This article focuses on the problem of maximal compliance design of a hyper-elastic solid with the optimal design of human skin grafts as the application in mind. The solution method is a phasefield-based topology optimization method that supposes multiple local phasefields and a minimum distance constraint in order to prevent the phasefields from merging. Consequently, structurally disintegrating solutions such as by the coalescence of voids can be prevented. The method is used to find an optimal graft meshing pattern for a sample that is subjected to a biaxial extension of up to 150%, which corresponds to an expansion ratio of 1 : 2.25. Three prospective unitcell solutions that exhibit meta-material behavior are proposed for a periodic graft pattern. The results are a step toward improving the skin graft meshing efficiency. This work does not cover experimental validation.
- MeSH
- Skin * MeSH
- Humans MeSH
- Prospective Studies MeSH
- Skin Transplantation * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Optimization of neural network topology, weights and neuron transfer functions for given data set and problem is not an easy task. In this article, we focus primarily on building optimal feed-forward neural network classifier for i.i.d. data sets. We apply meta-learning principles to the neural network structure and function optimization. We show that diversity promotion, ensembling, self-organization and induction are beneficial for the problem. We combine several different neuron types trained by various optimization algorithms to build a supervised feed-forward neural network called Group of Adaptive Models Evolution (GAME). The approach was tested on a large number of benchmark data sets. The experiments show that the combination of different optimization algorithms in the network is the best choice when the performance is averaged over several real-world problems.
While brain imaging tools like functional magnetic resonance imaging (fMRI) afford measurements of whole-brain activity, it remains unclear how best to interpret patterns found amid the data's apparent self-organization. To clarify how patterns of brain activity support brain function, one might identify metric spaces that optimally distinguish brain states across experimentally defined conditions. Therefore, the present study considers the relative capacities of several metric spaces to disambiguate experimentally defined brain states. One fundamental metric space interprets fMRI data topographically, that is, as the vector of amplitudes of a multivariate signal, changing with time. Another perspective compares the brain's functional connectivity, that is, the similarity matrix computed between signals from different brain regions. More recently, metric spaces that consider the data's topology have become available. Such methods treat data as a sample drawn from an abstract geometric object. To recover the structure of that object, topological data analysis detects features that are invariant under continuous deformations (such as coordinate rotation and nodal misalignment). Moreover, the methods explicitly consider features that persist across multiple geometric scales. While, certainly, there are strengths and weaknesses of each brain dynamics metric space, wefind that those that track topological features optimally distinguish experimentally defined brain states.
- Publication type
- Journal Article MeSH
Visualization of electrical activity in living cells represents an important challenge in context of basic neurophysiological studies. Here we report a new voltage sensitive fluorescent indicator which response could be detected by fluorescence monitoring in a single red channel. To the best of our knowledge, this is the first fluorescent protein-based voltage sensor which uses insertion-into-circular permutant topology to provide an efficient interaction between sensitive and reporter domains. Its fluorescent core originates from red fluorescent protein (FP) FusionRed, which has optimal spectral characteristics to be used in whole body imaging techniques. Indicators using the same domain topology could become a new perspective for the FP-based voltage sensors that are traditionally based on Förster resonance energy transfer (FRET).
- MeSH
- Biosensing Techniques methods MeSH
- Electrophysiological Phenomena MeSH
- Fluorescent Dyes metabolism MeSH
- HEK293 Cells MeSH
- Rats MeSH
- Humans MeSH
- Luminescent Proteins chemistry MeSH
- Cell Line, Tumor MeSH
- Protein Domains MeSH
- Protein Engineering methods MeSH
- Fluorescence Resonance Energy Transfer methods MeSH
- Animals MeSH
- Check Tag
- Rats MeSH
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
Density functional theory (DFT) studies on adsorption of several gaseous homo- and hetero-diatomic molecules (AB) including H2, O2, N2, NO and CO on external surface of H-capped pristine armchair (5, 5) single-walled carbon nanotube (SWCNT) were conducted. Structures of C70H10 and the corresponding C70H10-AB adducts were fully optimized at the B3LYP/6-311G* level of theory. Calculated HOMO/LUMO energy gaps (Eg), (13)C NMR chemical shifts and IR/Raman parameters were analyzed and critically compared with available experimental data. Significant changes of carbon NMR atom chemical shifts (up to -100 ppm) and shielding anisotropies (up to -180 ppm) at sites of addition were observed. Functionalized SWCNTs produced IR and Raman spectra different from the pristine nanotube model. The selective changes in vibrational spectra will help in assigning the topology of functionalization at the nanotube wall.
- MeSH
- Electrons MeSH
- Quantum Theory * MeSH
- Magnetic Resonance Spectroscopy MeSH
- Molecular Conformation MeSH
- Models, Molecular * MeSH
- Nanotubes, Carbon chemistry MeSH
- Spectrum Analysis, Raman * MeSH
- Spectroscopy, Fourier Transform Infrared MeSH
- Thermodynamics MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Cellular metabolism, the interconversion of small molecules by chemical reactions, is a tightly coordinated process that requires integration of diverse environmental and intracellular cues. While for many organisms the topology of the network of metabolic reactions is increasingly known, the regulatory principles that shape the network's adaptation to diverse and changing environments remain largely elusive. To investigate the principles of metabolic adaptation and regulation in metabolic pathways, we propose a computational approach based on in-silico evolution. Rather than analyzing existing regulatory schemes, we let a population of minimal, prototypical metabolic cells evolve rate constants and appropriate regulatory schemes that allow for optimal growth in static and fluctuating environments. Applying our approach to a small, but already sufficiently complex, minimal system reveals intricate transitions between metabolic modes. These results have implications for trade-offs in resource allocation. Going from static to varying environments, we show that for fluctuating nutrient availability, active metabolic regulation results in a significantly increased overall rate of metabolism.
- MeSH
- Algorithms MeSH
- Metabolic Flux Analysis methods MeSH
- Biological Clocks genetics MeSH
- Adaptation, Physiological genetics MeSH
- Humans MeSH
- Metabolome genetics MeSH
- Models, Genetic * MeSH
- Evolution, Molecular * MeSH
- Computer Simulation MeSH
- Signal Transduction genetics MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Naive use of molecular data may lead to ambiguous conclusions, especially within the context of "cryptic" species. Here, we integrated molecular and morphometric data to evaluate phylogenetic relationships in the widespread terrestrial micro-snail genus, Euconulus. We analyzed mitochondrial (16S + COII) and nuclear (ITS1 + ITS2) sequence across 94 populations from Europe, Asia and North America within the nominate species E. alderi, E. fulvus and E. polygyratus, and used the southeastern USA E. chersinus, E. dentatus, and E. trochulus as comparative outgroups. Phylogeny was reconstructed using four different reconstruction methods to identify robust, well-supported topological features. We then performed discriminant analysis on shell measurements between these genetically-identified species-level clades. These analyses provided evidence for a biologically valid North American "cryptic" species within E. alderi. However, while highly supported polyphyletic structure was also observed within E. fulvus, disagreement in placement of individuals between mtDNA and nDNA clades, lack of morphological differences, and presence of potential hybrids imply that these lineages do not rise to the threshold as biologically valid cryptic species, and rather appear to simply represent a complex of geographically structured populations within a single species. These results caution that entering into a cryptic species hypothesis should not be undertaken lightly, and should be optimally supported along multiple lines of evidence. Generally, post-hoc analyses of macro-scale features should be conducted to attempt identification of previously ignored diagnostic traits. If such traits cannot be found, i.e. in the case of potentially "fully cryptic" species, additional criteria should be met to propound a cryptic species hypothesis, including the agreement in tree topology among both mtDNA and nDNA, and little (or no) evidence of hybridization based on a critical analysis of sequence chromatograms. Even when the above conditions are satisfied, it only implies that the cryptic species hypothesis is plausible, but should optimally be subjected to further careful examination.
- MeSH
- Principal Component Analysis MeSH
- Cell Nucleus genetics MeSH
- Phylogeny MeSH
- Snails classification genetics MeSH
- Likelihood Functions MeSH
- Electron Transport Complex IV classification genetics MeSH
- RNA, Ribosomal, 16S classification genetics MeSH
- Base Sequence MeSH
- Sequence Analysis, DNA MeSH
- Sequence Alignment MeSH
- Animals MeSH
- Check Tag
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
An ultrasensitive impedimetric glycan-based biosensor for reliable and selective detection of inactivated, but intact influenza viruses H3N2 was developed. Such glycan-based approach has a distinct advantage over antibody-based detection of influenza viruses since glycans are natural viral receptors with a possibility to selectively distinguish between potentially pathogenic influenza subtypes by the glycan-based biosensors. Build-up of the biosensor was carefully optimized with atomic force microscopy applied for visualization of the biosensor surface after binding of viruses with the topology of an individual viral particle H3N2 analyzed. The glycan biosensor could detect a glycan binding lectin with a limit of detection (LOD) of 5 aM. The biosensor was finally applied for analysis of influenza viruses H3N2 with LOD of 13 viral particles in 1 μl, what is the lowest LOD for analysis of influenza viral particles by the glycan-based device achieved so far. The biosensor could detect H3N2 viruses selectively with a sensitivity ratio of 30 over influenza viruses H7N7. The impedimetric biosensor presented here is the most sensitive glycan-based device for detection of influenza viruses and among the most sensitive antibody or aptamer based biosensor devices.
- MeSH
- Biosensing Techniques methods MeSH
- Influenza, Human diagnosis virology MeSH
- Dielectric Spectroscopy methods MeSH
- Humans MeSH
- Limit of Detection MeSH
- Polysaccharides chemistry MeSH
- Influenza A Virus, H3N2 Subtype isolation & purification MeSH
- Influenza A Virus, H7N7 Subtype isolation & purification MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Evaluation Study MeSH
- Research Support, Non-U.S. Gov't MeSH
Data-driven cell tracking and segmentation methods in biomedical imaging require diverse and information-rich training data. In cases where the number of training samples is limited, synthetic computer-generated data sets can be used to improve these methods. This requires the synthesis of cell shapes as well as corresponding microscopy images using generative models. To synthesize realistic living cell shapes, the shape representation used by the generative model should be able to accurately represent fine details and changes in topology, which are common in cells. These requirements are not met by 3D voxel masks, which are restricted in resolution, and polygon meshes, which do not easily model processes like cell growth and mitosis. In this work, we propose to represent living cell shapes as level sets of signed distance functions (SDFs) which are estimated by neural networks. We optimize a fully-connected neural network to provide an implicit representation of the SDF value at any point in a 3D+time domain, conditioned on a learned latent code that is disentangled from the rotation of the cell shape. We demonstrate the effectiveness of this approach on cells that exhibit rapid deformations (Platynereis dumerilii), cells that grow and divide (C. elegans), and cells that have growing and branching filopodial protrusions (A549 human lung carcinoma cells). A quantitative evaluation using shape features and Dice similarity coefficients of real and synthetic cell shapes shows that our model can generate topologically plausible complex cell shapes in 3D+time with high similarity to real living cell shapes. Finally, we show how microscopy images of living cells that correspond to our generated cell shapes can be synthesized using an image-to-image model.
- MeSH
- Caenorhabditis elegans * MeSH
- Humans MeSH
- Mitosis MeSH
- Lung Neoplasms * MeSH
- Neural Networks, Computer MeSH
- Image Processing, Computer-Assisted methods MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
Competition in di- and tri-trophic food web modules with many competing species is studied. The food web modules considered are apparent competition between n species sharing a single predator and a diamond-like food web with a single resource, a single top predator and many competing middle species. The predators have either fixed preferences for their prey, or they switch between available prey in a way that maximizes their fitness. Dependence of these food web dynamics on environmental carrying capacity and food web connectance is studied. The results predict that optimal flexible foraging strongly weakens apparent competition and promotes species coexistence. Food web robustness (defined here as the proportion of surviving species) does not decrease with increased connectance in these food-webs. Moreover, it is shown that flexible prey switching leads to the same population equilibria as in corresponding food webs with highly specialized predators. The results show that flexible foraging behavior by predators can have very strong impact on species richness, as well as the response of communities to changes in resource enrichment and food-web connectance when compared to the same food-web topology with inflexible top predators. Several results on global stability using Lyapunov functions are provided.
