Generalized gene co-expression analysis via subspace clustering using low-rank representation

BACKGROUND: Gene Co-expression Network Analysis (GCNA) helps identify gene modules with potential biological functions and has become a popular method in bioinformatics and biomedical research. However, most current GCNA algorithms use correlation to build gene co-expression networks and identify modules with highly correlated genes. There is a need to look beyond correlation and identify gene modules using other similarity measures for finding novel biologically meaningful modules. RESULTS: We propose a new generalized gene co-expression analysis algorithm via subspace clustering that can identify biologically meaningful gene co-expression modules with genes that are not all highly correlated. We use low-rank representation to construct gene co-expression networks and local maximal quasi-clique merger to identify gene co-expression modules. We applied our method on three large microarray datasets and a single-cell RNA sequencing dataset. We demonstrate that our method can identify gene modules with different biological functions than current GCNA methods and find gene modules with prognostic values. CONCLUSIONS: The presented method takes advantage of subspace clustering to generate gene co-expression networks rather than using correlation as the similarity measure between genes. Our generalized GCNA method can provide new insights from gene expression datasets and serve as a complement to current GCNA algorithms

StrathSat-R : Deploying inflatable CubeSat structures in micro gravity

This paper presents the concepts, objectives and design of a student-led sounding rocket experiment which shall test novel inflatable devices in space conditions. This experiment is envisaged as the first step towards developing a CubeSat programme at the University of Strathclyde, which can exploit the novel concepts developed and the technical skills gained. The experiment itself aims to test novel, student developed, inflatable space structures in micro gravity and reduced pressure conditions. It consists of three distinct sections, the ejection housing on the rocket and the two ejectable modules that are based on CubeSat architecture. Shortly before reaching apogee, the two modules are ejected from the rocket and will deploy their own inflating structure during free flight. After landing, the ejectable modules recovery will rely upon a GPS position relayed to the team from the module by Globalstar transmission and a RF beacon for tracking with the recovery helicopter. The two modules carry two different structures resulting in distinct mission objectives: The aim of FRODO is to deploy an experimental passive de-orbiting system for high altitude spacecraft which will in the future utilise solar radiation pressure for orbit removal. The aim of SAM is to serve as a technology demonstrator for the residual air deployment method of a smart bio-inspired space structure. This paper contains details about the science objectives of the mission and how they will be achieved, its experimental design and the management of the student-led project

A hybrid architecture for robust parsing of german

This paper provides an overview of current research on a hybrid and robust parsing architecture for the morphological, syntactic and semantic annotation of German text corpora. The novel contribution of this research lies not in the individual parsing modules, each of which relies on state-of-the-art algorithms and techniques. Rather what is new about the present approach is the combination of these modules into a single architecture. This combination provides a means to significantly optimize the performance of each component, resulting in an increased accuracy of annotation

Work in progress: introduction of K-map based nano-logic synthesis as knowledge module in logic design course

This work in progress reports an effort of introducing knowledge module regarding novel nano-devices and novel logic primitives in undergraduate logic design class. Our motivation is to make our students aware of fundamental abstracted logical behaviors of future nano-devices, their functionality. This effort would also help the students use their existing knowledge of K-map based logical synthesis into constructing logic blocks for novel devices that uses majority logic as basic construct. Moreover, additional to stimulating our students' interests, we are also augmenting their learning by challenging them to use their existing knowledge to analyze, synthesize and comprehend novel nano-logic issues through the worksheets and lecture modules. Whereas many efforts are focusing on developing new courses on nanofabrication and even nano-computing, we intend to augment the existing standard EE and CS courses by inserting knowledge modules on nano-logic structure for stimulating their interest without significant diversion from the course framework

CORBYS cognitive control architecture for robotic follower

In this paper the novel generic cognitive robot control architecture CORBYS is presented. The objective of the CORBYS architecture is the integration of high-level cognitive modules to support robot functioning in dynamic environments including interacting with humans. This paper presents the preliminary integration of the CORBYS architecture to support a robotic follower. Experimental results on high-level empowerment-based trajectory planning have demonstrated the effectiveness of ROS-based communication between distributed modules developed in a multi-site research environment as typical for distributed collaborative projects such as CORBYS

Weak multiplier bimonoids

Based on the novel notion of `weakly counital fusion morphism', regular weak multiplier bimonoids in braided monoidal categories are introduced. They generalize weak multiplier bialgebras over fields and multiplier bimonoids in braided monoidal categories. Under some assumptions the so-called base object of a regular weak multiplier bimonoid is shown to carry a coseparable comonoid structure; hence to possess a monoidal category of bicomodules. In this case, appropriately defined modules over a regular weak multiplier bimonoid are proven to constitute a monoidal category with a strict monoidal forgetful type functor to the category of bicomodules over the base object. Braided monoidal categories considered include various categories of modules or graded modules, the category of complete bornological spaces, and the category of complex Hilbert spaces and continuous linear transformations.Comment: 56 page

Integration of disease-specific single nucleotide polymorphisms, expression quantitative trait loci and coexpression networks reveal novel candidate genes for type 2 diabetes.

Aims/hypothesisWhile genome-wide association studies (GWASs) have been successful in identifying novel variants associated with various diseases, it has been much more difficult to determine the biological mechanisms underlying these associations. Expression quantitative trait loci (eQTL) provide another dimension to these data by associating single nucleotide polymorphisms (SNPs) with gene expression. We hypothesised that integrating SNPs known to be associated with type 2 diabetes with eQTLs and coexpression networks would enable the discovery of novel candidate genes for type 2 diabetes.MethodsWe selected 32 SNPs associated with type 2 diabetes in two or more independent GWASs. We used previously described eQTLs mapped from genotype and gene expression data collected from 1,008 morbidly obese patients to find genes with expression associated with these SNPs. We linked these genes to coexpression modules, and ranked the other genes in these modules using an inverse sum score.ResultsWe found 62 genes with expression associated with type 2 diabetes SNPs. We validated our method by linking highly ranked genes in the coexpression modules back to SNPs through a combined eQTL dataset. We showed that the eQTLs highlighted by this method are significantly enriched for association with type 2 diabetes in data from the Wellcome Trust Case Control Consortium (WTCCC, p = 0.026) and the Gene Environment Association Studies (GENEVA, p = 0.042), validating our approach. Many of the highly ranked genes are also involved in the regulation or metabolism of insulin, glucose or lipids.Conclusions/interpretationWe have devised a novel method, involving the integration of datasets of different modalities, to discover novel candidate genes for type 2 diabetes

SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model

To realize human-like robot intelligence, a large-scale cognitive architecture is required for robots to understand the environment through a variety of sensors with which they are equipped. In this paper, we propose a novel framework named Serket that enables the construction of a large-scale generative model and its inference easily by connecting sub-modules to allow the robots to acquire various capabilities through interaction with their environments and others. We consider that large-scale cognitive models can be constructed by connecting smaller fundamental models hierarchically while maintaining their programmatic independence. Moreover, connected modules are dependent on each other, and parameters are required to be optimized as a whole. Conventionally, the equations for parameter estimation have to be derived and implemented depending on the models. However, it becomes harder to derive and implement those of a larger scale model. To solve these problems, in this paper, we propose a method for parameter estimation by communicating the minimal parameters between various modules while maintaining their programmatic independence. Therefore, Serket makes it easy to construct large-scale models and estimate their parameters via the connection of modules. Experimental results demonstrated that the model can be constructed by connecting modules, the parameters can be optimized as a whole, and they are comparable with the original models that we have proposed