Reflective practice and participant involvement in research

This article discusses the relationships between reflective practice and research for professionals who are research participants. It offers an analysis of the opportunities for reflective practice created for participants through their involvement in research. Three examples of research into professional’s perspectives on practice with children in Chile, Malta and Cyprus are presented and analysed. The analysis of the three examples shows the role research can have in creating particular kinds of spaces and relationships that facilitate reflection and how it can introduce dimensions that are normally excluded from critical reflection within a profession. The examples show this as involving: reparative and augmented critical reflection; new information and meaning making and the interactions between macro, meso and micro perspectives

Cyclization and Docking Protocol for Cyclic Peptide-Protein Modeling Using HADDOCK2.4

An emerging class of therapeutic molecules are cyclic peptides with over 40 cyclic peptide drugs currently in clinical use. Their mode of action is, however, not fully understood, impeding rational drug design. Computational techniques could positively impact their design, but modeling them and their interactions remains challenging due to their cyclic nature and their flexibility. This study presents a step-by-step protocol for generating cyclic peptide conformations and docking them to their protein target using HADDOCK2.4. A dataset of 30 cyclic peptide-protein complexes was used to optimize both cyclization and docking protocols. It supports peptides cyclized via an N- and C-terminus peptide bond and/or a disulfide bond. An ensemble of cyclic peptide conformations is then used in HADDOCK to dock them onto their target protein using knowledge of the binding site on the protein side to drive the modeling. The presented protocol predicts at least one acceptable model according to the critical assessment of prediction of interaction criteria for each complex of the dataset when the top 10 HADDOCK-ranked single structures are considered (100% success rate top 10) both in the bound and unbound docking scenarios. Moreover, its performance in both bound and fully unbound docking is similar to the state-of-the-art software in the field, Autodock CrankPep. The presented cyclization and docking protocol should make HADDOCK a valuable tool for rational cyclic peptide-based drug design and high-throughput screening

Incumbent Responses to an Entrant with a New Business Model: Resource Co-Deployment and Resource Re-Deployment Strategies

The constructs of re-deployment and co-deployment have been central to discussions of scope economies in diversified firms. We argue however that these constructs are also significant in the context of single business firms. Increasingly, changes in technology and demand preferences have provided opportunities for entrants to attack incumbents with a different business model, one that may neutralize the incumbent’s advantage for at least some set of customers (e.g. Netflix versus Blockbuster). In such a context incumbents often respond by modifying their business model. We note that several of the business model-altering responses of the incumbent can be characterized in terms of co-deployment and re-deployment benefits and costs, where co-deployment benefits/cost apply to the scope economies/diseconomies in running multiple business-models within the same firm and re-deployment benefits/costs apply to the implications of moving assets from one business model to another. We then examine the set of strategic choices faced by the incumbent in competing with an entrant with a different business model. We identify five set of factors that are likely to influence the decision to choose between these alternatives – uncertainty spawned by the new business model, market segment targeted by the new model, the within-business-across-business-model co-deployment and re-deployment benefits and costs, the across-business co-deployment and re-deployment benefits and costs, and the incumbent’s prior performance history. Although some of these choices have seen some work, most remain relatively underexplored in the strategy literature. We highlight the potential for research in this area with a set of propositions that identify key conditions that should hold true for a particular strategic choice to be picked by an incumbent

Extensive rewiring of the EGFR network in colorectal cancer cells expressing transforming levels of KRASG13D

Protein-protein-interaction networks (PPINs) organize fundamental biological processes, but how oncogenic mutations impact these interactions and their functions at a network-level scale is poorly understood. Here, we analyze how a common oncogenic KRAS mutation (KRASG13D) affects PPIN structure and function of the Epidermal Growth Factor Receptor (EGFR) network in colorectal cancer (CRC) cells. Mapping >6000 PPIs shows that this network is extensively rewired in cells expressing transforming levels of KRASG13D (mtKRAS). The factors driving PPIN rewiring are multifactorial including changes in protein expression and phosphorylation. Mathematical modelling also suggests that the binding dynamics of low and high affinity KRAS interactors contribute to rewiring. PPIN rewiring substantially alters the composition of protein complexes, signal flow, transcriptional regulation, and cellular phenotype. These changes are validated by targeted and global experimental analysis. Importantly, genetic alterations in the most extensively rewired PPIN nodes occur frequently in CRC and are prognostic of poor patient outcomes.This work was supported by European Union FP7 Grant No. 278568 “PRIMES” and Science Foundation Ireland Investigator Program Grant 14/IA/2395 to W.K. B.K. is supported by SmartNanoTox (Grant no. 686098), NanoCommons (Grant no. 731032), O.R. by MSCA-IF-2016 SAMNets (Grant no. 750688). D.M. is supported by Science Foundation Ireland Career Development award 15-CDA-3495. I.J. is supported by the Canada Research Chair Program (CRC #225404), Krembil Foundation, Ontario Research Fund (GL2-01-030 and #34876), Natural Sciences Research Council (NSERC #203475), Canada Foundation for Innovation (CFI #225404, #30865), and IBM. O.S. is supported by ERC investigator Award ColonCan 311301 and CRUK. I.S. is supported by the Canadian Cancer Society Research Institute (#703889), Genome Canada via Ontario Genomics (#9427 & #9428), Ontario Research fund (ORF/ DIG-501411 & RE08-009), Consortium Québécois sur la Découverte du Médicament (CQDM Quantum Leap) & Brain Canada (Quantum Leap), and CQDM Explore and OCE (#23929). T.C. was supported by a Teagasc Walsh Fellowshi

Machine Learning for Money Laundering Risk Detection in Online Gambling

This thesis addresses the issue of money laundering in online gambling. Over the years, the online gambling industry has evolved into one of the most profitable industries on the internet. While stringent new regulations have required the industry to become more vigilant, methods used to process proceeds from illicit activities have also advanced and have become more sophisticated. This research examines the application of machine learning for the detection of high-risk money laundering cases in online gambling. This work was part of a collaboration with Kindred Group, a major gambling operator. Money laundering as a fraud detection problem su↵ers from the binary class imbalance issue in data mining. This research focuses on investigating data and algorithmic level techniques to provide a solution to that issue. An in-depth analysis of supervised learning algorithms is carried out and a supervised learning framework is proposed to improve the detection rate of high-risk money laundering cases relative to the existing rule-based system. Results showed immediate improvement in the identification rate. Furthermore, it examines Generative Adversarial Networks (GANs) to provide a solution to the class imbalance problem by generating new synthetic data to oversample the minority class. Our GAN-based approach outperformed popular oversampling techniques when combined with supervised learning classifiers. Building on our GAN-based architecture, we then introduce a novel generative adversarial framework, based on semi-supervised learning and sparse auto-encoders, for the detection of fraud in online gambling. Experimental results show that the proposed framework outperforms mainstream discriminative techniques without the need of generating synthetic instances. We validated our system by applying it to other domains that suffer from the binary class imbalance problem. Finally, unsupervised anomaly detection (AD) framework based on encoder-decoder long short-term memory (LSTM-ATT) networks and Gaussian estimation is examined to discover new patterns in customer behaviours that could be related to money laundering risk, something which is not possible with a supervised framework. Our AD system is evaluated with the help of Kindred’s compliance team on specific cases. The feedback received from our research partners suggested that the detected anomalies indicated risk of money laundering and that the proposed framework can be included in their existing anti-money laundering (AML) process

Boards, Ownership Structure and Involuntary Delisting from the New York Stock Exchange

This study examines whether the likelihood of becoming involuntarily delisted from NYSE is associated with a firm’s board of directors and ownership characteristics. To this end we compare 161 firms that were delisted from NYSE between 1998 and 2004 to a set of industry and size-matched control firms. Consistent with our expectations, we find that the likelihood of delisting is related to a firm’s governance characteristics. Our results on the importance of the board of directors are new to this setting and add to a large body of evidence linking corporate boards and ownership characteristics to corporate performanc