@inproceedings{140, abstract = {Reachability analysis is difficult for hybrid automata with affine differential equations, because the reach set needs to be approximated. Promising abstraction techniques usually employ interval methods or template polyhedra. Interval methods account for dense time and guarantee soundness, and there are interval-based tools that overapproximate affine flowpipes. But interval methods impose bounded and rigid shapes, which make refinement expensive and fixpoint detection difficult. Template polyhedra, on the other hand, can be adapted flexibly and can be unbounded, but sound template refinement for unbounded reachability analysis has been implemented only for systems with piecewise constant dynamics. We capitalize on the advantages of both techniques, combining interval arithmetic and template polyhedra, using the former to abstract time and the latter to abstract space. During a CEGAR loop, whenever a spurious error trajectory is found, we compute additional space constraints and split time intervals, and use these space-time interpolants to eliminate the counterexample. Space-time interpolation offers a lazy, flexible framework for increasing precision while guaranteeing soundness, both for error avoidance and fixpoint detection. To the best of out knowledge, this is the first abstraction refinement scheme for the reachability analysis over unbounded and dense time of affine hybrid systems, which is both sound and automatic. We demonstrate the effectiveness of our algorithm with several benchmark examples, which cannot be handled by other tools.}, author = {Frehse, Goran and Giacobbe, Mirco and Henzinger, Thomas A}, issn = {03029743}, location = {Oxford, United Kingdom}, pages = {468 -- 486}, publisher = {Springer}, title = {{Space-time interpolants}}, doi = {10.1007/978-3-319-96145-3_25}, volume = {10981}, year = {2018}, } @article{14003, abstract = {Molecular chirality plays an essential role in most biochemical processes. The observation and quantification of chirality-sensitive signals, however, remains extremely challenging, especially on ultrafast timescales and in dilute media. Here, we describe the experimental realization of an all-optical and ultrafast scheme for detecting chiral dynamics in molecules. This technique is based on high-harmonic generation by a combination of two-color counterrotating femtosecond laser pulses with polarization states tunable from linear to circular. We demonstrate two different implementations of chiral-sensitive high-harmonic spectroscopy on an ensemble of randomly oriented methyloxirane molecules in the gas phase. Using two elliptically polarized fields, we observe that the ellipticities maximizing the harmonic signal reach up to 4.4 ± 0.2 % (at 17.6 eV). Using two circularly polarized fields, we observe circular dichroisms ranging up to 13 ± 6 % (28.3–33.1 eV). Our theoretical analysis confirms that the observed chiral response originates from subfemtosecond electron dynamics driven by the magnetic component of the driving laser field. This assignment is supported by the experimental observation of a strong intensity dependence of the chiral effects and its agreement with theory. We moreover report and explain a pronounced variation of the signal strength and dichroism with the driving-field ellipticities and harmonic orders. Finally, we demonstrate the sensitivity of the experimental observables to the shape of the electron hole. This technique for chiral discrimination will yield femtosecond temporal resolution when integrated in a pump-probe scheme and subfemtosecond resolution on chiral charge migration in a self-probing scheme.}, author = {Baykusheva, Denitsa Rangelova and Wörner, Hans Jakob}, issn = {2160-3308}, journal = {Physical Review X}, keywords = {General Physics and Astronomy}, number = {3}, publisher = {American Physical Society}, title = {{Chiral discrimination through bielliptical high-harmonic spectroscopy}}, doi = {10.1103/physrevx.8.031060}, volume = {8}, year = {2018}, } @inproceedings{141, abstract = {Given a model and a specification, the fundamental model-checking problem asks for algorithmic verification of whether the model satisfies the specification. We consider graphs and Markov decision processes (MDPs), which are fundamental models for reactive systems. One of the very basic specifications that arise in verification of reactive systems is the strong fairness (aka Streett) objective. Given different types of requests and corresponding grants, the objective requires that for each type, if the request event happens infinitely often, then the corresponding grant event must also happen infinitely often. All ω -regular objectives can be expressed as Streett objectives and hence they are canonical in verification. To handle the state-space explosion, symbolic algorithms are required that operate on a succinct implicit representation of the system rather than explicitly accessing the system. While explicit algorithms for graphs and MDPs with Streett objectives have been widely studied, there has been no improvement of the basic symbolic algorithms. The worst-case numbers of symbolic steps required for the basic symbolic algorithms are as follows: quadratic for graphs and cubic for MDPs. In this work we present the first sub-quadratic symbolic algorithm for graphs with Streett objectives, and our algorithm is sub-quadratic even for MDPs. Based on our algorithmic insights we present an implementation of the new symbolic approach and show that it improves the existing approach on several academic benchmark examples.}, author = {Chatterjee, Krishnendu and Henzinger, Monika H and Loitzenbauer, Veronika and Oraee, Simin and Toman, Viktor}, location = {Oxford, United Kingdom}, pages = {178--197}, publisher = {Springer}, title = {{Symbolic algorithms for graphs and Markov decision processes with fairness objectives}}, doi = {10.1007/978-3-319-96142-2_13}, volume = {10982}, year = {2018}, } @inproceedings{14198, abstract = {High-dimensional time series are common in many domains. Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations. However, most representation learning algorithms for time series data are difficult to interpret. This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time. To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling. This framework allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance. We introduce a new way to overcome the non-differentiability in discrete representation learning and present a gradient-based version of the traditional self-organizing map algorithm that is more performant than the original. Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the representation space. This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty. We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set. Our learned representations compare favorably with competitor methods and facilitate downstream tasks on the real world data.}, author = {Fortuin, Vincent and Hüser, Matthias and Locatello, Francesco and Strathmann, Heiko and Rätsch, Gunnar}, booktitle = {International Conference on Learning Representations}, location = {New Orleans, LA, United States}, title = {{SOM-VAE: Interpretable discrete representation learning on time series}}, year = {2018}, } @inproceedings{142, abstract = {We address the problem of analyzing the reachable set of a polynomial nonlinear continuous system by over-approximating the flowpipe of its dynamics. The common approach to tackle this problem is to perform a numerical integration over a given time horizon based on Taylor expansion and interval arithmetic. However, this method results to be very conservative when there is a large difference in speed between trajectories as time progresses. In this paper, we propose to use combinations of barrier functions, which we call piecewise barrier tube (PBT), to over-approximate flowpipe. The basic idea of PBT is that for each segment of a flowpipe, a coarse box which is big enough to contain the segment is constructed using sampled simulation and then in the box we compute by linear programming a set of barrier functions (called barrier tube or BT for short) which work together to form a tube surrounding the flowpipe. The benefit of using PBT is that (1) BT is independent of time and hence can avoid being stretched and deformed by time; and (2) a small number of BTs can form a tight over-approximation for the flowpipe, which means that the computation required to decide whether the BTs intersect the unsafe set can be reduced significantly. We implemented a prototype called PBTS in C++. Experiments on some benchmark systems show that our approach is effective.}, author = {Kong, Hui and Bartocci, Ezio and Henzinger, Thomas A}, location = {Oxford, United Kingdom}, pages = {449 -- 467}, publisher = {Springer}, title = {{Reachable set over-approximation for nonlinear systems using piecewise barrier tubes}}, doi = {10.1007/978-3-319-96145-3_24}, volume = {10981}, year = {2018}, } @inproceedings{14201, abstract = {Variational inference is a popular technique to approximate a possibly intractable Bayesian posterior with a more tractable one. Recently, boosting variational inference has been proposed as a new paradigm to approximate the posterior by a mixture of densities by greedily adding components to the mixture. However, as is the case with many other variational inference algorithms, its theoretical properties have not been studied. In the present work, we study the convergence properties of this approach from a modern optimization viewpoint by establishing connections to the classic Frank-Wolfe algorithm. Our analyses yields novel theoretical insights regarding the sufficient conditions for convergence, explicit rates, and algorithmic simplifications. Since a lot of focus in previous works for variational inference has been on tractability, our work is especially important as a much needed attempt to bridge the gap between probabilistic models and their corresponding theoretical properties.}, author = {Locatello, Francesco and Khanna, Rajiv and Ghosh, Joydeep and Rätsch, Gunnar}, booktitle = {Proceedings of the 21st International Conference on Artificial Intelligence and Statistics}, location = {Playa Blanca, Lanzarote}, pages = {464--472}, publisher = {ML Research Press}, title = {{Boosting variational inference: An optimization perspective}}, volume = {84}, year = {2018}, } @inproceedings{14202, abstract = {Approximating a probability density in a tractable manner is a central task in Bayesian statistics. Variational Inference (VI) is a popular technique that achieves tractability by choosing a relatively simple variational family. Borrowing ideas from the classic boosting framework, recent approaches attempt to \emph{boost} VI by replacing the selection of a single density with a greedily constructed mixture of densities. In order to guarantee convergence, previous works impose stringent assumptions that require significant effort for practitioners. Specifically, they require a custom implementation of the greedy step (called the LMO) for every probabilistic model with respect to an unnatural variational family of truncated distributions. Our work fixes these issues with novel theoretical and algorithmic insights. On the theoretical side, we show that boosting VI satisfies a relaxed smoothness assumption which is sufficient for the convergence of the functional Frank-Wolfe (FW) algorithm. Furthermore, we rephrase the LMO problem and propose to maximize the Residual ELBO (RELBO) which replaces the standard ELBO optimization in VI. These theoretical enhancements allow for black box implementation of the boosting subroutine. Finally, we present a stopping criterion drawn from the duality gap in the classic FW analyses and exhaustive experiments to illustrate the usefulness of our theoretical and algorithmic contributions.}, author = {Locatello, Francesco and Dresdner, Gideon and Khanna, Rajiv and Valera, Isabel and Rätsch, Gunnar}, booktitle = {Advances in Neural Information Processing Systems}, isbn = {9781510884472}, issn = {1049-5258}, location = {Montreal, Canada}, publisher = {Neural Information Processing Systems Foundation}, title = {{Boosting black box variational inference}}, volume = {31}, year = {2018}, } @inproceedings{14203, abstract = {We propose a conditional gradient framework for a composite convex minimization template with broad applications. Our approach combines smoothing and homotopy techniques under the CGM framework, and provably achieves the optimal O(1/k−−√) convergence rate. We demonstrate that the same rate holds if the linear subproblems are solved approximately with additive or multiplicative error. In contrast with the relevant work, we are able to characterize the convergence when the non-smooth term is an indicator function. Specific applications of our framework include the non-smooth minimization, semidefinite programming, and minimization with linear inclusion constraints over a compact domain. Numerical evidence demonstrates the benefits of our framework.}, author = {Yurtsever, Alp and Fercoq, Olivier and Locatello, Francesco and Cevher, Volkan}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, location = {Stockholm, Sweden}, pages = {5727--5736}, publisher = {ML Research Press}, title = {{A conditional gradient framework for composite convex minimization with applications to semidefinite programming}}, volume = {80}, year = {2018}, } @inproceedings{14204, abstract = {Two popular examples of first-order optimization methods over linear spaces are coordinate descent and matching pursuit algorithms, with their randomized variants. While the former targets the optimization by moving along coordinates, the latter considers a generalized notion of directions. Exploiting the connection between the two algorithms, we present a unified analysis of both, providing affine invariant sublinear O(1/t) rates on smooth objectives and linear convergence on strongly convex objectives. As a byproduct of our affine invariant analysis of matching pursuit, our rates for steepest coordinate descent are the tightest known. Furthermore, we show the first accelerated convergence rate O(1/t2) for matching pursuit and steepest coordinate descent on convex objectives.}, author = {Locatello, Francesco and Raj, Anant and Karimireddy, Sai Praneeth and Rätsch, Gunnar and Schölkopf, Bernhard and Stich, Sebastian U. and Jaggi, Martin}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {3198--3207}, publisher = {ML Research Press}, title = {{On matching pursuit and coordinate descent}}, volume = {80}, year = {2018}, } @inproceedings{14224, abstract = {Clustering is a cornerstone of unsupervised learning which can be thought as disentangling multiple generative mechanisms underlying the data. In this paper we introduce an algorithmic framework to train mixtures of implicit generative models which we particularize for variational autoencoders. Relying on an additional set of discriminators, we propose a competitive procedure in which the models only need to approximate the portion of the data distribution from which they can produce realistic samples. As a byproduct, each model is simpler to train, and a clustering interpretation arises naturally from the partitioning of the training points among the models. We empirically show that our approach splits the training distribution in a reasonable way and increases the quality of the generated samples.}, author = {Locatello, Francesco and Vincent, Damien and Tolstikhin, Ilya and Ratsch, Gunnar and Gelly, Sylvain and Scholkopf, Bernhard}, booktitle = {6th International Conference on Learning Representations}, location = {Vancouver, Canada}, title = {{Clustering meets implicit generative models}}, year = {2018}, } @article{14284, abstract = {Pore-forming toxins (PFT) are virulence factors that transform from soluble to membrane-bound states. The Yersinia YaxAB system represents a family of binary α-PFTs with orthologues in human, insect, and plant pathogens, with unknown structures. YaxAB was shown to be cytotoxic and likely involved in pathogenesis, though the molecular basis for its two-component lytic mechanism remains elusive. Here, we present crystal structures of YaxA and YaxB, together with a cryo-electron microscopy map of the YaxAB complex. Our structures reveal a pore predominantly composed of decamers of YaxA–YaxB heterodimers. Both subunits bear membrane-active moieties, but only YaxA is capable of binding to membranes by itself. YaxB can subsequently be recruited to membrane-associated YaxA and induced to present its lytic transmembrane helices. Pore formation can progress by further oligomerization of YaxA–YaxB dimers. Our results allow for a comparison between pore assemblies belonging to the wider ClyA-like family of α-PFTs, highlighting diverse pore architectures.}, author = {Bräuning, Bastian and Bertosin, Eva and Praetorius, Florian M and Ihling, Christian and Schatt, Alexandra and Adler, Agnes and Richter, Klaus and Sinz, Andrea and Dietz, Hendrik and Groll, Michael}, issn = {2041-1723}, journal = {Nature Communications}, keywords = {General Physics and Astronomy, General Biochemistry, Genetics and Molecular Biology, General Chemistry, Multidisciplinary}, publisher = {Springer Nature}, title = {{Structure and mechanism of the two-component α-helical pore-forming toxin YaxAB}}, doi = {10.1038/s41467-018-04139-2}, volume = {9}, year = {2018}, } @inproceedings{143, abstract = {Vector Addition Systems with States (VASS) provide a well-known and fundamental model for the analysis of concurrent processes, parameterized systems, and are also used as abstract models of programs in resource bound analysis. In this paper we study the problem of obtaining asymptotic bounds on the termination time of a given VASS. In particular, we focus on the practically important case of obtaining polynomial bounds on termination time. Our main contributions are as follows: First, we present a polynomial-time algorithm for deciding whether a given VASS has a linear asymptotic complexity. We also show that if the complexity of a VASS is not linear, it is at least quadratic. Second, we classify VASS according to quantitative properties of their cycles. We show that certain singularities in these properties are the key reason for non-polynomial asymptotic complexity of VASS. In absence of singularities, we show that the asymptotic complexity is always polynomial and of the form Θ(nk), for some integer k d, where d is the dimension of the VASS. We present a polynomial-time algorithm computing the optimal k. For general VASS, the same algorithm, which is based on a complete technique for the construction of ranking functions in VASS, produces a valid lower bound, i.e., a k such that the termination complexity is (nk). Our results are based on new insights into the geometry of VASS dynamics, which hold the potential for further applicability to VASS analysis.}, author = {Brázdil, Tomáš and Chatterjee, Krishnendu and Kučera, Antonín and Novotny, Petr and Velan, Dominik and Zuleger, Florian}, isbn = {978-1-4503-5583-4}, location = {Oxford, United Kingdom}, pages = {185 -- 194}, publisher = {IEEE}, title = {{Efficient algorithms for asymptotic bounds on termination time in VASS}}, doi = {10.1145/3209108.3209191}, volume = {F138033}, year = {2018}, } @phdthesis{14306, abstract = {Function and activity of biomolecules often depend on their spatial arrangement. The method introduced here allows genetically encoding the spatial arrangement of proteins and DNA. The approach relies on staple proteins that fold double-stranded DNA into user-defined shapes. This thesis describes the development of staple proteins based on the DNA recognition of TAL effectors and presents experimentally derived rules for designing a variety of self-assembling nanoscale shapes featuring structural motifs such as curvature, vertices, corners, and multilayer helix packing. }, author = {Praetorius, Florian M}, publisher = {Technische Universität München}, title = {{Genetically encoding the spatial arrangement of DNA and proteins in self-assembling nanostructures}}, year = {2018}, } @unpublished{14327, abstract = {A common assumption in causal modeling posits that the data is generated by a set of independent mechanisms, and algorithms should aim to recover this structure. Standard unsupervised learning, however, is often concerned with training a single model to capture the overall distribution or aspects thereof. Inspired by clustering approaches, we consider mixtures of implicit generative models that ``disentangle'' the independent generative mechanisms underlying the data. Relying on an additional set of discriminators, we propose a competitive training procedure in which the models only need to capture the portion of the data distribution from which they can produce realistic samples. As a by-product, each model is simpler and faster to train. We empirically show that our approach splits the training distribution in a sensible way and increases the quality of the generated samples.}, author = {Locatello, Francesco and Vincent, Damien and Tolstikhin, Ilya and Rätsch, Gunnar and Gelly, Sylvain and Schölkopf, Bernhard}, booktitle = {arXiv}, title = {{Competitive training of mixtures of independent deep generative models}}, doi = {10.48550/arXiv.1804.11130}, year = {2018}, } @article{145, abstract = {Aged proteins can become hazardous to cellular function, by accumulating molecular damage. This implies that cells should preferentially rely on newly produced ones. We tested this hypothesis in cultured hippocampal neurons, focusing on synaptic transmission. We found that newly synthesized vesicle proteins were incorporated in the actively recycling pool of vesicles responsible for all neurotransmitter release during physiological activity. We observed this for the calcium sensor Synaptotagmin 1, for the neurotransmitter transporter VGAT, and for the fusion protein VAMP2 (Synaptobrevin 2). Metabolic labeling of proteins and visualization by secondary ion mass spectrometry enabled us to query the entire protein makeup of the actively recycling vesicles, which we found to be younger than that of non-recycling vesicles. The young vesicle proteins remained in use for up to ~ 24 h, during which they participated in recycling a few hundred times. They were afterward reluctant to release and were degraded after an additional ~ 24–48 h. We suggest that the recycling pool of synaptic vesicles relies on newly synthesized proteins, while the inactive reserve pool contains older proteins.}, author = {Truckenbrodt, Sven M and Viplav, Abhiyan and Jähne, Sebsatian and Vogts, Angela and Denker, Annette and Wildhagen, Hanna and Fornasiero, Eugenio and Rizzoli, Silvio}, issn = {0261-4189}, journal = {The EMBO Journal}, number = {15}, publisher = {Wiley}, title = {{Newly produced synaptic vesicle proteins are preferentially used in synaptic transmission}}, doi = {10.15252/embj.201798044}, volume = {37}, year = {2018}, } @article{146, abstract = {The root cap protects the stem cell niche of angiosperm roots from damage. In Arabidopsis, lateral root cap (LRC) cells covering the meristematic zone are regularly lost through programmed cell death, while the outermost layer of the root cap covering the tip is repeatedly sloughed. Efficient coordination with stem cells producing new layers is needed to maintain a constant size of the cap. We present a signalling pair, the peptide IDA-LIKE1 (IDL1) and its receptor HAESA-LIKE2 (HSL2), mediating such communication. Live imaging over several days characterized this process from initial fractures in LRC cell files to full separation of a layer. Enhanced expression of IDL1 in the separating root cap layers resulted in increased frequency of sloughing, balanced with generation of new layers in a HSL2-dependent manner. Transcriptome analyses linked IDL1-HSL2 signalling to the transcription factors BEARSKIN1/2 and genes associated with programmed cell death. Mutations in either IDL1 or HSL2 slowed down cell division, maturation and separation. Thus, IDL1-HSL2 signalling potentiates dynamic regulation of the homeostatic balance between stem cell division and sloughing activity.}, author = {Shi, Chun Lin and Von Wangenheim, Daniel and Herrmann, Ullrich and Wildhagen, Mari and Kulik, Ivan and Kopf, Andreas and Ishida, Takashi and Olsson, Vilde and Anker, Mari Kristine and Albert, Markus and Butenko, Melinka A and Felix, Georg and Sawa, Shinichiro and Claassen, Manfred and Friml, Jirí and Aalen, Reidunn B}, journal = {Nature Plants}, number = {8}, pages = {596 -- 604}, publisher = {Nature Publishing Group}, title = {{The dynamics of root cap sloughing in Arabidopsis is regulated by peptide signalling}}, doi = {10.1038/s41477-018-0212-z}, volume = {4}, year = {2018}, } @article{147, abstract = {The trafficking of subcellular cargos in eukaryotic cells crucially depends on vesicle budding, a process mediated by ARF-GEFs (ADP-ribosylation factor guanine nucleotide exchange factors). In plants, ARF-GEFs play essential roles in endocytosis, vacuolar trafficking, recycling, secretion, and polar trafficking. Moreover, they are important for plant development, mainly through controlling the polar subcellular localization of PIN-FORMED (PIN) transporters of the plant hormone auxin. Here, using a chemical genetics screen in Arabidopsis thaliana, we identified Endosidin 4 (ES4), an inhibitor of eukaryotic ARF-GEFs. ES4 acts similarly to and synergistically with the established ARF-GEF inhibitor Brefeldin A and has broad effects on intracellular trafficking, including endocytosis, exocytosis, and vacuolar targeting. Additionally, Arabidopsis and yeast (Sacharomyces cerevisiae) mutants defective in ARF-GEF show altered sensitivity to ES4. ES4 interferes with the activation-based membrane association of the ARF1 GTPases, but not of their mutant variants that are activated independently of ARF-GEF activity. Biochemical approaches and docking simulations confirmed that ES4 specifically targets the SEC7 domain-containing ARF-GEFs. These observations collectively identify ES4 as a chemical tool enabling the study of ARF-GEF-mediated processes, including ARF-GEF-mediated plant development.}, author = {Kania, Urszula and Nodzyński, Tomasz and Lu, Qing and Hicks, Glenn R and Nerinckx, Wim and Mishev, Kiril and Peurois, Francois and Cherfils, Jacqueline and De, Rycke Riet Maria and Grones, Peter and Robert, Stéphanie and Russinova, Eugenia and Friml, Jirí}, issn = {1040-4651}, journal = {The Plant Cell}, number = {10}, pages = {2553 -- 2572}, publisher = {Oxford University Press}, title = {{The inhibitor Endosidin 4 targets SEC7 domain-type ARF GTPase exchange factors and interferes with sub cellular trafficking in eukaryotes}}, doi = {10.1105/tpc.18.00127}, volume = {30}, year = {2018}, } @phdthesis{539, abstract = {The whole life cycle of plants as well as their responses to environmental stimuli is governed by a complex network of hormonal regulations. A number of studies have demonstrated an essential role of both auxin and cytokinin in the regulation of many aspects of plant growth and development including embryogenesis, postembryonic organogenic processes such as root, and shoot branching, root and shoot apical meristem activity and phyllotaxis. Over the last decades essential knowledge on the key molecular factors and pathways that spatio-temporally define auxin and cytokinin activities in the plant body has accumulated. However, how both hormonal pathways are interconnected by a complex network of interactions and feedback circuits that determines the final outcome of the individual hormone actions is still largely unknown. Root system architecture establishment and in particular formation of lateral organs is prime example of developmental process at whose regulation both auxin and cytokinin pathways converge. To dissect convergence points and pathways that tightly balance auxin - cytokinin antagonistic activities that determine the root branching pattern transcriptome profiling was applied. Genome wide expression analyses of the xylem pole pericycle, a tissue giving rise to lateral roots, led to identification of genes that are highly responsive to combinatorial auxin and cytokinin treatments and play an essential function in the auxin-cytokinin regulated root branching. SYNERGISTIC AUXIN CYTOKININ 1 (SYAC1) gene, which encodes for a protein of unknown function, was detected among the top candidate genes of which expression was synergistically up-regulated by simultaneous hormonal treatment. Plants with modulated SYAC1 activity exhibit severe defects in the root system establishment and attenuate developmental responses to both auxin and cytokinin. To explore the biological function of the SYAC1, we employed different strategies including expression pattern analysis, subcellular localization and phenotypic analyses of the syac1 loss-of-function and gain-of-function transgenic lines along with the identification of the SYAC1 interaction partners. Detailed functional characterization revealed that SYAC1 acts as a developmentally specific regulator of the secretory pathway to control deposition of cell wall components and thereby rapidly fine tune elongation growth.}, author = {Hurny, Andrej}, issn = {2663-337X}, pages = {147}, publisher = {Institute of Science and Technology Austria}, title = {{Identification and characterization of novel auxin-cytokinin cross-talk components}}, doi = {10.15479/AT:ISTA:th_930}, year = {2018}, } @article{542, abstract = {The t-haplotype, a mouse meiotic driver found on chromosome 17, has been a model for autosomal segregation distortion for close to a century, but several questions remain regarding its biology and evolutionary history. A recently published set of population genomics resources for wild mice includes several individuals heterozygous for the t-haplotype, which we use to characterize this selfish element at the genomic and transcriptomic level. Our results show that large sections of the t-haplotype have been replaced by standard homologous sequences, possibly due to occasional events of recombination, and that this complicates the inference of its history. As expected for a long genomic segment of very low recombination, the t-haplotype carries an excess of fixed nonsynonymous mutations compared to the standard chromosome. This excess is stronger for regions that have not undergone recent recombination, suggesting that occasional gene flow between the t and the standard chromosome may provide a mechanism to regenerate coding sequences that have accumulated deleterious mutations. Finally, we find that t-complex genes with altered expression largely overlap with deleted or amplified regions, and that carrying a t-haplotype alters the testis expression of genes outside of the t-complex, providing new leads into the pathways involved in the biology of this segregation distorter.}, author = {Kelemen, Réka K and Vicoso, Beatriz}, journal = {Genetics}, number = {1}, pages = {365 -- 375}, publisher = {Genetics Society of America}, title = {{Complex history and differentiation patterns of the t-haplotype, a mouse meiotic driver}}, doi = {10.1534/genetics.117.300513}, volume = {208}, year = {2018}, } @article{543, abstract = {A central goal in theoretical neuroscience is to predict the response properties of sensory neurons from first principles. To this end, “efficient coding” posits that sensory neurons encode maximal information about their inputs given internal constraints. There exist, however, many variants of efficient coding (e.g., redundancy reduction, different formulations of predictive coding, robust coding, sparse coding, etc.), differing in their regimes of applicability, in the relevance of signals to be encoded, and in the choice of constraints. It is unclear how these types of efficient coding relate or what is expected when different coding objectives are combined. Here we present a unified framework that encompasses previously proposed efficient coding models and extends to unique regimes. We show that optimizing neural responses to encode predictive information can lead them to either correlate or decorrelate their inputs, depending on the stimulus statistics; in contrast, at low noise, efficiently encoding the past always predicts decorrelation. Later, we investigate coding of naturalistic movies and show that qualitatively different types of visual motion tuning and levels of response sparsity are predicted, depending on whether the objective is to recover the past or predict the future. Our approach promises a way to explain the observed diversity of sensory neural responses, as due to multiple functional goals and constraints fulfilled by different cell types and/or circuits.}, author = {Chalk, Matthew J and Marre, Olivier and Tkacik, Gasper}, journal = {PNAS}, number = {1}, pages = {186 -- 191}, publisher = {National Academy of Sciences}, title = {{Toward a unified theory of efficient, predictive, and sparse coding}}, doi = {10.1073/pnas.1711114115}, volume = {115}, year = {2018}, }