An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.

An insulin infusion advisory system for type 1 diabetes patients based on non-linear model predictive control methods

In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPC) which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the IIAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented IIAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays.

A reliable, traffic-adaptive and energy-efficient link layer for wireless sensor networks

The paper presents a link layer stack for wireless sensor networks, which consists of the Burst-aware Energy-efficient Adaptive Medium access control (BEAM) and the Hop-to-Hop Reliability (H2HR) protocol. BEAM can operate with short beacons to announce data transmissions or include data within the beacons. Duty cycles can be adapted by a traffic prediction mechanism indicating pending packets destined for a node and by estimating its wake-up times. H2HR takes advantage of information provided by BEAM such as neighbour information and transmission information to perform per-hop congestion control. We justify the design decisions by measurements in a real-world wireless sensor network testbed and compare the performance with other link layer protocols.

Perceived malleability of self versus the world and its impact on youths’ control orientation and well-being in the US, Germany, and India

Primary control is defined as changing the world to fit the self, while secondary control is defined as changing the self to fit the world. To understand why different individuals prefer different kinds of control processes, we proposed a research project looking at US, German and Indian young adults. We hypothesize that theories of self and the world (fixed vs. malleable; Dweck, 1999) affect the prevailing mode of control used. Furthermore, adolescents’ cultural background is assumed to affect their self-world theories as well as the adaptiveness of specific modes of control. For example, in the US, where the self is tended to be seen as fixed and the world as malleable, primary control prevails and is more adaptive than secondary control while the reverse is expected for India. We present the theoretical outline and methodology of the study as well as first results.

Personalized Tuning of a Reinforcement Learning Control Algorithm for Glucose Regulation

Artificial pancreas is in the forefront of research towards the automatic insulin infusion for patients with type 1 diabetes. Due to the high inter- and intra-variability of the diabetic population, the need for personalized approaches has been raised. This study presents an adaptive, patient-specific control strategy for glucose regulation based on reinforcement learning and more specifically on the Actor-Critic (AC) learning approach. The control algorithm provides daily updates of the basal rate and insulin-to-carbohydrate (IC) ratio in order to optimize glucose regulation. A method for the automatic and personalized initialization of the control algorithm is designed based on the estimation of the transfer entropy (TE) between insulin and glucose signals. The algorithm has been evaluated in silico in adults, adolescents and children for 10 days. Three scenarios of initialization to i) zero values, ii) random values and iii) TE-based values have been comparatively assessed. The results have shown that when the TE-based initialization is used, the algorithm achieves faster learning with 98%, 90% and 73% in the A+B zones of the Control Variability Grid Analysis for adults, adolescents and children respectively after five days compared to 95%, 78%, 41% for random initialization and 93%, 88%, 41% for zero initial values. Furthermore, in the case of children, the daily Low Blood Glucose Index reduces much faster when the TE-based tuning is applied. The results imply that automatic and personalized tuning based on TE reduces the learning period and improves the overall performance of the AC algorithm.

An adaptive Newton-method based on a dynamical systems approach

The traditional Newton method for solving nonlinear operator equations in Banach spaces is discussed within the context of the continuous Newton method. This setting makes it possible to interpret the Newton method as a discrete dynamical system and thereby to cast it in the framework of an adaptive step size control procedure. In so doing, our goal is to reduce the chaotic behavior of the original method without losing its quadratic convergence property close to the roots. The performance of the modified scheme is illustrated with various examples from algebraic and differential equations.

Robust meta-analytic-predictive priors in clinical trials with historical control information.

Historical information is always relevant for clinical trial design. Additionally, if incorporated in the analysis of a new trial, historical data allow to reduce the number of subjects. This decreases costs and trial duration, facilitates recruitment, and may be more ethical. Yet, under prior-data conflict, a too optimistic use of historical data may be inappropriate. We address this challenge by deriving a Bayesian meta-analytic-predictive prior from historical data, which is then combined with the new data. This prospective approach is equivalent to a meta-analytic-combined analysis of historical and new data if parameters are exchangeable across trials. The prospective Bayesian version requires a good approximation of the meta-analytic-predictive prior, which is not available analytically. We propose two- or three-component mixtures of standard priors, which allow for good approximations and, for the one-parameter exponential family, straightforward posterior calculations. Moreover, since one of the mixture components is usually vague, mixture priors will often be heavy-tailed and therefore robust. Further robustness and a more rapid reaction to prior-data conflicts can be achieved by adding an extra weakly-informative mixture component. Use of historical prior information is particularly attractive for adaptive trials, as the randomization ratio can then be changed in case of prior-data conflict. Both frequentist operating characteristics and posterior summaries for various data scenarios show that these designs have desirable properties. We illustrate the methodology for a phase II proof-of-concept trial with historical controls from four studies. Robust meta-analytic-predictive priors alleviate prior-data conflicts ' they should encourage better and more frequent use of historical data in clinical trials.

Closed-loop control of mean arterial blood pressure during surgery with alfentanil: clinical evaluation of a novel model-based predictive controller

BACKGROUND: In contrast to hypnosis, there is no surrogate parameter for analgesia in anesthetized patients. Opioids are titrated to suppress blood pressure response to noxious stimulation. The authors evaluated a novel model predictive controller for closed-loop administration of alfentanil using mean arterial blood pressure and predicted plasma alfentanil concentration (Cp Alf) as input parameters. METHODS: The authors studied 13 healthy patients scheduled to undergo minor lumbar and cervical spine surgery. After induction with propofol, alfentanil, and mivacurium and tracheal intubation, isoflurane was titrated to maintain the Bispectral Index at 55 (+/- 5), and the alfentanil administration was switched from manual to closed-loop control. The controller adjusted the alfentanil infusion rate to maintain the mean arterial blood pressure near the set-point (70 mmHg) while minimizing the Cp Alf toward the set-point plasma alfentanil concentration (Cp Alfref) (100 ng/ml). RESULTS: Two patients were excluded because of loss of arterial pressure signal and protocol violation. The alfentanil infusion was closed-loop controlled for a mean (SD) of 98.9 (1.5)% of presurgery time and 95.5 (4.3)% of surgery time. The mean (SD) end-tidal isoflurane concentrations were 0.78 (0.1) and 0.86 (0.1) vol%, the Cp Alf values were 122 (35) and 181 (58) ng/ml, and the Bispectral Index values were 51 (9) and 52 (4) before surgery and during surgery, respectively. The mean (SD) absolute deviations of mean arterial blood pressure were 7.6 (2.6) and 10.0 (4.2) mmHg (P = 0.262), and the median performance error, median absolute performance error, and wobble were 4.2 (6.2) and 8.8 (9.4)% (P = 0.002), 7.9 (3.8) and 11.8 (6.3)% (P = 0.129), and 14.5 (8.4) and 5.7 (1.2)% (P = 0.002) before surgery and during surgery, respectively. A post hoc simulation showed that the Cp Alfref decreased the predicted Cp Alf compared with mean arterial blood pressure alone. CONCLUSION: The authors' controller has a similar set-point precision as previous hypnotic controllers and provides adequate alfentanil dosing during surgery. It may help to standardize opioid dosing in research and may be a further step toward a multiple input-multiple output controller.

More adaptive versus less maladaptive coping: What is more predictive of symptom severity? Development of a new scale to investigate coping profiles across different psychopathological syndromes

BACKGROUND: Lack of adaptive and enhanced maladaptive coping with stress and negative emotions are implicated in many psychopathological disorders. We describe the development of a new scale to investigate the relative contribution of different coping styles to psychopathology in a large population sample. We hypothesized that the magnitude of the supposed positive correlation between maladaptive coping and psychopathology would be stronger than the supposed negative correlation between adaptive coping and psychopathology. We also examined whether distinct coping style patterns emerge for different psychopathological syndromes. METHODS: A total of 2200 individuals from the general population participated in an online survey. The Patient Health Questionnaire-9 (PHQ-9), the Obsessive-Compulsive Inventory revised (OCI-R) and the Paranoia Checklist were administered along with a novel instrument called Maladaptive and Adaptive Coping Styles (MAX) questionnaire. Participants were reassessed six months later. RESULTS: MAX consists of three dimensions representing adaptive coping, maladaptive coping and avoidance. Across all psychopathological syndromes, similar response patterns emerged. Maladaptive coping was more strongly related to psychopathology than adaptive coping both cross-sectionally and longitudinally. The overall number of coping styles adopted by an individual predicted greater psychopathology. Mediation analysis suggests that a mild positive relationship between adaptive and certain maladaptive styles (emotional suppression) partially accounts for the attenuated relationship between adaptive coping and depressive symptoms. LIMITATIONS: Results should be replicated in a clinical population. CONCLUSIONS: Results suggest that maladaptive and adaptive coping styles are not reciprocal. Reducing maladaptive coping seems to be more important for outcome than enhancing adaptive coping. The study supports transdiagnostic approaches advocating that maladaptive coping is a common factor across different psychopathologies.

Lake-effect snow as the dominant control of mesic-forest distribution in Michigan, USA

1Recent studies demonstrated the sensitivity of northern forest ecosystems to changes in the amount and duration of snow cover at annual to decadal time scales. However, the consequences of snowfall variability remain uncertain for ecological variables operating at longer time scales, especially the distributions of forest communities. 2The Great Lakes region of North America offers a unique setting to examine the long-term effects of variable snowfall on forest communities. Lake-effect snow produces a three-fold gradient in annual snowfall over tens of kilometres, and dramatic edaphic variations occur among landform types resulting from Quaternary glaciations. We tested the hypothesis that these factors interact to control the distributions of mesic (dominated by Acer saccharum, Tsuga canadensis and Fagus grandifolia) and xeric forests (dominated by Pinus and Quercus spp.) in northern Lower Michigan. 3We compiled pre-European-settlement vegetation data and overlaid these data with records of climate, water balance and soil, onto Landtype Association polygons in a geographical information system. We then used multivariate adaptive regression splines to model the abundance of mesic vegetation in relation to environmental controls. 4Snowfall is the most predictive among five variables retained by our model, and it affects model performance 29% more than soil texture, the second most important variable. The abundance of mesic trees is high on fine-textured soils regardless of snowfall, but it increases with snowfall on coarse-textured substrates. Lake-effect snowfall also determines the species composition within mesic forests. The weighted importance of A. saccharum is significantly greater than of T. canadensis or F. grandifolia within the lake-effect snowbelt, whereas T. canadensis is more plentiful outside the snowbelt. These patterns are probably driven by the influence of snowfall on soil moisture, nutrient availability and fire return intervals. 5Our results imply that a key factor dictating the spatio-temporal patterns of forest communities in the vast region around the Great Lakes is how the lake-effect snowfall regime responds to global change. Snowfall reductions will probably cause a major decrease in the abundance of ecologically and economically important species, such as A. saccharum.

Variability of lung function predicts loss of asthma control following withdrawal of inhaled corticosteroid treatment

BACKGROUND One aspect of a multidimensional approach to understanding asthma as a complex dynamic disease is to study how lung function varies with time. Variability measures of lung function have been shown to predict response to beta(2)-agonist treatment. An investigation was conducted to determine whether mean, coefficient of variation (CV) or autocorrelation, a measure of short-term memory, of peak expiratory flow (PEF) could predict loss of asthma control following withdrawal of regular inhaled corticosteroid (ICS) treatment, using data from a previous study. METHODS 87 adult patients with mild to moderate asthma who had been taking ICS at a constant dose for at least 6 months were monitored for 2-4 weeks. ICS was then withdrawn and monitoring continued until loss of control occurred as per predefined criteria. Twice-daily PEF was recorded during monitoring. Associations between loss of control and mean, CV and autocorrelation of morning PEF within 2 weeks pre- and post-ICS withdrawal were assessed using Cox regression analysis. Predictive utility was assessed using receiver operator characteristics. RESULTS 53 out of 87 patients had sufficient PEF data over the required analysis period. The mean (389 vs 370 l/min, p<0.0001) and CV (4.5% vs 5.6%, p=0.007) but not autocorrelation of PEF changed significantly from prewithdrawal to postwithdrawal in subjects who subsequently lost control, and were unaltered in those who did not. These changes were related to time to loss of control. CV was the most consistent predictor, with similar sensitivity and sensitivity to exhaled nitric oxide. CONCLUSION A simple, easy to obtain variability measure of daily lung function such as the CV may predict loss of asthma control within the first 2 weeks of ICS withdrawal.

Rapid parallel adaptive radiations from a single hybridogenic ancestral population

The Alpine lake whitefish (Coregonus lavaretus) species complex is a classic example of a recent radiation, associated with colonization of the Alpine lakes following the glacial retreat (less than 15 kyr BP). They have formed a unique array of endemic lake flocks, each with one to six described sympatric species differing in morphology, diet and reproductive ecology. Here, we present a genomic investigation of the relationships between and within the lake flocks. Comparing the signal between over 1000 AFLP loci and mitochondrial control region sequence data, we use phylogenetic tree-based and population genetic methods to reconstruct the phylogenetic history of the group and to delineate the principal centres of genetic diversity within the radiation. We find significant cytonuclear discordance showing that the genomically monophyletic Alpine whitefish clade arose from a hybrid swarm of at least two glacial refugial lineages. Within this radiation, we find seven extant genetic clusters centred on seven lake systems. Most interestingly, we find evidence of sympatric speciation within and parallel evolution of equivalent phenotypes among these lake systems. However, we also find the genetic signature of human-mediated gene flow and diversity loss within many lakes, highlighting the fragility of recent radiations.

Hybridization between distant lineages increases adaptive variation during a biological invasion: stickleback in Switzerland

The three-spined stickleback is a widespread Holarctic species complex that radiated from the sea into freshwaters after the retreat of the Pleistocene ice sheets. In Switzerland, sticklebacks were absent with the exception of the far northwest, but different introduced populations have expanded to occupy a wide range of habitats since the late 19th century. A well-studied adaptive phenotypic trait in sticklebacks is the number of lateral plates. With few exceptions, freshwater and marine populations in Europe are fixed for either the low plated phenotype or the fully plated phenotype, respectively. Switzerland, in contrast, harbours in close proximity the full range of phenotypic variation known from across the continent. We addressed the phylogeographic origins of Swiss sticklebacks using mitochondrial partial cytochrome b and control region sequences. We found only five different haplotypes but these originated from three distinct European regions, fixed for different plate phenotypes. These lineages occur largely in isolation at opposite ends of Switzerland, but co-occur in a large central part. Across the country, we found a strong correlation between a microsatellite linked to the high plate ectodysplasin allele and the mitochondrial haplotype from a region where the fully plated phenotype is fixed. Phylogenomic and population genomic analysis of 481 polymorphic amplified fragment length polymorphism loci indicate genetic admixture in the central part of the country. The same part of the country also carries elevated within-population phenotypic variation. We conclude that during the recent invasive range expansion of sticklebacks in Switzerland, adaptive and neutral between-population genetic variation was converted into within-population variation, raising the possibility that hybridization between colonizing lineages contributed to the ecological success of sticklebacks in Switzerland.

Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients

Prediction of glycemic profile is an important task for both early recognition of hypoglycemia and enhancement of the control algorithms for optimization of insulin infusion rate. Adaptive models for glucose prediction and recognition of hypoglycemia based on statistical and artificial intelligence techniques are presented.

Slc15a4, a gene required for pDC sensing of TLR ligands, is required to control persistent viral infection

Plasmacytoid dendritic cells (pDCs) are the major producers of type I IFN in response to viral infection and have been shown to direct both innate and adaptive immune responses in vitro. However, in vivo evidence for their role in viral infection is lacking. We evaluated the contribution of pDCs to acute and chronic virus infection using the feeble mouse model of pDC functional deficiency. We have previously demonstrated that feeble mice have a defect in TLR ligand sensing. Although pDCs were found to influence early cytokine secretion, they were not required for control of viremia in the acute phase of the infection. However, T cell priming was deficient in the absence of functional pDCs and the virus-specific immune response was hampered. Ultimately, infection persisted in feeble mice. We conclude that pDCs are likely required for efficient T cell priming and subsequent viral clearance. Our data suggest that reduced pDC functionality may lead to chronic infection.