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Accurately predicting total electron content (TEC) during geomagnetic storms is still a challenging task for ionospheric models. In this work, a neural-network (NN)-based model is proposed which predicts relative TEC with respect to the preceding 27-day median TEC, during storm time for the European region (with longitudes 30 degrees W-50 degrees E and latitudes 32.5 degrees N-70 degrees N). The 27-day median TEC (referred to as median TEC), latitude, longitude, universal time, storm time, solar radio flux index F10.7, global storm index SYM-H and geomagnetic activity index Hp30 are used as inputs and the output of the network is the relative TEC. The relative TEC can be converted to the actual TEC knowing the median TEC. The median TEC is calculated at each grid point over the European region considering data from the last 27 days before the storm using global ionosphere maps (GIMs) from international GNSS service (IGS) sources. A storm event is defined when the storm time disturbance index Dst drops below 50 nanotesla. The model was trained with storm-time relative TEC data from the time period of 1998 until 2019 (2015 is excluded) and contains 365 storms. Unseen storm data from 33 storm events during 2015 and 2020 were used to test the model. The UQRG GIMs were used because of their high temporal resolution (15 min) compared to other products from different analysis centers. The NN-based model predictions show the seasonal behavior of the storms including positive and negative storm phases during winter and summer, respectively, and show a mixture of both phases during equinoxes. The model's performance was also compared with the Neustrelitz TEC model (NTCM) and the NN-based quiet-time TEC model, both developed at the German Aerospace Agency (DLR). The storm model has a root mean squared error (RMSE) of 3.38 TEC units (TECU), which is an improvement by 1.87 TECU compared to the NTCM, where an RMSE of 5.25 TECU was found. This improvement corresponds to a performance increase by 35.6%. The storm-time model outperforms the quiet-time model by 1.34 TECU, which corresponds to a performance increase by 28.4% from 4.72 to 3.38 TECU. The quiet-time model was trained with Carrington averaged TEC and, therefore, is ideal to be used as an input instead of the GIM derived 27-day median. We found an improvement by 0.8 TECU which corresponds to a performance increase by 17% from 4.72 to 3.92 TECU for the storm-time model using the quiet-time-model predicted TEC as an input compared to solely using the quiet-time model.
The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-) processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.
CrashNet
(2021)
Destructive car crash tests are an elaborate, time-consuming, and expensive necessity of the automotive development process. Today, finite element method (FEM) simulations are used to reduce costs by simulating car crashes computationally. We propose CrashNet, an encoder-decoder deep neural network architecture that reduces costs further and models specific outcomes of car crashes very accurately. We achieve this by formulating car crash events as time series prediction enriched with a set of scalar features. Traditional sequence-to-sequence models are usually composed of convolutional neural network (CNN) and CNN transpose layers. We propose to concatenate those with an MLP capable of learning how to inject the given scalars into the output time series. In addition, we replace the CNN transpose with 2D CNN transpose layers in order to force the model to process the hidden state of the set of scalars as one time series. The proposed CrashNet model can be trained efficiently and is able to process scalars and time series as input in order to infer the results of crash tests. CrashNet produces results faster and at a lower cost compared to destructive tests and FEM simulations. Moreover, it represents a novel approach in the car safety management domain.
The actin cytoskeleton is an essential intracellular filamentous structure that underpins cellular transport and cytoplasmic streaming in plant cells. However, the system-level properties of actin-based cellular trafficking remain tenuous, largely due to the inability to quantify key features of the actin cytoskeleton. Here, we developed an automated image-based, network-driven framework to accurately segment and quantify actin cytoskeletal structures and Golgi transport. We show that the actin cytoskeleton in both growing and elongated hypocotyl cells has structural properties facilitating efficient transport. Our findings suggest that the erratic movement of Golgi is a stable cellular phenomenon that might optimize distribution efficiency of cell material. Moreover, we demonstrate that Golgi transport in hypocotyl cells can be accurately predicted from the actin network topology alone. Thus, our framework provides quantitative evidence for system-wide coordination of cellular transport in plant cells and can be readily applied to investigate cytoskeletal organization and transport in other organisms.
The Strange-tailed Tyrant Alectrurus risora (Aves: Tyrannidae) is an endemic species of southern South American grasslands that suffered a 90% reduction of its original distribution due to habitat transformation. This has led the species to be classified as globally Vulnerable. By the beginning of the last century, populations were partially migratory and moved south during the breeding season. Currently, the main breeding population inhabits the Ibera wetlands in the province of Corrientes, north-east Argentina, where it is resident all year round. There are two remaining small populations in the province of Formosa, north-east Argentina, and in southern Paraguay, which are separated from the main population by the Parana-Paraguay River and its continuous riverine forest habitat. The populations of Corrientes and Formosa are separated by 300 km and the grasslands between populations are non-continuous due to habitat transformation. We used mtDNA sequences and eight microsatellite loci to test if there were evidences of genetic isolation between Argentinean populations. We found no evidence of genetic structure between populations (Phi(ST) = 0.004, P = 0.32; Fst = 0.01, P = 0.06), which can be explained by either retained ancestral polymorphism or by dispersal between populations. We found no evidence for a recent demographic bottleneck in nuclear loci. Our results indicate that these populations could be managed as a single conservation unit on a regional scale. Conservation actions should be focused on preserving the remaining network of areas with natural grasslands to guarantee reproduction, dispersal and prevent further decline of populations.
The study of biological interaction networks is a central theme in systems biology. Here, we investigate common as well as differentiating principles of molecular interaction networks associated with different levels of molecular organization. They include metabolic pathway maps, protein-protein interaction networks as well as kinase interaction networks. First, we present an integrated analysis of metabolic pathway maps and protein-protein interaction networks (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzyme complexes. Inspecting high-throughput PIN data, it has been shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In this study, we expanded this line of research to include comparisons of the respective underlying network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency. Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and hence might be essential for the structural integrity of several biosynthetic systems. Besides metabolic aspects of PINs, we investigated the characteristic topological properties of protein interactions involved in signaling and regulatory functions mediated by kinase interactions. Characteristic topological differences between PINs associated with metabolism, and those describing phosphorylation networks were revealed and shown to reflect the different modes of biological operation of both network types. The construction of phosphorylation networks is based on the identification of specific kinase-target relations including the determination of the actual phosphorylation sites (P-sites). The computational prediction of P-sites as well as the identification of involved kinases still suffers from insufficient accuracies and specificities of the underlying prediction algorithms, and the experimental identification in a genome-scale manner is not (yet) doable. Computational prediction methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding P-sites. However the recognition of such motifs by the respective kinases is a spatial event. Therefore, we characterized the spatial distributions of amino acid residue types around P-sites and extracted signature 3D-profiles. We then tested the added value of spatial information on the prediction performance. When compared to sequence-only based predictors, a consistent performance gain was obtained. The availability of reliable training data of experimentally determined P-sites is critical for the development of computational prediction methods. As part of this thesis, we provide an assessment of false-positive rates of phosphoproteomic data.
Symmetric, elegantly entangled structures are a curious mathematical construction that has found their way into the heart of the chemistry lab and the toolbox of constructive geometry. Of particular interest are those structures—knots, links and weavings—which are composed locally of simple twisted strands and are globally symmetric. This paper considers the symmetric tangling of multiple 2-periodic honeycomb networks. We do this using a constructive methodology borrowing elements of graph theory, low-dimensional topology and geometry. The result is a wide-ranging enumeration of symmetric tangled honeycomb networks, providing a foundation for their exploration in both the chemistry lab and the geometers toolbox.
Symmetric, elegantly entangled structures are a curious mathematical construction that has found their way into the heart of the chemistry lab and the toolbox of constructive geometry. Of particular interest are those structures—knots, links and weavings—which are composed locally of simple twisted strands and are globally symmetric. This paper considers the symmetric tangling of multiple 2-periodic honeycomb networks. We do this using a constructive methodology borrowing elements of graph theory, low-dimensional topology and geometry. The result is a wide-ranging enumeration of symmetric tangled honeycomb networks, providing a foundation for their exploration in both the chemistry lab and the geometers toolbox.
Selfsustained oscillations are some of the most commonly observed phenomena in biological systems. They emanate from non-linear systems in a heterogeneous environment and can be described by the theory of dynamical systems. Part of this theory considers reduced models of the oscillator dynamics by means of amplitudes and a phase variable. Such variables are highly attractive for theoretical and experimental studies. Theoretically these variables correspond to an integrable linearization of the generally non-linear system. Experimentally, there exist well established approaches to extract phases from oscillator signals. Notably, one can define phase models also for networks of oscillators. One highly active field examines effects of non-local coupling among oscillators, which is thought to play a key role in networks with strong coupling. The dissertation introduces and expands the knowledge about high-order phase coupling in networks of oscillators. Mathematical calculations consider the Stuart-Landau oscillator. A novel phase estimation scheme for direct observations of an oscillator dynamics is introduced based on numerics. A numerical study of high-order phase coupling applies a Fourier fit for the Stuart-Landau and for the van-der-Pol oscillator. The numerical approach is finally tested on observation-based phase estimates of the Morris-Lecar neuron. A popular approach for the construction of phases from signals is based on phase demodulation by means of the Hilbert transform. Generally, observations of oscillations contain a small and generic variation of their amplitude. The work presents a way to quantify how much the variations of signal amplitude spoil a phase demodulation procedure. For the ideal case of phase modulated signals, amplitude modulations vanish. However, the Hilbert transform produces artificial variations of the reconstructed amplitude even in this case. The work proposes a novel procedure called Iterative Hilbert Transform Embedding to obtain an optimal demodulation of signals. The text presents numerous examples and tests of application for the method, covering multicomponent signals, observables of highly stable limit cycle oscillations and noisy phase dynamics. The numerical results are supported by a spectral theory of convergence for weak phase modulations.
Regulation of gene transcription plays a major role in mediating cellular responses and physiological behavior in all known organisms. The finding that similar genes are often regulated in a similar manner (co-regulated or "co-expressed") has directed several "guilt-by-association" approaches in order to reverse-engineer the cellular transcriptional networks using gene expression data as a compass. This kind of studies has been considerably assisted in the recent years by the development of high-throughput transcript measurement platforms, specifically gene microarrays and next-generation sequencing. In this thesis, I describe several approaches for improving the extraction and interpretation of the information contained in microarray based gene expression data, through four steps: (1) microarray platform design, (2) microarray data normalization, (3) gene network reverse engineering based on expression data and (4) experimental validation of expression-based guilt-by-association inferences. In the first part test case is shown aimed at the generation of a microarray for Thellungiella salsuginea, a salt and drought resistant close relative to the model plant Arabidopsis thaliana; the transcripts of this organism are generated on the combination of publicly available ESTs and newly generated ad-hoc next-generation sequencing data. Since the design of a microarray platform requires the availability of highly reliable and non-redundant transcript models, these issues are addressed consecutively, proposing several different technical solutions. In the second part I describe how inter-array correlation artifacts are generated by the common microarray normalization methods RMA and GCRMA, together with the technical and mathematical characteristics underlying the problem. A solution is proposed in the form of a novel normalization method, called tRMA. The third part of the thesis deals with the field of expression-based gene network reverse engineering. It is shown how different centrality measures in reverse engineered gene networks can be used to distinguish specific classes of genes, in particular essential genes in Arabidopsis thaliana, and how the use of conditional correlation can add a layer of understanding over the information flow processes underlying transcript regulation. Furthermore, several network reverse engineering approaches are compared, with a particular focus on the LASSO, a linear regression derivative rarely applied before in global gene network reconstruction, despite its theoretical advantages in robustness and interpretability over more standard methods. The performance of LASSO is assessed through several in silico analyses dealing with the reliability of the inferred gene networks. In the final part, LASSO and other reverse engineering methods are used to experimentally identify novel genes involved in two independent scenarios: the seed coat mucilage pathway in Arabidopsis thaliana and the hypoxic tuber development in Solanum tuberosum. In both cases an interesting method complementarity is shown, which strongly suggests a general use of hybrid approaches for transcript expression-based inferences. In conclusion, this work has helped to improve our understanding of gene transcription regulation through a better interpretation of high-throughput expression data. Part of the network reverse engineering methods described in this thesis have been included in a tool (CorTo) for gene network reverse engineering and annotated visualization from custom transcription datasets.