@article{62, abstract = {Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.}, author = {Shabazi, Ali and Kinnison, Jeffery and Vescovi, Rafael and Du, Ming and Hill, Robert and Jösch, Maximilian A and Takeno, Marc and Zeng, Hongkui and Da Costa, Nuno and Grutzendler, Jaime and Kasthuri, Narayanan and Scheirer, Walter}, journal = {Scientific Reports}, number = {1}, publisher = {Nature Publishing Group}, title = {{Flexible learning-free segmentation and reconstruction of neural volumes}}, doi = {10.1038/s41598-018-32628-3}, volume = {8}, year = {2018}, } @article{437, abstract = {Dendritic cells (DCs) are sentinels of the adaptive immune system that reside in peripheral organs of mammals. Upon pathogen encounter, they undergo maturation and up-regulate the chemokine receptor CCR7 that guides them along gradients of its chemokine ligands CCL19 and 21 to the next draining lymph node. There, DCs present peripherally acquired antigen to naïve T cells, thereby triggering adaptive immunity.}, author = {Leithner, Alexander F and Renkawitz, Jörg and De Vries, Ingrid and Hauschild, Robert and Haecker, Hans and Sixt, Michael K}, journal = {European Journal of Immunology}, number = {6}, pages = {1074 -- 1077}, publisher = {Wiley-Blackwell}, title = {{Fast and efficient genetic engineering of hematopoietic precursor cells for the study of dendritic cell migration}}, doi = {10.1002/eji.201747358}, volume = {48}, year = {2018}, } @article{617, abstract = {Insects are exposed to a variety of potential pathogens in their environment, many of which can severely impact fitness and health. Consequently, hosts have evolved resistance and tolerance strategies to suppress or cope with infections. Hosts utilizing resistance improve fitness by clearing or reducing pathogen loads, and hosts utilizing tolerance reduce harmful fitness effects per pathogen load. To understand variation in, and selective pressures on, resistance and tolerance, we asked to what degree they are shaped by host genetic background, whether plasticity in these responses depends upon dietary environment, and whether there are interactions between these two factors. Females from ten wild-type Drosophila melanogaster genotypes were kept on high- or low-protein (yeast) diets and infected with one of two opportunistic bacterial pathogens, Lactococcus lactis or Pseudomonas entomophila. We measured host resistance as the inverse of bacterial load in the early infection phase. The relationship (slope) between fly fecundity and individual-level bacteria load provided our fecundity tolerance measure. Genotype and dietary yeast determined host fecundity and strongly affected survival after infection with pathogenic P. entomophila. There was considerable genetic variation in host resistance, a commonly found phenomenon resulting from for example varying resistance costs or frequency-dependent selection. Despite this variation and the reproductive cost of higher P. entomophila loads, fecundity tolerance did not vary across genotypes. The absence of genetic variation in tolerance may suggest that at this early infection stage, fecundity tolerance is fixed or that any evolved tolerance mechanisms are not expressed under these infection conditions.}, author = {Kutzer, Megan and Kurtz, Joachim and Armitage, Sophie}, issn = {1420-9101}, journal = {Journal of Evolutionary Biology}, number = {1}, pages = {159 -- 171}, publisher = {Wiley}, title = {{Genotype and diet affect resistance, survival, and fecundity but not fecundity tolerance}}, doi = {10.1111/jeb.13211}, volume = {31}, year = {2018}, } @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}, } @article{321, abstract = {The twelve papers in this special section focus on learning systems with shared information for computer vision and multimedia communication analysis. In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes containing a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with shared information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different levels of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems.}, author = {Darrell, Trevor and Lampert, Christoph and Sebe, Nico and Wu, Ying and Yan, Yan}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, number = {5}, pages = {1029 -- 1031}, publisher = {IEEE}, title = {{Guest editors' introduction to the special section on learning with Shared information for computer vision and multimedia analysis}}, doi = {10.1109/TPAMI.2018.2804998}, volume = {40}, year = {2018}, } @misc{9841, 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 C. and Jongepier, Evelien and Robertson, Hugh M. and Arning, Nicolas and Bitard-Feildel, Tristan and Chao, Hsu and Childers, Christopher P. and Dinh, Huyen and Doddapaneni, Harshavardhan and Dugan, Shannon and Gowin, Johannes and Greiner, Carolin and Han, Yi and Hu, Haofu and Hughes, Daniel S. T. and Huylmans, Ann K and Kemena, Carsten and Kremer, Lukas P. M. and Lee, Sandra L. and Lopez-Ezquerra, Alberto and Mallet, Ludovic and Monroy-Kuhn, Jose M. and Moser, Annabell and Murali, Shwetha C. and Muzny, Donna M. and Otani, Saria and Piulachs, Maria-Dolors and Poelchau, Monica and Qu, Jiaxin and Schaub, Florentine and Wada-Katsumata, Ayako and Worley, Kim C. and Xie, Qiaolin and Ylla, Guillem and Poulsen, Michael and Gibbs, Richard A. and Schal, Coby and Richards, Stephen and Belles, Xavier and Korb, Judith and Bornberg-Bauer, Erich}, publisher = {Dryad}, title = {{Data from: Hemimetabolous genomes reveal molecular basis of termite eusociality}}, doi = {10.5061/dryad.51d4r}, year = {2018}, }