Functional Matrices Research Articles

Exponential Synchronization of Stochastic Memristive Neural Networks with Time-Varying Delays

This paper pays attention to the synchronization control methodology for stochastic memristive system. On the framework of Lyapunov functional, stability theory and free-weighting matrices technique, some brand-new solvability criteria are established to ensure the exponential synchronization goal of the target model. Considering the introduce of some free-weighting matrices, the obtained synchronization verdict will be much more applicable. Finally, the living example is included to show the effectiveness of the presented methodology.

On the Possibility of a Numerical Solution of the Heat and Mass Transfer Problem with the Combined Matrix&Generalized Powers of Bers Method

Some possibilities for the combined use of the matrix method and the method of generalized powers of Bers for the numerical solution of the heat and mass transfer problem is considered. Initially, the matrix method under consideration was used by us for the analytical solution of the problem of heat and mass transfer in a homogeneous or multilayered medium with translation, axial or central symmetry for an arbitrary number of layers. Simulation is reduced to the sequential multiplication of second-order functional matrices whose components at each point are determined by the physical and geometric parameters of the current layer. The main advantage of the matrix method is that it can be used for any number of layers. It is this property of the method that makes it possible to consider of its application as a numerical one if the medium is artificially splitted into many layers.For modelling, the problem of one-dimensional diffusion of minority charge carriers generated by a wide electron beam in a semiconductor material was taken. The relative error in the computations was estimated by a uniform norm in comparison with the analytical solution. The errors made were from 0.8% to 2.9%.

Multivariate Pattern Analysis of EEG-Based Functional Connectivity: A Study on the Identification of Depression

Resting-state electroencephalography (EEG) studies have shown significant group differences in functional connectivity networks between patients with depression and healthy controls. The present study aims to identify the altered EEG resting-state functional connectivity patterns of depressed patients, which can be used to test the feasibility of distinguishing individuals with depression from healthy controls. In the present study, the phase lag index was employed to construct functional connectivity matrices. An altered Kendall rank correlation coefficient was used to identify the features with high discriminative power, and several classifiers were employed to classify a total of 27 depressed patients and 28 demographically matched healthy volunteers. Permutation tests were used to evaluate classifier performance. The best classification results demonstrate that more than 92% of subjects were correctly classified by binary linear SVM through leave-one-out cross-validation for the full frequency band, and the AUC was 0.98. Our findings suggest that the depression affects brain activity in nearly the whole cortex and that changes in brain oscillation patterns in the delta, theta, and beta frequency bands are more significant than those in the alpha frequency band. The current study sheds new light on the pathological mechanism of depression and suggests that EEG resting-state functional connectivity analysis may identify potentially effective biomarkers for its clinical diagnosis.

Lyapunov’s first method: estimates of characteristic numbers of functional matrices

Lyapunov’s first method: estimates of characteristic numbers of functional matrices

Lyapunov’s first method: estimates of characteristic numbers of functional matrices

Lyapunov’s first method: estimates of characteristic numbers of functional matrices

Peptide-induced super-assembly of biocatalytic metal-organic frameworks for programmed enzyme cascades.

Despite the promise of metal-organic frameworks (MOFs) as functional matrices for enzyme stabilization, the development of a stimulus-responsive approach to induce a multi-enzyme cascade reaction in MOFs remains a critical challenge. Here, a novel method using peptide-induced super-assembly of MOFs is developed for programmed enzyme cascade reactions on demand. The super-assembled MOF particles containing different enzymes show remarkable 7.3-fold and 4.4-fold catalytic activity enhancements for the two-enzyme and three-enzyme cascade reactions, respectively, as compared with the unassembled MOF nanoparticles. Further digestion of the coiled-coil forming peptides on the MOF surfaces leads to the MOF superstructure disassembly and the programmed enzyme cascade reaction being "switched-off". Research on these stimuli-responsive materials with controllable and predictable biocatalytic functions/properties provide a concept to facilitate the fabrication of next-generation smart materials based on precision chemistry.

Macrobenthos at marine hotspots along the northwest Indian inner shelf: Patterns and drivers

Marine hotspots are areas prioritized for conservation and monitoring, based on their sensitivity or vulnerability. Understanding the natural variability of resident organisms in such critical areas is integral for deciphering human-induced perturbations to formulate appropriate management strategies. Five marine hotspots along northwest India, comprising three active harbours and two marine protected areas, were surveyed seasonally to understand the macrofaunal distribution patterns and functional traits. Among the 33 macrobenthic taxa, Polychaeta constituted the dominant taxon. Spatial variability was prominent due to differences in terms of polychaete species types, relative abundances and functional trait matrices. Monsoonal hypoxia altered the macrobenthic species and functional composition. CCA revealed a combination of natural (texture, DO, salinity) and anthropogenic (PHc, SS, ammonia) hydro-sedimentological variables as key drivers for the polychaete distribution patterns. The results are expected to improve the understanding of the variability and functioning of polychaete taxocommunity within the ecologically and economically significant “marine hotspots”.

Resting-state anticorrelated networks in Schizophrenia

Converging evidences from different lines of research suggest abnormalities in functional brain connectivity in schizophrenia. While positively correlated brain networks have been well researched, anticorrelated functional connectivity remains under explored. Hence, in this study we examined (1) the resting-state anticorrelated networks in schizophrenia, and (2) the accuracy of support vector machines (SVMs) in differentiating healthy individuals from schizophrenia patients using these anticorrelated networks. The sample consisted of 56 patients with DSM-IV schizophrenia and 56 healthy controls. We computed functional connectivity matrices and used Anticorrelation after Mean of Antilog method (AMA) to select predominantly anticorrelated networks. The basal ganglia, thalamus, lingual gyrus, and cerebellar vermis showed significantly different, Type A (decreased anticorrelation) connections. The medial temporal lobe and posterior cingulate gyri showed significantly different, Type B (increased anticorrelation) connections. Use of SVM on AMA networks showed moderate accuracy in differentiating schizophrenia and healthy controls. Our results suggest that anticorrelated networks between the sub-cortical and cortical areas are abnormal in schizophrenia and this has potential to be a differential biomarker. These preliminary findings, if replicated in future studies with larger number of patients, and advanced machine learning techniques could have potential clinical applications.

Cooperative contributions of structural and functional connectivity to successful memory in aging

Understanding the precise relation between functional connectivity and structural (white matter) connectivity and how these relationships account for cognitive changes in older adults are major challenges for neuroscience. We investigate these issues using an approach in which structural equation modeling (SEM) is employed to integrate functional and structural connectivity data from younger and older adults (n = 62), analyzed with a common framework based on regions connected by canonical tract groups (CTGs). CTGs (e.g., uncinate fasciculus) serve as a common currency between functional and structural connectivity matrices, and ensure equivalent sparsity in connectome information. We used this approach to investigate the neural mechanisms supporting memory for items and memory for associations, and how they are affected by healthy aging. We found that different structural and functional CTGs made independent contributions to source and item memory performance, suggesting that both forms of connectivity underlie age-related differences in specific forms of memory. Furthermore, the relationship between functional and structural connectivity was best explained by a general relationship between latent constructs—a relationship absent in any specific CTG group. These results provide insights into the relationship between structural and functional connectivity patterns, and elucidate their relative contribution to age-related differences in source memory performance.

Dynamic brain functional parcellation via sliding window and artificial bee colony algorithm

Dynamic brain functional parcellation is an important way to reveal the dynamics of brain function. However, current dynamic brain functional parcellation methods can not meet the need to clearly understand the dynamics. This paper presents a dynamic brain functional parcellation method based on sliding window and artificial bee colony (ABC) algorithm (called SWABC). In SWABC, a functional connectivity similarity minimum criterion (FCSMC) is firstly developed for determining the length of a sliding window and functional connectivity matrices are calculated with Pearson correlation and windowed time series of voxels. Then, an improved ABC is employed to identify functional states through clustering these matrices, where a hybrid search strategy and a dynamic radius constraint are respectively designed for employed bee search and scout bee search to enhance the search capability of ABC. Next, functional connectivity between voxels in each functional state is computed by concatenating time series belonging to the same state, and functional parcellation results for all functional states are achieved by performing the improved ABC. Finally, with comparison to other four algorithms, the experimental results on fMRI data of posterior cingulate cortex show that SWABC not only has better search capability, but also can yield reasonable functional states and corresponding functional parcellation results with stronger functional consistency and regional continuity. Moreover, the rationality of functional parcellation results from SWABC is also verified by functional connectivity fingerprints of subregions in each of them.

Time Temperature Indicator Label using Black Corn Extract and Chitosan Matrix

Time-temperature indicator (TTI) labels have been widely researched and some have been used commercially due to their ability to provide food product quality information through changes in label color when exposed to temperature over a specified period of time. The TTI label consists of functional dyes and matrices. The purpose of this study was to develop a TTI label that uses natural dyes derived from black corn extract and chitosan as the matrix. Labels were prepared using a simultaneous method then casted on top of PET substrate. Absorption properties of the black corn extract was investigated by using UV-Vis Spectrophotometer. The label colors were characterized using digital photography and analyzed with CIELab color code. The color of the labels were observed over time at temperature conditions of 10, 25 and 40 °C. The extract solution is red at pH 2-3, then pink at pH 4-5, purple at pH 6-7, violet at pH 8, and turned to yellow at pH 9-13 which indicates that the extract contains anthocyanin. Due to time period, the label changes color from purple to blue to yellow, with the fastest color changes occurred at higher temperature of 40 °C and slowest at lower temperature of 10 °C. The shelf life test on different RH conditions shows that the label stored at RH 33% is more stable than at higher RH of 76%. The labels show irreversible properties which is one of the requirements as a time temperature indicator label. These results indicate that the label of black corn extract with chitosan has the potential to be applied on smart packaging.

Identities for degenerate Bernoulli polynomials and Korobov polynomials of the first kind

In this paper, we derive five basic identities for Sheffer polynomials by using generalized Pascal functional and Wronskian matrices. Then we apply twelve basic identities for Sheffer polynomials, seven from previous results, to degenerate Bernoulli polynomials and Korobov polynomials of the first kind and get some new identities. In addition, letting $\lambda~\rightarrow~0$ in such identities gives usthose for Bernoulli polynomials and Bernoulli polynomials of the second kind.

Structural and Functional Brain Connectivity Changes Between People With Abdominal and Non-abdominal Obesity and Their Association With Behaviors of Eating Disorders

Abdominal obesity is important for understanding obesity, which is a worldwide medical problem. We explored structural and functional brain differences in people with abdominal and non-abdominal obesity by using multimodal neuroimaging and up-to-date analysis methods. A total of 274 overweight people, whose body mass index exceeded 25, were enrolled in this study. Participants were divided into abdominal and non-abdominal obesity groups using a waist–hip ratio threshold of 0.9 for males and 0.85 for females. Structural and functional brain differences were assessed with diffusion tensor imaging and resting-state functional magnetic resonance imaging. Centrality measures were computed from structural fiber tractography, and static and dynamic functional connectivity matrices. Significant inter-group differences in structural and functional connectivity were found using degree centrality (DC) values. The associations between the DC values of the identified regions/networks and behaviors of eating disorder scores were explored. The highest association was achieved by combining DC values of the cerebral peduncle, anterior corona radiata, posterior corona radiata (from structural connectivity), frontoparietal network (from static connectivity), and executive control network (from dynamic connectivity) compared to the use of structural or functional connectivity only. Our results demonstrated the effectiveness of multimodal imaging data and found brain regions or networks that may be responsible for behaviors of eating disorders in people with abdominal obesity.

Comprehensive analysis of multi-class mycotoxins in twenty different species of functional and medicinal herbs using liquid chromatography–tandem mass spectrometry

Analysis of mycotoxins in functional and medicinal herbs is a challenge because herbs have complicated and diverse matrices from different parts of plants as well as different species. Here, we introduce a rapid and comprehensive liquid chromatography–tandem mass spectrometry method for the determination of multi-class mycotoxins (aflatoxin B1, B2, G1, G2, ochratoxin A, zearalenone, deoxynivalenol, fumonisin B1, B2, B3 and T-2 toxin) in twenty different species of herbs that are used for both food and medicinal purposes. Various sample preparation methods were compared and optimized to obtain satisfactory extraction of mycotoxins in different matrices. The extraction efficiency was found to vary from not only extraction methods but also matrix types, demonstrating the complexity of herbal matrix. On the basis of optimization results, immunoaffinity column was selected as an extraction method to cover all types of samples. QuEChERS was also employed as a supplementary method to support the immunoaffinity column method. The developed method was validated by assessing specificity, linearity, precision, recovery, limit of quantification and uncertainty, and applied in a hundred of real samples to monitor target mycotoxins. The monitoring results were further verified using accurate high resolution mass measurements by Orbitrap mass spectrometry. As far as we know, this is the first time the multi-class mycotoxins were determined in a broad range of functional and medicinal herb matrices, with high selectivity and sensitivity. In addition, the proposed method was proven to be more efficient and sensitive than other methods.

Formal analysis of the kinematic Jacobian in screw theory

Abstract As robotic systems flourish, reliability has become a topic of paramount importance in the human–robot relationship. The Jacobian matrix in screw theory underpins the design and optimization of robotic manipulators. Kernel properties of robotic manipulators, including dexterity and singularity, are characterized with the Jacobian matrix. The accurate specification and the rigorous analysis of the Jacobian matrix are indispensable in guaranteeing correct evaluation of the kinematics performance of manipulators. In this paper, a formal method for analyzing the Jacobian matrix in screw theory is presented using the higher-order logic theorem prover HOL4. Formalizations of twists and the forward kinematics are performed using the product of exponentials formula and the theory of functional matrices. To the best of our knowledge, this work is the first to formally analyze the kinematic Jacobian using theorem proving. The formal modeling and analysis of the Stanford manipulator demonstrate the effectiveness and applicability of the proposed approach to the formal verification of the kinematic properties of robotic manipulators.

Trainable back-propagated functional transfer matrices

Connections between nodes of fully connected neural networks are usually represented by weight matrices. In this article, functional transfer matrices are introduced as alternatives to the weight matrices: Instead of using real weights, a functional transfer matrix uses real functions with trainable parameters to represent connections between nodes. Multiple functional transfer matrices are then stacked together with bias vectors and activations to form deep functional transfer neural networks. These neural networks can be trained within the framework of back-propagation, based on a revision of the delta rules and the error transmission rule for functional connections. In experiments, it is demonstrated that the revised rules can be used to train a range of functional connections: 20 different functions are applied to neural networks with up to 10 hidden layers, and most of them gain high test accuracies on the MNIST database. It is also demonstrated that a functional transfer matrix with a memory function can roughly memorise a non-cyclical sequence of 400 digits.

EEG Resting State Functional Connectivity in Adult Dyslexics Using Phase Lag Index and Graph Analysis.

Developmental dyslexia may involve deficits in functional connectivity across widespread brain networks that enable fluent reading. We investigated the large-scale organization of electroencephalography (EEG) functional networks at rest in 28 dyslexics and 36 typically reading adults. For each frequency band (delta, theta alpha and beta), we assessed functional connectivity strength with the phase lag index (PLI). Network topology was examined using minimum spanning tree (MST) graphs derived from the functional connectivity matrices. We found significant group differences in the alpha band (8–13 Hz). The graph analysis indicated more interconnected nodes, in dyslexics compared to typical readers. The graph metrics were significantly correlated with age in dyslexics but not in typical readers, which may indicate more heterogeneity in maturation of brain networks in dyslexics. The present findings support the involvement of alpha oscillations in higher cognition and the sensitivity of graph metrics to characterize functional networks in adult dyslexia. Finally, the current results extend our previous findings on children.

Ontogenia de las órbitas en Pan troglodytes: implicancias estructurales, funcionales y evolutivas

Las órbitas de los primates Haplorrhini son particulares dentro de los mamíferos por estar completamente separadas de la fenestra temporal y rodeadas por hueso, ser convergentes, estar frontalizadas y proveer la base para el desarrollo de un notorio torus supraorbitario. Estas características han sido descriptas, algunas como adaptaciones al hábito de vida y otras como resultado de reacomodaciones estructurales de las matrices funcionales del cráneo. El objetivo de este trabajo es analizar los cambios morfológicos de las órbitas en una muestra ontogenética transversal de Pan troglodytes y su asociación con estructuras adyacentes a fin de discutir implicancias estructurales y funcionales. Se utilizaron 52 imágenes de tomografías computadas de cráneos de Pan troglodytes desde 8 meses de vida postnatal hasta adultos. Se registraron landmarks y se tomaron medidas volumétricas y lineales que permitieron extraer variables angulares y proporciones. Se realizó ANOVA, regresiones lineales y parciales y Análisis de Componentes Principales. Los resultados indican que los rasgos de valor adaptativo (funcionales) de la órbita de Pan troglodytes se establecen tempranamente en la ontogenia y no varían durante el período postnatal, mientras que su relación con estructuras adyacentes muestra importantes cambios.

Excited-State Electronic Properties in Zr-Based Metal-Organic Frameworks as a Function of a Topological Network.

Molecular assemblies in metal-organic frameworks (MOFs) are reminiscent of natural light-harvesting (LH) systems and considered as emerging materials for energy conversion. Such applications require understanding the correlation between their excited-state properties and underlying topological net. Two chemically identical but topologically different tetraphenylpyrene (1,3,6,8-tetrakis( p-benzoicacid)pyrene; H4TBAPy)-based ZrIV MOFs, NU-901 ( scu) and NU-1000 ( csq), are chosen to computationally and spectroscopically interrogate the impact of topological difference on their excited-state electronic structures. Time-dependent density functional theory-computed transition density matrices for selected model compounds reveal that the optically relevant S1, S2, and S n states are delocalized over more than four TBAPy linkers with a maximum exciton size of ∼1.7 nm (i.e., two neighboring TBAPy linkers). Computational data further suggests the evolution of polar excitons (hole and electron residing in two different linkers); their oscillator strengths vary with the extent of interchromophoric interaction depending on their topological network. Femtosecond transient absorption (fs-TA) spectroscopic data of NU-901 highlight instantaneous spectral evolution of an intense S1 → S n transition at 750 nm, which diminishes with the emergence of a broad (580-1100 nm) induced absorption originating from a fast excimer formation. Although these ultrafast spectroscopic data reveal the first direct spectral observation of fast excimer formation (τ = 2 ps) in MOFs, the fs-TA features seen in NU-901 are clearly absent in NU-1000 and the free H4TBAPy linker. Furthermore, transient and steady-state fluorescence data collected as a function of solvent dielectrics reveal that the emissive states in both MOF samples are electronically nonpolar; however, low-lying polar excited states may get involved in the excited-state decay processes in polar solvents. The present work shows that the topological arrangement of the linkers critically controls the excited-state electronic structures.

Structure-function multi-scale connectomics reveals a major role of the fronto-striato-thalamic circuit in brain aging.

Physiological aging affects brain structure and function impacting morphology, connectivity, and performance. However, whether some brain connectivity metrics might reflect the age of an individual is still unclear. Here, we collected brain images from healthy participants (N = 155) ranging from 10 to 80 years to build functional (resting state) and structural (tractography) connectivity matrices, both data sets combined to obtain different connectivity features. We then calculated the brain connectome age-an age estimator resulting from a multi-scale methodology applied to the structure-function connectome, and compared it to the chronological age (ChA). Our results were twofold. First, we found that aging widely affects the connectivity of multiple structures, such as anterior cingulate and medial prefrontal cortices, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus, and temporal pole. Second, we found that the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto-striato-thalamic (FST) circuit, makes the major contribution to age estimation. In conclusion, our results highlight the key role played by the FST circuit in the process of healthy aging. Notably, the same methodology can be generally applied to identify the structural-functional connectivity patterns correlating to other biomarkers than ChA.