Neural Dynamic Principles for an Intentional Embodied Agent.

How situated embodied agents may achieve goals using knowledge is the classical question of natural and artificial intelligence. How organisms achieve this with their nervous systems is a central challenge for a neural theory of embodied cognition. To structure this challenge, we borrow terms from Searle's analysis of intentionality in its two directions of fit and six psychological modes (perception, memory, belief, intention-in-action, prior intention, desire). We postulate that intentional states are instantiated by neural activation patterns that are stabilized by neural interaction. Dynamic instabilities provide the neural mechanism for initiating and terminating intentional states and are critical to organizing sequences of intentional states. Beliefs represented by networks of concept nodes are autonomously learned and activated in response to desired outcomes. The neural dynamic principles of an intentional agent are demonstrated in a toy scenario in which a robotic agent explores an environment and paints objects in desired colors based on learned color transformation rules.

Using systems modeling to facilitate undergraduate physiology student learning and retention of difficult concepts.

Physiology concepts, such as acid-base balance, may be difficult for students to understand. Systems modeling, a cognitive tool, allows students to visualize their mental model of the problem space to enhance learning and retention. Methods: We performed a within-subjects three-period randomized control comparison of systems modeling versus written discussion activities in an undergraduate asynchronous online Anatomy and Physiology II course. Participants (n=108) were randomized to groups with differing treatment orders across three course units: endocrine, immune, and acid-base balance. Participants demonstrated content understanding either through constructing systems modeling diagrams (M) or written discussion posts (W) in a MWM, MMW, or WMM sequence. Results: For each of three units, student performance was assessed on six standardized multiple-choice questions embedded within a 45-question exam. The same six questions per unit, eighteen questions total, was again assessed on the 75-question final exam. The groups demonstrated no significant difference in performance in the endocrine unit exam (mean difference, MD=-0.036). However, the modeling group outperformed the writing group in the immune unit exam (MD=0.209) and widened the gap in the acid-base balance unit exam (MD=0.243). On the final exam, performance was again higher for the modeling group on acid-base balance content as mean difference increased to 0.306 despite the final exam content for acid-base balance being significantly more difficult compared to other units (modeling: F(2)=29.882, p<.001; writing: F(2)=25.450, p<.001). Conclusions: These results provide initial evidence that participation in systems modeling activities enhance student learning of difficult physiology content as evidenced by improved multiple-choice question performance.

Analysis of global nutrient gaps and their potential to be closed through redistribution and increased supply.

Global food systems are crucial for sustaining life on Earth. Although estimates suggest that the current production system can provide enough food and nutrients for everyone, equitable distribution remains challenging. Understanding global nutrient distribution is vital for addressing disparities and creating effective solutions for the present and future. This study analyzes global nutrient supply changes to address inadequacies in certain populations using the existing DELTA Model®, which uses aggregates of global food production to estimate nutrient adequacy. By examining the 2020 global food commodity and nutrient distribution, we project future food production in 2050 needs to ensure global adequate nutrition. Our findings reveal that while some nutrients appear to be adequately supplied on a global scale, many countries face national insufficiencies (% supply below the population reference intake) in essential vitamins and minerals, such as vitamins A, B12, B2, potassium, and iron. Closing these gaps will require significant increases in nutrient supply. For example, despite global protein supply surpassing basic needs for the 2050 population, significant shortages persist in many countries due to distribution variations. A 1% increase in global protein supply, specifically targeting countries with insufficiencies, could address the observed 2020 gaps. However, without consumption pattern changes, a 26% increase in global protein production is required by 2050 due to population growth. In this study, a methodology was developed, applying multi-decade linear convergence to sufficiency values at the country level. This approach facilitates a more realistic assessment of future needs within global food system models, such as the DELTA Model®, transitioning from idealized production scenarios to realistic projections. In summary, our study emphasizes understanding global nutrient distribution and adjusting minimum global nutrient supply targets to tackle country-level inequality. Incorporating these insights into global food balance models can improve projections and guide policy decisions for sustainable, healthy diets worldwide.

Interdependent water and power infrastructure model (IWPIM): A modeling approach for water and energy resource management in rural communities.

The importance of the dependencies between water and power systems is more acutely perceived when challenges emerge. As both energy and water supply are limited, efficient use is a must for any sustainable future, especially in rural areas. Although important, a modeling tool that can analyze water-energy systems interdependencies in rural systems, at the architectural level highlighting the physical interconnections and synergies of these systems, is still lacking. We present a multi-agent system model that captures the features of both systems, at the same levels of fidelity and resolution, with coordinated operations and contingency components represented. Unlike other models, ours captures architectural features of both systems and technical constraints of the systems' components, which is critical to capture physical intricacies of the interplay between systems components and shed light on the impacts of disruptions of either system on the other. This model, which includes multiple infrastructure components, shows the importance of a holistic understanding of the systems, for cooperation across systems physical boundaries and enhanced benefits at larger scales. This study looks to investigate water-power resource management in an irrigation system via the analysis of physical links and highlight strengths and vulnerabilities. The effects of water shortage, water re-allocation and load shedding are analyzed through scenarios designed to illustrate the utility of such a model. Results highlights the importance of inter-reservoir relationships for alleviating effects of disruption and unforeseen rise in energy demand. Water storage is also critical, helping to mitigate the impacts of water scarcity, and by extension, to keep the energy system unaffected. It can be a viable part of the solution to compensate for the negative impact of shortage for both resources.

A rapid approach with machine learning for quantifying the relative burden of antimicrobial resistance in natural aquatic environments.

The massive use and discharge of antibiotics have led to increasing concerns about antimicrobial resistance (AMR) in natural aquatic environments. Since the dose-response mechanisms of pathogens with AMR have not yet been fully understood, and the antibiotic resistance genes and bacteria-related data collection via field sampling and laboratory testing is time-consuming and expensive, designing a rapid approach to quantify the burden of AMR in the natural aquatic environment has become a challenge. To cope with such a challenge, a new approach involving an integrated machine-learning framework was developed by investigating the associations between the relative burden of AMR and easily accessible variables (i.e., relevant environmental variables and adjacent land-use patterns). The results, based on a real-world case analysis, demonstrate that the quantification speed has been reduced from 3-7 days, which is typical for traditional measurement procedures with field sampling and laboratory testing, to approximately 0.5 hours using the new approach. Moreover, all five metrics for AMR relative burden quantification exceed the threshold level of 85%, with F1-score surpassing 0.92. Compared to logistic regression, decision trees, and basic random forest, the adaptive random forest model within the framework significantly improves quantification accuracy without sacrificing model interpretability. Two environmental variables, dissolved oxygen and resistivity, along with the proportion of green areas were identified as three key feature variables for the rapid quantification. This study contributes to the enrichment of burden analyses and management practices for rapid quantification of the relative burden of AMR without dose-response information.

Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making.

Human decision-making is increasingly supported by artificial intelligence (AI) systems. From medical imaging analysis to self-driving vehicles, AI systems are becoming organically embedded in a host of different technologies. However, incorporating such advice into decision-making entails a human rationalization of AI outputs for supporting beneficial outcomes. Recent research suggests intermediate judgments in the first stage of a decision process can interfere with decisions in subsequent stages. For this reason, we extend this research to AI-supported decision-making to investigate how intermediate judgments on AI-provided advice may influence subsequent decisions. In an online experiment (N = 192), we found a consistent bolstering effect in trust for those who made intermediate judgments and over those who did not. Furthermore, violations of total probability were observed at all timing intervals throughout the study. We further analyzed the results by demonstrating how quantum probability theory can model these types of behaviors in human-AI decision-making and ameliorate the understanding of the interaction dynamics at the confluence of human factors and information features.

Spatially resolved land and grid model of carbon neutrality in China.

China has committed to achieve net carbon neutrality by 2060 to combat global climate change, which will require unprecedented deployment of negative emissions technologies, renewable energies (RE), and complementary infrastructure. At terawatt-scale deployment, land use limitations interact with operational and economic features of power systems. To address this, we developed a spatially resolved resource assessment and power systems planning optimization that models a full year of power system operations, sub-provincial RE siting criteria, and transmission connections. Our modeling results show that wind and solar must be expanded to 2,000 to 3,900 GW each, with one plausible pathway leading to 300 GW/yr combined annual additions in 2046 to 2060, a three-fold increase from today. Over 80% of solar and 55% of wind is constructed within 100 km of major load centers when accounting for current policies regarding land use. Large-scale low-carbon systems must balance key trade-offs in land use, RE resource quality, grid integration, and costs. Under more restrictive RE siting policies, at least 740 GW of distributed solar would become economically feasible in regions with high demand, where utility-scale deployment is limited by competition with agricultural land. Effective planning and policy formulation are necessary to achieve China's climate goals.

Dynamical models reveal anatomically reliable attractor landscapes embedded in resting state brain networks.

Analyses of functional connectivity (FC) in resting-state brain networks (RSNs) have generated many insights into cognition. However, the mechanistic underpinnings of FC and RSNs are still not well-understood. It remains debated whether resting state activity is best characterized as noise-driven fluctuations around a single stable state, or instead, as a nonlinear dynamical system with nontrivial attractors embedded in the RSNs. Here, we provide evidence for the latter, by constructing whole-brain dynamical systems models from individual resting-state fMRI (rfMRI) recordings, using the Mesoscale Individualized NeuroDynamic (MINDy) platform. The MINDy models consist of hundreds of neural masses representing brain parcels, connected by fully trainable, individualized weights. We found that our models manifested a diverse taxonomy of nontrivial attractor landscapes including multiple equilibria and limit cycles. However, when projected into anatomical space, these attractors mapped onto a limited set of canonical RSNs, including the default mode network (DMN) and frontoparietal control network (FPN), which were reliable at the individual level. Further, by creating convex combinations of models, bifurcations were induced that recapitulated the full spectrum of dynamics found via fitting. These findings suggest that the resting brain traverses a diverse set of dynamics, which generates several distinct but anatomically overlapping attractor landscapes. Treating rfMRI as a unimodal stationary process (i.e., conventional FC) may miss critical attractor properties and structure within the resting brain. Instead, these may be better captured through neural dynamical modeling and analytic approaches. The results provide new insights into the generative mechanisms and intrinsic spatiotemporal organization of brain networks.

Boolean model of the gene regulatory network of Pseudomonas aeruginosa CCBH4851.

Introduction: Pseudomonas aeruginosa infections are one of the leading causes of death in immunocompromised patients with cystic fibrosis, diabetes, and lung diseases such as pneumonia and bronchiectasis. Furthermore, P. aeruginosa is one of the main multidrug-resistant bacteria responsible for nosocomial infections worldwide, including the multidrug-resistant CCBH4851 strain isolated in Brazil. Methods: One way to analyze their dynamic cellular behavior is through computational modeling of the gene regulatory network, which represents interactions between regulatory genes and their targets. For this purpose, Boolean models are important predictive tools to analyze these interactions. They are one of the most commonly used methods for studying complex dynamic behavior in biological systems. Results and discussion: Therefore, this research consists of building a Boolean model of the gene regulatory network of P. aeruginosa CCBH4851 using data from RNA-seq experiments. Next, the basins of attraction are estimated, as these regions and the transitions between them can help identify the attractors, representing long-term behavior in the Boolean model. The essential genes of the basins were associated with the phenotypes of the bacteria for two conditions: biofilm formation and polymyxin B treatment. Overall, the Boolean model and the analysis method proposed in this work can identify promising control actions and indicate potential therapeutic targets, which can help pinpoint new drugs and intervention strategies.

Mesotrode chronic simultaneous mesoscale cortical imaging and subcortical or peripheral nerve spiking activity recording in mice.

Brain function originates from hierarchical spatial-temporal neural dynamics distributed across cortical and subcortical networks. However, techniques available to assess large-scale brain network activity with single-neuron resolution in behaving animals remain limited. Here, we present Mesotrode that integrates chronic wide-field mesoscale cortical imaging and compact multi-site cortical/subcortical cellular electrophysiology in head-fixed mice that undergo self-initiated running or orofacial movements. Specifically, we harnessed the flexibility of chronic multi-site tetrode recordings to monitor single-neuron activity in multiple subcortical structures while simultaneously imaging the mesoscale activity of the entire dorsal cortex. A mesoscale spike-triggered averaging procedure allowed the identification of cortical activity motifs preferentially associated with single-neuron spiking. Using this approach, we were able to characterize chronic single-neuron-related functional connectivity maps for up to 60 days post-implantation. Neurons recorded from distinct subcortical structures display diverse but segregated cortical maps, suggesting that neurons of different origins participate in distinct cortico-subcortical pathways. We extended the capability of Mesotrode by implanting the micro-electrode at the facial motor nerve and found that facial nerve spiking is functionally associated with the PTA, RSP, and M2 network, and optogenetic inhibition of the PTA area significantly reduced the facial movement of the mice. These findings demonstrate that Mesotrode can be used to sample different combinations of cortico-subcortical networks over prolonged periods, generating multimodal and multi-scale network activity from a single implant, offering new insights into the neural mechanisms underlying specific behaviors.

Can participatory processes lead to changes in the configuration of local mental health networks? A social network analysis.

Systems modeling offers a valuable tool to support strategic decision-making for complex problems because it considers the causal inter-relationships that drive population health outcomes. This tool can be used to simulate policies and initiatives to determine which combinations are likely to deliver the greatest impacts and returns on investment. Systems modeling benefits from participatory approaches where a multidisciplinary stakeholder group actively engages in mapping and contextualizing causal mechanisms driving complex system behaviors. Such approaches can have significant advantages, including that they may improve connection and coordination of the network of stakeholders operating across the system; however, these are often observed in practice as colloquial anecdotes and seldom formally assessed. We used a basic social network analysis to explore the impact on the configuration of the network of mental health providers, decision-makers, and other stakeholders in Bogota, Colombia active in a series of three workshops throughout 2021 and 2022. Overall, our analysis suggests that the participatory process of the systems dynamics exercise impacts the social network's structure, relationships, and dynamics.

Unpacking the modeling process for energy policy making.

This article explores how the modeling of energy systems may lead to an undue closure of alternatives by generating an excess of certainty around some of the possible policy options. We retrospectively exemplify the problem with the case of the International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s. We discuss different methodologies for quality assessment that may help mitigate this issue, which include Numeral Unit Spread Assessment Pedigree (NUSAP), diagnostic diagrams, and sensitivity auditing (SAUD). We illustrate the potential of these reflexive modeling practices in energy policy-making with three additional cases: (i) the case of the energy system modeling environment (ESME) for the creation of UK energy policy; (ii) the negative emission technologies (NETs) uptake in integrated assessment models (IAMs); and (iii) the ecological footprint indicator. We encourage modelers to adopt these approaches to achieve more robust, defensible, and inclusive modeling activities in the field of energy research.

Towards a Modular Pathological Tremor Simulation System Based on the Stewart Platform.

Wearable technologies have aided in reducing pathological tremor symptoms through non-intrusive solutions that aim to identify patterns in involuntary movements and suppress them using actuators positioned at specific joints. However, during the development of these devices, tests were primarily conducted on patients due to the difficulty of faithfully simulating tremors using simulation equipment. Based on studies characterizing tremors in Parkinson's disease, the development of a robotic manipulator based on the Stewart platform was initiated, with the goal of satisfactorily simulating resting tremor movements in the hands. In this work, a simulator was implemented in a computational environment using the multibody dynamics method. The platform structure was designed in a virtual environment using SOLIDWORKS® v2017 software and later exported to Matlab® R17a software using the Simulink environment and Simscape multibody library. The workspace was evaluated, and the Kalman filter was used to merge acceleration and angular velocity data and convert them into data related to the inclination and rotation of real patients' wrists, which were subsequently executed in the simulator. The results show a high correlation and low dispersion between real and simulated signals, demonstrating that the simulated mechanism has the capacity to represent Parkinson's disease resting tremors in all wrist movements. The system could contribute to conducting tremor tests in suppression devices without the need for the presence of the patient and aid in comparing suppression techniques, benefiting the development of new wearable devices.

Theoretical analysis reveals a role for RAF conformational autoinhibition in paradoxical activation.

RAF kinase inhibitors can, under certain conditions, increase RAF kinase signaling. This process, which is commonly referred to as 'paradoxical activation' (PA), is incompletely understood. We use mathematical and computational modeling to investigate PA and derive rigorous analytical expressions that illuminate the underlying mechanism of this complex phenomenon. We find that conformational autoinhibition modulation by a RAF inhibitor could be sufficient to create PA. We find that experimental RAF inhibitor drug dose-response data that characterize PA across different types of RAF inhibitors are best explained by a model that includes RAF inhibitor modulation of three properties: conformational autoinhibition, dimer affinity, and drug binding within the dimer (i.e., negative cooperativity). Overall, this work establishes conformational autoinhibition as a robust mechanism for RAF inhibitor-driven PA based solely on equilibrium dynamics of canonical interactions that comprise RAF signaling and inhibition.

Exploring progressive mental model representation of core physiology concepts in physician assistant students through word frequency and association analyses.

A well-developed mental model is crucial for effectively studying physiology core concepts. However, mental models can be difficult for students to represent and for instructors to evaluate and correct. Systems modeling as a visualization cognitive tool may facilitate mental model development. On the other hand, evidence of mental model development may also be represented verbally, in writing, and therefore, be evaluated. In this study, analysis of writing prompt completions illustrated progress in physician assistant student mental model formation of physiology core concepts, such as homeostasis and cell-cell communication, over time. Two cohorts of physician assistant students were invited to voluntarily submit completions of writing prompts five times over 16 months. Sessions included submissions pre- and post-small group systems modeling participation. Word frequency and word association cluster dendrogram analyses were conducted on submissions using the tm text mining package in R to provide insight into progressive changes in core concepts of word use and associations. Students demonstrated expanded core concepts systems thinking over time. This was apparent through the increased use of systems process terms, such as homeostasis, in submissions immediately following systems modeling activities. Students also increasingly included terms and associations emphasizing cell-cell communication and systems integration. The inclusion of these concepts within student mental models was demonstrably enhanced by participation in systems modeling activities.NEW & NOTEWORTHY This study applies text mining, an artificial intelligence form of natural language processing, to evaluate a series of physiology student-written prompt completions. Text mining of student writing in physiology has not yet been reported in the literature. Through the application of this technique, longitudinal trends in student development of mental models of core concepts were identified and visualized through word frequency distributions and cluster dendrograms.

From actin waves to mechanism and back: How theory aids biological understanding.

Actin dynamics in cell motility, division, and phagocytosis is regulated by complex factors with multiple feedback loops, often leading to emergent dynamic patterns in the form of propagating waves of actin polymerization activity that are poorly understood. Many in the actin wave community have attempted to discern the underlying mechanisms using experiments and/or mathematical models and theory. Here, we survey methods and hypotheses for actin waves based on signaling networks, mechano-chemical effects, and transport characteristics, with examples drawn from Dictyostelium discoideum, human neutrophils, Caenorhabditis elegans, and Xenopus laevis oocytes. While experimentalists focus on the details of molecular components, theorists pose a central question of universality: Are there generic, model-independent, underlying principles, or just boundless cell-specific details? We argue that mathematical methods are equally important for understanding the emergence, evolution, and persistence of actin waves and conclude with a few challenges for future studies.

A dynamical systems approach for multiscale synthesis of Alzheimer's pathogenesis.

Alzheimer's disease (AD) is a spatially dynamic pathology that implicates a growing volume of multiscale data spanning genetic, cellular, tissue, and organ levels of the organization. These data and bioinformatics analyses provide clear evidence for the interactions within and between these levels. The resulting heterarchy precludes a linear neuron-centric approach and necessitates that the numerous interactions are measured in a way that predicts their impact on the emergent dynamics of the disease. This level of complexity confounds intuition, and we propose a new methodology that uses non-linear dynamical systems modeling to augment intuition and that links with a community-wide participatory platform to co-create and test system-level hypotheses and interventions. In addition to enabling the integration of multiscale knowledge, key benefits include a more rapid innovation cycle and a rational process for prioritization of data campaigns. We argue that such an approach is essential to support the discovery of multilevel-coordinated polypharmaceutical interventions.

Single-Cell Kinetic Modeling of ß-Lapachone Metabolism in Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) cells are highly heterogeneous in their metabolism and typically experience elevated reactive oxygen species (ROS) levels such as superoxide and hydrogen peroxide (H2O2) in the tumor microenvironment. Tumor cells survive under these chronic oxidative conditions by upregulating antioxidant systems. To investigate the heterogeneity of cellular responses to chemotherapeutic H2O2 generation in tumor and healthy tissue, we leveraged single-cell RNA-sequencing (scRNA-seq) data to perform redox systems-level simulations of quinone-cycling ß-lapachone treatment as a source of NQO1-dependent rapid superoxide and hydrogen peroxide (H2O2) production. Transcriptomic data from 10 HNSCC patient tumors was used to populate over 4000 single-cell antioxidant enzymatic network models of drug metabolism. The simulations reflected significant systems-level differences between the redox states of healthy and cancer cells, demonstrating in some patient samples a targetable cancer cell population or in others statistically indistinguishable effects between non-malignant and malignant cells. Subsequent multivariate analyses between healthy and malignant cellular models pointed to distinct contributors of redox responses between these phenotypes. This model framework provides a mechanistic basis for explaining mixed outcomes of NAD(P)H:quinone oxidoreductase 1 (NQO1)-bioactivatable therapeutics despite the tumor specificity of these drugs as defined by NQO1/catalase expression and highlights the role of alternate antioxidant components in dictating drug-induced oxidative stress.

Data on the techno-economic and financial analyses of hybrid renewable energy systems in 634 Philippine off-grid islands.

This data article contains the location, energy consumption, renewable energy potential, techno-economics, and profitability of hybrid renewable energy systems (HRES) in 634 Philippine off-grid islands. The HRES under consideration consists of solar photovoltaics, wind turbines, lithium-ion batteries, and diesel generators. The islands were identified from Google Maps™, Bing Maps™, and the study of Meschede and Ocon et al. (2019) [1]. The peak loads of these islands were acquired from National Power Corporation - Small Power Utilities Group (NPC-SPUG), if available, or estimated from the island population otherwise. Hourly-resolution load profiles were synthesized using the normalized profiles reported by Bertheau and Blechinger (2018) [2]. Existing diesel generators in the islands were compiled from reports by NPC-SPUG, while monthly average global horizontal irradiance and wind speeds were taken from the Phil-LIDAR 2 database. Islands that are electrically interconnected were lumped into one microgrid, so the 634 islands were grouped into 616 microgrids. The HRES were optimized using Island System LCOEmin Algorithm (ISLA), our in-house energy systems modeling tool, which sized the energy components to minimize the net present cost. The component sizes and corresponding techno-economic metrics of the optimized HRES in each microgrid are included in the dataset. In addition, the net present value, internal rate of return, payback period, and subsidy requirements of the microgrid are reported at five different electricity rates. This data is valuable for researchers, policymakers, and stakeholders who are working to provide sustainable energy access to off-grid communities. A comprehensive analysis of the data can be found in our article "Techno-economic and Financial Analyses of Hybrid Renewable Energy System Microgrids in 634 Philippine Off-grid Islands: Policy Implications on Public Subsidies and Private Investments" [3].