Evaluating the effects of virulence genotype, swarming motility, and multi-locus sequence types of Escherichia coli on layer chicken embryos.

AIMS: To determine the effects of swarming motility (SM) and multi-locus sequence types (MLST) on the main effect of virulence genotype of Escherichia coli through an embryos lethality assay between the 12th and 18th days of incubation. METHODS AND RESULTS: We collected 58 E. coli isolates from asymptomatic commercial hens (n = 42) and lesions of colibacillosis cases (n = 16), then classified their virulence genotype as avirulent, moderately virulent, virulent-healthy, and virulent-colibacillosis categories by the presence of five virulence-associated genes (iroN, ompT, hlyF, iutA, and iss). These isolates were further classified as non-motile, motile, or hyper-motile by SM assay. From the 58 isolates, we selected 29 for ELA and determined their MLST. Each isolate was inoculated into 15 embryonated eggs through the allantoic cavity. We found the avirulent isolates reduced the relative embryo weight compared to virulent-colibacillosis and moderately virulent isolates (37.49 vs. 41.51 and 40.34%, P = 0.03). Among the moderately virulent and virulent-colibacillosis categories, embryo lethality was lower when isolates were non-motile. Yolk retention was unaffected by virulence categories, motility, or MLST. CONCLUSION: Interaction between virulence genotype and SM substantially influenced the embryo lethality assay of E. coli isolates.

The influence of synaptic plasticity on critical coupling estimates for neural populations.

The presence or absence of synaptic plasticity can dramatically influence the collective behavior of populations of coupled neurons. In this work, we consider spike-timing dependent plasticity (STDP) and its resulting influence on phase cohesion in computational models of heterogeneous populations of conductance-based neurons. STDP allows for the influence of individual synapses to change over time, strengthening or weakening depending on the relative timing of the relevant action potentials. Using phase reduction techniques, we derive an upper bound on the critical coupling strength required to retain phase cohesion for a network of synaptically coupled, heterogeneous neurons with STDP. We find that including STDP can significantly alter phase cohesion as compared to a network with static synaptic connections. Analytical results are validated numerically. Our analysis highlights the importance of the relative ordering of action potentials emitted in a population of tonically firing neurons and demonstrates that order switching can degrade the synchronizing influence of coupling when STDP is considered.

Optimal phase-based control of strongly perturbed limit cycle oscillators using phase reduction techniques.

Phase reduction is a well-established technique for analysis and control of weakly perturbed limit cycle oscillators. However, its accuracy is diminished in a strongly perturbed setting where information about the amplitude dynamics must also be considered. In this paper, we consider phase-based control of general limit cycle oscillators in both weakly and strongly perturbed regimes. For use at the strongly perturbed end of the continuum, we propose a strategy for optimal phase control of general limit cycle oscillators that uses an adaptive phase-amplitude reduced order model in conjunction with dynamic programming. This strategy can accommodate large magnitude inputs at the expense of requiring additional dimensions in the reduced order equations, thereby increasing the computational complexity. We apply this strategy to two biologically motivated prototype problems and provide direct comparisons to two related phase-based control algorithms. In situations where other commonly used strategies fail due to the application of large magnitude inputs, the adaptive phase-amplitude reduction provides a viable reduced order model while still yielding a computationally tractable control problem. These results highlight the need for discernment in reduced order model selection for limit cycle oscillators to balance the trade-off between accuracy and dimensionality.

Case management for integrated care of older people with frailty in community settings.

BACKGROUND: Ageing populations globally have contributed to increasing numbers of people living with frailty, which has significant implications for use of health and care services and costs. The British Geriatrics Society defines frailty as "a distinctive health state related to the ageing process in which multiple body systems gradually lose their inbuilt reserves". This leads to an increased susceptibility to adverse outcomes, such as reduced physical function, poorer quality of life, hospital admissions, and mortality. Case management interventions delivered in community settings are led by a health or social care professional, supported by a multidisciplinary team, and focus on the planning, provision, and co-ordination of care to meet the needs of the individual. Case management is one model of integrated care that has gained traction with policymakers to improve outcomes for populations at high risk of decline in health and well-being. These populations include older people living with frailty, who commonly have complex healthcare and social care needs but can experience poorly co-ordinated care due to fragmented care systems. OBJECTIVES: To assess the effects of case management for integrated care of older people living with frailty compared with usual care. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, CINAHL, Health Systems Evidence, and PDQ Evidence and databases from inception to 23 September 2022. We also searched clinical registries and relevant grey literature databases, checked references of included trials and relevant systematic reviews, conducted citation searching of included trials, and contacted topic experts. SELECTION CRITERIA: We included randomised controlled trials (RCTs) that compared case management with standard care in community-dwelling people aged 65 years and older living with frailty. DATA COLLECTION AND ANALYSIS: We followed standard methodological procedures recommended by Cochrane and the Effective Practice and Organisation of Care Group. We used the GRADE approach to assess the certainty of the evidence. MAIN RESULTS: We included 20 trials (11,860 participants), all of which took place in high-income countries. Case management interventions in the included trials varied in terms of organisation, delivery, setting, and care providers involved. Most trials included a variety of healthcare and social care professionals, including nurse practitioners, allied healthcare professionals, social workers, geriatricians, physicians, psychologists, and clinical pharmacists. In nine trials, the case management intervention was delivered by nurses only. Follow-up ranged from three to 36 months. We judged most trials at unclear risk of selection and performance bias; this consideration, together with indirectness, justified downgrading the certainty of the evidence to low or moderate. Case management compared to standard care may result in little or no difference in the following outcomes. • Mortality at 12 months' follow-up (7.0% in the intervention group versus 7.5% in the control group; risk ratio (RR) 0.98, 95% confidence interval (CI) 0.84 to 1.15; I2 = 11%; 14 trials, 9924 participants; low-certainty evidence) • Change in place of residence to a nursing home at 12 months' follow-up (9.9% in the intervention group versus 13.4% in the control group; RR 0.73, 95% CI 0.53 to 1.01; I2 = 0%; 4 trials, 1108 participants; low-certainty evidence) • Quality of life at three to 24 months' follow-up (results not pooled; mean differences (MDs) ranged from -6.32 points (95% CI -11.04 to -1.59) to 6.1 points (95% CI -3.92 to 16.12) when reported; 11 trials, 9284 participants; low-certainty evidence) • Serious adverse effects at 12 to 24 months' follow-up (results not pooled; 2 trials, 592 participants; low-certainty evidence) • Change in physical function at three to 24 months' follow-up (results not pooled; MDs ranged from -0.12 points (95% CI -0.93 to 0.68) to 3.4 points (95% CI -2.35 to 9.15) when reported; 16 trials, 10,652 participants; low-certainty evidence) Case management compared to standard care probably results in little or no difference in the following outcomes. • Healthcare utilisation in terms of hospital admission at 12 months' follow-up (32.7% in the intervention group versus 36.0% in the control group; RR 0.91, 95% CI 0.79 to 1.05; I2 = 43%; 6 trials, 2424 participants; moderate-certainty evidence) • Change in costs at six to 36 months' follow-up (results not pooled; 14 trials, 8486 participants; moderate-certainty evidence), which usually included healthcare service costs, intervention costs, and other costs such as informal care. AUTHORS' CONCLUSIONS: We found uncertain evidence regarding whether case management for integrated care of older people with frailty in community settings, compared to standard care, improved patient and service outcomes or reduced costs. There is a need for further research to develop a clear taxonomy of intervention components, to determine the active ingredients that work in case management interventions, and identify how such interventions benefit some people and not others.

Profiling mechanisms that drive acute oral toxicity in mammals and its prediction via machine learning.

We present a mechanistic machine-learning quantitative structure-activity relationship (QSAR) model to predict mammalian acute oral toxicity. We trained our model using a rat acute toxicity database compiled by the US National Toxicology Program. We profiled the database using new and published profilers and identified the most plausible mechanisms that drive high acute toxicity (LD50 ≤ 50 mg/kg; GHS categories 1 or 2). Our QSAR model assigns primary mechanisms to compounds, followed by predicting their acute oral LD50 using a random-forest machine-learning model. These predictions were further refined based on structural and mechanistic read-across to substances within the training set. Our model is optimized for sensitivity and aims to minimize the likelihood of underpredicting the toxicity of assessed compounds. It displays high sensitivity (76.1% or 76.6% for compounds in GHS 1-2 or GHS 1-3 categories, respectively), coupled with ≥73.7% balanced accuracy. We further demonstrate the utility of undertaking a mechanistic approach when predicting the toxicity of compounds acting via a rare mode of action (MOA) (aconitase inhibition). The mechanistic profilers and framework of our QSAR model are route- and toxicity endpoint-agnostic, allowing for future applications to other endpoints and routes of administration. Furthermore, we present a preliminary exploration of the potential role of metabolic clearance in acute toxicity. To the best of our knowledge, this effort represents the first accurate mechanistic QSAR model for acute oral toxicity that combines machine learning with MOA assignment, while also seeking to minimize underprediction of more highly potent substances.

Shared decision-making in persons living with dementia: A scoping review.

Evidence supports that older adults with cognitive impairment can reliably communicate their values and choices, even as cognition may decline. Shared decision-making, including the patient, family members, and healthcare providers, is critical to patient-centered care. The aim of this scoping review was to synthesize what is known about shared decision-making in persons living with dementia. A scoping review was completed in PubMed, CINAHL, and Web of Science. Keywords included content areas of dementia and shared decision-making. Inclusion criteria were as follows: description of shared or cooperative decision making, cognitively impaired patient population, adult patient, and original research. Review articles were excluded, as well as those for which the formal healthcare provider was the only team member involved in the decision-making (e.g., physician), and/or the patient sample was not cognitively impaired. Systematically extracted data were organized in a table, compared, and synthesized. The search yielded 263 non-duplicate articles that were screened by title and abstract. Ninety-three articles remained, and the full text was reviewed; 32 articles were eligible for this review. Studies were from across Europe (n = 23), North America (n = 7), and Australia (n = 2). The majority of the articles used a qualitative study design, and 10 used a quantitative study design. Categories of similar shared decision-making topics emerged, including health promotion, end-of-life, advanced care planning, and housing decisions. The majority of articles focused on shared decision-making regarding health promotion for the patient (n = 16). Findings illustrate that shared decision-making requires deliberate effort and is preferred among family members, healthcare providers, and patients with dementia. Future research should include more robust efficacy testing of decision-making tools, incorporation of evidence-based shared decisionmaking approaches based on cognitive status/diagnosis, and consideration of geographical/cultural differences in healthcare delivery systems.

Optimal entrainment for removal of pinned spiral waves.

Cardiac fibrillation is caused by self-sustaining spiral waves that occur in the myocardium, some of which can be pinned to anatomical obstacles, making them more difficult to eliminate. A small electrical stimulation is often sufficient to unpin these spirals but only if it is applied during the vulnerable unpinning window. Even if these unpinning windows can be inferred from data, when multiple pinned spirals exist, their unpinning windows will not generally overlap. Using phase-based reduction techniques, we formulate and solve an optimal control problem to yield a time-varying external voltage gradient that can synchronize a collection of spiral waves that are pinned to a collection of heterogeneous obstacles. Upon synchronization, the unpinning windows overlap so that they can be simultaneously unpinned by applying an external voltage gradient pulse at an appropriate moment. Numerical validation is presented in bidomain model simulations. Results represent a proof-of-concept illustration of the proposed unpinning strategy which explicitly incorporates heterogeneity in the problem formulation and requires no real-time feedback about the system state.

Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches.

Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.

Control of coupled neural oscillations using near-periodic inputs.

Deep brain stimulation (DBS) is a commonly used treatment for medication resistant Parkinson's disease and is an emerging treatment for other neurological disorders. More recently, phase-specific adaptive DBS (aDBS), whereby the application of stimulation is locked to a particular phase of tremor, has been proposed as a strategy to improve therapeutic efficacy and decrease side effects. In this work, in the context of these phase-specific aDBS strategies, we investigate the dynamical behavior of large populations of coupled neurons in response to near-periodic stimulation, namely, stimulation that is periodic except for a slowly changing amplitude and phase offset that can be used to coordinate the timing of applied input with a specified phase of model oscillations. Using an adaptive phase-amplitude reduction strategy, we illustrate that for a large population of oscillatory neurons, the temporal evolution of the associated phase distribution in response to near-periodic forcing can be captured using a reduced order model with four state variables. Subsequently, we devise and validate a closed-loop control strategy to disrupt synchronization caused by coupling. Additionally, we identify strategies for implementing the proposed control strategy in situations where underlying model equations are unavailable by estimating the necessary terms of the reduced order equations in real-time from observables.

Data-driven identification of dynamical models using adaptive parameter sets.

This paper presents two data-driven model identification techniques for dynamical systems with fixed point attractors. Both strategies implement adaptive parameter update rules to limit truncation errors in the inferred dynamical models. The first strategy can be considered an extension of the dynamic mode decomposition with control (DMDc) algorithm. The second strategy uses a reduced order isostable coordinate basis that captures the behavior of the slowest decaying modes of the Koopman operator. The accuracy and robustness of both model identification algorithms is considered in a simple model with dynamics near a Hopf bifurcation. A more complicated model for nonlinear convective flow past an obstacle is also considered. In these examples, the proposed strategies outperform a collection of other commonly used data-driven model identification algorithms including Koopman model predictive control, Galerkin projection, and DMDc.

Exploiting circadian memory to hasten recovery from circadian misalignment.

Recent years have seen a sustained interest in the development of circadian reentrainment strategies to limit the deleterious effects of jet lag. Due to the dynamical complexity of many circadian models, phase-based model reduction techniques are often an imperative first step in the analysis. However, amplitude coordinates that capture lingering effects (i.e., memory) from past inputs are often neglected. In this work, we focus on these amplitude coordinates using an operational phase and an isostable coordinate framework in the context of the development of jet-lag amelioration strategies. By accounting for the influence of circadian memory, we identify a latent phase shift that can prime one's circadian cycle to reentrain more rapidly to an expected time-zone shift. A subsequent optimal control problem is proposed that balances the trade-off between control effort and the resulting latent phase shift. Data-driven model identification techniques for the inference of necessary reduced order, phase-amplitude-based models are considered in situations where the underlying model equations are unknown, and numerical results are illustrated in both a simple planar model and in a coupled population of circadian oscillators.

Data-driven inference of high-accuracy isostable-based dynamical models in response to external inputs.

Isostable reduction is a powerful technique that can be used to characterize behaviors of nonlinear dynamical systems using a basis of slowly decaying eigenfunctions of the Koopman operator. When the underlying dynamical equations are known, previously developed numerical techniques allow for high-order accuracy computation of isostable reduced models. However, in situations where the dynamical equations are unknown, few general techniques are available that provide reliable estimates of the isostable reduced equations, especially in applications where large magnitude inputs are considered. In this work, a purely data-driven inference strategy yielding high-accuracy isostable reduced models is developed for dynamical systems with a fixed point attractor. By analyzing steady-state outputs of nonlinear systems in response to sinusoidal forcing, both isostable response functions and isostable-to-output relationships can be estimated to arbitrary accuracy in an expansion performed in the isostable coordinates. Detailed examples are considered for a population of synaptically coupled neurons and for the one-dimensional Burgers' equation. While linear estimates of the isostable response functions are sufficient to characterize the dynamical behavior when small magnitude inputs are considered, the high-accuracy reduced order model inference strategy proposed here is essential when considering large magnitude inputs.

Data-driven phase-isostable reduction for optimal nonfeedback stabilization of cardiac alternans.

Phase-isostable reduction is an emerging model reduction strategy that can be used to accurately replicate nonlinear behaviors in systems for which standard phase reduction techniques fail. In this work, we derive relationships between the cycle-to-cycle variance of the reduced isostable coordinates for systems subject to both additive white noise and periodic stimulation. Using this information, we propose a data-driven technique for inferring nonlinear terms of the phase-isostable coordinate reduction framework. We apply the proposed model inference strategy to the biologically motivated problem of eliminating cardiac alternans, an arrhythmia that is widely considered to be a precursor to more deadly cardiac arrhythmias. Using this strategy, by simply measuring a series of action potential durations in response to periodic stimulation, we are able to identify energy-optimal, nonfeedback control inputs to stabilize a period-1, alternans-free solution.

Analysis of input-induced oscillations using the isostable coordinate framework.

Many reduced order modeling techniques for oscillatory dynamical systems are only applicable when the underlying system admits a stable periodic orbit in the absence of input. By contrast, very few reduction frameworks can be applied when the oscillations themselves are induced by coupling or other exogenous inputs. In this work, the behavior of such input-induced oscillations is considered. By leveraging the isostable coordinate framework, a high-accuracy reduced set of equations can be identified and used to predict coupling-induced bifurcations that precipitate stable oscillations. Subsequent analysis is performed to predict the steady state phase-locking relationships. Input-induced oscillations are considered for two classes of coupled dynamical systems. For the first, stable fixed points of systems with parameters near Hopf bifurcations are considered so that the salient dynamical features can be captured using an asymptotic expansion of the isostable coordinate dynamics. For the second, an adaptive phase-amplitude reduction framework is used to analyze input-induced oscillations that emerge in excitable systems. Examples with relevance to circadian and neural physiology are provided that highlight the utility of the proposed techniques.

Degenerate isostable reduction for fixed-point and limit-cycle attractors with defective linearizations.

Isostable coordinates provide a convenient framework for understanding the transient behavior of dynamical systems with stable attractors. These isostable coordinates are often used to characterize the slowest decaying eigenfunctions of the Koopman operator; by neglecting the rapidly decaying Koopman eigenfunctions a reduced order model can be obtained. Existing work has focused primarily on nondegenerate isostable coordinates, that is, isostable coordinates that are associated with eigenvalues that have identical algebraic and geometric multiplicities. Current isostable reduction methods cannot be applied to characterize the decay associated with a defective eigenvalue. In this work, a degenerate isostable framework is proposed for use when eigenvalues are defective. These degenerate isostable coordinates are investigated in the context of various reduced order modeling frameworks that retain many of the important properties of standard (nondegenerate) isostable reduced modeling strategies. Reduced order modeling examples that require the use of degenerate isostable coordinates are presented with relevance to both circadian physiology and nonlinear fluid flows.

Optimal open-loop desynchronization of neural oscillator populations.

Deep brain stimulation (DBS) is an increasingly used medical treatment for various neurological disorders. While its mechanisms are not fully understood, experimental evidence suggests that through application of periodic electrical stimulation DBS may act to desynchronize pathologically synchronized populations of neurons resulting desirable changes to a larger brain circuit. However, the underlying mathematical mechanisms by which periodic stimulation can engender desynchronization in a coupled population of neurons is not well understood. In this work, a reduced phase-amplitude reduction framework is used to characterize the desynchronizing influence of periodic stimulation on a population of coupled oscillators. Subsequently, optimal control theory allows for the design of periodic, open-loop stimuli with the capacity to destabilize completely synchronized solutions while simultaneously stabilizing rotating block solutions. This framework exploits system nonlinearities in order to strategically modify unstable Floquet exponents. In the limit of weak neural coupling, it is shown that this method only requires information about the phase response curves of the individual neurons. The effects of noise and heterogeneity are also considered and numerical results are presented. This framework could ultimately be used to inform the design of more efficient deep brain stimulation waveforms for the treatment of neurological disease.

Stabilization of Weakly Unstable Fixed Points as a Common Dynamical Mechanism of High-Frequency Electrical Stimulation.

While high-frequency electrical stimulation often used to treat various biological diseases, it is generally difficult to understand its dynamical mechanisms of action. In this work, high-frequency electrical stimulation is considered in the context of neurological and cardiological systems. Despite inherent differences between these systems, results from both theory and computational modeling suggest identical dynamical mechanisms responsible for desirable qualitative changes in behavior in response to high-frequency stimuli. Specifically, desynchronization observed in a population of periodically firing neurons and reversible conduction block that occurs in cardiomyocytes both result from bifurcations engendered by stimulation that modifies the stability of unstable fixed points. Using a reduced order phase-amplitude modeling framework, this phenomenon is described in detail from a theoretical perspective. Results are consistent with and provide additional insight for previously published experimental observations. Also, it is found that sinusoidal input is energy-optimal for modifying the stability of weakly unstable fixed points using periodic stimulation.

Phase-amplitude reduction far beyond the weakly perturbed paradigm.

While phase reduction is a well-established technique for the analysis of perturbed limit cycle oscillators, practical application requires perturbations to be sufficiently weak thereby limiting its utility in many situations. Here, a general strategy is developed for constructing a set of phase-amplitude reduced equations that is valid to arbitrary orders of accuracy in the amplitude coordinates. This reduction framework can be used to investigate the behavior of oscillatory dynamical systems far beyond the weakly perturbed paradigm. Additionally, a patchwork phase-amplitude reduction method is suggested that is useful when exceedingly large magnitude perturbations are considered. This patchwork method incorporates the high-accuracy phase-amplitude reductions of multiple nearby periodic orbits that result from modifications to nominal parameters. The proposed method of high-accuracy phase-amplitude reduction can be readily implemented numerically and examples are provided where reductions are computed up to fourteenth order accuracy.

Using a multi-stakeholder experience-based design process to co-develop the Creating Active Schools Framework.

BACKGROUND: UK and global policies recommend whole-school approaches to improve childrens' inadequate physical activity (PA) levels. Yet, recent meta-analyses establish current interventions as ineffective due to suboptimal implementation rates and poor sustainability. To create effective interventions, which recognise schools as complex adaptive sub-systems, multi-stakeholder input is necessary. Further, to ensure 'systems' change, a framework is required that identifies all components of a whole-school PA approach. The study's aim was to co-develop a whole-school PA framework using the double diamond design approach (DDDA). METHODOLOGY: Fifty stakeholders engaged in a six-phase DDDA workshop undertaking tasks within same stakeholder (n = 9; UK researchers, public health specialists, active schools coordinators, headteachers, teachers, active partner schools specialists, national organisations, Sport England local delivery pilot representatives and international researchers) and mixed (n = 6) stakeholder groupings. Six draft frameworks were created before stakeholders voted for one 'initial' framework. Next, stakeholders reviewed the 'initial' framework, proposing modifications. Following the workshop, stakeholders voted on eight modifications using an online questionnaire. RESULTS: Following voting, the Creating Active Schools Framework (CAS) was designed. At the centre, ethos and practice drive school policy and vision, creating the physical and social environments in which five key stakeholder groups operate to deliver PA through seven opportunities both within and beyond school. At the top of the model, initial and in-service teacher training foster teachers' capability, opportunity and motivation (COM-B) to deliver whole-school PA. National policy and organisations drive top-down initiatives that support or hinder whole-school PA. To the authors' knowledge, this is the first time practitioners, policymakers and researchers have co-designed a whole-school PA framework from initial conception. The novelty of CAS resides in identifying the multitude of interconnecting components of a whole-school adaptive sub-system; exposing the complexity required to create systems change. The framework can be used to shape future policy, research and practice to embed sustainable PA interventions within schools. To enact such change, CAS presents a potential paradigm shift, providing a map and method to guide future co-production by multiple experts of PA initiatives 'with' schools, while abandoning outdated traditional approaches of implementing interventions 'on' schools.