3D Physical Space Research Articles

The k-ary n-direct s-indirect family of topologies for large-scale interconnection networks

In large-scale supercomputers, the interconnection network plays a key role in system performance. Network topology highly defines the performance and cost of the interconnection network. Direct topologies are sometimes used due to its reduced hardware cost, but the number of network dimensions is limited by the physical 3D space, which leads to an increase of the communication latency and a reduction of network throughput for large machines. Indirect topologies can provide better performance for large machines, but at higher hardware cost. In this paper, we propose a new family of hybrid topologies, the k-ary n-direct s-indirect, that combines the best features from both direct and indirect topologies to efficiently connect an extremely high number of processing nodes. The proposed network is an n-dimensional topology where the k nodes of each dimension are connected through a small indirect topology of s stages. This combination results in a family of topologies that provides high performance, with latency and throughput figures of merit close to indirect topologies, but at a lower hardware cost. In particular, it doubles the throughput obtained per cost unit compared with indirect topologies in most of the cases. Moreover, their fault-tolerance degree is similar to the one achieved by direct topologies built with switches with the same number of ports.

Detailed Computational Modeling of the Developmental Self-Organization of Neuronal Structure and Function

Event Abstract Back to Event Detailed Computational Modeling of the Developmental Self-Organization of Neuronal Structure and Function Roman Bauer1* 1 Newcastle University, United Kingdom The structure and function of neuronal networks is the result of a complex developmental process, which is implicitly encoded in the genes. A better understanding of neural development is therefore crucial for a comprehensive picture of the workings of the brain. Computational simulations allow to test models of how neural development occurs, and in particular how it relies on the interaction between genetic instructions and the local, external environment of cells. Hence, these simulations should ideally take into account a number of mechanisms, such as gene regulatory networks, extracellular substance gradients, physical forces and electrical activity. Here, I present work comprising the detailed modeling of neural development in cortex, as well as preliminary results on retinal development. In particular, I show how certain reaction-diffusion systems can give rise to spatial patterns of chemical guidance cues, enabling the development of a patchy motif of clustered axonal projections in the superficial layers of cortex [1]. Furthermore, neuronal outgrowth processes and synaptic learning rules that give rise to realistic synaptic connectivity as well as winner-take-all (WTA) functionality are presented [2]. Finally, simulations of cortical [3] and retinal lamination (i.e. the sequential layering of different cell types) are shown, providing a powerful computational framework for studying normal and pathological brain development. Overall, the results of these computational models emphasize the importance of taking into account genetic encoding as well as the 3D physical space within which cells interact, to study brain development. Moreover, this work highlights the necessity of appropriate software for these kinds of simulations, which also require significant computing resources. Acknowledgements This work was first supported by the EU grant 216593 SECO (http://www.seco-project.eu), and afterwards by the Engineering and Physical Sciences Research Council of the United Kingdom (EP/K026992/1) as part of the Human Brain Development Project (http://www.greenbrainproject.org/) References [1] Bauer R, Zubler F, Hauri A, Muir D, Douglas RJ, 2014. Developmental origin of patchy axonal connectivity in the neocortex: A computational model. Cerebral Cortex. [2] Bauer R, Pfister S, Hauri A, Zubler F, Pfeiffer M, Douglas RJ, 2014. Developmental self-construction and -configuration of functional neocortical neuronal networks. PLoS Computational Biology. [3] Zubler F, Hauri A, Pfister S, Bauer R, Anderson JC, Whatley AM, Douglas RJ, 2013: Simulating cortical development as a self constructing process: a novel multi-scale approach combining molecular and physical aspects. PLoS Computational Biology. Keywords: Guidance cues, Gene Regulatory Networks, neuronal self-organization, computational modeling, neurite growth Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016. Presentation Type: Investigator presentations Topic: Normal development / cognition Citation: Bauer R (2016). Detailed Computational Modeling of the Developmental Self-Organization of Neuronal Structure and Function. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00004 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 30 Apr 2016; Published Online: 18 Jul 2016. * Correspondence: Dr. Roman Bauer, Newcastle University, Newcastle upon Tyne, United Kingdom, r.bauer@surrey.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Roman Bauer Google Roman Bauer Google Scholar Roman Bauer PubMed Roman Bauer Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Computational modeling of pathological layer formation in neurodevelopmental disorders

Event Abstract Back to Event Computational modeling of pathological layer formation in neurodevelopmental disorders Roman Bauer1* and Marcus Kaiser1 1 Newcastle University, United Kingdom The mammalian cortex is composed of six main cell layers with distinct form and function. This laminar structure is the result of a complex developmental process, which is implicitly specified in the genetic code, but contingent upon extracellular factors. Computational modeling provides a valuable approach for a better understanding of this dynamic process [1]. Here, we present a computer model to put in comparison the development of normal and pathological laminar organization. In particular, we investigated scenarios where the migration of certain cells does not take place properly, such as for example in the neurodevelopmental disease periventricular heterotopia (PH) [2]. In PH, certain neurons that have failed to migrate into the developing cerebral cortex remain as nodules or neurons that line the ventricular surface. To implement our model we used the software framework Cx3D [3], which is a software framework for simulations of neural development in 3D physical space. Based on this model, we simulate a gene regulatory network, which by differentiation, proliferation and migration of cells gives rise to a laminated structure. By introducing defects to our artificial genetic code, our simulations yield results that are in good accordance with experimental observations. Overall, our results constitute a comprehensive model for the laminar disorganization in PH, which can also be applied to better understand many other neurodevelopmental trajectories of brain disorders. Acknowledgements This work was supported by the Engineering and Physical Sciences Research Council of the United Kingdom (EP/K026992/1) as part of the Human Brain Development Project (http://www.greenbrainproject.org/) References [1] Zubler, F., Hauri, A., Pfister, S., Bauer, R., Anderson, J. C., Whatley, A. M., & Douglas, R. J. (2013). Simulating cortical development as a self constructing process: a novel multi-scale approach combining molecular and physical aspects. PLoS Comput Biol, 9(8), e1003173. [2] Fox, J. W., Lamperti, E. D., Ekşioğlu, Y. Z., Hong, S. E., Feng, Y., Graham, D. A., ... & Berkovic, S. F. (1998). Mutations in filamin 1 prevent migration of cerebral cortical neurons in human periventricular heterotopia. Neuron, 21(6), 1315-1325. [3] Zubler, F., & Douglas, R. (2009). A framework for modeling the growth and development of neurons and networks. Frontiers in computational neuroscience, 3, 25. Keywords: computational modeling, gene regulatory networks (GRN), layer formation, neurodevelopmental diseases, Subcortical heterotopia Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016. Presentation Type: Poster Topic: Computational neuroscience Citation: Bauer R and Kaiser M (2016). Computational modeling of pathological layer formation in neurodevelopmental disorders. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00065 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 30 Apr 2016; Published Online: 18 Jul 2016. * Correspondence: Dr. Roman Bauer, Newcastle University, Newcastle upon Tyne, United Kingdom, r.bauer@surrey.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Roman Bauer Marcus Kaiser Google Roman Bauer Marcus Kaiser Google Scholar Roman Bauer Marcus Kaiser PubMed Roman Bauer Marcus Kaiser Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Molecular dynamics-based triangulation algorithm of free-form parametric surfaces for computer-aided engineering

A new computational approach for automated triangulation of Computer-Aided Design (CAD) surface models, applicable to various CFD (Computational Fluid Dynamics) problems of practical interest is proposed. The complex shaped product configurations are represented by a set of Non-Uniform Rational B-Splines (NURBS) surface patches. The suggested technique is based on the molecular dynamics method. The main idea of the approach is that the mesh nodes are considered as similarly charged interacting particles which move within the region to be meshed under the influence of internal (such as particle–particle interaction forces) and external forces as well as optional additional forces. Moreover, the particles experience a medium resistance due to which the system comes to equilibrium within a relatively short period of time. The proposed 3D surface mesh generation algorithm uses a parametric NURBS representation as initial definition of the domain boundary. This method first distributes the interacting nodes into optimal locations in the parametric domain of the NURBS surface patch using molecular dynamics simulation. Then, the well-shaped triangles can be created after connecting the nodes by Delaunay triangulation. Finally, the mapping from parametric space to 3D physical space is performed. Since the presented interactive algorithm allows to control the distance between a pair of nodes depending on the curvature of the NURBS surface, the method generates high quality triangular mesh. The algorithm enables to produce uniform mesh, as well as anisotropic adaptive mesh with refinement in the large gradient regions. The mesh generation approach has the abilities to preserve the representation accuracy of the input geometry model, create a close relationship between geometry modeling and grid generation process, be automated to a large degree. Some examples are considered in order to illustrate the method’s ability to generate a surface mesh for a complicated CAD model.

Tangible displays for the masses: spatial interaction with handheld displays by using consumer depth cameras

Spatially aware handheld displays are a promising approach to interact with complex information spaces in a more natural way by extending the interaction space from the 2D surface to the 3D physical space around them. This is achieved by utilizing their spatial position and orientation for interaction purposes. Technical solutions for spatially tracked displays already exist in research laboratories, e.g., embedded in a tabletop environment. Along with a large stationary screen, such multi-display systems provide a rich design space with a variety of benefits to users, e.g., the explicit support of co-located parallel work and collaboration. As we see a great future in the underlying interaction principles, the question is how the technology can be made accessible to the public. With our work, we want to address this issue. In the long term, we envision a low-cost tangible display ecosystem that is suitable for everyday usage and supports both active displays (e.g., the iPad) and passive projection media (e.g., paper screens and everyday objects such as a mug). The two major contributions of this article are a presentation of an exciting design space and a requirement analysis regarding its technical realization with special focus on a broad adoption by the public. In addition, we present a proof of concept system that addresses one technical aspect of this ecosystem: the spatial tracking of tangible displays with a consumer depth camera (Kinect).

Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface

Objective. At the balanced intersection of human and machine adaptation is found the optimally functioning brain–computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional (3D) physical space using noninvasive scalp electroencephalogram (EEG) in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that the operation of a real world device has on subjects' control in comparison to a 2D virtual cursor task. Approach. Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a 3D physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Main results. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m s−1. Significance. Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain–computer interfaces are systems that aim to restore or enhance a user's ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in 3D physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG-based BCI systems for accomplish complex control in 3D physical space. The present study may serve as a framework for the investigation of multidimensional noninvasive BCI control in a physical environment using telepresence robotics.

Fundamental properties of quaternion spinors

The interior structure of arbitrary sets of quaternion units is analyzed using general methods of the theory of matrices. It is shown that the units are composed of quadratic combinations of fundamental objects having a dual mathematical meaning as spinor couples and dyads locally describing 2D surfaces. A detailed study of algebraic relationships between the spinor sets belonging to different quaternion units is suggested as an initial step aimed at producing a self-consistent geometric image of spinor-surface distribution on the physical 3D space background.

Microcontroller based fibre-optic visual presentation system for multisensory neuroimaging

Presenting visual stimuli in physical 3D space during fMRI experiments carries significant technical challenges. Certain types of multisensory visuotactile experiments and visuomotor tasks require presentation of visual stimuli in peripersonal space, which cannot be accommodated by ordinary projection screens or binocular goggles. However, light points produced by a group of LEDs can be transmitted through fibre-optic cables and positioned anywhere inside the MRI scanner. Here we describe the design and implementation of a microcontroller-based programmable digital device for controlling fibre-optically transmitted LED lights from a PC. The main feature of this device is the ability to independently control the colour, brightness, and timing of each LED. Moreover, the device was designed in a modular and extensible way, which enables easy adaptation for various experimental paradigms. The device was tested and validated in three fMRI experiments involving basic visual perception, a simple colour discrimination task, and a blocked multisensory visuo-tactile task. The results revealed significant lateralized activation in occipital cortex of all participants, a reliable response in ventral occipital areas to colour stimuli elicited by the device, and strong activations in multisensory brain regions in the multisensory task. Overall, these findings confirm the suitability of this device for presenting complex fibre-optic visual and cross-modal stimuli inside the scanner.

Population Coding of Visual Space: Comparison of Spatial Representations in Dorsal and Ventral Pathways

Although the representation of space is as fundamental to visual processing as the representation of shape, it has received relatively little attention from neurophysiological investigations. In this study we characterize representations of space within visual cortex, and examine how they differ in a first direct comparison between dorsal and ventral subdivisions of the visual pathways. Neural activities were recorded in anterior inferotemporal cortex (AIT) and lateral intraparietal cortex (LIP) of awake behaving monkeys, structures associated with the ventral and dorsal visual pathways respectively, as a stimulus was presented at different locations within the visual field. In spatially selective cells, we find greater modulation of cell responses in LIP with changes in stimulus position. Further, using a novel population-based statistical approach (namely, multidimensional scaling), we recover the spatial map implicit within activities of neural populations, allowing us to quantitatively compare the geometry of neural space with physical space. We show that a population of spatially selective LIP neurons, despite having large receptive fields, is able to almost perfectly reconstruct stimulus locations within a low-dimensional representation. In contrast, a population of AIT neurons, despite each cell being spatially selective, provide less accurate low-dimensional reconstructions of stimulus locations. They produce instead only a topologically (categorically) correct rendition of space, which nevertheless might be critical for object and scene recognition. Furthermore, we found that the spatial representation recovered from population activity shows greater translation invariance in LIP than in AIT. We suggest that LIP spatial representations may be dimensionally isomorphic with 3D physical space, while in AIT spatial representations may reflect a more categorical representation of space (e.g., “next to” or “above”).

Population Coding of Visual Space: Modeling

We examine how the representation of space is affected by receptive field (RF) characteristics of the encoding population. Spatial responses were defined by overlapping Gaussian RFs. These responses were analyzed using multidimensional scaling to extract the representation of global space implicit in population activity. Spatial representations were based purely on firing rates, which were not labeled with RF characteristics (tuning curve peak location, for example), differentiating this approach from many other population coding models. Because responses were unlabeled, this model represents space using intrinsic coding, extracting relative positions amongst stimuli, rather than extrinsic coding where known RF characteristics provide a reference frame for extracting absolute positions. Two parameters were particularly important: RF diameter and RF dispersion, where dispersion indicates how broadly RF centers are spread out from the fovea. For large RFs, the model was able to form metrically accurate representations of physical space on low-dimensional manifolds embedded within the high-dimensional neural population response space, suggesting that in some cases the neural representation of space may be dimensionally isomorphic with 3D physical space. Smaller RF sizes degraded and distorted the spatial representation, with the smallest RF sizes (present in early visual areas) being unable to recover even a topologically consistent rendition of space on low-dimensional manifolds. Finally, although positional invariance of stimulus responses has long been associated with large RFs in object recognition models, we found RF dispersion rather than RF diameter to be the critical parameter. In fact, at a population level, the modeling suggests that higher ventral stream areas with highly restricted RF dispersion would be unable to achieve positionally-invariant representations beyond this narrow region around fixation.

WE‐D‐204B‐02: Correlated 3D Respiratory Motion Prediction with Low‐Dimensional Feature‐Based Learning

Purpose: Current methods of respiratory motion prediction treat each dimension independently. In this study we develop and investigate a low dimensional inference methodology that takes advantage of the correlative relation among various physical dimensions for respiratory motion prediction. Method and Materials: Covariate‐response training pairs are constructed in the original 3D physical space, via state augmentation. Unlike existing methods where independent inference models are built for each of the x,y,z coordinates, we first map these high dimensional paired samples into a lower dimensional feature space, spanned by the principle components. In this feature space, a Gaussian kernel with block‐diagonal covariance is employed for learning the joint covariate‐response pdf, which upon marginalization yields the distribution for the projected prediction value. Finally, the estimate in the feature space is mapped back into the original physical space to recover the prediction representation in the (x,y,z) coordinate. The proposed method is compared against its counterpart with independent prediction, using 159 lung motion traces from the Synchrony respiratory tracking system. Both methods were tested for predicting 160ms and 570ms, spanning the range of measured DMLC tracing system latencies. Performance is quantified with 3D RMSE and 90% percentile 3D error. Results: Under fair setup conditions, the proposed method achieves better performance than its independent counterpart. The case‐wise 3D RMSE was reduced by 30–40%, with p‐values in the order of 10̂(−14) for both lookahead lengths. The 90% percentile 3D error was reduced from 1.8mm to 1.1mm for 160ms prediction and from 2.8mm to 2.0mm for 570ms prediction. Conclusion: We have proposed a general low dimensional feature learning methodology to perform correlated high dimensional inference. Combining it with the already effective kernel density estimation yields a highly efficient approach for real‐time respiratory motion prediction. Research supported by NIH/NCI R01 93626, Varian Medical Systems, and AAPM Research Seed Funding initiative.

Tool-Path Generation Based on Angle-Based Flattening

Free-form surfaces represented by triangular meshes are widely used in modern product design and manufacture. However, most existing methods are very limited to machining mesh surfaces. In this paper, a new tool-path generation method is presented for machining mesh surfaces by three-axis ball-end milling. First, angle-based flattening, a recently emerging parameterization technique, is used to map triangular meshes from a three-dimensional (3D) physical space to a two-dimensional (2D) parametric plane. Then, the cutter contact points are calculated by continuously offsetting a drive curve. The main advantage of the method is that it reduces the task of tool-path generation from 3D to 2D space. Therefore, the tool-path generation can be analysed and calculated in a plane. This results in an efficient tool-path generation method, without complex geometric calculation such as determining drive plane, surface slicing, and intersection, in a 3D space. Its feasibility and efficiency are demonstrated by several experiments.

Augmented reality for collaborative product design and development

This paper presents the application of Augmented Reality (AR) to support concurrent collaborative product design among members of a multi-disciplinary team. A client/server framework has been developed to enable users in a distributed environment to carry out product design collaboratively. An intuitive interface, consisting of virtual and tangible interfaces, and a tri-layer model representation scheme have been designed and developed to support solid modeling and collaborative design activities in the AR-based environment. Using AR technology, the users wearing head-mounted devices can move about in a physical 3D space to view a part that is being designed from different angles and perspectives. The users can observe the design effects in real-time as the modifications are being made in the 3D space.

A framework for modeling the growth and development of neurons and networks.

The development of neural tissue is a complex organizing process, in which it is difficult to grasp how the various localized interactions between dividing cells leads relentlessly to global network organization. Simulation is a useful tool for exploring such complex processes because it permits rigorous analysis of observed global behavior in terms of the mechanistic axioms declared in the simulated model. We describe a novel simulation tool, CX3D, for modeling the development of large realistic neural networks such as the neocortex, in a physical 3D space. In CX3D, as in biology, neurons arise by the replication and migration of precursors, which mature into cells able to extend axons and dendrites. Individual neurons are discretized into spherical (for the soma) and cylindrical (for neurites) elements that have appropriate mechanical properties. The growth functions of each neuron are encapsulated in set of pre-defined modules that are automatically distributed across its segments during growth. The extracellular space is also discretized, and allows for the diffusion of extracellular signaling molecules, as well as the physical interactions of the many developing neurons. We demonstrate the utility of CX3D by simulating three interesting developmental processes: neocortical lamination based on mechanical properties of tissues; a growth model of a neocortical pyramidal cell based on layer-specific guidance cues; and the formation of a neural network in vitro by employing neurite fasciculation. We also provide some examples in which previous models from the literature are re-implemented in CX3D. Our results suggest that CX3D is a powerful tool for understanding neural development.

A mixed reality environment for collaborative product design and development

This paper presents a mixed reality (MR) environment for collaborative product design and development. This MR collaborative environment is based on a client/server architecture where multiple users can create and modify product features in a 3D physical space simultaneously. A tri-layer model representation scheme is designed to facilitate product creation and visualization. Intuitive feature manipulation methods and grid-and-snap modes have been designed to support solid modelling in the MR environment. Bi-directional communication between the MR environment and the CAD system ensures any modifications made by one user are propagated to the views of other users for maintaining design data consistency.

Procedimiento Geométrico para la Síntesis Automática de Prensiones con Equilibrio de Fuerzas de Cuatro Dedos sobre Objetos Poliédricos

The synthesis of Force-Closure Grasps is one of the fundamental problems in the grasp and manipulation of objects by means of mechanical hands. In this work a geometric method to determine force-closure grasps on polyhedral objects is presented. First the sets of faces whose relative orientations and positions allow obtaining force-closure grasps (concurrent prensiones, flat-pencils and regulus) are determined. Second, these sets are evaluated using a quality function and the best one is selected for the grasp; and third, on the selected set faces the fingertip contact points are determined such that they assure the force-closure property. This method is based on geometric operation in the 3D physical space and do not present iterative loops. The article includes a comparison of the resulting grasps with the optimum ones in different cases.

Noncontact Binocular Eye-Gaze Tracking for Point-of-Gaze Estimation in Three Dimensions

Binocular eye-gaze tracking can be used to estimate the point-of-gaze (POG) of a subject in real-world 3-D space using the vergence of the eyes. In this paper, a novel noncontact model-based technique for 3-D POG estimation is presented. The noncontact system allows people to select real-world objects in 3-D physical space using their eyes, without the need for head-mounted equipment. Remote 3-D POG estimation may be especially useful for persons with quadriplegia or Amyotrophic Lateral Sclerosis. It would also enable a user to select 3-D points in space generated by 3-D volumetric displays, with potential applications to medical imaging and telesurgery. Using a model-based POG estimation algorithm allows for free head motion and a single stage of calibration. It is shown that an average accuracy of 3.93 cm was achieved over a workspace volume of 30 x 23 x 25 cm (W x H x D) with a maximum latency of 1.5 s due to the digital filtering employed. The users were free to naturally move and reorient their heads while operating the system, within an allowable headspace of 3 cm x 9 cm x 14 cm.

MO‐SAMS‐AUD B‐02: Stereotactic Body Radiation Therapy (SBRT) II: Physics and Dosimetry Considerations

SBRT of well defined extracranial targets in various anatomical locations demands special treatment planning considerations due to limitations of dose calculation algorithms in heterogeneous medium and small field dosimetry coupled with tumor/organ motion and multiple radiobiological constraints. Unlike conventional intracranial radiosurgery, SBRT treatment planning and delivery are confounded by reduced 3D physical space for beam placement due to possibility of treatment machine and patient collision as well as a multiplicity of surrounding serial and parallel architecture organs. Currently advanced treatment planning techniques with radiobiological considerations and precise image‐guided treatment delivery methods are practiced for SBRT delivery to a wide variety of tumor types.The physics and dosimetry presentation will include an overview of the available technology for SBRT with an emphasis on image guidance, Quality Assurance, and clinical implementation of SBRT. The presentation will also include site specific treatment planning techniques with considerations to accuracy of dose calculation algorithms, radiobiological issues, and physics reporting strategies.Educational Objectives:1. Understand the issues related to the clinical implementation and technical aspects of SBRT techniques and technology and become familiar with the preliminary reporting of AAPM Task Group 101.2. Understand the importance of QA procedures and guidelines and reporting requirements for SBRT.3. Understand the practical aspects of SBRT treatment planning for paraspinal, lung, liver, and abdominal tumors and recognize the critical issues related to each site.4. Understand the importance and limitations of dose calculation algorithms and heterogeneity correction methods for SBRT in various anatomical sites.

Hierarchical coexistence of universality and diversity controls robustness and multi-functionality in intermediate filament protein networks

AbstractProteins constitute the elementary building blocks of a vast variety of biological materials such as cellular protein networks, spider silk or bone, where they create extremely robust, multi-functional materials by self-organization of structures over many length- and time scales, from nano to macro. Some of the structural features are commonly found in a many different tissues, that is, they are highly conserved. Examples of such universal building blocks include alpha-helices, beta-sheets or tropocollagen molecules. In contrast, other features are highly specific to tissue types, such as particular filament assemblies, beta-sheet nanocrystals in spider silk or tendon fascicles. These examples illustrate that the coexistence of universality and diversity – in the following referred to as the universality-diversity paradigm (UDP) – is an overarching feature in protein materials. This paradigm is a paradox: How can a structure be universal and diverse at the same time? In protein materials, the coexistence of universality and diversity is enabled by utilizing hierarchies, which serve as an additional dimension beyond the 3D or 4D physical space. This may be crucial to understand how their structure and properties are linked, and how these materials are capable of combining seemingly disparate properties such as strength and robustness. Here we illustrate how the UDP enables to unify universal building blocks and highly diversified patterns through formation of hierarchical structures that lead to multi-functional, robust yet highly adapted structures. We illustrate these concepts in an analysis of three types of intermediate filament proteins, including vimentin, lamin and keratin.

On physics at the Planck scale: Space as a network

It is shown that the one-dimensional quantum field theory can be modeled as a chain of classical oscillators in a thermal bath provided that the Gibbs measure is identified with the phase-space volume measure, the chain being in a nonequilibrium state. Quantized strings and p-branes are also modeled by ordered systems of oscillators. The model of a one-dimensional superspace is constructed. It is shown that the Ramond-Neveu-Schwarz superstring is modeled by a helix formed of a bosonic-string in a multidimensional space. The physical 3D space is represented by a superstring structure (3D “network”), which is described by some Lagrangian. Thus, the unified description of all interactions including gravity is achieved because the superstring excitations involve all fields. In view of the discrete character of the initial structure, the theory is free of ultraviolet divergences. The essential element of the model is the occurrence of the cosmological constant in the gravity equations. A black hole model giving reasonable values for its temperature and entropy is proposed.