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.

Analyzing the use of non-pharmaceutical personal protective measures through self-interest and social optimum for the control of an emerging disease.

Non-pharmaceutical personal protective (NPP) measures such as face masks use, and hand and respiratory hygiene can be effective measures for mitigating the spread of aerosol/airborne diseases, such as COVID-19, in the absence of vaccination or treatment. However, the usage of such measures is constrained by their inherent perceived cost and effectiveness for reducing transmission risk. To understand the complex interaction of disease dynamics and individuals decision whether to adopt NPP or not, we incorporate evolutionary game theory into an epidemic model such as COVID-19. To compare how self-interested NPP use differs from social optimum, we also investigated optional control from a central planner's perspective. We use Pontryagin's maximum principle to identify the population-level NPP uptake that minimizes disease incidence by incurring the minimum costs. The evolutionary behavior model shows that NPP uptake increases at lower perceived costs of NPP, higher transmission risk, shorter duration of NPP use, higher effectiveness of NPP, and shorter duration of disease-induced immunity. Though social optimum NPP usage is generally more effective in reducing disease incidence than self-interested usage, our analysis identifies conditions under which both strategies get closer. Our model provides new insights for public health in mitigating a disease outbreak through NPP.

Altered structural connectivity and functional brain dynamics in individuals with heavy alcohol use elucidated via network control theory.

BACKGROUND: Heavy alcohol use (HAU) and its associated conditions, such as alcohol use disorder (AUD), impact millions of individuals worldwide. While our understanding of the neurobiological correlates of alcohol use has evolved substantially, we still lack models incorporating whole-brain neuroanatomical, functional, and pharmacological information under one framework. METHODS: Here, we utilize diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in N = 130 individuals with a high amount of current alcohol use. We compared these alcohol using individuals to N = 308 individuals with minimal use of any substances. RESULTS: We find that individuals with HAU had less dynamic and complex brain activity, and through leveraging network control theory, had increased control energy to complete transitions between activation states. Further, using separately acquired positron emission tomography (PET) data, we deploy an in silico evaluation demonstrating that decreased D2 receptor levels, as found previously in individuals with AUD, may relate to our observed findings. CONCLUSIONS: This work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of heavy alcohol use.

Quantum Brain Dynamics and Virtual Reality.

In this paper we propose a control theory of manipulating holograms in Quantum Brain Dynamics (QBD) involving our subjective experiences, i.e. qualia. We begin with the Lagrangian density in QBD and extend our theory to a hierarchical model involving multiple layers covering the neocortex. We adopt reservoir computing approach or morphological computation to manipulate waveforms of holograms involving our subjective experiences. Numerical simulations performed indicate that the convergence to target waveforms of holograms is realized by external electric fields in QBD in a hierarchy. Our theory can be applied to non-invasive neuronal stimulation of the neocortex and adopted to check whether or not our brain adopts the language of holography. In case the protocol in a brain is discovered and the brain adopts the language of holography, our control theory will be applied to develop virtual reality devices by which our subjective experiences provided by the five senses in the form of qualia are manipulated non-invasively. Then, the information content of qualia might be directly transmitted into our brain without passing through sensory organs.

A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies.

Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.

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.

Composite control based on FNTSMC and adaptive neural network for PMSM system.

In this paper, a novel fixed-time non-singular terminal sliding mode control (NFNTSMC) method with an adaptive neural network (ANN) is proposed for permanent magnet synchronous motor (PMSM) system to improve PMSM performance. For nominal PMSM system without disturbance, a novel fixed-time non-singular terminal sliding mode control is designed to achieve fixed-time convergence property to improve the dynamic performance of the system. However, parameters mismatch and external load disturbances generally exist in PMSM system, the controller designed by NFNTSMC requires a large switching gain to ensure the robustness of the system, which will cause high-frequency sliding mode chattering. Therefore, an adaptive radial basis function (RBF) neural network is designed to approximate the unknown nonlinear lumped disturbance including parameters mismatch and external load disturbances online, and then the output of the neural network can be compensated to the NFNTSMC controller to reduce the switching gain and sliding mode chattering. Finally, the fixed-time convergence property and stability of the system are proved by Lyapunov method. The simulation and experimental results show that the presented strategy possesses satisfactory dynamic performance and strong robustness for PMSM system. And the proposed control scheme also provides an effective and systematic idea of the controller design for PMSM.

Sensorimotor adaptation to destabilizing dynamics in weakly electric fish.

Humans and other animals can readily learn to compensate for changes in the dynamics of movement. Such changes can result from an injury or changes in the weight of carried objects. These changes in dynamics can lead not only to reduced performance but also to dramatic instabilities. We evaluated the impacts of compensatory changes in control policies in relation to stability and robustness in Eigenmannia virescens, a species of weakly electric fish. We discovered that these fish retune their sensorimotor control system in response to experimentally generated destabilizing dynamics. Specifically, we used an augmented reality system to manipulate sensory feedback during an image stabilization task in which a fish maintained its position within a refuge. The augmented reality system measured the fish's movements in real time. These movements were passed through a high-pass filter and multiplied by a gain factor before being fed back to the refuge motion. We adjusted the gain factor to gradually destabilize the fish's sensorimotor loop. The fish retuned their sensorimotor control system to compensate for the experimentally induced destabilizing dynamics. This retuning was partially maintained when the augmented reality feedback was abruptly removed. The compensatory changes in sensorimotor control improved tracking performance as well as control-theoretic measures of robustness, including reduced sensitivity to disturbances and improved phase margins.

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.

Embedding Bifurcations into Pneumatic Artificial Muscle.

Harnessing complex body dynamics has long been a challenge in robotics, particularly when dealing with soft dynamics, which exhibit high complexity in interacting with the environment. Recent studies indicate that these dynamics can be used as a computational resource, exemplified by the McKibben pneumatic artificial muscle, a common soft actuator. This study demonstrates that bifurcations, including periodic and chaotic dynamics, can be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics not present in training data can be embedded through bifurcation embedment, implying the capability to incorporate various qualitatively different patterns into pneumatic artificial muscle without the need to design and learn all required patterns explicitly. Thus, this study introduces a novel approach to simplify robotic devices and control training by reducing reliance on external pattern generators and the amount and types of training data needed for control.

Stress-induced failure of embodied cognition: A general model.

We derive the classic, ubiquitous, but enigmatic Yerkes-Dodson effect of applied stress on real-world performance in a highly natural manner from fundamental assumptions on cognition and its dynamics, as constrained by the asymptotic limit theorems of information and control theories. We greatly extend the basic approach by showing how differences in an underlying probability model can affect the dynamics of decision across a broad range of cognitive enterprise. Most particularly, however, this development may help inform our understanding of the different expressions of human psychopathology. A 'thin tailed' underlying distribution appears to characterize expression of 'ordinary' situational depression/anxiety symptoms of conditions like burnout induced by toxic stress. A 'fat tailed' underlying distribution appears to be associated with brain structure and function abnormalities leading to serious mental illness and poor decision making where symptoms are not only emerging in the setting of severe stress but may also appear in a highly punctuated manner at relatively lower levels of stress. A simple hierarchical optimization shows how environmental 'shadow price' constraints can buffer or aggravate the effects of stress and arousal. Extension of the underlying theory to other patterns of pathology, like immune disorders and premature aging, seems apt. Applications to the punctuated dynamics of institutional cognition under stress also appear possible. Ultimately, the probability models studied here can be converted to new statistical tools for the analysis of observational and experimental data.

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.

Optimal strategies of oncolytic virus-bortezomib therapy via the apoptotic, necroptotic, and oncolysis signaling network.

Bortezomib and oncolytic virotherapy are two emerging targeted cancer therapies. Bortezomib, a proteasome inhibitor, disrupts protein degradation in cells, leading to the accumulation of unfolded proteins that induce apoptosis. On the other hand, virotherapy uses genetically modified oncolytic viruses (OVs) to infect cancer cells, trigger cell lysis, and activate anti-tumor response. Despite progress in cancer treatment, identifying administration protocols for therapeutic agents remains a significant concern, aiming to strike a balance between efficacy, minimizing toxicity, and administrative costs. In this work, optimal control theory was employed to design a cost-effective and efficient co-administration protocols for bortezomib and OVs that could significantly diminish the population of cancer cells via the cell death program with the NF$ \kappa $B-BAX-RIP1 signaling network. Both linear and quadratic control strategies were explored to obtain practical treatment approaches by adapting necroptosis protocols to efficient cell death programs. Our findings demonstrated that a combination therapy commencing with the administration of OVs followed by bortezomib infusions yields an effective tumor-killing outcome. These results could provide valuable guidance for the development of clinical administration protocols in cancer treatment.

What are the key stability challenges in high-bandwidth, non-minimum phase systems with time-varying, and non-smooth delays?

The analysis and control of stability in high-bandwidth systems characterized by non-minimum phase delays represent a formidable challenge within the realm of control theory and engineering. This research aims to address the pivotal question of whether it is feasible to enhance the stability of such intricate systems. These systems inherently possess uncertain and swiftly changing delay characteristics, rendering them exceptionally demanding to control effectively. In the course of this investigation, we embark on a comprehensive exploration of the theoretical underpinnings of the stability of high-bandwidth, non-minimum phase delay systems. This encompassing inquiry encompasses a meticulous consideration of both derivative-delay and piecewise continuous delay components. To underpin our analysis, we judiciously incorporate feedback mechanisms, drawing upon mathematical tools such as the Jensen inequality and Lyapunov-based methodologies to rigorously establish stability conditions. Furthermore, our exploration extends to encompass the concept of input-output stability and complements it with the notion of asymptotic stability, thereby ensuring that the systems in question exhibit uniform stability across diverse temporal domains. The outcomes of our investigation furnish compelling evidence that by harnessing the power of discrete-time Lyapunov-Krasovskii functionals, it becomes conceivable to circumscribe the maximum delay within predefined thresholds. This achievement holds the promise of enhancing stability in non-minimum phase delay systems characterized by high bandwidth. These findings have far-reaching implications, profoundly influencing the design and control paradigms across a spectrum of engineering applications. Notably, this impact extends to areas such as communication networks, real-time control systems, and robotics, where the mitigation of instability due to non-minimum phase delays has been an enduring challenge.

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.

The Reflective Mind of the Anxious in Action: Metacognitive Beliefs and Maladaptive Emotional Regulation Strategies Constrain Working Memory Efficiency.

The Attentional Control Theory (ACT) posits that, while trait anxiety may not directly impact performance, it can influence processing efficiency by prompting the use of compensatory mechanisms. The specific nature of these mechanisms, which might be reflective, is not detailed by the ACT. In a study involving 110 students (M = 20.12; SD = 2.10), surveys were administered to assess the students' metacognitive beliefs, trait anxiety, and emotion regulation strategies (ERSs). The participants engaged in two working memory exercises: the digit span task from the WAIS-IV and an emotional n-back task. The findings indicated that anxiety, metacognitive beliefs, and maladaptive ERSs did not affect task performance but were correlated with increased response times. Several regression analyses demonstrated that a lack of confidence in one's cognitive abilities and maladaptive ERSs predict higher reaction times (RT) in the n-back task. Additionally, maladaptive ERSs also predict an increased use of strategies in the digit span task. Finally, two mediation analyses revealed that anxiety increases processing efficiency, and this relation is mediated by the use of maladaptive ERSs. These results underscore the importance of the reflective level in mediating the effects of trait anxiety on efficiency. They highlight the necessity of incorporating metacognitive beliefs and maladaptive emotion regulation strategies for a thorough comprehension of the Attentional Control Theory. Recognizing these factors offers valuable perspectives for enhancing cognitive capabilities and fostering academic achievement.

The Effect of Peer Influence and Neighborhood Quality on Incarcerated Fathers' Attachment.

Commonly referred to as the "hidden victims" of incarceration, children with a parent who is intermittently or repeatedly incarcerated face various challenges that exacerbate behavioral and psychological development. Using a baseline adaptation of the Multi-site Family Study on Incarceration, Parenting and Partnering (MFS-IP), we sought to clarify how peer influence and neighborhood quality can predict the extent of an incarcerated father's attachment to the focal child and partner. Results showed a negative association between negative peer influence and poor neighborhood quality. Conversely, incarcerated fathers' relationship with their biological mother and fathers produced a significant positive association. These findings propose that risk and protective factors can directly influence attachment levels with the focal child, as suggested by Social Control Theory. This article provides a basis for a more comprehensive understanding of clinical support that can be offered to children and families who bear the systemic societal mechanisms of incarceration.

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.

A Berlin-sided retrospective of the origins of metabolic control theory.

Metabolic control theory (MCA) is celebrating its 50th anniversary. The theory introduced quantitative terms that describe the importance of an enzyme for the regulation of the overall flux and of metabolite concentrations. MCA was developed independently by two groups. The Berlin group included Reinhart Heinrich, Tom A. Rapoport and Samuel M. Rapoport, and the Edinburgh group Henrik Kacser and James A. Burns. Here, I provide a brief reminiscence from the perspective of the Berlin group.

Preserving the self with artificial intelligence using VIPCare-a virtual interaction program for dementia caregivers.

Introduction: Assistive technology is increasingly used to support the physical needs of differently abled persons but has yet to make inroads on support for cognitive or psychological issues. This gap is an opportunity to address another-the lack of contribution from theoretical social science that can provide insights into problems that cannot be seen. Using Affect Control Theory (ACT), the current project seeks to close that gap with an artificially intelligent application to improve interaction and affect for people with Alzheimer's Disease and Related Dementias (ADRD). Using sociological theory, it models interactions with persons with ADRD based on self-sentiments, rather than cognitive memory, and informs a cellphone-based assistive tool called VIPCare for supporting caregivers. Methods: Staff focus groups and interviews with family members of persons with ADRD in a long-term residential care facility collected residents' daily needs and personal histories. Using ACT's evaluation, potency, and activity dimensions, researchers used these data to formulate a self-sentiment profile for each resident and programmed that profile into the VIPCare application. VIPCare used that profile to simulate affectively intelligent social interactions with each unique resident that reduce deflection from established sentiments and, thus, negative emotions. Results: We report on the data collection to design the application, develop self-sentiment profiles for the resident, and generate assistive technology that applies a sociological theory of affect to real world management of interaction, emotion, and mental health. Discussion: By reducing trial and error in learning to engage people with dementia, this tool has potential to smooth interaction and improve wellbeing for a population vulnerable to distress.