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In this study, we investigated galacturonic (GalAc)- and mannuronic (ManAc) acids as novel targeting ligands for receptor-mediated gene delivery. GalAc and ManAc were coupled to either polyethyleneimine (PEI) or PEIpolyethyleneglycol (PEG). Furthermore, lactobionic acid (LacAc), which comprises a GalAc-related carbohydrate ring, was coupled to each of the polymers through its open-chain gluconic acid moiety. The molar mass distributions of the polymers were characterized by analytical ultracentrifugation and size exclusion chromatography. PEI-conjugatepDNA complexes were transfected into HepG2-, HeLa-, and 16HBE14o--cells. Gene expression mediated by GalAc- and LacAc-functionalized PEI-conjugates was lower than for PEI. In contrast, gene expression mediated by ManAc-functionalized PEI-conjugates was up to three orders of magnitude higher than for the other tested PEI-conjugates, in particular for negatively charged gene vectors at low N/P ratios, independent of the cell line. Pre-incubation of cells with an excess of ManAc before transfection significantly inhibited transfection rates only for ManAc-functionalized PEI-conjugates. Coupling of methyl-α-D-mannuronic acid to PEI resulted in significantly lower transfection rates than for ManAc-PEI based complexes. Together with fluorescence microscopy images of fluorescein-labelled ManAc-functionalized dextrans and FACS analyses of cells, these results demonstrate that receptor-mediated endocytosis of ManAcPEI-conjugatepDNA complexes via ManAc-specific receptors was involved in gene transfer. In conclusion, ManAc-modification of PEI-polymers represents a novel strategy for receptor-mediated gene delivery which could be promising for in vivo application.
Biofilms cause complications and high costs in both industry and medicine. Of particular interest are bacterial infections of prosthetic materials, which usually cannot be eliminated due to the high antibiotic resistance known for bacteria forming biofilms. The search for new materials and coatings with lower colonization potential and antibacterial activity is of great importance to reduce biofilm formation. However, there is no standardized procedure to examine the colonization characteristics of bacteria in the Biofilm state in situ. Here, we describe an automated epifluorescence microscopy system for the semi-quantitative analysis of three-dimensional (3D) biofilms on various surfaces. To analyze adherent bacteria, three materials (glass, steel and titanium) were incubated with bacteria in a flow chamber system. After fluorescence staining of the bacteria, automated image capturing, quantification of the bacteria, measurement of the colonized area and determination of the 3D biofilm height were carried out by using novel software.
Furthermore, the materials were examined for their surface topography using white light scanning interferometry. Titanium compared to glass showed a significantly higher number of adherent bacteria.
We argue that this was due to the higher microroughness of titanium. The colonized area was in accordance with the number of adherent bacteria and was also significantly larger on titanium coupons compared to glass. Maximum 3D biofilm height on glass coupons was significantly lower compared to the ones on steel and titanium. This novel method enables the standardized, automated investigation of the colonization with bacteria on different materials. This approach can considerably support the characterization of new material surfaces and their innovative coatings by analyzing the amount of attached Bacteria and thickness of biofilms in situ and eliminates the need of conventional cultivation.
The ability of industrial X-ray computed tomography (CT) to scan an object with several internal and external features at once causes increasing adoption in dimensional metrology. In order to evaluate the quality of a measurement value, the task-specific measurement uncertainty has to be determined. Currently, VDI/VDE 2630 part 2.1 gives a guideline to determine the uncertainty of CT measurements experimentally by conducting repeated measurements. This is costly and time-consuming. Thus, the aim is to determine the task-specific measurement uncertainty numerically by simulations (e. g. according to the guide to expression of uncertainty in measurement (GUM) Supplement 1). To achieve that, a digital twin is necessary. This contribution presents a simple first approach how a digital twin can be built. In order to evaluate this approach, a study comparing measurements and simulations of different real CT systems was carried out by determining the differences between the measurement results of the digital twin and of the measurement results of the real-world CT systems. The results have shown a moderate agreement between real and simulated data. To improve on this aspect, a standardized method to characterize CT systems and methods to implement CT parameters into the simulation with sufficient accuracy will be developed.
The objective of this paper is to demonstrate a new simulation technique which allows fast and automatic generation of temperature fields as input for subsequent thermomechanical welding simulation. The basic idea is to decompose the process model into an empirical part based on neural networks and a phenomenological part that describes the physical phenomena. The strength of this composite modelling approach is the automatic calibration of mathematical models against experimental data without the need for manual interference by an experienced user. As an example for typical applications in laser beam and GMA-laser hybrid welding, it is shown that even 3D heat conduction models of a low complexity can approximate measured temperature fields with a sufficient accuracy. In general, any derivation of model fitting parameters from the real process adds uncertainties to the simulation independent of the complexity of the underlying phenomenological model. The modelling technique presented hybridises empirical and phenomenological models. It reduces the model uncertainties by exploiting additional information which keeps normally hidden in the data measured when the model calibration is performed against few experimental data sets. In contrast, here the optimal model parameter set corresponding to a given process parameter is computed by means of an empirical submodel based on relatively large set of experimental data. The approach allows making a contribution to an efficient compensation of modelling inaccuracies and lack of knowledge about thermophysical material properties or boundary conditions. Two illustrating examples are provided.
Fast temperature field generation for welding simulation and reduction of experimental effort
(2011)
The quality of welding processes is governed by the occurring induced distortions yielding an increase in production costs due to necessary reworking. Especially for more complex specimens, it is difficult to evaluate the optimal configuration of welding sequences in order to minimize the distortion. Even experienced welding operators can solve this task only by trial and error which is time and cost consuming. In modern engineering the application of welding simulation is already known to be able to analyse the heat effects of welding virtually. However, the welding process is governed by complex physical interactions. Thus, recent weld thermal models are based on many simplifications. The state of the art is to apply numerical methods in order to solve the transient heat conduction equation. Therefore, it is not possible to use the real process parameters as input for the mathematical model. The model parameters which allow calculating a temperature field that is in best agreement with the experiments cannot be defined directly but inversely by multiple simulations runs. In case of numerical simulation software based on finite discretization schemes this approach is very time consuming and requires expert users. The weld thermal model contains an initial weakness which has to be adapted by finding an optimal set of model parameters. This process of calibration is often done against few experiments. The range of model validity is limited. An extension can be obtained by performing a calibration against multiple experiments. The focus of the paper is to show a combined modelling technique which provides an efficient solution of the inverse heat conduction problem mentioned above. On the one hand the inverse problem is solved by application of fast weld thermal models which are closed form solutions of the heat conduction equation. In addition, a global optimization algorithm allows an automated calibration of the weld thermal model. This technique is able to provide a temperature field automatically that fits the experimental one with high accuracy within minutes on ordinary office computers. This fast paradigm permits confirming the application of welding simulation in an industrial environment as automotive industry. On the other hand, the initial model weakness is compensated by calibrating the model against multiple experiments. The unknown relationship between model and process parameters is approximated by a neural network. The validity of the model is increased successively and enables to decrease experimental effort, For a test case, it is shown that this approach yields accurate temperature fields within very short amount of time for unknown process parameters as input data to the model contributing to the requirement to construct a substitute system of the real welding process.
The objective of this paper is to demonstrate a new simulation technique which allows the fast and automatic generation to temperature fields based on a combination of empirical and phenomenological modelling techniques. The automatic calibration of the phenomenological model is performed by a multi-variable global optimisation routine which yields the optimal fit between simulated and experimental weld charcteristics without the need for initial model parameters. For exemplary welding processes it is shown that linear 3D heat conduction models can approximate measured temperature fields with a high accuracy. The modelling approach presented comprises the automatic calibration against multiple experiments which permits simulating the temperature field for unknown process parameters. The validation of this composite simulation model is performed for exemplary welding processes and includes the prediction of the fusion line in the cross section and the corresponding thermal cycles.
Fast temperature field generation for welding simulation and reduction of experimental effort
(2009)
The quality of welding processes is governed by the occurring induced distortions yielding an
increase in production costs due to necessary reworking. Especially for more complex specimens
it is difficult to evaluate the optimal configuration of welding sequences in order to
minimise the distortion. Even experienced welding operators can solve this task only by trial
and error which is time and cost consuming.
In modern engineering the application of welding simulation is already known to be able to
analyse the heat effects of welding virtually. However, the welding process is governed by
complex physical interactions. Thus, recent weld thermal models are based on many simplifications.
The state of the art is to apply numerical methods in order to solve the transient heat
conduction equation. Therefore, it is not possible to use the real process parameters as input
for the mathematical model. The model parameters which allow calculating a temperature
field that is in best agreement with the experiments cannot be defined directly but inversely by
multiple simulations runs. In case of numerical simulation software based on finite discretisation
schemes this approach is very time consuming and requires expert users. The weld
thermal model contains an initial weakness which has to be adapted by finding an optimal set
of model parameters. This process of calibration is often done against few experiments. The
range of model validity is limited. An extension can be obtained by performing a calibration
against multiple experiments.
The focus of the paper is to show a combined modelling technique which provides an efficient
solution of the inverse heat conduction problem mentioned above. On the one hand the inverse
problem is solved by application of fast weld thermal models which are closed form
solutions of the heat conduction equation. In addition, a global optimisation algorithm allows
an automated calibration of the weld thermal model. This technique is able to provide a temperature
field automatically that fits the experimental one with high accuracy within minutes
on ordinary office computers. This fast paradigm permits confirming the application of welding
simulation in an industrial environment as automotive industry.
On the other hand, the initial model weakness is compensated by calibrating the model
against multiple experiments. The unknown relationship between model and process parameters
is approximated by a neural network. The validity of the model is increased successively
and enables to decrease experimental effort. For a test case it is shown, that this approach
yields accurate temperature fields within very short amount of time for unknown process parameters
as input data to the model contributing to the requirement to construct a substitute
system of the real welding process.