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A central motivation for the development of x-ray free-electron lasers has been the prospect of time-resolved single-molecule imaging with atomic resolution. Here, we show that x-ray photoelectron diffraction—where a photoelectron emitted after x-ray absorption illuminates the molecular structure from within—can be used to image the increase of the internuclear distance during the x-ray-induced fragmentation of an O2 molecule. By measuring the molecular-frame photoelectron emission patterns for a two-photon sequential K-shell ionization in coincidence with the fragment ions, and by sorting the data as a function of the measured kinetic energy release, we can resolve the elongation of the molecular bond by approximately 1.2 a.u. within the duration of the x-ray pulse. The experiment paves the road toward time-resolved pump-probe photoelectron diffraction imaging at high-repetition-rate x-ray free-electron lasers.
Long-range angular correlations on the near and away side in p–Pb collisions at √sNN=5.02 TeV
(2013)
Angular correlations between charged trigger and associated particles are measured by the ALICE detector in p–Pb collisions at a nucleon–nucleon centre-of-mass energy of 5.02 TeV for transverse momentum ranges within 0.5<pT,assoc<pT,trig<4 GeV/c. The correlations are measured over two units of pseudorapidity and full azimuthal angle in different intervals of event multiplicity, and expressed as associated yield per trigger particle. Two long-range ridge-like structures, one on the near side and one on the away side, are observed when the per-trigger yield obtained in low-multiplicity events is subtracted from the one in high-multiplicity events. The excess on the near-side is qualitatively similar to that recently reported by the CMS Collaboration, while the excess on the away-side is reported for the first time. The two-ridge structure projected onto azimuthal angle is quantified with the second and third Fourier coefficients as well as by near-side and away-side yields and widths. The yields on the near side and on the away side are equal within the uncertainties for all studied event multiplicity and pT bins, and the widths show no significant evolution with event multiplicity or pT. These findings suggest that the near-side ridge is accompanied by an essentially identical away-side ridge.
Um die kontinuierlich auf uns einströmende Menge an Reizen zu verarbeiten, ist es wichtig, die Informationen genau zu selektieren. Ein hilfreicher Mechanismus ist hierbei die Aufmerksamkeit gezielt nur auf eine Informationsquelle zu richten und zu verarbeiten.
So ist es möglich, selbst in komplexen Situationen, wie zum Beispiel einer Feier mit vielen verschiedenen Gesprächen, ganz gezielt ein einzelnes herauszufiltern.
Der Effekt von Aufmerksamkeit auf auditory steady-state Responses (aSSR) wurde in der Vergangenheit schon in verschiedenen Studien mit unterschiedlichen Messverfahren und Stimuli untersucht. Dabei wurden teils widersprüchliche Ergebnisse gefunden, die entweder gar keine oder auf sehr kleine Frequenzbereiche beschränkte Aufmerksamkeitseffekte fanden.
Das Ziel dieser Studie war es, die Auswirkungen von Aufmerksamkeit auf die aSSR innerhalb eines größeren Frequenzspektrums (11 Hz, 23 Hz, 41 Hz, 73 Hz und 97 Hz) zu analysieren. Für diesen Zweck wurden den Probanden nach Instruktion der zu beachtenden Seite jeweils zwei benachbarte Modulationsfrequenzen während vier aufeinander folgenden Blöcken dichotisch präsentiert. Die Probanden wurden angewiesen, Trägerfrequenzänderungen auf der zu beachtenden Seite durch Betätigen einer Maustaste anzugeben. Die Registrierung der aSSR-Antworten geschah mittels Elektroenzephalogramm (EEG). Auch die durch die Stimulation hervorgerufene P300 wurde dargestellt.
Bezüglich des Effekts von Aufmerksamkeit auf die aSSRs zeigte sich nach Analyse der gewonnenen Daten im Frequenzbereich von 23 Hz eine Auswirkung. Diese stellte sich in Form einer Amplitudensteigerung auf der jeweils durch den Probanden beachteten Präsentationsseite dar. Bei einer Modulationsfrequenz von 41 Hz kam es bei Präsentation auf der rechten Seite zu höheren Amplituden als bei Präsentation auf der linken Seite. Bei 73 Hz und 97 Hz konnte keinerlei Auswirkung weder der Aufmerksamkeit noch der Präsentationsseite registriert werden. Auffällig war bei den präsentierten Tönen im 20 Hz und 70 Hz Bereich auch eine im Vergleich zu den anderen Frequenzbereichen (11 Hz, 41 Hz und 97 Hz) verlängerte Reaktionszeit der Probanden. In Kombination mit der Modulation der aSSR-Amplitude durch Aufmerksamkeit bei 23 Hz könnte dies ein Hinweis auf einen förderlichen Einfluss der Aufgabenschwierigkeit auf die Detektierbarkeit von Aufmerksamkeitseffekten sein.
Im Gegensatz hierzu zeigte die dargestellte P300 in allen präsentierten Blöcken einen deutlichen Effekt der Aufmerksamkeit. Dieser äußerte sich ebenfalls in einer Steigerung der Amplitude.
Es scheint also zumindest ein moderater Einfluss von Aufmerksamkeit auf die aSSRs zu existieren. Gleichzeitig wirkt dieser allerdings stark abhängig von gewähltem Stimulus und Messmethode. Der Effekt von Aufmerksamkeit auf die P300 konnte dagegen gut repliziert werden und scheint daher bei dichotischer Stimulation ein deutlicher Marker für Aufmerksamkeit zu sein.
We show that market discipline, defined as the extent to which firm specific risk characteristics are reflected in market prices, eroded during the recent financial crisis in 2008. We design a novel test of changes in market discipline based on the relation between firm specific risk characteristics and debt-to-equity hedge ratios. We find that market discipline already weakened after the rescue of Bear Stearns before disappearing almost entirely after the failure of Lehman Brothers. The effect is stronger for investment banks and large financial institutions, while there is no comparable effect for non-financial firms.
Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs).
Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups.
Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier built based upon data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal.
Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697).
Background: Enhancers play a fundamental role in orchestrating cell state and development. Although several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of enhancers, which triggered the development of various methods to infer promoter–enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organization of the chromatin, paved the way to pinpoint long-range PEIs. Here we investigated whether including PEIs in computational models for the prediction of gene expression improves performance and interpretability.
Results: We have extended our TEPIC framework to include DNA contacts deduced from chromatin conformation capture experiments and compared various methods to determine PEIs using predictive modelling of gene expression from chromatin accessibility data and predicted transcription factor (TF) motif data. We designed a novel machine learning approach that allows the prioritization of TFs binding to distal loop and promoter regions with respect to their importance for gene expression regulation. Our analysis revealed a set of core TFs that are part of enhancer–promoter loops involving YY1 in different cell lines.
Conclusion: We present a novel approach that can be used to prioritize TFs involved in distal and promoter-proximal regulatory events by integrating chromatin accessibility, conformation, and gene expression data. We show that the integration of chromatin conformation data can improve gene expression prediction and aids model interpretability.
his paper studies heterogeneity in the reaction to rank feedback. In a laboratory experiment, individuals take part in a series of dynamic real-effort contests with intermediate feedback. To solve the identification problem in estimating the causal effect of rank feedback on subsequent effort provision we implement a random multiplier in the first round of each contest. The realization of this multiplier then serves as a valid instrument for rank feedback. While rank feedback has a robust effect on subsequent effort provision on average, an explicit analysis of between-subject heterogeneity reveals that a substantial fraction of participants in fact react entirely opposite than the aggregated results indicate. We further show that this heterogeneity has consequences for overall outcomes, thereby arguing that heterogeneous sensitivities to rank feedback could have implications for the design of various policies in education and organizations.
Despite the recent availability of vaccines against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), there is an urgent need for specific anti-SARS-CoV-2 drugs. Monoclonal neutralizing antibodies are an important drug class in the global fight against the SARS-CoV-2 pandemic due to their ability to convey immediate protection and their potential to be used as both prophylactic and therapeutic drugs. Clinically used neutralizing antibodies against respiratory viruses are currently injected intravenously, which can lead to suboptimal pulmonary bioavailability and thus to a lower effectiveness. Here we describe DZIF-10c, a fully human monoclonal neutralizing antibody that binds the receptor-binding domain of the SARS-CoV-2 spike protein. DZIF-10c displays an exceptionally high neutralizing potency against SARS-CoV-2, retains full activity against the variant of concern (VOC) B.1.1.7 and still neutralizes the VOC B.1.351, although with reduced potency. Importantly, not only systemic but also intranasal application of DZIF-10c abolished the presence of infectious particles in the lungs of SARS-CoV-2 infected mice and mitigated lung pathology when administered prophylactically. Along with a favorable pharmacokinetic profile, these results highlight DZIF-10c as a novel human SARS-CoV-2 neutralizing antibody with high in vitro and in vivo antiviral potency. The successful intranasal application of DZIF-10c paves the way for clinical trials investigating topical delivery of anti-SARS-CoV-2 antibodies.
Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs).
Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups.
Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier applied to the data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal.
Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697)
The success of social insects is largely intertwined with their highly advanced chemical communication system that facilitates recognition and discrimination of species and nest-mates, recruitment, and division of labor. Hydrocarbons, which cover the cuticle of insects, not only serve as waterproofing agents but also constitute a major component of this communication system. Two cryptic Crematogaster species, which share their nest with Camponotus ants, show striking diversity in their cuticular hydrocarbon (CHC) profile. This mutualistic system therefore offers a great opportunity to study the genetic basis of CHC divergence between sister species. As a basis for further genome-wide studies high-quality genomes are needed. Here, we present the annotated draft genome for Crematogaster levior A. By combining the three most commonly used sequencing techniques—Illumina, PacBio, and Oxford Nanopore—we constructed a high-quality de novo ant genome. We show that even low coverage of long reads can add significantly to overall genome contiguity. Annotation of desaturase and elongase genes, which play a role in CHC biosynthesis revealed one of the largest repertoires in ants and a higher number of desaturases in general than in other Hymenoptera. This may provide a mechanistic explanation for the high diversity observed in C. levior CHC profiles.