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

Optimization of sensor locations for measurement of flue gas flow in industrial ducts and stacks using neural networks

This paper presents a novel application of neural network modeling in the optimization of sensor locations for the measurement of flue gas flow in industrial ducts and stacks. The proposed neural network model has been validated with an experiment based upon a case-study power plant. The results have shown that the optimized sensor location can be easily determined with this model. The industry can directly benefit from the improvement of measurement accuracy of the flue gas flow in the optimized sensor location and the reduction of manual measurement operation with Pitot tube

Disclosure of environmental violations and the stock market in the Republic of Korea

For almost 20 years, the Ministry of Environment of the Republic of Korea has published on a monthly basis a list of enterprises that fail to comply with national environmental laws and regulations. In this paper, the authors examine the reaction of investors to the publication of these lists and show that enterprises appearing on these lists have experienced a significant decline in their market valuation. Firms in developing countries are often said to have no incentives to invest in pollution control because they typically face weak monitoring and enforcement of environmental regulations. The findings of the authors, however, indicate that the inability of formal institutions to control pollution through fines and penalties may not be as serious an impediment to pollution control as is generally argued. Environmental regulators in developing countries could harness market forces by introducing structured programs to release firm-specific information about environmental performance.Pollution Management&Control,Health Economics&Finance,Environmental Economics&Policies,Water and Industry,Decentralization,Environmental Economics&Policies,Energy and Environment,Health Economics&Finance,Access to Markets,Markets and Market Access

Benders decomposition for congested partial set covering location with uncertain demand

In this paper, we introduce a mixed integer quadratic formulation for the congested variant of the partial set covering location problem, which involves determining a subset of facility locations to open and efficiently allocating customers to these facilities to minimize the combined costs of facility opening and congestion while ensuring target coverage. To enhance the resilience of the solution against demand fluctuations, we address the case under uncertain customer demand using $\Gamma$-robustness. We formulate the deterministic problem and its robust counterpart as mixed-integer quadratic problems. We investigate the effect of the protection level in adapted instances from the literature to provide critical insights into how sensitive the planning is to the protection level. Moreover, since the size of the robust counterpart grows with the number of customers, which could be significant in real-world contexts, we propose the use of Benders decomposition to effectively reduce the number of variables by projecting out of the master problem all the variables dependent on the number of customers. We illustrate how to incorporate our Benders approach within a mixed-integer second-order cone programming (MISOCP) solver, addressing explicitly all the ingredients that are instrumental for its success. We discuss single-tree and multi-tree approaches and introduce a perturbation technique to deal with the degeneracy of the Benders subproblem efficiently. Our tailored Benders approaches outperform the perspective reformulation solved using the state-of-the-art MISOCP solver Gurobi on adapted instances from the literature

Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool

Lack of a dedicated integrated pipeline for neoantigen discovery in mice hinders cancer immunotherapy research. Novel sequential approaches through recurrent neural networks can improve the accuracy of T-cell epitope binding affinity predictions in mice, and a simplified variant selection process can reduce operational requirements. We have developed a web server tool (NAP-CNB) for a full and automatic pipeline based on recurrent neural networks, to predict putative neoantigens from tumoral RNA sequencing reads. The developed software can estimate H-2 peptide ligands, with an AUC comparable or superior to state-of-the-art methods, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the system's predictive capabilities, and we report its putative neoantigens. NAP-CNB web server is freely available at http://biocomp.cnb.csic.es/NeoantigensApp/ with scripts and datasets accessible through the download section

INRIASAC: Simple Hypernym Extraction Methods

Given a set of terms from a given domain, how can we structure them into a taxonomy without manual intervention? This is the task 17 of SemEval 2015. Here we present our simple taxonomy structuring techniques which, despite their simplicity, ranked first in this 2015 benchmark. We use large quantities of text (English Wikipedia) and simple heuristics such as term overlap and document and sentence co-occurrence to produce hypernym lists. We describe these techniques and pre-sent an initial evaluation of results.Comment: SemEval 2015, Jun 2015, Denver, United State

Pesticide Ground Water Monitoring Project Phase VIII

During Phase VIII monitoring (March 24, 2000 to June 30, 2001 ), 86 samples were collected from 77 new wells and 9 samples were collected from wells with previous detectable herbicide

BUSINESS ANALYSIS SUMMARY FOR SWINE FARMS

Livestock Production/Industries,