Classification of activity

The three significant OSD attributes are its sequential flow, its classification of activity type, and its ability to describe interactions between people and machines. In these respects, OSDs are similar to the Decision/Action charts, but more complex. The OSD can be seen as a static simulation of the system operations. This is also the reason why OSDs can become tedious to develop in the analysis of complex systems. 

Rusinko, A., Farmen, M. W., Lambert, C. G., and Young, S. S. (1997) SCAM Statistical classification of activities of molecules using recursive partitioning. 213th ACS Natl. Meeting, San Francisco, CA, CINF 068. 

Classification of Active Compounds by Median and Cell-Based Partitioning ... 

Table 18.2 shows the results of testing of the 14 chemicals classes, including the classification of active/inactive and the mean relative binding efficiency (RBA). Observed RBAs were strongest to weakest ... 

It is easy to show that for small t values, Eq. (2.29) transforms to Eq. (2.28). However, it is interesting to follow the pattern of P(t) dependence for the full range of p, from which it can be seen this dependence looks like a kinetic curve with self-acceleration. Thus the phenomenon of selfacceleration, which is observed in some cases, may be treated as a consequence of a two-stage polymerization reaction if the rate constants of both stages have values of the same order. The reaction rate at the initiation stage can be limited by various factors, and in this case, classification of activators as direct or indirect becomes meaningless.41... 

SCAMPI (Statistical Classification of Activities of Molecules for Pharmacophore Identification) is a program developed in C language by Chen et al. [104]. According to the authors, it allows the use of datasets of approximately 1000-2000 compounds. The SCAMPI program s implementation has been done to allow users to visualize the molecules and the generated pharmacophores in the Sybyl environment. 

Here, we provide an example of a previously published study [35] of an application of our analysis strategy to an HTS data set for DHFR of E. coli [36]. The goal was to find a good predictor for a correct classification of active and inactive molecules using MTree models. The data set consisted of 100000 compounds split in two equal-sized parts a training set and a test set both with bio-... 

From the author s immersion experiences as well as the wide ambit of issues discussed in the research literature, several frames of analysis can be identified. First, the focus of attention is on academic tourism research and its purposes. This kind of research can be distinguished from government and industry-based research efforts that have some different overall agendas (Jafari, 1990 Pearce, 1993a). The agendas for government-based studies are much more directed towards market analysis, statistics collection schemes that inform policy and provide classifications of activity. The consultancy-based work conducted for industry organisations and busi-... 

Using a set of 40 naphthyl-isoquinoline alkaloids, Bringmann and Rummey (56) compared two different methods of generating alignments automatically. Typically, data are divided into a training set used to optimize the parameters of the model and a test set that does not influence the model in any way but is used for evaluation of the predictive abilities of the final model. In this study, all available compounds were used for model development, and given that the researchers synthesized and tested new compounds on a continuous basis, the test set consisted of newly synthesized chemical compounds. When seven newly synthesized compounds were evaluated and predictions were made, a poor correlation was seen between actual and predicted IC50 values (r 0.279). If instead, the results are evaluated as a classification of active or inactive, five of the seven structures are predicted correctly. 

The MCC makes recommendations to the Minister of Hecdth in respect of the classification of active ingredients in intermediate-risk or lower-risk medicines where the active substance(s) have not previously been scheduled. 

Diluents for powders for injections require separate applications, cis these are regarded as separate formulations. As the classification of active or inactive refers to the pharmacological action of the ingredient, the classification of the diluent ingredient(s) in the appropriate column may be reflected as inactive -diluent . If reflected as active , Annexures 1, 7A, 7B and 10 would be affected, implying assay of the relevant ingredient. 

Classification of active pharmaceutical ingredients according to the Biopharmaceutics Classification System... 

Classification of activity eruptive (intensity), intensity scale after Tsuya (1955) in terms of total volume of ejected material 1 ( = 10 5 km3)-IX ( = 102 km3) preeruptive, intensification of activity before an eruption intraeruptive, phase of repose between two paroxysmal eruptions posteruptive, permanent fuming or fumarolic activity extraeruptive, exclusive fumarolic and solfataric activity. [From Berresheim and Jaeschke (1983).] h Calculated from the SO4- content of ash particles. c Estimated value. 

Statistical classification of activities of molecules for pharmacophore identification. IHandles large heterogeneous data sets. A random conformational search is combined. (144)... 

Abstract. Artificial neural networks (ANN) are useful components in today s data analysis toolbox. They were initially inspired by the brain but are today accepted to be quite different from it. ANN typically lack scalability and mostly rely on supervised learning, both of which are biologically implausible features. Here we describe and evaluate a novel cortex-inspired hybrid algorithm. It is found to perform on par with a Support Vector Machine (SVM) in classification of activation patterns from the rat olfactory bulb. On-line unsupervised learning is shown to provide significant tolerance to sensor drift, an important property of algorithms used to analyze chemo-sensor data. Scalability of the approach is illustrated on the MNIST dataset of handwritten digits. 

TABLE 9.6. Classification of Active Centres for Ethyne Hydrogenation in the Presence of Excess Ethene, and Their Functions... 

Table 20 suggests that van der Waajs and repulsive forces contribute to the temperature coefficient of fiow (in cal./mol.) just as they do to the latent heat of sublimation in the latter instance the attractive forces, which are small, are predominant, but in the former the much larger repulsive forces are predominant. This provides a basis for the classification of activated diffusion processes into specific types (Hg-Pd) and non-specific types (He-SiOg), the basis being the nature of the... 

Amylases degrade starch to a diversity of oligodextrins according to their individual substrate specificities and action patterns (1,2). Classification of activity can be made by the following distinctions i) endo- versus exo-mode of attack, ii) inversion versus retention of the product anomeric configuration, iii) poly-, oligo-, or disaccharide/disaccharide-analogue substrate preference, and iv) a-(l- 4), a-(l- 6) or dual bond-type specificity. 

TABLE 5 Classification of active and intelligent packaging systems... 

This is mainly a classification of active disease and does not take complications of gastro-oesophageal reflux disease into account, i.e. scar formation and strictures. In dysphagic patients with no other symptom or sign of reflux oesophagitis, carcinoma of the oesophagus must be excluded. 

Table 9.3 Classification of activities where potential active failures were identified - underground mining operations...

TABLE 5.3 Classification of Active Surface Mount Devices (SMD) by Arrangement of their ... 

---