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Summary: Predicting vigilance impairment in high‐risk shift work occupations is critical to help to reduce workplace errors and accidents. Current methods rely on multi‐night, often manually entered, sleep data. This study developed a machine learning model for predicting vigilance errors based on a single prior sleep period, derived from an under‐mattress sensor. Twenty‐four healthy volunteers (mean [SD] age = 27.6 [9.5] years, 12 male) attended the laboratory on two separate occasions, 1 month apart, to compare wake performance and sleep under two different lighting conditions. Each condition occurred over an 8 day protocol comprising a baseline sleep opportunity from 10 p.m. to 7 a.m., a 27 h wake period, then daytime sleep opportunities from 10 a.m. to 7 p.m. on days 3–7. From 12 a.m. to 8 a.m. on each of days 4–7, participants completed simulated night shifts that included six 10 min psychomotor vigilance task (PVT) trials per shift. Sleep was assessed using an under‐mattress sensor. Using extra‐trees machine learning models, PVT performance (reaction times <500 ms, reaction, and lapses) during each night shift was predicted based on the preceding daytime sleep. The final extra‐trees model demonstrated moderate accuracy for predicting PVT performance, with standard errors (RMSE) of 19.9 ms (reaction time, 359 [41.6]ms), 0.42 reactions/s (reaction speed, 2.5 [0.6] reactions/s), and 7.2 (lapses, 10.5 [12.3]). The model also correctly classified 84% of trials containing ≥5 lapses (Matthews correlation coefficient = 0.59, F1 = 0.83). Model performance is comparable to current fatigue prediction models that rely upon self‐report or manually entered data. This efficient approach may help to manage fatigue and safety in non‐standard work schedules. [ABSTRACT FROM AUTHOR]

Fatigue risk in humans has few biomathematical models, and existing biomathematical models have many shortcomings. We developed a biomathematical model of fatigue risk based on the concept of energy to quantify human alertness from the perspective of energy and used alertness to characterise human psychological fatigue risk. The model allows mathematical modelling of activity processes that concern sleep intensity and quality and distinguishes between intrinsic exertion in the waking state and workload exertion in the workload state. This alertness and fatigue risk biomathematical model predicts changes in human alertness and fatigue risk indices at any point in the day. We applied numerical simulation and model analysis to five cases to validate the potential value of the alertness energy fatigue risk biomathematical model. Practitioner summary: To overcome the shortcomings of current biomathematical models that evaluate fatigue risk, this study developed a biomathematical model of fatigue risk based on the concept of energy to quantify human alertness from the perspective of energy and used alertness to characterise human fatigue risk. Abbreviations: S: The sleep homeostatic process; C: The circadian process; SAFTE: The sleep activity fatigue and task effectiveness model; FAID: The fatigue audit interdyne model; EEG: Electroencephalogram [ABSTRACT FROM AUTHOR]

In order to study the applicability of classical biomathematical models in the operating environment of Chinese airlines, firstly, in the operating environment of Chinese airlines, continuous performance test (CPT), Karolinska sleepiness scale (KSS) and Samn-Perelli(SP) fatigue scale were used to observe pilots'alertness performance and self-assessed subjective fatigue during flight. Secondly, the corresponding pilot fatigue was simulated and quantified using the system for aircrew fatigue evaluation(SAFE). Finally, the difference between the crew alertness value predicted by the SAFE model and the subjective self-assessment and objective performance of fatigue was compared, and the analysis of variance was used to explore the difference of the influence factors of fatigue on the model prediction and subjective and objective fatigue. The results show that the SAFE model predicts crew fatigue and subjective fatigue has a strong correlation. Overall, the model's prediction of crew fatigue reflects the change trend of factors related to duty hours and circadian rhythm, but the predicted value is high after 18 h of continuous duty. With the increase in the amount of validation data of the model and the adjustment of relevant parameters of the model to adapt to Chinese flight operating environment, the model can be used as an objective tool for the predictive hazard identification of Chinese airline fatigue risk management system(FRMS). It can also provide reference for the theoretical research and engineering application of the localized biomathematical model in China. [ABSTRACT FROM AUTHOR]

Background Clinical trials, epidemiological studies, clinical registries, and other prospective research projects, together with patient care services, are main sources of data in the medical research domain. They serve often as a basis for secondary research in evidence-based medicine, prediction models for disease, and its progression. This data are often neither sufficiently described nor accessible. Related models are often not accessible as a functional program tool for interested users from the health care and biomedical domains. Objective The interdisciplinary project Leipzig Health Atlas (LHA) was developed to close this gap. LHA is an online platform that serves as a sustainable archive providing medical data, metadata, models, and novel phenotypes from clinical trials, epidemiological studies, and other medical research projects. Methods Data, models, and phenotypes are described by semantically rich metadata. The platform prefers to share data and models presented in original publications but is also open for nonpublished data. LHA provides and associates unique permanent identifiers for each dataset and model. Hence, the platform can be used to share prepared, quality-assured datasets and models while they are referenced in publications. All managed data, models, and phenotypes in LHA follow the FAIR principles, with public availability or restricted access for specific user groups. Results The LHA platform is in productive mode (). It is already used by a variety of clinical trial and research groups and is becoming increasingly popular also in the biomedical community. LHA is an integral part of the forthcoming initiative building a national research data infrastructure for health in Germany. [ABSTRACT FROM AUTHOR]

Abstract Background Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences. Methods Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females). Results Among males, tumor (T1Gd) radius was a predictor of overall survival (HR = 1.027, p = 0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR = 1.011, p

Background: Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences.Methods: Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females).Results: Among males, tumor (T1Gd) radius was a predictor of overall survival (HR = 1.027, p = 0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR = 1.011, p < 0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p = 0.010 t-test), but tumor size was not correlated with female overall survival (p = 0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p = 0.004, F p = 0.001, t-test).Conclusion: Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes. [ABSTRACT FROM AUTHOR]

In this paper, a special subclass of reaction diffusion systems with two arbitrary constitutive functions Γ(v) and H(u,v) is considered in the framework of transformation groups. These systems arise, quite often, as mathematical models, in several biological problems and in population dynamics. By using weak equivalence transformation the principal Lie algebra, LP, is written and the classifying equations obtained. Then the extensions of LP are derived and classified with respect to Γ(v) and H(u,v). Some wide special classes of special solutions are carried out.

In this paper, we consider a quite general class of advection reaction diffusion systems. By using an equivalence generator, derived in a previous paper, the authors apply a projection theorem to determine some special forms of the constitutive functions that allow the extension by one of the two-dimensional principal Lie algebra. As an example, a special case is discussed at the end of the paper.

ABSTRACT Biomathematical models offer a powerful method of clarifying complex temporal interactions and the relationships among multiple variables in a system. We present a coupled in silico biomathematical model of articular cartilage degeneration in response to impact and/or aberrant loading such as would be associated with injury to an articular joint. The model incorporates fundamental biological and mechanical information obtained from explant and small animal studies to predict post-traumatic osteoarthritis (PTOA) progression, with an eye toward eventual application in human patients. In this sense, we refer to the mathematics as a 'conduit of translation.' The new in silico framework presented in this paper involves a biomathematical model for the cellular and biochemical response to strains computed using finite element analysis. The model predicts qualitative responses presently, utilizing system parameter values largely taken from the literature. To contribute to accurate predictions, models need to be accurately parameterized with values that are based on solid science. We discuss a parameter identification protocol that will enable us to make increasingly accurate predictions of PTOA progression using additional data from smaller scale explant and small animal assays as they become available. By distilling the data from the explant and animal assays into parameters for biomathematical models, mathematics can translate experimental data to clinically relevant knowledge. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:566-572, 2017. [ABSTRACT FROM AUTHOR]

In this paper, we consider a quite general class of advection reaction diffusion systems. By using an equivalence generator, derived in a previous paper, the authors apply a projection theorem to determine some special forms of the constitutive functions that allow the extension by one of the two-dimensional principal Lie algebra. As an example, a special case is discussed at the end of the paper. [ABSTRACT FROM AUTHOR]

Maintaining human alertness and behavioral capability under conditions of sleep loss and circadian misalignment requires fatigue management technologies due to: (i) dynamic nonlinear modulation of performance capability by the interaction of sleep homeostatic drive and circadian regulation; (ii) large differences among people in neurobehavioral vulnerability to sleep loss; (iii) error in subjective estimates of fatigue on performance; and (iv) to inform people of the need for recovery sleep. Two promising areas of technology have emerged for managing fatigue risk in safety-sensitive occupations. The first involves preventing fatigue by optimizing work schedules using biomathematical models of performance changes associated with sleep homeostatic and circadian dynamics. Increasingly these mathematical models account for individual differences to achieve a more accurate estimate of the timing and magnitude of fatigue effects on individuals. The second area involves technologies for detecting transient fatigue from drowsiness. The Psychomotor Vigilance Test ( PVT), which has been extensively validated to be sensitive to deficits in attention from sleep loss and circadian misalignment, is an example in this category. Two shorter-duration versions of the PVT recently have been developed for evaluating whether operators have sufficient behavioral alertness prior to or during work. Another example is online tracking the percent of slow eyelid closures ( PERCLOS), which has been shown to reflect momentary fluctuations of vigilance. Technologies for predicting and detecting sleepiness/fatigue have the potential to predict and prevent operator errors and accidents in safety-sensitive occupations, as well as physiological and mental diseases due to inadequate sleep and circadian misalignment. [ABSTRACT FROM AUTHOR]

Abstract: Performance prediction models based on the classical two-process model of sleep regulation are reasonably effective at predicting alertness and neurocognitive performance during total sleep deprivation (TSD). However, during sleep restriction (partial sleep loss) performance predictions based on such models have been found to be less accurate. Because most modern operational environments are predominantly characterized by chronic sleep restriction (CSR) rather than by episodic TSD, the practical utility of this class of models has been limited. To better quantify performance during both CSR and TSD, we developed a unified mathematical model that incorporates extant sleep debt as a function of a known sleep/wake history, with recent history exerting greater influence. This incorporation of sleep/wake history into the classical two-process model captures an individual's capacity to recover during sleep as a function of sleep debt and naturally bridges the continuum from CSR to TSD by reducing to the classical two-process model in the case of TSD. We validated the proposed unified model using psychomotor vigilance task data from three prior studies involving TSD, CSR, and sleep extension. We compared and contrasted the fits, within-study predictions, and across-study predictions from the unified model against predictions generated by two previously published models, and found that the unified model more accurately represented multiple experimental studies and consistently predicted sleep restriction scenarios better than the existing models. In addition, we found that the model parameters obtained by fitting TSD data could be used to predict performance in other sleep restriction scenarios for the same study populations, and vice versa. Furthermore, this model better accounted for the relatively slow recovery process that is known to characterize CSR, as well as the enhanced performance that has been shown to result from sleep banking. [Copyright &y& Elsevier]

Both circadian rhythmicity and sleep play significant roles in the regulation of plasma cortisol concentration by the hypothalamopituitary- adrenal (HPA) axis. Numerous studies have found links between sleep and changes in cortisol concentration, but the implications of these results have remained largely qualitative. In this article, we present a quantitative phenomenological model to describe the effects of different sleep durations on cortisol concentration. We constructed the proposed model by incorporating the circadian and sleep allostatic effects on cortisol concentration, the pulsatile nature of cortisol secretion, and cortisol's negative autoregulation of its own production and validated its performance on three study groups that experienced four distinct sleep durations. The model captured many disparate effects of sleep on cortisol dynamics, such as the inhibition of cortisol secretion after the wake-to-sleep transition and the rapid rise of cortisol concentration before morning awakening. Notably, the model reconciled the seemingly contradictory findings between studies that report an increase in cortisol concentration following total sleep deprivation and studies that report no change in concentration. This work provides a biomathematical approach to combine the results on the effects of sleep on cortisol concentration into a unified framework and predict the impact of varying sleep durations on the cortisol profile. [ABSTRACT FROM AUTHOR]

In this paper, a special subclass of reaction diffusion systems with two arbitrary constitutive functions Γ (v) and H (u , v) is considered in the framework of transformation groups. These systems arise, quite often, as mathematical models, in several biological problems and in population dynamics. By using weak equivalence transformation the principal Lie algebra, L P , is written and the classifying equations obtained. Then the extensions of L P are derived and classified with respect to Γ (v) and H (u , v) . Some wide special classes of special solutions are carried out. [ABSTRACT FROM AUTHOR]

The regulation of cellular growth and proliferation is perhaps the most investigated and elusive problem in cell biology and seems to be possible to solve from almost any angle of study chosen. Among the non-systemic factors that have been discussed are genetic damage, genomic control, regulation by stimulatory and inhibitory peptide factors such as EGF, chalones, and fibronectin, protein kinase activition with tyrosine phosphorylation, adenylylcyclase and cAMP, cGMP, membrane perturbations and specifically in tumours the failure of the Pasteur effect in control of glycolysis, excessive membrane ATPase activity, and excessive hydrolytic and proteolytic activities at the cell surface. This article focusses on the central role of fluxes within the plasma membrane and re-examines the possibility that changes of flux of metabolites, ions, and reducing equivalents may be the common denominator regulating cellular proliferation. [ABSTRACT FROM AUTHOR]

A model for cellular proliferation is described according to which proliferation ensues when metabolism evolves towards commitment to DNA synthesis, and inhibition of proliferation occurs when enzymic interactions are iterated within a few metabolic pathways, another limiting factor being the supply of metabolites. The model successfully describes cellular growth and division as a 'cognitive process' based on interaction within enzymic elements and the genome, and affords an explanation in these terms of some empirical phenomena which have previously been understood only as isolated observations. [ABSTRACT FROM AUTHOR]