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How long does it take to emit an electron from an atom? This question has intrigued scientists for decades. As such emission times are in the attosecond regime, the advent of attosecond metrology using ultrashort and intense lasers has re-triggered strong interest on the topic from an experimental standpoint. Here, we present an approach to measure such emission delays, which does not require attosecond light pulses, and works without the presence of superimposed infrared laser fields. We instead extract the emission delay from the interference pattern generated as the emitted photoelectron is diffracted by the parent ion’s potential. Targeting core electrons in CO, we measured a 2d map of photoelectron emission delays in the molecular frame over a wide range of electron energies. The emission times depend drastically on the photoelectrons’ emission directions in the molecular frame and exhibit characteristic changes along the shape resonance of the molecule.
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.
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.
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 organisation 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 found that including long-range PEIs deduced from both HiC and HiChIP data indeed improves model performance. We designed a novel machine learning approach that allows to prioritize TFs in 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 show that the integration of chromatin conformation data improves gene expression prediction, underlining the importance of enhancer looping for gene expression regulation. Our general approach can be used to prioritize TFs that are involved in distal and promoter-proximal regulation using accessibility, conformation and expression data.
An ontology-based method for assessing batch effect adjustment approaches in heterogeneous datasets
(2018)
Motivation: International consortia such as the Genotype-Tissue Expression (GTEx) project, The Cancer Genome Atlas (TCGA) or the International Human Epigenetics Consortium (IHEC) have produced a wealth of genomic datasets with the goal of advancing our understanding of cell differentiation and disease mechanisms. However, utilizing all of these data effectively through integrative analysis is hampered by batch effects, large cell type heterogeneity and low replicate numbers. To study if batch effects across datasets can be observed and adjusted for, we analyze RNA-seq data of 215 samples from ENCODE, Roadmap, BLUEPRINT and DEEP as well as 1336 samples from GTEx and TCGA. While batch effects are a considerable issue, it is non-trivial to determine if batch adjustment leads to an improvement in data quality, especially in cases of low replicate numbers.
Results: We present a novel method for assessing the performance of batch effect adjustment methods on heterogeneous data. Our method borrows information from the Cell Ontology to establish if batch adjustment leads to a better agreement between observed pairwise similarity and similarity of cell types inferred from the ontology. A comparison of state-of-the art batch effect adjustment methods suggests that batch effects in heterogeneous datasets with low replicate numbers cannot be adequately adjusted. Better methods need to be developed, which can be assessed objectively in the framework presented here.
Understanding the complexity of transcriptional regulation is a major goal of computational biology. Because experimental linkage of regulatory sites to genes is challenging, computational methods considering epigenomics data have been proposed to create tissue-specific regulatory maps. However, we showed that these approaches are not well suited to account for the variations of the regulatory landscape between cell-types. To overcome these drawbacks, we developed a new method called STITCHIT, that identifies and links putative regulatory sites to genes. Within STITCHIT, we consider the chromatin accessibility signal of all samples jointly to identify regions exhibiting a signal variation related to the expression of a distinct gene. STITCHIT outperforms previous approaches in various validation experiments and was used with a genome-wide CRISPR-Cas9 screen to prioritize novel doxorubicin-resistance genes and their associated non-coding regulatory regions. We believe that our work paves the way for a more refined understanding of transcriptional regulation at the gene-level.
Systemic sclerosis (SSc) is a rare multi-organ autoimmune disease characterized by progressive skin fibrosis. Inflammation, type 2 immunity, and fibrogenic processes are involved in disease development and may be affected by sphingolipids. However, details about early-stage pathophysiological mechanisms and implicated mediators remain elusive. The sphingolipid sphingosine-1-phosphate (S1P) is elevated in the sera of SSc patients, and its receptor S1P5 is expressed in skin tissue. Nevertheless, almost nothing is known about the dermatological contribution of S1P5 to inflammatory and pro-fibrotic processes leading to the pathological changes seen in SSc. In this study, we observed a novel effect of S1P5 on the inflammatory processes during low-dose bleomycin (BLM)-induced fibrogenesis in murine skin. By comparing 2-week-treated skin areas of wild-type (WT) and S1P5-deficient mice, we found that S1P5 is important for the transcriptional upregulation of the Th2 characteristic transcription factor GATA-3 under treatment-induced inflammatory conditions, while T-bet (Th1) and FoxP3 (Treg) mRNA expression was regulated independently of S1P5. Additionally, treatment caused a regulation of S1P receptor 1 and S1P receptor 3 mRNA as well as a regulation of long-chain ceramide profiles, which both differ significantly between the genotypes. Despite S1P5-dependent differences regarding inflammatory processes, similar macroscopic evidence of fibrosis was detected in the skin histology of WT and S1P5-deficient mice after 4 weeks of subcutaneous BLM treatment. However, at the earlier 2-week point in time, the mRNA data of pro-collagen type 1 and SMAD7 indicate a pro-fibrotic S1P5 contribution in the applied SSc mouse model. In conclusion, we propose that S1P5 plays a role as a novel modulator during the early phase of BLM-caused fibrogenesis in murine skin. An immediate relationship between dermal S1P5 expression and fibrotic processes leading to skin alterations, such as formative for SSc pathogenesis, is indicated but should be studied more profound in further investigations. Therefore, this study is an initial step in understanding the role of S1P5-mediated effects during early stages of fibrogenesis, which may encourage the ongoing search for new therapeutic options for SSc patients.
Knowledge about the biogeographic affinities of the world’s tropical forests helps to better understand regional differences in forest structure, diversity, composition, and dynamics. Such understanding will enable anticipation of region-specific responses to global environmental change. Modern phylogenies, in combination with broad coverage of species inventory data, now allow for global biogeographic analyses that take species evolutionary distance into account. Here we present a classification of the world’s tropical forests based on their phylogenetic similarity. We identify five principal floristic regions and their floristic relationships: (i) Indo-Pacific, (ii) Subtropical, (iii) African, (iv) American, and (v) Dry forests. Our results do not support the traditional neo- versus paleotropical forest division but instead separate the combined American and African forests from their Indo-Pacific counterparts. We also find indications for the existence of a global dry forest region, with representatives in America, Africa, Madagascar, and India. Additionally, a northern-hemisphere Subtropical forest region was identified with representatives in Asia and America, providing support for a link between Asian and American northern-hemisphere forests.
Introduction: Evidence from a number of open-label, uncontrolled studies has suggested that rituximab may benefit patients with autoimmune diseases who are refractory to standard-of-care. The objective of this study was to evaluate the safety and clinical outcomes of rituximab in several standard-of-care-refractory autoimmune diseases (within rheumatology, nephrology, dermatology and neurology) other than rheumatoid arthritis or non-Hodgkin's lymphoma in a real-life clinical setting.
Methods: Patients who received rituximab having shown an inadequate response to standard-of-care had their safety and clinical outcomes data retrospectively analysed as part of the German Registry of Autoimmune Diseases. The main outcome measures were safety and clinical response, as judged at the discretion of the investigators.
Results: A total of 370 patients (299 patient-years) with various autoimmune diseases (23.0% with systemic lupus erythematosus, 15.7% antineutrophil cytoplasmic antibody-associated granulomatous vasculitides, 15.1% multiple sclerosis and 10.0% pemphigus) from 42 centres received a mean dose of 2,440 mg of rituximab over a median (range) of 194 (180 to 1,407) days. The overall rate of serious infections was 5.3 per 100 patient-years during rituximab therapy. Opportunistic infections were infrequent across the whole study population, and mostly occurred in patients with systemic lupus erythematosus. There were 11 deaths (3.0% of patients) after rituximab treatment (mean 11.6 months after first infusion, range 0.8 to 31.3 months), with most of the deaths caused by infections. Overall (n = 293), 13.3% of patients showed no response, 45.1% showed a partial response and 41.6% showed a complete response. Responses were also reflected by reduced use of glucocorticoids and various immunosuppressives during rituximab therapy and follow-up compared with before rituximab. Rituximab generally had a positive effect on patient well-being (physician's visual analogue scale; mean improvement from baseline of 12.1 mm).
Conclusions: Data from this registry indicate that rituximab is a commonly employed, well-tolerated therapy with potential beneficial effects in standard of care-refractory autoimmune diseases, and support the results from other open-label, uncontrolled studies.