yield declined linearly as blackleg severity increased. Seed yield per plant decreased by 1.8 g for each unit increase in disease severity, corresponding to a decline in yield of 17.2% for each unit increase in disease severity. Pyraclostrobin fungicide reduced disease severity in all site-years and increased yield. These results show that the reduction of blackleg in canola crops substantially improves yields. PMID:27447676

Relationships of concentrations of certain blood constituents with milk yield and age of cows in dairy herds.

PubMed

Kitchenham, B A; Rowlands, G J; Shorbagi, H

1975-05-01

Regression analyses were performed on data from 48 Compton metabolic profile tests relating the concentrations of certain constituents in the blood of dairy cows to their milk yield, age and stage of lactation. The common partial regression coefficients for milk yield, age and stage of lactation were estimated for each blood constituent. The relationships of greatest statistical significance were between the concentrations of inorganic phosphate and globulin and age, and the concentration of albumin and milk yield.

New insights into old methods for identifying causal rare variants.

PubMed

Wang, Haitian; Huang, Chien-Hsun; Lo, Shaw-Hwa; Zheng, Tian; Hu, Inchi

2011-11-29

The advance of high-throughput next-generation sequencing technology makes possible the analysis of rare variants. However, the investigation of rare variants in unrelated-individuals data sets faces the challenge of low power, and most methods circumvent the difficulty by using various collapsing procedures based on genes, pathways, or gene clusters. We suggest a new way to identify causal rare variants using the F-statistic and sliced inverse regression. The procedure is tested on the data set provided by the Genetic Analysis Workshop 17 (GAW17). After preliminary data reduction, we ranked markers according to their F-statistic values. Top-ranked markers were then subjected to sliced inverse regression, and those with higher absolute coefficients in the most significant sliced inverse regression direction were selected. The procedure yields good false discovery rates for the GAW17 data and thus is a promising method for future study on rare variants.

Analysis of the inter- and extracellular formation of platinum nanoparticles by Fusarium oxysporum f. sp. lycopersici using response surface methodology

NASA Astrophysics Data System (ADS)

Riddin, T. L.; Gericke, M.; Whiteley, C. G.

2006-07-01

Fusarium oxysporum fungal strain was screened and found to be successful for the inter- and extracellular production of platinum nanoparticles. Nanoparticle formation was visually observed, over time, by the colour of the extracellular solution and/or the fungal biomass turning from yellow to dark brown, and their concentration was determined from the amount of residual hexachloroplatinic acid measured from a standard curve at 456 nm. The extracellular nanoparticles were characterized by transmission electron microscopy. Nanoparticles of varying size (10-100 nm) and shape (hexagons, pentagons, circles, squares, rectangles) were produced at both extracellular and intercellular levels by the Fusarium oxysporum. The particles precipitate out of solution and bioaccumulate by nucleation either intercellularly, on the cell wall/membrane, or extracellularly in the surrounding medium. The importance of pH, temperature and hexachloroplatinic acid (H2PtCl6) concentration in nanoparticle formation was examined through the use of a statistical response surface methodology. Only the extracellular production of nanoparticles proved to be statistically significant, with a concentration yield of 4.85 mg l-1 estimated by a first-order regression model. From a second-order polynomial regression, the predicted yield of nanoparticles increased to 5.66 mg l-1 and, after a backward step, regression gave a final model with a yield of 6.59 mg l-1.

Yield of illicit indoor cannabis cultivation in the Netherlands.

PubMed

Toonen, Marcel; Ribot, Simon; Thissen, Jac

2006-09-01

To obtain a reliable estimation on the yield of illicit indoor cannabis cultivation in The Netherlands, cannabis plants confiscated by the police were used to determine the yield of dried female flower buds. The developmental stage of flower buds of the seized plants was described on a scale from 1 to 10 where the value of 10 indicates a fully developed flower bud ready for harvesting. Using eight additional characteristics describing the grow room and cultivation parameters, regression analysis with subset selection was carried out to develop two models for the yield of indoor cannabis cultivation. The median Dutch illicit grow room consists of 259 cannabis plants, has a plant density of 15 plants/m(2), and 510 W of growth lamps per m(2). For the median Dutch grow room, the predicted yield of female flower buds at the harvestable developmental stage (stage 10) was 33.7 g/plant or 505 g/m(2).

Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma. A systematic review and meta-regression analysis.

PubMed

Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos

2017-06-01

We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A comparison of two adaptive multivariate analysis methods (PLSR and ANN) for winter wheat yield forecasting using Landsat-8 OLI images

NASA Astrophysics Data System (ADS)

Chen, Pengfei; Jing, Qi

2017-02-01

An assumption that the non-linear method is more reasonable than the linear method when canopy reflectance is used to establish the yield prediction model was proposed and tested in this study. For this purpose, partial least squares regression (PLSR) and artificial neural networks (ANN), represented linear and non-linear analysis method, were applied and compared for wheat yield prediction. Multi-period Landsat-8 OLI images were collected at two different wheat growth stages, and a field campaign was conducted to obtain grain yields at selected sampling sites in 2014. The field data were divided into a calibration database and a testing database. Using calibration data, a cross-validation concept was introduced for the PLSR and ANN model construction to prevent over-fitting. All models were tested using the test data. The ANN yield-prediction model produced R2, RMSE and RMSE% values of 0.61, 979 kg ha-1, and 10.38%, respectively, in the testing phase, performing better than the PLSR yield-prediction model, which produced R2, RMSE, and RMSE% values of 0.39, 1211 kg ha-1, and 12.84%, respectively. Non-linear method was suggested as a better method for yield prediction.

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

PubMed

Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

2013-06-01

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric Society.

Metabolite Profiles in Leaves and Spikes of Wheat under Constrasting Field-growing Environments Are Derived from Hyperspectral Readings

NASA Astrophysics Data System (ADS)

Vergara-Diaz, O.; Obata, T., Sr.; Kefauver, S. C.; Fernie, A., Sr.; Araus, J. L.

2017-12-01

The advance on metabolomics has led to a better understanding of plant-environment interactions and how the levels of specific metabolites may be used as indicators of plant performance. In cereals, the accumulation of certain metabolites -such as proline and sugars- has been related with water stress and drought tolerance/susceptibility, even revealing significant relationships with yield. On the other hand, recent studies relating plant biochemicals with spectral reflectance open the door to a deep assessment of plant status which would have implications on plant breeding and ecosystem studies. In this study, we investigated in durum wheat the relationship between the reflectance in the visible and near infrared regions (400-2500 µm wavelength) of the spectrum of the flag leaf, the ears and canopy levels with their respective metabolite profiles as well as its relationship with yield. To this aim, five durum wheat genotypes grown in four environments in the field were examined. PLS regression models indicated a strong determination of yield by using the spectrum of either leaves, ears and canopy. Additionally, grain yield was strongly predicted by the metabolite content of leaves and ears with multivariate regression analysis. Further preliminary results showed a promising performance of hyperspectral remote-proximal sensing for the calibration of plant metabolite content.

Genetic analysis of fat-to-protein ratio, milk yield and somatic cell score of Holstein cows in Japan in the first three lactations by using a random regression model.

PubMed

Nishiura, Akiko; Sasaki, Osamu; Aihara, Mitsuo; Takeda, Hisato; Satoh, Masahiro

2015-12-01

We estimated the genetic parameters of fat-to-protein ratio (FPR) and the genetic correlations between FPR and milk yield or somatic cell score in the first three lactations in dairy cows. Data included 3,079,517 test-day records of 201,138 Holstein cows in Japan from 2006 to 2011. Genetic parameters were estimated with a multiple-trait random regression model in which the records within and between parities were treated as separate traits. The phenotypic values of FPR increased soon after parturition and peaked at 10 to 20 days in milk, then decreased slowly in mid- and late lactation. Heritability estimates for FPR yielded moderate values. Genetic correlations of FPR among parities were low in early lactation. Genetic correlations between FPR and milk yield were positive and low in early lactation, but only in the first lactation. Genetic correlations between FPR and somatic cell score were positive in early lactation and decreased to become negative in mid- to late lactation. By using these results for genetic evaluation it should be possible to improve energy balance in dairy cows. © 2015 Japanese Society of Animal Science.

Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

PubMed

Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L

2017-02-06

Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.

Factor regression for interpreting genotype-environment interaction in bread-wheat trials.

PubMed

Baril, C P

1992-05-01

The French INRA wheat (Triticum aestivum L. em Thell.) breeding program is based on multilocation trials to produce high-yielding, adapted lines for a wide range of environments. Differential genotypic responses to variable environment conditions limit the accuracy of yield estimations. Factor regression was used to partition the genotype-environment (GE) interaction into four biologically interpretable terms. Yield data were analyzed from 34 wheat genotypes grown in four environments using 12 auxiliary agronomic traits as genotypic and environmental covariates. Most of the GE interaction (91%) was explained by the combination of only three traits: 1,000-kernel weight, lodging susceptibility and spike length. These traits are easily measured in breeding programs, therefore factor regression model can provide a convenient and useful prediction method of yield.

Quantitative Research on the Relationship between Yield of Winter Wheat and Agroclimatological Resources—the Case Study from Yanzhou District, Shandong Province, China

NASA Astrophysics Data System (ADS)

Yan, Maoling; Liu, Pingzeng; Zhang, Chao; Zheng, Yong; Wang, Xizhi; Zhang, Yan; Chen, Weijie; Zhao, Rui

2018-01-01

Agroclimatological resources provide material and energy for agricultural production. This study is aimed to analyze the impact of selected climate factors change on wheat yield over the different growth period applied quantitatively method, by comparing two different time division modules of wheat growth cycle- monthly empirical-statistical multiple regression models ( From October to June of next year ) and growth stage empirical-statistical multiple regression models (Including sowing stage, seedling stage, tillering stage, overwintering period, regreening period, jointing stage, heading stage, maturity stage) analysis of relationship between agrometeorological data and growth stage records and winter wheat production in Yanzhou, Shandong Province of China. Correlation analysis(CA)was done for 35 years (from 1981 to 2015) between crop yield and corresponding weather parameters including daily mean temperature, sunshine duration, and average daily precipitation selected from 18 different meteorological factors. The results shows that the greatest impact on the winter wheat yield is the precipitation overwintering period in this area, each 1mm increase in daily mean rainfall was associated with 201.64 kg/hm2 lowered output. Moreover, the temperature and sunshine duration in heading period and maturity stage also exert significant influence on the output, every 1°C increase in daily mean temperature was associated with 199.85kg/hm2 adding output, every 1h increase in mean sunshine duration was associated with 130.68kg/hm2 reduced output. Comparing with the results of experiment which using months as step sizes and using farming as step sizes was in better agreement with the fluctuation in meteorological yield, offered a better explanation on the growth mechanism of wheat. Eventually the results indicated that 3 factors affects the yield during different growing periods of wheat in different extent and provided more specific reference to guide the agricultural production management in this area.

Predictors of course in obsessive-compulsive disorder: logistic regression versus Cox regression for recurrent events.

PubMed

Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M

2007-09-01

Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.

Detection of meteorological extreme effect on historical crop yield anomaly

NASA Astrophysics Data System (ADS)

Kim, W.; Iizumi, T.; Nishimori, M.

2017-12-01

Meteorological extremes of temperature and precipitation are a critical issue in the global climate change, and some studies investigating how the extreme changes in accordance with the climate change are continuously reported. However, it is rarely understandable that the extremes affect crop yield worldwide as heatwave, coolwave, drought, and flood, albeit some local or national reports are available. Therefore, we globally investigated the extremes effects on the variability of historical yield of maize, rice, soy, and wheat with a standardized index and a historical yield anomaly. For the regression analysis, the standardized index is annually aggregated in the consideration of a crop calendar, and the historical yield is detrended with 5-year moving average. Throughout this investigation, we found that the relationship between the aggregated standardized index and the historical yield anomaly shows not merely positive correlation but also negative correlation in all crops in the globe. Namely, the extremes cause decrease of crop yield as a matter of course, but increase in some regions contrastingly. These results help us to quantify the extremes effect on historical crop yield anomaly.

Satellite remote sensing of fine particulate air pollutants over Indian mega cities

NASA Astrophysics Data System (ADS)

Sreekanth, V.; Mahesh, B.; Niranjan, K.

2017-11-01

In the backdrop of the need for high spatio-temporal resolution data on PM2.5 mass concentrations for health and epidemiological studies over India, empirical relations between Aerosol Optical Depth (AOD) and PM2.5 mass concentrations are established over five Indian mega cities. These relations are sought to predict the surface PM2.5 mass concentrations from high resolution columnar AOD datasets. Current study utilizes multi-city public domain PM2.5 data (from US Consulate and Embassy's air monitoring program) and MODIS AOD, spanning for almost four years. PM2.5 is found to be positively correlated with AOD. Station-wise linear regression analysis has shown spatially varying regression coefficients. Similar analysis has been repeated by eliminating data from the elevated aerosol prone seasons, which has improved the correlation coefficient. The impact of the day to day variability in the local meteorological conditions on the AOD-PM2.5 relationship has been explored by performing a multiple regression analysis. A cross-validation approach for the multiple regression analysis considering three years of data as training dataset and one-year data as validation dataset yielded an R value of ∼0.63. The study was concluded by discussing the factors which can improve the relationship.

Ultrasonic-assisted extraction and in-vitro antioxidant activity of polysaccharide from Hibiscus leaf.

PubMed

Afshari, Kasra; Samavati, Vahid; Shahidi, Seyed-Ahmad

2015-03-01

The effects of ultrasonic power, extraction time, extraction temperature, and the water-to-raw material ratio on extraction yield of crude polysaccharide from the leaf of Hibiscus rosa-sinensis (HRLP) were optimized by statistical analysis using response surface methodology. The response surface methodology (RSM) was used to optimize HRLP extraction yield by implementing the Box-Behnken design (BBD). The experimental data obtained were fitted to a second-order polynomial equation using multiple regression analysis and also analyzed by appropriate statistical methods (ANOVA). Analysis of the results showed that the linear and quadratic terms of these four variables had significant effects. The optimal conditions for the highest extraction yield of HRLP were: ultrasonic power, 93.59 W; extraction time, 25.71 min; extraction temperature, 93.18°C; and the water to raw material ratio, 24.3 mL/g. Under these conditions, the experimental yield was 9.66±0.18%, which is well in close agreement with the value predicted by the model 9.526%. The results demonstrated that HRLP had strong scavenging activities in vitro on DPPH and hydroxyl radicals. Copyright © 2014 Elsevier B.V. All rights reserved.

Learning Models and Real-Time Speech Recognition.

ERIC Educational Resources Information Center

Danforth, Douglas G.; And Others

This report describes the construction and testing of two "psychological" learning models for the purpose of computer recognition of human speech over the telephone. One of the two models was found to be superior in all tests. A regression analysis yielded a 92.3% recognition rate for 14 subjects ranging in age from 6 to 13 years. Tests…

Role Stress and Aggression among Young Adults: The Moderating Influences of Gender and Adolescent Aggression

ERIC Educational Resources Information Center

Liu, Ruth X.; Kaplan, Howard B.

2004-01-01

Using data provided by a panel of non-Hispanic white respondents, this study explored whether aggressive response to severe role stress during early adulthood depends on gender and on an adolescent history of aggression. Logistic regression analysis yielded these findings: Men who reported aggression during early adolescence were significantly…

Research in the application of spectral data to crop identification and assessment, volume 2

NASA Technical Reports Server (NTRS)

Daughtry, C. S. T. (Principal Investigator); Hixson, M. M.; Bauer, M. E.

1980-01-01

The development of spectrometry crop development stage models is discussed with emphasis on models for corn and soybeans. One photothermal and four thermal meteorological models are evaluated. Spectral data were investigated as a source of information for crop yield models. Intercepted solar radiation and soil productivity are identified as factors related to yield which can be estimated from spectral data. Several techniques for machine classification of remotely sensed data for crop inventory were evaluated. Early season estimation, training procedures, the relationship of scene characteristics to classification performance, and full frame classification methods were studied. The optimal level for combining area and yield estimates of corn and soybeans is assessed utilizing current technology: digital analysis of LANDSAT MSS data on sample segments to provide area estimates and regression models to provide yield estimates.

[Optimization of cultivation conditions in se-enriched Spirulina platensis].

PubMed

Huang, Zhi; Zheng, Wen-Jie; Guo, Bao-Jiang

2002-05-01

Orthogonal combination design was adopted in examining the Spirulina platensis (S. platensis) yield and the influence of four factors (Se content, Se-adding method, S content and NaHCO3 content) on algae growth. The results showed that Se content, Se-adding method and NaHCO3 content were key factors in cultivation conditions of Se-enriched S. platensis with the optimal combination being Se at 300 mg/L, Se-adding amount equally divided into three times and NaHCO3 at 16.8 g/L. Algae yield had a remarkable correlation with OD560 and floating rate by linear regression analysis. There was a corresponding relationship between effects of the four factors on algae yield and on OD560, floating rate too. In conclusion, OD560 and floating rate could be served as yield-forming factors.

Estimation of crown closure from AVIRIS data using regression analysis

NASA Technical Reports Server (NTRS)

Staenz, K.; Williams, D. J.; Truchon, M.; Fritz, R.

1993-01-01

Crown closure is one of the input parameters used for forest growth and yield modelling. Preliminary work by Staenz et al. indicates that imaging spectrometer data acquired with sensors such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) have some potential for estimating crown closure on a stand level. The objectives of this paper are: (1) to establish a relationship between AVIRIS data and the crown closure derived from aerial photography of a forested test site within the Interior Douglas Fir biogeoclimatic zone in British Columbia, Canada; (2) to investigate the impact of atmospheric effects and the forest background on the correlation between AVIRIS data and crown closure estimates; and (3) to improve this relationship using multiple regression analysis.

Evaluation of trends in wheat yield models

NASA Technical Reports Server (NTRS)

Ferguson, M. C.

1982-01-01

Trend terms in models for wheat yield in the U.S. Great Plains for the years 1932 to 1976 are evaluated. The subset of meteorological variables yielding the largest adjusted R(2) is selected using the method of leaps and bounds. Latent root regression is used to eliminate multicollinearities, and generalized ridge regression is used to introduce bias to provide stability in the data matrix. The regression model used provides for two trends in each of two models: a dependent model in which the trend line is piece-wise continuous, and an independent model in which the trend line is discontinuous at the year of the slope change. It was found that the trend lines best describing the wheat yields consisted of combinations of increasing, decreasing, and constant trend: four combinations for the dependent model and seven for the independent model.

Time series regression-based pairs trading in the Korean equities market

NASA Astrophysics Data System (ADS)

Kim, Saejoon; Heo, Jun

2017-07-01

Pairs trading is an instance of statistical arbitrage that relies on heavy quantitative data analysis to profit by capitalising low-risk trading opportunities provided by anomalies of related assets. A key element in pairs trading is the rule by which open and close trading triggers are defined. This paper investigates the use of time series regression to define the rule which has previously been identified with fixed threshold-based approaches. Empirical results indicate that our approach may yield significantly increased excess returns compared to ones obtained by previous approaches on large capitalisation stocks in the Korean equities market.

Multitrait, Random Regression, or Simple Repeatability Model in High-Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain Yield.

PubMed

Sun, Jin; Rutkoski, Jessica E; Poland, Jesse A; Crossa, José; Jannink, Jean-Luc; Sorrells, Mark E

2017-07-01

High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat ( L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect selection for grain yield. In this study, we evaluated three statistical models, simple repeatability (SR), multitrait (MT), and random regression (RR), for the longitudinal data of secondary traits and compared the impact of the proposed models for secondary traits on their predictive abilities for grain yield. Grain yield and secondary traits, canopy temperature (CT) and normalized difference vegetation index (NDVI), were collected in five diverse environments for 557 wheat lines with available pedigree and genomic information. A two-stage analysis was applied for pedigree and genomic selection (GS). First, secondary traits were fitted by SR, MT, or RR models, separately, within each environment. Then, best linear unbiased predictions (BLUPs) of secondary traits from the above models were used in the multivariate prediction models to compare predictive abilities for grain yield. Predictive ability was substantially improved by 70%, on average, from multivariate pedigree and genomic models when including secondary traits in both training and test populations. Additionally, (i) predictive abilities slightly varied for MT, RR, or SR models in this data set, (ii) results indicated that including BLUPs of secondary traits from the MT model was the best in severe drought, and (iii) the RR model was slightly better than SR and MT models under drought environment. Copyright © 2017 Crop Science Society of America.

Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy.

PubMed

Hoffman, Haydn; Lee, Sunghoon I; Garst, Jordan H; Lu, Derek S; Li, Charles H; Nagasawa, Daniel T; Ghalehsari, Nima; Jahanforouz, Nima; Razaghy, Mehrdad; Espinal, Marie; Ghavamrezaii, Amir; Paak, Brian H; Wu, Irene; Sarrafzadeh, Majid; Lu, Daniel C

2015-09-01

This study introduces the use of multivariate linear regression (MLR) and support vector regression (SVR) models to predict postoperative outcomes in a cohort of patients who underwent surgery for cervical spondylotic myelopathy (CSM). Currently, predicting outcomes after surgery for CSM remains a challenge. We recruited patients who had a diagnosis of CSM and required decompressive surgery with or without fusion. Fine motor function was tested preoperatively and postoperatively with a handgrip-based tracking device that has been previously validated, yielding mean absolute accuracy (MAA) results for two tracking tasks (sinusoidal and step). All patients completed Oswestry disability index (ODI) and modified Japanese Orthopaedic Association questionnaires preoperatively and postoperatively. Preoperative data was utilized in MLR and SVR models to predict postoperative ODI. Predictions were compared to the actual ODI scores with the coefficient of determination (R(2)) and mean absolute difference (MAD). From this, 20 patients met the inclusion criteria and completed follow-up at least 3 months after surgery. With the MLR model, a combination of the preoperative ODI score, preoperative MAA (step function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.452; MAD=0.0887; p=1.17 × 10(-3)). With the SVR model, a combination of preoperative ODI score, preoperative MAA (sinusoidal function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.932; MAD=0.0283; p=5.73 × 10(-12)). The SVR model was more accurate than the MLR model. The SVR can be used preoperatively in risk/benefit analysis and the decision to operate. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application guide for AFINCH (Analysis of Flows in Networks of Channels) described by NHDPlus

USGS Publications Warehouse

Holtschlag, David J.

2009-01-01

AFINCH (Analysis of Flows in Networks of CHannels) is a computer application that can be used to generate a time series of monthly flows at stream segments (flowlines) and water yields for catchments defined in the National Hydrography Dataset Plus (NHDPlus) value-added attribute system. AFINCH provides a basis for integrating monthly flow data from streamgages, water-use data, monthly climatic data, and land-cover characteristics to estimate natural monthly water yields from catchments by user-defined regression equations. Images of monthly water yields for active streamgages are generated in AFINCH and provide a basis for detecting anomalies in water yields, which may be associated with undocumented flow diversions or augmentations. Water yields are multiplied by the drainage areas of the corresponding catchments to estimate monthly flows. Flows from catchments are accumulated downstream through the streamflow network described by the stream segments. For stream segments where streamgages are active, ratios of measured to accumulated flows are computed. These ratios are applied to upstream water yields to proportionally adjust estimated flows to match measured flows. Flow is conserved through the NHDPlus network. A time series of monthly flows can be generated for stream segments that average about 1-mile long, or monthly water yields from catchments that average about 1 square mile. Estimated monthly flows can be displayed within AFINCH, examined for nonstationarity, and tested for monotonic trends. Monthly flows also can be used to estimate flow-duration characteristics at stream segments. AFINCH generates output files of monthly flows and water yields that are compatible with ArcMap, a geographical information system analysis and display environment. Chloropleth maps of monthly water yield and flow can be generated and analyzed within ArcMap by joining NHDPlus data structures with AFINCH output. Matlab code for the AFINCH application is presented.

Yield of bedrock wells in the Nashoba terrane, central and eastern Massachusetts

USGS Publications Warehouse

DeSimone, Leslie A.; Barbaro, Jeffrey R.

2012-01-01

The yield of bedrock wells in the fractured-bedrock aquifers of the Nashoba terrane and surrounding area, central and eastern Massachusetts, was investigated with analyses of existing data. Reported well yield was compiled for 7,287 wells from Massachusetts Department of Environmental Protection and U.S. Geological Survey databases. Yield of these wells ranged from 0.04 to 625 gallons per minute. In a comparison with data from 103 supply wells, yield and specific capacity from aquifer tests were well correlated, indicating that reported well yield was a reasonable measure of aquifer characteristics in the study area. Statistically significant relations were determined between well yield and a number of cultural and hydrogeologic factors. Cultural variables included intended water use, well depth, year of construction, and method of yield measurement. Bedrock geology, topography, surficial geology, and proximity to surface waters were statistically significant hydrogeologic factors. Yield of wells was higher in areas of granites, mafic intrusive rocks, and amphibolites than in areas of schists and gneisses or pelitic rocks; higher in valleys and low-slope areas than on hills, ridges, or high slopes; higher in areas overlain by stratified glacial deposits than in areas overlain by till; and higher in close proximity to streams, ponds, and wetlands than at greater distances from these surface-water features. Proximity to mapped faults and to lineaments from aerial photographs also were related to well yield by some measures in three quadrangles in the study area. Although the statistical significance of these relations was high, their predictive power was low, and these relations explained little of the variability in the well-yield data. Similar results were determined from a multivariate regression analysis. Multivariate regression models for the Nashoba terrane and for a three-quadrangle subarea included, as significant variables, many of the cultural and hydrogeologic factors that were individually related to well yield, in ways that are consistent with conceptual understanding of their effects, but the models explained only 21 percent (regional model for the entire terrane) and 30 percent (quadrangle model) of the overall variance in yield. Moreover, most of the explained variance was due to well characteristics rather than hydrogeologic factors. Hydrogeologic factors such as topography and geology are likely important. However, the overall high variability in the well-yield data, which results from the high variability in aquifer hydraulic properties as well as from limitations of the dataset, would make it difficult to use hydrogeologic factors to predict well yield in the study area. Geostatistical analysis (variograms), on the other hand, indicated that, although highly variable, the well-yield data are spatially correlated. The spatial continuity appears greater in the northeast-southwest direction and less in the southeast-northwest direction, directions that are parallel and perpendicular, respectively, to the regional geologic structural trends. Geostatistical analysis (kriging), used to estimate yield values throughout the study area, identified regional-scale areas of higher and lower yield that may be related to regional structural features—in particular, to a northeast-southwest trending regional fault zone within the Nashoba terrane. It also would be difficult to use kriging to predict yield at specific locations, however, because of the spatial variability in yield, particularly at small scales. The regional-scale analyses in this study, both with hydrogeologic variables and geostatistics, provide a context for understanding the variability in well yield, rather a basis for precise predictions, and site-specific information would be needed to understand local conditions.

Mathematical and statistical analysis of the effect of boron on yield parameters of wheat

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

Rawashdeh, Hamzeh; Sala, Florin; Boldea, Marius

The main objective of this research is to investigate the effect of foliar applications of boron at different growth stages on yield and yield parameters of wheat. The contribution of boron in achieving yield parameters is described by second degree polynomial equations, with high statistical confidence (p<0.01; F theoretical < F calculated, according to ANOVA test, for Alfa = 0.05). Regression analysis, based on R{sup 2} values obtained, made it possible to evaluate the particular contribution of boron to the realization of yield parameters. This was lower for spike length (R{sup 2} = 0.812), thousand seeds weight (R{sup 2} =more » 0.850) and higher in the case of the number of spikelets (R{sup 2} = 0.936) and the number of seeds on a spike (R{sup 2} = 0.960). These results confirm that boron plays an important part in achieving the number of seeds on a spike in the case of wheat, as the contribution of this element to the process of flower fertilization is well-known. In regards to productivity elements, the contribution of macroelements to yield quantity is clear, the contribution of B alone being R{sup 2} = 0.868.« less

Estimating V0[subscript 2]max Using a Personalized Step Test

ERIC Educational Resources Information Center

Webb, Carrie; Vehrs, Pat R.; George, James D.; Hager, Ronald

2014-01-01

The purpose of this study was to develop a step test with a personalized step rate and step height to predict cardiorespiratory fitness in 80 college-aged males and females using the self-reported perceived functional ability scale and data collected during the step test. Multiple linear regression analysis yielded a model (R = 0.90, SEE = 3.43…

SU-F-T-130: [18F]-FDG Uptake Dose Response in Lung Correlates Linearly with Proton Therapy Dose

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

Kim, D; Titt, U; Mirkovic, D

2016-06-15

Purpose: Analysis of clinical outcomes in lung cancer patients treated with protons using 18F-FDG uptake in lung as a measure of dose response. Methods: A test case lung cancer patient was selected in an unbiased way. The test patient’s treatment planning and post treatment positron emission tomography (PET) were collected from picture archiving and communication system at the UT M.D. Anderson Cancer Center. Average computerized tomography scan was registered with post PET/CT through both rigid and deformable registrations for selected region of interest (ROI) via VelocityAI imaging informatics software. For the voxels in the ROI, a system that extracts themore » Standard Uptake Value (SUV) from PET was developed, and the corresponding relative biological effectiveness (RBE) weighted (both variable and constant) dose was computed using the Monte Carlo (MC) methods. The treatment planning system (TPS) dose was also obtained. Using histogram analysis, the voxel average normalized SUV vs. 3 different doses was obtained and linear regression fit was performed. Results: From the registration process, there were some regions that showed significant artifacts near the diaphragm and heart region, which yielded poor r-squared values when the linear regression fit was performed on normalized SUV vs. dose. Excluding these values, TPS fit yielded mean r-squared value of 0.79 (range 0.61–0.95), constant RBE fit yielded 0.79 (range 0.52–0.94), and variable RBE fit yielded 0.80 (range 0.52–0.94). Conclusion: A system that extracts SUV from PET to correlate between normalized SUV and various dose calculations was developed. A linear relation between normalized SUV and all three different doses was found.« less

Above-ground biomass of mangrove species. I. Analysis of models

NASA Astrophysics Data System (ADS)

Soares, Mário Luiz Gomes; Schaeffer-Novelli, Yara

2005-10-01

This study analyzes the above-ground biomass of Rhizophora mangle and Laguncularia racemosa located in the mangroves of Bertioga (SP) and Guaratiba (RJ), Southeast Brazil. Its purpose is to determine the best regression model to estimate the total above-ground biomass and compartment (leaves, reproductive parts, twigs, branches, trunk and prop roots) biomass, indirectly. To do this, we used structural measurements such as height, diameter at breast-height (DBH), and crown area. A combination of regression types with several compositions of independent variables generated 2.272 models that were later tested. Subsequent analysis of the models indicated that the biomass of reproductive parts, branches, and prop roots yielded great variability, probably because of environmental factors and seasonality (in the case of reproductive parts). It also indicated the superiority of multiple regression to estimate above-ground biomass as it allows researchers to consider several aspects that affect above-ground biomass, specially the influence of environmental factors. This fact has been attested to the models that estimated the biomass of crown compartments.

Comparison of CEAS and Williams-type models for spring wheat yields in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Barnett, T. L. (Principal Investigator)

1982-01-01

The CEAS and Williams-type yield models are both based on multiple regression analysis of historical time series data at CRD level. The CEAS model develops a separate relation for each CRD; the Williams-type model pools CRD data to regional level (groups of similar CRDs). Basic variables considered in the analyses are USDA yield, monthly mean temperature, monthly precipitation, and variables derived from these. The Williams-type model also used soil texture and topographic information. Technological trend is represented in both by piecewise linear functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test of each model (1970-1979) demonstrate that the models are very similar in performance in all respects. Both models are about equally objective, adequate, timely, simple, and inexpensive. Both consider scientific knowledge on a broad scale but not in detail. Neither provides a good current measure of modeled yield reliability. The CEAS model is considered very slightly preferable for AgRISTARS applications.

The costs and utility of imaging in evaluating dizzy patients in the emergency room.

PubMed

Ahsan, Syed F; Syamal, Mausumi N; Yaremchuk, Kathleen; Peterson, Edward; Seidman, Michael

2013-09-01

To determine the usefulness and the costs of computed tomography (CT) and magnetic resonance imaging (MRI) in the evaluation of patients with dizziness in the emergency department (ED). Retrospective chart review. Charts of patients with a specific health maintenance insurance plan presenting with dizziness and vertigo to a large health system's ED between January 2008 and January 2011 were reviewed. Patient demographics, signs/symptoms, and CT and MRI results were assessed. CT and MRI charges were determined based on positive versus unremarkable findings. Data analysis included stepwise logistic regressions. Of 1681 patients identified, 810 (48%) received CT brain/head scan totaling $988,200 in charges. Of these, only 0.74% yielded clinically significant pathology requiring intervention. However, 12.2% of MRI studies yielded discovery of significant abnormalities. Logistic regression analysis revealed that older patients (P = .001) were more likely to receive a CT scan. In the 3-year period studied, CT scans for ED patients with dizziness and vertigo yielded a low predictive value for significant pathology. These data reveal a great opportunity for cost savings by developing stricter guidelines for ordering CT scans for this set of ED patients. The use of MRI in all cases of dizziness was found to be neither practical nor useful. However, appropriately directed MRI of the brain is recommended in patients with dizziness and other neurological signs or symptoms. Copyright © 2013 The American Laryngological, Rhinological and Otological Society, Inc.

[Optimization of ultrasonic-assisted extraction of total flavonoids from leaves of the Artocarpus heterophyllus by response surface methodology].

PubMed

Wang, Hong-wu; Liu, Yan-qing; Wang, Yuan-hong

2011-07-01

To investigate the ultrasonic-assisted extract on of total flavonoids from leaves of the Artocarpus heterophyllus. Investigated the effects of ethanol concentration, extraction time, and liquid-solid ratio on flavonoids yield. A 17-run response surface design involving three factors at three levels was generated by the Design-Expert software and experimental data obtained were subjected to quadratic regression analysis to create a mathematical model describing flavonoids extraction. The optimum ultrasonic assisted extraction conditions were: ethanol volume fraction 69.4% and liquid-solid ratio of 22.6:1 for 32 min. Under these optimized conditions, the yield of flavonoids was 7.55 mg/g. The Box-Behnken design and response surface analysis can well optimize the ultrasonic-assisted extraction of total flavonoids from Artocarpus heterophyllus.

Estimates of nitrate loads and yields from groundwater to streams in the Chesapeake Bay watershed based on land use and geology

USGS Publications Warehouse

Terziotti, Silvia; Capel, Paul D.; Tesoriero, Anthony J.; Hopple, Jessica A.; Kronholm, Scott C.

2018-03-07

The water quality of the Chesapeake Bay may be adversely affected by dissolved nitrate carried in groundwater discharge to streams. To estimate the concentrations, loads, and yields of nitrate from groundwater to streams for the Chesapeake Bay watershed, a regression model was developed based on measured nitrate concentrations from 156 small streams with watersheds less than 500 square miles (mi2 ) at baseflow. The regression model has three predictive variables: geologic unit, percent developed land, and percent agricultural land. Comparisons of estimated and actual values within geologic units were closely matched. The coefficient of determination (R2 ) for the model was 0.6906. The model was used to calculate baseflow nitrate concentrations at over 83,000 National Hydrography Dataset Plus Version 2 catchments and aggregated to 1,966 total 12-digit hydrologic units in the Chesapeake Bay watershed. The modeled output geospatial data layers provided estimated annual loads and yields of nitrate from groundwater into streams. The spatial distribution of annual nitrate yields from groundwater estimated by this method was compared to the total watershed yields of all sources estimated from a Chesapeake Bay SPAtially Referenced Regressions On Watershed attributes (SPARROW) water-quality model. The comparison showed similar spatial patterns. The regression model for groundwater contribution had similar but lower yields, suggesting that groundwater is an important source of nitrogen for streams in the Chesapeake Bay watershed.

A comparison of three methods of assessing differential item functioning (DIF) in the Hospital Anxiety Depression Scale: ordinal logistic regression, Rasch analysis and the Mantel chi-square procedure.

PubMed

Cameron, Isobel M; Scott, Neil W; Adler, Mats; Reid, Ian C

2014-12-01

It is important for clinical practice and research that measurement scales of well-being and quality of life exhibit only minimal differential item functioning (DIF). DIF occurs where different groups of people endorse items in a scale to different extents after being matched by the intended scale attribute. We investigate the equivalence or otherwise of common methods of assessing DIF. Three methods of measuring age- and sex-related DIF (ordinal logistic regression, Rasch analysis and Mantel χ(2) procedure) were applied to Hospital Anxiety Depression Scale (HADS) data pertaining to a sample of 1,068 patients consulting primary care practitioners. Three items were flagged by all three approaches as having either age- or sex-related DIF with a consistent direction of effect; a further three items identified did not meet stricter criteria for important DIF using at least one method. When applying strict criteria for significant DIF, ordinal logistic regression was slightly less sensitive. Ordinal logistic regression, Rasch analysis and contingency table methods yielded consistent results when identifying DIF in the HADS depression and HADS anxiety scales. Regardless of methods applied, investigators should use a combination of statistical significance, magnitude of the DIF effect and investigator judgement when interpreting the results.

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

USGS Publications Warehouse

Anderson, Ryan; Clegg, Samuel M.; Frydenvang, Jens; Wiens, Roger C.; McLennan, Scott M.; Morris, Richard V.; Ehlmann, Bethany L.; Dyar, M. Darby

2017-01-01

Accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response of an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “sub-model” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. The sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.

Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions

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

Iavarone, Salvatore; Smith, Sean T.; Smith, Philip J.

Oxy-coal combustion is an emerging low-cost “clean coal” technology for emissions reduction and Carbon Capture and Sequestration (CCS). The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of cost-effective oxy-fuel technologies and the minimization of environmental concerns at industrial scale. The coupling of detailed chemistry models and CFD simulations is still challenging, especially for large-scale plants, because of the high computational efforts required. The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision. This paper presents a procedure for scale-bridging modeling of coal devolatilization, inmore » the presence of experimental error, that puts emphasis on the thermodynamic aspect of devolatilization, namely the final volatile yield of coal, rather than kinetics. The procedure consists of an engineering approach based on dataset consistency and Bayesian methodology including Gaussian-Process Regression (GPR). Experimental data from devolatilization tests carried out in an oxy-coal entrained flow reactor were considered and CFD simulations of the reactor were performed. Jointly evaluating experiments and simulations, a novel yield model was validated against the data via consistency analysis. In parallel, a Gaussian-Process Regression was performed, to improve the understanding of the uncertainty associated to the devolatilization, based on the experimental measurements. Potential model forms that could predict yield during devolatilization were obtained. The set of model forms obtained via GPR includes the yield model that was proven to be consistent with the data. Finally, the overall procedure has resulted in a novel yield model for coal devolatilization and in a valuable evaluation of uncertainty in the data, in the model form, and in the model parameters.« less

Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions

DOE PAGES

Iavarone, Salvatore; Smith, Sean T.; Smith, Philip J.; ...

2017-06-03

Oxy-coal combustion is an emerging low-cost “clean coal” technology for emissions reduction and Carbon Capture and Sequestration (CCS). The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of cost-effective oxy-fuel technologies and the minimization of environmental concerns at industrial scale. The coupling of detailed chemistry models and CFD simulations is still challenging, especially for large-scale plants, because of the high computational efforts required. The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision. This paper presents a procedure for scale-bridging modeling of coal devolatilization, inmore » the presence of experimental error, that puts emphasis on the thermodynamic aspect of devolatilization, namely the final volatile yield of coal, rather than kinetics. The procedure consists of an engineering approach based on dataset consistency and Bayesian methodology including Gaussian-Process Regression (GPR). Experimental data from devolatilization tests carried out in an oxy-coal entrained flow reactor were considered and CFD simulations of the reactor were performed. Jointly evaluating experiments and simulations, a novel yield model was validated against the data via consistency analysis. In parallel, a Gaussian-Process Regression was performed, to improve the understanding of the uncertainty associated to the devolatilization, based on the experimental measurements. Potential model forms that could predict yield during devolatilization were obtained. The set of model forms obtained via GPR includes the yield model that was proven to be consistent with the data. Finally, the overall procedure has resulted in a novel yield model for coal devolatilization and in a valuable evaluation of uncertainty in the data, in the model form, and in the model parameters.« less

Random Forests for Global and Regional Crop Yield Predictions.

PubMed

Jeong, Jig Han; Resop, Jonathan P; Mueller, Nathaniel D; Fleisher, David H; Yun, Kyungdahm; Butler, Ethan E; Timlin, Dennis J; Shim, Kyo-Moon; Gerber, James S; Reddy, Vangimalla R; Kim, Soo-Hyung

2016-01-01

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.

Partial least squares analysis of rocket propulsion fuel data using diaphragm valve-based comprehensive two-dimensional gas chromatography coupled with flame ionization detection.

PubMed

Freye, Chris E; Fitz, Brian D; Billingsley, Matthew C; Synovec, Robert E

2016-06-01

The chemical composition and several physical properties of RP-1 fuels were studied using comprehensive two-dimensional (2D) gas chromatography (GC×GC) coupled with flame ionization detection (FID). A "reversed column" GC×GC configuration was implemented with a RTX-wax column on the first dimension ((1)D), and a RTX-1 as the second dimension ((2)D). Modulation was achieved using a high temperature diaphragm valve mounted directly in the oven. Using leave-one-out cross-validation (LOOCV), the summed GC×GC-FID signal of three compound-class selective 2D regions (alkanes, cycloalkanes, and aromatics) was regressed against previously measured ASTM derived values for these compound classes, yielding root mean square errors of cross validation (RMSECV) of 0.855, 0.734, and 0.530mass%, respectively. For comparison, using partial least squares (PLS) analysis with LOOCV, the GC×GC-FID signal of the entire 2D separations was regressed against the same ASTM values, yielding a linear trend for the three compound classes (alkanes, cycloalkanes, and aromatics), yielding RMSECV values of 1.52, 2.76, and 0.945 mass%, respectively. Additionally, a more detailed PLS analysis was undertaken of the compounds classes (n-alkanes, iso-alkanes, mono-, di-, and tri-cycloalkanes, and aromatics), and of physical properties previously determined by ASTM methods (such as net heat of combustion, hydrogen content, density, kinematic viscosity, sustained boiling temperature and vapor rise temperature). Results from these PLS studies using the relatively simple to use and inexpensive GC×GC-FID instrumental platform are compared to previously reported results using the GC×GC-TOFMS instrumental platform. Copyright © 2016 Elsevier B.V. All rights reserved.

Orthogonal Regression: A Teaching Perspective

ERIC Educational Resources Information Center

Carr, James R.

2012-01-01

A well-known approach to linear least squares regression is that which involves minimizing the sum of squared orthogonal projections of data points onto the best fit line. This form of regression is known as orthogonal regression, and the linear model that it yields is known as the major axis. A similar method, reduced major axis regression, is…

a Comparison Between Two Ols-Based Approaches to Estimating Urban Multifractal Parameters

NASA Astrophysics Data System (ADS)

Huang, Lin-Shan; Chen, Yan-Guang

Multifractal theory provides a new spatial analytical tool for urban studies, but many basic problems remain to be solved. Among various pending issues, the most significant one is how to obtain proper multifractal dimension spectrums. If an algorithm is improperly used, the parameter spectrums will be abnormal. This paper is devoted to investigating two ordinary least squares (OLS)-based approaches for estimating urban multifractal parameters. Using empirical study and comparative analysis, we demonstrate how to utilize the adequate linear regression to calculate multifractal parameters. The OLS regression analysis has two different approaches. One is that the intercept is fixed to zero, and the other is that the intercept is not limited. The results of comparative study show that the zero-intercept regression yields proper multifractal parameter spectrums within certain scale range of moment order, while the common regression method often leads to abnormal multifractal parameter values. A conclusion can be reached that fixing the intercept to zero is a more advisable regression method for multifractal parameters estimation, and the shapes of spectral curves and value ranges of fractal parameters can be employed to diagnose urban problems. This research is helpful for scientists to understand multifractal models and apply a more reasonable technique to multifractal parameter calculations.

Bark yields of 11-year-old loblolly pine as influenced by competition control and fertilization

Treesearch

Allan E. Tiarks; James D. Haywood

1992-01-01

Bolts cut from 11-year-old loblolly pines (Pinus taeda L.) were measured to determine the effects of applications of fertilizer and competition control treatments on the amount of pine bark produced. Bark thickness at breast height was not significantly affected by any of the treatments. Regression analysis showed that the dry weight of bark per unit...

The Norwegian Healthier Goats program--modeling lactation curves using a multilevel cubic spline regression model.

PubMed

Nagel-Alne, G E; Krontveit, R; Bohlin, J; Valle, P S; Skjerve, E; Sølverød, L S

2014-07-01

In 2001, the Norwegian Goat Health Service initiated the Healthier Goats program (HG), with the aim of eradicating caprine arthritis encephalitis, caseous lymphadenitis, and Johne's disease (caprine paratuberculosis) in Norwegian goat herds. The aim of the present study was to explore how control and eradication of the above-mentioned diseases by enrolling in HG affected milk yield by comparison with herds not enrolled in HG. Lactation curves were modeled using a multilevel cubic spline regression model where farm, goat, and lactation were included as random effect parameters. The data material contained 135,446 registrations of daily milk yield from 28,829 lactations in 43 herds. The multilevel cubic spline regression model was applied to 4 categories of data: enrolled early, control early, enrolled late, and control late. For enrolled herds, the early and late notations refer to the situation before and after enrolling in HG; for nonenrolled herds (controls), they refer to development over time, independent of HG. Total milk yield increased in the enrolled herds after eradication: the total milk yields in the fourth lactation were 634.2 and 873.3 kg in enrolled early and enrolled late herds, respectively, and 613.2 and 701.4 kg in the control early and control late herds, respectively. Day of peak yield differed between enrolled and control herds. The day of peak yield came on d 6 of lactation for the control early category for parities 2, 3, and 4, indicating an inability of the goats to further increase their milk yield from the initial level. For enrolled herds, on the other hand, peak yield came between d 49 and 56, indicating a gradual increase in milk yield after kidding. Our results indicate that enrollment in the HG disease eradication program improved the milk yield of dairy goats considerably, and that the multilevel cubic spline regression was a suitable model for exploring effects of disease control and eradication on milk yield. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Empirical models based on the universal soil loss equation fail to predict sediment discharges from Chesapeake Bay catchments.

PubMed

Boomer, Kathleen B; Weller, Donald E; Jordan, Thomas E

2008-01-01

The Universal Soil Loss Equation (USLE) and its derivatives are widely used for identifying watersheds with a high potential for degrading stream water quality. We compared sediment yields estimated from regional application of the USLE, the automated revised RUSLE2, and five sediment delivery ratio algorithms to measured annual average sediment delivery in 78 catchments of the Chesapeake Bay watershed. We did the same comparisons for another 23 catchments monitored by the USGS. Predictions exceeded observed sediment yields by more than 100% and were highly correlated with USLE erosion predictions (Pearson r range, 0.73-0.92; p < 0.001). RUSLE2-erosion estimates were highly correlated with USLE estimates (r = 0.87; p < 001), so the method of implementing the USLE model did not change the results. In ranked comparisons between observed and predicted sediment yields, the models failed to identify catchments with higher yields (r range, -0.28-0.00; p > 0.14). In a multiple regression analysis, soil erodibility, log (stream flow), basin shape (topographic relief ratio), the square-root transformed proportion of forest, and occurrence in the Appalachian Plateau province explained 55% of the observed variance in measured suspended sediment loads, but the model performed poorly (r(2) = 0.06) at predicting loads in the 23 USGS watersheds not used in fitting the model. The use of USLE or multiple regression models to predict sediment yields is not advisable despite their present widespread application. Integrated watershed models based on the USLE may also be unsuitable for making management decisions.

Use of empirical likelihood to calibrate auxiliary information in partly linear monotone regression models.

PubMed

Chen, Baojiang; Qin, Jing

2014-05-10

In statistical analysis, a regression model is needed if one is interested in finding the relationship between a response variable and covariates. When the response depends on the covariate, then it may also depend on the function of this covariate. If one has no knowledge of this functional form but expect for monotonic increasing or decreasing, then the isotonic regression model is preferable. Estimation of parameters for isotonic regression models is based on the pool-adjacent-violators algorithm (PAVA), where the monotonicity constraints are built in. With missing data, people often employ the augmented estimating method to improve estimation efficiency by incorporating auxiliary information through a working regression model. However, under the framework of the isotonic regression model, the PAVA does not work as the monotonicity constraints are violated. In this paper, we develop an empirical likelihood-based method for isotonic regression model to incorporate the auxiliary information. Because the monotonicity constraints still hold, the PAVA can be used for parameter estimation. Simulation studies demonstrate that the proposed method can yield more efficient estimates, and in some situations, the efficiency improvement is substantial. We apply this method to a dementia study. Copyright © 2013 John Wiley & Sons, Ltd.

Quantifying the sensitivity of feedstock properties and process conditions on hydrochar yield, carbon content, and energy content.

PubMed

Li, Liang; Wang, Yiying; Xu, Jiting; Flora, Joseph R V; Hoque, Shamia; Berge, Nicole D

2018-08-01

Hydrothermal carbonization (HTC) is a wet, low temperature thermal conversion process that continues to gain attention for the generation of hydrochar. The importance of specific process conditions and feedstock properties on hydrochar characteristics is not well understood. To evaluate this, linear and non-linear models were developed to describe hydrochar characteristics based on data collected from HTC-related literature. A Sobol analysis was subsequently conducted to identify parameters that most influence hydrochar characteristics. Results from this analysis indicate that for each investigated hydrochar property, the model fit and predictive capability associated with the random forest models is superior to both the linear and regression tree models. Based on results from the Sobol analysis, the feedstock properties and process conditions most influential on hydrochar yield, carbon content, and energy content were identified. In addition, a variational process parameter sensitivity analysis was conducted to determine how feedstock property importance changes with process conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Climate Change Impact on Rainfall: How will Threaten Wheat Yield?

NASA Astrophysics Data System (ADS)

Tafoughalti, K.; El Faleh, E. M.; Moujahid, Y.; Ouargaga, F.

2018-05-01

Climate change has a significant impact on the environmental condition of the agricultural region. Meknes has an agrarian economy and wheat production is of paramount importance. As most arable area are under rainfed system, Meknes is one of the sensitive regions to rainfall variability and consequently to climate change. Therefore, the use of changes in rainfall is vital for detecting the influence of climate system on agricultural productivity. This article identifies rainfall temporal variability and its impact on wheat yields. We used monthly rainfall records for three decades and wheat yields records of fifteen years. Rainfall variability is assessed utilizing the precipitation concentration index and the variation coefficient. The association between wheat yields and cumulative rainfall amounts of different scales was calculated based on a regression model. The analysis shown moderate seasonal and irregular annual rainfall distribution. Yields fluctuated from 210 to 4500 Kg/ha with 52% of coefficient of variation. The correlation results shows that wheat yields are strongly correlated with rainfall of the period January to March. This investigation concluded that climate change is altering wheat yield and it is crucial to adept the necessary adaptation to challenge the risk.

Development of a traveltime prediction equation for streams in Arkansas

USGS Publications Warehouse

Funkhouser, Jaysson E.; Barks, C. Shane

2004-01-01

During 1971 and 1981 and 2001 and 2003, traveltime measurements were made at 33 sample sites on 18 streams throughout northern and western Arkansas using fluorescent dye. Most measurements were made during steady-state base-flow conditions with the exception of three measurements made during near steady-state medium-flow conditions (for the study described in this report, medium-flow is approximately 100-150 percent of the mean monthly streamflow during the month the dye trace was conducted). These traveltime data were compared to the U.S. Geological Survey?s national traveltime prediction equation and used to develop a specific traveltime prediction equation for Arkansas streams. In general, the national traveltime prediction equation yielded results that over-predicted the velocity of the streams for 29 of the 33 sites measured. The standard error for the national traveltime prediction equation was 105 percent. The coefficient of determination was 0.78. The Arkansas prediction equation developed from a regression analysis of dye-tracing results was a significant improvement over the national prediction equation. This regression analysis yielded a standard error of 46 percent and a coefficient of determination of 0.74. The predicted velocities using this equation compared better to measured velocities. Using the variables in a regression analysis, the Arkansas prediction equation derived for the peak velocity in feet per second was: (Actual Equation Shown in report) In addition to knowing when the peak concentration will arrive at a site, it is of great interest to know when the leading edge of a contaminant plume will arrive. The traveltime of the leading edge of a contaminant plume indicates when a potential problem might first develop and also defines the overall shape of the concentration response function. Previous USGS reports have shown no significant relation between any of the variables and the time from injection to the arrival of the leading edge of the dye plume. For this report, the analysis of the dye-tracing data yielded a significant correlation between traveltime of the leading edge and traveltime of the peak concentration with an R2 value of 0.99. These data indicate that the traveltime of the leading edge can be estimated from: (Actual Equation Shown in Report)

Optimization of critical quality attributes in continuous twin-screw wet granulation via design space validated with pilot scale experimental data.

PubMed

Liu, Huolong; Galbraith, S C; Ricart, Brendon; Stanton, Courtney; Smith-Goettler, Brandye; Verdi, Luke; O'Connor, Thomas; Lee, Sau; Yoon, Seongkyu

2017-06-15

In this study, the influence of key process variables (screw speed, throughput and liquid to solid (L/S) ratio) of a continuous twin screw wet granulation (TSWG) was investigated using a central composite face-centered (CCF) experimental design method. Regression models were developed to predict the process responses (motor torque, granule residence time), granule properties (size distribution, volume average diameter, yield, relative width, flowability) and tablet properties (tensile strength). The effects of the three key process variables were analyzed via contour and interaction plots. The experimental results have demonstrated that all the process responses, granule properties and tablet properties are influenced by changing the screw speed, throughput and L/S ratio. The TSWG process was optimized to produce granules with specific volume average diameter of 150μm and the yield of 95% based on the developed regression models. A design space (DS) was built based on volume average granule diameter between 90 and 200μm and the granule yield larger than 75% with a failure probability analysis using Monte Carlo simulations. Validation experiments successfully validated the robustness and accuracy of the DS generated using the CCF experimental design in optimizing a continuous TSWG process. Copyright © 2017 Elsevier B.V. All rights reserved.

Estimates of Flow Duration, Mean Flow, and Peak-Discharge Frequency Values for Kansas Stream Locations

USGS Publications Warehouse

Perry, Charles A.; Wolock, David M.; Artman, Joshua C.

2004-01-01

Streamflow statistics of flow duration and peak-discharge frequency were estimated for 4,771 individual locations on streams listed on the 1999 Kansas Surface Water Register. These statistics included the flow-duration values of 90, 75, 50, 25, and 10 percent, as well as the mean flow value. Peak-discharge frequency values were estimated for the 2-, 5-, 10-, 25-, 50-, and 100-year floods. Least-squares multiple regression techniques were used, along with Tobit analyses, to develop equations for estimating flow-duration values of 90, 75, 50, 25, and 10 percent and the mean flow for uncontrolled flow stream locations. The contributing-drainage areas of 149 U.S. Geological Survey streamflow-gaging stations in Kansas and parts of surrounding States that had flow uncontrolled by Federal reservoirs and used in the regression analyses ranged from 2.06 to 12,004 square miles. Logarithmic transformations of climatic and basin data were performed to yield the best linear relation for developing equations to compute flow durations and mean flow. In the regression analyses, the significant climatic and basin characteristics, in order of importance, were contributing-drainage area, mean annual precipitation, mean basin permeability, and mean basin slope. The analyses yielded a model standard error of prediction range of 0.43 logarithmic units for the 90-percent duration analysis to 0.15 logarithmic units for the 10-percent duration analysis. The model standard error of prediction was 0.14 logarithmic units for the mean flow. Regression equations used to estimate peak-discharge frequency values were obtained from a previous report, and estimates for the 2-, 5-, 10-, 25-, 50-, and 100-year floods were determined for this report. The regression equations and an interpolation procedure were used to compute flow durations, mean flow, and estimates of peak-discharge frequency for locations along uncontrolled flow streams on the 1999 Kansas Surface Water Register. Flow durations, mean flow, and peak-discharge frequency values determined at available gaging stations were used to interpolate the regression-estimated flows for the stream locations where available. Streamflow statistics for locations that had uncontrolled flow were interpolated using data from gaging stations weighted according to the drainage area and the bias between the regression-estimated and gaged flow information. On controlled reaches of Kansas streams, the streamflow statistics were interpolated between gaging stations using only gaged data weighted by drainage area.

Genetic improvement of total milk yield and total lactation persistency of the first three lactations in dairy cattle.

PubMed

Togashi, K; Lin, C Y

2008-07-01

The objective of this study was to compare 6 selection criteria in terms of 3-parity total milk yield and 9 selection criteria in terms of total net merit (H) comprising 3-parity total milk yield and total lactation persistency. The 6 selection criteria compared were as follows: first-parity milk estimated breeding value (EBV; M1), first 2-parity milk EBV (M2), first 3-parity milk EBV (M3), first-parity eigen index (EI(1)), first 2-parity eigen index (EI(2)), and first 3-parity eigen index (EI(3)). The 9 selection criteria compared in terms of H were M1, M2, M3, EI(1), EI(2), EI(3), and first-parity, first 2-parity, and first 3-parity selection indices (I(1), I(2), and I(3), respectively). In terms of total milk yield, selection on M3 or EI(3) achieved the greatest genetic response, whereas selection on EI(1) produced the largest genetic progress per day. In terms of total net merit, selection on I(3) brought the largest response, whereas selection EI(1) yielded the greatest genetic progress per day. A multiple-lactation random regression test-day model simultaneously yields the EBV of the 3 lactations for all animals included in the analysis even though the younger animals do not have the opportunity to complete the first 3 lactations. It is important to use the first 3 lactation EBV for selection decision rather than only the first lactation EBV in spite of the fact that the first-parity selection criteria achieved a faster genetic progress per day than the 3-parity selection criteria. Under a multiple-lactation random regression animal model analysis, the use of the first 3 lactation EBV for selection decision does not prolong the generation interval as compared with the use of only the first lactation EBV. Thus, it is justified to compare genetic response on a lifetime basis rather than on a per-day basis. The results suggest the use of M3 or EI(3) for genetic improvement of total milk yield and the use of I(3) for genetic improvement of total net merit H. Although this study deals with selection for 3-parity milk production, the same principle applies to selection for lifetime milk production.

[Regional scale remote sensing-based yield estimation of winter wheat by using MODIS-NDVI data: a case study of Jining City in Shandong Province].

PubMed

Ren, Jianqiang; Chen, Zhongxin; Tang, Huajun

2006-12-01

Taking Jining City of Shandong Province, one of the most important winter wheat production regions in Huanghuaihai Plain as an example, the winter wheat yield was estimated by using the 250 m MODIS-NDVI data smoothed by Savitzky-Golay filter. The NDVI values between 0. 20 and 0. 80 were selected, and the sum of NDVI value for each county was calculated to build its relation with winter wheat yield. By using stepwise regression method, the linear regression model between NDVI and winter wheat yield was established, with the precision validated by the ground survey data. The results showed that the relative error of predicted yield was between -3.6% and 3.9%, suggesting that the method was relatively accurate and feasible.

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

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

Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens

We report that accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response ofmore » an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “submodel” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. Lastly, the sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.« less

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

DOE PAGES

Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens; ...

2016-12-15

We report that accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response ofmore » an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “submodel” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. Lastly, the sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.« less

Identification of molecular markers associated with mite resistance in coconut (Cocos nucifera L.).

PubMed

Shalini, K V; Manjunatha, S; Lebrun, P; Berger, A; Baudouin, L; Pirany, N; Ranganath, R M; Prasad, D Theertha

2007-01-01

Coconut mite (Aceria guerreronis 'Keifer') has become a major threat to Indian coconut (Coçcos nucifera L.) cultivators and the processing industry. Chemical and biological control measures have proved to be costly, ineffective, and ecologically undesirable. Planting mite-resistant coconut cultivars is the most effective method of preventing yield loss and should form a major component of any integrated pest management stratagem. Coconut genotypes, and mite-resistant and -susceptible accessions were collected from different parts of South India. Thirty-two simple sequence repeat (SSR) and 7 RAPD primers were used for molecular analyses. In single-marker analysis, 9 SSR and 4 RAPD markers associated with mite resistance were identified. In stepwise multiple regression analysis of SSRs, a combination of 6 markers showed 100% association with mite infestation. Stepwise multiple regression analysis for RAPD data revealed that a combination of 3 markers accounted for 83.86% of mite resistance in the selected materials. Combined stepwise multiple regression analysis of RAPD and SSR data showed that a combination of 5 markers explained 100% of the association with mite resistance in coconut. Markers associated with mite resistance are important in coconut breeding programs and will facilitate the selection of mite-resistant plants at an early stage as well as mother plants for breeding programs.

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

Estimating the Depth of the Navy Recruiting Market

DTIC Science & Technology

2016-09-01

recommend that NRC make use of the Poisson regression model in order to determine high-yield ZIP codes for market depth. 14. SUBJECT...recommend that NRC make use of the Poisson regression model in order to determine high-yield ZIP codes for market depth. vi THIS PAGE INTENTIONALLY LEFT...DEPTH OF THE NAVY RECRUITING MARKET by Emilie M. Monaghan September 2016 Thesis Advisor: Lyn R. Whitaker Second Reader: Jonathan K. Alt

Empirical Behavioral Models to Support Alternative Tools for the Analysis of Mixed-Priority Pedestrian-Vehicle Interaction in a Highway Capacity Context

PubMed Central

Rouphail, Nagui M.

2011-01-01

This paper presents behavioral-based models for describing pedestrian gap acceptance at unsignalized crosswalks in a mixed-priority environment, where some drivers yield and some pedestrians cross in gaps. Logistic regression models are developed to predict the probability of pedestrian crossings as a function of vehicle dynamics, pedestrian assertiveness, and other factors. In combination with prior work on probabilistic yielding models, the results can be incorporated in a simulation environment, where they can more fully describe the interaction of these two modes. The approach is intended to supplement HCM analytical procedure for locations where significant interaction occurs between drivers and pedestrians, including modern roundabouts. PMID:21643488

The impact of furfural concentrations and substrate-to-biomass ratios on biological hydrogen production from synthetic lignocellulosic hydrolysate using mesophilic anaerobic digester sludge.

PubMed

Akobi, Chinaza; Hafez, Hisham; Nakhla, George

2016-12-01

This study evaluated the impact of furfural (a furan derivative) on hydrogen production rates and yields at initial substrate-to-microorganism ratios (S°/X°) of 4, 2, 1, and 0.5gCOD/gVSS and furfural concentrations of 4, 2, 1, and 0.5g/L. Fermentation studies were carried out in batches using synthetic lignocellulosic hydrolysate as substrate and mesophilic anaerobic digester sludge as seed. Contrary to other literature studies where furfural was inhibitory, this study showed that furfural concentrations of up to 1g/L enhanced hydrogen production with yields as high as 19% from the control (batch without furfural). Plots of hydrogen yields against gfurfural/gsugars and hydrogen yields versus gfurfural/gbiomass showed negative linear correlation indicating that these parameters influence biohydrogen production. Regression analysis indicated that gfurfural/gsugars initial exerted a greater effect on the degree of inhibition of hydrogen production than gfurfural/gVSS final . Copyright © 2016 Elsevier Ltd. All rights reserved.

Effects of Hydrological Parameters on Palm Oil Fresh Fruit Bunch Yield)

NASA Astrophysics Data System (ADS)

Nda, M.; Adnan, M. S.; Suhadak, M. A.; Zakaria, M. S.; Lopa, R. T.

2018-04-01

Climate change effects and variability have been studied by many researchers in diverse geophysical fields. Malaysia produces large volume of palm oil, the effects of climate change on hydrological parameters (rainfall and precipitation) could have adverse effects on palm oil fresh fruit bunch (FFB) production with implications at both local and international market. It is important to understand the effects of climate change on crop yield to adopt new cultivation techniques and guaranteeing food security globally. Based on this background, the paper’s objective is to investigate the effects of rainfall and temperature pattern on crop yield (FFB) within five years period (2013 - 2017) at Batu Pahat District. The Man - Kendall rank technique (trend test) and statistical analyses (correlation and regression) were applied to the dataset used for the study. The results reveal that there are variabilities in rainfall and temperature from one month to the other and the statistical analysis reveals that the hydrological parameters have an insignificant effect on crop yield.

Advantage of multiple spot urine collections for estimating daily sodium excretion: comparison with two 24-h urine collections as reference.

PubMed

Uechi, Ken; Asakura, Keiko; Ri, Yui; Masayasu, Shizuko; Sasaki, Satoshi

2016-02-01

Several estimation methods for 24-h sodium excretion using spot urine sample have been reported, but accurate estimation at the individual level remains difficult. We aimed to clarify the most accurate method of estimating 24-h sodium excretion with different numbers of available spot urine samples. A total of 370 participants from throughout Japan collected multiple 24-h urine and spot urine samples independently. Participants were allocated randomly into a development and a validation dataset. Two estimation methods were established in the development dataset using the two 24-h sodium excretion samples as reference: the 'simple mean method' estimated by multiplying the sodium-creatinine ratio by predicted 24-h creatinine excretion, whereas the 'regression method' employed linear regression analysis. The accuracy of the two methods was examined by comparing the estimated means and concordance correlation coefficients (CCC) in the validation dataset. Mean sodium excretion by the simple mean method with three spot urine samples was closest to that by 24-h collection (difference: -1.62  mmol/day). CCC with the simple mean method increased with an increased number of spot urine samples at 0.20, 0.31, and 0.42 using one, two, and three samples, respectively. This method with three spot urine samples yielded higher CCC than the regression method (0.40). When only one spot urine sample was available for each study participant, CCC was higher with the regression method (0.36). The simple mean method with three spot urine samples yielded the most accurate estimates of sodium excretion. When only one spot urine sample was available, the regression method was preferable.

Impacts of land use change on watershed streamflow and sediment yield: An assessment using hydrologic modelling and partial least squares regression

NASA Astrophysics Data System (ADS)

Yan, B.; Fang, N. F.; Zhang, P. C.; Shi, Z. H.

2013-03-01

SummaryUnderstanding how changes in individual land use types influence the dynamics of streamflow and sediment yield would greatly improve the predictability of the hydrological consequences of land use changes and could thus help stakeholders to make better decisions. Multivariate statistics are commonly used to compare individual land use types to control the dynamics of streamflow or sediment yields. However, one issue with the use of conventional statistical methods to address relationships between land use types and streamflow or sediment yield is multicollinearity. In this study, an integrated approach involving hydrological modelling and partial least squares regression (PLSR) was used to quantify the contributions of changes in individual land use types to changes in streamflow and sediment yield. In a case study, hydrological modelling was conducted using land use maps from four time periods (1978, 1987, 1999, and 2007) for the Upper Du watershed (8973 km2) in China using the Soil and Water Assessment Tool (SWAT). Changes in streamflow and sediment yield across the two simulations conducted using the land use maps from 2007 to 1978 were found to be related to land use changes according to a PLSR, which was used to quantify the effect of this influence at the sub-basin scale. The major land use changes that affected streamflow in the studied catchment areas were related to changes in the farmland, forest and urban areas between 1978 and 2007; the corresponding regression coefficients were 0.232, -0.147 and 1.256, respectively, and the Variable Influence on Projection (VIP) was greater than 1. The dominant first-order factors affecting the changes in sediment yield in our study were: farmland (the VIP and regression coefficient were 1.762 and 14.343, respectively) and forest (the VIP and regression coefficient were 1.517 and -7.746, respectively). The PLSR methodology presented in this paper is beneficial and novel, as it partially eliminates the co-dependency of the variables and facilitates a more unbiased view of the contribution of the changes in individual land use types to changes in streamflow and sediment yield. This practicable and simple approach could be applied to a variety of other watersheds for which time-sequenced digital land use maps are available.

BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES1

PubMed Central

Zhu, Xiang; Stephens, Matthew

2017-01-01

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a “Regression with Summary Statistics” (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss. PMID:29399241

Regression analysis of case K interval-censored failure time data in the presence of informative censoring.

PubMed

Wang, Peijie; Zhao, Hui; Sun, Jianguo

2016-12-01

Interval-censored failure time data occur in many fields such as demography, economics, medical research, and reliability and many inference procedures on them have been developed (Sun, 2006; Chen, Sun, and Peace, 2012). However, most of the existing approaches assume that the mechanism that yields interval censoring is independent of the failure time of interest and it is clear that this may not be true in practice (Zhang et al., 2007; Ma, Hu, and Sun, 2015). In this article, we consider regression analysis of case K interval-censored failure time data when the censoring mechanism may be related to the failure time of interest. For the problem, an estimated sieve maximum-likelihood approach is proposed for the data arising from the proportional hazards frailty model and for estimation, a two-step procedure is presented. In the addition, the asymptotic properties of the proposed estimators of regression parameters are established and an extensive simulation study suggests that the method works well. Finally, we apply the method to a set of real interval-censored data that motivated this study. © 2016, The International Biometric Society.

Comparison of 21-Gauge and 22-Gauge Aspiration Needle in Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration

PubMed Central

Akulian, Jason; Lechtzin, Noah; Yasin, Faiza; Kamdar, Biren; Ernst, Armin; Ost, David E.; Ray, Cynthia; Greenhill, Sarah R.; Jimenez, Carlos A.; Filner, Joshua; Feller-Kopman, David

2013-01-01

Background: Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a minimally invasive procedure originally performed using a 22-gauge (22G) needle. A recently introduced 21-gauge (21G) needle may improve the diagnostic yield and sample adequacy of EBUS-TBNA, but prior smaller studies have shown conflicting results. To our knowledge, this is the largest study undertaken to date to determine whether the 21G needle adds diagnostic benefit. Methods: We retrospectively evaluated the results of 1,299 patients from the American College of Chest Physicians Quality Improvement Registry, Education, and Evaluation (AQuIRE) Diagnostic Registry who underwent EBUS-TBNA between February 2009 and September 2010 at six centers throughout the United States. Data collection included patient demographics, sample adequacy, and diagnostic yield. Analysis consisted of univariate and multivariate hierarchical logistic regression comparing diagnostic yield and sample adequacy of EBUS-TBNA specimens by needle gauge. Results: A total of 1,235 patients met inclusion criteria. Sample adequacy was obtained in 94.9% of the 22G needle group and in 94.6% of the 21G needle group (P = .81). A diagnosis was made in 51.4% of the 22G and 51.3% of the 21G groups (P = .98). Multivariate hierarchical logistic regression showed no statistical difference in sample adequacy or diagnostic yield between the two groups. The presence of rapid onsite cytologic evaluation was associated with significantly fewer needle passes per procedure when using the 21G needle (P < .001). Conclusions: There is no difference in specimen adequacy or diagnostic yield between the 21G and 22G needle groups. EBUS-TBNA in conjunction with rapid onsite cytologic evaluation and a 21G needle is associated with fewer needle passes compared with a 22G needle. PMID:23632441

Partial Least Squares Regression Can Aid in Detecting Differential Abundance of Multiple Features in Sets of Metagenomic Samples

PubMed Central

Libiger, Ondrej; Schork, Nicholas J.

2015-01-01

It is now feasible to examine the composition and diversity of microbial communities (i.e., “microbiomes”) that populate different human organs and orifices using DNA sequencing and related technologies. To explore the potential links between changes in microbial communities and various diseases in the human body, it is essential to test associations involving different species within and across microbiomes, environmental settings and disease states. Although a number of statistical techniques exist for carrying out relevant analyses, it is unclear which of these techniques exhibit the greatest statistical power to detect associations given the complexity of most microbiome datasets. We compared the statistical power of principal component regression, partial least squares regression, regularized regression, distance-based regression, Hill's diversity measures, and a modified test implemented in the popular and widely used microbiome analysis methodology “Metastats” across a wide range of simulated scenarios involving changes in feature abundance between two sets of metagenomic samples. For this purpose, simulation studies were used to change the abundance of microbial species in a real dataset from a published study examining human hands. Each technique was applied to the same data, and its ability to detect the simulated change in abundance was assessed. We hypothesized that a small subset of methods would outperform the rest in terms of the statistical power. Indeed, we found that the Metastats technique modified to accommodate multivariate analysis and partial least squares regression yielded high power under the models and data sets we studied. The statistical power of diversity measure-based tests, distance-based regression and regularized regression was significantly lower. Our results provide insight into powerful analysis strategies that utilize information on species counts from large microbiome data sets exhibiting skewed frequency distributions obtained on a small to moderate number of samples. PMID:26734061

Dose-Dependent Effects of Statins for Patients with Aneurysmal Subarachnoid Hemorrhage: Meta-Regression Analysis.

PubMed

To, Minh-Son; Prakash, Shivesh; Poonnoose, Santosh I; Bihari, Shailesh

2018-05-01

The study uses meta-regression analysis to quantify the dose-dependent effects of statin pharmacotherapy on vasospasm, delayed ischemic neurologic deficits (DIND), and mortality in aneurysmal subarachnoid hemorrhage. Prospective, retrospective observational studies, and randomized controlled trials (RCTs) were retrieved by a systematic database search. Summary estimates were expressed as absolute risk (AR) for a given statin dose or control (placebo). Meta-regression using inverse variance weighting and robust variance estimation was performed to assess the effect of statin dose on transformed AR in a random effects model. Dose-dependence of predicted AR with 95% confidence interval (CI) was recovered by using Miller's Freeman-Tukey inverse. The database search and study selection criteria yielded 18 studies (2594 patients) for analysis. These included 12 RCTs, 4 retrospective observational studies, and 2 prospective observational studies. Twelve studies investigated simvastatin, whereas the remaining studies investigated atorvastatin, pravastatin, or pitavastatin, with simvastatin-equivalent doses ranging from 20 to 80 mg. Meta-regression revealed dose-dependent reductions in Freeman-Tukey-transformed AR of vasospasm (slope coefficient -0.00404, 95% CI -0.00720 to -0.00087; P = 0.0321), DIND (slope coefficient -0.00316, 95% CI -0.00586 to -0.00047; P = 0.0392), and mortality (slope coefficient -0.00345, 95% CI -0.00623 to -0.00067; P = 0.0352). The present meta-regression provides weak evidence for dose-dependent reductions in vasospasm, DIND and mortality associated with acute statin use after aneurysmal subarachnoid hemorrhage. However, the analysis was limited by substantial heterogeneity among individual studies. Greater dosing strategies are a potential consideration for future RCTs. Copyright © 2018 Elsevier Inc. All rights reserved.

Satellite-based studies of maize yield spatial variations and their causes in China

NASA Astrophysics Data System (ADS)

Zhao, Y.

2013-12-01

Maize production in China has been expanding significantly in the past two decades, but yield has become relatively stagnant in the past few years, and needs to be improved to meet increasing demand. Multiple studies found that the gap between potential and actual yield of maize is as large as 40% to 60% of yield potential. Although a few major causes of yield gap have been qualitatively identified with surveys, there has not been spatial analysis aimed at quantifying relative importance of specific biophysical and socio-economic causes, information which would be useful for targeting interventions. This study analyzes the causes of yield variation at field and village level in Quzhou county of North China Plain (NCP). We combine remote sensing and crop modeling to estimate yields in 2009-2012, and identify fields that are consistently high or low yielding. To establish the relationship between yield and potential factors, we gather data on those factors through a household survey. We select targeted survey fields such that not only both extremes of yield distribution but also all soil texture categories in the county is covered. Our survey assesses management and biophysical factors as well as social factors such as farmers' access to agronomic knowledge, which is approximated by distance to the closest demonstration plot or 'Science and technology backyard'. Our survey covers 10 townships, 53 villages and 180 fields. Three to ten farmers are surveyed depending on the amount of variation present among sub pixels of each field. According to survey results, we extract the amount of variation within as well as between villages and or soil type. The higher within village or within field variation, the higher importance of management factors. Factors such as soil type and access to knowledge are more represented by between village variation. Through regression and analysis of variance, we gain more quantitative and thorough understanding of causes to yield variation at village scale, which further explains the gap between average and highest achieved yield.

Photosynthetic and Canopy Characteristics of Different Varieties at the Early Elongation Stage and Their Relationships with the Cane Yield in Sugarcane

PubMed Central

Luo, Jun; Pan, Yong-Bao; Xu, Liping; Zhang, Yuye; Zhang, Hua; Chen, Rukai

2014-01-01

During sugarcane growth, the Early Elongation stage is critical to cane yield formation. In this study, parameters of 17 sugarcane varieties were determined at the Early Elongation stage using CI-301 photosynthesis measuring system and CI-100 digital plant canopy imager. The data analysis showed highly significant differences in leaf area index (LAI), mean foliage inclination angle (MFIA), transmission coefficient for diffused light penetration (TD), transmission coefficient for solar beam radiation penetration (TR), leaf distribution (LD), net photosynthetic rate (PN), transpiration rate (E), and stomatal conductance (GS) among sugarcane varieties. Based on the photosynthetic or canopy parameters, the 17 sugarcane varieties were classified into four categories. Through the factor analysis, nine parameters were represented by three principal factors, of which the cumulative rate of variance contributions reached 85.77%. A regression for sugarcane yield, with relative error of yield fitting less than 0.05, was successfully established: sugarcane yield = −27.19 − 1.69 × PN + 0.17 ×  E + 90.43 × LAI − 408.81 × LD + 0.0015 × NSH + 101.38 ×  D (R 2 = 0.928**). This study helps provide a theoretical basis and technical guidance for the screening of new sugarcane varieties with high net photosynthetic rate and ideal canopy structure. PMID:25045742

Statistical approaches to account for missing values in accelerometer data: Applications to modeling physical activity.

PubMed

Yue Xu, Selene; Nelson, Sandahl; Kerr, Jacqueline; Godbole, Suneeta; Patterson, Ruth; Merchant, Gina; Abramson, Ian; Staudenmayer, John; Natarajan, Loki

2018-04-01

Physical inactivity is a recognized risk factor for many chronic diseases. Accelerometers are increasingly used as an objective means to measure daily physical activity. One challenge in using these devices is missing data due to device nonwear. We used a well-characterized cohort of 333 overweight postmenopausal breast cancer survivors to examine missing data patterns of accelerometer outputs over the day. Based on these observed missingness patterns, we created psuedo-simulated datasets with realistic missing data patterns. We developed statistical methods to design imputation and variance weighting algorithms to account for missing data effects when fitting regression models. Bias and precision of each method were evaluated and compared. Our results indicated that not accounting for missing data in the analysis yielded unstable estimates in the regression analysis. Incorporating variance weights and/or subject-level imputation improved precision by >50%, compared to ignoring missing data. We recommend that these simple easy-to-implement statistical tools be used to improve analysis of accelerometer data.

Active sensing: An innovative tool for evaluating grain yield and nitrogen use efficiency of multiple wheat genotypes

NASA Astrophysics Data System (ADS)

Naser, Mohammed Abdulridha

Precision agricultural practices have significantly contributed to the improvement of crop productivity and profitability. Remote sensing based indices, such as Normalized Difference Vegetative Index (NDVI) have been used to obtain crop information. It is used to monitor crop development and to provide rapid and nondestructive estimates of plant biomass, nitrogen (N) content and grain yield. Remote sensing tools are helping improve nitrogen use efficiency (NUE) through nitrogen management and could also be useful for high NUE genotype selection. The objectives of this study were: (i) to determine if active sensor based NDVI readings can differentiate wheat genotypes, (ii) to determine if NDVI readings can be used to classify wheat genotypes into grain yield productivity classes, (iii) to identify and quantify the main sources of variation in NUE across wheat genotypes, and (iv) to determine if normalized difference vegetation index (NDVI) could characterize variability in NUE across wheat genotypes. This study was conducted in north eastern Colorado for two years, 2010 and 2011. The NDVI readings were taken weekly during the winter wheat growing season from March to late June, in 2010 and 2011 and NUE were calculated as partial factor productivity and as partial nitrogen balance at the end of the season. For objectives i and ii, the correlation between NDVI and grain yield was determined using Pearson's product-moment correlation coefficient (r) and linear regression analysis was used to explain the relationship between NDVI and grain yield. The K-means clustering algorithm was used to classify mean NDVI and mean grain yield into three classes. For objectives iii and iv, the parameters related to NUE were also calculated to measure their relative importance in genotypic variation of NUE and power regression analysis between NDVI and NUE was used to characterize the relationship between NDVI and NUE. The results indicate more consistent association between grain yield and NDVI and between NDVI and NUE later in the season, after anthesis and during mid-grain filling stage under dryland and a poor association in wheat grown in irrigated conditions. The results suggest that below saturation of NDVI values (about 0.9), (i.e. prior to full canopy closure and after the beginning of senescence or most of the season under dryland conditions) NDVI could assess grain yield and NUE. The results also indicate that nitrogen uptake efficiency was the main source of variation of NUE among genotypes grown in site-years with lower yield. Overall, results from this study demonstrate that NDVI readings successfully classified wheat genotypes into grain yield classes across dryland and irrigated conditions and characterized variability in NUE across wheat genotypes.

A non-destructive selection criterion for fibre content in jute : II. Regression approach.

PubMed

Arunachalam, V; Iyer, R D

1974-01-01

An experiment with ten populations of jute, comprising varieties and mutants of the two species Corchorus olitorius and C.capsularis was conducted at two different locations with the object of evolving an effective criterion for selecting superior single plants for fibre yield. At Delhi, variation existed only between varieties as a group and mutants as a group, while at Pusa variation also existed among the mutant populations of C. capsularis.A multiple regression approach was used to find the optimum combination of characters for prediction of fibre yield. A process of successive elimination of characters based on the coefficient of determination provided by individual regression equations was employed to arrive at the optimal set of characters for predicting fibre yield. It was found that plant height, basal and mid-diameters and basal and mid-dry fibre weights would provide such an optimal set.

REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits.

PubMed

Olivoto, T; Nardino, M; Carvalho, I R; Follmann, D N; Ferrari, M; Szareski, V J; de Pelegrin, A J; de Souza, V Q

2017-03-22

Methodologies using restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) in combination with sequential path analysis in maize are still limited in the literature. Therefore, the aims of this study were: i) to use REML/BLUP-based procedures in order to estimate variance components, genetic parameters, and genotypic values of simple maize hybrids, and ii) to fit stepwise regressions considering genotypic values to form a path diagram with multi-order predictors and minimum multicollinearity that explains the relationships of cause and effect among grain yield-related traits. Fifteen commercial simple maize hybrids were evaluated in multi-environment trials in a randomized complete block design with four replications. The environmental variance (78.80%) and genotype-vs-environment variance (20.83%) accounted for more than 99% of the phenotypic variance of grain yield, which difficult the direct selection of breeders for this trait. The sequential path analysis model allowed the selection of traits with high explanatory power and minimum multicollinearity, resulting in models with elevated fit (R 2 > 0.9 and ε < 0.3). The number of kernels per ear (NKE) and thousand-kernel weight (TKW) are the traits with the largest direct effects on grain yield (r = 0.66 and 0.73, respectively). The high accuracy of selection (0.86 and 0.89) associated with the high heritability of the average (0.732 and 0.794) for NKE and TKW, respectively, indicated good reliability and prospects of success in the indirect selection of hybrids with high-yield potential through these traits. The negative direct effect of NKE on TKW (r = -0.856), however, must be considered. The joint use of mixed models and sequential path analysis is effective in the evaluation of maize-breeding trials.

Factors related to well yield in the fractured-bedrock aquifer of New Hampshire

USGS Publications Warehouse

Moore, Richard Bridge; Schwartz, Gregory E.; Clark, Stewart F.; Walsh, Gregory J.; Degnan, James R.

2002-01-01

The New Hampshire Bedrock Aquifer Assessment was designed to provide information that can be used by communities, industry, professional consultants, and other interests to evaluate the ground-water development potential of the fractured-bedrock aquifer in the State. The assessment was done at statewide, regional, and well field scales to identify relations that potentially could increase the success in locating high-yield water supplies in the fractured-bedrock aquifer. statewide, data were collected for well construction and yield information, bedrock lithology, surficial geology, lineaments, topography, and various derivatives of these basic data sets. Regionally, geologic, fracture, and lineament data were collected for the Pinardville and Windham quadrangles in New Hampshire. The regional scale of the study examined the degree to which predictive well-yield relations, developed as part of the statewide reconnaissance investigation, could be improved by use of quadrangle-scale geologic mapping. Beginning in 1984, water-well contractors in the State were required to report detailed information on newly constructed wells to the New Hampshire Department of Environmental Services (NHDES). The reports contain basic data on well construction, including six characteristics used in this study?well yield, well depth, well use, method of construction, date drilled, and depth to bedrock (or length of casing). The NHDES has determined accurate georeferenced locations for more than 20,000 wells reported since 1984. The availability of this large data set provided an opportunity for a statistical analysis of bedrock-well yields. Well yields in the database ranged from zero to greater than 500 gallons per minute (gal/min). Multivariate regression was used as the primary statistical method of analysis because it is the most efficient tool for predicting a single variable with many potentially independent variables. The dependent variable that was explored in this study was the natural logarithm (ln) of the reported well yield. One complication with using well yield as a dependent variable is that yield also is a function of demand. An innovative statistical technique that involves the use of instrumental variables was implemented to compensate for the effect of demand on well yield. Results of the multivariate-regression model show that a variety of factors are either positively or negatively related to well yields. Using instrumental variables, well depth is positively related to total well yield. Other factors that were found to be positively related to well yield include (1) distance to the nearest waterbody; (2) size of the drainage area upgradient of a well; (3) well location in swales or valley bottoms in the Massabesic Gneiss Complex and Breakfast Hill Granite; (4) well proximity to lineaments, identified using high-altitude (1:80,000-scale) aerial photography, which are correlated with the primary fracture direction (regional analysis); (5) use of a cable tool rig for well drilling; and (6) wells drilled for commercial or public supply. Factors negatively related to well yields include sites underlain by foliated plutons, sites on steep slopes sites at high elevations, and sites on hilltops. Additionally, seven detailed geologic map units, identified during the detailed geologic mapping of the Pinardville and Windham quadrangles, were found to be positively or negatively related to well yields. Twenty-four geologic map units, depicted on the Bedrock Geologic Map of New Hampshire, also were found to be positively or negatively related to well yields. Maps or geographic information system (GIS) data sets identifying areas of various yield probabilities clearly display model results. Probability criteria developed in this investigation can be used to select areas where other techniques, such as geophysical techniques, can be applied to more closely identify potential drilling sites for high-yielding

Climatically driven yield variability of major crops in Khakassia (South Siberia)

NASA Astrophysics Data System (ADS)

Babushkina, Elena A.; Belokopytova, Liliana V.; Zhirnova, Dina F.; Shah, Santosh K.; Kostyakova, Tatiana V.

2018-06-01

We investigated the variability of yield of the three main crop cultures in the Khakassia Republic: spring wheat, spring barley, and oats. In terms of yield values, variability characteristics, and climatic response, the agricultural territory of Khakassia can be divided into three zones: (1) the Northern Zone, where crops yield has a high positive response to the amount of precipitation, May-July, and a moderately negative one to the temperatures of the same period; (2) the Central Zone, where crops yield depends mainly on temperatures; and (3) the Southern Zone, where climate has the least expressed impact on yield. The dominant pattern in the crops yield is caused by water stress during periods of high temperatures and low moisture supply with heat stress as additional reason. Differences between zones are due to combinations of temperature latitudinal gradient, precipitation altitudinal gradient, and the presence of a well-developed hydrological network and the irrigational system as moisture sources in the Central Zone. More detailed analysis shows differences in the climatic sensitivity of crops during phases of their vegetative growth and grain development and, to a lesser extent, during harvesting period. Multifactor linear regression models were constructed to estimate climate- and autocorrelation-induced variability of the crops yield. These models allowed prediction of the possibility of yield decreasing by at least 2-11% in the next decade due to increasing of the regional summer temperatures.

Climatically driven yield variability of major crops in Khakassia (South Siberia)

NASA Astrophysics Data System (ADS)

Babushkina, Elena A.; Belokopytova, Liliana V.; Zhirnova, Dina F.; Shah, Santosh K.; Kostyakova, Tatiana V.

2017-12-01

We investigated the variability of yield of the three main crop cultures in the Khakassia Republic: spring wheat, spring barley, and oats. In terms of yield values, variability characteristics, and climatic response, the agricultural territory of Khakassia can be divided into three zones: (1) the Northern Zone, where crops yield has a high positive response to the amount of precipitation, May-July, and a moderately negative one to the temperatures of the same period; (2) the Central Zone, where crops yield depends mainly on temperatures; and (3) the Southern Zone, where climate has the least expressed impact on yield. The dominant pattern in the crops yield is caused by water stress during periods of high temperatures and low moisture supply with heat stress as additional reason. Differences between zones are due to combinations of temperature latitudinal gradient, precipitation altitudinal gradient, and the presence of a well-developed hydrological network and the irrigational system as moisture sources in the Central Zone. More detailed analysis shows differences in the climatic sensitivity of crops during phases of their vegetative growth and grain development and, to a lesser extent, during harvesting period. Multifactor linear regression models were constructed to estimate climate- and autocorrelation-induced variability of the crops yield. These models allowed prediction of the possibility of yield decreasing by at least 2-11% in the next decade due to increasing of the regional summer temperatures.

Steep discounting of delayed monetary and food rewards in obesity: a meta-analysis.

PubMed

Amlung, M; Petker, T; Jackson, J; Balodis, I; MacKillop, J

2016-08-01

An increasing number of studies have investigated delay discounting (DD) in relation to obesity, but with mixed findings. This meta-analysis synthesized the literature on the relationship between monetary and food DD and obesity, with three objectives: (1) to characterize the relationship between DD and obesity in both case-control comparisons and continuous designs; (2) to examine potential moderators, including case-control v. continuous design, money v. food rewards, sample sex distribution, and sample age (18 years); and (3) to evaluate publication bias. From 134 candidate articles, 39 independent investigations yielded 29 case-control and 30 continuous comparisons (total n = 10 278). Random-effects meta-analysis was conducted using Cohen's d as the effect size. Publication bias was evaluated using fail-safe N, Begg-Mazumdar and Egger tests, meta-regression of publication year and effect size, and imputation of missing studies. The primary analysis revealed a medium effect size across studies that was highly statistically significant (d = 0.43, p < 10-14). None of the moderators examined yielded statistically significant differences, although notably larger effect sizes were found for studies with case-control designs, food rewards and child/adolescent samples. Limited evidence of publication bias was present, although the Begg-Mazumdar test and meta-regression suggested a slightly diminishing effect size over time. Steep DD of food and money appears to be a robust feature of obesity that is relatively consistent across the DD assessment methodologies and study designs examined. These findings are discussed in the context of research on DD in drug addiction, the neural bases of DD in obesity, and potential clinical applications.

Impact of Uncertainties in Exposure Assessment on Thyroid Cancer Risk among Persons in Belarus Exposed as Children or Adolescents Due to the Chernobyl Accident.

PubMed

Little, Mark P; Kwon, Deukwoo; Zablotska, Lydia B; Brenner, Alina V; Cahoon, Elizabeth K; Rozhko, Alexander V; Polyanskaya, Olga N; Minenko, Victor F; Golovanov, Ivan; Bouville, André; Drozdovitch, Vladimir

2015-01-01

The excess incidence of thyroid cancer in Ukraine and Belarus observed a few years after the Chernobyl accident is considered to be largely the result of 131I released from the reactor. Although the Belarus thyroid cancer prevalence data has been previously analyzed, no account was taken of dose measurement error. We examined dose-response patterns in a thyroid screening prevalence cohort of 11,732 persons aged under 18 at the time of the accident, diagnosed during 1996-2004, who had direct thyroid 131I activity measurement, and were resident in the most radio-actively contaminated regions of Belarus. Three methods of dose-error correction (regression calibration, Monte Carlo maximum likelihood, Bayesian Markov Chain Monte Carlo) were applied. There was a statistically significant (p<0.001) increasing dose-response for prevalent thyroid cancer, irrespective of regression-adjustment method used. Without adjustment for dose errors the excess odds ratio was 1.51 Gy- (95% CI 0.53, 3.86), which was reduced by 13% when regression-calibration adjustment was used, 1.31 Gy- (95% CI 0.47, 3.31). A Monte Carlo maximum likelihood method yielded an excess odds ratio of 1.48 Gy- (95% CI 0.53, 3.87), about 2% lower than the unadjusted analysis. The Bayesian method yielded a maximum posterior excess odds ratio of 1.16 Gy- (95% BCI 0.20, 4.32), 23% lower than the unadjusted analysis. There were borderline significant (p = 0.053-0.078) indications of downward curvature in the dose response, depending on the adjustment methods used. There were also borderline significant (p = 0.102) modifying effects of gender on the radiation dose trend, but no significant modifying effects of age at time of accident, or age at screening as modifiers of dose response (p>0.2). In summary, the relatively small contribution of unshared classical dose error in the current study results in comparatively modest effects on the regression parameters.

[Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].

PubMed

Han, Zhao-ying; Zhu, Xi-cun; Fang, Xian-yi; Wang, Zhuo-yuan; Wang, Ling; Zhao, Geng-Xing; Jiang, Yuan-mao

2016-03-01

Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.

Advanced imaging technologies increase detection of dysplasia and neoplasia in patients with Barrett's esophagus: a meta-analysis and systematic review.

PubMed

Qumseya, Bashar J; Wang, Haibo; Badie, Nicole; Uzomba, Rosemary N; Parasa, Sravanthi; White, Donna L; Wolfsen, Herbert; Sharma, Prateek; Wallace, Michael B

2013-12-01

US guidelines recommend surveillance of patients with Barrett's esophagus (BE) to detect dysplasia. BE conventionally is monitored via white-light endoscopy (WLE) and a collection of random biopsy specimens. However, this approach does not definitively or consistently detect areas of dysplasia. Advanced imaging technologies can increase the detection of dysplasia and cancer. We investigated whether these imaging technologies can increase the diagnostic yield for the detection of neoplasia in patients with BE, compared with WLE and analysis of random biopsy specimens. We performed a systematic review, using Medline and Embase, to identify relevant peer-review studies. Fourteen studies were included in the final analysis, with a total of 843 patients. Our metameter (estimate) of interest was the paired-risk difference (RD), defined as the difference in yield of the detection of dysplasia or cancer using advanced imaging vs WLE. The estimated paired-RD and 95% confidence interval (CI) were obtained using random-effects models. Heterogeneity was assessed by means of the Q statistic and the I(2) statistic. An exploratory meta-regression was performed to look for associations between the metameter and potential confounders or modifiers. Overall, advanced imaging techniques increased the diagnostic yield for detection of dysplasia or cancer by 34% (95% CI, 20%-56%; P < .0001). A subgroup analysis showed that virtual chromoendoscopy significantly increased the diagnostic yield (RD, 0.34; 95% CI, 0.14-0.56; P < .0001). The RD for chromoendoscopy was 0.35 (95% CI, 0.13-0.56; P = .0001). There was no significant difference between virtual chromoendoscopy and chromoendoscopy, based on Student t test analysis (P = .45). Based on a meta-analysis, advanced imaging techniques such as chromoendoscopy or virtual chromoendoscopy significantly increase the diagnostic yield for identification of dysplasia or cancer in patients with BE. Copyright © 2013 AGA Institute. Published by Elsevier Inc. All rights reserved.

The yield of colorectal cancer among fast track patients with normocytic and microcytic anaemia.

PubMed

Panagiotopoulou, I G; Fitzrol, D; Parker, R A; Kuzhively, J; Luscombe, N; Wells, A D; Menon, M; Bajwa, F M; Watson, M A

2014-05-01

We receive fast track referrals on the basis of iron deficiency anaemia (IDA) for patients with normocytic anaemia or for patients with no iron studies. This study examined the yield of colorectal cancer (CRC) among fast track patients to ascertain whether awaiting confirmation of IDA is necessary prior to performing bowel investigations. A review was undertaken of 321 and 930 consecutive fast track referrals from Centre A and Centre B respectively. Contingency tables were analysed using Fisher's exact test. Logistic regression analyses were performed to investigate significant predictors of CRC. Overall, 229 patients were included from Centre A and 689 from Centre B. The odds ratio for microcytic anaemia versus normocytic anaemia in the outcome of CRC was 1.3 (95% confidence interval [CI]: 0.5-3.9) for Centre A and 1.6 (95% CI: 0.8-3.3) for Centre B. In a logistic regression analysis (Centre B only), no significant difference in CRC rates was seen between microcytic and normocytic anaemia (adjusted odds ratio: 1.9, 95% CI: 0.9-3.9). There was no statistically significant difference in the yield of CRC between microcytic and normocytic anaemia (p=0.515, Fisher's exact test) in patients with anaemia only and no colorectal symptoms. Finally, CRC cases were seen in both microcytic and normocytic groups with or without low ferritin. There is no significant difference in the yield of CRC between fast track patients with microcytic and normocytic anaemia. This study provides insufficient evidence to support awaiting confirmation of IDA in fast track patients with normocytic anaemia prior to requesting bowel investigations.

Modelling crop yield in Iberia under drought conditions

NASA Astrophysics Data System (ADS)

Ribeiro, Andreia; Páscoa, Patrícia; Russo, Ana; Gouveia, Célia

2017-04-01

The improved assessment of the cereal yield and crop loss under drought conditions are essential to meet the increasing economy demands. The growing frequency and severity of the extreme drought conditions in the Iberian Peninsula (IP) has been likely responsible for negative impacts on agriculture, namely on crop yield losses. Therefore, a continuous monitoring of vegetation activity and a reliable estimation of drought impacts is crucial to contribute for the agricultural drought management and development of suitable information tools. This works aims to assess the influence of drought conditions in agricultural yields over the IP, considering cereal yields from mainly rainfed agriculture for the provinces with higher productivity. The main target is to develop a strategy to model drought risk on agriculture for wheat yield at a province level. In order to achieve this goal a combined assessment was made using a drought indicator (Standardized Precipitation Evapotranspiration Index, SPEI) to evaluate drought conditions together with a widely used vegetation index (Normalized Difference Vegetation Index, NDVI) to monitor vegetation activity. A correlation analysis between detrended wheat yield and SPEI was performed in order to assess the vegetation response to each time scale of drought occurrence and also identify the moment of the vegetative cycle when the crop yields are more vulnerable to drought conditions. The time scales and months of SPEI, together with the months of NDVI, better related with wheat yield were chosen to perform a multivariate regression analysis to simulate crop yield. Model results are satisfactory and highlighted the usefulness of such analysis in the framework of developing a drought risk model for crop yields. In terms of an operational point of view, the results aim to contribute to an improved understanding of crop yield management under dry conditions, particularly adding substantial information on the advantages of combining vegetation and hydro-meteorological drought indices for the assessment of cereal yield. Moreover, the present study will provide some guidance on user's decision making process in agricultural practices in the IP, assisting farmers in deciding whether to purchase crop insurance. Acknowledgements: This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project IMDROFLOOD (WaterJPI/0004/2014). Ana Russo thanks FCT for granted support (SFRH/BPD/99757/2014). Andreia Ribeiro also thanks FCT for grant PD/BD/114481/2016.

Trochanteric entry femoral nails yield better femoral version and lower revision rates-A large cohort multivariate regression analysis.

PubMed

Yoon, Richard S; Gage, Mark J; Galos, David K; Donegan, Derek J; Liporace, Frank A

2017-06-01

Intramedullary nailing (IMN) has become the standard of care for the treatment of most femoral shaft fractures. Different IMN options include trochanteric and piriformis entry as well as retrograde nails, which may result in varying degrees of femoral rotation. The objective of this study was to analyze postoperative femoral version between three types of nails and to delineate any significant differences in femoral version (DFV) and revision rates. Over a 10-year period, 417 patients underwent IMN of a diaphyseal femur fracture (AO/OTA 32A-C). Of these patients, 316 met inclusion criteria and obtained postoperative computed tomography (CT) scanograms to calculate femoral version and were thus included in the study. In this study, our main outcome measure was the difference in femoral version (DFV) between the uninjured limb and the injured limb. The effect of the following variables on DFV and revision rates were determined via univariate, multivariate, and ordinal regression analyses: gender, age, BMI, ethnicity, mechanism of injury, operative side, open fracture, and table type/position. Statistical significance was set at p<0.05. A total of 316 patients were included. Piriformis entry nails made up the majority (n=141), followed by retrograde (n=108), then trochanteric entry nails (n=67). Univariate regression analysis revealed that a lower BMI was significantly associated with a lower DFV (p=0.006). Controlling for possible covariables, multivariate analysis yielded a significantly lower DFV for trochanteric entry nails than piriformis or retrograde nails (7.9±6.10 vs. 9.5±7.4 vs. 9.4±7.8°, p<0.05). Using revision as an endpoint, trochanteric entry nails also had a significantly lower revision rate, even when controlling for all other variables (p<0.05). Comparative, objective comparisons between DFV between different nails based on entry point revealed that trochanteric nails had a significantly lower DFV and a lower revision rate, even after regression analysis. However, this is not to state that the other nail types exhibited abnormal DFV. Translation to the clinical impact of a few degrees of DFV is also unknown. Future studies to more in-depth study the intricacies of femoral version may lead to improved technology in addition to potentially improved clinical outcomes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Genome wide association study (GWAS) for grain yield in rice cultivated under water deficit.

PubMed

Pantalião, Gabriel Feresin; Narciso, Marcelo; Guimarães, Cléber; Castro, Adriano; Colombari, José Manoel; Breseghello, Flavio; Rodrigues, Luana; Vianello, Rosana Pereira; Borba, Tereza Oliveira; Brondani, Claudio

2016-12-01

The identification of rice drought tolerant materials is crucial for the development of best performing cultivars for the upland cultivation system. This study aimed to identify markers and candidate genes associated with drought tolerance by Genome Wide Association Study analysis, in order to develop tools for use in rice breeding programs. This analysis was made with 175 upland rice accessions (Oryza sativa), evaluated in experiments with and without water restriction, and 150,325 SNPs. Thirteen SNP markers associated with yield under drought conditions were identified. Through stepwise regression analysis, eight SNP markers were selected and validated in silico, and when tested by PCR, two out of the eight SNP markers were able to identify a group of rice genotypes with higher productivity under drought. These results are encouraging for deriving markers for the routine analysis of marker assisted selection. From the drought experiment, including the genes inherited in linkage blocks, 50 genes were identified, from which 30 were annotated, and 10 were previously related to drought and/or abiotic stress tolerance, such as the transcription factors WRKY and Apetala2, and protein kinases.

Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran

2010-01-01

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

Food Crops Response to Climate Change

NASA Astrophysics Data System (ADS)

Butler, E.; Huybers, P.

2009-12-01

Projections of future climate show a warming world and heterogeneous changes in precipitation. Generally, warming temperatures indicate a decrease in crop yields where they are currently grown. However, warmer climate will also open up new areas at high latitudes for crop production. Thus, there is a question whether the warmer climate with decreased yields but potentially increased growing area will produce a net increase or decrease of overall food crop production. We explore this question through a multiple linear regression model linking temperature and precipitation to crop yield. Prior studies have emphasised temporal regression which indicate uniformly decreased yields, but neglect the potentially increased area opened up for crop production. This study provides a compliment to the prior work by exploring this spatial variation. We explore this subject with a multiple linear regression model from temperature, precipitation and crop yield data over the United States. The United States was chosen as the training region for the model because there are good crop data available over the same time frame as climate data and presumably the yield from crops in the United States is optimized with respect to potential yield. We study corn, soybeans, sorghum, hard red winter wheat and soft red winter wheat using monthly averages of temperature and precipitation from NCEP reanalysis and yearly yield data from the National Agriculture Statistics Service for 1948-2008. The use of monthly averaged temperature and precipitation, which neglect extreme events that can have a significant impact on crops limits this study as does the exclusive use of United States agricultural data. The GFDL 2.1 model under a 720ppm CO2 scenario provides temperature and precipitation fields for 2040-2100 which are used to explore how the spatial regions available for crop production will change under these new conditions.

Simple agrometeorological models for estimating Guineagrass yield in Southeast Brazil.

PubMed

Pezzopane, José Ricardo Macedo; da Cruz, Pedro Gomes; Santos, Patricia Menezes; Bosi, Cristiam; de Araujo, Leandro Coelho

2014-09-01

The objective of this work was to develop and evaluate agrometeorological models to simulate the production of Guineagrass. For this purpose, we used forage yield from 54 growing periods between December 2004-January 2007 and April 2010-March 2012 in irrigated and non-irrigated pastures in São Carlos, São Paulo state, Brazil (latitude 21°57'42″ S, longitude 47°50'28″ W and altitude 860 m). Initially we performed linear regressions between the agrometeorological variables and the average dry matter accumulation rate for irrigated conditions. Then we determined the effect of soil water availability on the relative forage yield considering irrigated and non-irrigated pastures, by means of segmented linear regression among water balance and relative production variables (dry matter accumulation rates with and without irrigation). The models generated were evaluated with independent data related to 21 growing periods without irrigation in the same location, from eight growing periods in 2000 and 13 growing periods between December 2004-January 2007 and April 2010-March 2012. The results obtained show the satisfactory predictive capacity of the agrometeorological models under irrigated conditions based on univariate regression (mean temperature, minimum temperature and potential evapotranspiration or degreedays) or multivariate regression. The response of irrigation on production was well correlated with the climatological water balance variables (ratio between actual and potential evapotranspiration or between actual and maximum soil water storage). The models that performed best for estimating Guineagrass yield without irrigation were based on minimum temperature corrected by relative soil water storage, determined by the ratio between the actual soil water storage and the soil water holding capacity.irrigation in the same location, in 2000, 2010 and 2011. The results obtained show the satisfactory predictive capacity of the agrometeorological models under irrigated conditions based on univariate regression (mean temperature, potential evapotranspiration or degree-days) or multivariate regression. The response of irrigation on production was well correlated with the climatological water balance variables (ratio between actual and potential evapotranspiration or between actual and maximum soil water storage). The models that performed best for estimating Guineagrass yield without irrigation were based on degree-days corrected by the water deficit factor.

Genetic analyses of protein yield in dairy cows applying random regression models with time-dependent and temperature x humidity-dependent covariates.

PubMed

Brügemann, K; Gernand, E; von Borstel, U U; König, S

2011-08-01

Data used in the present study included 1,095,980 first-lactation test-day records for protein yield of 154,880 Holstein cows housed on 196 large-scale dairy farms in Germany. Data were recorded between 2002 and 2009 and merged with meteorological data from public weather stations. The maximum distance between each farm and its corresponding weather station was 50 km. Hourly temperature-humidity indexes (THI) were calculated using the mean of hourly measurements of dry bulb temperature and relative humidity. On the phenotypic scale, an increase in THI was generally associated with a decrease in daily protein yield. For genetic analyses, a random regression model was applied using time-dependent (d in milk, DIM) and THI-dependent covariates. Additive genetic and permanent environmental effects were fitted with this random regression model and Legendre polynomials of order 3 for DIM and THI. In addition, the fixed curve was modeled with Legendre polynomials of order 3. Heterogeneous residuals were fitted by dividing DIM into 5 classes, and by dividing THI into 4 classes, resulting in 20 different classes. Additive genetic variances for daily protein yield decreased with increasing degrees of heat stress and were lowest at the beginning of lactation and at extreme THI. Due to higher additive genetic variances, slightly higher permanent environment variances, and similar residual variances, heritabilities were highest for low THI in combination with DIM at the end of lactation. Genetic correlations among individual values for THI were generally >0.90. These trends from the complex random regression model were verified by applying relatively simple bivariate animal models for protein yield measured in 2 THI environments; that is, defining a THI value of 60 as a threshold. These high correlations indicate the absence of any substantial genotype × environment interaction for protein yield. However, heritabilities and additive genetic variances from the random regression model tended to be slightly higher in the THI range corresponding to cows' comfort zone. Selecting such superior environments for progeny testing can contribute to an accurate genetic differentiation among selection candidates. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Relationship between blood manganese and blood pressure in the Korean general population according to KNHANES 2008

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

Lee, Byung-Kook; Kim, Yangho, E-mail: yanghokm@nuri.net

Introduction: We present data on the association of manganese (Mn) level with hypertension in a representative sample of the adult Korean population who participated in the Korean National Health and Nutrition Examination Survey (KNHANES) 2008. Methods: This study was based on the data obtained by KNHANES 2008, which was conducted for three years (2007-2009) using a rolling sampling design involving a complex, stratified, multistage, probability-cluster survey of a representative sample of the noninstitutionalized civilian population of South Korea. Results: Multiple regression analysis after controlling for covariates, including gender, age, regional area, education level, smoking, drinking status, hemoglobin, and serum creatinine,more » showed that the beta coefficients of log blood Mn were 3.514, 1.878, and 2.517 for diastolic blood pressure, and 3.593, 2.449, and 2.440 for systolic blood pressure in female, male, and all participants, respectively. Multiple regression analysis including three other blood metals, lead, mercury, and cadmium, revealed no significant effects of the three metals on blood pressure and showed no effect on the association between blood Mn and blood pressure. In addition, doubling the blood Mn increased the risk of hypertension 1.828, 1.573, and 1.567 fold in women, men, and all participants, respectively, after adjustment for covariates. The addition of blood lead, mercury, and cadmium as covariates did not affect the association between blood Mn and the prevalence of hypertension. Conclusion: Blood Mn level was associated with an increased risk of hypertension in a representative sample of the Korean adult population. - Highlights: {yields} We showed the association of manganese with hypertension in Korean population. {yields} This study was based on the data obtained by KNHANES 2008. {yields} Blood manganese level was associated with an increased risk of hypertension.« less

Genetic Analysis of Milk Yield in First-Lactation Holstein Friesian in Ethiopia: A Lactation Average vs Random Regression Test-Day Model Analysis

PubMed Central

Meseret, S.; Tamir, B.; Gebreyohannes, G.; Lidauer, M.; Negussie, E.

2015-01-01

The development of effective genetic evaluations and selection of sires requires accurate estimates of genetic parameters for all economically important traits in the breeding goal. The main objective of this study was to assess the relative performance of the traditional lactation average model (LAM) against the random regression test-day model (RRM) in the estimation of genetic parameters and prediction of breeding values for Holstein Friesian herds in Ethiopia. The data used consisted of 6,500 test-day (TD) records from 800 first-lactation Holstein Friesian cows that calved between 1997 and 2013. Co-variance components were estimated using the average information restricted maximum likelihood method under single trait animal model. The estimate of heritability for first-lactation milk yield was 0.30 from LAM whilst estimates from the RRM model ranged from 0.17 to 0.29 for the different stages of lactation. Genetic correlations between different TDs in first-lactation Holstein Friesian ranged from 0.37 to 0.99. The observed genetic correlation was less than unity between milk yields at different TDs, which indicated that the assumption of LAM may not be optimal for accurate evaluation of the genetic merit of animals. A close look at estimated breeding values from both models showed that RRM had higher standard deviation compared to LAM indicating that the TD model makes efficient utilization of TD information. Correlations of breeding values between models ranged from 0.90 to 0.96 for different group of sires and cows and marked re-rankings were observed in top sires and cows in moving from the traditional LAM to RRM evaluations. PMID:26194217

Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper-spectral image data.

PubMed

Montesinos-López, Abelardo; Montesinos-López, Osval A; Cuevas, Jaime; Mata-López, Walter A; Burgueño, Juan; Mondal, Sushismita; Huerta, Julio; Singh, Ravi; Autrique, Enrique; González-Pérez, Lorena; Crossa, José

2017-01-01

Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. With the bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield and biomass. However, since vegetation indices only use some wavelengths (referred to as bands), we propose using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize (Aguate et al. in Crop Sci 57(5):1-8, 2017) and wheat (Montesinos-López et al. in Plant Methods 13(4):1-23, 2017) breeding trials indicated that using all bands produced better prediction accuracy than vegetation indices. However, until now, these prediction models have not accounted for the effects of genotype × environment (G × E) and band × environment (B × E) interactions incorporating genomic or pedigree information. In this study, we propose Bayesian functional regression models that take into account all available bands, genomic or pedigree information, the main effects of lines and environments, as well as G × E and B × E interaction effects. The data set used is comprised of 976 wheat lines evaluated for grain yield in three environments (Drought, Irrigated and Reduced Irrigation). The reflectance data were measured in 250 discrete narrow bands ranging from 392 to 851 nm (nm). The proposed Bayesian functional regression models were implemented using two types of basis: B-splines and Fourier. Results of the proposed Bayesian functional regression models, including all the wavelengths for predicting grain yield, were compared with results from conventional models with and without bands. We observed that the models with B × E interaction terms were the most accurate models, whereas the functional regression models (with B-splines and Fourier basis) and the conventional models performed similarly in terms of prediction accuracy. However, the functional regression models are more parsimonious and computationally more efficient because the number of beta coefficients to be estimated is 21 (number of basis), rather than estimating the 250 regression coefficients for all bands. In this study adding pedigree or genomic information did not increase prediction accuracy.

Relationships between locus of control and paranormal beliefs.

PubMed

Newby, Robert W; Davis, Jessica Boyette

2004-06-01

The present study investigated the associations between scores on paranormal beliefs, locus of control, and certain psychological processes such as affect and cognitions as measured by the Linguistic Inquiry and Word Count. Analysis yielded significant correlations between scores on Locus of Control and two subscales of Tobacyk's (1988) Revised Paranormal Beliefs Scale, New Age Philosophy and Traditional Paranormal Beliefs. A step-wise multiple regression analysis indicated that Locus of Control was significantly related to New Age Philosophy. Other correlations were found between Tobacyk's subscales, Locus of Control, and three processes measured by the Linguistic Inquiry and Word Count.

Quantifying ruminal nitrogen metabolism using the omasal sampling technique in cattle--a meta-analysis.

PubMed

Broderick, G A; Huhtanen, P; Ahvenjärvi, S; Reynal, S M; Shingfield, K J

2010-07-01

Mixed model analysis of data from 32 studies (122 diets) was used to evaluate the precision and accuracy of the omasal sampling technique for quantifying ruminal-N metabolism and to assess the relationships between nonammonia-N flow at the omasal canal and milk protein yield. Data were derived from experiments in cattle fed North American diets (n=36) based on alfalfa silage, corn silage, and corn grain and Northern European diets (n=86) composed of grass silage and barley-based concentrates. In all studies, digesta flow was quantified using a triple-marker approach. Linear regressions were used to predict microbial-N flow to the omasum from intake of dry matter (DM), organic matter (OM), or total digestible nutrients. Efficiency of microbial-N synthesis increased with DM intake and there were trends for increased efficiency with elevated dietary concentrations of crude protein (CP) and rumen-degraded protein (RDP) but these effects were small. Regression of omasal rumen-undegraded protein (RUP) flow on CP intake indicated that an average 32% of dietary CP escaped and 68% was degraded in the rumen. The slope from regression of observed omasal flows of RUP on flows predicted by the National Research Council (2001) model indicated that NRC predicted greater RUP supply. Measured microbial-N flow was, on average, 26% greater than that predicted by the NRC model. Zero ruminal N-balance (omasal CP flow=CP intake) was obtained at dietary CP and RDP concentrations of 147 and 106 g/kg of DM, corresponding to ruminal ammonia-N and milk urea N concentrations of 7.1 and 8.3mg/100mL, respectively. Milk protein yield was positively related to the efficiency of microbial-N synthesis and measured RUP concentration. Improved efficiency of microbial-N synthesis and reduced ruminal CP degradability were positively associated with efficiency of capture of dietary N as milk N. In conclusion, the results of this study indicate that the omasal sampling technique yields valuable estimates of RDP, RUP, and ruminal microbial protein supply in cattle. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Critical configurations (determinantal loci) for range and range difference satellite networks

NASA Technical Reports Server (NTRS)

Tsimis, E.

1973-01-01

The observational modes of Geometric Satellite Geodesy are discussed. The geometrical analysis of the problem yielded a regression model for the adjustment of the observations along with a suitable and convenient metric for the least-squares criterion. The determinantal loci (critical configurations) for range networks are analyzed. An attempt is made to apply elements of the theory of variants for this purpose. The use of continuously measured range differences for loci determination is proposed.

Visual Analysis of North Atlantic Hurricane Trends Using Parallel Coordinates and Statistical Techniques

DTIC Science & Technology

2008-07-07

analyzing multivariate data sets. The system was developed using the Java Development Kit (JDK) version 1.5; and it yields interactive performance on a... script and captures output from the MATLAB’s “regress” and “stepwisefit” utilities that perform simple and stepwise regression, respectively. The MATLAB...Statistical Association, vol. 85, no. 411, pp. 664–675, 1990. [9] H. Hauser, F. Ledermann, and H. Doleisch, “ Angular brushing of extended parallel coordinates

Approximate Probabilistic Methods for Survivability/Vulnerability Analysis of Strategic Structures.

DTIC Science & Technology

1978-07-15

weapon yield, in kilotons; K = energy coupling factor; C = coefficient determined from linear regression; a, b = exponents determined from linear...hn(l + .582 00 = 0.54 In the case of the applied pressure, according to Perret and Bass (1975), the variabilities in the exponents a and b of Eq. 32...ATTN: WESSF, L. Ingram ATTN: ATC-T ATTN: Library ATTN: F. Brown BMD Systems Command ATTN: J. Strange Deoartment of the Army ATTN: BMDSC-H, N. Hurst

The Role of Inflation and Price Escalation Adjustments in Properly Estimating Program Costs: F-35 Case Study

DTIC Science & Technology

2016-03-01

regression models that yield hedonic price indexes is closely related to standard techniques for developing cost estimating relationships ( CERs ...October 2014). iii analysis) and derives a price index from the coefficients on variables reflecting the year of purchase. In CER development, the...index. The relevant cost metric in both cases is unit recurring flyaway (URF) costs. For the current project, we develop a “Baseline” CER model, taking

Relationships among ultrasonic and mechanical properties of cancellous bone in human calcaneus in vitro.

PubMed

Wear, Keith A; Nagaraja, Srinidhi; Dreher, Maureen L; Sadoughi, Saghi; Zhu, Shan; Keaveny, Tony M

2017-10-01

Clinical bone sonometers applied at the calcaneus measure broadband ultrasound attenuation and speed of sound. However, the relation of ultrasound measurements to bone strength is not well-characterized. Addressing this issue, we assessed the extent to which ultrasonic measurements convey in vitro mechanical properties in 25 human calcaneal cancellous bone specimens (approximately 2×4×2cm). Normalized broadband ultrasound attenuation, speed of sound, and broadband ultrasound backscatter were measured with 500kHz transducers. To assess mechanical properties, non-linear finite element analysis, based on micro-computed tomography images (34-micron cubic voxel), was used to estimate apparent elastic modulus, overall specimen stiffness, and apparent yield stress, with models typically having approximately 25-30 million elements. We found that ultrasound parameters were correlated with mechanical properties with R=0.70-0.82 (p<0.001). Multiple regression analysis indicated that ultrasound measurements provide additional information regarding mechanical properties beyond that provided by bone quantity alone (p≤0.05). Adding ultrasound variables to linear regression models based on bone quantity improved adjusted squared correlation coefficients from 0.65 to 0.77 (stiffness), 0.76 to 0.81 (apparent modulus), and 0.67 to 0.73 (yield stress). These results indicate that ultrasound can provide complementary (to bone quantity) information regarding mechanical behavior of cancellous bone. Published by Elsevier Inc.

Impact of covariate models on the assessment of the air pollution-mortality association in a single- and multipollutant context.

PubMed

Sacks, Jason D; Ito, Kazuhiko; Wilson, William E; Neas, Lucas M

2012-10-01

With the advent of multicity studies, uniform statistical approaches have been developed to examine air pollution-mortality associations across cities. To assess the sensitivity of the air pollution-mortality association to different model specifications in a single and multipollutant context, the authors applied various regression models developed in previous multicity time-series studies of air pollution and mortality to data from Philadelphia, Pennsylvania (May 1992-September 1995). Single-pollutant analyses used daily cardiovascular mortality, fine particulate matter (particles with an aerodynamic diameter ≤2.5 µm; PM(2.5)), speciated PM(2.5), and gaseous pollutant data, while multipollutant analyses used source factors identified through principal component analysis. In single-pollutant analyses, risk estimates were relatively consistent across models for most PM(2.5) components and gaseous pollutants. However, risk estimates were inconsistent for ozone in all-year and warm-season analyses. Principal component analysis yielded factors with species associated with traffic, crustal material, residual oil, and coal. Risk estimates for these factors exhibited less sensitivity to alternative regression models compared with single-pollutant models. Factors associated with traffic and crustal material showed consistently positive associations in the warm season, while the coal combustion factor showed consistently positive associations in the cold season. Overall, mortality risk estimates examined using a source-oriented approach yielded more stable and precise risk estimates, compared with single-pollutant analyses.

Estimating the responses of winter wheat yields to moisture variations in the past 35 years in Jiangsu Province of China

PubMed Central

Ding, Jinfeng; Li, Chunyan

2018-01-01

Jiangsu is an important agricultural province in China. Winter wheat, as the second major grain crop in the province, is greatly affected by moisture variations. The objective of this study was to investigate whether there were significant trends in changes in the moisture conditions during wheat growing seasons over the past decades and how the wheat yields responded to different moisture levels by means of a popular drought index, the Standardized Precipitation Evapotranspiration Index (SPEI). The study started with a trend analysis and quantification of the moisture conditions with the Mann-Kendall test and Sen’s Slope method, respectively. Then, correlation analysis was carried out to determine the relationship between de-trended wheat yields and multi-scalar SPEI. Finally, a multivariate panel regression model was established to reveal the quantitative yield responses to moisture variations. The results showed that the moisture conditions in Jiangsu were generally at a normal level, but this century appeared slightly drier in because of the relatively high temperatures. There was a significant correlation between short time scale SPEI values and wheat yields. Among the three critical stages of wheat development, the SPEI values in the late growth stage (April-June) had a closer linkage to the yields than in the seedling stage (October-November) and the over-wintering stage (December-February). Moreover, the yield responses displayed an asymmetric characteristic, namely, moisture excess led to higher yield losses compared to moisture deficit in this region. The maximum yield increment could be obtained under the moisture level of slight drought according to the 3-month SPEI at the late growth stage, while extreme wetting resulted in the most severe yield losses. The moisture conditions in the first 15 years of the 21st century were more favorable than in the last 20 years of the 20th century for wheat production in Jiangsu. PMID:29329353

Estimating the responses of winter wheat yields to moisture variations in the past 35 years in Jiangsu Province of China.

PubMed

Xu, Xiangying; Gao, Ping; Zhu, Xinkai; Guo, Wenshan; Ding, Jinfeng; Li, Chunyan

2018-01-01

Jiangsu is an important agricultural province in China. Winter wheat, as the second major grain crop in the province, is greatly affected by moisture variations. The objective of this study was to investigate whether there were significant trends in changes in the moisture conditions during wheat growing seasons over the past decades and how the wheat yields responded to different moisture levels by means of a popular drought index, the Standardized Precipitation Evapotranspiration Index (SPEI). The study started with a trend analysis and quantification of the moisture conditions with the Mann-Kendall test and Sen's Slope method, respectively. Then, correlation analysis was carried out to determine the relationship between de-trended wheat yields and multi-scalar SPEI. Finally, a multivariate panel regression model was established to reveal the quantitative yield responses to moisture variations. The results showed that the moisture conditions in Jiangsu were generally at a normal level, but this century appeared slightly drier in because of the relatively high temperatures. There was a significant correlation between short time scale SPEI values and wheat yields. Among the three critical stages of wheat development, the SPEI values in the late growth stage (April-June) had a closer linkage to the yields than in the seedling stage (October-November) and the over-wintering stage (December-February). Moreover, the yield responses displayed an asymmetric characteristic, namely, moisture excess led to higher yield losses compared to moisture deficit in this region. The maximum yield increment could be obtained under the moisture level of slight drought according to the 3-month SPEI at the late growth stage, while extreme wetting resulted in the most severe yield losses. The moisture conditions in the first 15 years of the 21st century were more favorable than in the last 20 years of the 20th century for wheat production in Jiangsu.

Screening for ketosis using multiple logistic regression based on milk yield and composition.

PubMed

Kayano, Mitsunori; Kataoka, Tomoko

2015-11-01

Multiple logistic regression was applied to milk yield and composition data for 632 records of healthy cows and 61 records of ketotic cows in Hokkaido, Japan. The purpose was to diagnose ketosis based on milk yield and composition, simultaneously. The cows were divided into two groups: (1) multiparous, including 314 healthy cows and 45 ketotic cows and (2) primiparous, including 318 healthy cows and 16 ketotic cows, since nutritional status, milk yield and composition are affected by parity. Multiple logistic regression was applied to these groups separately. For multiparous cows, milk yield (kg/day/cow) and protein-to-fat (P/F) ratio in milk were significant factors (P<0.05) for the diagnosis of ketosis. For primiparous cows, lactose content (%), solid not fat (SNF) content (%) and milk urea nitrogen (MUN) content (mg/dl) were significantly associated with ketosis (P<0.01). A diagnostic rule was constructed for each group of cows: (1) 9.978 × P/F ratio + 0.085 × milk yield <10 and (2) 2.327 × SNF - 2.703 × lactose + 0.225 × MUN <10. The sensitivity, specificity and the area under the curve (AUC) of the diagnostic rules were (1) 0.800, 0.729 and 0.811; (2) 0.813, 0.730 and 0.787, respectively. The P/F ratio, which is a widely used measure of ketosis, provided the sensitivity, specificity and AUC values of (1) 0.711, 0.726 and 0.781; and (2) 0.678, 0.767 and 0.738, respectively.

The comparison of robust partial least squares regression with robust principal component regression on a real

NASA Astrophysics Data System (ADS)

Polat, Esra; Gunay, Suleyman

2013-10-01

One of the problems encountered in Multiple Linear Regression (MLR) is multicollinearity, which causes the overestimation of the regression parameters and increase of the variance of these parameters. Hence, in case of multicollinearity presents, biased estimation procedures such as classical Principal Component Regression (CPCR) and Partial Least Squares Regression (PLSR) are then performed. SIMPLS algorithm is the leading PLSR algorithm because of its speed, efficiency and results are easier to interpret. However, both of the CPCR and SIMPLS yield very unreliable results when the data set contains outlying observations. Therefore, Hubert and Vanden Branden (2003) have been presented a robust PCR (RPCR) method and a robust PLSR (RPLSR) method called RSIMPLS. In RPCR, firstly, a robust Principal Component Analysis (PCA) method for high-dimensional data on the independent variables is applied, then, the dependent variables are regressed on the scores using a robust regression method. RSIMPLS has been constructed from a robust covariance matrix for high-dimensional data and robust linear regression. The purpose of this study is to show the usage of RPCR and RSIMPLS methods on an econometric data set, hence, making a comparison of two methods on an inflation model of Turkey. The considered methods have been compared in terms of predictive ability and goodness of fit by using a robust Root Mean Squared Error of Cross-validation (R-RMSECV), a robust R2 value and Robust Component Selection (RCS) statistic.

Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield

NASA Astrophysics Data System (ADS)

Suarez, L. A.; Apan, A.; Werth, J.

2016-10-01

Yield loss in crops is often associated with plant disease or external factors such as environment, water supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation. Herbicide drift can be a combination of improper practices and environmental conditions which can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLS-R) models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality, photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA) were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables were highly affected by the chemical. Four PLS-R models for predicting yield were developed according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes (RMSEP = 2.6 and R2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R2 = 0.84). In summary, the results of this study show that it is possible to accurately predict yield after a simulated herbicide drift of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective and non-destructive alternative based on the internal response of the cotton leaves.

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...

Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects

NASA Astrophysics Data System (ADS)

Suhartono, Lee, Muhammad Hisyam; Prastyo, Dedy Dwi

2015-12-01

The aim of this research is to develop a calendar variation model for forecasting retail sales data with the Eid ul-Fitr effect. The proposed model is based on two methods, namely two levels ARIMAX and regression methods. Two levels ARIMAX and regression models are built by using ARIMAX for the first level and regression for the second level. Monthly men's jeans and women's trousers sales in a retail company for the period January 2002 to September 2009 are used as case study. In general, two levels of calendar variation model yields two models, namely the first model to reconstruct the sales pattern that already occurred, and the second model to forecast the effect of increasing sales due to Eid ul-Fitr that affected sales at the same and the previous months. The results show that the proposed two level calendar variation model based on ARIMAX and regression methods yields better forecast compared to the seasonal ARIMA model and Neural Networks.

Growth and yield in Eucalyptus globulus

Treesearch

James A. Rinehart; Richard B. Standiford

1983-01-01

A study of the major Eucalyptus globulus stands throughout California conducted by Woodbridge Metcalf in 1924 provides a complete and accurate data set for generating variable site-density yield models. Two models were developed using linear regression techniques. Model I depicts a linear relationship between age and yield best used for stands between five and fifteen...

The potential for using canopy spectral reflectance as an indirect selection tool for yield improvement in winter wheat

NASA Astrophysics Data System (ADS)

Prasad, Bishwajit

Scope and methods of study. Complementing breeding effort by deploying alternative methods of identifying higher yielding genotypes in a wheat breeding program is important for obtaining greater genetic gains. Spectral reflectance indices (SRI) are one of the many indirect selection tools that have been reported to be associated with different physiological process of wheat. A total of five experiments (a set of 25 released cultivars from winter wheat breeding programs of the U.S. Great Plains and four populations of randomly derived recombinant inbred lines having 25 entries in each population) were conducted in two years under Great Plains winter wheat rainfed environments at Oklahoma State University research farms. Grain yield was measured in each experiment and biomass was measured in three experiments at three growth stages (booting, heading, and grainfilling). Canopy spectral reflectance was measured at three growth stages and eleven SRI were calculated. Correlation (phenotypic and genetic) between grain yield and SRI, biomass and SRI, heritability (broad sense) of the SRI and yield, response to selection and correlated response, relative selection efficiency of the SRI, and efficiency in selecting the higher yielding genotypes by the SRI were assessed. Findings and conclusions. The genetic correlation coefficients revealed that the water based near infrared indices (WI and NWI) were strongly associated with grain yield and biomass production. The regression analysis detected a linear relationship between the water based indices with grain yield and biomass. The two newly developed indices (NWI-3 and NWI-4) gave higher broad sense heritability than grain yield, higher direct response to selection compared to grain yield, correlated response equal to or higher than direct response for grain yield, relative selection efficiency greater than one, and higher efficiency in selecting higher yielding genotypes. Based on the overall genetic analysis required to establish any trait as an efficient indirect selection tool, the water based SRI (especially NWI-3 and NWI-4) have the potential to complement the classical breeding effort for selecting genotypes with higher yield potential in a winter wheat breeding program.

Similar Estimates of Temperature Impacts on Global Wheat Yield by Three Independent Methods

NASA Technical Reports Server (NTRS)

Liu, Bing; Asseng, Senthold; Muller, Christoph; Ewart, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.;

Similar estimates of temperature impacts on global wheat yield by three independent methods

NASA Astrophysics Data System (ADS)

Liu, Bing; Asseng, Senthold; Müller, Christoph; Ewert, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.; Rosenzweig, Cynthia; Aggarwal, Pramod K.; Alderman, Phillip D.; Anothai, Jakarat; Basso, Bruno; Biernath, Christian; Cammarano, Davide; Challinor, Andy; Deryng, Delphine; Sanctis, Giacomo De; Doltra, Jordi; Fereres, Elias; Folberth, Christian; Garcia-Vila, Margarita; Gayler, Sebastian; Hoogenboom, Gerrit; Hunt, Leslie A.; Izaurralde, Roberto C.; Jabloun, Mohamed; Jones, Curtis D.; Kersebaum, Kurt C.; Kimball, Bruce A.; Koehler, Ann-Kristin; Kumar, Soora Naresh; Nendel, Claas; O'Leary, Garry J.; Olesen, Jørgen E.; Ottman, Michael J.; Palosuo, Taru; Prasad, P. V. Vara; Priesack, Eckart; Pugh, Thomas A. M.; Reynolds, Matthew; Rezaei, Ehsan E.; Rötter, Reimund P.; Schmid, Erwin; Semenov, Mikhail A.; Shcherbak, Iurii; Stehfest, Elke; Stöckle, Claudio O.; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tao, Fulu; Thorburn, Peter; Waha, Katharina; Wall, Gerard W.; Wang, Enli; White, Jeffrey W.; Wolf, Joost; Zhao, Zhigan; Zhu, Yan

2016-12-01

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify `method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.

Genetic analysis and association of simple sequence repeat markers with storage root yield, dry matter, starch and β-carotene content in sweetpotato.

PubMed

Yada, Benard; Brown-Guedira, Gina; Alajo, Agnes; Ssemakula, Gorrettie N; Owusu-Mensah, Eric; Carey, Edward E; Mwanga, Robert O M; Yencho, G Craig

2017-03-01

Molecular markers are needed for enhancing the development of elite sweetpotato ( Ipomoea batatas (L.) Lam) cultivars with a wide range of commercially important traits in sub-Saharan Africa. This study was conducted to estimate the heritability and determine trait correlations of storage root yield, dry matter, starch and β-carotene content in a cross between 'New Kawogo' × 'Beauregard'. The study was also conducted to identify simple sequence repeat (SSR) markers associated with these traits. A total of 287 progeny and the parents were evaluated for two seasons at three sites in Uganda and genotyped with 250 SSR markers. Broad sense heritability (H 2 ) for storage root yield, dry matter, starch and β-carotene content were 0.24, 0.68, 0.70 and 0.90, respectively. Storage root β-carotene content was negatively correlated with dry matter (r = -0.59, P < 0.001) and starch (r = -0.93, P < 0.001) content, while storage root yield was positively correlated with dry matter (r = 0.57, P = 0.029) and starch (r = 0.41, P = 0.008) content. Through logistic regression, a total of 12, 4, 6 and 8 SSR markers were associated with storage root yield, dry matter, starch and β-carotene content, respectively. The SSR markers used in this study may be useful for quantitative trait loci analysis and selection for these traits in future.

Genetic analysis and association of simple sequence repeat markers with storage root yield, dry matter, starch and β-carotene content in sweetpotato

PubMed Central

Yada, Benard; Brown-Guedira, Gina; Alajo, Agnes; Ssemakula, Gorrettie N.; Owusu-Mensah, Eric; Carey, Edward E.; Mwanga, Robert O.M.; Yencho, G. Craig

2017-01-01

Molecular markers are needed for enhancing the development of elite sweetpotato (Ipomoea batatas (L.) Lam) cultivars with a wide range of commercially important traits in sub-Saharan Africa. This study was conducted to estimate the heritability and determine trait correlations of storage root yield, dry matter, starch and β-carotene content in a cross between ‘New Kawogo’ × ‘Beauregard’. The study was also conducted to identify simple sequence repeat (SSR) markers associated with these traits. A total of 287 progeny and the parents were evaluated for two seasons at three sites in Uganda and genotyped with 250 SSR markers. Broad sense heritability (H2) for storage root yield, dry matter, starch and β-carotene content were 0.24, 0.68, 0.70 and 0.90, respectively. Storage root β-carotene content was negatively correlated with dry matter (r = −0.59, P < 0.001) and starch (r = −0.93, P < 0.001) content, while storage root yield was positively correlated with dry matter (r = 0.57, P = 0.029) and starch (r = 0.41, P = 0.008) content. Through logistic regression, a total of 12, 4, 6 and 8 SSR markers were associated with storage root yield, dry matter, starch and β-carotene content, respectively. The SSR markers used in this study may be useful for quantitative trait loci analysis and selection for these traits in future. PMID:28588391

A toolbox to visually explore cerebellar shape changes in cerebellar disease and dysfunction.

PubMed

Abulnaga, S Mazdak; Yang, Zhen; Carass, Aaron; Kansal, Kalyani; Jedynak, Bruno M; Onyike, Chiadi U; Ying, Sarah H; Prince, Jerry L

2016-02-27

The cerebellum plays an important role in motor control and is also involved in cognitive processes. Cerebellar function is specialized by location, although the exact topographic functional relationship is not fully understood. The spinocerebellar ataxias are a group of neurodegenerative diseases that cause regional atrophy in the cerebellum, yielding distinct motor and cognitive problems. The ability to study the region-specific atrophy patterns can provide insight into the problem of relating cerebellar function to location. In an effort to study these structural change patterns, we developed a toolbox in MATLAB to provide researchers a unique way to visually explore the correlation between cerebellar lobule shape changes and function loss, with a rich set of visualization and analysis modules. In this paper, we outline the functions and highlight the utility of the toolbox. The toolbox takes as input landmark shape representations of subjects' cerebellar substructures. A principal component analysis is used for dimension reduction. Following this, a linear discriminant analysis and a regression analysis can be performed to find the discriminant direction associated with a specific disease type, or the regression line of a specific functional measure can be generated. The characteristic structural change pattern of a disease type or of a functional score is visualized by sampling points on the discriminant or regression line. The sampled points are used to reconstruct synthetic cerebellar lobule shapes. We showed a few case studies highlighting the utility of the toolbox and we compare the analysis results with the literature.

A toolbox to visually explore cerebellar shape changes in cerebellar disease and dysfunction

NASA Astrophysics Data System (ADS)

Abulnaga, S. Mazdak; Yang, Zhen; Carass, Aaron; Kansal, Kalyani; Jedynak, Bruno M.; Onyike, Chiadi U.; Ying, Sarah H.; Prince, Jerry L.

2016-03-01

The cerebellum plays an important role in motor control and is also involved in cognitive processes. Cerebellar function is specialized by location, although the exact topographic functional relationship is not fully understood. The spinocerebellar ataxias are a group of neurodegenerative diseases that cause regional atrophy in the cerebellum, yielding distinct motor and cognitive problems. The ability to study the region-specific atrophy patterns can provide insight into the problem of relating cerebellar function to location. In an effort to study these structural change patterns, we developed a toolbox in MATLAB to provide researchers a unique way to visually explore the correlation between cerebellar lobule shape changes and function loss, with a rich set of visualization and analysis modules. In this paper, we outline the functions and highlight the utility of the toolbox. The toolbox takes as input landmark shape representations of subjects' cerebellar substructures. A principal component analysis is used for dimension reduction. Following this, a linear discriminant analysis and a regression analysis can be performed to find the discriminant direction associated with a specific disease type, or the regression line of a specific functional measure can be generated. The characteristic structural change pattern of a disease type or of a functional score is visualized by sampling points on the discriminant or regression line. The sampled points are used to reconstruct synthetic cerebellar lobule shapes. We showed a few case studies highlighting the utility of the toolbox and we compare the analysis results with the literature.

Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network.

PubMed

Antwi, Philip; Li, Jianzheng; Boadi, Portia Opoku; Meng, Jia; Shi, En; Deng, Kaiwen; Bondinuba, Francis Kwesi

2017-03-01

Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW). Anaerobic process parameters were optimized to identify their importance on methanation. pH, total chemical oxygen demand, ammonium, alkalinity, total Kjeldahl nitrogen, total phosphorus, volatile fatty acids and hydraulic retention time selected based on principal component analysis were used as input variables, whiles biogas and methane yield were employed as target variables. Quasi-Newton method and conjugate gradient backpropagation algorithms were best among eleven training algorithms. Coefficient of determination (R 2 ) of the BP-ANN reached 98.72% and 97.93% whiles MnLR model attained 93.9% and 91.08% for biogas and methane yield, respectively. Compared with the MnLR model, BP-ANN model demonstrated significant performance, suggesting possible control of the anaerobic digestion process with the BP-ANN model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.

Spatially Explicit Estimates of Suspended Sediment and Bedload Transport Rates for Western Oregon and Northwestern California

NASA Astrophysics Data System (ADS)

O'Connor, J. E.; Wise, D. R.; Mangano, J.; Jones, K.

2015-12-01

Empirical analyses of suspended sediment and bedload transport gives estimates of sediment flux for western Oregon and northwestern California. The estimates of both bedload and suspended load are from regression models relating measured annual sediment yield to geologic, physiographic, and climatic properties of contributing basins. The best models include generalized geology and either slope or precipitation. The best-fit suspended-sediment model is based on basin geology, precipitation, and area of recent wildfire. It explains 65% of the variance for 68 suspended sediment measurement sites within the model area. Predicted suspended sediment yields range from no yield from the High Cascades geologic province to 200 tonnes/ km2-yr in the northern Oregon Coast Range and 1000 tonnes/km2-yr in recently burned areas of the northern Klamath terrain. Bed-material yield is similarly estimated from a regression model based on 22 sites of measured bed-material transport, mostly from reservoir accumulation analyses but also from several bedload measurement programs. The resulting best-fit regression is based on basin slope and the presence/absence of the Klamath geologic terrane. For the Klamath terrane, bed-material yield is twice that of the other geologic provinces. This model explains more than 80% of the variance of the better-quality measurements. Predicted bed-material yields range up to 350 tonnes/ km2-yr in steep areas of the Klamath terrane. Applying these regressions to small individual watersheds (mean size; 66 km2 for bed-material; 3 km2 for suspended sediment) and cumulating totals down the hydrologic network (but also decreasing the bed-material flux by experimentally determined attrition rates) gives spatially explicit estimates of both bed-material and suspended sediment flux. This enables assessment of several management issues, including the effects of dams on bedload transport, instream gravel mining, habitat formation processes, and water-quality. The combined fluxes can also be compared to long-term rock uplift and cosmogenically determined landscape erosion rates.

Estimation of 305 Day Milk Yield from Cumulative Monthly and Bimonthly Test Day Records in Indonesian Holstein Cattle

NASA Astrophysics Data System (ADS)

Rahayu, A. P.; Hartatik, T.; Purnomoadi, A.; Kurnianto, E.

2018-02-01

The aims of this study were to estimate 305 day first lactation milk yield of Indonesian Holstein cattle from cumulative monthly and bimonthly test day records and to analyze its accuracy.The first lactation records of 258 dairy cows from 2006 to 2014 consisted of 2571 monthly (MTDY) and 1281 bimonthly test day yield (BTDY) records were used. Milk yields were estimated by regression method. Correlation coefficients between actual and estimated milk yield by cumulative MTDY were 0.70, 0.78, 0.83, 0.86, 0.89, 0.92, 0.94 and 0.96 for 2-9 months, respectively, meanwhile by cumulative BTDY were 0.69, 0.81, 0.87 and 0.92 for 2, 4, 6 and 8 months, respectively. The accuracy of fitting regression models (R2) increased with the increasing in the number of cumulative test day used. The used of 5 cumulative MTDY was considered sufficient for estimating 305 day first lactation milk yield with 80.6% accuracy and 7% error percentage of estimation. The estimated milk yield from MTDY was more accurate than BTDY by 1.1 to 2% less error percentage in the same time.

Economic consequences of aviation system disruptions: A reduced-form computable general equilibrium analysis

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

Chen, Zhenhua; Rose, Adam Z.; Prager, Fynnwin

The state of the art approach to economic consequence analysis (ECA) is computable general equilibrium (CGE) modeling. However, such models contain thousands of equations and cannot readily be incorporated into computerized systems used by policy analysts to yield estimates of economic impacts of various types of transportation system failures due to natural hazards, human related attacks or technological accidents. This paper presents a reduced-form approach to simplify the analytical content of CGE models to make them more transparent and enhance their utilization potential. The reduced-form CGE analysis is conducted by first running simulations one hundred times, varying key parameters, suchmore » as magnitude of the initial shock, duration, location, remediation, and resilience, according to a Latin Hypercube sampling procedure. Statistical analysis is then applied to the “synthetic data” results in the form of both ordinary least squares and quantile regression. The analysis yields linear equations that are incorporated into a computerized system and utilized along with Monte Carlo simulation methods for propagating uncertainties in economic consequences. Although our demonstration and discussion focuses on aviation system disruptions caused by terrorist attacks, the approach can be applied to a broad range of threat scenarios.« less

Climate-based statistical regression models for crop yield forecasting of coffee in humid tropical Kerala, India

NASA Astrophysics Data System (ADS)

Jayakumar, M.; Rajavel, M.; Surendran, U.

2016-12-01

A study on the variability of coffee yield of both Coffea arabica and Coffea canephora as influenced by climate parameters (rainfall (RF), maximum temperature (Tmax), minimum temperature (Tmin), and mean relative humidity (RH)) was undertaken at Regional Coffee Research Station, Chundale, Wayanad, Kerala State, India. The result on the coffee yield data of 30 years (1980 to 2009) revealed that the yield of coffee is fluctuating with the variations in climatic parameters. Among the species, productivity was higher for C. canephora coffee than C. arabica in most of the years. Maximum yield of C. canephora (2040 kg ha-1) was recorded in 2003-2004 and there was declining trend of yield noticed in the recent years. Similarly, the maximum yield of C. arabica (1745 kg ha-1) was recorded in 1988-1989 and decreased yield was noticed in the subsequent years till 1997-1998 due to year to year variability in climate. The highest correlation coefficient was found between the yield of C. arabica coffee and maximum temperature during January (0.7) and between C. arabica coffee yield and RH during July (0.4). Yield of C. canephora coffee had highest correlation with maximum temperature, RH and rainfall during February. Statistical regression model between selected climatic parameters and yield of C. arabica and C. canephora coffee was developed to forecast the yield of coffee in Wayanad district in Kerala. The model was validated for years 2010, 2011, and 2012 with the coffee yield data obtained during the years and the prediction was found to be good.

The phenology of winter rye in Poland: an analysis of long-term experimental data

NASA Astrophysics Data System (ADS)

Blecharczyk, Andrzej; Sawinska, Zuzanna; Małecka, Irena; Sparks, Tim H.; Tryjanowski, Piotr

2016-09-01

The study of the phenology of crops, although quite popular, has limitations, mainly because of frequent changes to crop varieties and management practices. Here, we present data on the phenology and yield of winter rye in western Poland collected between 1957 and 2012 from a long-term field experiment. Data were examined for trends through time and compared to climatological factors using regression analysis. Both annual air temperature and precipitation increased during the study period, equivalent to 2 °C and 186 mm, respectively, over the 52-year period for which met data were available. We detected significant delays in sowing date and recently in emergence, but significant advances were apparent in full flowering date equivalent to 4 days/decade. Yield and plant density experienced a step like change in 1986; yield increasing by ca. 70 % and plant density increasing by ca. 50 %, almost coinciding with a similar change in annual mean temperature, but most likely caused by a changed seed rate and use of herbicides. Future climate change is expected to have a greater impact on this crop, but farmers may be able to adapt to these changes by modifying water regimes, using new machinery and sowing new rye varieties.

Regression Equations for Monthly and Annual Mean and Selected Percentile Streamflows for Ungaged Rivers in Maine

USGS Publications Warehouse

Dudley, Robert W.

2015-12-03

The largest average errors of prediction are associated with regression equations for the lowest streamflows derived for months during which the lowest streamflows of the year occur (such as the 5 and 1 monthly percentiles for August and September). The regression equations have been derived on the basis of streamflow and basin characteristics data for unregulated, rural drainage basins without substantial streamflow or drainage modifications (for example, diversions and (or) regulation by dams or reservoirs, tile drainage, irrigation, channelization, and impervious paved surfaces), therefore using the equations for regulated or urbanized basins with substantial streamflow or drainage modifications will yield results of unknown error. Input basin characteristics derived using techniques or datasets other than those documented in this report or using values outside the ranges used to develop these regression equations also will yield results of unknown error.

Modelling drought-related yield losses in Iberia using remote sensing and multiscalar indices

NASA Astrophysics Data System (ADS)

Ribeiro, Andreia F. S.; Russo, Ana; Gouveia, Célia M.; Páscoa, Patrícia

2018-04-01

The response of two rainfed winter cereal yields (wheat and barley) to drought conditions in the Iberian Peninsula (IP) was investigated for a long period (1986-2012). Drought hazard was evaluated based on the multiscalar Standardized Precipitation Evapotranspiration Index (SPEI) and three remote sensing indices, namely the Vegetation Condition (VCI), the Temperature Condition (TCI), and the Vegetation Health (VHI) Indices. A correlation analysis between the yield and the drought indicators was conducted, and multiple linear regression (MLR) and artificial neural network (ANN) models were established to estimate yield at the regional level. The correlation values suggested that yield reduces with moisture depletion (low values of VCI) during early-spring and with too high temperatures (low values of TCI) close to the harvest time. Generally, all drought indicators displayed greatest influence during the plant stages in which the crop is photosynthetically more active (spring and summer), rather than the earlier moments of plants life cycle (autumn/winter). Our results suggested that SPEI is more relevant in the southern sector of the IP, while remote sensing indices are rather good in estimating cereal yield in the northern sector of the IP. The strength of the statistical relationships found by MLR and ANN methods is quite similar, with some improvements found by the ANN. A great number of true positives (hits) of occurrence of yield-losses exhibiting hit rate (HR) values higher than 69% was obtained.

Predictors of mother and child DNA yields in buccal cell samples collected in pediatric cancer epidemiologic studies: a report from the Children's Oncology group.

PubMed

Poynter, Jenny N; Ross, Julie A; Hooten, Anthony J; Langer, Erica; Blommer, Crystal; Spector, Logan G

2013-08-12

Collection of high-quality DNA is essential for molecular epidemiology studies. Methods have been evaluated for optimal DNA collection in studies of adults; however, DNA collection in young children poses additional challenges. Here, we have evaluated predictors of DNA quantity in buccal cells collected for population-based studies of infant leukemia (N = 489 mothers and 392 children) and hepatoblastoma (HB; N = 446 mothers and 412 children) conducted through the Children's Oncology Group. DNA samples were collected by mail using mouthwash (for mothers and some children) and buccal brush (for children) collection kits and quantified using quantitative real-time PCR. Multivariable linear regression models were used to identify predictors of DNA yield. Median DNA yield was higher for mothers in both studies compared with their children (14 μg vs. <1 μg). Significant predictors of DNA yield in children included case-control status (β = -0.69, 50% reduction, P = 0.01 for case vs. control children), brush collection type, and season of sample collection. Demographic factors were not strong predictors of DNA yield in mothers or children in this analysis. The association with seasonality suggests that conditions during transport may influence DNA yield. The low yields observed in most children in these studies highlight the importance of developing alternative methods for DNA collection in younger age groups.

The Role of Interleukin-6/GP130 Signaling in Prostate Cancer Progression and its Contribution to Bone Metastasis Morbidity

DTIC Science & Technology

2008-11-01

cancer cell survival, post-androgen withdrawal Task 1. To obtain BM18 tumor tissue that has regressed post-androgen withdrawal +/- IL-6...for analysis of IL-6 pathway molecules. Remaining tumor tissue was processed for routine histology and stained with Masson’s Trichome (Fig 2): - 7...recombinant human IL-6 or TSU xenografts yielded sera with detectable human IL-6 levels by ELISA (3pg/mL and 30pg/mL, respectively) (Twillie et al

Standardization and validation of the body weight adjustment regression equations in Olympic weightlifting.

PubMed

Kauhanen, Heikki; Komi, Paavo V; Häkkinen, Keijo

2002-02-01

The problems in comparing the performances of Olympic weightlifters arise from the fact that the relationship between body weight and weightlifting results is not linear. In the present study, this relationship was examined by using a nonparametric curve fitting technique of robust locally weighted regression (LOWESS) on relatively large data sets of the weightlifting results made in top international competitions. Power function formulas were derived from the fitted LOWESS values to represent the relationship between the 2 variables in a way that directly compares the snatch, clean-and-jerk, and total weightlifting results of a given athlete with those of the world-class weightlifters (golden standards). A residual analysis of several other parametric models derived from the initial results showed that they all experience inconsistencies, yielding either underestimation or overestimation of certain body weights. In addition, the existing handicapping formulas commonly used in normalizing the performances of Olympic weightlifters did not yield satisfactory results when applied to the present data. It was concluded that the devised formulas may provide objective means for the evaluation of the performances of male weightlifters, regardless of their body weights, ages, or performance levels.

Screening for ketosis using multiple logistic regression based on milk yield and composition

PubMed Central

KAYANO, Mitsunori; KATAOKA, Tomoko

2015-01-01

Multiple logistic regression was applied to milk yield and composition data for 632 records of healthy cows and 61 records of ketotic cows in Hokkaido, Japan. The purpose was to diagnose ketosis based on milk yield and composition, simultaneously. The cows were divided into two groups: (1) multiparous, including 314 healthy cows and 45 ketotic cows and (2) primiparous, including 318 healthy cows and 16 ketotic cows, since nutritional status, milk yield and composition are affected by parity. Multiple logistic regression was applied to these groups separately. For multiparous cows, milk yield (kg/day/cow) and protein-to-fat (P/F) ratio in milk were significant factors (P<0.05) for the diagnosis of ketosis. For primiparous cows, lactose content (%), solid not fat (SNF) content (%) and milk urea nitrogen (MUN) content (mg/dl) were significantly associated with ketosis (P<0.01). A diagnostic rule was constructed for each group of cows: (1) 9.978 × P/F ratio + 0.085 × milk yield <10 and (2) 2.327 × SNF − 2.703 × lactose + 0.225 × MUN <10. The sensitivity, specificity and the area under the curve (AUC) of the diagnostic rules were (1) 0.800, 0.729 and 0.811; (2) 0.813, 0.730 and 0.787, respectively. The P/F ratio, which is a widely used measure of ketosis, provided the sensitivity, specificity and AUC values of (1) 0.711, 0.726 and 0.781; and (2) 0.678, 0.767 and 0.738, respectively. PMID:26074408

Logistic Stick-Breaking Process

PubMed Central

Ren, Lu; Du, Lan; Carin, Lawrence; Dunson, David B.

2013-01-01

A logistic stick-breaking process (LSBP) is proposed for non-parametric clustering of general spatially- or temporally-dependent data, imposing the belief that proximate data are more likely to be clustered together. The sticks in the LSBP are realized via multiple logistic regression functions, with shrinkage priors employed to favor contiguous and spatially localized segments. The LSBP is also extended for the simultaneous processing of multiple data sets, yielding a hierarchical logistic stick-breaking process (H-LSBP). The model parameters (atoms) within the H-LSBP are shared across the multiple learning tasks. Efficient variational Bayesian inference is derived, and comparisons are made to related techniques in the literature. Experimental analysis is performed for audio waveforms and images, and it is demonstrated that for segmentation applications the LSBP yields generally homogeneous segments with sharp boundaries. PMID:25258593

Computational tools for exact conditional logistic regression.

PubMed

Corcoran, C; Mehta, C; Patel, N; Senchaudhuri, P

Logistic regression analyses are often challenged by the inability of unconditional likelihood-based approximations to yield consistent, valid estimates and p-values for model parameters. This can be due to sparseness or separability in the data. Conditional logistic regression, though useful in such situations, can also be computationally unfeasible when the sample size or number of explanatory covariates is large. We review recent developments that allow efficient approximate conditional inference, including Monte Carlo sampling and saddlepoint approximations. We demonstrate through real examples that these methods enable the analysis of significantly larger and more complex data sets. We find in this investigation that for these moderately large data sets Monte Carlo seems a better alternative, as it provides unbiased estimates of the exact results and can be executed in less CPU time than can the single saddlepoint approximation. Moreover, the double saddlepoint approximation, while computationally the easiest to obtain, offers little practical advantage. It produces unreliable results and cannot be computed when a maximum likelihood solution does not exist. Copyright 2001 John Wiley & Sons, Ltd.

An Integrated Analysis of the Physiological Effects of Space Flight: Executive Summary

NASA Technical Reports Server (NTRS)

Leonard, J. I.

1985-01-01

A large array of models were applied in a unified manner to solve problems in space flight physiology. Mathematical simulation was used as an alternative way of looking at physiological systems and maximizing the yield from previous space flight experiments. A medical data analysis system was created which consist of an automated data base, a computerized biostatistical and data analysis system, and a set of simulation models of physiological systems. Five basic models were employed: (1) a pulsatile cardiovascular model; (2) a respiratory model; (3) a thermoregulatory model; (4) a circulatory, fluid, and electrolyte balance model; and (5) an erythropoiesis regulatory model. Algorithms were provided to perform routine statistical tests, multivariate analysis, nonlinear regression analysis, and autocorrelation analysis. Special purpose programs were prepared for rank correlation, factor analysis, and the integration of the metabolic balance data.

An experimental validation of genomic selection in octoploid strawberry

PubMed Central

Gezan, Salvador A; Osorio, Luis F; Verma, Sujeet; Whitaker, Vance M

2017-01-01

The primary goal of genomic selection is to increase genetic gains for complex traits by predicting performance of individuals for which phenotypic data are not available. The objective of this study was to experimentally evaluate the potential of genomic selection in strawberry breeding and to define a strategy for its implementation. Four clonally replicated field trials, two in each of 2 years comprised of a total of 1628 individuals, were established in 2013–2014 and 2014–2015. Five complex yield and fruit quality traits with moderate to low heritability were assessed in each trial. High-density genotyping was performed with the Affymetrix Axiom IStraw90 single-nucleotide polymorphism array, and 17 479 polymorphic markers were chosen for analysis. Several methods were compared, including Genomic BLUP, Bayes B, Bayes C, Bayesian LASSO Regression, Bayesian Ridge Regression and Reproducing Kernel Hilbert Spaces. Cross-validation within training populations resulted in higher values than for true validations across trials. For true validations, Bayes B gave the highest predictive abilities on average and also the highest selection efficiencies, particularly for yield traits that were the lowest heritability traits. Selection efficiencies using Bayes B for parent selection ranged from 74% for average fruit weight to 34% for early marketable yield. A breeding strategy is proposed in which advanced selection trials are utilized as training populations and in which genomic selection can reduce the breeding cycle from 3 to 2 years for a subset of untested parents based on their predicted genomic breeding values. PMID:28090334

Optimization of sample preparation variables for wedelolactone from Eclipta alba using Box-Behnken experimental design followed by HPLC identification.

PubMed

Patil, A A; Sachin, B S; Shinde, D B; Wakte, P S

2013-07-01

Coumestan wedelolactone is an important phytocomponent from Eclipta alba (L.) Hassk. It possesses diverse pharmacological activities, which have prompted the development of various extraction techniques and strategies for its better utilization. The aim of the present study is to develop and optimize supercritical carbon dioxide assisted sample preparation and HPLC identification of wedelolactone from E. alba (L.) Hassk. The response surface methodology was employed to study the optimization of sample preparation using supercritical carbon dioxide for wedelolactone from E. alba (L.) Hassk. The optimized sample preparation involves the investigation of quantitative effects of sample preparation parameters viz. operating pressure, temperature, modifier concentration and time on yield of wedelolactone using Box-Behnken design. The wedelolactone content was determined using validated HPLC methodology. The experimental data were fitted to second-order polynomial equation using multiple regression analysis and analyzed using the appropriate statistical method. By solving the regression equation and analyzing 3D plots, the optimum extraction conditions were found to be: extraction pressure, 25 MPa; temperature, 56 °C; modifier concentration, 9.44% and extraction time, 60 min. Optimum extraction conditions demonstrated wedelolactone yield of 15.37 ± 0.63 mg/100 g E. alba (L.) Hassk, which was in good agreement with the predicted values. Temperature and modifier concentration showed significant effect on the wedelolactone yield. The supercritical carbon dioxide extraction showed higher selectivity than the conventional Soxhlet assisted extraction method. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Box-Behnken design based statistical modeling for ultrasound-assisted extraction of corn silk polysaccharide.

PubMed

Prakash Maran, J; Manikandan, S; Thirugnanasambandham, K; Vigna Nivetha, C; Dinesh, R

2013-01-30

In this study, ultrasound assisted extraction (UAE) conditions on the yield of polysaccharide from corn silk were studied using three factors, three level Box-Behnken response surface design. Process parameters, which affect the efficiency of UAE such as extraction temperature (40-60 °C), time (10-30 min) and solid-liquid ratio (1:10-1:30 g/ml) were investigated. The results showed that, the extraction conditions have significant effects on extraction yield of polysaccharide. The obtained experimental data were fitted to a second-order polynomial equation using multiple regression analysis with high coefficient of determination value (R(2)) of 0.994. An optimization study using Derringer's desired function methodology was performed and the optimal conditions based on both individual and combinations of all independent variables (extraction temperature of 56 °C, time of 17 min and solid-liquid ratio of 1:20 g/ml) were determined with maximum polysaccharide yield of 6.06%, which was confirmed through validation experiments. Copyright © 2012 Elsevier Ltd. All rights reserved.

Optimization of isolation of cellulose from orange peel using sodium hydroxide and chelating agents.

PubMed

Bicu, Ioan; Mustata, Fanica

2013-10-15

Response surface methodology was used to optimize cellulose recovery from orange peel using sodium hydroxide (NaOH) as isolation reagent, and to minimize its ash content using ethylenediaminetetraacetic acid (EDTA) as chelating agent. The independent variables were NaOH charge, EDTA charge and cooking time. Other two constant parameters were cooking temperature (98 °C) and liquid-to-solid ratio (7.5). The dependent variables were cellulose yield and ash content. A second-order polynomial model was used for plotting response surfaces and for determining optimum cooking conditions. The analysis of coefficient values for independent variables in the regression equation showed that NaOH and EDTA charges were major factors influencing the cellulose yield and ash content, respectively. Optimum conditions were defined by: NaOH charge 38.2%, EDTA charge 9.56%, and cooking time 317 min. The predicted cellulose yield was 24.06% and ash content 0.69%. A good agreement between the experimental values and the predicted was observed. Copyright © 2013 Elsevier Ltd. All rights reserved.

Comparison of 21-gauge and 22-gauge aspiration needle in endobronchial ultrasound-guided transbronchial needle aspiration: results of the American College of Chest Physicians Quality Improvement Registry, Education, and Evaluation Registry.

PubMed

Yarmus, Lonny B; Akulian, Jason; Lechtzin, Noah; Yasin, Faiza; Kamdar, Biren; Ernst, Armin; Ost, David E; Ray, Cynthia; Greenhill, Sarah R; Jimenez, Carlos A; Filner, Joshua; Feller-Kopman, David

2013-04-01

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a minimally invasive procedure originally performed using a 22-gauge (22G) needle. A recently introduced 21-gauge (21G) needle may improve the diagnostic yield and sample adequacy of EBUS-TBNA, but prior smaller studies have shown conflicting results. To our knowledge, this is the largest study undertaken to date to determine whether the 21G needle adds diagnostic benefit. We retrospectively evaluated the results of 1,299 patients from the American College of Chest Physicians Quality Improvement Registry, Education, and Evaluation (AQuIRE) Diagnostic Registry who underwent EBUS-TBNA between February 2009 and September 2010 at six centers throughout the United States. Data collection included patient demographics, sample adequacy, and diagnostic yield. Analysis consisted of univariate and multivariate hierarchical logistic regression comparing diagnostic yield and sample adequacy of EBUS-TBNA specimens by needle gauge. A total of 1,235 patients met inclusion criteria. Sample adequacy was obtained in 94.9% of the 22G needle group and in 94.6% of the 21G needle group (P = .81). A diagnosis was made in 51.4% of the 22G and 51.3% of the 21G groups (P = .98). Multivariate hierarchical logistic regression showed no statistical difference in sample adequacy or diagnostic yield between the two groups. The presence of rapid onsite cytologic evaluation was associated with significantly fewer needle passes per procedure when using the 21G needle (P < .001). There is no difference in specimen adequacy or diagnostic yield between the 21G and 22G needle groups. EBUS-TBNA in conjunction with rapid onsite cytologic evaluation and a 21G needle is associated with fewer needle passes compared with a 22G needle.

Video image analysis in the Australian meat industry - precision and accuracy of predicting lean meat yield in lamb carcasses.

PubMed

Hopkins, D L; Safari, E; Thompson, J M; Smith, C R

2004-06-01

A wide selection of lamb types of mixed sex (ewes and wethers) were slaughtered at a commercial abattoir and during this process images of 360 carcasses were obtained online using the VIAScan® system developed by Meat and Livestock Australia. Soft tissue depth at the GR site (thickness of tissue over the 12th rib 110 mm from the midline) was measured by an abattoir employee using the AUS-MEAT sheep probe (PGR). Another measure of this thickness was taken in the chiller using a GR knife (NGR). Each carcass was subsequently broken down to a range of trimmed boneless retail cuts and the lean meat yield determined. The current industry model for predicting meat yield uses hot carcass weight (HCW) and tissue depth at the GR site. A low level of accuracy and precision was found when HCW and PGR were used to predict lean meat yield (R(2)=0.19, r.s.d.=2.80%), which could be improved markedly when PGR was replaced by NGR (R(2)=0.41, r.s.d.=2.39%). If the GR measures were replaced by 8 VIAScan® measures then greater prediction accuracy could be achieved (R(2)=0.52, r.s.d.=2.17%). A similar result was achieved when the model was based on principal components (PCs) computed from the 8 VIAScan® measures (R(2)=0.52, r.s.d.=2.17%). The use of PCs also improved the stability of the model compared to a regression model based on HCW and NGR. The transportability of the models was tested by randomly dividing the data set and comparing coefficients and the level of accuracy and precision. Those models based on PCs were superior to those based on regression. It is demonstrated that with the appropriate modeling the VIAScan® system offers a workable method for predicting lean meat yield automatically.

The contextual effects of social capital on health: a cross-national instrumental variable analysis.

PubMed

Kim, Daniel; Baum, Christopher F; Ganz, Michael L; Subramanian, S V; Kawachi, Ichiro

2011-12-01

Past research on the associations between area-level/contextual social capital and health has produced conflicting evidence. However, interpreting this rapidly growing literature is difficult because estimates using conventional regression are prone to major sources of bias including residual confounding and reverse causation. Instrumental variable (IV) analysis can reduce such bias. Using data on up to 167,344 adults in 64 nations in the European and World Values Surveys and applying IV and ordinary least squares (OLS) regression, we estimated the contextual effects of country-level social trust on individual self-rated health. We further explored whether these associations varied by gender and individual levels of trust. Using OLS regression, we found higher average country-level trust to be associated with better self-rated health in both women and men. Instrumental variable analysis yielded qualitatively similar results, although the estimates were more than double in size in both sexes when country population density and corruption were used as instruments. The estimated health effects of raising the percentage of a country's population that trusts others by 10 percentage points were at least as large as the estimated health effects of an individual developing trust in others. These findings were robust to alternative model specifications and instruments. Conventional regression and to a lesser extent IV analysis suggested that these associations are more salient in women and in women reporting social trust. In a large cross-national study, our findings, including those using instrumental variables, support the presence of beneficial effects of higher country-level trust on self-rated health. Previous findings for contextual social capital using traditional regression may have underestimated the true associations. Given the close linkages between self-rated health and all-cause mortality, the public health gains from raising social capital within and across countries may be large. Copyright © 2011 Elsevier Ltd. All rights reserved.

The contextual effects of social capital on health: a cross-national instrumental variable analysis

PubMed Central

Kim, Daniel; Baum, Christopher F; Ganz, Michael; Subramanian, S V; Kawachi, Ichiro

2011-01-01

Past observational studies of the associations of area-level/contextual social capital with health have revealed conflicting findings. However, interpreting this rapidly growing literature is difficult because estimates using conventional regression are prone to major sources of bias including residual confounding and reverse causation. Instrumental variable (IV) analysis can reduce such bias. Using data on up to 167 344 adults in 64 nations in the European and World Values Surveys and applying IV and ordinary least squares (OLS) regression, we estimated the contextual effects of country-level social trust on individual self-rated health. We further explored whether these associations varied by gender and individual levels of trust. Using OLS regression, we found higher average country-level trust to be associated with better self-rated health in both women and men. Instrumental variable analysis yielded qualitatively similar results, although the estimates were more than double in size in women and men using country population density and corruption as instruments. The estimated health effects of raising the percentage of a country's population that trusts others by 10 percentage points were at least as large as the estimated health effects of an individual developing trust in others. These findings were robust to alternative model specifications and instruments. Conventional regression and to a lesser extent IV analysis suggested that these associations are more salient in women and in women reporting social trust. In a large cross-national study, our findings, including those using instrumental variables, support the presence of beneficial effects of higher country-level trust on self-rated health. Past findings for contextual social capital using traditional regression may have underestimated the true associations. Given the close linkages between self-rated health and all-cause mortality, the public health gains from raising social capital within countries may be large. PMID:22078106

Social support and support groups among people with HIV/AIDS in Ghana.

PubMed

Abrefa-Gyan, Tina; Wu, Liyun; Lewis, Marilyn W

2016-01-01

HIV/AIDS, a chronic burden in Ghana, poses social and health outcome concerns to those infected. Examining the Medical Outcome Study Social Support Survey (MOS-SSS) instrument among 300 Ghanaians from a cross-sectional design, Principal Component Analysis yielded four factors (positive interaction, trust building, information giving, and essential support), which accounted for 85.73% of the total variance in the MOS-SSS. A logistic regression analysis showed that essential support was the strongest predictor of the length of time an individual stayed in the support group, whereas positive interaction indicated negative association. The study's implications for policy, research, and practice were discussed.

An asymptotic analysis of the laminar-turbulent transition of yield stress fluids in pipes

NASA Astrophysics Data System (ADS)

Myers, Tim G.; Mitchell, Sarah L.; Slatter, Paul

2017-02-01

The work in this paper concerns the axisymmetric pipe flow of a Herschel-Bulkley fluid, with the aim of determining a relation between the critical velocity (defining the transition between laminar and turbulent flow) and the pipe diameter in terms of the Reynolds number Re 3. The asymptotic behaviour for large and small pipes is examined and simple expressions for the leading order terms are presented. Results are then compared with experimental data. A nonlinear regression analysis shows that for the tested fluids the transition occurs at similar values to the Newtonian case, namely in the range 2100 < Re 3 < 2500.

Application of third molar development and eruption models in estimating dental age in Malay sub-adults.

PubMed

Mohd Yusof, Mohd Yusmiaidil Putera; Cauwels, Rita; Deschepper, Ellen; Martens, Luc

2015-08-01

The third molar development (TMD) has been widely utilized as one of the radiographic method for dental age estimation. By using the same radiograph of the same individual, third molar eruption (TME) information can be incorporated to the TMD regression model. This study aims to evaluate the performance of dental age estimation in individual method models and the combined model (TMD and TME) based on the classic regressions of multiple linear and principal component analysis. A sample of 705 digital panoramic radiographs of Malay sub-adults aged between 14.1 and 23.8 years was collected. The techniques described by Gleiser and Hunt (modified by Kohler) and Olze were employed to stage the TMD and TME, respectively. The data was divided to develop three respective models based on the two regressions of multiple linear and principal component analysis. The trained models were then validated on the test sample and the accuracy of age prediction was compared between each model. The coefficient of determination (R²) and root mean square error (RMSE) were calculated. In both genders, adjusted R² yielded an increment in the linear regressions of combined model as compared to the individual models. The overall decrease in RMSE was detected in combined model as compared to TMD (0.03-0.06) and TME (0.2-0.8). In principal component regression, low value of adjusted R(2) and high RMSE except in male were exhibited in combined model. Dental age estimation is better predicted using combined model in multiple linear regression models. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

A prediction model to select PCOS patients suitable for IVM treatment based on anti-Mullerian hormone and antral follicle count.

PubMed

Guzman, L; Ortega-Hrepich, C; Polyzos, N P; Anckaert, E; Verheyen, G; Coucke, W; Devroey, P; Tournaye, H; Smitz, J; De Vos, M

2013-05-01

Which baseline patient characteristics can help assisted reproductive technology practitioners to identify patients who are suitable for in-vitro maturation (IVM) treatment? In patients with polycystic ovary syndrome (PCOS) who undergo oocyte IVM in a non-hCG-triggered system, circulating anti-Müllerian hormone (AMH), antral follicle count (AFC) and total testosterone are independently related to the number of immature oocytes and hold promise as outcome predictors to guide the patient selection process for IVM. Patient selection criteria for IVM treatment have been described in normo-ovulatory patients, although patients with PCOS constitute the major target population for IVM. With this study, we assessed the independent predictive value of clinical and endocrine parameters that are related to oocyte yield in patients with PCOS undergoing IVM. Cohort study involving 124 consecutive patients with PCOS undergoing IVM whose data were prospectively collected. Enrolment took place between January 2010 and January 2012. Only data relating to the first IVM cycle of each patient were included. Patients with PCOS underwent oocyte retrieval for IVM after minimal gonadotrophin stimulation and no hCG trigger. Correlation coefficients were calculated to investigate which parameters are related to immature oocyte yield (patient's age, BMI, baseline hormonal profile and AMH, AFC). The independence of predictive parameters was tested using multivariate linear regression analysis. Finally, multivariate receiver operating characteristic (ROC) analyses for cumulus oocyte complexes (COC) yield were performed to assess the efficiency of the prediction model to select suitable candidates for IVM. Using multivariate regression analysis, circulating baseline AMH, AFC and baseline total testosterone serum concentration were incorporated into a model to predict the number of COC retrieved in an IVM cycle, with unstandardized coefficients [95% confidence interval (CI)] of 0.03 (0.02-0.03) (P < 0.001), 0.012 (0.008-0.017) (P < 0.001) and 0.37 (0.18-0.57) (P < 0.001), respectively. Logistic regression analysis shows that a prediction model based on AMH and AFC, with unstandardized coefficients (95% CI) of 0.148 (0.03-0.25) (P < 0.001) and 0.034 (-0.003-0.07) (P = 0.025), respectively, is a useful patient selection tool to predict the probability to yield at least eight COCs for IVM in patients with PCOS. In this population, patients with at least eight COC available for IVM have a statistically higher number of embryos of good morphological quality (2.9 ± 2.3; 0.9 ± 0.9; P < 0.001) and cumulative ongoing pregnancy rate [30.4% (24 out of 79); 11% (5 out of 45); P = 0.01] when compared with patients with less than eight COC. ROC curve analysis showed that this prediction model has an area under the curve of 0.7864 (95% CI = 0.6997-0.8732) for the prediction of oocyte yield in IVM. The proposed model has been constructed based on a genuine IVM system, i.e. no hCG trigger was given and none of the oocytes matured in vivo. However, other variables, such as needle type, aspiration technique and whether or not hCG-triggering is used, should be considered as confounding factors. The results of this study have to be confirmed using a second independent validation sample. The proposed model could be applied to patients with PCOS after confirmation through a further validation study. This study was supported by a research grant by the Institute for the Promotion of Innovation by Science and Technology in Flanders, Project number IWT 070719.

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

Smith, Braeton J.; Shaneyfelt, Calvin R.

A NISAC study on the economic effects of a hypothetical H1N1 pandemic was done in order to assess the differential impacts at the state and industry levels given changes in absenteeism, mortality, and consumer spending rates. Part of the analysis was to determine if there were any direct relationships between pandemic impacts and gross domestic product (GDP) losses. Multiple regression analysis was used because it shows very clearly which predictors are significant in their impact on GDP. GDP impact data taken from the REMI PI+ (Regional Economic Models, Inc., Policy Insight +) model was used to serve as the responsemore » variable. NISAC economists selected the average absenteeism rate, mortality rate, and consumer spending categories as the predictor variables. Two outliers were found in the data: Nevada and Washington, DC. The analysis was done twice, with the outliers removed for the second analysis. The second set of regressions yielded a cleaner model, but for the purposes of this study, the analysts deemed it not as useful because particular interest was placed on determining the differential impacts to states. Hospitals and accommodation were found to be the most important predictors of percentage change in GDP among the consumer spending variables.« less

Estimation Methods for Non-Homogeneous Regression - Minimum CRPS vs Maximum Likelihood

NASA Astrophysics Data System (ADS)

Gebetsberger, Manuel; Messner, Jakob W.; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Non-homogeneous regression models are widely used to statistically post-process numerical weather prediction models. Such regression models correct for errors in mean and variance and are capable to forecast a full probability distribution. In order to estimate the corresponding regression coefficients, CRPS minimization is performed in many meteorological post-processing studies since the last decade. In contrast to maximum likelihood estimation, CRPS minimization is claimed to yield more calibrated forecasts. Theoretically, both scoring rules used as an optimization score should be able to locate a similar and unknown optimum. Discrepancies might result from a wrong distributional assumption of the observed quantity. To address this theoretical concept, this study compares maximum likelihood and minimum CRPS estimation for different distributional assumptions. First, a synthetic case study shows that, for an appropriate distributional assumption, both estimation methods yield to similar regression coefficients. The log-likelihood estimator is slightly more efficient. A real world case study for surface temperature forecasts at different sites in Europe confirms these results but shows that surface temperature does not always follow the classical assumption of a Gaussian distribution. KEYWORDS: ensemble post-processing, maximum likelihood estimation, CRPS minimization, probabilistic temperature forecasting, distributional regression models

Brazil soybean yield covariance model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate soybean yields for the seven soybean-growing states of Brazil. The meteorological data of these seven states were pooled and the years 1975 to 1980 were used to model since there was no technological trend in the yields during these years. Predictor variables were derived from monthly total precipitation and monthly average temperature.

A new graphic plot analysis for determination of neuroreceptor binding in positron emission tomography studies.

PubMed

Ito, Hiroshi; Yokoi, Takashi; Ikoma, Yoko; Shidahara, Miho; Seki, Chie; Naganawa, Mika; Takahashi, Hidehiko; Takano, Harumasa; Kimura, Yuichi; Ichise, Masanori; Suhara, Tetsuya

2010-01-01

In positron emission tomography (PET) studies with radioligands for neuroreceptors, tracer kinetics have been described by the standard two-tissue compartment model that includes the compartments of nondisplaceable binding and specific binding to receptors. In the present study, we have developed a new graphic plot analysis to determine the total distribution volume (V(T)) and nondisplaceable distribution volume (V(ND)) independently, and therefore the binding potential (BP(ND)). In this plot, Y(t) is the ratio of brain tissue activity to time-integrated arterial input function, and X(t) is the ratio of time-integrated brain tissue activity to time-integrated arterial input function. The x-intercept of linear regression of the plots for early phase represents V(ND), and the x-intercept of linear regression of the plots for delayed phase after the equilibrium time represents V(T). BP(ND) can be calculated by BP(ND)=V(T)/V(ND)-1. Dynamic PET scanning with measurement of arterial input function was performed on six healthy men after intravenous rapid bolus injection of [(11)C]FLB457. The plot yielded a curve in regions with specific binding while it yielded a straight line through all plot data in regions with no specific binding. V(ND), V(T), and BP(ND) values calculated by the present method were in good agreement with those by conventional non-linear least-squares fitting procedure. This method can be used to distinguish graphically whether the radioligand binding includes specific binding or not.

Ranking contributing areas of salt and selenium in the Lower Gunnison River Basin, Colorado, using multiple linear regression models

USGS Publications Warehouse

Linard, Joshua I.

2013-01-01

Mitigating the effects of salt and selenium on water quality in the Grand Valley and lower Gunnison River Basin in western Colorado is a major concern for land managers. Previous modeling indicated means to improve the models by including more detailed geospatial data and a more rigorous method for developing the models. After evaluating all possible combinations of geospatial variables, four multiple linear regression models resulted that could estimate irrigation-season salt yield, nonirrigation-season salt yield, irrigation-season selenium yield, and nonirrigation-season selenium yield. The adjusted r-squared and the residual standard error (in units of log-transformed yield) of the models were, respectively, 0.87 and 2.03 for the irrigation-season salt model, 0.90 and 1.25 for the nonirrigation-season salt model, 0.85 and 2.94 for the irrigation-season selenium model, and 0.93 and 1.75 for the nonirrigation-season selenium model. The four models were used to estimate yields and loads from contributing areas corresponding to 12-digit hydrologic unit codes in the lower Gunnison River Basin study area. Each of the 175 contributing areas was ranked according to its estimated mean seasonal yield of salt and selenium.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models.

PubMed

Bignardi, A B; El Faro, L; Cardoso, V L; Machado, P F; Albuquerque, L G

2009-09-01

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

Peak-flow characteristics of Virginia streams

USGS Publications Warehouse

Austin, Samuel H.; Krstolic, Jennifer L.; Wiegand, Ute

2011-01-01

Peak-flow annual exceedance probabilities, also called probability-percent chance flow estimates, and regional regression equations are provided describing the peak-flow characteristics of Virginia streams. Statistical methods are used to evaluate peak-flow data. Analysis of Virginia peak-flow data collected from 1895 through 2007 is summarized. Methods are provided for estimating unregulated peak flow of gaged and ungaged streams. Station peak-flow characteristics identified by fitting the logarithms of annual peak flows to a Log Pearson Type III frequency distribution yield annual exceedance probabilities of 0.5, 0.4292, 0.2, 0.1, 0.04, 0.02, 0.01, 0.005, and 0.002 for 476 streamgaging stations. Stream basin characteristics computed using spatial data and a geographic information system are used as explanatory variables in regional regression model equations for six physiographic regions to estimate regional annual exceedance probabilities at gaged and ungaged sites. Weighted peak-flow values that combine annual exceedance probabilities computed from gaging station data and from regional regression equations provide improved peak-flow estimates. Text, figures, and lists are provided summarizing selected peak-flow sites, delineated physiographic regions, peak-flow estimates, basin characteristics, regional regression model equations, error estimates, definitions, data sources, and candidate regression model equations. This study supersedes previous studies of peak flows in Virginia.

Template based rotation: A method for functional connectivity analysis with a priori templates☆

PubMed Central

Schultz, Aaron P.; Chhatwal, Jasmeer P.; Huijbers, Willem; Hedden, Trey; van Dijk, Koene R.A.; McLaren, Donald G.; Ward, Andrew M.; Wigman, Sarah; Sperling, Reisa A.

2014-01-01

Functional connectivity magnetic resonance imaging (fcMRI) is a powerful tool for understanding the network level organization of the brain in research settings and is increasingly being used to study large-scale neuronal network degeneration in clinical trial settings. Presently, a variety of techniques, including seed-based correlation analysis and group independent components analysis (with either dual regression or back projection) are commonly employed to compute functional connectivity metrics. In the present report, we introduce template based rotation,1 a novel analytic approach optimized for use with a priori network parcellations, which may be particularly useful in clinical trial settings. Template based rotation was designed to leverage the stable spatial patterns of intrinsic connectivity derived from out-of-sample datasets by mapping data from novel sessions onto the previously defined a priori templates. We first demonstrate the feasibility of using previously defined a priori templates in connectivity analyses, and then compare the performance of template based rotation to seed based and dual regression methods by applying these analytic approaches to an fMRI dataset of normal young and elderly subjects. We observed that template based rotation and dual regression are approximately equivalent in detecting fcMRI differences between young and old subjects, demonstrating similar effect sizes for group differences and similar reliability metrics across 12 cortical networks. Both template based rotation and dual-regression demonstrated larger effect sizes and comparable reliabilities as compared to seed based correlation analysis, though all three methods yielded similar patterns of network differences. When performing inter-network and sub-network connectivity analyses, we observed that template based rotation offered greater flexibility, larger group differences, and more stable connectivity estimates as compared to dual regression and seed based analyses. This flexibility owes to the reduced spatial and temporal orthogonality constraints of template based rotation as compared to dual regression. These results suggest that template based rotation can provide a useful alternative to existing fcMRI analytic methods, particularly in clinical trial settings where predefined outcome measures and conserved network descriptions across groups are at a premium. PMID:25150630

What Is the Role of Land-Use Compositions and Spatial Configurations in Sediment Yield from Mountainous Watershed?

NASA Astrophysics Data System (ADS)

Shi, Z. H.

2014-12-01

There are strong ties between land use and sediment yield in watersheds. Many studies have used multivariate regression techniques to explore the response of sediment yield to land-use compositions and spatial configurations in watersheds. However, one issue with the use of conventional statistical methods to address relationships between land-use compositions and spatial configurations and sediment yield is multicollinearity. This paper examines the combined effects of land-use compositions and land-use spatial configurations of the watershed on the specific sediment yield of the Upper Du River watershed (8,973 km2) in China using the Soil and Water Assessment Tool (SWAT) and partial least-squares regression (PLSR). The land-use compositions and spatial configurations of the watershed were calculated at the sub-watershed scale. The sediment yields from sub-watershed were evaluated using SWAT model. The first-order factors were identified by calculating the variable importance for the projection (VIP). The results revealed that the land-use compositions exerted the largest effects on the specific sediment yield and explained 61.2% of the variation in the specific sediment yield. Land-use spatial configurations were also found to have a large effect on the specific sediment yield and explained 21.7% of the observed variation in the specific sediment yield. The following are the dominant first-order factors of the specific sediment yield at the sub-watershed scale: the areal percentages of agriculture and forest, patch density, value of the Shannon's diversity index, contagion. The VIP values suggested that the Shannon's diversity index and contagion are important factors for sediment delivery.

Acoustic emission from trabecular bone during mechanical testing: the effect of osteoporosis and osteoarthritis.

PubMed

Leichter, I; Bivas, A; Margulies, J Y; Roman, I; Simkin, A

1990-01-01

This study examines the relation between the nature of acoustic emission signals emitted from cancellous bone under compression and the mechanical properties of the tissue. The examined bone specimens were taken from 12 normal, 31 osteoporotic and six osteoarthritic femoral heads. The mechanical behaviour of the osteoporotic bone specimens was found to be significantly different from that of the normal specimens both in the pre-yield and post-yield ranges. In the osteoarthritic bones only the elastic behaviour was significantly different. The rates of acoustic events before yield and beyond it were found to be significantly higher both in the osteoporotic and osteoarthritic bone specimens. The average peak amplitude of the signals was also significantly higher in the diseased bones. Stepwise regression analysis showed that a combination of the acoustic emission parameters could significantly predict some mechanical properties of the bone. The energy absorbed during compression and the ultimate compressive stress of the specimens could be estimated from the rate of pre-yield acoustic events, the average amplitude of the signals and the rate of post-yield events. However, the explanation power of the acoustic emission parameters was only moderate. The nature of acoustic emission signals was thus demonstrated to be a potential tool for assessing bone quality.

Differential stemflow yield from European beech saplings: the role and respective importance of individual canopy structure metrics

NASA Astrophysics Data System (ADS)

Levia, Delphis; Michalzik, Beate

2013-04-01

Stemflow yield from individual trees varies as a function of both meteorological conditions and canopy structure. The importance and differential effects of various metrics of canopy structure in relation to stemflow yield is inadequately understood and the subject of debate among forest hydrologists. It is possible to evaluate the role and respective importance of individual canopy structure metrics by holding meteorological conditions constant. Twelve isolated experimental European beech (Fagus sylvatica L.) saplings in Jena, Germany were exposed to identical meteorological conditions to examine the effects of canopy structure on stemflow production during the 2012 growing season. The canopy structure metrics being evaluated include: trunk diameter, trunk lean, tree height, projected crown area, branch inclination angle, branch count, and biomass (foliar and woody). Principal components analysis and multiple regression are utilized to determine the relative importance of different canopy structure metrics on stemflow yield. Experimental results will provide insight as to which metrics of canopy structure most strongly govern stemflow production. Ultimately, with a more thorough understanding of the unique contributions of various canopy structural metrics to stemflow yield, a useful conceptual guide of stemflow generation can be formulated on the basis of canopy structure for management purposes. Sponsor note: This research was funded by the Alexander von Humboldt Foundation.

Quasi-experimental evidence on tobacco tax regressivity.

PubMed

Koch, Steven F

2018-01-01

Tobacco taxes are known to reduce tobacco consumption and to be regressive, such that tobacco control policy may have the perverse effect of further harming the poor. However, if tobacco consumption falls faster amongst the poor than the rich, tobacco control policy can actually be progressive. We take advantage of persistent and committed tobacco control activities in South Africa to examine the household tobacco expenditure burden. For the analysis, we make use of two South African Income and Expenditure Surveys (2005/06 and 2010/11) that span a series of such tax increases and have been matched across the years, yielding 7806 matched pairs of tobacco consuming households and 4909 matched pairs of cigarette consuming households. By matching households across the surveys, we are able to examine both the regressivity of the household tobacco burden, and any change in that regressivity, and since tobacco taxes have been a consistent component of tobacco prices, our results also relate to the regressivity of tobacco taxes. Like previous research into cigarette and tobacco expenditures, we find that the tobacco burden is regressive; thus, so are tobacco taxes. However, we find that over the five-year period considered, the tobacco burden has decreased, and, most importantly, falls less heavily on the poor. Thus, the tobacco burden and the tobacco tax is less regressive in 2010/11 than in 2005/06. Thus, increased tobacco taxes can, in at least some circumstances, reduce the financial burden that tobacco places on households. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predictors of mother and child DNA yields in buccal cell samples collected in pediatric cancer epidemiologic studies: a report from the Children’s Oncology group

PubMed Central

2013-01-01

Background Collection of high-quality DNA is essential for molecular epidemiology studies. Methods have been evaluated for optimal DNA collection in studies of adults; however, DNA collection in young children poses additional challenges. Here, we have evaluated predictors of DNA quantity in buccal cells collected for population-based studies of infant leukemia (N = 489 mothers and 392 children) and hepatoblastoma (HB; N = 446 mothers and 412 children) conducted through the Children’s Oncology Group. DNA samples were collected by mail using mouthwash (for mothers and some children) and buccal brush (for children) collection kits and quantified using quantitative real-time PCR. Multivariable linear regression models were used to identify predictors of DNA yield. Results Median DNA yield was higher for mothers in both studies compared with their children (14 μg vs. <1 μg). Significant predictors of DNA yield in children included case–control status (β = −0.69, 50% reduction, P = 0.01 for case vs. control children), brush collection type, and season of sample collection. Demographic factors were not strong predictors of DNA yield in mothers or children in this analysis. Conclusions The association with seasonality suggests that conditions during transport may influence DNA yield. The low yields observed in most children in these studies highlight the importance of developing alternative methods for DNA collection in younger age groups. PMID:23937514

Two models of the sound-signal frequency dependence on the animal body size as exemplified by the ground squirrels of Eurasia (mammalia, rodentia).

PubMed

Nikol'skii, A A

2017-11-01

Dependence of the sound-signal frequency on the animal body length was studied in 14 ground squirrel species (genus Spermophilus) of Eurasia. Regression analysis of the total sample yielded a low determination coefficient (R 2 = 26%), because the total sample proved to be heterogeneous in terms of signal frequency within the dimension classes of animals. When the total sample was divided into two groups according to signal frequency, two statistically significant models (regression equations) were obtained in which signal frequency depended on the body size at high determination coefficients (R 2 = 73 and 94% versus 26% for the total sample). Thus, the problem of correlation between animal body size and the frequency of their vocal signals does not have a unique solution.

Synthesis, spectral studies and antimicrobial activities of some 2-naphthyl pyrazoline derivatives

NASA Astrophysics Data System (ADS)

Sakthinathan, S. P.; Vanangamudi, G.; Thirunarayanan, G.

A series of 2-naphthyl pyrazolines were synthesized by the cyclization of 2-naphthyl chalcones and phenylhydrazine hydrochloride in the presence of sodium acetate. The yields of pyrazoline derivatives are more than 80%. The synthesized pyrazolines were characterized by their physical constants, IR, 1H, 13C and MS spectra. From the IR and NMR spectra the Cdbnd N (cm-1) stretches, the pyrazoline ring proton chemical shifts (ppm) of δ, Hb and Hc and also the carbon chemical shifts (ppm) of δCdbnd N are correlated with Hammett substituent constants, F and R, and Swain-Lupton's parameters using single and multi-regression analyses. From the results of linear regression analysis, the effect of substituents on the group frequencies has been predicted. The antimicrobial activities of all synthesized pyrazolines have been studied.

Boosted Regression Trees Outperforms Support Vector Machines in Predicting (Regional) Yields of Winter Wheat from Single and Cumulated Dekadal Spot-VGT Derived Normalized Difference Vegetation Indices

NASA Astrophysics Data System (ADS)

Stas, Michiel; Dong, Qinghan; Heremans, Stien; Zhang, Beier; Van Orshoven, Jos

2016-08-01

This paper compares two machine learning techniques to predict regional winter wheat yields. The models, based on Boosted Regression Trees (BRT) and Support Vector Machines (SVM), are constructed of Normalized Difference Vegetation Indices (NDVI) derived from low resolution SPOT VEGETATION satellite imagery. Three types of NDVI-related predictors were used: Single NDVI, Incremental NDVI and Targeted NDVI. BRT and SVM were first used to select features with high relevance for predicting the yield. Although the exact selections differed between the prefectures, certain periods with high influence scores for multiple prefectures could be identified. The same period of high influence stretching from March to June was detected by both machine learning methods. After feature selection, BRT and SVM models were applied to the subset of selected features for actual yield forecasting. Whereas both machine learning methods returned very low prediction errors, BRT seems to slightly but consistently outperform SVM.

An Investigation of Widespread Ozone Damage to the Soybean Crop in the Upper Midwest Determined From Ground-Based and Satellite Measurements

NASA Technical Reports Server (NTRS)

Fishman, Jack; Creilson, John K.; Parker, Peter A.; Ainsworth, Elizabeth A.; Vining, G. Geoffrey; Szarka, John; Booker, Fitzgerald L.; Xu, Xiaojing

2010-01-01

Elevated concentrations of ground-level ozone (O3) are frequently measured over farmland regions in many parts of the world. While numerous experimental studies show that O3 can significantly decrease crop productivity, independent verifications of yield losses at current ambient O3 concentrations in rural locations are sparse. In this study, soybean crop yield data during a 5-year period over the Midwest of the United States were combined with ground and satellite O3 measurements to provide evidence that yield losses on the order of 10% could be estimated through the use of a multiple linear regression model. Yield loss trends based on both conventional ground-based instrumentation and satellite-derived tropospheric O3 measurements were statistically significant and were consistent with results obtained from open-top chamber experiments and an open-air experimental facility (SoyFACE, Soybean Free Air Concentration Enrichment) in central Illinois. Our analysis suggests that such losses are a relatively new phenomenon due to the increase in background tropospheric O3 levels over recent decades. Extrapolation of these findings supports previous studies that estimate the global economic loss to the farming community of more than $10 billion annually.

Quality by Design approach to spray drying processing of crystalline nanosuspensions.

PubMed

Kumar, Sumit; Gokhale, Rajeev; Burgess, Diane J

2014-04-10

Quality by Design (QbD) principles were explored to understand spray drying process for the conversion of liquid nanosuspensions into solid nano-crystalline dry powders using indomethacin as a model drug. The effects of critical process variables: inlet temperature, flow and aspiration rates on critical quality attributes (CQAs): particle size, moisture content, percent yield and crystallinity were investigated employing a full factorial design. A central cubic design was employed to generate the response surface for particle size and percent yield. Multiple linear regression analysis and ANOVA were employed to identify and estimate the effect of critical parameters, establish their relationship with CQAs, create design space and model the spray drying process. Inlet temperature was identified as the only significant factor (p value <0.05) to affect dry powder particle size. Higher inlet temperatures caused drug surface melting and hence aggregation of the dried nano-crystalline powders. Aspiration and flow rates were identified as significant factors affecting yield (p value <0.05). Higher yields were obtained at higher aspiration and lower flow rates. All formulations had less than 3% (w/w) moisture content. Formulations dried at higher inlet temperatures had lower moisture compared to those dried at lower inlet temperatures. Published by Elsevier B.V.

Production of ethyl levulinate by direct conversion of wheat straw in ethanol media.

PubMed

Chang, Chun; Xu, Guizhuan; Jiang, Xiaoxian

2012-10-01

The production of ethyl levulinate from wheat straw by direct conversion in ethanol media was investigated. Response surface methodology (RSM) was applied to optimize the effects of processing parameters, and the regression analysis was performed on the data obtained. A close agreement between the experimental results and the model predictions was achieved. The optimal conditions for ethyl levulinate production from wheat straw were acid concentration 2.5%, reaction temperature 183°C, mass ratio of liquid to solid 19.8 and reaction time 36 min. Under the optimum conditions, the yield of ethyl levulinate 17.91% was obtained, representing a theoretical yield of 51.0%. The results suggest that wheat straw can be used as potential raw materials for the production of ethyl levulinate by direct conversion in ethanol media. Copyright © 2012 Elsevier Ltd. All rights reserved.

Mass production of polyhedral occlusion bodies of NPV of Helicoverpa armigera in relation to dose, age and larval weight.

PubMed

Narayanan, K; Jayaraj, S

2002-07-01

A significant difference was noticed in the yield of polyhedral occlusion bodies (POBs) in various larval instars of H. armigera when three different doses of the nuclear polyhedrosis virus (NPV) were administered. The yield of POBs from a single larva ranged from 0.35 x 10(6) to 25033.33 x 10(6) with a mean of 18422.33 x 10(6) for fourth instar inoculated. Positive correlation existed between larval weight and number of POBs recovered. The regression analysis indicated POBs recovered responded with predictable manner to the weight of different larval instars and the various concentration of virus administered. The medium lethal time increased in the instars of the larva advanced with a minimum of 3.5 and maximum of 8 days in the first and fifth instars respectively.

Dissection of niche competition between introduced and indigenous arbuscular mycorrhizal fungi with respect to soybean yield responses.

PubMed

Niwa, Rieko; Koyama, Takuya; Sato, Takumi; Adachi, Katsuki; Tawaraya, Keitaro; Sato, Shusei; Hirakawa, Hideki; Yoshida, Shigenobu; Ezawa, Tatsuhiro

2018-05-09

Arbuscular mycorrhizal (AM) fungi associate with most land plants and deliver phosphorus to the host. Identification of biotic/abiotic factors that determine crop responses to AM fungal inoculation is an essential step for successful application of the fungi in sustainable agriculture. We conducted three field trials on soybean with a commercial inoculum and developed a new molecular tool to dissect interactions between the inoculum and indigenous fungi on the MiSeq sequencing platform. Regression analysis indicated that sequence read abundance of the inoculum fungus was the most significant factor that determined soybean yield responses to the inoculation, suggesting that dominance of the inoculum fungus is a necessary condition for positive yield responses. Agricultural practices (fallow/cropping in the previous year) greatly affected the colonization levels (i.e. read abundances) of the inoculum fungus via altering the propagule density of indigenous AM fungi. Analysis of niche competition revealed that the inoculum fungus competed mainly with the indigenous fungi that are commonly distributed in the trial sites, probably because their life-history strategy is the same as that of the inoculum fungus. In conclusion, we provide a new framework for evaluating the significance of environmental factors towards successful application of AM fungi in agriculture.

Sono-assisted extraction of alcohol-insoluble extract from Althaea rosea: purification and chemical analysis.

PubMed

Eskandari, Meghdad; Samavati, Vahid

2015-01-01

A Box-Behnken design (BBD) was used to evaluate the effects of ultrasonic power, extraction time, extraction temperature, and water to raw material ratio on extraction yield of alcohol-insoluble polysaccharide of Althaea rosea leaf (ARLP). Purification was carried out by dialysis method. Chemical analysis of ARLP revealed contained 12.69 ± 0.48% moisture, 79.33 ± 0.51% total sugar, 3.82 ± 0.21% protein, 11.25 ± 0.37% uronic acid and 3.77 ± 0.15% ash. The response surface methodology (RSM) showed that the significant quadratic regression equation with high R(2) (=0.9997) was successfully fitted for extraction yield of ARLP as function of independent variables. The overall optimum region was found to be at the combined level of ultrasonic power 91.85 W, extraction time 29.94 min, extraction temperature 89.78 °C, and the ratio of water to raw material 28.77 (mL/g). At this optimum point, extraction yield of ARLP was 19.47 ± 0.41%. No significant (p>0.05) difference was found between the actual and predicted (19.30 ± 0.075%) values. The results demonstrated that ARLP had strong scavenging activities on DPPH and hydroxyl radicals. Copyright © 2014 Elsevier B.V. All rights reserved.

Investigation of the Process Conditions for Hydrogen Production by Steam Reforming of Glycerol over Ni/Al₂O₃ Catalyst Using Response Surface Methodology (RSM).

PubMed

Ebshish, Ali; Yaakob, Zahira; Taufiq-Yap, Yun Hin; Bshish, Ahmed

2014-03-19

In this work; a response surface methodology (RSM) was implemented to investigate the process variables in a hydrogen production system. The effects of five independent variables; namely the temperature (X₁); the flow rate (X₂); the catalyst weight (X₃); the catalyst loading (X₄) and the glycerol-water molar ratio (X₅) on the H₂ yield (Y₁) and the conversion of glycerol to gaseous products (Y₂) were explored. Using multiple regression analysis; the experimental results of the H₂ yield and the glycerol conversion to gases were fit to quadratic polynomial models. The proposed mathematical models have correlated the dependent factors well within the limits that were being examined. The best values of the process variables were a temperature of approximately 600 °C; a feed flow rate of 0.05 mL/min; a catalyst weight of 0.2 g; a catalyst loading of 20% and a glycerol-water molar ratio of approximately 12; where the H₂ yield was predicted to be 57.6% and the conversion of glycerol was predicted to be 75%. To validate the proposed models; statistical analysis using a two-sample t -test was performed; and the results showed that the models could predict the responses satisfactorily within the limits of the variables that were studied.

Analyzing the Commitment of College Students Using a Brief, Contextualized Measure of Need Satisfaction From the Perspective of Self-Determination Theory.

PubMed

Davidson, William; Beck, Hall P

2018-01-01

This study empirically confirmed the relationships between the degree to which students satisfied three basic needs (competence, relatedness, and autonomy) and the strength of their commitments to the university they attended and to obtaining a baccalaureate degree. A questionnaire was administered online to 1257 students at two 4-year universities. Regression analysis yielded statistically significant associations between the three needs and Institutional Commitment and Degree Commitment, explaining more than 20% of the variance in the latter two variables.

Exploring the Diversification Discount: A Focus on High-Technology Target Firms

DTIC Science & Technology

2003-03-01

26 3. Breusch - Pagan Test Analysis ……………………………………………………..39 ix AFIT/GCA/ENV/03-01 Abstract When firms choose to acquire...of fit test , the Durbin-Watson test , and the Breusch - Pagan test respectively. A further discussion of the validation of the three regression...addition, the Breusch - Pagan test was employed to objectively test the assumption (Neter, 1996). The test yielded a p-value of 0.990, again

Age estimation using pulp/tooth area ratio in maxillary canines-A digital image analysis.

PubMed

Juneja, Manjushree; Devi, Yashoda B K; Rakesh, N; Juneja, Saurabh

2014-09-01

Determination of age of a subject is one of the most important aspects of medico-legal cases and anthropological research. Radiographs can be used to indirectly measure the rate of secondary dentine deposition which is depicted by reduction in the pulp area. In this study, 200 patients of Karnataka aged between 18-72 years were selected for the study. Panoramic radiographs were made and indirectly digitized. Radiographic images of maxillary canines (RIC) were processed using a computer-aided drafting program (ImageJ). The variables pulp/root length (p), pulp/tooth length (r), pulp/root width at enamel-cementum junction (ECJ) level (a), pulp/root width at mid-root level (c), pulp/root width at midpoint level between ECJ level and mid-root level (b) and pulp/tooth area ratio (AR) were recorded. All the morphological variables including gender were statistically analyzed to derive regression equation for estimation of age. It was observed that 2 variables 'AR' and 'b' contributed significantly to the fit and were included in the regression model, yielding the formula: Age = 87.305-480.455(AR)+48.108(b). Statistical analysis indicated that the regression equation with selected variables explained 96% of total variance with the median of the residuals of 0.1614 years and standard error of estimate of 3.0186 years. There is significant correlation between age and morphological variables 'AR' and 'b' and the derived population specific regression equation can be potentially used for estimation of chronological age of individuals of Karnataka origin.

UAV based mapping of variation in grassland yield for forage production in Arctic environments

NASA Astrophysics Data System (ADS)

Davids, C.; Karlsen, S. R.; Jørgensen, M.; Ancin Murguzur, F. J.

2017-12-01

Grassland cultivation for animal feed is the key agricultural activity in northern Norway. Even though the growing season has increased by at least a week in the last 30 years, grassland yields appear to have declined, probably due to more challenging winter conditions and changing agronomy practices. The ability for local and regional crop productivity forecasting would assist farmers with management decisions and would provide local and national authorities with a better overview over productivity and potential problems due to e.g. winter damage. Remote sensing technology has long been used to estimate and map the variability of various biophysical parameters, but calibration is important. In order to establish the relationship between spectral reflectance and grass yield in northern European environments we combine Sentinel-2 time series, UAV-based multispectral measurements, and ground-based spectroradiometry, with biomass analyses and observations of species composition. In this presentation we will focus on the results from the UAV data acquisition. We used a multirotor UAV with different sensors (a multispectral Rikola camera, and NDVI and RGB cameras) to image a number of cultivated grasslands of different age and productivity in northern Norway in June/July 2016 and 2017. Following UAV data acquisition, 10 to 20 in situ measurements were made per field using a FieldSpec3 (350-2500 nm). In addition, samples were taken to determine biomass and grass species composition. The imaging and sampling was done immediately prior to harvesting. The Rikola camera, when used as a stand-alone camera mounted on a UAV, can collect 15 bands with a spectral width of 10-15 nm in the range between 500-890 nm. In the initial analysis of the 2016 data we investigated how well different vegetation indices correlated with biomass and showed that vegetation indices that include red edge bands perform better than widely used indices such as NDVI. We will extend the analysis with partial least square regression once the 2017 data becomes available and in this presentation we will show the results of both the partial least square regression analysis and vegetation indices for the pooled data from the 2016 and 2017 acquisition.

Random regression models using Legendre orthogonal polynomials to evaluate the milk production of Alpine goats.

PubMed

Silva, F G; Torres, R A; Brito, L F; Euclydes, R F; Melo, A L P; Souza, N O; Ribeiro, J I; Rodrigues, M T

2013-12-11

The objective of this study was to identify the best random regression model using Legendre orthogonal polynomials to evaluate Alpine goats genetically and to estimate the parameters for test day milk yield. On the test day, we analyzed 20,710 records of milk yield of 667 goats from the Goat Sector of the Universidade Federal de Viçosa. The evaluated models had combinations of distinct fitting orders for polynomials (2-5), random genetic (1-7), and permanent environmental (1-7) fixed curves and a number of classes for residual variance (2, 4, 5, and 6). WOMBAT software was used for all genetic analyses. A random regression model using the best Legendre orthogonal polynomial for genetic evaluation of milk yield on the test day of Alpine goats considered a fixed curve of order 4, curve of genetic additive effects of order 2, curve of permanent environmental effects of order 7, and a minimum of 5 classes of residual variance because it was the most economical model among those that were equivalent to the complete model by the likelihood ratio test. Phenotypic variance and heritability were higher at the end of the lactation period, indicating that the length of lactation has more genetic components in relation to the production peak and persistence. It is very important that the evaluation utilizes the best combination of fixed, genetic additive and permanent environmental regressions, and number of classes of heterogeneous residual variance for genetic evaluation using random regression models, thereby enhancing the precision and accuracy of the estimates of parameters and prediction of genetic values.

Random Regression Models Using Legendre Polynomials to Estimate Genetic Parameters for Test-day Milk Protein Yields in Iranian Holstein Dairy Cattle.

PubMed

Naserkheil, Masoumeh; Miraie-Ashtiani, Seyed Reza; Nejati-Javaremi, Ardeshir; Son, Jihyun; Lee, Deukhwan

2016-12-01

The objective of this study was to estimate the genetic parameters of milk protein yields in Iranian Holstein dairy cattle. A total of 1,112,082 test-day milk protein yield records of 167,269 first lactation Holstein cows, calved from 1990 to 2010, were analyzed. Estimates of the variance components, heritability, and genetic correlations for milk protein yields were obtained using a random regression test-day model. Milking times, herd, age of recording, year, and month of recording were included as fixed effects in the model. Additive genetic and permanent environmental random effects for the lactation curve were taken into account by applying orthogonal Legendre polynomials of the fourth order in the model. The lowest and highest additive genetic variances were estimated at the beginning and end of lactation, respectively. Permanent environmental variance was higher at both extremes. Residual variance was lowest at the middle of the lactation and contrarily, heritability increased during this period. Maximum heritability was found during the 12th lactation stage (0.213±0.007). Genetic, permanent, and phenotypic correlations among test-days decreased as the interval between consecutive test-days increased. A relatively large data set was used in this study; therefore, the estimated (co)variance components for random regression coefficients could be used for national genetic evaluation of dairy cattle in Iran.

Random Regression Models Using Legendre Polynomials to Estimate Genetic Parameters for Test-day Milk Protein Yields in Iranian Holstein Dairy Cattle

PubMed Central

Naserkheil, Masoumeh; Miraie-Ashtiani, Seyed Reza; Nejati-Javaremi, Ardeshir; Son, Jihyun; Lee, Deukhwan

2016-01-01

The objective of this study was to estimate the genetic parameters of milk protein yields in Iranian Holstein dairy cattle. A total of 1,112,082 test-day milk protein yield records of 167,269 first lactation Holstein cows, calved from 1990 to 2010, were analyzed. Estimates of the variance components, heritability, and genetic correlations for milk protein yields were obtained using a random regression test-day model. Milking times, herd, age of recording, year, and month of recording were included as fixed effects in the model. Additive genetic and permanent environmental random effects for the lactation curve were taken into account by applying orthogonal Legendre polynomials of the fourth order in the model. The lowest and highest additive genetic variances were estimated at the beginning and end of lactation, respectively. Permanent environmental variance was higher at both extremes. Residual variance was lowest at the middle of the lactation and contrarily, heritability increased during this period. Maximum heritability was found during the 12th lactation stage (0.213±0.007). Genetic, permanent, and phenotypic correlations among test-days decreased as the interval between consecutive test-days increased. A relatively large data set was used in this study; therefore, the estimated (co)variance components for random regression coefficients could be used for national genetic evaluation of dairy cattle in Iran. PMID:26954192

Systematic review of treatment modalities for gingival depigmentation: a random-effects poisson regression analysis.

PubMed

Lin, Yi Hung; Tu, Yu Kang; Lu, Chun Tai; Chung, Wen Chen; Huang, Chiung Fang; Huang, Mao Suan; Lu, Hsein Kun

2014-01-01

Repigmentation variably occurs with different treatment methods in patients with gingival pigmentation. A systemic review was conducted of various treatment modalities for eliminating melanin pigmentation of the gingiva, comprising bur abrasion, scalpel surgery, cryosurgery, electrosurgery, gingival grafts, and laser techniques, to compare the recurrence rates (Rrs) of these treatment procedures. Electronic databases, including PubMed, Web of Science, Google, and Medline were comprehensively searched, and manual searches were conducted for studies published from January 1951 to June 2013. After applying inclusion and exclusion criteria, the final list of articles was reviewed in depth to achieve the objectives of this review. A Poisson regression was used to analyze the outcome of depigmentation using the various treatment methods. The systematic review was based on case reports mainly. In total, 61 eligible publications met the defined criteria. The various therapeutic procedures showed variable clinical results with a wide range of Rrs. A random-effects Poisson regression showed that cryosurgery (Rr = 0.32%), electrosurgery (Rr = 0.74%), and laser depigmentation (Rr = 1.16%) yielded superior result, whereas bur abrasion yielded the highest Rr (8.89%). Within the limit of the sampling level, the present evidence-based results show that cryosurgery exhibits the optimal predictability for depigmentation of the gingiva among all procedures examined, followed by electrosurgery and laser techniques. It is possible to treat melanin pigmentation of the gingiva with various methods and prevent repigmentation. Among those treatment modalities, cryosurgery, electrosurgery, and laser surgery appear to be the best choices for treating gingival pigmentation. © 2014 Wiley Periodicals, Inc.

Assessment of parametric uncertainty for groundwater reactive transport modeling,

USGS Publications Warehouse

Shi, Xiaoqing; Ye, Ming; Curtis, Gary P.; Miller, Geoffery L.; Meyer, Philip D.; Kohler, Matthias; Yabusaki, Steve; Wu, Jichun

2014-01-01

The validity of using Gaussian assumptions for model residuals in uncertainty quantification of a groundwater reactive transport model was evaluated in this study. Least squares regression methods explicitly assume Gaussian residuals, and the assumption leads to Gaussian likelihood functions, model parameters, and model predictions. While the Bayesian methods do not explicitly require the Gaussian assumption, Gaussian residuals are widely used. This paper shows that the residuals of the reactive transport model are non-Gaussian, heteroscedastic, and correlated in time; characterizing them requires using a generalized likelihood function such as the formal generalized likelihood function developed by Schoups and Vrugt (2010). For the surface complexation model considered in this study for simulating uranium reactive transport in groundwater, parametric uncertainty is quantified using the least squares regression methods and Bayesian methods with both Gaussian and formal generalized likelihood functions. While the least squares methods and Bayesian methods with Gaussian likelihood function produce similar Gaussian parameter distributions, the parameter distributions of Bayesian uncertainty quantification using the formal generalized likelihood function are non-Gaussian. In addition, predictive performance of formal generalized likelihood function is superior to that of least squares regression and Bayesian methods with Gaussian likelihood function. The Bayesian uncertainty quantification is conducted using the differential evolution adaptive metropolis (DREAM(zs)) algorithm; as a Markov chain Monte Carlo (MCMC) method, it is a robust tool for quantifying uncertainty in groundwater reactive transport models. For the surface complexation model, the regression-based local sensitivity analysis and Morris- and DREAM(ZS)-based global sensitivity analysis yield almost identical ranking of parameter importance. The uncertainty analysis may help select appropriate likelihood functions, improve model calibration, and reduce predictive uncertainty in other groundwater reactive transport and environmental modeling.

Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat.

PubMed

Nachit, M M; Nachit, G; Ketata, H; Gauch, H G; Zobel, R W

1992-03-01

The joint durum wheat (Triticum turgidum L var 'durum') breeding program of the International Maize and Wheat Improvement Center (CIMMYT) and the International Center for Agricultural Research in the Dry Areas (ICARDA) for the Mediterranean region employs extensive multilocation testing. Multilocation testing produces significant genotype-environment (GE) interaction that reduces the accuracy for estimating yield and selecting appropriate germ plasm. The sum of squares (SS) of GE interaction was partitioned by linear regression techniques into joint, genotypic, and environmental regressions, and by Additive Main effects and the Multiplicative Interactions (AMMI) model into five significant Interaction Principal Component Axes (IPCA). The AMMI model was more effective in partitioning the interaction SS than the linear regression technique. The SS contained in the AMMI model was 6 times higher than the SS for all three regressions. Postdictive assessment recommended the use of the first five IPCA axes, while predictive assessment AMMI1 (main effects plus IPCA1). After elimination of random variation, AMMI1 estimates for genotypic yields within sites were more precise than unadjusted means. This increased precision was equivalent to increasing the number of replications by a factor of 3.7.

Information loss in approximately bayesian data assimilation: a comparison of generative and discriminative approaches to estimating agricultural yield

USDA-ARS?s Scientific Manuscript database

Data assimilation and regression are two commonly used methods for predicting agricultural yield from remote sensing observations. Data assimilation is a generative approach because it requires explicit approximations of the Bayesian prior and likelihood to compute the probability density function...

Distillation time as tool for improved antimalarial activity and differential oil composition of cumin seed oil

USDA-ARS?s Scientific Manuscript database

A steam distillation extraction kinetics experiment was conducted to estimate essential oil yield, composition, antimalarial, and antioxidant capacity of cumin (Cuminum cyminum L.) seed (fruits). Furthermore, regression models were developed to predict essential oil yield and composition for a given...

Using within-day hive weight changes to measure environmental effects on honey bee colonies

PubMed Central

Holst, Niels; Weiss, Milagra; Carroll, Mark J.; McFrederick, Quinn S.; Barron, Andrew B.

2018-01-01

Patterns in within-day hive weight data from two independent datasets in Arizona and California were modeled using piecewise regression, and analyzed with respect to honey bee colony behavior and landscape effects. The regression analysis yielded information on the start and finish of a colony’s daily activity cycle, hive weight change at night, hive weight loss due to departing foragers and weight gain due to returning foragers. Assumptions about the meaning of the timing and size of the morning weight changes were tested in a third study by delaying the forager departure times from one to three hours using screen entrance gates. A regression of planned vs. observed departure delays showed that the initial hive weight loss around dawn was largely due to foragers. In a similar experiment in Australia, hive weight loss due to departing foragers in the morning was correlated with net bee traffic (difference between the number of departing bees and the number of arriving bees) and from those data the payload of the arriving bees was estimated to be 0.02 g. The piecewise regression approach was then used to analyze a fifth study involving hives with and without access to natural forage. The analysis showed that, during a commercial pollination event, hives with previous access to forage had a significantly higher rate of weight gain as the foragers returned in the afternoon, and, in the weeks after the pollination event, a significantly higher rate of weight loss in the morning, as foragers departed. This combination of continuous weight data and piecewise regression proved effective in detecting treatment differences in foraging activity that other methods failed to detect. PMID:29791462

Using within-day hive weight changes to measure environmental effects on honey bee colonies.

PubMed

Meikle, William G; Holst, Niels; Colin, Théotime; Weiss, Milagra; Carroll, Mark J; McFrederick, Quinn S; Barron, Andrew B

2018-01-01

Patterns in within-day hive weight data from two independent datasets in Arizona and California were modeled using piecewise regression, and analyzed with respect to honey bee colony behavior and landscape effects. The regression analysis yielded information on the start and finish of a colony's daily activity cycle, hive weight change at night, hive weight loss due to departing foragers and weight gain due to returning foragers. Assumptions about the meaning of the timing and size of the morning weight changes were tested in a third study by delaying the forager departure times from one to three hours using screen entrance gates. A regression of planned vs. observed departure delays showed that the initial hive weight loss around dawn was largely due to foragers. In a similar experiment in Australia, hive weight loss due to departing foragers in the morning was correlated with net bee traffic (difference between the number of departing bees and the number of arriving bees) and from those data the payload of the arriving bees was estimated to be 0.02 g. The piecewise regression approach was then used to analyze a fifth study involving hives with and without access to natural forage. The analysis showed that, during a commercial pollination event, hives with previous access to forage had a significantly higher rate of weight gain as the foragers returned in the afternoon, and, in the weeks after the pollination event, a significantly higher rate of weight loss in the morning, as foragers departed. This combination of continuous weight data and piecewise regression proved effective in detecting treatment differences in foraging activity that other methods failed to detect.

Grassland and cropland net ecosystem production of the U.S. Great Plains: Regression tree model development and comparative analysis

USGS Publications Warehouse

Wylie, Bruce K.; Howard, Daniel; Dahal, Devendra; Gilmanov, Tagir; Ji, Lei; Zhang, Li; Smith, Kelcy

2016-01-01

This paper presents the methodology and results of two ecological-based net ecosystem production (NEP) regression tree models capable of up scaling measurements made at various flux tower sites throughout the U.S. Great Plains. Separate grassland and cropland NEP regression tree models were trained using various remote sensing data and other biogeophysical data, along with 15 flux towers contributing to the grassland model and 15 flux towers for the cropland model. The models yielded weekly mean daily grassland and cropland NEP maps of the U.S. Great Plains at 250 m resolution for 2000–2008. The grassland and cropland NEP maps were spatially summarized and statistically compared. The results of this study indicate that grassland and cropland ecosystems generally performed as weak net carbon (C) sinks, absorbing more C from the atmosphere than they released from 2000 to 2008. Grasslands demonstrated higher carbon sink potential (139 g C·m−2·year−1) than non-irrigated croplands. A closer look into the weekly time series reveals the C fluctuation through time and space for each land cover type.

Reconstruction of missing daily streamflow data using dynamic regression models

NASA Astrophysics Data System (ADS)

Tencaliec, Patricia; Favre, Anne-Catherine; Prieur, Clémentine; Mathevet, Thibault

2015-12-01

River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure.

Simulated peak inflows for glacier dammed Russell Fiord, near Yakutat, Alaska

USGS Publications Warehouse

Neal, Edward G.

2004-01-01

In June 2002, Hubbard Glacier advanced across the entrance to 35-mile-long Russell Fiord creating a glacier-dammed lake. After closure of the ice and moraine dam, runoff from mountain streams and glacial melt caused the level in ?Russell Lake? to rise until it eventually breached the dam on August 14, 2002. Daily mean inflows to the lake during the period of closure were estimated on the basis of lake stage data and the hypsometry of Russell Lake. Inflows were regressed against the daily mean streamflows of nearby Ophir Creek and Situk River to generate an equation for simulating Russell Lake inflow. The regression equation was used to produce 11 years of synthetic daily inflows to Russell Lake for the 1992-2002 water years. A flood-frequency analysis was applied to the peak daily mean inflows for these 11 years of record to generate a 100-year peak daily mean inflow of 235,000 cubic feet per second. Regional-regression equations also were applied to the Russell Lake basin, yielding a 100-year inflow of 157,000 cubic feet per second.

Comparison of random regression test-day models for Polish Black and White cattle.

PubMed

Strabel, T; Szyda, J; Ptak, E; Jamrozik, J

2005-10-01

Test-day milk yields of first-lactation Black and White cows were used to select the model for routine genetic evaluation of dairy cattle in Poland. The population of Polish Black and White cows is characterized by small herd size, low level of production, and relatively early peak of lactation. Several random regression models for first-lactation milk yield were initially compared using the "percentage of squared bias" criterion and the correlations between true and predicted breeding values. Models with random herd-test-date effects, fixed age-season and herd-year curves, and random additive genetic and permanent environmental curves (Legendre polynomials of different orders were used for all regressions) were chosen for further studies. Additional comparisons included analyses of the residuals and shapes of variance curves in days in milk. The low production level and early peak of lactation of the breed required the use of Legendre polynomials of order 5 to describe age-season lactation curves. For the other curves, Legendre polynomials of order 3 satisfactorily described daily milk yield variation. Fitting third-order polynomials for the permanent environmental effect made it possible to adequately account for heterogeneous residual variance at different stages of lactation.

Factors Controlling Sediment Load in The Central Anatolia Region of Turkey: Ankara River Basin.

PubMed

Duru, Umit; Wohl, Ellen; Ahmadi, Mehdi

2017-05-01

Better understanding of the factors controlling sediment load at a catchment scale can facilitate estimation of soil erosion and sediment transport rates. The research summarized here enhances understanding of correlations between potential control variables on suspended sediment loads. The Soil and Water Assessment Tool was used to simulate flow and sediment at the Ankara River basin. Multivariable regression analysis and principal component analysis were then performed between sediment load and controlling variables. The physical variables were either directly derived from a Digital Elevation Model or from field maps or computed using established equations. Mean observed sediment rate is 6697 ton/year and mean sediment yield is 21 ton/y/km² from the gage. Soil and Water Assessment Tool satisfactorily simulated observed sediment load with Nash-Sutcliffe efficiency, relative error, and coefficient of determination (R²) values of 0.81, -1.55, and 0.93, respectively in the catchment. Therefore, parameter values from the physically based model were applied to the multivariable regression analysis as well as principal component analysis. The results indicate that stream flow, drainage area, and channel width explain most of the variability in sediment load among the catchments. The implications of the results, efficient siltation management practices in the catchment should be performed to stream flow, drainage area, and channel width.

Factors Controlling Sediment Load in The Central Anatolia Region of Turkey: Ankara River Basin

NASA Astrophysics Data System (ADS)

Duru, Umit; Wohl, Ellen; Ahmadi, Mehdi

2017-05-01

Better understanding of the factors controlling sediment load at a catchment scale can facilitate estimation of soil erosion and sediment transport rates. The research summarized here enhances understanding of correlations between potential control variables on suspended sediment loads. The Soil and Water Assessment Tool was used to simulate flow and sediment at the Ankara River basin. Multivariable regression analysis and principal component analysis were then performed between sediment load and controlling variables. The physical variables were either directly derived from a Digital Elevation Model or from field maps or computed using established equations. Mean observed sediment rate is 6697 ton/year and mean sediment yield is 21 ton/y/km² from the gage. Soil and Water Assessment Tool satisfactorily simulated observed sediment load with Nash-Sutcliffe efficiency, relative error, and coefficient of determination ( R²) values of 0.81, -1.55, and 0.93, respectively in the catchment. Therefore, parameter values from the physically based model were applied to the multivariable regression analysis as well as principal component analysis. The results indicate that stream flow, drainage area, and channel width explain most of the variability in sediment load among the catchments. The implications of the results, efficient siltation management practices in the catchment should be performed to stream flow, drainage area, and channel width.

Antibacterial and antifungal activities of pyroligneous acid from wood of Eucalyptus urograndis and Mimosa tenuiflora.

PubMed

de Souza Araújo, E; Pimenta, A S; Feijó, F M C; Castro, R V O; Fasciotti, M; Monteiro, T V C; de Lima, K M G

2018-01-01

This work aimed to evaluate the antibacterial and antifungal activities of two types of pyroligneous acid (PA) obtained from slow pyrolysis of wood of Mimosa tenuiflora and of a hybrid of Eucalyptus urophylla × Eucalyptus grandis. Wood wedges were carbonized on a heating rate of 1·25°C min -1 until 450°C. Pyrolysis smoke was trapped and condensed to yield liquid products. Crude pyrolysis liquids were bidistilled under 5 mmHg vacuum yielding purified PA. Multi-antibiotic-resistant strains of Escherichia coli, Pseudomonas aeruginosa (ATCC 27853) and Staphylococcus aureus (ATCC 25923) had their sensitivity to PA evaluated using agar diffusion test. Two yeasts were evaluated as well, Candida albicans (ATCC 10231) and Cryptococcus neoformans. GC-MS analysis of both PAs was carried out to obtain their chemical composition. Regression analysis was performed, and models were adjusted, with diameter of inhibition halos and PA concentration (100, 50 and 20%) as parameters. Identity of regression models and equality of parameters in polynomial orthogonal equations were verified. Inhibition halos were observed in the range 15-25 mm of diameter. All micro-organisms were inhibited by both types of PA even in the lowest concentration of 20%. The feasibility of the usage of PAs produced with wood species planted in large scale in Brazil was evident and the real potential as a basis to produce natural antibacterial and antifungal agents, with real possibility to be used in veterinary and zootechnical applications. © 2017 The Society for Applied Microbiology.

Statistical and evolutionary optimization for enhanced production of an antileukemic enzyme, L-asparaginase, in a protease-deficient Bacillus aryabhattai ITBHU02 isolated from the soil contaminated with hospital waste.

PubMed

Singh, Yogendra; Srivastav, S K

2013-04-01

Over the past few decades, L-asparaginase has emerged as an excellent anti-neoplastic agent. In present study, a new strain ITBHU02, isolated from soil site near degrading hospital waste, was investigated for the production of extracellular L-asparaginase. Further, it was renamed as Bacillus aryabhattai ITBHU02 based on its phenotypical features, biochemical characteristics, fatty acid methyl ester (FAME) profile and phylogenetic similarity of 16S rDNA sequences. The strain was found protease-deficient and its optimal growth occurred at 37 degrees C and pH 7.5. The strain was capable of producing enzyme L-asparaginase with maximum specific activity of 3.02 +/- 0.3 Umg(-1) protein, when grown in un-optimized medium composition and physical parameters. In order to improve the production of L-asparaginase by the isolate, response surface methodology (RSM) and genetic algorithm (GA) based techniques were implemented. The data achieved through the statistical design matrix were used for regression analysis and analysis of variance studies. Furthermore, GA was implemented utilizing polynomial regression equation as a fitness function. Maximum average L-asparaginase productivity of 6.35 Umg(-1) was found at GA optimized concentrations of 4.07, 0.82, 4.91, and 5.2 gL(-1) for KH2PO4, MgSO4 x 7H2O, L-asparagine, and glucose respectively. The GA optimized yield of the enzyme was 7.8% higher in comparison to the yield obtained through RSM based optimization.

Robust regression for large-scale neuroimaging studies.

PubMed

Fritsch, Virgile; Da Mota, Benoit; Loth, Eva; Varoquaux, Gaël; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Brühl, Rüdiger; Butzek, Brigitte; Conrod, Patricia; Flor, Herta; Garavan, Hugh; Lemaitre, Hervé; Mann, Karl; Nees, Frauke; Paus, Tomas; Schad, Daniel J; Schümann, Gunter; Frouin, Vincent; Poline, Jean-Baptiste; Thirion, Bertrand

2015-05-01

Multi-subject datasets used in neuroimaging group studies have a complex structure, as they exhibit non-stationary statistical properties across regions and display various artifacts. While studies with small sample sizes can rarely be shown to deviate from standard hypotheses (such as the normality of the residuals) due to the poor sensitivity of normality tests with low degrees of freedom, large-scale studies (e.g. >100 subjects) exhibit more obvious deviations from these hypotheses and call for more refined models for statistical inference. Here, we demonstrate the benefits of robust regression as a tool for analyzing large neuroimaging cohorts. First, we use an analytic test based on robust parameter estimates; based on simulations, this procedure is shown to provide an accurate statistical control without resorting to permutations. Second, we show that robust regression yields more detections than standard algorithms using as an example an imaging genetics study with 392 subjects. Third, we show that robust regression can avoid false positives in a large-scale analysis of brain-behavior relationships with over 1500 subjects. Finally we embed robust regression in the Randomized Parcellation Based Inference (RPBI) method and demonstrate that this combination further improves the sensitivity of tests carried out across the whole brain. Altogether, our results show that robust procedures provide important advantages in large-scale neuroimaging group studies. Copyright © 2015 Elsevier Inc. All rights reserved.

The role of depression and anxiety in impulsive and obsessive-compulsive behaviors among anorexic and bulimic patients.

PubMed

Finzi-Dottan, Ricky; Zubery, Eynat

2009-01-01

Eating disorders are believed to range across a spectrum of varying degrees of obsessive-compulsive and impulsive behavior. Sixty anorexic (mean age = 19.8; sd = 5.9) and 109 bulimic (mean age = 26.9; sd = 11.3) female patients completed self-report questionnaires assessing obsessive-compulsiveness, impulsivity, depression and anxiety, as well as two eating disorder scales. Results yielded significantly higher levels of impulsivity and negative body image in the bulimic compared to the anorexic group. Regression analysis predicting impulsivity showed that bulimia and negative body image were the main contributors. Regression analysis for predicting obsessive-compulsive behavior suggested that depression and anxiety obscure the link between anorexia and obsessive-compulsive behavior, and a high BMI intensifies the association between anxiety and obsessive-compulsive behavior. The high rates of both impulsivity and obsessive-compulsiveness found in both groups, and their association with the severity of the eating disorder, may suggest that impulsivity and obsessive-compulsiveness are not mutually exclusive and can both be found among anorexic and bulimic patients.

Watch what happens: using a web-based multimedia platform to enhance intraoperative learning and development of clinical reasoning.

PubMed

Fingeret, Abbey L; Martinez, Rebecca H; Hsieh, Christine; Downey, Peter; Nowygrod, Roman

2016-02-01

We aim to determine whether observed operations or internet-based video review predict improved performance in the surgery clerkship. A retrospective review of students' usage of surgical videos, observed operations, evaluations, and examination scores were used to construct an exploratory principal component analysis. Multivariate regression was used to determine factors predictive of clerkship performance. Case log data for 231 students revealed a median of 25 observed cases. Students accessed the web-based video platform a median of 15 times. Principal component analysis yielded 4 factors contributing 74% of the variability with a Kaiser-Meyer-Olkin coefficient of .83. Multivariate regression predicted shelf score (P < .0001), internal clinical skills examination score (P < .0001), subjective evaluations (P < .001), and video website utilization (P < .001) but not observed cases to be significantly associated with overall performance. Utilization of a web-based operative video platform during a surgical clerkship is an independently associated with improved clinical reasoning, fund of knowledge, and overall evaluation. Thus, this modality can serve as a useful adjunct to live observation. Copyright © 2016 Elsevier Inc. All rights reserved.

Analysis of nonlinear relationships in dual epidemics, and its application to the management of grapevine downy and powdery mildews.

PubMed

Savary, Serge; Delbac, Lionel; Rochas, Amélie; Taisant, Guillaume; Willocquet, Laetitia

2009-08-01

Dual epidemics are defined as epidemics developing on two or several plant organs in the course of a cropping season. Agricultural pathosystems where such epidemics develop are often very important, because the harvestable part is one of the organs affected. These epidemics also are often difficult to manage, because the linkage between epidemiological components occurring on different organs is poorly understood, and because prediction of the risk toward the harvestable organs is difficult. In the case of downy mildew (DM) and powdery mildew (PM) of grapevine, nonlinear modeling and logistic regression indicated nonlinearity in the foliage-cluster relationships. Nonlinear modeling enabled the parameterization of a transmission coefficient that numerically links the two components, leaves and clusters, in DM and PM epidemics. Logistic regression analysis yielded a series of probabilistic models that enabled predicting preset levels of cluster infection risks based on DM and PM severities on the foliage at successive crop stages. The usefulness of this framework for tactical decision-making for disease control is discussed.

Life stress and atherosclerosis: a pathway through unhealthy lifestyle.

PubMed

Mainous, Arch G; Everett, Charles J; Diaz, Vanessa A; Player, Marty S; Gebregziabher, Mulugeta; Smith, Daniel W

2010-01-01

To examine the relationship between a general measure of chronic life stress and atherosclerosis among middle aged adults without clinical cardiovascular disease via pathways through unhealthy lifestyle characteristics. We conducted an analysis of The Multi-Ethnic Study of Atherosclerosis (MESA). The MESA collected in 2000 includes 5,773 participants, aged 45-84. We computed standard regression techniques to examine the relationship between life stress and atherosclerosis as well as path analysis with hypothesized paths from stress to atherosclerosis through unhealthy lifestyle. Our outcome was sub-clinical atherosclerosis measured as presence of coronary artery calcification (CAC). A logistic regression adjusted for potential confounding variables along with the unhealthy lifestyle characteristics of smoking, excessive alcohol use, high caloric intake, sedentary lifestyle, and obesity yielded no significant relationship between chronic life stress (OR 0.93, 95% CI 0.80-1.08) and CAC. However, significant indirect pathways between chronic life stress and CAC through smoking (p = .007), and sedentary lifestyle (p = .03) and caloric intake (.002) through obesity were found. These results suggest that life stress is related to atherosclerosis once paths of unhealthy coping behaviors are considered.

Detection of Butter Adulteration with Lard by Employing (1)H-NMR Spectroscopy and Multivariate Data Analysis.

PubMed

Fadzillah, Nurrulhidayah Ahmad; Man, Yaakob bin Che; Rohman, Abdul; Rosman, Arieff Salleh; Ismail, Amin; Mustafa, Shuhaimi; Khatib, Alfi

2015-01-01

The authentication of food products from the presence of non-allowed components for certain religion like lard is very important. In this study, we used proton Nuclear Magnetic Resonance ((1)H-NMR) spectroscopy for the analysis of butter adulterated with lard by simultaneously quantification of all proton bearing compounds, and consequently all relevant sample classes. Since the spectra obtained were too complex to be analyzed visually by the naked eyes, the classification of spectra was carried out.The multivariate calibration of partial least square (PLS) regression was used for modelling the relationship between actual value of lard and predicted value. The model yielded a highest regression coefficient (R(2)) of 0.998 and the lowest root mean square error calibration (RMSEC) of 0.0091% and root mean square error prediction (RMSEP) of 0.0090, respectively. Cross validation testing evaluates the predictive power of the model. PLS model was shown as good models as the intercept of R(2)Y and Q(2)Y were 0.0853 and -0.309, respectively.

Brazil wheat yield covariance model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate wheat yields for the wheat growing states of Rio Grande do Sul, Parana, and Santa Catarina in Brazil. The meteorological data of these three states were pooled and the years 1972 to 1979 were used to develop the model since there was no technological trend in the yields during these years. Predictor variables were derived from monthly total precipitation, average monthly mean temperature, and average monthly maximum temperature.

Argentina soybean yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate soybean yields for the country of Argentina. A meteorological data set was obtained for the country by averaging data for stations within the soybean growing area. Predictor variables for the model were derived from monthly total precipitation and monthly average temperature. A trend variable was included for the years 1969 to 1978 since an increasing trend in yields due to technology was observed between these years.

LAS bioconcentration is isomer specific

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

Tolls, J.; Haller, M.; Graaf, I. de

1995-12-31

The authors measured parent compound specific bioconcentration data for linear alkylbenzene sulfonates in Pimephales promelas. They did so by using cold, custom synthesized sulfophenyl alkanes. They observed that, within homologous series of isomers, the uptake rate constants (k{sub 1}) and the bioconcentration factor (BCF) increase with increasing number of carbon atoms in the alkyl chain (n{sub C-atoms}). In contrast, the elimination rate constant k{sub 2} appears to be independent of the alkyl chain length. Regressions of log BCF vs n{sub C-atoms} yielded different slopes for the homologous groups of the 5- and the 2-sulfophenyl alkane isomers. Regression of all logmore » BCF-data vs log 1/CMC yielded a good description of the data. However, when regressing the data for both homologous series separately again very different slopes are obtained. The results therefore indicate that hydrophobicity-bioconcentration relationships may be different for different homologous groups of sulfophenyl alkanes.« less

Photochemical redox reactions of copper(II)-alanine complexes in aqueous solutions.

PubMed

Lin, Chen-Jui; Hsu, Chao-Sheng; Wang, Po-Yen; Lin, Yi-Liang; Lo, Yu-Shiu; Wu, Chien-Hou

2014-05-19

The photochemical redox reactions of Cu(II)/alanine complexes have been studied in deaerated solutions over an extensive range of pH, Cu(II) concentration, and alanine concentration. Under irradiation, the ligand-to-metal charge transfer results in the reduction of Cu(II) to Cu(I) and the concomitant oxidation of alanine, which produces ammonia and acetaldehyde. Molar absorptivities and quantum yields of photoproducts for Cu(II)/alanine complexes at 313 nm are characterized mainly with the equilibrium Cu(II) speciation where the presence of simultaneously existing Cu(II) species is taken into account. By applying regression analysis, individual Cu(I) quantum yields are determined to be 0.094 ± 0.014 for the 1:1 complex (CuL) and 0.064 ± 0.012 for the 1:2 complex (CuL2). Individual quantum yields of ammonia are 0.055 ± 0.007 for CuL and 0.036 ± 0.005 for CuL2. Individual quantum yields of acetaldehyde are 0.030 ± 0.007 for CuL and 0.024 ± 0.007 for CuL2. CuL always has larger quantum yields than CuL2, which can be attributed to the Cu(II) stabilizing effect of the second ligand. For both CuL and CuL2, the individual quantum yields of Cu(I), ammonia, and acetaldehyde are in the ratio of 1.8:1:0.7. A reaction mechanism for the formation of the observed photoproducts is proposed.

Portable visible and near-infrared spectrophotometer for triglyceride measurements.

PubMed

Kobayashi, Takanori; Kato, Yukiko Hakariya; Tsukamoto, Megumi; Ikuta, Kazuyoshi; Sakudo, Akikazu

2009-01-01

An affordable and portable machine is required for the practical use of visible and near-infrared (Vis-NIR) spectroscopy. A portable fruit tester comprising a Vis-NIR spectrophotometer was modified for use in the transmittance mode and employed to quantify triglyceride levels in serum in combination with a chemometric analysis. Transmittance spectra collected in the 600- to 1100-nm region were subjected to a partial least-squares regression analysis and leave-out cross-validation to develop a chemometrics model for predicting triglyceride concentrations in serum. The model yielded a coefficient of determination in cross-validation (R2VAL) of 0.7831 with a standard error of cross-validation (SECV) of 43.68 mg/dl. The detection limit of the model was 148.79 mg/dl. Furthermore, masked samples predicted by the model yielded a coefficient of determination in prediction (R2PRED) of 0.6856 with a standard error of prediction (SEP) and detection limit of 61.54 and 159.38 mg/dl, respectively. The portable Vis-NIR spectrophotometer may prove convenient for the measurement of triglyceride concentrations in serum, although before practical use there remain obstacles, which are discussed.

Box-Behnken design for investigation of microwave-assisted extraction of patchouli oil

NASA Astrophysics Data System (ADS)

Kusuma, Heri Septya; Mahfud, Mahfud

2015-12-01

Microwave-assisted extraction (MAE) technique was employed to extract the essential oil from patchouli (Pogostemon cablin). The optimal conditions for microwave-assisted extraction of patchouli oil were determined by response surface methodology. A Box-Behnken design (BBD) was applied to evaluate the effects of three independent variables (microwave power (A: 400-800 W), plant material to solvent ratio (B: 0.10-0.20 g mL-1) and extraction time (C: 20-60 min)) on the extraction yield of patchouli oil. The correlation analysis of the mathematical-regression model indicated that quadratic polynomial model could be employed to optimize the microwave extraction of patchouli oil. The optimal extraction conditions of patchouli oil was microwave power 634.024 W, plant material to solvent ratio 0.147648 g ml-1 and extraction time 51.6174 min. The maximum patchouli oil yield was 2.80516% under these optimal conditions. Under the extraction condition, the experimental values agreed with the predicted results by analysis of variance. It indicated high fitness of the model used and the success of response surface methodology for optimizing and reflect the expected extraction condition.

Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers.

PubMed

Maniruzzaman, Md; Rahman, Md Jahanur; Al-MehediHasan, Md; Suri, Harman S; Abedin, Md Menhazul; El-Baz, Ayman; Suri, Jasjit S

2018-04-10

Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is projected that this will reach nearly 10% by 2045. The major challenge is that when machine learning-based classifiers are applied to such data sets for risk stratification, leads to lower performance. Thus, our objective is to develop an optimized and robust machine learning (ML) system under the assumption that missing values or outliers if replaced by a median configuration will yield higher risk stratification accuracy. This ML-based risk stratification is designed, optimized and evaluated, where: (i) the features are extracted and optimized from the six feature selection techniques (random forest, logistic regression, mutual information, principal component analysis, analysis of variance, and Fisher discriminant ratio) and combined with ten different types of classifiers (linear discriminant analysis, quadratic discriminant analysis, naïve Bayes, Gaussian process classification, support vector machine, artificial neural network, Adaboost, logistic regression, decision tree, and random forest) under the hypothesis that both missing values and outliers when replaced by computed medians will improve the risk stratification accuracy. Pima Indian diabetic dataset (768 patients: 268 diabetic and 500 controls) was used. Our results demonstrate that on replacing the missing values and outliers by group median and median values, respectively and further using the combination of random forest feature selection and random forest classification technique yields an accuracy, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve as: 92.26%, 95.96%, 79.72%, 91.14%, 91.20%, and 0.93, respectively. This is an improvement of 10% over previously developed techniques published in literature. The system was validated for its stability and reliability. RF-based model showed the best performance when outliers are replaced by median values.

Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

NASA Astrophysics Data System (ADS)

Berger, Lukas; Kleinheinz, Konstantin; Attili, Antonio; Bisetti, Fabrizio; Pitsch, Heinz; Mueller, Michael E.

2018-05-01

Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy simulations using a dataset of a direct numerical simulation of a non-premixed sooting turbulent flame.

Analysis of selective chopper radiometer data

NASA Technical Reports Server (NTRS)

Roe, J.; Hovland, D.; Wilcox, R.

1983-01-01

Data from SCR-B on Nimbus 5 have been processed to yield global, orbital temperatures at 10, 5, 2, 1, and 0.4 mb for the period January 1977 through April 1978 under the current task. In addition gridded values at 10 deg latitude by 20 deg longitude were prepared by space-time interpolation for the period January 1975 through April 1978. Temperature retrieval was based on regression of radiances against Meteorological Rocket Network data, with regressions recomputed at approximately six-month intervals. This data now completes a consistent time series from April 1970 to April 1978 for all available radiance data from SCR A and SCR B on Nimbus 4 and 5. The processing details for the current period are discussed, but is also applicable to the previous data periods. The accuracy of the temperature retrievals for each 6-month period for the entire eight years is given in the Appendices.

New 1,6-heptadienes with pyrimidine bases attached: Syntheses and spectroscopic analyses

NASA Astrophysics Data System (ADS)

Hammud, Hassan H.; Ghannoum, Amer M.; Fares, Fares A.; Abramian, Lara K.; Bouhadir, Kamal H.

2008-06-01

A simple, high yielding synthesis leading to the functionalization of some pyrimidine bases with a 1,6-heptadienyl moiety spaced from the N - 1 position by a methylene group is described. A key step in this synthesis involves a Mitsunobu reaction by coupling 3N-benzoyluracil and 3N-benzoylthymine to 2-allyl-pent-4-en-1-ol followed by alkaline hydrolysis of the 3N-benzoyl protecting groups. This protocol should eventually lend itself to the synthesis of a host of N-alkylated nucleoside analogs. The absorption and emission properties of these pyrimidine derivatives ( 3- 6) were studied in solvents of different physical properties. Computerized analysis and multiple regression techniques were applied to calculate the regression and correlation coefficients based on the equation that relates peak position λmax to the solvent parameters that depend on the H-bonding ability, refractive index, and dielectric constant of solvents.

Beef quality grading using machine vision

NASA Astrophysics Data System (ADS)

Jeyamkondan, S.; Ray, N.; Kranzler, Glenn A.; Biju, Nisha

2000-12-01

A video image analysis system was developed to support automation of beef quality grading. Forty images of ribeye steaks were acquired. Fat and lean meat were differentiated using a fuzzy c-means clustering algorithm. Muscle longissimus dorsi (l.d.) was segmented from the ribeye using morphological operations. At the end of each iteration of erosion and dilation, a convex hull was fitted to the image and compactness was measured. The number of iterations was selected to yield the most compact l.d. Match between the l.d. muscle traced by an expert grader and that segmented by the program was 95.9%. Marbling and color features were extracted from the l.d. muscle and were used to build regression models to predict marbling and color scores. Quality grade was predicted using another regression model incorporating all features. Grades predicted by the model were statistically equivalent to the grades assigned by expert graders.

Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation

PubMed Central

Nilssen, Ingunn; Eide, Ingvar; de Oliveira Figueiredo, Marcia Abreu; de Souza Tâmega, Frederico Tapajós; Nattkemper, Tim W.

2016-01-01

This paper presents a machine learning based approach for analyses of photos collected from laboratory experiments conducted to assess the potential impact of water-based drill cuttings on deep-water rhodolith-forming calcareous algae. This pilot study uses imaging technology to quantify and monitor the stress levels of the calcareous algae Mesophyllum engelhartii (Foslie) Adey caused by various degrees of light exposure, flow intensity and amount of sediment. A machine learning based algorithm was applied to assess the temporal variation of the calcareous algae size (∼ mass) and color automatically. Measured size and color were correlated to the photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, ΦPSIImax) and degree of sediment coverage using multivariate regression. The multivariate regression showed correlations between time and calcareous algae sizes, as well as correlations between fluorescence and calcareous algae colors. PMID:27285611

Simple method for quick estimation of aquifer hydrogeological parameters

NASA Astrophysics Data System (ADS)

Ma, C.; Li, Y. Y.

2017-08-01

Development of simple and accurate methods to determine the aquifer hydrogeological parameters was of importance for groundwater resources assessment and management. Aiming at the present issue of estimating aquifer parameters based on some data of the unsteady pumping test, a fitting function of Theis well function was proposed using fitting optimization method and then a unitary linear regression equation was established. The aquifer parameters could be obtained by solving coefficients of the regression equation. The application of the proposed method was illustrated, using two published data sets. By the error statistics and analysis on the pumping drawdown, it showed that the method proposed in this paper yielded quick and accurate estimates of the aquifer parameters. The proposed method could reliably identify the aquifer parameters from long distance observed drawdowns and early drawdowns. It was hoped that the proposed method in this paper would be helpful for practicing hydrogeologists and hydrologists.

PREDICTION OF MALIGNANT BREAST LESIONS FROM MRI FEATURES: A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION TECHNIQUES

PubMed Central

McLaren, Christine E.; Chen, Wen-Pin; Nie, Ke; Su, Min-Ying

2009-01-01

Rationale and Objectives Dynamic contrast enhanced MRI (DCE-MRI) is a clinical imaging modality for detection and diagnosis of breast lesions. Analytical methods were compared for diagnostic feature selection and performance of lesion classification to differentiate between malignant and benign lesions in patients. Materials and Methods The study included 43 malignant and 28 benign histologically-proven lesions. Eight morphological parameters, ten gray level co-occurrence matrices (GLCM) texture features, and fourteen Laws’ texture features were obtained using automated lesion segmentation and quantitative feature extraction. Artificial neural network (ANN) and logistic regression analysis were compared for selection of the best predictors of malignant lesions among the normalized features. Results Using ANN, the final four selected features were compactness, energy, homogeneity, and Law_LS, with area under the receiver operating characteristic curve (AUC) = 0.82, and accuracy = 0.76. The diagnostic performance of these 4-features computed on the basis of logistic regression yielded AUC = 0.80 (95% CI, 0.688 to 0.905), similar to that of ANN. The analysis also shows that the odds of a malignant lesion decreased by 48% (95% CI, 25% to 92%) for every increase of 1 SD in the Law_LS feature, adjusted for differences in compactness, energy, and homogeneity. Using logistic regression with z-score transformation, a model comprised of compactness, NRL entropy, and gray level sum average was selected, and it had the highest overall accuracy of 0.75 among all models, with AUC = 0.77 (95% CI, 0.660 to 0.880). When logistic modeling of transformations using the Box-Cox method was performed, the most parsimonious model with predictors, compactness and Law_LS, had an AUC of 0.79 (95% CI, 0.672 to 0.898). Conclusion The diagnostic performance of models selected by ANN and logistic regression was similar. The analytic methods were found to be roughly equivalent in terms of predictive ability when a small number of variables were chosen. The robust ANN methodology utilizes a sophisticated non-linear model, while logistic regression analysis provides insightful information to enhance interpretation of the model features. PMID:19409817

Stem cell collection in unmanipulated HLA-haploidentical/mismatched related transplantation with combined granulocyte-colony stimulating factor-mobilised blood and bone marrow for patients with haematologic malignancies: the impact of donor characteristics and procedural settings.

PubMed

Zhang, C; Chen, X-H; Zhang, X; Gao, L; Gao, L; Kong, P-Y; Peng, X-G; Sun, A-H; Gong, Y; Zeng, D-F; Wang, Q-Y

2010-06-01

Unmanipulated haploidentical/mismatched related transplantation with combined granulocyte-colony stimulating factor-mobilised peripheral blood stem cells (G-PBSCs) and granulocyte-colony stimulating factor-mobilised bone marrow (G-BM) has been developed as an alternative transplantation strategy for patients with haematologic malignancies. However, little information is available about the factors predicting the outcome of peripheral blood stem cell (PBSC) collection and bone marrow (BM) harvest in this transplantation. The effects of donor characteristics and procedure factors on CD34(+) cell yield were investigated. A total of 104 related healthy donors received granulocyte-colony stimulating factor (G-CSF) followed by PBSC collection and BM harvest. Male donors had significantly higher yields compared with female donors. In multiple regression analysis for peripheral blood collection, age and flow rate were negatively correlated with cell yield, whereas body mass index, pre-aphaeresis white blood cell (WBC) and circulating immature cell (CIC) counts were positively correlated with cell yields. For BM harvest, age was negatively correlated with cell yields, whereas pre-BM collection CIC counts were positively correlated with cell yield. All donors achieved the final product of >or=6 x10(6) kg(-1) recipient body weight. This transplantation strategy has been shown to be a feasible approach with acceptable outcomes in stem cell collection for patients who received HLA-haploidentical/mismatched transplantation with combined G-PBSCs and G-BM. In donors with multiple high-risk characteristics for poor aphaeresis CD34(+) cell yield, BM was an alternative source.

Fast, Low-Cost and Non-Destructive Physico-Chemical Analysis of Virgin Olive Oils Using Near-Infrared Reflectance Spectroscopy

PubMed Central

Garrido-Varo, Ana; Sánchez, María-Teresa; De la Haba, María-José; Torres, Irina; Pérez-Marín, Dolores

2017-01-01

Near-Infrared (NIR) Spectroscopy was used for the non-destructive assessment of physico-chemical quality parameters in olive oil. At the same time, the influence of the sample presentation mode (spinning versus static cup) was evaluated using two spectrophotometers with similar optical characteristics. A total of 478 olive oil samples were used to develop calibration models, testing various spectral signal pre-treatments. The models obtained by applying MPLS regression to spectroscopic data yielded promising results for olive oil quality measurements, particularly for acidity, the peroxide index and alkyl and ethyl ester content. The results obtained indicate that this non-invasive technology can be used successfully by the olive oil sector to categorize olive oils, to detect potential fraud and to provide consumers with more reliable information. Although both sample presentation modes yielded comparable results, equations constructed with samples scanned using the spinning mode provided greater predictive capacity. PMID:29144417

Substantiating the concept of work strain: its implication for the assessment of work stressors.

PubMed

Szerencsi, Karolina; van Amelsvoort, Ludovic; Kant, Ijmert

2012-03-01

To assess which work stressors are substantially contributing to work strain and examine their relative contribution. We prospectively examined the association between work stressors and work strain, which was defined as employees reporting ill due to work stress. Relevant work stressors were combined into a stressor score with each stressor having its own relevance. Standardized odds ratios (SORs) were calculated using logistic regression analysis and used to compare the associations obtained between already existing scales and the stressor score with work strain. The stressor score yielded an SOR of 1.89 (95% confidence interval: 1.49 to 2.41) for work strain, while psychological demands (JCQ) yielded an SOR of 1.46 (95% confidence interval: 1.09 to 1.88) for work strain. We were able to extend and substantiate the range of relevant work stressors into a more comprehensive measure, which should be used to optimize prevention strategies.

Economic injury level of the psyllid, Agonoscena pistaciae, on Pistachio, Pistacia vera cv. Ohadi.

PubMed

Reza Hassani, Mohammad; Nouri-Ganbalani, Gadir; Izadi, Hamzeh; Shojai, Mahmoud; Basirat, Mehdi

2009-01-01

The pistachio psylla, Agonoscena pistaciae Burckhardt and Lauterer (Hemiptera: Psyllidae) is a major pest of pistachio trees, Pistacia vera L. (Sapindalis: Anacardiaceae) throughout pistachio-producing regions in Iran. Different density levels of A. pistaciae nymphs were maintained on pistachio trees by different insecticide dosages to evaluate the relationship between nymph density and yield loss (weight of 1000 nuts). Psylla nymph densities were monitored weekly by counting nymphs on pistachio terminal leaflets. There was a significant reduction in weight of 1000 nuts as seasonal averages of nymphs increased. Regression analysis was used to determine the relationship between nymph density and weight of 1000 nuts. The economic injury levels varied as a function of market values, management costs, insecticide efficiency and yield loss rate and ranged from 7.7 to 30.7 nymphal days per terminal leaflet, based on weight of 1000 nuts.

Economic Injury Level of the Psyllid, Agonoscena pistaciae, on Pistachio, Pistacia vera cv. Ohadi

PubMed Central

Reza Hassani, Mohammad; Nouri-Ganbalani, Gadir; Izadi, Hamzeh; Basirat, Mehdi

2009-01-01

The pistachio psylla, Agonoscena pistaciae Burckhardt and Lauterer (Hemiptera: Psyllidae) is a major pest of pistachio trees, Pistacia vera L. (Sapindalis: Anacardiaceae) throughout pistachio-producing regions in Iran. Different density levels of A. pistaciae nymphs were maintained on pistachio trees by different insecticide dosages to evaluate the relationship between nymph density and yield loss (weight of 1000 nuts). Psylla nymph densities were monitored weekly by counting nymphs on pistachio terminal leaflets. There was a significant reduction in weight of 1000 nuts as seasonal averages of nymphs increased. Regression analysis was used to determine the relationship between nymph density and weight of 1000 nuts. The economic injury levels varied as a function of market values, management costs, insecticide efficiency and yield loss rate and ranged from 7.7 to 30.7 nymphal days per terminal leaflet, based on weight of 1000 nuts. PMID:19619034

Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

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

Dyar, M. Darby; McCanta, Molly; Breves, Elly

2016-03-01

Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yield accurate resultsmore » from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.« less

Regression Discontinuity Design in Gifted and Talented Education Research

ERIC Educational Resources Information Center

Matthews, Michael S.; Peters, Scott J.; Housand, Angela M.

2012-01-01

This Methodological Brief introduces the reader to the regression discontinuity design (RDD), which is a method that when used correctly can yield estimates of research treatment effects that are equivalent to those obtained through randomized control trials and can therefore be used to infer causality. However, RDD does not require the random…

7 CFR 275.23 - Determination of State agency program performance.

Code of Federal Regulations, 2011 CFR

2011-01-01

... NUTRITION SERVICE, DEPARTMENT OF AGRICULTURE FOOD STAMP AND FOOD DISTRIBUTION PROGRAM PERFORMANCE REPORTING... section, the adjusted regressed payment error rate shall be calculated to yield the State agency's payment error rate. The adjusted regressed payment error rate is given by r 1″ + r 2″. (ii) If FNS determines...

A comparison of time dependent Cox regression, pooled logistic regression and cross sectional pooling with simulations and an application to the Framingham Heart Study.

PubMed

Ngwa, Julius S; Cabral, Howard J; Cheng, Debbie M; Pencina, Michael J; Gagnon, David R; LaValley, Michael P; Cupples, L Adrienne

2016-11-03

Typical survival studies follow individuals to an event and measure explanatory variables for that event, sometimes repeatedly over the course of follow up. The Cox regression model has been used widely in the analyses of time to diagnosis or death from disease. The associations between the survival outcome and time dependent measures may be biased unless they are modeled appropriately. In this paper we explore the Time Dependent Cox Regression Model (TDCM), which quantifies the effect of repeated measures of covariates in the analysis of time to event data. This model is commonly used in biomedical research but sometimes does not explicitly adjust for the times at which time dependent explanatory variables are measured. This approach can yield different estimates of association compared to a model that adjusts for these times. In order to address the question of how different these estimates are from a statistical perspective, we compare the TDCM to Pooled Logistic Regression (PLR) and Cross Sectional Pooling (CSP), considering models that adjust and do not adjust for time in PLR and CSP. In a series of simulations we found that time adjusted CSP provided identical results to the TDCM while the PLR showed larger parameter estimates compared to the time adjusted CSP and the TDCM in scenarios with high event rates. We also observed upwardly biased estimates in the unadjusted CSP and unadjusted PLR methods. The time adjusted PLR had a positive bias in the time dependent Age effect with reduced bias when the event rate is low. The PLR methods showed a negative bias in the Sex effect, a subject level covariate, when compared to the other methods. The Cox models yielded reliable estimates for the Sex effect in all scenarios considered. We conclude that survival analyses that explicitly account in the statistical model for the times at which time dependent covariates are measured provide more reliable estimates compared to unadjusted analyses. We present results from the Framingham Heart Study in which lipid measurements and myocardial infarction data events were collected over a period of 26 years.

Multitrait, random regression, or simple repeatability model in high-throughput phenotyping data improve genomic prediction for wheat grain yield

USDA-ARS?s Scientific Manuscript database

High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat (Triticum aestivum L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect s...

Estimates of spatial and temporal variation of energy crops biomass yields in the US

NASA Astrophysics Data System (ADS)

Song, Y.; Jain, A. K.; Landuyt, W.; Kheshgi, H. S.

2013-12-01

Perennial grasses, such as switchgrass (Panicum viragatum) and Miscanthus (Miscanthus x giganteus) have been identified for potential use as biomass feedstocks in the US. Current research on perennial grass biomass production has been evaluated on small-scale plots. However, the extent to which this potential can be realized at a landscape-scale will depend on the biophysical potential to grow these grasses with minimum possible amount of land that needs to be diverted from food to fuel production. To assess this potential three questions about the biomass yield for these grasses need to be answered: (1) how the yields for different grasses are varied spatially and temporally across the US; (2) whether the yields are temporally stable or not; and (3) how the spatial and temporal trends in yields of these perennial grasses are controlled by limiting factors, including soil type, water availability, climate, and crop varieties. To answer these questions, the growth processes of the perennial grasses are implemented into a coupled biophysical, physiological and biogeochemical model (ISAM). The model has been applied to quantitatively investigate the spatial and temporal trends in biomass yields for over the period 1980 -2010 in the US. The bioenergy grasses considered in this study include Miscanthus, Cave-in-Rock switchgrass and Alamo switchgrass. The effects of climate, soil and topography on the spatial and temporal trends of biomass yields are quantitatively analyzed using principal component analysis and GIS based geographically weighted regression. The spatial temporal trend results are evaluated further to classify each part of the US into four homogeneous potential yield zones: high and stable yield zone (HS), high but unstable yield zone (HU), low and stable yield zone (LS) and low but unstable yield zone (LU). Our preliminary results indicate that the yields for perennial grasses among different zones are strongly related to the different controlling factors. For example, the yield in HS zone is depended on soil and topography factors. However, the yields in HU zone are more controlled by climate factors, leading to a large uncertainty in yield potential of bioenergy grasses under future climate change.

[Effects of Chemical Fertilizers and Organic Fertilizer on Yield of Ligusticum chuanxiong Rhizome].

PubMed

Liang, Qin; Chen, Xing-fu; Li, Yan; Zhang, Jun; Meng, Jie; Peng, Shi-ming

2015-10-01

To study the effects of different N, P, K and organic fertilizer (OF) on yield of Ligusticum chuanxiong rhizome, in order to provide the theoretical foundation for the establishment of standardization cultivation techniques. The field plot experiments used Ligusticum chuanxiong rhizome which planted in Pengshan as material, and were studied by the four factors and five levels with quadratic regression rotation-orthogonal combination design. According to the data obtained, a function model which could predict the fertilization and yield of Ligusticum chuanxiong rhizome accurately was established. The model analysis showed that the yields of Ligusticum chuanxiong rhizome were significantly influenced by the N, P, K and OF applications. Among these factors, the order of increase rates by the fertilizers was K > OF > N > P; The effect of interaction between N and K, N and OF, K and OF on the yield of Ligusticum chuanxiong rhizome were significantly different. High levels of N and P, N and organic fertilizer, K and organic fertilizer were conducive to improve the yield of Ligusticum chuanxiong rhizome. The results showed that the optimal fertilizer application rates of N was 148.20 - 172.28 kg/hm2, P was 511.92 - 599.40 kg/hm2, K was 249.70 - 282.37 kg/hm2, and OF was 940.00 - 1 104.00 kg/hm2. N, P, K and OF obviously affect the yield of Ligusticum chuanxiong rhizome. K and OF can significantly increase the yield of Ligusticum chuanxiong rhizome. Thus it is suggested that properly high mount of K and OF and appropriate increasing N are two favorable factors for cultivating Ligusticum chuanxiong.

Solving large test-day models by iteration on data and preconditioned conjugate gradient.

PubMed

Lidauer, M; Strandén, I; Mäntysaari, E A; Pösö, J; Kettunen, A

1999-12-01

A preconditioned conjugate gradient method was implemented into an iteration on a program for data estimation of breeding values, and its convergence characteristics were studied. An algorithm was used as a reference in which one fixed effect was solved by Gauss-Seidel method, and other effects were solved by a second-order Jacobi method. Implementation of the preconditioned conjugate gradient required storing four vectors (size equal to number of unknowns in the mixed model equations) in random access memory and reading the data at each round of iteration. The preconditioner comprised diagonal blocks of the coefficient matrix. Comparison of algorithms was based on solutions of mixed model equations obtained by a single-trait animal model and a single-trait, random regression test-day model. Data sets for both models used milk yield records of primiparous Finnish dairy cows. Animal model data comprised 665,629 lactation milk yields and random regression test-day model data of 6,732,765 test-day milk yields. Both models included pedigree information of 1,099,622 animals. The animal model ¿random regression test-day model¿ required 122 ¿305¿ rounds of iteration to converge with the reference algorithm, but only 88 ¿149¿ were required with the preconditioned conjugate gradient. To solve the random regression test-day model with the preconditioned conjugate gradient required 237 megabytes of random access memory and took 14% of the computation time needed by the reference algorithm.

Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes.

PubMed

Nateghi, Roshanak; Guikema, Seth D; Quiring, Steven M

2011-12-01

This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy. © 2011 Society for Risk Analysis.

Shea (Vitellaria paradoxa Gaertn C. F.) fruit yield assessment and management by farm households in the Atacora district of Benin

PubMed Central

Villamor, Grace B.; Nyarko, Benjamin Kofi; Wala, Kperkouma; Akpagana, Koffi

2018-01-01

Vitellaria paradoxa (Gaertn C. F.), or shea tree, remains one of the most valuable trees for farmers in the Atacora district of northern Benin, where rural communities depend on shea products for both food and income. To optimize productivity and management of shea agroforestry systems, or "parklands," accurate and up-to-date data are needed. For this purpose, we monitored120 fruiting shea trees for two years under three land-use scenarios and different soil groups in Atacora, coupled with a farm household survey to elicit information on decision making and management practices. To examine the local pattern of shea tree productivity and relationships between morphological factors and yields, we used a randomized branch sampling method and applied a regression analysis to build a shea yield model based on dendrometric, soil and land-use variables. We also compared potential shea yields based on farm household socio-economic characteristics and management practices derived from the survey data. Soil and land-use variables were the most important determinants of shea fruit yield. In terms of land use, shea trees growing on farmland plots exhibited the highest yields (i.e., fruit quantity and mass) while trees growing on Lixisols performed better than those of the other soil group. Contrary to our expectations, dendrometric parameters had weak relationships with fruit yield regardless of land-use and soil group. There is an inter-annual variability in fruit yield in both soil groups and land-use type. In addition to observed inter-annual yield variability, there was a high degree of variability in production among individual shea trees. Furthermore, household socioeconomic characteristics such as road accessibility, landholding size, and gross annual income influence shea fruit yield. The use of fallow areas is an important land management practice in the study area that influences both conservation and shea yield. PMID:29346406

Shea (Vitellaria paradoxa Gaertn C. F.) fruit yield assessment and management by farm households in the Atacora district of Benin.

PubMed

Aleza, Koutchoukalo; Villamor, Grace B; Nyarko, Benjamin Kofi; Wala, Kperkouma; Akpagana, Koffi

2018-01-01

Vitellaria paradoxa (Gaertn C. F.), or shea tree, remains one of the most valuable trees for farmers in the Atacora district of northern Benin, where rural communities depend on shea products for both food and income. To optimize productivity and management of shea agroforestry systems, or "parklands," accurate and up-to-date data are needed. For this purpose, we monitored120 fruiting shea trees for two years under three land-use scenarios and different soil groups in Atacora, coupled with a farm household survey to elicit information on decision making and management practices. To examine the local pattern of shea tree productivity and relationships between morphological factors and yields, we used a randomized branch sampling method and applied a regression analysis to build a shea yield model based on dendrometric, soil and land-use variables. We also compared potential shea yields based on farm household socio-economic characteristics and management practices derived from the survey data. Soil and land-use variables were the most important determinants of shea fruit yield. In terms of land use, shea trees growing on farmland plots exhibited the highest yields (i.e., fruit quantity and mass) while trees growing on Lixisols performed better than those of the other soil group. Contrary to our expectations, dendrometric parameters had weak relationships with fruit yield regardless of land-use and soil group. There is an inter-annual variability in fruit yield in both soil groups and land-use type. In addition to observed inter-annual yield variability, there was a high degree of variability in production among individual shea trees. Furthermore, household socioeconomic characteristics such as road accessibility, landholding size, and gross annual income influence shea fruit yield. The use of fallow areas is an important land management practice in the study area that influences both conservation and shea yield.

Using a time-series statistical framework to quantify trends and abrupt change in US corn, soybean, and wheat yields from 1970-2016

NASA Astrophysics Data System (ADS)

Zhang, J.; Ives, A. R.; Turner, M. G.; Kucharik, C. J.

2017-12-01

Previous studies have identified global agricultural regions where "stagnation" of long-term crop yield increases has occurred. These studies have used a variety of simple statistical methods that often ignore important aspects of time series regression modeling. These methods can lead to differing and contradictory results, which creates uncertainty regarding food security given rapid global population growth. Here, we present a new statistical framework incorporating time series-based algorithms into standard regression models to quantify spatiotemporal yield trends of US maize, soybean, and winter wheat from 1970-2016. Our primary goal was to quantify spatial differences in yield trends for these three crops using USDA county level data. This information was used to identify regions experiencing the largest changes in the rate of yield increases over time, and to determine whether abrupt shifts in the rate of yield increases have occurred. Although crop yields continue to increase in most maize-, soybean-, and winter wheat-growing areas, yield increases have stagnated in some key agricultural regions during the most recent 15 to 16 years: some maize-growing areas, except for the northern Great Plains, have shown a significant trend towards smaller annual yield increases for maize; soybean has maintained an consistent long-term yield gains in the Northern Great Plains, the Midwest, and southeast US, but has experienced a shift to smaller annual increases in other regions; winter wheat maintained a moderate annual increase in eastern South Dakota and eastern US locations, but showed a decline in the magnitude of annual increases across the central Great Plains and western US regions. Our results suggest that there were abrupt shifts in the rate of annual yield increases in a variety of US regions among the three crops. The framework presented here can be broadly applied to additional yield trend analyses for different crops and regions of the Earth.

Argentina wheat yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

Five models based on multiple regression were developed to estimate wheat yields for the five wheat growing provinces of Argentina. Meteorological data sets were obtained for each province by averaging data for stations within each province. Predictor variables for the models were derived from monthly total precipitation, average monthly mean temperature, and average monthly maximum temperature. Buenos Aires was the only province for which a trend variable was included because of increasing trend in yield due to technology from 1950 to 1963.

Argentina corn yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate corn yields for the country of Argentina. A meteorological data set was obtained for the country by averaging data for stations within the corn-growing area. Predictor variables for the model were derived from monthly total precipitation, average monthly mean temperature, and average monthly maximum temperature. A trend variable was included for the years 1965 to 1980 since an increasing trend in yields due to technology was observed between these years.

Crop-climate relationships of cereals in Greece and the impacts of recent climate trends

NASA Astrophysics Data System (ADS)

Mavromatis, Theodoros

2015-05-01

Notwithstanding technological developments, agricultural production is still affected by uncontrollable factors, such weather and climate. Within this context, the present study aims at exploring the relative influence of growing season climate on the yields of major cereals (hard and soft wheat, maize, and barley) on a regional scale in Greece. To this end, crop-climate relationships and the impacts of climate trends over the period 1978-2005 were explored using linear regression and change point analysis (CPA). Climate data used include maximum (Tx) and minimum temperature (Tn), diurnal temperature range (Tr), precipitation (Prec), and solar radiation (Rad). Temperature effects were the most substantial. Yields reduced by 1.8-7.1 %/°C with increasing Tx and by 1.4-6.1 %/°C with decreasing Tr. The warming trends of Tn caused bilateral yield effects (from -3.7 to 8.4 %/°C). The fewer significantly increasing Rad and decreasing Prec anomalies were associated with larger yield decreases (within the range of 2.2 % MJ/m2/day (for maize) to 4.9 % MJ/m2/day (for hard wheat)) and smaller yield increases (from 0.04 to 1.4 %/mm per decade), respectively. Wheat and barley—the most vulnerable cereals—were most affected by the trends of extreme temperatures and least by Tr. On the contrary, solar radiation has proven to be the least affecting climate variable on all cereals. Despite the similarity in the direction of crop responses with both analyses, yield changes were much more substantial in the case of CPA analysis. In conclusion, regional climate change has affected Greek cereal productivity, in a few, but important for cereal production, regions. The results of this study are expected to be valuable in anticipating the effects of weather/climate on other warm regions worldwide, where the upper temperature limit for some cereals and further changes in climate may push them past suitability for their cultivation.

A tutorial on the use of Excel 2010 and Excel for Mac 2011 for conducting delay-discounting analyses.

PubMed

Reed, Derek D; Kaplan, Brent A; Brewer, Adam T

2012-01-01

In recent years, researchers and practitioners in the behavioral sciences have profited from a growing literature on delay discounting. The purpose of this article is to provide readers with a brief tutorial on how to use Microsoft Office Excel 2010 and Excel for Mac 2011 to analyze discounting data to yield parameters for both the hyperbolic discounting model and area under the curve. This tutorial is intended to encourage the quantitative analysis of behavior in both research and applied settings by readers with relatively little formal training in nonlinear regression.

A TUTORIAL ON THE USE OF EXCEL 2010 AND EXCEL FOR MAC 2011 FOR CONDUCTING DELAY-DISCOUNTING ANALYSES

PubMed Central

Reed, Derek D; Kaplan, Brent A; Brewer, Adam T

2012-01-01

In recent years, researchers and practitioners in the behavioral sciences have profited from a growing literature on delay discounting. The purpose of this article is to provide readers with a brief tutorial on how to use Microsoft Office Excel 2010 and Excel for Mac 2011 to analyze discounting data to yield parameters for both the hyperbolic discounting model and area under the curve. This tutorial is intended to encourage the quantitative analysis of behavior in both research and applied settings by readers with relatively little formal training in nonlinear regression. PMID:22844143

Toward disentangling the effect of hydrologic and nitrogen source changes from 1992 to 2001 on incremental nitrogen yield in the contiguous United States

NASA Astrophysics Data System (ADS)

Alam, Md Jahangir; Goodall, Jonathan L.

2012-04-01

The goal of this research was to quantify the relative impact of hydrologic and nitrogen source changes on incremental nitrogen yield in the contiguous United States. Using nitrogen source estimates from various federal data bases, remotely sensed land use data from the National Land Cover Data program, and observed instream loadings from the United States Geological Survey National Stream Quality Accounting Network program, we calibrated and applied the spatially referenced regression model SPARROW to estimate incremental nitrogen yield for the contiguous United States. We ran different model scenarios to separate the effects of changes in source contributions from hydrologic changes for the years 1992 and 2001, assuming that only state conditions changed and that model coefficients describing the stream water-quality response to changes in state conditions remained constant between 1992 and 2001. Model results show a decrease of 8.2% in the median incremental nitrogen yield over the period of analysis with the vast majority of this decrease due to changes in hydrologic conditions rather than decreases in nitrogen sources. For example, when we changed the 1992 version of the model to have nitrogen source data from 2001, the model results showed only a small increase in median incremental nitrogen yield (0.12%). However, when we changed the 1992 version of the model to have hydrologic conditions from 2001, model results showed a decrease of approximately 8.7% in median incremental nitrogen yield. We did, however, find notable differences in incremental yield estimates for different sources of nitrogen after controlling for hydrologic changes, particularly for population related sources. For example, the median incremental yield for population related sources increased by 8.4% after controlling for hydrologic changes. This is in contrast to a 2.8% decrease in population related sources when hydrologic changes are included in the analysis. Likewise we found that median incremental yield from urban watersheds increased by 6.8% after controlling for hydrologic changes—in contrast to the median incremental nitrogen yield from cropland watersheds, which decreased by 2.1% over the same time period. These results suggest that, after accounting for hydrologic changes, population related sources became a more significant contributor of nitrogen yield to streams in the contiguous United States over the period of analysis. However, this study was not able to account for the influence of human management practices such as improvements in wastewater treatment plants or Best Management Practices that likely improved water quality, due to a lack of data for quantifying the impact of these practices for the study area.

Mapping Quantitative Trait Loci Associated with Toot Traits Using Sequencing-Based Genotyping Chromosome Segment Substitution Lines Derived from 9311 and Nipponbare in Rice (Oryza sativa L.).

PubMed

Zhou, Yong; Dong, Guichun; Tao, Yajun; Chen, Chen; Yang, Bin; Wu, Yue; Yang, Zefeng; Liang, Guohua; Wang, Baohe; Wang, Yulong

2016-01-01

Identification of quantitative trait loci (QTLs) associated with rice root morphology provides useful information for avoiding drought stress and maintaining yield production under the irrigation condition. In this study, a set of chromosome segment substitution lines derived from 9311 as the recipient and Nipponbare as donor, were used to analysis root morphology. By combining the resequencing-based bin-map with a multiple linear regression analysis, QTL identification was conducted on root number (RN), total root length (TRL), root dry weight (RDW), maximum root length (MRL), root thickness (RTH), total absorption area (TAA) and root vitality (RV), using the CSSL population grown under hydroponic conditions. A total of thirty-eight QTLs were identified: six for TRL, six for RDW, eight for the MRL, four for RTH, seven for RN, two for TAA, and five for RV. Phenotypic effect variance explained by these QTLs ranged from 2.23% to 37.08%, and four single QTLs had more than 10% phenotypic explanations on three root traits. We also detected the correlations between grain yield (GY) and root traits, and found that TRL, RTH and MRL had significantly positive correlations with GY. However, TRL, RDW and MRL had significantly positive correlations with biomass yield (BY). Several QTLs identified in our population were co-localized with some loci for grain yield or biomass. This information may be immediately exploited for improving rice water and fertilizer use efficiency for molecular breeding of root system architectures.

Genetic correlations between the cumulative pseudo-survival rate, milk yield, and somatic cell score during lactation in Holstein cattle in Japan using a random regression model.

PubMed

Sasaki, O; Aihara, M; Nishiura, A; Takeda, H

2017-09-01

Trends in genetic correlations between longevity, milk yield, and somatic cell score (SCS) during lactation in cows are difficult to trace. In this study, changes in the genetic correlations between milk yield, SCS, and cumulative pseudo-survival rate (PSR) during lactation were examined, and the effect of milk yield and SCS information on the reliability of estimated breeding value (EBV) of PSR were determined. Test day milk yield, SCS, and PSR records were obtained for Holstein cows in Japan from 2004 to 2013. A random subset of the data was used for the analysis (825 herds, 205,383 cows). This data set was randomly divided into 5 subsets (162-168 herds, 83,389-95,854 cows), and genetic parameters were estimated in each subset independently. Data were analyzed using multiple-trait random regression animal models including either the residual effect for the whole lactation period (H0), the residual effects for 5 lactation stages (H5), or both of these residual effects (HD). Milk yield heritability increased until 310 to 351 d in milk (DIM) and SCS heritability increased until 330 to 344 DIM. Heritability estimates for PSR increased with DIM from 0.00 to 0.05. The genetic correlation between milk yield and SCS increased negatively to under -0.60 at 455 DIM. The genetic correlation between milk yield and PSR increased until 342 to 355 DIM (0.53-0.57). The genetic correlation between the SCS and PSR was -0.82 to -0.83 at around 180 DIM, and decreased to -0.65 to -0.71 at 455 DIM. The reliability of EBV of PSR for sires with 30 or more recorded daughters was 0.17 to 0.45 when the effects of correlated traits were ignored. The maximum reliability of EBV was observed at 257 (H0) or 322 (HD) DIM. When the correlations of PSR with milk yield and SCS were considered, the reliabilities of PSR estimates increased to 0.31-0.76. The genetic parameter estimates of H5 were the same as those for HD. The rank correlation coefficients of the EBV of PSR between H0 and H5 or HD were greater than 0.9. Additionally, the reliabilities of EBV of PSR of H0 were similar to those for H5 and HD. Therefore, the genetic parameter estimates in H0 were not substantially different from those in H5 and HD. When milk yield and SCS, which were genetically correlated with PSR, were used, the reliability of PSR increased. Estimates of the genetic correlations between PSR and milk yield and between PSR and SCS are useful for management and breeding decisions to extend the herd life of cows. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Investigation of the Process Conditions for Hydrogen Production by Steam Reforming of Glycerol over Ni/Al2O3 Catalyst Using Response Surface Methodology (RSM)

PubMed Central

Ebshish, Ali; Yaakob, Zahira; Taufiq-Yap, Yun Hin; Bshish, Ahmed

2014-01-01

In this work; a response surface methodology (RSM) was implemented to investigate the process variables in a hydrogen production system. The effects of five independent variables; namely the temperature (X1); the flow rate (X2); the catalyst weight (X3); the catalyst loading (X4) and the glycerol-water molar ratio (X5) on the H2 yield (Y1) and the conversion of glycerol to gaseous products (Y2) were explored. Using multiple regression analysis; the experimental results of the H2 yield and the glycerol conversion to gases were fit to quadratic polynomial models. The proposed mathematical models have correlated the dependent factors well within the limits that were being examined. The best values of the process variables were a temperature of approximately 600 °C; a feed flow rate of 0.05 mL/min; a catalyst weight of 0.2 g; a catalyst loading of 20% and a glycerol-water molar ratio of approximately 12; where the H2 yield was predicted to be 57.6% and the conversion of glycerol was predicted to be 75%. To validate the proposed models; statistical analysis using a two-sample t-test was performed; and the results showed that the models could predict the responses satisfactorily within the limits of the variables that were studied. PMID:28788567

Characteristics of sediment data and annual suspended-sediment loads and yields for selected lower Missouri River mainstem and tributary stations, 1976-2008

USGS Publications Warehouse

Heimann, David C.; Rasmussen, Patrick P.; Cline, Teri L.; Pigue, Lori M.; Wagner, Holly R.

2010-01-01

Suspended-sediment data from 18 selected surface-water monitoring stations in the lower Missouri River Basin downstream from Gavins Point Dam were used in the computation of annual suspended-sediment and suspended-sand loads for 1976 through 2008. Three methods of suspended-sediment load determination were utilized and these included the subdivision method, regression of instantaneous turbidity with suspended-sediment concentrations at selected stations, and regression techniques using the Load Estimator (LOADEST) software. Characteristics of the suspended-sediment and streamflow data collected at the 18 monitoring stations and the tabulated annual suspended-sediment and suspended-sand loads and yields are presented.

Comparison of continuously acquired resting state and extracted analogues from active tasks.

PubMed

Ganger, Sebastian; Hahn, Andreas; Küblböck, Martin; Kranz, Georg S; Spies, Marie; Vanicek, Thomas; Seiger, René; Sladky, Ronald; Windischberger, Christian; Kasper, Siegfried; Lanzenberger, Rupert

2015-10-01

Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting-state data, the application to task-specific fMRI has received growing attention. Three major methods for extraction of resting-state data from task-related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in-between task blocks. Despite widespread application in current research, consensus on which method best resembles resting-state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting-state, two different task paradigms were assessed (emotion discrimination and right finger-tapping) and five well-described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting-state (Dice, Intraclass correlation coefficient (ICC), R(2) ) showed that regression against task effects yields functional connectivity networks most alike to resting-state. However, all methods exhibited significant differences when compared to continuous resting-state and similarity metrics were lower than test-retest of two resting-state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting-state when extracting signals from task designs, although functional connectivity computed from task-specific data may indeed yield interesting information. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Comparison of continuously acquired resting state and extracted analogues from active tasks

PubMed Central

Ganger, Sebastian; Hahn, Andreas; Küblböck, Martin; Kranz, Georg S.; Spies, Marie; Vanicek, Thomas; Seiger, René; Sladky, Ronald; Windischberger, Christian; Kasper, Siegfried

2015-01-01

Abstract Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting‐state data, the application to task‐specific fMRI has received growing attention. Three major methods for extraction of resting‐state data from task‐related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in‐between task blocks. Despite widespread application in current research, consensus on which method best resembles resting‐state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting‐state, two different task paradigms were assessed (emotion discrimination and right finger‐tapping) and five well‐described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting‐state (Dice, Intraclass correlation coefficient (ICC), R 2) showed that regression against task effects yields functional connectivity networks most alike to resting‐state. However, all methods exhibited significant differences when compared to continuous resting‐state and similarity metrics were lower than test‐retest of two resting‐state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting‐state when extracting signals from task designs, although functional connectivity computed from task‐specific data may indeed yield interesting information. Hum Brain Mapp 36:4053–4063, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. PMID:26178250

Modeling maximum daily temperature using a varying coefficient regression model

Treesearch

Han Li; Xinwei Deng; Dong-Yum Kim; Eric P. Smith

2014-01-01

Relationships between stream water and air temperatures are often modeled using linear or nonlinear regression methods. Despite a strong relationship between water and air temperatures and a variety of models that are effective for data summarized on a weekly basis, such models did not yield consistently good predictions for summaries such as daily maximum temperature...

Gastrointestinal Spatiotemporal mRNA Expression of Ghrelin vs Growth Hormone Receptor and New Growth Yield Machine Learning Model Based on Perturbation Theory.

PubMed

Ran, Tao; Liu, Yong; Li, Hengzhi; Tang, Shaoxun; He, Zhixiong; Munteanu, Cristian R; González-Díaz, Humberto; Tan, Zhiliang; Zhou, Chuanshe

2016-07-27

The management of ruminant growth yield has economic importance. The current work presents a study of the spatiotemporal dynamic expression of Ghrelin and GHR at mRNA levels throughout the gastrointestinal tract (GIT) of kid goats under housing and grazing systems. The experiments show that the feeding system and age affected the expression of either Ghrelin or GHR with different mechanisms. Furthermore, the experimental data are used to build new Machine Learning models based on the Perturbation Theory, which can predict the effects of perturbations of Ghrelin and GHR mRNA expression on the growth yield. The models consider eight longitudinal GIT segments (rumen, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum), seven time points (0, 7, 14, 28, 42, 56 and 70 d) and two feeding systems (Supplemental and Grazing feeding) as perturbations from the expected values of the growth yield. The best regression model was obtained using Random Forest, with the coefficient of determination R(2) of 0.781 for the test subset. The current results indicate that the non-linear regression model can accurately predict the growth yield and the key nodes during gastrointestinal development, which is helpful to optimize the feeding management strategies in ruminant production system.

Gastrointestinal Spatiotemporal mRNA Expression of Ghrelin vs Growth Hormone Receptor and New Growth Yield Machine Learning Model Based on Perturbation Theory

PubMed Central

Ran, Tao; Liu, Yong; Li, Hengzhi; Tang, Shaoxun; He, Zhixiong; Munteanu, Cristian R.; González-Díaz, Humberto; Tan, Zhiliang; Zhou, Chuanshe

2016-01-01

The management of ruminant growth yield has economic importance. The current work presents a study of the spatiotemporal dynamic expression of Ghrelin and GHR at mRNA levels throughout the gastrointestinal tract (GIT) of kid goats under housing and grazing systems. The experiments show that the feeding system and age affected the expression of either Ghrelin or GHR with different mechanisms. Furthermore, the experimental data are used to build new Machine Learning models based on the Perturbation Theory, which can predict the effects of perturbations of Ghrelin and GHR mRNA expression on the growth yield. The models consider eight longitudinal GIT segments (rumen, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum), seven time points (0, 7, 14, 28, 42, 56 and 70 d) and two feeding systems (Supplemental and Grazing feeding) as perturbations from the expected values of the growth yield. The best regression model was obtained using Random Forest, with the coefficient of determination R2 of 0.781 for the test subset. The current results indicate that the non-linear regression model can accurately predict the growth yield and the key nodes during gastrointestinal development, which is helpful to optimize the feeding management strategies in ruminant production system. PMID:27460882

Genetic evaluation of lactation persistency for five breeds of dairy cattle.

PubMed

Cole, J B; Null, D J

2009-05-01

Cows with high lactation persistency tend to produce less milk than expected at the beginning of lactation and more than expected at the end. Best prediction of lactation persistency is calculated as a function of trait-specific standard lactation curves and linear regressions of test-day deviations on days in milk. Because regression coefficients are deviations from a tipping point selected to make yield and lactation persistency phenotypically uncorrelated it should be possible to use 305-d actual yield and lactation persistency to predict yield for lactations with later endpoints. The objectives of this study were to calculate (co)variance components and breeding values for best predictions of lactation persistency of milk (PM), fat (PF), protein (PP), and somatic cell score (PSCS) in breeds other than Holstein, and to demonstrate the calculation of prediction equations for 400-d actual milk yield. Data included lactations from Ayrshire, Brown Swiss, Guernsey (GU), Jersey (JE), and Milking Shorthorn (MS) cows calving since 1997. The number of sires evaluated ranged from 86 (MS) to 3,192 (JE), and mean sire estimated breeding value for PM ranged from 0.001 (Ayrshire) to 0.10 (Brown Swiss); mean estimated breeding value for PSCS ranged from -0.01 (MS) to -0.043 (JE). Heritabilities were generally highest for PM (0.09 to 0.15) and lowest for PSCS (0.03 to 0.06), with PF and PP having intermediate values (0.07 to 0.13). Repeatabilities varied considerably between breeds, ranging from 0.08 (PSCS in GU, JE, and MS) to 0.28 (PM in GU). Genetic correlations of PM, PF, and PP with PSCS were moderate and favorable (negative), indicating that increasing lactation persistency of yield traits is associated with decreases in lactation persistency of SCS, as expected. Genetic correlations among yield and lactation persistency were low to moderate and ranged from -0.55 (PP in GU) to 0.40 (PP in MS). Prediction equations for 400-d milk yield were calculated for each breed by regression of both 305-d yield and 305-d yield and lactation persistency on 400-d yield. Goodness-of-fit was very good for both models, but the addition of lactation persistency to the model significantly improved fit in all cases. Routine genetic evaluations for lactation persistency, as well as the development of prediction equations for several lactation end-points, may provide producers with tools to better manage their herds.

Comparison of energy expenditure to walk or run a mile in adult normal weight and overweight men and women.

PubMed

Loftin, Mark; Waddell, Dwight E; Robinson, James H; Owens, Scott G

2010-10-01

We compared the energy expenditure to walk or run a mile in adult normal weight walkers (NWW), overweight walkers (OW), and marathon runners (MR). The sample consisted of 19 NWW, 11 OW, and 20 MR adults. Energy expenditure was measured at preferred walking speed (NWW and OW) and running speed of a recently completed marathon. Body composition was assessed via dual-energy x-ray absorptiometry. Analysis of variance was used to compare groups with the Scheffe's procedure used for post hoc analysis. Multiple regression analysis was used to predict energy expenditure. Results that indicated OW exhibited significantly higher (p < 0.05) mass and fat weight than NWW or MR. Similar values were found between NWW and MR. Absolute energy expenditure to walk or run a mile was similar between groups (NWW 93.9 ± 15.0, OW 98.4 ± 29.9, MR 99.3 ± 10.8 kcal); however, significant differences were noted when energy expenditure was expressed relative to mass (MR > NWW > OW). When energy expenditure was expressed per kilogram of fat-free mass, similar values were found across groups. Multiple regression analysis yielded mass and gender as significant predictors of energy expenditure (R = 0.795, SEE = 10.9 kcal). We suggest that walking is an excellent physical activity for energy expenditure in overweight individuals that are capable of walking without predisposed conditions such as osteoarthritis or cardiovascular risk factors. Moreover, from a practical perspective, our regression equation (kcal = mass (kg) × 0.789 - gender (men = 1, women = 2) × 7.634 + 51.109) allows for the prediction of energy expenditure for a given distance (mile) rather than predicting energy expenditure for a given time (minutes).

Environmental factors controlling spatial variation in sediment yield in a central Andean mountain area

NASA Astrophysics Data System (ADS)

Molina, Armando; Govers, Gerard; Poesen, Jean; Van Hemelryck, Hendrik; De Bièvre, Bert; Vanacker, Veerle

2008-06-01

A large spatial variability in sediment yield was observed from small streams in the Ecuadorian Andes. The objective of this study was to analyze the environmental factors controlling these variations in sediment yield in the Paute basin, Ecuador. Sediment yield data were calculated based on sediment volumes accumulated behind checkdams for 37 small catchments. Mean annual specific sediment yield (SSY) shows a large spatial variability and ranges between 26 and 15,100 Mg km - 2 year - 1 . Mean vegetation cover (C, fraction) in the catchment, i.e. the plant cover at or near the surface, exerts a first order control on sediment yield. The fractional vegetation cover alone explains 57% of the observed variance in ln(SSY). The negative exponential relation (SSY = a × e- b C) which was found between vegetation cover and sediment yield at the catchment scale (10 3-10 9 m 2), is very similar to the equations derived from splash, interrill and rill erosion experiments at the plot scale (1-10 3 m 2). This affirms the general character of an exponential decrease of sediment yield with increasing vegetation cover at a wide range of spatial scales, provided the distribution of cover can be considered to be essentially random. Lithology also significantly affects the sediment yield, and explains an additional 23% of the observed variance in ln(SSY). Based on these two catchment parameters, a multiple regression model was built. This empirical regression model already explains more than 75% of the total variance in the mean annual sediment yield. These results highlight the large potential of revegetation programs for controlling sediment yield. They show that a slight increase in the overall fractional vegetation cover of degraded land is likely to have a large effect on sediment production and delivery. Moreover, they point to the importance of detailed surface vegetation data for predicting and modeling sediment production rates.

Evidence for a weakening strength of temperature-corn yield relation in the United States during 1980–2010

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

Leng, Guoyong

Temperature is known to be correlated with crop yields, causing reduction of crop yield with climate warming without adaptations or CO2 fertilization effects. The historical temperature-crop yield relation has often been used for informing future changes. This relationship, however, may change over time following alternations in other environmental factors. Results show that the strength of the relationship between the interannual variability of growing season temperature and corn yield (RGST_CY) has declined in the United States between 1980 and 2010 with a loss in the statistical significance. The regression slope which represents the anomalies in corn yield that occur in associationmore » with 1 degree temperature anomaly has decreased significantly from -6.9%/K of the first half period to -2.4%/K~-3.5%/K of the second half period. This implies that projected corn yield reduction will be overestimated by a fact of 2 in a given warming scenario, if the corn-temperature relation is derived from the earlier historical period. Changes in RGST_CY are mainly observed in Midwest Corn Belt and central High Plains, and are well reproduced by 11 process-based crop models. In Midwest rain-fed systems, the decrease of negative temperature effects coincides with an increase in water availability by precipitation. In irrigated areas where water stress is minimized, the decline of beneficial temperature effects is significantly related to the increase in extreme hot days. The results indicate that an extrapolation of historical yield response to temperature may bias the assessment of agriculture vulnerability to climate change. Efforts to reduce climate impacts on agriculture should pay attention not only to climate change, but also to changes in climate-crop yield relations. There are some caveats that should be acknowledged as the analysis is restricted to the changes in the linear relation between growing season mean temperature and corn yield for the specific study period.« less

Evidence for a weakening strength of temperature-corn yield relation in the United States during 1980-2010.

PubMed

Leng, Guoyong

2017-12-15

Temperature is known to be correlated with crop yields, causing reduction of crop yield with climate warming without adaptations or CO 2 fertilization effects. The historical temperature-crop yield relation has often been used for informing future changes. This relationship, however, may change over time following alternations in other environmental factors. Results show that the strength of the relationship between the interannual variability of growing season temperature and corn yield (R GST_CY ) has declined in the United States between 1980 and 2010 with a loss in the statistical significance. The regression slope which represents the anomalies in corn yield that occur in association with 1 degree temperature anomaly has decreased significantly from -6.9%/K of the first half period to -2.4%/K--3.5%/K of the second half period. This implies that projected corn yield reduction will be overestimated by a fact of 2 in a given warming scenario, if the corn-temperature relation is derived from the earlier historical period. Changes in R GST_CY are mainly observed in Midwest Corn Belt and central High Plains, but are partly reproduced by 11 process-based crop models. In Midwest rain-fed systems, the decrease of negative temperature effects coincides with an increase in water availability by precipitation. In irrigated areas where water stress is minimized, the decline of beneficial temperature effects is significantly related to the increase in extreme hot days. The results indicate that an extrapolation of historical yield response to temperature may bias the assessment of agriculture vulnerability to climate change. Efforts to reduce climate impacts on agriculture should pay attention not only to climate change, but also to changes in climate-crop yield relations. There are some caveats that should be acknowledged as the analysis is restricted to the changes in the linear relation between growing season mean temperature and corn yield for the specific study period. Copyright © 2017 Elsevier B.V. All rights reserved.

Geodesic least squares regression on information manifolds

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

Verdoolaege, Geert, E-mail: geert.verdoolaege@ugent.be

We present a novel regression method targeted at situations with significant uncertainty on both the dependent and independent variables or with non-Gaussian distribution models. Unlike the classic regression model, the conditional distribution of the response variable suggested by the data need not be the same as the modeled distribution. Instead they are matched by minimizing the Rao geodesic distance between them. This yields a more flexible regression method that is less constrained by the assumptions imposed through the regression model. As an example, we demonstrate the improved resistance of our method against some flawed model assumptions and we apply thismore » to scaling laws in magnetic confinement fusion.« less

Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification.

PubMed

Ramírez, J; Górriz, J M; Segovia, F; Chaves, R; Salas-Gonzalez, D; López, M; Alvarez, I; Padilla, P

2010-03-19

This letter shows a computer aided diagnosis (CAD) technique for the early detection of the Alzheimer's disease (AD) by means of single photon emission computed tomography (SPECT) image classification. The proposed method is based on partial least squares (PLS) regression model and a random forest (RF) predictor. The challenge of the curse of dimensionality is addressed by reducing the large dimensionality of the input data by downscaling the SPECT images and extracting score features using PLS. A RF predictor then forms an ensemble of classification and regression tree (CART)-like classifiers being its output determined by a majority vote of the trees in the forest. A baseline principal component analysis (PCA) system is also developed for reference. The experimental results show that the combined PLS-RF system yields a generalization error that converges to a limit when increasing the number of trees in the forest. Thus, the generalization error is reduced when using PLS and depends on the strength of the individual trees in the forest and the correlation between them. Moreover, PLS feature extraction is found to be more effective for extracting discriminative information from the data than PCA yielding peak sensitivity, specificity and accuracy values of 100%, 92.7%, and 96.9%, respectively. Moreover, the proposed CAD system outperformed several other recently developed AD CAD systems. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level

NASA Astrophysics Data System (ADS)

Muller, Sybrand Jacobus; van Niekerk, Adriaan

2016-07-01

Soil salinity often leads to reduced crop yield and quality and can render soils barren. Irrigated areas are particularly at risk due to intensive cultivation and secondary salinization caused by waterlogging. Regular monitoring of salt accumulation in irrigation schemes is needed to keep its negative effects under control. The dynamic spatial and temporal characteristics of remote sensing can provide a cost-effective solution for monitoring salt accumulation at irrigation scheme level. This study evaluated a range of pan-fused SPOT-5 derived features (spectral bands, vegetation indices, image textures and image transformations) for classifying salt-affected areas in two distinctly different irrigation schemes in South Africa, namely Vaalharts and Breede River. The relationship between the input features and electro conductivity measurements were investigated using regression modelling (stepwise linear regression, partial least squares regression, curve fit regression modelling) and supervised classification (maximum likelihood, nearest neighbour, decision tree analysis, support vector machine and random forests). Classification and regression trees and random forest were used to select the most important features for differentiating salt-affected and unaffected areas. The results showed that the regression analyses produced weak models (<0.4 R squared). Better results were achieved using the supervised classifiers, but the algorithms tend to over-estimate salt-affected areas. A key finding was that none of the feature sets or classification algorithms stood out as being superior for monitoring salt accumulation at irrigation scheme level. This was attributed to the large variations in the spectral responses of different crops types at different growing stages, coupled with their individual tolerances to saline conditions.

Use of partial least squares regression to impute SNP genotypes in Italian cattle breeds.

PubMed

Dimauro, Corrado; Cellesi, Massimo; Gaspa, Giustino; Ajmone-Marsan, Paolo; Steri, Roberto; Marras, Gabriele; Macciotta, Nicolò P P

2013-06-05

The objective of the present study was to test the ability of the partial least squares regression technique to impute genotypes from low density single nucleotide polymorphisms (SNP) panels i.e. 3K or 7K to a high density panel with 50K SNP. No pedigree information was used. Data consisted of 2093 Holstein, 749 Brown Swiss and 479 Simmental bulls genotyped with the Illumina 50K Beadchip. First, a single-breed approach was applied by using only data from Holstein animals. Then, to enlarge the training population, data from the three breeds were combined and a multi-breed analysis was performed. Accuracies of genotypes imputed using the partial least squares regression method were compared with those obtained by using the Beagle software. The impact of genotype imputation on breeding value prediction was evaluated for milk yield, fat content and protein content. In the single-breed approach, the accuracy of imputation using partial least squares regression was around 90 and 94% for the 3K and 7K platforms, respectively; corresponding accuracies obtained with Beagle were around 85% and 90%. Moreover, computing time required by the partial least squares regression method was on average around 10 times lower than computing time required by Beagle. Using the partial least squares regression method in the multi-breed resulted in lower imputation accuracies than using single-breed data. The impact of the SNP-genotype imputation on the accuracy of direct genomic breeding values was small. The correlation between estimates of genetic merit obtained by using imputed versus actual genotypes was around 0.96 for the 7K chip. Results of the present work suggested that the partial least squares regression imputation method could be useful to impute SNP genotypes when pedigree information is not available.

Matched cohort comparison of endovascular abdominal aortic aneurysm repair with and without EndoAnchors.

PubMed

Muhs, Bart E; Jordan, William; Ouriel, Kenneth; Rajaee, Sareh; de Vries, Jean-Paul

2018-06-01

The objective of this study was to examine whether prophylactic use of EndoAnchors (Medtronic, Santa Rosa, Calif) contributes to improved outcomes after endovascular aneurysm repair (EVAR) of abdominal aortic aneurysms through 2 years. The Aneurysm Treatment Using the Heli-FX Aortic Securement System Global Registry (ANCHOR) subjects who received prophylactic EndoAnchors during EVAR were considered for this analysis. Imaging data of retrospective subjects who underwent EVAR at ANCHOR enrolling institutions were obtained to create a control sample. Nineteen baseline anatomic measurements were used to perform propensity score matching, yielding 99 matched pairs. Follow-up imaging of the ANCHOR and control cohorts was then compared to examine outcomes through 2 years, using Kaplan-Meier survival analysis. Freedom from type Ia endoleak was 97.0% ± 2.1% in the ANCHOR cohort and 94.1% ± 2.5% in the control cohort through 2 years (P = .34). The 2-year freedom from neck dilation in the ANCHOR and control cohorts was 90.4% ± 5.6% and 87.3% ± 4.3%, respectively (P = .46); 2-year freedom from sac enlargement was 97.0% ± 2.1% and 94.0% ± 3.0%, respectively (P = .67). No device migration was observed. Aneurysm sac regression was observed in 81.1% ± 9.5% of ANCHOR subjects through 2 years compared with 48.7% ± 5.9% of control subjects (P = .01). Cox regression analysis found an inverse correlation between number of hostile neck criteria met and later sac regression (P = .05). Preoperative neck thrombus circumference and infrarenal diameter were also variables associated with later sac regression, although not to a significant degree (P = .10 and P = .06, respectively). Control subjects with thrombus were significantly less likely to experience later sac regression than those without thrombus (6% and 43%, respectively; P = .001). In ANCHOR subjects, rate of regression was not significantly different in subjects with or without thrombus (33% and 36%, respectively; P = .82). Control subjects with wide aortic necks (>28 mm) were observed to experience sac regression at a lower rate than subjects with smaller diameter necks (10% and 44%, respectively; P = .004). Wide neck and normal neck subjects implanted with EndoAnchors experienced later sac regression at roughly equivalent rates (44% and 33%, respectively; P = .50). In propensity-matched cohorts of subjects undergoing EVAR, the rate of sac regression in subjects treated with EndoAnchors was significantly higher. EndoAnchors may mitigate the adverse effect of wide infrarenal necks and neck thrombus on sac regression, although further studies are needed to evaluate the long-term effect of EndoAnchors. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes.

PubMed

Borquis, Rusbel Raul Aspilcueta; Neto, Francisco Ribeiro de Araujo; Baldi, Fernando; Hurtado-Lugo, Naudin; de Camargo, Gregório M F; Muñoz-Berrocal, Milthon; Tonhati, Humberto

2013-09-01

In this study, genetic parameters for test-day milk, fat, and protein yield were estimated for the first lactation. The data analyzed consisted of 1,433 first lactations of Murrah buffaloes, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, with calvings from 1985 to 2007. Ten-month classes of lactation days were considered for the test-day yields. The (co)variance components for the 3 traits were estimated using the regression analyses by Bayesian inference applying an animal model by Gibbs sampling. The contemporary groups were defined as herd-year-month of the test day. In the model, the random effects were additive genetic, permanent environment, and residual. The fixed effects were contemporary group and number of milkings (1 or 2), the linear and quadratic effects of the covariable age of the buffalo at calving, as well as the mean lactation curve of the population, which was modeled by orthogonal Legendre polynomials of fourth order. The random effects for the traits studied were modeled by Legendre polynomials of third and fourth order for additive genetic and permanent environment, respectively, the residual variances were modeled considering 4 residual classes. The heritability estimates for the traits were moderate (from 0.21-0.38), with higher estimates in the intermediate lactation phase. The genetic correlation estimates within and among the traits varied from 0.05 to 0.99. The results indicate that the selection for any trait test day will result in an indirect genetic gain for milk, fat, and protein yield in all periods of the lactation curve. The accuracy associated with estimated breeding values obtained using multi-trait random regression was slightly higher (around 8%) compared with single-trait random regression. This difference may be because to the greater amount of information available per animal. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Patient casemix classification for medicare psychiatric prospective payment.

PubMed

Drozd, Edward M; Cromwell, Jerry; Gage, Barbara; Maier, Jan; Greenwald, Leslie M; Goldman, Howard H

2006-04-01

For a proposed Medicare prospective payment system for inpatient psychiatric facility treatment, the authors developed a casemix classification to capture differences in patients' real daily resource use. Primary data on patient characteristics and daily time spent in various activities were collected in a survey of 696 patients from 40 inpatient psychiatric facilities. Survey data were combined with Medicare claims data to estimate intensity-adjusted daily cost. Classification and Regression Trees (CART) analysis of average daily routine and ancillary costs yielded several hierarchical classification groupings. Regression analysis was used to control for facility and day-of-stay effects in order to compare hierarchical models with models based on the recently proposed payment system of the Centers for Medicare & Medicaid Services. CART analysis identified a small set of patient characteristics strongly associated with higher daily costs, including age, psychiatric diagnosis, deficits in daily living activities, and detox or ECT use. A parsimonious, 16-group, fully interactive model that used five major DSM-IV categories and stratified by age, illness severity, deficits in daily living activities, dangerousness, and use of ECT explained 40% (out of a possible 76%) of daily cost variation not attributable to idiosyncratic daily changes within patients. A noninteractive model based on diagnosis-related groups, age, and medical comorbidity had explanatory power of only 32%. A regression model with 16 casemix groups restricted to using "appropriate" payment variables (i.e., those with clinical face validity and low administrative burden that are easily validated and provide proper care incentives) produced more efficient and equitable payments than did a noninteractive system based on diagnosis-related groups.

Photoacoustic spectroscopy based investigatory approach to discriminate breast cancer from normal: a pilot study

NASA Astrophysics Data System (ADS)

Priya, Mallika; Rao, Bola Sadashiva Satish; Chandra, Subhash; Ray, Satadru; Mathew, Stanley; Datta, Anirbit; Nayak, Subramanya G.; Mahato, Krishna Kishore

2016-02-01

In spite of many efforts for early detection of breast cancer, there is still lack of technology for immediate implementation. In the present study, the potential photoacoustic spectroscopy was evaluated in discriminating breast cancer from normal, involving blood serum samples seeking early detection. Three photoacoustic spectra in time domain were recorded from each of 20 normal and 20 malignant samples at 281nm pulsed laser excitations and a total of 120 spectra were generated. The time domain spectra were then Fast Fourier Transformed into frequency domain and 116.5625 - 206.875 kHz region was selected for further analysis using a combinational approach of wavelet, PCA and logistic regression. Initially, wavelet analysis was performed on the FFT data and seven features (mean, median, area under the curve, variance, standard deviation, skewness and kurtosis) from each were extracted. PCA was then performed on the feature matrix (7x120) for discriminating malignant samples from the normal by plotting a decision boundary using logistic regression analysis. The unsupervised mode of classification used in the present study yielded specificity and sensitivity values of 100% in each respectively with a ROC - AUC value of 1. The results obtained have clearly demonstrated the capability of photoacoustic spectroscopy in discriminating cancer from the normal, suggesting its possible clinical implications.

Visuoconstructional Impairment in Subtypes of Mild Cognitive Impairment

PubMed Central

Ahmed, Samrah; Brennan, Laura; Eppig, Joel; Price, Catherine C.; Lamar, Melissa; Delano-Wood, Lisa; Bangen, Katherine J.; Edmonds, Emily C.; Clark, Lindsey; Nation, Daniel A.; Jak, Amy; Au, Rhoda; Swenson, Rodney; Bondi, Mark W.; Libon, David J.

2018-01-01

Clock Drawing Test performance was examined alongside other neuropsychological tests in mild cognitive impairment (MCI). We tested the hypothesis that clock-drawing errors are related to executive impairment. The current research examined 86 patients with MCI for whom, in prior research, cluster analysis was used to sort patients into dysexecutive (dMCI, n=22), amnestic (aMCI, n=13), and multi-domain (mMCI, n=51) subtypes. First, principal components analysis (PCA) and linear regression examined relations between clock-drawing errors and neuropsychological test performance independent of MCI subtype. Second, between-group differences were assessed with analysis of variance (ANOVA) where MCI subgroups were compared to normal controls (NC). PCA yielded a 3-group solution. Contrary to expectations, clock-drawing errors loaded with lower performance on naming/lexical retrieval, rather than with executive tests. Regression analyses found increasing clock-drawing errors to command were associated with worse performance only on naming/lexical retrieval tests. ANOVAs revealed no differences in clock-drawing errors between dMCI versus mMCI or aMCI versus NCs. Both the dMCI and mMCI groups generated more clock-drawing errors than the aMCI and NC groups in the command condition. In MCI, language-related skills contribute to clock-drawing impairment. PMID:26397732

Risky decision making in Attention-Deficit/Hyperactivity Disorder: A meta-regression analysis.

PubMed

Dekkers, Tycho J; Popma, Arne; Agelink van Rentergem, Joost A; Bexkens, Anika; Huizenga, Hilde M

2016-04-01

ADHD has been associated with various forms of risky real life decision making, for example risky driving, unsafe sex and substance abuse. However, results from laboratory studies on decision making deficits in ADHD have been inconsistent, probably because of between study differences. We therefore performed a meta-regression analysis in which 37 studies (n ADHD=1175; n Control=1222) were included, containing 52 effect sizes. The overall analysis yielded a small to medium effect size (standardized mean difference=.36, p<.001, 95% CI [.22, .51]), indicating that groups with ADHD showed more risky decision making than control groups. There was a trend for a moderating influence of co-morbid Disruptive Behavior Disorders (DBD): studies including more participants with co-morbid DBD had larger effect sizes. No moderating influence of co-morbid internalizing disorders, age or task explicitness was found. These results indicate that ADHD is related to increased risky decision making in laboratory settings, which tended to be more pronounced if ADHD is accompanied by DBD. We therefore argue that risky decision making should have a more prominent role in research on the neuropsychological and -biological mechanisms of ADHD, which can be useful in ADHD assessment and intervention. Copyright © 2016 Elsevier Ltd. All rights reserved.

H. Pylori as a predictor of marginal ulceration: A nationwide analysis.

PubMed

Schulman, Allison R; Abougergi, Marwan S; Thompson, Christopher C

2017-03-01

Helicobacter pylori has been implicated as a risk factor for development of marginal ulceration following gastric bypass, although studies have been small and yielded conflicting results. This study sought to determine the relationship between H. pylori infection and development of marginal ulceration following bariatric surgery in a nationwide analysis. This was a retrospective cohort study using the 2012 Nationwide Inpatient Sample (NIS) database. Discharges with ICD-9-CM code indicating marginal ulceration and a secondary ICD-9-CM code for bariatric surgery were included. Primary outcome was incidence of marginal ulceration. A stepwise forward selection model was used to build the multivariate logistic regression model based on known risk factors. A P value of 0.05 was considered significant. There were 253,765 patients who met inclusion criteria. Prevalence of marginal ulceration was 3.90%. Of those patients found to have marginal ulceration, 31.20% of patients were H. pylori-positive. Final multivariate regression analysis revealed that H. pylori was the strongest independent predictor of marginal ulceration. H. pylori is an independent predictor of marginal ulceration using a large national database. Preoperative testing for and eradication of H. pylori prior to bariatric surgery may be an important preventive measure to reduce the incidence of ulcer development. © 2017 The Obesity Society.

Carcass yield and meat quality in broilers fed with canola meal.

PubMed

Gopinger, E; Xavier, E G; Lemes, J S; Moraes, P O; Elias, M C; Roll, V F B

2014-01-01

1. This study evaluated the effects of canola meal in broiler diets on carcass yield, carcass composition, and instrumental and sensory analyses of meat. 2. A total of 320 one-day-old Cobb broilers were used in a 35-d experiment using a completely randomised design with 5 concentrations of canola meal (0, 10, 20, 30 and 40%) as a dietary substitute for soya bean meal. 3. Polynomial regression at 5% significance was used to evaluate the effects of canola meal content. The following variables were measured: carcass yield, chemical composition of meat, and instrumental and sensorial analyses. 4. The results showed that carcass yield exhibited a quadratic effect that was crescent to the level of 18% of canola meal based on the weight of the leg and a quadratic increase at concentrations up to 8.4% of canola meal based on the weight of the chest. The yield of the chest exhibited a linear behaviour. 5. The chemical composition of leg meat, instrumental analysis of breast meat and sensory characteristics of the breast meat was not significantly affected by the inclusion of canola meal. The chemical composition of the breast meat exhibited an increased linear effect in terms of dry matter and ether extract and a decreased linear behaviour in terms of the ash content. 6. In conclusion, soya bean meal can be substituted with canola meal at concentrations up to 20% of the total diet without affecting carcass yield, composition of meat or the instrumental or sensory characteristics of the meat of broilers.

Analyzing prospective teachers' images of scientists using positive, negative and stereotypical images of scientists

NASA Astrophysics Data System (ADS)

Subramaniam, Karthigeyan; Esprívalo Harrell, Pamela; Wojnowski, David

2013-04-01

Background and purpose : This study details the use of a conceptual framework to analyze prospective teachers' images of scientists to reveal their context-specific conceptions of scientists. The conceptual framework consists of context-specific conceptions related to positive, stereotypical and negative images of scientists as detailed in the literature on the images, role and work of scientists. Sample, design and method : One hundred and ninety-six drawings of scientists, generated by prospective teachers, were analyzed using the Draw-A-Scientist-Test Checklist (DAST-C), a binary linear regression and the conceptual framework. Results : The results of the binary linear regression analysis revealed a statistically significant difference for two DAST-C elements: ethnicity differences with regard to drawing a scientist who was Caucasian and gender differences for indications of danger. Analysis using the conceptual framework helped to categorize the same drawings into positive, stereotypical, negative and composite images of a scientist. Conclusions : The conceptual framework revealed that drawings were focused on the physical appearance of the scientist, and to a lesser extent on the equipment, location and science-related practices that provided the context of a scientist's role and work. Implications for teacher educators include the need to understand that there is a need to provide tools, like the conceptual framework used in this study, to help prospective teachers to confront and engage with their multidimensional perspectives of scientists in light of the current trends on perceiving and valuing scientists. In addition, teacher educators need to use the conceptual framework, which yields qualitative perspectives about drawings, together with the DAST-C, which yields quantitative measure for drawings, to help prospective teachers to gain a holistic outlook on their drawings of scientists.

Improving mass-univariate analysis of neuroimaging data by modelling important unknown covariates: Application to Epigenome-Wide Association Studies.

PubMed

Guillaume, Bryan; Wang, Changqing; Poh, Joann; Shen, Mo Jun; Ong, Mei Lyn; Tan, Pei Fang; Karnani, Neerja; Meaney, Michael; Qiu, Anqi

2018-06-01

Statistical inference on neuroimaging data is often conducted using a mass-univariate model, equivalent to fitting a linear model at every voxel with a known set of covariates. Due to the large number of linear models, it is challenging to check if the selection of covariates is appropriate and to modify this selection adequately. The use of standard diagnostics, such as residual plotting, is clearly not practical for neuroimaging data. However, the selection of covariates is crucial for linear regression to ensure valid statistical inference. In particular, the mean model of regression needs to be reasonably well specified. Unfortunately, this issue is often overlooked in the field of neuroimaging. This study aims to adopt the existing Confounder Adjusted Testing and Estimation (CATE) approach and to extend it for use with neuroimaging data. We propose a modification of CATE that can yield valid statistical inferences using Principal Component Analysis (PCA) estimators instead of Maximum Likelihood (ML) estimators. We then propose a non-parametric hypothesis testing procedure that can improve upon parametric testing. Monte Carlo simulations show that the modification of CATE allows for more accurate modelling of neuroimaging data and can in turn yield a better control of False Positive Rate (FPR) and Family-Wise Error Rate (FWER). We demonstrate its application to an Epigenome-Wide Association Study (EWAS) on neonatal brain imaging and umbilical cord DNA methylation data obtained as part of a longitudinal cohort study. Software for this CATE study is freely available at http://www.bioeng.nus.edu.sg/cfa/Imaging_Genetics2.html. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Identification and functional characterization of HIV-associated neurocognitive disorders with large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

Chockanathan, Udaysankar; DSouza, Adora M.; Abidin, Anas Z.; Schifitto, Giovanni; Wismüller, Axel

2018-02-01

Resting-state functional MRI (rs-fMRI), coupled with advanced multivariate time-series analysis methods such as Granger causality, is a promising tool for the development of novel functional connectivity biomarkers of neurologic and psychiatric disease. Recently large-scale Granger causality (lsGC) has been proposed as an alternative to conventional Granger causality (cGC) that extends the scope of robust Granger causal analyses to high-dimensional systems such as the human brain. In this study, lsGC and cGC were comparatively evaluated on their ability to capture neurologic damage associated with HIV-associated neurocognitive disorders (HAND). Functional brain network models were constructed from rs-fMRI data collected from a cohort of HIV+ and HIV- subjects. Graph theoretic properties of the resulting networks were then used to train a support vector machine (SVM) model to predict clinically relevant parameters, such as HIV status and neuropsychometric (NP) scores. For the HIV+/- classification task, lsGC, which yielded a peak area under the receiver operating characteristic curve (AUC) of 0.83, significantly outperformed cGC, which yielded a peak AUC of 0.61, at all parameter settings tested. For the NP score regression task, lsGC, with a minimum mean squared error (MSE) of 0.75, significantly outperformed cGC, with a minimum MSE of 0.84 (p < 0.001, one-tailed paired t-test). These results show that, at optimal parameter settings, lsGC is better able to capture functional brain connectivity correlates of HAND than cGC. However, given the substantial variation in the performance of the two methods at different parameter settings, particularly for the regression task, improved parameter selection criteria are necessary and constitute an area for future research.

Ranking the Potential Yield of Salinity and Selenium from Subbasins in the Lower Gunnison River Basin Using Seasonal, Multi-parameter Regression Models

NASA Astrophysics Data System (ADS)

Linard, J.; Leib, K.; Colorado Water Science Center

2010-12-01

Elevated levels of salinity and dissolved selenium can detrimentally effect the quality of water where anthropogenic and natural uses are concerned. In areas, such as the lower Gunnison Basin of western Colorado, salinity and selenium are such a concern that control projects are implemented to limit their mobilization. To prioritize the locations in which control projects are implemented, multi-parameter regression models were developed to identify subbasins in the lower Gunnison River Basin that were most likely to have elevated salinity and dissolved selenium levels. The drainage area is about 5,900 mi2 and is underlain by Cretaceous marine shale, which is the most common source of salinity and dissolved selenium. To characterize the complex hydrologic and chemical processes governing constituent mobilization, geospatial variables representing 70 different environmental characteristics were correlated to mean seasonal (irrigation and nonirrigation seasons) salinity and selenium yields estimated at 154 sampling sites. The variables generally represented characteristics of the physical basin, precipitation, soil, geology, land use, and irrigation water delivery systems. Irrigation and nonirrigation seasons were selected due to documented effects of irrigation on constituent mobilization. Following a stepwise approach, combinations of the geospatial variables were used to develop four multi-parameter regression models. These models predicted salinity and selenium yield, within a 95 percent confidence range, at individual points in the Lower Gunnison Basin for irrigation and non-irrigation seasons. The corresponding subbasins were ranked according to their potential to yield salinity and selenium and rankings were used to prioritize areas that would most benefit from control projects.

The ability of video image analysis to predict lean meat yield and EUROP score of lamb carcasses.

PubMed

Einarsson, E; Eythórsdóttir, E; Smith, C R; Jónmundsson, J V

2014-07-01

A total of 862 lamb carcasses that were evaluated by both the VIAscan® and the current EUROP classification system were deboned and the actual yield was measured. Models were derived for predicting lean meat yield of the legs (Leg%), loin (Loin%) and shoulder (Shldr%) using the best VIAscan® variables selected by stepwise regression analysis of a calibration data set (n=603). The equations were tested on validation data set (n=259). The results showed that the VIAscan® predicted lean meat yield in the leg, loin and shoulder with an R 2 of 0.60, 0.31 and 0.47, respectively, whereas the current EUROP system predicted lean yield with an R 2 of 0.57, 0.32 and 0.37, respectively, for the three carcass parts. The VIAscan® also predicted the EUROP score of the trial carcasses, using a model derived from an earlier trial. The EUROP classification from VIAscan® and the current system were compared for their ability to explain the variation in lean yield of the whole carcass (LMY%) and trimmed fat (FAT%). The predicted EUROP scores from the VIAscan® explained 36% of the variation in LMY% and 60% of the variation in FAT%, compared with the current EUROP system that explained 49% and 72%, respectively. The EUROP classification obtained by the VIAscan® was tested against a panel of three expert classifiers (n=696). The VIAscan® classification agreed with 82% of conformation and 73% of the fat classes assigned by a panel of expert classifiers. It was concluded that VIAscan® provides a technology that can directly predict LMY% of lamb carcasses with more accuracy than the current EUROP classification system. The VIAscan® is also capable of classifying lamb carcasses into EUROP classes with an accuracy that fulfils minimum demands for the Icelandic sheep industry. Although the VIAscan® prediction of the Loin% is low, it is comparable to the current EUROP system, and should not hinder the adoption of the technology to estimate the yield of Icelandic lambs as it delivered a more accurate prediction for the Leg%, Shldr% and overall LMY% with negligible prediction bias.

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

Erosion and soil displacement related to timber harvesting in northwestern California, U.S.A.

Treesearch

R.M. Rice; D.J. Furbish

1984-01-01

The relationship between measures of site disturbance and erosion resulting from timber harvest was studied by regression analyses. None of the 12 regression models developed and tested yielded a coefficient of determination (R2) greater than 0.60. The results indicated that the poor fits to the data were due, in part, to unexplained qualitative...

"Erosion and soil displacement related to timber harvesting in northwestern California, U.S.A."

Treesearch

R. M. Rice; D. J. Furbish

1984-01-01

The relationship between measures of site disturbance and erosion resulting from timber harvest was studied by regression analyses. None of the 12 regression models developed and tested yielded a coefficient of determination (R 2) greater than 0.60. The results indicated that the poor fits to the data were due, in part, to unexplained qualitative differences in...

A collaborative comparison of objective structured clinical examination (OSCE) standard setting methods at Australian medical schools.

PubMed

Malau-Aduli, Bunmi Sherifat; Teague, Peta-Ann; D'Souza, Karen; Heal, Clare; Turner, Richard; Garne, David L; van der Vleuten, Cees

2017-12-01

A key issue underpinning the usefulness of the OSCE assessment to medical education is standard setting, but the majority of standard-setting methods remain challenging for performance assessment because they produce varying passing marks. Several studies have compared standard-setting methods; however, most of these studies are limited by their experimental scope, or use data on examinee performance at a single OSCE station or from a single medical school. This collaborative study between 10 Australian medical schools investigated the effect of standard-setting methods on OSCE cut scores and failure rates. This research used 5256 examinee scores from seven shared OSCE stations to calculate cut scores and failure rates using two different compromise standard-setting methods, namely the Borderline Regression and Cohen's methods. The results of this study indicate that Cohen's method yields similar outcomes to the Borderline Regression method, particularly for large examinee cohort sizes. However, with lower examinee numbers on a station, the Borderline Regression method resulted in higher cut scores and larger difference margins in the failure rates. Cohen's method yields similar outcomes as the Borderline Regression method and its application for benchmarking purposes and in resource-limited settings is justifiable, particularly with large examinee numbers.

Green lumber grade yields from factory grade logs of three oak species

Treesearch

Daniel A. Yaussy

1986-01-01

Multivariate regression models were developed to predict green board foot yields for the seven common factory lumber grades processed from white, black, and chestnut oak factory grade logs. These models use the standard log measurements of grade, scaling diameter, log length, and proportion of scaling defect. Any combination of lumber grades (such as 1 Common and...

Green lumber grade yields from black cherry and red maple factory grade logs sawed at band and circular mills

Treesearch

Daniel A. Yaussy

1989-01-01

Multivariate regression models were developed to predict green board-foot yields (1 board ft. = 2.360 dm 3) for the standard factory lumber grades processed from black cherry (Prunus serotina Ehrh.) and red maple (Acer rubrum L.) factory grade logs sawed at band and circular sawmills. The models use log...

Estimating yields of salt- and water-stressed forages with remote sensing in the visible and near infrared.

PubMed

Poss, J A; Russell, W B; Grieve, C M

2006-01-01

In arid irrigated regions, the proportion of crop production under deficit irrigation with poorer quality water is increasing as demand for fresh water soars and efforts to prevent saline water table development occur. Remote sensing technology to quantify salinity and water stress effects on forage yield can be an important tool to address yield loss potential when deficit irrigating with poor water quality. Two important forages, alfalfa (Medicago sativa L.) and tall wheatgrass (Agropyron elongatum L.), were grown in a volumetric lysimeter facility where rootzone salinity and water content were varied and monitored. Ground-based hyperspectral canopy reflectance in the visible and near infrared (NIR) were related to forage yields from a broad range of salinity and water stress conditions. Canopy reflectance spectra were obtained in the 350- to 1000-nm region from two viewing angles (nadir view, 45 degrees from nadir). Nadir view vegetation indices (VI) were not as strongly correlated with leaf area index changes attributed to water and salinity stress treatments for both alfalfa and wheatgrass. From a list of 71 VIs, two were selected for a multiple linear-regression model that estimated yield under varying salinity and water stress conditions. With data obtained during the second harvest of a three-harvest 100-d growing period, regression coefficients for each crop were developed and then used with the model to estimate fresh weights for preceding and succeeding harvests during the same 100-d interval. The model accounted for 72% of the variation in yields in wheatgrass and 94% in yields of alfalfa within the same salinity and water stress treatment period. The model successfully predicted yield in three out of four cases when applied to the first and third harvest yields. Correlations between indices and yield increased as canopy development progressed. Growth reductions attributed to simultaneous salinity and water stress were well characterized, but the corrections for effects of varying tissue nitrogen (N) and very low leaf area index (LAI) are necessary.

The impact of pneumatic tube system on routine laboratory parameters: a systematic review and meta-analysis.

PubMed

Kapoula, Georgia V; Kontou, Panagiota I; Bagos, Pantelis G

2017-10-26

Pneumatic tube system (PTS) is a widely used method of transporting blood samples in hospitals. The aim of this study was to evaluate the effects of the PTS transport in certain routine laboratory parameters as it has been implicated with hemolysis. A systematic review and a meta-analysis were conducted. PubMed and Scopus databases were searched (up until November 2016) to identify prospective studies evaluating the impact of PTS transport in hematological, biochemical and coagulation measurements. The random-effects model was used in the meta-analysis utilizing the mean difference (MD). Heterogeneity was quantitatively assessed using the Cohran's Q and the I2 index. Subgroup analysis, meta-regression analysis, sensitivity analysis, cumulative meta-analysis and assessment of publication bias were performed for all outcomes. From a total of 282 studies identified by the searching procedure, 24 were finally included in the meta-analysis. The meta-analysis yielded statistically significant results for potassium (K) [MD=0.04 mmol/L; 95% confidence interval (CI)=0.015-0.065; p=0.002], lactate dehydrogenase (LDH) (MD=10.343 U/L; 95% CI=6.132-14.554; p<10-4) and aspartate aminotransferase (AST) (MD=1.023 IU/L; 95% CI=0.344-1.702; p=0.003). Subgroup analysis and random-effects meta-regression analysis according to the speed and distance of the samples traveled via the PTS revealed that there is relation between the rate and the distance of PTS with the measurements of K, LDH, white blood cells and red blood cells. This meta-analysis suggests that PTS may be associated with alterations in K, LDH and AST measurements. Although these findings may not have any significant clinical effect on laboratory results, it is wise that each hospital validates their PTS.

Relation of watershed setting and stream nutrient yields at selected sites in central and eastern North Carolina, 1997-2008

USGS Publications Warehouse

Harden, Stephen L.; Cuffney, Thomas F.; Terziotti, Silvia; Kolb, Katharine R.

2013-01-01

Data collected between 1997 and 2008 at 48 stream sites were used to characterize relations between watershed settings and stream nutrient yields throughout central and eastern North Carolina. The focus of the investigation was to identify environmental variables in watersheds that influence nutrient export for supporting the development and prioritization of management strategies for restoring nutrient-impaired streams. Nutrient concentration data and streamflow data compiled for the 1997 to 2008 study period were used to compute stream yields of nitrate, total nitrogen (N), and total phosphorus (P) for each study site. Compiled environmental data (including variables for land cover, hydrologic soil groups, base-flow index, streams, wastewater treatment facilities, and concentrated animal feeding operations) were used to characterize the watershed settings for the study sites. Data for the environmental variables were analyzed in combination with the stream nutrient yields to explore relations based on watershed characteristics and to evaluate whether particular variables were useful indicators of watersheds having relatively higher or lower potential for exporting nutrients. Data evaluations included an examination of median annual nutrient yields based on a watershed land-use classification scheme developed as part of the study. An initial examination of the data indicated that the highest median annual nutrient yields occurred at both agricultural and urban sites, especially for urban sites having large percentages of point-source flow contributions to the streams. The results of statistical testing identified significant differences in annual nutrient yields when sites were analyzed on the basis of watershed land-use category. When statistical differences in median annual yields were noted, the results for nitrate, total N, and total P were similar in that highly urbanized watersheds (greater than 30 percent developed land use) and (or) watersheds with greater than 10 percent point-source flow contributions to streamflow had higher yields relative to undeveloped watersheds (having less than 10 and 15 percent developed and agricultural land uses, respectively) and watersheds with relatively low agricultural land use (between 15 and 30 percent). The statistical tests further indicated that the median annual yields for total P were statistically higher for watersheds with high agricultural land use (greater than 30 percent) compared to the undeveloped watersheds and watersheds with low agricultural land use. The total P yields also were higher for watersheds with low urban land use (between 10 and 30 percent developed land) compared to the undeveloped watersheds. The study data indicate that grouping and examining stream nutrient yields based on the land-use classifications used in this report can be useful for characterizing relations between watershed settings and nutrient yields in streams located throughout central and eastern North Carolina. Compiled study data also were analyzed with four regression tree models as a means of determining which watershed environmental variables or combination of variables result in basins that are likely to have high or low nutrient yields. The regression tree analyses indicated that some of the environmental variables examined in this study were useful for predicting yields of nitrate, total N, and total P. When the median annual nutrient yields for all 48 sites were evaluated as a group (Model 1), annual point-source flow yields had the greatest influence on nitrate and total N yields observed in streams, and annual streamflow yields had the greatest influence on yields of total P. The Model 1 results indicated that watersheds with higher annual point-source flow yields had higher annual yields of nitrate and total N, and watersheds with higher annual streamflow yields had higher annual yields of total P. When sites with high point-source flows (greater than 10 percent of total streamflow) were excluded from the regression tree analyses (Models 2–4), the percentage of forested land in the watersheds was identified as the primary environmental variable influencing stream yields for both total N and total P. Models 2, 3 and 4 did not identify any watershed environmental variables that could adequately explain the observed variability in the nitrate yields among the set of sites examined by each of these models. The results for Models 2, 3, and 4 indicated that watersheds with higher percentages of forested land had lower annual total N and total P yields compared to watersheds with lower percentages of forested land, which had higher median annual total N and total P yields. Additional environmental variables determined to further influence the stream nutrient yields included median annual percentage of point-source flow contributions to the streams, variables of land cover (percentage of forested land, agricultural land, and (or) forested land plus wetlands) in the watershed and (or) in the stream buffer, and drainage area. The regression tree models can serve as a tool for relating differences in select watershed attributes to differences in stream yields of nitrate, total N, and total P, which can provide beneficial information for improving nutrient management in streams throughout North Carolina and for reducing nutrient loads to coastal waters.

Crop status evaluations and yield predictions

NASA Technical Reports Server (NTRS)

Haun, J. R.

1975-01-01

A model was developed for predicting the day 50 percent of the wheat crop is planted in North Dakota. This model incorporates location as an independent variable. The Julian date when 50 percent of the crop was planted for the nine divisions of North Dakota for seven years was regressed on the 49 variables through the step-down multiple regression procedure. This procedure begins with all of the independent variables and sequentially removes variables that are below a predetermined level of significance after each step. The prediction equation was tested on daily data. The accuracy of the model is considered satisfactory for finding the historic dates on which to initiate yield prediction model. Growth prediction models were also developed for spring wheat.

Behavior of Three Metallic Alloys Under Combined Axial-Shear Stress at 650 C

NASA Technical Reports Server (NTRS)

Colaiuta, Jason F.; Lerch, Bradley (Technical Monitor)

2001-01-01

Three materials, Inconel 718, Haynes 188, and 316 stainless steel, were tested under an axial-torsional stress state at 650 C. The objective of this study was to quantify the evolution of the material while in the viscoplastic domain. Initial and subsequent yield surfaces were experimentally determined to quantify hardening. Subsequent yield surfaces (yield surfaces taken after a preload) had a well-defined front side, in the prestrain direction, but a poorly defined back side, opposite the prestrain direction. Subsequent yield surfaces exhibited isotropic hardening by expansion of the yield surface, kinematic hardening by translation of the yield surface, and distortional hardening by flattening of the yield surface in the direction opposite to the last prestrain. An existing yield function capable of representing isotropic, kinematic, and distortional hardening was used to fit each yield surface. Four variables are used to describe each surface. These variables evolve as the material state changes and have been regressed to the yield surface data.

Forecasting the remaining reservoir capacity in the Laurentian Great Lakes watershed

NASA Astrophysics Data System (ADS)

Alighalehbabakhani, Fatemeh; Miller, Carol J.; Baskaran, Mark; Selegean, James P.; Barkach, John H.; Dahl, Travis; Abkenar, Seyed Mohsen Sadatiyan

2017-12-01

Sediment accumulation behind a dam is a significant factor in reservoir operation and watershed management. There are many dams located within the Laurentian Great Lakes watershed whose operations have been adversely affected by excessive reservoir sedimentation. Reservoir sedimentation effects include reduction of flood control capability and limitations to both water supply withdrawals and power generation due to reduced reservoir storage. In this research, the sediment accumulation rates of twelve reservoirs within the Great Lakes watershed were evaluated using the Soil and Water Assessment Tool (SWAT). The estimated sediment accumulation rates by SWAT were compared to estimates relying on radionuclide dating of sediment cores and bathymetric survey methods. Based on the sediment accumulation rate, the remaining reservoir capacity for each study site was estimated. Evaluation of the anthropogenic impacts including land use change and dam construction on the sediment yield were assessed in this research. The regression analysis was done on the current and pre-European settlement sediment yield for the modeled watersheds to predict the current and natural sediment yield in un-modeled watersheds. These eleven watersheds are in the state of Indiana, Michigan, Ohio, New York, and Wisconsin.

Supercritical Carbon Dioxide Extraction of Flavonoids from Pomelo (Citrus grandis (L.) Osbeck) Peel and Their Antioxidant Activity

PubMed Central

He, Jin-Zhe; Shao, Ping; Liu, Jian-Hua; Ru, Qiao-Mei

2012-01-01

Supercritical carbon dioxide (SC-CO2) extraction of flavonoids from pomelo (Citrus grandis (L.) Osbeck) peel and their antioxidant activity were investigated. Box-Behnken design combined with response surface methodology was employed to maximize the extraction yield of flavonoids. Correlation analysis of the mathematical-regression model indicated that a quadratic polynomial model could be used to optimize the SC-CO2 extraction of flavonoids. The optimal conditions for obtaining the highest extraction yield of flavonoids from pomelo peel were a temperature of 80 °C, a pressure of 39 MPa and a static extraction time of 49 min in the presence of 85% ethanol as modifier. Under these conditions, the experimental yield was 2.37%, which matched positively with the value predicted by the model. Furthermore, flavonoids obtained by SC-CO2 extraction showed a higher scavenging activity on hydroxyl, 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzthiazoline-6-sulphonic acid) (ABTS) radicals than those obtained by conventional solvent extraction (CSE). Therefore, SC-CO2 extraction can be considered as a suitable technique for the obtainment of flavonoids from pomelo peel. PMID:23202938

A hierarchical spatial model for well yield in complex aquifers

NASA Astrophysics Data System (ADS)

Montgomery, J.; O'sullivan, F.

2017-12-01

Efficiently siting and managing groundwater wells requires reliable estimates of the amount of water that can be produced, or the well yield. This can be challenging to predict in highly complex, heterogeneous fractured aquifers due to the uncertainty around local hydraulic properties. Promising statistical approaches have been advanced in recent years. For instance, kriging and multivariate regression analysis have been applied to well test data with limited but encouraging levels of prediction accuracy. Additionally, some analytical solutions to diffusion in homogeneous porous media have been used to infer "effective" properties consistent with observed flow rates or drawdown. However, this is an under-specified inverse problem with substantial and irreducible uncertainty. We describe a flexible machine learning approach capable of combining diverse datasets with constraining physical and geostatistical models for improved well yield prediction accuracy and uncertainty quantification. Our approach can be implemented within a hierarchical Bayesian framework using Markov Chain Monte Carlo, which allows for additional sources of information to be incorporated in priors to further constrain and improve predictions and reduce the model order. We demonstrate the usefulness of this approach using data from over 7,000 wells in a fractured bedrock aquifer.

The extraction process optimization of antioxidant polysaccharides from Marshmallow (Althaea officinalis L.) roots.

PubMed

Pakrokh Ghavi, Peyman

2015-04-01

Response surface methodology (RSM) with a central composite rotatable design (CCRD) based on five levels was employed to model and optimize four experimental operating conditions of extraction temperature (10-90 °C) and time (6-30 h), particle size (6-24 mm) and water to solid (W/S, 10-50) ratio, obtaining polysaccharides from Althaea officinalis roots with high yield and antioxidant activity. For each response, a second-order polynomial model with high R(2) values (> 0.966) was developed using multiple linear regression analysis. Results showed that the most significant (P < 0.05) extraction conditions that affect the yield and antioxidant activity of extracted polysaccharides were the main effect of extraction temperature and the interaction effect of the particle size and W/S ratio. The optimum conditions to maximize yield (10.80%) and antioxidant activity (84.09%) for polysaccharides extraction from A. officinalis roots were extraction temperature 60.90 °C, extraction time 12.01 h, particle size 12.0mm and W/S ratio of 40.0. The experimental values were found to be in agreement with those predicted, indicating the models suitability for optimizing the polysaccharides extraction conditions. Copyright © 2015 Elsevier B.V. All rights reserved.

Perceived stress and resilience in Alzheimer's disease caregivers: testing moderation and mediation models of social support.

PubMed

Wilks, Scott E; Croom, Beth

2008-05-01

The study examined whether social support functioned as a protective, resilience factor among Alzheimer's disease (AD) caregivers. Moderation and mediation models were used to test social support amid stress and resilience. A cross-sectional analysis of self-reported data was conducted. Measures of demographics, perceived stress, family support, friend support, overall social support, and resilience were administered to caregiver attendees (N=229) of two AD caregiver conferences. Hierarchical regression analysis showed the compounded impact of predictors on resilience. Odds ratios generated probability of high resilience given high stress and social supports. Social support moderation and mediation were tested via distinct series of regression equations. Path analyses illustrated effects on the models for significant moderation and/or mediation. Stress negatively influenced and accounted for most variation in resilience. Social support positively influenced resilience, and caregivers with high family support had the highest probability of elevated resilience. Moderation was observed among all support factors. No social support fulfilled the complete mediation criteria. Evidence of social support as a protective, moderating factor yields implications for health care practitioners who deliver services to assist AD caregivers, particularly the promotion of identification and utilization of supportive familial and peer relations.

Prediction of Return-to-original-work after an Industrial Accident Using Machine Learning and Comparison of Techniques

PubMed Central

2018-01-01

Background Many studies have tried to develop predictors for return-to-work (RTW). However, since complex factors have been demonstrated to predict RTW, it is difficult to use them practically. This study investigated whether factors used in previous studies could predict whether an individual had returned to his/her original work by four years after termination of the worker's recovery period. Methods An initial logistic regression analysis of 1,567 participants of the fourth Panel Study of Worker's Compensation Insurance yielded odds ratios. The participants were divided into two subsets, a training dataset and a test dataset. Using the training dataset, logistic regression, decision tree, random forest, and support vector machine models were established, and important variables of each model were identified. The predictive abilities of the different models were compared. Results The analysis showed that only earned income and company-related factors significantly affected return-to-original-work (RTOW). The random forest model showed the best accuracy among the tested machine learning models; however, the difference was not prominent. Conclusion It is possible to predict a worker's probability of RTOW using machine learning techniques with moderate accuracy. PMID:29736160

Liver volume measurement: reason of the difference between in vivo CT-volumetry and intraoperative ex vivo determination and how to cope it.

PubMed

Niehues, Stefan M; Unger, J K; Malinowski, M; Neymeyer, J; Hamm, B; Stockmann, M

2010-08-20

Volumetric assessment of the liver regularly yields discrepant results between pre- and intraoperatively determined volumes. Nevertheless, the main factor responsible for this discrepancy remains still unclear. The aim of this study was to systematically determine the difference between in vivo CT-volumetry and ex vivo volumetry in a pig animal model. Eleven pigs were studied. Liver density assessment, CT-volumetry and water displacement volumetry was performed after surgical removal of the complete liver. Known possible errors of volume determination like resection or segmentation borders were eliminated in this model. Regression analysis was performed and differences between CT-volumetry and water displacement determined. Median liver density was 1.07g/ml. Regression analysis showed a high correlation of r(2) = 0.985 between CT-volumetry and water displacement. CT-volumetry was found to be 13% higher than water displacement volumetry (p<0.0001). In this study the only relevant factor leading to the difference between in vivo CT-volumetry and ex vivo water displacement volumetry seems to be blood perfusion of the liver. The systematic difference of 13 percent has to be taken in account when dealing with those measures.

[Comparative evaluation of the sensitivity of Acinetobacter to colistin, using the prediffusion and minimum inhibitory concentration methods: detection of heteroresistant isolates].

PubMed

Herrera, Melina E; Mobilia, Liliana N; Posse, Graciela R

2011-01-01

The objective of this study is to perform a comparative evaluation of the prediffusion and minimum inhibitory concentration (MIC) methods for the detection of sensitivity to colistin, and to detect Acinetobacter baumanii-calcoaceticus complex (ABC) heteroresistant isolates to colistin. We studied 75 isolates of ABC recovered from clinically significant samples obtained from various centers. Sensitivity to colistin was determined by prediffusion as well as by MIC. All the isolates were sensitive to colistin, with MIC = 2µg/ml. The results were analyzed by dispersion graph and linear regression analysis, revealing that the prediffusion method did not correlate with the MIC values for isolates sensitive to colistin (r² = 0.2017). Detection of heteroresistance to colistin was determined by plaque efficiency of all the isolates with the same initial MICs of 2, 1, and 0.5 µg/ml, which resulted in 14 of them with a greater than 8-fold increase in the MIC in some cases. When the sensitivity of these resistant colonies was determined by prediffusion, the resulting dispersion graph and linear regression analysis yielded an r² = 0.604, which revealed a correlation between the methodologies used.

Milk losses associated with somatic cell counts by parity and stage of lactation.

PubMed

Gonçalves, Juliano L; Cue, Roger I; Botaro, Bruno G; Horst, José A; Valloto, Altair A; Santos, Marcos V

2018-05-01

The reduction of milk production caused by subclinical mastitis in dairy cows was evaluated through the regression of test-day milk yield on log-transformed somatic cell counts (LnSCC). Official test-day records (n = 1,688,054) of Holstein cows (n = 87,695) were obtained from 719 herds from January 2010 to December 2015. Editing was performed to ensure both reliability and consistency for the statistical analysis, and the final data set comprised 232,937 test-day records from 31,692 Holstein cows in 243 herds. A segmented regression was fitted to estimate the cutoff point in the LnSCC scale where milk yield started to be affected by mastitis. The statistical model used to explain daily milk yield included the effect of herd as a random effect and days in milk and LnSCC as fixed effects regressions, and analyses were performed by parity and stage of lactation. The cutoff point where milk yield starts to be affected by changes in LnSCC was estimated to be around 2.52 (the average of all estimates of approximately 12,400 cells/mL) for Holsteins cows from Brazilian herds. For first-lactation cows, milk losses per unit increase of LnSCC had estimates around 0.68 kg/d in the beginning of the lactation [5 to 19 d in milk (DIM)], 0.55 kg/d in mid-lactation (110 to 124 DIM), and 0.97 kg/d at the end of the lactation (289 to 304 DIM). For second-lactation cows, milk losses per unit increase of LnSCC had estimates around 1.47 kg/d in the beginning of the lactation (5 to 19 DIM), 1.09 kg/d in mid-lactation (110 to 124 DIM), and 2.45 kg/d at the end of the lactation (289 to 304 DIM). For third-lactation cows, milk losses per unit increase of LnSCC had estimates around 2.22 kg/d in the beginning of the lactation (5 to 19 DIM), 1.13 kg/d in mid-lactation (140 to 154 DIM), and 2.65 kg/d at the end of the lactation (289 to 304 DIM). Daily milk losses caused by increased LnSCC were dependent on parity and stage of lactation, and these factors should be considered when estimating losses associated with subclinical mastitis. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A method of determining bending properties of poultry long bones using beam analysis and micro-CT data.

PubMed

Vaughan, Patrick E; Orth, Michael W; Haut, Roger C; Karcher, Darrin M

2016-01-01

While conventional mechanical testing has been regarded as a gold standard for the evaluation of bone heath in numerous studies, with recent advances in medical imaging, virtual methods of biomechanics are rapidly evolving in the human literature. The objective of the current study was to evaluate the feasibility of determining the elastic and failure properties of poultry long bones using established methods of analysis from the human literature. In order to incorporate a large range of bone sizes and densities, a small number of specimens were utilized from an ongoing study of Regmi et al. (2016) that involved humeri and tibiae from 3 groups of animals (10 from each) including aviary, enriched, and conventional housing systems. Half the animals from each group were used for 'training' that involved the development of a regression equation relating bone density and geometry to bending properties from conventional mechanical tests. The remaining specimens from each group were used for 'testing' in which the mechanical properties from conventional tests were compared to those predicted by the regression equations. Based on the regression equations, the coefficients of determination for the 'test' set of data were 0.798 for bending bone stiffness and 0.901 for the yield (or failure) moment of the bones. All regression slopes and intercepts values for the tests versus predicted plots were not significantly different from 1 and 0, respectively. The study showed the feasibility of developing future methods of virtual biomechanics for the evaluation of poultry long bones. With further development, virtual biomechanics may have utility in future in vivo studies to assess laying hen bone health over time without the need to sacrifice large groups of animals at each time point. © 2016 Poultry Science Association Inc.

Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: Insights into spatial variability using high-resolution satellite data

PubMed Central

Alexeeff, Stacey E.; Schwartz, Joel; Kloog, Itai; Chudnovsky, Alexandra; Koutrakis, Petros; Coull, Brent A.

2016-01-01

Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1km x 1km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R2 yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with greater than 0.9 out-of-sample R2 yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the standard errors. Land use regression models performed better in chronic effects simulations. These results can help researchers when interpreting health effect estimates in these types of studies. PMID:24896768

Developing a Study Orientation Questionnaire in Mathematics for primary school students.

PubMed

Maree, Jacobus G; Van der Walt, Martha S; Ellis, Suria M

2009-04-01

The Study Orientation Questionnaire in Mathematics (Primary) is being developed as a diagnostic measure for South African teachers and counsellors to help primary school students improve their orientation towards the study of mathematics. In this study, participants were primary school students in the North-West Province of South Africa. During the standardisation in 2007, 1,013 students (538 boys: M age = 12.61; SD = 1.53; 555 girls: M age = 11.98; SD = 1.35; 10 missing values) were assessed. Factor analysis yielded three factors. Analysis also showed satisfactory reliability coefficients and item-factor correlations. Step-wise linear regression indicated that three factors (Mathematics anxiety, Study attitude in mathematics, and Study habits in mathematics) contributed significantly (R2 = .194) to predicting achievement in mathematics as measured by the Basic Mathematics Questionnaire (Primary).

Reducing Production Basis Risk through Rainfall Intensity Frequency (RIF) Indexes: Global Sensitivity Analysis' Implication on Policy Design

NASA Astrophysics Data System (ADS)

Muneepeerakul, Chitsomanus; Huffaker, Ray; Munoz-Carpena, Rafael

2016-04-01

The weather index insurance promises financial resilience to farmers struck by harsh weather conditions with swift compensation at affordable premium thanks to its minimal adverse selection and moral hazard. Despite these advantages, the very nature of indexing causes the presence of "production basis risk" that the selected weather indexes and their thresholds do not correspond to actual damages. To reduce basis risk without additional data collection cost, we propose the use of rain intensity and frequency as indexes as it could offer better protection at the lower premium by avoiding basis risk-strike trade-off inherent in the total rainfall index. We present empirical evidences and modeling results that even under the similar cumulative rainfall and temperature environment, yield can significantly differ especially for drought sensitive crops. We further show that deriving the trigger level and payoff function from regression between historical yield and total rainfall data may pose significant basis risk owing to their non-unique relationship in the insured range of rainfall. Lastly, we discuss the design of index insurance in terms of contract specifications based on the results from global sensitivity analysis.

Why is everyone so anxious?: an exploration of stress and anxiety in genetic counseling graduate students.

PubMed

Jungbluth, Chelsy; Macfarlane, Ian M; Veach, Patricia McCarthy; Leroy, Bonnie S

2011-06-01

Stress is an inevitable part of daily life. Studies of graduate student stress exist, but none include genetic counseling students. The present mixed-methods study investigated 225 genetic counseling students' stress and anxiety levels using the State-Trait Anxiety Inventory (STAI; Spielberger et al. 1983), frequency and intensity of stressors associated with their graduate experience, positive and challenging aspects of their experience, and their stress management advice for prospective students. Principal axis factor analysis yielded five conceptual factors underlying the stressors: Professional Uncertainty, Personal Life Events, Interpersonal Demands, Academic Demands, and Isolating Circumstances. Exploratory model fitting using regression yielded four significant predictors accounting for 19% of the variance in state anxiety: (1) trait anxiety, (2) the Interpersonal Demands factor, (3) the Isolating Circumstances factor, and (4) the interaction between the Professional Uncertainty factor and advanced student status. Content analysis of open-ended responses identified several themes. For instance, most students enjoyed what they were learning, interactions with colleagues, and affirmation of their career choice, while certain academic and professional challenges were particularly stressful (e.g., workload, time constraints, clinical rotations). Additional findings, program implications, and research recommendations are provided.

Grid-cell-based crop water accounting for the famine early warning system

USGS Publications Warehouse

Verdin, J.; Klaver, R.

2002-01-01

Rainfall monitoring is a regular activity of food security analysts for sub-Saharan Africa due to the potentially disastrous impact of drought. Crop water accounting schemes are used to track rainfall timing and amounts relative to phenological requirements, to infer water limitation impacts on yield. Unfortunately, many rain gauge reports are available only after significant delays, and the gauge locations leave large gaps in coverage. As an alternative, a grid-cell-based formulation for the water requirement satisfaction index (WRSI) was tested for maize in Southern Africa. Grids of input variables were obtained from remote sensing estimates of rainfall, meteorological models, and digital soil maps. The spatial WRSI was computed for the 1996–97 and 1997–98 growing seasons. Maize yields were estimated by regression and compared with a limited number of reports from the field for the 1996–97 season in Zimbabwe. Agreement at a useful level (r = 0·80) was observed. This is comparable to results from traditional analysis with station data. The findings demonstrate the complementary role that remote sensing, modelling, and geospatial analysis can play in an era when field data collection in sub-Saharan Africa is suffering an unfortunate decline.

Peak oxygen consumption measured during the stair-climbing test in lung resection candidates.

PubMed

Brunelli, Alessandro; Xiumé, Francesco; Refai, Majed; Salati, Michele; Di Nunzio, Luca; Pompili, Cecilia; Sabbatini, Armando

2010-01-01

The stair-climbing test is commonly used in the preoperative evaluation of lung resection candidates, but it is difficult to standardize and provides little physiologic information on the performance. To verify the association between the altitude and the V(O2peak) measured during the stair-climbing test. 109 consecutive candidates for lung resection performed a symptom-limited stair-climbing test with direct breath-by-breath measurement of V(O2peak) by a portable gas analyzer. Stepwise logistic regression and bootstrap analyses were used to verify the association of several perioperative variables with a V(O2peak) <15 ml/kg/min. Subsequently, multiple regression analysis was also performed to develop an equation to estimate V(O2peak) from stair-climbing parameters and other patient-related variables. 56% of patients climbing <14 m had a V(O2peak) <15 ml/kg/min, whereas 98% of those climbing >22 m had a V(O2peak) >15 ml/kg/min. The altitude reached at stair-climbing test resulted in the only significant predictor of a V(O2peak) <15 ml/kg/min after logistic regression analysis. Multiple regression analysis yielded an equation to estimate V(O2peak) factoring altitude (p < 0.0001), speed of ascent (p = 0.005) and body mass index (p = 0.0008). There was an association between altitude and V(O2peak) measured during the stair-climbing test. Most of the patients climbing more than 22 m are able to generate high values of V(O2peak) and can proceed to surgery without any additional tests. All others need to be referred for a formal cardiopulmonary exercise test. In addition, we were able to generate an equation to estimate V(O2peak), which could assist in streamlining the preoperative workup and could be used across different settings to standardize this test. Copyright (c) 2010 S. Karger AG, Basel.

Validity and reliability of dental age estimation of teeth root translucency based on digital luminance determination.

PubMed

Ramsthaler, Frank; Kettner, Mattias; Verhoff, Marcel A

2014-01-01

In forensic anthropological casework, estimating age-at-death is key to profiling unknown skeletal remains. The aim of this study was to examine the reliability of a new, simple, fast, and inexpensive digital odontological method for age-at-death estimation. The method is based on the original Lamendin method, which is a widely used technique in the repertoire of odontological aging methods in forensic anthropology. We examined 129 single root teeth employing a digital camera and imaging software for the measurement of the luminance of the teeth's translucent root zone. Variability in luminance detection was evaluated using statistical technical error of measurement analysis. The method revealed stable values largely unrelated to observer experience, whereas requisite formulas proved to be camera-specific and should therefore be generated for an individual recording setting based on samples of known chronological age. Multiple regression analysis showed a highly significant influence of the coefficients of the variables "arithmetic mean" and "standard deviation" of luminance for the regression formula. For the use of this primer multivariate equation for age-at-death estimation in casework, a standard error of the estimate of 6.51 years was calculated. Step-by-step reduction of the number of embedded variables to linear regression analysis employing the best contributor "arithmetic mean" of luminance yielded a regression equation with a standard error of 6.72 years (p < 0.001). The results of this study not only support the premise of root translucency as an age-related phenomenon, but also demonstrate that translucency reflects a number of other influencing factors in addition to age. This new digital measuring technique of the zone of dental root luminance can broaden the array of methods available for estimating chronological age, and furthermore facilitate measurement and age classification due to its low dependence on observer experience.

Root Traits Enhancing Rice Grain Yield under Alternate Wetting and Drying Condition

PubMed Central

Sandhu, Nitika; Subedi, Sushil R.; Yadaw, Ram B.; Chaudhary, Bedanand; Prasai, Hari; Iftekharuddaula, Khandakar; Thanak, Tho; Thun, Vathany; Battan, Khushi R.; Ram, Mangat; Venkateshwarlu, Challa; Lopena, Vitaliano; Pablico, Paquito; Maturan, Paul C.; Cruz, Ma. Teresa Sta.; Raman, K. Anitha; Collard, Bertrand; Kumar, Arvind

2017-01-01

Reducing water requirements and lowering environmental footprints require attention to minimize risks to food security. The present study was conducted with the aim to identify appropriate root traits enhancing rice grain yield under alternate wetting and drying conditions (AWD) and identify stable, high-yielding genotypes better suited to the AWD across variable ecosystems. Advanced breeding lines, popular rice varieties and drought-tolerant lines were evaluated in a series of 23 experiments conducted in the Philippines, India, Bangladesh, Nepal and Cambodia in 2015 and 2016. A large variation in grain yield under AWD conditions enabled the selection of high-yielding and stable genotypes across locations, seasons and years. Water savings of 5.7–23.4% were achieved without significant yield penalty across different ecosystems. The mean grain yield of genotypes across locations ranged from 3.5 to 5.6 t/ha and the mean environment grain yields ranged from 3.7 (Cambodia) to 6.6 (India) t/ha. The best-fitting Finlay-Wilkinson regression model identified eight stable genotypes with mean grain yield of more than 5.0 t/ha across locations. Multidimensional preference analysis represented the strong association of root traits (nodal root number, root dry weight at 22 and 30 days after transplanting) with grain yield. The genotype IR14L253 outperformed in terms of root traits and high mean grain yield across seasons and six locations. The 1.0 t/ha yield advantage of IR14L253 over the popular cultivar IR64 under AWD shall encourage farmers to cultivate IR14L253 and also adopt AWD. The results suggest an important role of root architectural traits in term of more number of nodal roots and root dry weight at 10–20 cm depth on 22–30 days after transplanting (DAT) in providing yield stability and preventing yield reduction under AWD compared to continuous flooded conditions. Genotypes possessing increased number of nodal roots provided higher yield over IR64 as well as no yield reduction under AWD compared to flooded irrigation. The identification of appropriate root architecture traits at specific depth and specific growth stage shall help breeding programs develop better rice varieties for AWD conditions. PMID:29163604

A spectral-spatial-dynamic hierarchical Bayesian (SSD-HB) model for estimating soybean yield

NASA Astrophysics Data System (ADS)

Kazama, Yoriko; Kujirai, Toshihiro

2014-10-01

A method called a "spectral-spatial-dynamic hierarchical-Bayesian (SSD-HB) model," which can deal with many parameters (such as spectral and weather information all together) by reducing the occurrence of multicollinearity, is proposed. Experiments conducted on soybean yields in Brazil fields with a RapidEye satellite image indicate that the proposed SSD-HB model can predict soybean yield with a higher degree of accuracy than other estimation methods commonly used in remote-sensing applications. In the case of the SSD-HB model, the mean absolute error between estimated yield of the target area and actual yield is 0.28 t/ha, compared to 0.34 t/ha when conventional PLS regression was applied, showing the potential effectiveness of the proposed model.

Channel morphology and bed-load yield in fluvial, formerly-glaciated headwater streams of the Columbia Mountains, Canada

NASA Astrophysics Data System (ADS)

Green, K. C.; Brardinoni, F.; Alila, Y.

2013-04-01

This study examines channel-reach morphology and bedload yield dynamics in relation to landscape structure and snowmelt hydrology in headwater streams of the Columbia Mountains, Canada. Data collection relies on field surveys and geographic information systems analysis in conjunction with a nested monitoring network of water discharge and bedload transfer. The landscape is characterized by subdued, formerly-glaciated upland topography in which the geomorphic significance of landslides and debris flows is negligible and fluvial processes prevail. While the spatial organization of channel morphology is chiefly controlled by glacially imposed local slope in conjunction with wood abundance and availability of glacigenic deposits, downstream patterns of the coarse grain-size fraction, bankfull width, bankfull depth, and stream power are all insensitive to systematic changes of local slope along the typically stepped long profiles. This is an indication that these alluvial systems have adjusted to the contemporary snowmelt-driven water and sediment transport regimes, and as such are able to compensate for the glacially-imposed boundary conditions. Bedload specific yield increases with drainage area suggesting that fluvial re-mobilization of glacial and paraglacial deposits dominate the sedimentary dynamics of basins as small as 2 km2. Stepwise multiple regression analysis shows that annual rates of sediment transfer are mainly controlled by the number of peak events over threshold discharge. During such events, repeated destabilization of channel bed armoring and re-mobilization of sediment temporarily stored behind LWD structures can generate bedload transport across the entire snowmelt season. In particular, channel morphology controls the variability of bedload response to hydrologic forcing. In the present case studies, we show that the observed spatial variability in annual bedload yield appears to be modulated by inter-basin differences in morphometric characteristics, among which slope aspect plays a critical part.

Gas detection by correlation spectroscopy employing a multimode diode laser.

PubMed

Lou, Xiutao; Somesfalean, Gabriel; Zhang, Zhiguo

2008-05-01

A gas sensor based on the gas-correlation technique has been developed using a multimode diode laser (MDL) in a dual-beam detection scheme. Measurement of CO(2) mixed with CO as an interfering gas is successfully demonstrated using a 1570 nm tunable MDL. Despite overlapping absorption spectra and occasional mode hops, the interfering signals can be effectively excluded by a statistical procedure including correlation analysis and outlier identification. The gas concentration is retrieved from several pair-correlated signals by a linear-regression scheme, yielding a reliable and accurate measurement. This demonstrates the utility of the unsophisticated MDLs as novel light sources for gas detection applications.

Optimization of grapevine yield by applying mathematical models to obtain quality wine products

NASA Astrophysics Data System (ADS)

Alina, Dobrei; Alin, Dobrei; Eleonora, Nistor; Teodor, Cristea; Marius, Boldea; Florin, Sala

2016-06-01

Relationship between the crop load and the grape yield and quality is a dynamic process, specific for wine cultivars and for fresh consumption varieties. Modeling these relations is important for the improvement of technological works. This study evaluated the interrelationship of crop load (B - buds number) and several production parameters (Y - yield; S - sugar; A - acidity; GaI - Glucoacidimetric index; AP - alcoholic potential; F - flavorings, WA - wine alcohol; SR - sugar residue, in Muscat Ottonel wine cultivar and Y - yield; S - sugar; A - acidity; GaI - Glucoacidimetric Index; CP - commercial production; BS - berries size in the Victoria table grape cultivar). In both varieties have been identified correlations between the independent variable (B - buds number as a result of pruning and training practices) and quality parameters analyzed (r = -0.699 for B vsY relationship; r = 0.961 for the relationship B vs S; r = -0.959 for B vs AP relationship; r = 0.743 for the relationship Y vs S, p <0.01, in the Muscat Ottonel cultivar, respectively r = -0.907 for relationship B vs Y; r = -0.975 for B vs CP relationship; r = -0.971 for relationship B vs BS; r = 0.990 for CP vs BS relationship in the Victoria cultivar. Through regression analysis were obtained models that describe the variation concerning production and quality parameters in relation to the independent variable (B - buds number) with statistical significance results.

Multivariate classification of the infrared spectra of cell and tissue samples

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

Haaland, D.M.; Jones, H.D.; Thomas, E.V.

1997-03-01

Infrared microspectroscopy of biopsied canine lymph cells and tissue was performed to investigate the possibility of using IR spectra coupled with multivariate classification methods to classify the samples as normal, hyperplastic, or neoplastic (malignant). IR spectra were obtained in transmission mode through BaF{sub 2} windows and in reflection mode from samples prepared on gold-coated microscope slides. Cytology and histopathology samples were prepared by a variety of methods to identify the optimal methods of sample preparation. Cytospinning procedures that yielded a monolayer of cells on the BaF{sub 2} windows produced a limited set of IR transmission spectra. These transmission spectra weremore » converted to absorbance and formed the basis for a classification rule that yielded 100{percent} correct classification in a cross-validated context. Classifications of normal, hyperplastic, and neoplastic cell sample spectra were achieved by using both partial least-squares (PLS) and principal component regression (PCR) classification methods. Linear discriminant analysis applied to principal components obtained from the spectral data yielded a small number of misclassifications. PLS weight loading vectors yield valuable qualitative insight into the molecular changes that are responsible for the success of the infrared classification. These successful classification results show promise for assisting pathologists in the diagnosis of cell types and offer future potential for {ital in vivo} IR detection of some types of cancer. {copyright} {ital 1997} {ital Society for Applied Spectroscopy}« less

Yield estimation of sugarcane based on agrometeorological-spectral models

NASA Technical Reports Server (NTRS)

Rudorff, Bernardo Friedrich Theodor; Batista, Getulio Teixeira

1990-01-01

This work has the objective to assess the performance of a yield estimation model for sugarcane (Succharum officinarum). The model uses orbital gathered spectral data along with yield estimated from an agrometeorological model. The test site includes the sugarcane plantations of the Barra Grande Plant located in Lencois Paulista municipality in Sao Paulo State. Production data of four crop years were analyzed. Yield data observed in the first crop year (1983/84) were regressed against spectral and agrometeorological data of that same year. This provided the model to predict the yield for the following crop year i.e., 1984/85. The model to predict the yield of subsequent years (up to 1987/88) were developed similarly, incorporating all previous years data. The yield estimations obtained from these models explained 69, 54, and 50 percent of the yield variation in the 1984/85, 1985/86, and 1986/87 crop years, respectively. The accuracy of yield estimations based on spectral data only (vegetation index model) and on agrometeorological data only (agrometeorological model) were also investigated.

Measurement of rheology of distiller's grain slurries using a helical impeller viscometer.

PubMed

Houchin, Tiffany L; Hanley, Thomas R

2004-01-01

Current research is focused on developing a process to convert the cellulose and hemicellulose in distiller's grains into fermentable sugars, increasing both ethanol yield and the amount of protein in the remaining solid product. The rheologic properties of distiller's grain slurries were determined for concentrations of 21, 23, and 25%. Distiller's grain slurries are non-Newtonian, heterogeneous fluids subject to particle settling. Traditional methods of viscosity measurement, such as cone-and-plate and concentric cylinder viscometers, are not adequate for these fluids. A helical impeller viscometer was employed to measure impeller torque over a range of rotational speeds. Newtonian and non-Newtonian calibration fluids were utilized to obtain constants that relate shear stresses and shear rates to the experimental data. The Newtonian impeller constant, c, was 151; the non-Newtonian shear rate constant, k, was 10.30. Regression analysis of experimental data was utilized for comparison to power law, Herschel-Bulkley, and Casson viscosity models with regression coefficients exceeding 0.99 in all cases.

Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression.

PubMed

Meng, Yilin; Roux, Benoît

2015-08-11

The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost.

Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression

PubMed Central

2015-01-01

The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost. PMID:26574437

Methods for estimating drought streamflow probabilities for Virginia streams

USGS Publications Warehouse

Austin, Samuel H.

2014-01-01

Maximum likelihood logistic regression model equations used to estimate drought flow probabilities for Virginia streams are presented for 259 hydrologic basins in Virginia. Winter streamflows were used to estimate the likelihood of streamflows during the subsequent drought-prone summer months. The maximum likelihood logistic regression models identify probable streamflows from 5 to 8 months in advance. More than 5 million streamflow daily values collected over the period of record (January 1, 1900 through May 16, 2012) were compiled and analyzed over a minimum 10-year (maximum 112-year) period of record. The analysis yielded the 46,704 equations with statistically significant fit statistics and parameter ranges published in two tables in this report. These model equations produce summer month (July, August, and September) drought flow threshold probabilities as a function of streamflows during the previous winter months (November, December, January, and February). Example calculations are provided, demonstrating how to use the equations to estimate probable streamflows as much as 8 months in advance.

Measurement equivalence of the KINDL questionnaire across child self-reports and parent proxy-reports: a comparison between item response theory and ordinal logistic regression.

PubMed

Jafari, Peyman; Sharafi, Zahra; Bagheri, Zahra; Shalileh, Sara

2014-06-01

Measurement equivalence is a necessary assumption for meaningful comparison of pediatric quality of life rated by children and parents. In this study, differential item functioning (DIF) analysis is used to examine whether children and their parents respond consistently to the items in the KINDer Lebensqualitätsfragebogen (KINDL; in German, Children Quality of Life Questionnaire). Two DIF detection methods, graded response model (GRM) and ordinal logistic regression (OLR), were applied for comparability. The KINDL was completed by 1,086 school children and 1,061 of their parents. While the GRM revealed that 12 out of the 24 items were flagged with DIF, the OLR identified 14 out of the 24 items with DIF. Seven items with DIF and five items without DIF were common across the two methods, yielding a total agreement rate of 50 %. This study revealed that parent proxy-reports cannot be used as a substitute for a child's ratings in the KINDL.

Optimization by response surface methodology of lutein recovery from paprika leaves using accelerated solvent extraction.

PubMed

Kang, Jae-Hyun; Kim, Suna; Moon, BoKyung

2016-08-15

In this study, we used response surface methodology (RSM) to optimize the extraction conditions for recovering lutein from paprika leaves using accelerated solvent extraction (ASE). The lutein content was quantitatively analyzed using a UPLC equipped with a BEH C18 column. A central composite design (CCD) was employed for experimental design to obtain the optimized combination of extraction temperature (°C), static time (min), and solvent (EtOH, %). The experimental data obtained from a twenty sample set were fitted to a second-order polynomial equation using multiple regression analysis. The adjusted coefficient of determination (R(2)) for the lutein extraction model was 0.9518, and the probability value (p=0.0000) demonstrated a high significance for the regression model. The optimum extraction conditions for lutein were temperature: 93.26°C, static time: 5 min, and solvent: 79.63% EtOH. Under these conditions, the predicted extraction yield of lutein was 232.60 μg/g. Copyright © 2016 Elsevier Ltd. All rights reserved.

Bio hydrogen production from cassava starch by anaerobic mixed cultures: Multivariate statistical modeling

NASA Astrophysics Data System (ADS)

Tien, Hai Minh; Le, Kien Anh; Le, Phung Thi Kim

2017-09-01

Bio hydrogen is a sustainable energy resource due to its potentially higher efficiency of conversion to usable power, high energy efficiency and non-polluting nature resource. In this work, the experiments have been carried out to indicate the possibility of generating bio hydrogen as well as identifying effective factors and the optimum conditions from cassava starch. Experimental design was used to investigate the effect of operating temperature (37-43 °C), pH (6-7), and inoculums ratio (6-10 %) to the yield hydrogen production, the COD reduction and the ratio of volume of hydrogen production to COD reduction. The statistical analysis of the experiment indicated that the significant effects for the fermentation yield were the main effect of temperature, pH and inoculums ratio. The interaction effects between them seem not significant. The central composite design showed that the polynomial regression models were in good agreement with the experimental results. This result will be applied to enhance the process of cassava starch processing wastewater treatment.

Random regression models to account for the effect of genotype by environment interaction due to heat stress on the milk yield of Holstein cows under tropical conditions.

PubMed

Santana, Mário L; Bignardi, Annaiza Braga; Pereira, Rodrigo Junqueira; Menéndez-Buxadera, Alberto; El Faro, Lenira

2016-02-01

The present study had the following objectives: to compare random regression models (RRM) considering the time-dependent (days in milk, DIM) and/or temperature × humidity-dependent (THI) covariate for genetic evaluation; to identify the effect of genotype by environment interaction (G×E) due to heat stress on milk yield; and to quantify the loss of milk yield due to heat stress across lactation of cows under tropical conditions. A total of 937,771 test-day records from 3603 first lactations of Brazilian Holstein cows obtained between 2007 and 2013 were analyzed. An important reduction in milk yield due to heat stress was observed for THI values above 66 (-0.23 kg/day/THI). Three phases of milk yield loss were identified during lactation, the most damaging one at the end of lactation (-0.27 kg/day/THI). Using the most complex RRM, the additive genetic variance could be altered simultaneously as a function of both DIM and THI values. This model could be recommended for the genetic evaluation taking into account the effect of G×E. The response to selection in the comfort zone (THI ≤ 66) is expected to be higher than that obtained in the heat stress zone (THI > 66) of the animals. The genetic correlations between milk yield in the comfort and heat stress zones were less than unity at opposite extremes of the environmental gradient. Thus, the best animals for milk yield in the comfort zone are not necessarily the best in the zone of heat stress and, therefore, G×E due to heat stress should not be neglected in the genetic evaluation.

Causes and implications of the unforeseen 2016 extreme yield loss in the breadbasket of France.

PubMed

Ben-Ari, Tamara; Boé, Julien; Ciais, Philippe; Lecerf, Remi; Van der Velde, Marijn; Makowski, David

2018-04-24

In 2016, France, one of the leading wheat-producing and wheat-exporting regions in the world suffered its most extreme yield loss in over half a century. Yet, yield forecasting systems failed to anticipate this event. We show that this unprecedented event is a new type of compound extreme with a conjunction of abnormally warm temperatures in late autumn and abnormally wet conditions in the following spring. A binomial logistic regression accounting for fall and spring conditions is able to capture key yield loss events since 1959. Based on climate projections, we show that the conditions that led to the 2016 wheat yield loss are projected to become more frequent in the future. The increased likelihood of such compound extreme events poses a challenge: farming systems and yield forecasting systems, which often support them, must adapt.

Robust Variable Selection with Exponential Squared Loss.

PubMed

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-04-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are [Formula: see text] and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods.

Robust Variable Selection with Exponential Squared Loss

PubMed Central

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-01-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are n-consistent and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods. PMID:23913996

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach.

PubMed

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate - adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research.

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach

PubMed Central

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Introduction Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. Aim The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Methods Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate – adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Results Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. Conclusion To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research. PMID:26080057

Exploring relationships between Dairy Herd Improvement monitors of performance and the Transition Cow Index in Wisconsin dairy herds.

PubMed

Schultz, K K; Bennett, T B; Nordlund, K V; Döpfer, D; Cook, N B

2016-09-01

Transition cow management has been tracked via the Transition Cow Index (TCI; AgSource Cooperative Services, Verona, WI) since 2006. Transition Cow Index was developed to measure the difference between actual and predicted milk yield at first test day to evaluate the relative success of the transition period program. This project aimed to assess TCI in relation to all commonly used Dairy Herd Improvement (DHI) metrics available through AgSource Cooperative Services. Regression analysis was used to isolate variables that were relevant to TCI, and then principal components analysis and network analysis were used to determine the relative strength and relatedness among variables. Finally, cluster analysis was used to segregate herds based on similarity of relevant variables. The DHI data were obtained from 2,131 Wisconsin dairy herds with test-day mean ≥30 cows, which were tested ≥10 times throughout the 2014 calendar year. The original list of 940 DHI variables was reduced through expert-driven selection and regression analysis to 23 variables. The K-means cluster analysis produced 5 distinct clusters. Descriptive statistics were calculated for the 23 variables per cluster grouping. Using principal components analysis, cluster analysis, and network analysis, 4 parameters were isolated as most relevant to TCI; these were energy-corrected milk, 3 measures of intramammary infection (dry cow cure rate, linear somatic cell count score in primiparous cows, and new infection rate), peak ratio, and days in milk at peak milk production. These variables together with cow and newborn calf survival measures form a group of metrics that can be used to assist in the evaluation of overall transition period performance. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Effects of feeding organic trace minerals on milk production and reproductive performance in lactating dairy cows: a meta-analysis.

PubMed

Rabiee, A R; Lean, I J; Stevenson, M A; Socha, M T

2010-09-01

The objectives of this meta-analysis were to evaluate the effectiveness of supplementation with the organic trace minerals (OTM; Availa-4 and 4-Plex, Zinpro Corp., Eden Prairie, MN) on milk yield, composition, and component yields and reproductive performance in dairy cows. Twenty research papers and reports on the effects of OTM were considered in this meta-analysis. Criteria for inclusion in the study were information on the form of OTM, an adequate description of randomization, production and reproduction data, and associated measures of variance (SE or SD) and P-values. The OTM increased milk production by 0.93 kg [95% confidence interval (CI)=0.61 to 1.25], milk fat by 0.04 kg (95% CI=0.02 to 0.05), and milk protein by 0.03 kg (95% CI=0.02 to 0.04) per day. Milk SCC was not different in cows supplemented with OTM. All production outcomes except milk solids (yield) and milk SCC were heterogeneous. Meta-regression analysis showed that feeding before calving, feeding for a full lactation after calving, and the use of other supplements increased responses over feeding after calving only, feeding for part of lactation, or not using other supplements, respectively. Supplementation of cows with OTM reduced days open (weighted mean difference=13.5 d) and number of services per conception (weighted mean difference=0.27) in lactating dairy cows. The risk of pregnancy on d 150 of lactation was greater in cows fed OTM (risk ratio=1.07), but OTM had no significant effect on the interval from calving to first service and 21-d pregnancy rate. There was no evidence of heterogeneity for any of the reproductive outcomes evaluated. The results of this meta-analysis showed that organic trace mineral supplementation could improve production and reproduction in lactating dairy cows. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Effects of stage of pregnancy on variance components, daily milk yields and 305-day milk yield in Holstein cows, as estimated by using a test-day model.

PubMed

Yamazaki, T; Hagiya, K; Takeda, H; Osawa, T; Yamaguchi, S; Nagamine, Y

2016-08-01

Pregnancy and calving are elements indispensable for dairy production, but the daily milk yield of cows decline as pregnancy progresses, especially during the late stages. Therefore, the effect of stage of pregnancy on daily milk yield must be clarified to accurately estimate the breeding values and lifetime productivity of cows. To improve the genetic evaluation model for daily milk yield and determine the effect of the timing of pregnancy on productivity, we used a test-day model to assess the effects of stage of pregnancy on variance component estimates, daily milk yields and 305-day milk yield during the first three lactations of Holstein cows. Data were 10 646 333 test-day records for the first lactation; 8 222 661 records for the second; and 5 513 039 records for the third. The data were analyzed within each lactation by using three single-trait random regression animal models: one model that did not account for the stage of pregnancy effect and two models that did. The effect of stage of pregnancy on test-day milk yield was included in the model by applying a regression on days pregnant or fitting a separate lactation curve for each days open (days from calving to pregnancy) class (eight levels). Stage of pregnancy did not affect the heritability estimates of daily milk yield, although the additive genetic and permanent environmental variances in late lactation were decreased by accounting for the stage of pregnancy effect. The effects of days pregnant on daily milk yield during late lactation were larger in the second and third lactations than in the first lactation. The rates of reduction of the 305-day milk yield of cows that conceived fewer than 90 days after the second or third calving were significantly (P<0.05) greater than that after the first calving. Therefore, we conclude that differences between the negative effects of early pregnancy in the first, compared with later, lactations should be included when determining the optimal number of days open to maximize lifetime productivity in dairy cows.

Regression analysis on the variation in efficiency frontiers for prevention stage of HIV/AIDS.

PubMed

Kamae, Maki S; Kamae, Isao; Cohen, Joshua T; Neumann, Peter J

2011-01-01

To investigate how the cost effectiveness of preventing HIV/AIDS varies across possible efficiency frontiers (EFs) by taking into account potentially relevant external factors, such as prevention stage, and how the EFs can be characterized using regression analysis given uncertainty of the QALY-cost estimates. We reviewed cost-effectiveness estimates for the prevention and treatment of HIV/AIDS published from 2002-2007 and catalogued in the Tufts Medical Center Cost-Effectiveness Analysis (CEA) Registry. We constructed efficiency frontier (EF) curves by plotting QALYs against costs, using methods used by the Institute for Quality and Efficiency in Health Care (IQWiG) in Germany. We stratified the QALY-cost ratios by prevention stage, country of study, and payer perspective, and estimated EF equations using log and square-root models. A total of 53 QALY-cost ratios were identified for HIV/AIDS in the Tufts CEA Registry. Plotted ratios stratified by prevention stage were visually grouped into a cluster consisting of primary/secondary prevention measures and a cluster consisting of tertiary measures. Correlation coefficients for each cluster were statistically significant. For each cluster, we derived two EF equations - one based on the log model, and one based on the square-root model. Our findings indicate that stratification of HIV/AIDS interventions by prevention stage can yield distinct EFs, and that the correlation and regression analyses are useful for parametrically characterizing EF equations. Our study has certain limitations, such as the small number of included articles and the potential for study populations to be non-representative of countries of interest. Nonetheless, our approach could help develop a deeper appreciation of cost effectiveness beyond the deterministic approach developed by IQWiG.

Comparison of the Chiron Quantiplex branched DNA (bDNA) assay and the Abbott Genostics solution hybridization assay for quantification of hepatitis B viral DNA.

PubMed

Kapke, G E; Watson, G; Sheffler, S; Hunt, D; Frederick, C

1997-01-01

Several assays for quantification of DNA have been developed and are currently used in research and clinical laboratories. However, comparison of assay results has been difficult owing to the use of different standards and units of measurements as well as differences between assays in dynamic range and quantification limits. Although a few studies have compared results generated by different assays, there has been no consensus on conversion factors and thorough analysis has been precluded by small sample size and limited dynamic range studied. In this study, we have compared the Chiron branched DNA (bDNA) and Abbott liquid hybridization assays for quantification of hepatitis B virus (HBV) DNA in clinical specimens and have derived conversion factors to facilitate comparison of assay results. Additivity and variance stabilizing (AVAS) regression, a form of non-linear regression analysis, was performed on assay results for specimens from HBV clinical trials. Our results show that there is a strong linear relationship (R2 = 0.96) between log Chiron and log Abbott assay results. Conversion factors derived from regression analyses were found to be non-constant and ranged from 6-40. Analysis of paired assay results below and above each assay's limit of quantification (LOQ) indicated that a significantly (P < 0.01) larger proportion of observations were below the Abbott assay LOQ but above the Chiron assay LOQ, indicating that the Chiron assay is significantly more sensitive than the Abbott assay. Testing of replicate specimens showed that the Chiron assay consistently yielded lower per cent coefficients of variance (% CVs) than the Abbott assay, indicating that the Chiron assay provides superior precision.

Human impact on sediment fluxes within the Blue Nile and Atbara River basins

NASA Astrophysics Data System (ADS)

Balthazar, Vincent; Vanacker, Veerle; Girma, Atkilt; Poesen, Jean; Golla, Semunesh

2013-01-01

A regional assessment of the spatial variability in sediment yields allows filling the gap between detailed, process-based understanding of erosion at field scale and empirical sediment flux models at global scale. In this paper, we focus on the intrabasin variability in sediment yield within the Blue Nile and Atbara basins as biophysical and anthropogenic factors are presumably acting together to accelerate soil erosion. The Blue Nile and Atbara River systems are characterized by an important spatial variability in sediment fluxes, with area-specific sediment yield (SSY) values ranging between 4 and 4935 t/km2/y. Statistical analyses show that 41% of the observed variation in SSY can be explained by remote sensing proxy data of surface vegetation cover, rainfall intensity, mean annual temperature, and human impact. The comparison of a locally adapted regression model with global predictive sediment flux models indicates that global flux models such as the ART and BQART models are less suited to capture the spatial variability in area-specific sediment yields (SSY), but they are very efficient to predict absolute sediment yields (SY). We developed a modified version of the BQART model that estimates the human influence on sediment yield based on a high resolution composite measure of local human impact (human footprint index) instead of countrywide estimates of GNP/capita. Our modified version of the BQART is able to explain 80% of the observed variation in SY for the Blue Nile and Atbara basins and thereby performs only slightly less than locally adapted regression models.

Use of multiple regression models to evaluate the formation of halonitromethane via chlorination/chloramination of water from Tai Lake and the Qiantang River, China.

PubMed

Hong, Huachang; Qian, Lingya; Xiong, Yujing; Xiao, Zhuoqun; Lin, Hongjun; Yu, Haiying

2015-01-01

The deterioration of water quality, especially organic pollution in Tai Lake and the Qiantang River, have recently received attention in China. The objectives of this study were to evaluate the formation of halonitromethanes (HNMs) using multiple regression models for chlorination and chloramination and to identify the key factors that influence the formation of HNMs in Tai Lake and the Qiantang River. The results showed that the total formation of HNMs (T-HNMs) during chlorination and chloramination could be described using the following models: (1) [Formula: see text] =(10)(5.267)(DON)(6.645)(Br(-))(0.737)(DOC)(-)(5.537)(Cl2)(0.333)(t)(0.165) (R(2)=0.974, p<0.01, n=33), and (2) T-HNMNH2Cl=(10)(-)(2.481)(Cl2)(0.451)(NO2(-))(0.382)(Br(-))(0.630)(t)(0.640)(Temp)(0.581) (R(2)=0.961, p<0.05, n=33), respectively. The key factors that influenced the T-HNM yields during chlorination were dissolved organic nitrogen (DON), bromide and dissolved organic carbon (DOC). The nitrite and bromide concentrations and the reaction time mainly affected the T-HNM yields during chloramination. Additional analysis indicated that the bromine incorporation factors (BIFs) for trihalogenated HNMs generally decreased as the chlorine/chloramine dose, temperature and reaction time decreased and increased as the bromide concentration increased. Copyright © 2014 Elsevier Ltd. All rights reserved.

A comparative analysis of conventional and pretargeted radioimmunotherapy of B-cell lymphomas by targeting CD20, CD22, and HLA-DR singly and in combinations

PubMed Central

Orgun, Nural; Hamlin, Donald K.; Wilbur, D. Scott; Gooley, Theodore A.; Gopal, Ajay K.; Park, Steven I.; Green, Damian J.; Lin, Yukang; Press, Oliver W.

2009-01-01

Relapsed B-cell lymphomas are currently incurable with conventional chemotherapy and radiation treatments. Radiolabeled antibodies directed against B-cell surface antigens have emerged as effective and safe therapies for relapsed lymphomas. We therefore investigated the potential utility of both directly radiolabeled 1F5 (anti-CD20), HD39 (anti-CD22), and Lym-1 (anti-DR) antibodies (Abs) and of pretargeted radioimmunotherapy (RIT) using Ab-streptavidin (SA) conjugates, followed by an N-acetylgalactosamine dendrimeric clearing agent and radiometal-labeled DOTA-biotin, for treatment of lymphomas in mouse models using Ramos, Raji, and FL-18 human lymphoma xenografts. This study demonstrates the marked superiority of pretargeted RIT for each of the antigenic targets with more complete tumor regressions and longer mouse survival compared with conventional one-step RIT. The Ab-SA conjugate yielding the best tumor regression and progression-free survival after pretargeted RIT varied depending upon the lymphoma cell line used, with 1F5 Ab-SA and Lym-1 Ab-SA conjugates yielding the most promising results overall. Contrary to expectations, the best rates of mouse survival were obtained using optimal single Ab-SA conjugates rather than combinations of conjugates targeting different antigens. We hypothesize that clinical implementation of pretargeted RIT methods will provide a meaningful prolongation of survival for patients with relapsed lymphomas compared with currently available treatment strategies. PMID:19124831

Prediction-Oriented Marker Selection (PROMISE): With Application to High-Dimensional Regression.

PubMed

Kim, Soyeon; Baladandayuthapani, Veerabhadran; Lee, J Jack

2017-06-01

In personalized medicine, biomarkers are used to select therapies with the highest likelihood of success based on an individual patient's biomarker/genomic profile. Two goals are to choose important biomarkers that accurately predict treatment outcomes and to cull unimportant biomarkers to reduce the cost of biological and clinical verifications. These goals are challenging due to the high dimensionality of genomic data. Variable selection methods based on penalized regression (e.g., the lasso and elastic net) have yielded promising results. However, selecting the right amount of penalization is critical to simultaneously achieving these two goals. Standard approaches based on cross-validation (CV) typically provide high prediction accuracy with high true positive rates but at the cost of too many false positives. Alternatively, stability selection (SS) controls the number of false positives, but at the cost of yielding too few true positives. To circumvent these issues, we propose prediction-oriented marker selection (PROMISE), which combines SS with CV to conflate the advantages of both methods. Our application of PROMISE with the lasso and elastic net in data analysis shows that, compared to CV, PROMISE produces sparse solutions, few false positives, and small type I + type II error, and maintains good prediction accuracy, with a marginal decrease in the true positive rates. Compared to SS, PROMISE offers better prediction accuracy and true positive rates. In summary, PROMISE can be applied in many fields to select regularization parameters when the goals are to minimize false positives and maximize prediction accuracy.

Quantifying components of the hydrologic cycle in Virginia using chemical hydrograph separation and multiple regression analysis

USGS Publications Warehouse

Sanford, Ward E.; Nelms, David L.; Pope, Jason P.; Selnick, David L.

2012-01-01

This study by the U.S. Geological Survey, prepared in cooperation with the Virginia Department of Environmental Quality, quantifies the components of the hydrologic cycle across the Commonwealth of Virginia. Long-term, mean fluxes were calculated for precipitation, surface runoff, infiltration, total evapotranspiration (ET), riparian ET, recharge, base flow (or groundwater discharge) and net total outflow. Fluxes of these components were first estimated on a number of real-time-gaged watersheds across Virginia. Specific conductance was used to distinguish and separate surface runoff from base flow. Specific-conductance data were collected every 15 minutes at 75 real-time gages for approximately 18 months between March 2007 and August 2008. Precipitation was estimated for 1971–2000 using PRISM climate data. Precipitation and temperature from the PRISM data were used to develop a regression-based relation to estimate total ET. The proportion of watershed precipitation that becomes surface runoff was related to physiographic province and rock type in a runoff regression equation. Component flux estimates from the watersheds were transferred to flux estimates for counties and independent cities using the ET and runoff regression equations. Only 48 of the 75 watersheds yielded sufficient data, and data from these 48 were used in the final runoff regression equation. The base-flow proportion for the 48 watersheds averaged 72 percent using specific conductance, a value that was substantially higher than the 61 percent average calculated using a graphical-separation technique (the USGS program PART). Final results for the study are presented as component flux estimates for all counties and independent cities in Virginia.

Methodology for constructing a colour-difference acceptability scale.

PubMed

Laborie, Baptiste; Viénot, Françoise; Langlois, Sabine

2010-09-01

Observers were invited to report their degree of satisfaction on a 6-point semantic scale with respect to the conformity of a test colour with a white reference colour, simultaneously presented on a PDP display. Eight test patches were chosen along each of the +a*, -a*, +b*, -b* axes of the CIELAB chromaticity plane, at Y = 80 ± 2 cd.m(-2) . Experimental conditions reliably represented the automotive environment (patch size, angular distance between patches) and observers could move their head and eyes freely. We have compared several methods of category scaling, the Torgerson-DMT method (Torgerson, W. S. (1958). Theory and methods of scaling. Wiley, New York, USA); two versions of the regression method i.e. Bonnet's (Bonnet, C. (1986). Manuel pratique de psychophysique. Armand Colin, Paris, France) and logistic regression; and the medians method. We describe in detail a case where all methods yield substantial but slightly different results. The solution proposed by the regression method which works with incomplete matrices and yields results directly on a colorimetric scale is probably the most useful in this industrial context. Finally we summarize the implementation of the logistic regression method over four hues and for one experimental condition. © 2010 The Authors, Ophthalmic and Physiological Optics © 2010 The College of Optometrists.

Etiologic yield of subspecialists' evaluation of young children with global developmental delay.

PubMed

Shevell, M I; Majnemer, A; Rosenbaum, P; Abrahamowicz, M

2000-05-01

To determine the etiologic yield of subspecialists' evaluation of young children with global developmental delay. In addition, variables that may predict finding an underlying etiology were also identified. All children <5 years of age, referred over an 18-month period to subspecialty services for initial evaluation of a suspected developmental delay, were prospectively enrolled. Diagnostic yield was ascertained after the completion of clinical assessments and laboratory investigations requested by the evaluating physician. Ninety-nine children (71 boys) were found to have global developmental delay; 96% had a mild or moderate delay documented. An etiologic diagnosis was determined in 44. Four diagnoses (cerebral dysgenesis, hypoxic-ischemic encephalopathy, toxin exposure, chromosomal abnormalities) accounted for 34 of 44 (77%) of the diagnoses made. The presence of co-existing autistic traits was associated with significantly decreased diagnostic yield (0/19 vs 44/80, P <.0001), whereas specific historical features (eg, family history, toxin exposure, and perinatal difficulty; 23/32 vs 21/67, P =.0002) and findings on physical examination (eg, dysmorphology, microcephaly, and focal motor findings; 35/48 vs 9/51, P <.0001) were significantly associated with identifying a diagnosis. Multiple logistic regression analysis identified antenatal toxin exposure, microcephaly, focal motor findings, and the absence of autistic traits as significant predictor variables for the identification of an etiology. An etiologic diagnosis is often possible in the young child with global developmental delay, particularly in the absence of autistic features. Etiologic yield is augmented by presence of specific findings on history or physical examination on initial assessment.

Corn response to climate stress detected with satellite-based NDVI time series

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

Wang, Ruoyu; Cherkauer, Keith; Bowling, Laura

Corn growth conditions and yield are closely dependent on climate variability. Leaf growth, measured as the leaf area index, can be used to identify changes in crop growth in response to climate stress. This research was conducted to capture patterns of spatial and temporal corn leaf growth under climate stress for the St. Joseph River watershed, in northeastern Indiana. Leaf growth is represented by the Normalized Difference Vegetative Index (NDVI) retrieved from multiple years (2000–2010) of Landsat 5 TM images. By comparing NDVI values for individual image dates with the derived normal curve, the response of crop growth to environmentalmore » factors is quantified as NDVI residuals. Regression analysis revealed a significant relationship between yield and NDVI residual during the pre-silking period, indicating that NDVI residuals reflect crop stress in the early growing period that impacts yield. Both the mean NDVI residuals and the percentage of image pixels where corn was under stress (risky pixel rate) are significantly correlated with water stress. Dry weather is prone to hamper potential crop growth, with stress affecting most of the observed corn pixels in the area. Oversupply of rainfall at the end of the growing season was not found to have a measurable effect on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. Furthermore, the spatial extent of stress is much lower when precipitation is above normal than under dry conditions, masking the impact of small areas of yield loss at the watershed scale.« less

Corn response to climate stress detected with satellite-based NDVI time series

DOE PAGES

Wang, Ruoyu; Cherkauer, Keith; Bowling, Laura

2016-03-23

Corn growth conditions and yield are closely dependent on climate variability. Leaf growth, measured as the leaf area index, can be used to identify changes in crop growth in response to climate stress. This research was conducted to capture patterns of spatial and temporal corn leaf growth under climate stress for the St. Joseph River watershed, in northeastern Indiana. Leaf growth is represented by the Normalized Difference Vegetative Index (NDVI) retrieved from multiple years (2000–2010) of Landsat 5 TM images. By comparing NDVI values for individual image dates with the derived normal curve, the response of crop growth to environmentalmore » factors is quantified as NDVI residuals. Regression analysis revealed a significant relationship between yield and NDVI residual during the pre-silking period, indicating that NDVI residuals reflect crop stress in the early growing period that impacts yield. Both the mean NDVI residuals and the percentage of image pixels where corn was under stress (risky pixel rate) are significantly correlated with water stress. Dry weather is prone to hamper potential crop growth, with stress affecting most of the observed corn pixels in the area. Oversupply of rainfall at the end of the growing season was not found to have a measurable effect on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. Furthermore, the spatial extent of stress is much lower when precipitation is above normal than under dry conditions, masking the impact of small areas of yield loss at the watershed scale.« less

[Predicting the impact of climate change in the next 40 years on the yield of maize in China].

PubMed

Ma, Yu-ping; Sun, Lin-li; E, You-hao; Wu, Wei

2015-01-01

Climate change will significantly affect agricultural production in China. The combination of the integral regression model and the latest climate projection may well assess the impact of future climate change on crop yield. In this paper, the correlation model of maize yield and meteorological factors was firstly established for different provinces in China by using the integral regression method, then the impact of climate change in the next 40 years on China's maize production was evaluated combined the latest climate prediction with the reason be ing analyzed. The results showed that if the current speeds of maize variety improvement and science and technology development were constant, maize yield in China would be mainly in an increasing trend of reduction with time in the next 40 years in a range generally within 5%. Under A2 climate change scenario, the region with the most reduction of maize yield would be the Northeast except during 2021-2030, and the reduction would be generally in the range of 2.3%-4.2%. Maize yield reduction would be also high in the Northwest, Southwest and middle and lower reaches of Yangtze River after 2031. Under B2 scenario, the reduction of 5.3% in the Northeast in 2031-2040 would be the greatest across all regions. Other regions with considerable maize yield reduction would be mainly in the Northwest and the Southwest. Reduction in maize yield in North China would be small, generally within 2%, under any scenarios, and that in South China would be almost unchanged. The reduction of maize yield in most regions would be greater under A2 scenario than under B2 scenario except for the period of 2021-2030. The effect of the ten day precipitation on maize yield in northern China would be almost positive. However, the effect of ten day average temperature on yield of maize in all regions would be generally negative. The main reason of maize yield reduction was temperature increase in most provinces but precipitation decrease in a few provinces. Assessments of the future change of maize yield in China based on the different methods were not consistent. Further evaluation needs to consider the change of maize variety and scientific and technological progress, and to enhance the reliability of evaluation models.

Raman spectroscopy-based screening of hepatitis C and associated molecular changes

NASA Astrophysics Data System (ADS)

Bilal, Maria; Bilal, M.; Saleem, M.; Khan, Saranjam; Ullah, Rahat; Fatima, Kiran; Ahmed, M.; Hayat, Abbas; Shahzada, Shaista; Ullah Khan, Ehsan

2017-09-01

This study presents the optical screening of hepatitis C and its associated molecular changes in human blood sera using a partial least-squares regression model based on their Raman spectra. In total, 152 samples were tested through enzyme-linked immunosorbent assay for confirmation. This model utilizes minor spectral variations in the Raman spectra of the positive and control groups. Regression coefficients of this model were analyzed with reference to the variations in concentration of associated molecules in these two groups. It was found that trehalose, chitin, ammonia, and cytokines are positively correlated while lipids, beta structures of proteins, and carbohydrate-binding proteins are negatively correlated with hepatitis C. The regression vector yielded by this model is utilized to predict hepatitis C in unknown samples. This model has been evaluated by a cross-validation method, which yielded a correlation coefficient of 0.91. Moreover, 30 unknown samples were screened for hepatitis C infection using this model to test its performance. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve from these predictions were found to be 93.3%, 100%, 96.7%, and 1, respectively.

Determining degree of optic nerve edema from color fundus photography

NASA Astrophysics Data System (ADS)

Agne, Jason; Wang, Jui-Kai; Kardon, Randy H.; Garvin, Mona K.

2015-03-01

Swelling of the optic nerve head (ONH) is subjectively assessed by clinicians using the Frisén scale. It is believed that a direct measurement of the ONH volume would serve as a better representation of the swelling. However, a direct measurement requires optic nerve imaging with spectral domain optical coherence tomography (SD-OCT) and 3D segmentation of the resulting images, which is not always available during clinical evaluation. Furthermore, telemedical imaging of the eye at remote locations is more feasible with non-mydriatic fundus cameras which are less costly than OCT imagers. Therefore, there is a critical need to develop a more quantitative analysis of optic nerve swelling on a continuous scale, similar to SD-OCT. Here, we select features from more commonly available 2D fundus images and use them to predict ONH volume. Twenty-six features were extracted from each of 48 color fundus images. The features include attributes of the blood vessels, optic nerve head, and peripapillary retina areas. These features were used in a regression analysis to predict ONH volume, as computed by a segmentation of the SD-OCT image. The results of the regression analysis yielded a mean square error of 2.43 mm3 and a correlation coefficient between computed and predicted volumes of R = 0:771, which suggests that ONH volume may be predicted from fundus features alone.

The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques

NASA Astrophysics Data System (ADS)

Deng, Chengbin; Wu, Changshan

2013-12-01

Urban impervious surface information is essential for urban and environmental applications at the regional/national scales. As a popular image processing technique, spectral mixture analysis (SMA) has rarely been applied to coarse-resolution imagery due to the difficulty of deriving endmember spectra using traditional endmember selection methods, particularly within heterogeneous urban environments. To address this problem, we derived endmember signatures through a least squares solution (LSS) technique with known abundances of sample pixels, and integrated these endmember signatures into SMA for mapping large-scale impervious surface fraction. In addition, with the same sample set, we carried out objective comparative analyses among SMA (i.e. fully constrained and unconstrained SMA) and machine learning (i.e. Cubist regression tree and Random Forests) techniques. Analysis of results suggests three major conclusions. First, with the extrapolated endmember spectra from stratified random training samples, the SMA approaches performed relatively well, as indicated by small MAE values. Second, Random Forests yields more reliable results than Cubist regression tree, and its accuracy is improved with increased sample sizes. Finally, comparative analyses suggest a tentative guide for selecting an optimal approach for large-scale fractional imperviousness estimation: unconstrained SMA might be a favorable option with a small number of samples, while Random Forests might be preferred if a large number of samples are available.

Methods for estimating selected low-flow statistics and development of annual flow-duration statistics for Ohio

USGS Publications Warehouse

Koltun, G.F.; Kula, Stephanie P.

2013-01-01

This report presents the results of a study to develop methods for estimating selected low-flow statistics and for determining annual flow-duration statistics for Ohio streams. Regression techniques were used to develop equations for estimating 10-year recurrence-interval (10-percent annual-nonexceedance probability) low-flow yields, in cubic feet per second per square mile, with averaging periods of 1, 7, 30, and 90-day(s), and for estimating the yield corresponding to the long-term 80-percent duration flow. These equations, which estimate low-flow yields as a function of a streamflow-variability index, are based on previously published low-flow statistics for 79 long-term continuous-record streamgages with at least 10 years of data collected through water year 1997. When applied to the calibration dataset, average absolute percent errors for the regression equations ranged from 15.8 to 42.0 percent. The regression results have been incorporated into the U.S. Geological Survey (USGS) StreamStats application for Ohio (http://water.usgs.gov/osw/streamstats/ohio.html) in the form of a yield grid to facilitate estimation of the corresponding streamflow statistics in cubic feet per second. Logistic-regression equations also were developed and incorporated into the USGS StreamStats application for Ohio for selected low-flow statistics to help identify occurrences of zero-valued statistics. Quantiles of daily and 7-day mean streamflows were determined for annual and annual-seasonal (September–November) periods for each complete climatic year of streamflow-gaging station record for 110 selected streamflow-gaging stations with 20 or more years of record. The quantiles determined for each climatic year were the 99-, 98-, 95-, 90-, 80-, 75-, 70-, 60-, 50-, 40-, 30-, 25-, 20-, 10-, 5-, 2-, and 1-percent exceedance streamflows. Selected exceedance percentiles of the annual-exceedance percentiles were subsequently computed and tabulated to help facilitate consideration of the annual risk of exceedance or nonexceedance of annual and annual-seasonal-period flow-duration values. The quantiles are based on streamflow data collected through climatic year 2008.

Optimization of Extraction Process for Antidiabetic and Antioxidant Activities of Kursi Wufarikun Ziyabit Using Response Surface Methodology and Quantitative Analysis of Main Components.

PubMed

Edirs, Salamet; Turak, Ablajan; Numonov, Sodik; Xin, Xuelei; Aisa, Haji Akber

2017-01-01

By using extraction yield, total polyphenolic content, antidiabetic activities (PTP-1B and α -glycosidase), and antioxidant activity (ABTS and DPPH) as indicated markers, the extraction conditions of the prescription Kursi Wufarikun Ziyabit (KWZ) were optimized by response surface methodology (RSM). Independent variables were ethanol concentration, extraction temperature, solid-to-solvent ratio, and extraction time. The result of RSM analysis showed that the four variables investigated have a significant effect ( p < 0.05) for Y 1 , Y 2 , Y 3 , Y 4 , and Y 5 with R 2 value of 0.9120, 0.9793, 0.9076, 0.9125, and 0.9709, respectively. Optimal conditions for the highest extraction yield of 39.28%, PTP-1B inhibition rate of 86.21%, α -glycosidase enzymes inhibition rate of 96.56%, and ABTS inhibition rate of 77.38% were derived at ethanol concentration 50.11%, extraction temperature 72.06°C, solid-to-solvent ratio 1 : 22.73 g/mL, and extraction time 2.93 h. On the basis of total polyphenol content of 48.44% in this optimal condition, the quantitative analysis of effective part of KWZ was characterized via UPLC method, 12 main components were identified by standard compounds, and all of them have shown good regression within the test ranges and the total content of them was 11.18%.

Genome-Wide Association Analysis Reveals Different Genetic Control in Panicle Architecture Between and Rice.

PubMed

Bai, Xufeng; Zhao, Hu; Huang, Yong; Xie, Weibo; Han, Zhongmin; Zhang, Bo; Guo, Zilong; Yang, Lin; Dong, Haijiao; Xue, Weiya; Li, Guangwei; Hu, Gang; Hu, Yong; Xing, Yongzhong

2016-07-01

Panicle architecture determines the number of spikelets per panicle (SPP) and is highly associated with grain yield in rice ( L.). Understanding the genetic basis of panicle architecture is important for improving the yield of rice grain. In this study, we dissected panicle architecture traits into eight components, which were phenotyped from a germplasm collection of 529 cultivars. Multiple regression analysis revealed that the number of secondary branch (NSB) was the major factor that contributed to SPP. Genome-wide association analysis was performed independently for the eight particle architecture traits observed in the and rice subpopulations compared with the whole rice population. In total, 30 loci were associated with these traits. Of these, 13 loci were closely linked to known panicle architecture genes, and 17 novel loci were repeatedly identified in different environments. An association signal cluster was identified for NSB and number of spikelets per secondary branch (NSSB) in the region of 31.6 to 31.7 Mb on chromosome 4. In addition to the common associations detected in both and subpopulations, many associated loci were unique to one subpopulation. For example, and were specifically associated with panicle length (PL) in and rice, respectively. Moreover, the -mediated flowering genes and were associated with the formation of panicle architecture in rice. These results suggest that different gene networks regulate panicle architecture in and rice. Copyright © 2016 Crop Science Society of America.

Judo principles and practices: applications to conflict-solving strategies in psychotherapy.

PubMed

Gleser, J; Brown, P

1988-07-01

Jigoro Kano created judo from ju-jitsu techniques. He realized that the Ju principle of both judo and ju-jitsu as the art of yielding, was that of living and changing. The principle of yielding has been applied in dynamic and directive psychotherapies for many years and was recently linked to the Ju principle in martial arts. After several years of using a modified judo practice as a therapeutic tool, and applying the principle of yielding as a dynamic conflict-solving strategy, the authors discovered judo principles applicable to conflict solving, particularly for regressed and violent psychotic patients.

Epidemiology and impact of Fasciola hepatica exposure in high-yielding dairy herds

PubMed Central

Howell, Alison; Baylis, Matthew; Smith, Rob; Pinchbeck, Gina; Williams, Diana

2015-01-01

The liver fluke Fasciola hepatica is a trematode parasite with a worldwide distribution and is the cause of important production losses in the dairy industry. The aim of this observational study was to assess the prevalence of exposure to F. hepatica in a group of high yielding dairy herds, to determine the risk factors and investigate their associations with production and fertility parameters. Bulk milk tank samples from 606 herds that supply a single retailer with liquid milk were tested with an antibody ELISA for F. hepatica. Multivariable linear regression was used to investigate the effect of farm management and environmental risk factors on F. hepatica exposure. Higher rainfall, grazing boggy pasture, presence of beef cattle on farm, access to a stream or pond and smaller herd size were associated with an increased risk of exposure. Univariable regression was used to look for associations between fluke exposure and production-related variables including milk yield, composition, somatic cell count and calving index. Although causation cannot be assumed, a significant (p < 0.001) negative association was seen between F. hepatica exposure and estimated milk yield at the herd level, representing a 15% decrease in yield for an increase in F. hepatica exposure from the 25th to the 75th percentile. This remained significant when fertility, farm management and environmental factors were controlled for. No associations were found between F. hepatica exposure and any of the other production, disease or fertility variables. PMID:26093971

Thalamic functional connectivity predicts seizure laterality in individual TLE patients: application of a biomarker development strategy.

PubMed

Barron, Daniel S; Fox, Peter T; Pardoe, Heath; Lancaster, Jack; Price, Larry R; Blackmon, Karen; Berry, Kristen; Cavazos, Jose E; Kuzniecky, Ruben; Devinsky, Orrin; Thesen, Thomas

2015-01-01

Noninvasive markers of brain function could yield biomarkers in many neurological disorders. Disease models constrained by coordinate-based meta-analysis are likely to increase this yield. Here, we evaluate a thalamic model of temporal lobe epilepsy that we proposed in a coordinate-based meta-analysis and extended in a diffusion tractography study of an independent patient population. Specifically, we evaluated whether thalamic functional connectivity (resting-state fMRI-BOLD) with temporal lobe areas can predict seizure onset laterality, as established with intracranial EEG. Twenty-four lesional and non-lesional temporal lobe epilepsy patients were studied. No significant differences in functional connection strength in patient and control groups were observed with Mann-Whitney Tests (corrected for multiple comparisons). Notwithstanding the lack of group differences, individual patient difference scores (from control mean connection strength) successfully predicted seizure onset zone as shown in ROC curves: discriminant analysis (two-dimensional) predicted seizure onset zone with 85% sensitivity and 91% specificity; logistic regression (four-dimensional) achieved 86% sensitivity and 100% specificity. The strongest markers in both analyses were left thalamo-hippocampal and right thalamo-entorhinal cortex functional connection strength. Thus, this study shows that thalamic functional connections are sensitive and specific markers of seizure onset laterality in individual temporal lobe epilepsy patients. This study also advances an overall strategy for the programmatic development of neuroimaging biomarkers in clinical and genetic populations: a disease model informed by coordinate-based meta-analysis was used to anatomically constrain individual patient analyses.

Development of a noise annoyance sensitivity scale

NASA Technical Reports Server (NTRS)

Bregman, H. L.; Pearson, R. G.

1972-01-01

Examining the problem of noise pollution from the psychological rather than the engineering view, a test of human sensitivity to noise was developed against the criterion of noise annoyance. Test development evolved from a previous study in which biographical, attitudinal, and personality data was collected on a sample of 166 subjects drawn from the adult community of Raleigh. Analysis revealed that only a small subset of the data collected was predictive of noise annoyance. Item analysis yielded 74 predictive items that composed the preliminary noise sensitivity test. This was administered to a sample of 80 adults who later rate the annoyance value of six sounds (equated in terms of peak sound pressure level) presented in a simulated home, living-room environment. A predictive model involving 20 test items was developed using multiple regression techniques, and an item weighting scheme was evaluated.

Multiple-Instance Regression with Structured Data

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran; Roper, Alex

2008-01-01

We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.

Optimization of bio-ethanol autothermal reforming and carbon monoxide removal processes

NASA Astrophysics Data System (ADS)

Markova, D.; Bazbauers, G.; Valters, K.; Alhucema Arias, R.; Weuffen, C.; Rochlitz, L.

Experimental investigation of bio-ethanol autothermal reforming (ATR) and water-gas shift (WGS) processes for hydrogen production and regression analysis of the data is performed in the study. The main goal was to obtain regression relations between the most critical dependent variables such as hydrogen, carbon monoxide and methane content in the reformate gas and independent factors such as air-to-fuel ratio (λ), steam-to-carbon ratio (S/C), inlet temperature of reactants into reforming process (T ATRin), pressure (p) and temperature (T ATR) in the ATR reactor from the experimental data. Purpose of the regression models is to provide optimum values of the process factors that give the maximum amount of hydrogen. The experimental ATR system consisted of an evaporator, an ATR reactor and a one-stage WGS reactor. Empirical relations between hydrogen, carbon monoxide, methane content and the controlling parameters downstream of the ATR reactor are shown in the work. The optimization results show that within the considered range of the process factors the maximum hydrogen concentration of 42 dry vol. % and yield of 3.8 mol mol -1 of ethanol downstream of the ATR reactor can be achieved at S/C = 2.5, λ = 0.20-0.23, p = 0.4 bar, T ATRin = 230 °C, T ATR = 640 °C.

Chemical performance of multi-environment trials in lens (Lens culinaris M.).

PubMed

Karadavut, Ufuk; Palta, Cetin

2010-01-15

Genotype-environment (GE) interaction has been a major effect to determine stable lens (Lens culinaris (Medik.) Merr.) cultivars for chemical composition in Turkey. Utilization of the lines depends on their agronomic traits and stability of the chemical composition in diverse environments. The objectives of this study were: (i) to evaluate the influence of year and location on the chemical composition of lens genotypes; and (ii) to determine which cultivar is the most stable. Genotypes were evaluated over 3 years (2005, 2006 and 2007) at four locations in Turkey. Effects of year had the largest impact on all protein contents. GE interaction was analyzed by using linear regression techniques. Stability was estimated using the Eberhart and Russell method. 'Kişlik Kirmizi51' was the most stable cultivar for grain yield. The highest protein was obtained from 'Kişlik Kirmizi51' (4.6%) across environments. According to stability analysis, 'Firat 87' had the most stable chemical composition. This genotype had a regression coefficient (b(i) = 1) around unity, and deviations from regression values (delta(ij) = 0) around zero. Chemical composition was affected by year in this study. Temperature might have an effect on protein, oil, carbohydrate, fibre and ash. Firat 87 could be recommended for favourable environments. Copyright (c) 2009 Society of Chemical Industry.

Comparison of 3 Methods for Identifying Dietary Patterns Associated With Risk of Disease

PubMed Central

DiBello, Julia R.; Kraft, Peter; McGarvey, Stephen T.; Goldberg, Robert; Campos, Hannia

2008-01-01

Reduced rank regression and partial least-squares regression (PLS) are proposed alternatives to principal component analysis (PCA). Using all 3 methods, the authors derived dietary patterns in Costa Rican data collected on 3,574 cases and controls in 1994–2004 and related the resulting patterns to risk of first incident myocardial infarction. Four dietary patterns associated with myocardial infarction were identified. Factor 1, characterized by high intakes of lean chicken, vegetables, fruit, and polyunsaturated oil, was generated by all 3 dietary pattern methods and was associated with a significantly decreased adjusted risk of myocardial infarction (28%–46%, depending on the method used). PCA and PLS also each yielded a pattern associated with a significantly decreased risk of myocardial infarction (31% and 23%, respectively); this pattern was characterized by moderate intake of alcohol and polyunsaturated oil and low intake of high-fat dairy products. The fourth factor derived from PCA was significantly associated with a 38% increased risk of myocardial infarction and was characterized by high intakes of coffee and palm oil. Contrary to previous studies, the authors found PCA and PLS to produce more patterns associated with cardiovascular disease than reduced rank regression. The most effective method for deriving dietary patterns related to disease may vary depending on the study goals. PMID:18945692

Inner and outer coronary vessel wall segmentation from CCTA using an active contour model with machine learning-based 3D voxel context-aware image force

NASA Astrophysics Data System (ADS)

Sivalingam, Udhayaraj; Wels, Michael; Rempfler, Markus; Grosskopf, Stefan; Suehling, Michael; Menze, Bjoern H.

2016-03-01

In this paper, we present a fully automated approach to coronary vessel segmentation, which involves calcification or soft plaque delineation in addition to accurate lumen delineation, from 3D Cardiac Computed Tomography Angiography data. Adequately virtualizing the coronary lumen plays a crucial role for simulating blood ow by means of fluid dynamics while additionally identifying the outer vessel wall in the case of arteriosclerosis is a prerequisite for further plaque compartment analysis. Our method is a hybrid approach complementing Active Contour Model-based segmentation with an external image force that relies on a Random Forest Regression model generated off-line. The regression model provides a strong estimate of the distance to the true vessel surface for every surface candidate point taking into account 3D wavelet-encoded contextual image features, which are aligned with the current surface hypothesis. The associated external image force is integrated in the objective function of the active contour model, such that the overall segmentation approach benefits from the advantages associated with snakes and from the ones associated with machine learning-based regression alike. This yields an integrated approach achieving competitive results on a publicly available benchmark data collection (Rotterdam segmentation challenge).

Statistically extracted fundamental watershed variables for estimating the loads of total nitrogen in small streams

USGS Publications Warehouse

Kronholm, Scott C.; Capel, Paul D.; Terziotti, Silvia

2016-01-01

Accurate estimation of total nitrogen loads is essential for evaluating conditions in the aquatic environment. Extrapolation of estimates beyond measured streams will greatly expand our understanding of total nitrogen loading to streams. Recursive partitioning and random forest regression were used to assess 85 geospatial, environmental, and watershed variables across 636 small (<585 km2) watersheds to determine which variables are fundamentally important to the estimation of annual loads of total nitrogen. Initial analysis led to the splitting of watersheds into three groups based on predominant land use (agricultural, developed, and undeveloped). Nitrogen application, agricultural and developed land area, and impervious or developed land in the 100-m stream buffer were commonly extracted variables by both recursive partitioning and random forest regression. A series of multiple linear regression equations utilizing the extracted variables were created and applied to the watersheds. As few as three variables explained as much as 76 % of the variability in total nitrogen loads for watersheds with predominantly agricultural land use. Catchment-scale national maps were generated to visualize the total nitrogen loads and yields across the USA. The estimates provided by these models can inform water managers and help identify areas where more in-depth monitoring may be beneficial.

Prevalence and risk factors associated with tardive dyskinesia among Indian patients with schizophrenia.

PubMed

Achalia, Rashmin M; Chaturvedi, Santosh K; Desai, Geetha; Rao, Girish N; Prakash, Om

2014-06-01

Tardive dyskinesia (TD) is one of the most distressing side effects of antipsychotic treatment. As prevalence studies of TD in Asian population are scarce, a cross-sectional study was performed to assess the frequency of TD in Indian patients with schizophrenia and risk factors of TD. Cross-sectional study of 160 Indian patients fulfilling the DSM-IV TR criteria for schizophrenia and who received antipsychotics for at least one year, were examined with two validated scales for TD. Logistic regression analyses were used to examine the relationship between TD and clinical risk factors. The frequency of probable TD in the total sample was 26.4%. The logistic regression yielded significant odds ratios between TD and age, intermittent treatment, and total cumulative antipsychotic dose. The difference of TD between SGA and FGA disappeared after adjusting for important co-variables in regression analysis. Indian patients with schizophrenia and long-term antipsychotic treatment have a high risk of TD, and TD is associated with older age, intermittent antipsychotic treatment, and a high total cumulative antipsychotic dose. Our study findings suggest that there is no significant difference between SGAs with regards to the risk of causing TD as compared to FGAs. Copyright © 2014 Elsevier B.V. All rights reserved.

Random regression models using Legendre polynomials or linear splines for test-day milk yield of dairy Gyr (Bos indicus) cattle.

PubMed

Pereira, R J; Bignardi, A B; El Faro, L; Verneque, R S; Vercesi Filho, A E; Albuquerque, L G

2013-01-01

Studies investigating the use of random regression models for genetic evaluation of milk production in Zebu cattle are scarce. In this study, 59,744 test-day milk yield records from 7,810 first lactations of purebred dairy Gyr (Bos indicus) and crossbred (dairy Gyr × Holstein) cows were used to compare random regression models in which additive genetic and permanent environmental effects were modeled using orthogonal Legendre polynomials or linear spline functions. Residual variances were modeled considering 1, 5, or 10 classes of days in milk. Five classes fitted the changes in residual variances over the lactation adequately and were used for model comparison. The model that fitted linear spline functions with 6 knots provided the lowest sum of residual variances across lactation. On the other hand, according to the deviance information criterion (DIC) and bayesian information criterion (BIC), a model using third-order and fourth-order Legendre polynomials for additive genetic and permanent environmental effects, respectively, provided the best fit. However, the high rank correlation (0.998) between this model and that applying third-order Legendre polynomials for additive genetic and permanent environmental effects, indicates that, in practice, the same bulls would be selected by both models. The last model, which is less parameterized, is a parsimonious option for fitting dairy Gyr breed test-day milk yield records. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Thermochemical hydrolysis of macroalgae Ulva for biorefinery: Taguchi robust design method

PubMed Central

Jiang, Rui; Linzon, Yoav; Vitkin, Edward; Yakhini, Zohar; Chudnovsky, Alexandra; Golberg, Alexander

2016-01-01

Understanding the impact of all process parameters on the efficiency of biomass hydrolysis and on the final yield of products is critical to biorefinery design. Using Taguchi orthogonal arrays experimental design and Partial Least Square Regression, we investigated the impact of change and the comparative significance of thermochemical process temperature, treatment time, %Acid and %Solid load on carbohydrates release from green macroalgae from Ulva genus, a promising biorefinery feedstock. The average density of hydrolysate was determined using a new microelectromechanical optical resonator mass sensor. In addition, using Flux Balance Analysis techniques, we compared the potential fermentation yields of these hydrolysate products using metabolic models of Escherichia coli, Saccharomyces cerevisiae wild type, Saccharomyces cerevisiae RN1016 with xylose isomerase and Clostridium acetobutylicum. We found that %Acid plays the most significant role and treatment time the least significant role in affecting the monosaccharaides released from Ulva biomass. We also found that within the tested range of parameters, hydrolysis with 121 °C, 30 min 2% Acid, 15% Solids could lead to the highest yields of conversion: 54.134–57.500 gr ethanol kg−1 Ulva dry weight by S. cerevisiae RN1016 with xylose isomerase. Our results support optimized marine algae utilization process design and will enable smart energy harvesting by thermochemical hydrolysis. PMID:27291594

A Pilot Investigation of the Relationship between Climate Variability and Milk Compounds under the Bootstrap Technique

PubMed Central

Marami Milani, Mohammad Reza; Hense, Andreas; Rahmani, Elham; Ploeger, Angelika

2015-01-01

This study analyzes the linear relationship between climate variables and milk components in Iran by applying bootstrapping to include and assess the uncertainty. The climate parameters, Temperature Humidity Index (THI) and Equivalent Temperature Index (ETI) are computed from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis (2002–2010). Milk data for fat, protein (measured on fresh matter bases), and milk yield are taken from 936,227 milk records for the same period, using cows fed by natural pasture from April to September. Confidence intervals for the regression model are calculated using the bootstrap technique. This method is applied to the original times series, generating statistically equivalent surrogate samples. As a result, despite the short time data and the related uncertainties, an interesting behavior of the relationships between milk compound and the climate parameters is visible. During spring only, a weak dependency of milk yield and climate variations is obvious, while fat and protein concentrations show reasonable correlations. In summer, milk yield shows a similar level of relationship with ETI, but not with temperature and THI. We suggest this methodology for studies in the field of the impacts of climate change and agriculture, also environment and food with short-term data. PMID:28231215

Thermochemical hydrolysis of macroalgae Ulva for biorefinery: Taguchi robust design method

NASA Astrophysics Data System (ADS)

Jiang, Rui; Linzon, Yoav; Vitkin, Edward; Yakhini, Zohar; Chudnovsky, Alexandra; Golberg, Alexander

2016-06-01

Understanding the impact of all process parameters on the efficiency of biomass hydrolysis and on the final yield of products is critical to biorefinery design. Using Taguchi orthogonal arrays experimental design and Partial Least Square Regression, we investigated the impact of change and the comparative significance of thermochemical process temperature, treatment time, %Acid and %Solid load on carbohydrates release from green macroalgae from Ulva genus, a promising biorefinery feedstock. The average density of hydrolysate was determined using a new microelectromechanical optical resonator mass sensor. In addition, using Flux Balance Analysis techniques, we compared the potential fermentation yields of these hydrolysate products using metabolic models of Escherichia coli, Saccharomyces cerevisiae wild type, Saccharomyces cerevisiae RN1016 with xylose isomerase and Clostridium acetobutylicum. We found that %Acid plays the most significant role and treatment time the least significant role in affecting the monosaccharaides released from Ulva biomass. We also found that within the tested range of parameters, hydrolysis with 121 °C, 30 min 2% Acid, 15% Solids could lead to the highest yields of conversion: 54.134-57.500 gr ethanol kg-1 Ulva dry weight by S. cerevisiae RN1016 with xylose isomerase. Our results support optimized marine algae utilization process design and will enable smart energy harvesting by thermochemical hydrolysis.

Performance and carcass yield of crossbred dairy steers fed diets with different levels of concentrate.

PubMed

da Silva, Gabriel Santana; Chaves Véras, Antônia Sherlanea; de Andrade Ferreira, Marcelo; Moreira Dutra, Wilson; Menezes Wanderley Neves, Maria Luciana; Oliveira Souza, Evaristo Jorge; Ramos de Carvalho, Francisco Fernando; de Lima, Dorgival Morais

2015-10-01

The objective of this study was to evaluate the influence of diets with increasing concentrate levels (170, 340, 510 and 680 g/kg of total dry matter) on dry matter intake, digestibility, performance and carcass characteristics of 25 Holstein-Zebu crossbred dairy steers in a feedlot. A completely randomized design was used, and data were submitted to analysis of variance and regression. The dry matter intake and digestibility coefficients of all nutrients increased linearly. The total weight gain and average daily gain added 1.16 kg and 9.90 g, respectively, for each 10 g/kg increase in concentrate. The empty body weight, hot carcass weight and cold carcass weight responded linearly to increasing concentrate. The hot carcass yield and cold carcass yield, gains in empty body weight and carcass gain were also influenced, as were the efficiencies of carcass deposition and carcass deposition rate. It is concluded that increasing concentrate levels in feedlot diets increase the intake and digestibility of dry matter and other nutrients, improving the feed efficiency, performance and physical characteristics of the carcass. Furthermore and of importance concerning the climate change debate, evidence from the literature indicates that enteric methane production would be reduced with increasing concentrate levels such as those used.

Quasi-biennial (QBO), annual (AO), and semi-annual oscillation (SAO) in stratospheric SCIAMACHY O3, NO2, and BrO limb data using a multivariate least squares approach

NASA Astrophysics Data System (ADS)

Dikty, Sebastian; von Savigny, Christian; Sinnhuber, Bjoern-Martin; Rozanov, Alexej; Weber, Mark; Burrows, John P.

We use SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartog-raphY) ozone, nitrogen dioxide and bromine oxide profiles (20-50 km altitude, 2003-2008) to quantify the amplitudes of QBO, AO, and SAO signals with the help of a simple multivariate regression model. The analysis is being carried out with SCIAMACHY data covering all lat-itudes with the exception of polar nights, when measurements are not available. The overall global yield is approximately 10,000 profiles per month, which are binned into 10-steps with one zonal mean profile being calculated per day and per latitude bin.

Relationships among perceived career support, affective commitment, and work engagement.

PubMed

Poon, June M L

2013-01-01

This study sought to test the predictive effects of perceived career support and affective commitment on work engagement. It was hypothesized that perceived career support would relate positively to work engagement and this relationship would be transmitted through affective commitment. Survey data were collected from 115 full-time employees enrolled as part-time graduate students in a large public university in Malaysia. Multiple regression analysis yielded results indicating that the relationship between perceived career support and work engagement was mediated by affective commitment. This finding suggests that employers can promote employee work engagement by ensuring employees perceive their organization to be supportive of their career and increasing employees' level of affective commitment.

Solvent-free synthesis, spectral correlations and antimicrobial activities of some aryl E 2-propen-1-ones

NASA Astrophysics Data System (ADS)

Sathiyamoorthi, K.; Mala, V.; Sakthinathan, S. P.; Kamalakkannan, D.; Suresh, R.; Vanangamudi, G.; Thirunarayanan, G.

2013-08-01

Totally 38 aryl E 2-propen-1-ones including nine substituted styryl 4-iodophenyl ketones have been synthesised using solvent-free SiO2-H3PO4 catalyzed Aldol condensation between respective methyl ketones and substituted benzaldehydes under microwave irradiation. The yields of the ketones are more than 80%. The synthesised chalcones were characterized by their analytical, physical and spectroscopic data. The spectral frequencies of synthesised substituted styryl 4-iodophenyl ketones have been correlated with Hammett substituent constants, F and R parameters using single and multi-linear regression analysis. The antimicrobial activities of 4-iodophenyl chalcones have been studied using Bauer-Kirby method.

Synthesis, spectral studies, antimicrobial, antioxidant and insect antifeedant activities of some 9 H-fluorene-2-yl keto-oxiranes

NASA Astrophysics Data System (ADS)

Thirunarayanan, G.; Vanangamudi, G.

2011-10-01

Thirteen ee (α S, β R) 9 H-fluorene-2-yl keto-oxiranes (2-(9 H)-fluorene-4-yl[3-(substituted phenyl)oxiran-2-yl]methanones) have been synthesized by phase transfer catalysed epoxidation of 9 H-fluorene-2-yl chalcones. The yields of oxiranes are more than 95%. The synthesized oxiranes have been characterized by IR, 1H, 13C and GC-MS spectral data. The spectral data are correlated with Hammett substituent constants and Swain-Lupton parameters. From the regression analysis, the effect of substituents on the group frequencies has been predicted. The antimicrobial, antioxidant and insect antifeedant activities of all the synthesized oxiranes have been studied.

Models of subjective response to in-flight motion data

NASA Technical Reports Server (NTRS)

Rudrapatna, A. N.; Jacobson, I. D.

1973-01-01

Mathematical relationships between subjective comfort and environmental variables in an air transportation system are investigated. As a first step in model building, only the motion variables are incorporated and sensitivities are obtained using stepwise multiple regression analysis. The data for these models have been collected from commercial passenger flights. Two models are considered. In the first, subjective comfort is assumed to depend on rms values of the six-degrees-of-freedom accelerations. The second assumes a Rustenburg type human response function in obtaining frequency weighted rms accelerations, which are used in a linear model. The form of the human response function is examined and the results yield a human response weighting function for different degrees of freedom.

Longitudinal flying qualities criteria for single-pilot instrument flight operations

NASA Technical Reports Server (NTRS)

Stengel, R. F.; Bar-Gill, A.

1983-01-01

Modern estimation and control theory, flight testing, and statistical analysis were used to deduce flying qualities criteria for General Aviation Single Pilot Instrument Flight Rule (SPIFR) operations. The principal concern is that unsatisfactory aircraft dynamic response combined with high navigation/communication workload can produce problems of safety and efficiency. To alleviate these problems. The relative importance of these factors must be determined. This objective was achieved by flying SPIFR tasks with different aircraft dynamic configurations and assessing the effects of such variations under these conditions. The experimental results yielded quantitative indicators of pilot's performance and workload, and for each of them, multivariate regression was applied to evaluate several candidate flying qualities criteria.

Predicting RNA Duplex Dimerization Free-Energy Changes upon Mutations Using Molecular Dynamics Simulations.

PubMed

Sakuraba, Shun; Asai, Kiyoshi; Kameda, Tomoshi

2015-11-05

The dimerization free energies of RNA-RNA duplexes are fundamental values that represent the structural stability of RNA complexes. We report a comparative analysis of RNA-RNA duplex dimerization free-energy changes upon mutations, estimated from a molecular dynamics simulation and experiments. A linear regression for nine pairs of double-stranded RNA sequences, six base pairs each, yielded a mean absolute deviation of 0.55 kcal/mol and an R(2) value of 0.97, indicating quantitative agreement between simulations and experimental data. The observed accuracy indicates that the molecular dynamics simulation with the current molecular force field is capable of estimating the thermodynamic properties of RNA molecules.

Assessment of energy crops alternative to maize for biogas production in the Greater Region.

PubMed

Mayer, Frédéric; Gerin, Patrick A; Noo, Anaïs; Lemaigre, Sébastien; Stilmant, Didier; Schmit, Thomas; Leclech, Nathael; Ruelle, Luc; Gennen, Jerome; von Francken-Welz, Herbert; Foucart, Guy; Flammang, Jos; Weyland, Marc; Delfosse, Philippe

2014-08-01

The biomethane yield of various energy crops, selected among potential alternatives to maize in the Greater Region, was assessed. The biomass yield, the volatile solids (VS) content and the biochemical methane potential (BMP) were measured to calculate the biomethane yield per hectare of all plant species. For all species, the dry matter biomass yield and the VS content were the main factors that influence, respectively, the biomethane yield and the BMP. Both values were predicted with good accuracy by linear regressions using the biomass yield and the VS as independent variable. The perennial crop miscanthus appeared to be the most promising alternative to maize when harvested as green matter in autumn and ensiled. Miscanthus reached a biomethane yield of 5.5 ± 1 × 10(3)m(3)ha(-1) during the second year after the establishment, as compared to 5.3 ± 1 × 10(3)m(3)ha(-1) for maize under similar crop conditions. Copyright © 2014. Published by Elsevier Ltd.

Relationship between dairy cow genetic merit and profit on commercial spring calving dairy farms.

PubMed

Ramsbottom, G; Cromie, A R; Horan, B; Berry, D P

2012-07-01

Because not all animal factors influencing profitability can be included in total merit breeding indices for profitability, the association between animal total merit index and true profitability, taking cognisance of all factors associated with costs and revenues, is generally not known. One method to estimate such associations is at the herd level, associating herd average genetic merit with herd profitability. The objective of this study was to primarily relate herd average genetic merit for a range of traits, including the Irish total merit index, with indicators of performance, including profitability, using correlation and multiple regression analyses. Physical, genetic and financial performance data from 1131 Irish seasonal calving pasture-based dairy farms were available following edits; data on some herds were available for more than 1 year of the 3-year study period (2007 to 2009). Herd average economic breeding index (EBI) was associated with reduced herd average phenotypic milk yield but with greater milk composition, resulting in higher milk prices. Moderate positive correlations (0.26 to 0.61) existed between genetic merit for an individual trait and average herd performance for that trait (e.g. genetic merit for milk yield and average per cow milk yield). Following adjustment for year, stocking rate, herd size and quantity of purchased feed in the multiple regression analysis, average herd EBI was positively and linearly associated with net margin per cow and per litre as well as gross revenue output per cow and per litre. The change in net margin per cow per unit change in the total merit index was €1.94 (s.e. = 0.42), which was not different from the expectation of €2. This study, based on a large data set of commercial herds with accurate information on profitability and genetic merit, confirms that, after accounting for confounding factors, the change in herd profitability per unit change in herd genetic merit for the total merit index is within expectations.

Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics and comorbidities

PubMed Central

Gíslason, Magnús; Sigurðsson, Sigurður; Guðnason, Vilmundur; Harris, Tamara; Carraro, Ugo; Gargiulo, Paolo

2018-01-01

Sarcopenic muscular degeneration has been consistently identified as an independent risk factor for mortality in aging populations. Recent investigations have realized the quantitative potential of computed tomography (CT) image analysis to describe skeletal muscle volume and composition; however, the optimum approach to assessing these data remains debated. Current literature reports average Hounsfield unit (HU) values and/or segmented soft tissue cross-sectional areas to investigate muscle quality. However, standardized methods for CT analyses and their utility as a comorbidity index remain undefined, and no existing studies compare these methods to the assessment of entire radiodensitometric distributions. The primary aim of this study was to present a comparison of nonlinear trimodal regression analysis (NTRA) parameters of entire radiodensitometric muscle distributions against extant CT metrics and their correlation with lower extremity function (LEF) biometrics (normal/fast gait speed, timed up-and-go, and isometric leg strength) and biochemical and nutritional parameters, such as total solubilized cholesterol (SCHOL) and body mass index (BMI). Data were obtained from 3,162 subjects, aged 66–96 years, from the population-based AGES-Reykjavik Study. 1-D k-means clustering was employed to discretize each biometric and comorbidity dataset into twelve subpopulations, in accordance with Sturges’ Formula for Class Selection. Dataset linear regressions were performed against eleven NTRA distribution parameters and standard CT analyses (fat/muscle cross-sectional area and average HU value). Parameters from NTRA and CT standards were analogously assembled by age and sex. Analysis of specific NTRA parameters with standard CT results showed linear correlation coefficients greater than 0.85, but multiple regression analysis of correlative NTRA parameters yielded a correlation coefficient of 0.99 (P<0.005). These results highlight the specificities of each muscle quality metric to LEF biometrics, SCHOL, and BMI, and particularly highlight the value of the connective tissue regime in this regard. PMID:29513690

Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics and comorbidities.

PubMed

Edmunds, Kyle; Gíslason, Magnús; Sigurðsson, Sigurður; Guðnason, Vilmundur; Harris, Tamara; Carraro, Ugo; Gargiulo, Paolo

2018-01-01

Sarcopenic muscular degeneration has been consistently identified as an independent risk factor for mortality in aging populations. Recent investigations have realized the quantitative potential of computed tomography (CT) image analysis to describe skeletal muscle volume and composition; however, the optimum approach to assessing these data remains debated. Current literature reports average Hounsfield unit (HU) values and/or segmented soft tissue cross-sectional areas to investigate muscle quality. However, standardized methods for CT analyses and their utility as a comorbidity index remain undefined, and no existing studies compare these methods to the assessment of entire radiodensitometric distributions. The primary aim of this study was to present a comparison of nonlinear trimodal regression analysis (NTRA) parameters of entire radiodensitometric muscle distributions against extant CT metrics and their correlation with lower extremity function (LEF) biometrics (normal/fast gait speed, timed up-and-go, and isometric leg strength) and biochemical and nutritional parameters, such as total solubilized cholesterol (SCHOL) and body mass index (BMI). Data were obtained from 3,162 subjects, aged 66-96 years, from the population-based AGES-Reykjavik Study. 1-D k-means clustering was employed to discretize each biometric and comorbidity dataset into twelve subpopulations, in accordance with Sturges' Formula for Class Selection. Dataset linear regressions were performed against eleven NTRA distribution parameters and standard CT analyses (fat/muscle cross-sectional area and average HU value). Parameters from NTRA and CT standards were analogously assembled by age and sex. Analysis of specific NTRA parameters with standard CT results showed linear correlation coefficients greater than 0.85, but multiple regression analysis of correlative NTRA parameters yielded a correlation coefficient of 0.99 (P<0.005). These results highlight the specificities of each muscle quality metric to LEF biometrics, SCHOL, and BMI, and particularly highlight the value of the connective tissue regime in this regard.

Predictive capacity of sperm quality parameters and sperm subpopulations on field fertility after artificial insemination in sheep.

PubMed

Santolaria, P; Vicente-Fiel, S; Palacín, I; Fantova, E; Blasco, M E; Silvestre, M A; Yániz, J L

2015-12-01

This study was designed to evaluate the relevance of several sperm quality parameters and sperm population structure on the reproductive performance after cervical artificial insemination (AI) in sheep. One hundred and thirty-nine ejaculates from 56 adult rams were collected using an artificial vagina, processed for sperm quality assessment and used to perform 1319 AI. Analyses of sperm motility by computer-assisted sperm analysis (CASA), sperm nuclear morphometry by computer-assisted sperm morphometry analysis (CASMA), membrane integrity by acridine orange-propidium iodide combination and sperm DNA fragmentation using the sperm chromatin dispersion test (SCD) were performed. Clustering procedures using the sperm kinematic and morphometric data resulted in the classification of spermatozoa into three kinematic and three morphometric sperm subpopulations. Logistic regression procedures were used, including fertility at AI as the dependent variable (measured by lambing, 0 or 1) and farm, year, month of AI, female parity, female lambing-treatment interval, ram, AI technician and sperm quality parameters (including sperm subpopulations) as independent factors. Sperm quality variables remaining in the logistic regression model were viability and VCL. Fertility increased for each one-unit increase in viability (by a factor of 1.01) and in VCL (by a factor of 1.02). Multiple linear regression analyses were also performed to analyze the factors possibly influencing ejaculate fertility (N=139). The analysis yielded a significant (P<0.05) relationship between sperm viability and ejaculate fertility. The discriminant ability of the different semen variables to predict field fertility was analyzed using receiver operating characteristic (ROC) curve analysis. Sperm viability and VCL showed significant, albeit limited, predictive capacity on field fertility (0.57 and 0.54 Area Under Curve, respectively). The distribution of spermatozoa in the different subpopulations was not related to fertility. Copyright © 2015 Elsevier B.V. All rights reserved.

Estimated loads and yields of suspended soils and water-quality constituents in Kentucky streams

USGS Publications Warehouse

Crain, Angela S.

2001-01-01

Loads and yields of suspended solids, nutrients, major ions, trace elements, organic carbon, fecal coliform, dissolved oxygen, and alkalinity were estimated for 22 streams in 11 major river basins in Kentucky. Mean daily discharge was estimated at ungaged stations or stations with incomplete discharge records using drainage-area ratio, regression analysis, or a combination of the two techniques. Streamflow was partitioned into total and base flow and used to estimate loads and yields for suspended solids and water-quality constituents by use of the ESTIMATOR and FLUX computer programs. The relative magnitude of constituent transport to streams from groundand surface-water sources was determined for the 22 stations. Nutrient and suspended solids yields for drainage basins with relatively homogenous land use were used to estimate the total-flow and base-flow yields of nutrient and suspended solids for forested, agricultural, and urban land. Yields of nutrients?nitrite plus nitrate, ammonia plus organic nitrogen, and total phosphorus?in forested drainage basins were generally less than 1 ton per square mile per year ((ton/mi2)/yr) and were generally less than 2 (ton/mi2)/yr in agricultural drainage basins. The smallest total-flow yields for nitrogen (nitrite plus nitrate) was estimated at Levisa Fork at Paintsville in which 95 percent of the land is forested. This site also had one of the smallest total-flow yields for ammonia plus organic nitrogen. In general, nutrient yields from forested lands were lower than those from urban and agricultural land. Some of the largest estimated total-flow yields of nutrients among agricultural basins were for streams in the Licking River Basin, the North Fork Licking River near Milford, and the South Fork Licking River at Cynthiana. Agricultural land constitutes greater than 75 percent of the drainage area in these two basins. Possible sources of nutrients discharging into the Licking River are farm and residential fertilizers. Estimated base-flow yields of suspended solids and nutrients at several basins in the larger Green River and Lower Cumberland River Basins were about half of their estimated total-flow yields. The karst terrain in these basins makes the ground water highly susceptible to contamination, especially if a confining unit is thin or absent.

Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations

NASA Astrophysics Data System (ADS)

Zhang, Xiaoyang; Zhang, Qingyuan

2016-04-01

Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data have been extensively applied for crop yield prediction because of their daily temporal resolution and a global coverage. This study investigated global crop yield using daily two band Enhanced Vegetation Index (EVI2) derived from AVHRR (1981-1999) and MODIS (2000-2013) observations at a spatial resolution of 0.05° (∼5 km). Specifically, EVI2 temporal trajectory of crop growth was simulated using a hybrid piecewise logistic model (HPLM) for individual pixels, which was used to detect crop phenological metrics. The derived crop phenology was then applied to calculate crop greenness defined as EVI2 amplitude and EVI2 integration during annual crop growing seasons, which was further aggregated for croplands in each country, respectively. The interannual variations in EVI2 amplitude and EVI2 integration were combined to correlate to the variation in cereal yield from 1982-2012 for individual countries using a stepwise regression model, respectively. The results show that the confidence level of the established regression models was higher than 90% (P value < 0.1) in most countries in the northern hemisphere although it was relatively poor in the southern hemisphere (mainly in Africa). The error in the yield predication was relatively smaller in America, Europe and East Asia than that in Africa. In the 10 countries with largest cereal production across the world, the prediction error was less than 9% during past three decades. This suggests that crop phenology-controlled greenness from coarse resolution satellite data has the capability of predicting national crop yield across the world, which could provide timely and reliable crop information for global agricultural trade and policymakers.

Quantitative genetic studies relating to the domestication of meadowfoam (Limnanthes spp. ) as a new oil crop

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

Abuelgasim, E.H.

1982-01-01

Meadowfoam (Limnanthes spp.) has recently aroused interest as a promising new source of industrial oil that is a good substitute for sperm whale oil. Fourteen natural populations of Limnanthes, involving two species and five taxonomic varieties, were studied for 12 quantitative characters, during two seasons. The objectives were: to evaluate the potential use of the populations for domestication purposes, study the interrelationships among characters, and to obtain heritability estimates for some of the important characters. A great deal of variability among the populations was observed for all the characters studied. Although no single taxon or population had all the desiredmore » characters combined, nevertheless, three populations of L. douglasii var. nivea and one population of L. douglasii var. sulphurea, gave consistently higher values for seed yield, seed number, plant dry weight, harvest index and fertility index, than L. alba or L. douglasii var. douglasii populations. The nivea group was also the earliest to flower, however, this group together with var. sulphurea population, suffer from severe seed shattering at maturity. In both seasons, seed yield was positively and highly significantly correlated with all of the other characters with the exception of days to the first flower and days to flower, which showed a highly significant negative correlation with seed yield. Stepwise multiple regression and path-coefficient analyses showed that seed number was the most important character in contribution to variation in seed yield; it accounted for over 85% of the variation in seed yield. The four characters: seed number, 100-seed weight, plant dry weight and harvest index, in that order, were the most important characters to be included in a multiple regression equation for determination of seed yield.« less

Nutrient mass balance and trends, Mobile River Basin, Alabama, Georgia, and Mississippi

USGS Publications Warehouse

Harned, D.A.; Atkins, J.B.; Harvill, J.S.

2004-01-01

A nutrient mass balance - accounting for nutrient inputs from atmospheric deposition, fertilizer, crop nitrogen fixation, and point source effluents; and nutrient outputs, including crop harvest and storage - was calculated for 18 subbasins in the Mobile River Basin, and trends (1970 to 1997) were evaluated as part of the U.S. Geological Survey National Water Quality Assessment (NAWQA) Program. Agricultural nonpoint nitrogen and phosphorus sources and urban nonpoint nitrogen sources are the most important factors associated with nutrients in this system. More than 30 percent of nitrogen yield in two basins and phosphorus yield in eight basins can be attributed to urban point source nutrient inputs. The total nitrogen yield (1.3 tons per square mile per year) for the Tombigbee River, which drains a greater percentage of agricultural (row crop) land use, was larger than the total nitrogen yield (0.99 tons per square mile per year) for the Alabama River. Decreasing trends of total nitrogen concentrations in the Tombigbee and Alabama Rivers indicate that a reduction occurred from 1975 to 1997 in the nitrogen contributions to Mobile Bay from the Mobile River. Nitrogen concentrations also decreased (1980 to 1995) in the Black Warrior River, one of the major tributaries to the Tombigbee River. Total phosphorus concentrations increased from 1970 to 1996 at three urban influenced sites on the Etowah River in Georgia. Multiple regression analysis indicates a distinct association between water quality in the streams of the Mobile River drainage basin and agricultural activities in the basin.

Olive Actual "on Year" Yield Forecast Tool Based on the Tree Canopy Geometry Using UAS Imagery.

PubMed

Sola-Guirado, Rafael R; Castillo-Ruiz, Francisco J; Jiménez-Jiménez, Francisco; Blanco-Roldan, Gregorio L; Castro-Garcia, Sergio; Gil-Ribes, Jesus A

2017-07-30

Olive has a notable importance in countries of Mediterranean basin and its profitability depends on several factors such as actual yield, production cost or product price. Actual "on year" Yield (AY) is production (kg tree -1 ) in "on years", and this research attempts to relate it with geometrical parameters of the tree canopy. Regression equation to forecast AY based on manual canopy volume was determined based on data acquired from different orchard categories and cultivars during different harvesting seasons in southern Spain. Orthoimages were acquired with unmanned aerial systems (UAS) imagery calculating individual crown for relating to canopy volume and AY. Yield levels did not vary between orchard categories; however, it did between irrigated orchards (7000-17,000 kg ha -1 ) and rainfed ones (4000-7000 kg ha -1 ). After that, manual canopy volume was related with the individual crown area of trees that were calculated by orthoimages acquired with UAS imagery. Finally, AY was forecasted using both manual canopy volume and individual tree crown area as main factors for olive productivity. AY forecast only by using individual crown area made it possible to get a simple and cheap forecast tool for a wide range of olive orchards. Finally, the acquired information was introduced in a thematic map describing spatial AY variability obtained from orthoimage analysis that may be a powerful tool for farmers, insurance systems, market forecasts or to detect agronomical problems.

Bioconversion of hybrid poplar to ethanol and co-products using an organosolv fractionation process: optimization of process yields.

PubMed

Pan, Xuejun; Gilkes, Neil; Kadla, John; Pye, Kendall; Saka, Shiro; Gregg, David; Ehara, Katsunobu; Xie, Dan; Lam, Dexter; Saddler, Jack

2006-08-05

An organosolv process involving extraction with hot aqueous ethanol has been evaluated for bioconversion of hybrid poplar to ethanol. The process resulted in fractionation of poplar chips into a cellulose-rich solids fraction, an ethanol organosolv lignin (EOL) fraction, and a water-soluble fraction containing hemicellulosic sugars, sugar breakdown products, degraded lignin, and other components. The influence of four independent process variables (temperature, time, catalyst dose, and ethanol concentration) on product yields was analyzed over a broad range using a small composite design and response surface methodology. Center point conditions for the composite design (180 degrees C, 60 min, 1.25% H(2)SO(4), and 60% ethanol), yielded a solids fraction containing approximately 88% of the cellulose present in the untreated poplar. Approximately 82% of the total cellulose in the untreated poplar was recovered as monomeric glucose after hydrolysis of the solids fraction for 24 h using a low enzyme loading (20 filter paper units of cellulase/g cellulose); approximately 85% was recovered after 48 h hydrolysis. Total recovery of xylose (soluble and insoluble) was equivalent to approximately 72% of the xylose present in untreated wood. Approximately 74% of the lignin in untreated wood was recovered as EOL. Other cooking conditions resulted in either similar or inferior product yields although the distribution of components between the various fractions differed markedly. Data analysis generated regression models that describe process responses for any combination of the four variables. (c) 2006 Wiley Periodicals, Inc.

Web-based decision support and visualization tools for water quality management in the Chesapeake Bay watershed

USGS Publications Warehouse

Mullinix, C.; Hearn, P.; Zhang, H.; Aguinaldo, J.

2009-01-01

Federal, State, and local water quality managers charged with restoring the Chesapeake Bay ecosystem require tools to maximize the impact of their limited resources. To address this need, the U.S. Geological Survey (USGS) and the Environmental Protection Agency's Chesapeake Bay Program (CBP) are developing a suite of Web-based tools called the Chesapeake Online Assessment Support Toolkit (COAST). The goal of COAST is to help CBP partners identify geographic areas where restoration activities would have the greatest effect, select the appropriate management strategies, and improve coordination and prioritization among partners. As part of the COAST suite of tools focused on environmental restoration, a water quality management visualization component called the Nutrient Yields Mapper (NYM) tool is being developed by USGS. The NYM tool is a web application that uses watershed yield estimates from USGS SPAtially Referenced Regressions On Watershed (SPARROW) attributes model (Schwarz et al., 2006) [6] to allow water quality managers to identify important sources of nitrogen and phosphorous within the Chesapeake Bay watershed. The NYM tool utilizes new open source technologies that have become popular in geospatial web development, including components such as OpenLayers and GeoServer. This paper presents examples of water quality data analysis based on nutrient type, source, yield, and area of interest using the NYM tool for the Chesapeake Bay watershed. In addition, we describe examples of map-based techniques for identifying high and low nutrient yield areas; web map engines; and data visualization and data management techniques.

Olive Actual “on Year” Yield Forecast Tool Based on the Tree Canopy Geometry Using UAS Imagery

PubMed Central

Sola-Guirado, Rafael R.; Castillo-Ruiz, Francisco J.; Jiménez-Jiménez, Francisco; Blanco-Roldan, Gregorio L.; Gil-Ribes, Jesus A.

2017-01-01

Olive has a notable importance in countries of Mediterranean basin and its profitability depends on several factors such as actual yield, production cost or product price. Actual “on year” Yield (AY) is production (kg tree−1) in “on years”, and this research attempts to relate it with geometrical parameters of the tree canopy. Regression equation to forecast AY based on manual canopy volume was determined based on data acquired from different orchard categories and cultivars during different harvesting seasons in southern Spain. Orthoimages were acquired with unmanned aerial systems (UAS) imagery calculating individual crown for relating to canopy volume and AY. Yield levels did not vary between orchard categories; however, it did between irrigated orchards (7000–17,000 kg ha−1) and rainfed ones (4000–7000 kg ha−1). After that, manual canopy volume was related with the individual crown area of trees that were calculated by orthoimages acquired with UAS imagery. Finally, AY was forecasted using both manual canopy volume and individual tree crown area as main factors for olive productivity. AY forecast only by using individual crown area made it possible to get a simple and cheap forecast tool for a wide range of olive orchards. Finally, the acquired information was introduced in a thematic map describing spatial AY variability obtained from orthoimage analysis that may be a powerful tool for farmers, insurance systems, market forecasts or to detect agronomical problems. PMID:28758945

Factors influencing platelet clumping during peripheral blood hematopoietic stem cell collection

PubMed Central

Mathur, Gagan; Bell, Sarah L.; Collins, Laura; Nelson, Gail A.; Knudson, C. Michael; Schlueter, Annette J.

2018-01-01

BACKGROUND Platelet clumping is a common occurrence during peripheral blood hematopoietic stem cell (HSC) collection using the Spectra Optia mononuclear cell (MNC) protocol. If clumping persists, it may prevent continuation of the collection and interfere with proper MNC separation. This study is the first to report the incidence of clumping, identify precollection factors associated with platelet clumping, and describe the degree to which platelet clumping interferes with HSC product yield. STUDY DESIGN AND METHODS In total, 258 HSC collections performed on 116 patients using the Optia MNC protocol were reviewed. Collections utilized heparin in anticoagulant citrate dextrose to facilitate large-volume leukapheresis. Linear and logistic regression models were utilized to determine which precollection factors were predictive of platelet clumping and whether clumping was associated with product yield or collection efficiency. RESULTS Platelet clumping was observed in 63% of collections. Multivariable analysis revealed that a lower white blood cell count was an independent predictor of clumping occurrence. Chemotherapy mobilization and a lower peripheral blood CD34+ cell count were predictors of the degree of clumping. Procedures with clumping had higher collection efficiency but lower blood volume processed on average, resulting in no difference in collection yields. Citrate toxicity did not correlate with clumping. CONCLUSION Although platelet clumping is a common technical problem seen during HSC collection, the total CD34+ cell-collection yields were not affected by clumping. WBC count, mobilization approach, and peripheral blood CD34+ cell count can help predict clumping and potentially drive interventions to proactively manage clumping. PMID:28150319

Plasma amino acid profile associated with fatty liver disease and co-occurrence of metabolic risk factors.

PubMed

Yamakado, Minoru; Tanaka, Takayuki; Nagao, Kenji; Imaizumi, Akira; Komatsu, Michiharu; Daimon, Takashi; Miyano, Hiroshi; Tani, Mizuki; Toda, Akiko; Yamamoto, Hiroshi; Horimoto, Katsuhisa; Ishizaka, Yuko

2017-11-03

Fatty liver disease (FLD) increases the risk of diabetes, cardiovascular disease, and steatohepatitis, which leads to fibrosis, cirrhosis, and hepatocellular carcinoma. Thus, the early detection of FLD is necessary. We aimed to find a quantitative and feasible model for discriminating the FLD, based on plasma free amino acid (PFAA) profiles. We constructed models of the relationship between PFAA levels in 2,000 generally healthy Japanese subjects and the diagnosis of FLD by abdominal ultrasound scan by multiple logistic regression analysis with variable selection. The performance of these models for FLD discrimination was validated using an independent data set of 2,160 subjects. The generated PFAA-based model was able to identify FLD patients. The area under the receiver operating characteristic curve for the model was 0.83, which was higher than those of other existing liver function-associated markers ranging from 0.53 to 0.80. The value of the linear discriminant in the model yielded the adjusted odds ratio (with 95% confidence intervals) for a 1 standard deviation increase of 2.63 (2.14-3.25) in the multiple logistic regression analysis with known liver function-associated covariates. Interestingly, the linear discriminant values were significantly associated with the progression of FLD, and patients with nonalcoholic steatohepatitis also exhibited higher values.

Random forest regression for magnetic resonance image synthesis.

PubMed

Jog, Amod; Carass, Aaron; Roy, Snehashis; Pham, Dzung L; Prince, Jerry L

2017-01-01

By choosing different pulse sequences and their parameters, magnetic resonance imaging (MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield inconsistencies with MRI acquisitions across datasets or scanning sessions that can in turn cause inconsistent automated image analysis. Although image synthesis of MR images has been shown to be helpful in addressing this problem, an inability to synthesize both T 2 -weighted brain images that include the skull and FLuid Attenuated Inversion Recovery (FLAIR) images has been reported. The method described herein, called REPLICA, addresses these limitations. REPLICA is a supervised random forest image synthesis approach that learns a nonlinear regression to predict intensities of alternate tissue contrasts given specific input tissue contrasts. Experimental results include direct image comparisons between synthetic and real images, results from image analysis tasks on both synthetic and real images, and comparison against other state-of-the-art image synthesis methods. REPLICA is computationally fast, and is shown to be comparable to other methods on tasks they are able to perform. Additionally REPLICA has the capability to synthesize both T 2 -weighted images of the full head and FLAIR images, and perform intensity standardization between different imaging datasets. Copyright © 2016 Elsevier B.V. All rights reserved.

Optimization of cultural conditions for conversion of glycerol to ethanol by Enterobacter aerogenes S012

PubMed Central

2013-01-01

The aim of this research is to optimize the cultural conditions for the conversion of glycerol to ethanol by Enterobacter aerogenes S012. Taguchi method was used to screen the cultural conditions based on their signal to noise ratio (SN). Temperature (°C), agitation speed (rpm) and time (h) were found to have the highest influence on both glycerol utilization and ethanol production by the organism while pH had the lowest. Full factorial design, statistical analysis, and regression model equation were used to optimize the selected cultural parameters for maximum ethanol production. The result showed that fermentation at 38°C and 200 rpm for 48 h would be ideal for the bacteria to produce maximum amount of ethanol from glycerol. At these optimum conditions, ethanol production, yield and productivity were 25.4 g/l, 0.53 g/l/h, and 1.12 mol/mol-glycerol, repectively. Ethanol production increased to 26.5 g/l while yield and productivity decreased to 1.04 mol/mol-glycerol and 0.37 g/l/h, respectively, after 72 h. Analysis of the fermentation products was performed using HPLC, while anaerobic condition was created by purging the fermentation vessel with nitrogen gas. PMID:23388539

A novel, rapid and automated conductometric method to evaluate surfactant-cells interactions by means of critical micellar concentration analysis.

PubMed

Tiecco, Matteo; Corte, Laura; Roscini, Luca; Colabella, Claudia; Germani, Raimondo; Cardinali, Gianluigi

2014-07-25

Conductometry is widely used to determine critical micellar concentration and micellar aggregates surface properties of amphiphiles. Current conductivity experiments of surfactant solutions are typically carried out by manual pipetting, yielding some tens reading points within a couple of hours. In order to study the properties of surfactant-cells interactions, each amphiphile must be tested in different conditions against several types of cells. This calls for complex experimental designs making the application of current methods seriously time consuming, especially because long experiments risk to determine alterations of cells, independently of the surfactant action. In this paper we present a novel, accurate and rapid automated procedure to obtain conductometric curves with several hundreds reading points within tens of minutes. The method was validated with surfactant solutions alone and in combination with Saccharomyces cerevisiae cells. An easy-to use R script, calculates conductometric parameters and their statistical significance with a graphic interface to visualize data and results. The validations showed that indeed the procedure works in the same manner with surfactant alone or in combination with cells, yielding around 1000 reading points within 20 min and with high accuracy, as determined by the regression analysis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Grid-cell-based crop water accounting for the famine early warning system

NASA Astrophysics Data System (ADS)

Verdin, James; Klaver, Robert

2002-06-01

Rainfall monitoring is a regular activity of food security analysts for sub-Saharan Africa due to the potentially disastrous impact of drought. Crop water accounting schemes are used to track rainfall timing and amounts relative to phenological requirements, to infer water limitation impacts on yield. Unfortunately, many rain gauge reports are available only after significant delays, and the gauge locations leave large gaps in coverage. As an alternative, a grid-cell-based formulation for the water requirement satisfaction index (WRSI) was tested for maize in Southern Africa. Grids of input variables were obtained from remote sensing estimates of rainfall, meteorological models, and digital soil maps. The spatial WRSI was computed for the 1996-97 and 1997-98 growing seasons. Maize yields were estimated by regression and compared with a limited number of reports from the field for the 1996-97 season in Zimbabwe. Agreement at a useful level (r = 0·80) was observed. This is comparable to results from traditional analysis with station data. The findings demonstrate the complementary role that remote sensing, modelling, and geospatial analysis can play in an era when field data collection in sub-Saharan Africa is suffering an unfortunate decline. Published in 2002 by John Wiley & Sons, Ltd.

Optimization of a Coupling Process for Insulin Degludec According to a Quality by Design (QbD) Paradigm.

PubMed

Nie, Lei; Hu, Mingming; Yan, Xu; Guo, Tingting; Wang, Haibin; Zhang, Sheng; Qu, Haibin

2018-05-03

This case study described a successful application of the quality by design (QbD) principles to a coupling process development of insulin degludec. Failure mode effects analysis (FMEA) risk analysis was first used to recognize critical process parameters (CPPs). Five CPPs, including coupling temperature (Temp), pH of desB30 solution (pH), reaction time (Time), desB30 concentration (Conc), and molar equivalent of ester per mole of desB30 insulin (MolE), were then investigated using a fractional factorial design. The curvature effect was found significant, indicating the requirement of second-order models. Afterwards, a central composite design was built with an augmented star and center points study. Regression models were developed for the CPPs to predict the purity and yield of predegludec using above experimental data. The R 2 and adjusted R 2 were higher than 96 and 93% for the two models respectively. The Q 2 values were more than 80% indicating a good predictive ability of models. MolE was found to be the most significant factor affecting both yield and purity of predegludec. Temp, pH, and Conc were also significant for predegludec purity, while Time appeared to remarkably influence the yield model. The multi-dimensional design space and normal operating region (NOR) with a robust setpoint were determined using a probability-based Monte-Carlo simulation method. The verified experimental results showed that the design space was reliable and effective. This study enriches the understanding of acetylation process and is instructional to other complicated operations in biopharmaceutical engineering.

Kinetic analysis on precursors for iturin A production from Bacillus amyloliquefaciens BPD1.

PubMed

Wu, Jiun-Yan; Liao, Jen-Hung; Shieh, Chwen-Jen; Hsieh, Feng-Chia; Liu, Yung-Chuan

2018-06-12

In this study, the precursor effect for iturin A production was quantitatively analyzed. A strain identified as Bacillus amyloliquefaciens BPD1 (Ba-BPD1) was selected due to its ability to produce iturin A. The enhancement of iturin A production in a submerged culture was tested using various additives, including palmitic acid, oils, and complex amino acids. Among these, complex amino acids triggered the highest yield at 559 mg/L. The respective amino acids that contribute to the structure of iturin A were used as precursors. In fact, it was found that the addition of l-proline, l-glutamine, l-asparagine and l-serine could improve iturin A yield in the defined medium. However, during the kinetic analysis, all the amino acids exhibited a lower saturation level than l-serine, which exhibited a high saturation level at 1.2% resulting in an iturin A yield of 914 mg/L. In contrast, a negative effect was observed following the addition of l-tyrosine. To analyze the kinetic behavior of l-serine, three kinetic models were adopted: the kinetic order equation, the Langmuir kinetic equation, and a modified logistic equation. The regression results showed that the modified logistic model was the best fit for the kinetic behavior of l-serine as the major precursor, which could be further referred to the biosynthesis pathway of iturin A. Among the proposed processes for iturin A production, this study achieved the highest iturin A levels as a result of the addition of precursors. Copyright © 2018 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Optimization of microwave-assisted extraction (MAE) of coriander phenolic antioxidants - response surface methodology approach.

PubMed

Zeković, Zoran; Vladić, Jelena; Vidović, Senka; Adamović, Dušan; Pavlić, Branimir

2016-10-01

Microwave-assisted extraction (MAE) of polyphenols from coriander seeds was optimized by simultaneous maximization of total phenolic (TP) and total flavonoid (TF) yields, as well as maximized antioxidant activity determined by 1,1-diphenyl-2-picrylhydrazyl and reducing power assays. Box-Behnken experimental design with response surface methodology (RSM) was used for optimization of MAE. Extraction time (X1 , 15-35 min), ethanol concentration (X2 , 50-90% w/w) and irradiation power (X3 , 400-800 W) were investigated as independent variables. Experimentally obtained values of investigated responses were fitted to a second-order polynomial model, and multiple regression analysis and analysis of variance were used to determine fitness of the model and optimal conditions. The optimal MAE conditions for simultaneous maximization of polyphenol yield and increased antioxidant activity were an extraction time of 19 min, an ethanol concentration of 63% and an irradiation power of 570 W, while predicted values of TP, TF, IC50 and EC50 at optimal MAE conditions were 311.23 mg gallic acid equivalent per 100 g dry weight (DW), 213.66 mg catechin equivalent per 100 g DW, 0.0315 mg mL(-1) and 0.1311 mg mL(-1) respectively. RSM was successfully used for multi-response optimization of coriander seed polyphenols. Comparison of optimized MAE with conventional extraction techniques confirmed that MAE provides significantly higher polyphenol yields and extracts with increased antioxidant activity. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Photosynthetic Response of Soybean to Microclimate in 26-Year-Old Tree-Based Intercropping Systems in Southern Ontario, Canada.

PubMed

Peng, Xiaobang; Thevathasan, Naresh V; Gordon, Andrew M; Mohammed, Idris; Gao, Pengxiang

2015-01-01

In order to study the effect of light competition and microclimatic modifications on the net assimilation (NA), growth and yield of soybean (Glycine max L.) as an understory crop, three 26-year-old soybean-tree (Acer saccharinum Marsh., Populus deltoides X nigra, Juglans nigra L.) intercropping systems were examined. Tree competition reduced photosynthetically active radiation (PAR) incident on soybeans and reduced net assimilation, growth and yield of soybean. Soil moisture of 20 cm depth close (< 3 m) to the tree rows was also reduced. Correlation analysis showed that NA and soil water content were highly correlated with growth and yield of soybean. When compared with the monoculture soybean system, the relative humidity (RH) of the poplar-soybean, silver maple-soybean, and black walnut-soybean intercropped systems was increased by 7.1%, 8.0% and 5.9%, soil water content was reduced by 37.8%, 26.3% and 30.9%, ambient temperature was reduced by 1.3°C, 1.4°C and 1.0°C, PAR was reduced by 53.6%, 57.9% and 39.9%, and air CO2 concentration was reduced by 3.7μmol·mol(-1), 4.2μmol·mol(-1) and 2.8μmol·mol(-1), respectively. Compared to the monoculture, the average NA of soybean in poplar, maple and walnut treatments was also reduced by 53.1%, 67.5% and 46.5%, respectively. Multivariate stepwise regression analysis showed that PAR, ambient temperature and CO2 concentration were the dominant factors influencing net photosynthetic rate.

Photosynthetic Response of Soybean to Microclimate in 26-Year-Old Tree-Based Intercropping Systems in Southern Ontario, Canada

PubMed Central

Peng, Xiaobang; Thevathasan, Naresh V.; Gordon, Andrew M.; Mohammed, Idris; Gao, Pengxiang

2015-01-01

In order to study the effect of light competition and microclimatic modifications on the net assimilation (NA), growth and yield of soybean (Glycine max L.) as an understory crop, three 26-year-old soybean-tree (Acer saccharinum Marsh., Populus deltoides X nigra, Juglans nigra L.) intercropping systems were examined. Tree competition reduced photosynthetically active radiation (PAR) incident on soybeans and reduced net assimilation, growth and yield of soybean. Soil moisture of 20 cm depth close (< 3 m) to the tree rows was also reduced. Correlation analysis showed that NA and soil water content were highly correlated with growth and yield of soybean. When compared with the monoculture soybean system, the relative humidity (RH) of the poplar-soybean, silver maple-soybean, and black walnut-soybean intercropped systems was increased by 7.1%, 8.0% and 5.9%, soil water content was reduced by 37.8%, 26.3% and 30.9%, ambient temperature was reduced by 1.3°C, 1.4°C and 1.0°C, PAR was reduced by 53.6%, 57.9% and 39.9%, and air CO2 concentration was reduced by 3.7μmol·mol-1, 4.2μmol·mol-1 and 2.8μmol·mol-1, respectively. Compared to the monoculture, the average NA of soybean in poplar, maple and walnut treatments was also reduced by 53.1%, 67.5% and 46.5%, respectively. Multivariate stepwise regression analysis showed that PAR, ambient temperature and CO2 concentration were the dominant factors influencing net photosynthetic rate. PMID:26053375

Utility of gastric aspirates for diagnosing tuberculosis in children in a low prevalence area: predictors of positive cultures and significance of non-tuberculous mycobacteria.

PubMed

Kordy, Faisal; Richardson, Susan E; Stephens, Derek; Lam, Ray; Jamieson, Frances; Kitai, Ian

2015-01-01

In countries with low rates of tuberculosis (TB), yields of gastric aspirates (GAs) for Mycobacterium tuberculosis (MTB) culture are low. The significance of non-tuberculous mycobacteria (NTM) isolated from GA is uncertain. We reviewed clinical, microbiologic and radiologic data for children who underwent GA between 1999 and 2011 at Sick Kids, Toronto. Radiologic features of cases were compared with those of age matched controls. 785 GAs were obtained from 285 patients of whom 20 (7%) had positive MTB cultures: in 15 patients the GA was the only positive culture for MTB. Of 15 culture-positive patients who underwent exactly 3 GAs, MTB was isolated from the first lavage in 10 (67%), only from the second in 3 (20%) and only from the third in 2 (13%). On univariate analysis, miliary disease and intrathoracic lymphadenopathy were associated with a positive GA MTB culture. On multiple conditional logistic regression analysis, adenopathy remained significant (OR 10.2 [95% CI 2.0-51.4] p =0.005). Twelve patients had NTM isolated, most commonly M. avium complex: none had evidence of invasive NTM disease during a median duration of 12 months of follow-up. Causal pathogens different from the GA NTM culture were isolated from biopsies or bronchoalveolar lavage in 3. GAs continue to be important for TB diagnosis in children. Three GAs have a yield better than 1. Those with miliary or disseminated TB and intrathoracic lymphadenopathy have highest yields. NTM isolates from GA are likely unimportant and can be clinically misleading.

Principal component regression analysis with SPSS.

PubMed

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle

PubMed Central

Cho, C. I.; Alam, M.; Choi, T. J.; Choy, Y. H.; Choi, J. G.; Lee, S. S.; Cho, K. H.

2016-01-01

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3–L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of polynomials×3 types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea. PMID:26954184

Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle.

PubMed

Cho, C I; Alam, M; Choi, T J; Choy, Y H; Choi, J G; Lee, S S; Cho, K H

2016-05-01

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3-L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of polynomials×3 types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea.

Conduct urban agglomeration with the baton of transportation.

DOT National Transportation Integrated Search

2013-12-01

A key indicator of traffic activity patterns is commuting distance. Shorter commuting distances yield less traffic, fewer emissions, : and lower energy consumption. This study develops a spatial error seemingly unrelated regression model to investiga...

Risk factors for displaced abomasum or ketosis in Swedish dairy herds.

PubMed

Stengärde, L; Hultgren, J; Tråvén, M; Holtenius, K; Emanuelson, U

2012-03-01

Risk factors associated with high or low long-term incidence of displaced abomasum (DA) or clinical ketosis were studied in 60 Swedish dairy herds, using multivariable logistic regression modelling. Forty high-incidence herds were included as cases and 20 low-incidence herds as controls. Incidence rates were calculated based on veterinary records of clinical diagnoses. During the 3-year period preceding the herd classification, herds with a high incidence had a disease incidence of DA or clinical ketosis above the 3rd quartile in a national database for disease recordings. Control herds had no cows with DA or clinical ketosis. All herds were visited during the housing period and herdsmen were interviewed about management routines, housing, feeding, milk yield, and herd health. Target groups were heifers in late gestation, dry cows, and cows in early lactation. Univariable logistic regression was used to screen for factors associated with being a high-incidence herd. A multivariable logistic regression model was built using stepwise regression. A higher maximum daily milk yield in multiparous cows and a large herd size (p=0.054 and p=0.066, respectively) tended to be associated with being a high-incidence herd. Not cleaning the heifer feeding platform daily increased the odds of having a high-incidence herd twelvefold (p<0.01). Keeping cows in only one group in the dry period increased the odds of having a high incidence herd eightfold (p=0.03). Herd size was confounded with housing system. Housing system was therefore added to the final logistic regression model. In conclusion, a large herd size, a high maximum daily milk yield, keeping dry cows in one group, and not cleaning the feeding platform daily appear to be important risk factors for a high incidence of DA or clinical ketosis in Swedish dairy herds. These results confirm the importance of housing, management and feeding in the prevention of metabolic disorders in dairy cows around parturition and in early lactation. Copyright © 2011 Elsevier B.V. All rights reserved.

Use of vegetation health data for estimation of aus rice yield in bangladesh.

PubMed

Rahman, Atiqur; Roytman, Leonid; Krakauer, Nir Y; Nizamuddin, Mohammad; Goldberg, Mitch

2009-01-01

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991-2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March-April (weeks 8-13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost.

Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh

PubMed Central

Rahman, Atiqur; Roytman, Leonid; Krakauer, Nir Y.; Nizamuddin, Mohammad; Goldberg, Mitch

2009-01-01

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991–2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March–April (weeks 8–13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost. PMID:22574057

Spatial analysis of soybean canopy response to soybean cyst nematodes (Heterodera glycines) in eastern Arkansas: An approach to future precision agriculture technology application

NASA Astrophysics Data System (ADS)

Kulkarni, Subodh

2008-10-01

Heterodera glycines Ichinohe, commonly known as soybean cyst nematode (SCN) is a serious widespread pathogen of soybean in the US. Present research primarily investigated feasibility of detecting SCN infestation in the field using aerial images and ground level spectrometric sensing. Non-spatial and spatial linear regression analyses were performed to correlate SCN population densities with Normalized Difference Vegetation Index (NDVI) and Green NDVI (GNDVI) derived from soybean canopy spectra. Field data were obtained from two fields; Field A and B under different nematode control strategies in 2003 and 2004. Analysis of aerial image data from July 18, 2004 from the Field A showed a significant relationship between SCN population at planting and the GNDVI (R2=0.17 at p=0.0006). Linear regression analysis revealed that SCN had a little effect on yield (R2 =0.14, at p=0.0001, RMSEP=1052.42 kg ha-1) and GNDVI (R 2=0.17 at p=0.0006, RMSEP=0.087) derived from the aerial imagery on a single date. However, the spatial regression analysis based on spherical semivariogram showed that the RMSEP was 0.037 for the GNDVI on July 18, 2004 and 427.32 kg ha-1 for yield on October 14, 2003 indicating better model performance. For July 18, 2004 data from Field B, a relationship between NDVI and the cyst counts at planting was significant (R2=0.5 at p=0.0468). Non-spatial analyses of the ground level spectrometric data for the first field showed that NDVI and GNDVI were correlated with cyst counts at planting (R 2=0.34 and 0.27 at p=0.0015 and 0.0127, respectively), and GNDVI was correlated with eggs count at planting (R2= 0.27 at p=0.0118). Both NDVI and GNDVI were correlated with egg counts at flowering (R 2=0.34 and 0.27 at p=0.0013 and 0.0018, respectively). However, paired T test to validate the above relationships showed that, predicted values of NDVI and GNDVI were significantly different. The statistical evidences suggested that variability in vegetation indices was caused by SCN infestation. Comparison of estimators such as -2 RLL, AIC, and BIC of non-spatial and spatial models affirmed that incorporating spatial covariance structure of observations improved model performances. These results demonstrated a limited potential of aerial imaging and ground level spectrometry for detecting nematode infestation in the field. However, it is strongly recommended that more multisite-multiyear trials must be performed to establish and validate empirical models to quantify SCN population densities and their impact on soybean canopy reflectance.

Comparison of low‐dose, half‐rotation, cone‐beam CT with electronic portal imaging device for registration of fiducial markers during prostate radiotherapy

PubMed Central

Wee, Leonard; Hackett, Sara Lyons; Jones, Andrew; Lim, Tee Sin; Harper, Christopher Stirling

2013-01-01

This study evaluated the agreement of fiducial marker localization between two modalities — an electronic portal imaging device (EPID) and cone‐beam computed tomography (CBCT) — using a low‐dose, half‐rotation scanning protocol. Twenty‐five prostate cancer patients with implanted fiducial markers were enrolled. Before each daily treatment, EPID and half‐rotation CBCT images were acquired. Translational shifts were computed for each modality and two marker‐matching algorithms, seed‐chamfer and grey‐value, were performed for each set of CBCT images. The localization offsets, and systematic and random errors from both modalities were computed. Localization performances for both modalities were compared using Bland‐Altman limits of agreement (LoA) analysis, Deming regression analysis, and Cohen's kappa inter‐rater analysis. The differences in the systematic and random errors between the modalities were within 0.2 mm in all directions. The LoA analysis revealed a 95% agreement limit of the modalities of 2 to 3.5 mm in any given translational direction. Deming regression analysis demonstrated that constant biases existed in the shifts computed by the modalities in the superior–inferior (SI) direction, but no significant proportional biases were identified in any direction. Cohen's kappa analysis showed good agreement between the modalities in prescribing translational corrections of the couch at 3 and 5 mm action levels. Images obtained from EPID and half‐rotation CBCT showed acceptable agreement for registration of fiducial markers. The seed‐chamfer algorithm for tracking of fiducial markers in CBCT datasets yielded better agreement than the grey‐value matching algorithm with EPID‐based registration. PACS numbers: 87.55.km, 87.55.Qr PMID:23835391

Spatial regression analysis on 32 years of total column ozone data

NASA Astrophysics Data System (ADS)

Knibbe, J. S.; van der A, R. J.; de Laat, A. T. J.

2014-08-01

Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979-2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) ozone data (2009-2010). The two-dimensionality in this data set allows us to perform the regressions locally and investigate spatial patterns of regression coefficients and their explanatory power. Seasonal dependencies of ozone on regressors are included in the analysis. A new physically oriented model is developed to parameterize stratospheric ozone. Ozone variations on nonseasonal timescales are parameterized by explanatory variables describing the solar cycle, stratospheric aerosols, the quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO) and stratospheric alternative halogens which are parameterized by the effective equivalent stratospheric chlorine (EESC). For several explanatory variables, seasonally adjusted versions of these explanatory variables are constructed to account for the difference in their effect on ozone throughout the year. To account for seasonal variation in ozone, explanatory variables describing the polar vortex, geopotential height, potential vorticity and average day length are included. Results of this regression model are compared to that of a similar analysis based on a more commonly applied statistically oriented model. The physically oriented model provides spatial patterns in the regression results for each explanatory variable. The EESC has a significant depleting effect on ozone at mid- and high latitudes, the solar cycle affects ozone positively mostly in the Southern Hemisphere, stratospheric aerosols affect ozone negatively at high northern latitudes, the effect of QBO is positive and negative in the tropics and mid- to high latitudes, respectively, and ENSO affects ozone negatively between 30° N and 30° S, particularly over the Pacific. The contribution of explanatory variables describing seasonal ozone variation is generally large at mid- to high latitudes. We observe ozone increases with potential vorticity and day length and ozone decreases with geopotential height and variable ozone effects due to the polar vortex in regions to the north and south of the polar vortices. Recovery of ozone is identified globally. However, recovery rates and uncertainties strongly depend on choices that can be made in defining the explanatory variables. The application of several trend models, each with their own pros and cons, yields a large range of recovery rate estimates. Overall these results suggest that care has to be taken in determining ozone recovery rates, in particular for the Antarctic ozone hole.

Genetic Parameters for Milk Yield and Lactation Persistency Using Random Regression Models in Girolando Cattle

PubMed Central

Canaza-Cayo, Ali William; Lopes, Paulo Sávio; da Silva, Marcos Vinicius Gualberto Barbosa; de Almeida Torres, Robledo; Martins, Marta Fonseca; Arbex, Wagner Antonio; Cobuci, Jaime Araujo

2015-01-01

A total of 32,817 test-day milk yield (TDMY) records of the first lactation of 4,056 Girolando cows daughters of 276 sires, collected from 118 herds between 2000 and 2011 were utilized to estimate the genetic parameters for TDMY via random regression models (RRM) using Legendre’s polynomial functions whose orders varied from 3 to 5. In addition, nine measures of persistency in milk yield (PSi) and the genetic trend of 305-day milk yield (305MY) were evaluated. The fit quality criteria used indicated RRM employing the Legendre’s polynomial of orders 3 and 5 for fitting the genetic additive and permanent environment effects, respectively, as the best model. The heritability and genetic correlation for TDMY throughout the lactation, obtained with the best model, varied from 0.18 to 0.23 and from −0.03 to 1.00, respectively. The heritability and genetic correlation for persistency and 305MY varied from 0.10 to 0.33 and from −0.98 to 1.00, respectively. The use of PS7 would be the most suitable option for the evaluation of Girolando cattle. The estimated breeding values for 305MY of sires and cows showed significant and positive genetic trends. Thus, the use of selection indices would be indicated in the genetic evaluation of Girolando cattle for both traits. PMID:26323397

Biological behavior of tumors and associated retroviremia in cats inoculated with Snyder-Theilen fibrosarcoma virus and the phenomenon of tumor recurrence after primary regression.

PubMed Central

Pedersen, N C; Johnson, L; Theilen, G H

1984-01-01

The fate of tumors and associated retroviremia was studied in 111 cats infected with the Snyder-Theilen strain of feline sarcoma virus (FeSV). Tumors appeared at the site of inoculation within 7 to 10 days. A retroviremia, due mainly to the associated feline leukemia virus helper virus (FeLV-helper), developed at the same time as tumors. Of the cats, 44 developed progressively growing tumors and therefore had to be killed, and 67 developed tumors that regressed. There was a strong correlation between the persistence of the accompanying retroviremia and the growth of the tumors. The 44 cats with progressively growing fibrosarcomas remained retroviremic until death. Conversely, 53 of the 67 cats with solitary, regressing tumors were only transiently retroviremic. Tumor regression in these cats paralleled the disappearance of retrovirus from the blood. The fate of tumors and retroviremia was not always the same, however. Twelve cats remained persistently retroviremic after all signs of gross tumors disappeared. Two other kittens became nonviremic within 20 days after inoculation, yet tumors continued to grow and even metastasize for another 3 to 5 weeks before regressing. Fibrosarcomas recurred 3 weeks to 8 months later in 8 of 12 persistently retroviremic cats with regressed tumors. Although the blood and bone marrow from these cats contained predominantly FeLV-helper, tumor cells yielded both FeSV and FeLV-helper. Of 53 animals, 3 developed recurrent fibrosarcomas 5 weeks to 8 months after all signs of tumors and retroviremia had disappeared. Cells cultured from these tumors appeared initially like normal fibroblasts and were virus nonproducers. After one to three passages in culture, however, cells became malignantly transformed and replicated both FeSV and FeLV-helper. Cultures of the bone marrow from these and other nonviremic cats with regressed tumors yielded only FeLV-helper. PMID:6319286

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

NASA Technical Reports Server (NTRS)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

Regression Analysis by Example. 5th Edition

ERIC Educational Resources Information Center

Chatterjee, Samprit; Hadi, Ali S.

2012-01-01

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…

Duration-dependent effects of clinically relevant oral alendronate doses on cortical bone toughness in beagle dogs

PubMed Central

Burr, David B.; Liu, Ziyue; Allen, Matthew R.

2014-01-01

Bisphosphonates (BPs) have been shown to significantly reduce bone toughness in vertebrae within one year when given at clinical doses to dogs. Although BPs also reduce toughness in cortical bone when given at high doses, their effect on cortical bone material properties when given at clinical doses is less clear. In part, this may be due to the use of small sample sizes that were powered to demonstrate differences in bone mineral density rather than bone’s material properties. Our lab has conducted several studies in which dogs were treated with alendronate at a clinically relevant dose. The goal of this study was to examine these published and unpublished data collectively to determine whether there is a significant time-dependent effect of alendronate on toughness of cortical bone. This analysis seemed particularly relevant given the recent occurrence of atypical femoral fractures in humans. Differences in the toughness of ribs taken from dogs derived from five separate experiments were measured. The dogs were orally administered saline (CON, 1 ml/kg/day) or alendronate (ALN) at a clinical dose (0.2 mg/kg/day). Treatment duration ranged from 3 months to 3 years. Groups were compared using ANOVA, and time trends analyzed with linear regression analysis. Linear regressions of the percent difference in toughness between CON and ALN at each time point revealed a significant reduction in toughness with longer exposure to ALN. The downward trend was primarily driven by a downward trend in post-yield toughness, whereas toughness in the pre-yield region was not changed relative to CON. These data suggest that a longer duration of treatment with clinical doses of ALN results in deterioration of cortical bone toughness in a time-dependent manner. As the duration of treatment is lengthened, the cortical bone exhibits increasingly brittle behavior. This may be important in assessing the role that long-term BP treatments play in the risk of atypical fractures of femoral cortical bone in humans. PMID:25445446

The use of a tablet computer to complete the DASH questionnaire.

PubMed

Dy, Christopher J; Schmicker, Thomas; Tran, Quynh; Chadwick, Brian; Daluiski, Aaron

2012-12-01

To determine whether electronic self-administration of the Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire using a tablet computer increased completion rate compared with paper self-administration. We gave the DASH in self-administered paper form to 222 new patients in a single hand surgeon's practice. After a washout period of 5 weeks, we gave the DASH in self-administered tablet computer form to 264 new patients. A maximum of 3 questions could be omitted before the questionnaire was considered unscorable. We reviewed the submitted surveys to determine the number of scorable questionnaires and the number of omitted questions in each survey. We completed univariate analysis and regression modeling to determine the influence of survey administration type on respondent error while controlling for patient age and sex. Of the 486 total surveys, 60 (12%) were not scorable. A significantly higher proportion of the paper surveys (24%) were unscorable compared with electronic surveys (2%), with significantly more questions omitted in each paper survey (2.6 ± 4.4 questions) than in each electronic survey (0.1 ± 0.8 questions). Logistic regression analysis revealed survey administration mode to be significantly associated with DASH scorability while controlling for age and sex, with electronic survey administration being 14 times more likely than paper administration to yield a scorable DASH. In our retrospective series, electronic self-administration of the DASH decreased the number of omitted questions and yielded a higher number of scorable questionnaires. Prospective, randomized evaluation is needed to better delineate the effect of survey administration on respondent error. Administration of the DASH with a tablet computer may be beneficial for both clinical and research endeavors to increase completion rate and to gain other benefits from electronic data capture. Copyright © 2012 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.

Detection of Powdery Mildew in Two Winter Wheat Plant Densities and Prediction of Grain Yield Using Canopy Hyperspectral Reflectance

PubMed Central

Cao, Xueren; Luo, Yong; Zhou, Yilin; Fan, Jieru; Xu, Xiangming; West, Jonathan S.; Duan, Xiayu; Cheng, Dengfa

2015-01-01

To determine the influence of plant density and powdery mildew infection of winter wheat and to predict grain yield, hyperspectral canopy reflectance of winter wheat was measured for two plant densities at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 in the 2009–2010 and 2010–2011 seasons. Reflectance in near infrared (NIR) regions was significantly correlated with disease index at GS 10.5.3, 10.5.4, and 11.1 at two plant densities in both seasons. For the two plant densities, the area of the red edge peak (Σdr 680–760 nm), difference vegetation index (DVI), and triangular vegetation index (TVI) were significantly correlated negatively with disease index at three GSs in two seasons. Compared with other parameters Σdr 680–760 nm was the most sensitive parameter for detecting powdery mildew. Linear regression models relating mildew severity to Σdr 680–760 nm were constructed at three GSs in two seasons for the two plant densities, demonstrating no significant difference in the slope estimates between the two plant densities at three GSs. Σdr 680–760 nm was correlated with grain yield at three GSs in two seasons. The accuracies of partial least square regression (PLSR) models were consistently higher than those of models based on Σdr 680760 nm for disease index and grain yield. PLSR can, therefore, provide more accurate estimation of disease index of wheat powdery mildew and grain yield using canopy reflectance. PMID:25815468

Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice

PubMed Central

Liu, Xiaojun; Zhang, Ke; Zhang, Zeyu; Cao, Qiang; Lv, Zunfu; Yuan, Zhaofeng; Tian, Yongchao; Cao, Weixing; Zhu, Yan

2017-01-01

Canopy chlorophyll density (Chl) has a pivotal role in diagnosing crop growth and nutrition status. The purpose of this study was to develop Chl based models for estimating N status and predicting grain yield of rice (Oryza sativa L.) with Leaf area index (LAI) and Chlorophyll concentration of the upper leaves. Six field experiments were conducted in Jiangsu Province of East China during 2007, 2008, 2009, 2013, and 2014. Different N rates were applied to generate contrasting conditions of N availability in six Japonica cultivars (9915, 27123, Wuxiangjing 14, Wuyunjing 19, Yongyou 8, and Wuyunjing 24) and two Indica cultivars (Liangyoupei 9, YLiangyou 1). The SPAD values of the four uppermost leaves and LAI were measured from tillering to flowering growth stages. Two N indicators, leaf N accumulation (LNA) and plant N accumulation (PNA) were measured. The LAI estimated by LAI-2000 and LI-3050C were compared and calibrated with a conversion equation. A linear regression analysis showed significant relationships between Chl value and N indicators, the equations were as follows: PNA = (0.092 × Chl) − 1.179 (R2 = 0.94, P < 0.001, relative root mean square error (RRMSE) = 0.196), LNA = (0.052 × Chl) − 0.269 (R2 = 0.93, P < 0.001, RRMSE = 0.185). Standardized method was used to quantity the correlation between Chl value and grain yield, normalized yield = (0.601 × normalized Chl) + 0.400 (R2 = 0.81, P < 0.001, RRMSE = 0.078). Independent experimental data also validated the use of Chl value to accurately estimate rice N status and predict grain yield. PMID:29163568

Immune interference in the setting of same-day administration of two similar inactivated alphavirus vaccines: eastern equine and western equine encephalitis.

PubMed

Reisler, Ronald B; Gibbs, Paul H; Danner, Denise K; Boudreau, Ellen F

2012-11-26

We compared the effect on primary vaccination plaque-reduction neutralization 80% titers (PRNT80) responses of same-day administration (at different injection sites) of two similar investigational inactivated alphavirus vaccines, eastern equine encephalitis (EEE) vaccine (TSI-GSD 104) and western equine encephalitis (WEE) vaccine (TSI-GSD 210) to separate administration. Overall, primary response rate for EEE vaccine was 524/796 (66%) and overall primary response rate for WEE vaccine was 291/695 (42%). EEE vaccine same-day administration yielded a 59% response rate and a responder geometric mean titer (GMT)=89 while separate administration yielded a response rate of 69% and a responder GMT=119. WEE vaccine same-day administration yielded a 30% response rate and a responder GMT=53 while separate administration yielded a response rate of 54% and a responder GMT=79. EEE response rates for same-day administration (group A) vs. non-same-day administration (group B) were significantly affected by gender. A logistic regression model predicting response to EEE comparing group B to group A for females yielded an OR=4.10 (95% CL 1.97-8.55; p=.0002) and for males yielded an OR=1.25 (95% CL 0.76-2.07; p=.3768). WEE response rates for same-day administration vs. non-same-day administration were independent of gender. A logistic regression model predicting response to WEE comparing group B to group A yielded an OR=2.14 (95% CL 1.22-3.73; p=.0077). We report immune interference occurring with same-day administration of two completely separate formalin inactivated viral vaccines in humans. These findings combined with the findings of others regarding immune interference would argue for a renewed emphasis on studying the immunological mechanisms of induction of inactivated viral vaccine protection. Copyright © 2012. Published by Elsevier Ltd.

Lead in teeth from lead-dosed goats: Microdistribution and relationship to the cumulative lead dose

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

Bellis, David J.; Hetter, Katherine M.; Jones, Joseph

2008-01-15

Teeth are commonly used as a biomarker of long-term lead exposure. There appear to be few data, however, on the content or distribution of lead in teeth where data on specific lead intake (dose) are also available. This study describes the analysis of a convenience sample of teeth from animals that were dosed with lead for other purposes, i.e., a proficiency testing program for blood lead. Lead concentration of whole teeth obtained from 23 animals, as determined by atomic absorption spectrometry, varied from 0.6 to 80 {mu}g g{sup -1}. Linear regression of whole tooth lead ({mu}g g{sup -1}) on themore » cumulative lead dose received by the animal (g) yielded a slope of 1.2, with r{sup 2}=0.647 (p<0.0001). Laser ablation inductively coupled plasma mass spectrometry was employed to determine lead content at micrometer scale spatial resolution in the teeth of seven goats representing the dosing range. Highly localized concentrations of lead, ranging from about 10 to 2000 {mu}g g{sup -1}, were found in circumpulpal dentine. Linear regression of circumpulpal lead ({mu}g g{sup -1}) on cumulative lead dose (g) yielded a slope of 23 with r{sup 2}=0.961 (p=0.0001). The data indicated that whole tooth lead, and especially circumpulpal lead, of dosed goats increased linearly with cumulative lead exposure. These data suggest that circumpulpal dentine is a better biomarker of cumulative lead exposure than is whole tooth lead, at least for lead-dosed goats.« less

Predicting hypothetical willingness to participate (WTP) in a future phase III HIV vaccine trial among high-risk adolescents.

PubMed

Giocos, Georgina; Kagee, Ashraf; Swartz, Leslie

2008-11-01

The present study sought to determine whether the Theory of Planned Behaviour predicted stated hypothetical willingness to participate (WTP) in future Phase III HIV vaccine trials among South African adolescents. Hierarchical logistic regression analyses showed that The Theory of Planned Behaviour (TPB) significantly predicted WTP. Of all the predictors, Subjective norms significantly predicted WTP (OR = 1.19, 95% C.I. = 1.06-1.34). A stepwise logistic regression analysis revealed that Subjective Norms (OR = 1.19, 95% C.I. = 1.07-1.34) and Attitude towards participation in an HIV vaccine trial (OR = 1.32, 95% C.I. = 1.00-1.74) were significant predictors of WTP. The addition of Knowledge of HIV vaccines and HIV vaccine trials, Perceived self-risk of HIV infection, Health-promoting behaviours and Attitudes towards HIV/AIDS yielded non-significant results. These findings provide support for the Theory of Reasoned Action (TRA) and suggest that psychosocial factors may play an important role in WTP in Phase III HIV vaccine trials among adolescents.

Imaging genetics approach to predict progression of Parkinson's diseases.

PubMed

Mansu Kim; Seong-Jin Son; Hyunjin Park

2017-07-01

Imaging genetics is a tool to extract genetic variants associated with both clinical phenotypes and imaging information. The approach can extract additional genetic variants compared to conventional approaches to better investigate various diseased conditions. Here, we applied imaging genetics to study Parkinson's disease (PD). We aimed to extract significant features derived from imaging genetics and neuroimaging. We built a regression model based on extracted significant features combining genetics and neuroimaging to better predict clinical scores of PD progression (i.e. MDS-UPDRS). Our model yielded high correlation (r = 0.697, p <; 0.001) and low root mean squared error (8.36) between predicted and actual MDS-UPDRS scores. Neuroimaging (from 123 I-Ioflupane SPECT) predictors of regression model were computed from independent component analysis approach. Genetic features were computed using image genetics approach based on identified neuroimaging features as intermediate phenotypes. Joint modeling of neuroimaging and genetics could provide complementary information and thus have the potential to provide further insight into the pathophysiology of PD. Our model included newly found neuroimaging features and genetic variants which need further investigation.

Life-space mobility and social support in elderly adults with orthopaedic disorders.

PubMed

Suzuki, Tomoko; Kitaike, Tadashi; Ikezaki, Sumie

2014-03-01

The purpose of this cross-sectional survey was to explore relationships between life-space mobility and the related factors in elderly Japanese people who attend orthopaedic clinics. The study measures included surveys of life-space mobility (Life-space Assessment (LSA) score), social support (social network diversity and social ties), physical ability (instrumental self-maintenance, intellectual activity, social role), orthopaedic factors (diseases and symptoms) and demographic information. The questionnaire was distributed to 156 subjects; 152 persons responded, yielding 140 valid responses. Mean age of the sample was 76.0 ± 6.4 (range, 65-96 years), with 57.9% women (n = 81). In a multiple regression analysis, the six factors were significantly associated with LSA. Standardized partial regression coefficients (β) were gender (0.342), instrumental self-maintenance (0.297), social network diversity (0.217), age (-0.170), difficulty of motion (-0.156) and intellectual activity (0.150), with an adjusted R(2) = 0.488. These results suggest that outpatient health-care providers need to intervene in not only addressing orthopaedic factors but also promoting social support among elderly Japanese. © 2014 Wiley Publishing Asia Pty Ltd.

Socio-economic determinants of severe and moderate stunting among under-five children of rural Bangladesh.

PubMed

Mostafa, Kamal S M

2011-04-01

Malnutrition among under-five children is a chronic problem in developing countries. This study explores the socio-economic determinants of severe and moderate stunting among under-five children of rural Bangladesh. The study used data from the 2007 Bangladesh Demographic and Health Survey. Cross-sectional and multinomial logistic regression analyses were used to assess the effect of the socio-demographic variables on moderate and severe stunting over normal among the children. Findings revealed that over two-fifths of the children were stunted, of which 26.3% were moderately stunted and 15.1% were severely stunted. The multivariate multinomial logistic regression analysis yielded significantly increased risk of severe stunting (OR=2.53, 95% CI=1.34-4.79) and moderate stunting (OR=2.37, 95% CI=1.47-3.83) over normal among children with a thinner mother. Region, father's education, toilet facilities, child's age, birth order of children and wealth index were also important determinants of children's nutritional status. Development and poverty alleviation programmes should focus on the disadvantaged rural segments of people to improve their nutritional status.

Clinical laboratory investigation of the Sanofi ACCESS CK-MB procedure and comparison to electrophoresis and Abbott IMx.

PubMed

Mao, G D; Adeli, K; Eisenbrey, A B; Artiss, J D

1996-07-01

This evaluation was undertaken to verify the application protocol for the CK-MB assay on the ACCESS Immunoassay Analyzer (Sanofi Diagnostics Pasteur, Chaska, MN). The results show that the ACCESS CK-MB assay total imprecision was 6.8% to 9.1%. Analytical linearity of the ACCESS CK-MB assay was excellent in the range of < 1-214 micrograms/L. A comparison of the ACCESS CK-MB assay with the IMx (Abbott Laboratories, Abbott Park, IL) method shows good correlation r = 0.990 (n = 108). Linear regression analysis yielded Y = 1.36X-0.3, Sx/y = 7.2. ACCESS CK-MB values also correlated well with CK-MB by electrophoresis with r = 0.968 (n = 132). The linear regression equation for this comparison was Y = 1.08X + 1.4, Sx/y = 14.1. The expected non-myocardial infarction range of CK-MB determined by the ACCESS system was 1.3-9.4 micrograms/L (mean = 4.0, n = 58). The ACCESS CK-MB assay would appear to be rapid, precise and clinically useful.

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Bulk viscosity of water in acoustic modal analysis and experiment

NASA Astrophysics Data System (ADS)

Kůrečka, Jan; Habán, Vladimír; Himr, Daniel

2018-06-01

Bulk viscosity is an important factor in the damping properties of fluid systems and exhibits frequency dependent behaviour. A comparison between modal analysis in ANSYS Acoustics, custom code and experimental data is presented in this paper. The measured system consists of closed ended water-filled steel pipes of different lengths. The influence of a pipe wall, flanges on both ends and longitudinal waves in the structural part were included in measurement evaluation. Therefore, the obtained values of sound speed and bulk viscosity are parameters of the fluid. A numerical simulation was carried out only using fluid volume in a range of bulk viscosity. Damping characteristics in this range were compared to measured values. The results show a significant influence of sound speed and subsequently, the use of sound speed value regressed from experimental data yields a better fit between the measurement and the computation.

The Relationship of Gender and Self-Efficacy on Social Physique Anxiety among College Students.

PubMed

Rothberger, Sara M; Harris, Brandonn S; Czech, Daniel R; Melton, Bridget

The anxiety or fear associated with physique evaluation is defined as Social Physique Anxiety (SPA). Numerous studies have examined this construct, yet a gap exists exploring this phenomenon among current college students with SPA, self-efficacy, and gender concurrently. Therefore, the purposes of this study included quantitatively analyzing the association between SPA, gender, and self-efficacy. Participants included 237 students at a Southeastern university participating in jogging, body conditioning, or weight training courses. Analysis of Variance yielded a significant main effect for self-efficacy as well, as those with lower self-efficacy displayed higher levels of SPA ( p < 0.001). Stepwise regression analysis indicated self-efficacy and gender were both significant predictors of SPA. This information could aid in creating interventions designed to decrease the prevalence of SPA and increase levels of self-efficacy among the current college student population.

Estimating structural attributes of Douglas-fir/western hemlock forest stands from Landsat and SPOT imagery

NASA Technical Reports Server (NTRS)

Cohen, Warren B.; Spies, Thomas A.

1992-01-01

Relationships between spectral and texture variables derived from SPOT HRV 10 m panchromatic and Landsat TM 30 m multispectral data and 16 forest stand structural attributes is evaluated to determine the utility of satellite data for analysis of hemlock forests west of the Cascade Mountains crest in Oregon and Washington, USA. Texture of the HRV data was found to be strongly related to many of the stand attributes evaluated, whereas TM texture was weakly related to all attributes. Data analysis based on regression models indicates that both TM and HRV imagery should yield equally accurate estimates of forest age class and stand structure. It is concluded that the satellite data are a valuable source for estimation of the standard deviation of tree sizes, mean size and density of trees in the upper canopy layers, a structural complexity index, and stand age.

Recovery of zinc and manganese from alkaline and zinc-carbon spent batteries

NASA Astrophysics Data System (ADS)

De Michelis, I.; Ferella, F.; Karakaya, E.; Beolchini, F.; Vegliò, F.

This paper concerns the recovery of zinc and manganese from alkaline and zinc-carbon spent batteries. The metals were dissolved by a reductive-acid leaching with sulphuric acid in the presence of oxalic acid as reductant. Leaching tests were realised according to a full factorial design, then simple regression equations for Mn, Zn and Fe extraction were determined from the experimental data as a function of pulp density, sulphuric acid concentration, temperature and oxalic acid concentration. The main effects and interactions were investigated by the analysis of variance (ANOVA). This analysis evidenced the best operating conditions of the reductive acid leaching: 70% of manganese and 100% of zinc were extracted after 5 h, at 80 °C with 20% of pulp density, 1.8 M sulphuric acid concentration and 59.4 g L -1 of oxalic acid. Both manganese and zinc extraction yields higher than 96% were obtained by using two sequential leaching steps.

Analyzing effective municipal solid waste recycling programs: the case of county-level MSW recycling performance in Florida, USA.

PubMed

Park, Seejeen; Berry, Frances S

2013-09-01

Municipal solid waste (MSW) recycling performance, both nationally and in Florida, USA, has shown little improvement during the past decade. This research examines variations in the MSW recycling program performance in Florida counties in an attempt to identify effective recycling programs. After reviewing trends in the MSW management literature, we conducted an empirical analysis using cross-sectional multiple regression analysis. The findings suggest that the convenience-based hypothesis was supported by showing that curbside recycling had a positive effect on MSW recycling performance. Financial (cost-saving) incentive-based hypotheses were partially supported meaning that individual level incentives can influence recycling performance. Citizen environmental concern was found to positively affect the amount of county recycling, while education and political affiliation yielded no significant results. In conclusion, this article discusses the implications of the findings for both academic research and practice of MSW recycling programs.

Factors affecting the retrieval of famous names.

PubMed

Martins, Isabel Pavão; Loureiro, Clara; Rodrigues, Susana; Dias, Beatriz; Slade, Peter

2010-06-01

Tests of famous faces are used to study language and memory. Yet, the effect of stimulus properties on performance has not been fully investigated. To identify factors influencing proper name retrieval and to probe stimulus-specific parameters within proper name lexicon, we analysed the results obtained by 300 healthy participants on a test of famous faces that includes 74 personalities. A factor analysis yielded five main factors that were characterized by language (national or foreign names), epoch of peak popularity (current, recent or past) and occupation (politicians, entertainment and sports) of the personalities. Multiple regression analysis showed that participants' education, age and gender accounted for 10-32% of the variance in factor scores. These results indicate that there are variables of the stimulus and participants' that must be taken into account in proper name testing and in designing tests aimed to differentiate age-associated difficulties from cognitive decline.

Simultaneous determination of rifampicin, isoniazid and pyrazinamide in tablet preparations by multivariate spectrophotometric calibration.

PubMed

Goicoechea, H C; Olivieri, A C

1999-08-01

The use of multivariate spectrophotometric calibration is presented for the simultaneous determination of the active components of tablets used in the treatment of pulmonary tuberculosis. The resolution of ternary mixtures of rifampicin, isoniazid and pyrazinamide has been accomplished by using partial least squares (PLS-1) regression analysis. Although the components show an important degree of spectral overlap, they have been simultaneously determined with high accuracy and precision, rapidly and with no need of nonaqueous solvents for dissolving the samples. No interference has been observed from the tablet excipients. A comparison is presented with the related multivariate method of classical least squares (CLS) analysis, which is shown to yield less reliable results due to the severe spectral overlap among the studied compounds. This is highlighted in the case of isoniazid, due to the small absorbances measured for this component.

Analysis of the Einstein sample of early-type galaxies

NASA Technical Reports Server (NTRS)

Eskridge, Paul B.; Fabbiano, Giuseppina

1993-01-01

The EINSTEIN galaxy catalog contains x-ray data for 148 early-type (E and SO) galaxies. A detailed analysis of the global properties of this sample are studied. By comparing the x-ray properties with other tracers of the ISM, as well as with observables related to the stellar dynamics and populations of the sample, we expect to determine more clearly the physical relationships that determine the evolution of early-type galaxies. Previous studies with smaller samples have explored the relationships between x-ray luminosity (L(sub x)) and luminosities in other bands. Using our larger sample and the statistical techniques of survival analysis, a number of these earlier analyses were repeated. For our full sample, a strong statistical correlation is found between L(sub X) and L(sub B) (the probability that the null hypothesis is upheld is P less than 10(exp -4) from a variety of rank correlation tests. Regressions with several algorithms yield consistent results.

Production of biodiesel from coastal macroalgae (Chara vulgaris) and optimization of process parameters using Box-Behnken design.

PubMed

Siddiqua, Shaila; Mamun, Abdullah Al; Enayetul Babar, Sheikh Md

2015-01-01

Renewable biodiesels are needed as an alternative to petroleum-derived transport fuels, which contribute to global warming and are of limited availability. Algae biomass, are a potential source of renewable energy, and they can be converted into energy such as biofuels. This study introduces an integrated method for the production of biodiesel from Chara vulgaris algae collected from the coastal region of Bangladesh. The Box-Behnken design based on response surface methods (RSM) used as the statistical tool to optimize three variables for predicting the best performing conditions (calorific value and yield) of algae biodiesel. The three parameters for production condition were chloroform (X1), sodium chloride concentration (X2) and temperature (X3). Optimal conditions were estimated by the aid of statistical regression analysis and surface plot chart. The optimal condition of biodiesel production parameter for 12 g of dry algae biomass was observed to be 198 ml chloroform with 0.75 % sodium chloride at 65 °C temperature, where the calorific value of biodiesel is 9255.106 kcal/kg and yield 3.6 ml.

Optimization conditions for extracting polysaccharide from Angelica sinensis and its antioxidant activities.

PubMed

Tian, Suyang; Hao, Changchun; Xu, Guangkuan; Yang, Juanjuan; Sun, Runguang

2017-10-01

In this study, polysaccharides from Angelica sinensis were extracted using the ultrasound-assisted extraction method. Based on the results of single factor experiments and orthogonal tests, three independent variables-water/raw material ratio, ultrasound time, and ultrasound power-were selected for investigation. Then, we used response surface methodology to optimize the extraction conditions. The experimental data were fitted to a quadratic equation using multiple regression analysis, and the optimal conditions were as follows: water/raw material ratio, 43.31 mL/g; ultrasonic time, 28.06 minutes; power, 396.83 W. Under such conditions, the polysaccharide yield was 21.89±0.21%, which was well matched with the predicted yield. In vitro assays, scavenging activity of superoxide anion radicals, hydroxyl radicals, and 2,2-diphenyl-1-picry-hydrazyl radical showed that polysaccharides had certain antioxidant activities and that hydroxyl radicals have a remarkable scavenging capability. Therefore, these studies provide reference for further research and rational development of A. sinensis polysaccharide. Copyright © 2016. Published by Elsevier B.V.

Correlation between 96-h mortality and 24-h acetylcholinesterase inhibition in three grass shrimp larval life stages.

PubMed

Key, Peter B; Fulton, Michael H

2006-03-01

Three life stages of larval grass shrimp were tested to determine whether acetylcholinesterase (AChE) activity expressed as 24-h sublethal effect endpoints (EC20 and EC50) could be used to predict 96-h mortality (lowest observable effect concentration (LOEC) and LC50) for shrimp exposed to three organophosphate insecticides. With regard to mortality, newly hatched larvae and 18-day-old larvae were the most sensitive in the malathion and azinphosmethyl exposures. In the chlorpyrifos exposures, newly hatched larvae and postlarvae were the most sensitive life stages. Results of the 24-h AChE inhibition tests showed that newly hatched larvae were generally more sensitive in the three organophosphate exposures. A regression analysis of the EC50's and LC50's yielded the strongest correlation with R2=0.987 (correlation coefficient=0.994 and 95% confidence intervals 0.969-0.999). The LOEC/EC20 relationship yielded R2=0.962. For these grass shrimp life stages and pesticides, sublethal effect endpoints could be used as a predictor of 96-h mortality.

A modelling approach to evaluate the long-term effect of soil texture on spring wheat productivity under a rain-fed condition.

PubMed

He, Yong; Hou, Lingling; Wang, Hong; Hu, Kelin; McConkey, Brian

2014-07-30

Soil surface texture is an important environmental factor that influences crop productivity because of its direct effect on soil water and complex interactions with other environmental factors. Using 30-year data, an agricultural system model (DSSAT-CERES-Wheat) was calibrated and validated. After validation, the modelled yield and water use (WU) of spring wheat (Triticum aestivum L.) from two soil textures (silt loam and clay) under rain-fed condition were analyzed. Regression analysis showed that wheat grown in silt loam soil is more sensitive to WU than wheat grown in clay soil, indicating that the wheat grown in clay soil has higher drought tolerance than that grown in silt loam. Yield variation can be explained by WU other than by precipitation use (PU). These results demonstrated that the DSSAT-CERES-Wheat model can be used to evaluate the WU of different soil textures and assess the feasibility of wheat production under various conditions. These outcomes can improve our understanding of the long-term effect of soil texture on spring wheat productivity in rain-fed condition.

Preliminary investigation on the relationship of Raman spectra of sheep meat with shear force and cooking loss.

PubMed

Schmidt, Heinar; Scheier, Rico; Hopkins, David L

2013-01-01

A prototype handheld Raman system was used as a rapid non-invasive optical device to measure raw sheep meat to estimate cooked meat tenderness and cooking loss. Raman measurements were conducted on m. longissimus thoracis et lumborum samples from two sheep flocks from two different origins which had been aged for five days at 3-4°C before deep freezing and further analysis. The Raman data of 140 samples were correlated with shear force and cooking loss data using PLS regression. Both sample origins could be discriminated and separate correlation models yielded better correlations than the joint correlation model. For shear force, R(2)=0.79 and R(2)=0.86 were obtained for the two sites. Results for cooking loss were comparable: separate models yielded R(2)=0.79 and R(2)=0.83 for the two sites. The results show the potential usefulness of Raman spectra which can be recorded during meat processing for the prediction of quality traits such as tenderness and cooking loss. Copyright © 2012 Elsevier Ltd. All rights reserved.

The Impact of Global Warming on Precipitation Patterns in Ilorin and the Hydrological Balance of the Awun Basin

NASA Astrophysics Data System (ADS)

Ayanshola, Ayanniyi; Olofintoye, Oluwatosin; Obadofin, Ebenezer

2018-03-01

This study presents the impact of global warming on precipitation patterns in Ilorin, Nigeria, and its implications on the hydrological balance of the Awun basin under the prevailing climate conditions. The study analyzes 39 years of rainfall and temperature data of relevant stations within the study areas. Simulated data from the Coupled Global Climate model for historical and future datasets were investigated under the A2 emission scenario. Statistical regression and a Mann-Kendall analysis were performed to determine the nature of the trends in the hydrological variables and their significance levels, while a Soil and Water Assessment Tool (SWAT) was used to estimate the water balance and derive the stream flow and yield of the Awun basin. The study revealed that while minimum and maximum temperatures in Ilorin are increasing, rainfall is generally decreasing. The assessment of the trends in the water balance parameters in the basin indicates that there is no improvement in the water yield as the population increases. This may result in major stresses to the water supply in the near future.

Estimated suspended-sediment loads and yields in the French and Brandywine Creek Basins, Chester County, Pennsylvania, water years 2008-09

USGS Publications Warehouse

Sloto, Ronald A.; Olson, Leif E.

2011-01-01

Turbidity and suspended-sediment concentration data were collected by the U.S. Geological Survey (USGS) at four stream stations--French Creek near Phoenixville, West Branch Brandywine Creek near Honey Brook, West Branch Brandywine Creek at Modena, and East Branch Brandywine Creek below Downingtown--in Chester County, Pa. Sedimentation and siltation is the leading cause of stream impairment in Chester County, and these data are critical for quantifying sediment transport. This study was conducted by the USGS in cooperation with the Chester County Water Resources Authority and the Chester County Health Department. Data from optical turbidity sensors deployed at the four stations were recorded at 15- or 30-minute intervals by a data logger and uploaded every 1 to 4 hours to the USGS database. Most of the suspended-sediment samples were collected using automated samplers. The use of optical sensors to continuously monitor turbidity provided an accurate estimate of sediment fluctuations without the collection and analysis costs associated with intensive sampling during storms. Turbidity was used as a surrogate for suspended-sediment concentration (SSC), which is a measure of sedimentation and siltation. Regression models were developed between SSC and turbidity for each of the monitoring stations using SSC data collected from the automated samplers and turbidity data collected at each station. Instantaneous suspended-sediment loads (SSL) were computed from time-series turbidity and discharge data for the 2008 and 2009 water years using the regression equations. The instantaneous computations of SSL were summed to provide daily, storm, and water year annual loads. The annual SSL contributed from each basin was divided by the upstream drainage area to estimate the annual sediment yield. For all four basins, storms provided more than 96 percent of the annual SSL. In each basin, four storms generally provided over half the annual SSL each water year. Stormflows with the highest peak discharges generally carried the highest SSLs. For all stations, the greatest SSLs occurred during the late winter in February and March during the 2008 water year. During the 2009 water year, the greatest SSLs occurred during December and August. For French Creek near Phoenixville, the estimated annual SSL was 3,500 tons, and the estimated yield was 59.1 tons per square mile (ton/mi2) for the 2008 water year. For the 2009 water year, the annual SSL was 4,390 tons, and the yield was 74.3 ton/mi2. For West Branch Brandywine Creek near Honey Brook, the estimated annual SSL was 4,580 tons, and the estimated yield was 245 ton/mi2 for the 2008 water year. For the 2009 water year, the annual SSL was 2,300 tons, and the yield was 123 ton/mi2. For West Branch Brandywine Creek at Modena, the estimated annual SSL was 7,480 tons, and the estimated yield was 136 ton/mi2 for the 2008 water year. For the 2009 water year, the annual SSL was 4,930 tons, and the yield was 90 ton/mi2. For East Branch Brandywine Creek below Downingtown, the estimated annual SSL was 8,900 tons, and the estimated yield was 100 ton/mi2 for the 2008 water year. For the 2009 water year, the annual SSL was 7,590 tons, and the yield was 84 ton/mi2.

Classical and Bayesian Seismic Yield Estimation: The 1998 Indian and Pakistani Tests

NASA Astrophysics Data System (ADS)

Shumway, R. H.

2001-10-01

- The nuclear tests in May, 1998, in India and Pakistan have stimulated a renewed interest in yield estimation, based on limited data from uncalibrated test sites. We study here the problem of estimating yields using classical and Bayesian methods developed by Shumway (1992), utilizing calibration data from the Semipalatinsk test site and measured magnitudes for the 1998 Indian and Pakistani tests given by Murphy (1998). Calibration is done using multivariate classical or Bayesian linear regression, depending on the availability of measured magnitude-yield data and prior information. Confidence intervals for the classical approach are derived applying an extension of Fieller's method suggested by Brown (1982). In the case where prior information is available, the posterior predictive magnitude densities are inverted to give posterior intervals for yield. Intervals obtained using the joint distribution of magnitudes are comparable to the single-magnitude estimates produced by Murphy (1998) and reinforce the conclusion that the announced yields of the Indian and Pakistani tests were too high.

Classical and Bayesian Seismic Yield Estimation: The 1998 Indian and Pakistani Tests

NASA Astrophysics Data System (ADS)

Shumway, R. H.

The nuclear tests in May, 1998, in India and Pakistan have stimulated a renewed interest in yield estimation, based on limited data from uncalibrated test sites. We study here the problem of estimating yields using classical and Bayesian methods developed by Shumway (1992), utilizing calibration data from the Semipalatinsk test site and measured magnitudes for the 1998 Indian and Pakistani tests given by Murphy (1998). Calibration is done using multivariate classical or Bayesian linear regression, depending on the availability of measured magnitude-yield data and prior information. Confidence intervals for the classical approach are derived applying an extension of Fieller's method suggested by Brown (1982). In the case where prior information is available, the posterior predictive magnitude densities are inverted to give posterior intervals for yield. Intervals obtained using the joint distribution of magnitudes are comparable to the single-magnitude estimates produced by Murphy (1998) and reinforce the conclusion that the announced yields of the Indian and Pakistani tests were too high.

The effects of heat stress in Italian Holstein dairy cattle.

PubMed

Bernabucci, U; Biffani, S; Buggiotti, L; Vitali, A; Lacetera, N; Nardone, A

2014-01-01

The data set for this study comprised 1,488,474 test-day records for milk, fat, and protein yields and fat and protein percentages from 191,012 first-, second-, and third-parity Holstein cows from 484 farms. Data were collected from 2001 through 2007 and merged with meteorological data from 35 weather stations. A linear model (M1) was used to estimate the effects of the temperature-humidity index (THI) on production traits. Least squares means from M1 were used to detect the THI thresholds for milk production in all parities by using a 2-phase linear regression procedure (M2). A multiple-trait repeatability test-model (M3) was used to estimate variance components for all traits and a dummy regression variable (t) was defined to estimate the production decline caused by heat stress. Additionally, the estimated variance components and M3 were used to estimate traditional and heat-tolerance breeding values (estimated breeding values, EBV) for milk yield and protein percentages at parity 1. An analysis of data (M2) indicated that the daily THI at which milk production started to decline for the 3 parities and traits ranged from 65 to 76. These THI values can be achieved with different temperature/humidity combinations with a range of temperatures from 21 to 36°C and relative humidity values from 5 to 95%. The highest negative effect of THI was observed 4 d before test day over the 3 parities for all traits. The negative effect of THI on production traits indicates that first-parity cows are less sensitive to heat stress than multiparous cows. Over the parities, the general additive genetic variance decreased for protein content and increased for milk yield and fat and protein yield. Additive genetic variance for heat tolerance showed an increase from the first to third parity for milk, protein, and fat yield, and for protein percentage. Genetic correlations between general and heat stress effects were all unfavorable (from -0.24 to -0.56). Three EBV per trait were calculated for each cow and bull (traditional EBV, traditional EBV estimated with the inclusion of THI covariate effect, and heat tolerance EBV) and the rankings of EBV for 283 bulls born after 1985 with at least 50 daughters were compared. When THI was included in the model, the ranking for 17 and 32 bulls changed for milk yield and protein percentage, respectively. The heat tolerance genetic component is not negligible, suggesting that heat tolerance selection should be included in the selection objectives. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Exploring the social determinants of mental health service use using intersectionality theory and CART analysis.

PubMed

Cairney, John; Veldhuizen, Scott; Vigod, Simone; Streiner, David L; Wade, Terrance J; Kurdyak, Paul

2014-02-01

Fewer than half of individuals with a mental disorder seek formal care in a given year. Much research has been conducted on the factors that influence service use in this population, but the methods generally used cannot easily identify the complex interactions that are thought to exist. In this paper, we examine predictors of subsequent service use among respondents to a population health survey who met criteria for a past-year mood, anxiety or substance-related disorder. To determine service use, we use an administrative database including all physician consultations in the period of interest. To identify predictors, we use classification tree (CART) analysis, a data mining technique with the ability to identify unsuspected interactions. We compare results to those from logistic regression models. We identify 1213 individuals with past-year disorder. In the year after the survey, 24% (n=312) of these had a mental health-related physician consultation. Logistic regression revealed that age, sex and marital status predicted service use. CART analysis yielded a set of rules based on age, sex, marital status and income adequacy, with marital status playing a role among men and by income adequacy important among women. CART analysis proved moderately effective overall, with agreement of 60%, sensitivity of 82% and specificity of 53%. Results highlight the potential of data-mining techniques to uncover complex interactions, and offer support to the view that the intersection of multiple statuses influence health and behaviour in ways that are difficult to identify with conventional statistics. The disadvantages of these methods are also discussed.

The influence of training characteristics on the effect of aerobic exercise training in patients with chronic heart failure: A meta-regression analysis.

PubMed

Vromen, T; Kraal, J J; Kuiper, J; Spee, R F; Peek, N; Kemps, H M

2016-04-01

Although aerobic exercise training has shown to be an effective treatment for chronic heart failure patients, there has been a debate about the design of training programs and which training characteristics are the strongest determinants of improvement in exercise capacity. Therefore, we performed a meta-regression analysis to determine a ranking of the individual effect of the training characteristics on the improvement in exercise capacity of an aerobic exercise training program in chronic heart failure patients. We focused on four training characteristics; session frequency, session duration, training intensity and program length, and their product; total energy expenditure. A systematic literature search was performed for randomized controlled trials comparing continuous aerobic exercise training with usual care. Seventeen unique articles were included in our analysis. Total energy expenditure appeared the only training characteristic with a significant effect on improvement in exercise capacity. However, the results were strongly dominated by one trial (HF-action trial), accounting for 90% of the total patient population and showing controversial results compared to other studies. A repeated analysis excluding the HF-action trial confirmed that the increase in exercise capacity is primarily determined by total energy expenditure, followed by session frequency, session duration and session intensity. These results suggest that the design of a training program requires high total energy expenditure as a main goal. Increases in training frequency and session duration appear to yield the largest improvement in exercise capacity. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Quantitative analysis of triacylglycerol regioisomers in fats and oils using reversed-phase high-performance liquid chromatography and atmospheric pressure chemical ionization mass spectrometry.

PubMed

Fauconnot, Laëtitia; Hau, Jörg; Aeschlimann, Jean-Marc; Fay, Laurent-Bernard; Dionisi, Fabiola

2004-01-01

Positional distribution of fatty acyl chains of triacylglycerols (TGs) in vegetable oils and fats (palm oil, cocoa butter) and animal fats (beef, pork and chicken fats) was examined by reversed-phase high-performance liquid chromatography (RP-HPLC) coupled to atmospheric pressure chemical ionization using a quadrupole mass spectrometer. Quantification of regioisomers was achieved for TGs containing two different fatty acyl chains (palmitic (P), stearic (S), oleic (O), and/or linoleic (L)). For seven pairs of 'AAB/ABA'-type TGs, namely PPS/PSP, PPO/POP, SSO/SOS, POO/OPO, SOO/OSO, PPL/PLP and LLS/LSL, calibration curves were established on the basis of the difference in relative abundances of the fragment ions produced by preferred losses of the fatty acid from the 1/3-position compared to the 2-position. In practice the positional isomers AAB and ABA yield mass spectra showing a significant difference in relative abundance ratios of the ions AA(+) to AB(+). Statistical analysis of the validation data obtained from analysis of TG standards and spiked oils showed that, under repeatability conditions, least-squares regression can be used to establish calibration curves for all pairs. The regression models show linear behavior that allow the determination of the proportion of each regioisomer in an AAB/ABA pair, within a working range from 10 to 1000 microg/mL and a 95% confidence interval of +/-3% for three replicates. Copyright 2003 John Wiley & Sons, Ltd.

Potential for Bias When Estimating Critical Windows for Air Pollution in Children's Health.

PubMed

Wilson, Ander; Chiu, Yueh-Hsiu Mathilda; Hsu, Hsiao-Hsien Leon; Wright, Robert O; Wright, Rosalind J; Coull, Brent A

2017-12-01

Evidence supports an association between maternal exposure to air pollution during pregnancy and children's health outcomes. Recent interest has focused on identifying critical windows of vulnerability. An analysis based on a distributed lag model (DLM) can yield estimates of a critical window that are different from those from an analysis that regresses the outcome on each of the 3 trimester-average exposures (TAEs). Using a simulation study, we assessed bias in estimates of critical windows obtained using 3 regression approaches: 1) 3 separate models to estimate the association with each of the 3 TAEs; 2) a single model to jointly estimate the association between the outcome and all 3 TAEs; and 3) a DLM. We used weekly fine-particulate-matter exposure data for 238 births in a birth cohort in and around Boston, Massachusetts, and a simulated outcome and time-varying exposure effect. Estimates using separate models for each TAE were biased and identified incorrect windows. This bias arose from seasonal trends in particulate matter that induced correlation between TAEs. Including all TAEs in a single model reduced bias. DLM produced unbiased estimates and added flexibility to identify windows. Analysis of body mass index z score and fat mass in the same cohort highlighted inconsistent estimates from the 3 methods. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Sensory representation of typicality of Cabernet franc wines related to phenolic composition: impact of ripening stage and maceration time.

PubMed

Cadot, Yves; Caillé, Soline; Samson, Alain; Barbeau, Gérard; Cheynier, Véronique

2012-06-30

Phenolics are responsible for important sensory properties of red wines, including colour, astringency, and possibly bitterness. From a technical viewpoint, the harvest date and the maceration duration are critical decisions for producing red wine with a distinctive style. But little is known about the evolution of phenolics and of their extractability during ripening to predict the composition of the wine and related sensory properties. The aim of this study was to understand the relationship between the sensory profile of the wines and (i) the ripening stage of the berries (harvest date) and (ii) the extraction time (maceration duration). Phenolic acids, flavonols, anthocyanins and proanthocyanidins of Vitis Vinifera var. Cabernet franc were measured in grapes and in wines from two stages of maturity and with two maceration durations. Phenolic composition was analysed by high performance liquid chromatography, after fractionation and thiolysis for proanthocyanidins. The distinctive style of wines was investigated by descriptive analysis (trained panel), Just About Right profiles and typicality assessment (wine expert panel). Relationships between phenolics and sensory attributes were established by multidimensional analysis, and phenolics were classified according to sensory data by ANOVA and PLS regressions. Astringency, bitterness, colour intensity and alcohol significantly increased with ripening and astringency and colour intensity increased with maceration time. Grape anthocyanins increased and thiolysis yield significantly decreased with ripening. In wine, proanthocyanidins increased, and mean degree of polymerisation and thiolysis yield decreased with longer extraction time. The high impact of harvest date on the sensory profiles could be due to changes in anthocyanin and sugar contents, but also to an evolution of proanthocyanidins. Moreover, proanthocyanidin composition was affected by maceration time as suggested by the decrease of thiolysis yield. Our results suggest that the wine sensory quality established by the expert panel, is linked as expected to grape quality at harvest, reflected by sugar and anthocyanin contents, but also by thiolysis yield, which requires elucidation. Copyright © 2012 Elsevier B.V. All rights reserved.

Incorporating scale into digital terrain analysis

NASA Astrophysics Data System (ADS)

Dragut, L. D.; Eisank, C.; Strasser, T.

2009-04-01

Digital Elevation Models (DEMs) and their derived terrain attributes are commonly used in soil-landscape modeling. Process-based terrain attributes meaningful to the soil properties of interest are sought to be produced through digital terrain analysis. Typically, the standard 3 X 3 window-based algorithms are used for this purpose, thus tying the scale of resulting layers to the spatial resolution of the available DEM. But this is likely to induce mismatches between scale domains of terrain information and soil properties of interest, which further propagate biases in soil-landscape modeling. We have started developing a procedure to incorporate scale into digital terrain analysis for terrain-based environmental modeling (Drăguţ et al., in press). The workflow was exemplified on crop yield data. Terrain information was generalized into successive scale levels with focal statistics on increasing neighborhood size. The degree of association between each terrain derivative and crop yield values was established iteratively for all scale levels through correlation analysis. The first peak of correlation indicated the scale level to be further retained. While in a standard 3 X 3 window-based analysis mean curvature was one of the poorest correlated terrain attribute, after generalization it turned into the best correlated variable. To illustrate the importance of scale, we compared the regression results of unfiltered and filtered mean curvature vs. crop yield. The comparison shows an improvement of R squared from a value of 0.01 when the curvature was not filtered, to 0.16 when the curvature was filtered within 55 X 55 m neighborhood size. This indicates the optimum size of curvature information (scale) that influences soil fertility. We further used these results in an object-based image analysis environment to create terrain objects containing aggregated values of both terrain derivatives and crop yield. Hence, we introduce terrain segmentation as an alternative method for generating scale levels in terrain-based environmental modeling. Based on segments, R squared improved up to a value of 0.47. Before integrating the procedure described above into a software application, thorough comparison between the results of different generalization techniques, on different datasets and terrain conditions is necessary. This is the subject of our ongoing research as part of the SCALA project (Scales and Hierarchies in Landform Classification). References: Drăguţ, L., Schauppenlehner, T., Muhar, A., Strobl, J. and Blaschke, T., in press. Optimization of scale and parametrization for terrain segmentation: an application to soil-landscape modeling, Computers & Geosciences.

Early Yields Of Slash Pine Planted On a Cutover Site At Various Spacings

Treesearch

W.F. Mann

1971-01-01

Tabulates basal areas, cordwood and cubic-foot volumes, average d.b.h., and diameter distributions for 14-year-old slash pine planted in central Louisiana. Also gives regression equations developed to predict these parameters.

Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods

NASA Astrophysics Data System (ADS)

Erener, Arzu; Sivas, A. Abdullah; Selcuk-Kestel, A. Sevtap; Düzgün, H. Sebnem

2017-07-01

All of the quantitative landslide susceptibility mapping (QLSM) methods requires two basic data types, namely, landslide inventory and factors that influence landslide occurrence (landslide influencing factors, LIF). Depending on type of landslides, nature of triggers and LIF, accuracy of the QLSM methods differs. Moreover, how to balance the number of 0 (nonoccurrence) and 1 (occurrence) in the training set obtained from the landslide inventory and how to select which one of the 1's and 0's to be included in QLSM models play critical role in the accuracy of the QLSM. Although performance of various QLSM methods is largely investigated in the literature, the challenge of training set construction is not adequately investigated for the QLSM methods. In order to tackle this challenge, in this study three different training set selection strategies along with the original data set is used for testing the performance of three different regression methods namely Logistic Regression (LR), Bayesian Logistic Regression (BLR) and Fuzzy Logistic Regression (FLR). The first sampling strategy is proportional random sampling (PRS), which takes into account a weighted selection of landslide occurrences in the sample set. The second method, namely non-selective nearby sampling (NNS), includes randomly selected sites and their surrounding neighboring points at certain preselected distances to include the impact of clustering. Selective nearby sampling (SNS) is the third method, which concentrates on the group of 1's and their surrounding neighborhood. A randomly selected group of landslide sites and their neighborhood are considered in the analyses similar to NNS parameters. It is found that LR-PRS, FLR-PRS and BLR-Whole Data set-ups, with order, yield the best fits among the other alternatives. The results indicate that in QLSM based on regression models, avoidance of spatial correlation in the data set is critical for the model's performance.

Maternal Active Smoking During Pregnancy and Low Birth Weight in the Americas: A Systematic Review and Meta-analysis.

PubMed

Pereira, Priscilla Perez da Silva; Da Mata, Fabiana A F; Figueiredo, Ana Claudia Godoy; de Andrade, Keitty Regina Cordeiro; Pereira, Maurício Gomes

2017-05-01

Smoking during pregnancy may negatively impact newborn birth weight. This study investigates the relationship between maternal active smoking during pregnancy and low birth weight in the Americas through systematic review and meta-analysis. A literature search was conducted through indexed databases and the grey literature. Case-control and cohort studies published between 1984 and 2016 conducted within the Americas were included without restriction regarding publication language. The article selection process and data extraction were performed by two independent investigators. A meta-analysis of random effects was conducted, and possible causes of between-study heterogeneity were evaluated by meta-regressions and subgroup analyses. Publication bias was assessed by visual inspection of Begg's funnel plot and by Egger's regression test. The literature search yielded 848 articles from which 34 studies were selected for systematic review and 30 for meta-analysis. Active maternal smoking was associated with low birth weight, OR = 2.00 (95% CI: 1.77-2.26; I2 = 66.3%). The funnel plot and Egger's test (p = .14) indicated no publication bias. Meta-regression revealed that sample size, study quality, and the number of confounders in the original studies did not account for the between-study heterogeneity. Subgroup analysis indicated no significant differences when studies were compared by design, sample size, and regions of the Americas. Low birth weight is associated with maternal active smoking during pregnancy regardless of the region in the Americas or the studies' methodological aspects. A previous search of the major electronic databases revealed that no studies appear to have been conducted to summarize the association between maternal active smoking during pregnancy and low birth weight within the Americas. Therefore, this systematic review may help to fill the information gap. The region of the Americas contains some of the most populous countries in the world; therefore, this study may provide useful data from this massive segment of the world's population. © The Author 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Performance of Vegetation Indices for Wheat Yield Forecasting for Punjab, Pakistan

NASA Astrophysics Data System (ADS)

Dempewolf, J.; Becker-Reshef, I.; Adusei, B.; Barker, B.

2013-12-01

Forecasting wheat yield in major producer countries early in the growing season allows better planning for harvest deficits and surplus with implications for food security, world market transactions, sustaining adequate grain stocks, policy making and other matters. Remote sensing imagery is well suited for yield forecasting over large areas. The Normalized Difference Vegetation Index (NDVI) has been the most-used spectral index derived from remote sensing imagery for assessing crop condition of major crops and forecasting crop yield. Many authors have found that the highest correlation between NDVI and yield of wheat crops occurs at the height of the growing season when NDVI values and photosynthetic activity of the wheat plants are at their relative maximum. At the same time NDVI saturates in very dense and vigorous (healthy, green) canopies such as wheat fields during the seasonal peak and shows significantly reduced sensitivity to further increases in photosynthetic activity. In this study we compare the performance of different vegetation indices derived from space-borne red and near-infrared spectral reflectance measurements for wheat yield forecasting in the Punjab Province, Pakistan. Areas covered by wheat crop each year were determined using a time series of MODIS 8-day composites at 250 m resolution converted to temporal metrics and classified using a bagged decision tree approach, driven by classified multi-temporal Landsat scenes. Within the wheat areas we analyze and compare wheat yield forecasts derived from three different satellite-based vegetation indices at the peak of the growing season. We regressed in turn NDVI, Wide Dynamic Range Vegetation Index (WDRVI) and the Vegetation Condition Index (VCI) from the four years preceding the wheat growing season 2011/12 against reported yield values and applied the regression equations to forecast wheat yield for the 2011/12 season per district for each of 36 Punjab districts. Yield forecasts overall corresponded well with reported values. NDVI-based forecasts showed high correlations of r squared = 0.881 and RMSE 11%. The VCI performed similarly well with r squared = 0.886 and RMSE 11%. WDRVI performed better than either of the other indices with r squared = 0.909 and RMSE 10%, probably due to the increased sensitivity of the index at high values. Wheat yields in Pakistan show on average a slow but steady annual increase but overall are comparatively stable due to the fact that the majority of fields are irrigated. The next steps in this study will be to compare NDVI- with WDRVI-based yield forecasts in other environments dominated by rain-fed agriculture, such as Ukraine, Australia and the United States.

Nation-wide assessment of climate change impacts on crops in the Philippines and Peru as part of multi-disciplinary modelling framework

NASA Astrophysics Data System (ADS)

Fujisawa, Mariko; Kanamaru, Hideki

2016-04-01

Agriculture is vulnerable to environmental changes, and climate change has been recognized as one of the most devastating factors. In many developing countries, however, few studies have focused on nation-wide assessment of crop yield and crop suitability in the future, and hence there is a large pressure on science to provide policy makers with solid predictions for major crops in the countries in support of climate risk management policies and programmes. FAO has developed the tool MOSAICC (Modelling System for Agricultural Impacts of Climate Change) where statistical climate downscaling is combined with crop yield projections under climate change scenarios. Three steps are required to get the results: 1. The historical meteorological data such as temperature and precipitation for about 30 years were collected, and future climates were statistically downscaled to the local scale, 2. The historical crop yield data were collected and regression functions were made to estimate the yield by using observed climatic data and water balance during the growing period for each crop, and 3. The yield changes in the future were estimated by using the future climate data, produced by the first step, as an input to the yield regression functions. The yield was first simulated at sub-national scale and aggregated to national scale, which is intended to provide national policies with adaptation options. The methodology considers future changes in characteristics of extreme weather events as the climate projections are on daily scale while crop simulations are on 10-daily scale. Yields were simulated with two greenhouse gas concentration pathways (RCPs) for three GCMs per crop to account for uncertainties in projections. The crop assessment constitutes a larger multi-disciplinary assessment of climate change impacts on agriculture and vulnerability of livelihoods in terms of food security (e.g. water resources, agriculture market, household-level food security from socio-economic perspective). In our presentation we will show the cases of Peru and the Philippines, and discuss the implications for agriculture policies and risk management.

Metabolic control analysis using transient metabolite concentrations. Determination of metabolite concentration control coefficients.

PubMed Central

Delgado, J; Liao, J C

1992-01-01

The methodology previously developed for determining the Flux Control Coefficients [Delgado & Liao (1992) Biochem. J. 282, 919-927] is extended to the calculation of metabolite Concentration Control Coefficients. It is shown that the transient metabolite concentrations are related by a few algebraic equations, attributed to mass balance, stoichiometric constraints, quasi-equilibrium or quasi-steady states, and kinetic regulations. The coefficients in these relations can be estimated using linear regression, and can be used to calculate the Control Coefficients. The theoretical basis and two examples are discussed. Although the methodology is derived based on the linear approximation of enzyme kinetics, it yields reasonably good estimates of the Control Coefficients for systems with non-linear kinetics. PMID:1497632

Morphodynamic data assimilation used to understand changing coasts

USGS Publications Warehouse

Plant, Nathaniel G.; Long, Joseph W.

2015-01-01

Morphodynamic data assimilation blends observations with model predictions and comes in many forms, including linear regression, Kalman filter, brute-force parameter estimation, variational assimilation, and Bayesian analysis. Importantly, data assimilation can be used to identify sources of prediction errors that lead to improved fundamental understanding. Overall, models incorporating data assimilation yield better information to the people who must make decisions impacting safety and wellbeing in coastal regions that experience hazards due to storms, sea-level rise, and erosion. We present examples of data assimilation associated with morphologic change. We conclude that enough morphodynamic predictive capability is available now to be useful to people, and that we will increase our understanding and the level of detail of our predictions through assimilation of observations and numerical-statistical models.

Optimism and Planning for Future Care Needs among Older Adults

PubMed Central

Sörensen, Silvia; Hirsch, Jameson K.; Lyness, Jeffrey M.

2015-01-01

Aging is associated with an increase in need for assistance. Preparation for future care (PFC) is related to improved coping ability as well as better mental and physical health outcomes among older adults. We examined the association of optimism with components of PFC among older adults. We also explored race differences in the relationship between optimism and PFC. In Study 1, multiple regression showed that optimism was positively related to concrete planning. In Study 2, optimism was related to gathering information. An exploratory analysis combining the samples yielded a race interaction: For Whites higher optimism, but for Blacks lower optimism was associated with more planning. High optimism may be a barrier to future planning in certain social and cultural contexts. PMID:26045699

Biomotor structures in elite female handball players according to performance.

PubMed

Cavala, Marijana; Rogulj, Nenad; Srhoj, Vatromir; Srhoj, Ljerka; Katić, Ratko

2008-03-01

In order to identify biomotor structures in elite female handball players, factor structures of morphological characteristics and basic motor abilities, and of variables evaluating situation motor abilities of elite female handball players (n = 53) were determined first, followed by determination of differences and relations of the morphological, motor and specific motor space according to handball performance. Factor analysis of 16 morphological measures produced three morphological factors, i.e. factor of absolute voluminosity, i.e. mesoendomorphy, factor of longitudinal skeleton dimensionality, and factor of transverse hand dimensionality. Factor analysis of 15 motor variables yielded five basic motor dimensions, i.e. factor of agility, factor of throwing explosive strength, factor of running explosive strength (sprint), factor of jumping explosive strength and factor of movement frequency rate. Factor analysis of 5 situation motor variables produced two dimensions: factor of specific agility with explosiveness and factor of specific precision with ball manipulation. Analysis of variance yielded greatest differences relative to handball performance in the factor of specific agility and throwing strength, and the factor of basic motoricity that integrates the ability of coordination (agility) with upper extremity throwing explosiveness and lower extremity sprint (30-m sprint) and jumping (standing triple jump). Considering morphological factors, the factor of voluminosity, i.e. mesoendomorphy, which is defined by muscle mass rather than adipose tissue, was found to contribute significantly to the players'performance. Results of regression analysis indicated the handball performance to be predominantly determined by the general specific motor factor based on specific agility and explosiveness, and by the morphological factor based on body mass and volume, i.e. muscle mass. Concerning basic motor abilities, the factor of movement frequency rate, which is associated with the ability of ball manipulation, was observed to predict significantly the handball players' performance.

A meta-analysis of the effects of preweaned calf nutrition and growth on first-lactation performance.

PubMed

Gelsinger, S L; Heinrichs, A J; Jones, C M

2016-08-01

Several studies and a 2013 meta-analysis have proposed that increased feeding of milk or milk replacer to neonatal calves may improve subsequent milk production. However, data from individual studies are conflicting, and the meta-analysis was unable to assess the influence of calf starter intake. The objective of the current meta-analysis was to review newly published data and evaluate the effects of preweaning diet (including calf starter intake) and growth rate on first-lactation milk, fat, and protein yield. Data from 9 studies representing 21 treatment groups were included in the analysis. We created separate models for each outcome variable using regression methods in SAS (version 9.4, SAS Institute Inc., Cary, NC) to determine the effects of intake and growth rate. We then selected the best-fitting models using Akaike's information criterion. The effect of study explained 98, 85, and 96% of the variance in 305-d milk, fat, and protein yield in first lactation, respectively, indicating that other aspects of management are more important for determining first-lactation production than preweaning intake and growth rate. However, we found a synergistic relationship between preweaning liquid and starter dry matter intake for improving milk, fat, and protein production, and a positive relationship between first-lactation performance and preweaning average daily gain. These data indicate that provision of adequate nutrients from liquid and solid feeds and maintaining average daily gain above 0.5kg/d can enhance the first-lactation performance of heifers when combined with proper postweaning practices. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies

PubMed Central

Bulik-Sullivan, Brendan K.; Loh, Po-Ru; Finucane, Hilary; Ripke, Stephan; Yang, Jian; Patterson, Nick; Daly, Mark J.; Price, Alkes L.; Neale, Benjamin M.

2015-01-01

Both polygenicity (i.e., many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of test statistic inflation in many GWAS of large sample size. PMID:25642630