Clonal reproduction in fungi Taylor, John W; Christopher Hann-Soden; Sara Branco ...
Proceedings of the National Academy of Sciences - PNAS,
07/2015, Letnik:
112, Številka:
29
Journal Article
Recenzirano
Odprti dostop
Research over the past two decades shows that both recombination and clonality are likely to contribute to the reproduction of all fungi. This view of fungi is different from the historical and still ...commonly held view that a large fraction of fungi are exclusively clonal and that some fungi have been exclusively clonal for hundreds of millions of years. Here, we first will consider how these two historical views have changed. Then we will examine the impact on fungal research of the concept of restrained recombination Tibayrenc M, Ayala FJ (2012) Proc Natl Acad Sci USA 109 (48):E3305âE3313. Using animal and human pathogenic fungi, we examine extrinsic restraints on recombination associated with bottlenecks in genetic variation caused by geographic dispersal and extrinsic restraints caused by shifts in reproductive mode associated with either disease transmission or hybridization. Using species of the model yeast Saccharomyces and the model filamentous fungus Neurospora , we examine intrinsic restraints on recombination associated with mating systems that range from strictly clonal at one extreme to fully outbreeding at the other and those that lie between, including selfing and inbreeding. We also consider the effect of nomenclature on perception of reproductive mode and a means of comparing the relative impact of clonality and recombination on fungal populations. Last, we consider a recent hypothesis suggesting that fungi thought to have the most severe intrinsic constraints on recombination actually may have the fewest.
Abstract Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. ...Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose–insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, S I , the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to S I only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients ( N = 42,941 h in total) who received insulin while in the ICU and stayed for ≥72 h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% IQR 1.18, 6.41%. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7–12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose–insulin models render this article a mini-review of the state of model-based TGC in critical care.
Abstract Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial ...successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts. This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols. A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000 h of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric ( S I ). Variation in S I provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra- and inter-patient variability in insulin resistance (1/ S I ) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration.
Hyperglycaemia in critically ill patients increases the risk of further complications and mortality. This paper introduces a model capable of capturing the essential glucose and insulin kinetics in ...patients from retrospective data gathered in an intensive care unit (ICU). The model uses two time-varying patient specific parameters for glucose effectiveness and insulin sensitivity. The model is mathematically reformulated in terms of integrals to enable a novel method for identification of patient specific parameters. The method was tested on long-term blood glucose recordings from 17 ICU patients, producing 4% average error, which is within the sensor error. One-hour forward predictions of blood glucose data proved acceptable with an error of 2–11%. All identified parameter values were within reported physiological ranges. The parameter identification method is more accurate and significantly faster computationally than commonly used non-linear, non-convex methods. These results verify the model's ability to capture long-term observed glucose–insulin dynamics in hyperglycemic ICU patients, as well as the fitting method developed. Applications of the model and parameter identification method for automated control of blood glucose and medical decision support are discussed.
Fibrillin-rich microfibrils (FRMs) constitute integral components of the dermal elastic fibre network with a distinctive ultrastructural ‘beads-on-a-string’ appearance that can be visualised using ...atomic force microscopy and characterised by measurement of their length and inter-bead periodicity. Their deposition within the dermis in photoprotected skin appears to be contingent on skin ethnicity, and influences the ultrastructure of papillary – but not reticular – dermal FRMs. Truncation and depletion of FRMs at the dermal-epidermal junction of skin occurs early in photoageing in people with lightly pigmented skin; a process of accelerated skin ageing that arises due to chronic sun exposure. Accumulation of ultraviolet radiation (UVR)-induced damage, either by the action of enzymes, oxidation or direct photon absorption, results in FRM remodelling and changes to ultrastructure. In the current study, the direct effect of UVR exposure on FRM ultrastructure was assayed by isolating FRMs from the papillary and reticular dermis of photoprotected buttock skin of individuals of either black African or white Northern European ancestry and exposing them to solar-simulated radiation (SSR). Exposure to SSR resulted in significant reduction in nter-bead periodicity for reticular dermis-derived FRMs across both cohorts. In contrast, papillary dermal FRMs exhibited significantly increased inter-bead periodicity, with the magnitude of damage greater for African FRMs, as compared to Northern European FRMs. Our data suggest that FRMs of the dermis should be considered as two distinct populations that differentially accrue damage in response to SSR. Furthermore, papillary dermal FRMs derived from black African subjects show greater change following UVR challenge, when extracted from skin. Future studies should focus on understanding the consequences of UVR exposure
in vivo
, regardless of skin ethnicity, on the molecular composition of FRMs and how this UVR-induced remodelling may affect the role FRMs play in skin homeostasis.
The dermal elastic fibre network is the primary effector of skin elasticity, enabling it to extend and recoil many times over the lifetime of the individual. Fibrillin‐rich microfibrils (FRMs) ...constitute integral components of the elastic fibre network, with their distribution showing differential deposition in the papillary dermis across individuals of diverse skin ethnicity. Despite these differential findings in histological presentation, it is not known if skin ethnicity influences FRM ultrastructure. FRMs are evolutionarily highly conserved from jellyfish to man and, regardless of tissue type or species, isolated FRMs have a characteristic ‘beads‐on‐a‐string’ ultrastructural appearance, with an average inter‐bead distance (or periodicity) of 56 nm. Here, skin biopsies were obtained from the photoprotected buttock of healthy volunteers (18‐27 years; African: n = 5; European: n = 5), and FRMs were isolated from the superficial papillary dermis and deeper reticular dermis and imaged by atomic force microscopy. In the reticular dermis, there was no significant difference in FRM ultrastructure between European and African participants. In contrast, in the more superficial papillary dermis, inter‐bead periodicity was significantly larger for FRMs extracted from European participants than from African participants by 2.20 nm (p < .001). We next assessed whether these differences in FRM ultrastructure were present during early postnatal development by characterizing FRMs from full‐thickness neonatal foreskin. Analysis of FRM periodicity identified no significant difference between neonatal cohorts (p = .865). These data suggest that at birth, FRMs are developmentally invariant. However, in adults of diverse skin ethnicity, there is a deviation in ultrastructure for the papillary dermal FRMs that may be acquired during the passage of time from child to adulthood. Understanding the mechanism by which this difference in papillary dermal FRMs arises warrants further study.
Experimental isolation of FRMs from the papillary and reticular dermis of adult buttock skin was performed by cryosectioning bisected 6 mm skin biopsies en face. Atomic force microscopy images of FRM isolated from human papillary and reticular dermis reveal the “beads‐on‐a‐string” morphology. In the reticular dermis there was no significant difference in FRM ultrastructure between European and African participants. In contrast, in the more superficial papillary dermis, inter‐bead periodicity was significantly larger for FRMs extracted from European participants than from African participants.
Abstract Targeted, tight model-based glycemic control in critical care patients that can reduce mortality 18–45% is enabled by prediction of insulin sensitivity, SI . However, this parameter can vary ...significantly over a given hour in the critically ill as their condition evolves. A stochastic model of SI variability is constructed using data from 165 critical care patients. Given SI for an hour, the stochastic model returns the probability density function of SI for the next hour. Consequently, the glycemic distribution following a known intervention can be derived, enabling pre-determined likelihoods of the result and more accurate control. Cross validation of the SI variability model shows that 86.6% of the blood glucose measurements are within the 0.90 probability interval, and 54.0% are within the interquartile interval. “Virtual Patients” with SI behaving to the overall SI variability model achieved similar predictive performance in simulated trials (86.8% and 45.7%). Finally, adaptive control method incorporating SI variability is shown to produce improved glycemic control in simulated trials compared to current clinical results. The validated stochastic model and methods provide a platform for developing advanced glycemic control methods addressing critical care variability.
Abstract A majority of patients admitted to the Intensive Care Unit (ICU) require some form of respiratory support. In the case of Acute Respiratory Distress Syndrome (ARDS), the patient often ...requires full intervention from a mechanical ventilator. ARDS is also associated with mortality rate as high as 70%. Despite many recent studies on ventilator treatment of the disease, there are no well established methods to determine the optimal Positive End-Expiratory Pressure (PEEP) or other critical ventilator settings for individual patients. A model of fundamental lung mechanics is developed based on capturing the recruitment status of lung units. The main objective of this research is to develop a minimal model that is clinically effective in determining PEEP. The model was identified for a variety of different ventilator settings using clinical data. The fitting error was between 0.1% and 4% over the inflation limb and between 0.3% and 13% over the deflation limb at different PEEP settings. The model produces good correlation with clinical data, and is clinically applicable due to the minimal number of patient specific parameters to identify. The ability to use this identified patient specific model to optimize ventilator management is demonstrated by its ability to predict the patient specific response of PEEP changes before clinically applying them. Predictions of recruited lung volume change with change in PEEP have a median absolute error of 1.87% (IQR: 0.93–4.80%; 90% CI: 0.16–11.98%) for inflation and a median of 5.76% (IQR: 2.71–10.50%; 90% CI: 0.43–17.04%) for deflation, across all data sets and PEEP values ( N = 34 predictions). This minimal model thus provides a clinically useful and relatively simple platform for continuous patient specific monitoring of lung unit recruitment for a patient.
Abstract A previously validated mathematical model of the cardiovascular system (CVS) is made subject-specific using an iterative, proportional gain-based identification method. Prior works utilised ...a complete set of experimentally measured data that is not clinically typical or applicable. In this paper, parameters are identified using proportional gain-based control and a minimal, clinically available set of measurements. The new method makes use of several intermediary steps through identification of smaller compartmental models of CVS to reduce the number of parameters identified simultaneously and increase the convergence stability of the method. This new, clinically relevant, minimal measurement approach is validated using a porcine model of acute pulmonary embolism (APE). Trials were performed on five pigs, each inserted with three autologous blood clots of decreasing size over a period of four to five hours. All experiments were reviewed and approved by the Ethics Committee of the Medical Faculty at the University of Liege, Belgium. Continuous aortic and pulmonary artery pressures ( P ao , P pa ) were measured along with left and right ventricle pressure and volume waveforms. Subject-specific CVS models were identified from global end diastolic volume (GEDV), stroke volume (SV), P ao , and P pa measurements, with the mean volumes and maximum pressures of the left and right ventricles used to verify the accuracy of the fitted models. The inputs (GEDV, SV, P ao , P pa ) used in the identification process were matched by the CVS model to errors <0.5%. Prediction of the mean ventricular volumes and maximum ventricular pressures not used to fit the model compared experimental measurements to median absolute errors of 4.3% and 4.4%, which are equivalent to the measurement errors of currently used monitoring devices in the ICU (∼5–10%). These results validate the potential for implementing this approach in the intensive care unit.
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