Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements. The choice of a distance or similarity metric is, however, not trivial and can be highly dependent on the application at hand. In this work, we propose a novel metric learning method to evaluate distance between graphs that leverages the power of convolutional neural networks, while exploiting concepts from spectral graph theory to allow these operations on irregular graphs. We demonstrate the potential of our method in the field of connectomics, where neuronal pathways or functional connections between brain regions are commonly modelled as graphs. In this problem, the definition of an appropriate graph similarity function is critical to unveil patterns of disruptions associated with certain brain disorders. Experimental results on the ABIDE dataset show that our method can learn a graph similarity metric tailored for a clinical application, improving the performance of a simple k-nn classifier by 11.9% compared to a traditional distance metric.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Dendritic ion channel trafficking and plasticity.

Dendritic ion channels are essential for the regulation of intrinsic excitability as well as modulating the shape and integration of synaptic signals. Changes in dendritic channel function have been associated with many forms of synaptic plasticity. Recent evidence suggests that dendritic ion channel modulation and trafficking could contribute to plasticity-induced alterations in neuronal function. In this review we discuss our current knowledge of dendritic ion channel modulation and trafficking and their relationship to cellular and synaptic plasticity. We also consider the implications for neuronal function. We argue that to gain an insight into neuronal information processing it is essential to understand the regulation of dendritic ion channel expression and properties

The measurement of serum paracetamol using a discrete analyser.

Peer Reviewe

Preclinical characterization of zuranolone (SAGE-217), a selective neuroactive steroid GABAA receptor positive allosteric modulator

Zuranolone (SAGE-217) is a novel, synthetic, clinical stage neuroactive steroid GABAA receptor positive allosteric modulator designed with the pharmacokinetic properties to support oral daily dosing. In vitro, zuranolone enhanced GABAA receptor current at nine unique human recombinant receptor subtypes, including representative receptors for both synaptic (γ subunit-containing) and extrasynaptic (δ subunit-containing) configurations. At a representative synaptic subunit configuration, α1β2γ2, zuranolone potentiated GABA currents synergistically with the benzodiazepine diazepam, consistent with the non-competitive activity and distinct binding sites of the two classes of compounds at synaptic receptors. In a brain slice preparation, zuranolone produced a sustained increase in GABA currents consistent with metabotropic trafficking of GABAA receptors to the cell surface. In vivo, zuranolone exhibited potent activity, indicating its ability to modulate GABAA receptors in the central nervous system after oral dosing by protecting against chemo-convulsant seizures in a mouse model and enhancing electroencephalogram β-frequency power in rats. Together, these data establish zuranolone as a potent and efficacious neuroactive steroid GABAA receptor positive allosteric modulator with drug-like properties and CNS exposure in preclinical models. Recent clinical data support the therapeutic promise of neuroactive steroid GABAA receptor positive modulators for treating mood disorders; brexanolone is the first therapeutic approved specifically for the treatment of postpartum depression. Zuranolone is currently under clinical investigation for the treatment of major depressive episodes in major depressive disorder, postpartum depression, and bipolar depression

A global method for coupling transport with chemistry in heterogeneous porous media

Modeling reactive transport in porous media, using a local chemical equilibrium assumption, leads to a system of advection-diffusion PDE's coupled with algebraic equations. When solving this coupled system, the algebraic equations have to be solved at each grid point for each chemical species and at each time step. This leads to a coupled non-linear system. In this paper a global solution approach that enables to keep the software codes for transport and chemistry distinct is proposed. The method applies the Newton-Krylov framework to the formulation for reactive transport used in operator splitting. The method is formulated in terms of total mobile and total fixed concentrations and uses the chemical solver as a black box, as it only requires that on be able to solve chemical equilibrium problems (and compute derivatives), without having to know the solution method. An additional advantage of the Newton-Krylov method is that the Jacobian is only needed as an operator in a Jacobian matrix times vector product. The proposed method is tested on the MoMaS reactive transport benchmark.Comment: Computational Geosciences (2009) http://www.springerlink.com/content/933p55085742m203/?p=db14bb8c399b49979ba8389a3cae1b0f&pi=1

How functional programming mattered

In 1989 when functional programming was still considered a niche topic, Hughes wrote a visionary paper arguing convincingly ‘why functional programming matters’. More than two decades have passed. Has functional programming really mattered? Our answer is a resounding ‘Yes!’. Functional programming is now at the forefront of a new generation of programming technologies, and enjoying increasing popularity and influence. In this paper, we review the impact of functional programming, focusing on how it has changed the way we may construct programs, the way we may verify programs, and fundamentally the way we may think about programs

The CAP cancer protocols – a case study of caCORE based data standards implementation to integrate with the Cancer Biomedical Informatics Grid

BACKGROUND: The Cancer Biomedical Informatics Grid (caBIG™) is a network of individuals and institutions, creating a world wide web of cancer research. An important aspect of this informatics effort is the development of consistent practices for data standards development, using a multi-tier approach that facilitates semantic interoperability of systems. The semantic tiers include (1) information models, (2) common data elements, and (3) controlled terminologies and ontologies. The College of American Pathologists (CAP) cancer protocols and checklists are an important reporting standard in pathology, for which no complete electronic data standard is currently available. METHODS: In this manuscript, we provide a case study of Cancer Common Ontologic Representation Environment (caCORE) data standard implementation of the CAP cancer protocols and checklists model – an existing and complex paper based standard. We illustrate the basic principles, goals and methodology for developing caBIG™ models. RESULTS: Using this example, we describe the process required to develop the model, the technologies and data standards on which the process and models are based, and the results of the modeling effort. We address difficulties we encountered and modifications to caCORE that will address these problems. In addition, we describe four ongoing development projects that will use the emerging CAP data standards to achieve integration of tissue banking and laboratory information systems. CONCLUSION: The CAP cancer checklists can be used as the basis for an electronic data standard in pathology using the caBIG™ semantic modeling methodology

Identifying the unmet information and support needs of women with autoimmune rheumatic diseases during pregnancy planning, pregnancy and early parenting: mixed-methods study

Background Autoimmune rheumatic diseases (ARDs) such as inflammatory arthritis and Lupus, and many of the treatments for these diseases, can have a detrimental impact on fertility and pregnancy outcomes. Disease activity and organ damage as a result of ARDs can affect maternal and foetal outcomes. The safety and acceptability of hormonal contraceptives can also be affected. The objective of this study was to identify the information and support needs of women with ARDs during pregnancy planning, pregnancy and early parenting. Methods This mixed methods study included a cross-sectional online survey and qualitative narrative interviews. The survey was completed by 128 women, aged 18–49 in the United Kingdom with an ARD who were thinking of getting pregnant in the next five years, who were pregnant, or had young children (< 5 years old). The survey assessed quality-of-life and information needs (Arthritis Impact Measurement Scale Short Form and Educational Needs Assessment Tool), support received, what women found challenging, what was helpful, and support women would have liked. From the survey participants, a maximum variation sample of 22 women were purposively recruited for qualitative interviews. Interviews used a person-centered participatory approach facilitated by visual methods, which enabled participants to reflect on their experiences. Interviews were also carried out with seven health professionals purposively sampled from primary care, secondary care, maternity, and health visiting services. Results Survey findings indicated an unmet need for information in this population (ENAT total mean 104.85, SD 30.18). Women at the pre-conception stage reported higher needs for information on pregnancy planning, fertility, giving birth, and breastfeeding, whereas those who had children already expressed a higher need for information on pain and mobility. The need for high quality information, and more holistic, multi-disciplinary, collaborative, and integrated care consistently emerged as themes in the survey open text responses and interviews with women and health professionals. Conclusions There is an urgent need to develop and evaluate interventions to better inform, support and empower women of reproductive age who have ARDs as they navigate the complex challenges that they face during pregnancy planning, pregnancy and early parenting