Overall Coefficient Of Determination Research Articles

Multimorbidity in Medicare Beneficiaries: Performance of an ICD-Coded Multimorbidity-Weighted Index.

Most older adults have multimorbidity that impairs physical functioning, but it is difficult to quantify using claims data. We previously developed and validated a multimorbidity-weighted index (MWI) that embeds physical functioning through disease weightings. We mapped these conditions to International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and compared them with existing indices. Population-based prospective cohort. Respondents to the 2006-2016 waves of the Health and Retirement Study (HRS) with linked Medicare claims data and continuous enrollment in 2006. Community-dwelling Medicare-eligible HRS participants (N = 9923; mean age = 75.5 ± 8.5 y). Individuals were followed for future physical functioning (2006-2014) and mortality (2007-2016). MWI conditions were mapped to ICD-9-CM codes to produce an ICD-coded MWI (MWI-ICD). We compared MWI-ICD, simple disease count, Charlson, Elixhauser, and the health-related quality of life comorbidity index (HRQOL-CI) through distributions, hazard ratios for mortality, and relationships with future physical functioning. MWI-ICD exhibited the broadest distribution and most unique values (5891). Left censoring was most pronounced for Charlson (34.3% score = 0) and Elixhauser (13.1% score = 0) vs MWI (5.0% score = 0). Hazard ratios and concordance (C)-statistics for mortality across extreme quartiles were similar for MWI-ICD, Elixhauser, and Charlson but lower for disease count and the HRQOL-CI. For physical functioning, MWI-ICD yielded the greatest contrast across extreme quartiles and overall coefficient of determination (R2 ). MWI-ICD was significantly associated with mortality and future physical functioning and comparable with established metrics for mortality prediction although not weighted to mortality. MWI-ICD successfully captures diseases accumulation and functioning in claims data. J Am Geriatr Soc 68:999-1006, 2020.

Compressive Strength Evaluation of Fiber‐Reinforced High‐Strength Self‐Compacting Concrete with Artificial Intelligence

This paper describes the application of two artificial intelligence‐ (AI‐) based methods to predict the 28‐day compressive strength of fiber‐reinforced high‐strength self‐compacting concrete (FRHSSCC) from its ingredients. A series of 131 data samples collected from various published literature sources were used for training, validation, and testing models. Various AI models were developed with different training algorithms and a number of nodes in the hidden layer to obtain the optimal model for the FRHSSCC data. It is shown that the performances of the artificial neural network (ANN) were better than that of the adaptive neurofuzzy inference system (ANFIS) model. Specifically, the overall coefficient of determination (R2) of the ANN and ANFIS models was 0.9742 and 0.9584, respectively. The sensitivity analysis was also conducted with the ANN model to investigate the effects of input parameters on the output. The results from the sensitivity analysis revealed that the compressive strength of FRHSSCC at 28 days was more sensitive with the changes of water by cement ratio (WCR) parameter and insensitive with varying amounts of fiber (VOF). Finally, it can be concluded that the application of artificial intelligence shows the great potential in the prediction of compressive strength of FRHSSCC.

A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data

To apply deep learning technique for estimating hourly global solar radiation (GSR) from geostationary satellite observations, a hybrid deep network is proposed, relying on convolutional neural network (CNN) to extract spatial pattern from satellite imagery, multi-layer perceptron (MLP) to link the abstract patterns and additional time/location information to target hourly GSR. Its representative advantage lies in the ability to characterize changeable cloud morphology and simulate complex non-linear relationships. The deep network is trained using ground measured GSR values at 90 Chinese radiation stations in 2008 as well as the radiative transfer model simulation at the top of Mt. Everest which serves as constraints of extrapolation for high elevation regions. The extensibility of trained network is validated at 5 independent stations in 2008, yielding an overall coefficient of determination (R2) of 0.82, and at all stations in 2007 along with an R2 of 0.88. Comparative experiments confirm that the combination of spatial pattern and point information can lead to more accurate estimation of hourly GSR, achieving a minimum root mean square error (RMSE) of 84.18 W/m2 (0.30 MJ/m2), 1.92 MJ/m2 and 1.08 MJ/m2 in hourly, daily total and monthly total scales, respectively. Moreover, the deep network is capable of mapping spatially continuous hourly GSR which reflects the regional differences and reproduce the diurnal cycles of solar radiation properly.

Spatial variation in the association between NO2 concentrations and shipping emissions in the Red Sea

Air pollution from shipping emissions poses significant health and environmental risks, particularly in the coastal regions. For the first time, this region as one of the busiest seas and most important international shipping lane in the world with significant nitrogen dioxide (NO2) emissions has been analyzed comprehensively. This paper aims to characterize and quantify the contribution of maritime transport sector emissions to NO2 concentrations in the Red Sea using local Geographically Weighted Regression (GWR) model in a geographic information system (GIS) environment. Maritime traffic volume was estimated using SaudiSat satellite-based Automatic Identification System (S-AIS) data, and the remotely measured tropospheric NO2 concentrations data was acquired from the ozone monitoring instrument (OMI) satellite. A significant spatial variation in the NO2 values was detected across the Red Sea, with values ranging from 4.03 × 1014 to 41.39 × 1014 molecules/cm2. Most notably, the NO2 concentrations in international waters were more than double those in the western coastal regions, whereas the concentrations close to seaports were 100% higher than those over international waters. The results indicated that the local GWR model performed significantly better than the global ordinary least squares (OLS) regression model. The GWR model had a strong and significant overall coefficient of determination with an r2 of 0.94 (p < 0.005) in comparison to the OLS model with an r2 of 0.45 (p < 0.005). Maritime traffic volume and proximity to seaports weighted by shipping activities explained about 94% of the variations of NO2 concentrations in the Red Sea. The results of this study suggest that the S-AIS data and environmental satellite measurements can be used to assess the impacts of NO2 concentrations from shipping emissions. These findings should stimulate further research into using additional covariates to explain the NO2 concentrations in areas near seaports where the standardized residuals are high.

Assets Utilization and Performance of Manufacturing Firms in Nigeria

This study examined the effect of assets utilization on performance of selected manufacturing firms in Nigeria. Secondary data were collected from the annual report and accounts of the ten selected quoted firms for a period of five years spanning from 2012 to 2016. Data collected were analyzed using descriptive statistics, correlation and regression analyses. The empirical results revealed that asset turnover(ATR) has positive and significant effect on return on assets (ROA) of the selected manufacturing firms as confirmed by the coefficient and probability value of 0.235999(p=0.0000). Current assets ratio also has positive and significant effect on return on assets with the coefficient of 0.109040 (p=0.0035) while debt assets ratio has negative but insignificant effect on return on assets. The overall coefficient of determination (R2)of 0.84951showed that about 85 % of the total variation in the ROA is&amp;nbsp;explained by asset turnover (ATR), current ratio (CUR) and debt-assets ratio (DAR). The study concluded that assets utilization has positive and significant effect on the performance of manufacturing firms in Nigeria and therefore recommended that attention should be purposely paid to optimum asset utilization in the manufacturing firms in Nigeria.

Estimation of net primary productivity and its driving factors in the Ili River Valley, China

Net primary productivity (NPP), as an important variable and ecological indicator in grassland ecosystems, can reflect environmental change and the carbon budget level. The Ili River Valley is a wetland nestled in the hinterland of the Eurasian continent, which responds sensitively to the global climate change. Understanding carbon budget and their responses to climate change in the ecosystem of Ili River Valley has a significant effect on the adaptability of future climate change and sustainable development. In this study, we calculated the NPP and analyzed its spatio-temporal pattern of the Ili River Valley during the period 2000–2014 using the normalized difference vegetation index (NDVI) and an improved Carnegie-Ames-Stanford (CASA) model. Results indicate that validation showed a good performance of CASA over the study region, with an overall coefficient of determination (R2) of 0.65 and root mean square error (RMSE) of 20.86 g C/(m2•a). Temporally, annual NPP of the Ili River Valley was 599.19 g C/(m2•a) and showed a decreasing trend from 2000 to 2014, with an annual decrease rate of–3.51 g C/(m2•a). However, the spatial variation was not consistent, in which 55.69% of the areas showed a decreasing tendency, 12.60% of the areas remained relatively stable and 31.71% appeared an increasing tendency. In addition, the decreasing trends in NPP were not continuous throughout the 15-year period, which was likely being caused by a shift in climate conditions. Precipitation was found to be the dominant climatic factor that controlled the inter-annual variability in NPP. Furthermore, the correlations between NPP and climate factors differed along the vertical zonal. In the medium-high altitudes of the Ili River Valley, the NPP was positively correlated to precipitation and negatively correlated to temperature and net radiation. In the low-altitude valley and high-altitude mountain areas, the NPP showed a negative correlation with precipitation and a weakly positive correlation with temperature and net radiation. The results suggested that the vegetation of the Ili River Valley degraded in recent years, and there was a more complex mechanism of local hydrothermal redistribution that controlled the growth of vegetation in this valley ecosystem.

Modelling and analysis of net primary productivity and its response mechanism to climate factors in temperate grassland, northern China

ABSTRACTGrassland productivity is the most direct indicator of grassland’s ecological status; thus, precise and rapid estimation of grassland productivity has an important significance for evaluating regional ecological carrying capacity, utilizing and developing natural resources reasonably. In this study, based on Moderate Resolution Imaging Spectroradiometer remote-sensing data, meteorological data, and ground measured data, the light utilization efficiency parameter of Carnegie–Ames–Stanford Approach model was optimized, and the optimized model was used to estimate net primary productivity (NPP) and analyse its spatio-temporal distribution of Xilingol grassland between 2005 and 2014. In addition, we investigated the NPP response mechanism to climate factors from two aspects, i.e. the accumulation amount and time of NPP. The results indicated that (i) Maximum light utilization efficiency was calculated to be 0.539 g C MJ−1 for Xilingol grassland according to the least error criterion. (ii) Validation against NPP observations from 30 sites showed good performance of the optimized model in the research area, with an overall coefficient of determination (R2) of 0.72 and mean relative estimation error of 0.29. (iii) The annual mean NPP of Xilingol grassland was 161.23 g C m–2 year–1, with a decreasing trend from the northeast to the southwest. (iv) NPP presented increasing trend with a rate of 3.89 g C m–2 year–1 overall during 20052014, and the largest increase in NPP was found in temperate meadow-steppe. However, desert ecosystems showed the largest increasing rate after NPP standardization. (v) Precipitation was the main factor for driving carbon accumulation at the inter-annual scale, and the time to maximum NPP was significantly correlated with spring precipitation (R2 = 0.637), NPP peaked early as the spring precipitation increasing.

Estimation of daily global solar radiation using deep learning model

Solar radiation (SR) is an important data for various applications such as climate, energy and engineering. Because of this, determination and estimation of temporal and spatial variability of SR has critical importance in order to make plans and organizations for the present and the future. In this study, a deep learning method is employed for estimating the SR over 30 stations located in Turkey. The astronomical factor, extraterrestrial radiation and climatic variables, sunshine duration, cloud cover, minimum temperature and maximum temperature were used as input attributes and the output was obtained as SR. The datasets of 34 stations, spanning the dates from 2001 to 2007, were used for training and testing the model, respectively, and simulated values were compared with ground-truth values. The overall coefficient of determination, root mean square error and mean absolute error were calculated as 0.980, 0.78 MJm−2day−1 and 0.61 MJm−2day−1, respectively. Consequently, DL model has yielded very precise and comparable results for estimating daily global SR. These results are generally better than or they are comparable to many previous studies reported in literature, so one can conclude that the method can be a good alternative and be successfully applied to similar regions.

Quantitative Series: Factors Analysis Effects of Government Regulation Number 46 the Year 2013 For SME’s by Justice, Convenience, and Simplicity of Tax Aspects

In 2013 the business circle was struck by the new tax rules, namely Government Regulations PP No. 46, 2013 on taxation regulations among UKM (SME’s) / Small Medium Enterprises but the strong allegations issued PP 46 of 2013 is because the potential tax revenue from the sector of UKM has not been explored to the fullest. By the required by the taxpayer related PP. No. 46 of 2013 uses several variables, namely taxes, taxes, taxes and tax benefits. The object of this research is the perpetrators of UKM (SMEs) in Makassar City who do 40 people using quantitative analysis and quantitative analysis. The result of this research indicates that the perception of justice tax has a significant effect as well as the most dominant variable affecting taxpayer compliance, taxability perception has a significant adverse impact on taxpayer compliance, tax perception simplicity has no significant impact, taxpayer's judgment is not valid in testing. The overall coefficient of determination contributes 26.5%.

Application of the SHS-GC-FID method and HPLC-DAD method in the prediction of the shelf-life of gluten-free cookies

The aim of this study was to compare the sensitivity of two analytical methods for the prediction of the shelf-life of unpacked and packed gluten-free rice?buckwheat cookies kept at ambient (23?1?C) and elevated (40?1?C) temperature during storage, namely the static headspace gas chromatographic method with flame ionisation detection (SHS-GC-FID) for volatile saturated aldehydes (propanal (C3), pentanal (C5), hexanal (C6), heptanal (C7) and octanal (C8)) and the HPLC method for malondialdehyde (MDA) determination. Both methods resulted in obtaining the same end-points of cookie shelf-life, i.e., 3 and 5 months for unpacked and packed cookies kept at elevated temperature, respectively, and 11 and 14 months for unpacked and packed cookies kept at ambient temperature, respectively. Two computational approaches, i.e., the second order polynomial (SOP) and artificial neural network (ANN) models, were used accordingly. The calculations of the contents of aldehydes and MDA could be predicted with an overall coefficient of determination of 0.722 using the ANN model compared to 0.312?0.773 for SOP models. According to sensitivity analysis, it might be suggested that the relevant parameter for the prediction of the end-point of cookie shelf-life is the MDA rather than the C3, C5, C6, C7 and C8 content.

Variation in stratum corneum protein content as a function of anatomical site and ethnic group.

Quantification of stratum corneum (SC) protein levels from tape strippings is frequently used to investigate skin conditions, to correct for amounts of SC protein removed in SC biomarker studies and to determine distribution of topically applied ingredients. In recent years, a rapid and convenient method for SC protein quantification from tape strippings has become available using infrared densitometry (IRD). However, standard curves have only been generated for Caucasian forearm and shoulder SC and have been assumed to be correct not only for facial SC but also for SC samples of other ethnic groups. The aim of this study was to investigate whether the use of IRD for SC protein measurement is valid for other body sites such as the cheek and for measuring SC protein content of darkly pigmented skin types. Ten Caucasian and ten Black African female subjects with self-assessed normal skin participated in the study. Tape strippings were collected from two different body sites (forearm and cheek). First from the tape strippings, the SC optical absorption was determined densitometrically. This obtained absorption (%) was compared with absolute SC protein extracted from the same tapes using a colorimetric microbicinchoninic acid (μBCA) assay. Higher amounts of SC protein were removed from the forearm compared with the cheek (P < 0.01). The absolute SC protein concentration quantified by μBCA assay and the absorption of SC proteins by IRD followed a similar profile. There was no significant difference found between the two ethnicities in SC protein (P > 0.05). The overall coefficient of determination (R(2) ) shows a good fit to the regression line between the two methods in both sites (forearm = 0.82, cheek = 0.77). Also, both ethnicities showed good correlation (R(2) ≥ 0.69, P = 0.01). Facial SC is morphologically distinct from the forearm, as demonstrated by the differences in amounts of SC removed. Although the data distribution in different subject groups varied, the regression was always quite similar between the two body sites and both ethnic groups. Also, the correlations were similar to previously published data on other body sites. The resultant calibration curves can be used as a rapid indirect protein assessment of tape strippings from the cheek.

Analysis of spatial and temporal patterns of net primary production and their climate controls in China from 1982 to 2010

Ecosystem net primary production (NPP) represents vegetation biomass increment after accounting for autotrophic respiration and is recognized as an important component of the terrestrial carbon cycle. In this study, the spatial and temporal patterns of NPP and their climate controls in China’s ecosystems for the period of 1982–2010 were analyzed by using a remote sensing-based carbon model (i.e., the Carnegie–Ames–Stanford Approach, CASA) and multiple statistical methods. Validation against NPP observations from 335 forests sites showed good performance of CASA over the study region, with an overall coefficient of determination (R2) of 0.73 and root mean square error (RMSE) of 132.9gCm−2yr−1. Spatially, we found that the spatial pattern of China’s NPP showed gradients decreasing from the southeast toward northwest, which could be mainly explained by the spatial variability in annual precipitation. Temporally, China’s NPP showed a significant increasing trend at both the national and biome levels during 1982–2010, with an annual increase of 0.011PgC or 0.42%. However, the increasing trends in NPP were not continuous throughout the 29-year period at the national scale. On the other hand, it showed three periods where the trends changed, which was likely being caused by a shift in climate conditions and extensive drought. Air temperature was found to be the dominant climatic factor that controlled the interannual variability in NPP throughout the country except for arid and semi-arid regions in the middle-north and northwest parts of China, where the interannual variations in NPP were mainly explained by changes in precipitation. Similar results were also obtained at the seasonal scale that changes in NPP were generally controlled by that in air temperature except for summertime, in which higher NPP were favored by higher summer precipitation, whereas summer temperature was negatively correlated with NPP. At the monthly scale, NPP responded to change in temperature more rapidly than that in precipitation. However, temperature appeared to control NPP only in humid and semi-humid regions. For monthly NPP–precipitation relationship, the strongest positive relations were observed when NPP lagged behind precipitation by 1–3 months. However, deviating from the common hypothesis that plant in drier areas should respond to water availability more rapidly than in other regions, our analysis revealed a relative large time lag between monthly NPP and precipitation in arid ecosystems (i.e., 3 months). This result suggests that there may be a more complex mechanism of local water redistribution that controls vegetation water use (e.g., use of water from previous year precipitation) in extremely arid ecosystems.

Validation of a three-dimensional solar radiation interception model for tree crops

Solar irradiance is fundamental in estimating evapotranspiration and dry matter production from agricultural fields. In general, testing of radiation interception models is limited by the number of measurement points both in space and time and the few crop species used. We considered many nodes (points of measurement), more than in any other published work, more data points (days of measurements), and numerous tree crop species. A three-dimensional, hourly time-step tree-canopy radiation interception model is validated for different tree crops including Jatropha curcas (Jatropha), Leucaena leucocephela (Leucaena), Macadamia integrifolia (Macadamia) and Acacia mearnsii (Black Wattle). Planting patterns varied from isolated trees to hedgerows to trees arranged in tramline mode. Measurements were made using tube solarimeters 0.5m apart from the base of a tree trunk in four directions, along and perpendicular to the row to mid-way between trees and rows. For validation, tube solarimeters ranging from five to 22 in number were used for each experiment. Model validation was for each node using a range of input parameters. Model estimations of radiant transmittance were in agreement with measurements: overall coefficient of determination 0.91; index of agreement 0.96; general absolute standard deviation 17.66%; slope=0.97; intercept=18.10Wm−2. Agreement was influenced by distance and direction of the node from the tree trunk, sky conditions, canopy symmetry, stand uniformity and leaf distribution.

Assessment of Three Satellite-Estimated Land Surface Downwelling Shortwave Irradiance Data Sets

This letter assesses three satellite-estimated surface downwelling shortwave irradiance data sets: 1) GEWEX-SRB; 2) ISCCP-FD; and 3) CERES-FSW, using ground measurements collected at 36 globally distributed sites from 2000–2002. SRB and FD solar irradiance are available at three hourly intervals during daytime and FSW hourly solar irradiance is available at late morning. The data are compared to ground measurements at the temporal resolutions of the satellite measurements. Results indicate that the downwelling solar irradiances of the three products show good overall agreement with ground measurements. FSW has an overall coefficient of determination of <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$R^{2} = \hbox{0.69}$</tex></formula> , a bias of 29.7 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{Wm}^{-2}$</tex></formula> (6.0% in relative value), and a standard deviation (STD) of 123.2 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{Wm}^{-2}$</tex></formula> (25.1% in relative value). The values are 0.83, <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$-5.5\ \hbox{Wm}^{-2}$</tex></formula> ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$-$</tex></formula> 1.9%), 101.3 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$ \hbox{Wm}^{-2}$</tex></formula> (35.0%) for SRB, and 0.83, 2.8 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{Wm}^{-2}$ </tex></formula> (0.3%), 101.7 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{W m}^{-2}$</tex></formula> (35.0%) for FD. However, there are substantial uncertainties in these products in some regions. For example, large biases ranging from <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$-90.2\ \hbox{Wm}^{-2}$</tex></formula> to 45.8 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{Wm}^{-2}$</tex></formula> are found in SRB in Southeast Asia. FD has large biases in Southeast Asia (58.9 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{Wm}^{-2}$</tex></formula> ) and Greenland <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{(-33.2}\ \hbox{Wm}^{-2}\hbox{)}$</tex></formula> . FSW has substantial biases in Southeast Asia (72.6 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{Wm}^{-2}$</tex></formula> ) and Japan (66.5 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{Wm}^{-2}$</tex></formula> ); while <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX"> $R^{2}$</tex></formula> is 0.35 in Tibetan Plateau and 0.47 in Southeast Asia.

Natural History of Predominantly Classic, Minimally Classic, and Occult Subgroups in Exudative Age-related Macular Degeneration

We previously showed that the pattern of vision loss in eyes with subfoveal neovascularization in age-related macular degeneration (AMD) is uniform across a wide range of clinical trials, with apparent differences arising from differences in the time of entry of patients into clinical trials. In the current study, we used a similar analysis to compare the visual loss of untreated control eyes classified as predominantly classic (PC), minimally classic (MC), and occult with no classic (occult) based on fluorescein angiography. Meta-analysis of prior clinical trials. Data from patients enrolled in the Macular Photocoagulation Study (MPS), Treatment of Age-related Macular Degeneration with Photodynamic Therapy (TAP) Study, Verteporfin in Photodynamic Therapy (VIP) Study, Anecortave Acetate (AA) Trial, VEGF Inhibition Study in Ocular Neovascularization (VISION), and Minimally Classic/Occult Trial of the Anti-VEGF Antibody Ranibizumab in the Treatment of Neovascular Age-Related Macular Degeneration (MARINA) Trials. Visual acuity (VA) data of untreated control eyes for each study from appropriate subgroups were plotted on a double reciprocal (Lineweaver-Burke) plot of 1/[letters lost] versus 1/[months]. To correct for differences in time of entry into clinical trials, we introduced a horizontal translation factor to shift each data subset. We determined the coefficient of determination before and after adjustments for visual acuity at the time of enrollment. On a Lineweaver-Burke plot, the cumulative subgroups had an overall coefficient of determination of only r(2)<0.01 for the raw data but improved to a remarkably high r(2) = 0.90 when data were corrected for time of entry into clinical trials. For each subgroup there was excellent correlation between 1/[letters lost] versus 1/[months of exudative disease] for PC (r(2) = 0.91), MC (r(2) = 0.95), and occult (r(2) = 0.98) choroidal neovascularization. We were able to demonstrate a strong correlation for visual acuity as a function of time that is independent of the fluorescein angiography classification of a lesion, suggesting that initial protocol visual acuity, rather than angiographic classification, is the major determinant of the behavior of visual acuity as a function of time in exudative AMD.

Relationship between Low Ultraviolet B Irradiance and Higher Breast Cancer Risk in 107 Countries

Epidemiological data show an inverse relationship between vitamin D levels and breast cancer incidence. This study investigates the relationship of modeled and measured serum 25-hydroxyvitamin D [25(OH)D] levels with age-standardized incidence rates of breast cancer in 107 countries. The hypothesis being tested is that breast cancer incidence is inversely related to geographically-dependent cutaneous sunlight exposure. A multiple regression approach was used to examine the contributions of ultraviolet B (UVB) irradiance to age-standardized incidence rates of breast cancer in the 107 countries with data on these covariates-total column ozone thickness, per capita intake of alcohol and energy from animal and vegetable sources, cigarettes, proportion of female population overweight, and total fertility. Age-standardized incidence rates were substantially higher at latitudes distant from the equator (R2 = 0.43, p < 0.0001). The dose-response gradient between modeled serum 25(OH)D levels and incidence rates of breast cancer followed a standard inverse dose-response curve. Increasing increments in serum 25(OH)D in the range above 22 ng/mL were associated with incrementally lower incidence rates of breast cancer. According to multiple regression, UVB irradiance adjusted for cloud cover was inversely associated with incidence rates (p = 0.04) after controlling for covariates. Intake of energy from animal sources was also positively associated with incidence rates (p < 0.01). The overall coefficient of determination, R2, was 0.81 (p < 0.0001). There was a protective effect of UVB irradiance on risk of breast cancer that was independent of fertility rate, proportion of the population overweight, alcohol intake, animal energy intake, and other covariates.

Use of selected chemical markers in combination with a multiple regression model to assess the contribution of domesticated animal sources of fecal pollution in the environment

Human and animal wastes are major sources of environmental pollution. Reliable methods of identifying waste sources are necessary to specify the types and locations of measures that best prevent and mitigate pollution. This investigation demonstrates the use of chemical markers (fecal sterols and bile acids) to identify selected sources of fecal pollution in the environment. Fecal sterols and bile acids were determined for pig, horse, cow, and chicken feces (10–26 feces samples for each animal). Concentrations of major fecal sterols (coprostanol, epicoprostanol, cholesterol, cholestanol, stigmastanol, and stigmasterol) and bile acids (lithocholic acid, deoxycholic acid, cholic acid, chenodeoxycholic acid, ursodeoxycholic acid, and hyodeoxycholic acid) were determined using a gas chromatography and mass spectrometer (GC–MS) technique. The fecal sterol and bile acid concentration data were used to estimate parameters of a multiple linear regression model for fecal source identification. The regression model was calibrated using 75% of the available data validated against the remaining 25% of the data points in a jackknife process that was repeated 15 times. The regression results were very favorable in the validation data set, with an overall coefficient of determination between predicted and actual fecal source of 0.971. To check the potential of the proposed model, it was applied on a set of simulated runoff data in predicting the specific animal sources. Almost 100% accuracy was obtained between the actual and predicted fecal sources. While additional work using polluted water (as opposed to fresh fecal samples) as well as multiple pollution sources are needed, results of this study clearly indicate the potential of this model to be useful in identifying the individual sources of fecal pollution.

Efficient and simple quantification of stratum corneum proteins on tape strippings by infrared densitometry

The analysis of stratum corneum (SC) components is a widely accepted method to determine 'skin health' status or to follow the effects of topical treatments. These analytes are normally corrected to the amount of SC removed which can be determined gravimetrically or by extraction of SC proteins and their subsequent analysis. Unfortunately, this is a time consuming procedure and usually requires a laboratory. As a result there is a need for equipment that can be used in a clinical setting. Equally, a concurrent determination of total SC protein content and enzyme activity or any other analyte on the same tape stripping is difficult and also time consuming. Therefore a compact infrared densitometer was developed allowing a convenient and user friendly indirect measurement of SC protein content on tape strippings by optical absorption. As this is a non-destructive technique, the tape strippings can subsequently be utilized for any other bioassay. Using tape strippings from human subjects the SC optical absorption was determined densitometrically and after extraction of the tape strippings, their protein content was measured. A comparison between SC optical absorption and protein content was made between samples from different body sites, differing hydration and pH levels, different age groups and between the genders. The progression of absorption and protein curves was similar irrespective of tape strip number. The overall coefficient of determination (n=238) between absorption and protein content of forearm measurements was R(2)=0.852 and the corresponding overall linear regression 0.623x+2.703. Although the data distribution in the different subject groups varied, the regression was always quite similar and independent of gender, age, skin hydration rate, skin pH and varying skin areas. The correlations reached were statistically significant. Infrared densitometry is an easy to use and non-destructive technique for the convenient measurement of the optical absorption of SC tape strippings which was shown to be linearly proportional to their protein content. Thus the corresponding SC densitometric-protein content calibration curves can be used for a fast indirect protein evaluation of tape strippings. As this is a non-destructive method, the unmodified tapes can be used for further investigations.

Estimation of the Flow of Microbial Nitrogen to the Duodenum Using Urinary Uric Acid or Allantoin

Data were collected from six experiments using duodenally cannulated Holstein dairy cows (88 combinations of cow and period) to evaluate the relationship between urinary purine metabolites and microbial N flow. Experiments evaluated the effects of dietary factors on microbial N production, which included 1) varying concentrations of ruminally degradable protein and nonstructural carbohydrates, 2) supplemental sources of protected amino acids, 3) grass silage treated with fibrolytic enzymes, 4) bacterial inoculation of corn silage, and 5) ruminal starch availability as affected by corn silages of varying maturity. The coefficient of determination for individual experiments that measured the relationship between microbial N flow and allantoin or uric acid excretion in urine ranged from 0.01 to 0.68 and 0.02 to 0.82, respectively. Across all experiments, the coefficients of determination between microbial N flow and allantoin or uric acid excretion in urine were r2 = 0.002 and 0.11, respectively. Removal of data from one experiment improved the overall coefficient of determination between microbial N flow and urinary uric acid to r2 = 0.32. Urinary allantoin excretion across experiments was negatively correlated with microbial N flow, but urinary allantoin excretion within experiments was positively correlated with microbial N flow. Uric acid excretion in urine was positively correlated with microbial N flow across and within experiments, except for one experiment. Our data demonstrate that uric acid excretion in urine can be used to predict microbial N production, except in early lactation, and that urinary allantoin excretion cannot be used to predict microbial N production accurately among cows at different stages of lactation.

Effect of Extractable Soil Surface Phosphorus on Runoff Water Quality

Phosphorus (P) additions to surface water from agricultural nonpoint sources are of concern, because P often limits eutrophication of surface waters. Numerous sources of runoff P exist: indigenous soil and plant material, land-applied manure and sludge, and commercial fertilizer. In many soils receiving commercial fertilizer and manure, concentrations of P at the soil surface have been steadily rising due to either long-term or excessive applications of P. Critical levels of soil surface P may exist, above which runoff may promote eutrophication. Methods for rationally identifying these critical levels are needed to manage losses of P, which implies the need for accurate methods of relating soil surface P concentration (Ps) to runoff P concentration. A study was conducted on both pasture and tilled plots (with and without residue) to evaluate the relationship between Ps and dissolved reactive P in runoff (PR) using simulated rainfall. The data indicated that even for comparable storms, Ps alone was not a satisfactory estimator of PR. A model describing the kinetics of P release from surface soil to runoff was used to include additional variables in predicting PR. When used with uncalibrated parameters, the model explained a significant proportion of the variation in observed PR values for pasture plots (r2 = 0.43) but was less successful in predicting PR for tilled plots (with and without residue, r2 = 0.13). Calibration of (adjustments to) the extraction coefficients resulted in an overall coefficient of determination between observed and predicted PR values of 0.73. While the model was successful in describing how PR and the independent variables are related for the pasture plots, the extraction coefficients should be calibrated to obtain best estimates of PR. When used with calibrated extraction coefficients, the model provided realistic estimates of PR over the range of experimental conditions.