Network Interactions Research Articles

Identification of Potential Breast Cancer Stem Cell Biomarkers in the Secretome Using a Network Interaction Approach Analysis.

Breast cancer stem cells (BCSCs) play a role in the high rates of resistance, recurrence, and metastasis. The precise biomarkers of BCSCs can assist effectively in identifying cancer, assessing prognosis, diagnosing, and monitoring therapy. The aim of this study was to give a complete analysis for predicting specific biomarkers of BCSCs. We aggregated profile datasets in this work to shed light on the underlying critical genes and pathways of BCSCs. We obtained two expression profiling by array datasets (GSE7513 and GSE7515) from the Gene Expression Omnibus (GEO) database to identify biomarkers in BCSCs. Enrichr was used to do functional analysis, including gene ontology (GO) and reactome pathway. Furthermore, the protein-protein interaction (PPI) of these differential expression genes (DEGs) was visualized using Cytoscape with the search tool for the retrieval of interacting genes (STRING). The hub genes in the PPI network were chosen for further investigation. We identified 65 up-regulated and 190 down- regulated DEGs and the GO enrichment analysis revealed that these DEGs were enriched in biological process related to tumorigenesis and stemness, including alter the extracellular matrix's physicochemical properties, cytoskeletal reorganisation, adhesion, motility, migration, growth, and survival. The Reactome analysis indicated that these DEGs were also involved in modulating function of ECM, regulation cancer metabolism and angiogenesis, tumor growth, proliferation, and metastasis. Our bioinformatic study revealed that FYN, INADL, OCLN, F11R, and TOP2A were potential biomarker panel of BCSCs from secretome.

Shifts in host–parasitoid networks across community garden management and urban landscape gradients

AbstractBiological pest control relies on interactions between herbivores and their natural enemies. Maintaining this ecosystem service requires considering herbivore and natural enemy interactions and their response to anthropogenic change at multiple scales. In this study, we used ecological networks to quantify the network structure of interactions between herbivorous insects and their parasitoids. We examined how herbivore host abundance, parasitism rates, and shifts in network structure relate to changes in local habitat management and landscape context. We sampled herbivores and parasitoids in Brassica oleracea plants at 22 urban gardens in the Central Coast of California. At each site, we measured local management characteristics (e.g., vegetation, ground cover, canopy cover) and quantified surrounding landscape composition (e.g., urban, natural, open, and agricultural cover). For the eight sites with large enough networks, we calculated three network structure metrics (interaction richness, vulnerability, and functional complementarity). We then used generalized linear and mixed models to examine relationships between herbivore host abundance, parasitism rates, garden management and landscape characteristics, and network metrics. We found that both local management and landscape composition influenced parasitism, while only local factors affected host abundance and network structure. Higher network interaction richness was marginally associated with enhanced parasitism rates for two host species and lower parasitism rates for one host species. Our results suggest that local garden management decisions may shift the structure of host–parasitoid networks, which may subsequently affect host parasitism rates, but outcomes for biological pest control will likely vary across host species.

Identification of network interactions from time series data: An iterative approach.

The first step toward advancing our understanding of complex networks involves determining their connectivity structures from the time series data. These networks are often high-dimensional, and in practice, only a limited amount of data can be collected. In this work, we formulate the network inference task as a bilinear optimization problem and propose an iterative algorithm with sequential initialization to solve this bilinear program. We demonstrate the scalability of our approach to network size and its robustness against measurement noise, hyper-parameter variation, and deviations from the network model. Results across experimental and simulated datasets, comprising oscillatory, non-oscillatory, and chaotic dynamics, showcase the superior inference accuracy of our technique compared to existing methods.

Implementasi Program Keluarga Harapan Di Desa Ciwangi Kecamatan Balubur Limbangan Kabupaten Garut

This research was conducted with the aim of understanding the implementation of the Family Hope Program (PKH) in the Ciwangi Village, Balubur Limbangan District, Garut Regency. Several issues were identified in the implementation process, causing it to not run smoothly. This was due to various factors related to policy substance, the behavior of implementers, network interaction, target group participation, and resources in the implementation in Ciwangi Village, Balubur Limbangan Subdistrict, Garut Regency. The theory used in this research is the policy implementation theory proposed by Muchlis Hamdi (2014), which consists of policy substance, the behavior of implementers, network interaction, target group participation, and resources. This research utilized a qualitative research method with data collection techniques through observation, interviews, and documentation. The informant selection technique employed was purposive sampling, with a total of 8 informants. The findings indicate that the implementation of the Family Hope Program in Ciwangi Village, Balubur Limbangan District, Garut Regency, has not fulfilled several factors crucial for the success of policy implementation. However, there are also factors that have been fully met, such as policy substance and network interaction.

EventPro – Application for Events

Abstract: In recent years, the event management landscape has undergone a profound transformation, driven by the emergence of event apps that have played a pivotal role in this evolution. This abstract delves into the concept of event apps, shedding light on their transformative impact and their potential to revolutionize the entire event lifecycle, encompassing planning, execution, and the overall event experience. Event apps are specialized software applications tailored to streamline the comprehensive event management process. Leveraging the widespread adoption of smartphones and the connectivity of the internet, these digital tools provide a seamless, enhanced event experience for attendees, organizers, and stakeholders. A prominent advantage of event apps is their capacity to simplify the event registration and ticketing process. Attendees can swiftly and conveniently register for events, select sessions, and make payments, effectively reducing the administrative workload for event organizers. Furthermore, event apps offer an array of features, including personalized schedules, interactive maps, and real-time notifications, ensuring attendees can effortlessly navigate events and stay informed about the latest updates. Beyond convenience, event apps foster valuable networking interactions among participants. They facilitate connections, meeting scheduling, and the exchange of contact information, opportunities that might have been challenging to achieve in a traditional event setting. They track attendee engagement, preferences, and behaviors, equipping organizers with insights to tailor future events for maximum impact. Additionally, event apps enable real-time communication, simplifying logistics management and responses to unexpected changes or challenges. As technology continues to advance, the potential for event apps to further revolutionize event planning, execution, and overall experiences is virtually limitless.

Short-Term Charging Load Prediction of Electric Vehicles with Dynamic Traffic Information Based on a Support Vector Machine

This study proposes a charging demand forecasting model for electric vehicles (EVs) that takes into consideration the characteristics of EVs with transportation and mobile load. The model utilizes traffic information to evaluate the influence of traffic systems on driving and charging behavior, specifically focusing on the characteristics of EVs with transportation and mobile load. Additionally, it evaluates the effect of widespread charging on the distribution network. An urban traffic network model is constructed based on the multi-intersection features, and a traffic network–distribution network interaction model is determined according to the size of the urban road network. Type classification simplifies the charging and discharging characteristics of EVs, enabling efficient aggregation of EVs. The authors have built a singular EV transportation model and an EV charging queue model is established. The EV charging demand is forecasted and then used as an input in the support vector machine (SVM) model. The final projection value for EV charging load is determined by taking into account many influencing elements. Compared to the real load, the proposed method’s feasibility and effectiveness are confirmed.

Environmental Governance Networks and Geography: A Research Agenda at the Confluence of Critical Concepts for Navigating Rapid Environmental Change

Social networks are foundational to modern environmental governance, and particularly influential in supporting societal navigation of rapid environmental change. Social network science crosses disciplinary boundaries and provides theoretical and methodological tools to predict and assess relationships between network characteristics, structures, and the diffusion of information and innovations critical to navigating environmental governance challenges such as climate change and biodiversity loss. Most network studies examine networks that operate at a particular spatial scale, and research questions often rest on the relationships between simple network structures and characteristics of individual network actors and their associations. Geographers are poised to further develop theory underlying social network science specifically relevant to navigating human–environment governance challenges by disentangling the influences of scale, place, and identity in network studies. In this article, we advance the concept of environmental governance networks as multiscale, multilevel, and multi-identity social networks formed organically in response to environmental governance challenges. We propose that geographers are well positioned to expose the socially constructed nature of environmental governance networks as barriers and opportunities for societal navigation of rapid environmental change by explicitly theorizing critical influences across time, space, place, levels of social organization, and the multiple roles and social identities individual network actors hold. We develop a research agenda that leverages geographic theory to expand network science questions relative to concepts such as network proximity, affiliation, interaction, and intra-individual roles and identities to better understand the position and potential of environmental governance networks as a social structure for navigating rapid environmental change.

Partitioning the contribution of bees with different traits and hoverflies to flower-visitor interaction networks

Insect pollinators are key to maintaining biodiversity and providing important ecosystem services. Among them, bee species (i.e., Apidae) and hoverflies (i.e., Syrphidae) are two of the most important and well-studied taxa worldwide. Yet, their relative contribution to the structural and dynamic properties of plant-pollinator interaction networks remains poorly understood. This is an important gap in knowledge given that these phylogenetically and functionally different groups might play different roles within their communities and respond in contrasting ways to anthropogenic perturbations.Here, we study the relative contribution of bee species (with different traits) and syrphids to maintain network properties (i.e., nestedness, network specialization (H2), and interaction diversity) and their influence on the temporal dynamics of these network metrics. To this end, we simulate species removals in community-wide flower-arthropod visitor interaction networks. These interactions were extensively sampled across four years in two different near-pristine temperate ecosystems in Japan.We found that bee species contribute significantly more than syrphids or any random combination of species to network nestedness, low specialization, and interaction diversity across months and years. In addition, bees also played a particularly important role in maintaining the observed temporal dynamics over time. Conversely, although syrphids were an abundant and species-rich group, they did not contribute so prominently to network metrics or temporal dynamics, with the exception of network complementary specialization, to which they contributed positively. In addition, smaller bee species and those that were active for longer periods were particularly important for interaction diversity and the number of interactions, respectively, and to maintain observed network temporal dynamics.Our results support that bee species, and especially the small ones, are cornerstone contributors to plant-visitor communities by shaping network properties and network temporal dynamics in natural ecosystems.

Wireless EEG Recording of Audiogenic Seizure Activity in Freely Moving Krushinsky-Molodkina Rats.

This study investigates audiogenic epilepsy in Krushinsky-Molodkina (KM) rats, questioning the efficacy of conventional EEG techniques in capturing seizures during animal restraint. Using a wireless EEG system that allows unrestricted movement, our aim was to gather ecologically valid data. Nine male KM rats, prone to audiogenic seizures, received implants of wireless EEG transmitters that target specific seizure-related brain regions. These regions included the inferior colliculus (IC), pontine reticular nucleus, oral part (PnO), ventrolateral periaqueductal gray (VLPAG), dorsal area of the secondary auditory cortex (AuD), and motor cortex (M1), facilitating seizure observation without movement constraints. Our findings indicate that targeted neural intervention via electrode implantation significantly reduced convulsive seizures in approximately half of the subjects, suggesting therapeutic potential. Furthermore, the amplitude of brain activity in the IC, PnO, and AuD upon audiogenic stimulus onset significantly influenced seizure severity and nature, highlighting these areas as pivotal for epileptic propagation. Severe cases exhibited dual waves of seizure generalization, indicative of intricate neural network interactions. Distinctive interplay between specific brain regions, disrupted during convulsive activity, suggests neural circuit reconfiguration in response to escalating seizure intensity. These discoveries challenge conventional methodologies, opening avenues for novel approaches in epilepsy research and therapeutic interventions.

Local DER Control with Reduced Loop Interactions in Active Distribution Networks

Active Distribution Networks are Multi-Input Multi-Output (MIMO) systems with coupled dynamics, which cause interactions among the control loops of Distributed Energy Resources (DERs). This undesired effect leads to performance degradation of voltage control. To mitigate the effects of this unavoidable coupling, the present paper proposes a systematic design procedure based on the analysis of the interaction’s sources. In detail, each DER is equipped with a double-loop PI to control the active and reactive power output of the voltage source converter, which connects the DER to the network’s node. Furthermore, to guarantee ancillary services, the two loops are coupled by a simple mechanism of cooperation of the active power to voltage regulation realized by a filtered droop law. To achieve voltage regulation with reduced loop interactions, the PI parameters and the filter’s pulse are designed according to a procedure with two sequential steps based on the Internal Model Control (IMC) technique. Simulation studies are finally presented to demonstrate that the proposed design method achieves both reduction of the loop interaction and robust voltage control in the presence of model parameter uncertainty in the MIMO plant, modeling various operating conditions of the ADN, including a step connection of large loads, renewable energy source variations, and changes in the substation transformer ratio.

A Principal Components Analysis and Functional Annotation of Differentially Expressed Genes in Brain Regions of Gray Rats Selected for Tame or Aggressive Behavior.

The process of domestication, despite its short duration as it compared with the time scale of the natural evolutionary process, has caused rapid and substantial changes in the phenotype of domestic animal species. Nonetheless, the genetic mechanisms underlying these changes remain poorly understood. The present study deals with an analysis of the transcriptomes from four brain regions of gray rats (Rattus norvegicus), serving as an experimental model object of domestication. We compared gene expression profiles in the hypothalamus, hippocampus, periaqueductal gray matter, and the midbrain tegmental region between tame domesticated and aggressive gray rats and revealed subdivisions of differentially expressed genes by principal components analysis that explain the main part of differentially gene expression variance. Functional analysis (in the DAVID (Database for Annotation, Visualization and Integrated Discovery) Bioinformatics Resources database) of the differentially expressed genes allowed us to identify and describe the key biological processes that can participate in the formation of the different behavioral patterns seen in the two groups of gray rats. Using the STRING- DB (search tool for recurring instances of neighboring genes) web service, we built a gene association network. The genes engaged in broad network interactions have been identified. Our study offers data on the genes whose expression levels change in response to artificial selection for behavior during animal domestication.

Physics-guided spatio–temporal neural network for predicting dissolved oxygen concentration in rivers

The prediction of river water quality is key in water resource management. Data-driven machine learning models have been widely used for predicting river water quality. However, these models seldom consider the physical mechanisms of water quality variation, which degrades the accuracy and stability of the prediction results. Hence, we develop a physics-guided spatio–temporal neural network (PGSTNN) model to predict a critical parameter for water quality assessment, i.e. dissolved oxygen. Physical information regarding spatio–temporal interactions in a hydrological network is explicitly considered to construct the architecture of PGSTNN. Two physical rules of dissolved oxygen variation (i.e. Henry’s law and power-scaling law) are established for the loss function of PGSTNN to guarantee the physical consistency of the prediction results. Experiments on the 2020–2021 water quality dataset in Atlanta, USA show that PGSTNN outperforms seven baseline neural network models in terms of prediction accuracy and stability. PGSTNN typically brings at least 10% accuracy (e.g. root mean square error and mean absolute error) improvement over the comparison methods. The proposed PGSTNN may not only improve the emergency response ability of water resource management, but also provide useful ideas for integrating scientific knowledge with machine learning.

Towards low-carbon power networks: Optimal location and sizing of renewable energy sources and hydrogen storage

This paper proposes a systematic optimization framework to jointly determine the optimal location and sizing decisions of renewables and hydrogen storage in a power network to achieve the transition to low-carbon networks efficiently. We obtain these strategic decisions based on the multi-period alternating current optimal power flow (AC MOPF) problem that jointly analyzes power network, renewable, and hydrogen storage interactions at the operational level by considering the uncertainty of renewable output, seasonality of electricity demand, and electricity prices. We develop a tailored solution approach based on second-order cone programming within a Benders decomposition framework to provide globally optimal solutions. In a test case, we show that the joint integration of renewable sources and hydrogen storage and consideration of the AC MOPF model significantly reduces the operational cost of the power network. In turn, our findings can provide quantitative insights to decision-makers on how to integrate renewable sources and hydrogen storage under different settings of the hydrogen selling price, renewable curtailment cost, emission tax price, and conversion efficiency.

Brain functional connectivity alterations in patients with anterior cruciate ligament injury

Recent advancements in neuroimaging have illustrated that anterior cruciate ligament (ACL) injuries could impact the central nervous system (CNS), causing neuroplastic changes in the brain beyond the traditionally understood biomechanical consequences. While most of previous functional magnetic resonance imaging (fMRI) studies have focused on localized cortical activity changes post-injury, emerging research has suggested disruptions in functional connectivity across the brain. However, these prior investigations, albeit pioneering, have been constrained by two limitations: a reliance on small-sample participant cohorts, often limited to two to three patients, potentially limiting the generalizability of findings, and an adherence to region of interest based analysis, which may overlook broader network interactions. To address these limitations, our study employed resting-state fMRI to assess whole-brain functional connectivity in 15 ACL-injured patients, comparing them to matched controls using two distinct network analysis methods. Using Network-Based Statistics, we identified widespread reductions in connectivity that spanned across multiple brain regions. Further modular connectivity analysis showed significant decreases in inter-modular connectivity between the sensorimotor and cerebellar modules, and intra-modular connectivity within the default-mode network in ACL-injured patients. Our results thus highlight a shift from localized disruptions to network-wide dysfunctions, suggesting that ACL injuries induce widespread CNS changes. This enhanced understanding has the potential to stimulate the development of strategies aiming to restore functional connectivity and improve recovery outcomes.

The social and moral ecology of education for flourishing

ABSTRACT Educational institutions exist in reciprocal relations with broader social and moral ecologies. These ecologies involve interactions of networks of individuals and groups with wider aspects of culture, and are therefore broadly social, and they contain explicit or implicit content with regard to right and wrong, and are therefore moral. There is growing recognition that educational institutions could do more to promote full flourishing for students, teachers, staff, society, and planet, but there has been relatively little attention to the role played by such social and moral ecologies in fostering this desired change. This paper argues that ecosystem stewards can inspire and lead educational institutions in this direction by loving more fully into being a cultural climate that promotes morally good forms of ecosystem-wide flourishing. This includes an exploration of a systems perspective in order to encourage a more intentional and skillful integration of healthy social and moral influences.

Genomic linkages dictate cancer evolution

Aneuploidy, a state of chromosome imbalance frequently found in cancer, results in convoluted cancer genomes. Here, Kuzmin and colleagues1 identify how the aneuploid genome in triple-negative breast cancer is being shaped by unique genome network interactions.

CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression.

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies.

Simultaneous EEG-fMRI Investigation of Rhythm-Dependent Thalamo-Cortical Circuits Alteration in Schizophrenia.

Schizophrenia is accompanied by aberrant interactions of intrinsic brain networks. However, the modulatory effect of electroencephalography (EEG) rhythms on the functional connectivity (FC) in schizophrenia remains unclear. This study aims to provide new insight into network communication in schizophrenia by integrating FC and EEG rhythm information. After collecting simultaneous resting-state EEG-functional magnetic resonance imaging data, the effect of rhythm modulations on FC was explored using what we term "dynamic rhythm information." We also investigated the synergistic relationships among three networks under rhythm modulation conditions, where this relationship presents the coupling between two brain networks with other networks as the center by the rhythm modulation. This study found FC between the thalamus and cortical network regions was rhythm-specific. Further, the effects of the thalamus on the default mode network (DMN) and salience network (SN) were less similar under alpha rhythm modulation in schizophrenia patients than in controls ([Formula: see text]). However, the similarity between the effects of the central executive network (CEN) on the DMN and SN under gamma modulation was greater ([Formula: see text]), and the degree of coupling was negatively correlated with the duration of disease ([Formula: see text], [Formula: see text]). Moreover, schizophrenia patients exhibited less coupling with the thalamus as the center and greater coupling with the CEN as the center. These results indicate that modulations in dynamic rhythms might contribute to the disordered functional interactions seen in schizophrenia.

Causal interactions in brain networks predict pain levels in trigeminal neuralgia

Trigeminal neuralgia (TN) is a highly debilitating facial pain condition. Magnetic resonance imaging (MRI) is the main method for generating insights into the central mechanisms of TN pain in humans. Studies have found both structural and functional abnormalities in various brain structures in TN patients as compared with healthy controls. Whereas studies have also examined aberrations in brain networks in TN, no studies have to date investigated causal interactions in these brain networks and related these causal interactions to the levels of TN pain. We recorded fMRI data from 39 TN patients who either rested comfortably in the scanner during the resting state session or tracked their pain levels during the pain tracking session. Applying Granger causality to analyze the data and requiring consistent findings across the two scanning sessions, we found 5 causal interactions, including: (1) Thalamus → dACC, (2) Caudate → Inferior temporal gyrus, (3) Precentral gyrus → Inferior temporal gyrus, (4) Supramarginal gyrus → Inferior temporal gyrus, and (5) Bankssts → Inferior temporal gyrus, that were consistently associated with the levels of pain experienced by the patients. Utilizing these 5 causal interactions as predictor variables and the pain score as the predicted variable in a linear multiple regression model, we found that in both pain tracking and resting state sessions, the model was able to explain ∼36 % of the variance in pain levels, and importantly, the model trained on the 5 causal interaction values from one session was able to predict pain levels using the 5 causal interaction values from the other session, thereby cross-validating the models. These results, obtained by applying novel analytical methods to neuroimaging data, provide important insights into the pathophysiology of TN and could inform future studies aimed at developing innovative therapies for treating TN.

Unraveling biogeographical patterns and environmental drivers of soil fungal diversity at the French national scale

Abstract. The fungal kingdom is among the most diversified kingdoms on Earth, with estimations of up to 12 million species. However, it remains poorly understood, with only 150 000 fungal species currently described. Given the major ecological role of fungi in ecosystem functioning, these numbers stress the importance of investigating fungal diversity description across different ecosystem types. Here, we explored the spatial distribution of the soil fungal diversity on a broad geographical scale, using the French Soil Quality Monitoring Network that covers the whole French territory (2171 soils sampled along a systematic grid). Fungal alpha diversity was assessed directly from soil DNA using a meta-barcoding approach by targeting the 18S rDNA gene. The total accumulated fungal diversity across France included 136 219 operational taxonomic units (OTUs), i.e., about 1 % of worldwide soil fungal diversity (based on a maximum diversity estimate of 12 million) for a territory representing only 0.3 % of the terrestrial surface on Earth. Based on this dataset, the first extensive map of fungal alpha diversity was drawn and showed a heterogeneous and spatially structured distribution in large biogeographical patterns of 231 km radius for richness (Hill diversity of order 0) and smaller patterns of 36 km radius for dominant fungi (Hill diversity of order 2). As related to other environmental parameters, the spatial distribution of fungal diversity (Hill numbers based on different orders of diversity) was mainly influenced by local filters such as soil characteristics and land management and also by global filters such as climate conditions with various relative influences. Interestingly, cropped soils exhibited the highest pool of fungal diversity relative to forest and vineyard soils. To complement this, soil fungal OTU network interactions were calculated for the different land uses across France. They varied hugely and showed a loss of 75 % of the complexity in crop systems and grasslands compared to forests and up to 83 % in vineyard systems. Overall, our study revealed that a nationwide survey with a high spatial-resolution approach is relevant for deeply investigating the spatial distribution and determinism of soil fungal diversity. Our findings provide novel insights for a better understanding of soil fungal ecology across the 18S rDNA gene and upgrade biodiversity conservation policies by supplying representative repositories dedicated to soil fungi.