Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology

Abstract Background In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN). Results The RNN topology is a weighted directed graph derived from PPI network, and it is a natural representation of the topological dependences between proteins within the PPI network. Similar to the original PPI network, we have observed that essential proteins tend to be hub proteins in RNN topology. Additionally, essential genes are enriched in clusters containing many hub proteins in RNN topology (RNN protein clusters). Based on these two properties of essential genes in RNN topology, we have proposed a new measure; the RNN cluster centrality. Results from a variety of PPI networks demonstrate that RNN cluster centrality outperforms other centrality measures with regard to the proportion of selected proteins that are essential proteins. We also investigated the biological importance of RNN clusters. Conclusions This study reveals that RNN cluster centrality provides the best correlation of protein essentiality and placement of proteins in PPI network. Additionally, merged RNN clusters were found to be topologically important in that essential proteins are significantly enriched in RNN clusters, and biologically important because they play an important role in many Gene Ontology (GO) processes.http://deepblue.lib.umich.edu/bitstream/2027.42/78257/1/1471-2105-11-505.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/2/1471-2105-11-505-S1.DOChttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/3/1471-2105-11-505.pdfPeer Reviewe

Green Gasification Technology for Wet Biomass

The world now is facing two energy related threats which are lack of sustainable, secure and affordable energy supplies and the environmental damage acquired in producing and consuming ever-increasing amount of energy. In the first decade of the twenty-first century, increasing energy prices reminds us that an affordable energy plays an important role in economic growth and human development. To overcome the abovementioned problem, we cannot continue much longer to consume finite reserves of fossil fuels, the use of which contributes to global warming. Preferably, the world should move towards more sustainable energy sources such as wind energy, solar energy and biomass. However, the abovementioned challenges may not be met solely by introduction of sustainable energy forms. We also need to use energy more efficiently. Developing and introducing more efficient energy conversion technologies is therefore important, for fossil fuels as well as renewable fuels. This assignment addresses the question how biomass may be used more efficiently and economically than it is being used today. Wider use of biomass, a clean and renewable feedstock may extend the lifetime of our fossil fuels resources and alleviate global warming problems. Another advantage of using of biomass as a source of energy is to make developed countries less interdependent on oil-exporting countries, and thereby reduce political tension. Furthermore, the economies of agricultural regions growing energy crops benefit as new jobs are created.Keywords: energy, gasification, sustainable, wet biomas

Campus Mobility for the Future: The Electric Bicycle

Sustainable and practical personal mobility solutions for campus environments have traditionally revolved around the use of bicycles, or provision of pedestrian facilities. However many campus environments also experience traffic congestion, parking difficulties and pollution from fossil-fuelled vehicles. It appears that pedal power alone has not been sufficient to supplant the use of petrol and diesel vehicles to date, and therefore it is opportune to investigate both the reasons behind the continual use of environmentally unfriendly transport, and consider potential solutions. This paper presents the results from a year-long study into electric bicycle effectiveness for a large tropical campus, identifying barriers to bicycle use that can be overcome through the availability of public use electric bicycles

Multipartite entanglement in quantum spin chains

We study the occurrence of multipartite entanglement in spin chains. We show that certain genuine multipartite entangled states, namely W states, can be obtained as ground states of simple XX type ferromagnetic spin chains in a transverse magnetic field, for any number of sites. Moreover, multipartite entanglement is proven to exist even at finite temperatures. A transition from a product state to a multipartite entangled state occurs when decreasing the magnetic field to a critical value. Adiabatic passage through this point can thus lead to the generation of multipartite entanglement.Comment: 4 pages, 1 figur

Interpersonal Neural Entrainment during Early Social Interaction

Currently, we understand much about how children’s brains attend to and learn from information presented while they are alone, viewing a screen – but less about how interpersonal social influences are substantiated in the brain. Here, we consider research that examines how social behaviors affect not one, but both partners in a dyad. We review studies that measured interpersonal neural entrainment during early social interaction, considering two ways of measuring entrainment: concurrent entrainment (e.g., ‘when A is high, B is high’ – also known as synchrony) and sequential entrainment (‘changes in A forward-predict changes in B’). We discuss possible causes of interpersonal neural entrainment, and consider whether it is merely an epiphenomenon, or whether it plays an independent, mechanistic role in early attention and learning

The Effect of Electronic Structure on the Phases Present in High Entropy Alloys

Multicomponent systems, termed High Entropy Alloys (HEAs), with predominantly single solid solution phases are a current area of focus in alloy development. Although different empirical rules have been introduced to understand phase formation and determine what the dominant phases may be in these systems, experimental investigation has revealed that in many cases their structure is not a single solid solution phase, and that the rules may not accurately distinguish the stability of the phase boundaries. Here, a combined modelling and experimental approach that looks into the electronic structure is proposed to improve accuracy of the predictions of the majority phase. To do this, the Rigid Band model is generalised for magnetic systems in prediction of the majority phase most likely to be found. Good agreement is found when the predictions are confronted with data from experiments, including a new magnetic HEA system (CoFeNiV). This also includes predicting the structural transition with varying levels of constituent elements, as a function of the valence electron concentration, n, obtained from the integrated spin-polarised density of states. This method is suitable as a new predictive technique to identify compositions for further screening, in particular for magnetic HEAs

Design and fabrication of 3D-printed anatomically shaped lumbar cage for intervertebra disc (IVD) degeneration treatment

Spinal fusion is the gold standard surgical procedure for degenerative spinal conditions when conservative therapies have been unsuccessful in rehabilitation of patients. Novel strategies are required to improve biocompatibility and osseointegration of traditionally used materials for lumbar cages. Furthermore, new design and technologies are needed to bridge the gap due to the shortage of optimal implant sizes to fill the intervertebral disc defect. Within this context, additive manufacturing technology presents an excellent opportunity to fabricate ergonomic shape medical implants. The goal of this study is to design and manufacture a 3D-printed lumbar cage for lumbar interbody fusion. Optimisations of the proposed implant design and its printing parameters were achieved via in silico analysis. The final construct was characterised via scanning electron microscopy, contact angle, x-ray micro computed tomography (μCT), atomic force microscopy, and compressive test. Preliminary in vitro cell culture tests such as morphological assessment and metabolic activities were performed to access biocompatibility of 3D-printed constructs. Results of in silico analysis provided a useful platform to test preliminary cage design and to find an optimal value of filling density for 3D printing process. Surface characterisation confirmed a uniform coating of nHAp with nanoscale topography. Mechanical evaluation showed mechanical properties of final cage design similar to that of trabecular bone. Preliminary cell culture results showed promising results in terms of cell growth and activity confirming biocompatibility of constructs. Thus for the first time, design optimisation based on computational and experimental analysis combined with the 3D-printing technique for intervertebral fusion cage has been reported in a single study. 3D-printing is a promising technique for medical applications and this study paves the way for future development of customised implants in spinal surgical applications

Automatic classification of ICA components from infant EEG using MARA.

Automated systems for identifying and removing non-neural ICA components are growing in popularity among EEG researchers of adult populations. Infant EEG data differs in many ways from adult EEG data, but there exists almost no specific system for automated classification of source components from paediatric populations. Here, we adapt one of the most popular systems for adult ICA component classification for use with infant EEG data. Our adapted classifier significantly outperformed the original adult classifier on samples of naturalistic free play EEG data recorded from 10 to 12-month-old infants, achieving agreement rates with the manual classification of over 75% across two validation studies (n = 44, n = 25). Additionally, we examined both classifiers' ability to remove stereotyped ocular artifact from a basic visual processing ERP dataset compared to manual ICA data cleaning. Here, the new classifier performed on level with expert manual cleaning and was again significantly better than the adult classifier at removing artifact whilst retaining a greater amount of genuine neural signal operationalised through comparing ERP activations in time and space. Our new system (iMARA) offers developmental EEG researchers a flexible tool for automatic identification and removal of artifactual ICA components

Measuring the temporal dynamics of inter-personal neural entrainment in continuous child-adult EEG hyperscanning data.

Current approaches to analysing EEG hyperscanning data in the developmental literature typically consider interpersonal entrainment between interacting physiological systems as a time-invariant property. This approach obscures crucial information about how entrainment between interacting systems is established and maintained over time. Here, we describe methods, and present computational algorithms, that will allow researchers to address this gap in the literature. We focus on how two different approaches to measuring entrainment, namely concurrent (e.g., power correlations, phase locking) and sequential (e.g., Granger causality) measures, can be applied to three aspects of the brain signal: amplitude, power, and phase. We guide the reader through worked examples using simulated data on how to leverage these methods to measure changes in interbrain entrainment. For each, we aim to provide a detailed explanation of the interpretation and application of these analyses when studying neural entrainment during early social interactions

Structural dependency of some multiple principal component alloys with the Thomas-Fermi-Dirac electron density

The interplay between semi-empirical parameters for multi-principle-component alloy (or High-entropy alloy) phase prediction may be partly attributed to deviations in the Miedema mixing enthalpy from quantum principles. Thus, the electron density, n(r ws ) is investigated using a Runge-Kutta solution of the Thomas-Fermi-Dirac equation from Wigner-Seitz radius values approximated experimentally from the weighted mean volume-per-atom, following the fraction of each phase present. The results show that 1) Phase stability may be affected by alloy periodicity; and 2) A rapid drop of n(r ws ) is observed even when small amounts of the complex phase are detected, < 1%), indicating the importance of understanding electronic effects