Tally Erp 9 47 Full Version 13
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Research published in 2011 based on a survey of 225 manufacturers, retailers and distributors found "high" rates of interest and adoption of ERP systems and that very few businesses were "completely untouched" by the concept of an ERP system. 27% of the companies survey had a fully operational system, 12% were at that time rolling out a system and 26% had an existing ERP system which they were extending or upgrading.
This is a study, conducted in an academic medical center electroencephalography laboratory, of 632 subjects: 390 healthy normal controls, 70 patients with carefully defined CFS, 24 with major depression, and 148 with general fatigue. Aside from fatigue, all patients were medically healthy by history and examination. EEGs were obtained and spectral coherences calculated after extensive artifact removal. Principal Components Analysis identified coherence factors and corresponding factor loading patterns. Discriminant analysis determined whether spectral coherence factors could reliably discriminate CFS patients from healthy control subjects without misclassifying depression as CFS.
Since common psychiatric disorders, particularly depression, often cause fatigue and since psychiatric diagnoses may be difficult to objectively and reliably confirm, many continued to reasonably wonder about the role of an as of yet identified form of depression as the cause of CFS. However, it was found that many patients with CFS suffer from co-existing psychiatric disorders only after becoming ill with CFS. Moreover, in 30-50% of patients no co-existing psychiatric disorders [4, 5] can be demonstrated. In addition, a carefully controlled trial of fluoxetine in patients with CFS failed to improve fatigue, even in those patients with a concomitant major depression .
Data for all electrodes and for all EEG frequencies produce a large variable number-7936 for our study. To facilitate subsequent statistical analyses, we undertook Principal Components Analysis (PCA) as an objective technique to meaningfully reduce variable number . Our coherence data were first normalized (centered and shifted to have unit variance) so that eventual factors reflect deviations from the average response. To avoid loss of sensitivity by a priori data limitation, an 'unrestricted' form of PCA was applied  allowing all coherence variables per subject to enter analysis. By employment of an algorithm based upon singular value decomposition (SVD) , a data set of uncorrelated (orthogonal) principal components or factors [45, 59, 60] was developed in which the identification of a small number of factors (following Varimax rotation ) describe an acceptably large amount of variance . Varimax rotation enhances factor contrast yielding higher loadings for fewer factors whilst retaining factor orthogonality and has become "...the most widely accepted and employed standard for orthogonal rotation of factors..." (p.145) . Although not the only PCA method applicable to large, asymmetrical matrices (7936 variables by 632 cases as in the current study), SVD (which can be used to solve undetermined and over determined systems of linear equations ) is among the most efficient in our experience . This approach to variable number reduction has been successfully used in a prior study of EEG spectral coherence in infants .
Utilizing the full subject population (Table 1, n = 632) we were successful in reducing the initial 7936 coherence variables per subject to 40 orthogonal (uncorrelated) factors per subject which described 55.6% of the total, initial variance. In other words, PCA condensed over half the information (variance) contained in the initial 7936 variables into just 40 new variables (outcome factors). One benefit of this almost 7936:40 or 200 fold reduction in data dimensionality over the entire population is a parallel reduction in the likelihood for capitalization on chance of the sort that may occur during subsequent statistical analyses when they involve large numbers of variables . An additional benefit to this 'hands-off' data reduction is that it requires no advance or a priori coherence variable selection by the investigators, eliminating any possible variable selection bias. Bartels refers to this as allowing the intrinsic data structure of the population to select variables .
The less than 100% accuracy of our spectral coherence based classification function could reflect a deficiency in the CDC criteria for CFS, and/or a deficiency in the coherence-based discriminant itself, and/or unexplored physiological variability even within carefully CDC-defined CFS. For example, multiple etiologic agents have been identified as potential triggers of the CFS phenotype , each with the potential for a slightly differing impact upon the central nervous system (CNS) and, hence, on EEG spectral coherence. The possibility of sub-grouping  CFS on the basis of coherence and other objective CNS measures (e.g., MRI, SPECT/PET, neuropsychology) may be a fruitful area for further exploration. Subgrouping could result in a broader set of objectively derived CNS measures from neurophysiology and other neuroimaging techniques that might eventually become the diagnostic 'gold standard' for CFS.
Our immediate plans call for enlarging our population to prospectively test and refine current findings. This will primarily involve recruiting additional patients with depression and non-CFS prolonged fatigue as well as additional patients with CDC-defined CFS-especially males. All patients will have equivalent evaluations: clinical and behavioral as well and neurophysiological. We plan to evaluate a population of CFS patients before and after beginning medications. We also hope to develop specific classification rules to separate four diagnostic groups: CFS, non-CFS prolonged fatigue, depression, and healthy controls. We plan to search for CFS-gender interactions. All this will require substantially larger populations than now available to us. Finally, within the CFS population we will employ cluster analysis, as successfully applied by Montironi and Bartels  in another research area, to search for consistent CFS subpopulat