Structural Connectivity Networks Research Articles

Alterations of functional and structural connectivity in patients with brain metastases.

Metastases are the most prevalent tumors in the brain and are commonly associated with high morbidity and mortality. Previous studies have suggested that brain tumors can induce a loss of functional connectivity and alter the brain network architecture. Little is known about the effect of brain metastases on whole-brain functional and structural connectivity networks. In this study, 14 patients with brain metastases and 16 healthy controls underwent resting state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). We constructed functional connectivity network using rs-fMRI signal correlations and structural connectivity network using DTI tractography. Graph theoretical analysis was employed to calculate network properties. We further evaluated the performance of brain networks after metastases resection by a simulated method. Compared to healthy controls, patients with brain metastases showed an altered “small-world” architecture in both functional and structural connectivity networks, shifting to a more randomness organization. Besides, the coupling strength of functional-structural connectivity was decreased in patients. After removing nodes infiltrated by metastases, aggravated disruptions were found in both functional and structural connectivity networks, and the alterations of network properties correlated with the removed hubs number. Our findings suggest that brain metastases interfere with the optimal network organization and relationship of functional and structural connectivity networks, and tumor resection involving hubs could cause a worse performance of brain networks. This study provides neuroimaging guidance for neurosurgical planning and postoperative assessment of brain metastases from the aspect of brain networks.

Auxiliary α2δ1 and α2δ3 Subunits of Calcium Channels Drive Excitatory and Inhibitory Neuronal Network Development.

VGCCs are multisubunit complexes that play a crucial role in neuronal signaling. Auxiliary α2δ subunits of VGCCs modulate trafficking and biophysical properties of the pore-forming α1 subunit and trigger excitatory synaptogenesis. Alterations in the expression level of α2δ subunits were implicated in several syndromes and diseases, including chronic neuropathic pain, autism, and epilepsy. However, the contribution of distinct α2δ subunits to excitatory/inhibitory imbalance and aberrant network connectivity characteristic for these pathologic conditions remains unclear. Here, we show that α2δ1 overexpression enhances spontaneous neuronal network activity in developing and mature cultures of hippocampal neurons. In contrast, overexpression, but not downregulation, of α2δ3 enhances neuronal firing in immature cultures, whereas later in development it suppresses neuronal activity. We found that α2δ1 overexpression increases excitatory synaptic density and selectively enhances presynaptic glutamate release, which is impaired on α2δ1 knockdown. Overexpression of α2δ3 increases the excitatory synaptic density as well but also facilitates spontaneous GABA release and triggers an increase in the density of inhibitory synapses, which is accompanied by enhanced axonaloutgrowth in immature interneurons. Together, our findings demonstrate that α2δ1 and α2δ3 subunits play distinct but complementary roles in driving formation of structural and functional network connectivity during early development. An alteration in α2δ surface expression during critical developmental windows can therefore play a causal role and have a profound impact on the excitatory-to-inhibitory balance and network connectivity.SIGNIFICANCE STATEMENT The computational capacity of neuronal networks is determined by their connectivity. Chemical synapses are the main interface for transfer of information between individual neurons. The initial formation of network connectivity requires spontaneous electrical activity and the calcium channel-mediated signaling. We found that, in early development, auxiliary α2δ3 subunits of calcium channels foster presynaptic release of GABA, trigger formation of inhibitory synapses, and promote axonal outgrowth in inhibitory interneurons. In contrast, later in development, α2δ1 subunits promote the glutamatergic neurotransmission and synaptogenesis, as well as strongly enhance neuronal network activity. We propose that formation of connectivity in neuronal networks is associated with a concerted interplay of α2δ1 and α2δ3 subunits of calcium channels.

Dynamic Mechanical, Dielectrical, and Rheological Analysis of Polyethylene Terephthalate/Carbon Nanotube Nanocomposites Prepared by Melt Processing

The polyethylene terephthalate/carbon nanotube (PET/CNT) nanocomposites were prepared by melt mixing using a twin screw extruder. CNT content was varied up to 5 wt. %. Morphology as well as dynamic mechanical, calorimetric, and rheological properties of the PET/CNT nanocomposites was investigated. Morphological studies indicated that CNT bundles are regularly distributed within the polymer matrix creating a connected network structure which significantly affects the nanocomposite properties. Dynamic mechanical thermal analysis revealed increase in storage and loss modules of the investigated PET nanocomposites by increasing the content of CNTs. Differential scanning calorimetry results demonstrated increase in crystallinity of the investigated PET nanocomposites upon addition of the nanofiller. Rheological studies demonstrated that CNT addition up to 5 wt. % caused increment in complex viscosity and storage modulus. Rheological percolation threshold was observed to be 0.83 wt. % of CNT concentration, respectively.

The Effect of Carbonization Temperature on Carbon Aerogels Structure

Phloroglucinol–resorcinol–formaldehyde (PRF) carbon aerogels were synthesized by sol–gel reaction. The results of condition of shrinkage showed a degree of crosslinking more obvious with the increasing of the carbonization temperature. CA900 displays the highest adsorption amount and the biggest loops, as well as an increase in pore size and volume in comparison to the others samples. The SEM images of carbon aerogels under different carbonization temperature possess closer network structure and excellent connectivity, the holes between the network are abundant. The XRD patterns show that the high carbonization temperature can enhance the graphitization degree of carbon aerogels.

Altered structural connectivity networks in a mouse model of complete and partial dysgenesis of the corpus callosum

Corpus callosum dysgenesis (CCD) describes a collection of brain malformations in which the main fiber tract connecting the two hemispheres is either absent (complete CCD, or ‘agenesis of the corpus callosum’) or reduced in size (partial CCD). Humans with these neurodevelopmental disorders have a wide range of cognitive outcomes, including seemingly preserved features of interhemispheric communication in some cases. However, the structural substrates that could underlie this variability in outcome remain to be fully elucidated. Here, for the first time, we characterize the global brain connectivity of a mouse model of complete and partial CCD. We demonstrate features of structural brain connectivity that model those predicted in humans with CCD, including Probst bundles in complete CCD and heterotopic sigmoidal connections in partial CCD. Crucially, we also histologically validate the recently predicted ectopic sigmoid bundle present in humans with partial CCD, validating the utility of this mouse model for fine anatomical studies of this disorder. Taken together, this work describes a mouse model of altered structural connectivity in variable severity CCD and forms a foundation for future studies investigating the function and mechanisms of development of plastic tracts in developmental disorders of brain connectivity.

Advanced neuroimaging in neuropsychiatric systemic lupus erythematosus.

Purpose of reviewNeuropsychiatric lupus (NPSLE) comprises a disparate collection of syndromes affecting the central and peripheral nervous systems. Progress in the attribution of neuropsychiatric syndromes to SLE-related mechanisms and development of targeted treatment strategies has been impeded by a lack of objective imaging biomarkers that reflect specific neuropsychiatric syndromes and/or pathologic mechanisms. The present review addresses recent publications of neuroimaging techniques in NPSLE.Recent findingsImaging studies grouping all NPSLE syndromes together are unable to differentiate between NPSLE and non-NPSLE. In contrast, diffusion tensor imaging, FDG-PET, resting, and functional MRI techniques in patients with stable non-NPSLE demonstrate abnormal network structural and functional connectivity and regional brain activity in multiple cortical areas involving the limbic system, hippocampus, frontal, parietal, and temporal lobes. Some of these changes associate with impaired cognitive performance or mood disturbance, autoantibodies or inflammatory proteins. Longitudinal data suggest progression over time. DCE-MRI demonstrates increased Blood–brain barrier permeability.SummaryStudy design issues related to patient selection (non-NPSLE vs. NPSLE syndromes, SLE disease activity, medications) are critical for biomarker development. Regional and network structural and functional changes identified with advanced brain imaging techniques in patients with non-NPSLE may be further developed as biomarkers for cognitive and mood disorders attributable to SLE-related mechanisms.

Soft foam-like UiO-66/Polydopamine/Bacterial cellulose composite for the removal of aspirin and tetracycline hydrochloride

Recently, the alarming level of pharmaceuticals active compounds (PhACs) pollution has exerted serious threat to the environment. It is important but still a great challenge to develop an effective and efficient adsorbent for PhACs treatment due to their bioaccumulation, persistence, as well as potential toxicity. Here, soft foam-like UiO-66/polydopamine/bacterial cellulose (UiO-66/PDA/BC) with three-dimensional connected network structure and high hydrophilicity were prepared using biomass BC as scaffold. The uniform coating of UiO-66 nanoparticles with uniform size guarantees high surface area as well as active sites for the adsorption of target pollutants. Excellent adsorption performances are obtained towards aspirin (149 mg g−1) and TC (184 mg g−1), which are higher than a variety of similar materials previously reported. We further evaluated the adsorption mechanism by XPS and FTIR spectra. The results revealed that the efficient adsorption ability was mainly depended on the synergetic effect of chemical adsorption and π − π interaction. With the excellent reusability as well as stability, the aerogel shows great potential for removal of organic contaminant from water.

BRAIN WHITE MATTER ALTERATIONS IN STRUCTURAL INTEGRITY AND CONNECTIVITY FOLLOWING A SPORTS-RELATED CONCUSSION: EVALUATION OF THE POTENTIAL AMELIORATING EFFECT OF A JUGULAR VEIN COMPRESSION COLLAR DEVICE

Background:Sports-related concussion is a significant public health problem which may result in exacerbated or prolonged neurologic deficits. A neck collar device has been developed to apply mild jugular vein compression, increasing intracranial blood volume, with the intent of reducing brain slosh and preventing damage to brain microstructure from head impact exposure.Hypothesis/Purpose:We aimed to quantify the alterations in white matter (WM) diffusion properties and network structural connectivity in high school athletes after sports-related concussion and to test the effect of a jugular vein compression collar device worn during the concussive event.Methods:379 athletes (male American football and female Soccer) were included in this report. Of the 16693 total athlete exposures, 40 resulted in a diagnosed concussion and were included in these analyses. Prospective longitudinal MRI data were collected at pre-season and post-concussion: 20 athletes were in the non-collar group (12M, age =15.93±1.27yrs) and 20 athletes were in the non-collar group (16M, age=16.37±1.06yrs). DTI parameters, including fractional anisotropy (FA), and mean, axial and radial diffusivity (MD/AD/RD), were calculated to determine post-concussion alterations in WM diffusion properties. Graph analysis was utilized to quantify post-concussion changes in WM structural connectivity.Results:Diffusion property results revealed significant pre-season to post-concussion increases in MD, AD and RD within the non-collar group (p<0.05), but no change was found in the collar group (p>0.05). The pre-season to post-concussion MD, AD, or RD increase was significantly greater (p<0.05, Figure 1) in the non-collar group than the collar group. Graph analysis revealed a pre-season to post-concussion decrease in mean local efficiency in the non-collar group (p=0.036) and a significant increase in mean local efficiency in the collar group (p=0.017). The pre-season to post-concussion change in the mean local efficiency was significantly different in the non-collar group, when compared to the collar group (p=0.0024, Figure 2).Discussion & Conclusion:Results indicate that sports related concussion is associated with alterations in WM diffusion properties. The mean local efficiency of the network, a structural connectivity index reflecting network wiring redundancy and tolerance to brain injury, was also found to decrease after the concussion. Results indicate that wearing a neck collar device has the potential to limit these WM changes following a concussive event. Further studies are warranted to investigate the temporal progression of these changes for preventing long-term, neurologic squeala.Figure 1.White matter regions with significant greater increase in MD, AD, and/or RD in the non-collar group than the collar group.Figure 2.Mean local efficiency of brain network showed significant decrease in the non-collar group (p=0.036) and significant increase in the collar group (0.017), with a significant group difference in the change (p=0.0024).

VENUS AND MARS: THE IMPORTANCE OF SEX IN LATE-LIFE NEUROPSYCHIATRIC DISORDERS

Sex has increasingly become recognized as an important biological variable in brain disorders. Differences in the risk for both cognitive and emotional disorders by sex and gender are an increasing area of investigation. While women are at higher risk for depression and Alzheimer's disease, the causes of these differences are poorly understood and the implications for therapy not exploited in a systematic way. Advances in clinical and translational neuroscience have begun to reveal potential reasons for these sex differences. Differences in how men and women process emotional information and react to psychological and psychosocial stress as well as the unique hormone environment of women versus men have identified potential causative factors in the preponderance of mood disorders in women. New advances in brain imaging and analysis have revealed novel sex-related differences in structural network connectivity that affects the spread of abnormal proteins, which may help to explain sex-related differences in neurodegenerative disease. Hormonal changes that occur at menopause and beyond may have significant implications for how neurotransmitter system activity changes with normal and pathologic aging and may help to explain why women may be at higher risk for developing Alzheimer's disease. This session will explore advances in our understanding of sex differences in late life neuropsychiatric disorders including depression and neurodegenerative disease. For example, research has identified specific circuits in the brain that are sensitive to sex hormones and are involved in the processing of negative emotional information and in modulating response to psychological stress. These circuits react differentially to the presence of sex hormones and change following menopause and aging. These results may help explain sex differences in mood disorders and changes in sex ratios with normal aging. New research has identified sex-related differences in the spread of tau protein in the brains of patients with normal aging, with women showing more advanced spread and greater structural network connectivity. Whether this sex difference extends to early syndromes such as subjective cognitive decline is under investigation. Women may differ from men in the earliest expression of self-perceived cognitive symptoms due to changes at menopause. The role of gonadal steroids and other individual difference factors like lifetime hormone exposure as well mood and lifetime stress have the potential to modulate these relationships. Discussion will center on how this research may point the way towards sex-specific treatments, prevention strategies, and specific therapies.

Development of Brain Structural Networks Over Age 8: A Preliminary Study Based on Diffusion Weighted Imaging.

Brain structural network changes provide key information about the aging process of the brain. Unfortunately, there has yet to be a detailed characterization of these structural networks across different age groups. Efforts to classify these networks have also been hampered by their reliance on technically limited traditional methods, which are unable to track multiple fiber orientations within a voxel and consequently are prone to false detection and artifacts. In this study, a newly developed Ensemble Average Propagator (EAP) based probabilistic tractography method was applied to construct a structural network, with the strength of the link between any two brain functional regions estimated according to the alignment of the EAP along connecting pathways. Age-related changes in the topological organization of human brain structural networks were thereby characterized across a broad age range (ages 8–75 years). The data from 48 healthy participants were divided into four age groups (Group 1 aged 8–15 years; Group 2 aged 25–35 years; Group 3 aged 45–55 years; and, Group 4 aged 65–75 years; N = 12 per group). We found that the brain structural network continues to strengthen during later adolescence and adulthood, through the first 20–30 years of life. Older adults, aged 65–75, had a significantly less optimized topological organization in their structural network, with decreased global efficiency and increased path lengths versus subjects in other groups. This study suggests that probabilistic tractography based on EAP provides a reliable method to construct macroscale structural connectivity networks to capture the age-associated changes of brain structures.

Embedding the intrinsic relevance of vertices in network analysis: the case of centrality metrics

Complex network theory (CNT) is gaining a lot of attention in the scientific community, due to its capability to model and interpret an impressive number of natural and anthropic phenomena. One of the most active CNT field concerns the evaluation of the centrality of vertices and edges in the network. Several metrics have been proposed, but all of them share a topological point of view, namely centrality descends from the local or global connectivity structure of the network. However, vertices can exhibit their own intrinsic relevance independent from topology; e.g., vertices representing strategic locations (e.g., hospitals, water and energy sources, etc.) or institutional roles (e.g., presidents, agencies, etc.). In these cases, the connectivity network structure and vertex intrinsic relevance mutually concur to define the centrality of vertices and edges. The purpose of this work is to embed the information about the intrinsic relevance of vertices into CNT tools to enhance the network analysis. We focus on the degree, closeness and betweenness metrics, being among the most used. Two examples, concerning a social (the historical Florence family’s marriage network) and an infrastructure (a water supply system) network, demonstrate the effectiveness of the proposed relevance-embedding extension of the centrality metrics.

Push–Pull Gradient Methods for Distributed Optimization in Networks

In this article, we focus on solving a distributed convex optimization problem in a network, where each agent has its own convex cost function and the goal is to minimize the sum of the agents' cost functions while obeying the network connectivity structure. In order to minimize the sum of the cost functions, we consider new distributed gradient-based methods where each node maintains two estimates, namely an estimate of the optimal decision variable and an estimate of the gradient for the average of the agents' objective functions. From the viewpoint of an agent, the information about the gradients is pushed to the neighbors, whereas the information about the decision variable is pulled from the neighbors, hence giving the name “push-pull gradient methods.” The methods utilize two different graphs for the information exchange among agents and, as such, unify the algorithms with different types of distributed architecture, including decentralized (peer to peer), centralized (master-slave), and semicentralized (leader-follower) architectures. We show that the proposed algorithms and their many variants converge linearly for strongly convex and smooth objective functions over a network (possibly with unidirectional data links) in both synchronous and asynchronous random-gossip settings. In particular, under the random-gossip setting, “push-pull” is the first class of algorithms for distributed optimization over directed graphs. Moreover, we numerically evaluate our proposed algorithms in both scenarios, and show that they outperform other existing linearly convergent schemes, especially for ill-conditioned problems and networks that are not well balanced.

Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease.

Alzheimer's disease is the most common neurodegenerative disease. The aim of this study is to infer structural changes in brain connectivity resulting from disease progression using cortical thickness measurements from a cohort of participants who were either healthy control, or with mild cognitive impairment, or Alzheimer's disease patients. For this purpose, we develop a novel approach for inference of multiple networks with related edge values across groups. Specifically, we infer a Gaussian graphical model for each group within a joint framework, where we rely on Bayesian hierarchical priors to link the precision matrix entries across groups. Our proposal differs from existing approaches in that it flexibly learns which groups have the most similar edge values, and accounts for the strength of connection (rather than only edge presence or absence) when sharing information across groups. Our results identify key alterations in structural connectivity that may reflect disruptions to the healthy brain, such as decreased connectivity within the occipital lobe with increasing disease severity. We also illustrate the proposed method through simulations, where we demonstrate its performance in structure learning and precision matrix estimation with respect to alternativeapproaches.

Disrupted Brain Structural Connectivity Network in Subcortical Ischemic Vascular Cognitive Impairment With No Dementia

The alteration of the functional topological organization in subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND) patients has been illuminated by previous neuroimaging studies. However, in regard to the changes in the structural connectivity of brain networks, little has been reported. In this study, a total of 27 subjects, consisting of 13 SIVCIND patients, and 14 normal controls, were recruited. Each of the structural connectivity networks was constructed by diffusion tensor tractography. Subsequently, graph theory, and network-based statistics (NBS) were employed to analyze the whole-brain mean factional anisotropy matrix. After removing the factor of age, gender, and duration of formal education, the clustering coefficients (Cp) and global efficiency (Eglob) were significantly decreased and the mean path length (Lp) was significantly increased in SIVCIND patients compared with normal controls. Using the combination of four network topological parameters as the classification feature, a classification accuracy of 78% was obtained by leave-one-out cross-validation for all subjects with a sensitivity of 69% and a specificity of 86%. Moreover, we also found decreased structural connections in the SIVCIND patients, which mainly concerned fronto-occipital, fronto-subcortical, and tempo-occipital connections (NBS corrected, p < 0.01). Additionally, significantly altered nodal centralities were found in several brain regions of the SIVCIND patients, mainly located in the prefrontal, subcortical, and temporal cortices. These results suggest that cognitive impairment in SIVCIND patients is associated with disrupted topological organization and provide structural evidence for developing reliable biomarkers related to cognitive decline in SIVCIND.

Local Electrodynamics of a Disordered Conductor Model System Measured with a Microwave Impedance Microscope

We study the electrodynamic impedance of percolating conductors with a pre-defined network topology using a scanning microwave impedance microscope (sMIM) at GHz frequencies. For a given percolation number we observe strong spatial variations across a sample which correlate with the connected regions (clusters) in the network when the resistivity is low such as in Aluminum. For the more resistive material NbTiN the impedance becomes dominated by the local structure of the percolating network (connectivity). The results can qualitatively be understood and reproduced with a network current spreading model based on the pseudo-inverse Laplacian of the underlying network graph.

A Nesterov-Like Gradient Tracking Algorithm for Distributed Optimization Over Directed Networks

In this article, we concentrate on dealing with the distributed optimization problem over a directed network, where each unit possesses its own convex cost function and the principal target is to minimize a global cost function (formulated by the average of all local cost functions) while obeying the network connectivity structure. Most of the existing methods, such as push-sum strategy, have eliminated the unbalancedness induced by the directed network via utilizing column-stochastic weights, which may be infeasible if the distributed implementation requires each unit to gain access to (at least) its out-degree information. In contrast, to be suitable for the directed networks with row-stochastic weights, we propose a new directed distributed Nesterov-like gradient tracking algorithm, named as D-DNGT, that incorporates the gradient tracking into the distributed Nesterov method with momentum terms and employs nonuniform step-sizes. D-DNGT extends a number of outstanding consensus algorithms over strongly connected directed networks. The implementation of D-DNGT is straightforward if each unit locally chooses a suitable step-size and privately regulates the weights on information that acquires from in-neighbors. If the largest step-size and the maximum momentum coefficient are positive and small sufficiently, we can prove that D-DNGT converges linearly to the optimal solution provided that the cost functions are smooth and strongly convex. We provide numerical experiments to confirm the findings in this article and contrast D-DNGT with recently proposed distributed optimization approaches.

Using structural connectivity to augment community structure in EEG functional connectivity.

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.

Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain.

Intrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain’s underlying structural connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale- related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6,000 time series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region’s spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

CNN-Based Health Model for Regular Health Factors Analysis in Internet-of-Medical Things Environment

Remote health monitoring applications with the advent of Internet of Things (IoT) technologies have changed traditional healthcare services. Additionally, in terms of personalized healthcare and disease prevention services, these depend primarily on the strategy used to derive knowledge from the analysis of lifestyle factors and activities. Through the use of intelligent data retrieval and classification models, it is possible to study disease, or even predict any abnormal health conditions. To predict such abnormality, the Convolutional neural network (CNN) model is used, which can detect the knowledge related to disease prediction accurately from unstructured medical health records. However, CNN uses a large amount of memory if it uses a fully connected network structure. Moreover, the increase in the number of layers can lead to an increase in the complexity analysis of the model. Therefore, to overcome these limitations of the CNN-model, we propose a CNN-regular target detection and recognition model based on the Pearson Correlation Coefficient and regular pattern behavior, where the term “regular” denotes objects that generally appear in similar contexts and have structures with low variability. In this framework, we develop a CNN-regular pattern discovery model for data classification. First, the most important health-related factors are selected in the first hidden layer, then in the second layer, a correlation coefficient analysis is conducted to classify the positively and negatively correlated health factors. Moreover, regular patterns' behaviors are discovered through mining the regular pattern occurrence among the classified health factors. The output of the model is subdivided into regular-correlated parameters related to obesity, high blood pressure, and diabetes. Two distinct datasets are adopted to mitigate the effects of the CNN-regular knowledge discovery model. The experimental results show that the proposed model has better accuracy, and low computational load, compared with three different machine learning techniques methods.

The synthesis of a perovskite CsPbBr3 quantum dot superlattice in borosilicate glass.

For the first-time we have synthesised perovskite CsPbBr3 quantum dot (QD) superlattices in borosilicate glass, which play a key role in controllable network structure connectivity. The structural and optical properties of the CsPbBr3 QD superlattices embedded in borosilicate glass were investigated.