Repressive Gene Regulation Synchronizes Development with Cellular Metabolism.

Metabolic conditions affect the developmental tempo of animals. Developmental gene regulatory networks (GRNs) must therefore synchronize their dynamics with a variable timescale. We find that layered repression of genes couples GRN output with variable metabolism. When repressors of transcription or mRNA and protein stability are lost, fewer errors in Drosophila development occur when metabolism is lowered. We demonstrate the universality of this phenomenon by eliminating the entire microRNA family of repressors and find that development to maturity can be largely rescued when metabolism is reduced. Using a mathematical model that replicates GRN dynamics, we find that lowering metabolism suppresses the emergence of developmental errors by curtailing the influence of auxiliary repressors on GRN output. We experimentally show that gene expression dynamics are less affected by loss of repressors when metabolism is reduced. Thus, layered repression provides robustness through error suppression and may provide an evolutionary route to a shorter reproductive cycle.

Basic and Translational Neuroscience of Childhood-Onset Dystonia: A Control-Theory Perspective.

Dystonia is a collection of symptoms with involuntary muscle activation causing hypertonia, hyperkinetic movements, and overflow. In children, dystonia can have numerous etiologies with varying neuroanatomic distribution. The semiology of dystonia can be explained by gain-of-function failure of a feedback controller that is responsible for stabilizing posture and movement. Because postural control is maintained by a widely distributed network, many different anatomic regions may be responsible for symptoms of dystonia, although all features of dystonia can be explained by uncontrolled activation or hypersensitivity of motor cortical regions that can cause increased reflex gain, inserted postures, or sensitivity to irrelevant sensory variables. Effective treatment of dystonia in children requires an understanding of the relationship between etiology, anatomy, and the specific mechanism of failure of postural stabilization.

Information-theoretic principles in incremental language production.

I apply a recently emerging perspective on the complexity of action selection, the rate-distortion theory of control, to provide a computational-level model of errors and difficulties in human language production, which is grounded in information theory and control theory. Language production is cast as the sequential selection of actions to achieve a communicative goal subject to a capacity constraint on cognitive control. In a series of calculations, simulations, corpus analyses, and comparisons to experimental data, I show that the model directly predicts some of the major known qualitative and quantitative phenomena in language production, including semantic interference and predictability effects in word choice; accessibility-based ("easy-first") production preferences in word order alternations; and the existence and distribution of disfluencies including filled pauses, corrections, and false starts. I connect the rate-distortion view to existing models of human language production, to probabilistic models of semantics and pragmatics, and to proposals for controlled language generation in the machine learning and reinforcement learning literature.

Plants sum and subtract stimuli over different timescales.

Mounting evidence suggests that plants engage complex computational processes to quantify and integrate sensory information over time, enabling remarkable adaptive growth strategies. However, quantitative understanding of these computational processes is limited. We report experiments probing the dependence of gravitropic responses of wheat coleoptiles on previous stimuli. First, building on a mathematical model that identifies this dependence as a form of memory, or a filter, we use experimental observations to reveal the mathematical principles of how coleoptiles integrate multiple stimuli over time. Next, we perform two-stimulus experiments, informed by model predictions, to reveal fundamental computational processes. We quantitatively show that coleoptiles respond not only to sums but also to differences between stimuli over different timescales, constituting evidence that plants can compare stimuli-crucial for search and regulation processes. These timescales also coincide with oscillations observed in gravitropic responses of wheat coleoptiles, suggesting shoots may combine memory and movement in order to enhance posture control and sensing capabilities.

Mindful attention promotes control of brain network dynamics for self-regulation and discontinues the past from the present.

Mindful attention is characterized by acknowledging the present experience as a transient mental event. Early stages of mindfulness practice may require greater neural effort for later efficiency. Early effort may self-regulate behavior and focalize the present, but this understanding lacks a computational explanation. Here we used network control theory as a model of how external control inputs-operationalizing effort-distribute changes in neural activity evoked during mindful attention across the white matter network. We hypothesized that individuals with greater network controllability, thereby efficiently distributing control inputs, effectively self-regulate behavior. We further hypothesized that brain regions that utilize greater control input exhibit shorter intrinsic timescales of neural activity. Shorter timescales characterize quickly discontinuing past processing to focalize the present. We tested these hypotheses in a randomized controlled study that primed participants to either mindfully respond or naturally react to alcohol cues during fMRI and administered text reminders and measurements of alcohol consumption during 4 wk postscan. We found that participants with greater network controllability moderated alcohol consumption. Mindful regulation of alcohol cues, compared to one's own natural reactions, reduced craving, but craving did not differ from the baseline group. Mindful regulation of alcohol cues, compared to the natural reactions of the baseline group, involved more-effortful control of neural dynamics across cognitive control and attention subnetworks. This effort persisted in the natural reactions of the mindful group compared to the baseline group. More-effortful neural states had shorter timescales than less effortful states, offering an explanation for how mindful attention promotes being present.

Optimal Control Costs of Brain State Transitions in Linear Stochastic Systems.

The brain is a system that performs numerous functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be controlled based on the minimization of the control cost, and which brain regions are most important to the optimal control of transitions. Despite its great potential, the current control paradigm in neuroscience uses a deterministic framework and is therefore unable to consider stochasticity, severely limiting its application to neural data. Here, to resolve this limitation, we propose a novel framework for the evaluation of control costs based on a linear stochastic model. Following our previous work, we quantified the optimal control cost as the minimal Kullback-Leibler divergence between the uncontrolled and controlled processes. In the linear model, we established an analytical expression for minimal cost and showed that we can decompose it into the cost for controlling the mean and covariance of brain activity. To evaluate the utility of our novel framework, we examined the significant brain regions in the optimal control of transitions from the resting state to seven cognitive task states in human whole-brain imaging data of either sex. We found that, in realizing the different transitions, the lower visual areas commonly played a significant role in controlling the means, while the posterior cingulate cortex commonly played a significant role in controlling the covariances.SIGNIFICANCE STATEMENT The brain performs many cognitive functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be optimally controlled in terms of the cost, and which brain regions are most important to the optimal control of transitions. Here, we built a novel framework to quantify control cost that takes account of stochasticity of neural activity, which is ignored in previous studies. We established the analytical expression of the stochastic control cost, which enables us to compute the cost in high-dimensional neural data. We identified the significant brain regions for the optimal control in cognitive tasks in human whole-brain imaging data.

Ultrashort echo time magnetic resonance elastography for quantification of the mechanical properties of short T2 tissues via optimal control-based radiofrequency pulses.

The aim of the current study is to demonstrate the feasibility of radiofrequency (RF) pulses generated via an optimal control (OC) algorithm to perform magnetic resonance elastography (MRE) and quantify the mechanical properties of materials with very short transverse relaxation times (T2 < 5 ms) for the first time. OC theory applied to MRE provides RF pulses that bring isochromats from the equilibrium state to a fixed target state, which corresponds to the phase pattern of a conventional MRE acquisition. Such RF pulses applied with a constant gradient allow to simultaneously perform slice selection and motion encoding in the slice direction. Unlike conventional MRE, no additional motion-encoding gradients (MEGs) are needed, enabling shorter echo times. OC pulses were implemented both in turbo spin echo (OC rapid acquisition with refocused echoes [RARE]) and ultrashort echo time (OC UTE) sequences to compare their motion-encoding efficiency with the conventional MEG encoding (classical MEG MRE). MRE experiments were carried out on agar phantoms with very short T2 values and on an ex vivo bovine tendon. Magnitude images, wave field images, phase-to-noise ratio (PNR), and shear storage modulus maps were compared between OC RARE, OC UTE, and classical MEG MRE in samples with different T2 values. Shear storage modulus values of the agar phantoms were in agreement with values found in the literature, and that of the bovine tendon was corroborated with rheometry measurements. Only the OC sequences could encode motion in very short T2 samples, and only OC UTE sequences yielded magnitude images enabling proper visualization of short T2 samples and tissues. The OC UTE sequence produced the best PNRs, demonstrating its ability to perform anatomical and mechanical characterization. Its success warrants in vivo confirmation in further studies.

Optimal Oriented External Electric Fields to Trigger a Barrierless Oxaphosphetane Ring Opening Step of the Wittig Reaction.

The Wittig reaction is one of the most important processes in organic chemistry for the asymmetric synthesis of olefinic compounds. In view of the increasingly acknowledged potentiality of the electric fields in promoting reactions, here we will consider the effect of the oriented external electric field (OEEF) on the second step of Wittig reaction (i. e. the ring opening oxaphosphetane) in a model system for non-stabilized ylides. In particular, we have determined the optimal direction and strength of the electric field that should be applied to annihilate the reaction barrier of the ring opening through the polarizable molecular electric dipole (PMED) model that we have recently developed. We conclude that the application of the optimal external electric field for the oxaphosphetane ring opening favours a Bestmann-like mechanism.

Indirect structural changes and reduced controllability after temporal lobe epilepsy resection.

OBJECTIVE: To understand the potential behavioral and cognitive effects of mesial temporal resection for temporal lobe epilepsy (TLE) a method is required to characterize network-wide functional alterations caused by a discrete structural disconnection. The objective of this study was to investigate network-wide alterations in brain dynamics of patients with TLE before and after surgical resection of the seizure focus using average regional controllability (ARC), a measure of the ability of a node to influence network dynamics. METHODS: Diffusion-weighted imaging (DWI) data were acquired in 27 patients with drug-resistant unilateral mesial TLE who underwent selective amygdalohippocampectomy. Imaging data were acquired before and after surgery and a presurgical and postsurgical structural connectome was generated from whole-brain tractography. Edge-wise strength, node strength, and node ARC were compared before and after surgery. Direct and indirect edge-wise strength changes were identified using patient-specific simulated resections. Direct edges were defined as primary edges disconnected by the resection zone itself. Indirect edges were secondary measured edge strength changes. Changes in node strength and ARC were then related to both direct and indirect edge changes. RESULTS: We found nodes with significant postsurgical changes in both node strength and ARC surrounding the resection zone (paired t tests, p < .05, Bonferroni corrected). ARC identified additional postsurgical changes in nodes outside of the resection zone within the ipsilateral occipital lobe, which were associated with indirect edge-wise strength changes of the postsurgical network (Fisher's exact test, p < .001). These indirect edge-wise changes were facilitated through the "hub" nodes including the thalamus, putamen, insula, and precuneus. SIGNIFICANCE: Discrete network disconnection from TLE resection results in widespread structural and functional changes not predicted by disconnection alone. These can be well characterized by dynamic controllability measures such as ARC and may be useful for investigating changes in brain function that may contribute to seizure recurrence and behavioral or cognitive changes after surgery.

Bumblebees compensate for the adverse effects of sidewind during visually guided landings.

Flying animals often encounter winds during visually guided landings. However, how winds affect their flight control strategy during landing is unknown. Here, we investigated how sidewind affects the landing performance and sensorimotor control of foraging bumblebees (Bombus terrestris). We trained bumblebees to forage in a wind tunnel, and used high-speed stereoscopic videography to record 19,421 landing maneuvers in six sidewind speeds (0 to 3.4 m s-1), which correspond to winds encountered in nature. Bumblebees landed less often in higher windspeeds, but the landing durations from free flight were not increased by wind. By testing how bumblebees adjusted their landing control to compensate for adverse effects of sidewind on landing, we showed that the landing strategy in sidewind resembled that in still air, but with important adaptations. Bumblebees landing in a sidewind tended to drift downwind, which they controlled for by performing more hover maneuvers. Surprisingly, the increased hover prevalence did not increase the duration of free-flight landing maneuvers, as these bumblebees flew faster towards the landing platform outside the hover phases. Hence, by alternating these two flight modes along their flight path, free-flying bumblebees negated the adverse effects of high windspeeds on landing duration. Using control theory, we hypothesize that bumblebees achieve this by integrating a combination of direct aerodynamic feedback and a wind-mediated mechanosensory feedback control, with their vision-based sensorimotor control loop. The revealed landing strategy may be commonly used by insects landing in windy conditions, and may inspire the development of landing control strategies onboard autonomously flying robots.

Connection of social anxiety to impaired pattern of cognitive control and underlying motivational deficiencies: Evidence from event-related potentials.

Numerous studies have established a correlation between social anxiety and poor cognitive control. However, little is known about the cognitive control pattern of individuals with high social anxiety (HSAs) and the underlying mechanisms. Based on the Dual Mechanisms of Control framework and the Expected Value of Control theory, this study explored whether HSAs have an impaired cognitive control pattern (Experiment 1) and whether motivational deficiencies underlie the impaired control pattern (Experiment 2). In Experiment 1, 21 individuals with low social anxiety (LSAs) and 21 HSAs completed an AX-Continuous Performance Task. Results showed that HSAs had a smaller P3b amplitude than LSAs, indicating their weakened proactive control in the cue processing stage, but a larger contingent negative variation (CNV) on cue B as compensation for the negative effects of anxiety in the response preparation stage. No group difference was found in N2 and P3a amplitude on probes, suggesting that reactive control in HSAs was not affected compared to LSAs. In Experiment 2, 21 LSAs and 21 HSAs completed a cued-flanker task, where the likelihood of proactive control engagement was manipulated. The results revealed that HSAs exhibited motivation deficiencies in engaging in proactive control, as evidenced by P3b, CNV amplitude, and response times. These findings shed light on the impaired cognitive control pattern of HSAs and suggest that motivational deficiencies may be the crucial underlying factor.

Passive green space exposure leading to lower aggression: The mediating role of sense of control.

Green spaces, integral to natural environments, have been extensively studied for their positive impact on mental health, yet their influence on social behavior, particularly aggression, is less explored. While prior research has predominantly emphasized the effects of actively engaging with nature, the significant role of passive nature exposure-a more common daily occurrence-has often been overlooked. We conducted two studies to explore the influence of passive green space exposure on aggression and the mediating effect of the sense of control. Study 1 (N = 240) utilized a cross-sectional survey to assess the relationship between passive green space exposure, sense of control, and aggression. Study 2 (N = 260) employed a single-factor between-subjects experimental design to further explore these relationships in a controlled environment. The results from both studies indicated that passive green space exposure is negatively related to aggression, and that this relationship is partially mediated by an increased sense of control. Specifically, passive green space exposure was found to negatively predict aggression by bolstering individuals' sense of control. These findings underscore the potential of enhancing the sense of control through environmental factors like green spaces as an effective strategy to reduce aggression. This study enriches our understanding of the broader impacts of green spaces, extending beyond mental health to include social behaviors. We discussed both the theoretical and practical implications of our findings, highlighting how urban planning and environmental design can incorporate green spaces to foster community well-being and mitigate aggressive behaviors.

Holographic Brain Theory: Super-Radiance, Memory Capacity and Control Theory.

We investigate Quantum Electrodynamics corresponding to the holographic brain theory introduced by Pribram to describe memory in the human brain. First, we derive a super-radiance solution in Quantum Electrodynamics with non-relativistic charged bosons (a model of molecular conformational states of water) for coherent light sources of holograms. Next, we estimate memory capacity of a brain neocortex, and adopt binary holograms to manipulate optical information. Finally, we introduce a control theory to manipulate holograms involving biological water's molecular conformational states. We show how a desired waveform in holography is achieved in a hierarchical model using numerical simulations.

Excitatory and Inhibitory Neurons of the Spinal Cord Superficial Dorsal Horn Diverge in Their Somatosensory Responses and Plasticity in Vivo.

The superficial dorsal horn (SDH) of the spinal cord represents the first site of integration between innocuous and noxious somatosensory stimuli. According to gate control theory, diverse populations of excitatory and inhibitory interneurons within the SDH are activated by distinct sensory afferents, and their interplay determines the net nociceptive output projecting to higher pain centers. Although specific SDH cell types are ill defined, numerous classifications schemes find that excitatory and inhibitory neurons fundamentally differ in their morphology, electrophysiology, neuropeptides, and pain-associated plasticity; yet little is known about how these neurons respond over a range of natural innocuous and noxious stimuli. To address this question, we applied an in vivo imaging approach in male mice where the genetically encoded calcium indicator GCaMP6s was expressed either in vGluT2-positive excitatory or vIAAT-positive inhibitory neurons. We found that inhibitory neurons were markedly more sensitive to innocuous touch than excitatory neurons but still responded dynamically over a wide range of noxious mechanical stimuli. Inhibitory neurons were also less sensitive to thermal stimuli than their excitatory counterparts. In a capsaicin model of acute pain sensitization, the responses of excitatory neurons were significantly potentiated to innocuous and noxious mechanical stimuli, whereas inhibitory neural responses were only depressed to noxious stimuli. These in vivo findings show that excitatory and inhibitory SDH neurons diverge considerably in their somatosensory responses and plasticity, as postulated by gate control theory.SIGNIFICANCE STATEMENT Gate control theory posits that opposing spinal excitatory and inhibitory neurons, differently tuned across somatosensory modalities, determine the net nociceptive output to higher pain centers. Little is known about how natural stimuli activate these two neural populations. This study applied an in vivo calcium imaging approach to genetically target these neurons and contrast their responses over a range of innocuous and noxious mechanical and thermal stimuli. Compared with excitatory neurons, we found that inhibitory neurons are more sensitive to innocuous touch and far less sensitive to thermal stimuli. An acute model of pain also revealed that these subtypes undergo divergent mechanosensory plasticity. Our data provide important and novel insights for gate-control inspired models of pain processing.

Reduced emergent character of neural dynamics in patients with a disrupted connectome.

High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems - including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients' structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.

Math anxiety and the shifting function: An event-related potential study of arithmetic task switching.

Why is math anxiety usually related to less efficient math processing? According to attentional control theory, anxiety leads to reduced attentional control, which often entails a greater investment of resources (e.g., more time or effort) to carry out a cognitive task. The executive functions mainly affected by anxiety are inhibition and shifting. Previous studies suggest that math anxiety may impair the inhibitory function. In the present study, the relationship between math anxiety and shifting efficiency when switching between two-digit additions and subtractions was examined. Twenty highly math-anxious and 20 low math-anxious individuals participated in an event-related potential (ERP) transition-cueing experiment. Math anxiety was expected to delay the shifting process, leading to a larger switch cost in response time and no centroparietal cue-locked switch-specific positivity registered in the electroencephalogram during the cue-target interval. Highly math-anxious individuals showed a larger switch cost than their low math-anxious peers. Asymmetrical switch effects between operations in response time were found in both groups, which might be due to larger sequential difficulty effects after subtractions than after additions. The cue-locked switch-specific positivity was present only in the low math-anxious group. The present results suggest that highly math-anxious individuals take longer to shift task sets. Additionally, the highly math-anxious group showed a more positive frontal P2 after the cue that announced a switch to subtraction, probably indicating stronger attentional capture by this cue, because the most threatening condition is anticipated. Taken together, these data suggest that math anxiety also impairs attentional control when switching between arithmetic tasks.

Tumour growth control: analysis of alternative approaches.

In this work we address the problem of tumour growth control by properly exploiting a low-dimensional model that grounds on the Chemical Reaction Network (CRN) formalism. Originally conceived to work both in deterministic and stochastic frameworks, it is shown that, except for the case of very low number of tumour cells, the deterministic approach is appropriate to characterize the system behaviour, especially for control planning purposes. Two alternative control approaches are here investigated. One trivially assumes a constant infusion of external drug administration, the other is designed according to a state-feedback control scheme, with complete or partial knowledge of the state. Pros and cons of both control laws are investigated, showing that the tumour size at the beginning of the therapy plays a role of paramount importance for fixed infusion therapies, whilst only state-feedback laws can eradicate arbitrarily large tumours.

A model-based strategy for the COVID-19 vaccine roll-out in the Philippines.

COVID-19 has affected millions of people worldwide, causing illness and death, and disrupting daily life while imposing a significant social and economic burden. Vaccination is an important control measure that significantly reduces mortality if properly and efficiently distributed. In this work, an age-structured model of COVID-19 transmission, incorporating an unreported infectious compartment, is developed. Three age groups are considered: young (0-19 years), adult (20-64 years), and elderly (65+ years). The transmission rate and reporting rate are determined for each group by utilizing the number of COVID-19 cases in the National Capital Region in the Philippines. Optimal control theory is employed to identify the best vaccine allocation to different age groups. Further, three different vaccination periods are considered to reflect phases of vaccination priority groups: the first, second, and third account for the inoculation of the elderly, adult and elderly, and all three age groups, respectively. This study could guide in making informed decisions in mitigating a population-structured disease transmission under limited resources.

Phenotype Control techniques for Boolean gene regulatory networks.

Modeling cell signal transduction pathways via Boolean networks (BNs) has become an established method for analyzing intracellular communications over the last few decades. What's more, BNs provide a course-grained approach, not only to understanding molecular communications, but also for targeting pathway components that alter the long-term outcomes of the system. This has come to be known as phenotype control theory. In this review we study the interplay of various approaches for controlling gene regulatory networks such as: algebraic methods, control kernel, feedback vertex set, and stable motifs. The study will also include comparative discussion between the methods, using an established cancer model of T-Cell Large Granular Lymphocyte Leukemia. Further, we explore possible options for making the control search more efficient using reduction and modularity. Finally, we will include challenges presented such as the complexity and the availability of software for implementing each of these control techniques.

Resource allocation in a PDE ecosystem model.

The effects of habitat heterogeneity on a diffusing population are investigated here. We formulate a reaction-diffusion system of partial differential equations to analyze the effect of resource allocation in an ecosystem with resource having its own dynamics in space and time. We show a priori estimates to prove the existence of state solutions given a control. We formulate an optimal control problem of our ecosystem model such that the abundance of a single species is maximized while minimizing the cost of inflow resource allocation. In addition, we show the existence and uniqueness of the optimal control as well as the optimal control characterization. We also establish the existence of an optimal intermediate diffusion rate. Moreover, we illustrate several numerical simulations with Dirichlet and Neumann boundary conditions with the space domain in 1D and 2D.