Delaying high school start times impacts depressed mood among students: evidence from a natural experiment.

PURPOSE: Delaying high school start times prolongs weekday sleep. However, it is not clear if longer sleep reduces depression symptoms and if the impact of such policy change is the same across groups of adolescents. METHODS: We examined how gains in weekday sleep impact depression symptoms in 2,134 high school students (mean age 15.16 ± 0.35 years) from the Minneapolis metropolitan area. Leveraging a natural experiment design, we used the policy change to delay school start times as an instrument to estimate the effect of a sustained gain in weekday sleep on repeatedly measured Kandel-Davies depression symptoms. We also evaluated whether allocating the policy change to subgroups with expected benefit could improve the impact of the policy. RESULTS: Over 2 years, a sustained half-hour gain in weekday sleep expected as a result of the policy change to delay start times decreased depression symptoms by 0.78 points, 95%CI (-1.32,-0.28), or 15.6% of a standard deviation. The benefit was driven by a decrease in fatigue and sleep-related symptoms. While symptoms of low mood, hopelessness, and worry were not affected by the policy on average, older students with greater daily screen use and higher BMI experienced greater improvements in mood symptoms than would be expected on average, signaling heterogeneity. Nevertheless, universal implementation outperformed prescriptive strategies. CONCLUSION: High school start time delays are likely to universally decrease fatigue and overall depression symptoms in adolescents. Students who benefit most with respect to mood are older, spend more time on screens and have higher BMI.

Identification, influencing factors and outcomes of time delays in the management pathway of diabetic foot: A systematic review.

OBJECTIVE: A systematic review was conducted to evaluate the time delays in the management of diabetic foot and explore influencing factors of these delays and potential outcomes. METHODS: The researchers searched several electronic databases (Pubmed, Web of Science, Cochrane Library, EMbase, CNKI, WanFang, CBM and VIP) for English and Chinese studies that examined time delays in the management pathway of diabetic foot. Two authors independently screened and extracted data, and assessed the quality of the included studies using the Newcastle-Ottawa Scale and the Agency for Health Research and Quality checklist. Due to heterogeneity among the studies, descriptive analysis was performed. RESULTS: The review included 28 articles, comprising 20 cohort studies and 8 cross-sectional studies, that met the inclusion criteria. Among these, 14 were deemed of high quality. The median times from symptom onset to primary health care or specialist care varied from 3 to 46.69 days. The median delay in referral by primary care specialists ranged from 7 to 31 days, and subsequent median times to definitive treatment ranged from 6.2 to 56 days. Multiple complex factors were found to contribute to these delays, including patient demographics (older age, lower education level and income level) and poor patient health-seeking behaviors (inaccurate self-treatment, incorrect recognition and interpretation of symptoms), inaccurate assessment or initial treatment by health primary professionals, complex referral pathways and clinical characteristics of diabetic foot (number of foot ulcers, Wagner grade scale, and hemoglobin A1c index). Negative outcomes associated with these delays included increased risk of major amputation and mortality, decreased wound healing rate, prolonged hospital stay, and increased hospital costs. CONCLUSIONS: Time delays in the diabetic foot management pathway were both common and serious, contributing to negative health outcomes for patients with diabetic foot. Many complex factors related to patient's poor patient health-seeking behaviors, health system, and clinical characteristics of diabetic foot are responsible for these delays. Therefore, it is necessary to develop new strategies for standard referral practices and strengthen patient awareness of seeking care.

Dynamic Reconfiguration of Dominant Intrinsic Coupling Modes in Elderly at Prodromal Alzheimer's Disease Risk.

Large-scale human brain networks interact across both spatial and temporal scales. Especially for electro- and magnetoencephalography (EEG/MEG), there are many evidences that there is a synergy of different subnetworks that oscillate on a dominant frequency within a quasi-stable brain temporal frame. Intrinsic cortical-level integration reflects the reorganization of functional brain networks that support a compensation mechanism for cognitive decline. Here, a computerized intervention integrating different functions of the medial temporal lobes, namely, object-level and scene-level representations, was conducted. One hundred fifty-eight patients with mild cognitive impairment underwent 90 min of training per day over 10 weeks. An active control (AC) group of 50 subjects was exposed to documentaries, and a passive control group of 55 subjects did not engage in any activity. Following a dynamic functional source connectivity analysis, the dynamic reconfiguration of intra- and cross-frequency coupling mechanisms before and after the intervention was revealed. After the neuropsychological and resting state electroencephalography evaluation, the ratio of inter versus intra-frequency coupling modes and also the contribution of ß1 frequency was higher for the target group compared to its pre-intervention period. These frequency-dependent contributions were linked to neuropsychological estimates that were improved due to intervention. Additionally, the time-delays of the cortical interactions were improved in {δ, θ, α2, ß1} compared to the pre-intervention period. Finally, dynamic networks of the target group further improved their efficiency over the total cost of the network. This is the first study that revealed a dynamic reconfiguration of intrinsic coupling modes and an improvement of time-delays due to a target intervention protocol.

Perceived patient safety culture and its associated factors among clinical managers of tertiary hospitals: a cross-sectional survey.

BACKGROUND: Patient safety is a global challenge influenced by perceived patient safety culture. However, limited knowledge exists regarding the patient safety culture perceived by hospital clinical managers and its associated factors. This study aims to investigate the perceptions of patient safety culture and associated factors among clinical managers of tertiary hospitals in China. METHODS: A cross-sectional survey was conducted from June 19 to July 16, 2021, involving 539 clinical managers from four tertiary hospitals in Changsha City of Hunan Province. The Hospital Survey on Patient Safety Culture (HSOPSC) was utilized to assess perceived patient safety culture. Bivariate, multivariable linear regression, and logistic regression analyses were performed. RESULTS: The mean score for the total HSOPSC was 72.5 ± 7.6, with dimensional scores ranging from 62.1 (14.9) to 86.6 (11.7). Three dimensions exhibited positive response rates (PRRs) < 50%, indicating areas that need to be improved: "nonpunitive response to errors" (40.5%), "staffing" (41.9%), and "frequency of events reported" (47.4%). Specialized hospitals (ß = 1.744, P = 0.037), female gender (ß = 2.496, P = 0.003), higher professional title (ß = 1.413, P = 0.049), a higher education level (ß = 1.316, P = 0.001), and shorter time delays per shift (ß=-1.13, P < 0.001) were correlated with higher perceived patient safety culture. Education level, work department, "teamwork within a unit", "management support for patient safety", "communication openness", and "staffing" dimensions were associated with patient safety grades (all P < 0.05). Years worked in hospitals, occupation, education level, work department, hospital nature, professional title, "communication openness", and "handoffs & transitions" were associated with the number of adverse events reported (all P < 0.05). CONCLUSIONS: Our study revealed a generally low level of patient safety culture perceived by clinical managers and identified priority areas requiring urgent improvement. The associated factors of patient safety culture provide important guidance for the development of targeted interventions in the future. Promoting patient safety by optimizing the patient safety culture perceived by clinical managers should be prioritized.

Dynamics of a cross-superdiffusive SIRS model with delay effects in transmission and treatment.

We investigate the dynamics of a SIRS epidemiological model taking into account cross-superdiffusion and delays in transmission, Beddington-DeAngelis incidence rate and Holling type II treatment. The superdiffusion is induced by inter-country and inter-urban exchange. The linear stability analysis for the steady-state solutions is performed, and the basic reproductive number is calculated. The sensitivity analysis of the basic reproductive number is presented, and we show that some parameters strongly influence the dynamics of the system. A bifurcation analysis to determine the direction and stability of the model is carried out using the normal form and center manifold theorem. The results reveal a proportionality between the transmission delay and the diffusion rate. The numerical results show the formation of patterns in the model, and their epidemiological implications are discussed.

Almost Surely Exponential Convergence Analysis of Time Delayed Uncertain Cellular Neural Networks Driven by Liu Process via Lyapunov-Krasovskii Functional Approach.

As with probability theory, uncertainty theory has been developed, in recent years, to portray indeterminacy phenomena in various application scenarios. We are concerned, in this paper, with the convergence property of state trajectories to equilibrium states (or fixed points) of time delayed uncertain cellular neural networks driven by the Liu process. By applying the classical Banach's fixed-point theorem, we prove, under certain conditions, that the delayed uncertain cellular neural networks, concerned in this paper, have unique equilibrium states (or fixed points). By carefully designing a certain Lyapunov-Krasovskii functional, we provide a convergence criterion, for state trajectories of our concerned uncertain cellular neural networks, based on our developed Lyapunov-Krasovskii functional. We demonstrate under our proposed convergence criterion that the existing equilibrium states (or fixed points) are exponentially stable almost surely, or equivalently that state trajectories converge exponentially to equilibrium states (or fixed points) almost surely. We also provide an example to illustrate graphically and numerically that our theoretical results are all valid. There seem to be rare results concerning the stability of equilibrium states (or fixed points) of neural networks driven by uncertain processes, and our study in this paper would provide some new research clues in this direction. The conservatism of the main criterion obtained in this paper is reduced by introducing quite general positive definite matrices in our designed Lyapunov-Krasovskii functional.

Evaluation of delays during diagnosis and management of lung cancer in India: A prospective observational study.

OBJECTIVE: The majority of lung cancers are diagnosed at an advanced stage; the reasons for which are variable and unclear. METHODS: Lung cancer patients were evaluated prospectively to quantify various timelines and establish reasons for delays. Timelines were defined as time intervals between symptom onset, first physician visit, first specialist visit, date of diagnosis and treatment. RESULTS: A total 410 patients were included, majority having advanced disease. The median period for a first visit to a physician was 30 days (interquartile range [IQR] 20-90), 50 days (IQR 20-110) for referral to our centre, 23 days (IQR 14-33) to reach diagnosis, and 24 days (IQR 14.5-34) to initiate treatment. Administration ofanti-tuberculosis treatment further delayed referral to specialist centre. Treatment delays were related to performance status, disease stage and treatment type. On multivariate analysis, education and histology affected diagnosis delay and treatment delay. Treatment delay was less in those who received targeted therapy compared to chemotherapy. Various time delays did not affect the overall survival. CONCLUSION: Poor education status and inappropriate anti-tubercular treatment were primary factors associated with longer diagnostic delays. Creating disease awareness and high clinical suspicion are essential to overcome these lacunae in lung cancer care.

Reducing neurosurgical theatre start time delays by seventy minutes through application of the 'Golden Patient' initiative.

BACKGROUND: This project's focus was on improving neurosurgical theatre efficiency through the application of Javed et al's Golden Patient initiative to the emergency theatre setting. This initiative has not previously been used in neurosurgery, so we have had to consider how to adapt it. Phase I's primary objective was to quantify theatre start time delays. Phase II assessed whether introducing the initiative reduced the delays. METHODOLOGY: We performed an observational retrospective service evaluation project. Data was collected on weekday theatre start times over 12-week periods pre- and post-initiative. We quantified the delay in theatre start times and recorded the reasons for delays. Following the initiative's introduction, we repeated the evaluation process. Mean and median theatre start times were compared. An ANOVA test was used to confirm statistical significance. RESULTS: Data was collected on 49 days and on 48 days over 12-week periods in both Phase I and II respectively. Phase I of this project identified that there was on average an 86.7 minute delay in starting the theatre each day. The theatre start time was delayed in 91.7% of cases. A 72.3 minute reduction in the theatre start time delay was noted following the initiative's introduction (p < .0005), with an improvement in the average emergency theatre start time from 09:56 to 08:44 (08:30 is the recognised theatre start time). We have identified hospital-wide and doctor-related contributing factors which require further attention, most notably, relating to issues around transferring patients from the ward to theatre. CONCLUSIONS: We have identified a statistically significant improvement in reducing theatre start time delays following the introduction of the initiative. This relatively simple intervention improved communication amongst the multidisciplinary team and led to a notable improvement in the service provided to patients by reducing start time delays. Through tackling identified areas, we hope to further reduce theatre start time delays leading not only to financial savings but also to further improvements in the quality of care provided to our neurosurgical patients.

Nonlinear Analytics for Electrochemical Biosensor Design Using Enzyme Aggregates and Delayed Mass Action.

The paper is devoted to the extension of Brown's model of enzyme kinetics to the case with distributed delays. Firstly, we construct a multi-substrate multi-inhibitor model using discrete and distributed delays. Furthermore, we consider simplified models including one substrate and one inhibitor, for which an experimental study has been performed. The algorithm of parameter identifications was developed which was tested on the experimental data of solution conductivity. Both the model and Kohlrausch's law parameters are obtained as a result of the optimization procedure. Comparison of plots constructed with the help of the estimated parameters has shown that in such case the model with distributed delays is more chemically adequate in comparison with the discrete one. The methods of generalization of the results to the multi-substrate multi-inhibitor cases are discussed.

Malleability of gamma rhythms enhances population-level correlations.

An important problem in systems neuroscience is to understand how information is communicated among brain regions, and it has been proposed that communication is mediated by neuronal oscillations, such as rhythms in the gamma band. We sought to investigate this idea by using a network model with two components, a source (sending) and a target (receiving) component, both built to resemble local populations in the cerebral cortex. To measure the effectiveness of communication, we used population-level correlations in spike times between the source and target. We found that after correcting for a response time that is independent of initial conditions, spike-time correlations between the source and target are significant, due in large measure to the alignment of firing events in their gamma rhythms. But, we also found that regular oscillations cannot produce the results observed in our model simulations of cortical neurons. Surprisingly, it is the irregularity of gamma rhythms, the absence of internal clocks, together with the malleability of these rhythms and their tendency to align with external pulses - features that are known to be present in gamma rhythms in the real cortex - that produced the results observed. These findings and the mechanistic explanations we offered are our primary results. Our secondary result is a mathematical relationship between correlations and the sizes of the samples used for their calculation. As improving technology enables recording simultaneously from increasing numbers of neurons, this relationship could be useful for interpreting results from experimental recordings.

Coexistence of Hopf-born rotation and heteroclinic cycling in a time-delayed three-gene auto-regulated and mutually-repressed core genetic regulation network.

In this work, we study the behavior of a time-delayed mutually repressive auto-activating three-gene system. Delays are introduced to account for the location difference between DNA transcription that leads to production of messenger RNA and its translation that result in protein synthesis. We study the dynamics of the system using numerical simulations, computational bifurcation analysis and mathematical analysis. We find Hopf bifurcations leading to stable and unstable rotation in the system, and we study the rotational behavior as a function of cyclic mutual repression parameter asymmetry between each gene pair in the network. We focus on how rotation co-exists with a stable heteroclinic flow linking the three saddles in the system. We find that this coexistence allows for a transition between two markedly different types of rotation leading to strikingly different phenotypes. One type of rotation belongs to Hopf-induced rotation while the other type, belongs to heteroclinic cycling between three saddle nodes in the system. We discuss the evolutionary and biological implications of our findings.

A process-based, stage-structured model of potato cyst nematode population dynamics: Effects of temperature and resistance.

Potato cyst nematodes (PCN) are responsible for large losses in potato yields in many of the world's potato-growing regions. As soil temperatures increase due to climate change, there is potential for faster growth rates of PCN, allowing development of multiple generations in a growing season. We develop a process-based temperature-dependent model representing the life cycle of Globodera pallida, comprising juvenile, adult and cyst/diapause stages. To incorporate variability in the amount of time spent in each stage caused by genetic/environmental variation, the model is based on a mix of ordinary differential equations (ODEs) with sub-stages, and delay differential equations (DDEs). The effect of climate change is incorporated through the influence of soil temperature on the rate of development and survival in the hatching and juvenile stages. The level of the plant resistance to PCN is incorporated via the proportion of juveniles which become adults. After comparing the model with field data we run simulations to explore the effects of temperature and resistance on PCN populations. We find that with higher temperatures and longer growing seasons multiple generations of PCN can develop within a season, provided any required diapause period is short. Despite this, we show that growing resistant potatoes is a very effective control strategy and planting potatoes with even moderate levels of resistance can counter the effects of climate change.

Design of Adaptive-Robust Controller for Multi-State Synchronization of Chaotic Systems with Unknown and Time-Varying Delays and Its Application in Secure Communication.

In this paper, the multi-state synchronization of chaotic systems with non-identical, unknown, and time-varying delay in the presence of external perturbations and parametric uncertainties was studied. The presence of unknown delays, unknown bounds of disturbance and uncertainty, as well as changes in system parameters complicate the determination of control function and synchronization. During a synchronization scheme using a robust-adaptive control procedure with the help of the Lyapunov stability theorem, the errors converged to zero, and the updating rules were set to estimate the system parameters and delays. To investigate the performance of the proposed design, simulations have been carried out on two Chen hyper-chaotic systems as the slave and one Chua hyper-chaotic system as the master. Our results showed that the proposed controller outperformed the state-of-the-art techniques in terms of convergence speed of synchronization, parameter estimation, and delay estimation processes. The parameters and time delays were achieved with appropriate approximation. Finally, secure communication was realized with a chaotic masking method, and our results revealed the effectiveness of the proposed method in secure telecommunications.

Mathematical Modelling of p53 Signalling during DNA Damage Response: A Survey.

No gene has garnered more interest than p53 since its discovery over 40 years ago. In the last two decades, thanks to seminal work from Uri Alon and Ghalit Lahav, p53 has defined a truly synergistic topic in the field of mathematical biology, with a rich body of research connecting mathematic endeavour with experimental design and data. In this review we survey and distill the extensive literature of mathematical models of p53. Specifically, we focus on models which seek to reproduce the oscillatory dynamics of p53 in response to DNA damage. We review the standard modelling approaches used in the field categorising them into three types: time delay models, spatial models and coupled negative-positive feedback models, providing sample model equations and simulation results which show clear oscillatory dynamics. We discuss the interplay between mathematics and biology and show how one informs the other; the deep connections between the two disciplines has helped to develop our understanding of this complex gene and paint a picture of its dynamical response. Although yet more is to be elucidated, we offer the current state-of-the-art understanding of p53 response to DNA damage.

Dynamic Analysis of the Time-Delayed Genetic Regulatory Network Between Two Auto-Regulated and Mutually Inhibitory Genes.

Time delays play important roles in genetic regulatory networks. In this paper, a gene regulatory network model with time delays and mutual inhibition is considered, where time delays are regarded as bifurcation parameters. In the first part of this paper, we analyze the associated characteristic equations and obtain the conditions for the stability of the system and the existence of Hopf bifurcations in five special cases. Explicit formulas are given to determine the direction and stability of the Hopf bifurcation by using the normal form method and the center manifold theorem. Numerical simulations are then performed to illustrate the results we obtained. In the second part of the paper, using time-delayed stochastic numerical simulations, we study the impact of biological fluctuations on the system and observe that, in modest noise regimes, unexpectedly, noise acts to stabilize the otherwise destabilized oscillatory system.

Dynamics of unidirectionally-coupled ring neural network with discrete and distributed delays.

In this paper, we consider a ring neural network with one-way distributed-delay coupling between the neurons and a discrete delayed self-feedback. In the general case of the distribution kernels, we are able to find a subset of the amplitude death regions depending on even (odd) number of neurons in the network. Furthermore, in order to show the full region of the amplitude death, we use particular delay distributions, including Dirac delta function and gamma distribution. Stability conditions for the trivial steady state are found in parameter spaces consisting of the synaptic weight of the self-feedback and the coupling strength between the neurons, as well as the delayed self-feedback and the coupling strength between the neurons. It is shown that both Hopf and steady-state bifurcations may occur when the steady state loses stability. We also perform numerical simulations of the fully nonlinear system to confirm theoretical findings.

Ultimately Bounded Filtering for Time-Delayed Nonlinear Stochastic Systems with Uniform Quantizations under Random Access Protocol.

This paper investigates the ultimately bounded filtering problem for a kind of time-delay nonlinear stochastic systems with random access protocol (RAP) and uniform quantization effects (UQEs). In order to reduce the occurrence of data conflicts, the RAP is employed to regulate the information transmissions over the shared communication channel. The scheduling behavior of the RAP is characterized by a Markov chain with known transition probabilities. On the other hand, the measurement outputs are quantized by the uniform quantizer before being transmitted via the communication channel. The objective of this paper is to devise a nonlinear filter such that, in the simultaneous presence of RAP and UQEs, the filtering error dynamics is exponentially ultimately bounded in mean square (EUBMS). By resorting to the stochastic analysis technique and the Lyapunov stability theory, sufficient conditions are obtained under which the desired nonlinear filter exists, and then the filter design algorithm is presented. At last, two simulation examples are given to validate the proposed filtering strategy.

The role of network structure and time delay in a metapopulation Wilson--Cowan model.

We study the dynamics of a network Wilson--Cowan model (a system of connected Wilson--Cowan oscillators) for interacting excitatory and inhibitory neuron populations with time delays. Each node in this model corresponds to a population of neurons, including excitatory and inhibitory subpopulations, and hence it can be viewed as a metapopulation model. It is known that information transfer within each cortical area is not instantaneous, and therefore we consider a system of delay differential equations with two different kinds of discrete delays. We account for the time delay in information propagation between individual excitatory and inhibitory subpopulations at each node via intra-node time delays, and we account for time delay in information propagation between neuron populations at different nodes with inter-node time delays. The biologically relevant resting state solutions are oscillatory (stable limit cycles). After determining the influence of the coupling parameters between nodes, the intra-node delays, and the inter-node delays on the dynamics of the two coupled Wilson--Cowan oscillators, we then explore a variety of larger networks of 16 and 100 nodes, in order to determine how the network topology will influence time delayed Wilson--Cowan dynamics. We find that network structure can regularize or deregularize the dynamics, with networks of higher mean degree permitting stable limit cycles and networks with smaller mean degree yielding less regular dynamics (which may range from chaotic solutions, to solutions for which limit cycles collapse into steady states, which are biologically undesirable compared with the preferred stable limit cycles). Furthermore, heterogeneity in the degree distribution of the network (resulting from networks with nodes of varying degree) can result in asynchronous dynamics, even if at each node the local dynamics are that of a limit cycle, in contrast to the synchronization of dynamics between nodes seen when the degree of all nodes is equal. This suggests that homogeneous and well-connected networks permit robust limit cycles under time-delayed Wilson--Cowan dynamics, whereas heterogeneous or poorly connected networks may fail to provide such desirable dynamics, a phenomena akin to structural loss of neuron connections in neurodegenerative diseases.

Transmission time delays organize the brain network synchronization.

The timing of activity across brain regions can be described by its phases for oscillatory processes, and is of crucial importance for brain functioning. The structure of the brain constrains its dynamics through the delays due to propagation and the strengths of the white matter tracts. We use self-sustained delay-coupled, non-isochronous, nonlinearly damped and chaotic oscillators to study how spatio-temporal organization of the brain governs phase lags between the coherent activity of its regions. In silico results for the brain network model demonstrate a robust switching from in- to anti-phase synchronization by increasing the frequency, with a consistent lagging of the stronger connected regions. Relative phases are well predicted by an earlier analysis of Kuramoto oscillators, confirming the spatial heterogeneity of time delays as a crucial mechanism in shaping the functional brain architecture. Increased frequency and coupling are also shown to distort the oscillators by decreasing their amplitude, and stronger regions have lower, but more synchronized activity. These results indicate specific features in the phase relationships within the brain that need to hold for a wide range of local oscillatory dynamics, given that the time delays of the connectome are proportional to the lengths of the structural pathways. This article is part of the theme issue 'Nonlinear dynamics of delay systems'.

Analysis of an Epidemic System with Two Response Delays in Media Impact Function.

A functional differential model of SEIS-M type with two time delays, representing the response time for mass media to cover the current infection and for individuals' behavior changes to media coverage, was proposed to examine the delayed media impact on the transmission dynamics of emergent infectious diseases. The threshold dynamics were established in terms of the basic reproduction number [Formula: see text]. When there are no time delays, we showed that if the media impact is low, the endemic equilibrium is globally asymptotically stable for [Formula: see text], while the endemic equilibrium may become unstable and Hopf bifurcation occurs for some appropriate conditions by taking the level of media impact as bifurcation parameter. With two time delays, we comprehensively investigated the local and global bifurcation by considering the summation of delays as a bifurcation parameter, and theoretically and numerically examined the onset and termination of Hopf bifurcations from the endemic equilibrium. Main results show that either the media described feedback cycle, from infection to the level of mass media and back to disease incidence, or time delays can induce Hopf bifurcation and result in periodic oscillations. The findings indicate that the delayed media impact leads to a richer dynamics that may significantly affect the disease infections.