Hypothalamic structural integrity and temporal complexity of cortical information processing at rest in migraine without aura patients between attacks

The hypothalamus has been attributed an important role during the premonitory phase of a migraine attack. Less is known about the role played by the hypothalamus in the interictal period and its relationship with the putative neurocognitive networks previously identified in the pathophysiology of migraine. Our aim was to test whether the hypothalamic microstructure would be altered during the interictal period and whether this co-existed with aberrant connectivity at cortical level. We collected multimodal MRI data from 20 untreated patients with migraine without aura between attacks (MO) and 20 healthy controls (HC) and studied fractional anisotropy, mean (MD), radial (RD), and axial diffusivity of the hypothalamus ROI as a whole from diffusion tensor imaging (DTI). Moreover, we performed an exploratory analysis of the same DTI metrics separately for the anterior and posterior hypothalamic ROIs bilaterally. From resting-state functional MRI, we estimated the Higuchi’s fractal dimension (FD), an index of temporal complexity sensible to describe non-periodic patterns characterizing BOLD signature. Finally, we correlated neuroimaging findings with migraine clinical features. In comparison to HC, MO had significantly higher MD, AD, and RD values within the hypothalamus. These findings were confirmed also in the exploratory analysis on the sub-regions of the hypothalamus bilaterally, with the addition of lower FA values on the posterior ROIs. Patients showed higher FD values within the salience network (SN) and the cerebellum, and lower FD values within the primary visual (PV) network compared to HC. We found a positive correlation between cerebellar and SN FD values and severity of migraine. Our findings of hypothalamic abnormalities between migraine attacks may form part of the neuroanatomical substrate that predisposes the onset of the prodromal phase and, therefore, the initiation of an attack. The peculiar fractal dimensionality we found in PV, SN, and cerebellum may be interpreted as an expression of abnormal efficiency demand of brain networks devoted to the integration of sensory, emotional, and cognitive information related to the severity of migraine.

Devising a new filtration method and proof of self-similarity of electromyograms

The main attention is paid to the analysis of electromyogram (EMG) signals using Poincaré plots (PP). It was established that the shapes of the plots are related to the diagnoses of patients. To study the fractal dimensionality of the PP, the method of counting the coverage figures was used. The PP filtration was carried out with the help of Haar wavelets. The self-similarity of Poincaré plots for the studied electromyograms was established, and the law of scaling was used in a fairly wide range of coverage figures. Thus, the entire Poincaré plot is statistically similar to its own parts. The fractal dimensionalities of the PP of the studied electromyograms belong to the range from 1.36 to 1.48. This, as well as the values of indicators of Hurst exponent of Poincaré plots for electromyograms that exceed the critical value of 0.5, indicate the relative stability of sequences. The algorithm of the filtration method proposed in this research involves only two simple stages: Conversion of the input data matrix for the PP using the Jacobi rotation. Decimation of both columns of the resulting matrix (the so-called "lazy wavelet-transformation", or double downsampling). The algorithm is simple to program and requires less machine time than existing filters for the PP. Filtered Poincaré plots have several advantages over unfiltered ones. They do not contain extra points, allow direct visualization of short-term and long-term variability of a signal. In addition, filtered PPs retain both the shape of their prototypes and their fractal dimensionality and variability descriptors. The detected features of electromyograms of healthy patients with characteristic low-frequency signal fluctuations can be used to make clinical decisions.

Study of the hardening kinetics of a mineral binder using electrical and optical methods

Hardening of mineral binders (cement, gypsum, lime, clay) is accompanied by the dissolution of minerals from the binder surface, their chemical interaction with water (the reaction of hydration and hydrolysis), and the formation of a solution saturated with respect to new hydrates. The reactions of minerals with water continue for some time even after saturation when water molecules are adsorbed by the solid phase of the binder. An «intermediate» colloidal system thus formed is characterized by the viscosity or plasticity depending on the water content in it. At the final stage, the processes of recrystallization and coalescence of the particles in a colloidal solution occur resulting in solidification and hardening of the solution and increased strength of the formed stone. We present the results of studying the hardening kinetics of the aqueous solution of a mineral binder using electrical and optical methods with high time resolution. Semi-aqueous gypsum was selected as a mineral binder. During hardening, the resistance and the capacitance of the samples were measured along with the visualization of the spatial structure of the solution. The mineral composition of water significantly affected the character of hardening. Noticeable fluctuations of the electrical parameters were detected in the experiments with mineral water. Optical measurements showed that solidifying solution is similar in structure to dendrites and fractal dimensionality of the structure almost remains the same during growth. It is also shown that at the initial stage the hardening proceeds by the logistics law. The results obtained can be used and recommended for practical application for determination of the kinetic parameters of hardening and in diagnostics of the structure of materials based on mineral binders.

Fractal Characteristic Analysis of Urban Land use Evolution with Remote Sensing Images in Shenzhen City

Changes in urban land use/land cover (LULC) are the result of national policy and the economic activities of the urban population.Determining the spatial pattern of land cover types in cities is of particular significance for sustainable regional development.To achieve a better understand the spatiotemporal patterns of land cover types, this study uses the fractal dimension of spatial distributions as an index of the complex evolution of urban land-use.A long-term sequences LULC datasets are collected to do analysis, which covers the period 1988-2015 by employing Landsat TM/ETM+/OLI.Last but not least, a granularity analysis is adopted to study the structural changes of each land cover.Over this period, the fractal dimension of grassland, waterbody, and bare land exhibited bi-fractal dimensionality, but grassland, and bare land showed a consistently increasing bi-fractal trend, while the waterbody bi-fractals trend is weakened. The development of urban land saw a process of a multi-scale differential development with a hierarchical spatial system, and information entropy indicates that the urban land-use structure was unevenly distributed. These findings offer a scientific references for regional planning decisions on the evolution of urban land use in Shenzhen.

Dynamic signatures of the Eureka effect: An EEG study

The Eureka effect refers to the common experience of suddenly solving a problem. Here we study this effect in a pattern recognition paradigm that requires the segmentation of complex scenes and recognition of objects on the basis of Gestalt rules and prior knowledge. In the experiments both sensory evidence and prior knowledge were manipulated in order to obtain trials that do or do not converge towards a perceptual solution. Subjects had to detect objects in blurred scenes and signal recognition with manual responses. Neural dynamics were analyzed with high-density Electroencephalography (EEG) recordings. The results show significant changes of neural dynamics with respect to spectral distribution, coherence, phase locking, and fractal dimensionality. The Eureka effect was associated with increased coherence of oscillations in the alpha and theta band over widely distributed regions of the cortical mantle predominantly in the right hemisphere. This increase in coherence was associated with a decrease of beta band activity over parietal and central regions, and with a decrease of alpha power over frontal and occipital areas. In addition, there was a lateralized reduction of fractal dimensionality for activity recorded from the right hemisphere. These results suggest that the transition towards the solution of a perceptual task is mainly associated with a change of network dynamics in the right hemisphere that is characterized by enhanced coherence and reduced complexity. We propose that the Eureka effect requires cooperation of cortical regions involved in working memory, creative thinking, and the control of attention.

Study of Microstructure Features and Properties of Metals Obtained by Hot Isostatic Pressing

The aim of this research is to study the features of the structure and properties of alloys obtained using the technology of hot isostatic pressing (HIP) of metal powders. The study was carried out in the temperature range of interruption of the HIP cycle from 670 to 1150 °C on alloys 08Cr18Ni10Ti and Cr12MoV. For processing images of microstructures and assessing their fractal dimension, software has been developed in the MATLAB environment. The results of microstructural analysis of the metals under study showed that complete sintering of powders is observed at a HIP temperature of 1150 °C; at lower temperatures, pores and unsintered spherical particles of metal powder are observed in the microstructure of the alloys. The grain size of alloys obtained by HIP is determined, first of all, by the size of the initial fraction of the metal powder. Based on the results of evaluating the density of alloys obtained at different temperatures of the HIP, a relationship was established between the relative density of the alloy and the process temperature. Based on the results of fractal analysis, the relationship between the fractal dimensionality of the microstructure of the alloy and the HIP temperature and the relative density of the metals under study has been established. The obtained dependences are linear. The error in estimating the relative density from the obtained dependencies is, on average, 5 %. The data obtained in the course of the study make it possible to estimate the density of metals obtained by hot isostatic pressing of metal powders by evaluating the fractal dimension of the microstructure image.

Controlling the fractal dimensionality in self-assembly of terpyridine modified human insulin by Fe2+ and Eu3+ to direct in vivo effects

Metal ion-induced self-assembly (SA) of proteins into higher-order structures can provide new, dynamic nano-assemblies. Here, the synthesis and characterization of a human insulin (HI) analog modified at LysB29 with the...

Fault Classification of Nonlinear Small Sample Data through Feature Sub-Space Neighbor Vote

The fault classification of a small sample of high dimension is challenging, especially for a nonlinear and non-Gaussian manufacturing process. In this paper, a similarity-based feature selection and sub-space neighbor vote method is proposed to solve this problem. To capture the dynamics, nonlinearity, and non-Gaussianity in the irregular time series data, high order spectral features, and fractal dimension features are extracted, selected, and stacked in a regular matrix. To address the problem of a small sample, all labeled fault data are used for similarity decisions for a specific fault type. The distances between the new data and all fault types are calculated in their feature subspaces. The new data are classified to the nearest fault type by majority probability voting of the distances. Meanwhile, the selected features, from respective measured variables, indicate the cause of the fault. The proposed method is evaluated on a publicly available benchmark of a real semiconductor etching dataset. It is demonstrated that by using the high order spectral features and fractal dimensionality features, the proposed method can achieve more than 84% fault recognition accuracy. The resulting feature subspace can be used to match any new fault data to the fingerprint feature subspace of each fault type, and hence can pinpoint the root cause of a fault in a manufacturing process.

A Novel Functional Link Network Stacking Ensemble with Fractal Features for Multichannel Fall Detection

Falls are a major health concern and result in high morbidity and mortality rates in older adults with high costs to health services. Automatic fall classification and detection systems can provide early detection of falls and timely medical aid. This paper proposes a novel Random Vector Functional Link (RVFL) stacking ensemble classifier with fractal features for classification of falls. The fractal Hurst exponent is used as a representative of fractal dimensionality for capturing irregularity of accelerometer signals for falls and other activities of daily life. The generalised Hurst exponents along with wavelet transform coefficients are leveraged as input feature space for a novel stacking ensemble of RVFLs composed with an RVFL neural network meta-learner. Novel fast selection criteria are presented for base classifiers founded on the proposed diversity indicator, obtained from the overall performance values during the training phase. The proposed features and the stacking ensemble provide the highest classification accuracy of 95.71% compared with other machine learning techniques, such as Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine. The proposed ensemble classifier is 2.3× faster than a single Decision Tree and achieves the highest speedup in training time of 317.7× and 198.56× compared with a highly optimised ANN and RF ensemble, respectively. The significant improvements in training times of the order of 100× and high accuracy demonstrate that the proposed RVFL ensemble is a prime candidate for real-time, embedded wearable device–based fall detection systems.