The Shallowest Possible Water Extraction Profile: A Null Model for Global Root Distributions

2008 ◽  
Vol 7 (3) ◽  
pp. 1119-1124 ◽  
Author(s):  
H. Jochen Schenk
Keyword(s):  
Null Model ◽  
Water Extraction ◽  
Root Distributions

A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽  
2005 ◽  
Vol 1 (2) ◽  
pp. 81-85 ◽  
Author(s):  
Stefan C. Schmukle ◽  
Jochen Hardt
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Null Model ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Fit Indices ◽  
Cautionary Note ◽  
Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

Counter culture: Causes, extent and solutions of systematic bias in the analysis of behavioural counts

2019 ◽  
Author(s):  
Joel L Pick ◽  
Nyil Khwaja ◽  
Michael A. Spence ◽  
Malika Ihle ◽  
Shinichi Nakagawa
Keyword(s):  
Recent Literature ◽  
Null Model ◽  
Mechanistic Explanation ◽  
Effect Sizes ◽  
Systematic Bias ◽  
Intra Class Correlation ◽  
Counter Culture ◽  
Provisioning Rate ◽  
Parental Provisioning ◽  
Observation Period

We often quantify a behaviour by counting the number of times it occurs within a specific, short observation period. Measuring behaviour in such a way is typically unavoidable but induces error. This error acts to systematically reduce effect sizes, including metrics of particular interest to behavioural and evolutionary ecologists such as R2, repeatability (intra-class correlation, ICC) and heritability. Through introducing a null model, the Poisson process, for modelling the frequency of behaviour, we give a mechanistic explanation of how this problem arises and demonstrate how it makes comparisons between studies and species problematic, because the magnitude of the error depends on how frequently the behaviour has been observed (e.g. as a function of the observation period) as well as how biologically variable the behaviour is. Importantly, the degree of error is predictable and so can be corrected for. Using the example of parental provisioning rate in birds, we assess the applicability of our null model for modelling the frequency of behaviour. We then review recent literature and demonstrate that the error is rarely accounted for in current analyses. We highlight the problems that arise from this and provide solutions. We further discuss the biological implications of deviations from our null model, and highlight the new avenues of research that they may provide. Adopting our recommendations into analyses of behavioural counts will improve the accuracy of estimated effect sizes and allow meaningful comparisons to be made between studies.

Niche Sharing in Intertidal Mollusks and Decapods in Rocky Shore of Easter Island

2019 ◽  
Vol 53 (5) ◽  
pp. 417-422
Author(s):  
P. De los Ríos ◽  
E. Ibáñez Arancibia
Keyword(s):  
Spatial Distribution ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Rocky Shore ◽  
Negative Binomial ◽  
Null Model ◽  
Easter Island ◽  
Published Data ◽  
Distribution Analysis ◽  
Field Works

Abstract The coastal marine ecosystems in Easter Island have been poorly studied, and the main studies were isolated species records based on scientific expeditions. The aim of the present study is to apply a spatial distribution analysis and niche sharing null model in published data on intertidal marine gastropods and decapods in rocky shore in Easter Island based in field works in 2010, and published information from CIMAR cruiser in 2004. The field data revealed the presence of decapods Planes minutus (Linnaeus, 1758) and Leptograpsus variegatus (Fabricius, 1793), whereas it was observed the gastropods Nodilittorina pyramidalis pascua Rosewater, 1970 and Nerita morio (G. B. Sowerby I., 1833). The available information revealed the presence of more species in data collected in 2004 in comparison to data collected in 2010, with one species markedly dominant in comparison to the other species. The spatial distribution of species reported in field works revealed that P. minutus and N. morio have aggregated pattern and negative binomial distribution, L. variegatus had uniform pattern with binomial distribution, and finally N. pyramidalis pascua, in spite of aggregated distribution pattern, had not negative binomial distribution. Finally, the results of null model revealed that the species reported did not share ecological niche due to competition absence. The results would agree with other similar information about littoral and sub-littoral fauna for Easter Island.

GRAM: A True Null Model for Relative Binding Affinity Predictions

2019 ◽  
Author(s):  
Guanglei Cui ◽  
Alan P. Graves ◽  
Eric S. Manas
Keyword(s):  
Binding Affinity ◽  
Performance Metrics ◽  
Null Model ◽  
Performance Measure ◽  
Interval Estimate ◽  
Empirical Observation ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Relative Binding Affinity ◽  
Relative Binding

Relative binding affinity prediction is a critical component in computer aided drug design. Significant amount of effort has been dedicated to developing rapid and reliable in silico methods. However, robust assessment of their performance is still a complicated issue, as it requires a performance measure applicable in the prospective setting and more importantly a true null model that defines the expected performance of random in an objective manner. Although many performance metrics, such as correlation coefficient (r2), mean unsigned error (MUE), and room mean square error (RMSE), are frequently used in the literature, a true and non-trivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely prediction interval (PI), which can be estimated from the error distribution of the predictions. The benefits of using the interval estimate are 1) it provides the uncertainty range in the predicted activities, which is important in prospective applications; 2) a true null model with well-defined PI can be established. We provide one such example termed Gaussian Random Affinity Model (GRAM), which is based on the empirical observation that the affinity change in a typical lead optimization effort has the tendency to distribute normally N (0, s). Having an analytically defined PI that only depends on the variation in the activities, GRAM should in principle allow us to compare the performance of relative binding affinity prediction methods in a standard way, ultimately critical to measuring the progress made in algorithm development.<br>

Deterioration in the Water Quality of Groundwater in Some Municipal Water Extraction Plants in Finland

1979 ◽  
Vol 10 (2-3) ◽  
pp. 171-190
Author(s):  
Pertti Lahermo ◽  
Jouko Parviainen
Keyword(s):  
Water Quality ◽  
Urban Areas ◽  
Water Extraction ◽  
Groundwater Extraction ◽  
Dissolved Solids ◽  
Municipal Water ◽  
The 1960S

In this study the changes in the quality of groundwater are described on the basis of material collected at some groundwater extraction plants situated mainly in urban areas. The causes of the marked increase in the content of dissolved solids are evaluated from the 1960s onwards.

Application of Tea Extract in Food Industry

2020 ◽  
Vol 16 (7) ◽  
pp. 998-1004
Author(s):  
Aziz H. Rad ◽  
Raana B. Fathipour ◽  
Fariba K. Bidgoli ◽  
Aslan Azizi
Keyword(s):  
Food Industry ◽  
Health Benefits ◽  
Epigallocatechin Gallate ◽  
Water Extraction ◽  
Ultrasound Assisted Extraction ◽  
Epicatechin Gallate ◽  
Microwave Assisted ◽  
Assisted Extraction ◽  
Main Components ◽  
Tea Extract

Background and Objectives: Tea is considered one of the most consumed drinks around the world and the health benefits of it have recently attracted the attention of different researchers. It has also been proven beneficial in preventing the danger of some diseases like cancer and cardiovascular problems. Further, lipid oxidation is one of the major problems in food products. Considering the above-mentioned issues, the present review focused on various techniques used to extract polyphenols from different kinds of tea, as well as their use in the food industry. Results and Conclusion: Based on our findings in this review, the main components of tea are polyphenols that have health benefits and include catechins, epicatechin, epigallocatechin, epicatechin gallate, epigallocatechin gallate, gallic acid, flavonoids, flavonols, and theophlavins. From these components, catechin is regarded as the most beneficial component. Many techniques have been discovered and reformed to extract tea compounds such as solvent-based extraction, microwave-assisted water extraction, and ultrasound-assisted extraction techniques. Overall, the microwave-assisted water extraction method is a useful method for extracting tea polyphenols, which may be used in the meat, oil, and dairy industries.

Intra-specific variability in deep water extraction between trees growing on a Mediterranean karst

2020 ◽  
Vol 590 ◽  
pp. 125428 ◽  
Author(s):  
Simon Damien Carrière ◽  
Julien Ruffault ◽  
Coffi Belmys Cakpo ◽  
Albert Olioso ◽  
Claude Doussan ◽  
...  
Keyword(s):  
Deep Water ◽  
Water Extraction
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