СТЕЙКХОЛДЕРСЬКИЙ ПІДХІД ДО ВАРТІСНО–ОРІЄНТОВАНОГО УПРАВЛІННЯ ІННОВАЦІЙНОЮ ДІЯЛЬНІСТЮ ПІДПРИЄМСТВ

Fundamental researches in the sphere of innovation management state that the problem of development of the radically new paradigm of the value–based management of innovation development becomes more urgent. It could provide the increase of aggregate price of enterprise as the result of innovation implementation, through the growth of its efficiency and risk tolerance taking into account the interests of all stakeholders. Results.In this article on the ground of generalization of basis and rules of innovation activity management, scientific approaches to the interpretation of economic categories, the new definition of the notion of the value–based management of enterprise innovation activity was offered and theoretically substantiated. Depending on the level of representation of interests of the main groups of stakeholders, three levels of such management were distinguished. They are: level of strategic management, level of current management, level of external management. For each of these levels the indexes of value increment and indexes of enterprise risk tolerance provision within the accounting of interest of different groups of stakeholders were analyzed. Conclusion.The value–based management of enterprise innovation activity is the range of functions and rules as well as the methodological tools for making strategic and tactical decisions, concerning the development, introduction and commercialization of innovations that provide the growth of corporative value, accounting the monetized for the enter.В статье теоретически обосновано новое определение понятия стоимостно–ориентированное управление инновационной деятельностью предприятия. В зависимости от степени представления интересов основных групп стейкхолдеров выделены три уровня такого управления: уровень стратегического управления, уровень текущего управления и уровень внешнего управления. Для каждого из этих уровней проанализированы индикаторы прироста стоимости и индикаторы обеспечения рискоустойчивости предприятия в контексте учета интересов различных групп стейкхолдеров. Сделаны выводы, что стоимостно–ориентированное управление инновационной деятельностью предприятия должна обеспечивать рост его корпоративной стоимости с учетом монетизированной общественной ценности при условии согласования интересов всех групп стейкхолдеров.У статті теоретично обґрунтовано нове визначення поняття вартісно–орієнтоване управління інноваційною діяльністю підприємства. Залежно від ступеня представлення інтересів основних груп стейкхолдерів виокремлено три рівня такого управління: рівень стратегічного, поточного та зовнішнього управління. Для кожного з них проаналізовані індикатори приросту вартості та індикатори забезпечення ризикостійкості підприємства в контексті врахування інтересів різних груп стейкхолдерів. Зроблено висновок, що вартісно–орієнтоване управління інноваційною діяльністю підприємства має забезпечувати зростання його корпоративної вартості з урахуванням монетизованої для підприємства суспільної цінності при умові узгодження інтересів всіх груп стейкхолдерів

Mapping the ρ1 GABAC Receptor Agonist Binding Pocket

γ-Aminobutyric acid (GABA) is the major inhibitory neurotransmitter in the mammalian brain. The GABA receptor type C (GABAC) is a ligand-gated ion channel with pharmacological properties distinct from the GABAA receptor. To date, only three binding domains in the recombinant ρ1 GABAC receptor have been recognized among six potential regions. In this report, using the substituted cysteine accessibility method, we scanned three potential regions previously unexplored in the ρ1 GABAC receptor, corresponding to the binding loops A, E, and F in the structural model for ligand-gated ion channels. The cysteine accessibility scanning and agonist/antagonist protection tests have resulted in the identification of residues in loops A and E, but not F, involved in forming the GABAC receptor agonist binding pocket. Three of these newly identified residues are in a novel region corresponding to the extended stretch of loop E. In addition, the cysteine accessibility pattern suggests that part of loop A and part of loop E have a β-strand structure, whereas loop F is a random coil. Finally, when all of the identified ligand binding residues are mapped onto a three-dimensional homology model of the amino-terminal domain of the ρ1 GABAC receptor, they are facing toward the putative binding pocket. Combined with previous findings, a complete model of the GABAC receptor binding pocket was proposed and discussed in comparison with the GABAA receptor binding pocket

Sources of variation in Affymetrix microarray experiments

BACKGROUND: A typical microarray experiment has many sources of variation which can be attributed to biological and technical causes. Identifying sources of variation and assessing their magnitude, among other factors, are important for optimal experimental design. The objectives of this study were: (1) to estimate relative magnitudes of different sources of variation and (2) to evaluate agreement between biological and technical replicates. RESULTS: We performed a microarray experiment using a total of 24 Affymetrix GeneChip(® )arrays. The study included 4(th )mammary gland samples from eight 21-day-old Sprague Dawley CD female rats exposed to genistein (soy isoflavone). RNA samples from each rat were split to assess variation arising at labeling and hybridization steps. A general linear model was used to estimate variance components. Pearson correlations were computed to evaluate agreement between technical and biological replicates. CONCLUSION: The greatest source of variation was biological variation, followed by residual error, and finally variation due to labeling when *.cel files were processed with dChip and RMA image processing algorithms. When MAS 5.0 or GCRMA-EB were used, the greatest source of variation was residual error, followed by biology and labeling. Correlations between technical replicates were consistently higher than between biological replicates

Evaluation morphological changes in temporo-mandibular joints with experimental animals in the process of modeling fibrous ankylosis and the impact of transkranial electric stimulation on them

The results of morphological changes in temporomandibular joint (TMJ) with 30 experimental animals in the process of modeling fibrous ankylosis are given in the article. The analysis of the results obtained has shown the positive impact of transkranial electric stimulation (TES-therapy) which reduced the destructive processes in bone tissue of mandibular head and mandibular fossa thus accelerating the process of their restructuring and the promotion of osteogenesis process compared to the fabrics of TMJ with experimental animals to whom the TES-therapy has not been applied

Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations

BACKGROUND: Microarrays measure the binding of nucleotide sequences to a set of sequence specific probes. This information is combined with annotation specifying the relationship between probes and targets and used to make inferences about transcript- and, ultimately, gene expression. In some situations, a probe is capable of hybridizing to more than one transcript, in others, multiple probes can target a single sequence. These 'multiply targeted' probes can result in non-independence between measured expression levels. RESULTS: An analysis of these relationships for Affymetrix arrays considered both the extent and influence of exact matches between probe and transcript sequences. For the popular HGU133A array, approximately half of the probesets were found to interact in this way. Both real and simulated expression datasets were used to examine how these effects influenced the expression signal. It was found not only to lead to increased signal strength for the affected probesets, but the major effect is to significantly increase their correlation, even in situations when only a single probe from a probeset was involved. By building a network of probe-probeset-transcript relationships, it is possible to identify families of interacting probesets. More than 10% of the families contain members annotated to different genes or even different Unigene clusters. Within a family, a mixture of genuine biological and artefactual correlations can occur. CONCLUSION: Multiple targeting is not only prevalent, but also significant. The ability of probesets to hybridize to more than one gene product can lead to false positives when analysing gene expression. Comprehensive annotation describing multiple targeting is required when interpreting array data

A new method for class prediction based on signed-rank algorithms applied to Affymetrix® microarray experiments

<p>Abstract</p> <p>Background</p> <p>The huge amount of data generated by DNA chips is a powerful basis to classify various pathologies. However, constant evolution of microarray technology makes it difficult to mix data from different chip types for class prediction of limited sample populations. Affymetrix^®technology provides both a quantitative fluorescence signal and a decision (<it>detection call</it>: absent or present) based on signed-rank algorithms applied to several hybridization repeats of each gene, with a per-chip normalization. We developed a new prediction method for class belonging based on the detection call only from recent Affymetrix chip type. Biological data were obtained by hybridization on U133A, U133B and U133Plus 2.0 microarrays of purified normal B cells and cells from three independent groups of multiple myeloma (MM) patients.</p> <p>Results</p> <p>After a call-based data reduction step to filter out non class-discriminative probe sets, the gene list obtained was reduced to a predictor with correction for multiple testing by iterative deletion of probe sets that sequentially improve inter-class comparisons and their significance. The error rate of the method was determined using leave-one-out and 5-fold cross-validation. It was successfully applied to (i) determine a sex predictor with the normal donor group classifying gender with no error in all patient groups except for male MM samples with a Y chromosome deletion, (ii) predict the immunoglobulin light and heavy chains expressed by the malignant myeloma clones of the validation group and (iii) predict sex, light and heavy chain nature for every new patient. Finally, this method was shown powerful when compared to the popular classification method Prediction Analysis of Microarray (PAM).</p> <p>Conclusion</p> <p>This normalization-free method is routinely used for quality control and correction of collection errors in patient reports to clinicians. It can be easily extended to multiple class prediction suitable with clinical groups, and looks particularly promising through international cooperative projects like the "Microarray Quality Control project of US FDA" MAQC as a predictive classifier for diagnostic, prognostic and response to treatment. Finally, it can be used as a powerful tool to mine published data generated on Affymetrix systems and more generally classify samples with binary feature values.</p

Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages

BACKGROUND: With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing data sets. In this new context, analysis itself is no longer the problem, but retrieving and consistently integrating all this data before delivering it to the wide variety of existing analysis tools becomes the new bottleneck. RESULTS: We present the newly released inSilicoMerging R/Bioconductor package which, together with the earlier released inSilicoDb R/Bioconductor package, allows consistent retrieval, integration and analysis of publicly available microarray gene expression data sets. Inside the inSilicoMerging package a set of five visual and six quantitative validation measures are available as well. CONCLUSIONS: By providing (i) access to uniformly curated and preprocessed data, (ii) a collection of techniques to remove the batch effects between data sets from different sources, and (iii) several validation tools enabling the inspection of the integration process, these packages enable researchers to fully explore the potential of combining gene expression data for downstream analysis. The power of using both packages is demonstrated by programmatically retrieving and integrating gene expression studies from the InSilico DB repository [https://insilicodb.org/app/]

The Annotation, Mapping, Expression and Network (AMEN) suite of tools for molecular systems biology

<p>Abstract</p> <p>Background</p> <p>High-throughput genome biological experiments yield large and multifaceted datasets that require flexible and user-friendly analysis tools to facilitate their interpretation by life scientists. Many solutions currently exist, but they are often limited to specific steps in the complex process of data management and analysis and some require extensive informatics skills to be installed and run efficiently.</p> <p>Results</p> <p>We developed the Annotation, Mapping, Expression and Network (AMEN) software as a stand-alone, unified suite of tools that enables biological and medical researchers with basic bioinformatics training to manage and explore genome annotation, chromosomal mapping, protein-protein interaction, expression profiling and proteomics data. The current version provides modules for (i) uploading and pre-processing data from microarray expression profiling experiments, (ii) detecting groups of significantly co-expressed genes, and (iii) searching for enrichment of functional annotations within those groups. Moreover, the user interface is designed to simultaneously visualize several types of data such as protein-protein interaction networks in conjunction with expression profiles and cellular co-localization patterns. We have successfully applied the program to interpret expression profiling data from budding yeast, rodents and human.</p> <p>Conclusion</p> <p>AMEN is an innovative solution for molecular systems biological data analysis freely available under the GNU license. The program is available via a website at the Sourceforge portal which includes a user guide with concrete examples, links to external databases and helpful comments to implement additional functionalities. We emphasize that AMEN will continue to be developed and maintained by our laboratory because it has proven to be extremely useful for our genome biological research program.</p