Gorana Mijatovic

Biogeographical patterns of the genus Merodon Meigen, 1803 (Diptera: Syrphidae) in islands of the eastern Mediterranean and adjacent mainland

The objective of this study was to obtain a biogeographical perspective on the hoverfly genus Merodon (Diptera, Syrphidae) based on data from 32 islands in the Aegean and Ionian archipelagoes vis‐à‐vis the adjacent mainland. In this part of the world, the genus comprises 57 species, out of more than 160 species described worldwide. The importance of eco‐geographical variables (area, elevation, distance to the nearest island and distance to the nearest mainland) and the species–area relationship (SAR) were studied in order to explain patterns of species richness. All tests supported the dynamic equilibrium concept. The area and distance to closest island were found to be the most important drivers of species richness on the Aegean and Ionian archipelagoes. Out of three SAR models evaluated in this study, the exponential function fitted our data best. It was found that a power model with no intercept value (C = 1) performed even better by using symbolic regression for non‐linear equation optimisation. The cluster and null‐model analyses performed to detect inter‐island similarities and origins of the insular Merodon fauna indicated a clear influence of colonisation history of the species on different islands. The results imply that the current distributions of Merodon species in the study area exhibit the combined effects of historical and present‐day processes.

Heart rate dynamics in doxorubicin-induced cardiomyopathy

The clinical use of doxorubicin, an effective chemotherapeutic is hampered by the development of irreversible cardiotoxicity. Here we test time-frequency analysis of heart rate (HR) variability (HRV) for early detection of doxorubicin-induced cardiotoxicity. Experiments were conducted in adult male Wistar rats treated for 15 days with doxorubicin (DOXO, total dose 15 mg kg(-1), i.p.) or saline (CONT). DOXO rats exhibited cardiotoxicity confirmed by histological examination without developing heart failure as estimated by echocardiography. However, HR variability increase reflected subtle microscopic changes of cardiac toxicity in DOXO rats. The results recommend time-frequency analysis of HRV for early detection of doxorubicin-induced cardiomyopathy.

The variable geometry inductors for the energy harvesting applications

The energy harvesting applications becomes omnipresent, utilizing different harvesting techniques to collect energy from ambient. Recently, the electromagnetic field energy harvesting attracted attention, with objective to provide nearly unlimited power supply, particularly for variety of the low-power electronic devices. Furthermore, the inductive assemblies have been recognized as reliable, low cost and adaptive collectors for such kind of energy. Therefore, in this preliminary research paper, the inductive sensors with variable geometry have been analyzed, regarding increase of their overall inductance, with a basic idea to try in that way to collect and store more energy from surrounding electro magnetic field. The approach of partial inductance modeling has been used for the performance analyses of those inductors.

Explicit Markov counting model of inter-spike interval time series

In this paper the inter-spike intervals (ISI) time series are recorded in awake, behaving macaque monkeys and their differences are modeled as a counting explicit finite Markov chain. The average length of time series was 3050 samples. The parameters investigated were: the state probability, the transition probability and normalized count histogram of the Markov chain, as well as ISI interval and ISI difference associated to each state of Markov model separately. As a control parameter, for each series pseudorandom Gaussian and uniform series with same mean and standard deviation, as well as isodistributional surrogates were generated. An unexpected conclusion is that the state and the transition probabilities, as well as the count histogram, correspond to the exact formulae that are derived for the differentials of independent and identically distributed (i.i.d.) random data series.

A novel approach to probabilistic characterisation of neural firing patterns

BACKGROUND The advances in extracellular neural recording techniques result in big data volumes that necessitate fast, reliable, and automatic identification of statistically similar units. This study proposes a single framework yielding a compact set of probabilistic descriptors that characterise the firing patterns of a single unit. NEW METHOD Probabilistic features are estimated from an inter-spike-interval time series, without assumptions about the firing distribution or the stationarity. The first level of proposed firing patterns decomposition divides the inter-spike intervals into bursting, moderate and idle firing modes, yielding a coarse feature set. The second level identifies the successive bursting spikes, or the spiking acceleration/deceleration in the moderate firing mode, yielding a refined feature set. The features are estimated from simulated data and from experimental recordings from the lateral prefrontal cortex in awake, behaving rhesus monkeys. RESULTS An efficient and stable partitioning of neural units is provided by the ensemble evidence accumulation clustering. The possibility of selecting the number of clusters and choosing among coarse and refined feature sets provides an opportunity to explore and compare different data partitions. CONCLUSIONS The estimation of features, if applied to a single unit, can serve as a tool for the firing analysis, observing either overall spiking activity or the periods of interest in trial-to-trial recordings. If applied to massively parallel recordings, it additionally serves as an input to the clustering procedure, with the potential to compare the functional properties of various brain structures and to link the types of neural cells to the particular behavioural states.

The COMSOL support for analysis of inductors with variable geometry

The inductive assemblies with variable geometry have recently been proposed for potential electromagnetic (EM) energy harvesting utilization. Such inductors are capable of changing their initial geometry under the influence of some external force, collecting additional magnetic flux and producing increased induced voltage. In order to evaluate performance of such inductors, regarding increase of its total inductance, as well as EM field distribution and distribution of potential along its segments, the support of the COMSOL simulation framework has been used. In this paper, the case study of simple, planar inductors, which can change geometry along z-axis, has been presented.

The inductors with adjustable surface area for energy harvesting utilization

The concept of energy harvesting has been widely accepted as future necessity for the society, accelerating its transition to the environment of the so-called green technologies. The energy harvesting approaches in various domains of human surroundings have been considered, among them those related to the energy of ambient electromagnetic field (EMF). In this early research paper, the inductor with the adjustable surface area was analyzed, in order to maximize the magnetic flux through its surface and subsequently induced electromotive force (emf). Additionally, the overall inductance of such assemblies can be considerably increased. The case study of simple, planar, rectangular inductor has been examined using partial inductance approach, supported by MATLAB software framework, showing considerable increase in overall inductance of such inductors.

Time-frequency characterization of local field potential in a decision making task

This study seeks to characterize the neuronal mechanisms underlying voluntary decisions to check/verify. In order to describe and potentially decode decisions from brain signals we analyzed intracortical recordings from monkey prefrontal regions obtained during a cognitive task requiring self-initiated as well as cue-instructed decisions. Using local field potentials (LFP) and single units, we analyzed power spectral density, oscillatory modes, power profiles in time, single unit firing rate, and spike-phase relationships in the β band. Our results point toward specific but variable activation patterns of oscillations in β band from separate recordings, with task-dependent frequency preference and amplitude modulation of power. The results suggest relationships between particular LFP oscillations and functions engaged at specific time in the task.

Statistical approach to inter-spike interval ramps

This study presents a statistical analysis of single unit neural activity in the dorsal anterior cingulate cortex (dACC) in awake macaque monkeys. Based on spiking activity, a series of inter-spike intervals (ISI) are obtained. ISI series carries information on neural activities and instantaneous firing frequency. These series are analyzed using an explicit, finite Markov chain with memory. Model states represent the length of ISI ramp. The statistics of increasing and decreasing ISI ramps are observed separately. This paper analyzes the probability density function (pdf) and the model parameters obtained from original ISI series: the stationary state probabilities, the transition probabilities and normalized histogram of Markov chain per 1000 samples. As a control parameter, for each one of ISI time series 40 surrogate signals were generated. We have obtained the presentation of neural activity over the states as a function of time and typical shapes of the pdf for increasing and decreasing branch of the model.

Time-Frequency Analysis of Error Related Activity in Anterior Cingulate Cortex

Error related activity in local field potentials recorded in dorsal anterior cingulate cortex (dACC) of non-human primates is analyzed using ensemble empirical mode decomposition (EEMD). Neuronal activity in this region was recorded in a male rhesus monkey trained in the problem solving task (PS). It was hypothesized that activity in this region could reflect a modification of control just before execution errors, hence predicting the error about to be committed. The features obtained from time-frequency energy distribution, intrinsic mode functions (IMF) and higher order statistics calculated over IMFs were used to distinguish error related activity. The analysis has revealed the significant increase in energy of IMF4-IMF7, resulting in significant total energy increase immediately before and after execution errors. Higher order spectra analysis determined no difference in symmetry and shape of sample distribution when compared to control segments from successful trials. However, increased sample variance during errors combined with increase in energy indicates the unstable neural activation in this cortical region during execution errors resulting in less control over the current processing.