Supercell convergence of charge-transfer energies in pentacene molecular crystals from constrained DFT

Singlet fission (SF) is a multi-exciton generation process that could be harnessed to improve the efficiency of photovoltaic devices. Experimentally, systems derived from the pentacene molecule have been shown to exhibit ultrafast SF with high yields. Charge-transfer (CT) configurations are likely to play an important role as intermediates in the SF process in these systems. In molecular crystals, electrostatic screening effects and band formation can be significant in lowering the energy of CT states, enhancing their potential to effectively participate in SF. In order to simulate these, it desirable to adopt a computational approach which is acceptably accurate, relatively inexpensive, which and scales well to larger systems, thus enabling the study of screening effects. We propose a novel, electrostatically-corrected constrained Density Functional Theory (cDFT) approach as a low-cost solution to the calculation of CT energies in molecular crystals such as pentacene. Here we consider an implementation in the context of the ONETEP linear-scaling DFT code, but our electrostatic correction method is in principle applicable in combination with any constrained DFT implementation, also outside the linear-scaling framework. Our newly developed method allows us to estimate CT energies in the infinite crystal limit, and with these to validate the accuracy of the cluster approximation

Melanocytic naevi and melanoma in survivors of childhood cancer.

There is evidence from previous studies of small numbers of children who received cytotoxic therapy for cancer, that they may develop increased numbers of melanocytic naevi (moles), the strongest known risk factors for melanoma. Our aim was to investigate a large number of survivors of childhood cancer in order to test the hypothesis that they have more melanocytic naevi than matched controls. Total-body naevus counts were obtained from 263 oncology patients ascertained in paediatric oncology departments in Queensland, Australia, and from 263 hospital controls matched for age and sex. Additional information was gathered from children's parents about concurrent factors influencing naevus development such as type of complexion and history of sun exposure. Matched analyses, both crude and adjusted for possible confounding factors, revealed no significant difference in overall density of naevi among oncology patients and control subjects, according to diagnosis or to duration or type of chemotherapy. However significantly more oncology patients had atypical naevi (P < 0.05) and acral naevi (P < 0.0001) than controls. One patient developed a malignant melanoma 13 years after chemotherapy and radiotherapy for rhabdomyosarcoma. These findings support an association between treatment for childhood cancer and acral naevi and suggest that atypical naevi may also be associated with chemotherapy in childhood

3D High-Resolution Cardiac Segmentation Reconstruction From 2D Views Using Conditional Variational Autoencoders

Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imaging but are time-consuming, expensive to acquire and they often require long breath holds that are not suitable for patients. Consequently, multiplanar breath-hold 2D cines sequences are standard practice but are disadvantaged by lack of whole-heart coverage and low through-plane resolution. To address this, we propose a conditional variational autoencoder architecture able to learn a generative model of 3D high-resolution left ventricular (LV) segmentations which is conditioned on three 2D LV segmentations of one short-axis and two long-axis images. By only employing these three 2D segmentations, our model can efficiently reconstruct the 3D high-resolution LV segmentation of a subject. When evaluated on 400 unseen healthy volunteers, our model yielded an average Dice score of 87.92 ± 0.15 and outperformed competing architectures (TL-net, Dice score = 82.60 ± 0.23, p = 2.2 · 10 -16 )

Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models

Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging

Low resistivity Pt interconnects developed by electron beam assisted deposition using novel gas injector system

Electron beam-induced deposition (EBID) is a direct write process where an electron beam locally decomposes a precursor gas leaving behind non-volatile deposits. It is a fast and relatively in-expensive method designed to develop conductive (metal) or isolating (oxide) nanostructures. Unfortunately the EBID process results in deposition of metal nanostructures with relatively high resistivity because the gas precursors employed are hydrocarbon based. We have developed deposition protocols using novel gas-injector system (GIS) with a carbon free Pt precursor. Interconnect type structures were deposited on preformed metal architectures. The obtained structures were analysed by cross-sectional TEM and their electrical properties were analysed ex-situ using four point probe electrical tests. The results suggest that both the structural and electrical characteristics differ significantly from those of Pt interconnects deposited by conventional hydrocarbon based precursors, and show great promise for the development of low resistivity electrical contacts

Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta

Background and Objective: Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements, still makes researchers resort to idealized BCs. The aim of this study was to generate and thoroughly characterize a large dataset of synthetic 4D aortic velocity profiles sampled on a 2D cross-section along the ascending aorta with features similar to clinical cohorts of patients with ATAA. Methods: Time-resolved 3D phase contrast magnetic resonance (4D flow MRI) scans of 30 subjects with ATAA were processed through in-house code to extract anatomically consistent cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. Results: Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors. Conclusions: We built a data-driven generative model of 4D aortic inlet velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set

Cultural relativism and the discourse of intercultural communication: aporias of praxis in the intercultural public sphere

The premise of much intercultural communication pedagogy and research is to educate people from different cultures towards open and transformative positions of mutual understanding and respect. This discourse in the instance of its articulation realises and sustains Intercultural Communication epistemologically – as an academic field of social enquiry, and judgementally – as one which locates itself on a moral terrain. By adopting an ethical stance towards difference, the discourse of intercultural communication finds itself caught in a series of aporias, or performative contradictions, where interculturalists are projected simultaneously into positions of cultural relativism on the one hand and ideological totalism on the other. Such aporias arise because the theoretical premises upon which the discourse relies are problematic. We trace these thematics to a politics of presence operating within the discourse of intercultural communication and links this to questions of judgement and truth in the intercultural public sphere. We propose that the politics of presence be set aside in favour of an intercultural praxis which is oriented to responsibility rather than to truth

Pure iterative reconstruction improves image quality in computed tomography of the abdomen and pelvis acquired at substantially reduced radiation doses in patients with active Crohn disease

Objective: We assessed diagnostic accuracy and image quality of modified protocol (MP) computed tomography (CT) of the abdomen and pelvis reconstructed using pure iterative reconstruction (IR) in patients with Crohn disease (CD). Methods: Thirty-four consecutive patients with CD were referred with suspected extramural complications. Two contemporaneous CT datasets were acquired in all patients: standard protocol (SP) and MP. The MP and SP protocols were designed to impart radiation exposures of 10% to 20% and 80% to 90% of routine abdominopelvic CT, respectively. The MP images were reconstructed with model-based IR (MBIR) and adaptive statistical IR (ASIR). Results: The MP-CT and SP-CT dose length product were 88 (58) mGy.cm (1.27 [0.87] mSv) and 303 [204] mGy.cm (4.8 [2.99] mSv), respectively (P < 0.001). Median diagnostic acceptability, spatial resolution, and contrast resolution were significantly higher and subjective noise scores were significantly lower on SP-ASIR 40 compared with all MP datasets. There was perfect clinical agreement between MP-MBIR and SP-ASIR 40 images for detection of extramural complications. Conclusions: Modified protocol CT using pure IR is feasible for assessment of active CD