Jean-Marie Aerts

Energy and protein metabolism between 3 and 6 weeks of age of male broiler chickens selected for growth rate or for improved food efficiency.

1. A new open-circuit respiration unit consisting of 6 respiration chambers, gas analysis unit and data-acquisition system is briefly described. 2. Energy and protein metabolism in broiler lines selected for improved food efficiency (FC) or for growth rate (GL) were measured weekly from 3 to 6 weeks of age. 3. Gross and apparent metabolisable energy intake per kg W0.75 was on average higher for GL than for FC chickens without differences in metabolisability. Fed and fasted heat production per kg W0.75 did not differ between the lines. FC chickens retained less energy per kg W0.75 than GL chickens. 4. FC chickens deposited much less of the retained energy as fat than their GL counterparts and also showed greater protein conversion efficiency. The leaner composition of the body weight gain in FC chickens was confirmed by the estimated lower fat deposition per kg W0.75 and by the lower fat: protein ratio.

Dynamic Data-based Modelling of Heat Production and Growth of Broiler Chickens: Development of an Integrated Management System

An application of modern process control techniques to poultry production is outlined. Compact dynamic data-based models are proposed to describe and control the metabolic response of broiler chickens to the micro-environment. The dynamic response of heat production to step changes in air temperature and light intensity could be modelled with an average coefficient of determination RT2 of 0·83 and 0·93, respectively. Using recursive parameter estimation techniques, the time-variant response of animal growth to food supply could be predicted on-line with a maximum prediction error of 5%, 3–7 days ahead depending on the type of feeding schedule. Compact data-based models were shown to be suitable for control of broiler growth. Overall, the studies suggest that the potential conflicts between the environmental, financial and biological pressures on sustainable poultry production can be resolved through the development of integrated management systems using modern process control techniques.

Quantification of the Spatial Distribution of Surface Temperatures of Broilers

Thermal comfort is of great importance in chickens to preserve body temperature homeostasis during the growth period and during environmental thermal challenges. Because surface temperatures contribute much to thermal comfort, this research is aimed at studying spatial distribution of surface temperatures of broiler chickens. For this purpose, temperatures of 26 different parts on the chicken body surface were measured using thermography during the growth period of 6 wk. It was observed that there were significant differences in spatial distribution of broiler surface temperatures. The greatest temperatures were measured at the positions with little or no feathering (i.e., cheek, skull, and inner thigh). The least temperatures were observed on the places with thickest feather cover (i.e., wing and breast). The surface temperatures decreased as a function of age from approximately 36 to 28 degrees C. The spatial temperature range on the surface of the bird varied from 6 degrees C in wk 1 to 15 degrees C on wk 6. Temperature differences between the surface of the chicken and its surroundings were also studied, and it was found that in the range of 1 to 6 wk the age of the bird had significant effects on temperature difference (P < 0.0001). The temperature difference between the surface of the chicken and environment was at a maximum on wk 4 during the growth period of 6 wk.

Modelling the static and dynamic responses of total heat production of broiler chickens to step changes in air temperature and light intensity

1. The objective of this work was to explore the possibilities of modelling the static and dynamic responses of total heat production of broiler chickens to step changes in temperature and light intensity (light-dark alterations) using compact dynamic model structures. 2. Seventy-seven experiments were performed in an open-circuit respiration chamber to measure the dynamic response of heat production to step variations in temperature and light (on/off). The animal responses were modelled using transfer function model structures. 3. It was demonstrated that the complex process of the dynamic response of heat production of broiler chickens to step changes in air temperature and light-dark alterations can be modelled assuming 1st order dynamics. The coefficient of determination between measured and simulated heat production was on average 0.83 for responses to air temperature and 0.93 for responses to light-dark alterations.

Computer-assisted image analysis to quantify daily growth rates of broiler chickens.

1. The objective was to investigate the possibility of detecting daily body weight changes of broiler chickens with computer-assisted image analysis. 2. The experiment included 50 broiler chickens reared under commercial conditions. Ten out of 50 chickens were randomly selected and video recorded (upper view) 18 times during the 42-d growing period. The number of surface and periphery pixels from the images was used to derive a relationship between body dimension and live weight. 3. The relative error in weight estimation, expressed in terms of the standard deviation of the residuals from image surface data was 10%, while it was found to be 15% for the image periphery data. 4. Image-processing systems could be developed to assist the farmer in making important management and marketing decisions.

The use of Tubificidae in a biological early warning system

Tubificidae are aquatic annelids which occur abundantly in organically polluted waters. The anterior end of their body is buried in the sediment, while the posterior end moves in the water. This posterior end is (partly) retracted in the sediment in the presence of defined concentrations of pollutants. The retraction can be followed visually (qualitative) or can be monitored by videocamera and quantified by digital image analysis. The latter presents the advantage of a continuous recording that is always processed the same way. Our preliminary results indicate that digital image analysis is an accurate technique for the quantification of the behavioural response of Tubificidae to pollutants.

Reverse engineering of metabotropic glutamate receptor-dependent long-term depression in the hippocampus

This study focused on metabotropic glutamate receptor-dependent long-term depression (mGluR–LTD) in the hippocampus. This form of LTD is suggested to play a key role in learning, memory and the plasticity of behaviour. Recent advances have started to uncover the underlying mechanisms of mGluR-LTD [1]. However, it is not completely clear how these mechanisms are linked and it is believed that several crucial mechanisms still remain to be revealed. The two main objectives of this study were (i) to quantify the dynamics of mGluR-LTD responses by dynamic data-based models and (ii) to identify underlying dominant processes of mGluR-LTD by applying mathematical system identification methods. In recent years, more and more researchers advocate the use of a top-down modelling approach (reverse engineering) for improving the knowledge of biological systems [2,3]. The drug dihydroxyphenylglycine (DHPG) was used to induce mGluR-LTD in rat brain slices (table ​(table1).1). The drug was applied for different durations (5min, 15min, 2 hours) and in different concentrations (15mM, 30mM). In addition, also different sampling intervals (5min, 30s, 90s) were used. Table 1 Overview of the experiments For the modelling, discrete-time Transfer Functions (TF) models were used. The models described the relation between the DHPG application (input) and the long-term depression responses (output). All models were very accurate (all RT2-values higher than 0,94) and reliably estimated. For a 2 hours application of 30 µM DHPG sampled with a frequency of 1/30s, the time-constant of the mGluR-LTD response was 92s. Thus, the models for high sampling rate indicated that a sampling interval of 30s would be ideal to minimize information loss of the dynamics of mGluR-LTD responses. Interestingly, it was suggested that there are three dominant sub-processes underlying mGluR-LTD: one fast sub-process, one slow sub-process and an immediate sub-process. This study suggests that the dynamic data-based modelling approach can be a valuable tool for reverse engineering of mGluR-dependent LTD responses.

Heart rate and heart rate variability in chicken embryos at the end of incubation

Our immediate goal was to study heart rate variability (HRV) in chicken embryos in the egg. Instantaneous heart rate data were needed for this purpose, and accordingly an ECG recording method in the egg was developed. The aim of this work was to test the hypothesis that autonomic nervous cardiac modulation, as shown from HRV parameters, is present at the end of development and that it reaches a constant value during the last days of incubation. Embryonic chicken heart rate was obtained at the final incubation period (days 19 and 20) from ECG recordings. Tachograms were computed and time‐ and frequency‐domain indices of HRV were determined. No significant differences were found between HRV indices from day 19 and day 20. The power spectra extended in two frequency bands with centre frequency around 0.6–0.7 Hz (low frequency (LF) component), and another around 1.2–1.5 Hz (high frequency (HF) component); the latter was shown to reflect respiratory sinus arrhythmia. A relation between mean RR interval and some HRV parameters (rMSSD, pNN5 and HF power) was shown. HRV results obtained from embryonic chickens, showed the presence of modulation of cardiovascular function by the autonomic nervous system. The results suggested that sympathetic and parasympathetic activities have already reached a constant level at day 19 of incubation. High frequency oscillations (0.78–2.5 Hz) were detected and are considered to reflect respiratory sinus arrhythmia.

From data patterns to mechanistic models in acute critical illness

The complexity of the physiologic and inflammatory response in acute critical illness has stymied the accurate diagnosis and development of therapies. The Society for Complex Acute Illness was formed a decade ago with the goal of leveraging multiple complex systems approaches to address this unmet need. Two main paths of development have characterized the societys approach: (i) data pattern analysis, either defining the diagnostic/prognostic utility of complexity metrics of physiologic signals or multivariate analyses of molecular and genetic data and (ii) mechanistic mathematical and computational modeling, all being performed with an explicit translational goal. Here, we summarize the progress to date on each of these approaches, along with pitfalls inherent in the use of each approach alone. We suggest that the next decade holds the potential to merge these approaches, connecting patient diagnosis to treatment via mechanism-based dynamical system modeling and feedback control and allowing extrapolation from physiologic signals to biomarkers to novel drug candidates. As a predicate example, we focus on the role of data-driven and mechanistic models in neuroscience and the impact that merging these modeling approaches can have on general anesthesia.

Model-based prediction of nephropathia epidemica outbreaks based on climatological and vegetation data and bank vole population dynamics.

Wildlife‐originated zoonotic diseases in general are a major contributor to emerging infectious diseases. Hantaviruses more specifically cause thousands of human disease cases annually worldwide, while understanding and predicting human hantavirus epidemics pose numerous unsolved challenges. Nephropathia epidemica (NE) is a human infection caused by Puumala virus, which is naturally carried and shed by bank voles (Myodes glareolus). The objective of this study was to develop a method that allows model‐based predicting 3 months ahead of the occurrence of NE epidemics. Two data sets were utilized to develop and test the models. These data sets were concerned with NE cases in Finland and Belgium. In this study, we selected the most relevant inputs from all the available data for use in a dynamic linear regression (DLR) model. The number of NE cases in Finland were modelled using data from 1996 to 2008. The NE cases were predicted based on the time series data of average monthly air temperature (°C) and bank voles’ trapping index using a DLR model. The bank voles’ trapping index data were interpolated using a related dynamic harmonic regression model (DHR). Here, the DLR and DHR models used time‐varying parameters. Both the DHR and DLR models were based on a unified state‐space estimation framework. For the Belgium case, no time series of the bank voles’ population dynamics were available. Several studies, however, have suggested that the population of bank voles is related to the variation in seed production of beech and oak trees in Northern Europe. Therefore, the NE occurrence pattern in Belgium was predicted based on a DLR model by using remotely sensed phenology parameters of broad‐leaved forests, together with the oak and beech seed categories and average monthly air temperature (°C) using data from 2001 to 2009. Our results suggest that even without any knowledge about hantavirus dynamics in the host population, the time variation in NE outbreaks in Finland could be predicted 3 months ahead with a 34% mean relative prediction error (MRPE). This took into account solely the population dynamics of the carrier species (bank voles). The time series analysis also revealed that climate change, as represented by the vegetation index, changes in forest phenology derived from satellite images and directly measured air temperature, may affect the mechanics of NE transmission. NE outbreaks in Belgium were predicted 3 months ahead with a 40% MRPE, based only on the climatological and vegetation data, in this case, without any knowledge of the bank vole’s population dynamics. In this research, we demonstrated that NE outbreaks can be predicted using climate and vegetation data or the bank vole’s population dynamics, by using dynamic data‐based models with time‐varying parameters. Such a predictive modelling approach might be used as a step towards the development of new tools for the prevention of future NE outbreaks.