7 research outputs found
Ensayo aleatorizado del cierre de orejuela izquierda vs varfarina para la prevenciĆ³n de accidentes cerebrovasculares tromboembĆ³licos en pacientes con fibrilaciĆ³n auricular no relacionada con valvulopatĆa. Estudio PREVAIL
The successful application of polyĀ(<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>ā²-methylenebisĀ(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>, 3096ā3108] 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) āsingle reactant ā single productā activation. (B) AND gate with two reacting species. (C) OR gate with two signaling species., (D) āsingle reactant ā single productā inhibition. In all instances, function <i>f(x)</i> refers to the normalized hill function, with <i>pā=ā0.5, aā=ā1.0</i> and <i>nā=ā4</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 (TGFĪ±, TNFĪ±, 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<sub>i</sub><sup>k</sup></i>) (caused by <i>a</i> parameters being close to 0). The opacity of each edge corresponds to the activity (<i>z<sub>i</sub><sup>k</sup></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