Multiple factor interactions

Multiple factors interact in complex ways to result in opioid dependence. It is difficult to delineate, even for a specific individual, the precise etiology of dependence. In addition, each of the etiologic factors discussed in this section may play variable roles in initiation of use, maintenance of use, relapse, and recovery. Keeping in mind all of these potential factors is essential when formulating a treatment plan for each individual. 

Using the effects of multiple-factor interactions from full or fractional factorial designs. Multiple-factor interactions (e.g. three- and four-factor interactions) are considered to have a negligible effect. It is then considered that these higher-order interaction effects measure differences arising from experimental error [31]. 

ExjXjXjj are the effects of the multiple-factor interactions and XjXjX]f... 

The equations used to estimate (SE)g from negligible effects can be explained as follows. The mean effect of the multiple-factor interactions or of the dummies is expected to be zero. The variance of these effects can then be calculated as ... 

Multiple-factor interaction effects can be used to determine a critical effect value for the interpretation of full and fractional factorial designs [29] ... 

The error term can be approximated in different ways. A first possibility is that, analogous to the above, it is estimated from the multiple-factor interactions (two-, three-factor interactions, etc.) for (fractional) factorial designs [29]. In the example of Table 3.19 the sums of squares of the interactions AB, AC, BC and ABC are summed giving a MS error with 4 degrees of freedom. From this iSmain effects. The ANOVA table and equation (20) give of course the same results. 

Multiple-factor Interactions. Each potential regulatory factor may Interact synergistically with the others and enhance their effectiveness. For example, plant chemistry can influence the effectiveness of predators, parasitoids and diseases in a variety of ways (, , ). However, the selective pressure exerted by uniform chemical defenses should be strengthened by interactions with natural enemies, and their useful life will be shortened. 

Not all v/oods i< y show a predisposition to invasion by disease pathogens and insects. As Scheffer and Cowling (51.) pointed out, woods do vary in the extent to which they will inherently resist heartwood decay. Certain types of oak and redwood are resistant to decay while some pines, birches and hickories are slightly or not resistant to heartwood decay. Two of the members of this slightly or not resistant decay category did show surface deterioration in work done by Banks et al. (49). It seems feasible that wood from these trees could be affected by acid rain and possibly other pollutants in combination with light and water. The result of this multiple factor interaction may then be impacted by insects or diseases. 

The research available to date presents a partial view of the impacts of acid rain on woody plants. Many of the impacts are still only potential impacts, as simulation studies versus field studies present a conflicting view. However, one thing appears quite clear - more research is needed. As many researchers have found, the effect of acid rain is not going to be one of simple cause and effect, but rather one of a multiple factor interaction. Thus, future work should be statistically designed to test the inter-action(s) rather than main effects. Work needs to be done over both the short and long term to assess injury. Basic physiological work across disciplines with the standardization of techniques used (e.g. one set type of simulator for all researchers to produce simulated acid rain) must be employed in order for different experimental results to be comparable. If we can discover how plants will react to given combinations of stresses, only then will we be able to propose an appropriate course of action. 

Active chromatin. The DNA is shown as a pair of lines, in purple and gray. The nucleosomes are indicated by small, shaded gray spheres. The other shapes represent RNA polymerase and three transcription factors interacting with one another along the DNA. The diagram is not to scale, and transcription complexes probably contain more components than shown, but this illustrates the principle of multiple factors interacting with one another and with DNA and RNA polymerase. 

The practice of estabHshing empirical equations has provided useflil information, but also exhibits some deficiencies. Eor example, a single spray parameter, such as may not be the only parameter that characterizes the performance of a spray system. The effect of cross-correlations or interactions between variables has received scant attention. Using the approach of varying one parameter at a time to develop correlations cannot completely reveal the tme physics of compHcated spray phenomena. Hence, methods employing the statistical design of experiments must be utilized to investigate multiple factors simultaneously. 

Chin-A-Woeng TFC, D van den Broek, G de Voer, KMGK van der Drift, S Tuinman, JE Thomas-Oates, BJJ Lugtenberg, GV Bloemberg (2001) Phenazine-l-carboxamide production in the biocontrol strain Pseudomonas chlororaphis PC L 1391 is regulated by multiple factors secreted into the growth medium. Mol Plant-Microbe Interact 14 869-879. 

Airway obstruction manifests itself as symptoms such as chest tightness, cough, and wheezing. Airway obstruction can be caused by multiple factors including airway smooth muscle constriction, airway edema, mucus hypersecretion, and airway remodeling. Airway smooth muscle tone is maintained by an interaction between sympathetic, parasympathetic, and non-adrenergic mechanisms. Acute bronchoconstriction usually... 

The factorial approach to the design of experiments allows all the tests involving several factors to be combined in the calculation of the main effects and their interactions. For a 23 design, there are 3 main effects, 3 two-factor interactions, and 1 three-factor interaction. Yates algorithm can be used to determine the main effects and their interactions (17). The data can also be represented as a multiple linear regression model... 

When one tries to rationalize the effect of the solvent on any type of chemical reactivity, considerable problems are encountered due to the multiple factors responsible for the solute/solvent interaction.2 7 One of the common ways to take into account the solvent effect is to consider it in terms of polarity (or polarizability) of the solvent. However, this concept is vague and difficult to define precisely. A first tentative... 

Clearly, further studies will be necessary to sort out the multiple factors involved in the in vivo immune response to C. neoformans carbohydrate-mimetic peptides. Several conclusions may be drawn from the results to date. Peptides that mimic the cryptococcal capsular polysaccharide show specificity, in that each peptide binds with differing affinity to closely related mAbs [140,149]. The pattern of binding to protective and nonprotective mAbs differs between the mimetic peptides and the polysaccharide [140]. Protective efficacy is related to the location of carbohydrate epitopes recognized by these mAbs, within the polysaccharide capsule, but hkely also depends on interactions between mAbs and cellular responses [149]. Peptides have been shown to be functional, immunogenic mimics, but their protective efficacy depends on multiple factors, including the type of Abs elicited and interactions with the cellular immune system. Protective efficacy does not correlate with binding affinity to representative mAbs, but rather depends on the nature of these interactions. 

Sen, R. and Baltimore, D. (1986). Multiple nuclear factors interact with the immunoglobulin enhancer sequences. Cell 46, 705-715. 

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