The potential of metabolomics in the diagnosis of thyroid cancer

Thyroid cancer is the most common endocrine system malignancy. However, there is still a lack of reliable and specific markers for the detection and staging of this disease. Fine needle aspiration biopsy is the current gold standard for diagnosis of thyroid cancer, but drawbacks to this technique include indeterminate results or an inability to discriminate different carcinomas, thereby requiring additional surgical procedures to obtain a final diagnosis. It is, therefore, necessary to seek more reliable markers to complement and improve current methods. “Omics” approaches have gained much attention in the last decade in the field of biomarker discovery for diagnostic and prognostic characterisation of various pathophysiological conditions. Metabolomics, in particular, has the potential to identify molecular markers of thyroid cancer and identify novel metabolic profiles of the disease, which can, in turn, help in the classification of pathological conditions and lead to a more personalised therapy, assisting in the diagnosis and in the prediction of cancer behaviour. This review considers the current results in thyroid cancer biomarker research with a focus on metabolomics

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

Stakeholder perspectives on the cost requirements of Small Modular Reactors

The cost of a nuclear power plant (NPP) is an important influence on the future commercial success of Small Modular Reactors (SMRs). At the early design stage, the cost requirements of SMRs can be derived from an analysis of the factors driving the Levelized Cost of Electricity (LCOE). It is often much later into the development process before customers are engaged and their cost requirements are known, by which time key design decisions which influence the lifecycle cost have already been locked-in. A clear understanding is required of the cost priorities for the key stakeholders who are to invest in the SMR. This paper presents a novel approach to ranking the relative importance of different cost factors used to calculate the LCOE. Using a dynamic stakeholder analysis, the key decision-makers for each stage of the SMR product lifecycle are identified. The Analytic Hierarchy Process (AHP) with pair-wise comparisons obtained from nuclear cost experts is employed to rank the different factors in terms of their relative importance on the commercial success of a near-term deployable SMR. Each expert provides a different set of rankings, although project financing cost is consistently the most important for the successful commercial deployment of the SMR. The approach presented in this paper can be used as a verification method for any power generation technology to provide confidence that cost requirements are adequately captured to design for life cycle cost competitiveness from the perspective of different stakeholders.</p

Stakeholder perspectives on the cost requirements of Small Modular Reactors

This paper is in closed access until 11th Dec 2019.© 2018 Elsevier Ltd The cost of a nuclear power plant (NPP) is an important influence on the future commercial success of Small Modular Reactors (SMRs). At the early design stage, the cost requirements of SMRs can be derived from an analysis of the factors driving the Levelized Cost of Electricity (LCOE). It is often much later into the development process before customers are engaged and their cost requirements are known, by which time key design decisions which influence the lifecycle cost have already been locked-in. A clear understanding is required of the cost priorities for the key stakeholders who are to invest in the SMR. This paper presents a novel approach to ranking the relative importance of different cost factors used to calculate the LCOE. Using a dynamic stakeholder analysis, the key decision-makers for each stage of the SMR product lifecycle are identified. The Analytic Hierarchy Process (AHP) with pair-wise comparisons obtained from nuclear cost experts is employed to rank the different factors in terms of their relative importance on the commercial success of a near-term deployable SMR. Each expert provides a different set of rankings, although project financing cost is consistently the most important for the successful commercial deployment of the SMR. The approach presented in this paper can be used as a verification method for any power generation technology to provide confidence that cost requirements are adequately captured to design for life cycle cost competitiveness from the perspective of different stakeholders

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.Comment: This is the extended version of a paper with the same title that appeared at CAV 201

Neural Networks for Information Retrieval

Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many different approaches for many different IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. Additionally, it is interesting to see what key insights into IR problems the new technologies are able to give us. The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they benefit IR research. It covers key architectures, as well as the most promising future directions.Comment: Overview of full-day tutorial at SIGIR 201

Backpropagated Gradient Representations for Anomaly Detection

Learning representations that clearly distinguish between normal and abnormal data is key to the success of anomaly detection. Most of existing anomaly detection algorithms use activation representations from forward propagation while not exploiting gradients from backpropagation to characterize data. Gradients capture model updates required to represent data. Anomalies require more drastic model updates to fully represent them compared to normal data. Hence, we propose the utilization of backpropagated gradients as representations to characterize model behavior on anomalies and, consequently, detect such anomalies. We show that the proposed method using gradient-based representations achieves state-of-the-art anomaly detection performance in benchmark image recognition datasets. Also, we highlight the computational efficiency and the simplicity of the proposed method in comparison with other state-of-the-art methods relying on adversarial networks or autoregressive models, which require at least 27 times more model parameters than the proposed method.Comment: European Conference on Computer Vision (ECCV) 202

On the equivalence of pairing correlations and intrinsic vortical currents in rotating nuclei

The present paper establishes a link between pairing correlations in rotating nuclei and collective vortical modes in the intrinsic frame. We show that the latter can be embodied by a simple S-type coupling a la Chandrasekhar between rotational and intrinsic vortical collective modes. This results from a comparison between the solutions of microscopic calculations within the HFB and the HF Routhian formalisms. The HF Routhian solutions are constrained to have the same Kelvin circulation expectation value as the HFB ones. It is shown in several mass regions, pairing regimes, and for various spin values that this procedure yields moments of inertia, angular velocities, and current distributions which are very similar within both formalisms. We finally present perspectives for further studies.Comment: 8 pages, 4 figures, submitted to Phys. Rev.

Reclaiming literacies: competing textual practices in a digital higher education

This essay examines the implications of the ubiquitous use of the term ‘digital literacies’ in higher education and its increasing alignment with institutional and organisational imperatives. It suggests that the term has been stripped of its provenance and association with disciplinary knowledge production and textual practice. Instead it is called into service rhetorically in order to promote competency based agendas both in and outside the academy. The piece also points to a tendency to position teachers in deficit with regard to their technological capabilities and pay scant attention to their own disciplinary and scholarly practices in a digital world. It concludes that there is a case for building on established theoretical and conceptual frameworks from literacy studies if we wish to integrate advantages of the digital landscape with thoughtful teaching practice

Interaction of Ruthenium(II)-dipyridophenazine Complexes with CT-DNA: Effects of the Polythioether Ancillary Ligands

The complexes [Ru([9]aneS3)(dppz)Cl]Cl 1 and [Ru([12]aneS4)(dppz)]Cl2 2 ([9]aneS3 = 1,4,7- trithiaciclononane and [12]aneS4 = 1,4,7,10-tetrathiaciclododecane) were synthesised and fully characterised. These complexes belong to a small family of dipyridophenazine complexes with non-polypyridyl ancillary ligands . Interaction studies of these complexes with CT-DNA (UV/Vis titrations, steady-state emission and thermal denaturation) revealed their high affinity for DNA . Intercalation constants determined by UV/Vis titrations are of the same order of magnitude (106) as other dppz metallointercalators, namely [Ru(II)(bpy)2dppz]S2+. Differences between l and2 were identified by steady-state emission and thermal denaturation studies . Emission results are in accordance with structural data, which indicate how geometric distortions and different donor and/or acceptor ligand abilities affect luminescence. The possibility of noncovalent interactions between ancillary ligands and nucleobases by van der Waals contacts and H-bridges is discussed . Furthermore, complex l undergoes aquation under intra-cellular conditions and an equilibrium with the aquated form l' is attained . This behaviour may increase the diversity of available interaction modes