Network Of Edges Research Articles

Na2.64Mn1.64(MoO4)3 and Na2.62Ni1.69(MoO4)3: Physicochemical investigations and electrochemical properties as 3.5 V class positive electrode material for Na-batteries

Powders of two new mixed molybdates Na2.64Mn1.64(MoO4)3 (1) and Na2.62Ni1.69(MoO4)3 (2) have been prepared via a solid-solid reaction. They are isostructural, both crystallizing in monoclinic system (S. G.: C2/c), Z = 1, and the unit cell parameters a = 12.74109 (18) Å, b = 13.69390 (15) Å, c = 7.20028 (8) Å, β = 112.4314 (7) °, V = 1161.22 (2) Å3 (1), and a = 12.6368 (2) Å, b = 13.3633 (2) Å, c = 7.08501 (10) Å, β = 111.9248 (10) °, V = 1109.91 (3) Å3 (2). The structure consists of a 3D network of corners and edges sharing [MoO4] and [MO6] polyhedral, delimiting tunnels in which the Na ions partly site. The samples were investigated by powder X-ray diffraction, infrared spectroscopy, and scanning electron microscopy coupled to energy-dispersive X-ray spectroscopy (SEM-EDX). The vibrational study confirms the existence of the MoO42− functional groups. Laser Raman spectroscopy showed that the MoO42− tetrahedra in the two compounds are more regular, suggesting high symmetry for the compound. The as-synthesized compounds are fiber-like grains with an average length of 10−1 μm. FT-IR spectra showed the presence of all the functional groups present in the compounds. Magnetisation measurements showed their paramagnetic behaviour. The title compounds were characterized by cyclic voltammetry (CV), electrochemical performance and limiting factors are also applied as electrode material for Na-ion batteries at room temperature. Benefiting from these advantages, between 1.5 V and 4.6 V, (1) and (2) deliver high specific capacities of 230 and 160 mA h g−1 after 60 cycles, and good rate performances, respectively. It can also be inferred that the prepared electrocatalyst showed for both phases excellent Hydrogen Evolution Reaction (HER) performance with an overpotential of 531 mV for Mn and 512 mV for Ni, required to afford a current density of 10 mA cm−2.

Towards a biologically annotated brain connectome.

The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and toconstruct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.

A Comparative Analysis of Community Detection Agglomerative Technique Algorithms and Metrics on Citation Network

Social Network Analysis is a discipline that represents social relationships as a network of nodes and edges. The construction of social network with clusters will contribute in sharing the common characteristics or behaviour of a group. Partitioning the graph into modules is said to be a community. Communities are meant to symbolize actual social groups that share common characteristics. Citation network is one of the social networks with directed graphs where one paper will cite another paper and so on. Citation networks will assist the researcher in choosing research directions and evaluating research impacts. By constructing the citation networks with communities will direct the user to identify the similarity of documents which are interrelated to one or more domains. This paper introduces the agglomerative technique algorithms and metrics to a directed graph which determines the most influential nodes and group of similar nodes. The two stages required to construct the communities are how to generate network with communities and how to quantify the network performance. The strength and a quality of a network is quantified in terms of metrics like modularity, normalized mutual information (NMI), betweenness centrality, and F-Measure. The suitable community detection techniques and metrics for a citation graph were introduced in this paper. In the field of community detection, it is common practice to categorize algorithms according to the mathematical techniques they employ, and then compare them on benchmark graphs featuring a particular type of assortative community structure. The algorithms are applied for a sample citation sub data is extracted from DBLP, ACM, MAG and some additional sources which is taken from and consists of 101 nodes (nc) with 621 edges € and formed 64 communities. The key attributes in dataset are id, title, abstract, references SLM uses local optimisation and scalability to improve community detection in complicated networks. Unlike traditional methods, the proposed LS-SLM algorithm is identified that the modularity is increased by 12.65%, NMI increased by 2.31%, betweenness centrality by 3.18% and F-Score by 4.05%. The SLM algorithm outperforms existing methods in finding significant and well-defined communities, making it a promising community detection breakthrough.

Map-based experience replay: a memory-efficient solution to catastrophic forgetting in reinforcement learning.

Deep reinforcement learning (RL) agents often suffer from catastrophic forgetting, forgetting previously found solutions in parts of the input space when training new data. Replay memories are a common solution to the problem by decorrelating and shuffling old and new training samples. They naively store state transitions as they arrive, without regard for redundancy. We introduce a novel cognitive-inspired replay memory approach based on the Grow-When-Required (GWR) self-organizing network, which resembles a map-based mental model of the world. Our approach organizes stored transitions into a concise environment-model-like network of state nodes and transition edges, merging similar samples to reduce the memory size and increase pair-wise distance among samples, which increases the relevancy of each sample. Overall, our study shows that map-based experience replay allows for significant memory reduction with only small decreases in performance.

EXPANSION AND NATURALIZATION OF ADVENTIVE BUTTERFLY SPECIES (LEPIDOPTERA: PAPILIONOIDEA) ON THE NORTHEASTERN PART OF THE RUSSIAN PLAIN

Data on composition and naturalization patterns of more than twenty adventive butterfly species expanded to the northeastern part of the Russian Plain during the period of 1990-2021 are compiled. It is shown that lines of transport communications such as highways, railways, power lines, gas and oil pipelines serve as main immigration corridors for butterfly species from southern regions. Anthropogenic transformation of indigenous taiga forests through industrial logging operations, largely expanding areas of open habitats, which are more suitable for butterflies, promotes successful naturalization of adventive species. Linear systems of technical and industrial constructions together with river valleys play a role of semi-natural corridors for the expansion of adventive species. Furthermore, secondary small-leaved and mixed forests surrounded by a network of forest edges and meadows represent a kind of staging areas for the establishment of adventive species’ populations. Groups of adventive species are delineated based on their naturalization status that was estimated on the basis of an integral assessment of their biocenotic stability and landscape activity. It is drawn a conclusion that ongoing expansion and naturalization of adventive butterfly species on the northeastern part of the Russian Plain will increase changes in the composition of local and zonal faunas and the spatial and typological structure of their populations.

Automatic 3D CAD models reconstruction from 2D orthographic drawings

This paper introduces a two-stage approach that automatically generates 3D CAD models from 2D orthographic drawings. First, a pattern-matching algorithm is proposed to reconstruct a network of 3D edges by matching 2D edge features extracted from the multiple views of the 2D drawing. Second, starting from the resulting 3D wireframe and generated graph, a loop detection algorithm allows identifying possible loops of faces. Then, a clustering algorithm recognizes the faces from the set of detected loops. The reconstruction ends while trimming and stitching the faces to get a watertight ready-to-use 3D CAD model. This approach has been validated on a public dataset composed of several thousands of 3D shapes, and it achieved 99.59% of well-reconstructed models in F-score.

Assortative mixing in micro-architecturally annotated brain connectomes

The wiring of the brain connects micro-architecturally diverse neuronal populations, but the conventional graph model, which encodes macroscale brain connectivity as a network of nodes and edges, abstracts away the rich biological detail of each regional node. Here, we annotate connectomes with multiple biological attributes and formally study assortative mixing in annotated connectomes. Namely, we quantify the tendency for regions to be connected based on the similarity of their micro-architectural attributes. We perform all experiments using four cortico-cortical connectome datasets from three different species, and consider a range of molecular, cellular, and laminar annotations. We show that mixing between micro-architecturally diverse neuronal populations is supported by long-distance connections and find that the arrangement of connections with respect to biological annotations is associated to patterns of regional functional specialization. By bridging scales of cortical organization, from microscale attributes to macroscale connectivity, this work lays the foundation for next-generation annotated connectomics.

Biomass-Derived Hard Carbon and Nitrogen-Sulfur Co-Doped Graphene for High-Performance Symmetric Sodium Ion Capacitor Devices

An inexpensive bio-mass-derived hard carbon from tamarind pods was used as an anode, and nitrogen and nitrogen (N)/sulfur (S) co-doped graphene were used as a cathode for novel hybrid Na-ion supercapacitors. The structural and surface morphological analyses are investigated using a range of techniques. The 3D network of the heteroatom-doped graphene skeleton edges for N and NS-doping conformations were assigned as N-RGOs (N1s-5.09 at.%) and NS-RGOs (N1s-7.66 at.% and S1s-2.22 at.%) based on energy dispersive X-ray spectroscopy elemental mapping. The negative electrode (T-HC) hard carbon was pre-treated by pre-sodiation with a half-cell process by galvanostatic charge–discharge in a sodium-ion battery at 0.01–2.5 V vs. Na/Na+. The T-HC//NS-RGO, T-HC//N-RGO, and T-HC//RGO were used to construct the Na-ion supercapacitor device. In the CV experiments, the electrochemical galvanostatic charge–discharge was studied at 1.0–4.2 V. The specific capacitance was 352.18 F/g for the T.HC/NS-RGO device and 180.93 F/g for the T.HC/N-RGO device; both were symmetric devices. T.HC/NS-RGO device performance revealed excellent cycling stability, with T-HC//NS-RGO showing 89.26% capacitance retention over 5000 cycles. A carbon–carbon symmetric device, such as a Na-ion hybrid capacitor, can exhibit the characteristics of both batteries and supercapacitors for future electric vehicles.

Predicting Transdiagnostic Social Impairments in Childhood Using Connectome-Based Predictive Modeling

Social impairments are core features of multiple neurodevelopmental disorders. Previous neuroimaging studies have focused on predicting associations between brain function within disorders but rarely use brain-based models for the transdiagnostic prediction of symptoms. This study used a machine learning approach to examine functional connectivity that predicts elevated social impairments in a transdiagnostic sample of youths. We hypothesized a widespread network of edges would predict social impairments, across diagnoses, particularly in brain regions involved in social cognition.

Clar’s Goblet on Graphene: Field-Modulated Charge Transfer in a Hydrocarbon Heterostructure

In certain configurations, the aromatic properties of benzene ring structured molecules allow for unpaired, reactive valence electrons (known as radicals). Clar's goblets are such molecules. With an even number of unpaired radicals, these nanographenes are topologically frustrated hydrocarbons in which pi-bonding network and topology of edges give rise to the magnetism. Clar's goblets are therefore valued as prospective qubits provided they can be modulated between magnetic states. Using first principles DFT, we demonstrate the effects of adsorption on both molecule and substrate in a graphene-Clar's goblet heterostructure. We look at the energy difference bewteen FM and AFM states of the system and discuss underlying physical and chemical mechanisms in reference to the highest occupied molecular orbital (HOMO) and second HOMO (HOMO-1). We find that the HOMO of the molecule in the FM state is right at the Fermi surface, which leads to the hybridization between molecular state and the graphene state near the Dirac point. Furthermore, we investigate qualitative changes in charge realignment and magnetic state under variable electric field. Transitions from FM to AFM and back to FM states are observed.

Establishing the allosteric mechanism in CRISPR-Cas9

Allostery is a fundamental property of the CRISPR-Cas9 molecular machinery, which regulates biochemical information transfer between spatially distant sites. We report computational investigations to describe the signal transfer, connecting DNA recognition to cleavage, in the Cas9 protein. By using advanced network theory, we described CRISPR-Cas9 as a network of nodes and edges to quantify the signal transduction between the HNH and RuvC nucleases. Similar to social media analysis, our methods enabled the identification of the “hubs” in the protein network that are major players in the information transfer.

Variability in higher order structure of noise added to weighted networks

The complex behavior of many real-world systems depends on a network of both strong and weak edges. Distinguishing between true weak edges and low-weight edges caused by noise is a common problem in data analysis, and solutions tend to either remove noise or study noise in the absence of data. In this work, we instead study how noise and data coexist, by examining the structure of noisy, weak edges that have been synthetically added to model networks. We find that the structure of low-weight, noisy edges varies according to the topology of the model network to which it is added, that at least three qualitative classes of noise structure emerge, and that these noisy edges can be used to classify the model networks. Our results demonstrate that noise does not present as a monolithic nuisance, but rather as a nuanced, topology-dependent, and even useful entity in characterizing higher-order network interactions.

Roads and forest edges facilitate yellow fever virus dispersion

Abstract Landscape connectivity is important for a wide range of ecological processes, including to disease spread, once it describes the degree to which landscapes facilitate or impede vector and hosts dispersion. Understanding connectivity is extremely important to identify where pathogens can move, and at what speed, allowing the organization of vaccination campaigns or other preventive measures. To better understand the effects of landscape connectivity on yellow fever virus (YFV) dispersion in Brazil, we used a network approach and modelled the movement of non‐human primates' cases, the so‐called epizootic events, over time. The networks consider each epizootic event as a node and the dispersion between nodes as links. Those links were established considering, respectively, the date of each epizootic event, the distance among the nodes and the permeability of the landscape between each pair of nodes. Our results demonstrated that on average YFV dispersed 1.42 km/day, with the largest movement being 6.9 km/day. Dispersions were longer in summer (1.2 km/day) than in winter (0.22 km/day). Most dispersal movements occurred up to 1 km/day (71%) and within a week after the arrival of the virus in the source node (73%), except in winter, where dispersions occurred within a period of up to 20 days. The best model indicates that YFV disperses mainly through roads adjacent to forest areas, and along forest edges (within a range of 100 m) in interface with agricultural areas, water and forestry areas. Core areas of urban, agricultural and forest regions were important barriers for virus movement. Synthesis and applications. Through landscape connectivity analyses, we provided here the first evidence that highly fragmented landscapes with a wide road network and large densities of forest edges facilitate yellow fever virus propagation, and that the maintenance of large blocks of forest can help to inhibit this spread. These results can contribute to guide forest restoration and landscape management actions in order to amplify health benefits related to restoration projects, in addition to their benefits for biodiversity conservation and climate change mitigation.

Parameterizing elastic network models to capture the dynamics of proteins

Coarse-grained normal mode analyses of protein dynamics rely on the idea that the geometry of a protein structure contains enough information for computing its fluctuations around its equilibrium conformation. This geometry is captured in the form of an elastic network (EN), namely a network of edges between its residues. The normal modes of a protein are then identified with the normal modes of its EN. Different approaches have been proposed to construct ENs, focusing on the choice of the edges that they are comprised of, and on their parameterizations by the force constants associated with those edges. Here we propose new tools to guide choices on these two facets of EN. We study first different geometric models for ENs. We compare cutoff-based ENs, whose edges have lengths that are smaller than a cutoff distance, with Delaunay-based ENs and find that the latter provide better representations of the geometry of protein structures. We then derive an analytical method for the parameterization of the EN such that its dynamics leads to atomic fluctuations that agree with experimental B-factors. To limit overfitting, we attach a parameter referred to as flexibility constant to each atom instead of to each edge in the EN. The parameterization is expressed as a non-linear optimization problem whose parameters describe both rigid-body and internal motions. We show that this parameterization leads to improved ENs, whose dynamics mimic MD simulations better than ENs with uniform force constants, and reduces the number of normal modes needed to reproduce functional conformational changes.

Connectome-Based Predictive Modeling of Creativity Anxiety

While a recent upsurge in the application of neuroimaging methods to creative cognition has yielded encouraging progress toward understanding the neural underpinnings of creativity, the neural basis of barriers to creativity are as yet unexplored. Here, we report the first investigation into the neural correlates of one such recently identified barrier to creativity: anxiety specific to creative thinking, or creativity anxiety (Daker et al., 2019). We employed a machine-learning technique for exploring relations between functional connectivity and behavior (connectome-based predictive modeling; CPM) to investigate the functional connections underlying creativity anxiety. Using whole-brain resting-state functional connectivity data, we identified a network of connections or “edges” that predicted individual differences in creativity anxiety, largely comprising connections within and between regions of the executive and default networks and the limbic system. We then found that the edges related to creativity anxiety identified in one sample generalize to predict creativity anxiety in an independent sample. We additionally found evidence that the network of edges related to creativity anxiety were largely distinct from those found in previous work to be related to divergent creative ability (Beaty et al., 2018). In addition to being the first work on the neural correlates of creativity anxiety, this research also included the development of a new Chinese-language version of the Creativity Anxiety Scale, and demonstrated that key behavioral findings from the initial work on creativity anxiety are replicable across cultures and languages.

Electrochemical performance of graphene oxide modified graphite felt as a positive electrode in all-iron redox flow batteries

In this study, we demonstrate that coating a layer of graphene oxide (GO) onto graphite felts (GF) by electrostatic spraying can substantially increase the performance of all-iron redox flow batteries (IRFBs). Graphite felts are extensively used as electrodes but they do not have the desired electrochemical properties. GO has good electrochemical features. Hence, GO was synthesized from graphite powder and applied onto graphite felts. Chemical and structural features of the bare graphite felt electrode (BGF), thermally treated graphite felt electrode (TTGF), and graphene oxide modified graphite felt electrode (GOMGF) were characterized using X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray Analysis (EDX), Transmission Electron Microscopy (TEM), Raman Spectroscopy (RS), X-Ray Photoelectron Spectroscopy (XPS) and Brunauer–Emmett–Teller (BET) surface area analysis. Similarly, the electrochemical performance was evaluated using Cyclic Voltammetry (CV), Electrochemical Impedance Spectroscopy (EIS), Tafel analysis and charge–discharge experiments. The Charge–discharge experiments were performed at 1 to 5 mgcm−2 weight of GO on the modified graphite felt electrode and varying the current densities from 10 to 40 mAcm−2. The coulombic efficiency (ηC) and energy efficiency (ηE) of the cell determined at 20 mAcm−2 for 4 mgcm−2-GOMGF electrodes were found to be 64.61% and 50.27%, respectively. Among the three different types of electrodes, the GOMGF electrode showed better electrocatalytic performance mainly due to the excellent conducting network of the defective edges of oxygen on the surface of layered flakes of the GO. After twenty cycles, the average ηC and ηE of the cell using a 4 mgcm−2-GOMGF electrode were found to be 62.06% and 42.02%, respectively.

Visualizing the Process of Disaster Mental Health Services in the Joso Flood by Network Analyses of Emails.

Joso City, Ibaraki Prefecture, Japan was severely affected by flooding of the River Kinugawa in September 2015. Local psychiatric organizations immediately began providing disaster mental health services (DMHS). In post-disaster settings, DMHS involving organizational interventions by multiple regional institutions are required to support disaster victims. However, little is known about the process of coordinating multiple institutions or determining whether appropriate support has been provided. To elucidate the characteristics of communications that enable effective disaster medical team formation, we conducted network analyses of sender-recipient pairs of emails during the period of DMHS activity. The network analysis is a research method that represents various objects as a network of nodes and edges and explores their structural characteristics. We obtained 2,450 time-series emails from five core members of DMHS, including 32,865 pairs of senders and recipients. The network generated by the emails was scale-free, and its structure changed according to the phases of disaster recovery. In the ultra-acute phase, which lasted about 1 week, spreading information and recruiting people to provide disaster support was given the highest priority. In the acute phase, which lasted about 1 month, support and swift decision-making were essential for directing large numbers of staff. In the mid- to long-term phase, support for staff to share information and experience in small groups was observed. Network analyses have revealed that disaster medical teams must change their communication styles during the mission to adapt to different health needs corresponding to each post-disaster phase.

Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization.

Despite large experimental and computational efforts aiming to dissect the mechanisms underlying disease risk, mapping cis-regulatory elements to target genes remains a challenge. Here, we introduce a matrix factorization framework to integrate physical and functional interaction data of genomic segments. The framework was used to predict a regulatory network of chromatin interaction edges linking more than 20 000 promoters and 1.8 million enhancers across 127 human reference epigenomes, including edges that are present in any of the input datasets. Our network integrates functional evidence of correlated activity patterns from epigenomic data and physical evidence of chromatin interactions. An important contribution of this work is the representation of heterogeneous data with different qualities as networks. We show that the unbiased integration of independent data sources suggestive of regulatory interactions produces meaningful associations supported by existing functional and physical evidence, correlating with expected independent biological features.

(264) Altered Brain Network Topology in Chronic Low Back Pain Patients on Prescription Opioid Analgesics

Opioid prescribing for chronic pain conditions such as Chronic Low Back Pain (CLBP) in the United States has increased substantially in the past two decades. However, the effects of opioid analgesics on the brain network topology in CLBP remains unknown. The present study therefore provides the first test of the hypothesis that opioid status impacts the activity of the whole-brain network using graph theory methods. Resting state fMRI data were collected on a 3T scanner from 10 CLBP patients on long-term opioid regimens (CLBP+; 5 males, mean age ± SD = 48.5 ± 14.8 years) and 10 matched opioid-naive CLBP patients (CLBP-, 5 males, mean age ± SD = 43.6 ± 12.6 years) according to a protocol approved by the Stanford IRB. Following image quality assurance in MRIQC, and preprocessing in SPM12, we performed graph theoretical network analysis using CONN toolbox. For each participant, global network efficiency — a graph theory measure for integrative capacity of complex systems, was calculated and correlated with individual differences in sensory pain scores from Short Form McGill Pain Questionnaire (SF-MPQ). We focused on global efficiency as it reflects effective information transfer (i.e., small-worldness) within a network of nodes (i.e., regions of interests) and edges (i.e., correlation). Results revealed that the global efficiency values were positively correlated with pain in CLBP- but not in CLBP+ (r = 0.49 vs r = - 0.06, p = 0.05). This suggests that as sensory dimension of the pain intensity increased, CLBP- exhibited more efficient information transfer across a network of brain regions, including the core nodes of the salience network (anterior insula), frontoparietal central executive network (dorsolateral prefrontal cortices), and bilateral sensorimotor networks. Follow up studies with larger sample size are required to corroborate these observations and to formulate appropriate strategies for opioid prescribing guidelines, accordingly. Supported by NIH P01AT006651, and NIH T32DA035165.

Validation and quality assessment of macromolecular structures using complex network analysis

Validation of three-dimensional structures is at the core of structural determination methods. The local validation criteria, such as deviations from ideal bond length and bonding angles, Ramachandran plot outliers and clashing contacts, are a standard part of structure analysis before structure deposition, whereas the global and regional packing may not yet have been addressed. In the last two decades, three-dimensional models of macromolecules such as proteins have been successfully described by a network of nodes and edges. Amino acid residues as nodes and close contact between the residues as edges have been used to explore basic network properties, to study protein folding and stability and to predict catalytic sites. Using complex network analysis, we introduced common network parameters to distinguish between correct and incorrect three-dimensional protein structures. The analysis showed that correct structures have a higher average node degree, higher graph energy, and lower shortest path length than their incorrect counterparts. Thus, correct protein models are more densely intra-connected, and in turn, the transfer of information between nodes/amino acids is more efficient. Moreover, protein graph spectra were used to investigate model bias in protein structure.