A unified data representation theory for network visualization, ordering and coarse-graining

Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable both efficient data compression and hierarchical visualization to achieve high quality representations of larger data sets. Our unified data representation theory will help the analysis of extensive data sets, by revealing the large-scale structure of complex networks in a comprehensible form.Comment: 13 pages, 5 figure

Identification of genes required for Cf-dependent hypersensitive cell death by combined proteomic and RNA interfering analyses

Identification of hypersensitive cell death (HCD) regulators is essential to dissect the molecular mechanisms underlying plant disease resistance. In this study, combined proteomic and RNA interfering (RNAi) analyses were employed to identify genes required for the HCD conferred by the tomato resistance gene Cf-4 and the Cladosporium fulvum avirulence gene Avr4. Forty-nine proteins differentially expressed in the tomato seedlings mounting and those not mounting Cf-4/Avr4-dependent HCD were identified through proteomic analysis. Among them were a variety of defence-related proteins including a cysteine protease, Pip1, an operative target of another C. fulvum effector, Avr2. Additionally, glutathione-mediated antioxidation is a major response to Cf-4/Avr4-dependent HCD. Functional analysis through tobacco rattle virus-induced gene silencing and transient RNAi assays of the chosen 16 differentially expressed proteins revealed that seven genes, which encode Pip1 homologue NbPip1, a SIPK type MAP kinase Nbf4, an asparagine synthetase NbAsn, a trypsin inhibitor LeMir-like protein NbMir, a small GTP-binding protein, a late embryogenesis-like protein, and an ASR4-like protein, were required for Cf-4/Avr4-dependent HCD. Furthermore, the former four genes were essential for Cf-9/Avr9-dependent HCD; NbPip1, NbAsn, and NbMir, but not Nbf4, affected a nonadaptive bacterial pathogen Xanthomonas oryzae pv. oryzae-induced HCD in Nicotiana benthamiana. These data demonstrate that Pip1 and LeMir may play a general role in HCD and plant immunity and that the application of combined proteomic and RNA interfering analyses is an efficient strategy to identify genes required for HCD, disease resistance, and probably other biological processes in plants

Effects of Heterogeneous and Clustered Contact Patterns on Infectious Disease Dynamics

The spread of infectious diseases fundamentally depends on the pattern of contacts between individuals. Although studies of contact networks have shown that heterogeneity in the number of contacts and the duration of contacts can have far-reaching epidemiological consequences, models often assume that contacts are chosen at random and thereby ignore the sociological, temporal and/or spatial clustering of contacts. Here we investigate the simultaneous effects of heterogeneous and clustered contact patterns on epidemic dynamics. To model population structure, we generalize the configuration model which has a tunable degree distribution (number of contacts per node) and level of clustering (number of three cliques). To model epidemic dynamics for this class of random graph, we derive a tractable, low-dimensional system of ordinary differential equations that accounts for the effects of network structure on the course of the epidemic. We find that the interaction between clustering and the degree distribution is complex. Clustering always slows an epidemic, but simultaneously increasing clustering and the variance of the degree distribution can increase final epidemic size. We also show that bond percolation-based approximations can be highly biased if one incorrectly assumes that infectious periods are homogeneous, and the magnitude of this bias increases with the amount of clustering in the network. We apply this approach to model the high clustering of contacts within households, using contact parameters estimated from survey data of social interactions, and we identify conditions under which network models that do not account for household structure will be biased

Significance of Cuscutain, a cysteine protease from Cuscuta reflexa, in host-parasite interactions

<p>Abstract</p> <p>Background</p> <p>Plant infestation with parasitic weeds like <it>Cuscuta reflexa </it>induces morphological as well as biochemical changes in the host and the parasite. These modifications could be caused by a change in protein or gene activity. Using a comparative macroarray approach <it>Cuscuta </it>genes specifically upregulated at the host attachment site were identified.</p> <p>Results</p> <p>One of the infestation specific <it>Cuscuta </it>genes encodes a cysteine protease. The protein and its intrinsic inhibitory peptide were heterologously expressed, purified and biochemically characterized. The haustoria specific enzyme was named cuscutain in accordance with similar proteins from other plants, e.g. papaya. The role of cuscutain and its inhibitor during the host parasite interaction was studied by external application of an inhibitor suspension, which induced a significant reduction of successful infection events.</p> <p>Conclusions</p> <p>The study provides new information about molecular events during the parasitic plant - host interaction. Inhibition of cuscutain cysteine proteinase could provide means for antagonizing parasitic plants.</p

Evidence-Based PET for Neurological Diseases

Over the past two decades, one of the major breakthroughs for the approach to neurological diseases both in the clinical and research settings has been represented by the validation of diagnostic biomarkers able to demonstrate the presence of pathological mechanisms, alteration in neurotransmission as well as to predict disease progression [1, 2]. The use of PET with different tracers as well as other imaging biomarkers support the etiological diagnosis of neurological disorders in vivo. This approach is particularly relevant in the field of neurodegenerative diseases. In fact, neurodegenerative diseases are characterized by the progressive degeneration and death of neurons. They represent a heterogeneous group of conditions characterized by different etiologies, different neuropathological and neurochemical alterations leading to different clinical pictures and courses [3]. Indeed, an early accurate diagnosis allows to tackle the disease with available or experimental intervention, lifestyle changes, or logistical arrangements, before disability has developed. Early intervention is expected to have greater clinical impact, extend independent and active life, improve its quality, and decrease the burden and costs of the disease [4]. However, the validation of PET tracers in neurological disease is still ongoing, and evidence on its comparative and combined diagnostic value with respect to other biomarkers is incomplete [4, 5]. As a matter of fact, the increasing pressure for cost-effectiveness requires systematic assessment and validation of all biomarker performance in the clinical settings. Similarly only an evidence-based approach to new PET tracers can allow to select the most promising tracers for PET imaging in the research field both for pathophysiological investigations and for upcoming diagnostic approaches

Therapeutic targeting of cathepsin C::from pathophysiology to treatment

Cathepsin C (CatC) is a highly conserved tetrameric lysosomal cysteine dipeptidyl aminopeptidase. The best characterized physiological function of CatC is the activation of pro-inflammatory granule-associated serine proteases. These proteases are synthesized as inactive zymogens containing an N-terminal pro-dipeptide, which maintains the zymogen in its inactive conformation and prevents premature activation, which is potentially toxic to the cell. The activation of serine protease zymogens occurs through cleavage of the N-terminal dipeptide by CatC during cell maturation in the bone marrow. In vivo data suggest that pharmacological inhibition of pro-inflammatory serine proteases would suppress or attenuate deleterious effects of inflammatory/auto-immune disorders mediated by these proteases. The pathological deficiency in CatC is associated with Papillon-Lefèvre syndrome. The patients however do not present marked immunodeficiency despite the absence of active serine proteases in immune defense cells. Hence, the transitory pharmacological blockade of CatC activity in the precursor cells of the bone marrow may represent an attractive therapeutic strategy to regulate activity of serine proteases in inflammatory and immunologic conditions. A variety of CatC inhibitors have been developed both by pharmaceutical companies and academic investigators, some of which are currently being employed and evaluated in preclinical/clinical trials

Synthese und Umwandlungen 2-substituierter und 1,2-disubstituierter Indoxyl-Derivate /

Inaugural-Dissertation zur Erlangung der Würde eines Doktors rerum naturaliu