Unlocking legal validity. Some remarks on the artificial ontology of law

Following Kelsen’s influential theory of law, the concept of validity has been used in the literature to refer to different properties of law (such as existence, membership, bindingness, and more) and so it is inherently ambiguous. More importantly, Kelsen’s equivalence between the existence and the validity of law prevents us from accounting satisfactorily for relevant aspects of our current legal practices, such as the phenomenon of ‘unlawful law’. This chapter addresses this ambiguity to argue that the most important function of the concept of validity is constituting the complex ontological paradigm of modern law as an institutional-normative practice. In this sense validity is an artificial ontological status that supervenes on that of existence of legal norms, thus allowing law to regulate its own creation and creating the logical space for the occurrence of ‘unlawful law’. This function, I argue in the last part, is crucial to understanding the relationship between the ontological and epistemic dimensions of the objectivity of law. For given the necessary practice-independence of legal norms, it is the epistemic accessibility of their creation that enables the law to fulfill its general action-guiding (and thus coordinating) function

New targets for therapy in breast cancer: Small molecule tyrosine kinase inhibitors

Over the past several years many advances have been made in our understanding of critical pathways involved in carcinogenesis and tumor growth. These advances have led to the investigation of small molecule inhibitors of the ErbB family of receptor tyrosine kinases across a broad spectrum of malignancies. In this article we summarize the rationale for targeting members of the ErbB family in breast cancer, and review the preclinical and clinical data for the agents that are furthest in development. In addition, we highlight directions for future research, such as exploration of the potential crosstalk between the ErbB and hormone receptor signal transduction pathways, identification of predictive markers for tumor sensitivity, and development of rational combination regimens that include the tyrosine kinase inhibitors

PhenoFam-gene set enrichment analysis through protein structural information

<p>Abstract</p> <p>Background</p> <p>With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, PhenoFam.</p> <p>Results</p> <p>PhenoFam performs gene set enrichment analysis by employing structural and functional information on families of protein domains as annotation terms. Our tool is designed to analyse complete sets of results from quantitative high-throughput studies (gene expression microarrays, functional RNAi screens, <it>etc</it>.) without prior pre-filtering or hits-selection steps. PhenoFam utilizes Ensembl databases to link a list of user-provided identifiers with protein features from the InterPro database, and assesses whether results associated with individual domains differ significantly from the overall population. To demonstrate the utility of PhenoFam we analysed a genome-wide RNA interference screen and discovered a novel function of plexins containing the cytoplasmic RasGAP domain. Furthermore, a PhenoFam analysis of breast cancer gene expression profiles revealed a link between breast carcinoma and altered expression of PX domain containing proteins.</p> <p>Conclusions</p> <p>PhenoFam provides a user-friendly, easily accessible web interface to perform GSEA based on high-throughput data sets and structural-functional protein information, and therefore aids in functional annotation of genes.</p

Evaluating methods for ranking differentially expressed genes applied to microArray quality control data

<p>Abstract</p> <p>Background</p> <p>Statistical methods for ranking differentially expressed genes (DEGs) from gene expression data should be evaluated with regard to high sensitivity, specificity, and reproducibility. In our previous studies, we evaluated eight gene ranking methods applied to only Affymetrix GeneChip data. A more general evaluation that also includes other microarray platforms, such as the Agilent or Illumina systems, is desirable for determining which methods are suitable for each platform and which method has better inter-platform reproducibility.</p> <p>Results</p> <p>We compared the eight gene ranking methods using the MicroArray Quality Control (MAQC) datasets produced by five manufacturers: Affymetrix, Applied Biosystems, Agilent, GE Healthcare, and Illumina. The area under the curve (AUC) was used as a measure for both sensitivity and specificity. Although the highest AUC values can vary with the definition of "true" DEGs, the best methods were, in most cases, either the weighted average difference (WAD), rank products (RP), or intensity-based moderated <it>t </it>statistic (ibmT). The percentages of overlapping genes (POGs) across different test sites were mainly evaluated as a measure for both intra- and inter-platform reproducibility. The POG values for WAD were the highest overall, irrespective of the choice of microarray platform. The high intra- and inter-platform reproducibility of WAD was also observed at a higher biological function level.</p> <p>Conclusion</p> <p>These results for the five microarray platforms were consistent with our previous ones based on 36 real experimental datasets measured using the Affymetrix platform. Thus, recommendations made using the MAQC benchmark data might be universally applicable.</p

Mammalian Stem Cells Reprogramming in Response to Terahertz Radiation

We report that extended exposure to broad-spectrum terahertz radiation results in specific changes in cellular functions that are closely related to DNA-directed gene transcription. Our gene chip survey of gene expression shows that whereas 89% of the protein coding genes in mouse stem cells do not respond to the applied terahertz radiation, certain genes are activated, while other are repressed. RT-PCR experiments with selected gene probes corresponding to transcripts in the three groups of genes detail the gene specific effect. The response was not only gene specific but also irradiation conditions dependent. Our findings suggest that the applied terahertz irradiation accelerates cell differentiation toward adipose phenotype by activating the transcription factor peroxisome proliferator-activated receptor gamma (PPARG). Finally, our molecular dynamics computer simulations indicate that the local breathing dynamics of the PPARG promoter DNA coincides with the gene specific response to the THz radiation. We propose that THz radiation is a potential tool for cellular reprogramming