From a theoretical point of view, it has often been argued that the model of independent action (IA) is the most correct reference model to use for predicting the joint effect of mixtures of chemicals with different molecular target sites. The theory of IA, however, relies on a number of assumptions that are rarely fulfilled in practice. It has even been argued that, theoretically, the concentration addition (CA) model could be just as correct. In the present study, we tested the accuracy of both IA and CA in describing binary dose-response surfaces of chemicals with different molecular targets using statistical software. We compared the two models to determine which best describes data for 158 data sets. The data sets represented 98 different mixtures of, primarily, pesticides and pharmaceuticals tested on one or several of seven test systems containing one of the following: Vibrio fischeri, activated sludge microorganisms, Daphnia magna, Pseudokirchneriella subcapitata, Lemna minor, Tripleurospermum inodorum, or Stellaria media. The analyses showed that approximately 20% of the mixtures were adequately predicted only by IA, 10% were adequately predicted only by CA, and both models could predict the outcome of another 20% of the experiment. Half of the experiments could not be correctly described with either of the two models. When quantifying the maximal difference between modeled synergy or antagonism and the reference model predictions at a 50% effect concentration, neither of the models proved significantly better than the other. Thus, neither model can be selected over the other on the basis of accuracy alone.

Social interactions in which bacteria respond to one another by modifying their phenotype are central determinants of microbial communities. It is known that interspecific interactions influence the biofilm phenotype of bacteria; a phenotype that is central to the fitness of bacteria. However, the underlying role of fundamental ecological factors, specifically coexistence and phylogenetic history, in biofilm formation remains unclear. This study examines how social interactions affect biofilm formation in multi-species co-cultures from five diverse environments. We found prevalence of increased biofilm formation among co-cultured bacteria that have coexisted in their original environment. Conversely, when randomly co-culturing bacteria across these five consortia, we found less biofilm induction and a prevalence of biofilm reduction. Reduction in biofilm formation was even more predominant when co-culturing bacteria from environments where long-term coexistence was unlikely to have occurred. Phylogenetic diversity was not found to be a strong underlying factor but a relation between biofilm induction and phylogenetic history was found. The data indicates that biofilm reduction is typically correlated with an increase in planktonic cell numbers, thus implying a behavioral response rather than mere growth competition. Our findings suggest that an increase in biofilm formation is a common adaptive response to long-term coexistence.

This paper is a survey of estimation techniques for stationary and ergodic diffusion processes observed at discrete points in time. The reader is introduced to the following techniques: (i) estimating functions with special emphasis on martingale estimating functions and so-called simple estimating functions; (ii) analytical and numerical approximations of the likelihood function which can in principle be made arbitrarily accurate; (iii) Bayesian analysis and MCMC methods; and (iv) indirect inference and EMM which both introduce auxiliary (but wrong) models and correct for the implied bias by simulation.

Synergism and antagonism are often defined in relation to the model of Concentration Addition (CA). Hence, it is vital for the conclusion of mixture toxicity studies to be able to test whether an observed deviation from CA reflects a true deviation or whether it is simply due to random variation. In this paper we consider a non-linear regression model for the classical ray designs for binary mixture experiments. The model combines dose-response curves for each mixture in the experiment with an isobole model, describing possible deviations from CA. The method allows us to test whether the chosen isobole model is reasonable for the data and to test the hypothesis of CA. Furthermore, it provides us with a measure of the degree of synergism/antagonism. The method is flexible since both the dose-response relationships and the isobole model can be chosen arbitrarily. We demonstrate the use of the method on datasets where combinations of pesticides are tested on a floating plant, Lemna minor, and an algae, Pseudokirchneriella subcapitata. Furthermore, we conduct a simulation study in order to explore the power with which a specific deviation from CA can be distinguished in different test-systems.

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We found reduced 25(OH)D concentrations in patients with small intestinal resection, and showed that a deficient 25(OH)D concentration is associated with significantly increased markers of bone resorption and decreased BMD values.

Plankton community structure and major pools and fluxes of carbon were observed before and after culmination of a bloom of cyanobacteria in eutrophic Frederiksborg Slotssø, Denmark. Biomass changes of heterotrophic nanoflagellates, ciliates, microzooplankton (50 to 140 μm), and macrozooplankton (larger than 140 μm) were compared to phytoplankton and bacterial production as well as micro- and macrozooplankton ingestion rates of phytoplankton and bacteria. The carbon budget was used as a means to examine causal relationships in the plankton community. Phytoplankton biomass decreased and algae smaller than 20 μm replacedAphanizomenon after the culmination of cyanobacteria. Bacterial net production peaked shortly after the culmination of the bloom (510 μg C liter(-1) d(-1) and decreased thereafter to a level of approximately 124 μg C liter(-1) d(-1). Phytoplankton extracellular release of organic carbon accounted for only 4-9% of bacterial carbon demand. Cyclopoid copepods and small-sized cladocerans started to grow after the culmination, but food limitation probably controlled the biomass after the collapse of the bloom. Grazing of micro- and macrozooplankton were estimated from in situ experiments using labeled bacteria and algae. Macrozooplankton grazed 22% of bacterial net production during the bloom and 86% after the bloom, while microzooplankton (nauplii, rotifers and ciliates larger than 50 μm) ingested low amounts of bacteria and removed 10-16% of bacterial carbon. Both macro-and microzooplankton grazed algae smaller than 20 μm, although they did not control algal biomass. From calculated clearance rates it was found that heterotrophic nanoflagellates (40-440 ml(-1)) grazed 3-4% of the bacterial production, while ciliates smaller than 50 μm removed 19-39% of bacterial production, supporting the idea that ciliates are an important link between bacteria and higher trophic levels. During and after the bloom ofAphanizomenon, major fluxes of carbon between bacteria, ciliates and crustaceans were observed, and heterotrophic nanoflagellates played a minor role in the pelagic food web.

From a theoretical point of view, it has often been argued that the model of independent action (IA) is the most correct reference model to use for predicting the joint effect of mixtures of chemicals with different molecular target sites. The theory of IA, however, relies on a number of assumptions that are rarely fulfilled in practice. It has even been argued that, theoretically, the concentration addition (CA) model could be just as correct. In the present study, we tested the accuracy of both IA and CA in describing binary dose-response surfaces of chemicals with different molecular targets using statistical software. We compared the two models to determine which best describes data for 158 data sets. The data sets represented 98 different mixtures of, primarily, pesticides and pharmaceuticals tested on one or several of seven test systems containing one of the following: Vibrio fischeri, activated sludge microorganisms, Daphnia magna, Pseudokirchneriella subcapitata, Lemna minor, Tripleurospermum inodorum, or Stellaria media. The analyses showed that approximately 20% of the mixtures were adequately predicted only by IA, 10% were adequately predicted only by CA, and both models could predict the outcome of another 20% of the experiment. Half of the experiments could not be correctly described with either of the two models. When quantifying the maximal difference between modeled synergy or antagonism and the reference model predictions at a 50% effect concentration, neither of the models proved significantly better than the other. Thus, neither model can be selected over the other on the basis of accuracy alone.