Ana Leonor Rivera

Physical methods for genetic plant transformation.

Production of transgenic plants is a routine process for many crop species. Transgenes are introduced into plants to confer novel traits such as improved nutritional qualities, tolerance to pollutants, resistance to pathogens and for studies of plant metabolism. Nowadays, it is possible to insert genes from plants evolutionary distant from the host plant, as well as from fungi, viruses, bacteria and even animals. Genetic transformation requires penetration of the transgene through the plant cell wall, facilitated by biological or physical methods. The objective of this article is to review the state of the art of the physical methods used for genetic plant transformation and to describe the basic physics behind them.

Physical methods for genetic transformation of fungi and yeast

The production of transgenic fungi is a routine process. Currently, it is possible to insert genes from other fungi, viruses, bacteria and even animals, albeit with low efficiency, into the genomes of a number of fungal species. Genetic transformation requires the penetration of the transgene through the fungal cell wall, a process that can be facilitated by biological or physical methods. Novel methodologies for the efficient introduction of specific genes and stronger promoters are needed to increase production levels. A possible solution to this problem is the recently discovered shock-wave-mediated transformation. The objective of this article is to review the state of the art of the physical methods used for genetic fungi transformation and to describe some of the basic physics and molecular biology behind them.

On the phase space description of quantum nonlinear dynamics

Abstract We analyze the difference between classical dynamics (geometric optics) and quantum dynamics (wave optics) by calculating the time history of the Wigner function for the simplest nonlinear Hamiltonians which are fourth-degree polynomials in p and q . It is shown that the moments of the Wigner function carry important information about the state of a system and can be used to distinguish between quasiclassical and quantum evolution.

Heart Rate and Systolic Blood Pressure Variability in the Time Domain in Patients with Recent and Long-Standing Diabetes Mellitus

Diabetes Mellitus (DM) affects the cardiovascular response of patients. To study this effect, interbeat intervals (IBI) and beat-to-beat systolic blood pressure (SBP) variability of patients during supine, standing and controlled breathing tests were analyzed in the time domain. Simultaneous noninvasive measurements of IBI and SBP for 30 recently diagnosed and 15 long-standing DM patients were compared with the results for 30 rigorously screened healthy subjects (control). A statistically significant distinction between control and diabetic subjects was provided by the standard deviation and the higher moments of the distributions (skewness, and kurtosis) with respect to the median. To compare IBI and SBP for different populations, we define a parameter, α, that combines the variability of the heart rate and the blood pressure, as the ratio of the radius of the moments for IBI and the same radius for SBP. As diabetes evolves, α decreases, standard deviation of the IBI detrended signal diminishes (heart rate signal becomes more “rigid”), skewness with respect to the median approaches zero (signal fluctuations gain symmetry), and kurtosis increases (fluctuations concentrate around the median). Diabetes produces not only a rigid heart rate, but also increases symmetry and has leptokurtic distributions. SBP time series exhibit the most variable behavior for recently diagnosed DM with platykurtic distributions. Under controlled breathing, SBP has symmetric distributions for DM patients, while control subjects have non-zero skewness. This may be due to a progressive decrease of parasympathetic and sympathetic activity to the heart and blood vessels as diabetes evolves.

Loss of Breathing Modulation of Heart Rate Variability in Patients with Recent and Long Standing Diabetes Mellitus Type II

Healthy subjects under rhythmic breathing have heart interbeat intervals with a respiratory band in the frequency domain that can be an index of vagal activity. Diabetes Mellitus Type II (DM) affects the autonomic nervous system of patients, thus it can be expected changes on the vagal activity. Here, the influence of DM on the breathing modulation of the heart rate is evaluated by analyzing in the frequency domain heart interbeat interval (IBI) records obtained from 30 recently diagnosed, 15 long standing DM patients, and 30 control subjects during standardized clinical tests of controlled breathing at 0.1 Hz, supine rest and standing upright. Fourier spectral analysis of IBI records quantifies heart rate variability in different regions: low-frequencies (LF, 0.04–0.15 Hz), high-frequencies (HF, 0.15–0.4 Hz), and a controlled breathing peak (RP, centered around 0.1 Hz). Two new parameters are introduced: the frequency radius rf (square root of the sum of LF and HF squared) and β (power of RP divided by the sum of LF and HF). As diabetes evolves, the controlled breathing peak loses power and shifts to smaller frequencies, indicating that heart rate modulation is slower in diabetic patients than in controls. In contrast to the traditional parameters LF, HF and LF/HF, which do not show significant differences between the three populations in neither of the clinical tests, the new parameters rf and β, distinguish between control and diabetic subjects in the case of controlled breathing. Sympathetic activity that is driven by the baroreceptor reflex associated with the 0.1 Hz breathing modulations is affected in DM patients. Diabetes produces not only a rigid heartbeat with less autonomic induced variability (rf diminishes), but also alters the coupling between breathing and heart rate (reduced β), due to a progressive decline of vagal and sympathetic activity.

Behavior of Early Warnings near the Critical Temperature in the Two-Dimensional Ising Model

Among the properties that are common to complex systems, the presence of critical thresholds in the dynamics of the system is one of the most important. Recently, there has been interest in the universalities that occur in the behavior of systems near critical points. These universal properties make it possible to estimate how far a system is from a critical threshold. Several early-warning signals have been reported in time series representing systems near catastrophic shifts. The proper understanding of these early-warnings may allow the prediction and perhaps control of these dramatic shifts in a wide variety of systems. In this paper we analyze this universal behavior for a system that is a paradigm of phase transitions, the Ising model. We study the behavior of the early-warning signals and the way the temporal correlations of the system increase when the system is near the critical point.

Multiscale adaptive analysis of circadian rhythms and intradaily variability: Application to actigraphy time series in acute insomnia subjects

Circadian rhythms become less dominant and less regular with chronic-degenerative disease, such that to accurately assess these pathological conditions it is important to quantify not only periodic characteristics but also more irregular aspects of the corresponding time series. Novel data-adaptive techniques, such as singular spectrum analysis (SSA), allow for the decomposition of experimental time series, in a model-free way, into a trend, quasiperiodic components and noise fluctuations. We compared SSA with the traditional techniques of cosinor analysis and intradaily variability using 1-week continuous actigraphy data in young adults with acute insomnia and healthy age-matched controls. The findings suggest a small but significant delay in circadian components in the subjects with acute insomnia, i.e. a larger acrophase, and alterations in the day-to-day variability of acrophase and amplitude. The power of the ultradian components follows a fractal 1/f power law for controls, whereas for those with acute insomnia this power law breaks down because of an increased variability at the 90min time scale, reminiscent of Kleitman’s basic rest-activity (BRAC) cycles. This suggests that for healthy sleepers attention and activity can be sustained at whatever time scale required by circumstances, whereas for those with acute insomnia this capacity may be impaired and these individuals need to rest or switch activities in order to stay focused. Traditional methods of circadian rhythm analysis are unable to detect the more subtle effects of day-to-day variability and ultradian rhythm fragmentation at the specific 90min time scale.

A physicist’s view of homeostasis: how time series of continuous monitoring reflect the function of physiological variables in regulatory mechanisms

OBJECTIVE Homeostasis is one of the key concepts of physiology and the basis to understand chronic-degenerative disease and human ageing, but is difficult to quantify in clinical practice. The variability of time series resulting from continuous and non-invasive physiological monitoring is conjectured to reflect the underlying homeostatic regulatory processes, but it is not clear why the variability of some variables such as heart rate gives a favourable health prognosis whereas the variability of other variables such as blood pressure implies an increased risk factor. The purpose of the present contribution is to quantify homeostasis using time-series analysis and to offer an explanation for the phenomenology of physiological time series. APPROACH Within the context of network physiology, which focusses on the interactions between various variables at multiple scales of time and space, it may be understood that different physiological variables may play distinct roles in their respective regulatory mechanisms. In the present contribution, we distinguish between regulated variables, such as blood pressure or core temperature, and physiological responses, such as heart rate and skin temperature. MAIN RESULTS We give evidence that in optimal conditions of youth and health the former are characterized by Gaussian statistics, low variability and represent the stability of the internal environment, whereas the latter are characterized by non-Gaussian distributions, large variability and reflect the adaptive capacity of the human body; in the adverse conditions of ageing and/or disease, adaptive capacity is lost and the variability of physiological responses is diminished, and as a consequence the stability of the internal environment is compromised and its variability increases. SIGNIFICANCE Time-series analysis allows one to quantify homeostasis in the optimal conditions of youth and health and the degradation of homeostasis or homeostenosis in the adverse conditions of ageing and/or disease, and may offer an alternative approach to diagnosis in clinical practice.

Homeostasis from a Time-Series Perspective: An Intuitive Interpretation of the Variability of Physiological Variables

Homeostasis implies the approximate constancy of specific regulated variables, where the independence of the internal from the external environment is ensured by adaptive physiological responses carried out by other so-called effector variables. The loss of homeostasis is the basis to understand chronic-degenerative disease and age-associated frailty. Technological advances presently allow to monitor a large variety of physiological variables in a non-invasive and continuous way and the statistics of the resulting physiological time series is thought to reflect the dynamics of the underlying control mechanisms. Recent years have seen an increased interest in the variability and/or complexity analysis of physiological time series with possible applications in pathophysiology. However, a general understanding is lacking for which variables variability is an indicator of good health (e.g., heart rate variability) and when on the contrary variability implies a risk factor (e.g., blood pressure variability). In the present contribution, we argue that in optimal conditions of youth and health regulated variables and effector variables necessarily exhibit very different statistics, with small and large variances, respectively, and that under adverse circumstances such as ageing and/or chronic-degenerative disease these statistics degenerate in opposite directions, i.e. towards an increased variability in the case of regulated variables and towards a decreased variability for effector variables. We demonstrate this hypothesis for a simple mathematical model of a thermostat, and for blood pressure and body temperature homeostasis for healthy controls and patients with metabolic disease, and suggest that this scheme may explain the general phenomenology of physiological variables of homeostatic regulatory mechanisms.

Looking for Biomarkers in Physiological Time Series

From the point of view of Complexity Sciences, health can be considered as the state of dynamical balance between robustness and adaptability to the changes in the environment. We consider that any human disease can be found in physiological time series by deviations from this point that reflects the loss of this balance. Thus, it is possible to find biomarkers based on non-invasive physiological parameters that characterize the critical healthy state, and could help as early warnings auxiliary for clinical diagnoses of different diseases. In this work, we present a time-domain analysis using the distribution moments, autocorrelation function, Poincare diagrams, and the spectral analysis of interbeat intervals and blood pressure time series for control subjects of different age and gender, and diabetic patients. As a preliminary result, a statistical significant difference was found between health and disease in the statistical moments of blood pressure and heart rate variability that can be proposed as biomarkers.