Development and Application of a Predictive Model of Freshwater Fish Assemblage Composition to Evaluate River Health in Eastern Australia

Multivariate predictive models are widely used tools for assessment of aquatic ecosystem health and models have been successfully developed for the prediction and assessment of aquatic macroinvertebrates, diatoms, local stream habitat features and fish. We evaluated the ability of a modelling method based on the River InVertebrate Prediction and Classification System (RIVPACS) to accurately predict freshwater fish assemblage composition and assess aquatic ecosystem health in rivers and streams of south-eastern Queensland, Australia. The predictive model was developed, validated and tested in a region of comparatively high environmental variability due to the unpredictable nature of rainfall and river discharge. The model was concluded to provide sufficiently accurate and precise predictions of species composition and was sensitive enough to distinguish test sites impacted by several common types of human disturbance (particularly impacts associated with catchment land use and associated local riparian, in-stream habitat and water quality degradation). The total number of fish species available for prediction was low in comparison to similar applications of multivariate predictive models based on other indicator groups, yet the accuracy and precision of our model was comparable to outcomes from such studies. In addition, our model developed for sites sampled on one occasion and in one season only (winter), was able to accurately predict fish assemblage composition at sites sampled during other seasons and years, provided that they were not subject to unusually extreme environmental conditions (e.g. extended periods of low flow that restricted fish movement or resulted in habitat desiccation and local fish extinctions).     

Vertical structure of Erica umbellata,a representative species of European Ibero-Atlantic dry heaths

The canopy structure of Erica umbellata was studied in order to (a) quantify biomass allocation among several organ types, (b) analyse the possible changes in vertical structure related to season and plant size, and (c) evaluate the effectiveness of non-destructive measures to estimate biomass for a species that is declining in some areas, due to fire and other human disturbances. The study was conducted in NW Spain, sampling E. umbellata plants belonging to three size groups. Destructive (biomass) and non-destructive (frequency, height, diameter) measures were used to characterise the vertical distribution and abundance of photosynthetic, woody, reproductive and dead organs. Allometric equations were calculated to estimate total mass using non-destructive measurements. As E. umbellata increases in size, those organs with a higher renewal rate (leaves, new stems, flowers) increase in the upper strata. Seasonal differences are recorded for the reproductive organs and new stems. A sharp decrease in the green/total phytomass ratio (from 0.51 to 0.17) is observed as plant size increases. Organ biomass can be accurately predicted from total weight and it is also possible to estimate the total weight from non-destructive measures, which provides an easier way of recording data in the field.     

5 岁以下儿童ORMDL3基因表达与哮喘预测指数相关性分析

目的:为了探究ORMDL3(orosomucoid 1-like 3)基因表达量与哮喘预测指数(Asthma predictive index,API)、性别、年龄、遗传和环境等因素之间的相关性。方法:从2013年4月-2014年8月,我们收集了115位数据有效的5岁以下儿童的资料,并依据API严格标准对其进行分类:API+(过去一年内喘息次数≥4)、API-(过去一年内喘息次数1-3次)和API0(无喘息)组,统计三组之间的基本资料,并检测ORMDL3基因表达量与API、性别、年龄、家族史、喘息次数、和鼻炎之间的相关性。结果:API+组内男女性别比例显著高于其他组,ORMDL3基因表达量在API0内的表达量显著高于API+和API-组内表达量(P=0.044),并且ORMDL3基因表达量与API指数之间存在一定的相关性(相关系数:-0.232,P=0.020),但是ORMDL3基因表达量与性别、年龄、家族史、喘息次数、和鼻炎之间无统计学相关性(P0.05)。结论:ORMDL3基因表达量与API指数之间存在微弱的负相关性,API阳性鉴定标准需要进一步的改进。     

Classification of macroinvertebrate communities across drainage basins in Victoria, Australia: consequences of sampling on a broad spatial scale for predictive modelling

1. Spatial scale may influence the interpretation of environmental gradients that underlie classification and ordination analyses of lotic macroinvertebrate communities. This could have important consequences for the spatial scale over which predictive models derived from these multivariate analyses can be applied. 2. Macroinvertebrate community data (identified to genus or species) from edge and main-channel habitats were obtained for sites on rivers from 25 of the 29 drainage basins in Victoria. Trends in community similarity were analysed by carrying out separate multivariate analyses on data from the edge habitats (199 sites) and the main-channel habitats (163 sites). 3. Hierarchical classification (UPGMA) showed that the edge data could be placed into 11 site groups and the main-channel data into 12 site groups. 4. Ordination analysis (hybrid multidimensional scaling) showed no sharp disjunctions between site groups in either habitat; overlap was frequent. Correlation of the ordination patterns with environmental variables showed that edge communities varied longitudinally within a drainage basin and from the east to the west of Victoria. These two trends were superimposed on one another to form a single gradient on the ordination. The taxon richness of edge communities was also related to the species richness of macrophytes at a site. Main-channel communities also displayed a longitudinal and a geographic gradient, but these two gradients were uncorrelated on the ordination. 5. Community similarity only weakly reflected geographic proximity in either habitat. A preliminary subdivision of Victoria into a series of biogeographic regions did not match the pattern of distribution of site groups for the edge habitat, illustrating the difficulties of applying to lotic communities a priori regionalizations based on terrestrial features of the landscape. 6. The longitudinal gradients in the two data sets were commonly observed in data gathered at smaller spatial scales in Victoria. The other gradients (geographic, macrophyte), however, were either not consistently repeated or not evident at smaller spatial scales. At small spatial scales (i.e. within a single drainage basin) gradients were related to variables that varied over restricted ranges, e.g. mean particle size of the substratum. 7. Species richness was very variable when plotted against river slope or distance of site from source; both of these are measures of position on the longitudinal gradients. In contrast to suggestions in the literature, species richness did not show a unimodal trend on these gradients, or any other trend. 8. Environmental gradients (apart from longitudinal gradients) that underlie predictive models of macroinvertebrate distribution are reflections of the spatial scale on which the model has been constructed and cannot be extrapolated to different scales. Models must be suited to the spatial scale over which predictions are required.     

Application of in silico and in vitro methods in the development of adverse outcome pathway constructs in wildlife

There is a long history of using both in silico and in vitro methods to predict adverse effects in humans and environmental species where toxicity data are lacking. Currently, there is a great deal of interest in applying these methods to the development of so-called ‘adverse outcome pathway’ (AOP) constructs. The AOP approach provides a framework for organizing information at the chemical and biological level, allowing evidence from both in silico and in vitro studies to be rationally combined to fill gaps in knowledge concerning toxicological events. Fundamental to this new paradigm is a greater understanding of the mechanisms of toxicity and, in particular, where these mechanisms may be conserved across taxa, such as between model animals and related wild species. This presents an opportunity to make predictions across diverse species, where empirical data are unlikely to become available as is the case for most species of wildlife.     

A new generation of predictive models: The added value of hybrid models for manufacturing processes of therapeutic proteins

Due to the lack of complete understanding of metabolic networks and reaction pathways, establishing a universal mechanistic model for mammalian cell culture processes remains a challenge. Contrarily, data-driven approaches for modeling these processes lack extrapolation capabilities. Hybrid modeling is a technique that exploits the synergy between the two modeling methods. Although mammalian cell cultures are among the most relevant processes in biotechnology and indeed looks ideal for hybrid modeling, their application has only been proposed but never developed in the literature. This study provides a quantitative assessment of the improvement brought by hybrid models with respect to the state-of-the-art statistical predictive models in the context of therapeutic protein production. This is illustrated using a dataset obtained from a 3.5 L fed-batch experiment. With the goal to robustly define the process design space, hybrid models reveal a superior capability to predict the time evolution of different process variables using only the initial and process conditions in comparison to the statistical models. Hybrid models not only feature more accurate prediction results but also demonstrate better robustness and extrapolation capabilities. For the future application, this study highlights the added value of hybrid modeling for model-based process optimization and design of experiments.     

Uncertainty and Surprise Jointly Predict Musical Pleasure and Amygdala,Hippocampus, and Auditory Cortex Activity

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The effect of the native bacterial community structure on the predictability of E. coli O157:H7 survival in manure‐amended soil

Aims: The survival capability of pathogens like Escherichia coli O157:H7 in manure‐amended soil is considered to be an important factor for the likelihood of crop contamination. The aim of this study was to reveal the effects of the diversity and composition of soil bacterial community structure on the survival time (ttd) and stability (irregularity, defined as the intensity of irregular dynamic changes in a population over time) of an introduced E. coli O157:H7 gfp‐strain were investigated for 36 different soils by means of bacterial PCR‐DGGE fingerprints. Methods and Results: Bacterial PCR‐DGGE fingerprints made with DNA extracts from the different soils using bacterial 16S‐rRNA‐gene‐based primers were grouped by cluster analysis into two clusters consisting of six and 29 soils and one single soil at a cross‐correlation level of 16% among samples per cluster. Average irregularity values for E. coli O157:H7 survival in the same soils differed significantly between clusters (P = 0·05), whereas no significant difference was found for the corresponding average ttd values (P = 0·20). The irregularity was higher for cluster 1, which consisted primarily of soils that had received liquid manure and artificial fertilizer and had a significant higher bacterial diversity and evenness values (P < 0·001). Conclusions: Bacterial PCR‐DGGE fingerprints of 36 manure‐amended soils revealed two clusters which differed significantly in the stability (irregularity) of E. coli O157 decline. The cluster with the higher irregularity was characterized by higher bacterial diversity and evenness. Significance and Impact of the Study: The consequence of a high temporal irregularity is a lower accuracy of predictions of population behaviour, which results in higher levels of uncertainty associated with the estimates of model parameters when modelling the behaviour of E. coli O157:H7 in the framework of risk assessments. Soil community structure parameters like species diversity and evenness can be indicative for the reliability of predictive models describing the fate of pathogens in (agricultural) soil ecosystems.     

Modelling the effect of temperature and water activity on growth of Aspergillus niger strains and applications for food spoilage moulds

AIMS: To develop a model for the combined effect of water activity (a(w)) and temperature on growth of strains of Aspergillus niger, and comparison with data on food spoilage moulds in the literature. METHODS AND RESULTS: An extended combined model describing the growth of two strains of A. niger, as a function of temperature (25-30 degrees C) and a(w) (0.90-0.99) was developed. The growth rate (micro) was expressed as the increase in colony radial growth per unit of time. This extends the previous square root model showing the relationship between temperature and bacterial growth rate developed by Ratkowsky et al. (1983) and the parabolic relationship between the logarithm of the growth rate and a(w) developed by Gibson et al. (1994). A good correlation between the experimental data and the model predictions was obtained, with regression coefficients (r(2)) > 0.99. In addition, the use of this model allowed predictions of the cardinal a(w) levels: a(w(min)), and a(w(opt)). The estimation of the minimum a(w) levels (a(w(min))) was in accordance with data in the literature for similar and a range of other Aspergillus and related species, regardless of the solutes used for a(w) modification. The estimation of the optimal a(w) (a(w(opt))) and the optimal growth rate (micro(opt)) were in good agreement with the experimental results and data from the literature. CONCLUSIONS: This approach enables accurate prediction of the combined effects of environmental factors on growth of spoilage fungi for rapid prediction of cardinal limits using surface response curves. SIGNIFICANCE AND IMPACT OF THE STUDY: This approach is a rapid method for predicting optimal and marginal conditions for growth of a wide range of spoilage micro-organisms in relation to interacting environmental conditions and will have applications for improving shelf-life of intermediate moisture foods.