The viscoelastic properties of the cytoplasm of living yeast cells were investigated by studying the motion of lipid granules naturally occurring in the cytoplasm. A large frequency range of observation was obtained by a combination of video-based and laser-based tracking methods. At time scales from 10(-4) to 10(2) s, the granules typically perform subdiffusive motion with characteristics different from previous measurements in living cells. This subdiffusive behavior is thought to be due to the presence of polymer networks and membranous structures in the cytoplasm. Consistent with this hypothesis, we observe that the motion becomes less subdiffusive upon actin disruption.

Anomalous diffusion in living yeast cells

The selection of periodical or chaotic attractors becomes initial-dependent in that setting different initial values can trigger a different profile of attractors in a dynamical system with memory by adding a nonlinear term such as z2y in the Rossler system. The memory effect means that the outputs are very dependent on the initial value for variable z, e.g. magnetic flux for a memristor. In this study, standard nonlinear analyses, including phase portrait, bifurcation analysis, and Lyapunov exponent analysis were carried out. Synchronization between two coupled oscillators and a network was investigated by resetting initial states. A statistical synchronization factor was calculated to find the dependence of synchronization on the coupling intensity when different initial values were selected. Our results show that the dynamics of the attractor depends on the selection of the initial value for one variable z. In the case of coupling between two oscillators, appropriate initial values are selected to trigger two different nonlinear oscillators (periodical and chaotic). Results show that complete synchronization between periodical oscillators, chaotic oscillators, and periodical and chaotic oscillators can be realized by applying an appropriate unidirectional coupling intensity. In particular, two periodical oscillators can be coupled bidirectionally to reach chaotic synchronization so that periodical oscillation is modulated to become chaotic. When the memory effect is considered on some nodes of a chain network, enhancement of memory function can decrease the synchronization, while a small region for intensity of memory function can contribute to the synchronization of the network. Finally, dependence of attractor formation on the initial setting was verified on the field programmable gate array (FPGA) circuit in digital signal processing (DSP) builder block under Matlab/Simulink.

Synchronization stability between initial-dependent oscillators with periodical and chaotic oscillation

Seagrass meadows are key elements of marine ecosystems as they affect the physical, chemical and biological environment and provide habitats for fish and invertebrates. Human activities have caused...

https://www.tandfonline.com/doi/pdf/10.1080/22797254.2018.1544838

A review of seagrass detection, mapping and monitoring applications using acoustic systems

. In this study, high-resolution bathymetric multibeam and optical image data,
both obtained within the Belgian manganese (Mn) nodule mining license area by
the autonomous underwater vehicle (AUV) Abyss, were combined in
order to create a predictive random
forests (RF) machine learning model. AUV bathymetry reveals small-scale
terrain variations, allowing slope estimations and calculation of bathymetric
derivatives such as slope, curvature, and ruggedness. Optical AUV imagery
provides quantitative information regarding the distribution (number and
median size) of Mn nodules. Within the area considered in this study, Mn
nodules show a heterogeneous and spatially clustered pattern, and their
number per square meter is negatively correlated with their median size. A
prediction of the number of Mn nodules was achieved by combining information
derived from the acoustic and optical data using a RF model. This model was
tuned by examining the influence of the training set size, the number of
growing trees (ntree), and the number of predictor variables to be
randomly selected at each node (mtry) on the RF prediction accuracy. The use of larger training
data sets with higher ntree and mtry values increases the
accuracy. To estimate the Mn-nodule abundance, these predictions were linked
to ground-truth data acquired by box coring. Linking optical and
hydroacoustic data revealed a nonlinear relationship between the Mn-nodule
distribution and topographic characteristics. This highlights the importance
of a detailed terrain reconstruction for a predictive modeling of Mn-nodule
abundance. In addition, this study underlines the necessity of a sufficient
spatial distribution of the optical data to provide reliable modeling input
for the RF.

/pdf/quantitative-mapping-and-predictive-modeling-of-mn-nodules-1m6ngzrx9a.pdf

Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning

The number and areal extent of marine protected areas worldwide is rapidly increasing as a result of numerous national targets that aim to see up to 30% of their waters protected by 2030. Automated seabed classification algorithms are arising as faster and objective methods to generate benthic habitat maps to monitor these areas. However, no study has yet systematically compared their repeatability. Here we aim to address that problem by comparing the repeatability of maps derived from acoustic datasets collected on consecutive days using three automated seafloor classification algorithms: (1) Random Forest (RF), (2) K–Nearest Neighbour (KNN) and (3) K means (KMEANS). The most robust and repeatable approach is then used to evaluate the change in seafloor habitats between 2012 and 2015 within the Greater Haig Fras Marine Conservation Zone, Celtic Sea, UK. Our results demonstrate that only RF and KNN provide statistically repeatable maps, with 60.3% and 47.2% agreement between consecutive days. Additionally, this study suggests that in low-relief areas, bathymetric derivatives are non-essential input parameters, while backscatter textural features, in particular Grey Level Co-occurrence Matrices, are substantially more effective in the detection of different habitats. Habitat persistence in the test area between 2012 and 2015 was 48.8%, with swapping of habitats driving the changes in 38.2% of the area. Overall, this study highlights the importance of investigating the repeatability of automated seafloor classification methods before they can be fully used in the monitoring of benthic habitats.

/pdf/assessing-the-repeatability-of-automated-seafloor-1kn4rff1nc.pdf

Assessing the Repeatability of Automated Seafloor Classification Algorithms, with Application in Marine Protected Area Monitoring

Both basic science and marine spatial planning are in a need of high resolution spatially continuous data on seabed habitats and biota. As conventional point-wise sampling is unable to cover large spatial extents in high detail, it must be supplemented with remote sensing and modeling in order to fulfill the scientific and management needs. The combined use of in situ sampling, sonar scanning, and mathematical modeling is becoming the main method for mapping both abiotic and biotic seabed features. Further development and testing of the methods in varying locations and environmental settings is essential for moving towards unified and generally accepted methodology. To fill the relevant research gap in the Baltic Sea, we used multibeam sonar and mathematical modeling methods – generalized additive models (GAM) and random forest (RF) – together with underwater video to map seabed substrate and epibenthos of offshore shallows. In addition to testing the general applicability of the proposed complex of techniques, the predictive power of different sonar-based variables and modeling algorithms were tested. Mean depth, followed by mean backscatter, were the most influential variables in most of the models. Generally, mean values of sonar-based variables had higher predictive power than their standard deviations. The predictive accuracy of RF was higher than that of GAM. To conclude, we found the method to be feasible and with predictive accuracy similar to previous studies of sonar-based mapping.

Applying multibeam sonar and mathematical modeling for mapping seabed substrate and biota of offshore shallows

The problem of random motion of harmonically trapped charged particles in a constant external magnetic field is studied. A generalized three-dimensional Langevin equation with a power-law memory kernel is used to model the interaction of Brownian particles with the complex structure of viscoelastic media (e.g., dusty plasmas). The influence of a fluctuating environment is modeled by an additive fractional Gaussian noise. In the long-time limit the exact expressions of the first-order and second-order moments of the fluctuating position for the Brownian particle subjected to an external periodic force in the plane perpendicular to the magnetic field have been calculated. Also, the particle's angular momentum is found. It is shown that an interplay of external periodic forcing, memory, and colored noise can generate a variety of cooperation effects, such as memory-induced sign reversals of the angular momentum, multiresonance versus Larmor frequency, and memory-induced particle confinement in the absence of an external trapping field. Particularly in the case without external trapping, if the memory exponent is lower than a critical value, we find a resonancelike behavior of the anisotropy in the particle position distribution versus the driving frequency, implying that it can be efficiently excited by an oscillating electric field. Similarities and differences between the behaviors of the models with internal and external noises are also discussed.

Memory effects for a stochastic fractional oscillator in a magnetic field.

The behavior of a stochastic resonate-and-fire neuron model based on a reduction of a fractional noise-driven generalized Langevin equation (GLE) with a power-law memory kernel is considered. The effect of temporally correlated random activity of synaptic inputs, which arise from other neurons forming local and distant networks, is modeled as an additive fractional Gaussian noise in the GLE. Using a first-passage-time formulation, in certain system parameter domains exact expressions for the output interspike interval (ISI) density and for the survival probability (the probability that a spike is not generated) are derived and their dependence on input parameters, especially on the memory exponent, is analyzed. In the case of external white noise, it is shown that at intermediate values of the memory exponent the survival probability is significantly enhanced in comparison with the cases of strong and weak memory, which causes a resonancelike suppression of the probability of spike generation as a function of the memory exponent. Moreover, an examination of the dependence of multimodality in the ISI distribution on input parameters shows that there exists a critical memory exponent α_{c}≈0.402, which marks a dynamical transition in the behavior of the system. That phenomenon is illustrated by a phase diagram describing the emergence of three qualitatively different structures of the ISI distribution. Similarities and differences between the behavior of the model at internal and external noises are also discussed.

Memory-induced resonancelike suppression of spike generation in a resonate-and-fire neuron model.

A subordinated Langevin process, with a random operational time in the form of an inverse strictly increasing Levy-type subordinator, is considered as a generalization of the conventional perfect and leaky integrate-and-fire neuron models. The parent process is given by standard Brownian motion. The effect of the random activity of synaptic inputs, which arises from other neurons forming local and distant networks, is modeled via a Levy exponent of the subordinator. Using a first-passage-time formulation in an external force field, we find exact expressions for the Laplace transform of the output interspike interval (ISI) density. More detailed analysis is presented on the properties of the ISI distribution in the case of the Levy exponent which corresponds to the truncated double-order time-fractional diffusion equation for the probability density of the membrane potential. Particularly, it is shown that at some parameter regimes the ISI density exhibits a bimodal structure. Moreover, it is demonstrated that the ISIs regularity is maximized at an intermediate value of the mean input current.

Bimodality of the interspike interval distributions for subordinated diffusion models of integrate-and-fire neurons

The influence of a colored three-level input noise (trichotomous noise) on the spike generation of a perfect integrate-and-fire (PIF) model of neurons is studied. Using a first-passage-time formulation, exact expressions for the Laplace transform of the output interspike interval (ISI) density and for the statistical moments of the ISIs (such as the coefficient of variation, the skewness, the serial correlation coefficient, and the Fano factor) are derived. To model the anomalous subdiffusion that can arise from, e.g., the trapping properties of dendritic spines, the model is extended by including a random operational time in the form of an inverse strictly increasing L\'evy-type subordinator, and exact formulas for ISI statistics are given for this case as well. Particularly, it is shown that at some parameter regimes, the ISI density exhibits a three-modal structure. The results for the extended model show that the ISI serial correlation coefficient and the Fano factor are nonmonotonic with respect to the input current, which indicates that at an intermediate value of the input current the variability of the output spike trains is minimal. Similarities and differences between the behavior of the presented models and the previously investigated PIF models driven by dichotomous noise are also discussed.

Sander Paekivi

Papers

Applying multibeam sonar and mathematical modeling for mapping seabed substrate and biota of offshore shallows

Memory effects for a stochastic fractional oscillator in a magnetic field.

Memory-induced resonancelike suppression of spike generation in a resonate-and-fire neuron model.

Bimodality of the interspike interval distributions for subordinated diffusion models of integrate-and-fire neurons

Statistical moments of the interspike intervals for a neuron model driven by trichotomous noise.