CrossLink: visualization and exploration of sequence relationships between (micro) RNAs

CrossLink is a versatile tool for the exploration of relationships between RNA sequences. After a parametrization phase, CrossLink delegates the determination of sequence relationships to established tools (BLAST, Vmatch and RNAhybrid) and then constructs a network. Each node in this network represents a sequence and each link represents a match or a set of matches. Match attributes are reflected by graphical attributes of the links and corresponding alignments are displayed on a mouse-click. The distributions of match attributes such as E-value, match length and proportion of identical nucleotides are displayed as histograms. Sequence sets can be highlighted and visibility of designated matches can be suppressed by real-time adjustable thresholds for attribute combinations. Powerful network layout operations (such as spring-embedding algorithms) and navigation capabilities complete the exploration features of this tool. CrossLink can be especially useful in a microRNA context since Vmatch and RNAhybrid are suitable tools for determining the antisense and hybridization relationships, which are decisive for the interaction between microRNAs and their targets. CrossLink is available both online and as a standalone version at

A dataset of clinically recorded radar vital signs with synchronised reference sensor signals

Using Radar it is possible to measure vital signs through clothing or a mattress from the distance. This allows for a very comfortable way of continuous monitoring in hospitals or home environments. The dataset presented in this article consists of 24 h of synchronised data from a radar and a reference device. The implemented continuous wave radar system is based on the Six-Port technology and operates at 24 GHz in the ISM band. The reference device simultaneously measures electrocardiogram, impedance cardiogram and non-invasive continuous blood pressure. 30 healthy subjects were measured by physicians according to a predefined protocol. The radar was focused on the chest while the subjects were lying on a tilt table wired to the reference monitoring device. In this manner five scenarios were conducted, the majority of them aimed to trigger hemodynamics and the autonomic nervous system of the subjects. Using the database, algorithms for respiratory or cardiovascular analysis can be developed and a better understanding of the characteristics of the radar-recorded vital signs can be gained

Providing AI expertise as an infrastructure in academia

Artificial intelligence (AI) is proliferating and developing faster than any domain scientist can adapt. To support the scientific enterprise in the Helmholtz association, a network of AI specialists has been set up to disseminate AI expertise as an infrastructure among domain scientists. As this effort exposes an evolutionary step in science organization in Germany, this article aspires to describe our setup, goals, and motivations. We comment on past experiences, current developments, and future ideas as we bring our expertise as an infrastructure closer to scientists across our organization. We hope that this offers a brief yet insightful view of our activities as well as inspiration for other science organizations

Explainable machine learning for labquake prediction using catalog-driven features

Recently, Machine learning (ML) has been widely utilized for laboratory earthquake (labquake) prediction using various types of data. This study pioneers in time to failure (TTF) prediction based on ML using acoustic emission (AE) records from three laboratory stick-slip experiments performed on Westerly granite samples with naturally fractured rough faults, more similar to the heterogeneous fault structures in the nature. 47 catalog-driven seismo-mechanical and statistical features are extracted introducing some new features based on focal mechanism. A regression voting ensemble of Long-Short Term Memory (LSTM) networks predicts TTF with a coefficient of determination (R2) of 70% on the test dataset. Feature importance analysis revealed that AE rate, correlation integral, event proximity, and focal mechanism-based features are the most important features for TTF prediction. Results reveal that the network uses all information among the features for prediction, including general trends in high correlated features as well as fine details about local variations and fault evolution involved in low correlated features. Therefore, some highly correlated and physically meaningful features may be considered less important for TTF prediction due to their correlation with other important features. Our study provides a ground for applying catalog-driven to constrain TTF of complex heterogeneous rough faults, which is capable to be developed for real application

Model-Informed Machine Learning for Multi-component T2 Relaxometry

Recovering the T2 distribution from multi-echo T2 magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to combine machine learning and aspects of parametric (fitting from the MRI signal using biophysical models) and non-parametric (model-free fitting of the T2 distribution from the signal) approaches to T2 relaxometry in brain tissue by using a multi-layer perceptron (MLP) for the distribution reconstruction. For training our network, we construct an extensive synthetic dataset derived from biophysical models in order to constrain the outputs with \textit{a priori} knowledge of \textit{in vivo} distributions. The proposed approach, called Model-Informed Machine Learning (MIML), takes as input the MR signal and directly outputs the associated T2 distribution. We evaluate MIML in comparison to non-parametric and parametric approaches on synthetic data, an ex vivo scan, and high-resolution scans of healthy subjects and a subject with Multiple Sclerosis. In synthetic data, MIML provides more accurate and noise-robust distributions. In real data, MWF maps derived from MIML exhibit the greatest conformity to anatomical scans, have the highest correlation to a histological map of myelin volume, and the best unambiguous lesion visualization and localization, with superior contrast between lesions and normal appearing tissue. In whole-brain analysis, MIML is 22 to 4980 times faster than non-parametric and parametric methods, respectively.Comment: Preprint submitted to Medical Image Analysis (July 14, 2020

Precision Measurement of the Proton and Deuteron Spin Structure Functions g2 and Asymmetries A2

We have measured the spin structure functions g2p and g2d and the virtual photon asymmetries A2p and A2d over the kinematic range 0.02 < x < 0.8 and 0.7 < Q^2 < 20 GeV^2 by scattering 29.1 and 32.3 GeV longitudinally polarized electrons from transversely polarized NH3 and 6LiD targets. Our measured g2 approximately follows the twist-2 Wandzura-Wilczek calculation. The twist-3 reduced matrix elements d2p and d2n are less than two standard deviations from zero. The data are inconsistent with the Burkhardt-Cottingham sum rule if there is no pathological behavior as x->0. The Efremov-Leader-Teryaev integral is consistent with zero within our measured kinematic range. The absolute value of A2 is significantly smaller than the sqrt[R(1+A1)/2] limit.Comment: 12 pages, 4 figures, 2 table

Measurement of the Proton and Deuteron Spin Structure Functions g2 and Asymmetry A2

We have measured the spin structure functions g2p and g2d and the virtual photon asymmetries A2p and A2d over the kinematic range 0.02 < x < 0.8 and 1.0 < Q^2 < 30(GeV/c)^2 by scattering 38.8 GeV longitudinally polarized electrons from transversely polarized NH3 and 6LiD targets.The absolute value of A2 is significantly smaller than the sqrt{R} positivity limit over the measured range, while g2 is consistent with the twist-2 Wandzura-Wilczek calculation. We obtain results for the twist-3 reduced matrix elements d2p, d2d and d2n. The Burkhardt-Cottingham sum rule integral - int(g2(x)dx) is reported for the range 0.02 < x < 0.8.Comment: 12 pages, 4 figures, 1 tabl

Interaction of STAT6 with its co-activator SRC-1/NCoA-1 is regulated by dephosphorylation of the latter via PP2A

Regulation of gene expression represents a central issue in signal-regulated cellular responses. STAT6 is a critical mediator of IL-4 stimulated gene activation. To mediate this function, STAT6 recruits co-activator complexes. We have previously shown that STAT6 binds the PAS-B domain of the co-activator NCoA-1 via an LXXLL motif in its transactivation domain. Our recent finding that the PAS-B domain of NCoA-1 is also essential for co-activator complex formation points to an additional level of regulation of the co-activator assembly. In this study, we discovered that dephosphorylation of NCoA-1 is essential for the interaction with STAT6 and for IL-4-dependent transcriptional activation. PP2A dephosphorylates NCoA-1 and facilitates the activation of STAT6 target genes. Interestingly, simultaneous inhibition of phosphatase and cyclin-dependent kinases rescues the NCoA-1/STAT6 interaction. Moreover, arrest of cells at G1/S results in enhanced NCoA-1 phosphorylation. In summary, our results indicate that the interaction of NCoA-1 and STAT6 is dynamically regulated by the phosphatase PP2A and by cyclin-dependent kinases. This provides a mechanism for integrating transcriptional regulation by STAT6 with cell cycle progression

Reconciliation of essential process parameters for an enhanced predictability of Arctic stratospheric ozone loss and its climate interactions

Significant reductions in stratospheric ozone occur inside the polar vortices each spring when chlorine radicals produced by heterogeneous reactions on cold particle surfaces in winter destroy ozone mainly in two catalytic cycles, the ClO dimer cycle and the ClO/BrO cycle. Chlorofluorocarbons (CFCs), which are responsible for most of the chlorine currently present in the stratosphere, have been banned by the Montreal Protocol and its amendments, and the ozone layer is predicted to recover to 1980 levels within the next few decades. During the same period, however, climate change is expected to alter the temperature, circulation patterns and chemical composition in the stratosphere, and possible geo-engineering ventures to mitigate climate change may lead to additional changes. To realistically predict the response of the ozone layer to such influences requires the correct representation of all relevant processes. The European project RECONCILE has comprehensively addressed remaining questions in the context of polar ozone depletion, with the objective to quantify the rates of some of the most relevant, yet still uncertain physical and chemical processes. To this end RECONCILE used a broad approach of laboratory experiments, two field missions in the Arctic winter 2009/10 employing the high altitude research aircraft M55-Geophysica and an extensive match ozone sonde campaign, as well as microphysical and chemical transport modelling and data assimilation. Some of the main outcomes of RECONCILE are as follows: (1) vortex meteorology: the 2009/10 Arctic winter was unusually cold at stratospheric levels during the six-week period from mid-December 2009 until the end of January 2010, with reduced transport and mixing across the polar vortex edge; polar vortex stability and how it is influenced by dynamic processes in the troposphere has led to unprecedented, synoptic-scale stratospheric regions with temperatures below the frost point; in these regions stratospheric ice clouds have been observed, extending over >106km2 during more than 3 weeks. (2) Particle microphysics: heterogeneous nucleation of nitric acid trihydrate (NAT) particles in the absence of ice has been unambiguously demonstrated; conversely, the synoptic scale ice clouds also appear to nucleate heterogeneously; a variety of possible heterogeneous nuclei has been characterised by chemical analysis of the non-volatile fraction of the background aerosol; substantial formation of solid particles and denitrification via their sedimentation has been observed and model parameterizations have been improved. (3) Chemistry: strong evidence has been found for significant chlorine activation not only on polar stratospheric clouds (PSCs) but also on cold binary aerosol; laboratory experiments and field data on the ClOOCl photolysis rate and other kinetic parameters have been shown to be consistent with an adequate degree of certainty; no evidence has been found that would support the existence of yet unknown chemical mechanisms making a significant contribution to polar ozone loss. (4) Global modelling: results from process studies have been implemented in a prognostic chemistry climate model (CCM); simulations with improved parameterisations of processes relevant for polar ozone depletion are evaluated against satellite data and other long term records using data assimilation and detrended fluctuation analysis. Finally, measurements and process studies within RECONCILE were also applied to the winter 2010/11, when special meteorological conditions led to the highest chemical ozone loss ever observed in the Arctic. In addition to quantifying the 2010/11 ozone loss and to understand its causes including possible connections to climate change, its impacts were addressed, such as changes in surface ultraviolet (UV) radiation in the densely populated northern mid-latitudes

Reconciliation of essential process parameters for an enhanced predictability of Arctic stratospheric ozone loss and its climate interactions : (RECONCILE) ; activities and results

The international research project RECONCILE has addressed central questions regarding polar ozone depletion, with the objective to quantify some of the most relevant yet still uncertain physical and chemical processes and thereby improve prognostic modelling capabilities to realistically predict the response of the ozone layer to climate change. This overview paper outlines the scope and the general approach of RECONCILE, and it provides a summary of observations and modelling in 2010 and 2011 that have generated an in many respects unprecedented dataset to study processes in the Arctic winter stratosphere. Principally, it summarises important outcomes of RECONCILE including (i) better constraints and enhanced consistency on the set of parameters governing catalytic ozone destruction cycles, (ii) a better understanding of the role of cold binary aerosols in heterogeneous chlorine activation, (iii) an improved scheme of polar stratospheric cloud (PSC) processes that includes heterogeneous nucleation of nitric acid trihydrate (NAT) and ice on non-volatile background aerosol leading to better model parameterisations with respect to denitrification, and (iv) long transient simulations with a chemistry-climate model (CCM) updated based on the results of RECONCILE that better reproduce past ozone trends in Antarctica and are deemed to produce more reliable predictions of future ozone trends. The process studies and the global simulations conducted in RECONCILE show that in the Arctic, ozone depletion uncertainties in the chemical and microphysical processes are now clearly smaller than the sensitivity to dynamic variability