Identification of gene networks associated with acute myeloid leukemia by comparative molecular methylation and expression profiling

Around 80% of acute myeloid leukemia (AML) patients achieve a complete remission, however many will relapse and ultimately die of their disease. The association between karyotype and prognosis has been studied extensively and identified patient cohorts as having favourable [e.g. t(8; 21), inv (16)/t(16; 16), t(15; 17)], intermediate [e.g. cytogenetically normal (NK-AML)] or adverse risk [e.g. complex karyotypes]. Previous studies have shown that gene expression profiling signatures can classify the sub-types of AML, although few reports have shown a similar feature by using methylation markers. The global methylation patterns in 19 diagnostic AML samples were investigated using the Methylated CpG Island Amplification Microarray (MCAM) method and CpG island microarrays containing 12,000 CpG sites. The first analysis, comparing favourable and intermediate cytogenetic risk groups, revealed significantly differentially methylated CpG sites (594 CpG islands) between the two subgroups. Mutations in the NPM1 gene occur at a high frequency (40%) within the NK-AML subgroup and are associated with a more favourable prognosis in these patients. A second analysis comparing the NPM1 mutant and wild-type research study subjects again identified distinct methylation profiles between these two subgroups. Network and pathway analysis revealed possible molecular mechanisms associated with the different risk and/or mutation sub-groups. This may result in a better classification of the risk groups, improved monitoring targets, or the identification of novel molecular therapies.

On the Distribution of Signal Phase in Body Area Networks

In this letter, we investigate the distribution of the phase component of the complex received signal observed in practical experiments using body area networks. Two phase distributions, the recently proposed kappa-mu and eta-mu probability densities, which together encompass the most widely used fading models, namely Semi-Gaussian, Rayleigh, Hoyt, Rice, and Nakagami-m, have been compared with measurement data. The kappa-mu distribution has been found to provide the best fit over a range of on-body links, while the user was mobile. The experiments were carried out in two dissimilar indoor environments at opposite ends of the multipath spectrum. It has also been found that the uniform phase distribution has not arisen in anyone of the experiments.

Phylogeographic analysis of North American populations of the parasitic herbaceous plant Monotropa hypopitys reveals a complex history of range expansion from multiple late glacial refugia

Aim We carried out a phylogeographic study across the range of the herbaceous plant species Monotropa hypopitys L. in North America to determine whether its current disjunct distribution is due to recolonization from separate eastern and western refugia after the Last Glacial Maximum (LGM). Location North America: Pacific Northwest and north-eastern USA/south-eastern Canada. Methods Palaeodistribution modelling was carried out to determine suitable climatic regions for M. hypopitys at the LGM. We analysed between 155 and 176 individuals from 39 locations spanning the species' entire range in North America. Sequence data were obtained for the chloroplast rps2 gene (n=168) and for the nuclear ITS region (n=158). Individuals were also genotyped for eight microsatellite loci (n=176). Interpolation of diversity values was used to visualize the range-wide distribution of genetic diversity for each of the three marker classes. Minimum spanning networks were constructed showing the relationships between the rps2 and ITS haplotypes, and the geographical distributions of these haplotypes were plotted. The numbers of genetic clusters based on the microsatellite data were estimated using Bayesian clustering approaches. Results The palaeodistribution modelling indicated suitable climate envelopes for M. hypopitys at the LGM in both the Pacific Northwest and south-eastern USA. High levels of genetic diversity and endemic haplotypes were found in Oregon, the Alexander Archipelago, Wisconsin, and in the south-eastern part of the species' distribution range. Main conclusions Our results suggest a complex recolonization history for M. hypopitys in North America, involving persistence in separate eastern and western refugia. A generally high degree of congruence between the different marker classes analysed indicated the presence of multiple refugia, with at least two refugia in each area. In the west, putative refugia were identified in Oregon and the Alexander Archipelago, whereas eastern refugia may have been located in the southern part of the species' current distribution, as well as in the 'Driftless Area'. These findings are in contrast to a previous study on the related species Orthilia secunda, which has a similar disjunct distribution to M. hypopitys, but which appears to have recolonized solely from western refugia. © 2011 Blackwell Publishing Ltd.

N1, P2 and T-complex of the auditory brain event-related potentials to tones with varying rise times in adults with and without dyslexia

Dyslexia is a learning difficulty affecting the acquisition of fluent reading and spelling skills due to poor phonological processing. Underlying deficits in processing sound rise time have also been found in children and adults with dyslexia. However, the neural basis for these deficits is unknown. In the present study event-related potentials were used to index neural processing and examine the effect of rise time manipulation on the obligatory N1. T-complex and P2 responses in English speaking adults with and without dyslexia. The Tb wave of the T-complex showed differences between groups, with the amplitudes for Tb becoming less negative with increased rise time for the participants with dyslexia only. Frontocentral N1 and P2 did not show group effects. Enhanced Tb amplitude that is modulated by rise time could indicate altered neural networks at the lateral surface of the superior temporal gyrus in adults with dyslexia. (C) 2011 Elsevier B.V. All rights reserved.

A heterosynaptic learning rule for neural networks

In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre-and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean leaxning time increases with the number of patterns to be learned polynomially, indicating efficient learning.

Perturbations to trophic interactions and the stability of complex food webs

The pattern of predator-prey interactions is thought to be a key determinant of ecosystem processes and stability. Complex ecological networks are characterized by distributions of interaction strengths that are highly skewed, with many weak and few strong interactors present. Theory suggests that this pattern promotes stability as weak interactors dampen the destabilizing potential of strong interactors. Here, we present an experimental test of this hypothesis and provide empirical evidence that the loss of weak interactors can destabilize communities in nature. We ranked 10 marine consumer species by the strength of their trophic interactions. We removed the strongest and weakest of these interactors from experimental food webs containing >100 species. Extinction of strong interactors produced a dramatic trophic cascade and reduced the temporal stability of key ecosystem process rates, community diversity and resistance to changes in community composition. Loss of weak interactors also proved damaging for our experimental ecosystems, leading to reductions in the temporal and spatial stability of ecosystem process rates, community diversity, and resistance. These results highlight the importance of conserving species to maintain the stabilizing pattern of trophic interactions in nature, even if they are perceived to have weak effects in the system.

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, such as predation, competition, mutualism and facilitation. Understanding the resulting interaction networks is a challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks from field data. In the present study, we propose a novel Bayesian regression and multiple changepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions. (C) 2012 Elsevier B.V. All rights reserved.

Development of small and medium-sized enterprise horizontal innovation networks: UK agri-food sector study

Within the management literature, there is an emergent discourse on horizontal collaboration among small and medium-sized enterprises (SMEs), whereby individual rivalries are overcome by the need for more resources and innovation, leading to increased competitiveness through joint product development. In particular, a number of these horizontal collaborations between SMEs have occurred within the agri-food sector. As a consequence, this article aims to explore the longitudinal development of horizontal innovation networks within an artisan bakers’ network as part of the UK SME agri-food sector. An interpretivist research approach was used, whereby the development and evolution of an artisan bakers’ horizontal network was studied over a 27-month period. The findings, as summarised in conceptual models which draw upon knowledge-based open innovation and social network constructs, illustrate that a complex three-stage life cycle development occurred within the bakers’ horizontal network.

Structural reinforcement of microvascular networks using electrostatic layer-by-layer assembly with halloysite nanotubes

We demonstrate a method for tailoring local mechanical properties near channel surfaces of vascular structural polymers in order to achieve high structural performance in microvascular systems. While synthetic vascularized materials have been created by a variety of manufacturing techniques, unreinforced microchannels act as stress concentrators and lead to the initiation of premature failure. Taking inspiration from biological tissues such as dentin and bone, these mechanical deficiencies can be mitigated by complex hierarchical structural features near to channel surfaces. By employing electrostatic layer-by-layer assembly (ELbL) to deposit films containing halloysite nanotubes onto scaffold surfaces followed by matrix infiltration and scaffold removal, we are able to controllably deposit nanoscale reinforcement onto 200 micron diameter channel surface interiors in microvascular networks. High resolution strain measurements on reinforced networks under load verify that the halloysite reduces strain concentrations and improves mechanical performance.

TLM2.0 based timing accurate modeling method for complex NoC systems

Scalability and efficiency of on-chip communication of emerging Multiprocessor System-on-Chip (MPSoC) are critical design considerations. Conventional bus based interconnection schemes no longer fit for MPSoC with a large number of cores. Networks-on-Chip (NoC) is widely accepted as the next generation interconnection scheme for large scale MPSoC. The increase of MPSoC complexity requires fast and accurate system-level modeling techniques for rapid modeling and veri-fication of emerging MPSoCs. However, the existing modeling methods are limited in delivering the essentials of timing accuracy and simulation speed. This paper proposes a novel system-level Networks-on-Chip (NoC) modeling method, which is based on SystemC and TLM2.0 and capable of delivering timing accuracy close to cycle accurate modeling techniques at a significantly lower simulation cost. Experimental results are presented to demonstrate the proposed method. ©2010 IEEE.

Mechanochemical interconversion between discrete complexes and coordination networks - formal hydration/dehydration by LAG

Using a small planetary ball mill, liquid-assisted grinding (LAG) of metal salts or oxides (ZnO, CdO, CdCO3, Cu(OAc)(2)center dot H2O, Co(OAc)(2)center dot 4H(2)O, Mn(OAc)(2)center dot 4H(2)O, Ni(OAc)(2)center dot 4H(2)O, FeSO4 center dot 7H(2)O) with two equivalents of isonicotinic acid (HINA) and small amounts of water ( up to 5.6 molar equivalents) gave discrete aquo complexes trans-[M(INA)(2)(OH2)(4)] (M = Zn, Cd, Cu, Fe, Co, Ni, Mn) efficiently within 30 min. For M = Zn, Cd and Cu these complexes readily undergo reversible formal dehydration to the extended network structures [M(INA)(2)] (M = Zn, Cu) or [Cd(INA)(2)(OH2)]center dot DMF by further LAG with non-aqueous liquids such as methanol or DMF. Overall, the mechanochemical dehydrations are more effective than heating or immersion in bulk solvents. The work demonstrates a two-step mechanochemical synthesis of coordination networks via discrete aquo complexes which may be preferable to single step reactions or grinding-annealing procedures in some cases. For example, the two step method was the only way to prepare [Cd(INA)(2)(OH2)]center dot DMF mechanochemically and the porous network Cu(INA)(2) could not be obtained from the aquo complex by heating.

Generalized loopy 2U: A new algorithm for approximate inference in credal networks

Credal networks generalize Bayesian networks by relaxing the requirement of precision of probabilities. Credal networks are considerably more expressive than Bayesian networks, but this makes belief updating NP-hard even on polytrees. We develop a new efficient algorithm for approximate belief updating in credal networks. The algorithm is based on an important representation result we prove for general credal networks: that any credal network can be equivalently reformulated as a credal network with binary variables; moreover, the transformation, which is considerably more complex than in the Bayesian case, can be implemented in polynomial time. The equivalent binary credal network is then updated by L2U, a loopy approximate algorithm for binary credal networks. Overall, we generalize L2U to non-binary credal networks, obtaining a scalable algorithm for the general case, which is approximate only because of its loopy nature. The accuracy of the inferences with respect to other state-of-the-art algorithms is evaluated by extensive numerical tests.

Generalized Loopy 2U: A New Algorithm for Approximate Inference in Credal Networks

Credal nets generalize Bayesian nets by relaxing the requirement of precision of probabilities. Credal nets are considerably more expressive than Bayesian nets, but this makes belief updating NP-hard even on polytrees. We develop a new efficient algorithm for approximate belief updating in credal nets. The algorithm is based on an important representation result we prove for general credal nets: that any credal net can be equivalently reformulated as a credal net with binary variables; moreover, the transformation, which is considerably more complex than in the Bayesian case, can be implemented in polynomial time. The equivalent binary credal net is updated by L2U, a loopy approximate algorithm for binary credal nets. Thus, we generalize L2U to non-binary credal nets, obtaining an accurate and scalable algorithm for the general case, which is approximate only because of its loopy nature. The accuracy of the inferences is evaluated by empirical tests.

Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data

BACKGROUND: Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis.

METHODS: In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer.

RESULTS: We found that the urothelial cancer (UC) GRNs show a significant enrichment of subnetworks that are associated with known cancer hallmarks including cell cycle, immune response, signaling, differentiation and translation. Interestingly, the most prominent subnetworks of co-located genes were found on chromosome regions 5q31.3 (RNAseq), 8q24.3 (Oligo) and 1q23.3 (Bead), which all represent known genomic regions frequently deregulated or aberated in urothelial cancer and other cancer types. Furthermore, the identified hub genes of the individual GRNs, e.g., HID1/DMC1 (tumor development), RNF17/TDRD4 (cancer antigen) and CYP4A11 (angiogenesis/ metastasis) are known cancer associated markers. The GRNs were highly dataset specific on the interaction level between individual genes, but showed large similarities on the biological function level represented by subnetworks. Remarkably, the RNAseq UC GRN showed twice the proportion of significant functional subnetworks. Based on our analysis of inferential and experimental networks the Bead UC GRN showed the lowest performance compared to the RNAseq and Oligo UC GRNs.

CONCLUSION: To our knowledge, this is the first study investigating genome-scale UC GRNs. RNAseq based gene expression data is the data platform of choice for a GRN inference. Our study offers new avenues for the identification of novel putative diagnostic targets for subsequent studies in bladder tumors.