Enhancement of collagen deposition and cross-linking by coupling lysyl oxidase with bone morphogenetic protein-1 and its application in tissue engineering

Cultured cell-derived extracellular matrices (ECM)-based biomaterials exploit the inherent capacity of cells to create highly sophisticated supramolecular assemblies. However, standard cell culture conditions are far from ideal given the fact that the diluted microenvironment does not favor the production of ECM components, a circumstance particularly relevant for collagen. An incomplete conversion of procollagen by C-proteinase/bone morphogenetic protein 1 (BMP1) has been proposed to severely limit in vitro collagen deposition. BMP1 also catalyzes the proteolytic activation of the precursor of the collagen cross-linking enzyme, lysyl oxidase (LOX) to yield the active form, suggesting a deficit in cross-linking activity under standard conditions. We hypothesized that the implementation of fibroblast cultures with LOX and BMP1 may be an effective way to increase collagen deposition. To test it, we have generated stable cell lines overexpressing LOX and BMP1 and studied the effect of supernatants enriched in LOX and BMP1 on collagen synthesis and deposition from fibroblasts. Herein, we demonstrate that the supplementation with LOX and BMP1 strongly increased the deposition of collagen onto the insoluble matrix at the expense of the soluble fraction in the extracellular medium. Using decellularization protocols, we also show that fibroblast-derived matrices regulate adipogenic and osteogenic differentiation of human mesenchymal stem cells (MSC), and this effect was modulated by LOX/BMP1. Collectively, these data demonstrate that we have developed a convenient protocol to enhance the capacity of in vitro cell cultures to deposit collagen in the ECM, representing this approach a promising technology for application in tissue engineeringTis work was supported by grants from Ministerio de Economía y Competitividad (Plan Nacional de I+D+I: SAF2012-34916, and SAF2015-65679-R to F.R-P

Reduced Switching Connectivity for Large Scale Antenna Selection

In this paper, we explore reduced-connectivity radio frequency (RF) switching networks for reducing the analog hardware complexity and switching power losses in antenna selection (AS) systems. In particular, we analyze different hardware architectures for implementing the RF switching matrices required in AS designs with a reduced number of RF chains. We explicitly show that fully-flexible switching matrices, which facilitate the selection of any possible subset of antennas and attain the maximum theoretical sum rates of AS, present numerous drawbacks such as the introduction of significant insertion losses, particularly pronounced in massive multiple-input multiple-output (MIMO) systems. Since these disadvantages make fully-flexible switching suboptimal in the energy efficiency sense, we further consider partially-connected switching networks as an alternative switching architecture with reduced hardware complexity, which we characterize in this work. In this context, we also analyze the impact of reduced switching connectivity on the analog hardware and digital signal processing of AS schemes that rely on channel power information. Overall, the analytical and simulation results shown in this paper demonstrate that partially-connected switching maximizes the energy efficiency of massive MIMO systems for a reduced number of RF chains, while fully-flexible switching offers sub-optimal energy efficiency benefits due to its significant switching power losses.Comment: 14 pages, 11 figure

TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors

Tactile sensors provide useful contact data during the interaction with an object which can be used to accurately learn to determine the stability of a grasp. Most of the works in the literature represented tactile readings as plain feature vectors or matrix-like tactile images, using them to train machine learning models. In this work, we explore an alternative way of exploiting tactile information to predict grasp stability by leveraging graph-like representations of tactile data, which preserve the actual spatial arrangement of the sensor's taxels and their locality. In experimentation, we trained a Graph Neural Network to binary classify grasps as stable or slippery ones. To train such network and prove its predictive capabilities for the problem at hand, we captured a novel dataset of approximately 5000 three-fingered grasps across 41 objects for training and 1000 grasps with 10 unknown objects for testing. Our experiments prove that this novel approach can be effectively used to predict grasp stability

Innovation and jobs: evidence from manufacturing firms

This paper is aimed at structurally assessing the employment effects of the innovative activities of firms. We estimate firm level displacement and compensation effects in a model in which the stock of knowledge capital raises firm relative efficiency through process innovations and firm demand through product innovations. Displacement is estimated from the elasticity of employment with respect to innovation in the (conditional or Hicksian) demand for labour. Compensation effects are estimated from a firm-specific demand relationship. We also assess the enlargement and weakening of these effects due to firm agents’ behaviour aimed at appropriating innovation rents. We find that the potential employment compensation effect of process innovations surpasses the displacement effect, both in the short and long run (when competitors react), and that product innovation doubles the expanding impact by unit of expenditure, but also that agents’ behaviour can seriously reduce these effects. The actual elasticity of employment to knowledge capital is estimated, however, not far from unity, while “passive” productivity growth is suggested to have null or negative employment effects.

Heart Rate Extraction from Novel Neck Photoplethysmography Signals.

This paper demonstrates for the first time how heart rate (HR) can be extracted from novel neck photoplethysmography (PPG). A novel algorithm is presented, which when tested in neck PPG signals recorded from 9 subjects at different respiratory rates, obtained good precision with respect to gold standard ECG signals. Mean absolute error (MAE), standard deviation error (SDAE) and root-mean-square error (RMSE) resulted in 1.22, 1.54 and 1.98 beats per minute (BPM), respectively. HRneck estimation showed strong correlation (R=0.94) with reference HRECG. Good agreement between both techniques was also demonstrated by Bland-Altman analysis. The bias between mean HR paired differences was -0.16 BPM and 95% limits of agreement (LoA) were (-4.7, 4.4). Comparatively, for widely used finger PPG, errors were slightly smaller (MAE=0.38 BPM, SDAE=0.48 BPM, RMSE=0.62BPM) and the correlation with reference ECG was also very close to 1 (R=0.99). Bias of -0.04 BPM and 95% LoA (-1.5, 1.4), also showed high degree of agreement. However, these findings show the potential the neck could have as an alternative body location for wearable monitors, aiming to reduce the number of sensing sites whilst still providing access to a wide variety of physiological parameters

A primer of international migration: The Latin American experience and a proposal for a research agenda

Although the phenomenon of international migration has been around for a while, in the last decades there has been a world-wide resurgence of it, which is larger in scale, wider is scope and is frequently accompanied by large flows of monetary remittances. These tendencies have revived the debate in the academic and policy spheres over their potential social and economic consequences. In this paper we develop a ‘Primer’ that presents the ‘state of the art’ in the study of international migration and make special emphasis on how the Latin American experience fits in it. We first present an overview of migration patterns in the region and highlight the importance of the United States as the prime destination for Latin American migrants. We then develop the core of the paper which reviews the different theories that have been proposed in the academic literature for explaining both the determinants of migration and its potential impacts, particularly from the perspective of the source country. In the process, we accentuate the central role played by international remittances. Finally, based on the current knowledge frontier in the subject, we propose a research agenda in order to fill some of the current shortages in the analysis of migration in the region.Migration; Remittances; Latin America

Dynamic tax revenue buoyancy estimates for a panel of OECD countries. ESRI WP592, March 2018

In this paper we provide short- and long-run tax buoyancy estimates for a panel of OECD countries. Our results indicate that total tax revenue estimates are not different from unity, corporate income tax buoyancies exceed unity both in the long- and the short-run, while personal income tax buoyancies are smaller than unity; these results are robust to controlling for changes in the respective tax rates. Moreover, after taking into account the fluctuations of the business cycle, we observe that CIT estimates are larger during periods of contraction rather than periods of economic expansion; these results hold both for the whole panel and the Irish economy. Moreover, we examine the effects of using GNP instead of GDP as a base of economic activity for the Irish economy. Although the results are qualitatively the same, the differences need to be taken into account, especially form an economic policy point of view

Hybrid Analog-Digital Precoding Revisited under Realistic RF Modeling

In this paper we revisit hybrid analog-digital precoding systems with emphasis on their modelling and radio-frequency (RF) losses, to realistically evaluate their benefits in 5G system implementations. For this, we decompose the analog beamforming networks (ABFN) as a bank of commonly used RF components and formulate realistic model constraints based on their S-parameters. Specifically, we concentrate on fully-connected ABFN (FC-ABFN) and Butler networks for implementing the discrete Fourier transform (DFT) in the RF domain. The results presented in this paper reveal that the performance and energy efficiency of hybrid precoding systems are severely affected, once practical factors are considered in the overall design. In this context, we also show that Butler RF networks are capable of providing better performances than FC-ABFN for systems with a large number of RF chains.Comment: 12 pages, 5 figure