HIF-1α and Pro-Inflammatory Signaling Improves the Immunomodulatory Activity of MSC-Derived Extracellular Vesicles

Despite the strong evidence for the immunomodulatory activity of mesenchymal stromal cells (MSCs), clinical trials have so far failed to clearly show benefit, likely reflecting methodological shortcomings and lack of standardization. MSC-mediated tissue repair is commonly believed to occur in a paracrine manner, and it has been stated that extracellular vesicles (EVs) secreted by MSCs (EVMSC) are able to recapitulate the immunosuppressive properties of parental cells. As a next step, clinical trials to corroborate preclinical studies should be performed. However, effective dose in large mammals, including humans, is quite high and EVs industrial production is hindered by the proliferative senescence that affects MSCs during massive cell expansion. We generated a genetically modified MSC cell line overexpressing hypoxia-inducible factor 1-alpha and telomerase to increase the therapeutic potency of EVMSC and facilitate their large-scale production. We also developed a cytokine-based preconditioning culture medium to prime the immunomodulatory response of secreted EVs (EV_MSC-T-HIF^c). We tested the efficacy of this system in vitro and in a delayed-type hypersensitivity mouse model. MSC-T with an HIF-1α-GFP lentiviral vector (MSC-T-HIF) can be effectively expanded to obtain large amounts of EVs without major changes in cell phenotype and EVs composition. EV_MSC-T-HIF^c suppressed the proliferation of activated T-cells more effectively than did EVs from unmodified MSC in vitro, and significantly blunted the ear-swelling response in vivo by inhibiting cell infiltration and improving tissue integrity. We have developed a long-lived EV source that secretes high quantities of immunosuppressive EVs, facilitating a more standard and cost-effective therapeutic product.     

Direct-Ink-Writing of Electroactive Polymers for Sensing and Energy Storage Applications

Considering the high levels of materials used in the fields of electronics and energy storage systems, it is increasingly necessary to take into consideration environmental impact. Thus, it is important to develop devices based on environmentally friendlier materials and/or processes, such as additive manufacturing techniques. In this work, poly(vinylidene fluoride) (PVDF) and poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) are prepared by direct-ink-writing (DIW) by varying solvent evaporation temperature and fill density percentage. Different morphologies for both polymers are obtained, including dense films and porous membranes, as well as different electroactive β-phase content, thermal and mechanical properties. The dielectric constant and piezoelectric d₃₃ coefficient for dense films reaches up to 16 at 1 kHz and 4 pC N⁻¹, respectively for PVDF-HFP with a fill density of 80 and a solvent evaporation temperature of 50 °C. Porous structures are developed for battery separator membranes in lithium-ion batteries, with a highest ionic conductivity value of 3.8 mS cm⁻¹ for the PVDF-HFP sample prepared with a fill density of 100 and a solvent evaporation temperature of 25 °C, the sample showing an excellent cycling performance. It is demonstrated that electroactive films and membranes can be prepared by direct-ink writing suitable for sensors/actuators and energy storage systems.     

Nonextensive lattice gauge theories: Algorithms and methods

High-energy phenomena presenting strong dynamical correlations, long-range interactions and microscopic memory effects are well described by nonextensive versions of the canonical Boltzmann–Gibbs statistical mechanics. After a brief theoretical review, we introduce a class of generalized heat-bath algorithms that enable Monte Carlo lattice simulations of gauge fields on the nonextensive statistical ensemble of Tsallis. The algorithmic performance is evaluated as a function of the Tsallis parameter q$q$ in equilibrium and nonequilibrium setups. Then, we revisit short-time dynamic techniques, which in contrast to usual simulations in equilibrium present negligible finite-size effects and no critical slowing down. As an application, we investigate the short-time critical behaviour of the nonextensive hot Yang–Mills theory at q$q$-values obtained from heavy-ion collision experiments. Our results imply that, when the equivalence of statistical ensembles is obeyed, the long-standing universality arguments relating gauge theories and spin systems hold also for the nonextensive framework.     

A complete and efficient CUDA-sharing solution for HPC clusters

In this paper we detail the key features, architectural design, and implementation of rCUDA, an advanced framework to enable remote and transparent GPGPU acceleration in HPC clusters. rCUDA allows decoupling GPUs from nodes, forming pools of shared accelerators, which brings enhanced flexibility to cluster configurations. This opens the door to configurations with fewer accelerators than nodes, as well as permits a single node to exploit the whole set of GPUs installed in the cluster. In our proposal, CUDA applications can seamlessly interact with any GPU in the cluster, independently of its physical location. Thus, GPUs can be either distributed among compute nodes or concentrated in dedicated GPGPU servers, depending on the cluster administrator’s policy. This proposal leads to savings not only in space but also in energy, acquisition, and maintenance costs. The performance evaluation in this paper with a series of benchmarks and a production application clearly demonstrates the viability of this proposal. Concretely, experiments with the matrix–matrix product reveal excellent performance compared with regular executions on the local GPU; on a much more complex application, the GPU-accelerated LAMMPS, we attain up to 11x speedup employing 8 remote accelerators from a single node with respect to a 12-core CPU-only execution. GPGPU service interaction in compute nodes, remote acceleration in dedicated GPGPU servers, and data transfer performance of similar GPU virtualization frameworks are also evaluated.     

Amino-acids in humic,nitrohumic and nitrofuvic acids from coal

The humic acids, HA, extracted from a Spanish lignite and the nitrohumic acids, NHA, and nitrofulvic acids, NFA, extracted from the same lignite after its nitration, are hydrolysed with 6N HCI under reflux. The α-amino acids present in the hydrolysates are determined and thirteen α-amino acids are identified. The amounts of nitrogen in the amino-acids released by the hydrolysis increase according to the sequence: NFA > NHA > HA.     

Agricultural use of three organic residues: effect on orange production and on properties of a soil of the ‘Comarca Costa de Huelva’ (SW Spain)

Disposal of urban, agricultural and industrial organic residues impliesan increasing problem because of all the economic and environmentalrepercussions involved. One of the most adequate ways of managing this problemis the agricultural use of these wastes as organic amendments. Three organicresidues (AC, olive mill waste water sludge compost; MWC, municipal solid wastecompost; and PS, paper mill sludge) were used in a 3-year field experimentinvolving orange production. The effect of their application on crop productionand on soil quality was investigated. Soil samples (0–20 cm depth)collected 11 months after the last soil amendment were analysed for: pH and EC,Kjeldahl-N, available-P, available-K, total organic carbon, humic substances,dehydrogenase, phosphatase, -glucosidase, urease andbenzoyl-argininamidehydrolysing protease (BAA-protease) activities. Generally, the application of the MWC and PSincreased orange yield when compared to control. Moreover, total organic carbonand humic substances significantly increased in soils treated with all theorganic amendments. Organic fertilisation increased the Kjeldahl-N andavailable-P contents of the soil. The application of the organic residues also causedsignificant increases in dehydrogenase, -glucosidase, urease andBAA-protease activities of the soil. Significant positive correlations (p <0.01) between these enzymatic activities and total organic carbon were foundforall treatments. Significant positive correlation between dehydrogenase, urease,-glucosidase, and BAA-protease and orange yield was also found. However,a clear inhibition of phosphatase activity was observed in soils treated withPS. The results indicate that the repeated application to the soil of moderateamounts of organic amendments has positive effects on the chemical andbiochemical properties of the soil, as well as on the orange yield.     

Effect of water on the oxygen barrier properties of poly(ethylene terephthalate) and polylactide films

The aim of this work was to study the variations in the oxygen diffusion, solubility, and permeability coefficients of polylactide (PLA) films at different temperatures (5, 23, and 40°C) and water activities (0–0.9). The results were compared with the oxygen diffusion, solubility, and permeability coefficients obtained for poly(ethylene terephthalate) (PET) films under the same experimental conditions. The water sorption isotherm for PLA films was also determined. Diffusion coefficients were determined with the half‐sorption time method. Also, a consistency test for continuous‐flow permeability experimental data was run to obtain the diffusion coefficient with the lowest experimental error and to confirm that oxygen underwent Fickian diffusion in the PLA films. The permeability coefficients were obtained from steady‐state permeability experiments. The results indicated that the PLA films absorbed very low amounts of water, and no significant variation of the absorbed water with the temperature was found. The oxygen permeability coefficients obtained for PLA films (2–12 × 10^?18 kg m/m² s Pa) were higher than those obtained for PET films (1–6 × 10^?19 kg m/m² s Pa) at different temperatures and water activities. Moreover, the permeability coefficients for PLA and PET films did not change significantly with changes in the water activity at temperatures lower than 23°C. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 1790–1803, 2004     

The effectiveness of the combined use of VIX and Support Vector Machines on the prediction of S&P 500

The aim of this research is to analyse the effectiveness of the Chicago Board Options Exchange Market Volatility Index (VIX) when used with Support Vector Machines (SVMs) in order to forecast the weekly change in the S&P 500 index. The data provided cover the period between 3 January 2000 and 30 December 2011. A trading simulation is implemented so that statistical efficiency is complemented by measures of economic performance. The inputs retained are traditional technical trading rules commonly used in the analysis of equity markets such as Relative Strength Index, Moving Average Convergence Divergence, VIX and the daily return of the S&P 500. The SVM identifies the best situations in which to buy or sell in the market. The two outputs of the SVM are the movement of the market and the degree of set membership. The obtained results show that SVM using VIX produce better results than the Buy and Hold strategy or SVM without VIX. The influence of VIX in the trading system is particularly significant when bearish periods appear. Moreover, the SVM allows the reduction in the Maximum Drawdown and the annualised standard deviation.     

Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network

Algorithms for numeric data classification have been applied for text classification. Usually the vector space model is used to represent text collections. The characteristics of this representation such as sparsity and high dimensionality sometimes impair the quality of general-purpose classifiers. Networks can be used to represent text collections, avoiding the high sparsity and allowing to model relationships among different objects that compose a text collection. Such network- based representations can improve the quality of the classification results. One of the simplest ways to represent textual collections by a network is through a bipartite heterogeneous network, which is composed of objects that represent the documents connected to objects that represent the terms. Heterogeneous bipartite networks do not require computation of similarities or relations among the objects and can be used to model any type of text collection. Due to the advantages of representing text collections through bipartite heterogeneous networks, in this article we present a text classifier which builds a classification model using the structure of a bipartite heterogeneous network. Such an algorithm, referred to as IMBHN （Inductive Model Based on Bipartite Heterogeneous Network）, induces a classification model assigning weights to objects that represent the terms for each class of the text collection. An empirical evaluation using a large amount of text collections from different domains shows that the proposed IMBHN algorithm produces significantly better results than k-NN, C4.5, SVM, and Naive Bayes algorithms.