Controllable Multi-domain Semantic Artwork Synthesis

We present a novel framework for multi-domain synthesis of artwork from semantic layouts. One of the main limitations of this challenging task is the lack of publicly available segmentation datasets for art synthesis. To address this problem, we propose a dataset, which we call ArtSem, that contains 40,000 images of artwork from 4 different domains with their corresponding semantic label maps. We generate the dataset by first extracting semantic maps from landscape photography and then propose a conditional Generative Adversarial Network (GAN)-based approach to generate high-quality artwork from the semantic maps without necessitating paired training data. Furthermore, we propose an artwork synthesis model that uses domain-dependent variational encoders for high-quality multi-domain synthesis. The model is improved and complemented with a simple but effective normalization method, based on normalizing both the semantic and style jointly, which we call Spatially STyle-Adaptive Normalization (SSTAN). In contrast to previous methods that only take semantic layout as input, our model is able to learn a joint representation of both style and semantic information, which leads to better generation quality for synthesizing artistic images. Results indicate that our model learns to separate the domains in the latent space, and thus, by identifying the hyperplanes that separate the different domains, we can also perform fine-grained control of the synthesized artwork. By combining our proposed dataset and approach, we are able to generate user-controllable artwork that is of higher quality than existingComment: 15 pages, accepted by CVMJ, to appea

Fire analysis of steel frames with the use of artificial neural networks

The paper presents an alternative approach to the modelling of the mechanical behaviour of steel frame material when exposed to the high temperatures expected in fires. Based on a series of stress-strain curves obtained experimentally for various temperature levels, an artificial neural network (ANN) is employed in the material modelling of steel. Geometrically and materially, a non-linear analysis of plane frame structures subjected to fire is performed by FEM. The numerical results of a simply supported beam are compared with our measurements, and show a good agreement, although the temperature-displacement curves exhibit rather irregular shapes. It can be concluded that ANN is an efficient tool for modelling the material properties of steel frames in fire engineering design studies. (c) 2007 Elsevier Ltd. All rights reserved

Pushover analysis for the seismic response prediction of cable-stayed bridges under multi-directional excitation

Cable-stayed bridges represent nowadays key points in transport networks and their seismic behavior needs to be fully understood, even beyond the elastic range of materials. Both nonlinear dynamic (NL-RHA) and static (pushover) procedures are currently available to face this challenge, each with intrinsic advantages and disadvantages, and their applicability in the study of the nonlinear seismic behavior of cable-stayed bridges is discussed here. The seismic response of a large number of finite element models with different span lengths, tower shapes and class of foundation soil is obtained with different procedures and compared. Several features of the original Modal Pushover Analysis (MPA) are modified in light of cable-stayed bridge characteristics, furthermore, an extension of MPA and a new coupled pushover analysis (CNSP) are suggested to estimate the complex inelastic response of such outstanding structures subjected to multi-axial strong ground motions

A full Eulerian finite difference approach for solving fluid-structure coupling problems

A new simulation method for solving fluid-structure coupling problems has been developed. All the basic equations are numerically solved on a fixed Cartesian grid using a finite difference scheme. A volume-of-fluid formulation (Hirt and Nichols (1981, J. Comput. Phys., 39, 201)), which has been widely used for multiphase flow simulations, is applied to describing the multi-component geometry. The temporal change in the solid deformation is described in the Eulerian frame by updating a left Cauchy-Green deformation tensor, which is used to express constitutive equations for nonlinear Mooney-Rivlin materials. In this paper, various verifications and validations of the present full Eulerian method, which solves the fluid and solid motions on a fixed grid, are demonstrated, and the numerical accuracy involved in the fluid-structure coupling problems is examined.Comment: 38 pages, 27 figures, accepted for publication in J. Comput. Phy

Functional characterisation of a nicotinic acetylcholine receptor alpha subunit from the brown dog tick, Rhipicephalus sanguineus

Open Access funded by Biotechnology and Biological Sciences Research Council Under a Creative Commons license This work was supported by a Biotechnology and Biological Sciences Research Council (UK) Industrial CASE studentship award (BBSSM200411428) to K.L. with co-funding from Pfizer Animal Health, UK. We thank Dr. Andy Ball for help with rearing of ticks.Peer reviewedPublisher PD

Collagenous Fibroma (Desmoplastic Fibroblastoma) of the Neck Presenting with Neurological Symptoms

Collagenous fibromas are rare fibrous soft tissue tumours that usually arise in subcutaneous tissue or skeletal muscle at a variety of anatomical sites. These lesions commonly present as painless, slow-growing mobile masses. We describe a unique case of a 41-year-old woman presenting with a posterior neck swelling and longstanding history of severe ongoing pain in the right scapular region, shoulder and neck, weakness of the palmar grip and limited right lateral neck flexion and rotation. A history of trauma to the right neck in adolescence was noted. Histological analysis revealed a paucicellular lesion with spindle and stellate-shaped fibroblasts involving the cervical nerve roots, typical of collagenous fibroma. In a literature search on Medline and Pubmed, we found no reported cases of collagenous fibromas presenting with neurological symptoms. This report highlights the potential of these lesions to present with neurological symptoms due to infiltration of surrounding tissues, and that preceding trauma may contribute to the aetiology

Differentiation and Loss of Malignant Character of Spontaneous Pulmonary Metastases in Patient-Derived Breast Cancer Models

Patient-derived human-in-mouse xenograft models of breast cancer (PDX models) that exhibit spontaneous lung metastases offer a potentially powerful model of cancer metastasis. In this study, we evaluated the malignant character of lung micro-metastases that emerge in such models after orthotopic implantation of human breast tumor cells into the mouse mammary fat pad. Interestingly, relative to the parental primary breast tumors, the lung metastasis (met)-derived mammary tumors exhibited a slower growth rate and a reduced metastatic potential with a more differentiated epithelial status. Epigenetic correlates were determined by gene array analyses. Lung met-derived tumors displayed differential expression of negative regulators of cell proliferation and metabolism and positive regulators of mammary epithelial differentiation. Clinically, this signature correlated with breast tumor subtypes. We identified microRNA-138 as a novel regulator of invasion and epithelial-mesenchymal transition in breast cancer cells, acting by directly targeting the polycomb epigenetic regulator EZH2. Mechanistic investigations showed that GATA3 transcriptionally controlled miR-138 levels in lung metastases. Notably, the miR-138 activity signature served as a novel independent prognostic marker for patient survival beyond traditional pathologic variables, intrinsic subtypes or a proliferation gene signature. Our results highlight the loss of malignant character in some lung micro-metastatic lesions and the epigenetic regulation of this phenotype