On supersymmetric quantum mechanics

This paper constitutes a review on N=2 fractional supersymmetric Quantum Mechanics of order k. The presentation is based on the introduction of a generalized Weyl-Heisenberg algebra W_k. It is shown how a general Hamiltonian can be associated with the algebra W_k. This general Hamiltonian covers various supersymmetrical versions of dynamical systems (Morse system, Poschl-Teller system, fractional supersymmetric oscillator of order k, etc.). The case of ordinary supersymmetric Quantum Mechanics corresponds to k=2. A connection between fractional supersymmetric Quantum Mechanics and ordinary supersymmetric Quantum Mechanics is briefly described. A realization of the algebra W_k, of the N=2 supercharges and of the corresponding Hamiltonian is given in terms of deformed-bosons and k-fermions as well as in terms of differential operators.Comment: Review paper (31 pages) to be published in: Fundamental World of Quantum Chemistry, A Tribute to the Memory of Per-Olov Lowdin, Volume 3, E. Brandas and E.S. Kryachko (Eds.), Springer-Verlag, Berlin, 200

Time lagged information theoretic approaches to the reverse engineering of gene regulatory networks

Background: A number of models and algorithms have been proposed in the past for gene regulatory network (GRN) inference; however, none of them address the effects of the size of time-series microarray expression data in terms of the number of time-points. In this paper, we study this problem by analyzing the behaviour of three algorithms based on information theory and dynamic Bayesian network (DBN) models. These algorithms were implemented on different sizes of data generated by synthetic networks. Experiments show that the inference accuracy of these algorithms reaches a saturation point after a specific data size brought about by a saturation in the pair-wise mutual information (MI) metric; hence there is a theoretical limit on the inference accuracy of information theory based schemes that depends on the number of time points of micro-array data used to infer GRNs. This illustrates the fact that MI might not be the best metric to use for GRN inference algorithms. To circumvent the limitations of the MI metric, we introduce a new method of computing time lags between any pair of genes and present the pair-wise time lagged Mutual Information (TLMI) and time lagged Conditional Mutual Information (TLCMI) metrics. Next we use these new metrics to propose novel GRN inference schemes which provides higher inference accuracy based on the precision and recall parameters. Results: It was observed that beyond a certain number of time-points (i.e., a specific size) of micro-array data, the performance of the algorithms measured in terms of the recall-to-precision ratio saturated due to the saturation in the calculated pair-wise MI metric with increasing data size. The proposed algorithms were compared to existing approaches on four different biological networks. The resulting networks were evaluated based on the benchmark precision and recall metrics and the results favour our approach. Conclusions: To alleviate the effects of data size on information theory based GRN inference algorithms, novel time lag based information theoretic approaches to infer gene regulatory networks have been proposed. The results show that the time lags of regulatory effects between any pair of genes play an important role in GRN inference schemes

Repeated PTZ Treatment at 25-Day Intervals Leads to a Highly Efficient Accumulation of Doublecortin in the Dorsal Hippocampus of Rats

BACKGROUND: Neurogenesis persists throughout life in the adult mammalian brain. Because neurogenesis can only be assessed in postmortem tissue, its functional significance remains undetermined, and identifying an in vivo correlate of neurogenesis has become an important goal. By studying pentylenetetrazole-induced brain stimulation in a rat model of kindling we accidentally discovered that 25±1 days periodic stimulation of Sprague-Dawley rats led to a highly efficient increase in seizure susceptibility. METHODOLOGY/PRINCIPAL FINDINGS: By EEG, RT-PCR, western blotting and immunohistochemistry, we show that repeated convulsive seizures with a periodicity of 25±1 days led to an enrichment of newly generated neurons, that were BrdU-positive in the dentate gyrus at day 25±1 post-seizure. At the same time, there was a massive increase in the number of neurons expressing the migratory marker, doublecortin, at the boundary between the granule cell layer and the polymorphic layer in the dorsal hippocampus. Some of these migrating neurons were also positive for NeuN, a marker for adult neurons. CONCLUSION/SIGNIFICANCE: Our results suggest that the increased susceptibility to seizure at day 25±1 post-treatment is coincident with a critical time required for newborn neurons to differentiate and integrate into the existing hippocampal network, and outlines the importance of the dorsal hippocampus for seizure-related neurogenesis. This model can be used as an in vivo correlate of neurogenesis to study basic questions related to neurogenesis and to the neurogenic mechanisms that contribute to the development of epilepsy

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

YKL-40 tissue expression and plasma levels in patients with ovarian cancer

<p>Abstract</p> <p>Background</p> <p>YKL-40 (chitinase-3-like-1) is a member of "mammalian chitinase-like proteins". The protein is expressed in many types of cancer cells and the highest plasma YKL-40 levels have been found in patients with metastatic disease, short recurrence/progression-free intervals, and short overall survival. The aim of the study was to determine the expression of YKL-40 in tumor tissue and plasma in patients with borderline ovarian tumor or epithelial ovarian cancer (OC), and investigate prognostic value of this marker.</p> <p>Methods</p> <p>YKL-40 protein expression was determined by immunohistochemistry in tissue arrays from 181 borderline tumors and 473 OC. Plasma YKL-40 was determined by ELISA in preoperative samples from 19 patients with borderline tumor and 76 OC patients.</p> <p>Results</p> <p>YKL-40 protein expression was found in cancer cells, tumor associated macrophages, neutrophils and mast cells. The tumor cell expression was higher in OC than in borderline tumors (p = 0.001), and associated with FIGO stage (p < 0.0001) and histological subtype (p = 0.0009). Positive YKL-40 expression (≥ 5% staining) was not associated with reduced survival. Plasma YKL-40 was also higher in patients with OC than in patients with borderline tumors (p < 0.0001), and it was positively correlated to serum CA-125 (p < 0.0001) and FIGO stage (p = 0.0001). Univariate Cox analysis of plasma YKL-40 showed association with overall survival (p < 0.0001). Multivariate Cox analysis, including plasma YKL-40, serum CA125, FIGO stage, age and radicality after primary surgery as variables, showed that elevated plasma YKL-40 was associated with a shorter survival (HR = 2.13, 95% CI: 1.40–3.25, p = 0.0004).</p> <p>Conclusion</p> <p>YKL-40 in OC tissue and plasma are related to stage and histology, but only plasma YKL-40 is a prognostic biomarker in patients with OC.</p

Modularization of biochemical networks based on classification of Petri net t-invariants

<p>Abstract</p> <p>Background</p> <p>Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.</p> <p>With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system.</p> <p>Methods</p> <p>Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied.</p> <p>Results</p> <p>We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in <it>Saccharomyces cerevisiae</it>) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability.</p> <p>Conclusion</p> <p>We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.</p

Specific ant-pollination in an alpine orchid and the role of floral scent in attracting pollinating ants

Several studies have recently shown that floral scent can deter ants from flowers. However, when ants serve as reliable pollen vectors, for example in harsh, windy habitats, were flying insects are less active, plants should have evolved floral signals to attract them to the flowers. We tested this hypothesis in the alpine orchid, Chamorchis alpina. C. alpina was found to be predominantly ant pollinated, with some occasional pollination by ichneumonid wasps. In all three investigated populations, only two species of ants, Formica lemani and Leptothorax acervorum visited the flowers and removed pollinaria. These two pollinator ants were found to be among the most common ant species in all habitats, but other, non-pollinating ants were also frequently found, suggesting a factor that mediates specific pollination. Floral morphology was found to be compatible with at least one of the common non-pollinator ants. Floral scent consistently comprised five terpenoid compounds, β-phellandrene, 1,8-cineole, linalool, α-terpineol, and β-caryophyllene. A synthetic blend of these five compounds emitting from rubber septa, was found to be attractive to one pollinator ant-species, F. lemani, in the field. The floral scent of C. alpina, through attracting only specific ants, may thus play a role in filtering floral visitors

Transcriptome analysis of orange-spotted grouper (Epinephelus coioides) spleen in response to Singapore grouper iridovirus

<p>Abstract</p> <p>Background</p> <p>Orange-spotted grouper (<it>Epinephelus coioides</it>) is an economically important marine fish cultured in China and Southeast Asian countries. The emergence of infectious viral diseases, including iridovirus and betanodavirus, have severely affected food products based on this species, causing heavy economic losses. Limited available information on the genomics of <it>E. coioides </it>has hampered the understanding of the molecular mechanisms that underlie host-virus interactions. In this study, we used a 454 pyrosequencing method to investigate differentially-expressed genes in the spleen of the <it>E. coioides </it>infected with Singapore grouper iridovirus (SGIV).</p> <p>Results</p> <p>Using 454 pyrosequencing, we obtained abundant high-quality ESTs from two spleen-complementary DNA libraries which were constructed from SGIV-infected (V) and PBS-injected fish (used as a control: C). A total of 407,027 and 421,141 ESTs were produced in control and SGIV infected libraries, respectively. Among the assembled ESTs, 9,616 (C) and 10,426 (V) ESTs were successfully matched against known genes in the NCBI non-redundant (nr) database with a cut-off E-value above 10^-5. Gene ontology (GO) analysis indicated that "cell part", "cellular process" and "binding" represented the largest category. Among the 25 clusters of orthologous group (COG) categories, the cluster for "translation, ribosomal structure and biogenesis" represented the largest group in the control (185 ESTs) and infected (172 ESTs) libraries. Further KEGG analysis revealed that pathways, including cellular metabolism and intracellular immune signaling, existed in the control and infected libraries. Comparative expression analysis indicated that certain genes associated with mitogen-activated protein kinase (MAPK), chemokine, toll-like receptor and RIG-I signaling pathway were alternated in response to SGIV infection. Moreover, changes in the pattern of gene expression were validated by qRT-PCR, including cytokines, cytokine receptors, and transcription factors, apoptosis-associated genes, and interferon related genes.</p> <p>Conclusion</p> <p>This study provided abundant ESTs that could contribute greatly to disclosing novel genes in marine fish. Furthermore, the alterations of predicted gene expression patterns reflected possible responses of these fish to the virus infection. Taken together, our data not only provided new information for identification of novel genes from marine vertebrates, but also shed new light on the understanding of defense mechanisms of marine fish to viral pathogens.</p

The Effect of Macromolecular Crowding, Ionic Strength and Calcium Binding on Calmodulin Dynamics

The flexibility in the structure of calmodulin (CaM) allows its binding to over 300 target proteins in the cell. To investigate the structure-function relationship of CaM, we combined methods of computer simulation and experiments based on circular dichroism (CD) to investigate the structural characteristics of CaM that influence its target recognition in crowded cell-like conditions. We developed a unique multiscale solution of charges computed from quantum chemistry, together with protein reconstruction, coarse-grained molecular simulations, and statistical physics, to represent the charge distribution in the transition from apoCaM to holoCaM upon calcium binding. Computationally, we found that increased levels of macromolecular crowding, in addition to calcium binding and ionic strength typical of that found inside cells, can impact the conformation, helicity and the EF hand orientation of CaM. Because EF hand orientation impacts the affinity of calcium binding and the specificity of CaM's target selection, our results may provide unique insight into understanding the promiscuous behavior of calmodulin in target selection inside cells.Comment: Accepted to PLoS Comp Biol, 201