@article{691, abstract = {Background: Transport protein particle (TRAPP) is a multisubunit complex that regulates membrane trafficking through the Golgi apparatus. The clinical phenotype associated with mutations in various TRAPP subunits has allowed elucidation of their functions in specific tissues. The role of some subunits in human disease, however, has not been fully established, and their functions remain uncertain. Objective: We aimed to expand the range of neurodevelopmental disorders associated with mutations in TRAPP subunits by exome sequencing of consanguineous families. Methods: Linkage and homozygosity mapping and candidate gene analysis were used to identify homozygous mutations in families. Patient fibroblasts were used to study splicing defect and zebrafish to model the disease. Results: We identified six individuals from three unrelated families with a founder homozygous splice mutation in TRAPPC6B, encoding a core subunit of the complex TRAPP I. Patients manifested a neurodevelopmental disorder characterised by microcephaly, epilepsy and autistic features, and showed splicing defect. Zebrafish trappc6b morphants replicated the human phenotype, displaying decreased head size and neuronal hyperexcitability, leading to a lower seizure threshold. Conclusion: This study provides clinical and functional evidence of the role of TRAPPC6B in brain development and function.}, author = {Marin Valencia, Isaac and Novarino, Gaia and Johansen, Anide and Rosti, Başak and Issa, Mahmoud and Musaev, Damir and Bhat, Gifty and Scott, Eric and Silhavy, Jennifer and Stanley, Valentina and Rosti, Rasim and Gleeson, Jeremy and Imam, Farhad and Zaki, Maha and Gleeson, Joseph}, issn = {0022-2593}, journal = {Journal of Medical Genetics}, number = {1}, pages = {48 -- 54}, publisher = {BMJ Publishing Group}, title = {{A homozygous founder mutation in TRAPPC6B associates with a neurodevelopmental disorder characterised by microcephaly epilepsy and autistic features}}, doi = {10.1136/jmedgenet-2017-104627}, volume = {55}, year = {2018}, } @article{284, abstract = {Borel probability measures living on metric spaces are fundamental mathematical objects. There are several meaningful distance functions that make the collection of the probability measures living on a certain space a metric space. We are interested in the description of the structure of the isometries of such metric spaces. We overview some of the recent results of the topic and we also provide some new ones concerning the Wasserstein distance. More specifically, we consider the space of all Borel probability measures on the unit sphere of a Euclidean space endowed with the Wasserstein metric W_p for arbitrary p >= 1, and we show that the action of a Wasserstein isometry on the set of Dirac measures is induced by an isometry of the underlying unit sphere.}, author = {Virosztek, Daniel}, issn = {2064-8316}, journal = {Acta Scientiarum Mathematicarum}, number = {1-2}, pages = {65 -- 80}, publisher = {Springer Nature}, title = {{Maps on probability measures preserving certain distances - a survey and some new results}}, doi = {10.14232/actasm-018-753-y}, volume = {84}, year = {2018}, } @article{180, abstract = {In this paper we define and study the classical Uniform Electron Gas (UEG), a system of infinitely many electrons whose density is constant everywhere in space. The UEG is defined differently from Jellium, which has a positive constant background but no constraint on the density. We prove that the UEG arises in Density Functional Theory in the limit of a slowly varying density, minimizing the indirect Coulomb energy. We also construct the quantum UEG and compare it to the classical UEG at low density.}, author = {Lewi, Mathieu and Lieb, Élliott and Seiringer, Robert}, issn = {2270-518X}, journal = {Journal de l'Ecole Polytechnique - Mathematiques}, pages = {79 -- 116}, publisher = {Ecole Polytechnique}, title = {{Statistical mechanics of the uniform electron gas}}, doi = {10.5802/jep.64}, volume = {5}, year = {2018}, } @article{163, abstract = {For ultrafast fixation of biological samples to avoid artifacts, high-pressure freezing (HPF) followed by freeze substitution (FS) is preferred over chemical fixation at room temperature. After HPF, samples are maintained at low temperature during dehydration and fixation, while avoiding damaging recrystallization. This is a notoriously slow process. McDonald and Webb demonstrated, in 2011, that sample agitation during FS dramatically reduces the necessary time. Then, in 2015, we (H.G. and S.R.) introduced an agitation module into the cryochamber of an automated FS unit and demonstrated that the preparation of algae could be shortened from days to a couple of hours. We argued that variability in the processing, reproducibility, and safety issues are better addressed using automated FS units. For dissemination, we started low-cost manufacturing of agitation modules for two of the most widely used FS units, the Automatic Freeze Substitution Systems, AFS(1) and AFS2, from Leica Microsystems, using three dimensional (3D)-printing of the major components. To test them, several labs independently used the modules on a wide variety of specimens that had previously been processed by manual agitation, or without agitation. We demonstrate that automated processing with sample agitation saves time, increases flexibility with respect to sample requirements and protocols, and produces data of at least as good quality as other approaches.}, author = {Reipert, Siegfried and Goldammer, Helmuth and Richardson, Christine and Goldberg, Martin and Hawkins, Timothy and Hollergschwandtner, Elena and Kaufmann, Walter and Antreich, Sebastian and Stierhof, York}, issn = {0022-1554}, journal = {Journal of Histochemistry and Cytochemistry}, number = {12}, pages = {903--921}, publisher = {SAGE Publications}, title = {{Agitation modules: Flexible means to accelerate automated freeze substitution}}, doi = {10.1369/0022155418786698}, volume = {66}, year = {2018}, } @inproceedings{6012, abstract = {We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understanding functional relations and generalizing them from observed data to unseen parts of the parameter space. We show how to extend the class of learnable equations for a recently proposed equation learning network to include divisions, and we improve the learning and model selection strategy to be useful for challenging real-world data. For systems governed by analytical expressions, our method can in many cases identify the true underlying equation and extrapolate to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum system, where only 2 random rollouts are required to learn the forward dynamics and successfully achieve the swing-up task.}, author = {Sahoo, Subham and Lampert, Christoph and Martius, Georg S}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, location = {Stockholm, Sweden}, pages = {4442--4450}, publisher = {ML Research Press}, title = {{Learning equations for extrapolation and control}}, volume = {80}, year = {2018}, } @inproceedings{6011, abstract = {We establish a data-dependent notion of algorithmic stability for Stochastic Gradient Descent (SGD), and employ it to develop novel generalization bounds. This is in contrast to previous distribution-free algorithmic stability results for SGD which depend on the worst-case constants. By virtue of the data-dependent argument, our bounds provide new insights into learning with SGD on convex and non-convex problems. In the convex case, we show that the bound on the generalization error depends on the risk at the initialization point. In the non-convex case, we prove that the expected curvature of the objective function around the initialization point has crucial influence on the generalization error. In both cases, our results suggest a simple data-driven strategy to stabilize SGD by pre-screening its initialization. As a corollary, our results allow us to show optimistic generalization bounds that exhibit fast convergence rates for SGD subject to a vanishing empirical risk and low noise of stochastic gradient. }, author = {Kuzborskij, Ilja and Lampert, Christoph}, booktitle = {Proceedings of the 35 th International Conference on Machine Learning}, location = {Stockholm, Sweden}, pages = {2815--2824}, publisher = {ML Research Press}, title = {{Data-dependent stability of stochastic gradient descent}}, volume = {80}, year = {2018}, } @techreport{5686, author = {Danowski, Patrick}, pages = {5}, title = {{An Austrian proposal for the Classification of Open Access Tuples (COAT) - Distinguish different Open Access types beyond colors}}, doi = {10.5281/zenodo.1244154}, year = {2018}, } @inproceedings{6589, abstract = {Distributed training of massive machine learning models, in particular deep neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace. Several families of communication-reduction methods, such as quantization, large-batch methods, and gradient sparsification, have been proposed. To date, gradient sparsification methods--where each node sorts gradients by magnitude, and only communicates a subset of the components, accumulating the rest locally--are known to yield some of the largest practical gains. Such methods can reduce the amount of communication per step by up to \emph{three orders of magnitude}, while preserving model accuracy. Yet, this family of methods currently has no theoretical justification. This is the question we address in this paper. We prove that, under analytic assumptions, sparsifying gradients by magnitude with local error correction provides convergence guarantees, for both convex and non-convex smooth objectives, for data-parallel SGD. The main insight is that sparsification methods implicitly maintain bounds on the maximum impact of stale updates, thanks to selection by magnitude. Our analysis and empirical validation also reveal that these methods do require analytical conditions to converge well, justifying existing heuristics.}, author = {Alistarh, Dan-Adrian and Hoefler, Torsten and Johansson, Mikael and Konstantinov, Nikola H and Khirirat, Sarit and Renggli, Cedric}, booktitle = {Advances in Neural Information Processing Systems 31}, location = {Montreal, Canada}, pages = {5973--5983}, publisher = {Neural Information Processing Systems Foundation}, title = {{The convergence of sparsified gradient methods}}, volume = {Volume 2018}, year = {2018}, } @article{7, abstract = {Animal social networks are shaped by multiple selection pressures, including the need to ensure efficient communication and functioning while simultaneously limiting disease transmission. Social animals could potentially further reduce epidemic risk by altering their social networks in the presence of pathogens, yet there is currently no evidence for such pathogen-triggered responses. We tested this hypothesis experimentally in the ant Lasius niger using a combination of automated tracking, controlled pathogen exposure, transmission quantification, and temporally explicit simulations. Pathogen exposure induced behavioral changes in both exposed ants and their nestmates, which helped contain the disease by reinforcing key transmission-inhibitory properties of the colony's contact network. This suggests that social network plasticity in response to pathogens is an effective strategy for mitigating the effects of disease in social groups.}, author = {Stroeymeyt, Nathalie and Grasse, Anna V and Crespi, Alessandro and Mersch, Danielle and Cremer, Sylvia and Keller, Laurent}, issn = {1095-9203}, journal = {Science}, number = {6417}, pages = {941 -- 945}, publisher = {AAAS}, title = {{Social network plasticity decreases disease transmission in a eusocial insect}}, doi = {10.1126/science.aat4793}, volume = {362}, year = {2018}, } @article{19, abstract = {Bacteria regulate genes to survive antibiotic stress, but regulation can be far from perfect. When regulation is not optimal, mutations that change gene expression can contribute to antibiotic resistance. It is not systematically understood to what extent natural gene regulation is or is not optimal for distinct antibiotics, and how changes in expression of specific genes quantitatively affect antibiotic resistance. Here we discover a simple quantitative relation between fitness, gene expression, and antibiotic potency, which rationalizes our observation that a multitude of genes and even innate antibiotic defense mechanisms have expression that is critically nonoptimal under antibiotic treatment. First, we developed a pooled-strain drug-diffusion assay and screened Escherichia coli overexpression and knockout libraries, finding that resistance to a range of 31 antibiotics could result from changing expression of a large and functionally diverse set of genes, in a primarily but not exclusively drug-specific manner. Second, by synthetically controlling the expression of single-drug and multidrug resistance genes, we observed that their fitness-expression functions changed dramatically under antibiotic treatment in accordance with a log-sensitivity relation. Thus, because many genes are nonoptimally expressed under antibiotic treatment, many regulatory mutations can contribute to resistance by altering expression and by activating latent defenses.}, author = {Palmer, Adam and Chait, Remy P and Kishony, Roy}, issn = {0737-4038}, journal = {Molecular Biology and Evolution}, number = {11}, pages = {2669 -- 2684}, publisher = {Oxford University Press}, title = {{Nonoptimal gene expression creates latent potential for antibiotic resistance}}, doi = {10.1093/molbev/msy163}, volume = {35}, year = {2018}, }