A six-compartment model for COVID-19 with transmission dynamics and public health strategies.

The global crisis of the COVID-19 pandemic has highlighted the need for mathematical models to inform public health strategies. The present study introduces a novel six-compartment epidemiological model that uniquely incorporates a higher isolation rate for unreported symptomatic cases of COVID-19 compared to reported cases, aiming to enhance prediction accuracy and address the challenge of initial underreporting. Additionally, we employ optimal control theory to assess the cost-effectiveness of interventions and adapt these strategies to specific epidemiological scenarios, such as varying transmission rates and the presence of asymptomatic carriers. By applying this model to COVID-19 data from India (30 January 2020 to 24 November 2020), chosen to capture the initial outbreak and subsequent waves, we calculate a basic reproduction number of 2.147, indicating the high transmissibility of the virus during this period in India. A sensitivity analysis reveals the critical impact of detection rates and isolation measures on disease progression, showing the robustness of our model in estimating the basic reproduction number. Through optimal control simulations, we demonstrate that increasing isolation rates for unreported cases and enhancing detection reduces the spread of COVID-19. Furthermore, our cost-effectiveness analysis establishes that a combined strategy of isolation and treatment is both more effective and economically viable. This research offers novel insights into the efficacy of non-pharmaceutical interventions, providing a tool for strategizing public health interventions and advancing our understanding of infectious disease dynamics.

Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics.

Prey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism's traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based models are limited in their ability to handle multi-modal interactions, which are believed to be crucial for understanding living organisms. Conversely, prevailing prey-predator integration studies often rely on mathematical models and computer simulations, neglecting real-world constraints and noise. These elusive attributes, challenging to model, can lead to emergent behaviors and embodied intelligence. To bridge these gaps, our study designs and implements a prey-predator interaction scenario that incorporates visual and olfactory sensory cues not only in computer simulations but also in a real multi-robot system. Observed emergent spatial-temporal dynamics demonstrate successful transitioning of investigating prey-predator interactions from virtual simulations to the tangible world. It highlights the potential of multi-robotics approaches for studying prey-predator interactions and lays the groundwork for future investigations involving multi-modal sensory processing while considering real-world constraints.

The emergence of lines of hierarchy in collective motion of biological systems.

The emergence of large-scale structures in biological systems, and in particular the formation of lines of hierarchy, is observed at many scales, from collections of cells to groups of insects to herds of animals. Motivated by phenomena in chemotaxis and phototaxis, we present a new class of alignment models that exhibit alignment into lines. The spontaneous formation of such 'fingers' can be interpreted as the emergence of leaders and followers in a system of identically interacting agents. Various numerical examples are provided, which demonstrate emergent behaviors similar to the 'fingering' phenomenon observed in some phototaxis and chemotaxis experiments; this phenomenon is generally known to be a challenging pattern for existing models to capture. A novel protocol for pairwise interactions provides a fundamental alignment mechanism by which agents may form lines of hierarchy across a wide range of biological systems.

The power of weak, transient interactions across biology: A paradigm of emergent behavior.

A growing list of diverse biological systems and their equally diverse functionalities provides realizations of a paradigm of emergent behavior. In each of these biological systems, pervasive ensembles of weak, short-lived, spatially local interactions act autonomously to convey functionalities at larger spatial and temporal scales. In this article, a range of diverse systems and functionalities are presented in a cursory manner with literature citations for further details. Then two systems and their properties are discussed in more detail: yeast chromosome biology and human respiratory mucus.

Distributed cognition for collaboration between human drivers and self-driving cars.

This paper focuses on the collaboration between human drivers and intelligent vehicles. We propose a collaboration mechanism grounded on the concept of distributed cognition. With distributed cognition, intelligence does not lie just in the single entity but also in the interaction with the other cognitive components in a system. We apply this idea to vehicle intelligence, proposing a system distributed into two cognitive entities-the human and the autonomous agent-that together contribute to drive the vehicle. This account of vehicle intelligence differs from the mainstream research effort on highly autonomous cars. The proposed mechanism follows one of the paradigm derived from distributed cognition, the rider-horse metaphor: just like the rider communicates their intention to the horse through the reins, the human influences the agent using the pedals and the steering wheel. We use a driving simulator to demonstrate the collaboration in action, showing how the human can communicate and interact with the agent in various ways with safe outcomes.

Modeling cancer immunoediting in tumor microenvironment with system characterization through the ising-model Hamiltonian.

BACKGROUND AND OBJECTIVE: Cancer Immunoediting (CI) describes the cellular-level interaction between tumor cells and the Immune System (IS) that takes place in the Tumor Micro-Environment (TME). CI is a highly dynamic and complex process comprising three distinct phases (Elimination, Equilibrium and Escape) wherein the IS can both protect against cancer development as well as, over time, promote the appearance of tumors with reduced immunogenicity. Herein we present an agent-based model for the simulation of CI in the TME, with the objective of promoting the understanding of this process. METHODS: Our model includes agents for tumor cells and for elements of the IS. The actions of these agents are governed by probabilistic rules, and agent recruitment (including cancer growth) is modeled via logistic functions. The system is formalized as an analogue of the Ising model from statistical mechanics to facilitate its analysis. The model was implemented in the Netlogo modeling environment and simulations were performed to verify, illustrate and characterize its operation. RESULTS: A main result from our simulations is the generation of emergent behavior in silico that is very difficult to observe directly in vivo or even in vitro. Our model is capable of generating the three phases of CI; it requires only a couple of control parameters and is robust to these. We demonstrate how our simulated system can be characterized through the Ising-model energy function, or Hamiltonian, which captures the "energy" involved in the interaction between agents and presents it in clear and distinct patterns for the different phases of CI. CONCLUSIONS: The presented model is very flexible and robust, captures well the behaviors of the target system and can be easily extended to incorporate more variables such as those pertaining to different anti-cancer therapies. System characterization via the Ising-model Hamiltonian is a novel and powerful tool for a better understanding of CI and the development of more effective treatments. Since data of CI at the cellular level is very hard to procure, our hope is that tools such as this may be adopted to shed light on CI and related developing theories.

Teaching a small foreign language vocabulary to children using tact and listener instruction with a prompt delay.

This study consisted of a systematic replication of previous research examining the effects of tact and listener instruction on the emergence of native-to-foreign (NF) and foreign-to-native (FN) intraverbals in children who had experienced difficulties learning to read and write. We assigned different sets of stimuli to tact and listener conditions, and taught 4 children to tact or respond as listeners in a foreign language using a progressive prompt delay with differential reinforcement. All participants mastered tacts and listener responses in the foreign language. For all participants, tact instruction yielded greater emergence of intraverbals compared to listener instruction. Tact instruction also produced all possible bidirectional (NF and FN) intraverbals relations for 3 of 4 participants, but listener instruction never resulted in the emergence of all possible relations. These results replicate previous findings suggesting that tact instruction is a more efficient way to teach a foreign language and extend them to progressive prompt-delay procedures.

Phototaxis in Cyanobacteria: From Mutants to Models of Collective Behavior.

Cyanobacteria rely on photosynthesis, and thus have evolved complex responses to light. These include phototaxis, the ability of cells to sense light direction and move towards or away from it. Analysis of mutants has demonstrated that phototaxis requires the coordination of multiple photoreceptors and signal transduction networks. The output of these networks is relayed to type IV pili (T4P) that attach to and exert forces on surfaces or other neighboring cells to drive "twitching" or "gliding" motility. This, along with the extrusion of polysaccharides or "slime" by cells, facilitates the emergence of group behavior. We evaluate recent models that describe the emergence of collective colony-scale behavior from the responses of individual, interacting cells. We highlight the advantages of "active matter" approaches in the study of bacterial communities, discussing key differences between emergent behavior in cyanobacterial phototaxis and similar behavior in chemotaxis or quorum sensing.

Spontaneous circulation of active microtubules confined by optical traps.

We propose an experiment to demonstrate spontaneous ordering and symmetry breaking of kinesin-driven microtubules confined to an optical trap. Calculations involving the feasibility of such an experiment are first performed which analyze the power needed to confine microtubules and address heating concerns. We then present the results of first-principles simulations of active microtubules confined in such a trap and analyze the types of motion observed by the microtubules as well as the velocity of the surrounding fluid, both near the trap and in the far-field. We find three distinct phases characterized by breaking of distinct symmetries and also analyze the power spectrum of the angular momenta of polymers to further quantify the differences between these phases. Under the correct conditions, microtubules were found to spontaneously align with one another and circle the trap in one direction.

An Evaluation of the Emergence of Untrained Academic and Applied Skills After Instruction With Video Vignettes.

Applied behavior-analytic skills are derived from precise, technical, objective operational definitions and exemplars of natural phenomena. In some cases, technical behavior-analytic terminology can be challenging for students and practitioners to learn and apply given a person's individual history with the concepts. One of the conceptual areas of behavior analysis that learners tend to struggle with more than other areas is the functional account of human language or verbal behavior. We used an emergent-responding training protocol with freely available and easy-to-implement web-based learning tools to teach the terms and definitions of Skinner's taxonomy of verbal operants using video exemplars and mixed response forms to six graduate students. We also tested for the emergence of untrained applied clinical skills in the form of collecting data while watching novel real-world video exemplars. We found that the video-based training system reliably resulted in the emergence of untrained responding and generalization to novel stimuli and responses and that the skills were maintained by four out of six participants for 2 weeks. In addition, the applied skills performances of the participants were comparable to students who received traditional training in verbal behavior, slightly lower than the performances of Board Certified Behavior Analysts, and considerably lower than the performances of doctoral-level BCBAs. When compared to other published research that used emergent-responding training protocols, the current study required more training time on average but resulted in better performances during some maintenance probes. A brief conceptual analysis of our data is presented, as well as recommendations for future research. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40616-020-00140-3.

Modular microenvironment components reproduce vascular dynamics de novo in a multi-scale agent-based model.

Cells do not exist in isolation; they continuously act within and react to their environment. And this environment is not static; it continuously adapts and responds to cells. Here, we investigate how vascular structure and function impact emergent cell population behavior using an agent-based model (ABM). Our ABM enables researchers to "mix and match" cell agents, subcellular modules, and microenvironment components ranging from simple nutrient sources to complex, realistic vascular architectures that accurately capture hemodynamics. We use this ABM to highlight the bilateral relationship between cells and nearby vasculature, demonstrate the effect of vascular structure on environmental heterogeneity, and emphasize the non-linear, non-intuitive relationship between vascular function and the behavior of cell populations over time. Our ABM is well suited to characterizing in vitro and in vivo studies, with applications from basic science to translational synthetic biology and medicine. The model is freely available at https://github.com/bagherilab/ARCADE. A record of this paper's transparent peer review process is included in the supplemental information.

Continuous learning of emergent behavior in robotic matter.

One of the main challenges in robotics is the development of systems that can adapt to their environment and achieve autonomous behavior. Current approaches typically aim to achieve this by increasing the complexity of the centralized controller by, e.g., direct modeling of their behavior, or implementing machine learning. In contrast, we simplify the controller using a decentralized and modular approach, with the aim of finding specific requirements needed for a robust and scalable learning strategy in robots. To achieve this, we conducted experiments and simulations on a specific robotic platform assembled from identical autonomous units that continuously sense their environment and react to it. By letting each unit adapt its behavior independently using a basic Monte Carlo scheme, the assembled system is able to learn and maintain optimal behavior in a dynamic environment as long as its memory is representative of the current environment, even when incurring damage. We show that the physical connection between the units is enough to achieve learning, and no additional communication or centralized information is required. As a result, such a distributed learning approach can be easily scaled to larger assemblies, blurring the boundaries between materials and robots, paving the way for a new class of modular "robotic matter" that can autonomously learn to thrive in dynamic or unfamiliar situations, for example, encountered by soft robots or self-assembled (micro)robots in various environments spanning from the medical realm to space explorations.

Mechanosensitive smooth muscle cell phenotypic plasticity emerging from a null state and the balance between Rac and Rho.

Reversible differentiation of vascular smooth muscle cells (VSMCs) plays a critical role in vascular biology and disease. Changes in VSMC differentiation correlate with stiffness of the arterial extracellular matrix (ECM), but causal relationships remain unclear. We show that VSMC plasticity is mechanosensitive and that both the de-differentiated and differentiated fates are promoted by the same ECM stiffness. Differential equations developed to model this behavior predicted that a null VSMC state generates the dual fates in response to ECM stiffness. Direct measurements of cellular forces, proliferation, and contractile gene expression validated these predictions and showed that fate outcome is mediated by Rac-Rho homeostasis. Rac, through distinct effects on YAP and TAZ, is required for both fates. Rho drives the contractile state alone, so its level of activity, relative to Rac, drives phenotypic choice. Our results show how the cellular response to a single ECM stiffness generates bi-stability and VSMC plasticity.

Application of time-series regularity metrics to ion flux data from a population of pollen tubes.

Detecting the presence of an irregularity/regularity or chaos in the ion flows of an evolving plant cell is an important task that can be unraveled by performing the analyses by different metrics. Here I show that the results of the advanced fluctuation estimation methods that are obtained from the time series that is generated by the extracellular ion fluxes of tobacco pollen tubes (Nicotiana tabacum L.) have long-range correlations at critical temperatures. Further experimental evidence has been found to support the claim that the autonomous growth organization of extreme plant cell expansion is accomplished by self-organizing criticality (SOC), which is an orchestrated instability that occurs in an optimally evolving cell. The temperature-induced synchronous action of the ionic fluxes that are manifested, inter alia, by minimal dynamic entropy enabled the molecularly encoded information about germination and optimal growth temperatures of tobacco pollen tubes to be determined.

A Design of the Resilient Enterprise: A Reference Architecture for Emergent Behaviors Control.

The sooner disruptive emergent behaviors are detected, the sooner preventive measures can be taken to ensure the resilience of business processes execution. Therefore, organizations need to prepare for emergent behaviors by embedding corrective control mechanisms, which help coordinate organization-wide behavior (and goals) with the behavior of local autonomous entities. Ongoing technological advances, brought by the Industry 4.0 and cyber-physical systems of systems paradigms, can support integration within complex enterprises, such as supply chains. In this paper, we propose a reference enterprise architecture for the detection and monitoring of emergent behaviors in enterprises. We focus on addressing the need for an adequate reaction to disruptions. Based on a systematic review of the literature on the topic of current architectural designs for understanding emergent behaviors, we distill architectural requirements. Our architecture is a hybrid as it combines distributed autonomous business logic (expressed in terms of simple business rules) and some central control mechanisms. We exemplify the instantiation and use of this architecture by means of a proof-of-concept implementation, using a multimodal logistics case study. The obtained results provide a basis for achieving supply chain resilience "by design", i.e., through the design of coordination mechanisms that are well equipped to absorb and compensate for the effects of emergent disruptive behaviors.

Agent-Based Models Predict Emergent Behavior of Heterogeneous Cell Populations in Dynamic Microenvironments.

Computational models are most impactful when they explain and characterize biological phenomena that are non-intuitive, unexpected, or difficult to study experimentally. Countless equation-based models have been built for these purposes, but we have yet to realize the extent to which rules-based models offer an intuitive framework that encourages computational and experimental collaboration. We develop ARCADE, a multi-scale agent-based model to interrogate emergent behavior of heterogeneous cell agents within dynamic microenvironments and demonstrate how complexity of intracellular metabolism and signaling modules impacts emergent dynamics. We perform in silico case studies on context, competition, and heterogeneity to demonstrate the utility of our model for gaining computational and experimental insight. Notably, there exist (i) differences in emergent behavior between colony and tissue contexts, (ii) linear, non-linear, and multimodal consequences of parameter variation on competition in simulated co-cultures, and (iii) variable impact of cell and population heterogeneity on emergent outcomes. Our extensible framework is easily modified to explore numerous biological systems, from tumor microenvironments to microbiomes.

Towards systems tissue engineering: Elucidating the dynamics, spatial coordination, and individual cells driving emergent behaviors.

Biomaterial systems have enabled the in vitro production of complex, emergent tissue behaviors that were not possible with conventional two-dimensional culture systems, allowing for analysis of both normal development and disease processes. We propose that the path towards developing the design parameters for biomaterial systems lies with identifying the molecular drivers of emergent behavior through leveraging technological advances in systems biology, including single cell omics, genetic engineering, and high content imaging. This growing research opportunity at the intersection of the fields of tissue engineering and systems biology - systems tissue engineering - can uniquely interrogate the mechanisms by which complex tissue behaviors emerge with the potential to capture the contribution of i) dynamic regulation of tissue development and dysregulation, ii) single cell heterogeneity and the function of rare cell types, and iii) the spatial distribution and structure of individual cells and cell types within a tissue. By leveraging advances in both biological and materials data science, systems tissue engineering can facilitate the identification of biomaterial design parameters that will accelerate basic science discovery and translation.

Mind as a Behavioral Inhibition Network.

This study aimed to propose to add a new perspective on what may create the impression of "mind" in other beings. The conventional is perspective is that when we observe mental activities in animals, this creates in us the impression that they have a mind. On the other hand, the authors' proposal is that when we observe unpredictable activities in living beings, this creates in us the impression of mind. This "unpredictability" is a characteristic product of all living things and is not limited to animals. In response to this additional perspective of mind, we assumed that the following questions would arise, "Is mind as the source of unpredictability an imaginary thing? Does it really exist?" To answer this question, a conceptual model of mind was proposed, and its validity was investigated by introducing studies on the relationship between animals' unpredictability and emergent behavior. In section "Animal Mind as a Behavioral Inhibition Network," we examined the question from the perspectives of comparative psychology, ethology, and neurophysiology. As a result, we obtained the hypothesis that every animal can have a "behavioral inhibition network" and that this corresponds with the source of unpredictability. The function of the behavioral inhibition network is to create "unpredictable behavior." It makes an observer facing the animal feel unpredictability of the animal. However, unpredictable behavior may arise from exogenous factors such as congenital malfunction in the mechanism to generate an innately acquired behavior, as well as environmental disturbances. Therefore, in the section "Innate and Emergent Behavior of Animals," we introduce studies where unpredictable behavior seems to occur endogenously. In these studies, various animal species were examined in unexperienced problem-solving tasks that could not be solved by innately acquired behaviors. As a result, each animal solved the problem by generating unpredictable behaviors with high frequency. Such biologically significant unpredictable behaviors are referred to as "emergent behaviors." In the section "Discussion," we investigate whether the behavioral inhibition network matches the mind that one experiences in their daily life. Finally, toward a science of universal mind, we introduce experimental results suggesting the possibility that plants and materials such as stones have a similar structure to a behavioral inhibition network.

The Emergent Yo-yo Movement of Nuclei Driven by Cytoskeletal Remodeling in Pseudo-synchronous Mitotic Cycles.

Many aspects in tissue morphogenesis are attributed to a collective behavior of the participating cells. Yet, the mechanism for emergence of dynamic tissue behavior is not well understood. Here, we report that the "yo-yo"-like nuclear movement in the Drosophila syncytial embryo displays emergent features indicative of collective behavior. Following mitosis, the array of nuclei moves away from the wave front by several nuclear diameters only to return to its starting position about 5 min later. Based on experimental manipulations and numerical simulations, we find that the ensemble of elongating and isotropically oriented spindles, rather than individual spindles, is the main driving force for anisotropic nuclear movement. ELMO-dependent F-actin restricts the time for the forward movement and ELMO- and dia-dependent F-actin is essential for the return movement. Our study provides insights into how the interactions among the cytoskeleton as individual elements lead to collective movement of the nuclear array on a macroscopic scale.