A framework for digital sunken relief generation based on 3D geometric models

Sunken relief is a special art form of sculpture whereby the depicted shapes are sunk into a given surface. This is traditionally created by laboriously carving materials such as stone. Sunken reliefs often utilize the engraved lines or strokes to strengthen the impressions of a 3D presence and to highlight the features which otherwise are unrevealed. In other types of reliefs, smooth surfaces and their shadows convey such information in a coherent manner. Existing methods for relief generation are focused on forming a smooth surface with a shallow depth which provides the presence of 3D figures. Such methods unfortunately do not help the art form of sunken reliefs as they omit the presence of feature lines. We propose a framework to produce sunken reliefs from a known 3D geometry, which transforms the 3D objects into three layers of input to incorporate the contour lines seamlessly with the smooth surfaces. The three input layers take the advantages of the geometric information and the visual cues to assist the relief generation. This framework alters existing techniques in line drawings and relief generation, and then combines them organically for this particular purpose

A multiscale hybrid model for pro-angiogenic calcium signals in a vascular endothelial cell

Cytosolic calcium machinery is one of the principal signaling mechanisms by which endothelial cells (ECs) respond to external stimuli during several biological processes, including vascular progression in both physiological and pathological conditions. Low concentrations of angiogenic factors (such as VEGF) activate in fact complex pathways involving, among others, second messengers arachidonic acid (AA) and nitric oxide (NO), which in turn control the activity of plasma membrane calcium channels. The subsequent increase in the intracellular level of the ion regulates fundamental biophysical properties of ECs (such as elasticity, intrinsic motility, and chemical strength), enhancing their migratory capacity. Previously, a number of continuous models have represented cytosolic calcium dynamics, while EC migration in angiogenesis has been separately approached with discrete, lattice-based techniques. These two components are here integrated and interfaced to provide a multiscale and hybrid Cellular Potts Model (CPM), where the phenomenology of a motile EC is realistically mediated by its calcium-dependent subcellular events. The model, based on a realistic 3-D cell morphology with a nuclear and a cytosolic region, is set with known biochemical and electrophysiological data. In particular, the resulting simulations are able to reproduce and describe the polarization process, typical of stimulated vascular cells, in various experimental conditions.Moreover, by analyzing the mutual interactions between multilevel biochemical and biomechanical aspects, our study investigates ways to inhibit cell migration: such strategies have in fact the potential to result in pharmacological interventions useful to disrupt malignant vascular progressio

Digital relief generation from 3D models

It is difficult to extend image-based relief generation to high-relief generation, as the images contain insufficient height information. To generate reliefs from three-dimensional (3D) models, it is necessary to extract the height fields from the model, but this can only generate bas-reliefs. To overcome this problem, an efficient method is proposed to generate bas-reliefs and high-reliefs directly from 3D meshes. To produce relief features that are visually appropriate, the 3D meshes are first scaled. 3D unsharp masking is used to enhance the visual features in the 3D mesh, and average smoothing and Laplacian smoothing are implemented to achieve better smoothing results. A nonlinear variable scaling scheme is then employed to generate the final bas-reliefs and high-reliefs. Using the proposed method, relief models can be generated from arbitrary viewing positions with different gestures and combinations of multiple 3D models. The generated relief models can be printed by 3D printers. The proposed method provides a means of generating both high-reliefs and bas-reliefs in an efficient and effective way under the appropriate scaling factors

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine: Brussels, Belgium. 15-18 March 2016.

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

How do undergraduate medical students perceive social accountability?

Aim: The concept of social accountability within undergraduate training is embedded within the remit of medical schools. Little is known of how medical students perceive social accountability, recognize aspects of their training contributing to the development of this concept and cultivate the underpinning values. Methods: Students nearing graduation were recruited to participate in focus groups designed to explore their perceptions of social accountability, which curricular aspects had contributed to their understanding, and to investigate the implications of individual variations in training. Results: Students expressed limited appreciation of the concept of social accountability and acknowledged little explicit teaching around underpinning core concepts such as awareness of local health needs, advocacy and nurturing of altruism. However, participants recognized numerous aspects of the course and learning initiatives as impacting on their attitudes towards this concept implicitly. Conclusion: This study highlights areas of their undergraduate training that students recognize as having the greatest impact on their development into socially accountable professionals. It poses some significant challenges for health care educators in addressing unintended consequences, including an outcome-driven educational approach, in reducing students’ capacity or willingness to engage in curricular challenges often designed to embed this concept

On the Peripheries of Planetary Urbanization: Globalizing Manaus and its Expanding Impact

In this paper I argue that global urbanism produces peripherality in ways that cannot be adequately problematized without taking into account its actual extent and geographically uneven development. Therefore, planetary urbanization needs to engage scholarly traditions attuned to regional urbanization if the discourse is to move past limitations in the urban globalization canon and its narrow focus on cities. To that end, I examine research on extensive urbanization in the Amazon region. Illustrative case studies show how attempts to globalize Manaus precipitated territorial restructuring and sociospatial change far beyond the city's boundaries. Manaus is now a more unequal city. Selective metropolitan expansion to the Rio Negro's south bank has led to the simultaneous upgrading and peripheralization of Iranduba. Yet, the building of a city-centric regional network of roadways also shaped Roraima State's transformation from isolated borderland to bypassed periphery. Moreover, financial and symbolic appropriations of standing rainforests by metropolitan conservationism marginalize remote communities even in the absence of exploitative deforestation and resource extraction. Final remarks emphasize the need for further research on the hybrid (urban—rural) conditions and functional articulations of distant-yet-impacted peripheries. Such efforts may broaden the political horizons of planetary urbanization by informing extensive contestations of entrepreneurial urbanism

Subcellular optogenetic inhibition of G proteins generates signaling gradients and cell migration

Cells sense gradients of extracellular cues and generate polarized responses such as cell migration and neurite initiation. There is static information on the intracellular signaling molecules involved in these responses, but how they dynamically orchestrate polarized cell behaviors is not well understood. A limitation has been the lack of methods to exert spatial and temporal control over specific signaling molecules inside a living cell. Here we introduce optogenetic tools that act downstream of native G protein–coupled receptor (GPCRs) and provide direct control over the activity of endogenous heterotrimeric G protein subunits. Light-triggered recruitment of a truncated regulator of G protein signaling (RGS) protein or a Gβγ-sequestering domain to a selected region on the plasma membrane results in localized inhibition of G protein signaling. In immune cells exposed to spatially uniform chemoattractants, these optogenetic tools allow us to create reversible gradients of signaling activity. Migratory responses generated by this approach show that a gradient of active G protein αi and βγ subunits is sufficient to generate directed cell migration. They also provide the most direct evidence so for a global inhibition pathway triggered by Gi signaling in directional sensing and adaptation. These optogenetic tools can be applied to interrogate the mechanistic basis of other GPCR-modulated cellular functions

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

DHGCN: Dynamic hop graph convolution network for self-supervised point cloud learning

Recent works attempt to extend Graph Convolution Networks (GCNs) to point clouds for classification and segmentation tasks. These works tend to sample and group points to create smaller point sets locally and mainly focus on extracting local features through GCNs, while ignoring the relationship between point sets. In this paper, we propose the Dynamic Hop Graph Convolution Network (DHGCN) for explicitly learning the contextual relationships between the voxelized point parts, which are treated as graph nodes. Motivated by the intuition that the contextual information between point parts lies in the pairwise adjacent relationship, which can be depicted by the hop distance of the graph quantitatively, we devise a novel self-supervised part-level hop distance reconstruction task and design a novel loss function accordingly to facilitate training. In addition, we propose the Hop Graph Attention (HGA), which takes the learned hop distance as input for producing attention weights to allow edge features to contribute distinctively in aggregation. Eventually, the proposed DHGCN is a plug-and-play module that is compatible with point-based backbone networks. Comprehensive experiments on different backbones and tasks demonstrate that our self-supervised method achieves state-of-the-art performance. Our source code is available at: https://github.com/Jinec98/DHGCN