Fossil insect eyes shed light on trilobite optics and the arthropod pigment screen.

Fossilized eyes permit inferences of the visual capacity of extinct arthropods1-3. However, structural and/or chemical modifications as a result of taphonomic and diagenetic processes can alter the original features, thereby necessitating comparisons with modern species. Here we report the detailed molecular composition and microanatomy of the eyes of 54-million-year-old crane-flies, which together provide a proxy for the interpretation of optical systems in some other ancient arthropods. These well-preserved visual organs comprise calcified corneal lenses that are separated by intervening spaces containing eumelanin pigment. We also show that eumelanin is present in the facet walls of living crane-flies, in which it forms the outermost ommatidial pigment shield in compound eyes incorporating a chitinous cornea. To our knowledge, this is the first record of melanic screening pigments in arthropods, and reveals a fossilization mode in insect eyes that involves a decay-resistant biochrome coupled with early diagenetic mineralization of the ommatidial lenses. The demonstrable secondary calcification of lens cuticle that was initially chitinous has implications for the proposed calcitic corneas of trilobites, which we posit are artefacts of preservation rather than a product of in vivo biomineralization4-7. Although trilobite eyes might have been partly mineralized for mechanical strength, a (more likely) organic composition would have enhanced function via gradient-index optics and increased control of lens shape.

Assimilating flow and level data into an urban drainage surrogate model for forecasting flows and overflows.

It is crucial to be able to forecast flows and overflows in urban drainage systems to build good and effective real-time control and warning systems. Due to computational constraints, it may often be unfeasible to employ detailed 1D hydrodynamic models for real-time purposes, and surrogate models can be used instead. In rural hydrology, forecast models are usually built or calibrated using long historical time series of, for example, flow or level observations, but such series are typically not available for the ever-changing urban drainage systems. In the current study, we therefore used a fast, reservoir-based surrogate forecast model constructed from a 1D hydrodynamic urban drainage model. Thus, we did not rely directly on historical time series data. Forecast models should preferably be able to update their internal states based on observations to ensure the best initial conditions for each forecast. We therefore used the Ensemble Kalman filter to update the surrogate model before each forecast. Water level or flow observations were assimilated into the model either directly, or indirectly using rating curves. The model forecasts were validated against observed flows and overflows. The results showed that model updating improved the forecasts up to 2 h ahead, but also that updating using water level observations resulted in better flow forecasts than assimilation based on flow data. Furthermore, updating with water level observations was insensitive to changes in the noise formulation used for the Ensemble Kalman filter, meaning that the method is suitable for operational settings where there is often little time and data for fine-tuning.

Towards model predictive control: online predictions of ammonium and nitrate removal by using a stochastic ASM.

Online model predictive control (MPC) of water resource recovery facilities (WRRFs) requires simple and fast models to improve the operation of energy-demanding processes, such as aeration for nitrogen removal. Selected elements of the activated sludge model number 1 modelling framework for ammonium and nitrate removal were included in discretely observed stochastic differential equations in which online data are assimilated to update the model states. This allows us to produce model-based predictions including uncertainty in real time while it also reduces the number of parameters compared to many detailed models. It introduces only a small residual error when used to predict ammonium and nitrate concentrations in a small recirculating WRRF facility. The error when predicting 2 min ahead corresponds to the uncertainty from the sensors. When predicting 24 hours ahead the mean relative residual error increases to ∼10% and ∼20% for ammonium and nitrate concentrations respectively. Consequently this is considered a first step towards stochastic MPC of the aeration process. Ultimately this can reduce electricity demand and cost for water resource recovery, allowing the prioritization of aeration during periods of cheaper electricity.

Evaluation of pharmacokinetic model designs for subcutaneous infusion of insulin aspart.

Effective mathematical modelling of continuous subcutaneous infusion pharmacokinetics should aid understanding and control in insulin therapy. Thorough analysis of candidate model performance is important for selecting the appropriate models. Eight candidate models for insulin pharmacokinetics included a range of modelled behaviours, parameters and complexity. The models were compared using clinical data from subjects with type 1 diabetes with continuous subcutaneous insulin infusion. Performance of the models was compared through several analyses: R2 for goodness of fit; the Akaike Information Criterion; a bootstrap analysis for practical identifiability; a simulation exercise for predictability. The simplest model fit poorly to the data (R2 = 0.53), had the highest Akaike score, and worst prediction. Goodness of fit improved with increasing model complexity (R2 = 0.85-0.92) but Akaike scores were similar for these models. Complexity increased practical non-identifiability, where small changes in the dataset caused large variation (CV > 10%) in identified parameters in the most complex models. Best prediction was achieved in a relatively simple model. Some model complexity was necessary to achieve good data fit but further complexity introduced practical non-identifiability and worsened prediction capability. The best model used two linear subcutaneous compartments, an interstitial and plasma compartment, and two identified variables for interstitial clearance and subcutaneous transfer rate. This model had optimal performance trade-off with reasonable fit (R2 = 0.85) and parameterisation, and best prediction and practical identifiability (CV < 2%).

State-space adjustment of radar rainfall and skill score evaluation of stochastic volume forecasts in urban drainage systems.

Merging of radar rainfall data with rain gauge measurements is a common approach to overcome problems in deriving rain intensities from radar measurements. We extend an existing approach for adjustment of C-band radar data using state-space models and use the resulting rainfall intensities as input for forecasting outflow from two catchments in the Copenhagen area. Stochastic grey-box models are applied to create the runoff forecasts, providing us with not only a point forecast but also a quantification of the forecast uncertainty. Evaluating the results, we can show that using the adjusted radar data improves runoff forecasts compared with using the original radar data and that rain gauge measurements as forecast input are also outperformed. Combining the data merging approach with short-term rainfall forecasting algorithms may result in further improved runoff forecasts that can be used in real time control.

Preoperative staging of lung cancer with combined PET-CT.

BACKGROUND: Fast and accurate staging is essential for choosing treatment for non-small-cell lung cancer (NSCLC). The purpose of this randomized study was to evaluate the clinical effect of combined positron-emission tomography and computed tomography (PET-CT) on preoperative staging of NSCLC. METHODS: We randomly assigned patients who were referred for preoperative staging of NSCLC to either conventional staging plus PET-CT or conventional staging alone. Patients were followed until death or for at least 12 months. The primary end point was the number of futile thoracotomies, defined as any one of the following: a thoracotomy with the finding of pathologically confirmed mediastinal lymph-node involvement (stage IIIA [N2]), stage IIIB or stage IV disease, or a benign lung lesion; an exploratory thoracotomy; or a thoracotomy in a patient who had recurrent disease or death from any cause within 1 year after randomization. RESULTS: From January 2002 through February 2007, we randomly assigned 98 patients to the PET-CT group and 91 to the conventional-staging group. Mediastinoscopy was performed in 94% of the patients. After PET-CT, 38 patients were classified as having inoperable NSCLC, and after conventional staging, 18 patients were classified thus. Sixty patients in the PET-CT group and 73 in the conventional-staging group underwent thoracotomy (P=0.004). Among these thoracotomies, 21 in the PET-CT group and 38 in the conventional-staging group were futile (P=0.05). The number of justified thoracotomies and survival were similar in the two groups. CONCLUSIONS: The use of PET-CT for preoperative staging of NSCLC reduced both the total number of thoracotomies and the number of futile thoracotomies but did not affect overall mortality. (ClinicalTrials.gov number, NCT00867412.)

Meta-barcoding of 'dirt' DNA from soil reflects vertebrate biodiversity.

DNA molecules originating from animals and plants can be retrieved directly from sediments and have been used for reconstructing both contemporary and past ecosystems. However, the extent to which such 'dirt' DNA reflects taxonomic richness and structural diversity remains contentious. Here, we couple second generation high-throughput sequencing with 16S mitochondrial DNA (mtDNA) meta-barcoding, to explore the accuracy and sensitivity of 'dirt' DNA as an indicator of vertebrate diversity, from soil sampled at safari parks, zoological gardens and farms with known species compositions. PCR amplification was successful in the full pH range of the investigated soils (6.2 ± 0.2 to 8.3 ± 0.2), but inhibition was detected in extracts from soil of high organic content. DNA movement (leaching) through strata was evident in some sporadic cases and is influenced by soil texture and structure. We find that DNA from the soil surface reflects overall taxonomic richness and relative biomass of individual species. However, one species that was recently introduced was not detected. Furthermore, animal behaviour was shown to influence DNA deposition rates. The approach potentially provides a quick methodological alternative to classical ecological surveys of biodiversity, and most reliable results are obtained with spatial sample replicates, while relative amounts of soil processed per site is of less importance.

Development of a restricted state space stochastic differential equation model for bacterial growth in rich media.

In the present study, bacterial growth in a rich media is analysed in a Stochastic Differential Equation (SDE) framework. It is demonstrated that the SDE formulation and smoothened state estimates provide a systematic framework for data driven model improvements, using random walk hidden states. Bacterial growth is limited by the available substrate and the inclusion of diffusion must obey this natural restriction. By inclusion of a modified logistic diffusion term it is possible to introduce a diffusion term flexible enough to capture both the growth phase and the stationary phase, while concentration is restricted to the natural state space (substrate and bacteria non-negative). The case considered is the growth of Salmonella and Enterococcus in a rich media. It is found that a hidden state is necessary to capture the lag phase of growth, and that a flexible logistic diffusion term is needed to capture the random behaviour of the growth model. Further, it is concluded that the Monod effect is not needed to capture the dynamics of bacterial growth in the data presented.

Use of ESI-MS to determine reaction pathway for hydrogen sulphide scavenging with 1,3,5-tri-(2-hydroxyethyl)-hexahydro-s-triazine.

To study the reaction between hydrogen sulphide and 1,3,5-tri-(2-hydroxyethyl)-hexahydro-s-triazine, which is an often used hydrogen sulphide scavenger, electro spray ionisation mass spectrometry (ESI-MS) was used. The investigation was carried out in positive mode, and tandem mass spectrometry was used to investigate the nature of unknown peaks in the mass spectra. The reaction was found to proceed as expected from theory with the triazine reacting with hydrogen sulphide to form the corresponding thiadiazine. This species subsequently reacted with a second hydrogen sulphide molecule to form the dithiazine species, hereby confirming previously obtained results and showing the ability of the ESI-MS method for studying the scavenging reaction. The final theoretical product s-trithiane was not detected. Furthermore, fragmentation products of thiadiazine and dithiazine were detected in the solution, and possible pathways and structures were suggested to describe the observed fragments. In these, thiadiazine fragmented to 2-(methylidene amino)-ethanol and 2-(1,3-thiazetidin-3-yl)-ethanol and N-(2-hydroxyethyl)-N-(sulfanylmethyl)-ethaniminium, which underwent a further fragmentation to N-methyl-N-(2-oxoethyl)-methaniminium. Dithiazine fragmented to N-methyl-N-(2-oxoethyl)-methaniminium as well. The by-product from this reaction is methanedithiol, which was not detected due to its low polarity.

Physical-stochastic continuous-time identification of a forced Duffing oscillator.

Despite the simplicity of the Duffing oscillator, its dynamical behaviour is extremely rich. Hence, the Duffing equations are used to describe the dynamic behaviour of many real-world nonlinear systems for a wide range of frequency bands and amplitude of the excitation signal in basic sciences and engineering. For example, the Duffing oscillator has been successfully used to model a variety of physical processes such as stiffening springs, beam buckling, nonlinear electronic circuits, superconducting Josephson parametric amplifiers, and ionisation waves in plasmas etc. Therefore, the identification of the Duffing oscillator model/parameters directly from the measured input-output data is a topic of active research in many scientific fields In this paper, we use the concept of stochastic differential equations (SDEs) to identify a model of the Duffing oscillator. SDE-based grey-box models allow us to capture the underlying mathematical structure describing the physics of the system (e.g. the original Duffing equations) using the drift term and explicitly handling of model uncertainty (or the process noise) using the diffusion term whereas the measurement uncertainty is modelled using the measurement noise term respectively. In this paper, we propose a slight variation of the maximum likelihood estimation framework used for the identification of SDEs based grey-box models yielding improved performance for long-term predictions. The proposed framework is combined with an iterative residual analysis to develop a grey-box model of the forced Duffing oscillator. The benchmark data from the so-called Brussels "Silverbox system", which is an electrical circuit mimicking the forced Duffing oscillator dynamics is used for the identification purpose. Finally, the identified model performance (the simulation errors) is compared with the existing results available in the literature.

An ancestral hard-shelled sea turtle with a mosaic of soft skin and scutes.

The transition from terrestrial to marine environments by secondarily aquatic tetrapods necessitates a suite of adaptive changes associated with life in the sea, e.g., the scaleless skin in adult individuals of the extant leatherback turtle. A partial, yet exceptionally preserved hard-shelled (Pan-Cheloniidae) sea turtle with extensive soft-tissue remains, including epidermal scutes and a virtually complete flipper outline, was recently recovered from the Eocene Fur Formation of Denmark. Examination of the fossilized limb tissue revealed an originally soft, wrinkly skin devoid of scales, together with organic residues that contain remnant eumelanin pigment and inferred epidermal transformation products. Notably, this stem cheloniid-unlike its scaly living descendants-combined scaleless limbs with a bony carapace covered in scutes. Our findings show that the adaptive transition to neritic waters by the ancestral pan-chelonioids was more complex than hitherto appreciated, and included at least one evolutionary lineage with a mosaic of integumental features not seen in any living turtle.

Mechanism-based population modelling for assessment of L-cell function based on total GLP-1 response following an oral glucose tolerance test.

GLP-1 is an insulinotropic hormone that synergistically with glucose gives rise to an increased insulin response. Its secretion is increased following a meal and it is thus of interest to describe the secretion of this hormone following an oral glucose tolerance test (OGTT). The aim of this study was to build a mechanism-based population model that describes the time course of total GLP-1 and provides indices for capability of secretion in each subject. The goal was thus to model the secretion of GLP-1, and not its effect on insulin production. Single 75 g doses of glucose were administered orally to a mixed group of subjects ranging from healthy volunteers to patients with type 2 diabetes (T2D). Glucose, insulin, and total GLP-1 concentrations were measured. Prior population data analysis on measurements of glucose and insulin were performed in order to estimate the glucose absorption rate. The individual estimates of absorption rate constants were used in the model for GLP-1 secretion. Estimation of parameters was performed using the FOCE method with interaction implemented in NONMEM VI. The final transit/indirect-response model obtained for GLP-1 production following an OGTT included two stimulation components (fast, slow) for the zero-order production rate. The fast stimulation was estimated to be faster than the glucose absorption rate, supporting the presence of a proximal-distal loop for fast secretion from L: -cells. The fast component (st3) = 8.64·10⁻5 [mg⁻¹]) was estimated to peak around 25 min after glucose ingestion, whereas the slower component (st4 = 26.2·10⁻5 [mg⁻¹]) was estimated to peak around 100 min. Elimination of total GLP-1 was characterised by a first-order loss. The individual values of the early phase GLP-1 secretion parameter (st3) were correlated (r = 0.52) with the AUC(0-60 min.) for GLP-1. A mechanistic population model was successfully developed to describe total GLP-1 concentrations over time observed after an OGTT. The model provides indices related to different mechanisms of subject abilities to secrete GLP-1. The model provides a good basis to study influence of different demographic factors on these components, presented mainly by indices of the fast- and slow phases of GLP-1 response.

Predictive performance for population models using stochastic differential equations applied on data from an oral glucose tolerance test.

Several articles have investigated stochastic differential equations (SDEs) in PK/PD models, but few have quantitatively investigated the benefits to predictive performance of models based on real data. Estimation of first phase insulin secretion which reflects beta-cell function using models of the OGTT is a difficult problem in need of further investigation. The present work aimed at investigating the power of SDEs to predict the first phase insulin secretion (AIR (0-8)) in the IVGTT based on parameters obtained from the minimal model of the OGTT, published by Breda et al. (Diabetes 50(1):150-158, 2001). In total 174 subjects underwent both an OGTT and a tolbutamide modified IVGTT. Estimation of parameters in the oral minimal model (OMM) was performed using the FOCE-method in NONMEM VI on insulin and C-peptide measurements. The suggested SDE models were based on a continuous AR(1) process, i.e. the Ornstein-Uhlenbeck process, and the extended Kalman filter was implemented in order to estimate the parameters of the models. Inclusion of the Ornstein-Uhlenbeck (OU) process caused improved description of the variation in the data as measured by the autocorrelation function (ACF) of one-step prediction errors. A main result was that application of SDE models improved the correlation between the individual first phase indexes obtained from OGTT and AIR (0-8) (r = 0.36 to r = 0.49 and r = 0.32 to r = 0.47 with C-peptide and insulin measurements, respectively). In addition to the increased correlation also the properties of the indexes obtained using the SDE models more correctly assessed the properties of the first phase indexes obtained from the IVGTT. In general it is concluded that the presented SDE approach not only caused autocorrelation of errors to decrease but also improved estimation of clinical measures obtained from the glucose tolerance tests. Since, the estimation time of extended models was not heavily increased compared to basic models, the applied method is concluded to have high relevance not only in theory but also in practice.

A novel hybrid concept for implementation in drinking water treatment targets micropollutant removal by combining membrane filtration with biodegradation.

Groundwater extracted for drinking water production is commonly treated by aeration and sand filtration. However, this simple treatment is typically unable to remove pesticide residues. As a solution, bioaugmentation of sand filter units (i.e., the addition of specific degrader strains) has been proposed as an alternative "green" technology for targeted pesticide removal. However, the introduced degraders are challenged by (i) micropollutant levels of target residue, (ii) the oligotrophic environment and (iii) competition and predation by the native microorganisms, leading to loss of population and degradation potential. To overcome these challenges, we propose the introduction of a novel hybrid treatment step to the overall treatment process in which reverse osmosis filtration and biodegradation are combined to remove a target micropollutant. Here, the reverse osmosis produces a concentrated retentate that will act as a feed to a dedicated biofilter unit, intended to promote biodegradation potential and stability of an introduced degrader. Subsequently, the purified retentate will be re-mixed with the permeate from reverse osmosis, for re-mineralization and downstream consumption. In our study, we investigated the effect of reverse osmosis retentates on the degradation potential of an introduced degrader. This paper provides the first promising results of this hybrid concept using the 2,6-dichlorobenzamide (BAM)-degrading bacteria Aminobacter sp. MSH1 in batch experiments, spiked with radiolabeled BAM. The results showed an increased degradation potential of MSH1 in retentate waters versus untreated water. Colony-forming units and qPCR showed a stable MSH1 population, despite higher concentrations of salts and metals, and increased growth of native bacteria.

Relationship between Optimum Mini-doses of Glucagon and Insulin Levels when Treating Mild Hypoglycaemia in Patients with Type 1 Diabetes - A Simulation Study.

Hypoglycaemia remains the main limiting factor in type 1 diabetes management. We developed an insulin-dependent glucagon dosing regimen for treatment of mild hypoglycaemia based on simulations. A validated glucose-insulin-glucagon model was used to describe seven virtual patients with insulin pump-treated type 1 diabetes. In each simulation, one of ten different and individualized subcutaneous insulin boluses was administered to decrease plasma glucose (PG) from 7.0 to ≤3.9 mmol/l. Insulin levels were estimated as ratio of actual to baseline serum insulin concentration (se/ba-insulin), insulin on board (IOB) or percentage of IOB to total daily insulin dose (IOB/TDD). Insulin bolus sizes were chosen to provide pre-defined insulin levels when PG reached 3.9 mmol/l, where one of 17 subcutaneous glucagon boluses was administered. Optimum glucagon bolus to treat mild hypoglycaemia at varying insulin levels was the lowest dose that in most patients caused PG peak between 5.0 and 10.0 mmol/l and sustained PG ≥ 3.9 mmol/l for 2 hr after the bolus. PG response to glucagon declined with increasing insulin levels. The glucagon dose to optimally treat mild hypoglycaemia depended exponentially on insulin levels, regardless of how insulin was estimated. A 125-µg glucagon dose was needed to optimally treat mild hypoglycaemia when insulin levels were equal to baseline levels. In contrast, glucagon doses >500 µg were needed when se/ba-insulin >2.5, IOB >2.0 U or IOB/TDD >6%. Although the proposed model-based glucagon regimen needs confirmation in clinical trials, this is the first attempt to develop an insulin-dependent glucagon dosing regimen for treatment of insulin-induced mild hypoglycaemia in patients with type 1 diabetes.

Evaluation of direct membrane filtration and direct forward osmosis as concepts for compact and energy-positive municipal wastewater treatment.

Municipal wastewater treatment commonly involves mechanical, biological and chemical treatment steps to protect humans and the environment from adverse effects. Membrane technology has gained increasing attention as an alternative to conventional wastewater treatment due to increased urbanization. Among the available membrane technologies, microfiltration (MF) and forward osmosis (FO) have been selected for this study due to their specific characteristics, such as compactness and efficient removal of particles. In this study, two treatment concepts were evaluated with regard to their specific electricity, energy and area demands. Both concepts would fulfil the Swedish discharge demands for small- and medium-sized wastewater treatment plants at full scale: (1) direct MF and (2) direct FO with seawater as the draw solution. The framework of this study is based on a combination of data obtained from bench- and pilot-scale experiments applying direct MF and FO, respectively. Additionally, available complementary data from a Swedish full-scale wastewater treatment plant and the literature were used to evaluate the concepts in depth. The results of this study indicate that both concepts are net positive with respect to electricity and energy, as more biogas can be produced compared to that using conventional wastewater treatment. Furthermore, the specific area demand is significantly reduced. This study demonstrates that municipal wastewater could be treated in a more energy- and area-efficient manner with techniques that are already commercially available and with future membrane technology.

Severe hypokalaemia as a cause of acute transient quadriparesis.

Hypokalaemic paralysis covers a heterogeneous group of disorders caused either by an enhanced shift of potassium into the cells or following a significant renal or gastrointestinal loss of potassium. We present the case of a 48-year-old Caucasian man with paralysis of both upper and lower extremities. ECG showed sinus rhythm and characteristic changes of hypokalaemia with depression of the ST segment, prolonged QTc interval of 581ms and U waves seen as a small positive deflection at the T wave in the middle precordial leads. We suspected the cause of hypokalaemia leading to paralysis to be due to administration of high doses of furosemide without oral potassium supplementation coupled with regular use of insulin. Initial therapy included both oral and intravenous potassium replacement and close monitoring of cardiac rhythm and serum potassium levels. Twenty-four hours after admission, the potassium level had normalised and the patient slowly recovered and gained strength. The patient was discharged after 1 week of careful follow-up and did not experience any serious degree of rebound hyperkalaemia. At the time of discharge, all laboratory tests were normal and ECG revealed a normal sinus rhythm and normal QTc intervals.

An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes.

BACKGROUND: Bolus calculators help patients with type 1 diabetes to mitigate the effect of meals on their blood glucose by administering a large amount of insulin at mealtime. Intraindividual changes in patients physiology and nonlinearity in insulin-glucose dynamics pose a challenge to the accuracy of such calculators. METHOD: We propose a method based on a continuous-discrete unscented Kalman filter to continuously track the postprandial glucose dynamics and the insulin sensitivity. We augment the Medtronic Virtual Patient (MVP) model to simulate noise-corrupted data from a continuous glucose monitor (CGM). The basal rate is determined by calculating the steady state of the model and is adjusted once a day before breakfast. The bolus size is determined by optimizing the postprandial glucose values based on an estimate of the insulin sensitivity and states, as well as the announced meal size. Following meal announcements, the meal compartment and the meal time constant are estimated, otherwise insulin sensitivity is estimated. RESULTS: We compare the performance of a conventional linear bolus calculator with the proposed bolus calculator. The proposed basal-bolus calculator significantly improves the time spent in glucose target ( P < .01) compared to the conventional bolus calculator. CONCLUSION: An adaptive nonlinear basal-bolus calculator can efficiently compensate for physiological changes. Further clinical studies will be needed to validate the results.

Biochemistry and adaptive colouration of an exceptionally preserved juvenile fossil sea turtle.

The holotype (MHM-K2) of the Eocene cheloniine Tasbacka danica is arguably one of the best preserved juvenile fossil sea turtles on record. Notwithstanding compactional flattening, the specimen is virtually intact, comprising a fully articulated skeleton exposed in dorsal view. MHM-K2 also preserves, with great fidelity, soft tissue traces visible as a sharply delineated carbon film around the bones and marginal scutes along the edge of the carapace. Here we show that the extraordinary preservation of the type of T. danica goes beyond gross morphology to include ultrastructural details and labile molecular components of the once-living animal. Haemoglobin-derived compounds, eumelanic pigments and proteinaceous materials retaining the immunological characteristics of sauropsid-specific ß-keratin and tropomyosin were detected in tissues containing remnant melanosomes and decayed keratin plates. The preserved organics represent condensed remains of the cornified epidermis and, likely also, deeper anatomical features, and provide direct chemical evidence that adaptive melanism - a biological means used by extant sea turtle hatchlings to elevate metabolic and growth rates - had evolved 54 million years ago.

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes.

BACKGROUND: Currently, no consensus exists on a model describing endogenous glucose production (EGP) as a function of glucagon concentrations. Reliable simulations to determine the glucagon dose preventing or treating hypoglycemia or to tune a dual-hormone artificial pancreas control algorithm need a validated glucoregulatory model including the effect of glucagon. METHODS: Eight type 1 diabetes (T1D) patients each received a subcutaneous (SC) bolus of insulin on four study days to induce mild hypoglycemia followed by a SC bolus of saline or 100, 200, or 300 µg of glucagon. Blood samples were analyzed for concentrations of glucagon, insulin, and glucose. We fitted pharmacokinetic (PK) models to insulin and glucagon data using maximum likelihood and maximum a posteriori estimation methods. Similarly, we fitted a pharmacodynamic (PD) model to glucose data. The PD model included multiplicative effects of insulin and glucagon on EGP. Bias and precision of PD model test fits were assessed by mean predictive error (MPE) and mean absolute predictive error (MAPE). RESULTS: Assuming constant variables in a subject across nonoutlier visits and using thresholds of ±15% MPE and 20% MAPE, we accepted at least one and at most three PD model test fits in each of the seven subjects. Thus, we successfully validated the PD model by leave-one-out cross-validation in seven out of eight T1D patients. CONCLUSIONS: The PD model accurately simulates glucose excursions based on plasma insulin and glucagon concentrations. The reported PK/PD model including equations and fitted parameters allows for in silico experiments that may help improve diabetes treatment involving glucagon for prevention of hypoglycemia.