Estimation and comparison of flowering curves

Background: Many researchers have simply recorded first flowering dates, while others have recorded the full extent of flowering. Such flowering curves show the rate of increase and decrease in flowering, as well as the day on which flowering is a maximum.

Aim: To develop objective statistical methods for the estimation and comparison of flowering curves, with particular emphasis on the date of maximal flowering.

Methods: We considered data collected either as percentages or as actual counts of numbers of flowers. We developed appropriate techniques for fitting regression curves involving non-linear least squares and Poisson regression, including a new generalisation of the epsilon-skew-normal curve.

Results: Our generalised regression curve was found to be sufficiently flexible to provide good estimates of flowering in a wide variety of situations. The five parameters of this curve have a direct and straightforward interpretation, namely the date and magnitude of maximum flowering, along with the spread, skewness and kurtosis of flowering. The method of maximum likelihood was used to provide estimates and confidence limits for the parameters and to compare Crocosmia flowering curves over eight consecutive years.

Conclusions: Regression curves, particularly those of the generalised skew-normal, give an effective, practical and objective procedure for estimating and comparing flower curves.     

On the Probabilities of Obtaining Negative Estimates of Genetic Variance Components

This paper derives the probabilities of obtaining negative estimates of additive and dominance genetic variances when one uses the traditional weighted least square method for estimating genetic variances as given in MATHER and JINKS (1971). The model considered involves P₁, P₂, F₂, B₁ (Backcross to P₁) and B₂ (Backcross to P₂). The results are derived under the ordinary assumptions as made in the genetic literatures. It is shown that unless the genetic effects are very large and environmental effects small, the probabilities of obtaining negative estimates of additive and dominance variances are in general quite large.     

Entwicklung eines dynamischen modells der stoffproduktion von zuckerrüben mit hilfe der multiplen regressionsanalyse

If data are available which fulfil sufficiently the demands of statistics, and if physiologically plausible hypotheses can be formulated then it is possible to identify models by means of multiple regression analysis which simulate approximatively the behaviour of a crop within the range of the used data. A statistical as well as a physiological evaluation of the numerical results is necessary at every step of the calculation. The model consists of two equations describing the net assimilation rate and the distribution of the produced dry matter to leaves and roots. Some modification of the model are discussed, one of them with time-dependent parameters. The following influencing variables are taken into account: leaf area or leaf mass, global radiation, nitrogen content of the leaves, and water stress, the latter being calculated from available soil moisture and potential evaporation. One version of the model together with an algorithm of dynamic optimization was used to control the water status of sugar beet in field trials.     

Monitoring a bioprocess for ethanol production using FT-MIR and FT-Raman spectroscopy

The application of Fourier transform mid-infrared (FT-MIR) spectroscopy and Fourier transform Raman (FT-Raman) spectroscopy for process and quality control of fermentative production of ethanol was investigated. FT-MIR and FT-Raman spectroscopy along with multivariate techniques were used to determine simultaneously glucose, ethanol, and optical cell density of Saccharomyces cerevisiae during ethanol fermentation. Spectroscopic measurement of glucose and ethanol were compared and validated with the high-performance liquid chromatography (HPLC) method. Spectral wave number regions were selected for partial least-squares (PLS) regression and principal component regression (PCR) and calibration models for glucose, ethanol, and optical cell density were developed for culture samples. Correlation coefficient (R ²) value for the prediction for glucose and ethanol was more than 0.9 using various calibration methods. The standard error of prediction for the PLS first-derivative calibration models for glucose, ethanol, and optical cell density were 1.938 g/l, 1.150 g/l, and 0.507, respectively. Prediction errors were high with FT-Raman because the Raman scattering of the cultures was weak. Results indicated that FT-MIR spectroscopy could be used for rapid detection of glucose, ethanol, and optical cell density in S. cerevisiae culture during ethanol fermentation. Journal of Industrial Microbiology & Biotechnology (2001) 26, 185–190. Received 16 November 2000/ Accepted in revised form 12 January 2001     

Statistical method for prediction of gait kinematics with Gaussian process regression

We propose a novel methodology for predicting human gait pattern kinematics based on a statistical and stochastic approach using a method called Gaussian process regression (GPR). We selected 14 body parameters that significantly affect the gait pattern and 14 joint motions that represent gait kinematics. The body parameter and gait kinematics data were recorded from 113 subjects by anthropometric measurements and a motion capture system. We generated a regression model with GPR for gait pattern prediction and built a stochastic function mapping from body parameters to gait kinematics based on the database and GPR, and validated the model with a cross validation method. The function can not only produce trajectories for the joint motions associated with gait kinematics, but can also estimate the associated uncertainties. Our approach results in a novel, low-cost and subject-specific method for predicting gait kinematics with only the subject's body parameters as the necessary input, and also enables a comprehensive understanding of the correlation and uncertainty between body parameters and gait kinematics.     

Structural alterations in proliferating,remodeling, and regressing tooth pulp arterioles

Summary In the continuously growing upper incisor of 100 g rats about 25 arterioles arise from an artery outside the tooth and pass through the apical foramen to run parallel to one another in the central part of the pulp, each supplying a well-defined sector of the migrating odontoblast layer. The arterioles pass through a cycle of proliferation, growth, remodeling, regression and decay, phase displaced in relation to each other. Proliferative and degenerative processes occur in the arteriole wall throughout the cycle, but vary considerably in intensity at different phases. Proliferation takes place by mitosis in the endothelium and the innermost smooth muscle cells. The degenerative process consists of reduction in size of smooth muscle cells by partial autodigestion and by cell death. When the odontoblasts reach the incisal extremity of the tooth, they die, and the associated regressed arteriole disappears. The system of pulpal arterioles has remarkable spatio-temporal features and each of its vessels appears to be in a state of sensitive structural equilibrium.Dedicated to the University of Copenhagen on the occasion of its 500th anniversary