ResearchPad - control-theory https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Complex interactions can create persistent fluctuations in high-diversity ecosystems]]> https://www.researchpad.co/article/elastic_article_14702 Large abundance fluctuations are well-documented in natural populations. Yet, it is still not known to what extent these fluctuations stem from multi-species interactions, rather than environmental perturbations or demographic processes. There have been long-standing debates on these issues, questioning even the possibility of interaction-driven fluctuations, as they might induce species extinctions until equilibrium is reached.

The situation is all the more challenging and richer in complex high-dimensional settings (many interacting species, many niches, etc.), which feature qualitatively new phenomena, and where theory is still lacking. Here we show that high-diversity metacommunities can persist in dynamically-fluctuating states for extremely long periods of time without extinctions, and with a diversity well above that attained at equilibrium. We describe the quantitative conditions for these endogenous fluctuations, and the key fingerprints which would distinguish them from external perturbations.

We establish a theoretical framework for the many-species dynamics, derived from statistical physics of out-of-equilibrium systems. These settings present unique challenges, and observed behaviors may be counter-intuitive, making specialized theoretical techniques an indispensable tool. Our theory exactly maps the many-species problem to that of a single representative species (metapopulation). This allows us to draw connections with existing theory on perturbed metapopulations, while accounting for unique properties of endogenous feedbacks at high diversity.

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<![CDATA[The mediating role of coping behavior on the age-technostress relationship: A longitudinal multilevel mediation model]]> https://www.researchpad.co/article/5c8823acd5eed0c484638de4

This study seeks to explain the interplay between chronological age and technology-related strain through techno-stressors and coping strategy choices in organizational settings. Grounded in Lazarus´ stress theory, theories of cognitive aging, the life span theory of control and socioemotional selectivity theory, this study argues that even though older workers are more prone to techno-stressors, aging is connected to gaining coping skills, which in turn reduce technology-related strain over time. Understanding these processes enables modifying employees’ coping strategy choices and mitigating negative outcomes of technostress at the workplace. Longitudinal data from 1,216 employees over a time period of 8 months were used to perform multilevel mediation modeling. The findings reveal that age was negatively related to technology-related strain. The link between age and technology-related strain was explained through behavioral disengagement, which older workers used less than younger workers. Active coping and social coping did not act as mediators of this relationship across time points. These relationships were stable after controlling for dependency on technology.

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<![CDATA[Tuft dendrites of pyramidal neurons operate as feedback-modulated functional subunits]]> https://www.researchpad.co/article/5c897706d5eed0c4847d22c8

Dendrites of pyramidal cells exhibit complex morphologies and contain a variety of ionic conductances, which generate non-trivial integrative properties. Basal and proximal apical dendrites have been shown to function as independent computational subunits within a two-layer feedforward processing scheme. The outputs of the subunits are linearly summed and passed through a final non-linearity. It is an open question whether this mathematical abstraction can be applied to apical tuft dendrites as well. Using a detailed compartmental model of CA1 pyramidal neurons and a novel theoretical framework based on iso-response methods, we first show that somatic sub-threshold responses to brief synaptic inputs cannot be described by a two-layer feedforward model. Then, we relax the core assumption of subunit independence and introduce non-linear feedback from the output layer to the subunit inputs. We find that additive feedback alone explains the somatic responses to synaptic inputs to most of the branches in the apical tuft. Individual dendritic branches bidirectionally modulate the thresholds of their input-output curves without significantly changing the gains. In contrast to these findings for precisely timed inputs, we show that neuronal computations based on firing rates can be accurately described by purely feedforward two-layer models. Our findings support the view that dendrites of pyramidal neurons possess non-linear analog processing capabilities that critically depend on the location of synaptic inputs. The iso-response framework proposed in this computational study is highly efficient and could be directly applied to biological neurons.

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<![CDATA[Plant-soil feedbacks promote coexistence and resilience in multi-species communities]]> https://www.researchpad.co/article/5c6b26b6d5eed0c484289eef

Both ecological theory and empirical evidence suggest that negative frequency dependent feedbacks structure plant communities, but integration of these findings has been limited. Here we develop a generic model of frequency dependent feedback to analyze coexistence and invasibility in random theoretical and real communities for which frequency dependence through plant-soil feedbacks (PSFs) was determined empirically. We investigated community stability and invasibility by means of mechanistic analysis of invasion conditions and numerical simulations. We found that communities fall along a spectrum of coexistence types ranging from strict pair-wise negative feedback to strict intransitive networks. Intermediate community structures characterized by partial intransitivity may feature “keystone competitors” which disproportionately influence community stability. Real communities were characterized by stronger negative feedback and higher robustness to species loss than randomly assembled communities. Partial intransitivity became increasingly likely in more diverse communities. The results presented here theoretically explain why more diverse communities are characterized by stronger negative frequency dependent feedbacks, a pattern previously encountered in observational studies. Natural communities are more likely to be maintained by strict negative plant-soil feedback than expected by chance, but our results also show that community stability often depends on partial intransitivity. These results suggest that plant-soil feedbacks can facilitate coexistence in multi-species communities, but that these feedbacks may also initiate cascading effects on community diversity following from single-species loss.

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<![CDATA[Regulation of harvester ant foraging as a closed-loop excitable system]]> https://www.researchpad.co/article/5c102880d5eed0c484247563

Ant colonies regulate activity in response to changing conditions without using centralized control. Desert harvester ant colonies forage for seeds, and regulate foraging to manage a tradeoff between spending and obtaining water. Foragers lose water while outside in the dry air, but ants obtain water by metabolizing the fats in the seeds they eat. Previous work shows that the rate at which an outgoing forager leaves the nest depends on its recent rate of brief antennal contacts with incoming foragers carrying food. We examine how this process can yield foraging rates that are robust to uncertainty and responsive to temperature and humidity across minute-to-hour timescales. To explore possible mechanisms, we develop a low-dimensional analytical model with a small number of parameters that captures observed foraging behavior. The model uses excitability dynamics to represent response to interactions inside the nest and a random delay distribution to represent foraging time outside the nest. We show how feedback from outgoing foragers returning to the nest stabilizes the incoming and outgoing foraging rates to a common value determined by the volatility of available foragers. The model exhibits a critical volatility above which there is sustained foraging at a constant rate and below which foraging stops. To explain how foraging rates adjust to temperature and humidity, we propose that foragers modify their volatility after they leave the nest and become exposed to the environment. Our study highlights the importance of feedback in the regulation of foraging activity and shows how modulation of volatility can explain how foraging activity responds to conditions and varies across colonies. Our model elucidates the role of feedback across many timescales in collective behavior, and may be generalized to other systems driven by excitable dynamics, such as neuronal networks.

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<![CDATA[Adaptive management of energy consumption, reliability and delay of wireless sensor node: Application to IEEE 802.15.4 wireless sensor node]]> https://www.researchpad.co/article/5989db4fab0ee8fa60bdbc3d

Designing a Wireless Sensor Network (WSN) to achieve a high Quality of Service (QoS) (network performance and durability) is a challenging problem. We address it by focusing on the performance of the 802.15.4 communication protocol because the IEEE 802.15.4 Standard is actually considered as one of the reference technologies in WSNs. In this paper, we propose to control the sustainable use of resources (i.e., energy consumption, reliability and timely packet transmission) of a wireless sensor node equipped with photovoltaic cells by an adaptive tuning not only of the MAC (Medium Access Control) parameters but also of the sampling frequency of the node. To do this, we use one of the existing control approaches, namely the viability theory, which aims to preserve the functions and the controls of a dynamic system in a set of desirable states. So, an analytical model, describing the evolution over time of nodal resources, is derived and used by a viability algorithm for the adaptive tuning of the IEEE 802.15.4 MAC protocol. The simulation analysis shows that our solution allows ensuring indefinitely, in the absence of hardware failure, the operations (lifetime duration, reliability and timely packet transmission) of an 802.15.4 WSN and one can temporarily increase the sampling frequency of the node beyond the regular sampling one. This latter brings advantages for agricultural and environmental applications such as precision agriculture, flood or fire prevention. Main results show that our current approach enable to send more information when critical events occur without the node runs out of energy. Finally, we argue that our approach is generic and can be applied to other types of WSN.

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<![CDATA[Reconstruction of Gene Regulatory Modules in Cancer Cell Cycle by Multi-Source Data Integration]]> https://www.researchpad.co/article/5989db15ab0ee8fa60bccfce

Background

Precise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells.

Results and Principal Findings

We propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction. Among 846 human genes with putative roles in cell cycle regulation, we identified 46 transcription factors and 39 gene ontology groups. We reconstructed regulatory modules to infer the underlying regulatory relationships. Four regulatory network motifs were identified from the interaction network. The relationship between each transcription factor and predicted target gene groups was examined by training a recurrent neural network whose topology mimics the network motif(s) to which the transcription factor was assigned. Inferred network motifs related to eight well-known cell cycle genes were confirmed by gene set enrichment analysis, binding site enrichment analysis, and comparison with previously published experimental results.

Conclusions

We established a robust method that can accurately infer underlying relationships between a given transcription factor and its downstream target genes by integrating different layers of biological data. Our method could also be beneficial to biologists for predicting the components of regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these modules will shed light on the processes that occur in cancer cells resulting from errors in cell cycle regulation.

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<![CDATA[Efficient target control of complex networks based on preferential matching]]> https://www.researchpad.co/article/5989db51ab0ee8fa60bdc286

Controlling a complex network towards a desired state is of great importance in many applications. Existing works present an approximate algorithm to find the input nodes used to control partial nodes of the network. However, the input nodes obtained by this algorithm depend on the node matching order and cannot achieve optimum results. Here we present a novel algorithm to find the input nodes for target control based on preferential matching. The algorithm elaborately arranges the matching order of the nodes to reduce the size of the input node set. The results on both synthetic and real networks indicate that the proposed algorithm outperforms the previous algorithm.

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<![CDATA[Uncovering Interactions in the Frequency Domain]]> https://www.researchpad.co/article/5989da77ab0ee8fa60b9717e

Oscillatory activity plays a critical role in regulating biological processes at levels ranging from subcellular, cellular, and network to the whole organism, and often involves a large number of interacting elements. We shed light on this issue by introducing a novel approach called partial Granger causality to reliably reveal interaction patterns in multivariate data with exogenous inputs and latent variables in the frequency domain. The method is extensively tested with toy models, and successfully applied to experimental datasets, including (1) gene microarray data of HeLa cell cycle; (2) in vivo multi-electrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of a sheep; and (3) in vivo LFPs recorded from distributed sites in the right hemisphere of a macaque monkey.

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<![CDATA[Multi-Criteria Optimization of Regulation in Metabolic Networks]]> https://www.researchpad.co/article/5989dabcab0ee8fa60baf31d

Determining the regulation of metabolic networks at genome scale is a hard task. It has been hypothesized that biochemical pathways and metabolic networks might have undergone an evolutionary process of optimization with respect to several criteria over time. In this contribution, a multi-criteria approach has been used to optimize parameters for the allosteric regulation of enzymes in a model of a metabolic substrate-cycle. This has been carried out by calculating the Pareto set of optimal solutions according to two objectives: the proper direction of flux in a metabolic cycle and the energetic cost of applying the set of parameters. Different Pareto fronts have been calculated for eight different “environments” (specific time courses of end product concentrations). For each resulting front the so-called knee point is identified, which can be considered a preferred trade-off solution. Interestingly, the optimal control parameters corresponding to each of these points also lead to optimal behaviour in all the other environments. By calculating the average of the different parameter sets for the knee solutions more frequently found, a final and optimal consensus set of parameters can be obtained, which is an indication on the existence of a universal regulation mechanism for this system.The implications from such a universal regulatory switch are discussed in the framework of large metabolic networks.

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<![CDATA[Dynamics of Opinion Forming in Structurally Balanced Social Networks]]> https://www.researchpad.co/article/5989daedab0ee8fa60bc000f

A structurally balanced social network is a social community that splits into two antagonistic factions (typical example being a two-party political system). The process of opinion forming on such a community is most often highly predictable, with polarized opinions reflecting the bipartition of the network. The aim of this paper is to suggest a class of dynamical systems, called monotone systems, as natural models for the dynamics of opinion forming on structurally balanced social networks. The high predictability of the outcome of a decision process is explained in terms of the order-preserving character of the solutions of this class of dynamical systems. If we represent a social network as a signed graph in which individuals are the nodes and the signs of the edges represent friendly or hostile relationships, then the property of structural balance corresponds to the social community being splittable into two antagonistic factions, each containing only friends.

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<![CDATA[Frequency Domain Analysis Reveals External Periodic Fluctuations Can Generate Sustained p53 Oscillation]]> https://www.researchpad.co/article/5989da0dab0ee8fa60b786ff

p53 is a well-known tumor suppressor protein that regulates many pathways, such as ones involved in cell cycle and apoptosis. The p53 levels are known to oscillate without damping after DNA damage, which has been a focus of many recent studies. A negative feedback loop involving p53 and MDM2 has been reported to be responsible for this oscillatory behavior, but questions remain as how the dynamics of this loop alter in order to initiate and maintain the sustained or undamped p53 oscillation. Our frequency domain analysis suggests that the sustained p53 oscillation is not completely dictated by the negative feedback loop; instead, it is likely to be also modulated by periodic DNA repair-related fluctuations that are triggered by DNA damage. According to our analysis, the p53-MDM2 feedback mechanism exhibits adaptability in different cellular contexts. It normally filters noise and fluctuations exerted on p53, but upon DNA damage, it stops performing the filtering function so that DNA repair-related oscillatory signals can modulate the p53 oscillation. Furthermore, it is shown that the p53-MDM2 feedback loop increases its damping ratio allowing p53 to oscillate at a frequency more synchronized with the other cellular efforts to repair the damaged DNA, while suppressing its inherent oscillation-generating capability. Our analysis suggests that the overexpression of MDM2, observed in many types of cancer, can disrupt the operation of this adaptive mechanism by making it less responsive to the modulating signals after DNA damage occurs.

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<![CDATA[Performance Feedback Processing Is Positively Biased As Predicted by Attribution Theory]]> https://www.researchpad.co/article/5989db0dab0ee8fa60bcaf3c

A considerable literature on attribution theory has shown that healthy individuals exhibit a positivity bias when inferring the causes of evaluative feedback on their performance. They tend to attribute positive feedback internally (e.g., to their own abilities) but negative feedback externally (e.g., to environmental factors). However, all empirical demonstrations of this bias suffer from at least one of the three following drawbacks: First, participants directly judge explicit causes for their performance. Second, participants have to imagine events instead of experiencing them. Third, participants assess their performance only after receiving feedback and thus differences in baseline assessments cannot be excluded. It is therefore unclear whether the classically reported positivity bias generalizes to setups without these drawbacks. Here, we aimed at establishing the relevance of attributions for decision-making by showing an attribution-related positivity bias in a decision-making task. We developed a novel task, which allowed us to test how participants changed their evaluations in response to positive and negative feedback about performance. Specifically, we used videos of actors expressing different facial emotional expressions. Participants were first asked to evaluate the actors’ credibility in expressing a particular emotion. After this initial rating, participants performed an emotion recognition task and did—or did not—receive feedback on their veridical performance. Finally, participants re-rated the actors’ credibility, which provided a measure of how they changed their evaluations after feedback. Attribution theory predicts that participants change their evaluations of the actors’ credibility toward the positive after receiving positive performance feedback and toward the negative after negative performance feedback. Our results were in line with this prediction. A control condition without feedback showed that correct or incorrect performance alone could not explain the observed positivity bias. Furthermore, participants’ behavior in our task was linked to the most widely used measure of attribution style. In sum, our findings suggest that positive and negative performance feedback influences the evaluation of task-related stimuli, as predicted by attribution theory. Therefore, our study points to the relevance of attribution theory for feedback processing in decision-making and provides a novel outlook for decision-making biases.

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<![CDATA[Optimal Control Predicts Human Performance on Objects with Internal Degrees of Freedom]]> https://www.researchpad.co/article/5989dae6ab0ee8fa60bbd9fc

On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.

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<![CDATA[Human Leg Model Predicts Ankle Muscle-Tendon Morphology, State, Roles and Energetics in Walking]]> https://www.researchpad.co/article/5989daa8ab0ee8fa60ba8401

A common feature in biological neuromuscular systems is the redundancy in joint actuation. Understanding how these redundancies are resolved in typical joint movements has been a long-standing problem in biomechanics, neuroscience and prosthetics. Many empirical studies have uncovered neural, mechanical and energetic aspects of how humans resolve these degrees of freedom to actuate leg joints for common tasks like walking. However, a unifying theoretical framework that explains the many independent empirical observations and predicts individual muscle and tendon contributions to joint actuation is yet to be established. Here we develop a computational framework to address how the ankle joint actuation problem is resolved by the neuromuscular system in walking. Our framework is founded upon the proposal that a consideration of both neural control and leg muscle-tendon morphology is critical to obtain predictive, mechanistic insight into individual muscle and tendon contributions to joint actuation. We examine kinetic, kinematic and electromyographic data from healthy walking subjects to find that human leg muscle-tendon morphology and neural activations enable a metabolically optimal realization of biological ankle mechanics in walking. This optimal realization (a) corresponds to independent empirical observations of operation and performance of the soleus and gastrocnemius muscles, (b) gives rise to an efficient load-sharing amongst ankle muscle-tendon units and (c) causes soleus and gastrocnemius muscle fibers to take on distinct mechanical roles of force generation and power production at the end of stance phase in walking. The framework outlined here suggests that the dynamical interplay between leg structure and neural control may be key to the high walking economy of humans, and has implications as a means to obtain insight into empirically inaccessible features of individual muscle and tendons in biomechanical tasks.

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<![CDATA[Dynamics of a Cytokine Storm]]> https://www.researchpad.co/article/5989da22ab0ee8fa60b7f439

Six volunteers experienced severe inflammatory response during the Phase I clinical trial of a monoclonal antibody that was designed to stimulate a regulatory T cell response. Soon after the trial began, each volunteer experienced a “cytokine storm”, a dramatic increase in cytokine concentrations. The monoclonal antibody, TGN1412, raised serum concentrations of both pro- and anti-inflammatory cytokines το very hiγh values during the first day, while lymphocyte and monocyte concentrations plummeted. Because the subjects were healthy and had no prior indications of immune deficiency, this event provided an unusual opportunity to study the dynamic interactions of cytokines and other measured parameters. Here, the response histories of nine cytokines have been modeled by a set of linear ordinary differential equations. A general search procedure identifies parameters of the model, whose response fits the data well during the five-day measurement period. The eighteenth-order model reveals plausible cause-and-effect relationships among the cytokines, showing how each cytokine induces or inhibits other cytokines. It suggests that perturbations in IL2, IL8, and IL10 have the most significant inductive effect, while IFN-γ and IL12 have the greatest inhibiting effect on other cytokine concentrations. Although TNF-α is a major pro-inflammatory factor, IFN-γ and three other cytokines have faster initial and median response to TGN1412 infusion. Principal-component analysis of the data reveals three clusters of similar cytokine responses: [TNF-α, IL1, IL10], [IFN-γ, IL2, IL4, IL8, and IL12], and [IL6]. IL1, IL6, IL10, and TNF-α have the highest degree of variability in response to uncertain initial conditions, exogenous effects, and parameter estimates. This study illuminates details of a cytokine storm event, and it demonstrates the value of linear modeling for interpreting complex, coupled biological system dynamics from empirical data.

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<![CDATA[Gene Expression Prediction by Soft Integration and the Elastic Net—Best Performance of the DREAM3 Gene Expression Challenge]]> https://www.researchpad.co/article/5989da7cab0ee8fa60b98e17

Background

To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance.

Methodology/Principal Findings

We develop an algorithm by exploring various regression schemes with different model selection procedures. It turns out that the most effective scheme is based on least squares, with a penalty term of a recently developed form called the “elastic net”. Key components in the algorithm are the integration of expression data from other experimental conditions than those presented for the challenge and the utilization of transcription factor binding data for guiding the inference process towards known interactions. Of importance is also a cross-validation procedure where each form of external data is used only to the extent it increases the expected performance.

Conclusions/Significance

Our algorithm proves both the possibility to extract information from large-scale expression data concerning prediction of gene levels, as well as the benefits of integrating different data sources for improving the inference. We believe the former is an important message to those still hesitating on the possibilities for computational approaches, while the latter is part of an important way forward for the future development of the field of computational systems biology.

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<![CDATA[Node Vulnerability under Finite Perturbations in Complex Networks]]> https://www.researchpad.co/article/5989daf2ab0ee8fa60bc17e6

A measure to quantify vulnerability under perturbations (attacks, failures, large fluctuations) in ensembles (networks) of coupled dynamical systems is proposed. Rather than addressing the issue of how the network properties change upon removal of elements of the graph (the strategy followed by most of the existing methods for studying the vulnerability of a network based on its topology), here a dynamical definition of vulnerability is introduced, referring to the robustness of a collective dynamical state to perturbing events occurring over a fixed topology. In particular, we study how the collective (synchronized) dynamics of a network of chaotic units is disrupted under the action of a finite size perturbation on one of its nodes. Illustrative examples are provided for three systems of identical chaotic oscillators coupled according to three distinct well-known network topologies. A quantitative comparison between the obtained vulnerability rankings and the classical connectivity/centrality rankings is made that yields conclusive results. Possible applications of the proposed strategy and conclusions are also discussed.

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<![CDATA[Dynamic Health Policies for Controlling the Spread of Emerging Infections: Influenza as an Example]]> https://www.researchpad.co/article/5989dac2ab0ee8fa60bb136a

The recent appearance and spread of novel infectious pathogens provide motivation for using models as tools to guide public health decision-making. Here we describe a modeling approach for developing dynamic health policies that allow for adaptive decision-making as new data become available during an epidemic. In contrast to static health policies which have generally been selected by comparing the performance of a limited number of pre-determined sequences of interventions within simulation or mathematical models, dynamic health policies produce “real-time” recommendations for the choice of the best current intervention based on the observable state of the epidemic. Using cumulative real-time data for disease spread coupled with current information about resource availability, these policies provide recommendations for interventions that optimally utilize available resources to preserve the overall health of the population. We illustrate the design and implementation of a dynamic health policy for the control of a novel strain of influenza, where we assume that two types of intervention may be available during the epidemic: (1) vaccines and antiviral drugs, and (2) transmission reducing measures, such as social distancing or mask use, that may be turned “on” or “off” repeatedly during the course of epidemic. In this example, the optimal dynamic health policy maximizes the overall population's health during the epidemic by specifying at any point of time, based on observable conditions, (1) the number of individuals to vaccinate if vaccines are available, and (2) whether the transmission-reducing intervention should be either employed or removed.

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<![CDATA[A Fully Automated Robotic System for Microinjection of Zebrafish Embryos]]> https://www.researchpad.co/article/5989dafcab0ee8fa60bc5273

As an important embodiment of biomanipulation, injection of foreign materials (e.g., DNA, RNAi, sperm, protein, and drug compounds) into individual cells has significant implications in genetics, transgenics, assisted reproduction, and drug discovery. This paper presents a microrobotic system for fully automated zebrafish embryo injection, which overcomes the problems inherent in manual operation, such as human fatigue and large variations in success rates due to poor reproducibility. Based on computer vision and motion control, the microrobotic system performs injection at a speed of 15 zebrafish embryos (chorion unremoved) per minute, with a survival rate of 98% (n = 350 embryos), a success rate of 99% (n = 350 embryos), and a phenotypic rate of 98.5% (n = 210 embryos). The sample immobilization technique and microrobotic control method are applicable to other biological injection applications such as the injection of mouse oocytes/embryos and Drosophila embryos to enable high-throughput biological and pharmaceutical research.

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