A Procedure for the Common Estimation of Parameters Corresponding to Several Treatment Groups

In this article we give a procedure for the common estimation of parameters corresponding to several treatment groups. Thereby we assume that the distribution functions of the groups belong to the same family and differ only in the parameter values. The procedure allows the common estimation of some of these parameters. The parameters themselves will be estimated by the maximum likelihood method; the estimators will be calculated iteratively by the Newton-Raphson method. To prove if the common estimation is possible, we propose as a suitable test the maximum likelihood ratio test. Finally we show the application of our procedure in the case of the probit analysis.     

Kinetics of lactose fermentation using a recombinant Saccharomyces cerevisiae strain

This work presents a multi-route, non-structural kinetic model for interpretation of ethanol fermentation of lactose using a recombinant flocculent Saccharomyces cerevisiae strain expressing both the LAC4 (coding for beta-galactosidase) and LAC12 (coding for lactose permease) genes of Kluyveromyces lactis. In this model, the values of different metabolic pathways are calculated applying a modified Monod equation rate in which the growth rate is proportional to the concentration of a key enzyme controlling the single metabolic pathway. In this study, three main metabolic routes for S. cerevisiae are considered: oxidation of lactose, reduction of lactose (producing ethanol), and oxidation of ethanol. The main bioprocess variables determined experimentally were lactose, ethanol, biomass, and dissolved oxygen concentrations. Parameters of the proposed kinetic model were established by fitting the experimental data obtained in a small lab-scale fermentor with the initial lactose concentrations ranging from 5 g/dm3 to 50 g/dm3. A very good agreement between experimental data and simulated profiles of the main variables (lactose, ethanol, biomass, and dissolved oxygen concentrations) was achieved.     

Estimation of Parameters in Mixtures of Distributions Arising in Cultivation Problems

In many processes one has to describe events in which given particles or elements divide themselves so that these elements are to observe only after the division. In biological problems one meets such a situation i.e. in the cultivation of bacteria. In this paper we investigate a mathematical model which describes such a phenomenon. We give estimators for the expectation μ and the variance σ² of the distribution of the undivided particles. Therefore we give a best linear unbiased estimator for μ and an estimator μ where a clustering of the observation is essentially. In an example for the judgment of a cultivation method we demonstrate the given estimation procedures.     

Simultaneous versus sequential optimal experiment design for the identification of multi-parameter microbial growth kinetics as a function of temperature

Optimal experiment design for parameter estimation (OED/PE) has become a popular tool for efficient and accurate estimation of kinetic model parameters. When the kinetic model under study encloses multiple parameters, different optimization strategies can be constructed. The most straightforward approach is to estimate all parameters simultaneously from one optimal experiment (single OED/PE strategy). However, due to the complexity of the optimization problem or the stringent limitations on the system's dynamics, the experimental information can be limited and parameter estimation convergence problems can arise. As an alternative, we propose to reduce the optimization problem to a series of two-parameter estimation problems, i.e., an optimal experiment is designed for a combination of two parameters while presuming the other parameters known. Two different approaches can be followed: (i) all two-parameter optimal experiments are designed based on identical initial parameter estimates and parameters are estimated simultaneously from all resulting experimental data (global OED/PE strategy), and (ii) optimal experiments are calculated and implemented sequentially whereby the parameter values are updated intermediately (sequential OED/PE strategy).This work exploits OED/PE for the identification of the Cardinal Temperature Model with Inflection (CTMI) (Rosso et al., 1993). This kinetic model describes the effect of temperature on the microbial growth rate and encloses four parameters. The three OED/PE strategies are considered and the impact of the OED/PE design strategy on the accuracy of the CTMI parameter estimation is evaluated. Based on a simulation study, it is observed that the parameter values derived from the sequential approach deviate more from the true parameters than the single and global strategy estimates. The single and global OED/PE strategies are further compared based on experimental data obtained from design implementation in a bioreactor. Comparable estimates are obtained, but global OED/PE estimates are, in general, more accurate and reliable.     

Estimating subject-specific body segment parameters using a 3-dimensional modeller program

The estimation of body segment properties is important in the biomechanical analysis of movement. Current subject-specific estimation methods however can be expensive and time-consuming, while other methods do not adequately take into account individual or group variability. We describe a simple procedure for estimating subject-specific geometric properties, independent of joint centres. The method requires only a small number of anthropometric measurements and digital images of the segment or subject, a 3-dimensional modeller program and simple mathematical calculations to estimate segment volumes and centroids. Assuming that the segment is of uniform density, it's mass and moment of inertia can also be derived. Future work should include generating segment density profiles for particular populations, to increase the accuracy of the method, and comparing the accuracy of the results obtained with those produced by other techniques.     

colonise: a computer program to study colonization processes in metapopulations

colonise is a user‐friendly Microsoft® Windows software for the study of colonization events in natural populations. It determines the composition of the newly colonized population and makes inferences about the factors that influenced individuals to establish a new population. colonise uses a hierarchical Bayesian model to combine multilocus genotype data with demographic and environmental data, and a reversible jump Markov chain Monte Carlo (MCMC) algorithm to perform posterior density estimations. The software is written in C and C + + for the graphical user interface and is available at http://www2.ujf‐grenoble.fr/leca/presentation.html . The software integrates a tool to draw posterior density functions (histogram, running mean, traces, etc.) and to estimate parameters from them (mean, mode, variance, HPDI, etc.).     

Constructing a Cotton-Ball Catapult: An Interdisciplinary STS Learning Experience

The need for all students to develop a stronger ability to express their science knowledge in writing is important. In this article, the authors take you on a journey in an elementary school classroom with tools to help foster deeper learning and stronger writing skills in science content. With many students in high school required to pass end-of-semester science exams to receive a diploma, teaching writing at an early age across various content areas is an even more critical component of today’s curriculum. Presenting curriculum material through multiple means (Universal Design for Learning-Representation) allows students to gain information through a learning approach that best fits the students’ learning needs. The authors examine multiple means of representing science curriculum to engage students in creating detailed and comprehensive concept maps and to provide supportive scientific evidence in written explanations as they gain more content knowledge in science.     

Estimation of random regression parameters

The problem of the best linear unbiased estimation (BLUE) of random regression parameters is considered. It is proved that increasing informations about the mean value of the parameters both extend the class of estimable linear functionals and improve on the estimation. In all investigated cases the uniqueness of BLUE is proved. In the case of known mean values the BLUE is shown to be numerically equivalent with the MMSEE almost everywhere. A numerical example shows the improvements of BLUE due to increasing informations about the mean values of the parameters.     

解放军疾控所SPF动物房的工艺设计

解放军疾病预防控制所,为履行职能和任务,在业务办公大楼12层建设了SPF动物实验设施,面积约240 m^2。文章介绍了作者的设计思路和建设方案。同时还介绍了SPF动物房的建设经验。     

Design and characterization of novel hybrid peptides from LFB15(W4,10), HP(2-20), and cecropin A based on structure parameters by computer-aided method

The increasing problem of antibiotic resistance among pathogenic bacteria requires development of new antimicrobial agents. The pivotal assets of the antimicrobial peptide include potential for rapid bactericidal activity and low propensity for resistance. The four new antimicrobial hybrid peptides were designed based on peptides LFB15(W4,10), HP(2-20), and cecropin A according to the structure–activity relationship of the amphipathic and cationic antimicrobial peptides. Their structural parameters were accessed by bioinformatics tools, and then two hybrids with the most potential candidates were synthesized. The hybrid peptide LH28 caused an increase in antibiotic activity (MIC₅₀ = 1.56–3.13 μM) against given bacterial strains and did not cause obvious hemolysis of rabbit erythrocytes at concentration of 3.13 μM with effective antimicrobial activity. The results demonstrate that evaluating the structural parameters could be useful for designing novel antimicrobial peptides. Zi-gang Tian and Tian-tang Dong contributed equally to this paper     

Nonparametric Analysis of Cyclic Curves with an Application in the Psychiatry

A nonparametric procedure is described which can be used to distinguish certain types of very irregular curves. The lengths of some waves of the curves resp. their frequencies are estimated unbiasedly. An example of such curves are the so called arterypulse-waves which can be used to diagnose some psychiatric deseases.     

Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters

Accurate and fast estimation of genetic parameters that underlie quantitative traits using mixed linear models with additive and dominance effects is of great importance in both natural and breeding populations. Here, we propose a new fast adaptive Markov chain Monte Carlo (MCMC) sampling algorithm for the estimation of genetic parameters in the linear mixed model with several random effects. In the learning phase of our algorithm, we use the hybrid Gibbs sampler to learn the covariance structure of the variance components. In the second phase of the algorithm, we use this covariance structure to formulate an effective proposal distribution for a Metropolis-Hastings algorithm, which uses a likelihood function in which the random effects have been integrated out. Compared with the hybrid Gibbs sampler, the new algorithm had better mixing properties and was approximately twice as fast to run. Our new algorithm was able to detect different modes in the posterior distribution. In addition, the posterior mode estimates from the adaptive MCMC method were close to the REML (residual maximum likelihood) estimates. Moreover, our exponential prior for inverse variance components was vague and enabled the estimated mode of the posterior variance to be practically zero, which was in agreement with the support from the likelihood (in the case of no dominance). The method performance is illustrated using simulated data sets with replicates and field data in barley.     

Translational stiffness of the replaced shoulder joint

Results after a total shoulder arthroplasty in rheumatoid patients are poor, indicated by loosening of especially the glenoid component, bad joint functionality and the possibility of a joint dislocation. The failure mechanisms behind this are multiple, including patient, surgical and design factors. These results must be improved. At present, the optimal geometrical prosthesis component design, focused on joint conformity and constraint, still has to be investigated.

Proper understanding of the effect of geometrical design parameters on the theoretical relationship between joint translations and joint forces may contribute to improved designs. The main objective of this study is to theoretically describe this relationship and to investigate the joint translational stiffness, which can be used to investigate the effect of design parameters on joint motion. Joint translational stiffness is the gradient of the subluxation force with respect to the humeral head displacement.

For this static analysis a potential field is introduced, as the result of a joint compressive force (muscle forces) and a subluxation force (external forces). The positive and negative stiffness during articulation inside and subluxation outside the glenoid cavity, lead to stable and unstable equilibrium joint positions, respectively. A most lateral position of the humeral head centre coincides with a zero subluxation force; at this position the humerus is dislocated and a restoring force is needed to relocate the humeral head.

Joint conformity and compression force influence the joint translational stiffness during articulation inside the glenoid cavity, whereas during articulating outside the glenoid cavity this is influenced by the joint compression force and humeral radius of curvature. The glenoid radius of curvature influences the contact point and, in combination with the glenoid superior–inferior chord length, it also influences the constraintness angle, which influences the maximum allowable subluxation load to prevent a joint dislocation. This constraintness angle together with the joint conformity also influences maximum joint translations before articulation outside the glenoid cavity. Furthermore, the sign of the joint translational stiffness determines the stability of shoulder motion, which is stable and unstable if this stiffness is positive and negative, respectively.