Joint action model

The applicability of using these interdisciplinary approaches, which include incorporation of various physical and chemical properties of the pollutants, QSARs/QSPRs and multicomponent joint action modeling are discussed and evaluated using a group of toxic and carcinogenic pollutants, i. e., polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs). 

Depending on study goals, a general linear modeling approach might be applied to these kinds of data. If there are more metals in the mixture, the independent joint action model can be expanded to Equation (1.5). 

An estimated interaction coefficient, p, was used in Chapter 1 to quantify potential interactions in binary mixtures of metal ions based on the independent joint action model (Figure 1.8, top panel). The associated data set was generated with a matrix of binary metal mixtures. The SAS code in Appendix 8.2 shows an example La + and Ce + data set with five La + concentrations (including 0) combined with five Ce + concentrations (including 0). The top row of data includes those for different concentrations of Ce + and no added La +. The leftmost column of data includes those for different concentrations of La + and no added Ce +. All other data reflect mixtures of the two metal ions at different concentrations. The first row and column of data can be used to generate the probit models for the bioactivity of each metal... 

Objects that have similar behaviors are members of the same type they satisfy the specification of that type. Behaviors are specified in terms of attributes that are a valid abstract model, called a type model, of many possible implementations. Each action is described in terms of its effect on the attributes of the participating objects and the outputs it produces. The most interesting aspects of a design are the interactions between objects. You can abstract away detailed interaction protocols between objects by using joint actions and collaborations and you can describe specific interactions as refinements of a more abstract description. 

However, there is nothing to stop you from using a type in a model even though the type happens to have an implementation somewhere. In fact, good implementations of domain objects will often have their specs reused in this way. The more important design decision hinges on how the types in an implementation will be used, and those decisions involve joint actions recorded in collaboration diagrams. 

We describe designs for objects using collaborations—collections of actions. A joint action defines a goal achieved collaboratively between the participant objects, using postconditions whose vocabulary is the model of each of the participants. Collaborations range from business interactions ( banks trade stocks ) to hardware ( fax sender sends document to receiver ) to software ( scrollbar displays file position ). 

But makeOrder is an action a specification of something that must be achievable with the system, although not necessarily something it must take the entire responsibility for. Remember that we have modeled it as a joint action (see Section 4.2.3, Joint Actions), and the responsibility partition has not been decided. Our implementation of makeOrder is shown on the next page. 

The Catalysis approach to design is about standing back from the detail so that you can discuss the most important parts without the clutter of line detail. You prolong the life of a design by making your overall vision clear to maintained, enhancers, and extenders. Earlier in this book we saw how to use models to abstract away implementation details. Pre/ post specifications abstracted what was required of an object rather than how it achieved it. Joint actions represent, as one thing, an interaction that may be implemented by a series of messages. 

The idea of joint actions between stateful objects was inspired by the work done in Disco [Kurki-Suonio90]. These authors describe how joint actions provide powerful abstractions, provide the first precise semantics for state charts in the context of object modeling, and show how entire object behavior models might be refined. 

If the response of the organism is produced by a combination of the two compounds, then they are said to exert joint action. This joint action can be further classified into simply additive, more than additive (i.e., synergistic), and less than additive (i.e., antagonistic). When this scheme is applied to multicomponent mixtures present in leachates of solid wastes, the analysis becomes more complex because the joint actions of different compound pairs may fall into different types of joint action. In the next section, three different modeling schemes are presented. 

Thus, when Aboul-Kassim [1] demonstrated synergism rather than simple additivity using the PAH MOLCONN-QSAR models (Eqs. 57-59), the concentrations of the PAH components in mixtures that would cause 50% inhibition by joint action were accurately predicted. This can be easily seen from the associations of data points representing predicted vs experimental concentrations along the line of perfect prediction (Fig. 14). 

For joint action in innovation systems and especially also for outlining the framework conditions by the political actors, it is expedient to understand the itmovation systems. The model developed in the scope of the project serves to create a systematic hnk between the framework conditions, the influential factors and the correlations between the participants. The various contributory parties can better assess their own options for influence, existing resistance to new ideas, possible coahtions or the significance of market trends, and can approach change processes in a more purposeful way as a resrrlt of this. 

Ashford, J.R. 1981. General models for the joint action of drugs. Biometrics 37 457 74. 

Four possible mechanisms of joint action for mixtures as defined by Plackett and Hewlett (1952), with associated models... 

For noninteractive types of joint action, it is assumed that the chemicals in the mixture do not affect the toxicity of one another. Two different reference models are available for the analysis of noninteractive joint action, depending on the mode of action of the chemicals in the mixture. The modeling approach commonly known as concentration addition (CA) is used for mixtures with 2 or more compounds with a similar mode of action. The modeling approach called response addition (RA) is used for mixtures with 2 or more compounds with different modes of action. 

For the analysis of interactive joint action (for either similar or dissimilar modes of action), no general models are available, but empirical descriptions may be used. The concentration addition and response addition models are often used in the analysis of experimental data from mixtures with compounds having known but different modes of action. 

Recently, some models have been derived to analyze the occurrence of interactive joint action in binary single-species toxicity experiments (Jonker 2003). Such detailed analysis models are well equipped to serve as null models for a precision analysis of experimental data, next to the generalized use of concentration addition and response addition as alternative null models. However, in our opinion these models are not applicable to quantitatively predict the combined toxicity of mixtures with a complexity that is prevalent in a contaminated environment, because the parameters of such models are typically not known. Recently a hazard index (Hertzberg and Teus-chler 2002) was developed for human risk assessment for exposure to multiple chemicals. Based on a weight-of-evidence approach, this index can be equipped with an option to adjust the index value for possible interactions between toxicants. It seems plausible that a comparable kind of technique could be applied in ecotoxicological risk assessments of mixtures for single species. However, at present, the widespread application of this approach is prevented by lack of available information. 

We have reviewed current conceptual and modeling approaches in mixture eco-toxicology as well as current experimental evidence to derive practical risk assessment protocols for species and species assemblages. From the review of conceptual approaches in mixture ecotoxicology, it appears that there is a difference between a mechanistic view of joint action from a compound mixture and a probabilistic perspective on combined toxicity and mixture risk. A mechanistic view leads to emphasis on the distinction of modes of action and physicochemical properties first, then on the choice of the appropriate joint toxicity model, followed by a comparison of the models prediction with experimental observations. A probabilistic orientation leads to the observation that concentration addition often yields a relatively satisfactory quantitative prediction of observations for the integral level of effects as observed in individual organisms or populations. In these applications, concentration addition is frequently connected with a slight bias to conservatism, especially for compounds with different modes of action (Backhaus et al. 2000,2004 Faust et al. 2003). 

Our current understanding of mixture extrapolation is based on simple pharmacodynamic concepts of noninteractive joint action, such as simple similar action and simple independent action, with the associated extrapolation models concentration addition and response addition. These models are used for various types of extrapolations. Although mode of action is important when considering possible mixture interactions and extrapolations, the concept of the ecological mode of action needs to be expanded, as was also concluded for extrapolation across levels of biological organization. Mixture extrapolation should consider environmental (matrix)-chemical... 

Alternatively, response additivity (RA) for independently acting chemicals as a mathematical null model for testing observed responses (associated with the pharmacological concept of independent joint action) and with an assumed correlation of sensitivities of 0 also often fits the data well. Again, misfits occur (e.g., when the test mixture consists of compounds with the same MOA at concentrations below the individual compound s no-effect concentrations), and when they occur, they are often in the tails of the response curves. 

Barton CN. 1993. Nonlinear statistical models for the joint action of toxins. Biometrics 49 95-105. 

De March BGE. 1987. Simple similar action and independent joint action—two similar models for the joint effects of toxicants applied as mixtures. Aquat Toxicol 9 291-304. 

More precise verification of the theory was achieved with films studied by the microinterferometric technique. Though performed a long time ago (1960), these experiments deserve attention, since they represent the first quantitative proof of the DLVO-theory conducted on a model system (foam film) which still hold true. Independent studies were performed of the X ei(k) and Vlvw(h) as well as of their joint action at various electrolyte concentrations. At very low Cei equilibrium films of large thickness formed in which the electrostatic interaction was prevailing and their behaviour could be described completely with this interaction. At such film thickness Y vw was still very low so that the equilibrium film state was reached at equal electrostatic disjoining and capillary pressure (n, = p ). Fig. 3.15 depicts the equilibrium thickness dependence on electrolyte concentration for saponin microscopic foam films. 

In this paper, we report on simultaneous consideration of incident field enhancement and local density of photon states enhancement near a metal particle with spherical shape as a reasonable primary model for single molecule Raman spectroscopy. Joint action of these two factors at the same point of space is found to offer up to lO -fold enhancement of Raman scattering rate. To the best of our knowledge this is the first evidence that consistent theory of single molecule Raman spectroscopy and comprehensive description of so-called hot points in surface enhanced spectroscopies can be constructed without necessarily involvement of chemical mechanisms. 

Olmstead AW, LeBlanc GA. Joint action of polycyclic aromatic hydrocarbons Predictive modeling of sublethal toxicity. Aquat Toxicol 2005 75 253-62. 

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