Anthropogenic vectors enhance the natural dispersal capacity of plant seeds significantly in terms of quantity and distance. Human-mediated seed dispersal (i.e. anthropochory) greatly increases the dispersal of crop species across agroecosystems. In the case of oilseed rape (OSR), spillage of seeds from grain trailers during harvest has never been quantified.Our experimental approach involved establishing 85 seed trap-sites on the road verges of an agricultural area around the grain silo of Selommes . We recorded OSR spillage during harvest and applied a linear model to the data.The amount of seed spilled was related positively to the area of the OSR fields served by the road, whereas the amount of seed spilled decreased with other variables, such as distance from the trap-site to the verge of the road and to the nearest field.The distance to the grain silo, through local and regional effects, affected seed loss. Local effects from fields adjacent to the road resulted in a cumulative spillage on one-lane roads. On two-lane roads, spillage was nearly constant whatever the distance to the silo due to a mixture of these local effects and of grain trailers that joined the road from more distant fields.6 seeds (±4.76×105) along the road length, which represented a mean of 404±94 seeds per m2.From the data, we predicted the number of seeds lost from grain trailers on one road verge in the study area. We predicted a total spillage of 2.05×10Containment of OSR seeds will always be challenging. However, seed spillage could be reduced if grain trailers were covered and filled with less seed. Reducing distances travelled between fields and silos could also limit seed loss. Quantifying dispersal is crucial for understanding the population dynamics and spatial distribution of species in situ or while being driven et al.Human-mediated seed dispersal (i.e. anthropochory) influences plant dispersal greatly in areas where human activities are intense Brassica napus L.) is one of the most commonly cultivated crops in the European Union , who conducted seed trapping experiments in tunnels on a motorway between Berlin (Germany) and its agricultural suburbs. They found 6252 viable seeds, of which the most abundant were wheat, cereal rye, and OSR. The type of seed trapped was associated with the direction of traffic flow through the tunnels To improve knowledge of the dispersal of OSR seed in agroecosystems, we performed an experiment using traps. We laid seed traps on road verges located near OSR fields to measure seed spillage from grain trailers during harvest. We then used statistical models to explain the amount of trapped seed according to several covariables that described the road network, as well as local and landscape elements. We therefore make the five following assumptions:The number of seeds spilled from grain trailers onto the road verge should be related positively to the total area of OSR fields served by the selected road. Indeed, the number of grain trailers needed to transport the harvest from the fields to the grain silo should be proportional to the area of OSR fields harvested.Seed spillage from grain trailers should be greater on two-lane roads (main roads) than on one-lane roads. Drivers of grain trailers heading to the grain silo are expected to use more rapid and direct two-lane roads where possible.The number of seeds spilled from grain trailers should decrease with distance from the nearest OSR field. Grain trailers that have been overfilled with seeds in the field would lose increasingly less seed on the roads as the excess of seed decreases.The amount of seed spilled on road verges should decrease with increasing distance to the edge of the road because more seed is spilled on and near the road.Seed spillage from grain trailers should increase near the grain silo. All grain trailers converge at the grain silo so seed spillage from trailers should also converge at the grain silo.We collected a total of 7710 seeds among the 85 trap-sites. We found seeds every day on every road. The amount of seed spilled during the 8-day experiment varied greatly among trap-sites: it ranged from 0 , and not significant between 0 and 400 m . Data for sites at 40 m and 400 m were thus combined into a new variable: faraway.The stepwise procedure based on the AIC criterium selected the best-fit linear regression model included all the principal variables and the interaction between Silo see . The refSeeds+1) and Surface_OSR was positive  and negative with ln(Dist_road) . As for the interaction between Dist_Silo and Lanes, these results were interpreted as followed: the number of lanes was not significant (P>0.05) thus Lanes had no effect on the origin of the regression line. As for the slope, on a one-lane road, when Dist_field was 0 m, the expected response decreased by 4.45×10−4 with Dist_Silo (P<0.05) while on a two-lane road the slope was negative  but was not significant and thus interpreted as null.The amount of seed lost on road verges increased with the surface of OSR fields serviced and decreased with distance to the field, distance to the road and distance to the silo see . Indeed,P = 0.34): residuals of the model were spatially independent according to a neighborhood defined as the inverse of the shortest distance by the road network between two trap-sites and due to the introduction of Dist_Silo.The graphic of standardized residuals versus fitted values does not show any particular structure, confirming thus that the logarithm transformation stabilized well the variance of the observed response. Moran's test for regression residuals was not significant  were predicted by our model, which represented the harvest of 26 m2 of OSR . The mean rate of emergence for the collected seeds was 80.7%. Some seeds (11.9%) were damaged: half broken or a portion of their tegument detached. Damaged seeds had a lower emergence rate than undamaged seeds (36.8% vs 90.8%).Spillage of OSR seed from grain trailers during harvest appears to be a very common phenomenon, affecting both main roads to the grain silo and other roads. We showed that seed spillage increased with the surface area of OSR fields served, whereas local elements (e.g. distance from the trap-site to the verge of the road and to the nearest field) decreased the amount of seed spilled at a given site. We found no effect of the number of lanes of the roads alone on the number of seeds lost. We detected an interaction between the number of lanes and the distance to the main silo that influenced the slope but not the origin of the regression line. Seed loss decreased with distance to the main silo on one-lane roads but was constant on two-lane roads. On a road that serviced 66 ha of OSR fields, we predicted the spillage on the verge in the direction of transportation to be nearly two million seeds. In the context of GM crops cultivation in agroecosystems, these results emphasise the need to introduce the landscape complexity in models predicting the presence and persistence of GM OSR feral populations.2 and 0.4 to 14.4 seeds per trap, whereas we obtained an average of 11 seeds per trap-site per day with smaller traps, so approximately 66 seeds per m2 per day. Thus, if comparison is possible between these three studies, grain trailers appear to contribute more to seed dispersal than cars and seed dispersal seems to be more abundant in agricultural areas than in suburban areas.To our knowledge, there have been no previous studies on the amount of crop seed lost on road verges from grain trailers. Few studies have quantified seed dispersal by vehicles. For example, a study of car-mediated dispersal of seeds stuck into mud attached to vehicles was conducted on Flemish roads On verges, seeds were spilled mostly near the road, regardless of the type of road, despite our preliminary hypothesis. No previous study on dispersal is dealing with road type and grain trailers or trucks to our knowledge either. From what we observed on field, the absence of interaction between the distance to the road and the type of road on the amount of seed collected could be explained by different styles of driving on the different types of road. Seed losses through cracks in the back doors are presumed negligible and must occur mostly from the top of the trailer, which was rarely covered in our study area. Wind turbulence across the top increased with speed and so, seeds spilled. On narrow one-lane roads, grain trailers were driving slower and nearer to the verge, whereas on two-lane roads, grain trailers drove faster and in the middle of the road (pers. obs.) but also seemed to allow seeds to disperse further.As for the relationship between the number of seeds spilled and the distance to the field, our hypothesis was confirmed by the statistical analysis. Less seed was spilled at trap-sites located 40 m and 400 m from the field than at field borders. Indeed, grain trailers are commonly overfilled with seeds. The excess seed seems to be usually spilled within the first 10 m that the grain trailers drove on the road. When the excess had been lost and the seeds had been compacted in the trailer, spillage decreased. Given that the amount of seeds lost decreased with distance from the field, we presumed that at a certain distance from the field, spillage should cease.Distance to the silo was a predominant variable that interacted with the number of lanes and the geography of the area. The result that seed spillage decreased with the distance to the silo on one-lane roads was expected. However, seed spillage on two-lane roads was constant with the distance to the silo. Few grain trailers travelled on one-lane roads, and for short distances because the grain silo was at the centre of the area and one-lane roads were in general peripheral. Therefore, seed spillage on one-lane roads should be related only to local fields. Two-lane roads always led to the grain silo and thus received also seed spillage from all the neighbouring fields from other roads. This led to a non-structured and more heterogeneous cumulative pattern than on one-lane roads.The unique event that spilled 1500 seeds  at a single trap-site could not be correlated with any OSR field in the study area. The roads that were affected by the high levels of spillage on day 3 of the study period (R11 and R12) were on the edge of the study area and the grain trailers involved in the spillage probably originated from a farm outside the study area. This massive deposition of seeds also demonstrated that grain trailers can disperse seeds over distances greater than 400 m. Despite the large amounts of seed spilled and the high overall emergence rate under greenhouse conditions (80.7%), no feral populations were observed on road R12 in 2011, or during the previous 10 years. The road R12 is bordered by forest and low availability of light could explain the absence of feral OSR. Indeed, the deposition of seed is not necessarily related to adult recruitment, because the environmental conditions where seeds fall play a major role We showed that grain trailers frequently dispersed seeds up to a distance of 400 m. This is the farthest dispersal distance for OSR seed ever quantified. Environmental conditions such as traffic Despite its limitations, this study is the first to quantify the spillage of OSR seed from grain trailers, highlight the importance of this phenomenon, and explain seed loss on the basis of local and landscape elements.et al.in situ will determine rates of seed germination, survival or secondary dormancy. In a context where GM plants are cultivated, GM OSR seeds will be dispersed farther than expected by actual estimations. Road verges management should take into account these dispersal distances. Although it might be impossible to eliminate the spillage of small seeds, such as OSR, during transportation, spillage from grain trailers could be reduced by the introduction of appropriate policies. For example, covering the top of grain trailers, not overfilling trailers, or reducing the distances travelled between fields and silos could limit the loss of seed.According to Pivard In general, seed spillage from grain trailers is not considered or is underestimated in models for seed dispersal and the persistence of feral populations of GM OSR 2 centred on the village of Selommes, Loir-et-Cher, France  which contains a grain silo where most local farmers take their harvested grains , town mayors and farmers were informed about the study before the experiment.We focused on the study of paved roads more than 600 m long as this length was sufficient to put several traps at various distances from fields. We used as reference a survey from local farmers about grain trailer trips between fields and the main silo  and emptied of seeds. At the end of the experiment, all the surrounding fields have been harvested. Grain trailers are the only seed transportation vehicles circulating during harvest.Each trap consisted of two rectangular plastic saucers (172×425 mm) that were positioned side by side to form a 1-m long trap, parallel to the road, the nearest as possible to the road, and secured to the ground with two camping pegs each. A fine layer of cotton wool was placed in the base of each trap to prevent seeds from ricocheting out. Vegetation around the traps was cut back before installation to prevent it from disturbing the fall of the seeds into the traps We pooled the number of seeds collected in each trap over the eight days. Thus, the dependent variable in our model was the total number of seeds.Seeds, quantitative variable) found in the traps over the eight days, five explanatory variables were defined . The model included all the variables . We chose the model that fit our data best according to Akaike's information criteria (AIC) and analysis of residuals and partial residuals of the explanatory variables.The relation between the observed amounts of seeds and the covariates is modeled via a linear model where the dependent variable is ln) as a function of the variables of the study.(TIF)Click here for additional data file.


Electrothermal heating is one of radio frequency tuning method in nanoelectromechanical resonators with magnetomotive transduction. This study confirmed that the surface roughness of the nanoresonator affects the electrothermal tuning performance under moderate conditions at room temperature. The effect of surface roughness on electrothermal tuning is complicated and involves interactions of mechanical and electrical properties. In addition, the electrothermal damping varied depending on the nanoscale molecular solid structure. These factors affect the signal-to-noise ratio, the effective stress of the beam, and the quality Q-factor of the nanoresonator. In nanoelectromechanical systems (NEMS), there are many demands such as a low power consumption, high signal-to-noise ratio (SNR), wide dynamic range, high critical value, and improved Q-factors. These issues are becoming increasingly important as resonators are developed for smaller devices in extremely sensitive applications includinMoreover, in the nanoscale structure, the surface roughness can be a significant issue for electron and phonon transmission or scattering,15 sinceAfter the nanomechanical resonator showed successful operation of the radio frequency (RF) resonance, deepening research topics of various working conditions have been investigated including frequency tuning, controlThe dissipation and damping effects which limit the performance of beam resonance are mainly induced from various causes such as physical defects from the beam structure, thermal elastic damping from the surroundings, clamping losses from supporting clamps on the substrate, signal loss due to electrical feedthrough, and phonon–electron couplings. Therefore, in our study, much effort was made to carefully construct and test the experimental conditions in order to minimize the dissipation factors except for the surface roughness of the resonator.SiC provides superior material properties for high-frequency applications due to its high stiffness and low density, as well as its good tunability due to its higher thermal conductivity than other NEMS materials such as silicon and silicon nitride. Even though it has excellent mechanical properties including a high Young's modulus and low density, a drawback of SiC is its low electric conductivity. In this work, Al layers were applied to the surface of SiC to improve its conductance. This hybrid layer structure (Al/SiC) is a main loss factor but still results in comparable performance to other materials, which must be produced via careful fabrication processes.e is the equivalent resistance due to the substrate and other environment including the energy loss or thermal dissipation. Also, there are parasitic capacitance and inductance, Cp and Lp, from the beam structure or metal pad and read out. RT, the thermal resistance represents the energy dissipation due to the DC thermal voltage applied for the frequency tuning. The composite nanoresonators are 12-μm long, 100-nm wide, and 130-nm  thick as shown in Figure 4 and C3H8 were used as precursors[Scanning electron microscopy (SEM) images of the experimental apparatus and a fully suspended beam are presented in Figure ecursors followedIn the setup, the nanoscale doubly clamped resonator is loaded onto a printed circuit board (PCB) and connected to a moderate vacuum chamber at room temperature, which is affected vertically by a magnetic field (0.9 T). An analog current drive of at least a few tens of microvolts is sent through two ports of the PCB board, which are connected to the beam ends. The electromagnetic field voltage, which is induced by the Lorentzian excitation principles of the resonators, is detected by an amplifier-powered readout port connected to a network analyzer , as shown in Figure The resonant frequency of a doubly clamped beam under thermal stress induced by electrothermal power can be represented as follows:A is the beam cross-sectional area, L is the length of the beam, ρ is the effective density of the beam, E is the effective Young's modulus, and Tf is the beam tension which is proportional to the temperature change of the beam as below:where As presented in the equation, the beam stress is closely related to the resonance frequency and the Q-factor is also affected by changes of the beam stress via electrothermal stress due to critical parameters such as the thermal time constants and thermal conductivity. The resonant frequency is subject to variations despite the identical geometries and materials of the beams because of the different effective beam stresses. Predicting the resonant frequency is difficult due to the stress distributions over the beam structure, which is primarily caused by the different layer deposition conditions and the resulting molecular compositions.Figure The results also demonstrate how electrothermal-powered frequency tuning is affected by the surface conditions of the beam, which results in the determination of the tuning ratio's stability and linearity, based on the input power. Figures The initial resonant frequencies, which are different for each beam, do not affect the frequency tuning ratio, as shown in Figure Figure The tuning performance is primarily decided by the effective beam stress of resonator, which controls not only the resonant frequency but also the resonant properties of the Q-factor, dynamic range, and SNR. The beam stress distributions may be critically determined by the surface roughness, especially at the nanoscale since the surface roughness suggests not only the defects on the surface but also the intermolecular binding condition beneath the surface in the very thin structure. There are two main issues regarding the effects of the surface roughness on the electrothermal tuning performance. One is that the electric conductivity and thermal conductivity are closely related to the tuning performance, which is induced from decreasing electron and phonon transfer through a conducting layer. The other factor is the mechanical stress distribution with thermal stress provided by the electrothermal power. Intermolecular expansion or subtraction interaction occur either regularly or irregularly, which is decided by isotropic or anisotropic molecular bindings. These mostly depend on the surface roughness and sub-layer structure, which affect the boundary between the SiC and Al composite layers. The Al layer tends to be affected by tensile stress whereas SiC is dominated by compression stress while undergoing electrothermal tuning. Those opposite stress distributions from composite layers, especially at the boundary layer, make the tuning effects clearly different from other various molecular structures.Because the thermal damping effects on mechanical resonant motions over a megahertz resonant range are not trivial and many complicated effects exist regarding the thermal expansion among intermolecular bonding, the thermal stress over tight-binding solid structures is increased. These effects are mainly concentrated on the top metal layer of the composite resonator beam with a thickness of a few tens of nanometers, which is small enough to be sensitive to intermolecular stress changes induced by thermal stress. The nanoscale mechanical structure of a beam atomically deposited by chemical vapor deposition is highly related to the top layer surface roughness. From another point of view, the mechanical motion is primarily determined by a balanced weight distribution, especially in high frequency motion. Various unbalanced weight bumps distributed on the top of the surface increase the surface roughness, which strongly affects the resonant motions, contributing to Q-factor degradation. In the case of a nanoscaled beam, the roughness effects play a non-trivial role in RF motion.We demonstrated that as the size of the NEMS beam decreases, the effect related to the beam surface roughness becomes the dominant characteristic due to a large surface-to-volume ratio. The frequency tuning performance was improved with less electrothermal power consumption by improving the surface roughness of the Al-SiC nanobeam. The surface roughness should be controlled in order to minimize the loss of the RF tuning performance. The surface roughness effects are related to not only electromechanical resonance performance but also to electrothermal conductance and dissipation, which are emphasized more in nanoscaled devices because electron and phonon interactions are complicated with scattering issues.The authors declare that they have no competing interests.HY carried out the resonator operation and drafted the manuscript. BP carried out the resonator fabrication and AFM measurement. SJ supervised the experiment and conceived of the study. All authors read and approved the final manuscript.


Malaria is a global health problem responsible for nearly one million deaths every year around 85% of which concern children younger than five years old in Sub-Saharan Africa. In addition, around  Here, we studied the statistical properties  of parasite density estimators of commonly used threshold-based counting techniques depending on variable threshold values. We also assessed the influence of the thresholds on the cost-effectiveness of parasite density estimation methods. In addition, we gave more insights on the behavior of measurement errors according to varying threshold values, and on what should be the optimal threshold values that minimize this variability. Plasmodia: Plasmodium falciparum, P. vivax, P. malariae, and P. ovale. P. falciparum is the Plasmodium species responsible for 85% of the malaria cases and causes the most severe form of the disease. The three other species are less common and less dangerous. The usual and most reliable diagnosis of malaria is microscopic examination of blood films Malaria is caused by one or a combination of four species of m first HPFs, then do this, else do that. In the second ones, the m first HPFs are replaced by “when Parasite density estimation methods usually involve threshold-based counting techniques Epidemiological interpretations must rely on solid evidence, hence the importance of reproducibility for parasite density data. However, all the methods used to determine PD potentially induce variability. To deal with this potential inaccurate estimation of PD, research teams tend to analyze more slides and subjects. By taking duplicate readings or larger sample sizes, we can statistically improve our knowledge of the PD being measured. Then, we can decrease the variability in microscope slide readings and improve the accuracy and reproducibility of the measurements. However, one of the problems the research teams have to deal with is that during large scale studies the number of thick blood smears performed can be greater than To our knowledge, none of the studies of variability have dealt with the sampling error generated by the threshold-based counting techniques or evaluated the impact of the existing threshold values in endpoint measurements. In addition, the accuracy and consistency of these methods have largely been overlooked. Furthermore, there is no general agreement on the optimal method for estimating parasite density according to threshold values. Further experimental evidence is needed to determine which parasite counting technique is most accurate, reproducible, and efficient. The aim of this paper was to explore the variability of four frequently used threshold-based counting methods of determination of PD. For each of these methods, we assessed the consequences that a modification of the threshold can have on variability.PD estimates accuracy vary significantly depending on the methodology from which they are derived. The estimation method differs from one health care organisation to another. To summarize, there are four basic types of threshold-based counting techniques commonly used in epidemiological surveys.Dowling & Shute (1966) The World Health Organization (WHO) Trape (1985) According to the WHO recommendations Tori Bossito area in Southern Benin. In this program, the PD is determined by simultaneously counting parasites and leukocytes. The counting stops when either Finally, the last method presented in this paper was used during a research program conducted in the Unlike methods A and B, methods C and D are adaptative methods. In these methods, counting stops when parasites are found in sufficient number. Hence, their cost is reduced for high parasitemias.The source and scale of measurement error depends on several parameters, such as sample preparation, staining process, counting technique, microscopist performance, etc. However, variation of parasite density within a slide is expected even when prepared from a homogeneous sample Let In order to define the variability measures, we need to introduce the concept of mathematical expectation. The expected value of the estimator bias is the difference between the estimator's expected value unbiased. Mean error is the bias expressed as a percentage of The sampling bias occurs when the true value (in the population) differs from the observed value (in the study) due to a flaw in the sample selection process. An estimator A measure of the sampling error is the standard deviation which is the square root of its variance. Standard deviation is a measure of dispersion from the mean, or the expected value and it is commonly used to compute confidence intervals in statistical inferences. The reported margin of error is typically about twice the standard deviation (1.96), the radius of a 95 percent confidence interval. Sampling variability can also be expressed relative to the estimate itself through the coefficient of variation (CV), which is defined as the ratio of the standard deviation Then, CV is expressed as a percentage of The last measure of variability we study is the false negative rate (FNR), also known as Type II error or In addition to the variability, we are also interested in the cost-effectiveness of each method. We define the method's cost as the number of HPFs that has to be read to reach the threshold value. Once it is reached, we stop the examination of the smear. Depending on the method being used, the cost is based on the number of parasites (or WBCs) required to stop the reading of the thick blood smear. Let T denote the required number of HPFs to stop the counting. We first compute The following methodology is used to compute the three measures of variability  and to assess cost-effectiveness of methods. Firstly, the exact distribution of A1. The distribution of the thickness of the smear, and hence of the parasites within the smear, is homogeneous.A2. The distribution of the parasites in the HPFs is uniform, and thus can be modeled through a Poisson distribution Let Under an assumption of uniformity, the number of parasites per field can be modeled using Poisson distribution (assumption A2). If the expected number of parasites per HPF is -i.i.d.). Leukocytes are supposed evenly distributed over the thick smear. Therefore, the number of leukocytes per field can be modeled using the Poisson distribution. If the expected number of parasites per HPF is Let Let Let Then, Let Then, Let Probability of Let The probability mass function of For method A, natural estimator of unbiased. Thus, the ME is null.Let Cramer-Rao Bound (CRB). We show that the variance of the proposed estimation technique reaches the Cramer-Rao lower bound. Hence, In order to evaluate the efficiency of this estimator, the variance is to be compared against the Fisher Information I(The coefficient of variation (CV) is defined as the ratio of the standard deviation In practice, false negatives occur when diagnosing by mistake PD as null after reading For Method B, To derive statistical properties of the PD estimate, we first need to compute the probability of seeing The probability density function of Let Probabilities of Probability of Then, The probability density function of For Method D, Probabilities of Then, The probability density function of Simulations are used to study the accuracy of our mathematical models, and to validate the theoretical results derived from estimators' probability functions. For the purpose of simulations, we predefine a data-generating model of The recursive formulas described above are used to compute the exact distribution of variables. Each computation takes as input: We believe that if data mappings are addressed simultaneously in a single framework, the resulting approach will facilitate visual comparisons of methods. At that point, we consider the problem of scale. The methods use either different numbers of arguments or different types of arguments. We got rid of this by expressing variability measures for all methods as functions of parasity density and WBCs count. To do so, we convert threshold values used in each method into WBCs count. For Method A, we assume an average of In the following, parasitemia was categorized as either low , this estimator also satisfies the Cramér-Rao bound, which is an absolute lower bound on variance for statistics of a variable and thus is an unbiased efficient estimator. Analytic proofs are given in Section 1 in As shown in For Method C, three parts can be distinguished. For Note that the same ME level may be reached by more than one threshold value  for commonly used threshold values. We used 200 HPFs for Method A, 200 WBCs for Method B, 100 parasites and 200 WBCs for Method C and 500 WBCs or 500 parasites for Method D.To the best of our knowledge, this is the first study of threshold-based counting technique performance using the theoretical properties of PD estimators. We considered four commonly used threshold-based counting techniques, and assessed the performances of these methods according to threshold values. These thresholds may be fixed or variable. We showed that adaptative methods are more efficient than the ones involving fixed threshold values. To define the theoretical properties of the estimators we hypothesized that the distribution of parasites within HPFs follows a Poisson distribution. We demonstrated that Method A estimator is unbiased and efficient. However, this estimator generates a high number of false negatives, especially for low parasitemia levels when the counting does not exceed few HPFs. Moreover, Method A is time-consuming. We showed that the ME of Method B is independent from PD, and only depends on the threshold value. This helps to handle the amount of bias with an appropriate choice of the WBC threshold value. We showed that adding a new parameter to the stopping rules (the number of parasites seen) implies more accuracy and precision without increasing the method's cost for low and intermediate parasitemias. Method B and Method D have similar behaviors for low and intermediate parasitemia levels while Method D is more accurate and precise in the considered PD intervals. For high parasitemia levels, Method B and Method C have similar behaviors and are more accurate and precise than Method D. However, for high parasitemias, Method D outperformed the three other methods in terms of cost. For each method, different threshold values may be fixed, which raises questions regarding the accuracy and reproducibility of these parasite counting techniques.kappa coefficient of CohenScott's piThe importance of parasite density data reproducibility stems from the need for epidemiological interpretations to be based on solid evidence. However, variation of parasite density within a slide is expected even when prepared from a homogeneous sample P. falciparum, by 24% for P. malariae and by 21% for P. ovale. Trape (1985) However, very few studies have examined the threshold-based counting techniques or evaluated the impact of the sampling error in endpoint measurements. In Nigeria, Dowling & Shute (1966) In addition, the accuracy and consistency of these methods have been generally overlooked. There is no general agreement on the optimal method for estimating parasite density according to threshold values. Further experimental evidence is needed to determine which parasite counting technique is most accurate, reproducible, and efficient. Ultimately, the question is: to which extent would threshold values  influence the variability in parasite density estimates? However, there remains the issue of homogeneity. The distribution of the thickness of the smear and hence the distribution of parasites within the smear is not completely homogeneous To understand how the thresholds involved in parasite enumeration methods contribute to the magnitude of discrepancies in density determination, we studied their impact in variability measures generated by commonly used threshold-based counting techniques. We showed that estimators perform quite differently according to threshold values, and that an overall performance measure probably hides a lot of complexity in the behavior of each estimator. Another important aspect of this study is that we observed how estimators perform at different parasitemia levels, and how much the choice of threshold values may influence the performance of estimators relative to each parasitemia level. In summary, while all four estimators had some deficiencies, Method D outperformed all the other estimators for accuracy, precision measures and cost-effectiveness, and should therefore be seriously considered in future studies of comparative performance of PD estimators with field-collected data. In this paper, we explored the duality between cost-effectiveness and precision implied by estimation methods. An open question remains: To what extent is it possible to reduce methods' cost while staying accurate and precise in estimation measures?Accurate estimation of PD is an important endpoint in epidemiological studies and clinical trials, both as a direct measure of the level of infection in a population and when defining parasitemia thresholds to diagnose malaria in case of fever episodes. Malaria PD estimates are also used to assess the development of naturally acquired immunity However, two approaches must be distinguished. The first approach concerns clinical malaria diagnosis and two problems can be pointed out depending on whether the question concerns an individual or a population. At the individual level, recent studies have highlighted the massive problem of misdiagnosis in malaria endemic countries P. falciparum parasite/leukocyte ratio higher than an age-dependent pyrogenic threshold of PD previously identified in the patient From an epidemiological point of view  clinical malaria is often considered as any case of fever or fever-related symptoms  associated with a P. falciparum infection during a follow-up period A second approach concerns the assessment of parasite density in epidemiological studies, when PD is used as the variable of interest, independently of clinical disease. For example, genetic epidemiology studies often focused on a mean level of In further support of the arguments cited in this paper, empirical validation of the theoretical results is needed through a rereading experience conducted in the field. And toward a better understanding of threshold effects, we are interested in the study of the consequences of the quality of these estimators in models classically used and starting from these measures . These aspects of the problem are now under consideration.File S1Supporting Information File.(PDF)Click here for additional data file.


Many physiological processes within the human body can be perceived and modeled as large systems of interacting particles or swarming agents. The complex processes of the human immune system prove to be challenging to capture and illustrate without proper reference to the spacial distribution of immune-related organs and systems. Our work focuses on physical aspects of immune system processes, which we implement through swarms of agents. This is our first prototype for integrating different immune processes into one comprehensive virtual physiology simulation.Using agent-based methodology and a 3-dimensional modeling and visualization environment (LINDSAY Composer), we present an agent-based simulation of the decentralized processes in the human immune system. The agents in our model - such as immune cells, viruses and cytokines - interact through simulated physics in two different, compartmentalized and decentralized 3-dimensional environments namely, (1) within the tissue and (2) inside a lymph node. While the two environments are separated and perform their computations asynchronously, an abstract form of communication is allowed in order to replicate the exchange, transportation and interaction of immune system agents between these sites. The distribution of simulated processes, that can communicate across multiple, local CPUs or through a network of machines, provides a starting point to build decentralized systems that replicate larger-scale processes within the human body, thus creating integrated simulations with other physiological systems, such as the circulatory, endocrine, or nervous system. Ultimately, this system integration across scales is our goal for the LINDSAY Virtual Human project.in silico experiments and their associated modeling and optimization techniques as essential components in further enhancing our capabilities of simulating a whole-body, decentralized immune system, to be used both for medical education and research as well as for virtual studies in immunoinformatics.Our current immune system simulations extend our previous work on agent-based simulations by introducing advanced visualizations within the context of a virtual human anatomy model. We also demonstrate how to distribute a collection of connected simulations over a network of computers. As a future endeavour, we plan to use parameter tuning techniques on our model to further enhance its biological credibility. We consider these  Recent years have witnessed a growing interest in systems biology -7. Not oUsing agent-based approaches in simulations is another aspect to increase realism in computer simulations. Rules or simple programs and attributes for agents can then drive the overall dynamics of a system of interacting entities, which result in emergent observable patterns ,10. An aIn this work, we present our latest simulation of the decentralized processes of the human immune system ,13. Our The adaptive immune response results in the elimination of various pathogens such as viruses and other foreign particles. It is also responsible for developing a memory response for future infections with the same antigens. The mechanism through which humans develop immunity to disease-causing pathogens is through the cellular interactions of two different branches of immunity: (1) humoral and (2) cell-mediated. Humoral immunity acts via the production and secretion of antibodies which neutralize harmful antigens. On the other hand, cell-mediated immunity functions via the destruction of infected cells, in order to suppress any further spreading of a virus. Both branches have the ability to create memory cells that can prevent secondary infections with previously encountered viruses .Lymphocytes have a very important role in the adaptive immune system: T cells are responsible for cell-mediated immunity and B cells are responsible for humoral immunity. T cells and B cells are mainly found throughout the different lymphatic organelles, where they are most likely to encounter antigens. Both types of lymphocytes have antigen-specific receptors on their surface that allows them to detect specific antigen molecules. The binding of an antigen to a naive lymphocyte results in the lymphocyte becoming an effector cell, a cell that is directly involved in the immune response.The infection is initially detected by professional antigen-presenting cells such as macrophages and dendridic cells that are nearby. Upon detection, the antigen-bearing cells migrate to nearby lymph nodes to trigger the immune response. Within the lymph nodes, B cells and T cells interact with the antigen-presenting cells as well as free antigen molecules. Some activated T cells, namely the cytotoxic T cells, travel to the tissue to eliminate infected cells, while a subpopulation, called helper T cells, remain in the lymph node to promote further proliferation of nearby lymphocytes. B cells that develop into plasma cells produce large amounts of antibodies, which travel via the blood stream to the site of infection in order to neutralize virus particles. Neutralization involves the blocking of surface receptors on a virus responsible for entry into a healthy cell. After the infection is successfully defended, some B and T cells remain in the body as long lived memory cells. Upon secondary exposure to the same virus, the memory cells can quickly divide and mobilize to form a swift response, eliminating the virus before any serious effects are noticed by the host organism -17.Orthomyxoviridae family of viruses which affect humans, other mammals and some species of birds. The virus is generally transmitted from infected individuals through the air via coughs or sneezes and infects the epithelial cells lining the lung and throat. The Influenza A virus multiplies by utilizing the host cell's own replication and protein synthesis machinery to produce copies of the virus, which spread out to infect more tissue [Influenza A is a member of the e tissue . Figure Natural immune system processes have been inspirational for the modeling of adaptive and learning systems in the field of artificial intelligence ,20. We dAgent-based modeling, in the form of cellular automata has been used to simulate the processes of the immune system . This woJacob et al.  present For programming the behaviours and properties of agents we use an object-oriented architecture. In this approach, objects can have internal information (such as states) and are able to send and receive certain messages. Furthermore, the inheritance model is of high importance as it allows common behaviors, such as cell motility, to be implemented at a higher level and shared by the sub-class implementations of the different types of individual cells. Bersini provides some insight of how to use object-oriented programming to build the different agents of the human immune system .Integrating different models across many levels of scale is beginning to eliminate what most researchers consider the "bottom-up" approach, where systems are built from atomic parts. In the "bottom-up" approach systems become problematic when attempting to model a realistic process, because of the extremely large number of entities that simultaneously interact. As suggested by Noble , buildinLINDSAY Virtual Human project [LINDSAY is the integration of computational models across scales in order to simulate physiological processes from the body level to the level of organs, tissues, cells, and sub-cellular structures. This is similar to the Physiome project [As part of the  project , we are  project ,30, yet LINDSAY Composer (LC) [LINDSAY Composer with its own engine loaded at start-up.ser (LC)  is a sofThe Lindsay Composer allows a user to construct and add objects to a simulation using a static component library. The component library contains templates of possible agents and other controllers which can interact with the current simulation. Figure Entities simulated by the LC are represented as hierarchies of components that define their respective states and behaviors. For example, Figure ImmuneSystem_EpithelialCell defines the state and interaction rules of the epithelial cell agent.- LCTransformComponent defines the location, size and orientation of the agent in 3-dimensional space.- LCSphereGhostObject allows the agent to be aware of other physics objects inside the radius of the ghost object, which is used to create a virtual awareness space around an object. This object contains a child--the ImmuneSystem_AttractionField component-- which attracts certain other agents (such as viruses) towards the current epithelial cell.- OEMeshNode gives the agent a graphical representation, which contains a 3-dimensional mesh, textures, colours, shadows and animations.- LCRigidBodySphere contains all of the physics related information such as mass, acceleration and collision shape.- The human immune system encompasses processes across a wide range of scale: starting with the organismal level, one can integrate interaction processes at the organ level , at the tissue level , at the intra-cellular interaction level  and, finally, at the inner-cell processes such as protein synthesis and gene regulation. The LC framework allows the developer to integrate different modeling techniques across multiple scales Figure  into oneThe agents in our current immune system model are: epithelial cells, viruses, antibodies, B cells, T cells, macrophages and dendridic cells Figure . The behα will break out of the attraction with probability break∅(α) to ensure that overall stochastic motion is preserved. This approach gave us more reproducible results over multiple simulations, rather than basing the physical interactions solely on random motion.Due to current computational limitations, it is impossible to construct agent-based simulations that represent the exact number of entities found in nature. Therefore, we employed some constraints on our agents to simulate qualitatively correct interactions on a large scale with only a limited number of agents (in our case a few thousand). Since we have such a relatively small number of agents, the chance that two agents will collide randomly is low. Therefore, all our agents have attraction spheres around their actual physical meshes. This allows agents in close proximity to have a higher chance of becoming aware of and interacting with each other. For example, an epithelial cell can attract a virus, and similarly, a virus can attract an antibody  with a given probability intp upon direct mesh-to-mesh collision. After infection, the cell lives for another ∈incubation = 200 iterations, followed by cell death and subsequent release of ∈virusesreleased = 5 new virus particles. In addition, the lung tissue contains 12 initial immature dendridic cells residing in the tissue. These cells become activated upon contact with viral particles, and are able to migrate to the lymph nodes to activate the B and T lymphocytes.Epithelial cells (∈) are susceptible to be infected by an Influenza A Virus , which not only work over a network but also between threads and CPUs, whether on the same computer or not. Therefore, the system is not restricted to one simulation per machine, but rather one simulation per computing core over a large network, which maximizes efficiency with respect to message exchange.In our simulation, the information exchanged between the lymph node and tissue is rather basic. The lymph node distributed controller feeds its agent numbers and properties to other controllers that are connected to it. Since the tissue is connected, it will get informed of changes in numbers of certain agents that it is interested in. For example, the tissue controller is interested in knowing about the number of B cells, T cells and antibodies produced in the lymph node. On the other hand, the lymph node controller is interested in the number of contacts of dendridic cells with viral particles and activated antigen presenting cells in the tissue. At each simulation step, the respective controllers broadcast their current states over the network, allowing asynchronous coordination. Figure The information received by each distributed controller affects the state of the simulation environment or, in this case, the state of the immune system engine of the respective simulation. The agents controlled by the engine are then able to use the new state provided by the engine to inform their decisions, aside from their current physical interactions.The distributed approach to our system of simulation is highly flexible for extending it over multiple scales. The distributed controllers are not restricted to communicating across simulations of the same type, but can connect data from simulations over multiple scales. For example, one can introduce a differential equation-based model of lymphocyte growth and development in the bone marrow and link it to the agent population in the circulation and in the lymph nodes.A limitation of our system lies in different time offsets that can arise from a distributed simulation. Presently, at each distributed controller we make no adjustment with regard to time. That is, if a node is slowing down  the other nodes connected to it still run at their normal rates. The discrepancy is somewhat accounted for in the fact that the slow node does not receive any updates, and therefore does not broadcast any updates outside of its own rate of execution. This stabilizes the system such that the nodes that require information receive the information more slowly, thus eventually matching the same "simulation" speed as the slowest node. It is worth noting that the "simulation" speed is not the frame rate or execution time, but rather how fast certain events happen inside the context of the simulation.Table Figures We performed 20 simulation runs using the parameters in Table To test the adaptive capabilities of our immune system model, we re-introduce the same virus into the tissue after the initial infection has been cleared  those found in natural biological processes, we are currently applying evolutionary optimization techniques to fine tune the multitude of parameters of our models. At this point, we have gathered some preliminary, yet promising results with other agent-based models .in silico experiments and their associated modeling and optimization techniques as essential components in further enhancing our capabilities of simulating a whole-body, decentralized immune system, to be used both for medical education and research as well as for virtual studies in immunoinformatics.We consider The authors declare that they have no competing interests.Vladimir Sarpe (VS) is responsible for building the immune system model. VS was also tasked with designing and evaluating the experiments related to the immune system as well as parameter optimization. Christian Jacob (CJ) has provided supervision, feedback and previous work on simulating immune systems and has contributed to the writing of this paper.


This article describes an image processing system based on an intelligent ad-hoc camera, whose two principle elements are a high speed 1.2 megapixel Complementary Metal Oxide Semiconductor (CMOS) sensor and a Field Programmable Gate Array (FPGA). The latter is used to control the various sensor parameter configurations and, where desired, to receive and process the images captured by the CMOS sensor. The flexibility and versatility offered by the new FPGA families makes it possible to incorporate microprocessors into these reconfigurable devices, and these are normally used for highly sequential tasks unsuitable for parallelization in hardware. For the present study, we used a Xilinx XC4VFX12 FPGA, which contains an internal Power PC (PPC) microprocessor. In turn, this contains a standalone system which manages the FPGA image processing hardware and endows the system with multiple software options for processing the images captured by the CMOS sensor. The system also incorporates an Ethernet channel for sending processed and unprocessed images from the FPGA to a remote node. Consequently, it is possible to visualize and configure system operation and captured and/or processed images remotely. For example, they are used in door intercom  and videConsequently, an important contribution of this study is the construction of a specific image capture and processing platform on which to implement image processing techniques which are adaptable to different situations . These ketc.).Data acquisition: essentially comprising the image sensor, although it may include other devices used to complete the information required by the application  can be divided into three main blocks:As regards the processing devices, the most common are microcontrollers or microprocessors, Digital Signal Processors (DSPs), Field Programmable Gate Arrays (FPGAs) and multimedia processors. Application Specific Integrated Circuit (ASIC) devices, Single Instruction Multiple Data (SIMD) processors and embedded FPGA processors (soft/hard cores) are also used. The choice of which to use will depend on the application, and basic considerations include flexibility, processing performance, low Non-Recursive Engineering (NRE) cost, low energy consumption and level of programmability. This means that because the HW used is versatile, it can be used for different applications, adapting only the software (SW). The opposite situation would be systems based on ASIC devices whose functionality is only for which they were designed. If you want to reuse them for new applications, this will result in a higher NRE cost because they require at a minimum some new HW.From among these options, FPGAs represent an excellent choice as regards high performance applications and low production level. Indeed, FPGAs have undergone remarkable technological development in recent years and their use has increased considerably in fields such as aeronautics, the military, industry and research. Due to the increasing number of logic elements per device, increased clock frequencies, and to the possibility of using massive parallel processing, FPGAs now come close to ASICs in terms of data processing tasks. Furthermore, if we consider the possibilities they offer of instantiating DSP blocks, embedded processors (soft/hard cores) and memory controllers, among other features, FPGAs constitute a very interesting device for implementing intelligent camera architecture.The implementation of image processing algorithms on reconfigurable hardware permits an increase in algorithm efficiency. Previous studies such as  have demetc.), but also to implement processing algorithms capable of extracting more abstract information . The system was completed with a PowerPC processor for implementing/managing mixed processing, which combined the execution of hardware processing blocks with software processing blocks. As regards image capture, we chose the Aptina (formerly Micron) MT9M413C36STM  and image compression  via implementation of an efficient pipeline which provides a continuous flow of images. Pre-processing and compression is carried out by the FPGA, which employs shared memory to send the images to the ETRAX processor for transmission via Ethernet.In addition to comprising Open Source software (GNU/GPL license), Elphel’s NC3x3 project  also useThere are many other OEM cameras developed by manufacturers all over the world: the VC44xx, developed by VC Vision Components, the NI 17xx from National Instruments, the IVC-2D and IVC-3D from SICK IVP and the XCI from Sony, to name but a few. The majority of these cameras are industrial, which implies that they are to a large extent closed source and inflexible in terms of implementing new processing algorithms, in addition to being very expensive.The majority of the systems described here share in common the use of a device dedicated to pixel level image processing together with an external processor which executes high level software processing and serves as the communication interface. The capacity to implement efficient processing algorithms on hardware simplifies the software processing requirements. Thus, the implementation of these algorithms on reconfigurable hardware in symbiosis with an embedded processor in the same chip (hard/soft core) constitutes a system which eliminates the need for an external processor, since the same embedded processor can manage both purely sequential processing and the communication interface. In addition, internal FPGA resources allow hardware reconfiguration without the need to completely stop device applications.3.As regards cameras and vision systems, the image sensor plays a crucial role in system performance. Although image quality  and resolution (pixel number) are important parameters which characterize the image sensor, other considerations include pixel rate (number of pixels or images per second), addressing modes , ease of integration into the system, and the logic necessary to control acquisition.Full frame. The pixel matrix is formed entirely by photodiodes. The sensor takes exposures via a mechanical shutter release, and it is necessary to displace the entire matrix to the data acquisition electronics.Frame Transfer. Half of the matrix is covered by an opaque film. Shutter release in this case is electronic, and the charge from all the photodiodes is simultaneously displaced to the opaque area for subsequent readout. This type of CCD achieves lower exposure times and reduces luminous sensitivity.Interline CCD. The matrix includes intermediate storage devices in each column and row, which avoids noise accumulation due to charge displacement and permits rapid readout. Due to the type of construction, these CCD sensors suffer from a problem known as Blooming. If one of the photodiodes is overexposed, this triggers spontaneous charge displacement, affecting all the other photodiodes in the same column. Some CCDs implement fabrication techniques which mitigate the Blooming effect.CCD and CMOS are the two most frequently used technologies in image sensors today ,28. CCD CMOS sensors use the latter technology to implement a photosensitive matrix. They are based on digital memory readout techniques, using row decoders which permit random access and the selection of regions or windows of interest. Each pixel contains the electronic circuitry necessary to convert the electrical charge into tension, and this affects the fill factor which relates the data acquisition area to the pixel matrix conversion area. Thus, CMOS sensors usually have a fill factor of around 50% compared to the factor of around 100% which CCD sensors can reach. Due to their architecture, these sensors attain very low readout times and consequently, in order to characterize the image acquisition task, pixel number per second is used rather than the image number per second used by CCD sensors. Thus, CMOS sensors are characterized by obtaining high pixel per second rates. In addition, by selecting small pixel subregions, it is possible to obtain image per second rates which are above the nominal rate for the entire matrix. Other characteristic advantages of CMOS sensors include a high dynamic range, ease of integration and low Blooming effect. However, a disadvantage is that the image performance of CMOS sensors is low compared to CCD sensors, mainly due to Fixed Noise Pattern (FNP). This noise is the consequence of uncertainty in the electrical characteristics of the electronic circuitry associated with each pixel and each column output amplifier, resulting in an image presenting static offset. However, the FNP can be eliminated using digital processing, and with CMOS technology, additional hardware can be integrated into the same chip for this filtering process. Many of the current CMOS sensors include additional logic which adds more complex readouts (inverted or rotated images), communication interfaces (I2C), or even prior image processing .Cameras employed for vision applications most frequently use CMOS sensors. The lower image resolution and performance of these sensors is considered almost irrelevant compared to their image per second rate, selective acquisition of areas of interest, ease of integration and additional features provided by hardware embedded in the sensor. Furthermore, given that the images produced by this type of system will be digitally processed, the need for processing to eliminate FNP noise implies a negligible computational load.4.The camera presented in this article uses the monochrome version of the Aptina MT9M413C36STM sensor . This isThis sensor was chosen primarily because of its high speed image capture performance, thus the final rate of the system (the camera) is defined by hardware processing efficiency. Another interesting advantage of this system from the point of view of handling is the possibility of exporting the Hardware Description Language (HDL) controller implemented on the FPGA to other Aptina models, since the sensors made by this company are highly compatible. This is the case of the Aptina MT9P001sensor, for example, implemented on the Elphel NC353camera. The basic characteristics of this latter include 5 Mpixel (2.592 H × 1.944 V) resolution, sensitivity to the entire visible spectrum and an image per second rate of up to 15 fps (70 fps for VGA resolution). Despite being a CMOS sensor, its low noise and high sensitivity mean that it is possible to obtain images of a quality comparable to that of CCD sensors at a low cost and with the ease of integration provided by CMOS technology.Returning to the MT9M413C36STM sensor, this offers maximum response to the visible spectrum and reasonable sensitivity to the infrared wavelength, thus making it especially useful for certain applications .The sensor’s address system is partial, only permitting rows of interest to be selected, not columns, and it uses a 10-bit address bus for this function. With this system, it is possible to read specific rows whilst ignoring others, which increases the number of frames per second when only a certain area of the image is of interest.Simultaneous Mode: frame exposure is carried out whilst the previous frame is being read. This mode increases the image per second rate.Sequential Mode: this mode also produces continuous video, but the exposure of an image only begins once the previous image has been read, producing lower speeds than the simultaneous mode.Single Frame: in this mode, images are acquired at the end of the process, and all exposures must be executed before the image can be read.Two shutter release mechanisms are possible, Rolling Shutter (RS) or Global Shutter (GS). The first executes exposures row by row, whilst the second executes exposure of the entire matrix at the same time. With the RS method, higher image per second rates are achieved than with the GS method, but it also has the disadvantage of a time lag between rows which produces distortions to the image if the scene is not static. In turn, the GS system offers three different shutter and acquisition modes:On the basis of the characteristics described above, the sensor chosen made it possible to cover a very wide variety of applications with requirements that could range from high image capture speed to high resolution.5.Cell Logic Block (CLB), which implements the sequential and combinational logic functions. A CLB is composed by several slices where each slice contains Look Up Tables (LUT), multiplexers and Flip-Flop registers.Input-Output Block (IOB). These elements are related to the input/output terminals of the FPGA.Digital Clock Management (DCM). Thanks to this one, an efficient clock control can be done.Block RAM (BRAM): These blocks work as internal RAM of the FPGAHardware Multipliers: Multiply operation is frequently used in many applications. Due to this, hardware multipliers are available inside of FPGA in order to perform this operation as quickly as possible.The architecture proposed is an intrinsic part of an FPGA, specifically, a Virtex 4 FX (XC4VFX12) with internal implementation of an embedded system, consisting of a PowerPC (PPC) 405 processor integrated into the same chip (hard core). In addition to PPC, this FPGA has mainly the following internal resources:The FPGA is connected to the Aptina MT9M413C36STM CMOS sensor, 32 Mb of DDR SDRAM memory, 8 Mb of Flash memory, a 100 Mbit Ethernet transceiver and other I/O peripherals such as an RS-232 port, LEDS and switches. As regards the FPGA logic design . The input FIFO data are truncated to 8 bit in order to optimize the access to external memory. Image size can be reduced by up to ten during output by a decimator. Subsequently, the data are packaged and transferred to memory in burst mode.An acquisition controller manages the FIFO line memory, decimation and the CMOS sensor control bus, implementing the different image acquisition modes mentioned previously, which are parameterized by the register interface. Burst mode transfer of the image to memory is also parameterized by register interfaces. Using an interrupt line, the CMOS controller indicates when the writing burst to memory has finished and a complete frame has been written.The asymmetric and asynchronous FIFO memory has a modular, isolated design which facilitates use of another CMOS sensor module, for example the 5 Mpixel Aptina MT9P001 sensor. Integrating a new image sensor merely requires specification of the “Acquisition Controller” block specific to the sensor, adapting the datapath width via the FIFO line memory to the work frequency of the new sensor.The internal logic of the HW processing block in Writing image into memory stage: images captured by the CMOS sensor are read by the dedicated controller, pre-processed at bit level (decimated) and subsequently packaged in discrete pixel blocks and directly transferred in burst mode to the memory.Hardware processing of image stage: once the first block of pixels has been transferred to the memory, an interrupt to the PPC processor, which manages the hardware processing of that pixel block, is generated.Software processing of image stage: once hardware processing is completed, the corresponding block generates an interrupt which triggers software processing.Sending processing results stage: Lastly, the information obtained from the scene or image after processing can be sent via Ethernet.All the logic elements forming part of the design implemented on the FPGA are concurrent due to the hybrid pipeline system that combines the hardware and software domains. System execution flow can be divided into the following stages:In order to simplify analysis and validation of the system, execution of image processing is sequential: the hardware processing is executed first, and then the software processing. As regards executing a specific processing algorithm, the hardware and software processing phases can be executed conditionally and/or alternately if necessary to optimize algorithm execution time.FRAME_MEM: period of the stage where image is written into memory.TFRAME_HW: period of the hardware processing stage.TFRAME_SW: period of the software processing stage.TSEND: period associated with the stage of transmitting image processing results by Ethernet.TThe total image per second rate the system is capable of achieving is obtained from a partial analysis of each stage in execution flow. Thus, for each stage an fps rate is defined or, what is the same, the execution period of each stage:When calculating the total frames per second rate, in addition to considering periods associated with execution stages, sensor exposure time should also be taken into account. Where this exists, the total frame per second rate is given by FRAME_MEM is given by 5, the time necessary to package a pixel burst in the controller’s FIFO buffer. The expression includes the possible latency in memory write access ΔT6:The period TFRAME_HW is principally due to the size of one pixel burst and the time taken to process it, T8, this time depending on the processing algorithm implemented.. Note that the hardware processing datapath is 32 bits or 4 pixels, and high hardware processing performance is obtained by implementing parallel data processing algorithms. Memory access time is represented by the sum of time, T9, consisting of the system’s read/write access times and the associated latencies:For the rate obtained following the hardware processing represented by FRAME_SW period of (1) associated with the software processing rate, this mainly depends on algorithm execution time T10 and, as in previous stages, on latencies due to system memory or Cache access represented by the sum of T11:As regards the TSEND period depends, in the last instance, on the quality of data to be sent. Given that the system is oriented to internal processing, the quantity of data to transmit is low compared to the quantity of data needed to transmit a complete image. Furthermore, transmission of a complete processed image is only necessary during validation and refinement of the processing algorithm, which does not require real time image transmission. Thus, the data transmission time TSEND does not represent a limitation on the execution flow of the system.Finally, as regards data transmission time, the bandwidth is defined by the interface and communication protocol used—in this case, Ethernet 100 Mbps and TCP/IP. The T6.Pixel burst size of 512 words for the CMOS Sensor Controller and 128 words for the processing HW system.The algorithms implemented for hard- and software processing were thresholding. This has made it possible to compare the two implementation domains.The system for capture and processing of images using a CMOS sensor controlled by an FPGA described above was validated under the following conditions:The FPGA model employed was the Xilinx XC4VFX12 0, using the ISE, EDK and SDK Xilinx tools, version 10.1. The present system is limited by the number of occupied slices , and it is important to note that the FPGA used has the lowest number of slices of all the FX family . Therefore, in order to implement more complex image processing algorithms it would be necessary to choose a superior FPGA model.An analysis of the number of slices occupied in the different components of the system is given in As regards timing and frequency constraints, As for the actual tests used, the bank of tests employed to validate the camera is given in It is possible to configure the various camera parameters , refine operation at application level and control image acquisition and processing via the command interface. Images are sent using an image server. Ethernet bandwidth was tested using the Iperf tool, which measures maximum bandwidth performance for TCP and UDP connections. Transmission and reception was sampled at 5 seconds intervals over a 100 seconds period. Given the fact that microprocessor is working in several tasks (included Ethernet data transmission) the real Ethernet bandwidth decreases until 60 Mbpps. This situation can be checked in Test 1 (Capture): In this one a simple image capture and loading has been done by the FPGA. Thanks to this test the achieved frame rate will be the best case because no HW or SW processing is done.Test 2 (Capture + SW processing): This situation emulates an application in which the HW FPGA elements are only used for image capture and the microprocessor (SW stage) runs a SW image processing application (in our case Thresholding).Test 3 (Capture + HW processing): In this case, internal HW FPGA resources are also used to implement an image processing algorithm (Thresholding). The Microprocessor activity is reduced to manage and control the different HW enable signals and Ethernet Transmission.Test 4 (Capture + SW processing + HW processing): This situation will be the best case to apply a FPGA as a smart camera. All internal resources (HW and SW) are ready for image capture and processing application. Thus, HW FPGA internal resources are in charge to capture and processing image and the Microprocessor also implements a image processing algorithm. In HW and SW cases, a Thresholding application has been used.Frame rate is one of the most typical index to validate the efficacy of an image processing system. Due to this fact, the architecture implemented has been tested with four different tests which are continuously running, in order to extract different frame rates:All previous tests have been launched for two different modes: First one is a complete sequential system in which no parallel process (pipelined) has been implemented. Second one is the opposite, a system with the maximum parallel level with the capacity to develop. The number of frames per second achieved by the parallel mode is higher for all modes presented in It can be verified the degree of improvement is fairly constant in all cases. This situation is due to the improvement does not depend on the size of the image. If it is greater, the system with parallel mode is at full capacity after an initial latency. In the situations shown in 7.This article presents the architecture for image capture and processing using a high speed CMOS sensor implemented on an FPGA. In the present proposal, a versatile hardware/software system was implemented on the above platform, which enabled us to develop an intelligent camera based on an FPGA, capable of internal image processing without need to use an external PC. The existence of a embedded microprocessor in the FPGA provides the system with the flexibility to choose which parts of an image processing algorithm to implement on the software (programme housed in the microprocessor) or hardware : Thus, and with the aim of parallelizing tasks in order to attain concurrent execution, it is possible to attain high speed image processing. In order to obtain the highest fps rate possible, an exhaustive and in-depth analysis was carried out of the time consumed by each FPGA internal logic block.Due to the hardware features of the architecture developed here, an Ethernet channel is available, providing the system with the possibility of remote configuration of the CMOS sensor’s internal parameters and other parameters of the image to be processed. Furthermore, and due to this possibility, images can be sent to a remote location with or without FPGA processing. The present study also included an in-depth test of the Xilinx Ethernet controller function, demonstrating that with a real-life platform and a specific application, the maximum transmission and reception rates supplied by Xilinx are not viable.The use of Aptina CMOS sensors, and consequently, the use of HDL modules to control them, means that these can be reused in other sensor models without requiring more than minor modifications to the control and data buses connecting the FPGA and the CMOS sensor.


Escherichia coli and Bacillus subtilis, with the general belief that division site positioning occurs as a result of the combination of two negative regulatory systems, Min and nucleoid occlusion. These systems influence division by preventing the cytokinetic Z ring from forming anywhere other than midcell. However, evidence is accumulating for the existence of additional mechanisms that are involved in controlling Z ring positioning both in these organisms and in several other bacteria. In some cases the decision of where to divide is solved by variations on a common evolutionary theme, and in others completely different proteins and mechanisms are involved. Here we review the different ways bacteria solve the problem of finding the right place to divide. It appears that a one-size-fits-all model does not apply, and that individual species have adapted a division-site positioning mechanism that best suits their lifestyle, environmental niche and mode of growth to ensure equal partitioning of DNA for survival of the next generation.Spatial regulation of cell division in bacteria has been a focus of research for decades. It has been well studied in two model rod-shaped organisms,  The location of proteins inside bacterial cells is tightly regulated, with different proteins having specific cellular addresses that can change dynamically with time . This exThe first protein to localize to the division site in bacteria is the highly conserved tubulin-like protein, FtsZ. FtsZ polymerizes at this site to form a ring, called the Z ring, which then recruits all other known division proteins, about 20 in all, to form a protein complex called the divisome . The divEscherichia coli and Bacillus subtilis, Z ring formation occurs precisely at the cell midpoint .The Min system blocks Z ring assembly at the cell poles by inhibiting the polymerization of FtsZ . In bothpolymers . MinC isein MinJ , while iB. subtilis .Nucleoid occlusion prevents Z ring assembly over the nucleoid or chromosome . This efsubtilis  and SlmA and the cell poles  occurred in the central region of the cell. FtsZ production was then switched on  is consistent with a progressive step-wise potentiation of midcell , further highlighting the species-specific nature of bacterial division site placement. Below we describe the different positioning mechanisms at play in a range of recently studied organisms.The importance of Z ring positioning mechanisms other than Min and NO is further exemplified by the fact that many bacteria lack Noc/SlmA and/or Min protein homologues . Recent Caulobacter crescentus, which lacks both Min and NO proteins, Z ring positioning is governed by a bipolar gradient of the FtsZ inhibitor MipZ . Replication and segregation of the chromosomal origins then sets up a bipolar gradient in pre-divisional cells, which dislodges FtsZ from the pole and restricts Z ring formation to midcell .Importantly, MipZ provides both spatial and temporal cues for Z ring formation. In newborn Streptomyces coelicolor, which also lacks the Min and NO systems, FtsZ is recruited and tethered to the division site directly via interaction with the membrane-associated protein SsgB  may not be uniformly localized throughout the cell wall, at least during septum formation . WTA loc. aureus  indicatiS. pneumoniae frequently form over nucleoids and that cell constriction occurs concurrently with the separation of the DNA  can serve as a marker for the cell equator and hence the site for future cell division  is a Gram-negative coccus that divides in two alternating perpendicular planes , as well as the repertoire of FtsZ-binding proteins present in the organism. A striking recent example of niche-specific division site selection comes from the study of a rod-shaped γ-proteobacterium that adheres via one of its poles to the nematode Laxus oneistus. To ensure that both daughter cells remain attached to the host, this bacterium grows by increasing in width and forms its septum parallel rather than perpendicular to the length axis , mode of peptidoglycan synthesis (elongation/division modes), lifestyle (division occurs asymmetrically in gth axis . We are The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Ongoing research and technology developments hold the promise of rapid production and large-scale deployment of strain-specific or cross-protective vaccines for novel influenza viruses. We sought to investigate the impact of early vaccination on age-specific attack rates and evaluate the outcomes of different vaccination strategies that are influenced by the level of single or two-dose vaccine-induced protections. We developed and parameterized an agent-based model for two population demographics of urban and remote areas in Canada. Our results demonstrate that there is a time period before and after the onset of epidemic, during which the outcomes of vaccination strategies may differ significantly and are highly influenced by demographic characteristics. For the urban population, attack rates were lowest for children younger than 5 years of age in all vaccination strategies. However, for the remote population, the lowest attack rates were obtained for adults older than 50 years of age in most strategies. We found that the reduction of attack rates following the start of vaccination campaigns during the epidemic depends critically on the disease transmissibility, suggesting that for a sufficiently high transmissibility, vaccine delivery after the onset of epidemic has little or no effect, regardless of the population demographics. Vaccination remains the most effective public health measure to prevent influenza infection and its related complications12412The 2014–2015 Northern hemisphere influenza season reminds everyone, for yet another time, how efficacy of current vaccines can be affected by a strain mismatch such as the one observed for H3N2. Seasonal influenza vaccine manufacturing using traditional technologies such as egg-based is a time-consuming processTo address these global challenges associated with speed and better efficacy, much research and technology development have been devoted to the identification of novel means of vaccine manufacturing that will allow for the rapid production and large-scale deployment of a strain-specific or cross-protective vaccine in the event of an influenza pandemic or emergence of a new strain during yearly epidemics1393393143While each vaccine technology has various benefits and drawbacks, these technologies show significant promise to address the challenges of conventional egg-based vaccine production. This enables the development of a vaccine for testing and distribution possible within a few weeks following the identification and genetic sequencing of a new influenza strain, whether pandemic or emerging shortly before the yearly influenza season3Does the level of vaccine-induced protection influence the outcomes of vaccination strategies?How significantly do vaccination strategies differ with delay in vaccine availability?What is the impact of vaccination strategies on the reduction of hospitalizations?Given the availability of a strain-specific or cross-protective vaccine during the early stages of a pandemic or seasonal epidemic, we sought to evaluate the effectiveness of different vaccination strategies in reducing attack rates in the population. For this evaluation, we developed an agent-based simulation model from a previously established framework to include the effect of vaccination. For vaccination strategies evaluated here, we considered the possibility that the first dose of vaccine may provide low protection levels in all age groups, and therefore a second dose would be required during a pandemic. Considering the time for start of vaccination compared to the onset of epidemic , and the lag-time between the first and second vaccine doses in the case of low-efficacy vaccines, we specifically addressed the following questions:All individuals were considered eligible to receive vaccine during the outbreak, except those who were identified as infectious cases. In the model context, infectious cases are identified when they seek and receive care  with a probability that varied in simulations. For the implementation of vaccination, we included two key parameters: the time at which the vaccination campaign starts and the efficacy of vaccine. The time for start of the vaccination campaign was in the window of 2 weeks before the identification of the first infectious case to 4 weeks after the onset of epidemic in the population. Since vaccination may not be 100% effective, especially against a pandemic strain17181920When the vaccination campaign starts two weeks before the onset of epidemic  for mitigating the impact of a novel influenza virus. Our comparisons across population age groups have several important implications for vaccination policies. First and foremost is the fact that for a sufficiently high reproduction number, starting the vaccination campaign after the onset of epidemic has little or no effect in reducing attack rates, regardless of the population demographics. Even for relatively low reproduction numbers comparable to seasonal influenza, if the vaccination campaign begins too late with respect to the onset of the epidemic, then attack rates remain independent of the choice of vaccination strategy. Estimates of the reproduction number of a pandemic strain may therefore be essential for determining the most effective vaccination policies. Second, early vaccination, and optimally before the onset of disease outbreak, leads to lowest attack rates for both high and low efficacy vaccines, which highlights the importance of timely availability of vaccine. We observed that for early vaccination, a strategy that aims to reduce transmissibility of the disease in the population  has the highest impact on lowering attack rates in all age groups, and provides the highest percentage reduction of hospitalization compared to other vaccination strategies. However, there is a time period before and after the onset of epidemic, during which the outcomes of vaccination strategies may differ significantly. Furthermore, demographic variables could influence these outcomes, suggesting that vaccination policies should be tailored to fit the demographic and geographic characteristics of the populations.In the event of an influenza pandemic, timelines for the global spread of the disease may still be too short to allow for large-scale vaccine production, distribution, and implementation during the first pandemic wave. However, technologies that can deliver the first doses of vaccines within 12 weeks after the identification of a new pandemic strain could play a significant role for mitigating disease outcomes in subsequent pandemic wavesOur findings demonstrate that the choice of vaccination strategy is highly important when vaccines become available during the critical period around the onset of the epidemic, a volatile time with much uncertainty about the disease and its potential impact on the population. To date, no study has assessed the determinants  of vaccination produced using a novel technology with early distribution during an outbreak, although such technologies will be the reality for vaccine production in the near future. This assessment, while beyond the scope of this study, is essential to inform vaccination policies and optimize roll-out strategies that could maximize the population-wide benefits of early vaccination by raising herd immunity.Vaccination scheduling is also important for vaccines against annual epidemics as a strain mismatch can have significant impact on vaccine efficacy. The use of newer technologies to manufacture influenza vaccines in shorter timelines compared to the traditional egg-based method could allow the WHO to delay strain recommendations to be included in a vaccine, and yet still have the vaccines ready ahead of the influenza season to allow for optimal planning of vaccination campaigns.Our model combines the available information, data, and evidence in order to investigate the synergistic effects of vaccine, treatment, and isolation strategies. However, there are limitations that should be considered for interpretation of the results. The structure of the model for contact patterns is based on reasonable, general assumptions about human behavior consistent with those used in previous work29171819203334For simulated vaccination strategies, we relied on previous reports and data pertaining to the populations studied here. For example, for the morbidity-based strategy, it is assumed that most transmission occurs through contacts with young (5–19 and 20–49 years) age groups, which were prioritized in the model simulations. However, in the context of vaccination, the degree to which disrupting disease transmission through vaccination of younger individuals would affect morbidity and disease outcomes in those at high risk of infection remains undetermined. The risk-based strategy has some realistic limitations on the availability of information when deciding on which age groups to prioritize for vaccination. We assumed that the counts of confirmed cases in each age group are delayed by up to two weeks, and considered only individuals who seek care. While frequent changes in vaccine prioritization can be implemented during an outbreakDespite these limitations, our results remain robust in projecting that there is no significant difference in attack rates obtained from different vaccination strategies when vaccination starts sufficiently late after the onset of epidemic. For early vaccination, however, the choice of vaccination strategy can have a significant impact on attack rates in the population, underscoring the importance of research and development in technologies for rapid vaccine production against novel influenza viruses.A previously validated agent-based model28We implemented the model for an urban centre in the province of Manitoba, Canada. To evaluate the effect of demographic and geographic variables, we also simulated the model with shifted demographics to resemble a remote community in northern Manitoba. Statistics Canada census data37The model consists of different states corresponding to a conceptual framework developed for the natural history of influenza infectionConsidering the interaction of two individuals, one susceptible and the other infectious, disease transmission occurs as a result of rejection sampling based trials where the chance of success is defined by a transmission probability distributionThe probability of disease transmission between each pair of infectious and susceptible individuals co-located in the environment for the duration of β is the baseline transmission rate per time-step, β was obtained by calibrating the model to achieve an estimated reproduction number where Individuals with symptomatic infection may seek care with the probability , Canada . HospitaIndividuals who received vaccines (in either single-dose or two-dose strategy) may become infected, but at a reduced rate of acquiring infection compared to unvaccinated individuals. The probability of transmission depends on the vaccine-induced protection level at the time of exposure. We assumed a linear increase in the vaccine-induced protection to the sampled level over a two-week period following vaccination .We assumed that a certain number of vaccine doses are available per day , and theRandom: the probability of vaccination in a particular age group depends only on the number of eligible, unvaccinated individuals in that age group.Morbidity-based: prioritization of age groups is based on the highest attack rates observed in previous studies. The first priority group was school-aged children .Outcome-based: prioritization of age groups is based on the highest rates of hospitalization observed in laboratory confirmed data collected during H1N1pdm09 in the province of Manitoba, Canada. The first priority groups were pre-school children (0–4 years of age) and older adults (50+ years of age).Risk-based: prioritization of age groups is probabilistically based on estimates of the risk within each age group. This risk is calculated using cumulative confirmed case counts within the model for each week during the pandemic:Four different vaccine prioritization strategies were considered:Vaccination of age groups is then prioritized proportional to their risk.In all strategies, vaccination of the next priority group was initiated when there were no more eligible individuals in the current priority group. For vaccination campaigns beginning before there are any confirmed cases, prioritization is implemented the same as in the morbidity-based strategy. The priority order of age groups for vaccination is detailed in μ = −0.775 days, and shape parameter μ = 1 day, and the shape parameter We parameterized simulations with transmission rates that were calibrated to estimates of the reproduction number222333The effect of antiviral treatment in reducing infectiousness was included as a reduction factor for disease transmissibility following the initiation of treatment for symptomatic infections. We assumed that treatment reduces the transmissibility by 60%33343541To evaluate vaccination strategies, we compared age-specific attack rates in simulated scenarios. Simulations were seeded with a randomly selected initial infection when vaccination starts 2 weeks before the onset of epidemic, at the onset of epidemic, and 2 and 4 weeks after the start of epidemic. For the urban centre, we assumed the reproduction numbers How to cite this article: Laskowski, M. et al. Strategies for Early Vaccination During Novel Influenza Outbreaks. Sci. Rep.5, 18062; doi: 10.1038/srep18062 (2015).