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134 results on '"Kursa M"'

13. More than a feeling: central amygdala mediates social transfer of information about proximity of danger

Author

Andraka, K., primary, Kondrakiewicz, K., additional, Rojek-Sito, K., additional, Ziegart-Sadowska, K., additional, Meyza, K., additional, Nikolaev, T., additional, Hamed, A., additional, Kursa, M., additional, Wójcik, M., additional, Danielewski, K., additional, Wiatrowska, M., additional, Kublik, E., additional, Bekisz, M., additional, Lebitko, T., additional, Duque, D., additional, Jaworski, T., additional, Madej, H., additional, Konopka, W., additional, Boguszewski, P. M., additional, and Knapska, E., additional

Published
2020
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18. Evaluation of methods for modeling transcription factor sequence specificity

Author

Weirauch, M., Cote, A., Norel, R., Annala, M., Zhao, Y., Riley, T., Saez-Rodriguez, J., Cokelaer, T., Vedenko, A., Talukder, S., Consortium, D., Agius, P., Arvey, A., Bucher, P., Callan, C., Chang, C., Chen, C., Chen, Y., Chu, Y., Grau, J., Grosse, I., Jagannathan, V., Keilwagen, J., Kielbasa, S., Kinney, J., Klein, H., Kursa, M., Lahdesmaki, H., Laurila, K., Lei, C., Leslie, C., Linhart, C., Murugan, A., Mysickova, A., Noble, W., Nykter, M., Orenstein, Y., Posch, S., Ruan, J., Rudnicki, W., Schmid, C., Shamir, R., Sung, W., Vingron, M., Zhang, Z., Bussemaker, H., Morris, Q., Bulyk, M., Stolovitzky, G., and Hughes, T.

Subjects
Biomedical Engineering, Protein Array Analysis, Bioengineering, Biology, Applied Microbiology and Biotechnology, Genome, DNA-binding protein, Article, 03 medical and health sciences, Mice, 0302 clinical medicine, Animals, Position-Specific Scoring Matrices, Degeneracy (biology), Binding site, Nucleotide Motifs, Transcription factor, 030304 developmental biology, Genetics, 0303 health sciences, Microarray analysis techniques, Computational Biology, DNA-Binding Proteins, Molecular Medicine, Functional genomics, 030217 neurology & neurosurgery, Algorithms, Biotechnology, Transcription Factors
Abstract

Genomic analyses often involve scanning for potential transcription factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein's DNA-binding specificity, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For nine TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro-derived motifs performed similarly to motifs derived from the in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices trained by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases (

Published
2013
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20. Prediction of human population responses to toxic compounds by a collaborative competition

Author

Eduati, F, Mangravite, L, Wang, T, Tang, Hongxiang, Bare, J, Huang, R, Norman, T, Kellen, M, Menden, M, Yang, J, Zhan, X, Zhong, R, Xiao, G, Xia, M, Abdo, N, Kosyk, O, Friend, S, Stolovitzky, G, Dearry, A, Tice, R, Simeonov, S, Rusyn, I, Wright, F, Xie, Y, Alaimo, S, Amadoz, A, Ammad-ud-din, M, Chloé-Azencott, A, Bacardit, J, Barron, P, Bernard, E, Beyer, A, Bin, S, van Bömmel, A, Borgwardt, K, Brys, A, Caffrey, B, Chang, J, Christodoulou, E, Clément-Ziza, M, Cohen, T, Cowherd, M, Demeyer, S, Dopazo, J, Elhard, J, Falcao, A, Ferro, A, Friedenberg, D, Giugno, R, Gong, Y, Gorospe, J, Granville, C, Grimm, D, Heinig, M, Hernansaiz, R, Hochreiter, S, Liang-Huang, C, Huska, M, Jiao, Y, Klambauer, G, Kuhn, M, Kursa, M, Kutum, R, Lazzarini, N, Lee, I, Leung, M, K W, Lim, Liu, C, F L, López, Mammana, A, Mayr, A, Michoel, T, Mongiovì, M, Moore, J, Narasimhan, R, Opiyo, S, Pandey, G, Peabody, A, Perner, J, Pulvirenti, A, Rawlik, K, Reinhardt, S, Riffle, C, Ruderfer, D, Sander, A, Savage, R, Scornet, E, Sebastian-Leon, P, Sharan, R, Simon-Gabriel, C, Stoven, V, Sun, J, Tang, H, Teixeira, A, Tenesa, A, Jean-Vert, P, Vingron, M, Walter, T, Whalen, S, Wiśniewska, Z, Wu, Y, Xu, H, Zhang, S, Zhao, J, W Zheng J, Ziwei, D, Simeonov, A, Raymond, R Tice, Saez-Rodriguez, J., NIEHS-NCATS-UNC DREAM Toxicogenetics Collaboration, Institut de Recherche pour le Développement (IRD), National Center for Advancing Translational Sciences (NCATS), National Institutes of Health [Bethesda] (NIH), Data Storage Institute - A*STAR, Capital Normal University [Beijing], Southwest University of China, sans affiliation, Shanghai Ocean University, Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University (PSL), Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris-Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de l'Information Géographique et Forestière [IGN] (IGN), Systèmes Productifs, Logistique, Organisation des Transports et Travail (IFSTTAR/AME/SPLOTT), Communauté Université Paris-Est-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), Biotechnology Center [Dresden] (BIOTEC), Technische Universität Dresden (TUD), Laboratoire National de Métrologie et d'Essais [Trappes] (LNE ), Centre for Biomedical Network Research on Rare Diseases (CIBERER), Department of Computational Genomics, Centro de Investigación Príncipe Felipe (CIPF), Functional Genomics Node (INB), CIPF, Computational Intelligence Research Group (CA3), Centre of Technology and Systems (CTS), Faculty of Sciences and Technology (FCT NOVA), Universidade Nova de Lisboa (NOVA)-Universidade Nova de Lisboa (NOVA)-Faculty of Sciences and Technology (FCT NOVA), Universidade Nova de Lisboa (NOVA)-Universidade Nova de Lisboa (NOVA)-Faculdade de Ciências e Tecnologia (FCT NOVA), Universidade Nova de Lisboa (NOVA), ACEEE, The Institute of Doctors Engineers and Scientists - IDES, Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Laboratory of Information, Network and Communication Sciences (LINCS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Mines-Télécom [Paris] (IMT), Institute of Computational Biology, Helmholtz-Zentrum München (HZM), Stony Brook University [SUNY] (SBU), State University of New York (SUNY), University of Pensylvania, Institute for Infocomm Research - I²R [Singapore], Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, Department of Mechanical and Aerospace Engineering [Davis], University of California [Davis] (UC Davis), University of California-University of California, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Ludwig-Maximilians-Universität München (LMU), Laboratoire Architecture, Ville, Urbanisme, Environnement (LAVUE), École nationale supérieure d'architecture de Paris-La Villette (ENSAPLV)-École nationale supérieure d'architecture de Paris Val-de-Seine (ENSA PVDS)-Université Paris 8 Vincennes-Saint-Denis (UP8)-Ministère de la Culture (MC)-Université Paris Nanterre (UPN)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Statistique Théorique et Appliquée (LSTA), Université Pierre et Marie Curie - Paris 6 (UPMC), Agricultural Technological Extensive Station of Luntai County in Xinjiang, Medstar Research Institute, Huawei Technologies [Shanghaï], School of Economics and Business Administration (School of Economics and Business Administration), Beijing Normal University, National Center for Mathematics and Interdisciplinary Sciences [Beijing] (NCMIS), Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences [Beijing] (CAS)-Chinese Academy of Sciences [Beijing] (CAS), Division of Biostatistics, Helmholtz-Institute for Biomedical Engineering [RWTH Aachen University], European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Agricultural Information Institute (AII), Chinese Academy of Agricultural Sciences (CAAS), Department of Computer Science - Lafayette College, Lafayette College [Easton], MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Communauté Université Paris-Est, Technische Universität Dresden = Dresden University of Technology (TU Dresden), Faculdade de Ciências e Tecnologia = School of Science & Technology (FCT NOVA), Universidade Nova de Lisboa = NOVA University Lisbon (NOVA)-Universidade Nova de Lisboa = NOVA University Lisbon (NOVA)-Faculdade de Ciências e Tecnologia = School of Science & Technology (FCT NOVA), Universidade Nova de Lisboa = NOVA University Lisbon (NOVA)-Universidade Nova de Lisboa = NOVA University Lisbon (NOVA), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Paris 8 Vincennes-Saint-Denis (UP8)-École nationale supérieure d'architecture de Paris-La Villette (ENSAPLV)-Université Paris Nanterre (UPN)-École nationale supérieure d'architecture de Paris Val-de-Seine (ENSA PVDS)-Centre National de la Recherche Scientifique (CNRS)-Ministère de la Culture (MC), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Beijing Normal University (BNU), Sans affiliation, Mines Paris - PSL (École nationale supérieure des mines de Paris), Helmholtz Zentrum München = German Research Center for Environmental Health, Université Fédérale Toulouse Midi-Pyrénées-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Department of Mechanical and Aerospace Engineering [Univ California Davis] (MAE - UC Davis), University of California (UC)-University of California (UC), Université Paris 8 Vincennes-Saint-Denis (UP8)-École nationale supérieure d'architecture de Paris-La Villette (ENSAPLV), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Paris Nanterre (UPN)-École nationale supérieure d'architecture de Paris Val-de-Seine (ENSA PVDS)-Centre National de la Recherche Scientifique (CNRS)-Ministère de la Culture (MC), Centre National de la Recherche Scientifique (CNRS)-Université Paris Nanterre (UPN)-Ministère de la Culture (MC)-Université Paris 8 Vincennes-Saint-Denis (UP8)-École nationale supérieure d'architecture de Paris Val-de-Seine (ENSA PVDS)-École nationale supérieure d'architecture de Paris-La Villette (ENSAPLV), Institut Mines-Télécom [Paris] (IMT)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Pierre et Marie Curie - Paris 6 (UPMC), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT), and MINES ParisTech - École nationale supérieure des mines de Paris-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Bioinformatics, MESH: Risk Assessment, Applied Microbiology and Biotechnology, MESH: Dose-Response Relationship, Drug, Genotype, MESH: Incidence, MESH: Models, Genetic, Cytotoxicity, ComputingMilieux_MISCELLANEOUS, media_common, education.field_of_study, High-throughput screening, MESH: Genetic Predisposition to Disease, drug, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Trait, Molecular Medicine, Health occupations, Predictive medicine, Risk factors, Risk assessment, Biotechnology, data mining, expression, MESH: High-Throughput Screening Assays, media_common.quotation_subject, Population, Biomedical Engineering, MESH: Genetics, Population, Bioengineering, Computational biology, Biology, Competition (biology), Article, MESH: Computer Simulation, ddc:570, 1000 Genomes Project, education, MESH: Toxicity Tests, ta113, MESH: Humans, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], MESH: Hazardous Substances, MESH: Lymphocytes, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], RA
Abstract

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal., Nature Biotechnology, 33 (9), ISSN:1546-1696, ISSN:1087-0156

Published
2015

22. Critical assessment of automated flow cytometry data analysis techniques

Author

Aghaeepour, N, Finak, G, Flowcap, Consortium, Dougall, D, Khodabakhshi, Ah, Mah, P, Obermoser, G, Spidlen, J, Taylor, I, Wuensch, Sa, Bramson, J, Eaves, C, Weng, Ap, Iii, Es, Ho, K, Kollmann, T, Rogers, W, De Rosa, S, Dalal, B, Azad, A, Pothen, A, Brandes, A, Bretschneider, H, Bruggner, R, Finck, R, Jia, R, Zimmerman, N, Linderman, M, Dill, D, Nolan, G, Chan, C, Khettabi, Fe, O'Neill, K, Chikina, M, Ge, Y, Sealfon, S, Sugár, I, Gupta, A, Shooshtari, P, Zare, H, De Jager PL, Jiang, M, Keilwagen, J, Maisog, Jm, Luta, G, Barbo, Aa, Májek, P, Vilček, J, Manninen, T, Huttunen, H, Ruusuvuori, P, Nykter, M, Mclachlan, Gj, Wang, K, Naim, I, Sharma, G, Nikolic, R, Pyne, S, Qian, Y, Qiu, P, Quinn, J, Roth, A, Dream, Consortium, Meyer, P, Stolovitzky, G, Saez Rodriguez, J, Norel, R, Bhattacharjee, M, Biehl, M, Bucher, P, Bunte, K, DI CAMILLO, Barbara, Sambo, Francesco, Sanavia, Tiziana, Trifoglio, Emanuele, Toffolo, GIANNA MARIA, Dimitrieva, S, Dreos, R, Ambrosini, G, Grau, J, Grosse, I, Posch, S, Guex, N, Kursa, M, Rudnicki, W, Liu, B, Maienschein Cline, M, Schneider, P, Seifert, M, Strickert, M, Vilar, Jm, Hoos, H, Mosmann, Tr, Brinkman, R, Gottardo, R, Scheuermann, Rh, Dialogue for Reverse Engineering Assessment and Methods (DREAM) Consortium, Guex, Nicolas, and Abrosini, Giovanna

Subjects
Computer science, Population, Immunology, Graft vs Host Disease, Image processing, Gating, computer.software_genre, Bioinformatics, Biochemistry, Sensitivity and Specificity, Biotechnology, Cell Biology, Molecular Biology, Article, Flow cytometry, 03 medical and health sciences, 0302 clinical medicine, Software, Computational biology and bioinformatics, Immunology, Cancer, Flow cytometry, medicine, Image Processing, Computer-Assisted, Animals, Cluster Analysis, Humans, education, 030304 developmental biology, Cancer, 0303 health sciences, Data processing, education.field_of_study, medicine.diagnostic_test, business.industry, Computational Biology, Reproducibility of Results, Computational biology and bioinformatics, Identification (information), Data Interpretation, Statistical, Data analysis, Leukocytes, Mononuclear, Data mining, Lymphoma, Large B-Cell, Diffuse, business, computer, Algorithms, West Nile Fever, 030215 immunology
Abstract

Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.

Published
2013

24. Mechanical and microstructural behavior in friction stir welds of copper and brass

Author

Kursa, M, Kliber, J, Sarvghad Moghaddam, Madjid, Kursa, M, Kliber, J, and Sarvghad Moghaddam, Madjid

Abstract

The effect of friction stir welding on the microstructure and mechanical properties of copper and Cu-30Zn brass alloy was investigated by choosing fixed rotational speed of 950 rpm with welding travel speeds of 190 and 235 mm/min for both the copper and brass alloys. Microstructural investigations showed very fine grains with some deformed grains in the stirred zone and some coarser grains in the thermo-mechanically affected zone and base metal; whereas, no obvious HAZ was detected in welds. Results show that the grain size profile, hardness values and mechanical strength were not highly affected by the welding travel speed. In fact, the mechanical properties of both copper and brass should be determined as a result of mutual interaction between involving mechanisms. Depending on welding parameters, either they mitigate each other or some of them overcome the others and form a certain hardness or strength behavior in welds.

Published
2014

31. STRUCTURAL CHARACTERISTICS OF Ni3 Al BASED ALLOYS DEPENDING ON THE PREPARATION CONDITIONS.

Author

MALCHARCZIKOVÁ, J. and KURSA, M.

Subjects
*NICKEL alloys, *METAL microstructure, *SOLIDIFICATION, *MECHANICAL properties of metals, *MULTIPHASE flow, *STOICHIOMETRY
Abstract

The paper presents an evaluation of the influence of the composition of alloys based on Ni3Al on their mechanical characteristics. The structure of the alloy was controlled through directional solidification. The achieved values of mechanical characteristics are in good agreement with the material structure. The alloys with sub-stoichiometric contents of aluminium have a multiphase structure. These alloys contain network with high values of tensile strain. The microstructure of the samples was investigated and behaviour of dislocations in the alloys was analysed by Transmission electron microscopy methods (TEM). [ABSTRACT FROM AUTHOR]

Published
2015

37. FEA of Plastic Flow in AZ63 Alloy during ECAP Process.

Author

Kocicha, R., Kursa, M., and Macháčková, A.

Subjects
*MAGNESIUM alloys, *DEFORMATIONS (Mechanics), *ALLOYS, *ELASTIC solids, *MECHANICS (Physics)
Abstract

Magnesium alloys based on Mg-Al-Zn system are very attractive materials in many industries due to their specific properties. One of common forming manners suitable for this kind of material is the application of severe plastic deformation method. This paper is focused on the deformation behavior of AZ63 alloy at equal channel angular pressing process in the temperature range of 250-320 °. C. For this purpose finite element analysis was used. Main attention was devoted to the influence of processing parameters on the material flow. Among other, changes of the flow during equal channel angular pressing in chosen regions of the pressed samples were monitored. As variables, temperature, friction coefficient, strain rate, φ-angle or back pressure application were chosen. The comparison of the material flow lines at different conditions shows the affecting of material flow. There are obvious considerable differences between regions adjacent to the inner radius and those adjacent to the outer radius. For example in the case μ = 0 the dead zone is delimited by the angle ψ= 36 °. , while in the case of friction given by the coefficient μ = 0.04, the value of the angle was ψ= 26.5 °. [ABSTRACT FROM AUTHOR]

Published
2012

39. Influence of crystallization process on Ni3Al properties.

Author

Kursa, M., Drápala, J., Tyleček, I., and Hyspecká, L.

Abstract

The paper relates to the preceding works dealing with structural characteristics of Ni3Al and phases that are generated at crystallization of Al-Ni alloys in application of various melting technologies and thermal processing. Attention was paid to the process of crystallization and cooling at melting and casting, comprehensive chemical analysis in the context of fracture surfaces analyses, grain boundaries and phases with stress to determination of behaviour and occurrence of possible admixtures in Ni3Al. Such mechanical properties as tensile strength and high-temperature creep were investigated as well. [ABSTRACT FROM AUTHOR]

Published
1997
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40. Novel Shielded Transferrin−Polyethylene Glycol−Polyethylenimine/DNA Complexes for Systemic Tumor-Targeted Gene Transfer

Author

Kursa, M., Walker, G. F., Roessler, V., Ogris, M., Roedl, W., Kircheis, R., and Wagner, E.

Abstract

Tumor-targeting DNA complexes which can readily be generated by the mixing of stable components and freeze−thawed would be very advantageous for their subsequent application as medical products. Complexes were generated by the mixing of plasmid DNA, linear polyethylenimine (PEI22, 22 kDa) as the main DNA condensing agent, PEG−PEI (poly(ethylene glycol)-conjugated PEI) for surface shielding, and Tf−PEG−PEI (transferrin−PEG−PEI) to provide a ligand for receptor-mediated cell uptake. Within the shielding conjugates, PEG chains of varying size (5, 20, or 40 kDa) were conjugated with either linear PEI22 (22 kDa) or branched PEI25 (25 kDa). The three polymer components were mixed together at various ratios with DNA; particle size, surface charge, in vitro transfection activity, and systemic gene delivery to tumors was investigated. In general, increasing the proportion of shielding conjugate in the complex reduced surface charge, particle size, and in vitro transfection efficiency in transferrin receptor-rich K562 cells. The particle size or surface charge of the complexes containing the PEG−PEI conjugate did not significantly change after freeze−thawing, while complexes without the shielding conjugate aggregated. Complexes containing PEG−PEI conjugate efficiently transfected K562 cells after freeze−thawing. Furthermore the systemic application of freeze−thawed complexes exhibited in vivo tumor targeted expression. For complexes containing the luciferase reporter gene the highest expression was found in tumor tissue of mice. An optimum formulation for in vivo application, PEI22/Tf−PEG−PEI/PEI22−PEG5, containing plasmid DNA encoding for the tumor necrosis factor (TNF-α), inhibited tumor growth in three different murine tumor models. These new DNA complexes offer simplicity and convenience, with tumor targeting activity in vivo after freeze−thawing.

Published
2003

41. Different Strategies for Formation of PEGylated EGF-Conjugated PEI/DNA Complexes for Targeted Gene Delivery

Author

Blessing, T., Kursa, M., Holzhauser, R., Kircheis, R., and Wagner, E.

Abstract

With the aim of generating gene delivery systems for tumor targeting, we have synthesized a conjugate consisting of polyethylenimine (PEI) covalently modified with epidermal growth factor (EGF) peptides. Transfection efficiency of the conjugate was evaluated and compared to native PEI in three tumor cell lines:  KB epidermoid carcinoma cells, CMT-93 rectum carcinoma cells, and Renca-EGFR renal carcinoma cells. Depending on the tumor cell line, incorporation of EGF resulted in an up to 300-fold increased transfection efficiency. This ligand-mediated enhancement and competition with free EGF strongly suggested uptake of the complexes through the EGF receptor-mediated endocytosis pathway. Shielded particles being crucial for systemic gene delivery, we studied the effect of covalent surface modification of EGF−PEI/DNA complexes with a poly(ethylene glycol) (PEG) derivative. An alternative way for the formation of PEGylated EGF-containing complexes was also evaluated where EGF was projected away from PEI/DNA core complexes through a PEG linker. Both strategies led to shielded particles still able to efficiently transfect tumor cells in a receptor-dependent fashion. These PEGylated EGF-containing complexes were 10- to 100-fold more efficient than PEGylated complexes without EGF.

Published
2001

42. Mannose polyethylenimine conjugates for targeted DNA delivery into dendritic cells.

Author

Diebold, S S, Kursa, M, Wagner, E, Cotten, M, and Zenke, M

Abstract

Cell surface-bound receptors represent suitable entry sites for gene delivery into cells by receptor-mediated endocytosis. Here we have taken advantage of the mannose receptor that is highly expressed on antigen-presenting dendritic cells for targeted gene transfer by employing mannosylpolyethylenimine (ManPEI) conjugates. Several ManPEI conjugates were synthesized and used for formation of ManPEI/DNA transfection complexes. Conjugates differed in the linker between mannose and polyethylenimine (PEI) and in the size of the PEI moiety. We demonstrate that ManPEI transfection is effective in delivering DNA into mannose receptor-expressing cells. Uptake of ManPEI/DNA complexes is receptor-specific, since DNA delivery can be competed with mannosylated albumin. Additionally, incorporation of adenovirus particles into transfection complexes effectively enhances transgene expression. This is particularly important for primary immunocompetent dendritic cells. It is demonstrated here that dendritic cells transfected with ManPEI/DNA complexes containing adenovirus particles are effective in activating T cells of T cell receptor transgenic mice in an antigen-specific fashion.

Published
1999

44. Leaching refuse after sphalerite mineral for extraction zinc and cobalt

Author

Silvie Brožová, Drápala, J., Kursa, M., Pustějovská, P., and Jursová, S.

Subjects
lcsh:TN1-997, sphalerite minerals, zinc, hydrometallurgical processes, leaching conditions, cobalt, lcsh:Mining engineering. Metallurgy
Abstract

The paper deals with a possibility of zinc and cobalt extraction from refuse after sphalerite mineral leaching. It contains theoretic analysis of hydrometallurgical processes. Practical part describes samples and their leaching in 10 % and 20 % sulphuric acid. In the end of the paper it is evaluated under which conditions the highest yield of zinc and cobalt from refuse after sulphide ore leaching is reached. Web of Science 55 3 499 497

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