Unifying the essential concepts of biological networks: biological insights and philosophical foundations

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies

Perspective: network-guided pattern formation of neural dynamics

The understanding of neural activity patterns is fundamentally linked to an understanding of how the brain's network architecture shapes dynamical processes. Established approaches rely mostly on deviations of a given network from certain classes of random graphs. Hypotheses about the supposed role of prominent topological features (for instance, the roles of modularity, network motifs, or hierarchical network organization) are derived from these deviations. An alternative strategy could be to study deviations of network architectures from regular graphs (rings, lattices) and consider the implications of such deviations for self-organized dynamic patterns on the network. Following this strategy, we draw on the theory of spatiotemporal pattern formation and propose a novel perspective for analyzing dynamics on networks, by evaluating how the self-organized dynamics are confined by network architecture to a small set of permissible collective states. In particular, we discuss the role of prominent topological features of brain connectivity, such as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the notion of network-guided pattern formation with numerical simulations and outline how it can facilitate the understanding of neural dynamics

Localized Linear Discriminant Analysis

Despite its age, the Linear Discriminant Analysis performs well even in situations where the underlying premises like normally distributed data with constant covariance matrices over all classes are not met. It is, however, a global technique that does not regard the nature of an individual observation to be classified. By weighting each training observation according to its distance to the observation of interest, a global classifier can be transformed into an observation specific approach. So far, this has been done for logistic discrimination. By using LDA instead, the computation of the local classifier is much simpler. Moreover, it is ready for applications in multi-class situations. --classification,local models,LDA

Unifying the essential concepts of biological networks: biological insights and philosophical foundations

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies

Unifying the essential concepts of biological networks: biological insights and philosophical foundations

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies

10371 Abstracts Collection -- Dynamic Maps

From September 12th to 17th, 2010, the Dagstuhl Seminar 10371 ``Dynamic Maps \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Sequential and direct ionic excitation in the strong-field ionization of 1-butene molecules

We study the Strong-Field Ionization (SFI) of the hydrocarbon 1-butene as a function of wavelength using photoion-photoelectron covariance and coincidence spectroscopy. We observe a striking transition in the fragment-associated photoelectron spectra: from a single Above Threshold Ionization (ATI) progression for photon energies less than the cation D0–D1 gap to two ATI progressions for a photon energy greater than this gap. For the first case, electronically excited cations are created by SFI populating the ground cationic state D0, followed by sequential post-ionization excitation. For the second case, direct sub-cycle SFI to the D1 excited cation state contributes significantly. Our experiments access ionization dynamics in a regime where strong-field and resonance-enhanced processes can interplay

Towards a safe MLOps Process for the Continuous Development and Safety Assurance of ML-based Systems in the Railway Domain

Traditional automation technologies alone are not sufficient to enable driverless operation of trains (called Grade of Automation (GoA) 4) on non-restricted infrastructure. The required perception tasks are nowadays realized using Machine Learning (ML) and thus need to be developed and deployed reliably and efficiently. One important aspect to achieve this is to use an MLOps process for tackling improved reproducibility, traceability, collaboration, and continuous adaptation of a driverless operation to changing conditions. MLOps mixes ML application development and operation (Ops) and enables high frequency software releases and continuous innovation based on the feedback from operations. In this paper, we outline a safe MLOps process for the continuous development and safety assurance of ML-based systems in the railway domain. It integrates system engineering, safety assurance, and the ML life-cycle in a comprehensive workflow. We present the individual stages of the process and their interactions. Moreover, we describe relevant challenges to automate the different stages of the safe MLOps process

Towards a Continuous Manufacturing Process of Protein-Loaded Polymeric Nanoparticle Powders

To develop a scalable and efficient process suitable for the continuous manufacturing of poly(lactic-co-glycolic acid) (PLGA) nanoparticles containing ovalbumin as the model protein. PLGA nanoparticles were prepared using a double emulsification spray-drying method. Emulsions were prepared using a focused ultrasound transducer equipped with a flow cell. Either poly(vinyl alcohol) (PVA) or poloxamer 407 (P-407) was used as a stabilizer. Aliquots of the emulsions were blended with different matrix excipients and spray dried, and the yield and size of the resuspended nanoparticles was determined and compared against solvent displacement. Nanoparticle sizes of spray-dried PLGA/PVA emulsions were independent of the matrix excipient and comparable with sizes from the solvent displacement method. The yield of the resuspended nanoparticles was highest for emulsions containing trehalose and leucine (79%). Spray drying of PLGA/P-407 emulsions led to agglomerated nanoparticles independent of the matrix excipient. PLGA/P-407 nanoparticles pre-formed by solvent displacement could be spray dried with limited agglomeration when PVA was added as an additional stabilizer. A comparably high and economically interesting nanoparticle yield could be achieved with a process suitable for continuous manufacturing. Further studies are needed to understand the robustness of a continuous process at commercial scale