Utility of sarcoma-specific fusion gene analysis in paraffin-embedded material for routine diagnosis at a specialist centre.

AIMS: Diagnosis of soft tissue sarcomas can be difficult. It can be aided by detection of specific genetic aberrations in many cases. This study assessed the utility of a molecular genetics/cytogenetics service as part of the routine diagnostic service at the Royal Marsden Hospital. METHODS: A retrospective audit was performed over a 15-month period to evaluate the diagnostic usefulness for soft tissue sarcomas with translocations of fluorescence in situ hybridisation (FISH) and reverse-transcriptase PCR (RT-PCR) in paraffin-embedded (PE) material. Results were compared with histology, and evaluated. RESULTS: Molecular investigations were performed on PE material in 158 samples (total 194 RT-PCR and 174 FISH tests), of which 85 were referral cases. Synovial sarcoma, Ewing sarcoma and low-grade fibromyxoid sarcoma were the most commonly tested tumours. Myxoid liposarcoma showed the best histological and molecular concordance, and alveolar rhabdomyosarcoma showed the best agreement between methods. FISH had a higher sensitivity for detecting tumours (73%, compared with 59% for RT-PCR) with a better success rate than RT-PCR, although the latter was specific in identifying the partner gene for each fusion. In particular, referral blocks in which methods of tissue fixation and processing were not certain resulted in higher RT-PCR failure rates. CONCLUSIONS: FISH and RT-PCR on PE tissue are practical and effective ancillary tools in the diagnosis of soft tissue sarcomas. They are useful in confirming doubtful histological diagnoses and excluding malignant diagnoses. PCR is less sensitive than FISH, and the use of both techniques is optimal for maximising the detection rate of translocation-positive sarcomas.

The making of professionals: Embedded civil society development in a transition country

The paper addresses the development of non-governmental organisations (NGOs) in transition settings. Caught in the balance of knowledge exchange and translation of ideas from abroad, organisations in turbulent setting legitimise their existence by learning through professional networks. By association, organisational actors gain acknowledgement by their sector by traversing the corridors of influence provided by international partnerships. What they learn is how to conduct themselves as agents of change in society, and how to deliver on stated missions and goals, therefore, legitimising their presence in a budding civil society at home. The paper presents a knowledge production and learning practices framework which indicates a presence of dual identity of NGOs - their “embeddedness” locally and internationally. Selected framework dimensions and qualitative case study themes are discussed with respect to the level of independence of organisational actors in the East from their partners in the West in a post-socialist context. A professional global civil society as organisations are increasingly managed in similar, professional ways (Anheier & Themudo 2002). Here knowledge “handling” and knowledge “translation” take place through partnership exchanges fostering capable and/or competitive change-inducing institutions (Czarniawska & Sevon 2005; Hwang & Suarez 2005). How professional identity presents itself in the third sector, as well as the sector’s claim to expertise, need further attention, adding to ongoing discussions on professions in institutional theory (Hwang & Powell 2005; Scott 2008; Noordegraaf 2011). A conceptual framework on the dynamic involved for the construction professional fields follows: • Multiple case analysis provides a taxonomy for understanding what is happening in knowledge transition, adaptation, and organisational learning capacity for NGOs with respect to their role in a networked civil society. With the model we can observe the types of knowledge produced and learning employed by organisations. • There are elements of professionalisation in third sector work organisational activity with respect to its accreditation, sources and routines of learning, knowledge claims, interaction with the statutory sector, recognition in cross-sector partnerships etc. • It signals that there is a dual embeddedness in the development of the sector at the core to the shaping the sector’s professional status. This is instrumental in the NGOs’ goal to gain influence as institutions, as they are only one part of a cross-sector mission to address complex societal problems The case study material highlights nuances of knowledge production and learning practices in partnerships, with dual embeddedness a main feature of the findings. This provides some clues to how professionalisation as expert-making takes shape in organisations: • Depending on the type of organisations’ purpose, over its course of development there is an increase in participation in multiple networks, as opposed to reliance on a single strategic partner for knowledge artefacts and practices; • Some types of organisations are better connected within international and national networks than others and there seem to be preferences for each depending on the area of work; • The level of interpretation or adaptation of the knowledge artefacts is related to an organisation’s embeddedness locally, in turn giving it more influence within the network of key institutions; An overreaching theme across taxonomy categories (Table 1)is “professionalisation” or developing organisational “expertise”, embodied at the individual, organisational, and sector levels. Questions relevant to the exercise of power arise: Is competence in managing a dual embeddedness signals the development of a dual identity in professionalisation? Is professionalisation in this sense a sign of organisations maturing into more capable partners to the arguably more experienced (Western) institutions, shifting the power balance? Or is becoming more professional a sign of domestication to the agenda of certain powerful stakeholders, who define the boundaries of the profession? Which dominant dynamics can be observed in a broadly-defined transition country civil society, where individual participation in the form of activism may be overtaking the traditional forms of organised development work, especially with the spread of social media?

CoO nanoparticles embedded in three-dimensional nitrogen/sulfur co-doped carbon nanofiber networks as a bifunctional catalyst for oxygen reduction/evolution reactions

High-performance and low-cost bifunctional electrocatalysts play crucial roles in oxygen reduction and evolution reactions. Herein, a novel three-dimensional (3D) bifunctional electrocatalyst was prepared by embedding CoO nanoparticles into nitrogen and sulfur co-doped carbon nanofiber networks (denoted as CoO@N/S-CNF) through a facile approach. The carbon nanofiber networks were derived from a nanostructured biological material which provided abundant functional groups to nucleate and anchor nanoparticles while retaining its interconnected 3D porous structure. The composite possesses a high specific surface area and graphitization degree, which favors both mass transport and charge transfer for electrochemical reaction. The CoO@N/S-CNF not only exhibits highly efficient catalytic activity towards oxygen reduction reaction (ORR) in alkaline media with an onset potential of about 0.84 V, but also shows better stability and stronger resistance to methanol than Pt/C. Furthermore, it only needs an overpotential of 1.55 V to achieve a current density of 10 mA cm^-2, suggesting that it is an efficient electrocatalyst for oxygen evolution reaction (OER). The ΔE value (oxygen electrode activity parameter) of CoO@N/S-CNF is calculated to be 0.828 V, which demonstrates that the composite could be a promising bifunctional electrocatalyst for both ORR and OER.

A Phase II Biomarker-Embedded Study of Lapatinib plus Capecitabine as First-line Therapy in Patients with Advanced or Metastatic Gastric Cancer

An exploratory phase II biomarker-embedded trial (LPT109747; NCT00526669) designed to determine the association of lapatinib-induced fluoropyrimidine gene changes with efficacy of lapatinib plus capecitabine as first-line treatment for advanced gastric cancer or gastroesophageal junction adenocarcinoma independent of tumor HER2 status. Tumor biopsies obtained before and after 7-day lapatinib (1,250 mg) to analyze changes in gene expression, followed by a 14-day course of capecitabine (1,000 mg/m(2) twice daily, 14/21 days) plus lapatinib 1,250 mg daily. Blood samples were acquired for pharmacokinetic analysis. Primary clinical objectives were response rate (RR) and 5-month progression-free survival (PFS). Secondary objectives were overall survival (OS), PFS, time to response, duration of response, toxicity, and identification of associations between lapatinib pharmacokinetics and biomarker endpoints. Primary biomarker objectives were modulation of 5-FU-pathway genes by lapatinib, effects of germline SNPs on treatment outcome, and trough steady-state plasma lapatinib concentrations. Sixty-eight patients were enrolled; (75% gastric cancer, 25% gastroesophageal junction). Twelve patients (17.9%) had confirmed partial response, 31 (46.3%) had stable disease, and 16 (23.9%) had progressive disease. Median PFS and OS were 3.3 and 6.3 months, respectively. Frequent adverse events included diarrhea (45%), decreased appetite (39%), nausea (36%), and fatigue (36%). Lapatinib induced no changes in gene expression from baseline and no significant associations were found for SNPs analyzed. Elevated baseline HER3 mRNA expression was associated with a higher RR (33% vs. 0%; P = 0.008). Lapatinib plus capecitabine was well tolerated, demonstrating modest antitumor activity in patients with advanced gastric cancer. The association of elevated HER3 and RR warrants further investigation as an important player for HER-targeted regimens in combination with capecitabine

Decision Making for Teams of Mobile Robots

In the past years, we could observe a significant amount of new robotic systems in science, industry, and everyday life. To reduce the complexity of these systems, the industry constructs robots that are designated for the execution of a specific task such as vacuum cleaning, autonomous driving, observation, or transportation operations. As a result, such robotic systems need to combine their capabilities to accomplish complex tasks that exceed the abilities of individual robots. However, to achieve emergent cooperative behavior, multi-robot systems require a decision process that copes with the communication challenges of the application domain. This work investigates a distributed multi-robot decision process, which addresses unreliable and transient communication. This process composed by five steps, which we embedded into the ALICA multi-agent coordination language guided by the PROViDE negotiation middleware. The first step encompasses the specification of the decision problem, which is an integral part of the ALICA implementation. In our decision process, we describe multi-robot problems by continuous nonlinear constraint satisfaction problems. The second step addresses the calculation of solution proposals for this problem specification. Here, we propose an efficient solution algorithm that integrates incomplete local search and interval propagation techniques into a satisfiability solver, which forms a satisfiability modulo theories (SMT) solver. In the third decision step, the PROViDE middleware replicates the solution proposals among the robots. This replication process is parameterized with a distribution method, which determines the consistency properties of the proposals. In a fourth step, we investigate the conflict resolution. Therefore, an acceptance method ensures that each robot supports one of the replicated proposals. As we integrated the conflict resolution into the replication process, a sound selection of the distribution and acceptance methods leads to an eventual convergence of the robot proposals. In order to avoid the execution of conflicting proposals, the last step comprises a decision method, which selects a proposal for implementation in case the conflict resolution fails. The evaluation of our work shows that the usage of incomplete solution techniques of the constraint satisfaction solver outperforms the runtime of other state-of-the-art approaches for many typical robotic problems. We further show by experimental setups and practical application in the RoboCup environment that our decision process is suitable for making quick decisions in the presence of packet loss and delay. Moreover, PROViDE requires less memory and bandwidth compared to other state-of-the-art middleware approaches.

Approaches to Developing Educational Resources Using AI Embedded in Computer Game Technology. A Case Study: Primary School Literacy

In this extended abstract, we discuss recent research at Worcester into the inclusion of AI into ‘Serious Games’. Serious Games research intends to harness the power of computer game technology to produce educational and training materials. We prefer the name ‘Immersive Environments’ (IEs) since this emphasises the human psychological dimension. Creation of compelling and convincing learning software requires a rich engagement of the learner, and a convincing learning experience. We believe that various aspects of the AI tradition can inform the production of such learning.

A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics

This paper outlines the development of a crosscorrelation algorithm and a spiking neural network (SNN) for sound localisation based on real sound recorded in a noisy and dynamic environment by a mobile robot. The SNN architecture aims to simulate the sound localisation ability of the mammalian auditory pathways by exploiting the binaural cue of interaural time difference (ITD). The medial superior olive was the inspiration for the SNN architecture which required the integration of an encoding layer which produced biologically realistic spike trains, a model of the bushy cells found in the cochlear nucleus and a supervised learning algorithm. The experimental results demonstrate that biologically inspired sound localisation achieved using a SNN can compare favourably to the more classical technique of cross-correlation.

Online Alpha Wave detector: an Embedded hardware-software implementation

The recent trend on embedded system development opens a new prospect for applications that in the past were not possible. The eye tracking for sleep and fatigue detection has become an important and useful application in industrial and automotive scenarios since fatigue is one of the most prevalent causes of earth-moving equipment accidents. Typical applications such as cameras, accelerometers and dermal analyzers are present on the market but have some inconvenient. This thesis project has used EEG signal, particularly, alpha waves, to overcome them by using an embedded software-hardware implementation to detect these signals in real time

Development of a Wireless MEMS Multifunction Sensor System and Field Demonstration of Embedded Sensors for Monitoring Concrete Pavements, TR-637, 2016

Pavements tend to deteriorate with time under repeated traffic and/or environmental loading. By detecting pavement distresses and damage early enough, it is possible for transportation agencies to develop more effective pavement maintenance and rehabilitation programs and thereby achieve significant cost and time savings. The structural health monitoring (SHM) concept can be considered as a systematic method for assessing the structural state of pavement infrastructure systems and documenting their condition. Over the past several years, this process has traditionally been accomplished through the use of wired sensors embedded in bridge and highway pavement. However, the use of wired sensors has limitations for long-term SHM and presents other associated cost and safety concerns. Recently, micro-electromechanical sensors and systems (MEMS) and nano-electromechanical systems (NEMS) have emerged as advanced/smart-sensing technologies with potential for cost-effective and long-term SHM. This two-pronged study evaluated the performance of commercial off-the-shelf (COTS) MEMS sensors embedded in concrete pavement (Final Report Volume I) and developed a wireless MEMS multifunctional sensor system for health monitoring of concrete pavement (Final Report Volume II).

Resistive Embedded Heating for Homogeneous Curing of Composite Damage Repair

Thesis (Ph.D.)--University of Washington, 2016-08

Techniques for batch reinforcement learning in robotics

This thesis addresses the Batch Reinforcement Learning methods in Robotics. This sub-class of Reinforcement Learning has shown promising results and has been the focus of recent research. Three contributions are proposed that aim to extend the state-of-art methods allowing for a faster and more stable learning process, such as required for learning in Robotics. The Q-learning update-rule is widely applied, since it allows to learn without the presence of a model of the environment. However, this update-rule is transition-based and does not take advantage of the underlying episodic structure of collected batch of interactions. The Q-Batch update-rule is proposed in this thesis, to process experiencies along the trajectories collected in the interaction phase. This allows a faster propagation of obtained rewards and penalties, resulting in faster and more robust learning. Non-parametric function approximations are explored, such as Gaussian Processes. This type of approximators allows to encode prior knowledge about the latent function, in the form of kernels, providing a higher level of exibility and accuracy. The application of Gaussian Processes in Batch Reinforcement Learning presented a higher performance in learning tasks than other function approximations used in the literature. Lastly, in order to extract more information from the experiences collected by the agent, model-learning techniques are incorporated to learn the system dynamics. In this way, it is possible to augment the set of collected experiences with experiences generated through planning using the learned models. Experiments were carried out mainly in simulation, with some tests carried out in a physical robotic platform. The obtained results show that the proposed approaches are able to outperform the classical Fitted Q Iteration.

Unlinked Embedded Graphs

This paper is a self-contained development of an invariant of graphs embedded in three-dimensional Euclidean space using the Jones polynomial and skein theory. Some examples of the invariant are computed. An unlinked embedded graph is one that contains only trivial knots or links. Examples show that the invariant is sufficiently powerful to distinguish some different unlinked embeddings of the same graph.

Decision making models embedded into a web-based tool for assessing pest infestation risk

Current practices in agricultural management involve the application of rules and techniques to ensure high quality and environmentally friendly production. Based on their experience, agricultural technicians and farmers make critical decisions affecting crop growth while considering several interwoven agricultural, technological, environmental, legal and economic factors. In this context, decision support systems and the knowledge models that support them, enable the incorporation of valuable experience into software systems providing support to agricultural technicians to make rapid and effective decisions for efficient crop growth. Pest control is an important issue in agricultural management due to crop yield reductions caused by pests and it involves expert knowledge. This paper presents a formalisation of the pest control problem and the workflow followed by agricultural technicians and farmers in integrated pest management, the crop production strategy that combines different practices for growing healthy crops whilst minimising pesticide use. A generic decision schema for estimating infestation risk of a given pest on a given crop is defined and it acts as a metamodel for the maintenance and extension of the knowledge embedded in a pest management decision support system which is also presented. This software tool has been implemented by integrating a rule-based tool into web-based architecture. Evaluation from validity and usability perspectives concluded that both agricultural technicians and farmers considered it a useful tool in pest control, particularly for training new technicians and inexperienced farmers.

Multirobot Tethering for Localization and Control

Particle filtering has proven to be an effective localization method for wheeled autonomous vehicles. For a given map, a sensor model, and observations, occasions arise where the vehicle could equally likely be in many locations of the map. Because particle filtering algorithms may generate low confidence pose estimates under these conditions, more robust localization strategies are required to produce reliable pose estimates. This becomes more critical if the state estimate is an integral part of system control. We investigate the use of particle filter estimation techniques on a hovercraft vehicle. The marginally stable dynamics of a hovercraft require reliable state estimates for proper stability and control. We use the Monte Carlo localization method, which implements a particle filter in a recursive state estimate algorithm. An H-infinity controller, designed to accommodate the latency inherent in our state estimation, provides stability and controllability to the hovercraft. In order to eliminate the low confidence estimates produced in certain environments, a multirobot system is designed to introduce mobile environment features. By tracking and controlling the secondary robot, we can position the mobile feature throughout the environment to ensure a high confidence estimate, thus maintaining stability in the system. A laser rangefinder is the sensor the hovercraft uses to track the secondary robot, observe the environment, and facilitate successful localization and stability in motion.