Network Structure Research Articles

The network structures of mental and behavioral problems among children and adolescents in China using propensity score matching: A comparison between one-child and multi-child families based on a nationwide survey

BackgroundExploring networks of mental and behavioral problems in children and adolescents may identify differences between one-child and multi-child families. This study compared the network structures of mental and behavioral problems in children and adolescents in one-child families versus multi-child families based on a nationwide survey. MethodsPropensity score matching (PSM) was used to match children and adolescents from one-child families with those from multi-child families. Mental and behavioral problems were assessed using the Achenbach's Child Behavior Checklist (CBCL) with eight syndromal subscales. In the network analysis, strength centrality index was used to estimate central symptoms, and case-dropping bootstrap method was used to assess network stability. ResultsThe study included 39,648 children and adolescents (19,824 from one-child families and 19,824 from multi-child families). Children and adolescents from multi-child families exhibited different network structure and higher global strength compared to those from one-child families. In one-child families, the most central symptoms were “Social problems”, “Anxious/depressed” and “Withdrawn/depressed”, while in multi-child families, the most central symptoms were “Social problems”, “Rule-breaking behavior” and “Anxious/depressed”. ConclusionDifferences in mental and behavioral problems among children and adolescents between one-child and multi-child families were found. To address these problems, interventions targeting “Social problems” and “Anxious/depressed” symptoms should be developed for children and adolescents in both one-child and multi-child families, while other interventions targeting “Withdrawn/depressed” and “Rule-breaking behavior” symptoms could be useful for those in one-child and multi-child families, respectively.

My cave, my rules: Specialization, modularity, and topology of bat‐fly interactions in hot and cold caves from eastern South America

AbstractCaves provide relatively stable and advantageous roosting sites for bats compared to more open roosts, like tree foliage. This environment may have the drawback of facilitating interactions with their ectoparasites due to the confined spaces. Understanding the structure of interactions between bats, acting as hosts, and bat flies, serving as parasites in cave ecosystems, is a crucial first step in deciphering the roles of each species (pullers and pushers) within the networks that form in subterranean ecosystems. Here, we describe and evaluate the network structures of bat‐fly interactions in two distinct cave systems: cold caves (n = 10), also known as bat caves, and hot caves (n = 6). Based on the records of 700 bats from 16 species and 1.412 bat flies from 30 species we uncovered highly distinct topologies comparing hot and cold bat caves that differed also in terms of interactions, specializations, and modularity. We found relatively lower specialization and modularity in hot caves compared to the cold caves, which may be associated to the bat composition and the cave microclimate. Bat flies were highly species‐specific in relation to their bat hosts and dependent on the bats in both hot and cold caves systems. The differences in network structure and at the species level between the bat (cold) and hot caves systems suggest that bat‐fly interactions are shaped by the host species' composition and by the cave system type. Those differences stem from each bat species' adaptation to extreme cave microclimates and their species‐specific roosting behaviors.Abstract in Portuguese is available with online material.

ClusterLP: A novel Cluster-aware Link Prediction model in undirected and directed graphs

Link prediction models endeavor to understand the distribution of links within graphs and forecast the presence of potential links. With the advancements in deep learning, prevailing methods typically strive to acquire low-dimensional representations of nodes in networks, aiming to capture and retain the structure and inherent characteristics of networks. However, the majority of these methods primarily focus on preserving the microscopic structure, such as the first- and second-order proximities of nodes, while largely disregarding the mesoscopic cluster structure, which stands out as one of the network's most prominent features. Following the homophily principle, nodes within the same cluster exhibit greater similarity to each other compared to those from different clusters, suggesting that they should possess analogous vertex representations and higher probabilities of linkage. In this study, we develop a straightforward yet efficient Cluster-aware Link Prediction framework (ClusterLP), with the objective of directly leveraging cluster structures to predict links among nodes with maximum accuracy in both undirected and directed graphs. Specifically, we posit that establishing links between nodes with similar representation vectors and cluster tendencies is more feasible in undirected graphs, whereas nodes in directed graphs are inclined to point towards nodes with akin representation vectors and greater influence. We tailor the implementation of ClusterLP for undirected and directed graphs, respectively, and experimental findings using multiple real-world networks demonstrate the high competitiveness of our models in the realm of link prediction tasks. The code utilized in our implementation is accessible at https://github.com/ZINUX1998/ClusterLP.

On the Comparative Analysis among Topological Indices for Rhombus Silicate and Oxide Structures

A topological index (TI) is a numeric digit that signalizes the whole chemical structure of a molecular network. TIs are helpful in predicting the bioactivity of molecular substances in investigations of quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR). TIs correlate various chemical and physical attributes of chemical substances such as melting and freezing point, strain energy, stability, temperature, volume, density, and pressure. There are several distance-based descriptors available in the literature, but connection-based TIs are considered more effective than degree-based TIs in measuring the chemical characteristics of molecular compounds. The present study focuses on computing the connection-based TIs for the most significant type of chemical structures, namely, rhombus silicate and rhombus oxide networks. At the end, we compare these structures on the basis of their computed result.

Unsupervised Anomaly Detection for Power Batteries: A Temporal Convolution Autoencoder Framework

Abstract To prevent potential abnormalities from escalating into critical faults, a rapid and precise algorithm should be employed for detecting power battery anomalies. An unsupervised model based on a temporal convolutional autoencoder was proposed. It can quickly and accurately identify abnormal power battery data. Its encoder utilized a temporal convolutional network (TCN) structure with residuals to parallelly process data while capturing time dependencies. A novel TCN structure with an effect–cause relationship was developed for the decoder. The same-timescale connection was established between the encoder and decoder to improve the model performance. The validity of the proposed model was confirmed using a real-world car dataset. Compared with the GRU-AE model, the proposed approach reduced the parameter count and mean square error by 19.5% and 71.9%, respectively. This study provides insights into the intelligent battery pack abnormality detection technology.

Ultrasound-induced yield increase of yuba (protein-lipid film from soybean milk) and changes in structural, physicochemical, and digestive properties during film-peeling process

Yuba, as a traditional soybean food, is protein-lipid film from soybean milk. This study proposed a novel ultrasound-assisted yuba preparation strategy and investigated the structural, physicochemical, and digestive properties of yuba during the film-peeling process. The process parameters of yuba preparation were optimized using response surface method and Box-Behnken design. The results showed that total yield of 5 yuba films was 39.8 ± 0.3% under the optimum conditions (315 W ultrasound power, 8 min ultrasound time, and 1:9 solid-to-liquid ratio) with an increase rate of 12.6%, compared with that of the control group. With the increasing yuba film-peeling times, protein and lipid contents in yuba decreased, whereas carbohydrate content displayed an increasing trend. The analyses of mechanical properties, protein secondary structure, X-ray diffraction, disulfide bond, and microstructure showed that the protein network structure of yuba films became looser with film-peeling progression. Furthermore, in vitro simulated digestion experiments indicated that yuba obtained in the early film-peeling stage (the first four films) exhibited better protein degradation performance and higher degree of lipid hydrolysis. This study provides technical references for the process innovations of yuba and theoretical basis for systematically evaluating yuba quality and better utilizing its nutritional value.

Local Cultures of Advice Tie Formation

Abstract Informal social networks and especially advice networks are a key subject of organizational sociological research. The formation of these networks has so far been explained on the basis of universal tie-formation mechanisms. However, recent conceptual contributions suggest that tie formation practices follow cultural rules that may vary across social contexts. In our paper, we substantiate this argument empirically. Based on rich qualitative data, we compare advice tie formation (a) between two entrepreneurial communities and (b) between three research and development departments of large enterprises. We show that both impact and configuration of tie formation mechanisms systematically differ between comparable contexts. Building on our findings, we finally formulate propositions describing which advice network structures can be expected in specific cultures.

Optimizing Eriochrome Black T adsorption through In-Situ polymerization of Poly(aniline-co-formaldehyde) on biochar: Multivariate approach using full factorial Design, Box-Behnken, Artificial intelligence, and DFT

Over the past few years, using organic–inorganic composites in wastewater treatment through adsorption has become increasingly widespread. The combination of activated carbon and polyaniline (AC@PANI) has garnered significant interest in these composites. This study introduces a novel type of coating known as poly(aniline-co-formaldehyde) (PAF), which is classified as a copolymer and exhibits crosslinking properties. Compared to polyaniline, PAF forms a three-dimensional network structure, a rapid process for the synthesis of AC@PAF. Moreover, PAF demonstrates a notable enhancement in adsorption capacity for the anionic Eriochrome Black T (EBT) dye. In this study, we coated activated carbon, previously activated using H3PO4 (ACNgG-P), with PANI and PAF. Our findings reveal that the PAF coating exhibits an exceptional adsorption capacity of 700.01 mg/g for EBT, surpassing ACNgG-P and ACNgG-P@PANI.Furthermore, the adsorption of EBT onto the surface of ACNgG-P@PAF was optimized and modeled using artificial intelligence (AI), specifically an artificial neural network (ANN). The ANN was employed to predict and maximize adsorption by analyzing the complex relationships between various parameters. Finally, the Density Functional Theory calculations were used to elucidate the adsorption mechanism by identifying the active sites on the surface of ACNgG-P@PAF and analyzing the molecular structure of EBT.

Interactions between corn starch and lingonberry polyphenols and their effects on starch digestion and glucose transport

This study investigated the interaction mechanism between corn starch (CS) and lingonberry polyphenols (LBP) during starch gelatinization, focusing on their effects on starch structure and physicochemical properties. Moreover, it explored the effect of this interaction on starch digestion and glucose transport. The results indicated that LBP interacted non-covalently with CS during starch gelatinization, disrupted the short-range ordered structure of starch, decreased gelatinization enthalpy of starch, and formed a dense network structure. Furthermore, the incorporation of LBP remarkably reduced the digestibility of CS. In particular, the addition of 10 % LBP decreased the terminal digestibility (C∞) from 77.87 % to 60.43 % and increased the amount of resistant starch (RS) by 21.63 %. LBP was found to inhibit α-amylase and α-glucosidase in a mixed manner. Additionally, LBP inhibited glucose transport in Caco-2 cells following starch digestion. When 10 % LBP was added, there was a 34.17 % decrease in glucose transport compared with starch digestion without LBP. This study helps establish the foundation for the development of LBP-containing starch or starch-based healthy foods and provides new insights into the mechanism by which LBP lowers blood glucose.

Mapping social movements momentum: unveiling networks in the movement for the right to abortion in Mexico

In this case study, we propose a network analysis approach to map social movements through an intersectional feminist lens. We aim to gain a more in-depth understanding of movements' network structures to examine the roles and relationships of movement actors, the flow of resources between them, and potential areas of collaboration and conflict. By incorporating Social Network Analysis (SNA) techniques and visually mapping dynamics within social movements, this approach can assess the significance of small actors in creating change and stresses the need for their perspectives to be heard. Furthermore, our methodology adopts a feminist intersectional framework that recognizes the role of different identities in shaping the movement and its actors. To demonstrate the practical application of this approach, we examined the movement for the right to abortion in Mexico between September and December 2021. Our multi-step process included (1) designing a survey tool adapted to the context of the movement, (2) collecting and analyzing survey responses on movement actors' relationships and interactions, including their priorities, activities, needs, risks, and challenges, (3) visualizing the network using SNA techniques that account for the complexity and diversity of the movement, (4) interpreting the findings through insights collected in semi-structured interviews and validation meetings with key movement actors, and (5) implementing safety and security guidelines to safeguard the identity of individuals whose activities could put them at risk of suffering institutional, emotional, and physical violence. Our case study offers valuable insights that not only encourage the integration of feminist and intersectional perspectives into data collection processes but also provide a roadmap for accompanying social movements and supporting meaningful and contextually responsive activities.

Spider-silk-inspired strong and tough hydrogel fibers with anti-freezing and water retention properties

Ideal hydrogel fibers with high toughness and environmental tolerance are indispensable for their long-term application in flexible electronics as actuating and sensing elements. However, current hydrogel fibers exhibit poor mechanical properties and environmental instability due to their intrinsically weak molecular (chain) interactions. Inspired by the multilevel adjustment of spider silk network structure by ions, bionic hydrogel fibers with elaborated ionic crosslinking and crystalline domains are constructed. Bionic hydrogel fibers show a toughness of 162.25 ± 21.99 megajoules per cubic meter, comparable to that of spider silks. The demonstrated bionic structural engineering strategy can be generalized to other polymers and inorganic salts for fabricating hydrogel fibers with broadly tunable mechanical properties. In addition, the introduction of inorganic salt/glycerol/water ternary solvent during constructing bionic structures endows hydrogel fibers with anti-freezing, water retention, and self-regeneration properties. This work provides ideas to fabricate hydrogel fibers with high mechanical properties and stability for flexible electronics.

Identifying symptom clusters and temporal interconnections in patients with lung tumors after CT-guided microwave ablation: A network analysis.

To explore symptom clusters and interrelationships using a network analysis approach among symptoms in patients with lung tumors who underwent computed tomography (CT)-guided microwave ablation (MWA). A longitudinal study was conducted, and 196 lung tumor patients undergoing MWA were recruited and were measured at 24h, 48h, and 72h after MWA. The Chinese version of the MD Anderson Symptom Inventory and the Revised Lung Cancer Module were used to evaluate symptoms. Network analyses were performed to explore the symptom clusters and interrelationships among symptoms. Four stable symptom communities were identified within the networks. Distress, weight loss, and chest tightness were the central symptoms. Distress, and weight loss were also the most key bridge symptoms, followed by cough. Three symptom networks were temporally stable in terms of symptom centrality, global connectivity, and network structure. Our findings identified the central symptoms, bridge symptoms, and the stability of symptom networks of patients with lung tumors after MWA. These network results will have important implications for future targeted symptom management intervention development. Future research should focus on developing precise interventions for targeting central symptoms and bridge symptoms to promote patients' health.

Quantifying Variability in Zebrafish Larvae Locomotor Behavior across Experimental Conditions: A Learning-Based Tracker

This study investigated the effects of environmental changes on zebrafish larval behavior, using single-factor and orthogonal experiments to assess locomotion during temperature and pH changes. In single-factor experiments, zebrafish larvae were exposed to variations in temperature (22 to 30 °C) and pH levels (6.0, 7.0, 9.0). The simultaneous temperature and pH changes were investigated by orthogonal tests. In both experiments, each zebrafish larva was recorded in three 5 min videos at different stages (before exposure, during short-term exposure (10 min), and after long-term exposure (60 min)). You Look Only Once (YOLOv5) and Deep Simple Online Real Time Tracking (DeepSORT) models were adopted to develop a zebrafish larva tracking system, and YOLOv5 was improved in two aspects of anchor clustering and network structure. The tracking accuracy of the tracking system for small targets effectively improved, reaching more than 98% MOTA (Multiple Object Tracking Accuracy). Principal Component Analysis (PCA) was employed to extract three behavioral features from 13 motion parameters, namely motion activity, edge behavior, and motion direction preference. Our findings reveal that lower temperatures and acidic conditions both led to a decrease in motion behavioral activity, and the former also increased edge behavior. Conversely, elevated temperatures and alkaline conditions had a muted impact on these behaviors. Interestingly, concurrent changes in temperature and pH significantly altered directional preference. Additionally, we observed that lower temperatures elicited distinct temporal behavioral patterns at a constant pH level. In summary, we recommend the precise control and explicit reporting of ambient temperature and pH in both breeding devices and experimental wells to minimize the environmental impact on zebrafish behavior and enhance experiment repeatability and reliability.

MNMST: topology of cell networks leverages identification of spatial domains from spatial transcriptomics data

Advances in spatial transcriptomics provide an unprecedented opportunity to reveal the structure and function of biology systems. However, current algorithms fail to address the heterogeneity and interpretability of spatial transcriptomics data. Here, we present a multi-layer network model for identifying spatial domains in spatial transcriptomics data with joint learning. We demonstrate that spatial domains can be precisely characterized and discriminated by the topological structure of cell networks, facilitating identification and interpretability of spatial domains, which outperforms state-of-the-art baselines. Furthermore, we prove that network model offers an effective and efficient strategy for integrative analysis of spatial transcriptomics data from various platforms.

#2474 TPPP3 maintains the stability of foot cytoskeleton architecture

Abstract Background and Aims Podocytes have a unique cytoskeleton architecture, and the disturbance of the skeleton architecture can lead to foot processes fusion and then proteinuria. Actin microfilaments dominate the cytoskeleton of the foot process, and microtubules and intermediate filaments dominate the synapses of the foot cells. The structural destruction of foot process actin is closely related to the injury of podocytes. The microtubule and actin often work together in cellular processes through stable connections. As the core structure of the skeleton network, microtubules play a key role in supporting and maintaining the skeleton network. However, the mechanism of microtubule structure destruction and podocyte injury remains unclear. Method Immunofluorescence and western blot were used to confirm the correlation between actin injury and decreased acetylation of tubulin in podocytes in vitro and in vivo. Single-cell RNA sequencing comprehensively characterized the expression of TPPP3 in human glomeruli. Immunofluorescence, immunohistochemical, western blot were used to determine the expression of TPPP3, α-tubulin, acetylated tubulin, actin microfilaments. Results In the case of podocyte injury, the acetylation level of tubulin decreased (P < 0.05). Reducing tubulin acetylation can lead to microfilaments injury (P < 0.05) and weakened podocyte motility (P < 0.05). TPPP3 was specifically expressed in human glomerular podocytes, and its expression level was significantly decreased after podocyte injury (P < 0.05). Knockdown of TPPP3 leaded to the level of microtubule acetylation decreased (P < 0.05) and microfilaments were damaged (P < 0.05) in podocytes. High acetylation levels of tubulin can partially restore actin microfilament damage caused by TPPP3 reduction in vivo and in vitro (P < 0.05). Conclusion TPPP3 is involved in maintaining the normal morphology and function of podocytes by stabilizing the actin microfilament skeleton structure by regulating the acetylation level of microtubules.

Citrus Pest Identification Model Based on Improved ShuffleNet

To address the current issues of complex structures and low accuracies in citrus pest identification models, a lightweight pest identification model was proposed. First, a parameterized linear rectification function was introduced to avoid neuronal death. Second, the model’s attention to pest characteristics was improved by incorporating an improved mixed attention mechanism. Subsequently, the network structure of the original model was adjusted to reduce architectural complexity. Finally, by employing transfer learning, an SCHNet model was developed. The experimental results indicated that the proposed model achieved an accuracy rate of 94.48% with a compact size of 3.84 MB. Compared to the original ShuffleNet V2 network, the SCHNet model showed a 3.12% accuracy improvement while reducing the model size by 22.7%. The SCHNet model exhibited an excellent classification performance for citrus pest identification, enabling the accurate identification of citrus pests.

Spatial Heterogeneity Analysis of Factors on Commuting Carbon Emissions: Evidence from the Shenzhen Metropolitan Area in China

As an essential part of daily life, commuting produces considerable carbon emissions and is currently receiving increased amounts of attention. Comprehensive explorations of carbon emissions and the spatial distribution of their effects based on previous studies are lacking. First, we adopt stepwise regression and geographically weighted regression (GWR) to explore the diverse impacts of carbon emissions on the different layers of metropolitan areas, employing factors from the perspectives of socioeconomics, transportation services, and road networks. Our findings show that optimizing the road network structure could be an effective approach to reducing carbon emissions from commuting, especially in the periphery of metropolitan areas. In addition, the mixed use of land contributes to reducing carbon emissions from commuting, especially in the central areas. Thus, the coverage of public transport should be improved, especially in peripheral regions. Policymakers should monitor the spatial heterogeneity of variables and develop suitable policies to adapt to the conditions of the different layers of metropolitan areas.

Improving the Oil Separation of Composite Lubricating Polyurea Grease via Regulating the Thickener Network Structure

Improving the Oil Separation of Composite Lubricating Polyurea Grease via Regulating the Thickener Network Structure

The durability of slag and tannery sludge based geopolymer corroded by acid and base solutions

The current treatment (landfill, composting, and incineration) of chromium-containing sludge can contaminate soil, surface water and groundwater. In this study, tannery sludge and granulated blast furnace slag was chosen to make a geopolymer and its ability to resist acid and alkali was investigated by immersing them in different solutions for 28 d. The experimental results showed that when pH value increased from 1.0 to 13.0, the compressive strength linearly increased from 36.7 MPa to 68.4 MPa while the porosity decreased from 4.37 % to 3.4 %. The results of NMR, SEM and FIR showed that the H+ in HCl acid solution depolymerized the three-dimensional network structure of the geopolymer and acicular product (CaCl2) was generated, which leaded to the increase in small pores and large pores. However, when immersed in alkaline solution the secondary polymerization occurred and more C-S-H appeared, making the geopolymer denser. In addition, the environmental benefits of the geopolymer were investigated and the results showed that the leaching concentration ranges of total chromium and Cr(VI) were 1.21–0.97 mg/L and 0.04–0.06 mg/L, both of which were far below the requirement of standards. The CO2 emission was 8.06 kg/(m3 Mpa), which was roughly half of the ordinary Portland cement (18 kg/(m³ MPa)). Therefore, the geopolymer could be a way to treat tannery sludge and the geopolymer made of tannery sludge and slag had the potential to be environmentally friendly building materials.

Constructing 3D magnetic MXenes/chitosan composites to reinforce U(VI) and Cr(VI) trapping: Excavating from performance to mechanism

Herein, we proposed a novel chitosan-induced self-assembly strategy to construct three-dimensional magnetic MXenes/chitosan composites to eliminate U(VI)/Cr(VI). Spectroscopic analysis elucidated that the magnetic composites exhibited a three-dimensional network structure, and the abundant –OH and –NH2 groups played a pivotal role in boosting elimination performance of the composites. Batch experiment demonstrated that the synergistic effect of the amino and hydroxyl groups contributed greatly to adsorption, and the maximum adsorption capacity of U(VI) and Cr(VI) was up to 106.2 and 83.1 mg/g, respectively. Meanwhile, the kinetic studies showed that an obvious transition between diffusion control and mass transfer control, and the adsorption behavior was endothermic and spontaneous. Furthermore, a comprehensive synergistic adsorption mechanism was proposed based on the systematic spectra analysis. Namely, the target contaminant was rapidly immobilized on the 3D composites via electrostatic interaction, then the U(VI) was complexed with the amino groups, while a part of Cr(VI) was reduced to Cr(III) by the hydroxyl groups. Thus, this study not only paved the way for mitigating the increasing heavy metal/radionuclide contamination, but also promoted the application of MXexes-based materials in the field of environmental remediation.