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Institution

University of Cologne

EducationCologne, Germany
About: University of Cologne is a education organization based out in Cologne, Germany. It is known for research contribution in the topics: Population & Transplantation. The organization has 32050 authors who have published 66350 publications receiving 2210092 citations. The organization is also known as: Universität zu Köln & Universitatis Coloniensis.


Papers
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Journal ArticleDOI
05 Dec 2008-Science
TL;DR: This work was able to directly measure the compressibility of the quantum gas in the trap using in situ imaging and independent control of external confinement and lattice depth, to demonstrate the potential to model interacting condensed-matter systems using ultracold fermionic atoms.
Abstract: The fermionic Hubbard model plays a fundamental role in the description of strongly correlated materials. We have realized this Hamiltonian in a repulsively interacting spin mixture of ultracold 40 K atoms in a three-dimensional (3D) optical lattice. Using in situ imaging and independent control of external confinement and lattice depth, we were able to directly measure the compressibility of the quantum gas in the trap. Together with a comparison to ab initio dynamical mean field theory calculations, we show how the system evolves for increasing confinement from a compressible dilute metal over a strongly interacting Fermi liquid into a band-insulating state. For strong interactions, we find evidence for an emergent incompressible Mott insulating phase. This demonstrates the potential to model interacting condensed-matter systems using ultracold fermionic atoms.

569 citations

Journal ArticleDOI
TL;DR: Heterogeneous pretreatment patient cohorts have been defined by the International Neuroblastoma Risk Group classification system and led to advances in understanding of neuroblastoma biology, refinements in risk classification, and stratified treatment strategies, resulting in improved outcome.
Abstract: Risk-based treatment approaches for neuroblastoma have been ongoing for decades. However, the criteria used to define risk in various institutional and cooperative groups were disparate, limiting the ability to compare clinical trial results. To mitigate this problem and enhance collaborative research, homogenous pretreatment patient cohorts have been defined by the International Neuroblastoma Risk Group classification system. During the past 30 years, increasingly intensive, multimodality approaches have been developed to treat patients who are classified as high risk, whereas patients with low- or intermediate-risk neuroblastoma have received reduced therapy. This treatment approach has resulted in improved outcome, although survival for high-risk patients remains poor, emphasizing the need for more effective treatments. Increased knowledge regarding the biology and genetic basis of neuroblastoma has led to the discovery of druggable targets and promising, new therapeutic approaches. Collaborative efforts of institutions and international cooperative groups have led to advances in our understanding of neuroblastoma biology, refinements in risk classification, and stratified treatment strategies, resulting in improved outcome. International collaboration will be even more critical when evaluating therapies designed to treat small cohorts of patients with rare actionable mutations.

568 citations

Journal ArticleDOI
TL;DR: Data indicate that BCR signal strength, rather than antigen specificity, determines mature B cell fate, and spontaneous germinal centers developed in gut-associated lymphoid tissue of LMP2A mice, indicating that microbial antigens can promote germineal centers independently of BCR-mediated antigen recognition.
Abstract: B cell receptor (BCR)-mediated antigen recognition is thought to regulate B cell differentiation. BCR signal strength may also influence B cell fate decisions. Here, we used the Epstein-Barr virus protein LMP2A as a constitutively active BCR surrogate to study the contribution of BCR signal strength in B cell differentiation. Mice carrying a targeted replacement of Igh by LMP2A leading to high or low expression of the LMP2A protein developed B-1 or follicular and marginal zone B cells, respectively. These data indicate that BCR signal strength, rather than antigen specificity, determines mature B cell fate. Furthermore, spontaneous germinal centers developed in gut-associated lymphoid tissue of LMP2A mice, indicating that microbial antigens can promote germinal centers independently of BCR-mediated antigen recognition.

567 citations

Journal ArticleDOI
06 Feb 2020-Nature
TL;DR: Whole-genome sequencing data for 2,778 cancer samples from 2,658 unique donors is used to reconstruct the evolutionary history of cancer, revealing that driver mutations can precede diagnosis by several years to decades.
Abstract: Cancer develops through a process of somatic evolution1,2. Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes3. Here, by whole-genome sequencing analysis of 2,658 cancers as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)4, we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection.

565 citations

Journal ArticleDOI
TL;DR: CUPSAT (Cologne University Protein Stability Analysis Tool) is a web tool to analyse and predict protein stability changes upon point mutations (single amino acid mutations) that gives >80% prediction accuracy for most of these validation tests.
Abstract: CUPSAT (Cologne University Protein Stability Analysis Tool) is a web tool to analyse and predict protein stability changes upon point mutations (single amino acid mutations). This program uses structural environment specific atom potentials and torsion angle potentials to predict ΔΔG, the difference in free energy of unfolding between wild-type and mutant proteins. It requires the protein structure in Protein Data Bank format and the location of the residue to be mutated. The output consists information about mutation site, its structural features (solvent accessibility, secondary structure and torsion angles), and comprehensive information about changes in protein stability for 19 possible substitutions of a specific amino acid mutation. Additionally, it also analyses the ability of the mutated amino acids to adapt the observed torsion angles. Results were tested on 1538 mutations from thermal denaturation and 1603 mutations from chemical denaturation experiments. Several validation tests (split-sample, jack-knife and k-fold) were carried out to ensure the reliability, accuracy and transferability of the prediction method that gives >80% prediction accuracy for most of these validation tests. Thus, the program serves as a valuable tool for the analysis of protein design and stability. The tool is accessible from the link http://cupsat.uni-koeln.de.

562 citations


Authors

Showing all 32558 results

NameH-indexPapersCitations
Julie E. Buring186950132967
Stuart H. Orkin186715112182
Cornelia M. van Duijn1831030146009
Dorret I. Boomsma1761507136353
Frederick W. Alt17157795573
Donald E. Ingber164610100682
Klaus Müllen1642125140748
Klaus Rajewsky15450488793
Frederik Barkhof1541449104982
Stefanie Dimmeler14757481658
Detlef Weigel14251684670
Hidde L. Ploegh13567467437
Luca Valenziano13043794728
Peter Walter12684171580
Peter G. Martin12555397257
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023324
2022634
20214,225
20204,051
20193,526
20183,078