Marine inspired biosilica-filled hydrogels for hard tissue repair

• P.J Walsh, Susan Clarke, Iossif Strehin, Phillip Messersmith. “Marine inspired biosilica-filled hydrogels for hard tissue repair”. 26th European Conference on Biomaterials 2014, Liverpool, UK

Free fatty acid receptor 1 (GPR40) agonists containing spirocyclic periphery inspired by LY2881835

The free fatty acid receptor 1 (FFA1), a G protein-coupled receptor (GPCR) naturally activated by long-chain fatty acids is a novel target for the treatment of metabolic diseases. The basic amine spirocyclic periphery of Eli Lilly's drug candidate LY2881835 for treatment of type 2 diabetes mellitus (which reached phase I clinical trials) inspired a series of novel FFA1 agonists. These were designed to incorporate the 3-[4-(benzyloxy)phenyl]propanoic acid pharmacophore core decorated with a range of spirocyclic motifs. The latter were prepared via the Prins cyclization and subsequent modification of the 4-hydroxytetrahydropyran moiety in the Prins product. Here, we synthesize 19 compounds and test for FFA1 activity. Within this pilot set, a nanomolar potency (EC50=55nM) was reached. Four lead compounds (EC50 range 55-410nM) were characterized for aqueous solubility, metabolic stability, plasma protein binding and Caco-2 permeability. While some instability in the presence of mouse liver microsomes was noted, mouse pharmacokinetic profile of the compound having the best overall ADME properties was evaluated to reveal acceptable bioavailability (F=10.3%) and plasma levels achieved on oral administration.

Updating Beliefs: Are economic agents inspired by rational action or according to ones hopes and fears?.

The purpose of this investigationwas to simulate a real life scenarioand explore the way economicagents update their beliefs. Do theyupdate according to what theyhope? Or do they update inspired byrational behavior?We mimicked the environment whicha recently high school graduate faceswhen entering college to see how astudent updates his beliefs in regardsto his classroom position. We examinedhow economic agents envisagethemselves through and through collegeand see if they update their beliefsabout a hypothesis A in the lightof new evidence B, or if they updatetheir beliefs subject to what theychoose they hope. In this sense weexplored the possibility of setting asidethe neoclassical assumption thatagents are anything more than hyperrational naïve optimizers acting on perfect (and in some cases, limited information)in order to turn back to anolder tradition in economic theory, thatis agents are recognizably human.

'Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection'

Abstract. Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.

'Cooperative Automated worm Response and Detection ImmuNe ALgorithm (CARDINAL) inspired by T-cell Immunity and Tolerance'

The role of T-cells within the immune system is to confirm and assess anomalous situations and then either respond to or tolerate the source of the effect. To illustrate how these mechanisms can be harnessed to solve real-world problems, we present the blueprint of a T-cell inspired algorithm for computer security worm detection. We show how the three central T-cell processes, namely T-cell maturation, differentiation and proliferation, naturally map into this domain and further illustrate how such an algorithm fits into a complete immune inspired computer security system and framework.

'Malicious Code Execution Detection and Response Immune System inspired by the Danger Theory'

The analysis of system calls is one method employed by anomaly detection systems to recognise malicious code execution. Similarities can be drawn between this process and the behaviour of certain cells belonging to the human immune system, and can be applied to construct an artificial immune system. A recently developed hypothesis in immunology, the Danger Theory, states that our immune system responds to the presence of intruders through sensing molecules belonging to those invaders, plus signals generated by the host indicating danger and damage. We propose the incorporation of this concept into a responsive intrusion detection system, where behavioural information of the system and running processes is combined with information regarding individual system calls.

Every picture tells a story: using selfie-inspired activities to enhance social relations and encourage self-reflexivity.

This chapter explores the results of a study in Thailand that capitalised on the popularity of the selfie, providing second-year English language students with an opportunity to practise their oral presentation and speaking skills. The selfie was used not in the usual sense of online picture-sharing, but as a visual aid in a face-to-face interaction, thus serving as a “currency for social interaction” (van Dijck 2008, p.62) and communication device (Saltz, 2014). Mining the rich insights gained from the Thai study, this chapter presents another selfie-inspired activity adapted for a different context and purpose at a UK university. Initially designed to facilitate recall of students’ names linked with faces, the initiative evolved into an effective conversation starter. It is suggested that both selfie-inspired initiatives have led to serendipitous results, such as encouraging self-reflexivity among the students and promoting the development of “rapid intimacy” in the classroom (Victoria 2011, p.72). Indeed, creating a space for students to share their personal stories and enact different identities can help enrich the learning and teaching experience. This chapter also demonstrates how aspects of visual methodologies can be employed as a resource for theorising visual data, such as the selfie, for classroom application.

A flexible bio-inspired hierarchical model for analyzing musical timbre

A flexible and multipurpose bio-inspired hierarchical model for analyzing musical timbre is presented in this paper. Inspired by findings in the fields of neuroscience, computational neuroscience, and psychoacoustics, not only does the model extract spectral and temporal characteristics of a signal, but it also analyzes amplitude modulations on different timescales. It uses a cochlear filter bank to resolve the spectral components of a sound, lateral inhibition to enhance spectral resolution, and a modulation filter bank to extract the global temporal envelope and roughness of the sound from amplitude modulations. The model was evaluated in three applications. First, it was used to simulate subjective data from two roughness experiments. Second, it was used for musical instrument classification using the k-NN algorithm and a Bayesian network. Third, it was applied to find the features that characterize sounds whose timbres were labeled in an audiovisual experiment. The successful application of the proposed model in these diverse tasks revealed its potential in capturing timbral information.

Motif detection inspired by immune memory

The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify variable length unknown motifs which repeat within time series data. The algorithm searches from a completely neutral perspective that is independent of the data being analysed and the underlying motifs. In this paper we test the flexibility of the motif tracking algorithm by applying it to the search for patterns in two industrial data sets. The algorithm is able to identify a population of motifs successfully in both cases, and the value of these motifs is discussed.

Price Trackers Inspired by Immune Memory

In this paper we outline initial concepts for an immune inspired algorithm to evaluate price time series data. The proposed solution evolves a short term pool of trackers dynamically through a process of proliferation and mutation, with each member attempting to map to trends in price movements. Successful trackers feed into a long term memory pool that can generalise across repeating trend patterns. Tests are performed to examine the algorithm’s ability to successfully identify trends in a small data set. The influence of the long term memory pool is then examined. We find the algorithm is able to identify price trends presented successfully and efficiently.

Introducting Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound imnological concepts.

An Immune Inspired Approach to Anomaly Detection

The immune system provides a rich metaphor for computer security: anomaly detection that works in nature should work for machines. However, early artificial immune system approaches for computer security had only limited success. Arguably, this was due to these artificial systems being based on too simplistic a view of the immune system. We present here a second generation artificial immune system for process anomaly detection. It improves on earlier systems by having different artificial cell types that process information. Following detailed information about how to build such second generation systems, we find that communication between cells types is key to performance. Through realistic testing and validation we show that second generation artificial immune systems are capable of anomaly detection beyond generic system policies. The paper concludes with a discussion and outline of the next steps in this exciting area of computer security.

Oil Price Trackers Inspired by Immune Memory

We outline initial concepts for an immune inspired algorithm to evaluate and predict oil price time series data. The proposed solution evolves a short term pool of trackers dynamically, with each member attempting to map trends and anticipate future price movements. Successful trackers feed into a long term memory pool that can generalise across repeating trend patterns. The resulting sequence of trackers, ordered in time, can be used as a forecasting tool. Examination of the pool of evolving trackers also provides valuable insight into the properties of the crude oil market.

'Cooperative Automated worm Response and Detection ImmuNe ALgorithm (CARDINAL) inspired by T-cell Immunity and Tolerance'

The role of T-cells within the immune system is to confirm and assess anomalous situations and then either respond to or tolerate the source of the effect. To illustrate how these mechanisms can be harnessed to solve real-world problems, we present the blueprint of a T-cell inspired algorithm for computer security worm detection. We show how the three central T-cell processes, namely T-cell maturation, differentiation and proliferation, naturally map into this domain and further illustrate how such an algorithm fits into a complete immune inspired computer security system and framework.

'Malicious Code Execution Detection and Response Immune System inspired by the Danger Theory'

The analysis of system calls is one method employed by anomaly detection systems to recognise malicious code execution. Similarities can be drawn between this process and the behaviour of certain cells belonging to the human immune system, and can be applied to construct an artificial immune system. A recently developed hypothesis in immunology, the Danger Theory, states that our immune system responds to the presence of intruders through sensing molecules belonging to those invaders, plus signals generated by the host indicating danger and damage. We propose the incorporation of this concept into a responsive intrusion detection system, where behavioural information of the system and running processes is combined with information regarding individual system calls.