Mechanism‐based selection of a potent kallikrein‐related peptidase 7 inhibitor from a versatile library based on the sunflower trypsin inhibitor SFTI‐1

Potent and specific enzyme inhibition is a key goal in the development of therapeutic inhibitors targeting proteolytic activity. The backbone-cyclized peptide, Sunflower Trypsin Inhibitor (SFTI-1) affords a scaffold that can be engineered to achieve both these aims. SFTI-1's mechanism of inhibition is unusual in that it shows fast-on/slow-off kinetics driven by cleavage and religation of a scissile bond. This phenomenon was used to select a nanomolar inhibitor of kallikrein-related peptidase 7 (KLK7) from a versatile library of SFTI variants with diversity tailored to exploit distinctive surfaces present in the active site of serine proteases. Inhibitor selection was achieved through the use of size exclusion chromatography to separate protease/inhibitor complexes from unbound inhibitors followed by inhibitor identification according to molecular mass ascertained by mass spectrometry. This approach identified a single dominant inhibitor species with molecular weight of 1562.4 Da, which is consistent with the SFTI variant SFTI-WCTF. Once synthesized individually this inhibitor showed an IC50 of 173.9 ± 7.6 nM against chromogenic substrates and could block protein proteolysis. Molecular modeling analysis suggested that selection of SFTI-WCTF was driven by specific aromatic interactions and stabilized by an enhanced internal hydrogen bonding network. This approach provides a robust and rapid route to inhibitor selection and design.

Long time, no see?

Long Time, No See? is a crowd-sourced project that asks people to reflect upon what kind of long term future they would each like to promote. It is an evolving experiment in the social practice of ‘everyday futuring’. To participate download the Long Time, No See? IPhone APP that gently guides you during a short walk, encouraging you to experience new places, sensations and thoughts in your locality. At nine stages along that journey you donate ‘field notes’ as images, texts, sounds and ‘themes’, offering a unique opportunity to reveal possible pathways towards more sustaining futures. The APP records the shape of your walk on the ground and draws an island on the ‘map’ shown here, populated by your nine sets of responses. The themes you have chosen then connect your island into an evolving ‘world’ map of connections and possibilities, which you can then explore at your leisure. In these ways, Long Time, No See? doesn’t ask you for lofty visions or ask you to lay out a program of action, but instead asks you to consider what is around you today, steering your eyes, ears and embodied experiences towards new futures that demonstrate your ‘care’ for what comes after you. Please use the contribute tab below to learn how to add your voice! PARTICIPATE To contribute 1: Download the APP {bit.do/ltns}, iPhone/iPad is supported right now. 2: Register a ‘walker name’. 3: Take a leisurely walk (30 -60mins) and contribute image, text, sound and themes when asked. 4: Wait while we verify and upload your walk (allow about 24 hours) 5: View your contributions via your ‘walker name’ and discover how it relates to others, here at the Cube and at www.long-time-no-see.org. NB You can undertake each walk over more than one day if that suits. You may even drive, cycle or move by other modes. DOWNLOAD THE APP: bit.do/ltns (insert QI Code) FIND OUT MORE www.long-time-no-see.org

Entrepreneurship in the public sector : new possibilities?

Previous research has described potential roles for entrepreneurs in public sector organisations as either closely related to corporate entrepreneurship, or as normative prescriptions regarding the importance of entrepreneurship in the public sector (Ireland, Covin & Kuratko, 2009: Morris & Jones, 1999). While some might argue that entrepreneurship in the public sector context is an oxymoron, recent studies have demonstrated that entrepreneurship in the public sector is alive and well (Currie, Humphreys, Ucbasaran & McManus 2008; Kim, 2010). Entrepreneurship in the public sector can take many forms and generate a range of benefits but to date less attention has been given to the potential to generate new public value (Moore, 1995). The purpose of this paper is to increase our knowledge and understanding of the types of strategies and activities the public sector is using to capture initiative, create new public value, and generate new economic activity for the benefit of multiple stakeholders. This paper explores entrepreneurship in one public sector context. Findings indicate that entrepreneurship and commercialisation is more likely to be encouraged in contexts where contestability in develop and exploit capabilities.

Joint 2D and 3D cues for image segmentation towards robotic applications

This thesis investigates the fusion of 3D visual information with 2D image cues to provide 3D semantic maps of large-scale environments in which a robot traverses for robotic applications. A major theme of this thesis was to exploit the availability of 3D information acquired from robot sensors to improve upon 2D object classification alone. The proposed methods have been evaluated on several indoor and outdoor datasets collected from mobile robotic platforms including a quadcopter and ground vehicle covering several kilometres of urban roads.

Transcript and metabolite profiling for the evaluation of tobacco tree and poplar as feedstock for the bio-based industry

The global demand for food, feed, energy and water poses extraordinary challenges for future generations. It is evident that robust platforms for the exploration of renewable resources are necessary to overcome these challenges. Within the multinational framework MultiBioPro we are developing biorefinery pipelines to maximize the use of plant biomass. More specifically, we use poplar and tobacco tree (Nicotiana glauca) as target crop species for improving saccharification, isoprenoid, long chain hydrocarbon contents, fiber quality, and suberin and lignin contents. The methods used to obtain these outputs include GC-MS, LC-MS and RNA sequencing platforms. The metabolite pipelines are well established tools to generate these types of data, but also have the limitations in that only well characterized metabolites can be used. The deep sequencing will allow us to include all transcripts present during the developmental stages of the tobacco tree leaf, but has to be mapped back to the sequence of Nicotiana tabacum. With these set-ups, we aim at a basic understanding for underlying processes and at establishing an industrial framework to exploit the outcomes. In a more long term perspective, we believe that data generated here will provide means for a sustainable biorefinery process using poplar and tobacco tree as raw material. To date the basal level of metabolites in the samples have been analyzed and the protocols utilized are provided in this article.

Beyond tasks and gateways: discovering BPMN models with subprocesses, boundary events and activity markers

Existing techniques for automated discovery of process models from event logs largely focus on extracting flat process models. In other words, they fail to exploit the notion of subprocess, as well as structured error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique for automated discovery of BPMN models containing subprocesses, interrupting and non-interrupting boundary events, and loop and multi-instance markers. The technique analyzes dependencies between data attributes associated with events, in order to identify subprocesses and to extract their associated logs. Parent process and subprocess models are then discovered separately using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. A validation with one synthetic and two real-life logs shows that process models derived using the proposed technique are more accurate and less complex than those derived with flat process model discovery techniques.

Endothelial precursor cells home to a vascularized tissue engineering chamber by application of the angiogenic chemokine CXCl12

Tissue engineering of vascularized constructs has great utility in reconstructive surgery. While we have been successful in generating vascularized granulation-like tissue and adipose tissue in an in vivo tissue engineering chamber, production of other differentiated tissues in a stable construct remains a challenge. One approach is to utilize potent differentiation factors, which can influence the base tissue. Endothelial precursor cells (EPCs) have the ability to both carry differentiation factors and home to developing vasculature. In this study, proof-of-principle experiments demonstrate that such cells can be recruited from the circulation into an in vivo tissue engineering chamber. CXC chemokine ligand 12 (CXCL12)/stromal cell-derived factor 1 was infused into the chamber through Alzet osmotic pumps and chamber cannulation between days 0 and 7, and facilitated recruitment of systemically inoculated exogenous human EPCs injected on day 6. CXCL12 infusion resulted in an eightfold increase in EPC recruitment, 2 (p = 0.03) and 7 days postinfusion (p = 0.008). Delivery of chemotactic/proliferation and/or differentiation factors and appropriately timed introduction of effective cells may allow us to better exploit the regenerative potential of the established chamber construct. © Copyright 2009, Mary Ann Liebert, Inc. 2009.

Integrated access control for smart buildings using building information models

This thesis investigates the use of building information models for access control and security applications in critical infrastructures and complex building environments. It examines current problems in security management for physical and logical access control and proposes novel solutions that exploit the detailed information available in building information models. The project was carried out as part of the Airports of the Future Project and the research was modelled based on real-world problems identified in collaboration with our industry partners in the project.

Effect of atmospheric gas plasmas on cancer cell signaling

Cancer is one of the most life-threatening diseases with many forms still regarded as incurable. The conventional cancer treatments have unwanted side effects such as the death of normal cells. A therapy that can accurately target and effectively kill tumor cells could address the inadequacies of the available therapies. Atmospheric gas plasmas (AGP) that are able to specifically kill cancerous cells offer a promising alternative approach compared to conventional therapies. AGP have been shown to exploit tumor-specific genetic defects and a recent trial in mice has confirmed its antitumor effects. The mechanism by which the AGP act on tumor cells but not normal cells is not fully understood. A review of the current literature suggests that reactive oxygen species (ROS) generated by AGP induce death of cancer cells by impairing the function of intracellular regulatory factors. The majority of cancer cells are defective in tumor suppressors that interfere normal cell growth pathways. It appears that pro-oncogene or tumor suppressor-dependent regulation of antioxidant/or ROS signaling pathways may be involved in AGP-induced cancer cell death. The toxic effects of ROS are mitigated by normal cells by adjustment of their metabolic pathways. On the other hand, tumor cells are mostly defective in several regulatory signaling pathways which lead to the loss of metabolic balance within the cells and consequently, the regulation of cell growth. This review article evaluates the impact of AGP on the activation of cellular signaling and its importance for exploring mechanisms for safe and efficient anticancer therapies.

Carbon nanotube membranes with ultrahigh specific adsorption capacity for water desalination and purification

Development of technologies for water desalination and purification is critical to meet the global challenges of insufficient water supply and inadequate sanitation, especially for point-of-use applications. Conventional desalination methods are energy and operationally intensive, whereas adsorption-based techniques are simple and easy to use for point-of-use water purification, yet their capacity to remove salts is limited. Here we report that plasma-modified ultralong carbon nanotubes exhibit ultrahigh specific adsorption capacity for salt (exceeding 400% by weight) that is two orders of magnitude higher than that found in the current state-of-the-art activated carbon-based water treatment systems. We exploit this adsorption capacity in ultralong carbon nanotube-based membranes that can remove salt, as well as organic and metal contaminants. These ultralong carbon nanotube-based membranes may lead to next-generation rechargeable, point-of-use potable water purification appliances with superior desalination, disinfection and filtration properties. © 2013 Macmillan Publishers Limited.

Asymptotic minimax robust and misspecified Lorden quickest change detection for dependent stochastic processes

The quick detection of an abrupt unknown change in the conditional distribution of a dependent stochastic process has numerous applications. In this paper, we pose a minimax robust quickest change detection problem for cases where there is uncertainty about the post-change conditional distribution. Our minimax robust formulation is based on the popular Lorden criteria of optimal quickest change detection. Under a condition on the set of possible post-change distributions, we show that the widely known cumulative sum (CUSUM) rule is asymptotically minimax robust under our Lorden minimax robust formulation as a false alarm constraint becomes more strict. We also establish general asymptotic bounds on the detection delay of misspecified CUSUM rules (i.e. CUSUM rules that are designed with post- change distributions that differ from those of the observed sequence). We exploit these bounds to compare the delay performance of asymptotically minimax robust, asymptotically optimal, and other misspecified CUSUM rules. In simulation examples, we illustrate that asymptotically minimax robust CUSUM rules can provide better detection delay performance at greatly reduced computation effort compared to competing generalised likelihood ratio procedures.

Understanding the value of social media in organisations : a taxonomic approach

While organizations strive to leverage the vast information generated daily from social media platforms and both decision makers and consultants are keen to identify and exploit this information’s value, there has been little research into social media in the business context. Social media are diverse, varying in scope and functionality, this diversity entailing a complex of attributes and characteristics, resulting in confusion for both researchers and organizations. Taxonomies are important precursors in emerging fields and are foundational for rigorous theory building. Though aspects of social media have been studied from various discipline perspectives, this work has been largely descriptive. Thus, while the need for a rigorous taxonomy of social media is strong, previous efforts to classify social media suffer limitations – e.g. lack of a systematic taxonomic method, overreliance on intuition, disregard for the users’ perspective, and inadequate consideration of purpose. Thus, this study was mainly initiated by the overarching question “How can social media in the business context be usefully classified?” In order to address this gap, the current paper proposes a systematic method for developing a taxonomy appropriate to study social media in organizations context, combining Nickerson et al,’s (2012) IS taxonomy building guidelines and a Repertory grid (RepGrid) approach.

A runtime integrity monitoring framework for real-time relative positioning systems based on GPS and DSRC

This paper provides a three-layered framework to monitor the positioning performance requirements of Real-time Relative Positioning (RRP) systems of the Cooperative Intelligent Transport Systems (C-ITS) that support Cooperative Collision Warning (CCW) applications. These applications exploit state data of surrounding vehicles obtained solely from the Global Positioning System (GPS) and Dedicated Short-Range Communications (DSRC) units without using other sensors. To this end, the paper argues the need for the GPS/DSRC-based RRP systems to have an autonomous monitoring mechanism, since the operation of CCW applications is meant to augment safety on roads. The advantages of autonomous integrity monitoring are essential and integral to any safety-of-life system. The autonomous integrity monitoring framework proposed necessitates the RRP systems to detect/predict the unavailability of their sub-systems and of the integrity monitoring module itself, and, if available, to account for effects of data link delays and breakages of DSRC links, as well as of faulty measurement sources of GPS and/or integrated augmentation positioning systems, before the information used for safety warnings/alarms becomes unavailable, unreliable, inaccurate or misleading. Hence, a monitoring framework using a tight integration and correlation approach is proposed for instantaneous reliability assessment of the RRP systems. Ultimately, using the proposed framework, the RRP systems will provide timely alerts to users when the RRP solutions cannot be trusted or used for the intended operation.

An intentional class design model to engage first year students with threshold concepts using the academic discourse theories of Vygotsky and Laurillard

In the context of the first-year university classroom, this paper develops Vygotsky’s claim that ‘the relations between the higher mental functions were at one time real relations between people’. By taking the main horizontal and hierarchical levels of classroom discourse and dialogue (student-student, student-teacher, teacher-teacher) and marrying these with the possibilities opened up by Laurillard’s conversational framework, we argue that the learning challenge of a ‘troublesome’ threshold concept might be met by a carefully designed sequence of teaching events and experiences for first year students, and we provide a number of strategies that exploit each level of these ‘hierarchies of discourse’. We suggest that an analytical approach to classroom design that embodies these levels of discourse in sequenced dialogic methods could be used by teachers as a strategy to interrogate and adjust teaching-in-practice especially in the first year of university study.

Learned stochastic mobility prediction for planning with control uncertainty on unstructured terrain

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This mobility prediction model is trained using sample executions of motion primitives on representative terrain, and predicts the future outcome of control actions on similar terrain. Using Gaussian process regression allows us to exploit its inherent measure of prediction uncertainty in planning. We integrate mobility prediction into a Markov decision process framework and use dynamic programming to construct a control policy for navigation to a goal region in a terrain map built using an on-board depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and non-traversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and report results from nearly 300 experimental trials using a planetary rover platform in a Mars-analogue environment. Our results validate the approach and demonstrate the value of planning under uncertainty for safe and reliable navigation.