@article{JiangOronClarketal.2016,
  author    = {Jiang, Yuxiang and Oron, Tal Ronnen and Clark, Wyatt T. and Bankapur, Asma R. and D'Andrea, Daniel and Lepore, Rosalba and Funk, Christopher S. and Kahanda, Indika and Verspoor, Karin M. and Ben-Hur, Asa and Koo, Da Chen Emily and Penfold-Brown, Duncan and Shasha, Dennis and Youngs, Noah and Bonneau, Richard and Lin, Alexandra and Sahraeian, Sayed M. E. and Martelli, Pier Luigi and Profiti, Giuseppe and Casadio, Rita and Cao, Renzhi and Zhong, Zhaolong and Cheng, Jianlin and Altenhoff, Adrian and Skunca, Nives and Dessimoz, Christophe and Dogan, Tunca and Hakala, Kai and Kaewphan, Suwisa and Mehryary, Farrokh and Salakoski, Tapio and Ginter, Filip and Fang, Hai and Smithers, Ben and Oates, Matt and Gough, Julian and T{\"o}r{\"o}nen, Petri and Koskinen, Patrik and Holm, Liisa and Chen, Ching-Tai and Hsu, Wen-Lian and Bryson, Kevin and Cozzetto, Domenico and Minneci, Federico and Jones, David T. and Chapman, Samuel and BKC, Dukka and Khan, Ishita K. and Kihara, Daisuke and Ofer, Dan and Rappoport, Nadav and Stern, Amos and Cibrian-Uhalte, Elena and Denny, Paul and Foulger, Rebecca E. and Hieta, Reija and Legge, Duncan and Lovering, Ruth C. and Magrane, Michele and Melidoni, Anna N. and Mutowo-Meullenet, Prudence and Pichler, Klemens and Shypitsyna, Aleksandra and Li, Biao and Zakeri, Pooya and ElShal, Sarah and Tranchevent, L{\´e}on-Charles and Das, Sayoni and Dawson, Natalie L. and Lee, David and Lees, Jonathan G. and Sillitoe, Ian and Bhat, Prajwal and Nepusz, Tam{\´a}s and Romero, Alfonso E. and Sasidharan, Rajkumar and Yang, Haixuan and Paccanaro, Alberto and Gillis, Jesse and Sede{\~n}o-Cort{\´e}s, Adriana E. and Pavlidis, Paul and Feng, Shou and Cejuela, Juan M. and Goldberg, Tatyana and Hamp, Tobias and Richter, Lothar and Salamov, Asaf and Gabaldon, Toni and Marcet-Houben, Marina and Supek, Fran and Gong, Qingtian and Ning, Wei and Zhou, Yuanpeng and Tian, Weidong and Falda, Marco and Fontana, Paolo and Lavezzo, Enrico and Toppo, Stefano and Ferrari, Carlo and Giollo, Manuel and Piovesan, Damiano and Tosatto, Silvio C. E. and del Pozo, Angela and Fern{\´a}ndez, Jos{\´e} M. and Maietta, Paolo and Valencia, Alfonso and Tress, Michael L. and Benso, Alfredo and Di Carlo, Stefano and Politano, Gianfranco and Savino, Alessandro and Rehman, Hafeez Ur and Re, Matteo and Mesiti, Marco and Valentini, Giorgio and Bargsten, Joachim W. and van Dijk, Aalt D. J. and Gemovic, Branislava and Glisic, Sanja and Perovic, Vladmir and Veljkovic, Veljko and Almeida-e-Silva, Danillo C. and Vencio, Ricardo Z. N. and Sharan, Malvika and Vogel, J{\"o}rg and Kansakar, Lakesh and Zhang, Shanshan and Vucetic, Slobodan and Wang, Zheng and Sternberg, Michael J. E. and Wass, Mark N. and Huntley, Rachael P. and Martin, Maria J. and O'Donovan, Claire and Robinson, Peter N. and Moreau, Yves and Tramontano, Anna and Babbitt, Patricia C. and Brenner, Steven E. and Linial, Michal and Orengo, Christine A. and Rost, Burkhard and Greene, Casey S. and Mooney, Sean D. and Friedberg, Iddo and Radivojac, Predrag and Veljkovic, Nevena},
  title     = {An expanded evaluation of protein function prediction methods shows an improvement in accuracy},
  series = {Genome Biology},
  volume    = {17},
  journal   = {Genome Biology},
  number    = {184},
  doi       = {10.1186/s13059-016-1037-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-166293},
  year      = {2016},
  abstract  = {Background A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.},
  language  = {en}
}
@article{MarenholzEsparzaGordilloRueschendorfetal.2015,
  author    = {Marenholz, Ingo and Esparza-Gordillo, Jorge and R{\"u}schendorf, Franz and Bauerfeind, Anja and Strachan, David P. and Spycher, Ben D. and Baurecht, Hansj{\"o}rg and Magaritte-Jeannin, Patricia and S{\"a}{\"a}f, Annika and Kerkhof, Marjan and Ege, Markus and Baltic, Svetlana and Matheson, Melanie C. and Li, Jin and Michel, Sven and Ang, Wei Q. and McArdle, Wendy and Arnold, Andreas and Homuth, Georg and Demenais, Florence and Bouzigon, Emmanuelle and S{\"o}derh{\"a}ll, Cilla and Pershagen, G{\"o}ran and de Jongste, Johan C. and Postma, Dirkje S. and Braun-Fahrl{\"a}nder, Charlotte and Horak, Elisabeth and Ogorodova, Ludmila M. and Puzyrev, Valery P. and Bragina, Elena Yu and Hudson, Thomas J. and Morin, Charles and Duffy, David L. and Marks, Guy B. and Robertson, Colin F. and Montgomery, Grant W. and Musk, Bill and Thompson, Philip J. and Martin, Nicholas G. and James, Alan and Sleiman, Patrick and Toskala, Elina and Rodriguez, Elke and F{\"o}lster-Holst, Regina and Franke, Andre and Lieb, Wolfgang and Gieger, Christian and Heinzmann, Andrea and Rietschel, Ernst and Keil, Thomas and Cichon, Sven and N{\"o}then, Markus M. and Pennel, Craig E. and Sly, Peter D. and Schmidt, Carsten O. and Matanovic, Anja and Schneider, Valentin and Heinig, Matthias and H{\"u}bner, Norbert and Holt, Patrick G. and Lau, Susanne and Kabesch, Michael and Weidinger, Stefan and Hakonarson, Hakon and Ferreira, Manuel A. R. and Laprise, Catherine and Freidin, Maxim B. and Genuneit, Jon and Koppelman, Gerard H. and Mel{\´e}n, Erik and Dizier, Marie-H{\´e}l{\`e}ne and Henderson, A. John and Lee, Young Ae},
  title     = {Meta-analysis identifies seven susceptibility loci involved in the atopic march},
  series = {Nature Communications},
  volume    = {6},
  journal   = {Nature Communications},
  number    = {8804},
  doi       = {10.1038/ncomms9804},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-139835},
  year      = {2015},
  abstract  = {Eczema often precedes the development of asthma in a disease course called the 'atopic march'. To unravel the genes underlying this characteristic pattern of allergic disease, we conduct a multi-stage genome-wide association study on infantile eczema followed by childhood asthma in 12 populations including 2,428 cases and 17,034 controls. Here we report two novel loci specific for the combined eczema plus asthma phenotype, which are associated with allergic disease for the first time; rs9357733 located in EFHC1 on chromosome 6p12.3 (OR 1.27; P = 2.1 x 10(-8)) and rs993226 between TMTC2 and SLC6A15 on chromosome 12q21.3 (OR 1.58; P = 5.3 x 10(-9)). Additional susceptibility loci identified at genome-wide significance are FLG (1q21.3), IL4/KIF3A (5q31.1), AP5B1/OVOL1 (11q13.1), C11orf30/LRRC32 (11q13.5) and IKZF3 (17q21). We show that predominantly eczema loci increase the risk for the atopic march. Our findings suggest that eczema may play an important role in the development of asthma after eczema.},
  language  = {en}
}
@phdthesis{Martin2010,
  author    = {Martin, Lee C.},
  title     = {The Kondo Lattice Model: a Dynamical Cluster Approximation Approach},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-49446},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2010},
  abstract  = {We apply an antiferromagnetic symmetry breaking implementation of the dynamical cluster approximation (DCA) to investigate the two-dimensional hole-doped Kondo lattice model (KLM) with hopping \$t\$ and coupling \$J\$. The DCA is an approximation at the level of the self-energy. Short range correlations on a small cluster, which is self-consistently embedded in the remaining bath electrons of the system, are handled exactly whereas longer ranged spacial correlations are incorporated on a mean-field level. The dynamics of the system, however, are retained in full. The strong temporal nature of correlations in the KLM make the model particularly suitable to investigation with the DCA. Our precise DCA calculations of single particle spectral functions compare well with exact lattice QMC results at the particle-hole symmetric point. However, our DCA version, combined with a QMC cluster solver, also allows simulations away from particle-hole symmetry and has enabled us to map out the magnetic phase diagram of the model as a function of doping and coupling \$J/t\$. At half-filling, our results show that the linear behaviour of the quasi-particle gap at small values of \$J/t\$ is a direct consequence of particle-hole symmetry, which leads to nesting of the Fermi surface. Breaking the symmetry, by inclusion of a diagonal hopping term, results in a greatly reduced gap which appears to follow a Kondo scale. Upon doping, the magnetic phase observed at half-filling survives and ultimately gives way to a paramagnetic phase. Across this magnetic order-disorder transition, we track the topology of the Fermi surface. The phase diagram is composed of three distinct regions: Paramagnetic with {\it large} Fermi surface, in which the magnetic moments are included in the Luttinger sum rule, lightly antiferromagnetic with large Fermi surface topology, and strongly antiferromagnetic with {\it small} Fermi surface, where the magnetic moments drop out of the Luttinger volume. We draw on a mean-field Hamiltonian with order parameters for both magnetisation and Kondo screening as a tool for interpretation of our DCA results. Initial results for fixed coupling and doping but varying temperature are also presented, where the aim is look for signals of the energy scales in the system: the Kondo temperature \$T_{K}\$ for initial Kondo screening of the magnetic moments, the Neel temperature \$T_{N}\$ for antiferromagnetic ordering, a possible \$T^{*}\$ at which a reordering of the Fermi surface is observed, and finally, the formation of the coherent heavy fermion state at \$T_{coh}\$.},
  subject      = {Gittermodell},
  language  = {en}
}
@article{ThornChaoGeorgievetal.2020,
  author    = {Thorn, Simon and Chao, Anne and Georgiev, Konstadin B. and M{\"u}ller, J{\"o}rg and B{\"a}ssler, Claus and Campbell, John L. and Jorge, Castro and Chen, Yan-Han and Choi, Chang-Yong and Cobb, Tyler P. and Donato, Daniel C. and Durska, Ewa and Macdonald, Ellen and Feldhaar, Heike and Fontaine, Jospeh B. and Fornwalt, Paula J. and Hern{\´a}ndez Hern{\´a}ndez, Raquel Mar{\´i}a and Hutto, Richard L. and Koivula, Matti and Lee, Eun-Jae and Lindenmayer, David and Mikusinski, Grzegorz and Obrist, Martin K. and Perl{\´i}k, Michal and Rost, Josep and Waldron, Kaysandra and Wermelinger, Beat and Weiß, Ingmar and Zmihorski, Michal and Leverkus, Alexandro B.},
  title     = {Estimating retention benchmarks for salvage logging to protect biodiversity},
  series = {Nature Communications},
  volume    = {11},
  journal   = {Nature Communications},
  doi       = {10.1038/s41467-020-18612-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-230512},
  year      = {2020},
  abstract  = {Forests are increasingly affected by natural disturbances. Subsequent salvage logging, a widespread management practice conducted predominantly to recover economic capital, produces further disturbance and impacts biodiversity worldwide. Hence, naturally disturbed forests are among the most threatened habitats in the world, with consequences for their associated biodiversity. However, there are no evidence-based benchmarks for the proportion of area of naturally disturbed forests to be excluded from salvage logging to conserve biodiversity. We apply a mixed rarefaction/extrapolation approach to a global multi-taxa dataset from disturbed forests, including birds, plants, insects and fungi, to close this gap. We find that 757\% (mean +/- SD) of a naturally disturbed area of a forest needs to be left unlogged to maintain 90\% richness of its unique species, whereas retaining 50\% of a naturally disturbed forest unlogged maintains 73 +/- 12\% of its unique species richness. These values do not change with the time elapsed since disturbance but vary considerably among taxonomic groups. Salvage logging has become a common practice to gain economic returns from naturally disturbed forests, but it could have considerable negative effects on biodiversity. Here the authors use a recently developed statistical method to estimate that ca. 75\% of the naturally disturbed forest should be left unlogged to maintain 90\% of the species unique to the area.},
  language  = {en}
}
@article{RaynerColemanPurvesetal.2019,
  author    = {Rayner, Christopher and Coleman, Jonathan R. I. and Purves, Kirstin L. and Hodsoll, John and Goldsmith, Kimberley and Alpers, Georg W. and Andersson, Evelyn and Arolt, Volker and Boberg, Julia and B{\"o}gels, Susan and Creswell, Cathy and Cooper, Peter and Curtis, Charles and Deckert, J{\"u}rgen and Domschke, Katharina and El Alaoui, Samir and Fehm, Lydia and Fydrich, Thomas and Gerlach, Alexander L. and Grocholewski, Anja and Hahlweg, Kurt and Hamm, Alfons and Hedman, Erik and Heiervang, Einar R. and Hudson, Jennifer L. and J{\"o}hren, Peter and Keers, Robert and Kircher, Tilo and Lang, Thomas and Lavebratt, Catharina and Lee, Sang-hyuck and Lester, Kathryn J. and Lindefors, Nils and Margraf, J{\"u}rgen and Nauta, Maaike and Pan{\´e}-Farr{\´e}, Christiane A. and Pauli, Paul and Rapee, Ronald M. and Reif, Andreas and Rief, Winfried and Roberts, Susanna and Schalling, Martin and Schneider, Silvia and Silverman, Wendy K. and Str{\"o}hle, Andreas and Teismann, Tobias and Thastum, Mikael and Wannem{\"u}ller, Andre and Weber, Heike and Wittchen, Hans-Ulrich and Wolf, Christiane and R{\"u}ck, Christian and Breen, Gerome and Eley, Thalia C.},
  title     = {A genome-wide association meta-analysis of prognostic outcomes following cognitive behavioural therapy in individuals with anxiety and depressive disorders},
  series = {Translational Psychiatry},
  volume    = {9},
  journal   = {Translational Psychiatry},
  number    = {150},
  doi       = {10.1038/s41398-019-0481-y},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-225048},
  pages     = {1-13},
  year      = {2019},
  abstract  = {Major depressive disorder and the anxiety disorders are highly prevalent, disabling and moderately heritable. Depression and anxiety are also highly comorbid and have a strong genetic correlation (r(g) approximate to 1). Cognitive behavioural therapy is a leading evidence-based treatment but has variable outcomes. Currently, there are no strong predictors of outcome. Therapygenetics research aims to identify genetic predictors of prognosis following therapy. We performed genome-wide association meta-analyses of symptoms following cognitive behavioural therapy in adults with anxiety disorders (n = 972), adults with major depressive disorder (n = 832) and children with anxiety disorders (n = 920; meta-analysis n = 2724). We (h(SNP)(2)) and polygenic scoring was used to examine genetic associations between therapy outcomes and psychopathology, personality and estimated the variance in therapy outcomes that could be explained by common genetic variants learning. No single nucleotide polymorphisms were strongly associated with treatment outcomes. No significant estimate of h(SNP)(2) could be obtained, suggesting the heritability of therapy outcome is smaller than our analysis was powered to detect. Polygenic scoring failed to detect genetic overlap between therapy outcome and psychopathology, personality or learning. This study is the largest therapygenetics study to date. Results are consistent with previous, similarly powered genome-wide association studies of complex traits.},
  language  = {en}
}