Variation in Marginal Response to Nitrogen Fertilizer between Locations

AbstractA logistic growth equation with time and location varying parameters was used to model corn response to applied nitrogen. A nonlinear dummy-variable regression model provided a parsimonious representation of site and time effects on parameter values. The model was used to test for the equality of the mean marginal product of nitrogen fertilizer between locations on the coastal plain of North Carolina. Monte Carlo simulation and bootstrap simulation were used to construct finite sample covariance estimates. Results support rejection of the hypothesis that mean marginal products are equal when nitrogen is applied at 168 kg/ac. A comparison of bootstrapped errors and asymptotic errors suggests that results based on asymptotic theory are fairly reliable in this case.

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Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random

Discrete Dynamics in Nature and Society ◽

10.1155/2020/8893594 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Hanji He ◽

Guangming Deng

Keyword(s):

Empirical Likelihood ◽

Missing At Random ◽

Confidence Regions ◽

Likelihood Inference ◽

High Dimensional ◽

Finite Sample ◽

The Mean ◽

Data Missing ◽

Consistency And Asymptotic Normality ◽

The Impact

We extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean. As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset. The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse. Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established. The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.

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Basis Expansions for Functional Snippets

Biometrika ◽

10.1093/biomet/asaa088 ◽

2020 ◽

Author(s):

Zhenhua Lin ◽

Jane-Ling Wang ◽

Qixian Zhong

Keyword(s):

Data Analysis ◽

Convergence Rate ◽

Functional Data Analysis ◽

Functional Data ◽

Covariance Function ◽

Covariance Estimation ◽

Simulation Studies ◽

Finite Sample ◽

Covariance Functions ◽

The Mean

Summary Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that there are enough data in the domain of interest to estimate both the mean and covariance functions. In this paper, we investigate mean and covariance estimation for functional snippets in which observations from a subject are available only in an interval of length strictly (and often much) shorter than the length of the whole interval of interest. For such a sampling plan, no data is available for direct estimation of the off-diagonal region of the covariance function. We tackle this challenge via a basis representation of the covariance function. The proposed estimator enjoys a convergence rate that is adaptive to the smoothness of the underlying covariance function, and has superior finite-sample performance in simulation studies.

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Rapid review of available evidence on the serial interval and generation time of COVID-19

BMJ Open ◽

10.1136/bmjopen-2020-040263 ◽

2020 ◽

Vol 10 (11) ◽

pp. e040263

Author(s):

John Griffin ◽

Miriam Casey ◽

Áine Collins ◽

Kevin Hunt ◽

David McEvoy ◽

...

Keyword(s):

Generation Time ◽

Social Contact ◽

Careful Consideration ◽

Rapid Review ◽

Eligibility Criteria ◽

Reproduction Numbers ◽

Serial Interval ◽

Scientific Papers ◽

The Mean ◽

Parameter Values

The serial interval is the time between symptom onsets in an infector–infectee pair. The generation time, also known as the generation interval, is the time between infection events in an infector–infectee pair. The serial interval and the generation time are key parameters for assessing the dynamics of a disease. A number of scientific papers reported information pertaining to the serial interval and/or generation time for COVID-19. Objective Conduct a review of available evidence to advise on appropriate parameter values for serial interval and generation time in national COVID-19 transmission models for Ireland and on methodological issues relating to those parameters. Methods We conducted a rapid review of the literature covering the period 1 January 2020 and 21 August 2020, following predefined eligibility criteria. Forty scientific papers met our inclusion criteria and were included in the review. Results The mean of the serial interval ranged from 3.03 to 7.6 days, based on 38 estimates, and the median from 1.0 to 6.0 days (based on 15 estimates). Only three estimates were provided for the mean of the generation time. These ranged from 3.95 to 5.20 days. One estimate of 5.0 days was provided for the median of the generation time. Discussion Estimates of the serial interval and the generation time are very dependent on the specific factors that apply at the time that the data are collected, including the level of social contact. Consequently, the estimates may not be entirely relevant to other environments. Therefore, local estimates should be obtained as soon as possible. Careful consideration should be given to the methodology that is used. Real-time estimations of the serial interval/generation time, allowing for variations over time, may provide more accurate estimates of reproduction numbers than using conventionally fixed serial interval/generation time distributions.

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Effects of Elevated Atmospheric CO2 and its Interaction with Temperature and Nitrogen on Yield of Barley (Hordeum vulgare L.): A Meta-analysis

10.21203/rs.3.rs-466735/v1 ◽

2021 ◽

Author(s):

Mekides Woldegiorgis Gardi ◽

Bettina I.G Haussmann ◽

Waqas Ahmed Malik ◽

Petra Högy

Keyword(s):

Grain Yield ◽

Nitrogen Fertilizer ◽

Meta Analysis ◽

N Fertilization ◽

Additional Stress ◽

Hordeum Vulgare L ◽

Grain Number ◽

Experimental Conditions ◽

Elevated Atmospheric Co2 ◽

The Mean

Abstract AimsThe general aim of this meta-analysis is to synthesize and summarise the mean response of barley yield variables to elevated CO2 (eCO2) and its interaction with temperature and N fertilization. Methods The present study quantitatively synthesized the response of barley to eCO2 and its interaction with temperature, and Nitrogen (N). A meta-analysis procedure was used to analyse five yield variables of barley extracted from 76 articles to determine the effect size and the magnitude in relation to eCO2 and its interaction with temperature and N. Results CO2 enrichment increased biomass (23.8%), grain number (24.8%), grain yield (27.4%), and thousand-grain weight (5.6%). However, responses to eCO2 were affected by genotype, additional stress, and experimental conditions. In comparison, genotype “Anakin” shows the highest response of biomass (47.1%), while “Genebank accessions” had a higher grain number (46.1%) and grain yield (57.1%) under eCO2. The maximal enhancement of barley yield was observed when plants grow under a combination of eCO2 and higher nitrogen fertilizer (>100 kg ha-1). Nevertheless, biomass (-12%), and grain yield (-17%) responses were lower when eCO2 is combined with high temperature (>25 °C). It was further noted the response of barley yield to eCO2 was higher in the growth chamber than in other CO2 exposure methods. Moreover, comparing pot-rooted versus field-rooted barley plants, a higher response of biomass and grain yield was observed for pot-rooted plants. ConclusionsOverall, results suggest that the maximal barley production under eCO2 will be obtained in combination with high nitrogen fertilizer and optimal temperature (21-25 °C).

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Infarcted Left Ventricles Have Stiffer Material Properties and Lower Stiffness Variation: Three-Dimensional Echo-Based Modeling to Quantify In Vivo Ventricle Material Properties

Journal of Biomechanical Engineering ◽

10.1115/1.4030668 ◽

2015 ◽

Vol 137 (8) ◽

Cited By ~ 8

Author(s):

Longling Fan ◽

Jing Yao ◽

Chun Yang ◽

Dalin Tang ◽

Di Xu

Keyword(s):

Wall Thickness ◽

Material Properties ◽

Strain Curve ◽

Stress And Strain ◽

Stress Strain ◽

Material Stiffness ◽

The Mean ◽

Lv Volume ◽

Parameter Values

Methods to quantify ventricle material properties noninvasively using in vivo data are of great important in clinical applications. An ultrasound echo-based computational modeling approach was proposed to quantify left ventricle (LV) material properties, curvature, and stress/strain conditions and find differences between normal LV and LV with infarct. Echo image data were acquired from five patients with myocardial infarction (I-Group) and five healthy volunteers as control (H-Group). Finite element models were constructed to obtain ventricle stress and strain conditions. Material stiffening and softening were used to model ventricle active contraction and relaxation. Systolic and diastolic material parameter values were obtained by adjusting the models to match echo volume data. Young's modulus (YM) value was obtained for each material stress–strain curve for easy comparison. LV wall thickness, circumferential and longitudinal curvatures (C- and L-curvature), material parameter values, and stress/strain values were recorded for analysis. Using the mean value of H-Group as the base value, at end-diastole, I-Group mean YM value for the fiber direction stress–strain curve was 54% stiffer than that of H-Group (136.24 kPa versus 88.68 kPa). At end-systole, the mean YM values from the two groups were similar (175.84 kPa versus 200.2 kPa). More interestingly, H-Group end-systole mean YM was 126% higher that its end-diastole value, while I-Group end-systole mean YM was only 29% higher that its end-diastole value. This indicated that H-Group had much greater systole–diastole material stiffness variations. At beginning-of-ejection (BE), LV ejection fraction (LVEF) showed positive correlation with C-curvature, stress, and strain, and negative correlation with LV volume, respectively. At beginning-of-filling (BF), LVEF showed positive correlation with C-curvature and strain, but negative correlation with stress and LV volume, respectively. Using averaged values of two groups at BE, I-Group stress, strain, and wall thickness were 32%, 29%, and 18% lower (thinner), respectively, compared to those of H-Group. L-curvature from I-Group was 61% higher than that from H-Group. Difference in C-curvature between the two groups was not statistically significant. Our results indicated that our modeling approach has the potential to determine in vivo ventricle material properties, which in turn could lead to methods to infer presence of infarct from LV contractibility and material stiffness variations. Quantitative differences in LV volume, curvatures, stress, strain, and wall thickness between the two groups were provided.

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Progress curve analysis of qRT-PCR reactions using the logistic growth equation

Biotechnology Progress ◽

10.1002/btpr.666 ◽

2011 ◽

Vol 27 (5) ◽

pp. 1407-1414 ◽

Cited By ~ 5

Author(s):

Meile Liu ◽

Claudia Udhe-Stone ◽

Chetan T. Goudar

Keyword(s):

Curve Analysis ◽

Logistic Growth ◽

Growth Equation ◽

Qrt Pcr ◽

Progress Curve

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Selection of Pairings Reaching Evenly Across the Data (SPREAD): A simple algorithm to design maximally informative fully crossed mating experiments

10.1101/009720 ◽

2014 ◽

Author(s):

Kolea Zimmerman ◽

Daniel Levitis ◽

Ethan Addicott ◽

Anne Pringle

Keyword(s):

Nearest Neighbor ◽

Simple Algorithm ◽

Two Dimensional ◽

Neighbor Distance ◽

Crossing Experiments ◽

The Mean ◽

Parameter Values ◽

Selection Of ◽

Novel Algorithm ◽

Trait Space

We present a novel algorithm for the design of crossing experiments. The algorithm identifies a set of individuals (a ?crossing-set?) from a larger pool of potential crossing-sets by maximizing the diversity of traits of interest, for example, maximizing the range of genetic and geographic distances between individuals included in the crossing-set. To calculate diversity, we use the mean nearest neighbor distance of crosses plotted in trait space. We implement our algorithm on a real dataset ofNeurospora crassastrains, using the genetic and geographic distances between potential crosses as a two-dimensional trait space. In simulated mating experiments, crossing-sets selected by our algorithm provide better estimates of underlying parameter values than randomly chosen crossing-sets.

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Statistics of nascent and mature RNA fluctuations in a stochastic model of transcriptional initiation, elongation, pausing, and termination

10.1101/2020.05.13.092650 ◽

2020 ◽

Author(s):

Tatiana Filatova ◽

Nikola Popovic ◽

Ramon Grima

Keyword(s):

Experimental Data ◽

Fluorescence Microscopy ◽

Stochastic Model ◽

Explicit Dependence ◽

Transcriptional Initiation ◽

Estimate Parameter ◽

Mean And Variance ◽

The Mean ◽

Nascent Rna ◽

Parameter Values

AbstractRecent advances in fluorescence microscopy have made it possible to measure the fluctuations of nascent (actively transcribed) RNA. These closely reflect transcription kinetics, as opposed to conventional measurements of mature (cellular) RNA, whose kinetics is affected by additional processes downstream of transcription. Here, we formulate a stochastic model which describes promoter switching, initiation, elongation, premature detachment, pausing, and termination while being analytically tractable. By computational binning of the gene into smaller segments, we derive exact closed-form expressions for the mean and variance of nascent RNA fluctuations in each of these segments, as well as for the total nascent RNA on a gene. We also derive exact expressions for the first two moments of mature RNA fluctuations, and approximate distributions for total numbers of nascent and mature RNA. Our results, which are verified by stochastic simulation, uncover the explicit dependence of the statistics of both types of RNA on transcriptional parameters and potentially provide a means to estimate parameter values from experimental data.

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Psi-Caputo Logistic Population Growth Model

Journal of Mathematics ◽

10.1155/2021/8634280 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Muath Awadalla ◽

Yves Yannick Yameni Noupoue ◽

Kinda Abu Asbeh

Keyword(s):

Population Growth ◽

Fractional Derivative ◽

Carrying Capacity ◽

Chinese Population ◽

Logistic Model ◽

Logistic Equation ◽

Logistic Growth ◽

Growth Equation ◽

Uniqueness Of The Solution ◽

Better Than

This article studies modeling of a population growth by logistic equation when the population carrying capacity K tends to infinity. Results are obtained using fractional calculus theories. A fractional derivative known as psi-Caputo plays a substantial role in the study. We proved existence and uniqueness of the solution to the problem using the psi-Caputo fractional derivative. The Chinese population, whose carrying capacity, K, tends to infinity, is used as evidence to prove that the proposed approach is appropriate and performs better than the usual logistic growth equation for a population with a large carrying capacity. A psi-Caputo logistic model with the kernel function x + 1 performed the best as it minimized the error rate to 3.20% with a fractional order of derivative α = 1.6455.

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Karyotypes and morphological variation in the sessile-flowered trilliums of North Carolina

Canadian Journal of Botany ◽

10.1139/b69-173 ◽

1969 ◽

Vol 47 (8) ◽

pp. 1223-1232 ◽

Cited By ~ 4

Author(s):

Cornelia Ann Serota

Keyword(s):

North Carolina ◽

Cold Treatment ◽

Morphological Data ◽

Geographic Isolation ◽

Flower Scent ◽

Cross Pollination ◽

Western North Carolina ◽

Wide Range ◽

Coefficient Of Variability ◽

The Mean

Six populations of apparently morphologically similar sessile-flowered Trillium (known taxonomically as T. cuneatum Raf. and T. luteum (Muhl.) Harb.) observed growing at elevations between 800 and 1700 ft in the Appalachians of western North Carolina and eastern Tennessee exhibit a wide range of variability by observation. The extent of variability was assessed by using statistical and karyotypic methods of analysis and by intraspecific cross-pollination. A correlation exists between the mean coefficient of variability and (1) the size of the population and (2) the degree of geographic isolation. The number of types of each kind of chromosome (A–E), as resolved by cold treatment, varies by one type from population to population. The homozygosity for the six populations is 85.8%, and the distribution of euchromatin is not complex. Intraspecific cross-pollination was effective among 76% of the test crosses. A review of the statistics and other morphological data indicates that the presence or absence of anthocyanins, flower scent, and the degree of geographic isolation are the three major variants among the six populations. These kinds of Trillium grow sympatrically with T. grandiflorum (Michx.) Salisb., T. simile Gleason, T. erectum var. album (Michx.) Pursh, T. flexipes Raf. (T. gleasoni Fern.), and T. erectum L. The cumulative karyotype for the six stands indicates that these plants have not hybridized with any of the above species and varieties. Although these plants have been classified as T. cuneatum Raf. in some stands and as T. luteum (Muhl.) Harb. in others the data demonstrate that these six stands represent one discontinuous population of a sessile-flowered Trillium. Trillium discolor Wray, although relatively rare in western North Carolina, also grows here. Although T. discolor is not directly involved in this problem, its description and distinctive karyotype are included.

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