Faraday-cage screening reveals intrinsic aspects of the van der Waals attraction

General properties of the recently observed screening of the van der Waals (vdW) attraction between a silica substrate and silica tip by insertion of graphene are predicted using basic theory and first-principles calculations. Results are then focused on possible practical applications, as well as an understanding of the nature of vdW attraction, considering recent discoveries showing it competing against covalent and ionic bonding. The traditional view of the vdW attraction as arising from pairwise-additive London dispersion forces is considered using Grimme's "D3" method, comparing results to those from Tkatchenko's more general many-body dispersion (MBD) approach, all interpreted in terms of Dobson's general dispersion framework. Encompassing the experimental results, MBD screening of the vdW force between two silica bilayers is shown to scale up to medium separations as 1.25 de/d, where d is the bilayer separation and de its equilibrium value, depicting antiscreening approaching and inside de. Means of unifying this correlation effect with those included in modern density functionals are urgently required

Reliable density functional and G_0 W_0 approaches to the calculation of bandgaps in 2D materials

Optimizing density-functional theory (DFT) and G0W0 calculations present coupled problems as orbitals from DFT are needed as G0W0 starting points. Applied to 341 two-dimensional (2D) materials, we demonstrate that CAM-B3LYP provides minimal changes in bandgap (e.g., mean absolute deviation of 0.23 eV) when used to start G0W0 calculations, compared to traditional functionals such as PBE, PBE0, and HSE06 (1.07 eV, 1.48 eV, and 1.51 eV, respectively). CAM-B3LYP also delivers the smallest changes in orbital representation. These and other results indicate the suitability of CAM-B3LYP as a density-functional approach for modelling 2D materials, as well as for use in optimizing G0W0 calculations. Our findings parallel well established features of applications to molecules, as well as for spectroscopic applications involving 3D materials

Identification of the Chromophores in Prussian blue

Prussian blue was the world's first synthetic dye. Its structural, optical and magnetic properties have led to many applications in technology and medicine, and provide paradigms for understanding coordination polymers, framework materials and mixed-valence compounds. The intense red absorption of Prussian blue that characterises chemical and physical properties critical to many of these applications is now shown to arise from localised intervalence charge transfer transitions within two chromophoric variants (ligand isomers) of an idealised "dimer" fragment {(NC)5FeII}(mu-CN){FeIII(NC)3(H2O)2}. This fragment is only available in modern interpretations of the material's crystal structure, with the traditional motif {(NC)5FeII}(mu-CN){FeIII(NC)5} shown not to facilitate visible absorption. Essential to the analysis is the demonstration, obtained independently using absorption and magnetic circular dichroism spectroscopies, that spectra of Prussian blues are strongly influenced by particle size and (subsequent) light scattering. These interpretations are guided and supported by density functional theory calculations (CAM-B3LYP), supplemented by coupled cluster and Bethe-Salpeter spectral simulations, as well as electron paramagnetic resonance spectroscopy of Prussian blue and a model molecular dimeric ion [Fe2(CN)11]6-

Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease

Functional brain networks detected in task-free (“resting-state”) functional magnetic resonance imaging (fMRI) have a small-world architecture that reflects a robust functional organization of the brain. Here, we examined whether this functional organization is disrupted in Alzheimer's disease (AD). Task-free fMRI data from 21 AD subjects and 18 age-matched controls were obtained. Wavelet analysis was applied to the fMRI data to compute frequency-dependent correlation matrices. Correlation matrices were thresholded to create 90-node undirected-graphs of functional brain networks. Small-world metrics (characteristic path length and clustering coefficient) were computed using graph analytical methods. In the low frequency interval 0.01 to 0.05 Hz, functional brain networks in controls showed small-world organization of brain activity, characterized by a high clustering coefficient and a low characteristic path length. In contrast, functional brain networks in AD showed loss of small-world properties, characterized by a significantly lower clustering coefficient (p<0.01), indicative of disrupted local connectivity. Clustering coefficients for the left and right hippocampus were significantly lower (p<0.01) in the AD group compared to the control group. Furthermore, the clustering coefficient distinguished AD participants from the controls with a sensitivity of 72% and specificity of 78%. Our study provides new evidence that there is disrupted organization of functional brain networks in AD. Small-world metrics can characterize the functional organization of the brain in AD, and our findings further suggest that these network measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging

A Mathematical Model for Interpretable Clinical Decision Support with Applications in Gynecology

Over time, methods for the development of clinical decision support (CDS) systems have evolved from interpretable and easy-to-use scoring systems to very complex and non-interpretable mathematical models. In order to accomplish effective decision support, CDS systems should provide information on how the model arrives at a certain decision. To address the issue of incompatibility between performance, interpretability and applicability of CDS systems, this paper proposes an innovative model structure, automatically leading to interpretable and easily applicable models. The resulting models can be used to guide clinicians when deciding upon the appropriate treatment, estimating patient-specific risks and to improve communication with patients.We propose the interval coded scoring (ICS) system, which imposes that the effect of each variable on the estimated risk is constant within consecutive intervals. The number and position of the intervals are automatically obtained by solving an optimization problem, which additionally performs variable selection. The resulting model can be visualised by means of appealing scoring tables and color bars. ICS models can be used within software packages, in smartphone applications, or on paper, which is particularly useful for bedside medicine and home-monitoring. The ICS approach is illustrated on two gynecological problems: diagnosis of malignancy of ovarian tumors using a dataset containing 3,511 patients, and prediction of first trimester viability of pregnancies using a dataset of 1,435 women. Comparison of the performance of the ICS approach with a range of prediction models proposed in the literature illustrates the ability of ICS to combine optimal performance with the interpretability of simple scoring systems.The ICS approach can improve patient-clinician communication and will provide additional insights in the importance and influence of available variables. Future challenges include extensions of the proposed methodology towards automated detection of interaction effects, multi-class decision support systems, prognosis and high-dimensional data

Dissecting the Shared and Context-Dependent Pathways Mediated by the p140Cap Adaptor Protein in Cancer and in Neurons

International audienc

Методология синтеза архитектуры программно-технического комплекса автоматизированной системы мониторинга обстановки

Предложен подход к проектированию архитектуры программно-технического комплекса автоматизированной системы мониторинга обстановки в реальном времени, основанный на классификации решаемых функциональных задач на основе методов кластерного анализа и выбранного множества признаков подобия. Разработанный подход позволяет из множества функций системы выделить подобные (по определенным признакам) и объединить их в архитектурные компоненты (унифицированные функциональные модули).Запропоновано підхід до проектування архітектури центру обробки інформації автоматизованої системи моніторингу середовища в реальному часі, що заснований на класифікації функціональних задач на підставі методів кластерного аналізу і обраної множини ознак схожості. Розроблений підхід дозволяє вибрати із множини функцій системи схожі (за певними ознаками) і поєднати їх в архітектурні компоненти (уніфіковані функціональні модулі).The approach to designing architecture of the information processing complex of the automated real time conditions monitoring system based on classification of functional tasks on the basis of methods of cluster analysis and the chosen set of similarity attributes is offered. The developed approach allows to allocate from a set of functions the systems similar (on certain attributes) and to unite them in architectural components (unified functional modules)

Data integration for offshore decommissioning waste management

Offshore decommissioning represents significant business opportunities for oil and gas service companies. However, for owners of offshore assets and regulators, it is a liability because of the associated costs. One way of mitigating decommissioning costs is through the sales and reuse of decommissioned items. To achieve this effectively, reliability assessment of decommissioned items is required. Such an assessment relies on data collected on the various items over the lifecycle of an engineering asset. Considering that offshore platforms have a design life of about 25 years and data management techniques and tools are constantly evolving, data captured about items to be decommissioned will be in varying forms. In addition, considering the many stakeholders involved with a facility over its lifecycle, information representation of the items will have variations. These challenges make data integration difficult. As a result, this research developed a data integration framework that makes use of Semantic Web technologies and ISO 15926 - a standard for process plant data integration - for rapid assessment of decommissioned items. The proposed solution helps in determining the reuse potential of decommissioned items, which can save on cost and benefit the environment