Ensayo aleatorizado del cierre de orejuela izquierda vs varfarina para la prevención de accidentes cerebrovasculares tromboembólicos en pacientes con fibrilación auricular no relacionada con valvulopatía. Estudio PREVAIL

The successful application of poly­(<i>N</i>-vinylcaprolactam)-based microgels requires a profound understanding of their synthesis. For this purpose, a validated process model for the microgels synthesis by precipitation copolymerization with the cross-linker <i>N</i>,<i>N</i>′-methylenebis­(acrylamide) is formulated. Unknown reaction rate constants, reaction enthalpies, and partition coefficients are obtained by quantum mechanical calculations. The remaining parameter values are estimated from reaction calorimetry and Raman spectroscopy measurements of experiments with different monomer/cross-linker compositions. Because of high cross-propagation reaction rate constants, simulations predict a fast incorporation of the cross-linker. This agrees with reaction calorimetry measurements. Furthermore, the gel phase is predicted as the major reaction locus. The model is utilized for a prediction of the internal particle structure regarding its cross-link distribution. The highly cross-linked core reported in the literature corresponds to the predictions of the model

Optimization of Multiproduct Biorefinery Processes under Consideration of Biomass Supply Chain Management and Market Developments

Even though a shift from conventional to renewable resources is often envisaged, lignocellulosic biorefinery concepts struggle with economic viability and sustainability. In order to overcome these barriers, a full analysis from biomass supply chain, process performance optimization, and product-portfolio selection is targeted. Addressing the economic viability and sustainability already at an early process development stage when only limited knowledge is available, Process Network Flux Analysis (PNFA) [Ulonska et al., <i>AIChE J.</i> <b>2017</b>, <i>62</i>, 3096–3108] is capable of systematically identifying the most valuable processing pathways. This enables a first performance ranking based on the profit or global warming potential of pathways, thereby accelerating process development. While so far only processing networks have been considered, the methodology is herein extended to consider biomass supply chain optimization and market-dependent price developments such that all main influencing factors are considered simultaneously. The extended methodology is validated identifying reasonable plant locations in North Rhine-Westphalia, Germany. Enhancing economic viability of the best performing biofuel ethanol, a multiproduct biorefinery is targeted coproducing value-added chemicals. Herein, a coproduction of iso-butanol raises the profit significantly: a mass ratio of at most 1.9 (ethanol:iso-butanol) is required to break even

Reducing the Fouling Potential in a Continuous Polymerization Millireactor via Geometry Modification

Continuous milli-scale reactors with internal mixing elements are increasingly used in the chemical industry and considered for polymerization processes. Their small scale makes them especially susceptible to fouling. Addressing this problem, this article presents a computational fluid dynamics (CFD) analysis of a Miprowa millireactor channel. The flow field is examined with the finite-element based software package COMSOL Multiphysics to identify areas of low velocity, generally prone to fouling during polymerization reactions. For simplicity, the flow is assumed incompressible and isothermal, using the material properties of water. Three geometry modifications to the mixing elements are proposed. Numerical simulations of the flow field around the proposed mixing elements show a reduction in areas with stagnating flow by 20%, reducing the fouling potential without substantially affecting other metrics such as the pressure loss

Mapping of the optimized model to the PKN.

<p>Mapping of the optimization results to the PKN by removing the compartmentalized components. Reactions within the same compartment are plotted in blue and were not included in the optimization procedure. Reactions in black are the ones whose parameters were interrogated. Their opacity corresponds to their activity in the optimized model, with reactions that propagate more signal downstream being more opaque than the rest.</p

Connectivity modules of signaling pathways in the proposed constrained fuzzy logic formulation.

<p>The transfer functions supported by the proposed constrained fuzzy logic (cFL) formulation are illustrated. (A) “single reactant – single product” activation. (B) AND gate with two reacting species. (C) OR gate with two signaling species., (D) “single reactant – single product” inhibition. In all instances, function <i>f(x)</i> refers to the normalized hill function, with <i>p = 0.5, a = 1.0</i> and <i>n = 4</i>.</p

Optimization of a large-scale signal transduction pathway.

<p>(A) optimized pathway upon compartmentalization based on the equivalent classes concept (right panel). The proposed compartmentalization scheme groups together nodes that share identical in-silico responses under all experimental conditions, thus decreasing the parameters space. (B) Signaling dataset, consisting of 15 cytokines in combinations of two, and 3 inhibitors (including the no-inhibitor treatment), total of 120 experimental treatments (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050085#pone.0050085-Melas2" target="_blank">[48]</a>). The red background color corresponds to the measurement prediction mismatch of the solution.To generate model predictions the optimized values of all model parameters were used (i.e., parameter values obtained from the optimization procedure)</p

Optimization of a toy model to signaling data.

<p>(A) Generic pathway is represented as a signed directed graph, also refers as PKN. Green nodes refer to different cytokines (ligands) where the signaling process initiates; Red nodes refer to inhibitors present in the in-silico dataset; Grey nodes refer to measured proteins; White nodes refer to latent species, i.e. proteins whose activation state is not measured. (B) In-silico signaling data under combinatorial treatment with stimuli (TGFα, TNFα, no-treatment) and inhibitors (mek12i, pi3ki, no-inhibitor). Each subplot shows the average activation level within 30 minutes upon stimulation <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050085#pone.0050085-Alexopoulos1" target="_blank">[37]</a>. Red background refers to model-prediction mismatches (C) Optimized pathway, grey arrows refer to reactions with limited activity (<i>z_i^k</i>) (caused by <i>a</i> parameters being close to 0). The opacity of each edge corresponds to the activity (<i>z_i^k</i>) of the corresponding reaction. (D) In silico signaling dataset and fitness error after the optimization procedure. Decrease in the red background color shows the optimized model is in accordance to the signaling dataset. (E) Optimized transfer functions presented in C.</p