PoMaMo—a comprehensive database for potato genome data

A database for potato genome data (PoMaMo, Potato Maps and More) was established. The database contains molecular maps of all twelve potato chromosomes with about 1000 mapped elements, sequence data, putative gene functions, results from BLAST analysis, SNP and InDel information from different diploid and tetraploid potato genotypes, publication references, links to other public databases like GenBank (http://www.ncbi.nlm.nih.gov/) or SGN (Solanaceae Genomics Network, http://www.sgn.cornell.edu/), etc. Flexible search and data visualization interfaces enable easy access to the data via internet (https://gabi.rzpd.de/PoMaMo.html). The Java servlet tool YAMB (Yet Another Map Browser) was designed to interactively display chromosomal maps. Maps can be zoomed in and out, and detailed information about mapped elements can be obtained by clicking on an element of interest. The GreenCards interface allows a text-based data search by marker-, sequence- or genotype name, by sequence accession number, gene function, BLAST Hit or publication reference. The PoMaMo database is a comprehensive database for different potato genome data, and to date the only database containing SNP and InDel data from diploid and tetraploid potato genotypes

Sensitivity and Generalization of a Neural Network for Estimating Left Atrial Fibrotic Volume Fractions from the 12-lead ECG

Features extracted from P waves of the 12-lead electrocardiogram (ECG) have proven valuable for non-invasively estimating the left atrial fibrotic volume fraction associated with the arrhythmogenesis of atrial fibrillation. However, feature extraction in the clinical context is prone to errors and oftentimes yields unreliable results in the presence of noise. This leads to inaccurate input values provided to machine learning algorithms tailored at estimating the amount of atrial fibrosis with clinical ECGs.Another important aspect for clinical translation is the network’s generalization ability regarding newECGs.To quantify a network’s sensitivity to inaccurately extracted P wave features, we added Gaussian noise to the features extracted from 540,000 simulated ECGs consisting of P wave duration, dispersion, terminal force in lead V1, peak-to-peak amplitudes, and additionallythoracic and atrial volumes. For assessing generalization, we evaluated the network performance for train-validation-test splits divided such that ECGs simulated with the same atria or torso geometry only belongedto either the trainingand validationor the test set. The root mean squared error (RMSE) of the network increased the most in case of noisy torso volumes and P wave durations. Large generalization errors witha RMSEdifference between training and test set of more than 2% fibrotic volume fraction only occurred ifveryhigh or low atria and torso volumes were left out during training.Our results suggest that P wave duration and thoracic volume are features that have to be measured accurately if employed for estimating atrial fibrosis with a neural network. Furthermore, our method is capable of generalizing wellto ECGs simulated with anatomical models excluded during training and thus meets an important requirement for clinical translation

Tests in a Case-Control Design Including Relatives

We present a new approach to handle dependencies within the general framework of case-control designs, illustrating our approach by a particular application from the field of genetic epidemiology. The method is derived for parent-offspring trios, which will later be relaxed to more general family structures. For applications in genetic epidemiology we consider tests on equality of allele frequencies among cases and controls utilizing well-known risk measures to test for independence of phenotype and genotype at the observed locus. These test statistics are derived as functions of the entries in the associated contingency table containing the numbers of the alleles under consideration in the case and the control group. We find the joint asymptotic distribution of these entries, which enables us to derive critical values for any test constructed on this basis. A simulation study reveals the finite sample behavior of our test statistics. --association tests,contingency tables,dependent data,risk measures

A bi-atrial statistical shape model for large-scale in silico studies of human atria: model development and application to ECG simulations

Large-scale electrophysiological simulations to obtain electrocardiograms (ECG) carry the potential to produce extensive datasets for training of machine learning classifiers to, e.g., discriminate between different cardiac pathologies. The adoption of simulations for these purposes is limited due to a lack of ready-to-use models covering atrial anatomical variability. We built a bi-atrial statistical shape model (SSM) of the endocardial wall based on 47 segmented human CT and MRI datasets using Gaussian process morphable models. Generalization, specificity, and compactness metrics were evaluated. The SSM was applied to simulate atrial ECGs in 100 random volumetric instances. The first eigenmode of our SSM reflects a change of the total volume of both atria, the second the asymmetry between left vs. right atrial volume, the third a change in the prominence of the atrial appendages. The SSM is capable of generalizing well to unseen geometries and 95% of the total shape variance is covered by its first 23 eigenvectors. The P waves in the 12-lead ECG of 100 random instances showed a duration of 104ms in accordance with large cohort studies. The novel bi-atrial SSM itself as well as 100 exemplary instances with rule-based augmentation of atrial wall thickness, fiber orientation, inter-atrial bridges and tags for anatomical structures have been made publicly available. The novel, openly available bi-atrial SSM can in future be employed to generate large sets of realistic atrial geometries as a basis for in silico big data approaches

Influence of tectonic perturbations on the migration of long-lived radionuclides from an underground repository of radioactive waste

We studied the influence of tectonic perturbations on the transport of potentially mobilized radionuclides in groundwater from a deep-mined repository of solid high-level radioactive waste. The study was carried out by the method of mathematical modeling. Key parameters of the model correspond to the site of a potential federal repository in Russia. The groundwater flow domain is delimited on one side by a water divide (i.e., boundary of the catchment basin) and on the other side by the river bank. 2D simulations of groundwater flow and radionuclide migration are carried out along a vertical cross-section normal to the water divide. The groundwater flows through the rock massif, which encloses the repository, and discharges into the adjacent river. It is supposed that tectonic activity may form a fault which is parallel to the river bank. We analyzed how repository safety depends on the time of the fault emergence and on the distance between the repository and the fault. The results of our simulations suggest that: (1) emergence of a fault due to tectonic perturbations is not inevitably associated with a substantial growth of radionuclides released from the repository to the environment; (2) influence of the fault on the repository safety depends on the distance between the fault and the repository as well as on the time interval between the repository development and the fault emergence; (3) the influence of the fault on the repository safety can depend substantially on local elevations of the relief at the repository site

Choice overload reduces neural signatures of choice set value in dorsal striatum and anterior cingulate cortex

Modern societies offer a large variety of choices, which is generally thought to be valuable. But having too much choice can be detrimental if the costs of choice outweigh its benefits due to ‘choice overload’. Current explanatory models of choice overload mainly derive from behavioural studies. A neuroscientific investigation could further inform these models by revealing the covert mental processes during decision-making. We explored choice overload using functional magnetic resonance imaging while subjects were either choosing from varying-sized choice sets or were browsing them. When choosing from sets of 6, 12 or 24 items, functional magnetic resonance imaging activity in the striatum and anterior cingulate cortex resembled an inverted U-shaped function of choice set size. Activity was highest for 12-item sets, which were perceived as having ‘the right amount’ of options and was lower for 6-item and 24-item sets, which were perceived as ‘too small’ and ‘too large’, respectively. Enhancing choice set value by adding a dominant option led to an overall increase of activity. When subjects were browsing, the decision costs were diminished and the inverted U-shaped activity patterns vanished. Activity in the striatum and anterior cingulate reflects choice set value and can serve as neural indicator of choice overload

Non-Invasive and Quantitative Estimation of Left Atrial Fibrosis Based on P Waves of the 12-Lead ECG—A Large-Scale Computational Study Covering Anatomical Variability

The arrhythmogenesis of atrial fibrillation is associated with the presence of fibrotic atrial tissue. Not only fibrosis but also physiological anatomical variability of the atria and the thorax reflect in altered morphology of the P wave in the 12-lead electrocardiogram (ECG). Distinguishing between the effects on the P wave induced by local atrial substrate changes and those caused by healthy anatomical variations is important to gauge the potential of the 12-lead ECG as a non-invasive and cost-effective tool for the early detection of fibrotic atrial cardiomyopathy to stratify atrial fibrillation propensity. In this work, we realized 54,000 combinations of different atria and thorax geometries from statistical shape models capturing anatomical variability in the general population. For each atrial model, 10 different volume fractions (0–45%) were defined as fibrotic. Electrophysiological simulations in sinus rhythm were conducted for each model combination and the respective 12-lead ECGs were computed. P wave features (duration, amplitude, dispersion, terminal force in V1) were extracted and compared between the healthy and the diseased model cohorts. All investigated feature values systematically in- or decreased with the left atrial volume fraction covered by fibrotic tissue, however value ranges overlapped between the healthy and the diseased cohort. Using all extracted P wave features as input values, the amount of the fibrotic left atrial volume fraction was estimated by a neural network with an absolute root mean square error of 8.78%. Our simulation results suggest that although all investigated P wave features highly vary for different anatomical properties, the combination of these features can contribute to non-invasively estimate the volume fraction of atrial fibrosis using ECG-based machine learning approaches

A Large-scale Virtual Patient Cohort to Study ECG Features of Interatrial Conduction Block

Interatrial conduction block refers to a disturbance in the propagation of electrical impulses in the conduction pathways between the right and the left atrium. It is a risk factor for atrial fibrillation, stroke, and premature death. Clinical diagnostic criteria comprise an increased P wave duration and biphasic P waves in lead II, III and aVF due to retrograde activation of the left atrium. Machine learning algorithms could improve the diagnosis but require a large-scale, well-controlled and balanced dataset. In silico electrocardiogram (ECG) signals, optimally obtained from a statistical shape model to cover anatomical variability, carry the potential to produce an extensive database meeting the requirements for successful machine learning application. We generated the first in silico dataset including interatrial conduction block of 9,800 simulated ECG signals based on a bi-atrial statistical shape model. Automated feature analysis was performed to evaluate P wave morphology, duration and P wave terminal force in lead V1. Increased P wave duration and P wave terminal force in lead V1 were found for models with interatrial conduction block compared to healthy models. A wide variability of P wave morphology was detected for models with interatrial conduction block. Contrary to previous assumptions, our results suggest that a biphasic P wave morphology seems to be neither necessary nor sufficient for the diagnosis of interatrial conduction block. The presented dataset is ready for a classification with machine learning algorithms and can be easily extended

Electrocardiogram Analysis Reveals Ionic Current Dysregulation Relevant for Atrial Fibrillation

Antiarrhythmic drug choice for atrial fibrillation (AF) neglects the individual ionic current profile of the patient, even though it determines drug safety and efficacy. We hypothesize that the electrocardiogram (ECG) might contain information critical for pharmacological treatment personalization. Thus, this study aims to identify the extent of atrial ionic information embedded in the ECG, using multi-scale modeling and simulation. A dataset of 1,000 simulated ECGs was computed using a population of human-based whole-atria models with 200 individual ionic profiles and 5 different torso-atria orientations. A regression neural network was built to predict key atrial ionic conductances based on P- and T$_a$ -wave biomarkers. The neural network predicted, with >80% precision, the density of seven ionic currents relevant for AF, namely, ultra-rapid (I$_{Kur}$ ), rapid (I$_{Kr}$ ), outward transient (I$_{to}$ ), inward rectifier K$^+$ (I$_{K1}$ ), L-type Ca$^{2+}$ (I$_{CaL}$ ), Na$^+$ /K$^+$ pump (I$_{NaK}$ ) and fast Na$^+$ (I$_{Na}$) currents. These ionic densities were identified through the P- (i.e., I$_{Na}$), Ta - (i.e., I$_{K1}$ , I$_{NaK}$) or both waves (i.e., I$_{Kur}$ , I$_{Kr}$ , I$_{to}$ , I$_{CaL}$), providing a non- invasive characterization of the atrial electrophysiology. This could improve patient stratification and cardiac safety and the efficacy of AF pharmacological treatment

Sensitivity Analysis of Electrocardiogram Features to Computational Model Input Parameters

Cardiac models of electrophysiology capable of generating simulated electrocardiogram (ECG) signals are an increasingly valuable tool for both personalised medicine and understanding cardiac pathologies. Sensitivity analysis (SA) can provide crucial insight into how simulation parameters affect ECG morphology. We use two SA methods, direct numerical evaluation of integrals and polynomial chaos expansion, to calculate main and total effects for ECG features extracted from QRS complexes generated by a cardiac ventricular model. The importance of stimulation site parameters on output ECG features is evaluated. SA methods can highlight and quantify important input parameters for different ECG morphology features, which in some cases can be linked to physiological explanations. For example R peak amplitude in lead II depends on apicobasal location of stimulation sites in the left ventricle. Furthermore, different SA methods have different strengths and weaknesses. Insight into parameter importance supports model development and allows for more nuanced and patient-specific simulation changes