DA) may contribute to total blood mercury (TBHg) levels of pregnant women in the UK.There is curiosity concerning the source of mercury that is absorbed into the mother's blood and which may affect the developing fetus. This study therefore sets out to determine the extent to which dental amalgam (n = 4484) enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC). The whole blood samples were assayed for total mercury levels using inductively coupled plasma dynamic reaction cell mass spectrometry (ICP‐DRC‐MS), and the women were retrospectively asked about features of their dental care during the pregnancy. Linear regression was used to estimate the relative contributions of DA to TBHg levels (log‐transformed) based on R2 values, compared to the results from dietary and socio‐demographic variables.Whole blood samples and information on diet and socio‐demographic factors were collected from pregnant women (TBHg levels by dental variables was 6.47%, a figure comparable to the 8.75% shown for seafood consumption in this population. Dietary and dental variables explained 20.16% of the variance, with socio‐demographic variables contributing only a further 3.40%. The number of amalgams in the mouth at the start of pregnancy accounted for most of the variance in dental variables.The contribution to the variance of the mothers' TBHg to seafood consumption in this population. However, because the measures of DA exposure were imprecise, these findings are likely to be an underestimate. There is no evidence to date in the literature that fetal exposures to mercury from maternal DAs have adverse effects on the developing child, but long‐term studies are warranted.Dental amalgam contributes a comparable amount of variance in  There has been considerable and ongoing concern as to the adverse consequences of mercury on health and development. Most concern relates to mercury from seafood, but we have shown, using a population of pregnant women, that seafood contributes a relatively small amount of the variance of total blood mercury (TBHg) levelsAlthough it is tempting to assume that the mercury in DA is inert, there is now evidence in humans that mercury vapor is released continuously from amalgams, and that this results in a marked increase during chewingMercury from DA is found in the uriner = 0.46 and 0.27, respectively). The authors concluded ‘our results are compatible with the hypothesis that amalgam fillings may be a continuous source of organic mercury, which is more toxic than inorganic mercury and almost completely absorbed by the human intestine’. At the same time, VahterThere has been some controversy concerning whether DA would have adverse effects as inorganic as opposed to methyl mercury is assumed to be relatively inert. However, LeistevuoIn our earlier publicationhttp://www.bris.ac.uk/alspac/researchers/data-access/data-dictionary/.All pregnant women resident in a geographic area (Avon) in the UK, whose expected date of delivery lay between the 1 April 1991 and 31 December 1992, were eligible to take part in the Avon Longitudinal Study of Parents and Children (ALSPAC). A total of 14 541 pregnancies were enrolled: over 80% of the eligible pregnanciesEthical approval for the study was obtained from the ALSPAC Law and Ethics Committee and the Local Research Ethics Committees. .Whole blood samples were collected in acid washed heparin vacutainers (Becton and Dickinson) by midwives as early as possible in pregnancy. This was purely voluntary on their part as the study had no funding for this. Samples were obtained from women in two of the three Health Authority areas of the recruitment region. Altogether, there were 4484 samples collected. The women who gave such samples were slightly older and slightly better educated than the rest of the cohortThe Centers for Disease Control and Prevention (CDC) developed methods to prepare the samples for analysis of whole blood mercury as well as of lead, selenium, and cadmium (CDC method 3009.1) as described in our previous publicationThe questions concerning dental treatment of the mother were not asked until 2 years after the study child was born. At this point, the mother was asked the following: (i) The number of fillings she had in her mouth at the time she became pregnant ; (ii) during the study pregnancy whether she had visited the dentist at all (yes/no); (iii) if yes to (ii), (a) did she have any teeth out? (yes/no); (b) did she have new amalgam fillings put in? (yes/no); (c) did she have old amalgam fillings removed? (yes/no); (d) did she have dental gas? (yes/no); (e) did she have a dental X‐ray (yes/no)? For questions (a) – (e) those who had not visited the dentist were coded ‘no’. These questions were answered by 90% or more of the women answering this questionnaire – the question with lowest response was that relating to the number of amalgams at the start of pregnancy, where 10% stated that they could not remember. It is important to note that pregnant women were provided with free dental treatment as part of the UK's National Health Service, in consequence of which many women took advantage of this and obtained treatment they otherwise would be likely to have postponed. Thus, there was unlikely to be any reduction in dental care in pregnancy due to lack of finance.An assessment of prenatal dietary consumption used a food frequency format, asking the number of occasions per time interval the woman currently ate specific types of food, and additionally the most frequently used milk, fats, and types of bread consumedThe following socio‐demographic factors were collected using questionnaires completed by the mother: (i) her age at the time of the delivery of the child; (ii) her social class based on her last/current occupation, graded from I  to IV (semi‐skilled) and V ; (iii) her highest education level achieved, using 5 ranked categories from minimal to university degree; (iv) housing tenure, distinguishing between mortgaged or owned outright, rented public housing, and other accommodation; (v) her ethnic background distinguishing white and non‐white; and (vi) her parity, defined as the number of previous pregnancies resulting in a live or stillbirth.P value to enter of 0.05, and eliminated those variables that did not enter.As the distribution of mercury levels was slightly skewed, we employed a natural log (ln) transformation for all analyses. To develop a final model for the dental variables, we offered them all to a stepwise multiple regression with R2 for simple linear regression models involving the dental variables available. To assess whether the dental components were a consequence of socio‐demographic or dietary influences, or vice versa, the R2 attributed to each, and combinations of the three were computed.An estimate of the contribution of DA to TBHg levels was based upon the Details of maternal dental care relevant to the current research question were available for between 2591 and 2855 women for whom prenatal TBHg levels were available. As shown in Table As anticipated, there were the expected unadjusted differences between the arithmetic mean TBHg levels and features of dental care Table . Women wR2 for these four dental variables remaining in the model was 6.47%. Although the final model reported in Table R2 = 6.46%; this is not surprising as removal of a filling usually results in insertion of another.The next step was to assess the independent relationships between the variables in Table n = 1690, P < 0.001). As a sensitivity analysis, we used the information collected at 2 months post‐delivery concerning whether the mother had visited a dentist during pregnancy. The regression model was similar to that shown with the 33‐month measure, but the proportion of variance explained (R2) had resulted in an increase to 7.67%, thus indicating an improvement in accuracy. However, this measure was only available for 1533 subjects.To assess the impact of recall bias, the responses to visiting the dentist and new fillings at 33 months were compared to a subgroup of the cohort who was asked these questions at 2 months postdelivery. Agreements were 88% and 79%, respectively . However, the actual value of R2 on combining all these variables into one model is 23.56%, implying that many of the variables are mutually associated. If we concentrate on the two types of variables that are shown in the literature to be associated with blood mercury , the R2 rises to 20.16%. Although on their own the socio‐demographic variables account for an R2 of 12.61% (B), addition of these variables to the model containing dental and dietary variables (G) results in an increase of only 3.40% (23.56–20.16%). This implies that the socio‐demographic variables, which we have shown elsewhere to indicate positive associations (more advantageous living conditions) with TBHgIn Table In this study, we have used a relatively large population of pregnant women to demonstrate that dental care, particularly the number of DA fillings, is responsible for at least 6.47% of their TBHg level. This may be compared with the 8.75% we have shown to be attributable to seafood consumption in the same populationImportantly, this study is concerned with population levels of TBHg and the factors that influence them. We have shown that it is likely that, in this population, the variation in maternal blood mercury levels attributable to seafood is similar to that attributable to dental care using DA.In accord with our study, there have been reports of positive associations between blood mercury and ageThe present study has a number of advantages and disadvantages. The disadvantages were (i) that the questions on DA were asked about 2 years after the end of pregnancy; consequently, the memory of the participating mothers is likely to be faulty. It is probable, however, that women who had ≤3 fillings would be able to remember and report this, but those with larger numbers of fillings would be less likely to recall the exact number. This was found in a validation study of pregnant women in Norway where the women were asked to use a mirror to count the number of amalgams in their mouths; the correlation with a dental examination was high for those with <5 fillings but much poorer for those with higher numbersAmong the advantages are that compared with most other studies: (i) the numbers are large, (ii) the participants were population based, (iii) questions on DA were inserted in the middle of the questionnaire without reference to any outcome, and thus, there was little chance of the study subject answering the questions in a biased way.There has been confusion as to how safe DA is for the population in general; for example, in 2006 BatesA phase down of the use of mercury was agreed at the Minamata Convention in 2013The authors have no competing interests.


Collective motion is one of the most ubiquitous behaviours displayed by social organisms and has led to the development of numerous models. Recent advances in the understanding of sensory system and information processing by animals impels one to revise classical assumptions made in decisional algorithms. In this context, we present a model describing the three-dimensional visual sensory system of fish that adjust their trajectory according to their perception field. Furthermore, we introduce a stochastic process based on a probability distribution function to move in targeted directions rather than on a summation of influential vectors as is classically assumed by most models. In parallel, we present experimental results of zebrafish  swimming in both homogeneous and heterogeneous environments. We use these experimental data to set the parameter values of our model and show that this perception-based approach can simulate the collective motion of species showing cohesive behaviour in heterogeneous environments. Finally, we discuss the advances of this multilayer model and its possible outcomes in biological, physical and robotic sciences. In the self-propelled particles (SPP) models . Multiple hypotheses have been proposed to calculate the subset of individuals that influence the motion of a focal fish: the metric perception includes all individuals situated within a defined distance; the topological perception includes the rception –46. In tThese theoretical and experimental studies highlighted that the visual sensory system of the fish is determinant for information transfer during collective motions. Indeed, the comparison of the interaction mechanisms showed that a vision-based model outperforms other mechanisms  in explaining experimental data . In para1.3Once the focal fish has perceived potential stimuli, this information has to be processed and translated into a movement of the individual. In the SPP and SF models considering only the influence of congeners, a vector of interaction is computed with all neighbours situated in a delimited range (metric models) or according to their proximity rank . Then, the focal fish moves along the resulting force computed by a weighted summation process of the different interaction forces applied on the focal fish. While it results in a coordinated motion of the agents, these models can also produce biological incoherent behaviours of the simulated individuals: the influence of some congeners can be omitted/overestimated or the resulting vector can point towards a direction where no stimulus is present . In addi1.4It would be interesting to develop a multilevel modelling approach that takes into account models for the perception and the information processing by the individuals. Beyond the existing SPP and RW models, it requires one to extend the description of the agents by biologically relevant properties. In this paper, we propose a new model at the crossroads of RW and SPP models that includes a probabilistic component (inspired by RW) in the behaviour of the agents that react to their perception field (inspired by SPP models). In the model, the agents choose a direction to move according to a PDF that is determined by the presence of stimuli in their visual perception field. This PDF is computed as a mixture distribution of von Mises distributions centred on each perceived stimulus. Once the PDF has been computed, the direction of the agent is chosen accordingly to this PDF. With this model, we simulate the motion of single individuals or groups of fish evolving in a bounded environment that can include other stimuli like spots of interest.To validate this model, we measured the individual (single fish) and the collective (group of 10 fish) motion of adult zebrafish in different environmental conditions. We observed the behaviour of zebrafish in a bounded tank with potential spots of interest to take into account the interactions with the environment. First, we analysed the locomotion of isolated individuals swimming in a uniform environment to determine the intrinsic characteristics of their motion . We performed similar measurements in a heterogeneous environment by placing two floating plastic discs acting as attractive spots. Then, we investigated the influence of conspecifics by observing the collective motion of groups of zebrafish in homogeneous and heterogeneous environments. We measured their probability of presence in each location in the experimental tank and their interindividual distances. These experimental data are used for setting the parameters’ values of the individual behaviours as well as the weights of the different types of stimulus on the directional choice of the fish.2.Xi with a velocity vector Vi though a discrete time process in a bounded two-dimensional space:vi the linear speed of the ith agent and Θi its orientation. As we focus on the decision-making process of the fish to choose its orientation in a complex environment  with other fish, we assume the simple hypothesis that the linear speed vi of the agent is randomly drawn from the instantaneous speed distribution measured in our experiments. The novelty of this model is the computation of the orientation Θi. We model the spherical visual perception field of the fish  and a measure of concentration κ . For a fish that perceives no stimulus, the distribution of probability is described by μ=0 while the value of κ0 is determined experimentally  for a fish to move in each potential direction θ in a bounded tank without perceptible stimulus is given byd the distance to the closest wall, dw the threshold distance determining the interaction with the walls, κ0 and κw the dispersion parameters, respectively, associated with the basic-swimming and wall-following behaviours, μw1 and μw2 the two possible directions along a considered wall and I0 the modified Bessel function of first kind of order zero. The values of dw, κ0 and κw are determined experimentally.Our aim is to model fish swimming in nearly two dimensions in a bounded environment with potential spots of interest that attract them. We simulate agents that update their position vector the fish  and descequation ). By doiEquations –2.4) ar ar2.4) a2.1a). Their visual perception is simulated as a cyclopean vision sensor that has a 270° spherical field of view extending frontally and laterally and an infinite effective radius (b). The fish perceive potential stimuli by the solid angles that the projection of their extremities captures in their spherical perception field (c).We simulate fish-agents that can perceive and react to three-dimensional stimuli perceived in their visual field. The fish are modelled as three-dimensional polygons with six vertices swimming on a two-dimensional plane space a. Their e radius b. The fion field c.To reflect our experimental conditions, we include two potential stimuli in our simulations: fish and spots of interest. The fish are considered as polygons of length=0.035 m, width=0.01 m and height=0.01 m whose extremities form the boundaries of the fish. The spots of interest are considered as discs with a radius of 0.1 m floating 0.05 m above the plane space.2.2θ the potential direction of movement of the fish, μfi the location of the perceived fish i and κf a measure of concentration.Once all potential stimuli have been perceived, the model computes a PDF for the focal fish to move according to each stimulus (fish or spots). For example, the probability of the focal fish to orient towards a perceived fish is given by a von Mises distribution clustered around this fish:Ai* captured by each stimulus to the sum of the solid angles captured by all stimuli AT*. For example, the PDF computed for all perceived fish is given byATf the sum of the solid angles Afi captured by each fish i and nf the number of perceived fish.The model computes a distribution for each stimulus (fish or spot) in the perception field of the focal fish. Then for each type of stimuli, it performs a weighted sum of all distributions proportional to the ratio of the solid angle μsi the location of the centre of the perceived spot i, κs the dispersion parameters associated with the spots, ATs the sum of the solid angles Asi captured by each spot and ns the number of perceived spots.We calculate a similar PDF for the spots of interest perceived by the focal fish:f(θ) of the focal fish to move towards a given direction. In this first approach, we assumed that the weight of the PDF associated with each type of stimuli is a linear function of the total solid angle AT* that they capture in the perception field of the focal fish (f(θ) is given byα0 and β0 the parameters weighting the influence of, respectively, the fish and the shelters for a fish distant from any wall and αW and βW for a fish following a wall. These parameters are fitted experimentally.Once the PDFs have been computed for each type of stimuli (fish and/or spots), we calculate a weighted sum of the PDFs to obtain the global probability distribution function cal fish . This imf(θ) by performing a cumulative trapezoidal numerical integration of the PDF in the interval . Finally, the model draws a random direction Θi in this distribution by inverse transform sampling. The position of the fish is then updated according to this direction and this velocity with equations . The distribution of the positions detected in the tank shows that the higher probability of the presence is situated along the walls and that the fish avoid the centre of the tank (a).We measured the positions of ten fish swimming individually in our 1.20×1.20 m experimental tank during 1 h. Based on this tracking, we built the trajectories of the fish and computed their speed and change in orientation. An example of a 10 min trajectory of a fish is given in figure 5the tank a.Figurei at time t and t+1. While this differs obviously from the reality, we do not take into account speed matching in this first parametrization of our model for simplicity. The experiments with single fish allow us to fit the parameter value characterizing the change of direction of fish κ0. To do so, we measured the change of direction of the fish when they were at least at 0.30 m from any walls. By doing so, we excluded the potential influence of the walls and considered only the intrinsic changes of direction. Then, we compared these experimental distributions to theoretical ones and selected the best fitting. In this first approach, we chose the sum of least-squares minimization as a rapid and low computational cost fitting method. The best fitting of this experimental distribution was given by κ0=6.3 , as shown in We used this experimental data to set the parameters of our model in order to simulate the movement of a single fish in a homogeneous tank. In the simulations, the speed of the agent is drawn from the experimental distribution of the instantaneous speed of the fish. Speeds are drawn independently from each other so that there is no correlation between the speed of agent dw and the measure of concentration κw of the PDFs associated with wall following. To estimate these parameter values, we performed simulations with different couples of values  and compared the experimental distributions of change in orientation and of probability of the presence with those generated by the simulations. The fitting of these parameters showed that dw=0.05 and κw=20 are the best values to reproduce our experimental data . A 10 min trajectory of a simulated fish with these parameter values is shown in figure 5c.For the experiments involving a single fish, only the interaction with the walls of the tank is present. Therefore, the other relevant parameters are the distance of interaction with the walls c). We measured the distance between all pairs of fish at each time step to characterize the cohesion of the group. The distribution of these interindividual distances shows an average of 0.394 m±0.38 m with the mode of the distribution between 0.1 and 0.2 m (a).Then, we performed experiments with 10 groups of 10 zebrafish. As observed for single fish experiments, fish were mostly detected along the walls of the tank c. We meand 0.2 m a.FigureκF associated with the PDF computed for each perceived fish and the parameters α0 and αW that weight the influence of other individuals on a fish that is far from (α0) or close (αW) to a wall. To reduce the parameters’ space and the complexity of the fitting, the value of κF is assumed to be similar to κw and equal to 20. By doing so, we consider that a fish orients towards a given target μ with a high accuracy. To determine the value of the weights α* we perform simulations with different values of  and compare the distributions of the interindividual distances and the probability of the presence with those obtained for the experiments. The best values to reproduce both distributions are α0=55 and αW=20 . Thus, the fish that follow a wall are less influenced by other congeners than the fish situated in the centre of the tank. With these parameter values, the model reproduces the probability of the presence displayed by groups of 10 zebrafish as shown in figure 8d. Concerning the distribution of interindividual distances, the model reproduces the decreasing distribution observed in the experiments except for the mode of the distribution that is between 0 and 0.1 m (b).To simulate experiments with 10 fish, we introduce three parameters describing the interaction with the fish: the measure of concentration nd 0.1 m b.3.2b. We calculated similar parameters from the individual positions of the fish (10×1 fish) moving alone in the tank. In the presence of two spots, the fish are mainly detected along the walls and under the spots as shown by their probability of the presence (a). The experiments with single fish also showed that the fish decrease their speed under these floating discs. Indeed, the separation of the speed distribution measured outside or inside the spots shows that the average speed was three times slower when the fish were located under a floating disc (a). On the contrary, the presence of the spots did not affect the distribution of the changes in orientation that were similar outside and inside (b).We added two spots of interest placed at presence a. The exing disc a. On thed inside b.Figuredw=0.05 and κw=20 were fitted by our experiments in homogeneous environment, we explore the parameter values of β0 and βW (the weight of influence of the spots) and assume that the measure of concentration associated with the spots κs=κw=20. We perform simulations for different values of  and compare their results with the experimental probability of the presence and distribution of change of orientation. The best values to reproduce our experimental data are β0=0.15 and βW=0.01 . As observed for the influence of the congeners, these values indicate that the fish following a wall are less influenced by the spots than the fish swimming in the centre of the tank. With these parameter values, the model reproduces the spatial distribution of the fish along the walls and under the floating discs (c).Only the interaction with the walls and the shelters of the tank are present in the experiments involving a single fish. As ng discs c.b). The measure of the interindividual distances shows that the presence of spots does not have a strong influence on the distance between the fish (c). Indeed, the average distance between the individuals is 0.439±0.331 m, which is 6 mm less than observed in the absence of floating discs.Finally, we observed 10 groups of 10 zebrafish swimming in the presence of two spots of interest during 1 h. In this case, the fish were also observed mainly under the spots and along the walls but showed a preference for the spots b. The methe fish c. Indeedα0, αW) and the spots of interest  were fitted independently but here, both stimuli are simultaneously present in the perception field of the fish. Therefore, we investigate the relative importance of both stimuli by weighting the influence of the congeners and the spots. To do so, we multiply the previously fitted values of  and  by different weighting factors wF,wS) and compare the distributions of the probability of the presence and the interindividual distances with those measured experimentally. The best fit is given by α* and β* to reproduce the experimental data are α0=27.5, αW=10, β0=0.016 and βW=0.0011. While these values give a correct fit of the experimental probability of the presence (d), the model does not perfectly reproduce the distribution of the interindividual distances (d). Similar to the groups of fish in a homogeneous environment, the mode of the interindividual distances measured in the simulation is between 0 and 0.1 m, whereas the mode of the experimental distances is between 0.1 and 0.2 m.We simulated these experiments with groups of 10 zebrafish with our model by integrating the interactions of the agents with the other fish and the spots of interest. In the previous experiments, the parameters ruling the interactions with the fish . Although the fish were attracted by the floating disc, they did not seem to avoid the light because their presence under the disk and along the wall (that are exposed to light) are similar. These observations are in accordance with the ecology of the species that shows a diurnal activity in Nepalese and Indian shallow water and rice paddies .4.2areae) in the fronto-dorsal region [Based on our observations and on the literature, we developed a model describing the individual behaviour of fish during collective motion. This model is based on a three-dimensional perception system accounting for the vision of zebrafish in which the objects are described by the solid angles that they capture in their perception field. By doing so, we are closer to a realistic description of the sensory system of the fish. In this first step, we assumed the simple hypothesis that the objects were homogeneously perceived in the three-dimensional sensory field. However, the recent characterization of the visual system of zebrafish highlighted the existence of centres of acute vision . Such choice between two concurrent stimuli has been evidenced in zebrafish larvae that orient towards one of two light sources rather than swimming towards their bisector [b,e), the two peaks of probability merge into a unique one pointing between both stimuli . In this case, the fish has a higher probability to orient between the stimuli rather than choosing one of them. Thus, the proposed algorithm is able to spontaneously transit from choice to compromise and vice versa according to the spatial distribution of the stimuli.In addition to this sensory system, we proposed a stochastic mechanism to determine the direction of an agent according to its visual field. Rather than computing a resulting vectorial force that is applied to the fish, we described the decision-making process of the fish to make an intentional movement according to its perception field. In this first step, we represented the choice of the individual by a stochastic process. The fish can potentially move in any direction, but it will favour directions associated with a perceived stimuli (e.g. congeners). This is made possible by the representation of the perception field of the fish and its translation into the normalized sum of the PDFs associated to each perceived stimulus. With this approach of PDFs’ summation, we can potentially include all kinds of stimuli  that are perceived by the fish and account for a choice or a compromise between potentially antagonist stimuli. Indeed, two stimuli distant from each other will generate two distinct peaks in the PDF, leading to a choice of the fish to orient preferentially towards one of the stimuli a,d. Suchbisector . On the  stimuli c,f. In tDanio rerio. The model correctly reproduces the behaviour of single individuals swimming in a bounded aquarium. The fish mainly follow the walls but sometimes swim in the centre of the tank. The probability of the presence of the fish is also correctly reproduced when simulating groups of 10 agents. However, the distribution of interindividual distances is biased towards short distances. This could originate from the absence of a preferred distance or an avoidance distance between the agents. Indeed, while the agents of the model can overlap, the fish have to respect a distance between them. Such distance was not introduced in this first parametrization of the model to limit the number of parameters. In a heterogeneous environment, the model was also able to reproduce the individual trajectories of single fish that transit between the walls and the spots of interest. As in the homogeneous environment, the probability of the presence of the groups of fish is also reflected in the simulations, but again, the distribution of the interindividual distances is biased towards short distances. These results indicate that further versions of the model should include additional biological variables that can be species dependent like a preferred distance.Moreover, most of the models consider motion in an unbounded homogeneous space (i.e. in a torus geometry). While this assumption is reasonable to study the collective behaviour of animals living in pelagic waters, the interactions with environmental features cannot be neglected for the species living in small streams. Therefore, our model takes into account a bounded and heterogeneous space to be closer to the natural conditions of et al. [D. rerio locomotion. In our model, we did not implement a function accounting for speed modulation in order to focus on the directional decision-making mechanism. The instantaneous speed was drawn from the experimental distribution with a time step corresponding to the tail-beat period of zebrafish. Then, future work should investigate the impact of the proposed stochastic mechanism for orientation on such KMs. Moreover, the development of a KM version of our IBM could be an intermediate step towards a continuum model description of the group of agents. This could then be used to perform large-scale analysis and predictions of the collective behaviour displayed by very large populations. Such multiscale modelling approach would allow us to identify the properties of the group that are preserved at all scales of analysis or on the contrary that are specific to a particular level of observation [In this study, we applied our decision-making algorithm to an individual-based model (IBM). Recently, a kinetic model (KM) was proposed to account for the individual movement of isolated zebrafish ,40. Thiset al. ,38 took ervation .In parallel to the understanding of information processing by the animals, this approach is also a new step towards bioinspired algorithms that can be implemented in robotic agents. Indeed, it is a major scientific challenge to build artificial systems composed of robots that can perceive, communicate to, interact with other agents  and adapt to their environment . To do s5.5.1D. rerio AB strain from Institut Curie, Paris). Fish were 18 months old at the time of the experiments. We kept fish under laboratory conditions, 27°C, 500 μS salinity with a 10 L:14 D cycle. The fish were reared in 55 l tanks and were fed two times per day . Water pH was maintained at 7.5 and nitrites (NO2−) were below 0.3 mg l−1.We acquired 150 adult wild-type zebrafish  was placed at each corner of the tank and oriented towards the wall to provide indirect lightning of the tank. The whole set-up was confined behind white sheets to isolate experiments and homogenize luminosity.We recorded the behaviour of zebrafish in a 120×120×30 cm experimental tank made of glass with internal walls covered with white adhesive. The water depth was kept at 10 cm during the experiments. A Logitech5.3We recorded the behaviour of zebrafish swimming in our tank during 1 h. We tested two numbers of individuals (single fish or groups of 10 fish) in two environmental conditions (homogeneous or heterogeneous). The heterogeneous environment was created by adding two floating discs made of blue coloured Plexiglas (200 mm diameter and 3 mm thick) suspended by nylon threads. The two spots were spaced from 70 cm and located on a diagonal of the square. Before the trials, fish were placed with a hand net in a cylindrical arena (20 cm diameter) made of Plexiglas placed in the centre of our experimental tank. Following a 5 min acclimatization period, this arena was removed and fish were able to freely swim in the experimental tank. We performed 10 replicates for each combination of parameters .5.4P of the fish were recorded at each time step t=1 s for all other experimental conditions. This allowed us to build the trajectory of each individual for experiments involving a single fish and to compute the speed of the individuals as well as their change in orientation between successive positions. The instantaneous speed vt was calculated on three positions and thus computed as the distance between P and P divided by two time steps, while change in orientation was computed as the angle between two successive speed vectors. The distributions of speed were computed only for parts of the trajectory during which the fish were not in freezing behaviour (i.e. immobile). This corresponds to a spontaneous speed higher than 1 mm s−1. As our tracking system could switch the identity of the fish after occlusions , we did not calculate individual measures for data obtained with groups of fish but characterized the aggregation level of the group.The recorded videos were analysed offline using a custom Matlab script developed to detect the position of the fish. This script performed a background subtraction on each frame and transformed it in a binary image according to a pixel intensity threshold given by the user. The software then identified the blobs on this image and kept only the blobs that were formed by more than 20 and less than 200 pixels . As this method did not allow a perfect detection of all the individuals, we developed a second script that was run after the first one and that plotted the frame where a fish (or more) was undetected for the user to manually identify the missing individual(s). While this analysis tool is time-costly, it allowed us to identify the fish that were partially hidden during a collision/superposition with another fish or the fish that were situated under the floating discs as our program was not able to detect them due to a lack of intensity of the pixel after the background subtraction. The positions 5.5x, y, 0). The positions of the other vertices are computed according to the position of the head and the direction of the fish. To compute the solid angle of the perceived stimuli in the perception field of the focal fish, we calculated the area of the spherical polygon delimited by the projection of the stimuli’s vertices on a unit sphere centred on the focal fish. To do so, we divided the polygon in two spherical triangles and computed their spherical excess using L’Huilier’s theorem:a, b and c the length of the arcs between the vertices expressed in spherical coordinates  and computed bys the semi-parameter given byThe model was implemented in a Matlab script. The simulations were run during 10 800 time steps, each time step representing an increment of


In delay tolerant WSNs mobile ferries can be used for collecting data from sensor nodes, especially in large-scale networks. Unlike data collection via multi-hop forwarding among the nodes, ferries travel across the sensing field and collect data from sensors. The advantage of using a ferry-based approach is that, it eliminates the need for multi-hop forwarding of data, and as a result energy consumption at the nodes is significantly reduced. However, this increases data delivery latency and as such might not be suitable for all applications. In this paper an efficient data collection algorithm using a ferry node is proposed while considering the overall ferry roundtrip travel time and the overall consumed energy in the network. To minimize the overall roundtrip travel time, we divided the sensing field area into virtual grids based on the assumed sensing range and assigned a checkpoint in each one. A Genetic Algorithm with weight metrics to solve the Travel Sales Man Problem (TSP) and decide on an optimum path for the ferry to collect data is then used. We utilized our previously published node ranking clustering algorithm (NRCA) in each virtual grid and in choosing the location for placing the ferry’s checkpoints. In NRCA the decision of selecting cluster heads is based on their residual energy and their distance from their associated checkpoint which acts as a temporary sink. We simulated the proposed algorithm in MATLAB and showed its performance in terms of the network lifetime, total energy consumption and the total travel time. Moreover, we showed through simulation that nonlinear trajectory achieves a better optimization in term of network lifetime, overall energy consumed and the roundtrip travel time of the ferry compared to linear predetermined trajectory. In additional to that, we compared the performance of your algorithm to other recent algorithms in terms of the network lifetime using same and different initial energy values. Recent improvements in hardware electronics technology have enabled manufacturers to develop low cost, low power and small size sensors ,2,3. HunIn general, a wireless sensor network is a collection of static nodes with sensing, computation, and wireless communication capabilities . Using mIn this paper, we propose a mobile ferry algorithm based on our previous published work, the Node Ranking Clustering Algorithm (NRCA) ,8,9. UsiThe rest of the paper is organized as follows: in Using mobile ferries in WSNs is a relatively new area of research which is gaining the attention of many researchers. Incorporating ferries in WSNs helps in eliminating the need for multi-hop forwarding of data . It alsoThe paths that the ferry takes to collect sensed data from sensors can be classified into two categories: random paths and planned paths. Usually in the case of a random path the ferry is attached to people or animals moving randomly to collect sensed data whenever they are in the communication range of the static sensor nodes. In  mobile eDue to the random mobility of the ferry it is difficult to gather sensed data from all deployed nodes. Unlike the random path approach, in the planned path approach a path is determined before the ferry is dispatched and thus the ferry is sent to cover a certain area of deployed sensors to collect data. In , a wirelIn Mobi-Route , a routiIn  the authThe authors in  used a si.e., the sink. By doing this, each checkpoint will act like a virtual sink within each virtual grid. We refer to the new modified NRAC algorithm as ferry based NRCA or FNRCA. In FNRCA distance used to rank the nodes is in reference to the checkpoint position rather than the position of the base station as was done in NRCA.Our proposed approach is different from the above as the ferry does not have to visit each node in the network to collect sensed information. Instead, the sensing field area will be divided into virtual grids and a checkpoint will be placed in each grid. The ferry will only visit these checkpoints to collect data. Our approach also uses TSP and genetic algorithm to choose the optimum path of the ferry which consists of visiting a list of sequenced checkpoints. The sequence of checkpoints that will be visited will be decided by assigning a weight to each checkpoint and deciding which checkpoint will be visited first. Moreover, the NRCA algorithm will be applied in each virtual grid to decide on the best placement position for each checkpoint in order to preserve the energy of the whole network. Our aim is to minimize the overall round trip travelling time of the ferry and to minimize consumed energy in the network. This is achieved by modifying NRCA to be applied in reference to the position of the checkpoint rather than the position of the base station The scheduling of when exactly to send the ferry to collect sensed data from nodes is a complicated task. In ,24 the aIn ,20 the aThe authors in  used a fIn  the authThe authors in  proposedSince data transmission can account for up to 70% of the power consumed by typical sensor nodes ,29,30,31In this paper a proposed ferry-based node ranking clustering algorithm (FNRCA) is used to collect data from nodes. The difference between this algorithm and other algorithms is that this one uses a more efficient mechanism to select cluster heads (CHs). This is done by considering the nodes’ distances and locations within the sensing field, current energy levels of nodes and by calculating the number of rounds that each node can be a cluster head for, to maximize the network lifetime and decrease excessive communication overheads used for electing new cluster heads. In this algorithm, nodes are ranked based on their current energy level (En) and their positions (Dn) with reference to the predetermined checkpoints on the mobile ferry’s trajectory. This ranking is used for choosing cluster heads which are also ranked into levels based on their position, the Euclidean distance from the ferry’s checkpoints. Therefore, each node is assigned a rank Rn  reflecting its candidacy for it being elected as a cluster head.In our algorithm, once the ferry reaches a checkpoint, it broadcasts a notification message to all nodes in its communication range informing them of its presence at the respective checkpoint they are associated with. Nodes within each cluster will then start sending to their associate cluster heads any sensed data to be carried to the base station. The ferry stopping time at each checkpoint is determined by the number of attached cluster heads to that particular checkpoint as will be shown later. Using this strategy, cluster-heads will not have to worry about the speed or the direction of the ferry and energy wasted in doing this will be preserved. The algorithm also is provided with efficient energy management strategy where cluster heads are only waking up when the ferry sends them a notification massage to inform them of its presence. The idea of using the ferry to collect data further preserved energy by reducing multi hopping which consumes energy. To optimize the ferry’s path, a weight was assigned to each checkpoint to be able to choose the best sequence, the order of checkpoints to be visited and the needed stopping time at each one. This eliminates the loss of messages due to any wrong prediction of the position of the ferry or its movement. Our algorithm uses three phases as shown below in The proposed algorithm is shown to be energy efficient because it aims to minimize the energy consumed in the network to collect and transfer data to the BS using a mobile ferry. In the next subsection we introduce the proposed algorithm in more details.In most previously proposed clustering algorithms a node is elected as a cluster head either randomly or based on having the highest residual energy in a cluster. This selection might lead to inefficiencies . One of In the proposed algorithm several assumptions are needed: first, the Base Station (BS) is placed at a fixed position and has unlimited energy. Thus no constraints are assumed with regards to power consumption due to data processing and communication. Second, all nodes are assumed to have the same energy level at the set up phase, which is known to the BS. Third, the sensing field dimensions are also assumed to be provided to the BS. Fourth, it is assumed that the mobile ferry is dispatched from the base station and returns back to it once its task is completed. In addition, it is assumed that there are no energy constraints with respect to the ferry which is assumed to be moving at a fixed speed. Nodes throughout the sensing field are randomly and uniformly distributed.The BS divides the sensing field into smaller partitions called clusters based on the assumed communication/sensing range of the nodes.The sensing field will then be divided into virtual square grids based on the specified sensing range. Each virtual grid will be of size Initially, a ferry checkpoint  is placed at the center of each virtual grid.Initially NRCA is used to choose CHs based on their location from the ferry’s checkpoints.Nodes and cluster heads will associate themselves with the ferry’s checkpoint based on their location within each virtual grid.Border line nodes and cluster heads will be associated with cluster heads and checkpoints closer to them based on distance respectively.After the initial phase, NRCA is applied on each virtual grid based on the position of the ferry’s checkpoint and energy values of the associated nodes. Therefore, the energy consumed per virtual grid will be minimized. This is explained below in the subsequent sections.The ferry will be dispatched from the BS to visit all checkpoints and return back to the BS using a Hamilton cycle as will be shown later.At each checkpoint the ferry stops to collect gathered data from cluster heads associated with this checkpoint. Gathered data consists of sensed data and control information like nodes energy values and nodes GPS locations.Dissemination of data from cluster heads to the ferry is triggered by a control message communicated by the ferry to the cluster heads associated with each checkpoint. The time the ferry will stay for at each checkpoint is determined based on several parameters as will be shown later.In the subsequent rounds of dispatching the ferry, the BS choses the new locations of the checkpoints based on the collected information to minimize the energy of the overall sensing field as will be shown later. The BS will then determine the new path of the ferry using Hamilton cycle as was done in the initial phase.The energy model used for sensing and dissemination of data in our simulation is the same used by ,36,37,38The proposed algorithm is an extension based on our previously published work NRCA ,8,9 withAfter the initial formation of clusters and based on the information collected through the first dispatching round of the ferry, nodes in each cluster are ranked by the BS based on their position with respect to the checkpoint they are attached to and on their current energy level. This information is dispatched back to the nodes through the next ferry visit. Nodes with the maximum residual energy and minimum distance will be chosen as a cluster head based on NodeRanking A cluster head in each cluster will be changed when its energy level reaches a pre-defined threshold or a calculated value and not every sensing round. This will make it possible for To decide the location of the ferry’s checkpoints, we first create a virtual grid based on the specified sensing range. Each virtual grid will be of size ○Input: a subset of cluster heads CPch in each virtual grid, the virtual grid dimensions and the sensing range r between the ferry and clusters heads;○Output: if the subset of all cluster heads which can be covered by a circle with a radius at most r, return the circle’s center (Equations (3) and (4)) or false otherwise and no change in the checkpoint position i.e., it will be its previous position.○if○thenradius > r ○return false;      // no change in checkpoint position○else○center = (Equations (3) and (4))○return center. // checkpoint position will be the center ○end ifFollowing is the pseudo code for choosing the checkpoint location in each grid: The stopping time (ST) is the time period the ferry will stay at each checkpoint, j, to allow the associated cluster heads to send gathered data to the ferry. This time period depends on the number of associated cluster heads, their buffer sizes and the transmission time of a bit based on the assumed medium physical characteristics: CHsCPsGiven a set of cluster-heads Goal one: Minimizing the overall tour time of the ferry (delay): Goal two: Minimize the overall energy consumption of the network by applying our FNRCA in each virtual grid i.e., finding the: In order to choose the optimum path of the ferry and achieve the above goal and constraints a weighting scheme is used to order the checkpoints into the sequence to be visited by the ferry.Checkpoints with larger number of attached cluster heads:To determine the path of the ferry or which checkpoints to visit first, a weighting scheme is used based on determining the following weights: Checkpoints closer to the Base Station:Checkpoints with larger number of attached cluster heads will contribute more to the amount of the data collected and in order to reduce data lost, they will have a higher priority of being visited first by the ferry. The weight for such a CP, j, is calculated as: Checkpoints closer to each other:Checkpoints closer to the Base Station will be given a higher weight in order for the ferry to start the collection process from them first and then move ones further away. Their weight will be calculated as: Checkpoint closer to each other will reduce the traveling time and distance of the ferry; therefore, they will have a higher priority of being visited first by the ferry and their weight will be calculated as: The overall weight ○Input: a set of checkpoints, their attached cluster heads.○Output: A sequence of checkpoint for the ferry to follow.○//Optimal Travelling_Salesman_Problem_tour○while there exist checkpoints do○for all CPj  dofind the weight W from Equation (15).○End for○Select CP with maximum weight○Add it to the TSPtour list {CPj,CPj + 1…}○Remove it from the set○end while○return TSPtourFollowing is the pseudo code for ordering the checkpoint in Travelling Salesman Problem sequence to be visited by the ferry according to the weight given to them: The output of the above pseudo code which is the ordered sequence of checkpoint will be used as an input to the next pseudo code of the genetic algorithm. In our work, TSP will be used to find a Hamilton cycle to decide the path of the ferry, where checkpoints represent vertices and the distance between them will represent the edges. The above weight will be used to choose which vertices will be visited first such that the overall consumed energy and roundtrip travelling time will be a minimum.Assuming a directed graph (G) with weights on the edges where The abovementioned problem can be solved easily in a short time if the number of checkpoints are four or less by trying all possible paths (4!) and finding the minimum weight among them. However, if the number of checkpoints is more than four there will be permutations of possible paths which will take much longer time and processing capabilities. Therefore, we used a genetic algorithm to find the best path based on our own fitness function, goals and weights as shown in the below subsections.Since TSP is an NP-hard problem ,11,38 we○Input: p(t) and c(t) are parents paths and offspring candidate paths in current generation t.○// Input will be taken from the previous pseudocode○Output: The optimum solution TSP.○T←0;○Initialize p(t);○Evaluate p(t);○While (there exist p(t)) do○Perform crossover and mutation p(t) to get c(t);○Evaluate c(t) with the fitness function(c(t));○Select p(t + 1) from p(t) and c(t);○T ← t + 1;○End While○EndIn this work, we represented the ferry’s path as a list of genes or chromosomes. Where each checkpoint will have a number to identify it. e.g., We used ordered crossover (OX) in our genetic implementation which was used in BERLIN52, the best known program for TSP so far ,38. GiveGiven the following two paths:1st Path = (1 2 3 4 5 6 7 8 9)2nd Path = (4 5 2 1 8 7 6 9 3) Partition each path into three segments 1st Path = (1 2 3 | 4 5 6 7 | 8 9)2nd Path = (4 5 2 | 1 8 7 6 | 9 3)Copy the middle segment of both paths, the two candidate paths become as follow: 1st candidate path = (- - - | 4 5 6 7 | - -)2nd candidate path = (- - - | 1 8 7 6 | - -) Reorder each of the sequences starting from the right segments according to their order in the second path without repeating the already copied numbers (9-3-2-1-8). As 4, 5 7 and 6 are already copied.Place the ordered sequence into the path starting from the right segment.1st candidate path = (2 1 8 | 4 5 6 7 | 9 3)Generate new candidate paths as:1st candidate path = (2 1 8 | 4 5 6 7 | 9 3)2nd candidate path = (3 4 5 | 1 8 7 6 | 9 2)Based on the used OX genetic implementation ,38, the Path 1       =   (1 2 3 4 5 6 7 )Candidate path 1   =   (1 2 3 4 5 6 7 )The resulting children from ordered crossover operation will now be subjected to the mutation operator in the final step to form a new generation. This operator randomly flips or alters one or more bit values at randomly selected locations in a chromosome. An example is shown below where 8 has been altered to 9: MutationFcn” command.For implementing mutation in MATLAB we used the “Time_Fitness_Fun” in Equation (16) will evaluate the travelling time where the shorter the travelling time the better the path will be, however if the travelling time is greater than Tmax, it will be eliminated by assigning a negative value to the function represented by, The fitness function is used to measure the goodness of the produced children in terms of predefined goals, where bad solutions will be eliminated and good solutions will be kept. Our two goals, as shown in Equations (7) and (9), are: first, to evaluate the total travelling time of the ferry and second, to evaluate the total energy consumed in the whole network subject to the constraint in Equation (11). Based on the first goal, the fitness function “For the implementation of the fitness functions we used the MATLAB “fitnessfcn” command given by: To evaluate the performance of the proposed FNRCA algorithm we used MATLAB to simulate the algorithm on a 200 m × 200 m sensing field. As shown in To evaluate the performance we looked at network lifetime, energy consumed and the time of the overall roundtrip as will be shown in the subsections below. We ran the simulation five times and took the average of the runs to present our results.Network lifetime is defined here as the time interval from the time the sensor network starts its operation until the death of the last node in the network. We compare the performance of using a four checkpoints TSP with a genetic algorithm, referred to as the optimized path, to the case of using a predetermined fixed path in the center of the sensing field and the multi-hop NRCA without the use of a ferry. From As shown in The total overall time of one round trip of the ferry is defined as the overall travelling time of the ferry from the base station to each checkpoint and returning back to the BS plus the stopping time at each checkpoint, once, to collect data. As shown in By changing the sensing range for the optimized path, the number of checkpoints will be changed as well. We changed the sensing range to be 20 m and the number of checkpoints to be 25 checkpoints, 40 m and the number of checkpoints to be nine checkpoints, 50 m and the number of checkpoints to be four and finally, to be 100 m and the number of checkpoints to be one. We looked into comparing our algorithm with other state of the art ones under not exact but similar operating conditions. We are using a random uncontrolled path, but in the literature, some of the well-known algorithms we found such as ) are usiIn this paper we have proposed a new efficient data collection algorithm using ferry nodes to collect data from WSN nodes based on a ferry’s path selection. Using this algorithm we achieved efficiency measured by two goals which are minimizing the overall total roundtrip travel time of the ferry and minimizing the overall energy consumed in the whole network. Through simulation we proved the efficiency of our algorithm compared to using traditional multi-hopping method to collect data and using fixed predetermined paths. We showed that nonlinear trajectory achieves a better optimization in term of network lifetime, overall energy consumed and the roundtrip travel time of the ferry. The results of the simulation also showed that using a larger number of checkpoints increases the network lifetime, however, it increases the roundtrip travel time of the ferry.In our future work, we are considering using multiple ferries to collect data which can be also an improvement over the current proposed algorithm and can decrease the time delay in case of emergency or non-delay tolerant applications. We are also considering changing the algorithm to have a speed controlled fly over ferry instead of stopping at each checkpoints, where the ferry can decrease its speed while flying over checkpoints to collect the data.


Cell signaling, gene expression, and metabolism are affected by cell-cell heterogeneity and random changes in the environment. The effects of such fluctuations on cell signaling and gene expression have recently been studied intensively using single-cell experiments. In metabolism heterogeneity may be particularly important because it may affect synchronisation of metabolic oscillations, an important example of cell-cell communication. This synchronisation is notoriously difficult to describe theoretically as the example of glycolytic oscillations shows: neither is the mechanism of glycolytic synchronisation understood nor the role of cell-cell heterogeneity. To pin down the mechanism and to assess its robustness and universality we have experimentally investigated the entrainment of glycolytic oscillations in individual yeast cells by periodic external perturbations. We find that oscillatory cells synchronise through phase shifts and that the mechanism is insensitive to cell heterogeneity (robustness) and similar for different types of external perturbations . Cellular signaling, gene expression, and metabolism are determined by chemical reactions within the cell. The discrete nature of molecular reactions as well as environmental fluctuations and heterogeneity cause fluctuations in these processes. The effect of such noise on cell signaling and gene expression has recently been studied intensively using single-cell analysis1234Saccharomyces cerevisiae7899121314789912131415161718820One of the most intensively studied metabolic dynamics is that of glycolytic oscillations in yeast cells There is a long history of theoretical investigations of oscillations in glycolytic networks222425262728242526In order to understand the synchronisation mechanism and to elucidate the role of heterogeneity it is necessary to quantify how individual cells respond to periodic perturbations of their environments, either due to an externally applied perturbation or through interactions with other cells. To achieve a qualitative and quantitative understanding of the phenomenon requires us to answer three fundamental questions.First, what is the mechanism of synchronisation? Experimental studies of macroscopic oscillations indicate that phase synchronisation may play a role914Second, how robust is the synchronisation mechanism? In a theoretical model for phase synchronisation, the efficiency of the mechanism is determined by the heterogeneity of the cells as well as the strength of the entrainmentThird, how universal is the synchronisation mechanism? Entrainment involves the entire glycolytic network, and not just a single reaction or chemical species. The effect might in fact be the result of the combined response to several different chemical species912131415161718In order to answer the questions raised above we report on the results of novel experiments subjecting individual yeast cells to periodic perturbations. Using an experimental setup that combines an optical trap for cell positioning with microfluidics for precise spatial and temporal control of the extracellular environment we induce glycolytic oscillations in individual cells and investigate their response to periodic perturbations. These experiments allow us to address the questions raised above.To enforce strict periodicity of the perturbations, a microfluidic device is used to achieve complete and reversible changes of the extracellular milieu. This makes it possible to experimentally investigate how the phase, the frequency and the amplitude of an individual-cell oscillation are affected by periodic external perturbations. Analysing these parameters from individual cells allows us to determine the mechanism of entrainment, and thus to answer the first question raised above.To answer the second question, we calculate the order parameter of the phases of the oscillations of the individual cells. This reveals the degree of synchronisation. Studying individual cells allows us to determine which fraction of cells becomes entrained by the perturbation. To ensure that no additional perturbations from cell-cell interactions complicate the analysis, we position the cells far apart from each other by means of an optical trap. Cell-cell interactions are further reduced by flushing out intercellular signaling agents by the flow in the microfluidic chamberTo answer the third question we investigate through which biochemical reactions in the glycolytic reaction network the perturbation causes entrainment. Starting out, we investigated the individual cell responses to periodic addition of ACA to gain information about the single-cell responses at conditions previously studied in populations132633913153334Alternating between a solution containing 20 mM glucose + 5 mM cyanide and a solution containing only glucose, we show that periodic removal of cyanide entrains the oscillations of individual cells in a similar way as addition of ACA  online. To investigate the mechanism responsible for the observed entrainment, the instantaneous phases were calculated  and 2b bPhase synchronisation assumes that the physical properties of the entrained oscillator remain essentially unchanged and that entrainment is due to phase shifts caused by the perturbation. To test whether the frequencies of the oscillations become affected by the perturbations, the mean and standard deviation of the frequencies were calculated before and after the perturbation interval . No signr(t) (see Methods). It increases very rapidly in the perturbation interval and already within the first minute of perturbations, the order parameter reaches a value close to unity, corresponding to complete synchrony  = r0eωt−Δ, see Methods. This expectation is borne out by our experiments . This indicates that the main cause of entrainment at cyanide removal is not due to respiratory reactions. However, care has to be taken when performing experiments using hypoxic media, since yeast cells can respire also in very oxygen poor environments. Even low concentrations of oxygen in the hypoxic medium could cause cell responses when cyanide is removed.et al. induced oscillations using 4 mM KOH and hypoxic solutions instead of cyanideA second reason for the response could be that the pH is altered when cyanide is added. In previous studies, Poulsen A third reason for the observed synchronisation could be cyanide binding to intracellular ACA. The phase dependence of the phase shift differs for different chemical speciesThe experiments reported here show that the mechanism behind the synchronisation of individual oscillatory yeast cells is phase synchronisation rather than frequency or amplitude modulation. Our data show that the strongest phase response is achieved when the concentration of NADH is at a minimum, where the removal of cyanide prolongs the subsequent period roughly by one third. Oscillatory cells continue to oscillate with frequencies and frequency distributions largely unaffected by the periodic perturbation. Also the amplitude of the oscillations remains relatively unaffected by the perturbations. These results support the paradigm originally put forward by Kuramoto and his collaborators , and that cyanide might affect the behavior of glycolytic oscillations in more ways than just by binding ACA and inhibiting respiration43et al., who found that oscillations disappear for strains with deletions of a gene coding for subunit VI of cytochrome c oxidaseOur results indicate that cyanide can work as a synchronising agent due to its ability to bind intracellular ACA. Removal of cyanide will then have the same effect as addition of ACA, where the response is transduced to the first part of glycolysis via NADH/NADIn summary, we report on experimental results that allow us to determine the mechanism, the robustness, and the universality of synchronisation of glycolytic oscillations in yeast cells. Our results pose challenges to theoretical modeling, namely to reproduce our results by simulations of the dynamics of glycolytic reaction networks, and to explain the robustness and universality of the synchronisation mechanism. A successful model must show (1) the phase responses to periodic perturbations reported in this work, (2) the robustness of phase synchronisation also for the cell heterogeneity reported here, and (3) the universality of the mechanism; a wide range of periodic perturbations entrain the oscillations. Our experiments are the first that show entrainment of individual yeast cells by periodic perturbations. They support the paradigm originally put forward by Kuramoto: that periodic perturbations can entrain oscillators by affecting just the phases of the oscillators, resulting in a robust and universal mechanism.Saccharomyces cerevisiae, X2180), were grown in a medium comprising 10 g/l glucose, 6.7 g/l yeast nitrogen base and 100 mM potassium phthalate (pH 5.0) on a rotary shaker at 30°C until glucose was exhausted, as described inYeast cells  and an EM-CCD camera , where images were acquired every other secondThe experimental setup and procedure was described in detail in4247nϕ(t), were extracted from the NADH signals by means of a standard procedure that is based upon evaluating the Hilbert transform of the signals in MATLABbeforeϕ, and 9 s after cyanide removal, afterϕ, were plotted for perturbations 1, 2, 4 and 8, for the experiments where entrainment was induced  and for control experiments where only the flow rates were changed but not the chemicals in the solutions . The 9 s delay was chosen to allow the cells time to respond to the perturbation.The acquired images were processed and analysed as described previouslynω, was calculated in time intervals 0–5, 5–10 min and 20–25 min as the frequency at the maximum peak of the Fourier transforms of the NADH signals multiplied by 2π, and is given as mean ± standard deviation for experiments where entrainment was induced  and for the control experiments .The oscillation frequency of the individual cells, ϕ = afterϕ − beforeϕ. If |Δϕ| > π, Δϕ was adjusted with ±2π to move it into the interval . The phase shifts were then corrected by subtracting the expected phase shifts of each cell, nωΔt, where Δt is the time between the measurement of the phase before and after the perturbation. This corrected phase shift was fitted by a second-degree polynomial on the form:The phase shifts of the data sets at the first perturbation were calculated as Δinterval .r(t), defined as3149N is the total number of cells in the experiment (N = 10 in N = 14 in The degree of synchronisation was characterised by the order parameter r0 is the order at t = 0 and Δω is the standard deviation of nω. The frequencies were calculated in time interval 20–25 min and the decay was set to start 18 s after the end of the last perturbation (When the cells are independent and there is no external perturbation, the order parameter is expected to decay asurbation .A.-K.G planned and performed the experiments, analysed the data and wrote the manuscript. All authors discussed the results and commented on the manuscript. C.B.A, B.M. and M.G provided guidance throughout.Supplementary Information


Development of more sustainable biofuel production processes is ongoing, and technology to run these processes at a high dry matter content, also called high-gravity conditions, is one option. This paper presents the results of a life cycle assessment (LCA) of such a technology currently in development for the production of bio-ethanol from spruce wood chips.The cradle-to-gate LCA used lab results from a set of 30 experiments (or process configurations) in which the main process variable was the detoxification strategy applied to the pretreated feedstock material. The results of the assessment show that a process configuration, in which washing of the pretreated slurry is the detoxification strategy, leads to the lowest environmental impact of the process. Enzyme production and use are the main contributors to the environmental impact in all process configurations, and strategies to significantly reduce this contribution are enzyme recycling and on-site enzyme production. Furthermore, a strong linear correlation between the ethanol yield of a configuration and its environmental impact is demonstrated, and the selected environmental impacts show a very strong cross-correlation  contains supplementary material, which is available to authorized users. More sustainable processes for the production of biofuels continue to be the focus of ongoing technology development. The industry has been aiming at improving the energy and material efficiency of existing bio-ethanol production technologies with the goal to make these more cost competitive (for some examples see chapter 12 in . HoweverLife cycle assessment (LCA) is a tool for assessing the environmental aspects and potential impacts associated with a product throughout its life cycle , 10. LCALCAs of second generation ethanol production have generally been done using data that describe industrial-scale operations  andCherubini and Strømman  pointed ee e.g., –32), butThe purpose of the current LCA was to assess the environmental impact of high-gravity conditions during the hydrolysis and fermentation steps in an ethanol production process with spruce wood chips as the feedstock, at a very early development stage. Furthermore, the main factors that determine the environmental impact of the technology in development were determined. From a methodological point-of-view, the possibility of streamlining the LCA was explored. Finally, the ethanol production from spruce wood chips using high-gravity technology was compared with its production from wheat straw using a similar technology. The results of the LCA are intended to help guide the development of the technology for high-gravity hydrolysis and fermentation by providing technology developers, researchers and industry decision makers the environmental hotspots from an environmental life cycle point-of-view.v/v). The solids in the bottom product are dried and made into pellets that mainly consist of lignin, and the water with dissolved fermentation by-products is fed to the anaerobic digester. The methane produced in the AD and the lignin pellets were assumed to be incinerated to provide the process with the required energy. A surplus of either by-product was exported. It should be noted that the non-digested cellulose was treated as a material loss (it is neither recycled nor incinerated). This was done in order to model the production of lignin pellets by-product with a high purity. The distribution of the produced ethanol to the pump and the use phase were excluded from the assessment because the focus is on the development of the new high-gravity technology for ethanol production.The LCA conducted in this study was defined as a cradle-to-gate system, from the extraction of spruce wood until the gate of the ethanol production plant, for a set of process configurations see “” sectionss basis ) is prodTwenty-one batch experiments in which four different detoxification strategies were tested using two process strategies, namely, separate hydrolysis and fermentation (SHF) and pre-saccharification and fermentation (PSSF) . The fouFive experiments in which batch and fed-batch strategies were tested for process configurations using the simultaneous saccharification and fermentation (SSF) process strategy . Two batThe LCA of various high-gravity process configurations was based on the results of two sets of laboratory-scale experiments , 8. The All batch experiments were run with a dry matter (DM) content of 30 % DM (corresponding to 20 % water-insoluble solids (WIS) content), except for the PSSF batch experiments in which the slurry was washed. In those experiments, the DM content was equal to the WIS content (20 %) because all water-soluble solids were removed. Furthermore, the enzyme preparation that was used in these experiments was Cellic CTec 2 and the enzyme load was 7.5 FPU gerred to . AdditioThe functional unit in this assessment was 1 L of ethanol produced from spruce by a process system that uses the high-gravity hydrolysis and fermentation technology. The LCA was carried out using an attributional approach, because the purpose was to identify improvement possibilities in the technologies in development (and to compare them to the base case process configuration), and thus on what to focus in the development.The life cycle impact assessment (LCIA) was carried out using the CML characterization method . The folGWP The main goal of the use of biofuels is to reduce the use of fossil-based fuels and thus reducing their potential impact on global warming. In this assessment, biogenic greenhouse gas (GHG) emissions were assumed to be climate neutral, and thus only the environmental impact of fossil GHG emissions of the analyzed system were taken into account.Eutrophication potential (EP) The use of nutrients during the production of the enzyme preparation can lead to increased eutrophication due to the emission of phosphorus and nitrogen.Acidification potential (AP) Combustion of fossil fuels, biogas, and lignin pellets can lead to increased acidification due to the emission of Photochemical ozone creation potential (POCP) Combustion of fossil fuels, biogas, and lignin pellets can lead to increased photochemical ozone creation due to the emission of volatile organic compounds (VOCs), CO and Our previous LCA study on the production of ethanol from wheat straw demonstrated that the ethanol yield is the main determinant of the environmental impact of a process configuration . This waA partitioning of the environmental burden based on the economic value of the main product (ethanol) and the by-products  was used  theion (AD) , and 85 ion (AD) . The CODentclass1pt{minimant}WIS-1 ; (g) thent}WIS-1    and tookAdditional nutrients see “” sectionThe fossil fuel mix used as additional fuel for the ethanol production process was assumed to be the Swedish mix from 2011 and consists of 11 % coal, 82 % oil and 7 % natural gas . This fuThe electricity used by the ethanol production process was produced in Sweden, and was modeled with the ecoinvent process ‘electricity, production mix SE’ .Several choices also had to be made for modeling forestry and sawmill operations, and enzymes and chemicals production  Fig. . This sues) Fig. .Fig. 5CrThe base case process configuration is the only configuration that is in need of additional fuel to meet its energy needs. It has been assumed that this additional energy is delivered in the form of fossil fuel, and amounts to 3.1 MJ LThe consumption of fossil fuel may also be reduced by increasing the combustion efficiency of the renewable fuels generated by the process itself, that is, lignin and methane. The combustion efficiency of methane is not considered here because it is already high at 90 %. The combustion efficiency of lignin needs to increase from 75 to 87 % in order to completely avoid the use of fossil fuel. Improving the energy efficiency of the pretreatment by 26 % would also lead to the elimination of fossil fuel use. Both improvements lead to a similar reduction of the GWP (approx. 7.5 %) as in the case of replacing fossil fuel with biomass at a combustion efficiency of 75 %. These process improvements will also positively affect the GWP of all the other process configurations. For instance, in the case of the configuration with the highest yield  is approx. 25 L Lmentclass2pt{minimThere are 12 process configurations that show a reduction in energy use during downstream processing when compared to the base case configuration Fig. a. The coThe ethanol future price is volatile and this affects the environmental impact that is allocated to the ethanol product. All of the impact categories show a significant sensitivity to varying the price of ethanol. Changing this price from 600 to 415 € teld see “” sectionThe water stream going from the detoxification and neutralization step to the anaerobic digester Fig.  may be cSHF with PEI added to the slurry before hydrolysis in one case, and PEI added after hydrolysis in the other case. The hydrolysis took place at 50 SSF with PEI added to the slurry before the SSF starts. The SSF was run at 34 The use of polyethylenimine (PEI) as a detoxifying agent was tested by Cannella et al. . Three pThe experiments were run at 19 % WIS. The enzyme preparation used was Cellic CTec2 and the enzyme load was 7.5 FPU gziridine , and aziziridine . These tziridine . 15 g\dolts, see .The results for the GWP of these three configurations Fig. a. This iThe production and use of PEI as a detoxifying agent have a higher environmental impact than the production and use of It should be noted that the ethanol yields for the PEI configurations as shown in Fig. Enzyme production and use are the largest contributors to all environmental impacts studied for all process configurations tested  . The enzct Table , and is The changes in the process configurations analyzed in “Janssen et al.  performeIn the case of both feedstocks, the REU and NREU allocated to the ethanol produced show a declining trend with increasing ethanol yield, as can be expected Fig. . The allBased on the suggestion made in “The total amount of REU and NREU used to produce 1 L of ethanol and its by-products , that iA LCA was performed for a high-gravity technology in development that produces ethanol from spruce wood chips. The LCA was based on lab experiments in which a total of 30 different process configurations using different detoxification strategies were tested. The results show that the configurations in which the pretreated wood slurry is washed have the lowest environmental impact. These configurations were run at 20 % DM, whereas the other experiments were run at 30 % DM. A lower dry matter content may thus improve the environmental impact, although an even lower DM content may lead to additional use of fossil fuel to meet the process’ energy needs, specifically in downstream processing in order to remove the water from the main product stream. The main contributor to the environmental impact is the production and use of enzyme in all tested process configurations. It was demonstrated that either enzyme recycling or on-site production of enzyme can significantly reduce the environmental impact of the technology in development.For the current case, the assessed impact categories  show a strong linear correlation with the ethanol yield of each process configuration: the higher the yield, the lower the environmental impact. Furthermore, these impact categories showed a very strong cross-correlation which suggests that using GWP would suffice in the current case to accurately assess the environmental impact of the technology in development.Finally, a comparison was done with an LCA of a similar technology in development that uses wheat straw as the feedstock based on the renewable (REU) and NREU of the tested process configurations. While a difference between the use of spruce wood chips or wheat straw can be discerned when considering REU and NREU, there is no statistically significant difference in the performance of the high-gravity technology between the use of these two feedstocks when considering the resulting GWP, AP and POCP. This is due to the high contribution of enzyme production and use to each of these impact categories for both spruce wood chips  and wheat straw . HoweveThe LCA points out the environmental hotspots where further development efforts will result in a reduction of the environmental impact of the process. The main identified hotspot in this study was enzyme production and use, which may be reduced by recycling the enzyme or by on-site production of the enzyme. While the results show a strong correlation among the impact categories in the current case study, one should be cautious to exclude impact categories in the assessment. Knowledge of the system under study and the causal links that may exist between inputs to and outputs from the system, and the impact categories provide further input for choosing the most relevant impact categories. The results in this study show that comparing an agricultural feedstock with a forestry feedstock needs to include EP, next to GWP.


Fish living around the coral reefs in the Philippines provide livelihoods for more than a million local fishers and are an important source of protein for coastal communities. However, this rich resource is at risk from myriad threats, which consequently threaten human livelihoods, nutrition, and health. In this paper, we examine the degree to which marine protected areas (MPAs), which aim to conserve marine biodiversity, are associated with improved nutritional outcomes in children under age 5. This analysis, which uses data from the 2008 Philippines Demographic and Health Survey and MPA data from the Coastal Conservation and Education Fund, found a positive association between MPAs and children’s dietary diversity when the MPAs were located closer than 2 km to a child’s community. MPA characteristics such as age or type of management were not consistently associated with dietary diversity. These results suggest a positive association of proximity to MPAs with certain aspects of children’s diet.The online version of this article (doi:10.1007/s11111-015-0240-9) contains supplementary material, which is available to authorized users. Coral reefs have the highest biodiversity of any marine ecosystem and provide critically important ecosystem services for coastal communities in the Philippines. It is estimated that fish living around the coral reefs in the Philippines, which is part of the biodiversity-rich Coral Triangle region, provide livelihoods for more than a million local fishers and are estimated to contribute almost USD 1.5 billion to the economy annually —“clearly defined geographical space[s], recognized, dedicated, and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values” Laffoley —have beeWhile the impacts of MPAs on marine ecosystems in the Philippines are generally considered to be positive . They range in size from <1 sq. km to a handful that are more than 1,000 sq. km  scale.” Two other recent assessments found that less than one-third of MPAs in the Philippines are meeting their conservation and associated ecosystem services goals  and children’s nutrition outcomes following the pathway presented in Fig. We hypothesize that proximity to effective MPAs will have a positive effect on children’s dietary diversity as a result of fish abundance. For this hypothesis to be true, we assume that those who live close to an MPA are primarily dependent on marine resources for their income and/or food, particularly as fishers. With greater fish catch as a result of the establishment of an MPA, it is expected that correlated increases in household incomes from sale of fish would in turn result in increased household spending on a more diverse array of foods for family consumption , which provided child nutrition indicators as well as sociodemographic control variables for the analysis; and georeferenced data on MPAs in the Philippines from the CCEF’s MPA database.The data on MPAs in the Philippines come from the CCEF Marine Protected Coast Reef and Management database, which maintains information on various characteristics of MPAs such as geographic coordinates, size, year of establishment, and an effectiveness rating. We did not use the CCEF effectiveness rating because the majority of the National Integrated Protected Area System (NIPAS) MPAs have not been evaluated. Although restricting the analysis to well-managed MPAs and eliminating the unrated MPAs would be ideal, it would reduce our sample sizes and could bias the analytical results. MPAs that are missing data on latitude/longitude coordinates, the year of establishment, or the size of the MPA are excluded from the analysis. Additionally, the Tañon Strait national MPA is excluded from the study due to the oil exploration activities within the Strait. MPAs that were established after 2008 are also excluded from the analysis, resulting in 526 MPAs included in this analysis. More specific details on the setup of the MPA dataset are presented in Appendix The DHS are nationally representative household surveys with large sample sizes that provide detailed information on social, demographic, and health indicators, including nutritional status of women and children by interviewing women of reproductive age 15–49 to obtain information on the health, nutrition, and socioeconomic characteristics of the respondents, their children, and other household members. Surveyed households are selected through a two-stage random sampling process. At the first stage, DHS clusters or primary sampling units (PSU) comprised of 100–300 households are identified. In the second stage, a prefixed number of households are selected from a list of households from each PSU. In 2008, the sample of interviewed women includes 13,594 women aged 15–49 residing in 12,469 households; the response rates for both the household and women’s interviews exceeded 99 % ; flesh foods ; eggs; vitamin A-rich fruits and vegetables; and other fruits and vegetables WHO . In thisThis analysis examines the relationship of MPA characteristics to dietary diversity using four different characteristics: (1) the categorical distance in kilometers of the index child’s community (sampling cluster) to the closest MPA; (2) the age of the MPA in years, based on the year of establishment; (3) the size of the MPA in hectares; and (4) whether the MPA is locally managed.For all four MPA-related variables, all MPAs at a distance >10 km from the child’s community are treated as the reference group; 88 % of all MPAs in the analysis sample were at a distance >10 km from the child’s community.The age of the MPA is categorized as follows: <3 years, 3–5 years, 6 or more years, no MPA close by. Although these categories were developed based on when the MPA was established, they reflect aspects of the Management Effectiveness Rating System that CCEF developed as part of their MPA database where age of the MPA is a key criteria in the definition of the rating system. According to this classification, MPAs <1 year old (Level 1) are treated as initiated MPAs, with little to no difference between MPA and no MPA. MPAs 1–2 years old are established MPAs which are expected to be managed and somewhat enforced (Level 2). MPAs 3–5 years old are those whose management is enforced and sustained (Levels 3 and 4). MPAs in existence for more than 5 years are expected to be institutionalized (Level 5). Because a large number of MPAs are located at a distance >10 km from households sampled by the DHS, thus falling into the reference group, the number of MPAs in each of the above-specified levels is small. To ensure a sufficiently large number of cases per categorical grouping, the first two categories covering new or recently established MPAs (Level 1 and Level 2) were collapsed, thus grouping all MPAs <3 years old into one category in the analysis.The other variables of interest include the proximity of the child’s community to the ocean (measured in km and serving as a control variable); child’s sex and relative household wealth status, which is an asset-based index that classifies the sample into five wealth quintiles; mother’s education; and number of children in the household. More specific details on the variables and the grouping for the categorical variables are presented in Appendix Broadly based on the pathway presented in Fig. All descriptive and regression analyses are conducted using the svy command in the statistical software Stata version 13, and regression results are presented as odds ratios. All results presented in the next section are based on the analytical sample in the regression taking into account missing information in covariates. Multivariate analytical results examining the relationship between MPA characteristics and minimum dietary diversity and also controlling for child and household characteristics are presented in Table In understanding the results of this analysis, it is important to consider some of the limitations of the study. Calculations based on both the DHS and MPA data were based on several assumptions and involved approximations. While data on the precise dimensions of national MPAs were available through the CCEF database, this information was not consistently available for locally managed MPAs. Thus, the decision was made to define the dimensions of all locally managed MPAs with a calculation based on the size of the MPA in hectares (available in the CCEF database) and the estimated radius.In addition to assumptions regarding MPA dimensions, the displacement of DHS clusters also affects the calculation of proximity to the MPA. Recognizing this, the analysis uses a categorical variable for proximity to the MPA with the smallest category not <2 km. It is possible that some households in the smallest category are in reality at a somewhat greater distance from MPAs, thus affecting the relationship with children’s nutritional outcomes. However, it is hard to know to what extent this is the case—a fact that highlights the difficulties displacement poses for spatial analyses of the DHS data. Finally, the relatively small number of children residing near the coast and proximate to an MPA must be acknowledged and caution therefore advised in interpreting the results of the analysis.Another issue to consider is the fact that there may be some built-in biases around areas with MPAs which cannot be measured in an analytical model. It is possible that households proximate to MPAs are exceptional in that they have the foresight to protect their areas. These communities may have access to greater resources, better Local Government Units, or even better coral reefs. Access to all of these factors may independently influence households’ and children’s nutrition.Weighted percent distributions of the four MPA characteristics used in the analysis are presented in Table Table Table We also examined children’s dietary patterns with respect to each of the seven food groups and distance to an MPA Fig. . The resThere is no clear pattern of association between children’s dietary diversity and the age of the MPA Table . ChildreUsing the non-proximity of MPAs (MPAs at a distance >10 km) from the child’s household as the reference group, the strong effect of distance to the MPA is clearly evident Table . This efThere are no consistent, statistically significant associations between MPA size and children’s diet (see Models 5–6), or between management status  and children’s diet (Models 7–8). No relationship between proximity to the ocean and children’s diet was observed.Coefficients for the child and household control variables included in the models are not presented in the tables, but are available in the supplement. Those findings reiterate the broader literature on nutrition outcomes demonstrating that household characteristics have a key role to play in influencing dietary diversity. Although, as expected, differences in diet among boys and girls in the Philippines are not significant, mother’s education and household wealth have strong positive effects on dietary diversity, while households with a greater number of children showed reduced diversity in children’s diets. With more children in the household, the household or mother’s ability to ensure better nutrition levels among children, in this case the most recently born child, is compromised.The health of fisheries in the Philippines has declined over the past few decades, raising concerns about potential economic and nutritional impacts on coastal communities FAO . In ordeThe results of this study shed light on the relationship between several MPA characteristics and children’s minimum dietary diversity in the Philippines. Dietary diversity is an important indicator associated both with child nutritional status . MPAs can not only improve fishermen’s incomes, by increasing their catch and yields, but also have a trickledown effect upon their children, by improving their nutritional outcomes, their overall health, and future well-being. Accordingly, this research demonstrates that MPAs can be a key strategy to improve well-being among vulnerable and impoverished fishing populations in the Philippines and demonstrates that there may be a multiplier effect on their children’s dietary diversity and health.Regarding the conclusion of Weeks et al.  that MPAAt the same time, while MPAs may represent a viable strategy to improve household nutrition and socioeconomic outcomes, there is need for more research to identify the specific set of MPA characteristics that contribute to their overall effectiveness in conserving marine resources and influencing coastal communities. Identifying the set of MPA characteristics that most contribute to their overall effectiveness, including the optimal legal and governance structures and design and socialization strategies, will be important for informing policy as well as in the management of current MPAs and the establishment of new ones.Both datasets used in the analysis were collected by different entities for different purposes. As a result, almost 90 % of all MPAs included in the study were located at a distance of 10 or more km from DHS clusters where children in the interview sample lived. Furthermore, as a result of the GPS data displacement in the DHS, it is harder to examine the effect of small distances and the proximity to MPAs especially if they are at a distance of <2 km from children’s residence. Even at distances >2 km, the possibility of classification error remains. This research demonstrates the need to take a coordinated, multidisciplinary approach to data collection, so that the appropriate data may be obtained and analyzed both for the explicit purposes of the organization implementing the data collection and for ensuring precise linkages to external but critically complementary datasets.Such a holistic and integrated approach, in which the health and environmental communities consciously collaborate, is essential both for building the evidence base for the importance of biodiversity conservation for human well-being, as well as for effectively advocating for the conservation of the environment in order to achieve optimal and sustainable outcomes for ecosystems and humans alike.Supplementary material 1 (XLS 35 kb)