Optimizational production of phenyllactic acid by a Lactobacillus buchneri strain via uniform design with overlay sampling methodology

A Lactobacillus buchneri GBS3 strain isolated from the traditional Chinese pickles was used for the production of 3-phenyllactic acid(PLA), an important compound with antimicrobial activities against a wide species of grampositive and gram-negative bacteria and some fungi. The growth performance of this strain in the de Man, Rogosa and Sharpe(MRS) medium, the production of metabolites of valuable organic acids, and the biosynthesis of PLA using this strain as the whole-cell biocatalyst and phenylpyruvic acid(PPA) as the precursor, were investigated experimentally. The uniform design method with overlay sampling was developed for the optimization of the biotransformation conditions. The results showed that although it produced naturally lactic acid with the maximum concentration of 1.84 g·L^(-1) and PLA with the concentration of 0.015 g·L^(-1) after 66 to 72 h cultivation in MRS broth by fermentation, the present strain displayed an effective utilization ability by transforming PPA to PLA. By the uniform design method with overlay sampling for the design and optimization of transformation conditions, a maximum yield of 10.93 g·L^(-1) PLA with the mole conversion ratio of 83.07% from PPA to PLA was achieved under the optimized condition, i.e., 20 g·L^(-1) glucose, 270 g·L^(-1) cells, 13 g·L^(-1) PPA, pH 8.0 and the reaction time of 15 h, indicating that Lactobacillus buchneri GBS3 was an interesting strain for the biosynthesis of PLA via the microbial transformation. The prediction of PLA yield under different conditions was achieved successfully based on the limited information of only a small number of experiments by the uniform design with overlay sampling. Therefore, the present methodology is effective and helpful for the optimization of the biosynthesis processes of PLA.

Impacts of the sampling design on the abundance index estimation of Portunus trituberculatus using bottom trawl

In the survey of fishery resources,the sampling design will directly impact the accuracy of the estimation of the abundance.Therefore,it is necessary to optimize the sampling design to increase the quality of fishery surveys.The distribution and abundance of fisheries resource estimated based on the bottom trawl survey data in the Changjiang River(Yangtze River)Estuary-Hangzhou Bay and its adjacent waters in 2007 were used to simulate the"true"situation.Then the abundance index of Portunus trituberculatus were calculated and compared with its true index to evaluate the impacts of different sampling designs on the abundance estimation.Four sampling methods(including fixed-station sampling,simple random sampling,stratified fixed-station sampling,and stratified random sampling)were simulated.Three numbers of stations(9,16 and 24)were assumed for the scenarios of fixed-station sampling and simple random sampling without stratification.While 16 stations were assumed for the scenarios with stratification.Three reaction distances(1.5 m,3 m and 5 m)of P.trituberculatus to the bottom line of trawl were also assumed to adapt to the movement ability of the P.trituberculatus for different ages,seasons and substrate conditions.Generally speaking,compared with unstratified sampling design,the stratified sampling design resulted in more accurate abundance estimation of P.trituberculatus,and simple random sampling design is better than fixed-station sampling design.The accuracy of the simulated results was improved with the increase of the station number.The maximum relative estimation error(REE)was 163.43%and the minimum was 49.40%for the fixed-station sampling scenario with 9 stations,while 38.62%and 4.15%for 24 stations.With the increase of reaction distance,the relative absolute bias(RAB)and REE gradually decreased.Resource-intensive area and the seasons with high density variances have significant impacts on simulation results.Thus,it will be helpful if there are prior information or pre-survey results about density distribution.The current study can provide reference for the future sampling design of bottom trawl of P.trituberculatus and other species.

Impacts of sampling design on the abundance estimation of Portunus trituberculatus using crab pots

Fishery-independent surveys can provide high-quality data and support fishery assessment and management.Optimization of sampling design is crucial to increase the quality of fishery surveys.Crab pots are important fishing gears used to catch crabs.We analyzed the impacts of sampling design of crab pots on the abundance of Portunus trituberculatus in the Changjiang(Yangtze)River estuary to the Hangzhou Bay and its adjacent waters in East China Sea.The crab pots were cylindrical,240 mm in height and 600 mm in diameter of the iron ring.Our sampling designs(including fixed-station sampling,simple random sampling,stratified fixed-station sampling,and stratified random sampling),three number of stations(9,16,and 24),and three numbers of crab pots(500,1000,and 3000)were simulated and compared with the“true”abundance that obtained from bottom trawl surveys in the study area in 2007.The scenarios with 16 stations were set in stratification as a control group for comparison with unstratified designs.Results show that simple random sampling can obtain more stable results than fixed-station sampling in the abundance estimation of P.trituberculatus.In addition,stratified sampling resulted in more accurate abundance than unstratified sampling.The accuracy of the simulated results improved with the increase of the number of stations.No remarkable differences in the results were found among the scenarios of different number of crab pots at each station.However,resource-intensive areas exerted great impacts on simulation results.Thus,prior information or pre-survey results about resource abundance and density distribution are necessary.This study may serve as a reference for future sampling designs of crab pots of P.trituberculatus and other species.

Estimation of genetic parameters and their sampling variances for quantitative traits in the type 2 modified augmented design

The type 2 modified augmented design(MAD2) is an efficient unreplicated experimental design used for evaluating large numbers of lines in plant breeding and for assessing genetic variation in a population. Statistical methods and data adjustment for soil heterogeneity have been previously described for this design. In the absence of replicated test genotypes in MAD2, their total variance cannot be partitioned into genetic and error components as required to estimate heritability and genetic correlation of quantitative traits, the two conventional genetic parameters used for breeding selection. We propose a method of estimating the error variance of unreplicated genotypes that uses replicated controls, and then of estimating the genetic parameters. Using the Delta method, we also derived formulas for estimating the sampling variances of the genetic parameters.Computer simulations indicated that the proposed method for estimating genetic parameters and their sampling variances was feasible and the reliability of the estimates was positively associated with the level of heritability of the trait. A case study of estimating the genetic parameters of three quantitative traits, iodine value, oil content, and linolenic acid content, in a biparental recombinant inbred line population of flax with 243 individuals, was conducted using our statistical models. A joint analysis of data over multiple years and sites was suggested for genetic parameter estimation. A pipeline module using SAS and Perl was developed to facilitate data analysis and appended to the previously developed MAD data analysis pipeline(http://probes.pw.usda.gov/bioinformatics_ tools/MADPipeline/index.html).

Toward Estimating the Variance in Acoustic Surveys Based on Sampling Design

This paper develops a sampling method to estimate the integral of a function of the area with a strategy to cover the area with parallel lines of observation. This sampling strategy is special in that lines very close to each other are selected much more seldom than under a uniformly random design for the positions of the parallel lines. It is also special in that the positions of some of the lines are deterministic. Two different variance estimators are derived and investigated by sampling different man made signal functions. They show different properties in that the estimator that estimate the biggest variance gives an error interval that, in some situations, may be more than ten times the error interval computed from the other estimator. It is also obvious that the second estimator underestimates the variance. The author has not succeeded to derive an expression for the expectation of this estimator. This work is motivated towards finding the variance of acoustic abundance estimates.

The Use of Spatial Sampling Designs in Business Surveys

An innovative use of spatial sampling designs is here presented. Sampling methods which consider spatial locations of statistical units are already used in agricultural and environmental contexts, while they have never been exploited for establishment surveys. However, the rapidly increasing availability of geo- referenced information about business units makes that possible. In business studies, it may indeed be important to take into account the presence of spatial autocorrelation or spatial trends in the variables of interest, in order to have more precise and efficient estimates. The opportunity of using the most innovative spatial sampling designs in business surveys, in order to produce samples that are well spread in space, is here tested by means of Monte Carlo experiments. For all designs, the Horvitz-Thompson estimator of the population total is used both with equal and unequal inclusion probabilities. The efficiency of sampling designs is evaluated in terms of relative RMSE and efficiency gain compared with designs ignoring the spatial information. Furthermore, an evaluation of spatially balancing samples is also conducted.

Assessing the Performance of Some Ranked Set Sampling Designs Using Hybrid Approach

In this paper,a joint analysis consisting of goodness-of-fit tests and Markov chain Monte Carlo simulations are used to assess the performance of some ranked set sampling designs.The Markov chain Monte Carlo simulations are conducted when Bayesian methods with Jeffery’s priors of the unknown parameters of Weibull distribution are used,while the goodness of fit analysis is conducted when the likelihood estimators are used and the corresponding empirical distributions are obtained.The ranked set sampling designs considered in this research are the usual ranked set sampling,extreme ranked set sampling,median ranked set sampling,and neoteric ranked set sampling designs.An intensive Monte Carlo simulation study is conducted using Lindley’s approximation algorithm to compute the different designs’-based estimators.The study showed that the dependent design“neoteric ranked set sampling design”is superior to other ranked set designs and the total relative efficiency is higher than the other designs’total relative efficiency.

Performance evaluation of fixed-station sampling design for a fishery-independent survey with multiple objectives

Fixed-station sampling design was widely used in fishery-independent surveys because of its characteristics of convenient sampling station setting,but the non-probabilistic(fixed)nature made it more uncertainty of drawing inferences on population.The performance of fixed-station sampling design for multispecies survey has not been evaluated,and we are uncertain if the design could detect the temporal trends of different populations in multispecies fishery-independent survey.In this study,spatial distribution of abundance indices for three species with different spatial distribution patterns including small yellow croaker(Larimichthys polyactis),whitespotted conger(Conger myriaster)and Fang’s blenny(Enedrias fangi)were simulated using ordinary kriging interpolation as the“true”population distribution.The performance of fixed-station sampling design was compared with simple random sampling design by resampling the simulated“true”populations in this simulation study.The results showed that the fixed-station sampling design had the power to detect the seasonal trends of species abundance.The effectiveness of fixed-station sampling design were different in different species distribution patterns.When the species had even distribution,fixed-station sampling design could get high quality abundance data;when the distribution was uneven with heterogeneity or patchiness,fixed-station sampling design tended to underestimate or overestimate the abundance.Evidently,the estimates of abundance index based on the fixedstation sampling design must be calibrated cautiously while applying them for fisheries stock assessment and management.This study suggested that fixed-station sampling design could catch the temporal dynamics of population abundance,but the abundance estimates from the fixed-station sampling design could not be treated as the absolute estimates of populations.

Event-triggered sampling and fault-tolerant control co-design based on fault diagnosis observer

A co-design scheme of event-triggered sampling mechanism and active fault tolerant control(FTC) is developed. Firstly,a fault diagnosis observer is designed to estimate both the fault and the state simultaneously by using the event-triggered sampled output. Some H∞constraints between the estimation errors and the event-triggered sampling mechanism are established to ensure the estimation accuracy. Then, based on the constraints and the obtained fault information, an event-triggered detector and a static fault tolerant controller are co-designed to guarantee the stability of the faulty system and to reduce the sensor communication cost.Furthermore, the problem of the event detector and dynamic FTC co-design is also investigated. Simulation results of an unstable batch reactor are finally provided to illustrate the effectiveness of the proposed method.

Sampling Designs with Linear and Quadratic Probability Functions

Fixed size without replacement sampling designs with probability functions that are linear or quadratic functions of the sampling indicators are defined and studied. Generality, simplicity, remarkable properties, and also somewhat restricted flexibility characterize these designs. It is shown that the families of linear and quadratic designs are closed with respect to sample complements and with respect to conditioning on sampling outcomes for specific units. Relations between inclusion probabilities and parameters of the probability functions are derived and sampling procedures are given.

Spatial Variability of Soil Properties at Capulin Volcano,New Mexico,USA:Implications for Sampling Strategy

Non-agricultural lands are surveyed sparsely in general.Meanwhile,soils in these areas usually exhibit strong spatial variability which requires more samples for producing acceptable estimates.Capulin Volcano National Monument,as a typical sparsely-surveyed area,was chosen to assess spatial variability of a variety of soil properties,and furthermore,to investigate its implications for sampling design.One hundred and forty one composited soil samples were collected across the Monument and the surrounding areas.Soil properties including pH,organic matter content,extractable elements such as calcium (Ca),magnesium (Mg),potassium (K),sodium (Na),phosphorus (P),sulfur (S),zinc (Zn),and copper (Cu),as well as sand,silt,and clay percentages were analyzed for each sample.Semivariograms of all properties were constructed,standardized,and compared to estimate the spatial variability of the soil properties in the area.Based on the similarity among standardized semivariograms,we found that the semivariograms could be generalized for physical and chemical properties,respectively.The generalized semivariogram for physical properties had a much greater sill value (2.635) and effective range (7 500 m) than that for chemical properties.Optimal sampling density (OSD),which is derived from the generalized semivariogram and defines the relationship between sampling density and expected error percentage,was proposed to represent,interpret,and compare soil spatial variability and to provide guidance for sample scheme design.OSDs showed that chemical properties exhibit a stronger local spatial variability than soil texture parameters,implying more samples or analysis are required to achieve a similar level of precision.

Drilling Power Consumption and Soil Conveying Volume Performances of Lunar Sampling Auger

The sampling auger used in lunar sampling and return mission is to transmit power and convey soil, and its performance is the key factor of the whole mission. However, there is currently a lack of the optimization research on soil conveying volume and power consumption models in auger structure design. To provide the drilled object, the simulation lunar soil, whose physical and mechanical property is the same as the real soil, is made by reducing soil void ratio. The models are formulated to analyze the influence of auger structure parameters on power consumption and soil conveying volume. To obtain the optimized structure parameters of auger, the multi-objective optimization functions of the maximum soil conveying volume and minimum power consumption are developed. To verify the correctness of the models, the performances of different augers drilling simulation soil are tested. The test results demonstrate that the power consumption of optimized auger is the lowest both in theory and test, and the experimental results of soil conveying volume are in agreement with theoretical analysis. Consequently, a new method for designing a lunar sampling auger is proposed which includes the models of soil conveying volume and transportation power consumption, the optimization of structure parameters and the comparison tests. This method provides a reference for sampling auger designing of the Chinese Lunar Sample Mission.

The sampling apparatus of volatile organic compounds for wood composites

Terpenes, aldehydes, ketones, benzene, and toluene are the important volatileorganic compounds (VOCs) emitted from wood composites. A sampling apparatus of VOCs for woodcomposites was designed and manufactured by Northeast Forestry University in China. Theconcentration of VOCs derived from wood based materials, such as flooring, panel wall, finishing,and furniture can be sampled in a small stainless steel chambers. A protocol is also developed inthis study to sample and measure the new and representative specimens. Preliminary research showedthat the properties of the equipment have good stability. The sort and the amount of differentcomponents can be detected from it. The apparatus is practicable.

Design of Full-Ocean-Depth Self-Floating Sampler and Analysis of Factors Affecting Core Penetration Depth

The hadal zone(ocean depths of 6 – 11 km) is one of the least-understood habitats on Earth because of its extreme conditions such as high pressure, darkness, and low temperature. With the development of deep-sea vehicles such as China's 7000 m manned submersible Jiaolong, abyssal science has received greater attention. For decades, gravity-piston corers have been widely used to collect loose subsea-sediment long-core samples. However, the weight and length of the gravity sampler cables and the operating environment limit sampling capacity at full ocean depths. Therefore, a new self-floating sediment sampler with a spring-loaded auto-trigger release and that incorporates characteristics from traditional gravity-driven samplers is designed. This study analyzes the process by which a gravity-piston corer penetrates the sediment and the factors that affect it. A formula for obtaining the penetration depth is deduced. A method of optimizing the sampling depth is then developed based on structure design and parametric factor modeling. The parameters considered in the modeling include the sampling depth, balance weight, ultimate stress friction coefficient, dimensions of the sampler, and material properties. Thus, a new deep-sea floating parametric sampler designed based on virtual prototyping is proposed. Accurate values for all the design factors are derived from calculations based on the conservation of energy with penetration depth, analyses of the factors affecting the penetration depth, and analyses of the pressure bar stability. Finally, experimental data are used to verify the penetration-depth function and to provide theoretical guidance for the design of sediment samplers.

Data-driven evolutionary sampling optimization for expensive problems

Surrogate models have shown to be effective in assisting evolutionary algorithms(EAs)for solving computationally expensive complex optimization problems.However,the effectiveness of the existing surrogate-assisted evolutionary algorithms still needs to be improved.A data-driven evolutionary sampling optimization(DESO)framework is proposed,where at each generation it randomly employs one of two evolutionary sampling strategies,surrogate screening and surrogate local search based on historical data,to effectively balance global and local search.In DESO,the radial basis function(RBF)is used as the surrogate model in the sampling strategy,and different degrees of the evolutionary process are used to sample candidate points.The sampled points by sampling strategies are evaluated,and then added into the database for the updating surrogate model and population in the next sampling.To get the insight of DESO,extensive experiments and analysis of DESO have been performed.The proposed algorithm presents superior computational efficiency and robustness compared with five state-of-the-art algorithms on benchmark problems from 20 to 200 dimensions.Besides,DESO is applied to an airfoil design problem to show its effectiveness.

Efficient sampling for decision making in materials discovery

Accelerating materials discovery crucially relies on strategies that efficiently sample the search space to label a pool of unlabeled data.This is important if the available labeled data sets are relatively small compared to the unlabeled data pool.Active learning with efficient sampling methods provides the means to guide the decision making to minimize the number of experiments or iterations required to find targeted properties.We review here different sampling strategies and show how they are utilized within an active learning loop in materials science.

Analytical quality-by-design approach for sample treatment of BSA-containing solutions

The sample preparation of samples conlaining bovine serum albumin（BSA）,e.g..as used in transdermal Franz diffusion cell（FDC） solutions,was evaluated using an analytical qualily-by-design（QbD）approach.Traditional precipitation of BSA by adding an equal volume of organic solvent,often successfully used with conventional HPLC-PDA,was found insufficiently robust when novel fused-core HPLC and/or UPLC-MS methods were used.In this study,three factors（acetonitrile（%）.formic acid（%） and boiling time（min）） were included in the experimental design to determine an optimal and more suitable sample treatment of BSAcontaining FDC solutions.Using a QbD and Derringer desirability（D） approach,combining BSA loss,dilution factor and variability,we constructed an optimal working space with the edge of failure defined as D〈0.9.The design space is modelled and is confirmed to have an ACN range of 83 ± 3% and FA content of 1 ±0.25%.

H_∞ Design for Sampled-Data Systems via Lifting Technique: Conditions and Limitation

The initial motivation of the lifting technique is to solve the H∞control problems. However, the conventional weighted H∞design does not meet the conditions required by lifting, so the result often leads to a misjudgement of the design. Two conditions required by using the lifting technique are presented based on the basic formulae of the lifting. It is pointed out that only the H∞disturbance attenuation problem with no weighting functions can meet these conditions, hence, the application of the lifting technique is quite limited.

Power Analysis and Sample Size Determination for Crossover Trials with Application to Bioequivalence Assessment of Topical Ophthalmic Drugs Using Serial Sampling Pharmacokinetic Data

Objective To develop methods for determining a suitable sample size for bioequivalence assessment of generic topical ophthalmic drugs using crossover design with serial sampling schemes.Methods The power functions of the Fieller-type confidence interval and the asymptotic confidence interval in crossover designs with serial-sampling data are here derived.Simulation studies were conducted to evaluate the derived power functions.Results Simulation studies show that two power functions can provide precise power estimates when normality assumptions are satisfied and yield conservative estimates of power in cases when data are log-normally distributed.The intra-correlation showed a positive correlation with the power of the bioequivalence test.When the expected ratio of the AUCs was less than or equal to 1, the power of the Fieller-type confidence interval was larger than the asymptotic confidence interval.If the expected ratio of the AUCs was larger than 1, the asymptotic confidence interval had greater power.Sample size can be calculated through numerical iteration with the derived power functions.Conclusion The Fieller-type power function and the asymptotic power function can be used to determine sample sizes of crossover trials for bioequivalence assessment of topical ophthalmic drugs.

Influence of parental sample sizes on the estimating genetic parameters in cultured clam Meretrix meretrix based on factorial mating designs

The precise and accurate knowledge of genetic parameters is a prerequisite for making efficient selection strategies in breeding programs.A number of estimators of heritability about important economic traits in many marine mollusks are available in the literature,however very few research have evaluated about the accuracy of genetic parameters estimated with different family structures.Thus,in the present study,the effect of parent sample size for estimating the precision of genetic parameters of four growth traits in clam M.meretrix by factorial designs were analyzed through restricted maximum likelihood（REML） and Bayesian.The results showed that the average estimated heritabilities of growth traits obtained from REML were 0.23-0.32 for 9 and 16 full-sib families and 0.19-0.22 for 25 full-sib families.When using Bayesian inference,the average estimated heritabilities were0.11-0.12 for 9 and 16 full-sib families and 0.13-0.16 for 25 full-sib families.Compared with REML,Bayesian got lower heritabilities,but still remained at a medium level.When the number of parents increased from 6 to 10,the estimated heritabilities were more closed to 0.20 in REML and 0.12 in Bayesian inference.Genetic correlations among traits were positive and high and had no significant difference between different sizes of designs.The accuracies of estimated breeding values from the 9 and 16 families were less precise than those from 25 families.Our results provide a basic genetic evaluation for growth traits and should be useful for the design and operation of a practical selective breeding program in the clam M.meretrix.