Growth Of Networks Research Articles

Electronic health records have been increasingly adopted as useful resources for genomic research. However, case-control labeling of clinical data from electronic health records is challenging and most studies utilize phenotype codes to define case/control labels, resulting in suboptimal downstream analyses. Here we describe the liability threshold phenotypic integration, a method combining genetic relatedness with phenotypic data, including binary and continuous traits such as diagnosis codes, family disease history, laboratory measurements and biomarkers, to derive new continuous phenotypes for target diseases. The model utilizes an automatic trait selection algorithm that increases performance in disease risk prediction and provides insights into nontarget traits associated with the target disease. Our simulations and applications to the eMERGE network and the UK Biobank data demonstrate consistent performance gains in disease risk prediction and genome-wide association study power compared to conventional phenotype codes, models that solely incorporate family history and the phenotype imputation method SoftImpute, with similar false-positive rate control.

Supramolecular hydrogels formed by the self-assembly of low-molecular-weight (LMW) agents are promising next-generation biomaterials for drug delivery, tissue engineering, and regenerative medicine. Phenylalanine (Phe) derivatives have emerged as a privileged class of LMW supramolecular gelators due to their strong propensity to self-assemble into emergent hydrogel networks with demonstrated biocompatibility. We have previously reported a series of cationic Phe-derived gelators in which fluorenylmethoxycarbonyl (Fmoc) phenylalanine (Phe), 3-fluorophenylalanine (3F-Phe), and pentafluorophenylalanine (F5-Phe) are functionalized at the C-terminus with diaminopropane (DAP). These gelators (Fmoc-Phe-DAP, Fmoc-3F-Phe-DAP, and Fmoc-F5-Phe-DAP) are water-soluble and undergo spontaneous self-assembly and gelation upon an increase in the ionic strength of the solution caused by addition of sodium chloride. Herein, we report the effects of pH on the self-assembly and gelation of Fmoc-Phe-DAP, Fmoc-3F-Phe-DAP, and Fmoc-F5-Phe-DAP. We also describe the effects that pH has on the emergent properties of these hydrogel networks, including assembly morphology and hydrogel viscoelasticity. These studies indicate that pH has varying effects on the properties of the hydrogels that are also dependent on the molecular structure of the Fmoc-Phe-DAP derivative. Fmoc-Phe-DAP hydrogels are highly sensitive to changes in solvent pH, forming strong hydrogels only near neutral pH. In contrast, hydrogels of Phe derivatives with fluorinated side chains (Fmoc-3F-Phe-DAP and Fmoc-F5-Phe-DAP) have consistent emergent viscoelastic properties across a wider range of acidic to basic pH values.

Adaptive UAV deployment for enhanced connectivity in disaster-stricken emergency networks: A multi-objective approach

Urban agriculture experiments in Asia's metropolises are often spontaneous assemblages that unfold with diverse trajectories. Rather than being confined to the laboratories and field sites of state and market institutions, many such experiments escape or exceed the knowledge production spaces of technocrats, scientific professionals, and planners. Instead, knowledge is co-produced within a network of agentic actors, which also includes fledgling gardeners, local government officials, non-profit organizations, experienced rural farmers, culinary professionals, and even nonhumans. Some emergent networks are unlikely alliances forged across class lines, rural-urban divides, and political spectra. Spontaneous, diffuse, and polycentric, urban agriculture experiments complicate our understanding of the governance of urban experiments and urban environments. This paper explores the polycentric governance of urban agriculture experiments in Metro Manila, a highly dense and fragmented megacity in Southeast Asia. Drawing on observations in urban agriculture spaces and in-depth interviews with urban farmers and representatives from state and non-state institutions, this paper illustrates how polycentric urban agriculture experiments in Metro Manila reflect the ambivalences—both the promises and struggles—of urban experimentation. On the one hand, the socio-material relations emerging from urban agriculture experiments demonstrate transformative promises. On the other hand, while creativity and innovation sprout out of the metropolis's cracks and fissures like tiny seedlings of hope, many of them are ultimately stunted by Metro Manila's adverse political and economic realities. Whether these experiments can eventually influence urban governance and planning to improve the lives of Manileños remains a question.

During the COVID-19 outbreak, military personnel were deployed on an unprecedented scale to overwhelmed healthcare institutions. In the Netherlands too, military medical personnel were deployed to cope with the major patient influx. This study focuses on the civil-military cooperation at the Dutch University Medical Centre Utrecht (UMCU). We explore collaboration experiences and identify lessons for future collaboration. A qualitative study was conducted based on a theoretical framework consisting of network theory, literature on emergent networks, and inter-organizational theory. Data were collected from semi-structured interviews with 34 participants; both civil and military personnel. An abductive thematic analysis was guided by five themes recovered from earlier research on civil-military cooperation. An ethical review and approval for non-medical research was obtained from the Research Ethics Review Committee Faculty of Science of the VU University Amsterdam (BETCHIE 2022.038). Military assistance at UMCU was perceived to be essential in the COVID-19 crisis and was characterized by its prolonged duration. Day-to-day obstacles arose and were overcome, including varying levels of medical skills and organizational culture differences. Yet, the prolonged duration of the deployment had a severe impact on the collaboration. The common goal became indistinct over time and the collaboration suffered from an ambiguous crisis definition and lacked a clear exit strategy. This uncertainty caused declining motivation amongst personnel. Military medical support was highly appreciated, but crisis support lacked clearly defined phases to demarcate an end to the crisis, which also hampered the collaboration. Therefore, more attention to the different phases of the Emergency Management Cycle (EMC) is needed. Collaborative actions in the preparedness phase can familiarize partners in an early stage. Distinction between the response and recovery phase can provide transparency on the exit strategy.

Connecting electronics to the brain and neural organoids is critical for establishing machine-human interfaces, exploring complex mechanisms of the nervous system, and developing theranostic approaches. However, electrophysiological monitoring of these three-dimensional (3D) nervous tissues remains challenging due to their highly irregular surfaces, which severely limit electrode-tissue contact. Here, we present a stretchable mesh electrode array integrated with elastic graphene microneedles for interfacing with human neural organoids and their assemblies. The device is fabricated via microfluidic patterning technology, enabling low-cost and reproducible production. Graphene microneedles (50-100 μm in height) seamlessly interconnected with liquid metal-polymer conductor (MPC) interconnects within the stretchable mesh architecture. Graphene microneedles and MPC interconnects retain structural integrity under 200% strain. This configuration enhances multisite electrode-tissue contact, enabling recordings of spontaneous and stimulus-evoked electrophysiological activity. Over 60% of channels were activated, surpassing the performance of commercial planar electrodes. This biocompatible interface overcomes the mechanical mismatch between flexible electronics and the surface irregularities of neural organoids, providing an avenue for investigating emergent neural network behaviors in 3D models.

Neuronal synchronization refers to the temporal coordination of activity across populations of neurons, a process that underlies coherent information processing, supports the encoding of diverse sensory stimuli, and facilitates adaptive behavior in dynamic environments. Previous studies of synchronization have predominantly emphasized rate coding and pairwise interactions between neurons, which have provided valuable insights into emergent network phenomena but remain insufficient for capturing the full complexity of temporal dynamics in spike trains, particularly the interspike interval. To address this limitation, we performed in vivo neural ensemble recording in the primary olfactory center—the antennal lobe (AL) of the hawk moth Manduca sexta—by stimulating with floral odor blends and systematically varying the concentration of an individual odorant within one of the mixtures. We then applied machine learning methods integrating modern attention mechanisms and generative normalizing flows, enabling the extraction of semi-interpretable attention weights that characterize dynamic neuronal interactions. These learned weights not only recapitulated the established principles of neuronal synchronization but also facilitated the functional classification of two major cell types in the antennal lobe (AL) [local interneurons (LNs) and projection neurons (PNs)]. Furthermore, by experimentally manipulating the excitation/inhibition balance within the circuit, our approach revealed the relationships between synchronization strength and odorant composition, providing new insight into the principles by which olfactory networks encode and integrate complex sensory inputs.

TopoLLM: LLM-driven adaptive tool learning for real-time emergency network topology planning

This study advances emergency management research by systematically examining the dynamic evolution of core–periphery structures in disaster response networks using the 2022 Luding earthquake as a case study. Unlike prior work focused on static comparisons of centralized versus decentralized networks, we employ longitudinal social network analysis to reveal how core–periphery configurations shift across disaster phases—a methodological innovation that captures real-time adaptation. Our findings demonstrate that effective emergency networks require phase-specific structures: highly centralized cores dominate rescue operations for rapid coordination while specialized, resource-controlling nodes emerge during recovery. In the case study, only three organizations maintained core status across both stages, highlighting the fluidity of network roles and challenging assumptions about fixed hierarchies. This study identifies an optimal balance between core dominance and peripheral flexibility, with excessive centralization or fragmentation impairing performance. These results provide empirical support for adaptive governance approaches and offer practical strategies for designing flexible emergency systems, including phase-sensitive resource allocation and dynamic role transition protocols. By bridging theoretical gaps in network evolution and delivering actionable insights, this research contributes to more resilient disaster response frameworks.

Timely access to trauma and urgent medical care within the “golden hour” is critical for survival and recovery in emergency situations. In rural Illinois, geographic barriers, hospital closures, and fragmented emergency communication networks have historically contributed to disparities in emergency care access. This study evaluates whether the implementation of Next Generation 911 (NG911) upgrades improves spatial and temporal access to trauma and urgent care facilities across rural regions of the state. Using geospatial network analysis, service area delineation, and isochrone modeling, we compared emergency travel times under legacy 911 networks with those under NG911-enhanced systems. Road network data, address points, hospital and trauma center locations, and gridded population estimates from WorldPop were integrated into a geographic information system (GIS) environment. We measured changes in the proportion of the rural population with access to Level I and Level II trauma centers within 30, 45, and 60-minute thresholds. Results show that NG911 upgrades significantly expand coverage within the golden hour, particularly in counties with sparse populations and limited health infrastructure. Populations previously outside 60-minute travel thresholds experienced marked improvements in accessibility, although disparities remain for extremely remote communities. The findings underscore the role of NG911 in enhancing rural health equity, while also highlighting the continued need for complementary strategies such as telemedicine, air ambulance expansion, and regionalized trauma system planning. This research contributes evidence to policy discussions on rural emergency preparedness and underscores the potential of spatial analysis for evaluating health system interventions.

ABSTRACT This study employs Actor-Network Theory (ANT) to investigate maternal agency in Family Language Policy (FLP) among six African-Chinese intermarriage families in Guangzhou, China. Drawing on seven years of ethnographic work, including participatory observations, interviews, and focus groups, the study traced how mothers negotiate FLP within dynamic networks of heterogeneous actors (fathers, children, resources, institutions, peers, auxiliary caregivers, and technologies). Findings reveal maternal agency as an emergent network effect, contingent on mother’s problematization of language development and continuous interessement, enrolment, and mobilisation of the actors. Yet these efforts were systematically disrupted by competing actor-networks: fathers prioritising provider roles, children asserting Mandarin-preferring counter-networks, and economic pressures capturing material resources. The study advances FLP scholarship by demonstrating ANT’s utility in tracing temporal, contested, and scale-blurring agency dynamics, while practically advocating for peer-integration initiatives and parental training to support African-Chinese intermarriage families in China.

Integrating smartwatch-based out-of-hospital cardiac arrest detection into resuscitation systems: a focus group study of community responder perspectives.

Understanding the intricate architecture of brain networks and its connection to brain function is essential for deciphering the underlying principles of cognition and disease. While traditional graph-theoretical measures have been widely used to characterize these networks, they often fail to fully capture the emergent properties of large-scale neural dynamics. Here, we introduce an alternative approach to quantify brain networks that is rooted in complex dynamics, fractal geometry, and asymptotic analysis. We apply these concepts to brain connectomes and demonstrate how quadratic iterations and geometric properties of Mandelbrot-like sets can provide novel insights into structural and functional network dynamics. Our findings reveal fundamental distinctions between structural (positive) and functional (signed) connectomes, such as the shift of cusp orientation and the variability in equi-M set geometry. Notably, structural connectomes exhibit more robust, predictable features, while functional connectomes show increased variability for non-trivial tasks. We further demonstrate that traditional graph-theoretical measures, when applied separately to the positive and negative sub-networks of functional connectomes, fail to fully capture their dynamic complexity. Instead, size and shape-based invariants of the equi-M set effectively differentiate between rest and emotional task states, which highlights their potential as superior markers of emergent network dynamics. These results suggest that incorporating fractal-based methods into network neuroscience provides a powerful tool for understanding how information flows in natural systems beyond static connectivity measures, while maintaining simplicity.

This study evaluates the feasibility and benefits of adopting the IEC 62034:2012 standard for Automatic Testing Systems (ATS) for emergency and escape lighting on the BorWin5 High Voltage Direct Current (HVDC) offshore converter platform. The system comprises approximately 1800 luminaires from multiple manufacturers that are integrated into an open-architecture 220 VDC emergency network. Life-cycle cost analysis (LCCA) and multi-criteria decision-making (MCDM) approaches were employed to evaluate four configurations, ranging from manual testing to fully automated, centrally powered systems, based on technical, economic, operational, and environmental criteria. The chosen solution, which combines centralized power with automated testing and real-time monitoring, represents a significant advancement in offshore safety infrastructure. Implementing this solution on BorWin5 enhances reliability and maintainability while ensuring compliance with international standards, supporting a projected service life of over 30 years for an emergency and escape lighting system in an extreme marine environment. The findings offer a scalable model for future offshore platforms operating in similarly challenging conditions.

Current studies regarding the secondary use of electronic health records (EHR) predominantly rely on domain expertise and existing medical knowledge. A powerful representation approach can unleash the potential of discovering new medical patterns underlying the EHR. Here, we introduce an unsupervised method for embedding high-dimensional EHR data at the patient level to characterize heterogeneity in complex diseases and identify novel disease patterns linked to disparities in clinical outcomes. We applied this approach to 34,851 unique medical codes across 1,046,649 longitudinal patient events, including 102,740 patients in the Electronic Medical Records and GEnomics (eMERGE) Network. The model achieved strong predictive performance in predicting future disease (median AUROC = 0.87 within one year) and bulk phenotyping (median AUROC = 0.84). Notably, these patient embeddings revealed diverse comorbidity profiles and health outcomes, including distinct subtypes and progression patterns in colorectal cancer and systemic lupus erythematosus.

ABSTRACTThis paper aimed to describe and analyze the factors that shaped the political priority of global neurosurgery from 2015 to 2022. The neurosurgery community formed a movement (that can be described as a global health network) to advocate for the neurosurgical patient to ensure safe, timely, and affordable neurosurgical care for all, and address disparities in care. Semi‐structured interviews with 15 key opinion leaders in neurosurgery explored perspectives on the political priority of global neurosurgery within the neurosurgical profession, both globally and locally. Based on previous frameworks, factors shaping political priority were divided into three categories: (1) network features, (2) policy environment, and (3) issue characteristics. Shared leadership, community cohesion, and guiding institutions helped increase political priority within the global neurosurgery network. Consensus and understanding of the problem and possible solutions have also increased. However, homogeneity of the network and unequal representation are present. Moreover, although there is an established definition for global neurosurgery, ambiguities remain regarding the applicability within the neurosurgery profession. The network, allied with the global surgery network, harnessed on the policy windows and positioned itself for global policy participation, within the framework of the World Health Assembly of the World Health Organization. The global surgery network has been a crucial ally for this positioning. Research‐generated knowledge has served as a tool for advocacy at a global level. Continuous collection of regional data comparing neurosurgical conditions with others is necessary to measure the issue's magnitude in each context and serve for local advocacy and policy change.

The rapid clustering of Chemical Industrial Parks (CIPs) has escalated the risk of cascading disasters (e.g., toxic leaks and explosions), underscoring the need for resilient emergency logistics systems. However, traditional two-stage optimization models often yield suboptimal outcomes due to decoupled facility location and routing decisions. To address this issue, we propose a unified mixed-integer nonlinear programming (MINLP) model that integrates site selection and routing decisions in a single framework. The model accounts for multi-source supply allocation, enforces minimum safety distance constraints, and incorporates heterogeneous economic factors (e.g., regional land costs) to ensure risk-aware, cost-efficient planning. Two deployment scenarios are considered: (1) incremental augmentation of an existing emergency network and (2) full network reconstruction after a systemic failure. Simulations on a regional CIP cluster (2400 × 2400 km) were conducted to validate the model. The integrated approach reduced facility and operational costs by 9.77% (USD 13.68 million saved) in the incremental scenario and achieved a 15.10% (USD 21.13 million saved) total cost reduction over decoupled planning in the reconstruction scenario while maintaining an 8 km minimum safety distance. This integrated approach can enhance cost-effectiveness and strengthen the resilience of high-risk industrial emergency response networks. Overall, the proposed modeling framework, which integrates spatial constraints, time-sensitive supply mechanisms, and disruption risk considerations, is not only tailored for hazardous chemical zones but also exhibits strong potential for adaptation to a variety of high-risk scenarios, such as natural disasters, industrial accidents, or critical infrastructure failures.

Emergency communication systems face unprecedented challenges during crises, where traditional infrastructure often fails to meet the escalating demands of coordinated response operations. This article presents the transformative integration of artificial intelligence technologies into emergency communication networks for emergency planners, city planners, and community leaders, addressing critical limitations through advanced machine learning algorithms, predictive analytics, and adaptive resource allocation mechanisms. The convergence of edge computing, Internet of Things architectures, and intelligent networking protocols creates sophisticated communication systems capable of real-time data processing and dynamic traffic management during emergencies. AI-enabled networks demonstrate substantial improvements across multiple domains, including disaster response, law enforcement coordination, healthcare emergency management, and autonomous vehicle integration through ensemble learning methods, convolutional neural networks, and reinforcement learning algorithms with documented performance improvements of 15-25% over traditional systems. The technological foundation encompasses mobile edge computing for latency reduction, distributed machine learning frameworks for pattern recognition, and advanced optimization strategies utilizing deep reinforcement learning for resource allocation. Implementation considerations include deployment costs ranging from $2-8 million per city, depending on population size, phased rollout timelines of 18-36 months, and comprehensive stakeholder coordination requirements. However, deployment requires careful consideration of data privacy protection through advanced encryption protocols, cybersecurity vulnerabilities, including adversarial attacks on AI models, and infrastructure resilience challenges during multi-hazard scenarios. Privacy-preserving techniques, including differential privacy and federated learning architectures, address regulatory compliance requirements while maintaining operational effectiveness. The article examines comprehensive risk mitigation strategies encompassing fault-tolerant design principles, redundant system architectures, and robust security frameworks essential for reliable deployment in critical emergency response scenarios.

Covering medical care costs for participants in the eMERGE Network: Challenges for equity and implementation.

Automated cardiac arrest detection and emergency service alerting using device-independent smartwatch technology: proof-of-principle.