Meta, definition

SRS attempts to meet all the challenges listed in the introduction to this chapter. Molecular biology resources - databases and applications - can be quickly and easily incorporated into the system. Using the meta definitions of the format and syntax of the resources, SRS creates indices of the data (both databases and the output of applications) which can then be quickly and easily queried. Once a query is performed against one or more databases, either the whole database entry or just the information which the user requires can be extracted from the databases and displayed. Links (both explicit and implicit) between those data and other databases can be made and hence navigation between resources is possible. 

To achieve this SRS utilizes the special purpose programming language, ICARUS (Interpreter of Commands and Recursive Syntax), which is used both for the meta definition of biological resources and the syntactic description of the data in the resources. The SRS core includes programs which use ICARUS meta definitions and syntactic descriptions to index and retrieve information from resources. In the case of applications, the meta definition includes information on how to run the programs as well as the meta description of the syntax and structure of the output. 

To index and retrieve data, SRS uses a token server. The meta definition is used to extract tokens, i.e. strings, from the database for indexing. When a query is performed, SRS searches for the query term against these indices and creates a set of entries which match that query. The fields of the entry which the user wants displayed are then extracted from the entry. This is done by using the connection made in the meta definition between a field and tokens received from the token server. 

Meta definition can be defined as a higher level definition and is widely used in SRS. Meta definition is used to describe both the resources to be... 

Meta definition has many advantages, foremost being that changes to the format of input and/or outputs only require minimal changes to the meta definition, and none to any core component of SRS. 

In SRS, meta definition is used to describe objects which the SRS core uses. In the case of a database, a library object must be defined. This object contains the name of the library, what sort of library it is (i.e. what group of databanks it belongs to), the name and whereabouts of the flat files containing the data. It also contains a link to a file containing a list of rules which describe the internal syntax of the databank. These syntax rules will be described below. 

From the meta definition layer, it is also possible to use alternative viewer adaptors. The view adaptor is given a meta definition, which describes the input that the viewer requires. This is currently used to allow graphical Java applets to use information from SRS for data visualization. 

The SRS core consists of several programs which use the meta definition layer to allow the resources to be queried, data from these resources to be returned and, in the case of applications, other programs run on selected data. This includes the creation of indices from the data resources. It is the creation of these indices which makes the querying of the databases quick. 

To create the indices, SRS uses the rules given in the syntax description to create tokens. In this case, a token is defined as a piece of text from the database entry. These tokens are stored in token tables which are linked to a particular field in the database by a meta definition in the library meta definition object. 

These rules are also used to extract data from the database. An object loader, another object in the SRS system, can be defined in the meta definition layer and describes the way a token table relates to a field to be extracted and viewed. 

Another advantage of the meta-definition approach which SRS has taken is that wrappers can be generated to provide a programming interface to SRS. This allows programmers to be able to link in the SRS system to their own... 

For each library a syntax description of the data must be supplied. This syntax description must describe the data in the database and takes the form of a set of rules, also called a grammar. This list of rules is written in the SRS programming language ICARUS. The rules in this case provide the syntax part of the ICARUS language (the other part being Meta Definition) and are called productions. Productions are used to extract pieces of text from a database entry, tokens which are written to token tables. It is the data which is in these token tables that is indexed and used when querying the database, or extracting parts of the database for display. 

The meta definition which describes the structure of the resource is also written using ICARUS. For the EMBL database this meta definition consists of the definition of several objects. 

The field information is related to a particular token table via the index-Token attribute in the Field object. Hence, when a particular piece of information from the description field of the EMBL databank is requested, SRS looks in the description field meta definition for the EMBL database... 

In the case of indexing databases, the token tables for each field which is to be indexed are constructed. This means that for each field defined in the LibFormat object, a token table is generated for that field based on the indexToken attribute in the meta definition. The values in the token table for each entry are then written to indices which are sorted and can be searched quickly. 

When our most recent - and most definitive - meta-analysis was published, the headlines in many newspapers blazoned that antidepressants don t work .1 The Daily Telegraph headline phrased it more specifically, clarifying that antidepressants are no better than dummy pills ,2 but even this headline was not entirely accurate. What our analyses actually showed was that antidepressants work statistically better than placebos, but that this statistical difference was not clinically meaningful. It was too small a difference to be of much importance in the life of a severely depressed person. 

The most famous mechanism, namely Cossets mechanism, in which the alkene inserts itself directly into the metal-carbon bond (Eq. 5), has been proposed, based on the kinetic study [134-136], This mechanism involves the intermediacy of ethylene coordinated to a metal-alkyl center and the following insertion of ethylene into the metal-carbon bond via a four-centered transition state. The olefin coordination to such a catalytically active metal center in this intermediate must be weak so that the olefin can readily insert itself into the M-C bond without forming any meta-stable intermediate. Similar alkyl-olefin complexes such as Cp2NbR( /2-ethylene) have been easily isolated and found not to be the active catalyst precursor of polymerization [31-33, 137]. In support of this, theoretical calculations recently showed the presence of a weakly ethylene-coordinated intermediate (vide infra) [12,13]. The stereochemistry of ethylene insertion was definitely shown to be cis by the evidence that the polymerization of cis- and trans-dideutero-ethylene afforded stereoselectively deuterated polyethylenes [138]. 

The approach taken is loosely based on the input-process-output meta-model utilized to transform a problem statement into a functional process. The section Scope definition discusses the intended purpose and potential constraints of the isolation effort, followed by an overview of the Toolbox available to the practitioner (input). The section Method development scouting and scale-up reviews platform-based, highly automated approaches to selectivity scouting, development of the isolation as well as options for scaling up the chromatographic separation depending on purpose and constraints (process). The final section. Performing the task, explores a work breakdown structure approach to the preparative isolation of impurities as a unit operation in the development process (output). 

In simple terms, meta-analysis is the practice of using statistical methods to combine and quantify the outcomes of a series of studies in a single, pooled analysis. What is crucial in this definition is the emphasis on the use of statistical methods. In most biomedical research, the scientific review has a lengthy history and is still widely used. However, insofar that it does not utilise statistical methods for pooling results, and tends to summarise more in qualitative rather than quantitative terms it cannot be regarded as meta-analysis. 

The largest meta-analysis is the Meta-Analysis of Chemotherapy in Head and Neck Cancer (MACH-NC) study evaluating 63 trials with a total of 10,741 patients (Table 2) (69). MACH-NC assessed individual data rather than literature-based data with the inclusion of updated data and unpublished trials. For two-thirds of the trials, individual data were updated to a median follow-up of 6.8 yr. The meta-analysis was subcategorized into locoregional treatment with and without concomitant chemotherapy, induction/adju-vant chemotherapy, and laryngeal preservation with induction chemotherapy rather than definitive treatment for laryngeal and hypopharyngeal tumors. 

These particular points relate to each individual trial, but equally there will be similar considerations needed at the level of the development plan. In order for the overall, ordered programme of clinical trials to be scientifically sound there needs to be a substantial amount of commonality across the trials in terms of endpoints, definitions of analysis sets, recording of covariates and so on. This will facilitate the use of integrated summaries and meta-analysis for the evaluation and presentation of the complete programme or distinct parts of that programme, and outside of that, will allow a consistency of approach to the evaluation of the different trials. 

In this context, Lilly (474) reported a meta-analysis of three controlled studies of patients with TD who were treated with olanzapine. These authors found an 11-fold decrease in TD on olanzapine versus haloperidol based on the AIMS scale. There were a few patients who developed TD in the first 6 weeks of olanzapine, but this could have been from previous drug exposure, now not suppressed by the neuroleptic. Interestingly, there were no new cases (0/375) of TD developing in patients on long-term olanzapine treatment, whereas there were three of 83 cases on haloperidol. It is very difficult to arrive at definitive evidence about TD because most patients have received previous neuroleptic therapy and because TD-like symptoms occur spontaneously, providing an alternative explanation. It is clear that it is difficult to prove that olanzapine causes TD but equally difficult to prove that it does not. The 11-fold decreased incidence, however, is strong evidence that at least it produces much less TD. 

These issues must be kept in mind when interpreting claims made on the basis of clinical trial results. The variability in scales and definition of terms used in antidepressant trials also confounds attempts to make comparisons across studies and to do meta-analyses. With these caveats in mind, the next several sections review the acute and maintenance efficacy of the available antidepressant options, as well as some currently investigational antidepressants. 

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