Jan Pieters

The HMI™ module: a new tool to study the Host-Microbiota Interaction in the human gastrointestinal tract in vitro

BackgroundRecent scientific developments have shed more light on the importance of the host-microbe interaction, particularly in the gut. However, the mechanistic study of the host-microbe interplay is complicated by the intrinsic limitations in reaching the different areas of the gastrointestinal tract (GIT) in vivo. In this paper, we present the technical validation of a new device - the Host-Microbiota Interaction (HMI) module - and the evidence that it can be used in combination with a gut dynamic simulator to evaluate the effect of a specific treatment at the level of the luminal microbial community and of the host surface colonization and signaling.ResultsThe HMI module recreates conditions that are physiologically relevant for the GIT: i) a mucosal area to which bacteria can adhere under relevant shear stress (3 dynes cm−2); ii) the bilateral transport of low molecular weight metabolites (4 to 150xa0kDa) with permeation coefficients ranging from 2.4 × 10−6 to 7.1 × 10−9xa0cmxa0sec−1; and iii) microaerophilic conditions at the bottom of the growing biofilm (PmO2 =u20092.5 × 10−4xa0cmxa0sec−1). In a long-term study, the host’s cells in the HMI module were still viable after a 48-hour exposure to a complex microbial community. The dominant mucus-associated microbiota differed from the luminal one and its composition was influenced by the treatment with a dried product derived from yeast fermentation. The latter - with known anti-inflammatory properties - induced a decrease of pro-inflammatory IL-8 production between 24 and 48xa0h.ConclusionsThe study of the in vivo functionality of adhering bacterial communities in the human GIT and of the localized effect on the host is frequently hindered by the complexity of reaching particular areas of the GIT. The HMI module offers the possibility of co-culturing a gut representative microbial community with enterocyte-like cells up to 48xa0h and may therefore contribute to the mechanistic understanding of host-microbiome interactions.

High Speed Stereovision Setup for Position and Motion Estimation of Fertilizer Particles Leaving a Centrifugal Spreader

A 3D imaging technique using a high speed binocular stereovision system was developed in combination with corresponding image processing algorithms for accurate determination of the parameters of particles leaving the spinning disks of centrifugal fertilizer spreaders. Validation of the stereo-matching algorithm using a virtual 3D stereovision simulator indicated an error of less than 2 pixels for 90% of the particles. The setup was validated using the cylindrical spread pattern of an experimental spreader. A 2D correlation coefficient of 90% and a Relative Error of 27% was found between the experimental results and the (simulated) spread pattern obtained with the developed setup. In combination with a ballistic flight model, the developed image acquisition and processing algorithms can enable fast determination and evaluation of the spread pattern which can be used as a tool for spreader design and precise machine calibration.

The Use of High-Speed Imaging Systems for Applications in Precision Agriculture

High speed imaging (HSI) has been widely used for industrial and military applications such as ballistics, hypervelocity impact, car crash studies, fluid mechanics, and others. In agriculture HSI is mainly used in two domains that both require fast processing: fertilization and spraying. uf0b7 Fertilization, be it organic or mineral, is essential to agriculture. Over-fertilization can reduce yield and lead to environmental pollution (Mulligan et al., 2006). To prevent these consequences, the fertilization process must be controlled. In Europe and worldwide, mineral fertilization is performed using centrifugal spreaders because they are more cost-efficient than pneumatic spreaders. The process of centrifugal spreading is based on spinning discs which eject large numbers of grains at high speeds (30 to 40 ms-1). To control the spreading process and to predict the distribution pattern on the soil, several characteristics need to be accurately evaluated, i.e., ejection parameters such as velocity and direction, plus granulometry and the angular distribution. uf0b7 The spray quality generated by agricultural nozzles plays an important role in the application of plant protection products. The ideal nozzle-pressure combination should maximize spray efficiency by increasing deposition and transfer of a lethal dose to the target (Smith et al., 2000) while minimizing residues (Derksen et al., 2008) and off-target losses such as spray drift (Nuyttens et al., 2007a) and user exposure (Nuyttens et al., 2009a). The most important spray characteristics influencing the efficiency of the pesticide application process are the droplet sizes, the droplet velocities and directions, the volume distribution pattern, the spray sheet structure and length, the structure of

Development of a reference method for airflow rate measurements through rectangular vents towards application in naturally ventilated animal houses

A naturally ventilated test facility was built.An airflow rate measuring method using 3D ultrasonic anemometers was developed.The method was successfully validated through the law of mass conservation.The effect of the wind incidence angle and speed on the airflow rate was studied.The necessity of measuring the 3D in-/outflow pattern was proven. In order to measure the airflow rate and emission rate of a naturally ventilated livestock building correctly, more reliable measuring techniques need to be developed. A test facility with a cross ventilated room was built at the Institute for Agricultural and Fisheries Research (Belgium) to study a new airflow rate measuring method. This method is based on an automated traverse movement of a 3D ultrasonic anemometer across 2 vents of 0.5mi?1.0m. To cope with the fluctuating wind velocity profile, a velocity measurement of 10s in 16 equally distributed measuring points is needed. Moreover, 10 traverse replicates are needed to obtain a representative average flow rate. Based on the law of mass conservation, the accuracy of the method was determined by calculating the relative deviation between the simultaneously measured airflow rates through both vents. A relative error of -1?11% was found, averaged over all wind incidence angles. However, wind angles parallel to the vent resulted in larger relative errors. A 3D velocity measurement in the in- or outlet opening of the test facility is necessary to obtain a correct flow rate. This was especially true in the outlet where up to 30% of the airflow rate was delivered by velocity components other than normal to the vent. The test facility and the developed ventilation rate measuring method can serve as a reference to study and validate new and existing ventilation rate measuring methods.

Two-step cross correlation–based algorithm for motion estimation applied to fertilizer granules’ motion during centrifugal spreading

Imaging systems are progressing in both accuracy and ro- bustness, and their use in precision agriculture is increasing accordingly. One application of imaging systems is to understand and control the cen- trifugal fertilizing spreading process. Predicting the spreading pattern on the ground relies on an estimation of the trajectories and velocities of ejected granules. The algorithms proposed to date have shown low ac- curacy, with an error rate of a few pixels. But a more accurate estimation of the motion of the granules can be achieved. Our new two-step cross- correlation-based algorithm is based on the technique used in particle image velocimetry (PIV), which has yielded highly accurate results in the field of fluid mechanics. In order to characterize and evaluate our new algorithm, we develop a simulator for fertilizer granule images that ob- tained a high correlation with the real fertilizer images. The results of our tests show a deviation of <0.2 pixels for 90% of estimated velocities. This subpixel accuracy allows for use of a smaller camera sensor, which decreases the acquisition and processing time and also lowers the cost. These advantages make it more feasible to install this system on existing centrifugal spreaders for real-time control and adjustment. C 2011 Society of

Multi-phase cross-correlation method for motion estimation of fertiliser granules during centrifugal spreading

Excessive fertiliser use has been a main contributor to the increasing environmental imbalance observed in the past 20xa0years. Better accuracy in spreading would limit excess fertiliser loss into the environment. Increased accuracy begins by understanding the fertiliser spreading process from the vane to the soil. Our work concentrates on the use of centrifugal spreaders, as these are most commonly used in Europe. Progress in imaging devices and image processing has resulted in the availability of new technologies to use when describing the behaviour of fertiliser granules during ejection from centrifugal spreaders. Fertiliser deposition on the soil can be predicted using a ballistic flight model, but this requires determination of the velocities and the directions of the granules when they leave the spinning disc. This paper presents improvements to the high speed imaging system that we had previously developed, i.e. enhancements to the illumination and the image processing. The illumination of the previous system, which used many separate flashes, did not give consistent illumination. We have improved it by using a stroboscope with power-LEDs, located at 1xa0m height around the digital camera and controlled by a Field-programmable gate array (FPGA) card. The image processing has been improved by development of a multi-phase method based on a cross-correlation algorithm. We have compared the cross-correlation method to the Markov Random Fields (MRF) method previously implemented. These tests, based on multi-exposure images, revealed that cross-correlation method gives more accurate results than the MRF technique, with guaranteed sub-pixel accuracy. Knowing that an error of one pixel can lead to a prediction error between 200 and 500xa0mm on the ground, the latter method gives an accuracy range between 0.1 and 0.4 pixels, whereas the MRFs technique is limited to 3 and 9 pixels for the vertical and horizontal components of the velocities, respectively. The sub-pixel accuracy of the new method was proven by applying it on simulated images with known displacements between the grains. By using a realistic spreading model, the simulated images are similar to those obtained with a high speed imaging system. This sub-pixel accuracy now makes it possible to decrease the resolution of the camera to that of a classical high-speed camera. These improvements have created an affordable and durable system appropriate for installation on a spreader. Farmers could use this system to both calibrate the spreader and verify the fertiliser distribution on the ground.

Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery

Abstract The developments in the use of unmanned aerial vehicles (UAVs) and advanced imaging sensors provide new opportunities for ultra-high resolution (e.g., less than a 10u202fcm ground sampling distance (GSD)) crop field monitoring and mapping in precision agriculture applications. In this study, we developed a strategy for inter- and intra-row weed detection in early season maize fields from aerial visual imagery. More specifically, the Hough transform algorithm (HT) was applied to the orthomosaicked images for inter-row weed detection. A semi-automatic Object-Based Image Analysis (OBIA) procedure was developed with Random Forests (RF) combined with feature selection techniques to classify soil, weeds and maize. Furthermore, the two binary weed masks generated from HT and OBIA were fused for accurate binary weed image. The developed RF classifier was evaluated by 5-fold cross validation, and it obtained an overall accuracy of 0.945, and Kappa value of 0.912. Finally, the relationship of detected weeds and their ground truth densities was quantified by a fitted linear model with a coefficient of determination of 0.895 and a root mean square error of 0.026. Besides, the importance of input features was evaluated, and it was found that the ratio of vegetation length and width was the most significant feature for the classification model. Overall, our approach can yield a satisfactory weed map, and we expect that the obtained accurate and timely weed map from UAV imagery will be applicable to realize site-specific weed management (SSWM) in early season crop fields for reducing spraying non-selective herbicides and costs.

Development of a High Irradiance LED Configuration for Small Field of View Motion Estimation of Fertilizer Particles

Better characterization of the fertilizer spreading process, especially the fertilizer pattern distribution on the ground, requires an accurate measurement of individual particle properties and dynamics. Both 2D and 3D high speed imaging techniques have been developed for this purpose. To maximize the accuracy of the predictions, a specific illumination level is required. This paper describes the development of a high irradiance LED system for high speed motion estimation of fertilizer particles. A spectral sensitivity factor was used to select the optimal LED in relation to the used camera from a range of commercially available high power LEDs. A multiple objective genetic algorithm was used to find the optimal configuration of LEDs resulting in the most homogeneous irradiance in the target area. Simulations were carried out for different lenses and number of LEDs. The chosen configuration resulted in an average irradiance level of 452 W/m2 with coefficient of variation less than 2%. The algorithm proved superior and more flexible to other approaches reported in the literature and can be used for various other applications.

Infrared Heating as a Disinfestation Method Against Sitophilus oryzae and Its Effect on Textural and Cooking Properties of Milled Rice

Infrared (IR) heating method against rice weevils (Sitophilus oryzae) in an egg stage was investigated. A kinetic model was developed to describe insect mortality in a temperature range of 40–60xa0°C. Effects of IR heating temperature (50–60xa0°C) and exposure time (1–3xa0min) on insect mortality and quality attributes of the treated rice were evaluated. The optimized condition obtained by means of the response surface method was used to analyze rice quality before and after IR treatment with storage. The results showed that the 0.5th-order thermal death kinetic equation was the most suitable model, and the S. oryzae eggs had less heat tolerance than the adults and some other species. Mortality achieved 100xa0% after 2xa0min for all temperatures. Both IR heating parameters significantly affected the treated milled rice qualities. The minimal changes in rice quality before and after storage could be obtained using optimized temperature and exposure time of 53.6xa0°C and 1.2xa0min, respectively.

Spray Droplet Characterization from a Single Nozzle by High Speed Image Analysis Using an In-Focus Droplet Criterion

Accurate spray characterization helps to better understand the pesticide spray application process. The goal of this research was to present the proof of principle of a droplet size and velocity measuring technique for different types of hydraulic spray nozzles using a high speed backlight image acquisition and analysis system. As only part of the drops of an agricultural spray can be in focus at any given moment, an in-focus criterion based on the gray level gradient was proposed to decide whether a given droplet is in focus or not. In a first experiment, differently sized droplets were generated with a piezoelectric generator and studied to establish the relationship between size and in-focus characteristics. In a second experiment, it was demonstrated that droplet sizes and velocities from a real sprayer could be measured reliably in a non-intrusive way using the newly developed image acquisition set-up and image processing. Measured droplet sizes ranged from 24 μm to 543 μm, depending on the nozzle type and size. Droplet velocities ranged from around 0.5 m/s to 12 m/s. The droplet size and velocity results were compared and related well with the results obtained with a Phase Doppler Particle Analyzer (PDPA).