Numerical investigation of the active role of the actin cytoskeleton in the compression resistance of cells.

Numerous in-vitro studies have established that cells react to their physical environment and to applied mechanical loading. However, the mechanisms underlying such phenomena are poorly understood. Previous modelling of cell compression considered the cell as a passive homogenous material, requiring an artificial increase in the stiffness of spread cells to replicate experimentally measured forces. In this study, we implement a fully 3D active constitutive formulation that predicts the distribution, remodelling, and contractile behaviour of the cytoskeleton. Simulations reveal that polarised and axisymmetric spread cells contain stress fibres which form dominant bundles that are stretched during compression. These dominant fibres exert tension; causing an increase in computed compression forces compared to round cells. In contrast, fewer stress fibres are computed for round cells and a lower resistance to compression is predicted. The effect of different levels of cellular contractility associated with different cell phenotypes is also investigated. Highly contractile cells form more dominant circumferential stress fibres and hence provide greater resistance to compression. Computed predictions correlate strongly with published experimentally observed trends of compression resistance as a function of cellular contractility and offer an insight into the link between cell geometry, stress fibre distribution and contractility, and cell deformability. Importantly, it is possible to capture the behaviour of both round and spread cells using a given, unchanged set of material parameters for each cell type. Finally, it is demonstrated that stress distributions in the cell cytoplasm and nucleus computed using the active formulation differ significantly from those computed using passive material models.

Templates and anchors for antenna-based wall following in cockroaches and robots

The interplay between robotics and neuromechanics facilitates discoveries in both fields: nature provides roboticists with design ideas, while robotics research elucidates critical features that confer performance advantages to biological systems. Here, we explore a system particularly well suited to exploit the synergies between biology and robotics: high-speed antenna-based wall following of the American cockroach (Periplaneta americana). Our approach integrates mathematical and hardware modeling with behavioral and neurophysiological experiments. Specifically, we corroborate a prediction from a previously reported wall-following template - the simplest model that captures a behavior - that a cockroach antenna-based controller requires the rate of approach to a wall in addition to distance, e.g., in the form of a proportional-derivative (PD) controller. Neurophysiological experiments reveal that important features of the wall-following controller emerge at the earliest stages of sensory processing, namely in the antennal nerve. Furthermore, we embed the template in a robotic platform outfitted with a bio-inspired antenna. Using this system, we successfully test specific PD gains (up to a scale) fitted to the cockroach behavioral data in a "real-world" setting, lending further credence to the surprisingly simple notion that a cockroach might implement a PD controller for wall following. Finally, we embed the template in a simulated lateral-leg-spring (LLS) model using the center of pressure as the control input. Importantly, the same PD gains fitted to cockroach behavior also stabilize wall following for the LLS model. © 2008 IEEE.

Direct comparison of the gravimetric responsivities of ZnO-based FBARs and SMRs

Film bulk acoustic resonators (FBARs) and solidly mounted resonators (SMRs) have the potential to significantly improve upon the sensitivity and minimum detection limit of traditional gravimetric sensors based on quartz crystal microbalances (QCMs) and surface acoustic wave resonators (SAWs). To date, neither FBAR nor SMR devices have been demonstrated to be superior to the other; hence the choice between them depends primarily on the users' ability to design/fabricate membranes and/or Bragg reflectors. In this work, it is shown that identically designed FBAR and SMR devices resonating at the same frequency exhibit different responsivities to mass loadings, Rm, and that the SMRs are less responsive than the FBARs. For the specific device design and resonant frequency (~2 GHz) of the resonators presented here, the FBARs' mass responsivity is ~20% greater than that of the SMRs', and although this value is not universal for all possible device designs, it clearly shows that FBAR devices should be favoured over SMRs in gravimetric sensing applications where the FBARs' fragility is not an issue. Numerical calculations based on Mason's model offer an insight into the physical mechanisms behind the greater FBARs responsivity, and it was shown that the Bragg reflector has an effect on the acoustic load at one of the facets of the piezoelectric films which is in turn responsible for the SMRs' lower responsivity to mass loadings. © 2013 Elsevier B.V.

The role of artificial intelligence as 'text' within design

The paper describes a new approach to artificial intelligence (AI) and its role in design. This approach argues that AI can be seen as 'text', or in other words as a medium for the communication of design knowledge and information between designers. This paper will apply these ideas to reinterpreting an existing knowledge-based system (KBS) design tool, that is, CADET - a product design evaluation tool. The paper will discuss the authorial issues, amongst others, involved in the development of AI and KBS design tools by adopting this new approach. Consequently, the designers' rights and responsibilities will be better understood as the knowledge medium, through its concern with authorship, returns control to users rather than attributing the system with agent status. © 1998 Elsevier Science Ltd. All rights reserved.

A conceptual client-designer framework: Inspiring the development of inclusive design interactive techniques

The adoption of inclusive design approach into design practice is compatible to the needs of an ageing society. However, tools and methods that promote inclusivity during new product development are scarcely used in industry. This paper is part of a research project that investigates ways to accommodate inclusive design into the design process in industrial context. The present paper is based on the finds from the observations and interviews with industrial designers and interviews with stakeholders. The outcomes from the study supported a better understanding of the client-designer dynamic as well as the stages in the design process where information related to inclusive design could be introduced. The findings were essential to inspire the development of an inclusive design interactive technique to be used by clients and designers. © 2013 Springer-Verlag Berlin Heidelberg.

Investigation of multilingual deep neural networks for spoken term detection

The development of high-performance speech processing systems for low-resource languages is a challenging area. One approach to address the lack of resources is to make use of data from multiple languages. A popular direction in recent years is to use bottleneck features, or hybrid systems, trained on multilingual data for speech-to-text (STT) systems. This paper presents an investigation into the application of these multilingual approaches to spoken term detection. Experiments were run using the IARPA Babel limited language pack corpora (∼10 hours/language) with 4 languages for initial multilingual system development and an additional held-out target language. STT gains achieved through using multilingual bottleneck features in a Tandem configuration are shown to also apply to keyword search (KWS). Further improvements in both STT and KWS were observed by incorporating language questions into the Tandem GMM-HMM decision trees for the training set languages. Adapted hybrid systems performed slightly worse on average than the adapted Tandem systems. A language independent acoustic model test on the target language showed that retraining or adapting of the acoustic models to the target language is currently minimally needed to achieve reasonable performance. © 2013 IEEE.

The industrial emergence of commercial inkjet printing

Purpose: The purpose of this paper is to investigate how supply and demand interact during industrial emergence. Design/methodology/approach: The paper builds on previous theorising about co-evolutionary dynamics, exploring the interaction between supply and demand in a study of the industrial emergence of the commercial inkjet cluster in Cambridge, UK. Data are collected through 13 interviews with professionals working in the industry. Findings: The paper shows that as new industries emerge, asynchronies between technology supply and market demand create opportunities for entrepreneurial activity. In attempting to match innovative technologies to particular applications, entrepreneurs adapt to the system conditions and shape the environment to their own advantage. Firms that successfully operate in emerging industries demonstrate the functionality of new technologies, reducing uncertainty and increasing customer receptiveness. Research limitations/implications: The research is geographically bounded to the Cambridge commercial inkjet cluster. Further studies could consider commercial inkjet from a global perspective or test the applicability of the findings in other industries. Practical implications: Technology-based firms are often innovating during periods of industrial emergence. The insights developed in this paper help such firms recognise the emerging context in which they operate and the challenges that need to overcome. Originality/value: As an in depth study of a single industry, this research responds to calls for studies into industrial emergence, providing insights into how supply and demand interact during this phase of the industry lifecycle. © Emerald Group Publishing Limited.

AI in locomotion: Challenges and perspectives of underactuated robots

This article discusses the issues of adaptive autonomous navigation as a challenge of artificial intelligence. We argue that, in order to enhance the dexterity and adaptivity in robot navigation, we need to take into account the decentralized mechanisms which exploit physical system-environment interactions. In this paper, by introducing a few underactuated locomotion systems, we explain (1) how mechanical body structures are related to motor control in locomotion behavior, (2) how a simple computational control process can generate complex locomotion behavior, and (3) how a motor control architecture can exploit the body dynamics through a learning process. Based on the case studies, we discuss the challenges and perspectives toward a new framework of adaptive robot control. © Springer-Verlag Berlin Heidelberg 2007.