Analysis of competition effects in mono- and mixed cultures of juvenile beech and spruce by means of the plant growth simulation model PLATHO

Inter- and intra-specific competition between plants for external resources is a critical process for plant growth in natural and managed ecosystems. We present a new approach to simulate competition for the resources light, water, and nitrogen between individual plants within a canopy. This approach was incorporated in a process-oriented plant growth simulation model. The concept of modelling competition is based on competition coefficients calculated from the overlap of occupied crown and soil volumes of each plant individual with the occupied volumes of its four nearest neighbours. The model was parameterised with data from a two-year phytotron experiment with juvenile beech and spruce trees growing in mono- and mixed cultures. For testing the model, an independent data set from this experiment and data from a second phytotron experiment with mixed cultures were used. The model was applied to analyse the consequences of start conditions and plant density on plant-plant competition. In both experiments, spruce dominated beech in mixed cultures. Based on model simulations, we postulate a large influence of start conditions and stand density on the outcome of the competition between the species. When both species have similar heights at the time of canopy closure, the model suggests a greater morphological plasticity of beech compared with spruce to be the crucial mechanism for competitiveness in mixed canopies. Similar to the experiment, in the model greater plasticity was a disadvantage for beech leading to it being outcompeted by the more persistent spruce.     

Model based design of a biochemical cultivation process

In this paper a simple design procedure is used to enhance the performance of a biotechnical cultivation process. A model supported approach is proposed. It starts with a simple classical process model obtained from literature data, which is used to design the first experiment. Then, the main procedure is an iteration of (i) improving the model making use of the deviations between the experimental data and the data predicted by the model, (ii) of designing the next experiment by determining the optimal control profiles from the current model, and (iii) of executing that designed experiment. Important for the success of the procedure is that the model development is oriented at the process performance. The procedure is demonstrated at the simple practical example of a laboratory-scale fed-batch cultivation of Escherichia coli.     

The application of a multistage model that incorporates DNA damage and repair to the analysis of initiation/promotion experiments.

In a previous article, a multistage model of carcinogenesis was introduced that takes into account the role of DNA damage, DNA repair, and cell replication on the incidence of malignancies. For this model the number of detectable clones of initiated cells is derived and model parameters are estimated using data arising from a two-stage skin-painting experiment in mice. The data from this experiment are interpretable in terms of the cellular events involved in initiation and promotion.     

Magnetic Force Transmission of a Reciprocating Motion

Magnetic force transmission of a reciprocating motion is studied by theoretical analysis and experiment. A mathematical model for calculating the magnetic force is derived using the theory of equivalent magnetic charges. An experimental rig is constructed to test the transmission and the model is verified by experiment. Effect of the transmission parameters on the magnetic force is analyzed theoretically from the model, and characteristic of the transmission is studied experimentally. Since the transmission is without direct contact between two elements, it is suitable for application in an organism.     

A mixed model for the effects of single gene,polygenes and their interaction on quantitative traits

Summary A model for the effects of single gene (SG), polygenes (PG) and their interaction on quantitative traits was developed. It is a mixed model where the SG is a fixed effect and the PG is a random effect. A two-way factorial experiment, in which the SG and the PG are the main effects, is proposed. The experimental material is comprised of F₃ families derived from F₂ plants heterozygous for the SG. For this experiment an ANOVA table with expected mean square is proposed, which facilitates estimation of the components of the model and testing of their significance. A detailed method for the interpretation of results from such an experiment is proposed, with emphasis on the analysis of the SG × PG interaction. Theoretical and applied aspects of SG × PG interaction is discussed.This paper is part of a Ph.D. Thesis of the senior author to be submitted to the Hebrew University of Jerusalem     

Combining multiple microarrays in the presence of controlling variables

MOTIVATION: Microarray technology enables the monitoring of expression levels for thousands of genes simultaneously. When the magnitude of the experiment increases, it becomes common to use the same type of microarrays from different laboratories or hospitals. Thus, it is important to analyze microarray data together to derive a combined conclusion after accounting for the differences. One of the main objectives of the microarray experiment is to identify differentially expressed genes among the different experimental groups. The analysis of variance (ANOVA) model has been commonly used to detect differentially expressed genes after accounting for the sources of variation commonly observed in the microarray experiment. RESULTS: We extended the usual ANOVA model to account for an additional variability resulting from many confounding variables such as the effect of different hospitals. The proposed model is a two-stage ANOVA model. The first stage is the adjustment for the effects of no interests. The second stage is the detection of differentially expressed genes among the experimental groups using the residuals obtained from the first stage. Based on these residuals, we propose a permutation test to detect the differentially expressed genes. The proposed model is illustrated using the data from 133 microarrays collected at three different hospitals. The proposed approach is more flexible to use, and it is easier to accommodate the individual covariates in this model than using the meta-analysis approach. AVAILABILITY: A set of programs written in R will be electronically sent upon request.     

神经自发整数倍峰放电节律的随机性和确定性模式的比较

为进一步区分神经自发整数倍峰放电节律的随机性和确定性模式的动力学性质,详细研究了实验神经起步点、随机理论模型(Chay模型)和确定性理论模型(Wang模型)产生的3种整数倍峰放电节律及其变化规律。结果发现,实验和随机模型经随机自共振产生的整数倍峰放电节律具有相同的统计性质(峰峰间期越大,出现的频度越低,约呈指数递减)和变化规律,与确定性Wang模型产生的整数倍节律明显不同。这提示,呈指数衰减分布的整数倍峰放电节律是经随机自共振产生的,是确定性因素和随机因素共同作用的结果。     

Using an Hebbian Learning Rule for Multi-Class SVM Classifiers

Regarding biological visual classification, recent series of experiments have enlighten the fact that data classification can be realized in the human visual cortex with latencies of about 100-150 ms, which, considering the visual pathways latencies, is only compatible with a very specific processing architecture, described by models from Thorpe et al. Surprisingly enough, this experimental evidence is in coherence with algorithms derived from the statistical learning theory. More precisely, there is a double link: on one hand, the so-called Vapnik theory offers tools to evaluate and analyze the biological model performances and on the other hand, this model is an interesting front-end for algorithms derived from the Vapnik theory. The present contribution develops this idea, introducing a model derived from the statistical learning theory and using the biological model of Thorpe et al. We experiment its performances using a restrained sign language recognition experiment. This paper intends to be read by biologist as well as statistician, as a consequence basic material in both fields have been reviewed.     

Known Knowns,Known Unknowns,and Unknown Unknowns: Coverage in MS Experiments

A mathematical model from ecology, namely, the capture–recapture model with a closed population and time‐varying and heterogeneous individual probabilities of capture, is implemented to model the number of protein identifications across the various cycles of a mass spectroscopy experiment. Rcapture, a package available in the R computing environment, can easily provide estimates of the cardinality of the proteome from such experiments. Alternatively, model fitting can be undertaken in other software platforms, such as Matlab, that can accommodate general linear models. It has not escaped our notice that capture–recapture models can be more broadly applied to other settings, so as to estimate the number of missing observations in an experiment.     

A Bayesian two-level model for fluctuation assay

The fluctuation experiment is an essential tool for measuring microbial mutation rates in the laboratory. When inferring the mutation rate from an experiment, one assumes that the number of mutants in each test tube follows a common distribution. This assumption conceptually restricts the scope of applicability of fluctuation assay. We relax this assumption by proposing a Bayesian two-level model, under which an experiment-wide average mutation rate can be defined. The new model suggests a gamma mixture of the Luria-Delbrück distribution, which coincides with a recently discovered discrete distribution. While the mixture model is of considerable independent interest in fluctuation assay, it also offers a practical Markov chain Monte Carlo method for estimating mutation rates. We illustrate the Bayesian approach with a detailed analysis of an actual fluctuation experiment.     

Identifiability: the first step in parameter estimation

The observations in an experiment define a set of observational parameters that are functions of the basic kinetic parameters of the model of the system. The problem of identifiability is concerned with whether the observational parameters uniquely specify the basic kinetic parameters. As such, it depends only on the functional relation between the two levels of parameters and not on errors of observation and the estimation procedure. It should be checked before doing the experiment. Given initial estimates of the basic kinetic parameters, identifiability can be checked, in a local sense, from data generated by simulating the experiment on the model.     

Modelling and parameter estimation of the enzymatic synthesis of oligosaccharides by β‐galactosidase from Bacillus circulans

The aim of this research is to develop a model to describe oligosaccharide synthesis and simultaneously lactose hydrolysis. Model A (engineering approach) and model B (biochemical approach) were used to describe the data obtained in batch experiments with β‐galactosidase from Bacillus circulans at various initial lactose concentrations (from 0.19 to 0.59 mol·kg⁻¹). A procedure was developed to fit the model parameters and to select the most suitable model. The procedure can also be used for other kinetically controlled reactions. Each experiment was considered as an independent estimation of the model parameters, and consequently, model parameters were fitted to each experiment separately. Estimation of the parameters per experiment preserved the time dependence of the measurements and yielded independent sets of parameters. The next step was to study by ordinary regression methods whether parameters were constant under the altering conditions examined. Throughout all experiments, the parameters of model B did not show a trend upon the initial lactose concentration when inhibition was included. Therefore model B, a galactosyl‐enzyme complex‐based model, was chosen to describe the oligosaccharide synthesis, and one parameter set was determined for various initial lactose concentrations. © 1999 John Wiley & Sons, Inc. Biotechnol Bioeng 64: 558–567, 1999.     

Parametric modeling of reaction time experiment data

A simple parametric model is proposed for data from a point-process version of a reaction time experiment. It is used to statistically check for the presence and nature of nonlinear inhibition in the eye-brain-hand system, as well as to study the nature of the reaction time delay distribution. The model tells us that, in principle, the second-order intensity estimate can be used to determine whether the experimental subject is systematically observing the first or the second of two flashes transmitted in short succession. Nonparametric estimates of second-order intensity functions are used in conjunction with this model. In particular, the model allows for the computation of good bandwidths for intensity curve estimation. A parametric bootstrap can also be implemented. Our methods are illustrated with 12 runs of data from a real reaction time experiment. It is found that nonlinear inhibition is present in the eye-brain-hand system. However, there are insufficient data to distinguish between log-normality and normality in the reaction time distribution, due partly to confounding with the particular kind of nonlinear inhibition present in the system.     

Selection for environmental variation: a statistical analysis and power calculations to detect response

Data from uterine capacity in rabbits (litter size) were analyzed to determine whether the environmental variance was partly genetically determined. The fit of a classical homogeneous variance mixed linear (HOM) model and that of a genetically structured heterogeneous variance mixed linear (HET) model were compared. Various methods to assess the quality of fit favor the HET model. The posterior mean (95% posterior interval) of the additive genetic variance affecting the environmental variance was 0.16 (0.10; 0.25) and the corresponding number for the coefficient of correlation between genes affecting mean and variance was -0.74 (-0.90;-0.52). It is argued that stronger support for the HET model than that derived from statistical analysis of data would be provided by a successful selection experiment designed to modify the environmental variance. A simple selection criterion is suggested (average squared deviation from the mean of repeated records within individuals) and its predicted response and variance under the HET model are derived. This is used to determine the appropriate size and length of a selection experiment designed to change the environmental variance. Results from the analytical expressions are compared with those obtained using simulation. There is good agreement provided selection intensity is not intense.     

Recognizing odd smells and ejection of brood parasitic eggs. An experimental test in magpies of a novel defensive trait against brood parasitism

One of the most important defensive host traits against brood parasitism is the detection and ejection of parasitic eggs from their nests. Here, we explore the possible role of olfaction in this defensive behaviour. We performed egg‐recognition tests in magpie Pica pica nests with model eggs resembling those of parasitic great spotted cuckoos Clamator glandarius. In one of the experiment, experimental model eggs were exposed to strong or moderate smell of tobacco smoke, whereas those of a third group (control) were cleaned with disinfecting wipes and kept in boxes containing odourless cotton. Results showed that model eggs with strong tobacco scent were more frequently ejected compared with control ones. In another experiment, models were smeared with scents from cloacal wash from magpies (control), cloacal wash or uropygial secretions from cuckoos, or human scents. This experiment resulted in a statistically significant effect of treatment in unparasitized magpie nests in which control model eggs handled by humans were more often rejected. These results provide the first evidence that hosts of brood parasites use their olfactory ability to detect and eject foreign eggs from their nests. These findings may have important consequences for handling procedures of experimental eggs used in egg‐recognition tests, in addition to our understanding of interactions between brood parasites and their hosts.     

Structural identifiability of the parameters of a nonlinear batch reactor model.

The similarity transformation approach is used to analyze the structural identifiability of the parameters of a nonlinear model of microbial growth in a batch reactor in which only the concentration of microorganisms is measured. It is found that some of the model parameters are unidentifiable from this experiment, thus providing the first example of a real-life nonlinear model that turns out not to be globally identifiable. If it is possible to measure the initial concentration of growth-limiting substrate as well, all model parameters are globally identifiable.     

RNA folding with soft constraints: reconciliation of probing data and thermodynamic secondary structure prediction

Thermodynamic folding algorithms and structure probing experiments are commonly used to determine the secondary structure of RNAs. Here we propose a formal framework to reconcile information from both prediction algorithms and probing experiments. The thermodynamic energy parameters are adjusted using 'pseudo-energies' to minimize the discrepancy between prediction and experiment. Our framework differs from related approaches that used pseudo-energies in several key aspects. (i) The energy model is only changed when necessary and no adjustments are made if prediction and experiment are consistent. (ii) Pseudo-energies remain biophysically interpretable and hold positional information where experiment and model disagree. (iii) The whole thermodynamic ensemble of structures is considered thus allowing to reconstruct mixtures of suboptimal structures from seemingly contradicting data. (iv) The noise of the energy model and the experimental data is explicitly modeled leading to an intuitive weighting factor through which the problem can be seen as folding with 'soft' constraints of different strength. We present an efficient algorithm to iteratively calculate pseudo-energies within this framework and demonstrate how this approach can be used in combination with SHAPE chemical probing data to improve secondary structure prediction. We further demonstrate that the pseudo-energies correlate with biophysical effects that are known to affect RNA folding such as chemical nucleotide modifications and protein binding.     

Locating and navigation mechanism based on place-cell and grid-cell models

Extensive experiments on rats have shown that environmental cues play an important role in goal locating and navigation. Major studies about locating and navigation are carried out based only on place cells. Nevertheless, it is known that navigation may also rely on grid cells. Therefore, we model locating and navigation based on both, thus developing a novel grid-cell model, from which firing fields of grid cells can be obtained. We found a continuous-time dynamic system to describe learning and direction selection. In our simulation experiment, according to the results from physiology experiments, we successfully rebuild place fields of place cells and firing fields of grid cells. We analyzed the factors affecting the locating accuracy. Results show that the learning rate, firing threshold and cell number can influence the outcomes from various tasks. We used our system model to perform a goal navigation task and showed that paths that are changed for every run in one experiment converged to a stable one after several runs.