570 Biowissenschaften; Biologie
Refine
Has Fulltext
- no (30) (remove)
Document Type
- Article (28)
- Doctoral Thesis (1)
- Other (1)
Language
- English (30) (remove)
Is part of the Bibliography
- yes (30)
Keywords
- ion mobility spectrometry (2)
- (PEDOT (1)
- APCI (1)
- Azobenzene containing cationic surfactants (1)
- Biodiversity (1)
- Boreal forest diebacks (1)
- Climatic tipping points (1)
- Conservation policy (1)
- Coupled oscillators (1)
- D. discoideum (1)
Institute
- Institut für Physik und Astronomie (30) (remove)
Due to their electrically polarized air-filled internal pores, optimized ferroelectrets exhibit a remarkable piezoelectric response, making them suitable for energy harvesting. Expanded polytetrafluoroethylene (ePTFE) ferroelectret films are laminated with two fluorinated-ethylene-propylene (FEP) copolymer films and internally polarized by corona discharge. Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS)-coated spandex fabric is employed for the electrodes to assemble an all-organic ferroelectret nanogenerator (FENG). The outer electret-plus-electrode double layers form active device layers with deformable electric dipoles that strongly contribute to the overall piezoelectric response in the proposed concept of wearable nanogenerators. Thus, the FENG with spandex electrodes generates a short-circuit current which is twice as high as that with aluminum electrodes. The stacking sequence spandex/FEP/ePTFE/FEP/ePTFE/FEP/spandex with an average pore size of 3 mu m in the ePTFE films yields the best overall performance, which is also demonstrated by the displacement-versus-electric-field loop results. The all-organic FENGs are stable up to 90 degrees C and still perform well 9 months after being polarized. An optimized FENG makes three light emitting diodes (LEDs) blink twice with the energy generated during a single footstep. The new all-organic FENG can thus continuously power wearable electronic devices and is easily integrated, for example, with clothing, other textiles, or shoe insoles.
Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows
(2021)
Amoeboid cell motility is essential for a wide range of biological processes including wound healing, embryonic morphogenesis, and cancer metastasis. It relies on complex dynamical patterns of cell shape changes that pose long-standing challenges to mathematical modeling and raise a need for automated and reproducible approaches to extract quantitative morphological features from image sequences. Here, we introduce a theoretical framework and a computational method for obtaining smooth representations of the spatiotemporal contour dynamics from stacks of segmented microscopy images. Based on a Gaussian process regression we propose a one-parameter family of regularized contour flows that allows us to continuously track reference points (virtual markers) between successive cell contours. We use this approach to define a coordinate system on the moving cell boundary and to represent different local geometric quantities in this frame of reference. In particular, we introduce the local marker dispersion as a measure to identify localized membrane expansions and provide a fully automated way to extract the properties of such expansions, including their area and growth time. The methods are available as an open-source software package called AmoePy, a Python-based toolbox for analyzing amoeboid cell motility (based on time-lapse microscopy data), including a graphical user interface and detailed documentation. Due to the mathematical rigor of our framework, we envision it to be of use for the development of novel cell motility models. We mainly use experimental data of the social amoeba Dictyostelium discoideum to illustrate and validate our approach. <br /> Author summary Amoeboid motion is a crawling-like cell migration that plays an important key role in multiple biological processes such as wound healing and cancer metastasis. This type of cell motility results from expanding and simultaneously contracting parts of the cell membrane. From fluorescence images, we obtain a sequence of points, representing the cell membrane, for each time step. By using regression analysis on these sequences, we derive smooth representations, so-called contours, of the membrane. Since the number of measurements is discrete and often limited, the question is raised of how to link consecutive contours with each other. In this work, we present a novel mathematical framework in which these links are described by regularized flows allowing a certain degree of concentration or stretching of neighboring reference points on the same contour. This stretching rate, the so-called local dispersion, is used to identify expansions and contractions of the cell membrane providing a fully automated way of extracting properties of these cell shape changes. We applied our methods to time-lapse microscopy data of the social amoeba Dictyostelium discoideum.
A symmetry-breaking mechanism is investigated that creates bistability between fully and partially synchronized states in oscillator networks. Two populations of oscillators with unimodal frequency distribution and different amplitudes, in the presence of weak global coupling, are shown to simplify to a modular network with asymmetrical coupling. With increasing the coupling strength, a synchronization transition is observed with an isolated fully synchronized state. The results are interpreted theoretically in the thermodynamic limit and confirmed in experiments with chemical oscillators.
Self-organized coherence-incoherence patterns, called chimera states, have first been reported in systems of Kuramoto oscillators. For coupled excitable units, similar patterns where coherent units are at rest are called bump states. Here, we study bumps in an array of active rotators coupled by nonlocal attraction and global repulsion. We demonstrate how they can emerge in a supercritical scenario from completely coherent Turing patterns: a single incoherent unit appears in a homoclinic bifurcation, undergoing subsequent transitions to quasiperiodic and chaotic behavior, which eventually transforms into extensive chaos with many incoherent units. We present different types of transitions and explain the formation of coherence-incoherence patterns according to the classical paradigm of short-range activation and long-range inhibition.
Characterization of binding interactions of SARS-CoV-2 spike protein and DNA-peptide nanostructures
(2022)
Binding interactions of the spike proteins of the severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) to a peptide fragment derived from the human angiotensin converting enzyme 2 (hACE2) receptor are investigated.
The peptide is employed as capture moiety in enzyme linked immunosorbent assays (ELISA) and quantitative binding interaction measurements that are based on fluorescence proximity sensing (switchSENSE).
In both techniques, the peptide is presented on an oligovalent DNA nanostructure, in order to assess the impact of mono- versus trivalent binding modes.
As the analyte, the spike protein and several of its subunits are tested as well as inactivated SARS-CoV-2 and pseudo viruses. While binding of the peptide to the full-length spike protein can be observed, the subunits RBD and S1 do not exhibit binding in the employed concentrations.
Variations of the amino acid sequence of the recombinant full-length spike proteins furthermore influence binding behavior. The peptide was coupled to DNA nanostructures that form a geometric complement to the trimeric structure of the spike protein binding sites.
An increase in binding strength for trimeric peptide presentation compared to single peptide presentation could be generally observed in ELISA and was quantified in switchSENSE measurements. Binding to inactivated wild type viruses could be shown as well as qualitatively different binding behavior of the Alpha and Beta variants compared to the wild type virus strain in pseudo virus models.
The contamination of barley by molds on the field or in storage leads to the spoilage of grain and the production of mycotoxins, which causes major economic losses in malting facilities and breweries. Therefore, on-site detection of hidden fungus contaminations in grain storages based on the detection of volatile marker compounds is of high interest. In this work, the volatile metabolites of 10 different fungus species are identified by gas chromatography (GC) combined with two complementary mass spectrometric methods, namely, electron impact (EI) and chemical ionization at atmospheric pressure (APCI)-mass spectrometry (MS). The APCI source utilizes soft X-radiation, which enables the selective protonation of the volatile metabolites largely without side reactions. Nearly 80 volatile or semivolatile compounds from different substance classes, namely, alcohols, aldehydes, ketones, carboxylic acids, esters, substituted aromatic compounds, alkenes, terpenes, oxidized terpenes, sesquiterpenes, and oxidized sesquiterpenes, could be identified. The profiles of volatile and semivolatile metabolites of the different fungus species are characteristic of them and allow their safe differentiation. The application of the same GC parameters and APCI source allows a simple method transfer from MS to ion mobility spectrometry (IMS), which permits on-site analyses of grain stores. Characterization of IMS yields limits of detection very similar to those of APCI-MS. Accordingly, more than 90% of the volatile metabolites found by APCI-MS were also detected in IMS. In addition to different fungus genera, different species of one fungus genus could also be differentiated by GC-IMS.
The movement of microswimmers is often described by active Brownian particle models. Here we introduce a variant of these models with several internal states of the swimmer to describe stochastic strategies for directional swimming such as run and tumble or run and reverse that are used by microorganisms for chemotaxis. The model includes a mechanism to generate a directional bias for chemotaxis and interactions with external fields (e.g., gravity, magnetic field, fluid flow) that impose forces or torques on the swimmer. We show how this modified model can be applied to various scenarios: First, the run and tumble motion of E. coli is used to establish a paradigm for chemotaxis and investigate how it is affected by external forces. Then, we study magneto-aerotaxis in magnetotactic bacteria, which is biased not only by an oxygen gradient towards a preferred concentration, but also by magnetic fields, which exert a torque on an intracellular chain of magnets. We study the competition of magnetic alignment with active reorientation and show that the magnetic orientation can improve chemotaxis and thereby provide an advantage to the bacteria, even at rather large inclination angles of the magnetic field relative to the oxygen gradient, a case reminiscent of what is expected for the bacteria at or close to the equator. The highest gain in chemotactic velocity is obtained for run and tumble with a magnetic field parallel to the gradient, but in general a mechanism for reverse motion is necessary to swim against the magnetic field and a run and reverse strategy is more advantageous in the presence of a magnetic torque. This finding is consistent with observations that the dominant mode of directional changes in magnetotactic bacteria is reversal rather than tumbles. Moreover, it provides guidance for the design of future magnetic biohybrid swimmers. Author summary In this paper, we propose a modified Active Brownian particle model to describe bacterial swimming behavior under the influence of external forces and torques, in particular of a magnetic torque. This type of interaction is particularly important for magnetic biohybrids (i.e. motile bacteria coupled to a synthetic magnetic component) and for magnetotactic bacteria (i.e. bacteria with a natural intracellular magnetic chain), which perform chemotaxis to swim along chemical gradients, but are also directed by an external magnetic field. The model allows us to investigate the benefits and disadvantages of such coupling between two different directionality mechanisms. In particular we show that the magnetic torque can speed chemotaxis up in some conditions, while it can hinder it in other cases. In addition to an understanding of the swimming strategies of naturally magnetotactic organisms, the results may guide the design of future biomedical devices.
How predictable is the next move of an animal? Specifically, which factors govern the short- and long-term motion patterns and the overall dynamics of land-bound, plant-eating animals in general and ruminants in particular? To answer this question, we here study the movement dynamics of springbok antelopes Antidorcas marsupialis. We propose several complementary statistical-analysis techniques combined with machine-learning approaches to analyze—across multiple time scales—the springbok motion recorded in long-term GPS tracking of collared springboks at a private wildlife reserve in Namibia. As a result, we are able to predict the springbok movement within the next hour with a certainty of about 20%. The remaining about 80% are stochastic in nature and are induced by unaccounted factors in the modeling algorithm and by individual behavioral features of springboks. We find that directedness of motion contributes approximately 17% to this predicted fraction. We find that the measure for directedeness is strongly dependent on the daily cycle of springbok activity. The previously known daily affinity of springboks to their water points, as predicted from our machine-learning algorithm, overall accounts for only about 3% of this predicted deterministic component of springbok motion. Moreover, the resting points are found to affect the motion of springboks at least as much as the formally studied effects of water points. The generality of these statements for the motion patterns and their underlying behavioral reasons for other ruminants can be examined on the basis of our statistical-analysis tools in the future.
We consider an array of nonlocally coupled oscillators on a ring, which for equally spaced units possesses a Kuramoto-Battogtokh chimera regime and a synchronous state. We demonstrate that disorder in oscillators positions leads to a transition from the synchronous to the chimera state. For a static (quenched) disorder we find that the probability of synchrony survival depends on the number of particles, from nearly zero at small populations to one in the thermodynamic limit. Furthermore, we demonstrate how the synchrony gets destroyed for randomly (ballistically or diffusively) moving oscillators. We show that, depending on the number of oscillators, there are different scalings of the transition time with this number and the velocity of the units.
Organic solar cells (OSCs) have progressed rapidly in recent years through the development of novel organic photoactive materials, especially non-fullerene acceptors (NFAs). Consequently, OSCs based on state-of-the-art NFAs have reached significant milestones, such as similar to 19% power conversion efficiencies (PCEs) and small energy losses (less than 0.5 eV). Despite these significant advances, understanding of the interplay between molecular structure and optoelectronic properties lags significantly behind. For example, despite the theoretical framework for describing the energetic disorder being well developed for the case of inorganic semiconductors, the question of the applicability of classical semiconductor theories in analyzing organic semiconductors is still under debate. A general observation in the inorganic field is that inorganic photovoltaic materials possessing a polycrystalline microstructure exhibit suppressed disorder properties and better charge carrier transport compared to their amorphous analogs. Accordingly, this principle extends to the organic semiconductor field as many organic photovoltaic materials are synthesized to pursue polycrystalline-like features. Yet, there appears to be sporadic examples that exhibit an opposite trend. However, full studies decoupling energetic disorder from aggregation effects have largely been left out. Hence, the potential role of the energetic disorder in OSCs has received little attention. Interestingly, recently reported state-of-the-art NFA-based devices could achieve a small energetic disorder and high PCE at the same time; and interest in this investigation related to the disorder properties in OSCs was revived. In this contribution, progress in terms of the correlation between molecular design and energetic disorder is reviewed together with their effects on the optoelectronic mechanism and photovoltaic performance. Finally, the specific challenges and possible solutions in reducing the energetic disorder of OSCs from the viewpoint of materials and devices are proposed.
Forest and steppe communities in the Altai region of Central Asia are threatened by changing climate and anthropogenic pressure. Specifically, increasing drought and grazing pressure may cause collapses of moisture-demanding plant communities, particularly forests. Knowledge about past vegetation and fire responses to climate and land use changes may help anticipating future ecosystem risks, given that it has the potential to disclose mechanisms and processes that govern ecosystem vulnerability. We present a unique paleoecological record from the high-alpine Tsambagarav glacier in the Mongolian Altai that provides novel large-scale information on vegetation, fire and pollution with an exceptional temporal resolution and precision. Our palynological record identifies several late-Holocene boreal forest expansions, contractions and subsequent recoveries. Maximum forest expansions occurred at 3000-2800 BC, 2400-2100 BC, and 1900-1800 BC. After 1800 BC mixed boreal forest communities irrecoverably declined. Fires reached a maximum at 1600 BC, 200 years after the final forest collapse. Our multiproxy data suggest that burning peaked in response to dead biomass accumulation resulting from forest diebacks. Vegetation and fire regimes partly decoupled from climate after 1700 AD, when atmospheric industrial pollution began, and land use intensified. We conclude that moisture availability was more important than temperature for past vegetation dynamics, in particular for forest loss and steppe expansion. The past Mongolian Altai evidence implies that in the future forests of the Russian Altai may collapse in response to reduced moisture availability.
How related are the ergodic properties of the over- and underdamped Langevin equations driven by fractional Gaussian noise? We here find that for massive particles performing fractional Brownian motion (FBM) inertial effects not only destroy the stylized fact of the equivalence of the ensemble-averaged mean-squared displacement (MSD) to the time-averaged MSD (TAMSD) of overdamped or massless FBM, but also dramatically alter the values of the ergodicity-breaking parameter (EB). Our theoretical results for the behavior of EB for underdamped or massive FBM for varying particle mass m, Hurst exponent H, and trace length T are in excellent agreement with the findings of stochastic computer simulations. The current results can be of interest for the experimental community employing various single-particle-tracking techniques and aiming at assessing the degree of nonergodicity for the recorded time series (studying, e.g., the behavior of EB versus lag time). To infer FBM as a realizable model of anomalous diffusion for a set single-particle-tracking data when massive particles are being tracked, the EBs from the data should be compared to EBs of massive (rather than massless) FBM.
We present a systematic study on the properties of Na(Y,Gd)F-4-based upconverting nanoparticles (UCNP) doped with 18% Yb3+, 2% Tm3+, and the influence of Gd3+ (10-50 mol% Gd3+). UCNP were synthesized via the solvothermal method and had a range of diameters within 13 and 50 nm. Structural and photophysical changes were monitored for the UCNP samples after a 24-month incubation period in dry phase and further redispersion. Structural characterization was performed by means of X-ray diffraction (XRD), transmission electron microscopy (TEM) as well as dynamic light scattering (DLS), and the upconversion luminescence (UCL) studies were executed at various temperatures (from 4 to 295 K) using time-resolved and steady-state spectroscopy. An increase in the hexagonal lattice phase with the increase of Gd3+ content was found, although the cubic phase was prevalent in most samples. The Tm3+-luminescence intensity as well as the Tm3+-luminescence decay times peaked at the Gd3+ concentration of 30 mol%. Although the general upconverting luminescence properties of the nanoparticles were preserved, the 24-month incubation period lead to irreversible agglomeration of the UCNP and changes in luminescence band ratios and lifetimes.
Predator-prey cycles rank among the most fundamental concepts in ecology, are predicted by the simplest ecological models and enable, theoretically, the indefinite persistence of predator and prey(1-4). However, it remains an open question for how long cyclic dynamics can be self-sustained in real communities. Field observations have been restricted to a few cycle periods(5-8) and experimental studies indicate that oscillations may be short-lived without external stabilizing factors(9-19). Here we performed microcosm experiments with a planktonic predator-prey system and repeatedly observed oscillatory time series of unprecedented length that persisted for up to around 50 cycles or approximately 300 predator generations. The dominant type of dynamics was characterized by regular, coherent oscillations with a nearly constant predator-prey phase difference. Despite constant experimental conditions, we also observed shorter episodes of irregular, non-coherent oscillations without any significant phase relationship. However, the predator-prey system showed a strong tendency to return to the dominant dynamical regime with a defined phase relationship. A mathematical model suggests that stochasticity is probably responsible for the reversible shift from coherent to non-coherent oscillations, a notion that was supported by experiments with external forcing by pulsed nutrient supply. Our findings empirically demonstrate the potential for infinite persistence of predator and prey populations in a cyclic dynamic regime that shows resilience in the presence of stochastic events.
Low-dimensional description for ensembles of identical phase oscillators subject to Cauchy noise
(2020)
We study ensembles of globally coupled or forced identical phase oscillators subject to independent white Cauchy noise. We demonstrate that if the oscillators are forced in several harmonics, stationary synchronous regimes can be exactly described with a finite number of complex order parameters. The corresponding distribution of phases is a product of wrapped Cauchy distributions. For sinusoidal forcing, the Ott-Antonsen low-dimensional reduction is recovered.
We report on rendering polyelectrolyte brushes photosensitive by loading them with azobenzene-containing cationic surfactants. Planar poly(methacrylic acid) (PMAA) brushes are synthesized using the “grafting from” free-radical polymerization scheme followed by exposure to a solution of photosensitive surfactants consisting of positively-charged head groups and hydrophobic tails into which azobenzene moieties are inserted. In this study the length of the hydrophobic methylene spacer connecting the azobenzene and the charged head group ranges from 4 to 10 CH2 groups. Under irradiation with UV light, the photo-isomerization of azobenzene integrated into a surfactant results in a change in size, geometry, dipole moment and free volume of the whole molecule. When the brush loaded with photosensitive surfactants is exposed to irradiation with UV interference patterns, the topography of the brush deforms following the distribution of the light intensity, exhibiting surface relief gratings (SRG). Since SRG formation is accompanied by a local rupturing of polymer chains in areas from which the polymer material is receding, most of the polymer material is removed from the surface during treatment with good solvent, leaving behind characteristic patterns of lines or dots. The azobenzene molecules still integrated within the polymer film can be removed by washing the brush with water. The remaining nano-structured brush can then be re-used for further functionalization. Although the opto-mechanically induced rupturing occurs for all surfactants, larger species do not penetrate deep enough into the brush such that after rupturing a leftover layer of polymer material remains on the substrate. This indicates that rupturing occurs predominantly in regions of high surfactant density.
We employ Langevin-dynamics simulations to unveil non-Brownian and non-Gaussian center-of-mass self-diffusion of massive flexible dumbbell-shaped particles in crowded two-dimensional solutions. We study the intradumbbell dynamics of the relative motion of the two constituent elastically coupled disks. Our main focus is on effects of the crowding fraction phi and of the particle structure on the diffusion characteristics. We evaluate the time-averaged mean-squared displacement (TAMSD), the displacement probability-density function (PDF), and the displacement autocorrelation function (ACF) of the dimers. For the TAMSD at highly crowded conditions of dumbbells, e.g., we observe a transition from the short-time ballistic behavior, via an intermediate subdiffusive regime, to long-time Brownian-like spreading dynamics. The crowded system of dimers exhibits two distinct diffusion regimes distinguished by the scaling exponent of the TAMSD, the dependence of the diffusivity on phi, and the features of the displacement-ACF. We attribute these regimes to a crowding-induced transition from viscous to viscoelastic diffusion upon growing phi. We also analyze the relative motion in the dimers, finding that larger phi suppress their vibrations and yield strongly non-Gaussian PDFs of rotational displacements. For the diffusion coefficients D(phi) of translational and rotational motion of the dumbbells an exponential decay with phi for weak and a power-law variation D(phi) proportional to (phi - phi(star))(2.4) for strong crowding is found. A comparison of simulation results with theoretical predictions for D(phi) is discussed and some relevant experimental systems are overviewed.
Random logic networks
(2021)
We investigate dynamical properties of a quantum generalization of classical reversible Boolean networks. The state of each node is encoded as a single qubit, and classical Boolean logic operations are supplemented by controlled bit-flip and Hadamard operations. We consider synchronous updating schemes in which each qubit is updated at each step based on stored values of the qubits from the previous step. We investigate the periodic or quasiperiodic behavior of quantum networks, and we analyze the propagation of single site perturbations through the quantum networks with input degree one. A nonclassical mechanism for perturbation propagation leads to substantially different evolution of the Hamming distance between the original and perturbed states.