A real time simulation model of glucose-insulin metabolism for type 1 diabetes patients

In this paper, a simulation model of glucose-insulin metabolism for Type 1 diabetes patients is presented. The proposed system is based on the combination of Compartmental Models (CMs) and artificial Neural Networks (NNs). This model aims at the development of an accurate system, in order to assist Type 1 diabetes patients to handle their blood glucose profile and recognize dangerous metabolic states. Data from a Type 1 diabetes patient, stored in a database, have been used as input to the hybrid system. The data contain information about measured blood glucose levels, insulin intake, and description of food intake, along with the corresponding time. The data are passed to three separate CMs, which produce estimations about (i) the effect of Short Acting (SA) insulin intake on blood insulin concentration, (ii) the effect of Intermediate Acting (IA) insulin intake on blood insulin concentration, and (iii) the effect of carbohydrate intake on blood glucose absorption from the gut. The outputs of the three CMs are passed to a Recurrent NN (RNN) in order to predict subsequent blood glucose levels. The RNN is trained with the Real Time Recurrent Learning (RTRL) algorithm. The resulted blood glucose predictions are promising for the use of the proposed model for blood glucose level estimation for Type 1 diabetes patients.

Teaching Cognitive Modeling Using PDP++

PDP++ is a freely available, open source software package designed to support the development, simulation, and analysis of research-grade connectionist models of cognitive processes. It supports most popular parallel distributed processing paradigms and artificial neural network architectures, and it also provides an implementation of the LEABRA computational cognitive neuroscience framework. Models are typically constructed and examined using the PDP++ graphical user interface, but the system may also be extended through the incorporation of user-written C++ code. This article briefly reviews the features of PDP++, focusing on its utility for teaching cognitive modeling concepts and skills to university undergraduate and graduate students. An informal evaluation of the software as a pedagogical tool is provided, based on the author’s classroom experiences at three research universities and several conference-hosted tutorials.

Investigation on the evolution of an indoor robotic localization system based on wireless networks

This work addresses the evolution of an artificial neural network (ANN) to assist in the problem of indoor robotic localization. We investigate the design and building of an autonomous localization system based on information gathered from wireless networks (WN). The article focuses on the evolved ANN, which provides the position of a robot in a space, as in a Cartesian coordinate system, corroborating with the evolutionary robotic research area and showing its practical viability. The proposed system was tested in several experiments, evaluating not only the impact of different evolutionary computation parameters but also the role of the transfer functions on the evolution of the ANN. Results show that slight variations in the parameters lead to significant differences on the evolution process and, therefore, in the accuracy of the robot position.

Oxygen isotopes, IRD and SST reconstruction for the interval MIS31-19 of IODP Site 306-U1314

Early and Mid-Pleistocene climate, ocean hydrography and ice sheet dynamics have been reconstructed using a high-resolution data set (planktonic and benthic d18O time series, faunal-based sea surface temperature (SST) reconstructions and ice-rafted debris (IRD)) record from a high-deposition-rate sedimentary succession recovered at the Gardar Drift formation in the subpolar North Atlantic (Integrated Ocean Drilling Program Leg 306, Site U1314). Our sedimentary record spans from late in Marine Isotope Stage (MIS) 31 to MIS 19 (1069-779 ka). Different trends of the benthic and planktonic oxygen isotopes, SST and IRD records before and after MIS 25 (~940 ka) evidence the large increase in Northern Hemisphere ice-volume, linked to the cyclicity change from the 41-kyr to the 100-kyr that occurred during the Mid-Pleistocene Transition (MPT). Beside longer glacial-interglacial (G-IG) variability, millennial-scale fluctuations were a pervasive feature across our study. Negative excursions in the benthic d18O time series observed at the times of IRD events may be related to glacio-eustatic changes due to ice sheets retreats and/or to changes in deep hydrography. Time series analysis on surface water proxies (IRD, SST and planktonic d18O) of the interval between MIS 31 to MIS 26 shows that the timing of these millennial-scale climate changes are related to half-precessional (10 kyr) components of the insolation forcing, which are interpreted as cross-equatorial heat transport toward high latitudes during both equinox insolation maxima at the equator.

Paleoceanographic record since 13kyr BP of sediment core LaPAS-KF02

In this paper we present a paleoceanographic reconstruction of the southwestern South Atlantic for the past 13 kyr based on faunal and isotopic analysis of planktonic foraminifera from a high-resolution core retrieved at the South Brazil Bight continental slope. Our record indicates that oceanographic changes in the southwestern South Atlantic during the onset of the Holocene were comparable in strength to those that occurred during the Younger Dryas. Full interglacial conditions started abruptly after 8.2 kyr BP with a sharp change in faunal composition and surface hydrography (SST and SSS). Part of the observed events may be explained in terms of changes in thermohaline circulation while the other part suggests a dominant role of winds. Our data indicate that during the Early Holocene upwelling was significantly strengthened in the South Brazil Bight promoting high productivity and preventing the establishment of the typically interglacial menardiiform species. In general terms, oceanographic changes recorded by core KF02 occurred in synchrony with Antarctica's climate.

Euphausiid respiration model revamped, link to model results

Euphausiids constitute major biomass component in shelf ecosystems and play a fundamental role in the rapid vertical transport of carbon from the ocean surface to the deeper layers during their daily vertical migration (DVM). DVM depth and migration patterns depend on oceanographic conditions with respect to temperature, light and oxygen availability at depth, factors that are highly dependent on season in most marine regions. Changes in the abiotic conditions also shape Euphausiid metabolism including aerobic and anaerobic energy production. Here we introduce a global krill respiration model which includes the effect of latitude (LAT), the day of the year of interest (DoY), and the number of daylight hours on the day of interest (DLh), in addition to the basal variables that determine ectothermal oxygen consumption (temperature, body mass and depth) in the ANN model (Artificial Neural Networks). The newly implemented parameters link space and time in terms of season and photoperiod to krill respiration. The ANN model showed a better fit (r**2=0.780) when DLh and LAT were included, indicating a decrease in respiration with increasing LAT and decreasing DLh. We therefore propose DLh as a potential variable to consider when building physiological models for both hemispheres. We also tested for seasonality the standard respiration rate of the most common species that were investigated until now in a large range of DLh and DoY with Multiple Linear Regression (MLR) or General Additive model (GAM). GAM successfully integrated DLh (r**2= 0.563) and DoY (r**2= 0.572) effects on respiration rates of the Antarctic krill, Euphausia superba, yielding the minimum metabolic activity in mid-June and the maximum at the end of December. Neither the MLR nor the GAM approach worked for the North Pacific krill Euphausia pacifica, and MLR for the North Atlantic krill Meganyctiphanes norvegica remained inconclusive because of insufficient seasonal data coverage. We strongly encourage comparative respiration measurements of worldwide Euphausiid key species at different seasons to improve accuracy in ecosystem modelling.

Distribution of planktonic foraminifera during the Mid-Pleistocene of ODP Site 181-1123

Planktonic foraminiferal assemblages and artificial neural network estimates of sea-surface temperature (SST) at ODP Site 1123 (41°47.2'S, 171°29.9'W; 3290 m deep), east of New Zealand, reveal a high-resolution history of glacial-interglacial (G-I) variability at the Subtropical Front (STF) for the last 1.2 million years, including the Mid-Pleistocene climate transition (MPT). Most G-I cycles of ~100 kyr duration have short periods of cold glacial and warm deglacial climate centred on glacial terminations, followed by long temperate interglacial periods. During glacial-deglacial transitions, maximum abundances of subantarctic and subtropical taxa coincide with SST minima and maxima, and lead ice volume by up to 8 kyrs. Such relationships reflect the competing influence of subantarctic and subtropical surface inflows during glacial and deglacial periods, respectively, suggesting alternate polar and tropical forcing of southern mid-latitude ocean climate. The lead of SSTs and subtropical inflow over ice volume points to tropical forcing of southern mid-latitude ocean-climate during deglacial warming. This contrasts with the established hypothesis that southern hemisphere ocean climate is driven by the influence of continental glaciations. Based on wholesale changes in subantarctic and subtropical faunas, the last 1.2 million years are subdivided into 4-distinct periods of ocean climate. 1) The pre-MPT (1185-870 ka) has high amplitude 41-kyr fluctuations in SST, superimposed on a general cooling trend and heightened productivity, reflecting long-term strengthening of subantarctic inflow under an invigorated Antarctic Circumpolar Current. 2) The early MPT (870-620 ka) is marked by abrupt warming during MIS 21, followed by a period of unstable periodicities within the 40-100 kyr orbital bands, decreasing SST amplitudes, and long intervals of temperate interglacial climate punctuated by short glacial and deglacial phases, reflecting lower meridional temperature gradients. 3) The late MPT (620-435 ka) encompasses an abrupt decrease in the subantarctic inflow during MIS 15, followed by a period of warm equable climate. Poorly defined, low amplitude G-I variations in SSTs during this interval are consistent with a relatively stable STF and evenly balanced subantarctic and subtropical inflows, possibly in response to smaller, less dynamic polar icesheets. 4) The post-MPT (435-0 ka) is marked by a major climatic deterioration during MIS 12, and a return to higher amplitude 100 kyr-frequency SST variations, superimposed on a long term trend towards cooler SSTs and increased mixed-layer productivity as the subantarctic inflow strengthened and polar icesheets expanded.

Global macrozoobenthos production and energy budget data base V150731, with link to database

I developed a new model for estimating annual production-to-biomass ratio P/B and production P of macrobenthic populations in marine and freshwater habitats. Self-learning artificial neural networks (ANN) were used to model the relationships between P/B and twenty easy-to-measure abiotic and biotic parameters in 1252 data sets of population production. Based on log-transformed data, the final predictive model estimates log(P/B) with reasonable accuracy and precision (r2 = 0.801; residual mean square RMS = 0.083). Body mass and water temperature contributed most to the explanatory power of the model. However, as with all least squares models using nonlinearly transformed data, back-transformation to natural scale introduces a bias in the model predictions, i.e., an underestimation of P/B (and P). When estimating production of assemblages of populations by adding up population estimates, accuracy decreases but precision increases with the number of populations in the assemblage.