1,094 research outputs found

    Combinatorial Kalman Filter and High Level Trigger Reconstruction for the Belle II Experiment

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    Deciphering the role of orphan nuclear receptor GCNF in germ layer specification and early neural induction by utilizing CRISPR/Cas9

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    Nuclear receptors are widely recognized as an important layer of transcriptional regulation and are usually activated by a signaling molecule. Nuclear receptors without a known ligand are designated orphan nuclear receptors. One of those receptors is GCNF. Whereas it is known that GCNF serves an important role in gonad development and its impact on silencing OCT4 upon differentiation of hPSCs is widely appreciated, less is known about its implication in germ layer specification and early neural induction. To tackle this question, the CRISPR/Cas9 technology was used to generate GCNF-deficient hiPSCs. All quality checks like stable pluripotency marker expression, as well as assessment of genomic integrity by SNP analysis, were passed successfully by the GCNF-deficient hiPSC clones. In self-renewing conditions GCNF ablation could be confirmed on mRNA as well as protein level. An undirected embryoid body-based differentiation experiment combined with a TaqMan-based analysis of differentiation potential, pointed towards an impaired ectodermal differentiation during germ layer specification. To investigate the impact of GCNF-deficiency more specifically on neuroectoderm formation, a directed neural induction experiment with GCNF-deficient hiPSC clones was performed and assessed by RNA-seq, qRT-PCR and immunocytochemistry. This approach revealed candidate genes that might be key players in GCNF downstream signaling. While OCT4 and CRIPTO, two known GCNF target genes, were found to be upregulated, the expression of key factors of early neurodevelopment like PAX6, FEZF2 and FOXG1 were significantly impaired upon GCNF ablation. Additionally, upregulation of WNT4 and reduced RSPO3 expression indicate GCNF being involved in WNT signaling. For future ChIP-seq experiments to identify GCNF target genes, CRISPR/Cas9 technology was utilized to generate hiPSC lines with ChIP-compatible tags (FLAG and AM) added to the C-terminus of the endogenous GCNF gene. In summary, CRISPR/Cas9 technology was successfully employed to generate an isogenic set of GCNF-deficient hiPSCs, that revealed a developmental impairment during germ layer specification and early neural induction

    Restrictions on infinite sequences of type IIB vacua

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    Ashok and Douglas have shown that infinite sequences of type IIB flux vacua with imaginary self-dual flux can only occur in so-called D-limits, corresponding to singular points in complex structure moduli space. In this work we refine this no-go result by demonstrating that there are no infinite sequences accumulating to the large complex structure point of a certain class of one-parameter Calabi-Yau manifolds. We perform a similar analysis for conifold points and for the decoupling limit, obtaining identical results. Furthermore, we establish the absence of infinite sequences in a D-limit corresponding to the large complex structure limit of a two-parameter Calabi-Yau. In particular, our results demonstrate analytically that the series of vacua recently discovered by Ahlqvist et al., seemingly accumulating to the large complex structure point, are finite. We perform a numerical study of these series close to the large complex structure point using appropriate approximations for the period functions. This analysis reveals that the series bounce out from the large complex structure point, and that the flux eventually ceases to be imaginary self-dual. Finally, we study D-limits for F-theory compactifications on K3\times K3 for which the finiteness of supersymmetric vacua is already established. We do find infinite sequences of flux vacua which are, however, identified by automorphisms of K3.Comment: 35 pages. v2. Typos corrected, ref. added. Matches published versio

    PALP - a User Manual

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    This article provides a complete user's guide to version 2.1 of the toric geometry package PALP by Maximilian Kreuzer and others. In particular, previously undocumented applications such as the program nef.x are discussed in detail. New features of PALP 2.1 include an extension of the program mori.x which can now compute Mori cones and intersection rings of arbitrary dimension and can also take specific triangulations of reflexive polytopes as input. Furthermore, the program nef.x is enhanced by an option that allows the user to enter reflexive Gorenstein cones as input. The present documentation is complemented by a Wiki which is available online.Comment: 71 pages, to appear in "Strings, Gauge Fields, and the Geometry Behind - The Legacy of Maximilian Kreuzer". PALP Wiki available at http://palp.itp.tuwien.ac.at/wiki/index.php/Main_Pag

    Weight estimations with time-reversed point-light displays

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    Interpreting other’s actions is a very important ability not only in social life, but also in interactive sports. Previous experiments have demonstrated good estimation performances for the weight of lifted objects through point-light displays. The basis for these performances is commonly assigned to the concept of motor simulation regarding observed actions. In this study, we investigated the weak version of the motor simulation hypothesis which claims that the goal of an observed action strongly influences its understanding (Fogassi, Ferrari, Gesierich, Rozzi, Chersi, & Rizzolatti, 2005). Therefore, we conducted a weight judgement task with point-light displays and showed participants videos of a model lifting and lowering three different weights. The experimental manipulation consisted of a goal change of these actions by showing the videos normal and in a time-reversed order of sequence. The results show a systematic overestimation of weights for time-reversed lowering actions (thus looking like lifting actions) while weight estimations for time-reversed lifting actions did not differ from the original playback direction. The results are discussed in terms of motor simulation and different kinematic profiles of the presented actions. © 2020, The Author(s)

    Dichte Packung durch Sechseckschichten : eine Packungsanalyse der Cambridge Structural Database

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    Die Eigenschaften von Molekülkristallen werden nicht nur durch die Eigenschaften der Moleküle bestimmt, welche die Kristallpackung aufbauen, sondern ebenso durch die Art der Packung im Kristall. Während die Eigenschaften der Moleküle im allgemeinen gut verstanden sind, ist die Art der Packung von Molekülen in einem Kristall wesentlich weniger gut bekannt. In vielen Fällen wird sie auch nicht weiter analysiert. Dies liegt wenigstens zum Teil daran, daß geeignete Werkzeuge für die Analyse molekularer Packungsmuster fehlen. Die vorliegende Arbeit beschreibt die Entwicklung von Algorithmen und Werkzeugen für die automatische Packungsanalyse großer Datenbestände. Von den verschiedenen Ansätzen, die hierbei verfolgt wurden, hat sich die Klassifzierung von Packungen in Bezug auf das Vorliegen und die Stapelung von dichtest gepackten Schichten besonders bewährt. Eine Korrelation zwischen der Molekülform, angenähert durch das Ellipsoid der zweiten Momente, und dem Packungsmuster läßt sich in vielen Fällen auffinden. Das aufregendste Ergebnis dieser Untersuchung ist die Erkenntnis, daß über 30% der analysierten Packungsmuster (130 000 Strukturen der Cambridge Structural Database) durch das Vorliegen dichtest gepackter Ebenen charakterisiert sind

    Distribution System Monitoring for Smart Power Grids with Distributed Generation Using Artificial Neural Networks

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    The increasing number of distributed generators connected to distribution grids requires a reliable monitoring of such grids. Economic considerations prevent a full observation of distribution grids with direct measurements. First approaches using a limited number of measurements to monitor such grids exist, some of which use artificial neural networks (ANN). The current ANN-based approaches, however, are limited to static topologies, only estimate voltage magnitudes, do not work properly when confronted with a high amount of distributed generation and often yield inaccurate results. These strong limitations have prevented a true applicability of ANN for distribution grid monitoring. The objective of this paper is to overcome the limitations of existing approaches. We do that by presenting an ANN-based scheme, which advances the state-of-the-art in several ways: Our scheme can cope with a very low number of measurements, far less than is traditionally required by the state-of-the-art weighted least squares state estimation (WLS SE). It can estimate both voltage magnitudes and line loadings with high precision and includes different switching states as inputs. Our contribution consists of a method to generate useful training data by using a scenario generator and a number of hyperparameters that define the ANN architecture. Both can be used for different grids even with a high amount of distributed generation. Simulations are performed with an elaborate evaluation approach on a real distribution grid and a CIGRE benchmark grid both with a high amount of distributed generation from photovoltaics and wind energy converters. They demonstrate that the proposed ANN scheme clearly outperforms state-of-the-art ANN schemes and WLS SE under normal operating conditions and different situations such as gross measurement errors when comparing voltage magnitude and line magnitude estimation errors.Comment: 12 pages, 10 figures, 5 tables, preprin
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