Design classification scheme

Primers may be divided into several broad categories based upon the type of interface they are designed to improve. Table 1 shows one such classification. Further discussion in this chapter is based upon this classification scheme. 

Classification of bacterial viruses In the bacterial viruses, a formal classification scheme is rarely used. Rather, each bacterial virus is designated in terms of its principal bacterial host, followed by an arbitrary alphanumeric. Thus, we speak of T4 virus of Escherichia coli or P22 virus of Salmonella typhimurium. An overview of some of the major types of bacterial viruses is given later. We should note, however, that although a bacterial virus may be designated in reference to its principal host, the actual host range of the virus may be broader. Thus, bacteriophage Mu, generally studied with Escherichia coli, also infects Citrobacter and Salmonella. 

The classical calorimetric methods addressed in chapters 7-9, 11, and 12 were designed to study thermally activated processes involving long-lived species. As discussed in chapter 10, some of those calorimeters were modified to allow the thermochemical study of radiation-activated reactions. However, these photocalorimeters are not suitable when reactants or products are shortlived molecules, such as most free radicals. To study the thermochemistry of those species, the technique of photoacoustic calorimetry was developed (see chapter 13). It may be labeled as a nonclassical calorimetric technique because it relies on concepts that do not fit into the classification schemes just outlined. 

An alternative classification scheme was introduced in 2002 and this divides lantibiotics into two subgroups, class I and class II. This scheme primarily classifies lantibiotics according to their biosynthetic enzymes and sequence homology of their leader peptides. Similar to most biosynthetic pathways in bacteria, the genes for lantibiotic biosynthesis are clustered. They have been designated the generic locus symbol Ian, with a more... 

In line with the situation found previously for the other receptor families, several classification schemes coexist for nuclear receptors. In particular, beyond the NC-IUPHAR system described above, an alternative nomenclature system has been proposed and is currently widely accepted and used by the research community working in this family. This annotation scheme consists of a 3-character code. The first character is a number that designates... 

According to IPCS [18] an exposure model is a conceptual or mathematical representation of the exposure process, designed to reflect real-world human exposure scenarios and processes. There are many different ways to classify exposure models. A consensus appears to be developing around the following classification scheme proposed by the World Health Organization [19], which has been adopted in this chapter (a) mechanistic or empirical and (b) deterministic or stochastic (probabilistic). Table 1 lists these model categories. However, alternative classifications may be considered as well. 

We see that for the (alkali metal + oxygen macrocycle) complexes, charge and relative size of the ion play an important part in determining the stability of the complex. However, for the (transition metal + aza- or thia-substituted macrocycle) complexes, the nature of the bonding seems to be the important effect. Sten Ahrland16 has used a classification scheme for metal ion acceptors that helps us understand this difference. He designates the metal ion as either hard or soft. The characteristics that determine the assignment are as follows. 

There also is difficulty in accommodating fused-salt systems in a classification scheme designed primarily for organic solvents, because the dielectric constants are not comparable and the measures of solvent polarity appropriate to organic solvents are not generally useful in fused salts. Therefore, fused-salt systems are not discussed, nor are they included in the general classification scheme. 

The term flow system, designated in our classification scheme by the letter F, is reserved for those methods employing integral flow because in no other case does flow alter the basic nature of separation. Two distinct classes of integral flow can be identified, F(=) and F( + ), as noted in our classification of Section 7.3. These classes are discussed separately below. 

In chromatography, one phase is held immobile or stationary, and the other one is passed over it (the mobile phase). The designations GC and LC refer to the physical state of the mobile phase. Further classifications can be made by naming the mobile and stationary phases thus we have gas-solid (GS), gas-liquid (GL), liquid-liquid (LL), and liquid-solid (LS) chromatography. More recently, supercritical fluids have been used as mobile phases, and these techniques have been named supercritical fluid chromatography (SFC) irrespective of the state of the stationary phase. Other names have also become popular, and Table 1 shows a complete classification scheme. Included in the classification scheme are not only the states of the two phases but also the configuration of the chromato-... 

To understand the important issues for a design, it is helpful to develop a classification scheme to characterize a design. If the scheme is appropriate, then the methodologies for designing should be related for designs similarly classified. We are using abduction to conjecture the usefulness of this classification scheme (Fig. 1). 

In the previous chapters the purposes of near miss reporting have been outlined and a framework of designing such a safety management tool has been presented. The importance of human behaviour as a dominant factor in incident sequences was stressed by developing a system failure classification scheme largely based on a theoretical model of operator behaviour. Also an overview was given of the organisational factors necessary for a successful implementation of a NMMS. 

There are two methods for estimating fiber quality, expert appraisal by a trained classer, and instrumentation. Many different classification systems are used worldwide in the various countries where cotton is grown. For example, the United States [601], China [3,594,602], Uzbekistan [603], and Egypt [594] all have different systems. All systems measure cotton quality using similar parameters, even though the actual classification scheme and classification designations may be different. The determinations, either manually or by instrument, may be different for individual parameters also. The quality parameters are the following ... 

At this point, it is instructive to introduce a system to classify the bonding around an atom the N-X-L system. First suggested in 1980, it is particularly useful for the species discussed in this article. In this classification scheme, N is the number of valence-shell electrons formally assigned to the central atom X and L is the number of ligands directly bonded to X. The N-X-L designations are illustrated in Figure 2 for each species listed. 

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