Networks-based Approach Research Articles

Identity Networks as a New Frontier: Conceptualizing, Mapping, and Analyzing Identity Structures and Their Impact on Well-Being

Sociological thought emphasizes the interdependencies between an individual’s multiple identities as crucial for understanding processes and outcomes at individual and societal levels. To describe such interdependencies, researchers often invoke the metaphor of a “network of identities.” However, sociology currently lacks a cohesive body of research that moves beyond the metaphor to actually conceptualize, measure, and study identity structures as networks, thus missing the potential benefits of this approach. In this article, we take a networks-based approach to capture identity structures and operationalize key concepts in the identities literature. We use role theory and identity theory to derive hypotheses that link well-being with the prevalence and intensity of interference and facilitation among identities. Using original survey data, we find that both the prevalence and intensity of interferences between identities and the prevalence of facilitation between identities are associated with well-being. Our findings demonstrate the potential of identity networks to advance multiple areas of sociological work.

Pathophysiology of the ascending aorta: Impact of dilation and valve phenotype on large-scale blood flow coherence detected by 4D flow MRI

Background and objectiveThe evidence on the role of hemodynamics in aorta pathophysiology has yet to be robustly translated into clinical applications, to improve risk stratification of aortic diseases. Motivated by the need to enrich the current understanding of the pathophysiology of the ascending aorta (AAo), this study evaluates in vivo how large-scale aortic flow coherence is affected by AAo dilation and aortic valve phenotype. MethodsA complex networks-based approach is applied to 4D flow MRI data to quantify subject-specific AAo flow coherence in terms of correlation between axial velocity waveforms and the aortic flow rate waveform along the cardiac cycle. The anatomical length of persistence of such correlation is quantified using the recently proposed network metric average weighted curvilinear distance (AWCD). The analysis considers 107 subjects selected to allow an ample stratification in terms of aortic valve morphology, absence/presence of AAo dilation and of aortic valve stenosis. ResultsThe analysis highlights that the presence of AAo dilation as well as of bicuspid aortic valve phenotype breaks the physiological AAo flow coherence, quantified in terms of AWCD. Of notice, it emerges that cycle-average blood flow rate and relative AAo dilation are main determinants of AWCD, playing opposite roles in promoting and hampering the persistence of large-scale flow coherence in AAo, respectively. ConclusionsThe findings of this study can contribute to broaden the current mechanistic link between large-scale blood flow coherence and aortic pathophysiology, with the prospect of enriching the existing tools for the in vivo non-invasive hemodynamic risk assessment for aortic diseases onset and progression.

Streamflow Prediction Using Complex Networks.

The reliable prediction of streamflow is crucial for various water resources, environmental, and ecosystem applications. The current study employs a complex networks-based approach for the prediction of streamflow. The approach consists of three major steps: (1) the formation of a network using streamflow time series; (2) the calculation of the clustering coefficient (CC) as a network measure; and (3) the use of a clustering coefficient-based nearest neighbor search procedure for streamflow prediction. For network construction, each timestep is considered as a node and the existence of link between any node pair is identified based on the difference (distance) between the streamflow values of the nodes. Different distance threshold values are used to identify the critical distance threshold to form the network. The complex networks-based approach is implemented for the prediction of daily streamflow at 142 stations in the contiguous United States. The prediction accuracy is quantified using three statistical measures: correlation coefficient (R), normalized root mean square error (NRMSE), and Nash-Sutcliffe efficiency (NSE). The influence of the number of neighbors on the prediction accuracy is also investigated. The results, obtained with the critical distance threshold, reveal that the clustering coefficients for the 142 stations range from 0.799 to 0.999. Overall, the prediction approach yields reasonably good results for all 142 stations, with R values ranging from 0.05 to 0.99, NRMSE values ranging from 0.1 to 12.3, and the NSE values ranging from -0.89 to 0.99. An attempt is also made to examine the relationship between prediction accuracy and the catchment characteristics/streamflow statistical properties (drainage area, mean flow, coefficient of variation of flow). The results suggest that the prediction accuracy does not have much of a relationship with the drainage area and the mean streamflow values, but with the coefficient of variation of flow. The outcomes from this study are certainly promising regarding the application of complex networks-based concepts for the prediction of streamflow (and other hydrologic) time series.

What predicts children’s perceptions of intergroup connections within the classroom?: A social networks-based approach

Intergroup connections play a role in fostering positive developmental outcomes. Scholarship has been limited in exploring children’s perceptions of such relationships. We apply cognitive social structures and network assortativity coefficients to capture third through eighth grade children’s perceptions of intergroup connections related to gender, socio-economic status, and race/ethnicity within their classroom networks. Of the 99 participants, 35.4% were African American/Black, 27.3% were Latino(a), and 26.3% were White. Additionally, 77.8% of students were eligible for free/reduced lunch. Regression analyses revealed that grade level and prosocial behaviors were associated with children’s perceptions of network assortativity based on gender. Findings indicated that free lunch status, perceived popularity, and peer-nominated prosocial behavior predicted children’s perceptions of network assortativity based on socio-economic status. There was also a relationship between grade level and children’s perceptions of network assortativity based on race/ethnicity. Results hold implications for understanding developmental perceptions of intergroup connections, as well as peer-based interventions.

Identification of linear flat outputs using neural networks—Examples of two-degree-of-freedom underactuated mechanical systems

This paper proposes a neural networks-based approach of finding flat output of linearized underactuated mechanical systems (UMS). Given that differential flatness and controllability are equivalent for linear systems, the problem is equivalent to finding the Brunovsky canonical form of linearized UMSs. We use a two degree-of-freedom (2DOF) system to illustrate the theoretical development. The proposed method identifies the local flat output of nonlinear mechanical systems from the measurements only, without a detailed mathematical model. The identification allows us to combine the well-known active disturbance rejection control (ADRC) and differential flatness control. A time-domain direct identification (TDDI) algorithm and its variant based on the algebraic method are proposed for flat output identification (FOID). The neural networks for implementing the TDDI algorithm called FOID-NN are created to evaluate flat output candidates and to use the flat output to reconstruct the system states in terms of a linear mapping. The neural networks are trained with the loss functions defined by reconstruction errors. Two special layers, namely tracking differentiator (TD) and algebraic layer, are inserted in the FOID-NN to handle noisy signal differentiations. Simulations of a cart–pole system and a stable 2DOF nonlinear UMS are carried out to show the range of applications of the FOID-NN. The experimental data of an underactuated rotary crane are used to validate the identified flat output.

Abstract 3344: Widespread BRCA1/2-independent homologous recombination defects are caused by alterations in RNA binding proteins

Abstract Summary: Defects in homologous recombination DNA repair (HRD) both predispose to cancer development and produce therapeutic vulnerabilities, making it critical to define the spectrum of genetic events that cause HRD. However, we found that mutations in BRCA1/2 and other canonical HR genes only identified 10-20% of tumors that display genomic evidence of HRD. Using a networks-based approach, we discovered that over half of putative genes causing HRD originated outside of canonical DNA damage response genes, with a particular enrichment for RNA binding protein (RBP)-encoding genes. These putative drivers of HRD were experimentally validated, cross-validated in an independent cohort, and enriched in cancer-associated GWAS loci. Mechanistic studies indicate that some RBPs are recruited to sites of DNA damage to facilitate repair, whereas others control the expression of canonical HR genes. Overall, this study greatly expands the repertoire of known drivers of HRD, with implications for basic biology, genetic screening, and therapy stratification. Citation Format: Babita Kaundal, Daniel J. McGrail, Yang Li, Roger S. Smith, Bin Feng, Hui Dai, Limei Hu, Briana Dennehey, Sharad Awasthi, Marc L. Mendillo, Anil K. Sood, Gordon B. Mills, Shiaw-Yih Lin, S. Stephen Yi, Nidhi Sahni. Widespread BRCA1/2-independent homologous recombination defects are caused by alterations in RNA binding proteins [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3344.

Data-Driven Accelerated Parameter Identification for Chaboche-Type Visco-Plastic Material Models to Describe the Relaxation Behavior of Copper Alloys

Abstract Background Calibrating material models to experimental measurements is crucial for realistic computational analysis of components. For complex material models, however, optimization-based identification procedures can become time-consuming, particularly if the optimization problem is ill-posed. Objective The objective of this paper is to assess the feasibility of using machine learning to identify the parameters of a Chaboche-type material model that describes copper alloys. Specifically, we apply and analyze this identification approach using short-term uniaxial relaxation tests on a C19010 copper alloy. Methods A genetic algorithm forms the basis for identifying the parameters of the Chaboche-type material model. The approach is accelerated by replacing the numerical simulation of the experimental setup by a neural network surrogate. The neural networks-based approach is compared against a classic approach using both, synthetic and experimental data. Results The results show that on the one hand, a sufficiently accurate identification of the material model parameters can be achieved by a classic but time-consuming genetic algorithm. On the other hand, it is shown that machine learning enables a much more time-efficient identification procedure, however, suffering from the ill-posedness of the identification problem. Conclusion Compared to classic parameter identification approaches, machine learning techniques can significantly accelerate the identification procedure for parameters of Chaboche-type material models with acceptable loss of accuracy.

A bilinear transformer interactive neural networks-based approach to fine-grained recognition and protection of plant diseases for gardening design

Landscape gardening design is an important part of urban ecology and urban image, which is of great significance for urban plant protection. Plant diseases are a major challenge that must be faced in the conservation of landscape plants. Having timely and comprehensive information about the types and developmental stages of plant diseases is crucial for effective disease control. Traditional plant disease differentiation, which relies on manual inspection by professionals, is both time-consuming and labor-intensive. The rapid development of deep artificial neural network technology, especially the rise of computer vision technology, provides efficient and accurate technical means for plant disease identification. In this paper, we introduce the Interactive Bilinear Transformer Network (IBTN) model, which utilizes fine-grained recognition technology for landscape plant disease identification. We validate the effectiveness of model using several public datasets, demonstrating its competitive performance.

New perspectives for the intelligent rolling stock classification in railways: an artificial neural networks-based approach

New perspectives for the intelligent rolling stock classification in railways: an artificial neural networks-based approach

Widespread BRCA1/2-independent homologous recombination defects are caused by alterations in RNA-binding proteins

Defects in homologous recombination DNA repair (HRD) both predispose to cancer development and produce therapeutic vulnerabilities, making it critical to define the spectrum of genetic events that cause HRD. However, we found that mutations in BRCA1/2 and other canonical HR genes only identified 10%-20% of tumors that display genomic evidence of HRD. Using a networks-based approach, we discovered that over half of putative genes causing HRD originated outside of canonical DNA damage response genes, with a particular enrichment for RNA-binding protein (RBP)-encoding genes. These putative drivers of HRD were experimentally validated, cross-validated in an independent cohort, and enriched in cancer-associated genome-wide association study loci. Mechanistic studies indicate that some RBPs are recruited to sites of DNA damage to facilitate repair, whereas others control the expression of canonical HR genes. Overall, this study greatly expands the repertoire of known drivers of HRD, with implications for basic biology, genetic screening, and therapy stratification.

Spatial-Temporal Cellular Traffic Prediction for 5G and Beyond: A Graph Neural Networks-Based Approach

During the past decade, Industry 4.0 has greatly promoted the improvement of industrial productivity by introducing advanced communication and network technologies in the manufacturing process. With the continuous emergence of new communication technologies and networking facilities, especially the rapid evolution of cellular networks for 5 G and beyond, the requirements for smarter, more reliable, and more efficient cellular network services have been raised from the Industry 5.0 blueprint. To meet these increasingly challenging requirements, proactive and effective allocation of cellular network resources becomes essential. As an integral part of the cellular network resource management system, cellular traffic prediction faces severe challenges with stringent requirements for accuracy and reliability. One of the most critical problems is how to improve the prediction performance by jointly exploring the spatial and temporal information within the cellular traffic data. A promising solution to this problem is provided by Graph Neural Networks (GNNs), which can jointly leverage the cellular traffic in the temporal domain and the physical or logical topology of cellular networks in the spatial domain to make accurate predictions. In this paper, we present the spatial-temporal analysis of a real-world cellular network traffic dataset and review the state-of-the-art researches in this field. Based on this, we further propose a time-series similarity-based graph attention network, TSGAN, for the spatial-temporal cellular traffic prediction. The simulation results show that our proposed TSGAN outperforms three classic prediction models based on GNNs or GRU on a real-world cellular network dataset in short-term, mid-term, and long-term prediction scenarios.

Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach.

Introduction: Of all the cancers that afflict women, breast cancer (BC) has the second-highest mortality rate, and it is also believed to be the primary cause of the high death rate. Breast cancer is the most common cancer that affects women globally. There are two types of breast tumors: benign (less harmful and unlikely to become breast cancer) and malignant (which are very dangerous and might result in aberrant cells that could result in cancer). Methods: To find breast abnormalities like masses and micro-calcifications, competent and educated radiologists often examine mammographic images. This study focuses on computer-aided diagnosis to help radiologists make more precise diagnoses of breast cancer. This study aims to compare and examine the performance of the proposed shallow convolutional neural network architecture having different specifications against pre-trained deep convolutional neural network architectures trained on mammography images. Mammogram images are pre-processed in this study's initial attempt to carry out the automatic identification of BC. Thereafter, three different types of shallow convolutional neural networks with representational differences are then fed with the resulting data. In the second method, transfer learning via fine-tuning is used to feed the same collection of images into pre-trained convolutional neural networks VGG19, ResNet50, MobileNet-v2, Inception-v3, Xception, and Inception-ResNet-v2. Results: In our experiment with two datasets, the accuracy for the CBIS-DDSM and INbreast datasets are 80.4%, 89.2%, and 87.8%, 95.1% respectively. Discussion: It can be concluded from the experimental findings that the deep network-based approach with precise tuning outperforms all other state-of-the-art techniques in experiments on both datasets.

Machine Learning and Artificial Neural Networks-Based Approach to Model and Optimize Ethyl Methanesulfonate and Sodium Azide Induced In Vitro Regeneration and Morphogenic Traits of Water Hyssops (Bacopa monnieri L.)

Machine Learning and Artificial Neural Networks-Based Approach to Model and Optimize Ethyl Methanesulfonate and Sodium Azide Induced In Vitro Regeneration and Morphogenic Traits of Water Hyssops (Bacopa monnieri L.)

An Artificial Neural Network-Based Approach for Predicting the COVID-19 Daily Effective Reproduction Number Rt in Italy

Since December 2019, the novel coronavirus disease (COVID-19) has had a considerable impact on the health and socio-economic fabric of Italy. The effective reproduction number Rt is one of the most representative indicators of the contagion status as it reports the number of new infections caused by an infected subject in a partially immunized population. The task of predicting Rt values forward in time is challenging and, historically, it has been addressed by exploiting compartmental models or statistical frameworks. The present study proposes an Artificial Neural Networks-based approach to predict the Rt temporal trend at a daily resolution. For each Italian region and autonomous province, 21 daily COVID-19 indicators were exploited for the 7-day ahead prediction of the Rt trend by means of different neural network architectures, i.e., Feed Forward, Mono-Dimensional Convolutional, and Long Short-Term Memory. Focusing on Lombardy, which is one of the most affected regions, the predictions proved to be very accurate, with a minimum Root Mean Squared Error (RMSE) ranging from 0.035 at day t + 1 to 0.106 at day t + 7. Overall, the results show that it is possible to obtain accurate forecasts in Italy at a daily temporal resolution instead of the weekly resolution characterizing the official Rt data.

A Convolutional Neural Networks-Based Approach for Texture Directionality Detection.

The perceived texture directionality is an important, not fully explored image characteristic. In many applications texture directionality detection is of fundamental importance. Several approaches have been proposed, such as the fast Fourier-based method. We recently proposed a method based on the interpolated grey-level co-occurrence matrix (iGLCM), robust to image blur and noise but slower than the Fourier-based method. Here we test the applicability of convolutional neural networks (CNNs) to texture directionality detection. To obtain the large amount of training data required, we built a training dataset consisting of synthetic textures with known directionality and varying perturbation levels. Subsequently, we defined and tested shallow and deep CNN architectures. We present the test results focusing on the CNN architectures and their robustness with respect to image perturbations. We identify the best performing CNN architecture, and compare it with the iGLCM, the Fourier and the local gradient orientation methods. We find that the accuracy of CNN is lower, yet comparable to the iGLCM, and it outperforms the other two methods. As expected, the CNN method shows the highest computing speed. Finally, we demonstrate the best performing CNN on real-life images. Visual analysis suggests that the learned patterns generalize to real-life image data. Hence, CNNs represent a promising approach for texture directionality detection, warranting further investigation.

Multiplex-Heterogeneous Network-Based Capturing Potential SNP "Switches" of Pathways Associating With Diverse Disease Characteristics of Asthma.

Asthma is a complex heterogeneous respiratory disorder. In recent years nubbly regions of the role of genetic variants and transcriptome including mRNAs, microRNAs, and long non-coding RNAs in the pathogenesis of asthma have been separately excavated and reported. However, how to systematically integrate and decode this scattered information remains unclear. Further exploration would improve understanding of the internal communication of asthma. To excavate new insights into the pathogenesis of asthma, we ascertained three asthma characteristics according to reviews, airway inflammation, airway hyperresponsiveness, and airway remodeling. We manually created a contemporary catalog of corresponding risk transcriptome, including mRNAs, miRNAs, and lncRNAs. MIMP is a multiplex-heterogeneous networks-based approach, measuring the relevance of disease characteristics to the pathway by examining the similarity between the determined vectors of risk transcriptome and pathways in the same low-dimensional vector space. It was developed to enable a more concentrated and in-depth exploration of potential pathways. We integrated experimentally validated competing endogenous RNA regulatory information and the SNPs with significant pathways into the ceRNA-mediated SNP switching pathway network (CSSPN) to analyze ceRNA regulation of pathways and the role of SNP in these dysfunctions. We discovered 11 crucial ceRNA regulations concerning asthma disease feature pathway and propose a potential mechanism of ceRNA regulatory SNP → gene → pathway → disease feature effecting asthma pathogenesis, especially for MALAT1 (rs765499057/rs764699354/rs189435941) → hsa-miR-155 → IL13 (rs201185816/rs1000978586/rs202101165) → Interleukin-4 and Interleukin-13 signaling → inflammation/airway remodeling and MALAT1 (rs765499057/rs764699354/rs189435941) → hsa-miR-155 → IL17RB (rs948046241) → Interleukin-17 signaling (airway remodeling)/Cytokine-cytokine receptor interaction (inflammation). This study showed a systematic and propagable workflow for capturing the potential SNP “switch” of asthma through text and database mining and provides further information on the pathogenesis of asthma.

A Networks-Based Approach Identifies DDR Genes Not Associated With TMB Yet Predictive of Immune Checkpoint Response

Immune checkpoint blockade (ICB) therapies have had remarkable success for treatment of solid tumors. However, as only a subset of patients exhibit responses, there is a continued need for biomarker development. Numerous reports have shown a link between tumor mutational burden (TMB) and ICB response, while others have identified a link between ICB response and mutation in DNA Damage Repair (DDR) genes. However, it remains unclear to what extent mutations in DDR genes hold predictive value above and beyond their association with TMB. Herein we present a novel, networks-based test and Network Based-Expected TMB Score (N-BETS) with higher specificity for discriminating DDR genes and pathways that are associated with elevated TMB. Moreover, we find that mutations in certain DDR genes that are not associated with elevated TMB are nevertheless predictive of ICB benefit in two separate studies, demonstrating that their inactivation contributes to ICB response in a TMB independent manner.

Convolutional Neural Networks-Based Approach to Detect Neonatal Respiratory System Anomalies with Limited Thermal Image

Respiratory system diseases in neonates are thought-about major causes of neonatal morbidity and mortality, particularly in developing countries Early diagnosis and management of these diseases is very important. Thermal imaging stands out as a harmless non-ionizing method, and monitoring of temperature changes or thermal symmetry is used as a diagnostic tool in medicine. This study aims to detect respiratory abnormalities of neonates by artificial intelligence using limited thermal image. Convolutional neural network (CNN) models, although a powerful classification tool, require a balanced and large amount of data. The conditions that require the attention of infants in neonatal intensive care units make medical imaging difficult. It may not always be possible to have much data in the neonatal thermal image database, as in some real-world problems. To overcome this, an effective deep learning model and various data enhancement techniques were used and their effects on the classification results were observed. Neonates with respiratory abnormalities were evaluated in one class, with cardiovascular diseases and abdominal abnormalities were evaluated in the other class. As a result, when the number of images is increased by 4 times with data augmentation, it was determined that the classification accuracy increased from 84.5% to 90.9%.

Spatio-temporal connections in streamflow: a complex networks-based approach

This study introduces a new complex networks-based method to examine the spatio-temporal connections in streamflow. The method involves reconstruction of two (or more) time series jointly in a multi-dimensional phase space using a nonlinear phase space embedding procedure and construction of the spatio-temporal streamflow network of nodes and links based on the reconstructed vectors. After this spatio-temporal network construction, the clustering property of the network is measured using the clustering coefficient, which quantifies the tendency of a network to cluster. The approach is applied to monthly streamflow time series observed at each of 639 streamflow stations in the United States. Different distance threshold values are used to identify the presence/absence of links in the streamflow network and, hence, to calculate the clustering coefficient. The clustering coefficient results help to identify the critical distance threshold and optimal embedding dimension of each streamflow time series using different distance threshold values. The optimal embedding dimensions and the clustering coefficient values of the 639 streamflow time series are also discussed in terms of the role of catchment and flow properties (drainage area, elevation, flow mean, and flow coefficient of variation). The dimensions for the 639 streamflow time series are generally found to range from 2 to 18 (but even up to 30 for a few stations), indicating a wide range of complexity in the spatio-temporal connections in streamflow across the United States. The clustering coefficient values for the 639 stations are found to be in the range of 0.53–0.99, which suggest generally strong connections. The outcomes of this study clearly indicate the usefulness of the networks-based approach for examining the spatio-temporal connections in streamflow.

Estimating Johnson-Cook Material Parameters using Neural Networks

The five-parameter Johnson-Cook (J-C) material model represents the behavior of a material under extreme mechanical loading, including high temperatures, strains, and strain rates. The goal of this study is to estimate five J-C material parameters and chip thickness jointly for a given set of force components, power, and temperature. The approach uses two neural network models on a dataset simulated by finite element analysis for orthogonal cutting of aluminum 6061-T6. The first model develops a function approximator to predict the force components, power, and temperature using a given set of J-C parameters and chip thickness for aluminum 6061-T6. The second model searches the input space of the first model to estimate the J-C parameter values and chip thickness, given a set of targeted force components, power, and temperature of interest. The performance of both neural network models is evaluated using mean absolute percentage error. The results suggest that the developed neural networks-based approach is capable of estimating multiple J-C parameters and chip thickness that will result in a targeted force components, power, and temperatures of interest, given starting ‘educated guesses’ about these values.