Network Response Research Articles

Unraveling emergent network indeterminacy in complex ecosystems: A random matrix approach

Indeterminacy of ecological networks-the unpredictability of ecosystem responses to persistent perturbations-is an emergent property of indirect effects a species has on another through interaction chains. Thus, numerous indirect pathways in large, complex ecological communities could make forecasting the long-term outcomes of environmental changes challenging. However, a comprehensive understanding of ecological structures causing indeterminacy has not yet been reached. Here, using random matrix theory (RMT), we provide mathematical criteria determining whether network indeterminacy emerges across various ecological communities. Our analytical and simulation results show that indeterminacy intricately depends on the characteristics of species interaction. Specifically, contrary to conventional wisdom, network indeterminacy is unlikely to emerge in large competitive and mutualistic communities, while it is a common feature in top-down regulated food webs. Furthermore, we found that predictable and unpredictable perturbations can coexist in the same community and that indeterminate responses to environmental changes arise more frequently in networks where predator-prey relationships predominate than competitive and mutualistic ones. These findings highlight the importance of elucidating direct species relationships and analyzing them with an RMT perspective on two fronts: It aids in 1) determining whether the network's responses to environmental changes are ultimately indeterminate and 2) identifying the types of perturbations causing less predictable outcomes in a complex ecosystem. In addition, our framework should apply to the inverse problem of network identification, i.e., determining whether observed responses to sustained perturbations can reconstruct their proximate causalities, potentially impacting other fields such as microbial and medical sciences.

Functional characterization of the language network of polyglots and hyperpolyglots with precision fMRI.

How do polyglots-individuals who speak five or more languages-process their languages, and what can this population tell us about the language system? Using fMRI, we identified the language network in each of 34 polyglots (including 16 hyperpolyglots with knowledge of 10+ languages) and examined its response to the native language, non-native languages of varying proficiency, and unfamiliar languages. All language conditions engaged all areas of the language network relative to a control condition. Languages that participants rated as higher proficiency elicited stronger responses, except for the native language, which elicited a similar or lower response than a non-native language of similar proficiency. Furthermore, unfamiliar languages that were typologically related to the participants' high-to-moderate-proficiency languages elicited a stronger response than unfamiliar unrelated languages. The results suggest that the language network's response magnitude scales with the degree of engagement of linguistic computations (e.g. related to lexical access and syntactic-structure building). We also replicated a prior finding of weaker responses to native language in polyglots than non-polyglot bilinguals. These results contribute to our understanding of how multiple languages coexist within a single brain and provide new evidence that the language network responds more strongly to stimuli that more fully engage linguistic computations.

System modeling of the impact of multiple capacitor coupled substations located at different proximities on a transmission network

Electricity plays a vital role in the economy of any nation, and its availability has transformed from being a luxury to being an essential requirement. However, the implementation of conventional distribution networks in sparsely populated rural areas is often deemed economically impractical. Consequently, the exploration of alternative technologies for rural electrification becomes imperative. One such technology is the Capacitor Coupled Substation (CCS), which taps electrical power from high-voltage lines through coupling capacitors. Given that capacitors can introduce interference in an electrical system, the deployment of a CCS necessitates consideration to minimize these network disturbances. This paper modelled and analyzed the impact of multiple CCSs on the electrical transmission network, with a specific focus on the impact on the transmission line voltage when multiple CCSs are located at different proximity to one another. A typical electrical transmission network with fixed supply voltage represented as 230kVac and downstream parameters with a 230kVac and 100 kW load was selected. A system model comprising three CCSs, located at known proximities from one another, was developed using MATLAB/Simulink to simulate the network's response when one or more CCS units are connected or disconnected from the electrical transmission network. Three prototypes were also constructed to represent real-world electrical systems with different loads. The model simulation was executed while connecting and disconnecting one or more CCS at a time and at different proximities from one another within the electrical transmission network. The same procedure was repeated on the prototypes where they were connected and disconnected from the selected network. The proximity to one another was represented by different resistances which represented the electrical transmission lines between the different locations of each CCS on the electrical network. The obtained test results were subjected to comparison across three platforms: the MATLAB/Simulink model, physical prototype testing using a multimeter, and digital testing using an oscilloscope. The results showed negligible disruptions on the supply side and downstream side of the transmission network. The primary goal of this research was to provide valuable insights into the potential application of CCS technology in rural electrification by examining its impact on the transmission network. Further analysis is recommended to understand the impact of each critical component within a CCS.Capacitor Coupled Substation; Rural Electrification; System Modeling; Multiple CCS

Voltage dip propagation in renewable‐rich power systems utilizing grid‐forming converters

AbstractThe growing integration of converter‐interfaced renewable energy sources (RESs) utilizing Grid‐Following (GFL) converters has displaced conventional synchronous generators (SGs) in central generation units. This shift presents challenges, including diminished system inertia, lower fault levels, and implications for system strength and network resilience. The propagation of voltage dips, particularly during disturbances like system Short Circuit (SC) faults, is adversely affected by the increased penetration of such RESs. This is attributed to the limited support capability of these sources and their distinct SC response compared to SGs. In response to these challenges, Grid‐Forming (GFM) converters emerge as a promising technology equipped with advanced functionalities that emulate SG operation. Consequently, they hold potential for mitigating the effects of voltage dip propagation in renewable‐rich power systems. This study aims to assess the impact of employing GFM converters in renewable‐rich power systems on voltage dip propagation across the network. The authors’ investigation begins by examining the SC response of GFM converters and comparing it with the responses of traditional GFL converters and SGs. The paper proceeds to analyze voltage dip propagation, considering various penetration scenarios involving RESs based on GFL and GFM converters. The IEEE 9‐BUS test system, implemented in the DIgSILENT PowerFactory software, serves as the basis for these evaluations. Through extensive simulations and analysis, the authors’ research provides valuable insights into the effectiveness of GFM converters in enhancing the network's response to voltage dips.

Lessons from the English primary care sentinel network's response to the COVID-19 pandemic

Lessons from the English primary care sentinel network's response to the COVID-19 pandemic

Microrheological characterisation of Cyanoflan in human blood plasma

Naturally occurring polysaccharidic biopolymers released by marine cyanobacteria are of great interest for numerous biomedical applications, such as wound healing and drug delivery. Such polymers generally exhibit high molecular weight and an entangled structure that impact the rheology of biological fluids. However, biocompatibility tests focus not so much on rheological properties as on immune response. In the present study, the rheological behaviour of native blood plasma as a function of the concentration of a cyanobacterium biopolymer is investigated via multiple particle tracking microrheology, which measures the Brownian motion of probes embedded in a sample, and cryogenic scanning electron microscope microstructural characterisation. We use Cyanoflan as the biopolymer of choice, and profit from our knowledge of its chemical structure and its exciting potential for biotechnological applications. A sol–gel transition is identified using time-concentration superposition and the power-law behaviour of the incipient network's viscoelastic response is observed in a variety of microrheological data. Our results point to rheology-based principles for blood compatibility tests by facilitating the assignment of quantitative values to specific properties, as opposed to more heuristic approaches.

ExistenceMap-PointPillars: A Multifusion Network for Robust 3D Object Detection with Object Existence Probability Map.

Recognition of surrounding objects is crucial for ensuring the safety of automated driving systems. In the realm of 3D object recognition through deep learning, several methods incorporate the fusion of Light Detection and Ranging (LiDAR) and camera data. The effectiveness of the LiDAR-camera fusion approach is widely acknowledged due to its ability to provide a richer source of information for object detection compared to methods that rely solely on individual sensors. Within the framework of the LiDAR-camera multistage fusion method, challenges arise in maintaining stable object recognition, especially under adverse conditions where object detection in camera images becomes challenging, such as during night-time or in rainy weather. In this research paper, we introduce "ExistenceMap-PointPillars", a novel and effective approach for 3D object detection that leverages information from multiple sensors. This approach involves a straightforward modification of the LiDAR-based 3D object detection network. The core concept of ExistenceMap-PointPillars revolves around the integration of pseudo 2D maps, which depict the estimated object existence regions derived from the fused sensor data in a probabilistic manner. These maps are then incorporated into a pseudo image generated from a 3D point cloud. Our experimental results, based on our proprietary dataset, demonstrate the substantial improvements achieved by ExistenceMap-PointPillars. Specifically, it enhances the mean Average Precision (mAP) by a noteworthy +4.19% compared to the conventional PointPillars method. Additionally, we conducted an evaluation of the network's response using Grad-CAM in conjunction with ExistenceMap-PointPillars, which exhibited a heightened focus on the existence regions of objects within the pseudo 2D map. This focus resulted in a reduction in the number of false positives. In summary, our research presents ExistenceMap-PointPillars as a valuable advancement in the field of 3D object detection, offering improved performance and robustness, especially in challenging environmental conditions.

Adaptive short-term plasticity in the typical reading network

The left temporo-parietal cortex (TPC) is crucial for phonological decoding, i.e., for learning and retaining sound-letter mappings, and appears hypoactive in dyslexia. Here, we tested the causal contribution of this area for reading in typical readers with transcranial magnetic stimulation (TMS) and explored the reading network's response with fMRI. By investigating the underlying neural correlates of stimulation-induced modulations of the reading network, we can help improve targeted interventions for individuals with dyslexia. 28 typical adult readers overtly read simple and complex words and pseudowords during fMRI after effective and sham TMS over the left TPC. To explore differences in functional activation and effective connectivity within the reading network, we performed univariate and multivariate analyses, as well as dynamic causal modeling. While TMS-induced effects on reading performance and brain activation showed large individual variability, multivariate analyses revealed a shift in activation in the left inferior frontal cortex for pseudoword reading after effective TMS. Furthermore, TMS increased effective connectivity from the left ventral occipito-temporal cortex to the left TPC. In the absence of effects on reading performance, the observed changes in task-related activity and the increase in functional coupling between the two core reading nodes suggest successful short-term compensatory reorganization in the reading network following TMS-induced disruption. This study is the first to explore neurophysiological changes induced by TMS to a core reading node in typical readers while performing an overt reading task. We provide evidence for remote stimulation effects and emphasize the relevance of functional interactions in the reading network.

Packaging style design based on visual semantic segmentation technology and intelligent cyber physical system.

The integration of image segmentation technology into packaging style design significantly amplifies both the aesthetic allure and practical utility of product packaging design. However, the conventional image segmentation algorithm necessitates a substantial amount of time for image analysis, rendering it susceptible to the loss of vital image features and yielding unsatisfactory segmentation results. Therefore, this study introduces a novel segmentation network, G-Lite-DeepLabV3+, which is seamlessly incorporated into cyber-physical systems (CPS) to enhance the accuracy and efficiency of product packaging image segmentation. In this research, the feature extraction network of DeepLabV3 is replaced with Mobilenetv2, integrating group convolution and attention mechanisms to proficiently process intricate semantic features and improve the network's responsiveness to valuable characteristics. These adaptations are then deployed within CPS, allowing the G-Lite-DeepLabV3+ network to be seamlessly integrated into the image processing module within CPS. This integration facilitates remote and real-time segmentation of product packaging images in a virtual environment.Experimental findings demonstrate that the G-Lite-DeepLabV3+ network excels at segmenting diverse graphical elements within product packaging images. Compared to the original DeepLabV3+ network, the intersection over union (IoU) metric shows a remarkable increase of 3.1%, while the mean pixel accuracy (mPA) exhibits an impressive improvement of 6.2%. Additionally, the frames per second (FPS) metric experiences a significant boost of 22.1%. When deployed within CPS, the network successfully accomplishes product packaging image segmentation tasks with enhanced efficiency, while maintaining high levels of segmentation accuracy.

From Alpha to Omicron: A Tertiary Care Hospital-Based Radiation Oncology Network's COVID-19 Response Experience

Purpose/Objective(s)The COVID-19 pandemic completely altered access to medical care, severely limiting or halting outpatient procedures over long periods of time. However, it was clear that radiation therapy needed to remain available to patients. We implemented asymptomatic testing algorithms and constructed negative air pressure CT simulator and treatment rooms within our tertiary care hospital-based Radiation Oncology network to accommodate the safe treatment of patients affected by the COVID-19 pandemic.Materials/MethodsOur Radiation Oncology department undertook dedicated construction to convert select CT simulator rooms and treatment vaults into negative pressure environments, including one CT simulator room and photon-based linear accelerator treatment vault at the main campus in 5/2020; and one CT simulator room, two photon-based linear accelerator vaults, and a single proton therapy gantry between two regional campuses in 12/2021. From 5/18/20 to 9/7/21, an asymptomatic screening protocol was implemented for all patients prior to initiation of radiation therapy. Patients with COVID-19 infection and Persons Under Investigation (PUIs) could undergo simulation and/or treatment in the newly constructed biocontainment environments at the discretion of the treating physician with guidance from a predefined priority scale. A biocontainment treatment protocol delineated specific roles within a treatment team of one Nursing Safety Officer, one Direct Contact Radiation Therapist, and one Indirect Contact Radiation Therapist. Safety drills were performed to establish and practice safe pathways for escorting patients directly from their car to the treatment room, and vice versa, for the last treatment slot(s) of the day. Patients who were treated under COVID-19 protocol from 6/2020 to 1/2022 were chart reviewed to assess utilization of our newly constructed and developed biocontainment resources.ResultsA total of 6,525 patients were treated from 6/2020 through 1/2022 across our 5 campuses. During this time, 42 patients were treated under the COVID-19 biocontainment protocol. All but 3 had documented personal COVID-19 positivity, while the others were designated as PUI, typically for COVID-positive household contacts. For 61.9% of patients, these safety measures mitigated the need for extended breaks during treatment courses that had already been initiated. The majority (64.3%) of patients were being treated with curative intent, while 10 patients (23.8%) were treated as a result of an inpatient palliative consult. A third (35.7%) were transferred from a regional campus to the main campus for management prior to expansion of biocontainment resources. The median number of biocontainment sessions required by each patient was 6 (range: 1-15).ConclusionThe construction of negative pressure environments and development of a COVID-19 biocontainment protocol have helped to mitigate the impact of the pandemic on our patients and to maximize efforts in protecting our staff.

Precision fMRI reveals that the language-selective network supports both phrase-structure building and lexical access during language production.

A fronto-temporal brain network has long been implicated in language comprehension. However, this network's role in language production remains debated. In particular, it remains unclear whether all or only some language regions contribute to production, and which aspects of production these regions support. Across 3 functional magnetic resonance imaging experiments that rely on robust individual-subject analyses, we characterize the language network's response to high-level production demands. We report 3 novel results. First, sentence production, spoken or typed, elicits a strong response throughout the language network. Second, the language network responds to both phrase-structure building and lexical access demands, although the response to phrase-structure building is stronger and more spatially extensive, present in every language region. Finally, contra some proposals, we find no evidence of brain regions-within or outside the language network-that selectively support phrase-structure building in production relative to comprehension. Instead, all language regions respond more strongly during production than comprehension, suggesting that production incurs a greater cost for the language network. Together, these results align with the idea that language comprehension and production draw on the same knowledge representations, which are stored in a distributed manner within the language-selective network and are used to both interpret and generate linguistic utterances.

KMFA2 based QoS improvement for multi‐channel IoT networks

AbstractInternet of Things (IoT) networks are designed to obtain the best possible end‐user experience. This experience is measured in terms of security and speed of operation. In order to achieve high security, encryption, and authentication algorithms are proposed by researchers. The speed of operation is generally measured using the network's response time, and it can be improved using light‐weighted algorithms for data processing and actuation. Most of these techniques are applied at the application level, making the network ineffective in its originally deployed form. Moreover, due to the addition of security and data processing layers, the overall IoT network's QoS reduces, reducing the end user's experience. In order to remove these issues, this work proposes a novel kernel‐level multi‐channel fuzzy‐authentication and aggregation framework (KMFA2). The KMFA2 framework can solve authentication issues by using a kernel‐level lightweight and secure authentication protocol for all IoT devices. KMFA2 also enables the IoT devices to possess multi‐channel capabilities, with inbuilt aggregation capabilities. The overall implementation is carried out on the NS2 tool. The proposed KMFA2 protocol is tested on different IoT networks, and it is observed that the protocol improves the QoS by almost 10% and simplifies security using the authentication framework. The comparisons were performed with state‐of‐the‐art IoT deployments.

PressPurt: network sensitivity to press perturbations under interaction uncertainty

While the use of networks to understand how complex systems respond to perturbations is pervasive across scientific disciplines, the uncertainty associated with estimates of pairwise interaction strengths (edge weights) remains rarely considered. Mischaracterizations of interaction strength can lead to qualitatively incorrect predictions regarding system responses as perturbations propagate through often counteracting direct and indirect effects. Here, we introduce PressPurt, a computational package for identifying the interactions whose strengths must be estimated most accurately in order to produce robust predictions of a network's response to press perturbations. The package provides methods for calculating and visualizing these edge-specific sensitivities (tolerances) when uncertainty is associated to one or more edges according to a variety of different error distributions. The software requires the network to be represented as a numerical (quantitative or qualitative) Jacobian matrix evaluated at stable equilibrium. PressPurt is open source under the MIT license and is available as both a Python package and an R package hosted at https://github.com/dkoslicki/PressPurt and on the CRAN repository https://CRAN.R-project.org/package=PressPurt.

Sustainability of the Aging Network's COVID-19 Responses: Isolated Impact or Transformative Change?

The COVID-19 pandemic mobilized individuals, organizations, and communities. Area Agencies and Aging (AAAs) and Title VI Native American Programs, core organizations of the network of community-based organizations (CBOs) that serve older adults and their families, pivoted their service delivery methods to provide life-sustaining services. Their long-standing expertise in community needs assessment, pre-existing cross-sectoral partnerships, and an infusion of emergency federal funding, enabled this rapid response. Recently concluded studies using key-informant interviews and national surveys of AAAs and Title VI programs highlight these service adaptations, from expansion of home-delivered meal programs to new partnerships in telehealth. These organizations also reported expansion of services offered and people served, and the emergence or strengthening of partnerships with other CBOs, businesses, and governmental organizations such as public health entities. For example, 78% of the respondents to the recent survey of AAAs reported that they have a role in vaccination outreach, scheduling support, or delivery. The papers in this symposium will use these new studies to describe the nature, origins, and potential sustainability of new and expanded services and partnerships. The Collective Impact Model for community change (introduced in the Stanford Social Innovation Review) will provide a framework for the discussion. Built on the importance of cross-sector coordination, the five pillars of success for collective rather than isolated impact are: a common agenda, mutually reinforcing activities, continuous communication, shared measurement, and a backbone organization. Each of these five pillars is relevant to the heightened community response during the pandemic, and to the likelihood of sustainability.

Selecting the Best Routing Traffic for Packets in LAN via Machine Learning to Achieve the Best Strategy

The application of machine learning touches all activities of human behavior such as computer network and routing packets in LAN. In the field of our research here, emphasis was placed on extracting weights that would affect the speed of the network's response and finding the best path, such as the number of nodes in the path and the congestion on each path, in addition to the cache used for each node. Therefore, the use of these elements in building the neural network is worthy, as is the exploitation of the feed forwarding and the backpropagation in the neural network in order to reach the best prediction for the best path. The goal of the proposed neural network is to minimize the network time delay within the optimization of the packet paths being addressed in this study. The shortest path is considered as the key issue in routing algorithm that can be carried out with real time of path computations. Exploiting the gaps in previous studies, which are represented in the lack of training of the system and the inaccurate prediction as a result of not taking into consideration the hidden layers' feedback, leads to great performance. This study aims to suggest an efficient algorithm that could help in selecting the shortest path to improve the existing methods using weights derived from packet ID and to change neural network iteration simultaneously. In this study, the design of the efficient neural network of appropriate output is discussed in detail including the principles of the network. The findings of the study revealed that exploiting the power of computational system to demonstrate computer simulation is really effective. It is also shown that the system achieved good results when training the neural network system to get 2.4% time delay with 5 nodes in local LAN. Besides, the results showed that the major features of the proposed model will be able to run in real time and are also adaptive to change with path topology.

Integrating the Food and Drug Administration Office of the Coordinated Outbreak Response and Evaluation Network's foodborne illness outbreak surveillance and response activities with principles of the National Incident Management System.

The Food Safety Modernization Act mandates building a national Integrated Food Safety System, which represents a seamless partnership among federal, state, local, territorial, and tribal agencies. During multistate foodborne illness outbreak investigations, local and state partners, the Centers for Disease Control and Prevention, the United States Food and Drug Administration (FDA), or the United States Department of Agriculture Food Safety Inspection Service, depending on the regulated food product, become engaged and assist in coordinating the efforts between partners involved and determine the allocation of resources. The FDA Center for Food Safety and Applied Nutrition (CFSAN) Office of the Coordinated Outbreak Response and Evaluation (CORE) Network coordinates foodborne illness outbreak surveillance, response, and post-response activities related to incidents involving multiple illnesses linked to FDA-regulated human food, dietary supplements, and cosmetic products. FDA has implemented the National Incident Management System (NIMS) Incident Command System (ICS) principles across the agency to coordinate federal response efforts, and CORE has adapted NIMS ICS principles for the emergency management of multistate foodborne illness outbreaks. CORE's implementation of ICS principles has provided several benefits to the operational cycle of foodborne illness outbreak investigations, including establishing a consistent, standardized, and transparent step-by-step approach to outbreak investigations. ICS principles have been instrumental in the development of a national platform for rapid and systematic laboratory, traceback, and epidemiologic information sharing, data analysis, and decision-making. This allows for partners across jurisdictions to reach a consensus regarding outbreak goals and objectives, deploy resources, and take regulatory and public health actions.

Planning of Reserve Branches to Increase Reconfiguration Capability in Distribution Systems: A Scenario-Based Convex Programming Approach

Distribution networks are usually designed with a fixed tree-shape topology, which limits its adapting capability against failures and excessive variation of load/generation. Reconfiguration is an important tool to optimize the steady-state operating point of the distribution system under normal operating conditions, as well as increasing the network's responsiveness under interruptions by minimizing non-supplied demand. This paper studies how to increase the capabilities of reconfiguration in distribution networks through the optimal installation of reserve branches even if few branches are switchable. For this purpose, a scenario-based convex programming model is proposed to minimize the operation cost under normal operating conditions along with the energy not supplied related to network interruptions. Instead of conventional simulation adopted by previous approaches, the formulation is based on a set of linear logical constraints that represent the post-fault network reconfiguration process. Hence, classical optimization techniques can be used to solve the proposed model, which provides a suitable framework for the attainment of global optimality using efficient off-the-shelf software. Results show a decrease in the cost of energy production (2.85% and 1.08% for the 33-node and 69-node test systems) along with a decrease in the cost of energy not supplied (58.17% and 72.42% for the 33-node and 69-node test systems) due to the greater restoration capacity of the system using reserve branches.

Leveraging the thermoresponsiveness of fluorinated poly(N-isopropylacrylamide) copolymers as a sensing tool for perfluorooctane sulfonate.

Due to mounting evidence of the negative health effects of persistent perfluoroalkyl acids (PFAAs) with long (i.e., >C7) tails, there is a need for convenient systems capable of sensing these contaminants at dilute aqueous concentrations. To address this concern, a thermoresponsive polymeric network composed of poly(N-isopropylacrylamide) copolymerized with fluorinated comonomers was studied to characterize the gel's physical response to fluorosurfactants in solution. Incorporating fluorinated comonomers into the polymer backbone raised their swelling in fluorocontaminant solutions relative to water - gels synthesized with 10.0 mol% 2,2,2-trifluoroethyl acrylate (TFEA) displayed a heightened maximum water-analyte swelling difference of 3761 ± 147% compared to 3201 ± 466% for non-fluorinated gels in the presence of 1 mM tetraethylammonium perfluorooctane sulfonate (TPFOS). The normalized area under the curve for gels with 12.5 mol% TFEA was further raised to 1.77 ± 0.09, indicating a broadened response window for the contaminant, but at the cost of reducing the overall swelling ratio to 3227 ± 166% and elongating the time required to reach swelling equilibrium. Overall, a copolymer fed with 10.7 mol% TFEA was predicted to maximize both the swelling and response window of the polymer toward TPFOS. Equilibration times followed a logarithmic increase as the percentage of comonomer was raised, noting gradual fluorosurfactant penetration into the gels impeded by initial gel compaction caused by the addition of fluorinated comonomers. Comparative study of gels containing 1H,1H,7H-dodecafluoroheptyl acrylate, TFEA, or 1,1,1,3,3,3-hexafluoroisopropyl acrylate identified careful selection of fluorinated comonomers and their feed ratios as useful tools for tailoring the network's swelling response to TPFOS.

Putting the network to work: Learning networks in rapid response situations.

IntroductionThe rapid response to COVID‐19 has necessitated infrastructural development and reorientation in order to safely meet patient care needs.MethodsA qualitative case study was constructed within a larger ethnographic field study. Document collection and fieldnotes and recordings from nonparticipant observation of network activities were compiled and chronologically ordered to chart the network's response to changes in epilepsy care resulting from COVID‐19 and the rapid transition to telemedicine.ResultsThe network's response to COVID‐19 was characterized by a predisposition to action, the role of sharing as both a group practice and shared value, and the identification of improvement science as the primary contribution of the group within the larger epilepsy community's response to COVID‐19. The findings are interpreted as an example of how group culture can shape action via a transparent and mundane shared infrastructure.ConclusionsThe case of one multi‐stakeholder epilepsy Learning Network provides an example of the use of infrastructure that is shaped by the group's culture. These findings contribute to the development of a social theory of infrastructure within Learning Health Systems.

How Retroactivity Affects the Behavior of Incoherent Feedforward Loops.

SummaryAn incoherent feedforward loop (IFFL) is a network motif known for its ability to accelerate responses and generate pulses. It remains an open question to understand the behavior of IFFLs in contexts with high levels of retroactivity, where an upstream transcription factor binds to numerous downstream binding sites. Here we study the behavior of IFFLs by simulating and comparing ODE models with different levels of retroactivity. We find that increasing retroactivity in an IFFL can increase, decrease, or keep the network's response time and pulse amplitude constant. This suggests that increasing retroactivity, traditionally considered an impediment to designing robust synthetic systems, could be exploited to improve the performance of IFFLs. In contrast, we find that increasing retroactivity in a negative autoregulated circuit can only slow the response. The ability of an IFFL to flexibly handle retroactivity may have contributed to its significant abundance in both bacterial and eukaryotic regulatory networks.