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Model development and simulation of biological networks is recognized as a key task in Systems Biology. Integrated with in vitro and in vivo experimental data, network simulation allows for the discovery of the dynamics that regul...
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Model development and simulation of biological networks is recognized as a key task in Systems Biology. Integrated with in vitro and in vivo experimental data, network simulation allows for the discovery of the dynamics that regulate biological systems. Stochastic Petri Nets (SPNs) have become a widespread and reference formalism to model metabolic networks thanks to their natural expressiveness to represent metabolites, reactions, molecule interactions, and simulation randomness due to system fluctuations and environmental noise. In the literature, starting from the network model and the complete set of system parameters, there exist frameworks that allow for dynamic system simulation. Nevertheless, they do not allow for automatic model parameterization, which is a crucial task to identify, in silico, the network configurations that lead the model to satisfy specific temporal properties. To cover such a gap, this work first presents a framework to implement SPN models into SystemC code. Then, it shows how the framework allows for automatic parameterization of the networks. The user formally defines the network properties to be observed and the framework automatically extrapolates, through Assertion-based Verification (ABV), the parameter configurations that satisfy such properties. We present the results obtained by applying the proposed framework to model the complex metabolic network of the purine metabolism. We show how the automatic extrapolation of the system parameters allowed us to simulate the model under different conditions, which led to the understanding of behavioral differences in the regulation of the entire purine network. We also show the scalability of the approach through the modeling and simulation of four biological networks, each one with different structural characteristics.
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Graph-based network modeling is becoming increasingly pervasive touching very different fields. Among these are social networks analysis and brain connectivity modeling. Though apparently very far apart, these two domains share th...
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Graph-based network modeling is becoming increasingly pervasive touching very different fields. Among these are social networks analysis and brain connectivity modeling. Though apparently very far apart, these two domains share the same questions about how the underlying network is structured and how this can be measured. This determines an a-priori unexpected convergence of the research efforts of two different communities, that is neurosciences and information technology. In this work, we put forth some basic issues emerging from the overlaps of the two domains and propose a first simple measure allowing to capture one among the features of interest: the transtopic closeness centrality. To this end, the related concepts are briefly recalled and two case studies are considered. Then, relying on social network analysis principles, the transposition to functional brain networks is proposed highlighting and discussing some of the inherent critical issues.
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The Burrows-Wheeler-Transform (BWT) is a reversible string transformation which plays a central role in text compression and is fundamental in many modern bioinformatics applications. The BWT is a permutation of the characters, wh...
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The Burrows-Wheeler-Transform (BWT) is a reversible string transformation which plays a central role in text compression and is fundamental in many modern bioinformatics applications. The BWT is a permutation of the characters, which is in general better compressible and allows to answer several different query types more efficiently than the original string.
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Energy management is a key topic for today's society, and a crucial challenge is the shift from a production system based on fossil fuel to sustainable energy. A key ingredient for this important step is the use of a highly automa...
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Energy management is a key topic for today's society, and a crucial challenge is the shift from a production system based on fossil fuel to sustainable energy. A key ingredient for this important step is the use of a highly automated power delivery network, where intelligent devices can communicate and collaborate to optimize energy management. This paper investigates a specific model for smart power grids initially proposed by Zdeborov et al. (Phys Rev E Stat Nonlinear Soft Matter Phys 80(4): 2009) where backup power lines connect a subset of loads to generators so to meet the demand of the whole network. Specifically, we extend such model to minimize CO2 emissions related to energy production. In more detail, we propose a formalization for this problem based on the Distributed Constraint Optimization Problem (DCOP) framework and a solution approach based on the min-sum algorithm. We empirically evaluate our approach on a set of benchmarking power grid instances comparing our proposed solution to simulated annealing and to the DSA algorithm. Our results show that min-sum favorably compares with simulated annealing and DSA providing a promising solution method for this model.
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Adaptive optics (AO) is an established technique to measure and compensate for optical aberrations. One of its key components is the wavefront sensor (WFS), which is typically a Shack-Hartmann sensor (SH) capturing an image relate...
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Adaptive optics (AO) is an established technique to measure and compensate for optical aberrations. One of its key components is the wavefront sensor (WFS), which is typically a Shack-Hartmann sensor (SH) capturing an image related to the aberrated wavefront. We propose an efficient implementation of the SH-WFS centroid extraction algorithm, tailored for edge computing. In the edge-computing paradigm, the data are elaborated close to the source (i.e., at the edge) through low-power embedded architectures, in which CPU computing elements are combined with heterogeneous accelerators (e.g., CPUs, field-programmable gate arrays). Since the control loop latency must be minimized to compensate for the wavefront aberration temporal dynamics, we propose an optimized algorithm that takes advantage of the unified CPU/GPU memory of recent low-power embedded architectures. Experimental results show that the centroid extraction latency obtained over spot images up to 700 x 700 pixels wide is smaller than 2 ms. Therefore, our approach meets the temporal requirements of small- to medium-sized AO systems, which are equipped with deformable mirrors having tens of actuators. (C) 2020 Optical Society of America
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We introduce a new method for non-rigid registration of 3D human shapes. Our proposed pipeline builds upon a given parametric model of the human, and makes use of the functional map representation for encoding and inferring shape ...
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We introduce a new method for non-rigid registration of 3D human shapes. Our proposed pipeline builds upon a given parametric model of the human, and makes use of the functional map representation for encoding and inferring shape maps throughout the registration process. This combination endows our method with robustness to a large variety of nuisances observed in practical settings, including non-isometric transformations, downsampling, topological noise and occlusions; further, the pipeline can be applied invariably across different shape representations (e.g. meshes and point clouds), and in the presence of (even dramatic) missing parts such as those arising in real-world depth sensing applications. We showcase our method on a selection of challenging tasks, demonstrating results in line with, or even surpassing, state-of-the-art methods in the respective areas.
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If a biological population is fragmented in small isolated groups, there may emerge an evolution phenomenon independent from natural selection. Limited number of individuals and their isolation allow a completely random variation ...
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If a biological population is fragmented in small isolated groups, there may emerge an evolution phenomenon independent from natural selection. Limited number of individuals and their isolation allow a completely random variation in their genetic frequencies, in such a way to have the predominance of some genes and the disappearance of others in next generations, independently on the convenience of the predominant genes for the individuals. This statistical phenomenon is called founder effect, or bottleneck effect (in ecological dynamics).
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PurposeAlthough ultrasound (US) images represent the most popular modality for guiding breast biopsy, malignant regions are often missed by sonography, thus preventing accurate lesion localization which is essential for a successf...
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PurposeAlthough ultrasound (US) images represent the most popular modality for guiding breast biopsy, malignant regions are often missed by sonography, thus preventing accurate lesion localization which is essential for a successful procedure. Biomechanical models can support the localization of suspicious areas identified on a preoperative image during US scanning since they are able to account for anatomical deformations resulting from US probe pressure. We propose a deformation model which relies on position-based dynamics (PBD) approach to predict the displacement of internal targets induced by probe interaction during US acquisition. MethodsThe PBD implementation available in NVIDIA FleX is exploited to create an anatomical model capable of deforming online. Simulation parameters are initialized on a calibration phantom under different levels of probe-induced deformations; then, they are fine-tuned by minimizing the localization error of a US-visible landmark of a realistic breast phantom. The updated model is used to estimate the displacement of other internal lesions due to probe-tissue interaction.ResultsThe localization error obtained when applying the PBD model remains below 11mm for all the tumors even for input displacements in the order of 30mm. This proposed method obtains results aligned with FE models with faster computational performance, suitable for real-time applications. In addition, it outperforms rigid model used to track lesion position in US-guided breast biopsies, at least halving the localization error for all the displacement ranges considered.ConclusionPosition-based dynamics approach has proved to be successful in modeling breast tissue deformations during US acquisition. Its stability, accuracy and real-time performance make such model suitable for tracking lesions displacement during US-guided breast biopsy.
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We compute simultaneously the translational speed, the magnitude and the phase of a quantum vortex ring for a wide range of radii, within the Gross-Pitaevskii model, by imposing its self preservation in a co-moving reference frame...
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We compute simultaneously the translational speed, the magnitude and the phase of a quantum vortex ring for a wide range of radii, within the Gross-Pitaevskii model, by imposing its self preservation in a co-moving reference frame. By providing such a solution as the initial condition for the time-dependent Gross-Pitaevskii equation, we verify a posteriori that the ring's radius and speed are well maintained in the reference frame moving at the computed speed. Convergence to the numerical solution is fast for large values of the radius, as the wavefunction tends to that of a straight vortex, whereas a continuation technique and interpolation of rough solutions are needed to reach convergence as the ring tends to a disk. Comparison with other strategies for generating a quantum ring reveals that all of them seem to capture quite well the translational speed, whereas none of them seems to preserve the radius with the accuracy reached in the present work.
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