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Mathematical modeling of Arabidopsis thaliana with focus on network decomposition and reduction
(2014)
Systems biology has become an important research field during the last decade. It focusses on the understanding of the systems which emit the measured data. An important part of this research field is the network analysis, investigating biological networks. An essential point of the inspection of these network models is their validation, i.e., the successful comparison of predicted properties to measured data. Here especially Petri nets have shown their usefulness as modeling technique, coming with sound analysis methods and an intuitive representation of biological network data.
A very important tool for network validation is the analysis of the Transition-invariants (TI), which represent possible steady-state pathways, and the investigation of the liveness property. The computational complexity of the determination of both, TI and liveness property, often hamper their investigation.
To investigate this issue, a metabolic network model is created. It describes the core metabolism of Arabidopsis thaliana, and it is solely based on data from the literature. The model is too complex to determine the TI and the liveness property.
Several strategies are followed to enable an analysis and validation of the network. A network decomposition is utilized in two different ways: manually, motivated by idea to preserve the integrity of biological pathways, and automatically, motivated by the idea to minimize the number of crossing edges. As a decomposition may not be preserving important properties like the coveredness, a network reduction approach is suggested, which is mathematically proven to conserve these important properties. To deal with the large amount of data coming from the TI analysis, new organizational structures are proposed. The liveness property is investigated by reducing the complexity of the calculation method and adapting it to biological networks.
The results obtained by these approaches suggest a valid network model. In conclusion, the proposed approaches and strategies can be used in combination to allow the validation and analysis of highly complex biological networks.
The efficient management of large multimedia databases requires the development of new techniques to process, characterize, and search for multimedia objects. Especially in the case of image data, the rapidly growing amount of documents prohibits a manual description of the images’ content. Instead, the automated characterization is highly desirable to support annotation and retrieval of digital images. However, this is a very complex and still unsolved task. To contribute to a solution of this problem, we have developed a mechanism for recognizing objects in images based on the query by example paradigm. Therefore, the most salient image features of an example image representing the searched object are extracted to obtain a scale-invariant object model. The use of this model provides an efficient and robust strategy for recognizing objects in images independently of their size. Further applications of the mechanism are classical recognition tasks such as scene decomposition or object tracking in video sequences.
In den Anwendungsbereichen der Mixed Reality (MR) werden die reale und die virtuelle Welt kombiniert, so dass ein Eindruck der Koexistenz beider Welten entsteht. Meist wird dabei die reale Umgebung durch virtuelle Objekte angereichert, die dem Anwender zusätzliche Informationen bieten sollen. Um die virtuellen Objekte richtig zu positionieren, muss die reale Umgebung erkannt werden. Diese Erkennung der realen Umgebung wird meist durch Bestimmung und Verfolgung von Orientierung und Positionierung der realen Objekte realisiert, was als Tracking bezeichnet wird und einen der wichtigsten Bestandteile für MR-Anwendung darstellt. Ohne die exakte Ausrichtung von realen und virtuellen Objekten, geht die Illusion verloren, dass die virtuellen Objekte Teil der realen Umgebung sind und mit ihr verschmelzen. Markerkombination Das markerbasierte Tracking ist ein Verfahren, das die Bestimmung der Positionierung von realen Objekten durch zusätzliche Markierungen in der realen Umgebung ermöglicht. Diese Markierungen können besonders gut durch Bildanalyseverfahren extrahiert werden und bieten anhand ihrer speziellen Form Positionierungsinformationen. Der Einsatz dieser Trackingtechnologie ist dabei denkbar einfache und kostengünstig. Ein breiter Anwendungsbereich ist durch den kostengünstigen Einsatz dieser Technologien gegeben, allerdings ist das Erstellen von MR-Anwendungen fast ausschließlich MR-Spezialisten vorbehalten, die über Programmierfertigkeiten und spezielle Kenntnisse aus dem MR-Bereich besitzen. Diese Arbeit beschreibt die Entwicklung und Umsetzung der Konzepte, die einem Personenkreis, der lediglich über geringe Kenntnisse von MR-Technologien und deren Anwendung verfügt, den kostengünstigen und einfachen Einsatz von markerbasierten Trackingtechnologien ermöglicht. Die im Rahmen der Arbeit durchgeführte Analyse verweist auf die problematischen Anwendungsfälle des markerbasierten Trackings, die durch die Verdeckung von Markern zustande kommen, in der Beschränkung der Markeranzahl begründet sind, oder durch die Schwankung der Trackingangaben entstehen. Diese Problembereiche sind bei der Entwicklung berücksichtigt worden und können mit Hilfe der entwickelten Konzepte vom Autor bewältigt werden. Das Konzept der Markerkategorien ermöglicht dabei den Einsatz von angepassten Filterungstechniken. Die redundante Markerkombination behebt das Verdeckungsproblem und eliminiert Schwankungen durch das Kombinieren von mehreren Trackinginformationen. Die Gütefunktion ermöglicht die Bewertung von Trackinginformationen und wird zur Gewichtung der Trackingangaben innerhalb einer Markerkombination genutzt. Das Konzept der Markertupel ermöglicht eine Wiederverwendung von Markern, durch den Ansatz der Bereichsunterteilung. Die Konzepte sind in der AMIRE-Umgebung vollständig implementiert und getestet worden. Zum Abschluss ist rückblickend eine kritische Betrachtung der Arbeit, in punkto Vorgehensweise und erreichter Ergebnisse durchgeführt worden.
Dieser Arbeit war zum Ziel gesetzt, Methoden zur Simulation von neuronalen Prozessen zu entwickeln, zu implementieren, einzusetzen und zu vergleichen. Ein besonderes Augenmerk lag dabei auf der Frage, wo eine volle räumliche Auflösung der Modelle benötigt wird und wo darauf zugunsten von vereinfachenden niederdimensionalen Modellen, die wesentlich weniger Ressourcen und mathematischen Sachverstand erfordern, verzichtet werden kann. Außerdem wurde speziell bei der Beschreibung der verschiedenen Modelle für die Elektrik der Nervenzellen das Anliegen verfolgt, deren Zusammenhänge und die Natur vereinfachender Annahmen herauszuarbeiten, um deutlich zu machen, an welchen Stellen Probleme bei der Benutzung der weniger komplexen Modelle auftreten können.
In etlichen Beispielen wurde daraufhin untersucht, inwieweit die Vereinfachung auf ein eindimensionales Kabelmodell sowie der Verzicht auf die Betrachtung einzelner Ionensorten die realistische Darstellung der zellulären Elektrik beeinträchtigen können. Dabei stellte sich heraus, dass alle betrachteten Modelle für das rein elektrische Verhalten der Neuronen im Wesentlichen dieselben Ergebnisse liefern, weshalb zu dessen Simulation in den allermeisten Fällen ein 1D-Kabelmodell völlig ausreichend und angezeigt sein dürfte.
Nur wenn Größen von Interesse sind, die in diesem Modell nicht erfasst werden, etwa das Außenraumpotential oder die Ionenkonzentrationen, muss auf genauere Modelle zurückgegriffen werden. Außerdem ist in einer Konvergenzstudie exemplarisch vorgeführt worden, dass bereits eine recht grobe Darstellung der zugrundeliegenden Rechengitter genügt, um korrekte Ergebnisse bei der Simulation der rein elektrischen Signale sicherzustellen.
In scharfem Kontrast steht hierzu die Simulation von einzelnen Ionen-Dynamiken. Bereits in der Untersuchung des Poisson-Nernst-Planck-Modells für das Membranpotential erwies sich, dass für eine korrekte Simulation der diffusiven Anteile der Ionenbewegung wesentlich feinere Gitter benötigt werden.
Noch viel deutlicher wurde dies in Simulationen von Calcium-Wellen in Dendriten, wo -- neben anderen Einsichten -- aufgezeigt werden konnte, dass nicht nur eine feine axiale
(und Zeit-) Auflösung der Dendritengeometrie zur Sicherstellung exakter Ergebnisse notwendig ist, sondern auch die räumliche Auflösung in die übrigen Dimensionen wichtig ist, weswegen eine eindimensionale Kabeldarstellung der Calcium-Dynamik erheblich fehlerbehaftet und
(jedenfalls im Zusammenhang mit Ryanodin-Rezeptorkanälen) von deren Nutzung dringend abzuraten ist. Auch die Darstellung von Kanälen als eine kontinuierliche Dichte in der Membran kann, wie darüber hinaus vorgeführt wurde, problematisch sein.
Ihre exaktere Modellierung, etwa durch Einbettung auch probabilistischer Einzelkanaldarstellungen in das räumliche Modell sollte in zukünftigen Arbeiten noch mehr thematisiert werden.
Mit Blick auf die Wiederverwendbarkeit bereits implementierter Funktionalität innerhalb dieser Arbeiten wurden spezielle Teile dieser Funktionalität hier in einem gesonderten
Kapitel genauer beschrieben. Als komplexes Beispiel für das, was simulationstechnisch bereits im Bereich des Machbaren
liegt, und gleichsam für eine Anwendung, die zeigt, wie möglichst viele der im Rahmen dieser Arbeit entwickelten Methoden miteinander kombiniert werden können, wurde die
Calcium-Dynamik eines kompletten Dendriten innerhalb eines großen aktiven neuronalen Netzwerks simuliert.
Background: Signal transduction pathways are important cellular processes to maintain the cell’s integrity. Their imbalance can cause severe pathologies. As signal transduction pathways feature complex regulations, they form intertwined networks. Mathematical models aim to capture their regulatory logic and allow an unbiased analysis of robustness and vulnerability of the signaling network. Pathway detection is yet a challenge for the analysis of signaling networks in the field of systems biology. A rigorous mathematical formalism is lacking to identify all possible signal flows in a network model.
Results: In this paper, we introduce the concept of Manatee invariants for the analysis of signal transduction networks. We present an algorithm for the characterization of the combinatorial diversity of signal flows, e.g., from signal reception to cellular response. We demonstrate the concept for a small model of the TNFR1-mediated NF- κB signaling pathway. Manatee invariants reveal all possible signal flows in the network. Further, we show the application of Manatee invariants for in silico knockout experiments. Here, we illustrate the biological relevance of the concept.
Conclusions: The proposed mathematical framework reveals the entire variety of signal flows in models of signaling systems, including cyclic regulations. Thereby, Manatee invariants allow for the analysis of robustness and vulnerability of signaling networks. The application to further analyses such as for in silico knockout was shown. The new framework of Manatee invariants contributes to an advanced examination of signaling systems.
We determine the magnetic susceptibility of thermal QCD matter by means of first principles lattice simulations using staggered quarks with physical masses. A novel method is employed that only requires simulations at zero background field, thereby circumventing problems related to magnetic flux quantization. After a careful continuum limit extrapolation, diamagnetic behavior (negative susceptibility) is found at low temperatures and strong paramagnetism (positive susceptibility) at high temperatures. We revisit the decomposition of the magnetic susceptibility into spin- and orbital angular momentum- related contributions. The spin term — related to the normalization of the photon lightcone distribution amplitude at zero temperature — is calculated non-perturbatively and extrapolated to the continuum limit. Having access to both the full magnetic susceptibility and the spin term, we calculate the orbital angular momentum contribution for the first time. The results reveal the opposite of what might be expected based on a free fermion picture. We provide a simple parametrization of the temperature- and magnetic field-dependence of the QCD equation of state that can be used in phenomenological studies.
We investigate the QCD phase diagram for nonzero background magnetic fields using first-principles lattice simulations. At the physical point (in terms of quark masses), the thermodynamics of this system is controlled by two opposing effects: magnetic catalysis (enhancement of the quark condensate) at low temperature and inverse magnetic catalysis (reduction of the condensate) in the transition region. While the former is known to be robust and independent of the details of the interactions, inverse catalysis arises as a result of a delicate competition, effective only for light quarks. By performing simulations at different quark masses, we determine the pion mass above which inverse catalysis does not take place in the transition region anymore. Even for pions heavier than this limiting value — where the quark condensate undergoes magnetic catalysis — our results are consistent with the notion that the transition temperature is reduced by the magnetic field. These findings will be useful to guide low-energy models and effective theories of QCD.
In the last years, much effort went into the design of robust anaphor resolution algorithms. Many algorithms are based on antecedent filtering and preference strategies that are manually designed. Along a different line of research, corpus-based approaches have been investigated that employ machine-learning techniques for deriving strategies automatically. Since the knowledge-engineering effort for designing and optimizing the strategies is reduced, the latter approaches are considered particularly attractive. Since, however, the hand-coding of robust antecedent filtering strategies such as syntactic disjoint reference and agreement in person, number, and gender constitutes a once-for-all effort, the question arises whether at all they should be derived automatically. In this paper, it is investigated what might be gained by combining the best of two worlds: designing the universally valid antecedent filtering strategies manually, in a once-for-all fashion, and deriving the (potentially genre-specific) antecedent selection strategies automatically by applying machine-learning techniques. An anaphor resolution system ROSANA-ML, which follows this paradigm, is designed and implemented. Through a series of formal evaluations, it is shown that, while exhibiting additional advantages, ROSANAML reaches a performance level that compares with the performance of its manually designed ancestor ROSANA.
LSTMVoter : chemical named entity recognition using a conglomerate of sequence labeling tools
(2019)
Background: Chemical and biomedical named entity recognition (NER) is an essential preprocessing task in natural language processing. The identification and extraction of named entities from scientific articles is also attracting increasing interest in many scientific disciplines. Locating chemical named entities in the literature is an essential step in chemical text mining pipelines for identifying chemical mentions, their properties, and relations as discussed in the literature. In this work, we describe an approach to the BioCreative V.5 challenge regarding the recognition and classification of chemical named entities. For this purpose, we transform the task of NER into a sequence labeling problem. We present a series of sequence labeling systems that we used, adapted and optimized in our experiments for solving this task. To this end, we experiment with hyperparameter optimization. Finally, we present LSTMVoter, a two-stage application of recurrent neural networks that integrates the optimized sequence labelers from our study into a single ensemble classifier.
Results: We introduce LSTMVoter, a bidirectional long short-term memory (LSTM) tagger that utilizes a conditional random field layer in conjunction with attention-based feature modeling. Our approach explores information about features that is modeled by means of an attention mechanism. LSTMVoter outperforms each extractor integrated by it in a series of experiments. On the BioCreative IV chemical compound and drug name recognition (CHEMDNER) corpus, LSTMVoter achieves an F1-score of 90.04%; on the BioCreative V.5 chemical entity mention in patents corpus, it achieves an F1-score of 89.01%.
Availability and implementation: Data and code are available at https://github.com/texttechnologylab/LSTMVoter.
We show lower bounds for the signature size of incremental schemes which are secure against substitution attacks and support single block replacement. We prove that for documents of n blocks such schemes produce signatures of \Omega(n^(1/(2+c))) bits for any constant c>0. For schemes accessing only a single block resp. a constant number of blocks for each replacement this bound can be raised to \Omega(n) resp. \Omega(sqrt(n)). Additionally, we show that our technique yields a new lower bound for memory checkers.
This paper gives a brief overview of computation models for data stream processing, and it introduces a new model for multi-pass processing of multiple streams, the so-called mp2s-automata. Two algorithms for solving the set disjointness problem with these automata are presented. The main technical contribution of this paper is the proof of a lower bound on the size of memory and the number of heads that are required for solving the set disjointness problem with mp2s-automata.
A new approach to optimize multilevel logic circuits is introduced. Given a multilevel circuit, the synthesis method optimizes its area while simultaneously enhancing its random pattern testability. The method is based on structural transformations at the gate level. New transformations involving EX-OR gates as well as Reed–Muller expansions have been introduced in the synthesis of multilevel circuits. This method is augmented with transformations that specifically enhance random-pattern testability while reducing the area. Testability enhancement is an integral part of our synthesis methodology. Experimental results show that the proposed methodology not only can achieve lower area than other similar tools, but that it achieves better testability compared to available testability enhancement tools such as tstfx. Specifically for ISCAS-85 benchmark circuits, it was observed that EX-OR gate-based transformations successfully contributed toward generating smaller circuits compared to other state-of-the-art logic optimization tools.
This paper presents the first measurements of the charge independent (CI) and charge dependent (CD) two-particle transverse momentum correlators GCI2 and GCD2 in Pb--Pb collisions at sNN−−−√=2.76\TeVe by the ALICE collaboration. The two-particle transverse momentum correlator G2 was introduced as a measure of the momentum current transfer between neighbouring system cells. The correlators are measured as a function of pair separation in pseudorapidity (Δη) and azimuth (Δφ) and as a function of collision centrality. From peripheral to central collisions, the correlator GCI2 exhibits a longitudinal broadening while undergoing a monotonic azimuthal narrowing. By contrast, GCD2 exhibits a narrowing along both dimensions. These features are not reproduced by models such as HIJING and AMPT. However, the observed narrowing of the correlators from peripheral to central collisions is expected to result from the stronger transverse flow profiles produced in more central collisions and the longitudinal broadening is predicted to be sensitive to momentum currents and the shear viscosity per unit of entropy density η/s of the matter produced in the collisions. The observed broadening is found to be consistent with the hypothesized lower bound of η/s and is in qualitative agreement with values obtained from anisotropic flow measurements.
This paper presents the first measurements of the charge independent (CI) and charge dependent (CD) two-particle transverse momentum correlators GCI2 and GCD2 in Pb-Pb collisions at sNN−−−√ = 2.76 TeV by the ALICE collaboration. The correlators are measured as a function of pair separation in pseudorapidity (Δη) and azimuth (Δφ) and as a function of collision centrality. The correlator GCI2 exhibits a longitudinal broadening while undergoing a monotonic azimuthal narrowing from peripheral to central collisions. By contrast, GCD2 exhibits a narrowing along both dimensions towards central events. These features are not reproduced by models such as HIJING and AMPT. However, the observed narrowing of the correlators is expected to result from the stronger transverse flow profiles produced in more central collisions and the longitudinal broadening is predicted to be sensitive to momentum currents and the shear viscosity per unit of entropy density η/s of the matter produced in the collisions. The observed broadening is found to be consistent with the hypothesized lower bound of η/s and is in qualitative agreement with values obtained from anisotropic flow measurements.
Poster presentation: Twenty Second Annual Computational Neuroscience Meeting: CNS*2013. Paris, France. 13-18 July 2013.
The synaptic cleft is an extracellular domain that is capable of relaying a presynaptically received electrical signal by diffusive neurotransmitters to the postsynaptic membrane. The cleft is trans-synaptically bridged by ring-like shaped clusters of pre- and postsynaptically localized calcium-dependent adhesion proteins of the N-Cadherin type and is possibly the smallest intercircuit in nervous systems [1]. The strength of association between the pre- and postsynaptic membranes can account for synaptic plasticity such as long-term potentiation [2]. Through neuronal activity the intra- and extracellular calcium levels are modulated through calcium exchangers embedded in the pre- and postsynaptic membrane. Variations of the concentration of cleft calcium induces changes in the N-Cadherin-zipper, that in synaptic resting states is rigid and tightly connects the pre- and postsynaptic domain. During synaptic activity calcium concentrations are hypothesized to drop below critical thresholds which leads to loosening of the N-Cadherin connections and subsequently "unzips" the Cadherin-mediated connection. These processes may result in changes in synaptic strength [2]. In order to investigate the calcium-mediated N-Cadherin dynamics at the synaptic cleft, we developed a three-dimensional model including the cleft morphology and all prominent calcium exchangers and corresponding density distributions [3-6]. The necessity for a fully three-dimensional model becomes apparent, when investigating the effects of the spatial architecture of the synapse [7], [8]. Our data show, that the localization of calcium channels with respect to the N-Cadherin ring has substantial effects on the time-scales on which the Cadherin-zipper switches between states, ranging from seconds to minutes. This will have significant effects on synaptic signaling. Furthermore we see, that high-frequency action potential firing can only be relayed to the Calcium/N-Cadherin-system at a synapse under precise spatial synaptic reorganization.
The cortical networks that underlie behavior exhibit an orderly functional organization at local and global scales, which is readily evident in the visual cortex of carnivores and primates1-6. Here, neighboring columns of neurons represent the full range of stimulus orientations and contribute to distributed networks spanning several millimeters2,7-11. However, the principles governing functional interactions that bridge this fine-scale functional architecture and distant network elements are unclear, and the emergence of these network interactions during development remains unexplored. Here, by using in vivo wide-field and 2-photon calcium imaging of spontaneous activity patterns in mature ferret visual cortex, we find widespread and specific modular correlation patterns that accurately predict the local structure of visually-evoked orientation columns from the spontaneous activity of neurons that lie several millimeters away. The large-scale networks revealed by correlated spontaneous activity show abrupt ‘fractures’ in continuity that are in tight register with evoked orientation pinwheels. Chronic in vivo imaging demonstrates that these large-scale modular correlation patterns and fractures are already present at early stages of cortical development and predictive of the mature network structure. Silencing feed-forward drive through either retinal or thalamic blockade does not affect network structure suggesting a cortical origin for this large-scale correlated activity, despite the immaturity of long-range horizontal network connections in the early cortex. Using a circuit model containing only local connections, we demonstrate that such a circuit is sufficient to generate large-scale correlated activity, while also producing correlated networks showing strong fractures, a reduced dimensionality, and an elongated local correlation structure, all in close agreement with our empirical data. These results demonstrate the precise local and global organization of cortical networks revealed through correlated spontaneous activity and suggest that local connections in early cortical circuits may generate structured long-range network correlations that underlie the subsequent formation of visually-evoked distributed functional networks.
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.
Two-particle angular correlations are measured in high-multiplicity proton-proton collisions at s√ = 13 TeV by the ALICE Collaboration. The yields of particle pairs at short-(∆η ∼ 0) and long-range (1.6 < |∆η| < 1.8) in pseudorapidity are extracted on the near-side (∆φ ∼ 0). They are reported as a function of transverse momentum (pT) in the range 1 < pT < 4 GeV/c. Furthermore, the event-scale dependence is studied for the first time by requiring the presence of high-pT leading particles or jets for varying pT thresholds. The results demonstrate that the long-range “ridge” yield, possibly related to the collective behavior of the system, is present in events with high-pT processes as well. The magnitudes of the short- and long-range yields are found to grow with the event scale. The results are compared to EPOS LHC and PYTHIA 8 calculations, with and without string-shoving interactions. It is found that while both models describe the qualitative trends in the data, calculations from EPOS LHC show a better quantitative agreement for the pT dependency, while overestimating the event-scale dependency.
Two-particle angular correlations are measured in high-multiplicity proton-proton collisions at s√=13 TeV by the ALICE Collaboration. The yields of particle pairs at short-(Δη ∼ 0) and long-range (1.6<|Δη|<1.8) in pseudorapidity are extracted on the near-side (Δφ ∼ 0). They are reported as a function of transverse momentum (pT) in the range 1<pT<4 GeV/c. Furthermore, the event-scale dependence is studied for the first time by requiring the presence of high-pT leading particles and jets for varying pT thresholds. The results demonstrate that the long-range "ridge" yield, possibly related to the collective behavior of the system, is present in events with high-pT processes. The magnitudes of the short- and long-range yields are found to grow with the event scale. The results are compared to EPOS LHC and PYTHIA 8 calculations, with and without string-shoving interactions. It is found that while both models describe the qualitative trends in the data, calculations from EPOS LHC show a better quantitative agreement, in particular for the pT and event-scale dependencies.