On Demand Solid Texture Synthesis Using Deep 3D Networks

This paper describes a novel approach for on demand volumetric texture synthesis based on a deep learning framework that allows for the generation of high-quality three-dimensional (3D) data at interactive rates. Based on a few example images of textures, a generative network is trained to synthesize coherent portions of solid textures of arbitrary sizes that reproduce the visual characteristics of the examples along some directions. To cope with memory limitations and computation complexity that are inherent to both high resolution and 3D processing on the GPU, only 2D textures referred to as ‘slices’ are generated during the training stage. These synthetic textures are compared to exemplar images via a perceptual loss function based on a pre-trained deep network. The proposed network is very light (less than 100k parameters), therefore it only requires sustainable training (i.e. few hours) and is capable of very fast generation (around a second for 256³ voxels) on a single GPU. Integrated with a spatially seeded pseudo-random number generator (PRNG) the proposed generator network directly returns a color value given a set of 3D coordinates. The synthesized volumes have good visual results that are at least equivalent to the state-of-the-art patch-based approaches. They are naturally seamlessly tileable and can be fully generated in parallel.     

[2.2]Paracyclophane-Based TCN-201 Analogs as GluN2A-Selective NMDA Receptor Antagonists

Recent studies have shown the involvement of GluN2A subunit-containing NMDA receptors in various neurological and pathological disorders. In the X-ray crystal structure, TCN-201 ( 1 ) and analogous pyrazine derivatives 2 and 3 adopt a U-shape (hairpin) conformation within the binding site formed by the ligand binding domains of the GluN1 and GluN2A subunits. In order to mimic the resulting π/π-interactions of two aromatic rings in the binding site, a [2.2]paracyclophane system was designed to lock these aromatic rings in a parallel orientation. Acylation of [2.2]paracyclophane ( 5 ) with oxalyl chloride and chloroacetyl chloride and subsequent transformations led to the oxalamide 7 , triazole 10 and benzamides 12 . The GluN2A inhibitory activities of the paracyclophane derivatives were tested with two-electrode voltage clamp electrophysiology using Xenopus laevis oocytes expressing selectively functional NMDA receptors with GluN2A subunit. The o-iodobenzamide 12 b with the highest similarity to TCN-201 showed the highest GuN2A inhibitory activity of this series of compounds. At a concentration of 10 μM, 12 b reached 36 % of the inhibitory activity of TCN-201 ( 1 ). This result indicates that the [2.2]paracyclophane system is well accepted by the TCN-201 binding site.     

Physics inspired algorithms for (co)homology computations of three-dimensional combinatorial manifolds with boundary

The issue of computing (co)homology generators of a cell complex is gaining a pivotal role in various branches of science. While this issue may be rigorously solved in polynomial time, it is still overly demanding for large scale problems. Drawing inspiration from low-frequency electrodynamics, this paper presents a physics inspired algorithm for first cohomology group computations on three-dimensional complexes. The algorithm is general and exhibits orders of magnitude speed up with respect to competing ones, allowing to handle problems not addressable before. In particular, when generators are employed in the physical modeling of magneto-quasistatic problems, this algorithm solves one of the most long-lasting problems in low-frequency computational electromagnetics. In this case, the effectiveness of the algorithm and its ease of implementation may be even improved by introducing the novel concept of lazy cohomology generators.     

Charge Migration in Organic Materials: Can Propagating Charges Affect the Key Physical Quantities Controlling Their Motion?

Charge migration is a ubiquitous phenomenon with profound implications throughout many areas of chemistry, physics, biology, and materials science. The long-term vision of designing functional materials with tailored molecular-scale properties has triggered an increasing quest to identify prototypical systems where truly molecular conduction pathways play a fundamental role. Such pathways can be formed due to the molecular organization of various organic materials and are widely used to discuss electronic properties at the nanometer scale. Here, we present a computational methodology to study charge propagation in organic molecular stacks at nano and sub-nanoscales and exploit this methodology to demonstrate that moving charge carriers strongly affect the values of the physical quantities controlling their motion. The approach is also expected to find broad application in the field of charge migration in soft matter systems.     

Lactic acid production using waste generated from sweet potato processing

Organic waste generated from industrial sweet potato canning is estimated to be 30% of incoming raw material with significant residual carbohydrate content. The purpose of this study was to evaluate the potential of waste generated from sweet potato processing material to support the growth of lactic acid bacteria and the production of lactic acid. The waste was comprised of 16.5% solids consisting of 18.5% ash, 4.4% protein, 20.5% simple sugars and 19% soluble starch. Following a screening of three lactic acid bacteria strains, Lactobacillus rhamnosus was deemed the best candidate for lactic acid production. The potential of various dilutions of the enzyme‐hydrolysed waste, with and without pH control, as a fermentation substrate was evaluated. Lactic acid production was highest in hydrolysed waste (without dilution) at pH set point 5.0, yielding 10 g L^?1 in 72 h. Thus, lactic acid, a valuable organic compound, can be generated from sweet potato waste.     

Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach

Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE) approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-)automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only.     

Exact 3D boundary representation in finite element analysis based on Cartesian grids independent of the geometry

This paper proposes a novel Immersed Boundary Method where the embedded domain is exactly described by using its Computer‐Aided Design (CAD) boundary representation with Non‐Uniform Rational B‐Splines (NURBS) or T‐splines. The common feature with other immersed methods is that the current approach substantially reduces the burden of mesh generation. In contrast, the exact boundary representation of the embedded domain allows to overcome the major drawback of existing immersed methods that is the inaccurate representation of the physical domain. A novel approach to perform the numerical integration in the region of the cut elements that is internal to the physical domain is presented and its accuracy and performance evaluated using numerical tests. The applicability, performance, and optimal convergence of the proposed methodology is assessed by using numerical examples in three dimensions. It is also shown that the accuracy of the proposed methodology is independent on the CAD technology used to describe the geometry of the embedded domain. Copyright © 2015 John Wiley & Sons, Ltd.     

Forum The role of CIB: some reflections

An explanation for the low innovation activity in construction is put forward. The central argument is that the current theory of construction is one root cause for low innovation activity. Instead, an explicit and more powerful theory of construction is needed for further innovation, which is ‘to manage new ideas into good currency’. There are three main mechanisms in the current theory of construction which are identified as causing this hindrance. Firstly, production theories in general, as well as construction theories specifically, have been implicit. Therefore, it has not been possible to transfer radical managerial innovations, such as lean production, from manufacturing to construction at a theoretical level. Direct application of this production template to construction has been limited due to the different context of construction in comparison with manufacturing. Secondly, the current theoretical model of construction is based on the transformation model of production. It is argued that the principles of this model are counterproductive, because uncertainty and interdependence are abstracted away. This leads to fragmented and myopic management and inflated variability. Practical examples show that these deficiencies and related practical constraints hinder the implementation of top-down innovations. Thirdly, empirical research shows that also bottom-up innovations - systematic learning and problem solving - are being hindered by the current theory. Thus, the advancement of innovations in construction requires that a new, explicit and valid theory of construction is created, and business models and control methods are developed on the basis of that new theory. Cet article propose une explication concernant le taux bas de l'activité d'innovation dans la secteur du bâtiment. L'argument central étant que la théorie actuelle de la construction est une des raisons fondamentales de cette activité basse d'innover. Au lieu de cela, on a besoin d'une théorie de la construction explicite et plus efficace afin d'innover. Dans la théorie actuelle du bâtiment, il y a trois mécanismes fondamentaux qui sont identifiés comme causes gênant cette innovation. Premièrement, les théories de production en général, ainsi que les théories de la construction en particulier, ont été implicites. Ainsi, il était impossible de transférer des innovations gestionnaires radicales, comme la production allégée (lean production), à partir du domaine de la fabrication à celui du bâtiment à un niveau théorique. L'application directe de ce modèle de production au secteur bâtiment a été limitée à cause du contexte différent de celui-ci en comparaison de celui de la fabrication. Deuxièmement, le modèle théorique actuel de la construction bâtiment est basé sur le modèle de la transformation de production. On avance que les principes de ce modèle agissent négativement sur la productivité parce que l'incertitude et l'interdépendance en sont exclues. Ceci mène à une gestion fragmentée et myope ainsi qu'à une inflation de la variabilité. Des exemples pratiques montrent que ces déficiences et les contraintes pratiques qui leur sont liées, gênent les implémentations des innovations de haut en bas. Troisièmement, une étude empirique montre que les innovations de bas en haut - c'est-à-dire l'apprentissage systématique et la résolution de problèmes - sont gênés par la théorie actuelle. Ainsi, le progrès dan les innovations du secteur bâtiment exige qu'une nouvelle théorie explicite et valide soit créée, et que des modèles d'affaires et des méthodes de contrôle soient développés sur la base de cette nouvelle théorie.