Aboveground community and species-specific plant biomass from the Jena Experiment (Main Experiment, time series since 2002)

This data set comprises a time series of aboveground community plant biomass (Sown plant community, Weed plant community, Dead plant material, and Unidentified plant material; all measured in biomass as dry weight) and species-specific biomass from the sown species of the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. Aboveground community biomass was harvested twice a year just prior to mowing (during peak standing biomass twice a year, generally in May and August; in 2002 only once in September) on all experimental plots of the main experiment. This was done by clipping the vegetation at 3 cm above ground in up to four rectangles of 0.2 x 0.5 m per large plot. The location of these rectangles was assigned by random selection of new coordinates every year within the core area of the plots (i.e. the central 10 x 15 m). The positions of the rectangles within plots were identical for all plots. The harvested biomass was sorted into categories: individual species for the sown plant species, weed plant species (species not sown at the particular plot), detached dead plant material (i.e., dead plant material in the data file), and remaining plant material that could not be assigned to any category (i.e., unidentified plant material in the data file). All biomass was dried to constant weight (70°C, >= 48 h) and weighed. Sown plant community biomass was calculated as the sum of the biomass of the individual sown species. The data for individual samples and the mean over samples for the biomass measures on the community level are given. Overall, analyses of the community biomass data have identified species richness as well as functional group composition as important drivers of a positive biodiversity-productivity relationship.

Aboveground community and species-specific plant biomass from the Jena Experiment (Dominance Experiment, time series since 2002)

This data set comprises a time series of aboveground community plant biomass (Sown plant community, Weed plant community, Dead plant material, and Unidentified plant material; all measured in biomass as dry weight) and species-specific biomass from the sown species of the dominance experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the dominance experiment, 206 grassland plots of 3.5 x 3.5 m were established from a pool of 9 species that can be dominant in semi-natural grassland communities of the study region. In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 3, 4, 6, and 9 species). Plots were maintained by bi-annual weeding and mowing. Aboveground community biomass was harvested twice a year, generally in May and August (in 2002 only once in September) on all experimental plots of the dominance experiment. This was done by clipping the vegetation at 3 cm above ground in two rectangles of 0.2 x 0.5 m per experimental plot. The location of these rectangles was assigned by random selection of new coordinates every year within the central area of the plots (excluding an outer edge of 50cm). The positions of the rectangles within plots were identical for all plots. The harvested biomass was sorted into categories: individual species for the sown plant species, weed plant species (species not sown at the particular plot), detached dead plant material, and remaining plant material that could not be assigned to any category. Biomass was dried to constant weight (70°C, >= 48 h) and weighed. Sown plant community biomass was calculated as the sum of the biomass of the individual sown species. The mean of both samples per plot and the individual measurements are provided in the data file. Overall, analyses of the community biomass data have identified species richness and the presence of particular species as an important driver of a positive biodiversity-productivity relationship.

Collection of aboveground community and species-specific plant biomass from the Jena Experiment (time series since 2002).

This data set comprises time series of aboveground community plant biomass (Sown plant community, Weed plant community, Dead plant material, and Unidentified plant material; all measured in biomass as dry weight) and species-specific biomass from the sown species of several experiments at the field site of a large grassland biodiversity experiment (the Jena Experiment; see further details below). Aboveground community biomass was normally harvested twice a year just prior to mowing (during peak standing biomass twice a year, generally in May and August; in 2002 only once in September) on all experimental plots in the Jena Experiment. This was done by clipping the vegetation at 3 cm above ground in up to four rectangles of 0.2 x 0.5 m per large plot. The location of these rectangles was assigned by random selection of new coordinates every year within the core area of the plots. The positions of the rectangles within plots were identical for all plots. The harvested biomass was sorted into categories: individual species for the sown plant species, weed plant species (species not sown at the particular plot), detached dead plant material (i.e., dead plant material in the data file), and remaining plant material that could not be assigned to any category (i.e., unidentified plant material in the data file). All biomass was dried to constant weight (70°C, >= 48 h) and weighed. Sown plant community biomass was calculated as the sum of the biomass of the individual sown species. The data for individual samples and the mean over samples for the biomass measures on the community level are given. Overall, analyses of the community biomass data have identified species richness as well as functional group composition as important drivers of a positive biodiversity-productivity relationship. The following series of datasets are contained in this collection: 1. Plant biomass form the Main Experiment: In the Main Experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). 2. Plant biomass from the Dominance Experiment: In the Dominance Experiment, 206 grassland plots of 3.5 x 3.5 m were established from a pool of 9 species that can be dominant in semi-natural grassland communities of the study region. In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 3, 4, 6, and 9 species). 3. Plant biomass from the monoculture plots: In the monoculture plots the sown plant community contains only a single species per plot and this species is a different one for each plot. Which species has been sown in which plot is stated in the plot information table for monocultures (see further details below). The monoculture plots of 3.5 x 3.5 m were established for all of the 60 plant species of the Jena Experiment species pool with two replicates per species like the other experiments in May 2002. All plots were maintained by bi-annual weeding and mowing.

Conflits de norms de justice distributive dans les fusions entre égaux Le cas de BioM.rieux-Pierre Fabre

Les strictes fusions entre égaux constituent un phénomène très rare. Pourtant, de nombreux dirigeants communiquent sur l’aspect égalitaire des fusions et acquisitions qu’ils conçoivent. Dans cet article, les auteurs expliquent pourquoi les dirigeants <aquillent>> leurs F&A en <aux>> ; montrent en quoi le postulat égalitaire initial accroît la probabilité de conflits entre deux normes de justice distributive pourtant compl.mentaires : l’égalité et l’équité ; et illustrent leurs propos avec un cas spectaculaire : la fusion égalitaire, puis la separation des entreprises BioMérieux et Pierre Fabre. Paradoxalement, la simple formulation en termes égalitaire des F&A favorise la diffusion de sentiments d’injustice distributive, qui nuit in fine à la performance de l’opération.

Bayesian experimental design for models with intractable likelihoods

In this paper we present a methodology for designing experiments for efficiently estimating the parameters of models with computationally intractable likelihoods. The approach combines a commonly used methodology for robust experimental design, based on Markov chain Monte Carlo sampling, with approximate Bayesian computation (ABC) to ensure that no likelihood evaluations are required. The utility function considered for precise parameter estimation is based upon the precision of the ABC posterior distribution, which we form efficiently via the ABC rejection algorithm based on pre-computed model simulations. Our focus is on stochastic models and, in particular, we investigate the methodology for Markov process models of epidemics and macroparasite population evolution. The macroparasite example involves a multivariate process and we assess the loss of information from not observing all variables.

Shrinkage empirical likelihood estimator in longitudinal analysis with time-dependent covariates-application to modeling the health of Filipino children

The method of generalized estimating equations (GEE) is a popular tool for analysing longitudinal (panel) data. Often, the covariates collected are time-dependent in nature, for example, age, relapse status, monthly income. When using GEE to analyse longitudinal data with time-dependent covariates, crucial assumptions about the covariates are necessary for valid inferences to be drawn. When those assumptions do not hold or cannot be verified, Pepe and Anderson (1994, Communications in Statistics, Simulations and Computation 23, 939–951) advocated using an independence working correlation assumption in the GEE model as a robust approach. However, using GEE with the independence correlation assumption may lead to significant efficiency loss (Fitzmaurice, 1995, Biometrics 51, 309–317). In this article, we propose a method that extracts additional information from the estimating equations that are excluded by the independence assumption. The method always includes the estimating equations under the independence assumption and the contribution from the remaining estimating equations is weighted according to the likelihood of each equation being a consistent estimating equation and the information it carries. We apply the method to a longitudinal study of the health of a group of Filipino children.

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

The Brain Research Institute (BRI) uses various types of indirect measurements, including EEG and fMRI, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the BRI asked the MISG 2010 participants to examine how the available EEG and fMRI data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was made with the following three aspects. (1) The science behind brain activity investigations depends crucially on the quality of the analysis and interpretation of, as well as the recovery of information from, EEG and fMRI measurements. A number of specific methodologies were discussed and formalised, including independent component analysis, principal component analysis, profile monitoring and change point analysis (hidden Markov modelling, time series analysis, discontinuity identification). (2) Even though EEG measurements accurately and very sensitively record the onset of an epileptic event or seizure, they are, from the perspective of understanding the internal initiation and localisation, of limited utility. They only record neuronal activity in the cortical (surface layer) neurons of the brain, which is a direct reflection of the type of electrical activity they have been designed to record. Because fMRI records, through the monitoring of blood flow activity, the location of localised brain activity within the brain, the possibility of combining fMRI measurements with EEG, as a joint inversion activity, was discussed and examined in detail. (3) A major goal for the BRI is to improve understanding about ``when'' (at what time) an epileptic seizure actually commenced before it is identified on an eeg recording, ``where'' the source of this initiation is located in the brain, and ``what'' is the initiator. Because of the general agreement in the literature that, in one way or another, epileptic events and seizures represent abnormal synchronisations of localised and/or global brain activity the modelling of synchronisations was examined in some detail. References C. M. Michel, G. Thut, S. Morand, A. Khateb, A. J. Pegna, R. Grave de Peralta, S. Gonzalez, M. Seeck and T. Landis, Electric source imaging of human brain functions, Brain Res. 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Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets

Analytically or computationally intractable likelihood functions can arise in complex statistical inferential problems making them inaccessible to standard Bayesian inferential methods. Approximate Bayesian computation (ABC) methods address such inferential problems by replacing direct likelihood evaluations with repeated sampling from the model. ABC methods have been predominantly applied to parameter estimation problems and less to model choice problems due to the added difficulty of handling multiple model spaces. The ABC algorithm proposed here addresses model choice problems by extending Fearnhead and Prangle (2012, Journal of the Royal Statistical Society, Series B 74, 1–28) where the posterior mean of the model parameters estimated through regression formed the summary statistics used in the discrepancy measure. An additional stepwise multinomial logistic regression is performed on the model indicator variable in the regression step and the estimated model probabilities are incorporated into the set of summary statistics for model choice purposes. A reversible jump Markov chain Monte Carlo step is also included in the algorithm to increase model diversity for thorough exploration of the model space. This algorithm was applied to a validating example to demonstrate the robustness of the algorithm across a wide range of true model probabilities. Its subsequent use in three pathogen transmission examples of varying complexity illustrates the utility of the algorithm in inferring preference of particular transmission models for the pathogens.

Alive SMC^2: Bayesian model selection for low-count time series models with intractable likelihoods

In this paper we present a new method for performing Bayesian parameter inference and model choice for low count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel pseudo-marginal algorithm, which we refer to as alive SMC^2. The advantages of this approach over competing approaches is that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series and the cumulative number of poison disease cases in mule deer.

Charred seeds in Mikkeli Orijärvi : A study of subsistence strategies of an Iron Age settlement in East Finland

In this study the over 350 macrofossil samples, containing over 2300 charred plant remains from an Iron Age settlement containing fossil fields in Mikkeli Orijärvi Kihlinpelto, were studied archaeobotanically. The aim was to get more information about subsistence strategies, especially agriculture and study differences in the plant combinations in the different structures and use the archaeobotanical theory to interpret these structures. The methodological question was to study the taphonomy of the charred plant material. The results gave a diverse impression of the agriculture and subsistence strategies of the settlement in Orijärvi, where barley was the most important cereal with rye, wheat and oat cultivated as minor crops. The arable weed assemblage indicates that the fields were situated in different kinds of soils and the crops were cultivated when different kind of weather conditions were prevailing. Ergot was found with the cereals, and it was growing on some of the arable crops and it also indicates wet climate. Hemp and flax were cultivated and wild plants were collected. The meadow and wetland plants found in the material derive most probably from animal fodder. Tubers of bulbous oat-grass were interesting, because they are usually found in graves. Comparison with other Iron Age settlements and graves indicates that the plant material found from the ancient field layers derives most probably from dwellings and graves, which were taken into cultivation.

退耕草地演替过程中的碳储量变化

退耕草地演替的研究对了解现有退耕草地的变化趋势有重要意义,也可以为退耕地的植被恢复提供科学依据。本研究采用以空间代替时间的方法,对处于不同演替时间阶段退耕草地的土壤碳储量以及植被的地上部分与根系生物碳储量变化进行了研究,结果表明,退耕草地演替过程中,地上部分生物碳储量呈阶梯式上升趋势,演替初期地上部分生物碳储量先降后升,并在演替的22~32年,保持相对平稳,之后在演替的40~60年,达到第2个相对平稳的阶段。根系生物碳储量也呈分阶段的阶梯式上升趋势,但第1个相对平稳的阶段出现在演替的第12~28年,在演替的第32~60年出现第2个相对平稳的阶段。退耕草地的土壤碳储量在退耕演替的初期下降,且在演替的第1~12年一直小于农地,在演替的第15年之后,土壤碳储量逐步上升。在0~150 cm的不同土层中,土壤有机碳含量以0~15 cm最高,在演替的1~12年,各土层有机碳含量均小于农地,之后在演替的第15~60年,各土层土壤有机碳含量均随演替时间的增加有所增加,且0~50 cm表层土壤有机碳含量在演替第34~60年迅速积累,增幅较大。在演替初期,草地地上部分生物碳储量、根系生物碳储量和土壤碳储量较演替第1年均表现为下降...

一株掷孢酵母利用红薯淀粉酶解液积累油脂研究

本论文以红薯淀粉的双酶法水解液为碳源，从19 株红色酵母中筛选出一株油脂含量较高的菌株掷孢酵母（Sporobolomyces reseus）As.2.618。为了提高掷孢酵母（S.reseus）As.2.618 的油脂产量，考察了培养基组成对该菌生长情况及油脂积累的影响。用均匀设计法对培养基组成进行了优化，由DPS软件得出的优化结果为：还原糖103g/L、酵母粉11.5g/L、磷酸二氢钾0.3g/L、硫酸镁0.15g/L。生物量可达19.23 g/L,油脂含量为3.875 g/L。研究了添加二价离子对该菌的生长及油脂积累的影响,结果表明Zn2+对该菌生长和油脂积累都有显著促进作用。研究了发酵条件以及添加氧载体正十二烷对该菌发酵的影响，表明添加正十二烷有利用于该菌生长与油脂积累。得出最佳发酵条件是：在还原糖103g/L、酵母粉11.5g/L、磷酸二氢钾0.3g/L、硫酸镁0.15g/L。添加30mg/L 硫酸锌,接种量为5％，在24h 后添加2g/L 的碳酸钙和2%(v/v)正十二烷，pH6.0 培养温度为27℃，转速为200r/min，培养时间为7 天的条件下，该菌生物量干重可达35.05g/L,油脂含量也达11.98g/L。Lipid is one of the basic material for life-sustaining activities andimportant industrial materials. As lipid resources mainly come from the animal andthe plant, the problem of lipid lack is encountered at times. The lipid frommicroorganisms is the substitute and superior to the above lipid with a short period ofproduction and much cheaper fermentation materials such as agricultural and sidelineproducts or wastes of crop.Thus large scale production and broad application ofmicrobial lipid will be efficient not only in substitute of the animal and the plant lipidfor food and industrial field , but also inducing a new way leading to solve the energyproblem.For the purpose of exploring the characteristics of lipid production of redyeasts from sweet potato starch hydrolysates. 19 red yeasts are screened for thecapability of lipid producing and one strain Sporobolomyces reseus As.2.618 withsuperior performance is sellected.To improve the Sporobolomyces reseus As.2.618’s capability of lipidaccumulation , the components of the medium, which may influence the growth of thestrain and the lipid yield have been studied. To get the optimum mediumcomponents ,the “uniform design” was used .The DPS software gave the optimummedium component is: reducing sugar 103 g/L、yeast extract 11.5 g/L、KH2PO4 0.3g/L、MgSO4 0.15 g/L. The biomass could reach up to 19.23 g/L and lipid yield 3.87g/L with the above composition of fermentation medium.Furthermore the fermentation conditions , addition of the divalent metal ionsand the oxygen vector to increase the strain’s lipid producing capability are tested.The optimum condition is : reducing sugar 103 g/L、yeast extract 11.5 g/L、KH2PO40.3 g/L、MgSO4 0.15 g/L，Adding 30mg/L ZnSO4,and adding 2g/L CaCO3 2％(v/v)n-dodecane after 24h’s fermentation. the optimal fermentation condition were asfollow :30ml medium in the 500ml flask with initial pH 6.0,the flasks with 5%inoculation volume were at 200r/min shaking speed for 7d’s fermentation at27 .Under this kind of condition the high biom ¡æ ass which reach to 35.05 g/L could begot ,the yield of lipid also could reach to 11.98g/L.

PenPC: A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs.

Estimation of the skeleton of a directed acyclic graph (DAG) is of great importance for understanding the underlying DAG and causal effects can be assessed from the skeleton when the DAG is not identifiable. We propose a novel method named PenPC to estimate the skeleton of a high-dimensional DAG by a two-step approach. We first estimate the nonzero entries of a concentration matrix using penalized regression, and then fix the difference between the concentration matrix and the skeleton by evaluating a set of conditional independence hypotheses. For high-dimensional problems where the number of vertices p is in polynomial or exponential scale of sample size n, we study the asymptotic property of PenPC on two types of graphs: traditional random graphs where all the vertices have the same expected number of neighbors, and scale-free graphs where a few vertices may have a large number of neighbors. As illustrated by extensive simulations and applications on gene expression data of cancer patients, PenPC has higher sensitivity and specificity than the state-of-the-art method, the PC-stable algorithm.