TY - JOUR AB - In plants, clathrin mediated endocytosis (CME) represents the major route for cargo internalisation from the cell surface. It has been assumed to operate in an evolutionary conserved manner as in yeast and animals. Here we report characterisation of ultrastructure, dynamics and mechanisms of plant CME as allowed by our advancement in electron microscopy and quantitative live imaging techniques. Arabidopsis CME appears to follow the constant curvature model and the bona fide CME population generates vesicles of a predominantly hexagonal-basket type; larger and with faster kinetics than in other models. Contrary to the existing paradigm, actin is dispensable for CME events at the plasma membrane but plays a unique role in collecting endocytic vesicles, sorting of internalised cargos and directional endosome movement that itself actively promote CME events. Internalized vesicles display a strongly delayed and sequential uncoating. These unique features highlight the independent evolution of the plant CME mechanism during the autonomous rise of multicellularity in eukaryotes. AU - Narasimhan, Madhumitha AU - Johnson, Alexander J AU - Prizak, Roshan AU - Kaufmann, Walter AU - Tan, Shutang AU - Casillas Perez, Barbara E AU - Friml, Jiří ID - 7490 JF - eLife TI - Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis in plants VL - 9 ER - TY - JOUR AB - Characteristic or classic phenotype of Cornelia de Lange syndrome (CdLS) is associated with a recognisable facial pattern. However, the heterogeneity in causal genes and the presence of overlapping syndromes have made it increasingly difficult to diagnose only by clinical features. DeepGestalt technology, and its app Face2Gene, is having a growing impact on the diagnosis and management of genetic diseases by analysing the features of affected individuals. Here, we performed a phenotypic study on a cohort of 49 individuals harbouring causative variants in known CdLS genes in order to evaluate Face2Gene utility and sensitivity in the clinical diagnosis of CdLS. Based on the profile images of patients, a diagnosis of CdLS was within the top five predicted syndromes for 97.9% of our cases and even listed as first prediction for 83.7%. The age of patients did not seem to affect the prediction accuracy, whereas our results indicate a correlation between the clinical score and affected genes. Furthermore, each gene presents a different pattern recognition that may be used to develop new neural networks with the goal of separating different genetic subtypes in CdLS. Overall, we conclude that computer-assisted image analysis based on deep learning could support the clinical diagnosis of CdLS. AU - Latorre-Pellicer, Ana AU - Ascaso, Ángela AU - Trujillano, Laura AU - Gil-Salvador, Marta AU - Arnedo, Maria AU - Lucia-Campos, Cristina AU - Antoñanzas-Pérez, Rebeca AU - Marcos-Alcalde, Iñigo AU - Parenti, Ilaria AU - Bueno-Lozano, Gloria AU - Musio, Antonio AU - Puisac, Beatriz AU - Kaiser, Frank J. AU - Ramos, Feliciano J. AU - Gómez-Puertas, Paulino AU - Pié, Juan ID - 7488 IS - 3 JF - International Journal of Molecular Sciences SN - 16616596 TI - Evaluating Face2Gene as a tool to identify Cornelia de Lange syndrome by facial phenotypes VL - 21 ER - TY - CONF AB - Neural networks have demonstrated unmatched performance in a range of classification tasks. Despite numerous efforts of the research community, novelty detection remains one of the significant limitations of neural networks. The ability to identify previously unseen inputs as novel is crucial for our understanding of the decisions made by neural networks. At runtime, inputs not falling into any of the categories learned during training cannot be classified correctly by the neural network. Existing approaches treat the neural network as a black box and try to detect novel inputs based on the confidence of the output predictions. However, neural networks are not trained to reduce their confidence for novel inputs, which limits the effectiveness of these approaches. We propose a framework to monitor a neural network by observing the hidden layers. We employ a common abstraction from program analysis - boxes - to identify novel behaviors in the monitored layers, i.e., inputs that cause behaviors outside the box. For each neuron, the boxes range over the values seen in training. The framework is efficient and flexible to achieve a desired trade-off between raising false warnings and detecting novel inputs. We illustrate the performance and the robustness to variability in the unknown classes on popular image-classification benchmarks. AU - Henzinger, Thomas A AU - Lukina, Anna AU - Schilling, Christian ID - 7505 T2 - 24th European Conference on Artificial Intelligence TI - Outside the box: Abstraction-based monitoring of neural networks VL - 325 ER - TY - JOUR AB - In this paper, we introduce a novel method for deriving higher order corrections to the mean-field description of the dynamics of interacting bosons. More precisely, we consider the dynamics of N d-dimensional bosons for large N. The bosons initially form a Bose–Einstein condensate and interact with each other via a pair potential of the form (N−1)−1Ndβv(Nβ·)forβ∈[0,14d). We derive a sequence of N-body functions which approximate the true many-body dynamics in L2(RdN)-norm to arbitrary precision in powers of N−1. The approximating functions are constructed as Duhamel expansions of finite order in terms of the first quantised analogue of a Bogoliubov time evolution. AU - Bossmann, Lea AU - Pavlović, Nataša AU - Pickl, Peter AU - Soffer, Avy ID - 7508 JF - Journal of Statistical Physics SN - 0022-4715 TI - Higher order corrections to the mean-field description of the dynamics of interacting bosons VL - 178 ER - TY - JOUR AB - Cryo electron tomography with subsequent subtomogram averaging is a powerful technique to structurally analyze macromolecular complexes in their native context. Although close to atomic resolution in principle can be obtained, it is not clear how individual experimental parameters contribute to the attainable resolution. Here, we have used immature HIV-1 lattice as a benchmarking sample to optimize the attainable resolution for subtomogram averaging. We systematically tested various experimental parameters such as the order of projections, different angular increments and the use of the Volta phase plate. We find that although any of the prominently used acquisition schemes is sufficient to obtain subnanometer resolution, dose-symmetric acquisition provides considerably better outcome. We discuss our findings in order to provide guidance for data acquisition. Our data is publicly available and might be used to further develop processing routines. AU - Turoňová, Beata AU - Hagen, Wim J.H. AU - Obr, Martin AU - Mosalaganti, Shyamal AU - Beugelink, J. Wouter AU - Zimmerli, Christian E. AU - Kräusslich, Hans Georg AU - Beck, Martin ID - 7511 JF - Nature Communications TI - Benchmarking tomographic acquisition schemes for high-resolution structural biology VL - 11 ER -