@article{5888, abstract = {Despite the remarkable number of scientific breakthroughs of the last 100 years, the treatment of neurodevelopmental disorders (e.g., autism spectrum disorder, intellectual disability) remains a great challenge. Recent advancements in genomics, such as whole-exome or whole-genome sequencing, have enabled scientists to identify numerous mutations underlying neurodevelopmental disorders. Given the few hundred risk genes that have been discovered, the etiological variability and the heterogeneous clinical presentation, the need for genotype — along with phenotype- based diagnosis of individual patients has become a requisite. In this review we look at recent advancements in genomic analysis and their translation into clinical practice.}, author = {Tarlungeanu, Dora-Clara and Novarino, Gaia}, issn = {2092-6413}, journal = {Experimental & Molecular Medicine}, number = {8}, publisher = {Springer Nature}, title = {{Genomics in neurodevelopmental disorders: an avenue to personalized medicine}}, doi = {10.1038/s12276-018-0129-7}, volume = {50}, year = {2018}, } @article{295, abstract = {We prove upper and lower bounds on the ground-state energy of the ideal two-dimensional anyon gas. Our bounds are extensive in the particle number, as for fermions, and linear in the statistics parameter (Formula presented.). The lower bounds extend to Lieb–Thirring inequalities for all anyons except bosons.}, author = {Lundholm, Douglas and Seiringer, Robert}, journal = {Letters in Mathematical Physics}, number = {11}, pages = {2523--2541}, publisher = {Springer}, title = {{Fermionic behavior of ideal anyons}}, doi = {10.1007/s11005-018-1091-y}, volume = {108}, year = {2018}, } @article{555, abstract = {Conventional wisdom has it that proteins fold and assemble into definite structures, and that this defines their function. Glycosaminoglycans (GAGs) are different. In most cases the structures they form have a low degree of order, even when interacting with proteins. Here, we discuss how physical features common to all GAGs — hydrophilicity, charge, linearity and semi-flexibility — underpin the overall properties of GAG-rich matrices. By integrating soft matter physics concepts (e.g. polymer brushes and phase separation) with our molecular understanding of GAG–protein interactions, we can better comprehend how GAG-rich matrices assemble, what their properties are, and how they function. Taking perineuronal nets (PNNs) — a GAG-rich matrix enveloping neurons — as a relevant example, we propose that microphase separation determines the holey PNN anatomy that is pivotal to PNN functions.}, author = {Richter, Ralf and Baranova, Natalia and Day, Anthony and Kwok, Jessica}, journal = {Current Opinion in Structural Biology}, pages = {65 -- 74}, publisher = {Elsevier}, title = {{Glycosaminoglycans in extracellular matrix organisation: Are concepts from soft matter physics key to understanding the formation of perineuronal nets?}}, doi = {10.1016/j.sbi.2017.12.002}, volume = {50}, year = {2018}, } @article{448, abstract = {Around 150 million years ago, eusocial termites evolved from within the cockroaches, 50 million years before eusocial Hymenoptera, such as bees and ants, appeared. Here, we report the 2-Gb genome of the German cockroach, Blattella germanica, and the 1.3-Gb genome of the drywood termite Cryptotermes secundus. We show evolutionary signatures of termite eusociality by comparing the genomes and transcriptomes of three termites and the cockroach against the background of 16 other eusocial and non-eusocial insects. Dramatic adaptive changes in genes underlying the production and perception of pheromones confirm the importance of chemical communication in the termites. These are accompanied by major changes in gene regulation and the molecular evolution of caste determination. Many of these results parallel molecular mechanisms of eusocial evolution in Hymenoptera. However, the specific solutions are remarkably different, thus revealing a striking case of convergence in one of the major evolutionary transitions in biological complexity.}, author = {Harrison, Mark and Jongepier, Evelien and Robertson, Hugh and Arning, Nicolas and Bitard Feildel, Tristan and Chao, Hsu and Childers, Christopher and Dinh, Huyen and Doddapaneni, Harshavardhan and Dugan, Shannon and Gowin, Johannes and Greiner, Carolin and Han, Yi and Hu, Haofu and Hughes, Daniel and Huylmans, Ann K and Kemena, Karsten and Kremer, Lukas and Lee, Sandra and López Ezquerra, Alberto and Mallet, Ludovic and Monroy Kuhn, Jose and Moser, Annabell and Murali, Shwetha and Muzny, Donna and Otani, Saria and Piulachs, Maria and Poelchau, Monica and Qu, Jiaxin and Schaub, Florentine and Wada Katsumata, Ayako and Worley, Kim and Xie, Qiaolin and Ylla, Guillem and Poulsen, Michael and Gibbs, Richard and Schal, Coby and Richards, Stephen and Belles, Xavier and Korb, Judith and Bornberg Bauer, Erich}, journal = {Nature Ecology and Evolution}, number = {3}, pages = {557--566}, publisher = {Springer Nature}, title = {{Hemimetabolous genomes reveal molecular basis of termite eusociality}}, doi = {10.1038/s41559-017-0459-1}, volume = {2}, year = {2018}, } @article{723, abstract = {Escaping local optima is one of the major obstacles to function optimisation. Using the metaphor of a fitness landscape, local optima correspond to hills separated by fitness valleys that have to be overcome. We define a class of fitness valleys of tunable difficulty by considering their length, representing the Hamming path between the two optima and their depth, the drop in fitness. For this function class we present a runtime comparison between stochastic search algorithms using different search strategies. The (1+1) EA is a simple and well-studied evolutionary algorithm that has to jump across the valley to a point of higher fitness because it does not accept worsening moves (elitism). In contrast, the Metropolis algorithm and the Strong Selection Weak Mutation (SSWM) algorithm, a famous process in population genetics, are both able to cross the fitness valley by accepting worsening moves. We show that the runtime of the (1+1) EA depends critically on the length of the valley while the runtimes of the non-elitist algorithms depend crucially on the depth of the valley. Moreover, we show that both SSWM and Metropolis can also efficiently optimise a rugged function consisting of consecutive valleys.}, author = {Oliveto, Pietro and Paixao, Tiago and Pérez Heredia, Jorge and Sudholt, Dirk and Trubenova, Barbora}, journal = {Algorithmica}, number = {5}, pages = {1604 -- 1633}, publisher = {Springer}, title = {{How to escape local optima in black box optimisation when non elitism outperforms elitism}}, doi = {10.1007/s00453-017-0369-2}, volume = {80}, year = {2018}, }