Social Jetlag and Related Risks for Human Health: A Timely Review

The term social jetlag is used to describe the discrepancy between biological time, determined by our internal body clock, and social times, mainly dictated by social obligations such as school or work. In industrialized countries, two-thirds of the studying/working population experiences social jetlag, often for several years. Described for the first time in 2006, a considerable effort has been put into understanding the effects of social jetlag on human physiopathology, yet our understanding of this phenomenon is still very limited. Due to its high prevalence, social jetlag is becoming a primary concern for public health. This review summarizes current knowledge regarding social jetlag, social jetlag associated behavior (e.g., unhealthy eating patterns) and related risks for human health.

Reconstruction of Phase Space and Eigenvalue Decomposition from a Biological Time Series: A Malayalam Speech Signal Case Study

Our objective is to describe the speech production system from a non-linear physiological system perspective and reconstruct the attractor from the experimental speech data. Mutual information method is utilized to find out the time delay for embedding. The False Nearest Neighbour (FNN) method and Principal Component Analysis (PCA) method are used for optimizing the embedding dimension of time series. The time series obtained from the typical non-linear systems, Lorenz system and Rössler system, is used to standardize the methods and the Malayalam speech vowel time series of both genders of different age groups, sampled at three sampling frequencies (16[Formula: see text]kHz, 32[Formula: see text]kHz, 44.1[Formula: see text]kHz), are taken for analysis. It was observed that time delay varies from sample to sample and, it ought to be better to figure out the time delay with the embedding dimension analysis. The embedding dimension is shown to be independent of gender, age and sampling frequency and can be projected as five. Hence a five-dimensional hyperspace will probably be adequate for reconstructing attractor of speech time series.

Bayesian inference of biomass growth characteristics for sugi (C. japonica) and hinoki (C. obtusa) forests in self-thinned and managed stands

Background Forests are an important sink for atmospheric carbon and could release that carbon upon deforestation and degradation. Knowing stand biomass dynamic of evergreen forests has become necessary to improve current biomass production models. The different growth processes of managed forests compared to self-managed forests imply an adaptation of biomass prediction models. Methods In this paper we model through three models the biomass growth of two tree species (Japanese cedar, Japanese cypress) at stand level whether they are managed or not (self-thinning). One of them is named self-thinned model which uses a specific self-thinning parameter α and adapted to self-managed forests and an other model is named thinned model adapted to managed forests. The latter is compared to a Mitscherlich model. The self-thinned model takes into account the light competition between trees relying on easily observable parameters (e.g. stand density). A Bayesian inference was carried out to determine parameters values according to a large database collected. Results In managed forest, Bayesian inference results showed obviously a lack of identifiability of Mitscherlich model parameters and a strong evidence for the thinned model in comparison to Mitscherlich model. In self-thinning forest, the results of Bayesian inference are in accordance with the self-thinning 3/2 rule (α=1.4). Structural dependence between stand density and stand yield in self-thinned model allows to qualifying the expression of biological time as a function of physical time and better qualify growth and mortality rate. Relative mortality rate is 2.5 times more important than relative growth rate after about 40 years old. Stand density and stand yield can be expressed as function of biological time, showing that yield is independent of initial density. Conclusions This paper addressed stand biomass dynamic models of evergreen forests in order to improve biomass growth dynamic assessment at regional scale relying on easily observable parameters. These models can be used to dynamically estimate forest biomass and more generally estimate the carbon balance and could contribute to a better understanding of climate change factors.

Time-Delayed Bioreactor Model of Phenol and Cresol Mixture Degradation with Interaction Kinetics

This paper is devoted to a mathematical model for phenol and p-cresol mixture degradation in a continuously stirred bioreactor. The biomass specific growth rate is presented as sum kinetics with interaction parameters (SKIP). A discrete time delay is introduced and incorporated into the biomass growth response. These two aspects—the mutual influence of the two substrates and the natural biological time delay in the biomass growth rate—are new in the scientific literature concerning bioreactor (chemostat) models. The equilibrium points of the model are determined and their local asymptotic stability as well as the occurrence of local Hopf bifurcations are studied in dependence on the delay parameter. The existence and uniqueness of positive solutions are established, and the global stabilizability of the model dynamics is proved for certain values of the delay. Numerical simulations illustrate the global behavior of the model solutions as well as the transient oscillations as a result of the Hopf bifurcation. The performed theoretical analysis and computer simulations can be successfully used to better understand the biodegradation dynamics of the chemical compounds in the bioreactor and to predict and control the system behavior in real life conditions.

Autonomous clocks that regulate organelle biogenesis, cytoskeletal organization, and intracellular dynamics

How do cells perceive time? Do cells use temporal information to regulate the production/degradation of their enzymes, membranes, and organelles? Does controlling biological time influence cytoskeletal organization and cellular architecture in ways that confer evolutionary and physiological advantages? Potential answers to these fundamental questions of cell biology have historically revolved around the discussion of ‘master’ temporal programs, such as the principal cyclin-dependent kinase/cyclin cell division oscillator and the circadian clock. In this review, we provide an overview of the recent evidence supporting an emerging concept of ‘autonomous clocks,’ which under normal conditions can be entrained by the cell cycle and/or the circadian clock to run at their pace, but can also run independently to serve their functions if/when these major temporal programs are halted/abrupted. We begin the discussion by introducing recent developments in the study of such clocks and their roles at different scales and complexities. We then use current advances to elucidate the logic and molecular architecture of temporal networks that comprise autonomous clocks, providing important clues as to how these clocks may have evolved to run independently and, sometimes at the cost of redundancy, have strongly coupled to run under the full command of the cell cycle and/or the circadian clock. Next, we review a list of important recent findings that have shed new light onto potential hallmarks of autonomous clocks, suggestive of prospective theoretical and experimental approaches to further accelerate their discovery. Finally, we discuss their roles in health and disease, as well as possible therapeutic opportunities that targeting the autonomous clocks may offer.

Multiple metals influence distinct properties of the Arabidopsis circadian clock

Circadian rhythms coordinate endogenous events with external signals, and are essential to biological function. When environmental contaminants affect these rhythms, the organism may experience fitness consequences such as reduced growth or increased susceptibility to pathogens. In their natural environment plants may be exposed to a wide range of industrial and agricultural pollutants. Here, we investigate how the addition of various metal salts to the environment can impact plant-circadian rhythms, via the promoter:luciferase system. The consequences of these environmental changes were found to be varied and complex. Therefore, in addition to Fourier-based analyses, we apply novel wavelet-based spectral hypothesis testing and clustering methodologies to organize and understand the data. We are able to classify broad sets of responses to environmental contaminants, including pollutants which increase, or decrease, the period, or which induce a lack of precision or disrupt any meaningful periodicity. The methods are general, and may be applied to discover common responses and hidden structures within a wide range of biological time series data.

Experimental and computational analysis of calcium dynamics in 22q11.2 deletion model astrocytes

ABSTRACTIntracellular calcium dynamics in spontaneously active cells such as neurons or astrocytes is an information-rich readout of the physiological state of the cell. Methods for deriving mechanistic information from biological time courses, as well as for extracting cellular activity time courses algorithmically from imaging data, have significantly advanced in recent years but been mostly applied to neuronal data. At the same time, the role of astrocytes, a type of glial brain cells, in enabling cognition and in psychiatric diseases is beginning to come into focus. In the present work, we analyze calcium dynamics in astrocytes from a transgenic mouse model of 22q11.2 deletion syndrome (DiGeorge syndrome), an inborn condition associated with psychiatric disorders and other abnormalities of development. Methods of calcium imaging, computer vision, and Bayesian inference are applied to compare normal and deletion-bearing cells. Inference of highest-likelihood molecular kinetic characteristics from the intracellular calcium time courses pinpoints a significant change in the activity of the sarcoendoplasmic reticulum calcium ATPase (SERCA). Applying a SERCA inhibitor to the normal cells reproduces the differences detected in the deletion-bearing cells. We conclude that Bayesian kinetic inference is a useful tool for mechanistic dissection of complex cellular phenotypes in neuropsychiatric glia research. Its application can allow rapid, rigorous formulation of specific hypotheses concerning the underlying molecular mechanisms, prioritization of experiments testing such hypotheses, and, in the future, individualized functional molecular diagnostics.

The endogenous circadian system worsens asthma at night independent of sleep and other daily behavioral or environmental cycles

Asthma often worsens at night. To determine if the endogenous circadian system contributes to the nocturnal worsening of asthma, independent of sleep and other behavioral and environmental day/night cycles, we studied patients with asthma (without steroid use) over 3 wk in an ambulatory setting (with combined circadian, environmental, and behavioral effects) and across the circadian cycle in two complementary laboratory protocols performed in dim light, which separated circadian from environmental and behavioral effects: 1) a 38-h “constant routine,” with continuous wakefulness, constant posture, 2-hourly isocaloric snacks, and 2) a 196-h “forced desynchrony” incorporating seven identical recurring 28-h sleep/wake cycles with all behaviors evenly scheduled across the circadian cycle. Indices of pulmonary function varied across the day in the ambulatory setting, and both laboratory protocols revealed significant circadian rhythms, with lowest function during the biological night, around 4:00 AM, uncovering a nocturnal exacerbation of asthma usually unnoticed or hidden by the presence of sleep. We also discovered a circadian rhythm in symptom-based rescue bronchodilator use (β2-adrenergic agonist inhaler) whereby inhaler use was four times more likely during the circadian night than day. There were additive influences on asthma from the circadian system plus sleep and other behavioral or environmental effects. Individuals with the lowest average pulmonary function tended to have the largest daily circadian variations and the largest behavioral cycle effects on asthma. When sleep was modeled to occur at night, the summed circadian, behavioral/environmental cycle effects almost perfectly matched the ambulatory data. Thus, the circadian system contributes to the common nocturnal worsening of asthma, implying that internal biological time should be considered for optimal therapy.

Dynamic Modeling of Carnobacterium Maltaromaticum CNCM I-3298 Growth and Metabolite Production and Model-Based Process Optimization

Carnobacterium maltaromaticum is a species of lactic acid bacteria found in dairy, meat, and fish, with technological properties useful in food biopreservation and flavor development. In more recent years, it has also proven to be a key element of biological time–temperature integrators for tracking temperature variations experienced by perishable foods along the cold-chain. A dynamic model for the growth of C. maltaromaticum CNCM I-3298 and production of four metabolites (formic acid, acetic acid, lactic acid, and ethanol) from trehalose in batch culture was developed using the reaction scheme formalism. The dependence of the specific growth and production rates as well as the product inhibition parameters on the operating conditions were described by the response surface method. The parameters of the model were calibrated from eight experiments, covering a broad spectrum of culture conditions (temperatures between 20 and 37 °C; pH between 6.0 and 9.5). The model was validated against another set of eight independent experiments performed under different conditions selected in the same range. The model correctly predicted the growth kinetics of C. maltaromaticum CNCM I-3298 as well as the dynamics of the carbon source conversion, with a mean relative error of 10% for biomass and 14% for trehalose and the metabolites. The paper illustrates that the proposed model is a valuable tool for optimizing the culture of C. maltaromaticum CNCM I-3298 by determining operating conditions that favor the production of biomass or selected metabolites. Model-based optimization may thus reduce the number of experiments and substantially speed up the process development, with potential applications in food technology for producing starters and improving the yield and productivity of the fermentation of sugars into metabolites of industrial interest.