Disability and Mere-Difference: An Exploration of the Relationship Between Disability and Well-Being

The objective of this thesis is to evaluate and defend the mere-difference view of disability. In order to do this, I will first review Elizabeth Barnes’s argument for a moderate social-constructivist understanding of disability. I will then review her presentation of the mere-difference view, and formally introduce a common and perhaps powerful objection to it—that it has unacceptable implications. Next, I will review some of Barnes’s responses to one of this objection’s common forms. I will then discuss objections raised against Barnes’s responses—specifically, those raised by Guy Kahane and Julian Savulescu, along with Vuko Andrić and Joachim Wündisch—before offering my own responses to these objections. Finally, I will broadly review additional objections that may be raised against the mere-difference view and offer additional responses. It is my aim to defend the position that disability is something which, in terms of well-being, is neither necessarily good, nor necessarily bad. Rather, that disability is a mere-difference; that it is, itself, a kind of difference which is neutral with regard to its effect on well-being

Beyond microarrays: Finding key transcription factors controlling signal transduction pathways

BACKGROUND: Massive gene expression changes in different cellular states measured by microarrays, in fact, reflect just an "echo" of real molecular processes in the cells. Transcription factors constitute a class of the regulatory molecules that typically require posttranscriptional modifications or ligand binding in order to exert their function. Therefore, such important functional changes of transcription factors are not directly visible in the microarray experiments. RESULTS: We developed a novel approach to find key transcription factors that may explain concerted expression changes of specific components of the signal transduction network. The approach aims at revealing evidence of positive feedback loops in the signal transduction circuits through activation of pathway-specific transcription factors. We demonstrate that promoters of genes encoding components of many known signal transduction pathways are enriched by binding sites of those transcription factors that are endpoints of the considered pathways. Application of the approach to the microarray gene expression data on TNF-alpha stimulated primary human endothelial cells helped to reveal novel key transcription factors potentially involved in the regulation of the signal transduction pathways of the cells. CONCLUSION: We developed a novel computational approach for revealing key transcription factors by knowledge-based analysis of gene expression data with the help of databases on gene regulatory networks (TRANSFAC(® )and TRANSPATH(®)). The corresponding software and databases are available at

FeatureScan: revealing property-dependent similarity of nucleotide sequences

FeatureScan is a software package aiming to reveal novel types of DNA sequence similarity by comparing physico-chemical properties. Thirty-eight different parameters of DNA double strands such as charge, melting enthalpy, conformational parameters and the like are provided. As input FeatureScan requires two sequences, a pattern sequence and a target sequence, search conditions are set by selecting a specific DNA parameter and a threshold value. Search results are displayed in FASTA format and directly linked to external genome databases/browsers (ENSEMBL, NCBI, UCSC). An Internet version of FeatureScan is accessible at . As part of the HOBIT initiative () FeatureScan is also accessible as a web service at its above home page. Currently, several preloaded genomes are provided at this Internet website (Homo sapiens, Mus musculus, Rattus norvegicus and four strains of Escherichia coli) as target sequences. Standalone executables of FeatureScan are available on request

Topological defects in spinor condensates

We investigate the structure of topological defects in the ground states of spinor Bose-Einstein condensates with spin F=1 or F=2. The type and number of defects are determined by calculating the first and second homotopy groups of the order-parameter space. The order-parameter space is identified with a set of degenerate ground state spinors. Because the structure of the ground state depends on whether or not there is an external magnetic field applied to the system, defects are sensitive to the magnetic field. We study both cases and find that the defects in zero and non-zero field are different.Comment: 10 pages, 1 figure. Published versio

TRANSFAC(®) and its module TRANSCompel(®): transcriptional gene regulation in eukaryotes

The TRANSFAC(®) database on transcription factors, their binding sites, nucleotide distribution matrices and regulated genes as well as the complementing database TRANSCompel(®) on composite elements have been further enhanced on various levels. A new web interface with different search options and integrated versions of Match™ and Patch™ provides increased functionality for TRANSFAC(®). The list of databases which are linked to the common GENE table of TRANSFAC(®) and TRANSCompel(®) has been extended by: Ensembl, UniGene, EntrezGene, HumanPSD™ and TRANSPRO™. Standard gene names from HGNC, MGI and RGD, are included for human, mouse and rat genes, respectively. With the help of InterProScan, Pfam, SMART and PROSITE domains are assigned automatically to the protein sequences of the transcription factors. TRANSCompel(®) contains now, in addition to the COMPEL table, a separate table for detailed information on the experimental EVIDENCE on which the composite elements are based. Finally, for TRANSFAC(®), in respect of data growth, in particular the gain of Drosophila transcription factor binding sites (by courtesy of the Drosophila DNase I footprint database) and of Arabidopsis factors (by courtesy of DATF, Database of Arabidopsis Transcription Factors) has to be stressed. The here described public releases, TRANSFAC(®) 7.0 and TRANSCompel(®) 7.0, are accessible under

Advanced Computational Biology Methods Identify Molecular Switches for Malignancy in an EGF Mouse Model of Liver Cancer

The molecular causes by which the epidermal growth factor receptor tyrosine kinase induces malignant transformation are largely unknown. To better understand EGFs' transforming capacity whole genome scans were applied to a transgenic mouse model of liver cancer and subjected to advanced methods of computational analysis to construct de novo gene regulatory networks based on a combination of sequence analysis and entrained graph-topological algorithms. Here we identified transcription factors, processes, key nodes and molecules to connect as yet unknown interacting partners at the level of protein-DNA interaction. Many of those could be confirmed by electromobility band shift assay at recognition sites of gene specific promoters and by western blotting of nuclear proteins. A novel cellular regulatory circuitry could therefore be proposed that connects cell cycle regulated genes with components of the EGF signaling pathway. Promoter analysis of differentially expressed genes suggested the majority of regulated transcription factors to display specificity to either the pre-tumor or the tumor state. Subsequent search for signal transduction key nodes upstream of the identified transcription factors and their targets suggested the insulin-like growth factor pathway to render the tumor cells independent of EGF receptor activity. Notably, expression of IGF2 in addition to many components of this pathway was highly upregulated in tumors. Together, we propose a switch in autocrine signaling to foster tumor growth that was initially triggered by EGF and demonstrate the knowledge gain form promoter analysis combined with upstream key node identification

Endothelial FGF signaling is protective in hypoxia-induced pulmonary hypertension

Hypoxia-induced pulmonary hypertension (PH) is one of the most common and deadliest forms of PH. Fibroblast growth factor receptors 1 and 2 (FGFR1/2) are elevated in patients with PH and in mice exposed to chronic hypoxia. Endothelial FGFR1/2 signaling is important for the adaptive response to several injury types and we hypothesized that endothelial FGFR1/2 signaling would protect against hypoxia-induced PH. Mice lacking endothelial FGFR1/2, mice with activated endothelial FGFR signaling, and human pulmonary artery endothelial cells (HPAECs) were challenged with hypoxia. We assessed the effect of FGFR activation and inhibition on right ventricular pressure, vascular remodeling, and endothelial-mesenchymal transition (EndMT), a known pathologic change seen in patients with PH. Hypoxia-exposed mice lacking endothelial FGFRs developed increased PH, while mice overexpressing a constitutively active FGFR in endothelial cells did not develop PH. Mechanistically, lack of endothelial FGFRs or inhibition of FGFRs in HPAECs led to increased TGF-β signaling and increased EndMT in response to hypoxia. These phenotypes were reversed in mice with activated endothelial FGFR signaling, suggesting that FGFR signaling inhibits TGF-β pathway-mediated EndMT during chronic hypoxia. Consistent with these observations, lung tissue from patients with PH showed activation of FGFR and TGF-β signaling. Collectively, these data suggest that activation of endothelial FGFR signaling could be therapeutic for hypoxia-induced PH

Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates

<p>Abstract</p> <p>Background</p> <p>The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published <it>in silico </it>method PAAS was applied for prediction of interactions between protein kinases and their substrates.</p> <p>Results</p> <p>We used the method for recognition of the protein classes defined by the interaction with the same protein partners. 1021 protein kinase substrates classified by 45 kinases were extracted from the Phospho.ELM database and used as a training set. The reasonable accuracy of prediction calculated by leave-one-out cross validation procedure was observed in the majority of kinase-specificity classes. The random multiple splitting of the studied set onto the test and training set had also led to satisfactory results. The kinase substrate specificity for 186 proteins extracted from TRANSPATH^®database was predicted by PAAS method. Several kinase-substrate interactions described in this database were correctly predicted. Using the previously developed ExPlain™ system for the reconstruction of signal transduction pathways, we showed that addition of the newly predicted interactions enabled us to find the possible path between signal trigger, TNF-alpha, and its target genes in the cell.</p> <p>Conclusions</p> <p>It was shown that the predictions of protein kinase substrates by PAAS were suitable for the enrichment of signaling pathway networks and identification of the novel signaling pathways. The on-line version of PAAS for prediction of protein kinase substrates is freely available at <url>http://www.ibmc.msk.ru/PAAS/</url>.</p

Leading-effect vs. Risk-taking in Dynamic Tournaments: Evidence from a Real-life Randomized Experiment

Two 'order effects' may emerge in dynamic tournaments with information feedback. First, participants adjust effort across stages, which could advantage the leading participant who faces a larger 'effective prize' after an initial victory (leading-effect). Second, participants lagging behind may increase risk at the final stage as they have 'nothing to lose' (risk-taking). We use a randomized natural experiment in professional two-game soccer tournaments where the treatment (order of a stage-specific advantage) and team characteristics, e.g. ability, are independent. We develop an identification strategy to test for leading-effects controlling for risk-taking. We find no evidence of leading-effects and negligible risk-taking effects