Search

In Saccharomyces cerevisiae, the FLO1gene encodes flocculins that lead to formation of multicellular flocs, that offer protection to the constituent cells. Flo1p was found to preferentially bind to fellow cooperators compared to defectors lacking FLO1expression, enriching cooperators within the flocs. Given this dual function in cooperation and kin recognition, FLO1has been termed a “green beard gene”. Because of the heterophilic nature of the Flo1p bond however, we hypothesize that kin recognition is permissive and depends on the relative stability of the FLO1+/flo1-versus FLO1+/FLO1+detachment force F. We combine single-cell measurements of adhesion, individual cell-based simulations of cluster formation, and in vitro flocculation to study the impact of relative bond stability on the evolutionary stability of cooperation. We identify a trade-off between both aspects of the green beard mechanism, with reduced relative bond stability leading to increased kin recognition at the expense of cooperative benefits. We show that the fitness of FLO1cooperators decreases as their frequency in the population increases, arising from the observed permissive character (F+-= 0.5 F++) of the Flo1p bond. Considering the costs associated with FLO1expression, this asymmetric selection often results in a stable coexistence between cooperators and defectors.

In this study, we report on the influence of mechanochemical activation on the chemical stability of amorphous solid dispersions made up of indomethacin and hydroxypropyl methyl cellulose (HPMC), poly(vinylpyrrolidone) (PVP), poly(vinylpyrrolidone vinylacetate) (PVPVA), or Soluplus. In agreement with our recently published work, all applied carriers were found to be prone to polymer degradation. Covalent bonds within the polymers were cleaved and mechanoradicals were generated. Furthermore, decomposition of indomethacin was also observed but occurred only in the presence of polymers. Hence, it is proposed that the generated mechanoradicals from the polymers are responsible for the chemical degradation of indomethacin. Our study also strongly suggests the existence of a critical polymer- and process-dependent molecular weight limit “M∞”, below which only limited mechanodegradation takes place since the lower-molecular-weight polymer PVP K12PF had a less profound influence on the degradation of indomethacin in comparison to PVP K25.

The interplay between cell-cell and cell-substrate interactions is complex yet necessary for the formation and healthy functioning of tissues. The same mechanosensing mechanisms used by the cell to sense its extracellular matrix also play a role in intercellular interactions. We used the discrete element method to develop a computational model of a deformable cell that includes subcellular components responsible for mechanosensing. We modeled a three-dimensional cell pair on a patterned (two-dimensional) substrate, a simple laboratory setup to study intercellular interactions. We explicitly modeled focal adhesions and adherens junctions. These mechanosensing adhesions matured, becoming stabilized by force. We also modeled contractile stress fibers that bind the discrete adhesions. The mechanosensing fibers strengthened upon stalling. Traction exerted on the substrate was used to generate traction maps (along the cell-substrate interface). These simulated maps are compared to experimental maps obtained via traction force microscopy. The model recreates the dependence on substrate stiffness of the tractions’ spatial distribution, contractile moment of the cell pair, intercellular force, and number of focal adhesions. It also recreates the phenomenon of cell decoupling, in which cells exert forces separately when substrate stiffness increases. More importantly, the model provides viable molecular explanations for decoupling: mechanosensing mechanisms are responsible for competition between different fiber-adhesion configurations present in the cell pair. The point at which an increasing substrate stiffness becomes as high as that of the cell-cell interface is the tipping point at which configurations that favor cell-substrate adhesion dominate over those favoring cell-cell adhesion. This competition is responsible for decoupling.

We study the self-assembly dynamics of human progenitor cells in agarose microwells that are used for production of chondrogenic organoids. Using image analysis on time-lapse microscopy, we estimate the aggregate area in function of time for a large number of aggregates. In control conditions, the aggregate radius follows an exponential relaxation that is consistent with the dewetting dynamics of a liquid film. Upon introducing Y-27632 Rho kinase inhibitor, the compatibility with the liquid model is lost, and slowed-down relaxation dynamics are observed. We demonstrate that these aggregates behave as granular piles undergoing compaction, with density relaxation that follows a stretched exponential. Using simulations with an individual cell-based model, we construct a phase diagram of cell aggregates that suggests that the aggregate in presence of Rho kinase inhibitor approaches glass transition.

We consider a cell as an elastic, contractile shell surrounding a liquid incompressible cytoplasm and with nonspecific adhesion. We perform numerical simulations of this model to study the mechanics of cell-cell separation. By variation of parameters, we are able to recover well-known limits of the Johnson-Kendall-Roberts theory, the Derjaguin-Muller-Toporov model, adhesive vesicles with surface tension (Brochard-Wyart and de Gennes derivation), and thin elastic shells. We further locate biological cells on this parameter space by comparison to existing experiments on S180 cells. Using this model, we show that mechanical parameters can be obtained that are consistent with both dual pipette aspiration and micropipette aspiration, a problem not successfully tackled so far. We estimate a cortex elastic modulus of Ec≈ 15 kPa, an effective cortex thickness of tc≈ 0.3 μm, and an active tension of γ≈ 0.4 nN/μm. With these parameters, a Johnson-Kendall-Roberts-like scaling of the separation force is recovered. Finally, the change of contact radius with applied force in a pull-off experiment was investigated. For small forces, a scaling similar to both the Brochard-Wyart and de Gennes derivation and the Derjaguin-Muller-Toporov model is found.

Cell division during early embryogenesis is linked to key morphogenic events such as embryo symmetry breaking and tissue patterning. It is thought that the physical surrounding of cells together with cell intrinsic cues act as a mechanical “mold“, guiding cell division to ensure these events are robust. To quantify how cell division is affected by the mechanical and geometrical environment, we present a novel computational mechanical model of cytokinesis, the final phase of cell division. Simulations with the model reproduced experimentally observed furrow dynamics and describe the volume ratio of daughter cells in asymmetric cell divisions, based on the position and orientation of the mitotic spindle. For dividing cells in geometrically confined environments, we show how the orientation of confinement relative to the division axis modulates the volume ratio in asymmetric cell division. Further, we quantified how cortex viscosity and surface tension determine the shape of a dividing cell and govern bubble-instabilities in asymmetric cell division. Finally, we simulated the formation of the three body axes via sequential (a)symmetric divisions up until the 6-cell stage of early C. elegansdevelopment, which proceeds within the confines of an eggshell. We demonstrate how model input parameters spindle position and orientation provide sufficient information to reliably predict the volume ratio of daughter cells during the cleavage phase of development. However, for egg geometries perturbed by compression, the model predicts that a change in confinement alone is insufficient to explain experimentally observed differences in cell volume. This points to an effect of the compression on the spindle positioning mechanism. Additionally, the model predicts that confinement stabilizes asymmetric cell divisions against bubble-instabilities.

Accurate knowledge of the optical properties of turbid media in the light path is important in NIR absorption spectroscopy of biological tissues where multiple scattering complexes the collected light signals due to the non-uniform tissue architecture. Several approaches such as time resolved spectroscopy and spatially resolved spectroscopy have been proposed to measure the bulk optical properties of turbid media. Among them, double integrating sphere (DIS) measurements are recognized as the "golden standard" for in vitro optical properties measurement of turbid media because of its high accuracy and robustness in different conditions. A DIS system is convenient to measure the in vitro optical properties of turbid media like intralipid solutions and biological tissues, since it measures the diffuse reflectance and transmittance simultaneously. However, DIS measurements have been mostly limited to the optical window region (400-1000 nm) or suffered from low signal levels on the detectors due to the absorption by water in the NIR region. In this study, we developed a DIS system for optical property measurement in the 1300-2350 nm region based on a novel wavelength tunable spectroscopic setup which incorporates a high power broadband supercontinuum laser and a high precision monochromator. With this system, optical properties of intralipid solutions were measured in the wavelength region of 1300-2350nm.

Optical measurement of fruit quality is challenging due to the presence of a skin around the fruit flesh and the multiple scattering by the structured tissues. To gain insight in the light-tissue interaction, the optical properties of apple skin and flesh tissue are estimated in the 350-2200 nm range for three cultivars. For this purpose, single integrating sphere measurements are combined with inverse adding-doubling. The observed absorption coefficient spectra are dominated by water in the near infrared and by pigments and chlorophyll in the visible region, whose concentrations are much higher in skin tissue. The scattering coefficient spectra show the monotonic decrease with increasing wavelength typical for biological tissues with skin tissue being approximately three times more scattering than flesh tissue. Comparison to the values from time-resolved spectroscopy reported in literature showed comparable profiles for the optical properties, but overestimation of the absorption coefficient values, due to light losses.

The finite element method was used to analyse the cutting process of a sandy loam soil with medium-deep subsoiler, used as a compaction sensor, aiming to calculate the subsoiler draught for various combinations of dry bulk density, moisture content and tillage depth. The finite element results showed that draught increased with depth and dry bulk density, whereas it decreased with moisture content. A multiple linear regression analysis was performed to establish a formula for relating subsoiler draught (dependent variable) with the three independent variables. The regression equation developed was simple and had a high determination coefficient close to 0.95. An equation for prediction of dry bulk density as a function of moisture content, depth and draught was derived from the regression equation developed. This equation was used to calculate dry bulk density, for measured depth, moisture content and draught at nine different points along a single line in a meadow field of a silty clay loam soil. The predicted dry bulk density indicated that there was a considerable variation in the degree of compaction throughout the measured line. However, the on-line depth control and measurement of moisture content still need to be integrated with the on-line measurement of draught, to govern the model suitability for performing on-line detection of the spatial distribution of soil compaction, assessed as dry bulk density.

The chlorophyll fluorescence kinetics of ‘Jonagold’ and ‘Cox’ apples, stored under different conditions to induce mealiness, were measured. Three different storage conditions were considered, causing 3 mealiness levels: not mealy, moderately and strongly mealy. Also destructive measurements of the firmness and texture (compression, juiciness and shear) were done. Classification into the different meatiness levels based on the fluorescence measurements was superior to a classification based on the destructive measurements. To estimate the mealiness level in a non-destructive way from the fluorescence features, a number of different classifiers was constructed. Quadratic discriminants and supervised and unsupervised neural networks were tested and compared. The SOM in general outperforms the other methods. The different advantages ofthe constructed classifiers suggest that fluorescence can be used in an automatic sorting line to assess the mealiness, and the senescence in general, and eventually to predict the time a certain quality level can be guaranteed to the consumers under certain conditions

One of the most important features of biological motion control is the connection of motor action to sensory perception. Sensorimotor coordination adapts and develops by actions of sensory and motor experience. For tasks in which a-priori knowledge is limited, self-organising controllers are proposed. A novel technique is described, that allows a robot to autonomously determine its sensorimotor mapping. Artificial neural networks are used to learn the sensorimotor coordination, trained using self-organizing learning algorithms. The inverse kinematic mapping is learned by the use of a series of trial movements. The relation of the proposed algorithm to biological motion control is discussed.

Vertical sprayer boom movements are studied with experimental modal analysis. A distinction is made between rigid-body motions and flexible deformations of the sprayer boom. Pure vertical and rolling rigid-body motions of the boom are mainly caused by unwanted tractor movements which are caused primarily by soil undulations and tyre characteristics. Flexible boom vibrations are induced by vertical and rolling angular accelerations of the tractor body.The influence of the driving speed and tyre pressure is investigated on the distinct boom motions and the effect of speed seems to dominate. Increase of driving speed (4–12 km/h) results in a rise of the pure vertical translations (130%), the rolling motions (×10) and the vertical flexible deformations (×4). A change of tyre pressure from 60 to 180 kPa leads to fall of the pure vertical translations (−20%), while the rolling and vertical flexible deformations go up by 2%. These phenomena are due to frequency shifts and larger force intensities in the frequency band between 0·5 and 3·5 Hz.The spray deposit distribution is calculated and, from the simulated spray patterns, the effect of the boom motions on uniformity of the spray pattern is examined. Simulation outcomes reveal a minor effect of the vertical translations, while rolling of the boom and vertical flexible deformations have larger influences, especially at the extremities of the boom owing to their larger amplitudes. Maximum deposits reach 760% and minimum 0% in the case of the largest sprayer boom motions. From these calculated spray patterns, an optimal range of use is determined for the tested sprayer: a spraying height of 0·5 m or more in combination with a fixed nozzle spacing of 0·5 m, a limited tractor speed and low tyre pressure leads to a good spray distribution (correct overlap) with the flat fan nozzle with top angle of 110°.

Chemical products for crop protection are usually distributed on the field as liquids by field sprayers. Unwanted horizontal and vertical sprayer boom movements create local under- and over-applications of spray liquid. A non-linear finite element model of a tractor and a mounted sprayer is developed to simulate these unwanted boom vibrations during field operations. Representative disturbance signals at the hitch point of the boom suspension are calculated for a tractor with a mounted sprayer driving over a standardized bumpy track. These disturbances are applied as input excitations to simulate boom movements for several types of suspensions (pendulum, single and double trapezium). Validation experiments with a simple 12 m boom on a hydraulic test rig indicate that this non-linear finite element modelling technique is a promising method for simulating boom movements of field sprayers. Further simulation results show that the modelled suspensions approximately halve the vertical motion of the boom compared with a rigidly mounted boom. Standard deviations of the vertical boom tip movements around the equilibrium position are, respectively, 0·44, 0·25 and 0·27 m for a fixed boom, a boom with pendulum suspension and a boom with trapezium suspension. This proves the effectiveness of existing vertical boom suspensions.

In the paper, an electro-hydraulic control system for a vibrations isolating platform which must behave like a bandpass filter, is designed. A good estimation of the frequency response is obtained by doing an identification on the low and the high frequency band. The ill conditionedness of the H∞algorithm caused by poles and zeros at the origin is solved by adding uncertainty. Accelerometer drift and vibrations of the excitation table put conflicting constraints on the controller design. Practical implementation issues which arise from numerical problems are tackled. Finally the performance and the robustness are evaluated on the set-up

The Self-Organizing Map Neural Network is used in a supervised way to represent a sensor-actuator mapping. The learning of the controller assumes no prior information, but only reward/failure signals that are produced by an evaluation criterion. The evaluation criterion used is based on the low-pass filtering of the gradient of a reward function and the local storing of the filtered gradient value. The control method is tested in vibration isolation of a flexible spray boom used in agriculture for pesticide application. The Neural Network learns to stabilise the boom on-line without any prior information and with a very high performance

Finite element simulations show that existing suspensions approximately halve vertical sprayer boom movements compared to a fixed boom. The reduction of horizontal boom vibrations however is negligible. This shows the need for a performant horizontal suspension. Because of the high mass and inertia of sprayer booms active suspensions would require too much engine power. Therefore the possibilities of a passive suspension are investigated. Optimal suspension settings (spring stiffness, damping constant) are determined by optimization of the singular value plot derived from a parameterized linear multi-body model of the boom and suspension. Validation experiments confirm that the used modelling techniques are reliable.

Existing boom suspensions are effective to reduce rolling boom motions, but reduction of horizontal boom vibrations is negligible. A prototype passive horizontal suspension is developed and different suspension set-ups are compared with regard to boom vibrations and spray pattern homogeneity. Boom vibrations are simulated with a non-linear finite element program. The corresponding spray pattern is obtained by combining the static spray pattern with the simulated boom movements. An appropriate passive suspension is able to almost halve the CV of spray deposition compared to a fixed boom.

The efficiency of crop treatments depends greatly on the homogeneity of the spray distribution. Boom vibrations cause local under- and over-applications. This paper describes how boom movements can be simulated with a non-linear finite element program for a 12-meter working width sprayer. Special attention must be paid to the dynamic characteristics of the hinges because they are detenninative for horizontal boom vibrations. The model is validated on a test rig that reproduces horizontal tractor vibrations. Boom velocities are measured by a laser and fit well with the simulated velocities.

A new neural network architecture for classification purposes is proposed. The Self-Organizing Map (SOM) Neural Network is used in a supervised way for a classification task. The neurons of the SOM become associated with Local Linear Mappings. Error information obtained during training is used in a novel learning algorithm to train the classifier. The proposed method achieves fast convergence and good generalisation. The classification method is then applied in a precision farming application, the classification of crops and weeds using spectral properties. The classification performance of the proposed method is proven superior compared to other statistical and neural classifiers.

The finite element and the discrete element method to model mechanical strutures are compared. A systematic approach for deriving the mass, damping, stiffnes and input distribution matrices is proposed for the discrete element method. It is proved that the allocation of mass to the discrete points can be done based upon shape functions instead of intuitive geometrical considerations. The two methods are compared theoretically, are applied on a prototype spray boom and validated on a laboratory experiment.

A new method for on-line system identification based on the Self-Organizing Map is presented. The standard Self-Organizing Map (SOM) is extended with Local Linear Mappings. To every node in the SOM along with the input weight two output weights are assigned: one that stores the output part of an input-output pair and one that stores the local gradient matrix (Jacobian) that is calculated from the training pairs. A training algorithm for the Jacobian matrices is derived. The method is tested in system identification of two Agricultural Machines: a flexible Spray Boom and a shaker with a nonlinear spring.

A hierarchical Self-Organizing Map has been developed for solving classification problems, where several measurements have been taken from one object. The algorithm will be used to classify eggs according to their shell state. Broken eggs will be separated from intact eggs. The classification architecture actually consists of two different SOMs. The first SOM clusters the data in an unsupervised way. Afterwards, the ordered activations of each object are collected and fed to the second SOM which assosiates them with a class. This class-vector is assigned to every node in the second map and it is learned with Kohonen's learning rule.

A novel technique is presented based on self-organizing neural networks for prediction of fertilizer distribution patterns of spreaders as a function of spreader settings and fertilizer properties. The main aim of the presented technique is to predict tendencies in the spreading distribution pattern as a function of machine configurations and physical fertilizer properties. The Self-Organizing Map is used in a novel way to represent input–output relationships between high-dimensional spaces. Other NN methods would be very difficult to use because of the high dimensions of the input and output spaces. In the case of a multilayer perceptron, the global connectivity would lead to a prohibitively large number of free parameters giving rise to learning time problems. The spreading distribution patterns are predicted with a high performance with the proposed technique. [Copyright &y& Elsevier]